Here is the agenda for the 2024 CMAS Conference. Each speaker is alloted 15 minutes for their oral presentation and 5 minutes for questions. We will strictly enforce these time allotments, so that we have time to accommodate everyone on the schedule.
Note: All times in Eastern Time (New York)
October 21, 2024 | ||
Grumman Auditorium | ||
7:30 AM | Registration and Continental Breakfast | |
8:00 AM | A/V Upload | |
8:30 AM | Opening Remarks : TBD | |
8:45 AM | State of the CMAS Center: Sarav Arunachalam (CMAS Center Director, UNC-CH) | |
9:00 AM | The 2024 PM NAAQS: Overview and Modeling Considerations, Jim Kelly, US Environmental Protection Agency | |
9:45 AM | Break | |
The Importance of Community in the Community Multiscale Air Quality Model, Facilitated by Rohit Mathur, US EPA Office of Research and Development | ||
10:15 AM | Summary and evaluation of the new Community Multiscale Air Quality (CMAQ) model version 5.5, Wyat Appel, US EPA Office of Research and Development | |
10:35 AM | Recent Advances in CMAQ ISAM and Their Application to the EQUATES Project, Christian Hogrefe, US EPA Office of Research and Development | |
10:50 AM | CRACMM chemistry and CMAQ: past, present, and future, Havala Pye, US EPA Office of Research and Development | |
11:15 AM | Public release of the coupled MPAS-CMAQ system for global domains, Jeff Willison, US EPA Office of Research and Development | |
11:30 AM | Panel Q&A session with the speakers | |
11:50 AM | CMAS Conference Logistics, Sedona Ryan | |
12:00 PM | Lunch in Trillium | |
Grumman Auditorium | Redbud Room | |
Model Development, Chaired by Emma D'Ambro, US EPA and Yang Zhang, Northeastern University | Multiscale Model Applications and Evaluations, Chaired by Christian Hogrefe, US EPA and Pradeepa Vennam, Ramboll | |
1:00 PM | Session Keynote: Next generation atmospheric chemistry modeling with the Multiscale Infrastructure for Chemistry and Aerosols (MUSICA) Gabriele Pfister NCAR |
Session Keynote: Connecting Disparate Scales in Air Pollution Modeling: Perspectives from Development and Application of the CMAQ Modeling System Rohit Mathur US EPA |
1:20 PM |
Influence of Chemical Mechanism on Carbonyl and Ozone Formation During Winter
Influence of Chemical Mechanism on Carbonyl and Ozone Formation During Winter
Loknath Dhar, Dr. Seth Lyman In Utah’s Uinta Basin, elevated ozone concentrations can occur during the winter months and form under conditions of thermal inversion, snow cover, and the emission of pollutants from the oil and gas industry. Carbonyl compounds are important precursors to winter ozone, but their chemistry during winter ozone episodes has not been thoroughly investigated. Box models can be useful in air quality studies because they simplify the atmosphere into a single box, allowing for detailed analysis of atmospheric chemistry. In this study, (1) the F0AM Box Model was used along with four different chemical mechanisms to identify the specific carbonyl species that act as important precursors to winter ozone formation and determine how they form; (2) modeled emission factors for carbonyl compounds were determined, (3) the ozone formation potential of different carbonyl compounds was assessed, and (4) the sensitivity of various alkanes, alkenes, and aromatic species to carbonyl compounds was analyzed to provide information about how different hydrocarbon species regulate the production of ozone and carbonyl compounds. These analyses were conducted using the MCMv331, RACM2, SAPRC07, and CB06 chemical mechanisms, and the results of the different mechanisms were compared. Emission factors were adjusted until the modeled carbonyl concentrations matched the measured values. Final emission factors were zero or nearly zero, which indicates carbonyl compounds were mostly secondary pollutants, created in the atmosphere from photochemistry. Throughout this study, MCMv331, RACM2, and SAPRC07 provided nearly similar results, showing formaldehyde to be the most important carbonyl species, contributing 33–42% of the total impact of carbonyls on ozone production. The same models agreed that acetaldehyde was the second most important contributor, with around 15–28%. However, the CB06 mechanism resulted in KET (a model species signifying ketone groups) as the most important carbonyl contributor to ozone formation (65%) and formaldehyde as the second most important with 17%. The CB06 mechanism uses functional groups as a stand-in for specific compounds, and this might cause the observed differences in the results compared to other chemical mechanisms. By incorporating heterogeneous chemistry in the F0AM model script with MCM, the ozone level decreased by less than 2 ppb, and the overall contribution of different carbonyl compounds to ozone production remained the same. The next step in this project will be to use CMAQ for a comprehensive, regional-scale analysis. The impact of different emission sources will be evaluated, and the results from the F0AM box model will be compared against CMAQ results. Baseline simulations will be run to understand the model's default behavior, followed by sensitivity simulations with different mechanisms and modified emissions to study the impacts of organic emissions on ozone formation, particularly focusing on carbonyl compounds. After the completion of simulations, results will be analyzed using tools like VERDI to compare different chemical mechanisms, helping to understand factors influencing winter ozone formation and the impact of various precursors and emission sources. This comparison will highlight the strengths and weaknesses of the mechanisms, guiding future improvements in atmospheric chemistry modeling and validating the F0AM box model results. Loknath Dhar Utah State University |
Overview of the EPA Dynamically Downscaled Ensemble (EDDE): Current Status and Future Plans
Overview of the EPA Dynamically Downscaled Ensemble (EDDE): Current Status and Future Plans
Tanya Spero, Megan Mallard, Jeff Willison, Jared Bowden, and Chris Nolte The EPA Dynamically Downscaled Ensemble (EDDE) is a collection of physics-based regional climate data that represent atmospheric conditions for historical and future periods under different greenhouse gas emission scenarios. The EDDE datasets were developed using the Weather Research and Forecasting (WRF) model driven by multiple global climate models. The EDDE datasets can be used to explore regional climate change and its influence on extreme weather events out to 2100 across the contiguous United States to quantify potential impacts on human health and the environment. Specifically, the EDDE datasets have been used with CMAQ to explore plausible impacts of climate change on air quality, pollutant deposition to sensitive ecosystems, and human health. This presentation will provide an overview of EDDE Version 1, a subset of which is available through Amazon Web Services Open Data Project. The technical details and utility of the dataset will be covered. In addition, EDDE Version 2 is under development, and details of that dataset and timelines will be presented. Tanya Spero U.S. EPA |
1:40 PM |
Examining the impacts of halogen chemistry and photolysis of particulate nitrate on ozone over the Northern Hemisphere
Examining the impacts of halogen chemistry and photolysis of particulate nitrate on ozone over the Northern Hemisphere
Golam Sarwar, William T. Hutzell, Jesse Bash, George Pouliot, David Wong, Robert Gilliam, Christian Hogrefe, Kristen Foley, Fahim Sidi, T. Nash Skipper, Havala Pye, Rohit Mathur, Jeff Willison, Ben Murphy, Barron Henderson, Kevin Talgo, William R. Stockwell, Alfonso Saiz-Lopez The Community Regional Atmospheric Chemistry Multiphase Mechanism (CRACMM) is a newly developed chemical mechanism that integrates reaction pathways for formation of ozone, secondary organic aerosol, and hazardous air pollutants and contains 508 chemical reactions. To extend the applicability of CRACMM to broader spatial and temporal scales, we implement 370 additional halogen related reactions and the photolysis of particulate nitrate into CRACMM and perform three different simulations for the year 2018 using the Community Multiscale Air Quality (CMAQv5.4) model over the Northern Hemisphere with 108-km horizontal grid spacing. The first simulation uses the base CRACMM without halogen chemistry and no particulate nitrate photolysis. The second simulation uses base CRACMM augmented with halogen chemistry, and the third simulation uses base CRACMM with both halogen chemistry and the particulate nitrate photolysis. The halogen chemistry generally reduces ozone with a larger reduction occurring over seawater than over land. In contrast, the photolysis of particulate nitrate enhances ozone and compensates the halogen mediated ozone loss with a larger enhancement occurring over seawater than over land. The halogen mediated ozone loss is the largest near the surface layer and decreases with altitude. In contrast, the particulate nitrate photolysis initiated ozone enhancement is larger aloft than near the surface. We compare model ozone mixing ratios with measurements from the Clean Air Status and Trends Network (CASTNET) and the Air Quality System (AQS) over the United States. The mean bias (MB) of the model without the halogen chemistry is generally negative in cooler months and positive in warmer months. The halogen chemistry deteriorates the MB in cooler months but improves it in warmer months. The photolysis of aerosol nitrate improves MB in cooler months but deteriorates it in warmer months. We also compare model predictions with available ozonesonde data and find that halogen chemistry generally deteriorates the comparison while the particulate nitrate photolysis improves the comparison with observed data. Disclaimer: The views expressed in this abstract are those of the authors and do not necessarily represent the views or policies of the U.S. Environmental Protection Agency (EPA). Golam Sarwar USEPA |
Quantifying CMAQ EQUATES improvements to Downscaler fused surfaces: A cross-validation study using 18 years of PM2.5 and ozone modeling
Quantifying CMAQ EQUATES improvements to Downscaler fused surfaces: A cross-validation study using 18 years of PM2.5 and ozone modeling
Kristen Foley1, Adam Reff2, Jeannette Reyes2, Sharon Philips2, Barron Henderson2, Christian Hogrefe1 1 US Environmental Protection Agency Office of Research and Development 2 US Environmental Protection Agency Office of Air and Radiation As part of the EPA’s Air Quality Time Series (EQUATES) project, we created measurement-model fused estimates of PM2.5 and maximum daily 8-hr average ozone (MDA8 O3) using the set of consistent CMAQv5.3.2 simulations and a Bayesian space-time Downscaler (DS) statistical model. We compare the 2002-2019 EQUATES DS surfaces to another set of DS fused surfaces that EPA developed to inform the CDC’s National Environmental Public Health Tracking Network. The CDC DS surfaces were developed over the last decade based on the best air quality modeling data available at the time. For example, the fused estimates for the early 2000s are based on older versions of the CMAQ system and use a courser grid resolution than estimates for more recent years. We used 10-fold cross-validation (CV) of both the EQUATES and CDC surfaces to quantify differences in estimation error at unmonitored locations from the two fused datasets. We find that the EQUATES DS and CDC DS have very similar aggregate cross validation errors (i.e., averaged across seasons and regions), even when the underlying CMAQ estimates in EQUATES had better performance than the older CMAQ versions used for the CDC timeseries, reinforcing the benefits of using data fusion as a bias adjustment tool. However, looking at individual days we see large differences can occur between the two datasets, highlighting that biases in the underlying air quality output can still be carried through to the fused surfaces. We also use a modified cross validation approach to investigate how the 1-in-3-day sampling at many PM2.5 monitoring sites impacts the fused estimates, i.e., how the CV error changes on days with less observational data available to adjust the model surfaces. The EQUATES and CDC DS cross validation metrics show that biases in PM2.5 are within ±4 µg/m3 across seasons and regions except for a persistent underestimation in the fall and winter in northwestern states. The seasonal/regional CV mean biases in MD8 O3 for both EQUATES and CDC are typically within ±3ppb. The EQUATES DS cross validation correlation is typically slightly higher than the CDC DS correlation. The fused cross validation metrics are substantially better than the metrics for the unadjusted EQUATES and CDC CMAQ output for most regions and seasons. The EQUATES DS fused PM2.5 and MD8 O3 offer the advantage of consistency in the underlying CMAQ modeling which is particularly important in the early years of the time series. We discuss the implication of these results on the use of Downscaler fused CMAQ surfaces for epidemiological studies linking health outcomes with pollutant concentrations. Kristen Foley US EPA |
2:00 PM |
GPU Implementation of a Gas-Phase Chemistry Solver in the CMAQ Chemical Transport Model
GPU Implementation of a Gas-Phase Chemistry Solver in the CMAQ Chemical Transport Model
Duncan Quevedo, Khanh Do, George Delic, José Rodríguez Borbón, Bryan M. Wong, and Cesunica E. Ivey The Community Multiscale Air Quality (CMAQ) model simulates atmospheric phenomena including advection, diffusion, gas-phase chemistry, aerosol physics and chemistry, and cloud processes. Gas-phase chemistry traditionally presents the major computational bottleneck due to its representation as large systems of coupled nonlinear stiff differential equations. We leverage the parallel computational performance of graphics processing unit (GPU) hardware to accelerate the numerical integration of these systems in CMAQ’s CHEM module. Our implementation, dubbed CMAQ-CUDA in reference to its use of the Compute Unified Device Architecture (CUDA) general purpose GPU (GPGPU) computing solution, migrates CMAQ’s Rosenbrock solver from Fortran to CUDA Fortran. CMAQ-CUDA accelerates the Rosenbrock solver by a factor of at least 3.5 and maintains an acceleration factor of at least 2.25 when compared to Euler Backwards Integration, CMAQ’s fastest solver. CMAQ-CUDA maintains fidelity to the standard Fortran implementation with discrepancies well below 0.1% throughout the simulation domain for the duration of the simulation period. Our results demonstrate that CMAQ is amenable to GPU acceleration and provide a Rosenbrock solver implementation that relieves the computational bottleneck imposed by the CHEM module. This work facilitates timely air quality modeling studies that can ultimately protect human and ecosystem health. I am a 4th year environmental engineering PhD student in Dr. Cesunica Ivey's Air Quality Modeling and Exposure Lab at the University of California, Berkeley. My background is in applied math and public health. I received my BS in Applied Mathematics and my BSPH in Environmental Health Sciences from the University of North Carolina at Chapel Hill. My research interests include air quality modeling with numerical and statistical tools to help reduce the burden of air pollution exposure around the country. Duncan Quevedo University of California, Berkeley |
Updates to NOAAs Unified Forecast System Air Quality Model (UFS-AQM): An Evaluation for Summer 2023 Case Studies
Updates to NOAAs Unified Forecast System Air Quality Model (UFS-AQM): An Evaluation for Summer 2023 Case Studies
Youhua Tang1,2, Patrick Campbell1,2, Beiming Tang1,2, Wei Li1,2, Cory Martin3, Hongli Wang4,5, Yunyao Li2, Tianfeng Chai1, Mariusz Pagowski4,5, Daryl Kleist3, Barry Baker1, Kai Wang3,6, Jianping Huang3, Jeff McQueen3, Raffaele Montuoro3, Daniel Tong1,2, Ivanka Stajner3, Youngsun Jung7, Shobha Kondragunta9, Ken Aikin10, Andrew Rollins10, Eleanor Waxman5,10, John Crounse11, Paul Wennberg11, Ryan Bennett12, Ilana Pollack5,10,13, Emily Lill13, Matt Coggon10, Kelvin Bates5,10, Amy Sullivan13, Mike Robinson10, Carsten Warneke10, Matthew Coggon10, Lu Xu5,10, Kristen Zuraski5,10, Jeff Peischl5,10, Chris Jernigan5,10, Wyndom Chace5, Steven S. Brown10, Caroline Womack10, Gordon Novak10, Patrick Veres10, Chelsea Stockwell10, Ann Middlebrook10, Rebecca Schwantes10, Charles Gatebe14 1. NOAA Air Resources Laboratory (ARL), College Park, MD. 2. Center for Spatial Information Science and Systems, George Mason University, Fairfax, VA. 3. NOAA/NCEP/EMC 4. NOAA Global Systems Laboratory 5. CIRES, University of Colorado, Boulder, CO 6. Lynker, Leesburg, VA 7. NOAA NWS/STI 8. National Center for Atmospheric Research, Boulder, CO 9 NOAA/NESDIS/STAR 10 NOAA Chemical Sciences Laboratory 11 California Institute of Technology 12 Bay Area Environmental Research Institute, California 13. Colorado State University, Fort Collins, CO 14. NASA Ames Research Center, MS 245-5, Moffett Field, CA The recently implemented NOAA National Air Quality Forecasting Capability (NAQFC) uses chemistry based on the Environmental Protection Agency (EPA)’s Community Multiscale Air Quality (CMAQ) model version 5.2. CMAQ serves as the basis for NOAA’s Unified Forecast System (UFS) air quality model (UFS-AQM), which is now online-coupled with the Finite Volume Cubed-Sphere Dynamic Core (FV3), and Common Community Physics Package (CCPP). Here we are working to upgrade UFS-AQM with multiple changes, including the newer CMAQ version ( 5.4+), increased resolution, physics changes, and data assimilation with Joint Effort for Data-Assimilation Integration (JEDI). This JEDI-based chemical DA system has been applied to constrain the UFS-AQM’s chemical initial conditions over an expanded North American domain at 13 km resolution. The assimilation of in-situ AIRNow data consistently improves the model's predictions for surface ozone and PM2.5. The change of UFS-AQM to use CMAQ version 5.4 mainly affects the dry deposition rates of ozone and water-soluble tracer gases, such as SO2 and NOx, and leads to lower concentrations of these species and higher concentrations of volatile organic compounds (VOCs). When compared to the aircraft data of Atmospheric Emissions and Reactions Observed from Megacities to Marine Areas (AEROMMA), the difference before and after the CMAQ 5.4 changes mainly appears in low altitude (<4km). Besides the primarily emitted species, the chemical mechanism change also affects the secondary oxidized compounds, such as acetaldehyde and glyoxal. Some systemic model biases are likely due to emissions and chemical background concentrations. Youhua Tang is senior research scientist in George Mason University and NOAA Air Resources Laboratory. His work focuses on air quality modeling, its associated data/process analysis and chemical data assimilation. He is one of major scientists to support the NOAA National Air Quality Forecasting Capability (NAQFC) system Youhua Tang George Mason University |
2:20 PM |
Atmospheric multiphase chemistry: a continuum
Atmospheric multiphase chemistry: a continuum
Simon Rosanka, Bill Hutzell, Kathleen Fahey, Ann Marie Carlton Clouds and in particular aerosol-cloud interactions represent one of the largest uncertainties in projections of future climate. Aqueous phase chemistry in clouds produces sulfate and carbonaceous aerosol mass aloft, where particles have a longer lifetime and scatter incoming solar radiation. While aqueous phase cloud chemistry occurs simultaneously with partitioning and gas-phase reactions, historically, most atmospheric models represent multiphase chemistry sequentially. In general, due to numerical reasons, most physical and chemical processes are conducted as consecutive rather than concurrent steps by using operator splitting. For example, the “SCIPROC” subroutine in the Community Multiscale Air Quality (CMAQ) model first calls aqueous phase processes for resolved and convective clouds in series, followed by the representation of gas and heterogeneous chemistry, and lastly aerosol processes. This fundamental assumption, however, contradicts the strong interconnection between these processes. In this work, we reduce CMAQ’s chemical operator splitting and develop a submodel that integrates chemical processes across gas- and aqueous-phases in parallel. This is achieved by representing gas and aqueous phase, and heterogeneous reactions in one single system of ordinary differential equations using the Kinetic PreProcessor. We evaluate the performance of the new chemical operator, with a particular focus on the representation of model variability of soluble gases and aerosols. Overall, the new development has the potential to improve the adjoint modelling capabilities of CMAQ as well as representing processes governing gas and aerosol species. Simon Rosanka Department of Chemistry, University of California, Irvine, California, USA |
Modeling air quality in California's South Coast and San Joaquin Valley in 2021
Modeling air quality in California's South Coast and San Joaquin Valley in 2021
Dazhong Yin, Majiong Jiang, Zhan Zhao, Maybelline Disuanco, Chenxia Cai, Jeremy Avise The Re-evaluating the Chemistry of Air Pollutants in California (RECAP-CA) 2021 field campaign gathered rich ambient air quality and meteorological data via aircraft, special ground site, and other measurements. Researchers from UC Berkeley conducted airborne in-situ and flux measurements of NOx using a thermal-dissociation laser-induced fluorescence (TD-LIF) and volatile organic compounds (VOCs) using a proton transfer reaction time-of-flight mass spectrometer (PTR-TOF-MS) on board a Twin-Otter aircraft from June 1 to June 22, 2021. The NOAA Chemical Sciences Laboratory deployed a trailer which housed the gas-phase instruments, including NOAA gas chromatography–mass spectrometry (GC-MS) from August 2 to September 7, 2021 at the ground site on the Caltech campus in Pasadena. Two NOAA LIDAR systems were set up at the site as well. The NOAA mobile laboratory periodically measured the spatial distributions of NOx and VOCs in the LA basin during that time. Air quality modeling for 2021 was conducted using the Community Multiscale Air Quality Modeling System (CMAQ). We will evaluate the model performance for ozone, particulate matter and their precursor species in the South Coast and San Joaquin Valley (SJV) against the measurement data collected from the RECAP field campaign as well as the routine monitoring network. Given the availability of observational data, we will be able to explore various issues. These include assessing the contrast between model capabilities in reproducing measured NOx and VOCs concentrations at the ground level and within the planetary boundary layer. We will also exam the emissions deficiencies due to the lack of understanding related to the volatile consumer products (VCPs); motor vehicle emissions deficiencies due to an influx of ecommerce warehouses in the LA basin; the growing contribution of VOC emissions from VCPs; and cooking emissions in urban areas. Moreover, the 2021 modeling platform will serve as a testbed for the model attainment demonstrations required for development of State Implementation Plans under the new lower annual PM2.5 NAAQS. In California, this means upwards of 15 non-attainment regions, many of which are smaller rural air districts that have never required a comprehensive emissions inventory or modeling evaluation. Dazhong Yin California Air Resources Board |
2:40 PM |
Evaluating new secondary organic aerosol (SOA) formation schemes in CAMx
Evaluating new secondary organic aerosol (SOA) formation schemes in CAMx
Katie Tuite1, Chris Emery1, Ling Huang2, Pradeepa Vennam1, Greg Yarwood1 1Ramboll Americas Engineering Solutions 7250 Redwood Blvd., Suite 105 Novato, CA 94945 2School of Environmental and Chemical Engineering, Shanghai University, Shanghai, 200444, China The U.S. Environmental Protection Agency (EPA) recently lowered the National Ambient Air Quality Standard for particulate matter with a diameter of 2.5 micrometers or smaller (PM2.5). Because of this, state air quality agencies are preparing for PM modeling that may be required to demonstrate compliance with the new standard. Secondary organic aerosol (SOA), which is formed chemically in the atmosphere from precursor emissions, can contribute a large fraction of PM2.5. In this study, we implement and test new SOA schemes in the Comprehensive Air quality Model with extensions (CAMx), a photochemical grid model which is used worldwide for modeling PM2.5, oxidants, and air toxics. Three new SOA schemes were developed and tested in CAMx alongside the existing scheme, SOAP2. Two of the new schemes have SOA yield parameters that emulate the AERO7 and CRACMM schemes in the Community Multiscale Air Quality (CMAQ) model. The third scheme (referred to as SIMPLE) assumes all SOA is non-volatile with SOA yields that align with multi-model means. Initial box model tests quantified the response of SOA concentrations to varying anthropogenic VOC and NOx emissions. The SOAP2, AERO7-based, and SIMPLE schemes respond similarly to variations in VOC and NOx emissions. The CRACMM-based scheme predicted a decrease in total SOA when NOx was reduced, which is opposite of the other schemes and experimental findings. Because this behavior is unique to CRACMM, we did not proceed to 3-D testing of the CRACMM-based scheme. CAMx 3-D simulations were conducted with the SIMPLE, AERO7-based (re-named to COMPLEX), and SOAP2 schemes and results were evaluated against observed organic carbon (OC) and PM2.5 from monitoring networks across the US. A statistical and graphical analysis showed comparable model performance among each of the SOA schemes. The SIMPLE and COMPLEX SOA schemes have replaced the SOAP2 scheme in CAMx version 7.3 because they are based on more recently published data for SOA yields. The SIMPLE scheme has additional practical advantages, including the ability to calculate emission-weighted SOA potential and the potential for efficiency gains with CAMx’s source apportionment probing tools by reducing the number of tracer species. The SOA schemes developed through this work were chosen considering that models used for State Implementation Plan (SIP) purposes must predict reasonable SOA concentrations and, importantly, that the SOA response to precursor reductions should be traceable to data that is known with some confidence. Katie Tuite Ramboll |
Future year Source Apportionment PM2.5 modeling in Continental US
Future year Source Apportionment PM2.5 modeling in Continental US
Lakshmi Pradeepa Vennam1, Prakash Karamchandani1, Tejas Shah1, Ralph Morris1, Karim Hamza2, Fred Turrati2, Yamada Taiga2 and Toru Kidokoro2
2Toyota Motor Corporation Recent reductions in the annual fine particulate matter (PM2.5) standards from 12 to 9 µg/m3 have created interest in understanding the contributions of specific source categories to PM2.5, particularly in areas in the US that are already in non-attainment or may potentially become nonattainment areas due to the new regulations. This understanding can be obtained by conducting future year photochemical grid modeling to quantify the contributions of various source types to PM2.5. In this study, we used the Comprehensive Air quality Model with extensions (CAMx) with the Particulate Source Apportionment Technology (PSAT) probing tool for a future year (2032) to calculate PM2.5 design values and the contributions of various source categories to primary, secondary, and total PM2.5 at monitoring locations in 24 key cities throughout the US. The future year design values indicated that the new PM NAAQS is estimated to be achieved in most of the 24 cities investigated in this study except for a few locations in California. In terms of source category contributions, our modeling showed that the other anthropogenic emission sector (consisting of EGU, locomotive, marine, agriculture/prescribed fires, fugitive dust emissions and other area sources) is the major contributor to the total PM2.5 followed by the off-road sector. The primary PM2.5 showed relatively higher contribution than secondary PM2.5 to the total PM2.5. The source apportionment results also showed that the other anthropogenic source sector is the dominant contributor to primary PM2.5, indicating that total PM2.5 is largely influenced by primary anthropogenic fugitive dust emissions and emissions from agricultural/prescribed fires. Pradeepa Vennam Ramboll Americas Engineering Solutions |
3:00 PM | Break | Break |
3:30 PM |
Development of an advanced regional air quality online prediction systems within the Unified Forecast System (UFS) framework
Development of an advanced regional air quality online prediction systems within the Unified Forecast System (UFS) framework
Jianping Huang (1), Fanglin, Yang (1), Ivanka Stajner (1), Raffaele Montuoro (1), Jeff McQueen (1), Cory Martin (1), Ho-Chun Huang (1, 2), Kai Wang (1,2), Brian Curtis (1, 2), Hyundeok Choi (1,2), Hiaxia Liu (1,2), Barry Baker (3), Daniel Tong (4), Youhua Tang (3, 4), Patrick Campbell (3,4), James Wilczak (5), Dave Allured (5, 6), Irina Djalalova (5,6), Shobha Kondragunta (7), Chuanyu Xu (7,8), and Fangjun Li (9)) 1) NOAA/NWS/NCEP/EMC, 2) Lynker, 3 NOAA/OAR/ARL, 4) GMU, 5) NOAA/OAR/PSL, 6) CIRES, University of Colorado Boulder, 7) NOAA/NESDIS/STAR, 8) IMSG, 9) South Dakota State University Wildfires are significant sources of numerous air pollutants in the United States (US), posing a major challenge to air quality predictions. In response, the National Oceanic and Atmospheric Administration (NOAA) has developed a regional air quality online prediction system (AQPS) within the Unified Forecast System (UFS) framework. This initiative aims to improve the representation of wildfire emissions and their impact on air quality. The online system integrates the Environmental Protection Agency's (EPA) Community Multiscale Air Quality (CMAQ) model as a column atmospheric chemistry model with the UFS-based atmospheric model. Wildfire emissions are computed utilizing real-time satellite-derived hourly high-resolution fire products, the Regional Hourly Advanced Baseline Imager (ABI), and the Visible Infrared Imaging Radiometer Suite (VIIRS) Emissions (RAVE). This presentation will provide an overview of the UFS-AQM online prediction system, emphasizing major scientific advancements, followed by a comprehensive evaluation of model performance across various seasons, regions, and intense fire events. The model has been extensively evaluated using various observational datasets, including surface monitoring networks and satellite retrievals. Additionally, a bias correction post-processing procedure has been implemented to refine prediction accuracy. Results show that the newly developed online system substantially improves predictions of daily 8-hour maximum ozone (O3) and 24-hour average particulate matter with diameters less than 2.5 micrometers (PM2.5), as well as alerting their exceedance events. Further ongoing efforts include the development of a data assimilation approach to constrain chemical initial conditions, using observations of PM2.5 from AirNow, Aerosol Optical Depth (AOD) retrievals from the Visible Infrared Imaging Radiometer Suite (VIIRS), and NO2 retrievals from the TROPOspheric Monitoring Instrument (TROPOMI). Additionally, efforts are directed towards developing machine learning emulators to improve computational efficiency and address the challenges posed by high-resolution predictions. Jianping Huang NOAA/NWS/NCEP/Environmental Modeling Center |
Surface ozone sensitivity to precursors emissions and the source apportionment in Southeast Asia
Surface ozone sensitivity to precursors emissions and the source apportionment in Southeast Asia
Hu Jie, Fang Tingting, Steve YIM Air pollution has become one of the most significant environmental risks to humans in Southeast Asia (SEA), detrimental to public health and the natural ecosystem. Ozone (O3) has recently risen to one of the top of the list among air pollutants. This study advances and applies the CMAQ-adjoint model to quantify the source-receptor (S-R) relationship between O3 concentration and the precursors’ emissions (i.e., VOC and NOx) as well as determines the contribution of each precursor’s emission source over different receptor regions in SEA. Eleven country-level receptor regions are defined, including Singapore, Malaysia, Indonesia, Brunei, Philippines, Vietnam, Cambodia, Thailand, Laos, Myanmar, and East Timor. The simulation results show high-level O3 concentration in Singapore, Jakarta, Bangkok and the Malacca Straits. Meanwhile, O3 has an overall NOx-limited formation regime in SEA due to high temperatures and large biogenic emissions. In addition, transboundary O3 is a large pollutant, while transportation, shipping and industry are the three dominant contributing sectors to the O3 in most countries in SEA. The study is anticipated to give a complete understanding of the S-R relationship of O3 in SEA. It may provide policymakers with the information to evaluate a variety of emission control schemes to reduce O3. Hu Jie Nanyang Technological University |
3:50 PM |
Dynamic Coupling of Meteorologically-induced (MetEmis) NH3 and VOC Livestock Emissions with CMAQv5.4
Dynamic Coupling of Meteorologically-induced (MetEmis) NH3 and VOC Livestock Emissions with CMAQv5.4
B.H. Baek, Siqi Ma, Irena Ivanova, Chi-Tsan Wang, Patrick Campbell, Youhua Tang, Daniel Tong Department of Atmospheric, Oceanic and Earth Sciences, George Mason University, Fairfax, VA 22030, USA The dynamic coupling of meteorological conditions and emissions of ammonia (NH₃) and volatile organic compound (VOC) from livestock sources with the Community Multiscale Air Quality (CMAQ) model version 5.4 could provide a more accurate temporal representation of air quality impacts. Traditional emission inventories often use static temporal profile factors that do not account for the variability in meteorological conditions, such as temperature and precipitations, leading to potential inaccuracies in air quality predictions in NOAA’s National Air Quality Forecasting Capability (NAQFC) system. This study integrates a dynamic meteorologically-induced emission coupler, known as MetEmis, considering temperature and precipitations, to better capture the emissions from livestock activities. Following up on the success with the CMAQ-dynamic coupler for onroad mobile emissions, we have developed the CMAQ-coupler for livestock emission sources, called CMAQ-MetEmis_LV. We conducted simulations over a region with significant livestock farming to evaluate the effects of this dynamic coupling on NH₃ and VOC emissions and their subsequent impacts on air quality. The outcome indicates that Incorporating meteorologically-driven emission variations has led to significant differences in the predicted emissions of NH₃ and VOCs, especially during the summer season and over the eastern U.S. Unlike the current static temporal allocation approach, the dynamic emissions modeling reflected the decrease of emissions during the warmer and rainy conditions, aligning better with observed concentration patterns. These changes are expected to impact the ambient concentrations of fine particulate matter (PM2.5) and ozone (O₃). We conduct a comparative analysis between static and dynamic emission scenarios to demonstrate that the dynamic coupling reduces the uncertainties in emissions and improves the performance of air quality models in predicting pollutant concentrations and their temporal distributions. These findings underscore the importance of considering meteorological variability in emission inventories to enhance the reliability of air quality forecasts and highlight the necessity for continuous advancements in emission modeling techniques to address the complexities of air pollution sources and their interactions with atmospheric processes. Dr. Baek is a research professor from George Mason University, and he has extensive experience in atmospheric chemistry monitoring and modeling as well as emissions modeling. Last 18 years, he has worked on developing the SMOKE modeling system with USEPA, and focusing on enhancing the emissions input for air quality modeling community. Especially, Dr. Baek has been recently working on enhancing the NOx, NH3, PM2.5 annd VOC emissions inventory using the machine-learning CTM model with satellites and other surface observations.
B.H. Baek George Mason University |
Condensed-phase hydrolysis of Per- and Polyfluoroalkyl Substances (PFAS)
Condensed-phase hydrolysis of Per- and Polyfluoroalkyl Substances (PFAS)
Emma L. D’Ambro, Benjamin N. Murphy, Havala O. T. Pye, and Ivan R. Piletic Per- and polyfluoroalkyl substances (PFAS) are a class of thousands of industrially-produced compounds that have been released into the environment for decades. They are long-lived and many do not readily degrade chemically, resulting in their distribution throughout the globe in all environmental media, and earning them the nickname “forever chemicals”. Several atmospheric chemical transformations have however been documented, and as our understanding of the present and historical atmospheric emissions of PFAS grows, so too does our understanding of important atmospheric chemical pathways. One such pathway is the hydrolysis of acyl fluoride-containing compounds into carboxylic acid-containing compounds. The hydrolysis of acyl fluorides to carboxylic acids is rapid in bulk condensed water, and the resulting carboxylic acids exhibit higher solubility in atmospheric particles resulting in decreased atmospheric lifetimes due to enhanced deposition. This process is relevant for one compound of particular regulatory and public interest: HFPO-DA (GenX). We apply the Community Multiscale Air Quality (CMAQ) model version 5.4 to a case study in Eastern North Carolina to model the PFAS emissions, transport, and chemical transformations at fine scale (1 km) from the Chemours Inc. Fayetteville-Works facility. We implement condensed-phase hydrolysis of hexafluoropropylene dimer acyl fluoride (HFPO-DAF) to hexafluoropropylene dimer acid (HFPO-DA) in aerosol and cloud liquid water. We evaluate the model’s deposition predictions with deposition measurements in North Carolina and discuss the impact of this chemical transformation on results. Disclaimer: The views expressed in this presentation are those of the authors and do not necessarily reflect the views or policies of the U.S. EPA. Emma D'Ambro US EPA |
4:10 PM |
Ending the half century monopoly of similarity functions in meteorology and air quality modeling
Ending the half century monopoly of similarity functions in meteorology and air quality modeling
Kiran Alapaty1, Jesse Bash1, Rob Gilliam1, Christian Hogrefe1, Daiwen Kang1, Barron Henderson1, Alan Vette1, Saravanan Arunachalam2, and Chris Nolte1 For the past half century similarity functions have been widely used as universal functions in atmospheric boundary layer modeling for meteorology and air quality models. Many box models that are driven by field-scale measurements don’t require usage of similarity functions since field measurements provide natural variability of the atmosphere. However, in 3-D models similarity functions are necessary for accurate model predictions of boundary layer evolution and associated processes. A proliferation of similarity methods has contributed to a divergence of formulations, which has contributed to differences in the outcomes among different 3-D atmospheric models. To address this cause of model divergence, we have developed a seamless universal formulation for use in box as well as 3-D atmospheric models. This innovative methodology avoids the usage of similarity functions and has the potential to unify models in representing turbulence effects in atmospheric boundary layer modeling. The basis of the new methodology was the development of a 3-D turbulence velocity scaling, which is parameterized using the surface turbulence kinetic energy and was validated using field scale measurements. Meteorology and air quality models were then used at 108 km grid spacing covering the northern hemisphere to perform two sets of numerical simulations: (1) Use standard similarity profile methods (SPM); and (2) Replace the SPM with the newly developed 3-D turbulence velocity-based method (TVM) in both WRF and CMAQ models (while prior work was limited to the implementation of TVM only in WRF model). Simulated meteorological parameters that drive an air quality model which simulates ozone and particulate matter (PM2.5) were intercompared and evaluated against measurements for the year 2018. Preliminary results indicate that the simulations using TVM are at least as accurate as those using similarity functions in both the meteorology and air quality models. Kiran Alapaty is the Senior Science Advisor in the Atmospheric & Environmental Systems Modeling Division in the ORD of US EPA. He is a part-time researcher for the past 20 years focusing on developing scale-aware cumulus convection scheme, development of new ways to model atmospheric boundary layer processes. Before joining EPA, Kiran was directing the Dept of Energy's national climate programs: ARM and ASR programs, and the global climate program at National Science Foundation. Kiran holds a Ph.D. degree from NC State and a M.S. from Indian Institute of Sciences. Kiran Alapaty US EPA |
Inter-model comparison of seasonal variability in gridded nitrogen and sulfur wet deposition from AQMEII4 over North America
Inter-model comparison of seasonal variability in gridded nitrogen and sulfur wet deposition from AQMEII4 over North America
Colleen B. Baublitz, Christian Hogrefe, Olivia E. Clifton, Paul Makar, Jesse O. Bash, Kathleen Fahey, Rohit Mathur, John T. Walker, Philip Cheung, Stefano Galmarini, Alma Hodzic, Aurelia Lupascu, Jon Pleim, Young-Hee Ryu, Roberto San Jose Reactive nitrogen and sulfur deposition can degrade natural resources and affect the lifetime of air pollutants that harm human health. The National Trends network offers long-term measurements of wet deposition to facilitate constraining regional chemical transport models over North America. Here we evaluate an ensemble of simulations from the fourth round of the Air Quality Model Evaluation International Initiative (AQMEII4) for their representation of the seasonal and spatial spread in nitrogen and sulfur wet deposition. A larger inter-model spread could indicate conditions or regions for which expanded measurement constraints may facilitate evaluation and prioritize directions for model development. Preliminary results using the 2010 simulations suggest that summer has the largest model spread (defined here as the standard deviation across models in space and/or time) in wet deposition for each of nitrate, ammonium and sulfate, corresponding to their summertime peak. Between spring and fall, the relative variation (across models, the standard deviation divided by the mean) is largest between the Baja Peninsula and California. Inter-model differences in the representation of aerosol species’ wet deposition could influence their representation of the aerosol lifetime. The inter-model spread in precipitation and each species’ wet deposition correlate for several months of the year, especially in the summer, suggesting that model differences in precipitation fields may contribute to the wet deposition spread. Future work will extend this analysis to an additional year (2016) to consider a potential role for interannual variability. We will compare these simulations with NTN data to evaluate individual ensemble member performance, with a special focus on the summer and in California, where the intermodel relative variation is largest. Colleen Baublitz is a Physical Scientist in the Air Quality Modeling Group in the EPA's Office of Air Quality Planning and Standards. Colleen Baublitz US Environmental Protection Agency |
Cloud Computing, Chaired by Fahim Sidi, US EPA | Multiscale Model Applications and Evaluations, cont. | |
4:30 PM |
Using Cloud Computing for CMAQ Modeling to Assess Air Quality in the Monterrey Metropolitan Area: Evaluation of Emission Reduction Strategies and the PIGECA Plan
Using Cloud Computing for CMAQ Modeling to Assess Air Quality in the Monterrey Metropolitan Area: Evaluation of Emission Reduction Strategies and the PIGECA Plan
Ana Yael Vanoye This study demonstrates the use of the Community Multiscale Air Quality (CMAQ) modeling system through a cloud computing approach to assess air quality in the Monterrey Metropolitan Area, the second-largest metropolitan area in Mexico and one of the most polluted zones in Latin America due to its extensive industrial operations and vehicle emissions. The utilization of cloud computing enables a versatile, scalable, and effective implementation of the CMAQ system, permitting resilient air quality simulations and analysis. We implemented the CMAQ model in a cloud environment to guarantee consistent and reproducible environments across various computational platforms. This configuration improves the model's usefulness and accessibility for academics and policymakers, particularly those in developing countries. The study examines the regional and temporal distribution of criteria pollutants, such as ozone and particulate matter, during a high-pollution episode. The cloud-based CMAQ application facilitates scenario analysis, which involves studying different emission reduction strategies and assessing their efficacy in enhancing air quality. The results are used to evaluate the 2023 state-official emission inventory and can be contrasted with the strategies proposed by the recent Comprehensive Strategic Air Quality Management Plan (PIGECA, in Spanish), presented by the Nuevo Leon State Government, in order to evaluate their feasibility. Ana Yael Vanoye Department of Sustainable Technologies and Civil Engineering School of Engineering and Sciences Tecnológico de Monterrey |
Examination of Modal Versus Sharp-cut PM2.5 Values Calculated by the CMAQ Model
Examination of Modal Versus Sharp-cut PM2.5 Values Calculated by the CMAQ Model
K. Wyat Appel, Christian Hogrefe, Kristen Foley, Fahim Sidi, Benjamin Murphy, Christopher Nolte Particulate matter (PM) is an important atmospheric pollutant comprised of particles of varying size and chemical properties. The Community Multiscale Air Quality (CMAQ) model uses three lognormal modes to represent the atmospheric particle distribution. These three modes correspond to the Aitken, accumulation, and coarse particle sizes. When summed together, these modes represent the full distribution (e.g., total PM) of respirable particles in the atmosphere. Often, however, the interest is in particles within a specific size range, such as PM2.5, which consists of only those particles less than 2.5 micrometers (µm) in diameter. Early versions of CMAQ did not provide a simple method to subset the three particle size modes in CMAQ into particles smaller than 2.5 µm; instead, PM2.5 was calculated by summing of the particles in the Aitken and accumulation modes. This approach may not accurately represent particles smaller than 2.5 µm, as the upper tail of the accumulation mode can contain particles larger than 2.5 µm. Additionally, the lower tail of the coarse mode can contain particles smaller than 2.5 µm, resulting in particles being erroneously excluded from the sum. Conceptually, a 'sharp-cut' PM2.5 approach is more appropriate when comparing to observed PM2.5 values. In practice, the three particle modes in CMAQ may not accurately represent the particle size distributions in the atmosphere, adding uncertainty to the model PM2.5 estimates. Nolte et al. (2015; https://gmd.copernicus.org/articles/8/2877/2015/) compared CMAQ PM2.5 values based on the modal sum and sharp-cut approach and found that while there could be relatively large differences (> 2 µgm-3) between the two calculations for a single day and site, the difference between the two values became relatively small (0.04 – 0.2 µgm-3) when aggregated over space and time. This indicated that aggregate evaluation metrics were likely to lead to the same conclusions about model bias and error using either approach. Based on the uncertainty in the particle size distributions and these findings, many subsequent CMAQ evaluations against observations continued to use the sum of the Aitken and accumulation modes to represent PM2.5. Since publication of that work in 2015 there have been significant updates to the CMAQ modeling system. This work will utilize output from the EPA’s Air QUAlity TimE Series (EQUATES) to reexamine the comparison of the modal sum and sharp-cut PM2.5 estimates using a newer version of the CMAQ modeling system (v5.3.2). The EQUATES data also allow for analysis over a long time period (2002-2019) to explore whether the differences between the two methods for computing PM2.5 have changed over time because of emissions-driven changes in aerosol composition. Comparisons of the two model PM2.5 values against observations from several air quality networks across the United States will also be presented to assess how the use of each method for computing PM2.5 compares to observed values. K. Wyat Appel U.S. EPA |
4:50 PM |
The Atmospheric Model Evaluation Tool (AMET) Amazon Machine Image
The Atmospheric Model Evaluation Tool (AMET) Amazon Machine Image
Robert Gilliam, Michael Morton and Wyat Appel The Atmospheric Model Evaluation Tool (AMET) was developed and first presented as a model evaluation option for air quality and meteorology at the 4th Annual CMAS conference in 2005. Since that time, AMET has evolved with periodic version updates, and more recently the distribution of new releases and more frequent bug fixes via Git-Hub (https://github.com/USEPA/AMET). In a recent effort to make AMET more user friendly, the US EPA has developed a Java-based Graphical User Interface (GUI) to broaden the analysis capabilities that are currently performed using Unix C shell scripts. The GUI essentially provides all the query criteria and analysis options in a single unified interface. Furthermore, an Amazon Web Services (AWS) AMET server instance has been configured including the AMETGUI with test datasets and distributed as an Amazon Machine Image (AMI). Traditionally AMET required users to install external software and other configuration steps on their local servers or systems that often require IT administrators and higher degree of Unix expertise. The AMI can instantly allow users to become familiar with AMET matching and analysis steps with a populated database and underlying model and observation datasets and quickly transition to evaluating their own model simulations. Robert has been involved in meteorological modeling at the US EPA for 20+ years with a primary focus on developing the Atmospheric Model Evaluation tool. Additionally, Robert has been closely involved in running, evaluating and improving model simulation for air quality applications using models such as MM5, WRF and MPAS from global to fine-scales. Robert Gilliam US EPA |
Multi-Scale Air Quality Modeling Using WRF-Chem-GHG over Northeastern Africa
Multi-Scale Air Quality Modeling Using WRF-Chem-GHG over Northeastern Africa
Yang Zhang, Daniel Schuch, and Khanh Do Department of Civil and Environmental Engineering, Northeastern University, Boston, MA, USA Air pollution has been a major concern in Africa where the exposure to ambient air pollution has caused more than 1 million premature deaths per annum. Particulate pollution in African cities resulted from vehicles, biomass burning (e.g., agricultural slash and burn and wildfires), industrial sources, and dust storms has large impacts on public human health, with daily and annual average concentrations of PM2.5 well above World Health Organization standards. Air pollution and its health impact in Africa are significantly understudied due to sparse air quality measurements, large uncertainties in emissions (e.g., biomass burning and dust storms), and limited computational resources for applications of 3-D air quality models. In this work, we perform multi-scale air quality modeling over triple-nested domains in Northeastern Africa, Egypt, and the city of Cairo at spatial resolutions of 36-, 6-, and 1-km, respectively, using the Weather Research and Forecasting Model coupled with Chemistry (WRF-Chem) with greenhouse gases (GHGs) (hereafter WRF-Chem-GHG). Unlike WRF-Chem and most other regional/urban air quality models, WRF-Chem-GHG can simultaneously simulate the emissions, transport, removal, and chemical and physical processes for both short-lived air pollutants and GHGs as well as their interactions via meteorology, radiation, cloud, and chemistry. Anthropogenic emissions are based on the Emissions Database for Global Atmospheric Research (EDGAR). Biogenic VOCs and CO2 emissions are generated online based on the Model of Emissions of Gases and Aerosols from Nature version 2 (MEGAN2) and the Vegetation Photosynthesis and Respiration model (VPRM), respectively, dust and sea-salt emissions are also generated online using available emissions modules in WRF-Chem-GHG. The initial and boundary conditions are derived from a global chemical transport model (e.g., CAM-Chem or CAMS). The simulated meteorology and air quality will be evaluated in terms of performance statistics, spatial overlays, temporal agreement using surface observations from the METeorological Aerodrome Reports (METAR), Egyptian Environmental Affairs Agency (EEAA), AirNow collected by US Embassy, and the Carbon Cycle Greenhouse Gases Air Sampling Network (CCCGASN). To complement limited surface measurements, satellite data (e.g., TROPOMI, OMI, GOSAT) will also be used for model evaluation. The likely causes of large model bias will be analyzed. As the first application of WRF-Chem-GHG in Africa, the work can shed light into its performance over Africa and provide a baseline to identify potential areas of model improvement and design sensitivity simulations to examine uncertainties and test improved model and inputs. Yang Zhang Northeastern University |
5:15 PM | Poster Introductions | |
5:30 PM | Reception and Poster Session, Chaired by Taciana Albuquerque, Federal University of Minas Gerais and Yang Zhang, Northeastern University
Air Quality, Climate and Energy
Model performance evaluation of CMAQ predictions of secondary organic aerosols from multiphase Oxidation of alpha-Pinene experiments.
Model performance evaluation of CMAQ predictions of secondary organic aerosols from multiphase Oxidation of alpha-Pinene experiments.
Dannys Ayala Terrones Department of Environmental Sciences and Engineering, University of North Carolina, Chapel Hill, NC Recent experimental findings have shown that monoterpene biogenic volatile organic compounds (BVOCs), such as alpha-pinene, contribute to the formation of organosulfates (OS) as part of secondary organic aerosol (SOA) production through multiphase oxidation processes. SOA is a significant contributor to fine particulate matter (PM2.5) impacting human health. Additionally, SOA can act as cloud condensation nuclei (CCN) and ice nucleation particles (INP), thereby influencing aerosol effective radiative forcing and affecting the climate. Monoterpenes account for approximately 15% of the BVOCs emitted into the troposphere, with alpha-pinene being the most abundant. Recent experiments conducted by collaborators at Texas A&M University demonstrated that multiphase reactions between alpha-pinene and hydroxyl radicals significantly increase OS formation under acidic conditions. In these experiments, ammonium sulfate seed particles were used to simulate non-acidic conditions, while ammonium bisulfate seed particles represented acidic environments. The goal of this work is to evaluate simulations of the alpha-pinene-derived SOA experiments utilizing the Community Multiscale Air Quality (CMAQ) Modeling System. Simulations are conducted using the Framework for 0D Atmospheric Modeling (F0AM) in MATLAB, with SOA formation represented as implemented in CMAQv5.3.2. Predicted organosulfate concentrations from the simulations are compared with those observed in the chamber experiments. Discrepancies between the model predictions and experimental results highlight the need for updates to current regional-scale models to improve predictions of alpha-pinene derived SOA and, more broadly, monoterpene-derived SOA predictions. Dannys Ayala Terrones UNC-CH
Identifying technological pathways for meeting decarbonization and air quality goals simultaneously
Identifying technological pathways for meeting decarbonization and air quality goals simultaneously
Joseph Bronstein, Chris Nolte, and Dan Loughlin Since carbon dioxide and air pollutants are co-emitted, achieving deep decarbonization is expected to result in significant air quality co-benefits. However, some decarbonization pathways yield more air quality benefits than others. For example, while low in carbon emissions, combustion of biomass and fossil fuels paired with carbon capture and sequestration result in particulate matter, nitrogen oxides, and other pollutants that can increase downwind air pollution. We hypothesize that considering air quality benefits explicitly when developing decarbonization strategies will achieve air quality benefits sooner and at a greater magnitude. To test this hypothesis, the air quality impacts of two scenarios are evaluated: a Net Zero scenario and another that also achieves Net Zero but also endogenizes the health costs associated with air pollution. The scenarios are evaluated using EPA’s GLIMPSE air-climate-energy planning tool, which is built upon the Global Change Analysis Model. For both scenarios, GCAM is allowed to identify a cost-effective pathway for achieving the Net Zero target. Comparing the results, we identify how these pathways differ with respect to technology choices, including exploring whether there are regional differences in technology response. Additionally, the health benefits associated with the two pathways are compared. Joe Bronstein is an ORISE research fellow in the EPA's Office of Research and Development in Research Triangle Park, NC. Joe's current work focuses on exploring how to use GLIMPSE and GCAM-USA to assist with regulatory efforts at the EPA. He received his bachelor's degree from the University of Arizona in Environmental Economics and his master's degree from the same institution in Applied Econometrics and Data Analytics. Joe Bronstein US EPA (ORISE fellow)
Mapping TOAR II Monthly Global Surface Level Ozone using Bayesian Maximum Entropy with Space Time Kernel Smoothing
Mapping TOAR II Monthly Global Surface Level Ozone using Bayesian Maximum Entropy with Space Time Kernel Smoothing
George Heidkamp Research into ground-level ozone pollution on a global scale has predominantly focused on annual estimates, which are computationally efficient but lack temporal fidelity to capture ozone's significant seasonal variability. This study aims to generate monthly global ground-level ozone estimates, increasing temporal resolution to better understand seasonal fluctuations in ozone pollution exposure. We employ the Bayesian Maximum Entropy (BME) modeling framework to integrate high-resolution information from TOAR II ground-level ozone monitoring data with a global offset calculated from a space-time kernel smoothing average. This novel application of BME for monthly global ground level ozone estimation allows for predictions with higher spatial and temporal clarity than previous annual analyses. Preliminary covariance modeling reveals a complex structure comprising three components in space and three in time, representing local, regional, and global contributions. Spatial ranges are 1°, 25°, and 3000°, while temporal ranges are 0.3, 0.5, and 10 years. Initial kriging maps for 2005 demonstrate clear seasonal and hemispheric patterns. While the study is ongoing, these early results suggest that our approach can capture fine-scale temporal variations in ozone concentrations. This research represents a significant step towards more temporally resolved global ozone estimates, which could enhance air quality management strategies and inform public health policies on a global scale. George Heidkamp Department of Environmental Sciences and Engineering, University of North Carolina, Chapel Hill, NC
Estimating the Impacts of Deforestation on Air Quality in Northern South America with an Improved Fire Characterization Algorithm
Estimating the Impacts of Deforestation on Air Quality in Northern South America with an Improved Fire Characterization Algorithm
Nafisa Islam, Grace Choi, Diego Rojas Neisa, Joshua Grey, Ricardo Morales, Fernando Garcia Menendez Adequately characterizing fire emissions and associated air quality impacts is critical to assess the detrimental effects of deforestation. In northern South America, specifically around the Amazonian region, deforestation and other types of biomass burning can significantly influence regional air pollution. Here, efforts to mitigate air pollution should consider strategies to reduce deforestation and associated emissions. Prior studies have used satellite-based estimates of burned area (BA) to predict the fire related emissions and air pollution impacts. However, large differences exist in BA estimates among different fire inventories, as well as important variations in fire attributed to deforestation. These uncertainties propagate to estimates of fire-related impacts on fine particulate matter (PM) and assessments of the benefits of reduced deforestation. In this study, we explore a new approach to improve estimates of deforestation area and emission in northern South America based on several remote sensing products. Specifically, we use an algorithm that relies on the Global Fire Atlas product; Landsat-based fire, forest cover, and forest change products; and MODIS-based fire products. The algorithm combines these datasets to better differentiate deforestation fires from other fires types. We then apply these results to refine emissions estimates from the Fire INventory from NCAR (FINN), one of the most widely used fire emission inventories. Finally, we use our updated fire emissions as inputs into the Weather Research Forecast model with Chemistry (WRF-Chem) and compare the model’s predictions of fire-related PM to those obtained using FINN’s original emissions. The modeling approach developed aims to more accurately identify and quantify deforestation fires in northern South American and enable better projections of the air quality benefits that can be obtained by eliminating deforestation in the region. Nafisa Islam North Carolina State University
Developing state-specific insights about greenhouse gas mitigation opportunities using a Human-Earth systems model
Developing state-specific insights about greenhouse gas mitigation opportunities using a Human-Earth systems model
Gyungwon Joyce Kim and Dan Loughlin As part of global efforts to mitigate climate change, achieving carbon neutrality by 2050 has become a critical goal. In the United States, while the federal government provides an overall guideline and financial support (e.g., Carbon Pollution Reduction Grant program under the Inflation Reduction Act of 2022) to achieve the goal, state and local governments play a significant role in its implementation. Recently, state governments have been developing Comprehensive Climate Action Plans (CCAPs) to contribute to this overarching objective. Understanding the factors contributing to CO2 emissions and analyzing available energy resources and strategic policy scenarios on a state-by-state basis are crucial steps in this endeavor. This study employs the Kaya Identity and Logarithmic Mean Divisia Index (LMDI) analysis methods to decompose CO2 emissions into components such as population growth, GDP growth, energy intensity, carbon intensity, and energy efficiency, thereby assessing their respective impacts on emissions. Additionally, a human-earth systems model, Global Change Analysis Model (GCAM) enables integrated scenario analysis for air pollutants, facilitating strategic planning towards decarbonization. Using GCAM-USA modeling data under 2050 decarbonization scenarios, this research focuses on conducting LMDI analysis across the 50 U.S. states to propose state-specific strategies for achieving carbon neutrality. It aims to identify key factors pivotal in reducing CO2 emissions in each state. The results suggest ideas such as optimizing energy market structure across electricity generation, industry, buildings, and transportation sectors, improvements in carbon intensity through biomass and renewable energy utilization, potential enhancements in energy intensity due to technology choices for energy efficiency, and the use of direct carbon capture technologies. This poster presentation will compare diverse approaches to decarbonization by state, aiming to provide actionable insights into effective strategies tailored to each state's unique characteristics and challenges. Gyungwon Joyce Kim U.S.EPA
Supporting state-level climate planning using EPA's GLIMPSE
Supporting state-level climate planning using EPA's GLIMPSE
Dan Loughlin, Kate Crowley, Gyungwon Joyce Kim, Shannon Koplitz, Chris Nolte, Jia Shi, and Trinity White(*) U.S. Environmental Protection Agency (*) ORISE fellow The Inflation Reduction Act (IRA) of 2022 created the EPA-administered Climate Pollution Reduction Grant (CPRG) program. Under the CPRG, EPA is providing funding for states to develop Comprehensive Climate Action Plans (CCAPs). CCAPs are intended to include a number of core components, including: (i) development of a greenhouse gas (GHG) emissions inventory, (ii) a projection of that inventory into the future, (iii) quantification of the GHG emission reduction benefits associated with specific measures, (iv) estimation of impacts on co-emitted air pollutants, and (v) assessment of impacts on low income and disadvantaged communities. States optionally can translate these air pollutant changes into air quality impacts and health benefits. EPA's Global Change Analysis Model Long-term Integrated Multi-Pollutant Scenario Evaluator (GLIMPSE) is a tool that can support these activities. In this poster, examples are provided for how GLIMPSE could be used in support of CCAPs. Disclaimer: The views expressed in this poster are those of the authors and do not necessarily reflect the views or policies of the U.S. Environmental Protection Agency Dan Loughlin is a senior scientist in the U.S. EPA’s Office of Research and Development, where he has conducted energy system and integrated assessment modeling for nearly twenty-three years. Dan leads the GLIMPSE project ( https://epa.gov/glimpse), an effort to develop a decision support tool with state-level resolution for long-term air quality, climate, and energy planning. GLIMPSE was released to the public in the summer of 2023. Dan was a contributing author to the Energy Chapter of the recent 5th National Climate Assessment. He has a doctoral degree from NC State University and is an adjunct professor at Duke University where he teaches a course on Integrated Assessment Modeling. Dan Loughlin US EPA
A Framework for Integrated Modeling of Agricultural Soil Nitrogen Emissions
A Framework for Integrated Modeling of Agricultural Soil Nitrogen Emissions
Grant A. Parajuli, Lina Luo, Daniel S. Cohan Agriculture is a major source of non-CO2 greenhouse gas pollution, including N2O and methane. Additionally, it is a critical source of NH3, an important PM2.5 precursor, and a source of nitrogen oxides (NOx, including NO and HONO). Despite this importance for climate change mitigation and pollution reduction, nitrogen emissions (N2O, NH3, NOx) from agriculture are still not well-constrained. Each nitrogen species is often studied individually rather than through integrated modeling, which would enable an examination of the co-benefits and trade-offs of farming practices. This poster will discuss the state of the science of soil nitrogen emissions estimates in CMAQ and recommend pathways toward more integrated approaches. CMAQ includes a bi-directional scheme for NH3 emissions that links with the mechanistic agroecosystem model EPIC (Environmental Policy Integrated Climate) and applies parametric approaches to model NOx emissions, such as Yienger-Levy or BDSNP (Berkeley-Dalhousie Soil NOx Parameterization) algorithms. However, there is a need to compare these estimates with integrated, mechanistic approaches from agroecosystem models. Agroecosystem models can also consider N2O alongside NOx and NH3, which is neglected by using CMAQ alone. Here, we present a framework for comparing emissions estimates from updated versions of CMAQ (including improved BDSNP representation of soil NOx responses to soil moisture and temperature) and EPIC, which we have enhanced with a better representation of nitrification and denitrification processes. We introduce our plans to compare these improvements in mechanistic and parametric approaches by linking EPIC and CMAQ using FEST-C (Fertilizer Emissions Scenario Tool for CMAQ) to analyze all nitrogen species, and identify field measurements that can be used to evaluate the performance of each approach. We explain the importance of developing consistent methods for modeling nitrogen transformations instead of with isolated methods. Grant Parajuli Rice University
Initial Evaluation of the AIRPACT5 3-day forecast
Initial Evaluation of the AIRPACT5 3-day forecast
Mohammadamin Vahidi Ghazvini Joseph Vaughan Jun Meng Von Walden The AIRPACT (Air Indicator Report for Public Awareness and Community Tracking) model, version 5, is a complex air quality forecasting system that predicts air pollutants concentrations and depositions. The simulation domain of this model is the northwestern United States, includes the entirety of the states of Washington, Oregon, and Idaho, as well as the northern parts of California, Nevada, and Utah, the western parts of Wyoming and Montana, and the southern parts of the Canadian provinces of British Columbia and Alberta. It was developed at Washington State University in 2001 and has been operating for over 20 years. The model currently provides a 48-hour (2-day) forecast. In this research we extended the forecast period to 72 hours (3-day) and also provided initial evaluation of forecast performance. Mohammadamin Vahidi Ghazvini Washington State University Model Development
What's new in CRACMM2
What's new in CRACMM2
Nash Skipper, Emma D’Ambro, Woods Wiser, Faye McNeill, Becky Schwantes, Barron Henderson, Ivan Piletic, Colleen Baublitz, Jesse Bash, Andrew Whitehill, Luke Valin, Asher Mouat, Jen Kaiser, Glenn Wolfe, Jason St. Clair, Thomas Hanisco, Alan Fried, Bryan Place, and Havala Pye The Community Regional Atmospheric Chemistry Multiphase Mechanism (CRACMM) is a chemical mechanism designed to integrate modeling of ozone, secondary organic aerosol (SOA), and toxic air pollutants. CRACMM version 2 (CRACMM2) has recently been developed and will be available in the Community Multiscale Air Quality (CMAQ) model version 5.5. Development of CRACMM2 primarily focused on improving the representation of secondary production of formaldehyde from isoprene and monoterpenes. Additional updates include adding heterogeneous uptake of radicals, tracking of locally emitted methane, reorganizing some aromatic species, and updating the chemistry of monoterpene-derived organic nitrates. We summarize the impacts of CRACMM2 compared to CRACMM1 in CMAQ for formaldehyde, ozone, and SOA. Formaldehyde is increased, particularly in the eastern US and compares more favorably with satellite-, aircraft-, and ground-based observations. Ozone is increased except during winter and is mostly affected by changes in the isoprene and monoterpene nitrate systems. SOA is decreased due to changes in the formation of highly oxygenated organic molecules (HOMs) from monoterpenes. Nash Skipper is an ORISE postdoc at EPA. He currently works on development of CRACMM with a specific emphasis on formaldehyde. Nash Skipper US EPA
Examine the CMAQ Model Response to VOC Emission Changes in Nonpoint Solvents using CRACMM and CB6 Chemical Mechanisms
Examine the CMAQ Model Response to VOC Emission Changes in Nonpoint Solvents using CRACMM and CB6 Chemical Mechanisms
Yuhong (Ruby) Tian1, Winston Hao1, Kristen Foley2, Havala Pye2, Jeongran Yun1, Eric Zalewsky1 and Kevin Civerolo1 1Division of Air Resources, New York State Department of Environmental Conservation 2U.S. Environmental Protection Agency It is well-documented that Volatile Organic Compounds (VOC) emissions from solvents play an important role in forming summer ozone (O3), especially in large urban areas. The Community Regional Atmospheric Chemistry Multiphase Mechanism (CRACMM) is a new chemical mechanism developed by EPA for air quality modeling. In this study we ran the Community Multiscale Air Quality (CMAQ) model v5.4, using 2018 emissions provided by EPA, to examine the response of O3 and fine particulate matter (PM2.5) due to emission changes using CRACMM and the Carbon Bond chemical mechanism (CB6). We examined this model sensitivity by tripling VOC emissions from the nonpoint solvents sector over the EPA’s 12US1 domain in July. In the base case, the simulated daily maximum 8-hour average (MDA8) O3 and 24-hour average PM2.5 from CRACMM were slightly higher than that from CB6. The spatial distribution of the O3 and PM2.5 differences between the emission addition runs and the base run were quantified in terms of monthly mean of MDA8 O3 and daily PM2.5. Such O3 and PM2.5 differences from CRACMM and CB6 were compared to examine which mechanism has a stronger response to VOC changes. Two monitoring sites in New York: New York Botanical Garden (NYBG), a site in emissions-rich New York City (NYC), and Flax Pond (East Setauket), approximately 60 km downwind of NYC, were selected to show how they responded to VOC modifications. Yuhong (Ruby) Tian Division of Air Resources, New York State Department of Environmental Conservation Multiscale Model Applications and Evaluations
Automatic Dispersion Modeling Tool for Complaints Evaluation
Automatic Dispersion Modeling Tool for Complaints Evaluation
Willian Lemker Andreao1, Rizzieri Pedruzzi2, Jose Gustavo da Costa1 , Rafael Sartim1,4, Otavio Medeiros Sobrinho3, Renata Drumond Correa4, Taciana Toledo de Almeida Albuquerque3,4 1ArcelorMittal Tubarao, Serra, ES, Brazil 2Universidade do Estado do Rio de Janeiro, Rio de Janeiro, RJ, Brazil 3Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil 4Universidade Federal do Espirito Santo, Vitoria, ES, Brazil Plume dispersion views are crucial for managing industrial emissions, protecting the environment, and ensuring healthier living conditions for nearby communities. An automatic dispersion modeling tool, developed in Python, has been designed to automatic run AERMOD modeling dispersion to evaluate past emission events and perform three-day forecasts for particulate matter and odor emissions. The tool produces plume concentration maps at receptor locations, which help determine the potential impact of emission sources on surrounding communities and facilitate the evaluation of community complaints. To run the meteorological preprocessor AERMET, a SAMSON format input file is generated using online METAR data from the Iowa Environmental Mesonet (IEM) (for past data) or Open-Meteo data for a 3-day forecast. Vertical profiles of pressure and temperature are estimated in a virtual sounding up to 5,000 meters using surface data. These variables are automatically incorporated into AERMET namelists based on user-specified dates. For AERMOD, emission source details, such as type, coordinates, altitude, and emission rates, are provided in a separate Excel file. The effects of buildings downwash on point source emissions are accounted for by running the PRIME downwash algorithm beforehand, with its output incorporated into the AERMOD namelist. Receptor information, including coordinates and altitude, is similarly prepared previous in a separate text file. Each hour of the selected day is processed individually, requiring a separate namelist for each hour, which is automatically generated by a coded loop. The final step involves defining output files that contain hourly average concentrations at each receptor. These files are used to create maps that visualize the plume over the domain, using a Google Earth image of the area as a background. This process enables detailed visualization of pollutant dispersion across specific hours of the selected day or forecast analysis. Taciana Albuquerque Federal University of Minas Gerais - Brazil
Evaluation of Concurrent Multiscale Simulations of Air Pollutants and Greenhouse Gases with WRF-Chem-GHG over the Contiguous US
Evaluation of Concurrent Multiscale Simulations of Air Pollutants and Greenhouse Gases with WRF-Chem-GHG over the Contiguous US
Eeshan Basu, Yang Zhang, Kai Wang, Daniel Tong, Siqi Ma, Daniel H. Loughlin and Ravan Ahmadov Evaluating how well atmospheric models represent meteorology, air pollutants, and greenhouse gases (GHGs) is essential. This helps improve air quality models’ capabilities to better support effective, relevant policy decisions. In this work, the Weather Research and Forecasting model coupled with Chemistry and Greenhouse Gases (WRF-Chem-GHG) is used to simulate air pollutants and GHGs over a triple-nested domain, ranging from the contiguous US (CONUS) at 36-km resolution to the Northeastern US (NE US) at 6-km, and to the Greater Boston area at 1-km, from 2012 to 2017. The primary objective of this research is to evaluate the model performance and identify the potential sources of biases to improve baseline for future year projections. The study uses different versions of the National Emissions Inventory (NEI) for criteria air pollutants (CAPs) and the Vulcan emission dataset for CO2. Annual evaluation of the maximum daily average 8-hour ozone (MDA-8hO3) shows the domain-wide NMBs of -4.7% to 11.6% and NMEs of 18-22.6%, which consistently fall within the acceptable benchmark of normalized mean bias (NMB) ≤± 15%, and normalized mean error (NME) ≤ 25%. While 2012 – 2014 PM2.5 results using NEI2011 perform well, the results from 2015 – 2017 using NEI2016 show underpredictions with NMBs of -30.4% to -22.1% at 36-km over the CONUS. Such underpredictions in PM2.5 concentrations can be attributed in part to possible underestimation of the emissions in NEI2016 and in part to overprediction of rainfall during 2016 - 2017. Overprediction of PM2.5 at 1-km in Greater Boston can be attributed to the significant organic carbon (OC) and unspeciated PM2.5 overpredictions. Underpredictions in toluene and isoprene may cause underpredictions in secondary organic carbon, indicating that OC overpredictions are likely driven by the primary OC. The annual CO2 results after subtracting dynamic background concentrations show good overall model performance with NMBs in the range of -24.2% to 15.5% over all domains. Domain-wide temperature underpredictions are likely to contribute to CO2 underpredictions as the biogenic CO2 production is temperature-dependent. These results emphasize the need for further refinement of emission estimates and other inputs, configurations, and atmospheric process representations in the model to improve CAPs and GHG predictions. Disclaimer: The views expressed in this abstract are those of the authors and do not necessarily represent the views or policies of the U.S. Environmental Protection Agency. Eeshan Basu Northeastern University
Characterization of the PBL structure and PM2.5 in Taiwan: T-POMDA field campaign and modeling
Characterization of the PBL structure and PM2.5 in Taiwan: T-POMDA field campaign and modeling
Fang-Yi Chenga, Yi-Cheng Lina, Shu-Chih Yanga, Sheng-Hsiang Wanga, Hsin-Chih Laib a Department of Atmospheric Sciences, National Central University, Taiwan b Department of Green Energy and Environmental Resources, Chang Jung Christian University, Tainan, Taiwan The T-POMDA (PBL Observation, Modeling, and Data Assimilation) field campaign was conducted in central to southwestern Taiwan to investigate the physical and chemical processes and emission sources contributing to the severe PM2.5 problem. This comprehensive experiment focused on boundary layer structures and their impact on air pollution dispersion. Various observation instruments, including wind profilers, radiosondes, unmanned aerial vehicles (UAVs), lidar, and surface datasets, were deployed to monitor PBL structures. A PBL ensemble data assimilation system was also developed using the Weather Research and Forecasting Local Ensemble Transform Kalman Filter (WRF-LETKF) framework, coupled with the Community Multiscale Air Quality (CMAQ) model. The WRF model was run with different PBL schemes (YSU, SH, MYNN, ACM) and at fine grid resolution (1 km and 200 m). Analysis of the observational and model results show that the development of the PBL depth was limited due to the enhanced atmospheric stability and the strong subsidence process. The model can reproduce observed features and assist in discussing the air pollution transport processes. The thermal and moisture fields do not show apparent differences among the various PBL schemes, yet the wind structures reveal notable differences depending on the PBL option used. Fang-Yi Cheng Department of Atmospheric Sciences, National Central University, Taiwan
Intercomparison of data products and population exposure for studying trends in PM2.5 and ozone air quality over space and time in China
Intercomparison of data products and population exposure for studying trends in PM2.5 and ozone air quality over space and time in China
Shreya Guha, Lucas R.F. Henneman Fine particulate matter (PM2.5) and surface ozone air pollution pose severe health risks. Research products that estimate spatial and temporal variations in PM2.5 and ozone is essential for informing air quality health studies. Rapid industrialization and urbanization in China during the 1990s and 2000s caused severe air pollution, making the region a critical area for studying PM2.5 and O3 trends. We compare publicly available modeled fine-scale pollutant concentration datasets with their nearest observed data from 2014 to 2023 at national, regional, and provincial levels. The five modelled datasets under investigation are: the CHAP (China High Air Pollutant) dataset, the TAP (Tracking Air Pollution) dataset, the Global/Regional estimates (V5.GL.03), the full-coverage 1 km daily ambient concentrations dataset developed by Ma et al., and the high-resolution Spatiotemporal Modeling for Ambient PM2.5 Exposure Assessment (SMAPE) dataset developed by Wang et al. SMAPE product performed best for PM2.5, with an R2 of 0.78 and root mean square error (RMSE) of 8.15µgm-3. For ozone, the performances of both TAP and CHAP products were comparable with R2 values of 0.77 and 0.82 and RMSE values of 17.4 and 18.7 µgm-3 respectively for the datasets. TAP has lower error in the north, while CHAP has lower error in the south. This comprehensive analysis aims to improve our understanding of air pollution trends and their impact on public health in China. Keywords: PM2.5, Ozone, Data comparison, Population exposure Shreya Guha George Mason University
Global Models and Satellite Informed Lateral Boundary Conditions
Global Models and Satellite Informed Lateral Boundary Conditions
Barron H. Henderson, R. Brad Pierce, Zac Adelman, Monica K Harkey, Kazuyuki Miyazaki, Emma Knowland, and the HAQAST LBC Tiger Team Air quality management has reduced local contributions to ozone, which increase the relative importance of “background” sources. A large portion of “background,” including global natural and transported anthropogenic contributions, is introduced to regional models through the lateral boundary conditions. Previous work has demonstrated improved ozone predictions from global models, and demonstrated that boundary conditions informed by satellite observations further improves regional model performance. Yet access to boundary conditions from global models remains a challenge, and satellite informed lateral boundaries are still uncommon. This poster highlights the NASA Health and Air Quality Applied Sciences Team (HAQAST) project to improve access to boundary conditions from multiple global models including satellite data assimilation, and the updates to the air quality model boundary condition (aqmbc; github.com/barronh/aqmbc) processor to facilitate that work. This poster describes the HAQAST Tiger Team project to compare LBCs derived from several global models, including UWisc’s RAQMS, NCAR’s WACCM, NASA’s TCR-2, and NASA’s GEOS-CF. The NASA JPL TROPESS Composition Reanalysis II (TCR-2) has 2005-2021 data that synthesize a multi-model ensemble with assimilation of multiple satellite data products. The NASA GMAO GEOS Composition Forecast (GEOS-CF) has analysis data from 2019 to present, includes nudging of the stratosphere to a satellite assimilated product, and the next version will include direct satellite data assimilation. As part of this HAQAST project, the aqmbc tool was updated to create boundary conditions from additional global models. Of those models, 4 are publicly available. This poster documents aqmbc capabilities, provides examples of how to use the tool, and evaluates the available model data for 2022. By documenting examples, potential users will better understand how to leverage publicly available tools and datasets and by comparing available datasets to sondes, users will have a better sense of the fidelity of dataset that feeds their boundary conditions. This helps meet the HAQAST Tiger Team project goals of enabling access, documenting the processors, and evaluating data sources. Barron Henderson is a Physical Scientist at the United States Environmental Protection Agency. He uses theory, computer simulation, and satellite data to explore scientific and societal issues related to air pollutants. Barron's body of work advances process-level understanding and quantitative constraints within air quality models, and uses those models to quantify integrated impacts of air pollution at local, regional and global scales. Barron H. Henderson US EPA
Diagnostic Intercomparison of Gridded Ozone Dry Deposition Fields in AQMEII4
Diagnostic Intercomparison of Gridded Ozone Dry Deposition Fields in AQMEII4
Christian Hogrefe1, Paul A. Makar2, Ioannis Kioutsioukis3, Ummugulsum Alyuz4, Jesse O. Bash1, Roberto Bellasio5, Roberto Bianconi5, Tim Butler6, Philip Cheung2, Olivia E. Clifton7,8, Alma Hodzic9, Richard Kranenburg10, Aurelia Lupascu11, Kester Momoh4, Juan Luis Perez-Camanyo12, Jonathan Pleim1, Young-Hee Ryu13, Roberto San Jose12, Ranjeet Sokhi4, and Stefano Galmarini14 [1] Center for Environmental Measurement & Modeling, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC, USA [2] Environment and Climate Change Canada, Toronto, Canada [3] Department of Physics, University of Patras, Patras, Greece [4] Centre for Climate Change Research (C3R), U. Hertfordshire, UK [5] Enviroware srl, Concorezzo, MB, Italy [6] Research Institute for Sustainability – Helmholtz Centre Potsdam, Germany [7] NASA Goddard Institute for Space Studies, New York, NY, USA [8] Center for Climate Systems Research, Columbia University, New York, NY, USA [9] NCAR, Boulder, CO, USA [10] TNO, Utrecht, the Netherlands [11] ECMWF, Bonn, Germany [12] Technical University of Madrid (UPM), Madrid, Spain [13] Yonsei University, Seoul, South Korea [14] JRC, European Commission, Ispra, Italy Phase 4 of the Air Quality Model Evaluation International Initiative (AQMEII4) is focused on the diagnostic intercomparison and evaluation of deposition simulated by regional-scale air quality modeling systems and employs both grid and box modeling techniques to accomplish this goal. This study presents an analysis of ozone dry deposition fluxes and velocities (Vd(O3)) from annual AQMEII4 photochemical grid modeling simulations that were performed over North America and Europe. All simulations used a common set of emissions and chemical boundary conditions. To gain mechanistic insights into modeled dry deposition, AQMEII4 employed additional diagnostic variables, i.e. grid-scale and land-use specific effective conductances and effective fluxes for the four major dry deposition pathways (stomatal, cuticular, branches/exposed surfaces, and soil). Analysis of these fields shows that models with similar total Vd(O3) can exhibit significant differences in the absolute and relative contributions of different pathways to Vd(O3). In other cases, differences in total Vd(O3) can be attributed to differences in specific pathway contributions, which enables differences in process representation driving the model-to-model variations in total Vd(O3) to be identified. Furthermore, AQMEII4 also collected detailed information on each model’s land use classification scheme and mapped them to a common set of land use categories. Analysis of the different models’ land use distributions identified their differences as causing as much model spread of dry deposition fields as the differences in dry deposition schemes. The two recommendations for future deposition-focused model studies emerging from the AQMEII4 analyses presented here are to 1) routinely generate diagnostic outputs to advance a process-based understanding of modeled deposition and support impact analysis (e.g quantify stomatal vs. non-stomatal deposition), and 2) recognize the importance of improving and harmonizing the representation of land use both for model development and for future model intercomparison studies, in addition to the current standard practice of harmonizing emissions and boundary conditions in intercomparison studies. Disclaimer: The views expressed in this abstract are those of the authors and do not necessarily represent the views or policies of the U.S. Environmental Protection Agency (EPA). Christian Hogrefe U.S. EPA, ORD, CEMM, AESMD, ADMB
Measurement and Modeling of Ultrafine Particles from Aircraft Landing and Takeoff Operations at Boston Logan
Measurement and Modeling of Ultrafine Particles from Aircraft Landing and Takeoff Operations at Boston Logan
Hyeongseok Kim1, MVS Ramarao1, Christos Efstathious1, Kevin J Lane2, Jonathan I. Levy2, and Saravanan Arunachalam1 1 Institute for the Environment, The University of North Carolina at Chapel Hill, Chapel Hill, NC 2 Department of Environmental Health, Boston University School of Public Health Aircraft emit particulate matter (PM) besides other pollutants from engine combustion during landing and takeoff (LTO) activities. These emissions include particles of various diameters, particularly in the ultrafine particle (UFP) size range (< 100 nm). These tiny particles penetrate deep into the lungs and can adversely impact human health in multiple ways. Moreover, aircraft emissions are one type of mobile source, affecting communities that live in the vicinity of airports. However, the aircraft emissions data compiled by the US Environmental Protection Agency (EPA) in the National Emissions Inventory (NEI) 2017 does not take aircraft pathways into account. Instead, the emissions are assigned to a single latitude and longitude at the surface layer. While there is an emerging interest in aviation-related UFPs, there are limitations in focusing only on measurements or models to study them. An integrated approach that combines field measurements and model-based approaches enables gaining a clearer understanding of aircraft contributions to ambient air quality. To achieve this objective and address the limitations above, we developed a new high-fidelity aircraft emissions inventory using real-time aircraft operations, by creating tracks with aircraft pathways, and specific taxi-in and taxi-out times for each aircraft by running the FAA’s (Federal Aviation Administration) Aviation Environmental Design Tool (AEDT). The Aircraft-specific Aerosol Size Distribution (ASASD) is applied in the Community Multiscale Air Quality (CMAQ) model (Version 5.3.3) because aircraft emissions have different geometric mean diameter (GMD) and standard deviation (SD) compared to other anthropogenic emissions sources that are typically treated in CMAQ. We utilized a nested resolution model application at 12 km, 4 km, and 1.33 km for January and July 2017 to capture seasonal variations. The model was run with and without aircraft emissions scenarios to assess the incremental contributions of LTO activity. In our presentation, we will present estimates of ultrafine particle number concentrations (UFPNC) attributable to Boston Logan International Airport (KBOS) for these two seasons. These estimates are compared to measurements taken near KBOS at seven different field monitoring stations, aligned with flight LTO paths and modeled approaches. We will also discuss the potential strengths and weaknesses of both measurement and modeling approaches in assessing airport related air quality with a focus on UFP. Hyeongseok Kim, a graduate research assistant with the UNC Institute for the Environment and a Ph.D. student at the UNC Department of Environmental Sciences and Engineering. Most of his research during this period has focused on modeling and analyses that support air quality modeling activities and health assessment for policy decisions. His recent research interests include ultrafine particles (UFPs) near airport area by using CMAQ. Hyeongseok Kim UNC-Chapel Hill
Environmental impact of mining on air quality by using CMAQ Modeling System
Environmental impact of mining on air quality by using CMAQ Modeling System
Rizzieri Pedruzzi1,Leonardo Hoinaski2, Robson Will2, Camilo Ribeiro2, Amanda K. C. Ribeiro3, Otavio Sobrinho3, Thiago Fonseca da Costa1, Renata Drumond Correa4, Taciana T. de A.Albuquerque3,4 . 1. DESMA - Rio de Janeiro State University (UERJ) - Brazil 2. DESA- Federal University of Santa Catarina (UFSC) - Brazil 3. DESA/SMARH - Federal University of Minas Gerais (UFMG) – Brazil 4. PPGEA- Federal University of Espírito Santo (UFES) - Brazil This work is focused on developing air quality management strategies for Congonhas City, a unique location in Minas Gerais State, Brazil, renowned for its iron ore mining industry. Simulating air quality in mining areas is crucial for managing mining operations' environmental and public health impacts. Mining activities, such as excavation and ore processing, often release particulate matter and gaseous pollutants that can degrade air quality and pose significant health risks to nearby communities and ecosystems. However, effective simulation models can predict the dispersion and concentration of these pollutants, enabling proactive measures to mitigate their effects. The local government can assess potential air quality scenarios under various operational conditions by incorporating meteorological data, emission sources, and topographical features into simulation models. The main objective of this work is to apply the WRF-SMOKE-CMAQ system to determine the industrial emissions, road emissions, and windblown dust caused by the exposed soil and stockpiles. For the local emissions inventory, 24 mining and steel sector facilities were considered in the study area. Therefore, the Congonhas region needs air quality management with emissions reduction strategies to reduce the harm to the population. Integrating air quality simulations into mining management practices is expected to contribute to more sustainable and responsible mining operations for a healthier and more balanced future, where economic benefits are harmonized with environmental stewardship. Rizzieri Pedruzzi Rio de Janeiro State University, Brazil
Multiscale Air Quality Modeling Using WRF-Chem: Model Evaluation, Sensitivity and Chemical Feedback to Meteorology
Multiscale Air Quality Modeling Using WRF-Chem: Model Evaluation, Sensitivity and Chemical Feedback to Meteorology
Libo Zhang, Yang Zhang, Kai Wang, Xiaoyang Chen, Daniel Schuch, Ying Wang, Kirsten Koehler, Misti Levy Zamora, Colby Buehler, Drew R. Gentner, and Michelle Bell Accurate modeling of meteorology and air quality is crucial for effective air quality management and public health improvement. This study aims to identify and reduce biases in the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) by evaluating its performance in meteorological and chemical predictions using surface and satellite data. Nested simulations for July, August, and December 2019 are conducted at 36-, 6-, and 1-km grid resolutions over the Contiguous United States (CONUS), a portion of Northeastern U.S., Baltimore metropolitan area and its surroundings, respectively. The model predicts well for major meteorological variables, such as 2-m temperature (T2), 2-m relative humidity, 10-m wind speed, and 10-m wind direction, with relatively lower biases at finer grid resolutions by better representing geographic characteristics and localized processes across all months and domains against the METeorological Aerodrome Reports. However, exceptions include larger biases for T2 in December, likely due to missing anthropogenic heating, and persistent biases in precipitation across all simulations. The baseline simulations predict O3 and PM2.5 well over three months and all domains except for PM2.5 in December at 36-km (with a normalized mean bias (NMB) of -39%) and 1-km (an NMB of 55%) grid resolutions against the observations from the Air Quality System due likely to uncertainties associated with primary organic carbon (OC) emissions and biases in meteorological predictions. The underprediction of PM2.5 over 36-km CONUS in July and August (NMBs of -28% to -23%), as well as December, is linked to the underprediction of inorganic and unspeciated PM concentrations, likely due to the low resolution of emissions data, underestimated emissions of SO2, NOx, element carbon (EC), and unspeciated PM, and underprediction of cloud fraction. As grid resolution becomes finer, more anthropogenic emissions are concentrated in the source areas, reducing negative biases for most PM components but increasing positive biases for OC across a large area of the eastern region. To reduce those biases, several sensitivity simulations are conducted. Activating anthropogenic heat in the urban canopy model reduces cold bias in T2, increases boundary layer height and vertical mixing, and decreases NMBs from 55% to 17% for PM2.5 in December at 1-km over the Baltimore region. In August, compared to the Fire INventory from NCAR version 2.5, the Quick Fire Emissions Dataset version 2.6 reduces CONUS domain averaged NMBs for PM2.5 from -27% to -17% at 36-km but increases NMBs for OC from 6% to 28%. This study highlights the importance of meteorological conditions, emissions, and anthropogenic heating in air quality predictions. Libo Zhang Northeastern University Wildfire Emissions and Air Quality
CMAQ estimates of nitrogen and phosphorus deposition from wildfires to lakes in the US Intermountain West
CMAQ estimates of nitrogen and phosphorus deposition from wildfires to lakes in the US Intermountain West
Anne Barkley, Uma Shankar, Nicole Olson, Stephen D. LeDuc, Janice Brahney, Jiahao Wen, Douglas Hamilton In the United States (US) Intermountain West, nutrient concentrations in many high elevation remote lakes have been increasing for the past few decades. It is currently unclear why multiple lakes across different states, which lack a common source of pollution or run-off, would simultaneously experience elevated nutrient concentrations. Many of these lakes are in Federal Class I Areas (e.g., National Parks) and are thus protected under the provisions of the Clean Air Act Amendments of 1990, and it is therefore important to examine impacts to these lakes from atmospheric deposition. Recent work has linked increases in phosphorus (P) in particulate matter below 2.5 microns in mean diameter (PM2.5) emitted in wildfire smoke to harmful algal blooms in downwind lakes in California. We expand upon that observation-based study in this modeling effort, using the Community Multiscale Air Quality Model (CMAQ) to examine nitrogen (N) and P deposition for the entirety of 2018 from wildfires over the conterminous US at a 12-km grid resolution. As P is not included in the emissions inputs to CMAQ, we estimate total P deposition using elemental carbon (EC) deposition from CMAQ as a proxy, along with the ratio of P to EC obtained from a previous modeling study with the Community Earth System Model. These results provide a baseline total N and P deposition estimates from all sources and are evaluated against observations from a P deposition measurement network across the Intermountain West. Differences between the baseline estimate and the results of a CMAQ sensitivity simulation without wildfire emissions yield an estimate of N and P deposition from wildfires. These results allow us to explore the linkage between N and P deposition and wildfire smoke emissions, and to quantify their potential impact on sensitive aquatic ecosystems. The views expressed in this abstract are those of the authors and do not necessarily represent the views or policies of the U.S. Environmental Protection Agency. Anne Barkley US EPA
Capturing longwave aerosol radiative feedback cycles downwind of U.S. wildfires in WRF-CMAQ
Capturing longwave aerosol radiative feedback cycles downwind of U.S. wildfires in WRF-CMAQ
Sara Farrell1,2,3, Jon Pleim2, Jeff Willison2, Fahim Sidi2, Robert Gilliam2, Bill Hutzell2, Havala Pye2, Kathleen Fahey2, Tanya Spero2, Rohit Mathur2, Christian Hogrefe2, Kristen Foley2, Wyat K. Appel2, William Vizuete3, David Wong2 1. Oak Ridge Institute for Science and Engineering (ORISE) Program at the Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, North Carolina, USA 2. Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, North Carolina, USA 3. Gillings School of Global Public Health at The University of North Carolina at Chapel Hill, North Carolina, USA Aerosol-radiative feedback cycles have been added across different coupled modelling platforms, including the Community Multiscale Air Quality (CMAQ) model coupled with the Weather Research Forecasting (WRF) model. Studies using previous iterations of this coupled model have demonstrated direct aerosol – shortwave (SW) radiative feedback cycles during wildfires where high aerosol loadings caused a reduction in SW radiation reaching the ground, causing surface cooling and a reduction in the modelled planetary boundary layer height (PBLH). This consequently also increased PM concentrations leading to a positive feedback cycle. Aerosol-longwave (LW) radiative feedback cycles, however, have not been prioritized and studied less in meteorology and air quality coupled models and warrant exploration. In exploring the complex refractive index (CRI), which dictates an aerosol’s wavelength dependent scattering and absorbing spectra, sulfate aerosols are the focal point since they were found to have a peak real (or scattering) CRI in the SW and a peak imaginary (or absorbing) CRI in the LW. Similarly, the imaginary CRIs of biomass burning aerosols and primary organic carbon indicate absorption of LW radiation as well. How LW absorbing aerosols alter the vertical temperature profile and PBLH during smoke events from wildfires have not yet been examined. In this work we added the LW-aerosol feedback feature in the WRF-CMAQ two-way coupled model with the WRF model version 4.5.2 and the U.S. Environmental Protection Agency’s CMAQ model version 5.4+ (12km resolution) to simulate LW-aerosol feedbacks across the Contiguous U.S., with the focus on the 2019 wildfire season, and taking advantage of the extensive set of measurements taken during the Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) field campaign (July-August, 2019) to evaluate the new model performance. While this work aims primarily to understand aerosol-LW feedback impacts on vertical mixing and air pollution, results may also help quantify aerosol-LW interactions and understand how they affect Earth’s radiative balance. Sara L. Farrell Department of Environmental Science and Engineering at Gillings School of Global Public Health and Office of Research and Development at the USEPA
Using CMAQ to Assess the Air Quality Impacts of Prescribed Burns in North Carolina State Parks
Using CMAQ to Assess the Air Quality Impacts of Prescribed Burns in North Carolina State Parks
Amir Ghajari, Fernando Garcia Menendez Prescribed burns are a critical strategy in the management of ecosystems and the prevention of wildfires. Moreover, these burns may mitigate severe air pollution episodes by lessening the severity and likelihood of uncontrolled wildfires. While chemical transport models included state-of-the-science representations of the chemical and physical processes that determine the transport and transformation of fire emissions, modeling small, short-lived prescribed burn plumes can be challenging with gridded regional-scale models. To better understand and quantify the air quality impacts of prescribed burns, particularly within the state parks of North Carolina, we implement an air quality modeling framework based on the CMAQ model. We utilize the BlueSky Pipeline modeling system to characterize emissions from prescribed burns based on their size, location, and duration. These emissions data are then integrated with meteorological information derived from the WRF simulations using the SMOKE model. This combined data is input into the CMAQ modeling system, enabling us to assess the impact of prescribed burns on air quality. Our research aims to evaluate the ability of the CMAQ modeling framework to represent the air quality impacts of prescribed burns conducted across all State Parks in North Carolina in 2022. By examining burns with varying sizes across diverse regions, we analyze how smoke dispersion is influenced by geographical location and seasonal timing. We identify common weather conditions during the burns and understanding their role in PM formation. With the modeling system, we assess the performance of CMAQ in capturing smoke plumes from prescribed fires at a fine resolution (1 km x 1 km) compared to larger scale model setups. We also investigate the duration and spatial extent of pollution plumes from specific prescribed fires to enable simulations to effectively capture pollution impacts on neighboring population while considering computational efficiency. Ultimately, this research aims to provide recommendations for adequate use of CTMs to model the air pollution impacts of specific prescribed fire projects. Amir Ghajari North Carolina State University |
|
October 22, 2024 | ||
Grumman Auditorium | Redbud Room | |
7:30 AM | Registration and Continental Breakfast | |
8:00 AM | A/V Upload | A/V Upload |
Air Quality, Climate and Energy, Chaired by Noah Kittner, UNC-Chapel Hill and Chris Nolte, US EPA | Wildfire Emissions and Air Quality, Chaired by Yijia Dietrich, US EPA and Amber Soja, NASA | |
8:30 AM | Session Keynote |
Session Keynote: Forecasting Smoke from Wildland Fires: Insights from NOAA's Realtime Smoke Models
Session Keynote: Forecasting Smoke from Wildland Fires: Insights from NOAA's Realtime Smoke Models
Ravan Ahmadov and the RRFS modeling team NOAA Global Systems Laboratory Wildland fires emit large amounts of smoke consisting of many different air pollutants. These pollutants can have profound effects on air quality and weather. It is crucial to provide timely and accurate prediction of smoke concentrations from wildland fires. During the last decade, NOAA Global Systems Laboratory, in collaboration with other laboratories and universities, has been developing coupled weather-smoke models to predict fire weather and smoke aerosol concentrations in 1-3 days time scales at high spatial resolution (3km). These models incorporate parameterizations of biomass burning emissions, fire plume rise, turbulent and convective mixing of smoke, dry/wet removal and smoke radiative feedback processes. In this talk NOAA's operational HighResolution Rapid Refresh (HRRR) coupled with smoke (HRRR-Smoke) and experimental Rapid Refresh Forecasting System (RRFS) coupled with smoke and dust (RRFS-SD) models will be presented. The HRRR-Smoke model has been operational since 2020 [https://rapidrefresh.noaa.gov/hrrr/HRRRsmoke/]. The new RRFS-Smoke-Dust (RRFS-SD) model is running in real time in an experimental mode [https://rapidrefresh.noaa.gov/RRFS-SD/]. This presentation will discuss the current state of NOAA's smoke forecasting capabilities in numerical weather prediction models. The performance of these models during FIREX-AQ (August 2019) and other case studies will be presented. Improvements in air quality models related to forecasting fire emissions and their composition, smoke transport, and feedback on weather will also be discussed. Finally, the remaining challenges in smoke forecasting models for future applications will be addressed. Dr. Ravan Ahmadov is a physical scientist at NOAA Global Systems Laboratory. Dr. Ahmadov is leading the atmospheric composition branch of the Earth Prediction Advancement Division at NOAA/GSL. He received a master's degree in Astrophysics at Baku State University in 2000 and PhD degree in atmospheric physics from Moscow State University in 2004. His research has been focused on the simulation of air pollution from various natural and anthropogenic sources. Currently, he is working on improving fire weather and atmospheric composition prediction capabilities in atmospheric models and applying these tools to understand and forecast various processes associated with wildland fires and smoke. These processes include wildland fire emissions and plume rise, smoke transport and mixing, smoke chemistry, the impact of smoke on radiation and microphysics. Dr. Ahmadov led the development of NOAA's operational coupled weather-smoke model HRRR-Smoke (https://rapidrefresh.noaa.gov/hrrr/HRRRsmoke/), which has been operational at NCEP since 2020. Currently he's leading the development of NOAA's next-generation high-resolution smoke and dust forecasting model RRFS-Smoke-Dust (https://rapidrefresh.noaa.gov/RRFS-SD/). Ravan Ahmadov NOAA Global Systems Laboratory |
8:50 AM |
Modeled Air Quality Impacts of Wind Energy Projects Off the Northern Atlantic U.S. Coast
Modeled Air Quality Impacts of Wind Energy Projects Off the Northern Atlantic U.S. Coast
Baker, Kirk It is useful to understand the potential air quality impacts related to new construction and operation/maintenance emissions from Outer Continental Shelf (OCS) wind energy projects located off the Northeast coast of the United States given the proximity to areas not compliant with the O3 and annual PM2.5 National Ambient Air Quality Standards. The Community Multiscale Air Quality (CMAQ) photochemical grid model was applied for 1) a representation of current pre-construction conditions, 2) a future year that includes construction emissions from offshore wind energy projects, and 3) a future year that reflects operation emissions for these same projects. An alternative future year representing operating emissions was also modeled to include projected reductions in onshore electrical generation resulting from the increased supply of offshore energy. Human health effects for each future year scenario were quantified based on the change in population-weighted air quality related to seasonal (April to September) average maximum daily 8-hr average (MDA8) O3 and annual average PM2.5. Kirk Baker US EPA |
Development of a Year 2022 Wildland Fires Emissions Inventory for United States
Development of a Year 2022 Wildland Fires Emissions Inventory for United States
Jeff Vukovich Office of Air Quality Planning and Standards U.S. Environmental Protection Agency, Research Triangle Park, NC 27711 James Beidler Center for Environmental Modeling and Measurement U.S. Environmental Protection Agency, Research Triangle Park, NC 27711 Christine Allen General Dynamics Information Technology 79 T.W. Alexander Drive, Research Triangle Park, NC 27709 Rhonda Payne Western States Air Resources Council (WESTAR) Helena, MT 59601 A year 2022 wildland fire emissions inventory was developed to support the 2022 Emissions Modeling Platform as part of a Collaborative effort that involved various federal, state, and local agencies. Over 30 government agencies submitted fire activity information that was organized and ingested into the Python-based SMARTFIRE2 tool to generate daily acres burned data. The daily acres burned data was input into the US Forest Service’s Bluesky Pipeline system to generate daily emissions for prescribed burns and wildfires for all 50 states. A summary of acres burned and emissions, lessons learned, and key areas needed for improvement will be presented. Jeff Vukovich is a Physical Scientist in the Emissions Inventory and Analysis Group at the Office of Air Quality Planning and Standards in Research Triangle Park, NC. He has been involved with the development and application of emissions inventories and modeling systems for about 30 years. He is currently the Emissions Inventory sector lead for wildland fires. Jeff has been leading EPA projects that produced emissions inventories for wildland fires for years 2016 through 2021 including the 2020 National Emissions Inventory wildlands fire inventory. Jeff Vukovich USEPA |
9:10 AM |
Assessing the air quality and environmental justice implications of deep decarbonization pathways
Assessing the air quality and environmental justice implications of deep decarbonization pathways
Dan Loughlin, Joyce Kim, and Trinity White Historically, low-income disadvantaged communities have been disproportionately impacted by environmental stressors, including exposure to poor air quality. As the United States seeks to decarbonize our energy system, air quality is expected to improve since carbon dioxide and air pollutants are co-emitted by many of the same sources. However, not all low-carbon technologies are also low in air pollutants. For example, biomass and fossil fuels, paired with carbon capture, could have significant particulate matter emissions that would impact downwind communities. Thus, when developing decarbonization strategies, there are important considerations about who would be impacted positively and negatively, as well as how these changes impact historical disparities. In the work presented here, we use EPA’s GLIMPSE air-climate-energy planning tool to simulate the emissions implications of several decarbonization pathways. The air quality impacts are then evaluated using the reduced-form air quality model within EPA’s COBRA tool. County-level impacts on ozone and fine particulate matter concentrations are compared with demographic data from EPA’s EJScreen and from the U.S. Census, and categorical regression trees are applied to develop insights about the environmental justice implications. Among the results that will be presented are an assessment of how these implications differ across pathways and for different areas of the country. Dan Loughlin is a senior scientist in the U.S. EPA’s Office of Research and Development, where he has conducted energy system and integrated assessment modeling for nearly twenty-three years. Dan leads the GLIMPSE project ( https://epa.gov/glimpse), an effort to develop a decision support tool with state-level resolution for long-term air quality, climate, and energy planning. GLIMPSE was released to the public in the summer of 2023. Dan was a contributing author to the Energy Chapter of the recent 5th National Climate Assessment. He has a doctoral degree from NC State University and is an adjunct professor at Duke University where he teaches a course on Integrated Assessment Modeling. Dan Loughlin U.S. EPA Office of Research and Development |
Development of Emissions from the Burning of Piled Woody Biomass in the 2022 Emissions Modeling Platform
Development of Emissions from the Burning of Piled Woody Biomass in the 2022 Emissions Modeling Platform
James Beidler, Jeff Vukovich, George Pouliot, Venkatesh Rao The burning of piled woody biomass, or pile burns, is a major method for disposing of cut and downed wood from logging operations and for wildland hazardous fuel reduction. Pile burning is the predominant prescribed fire practice in the northwestern United States and is extensively utilized throughout the west and southeast. Historically, pile burn emissions have not been included in the National Emissions Inventory because they have not been reported by the states, cannot be classified or detected through older methods of remote sensing, and/or have been difficult to estimate. For the first time in the 2022 collaborative emissions modeling effort, we have developed a method to estimate pile burn emissions, with assistance from a few key states and improved remote sensing capabilities to detect these fires. Over a dozen states, federal agencies, and tribes submitted pile burning activity for the 2022 modeling platform. The method for estimating pile burning emissions, utilizing various activity data sources, was applied as part of the EPA 2022 emissions modeling platform. Using this method pile burns were estimated to contribute approximately 11% of the total prescribed fire PM2.5 in the western United States. Here, we will describe the emissions estimation method and the resulting 2022 estimates, as well as discuss improvements for application in the 2023 NEI. James joined the emissions systems analysis branch of EPA's ORD earlier this year after nearly two decades as an emissions modeling contractor. He has contributed to the development of wildland fire emissions for the 2017 and 2020 National Emissions Inventories along with multiple intermediate platform years. James Beidler USEPA |
9:30 AM |
Whole Home Electrification Impacts on Ozone and PM2.5 in Northeast US Sensitivity Tests Using EPA 2016 V3 Emission Platform
Whole Home Electrification Impacts on Ozone and PM2.5 in Northeast US Sensitivity Tests Using EPA 2016 V3 Emission Platform
Yuhong (Ruby) Tian1, Winston Hao1, Eric Zalewsky1, Jeongran Yun1, Alexandra Karambelas2, and Kevin Civerolo1 1Division of Air Resources, New York State Department of Environmental Conservation 2Northeast States for Coordinated Air Use Management Approximately 30 million people in the mid-Atlantic and Northeast regions experience unhealthy levels of ozone (O3) and sometimes PM2.5. High ground-level concentrations of these pollutants can decrease lung function and trigger asthma attacks, which is a serious human health concern. US EPA’s tighter standards pose persistent challenges for our air quality management. In this study we used the Community Multiscale Air Quality (CMAQ) model v5.4 to examine the response of O3 and PM2.5 due to emission reductions resulting from Whole Home Electrification (WHE). The WHE refers to electrification of residential space heating and cooling, water heating, and appliances, and eliminates fossil fuel consumption and emissions for these needs. We ran CMAQ for summer (June and July) and winter (January and February) months using 2026 projected emissions from the EPA 2016 V3 Emission Platform. The Integrated Planning Model (IPM) projected electric generating unit (EGU) emissions (IPM PTEGU) were replaced with the Eastern Regional Technical Advisory Committee (ERTAC) projected EGU emissions (PTERTAC) in this study. The sensitivity tests contained two steps. Step 1 was to eliminate fossil fuel consumption and emissions for residential fuel combustion use. The residential fuel includes distillate oil, residual oil, natural gas, liquified petroleum gas and kerosene. Step 2 was to modify EGU emissions to account for the increased electricity demand due to WHE by applying adjustment factors. Such extra demand was split between heating season and cooling season with water heating spread evenly throughout the year based on studies from Northeast States for Coordinated Air Use Management (NESCAUM) using National Renewable Energy Laboratory (NREL) ResStock tool. The adjustment factors were applied to all model species in the PTERTAC EGU output files generated from the Sparse Matrix Operator Kernel Emission (SMOKE) processing system. The sensitivity tests showed that impacts on elevated summertime O3 are generally mild. The daily maximum 8-hour average (MDA8) O3 decreased by about 0.5 ppb on high (>60 ppb) O3 days, with isolated O3 increases near NYC due to reduced NOx titration. The added electricity caused some additional decreases in regions of elevated O3. Impacts on PM2.5 mass and species were larger during the winter months. PM2.5 decreased by as much as 0.5-1 ug/m3 and >1 ug/m3 in New York City. NO3 decreased by up to 0.5 ug/m3, which accounted for a substantial portion of the PM2.5 decrease. Yuhong (Ruby) Tian Division of Air Resources, New York State Department of Environmental Conservation |
The Tradeoffs Between Wildfires and Prescribed Fires: A Case Study of 2016 Gatlinburg Fires
The Tradeoffs Between Wildfires and Prescribed Fires: A Case Study of 2016 Gatlinburg Fires
M. Talat Odman, Zongrun Li, Kamal J. Maji, Yongtao Hu, Ambarish Vaidyanathan, Armistead G. Russell Prescribed burning is an effective land management tool for reducing wildfire risks. However, smoke from both wildfires and prescribed fires degrades air quality and impacts human health. Additionally, prescribed burning does not necessarily prevent wildfires; therefore, air quality impacts from post-prescribed burn wildfires should also be considered in evaluating the tradeoffs between wildfires and prescribed fires. In this study, we focused on the 2016 Gatlinburg fires and used a modeling framework to compare the impacts of actual wildfires to those of counterfactual prescribed fires followed by post-prescribed burn wildfires. While this framework has been used before, we improved on the design of counterfactual prescribed burns by considering fire breaks in setting the boundaries of burn units, practicability in deciding on fire sizes, and meteorological conditions in selecting burn days. We estimated all fire emissions based on high resolution fuel maps considering the heterogeneity of fuels within the fire boundaries. These fire emissions were then input to a chemical transport model to simulate the air quality impacts. Finally, population-weighted smoke concentrations and smoke impacted populations under different fire scenarios were analyzed to evaluate the tradeoffs between wildfires and prescribed fires. For the 2016 Gatlinburg fires, total emissions from prescribed fires and post-prescribed burn wildfires were less than the emissions from wildfires. This led to lower daily average PM2.5, MDA8-O3, and 1-hr max NO2 concentrations and exposures. In the county where the fires occurred the daily average PM2.5, MDA8-O3, and 1-hr max NO2 decreased by 5.28 g/m3, 1.68 ppb, and 0.26 ppb, respectively. Smoke exposure times-per person also decreased. Hence prescribed burning was beneficial for air quality and public health in this case study. The benefits came primarily from preventing the long-distance transport of smoke and reducing smoke concentrations. Talat Odman Georgia Institute of Technology |
9:50 AM | Break | Break |
10:20 AM |
Modeling the air quality impacts of energy system scenarios using CMAQ
Modeling the air quality impacts of energy system scenarios using CMAQ
Chris Nolte, Dan Loughlin, Ben Murphy, Jesse Bash Combustion sources that emit carbon dioxide (CO2) also emit reactive nitrogen species and other atmospheric pollutants. Thus, recent decarbonization targets at the national and state levels have the potential to bring about substantial co-benefits for air quality. Here we apply the Global Change Analysis Model with state-level resolution of the U.S. energy system (GCAM-USA) to project future emissions under the following two scenarios: a reference scenario that includes provisions of the Inflation Reduction Act and an alternative scenario that reaches net-zero CO2 emissions in 2050. We quantify the air quality impacts of these emissions changes associated for the years 2035 and 2050 using the Community Multiscale Air Quality (CMAQ) model v5.4. The Detailed Emissions Scaling, Isolation, and Diagnostic (DESID) module applies state, sector, and pollutant-specific scaling factors from GCAM-USA to emissions from EPA’s Air Quality Time Series (EQUATES) project for 2016. This presentation will discuss impacts on modeled ozone and PM2.5 concentrations and compare results to those from the EPA’s Avoided Emissions and Generation Tool (AVERT) reduced-complexity model. These scenarios will also be used with CMAQ’s Integrated Source Apportionment Method (ISAM) to isolate the impacts of specific economic sectors on nitrogen deposition to the Chesapeake Bay watershed. Chris Nolte EPA Office of Research and Development |
Quantifying Burned Area from Prescribed and Smaller Fires: Flint Hills, Kansas 2022 and Iowa 2019
Quantifying Burned Area from Prescribed and Smaller Fires: Flint Hills, Kansas 2022 and Iowa 2019
Amber Soja, Emily Gargulinski In the Central and Southeastern U.S. (C& SE), prescribed, small, and short-lived fires are underestimated by satellites. Accurately detecting and quantifying fires in the C& SE is challenging due to size, time of burning, cloud cover, and quick regrowth/plowing soon after burn. Satellite data are critical to defining fire in our National Emissions Inventory (NEI), because historically, ground-based inventories are incomplete and can be geographically inaccurate. Quantifying burned area (BA) and emissions in the C& SE will provide the basis for Health Impact Assessments, with the goal being to inform communities of potential health impacts. As part of NASA’s Health and Air Quality Applied Sciences (HAQAST) team, we are building a C& SE BA inventory, focusing on these often ‘missing’ small fires. We have sampled 100 random 5 x 5 km tiles from the Flint Hills and have digitized burn scars from the March – April 2022 burning season using Sentinel-2 10 meter data. Preliminary results of 355 burning events show 66% of the burn scars had at least one detection between VIIRS and MODIS satellites. Burn scars without detections averaged 57 acres and burn events that had detections averaged 750 acres. Amber Soja NASA Langley Research Center |
10:40 AM |
Impact of Severe Drought on Air Quality in California
Impact of Severe Drought on Air Quality in California
Huazhen Liu, Jared H. Bowden, Timothy Glotfelty, Jordan Kern, J. Jason West Drought is a complex and multivariate phenomenon influenced by diverse physical and biological processes. It occurs naturally, but climate change can lead to more frequent and intense drought events in some regions. The strong perturbation of drought on the atmospheric water cycle can influence air quality. The high electricity demand and low streamflow, which decreases hydropower, during drought periods lead to more power generated by fossil fuels, which exacerbates air pollution. Possible increases in wildfires during drought events also exacerbate air pollution. In this study, we model the effects of drought on air pollution in California, contrasting drought and non-drought years, separating the influences of atmospheric and natural processes, fire emissions, and emissions from fossil fuel electricity generation. We compare a severe drought year in California (2021) with a non-drought year (2019). According to observations in the dry season (April to September), the average temperature in 2021 is 0.68℃ higher than in 2019, and the relative humidity is lower (54.08% vs. 59.78%). The total power generated by fossil fuels is 39.90% higher than in 2019, leading to higher emissions of SO2 (63.18% higher) and NOx (50.23%). The total wildfire-burned area in 2021 is 14.32 times that in 2019. The averaged daily PM2.5 (particulate matter with diameter less than 2.5 μm) concentration in 2021 is 12.61 μg/m3 and the averaged ozone MDA8 (maximum daily 8-hour average) concentration is 48.88 ppb, both of which are higher than in 2019 (PM2.5: 6.41 μg/m3; O3: 45.95 ppb). To simulate meteorology and air quality, we used the WRF-Chem model with nested grids at 12 km (the outer domain, over the Western US and part of the Pacific Ocean) and 4 km (the inner domain, over California and Nevada) resolution. The high resolution allows for more detailed representation of key processes, including the effect of topography. WRF-Chem uses the MOZART-MOSAIC gas phase and aerosol mechanisms and the Noah land surface model. In simulations of the dry season in the drought year 2021 and the non-drought year 2019, the model generally performs well for meteorological parameters as well as PM2.5 and ozone. By replacing the meteorology of 2021 with 2019, we determine the contribution of meteorological changes on exacerbated air pollution. Similarly, the contributions of changes in power plant emissions and fire emissions are also determined. Huazhen Liu University of North Carolina at Chapel Hill |
SmokePath Explorer: A Decision-Support Tool for Prescribed Fire Planning and Wildfire Response
SmokePath Explorer: A Decision-Support Tool for Prescribed Fire Planning and Wildfire Response
Azimeh Zare, ShihMing Huang, Samantha Kramer, Farnaz Hosseinpour, Kayla Besong, Charles Scarborough, Naresh Kumar, and Tim Brown we developed a turnkey, online decision-support tool named SmokePath Explorer for prescribed fire planning and wildfire response. Sponsored by CAL FIRE, the tool allows users to specify a time window and a location for a planned prescribed fire or current wildfire and view the fire weather statistics and the probability of potential downwind smoke impacts. Based on 20 years (2001-2020) of high-resolution (2 km) climatological data for California and Nevada, fire weather statistics were computed, and air transport data were created through hourly air parcel trajectory modeling. For user-specified location, years, and months or weeks of the year, the SmokePath Explorer: a) Determines the probability of air parcel transport downwind, b) Displays the historical summary of weather parameters relevant to fire and smoke conditions, and c) Identifies the potential receptor locations by integrating air parcel transport probability with population, education facility, healthcare facility, and ZIP code data. Air and land managers can use the SmokePath Explorer to plan prescribed fires, allowing for more timely public messaging regarding potential smoke impacts, and proactive mitigation efforts to assist at-risk populations. In addition to the tool, the project team has identified multiple case studies based on past prescribed fire projects and wildfires to evaluate the transport modeling results and establish guidance for utilizing the tool. These cases include pile burns, broadcast burns, and typical wildfires in different air basins across California. Results of these case studies and the tool will be presented. Azimeh Zare Harofteh Desert Research Institute |
11:00 AM |
Environmental Justice in the Energy Transition: Challenges in Modeling Capacity Expansion and Air Pollutant Dispersion for Equitable Decarbonization Pathways
Environmental Justice in the Energy Transition: Challenges in Modeling Capacity Expansion and Air Pollutant Dispersion for Equitable Decarbonization Pathways
Jordan French; University of Texas at Austin; Department of Civil, Architectural and Environmental Engineering Capacity expansion models have proven valuable for developing cost-optimized decarbonization portfolios for grid systems; however, air pollution and environmental justice impacts are often neglected or oversimplified. This work attempts to address this gap in the literature by pairing SWITCH, an open-source capacity expansion model, with pollutant dispersion modeling through an iterative procedure. The Texas ERCOT grid is modeled as an example, with analysis conducted to determine the siting and emissions of future natural gas plants. NOx emissions dispersion is then modeled with disperseR and InMAP, two reduced complexity pollutant dispersion models. The results of these models are used to define environmental justice constraints, which are incorporated into SWITCH for equitable decarbonization pathways. In developing this framework, the results of disperseR and InMAP are compared to determine the sensitivity of environmental justice metrics to monthly wind patterns, particulate matter formation, interstate considerations, and facility siting decisions. This methodology lays a foundation for applying environmental justice constraints in the development of equitable grid decarbonization pathways, and highlights potential paths forward for more effective constraints. Jordan French University of Texas at Austin |
Expedited Modeling of Burn Events Results (EMBER)
Expedited Modeling of Burn Events Results (EMBER)
Heather Simon, James Beidler, Kirk Baker, Barron Henderson, Loren Fox, Chris Misenis, Jeff Vukovich, Norm Possiel, Alison Eyth The summer of 2023 was a record-breaking season for wildfire acres burned in Canada. Many of the wildfire plumes reached portions of the central and eastern United States that have not experienced frequent and severe wildfire impacts in recent years. At the same time, much of the US experienced ozone concentrations that were unusually high compared to the last decade. Photochemical modeling is a valuable tool for assessing the contribution of fire emissions to ozone formation. Here we present the Expedited Modeling of Burn Events Results (EMBER). EMBER is a rapid-turnaround screening-level CMAQ modeling dataset covering the summer of 2023. The modeling uses 36-km grid resolution and leverages existing publicly available datasets to obtain meteorology and boundary condition inputs. Fire emissions across the US and Canada were developed for this effort using the BlueSky pipeline modeling system. Emissions from a recent 2018 emissions modeling platform are used for emissions other than wildfires. Zero-out modeling was conducted to estimate the ozone concentrations resulting from Canadian Wildfire emissions as well as from the combination of US and Canadian prescribed and wildfire emissions. Heather Simon US EPA |
11:20 AM |
Fine particulate matter and ozone variability with regional and local meteorology in Beijing, China
Fine particulate matter and ozone variability with regional and local meteorology in Beijing, China
Shreya Guha, Ting Zhang, Patrick L. Kinney, Lucas R.F. Henneman Fine particulate matter (PM2.5) and surface ozone air pollution have severe health consequences. Since China implemented the nationwide Air Pollution Prevention and Control Action Plan (APPCAP) in 2013, annual average PM2.5 concentrations have decreased while O3 concentration have increased in Beijing. It is unclear, however, to the extent that short-term meteorological variability has influenced these trends. To separate the influence of meteorology from long-term emissions-induced changes, we quantify the portion of the long-term PM2.5 and O3 concentration signals associated with short-term meteorological variability. Building off recent literature highlighting the regional nature of pollutant variability, we incorporate both locally observed meteorology and average regional reanalysis. We isolated sub-seasonal variability using the Kolmogorov-Zurbenko filter. Next, we trained and compared the utility of three statistical models of varying complexity (a linear model, a general additive model with splines, and a random forest) in their ability to predict out-of-sample daily concentrations. The random forest model yields the best predictive capability in holdout tests and predicts changing meteorological contributions to PM2.5 and ozone concurrent with changing concentrations across the study period. Results show an overall decrease in meteorology induced PM2.5 concentration variability at daily scales since 2013, but variability from meteorology has increased relative to mean PM2.5 concentrations. In contrast, O3 shows the opposite trend, with increasing meteorology-induced variability (meteorology-induced variability normalized by mean observed O3 has decreased). Our results show the importance of including regional meteorology in explaining local PM2.5 and O3 variability and quantify links between long-term policy implementation and short-term meteorological contributions to pollutant concentrations. Shreya Guha George Mason University |
Improving numerical air quality forecasts in the western United States with an emphasis on wildfire smoke
Improving numerical air quality forecasts in the western United States with an emphasis on wildfire smoke
Taylor Y. Wilmot, Heather A. Holmes, Derek V. Mallia, Kerry E. Kelly, Ross T. Whitaker In light of the public health burden presented by particulate and ozone pollution, nationally and especially in the western U.S., it is evident that there is a need for high-quality and timely physics-based air quality forecasts. The state of Utah has elevated ozone and particulate matter (PM) concentrations and is currently in nonattainment or maintenance for the PM2.5, PM10, and ozone National Ambient Air Quality Standards (NAAQS). The sources contributing to these elevated pollutant concentrations vary significantly in space and time (e.g., dust, wildfire smoke), often resulting in acute air pollution episodes. Leveraging an NSF CIVIC project titled Community Resilience through Engaging, Actionable, Timely, high-rEsolution Air Quality Information (CREATE-AQI) we developed an air quality forecast system for Utah communities. Through CREATE-AQI our goal was to co-develop community-scale, actionable air-quality products for the state of Utah to help reduce exposure to air pollutant hazards, particularly for children. The CREATE-AQI public facing user interface displays both low-cost sensor observations (nowcasting) and three-day air quality predictions (forecasting) using visualizations that were co-developed with our community partners. Our objective is to provide actionable information about current and future air quality to local stakeholders. This talk will present results from the CREATE-AQI modeling platform showing the integration of the nowcasting and forecasting air quality products. The focus will primarily be on the development and evaluation of the numerical air quality forecasting platform. The forecasting framework uses the Weather Research and Forecasting (WRF) model to forecast meteorology, the U.S. EPA National Emissions Inventory (NEI) and the Sparse Matrix Operator Kernel Emissions (SMOKE) to provide and process emissions, and the Community Multiscale Air Quality (CMAQ) model to forecast air quality. In its first iteration, this air quality forecast provides outputs for the continental United States at 12 km horizontal resolution and hourly temporal resolution out to 72 hours. In future iterations the Utah forecasts will come from a nested 4km horizontal resolution domain to improve the simulation quality over mountainous terrain. The forecast system incorporates novel developments to improve wildfire smoke forecasting. Specifically we (1) forecast the temporal fire emissions to improve on the wildfire persistence assumption that is often used to forecast wildfire emissions, (2) incorporate plume top heights from the Freitas plume rise scheme, and (3) incorporate a forecasted vertical distribution of smoke emissions below the plume top. This vertical distribution of emissions is based on simulated wildfire energetics and atmospheric structure at the time of emission. Combined, these model developments improve the air quality forecasting capabilities in the western U.S. Taylor Wilmot Department of Chemical Engineering, University of Utah |
11:40 AM |
Title: Estimating Air Quality Exposure in 1940 and 2010 For Future Application to Health Analyses
Title: Estimating Air Quality Exposure in 1940 and 2010 For Future Application to Health Analyses
Xiaorong Shan1, Joan A. Casey2, Lucas Henneman1 1 Department of Civil, Environmental, and Infrastructure Engineering, George Mason University, Fairfax, VA, USA; xshan2@gmu.edu; lhennem@gmu.edu 2 Department of Environmental and Occupational Health Sciences, School of Public Health, University of Washington, Seattle, WA, United States Objectives Our team aims to test the relationship between 1940 air quality and dementia onset in the 1990s-2000s, but this work is limited by a lack of air quality data, including emissions information and historical measurements for evaluation, prior to 1970 in the United States. Here, we devised and implemented advanced methodologies to quantify the air quality in 1940 and compared our estimates to present-day where we could validate our methods using air quality monitors. Materials & Methods We developed several county-level source-specific exposure metrics across the contiguous United States in 1940 and 2010. We quantified exposure to traffic-related air pollution (TRAP), oil/gas wells, power plants, and fine particulate matter (PM2.5). For TRAP, we incorporated information on statewide gasoline sales and county-level population to estimate gasoline consumed. Using oil/gas well locations, we employed Inverse Distance Weighting (IDW). For fossil fuel power plant locations, we employed IDW. We quantified PM2.5 concentrations using climate model output from the Coupled Model Intercomparison Project Phase 6 (CMIP6). We evaluated these source-specific exposure estimates against observed 2010 annual concentrations from EPA Air Quality System monitors, using Pearson correlation coefficients and mean differences as evaluation metrics. Results The initial evaluation revealed that CMIP6-based simulations correlated moderately with observed PM2.5 concentrations (Pearson: 0.40). In contrast, automobile emissions showed a weak correlation with PM2.5 (Pearson: 0.04), but a stronger one with NOx (Pearson: 0.41). Well activity presented stronger correlations with VOCs (Pearson: 0.34). The IDW approach to power plant emissions indicated a fair correlation with PM2.5 (Pearson: 0.29) and a higher correlation with sulfate (Pearson: 0.33). Conclusions Positive correlations suggest expected relationships between the sources of interest and observed air pollutant concentrations. The somewhat lower than expected correlations, however, suggest some refinement to the exposure methods may be necessary. In addition, there may be utility in comparing the concentrations at finer spatial scale than county level. Xiaorong Shan George Mason University |
An Analysis of Uncertain Wind Impacts in Coupled Fire-Atmosphere Model WRF-SFIRE
An Analysis of Uncertain Wind Impacts in Coupled Fire-Atmosphere Model WRF-SFIRE
Zongrun Li, Yongtao Hu, Armistead G. Russell, M. Talat Odman Coupled fire-atmosphere models that simulate the fire propagation, smoke plume formation and transport under different ignition patterns are useful tools for the management of prescribed fires. Emissions and meteorology are the primary sources of uncertainty in these models, especially for fire simulations at high spatial and temporal resolution. Understanding the impacts of these uncertainty sources is important for improving model performance and increasing confidence in the model used for air quality forecasting. We designed and implemented numerical and statistical methods to quantitatively analyze the impacts of wind uncertainties on downwind smoke concentrations in prescribed fire simulations. Our numerical methods aim to mitigate wind uncertainty during meteorological simulations. Data assimilation with different intensities and modifying initial and boundary conditions are the numerical methods. The statistical methods provide an uncertainty range by considering the differences between observed and simulated winds in the post-analysis of modeled smoke concentrations. To illustrate and evaluate the designed methods, we simulated prescribed fire events with precise ignition information and downwind smoke measurements using the coupled fire-atmosphere model WRF-SFIRE at high spatiotemporal resolution. Among the numerical methods, modifying initial and boundary conditions by incorporating the regional observed winds is the most effective way to reduce the wind bias, especially the wind speed bias. The performance of the smoke concentration simulation is enhanced by improving the accuracy of the wind simulation. As algorithms for post-analysis of numerical modeling results, the statistical methods provide computationally efficient sensitivity analysis and uncertainty estimation for smoke concentrations by considering the simulated wind bias at monitoring locations. The statistical methods estimated the probability of smoke impacting the monitor location under uncertain wind simulations and demonstrated that the modeled concentrations are highly sensitive to wind conditions. The methods proposed in this presentation can also be applied to other chemical transport models for fire simulations as well as other situations involving point or area sources. Zongrun Li is currently a PhD student at Georgia Tech. His research interests include empirical modeling and numerical modeling related to air quality. His PhD project is focused on developing and improving air quality modeling frameworks for wildland fires, with a particular emphasis on prescribed fires. Zongrun Li Georgia Tech |
12:00 PM | Lunch in Trillium | |
Modeling to Support Exposure, Health and Environmental Justice Studies at MultiscalesChaired by Vlad Isakov, US EPA and Rebecca Saari, University of Waterloo | Wildfire Emissions and Air Quality cont. | |
1:00 PM | Session Keynote: Development and Demonstration of the Benefits Mapping and Analysis Program in R (BenMAPR) Jonathan Buonocore Boston University |
Landscape fires, nitrogen emissions, and deposition: Implications for downwind ecosystems in the U.S.
Landscape fires, nitrogen emissions, and deposition: Implications for downwind ecosystems in the U.S.
Patrick Campbell, Daniel Tong, Yunyao Li, Shinkuang Chang, Siqi Ma, B.H. Baek, Youhua Tang, Rick Saylor, and John Walker A consensus has emerged that climate-driven increases in the frequency and severity of wildfires can have serious ramifications for enhanced downwind nitrogen (N) emissions and deposition and associated ecosystem impacts like critical load exceedances (see the September 2023 WMO Air Quality and Climate bulletin: https://library.wmo.int/records/item/62090-no-3-september-2023). There is a need in the community to better understand the long term (~ multi-decadal) fire activity and implications on changes in N emissions, atmospheric deposition, and the impacts on downwind ecosystems across the contiguous U.S. (CONUS). Thus, to aid in this important effort, here we use two 20-year (2002 - 2021) coupled Weather Research and Forecasting (WRF) - Community Multiscale Air Quality (CMAQ) model simulations for both “with-fire” and “without-fire” sensitivity runs to quantify the change in relative trends and contributions of fire sources to the total emissions and deposition trends of reactive (i.e., oxidized) versus reduced forms of N over CONUS. The meteorological conditions are constrained by downscaling the ECMWF Reanalysis v5 (ERA-5) dataset, and the emissions (both for anthropogenic and fire sources) are based on the U.S. EPA’s Air QUAlity TimE Series Project (EQUATES) for 2002-2017, the 2020 National Emissions Inventory (NEI), and 2018v2, 2019, and 2021 emissions modeling platforms. Preliminary model results show that fire activity has increased substantially in the western U.S. fire climate regions, especially in the northwest U.S., and that this increase is associated with positive annual near-surface temperature and vapor pressure deficit anomalies compared to the 2002-2021 average. This has led to pronounced increases in both oxidized (i.e., NOx = NO + NO2) and reduced (i.e., NHx = NH3 + NH4+) forms of N emissions from fires, which can dominate the total amount of N emissions in parts of the western fire climate regions, especially after year 2012. We will further show that the increasing trend of enhanced N emissions from fires lead to increases in dry deposition of N compounds, especially NHx, which has implications for trends of increasing critical load exceedances for major vegetation types of the western U.S. between 2002 - 2021. We anticipate that future climate change may exacerbate such trends in the western U.S. Dr. Patrick Campbell is an Earth systems scientist and research associate professor at the George Mason University Center for Spatial Information Science and Systems and is the associate director of GMU’s Satellite and Earth System Studies (SESS) program. Dr. Campbell is also a NOAA Air Resources Laboratory Affiliate under the Cooperative Institute for Satellite Earth System Studies (CISESS). Dr. Campbell is working closely with the NOAA-Air Resources Laboratory Chemical Modeling and Emissions Group on scientific research, development, and application of next-generation atmospheric composition and air quality forecasting models that help protect human and ecosystem health. Patrick Campbell George Mason University |
1:20 PM |
Air toxics exposure disparities in disadvantaged communities
Air toxics exposure disparities in disadvantaged communities
Xue Meng Chen, Shuming Du, Abdullah Mahmud, Yiting Li, Yunle Chen, Pingkuan Di, and Jeremy Avise In this study, we quantified the disparity that exists across California in terms of exposure and health impact from major air toxics such as diesel particulate matter (DPM), volatile organic compounds (VOCs) and heavy metals, and how it has changed from 2012 to 2017. To help achieve environmental justice, disadvantaged communities that are overburdened by air pollutants have been identified by various programs such as the Community Air Protection Program (AB617) and CalEnviroScreen (SB535). CARB’s California Air Toxics Assessment (CATA) provides estimates of population exposure and associated cancer risks from major air toxics at the census tract level. Using CATA’s high resolution modeling results, this analysis aims to answer two main questions: 1) how do air toxics related cancer risks in AB617 and SB535 communities compare with the rest of the state, i.e., how bad is the disparity in California in terms of exposure to air toxics, and 2) has that disparity changed over time. Our results indicate that in both 2012 and 2017, most disadvantaged communities still bear a disproportionate cancer risk burden from air toxics. We also found that even though the average cancer risk from air toxics decreased across the state, disparity has not decreased substantially, especially in the southern part of the state. Xue Meng Chen California Air Resources Board |
Using Self Organizing Maps to Identify the Meteorology of Long-Range Transport of Wildland Fire Smoke
Using Self Organizing Maps to Identify the Meteorology of Long-Range Transport of Wildland Fire Smoke
Tsengel Nergui, Victor Geiser, and Zac Adelman The Midwest is a land-locked mid-latitude geographical setting where complex atmospheric processes take place in conjunction with local emissions and transported air pollutants. Periodically, upwind wildland and prescribed fire smoke is transported into the region and results in unhealthy concentrations of fine particulate matter (PM2.5) and ozone (O3) at the surface. Meteorological conditions associated with typical high pollution days, compared to those of fire smoke influenced days, are often less apparent to air quality forecasters and planners. To better understand the meteorological setting and pollutant transport pathways bringing wildland fire smoke into the region, LADCO applied a spatial classification technique called a Self-Organizing Map (SOM) to daily average PM2.5 and daily maximum 8-hour average O3 concentrations using the 3-km resolution High Resolution Rapid Refresh (HRRRv4) reanalysis dataset for 2019-2023. The objective of the analysis is to identify the primary features of the atmospheric conditions associated with air pollution episodes with and without the influence of fire smoke. We will present the results of SOM analysis of high pollution episodes caused by wildland fires originating in the southwestern US and southeastern Canada. We used SOM to identify the synoptic-scale meteorological conditions and the corresponding increases in PM2.5 and O3 during these fire events. In addition, we investigated a key aspect of whether the long-range transported smoke aloft reached the surface. Vertical atmospheric characteristics such as wind shear, stability, and 24-hour changes in geopotential height and temperature for the fire-influenced SOM nodes highlight key upper-air features for vertical mixing, and indicate whether air masses ascend or descend along the transport path between the fire smoke source and receptor monitors. Our study offers two practical applications for air quality forecasters. First, using SOM to identify the weather patterns associated with typical high-pollution days provides historical data for similar-day analysis for exceptional event applications. Second, the identified synoptic weather patterns linked to fire smoke-influenced days provide insights into the expected increases in PM2.5 and O3 concentrations due to the fire smoke in the Midwest. Tsengel Nergui Lake Michigan Air Directors Consortium |
1:40 PM |
Addressing PM2.5 Exposure Disparities: High-Resolution Modeling and Health Assessment for Environmental Justice Across the Continental U.S.
Addressing PM2.5 Exposure Disparities: High-Resolution Modeling and Health Assessment for Environmental Justice Across the Continental U.S.
Kai Fan, Zhen Qu Air pollution is the fifth leading risk factor for mortality globally. In the U.S., it disproportionately affects marginalized communities, with monitoring stations primarily situated in high-income, predominantly white regions. This leaves vulnerable communities unaware of their exposure levels. To address these challenges, we assessed air pollution exposure disparities in the top 50 populated counties in the contiguous U.S. (CONUS). Our approach combines high spatial-temporal air pollution predictions with health assessments to improve equity and environmental justice (EEJ) in the U.S. We utilized a U-Net-like model with three-dimensional spatiotemporal partial convolutions to fill nighttime AOD and spatial gaps in the missing GOES satellite AOD. Subsequently, a random forest model predicted hourly air pollutant concentrations at a 1-km horizontal resolution, using gap-filled AOD, ground measurements (AQS, PurpleAir), and other geographical data (e.g., population, terrain height, land cover). The gap-filled AOD was evaluated against AERONET observations, achieving a correlation coefficient of 0.8. Site-based cross-validation for daily PM2.5 predictions yielded an R2 value above 0.7. The health assessment estimated annual premature deaths due to PM2.5 exposure over CONUS using our air pollution predictions and the GBD Exchange Results tool. The overall premature death rate for the Black population is 6.1% higher than for the non-Hispanic white population, and low-income census tracts face a 7.0% higher premature death rate. In the top 50 most populated counties, premature death rates vary by racial groups due to the different residential locations of these groups. The premature death rate for the Black population is 12% higher than for the white population because the Black population resides in areas with higher PM2.5 concentrations compared to areas where the white population lives. Our research provides high spatial-temporal resolution air pollution predictions and offers insights into air pollution exposure disparities among races/ethnicities and locations, highlighting environmental justice issues in local communities. Kai Fan North Carolina State University |
Image-Based Wildfire Detection for Emission Control and Air Quality Improvement
Image-Based Wildfire Detection for Emission Control and Air Quality Improvement
Sambit Bhattacharya, Taylor Brown, Matthew Wilkerson Wildfire emissions significantly impact air quality, posing health risks and contributing to climate change. Early detection and rapid response are crucial for mitigating these effects. This study presents an image-based wildfire detection system, which is being developed under a project that is executed by a collaboration of three universities and the Jet Propulsion Laboratory (JPL) and is supported by the NASA funded Institute for Multi-agent Perception through Advanced Cyberphysical Technologies (IMPACT). Our approach leverages advanced machine learning algorithms and remote sensing technologies to detect wildfires with high accuracy and minimal latency. By integrating data from the UAVSAR platform and the IMPACT Early Exit framework, we are working on close to real-time monitoring capabilities that are essential for proactive wildfire management. Our system utilizes a combination of Synthetic Aperture Radar (SAR) imagery and optical data to identify wildfire signatures, even under challenging conditions such as dense smoke or cloud cover. The integration of these datasets enhances detection reliability and provides comprehensive situational awareness. This research underscores the importance of cutting-edge image processing techniques in wildfire detection, offering a promising tool for emission control and air quality improvement. Early wildfire detection not only helps in immediate response efforts but also plays a vital role in long-term air quality management, highlighting the intersection of technology and environmental stewardship. Sambit Bhattacharya is a Computer Scientist with more than 15 years of experience in teaching and research. He has a PhD in Computer Science and Engineering from the State University of New York at Buffalo. He is a tenured Full Professor of Computer Science at Fayetteville State University, North Carolina, USA. In 2023 he was honored by the University of North Carolina (UNC) Board of Governor’s Award for Teaching Excellence. He was a winner of NASA’s Space Tech Catalyst prize in 2024 which was awarded in recognition of work to increase diversity in the workforce. Dr. Bhattacharya is experienced in developing and executing innovative and use-inspired research in Artificial Intelligence and Machine Learning (AIML) with a broad range of techniques and applications, and with multidisciplinary teams. He has several peer reviewed publications and has delivered many oral presentations, including keynote lectures at conferences. As PI he leads projects funded by the National Science Foundation, the US Department of Defense (DoD), and NASA. While on teaching leave from the university, Dr. Bhattacharya has served as faculty research fellow in research labs of the DoD and in the National Geospatial Intelligence Agency (NGA). He is currently a Visiting Scientist part time at NGA. Recently he has started working as Co-PI and AI lead with the UNC Radiation Oncology department on an NIH funded project. He directs the Intelligent Systems Lab (ISL), which supports faculty and students, and collaborations with external partners. Sambit Bhattacharya Fayetteville State University |
Modeling to Support Exposure, Health and Environmental Justice Studies at Multiscales cont. | Remote Sensing/Sensor Technology and Measurements Studies, Chaired by Matthew Alvarado, AER and Barron Henderson, US EPA | |
2:00 PM |
Assessing Health Risks Associated with Exposure to Toxic Volatile Organic Compounds (VOCs) in California for 2017 Utilizing the Community Multi-scale Air Quality Model version 5.3.2 (CMAQv5.3.2)
Assessing Health Risks Associated with Exposure to Toxic Volatile Organic Compounds (VOCs) in California for 2017 Utilizing the Community Multi-scale Air Quality Model version 5.3.2 (CMAQv5.3.2)
Abdullah Mahmud, Pingkuan Di, and Jeremy Avise Exposure to toxic Volatile Organic Compounds (VOCs) through inhalation poses significant health risks. Some of the toxic VOCs such as formaldehyde, acetaldehyde, benzene, 1,3-butadiene, perchloroethylene, and p-dichlorobenzene have been identified as human carcinogens. The primary objectives of the current study were to assess the population health risks and, to some extent, to support identifying areas that are likely to experience disproportionately high concentrations of toxics as mandated by the California Community Air Protection Assembly Bill 617 (AB 617). CMAQv5.3.2 was utilized to simulate the physical and chemical processes that the toxic VOCs undergo in the atmosphere. The simulations were carried out at a horizontal resolution of 2-km over an area encompassing major air basins including the Sacramento Valley (SV), San Francisco Bay (SFB), San Joaquin Valley (SJV), South Coast (SC), San Diego (SD), and Imperial (IMP) in California for the entire 2017. The meteorological fields and the speciated and gridded emissions from anthropogenic, biogenic, and wildfire sources that drove the air quality simulations were generated by the Weather Research Forecast version 3.9.1 (WRFv3.9.1) model and the SMOKE emissions processing model, respectively. Gridded annual average concentrations of toxic VOCs were applied in calculating potential cancer risk and burden at the 30-yr and 70-yr exposure levels according to the California Office of Environmental Health Hazard Assessment (OEHHA) guidelines, respectively. These risks were further aggregated to census tract levels. The SC, SJV, SV, and SFB air basins showed to have high levels of cancer risks. Significant risks were also seen in areas in SD and IMP near the California-Mexico border due to transport of emissions from the Mexican side. The population weighted cancer risk in the South Coast was approximately 129 per million which was the highest followed by the risk of about 103 per million in San Diego. The cancer risks in the Sacramento Valley, San Francisco and San Joaquin Valley were 68, 74, and 65 per million, respectively. The comparisons between modeled and monitored annually averaged concentrations for four major VOCs (1,3-butadiene, benzene, formaldehyde, and acetaldehyde) were carried out using available data from monitors located throughout the state. The modeled results for 1,3-butadiene and benzene showed good agreements with the observed data, with index of agreements of 0.7 and 0.6, respectively. CMAQ underestimated the concentrations of formaldehyde and acetaldehyde, particularly during extreme events. The cause of such under-estimation could be attributed to a lack of appropriate representation of chemical processes of formation for secondary acetaldehydes and formaldehyde during wildfire events that occurred in significant number in 2017. The modeled concentrations of formaldehyde will further be evaluated against satellite measurements in the future. Abdullah Mahmud California Air Resources Board |
Session Keynote: Remote Sensing of Ammonia (NH3): How it works and what it can tell us Karen Cady-Pereira AER |
2:20 PM |
Can Electric Vehicles Adoption address Environmental Inequalities in Georgia?
Can Electric Vehicles Adoption address Environmental Inequalities in Georgia?
Xin He, Yakun Zhou, Armistead G. Russell, Jennifer Kaiser In this study, we employ the Community Multiscale Air Quality (CMAQ) CTM to characterize fine resolution at 1 km x 1 km, investigating how EV adoption would impact emissions of traffic related air pollutants PM2.5, NO2, O3, and estimating health impact disparities between racial/ethnic population at census tract-level. We investigate the effect of the spatial resolution on estimated health burdens. We focus on the most densely populated regions in Georgia, centered around the Atlanta metro area. We consider two EV adoption scenarios (no-EV and 30-EV) and our approach accounts for excess emissions from electricity generation units (EGUs) owed to additional battery charging demands by utilizing a power dispatch model. Xin He Georgia Institute of Technology |
Evaluating NOx Emissions using GCAS, TROPOMI and High-Resolution CAMx Source Apportionment Modeling
Evaluating NOx Emissions using GCAS, TROPOMI and High-Resolution CAMx Source Apportionment Modeling
Johnson, J.1, Goldberg, D.2, de Foy, B.3, Nawaz, M.O.2, Yarwood, G.1, Judd, L.4 1 Ramboll Americas Engineering Solutions 7250 Redwood Blvd., Suite 105 Novato, CA 94945 2 Department of Environmental and Occupational Health George Washington University Washington, DC 20052 3 Saint Louis University St. Louis, MO 63103 4 NASA Langley Research Center Hampton, VA 23681 Ozone formation is limited by nitrogen oxide (NOx) emissions in most North American cities and further reducing ozone production rates within cities will require accurate quantification of NOx emissions with source-sector precision. One major limitation of our current observing networks is the inability to accurately quantify NOx emissions on a sector-by-sector basis. In this study, we leverage spatially continuous column NO2 measurements from the NASA GeoCAPE Airborne Simulator (GCAS) at 250 × 560 m2 resolution, and coarser Tropospheric Monitoring Instrument (TROPOMI) NO2 columns, to validate a finely resolved 444 x 444 m2 CAMx model simulation of the Houston metropolitan area during the September 2021 NASA/TCEQ Tracking Aerosol Convection ExpeRiment - Air Quality (TRACER-AQ) field campaign. We compared CAMx NO2 columns with observations from the GCAS instrument, Pandora instruments, and TROPOMI to identify gaps in our understanding of NOx emissions within CAMx. We find that column NO2 from GCAS has excellent agreement with Pandora measurements (r2=0.81 and NMB of +2.4%), column NO2 from TROPOMI had good correlation with Pandora (r2=0.62), but with a low bias that was unresolved even when we applied a CAMx-based air mass factor (NMB of –11%). Column NO2 from CAMx showed a low bias when compared with Pandora (NMB of –20%) and GCAS measurements (NMB of –31%). We exploited the ability of CAMx to tag NO2 concentrations (and columns) by source sector, tagging 15 emission source categories, including 9 EGUs individually. We applied a flux divergence method to the GCAS measurements and CAMx source-apportioned NO2 columns to better distinguish emission sources, e.g., the linear shape of major highways is revealed by flux-divergence. Using a multiple linear regression model, we found that some tagged sources matched well with the GCAS measurements, including individual EGUs that have continuous emission monitors (CEMs). However, on-road, railyard and “other” NOx emissions were underestimated relative to the GCAS measurements and contribute to the overall low NO2 bias. Additionally, we identified a likely overestimate of marine shipping NOx emissions. We modified on-road and shipping NOx emissions in a new CAMx simulation and increased the background NO2 and found better agreement with GCAS measurements: bias improved from –31% to –10% and r2 improved from 0.78 to 0.80. We will also present preliminary findings from similar work for the New York City and Chicago areas. This study shows how remote sensing data, including fine spatial information from newer instruments such as Tropospheric Emissions Monitoring of Pollution satellite (TEMPO), can be used in combination with chemical transport models with source apportionment to provide actionable information for air quality managers to identify further opportunities to improve NOx emissions inventories. Jeremiah Johnson is a Managing Consultant at Ramboll’s Novato, California office. He has extensive experience applying the WRF meteorological model for regional and local-scale air quality modeling applications in the U.S. and internationally. Additionally, Jeremiah specializes in ozone forecasting, exceptional event analysis, evaluation of emission inventories using remote sensing, and wildland fire emissions. Jeremiah Johnson Ramboll |
2:40 PM |
US mortality impacts of PM2.5 associated with climate change and climate variability at midcentury using dynamically downscaled global climate and chemistry in WRF and CMAQ
US mortality impacts of PM2.5 associated with climate change and climate variability at midcentury using dynamically downscaled global climate and chemistry in WRF and CMAQ
Surendra Kunwar1, Jared Bowden2, George Milly3, Michael Previdi3, Arlene Fiore4, J. Jason West1 1University of North Carolina at Chapel Hill, Chapel Hill, NC, USA 2North Carolina State Climate Office, Raleigh, NC, USA 3Lamont-Doherty Earth Observatory and Columbia University, Palisades, NY, USA 4Massachussetts Institute of Technology, Cambridge, MA, USA Climate variability can confound the effects of climate change on future US PM2.5, leading to uncertainty in estimates of climate change induced mortality via PM2.5 changes. Many past studies have averaged atmospheric model output for fewer than 10 years, which can be insufficient to distinguish the effects of climate change on PM2.5 from natural variability. Here, we present a novel method to estimate the effects of both climate change and variability on U.S. PM2.5 under the RCP8.5 climate change scenario at 12km resolution, and estimate associated human mortalities. In this study, fine scale annual PM2.5 probability distributions (assumed Gaussian) of two periods of interest - present-day (2006-2020) and midcentury (2051-2065) - are defined by statistics from the combination of a large dataset of global chemistry-climate simulations (GFDL-CM3 model, 2 degree spatial resolution, years 2006-2100, 3 initial condition ensemble members for RCP8.5 climate change), and high resolution downscaling. We downscale eight selected GFDL years representative of medium and high PM2.5 in different regions of the US using the regional climate model WRF and regional air quality model CMAQ, to 12km spatial resolution. Across the models, anthropogenic PM2.5 and O3 precursor emissions are fixed at present day levels, thereby isolating the influence of climate change (and variability) on PM2.5. Natural feedback emissions of seasalt, biogenic VOCs and lightning NOx are activated in the regional chemistry simulations. At each 12km gridcell of the regional model US domain, a Monte Carlo simulation (1000 draws) of the difference between present-day and midcentury mean annual PM2.5 is used to estimate the impacts of climate change on PM2.5, in light of natural climate variability, in the form of a probability distribution. There is a wide range of PM2.5 changes due to climate change in the 2050s, with most areas within the US experiencing a mean increase of up to 1.25 μg/m3, throughout major cities of the US. The Southeastern US sees a large increase in mean annual PM2.5 from boosted OM levels in the future. We then use BenMAP (Benefits Mapping and Analysis) to assess probability distributions of premature deaths related to PM2.5 and attributable to climate change. Estimated deaths are represented as probability distributions that reflect the uncertainty in concentration-response functions, and the year-to-year meteorological variability. Due to the natural variability in PM2.5, there is uncertainty in health impacts at the grid cell level and aggregate (county, state, national) levels. Presenting the health impacts of PM2.5 from climate change in the form of probability distributions to incorporate natural climate variability is very informative and relevant for policymaking. Surendra Kunwar University of North Carolina at Chapel Hill |
Inferring Global Ground-level Ozone Concentrations Through Data Fusion of Chemical Reanalysis, Ozonesondes, and Lowermost Tropospheric Satellite Observations
Inferring Global Ground-level Ozone Concentrations Through Data Fusion of Chemical Reanalysis, Ozonesondes, and Lowermost Tropospheric Satellite Observations
Hantao Wang, Marc Serre, Kazuyuki Miyazaki, Juan Cuesta, J. Jason West Satellite observations have been widely used to monitor the global distribution of tropospheric ozone, but ground-level ozone is difficult to detect. This study proposes a method to infer global ground-level ozone concentrations by combining satellite Lowermost Tropospheric (LMT) observations, a chemical reanalysis, and ozonesonde profiles. We then aim to use this approach to better understand ozone in regions with few ground monitors and include the output in data fusion of global ozone concentrations. We use ozonesonde observations at 43 sites globally to fit a monthly trend of 0-3 km ozone concentrations by modeling time series of ozone concentrations at discrete pressure levels with associated seasonal and interannual variability, with a generalized additive model. Our analysis shows an inverse linear relationship with very high certainty between pressure and ozone concentration over 0–3 km altitude, and a geographic autocorrelation over a 15° range. We then use the Bayesian Maximum Entropy (BME) data fusion method with multispectral satellite LMT observations from IASI+GOME2 as the global offset, and monthly ozonesonde fitting data as observations, to produce monthly estimates of LMT ozone. Next, we generate a global map of the ratio of ground-level to LMT ozone, also using the BME method, where ratios from the Tropospheric Chemistry Reanalysis version 2 (TCR2) are input as the global offset, and ratios from monthly ozonesonde fitting data as the observations. Finally, this BME ratio map is combined with the monthly LMT ozone data output from the BME to produce a global 1° x 1° monthly ground-level ozone map. The resulting monthly ground-level ozone map was evaluated with respect to monthly average DMA8 (daily maximum 8 h average) of ground-level observations at 5228 sites in 2017 from the Tropospheric Ozone Assessment Report (TOAR-II) database. The global map demonstrates a robust R2 of 0.47 and a Root Mean Square Error (RMSE) of 8.77 ppb, significantly surpassing the R2 of 0.21 and RMSE of 10.17 ppb from the original satellite LMT data. When focusing on TOAR-II stations north of 30°N during the summer (June to August), the performance of the derived ozone map is more accurate than that of satellite LMT observations and the chemical reanalysis. I'm in third year of my PhD. My research primarily focuses on global aerosol mapping and utilizing satellite observations. Also, I am good at modeling using statistical methods. Hantao Wang The University of North Carolina at Chapel Hill |
3:00 PM | Break | Break |
3:30 PM |
Enhancing Urban Environmental Quality through Computational Fluid Dynamics: Case Studies and Methodologies in Green Infrastructure Design
Enhancing Urban Environmental Quality through Computational Fluid Dynamics: Case Studies and Methodologies in Green Infrastructure Design
Fleischer, Daniel; Penaloza Gutierrez, Juan This presentation explores environmental modeling with an emphasis on Computational Fluid Dynamics (CFD) applications, drawing on case studies from our nesting projects in West Oakland, CA, Dallas, TX, Louisville, KY, and our National Science Foundation (NSF) grant development efforts, which are pivotal to the development of the AdaptOS platform. The primary objective of our research is to enhance urban environmental quality through the implementation of green infrastructure. Our methodology encompasses extensive monitoring and modeling to establish baseline metrics, followed by strategic design interventions aimed at improving air quality and mitigating heat. The CFD modeling process requires inputs such as a domain model, meteorological data, and traffic data. The domain model, or digital twin, represents the urban environment's structure and may depict existing, historical, or proposed conditions. CFD can estimate the effects of design decisions on air pollution metrics by comparing different digital twins while keeping other inputs constant. Urban areas in particular, are characterized by complex interactions between various built and natural features, influencing the dispersion of pollutants. Our research employs CFD to analyze pollutant dispersion in urban environments, focusing on micro-scale simulations that capture detailed flow dynamics around buildings, streets, and trees. Understanding these local variations is crucial for mitigating pollution exposure and optimizing urban design. In our studies, we aim to address urban flows at the meter to sub-meter scales, resolving the interactions between airflow and urban features including buildings, trees, and vehicles. As we well know, trees play a significant role in pollution reduction by acting as physical barriers and facilitating deposition processes. Conversely, vehicle-induced turbulence enhances pollutant dispersion, creating flow patterns necessitating detailed analysis. A key consideration is the dual role of vegetation: by slowing down wind, trees can trap air pollution at ground level, increasing exposure, which might otherwise disperse pollutants vertically into less inhabited atmospheric strata. We will also examine the implications of this work on urban environmental justice, emphasizing potential enhancements and benefits to the community. Additionally, we will discuss the complexities of collaborating with regulatory agencies and the critical importance of democratizing environmental modeling via software development. This study entails a comparative analysis of various methods, objectives, and outcomes, uncovering diverse results from different modeling approaches and monitoring techniques. The findings highlight the importance of integrating detailed micro-scale simulations in urban planning to balance the benefits and drawbacks of various design features on pollutant dispersion -- as well as considering how our CFD can be utilized in partnership with existing CMAS models. Juan Penaloza Gutierrez, PhD CFD Specialist Hyphae Design Laboratory 456 8th St, Oakland, CA 94607 Email: juan@hyphae.net Professional Preparation: University of South Florida Tampa, FL, USA PhD Mechanical Engineering 2023 University of South Florida Tampa, FL, USA MS Mechanical Engineering 2020 Florida International University Miami, FL, USA BS Mechanical Engineering 2018 Professional Experience: 2021 - 2023 Principal instructor: Introduction to Design Graphics Appointments: March 2024 – Present Software Engineer (CFD modeler), Hyphae Design Laboratory, Oakland, CA Fall 2019 – Fall 2023 Graduate Research Assistant (advisor: Andrés E. Tejada-Martínez), University of South Florida,Department of Civil and Environmental Engineering, Tampa, Florida Fall 2021 – Summer 2023 Graduate Teaching Assistant Doctoral, Principal Instructor (Introduction to Design Graphics -EGN 1113), University of South Florida, Department of Civil and Environmental Engineering, Tampa, Florida Spring 2021 Graduate Teaching Assistant (Computational Fluid Dynamics - EML 4930), University of South Florida, Department of Mechanical Engineering, Tampa, Florida Spring 2020 Graduate Teaching Assistant (Thermodynamics - EGN 3343), University of South Florida, Department of Mechanical Engineering, Tampa, Florida Spring 2018 Teaching Assistant (Modeling and Control of Robots - EML 4806), Florida International University, Department of Mechanical Engineering, Miami, Florida Research Interests: Computational Fluid Dynamics (CFD). Reynolds Averaged Navier Stokes Simulation (RANSS). Large-eddy Simulation (LES). Finite Volume Method. Turbulent flows. Scalar transport. RANSS of wind-field and pollution dispersion in urban environments. RANSS of nearshore Langmuir circulation and the formation of Oil-Particle Aggregates. RANSS of aerosol dispersion in a ventilated hospital isolation room. Speaking Engagement/Abstracts: J.J. Peñaloza Gutierrez, along with A. E. Tejada-Martínez and collaborators, has extensively presented their research on Reynolds-Averaged Navier-Stokes (RANS) simulations of nearshore Langmuir circulation and its impacts, such as the formation of oil-particle aggregates and sediment transport, at various prominent conferences from 2020 to 2024. Their work has been featured at events like the Ocean Sciences Meeting (OSM), the Gulf of Mexico Conference (GOMCON), U.S. National Congress on Computational Mechanics (USNCCM), Engineering Mechanics Institute Conference (EMI), and the Annual Meetings of the APS Division of Fluid Dynamics. Notable presentations include analyses of Langmuir turbulence and its role in coastal ocean dynamics, with comparisons to large eddy simulations. Additionally, J.J. Peñaloza Gutierrez has contributed to studies on hospital isolation room aerosol dispersion modeling. Manuscripts in review/preparation: J.J. Peñaloza Gutierrez, A. E. Tejada-Martínez, “Reynolds Averaged Simulation Strategies for Langmuir Turbulence in the Coastal Ocean”, in preparation. A. J. Perez, J.J. Peñaloza Gutierrez, A. E. Tejada-Martínez, “Residence Time Distribution Analysis of Aerosol Transport and Associated Healthcare Worker Exposure in a Mock Hospital Isolation Room via Computational Fluid Dynamics”, submitted to Journal of Occupational & Environmental Hygiene. Publications: Perez, A. J., F. Cui, J.J. Peñaloza-Gutierrez, S. Zeidi, N. Sinha, M. Boufadel, C. Smith, D.W. Murphy, and A.E Tejada-Martínez (2021) “Simulation of Vertical Dispersion of Oil Droplets by Langmuir Supercells through a Reynolds-averaged Eulerian Formulation Combined with Lagrangian Particle Tracking”, Ocean Engineering, 235, 109043. J.J. Peñaloza Gutierrez, A. E. Tejada-Martínez, and M. C. Boufadel (2023) “Reynolds- averaged Navier-Stokes Simulation of Nearshore Langmuir Circulation and the Formation of Oil-Particle Aggregates”, Journal of Ocean Modelling, 187, 102306. Certification I certify that the information provided is current, accurate, and complete. This includes but is not limited to current, pending, and other support (both foreign and domestic) as defined in 42 U.S.C. §6605. I also certify that, at the time of submission, I am not a party to a malign foreign talent recruitmentprogram. Misrepresentations and/or omissions may be subject to prosecution and liability pursuant to, but not limited to, 18 U.S.C. §§ 287, 1001, 1031 and 31 U.S.C. §§ 3729-3733 and 3802. Signature: Juan Penaloza Gutierrez Date: 7/3/24 Juan Penaloza Gutierrez and Daniel Fleischer Hyphae Design Laboratory |
Improving CMAQ Representation of 2023 Wildfire Air Quality Impacts by Assimilating Satellite NO2 Data
Improving CMAQ Representation of 2023 Wildfire Air Quality Impacts by Assimilating Satellite NO2 Data
Joshua Kumm1,2, Barron Henderson3, Sergey Napelenok4, Heather Simon3, Zhen Qu2 1. ORISE Research Fellow @ US EPA 2. North Carolina State University, Department of Marine, Earth, and Atmospheric Sciences 3. US EPA Office of Air Quality Planning and Standards 4. US EPA Atmospheric & Environmental Systems Modeling Division This work explores the ability of satellite data assimilation to improve early modelling of the 2023 North American wildfire season. We assimilate Tropospheric Ozone Monitoring Instrument (TROPOMI) NO2 in the CMAQ chemical transport model using 3D variational data assimilation (3DVAR) and assess the changes in CMAQ simulation skill for ozone associated with the fires. Nitrogen oxides (NOx = NO + NO2) and volatile organic compounds (VOC) are emitted from forest fires and can be carried long distances in smoke plumes by atmospheric wind patterns, where they can react photochemically to form secondary pollutants such as tropospheric ozone and fine particulate matter. This process leads to increased concentrations of ozone and fine particulate matter even in remote locations, which was evident in the Eastern US as large wildfires raged in Canada during the summer and early fall of 2023. Proper estimation of NO2 and its byproducts is critical to understanding overall fire air quality impacts and informing policy decision making; however, fire emission inventories for 2023 are still under development due to the labor-intensive and time-consuming nature of fire inventory development. Preliminary inventories with high uncertainty have been incorporated into a rapid modeling effort known as Expedited Modeling of Burn Events Result (EMBER). Here, we attempt to improve the EMBER air quality state by developing and running a framework that uses CMAQ coupled with Gridpoint Statistical Interpolation (GSI) software for assimilating TROPOMI NO2 data. The resulting simulation is then compared to a CMAQ EMBER base simulation (with no assimilation) to analyze changes in NO2 and ozone, and how these changes compare to observations. We focus our analysis on the assimilation impact within the simulated fire plume and at downwind locations. Overall, this work seeks to aid the scientific understanding of wildfire air quality impacts, inform fire emissions inventory corrections, and enable swift regulatory decision making. Joshua Kumm 1. U.S. EPA ORISE; 2. North Carolina State University, Department of Marine, Earth, and Atmospheric Sciences |
3:50 PM |
AirFuse Real-time Application and Improved Integration of Geostationary Satellite Data
AirFuse Real-time Application and Improved Integration of Geostationary Satellite Data
Barron H. Henderson, Halil Cakir, Brett Gantt, Ben Wells, Janica Gordon, Phil Dickerson, Hai Zhang, Alqamah Sayeed, Shobha Kondragunta, Yang Liu, Meng Qi, and the rest of the HAQAST AirNow team This presentation provides an update on the AirFuse system that was introduced last year at CMAS. AirFuse integrates monitors, sensors, models, and satellites to make the best air quality estimates available for AirNow. Last year, we showed results from a historical period and highlighted the strengths and weaknesses of the system. This year will provide evaluation of a real-time pilot application that began in March of 2024, but only uses monitors, sensors, and models. We will also share updates on the more complete integration of GOES-R satellite PM25 data for the next release. AirFuse has been piloted the EPA running every hour of every day using just the monitors and sensors. As a standard part of AirFuse, it outputs a 10-fold cross validation that allows us to assess the performance. In October, we will have 8 months of live performance data. This is a good test of the data screening and will highlight how moving to AirFuse will increase the fidelity of air quality in AirNow. We will also share the updates on integration of GOES-R into the AirFuse system. The GOES-PM25 satellite data fusion has also been running every daylight hour of every day. The GOES-PM25 system has been updated to improve pixel quality assurance and averaging techniques on the AOD that improves the weighted regression-based PM25. In addition, the machine learning bias-correction algorithm has been updated to further improve the transition between subdomains. Finally, we’ve been improving the weighting methodologies to better integrate GEOS-PM25 with the monitors and sensors to create a final fused product. This talk will cover the updates to the methodology, real-time validation results, evaluate the new integration of GOES-PM25, and outline a plan for a next version in AirNow. Barron Henderson is a Physical Scientist at the United States Environmental Protection Agency. He uses theory, computer simulation, and satellite data to explore scientific and societal issues related to air pollutants. Barron's body of work advances process-level understanding and quantitative constraints within air quality models, and uses those models to quantify integrated impacts of air pollution at local, regional and global scales. Barron H. Henderson US EPA |
|
4:10 PM |
The Unified Platform - an evolution to the way we store and process air ambient air quality data, emissions data, and more…
The Unified Platform - an evolution to the way we store and process air ambient air quality data, emissions data, and more…
Martin Husk, Phil Dickerson, Rob Wildermann, Alison Eyth, Ketan Patel The Unified Platform envisioned by EPA’s Office of Air Quality Planning and Standards (OAQPS) is a single architecture that processes, stores, and distributes ambient, emissions, and regulatory data from one central location. This platform, funded by IRA resources, streamlines and modernizes air quality data systems, enhancing emission reporting, accountability, and data accessibility. The platform will integrate programmatic and community air quality information, improve access for permitting authorities and communities, thereby enabling better monitoring of air quality improvements over time. Alison Eyth has been working in the environmental field since 1993. She has been with EPA's Office of Air Quality Planning and Standards since 2009 and is the Emissions Modeling Team Leader. In this role, she is responsible for preparing emissions data for air quality modeling. She has degrees in Computer Science from the University of Pennsylvania and in Environmental Engineering from Syracuse University. Alison Eyth US EPA |
|
5:00 PM | Poster Introductions | |
5:15 PM | Reception and Poster Session, Chaired by Taciana Albuquerque, Federal University of Minas Gerais and Yang Zhang, Northeastern University
Emissions Inventories, Models, and Processes
Application of AERMOD in Air Dispersion Modeling of Methane Emissions: A Case Study from Karnes County in the Eagle Ford Shale
Application of AERMOD in Air Dispersion Modeling of Methane Emissions: A Case Study from Karnes County in the Eagle Ford Shale
Mohammadreza Bohloul, Lucas Henneman Methane (CH₄) is a potent greenhouse gas (GHG) and contributes to ozone formation. There are various natural and human-made sources that emit methane into the atmosphere, with one of the most significant being the fossil fuel industry. Methane leaks occur during the extraction, processing, and transport of oil and natural gas. The United States ranks among the top countries in the world in terms of the size and output of its fossil fuel industry, particularly in oil and natural gas production. One of the most important regions in this industry is the Eagle Ford Shale (EFS) with a total of 17,414 production sites in counties located in the EFS, which stretches across Southern Texas from the Mexican border northeast towards East Texas. Karnes County, TX produces the most oil, gas, and condensate within the EFS. We utilized the AERMOD modeling system to predict methane concentrations in Karnes County from individual well sites and well processes. We considered 402 well process locations area-weighted to their emissions centroids on a 36 x 36 km grid, each containing various emission sources such as tank batteries, pneumatic control valves, and leaks. There are 1,123 emission sources across these locations. We determined methane concentrations at multiple temporal scales across a high-resolution grid receptor network and at specific locations, using an emission inventory reported by Center for Energy and Environmental Resources of the University of Texas at Austin, to evaluate the model against observations. Furthermore, we estimated methane contributions from different source categories, allowing us to identify which categories had a significant impact on methane concentrations. Our findings provide insight into which Unconventional Oil and Natural Gas Development (UOGD) processes contribute to air pollution exposure in the EFS. Our results show that the maximum daily average contribution from these sources in Karnes City is 0.25 (μg/m^3 ), with pneumatic control valves contributing the most impact at about 64 percent across a 2-month study period (March-April 2020). We expect that this study will elucidate the adverse impacts of industrial activities on environmental and public health. Additionally, it will contribute valuable perspectives for making informed decisions and devising effective air pollution control strategies in future research. Mohammadreza Bohloul Department of Civil, Environmental, and Infrastructure Engineering, George Mason University.
Impacts on Emissions of Ozone Precursors in Arizona of Adopting the Ozone Transport Commissions Consumer Products Model Rules and Californias On-Road Mobile Source Regulations
Impacts on Emissions of Ozone Precursors in Arizona of Adopting the Ozone Transport Commissions Consumer Products Model Rules and Californias On-Road Mobile Source Regulations
Shih Ying “Changsy” Chang,1,2 Jeff Houk,1 Evan Couzo,1 and Isaac Afreh1 1 Sonoma Technology, Petaluma, CA 2 cchang@sonomatech.com, (707)-665-9900 The Phoenix-Mesa area in Arizona is not in attainment for the National Ambient Air Quality Standard (NAAQS) for ozone concentrations. A study was conducted to evaluate model rules and regulations aimed at reducing emissions of ozone precursors, specifically nitrogen oxides (NOx) and volatile organic compounds (VOC). The investigation focused on two main emission sectors: consumer products and on-road mobile sources. For consumer products, Phases IV and V of the Ozone Transport Commission’s (OTC) Consumer Product Model Rules were assessed. The OTC model rules establish standards for VOC content in consumer products; these standards were integrated with the methodology developed for the 2020 National Emissions Inventory’s (NEI) nonpoint solvent sector (np_solvent) to estimate changes in VOC emissions. For on-road mobile sources, the California Air Resources Board’s (CARB) Advanced Clean Cars II (ACC II) regulations, Advanced Clean Trucks (ACT) regulations, and Heavy-Duty Omnibus regulations were examined. These CARB regulations mandate the increased sales of electric vehicles (EVs). These CARB regulations mandate the increased sales of electric vehicles (EVs). For example, the ACC II regulations set a goal of having EVs as 100% of vehicles sold by 2035. The Motor Vehicle Emissions Simulator (MOVES) was used to model the emissions for Arizona's adoption of these regulations. The results indicated that implementing Phases IV and V of the OTC model rules would reduce VOC emissions statewide by 4,846 tons (8% of the np_solvent sector) per year in 2026 and 9,975 tons (16% of the np_solvent sector) per year in 2027. Adopting California’s on-road mobile source regulations could decrease carbon monoxide (CO) emissions by 46 tons (8% of the on-road mobile source sector), NOx emissions by 3 tons (6% of the on-road mobile source sector), and VOC emissions by 2 tons (5% of the on-road mobile source sector) per ozone season day by 2032. The methodologies used in this study can be applied by other states to evaluate potential emission reductions in these sectors. Shih Ying "Changsy" Chang Sonoma Technology
Comparison of Nonpoint Oil & Gas 2022 Modeling Inventories: TCEQ 2022v1 vs. EPA 2022v1
Comparison of Nonpoint Oil & Gas 2022 Modeling Inventories: TCEQ 2022v1 vs. EPA 2022v1
Rob Keirstead, Michael Ege, Bryce Kuchan Texas Commission on Environmental Quality (TCEQ) have developed emissions inventory for the Texas Oil and Gas sector for the 2022 modeling year. The Environmental Protection Agency (EPA) has released version 1 of their 2022 Emissions Modeling Platform, where nonpoint oil and gas emissions are estimated using EPA’s Oil and Gas Emissions Estimation tool. The TCEQ 2022 nonpoint oil and gas inventory was developed using TCEQ’s own Oil and Gas calculator, which estimates emissions differently than the EPA tool for some oil and gas categories. Emissions sources include drilling, compressor engines, storage tanks, pneumatic pumps, heaters, and hydraulic fracturing pumps. The different methodologies for deriving emissions as well as the processing steps to allocate them temporally and spatially between the TCEQ and EPA will be discussed. For example, for compressor engines EPA assumes a number of engines per well, while TCEQ bases the emissions on the amount of gas produced. The total amounts of oil & gas emissions by source category in the two inventories as well as the parameters that impact emission estimations (activity data, well counts, produced hydrocarbons, etc.) and processing factors (VOC lb/BBL for storage tanks, g/hp-hr for compressor engines, etc.) will be compared. Differences in projection methods for future year emissions between TCEQ and EPA will also be outlined as well as an analysis of the effectiveness of these projection methods based on comparisons of previous future year estimates and current emissions estimates. Finally, the impact of spatial and temporal processing on emissions rates will be reviewed in a broad sense. Rob Keirstead TCEQ
Developing a method to adjust NOx emissions in North Korea using machine learning, satellite observations, and simulations
Developing a method to adjust NOx emissions in North Korea using machine learning, satellite observations, and simulations
Eunhye Kim, Rak-hoon Son, Hyun Cheol Kim, Jung-Hun Woo, Soontae Kim Air pollution in North Korea is reported to be severe. However, North Korea does not provide official emission inventories, and accessible information for estimating emissions is very limited, resulting in high uncertainty in the emission inputs used for air quality models. Since air pollutants can transport long distances, the uncertainty in North Korea's emissions could affect air quality not only within North Korea but also in the Northeast Asian region. Therefore, estimating accurate emission data for North Korea is crucial for accurately predicting air quality in the Northeast Asia region. Previous studies have utilized satellite observation data to estimate long-term emissions trends in regions with high emission uncertainties, such as North Korea. However, due to differences in the concentration distribution between the surface and upper level, it is challenging to quantitatively estimate surface emissions or concentrations using vertical column densities (VCD) data. In this study, we developed surface concentration fields from Geostationary Environment Monitoring Spectrometer (GEMS) NO2 VCD using machine learning and adjusted NOx emissions which enables minimize discrepancies between machine learning-based NO2 concentration fields and emission inventory-based NO2 simulated concentrations. This methodology was initially applied to South Korea for December 2022, and the adjusted emission-based NO2 simulated concentrations were validated by comparing them with available ground and satellite observed concentrations in South Korea. The same methodology was applied to North Korea to estimate NOx emissions, and the simulated NO2 VCD based on the adjusted emissions was compared with observed NO2 VCD. This approach is significant as it allows for the estimation of emissions in regions with limited ground observations and emission information. This method can improve the simulation accuracy of not only NO2 but also secondary pollutants' concentrations, improving in air quality prediction in both the target and surrounding regions. Eunhye Kim Ajou University
Enhancing vertical allocation and temporal distributions of traffic emissions in Taiwan
Enhancing vertical allocation and temporal distributions of traffic emissions in Taiwan
Yi-Ju Lee, Bo-Sheng Huang, and Fang-Yi Cheng Taiwan's dense population, limited plain areas, and complex topography have led to the construction of several elevated highways and alternative roads to improve vehicle flow rates and reduce traffic congestion. Accurate air quality modeling of traffic emissions necessitates adequate vertical and temporal allocation. Traditionally, these emissions are assumed to occur at the surface level, neglecting road heights. Additionally, variations in vehicle activity across weekdays, weekends, and long weekends are not adequately accounted for in current temporal allocations. This study addresses these gaps by reallocating the vertical distribution of traffic emissions using measured road height data. Temporal variations of traffic emissions were captured using high-resolution traffic volume data from Taiwan's highway electronic toll collection system. The CMAQ model version 5.2 was employed to evaluate the impact of these adjustments over a simulation period from December 26, 2022, to March 5, 2023, which included holidays of varying lengths: three days, four days, and the ten-day Lunar New Year. Results indicate that the updated traffic emissions allocation significantly improves the model's accuracy, particularly in reducing the overestimation of NOX and the underestimation of O3, especially under weak synoptic weather conditions at night. Yi-Ju Lee Department of Atmospheric Sciences, National Central University, Taiwan
WRF-CMAQ modeling Evaluation over a mining area in the Minas Gerais state, Brazil, for the 2020 year
WRF-CMAQ modeling Evaluation over a mining area in the Minas Gerais state, Brazil, for the 2020 year
Otavio Medeiros Sobrinho, William Vizuete, Rizzieri Pedruzzi, Leila Droprinchinski Martins, Taciana Toledo de Almeida Albuquerque In Brazil, Minas Gerais state is responsible for more than 70% of Brazilian iron production, extracted by open pit mining. Due to this extraction method, emissions from mining areas significantly influence atmospheric particulate matter concentrations with 10 microns or less in diameter (PM10). Surface air quality station data of PM10 and field campaign measurements show significant proportions of crustal elements, featuring a robust presence of natural iron enrichment. These field measurements have focused on the Belo Horizonte’s urban area, resulting in a limited observational data set needed to evaluate the impacts of mining in the Minas Gerais state. This is further hampered by the limited number of surface monitoring stations found in the region. Given these limitations, air quality models are a critical tool in assessing mining emissions' source, fate, and transport. In this context, this work aims to evaluate model predictions of PM10 near an open pit mine in the Minas Gerais state and identify model performance issues related to mining activity represented in the emission inventory. Simulations were completed for 2020 using the spell out (WRF-CMAQ, version 4.1.3 and 5.3.2). The modeling systems' meteorological initial and boundary conditions were obtained from the Global Data Assimilation System and were also supplemented by observational data (GDAS/FNL), air quality initial and boundary conditions from Goddard Earth Observing System - Chemical transport model, anthropogenic emissions from Betim-Contagem-Belo Horizonte local emissions inventory and Emission Database for Global Atmospheric Research - Hemispheric Transport of Air Pollution version 2 for sources are not accounted and not spatially coverage for the local inventory, biogenic emissions from Model of Emissions of Gases and Aerosols from Nature version 3.1, and Fire Inventory of National Center for Atmospheric Research version 1.5 for fire outbreaks emissions data. To evaluate model performance and avoid potential emissions from Belo Horizonte City or other industrial areas, the mining area of Itabira City was selected. Three air quality stations in this city, with hourly data PM10 and wind data, are located along the mine’s fence line. For the 2020 modeling episode, the air quality model consistently underestimates PM10 concentrations observed at these three monitors with a normalized mean bias (NMB) of ~50% (NMB = -0.52). The model overestimated wind speeds with an NMB = 1.58 for these monitors. Wind speed overpredictions suggest that the PM10 emitted by these activities is incorrectly transported or diluted to lower concentrations. 92 PM10 concentration peaks were observed in the hourly observational data at the selected stations throughout this year. The model performed even worse during these periods, with an NMB of -0.95. When the fence line data was filtered to minimize wind errors, the model prediction errors in PM10 concentrations persisted. Thus, model performance issues can partly be attributed to uncertainties associated with mining activities and/or predictions of wind-blown dust. Otavio Sobrinho Federal University of Minas Gerais
TEMPO Measured High Spatial/Temporal Resolution of NO2 Column Concentration over NY Capital Regions
TEMPO Measured High Spatial/Temporal Resolution of NO2 Column Concentration over NY Capital Regions
Valerie Nadine Scull, Supervised by Dr. Jie Zhang University at Albany, State University of New York Nitrogen dioxide (NO2) is crucial due to its significant health impacts and role as a precursor to other pollutants. However, its measurements are largely limited in spatial and temporal resolution through the traditional ground measurements and the commonly used satellite observations (i.e., OMI and TROPOMI). This study uses NASA's new Tropospheric Emissions: Monitoring of Pollution (TEMPO) observations to explore the spatial/temporal tropospheric nitrogen dioxide (NO2) column concentrations in the NY Capital (Albany) regions, aiming to highlight the ways in which the new satellite will improve understanding of air quality variations in community levels, as well as for environmental justice purposes. The TEMPO data from August 2023, September 2023, and May 2024 are used to analyze NO2 patterns over Albany and the surrounding area. Preliminary results indicate distinct daily patterns in NO2 levels, with peaks during morning and evening rush hours, highlighting the hypothesized impact of commuter traffic. Comparisons between weekdays and weekends demonstrate significant differences in NO2 levels, emphasizing the influence of personal vehicle use. This study underscores the potential of TEMPO data in understanding NO2 dynamics and informing air quality policies. The findings support the need for continued and expanded use of high resolution satellite data to address environmental justice and public health concerns related to air pollution. Future research will benefit from more extensive datasets as TEMPO continues to collect and refine its measurements. Valerie Scull UNC-Chapel Hill
Revisiting Tire Wear Emissions from On-road Vehicles in MOVES4 in a Vehicle Electrification Era
Revisiting Tire Wear Emissions from On-road Vehicles in MOVES4 in a Vehicle Electrification Era
Aashish Aryal, Micah Craine, Salahuddin Setu, and Hosein Foroutan Non-exhaust particulate matter (PM) emissions have gained increasing attention due to stricter exhaust emission policies and the growing global vehicle fleet. As the automotive industry rapidly advances towards vehicle electrification, PM exhaust emissions are virtually eliminated, and non-exhaust brake wear PM is considerably reduced due to regenerative braking systems in electric vehicles (EVs). Consequently, tire wear particles (TWP) have become a critical component of total traffic PM emissions, particularly as EVs tend to be heavier than gasoline vehicles due to battery weight. There is an urgent need to accurately quantify tire wear emissions from on-road vehicles. However, the algorithm to estimate TWPs in the latest version of the U.S. EPA’s MOtor Vehicle Emission Simulator (MOVES4) has not been updated and still relies on outdated studies from the early 2000s, where the total tire weight loss (rather than the actual airborne particles) was measured. MOVES4 uses predefined fixed values for both PM10 fraction of total tire mass loss and the PM10-to-PM2.5 ratio to estimate airborne TWPs. In this work, we revisit these assumptions using data from more recent studies, including our own, where size-resolved measurements of actual airborne TWPs were taken. Leveraging insights from these measurements, we updated the MOVES4 database and investigated the implications of these changes on the total PM emissions from on-road vehicles at the national level. Our revised approach potentially provides a more accurate representation of TWP emissions in the context of increasing vehicle electrification, supporting the development of effective environmental policies and mitigation strategies. Salahuddin Setu Virginia Tech
Comparison of Biogenic Emission Estimates from BEIS and MEGAN Models for Texas and the Continental USA
Comparison of Biogenic Emission Estimates from BEIS and MEGAN Models for Texas and the Continental USA
Kira Sorochkina, Tejas Shah Biogenic emissions establish an important baseline for modeling air pollutants and have a substantial influence on air pollutants like ozone. Biogenic volatile organic compounds (BVOC) include emissions such as isoprene and terpenes emitted by vegetation and nitrous oxide compounds (NOx) emitted by soil microorganisms, all of which directly interact with the ozone cycle. Biogenic Emissions Inventory System (BEIS) is the current model of choice used to generate biogenic emissions by most states and the EPA. Model of Emissions of Gases and Aerosols from Nature (MEGAN) is another biogenic model that differs in several ways from BEIS. One major difference is that MEGAN is capable of using a soil NOx algorithm called BDSNP (Berkeley-Dalhousie Soil NOx Parametrization), in addition to the currently and the only used soil NOx algorithm YL (Yienger and Levy 1995) in BEIS. BDSNP incorporates a more continuous soil temperature/moisture dependence function, wet and dry nitrogen (N) deposition and utilizes soil moisture for more accurate pulsing compared to the YL algorithm in BEIS. In this study, biogenic models BEIS version 4 with BELD6 and MEGAN version 3.2 will be used to estimate air emissions including NOx and VOCs for U.S. 12-km and 4-km modeling domains that encompasses central and southeastern parts of Texas. Impacts of model type, soil NOx algorithm, drought sensitivity, and seasonality on biogenic emissions models will be evaluated qualitatively and quantitatively. Kira Sorochkina Texas Commission on Environmental Quality (TCEQ) Modeling to Support Exposure, Health and Environmental Justice Studies at Multiscales
Assessing Health Risks and Disparities in Air Pollution and Socioeconomic Status across the United States
Assessing Health Risks and Disparities in Air Pollution and Socioeconomic Status across the United States
Khanh Do and Yang Zhang Air quality in the United States (US) has significantly improved over the last few decades. Nevertheless, many parts of the country still suffer from high levels of air pollution, particularly in non-attainment areas. Human-induced air pollution is associated with various activities, including industrial operations, transportation, agriculture, and other residential activities, which emit primary gaseous pollutants such as sulfur dioxide, carbon monoxide, volatile organic compounds, and nitrogen oxides, and particulate matter. These polluted areas contribute to disparities in air pollution exposure among different population groups. This study systematically analyzes the impact of air pollution on human health across the US, focusing on high population density areas and assessing the correlation between criteria pollutants and diseases. Using inequality curves, we quantify the disparities faced by disadvantaged groups in terms of disease risks. Additionally, we assess the environmental health and air pollution disparities across the US by integrating socioeconomic and air quality data with health surveys by the Centers for Disease Control and Prevention. Our analysis indicates that fine particulate matter (PM2.5) is associated to most health problems, while ozone (O3) correlates strongly with cancer and kidney disease prevalence. Health risks significantly increase when the levels of PM2.5 and O3 exceed 14 µg m-3 and 40 ppb, respectively. Furthermore, a strong correlation exists between disease prevalence, air pollution levels, and socioeconomic status. Minority and low-income groups in the US are exposed to higher PM2.5 levels and are prone to greater health risks, including asthma, high blood pressure, mental health, physical health, diabetes, and kidney disease. Notably, individuals exposed to PM2.5 levels greater than 9.0 µg m-3 are at a greater risk of self-rating their health as poor. The analysis suggests that reducing air pollution leads to decreased environmental health risks, benefiting both disadvantaged and advantaged groups. Given that minority and low-income populations face higher exposure rates and prevalence of health issues, there is a need for local authorities to investigate environmental health and justice concerns within the communities. Khan Do Civil and Environmental Engineering Department, Northeastern University, Boston, MA 02115, USA
Enhancing Urban Environmental Quality through Computational Fluid Dynamics: Case Studies and Methodologies in Green Infrastructure Design
Enhancing Urban Environmental Quality through Computational Fluid Dynamics: Case Studies and Methodologies in Green Infrastructure Design
Fleischer, Daniel; Penaloza Gutierrez, Juan This presentation explores environmental modeling with an emphasis on Computational Fluid Dynamics (CFD) applications, drawing on case studies from our nesting projects in West Oakland, CA, Dallas, TX, Louisville, KY, and our National Science Foundation (NSF) grant development efforts, which are pivotal to the development of the AdaptOS platform. The primary objective of our research is to enhance urban environmental quality through the implementation of green infrastructure. Our methodology encompasses extensive monitoring and modeling to establish baseline metrics, followed by strategic design interventions aimed at improving air quality and mitigating heat. The CFD modeling process requires inputs such as a domain model, meteorological data, and traffic data. The domain model, or digital twin, represents the urban environment's structure and may depict existing, historical, or proposed conditions. CFD can estimate the effects of design decisions on air pollution metrics by comparing different digital twins while keeping other inputs constant. Urban areas in particular, are characterized by complex interactions between various built and natural features, influencing the dispersion of pollutants. Our research employs CFD to analyze pollutant dispersion in urban environments, focusing on micro-scale simulations that capture detailed flow dynamics around buildings, streets, and trees. Understanding these local variations is crucial for mitigating pollution exposure and optimizing urban design. In our studies, we aim to address urban flows at the meter to sub-meter scales, resolving the interactions between airflow and urban features including buildings, trees, and vehicles. As we well know, trees play a significant role in pollution reduction by acting as physical barriers and facilitating deposition processes. Conversely, vehicle-induced turbulence enhances pollutant dispersion, creating flow patterns necessitating detailed analysis. A key consideration is the dual role of vegetation: by slowing down wind, trees can trap air pollution at ground level, increasing exposure, which might otherwise disperse pollutants vertically into less inhabited atmospheric strata. We will also examine the implications of this work on urban environmental justice, emphasizing potential enhancements and benefits to the community. Additionally, we will discuss the complexities of collaborating with regulatory agencies and the critical importance of democratizing environmental modeling via software development. This study entails a comparative analysis of various methods, objectives, and outcomes, uncovering diverse results from different modeling approaches and monitoring techniques. The findings highlight the importance of integrating detailed micro-scale simulations in urban planning to balance the benefits and drawbacks of various design features on pollutant dispersion -- as well as considering how our CFD can be utilized in partnership with existing CMAS models. Daniel Fleischer and Juan Penaloza Gutierrez Hyphae Design Laboratory
Simulation of Neighborhood-Scale Air Quality by integrating CMAQ with WRF and SMOKE Over the Washington D.C. Metropolitan Area
Simulation of Neighborhood-Scale Air Quality by integrating CMAQ with WRF and SMOKE Over the Washington D.C. Metropolitan Area
Hoda Hallaji, Lucas Henneman Abstract: The Washington D.C. metropolitan area, encompassing the nation's capital and its densely populated suburbs, is an urban landscape with significant vehicular and industrial emissions. We use EPA’s Community Multiscale Air Quality model (CMAQ v5.4+) with a nested domain setup of 12 km2 covering the entire US, 4 km2 covering the eastern US, and 1 km2 across approximately 1,500 km² of the region. The fine-scale 1 km2 resolution enables us to capture detailed meteorological and chemical dynamics at neighborhood level scales that are not possible from typical resolutions. To make this fine-scale modeling possible, we derive meteorological data from the Weather Research Forecast (WRF) model version 4.2.1. Fine-scale gridded emissions come from processing emissions data using the Sparse Matrix Operator Kernel Emissions (SMOKE) model version 4.7, utilizing the 2017 National Emissions Inventory (NEI2017) alongside the Neighborhood Emission Mapping Operation (NEMO) dataset, which was developed using 1 km² emissions surrogates. The detailed 1 km2 resolution facilitates the identification and analysis of specific areas that may benefit or experience adverse effects from local regulatory policies, particularly zones near roads within regulated areas or regions outside these zones where traffic may increase. By employing this innovative modeling approach, we aim to provide insights that can support hypothesis-driven policy implementation studies and inform effective urban environmental policymaking, with a focus on the health and equity impacts of localized air quality changes. Hoda Hallaji Department of Civil, Environmental and Infrastructure Engineering, Georege Mason University
Source Apportionment of PM2.5 across Contiguous United States for 2017
Source Apportionment of PM2.5 across Contiguous United States for 2017
Sumaiya Hussain, Lucas RF Henneman, Ting Zhang PM2.5 accounted for 7.3% (4.1 million) of total global deaths in 2019. Control of secondary PM2.5 is more difficult as compared to primary PM2.5 as it requires identification of precursors and their sources, and understanding of the complex chemistry that causes formation of secondary PM2.5. Moreover, the relationship between the concentration of PM2.5 and health varies temporally and spatially due to different toxicities and chemical compositions of PM2.5. Most epidemiological studies do not differentiate between pollution sources as limited source specific data is available. To control the formation and reduce the effects of PM2.5, it is necessary to know emission sources contributing to release of PM2.5 or its precursors. This study aimed to perform source apportionment of PM2.5 over the Contiguous United States (CONUS) for 2017 by employing EPA’s CMAQ Model using ISAM for multiple point and non-point sectors for all PM2.5 species, including sulfate, nitrate, ammonium, EC, OC, and VOC. We calculated that EGUs contribute to an average of 0.13 ug/m3 PM2.5 across the contiguous United States, on-road and non-road gasoline contributes to 0.036 ug/m3 PM2.5 and on-road and non-road diesel contributes to 0.031 ug/m3 PM2.5. EGU sensitivities are larger in the east and near power plants and range from 0 to 12.5 ug/m3. On-road sensitivities are larger near cities; with gasoline-related sensitivities ranging from 0 to 11.5 ug/m3 and diesel-related sensitivities ranging from 0 to 11.9 ug/m3. The results of this study will provide detailed source specific PM2.5 data which can facilitate environmental justice and accountability studies. This data will also help understand the spatial and temporal variability in sectors contributing to ambient PM2.5 concentrations and their equitable distribution across populations. Sumaiya Hussain George Mason University Department of Civil, Environmental, and Infrastructure Engineering, Fairfax, VA
The impact of spatial resolution on evaluating air pollution exposure disparities from power generation
The impact of spatial resolution on evaluating air pollution exposure disparities from power generation
Bianca Meotti, Jeremiah J. Johnson, Fernando Garcia Menendez. Air pollution is one of the largest risk factors for disease and its effects are unevenly distributed across different ethnic and socioeconomic groups. The power sector significantly contributes to ambient fine particulate matter (PM2.5), which has been identified as the largest driver of the burden of disease associated with air pollution. While many studies have examined racial-ethnic and socioeconomic disparities in air pollution impacts from power plants, few have evaluated the uncertainty in estimates of disparity arising from the spatial resolution of air pollution modeling frameworks used. In this study, we assess how the spatial resolution of an air pollution modeling framework influences the assessment of racial-ethnic and income-based disparities in air pollution exposure from electricity generation in Texas. We utilize CALPUFF, an air pollution dispersion model, to simulate ambient concentrations of PM2.5 from coal and natural gas power plant emissions at different spatial resolutions. Additionally, we calculate the marginal health costs associated with premature deaths by race and income due to these PM2.5 concentrations. Here, we apply the modeling framework to Texas power plant to show how variations in spatial resolution of modeled air pollution fields affect assessments of exposure disparities in the power sector. Moreover, our findings explore the implications of spatial resolution and model uncertainty for decision-making aiming to integrate socioeconomic disparities into power system planning. Bianca Meotti North Carolina State University
Bias correction of PM2.5 for the application of epidemiological studies
Bias correction of PM2.5 for the application of epidemiological studies
Yu Morino, Chisato Kimura (National Institute for Environmental Studies) Regression or machine learning (ML) models with observational and land-use data have been applied for the estimate of concentration fields of air pollutants for epidemiological studies. Results from chemical transport models (CTM) contain uncertainties in reproducing concentrations, though it has recently been shown that CTM data can help to improve the estimation of concentration fields using ML models. This study analyzes the results of ML calculations with and without CMAQ data for the construction of daily and annual PM2.5 concentration fields. We compared PM2.5 concentration fields calculated with (1) ordinary and regression kriging for PM2.5 (KRIG(PM2.5)), (2) random forest (RF) with CCTM (concentration), MCIP (meteorology), EMIS (emissions) and kriging data for compounds other than PM2.5 (KRIG(other)), (3) RF with CCTM, MCIP and EMIS, (4) RF with MCIP, EMIS and kriging data, and (5) RF with MCIP and EMIS. Cross-validation shows that KRIG(PM2.5) best reproduces the observed PM2.5, although the performance of RF with CCTM and KRIG(other) is similar to KRIG(PM2.5). Performance of RF without CCTM or KRIG(other) is worse than the other models, suggesting that CCTM results are useful for estimating PM2.5 concentration fields for health studies. Yu Morino National Institute for Environmental Studies
CMAQ-AERMOD Hybrid Modeling for EPAs 2020 AirToxScreen Toxics Assessment
CMAQ-AERMOD Hybrid Modeling for EPAs 2020 AirToxScreen Toxics Assessment
Sharon Phillips, James Thurman, Alyssa Piliero, Art Diem, Alison Eyth, Caroline Farkas, Jeanette Reyes, Matt Woody, Chad Bailey, Grace Kuiper EPA has recently released the 2020 AirToxScreen results, a national level screening assessment of air toxics risk based on the EPA’s multi-pollutant platform. This platform includes the National Emissions Inventory (NEI) and air quality modeling of air toxics concentrations for the contiguous U.S., Alaska, Hawaii, Puerto Rico, and the U.S Virgin Islands. EPA has committed to releasing AirToxScreen on an annual basis with assessments for 2017-2019 being completed over the past two years along with the recent release of 2020 data. The multi-pollutant platform relies on air toxic emissions from the NEI to estimate concentrations using the photochemical Community Multiscale Air Quality Modeling System (CMAQ) at 12-km and the AMS/EPA Regulatory Model (AERMOD) dispersion model. Estimates from these two models are combined in a hybrid approach to calculate concentrations for cancer and non-cancer risk characterization. The hybrid approach leverages methodological strengths of both CMAQ – photochemistry, characterization of wild and prescribed fires, estimation of biogenic concentrations, and long-range transport of air pollutants – and AERMOD – simulation of near source impacts and concentration gradients using a network of gridded receptors, census block receptors, and monitor locations. We will present the methodology of this hybrid modeling approach and evaluations of CMAQ, AERMOD, and CMAQ-AERMOD hybrid concentrations compared to observed concentrations for the 2020 iteration of AirToxScreen. Sharon Phillips and James Thurman U.S. Environmental Protection Agency / Office of Air Quality Planning and Standards
Data fusion and linear regression models to obtain ambient air pollution exposure fields at fine resolution
Data fusion and linear regression models to obtain ambient air pollution exposure fields at fine resolution
Yifeng Wang, Dr. Armistead G. Russell Lower fertility is becoming an increasingly issue. Some previous studies have suggested that air pollution, specifically, traffic-related air pollution has adverse effects on human reproduction. However, there remain some unsolved issues, including the exposure windows, biological mechanisms, types of pollutants, etc. Our study applied a new model of the assisted reproductive technology (ART) to investigate the association between traffic related air pollution and lower fertility. To implement the epidemiology study, fine spatial-temporal resolution exposure fields are required. In our study, we developed exposure fields by fusing two air quality models: The Community Multiscale Air Quality (CMAQ) and the Research LINE-source model. Compared to using CMAQ model solely, fusing the CMAQ and RLINE can capture a higher resolution horizontal pollutant concentration gradient at near-roads area. We developed fused daily exposure fields of CO, NOx and PM2.5 at 250m resolution for Atlanta from 2005 to 2019. The data fusion model works good for non-near road sites, however, for CO and NOx, there exists discrepancies between predicted results and observed data at near road areas. We developed a linear regression-based model to enhance the performance and accuracy of exposure fields. Yifeng Wang Georgia Institute of Technology Regulatory Modeling and SIP Applications
Contribution assessment of meteorology vs. emissions in the ozone trend over China from 2014 to 2023 by an environmental meteorology index
Contribution assessment of meteorology vs. emissions in the ozone trend over China from 2014 to 2023 by an environmental meteorology index
Shuhua Lu1, 2, Sunling Gong1, 5, Yang Zhang2, Jianmin Chen3, Lei Zhang1, Huabin Ke4, Weijun Pan1, Jianyan Lu3, Yan You5 1 State Key Laboratory of Severe Weather & Key Laboratory of Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences, Beijing 100081, China 2 Department of Civil and Environmental Engineering, Northeastern University, Boston, MA 02115, USA 3 Department of Atmospheric and Oceanic Sciences and Institute of Atmospheric Sciences, Fudan University, Shanghai 200438, China 4 State Key Laboratory of Severe Weather & Institute of Artificial Intelligence for Meteorology, Chinese Academy of Meteorological Sciences, Beijing 100081, China 5 National Observation and Research Station of Coastal Ecological Environments in Macao, Macao Environmental Research Institute, Macau University of Science and Technology, Macao SAR 999078, China With the effective controls in anthropogenic emissions in recent years, PM2.5 decreased in most cities of China but the composite pollution of PM2.5 and O3 has become the main issue in atmospheric pollution. The rapid development of the economy and frequent occurrence of extreme weather events caused large temporal and spatial changes in O3 pollution in China. A new environmental meteorology index for O3 pollution (EMI-O3) has been developed to quantify the contributions of meteorological variations to the surface O3 trend in different areas based on the analyses of Weather Research and Forecasting model coupled to Chemistry (WRF-Chem) simulation results. Under fixed emissions (2016) and varying meteorology conditions from 2014 to 2023, several variables that influence the surface O3 trend in a region, i.e., chemical formation (chem_o3), vertical mixing couple with dry deposition (vmix_o3), horizontal advection (advh_o3 and advz_o3), and dry deposition velocity (dep_vel) are extracted from the simulation results and used to formulate the EMI-O3 index as a function of meteorology. Five representative cities in China are selected to test the usefulness of the index and evaluate its accuracy. The results show that EMI-O3 effectively explains the changes in O3 observations from 2014 to 2023, demonstrating its capability to quantify the meteorological impacts on O3 levels in China. Based on the EMI-O3, the mechanisms by which meteorological factors influence O3 anomalies have been explained. During July 2017 and July 2022, high EMI-O3 levels in Shanghai indicate that heat waves in these two summers had significant effects on O3 pollution through impacting its chemical formation process. The EMI-O3 framework also has been used to quantitatively assess individual contributions of meteorology and emissions in different regions. The changes of contributions by meteorology and emissions are quantified during the lockdown of COVID-19 in 2020. The results show that the contributions of emissions calculated by EMI-O3 were all decreased in the summer of 2020 in the eastern China compared with those during the summer of 2019, which is consistent with actual emission trends. Our EMI-O3 framework can effectively explain variations in O3 pollution by quantifying meteorological impacts on O3 levels, and it has notable applicability in the quantitative analysis of exploring the drivers of O3 pollution in China. Shuhua Lu Department of Civil and Environmental Engineering, Northeastern University
A study on the impact of different photochemical indicators and their transition values on the source apportionment of ozone pollution
A study on the impact of different photochemical indicators and their transition values on the source apportionment of ozone pollution
Chang-You Tsai, Tu-Fu Chen, Ken-Hui Chang The results of using photochemical indicators to determine O3 sensitivity can affect the source apportionment of O3 pollution in CMAQ-ISAM. Since O3 sensitivity in different regions is influenced by local meteorological conditions and emissions, this study compared the literature-recommended transition values of photochemical indicators such as O3/NOx and O3/NOY (Zhang et al., 2009; Ye et al., 2016; Tsai et al., 2024) with the transition value equations developed in this study for analyzing the source contributions of O3 pollution in Taiwan using CMAQ-ISAM. This study established a benchmark for O3 sensitivity to evaluate the ability of photochemical indicators to determine the sensitivity of daily maximum O3 (DMO3). The evaluation results showed that during pollution events (DMO3 ≥ 100 ppb), the overall accuracy of O3 sensitivity determination using the transition value equations developed in this study is 35% higher than that of the ISAM default transition values. The differences in O3 sensitivity determination also led to significant changes in the simulated O3 pollution source contributions by CMAQ-ISAM. This demonstrates that different transition value settings can influence the simulation of O3 pollution source contributions. Such differences could potentially lead to inappropriate pollution control strategies. The transition value equations developed in this study can improve the determination of O3 sensitivity, aiding CMAQ-ISAM in more accurately apportioning pollution sources and developing more precise strategies for ozone pollution control. Chang-You Tsai Department of Safety, Health and Environmental Engineering, National Yunlin University of Science and Technology
Impact of Light and Medium Duty Vehicle Rule on Air Quality in 2055
Impact of Light and Medium Duty Vehicle Rule on Air Quality in 2055
Margaret Zawacki (OTAQ), Sharon Phillips (OAQPS), Grace Kuiper (OTAQ) The Environmental Protection Agency (EPA) has recently finalized a rule to further reduce air pollution from light-and medium-duty vehicles across the United States; the rule will impact emissions of criteria and air toxic pollutants. EPA conducted an air quality modeling analysis for the year 2055 which included light-and medium-duty “onroad” vehicle emission reductions and changes in emissions from “upstream” sources like EGUs (electric generating units) and refineries. The analysis provides insight into the air quality impacts associated with emissions decreases and increases from these multiple sectors. In 2055 the final rule is projected to result in widespread decreases in air pollution. Due to increased electricity generation, the analysis suggests that some localized areas may experience an increase in ambient concentrations of criteria and air toxic pollutants. However, the Inflation Reduction Act (IRA) of 2022 and other future policies that were not accounted for in this analysis are expected to mitigate these impacts by making the power sector cleaner over time. Margaret Zawacki US EPA/OAR/OTAQ |
|
October 23, 2024 | ||
Grumman Auditorium | Redbud Room | |
7:30 AM | Registration and Continental Breakfast | |
8:00 AM | A/V Upload | A/V Upload |
Emissions Inventories, Models, and Processes, Chaired by B.H Baek, George Mason University and Jeff Vukovich, US EPA | Machine Learning and Reduced Form Models: Developments and Applications, Chaired by Jim Kelly, US EPA and Jia Xing, University of Tennessee-Knoxville | |
8:30 AM |
Session Keynote: Neighborhood Emission Mapping Operation (NEMO): 1-km Anthropogenic Emission Data for Multi-scale Air Quality Modeling and Environmental Justice Applications in the United States
Session Keynote: Neighborhood Emission Mapping Operation (NEMO): 1-km Anthropogenic Emission Data for Multi-scale Air Quality Modeling and Environmental Justice Applications in the United States
Daniel Tong, Siqi Ma, BH Baek, Jia Xing, Patrick Campbell, Youhua Tang, Barry Baker, Yang Zhang Emissions of air pollutants and greenhouse gases are the main driver of many Earth system processes behind some of the grandest environmental challenges today, such as air pollution and climate change. This work introduces a new effort to map anthropogenic emissions of air pollutants at 1-km spatial resolution across the entire Contiguous United States (CONUS). This high-resolution dataset, called the Neighborhood Emission Mapping Operation (NEMO), is produced at hourly intervals based on emission inventories from the United States Environmental Protection Agency (USEPA) for the years of 2017 and 2019. Fine-scale spatial allocation was achieved through distributing the emission sources using 108 spatial surrogates, factors representing the portion of a source in each 1km grid derived from survey or satellite data. Gaseous and particulate pollutants are speciated into model species for the Carbon Bond 6 chemical mechanism. All sources are grouped in 9 emission sectors and stored in the NetCDF format for air quality modeling, and in the Shapefile format for GIS users and air quality/public health managers. This dataset shows good consistency with the USEPA benchmark dataset, with a monthly difference in emissions less than 0.03% for any sector. NEMO provides the first 1 km mapping of air pollution over the CONUS, enabling new applications such as fine-scale air quality modeling, air pollution exposure assessment, and environmental justice studies. We will also discuss new development of NEMO, including recent updates to the years of 2021, and rapid refresh capability to generate near-real-time emission datasets to support national air quality forecast capability. Dr. Daniel Tong is Associate Professor of Atmospheric Chemistry and Director of Cooperative Institute of Satellite and Earth System Studies at George Mason University (GMU), Virginia, USA. He obtained B.S in Chemistry and B.A. in Finance from Ocean University of China, PhD in Atmospheric Sciences from North Carolina State University, and postdoctoral training in environmental policy at Princeton University. His research focuses on modeling and prediction of natural and anthropogenic emissions, and their effects on air quality, climate, and human health. Dr. Tong is a member of NASA Health and Air Quality Applied Science Team and an affiliated scientist of NOAA National Air Quality Forecast Capability (NAQFC) Team. He serves as the Chair of World Meteorological Organization (WMO) Sand and Dust Storms Warning Advisory and Assessment System, and the founding Co-Director of WMO Vegetation Fire Smoke Pollution North America Center. He is the President-Elect of American Geophysical Union (AGU) GeoHealth Section and a co-founder of the Dust Alliance for North America (DANA). Daniel Tong George Mason University |
Session Keynote: The Role of Physical Models in the Epic Advancement of Machine Learning
Session Keynote: The Role of Physical Models in the Epic Advancement of Machine Learning
Siwei Li School of Remote Sensing and Information Engineering, Wuhan University, Hubei, 430000, China With the rapid growth of machine learning techniques and their application in atmospheric science, traditional physical models are becoming even more crucial than we expected. They definitely cannot be replaced by machine learning. This presentation uses sample examples to illustrate the importance of physical models in AI applications and demonstrate how these models will be key to further improving AI model applications in atmospheric sciences. Firstly, physical models provide abundant datasets for training, which is crucial for data-driven models like machine learning to better represent the correlations among different atmospheric variables, including emissions, meteorological conditions, and concentrations. One application involves retrieving the vertical profile of NO2 concentration from column density. This demonstrates how datasets from physical models can establish the natural linkage between total column and specific vertical concentrations, enabling the retrieval of the NO₂ vertical profile from satellite measurements. Secondly, physical models provide a "ground-truth" dataset for evaluating and optimizing AI models. For example, when retrieving surface PM2.5 concentrations from satellite and ground measurements, an ignorable sample-imbalance problem is revealed using CMAQ simulation data as a testbed. Solutions to this problem are sought by conducting multiple testbed runs with the CMAQ simulation dataset. Lastly, physically constrained AI models will become increasingly important. Continuous improvements in physical models, through enhanced comprehensive physical schemes and chemical mechanisms, are more necessary than ever. These advancements will provide AI models with deeper scientific knowledge, leading to better applications in the future. Siwei Li received the Ph.D. degree in atmospheric sciences from the University at Albany, State University of New York, Albany, NY, USA, in 2012, with a focus on atmospheric remote sensing.
He is currently the Executive Vice-President of the Institute for Carbon Neutrality and also a professor of atmospheric sciences with the School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, China. He mainly works on satellite observation, model simulation and machine learning on air pollution and climate change. Siwei Li Wuhan University |
8:50 AM |
Impact of Major Updates to Canadian On-road Emissions Inventory and Spatial Surrogates on Regional Air Quality Model Predictions
Impact of Major Updates to Canadian On-road Emissions Inventory and Spatial Surrogates on Regional Air Quality Model Predictions
Junhua Zhang, Qiong Zheng, and Ali Katal Air Quality Research Division, Environment and Climate Change Canada, 4905 Dufferin Street, Toronto, ON, M3H 5T4, Canada junhua.zhang@ec.gc.ca Laurent Bruneau Cossette Air Quality Policy-Issue Response Section, Meteorological Service of Canada, Environment and Climate Change Canada, 2121 Rte Transcanadienne, Montreal, Quebec, H9P 1J3, Canada On-road motor vehicles are one of the important sources of pollutants that affect air quality. The current Environment and Climate Change Canada (ECCC) Regional Air Quality Deterministic Prediction System (RAQDPS) has been found to overestimate NO2 concentrations for Canada, particularly in large Canadian cities. This overestimation may be due to the spatial surrogates used for processing model-ready on-road emissions, which allocate excessive emissions to the urban areas. To address this, extensive efforts have been made to improve the on-road spatial surrogates, such as creating different off-network surrogates for different types of vehicles based on building types, e.g., residential building for passenger vehicles, commercial and industrial buildings for heavy duty vehicles, and truck rest stops for heavy duty vehicle hoteling. Additionally, on-road emissions reported in previous Canada’s Air Pollutant Emission Inventory (APEI) might have been overestimated. Recently in 2023, two major updates were made for APEI for estimating on-road vehicle emissions: 1) corrections to diesel fuel oil consumption determined from the Report on Energy Supply and Demand (RESD) in Canada, and 2) an upgrade of the MOtor Vehicle Emission Simulator (MOVES) model from MOVES2014 to MOVES3 (see, Table A3–4 of https://publications.gc.ca/collections/collection_2023/eccc/En81-30-2021-eng.pdf).These two updates resulted in approximately a 50% reduction in emissions for the major pollutants, such as NOx, PM2.5, and VOC. The updated spatial surrogates and revised on-road emission inventory will be evaluated using ECCC’s GEM-MACH (GEM–Modeling Air quality and CHemistry) model, which serves as the online one-way-coupled chemical transport model in ECCC’s RAQDPS. This presentation will discuss the impacts of those updates on model performance in predicting NO2, O3, and PM2.5 concentrations over different Canadian regions and large cities. Junhua Zhang Environment and Climate Change Canada |
A Deep Learning Method for Model-Measurement Fusion of Atmospheric Concentrations with Physical Constraints
A Deep Learning Method for Model-Measurement Fusion of Atmospheric Concentrations with Physical Constraints
Jia Xing1,2, Bok H. Baek1, Siwei Li3, Chi-Tsan Wang1, Ge Song3, Siqi Ma1, Daniel Tong1, Joshua S. Fu2 1 Center for Spatial Information Science and Systems, George Mason University, Fairfax, VA 22030, USA 2 Department of Civil and Environmental Engineering, the University of Tennessee, Knoxville, TN 37996, USA 3 Hubei Key Laboratory of Quantitative Remote Sensing of Land and Atmosphere, School of Remote Sensing and Information Engineering, Wuhan University, Hubei, 430000, China Accurate and efficient retrieval of atmospheric chemical concentrations across space and time is crucial for health and climate assessments. However, existing model-measurement fusion methods suffer from limitations due to imbalanced samples from ground measurements or less effective assimilation of satellite data along with numerical modeling. To address these limitations, this study introduces a novel Deep-learning Measurement-Model Fusion method (DeepMMF) constrained by physical and chemical laws inferred from numerical chemical transport models (CTM). This method is applied to NO₂ species over the Continental United States (CONUS) domain for the years 2019 and 2020. By pre-training with abundant CTM simulations, fine-tuning with satellite and ground measurements, and employing a novel optimization strategy for selecting proper prior emissions, the retrieved spatiotemporally continuous surface NO₂ concentrations present consistent values and daily variations with observations (NMB reduced from -0.3 to -0.1 compared to original CTM simulation). Importantly, the corresponding emissions have been simultaneously adjusted, showing good agreement with changes reported in the national emission inventory (NEI) between 2019 and 2020. Interpretation analysis suggests that the DeepMMF model effectively identifies the importance of satellite data at the regional level and ground measurements at the city level, which is scientifically sound. It exhibits consistent prediction of ground measurements while successfully avoiding the sample imbalance problem that leads to overestimation (up to +100%) of downwind/rural concentrations compared to other existing methods. These results demonstrate the great potential of DeepMMF in data assimilation and retrieval studies for other pollutants and regions, to better support health and climate studies. Jia Xing University of Tennessee-Knoxville |
9:10 AM |
Using ML to Enhance on-road Fleet Composition input for vehicle Emissions Inventory
Using ML to Enhance on-road Fleet Composition input for vehicle Emissions Inventory
Maha Shehadeh, Vahid Hosseini This presentation explores advanced methodologies for improving the accuracy of vehicle emissions inventories through machine learning. Using a case study from an urban area in Canada, we employ traffic camera data to achieve detailed temporal and spatial monitoring of fleet composition at the community level. The study compares observed fleet composition with the assumed homogenous fleet composition derived from insurance records. This comparison is particularly crucial for heavy-duty vehicles (HDVs), which frequently transit through the region without local insurance coverage. This work aims to improve the accuracy of emissions inventory inputs. Maha Shehadeh Simon Fraser University |
Long-term criteria and toxic pollutants trends in air quality and community exposures over the Marcellus Shale region in the U.S.
Long-term criteria and toxic pollutants trends in air quality and community exposures over the Marcellus Shale region in the U.S.
Chi-tsan Wang, B.H. Baek, Yunyao Li , Charles Chang , Jia Xing The oil and gas industry significantly impacts local and regional air quality, particularly in regions experiencing extensive unconventional oil and gas development (UOGD), such as the Marcellus Shale area. From 2008 to 2020, natural gas production in the Marcellus Shale region increased dramatically, growing from approximately 2 billion cubic feet per day to over 32 billion cubic feet per day, marking more than a 15-fold increase. In 2021, gas production in Marcellus Shale counties accounted for approximately 44% of nationwide gas production. Long- term trends of emissions from UOGD, including hazardous air pollutants (HAPs) and criteria air pollutants (CAPs) such as NOx, ozone, and PM2.5, are not well characterized. Therefore, understanding the long-term impact of these emissions is crucial for developing effective regulatory and industry practices to mitigate adverse environmental and public health impacts. To address these long-term air quality change issues, the Comprehensive Air Quality Model with Extensions (CAMx) was applied to simulate four target years (2008, 2014, 2019, and 2021) using the reactive tracer method in a 4km x 4km flexi-nesting domain. The CAMx model results include CAPs (such as ozone, NOx, and PM2.5) and selected HAPs concentrations (including styrene, benzene, toluene, ethylbenzene, xylenes, naphthalene, and butadiene). These four years of CAMx input and output data were then used as training data for the deep-learning enhanced CTM system (DeepCTM). After completing the training process, the DeepCTM model, which mimics the CAMx model processes but saves considerable simulation time, was used to generate 20-year (2002-2021) CAPs and HAPs concentrations. The input data for the DeepCTM model are the same as for the CAMx model, including daily emission, meteorological, and land data. In current preliminary results for HAPs hourly data, the DeepCTM model results closely matched the CAMx output (R-square: 0.72-0.79). Chi-tsan Wang George Mason University |
9:30 AM |
Recent Gaseous and Particulate Emission Trends in Northeast Asia Using Top-Down Emission Adjustment Method with Surface Observations
Recent Gaseous and Particulate Emission Trends in Northeast Asia Using Top-Down Emission Adjustment Method with Surface Observations
Seongeun Jeong, Eunhye kim, Yoon-Hee Kang ,Soontae Kim In Northeast Asia, PM2.5 concentrations had been rapidly changing due to stringent emission reduction policies and socioeconomic impacts. To accurately capture these changes, continuous efforts to update emission inventories were essential. However, keeping pace with these updates had been challenging. Previous studies recommended adjusting gaseous emissions using ground-based or satellite observations, but they had been limited in addressing primary particulate emissions, which directly influenced PM2.5 concentrations. To enhance the accuracy of emissions used in the air quality model, we implemented an emission adjustment method for both gaseous and particulate emissions using ground observations and simulation results from the Community Multiscale Air Quality (CMAQ) model. Considering the source-receptor relationship on a regional scale, we recognized that PM2.5 concentrations are transported to transboundary regions. Initially, we adjusted emissions in China and South Korea sequentially, given that South Korea is located downwind of China. To validate the simulation results using these adjusted emissions, we compared simulated concentrations with satellite and aircraft observations from the ASIA-AQ project. This comparison confirmed the accuracy and reliability of the adjusted emissions inventories. Seongeun Jeong Ajou University, South Korea |
Enhancing Meteorological and Air Quality prediction Accuracy through High-Resolution Data Assimilation Using Deep Learning
Enhancing Meteorological and Air Quality prediction Accuracy through High-Resolution Data Assimilation Using Deep Learning
Hossein Alizadeh, Ali Azimi, Hamid Delbari, Vahid Hosseini Accurate meteorological fields are crucial for effective air quality modeling due to their known impact on the dispersion, reaction, and deposition of atmospheric pollutants. This study uses a nudging method to enhance the accuracy of the Weather Research and Forecasting (WRF) model's outputs and demonstrates their impact on the Community Multiscale Air Quality (CMAQ) model outputs. A deep learning architecture is developed to downscale low-resolution (108 km2) ambient data of the National Centers for Environmental Prediction (NCEP) to a high-resolution (1 km2) grid using an encoder-decoder architecture with skip connections. The model employs bilinear interpolation to produce a preliminary high-resolution estimate, which is then refined by predicting residuals, resulting in more accurate and detailed outputs. An attention mechanism within the model captures complex patterns and variations, particularly in high-frequency variables like wind speed. The resulting high-resolution (1 km2) data is used to nudge the WRF model's state across the domain. This approach helps to rectify any discrepancies in the WRF model’s predictions, leading to more accurate representation of atmospheric conditions. The enhanced meteorological fields are utilized for emissions processing and air quality simulations using the CMAQ. The findings highlight that nudging the WRF results with high-resolution data significantly improves the model's accuracy. Furthermore, the use of these more precise meteorological data in CMAQ enhances the model's capability to predict pollutant concentrations. This study underscores the importance of high-resolution data assimilation using deep learning in improving the reliability of air quality predictions. Vahid Hosseini is an associate professor of Sustainable Energy Engineering at Simon Fraser University, Vancouver Canada. He has a Ph.D. in mechanical engineering - combustion generated air pollution from the University of Alberta, Canada and 10 years of previous experience as a professor at Sharif University of Technology in Tehran, Iran and head of Tehran's air qulaity monitoring network for 5 years. Vahid Hosseini Simon Fraser University |
9:50 AM | Break | Break |
10:20 AM |
On the development of an Energy-based Lightning NOx Model by Synergizing Satellite and Ground-based Lightning Datasets for Air Quality Modeling
On the development of an Energy-based Lightning NOx Model by Synergizing Satellite and Ground-based Lightning Datasets for Air Quality Modeling
Daiwen Kang, Yuling Wu, Arastoo Pour-Biazar, Rohit Mathur, David Wong, and Wyat Appel Lightning produced NOx emissions (LNOx) provide the most abundant NOx source in the mid-to-upper troposphere, a critical precursor for ozone and hydroxyl radical (OH), the primary tropospheric oxidants. Accurately quantifying LNOx emissions in time and space in chemistry transport models is challenging due to various uncertainties associated with lightning data and LNOx production rates. Currently, LNOx yield is mainly based on lightning flashes either detected by lightning detection networks or parameterized by lightning parameterization schemes based on modeled convective parameters (such as cloud-top height, cloud ice fluxes, or convective available potential energy). However, the definition of lightning flash varies from one network to another, confounding the use of datasets from different networks. In addition, it has long been recognized that LNOx yield is related to lightning energy levels, which varies across lightning types (e.g., cloud-to-ground, cloud-to-cloud) and exhibits large geographical variations. Along with flash and stroke counts, most networks also report information on energy associated with lightning strikes. Even though the registered energy values are only a tiny fraction of the sampled lightning strikes (either electromagnetic or optical) by different detection techniques, they represent the same lightning activities (if the networks are similarly purposed), thus some relationship should exist among them. Exploratory analysis of the lightning datasets from the World Wide Lightning Location Network (WWLLN) and the Geostationary Lightning Mapper (GLM) aboard the GOES-R satellites have indicated that even though both networks detect total lightning, the WWLLN network is far better at detecting cloud-to-ground lightning strikes, while the GLM technique is good at detecting total lightning. The energy aspects detected by the two networks display strong relationship when these energy values are aggregated over time and space, suggesting that synergies between the two datasets could be exploited to improve LNOx emissions estimate across temporal and spatial scales broader than those covered individually. An energy-based LNOx model will be presented and assessed for LNOx emissions estimate using a modeling domain covering the contiguous United States and surrounding areas discretized with a 12-km resolution grid. Daiwen Kang U.S. EPA |
Evaluating The Relationship of Modeled Meteorology and Ozone Bias using Random Forest Regression
Evaluating The Relationship of Modeled Meteorology and Ozone Bias using Random Forest Regression
Jonathan Brunton, Christian Hogrefe, K. Wyat Appel, Robert Gilliam, Fahim Sidi, Kristen Foley Meteorology and air quality modeling data from the EPA’s Air QUAlity TimE Series Project (EQUATES), spanning 2002-2019, was used for diagnostic evaluation of bias in modeled summertime ozone. Using paired modeled-observed data from the Weather Research and Forecasting (WRF) model and paired modeled-observed MDA8 O3 from the Community Multiscale Air Quality (CMAQ) model for the contiguous U.S., we conduct an exploratory statistical analysis of the impact of model bias in temperature, mixing ratio, windspeed, and precipitation on top ten modeled MDA8 O3 days for each year in the time series. To study this relationship further, we developed a machine learning workflow using Random Forest (RF) regression to predict MDA8 O3 bias and its trends based on meteorological and spatiotemporal variables. The spatiotemporal variables (year, month, latitude, longitude, elevation) are used to represent drivers of ozone bias not explicitly included in the statistical model, e.g., seasonal and regional patterns in emissions biases. An individual RF Regressor model was trained for each NOAA climate region in the EQUATES CMAQ domain, to optimize the model for regional characteristics. The RF models allow for exploration of complex relationships between the ozone bias and multiple model variables, or features. A Feature Importance analysis identifies regions where bias in meteorological variables had a comparable impact on MDA8 O3 bias as the non-meteorological features. Partial Dependence analysis shows whether the relationship between the ozone bias and the meteorological, spatial, and temporal features are linear, monotonic, or more complex. This workflow and RF model allow for future studies of the same nature involving additional CMAQ model inputs, such as emissions or additional meteorological data. Jonathan Brunton Oak Ridge Associated Universities |
10:40 AM |
TREND+PROJ21: Towards better granularity, production speed and quality control of Canadian inventories.
TREND+PROJ21: Towards better granularity, production speed and quality control of Canadian inventories.
Laurent Bruneau-Cossette, David Niemi, Maxim Bulat, Mourad Sassi The TREND and PROJ inventories are AQM-ready inventories of respectively past and future Canadian pollutant emissions. They are created and used by REQA for regulatory work and provided to various domestic and foreign governments and regulatory agencies. Recently, the work of updating, automating, and porting the existing methodology into a PostgreSQL + PostGIS database was undertaken. The resulting implementation greatly improves the quality control capabilities, scalability and production efficiency of TREND+PROJ inventories. TREND+PROJ21 is the first available inventory produced by this implementation. This increase in productivity also provides opportunities for further improvement. Laurent Bruneau-Cossette Environment and Climate Change Canada |
Air Quality Model Reduction Using Scientific Machine Learning
Air Quality Model Reduction Using Scientific Machine Learning
Christopher Tessum Assistant Professor, Civil and Environmental Engineering University of Illinois Urbana-Champaign Atmospheric chemical transport models (CTMs) are computationally intensive to run, and this limits their usefulness. Often, limited availability of computational resources limits scientific progress; additionally, these models are not available at all to scientists and stakeholders who do not have access to high-performance computing clusters. One way to way to decrease CTM computational intensity is to create reduced-complexity versions of each model component. However, manual model reduction to create reduced complexity models is time-consuming and can result in models only applicable in a limited range of situations. One promising approach to reconcile the need for reduced-order models with the need for models that work in a wide range of contexts is to use machine learning (ML) for the automated creation of “surrogate models” that reproduce the emergent behavior of a mechanistic reference model but with reduced computational expense. In this presentation, I will discuss recent progress in creating ML surrogate models for atmospheric chemistry and advection—typically the two most computationally intensive parts of a CTM—based on "Scientific Machine Learning" techniques which aim to create parsimonious, human-interpretable ML models. I will also describe efforts to build a "next generation" geoscientific modeling framework to enable and accelerate the integration of traditional and ML-based model development. Chris Tessum University of Illinois Urbana-Champaign |
11:00 AM |
Improved Day-to-Day Variations of US NOx emissions Inferred from TEMPO NO2 Retrievals
Improved Day-to-Day Variations of US NOx emissions Inferred from TEMPO NO2 Retrievals
Nana Wu, Zhen Qu, Amir Ghajari Bottom-up emission inventories face uncertainties at fine temporal scales (e.g., daily, hourly), posing challenges in chemical transport modeling. The newly launched TEMPO instrument has unprecedented hourly resolution over the US and therefore offers the capability to enhance our understanding of the temporal variations in NOx emissions from space. Here, we set up a pseudo-observation experiment with known “true” emissions to evaluate how the finite difference mass balance (FDMB) inversion methods and TEMPO NO2 retrievals improve day-to-day NOx emissions. TEMPO-constrained NOx emissions are then used for the weekend effect quantification of air pollution in different regions. We find that FDMB methods have the potential to recover day-to-day emissions at 12 km resolution grids. Integrating highly resolved TEMPO observations, the daily NOx emissions in the third week of December 2023 increased by an average of 27%, leading to a 33% rise in NO2 concentration. Compared to the prior emissions, the posterior emissions result in a 2.0 ug/m3 decline in the mean bias (MB) of simulated daily NO2 concentrations across the US against ground observations, a 1.5 ug/m3 reduction in the root mean square error (RMSE), and a 13.2% decrease in the normalized mean error (NME) by 13.2%. We will incorporate satellite measurements of additional species (e.g., formaldehyde) to further improve chemical transport modeling and support policy formulation. Nana Wu North Carolina Stare University |
Application of Response Surface Model in Optimizing Air Pollution Control Strategy
Application of Response Surface Model in Optimizing Air Pollution Control Strategy
Zhaoxin Dong1,2, Shuxiao Wang1,2*, Jia Xing3, Haotian Zheng1,2, Yueqi Jiang1,2,Dian Ding4, Jiming Hao1,2 1 State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China, 2 State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China, 3 Department of Civil and Environmental Engineering, The University of Tennessee, Knoxville, Tennessee 37996, U.S.A, 4 School of Environment and Energy, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou, 510006, China *Corresponding author, shxwang@tsinghua.edu.cn How to control the emissions of precursors so as to achieve the goal of synergistic PM2.5 and O3 control, and obtain as many health and ecological benefits as possible, has become an urgent scientific problem for the national and local government. Clarifying the response relationship of various environmental impacts to precursor emissions, and constructing a coordinated control strategy of PM2.5 and O3 based on health and ecological benefits is needed for the air pollution management. Here we set up the response surface model of PM2.5 and O3 concentrations, health effects of PM2.5 and O3, crop losses, nitrogen and sulfur deposition in the Yangtze River Delta (YRD) region, and the responses of multi-environmental benefits to changes in different precursor emissions from different cities and sectors in the YRD region were obtained. Based on the response surface model, an optimization model for emission reduction strategies that involves PM2.5 and O3 concentration goals, health benefits and ecological benefits was established comprehensively, with the short-term and long-term emission control strategy for various pollutants in the YRD region clarified. The study found that the contribution of anthropogenic emissions in the region to PM2.5 and O3 in the YRD was 56-86% and 12-35%, respectively, and the sectors that contributed the most were technological processes and on-road vehicles. The O3 in the central cities is in the VOC-limited area, and the minimum VOCs/NOx emission reduction ratio to avoid the rise of O3 is 0.5 on average. The PM2.5 exposure concentration in each city is slightly lower than the PM2.5 concentration, but the response relationship of precursor emissions changes is like that of the PM2.5 concentration. The O3 exposure concentration in the warm season is lower than the annual O3 concentration, and the VOC-limited feature is stronger. It shows that the emission control strategies based on the target of reducing annual O3 concentration may not protect the human health effectively. Zhaoxin Dong Tsinghua University |
11:20 AM |
Timely Updates of anthropogenic emissions with Satellite and Ground Observations: Comparing of multiple rapid emission refresh methods
Timely Updates of anthropogenic emissions with Satellite and Ground Observations: Comparing of multiple rapid emission refresh methods
Siqi Ma, Daniel Tong, Fei Liu, Jia Xing, Bok H. Baek, Chi-tsan Wang, Yunyao Li Emissions of air pollutants and greenhouse gases are the main driver of many Earth system processes behind some of the grandest environmental challenges today, such as air pollution and global warming. Creating emission inventories (NEI) typically lags by a few years, so that these datasets may not reflect the current emission status, especially during the periods of Economic Recession or COVID-19. Updating emissions from the base year to the specific forecast year using observational datasets, a process known as emission rapid refresh, is an effective approach to reduce the uncertainties of outdated emission inventories to increase forecasting accuracy. In this study, we applied four refresh methods to update the base year emission: state-level emission adjustment (ADJ), ADJ with budget-aware urban adjustment (BA), ADJ with TROPOMI city emissions (SatNOx), and adjustment with daily Variational AutoEncorder (VAE) physically-informed inverted emissions. Then we conducted summertime (July 2020) sensitivity experiments in a 12-km CMAQ modeling system using these three methods, alongside two no-adjustment runs—simulations with previous year (2019, control) and current year (2020, N2020) emissions—to evaluate the effectiveness of each approach. The results show that O3 generally decreases, with increases observed in the suburban and rural areas of New Mexico, southern Texas, and western New York in the adjusted runs. NO2 shows a consistent change pattern with NOx emissions. The highest O3 changes occur in suburban areas, while NO2 changes are most pronounced in urban areas. Compared to the control run, the simulations with adjustments generally reduce bias by 0.4–1.4 ppb in the eastern and southern US, while increasing by 0.2–0.8 ppb in the western US. The adjusted runs perform comparably to N2020 in the O3 simulation and better reproduce surface NO2 due to a more significant NOx emission reduction in N2020. However, N2020 outperforms others in NO2 vertical concentration density (VCD) simulation. Our findings indicate that emission refresh methods are capable of improving the O3 predictability, achieving performance comparable to simulations using current-year emissions. Siqi Ma George Mason University |
Understanding AIs place in environmental systems modeling
Understanding AIs place in environmental systems modeling
Douglas Lapham Artificial Intelligence (AI) is more than a single technology breakthrough. It's a complex integration of processes and algorithms that deliver transformational effects to organizations' missions. Now is the time to harness and integrate this incredible technology as it transforms our understanding of the environment at multiple scales. This session provides a high-level overview of AI and how it can integrate with, and enhance, current detection and risk prevention technologies. Potential use cases include how AI can be used to efficiently run and deploy environmental models, and the utilization of AI for understanding complex natural processes needed to formulate recommendations on new emerging air and water related risks. Emerging technologies, such as quantum sensors, may both accelerate and supplement the value added by AI, unlocking new sensor capabilities and analytic insights relevant to EPA's mission. Additionally, the session will discuss AI’s role in supporting model development through applications, such as cyber security, policy auditing, software and data quality assessment, data policy enforcement, and data model usage compliance, and make it useful for operations and regulatory purposes. For example, we will explore the use of machine learning (ML) to ingest, extract, and normalize data from Coupled Model Intercomparison Project (CMIP6) and the United States Geological Survey (USGS) REST water model. The platform follows repeatable patterns that build scalable, repeatable data pipelines to process climate data. This speeds redeployment and integration of additional services, ready for enterprise expansion as new Climate Intelligence use cases arise. Finally, the presentation will survey AI integration in other Federal Agencies also focused on climate science and modeling, including USGS, National Aeronautics and Space Administration (NASA), National Oceanic and Atmospheric Administration (NOAA), and the Department of Defense (DoD). For example, in support of a recently awarded Broad Agency Announcement (BAA) at NOAA we are exploring the feasibility and capability of emerging technologies focusing on data processing and dissemination of weather data. We are leveraging an AIOps framework and automated data pipelines to ingest NOAA data, process simultaneous AI/ ML algorithms at scale, and implementing a foundation for modernized, efficient research to operations workflows. Understanding and implementing the AI of today is key to unlocking the effective climate modeling of tomorrow. Douglas Lapham Booz Allen Hamilton |
Emissions Inventories, Models, and Processes, cont. | Regulatory Modeling and SIP Applications, Chaired by Zac Adelman, LADCO and Heather Simon, US EPA | |
11:40 AM |
Advancing sectoral emission estimates using TEMPO observations
Advancing sectoral emission estimates using TEMPO observations
Zhen Qu, Nana Wu Accurate quantification of air pollutant emissions from each human activity is critical for the planning and verification of emission reduction efforts. Top-down estimates with satellite data provide important information on the sources of air pollutants. We apply a newly developed sector-based inversion method based on 4D-Var and mass balance to quantify NOx, SO2, and VOC emissions from various activities, including transportation, industry, residential, aviation, shipping, energy, and biomass burning over the US. We incorporate TEMPO NO2 and HCHO, TROPOMI SO2, and MOPITT CO observations and leverage the co-emission of these gases to identify the source sectors. The framework improves emission estimates at the process level by optimizing emission factors and activity rates without relying on explicit knowledge of their values and resolves discrepancies with bottom-up inventories at the sector level. Previous applications of this approach show reduced normalized mean bias (NMB) by 7% (NO2)–15% (SO2) and normalized mean square error (NMSE) by 8% (SO2)–9% (NO2) compared to a species-based inversion when evaluated with surface measurements. We will evaluate the sectoral profile and temporal variations of our posterior emissions with bottom-up inventories (e.g., EPA NEI and CEDS) and EPA AQS measurements. Dr. Zhen Qu is an assistant professor in the Department of Marine, Earth and Atmospheric Sciences at North Carolina State University. She received her Ph.D. in Mechanical Engineering from the University of Colorado Boulder in 2019, B.S. in Physics from Peking University in 2014, and postdoctoral training at Harvard University. Her research interests include air quality, greenhouse gases, remote sensing, data assimilation, and inverse modeling. Her research aims to address the current challenges in air pollution and climate change by (1) exploiting observations to elucidate air pollutant and greenhouse gas emissions, concentrations, and chemistry; and (2) developing science-based strategies to improve air quality and abate climate forcing. Zhen Qu North Carolina State University |
Session Keynote: Unflattening the Trend: Challenges and Opportunities in Regulatory and SIP Modeling for Continuing to Improve Air Quality in the U.S.
Session Keynote: Unflattening the Trend: Challenges and Opportunities in Regulatory and SIP Modeling for Continuing to Improve Air Quality in the U.S.
Zac Adelman, LADCO 2025 is going to be a busy year for State Implementation Plan (SIP) modeling in the U.S. Attainment demonstrations for 2015 ozone National Ambient Air Quality Standard (NAAQS) serious nonattainment areas are due in early 2026. Final area designations for the 2024 annual PM2.5 NAAQS will also be made in early 2026. Exceptional event demonstrations for the historic wildfires of 2023 will be ongoing. A new national emissions modeling platform based on 2022, and model version updates will require many of the modeling centers at state/regional/national planning agencies to update their decision support systems to the latest data and tools. Along with the challenges, opportunities exist to confront the technical demands of complex air quality modeling and planning through cloud data and computing, machine learning tools, and ever-improving access to non-regulatory air quality data (e.g., satellites and sensors). In this talk I will present specific examples of where SIP modeling will be particularly challenging over the next few years, and where we need to head as a community to confront these challenges. With the new level of the annual PM2.5 NAAQS comes the need to understand the regulatory significance of the different precursor species to PM2.5. The current U.S. EPA PM2.5 precursor significance demonstration guidance prescribes the use of photochemical modeling to quantify the extent to which each precursor contributes to PM2.5 concentrations. The resources required for conducting the modeling and analysis to support these demonstrations will be significant and do not align well with the capabilities of photochemical models to provide reliable solutions for PM2.5 precursor significance testing. Another area that will challenge SIP planners is incorporating into attainment testing and demonstration projects all the recent federal rules targeting criteria and hazardous air pollutants, and the impacts of state and federal climate pollution reduction grant programs on NAAQS attainment. Understanding and quantifying the impacts of these rules and grant programs on ozone and PM2.5 precursors, NOx and VOCs in particular, at the county level between now the early 2030s will be needed for SIP modeling activities starting in 2025. Cooperation across the air pollution and climate planning communities is needed to develop a system for sharing inventory information and for holistically quantifying the co-benefits of pollution reduction programs. The SIP modeling systems that we've relied on over the past two decades have been critical tools in supporting the decisions that have produced significant improvements to U.S. air quality. The modeling systems of the next two decades will need to be developed with our current challenges at the top of mind. Capabilities to support co-benefits and health-based decision making, fuse datasets across a wide range of temporal and spatial scales, leverage machine learning algorithms, and to deploy across different operating system and hardware configurations, among others, will be needed to continue the momentum of progress that we've realized in air pollution planning in the U.S. Zac Adelman has been the Executive Director of LADCO since September 2017. He works at the interface of air quality modeling, ambient monitoring, and air quality planning. Before joining LADCO, he worked for 15 years as an air quality researcher and project manager at the University of North Carolina. Zac holds degrees from the UNC Dept of Environmental Sciences and Engineering. He is an atmospheric scientist with expertise in air pollution modeling and ozone chemistry. Zac Adelman LADCO |
12:00 PM | Lunch in Trillium and Hanna Student Poster Awards and User Forum Award | |
Emissions Inventories, Models, and Processes, cont. | Regulatory Modeling and SIP Applications, cont. | |
1:00 PM |
Emission Methods Improvements to support the National Emissions Inventory (NEI) and development of emissions data to support Air Quality Modeling
Emission Methods Improvements to support the National Emissions Inventory (NEI) and development of emissions data to support Air Quality Modeling
V. Rao1, K. Seltzer1, M. Strum1, Y. Dietrich1, R. Mason1, L. Dayton1, A. Eyth1, J. Vukovich1, A. Diem1, J. Bash2, A. Holder2, I. George2, H. Pye2, J. Beidler2, G. Pouliot2, 1 US EPA, OAQPS, EIAG 2 US EPA, ORD, CEMM The National Emissions Inventory (NEI) program within the EPA Office of Air Quality Planning and Standards continually works to update science related to emissions inventories. Efforts includes developing emission estimation methods for previously uncharacterized sources, upgrading methods for existing sources (including improved activity and emission factor data) and other ancillary data (e.g., modeling-related emissions transformations such as chemical speciation, temporalization, spatial allocation, and emission projections), and improving emission trends information. In this talk, we provide an overview of the new methods introduced for the 2020 NEI, planned updates for the 2023 NEI, and discuss the drivers of priorities in this arena as we move beyond the 2023 NEI. In doing this, we will summarize some of the impacts of past methods improvements on emissions, including added pollutant coverage, improved/added emissions source coverage, and ancillary data improvements for modeling. Specifically, this presentation will include emissions methods improvements related to: fires, solvents/asphalts, sources of NH3, residential wood combustion, VOC and PM2.5 speciation, and spatial/temporal upgrades. Tesh has worked at the US EPA for over 32 years. Tesh has worked for both the office of tranportation and air quality as well as the office of air quality planning and standards, both in EPA's OAR Office. Tesh's work has mainly involved working on emission inventories and associated atmospheric chemistry sectors. Current work that Tesh is engaged in at EPA/OAQPS include emissions methods development and working on specific sectors related to ammonia, fires, and emerging new emission sources. Venkatesh Rao U. S. Environmental Protection Agency |
Source Identification of Sub-10 nm Particles through Air Dispersion Modeling
Source Identification of Sub-10 nm Particles through Air Dispersion Modeling
Stephanie Bachman, Dr. Nicholas Meskhidze, Jonah Hazelwood, Lintong Cai, Dr. Markus Petters Ambient aerosol measurements conducted on the rooftop of Jordan Hall at North Carolina State University (NCSU) revealed episodic events where sub-10 nm particle number concentrations reached 373,086 #/cm^3. The particle modal diameter remained constant over time, suggesting these particles were not associated with new particle formation events but likely originated from primary sources. Increases in sub-10 nm particle number concentrations correlated with nighttime conditions and/or overcast skies. To investigate potential sources of these particles, simulations were performed using EPA's Gaussian plume-based model, AERMOD. Through campus drives and analysis of Google Earth images, three possible sources capable of emitting large numbers of sub-10 nm particles were identified in the vicinity of the measurement site: Cates Avenue Steam Plant, Yarbrough Central Utility Plant, and Centennial Campus Central Utility Plan. In AERMOD, different point sources were created for each power plant. Each plant was assigned an emission factor of 500 g/s, and concentrations were modeled at the Jordan Hall location at various heights from ground level to 20 meters above ground. The study explored source contributions and the effects of various meteorological conditions on modeled particle numbers, including wind speed, wind direction, solar radiation, and the Planetary Boundary Layer (PBL) height. This approach allowed for a comprehensive analysis of both the potential sources and the environmental factors influencing the observed sub-10 nm particle concentrations.
Preliminary results show a good correlation between the model-assigned and measured particle number concentrations. Based on wind speed and direction, it was possible to identify plumes originating from different power plants, reaching the Jordan Hall location at varying concentrations. The model also successfully reproduced the observed relationship between particle numbers and the PBL height. The model accurately replicated elevated numbers of particles during periods with shallow PBL heights.
Sub-10 nm-sized particles are very harmful, as they efficiently deposit in the nasopharyngeal, tracheobronchial, and alveolar regions. Within these regions, the particles cross the mucous membrane and reach sensitive organs, including the liver, heart, spleen, brain, lungs, and gastrointestinal tract. Given the proximity of both the NCSU student population and the wider Raleigh area to the power plants, further investigation is warranted. Additional measurements and modeling studies are necessary to more accurately determine the power plants’ contribution to the measured nanoparticle concentrations and analyze their potential impacts, especially within the exhaust plume. Stephanie Bachman North Carolina State University |
1:20 PM |
Development of Year 2022 Emissions for the 2022v1 Emissions Modeling Platform
Development of Year 2022 Emissions for the 2022v1 Emissions Modeling Platform
Karl Seltzer, Zac Adelman, Michael Aldridge, Christine Allen, James Beidler, Andrew Bollman, Art Diem, Yijia Dietrich, Lindsay Dayton, Alison Eyth, Janice Godfrey, Mark Janssen, Byeong Kim, Tom Moore, Rhonda Payne, Matt Roark, Sarah Roberts, Tom Richardson, Kevin Talgo, Mary Uhl, Jeff Vukovich The 2022v1 emissions modeling platform (EMP) is being developed by the National Emissions Collaborative, a constellation of staff at regional, state, local, and tribal air agencies, and the U.S. EPA. The platform will support both regulatory modeling and air quality related studies. As with the 2016 Emissions Inventory Collaborative, transparency is fundamental to the development and overall confidence in the platform. Emissions for the year 2022 were developed using state / local / and tribal submitted data for 2022, along with EPA-developed data based on the 2020 National Emissions Inventory and other data sources. In addition, ancillary data for the platform were reviewed and some improvements were implemented. Here, the methods and data sources used to develop the 2022 air quality model-ready emissions for the 2022v1 EMP will be discussed. We will also illustrate the magnitude of emissions from various sectors, discuss ancillary data updates made, and describe how updates were made to ensure the transparency of this development process. The development and values of the analytic year emission for 2026, 2032, and 2038 will be discussed in a separate talk. Karl Seltzer is a Physical Scientist in the U.S. EPA’s Emissions Inventory and Analysis Group within the Office of Air Quality Planning and Standards. He contributes to the development of the tri-annual National Emissions Inventory, the Agency’s photochemical emissions modeling platforms, is the speciation lead for the Agency’s emissions group, and helps develop the chemistry used in the Community Multiscale Air Quality modeling system. Karl Seltzer U.S. EPA |
Summertime Ozone Sensitivity to Biogenic VOCs in Utah
Summertime Ozone Sensitivity to Biogenic VOCs in Utah
Lexie Wilson, Rachel Edie Ph.D., Mark Sghiatti Understanding the impact of biogenic emissions on air quality modeling is crucial for effective environmental management in urban areas in the intermountain west. This study investigates the sensitivity of our photochemical model (Comprehensive Air Quality Model with extensions, CAMx) to different biogenic emissions inputs in Utah’s Northern Wasatch Front ozone nonattainment area. Building on previous work by the Utah Division of Air Quality (UDAQ) to improve model performance, we evaluate Biogenic Volatile Organic Compound (BVOC) emissions from Biogenic Emissions Inventory System version 4 (BEIS 4) against the Model of Emissions of Gases and Aerosols from Nature (MEGAN 3.2). MEGAN inputs were tailored to our region using satellite observations and local tree surveys. Our analysis focuses on comparing modeled ozone and BVOC concentrations from CAMx with MEGAN and BEIS inputs, and to available observations in the nonattainment area. Observational analysis will be both qualitative and quantitative due to PAMS expansion after our 2017 modeling year. Lexie Wilson is an air quality modeler, researcher, and lead GIS analyst at the Utah Division of Air Quality. Lexie's current work is focused on improving emissions modeling for Utah's ozone SIP using SMOKE. Lexie Wilson Utah Division of Air Quality |
1:40 PM |
Development of Analytic Year Emissions for the 2022v1 Emissions Modeling Platform
Development of Analytic Year Emissions for the 2022v1 Emissions Modeling Platform
Z. Adelman, M. Aldridge, C. Allen, A. Bollman, M. Collins, Y. Dietrich, L. Dayton, A. Eyth, J. Godfrey, M. Janssen, S. Kayin, B. Kim, S. McCusker, T. Moore, E. Murray, R. Payne, M. Roark, S. Roberts, T. Richardson, K. Seltzer, K. Talgo, M. Uhl, J. Vukovich The 2022v1 emissions modeling platform includes emissions for the base year of 2022 and for the analytic years of 2026, 2032, and 2038. These future years were selected for inclusion in this platform due to the near-term importance of regulatory modeling for ozone, particulate matter, and regional haze. In a separate talk, development of the year-2022 inventory was discussed. Here, development of the analytic year emissions is discussed. We cover the anticipated changes in activity data and emissions from anthropogenic sources from 2022 through 2038, the methods used to compute analytic year emissions, including proper reflection of applicable control programs and closures of facilities and units, when models were run to reflect changes between the base (i.e., 2022) and analytic years, and when emissions were held constant. It is important to note that the analytic year air quality modeling for these types of analyses uses the same 2022 meteorological data as the base year air quality modeling. Zac Adelman has been the Executive Director of LADCO since September 2017. He works at the interface of air quality modeling, ambient monitoring, and air quality planning. Before joining LADCO, he worked for 15 years as an air quality researcher and project manager at the University of North Carolina. Zac holds degrees from the UNC Dept of Environmental Sciences and Engineering. He is an atmospheric scientist with expertise in air pollution modeling and ozone chemistry. TBD TBD |
A comparison of ozone source apportionment air quality modeling results using emissions from two Electricity Generating Unit projection models and their impacts on upwind state contributions to downwind monitors
A comparison of ozone source apportionment air quality modeling results using emissions from two Electricity Generating Unit projection models and their impacts on upwind state contributions to downwind monitors
Jeongran Yun1, Winston Hao1, Yuhong (Ruby) Tian1, Eric Zalewsky1, Jin-Sheng Lin2, Doris McLeod2, Susan McCusker3, and Kevin Civerolo1 1 New York State Department of Environmental Conservation 2 Virginia Department of Environmental Quality 3 Mid Atlantic Regional Air Management Association In this study, we considered two sets of projected emissions for Electricity Generating Unit (EGU) point sources: one based on the Integrated Planning Model (IPM), and another based on the Eastern Regional Technical Advisory Committee (ERTAC) EGU model. Air quality modeling based on the two EGU projection approaches has been used to guide air quality planners in control strategy development and policy decision-making. EPA uses emissions from the IPM model, while member states of the Ozone Transport Commission use emissions from the ERTAC model for regulatory modeling. EPA performed CAMx ozone source apportionment modeling to determine the upwind contributions of total anthropogenic emissions from each state to projected ozone design values at downwind monitor sites for the Good Neighbor Plan to address attainment of the 2015 ozone National Ambient Air Quality Standard. In this study, we obtained EPA’s 2016 base year and 2026 analytical year version 3 modeling platform used for the Good Neighbor Plan, replaced the IPM-based EGU and non-EGU point sources with the ERTAC-based EGU and non-EGU point sources, and performed CAMx ozone source apportionment modeling. We discuss the spatial differences in our comparisons of the CAMx modeling results using the two projected EGU/non-EGU point source emissions and the impacts of using ERTAC-based emissions in place of IPM-based emissions on upwind ozone contributions by states to downwind monitors. Jeongran Yun New York State Department of Environmental Conservation |
2:00 PM |
Impacts of Chemical Mechanism Choice and Meteorology on Model Processes and Wintertime PM2.5 Modeling in the Western US
Impacts of Chemical Mechanism Choice and Meteorology on Model Processes and Wintertime PM2.5 Modeling in the Western US
Cam Phelan, Abi Lawal, William Vizuete, Jacob Boomsma, Heather Holmes, Cesunica Ivey The 2024 update to the annual PM2.5 standard will require both state and local entities to submit updated SIPs and potentially perform demonstration modeling for proposed emission controls. Many of the areas projected to be out of attainment for this new standard are in the western U.S., where CMAQ traditionally does not perform well. Previous work has shown that the choice of chemical mechanism significantly impacts organic carbon, nitrate, and ammonium PM2.5 concentrations, especially during wintertime temperature inversions. This creates an issue for regulatory modeling applications, where there is no official guidance on chemical mechanism choice, and we aim to characterize how and why different chemical mechanisms in CMAQ 5.3.3 and 5.4 impact simulated PM2.5. We use inline Integrated Reaction Rate (IRR) and Integrated Process Rate (IPR) in monthlong simulations to identify science processes and reactions of interest that can explain the discrepancies between mechanisms and explore the impact of meteorology inputs on these processes and model performance. Cam Phelan University of California Berkeley Department of Civil and Environmental Engineering |
|
2:20 PM | Break | Break |
3:00 PM | Adjourn |
Dr. Sarav Arunachalam, CMAS Director, Institute for the Environment, UNC-Chapel Hill
Sedona Ryan, Research Coordinator, Center for Environmental Modeling for Policy Development, UNC-Chapel Hill
Environmental Scientist, Office of Air Quality Planning & Standards, US Environmental Protection Agency
Jim is an Environmental Scientist with the U.S. Environmental Protection Agency where he leads the Air Quality Assessment Division's PM NAAQS Review Team. Prior to this, he held positions at the California Air Resources Board, Lawrence Livermore National Laboratory, and the CIIT Centers for Health Research related to air quality modeling and particle dosimetry. Jim earned a PhD from UC Davis with a dissertation on water uptake by atmospheric particles.
Jim Kelly, Office of Air Quality Planning & Standards, US Environmental Protection Agency, RTP, NC
On February 7, 2024, the U.S. Environmental Protection Agency strengthened the National Ambient Air Quality Standards (NAAQS) for fine particle pollution (PM2.5) by revising the level of the primary (health-based) annual PM2.5 standard to 9.0 ug/m3 (from 12 ug/m3). As required by the Clean Air Act, the PM2.5 NAAQS revision initiates a series of implementation steps including the development of state implementation plans (SIPs) to meet the revised standard in areas designated as nonattainment. Due to changes in emissions and air quality in the last several decades, a fresh look at the nature of PM challenges and needed data and modeling capabilities is warranted for attainment planning. In this presentation, an overview of the revisions to the PM NAAQS will be provided along with information on the nature of PM in areas of the US with elevated concentrations. Drawing from recent EPA assessments, examples of PM challenges will be discussed for areas projected to exceed the revised NAAQS in recent modeling. The large reductions in PM precursor emissions (e.g., SO2 and NOx) in recent decades have enhanced the importance of local sources of direct PM relative to regional sources. Scientific studies have also reported that location-specific emission reduction strategies are important for mitigating PM2.5 exposure disparities in the US.
You must be Registered for the Conference and Paid in order to view extended abstracts and conference presentations. Log In for full access.CMAQv5.5 includes CRACMMv2.0; updates to the photolysis calculation; updates to the M3Dry and STAGE dry deposition models; and an update to grid definitions and projection checking for fine scale (i.e. < 4km) simulations. A summary of these and other updates and code fixes will be provided along with an evaluation comparing version CMAQv5.5 simulations to similarly configured CMAQv5.4 simulations for regional (CONUS) and hemispheric domains, including a comparison of the CB6r5 and CRACMMv2.0 mechanisms. An evaluation of a series of sensitivity simulations is used to investigate the impact of using the CMAQ dust module with different land use datasets. The results of the simulations will be compared to observations from multiple sources, including ground-based networks, vertical profiles, and satellite data. The presentation will also provide details on updates to several existing CMAQ tools (e.g., ISAM, DDM) and new tools (e.g., shape2cmaq).
You must be Registered for the Conference and Paid in order to view extended abstracts and conference presentations. Log In for full access.Center for Environmental Measurement and Modeling, Office of Research and Development, U.S. Environmental Protection Agency
Dr. Rohit Mathur works in the Office of Research and Development, U.S. Environmental Protection Agency. His research deals with the development of methods to represent the physical and chemical behavior of atmospheric pollutants in comprehensive modeling frameworks. Through a multidisciplinary approach involving physical, numerical and computational modeling, his work has focused on continually enhancing the science in air quality models through exploring the development of novel new modeling methodologies. He has contributed to the development of several large-scale air pollution modeling systems, has published widely on a variety of topics related to air pollution modeling, characterization and data analysis, and has served in numerous leadership and science management positions.
The U.S. EPA will release its latest version (v5.5) of the Community Multiscale Air Quality (CMAQ) model in the fall of 2024. This new version of the model contains numerous updates to the modeling system, the most significant of which is the inclusion of version 2.0 of the Community Regional Atmospheric Chemistry Multiphase Mechanism (CRACMM). The release will also include, for the first time, support for running CMAQ coupled with the Model for Prediction Across Scales - Atmosphere (MPAS-A). The speakers in this session will provide a description and evaluation of these updates and will highlight many opportunities for community involvement in future model development. Following the three presentations there will be a panel Q&A session with the speakers. Audience members are encouraged to ask questions about current CMAQ development plans and offer ideas for future directions.
You must be Registered for the Conference and Paid in order to view extended abstracts and conference presentations. Log In for full access.Rohit Mathur and The CMAQ Modeling Team
Center for Environmental Measurement and Modeling, Office of Research and Development, U.S. Environmental Protection Agency
Atmospheric processes are inherently complex in that they occur over disparate space and time scales. Air quality assessments and management require characterization of pollutants in environments ranging from near source to those impacted by cumulative effects of emissions transported across inter-continental scales. Comprehensive atmospheric chemistry-transport models attempt to incorporate in as much detail as possible the interactions between various physical, chemical and dynamical processes over spatial scales ranging from a few to thousands of kilometers laterally, from the surface to the upper troposphere vertically, and on temporal scales ranging from a few seconds to annual cycles. To assist with the design of emission control strategies that yield compliance with more stringent air quality standards, such models must accurately simulate ambient pollutant levels across the entire spectrum from background to extreme concentrations, which in turn arise from multipollutant interactions at various spatial and temporal scales. Additionally, changing climate and emissions globally are modulating the dynamical and chemical state of the atmosphere, so that it is likely that process interactions across multiple scales are also changing with time, emphasizing the need to continuously quantify their impacts on evolving air pollution trends. In this presentation, we will discuss progress and challenges in multi-scale air pollution modeling based on perspectives developed from the application and scientific evolution of the Community Multiscale Air Quality (CMAQ) modeling system.
You must be Registered for the Conference and Paid in order to view extended abstracts and conference presentations. Log In for full access.Center for Environmental Measurement and Modeling, Office of Research and Development, U.S. Environmental Protection Agency
Dr. Rohit Mathur works in the Office of Research and Development, U.S. Environmental Protection Agency. His research deals with the development of methods to represent the physical and chemical behavior of atmospheric pollutants in comprehensive modeling frameworks. Through a multidisciplinary approach involving physical, numerical and computational modeling, his work has focused on continually enhancing the science in air quality models through exploring the development of novel new modeling methodologies. He has contributed to the development of several large-scale air pollution modeling systems, has published widely on a variety of topics related to air pollution modeling, characterization and data analysis, and has served in numerous leadership and science management positions.
CMAQv5.5 includes CRACMMv2.0; updates to the photolysis calculation; updates to the M3Dry and STAGE dry deposition models; and an update to grid definitions and projection checking for fine scale (i.e. < 4km) simulations. A summary of these and other updates and code fixes will be provided along with an evaluation comparing version CMAQv5.5 simulations to similarly configured CMAQv5.4 simulations for regional (CONUS) and hemispheric domains, including a comparison of the CB6r5 and CRACMMv2.0 mechanisms. An evaluation of a series of sensitivity simulations is used to investigate the impact of using the CMAQ dust module with different land use datasets. The results of the simulations will be compared to observations from multiple sources, including ground-based networks, vertical profiles, and satellite data. The presentation will also provide details on updates to several existing CMAQ tools (e.g., ISAM, DDM) and new tools (e.g., shape2cmaq).
You must be Registered for the Conference and Paid in order to view extended abstracts and conference presentations. Log In for full access.CRACMM is a chemical mechanism released for the first time in 2022 as part of CMAQv5.4 and is expected to become default in CMAQ in 2026. CRACMM aims to incorporate and leverage knowledge from the broad atmospheric science community to improve predictions of oxidants, hazardous air pollutants, and particulate matter. This overview talk will highlight scientific advancements and CRACMM milestones completed in the initial versions 1 (2022 release) and 2 (2024 release) as well as plans for the 2026 version 3 release. For those new to CRACMM, the talk will include resources available to leverage this chemistry. In addition, a vision for community-oriented development and a workflow for contributing to CRACMM will be presented.
You must be Registered for the Conference and Paid in order to view extended abstracts and conference presentations. Log In for full access.CMAQ simulations often rely on community-developed meteorological models developed and maintained by the National Center for Atmospheric Research (NCAR). NCAR has created MPAS as the successor to the decades-old Weather Research and Forecasting model (WRF). NCAR has signaled that WRF development will wind down over the next decade. In response, EPA is preparing for a gradual transition of the CMAQ ecosystem from WRF to MPAS. Unlike WRF, MPAS supports global domains with seamless regional resolution refinement over areas of interest. CMAQ version 5.5 will be the first publicly available version of CMAQ's chemical transport model that supports coupling with MPAS. This presentation will include a brief overview of initially supported features, available case studies, model performance, and opportunities for community contributions.
You must be Registered for the Conference and Paid in order to view extended abstracts and conference presentations. Log In for full access.Assistant Professor of Civil and Environmental Engineering, Virginia Tech
Hosein Foroutan is an Assistant Professor in the Charles Edward Via, Jr. Department of Civil and Environmental Engineering, and an affiliate faculty with the Global Change Center at Virginia Tech. Prior to that, he was a postdoctoral research fellow in the U.S. Environmental Protection Agency (US EPA), where he was a member of the CMAQ development team. He holds a doctorate degree in Mechanical Engineering (with a minor in Computational Science) from Penn State. Foroutan's research focuses on improving understanding of the processes behind emission, transport, and fate of pollutants of emerging importance in the atmosphere.
Research Scientist, Office of Research and Development, US EPA
Dan Loughlin has been a Research Scientist at the U.S. EPA for 20 years. His specialties include energy system modeling, technology assessment, estimating air pollutant and greenhouse gas emissions for technology and policy scenario, and sensitivity and uncertainty analyses. Dan is the co-lead of the GLIMPSE project with Chris Nolte. Previously, Dan was involved with EPA���s MARKAL database development and applications, focusing on the electric and transportation sectors. He is an adjunct professor at Duke University's Nicholas School of the Environment where he teaches a course on Integrated Assessment Modeling.
Assistant Professor, Department of Environmental Sciences and Engineering, UNC-Chapel Hill
Dr. Kittner studies energy systems at multiple scales, from regional and international power grids to community-owned micro-grids and household energy dynamics. Kittner's current work examines the relationship between energy systems, low-carbon development, and human health. Ongoing projects include energy system planning in Thailand, Laos, and Myanmar, the emissions and health effects of coal-fired power plants in Kosovo, and battery assessment for electric vehicle charging and energy storage applications. Kittner holds a PhD in Energy and Resources from UC Berkeley where he studied energy systems engineering, science, and policy. Kittner's research has been supported by NSF, NC Policy Collaboratory, NASA, and the California Energy Commission.
Physical Scientist, Office of Air Quality Planning and Standards, US EPA
Jeff Vukovich is an USEPA Physical Scientist in the Emissions Inventory and Analysis Group at the Office of Air Quality Planning and Standards in Research Triangle Park, NC. He has been involved with the development and application of emissions inventories and modeling systems for over 25 years. He is the emissions sector lead for wildland fires, oil and gas, and biogenic emissions sectors for EPA's National Emissions Inventory team. He is also currently providing technical support for emissions modeling platform development at EPA.
Associate Professor and Associate Department Head, Civil and Environmental Engineering, University of Connecticut
Dr. Astitha is an Associate Professor and the Associate Department Head for Graduate Education, Equity and Inclusion at the Department of Civil and Environmental Engineering, University of Connecticut (UConn). Dr. Astitha has 15 years of experience in atmospheric numerical modeling systems from regional to global scales. She is leading the Atmospheric Modeling and Air Quality Group (https://airmg.uconn.edu/) since joining UConn in 2013. The group currently consists of PhD, MS and undergraduate students in Environmental Engineering conducting research on extreme weather prediction, air quality modeling systems, and integration of numerical models with machine learning algorithms for error reduction and new model development (weather and water quality applications). Dr. Astitha's group is also conducting research related to renewable energy (offshore wind farms) and storm forecasting that impacts power distribution in the NE US.
Research Scientist, ORD, US EPA
I am a research scientist in the Office of Research and Development at the Environmental Protection Agency (EPA). I graduated with Master's in Mechanical Engineering from North Carolina State University with a focus on numerical methods and computational modeling of fluid and thermal systems. My research at the EPA focuses on the development, evaluation and integration of methods to represent the physical and chemical behavior of atmospheric pollutants in the Community Multiscale Air Quality Modeling (CMAQ) framework. Additionally, I am a technical co-lead on an initiative to develop an Air Quality Modeling framework on the Cloud. The goal of this effort is to advance the state of knowledge within the air quality modeling community on using the cloud to support regulatory and research modeling applications.
Senior Research Scientist, Office of Research and Development, US EPA
Dr. Vlad Isakov a senior research scientist at the U.S. EPA's Office of Research and Development (ORD)'s Center for Environmental Measurement & Modeling (CEMM). He provides scientific leadership in the Atmospheric and Environmental Systems Modeling Division (AESMD) on the development and testing through applications innovative approaches to model spatially and temporally resolved air quality concentrations in support of exposure and health studies. His areas of expertise include dispersion modeling, boundary layer meteorology, local-scale air quality modeling, web-based air quality models and visualization systems, and exposure characterization for environmental health assessments. Dr. Isakov earned a M.S. in Meteorology from South Dakota School of Mines & Technology and a Ph.D. in Atmospheric Science from the University of Nevada, Reno.
Associate Professor in Environmental Engineering and Associate Dean - Graduate Studies, Carleton University
Program Manager 1, Georgia Environmental Protection Division
Dr. Byeong-Uk Kim received his PhD degree from the Department of Environmental Sciences & Engineering at the University of North Carolina at Chapel Hill in 2006. After his PhD and three-month post-doctoral associate position, he joined Georgia Environmental Protection Division as an environmental modeler where he performed ozone, PM2.5, and regional haze SIP modeling for 13 years. He has spent the last three and half years as Manager of the Data and Modeling Unit where he supervises photochemical modeling, permit modeling, exceedance report development, and special modeling projects. He published multiple journal manuscripts and made presentations at many international and national modeling conferences.
Professor, University of North Carolina-Chapel Hill
A leading cause of global premature deaths are exposures to ozone and particulate matter. Particulate matter is also an important atmospheric radiative forcer and plays a critical role in our understanding of the impacts of climate change. Tackling problems of air pollution mortality and climate change are the global public health issues that motivate my research. In my research I focus on understanding how the atmosphere can change the formation processes of ozone and particulate matter, and its connection to human health. Through this research I have increased our scientific knowledge in these areas and produced new insights through air quality model predictions, field studies, laboratory experiments, and the development of a novel in vitro technology.
Senior Scientist, US EPA
Tesh has worked at EPA for almost 31 years. He has worked at both OTAQ and OAQPS, both OAR offices. At OTAQ, he was most involved with working on predictive models, MOBILE (predecessor to MOVES model), regulation development, and fuel effects on emissions. At OAQPS, he has worked on ambient data analysis, on NAAQS related issues and emission inventories. His focus in the emissions arena has been fires, ammonia sectors, speciation, and working with the office of research development on EF testing programs to support inventory (NEI) and air quality modeling needs.
Principal Research Engineer, Georgia Institute of Technology
Vice President of Research and Development, Verisk Atmospheric and Environmental Research
Dr. Matthew Alvarado leads an international team of scientists applying novel remote sensing strategies to the grand challenges of Earth science, including clouds/convection, greenhouse gases, oceans/cryosphere, and fire/smoke. This research guides the climate change, environmental protection, and severe weather decisions of regulators, industry, and the public.
As Vice President of R&D at Verisk Atmospheric and Environmental Research (Verisk AER), Matt ensures that the division's individual research projects align with the pressing needs of the scientific community, policymakers, and industry. He also leads several research projects on atmospheric chemistry, air quality, and radiative transfer modeling, with a focus on the chemistry of wildfire smoke. His research has resulted in dozens of articles in peer-reviewed scientific journals.
Research Scientist, Office of Research and Development, US EPA
Dr. Pye is a research scientist in the US EPA Office of Research and Development where she uses computer models including CMAQ to understand what governs chemicals in air: from emissions through chemical and physical transformation and ultimately removal. Dr. Pye is currently leading efforts to build a new chemical mechanism for use in CMAQ and other models that couples gas and organic aerosol chemistry. Dr. Pye is a recipient of a Presidential Early Career Award for Scientists and Engineers, the highest honor bestowed by the U.S. government on outstanding scientists and engineers beginning their independent careers. Dr. Pye received her PhD in 2011 in Chemical Engineering with a minor in Environmental Science and Engineering from the California Institute of Technology. More information about her work is available at https://havalapye.wordpress.com/.
Physical Scientist, Professor and Distinguished Fellow and Associate Chair for Research, Civil and Environmental Engineering, Northeastern University
Dr. Zhang is a Professor in the Department of Civil and Environmental Engineering at Northeastern University. She specializes in 3-D multi-scale modeling of energy- and health-relevant air pollutants and their environmental and health impacts, and has led or contributed to the development, improvement, application, and evaluation of several 3-D atmospheric/Earth system models. She authored or coauthored more than 200 journal publications. Dr. Zhang is a member of the External Advisory Committee of the Community Modeling and Analysis System center, the University of North Carolina at Chapel Hill, and a member of the World Meteorological Organization's Global Air Quality Forecasting and Information System (GAFIS) Steering Committee and co-chair for the capacity development group under the GAFIS. More detailed on her work can be found at https://coe.northeastern.edu/Research/CASCADE/.
Executive Director, LADCO
Zac Adelman has been the Executive Director of LADCO since September 2017. He works at the interface of air quality modeling, ambient monitoring, and air quality planning. Before joining LADCO, he worked for 15 years as an air quality researcher and project manager at the University of North Carolina.
Zac holds degrees from the UNC Dept of Environmental Sciences and Engineering. He is an atmospheric scientist with expertise in air pollution modeling and ozone chemistry..
Senior Adviser, U.S. Environmental Protection Agency's Office of Air and Radiation
Steve works on strategic information technology (IT) and information management (IM) issues affecting EPA's Office of Air and Radiation. This includes accelerating the adoption of cloud computing. One aspect of that work is helping to make it easier for the modeling community to conduct air quality simulations and related analyses in the cloud.
Steve has multiple degrees from The Pennsylvania State University, including a Ph.D. in meteorology and an M.S. in computer science. His professional background includes software development to support environmental modeling and analysis, leadership roles in the National Oceanic and Atmospheric Administration's research office, and leadership of core EPA IT/IM services.
Azure HPC Technology Specialist, Microsoft
In 2086, Steve taught the FORTRAN vectorization workshop at IBM's National Engineering and Scientific Support Center. He then spent 10 years as an AIX-RS/6000 technical Specialist. After moving to Microsoft over 22 years ago, Steve has spent the last 10 years focused on helping customers move their HPC workloads to the Azure Cloud.
Over the last couple of years researchers have been turning to public clouds like Microsoft Azure for running their scientific simulation workloads. The team from the UNC Institute for the Environment has published tutorials to enable the community to more easily run the CMAQ simulation on Microsoft Azure. Recent experiments have demonstrated dramatic performance improvements by optimizing the storage and processor pinning. This presentation will describe the underlying architecture of the Microsoft Azure platform and detail those performance improvements.
Research Statistician, US EPA
Dr. Kristen Foley is a Research Statistician with the EPA's Office of Research and Development (ORD). Kristen is part of the team at EPA that develops and applies the Community Multiscale Air Quality (CMAQ) modeling system. CMAQ is used to identify sources of air quality problems and assist in the design of effective strategies to reduce harmful air pollutants. Dr. Foley's research includes development and application of statistical techniques to evaluate output from the numerical model against different types of air quality measurements. She also provides statistical consulting to her fellow team members to help them visualize, analyze, and interpret their data. Dr. Foley has a M.S. degree and a Ph.D. in Statistics from NC State University.
Research Associate Professor, George Mason University
Dr. Campbell is a research faculty at George Mason University and is affiliated with NOAA's Air Resources Laboratory under the Cooperative Institute for Satellite Earth System Studies (CISESS). He currently leads air composition, emissions, and surface-atmospheric exchange research and development projects to support and advance NOAA's current and next-generation atmospheric composition and air quality prediction systems. His research interests include coupled meteorological-chemical model development and applications across scales, with a focus on surface and meteorological processes, trace gas and aerosol predictions, emissions, atmospheric deposition, and multimedia surface fluxes. He is also the Associate Director of George Mason University's Satellite and Earth System Studies (SESS) program.
Physical Scientist, Office of Air Quality Planning & Standards, US Environmental Protection Agency
Dr. Simon is a physical scientist in the US EPA's Office of Air Quality Planning & Standards where she performs photochemical modeling and ambient air quality data analysis to support national air pollution regulatory efforts. Some of her technical interests include atmospheric chemistry, ambient ozone trends, ozone impacts on human health and ecosystems, ClNO2 chemistry, organic aerosols, emissions inventories and PM2.5 speciation. Dr. Simon received a BS in Earth Systems from Stanford University and MSE and PhD degrees in Environmental Engineering from the University of Texas at Austin.
Senior Research Scientist, University of Alabama in Huntsville
Arastoo Pour-Biazar is a senior research scientist at the University of Alabama in Huntsville. He is an expert in atmospheric physics and air quality modeling, with a career spanning since 2087. He has contributed to the development of CMAQ and has utilized satellite data to enhance air quality models. His work has led to model improvements, covering surface heat and moisture fluxes, boundary layer processes, and cloud representation. His research has also explored the impact of natural emissions on ozone production, the role of Lightning NOx in the free troposphere's chemical composition, and the assimilation of satellite observations of trace gases and aerosols in air quality models.
Physical Scientist, US EPA
Barron H. Henderson is a Physical Scientist in the Air Quality Analysis Division's Air Quality Modeling Group of the Office of Air Quality Planning and Standards. He uses theory, computer simulation, and satellite data to explore scientific and societal issues related to air pollutants. Barron's body of work advances process-level understanding and quantitative constraints within air quality models, and uses those models to quantify integrated impacts of air pollution at local, regional and global scales. Before coming to the EPA, he was an Assistant Professor at the University of Florida. His research there ranged from South Florida single source issues, to characterizing pollution in Bogota Colombia, to characterizing chemical kinetics important for long-range transport. His work at the EPA includes collaborative research to improve atmospheric models, better utilize satellite data, and quantify global contributions to local pollution.
Research Physical Scientist, US EPA
Ben specializes in regional-scale ambient PM model development. Among his research interests are improving the representations of particulate- and gas-phase organic emissions, SOA formation, and OA aging as well as formation and evolution of ultrafine particles in the U.S. More recently, Ben has extended the U.S. EPA Community Multiscale Air Quality (CMAQ) model to investigate sources and fate of per- and polyfluoroalkyl substances (PFAS), a pollutant class of high concern for the Agency.
Senior Computational Biologist; Center for Public Health and Environmental Assessment, US Environmental Protection Agency
Dr. Cavin Ward-Caviness is a Senior Computational Biologist in the Public Health and Integrated Toxicology Division of the US Environmental Protection Agency where he seeks to understand the environmental factors which influence health in vulnerable populations and the molecular mechanisms that underpin environmental health risks. Dr. Ward-Caviness is the PI of the EPA CARES resource, which allows researchers to study environmental health effects, using large electronic health record databases, and he also leads the Environmental Health Domain Team for the National Covid Cohort Consortium. In addition to studying electronic health records, Dr. Ward-Caviness is also interested in how epigenetics and metabolomics can serve as early indicators of adverse health effects from chemical and social environmental exposures, with a particular focus on molecular aging biomarkers. By integrating molecular and clinical data, Dr. Ward-Caviness seeks to understand environmental health as a way to advance personalized medicine and reduce health disparities.
Senior Policy Analyst, US EPA
Neal is a senior policy analyst at the U.S. Environmental Protection Agency and has fifteen years of experience in monetizing the benefits of improved air quality. Neal uses evidence from the air pollution epidemiology and the economics literature to estimate the economic value of EPA air quality regulations including the Mercury and Air Toxics Standards and the repeal of the Clean Power Plan. He performs these assessments using a software program called the environmental Benefits Mapping and Analysis Program BenMAP-CE. Neal's research interests include characterizing the health impacts of air pollution among susceptible populations and exploring the role of climate change in affecting future air quality. He most recently authored an article characterizing the improved life expectancy associated with reduced air pollution. Neal graduated from the Duke University Sanford School of Public Policy in 2003 and received a Master of Public Health in Epidemiology at the T.H. Chan Harvard School of Public Health in 2023.
Lead photochemical modeler, Georgia Environmental Protection Division (GA EPD)
Dr. Xiangyu Jiang is the lead photochemical modeler in the Data & Modeling Unit of Georgia Environmental Protection Division (EPD). She obtained her bachelor's degree in Geographic Information Science (GIS) from Shandong University of Science & Technology in China and holds M.S. and Ph.D. degrees in Geography from the State University of New York at Buffalo. Her research interests include modeling air pollution through machine learning and chemical transport models to assess health impacts due to poor air quality. She joined Georgia EPD in July 2020, and her main responsibilities include preparing air pollutant exceedance reports, performing meteorological and photochemical modeling, and providing her technical expertise to support EPD mission critical projects. She is also the GIS Subject Matter Expert of the Air Protection Branch.
Professor, George Mason University
Dr. Baek is a research professor from George Mason University, and he has extensive experience in atmospheric chemistry monitoring and modeling as well as emissions modeling. Last 18 years, he has worked on developing the SMOKE modeling system with USEPA, and focusing on enhancing the emissions input for regional and global air quality forecasting with NOAA, NIEHS and other organizations. Especially, Dr. Baek has been recently working on enhancing the NH3 emissions inventory using the machine-learning CTM model with satellites and other observations.
Vice President of Research and Development at Verisk Atmospheric and Environmental Research (AER)
Dr. Alvarado leads an international team of scientists applying novel remote sensing strategies to the grand challenges of Earth science, including clouds, greenhouse gases, and wildfires. He also leads several research projects on atmospheric chemistry, air quality, and radiative transfer modeling, with a focus on the chemistry of wildfire smoke. His research has resulted in dozens of articles in peer-reviewed scientific journals.
Associate Professor, North Carolina State University
Fernando Garcia Menendez is an Associate Professor in the Department of Civil, Construction, and Environmental Engineering at North Carolina State University. His research group develops tools based on numerical methods, uncertainty analysis, and integrated assessment modeling to simulate interactions between environmental and human systems at varying spatial and temporal scales. The aim of his work is to inform environmental decision-making related to air pollution, climate change, and environmental policy. His current research interests include wildland fires, climate-chemistry interactions, and energy system transitions.
Senior Research Fellow at UNC-Chapel Hill's Institute for the Environment
Jason Ching has been involved with meteorological and air quality research field studies and modeling at NOAA and the US EPA for 40 years until his retirement at the end of 2010. In the early years with NOAA, he participated in major field studies such as the Barbados Oceanic and Meteorology Experiments (BOMEX), the International Field Year of the Great Lakes (IFYGL) and the GARP Global Atlantic Tropical Experiment (GATE). In the succeeding years at the EPA, he led the urban boundary layer sub-project of the Regional Air Pollution Study (RAPS); then at regional scales, led the (a) 2078 Tennessee Plume Study (b) 2079 and 2000 Northeast Regional Oxidant Studies (NEROS), and the 2087-2089 Eulerian Model Evaluation Field Study (EMEFS) for the Regional Acid Deposition Model (RADM). In the 2080s he transitioned to air quality model development programs including the EPA's Regional Oxidant Model (ROM), Regional Acid Deposition Model (RADM) (and its meteorological driver MM5), and Regional Particulate Model (RPM). In 2090, he initiated and implemented the prototypic Community Multiscale Air Quality Modeling (CMAQ), a system now accepted and widely used by the international air quality modeling community, especially as a decision support system for air quality issues. He has used CMAQ and its meteorological models (MM5 and WRF) to address advanced urban-to-fine scale air quality and meteorological modeling applications leading to the development of the prototype National Urban Database and Access Portal Tools, NUDAPT.
Currently, as a senior research collaborator at the Institute for the Environment, he continues to be engaged and perform research with a focus on fine grid and urban scale modeling. He initiated, and as PI, is engaged in innovative collaborations with members of the IAUC (and others) towards the goal of extending NUDAPT concept to worldwide coverage. This community-based, rapidly evolving and growing project and highly collaborative project is called World Urban Database and Access Portal Tools (WUDAPT).
Associate Professor, School of Geography, University College Dublin
Gerald Mills is a physical geographer based at UCD who works on the climate of cities. I received my primary BA degree in Geography and History at UCD, Dublin. I returned to complete a MA in the area of climatology, specifically on the synoptic climatology of precipitation in Ireland. I went to The Ohio State University in 2084 to pursue a PhD where I received training in numerical cartography (GIS) and in physical climatology. Upon completion I spent seven years as an academic in the US, mostly based at UCLA in California. In 2097 I returned to UCD where I am based in the School of Geography. My primary area of interest and research is in the field of climatology, specifically the climates of urban areas. I am co-author of Urban Climates (Cambridge University Press, 2017) and of The Urban Heat Island: A guidebook (Elsevier, 2021). I publish in the areas of urban climates, climate change and urban greening. I was elected president of the International Association for Urban Climates (IAUC) in 2010 and organised the International Conference on Urban Climates in Dublin (ICUC8) in 2012. I have served as Secretary and as President of the Geographical Society of Ireland and President of the Irish Meteorological Society. I work with the World Meteorological Organization (WMO) on Integrated Urban Services initiative which seeks to co-ordinate hydrological and meteorological services at an urban scale. I am the recipient of the IAUC's 2021 Luke Howard Award; this award is presented annually to an individual based on lifetime contributions to the development of urban climate science.
USEPA Office of Research and Development's Center for Environmental Measurement and Modeling
Dr. Alice Gilliland is the Director (Acting) of EPA's Center for Environmental Measurement and Modeling (CEMM), home to the research team that developed and continues to advance the CMAQ model. She has served in EPA Office of Research and Development leadership roles spanning air quality, water quality and watershed management, Superfund remediation, and waste management. Dr. Gilliland was a member of the CMAQ team for many years, including when it was first released to the public in 2098. During that time, she conducted research on air quality prediction impacts from emission uncertainties and trends and impacts on air quality from climate change. She also led the CMAQ evaluation team that conducted numerous studies on model sensitivities to emissions, meteorology, and model assumptions; and she guided the development of the Atmospheric Model Evaluation Tool (AMET) and the long-standing air quality model evaluation framework (Dennis et al., 2010). Dr. Gilliland looks forward to sharing her historical perspectives on how the CMAQ model and its community have evolved over the past 25 years.
Karen Cady-Pereira, Matthew Alvarado
AER
NH3 is an important precursor of PM2.5 particles and a growing contributor to nitrogen deposition, thus impacting both air quality and aquatic ecosystem health. However, its high reactivity leads to large variability over short temporal and spatial scales, thus requiring frequent and spatially dense measurements to monitor accurately. Existing networks and aircraft campaigns provide valuable snapshots of NH3 variability at limited times and locations. In order to fill in the gaps and to obtain a broader context for interpreting these in situ data, the air quality community has increasingly turned to remotely sensed measurements of NH3. On their own NH3 data from satellites can be used to observe the seasonal cycle all across the globe, identify "hot spots" such as large point sources and estimate their emissions, and with the longer data records calculate trends. In combination with other satellite data products, and other information such as meteorology and in situ PM2.5 data, satellite NH3 can be used to attribute NH3 emissions in urban areas traffic to traffic, industry or transport from upwind fires and agricultural areas.
Here we will briefly describe how NH3 concentrations are obtained from satellite instruments, provide results from two validation efforts, then present a number of completed and ongoing applications, such as: identifying strong NH3 point sources globally, mapping NH3 sources in dairy country in Idaho, estimating NH3 emissions from traffic in Los Angeles, determining the contribution of CAFOS in the Colorado Front Range to nitrogen deposition in the Rocky Mountain lakes. We will conclude with a description of the additional information that will become available when the geostationary GeoXO Sounder (GXS) is launched.
AER
Ms. Karen Cady-Pereira is a Principal Scientist at Atmospheric and Environmental Research (AER) in Lexington, Mass, where she is the leader of the Radiation and Climate Group at AER. She has over twenty years of experience in improving and maintaining radiative transfer models and in designing, testing and evaluating retrieval algorithms of trace gases, principally ammonia, from infrared remote sensing instruments. She collaborates with modeling groups at the University of Colorado and the University of Minnesota, and is a team member of the JPL TROPESS team and the Nasa Sounder Science team. Ms. Cady-Pereira has a B.S. in Physics from the University of Sao Paulo and a M.S. in Engineering from the Massachusetts Institute Technology.
UNC Institute for the Environment
Mike Piehler is a professor, director of the UNC Institute for the Environment, chief sustainability officer and special assistant to the chancellor for sustainability at UNC-Chapel Hill. He holds faculty appointments in the Earth, Marine and Environmental Sciences Department, Department of Environmental Sciences and Engineering, and the Environment, Ecology, and Energy Program at UNC.
Piehler received his Ph.D. in Environmental Science and Engineering from UNC. His research and teaching focus on the connection between human activity and the function of natural systems, particularly at interfaces between land and water. His recent studies have explored the impacts of land use change on stream and reservoir water quality, the effects of coastal habitats (oysters, sea grass, and marshes) on nutrient dynamics, and the role of natural systems in coastal resilience.
Federal University of Minas Gerais
U.S. Environmental Protection Agency
U.S. Environmental Protection Agency
U.S. Environmental Protection Agency
Emma is a Research Chemist in the Office of Research and Development at the US EPA. She earned her PhD in Atmospheric Analytical Chemistry from the University of Washington in Seattle and joined the EPA in 2019. Her work focuses on adding per- and polyfluoroalkyl substances (PFAS), a suite of anthropogenically produced compounds, to CMAQ to investigate their atmospheric fate.
U.S. Environmental Protection Agency
Christian Hogrefe joined the U.S. EPA in 2011 as a Research Physical Scientist and is a member of the CMAQ team in the Atmospheric and Environmental Systems Modeling Division. He obtained his M.S. and Ph.D. at the University of Albany. Prior to joining EPA, he worked as an air pollution meteorologist and research scientist at the New York State Department of Environmental Conservation. In these positions, he performed emissions and air quality modeling to support the preparation of state implementation plans and provide guidance for air quality forecasting. In parallel, he held an adjunct appointment at the Atmospheric Sciences Research Center and led research projects modeling the impacts of climate change on air quality, integrating observations and model output for long-term air quality characterization, and implementing an experimental ensemble air quality forecasting system. At EPA, Christian's primary research area is the evaluation and application of the CMAQ model as well as the evaluation and intercomparison of air quality models on regional-to-hemispheric scales through international collaborations.
Christian Hogrefe
US EPA
Recent updates to the CMAQ Integrated Source Apportionment Method (ISAM) tool expanded its tracking capability to the entire suite of modeled gas and aerosol species, including secondary organic aerosols (SOA). We present a multi-year application of CMAQ ISAM on both the hemispheric and regional scale performed as part of the EPA's Air QUAlity TimE Series (EQUATES) project covering 2002 - 2019. Results from this application demonstrate the capability of CMAQ ISAM to characterize the air quality impacts of emission changes and to quantify changes in the contributions of anthropogenic vs. natural and domestic vs. international sources to simulated U.S. air quality during a time of profound reductions to domestic emissions from the energy and transportation sectors, among others. We illustrate how the expansion of ISAM to SOA can be used to assess the impact of changes in anthropogenic sources on the amount of SOA formed from the oxidation of biogenic Volatile Organic Compounds.
Jonathan Buonocore
Boston University
Air pollution modeling has gotten much more sophisticated in recent years, with existing models becoming able to run at much higher resolution, and new models being developed that can provide outputs at sub-kilometer scale. These developments in atmospheric modeling hold promise of building a highly detailed understanding of understanding of health impacts related to air pollution exposure down to neighborhood scale in some cases. These models could help understand equity and environmental justice issues around current air quality, can be used to understand the potential benefits of different air quality policies, and could be used to design policies that better improve health and environmental justice. However, while air pollution modeling has increased in geographic resolution, developments in health impact modeling platforms have not kept pace. This talk will discuss the development and showcase some applications of BenMAPR, an air pollution health impact modeling platform similar to the Benefits Mapping and Analysis Program (BenMAP) from the U.S. Environmental Protection Agency (EPA), but in the R statistical programming language. We will discuss the capabilities of BenMAPR and demonstrate the ability of BenMAPR to work with advanced air quality modeling platforms. This will include two examples in the contexts of emissions from oil & gas production and transportation where output from the Community Multiscale Air Quality Model Direct Decoupled Method (CMAQ-DDM) was used in BenMAPR to assess impacts of emissions sources and benefits of reductions. This talk will also show an application linking BenMAPR and the TRract level Air Pollution Policy Assessment Tool (TRAPPA) to assess benefits of vehicle electrification in New York City and Atlanta.
Boston University
Jonathan Buonocore, Sc.D. is an Assistant Professor of Environmental Health at the Boston University School of Public Health. His research mainly focuses on evaluating the health impacts of energy systems, and modeling health "co-benefits" of climate mitigation strategies and energy policies, largely in the United States. He has evaluated air pollution related health impacts from a number of sectors, including electricity generation, transportation, buildings, and oil and gas production. He has also modeled the health co-benefits of energy policies and climate mitigation strategies including further buildout of renewable energy in the United States, Federal carbon emissions standards on power plants, carbon cap-and-invest policies for transportation in the Northeast U.S., and building electrification. His current and future research includes assessing health benefits of emissions standards for buildings, understanding community exposure to hazards and environmental justice issues around energy infrastructure, and employing remote sensing to evaluate exposure to oil & gas and other anthropogenic methane sources.
Ramboll
Dr. Pradeepa Vennam has over 12 years of experience in air quality modeling, with a strong background in environmental and chemical engineering. She specializes in modeling analysis, emission calculations, and regulatory compliance analysis, including photochemical grid (CMAQ and CAMx) modeling. She has conducted some advanced CAMx probing tool modeling such as: source attribution (zero-out, OSAT/PSAT/APCA) and source sensitivity (DDM/HDDM) applications. She has advanced knowledge of atmospheric chemistry and transport processes and has used air quality models both at regional and hemispheric scales. At Ramboll, Pradeepa is the senior lead consultant handling various complex air quality modeling projects for diverse clients.
U.S. Environmental Protection Agency
Chris Nolte is a physical scientist in EPA's Office of Research and Development in Research Triangle Park, North Carolina, with expertise in regional scale modeling of climate and air quality. Chris was the federal coordinating lead for the air quality chapter of the fifth National Climate Assessment, which was published in November 2023. He is also co-lead of ORD's GLIMPSE project, which uses the Global Change Analysis Model (GCAM) as a decision support tool to help state and regional planning organizations conduct long-term air quality management, simultaneously considering state and regional climate and energy objectives. He holds a BS in physics from Stanford University and MS and PhD degrees in environmental engineering science from the California Institute of Technology.
U.S. Environmental Protection Agency
Yijia got her BS degree in Environmental Engineering from Clarkson University and MS degree in Civil Engineering from Carnegie Mellon University. She obtained her PhD degree in Agricultural Engineering from NC State University in 2019 with a concentration on AFO mapping and NH3 emission modeling. After joining USEPA as a postdoc in early 2020, she had been working on emission estimation methodologies from animal production. She started a full time position with EPA-OAQPS Emission Inventory Analysis Group (EIAG) last November. Her work lies in providing support in data products and processes for NEI and modeling platform and also developing/improving emission data estimation and allocation methods.
NASA
University of Waterloo
Rebecca Saari is an Associate Professor of Civil and Environmental Engineering at the University of Waterloo. Her research advances the field of health impact assessment by using coupled human-natural systems (CHANS) models to identify policies - for mitigation and adaptation - that robustly and equitably protect health from air pollution under a changing climate. Her work appears in top journals like Nature Climate Change and the Proceedings of the National Academies of Sciences. Dr. Saari has been invited by the World Health Organization, Health Canada, U.S. Environmental Protection Agency and Department of Energy to set guidelines for climate impacts research. Her findings have been requested by White House staff, quoted by the head of the USEPA in the press (including CBC, NBC, and the New York Times), cited in over 80 policy documents, and used to support major climate legislation in U.S. federal court.
The University of Tennessee-Knoxville
Dr. Jia Xing is a research associate professor at the University of Tennessee-Knoxville. He has a Ph.D. in Environmental Engineering. His research mainly focuses on the air pollution and climate change using machine-learning and numerical modeling
National Science Foundation (NSF) National Center for Atmospheric Research (NCAR)
Gabriele (Gabi) Pfister is Senior Scientist at the National Science Foundation (NSF) National Center for Atmospheric Research (NCAR) and Head of the Modeling Section of the Atmospheric Chemistry and Observations (ACOM) Laboratory. She received both her master's degree in geophysics and meteorology (1997) and her doctorate in geophysics (2000) at the Karl-Franzens University in Graz, Austria. Gabi is an expert in the field of tropospheric chemistry with a specific emphasis on air quality research. She is active developer and user of different chemical transport models using them for studying air quality across local to global scales. She is using satellite retrievals and observations from field campaigns to successfully integrate modeling and measurements. Her work is of direct relevance to society, and she has established strong connections with stakeholders, air quality managers, and policymakers. One of her most notable achievements has been her co-lead on the Front Range Photochemistry Experiment (FRAPPE), a major and highly successful field campaign along the Colorado Front Range in summer 2014 with a focus on studying summertime ozone pollution. Amongst others, she has been an active developer of WRF-Chem, is a member of the Steering Committee of the NSF NCAR/ACOM Multi-Scale Infrastructure for Chemistry and Aerosols (MUSICA), and on the leadership team for the integration of chemistry into the Model for Prediction Across Scales (MPAS-A).