Modeling the Contributions of Major Sources to Atmospheric Nitrogen Deposition in the United States

Sharmin Akter, and Kristina Wagstrom

Atmospheric nitrogen deposition is a major contributor to nutrient loadings in the aquatic systems. Because of strict environmental regulations over the last few decades, atmospheric nitrogen deposition has decreased in the northeastern US. However, some parts of the US -mainly the Mid-US has experienced higher atmospheric nitrogen deposition due to higher anthropogenic activities- mostly from agriculture. Excess nitrogen can cause hypoxia, acidification, deforestation, eutrophication, harmful algal bloom, and loss of biodiversity. To combat these negative outcomes, it is important to identify the major species, source sectors, and source regions contributing to deposited atmospheric nitrogen - both spatially and temporally. We use the Comprehensive Air Quality Model with extensions (CAMx – v. 6.0), along with the Particulate Matter Source Apportionment Technology (PSAT), to quantify the contributions from major species, source sectors, and source regions contributing to atmospheric nitrogen deposition. We model the amount of atmospheric nitrogen deposition from electricity generating units, biogenic emissions, area fugitive dust, on-road, residential wood combustion, agricultural emissions, non-point source oil and gas, point source oil and gas, fires, non-road mobile equipment sources, marine vessels and locomotives, and other non-point sources in the contiguous United States, southern Canada, and northern Mexico. Agricultural emissions are the dominant sector for total nitrogen deposition throughout the United States. We also model the contributions of emissions from each 2-digit Hydrologic Unit Region (HUC2) and major metropolitan areas to atmospheric nitrogen deposition throughout the United States. The Upper Mississippi and Ohio regions are important contributors to total nitrogen deposition. New York City is the biggest metropolitan contributor to atmospheric nitrogen deposition. This information will help environmental regulators develop more effective measures and improve existing frameworks to protect human health and ecosystems. This could inform the development of effective management approaches that are transferrable to other regions of the globe. This study provides significant in-depth and comprehensive information about atmospheric nitrogen contributions in different places or times to improve watershed management plans.

Hemispheric-Scale Evaluation of CMAQv5.4

Christian Hogrefe, Wyat Appel, Kristen Foley, Golam Sarwar, Jonathan Pleim, Jesse Bash, Jeff Willison, Robert Gilliam, George Pouliot, and Rohit Mathur

The U.S. EPA is planning to release version 5.4 of the Community Multiscale Air Quality (CMAQ) model in September 2022. In this study, we present analyses of several CMAQv5.3.3 and CMAQv5.4 simulations performed over the Northern Hemisphere for the year 2018. The simulations were designed to quantify the effects of specific model updates and new science options available in CMAQv5.4. Specifically, analyses will show the effects of updates in halogen chemistry, windblown dust, aerosol dry deposition, and the newly added option to calculate biogenic VOC and soil NO emissions inline in CMAQ using version 3.2 of the Model of Emissions of Gases and Aerosols from Nature (MEGAN). Simulations with CMAQv5.3.3 use the Carbon Bond 6 chemical mechanism, version 3, with detailed halogen chemistry (CB6r3m), while simulations with CMAQv5.4 use the Carbon Bond 6 chemical mechanism, version 5, with updated detailed halogen chemistry (CB6r5m). Revisions to the detailed halogen chemistry cause a general increase of ozone, especially outside the summer months. Ozone results are also sensitive to the two soil NO emission options available in MEGAN. Revisions to the windblown dust calculations in CMAQv5.4 cause a general increase in dust-related aerosol concentrations, while changes to aerosol dry deposition cause a general decrease. Performance of these simulations is assessed using surface observations, satellite retrievals, and ozonesonde observations for different regions. The presentation will also include an analysis of ozone and aerosols simulated by hemispheric CMAQ at the boundaries of a 12km modeling domain covering the conterminous U.S. to assess the impacts of model updates and science options on estimated background conditions for continental-scale applications.

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 leads to more frequent and intense drought events in some regions. The strong perturbation of drought on the atmospheric water cycle can cause influences on air quality. Exposure to air pollution has a wide variety of adverse effects on human health. The low streamflow and high electricity demand during drought periods lead to more power generated by fossil fuels, which exacerbates air pollution. In addition, meteorological changes during drought periods, including high temperature and low humidity, have been demonstrated to increase the concentrations of PM2.5 and ozone. Drought is generally associated with increased atmospheric stagnation and less precipitation, which decrease the diffusion and wet removal of air pollutants. In this study, we explore connections between drought and air pollution and processes leading to these relationships. We present initial results from our work to conduct both statistical analysis on observational PM2.5 and ozone concentrations, and WRF-Chem model simulations of air pollution. We first classify the drought conditions of the past 20 years in California into three types using the Palmer Drought Severity Index (PDSI). By comparing air pollution levels during the drought, neutral, and wet periods, we investigate the extent to which air pollutant concentrations increase during drought periods. The WRF-Chem model is used to quantify the effects of droughts on air quality, and to separate the contributions of meteorological and biological processes, increased electricity demand, and increased fossil electricity generation to compensate for decreased hydropower. By replacing meteorology during drought periods with non-drought periods , the contribution of meteorological changes on exacerbated air pollution can be determined. Similarly, the contribution of changes in power plant emissions can also be determined, separating the contributions of increased demand vs. generation mix, based on output from electric grid system models.

Evaluation of Regional-Scale CMAQv5.4 for North America

K. Wyat Appel, Christian Hogrefe, Kristen Foley, Golam Sarwar, Jonathan Pleim, Jesse Bash, Havala Pye, Bryan Place, Robert Gilliam, George Pouliot, Sergey Napelenok, Kathleen Fahey, William Hutzell, Daiwen Kang, Benjamin Murphy, Chris Nolte, Fahim Sidi, Tanya Spero, Jeffery Willison, David Wong, Russell Bullock, Emma D'Ambro, Jerry Herwehe, Rohit Mathur

The U.S. EPA is planning to release version 5.4 of the Community Multiscale Air Quality (CMAQ) model in September 2022. We will present analyses of several CMAQv5.3.3 and CMAQv5.4 simulations performed over North America for the entire year 2018. The simulations were designed to quantify the effects of several model updates and new science options available in CMAQv5.4. Specifically, analyses will show the effects of updates in halogen chemistry, windblown dust, aerosol dry deposition, and the initial implementation of the Community Regional Atmospheric Chemistry Multiphase Mechanism (CRACMM). The presentation will include an analysis of ozone and aerosols simulated by CMAQ using 12-km horizontal grid spacing for a model domain covering the conterminous U.S. and Canada to assess the impacts of model updates and science options on the estimated concentrations of ambient and wet and dry deposition pollutants. Performance of these simulations is assessed using the various surface observations available across the U.S. and Canada, and where applicable/available, satellite retrievals and ozonesonde observations. Updates to the instrumented CMAQ tools (i.e., the Decoupled Direct Method and the Integrated Source Apportionment Method) will be briefly discussed as well.

Resilience Benefits of Replacing Diesel Backup Generators with PV-Plus-Storage Microgrids for California Public Buildings

Sunjoo Hwang, Department of Environmental Sciences and Engineering, Gillings School of Global Public Health, University of North Carolina at Chapel Hill

Sopitsuda Tongsopit, Independent Researcher, Sacramento, CA

Noah Kittner, Department of Environmental Sciences and Engineering, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Department of City and Regional Planning, University of North Carolina at Chapel Hill

Climate change has increased the frequency and intensity of extreme weather events, exacerbating economic losses and health concerns to communities. The western part of the U.S. has suffered from extreme heat and wildfires, and climate change is a key factor that accounts for the frequency and extent of those events. Research has demonstrated that climate changes create warmer and drier conditions, which in turn increases drought and contributes to a longer fire season. Wildfires are expected to double by 2050 due to higher average temperatures. These severe weather events have caused significant negative impacts on the U.S. grid infrastructure as evidenced by the 2021 Texas cold snap and California wildfires. California has experienced more sustained and frequent outages due to wildfires, and this has led to rapid surging demand for backup generators. However, diesel backup generators bear high life-cycle environmental costs, emitting greenhouse gases and air pollutants. They have a reliability issue during a sustained power outage, and the availability of diesel fuels during emergency periods remains susceptible to extreme weather due to road closures. Using REopt, an energy integration and optimization model, this study evaluates the resilience value of replacing diesel backup generators with PV-plus-storage microgrids for a public building in California by quantifying costs and benefits under different outage durations (12, 24, and 41 hours) over 20 years. We estimate the net present value by calculating upfront costs, grid electricity purchases, fuel costs, and emissions costs using the social cost of carbon and emissions factors. The result documents large benefits and public economic savings (~$2 M), with even higher values for more sustained outages. The benefits can increase up to $51 billion if the replacement were expanded over California’s two air districts. Although it would be challenging for decision-makers to finance or invest in high capital costs for PV and storage installations, this research provides a framework to dive into the economic value of resilience for distributed energy systems, including air quality, climate change, and public health. This study also demonstrates that installing more PV capacity does not necessarily add more resilience past a certain outage duration. Rather, the importance of securing more energy storage would be higher as the outage duration increases. This has implications for wildfire, drought, and extreme weather event planning. More focused attention on the parameterization of battery storage is helpful particularly when longer duration outages are becoming expected more often.

Recent advancement of EPAs global air quality modeling system: MPAS-CMAQ

Jeff Willison, Jonathan Pleim, David Wong, Robert Gilliam, Russ Bullock, Jerry Herwehe, Christian Hogrefe, George Pouliot, Golam Sarwar, and Rohit Mathur

We have coupled the National Center for Atmospheric Research (NCAR) Model for Prediction Across Scales – Atmosphere (MPAS-A) with the Community Multiscale Air Quality (CMAQ) model developed at the USEPA. This new tool enables modeling of air quality from global to regional to local scales.

In this presentation, we will describe the value and design of the MPAS-CMAQ system and present results from recent simulations. An EPA-modified version of MPAS-A provides meteorological forcing and horizontal transport for CMAQ. The EPA modifications to MPAS include the addition of four-dimensional data assimilation (FDDA), the ACM2 PBL model, and the PX land surface model. Anthropogenic emissions follow the approach used in EPA’s Air QUAlity TimE Series Project (EQUATES). Biogenic emissions are provided by inline Model of Emissions of Gases and Aerosol from Nature (MEGANv3.1). Global ozone fields from the ECMWF’s Copernicus Atmosphere Monitoring Service (CAMS) are used for initialization and for stratospheric ozone data assimilation in the upper layers of the model for the entire simulation periods.

We will show results from a multi-year simulation on a uniform mesh with 120 km cell spacing and from sub-seasonal simulations using a variable resolution mesh (60-12 km) with refinement over North America. Air quality evaluation includes comparisons with World Ozone and Ultraviolet Radiation Data Centre (WOUDC) ozonesondes and surface-based measurement networks. In addition, we contextualize our results with comparisons to atmospheric compositions simulated by alternative tools such as WRF-CMAQ with hemispheric coverage, the GEOS Composition Forecasting (GEOS-CF) system, and the Copernicus Atmospheric Monitoring Service (CAMS) global reanalysis.

Potential impact of future climate on winter time particulate matter in the western U.S.

Abi Lawal, Cam Phelan, Khanh Do, Yi Ji, Nash T Skipper, Huizhong Shen, Heather A. Holmes, Cesunica E. Ivey

Preliminary studies show that while emission regulations and policies are effective at curbing primary particulate matter (PM) emissions and secondary PM and ozone formation, the probability of a warmer climate could counteract the effect of these regulations. This is particularly important as past studies have shown that both the magnitude, duration, and frequency of elevated particulate matter concentrations are highly impacted by Persistent Cold Air Pools (PCAPs) during the wintertime. Given the difficulties of present-day studies in simulating these PCAP episodes, variability under future and different climate scenarios has not been explored. In this study, we assess the impact of climate, using two different future climate projections, Representative Concentration Pathways (RCP) 4.5 and 8.5 on wintertime particulate matter at a number of sites across the western U.S, focusing on mountainous cities where PCAPs tend to persist. We explore the sensitivity of meteorology on PCAPs using both air quality modeling (CMAQ) and statistical (fixed effects) methods. Our preliminary findings show that differences in simulated PCAP episodes are impacted by relative humidity levels and temperatures, both which have a noted effect on elevated particulate matter levels during PCAPs.

Retrospective, Multiannual Evaluation of the Canadian Operational Regional Air Quality Deterministic Prediction System against North American Surface Observations

Michael D. Moran¹, Alexandru Lupu¹, Verica Savic-Jovcic¹, Junhua Zhang¹, Qiong Zheng¹, Rabab Mashayekhi², Sylvain Ménard², Jack Chen¹, Konstantinos Menelaou², Rodrigo Munoz-Alpizar², and Dragana Kornic²

¹Air Quality Research Division, Environment and Climate Change Canada, Toronto, Ontario, Canada

²Canadian Centre for Meteorological and Environmental Prediction, Environment and Climate Change Canada, Montreal, Quebec, Canada

The central component of the Regional Air Quality Deterministic Prediction System (RAQDPS) employed operationally by Environment and Climate Change Canada (ECCC) is the GEM-MACH chemical weather model. GEM-MACH consists of an online tropospheric chemistry module embedded within the GEM model, which is the ECCC operational numerical weather prediction model. Physical and chemical processes represented in GEM-MACH include anthropogenic and biogenic emissions, atmospheric dispersion, gas- and aqueous-phase chemistry, inorganic heterogeneous chemistry, aerosol dynamics, and wet and dry removal. A comprehensive performance evaluation of the current version of the RAQDPS, version 023, has been carried out for a four-year (2013–16) period that was run retrospectively with year-specific Canadian, U.S., and Mexican emissions (based on Canada’s Air Pollutant Emissions Inventory, APEI 2018, and EPA’s Air Quality Time Series EQUATES dataset) on a 10-km North American horizontal grid. The model output is compared with a wide range of measurements from Canadian and U.S. air- and precipitation-chemistry surface networks. Such a comprehensive and consistent multi-year evaluation is not possible for the usual operational prospective forecasts due to the limited availability of near-real-time measurements and frequent model updates. Standard performance metrics are used to assess the retrospective annual simulations and to identify systematic shortcomings in the representation of various processes in the model for the four-year period.

Performance of an electrified U.S. passenger car fleet in 2050 under future climate scenarios on air quality

Abi Lawal, T. Nash Skipper, Jooyong Lee, Huizhong Shen, Yilin Chen, Cesunica E. Ivey, Anu Ramaswami, Kara M. Kockelman, Armistead G. Russell

The impact of electric vehicles (EVs) on energy demand, emissions and air quality has been explored in a number of studies, many of which assess EV impacts in the context of various energy supply scenarios along with increased travel demand. Most however, do not take into account the impact of self-driving vehicles, otherwise referred to as Autonomous Vehicles (AV) or Shared Autonomous Vehicles (SAVs) in quantifying EV effects, despite its added effect of additional Vehicle Miles Traveled (VMT) due to AV and SAV utilization. Many also do not consider the relative performance of EVs in different climate scenarios as well. In this study, we assess the future impact of AVs, SAVs and EVs under two energy policies (EP) where relaxed and stringent controls are enacted across multiple emission sectors. Two climate projections (moderate and adverse) under the Representative Concentration Pathways (RCPs) 4.5 and 8.5 are used respectively. The 2050 EV scenario is compared with an alternative 2050 scenario where passenger cars are gasoline powered. In all cases, the on-road fleet of internal combustion vehicles (ICVs) are expected to be more efficient and less polluting than they are at present, such that non-tailpipe emissions are a major fraction of the particulate matter emissions, potentially limiting the positive impacts of EVs on air quality. The efficacy of EVs is assessed using both computational models (CMAQ) and statistical analysis with random and fixed effects models. Initial findings show that in future, climate has a significant effect on EV efficacy more so than control technologies on air quality.

ECCCs first-generation 10 km North America reforecast dataset for air quality

Xihong Wang, Nedka Pentcheva, Maxim Bulat, Rabab Mashayekhi, Annie Duhamel, Mike Moran, Verica Savic-Jovcic, and Jack Chen

A 10-km reforecast air quality system over North American has been developed at the Environment and Climate Change Canada (ECCC) based on ECCC’s operational air quality forecast model, GEM-MACH (Global Environmental Multi-scale - Modelling Air quality and Chemistry). The core model selected for the reforecast is the version v2.4.6 of GEM-MACH model which is an in-line chemical transport model (CTM) with one-way coupling with the version of 4.8-LTS.17 of the Canadian weather forecast model GEM. A set of upgrades have been made to the model for reforecast purpose. The new reforecast model includes the emissions from both anthropogenic and biogenic sources, as well as the emissions from biomass burning. The anthropogenic emissions are based on the emissions inventories that are generated by ECCC for policy studies. Biomass burning emissions are provided by EPA’s air quality time series projects (EQUATES) and Canadian forest fire emissions prediction system (CFFEPS). The latest 10-km atmospheric reanalysis from the Regional Deterministic Reforecast System (RDRS) developed at ECCC is used to drive the model. This presentation will introduce the configuration of the reforecast system in detail. The evaluation of the reforecast performance against surface air-quality measurements and against the ECCC operational regional air quality deterministic prediction system (RAQDPS) will be presented. The reforecast datasets produced with EQUATES, CFFEPS wildfire emissions, respectively, and without wildfire emissions will be compared and evaluated against hourly surface measurements for the period 2014-2016.

Impacts of Future Energy Transition on the U.S. Air Quality and Mortality

Yang Zhang1, Kai Wang1, Xiao-Yang Chen1, Kiarash Farzad1, Shen Wang2, Benjamin Hobbs2, Hugh Ellis2, Ken Gillingham3, and Michelle Bell3

1Department of Civil and Environmental Engineering, Northeastern University, Boston, MA

2Department of Environmental Health and Engineering, Johns Hopkins University, Baltimore, MD

3School of Environment, Yale University, New Haven, CT

Transition from fossil fuel based energy to clean energy will impact air pollutant emissions, which in turn impact air quality and mortality caused by surface ozone (O3) and fine particulate matter (PM2.5). As part of the U.S. EPA program on Air, Climate, and Energy centers, the center of Solutions for Energy, AiR, Climate, and Health (SEARCH), multiple 5-year air quality simulations during 2008-2050 are performed under a suite of energy transition scenarios over the U.S. using an online-coupled meteorology and air quality model (WRF-Chem) and an offline model (CMAQ). The simulated energy transition scenarios include a reference scenario without the clean power plan (refnocpp), a scenario with abundant natural gas (highNG), a scenario with high electric vehicle market share (highEV), a scenario with port electrification (port), and a scenario with intensive building energy efficiency (highEE). The projected impacts of these scenarios on emissions, air quality, and mortality are examined along with their policy implications and co-benefits. Among the scenarios examined, the highEE scenario is projected to have the maximum co-benefit to both air quality and public health over the U.S.

Evaluating Methods of Representing Lightning NOx Emissions Across the Northern Hemisphere

J. Mike Madden, Daiwen Kang, James East, Golam Sarwar, Christian Hogrefe, Rohit Mathur, and Barron H. Henderson

As anthropogenic emissions decrease in the United States and many parts of the world, lightning-produced NO_x (LNO_x) emissions become increasingly important in model studies relating to atmospheric composition, climate change, and natural resource management. Considering that LNO_x is primarily produced in the mid to upper troposphere, the subsequent effects of these emissions can transcend long distances. To account for this important NO_x source, LNO_x emissions can be represented by different approaches, such as climatological datasets or parameterization from lightning flash data. For example, the Community Multiscale Air Quality (CMAQ) model’s inline LNO_x emissions have been shown to provide credible representations of LNO_x using data from the National Lightning Detection Network (NLDN), a dense, high-efficiency lightning flash dataset over the contiguous U.S. For hemispheric to global model applications, the use of lightning flash data from the World Wide Lightning Location Network (WWLLN) is attractive; however, WWLLN possesses a lower lightning detection efficiency.

To assess the LNO_x impact on simulated atmospheric composition in the Northern Hemisphere, four configurations of LNO_x are devised for the year 2016: the first with no LNO_x emissions, the second with monthly LNO_x climatology from the Global Emissions InitiAtive (GEIA) inventory, the third with WWLLN, and the fourth using a “scaled” version of WWLLN which contains adjustments that reflect the NLDN monthly climatology over the contiguous U.S. that are then extrapolated over the Northern Hemisphere. Comparisons are made against surface concentrations from the U.S. Air Quality System, deposition fluxes from the National Atmospheric Deposition Program, vertical ozone profiles from the World Ozone and Ultraviolet Radiation Data Centre (WOUDC), satellite products from the Ozone Monitoring Instrument, and aircraft samples from the Atmospheric Tomography Mission (ATom). The comparisons to observations demonstrate the value of using LNO_x data for simulating the atmospheric composition across the Northern Hemisphere.

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).

Development of air pollutant emission projections for alternative scenarios of the future using GLIMPSE/GCAM-USA 5.4

Daniel H. Loughlin, Christopher G. Nolte, and Uma Shankar U.S. Environmental Protection Agency 

Steven J. Smith, Maridee Weber, and Yang Ou Pacific Northwest National Laboratory

A version of the Global Change Analysis Model with state-level resolution (GLIMPSE/GCAM-USA 5.4) is used to develop state-, pollutant-, and source category-specific projections of air pollutant emissions for a Reference Case representing current legislation, as well as for two hypothetical Deep Decarbonization scenarios that take different technological pathways toward decarbonization. One pathway prioritizes wind, solar, and nuclear power, combined with end-use electrification, while the other prioritizes carbon capture utilization and storage (CCUS) and direct air capture (DAC). The projected emissions for these scenarios are compared at the national scale with EPA's 2016v2 emissions modeling platform, including for the years 2016, 2026, and 2032. We highlight the major trends in the Reference Case projection, including the underlying factors that drive those trends - increasing demands for energy, technology change, and environmental and energy policies. Next, we show how these factors differ under the Deep Decarbonization scenarios. The air quality impacts of the resulting emission projections can be examined using an air quality model, which is the subject of a companion presentation by Uma Shankar.

Integrating a reduced complexity model into a hybrid modeling framework

Britney Russell, Kristina Wagstrom

Hybrid modeling frameworks have been proposed to address the shortcomings of stand-alone air quality models. One such framework is HYCAMR (Parvez & Wagstrom, 2019). HYCAMR combines outputs from a regional chemical transport model (Comprehensive Air Quality Model with Extensions, CAMx), and a local-scale dispersion model (R-LINE) to estimate pollutant concentrations at a fine spatial and temporal scale. The current version of the model combines two models with vastly different horizontal spatial resolutions, which may affect the accuracy of the estimates. We will present a modification to the HYCAMR methodology that bridges the gap between the horizontal resolution of CAMx and R-LINE. We first start by using CAMx to estimate concentrations at a 12 x 12 km resolution for the contiguous United States. We then resolve these estimates to a higher spatial resolution (1 x 1 km) using a reduced complexity model (Intervention Model for Air Pollution, InMAP). Finally, the resolved outputs are combined with outputs from R-LINE to estimate pollutant concentrations at a 40 x 40 m spatial resolution. The proposed modification to HYCAMR will contribute to an improved understanding of the spatial variability of air pollutants.

1. Parvez, F., & Wagstrom, K. (2019). A hybrid modeling framework to estimate pollutant concentrations and exposures in near road environments. The Science of the total environment, 663, 144–153. https://doi.org/10.1016/j.scitotenv.2019.01.218

Examining the Air Quality Co-benefits of Two Alternative Future Emission Scenarios Modeled in GCAM-USA version 5.4

Uma Shankar, Benjamin N. Murphy, Maridee Weber, Yang Ou, Steven J. Smith, Daniel H. Loughlin, and Christopher G. Nolte

Human-Earth system models such as the Global Change Analysis Model (GCAM) can simulate the evolution of the energy system and estimate simultaneous changes in the associated emissions of greenhouse gases (GHGs) and traditional air pollutants. However, these models have limited use on their own in air quality management because their air pollutant emission outputs must be translated into model-ready inputs for an air quality model. As Human-Earth system models and air quality models typically have very different temporal and spatial resolution and emission sector representations, methods to perform this inter-model transformation are needed. Here, we demonstrate a method that makes use of the Detailed Emission Scaling, Isolation, and Diagnostics (DESID) module within the Community Multiscale Air Quality model (CMAQ). This module was designed for efficient assessment of air quality under hypothetical emission changes at the regional, sector and pollutant level. It is thus well suited to examine the air quality impacts of alternative future energy and associated emission scenarios modeled in GCAM, facilitating the coordination of long-term air, climate and energy planning. DESID is used here to examine the ozone and PM2.5 impacts in 2050 of two alternative future scenarios in GLIMPSE/GCAM-USA v5.4, a reference scenario continuing current legislation, and a deep decarbonization scenario, described in the companion presentation by Loughlin and co-authors. Our CMAQ results show broad regional co-benefits for both ozone and PM from an 80% reduction across all sectors in CO2 emissions. The reference case trajectory provides substantial air quality improvements by 2050, which are enhanced by deeper decarbonization. However, the increased regional use of carbon capture in fossil-fueled power plants in the latter scenario has the potential to increase SO2 and PM2.5 in some locations, pointing towards the need to further examine the air quality implications of these technological pathways. Disclaimer: The views expressed in this presentation are those of the authors and do not necessarily represent the views or policies of the U.S. Environmental Protection Agency.

Investigation of Impacts of Flaring and Venting from Onshore Oil and Gas Production in the United States on Air Quality using CMAQ-DDM

Huy Tran, Jonathan Buonocore, Ananya Roy, Beth Trask, Hillary Hull, Saravanan Arunachalam

Emissions from oil and gas production and processing have significant impacts on air quality as they were found in a recent study to have contributed to 29 and 634 instances of exceedances of national ambient standards for fine particulate matter and ozone, respectively, across various regions in the U.S. in 2016. Flaring and venting activities are considered as potentially significant emission contributors among oil and gas sectors. However, their emissions are difficult to quantify due to their being intermittent sources and lack of adequate monitoring and reporting. Their emissions are not explicitly estimated and not fully captured in the current 2017 national emission inventory (NEI) from the U.S. Environmental Protection Agency (EPA). Given potential uncertainties with the NEI 2017 in characterizing emissions from flaring and venting especially in the states with the highest oil and gas production, we rely on satellite observations and state and local reported flaring data to estimate emissions from the activities. We then used the Community Multiscale Air Quality Modeling System with Decoupled Direct Method in Three Dimensions (CMAQ-DDM-3D) to evaluate impacts on air quality and public health from flaring and venting practices in onshore oil and gas production in the U.S. We first developed a base case which relies only on satellite observations (from Visible Infrared Imaging Radiometer Suite (VIIRS)) and state/local reported data, and an alternative base case where NEI 2017 data was augmented with satellite observations to account for missing flares in VIIRS. Using a zero-out case where emissions from venting and flaring are excluded, we quantified the incremental air quality impacts of the flaring emissions using CMAQ, and subsequently quantified public health risk. We then used the CMAQ-DDM-3D technique to evaluate sensitivity of O3, PM2.5 and NO2 to the flaring emissions in the U.S. We will present results from the two approaches focusing on changes in air quality and public health risk due to this emissions sector.

Spatiotemporal Trends in PM2.5 Chemical Composition in the Continental U.S. during 2006-2021

Bin Cheng¹, Saravanan Arunachalam¹, and Kiran Alapaty²

¹Institute for the Environment, The University of North Carolina at Chapel Hill, Chapel Hill, NC

²U.S. Environmental Protection Agency, Research Triangle Park, NC

Major chemical constituents of atmospheric PM_2.5 in the United States (U.S.) include sulfate (SO₄^2-), ammonium (NH₄⁺), nitrate (NO₃^-), organic carbon (OC), elemental carbon (EC), and a spectrum of elements and ions, which can be attributed to a variety of emissions sources and different atmospheric processes. While the National Ambient Air Quality Standards (NAAQS) for PM_2.5 are mass-based, some previous research has implicated the different toxicity characteristics of PM_2.5 chemical components with evolving findings on the relative risks of the different species. Comprehensive explanation of the adverse impacts of PM_2.5 in the U.S. necessitates detailed analysis of spatiotemporal trends of PM_2.5 chemical composition, given the large-scale reductions in PM_2.5 precursors from various federal, state, and local regulatory programs. This research aims to (1) quantify the spatiotemporal trends of PM_2.5 chemical composition; (2) investigate the dynamic contributions of various chemical species to the spatial and temporal variability of PM_2.5 concentrations in the continental U.S.; and (3) provide insights for the development of PM_2.5 control strategies targeting specific chemical constituents. Data (2006-2021) for the concentrations of PM_2.5 mass and its chemical compositions covering the contiguous U.S. were extracted from U.S. Environmental Protection Agency (USEPA) Air Quality System (AQS) database. Preliminary data analyses (Tukey’s test) indicated that during 2006-2011 the PM_2.5 mass concentrations were higher in urban areas of California, the Ohio Valley region, and Mid-Atlantic States. Moreover, PM_2.5 concentrations have been significantly reduced across the contiguous U.S due to the reductions in SO₄^2-, NO₃^-, and NH₄⁺, which are attributed to large-scale reductions in SO_x and NO_x emissions from the coal-burning power plants. Before 2008, PM_2.5 concentrations peaked in summertime dominated by strong seasonal variations of SO₄^2-, while after 2008, PM_2.5 concentrations tend to peak in wintertime due to weaker seasonal variations of SO₄^2-. Based on Kendall’s tau temporal analysis, the concentrations of secondary inorganic PM_2.5 (SO₄^2- + NO₃^- + NH₄⁺) have been reduced significantly, and national annual average concentrations of SO₄^2-, NO₃^-, and NH₄⁺ were reduced from 2.26 to 0.71 µg m^-3, 1.03 to 0.59 µg m^-3, and 1.32 to 0.35 µg m^-3, respectively. EC and organic matter (OM) have grown to be the major contributors (~40% and ~43% in winter and summer, respectively) to PM_2.5 in recent years. The spatiotemporal variations in reductions of PM_2.5 chemical compositions observed in the past two decades were due to more stringent control strategies promulgated by U.S. EPA while also due to the inherent spatial heterogeneity of various emissions sources. This analysis revealed the spatial and temporal trends of various PM_2.5 chemical compositions in the contiguous U.S. during the past 15 years and provide insights to target specific precursors to aid in the development of PM_2.5 control strategies. Disclaimer: The views expressed in this article are those of the authors and do not necessarily represent the views or policies of US EPA.

Satellites, Machine Learning, and Numerical Weather Prediction: Applications to Lake Breeze Events in the Great Lakes Basin

Tsengel Nergui and Zac Adelman

Lake Michigan Air Directors Consortium (LADCO)

The highest surface ozone concentrations in the Great Lakes Basin are observed near the shorelines of the lakes. The lake-to-land breeze is a primary meteorological phenomenon that is associated with high ozone conditions in the region. When ozone attainment testing is conducted using air quality model outputs, the adequacy of the model performance and whether the model accurately simulates the lake breeze dynamics are often questioned. We will present an assessment of the LADCO 2016 WRF model skills for simulating lake breeze events using qualitative and quantitative approaches. LADCO used Classification and Regression Tree (CART) analysis to predict lake breeze days based on observations from the Visible Infrared Imaging Radiometer Suite (VIIRS) true color imagery on the MODIS satellite and Doppler radar. We then analyzed the surface meteorological observations during summer 2016 near the shorelines of Lake Michigan and Lake Erie to identify the observed surface variables that are most closely associated with lake breeze periods. We will present our CART analysis results of the typical indicators of surface meteorological conditions that are most associated with lake breeze days along the shorelines of Lake Michigan and Lake Erie, and contrast the differences in the lake breeze conditions between the Lakes. We will also show how the LADCO WRF 2016 model performance varies by grid resolutions on lake breeze and non-lake breeze days. Lastly, we will discuss our future work and operational plans for developing a tool for providing advisory of lake breezes into state’s air quality forecasts and for integrating this new diagnostic approach for lake breeze assessments into our model performance evaluations to support regulatory air quality modeling applications in the Midwest.

Impacts of accelerating decarbonization in China on the countrys power system and public health

Qian Luo, Fernando Garcia Menendez, Jiang Lin, Gang He, Jeremiah X. Johnson

China, the world’s largest greenhouse gas emitter, is aiming to achieve carbon neutrality by 2060. The power sector in China will be a major component of the decarbonization process due to its reliance on coal. Prior studies have investigated power sector decarbonization pathways and quantified their air quality co-benefits. However, few have analyzed the potential impacts of accelerating the decarbonization on both electric power systems and public health. Here, we design two accelerated decarbonization scenarios and apply a capacity expansion model that captures essential constraints in power system operations to project the future energy resource use in China. We then use a reduced-form air quality model to quantify health impacts of decarbonization under each scenario. We find that compared with a reference decarbonization pathway, a stricter cap on carbon emissions would yield co-benefits to public health, with a 20% lower carbon limit leading to 22% reduction in power sector health impacts. Under this scenario, climate and health benefits would exceed the capital investment increase required. However, targeting carbon neutrality earlier than 2050 can result in a more expensive power system and lower health benefits. Additionally, sensitivity analyses indicate that net benefits tend to increase initially and then drop as stricter carbon emission caps are applied to the system. We also find that if the capital costs of renewable energy decline rapidly, carbon emissions from the power sector would decrease quickly even without a limit on carbon emissions. This study investigates the power system impacts of accelerated decarbonization of China’s power sector and shows the benefits to climate and public health that would be derived from it. It also emphasizes the impact of different approaches to accelerated decarbonization can have on the benefits derived from it.

Modeling the wintertime meteorology for the 2022 Alaskan Layered Pollution and Chemical Analysis (ALPACA) campaign

Robert Gilliam, Kathleen Fahey, George Pouliot, Havala Pye, Nicole Briggs, Deanne Huff and Sara Farrell

Fairbanks, Alaska is a nonattainment area for the 24-hour PM2.5 National Ambient Air Quality Standards (NAAQS). Violations of the NAAQS typically occur in winter when the cold conditions are associated with strong temperature inversions and air stagnation that are often difficult to simulate. These weather regimes in urban areas of higher emissions (i.e.; residential wood combustion, mobile sources and energy production) result in a buildup of particulate pollution at the surface. The Alaskan Layered Pollution and Chemical Analysis (ALPACA) field campaign was conducted in January and February of 2022 to address some of the knowledge gaps with a focus on better understanding emissions, meteorology, and atmospheric chemistry.

This presentation details the meteorological modeling component of ALPACA, a principal input to the Community Multiscale Air Quality (CMAQ) model that is being used to characterize the atmospheric chemistry and transport of pollutants in and around Fairbanks. We employ the Weather Research and Forecasting (WRF) model to simulate meteorology at a grid scale of 1.33 km. More specifically, we will cover the WRF configuration including physics and data assimilation for this complex subarctic, mid-winter, problem as well as an evaluation that focuses on several extreme cold periods where observed PM2.5 was well above the NAAQS. Results of the preliminary evaluation indicate that WRF can simulate near-surface meteorology and vertical temperature and moisture gradients around Fairbanks with high confidence considering the complex meteorology of the area. This is accomplished with four-dimensional data assimilation using global model analyses, observational nudging of standard surface observation networks, mesonet and above-surface rawinsonde soundings in combination with the selection of land-surface and boundary layer physics options. The final modeling platform will incorporate the latest scientific understanding to provide an improved modeling tool for the state of Alaska to use in its air pollution program in Fairbanks.

Assessing the tradeoffs in emissions, air quality and health benefits from excess power generation due to climate-related policies for the transportation sector
Christos Efstathiou 1, Calvin Arter 1, Jonathan Buonocore 2 and Saravanan Arunachalam 1

1 Institute for the Environment, The University of North Carolina at Chapel Hill, Chapel Hill, NC
2 Boston University School of Public Health, Department of Environmental Health, Boston, MA

Twelve East Coast states and the District of Columbia have joined the Transportation Climate Initiative (TCI) to create a regional cap-and-invest program to reduce transportation-sector GHG emissions. The Transportation, Equity Climate and Health (TRECH) Study has previously evaluated the air quality related health co-benefits of five cap and invest scenarios developed by policymakers participating in the TCI initiative, with distinction from Pennsylvania’s participation in the Regional Greenhouse Gas Initiative (RGGI). The second phase of the TRECH study adds to previous efforts to quantify the impacts on emissions and public health solely from onroad sources, by adding the shift in energy demand related to changes in the onroad fleet composition toward a higher deployment of electric vehicles (EVs).

This work investigates the ways that policy-driven changes in the transportation sector affect emissions from the electrical grid, due to the need for additional generation to meet additional demand from the increased electrical load, and the potential impacts on air quality and public health over the Northeast and Mid-Atlantic United States. We used outputs from the Integrated Planning Model (IPM) to account for changes in the emission of pollutants related to the implementation of policies in the form of scenarios that reflect potential changes in technologies, infrastructure, pricing, and additional drivers. The core of the emissions calculation methodology is based on the 2016 National Emissions Inventory (NEI) and the Sparse Matrix Operator Kernel Emissions (SMOKE) Modeling System with a new interface (IPM2SMOKE) that links the two modeling systems and maintains consistent inventory assumptions between them using the National Electric Energy Data System (NEEDS) and other proprietary databases. Five illustrative scenarios were designed to capture the effect of different sets of policies under the following conditions: 1. Pennsylvania not part of RGGI, but other states subject to RGGI rule (same participation as in 2020) with emission estimates according to the TCI reference case for both energy and transportation sectors, 2. Pennsylvania not part of RGGI, but other states subject to RGGI rule with emission estimates following the TCI 25% hybrid portfolio case for both sectors, 3. Pennsylvania participating in RGGI, with all emission estimates according to the TCI reference case for both sectors, 4. Pennsylvania is part of RGGI, with all emission estimates according to the TCI 25% hybrid portfolio case for both sectors, and 5. Pennsylvania is part of RGGI, with transportation emissions following the TCI 25% hybrid portfolio case and power grid emissions developed under a Clean Energy Standards (CES) policy implementation.

CMAQ v5.2 simulations were performed using scenario-specific inputs for January and July of 2016 over a domain that covers the entire East US and most of the Central US (12EUS). Comparing differences in the distribution patterns and levels of O3, PM2.5 and NO2 reveals changes originating from implementing different policies, accounting for PA participation in RGGI or not. While the effect of transportation and energy sector policies (Scenario 2-1) on domain-wide concentrations is modest (max change PM2.5 ~ 0,06 μg/m3, change NO2 ~0.3 ppbv, change O3 ~ 0.15ppbv), we do find that if Pennsylvania does join RGGI, there are substantial concentration changes in Ohio and West Virginia (Scenario 4-3) but the overall pollutant levels over the TCI region remained comparable. If PA joins RGGI and a clean energy standard (CES) is enacted (Scenario 5-3), there are significant reductions in average concentrations (max change PM2.5 ~ 1,2 μg/m3, change NO2 ~ 1.1ppbv, change O3 ~ 1.7ppbv) with the exception of Louisiana and Mississippi where disbenefits in the same range are observed. When focusing exclusively on emissions reductions from transportation, this policy had health benefits of 530 avoided adult deaths, and 46,000 avoided asthma exacerbations. With a "business as usual" grid, these health benefits stay similar. However, if Pennsylvania joins RGGI, the total health benefits, and the location of benefits and impacts, changes substantially. The total deaths avoided gets cut by about 30%, avoided asthma exacerbations stays about the same, but a large portion of health impact moves from within the TCI states to Ohio and West Virginia, since coal fired power plants in those states respond to the increased electricity demand.

Impact of short-term and long-term COVID-19 intervention on power plant emissions in the United States using generalized synthetic control causal inference methods

Munshi Md Rasel^1*, Kevin L. Chen², Rachel C. Nethery², and Lucas R.F. Henneman¹

¹Department of Civil, Environmental, and Infrastructure Engineering, George Mason University, Fairfax, VA, USA

²Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA

*Corresponding Author: mrasel@gmu.edu

This study investigates the COVID-19 pandemic intervention impact on U.S. power plant SO₂, NO_x and CO₂ emissions under a causal inference framework. We use publicly available emissions data from 937 U.S. electricity generating units (EGUs) from EPA's Clean Air Markets Database from 2010-2020. We train the GSYNTH model on weekly power plants emissions data from 2010-2020 using meteorological and seasonal parameters as covariates to estimate hypothetical counterfactual emissions from each facility, i.e., expected business as usual (BAU) emissions assuming no pandemic in 2020. The model is evaluated against naive approaches such as long-term averaging of emissions. We identify the plants characteristics that play a role in actual and counterfactual emissions using linear models. Finally, we quantify ambient PM_2.5 concentration changes attributable to EGU SO₂ emissions using a reduced complexity model, HyADS. We find in 2020 that out of 937 EGUs, more than 60% of EGUs saw SO₂, NO_x, and CO₂ emissions increased relative to expected emissions under BAU and, with most of the increases occurring in the eastern parts of the U.S. Nationwide during the lockdown period, SO₂, NO_x, and CO₂ emissions increased by 47% (3,100 tons/week), 19% (300 tons/week), and 8% (0.88 million tons/week) respectively. During long-term period, March-December 2020, SO₂, NO_x, and CO₂ emissions increased by 44% (4,700 tons/week), 23% (2,300 tons/week), and 14% (2.3 million tons/week) respectively. We find facilities using coal as primary fuels, mostly in the eastern parts of the U.S., are the main driver for the increased SO₂, NO_x, and CO₂ emissions. An average increase over BAU of 13% (16%) coal PM_2.5 concentration was observed from coal facilities during the lockdown period (from March-December, 2020). We find large emissions increases across many facilities, especially coal/natural gas/other facilities located in the country's eastern region. The increases weren't ubiquitous, however, as many facilities did reduce their emissions. The results highlight that any sustained influence of population activity shifts—e.g., greater percentages of the workforce working from home—did not lead to reduced power plant emissions in the U.S. in 2020.

High-resolution modeling of air quality and health benefits of transportation policies in the Boston Metropolitan area

Manish Soni¹, Christos Efstathiou¹, Ramarao Mandavalli¹,Saravanan Arunachalam¹,Christopher Rick², Jonathan Levy², Pat Kinney², Laura Buckley², Matt Raifman², Jonathan Buonocore²

¹Institute for the Environment, The University of North Carolina at Chapel Hill, Chapel Hill, NC

²Boston University School of Public Health, Department of Environmental Health, Boston, MA

Onroad vehicular emissions can adversely affect the health of people both near-road and regionally through contributions to O₃, NO₂ and PM_2.5. While multiple studies have characterized the overall air quality and health benefits of emissions from the transportation sector, fewer studies have modeled the benefits of transportation policies at higher geographic resolution relevant to communities. We use a multi-scale nested (12/4/1.33km) application of EPA’s Community Multiscale Air Quality (CMAQ) model with the Carbon Bond 6 revision 3 mechanism (cb6r3) version with aero6 treatment for standard cloud chemistry. We use CMAQ Version 5.2.1 with the decoupled direct method (DDM) simulations for January and July of 2017 with spin up of 15 days using Northern Hemisphere 108-km domain initial and boundary conditions, meteorology derived from WRF model and updated emissions inventory from the EPA’s National Emissions Inventories for the year 2017 processed through SMOKE. Outer domain runs are done without DDM (12/4km), while the inner-most nest at 1.33 km resolution focuses on Boston neighborhoods and surrounding communities within the Metropolitan Area Planning Council (MAPC) inner core. We instrumented CMAQ-DDM to compute sensitivities of predicted O₃, NO₂ and PM_2.5 to a large matrix of input parameters including five vehicular classes (light-duty autos, light-duty trucks, medium-duty trucks, heavy-duty trucks, and buses), five precursor emissions (PM_2.5, NO_X, SO₂, NH3 and volatile organic compounds (VOC)), and 6 MAPC inner core sub regions. Our findings will emphasize the air quality and health impacts from the sensitivity matrix, with consideration of variability in impact per ton of precursor emissions. Results from our modeling platform will help in assessing both the public health benefits and the equity implications of local-scale transportation policies in the Boston metropolitan area.

The Pandemic Bottom-up National Emissions Inventory Development using Human Activity, Satellite Retrieval, and Chemical Transport Model

Chi-Tsan Wang, Bok Haeng Baek*, Daniel Tong, Kai Yang, Siqi Ma

The COVID-19 outbreak started in March 2020 and caused 1.02 million deaths. All Countries announced stay-at-home emergency orders for limiting in-person contact to limit the disease spread. The U.S. federal and state governments implemented these orders in multi-phases, significantly impacting human activity and behaviors. This caused human activities to dramatic reduction and decreased the anthropogenic emission processes. This phenomenon was confirmed by satellite remote sensing and as a controlled experiment that can study the anthropogenic activities affecting regional air quality. This study establishes the bottom-up 2020 emission inventory data by incorporating the 2019 National Emissions Inventory (NEI) and human activity data from various public databases, such as the U.S. Department of Transportation (US DOT), Federal Highway Administration (FHA), the Energy Information Administration (EIA), Federal Aviation Administration (FAA), U.S. Department of Agricultural (USDA) and other available data sources. This 2020 bottom-up emission data are then applied with the chemical transport model (CTM) to simulate the air pollutants during the pandemic and will be evaluated by surface Air Quality Stations (AQS) and satellite data (OMPS and TROPOMI). Further, the satellite data will be applied to calibrate the 2020 emission data using an assimilation inverse modeling approach for the calibrated 2020 NEI development for the quality modeling community and policymakers. The preliminary results show that the traffic activity data, and many energy exhausts or productions are affected by the COVID-19 pandemic. The daily traffic data indicates that the US daily total passenger vehicle VMT decreased 50% in 2020 April and May compared with the same month in 2019, but the fleet truck and long-haul vehicles only reduced by 15-20%. The 2020 U.S. monthly total electricity generation by fossil fuels combustion was reduced by 8% and 14% in 2020 April and May; the U.S. industry petroleum fuel consumption also decreased by 21% and 13% in the same month. However, the natural gas for residential heating usage increased by 11% and 15% in 2020, revealing human activity changes during the pandemic. This study applied these changes (county and state level) between 2019 and 2020 to the U.S. EPA 2019 NEI and generated the updated bottom-up 2020 NEI.

US coal power plant emissions changes and inequities since 1999

Lucas Henneman, Munshi Md Rasel, Christine Choirat, Susan Anenberg, Corwin Zigler

United States Coal power plant emissions have decreased over recent decades because of strict regulations and economics. The changes have led to improved regional air quality, but questions remain about 1) whether benefits have accrued equitably across population groups and 2) the extent to which the health burden has reduced as a consequence of the emissions decreases. To investigate these questions, we estimate exposure to PM2.5 related to SO2 emissions (“coal PM2.5”) from each of 1,237 US coal power plants from 1999-2020 using the HYSPLIT model and PM2.5 sensitivities derived from CMAQ-DDM. We find that US population-weighted PM2.5 decreased from 1.48 µg m 3 to 0.09 µg m 3 across 1999-2020. SO2 scrubber installations led to most of the exposure reductions between 2007-2010, and after 2010 most of the decrease is attributable to retirements. While exposure inequities decreased with falling emissions, facilities in states across the North Central US continue to inequitably expose Black populations, and Native populations are inequitably exposed to emissions from facilities in the West.

The Burden of Lightning NOx Emissions over the Contiguous United States and the Northern Hemisphere Derived Using Observational and Climatological Approaches

Daiwen Kang, J. Mike Madden, Christian Hogrefe, Golam Sarwar, Rohit Mathur, and Barron H. Henderson

Lightning induced nitrogen oxides (LNO_x) emissions are estimated to be in the range of 2 to 8 Tg N yr⁻¹ and contribute 10-15% of the total global NO_x emissions budget derived through limited observational and modeling approaches. The 2 to 8 Tg N yr^-1 range has become a de facto constraint for LNO_x emissions allocation of a constant total (e.g., 5 Tg N yr⁻¹) in many global modeling studies. We analyze the impact of using lightning flash data observed from National Lightning Detection Network (NLDN) and World Wide Lightning Location Network (WWLLN) as input to the Community Multiscale Air Quality (CMAQ) model’s inline LNO_x emissions module. We apply CMAQ over the contiguous United States (CONUS) and Northern Hemisphere to analyze the spatial and temporal distributions of LNO_x emissions and the contributions to the nitrogen oxide (NO_x) budget. Comparisons to the LNO_x emissions in the Global Emissions InitiAtive (GEIA) are made to examine the differences and similarities between the observational-based dynamic emissions and the static climatological-based emissions inventory in zonal and regional distributions. Preliminary results suggest that the GEIA LNO_x emissions are comparable to the observational-based LNO_x emissions in North America and Europe (when the WWLLN lightning flashes are scaled by those from NLDN), but large differences are observed in other regions. In generating LNO_x emissions, large uncertainty is associated with lightning production efficiency, a parameter used to convert lightning flashes into LNO_x emissions. Based on recent literature reported values and modeling studies on LNO_x impact on air quality, we provide reasonable confidential range of LNO_x emissions over different regions. Taking advantage of the EPA’s Air QUAlity TimE Series (EQUATES) project, we investigate the regional to global LNO_x burdens (e.g., the ratios between LNO_x emissions and the total NO_x emissions) in response to the decreasing trend of anthropogenic NO_x emissions over the decadal scale.

Incorporating Model-based Equity Analyses into Air Quality Planning and Environmental Justice Programs in the San Francisco Bay Area

Stephen Reid, David Holstius, Bonyoung Koo, Yuanyuan Fang, Yiqin Jia, James Cordova, Yuan Du, Song Bai, Saffet Tanrikulu, Phil Martien

In 2021, the Board of Directors of the Bay Area Air Quality Management District (BAAQMD) established a Community Equity, Health and Justice Committee to address environmental justice and inequities in health outcomes in Bay Area communities disproportionately impacted by air pollution. Though BAAQMD has long focused on environmental justice issues through its Community Air Risk Evaluation (CARE) program and related initiatives, this new Board committee signaled an intention to focus more intentionally on equity and justice concerns.

This presentation will highlight recent efforts to include equity analyses in BAAQMD’s rulemaking and air quality planning efforts. Examples include the use of air quality models to evaluate disparities in air pollution exposure due to emissions from fluidized catalytic cracking units (FCCU) at Bay Area refineries and from natural gas-fired appliances at residential and commercial locations. These equity analyses quantified differences in exposure among various races/ethnicities and helped to highlight the need for regulatory action.

In addition, the presentation will describe hyper-local modeling efforts being conducted in environmental justice communities identified as part of BAAQMD’s implementation of California Assembly Bill 617 (AB 617), which was adopted in 2017. AB 617 requires local air districts to partner with community groups and other stakeholders to develop community emission reduction plans in neighborhoods most impacted by air pollution.

Comparison of source apportionment methods using CMAQ for the Madrid Region

Rafael Borge1, David de la Paz1, Golam Sarwar2, Sergey Napelenok2

1-Universidad Politécnica de Madrid (UPM). Department of Chemical and Environmental Engineering, ETSII-UPM – Calle José Gutiérrez Abascal 2, 28006, Madrid, Spain

2- Center for Environmental Measurement & Modeling, U.S. Environmental Protection Agency, Research Triangle Park, NC, USA

Source apportionment is a key input in the process of designing measures and plans to improve air quality, especially for secondary pollutants. All available methods have advantages and drawbacks and the only way to accurately anticipate the effect of a given policy (within the intrinsic limitations of chemical-transport models) is to simulate that emissions scenario specifically. It is, however, of great interest to have a reasonable idea of the relative importance of different sources to prioritize interventions. Given the complexity of the problem, several methodologies have been developed to accomplish the task. With this contribution, we intend to improve our understanding on how the results of different source apportionment methodologies should be interpreted and how that information can be used for decision making processes. Specifically, we discuss the results for the most relevant pollutants (nitrogen dioxide (NO2), ozone (O3), particulate matter (PM)) for a summer month in Madrid, Spain. We compare the single-perturbation method with the recently developed Integrated Source Apportionment Method in CMAQ (CMAQ-ISAM). The first method, also known as brute force method, informs on the contribution of a given sector by zeroing out its emissions and comparing the results with a model run that considers the full emissions inventory. The second method tracks the relevant chemical species that have been previously labeled according to the emitting sector. There are, however, different approaches even within ISAM. In this study, we compare the results from CMAQv5.0.2 and CMAQv5.3.2, and find substantial differences. For instance, the brute force method using CMAQv5.3.2 identifies road traffic as the main source of NO2, with an average contribution of 67% over the Madrid region. This is in agreement with the outcomes from CMAQ-ISAM v5.0.2 (63%), but the ISAM version in CMAQv5.3.2 apportions only 32% of NO2 to this sector. In contrast, boundary conditions are given a much larger attribution due to the influence of O3 in CMAQv5.3.2.

Disclamer: The views expressed in this abstract are those of the authors and do not necessarily represent the views or policies of the U.S. EPA.

Accelerated Electric Vehicle Fleet Penetration: Emissions, Air Quality, and Environmental Justice Benefits

Shih Ying Chang, Jiaoyan Huang, Melissa Chaveste, Frederick Lurmann, Douglas Eisinger, Anondo Mukherjee, Garnet Erdakos, Marcus Alexander, Eladio Knipping

Accelerated penetration of on-road electric vehicles (EVs) offers regional and community-scale air quality benefits; however, these benefits have not been previously quantified in regard to environmental justice (EJ) communities near major roads. This study evaluated six 2040 EV scenarios, and quantified concentration reductions of nitrogen dioxide (NO₂) and particulate matter with diameter less than 2.5 µm (PM_2.5) for southern California EJ communities near Interstate 710. Findings showed that aggressive EV penetration (85% EV share) reduced NO₂ and PM_2.5 concentrations more in communities with more people of color (1.9 ppb and 1.1 μg/m3) than in communities with more white residents (1.6 ppb and 0.94 μg/m³). Aggressive EV penetration reduced pollution exposure disparity by 30% for NO₂ and 14% for PM_2.5. Disparity reductions were also found based on population educational attainment. Results suggest policies that encourage accelerated EV penetration will address inequalities in air pollution exposure and help achieve environmental justice.

Anthropogenic Secondary Organic Aerosol and Ozone from Asphalt-Related Emissions

Karl Seltzer, Venkatesh Rao, Havala Pye, Benjamin Murphy, Bryan Place, Peeyush Khare, Drew Gentner, Chris Allen, David Cooley, Rich Mason, Marc Houyoux

Liquid asphalt is a petroleum-derived substance commonly used as a binder in construction activities. In 2018, ~20 Tg of liquid asphalt was consumed in the United States for paving applications, such as the construction of roads and parking lots, as well as non-paving applications, such as the application of asphalt roofing coatings and manufacturing of roofing shingles. Recent work, utilizing advanced observation techniques to characterize gas-phase organics in the semi-to-intermediate volatility range, has identified reactive organic carbon from asphalt as a considerable and previously unaccounted source of secondary organic aerosol (SOA) precursor emissions in urban areas. These lower volatility, gas-phase emissions have historically been neglected in emissions inventories used for air quality modeling applications. Here, we leverage source measurements from recent work to construct a bottom-up inventory of asphalt-related emissions for the United States. In 2018, we estimate that hot-mix (~60 Gg), warm-mix (~10 Gg), emulsified (~193 Gg), cutback (~62 Gg), and roofing (~54 Gg) asphalt collectively generated ~380 Gg (95% CI: 317 Gg – 447 Gg) of organic emissions. We then comprehensively reflect the impacts of this inventory in the Community Multiscale Air Quality (CMAQ) model using the newly developed Community Regional Atmospheric Chemistry Multiphase Mechanism (CRACMM) to quantify the subsequent enhancements in anthropogenic SOA and ozone resolved throughout the United States. In addition, we explore trends in historical asphalt usage and estimated emissions by process to provide context on how secondary air pollutants have evolved from asphalt-related emissions over the last decade.

Estimating the Societal Benefits of Reducing PM25 and Precursor Emissions via Hemispheric Adjoint Analysis

Y. Burak Oztaner, ShunLiu Zhao, Amir Hakami (Carleton University), Rohit Mathur, Barron Henderson (U.S. EPA)

Chronic exposure to ambient PM2.5 is one of the leading causes of mortality across the globe. The latest Global Burden of Disease (GBD) project (2022) estimates over 4 million premature deaths per year worldwide due to long-term outdoor PM2.5 exposure. We use adjoint sensitivity analysis to estimate location-specific Benefit-Per-Ton (BPT) and mortality burdens due to exposure to outdoor fine particulate matter in the Northern Hemisphere.

We apply U.S. EPA’s CMAQv5.0 and its multiphase adjoint to estimate the societal benefits of reducing primary PM2.5 and its precursor emissions (NOX, SO2, and NH3). Meteorological inputs are driven from the Weather Research and Forecasting (WRFv3.8.1), and emissions are retrieved from the 2016 Hemispheric Emission modelling platform. The simulations are carried out over a 108-km resolution for the year 2016. We employ the Global Exposure Mortality Model (GEMM, Burnett, et al., 2018) which provides a generalized concentration-response function for chronic exposure mortality based on various cohorts worldwide. The population data is retrieved at 1 km and aggregated to model domain grid resolution. Wherever possible, the country-specific baseline mortality data retrieved from IHME (2020) is applied. We monetize premature mortality using country-specific values for statistical life (VSLs) to quantify hemispheric BPTs. We estimate a hemispheric burden of 460,000 premature mortality, valuated at above $1T for 10% across-the-board reduction in hemispheric emissions. These estimates are larger than those predicted through the GBD project, partly due to larger effect estimates in GEMM. India and China have the largest burden estimates, at approximately 140,000 and 160,000 annual premature deaths for 10% reduction in emissions.

Assessing the impact of Scrubber on shipping emissions in Canada

Mourad Sassi

The purpose of this work is to study the impacts of air pollutants from ships in Canada; with main focus on black carbon and particulate matter (PM) emission from different types of marine fuels in different locations within Canadian waters. We investigate the impacts of marine scrubber and other shipping regulations on air quality.

Assessment of Vulnerability to Wildfire-induced PM2.5 in the U.S. Using Community Multiscale Air Quality Modeling System

Jihoon Jung, PhD, University of North Carolina at Chapel Hill; climate@unc.edu Joseph Wilkins, PhD, Howard University, joseph.wilkins@howard.edu Claire Schollaert, PhD Candidate, University of Washington, Department of Environmental and Occupational Health Sciences, cscholla@uw.edu

The impact of wildfire smoke depends not only on biophysical aspects of the events (e.g., frequency, intensity, duration), but also demographic (e.g., age) and socio-economic aspects (e.g., income, education) of differential societies or population groups. In this study, we utilized vulnerability assessment to better understand the degree of susceptibility of a region, or population group. Here, we define vulnerability as a function of exposure, sensitivity, and adaptive capacity. Using that definition, we collected a total of 1 exposure variable (i.e., wildfire induced fine particulate matter 2.5 (PM2.5)) simulated through the Community Multiscale Air Quality (CMAQ) Model, 8 sensitivity variables (e.g., age, occupation, disease prevalence rates), and 15 adaptive capacity variables (e.g., education, poverty, unemployment) based on previous studies. We then normalized all variables from zero through one as each variable has its own unit and direction. With the normalized variables, we separately calculated exposure, sensitivity, and adaptive capacity sub-indices by taking the average of all variables with the same weight. Next, all of three sub-indices were summed to make a composite vulnerability index which represents the overall vulnerability to wildfire smoke. Finally, we calculated the population size at risk in terms of the level and duration of exposure to wildfire induced PM2.5 and sensitivity/adaptive capacity/vulnerability indices to understand how many people are at high risk. Results: Preliminary results showed that counties in the northwestern and southeastern U.S. tend to have higher vulnerability to wildfire induced PM2.5 than other counties. Such high vulnerabilities were mostly driven by high exposure (northwestern) and high sensitivity (southeastern) respectively. Specifically, each sub-index exhibited different spatial patterns. For example, we observed high exposure sub-index from counties in California, Montana, and Georgia; low adaptive capacity sub-index from counties in Texas, Mississippi, Alabama, and Louisiana; and high sensitivity sub-index from counties in Oklahoma, Arkansas, Alabama, Mississippi, and West Virginia. Our results also showed that areas of higher vulnerability, higher sensitivities, and less prepared counties were more likely to be exposed to high annual average ambient PM2.5 (>1.5μg/m3). For instance, 32.1% of counties in the U.S. with high sensitivity sub-index (>95 percentile) and 29.5% of counties with low adaptive capacity sub-index (<5 percentile) were exposed to high annual average ambient PM2.5, whereas only 8.2% and 4.4% of U.S. counties with below average sensitivity sub-index (<50 percentile) and above adaptive capacity sub-index (> 50 percentile) experienced high annual average ambient PM2.5. Similarly, 91.7% of counties with high vulnerability index (>95 percentile) were exposed to high annual average ambient PM2.5 (>1.5μg/m3), while only 0.1% of counties with below average vulnerability sub-index (<50 percentile) were exposed to high PM2.5 (>1.5μg/m3). Furthermore, we found that 7.9% of the population (32.2 million) resided in the areas where the annual average ambient PM2.5 was higher than 1.5μg/m3, and 11.8 million people lived with unhealthy air quality level (over 35 μg/m3) for more than 10 days. Discussion: Using this vulnerability assessment, we respectively identified the most sensitive, least prepared, and most vulnerable counties to wildfire smoke for the U.S. We also estimated how many people are at high risk of wildfire smoke. This information can help prepare region-specific plans to reduce vulnerability.

MARINER: Development and Application of a Shipping Emissions Estimation Program

Fiona Jiang, Ramboll;

Christian Lindhjem, Ramboll;

Beata Czader, Texas Commission on Environmental Quality;

Marco Rodriguez, Ramboll

The MARINe Emissions Resolver (MARINER) is a python-based program that uses Automatic Identification System (AIS) vessel location data to estimate emissions from the shipping sector. MARINER can produce gridded emissions and temporal profiles for input to the Emission Processor System Version 3 (EPS3) as well as detailed activity reports, e.g., by location or time period. The AIS system provides data on speed, location, direction, and vessel type for Commercial Marine Vessels (CMV) in the United States and international waters with a temporal resolution of minutes that is ideal for developing detailed emissions inventories of CMV at geographic locations of interest.

The Texas Commission on Environmental Quality uses MARINER and EPS3 to process shipping emissions for State Implementation Plan revisions in Texas. This presentation will give an overview of MARINER, show several of the tool’s capabilities to estimate shipping emissions for Texas waters, and contrast the methodology used with the EPA modeling platform shipping emission estimates.

2018 AirToxScreen: EPAs Screening-Level Assessment Tool for Outdoor Air Toxics

Caroline Farkas, Matthew Woody, Art Diem, Rod Truesdell, James Thurman, Sharon Phillips, Alison Eyth, Julia Black, Doug Solomon, Jeanette Reyes, Christine Allen, James Beidler

The Air Toxics Screening Assessment (AirToxScreen) is EPA’s national screening-level assessment for inhalation of air toxics and serves as a successor to the National Air Toxics Assessment (NATA). AirToxScreen uses air quality modeling to estimate chronic exposures to air toxics at census tract resolutions, assessing cancer risks and chronic noncancer health effects from over 100 air toxics. Emissions are based on the National Emissions Inventory (NEI). Ambient concentrations are estimated using a hybrid approach which combines results from the Community Multiscale Air Quality (CMAQ) model and the AERMOD dispersion model. Exposure concentrations are then estimated using the Hazardous Air Pollutant Exposure Model (HAPEM), which accounts for indoor/outdoor microenvironment concentration relationships, population data, and human activity pattern data. Results are produced at every populated census tract in the U.S., including Alaska, Hawaii, Puerto Rico and the Virgin Islands, by pollutant and emission source sector. To visualize results more easily, we have developed a new web-based mapping tool called the AirToxScreen Mapping Tool. The public uses AirToxScreen to better understand the potential impact of air toxics in their area. State, Local, and Tribal air agencies use AirToxScreen to aid in their ongoing review of outdoor air toxics and further evaluate and address specific potential issues.

Public health effects from the adoption of Californias Advanced Clean Cars II regulation in Oregon and Connecticut in 2030 and 2040

Jiaoyan (Joey) Huang, Eric Sussman, Jeff Houk, Shih-Ying Chang,

The Advanced Clean Cars phase I (ACCI) regulation was first adopted by the California Air Resources Board (CARB) in 2012. The three components of ACCI include low-emission vehicle (LEV) III criteria, LEV III greenhouse gas (GHG) criteria, and amendments to the zero-emission vehicle (ZEV) regulation. ACCI is projected to reduce 75% of criteria pollutants and 40% of greenhouse gas (GHG) emissions of vehicles sold in 2025 compared to the emissions from vehicles from the 2012 model year. CARB then proposed the ACC phase II (ACCII) regulation in June 2022 to further reduce light-duty vehicle (LDV) emissions starting in 2026. This proposal requires all new LDVs sold in California to be zero emission by 2035 and amends the LEV regulations to include more stringent standards for gasoline cars and trucks in order to reduce smog-forming emissions. In this study, the impacts of adopting ACCII in Oregon (OR) and Connecticut (CT) on emissions were quantified. For the on-road mobile source sector, the baseline emissions of NH3, SO2, NOx, PM2.5, VOCs, and CO2 equivalent (CO2e) for OR and CT were modeled using the U.S. Environmental Protection Agency’s (EPA) Motor Vehicle Emissions Simulator (MOVES3) model at the county level for calendar years 2017, 2030, and 2040. MOVES input data were provided by state agencies if available. For data not available from state agencies, data from the 2017 National Emissions Inventory (NEI) were used. The baseline emissions for 2030 and 2040 were further adjusted by ACCII emission reduction factors developed from projected California emissions to calculate the reduction of emissions from adopting ACCII. Upstream GHG and air pollutant emissions changes for the electrical generating unit (EGU) sector were estimated using the U.S. Department of Energy’s Greenhouse Gases, Regulated Emissions, and Energy Use in Technologies (GREET) Model-2021. Three emission scenarios were evaluated in this study: business-as-usual (BAU), ACCII implemented in 2026 (S2), and ACCII implemented in 2027 (S3). The BAU scenario reflects the current populations of ZEVs in each state and projected future populations based on CARB’s assumptions of ZEV fleet penetration absent the ACCII rule. We estimated the net monetized health benefits of vehicular and upstream emissions changes in 2040 using the EPA’s CO-Benefits Risk Assessment (COBRA) screening model. The COBRA model was run using default disease incidence rates. Input data, including human population and baseline emissions, were projected to 2040 levels using information from the U.S. Census, the Energy Information Administration (EIA), and MOVES3. The benefits were calculated for health endpoints such as mortality and hospital admission due to various diseases and conditions from exposure to PM2.5. Comparing the BAU and S2 scenarios, the reduced emissions from LDVs due to adopting ACCII in OR and CT could lead to large benefits in 2040. By 2040, concentrations of NOx were reduced by 42%, CO2e by 57%, PM2.5 by 16%, VOC by 22%, SO2 by 46%, and NH3 by 47%. However, the benefits are small in 2030 (2% for NOx and 10% for CO2e). The preliminary results showed that the emissions reduction differences between S2 and S3 are small (<2%) for all pollutants in 2040. This indicates that there are low benefits of implementing ACCII in 2026 instead of 2027. Overall, in 2040, the adoption of ACCII in CT avoids $16 million (2017 value dollars) of cost in-state, and $10 million of cost out-of-state. OR saves $9 million of cost in-state, and $4 million of cost out-of-state. In this presentation, detailed criteria pollutant and GHG pollutant emission reductions (vehicle emissions and upstream emissions) estimated for each state will be discussed, and the modeled future-year population of electric vehicles will be presented.

Mortality and Morbidity Attributable to Wildland Fire Smoke in California from 2008-2018

Additional Presenters/Authors:

Joseph Wilkins, PhD, Howard University, joseph.wilkins@howard.edu; Rachel Connolly, PhD Candidate, UCLA Department of Environmental Health Sciences, rachelconnolly@g.ucla.edu; Diane Garcia-Gonzales, Post-doctoral Scholar, UCLA Fielding School of Public Health, 510-366-3762, dgonzales98@ucla.edu; Miriam Marlier, PhD, UCLA Department of Environmental Health Sciences, mmarlier@ucla.edu; Jason Su, PhD, University of California at Berkeley, Division of Environmental Health Sciences, jasonsu@berkeley.edu; Rick Burnett, PhD, Health Canada, Santé Canada, rtburnett1@gmail.com; Michael Jerrett, PhD, UCLA Department of Environmental Health Sciences, mjerrett@ucla.edu; Jihoon Jung, PhD, University of North Carolina at Chapel Hill; climate@unc.edu; Claire Schollaert, PhD Candidate, University of Washington, Department of Environmental and Occupational Health Sciences, cscholla@uw.edu  

Background: In California, wildfire risk and severity have grown substantially in the last several decades due to climate change and other anthropogenic factors such as increasing development at the wildland-urban interface. Existing research has characterized substantial adverse health impacts from exposure to wildfire-associated fine particulate matter (PM2.5). Few existing studies, however, have quantified long-term health impacts from wildfires, and none have used a wildfire-specific long-term dose-response coefficient for mortality, which we have developed for this study. These findings are critical for guiding societal investments for wildfire prevention and suppression. Methods: We quantified the total mortality burden for exposure to PM2.5 due to wildland fires in California at the ZIP code scale, using 11 years of Community Multiscale Air Quality (CMAQ) model wildland fire-specific PM2.5 estimates, ZIP-code level mortality data from the California Department of Public Health, and a novel wildfire-specific long-term dose-response coefficient accounting for the increased toxicity of wildfire smoke, calculated using recently published coefficients in the peer-reviewed literature. We used the US EPA’s BenMAP tool and wildfire-specific dose-response coefficients to quantify morbidity outcomes for asthma-related hospitalizations and emergency room visits. Results: Preliminary results for 2008 – 2018 demonstrate that annual premature deaths due to wildland fire PM2.5 range from 1,150 in low-fire years to 11,500 in high-fire years. In 2018, in which wildfires burned almost two million acres in California, wildland fire PM2.5 was responsible for approximately 5,000 asthma-related emergency room visits and hospitalizations and approximately 11,500 premature deaths. The mortality burden for 2018 equates to an economic impact of approximately $100 billion, with respect to the EPA Value of a Statistical Life of $8.7 million (2015 dollars). Upon finalization, results will be publicly available via our interactive health scenario tool that allows users to visualize the results of various management scenarios to support wildfire mitigation decision-making. Discussion: These findings will add to a growing evidence base on the health impacts associated with wildfire exposure. To our knowledge, no studies have evaluated the long-term mortality and morbidity impacts of exposure to wildland fire PM2.5 in California over an extended period at a spatially granular level. Future analyses will explore the equity implications associated with wildland fire exposures throughout the state by using census data to characterize the populations living in the highest impacted regions during periods of varying fire intensity. These findings will enable decision-makers to identify potential future exposure concerns and develop an evidence base for policy interventions.

A Review of Emissions Modeling Platforms for the Years 2016 through 2019

A. Eyth, J. Vukovich, C. Farkas, J. Godfrey, S. Roberts, K. Seltzer, C. Allen, J. Beidler

The EPA has developed an updated version of the 2016 emissions modeling platform and additional emissions modeling platforms for the years 2017, 2018, and 2019 based on the 2017 National Emissions Inventory (NEI) and additional year-specific data. The modeling platforms have been used for studies related to ozone, particulate matter, and air toxics. The magnitude of emissions in the platforms will be reviewed along with some of recent improvements regarding the modeling of the emissions.

Distribution of Air Quality Health Benefits of Medium and Heavy-Duty Electric Vehicle (MHDEV) Policies in New York City and Atlanta

Saravanan Arunachalam1, Catherine Seppanen1, Brian Naess1, Dylan Morgan1, Bin Cheng1, Chet France2, Rick Rykowski2, Frederica Perera3, Katie Coomes3, Ananya Roy2, Jonathan Buonocore4

1Institute for the Environment, The University of North Carolina at Chapel Hill, Chapel Hill, NC

2Environmental Defense Fund, Washington, D.C.

3Columbia University Mailman School of Public Health, New York, NY

4Boston University School of Public Health, Boston, MA

The U.S. EPA and various states are considering medium and heavy-duty vehicle emission standards and rules, which may include electrification as a pathway to address GHG emissions and air quality. The potential health benefits of air pollution reductions in disproportionately burdened environmental justice (EJ) communities are key motivators for these strategies, but there exists little quantification of local-scale air quality health benefits and potential reduction in disparities. Here, we employ a novel framework to model, at census tract scale, the air pollution and health benefits due to reductions in PM2.5 and NO2 exposure due to electrification of medium and heavy-duty diesel vehicles (MHDDVs) in New York City. Using this framework that was initially based on the ZIP Code Air Pollution Policy Assessment (ZAPPA) tool, we refined it to model both PM2.5 and NO2 at census scales and find that full electrification of these vehicles in 2040 in NYC would prevent 250 adult deaths and 173 asthma ED visits in children, annually, valued at $2.4 billion per year. We also find that NO2 reductions are responsible for over 80% of these total benefits. These benefits tend to be concentrated in neighborhoods with disadvantaged populations – 68% of the asthma ED visit reductions occur in tracts with populations that are >85% minority, and 50% of the reductions in asthma ED visits occur in tracts with average income per capita under $20,000 / year. This analysis showcases the ability to perform air pollution health impact assessment for policies at an extremely fine scale, relevant for equity and environmental justice concerns, and demonstrates the importance of including NO2 exposures in health impact assessments. We are expanding this framework to perform a similar assessment for the 11-county Atlanta metropolitan area and will present results from NYC and Atlanta.

Applying satellite data assimilation to infer lightning-NOx emissions in CMAQ

James D. East, Mike Madden, Barron H. Henderson, Shannon N. Koplitz, Sergey L. Napelenok, Daiwen Kang, Fernando Garcia Menendez

Lightning emissions are an important source of oxides of nitrogen (NOx = NO + NO₂) in the troposphere, and lead to impacts on air pollution, health, and climate. On hemispheric scales, lightning NOx (LNOx) emissions contribute to ozone formation that can adversely impact air quality in the U.S. and elsewhere. Accurately simulating the effects of LNOx emissions in air quality models is important to ensure an accurate background condition. However, LNOx emissions from different emissions datasets diverge, leading to differences in simulated ground level ozone and in boundary conditions for regional scale models. To work towards reconciling differences between available LNOx emissions data sets, we apply satellite retrievals of NO₂ in a chemical data assimilation system to constrain various LNOx emissions estimates. In the satellite data assimilation system, we assimilate NO₂ retrievals in the hemispheric CMAQ model, and then scale LNOx emissions in a finite-difference mass-balance (FDMB) inversion based on the difference between CMAQ with and without assimilated NO₂. The system produces gridded LNOx emissions, constrained by satellite observations. We apply the system to four LNOx emissions cases: (1) climatological emissions from the Global Emissions InitiAtive (GEIA), (2) emissions derived from lightning flash observations in the World Wide Lightning Location Network (WWLLN), (3) WWLLN derived emissions scaled using scaling factors derived from the National Lightning Detection Network, and (4) LNOx emissions produced in the GEOS-Chem modeling platform. In this presentation, we compare the satellite-based inferences between each LNOx dataset. We evaluate hemispheric CMAQ simulations using each set of emissions against remote and sonde measurements of NO₂ and O₃, and characterize their simulated impacts on the troposphere and on U.S. air quality.

Disclaimer: The views expressed in this abstract are those of the author(s) and do not necessarily represent the views or policies of the U.S. Environmental Protection Agency.

Improving Estimates of Wind-Blown Dust from Natural and Agricultural Sources

Pradeepa Vennam, Chris Emery, and Greg Yarwood

The Regional Haze Rule established requirements for states to develop State Implementation Plans that demonstrate progress towards improving visibility in federally protected “Class I Areas.” Visibility degradation is primarily caused by particulate matter (PM). Photochemical grid modeling in Texas has significantly underpredicted soil-derived PM, particularly the coarse mode, which is heavily influenced by wind-blown dust (WBD) emissions. Under funding from the Texas Air Quality Research Program (AQRP), we improved a windblown dust emission model (WBDUST) with updated parameterizations and more locally specific and temporally resolved vegetation data. We identified, reviewed, and adapted alternative landcover and year/season-specific US cropland activity datasets to further improve the characterization of WBD from agricultural lands. We applied the Comprehensive Air Quality Model with extensions (CAMx) to assess the effects of all WBDUST updates on simulated particulate matter (PM) concentrations. Modeling results were compared against measurements from monitoring sites in Class I Areas throughout the south-central US. Model-observation agreement for fine and coarse WBD concentrations improved substantially with the updated WBDUST model.

Ozone Sensitivity Analysis to NOx and VOC Emissions and the Impact of International Anthropogenic Emissions in Maricopa County

Rene Nsanzineza

Maricopa County accounts for most of the Phoenix-Mesa Nonattainment Area (NA). The Nitrogen Oxides (NOx) and Volatile Organic Compound (VOC) emissions in the County are representative of the local emissions’ impact on the ozone exceedance in the Phoenix-Mesa NA. The Arizona Department of Environmental Quality (ADEQ) used the high order decoupled direct method (HDDM) and first-order decoupled direct method (DDM) tools within the Comprehensive Air quality Model with extensions (CAMx) to quantify the ozone response to changes in NOx and VOC emissions within the County. The model run focused on periods with measured ozone exceedances in the Spring and Summer. The results showed a moderate ozone response to changes in anthropogenic NOx and biogenic VOC emissions compared to a minimal/no response to changes in anthropogenic VOC and biogenic NOx emissions. The sensitivity analysis also showed a shift from non-linear ozone response to anthropogenic NOx emissions in the Spring, to a linear ozone response in the Summer at some monitors. The preliminary results of this study suggest that anthropogenic NOx emissions could play a key role in ozone reduction. In a separate CAMx analysis, ADEQ ran the model with and without anthropogenic emissions outside of the U.S. ADEQ found that without contributions from international anthropogenic emissions, most monitors in the Phoenix-Mesa NA would have attained the 2015 ozone National Ambient Air Quality Standard (NAAQS) in 2017.

Modeling agricultural reactive nitrogen emissions with soil carbon amendments using an enhanced version of Fertilizer Emissions Scenario Tool for CMAQ (FEST-C)

Lina Luo¹, Daniel S. Cohan¹, Caroline A. Masiello², and Taras E. Lychuk³

(1) Rice University, Department of Civil and Environmental Engineering, Houston, TX, United States,

(2) Rice University, Department of Earth, Environmental, and Planetary Sciences, Houston, TX, United States,

(3) Agriculture and Agri-Food Canada, Brandon Research and Development Centre, Brandon, MB, Canada

Inefficient management and overuse of fertilizers makes agriculture the leading contributor to reactive nitrogen emissions in the United States, imposing a range of adverse impacts on air quality, health, and climate. Reactive nitrogen emitted from agricultural soils includes air pollutants nitrous acid (HONO), nitric oxide (NO), and ammonia (NH₃), which contribute to health-damaging tropospheric ozone and particulate matter, and the potent greenhouse gas nitrous oxide (N₂O). High spatiotemporal variability and complex influences of soil properties, climate conditions, and farming practices complicate efforts to control these emissions, which are often neglected by policymakers. Biochar is carbon-rich material produced from biomass pyrolysis under oxygen-limited conditions. Novel materials produced from methane pyrolysis are also being developed. Applying carbonaceous soil amendments has gained considerable attention because of its ability to influence nitrogen emissions and crop yields. However, those impacts vary widely in various agricultural regions, from positive to negative and even neutral, depending on the properties of biochar and its interaction with soil properties, farming practices, and climate conditions. Previous studies either relied on field measurements or field-scale agroecosystem models that were inadequate to characterize conditions across U.S. agricultural lands.

In this study, we incorporate biochar algorithms into an agroecosystem model Fertilizer Emissions Scenario Tool for CMAQ (FEST-C) to investigate how nitrogen emissions vary with soil carbon amendments. FEST-C has a number of input data generation tools with built-in databases that enable simulation across U.S. agricultural lands. Our previous work (Luo et al., ES&T, 2022) enhanced FEST-C by updating its nitrogen schemes to estimate reactive nitrogen emissions in a consistent and mechanistic manner. We further enhance the functions of FEST-C to simulate soil carbon amendments by adapting the algorithms developed by Lychuk et al. These algorithms assume that the essence of biochar is organic matter and its additions to soil could change the soil carbon pool and the bulk density. Second, the high surface area and charge density of biochar could increase the soil pH and cation exchange capacity. To evaluate the FEST-C biochar model performance, we compare its estimates of the impacts of biochar with those observed in published field studies in the United States. We then perform scenario analyses with biochar application rates of 5 and 20 ton/ha across U.S. agricultural lands. Our results demonstrate that for most agricultural regions, high-dose applications of biochar could mitigate reactive nitrogen emissions while low-dose applications could stimulate them. The net impacts of biochar amendments on nitrogen emissions depend mostly on how they influence nitrification rates. We are also exploring the potential impacts of soil carbon amendments derived from pyrolysis of methane rather than biomass.

Sensitivity of Ozone to Emissions Changes in the Great Lakes Region

M. Talat Odman¹, Yongtao Hu¹, Armistead G. Russell¹, Mark Janssen², Tsengel Nergui², Angela F. Dickens², Zac Adelman²

1. School of Civil and Environmental Engineering, Georgia Institute of Technology Atlanta, GA 30332-0512, USA

2. Lake Michigan Air Directors Consortium, 4415 West Harrison St., Suite 548, Hillside, IL 60162, USA 

The US EPA designated fourteen areas in the Great Lakes Region as nonattainment or maintenance areas (NAA/MA) for the 2015 ozone National Ambient Air Quality Standards (NAAQS). To support ozone NAAQS attainment planning in the region, we estimated the impacts that reducing nitrogen oxides (NOx) and volatile organic compounds (VOC) emissions from different inventory sectors and at different times of the day would have on surface ozone concentrations. Our goal was to identify optimal ozone reduction strategies for each NAA/MA.

We used the Comprehensive Air Quality Model with Extensions (CAMx) equipped with High-order Decoupled Direct Method (HDDM) to simulate the first and second order sensitivity coefficients of ozone concentrations to NOx and VOCs emissions over a 4-km horizontal resolution grid, during high-ozone episodes in June–August, 2016. Using the sensitivity coefficients, we built reduced form models (RFMs) and constructed ozone isopleths for each high ozone day at each monitor of the fourteen NAA/MA. We then derived “optimal” control strategies for emissions from eight source sectors (onroad mobile gasoline vehicles, onroad mobile diesel vehicles, nonroad mobile, volatile chemical products, other nonpoint, electricity generating point, other point, and Canada sources) at six different time periods (early morning, late morning, early afternoon, late afternoon, evening, and night).

Our analysis revealed that NOx emissions reductions are most effective for avoiding ozone NAAQS exceedances in St. Louis (IL-MO), Louisville (IN-KY), Cincinnati (OH-KY); Cleveland (OH), Detroit (MI), Berrien County (MI), Allegan County (MI), and Muskegon County (MI). We found that while NOx emissions reductions are most effective in Columbus (OH) and coastal monitors in Chicago (IL-IN-WI), these areas also would benefit from VOC emissions reductions. Combination of NOx and VOC emissions reductions are most effective at monitors in Milwaukee (WI) Sheboygan County (WI), Manitowoc County (WI), and Door County (WI), and at the far north and inland monitors in Chicago (IL-IN-WI). Detailed analyses at sector and time-period levels will be presented for select NAA/MA.

Comparing the OH-reactivity of VOCs in three major chemical mechanisms and its impact on O3 and NO2

Siqi Ma, Daniel Tong, Havala Pye, Yang Zhang, Chi-tsan Wang, Xiaoyang Chen, Daiwen Kang, Benjamin Murphy, Bok Haeng Baek

Volatile Organic Compounds (VOCs) are key precursors to tropospheric ozone (O3) and secondary organic aerosols. An explicit representation of individual VOCs in chemical transport models is complex and computationally infeasible. Except for a few explicit species, VOCs are often lumped into model species based on either lumped molecule or chemical reactivity. Each mechanism uses its own grouping method to split the total VOCs from the National Emissions Inventories (NEIs) into corresponding lumped species for model-ready inputs. During this process, the chemical reactivity of emitted VOCs may be altered, causing unknown effects on the prediction of O3 and other key species. To address this issue, the emissions were prepared for three commonly-used chemical mechanisms: Carbon Bond version 6 revision 3 (CB6r3), SAPRC07 and the Regional Atmospheric Chemistry Mechanism version 2 (RACM2), based on the NEI 2016v1. Simulations with these mechanisms were conducted for 4 representative months (January, April, July, and October) in 2019 using the Community Multiscale Air Quality (CMAQ) model version 5.3.2. The total mass of anthropogenic VOCs in active model species are comparable among the three mechanisms, with the CB6 representing the highest (2971.5 tons/month), followed by SAPRC07 (2953.9 tons/month), and then RACM2 (2916.0 tons/month). In comparison, the hydroxyl radical (OH) reactivities of emitted VOCs in SAPRC07 and RACM2 are usually higher than CB6. Meanwhile, the biogenic VOCs vary largely among different months and mechanisms with a significantly higher mass of emitted VOCs represented in CB6, which is much larger than the other two mechanisms, as well as the reactivities. These emission differences are probably caused by different speciation profiles used in each mechanism. The predicted O3 and nitrogen dioxide (NO2) with the three mechanisms are compared with ground-based measurements over the contiguous United States. The results show that the correlation coefficients between observations and simulations are comparable among the mechanisms for all the 4 months, ranging from 0.52 (January) to 0.74 (October). The O3 mixing ratios with RACM2 are higher than those with SAPRC07 and CB6. The simulation with RACM2 performs better when the O3 prediction is biased-low (i.e., January and April) while that with CB6 performs better when O3 prediction is biased-high (i.e., July and October). The NO2 mixing ratios simulated by the three mechanisms are similar and the normalized mean bias (NMB) from SAPRC07 (-11%) is generally smaller compared to the other two mechanisms (-13% and -12%). In addition, RACM2 and SAPRC07 perform better during high O3 episodes than CB6. A preliminary analysis based on Integrated Process Rate (IPR) method indicated the chemical and physical processes related to O3 and NO2 in RACM2 are more active while those in CB6 present weaker. Finally, a case study using the process analysis method compares the OH-reactivity of VOCs in model and investigates the cause of the differences among the chemical mechanisms.

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. EPA.

Estimating Mexican EGU Impacts on Ozone in El Paso with SCICHEM

Chase Calkins and Matthew J. Alvarado, Verisk Atmospheric and Environmental Research

This work used v3.3 of the Second-Order Closure Integrated Puff Model with Chemistry (SCICHEM) to assess the impact of four EGUs near Ciudad Juarez on ozone levels in El Paso County, Texas. We examined how large the emissions from hypothetical new point sources in Juarez and surrounding regions would have to be to have a significant impact on ozone in El Paso. Four scenarios were simulated for August 2019, one base 2019 emissions case and three potential 2023 emission cases. Stack parameters and hourly speciated emissions estimates were developed for each EGU consistent with US EPA data and assumptions. WRF and CAMx output for the El Paso area (180 km x 180 km domain, 4 km resolution) was processed using MMIF and CTM2SCICHEM, respectively, to develop the necessary meteorological and background chemical input data for SCICHEM. A receptor gird at 2 km resolution covering the TCEQ El Paso CAMx domain was used. To speed up processing, each day was simulated separately on AWS cloud resources using a 6-hour spin-up period before performing the 24-hour simulation. The maximum daily 8-hour average (MDA8) O3 impacts at each receptor were calculated, along with the daily 1-hour maximum NO and NO2 concentrations, which were compared to available monitoring data in El Paso County. In this presentation we will discuss the results of the study and our recommendations for further work.

Comparisons of Air Quality Models for Prescribed Burning Simulations at a Military Base in Southeastern United States

Zongrun Li, M. Talat Odman, Yongtao Hu, Armistead G. Russell

Accurate simulation of prescribed burning is important for the management of fires and air quality in southeastern United States, as well as for the design of related field studies. We conducted simulations of prescribed burns in Fort Benning, Georgia with BlueSky-CMAQ and WRF-Fire-Chem and compared the differences between the results of these two modeling frameworks. The accuracy of the simulations is evaluated against the data collected by monitors deployed at Fort Benning.

BlueSky-CMAQ couples the BlueSky framework with the CMAQ model. BlueSky estimates the fuel type, fuel load, fuel moisture and emission factors, which are all related to the magnitude of emissions. BlueSky also estimates the vertical structure of emissions by the plume rise models it incorporates. The emissions calculated by BlueSky are provided to CMAQ that utilizes them along with meteorological conditions to predict the contribution of prescribed burning to local and regional air quality.

WRF-Fire-Chem, which is mostly used for modeling wildfires, considers the interactions among meteorology, fire and chemistry. WRF-Fire-Chem couples a meteorological model with models of fire, fuel moisture and chemistry. The fire model estimates fire propagation under real-time meteorological and fuel moisture condition and has feedback to the local meteorology. The emissions are calculated based on fuel consumption from the fire model results. The vertical structure of smoke is derived from buoyancy generated by the heat released from the fire. Finally, the calculated emissions are provided to WRF-Chem which can consider the interactions between chemistry and meteorology.

In this presentation, we compare the fuel types, time profiles and magnitudes of emissions, plume heights, and ground level concentrations of pollutants between the two modeling frameworks. Also, observations of winds and pollutants from monitors in Fort Benning are used as benchmarks to evaluate model performance and understand critical factors affecting simulation performance. The findings will inform model implementation for studying the prescribed burning impacts on air quality.

Predicted impacts of heterogeneous chemical pathways on particulate sulfur in Fairbanks, Alaska

Sara Farrell, Havala O. T. Pye, Robert Gilliam, George Pouliot, Deanna Huff, Golam Sarwar, William Vizuete, Kathleen Fahey

Fairbanks, Alaska, is currently in serious non-attainment of the 24-hour fine particulate matter (PM2.5) National Ambient Air Quality Standard, with mass concentrations reaching over 100 ug/m3 during extreme wintertime particulate pollution episodes characterized by strong temperature inversions, low winds, and high home heating emissions. While the particulate matter composition is dominated by organic carbon and driven by primary home heating emissions, sulfate is the second-largest contributor to particle mass. Mechanisms leading to high sulfate concentrations during cold and dark conditions, when the globally dominant aqueous and gas-phase photochemical SO2 oxidation pathways are limited, remain uncertain. In addition, these conditions may also favor the formation of hydroxymethanesulfonate (HMS), an organosulfur species which is detected in certain sulfate measurements (e.g., Moch et al., 2018). While the Community Multiscale Air Quality (CMAQ) modeling system and most current chemical transport models include gas-phase oxidation of SO2 by OH and in-cloud aqueous oxidation (e.g., via H2O2 and O3) leading to sulfate, there are conditions, such as those characteristic of Fairbanks winters, where these pathways do not reproduce the high sulfate concentrations that are observed (CMAQ sulfate NMB ~ -70% for January-February 2008). Implementation of additional heterogeneous sulfur chemistry in air quality models may ameliorate this model-measurement gap. In this work we implement heterogeneous sulfate and HMS chemistry in CMAQ, version 5.3.3, and investigate the potential impacts of high ionic strength on kinetic rate expressions and model parameters. With a more complete representation of sulfur chemistry, models like CMAQ can be better equipped to link secondary aerosol concentrations to precursor levels and identify effective pathways to attain air quality goals and protect human and ecosystem health.

Comparison of Global Fire Emission Inventories and Development of a New Python-based Fire Emission Inventory Processor

Jeremiah Johnson¹, Pradeepa Vennam¹, Chris Emery¹, and Greg Yarwood¹

¹ Ramboll US Consulting, Inc. 7250 Redwood Blvd., Suite 105 Novato, CA 94945

Fires are large emission sources and accurate Fire Emission Inventories (FEIs) are needed for exceptional event analyses and State Implementation Plan (SIP) modeling for ozone, PM, and regional haze. Currently available FEIs can differ by an order of magnitude, so a well-developed FEI processing tool is essential to help understand the ranges of air quality impacts predicted using alternative FEIs in regional air quality models. FEI processing is complex because the available inventories contain different information and omit some information needed by air quality models (e.g., daily temporal allocation, chemical speciation, and plume height profiles) requiring the use of supplementary data and assumptions. We developed a new literature-based Python FEI processor that provides a flexible fire emissions processing platform that can process four different FEIs: 1) FINN1.0; 2) FINN2.5; 3) GFAS1.2, and 4) QFED2.5. Comparisons of the FEIs revealed substantial differences in emissions of key pollutants for the 2019 ozone season in Texas as well as states that frequently contribute wildfire smoke into Texas. This finding is consistent with the literature review and highlights the uncertainties in fire detection and emissions estimations derived from satellite measurements. Future development plans include adding additional FEIs and testing and evaluation in air quality models to help refine the emissions estimates produced by the FEI processor.

Sensitivity of Ozone to Emission Reductions in New York City

Trang Tran, Naresh Kumar, and Eladio Knipping

Ozone sensitivity to emission changes in urban environments were examined using WRF and CAMx simulations for the New York City metropolitan area (NYC) with data from the 2018 Long Island Sound Tropospheric Ozone Study (LISTOS) measurements. The WRF simulation showed large positive biases of windspeed over entire domain, but those were significantly reduced with observational nudging. The base CAMx simulation showed large positive bias for NO2 concentrations in NYC, which was corrected after NOx emission adjustment (22% reduction) based on previous work. The improved modeling platform demonstrated reliable performance skills for regulatory applications. Pollution transport from NYC to the downwind region were well captured by the model. Ozone concentration increases in response to NOx emission reductions within NYC proximity revealed this area to be a VOC-limited ozone formation regime. Reducing anthropogenic emissions by 50% for all pollutants reduced the number of simulated ozone exceedances from 18 to 9 days for NYC. Emission reductions were even more effective for air quality improvements in the downwind region with ozone exceedances decreasing from 20 to 7 days. Source apportionment showed mobile and industrial solvent emission sectors to be contributing more to ozone exceedances than the electric sector in both NYC and the downwind region. The largest air quality improvement appeared to be associated with VOC emission reductions from the industrial solvent sector. All source sectors yielded higher ozone production efficiency when emissions were reduced by 50%, suggesting remaining emissions can be more potent in producing ozone per molecule of NOx.

A scenario analysis of climate-driven changes in wildland fire smoke in the Southeastern U.S.

Megan M. Johnson and Fernando Garcia Menendez

Land managers in the Southeastern U.S. use prescribed fire extensively to meet objectives such as wildfire risk reduction, maintaining wildlife habitat, and supporting fire-dependent species. However, climate change will likely affect the acceptable meteorological conditions for prescribed fire in the region, as well as wildfire risk. Smoke from wildland fire (wildfire and prescribed fire) is one of the largest sources of fine particulate matter air pollution (PM2.5) in the U.S. and is associated with negative impacts on human health. The Southeast has one of the largest populations living at the wildland-urban interface, which may be particularly vulnerable to health impacts from smoke. Using chemical transport modeling, U.S. EPA EQUATES datasets, and projections of changes in fire activity, we model possible mid-century scenarios of change in wildland fire PM2.5 across the Southeast. Projected changes in wildfire burned area are based on climate scenarios that incorporate population, land use, and economic changes. Altered prescribed fire activity is informed by projected changes in acceptable meteorological conditions, or “burn windows.” Changes in wildfire and prescribed fire PM2.5 and their contributions to regional air pollution are quantified and compared. Using census demographic data and U.S. EPA indices of existing social and environmental stress, we discuss the populations in the Southeast that most frequently experience wildland fire smoke and how these impacts may be altered due to future fire activity. By examining how smoke impacts may change under projected climate conditions, this work can help to identify where shifts in the benefits and detriments of wildland fire activity may occur and who may be most affected.

Modeling Summertime O3 Formation in the Salt Lake Valley: Model Performance and Sensitivity Analyses

Nancy Daher

The Salt Lake Valley in Utah continues to exceed the National Ambient Air Quality Standard (NAAQS) for ozone during the summer, with ozone having a mix of different sources, both local and non-local. These comprise sources from both anthropogenic and natural sources, including stratospheric transport, wildfires, biogenic emissions as well as US and international anthropogenic sources. A typical summertime O3 air pollution episode that occurred in the Salt Lake Valley in 2017 was simulated using the Comprehensive Air Quality Model with extensions (CAMxv7.1). Three 12/4/1.33 km nested grid domains were considered for this analysis. A comparison of measured and modeled O3 showed that while the model overall captures well the temporal variation in maximum daily 8-hr average O3 (MDA8), measured MDA8 was overall underestimated by about 5-15% on ozone exceedance days across the entire 1.33 km domain. This underprediction in O3 is accompanied by an underestimation of oxygenated and biogenic VOCs, particularly formaldehyde and isoprene. Model sensitivity to different versions of the Biogenic Emissions Inventory System (beis) and Biogenic Emissions Landcover Database (BELD) was also evaluated. Model sensitivity to halogens emissions from a point industrial source was also assessed, with results showing increased ozone formation across the valley. Findings have important policy implications for emissions control development.

Prescribed Fire Emissions and their Impacts on PM2.5 in Southeastern United States

Kamal J. Maji¹, Zongrun. Li¹, Yongtao Hu¹, Armistead G. Russell¹, Jennifer Stowell², Chad Milando², Patrick Kinney², Gregory A. Wellenius², Ambarish Vaidyanathan^{1, 3,} and M. Talat Odman¹

¹ School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA 30332-0512, USA

² School of Public Health, Boston University, Boston, MA 02118, USA

³ National Center for Environmental Health, Centers for Disease Control and Prevention, Atlanta, GA 30341, USA

Over the past decades, the world has witnessed unprecedented levels of smoke exposure from wildland fires, including from prescribed burning which is used as a management strategy to reduce the wildfire risk and for ecosystem health. However, prescribed fire is also a leading source of PM_2.5 emissions, particularly in the southeastern United States (US) with potential impacts on human health. While burn permit records are a reliable source for estimating prescribed fire emissions, they are not available in all states; therefore, satellites have been used for regional studies. However, since satellite detections do not differentiate the type of fires, we developed an algorithm to segregate Fire INventory from NCAR (FINN) emission product into wildfires, and prescribed and agricultural burns, based on their location and duration.

We used the BlueSky fire emission simulator to compute smoke emissions based on FINN burned area information. Then, we simulated the prescribed fire impacts on daily total PM_2.5 during the burn seasons (January–April) 2017-2020, using the Community Multiscale Air Quality (CMAQ) model at 12- and 4-km horizontal grid resolutions over the southeastern US. The inner domain includes two major metropolitan areas: Atlanta and Charlotte as well as the prescribed fire areas in South Georgia and the Florida Panhandle whose emissions impact those cities and other populated locations. The meteorological inputs were taken from daily weather forecast simulations over the same domains with the Weather Research and Forecasting (WRF) model. These simulated PM_2.5 fields were then fused with daily total PM_2.5 observations at ambient surface monitors. The resulting PM_2.5 fields include temporal information of the observations, as well as the spatiotemporal completeness provided by the air quality model. Finally, the observation-fused daily total PM_2.5 fields were scaled by the ratio of the burn impact to total PM_2.5 from CMAQ for each day and each grid cell to generate an “observation-adjusted burn impact” on PM_2.5.

In this presentation, prescribed fire emission estimates and their impacts on PM_2.5 concentrations will be evaluated with respect to total PM_2.5 in the southeastern US and their uncertainties will be discussed.

On the sensitivities driving predictions from Per- and polyfluoroalkyl substance (PFAS) chemical transport modeling

E.L. D’Ambro, H.O.T. Pye, C. Allen, K. Talgo, L. Reynolds, K. Brehme, R. Gilliam, J.O. Bash, B.N. Murphy

Per- and polyfluoroalkyl substances (PFAS) are a large class of human-made compounds that have contaminated the environment both near and far from sources. To date, much of the scientific and regulatory focus has been on PFAS in drinking and surface waters, with a paucity of studies on air emissions, transport, and deposition, which is likely to contribute in part to water contamination. In previous work, we implemented a comprehensive suite of atmospheric PFAS emissions from a fluoropolymer manufacturing facility in Eastern NC and investigated their transport and fate within ~150 km of the manufacturing facility at a horizontal resolution of 1 km. The level of detail in our previous study is unmatched, primarily due to the lack of reporting requirements and thus detailed information on emission speciation and rates from PFAS manufacturers and users. Herein, we test the sensitivity of our results, primarily the predicted air concentrations and deposition rates, to the level of detail in our model inputs and configuration. CMAQ v5.3.2 is utilized to test a simpler chemical speciation, uniform temporal specificity of emissions, and horizontal resolution relative to our original study. We find that the chemical specificity is important for accurate physicochemical properties which determine phase state and thus deposition rates of PFAS. We provide recommendations for future chemical transport modeling studies of PFAS.

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.

Wildfire-caused Exceedances of Critical Loads of Nitrogen Deposition at Class I Areas
Krish Vijayaraghavan, Marco Rodriguez, Tejas Shah, Pradeepa Vennam, Fianna Li

Ramboll, Novato, California

This paper presents results from regional photochemical modeling analysis of the contribution of wildland fires, both natural and prescribed, to atmospheric nitrogen deposition at selected Federal Class I areas. The Comprehensive Air Quality Model with Extensions, CAMx with Particulate Source Apportionment Technology (PSAT), is applied to estimate nitrogen deposition at selected national parks, wilderness areas, and wildlife refuges in the western United States. The modeling is used to assess to what extent wildfires are responsible for exceedances of critical loads of nitrogen deposition at these areas that have mandatory protection under the Clean Air Act. Critical loads represent the levels of deposition below which harmful effects do not occur to natural ecosystems. Critical loads applied here include those for aquatic acidification, forest ecosystems, herbaceous plants and shrubs, and mycorrhizal fungi. The source apportionment modeling indicates that wildfire emissions could contribute over 60% of the total nitrogen deposition at some Class I areas resulting in exceedances of critical loads.

Development and Evaluation of An Advanced Ozone Dry Deposition Model

Kiran Alapaty1, Jesse Bash1, Bin Cheng2, Saravanan Arunachalam2, and J. William Munger3

1U.S. Environmental Protection Agency, Research Triangle Park, NC

2Institute for the Environment, The University of North Carolina at Chapel Hill, Chapel Hill, NC

3Harvard School of Engineering and Applied Sciences, 24 Oxford Street, Cambridge, MA

Literature indicates that turbulence strength is under-represented in almost all dry deposition models and the different types of stability functions used are contributing to biases in the estimated dry deposition of ozone (O3) and are also a source for differences among models’ simulations. To alleviate these issues, we developed new and revised resistance formulations based on turbulence kinetic energy to accurately represent turbulence strength to improve estimation of O3 deposition fluxes. New formulations developed are for the aerodynamic and cuticle resistances, and relevant formulations for other resistances (e.g., leaf boundary layer resistance) were revised to include improved representation of turbulence strength. A revised stomatal resistance includes impacts of dew formation on adaxial surface and particle blockage of abaxial stoma. Decadal measurements (1991-2000) (referred to as OBS) available from the Harvard Forest site are used to drive a single-point (box) model and to evaluate O3 deposition flux estimation by STAGE (referred to as STAGE) as well as the revised STAGE (referred to as TKE-STAGE) formulations. We hypothesized and proved that a new turbulence velocity scale can effectively avoid the usage of stability functions, and that inclusion of an improved estimate of turbulence strength along with other revisions leads to a more representative simulation of O3 deposition. Decadal averaged monthly & hourly variations of simulated O3 fluxes by TKE-STAGE are much closer to OBS when compared to STAGE. For example, decadal averaged hourly minimum measured fluxes (ppb ms-1) occurred at midnight with OBS=0.18; STAGE=0.21; TKE-STAGE=0.17 while maximum measured fluxes occurred at local noon with OBS=0.6; STAGE=0.8; TKE-STAGE=0.7. We found that the bias reduction is attributable to improved representation of processes in the TKE-STAGE formulations. The findings from the research may help improve the capability of dry deposition schemes for better estimation of dry deposition fluxes and opens doors for the development of a community dry deposition model for use in regional/global air quality models. The TKE-STAGE formulations will be available as an additional option to choose from in a future release of Box model and CMAQ model.

Disclaimer: The views expressed in this article are those of the authors and do not necessarily represent the views or policies of US EPA.

Air quality impacts of increased port activity in Los Angeles, California investigated using satellite retrievals and chemical transport modelling

T. Nash Skipper¹, Jennifer Kaiser^1,2, Armistead G. Russell¹

¹ School of Civil & Environmental Engineering, Georgia Institute of Technology, Atlanta, GA

² School of Earth & Atmospheric Sciences, Georgia Institute of Technology, Atlanta, GA

The ports of Los Angeles and Long Beach experienced a large increase in activity during 2021 which resulted in an increase in ships idling offshore near the California coast. Increased emissions from ship traffic and associated emissions from truck and rail transport have raised questions about the impacts on air quality throughout the Los Angeles metro area, especially in areas near the port which are already disproportionately impacted by poor air quality.

We use a combination of CMAQ simulations and TROPOMI NO2 retrievals to investigate the impacts from increased port emissions in the Los Angeles metro area by comparing the conditions in 2021 to a historical baseline in 2018 and 2019 (2020 is excluded since it is heavily impacted by Covid-19). Emissions impacts cannot usually be inferred directly from satellite retrievals because of year-to-year fluctuations in the impacts of meteorology. We adjust the TROPOMI NO2 retrievals, using CMAQ to remove the effects of meteorology. Results indicate that meteorological conditions tended to increase the impacts of emissions in 2021 relative to the 2018-2019 baseline. In some areas, the inferred impacts from emissions decreased while the total impacts increased, indicating that the conclusions drawn from year-to-year comparisons of satellite retrievals can be significantly altered if meteorology is not taken into account.

Development of JEDI Based AIRNow and AOD Assimilations Capabilities to Improve RRFS-CMAQ Predictions

Youhua Tang^1,2, Cory Martin³, Min Huang^2,3, Tianfeng Chai^1,4, Mariusz Pagowski^5,6, Hongli Wang^5,6, Daryl Kleist³, Rajesh Kumar⁷, Shobha Kondragunta⁸, Barry Baker¹, Patrick Campbell^1,2, Jianping Huang^3,9, Jeff McQueen³, Raffaele Montuoro³, Daniel Tong^1,2, Ivanka Stajner³, Youngsun Jung¹⁰

1. NOAA Air Resources Laboratory (ARL), College Park, MD. 2. Center for Spatial Information Science and Systems, George Mason University, Fairfax, VA. 3. NOAA National Centers for Environmental Prediction (NCEP), College Park, MD 4. CISESS, University of Maryland, College Park 5. NOAA Global Systems Laboratory 6. CIRES, University of Colorado, Boulder, CO 7. National Center for Atmospheric Research, Boulder, CO 8 NOAA/NESDIS/STAR 9 I.M. Systems Group Inc., Rockville, MD 10. NOAA NWS/STI

A chemical data assimilation (DA) capability for CMAQ species is being developed under Joint Effort for Data-Assimilation Integration (JEDI) framework to improve the prediction results of the regional online air quality model, RRFS-CMAQ (Rapid Refresh Forecast System with CMAQ chemistry), which is based on the Limited Area Model (LAM) version of Finite Volume Cubed-Sphere (FV3) dynamic core. The existing JEDI has two major interfaces to FV3 and MPAS (Model for Prediction Across Scales) models. In this study, our focus is FV3-JEDI. Under this DA framework, we developed the observation processors and forward operators for the AIRNow in-situ measurements and VIIRS aerosol optical depth (AOD). The AOD operators for aerosol extinction calculation include the observation based reconstruction method (RM) and a mapping method (mapping CMAQ aerosol species to GOCART aerosol species to use its existing look-up table) (Tang et al, 2017). Both methods yield similar aerosol increments in AOD DA using the VIIRS AOD data from SNPP and NOAA-20 satellites. Our testing shows that assimilating in-situ measurements is a reliable method to reduce the bias in the aerosol initial conditions of RRFS-CMAQ, though its effect may not last long (~ several hours), especially over polluted areas with strong emissions. AOD DA is useful over remote regions, where the AIRNow data are relatively sparse or nonexistent. Both assimilations were sensitive to the observation/background error covariances and length scales, or B-Matrix. The AOD DA adjustment may not be consistent with AIRNow DA increment, depending on the aerosol types, size and vertical distributions. This inconsistency could be more obvious when the existing background model bias is relatively small.

Evaluation of global IASI-GOME2 observations of lowermost tropospheric ozone as a predictor of ground-level ozone concentration

Hantao Wang and J. Jason West

Remote sensing techniques have been widely used to monitor the global distribution of tropospheric ozone, but ground-level ozone is difficult to detect. Recently, researchers have combined observations from satellites to infer ozone in the lower troposphere. The main objective of this study is to evaluate satellite observations of ozone in the lowermost troposphere (0-3km) as predictors of ground-level ozone concentration. We then hope to use these observations to better understand ozone in regions with few ground monitors, and include it in data fusion of global ozone concentrations. We evaluate retrievals of lowermost troposphere ozone from the IASI+GOME2 database, which combines atmospheric radiances in the thermal infrared (TIR) detected by IASI and ultraviolet (UV) Earth reflectance measured by GOME2. The IASI+GOME2 database uses two validated methods to enhance the sensitivity of lower troposphere ozone through its altitude-dependent Phillips-Tikhonov type constraints. Monthly averages obtained from IASI+GOME2 observations at about 9:30 AM local time overpass were evaluated with respect to monthly average DMA8 (daily maximum 8 h average) of ground-level observations at 2364 sites in the 2017 Tropospheric Ozone Assessment Report (TOAR) database. Here, a generalized linear model was used to analyze the correlation between satellite and ground data, as well as the spatio-temporal interactions. Quantile regression was employed in this study to examine the reliability of satellite data at various ground-level ozone concentrations. Multiple linear regression was performed to investigate the effect of land cover on association between satellite and surface data. Our results highlight that in the summer, regions between 30° and 40° latitude had the strongest correlations between satellite and ground-level ozone data. In general, locations with higher ground-level ozone concentrations show better agreement between satellite and ground data. The study suggests that when satellite data are included in data fusion, the geographical and temporal variability in performance with respect to ground data should be considered.

Simulations of IEPOX Chemistry Using Laboratory Results and F0AM-CMAQ Model with ISORROPIA and AIOMFAC Thermodynamic Model Integration

Jaime R. Green, Alexandra E. Ng, Yuzhi Chen, Jason D. Surratt, and William Vizuete

The atmospheric formation of isoprene epoxydiol (IEPOX)-derived secondary organic aerosol (SOA) affects air quality and climate due to their substantial mass contribution to fine particulate matter. In determining the rates of IEPOX-SOA formation, acidity plays a critical role. In the Community Multiscale Air Quality Model (CMAQ) model version 5.2 phase separation was added, but CMAQ only has the capability to predict aerosol acidity based only on the inorganic species using the ISORROPIA thermodynamic model. Our previous work has implemented aerosol phase state predictions in CMAQ v5.3.2, but that model still lacks the ability to predict the acidity of the organic shell. Recent work has shown that the partitioning of gas phase IEPOX is influenced by the combined acidic activity of the organic and inorganic acid components of the aerosol. The combined acid activity influences particle growth rates, physical properties, and the change in overall chemical composition during atmospheric aging. This work describes a series of 0D box model simulations implementing both organic and inorganic species in acidity calculations by implementing Aerosol Inorganic-Organic Mixtures Functional Groups Activity Coefficients model (AIOMFAC 3.03) with the current parameterization in CMAQ v5.3.2. The 0D model is based on the Framework for 0-D Atmospheric Modeling (F0AM) box model with a module based (F0AM-CMAQ) on current implementation of SOA formation in CMAQ v5.3.2. The performance of any explicit aerosol formation mechanism developed based on field and laboratory data will be compared alongside the mechanisms in aerosol CMAQ, taking any explicit gas phase chemical output from the F0AM model. Results include the comparison of the predicted evolution of SOA mass loading between CMAQ and the observational data and an evaluation of which thermodynamic model can be used to efficiently represent the chemical activity within a given IEPOX-derived SOA. These model runs using the F0AM-CMAQ model system will guide future updates to the CMAQ model related to isoprene-derived SOA.

Introducing Geostationary Lightning Mapper Nitrogen Oxides (GLM-NOx) Emissions Processor and its Application in 2019 Air Quality Simulations
Arastoo Pour-Biazar, Peiyang Cheng, Yuling Wu, William Koshak, Andrew White, Maudood Khan, Richard T. McNider

With the reduction of anthropogenic nitrogen oxides (NOx=NO+NO2) in the United States, understanding the contribution of natural NOx emissions to pollution episodes is increasingly important. Lightning constitutes a significant natural source of NOx in the middle and upper troposphere and plays an important role in tropospheric ozone chemistry. However, estimates of Lightning-generated NOx (LNOx) are highly uncertain. Furthermore, the approach for spatial and temporal distribution of these estimates in numerical air quality models such as CMAQ greatly affects air quality simulations.

The newly launched Geostationary Lightning Mapper (GLM) onboard GOES-16 (east) and GOES17 (west) has created the opportunity for an alternate approach for estimating and incorporating LNOx in the air quality models. In this study, we first evaluate GLM observations with respect to their lightning detection efficiency over North America. Since the detection efficiency degrades with increasing satellite view angle, it is necessary to merge GLM-16 and GLM-17 observations over the contiguous United States (CONUS) to create a reliable dataset.

The methodology for estimating LNOx emissions, is based on the theoretical and laboratory estimates of LNOx production per unit energy, and uses the optical energy observed by the GLM to calculate the total column LNO. The procedure first establishes a calibration scaling factor by using multiyear GLM observations and assuming an average 250 mol/flash production rate. Then, the observed optical energy from the individual flashes is used to calculate LNO. Subsequently, NASA Lightning Nitrogen Oxides Model (LNOM) monthly derived vertical profiles, which are based on uniquely fused theoretical and laboratory results with multiyear North Alabama Lightning Mapping Array (LMA) and National Lightning Detection Network (NLDN) observations, are used to vertically distribute NO within a model column. A software package is developed to perform these steps and produce the final CMAQ-ready hourly emissions. LNOx emissions were estimated for 2019 and subsequently used in air quality simulations (June-September) over the CONUS using WRF/CMAQ modeling system. Based on our estimates, from June-September 2019, LNOx contributed 0.174 TgN, accounting for 12% of total NOx emissions and 45% of natural emissions during this period. Tropospheric column ozone was increased up to 5% over the south/southeastern U.S. The maximum ozone increase in the column was above 4 km altitude. The average increase was about 1 ppb over CONUS and 2 ppb for the S.E. Over Huntsville, Alabama, the ozone increase above 4 km was 2.5 ppb. Comparing model results to available lidar measurements during this period exhibited a reasonable agreement between the model and observations. Preliminary results from this activity will be presented.

Impacts of the halogen chemistry and CB6 chemical mechanism updates on air quality

Golam Sarwar, Jeff Willison, Kathleen Fahey, William Hutzell, Daiwen Kang, Kristen Foley, Christian Hogrefe, Barron Henderson, Rohit Mathur, Wyat Appel

The Community Multiscale Air Quality (CMAQv5.3) model contains the Carbon Bond chemical mechanism, version 3 (CB6r3) and a simple representation of ozone loss due to halogen chemistry. It also includes detailed halogen (bromine and iodine) chemistry which is combined with CB6r3 (CB6r3m). In CMAQv5.4, both the simplified and detailed halogen chemistry in CB6r3 are updated and dimethyl sulfide (DMS) chemistry is added. The detailed halogen chemistry is updated by removing bromine reactions in the cloud chemistry, revising heterogenous reactions, updating product yields, adding two gas-phase iodine reactions, and revising the spatial distribution of halocarbon emissions. The simple representation of ozone loss due to halogen chemistry was previously derived using results of the hemispheric CMAQ simulations without and with the detailed halogen chemistry. In CMAQv5.4, the simple halogen chemistry is re-derived. The updates in detailed and simple halogen chemistry increase ozone over seawater and land areas compared to the previous version. In CMAQv5.3, the simple halogen chemistry was activated whenever the fractional area of any grid-cell with open ocean and surf zone area was greater than zero causing the prescribed first order ozone loss rate to occur equally over open ocean and surf zone area. In CMAQv5.4, the prescribed first order ozone loss rate is scaled by the fractional area of the grid-cell with open ocean and surf zone area. The change causes no impacts over open ocean but reduces the impact over coastal areas. Ramboll, the developer of the Carbon Bond chemical mechanism, recently updated the Carbon Bond chemical mechanism (CB6r5) by revising rate constants of 41 reactions and by adding an extra reaction. The CB6r5 is now implemented into CMAQv5.4. It has mixed impacts on ozone, increasing mixing ratios over some areas while decreasing mixing ratios over other areas. In CMAQv5.4, DMS chemistry is combined with CB6r5, which increases sulfur dioxide and sulfate concentrations over seawater and adjacent land areas. Impact over the interior portion of the modeling domain is generally small. The CB6r5 is also coupled with the updated detailed halogen chemistry (CB6r5m) which also has mixed impacts (compared to CB6r3m), increasing ozone over some areas while decreasing ozone over other areas. Details and model results for each update are presented, along with a comparison of model predictions and observed data from several U.S. nationwide air quality networks.

Disclaimer: The views expressed in this paper are those of the authors and do not necessarily represent the views or policies of the U.S. EPA.

Using NWS Radars to Constrain Planetary Boundary Layer Height Simulations

John Henderson, Marikate Mountain, and Matthew Alvarado, Verisk Atmospheric and Environmental Research

We have developed a proof-of-concept system that applies the Quasi-Vertical Profile (QVP) methodology to NWS radar data to compute estimates of planetary boundary layer height (PBLH). The approach uses the Level 3 differential reflectivity field from the 88D radar network that became available following the upgrade in recent years to dual polarization. PBLH estimates are generated for every radar volume scan under quiescent meteorological conditions after applying quality control and smoothing algorithms. The underlying QVP scientific technique was refined by applying the system to approximately 50 late summer and autumn use cases at several radar sites in Texas, which made clearer its behavior and limitations under a broad range of weather conditions and local influences. Wrapper scripts in Python run the end-to-end system within Docker for portability. A more thorough comparison against radiosonde observations will guide further improvements to the science algorithm and will encourage its use in all seasons and for all NWS radar locations. We anticipate this will help alleviate the current dearth of PBLH observations and enable model PBLH validation through development of a forward operator applied to computed modeled PBLH fields.

Development and application of the Community Regional Atmospheric Chemistry Multiphase Mechanism (CRACMM) version 1.0

Havala O. T. Pye¹, Bryan K. Place², Ben N. Murphy¹, Karl M. Seltzer³, Emma L. D’Ambro¹, Chris Allen⁴, Ivan R. Piletic¹, Sara Farrell², Emily Saunders⁵, Golam Sarwar¹, Bill Hutzell¹, Forwood Wiser⁶, V. Faye McNeill⁶, Rebecca H. Schwantes⁷, Matthew M. Coggon⁷, Lu Xu^7,8, Kristen M. Foley¹, George Pouliot¹, Jesse Bash¹, Wyat Appel¹, Daniel M. Westervelt⁶, Arlene M. Fiore⁹, Daven Henze¹⁰, Siddhartha Sen¹¹, Luke Valin¹, Ana Torres-Vazquez², Jon Pleim¹, Heather Simon³, and William R. Stockwell¹²

¹Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, North Carolina, USA; ²Oak Ridge Institute for Science and Engineering (ORISE) Postdoctoral Program at the Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, North Carolina, USA; ³Office of Air and Radiation, US Environmental Protection Agency, Research Triangle Park, North Carolina, USA;  ⁴General Dynamics Information Technology, Research Triangle Park, North Carolina, USA;  ⁵Office of Chemical Safety and Pollution Prevention, US Environmental Protection Agency, Washington D.C, USA;  ⁶Columbia University, New York, New York, USA;  ⁷NOAA Chemical Science Laboratory (CSL), Boulder, Colorado, USA;  ⁸Cooperative Institute for Research in Environmental Science (CIRES), University of Colorado, Boulder, Colorado, USA;  ⁹Massachusettes Institute of Technology, Cambridge, Massachusetts, USA;  ¹⁰University of Colorado, Boulder, Colorado, USA;  ¹¹Microsoft Research, New York, New York, USA;  ¹²University of Texas at El Paso, El Paso, Texas, USA

Air pollution remains an important contributor to negative health outcomes in the U.S., and recent work indicates a strong association of secondary organic aerosol (SOA), an important component of fine particles, with cardiorespiratory mortality. The Community Multiscale Air Quality (CMAQ) model, however, has limited ability to provide source apportionment of SOA due to the empirical representation of anthropogenic SOA. This work focuses on the development of the Community Regional Atmospheric Chemistry Multiphase Mechanism (CRACMM) version 1.0 - which couples gas and particle chemistry and includes new explicit hazardous air pollutants (HAPs). Conservation of mass constraints introduced by integrating the chemistry enabled the implementation of new SOA precursor compounds not previously considered in CMAQ aerosol modules. As a result, CRACMM provides a bottom-up prediction of SOA which is required for accurate source apportionment. In addition, CRACMM shows higher ozone production efficiency than the Carbon Bond chemical mechanism (CB6r3) resulting in improved predictions of ozone over the Northeast U.S. for summer 2018. CRACMM is the first chemical mechanism to include autoxidation and fully couple ozone-forming radical chemistry in all SOA systems. CRACMM will be publicly available in two versions in CMAQv5.4: base CRACMM1 and CRACMM1AMORE with isoprene chemistry developed by the Columbia University Atmospheric Chemistry Model Reduction (AMORE) algorithm.

Tropospheric Emissions: Monitoring of Pollution (TEMPO) mission and Level 2 Data Product Validation Plans

Jim Szykman, Luke Valin, David Williams, and Eric Baumann, EPA Office of Research and Development, Center for Environmental Measurement and Modeling, RTP, NC

Thomas Hanisco and Nader Abuhassan, NASA Goddard Space Flight Center, Greenbelt, MD 20771

Alexander Cede and Manuel Gebetsberger, LuftBlick, Kreith, Austria

Kelly Chance, Harvard-Smithsonian Center for Astrophysics Cambridge, MA 02138

The TEMPO satellite mission, scheduled to launch in early 2023, will provide hourly to sub-hourly observations of air quality relevant pollutants (NO2, HCHO, O3, SO2, and aerosols) during daylight hours at neighborhood scales, 2 km × 4 km. These observations will provide an unprecedented view of air pollution across the U.S. in a mode similar too how the GOES weather satellites informed weather research and application with hourly and sub-hourly observations and are expected to greatly enhance our understanding of air quality. Anticipated research and application areas include long range pollutant transport, hot spot identification and network design, emission inventory evaluation, and exposure assessment. To aid in more routine and systematic validation and interpretation of TEMPO observations, researchers in EPA-ORD are involved in developing capacity for correlative geophysical measurements for validation of baseline Level 2 TEMPO data products. In collaboration with the NASA, European Space Agency (ESA), and State and Local agencies, ORD is a partner in establishing a network of ground-based remote sensing instruments (Pandora spectrometers) across the TEMPO field-of-view. Pandora spectrometer current data products include atmospheric column trace gas amounts of nitrogen dioxide (NO2), formaldehyde (CH2O), and ozone (O3), along with profiles in the boundary layer for NO2 and HCHO. When operated at the surface in a direct-sun observing mode, Pandora retrievals have much smaller uncertainties than retrievals from satellites, making the instrument a good candidate for data validation measurements. The Pandonia Global Network (PGN) and pandora spectrometer helps fill a critical gap on satellite data product validation and serves as key measurement for on-going routine and systematic validation for TEMPO Level 2 geophysical data products.

This talk will present EPA-ORD strategic approach to help EPA, state and local agencies on the incorporation of satellite and remote sensing data products to better meet additional air quality data information needs, by focusing on improved validation of satellite products, including at key locations within the national air quality monitoring network. This talk will discuss the TEMPO mission, including Level 2 data product validation plans, and the key role of the pandora spectrometer. This work is in part through the EPA-ORD Air, Climate, and Energy national research program.

Resolving the effect of roadside green infrastructure on near-road air quality in a multi-regime modeling framework
Authors: Khaled Hashad a, Jonathan T. Steffens a , Richard W. Baldauf b,c, David K. Heist b, and K. Max Zhang a

a Sibley School of Mechanical and Aerospace Engineering, Cornell University, Ithaca, NY 14853, USA
b Office of Research and Development, U.S. Environmental Protection Agency, Durham, NC, USA
c Office of Transportation and Air Quality, U.S. Environmental Protection Agency, Ann Arbor, MI, USA

Communities located in near-road environments experience elevated levels of traffic-related air pollution. Roadside green infrastructure such as trees, hedges, and bushes may help reduce pollution levels through enhanced deposition and mixing. Gaussian-based dispersion models are widely used by policy makers to evaluate mitigation strategies and develop regulatory actions. However, vegetation barriers are not included in those models, hindering air quality improvement at the community level. The main modeling challenge is the complexity of the deposition and mixing process within and downwind of the vegetation barrier. We propose a novel multi-regime Gaussian-based model that describes the parameters of the standard Gaussian equations in each regime to account for the physical mechanisms by which the vegetation barrier deposits and disperses pollutants. The four regimes include the vegetation, a downwind wake, a transition, and a recovery zone. For each regime, we fit the relevant Gaussian plume equation parameters as a function of the vegetation properties and the local wind speed. Furthermore, the model captures particle deposition, a major factor in pollutant reduction by vegetation barriers. We parameterized the multi-regime model using data generated from a fields-validated computational fluid dynamics (CFD) model, covering a wide range of vegetation properties and meteorological conditions. The proposed multi-regime Gaussian-based model was evaluated across 9 particle sizes and a tracer gas to assess its capability of capturing dispersion and deposition. The multi-regime model's normalized mean error (NME) ranged between 0.18-0.3, the fractional bias (FB) ranged between -0.12-0.09, and R2 value ranged from 0.47-0.75 across all particle sizes and the tracer gas for ground level concentrations, which are within acceptable ranges for air quality dispersion modeling. Even though the multi-regime model is parameterized for coniferous trees, our sensitivity study indicates that it can provide useful predictions for hedges/bushes vegetative barriers as well.

Improving the Computational Performance of Air Quality Models with Machine Learning Tools
Obin Sturm and Anthony Wexler

There is a growing community of researchers who are using machine learning tools to improve the computational speed and performance of chemical transport models at scales ranging from regional air quality to global climate change. Speed improvements can lead to improved accuracy if finer grid scales can be used or if the model can be run for longer times that encompass more conditions. Some of the machine learning efforts are purely aimed to increase speed, others incorporate physics and chemistry into the algorithm and yet others strive for both. The field has been around for 25 years, so is not really in its infancy, but the recent growth in the community of investigators working in this area is promising for breakthroughs that may alter how we think about air quality modeling. In this talk, I will review a few efforts of ours in this burgeoning area.

Anthony Wexler is distinguished professor of Mechanical and Aerospace Engineering, Civil and Environmental Engineering and Land, Air and Water Resources at UC Davis. He is also director of the Air Quality Research Center. He employs theory, mathematical modeling and laboratory and field measurements to understand the atmospheric processes that lead to the health effects of air pollution particles.

The WUDAPT approach to multi-scale intra-urban atmospheric modeling and analyses applications.

Jason Ching, Gerald Mills, Matthias Demuzere, Fei Chen, Benjamin Bechtel, Daniel Aliaga, Michael Wong, Dev Niyogi, Sarav Arunachalum

While urban areas occupy 3% of the total land coverage, and home to more than half the world’s population; its influence is significant based on its role and impact on climate change as well as to its local weather, the latter due to the influence of urbanization factors that contribute to the structure and dynamics of the urban boundary layer. Advanced meteorological models of the urban boundary layer now include canopy layer physics and urban canopy parameters (UCPs) to represent the aggregate effects of the underlying morphological elements of its built environment. to predicting weather impacts at intraurban scales. The NUDAPT was an initial prototype of generating UCP inputs for mesoscale weather models for the USA. It scope was extended to providing UCPs for cities on a global scale by an Initiative in 2012 of the urban climate community called WUDAPT (World Urban Database and Access Portal Tools. WUDAPT goal was to generate consistent information on urban form and function for cities worldwide that can support “fit for purpose” (FFP) scale dependent urban weather, climate, hydrology and air quality analyses and model applications. WUDAPT proceeded in two stages; the initial stage employed the Local Climate Zone classification scheme where each city is mapped into 10 universal Local Climate Zone (LCZ) classes; with range of UCPs values for each LCZ class. During this WUDAPT decade, the community has generated city specific LCZ maps, then geo-specific cities within regional maps and finally, as global LCZ maps, land use in non-urban areas employing 7 other LCZ classes. Advanced WUDAPT efforts provide an augmentation to the table lookup UCPs option with gridded UCPs based on NUDAPT prototype and paradigm; facilitated with cyber methodologies, called Digital Synthetic City (DSC) for generating grid specific scale dependent form based UCPs and an innovation using an Urban Building Energy Models (UBEM) based upon building dictionary metadata characteristics of building materials, energy usage for building archetypes representative of the city. A UCP tool has been devised to generate gridded UCPs; available on the WUDAPT Portal to facilitate running a variety of Fit for Purpose (FFP) applications based on availability of gridded UCP. Opportunities for collaborations with CMAS community using WUDAPT products are highlighted.

Updates on VERDIs New Functionality and Development

Daiwen Kang, Donna Schwede, Jerry Herwehe, Tony Howard, Yadong Xu, Liz Adams, Cody Simmons, Heidi Paulsen, and Steve Beaulieu

The Visualization Environment for Rich Data Interpretation (VERDI) is an important tool for visualization and analysis of the Community Multiscale Air Quality (CMAQ) model, Advanced Air Quality Modeling System (AAQMS), and various model input/output and observational data. While the existing features are continually enhanced across different computing environments, additional capabilities have been developed to meet the needs of new model applications and to adapt new observational datasets. For instance, the 3-D fields and curtain plots from model simulations are recently available and aircraft measurements as overlay features on top of model simulations are being implemented. Various satellite products such as OMI/TROPOMI O3 and NO2 columns will be handled by VERDI to compare with model simulations either through side-by-side visualizations or through difference plots. This presentation will showcase the various new features in VERDI and propose the future development.

A Machine Learning Approach to Quantify the Impact of Meteorology on Tropospheric Ozone in the Inland Empire, CA

Khanh Do, Manasi Mahish, Arash Kashfi Yeganeh, Ziqi Gao, Charles L. Blanchard, Cesunica E. Ivey

In air quality compliance, deterministic regulatory models are traditionally used for decision-making. However, by taking the advantage of historical air quality data spanning over several decades, we created a machine learning (ML) model that identified patterns of the natural processes, and with the model, we explored the links between meteorology, emissions, and ozone concentrations. We analyzed large meteorology and air quality datasets to train the ML models to enhance the prediction of peak hour (12:00-5:00 PM) ozone levels in the South Coast Air Basin (SoCAB), investigate the role of meteorology in peak ozone levels, and determine the factors influencing ozone exceedance hours. We used Random Forest Regression (RFR) with seven meteorological features from Los Angeles and Ontario International Airport stations and two air quality features from the Fontana, CA air monitoring station to train the model to estimate ozone concentrations in Fontana. Models were applied for data from 1994 to 2018. K-nearest neighbor was used as a binary classification to predict the ozone exceedances when the concentrations are greater than 70 ppb. In comparison with the ML model, we ran a high-resolution CMAQ simulation over Southern California to simulate peak hour ozone concentrations in the SoCAB. The correlation of the RFR model with CMAQ was 0.92, and the probability of detection for ozone exceedances was 0.81 for the most recent years of the analysis (2014-2018) using k-nearest neighbors. Overall, the RFR model had better statistical evaluation metrics. Both CMAQ and RFR overestimate ozone levels; however, the RFR model has a smaller bias, which ranges from 3.9 ppb to 10 ppb, while the CMAQ mean bias ranges from 11 ppb to 22 ppb. The R2 for the CMAQ simulation was 0.58, and the peak hour ozone concentrations were overestimated. The RFR model gave a better performance with an R2 of 0.85, a slope of 0.98, and an intercept of 7 ppb. A good ML model with appropriate algorithms provides scientists an alternative method to study air quality and mitigation, as well as accelerate research with a faster simulation time using a trained model.

The Impact of Altering Emission Data Precision on the Community Multiscale Air Quality Model

David Wong and Michael Walters

In this annual study, we will examine the altered emission by keeping a specific number of significant digits (between 3 and 5) on data compression as well as model accuracy.  

GPU-Assisted CMAQ Simulations

Khanh Do, George Delic, Bryan Wong, Cesunica Ivey

The computational efficiency of CMAQ largely suffers from solving a set of stiff differential equations when computing the gas phase chemical concentrations. For example, with the SAPRC07 chemical mechanism, the systems of photochemical reactions are computed using Euler Backward Iteration, SMV Gear, or Rosenbrock solver for every time step and grid cell (row x column x height) for all species in the SAPRC07 family until a specified convergence tolerance is met. In our latest work, we reduced the overall simulation time of CMAQ by porting the intensive computational process (CHEM solver) onto graphics processing units (GPUs). With thousands of Compute Unified Device Architecture (CUDA) cores in a single GPU, a large number of independent arithmetic operations can be carried out simultaneously. We have explored the computer architecture, the advantages, and the disadvantages of GPU programming. We successfully converted the partial derivative (grpderiv.F), decomposition (grdecomp.F), and back substitution (grbacksub.F) subroutines of the SMVGEAR solver to the CUDA platform. Our CPU-GPU version of the CMAQ model is tested with SAPRC07 for a simulation over Southern California with 102 x 156 x 11 grid cells. The GPU kernel is more than an order of magnitude faster than the conventional code with the same number of operations and with a BLKSIZE of 7,000. However, the overall performance of CMAQ declines due to the big BLKSIZE and the time for transferring data between the host (CPU) and the device (GPU). The use of GPUs in air quality modeling can be beneficial for large-scale simulations, given proper optimizations of subroutines and data transfers.

Community Multiscale Air Quality (CMAQ) Modeling and Analysis on the Cloud
Elizabeth Adams, Carlie Coats, Christos Efstathiou and Saravanan Arunachalam
UNC Institute for the Environment

Mark Reed, Robert Zelt and John McGee
UNC Information Technology Services

Cloud computing offers the CMAS community the ability to run high resolution CMAQ applications on high performance computing systems that they may not have access to within their organization. The Amazon Web Services (AWS) or Microsoft Azure (MSA) Cloud resources may be configured to be the equivalent of a High-Performance Computing (HPC) environment, including using job schedulers such as SLURM, running on multiple nodes using code compiled with Message Passing Interface (MPI), and reading and writing output to a high performance, low latency shared disk. This presentation will focus on two recent advances from the CMAS Center - updates to running CMAQ on the two cloud computing environments and a new data warehouse for distributing CMAQ datasets.

The CMAS Data Warehouse is now available to the public through the AWS Open Data Sponsorship Program (ODP) and currently hosts the data for the 12-km x 12-km horizontal resolution Continental U.S. (ConusUS2) domain for the benchmark suite used in our application. This allows for a more widespread sharing and use of high-value cloud-optimized datasets, by eliminating the cost of storage and waiving egress fees. We have plans to migrate a longer time-series of data to AWS ODP to support the CMAS community.

We installed and ran CMAQ v5.3.3 for the ConusUS2 Benchmark suite, both in AWS and MSA systems using a variety of different cloud configurations - ranging from single-node Virtual Machines (VMs) to multiple node environments such as the AWS Parallel Cluster and MSA CycleCloud environments. We also explored different hardware configurations both for the CPUs and the storage disks that were used for the computing and developed detailed hands-on tutorials for the CMAS user community and performed scaling tests on both environments. The AWS Parallel Cluster tutorial is available at https://pcluster-cmaq.readthedocs.io/en/latest/index.html. The MSA CycleCloud tutorial is available at https://cyclecloud-cmaq.readthedocs.io/en/latest/. Both tutorials include methods to perform scalability tests, verify the results, and conduct performance analysis of different CMAQ components.

A comparison of the performance on the MSA CycleCloud versus the AWS Parallel Cluster shows scalability up to 120 processors (AWS maxes out on 96) on a single VM (Virtual Machine) using AMD EPYC processor for the ConusUS2 domain benchmark case. The results corroborate with the evolution of processing power and its translation to HPC offers from each provider, and in general are comparable between the two systems and different configurations, with costs following a similar pattern that aligns more closely with CPU efficiency. AMD EPYC processor tier (120HBv3 on MZA and HPC6a.48xlarge on AWS) impressive price/performance benefits make it an ideal choice to power CMAQ applications compared to the Intel Xeon offerings in both environments. Creating and sharing reproducible workflow methods for installing and building CMAQ for the 12-km x 12-km ConUS benchmark cases using the Parallel Cluster and Azure Cycle Cloud will allow the CMAS community to learn how to provision and easily migrate to the best available resources, accurately forecast CMAQ modeling run time and storage requirements, and further create reliable cost estimates for performing air quality modeling in the cloud for their individual use cases.

CMAQ 5.3 PARALLEL PERFORMANCE FOR Q4 2016

George Delic, HiPERiSM Consulting, LLC, P.O. Box 569, Chapel Hill, NC 27514

This presentation continues with a review of thread parallel performance results for CMAQ 5.3 for the last quarter of 2016 (92 days). Attention is focused on the Rosenbrock and EBI solvers in the Chemistry Transport Model (CTM). Both FSparse [1] and the legacy JSparse [2] algorithms are compared. This year’s results used 16 MPI processes and 12 OpenMP threads of parallelism in the CTM and the horizontal advection science procedures (HADV). Data for the CONUS domain was provided by the U.S. EPA [3]. Both the legacy (EPA) JSparse and the FSparse thread parallel versions are compared in a hybrid MPI+OpenMP model on a heterogeneous cluster of 13 nodes using a total of 192 cores. Observed speedup with the FSparse version was 1.2 for EBI and 1.3 for the Rosenbrock solver, respectively, showing a strong improvement over last year’s 8 thread results. Following a ongoing upgrade to 330 cores the full 2016 year scenario should be completed in the next year with 24 MPI processes.

[1] G. Delic, Modern Environmental Science and Engineering, Vol. 5, Nr.9, 2019, pp. 775-791. Full text available at: https://www.researchgate.net/publication/338581080_A_Thread_Parallel_Sparse_Matrix_Chemistry_Algorithm_for_the_Community_Multiscale_Air_Quality_Model

[2] M. Jacobson and R.P. Turco (1994), Atmos. Environ. 28, 273-284.

[3] The author gratefully acknowledges help from Kristen Foley (EPA), Ed Anderson (GDIT), and Elizabeth Adams (UNC) in providing model data and resolving implementation issues.

Scaling NWP workloads on AWS to achieve your research goals
Timothy Brown
Amazon Web Services
tpbrown@amazon.com

ABSTRACT

The use of cloud computing technologies within HPC has grown considerably over the last few years. With these advances there's more options on how to run Numerical Weather Prediction (NWP) than ever. In this talk we distill the options and show how researchers can get started in an environment they're familiar with. We'll discuss cluster orchestration with AWS ParallelCluster and Slurm, parallel filesystem with Amazon FSx for Lustre, high performance networking with Elastic Fabric Adapter (EFA), software management with Spack and then we'll present scaling and cost analysis of NWP on AWS HPC6a (AMD Milan).

ABOUT THE AUTHOR

Timothy Brown is a Principal Solutions Architect for Compute & HPC at AWS. He has 15 years of HPC experience, spanning different roles related to compute, storage and network optimizations, with a focus on numerical weather prediction. Prior to AWS, Timothy was a Software Engineer at Spire Global. Timothy holds an MSc and BSc (Hons) from UWA, Australia.

Evaluating and Comparing the Effects of WRF Setup Variables

Corey L. Smithson, Natalie White, Hans Klomp, Bradley R. Adams

The increasing urbanization of the greater Salt Lake City area (GSLA) has contributed to the development of an urban canopy over the GSLA. This canopy refers to the effects of building profiles, varying land surface properties and anthropogenic heating on local meteorological conditions including temperature, humidity, and wind velocity. These meteorological conditions affect concentrations of pollutants such as PM2.5 and ozone. The Weather Research and Forecasting (WRF) model can be used to model these meteorological conditions in and around urban areas using an urban canopy model (UCM). UCMs use parameters associated with differing urban classifications to approximate the effects of building profiles, varying land surface properties and anthropogenic heating on a mesoscale.

Running a WRF simulation using a UCM requires a wide range of inputs, including various physics schemes, grid specifications, simulation time, land use classifications and other parameters. These inputs can have a significant impact on predicted values. For example, varying the number of urban classifications resulted in 10-degree differences in predicted 2-meter temperature in downtown Salt Lake City during the night. This shows that identifying appropriate inputs is critical to developing an accurate model. This study identifies the effects of adjusting some of these variables for the GSLA, including grid resolution, domain size, domain location, simulation lead time, land surface model, and the number of urban classifications used. Temperature, wind, and relative humidity comparisons were also made for simulations conducted with and without a UCM.

As inputs are varied, predicted meteorological conditions are compared to identify which inputs the model is most sensitive to. This can help researchers understand which inputs require more careful consideration when developing a model. Additionally, the results of the simulations are compared to existing meteorological measurements to identify which input values should be used for the GSLA. The results of this study are also compared to results of other studies conducted for other regions and models to inform how input sensitivities can change from one region to another.

Use of NOAAs Global Forecast System Data in the Cloud for Community Air Quality Modeling

Patrick C. Campbell, Weifeng (Rick) Jiang, Sonny Zinn, and Zachary Moon

Here we describe our initial implementation of the NOAA-EPA Atmosphere-Chemistry Coupler (NACC) in the cloud (“NACC-in-the-Cloud”), which allows the air quality modeling community to on-demand process NOAA’s next operational Global Forecast System version 16 (GFSv16) meteorology and output model-ready meteorological files needed to drive the widely used Community Multiscale Air Quality (CMAQ) model for any region across the world. NACC is adapted from the U.S. EPA’s Meteorology-Chemistry Interface Processor version 5 (MCIPv5), and is being used as the primary model coupler in the current operational NWS/NOAA National Air Quality Forecast Capability (NAQFC). The GFSv16 gridded (NetCDF) input data is very large (~ 10s of TBs/month), and thus it is very cumbersome to use basic transfer and pre-processing tools for usage in the scientific modeling community. Therefore, the use of the cloud in this work is critical to provide the scientific community streamlined access to NOAA’s operational GFSv16 data to facilitate user-defined NACC processing and download of model-ready, meteorological input for any regional CMAQ domain worldwide. Cloud computing and storage platforms are desirable as they are highly-customizable, on-demand, and much more scalable than traditional local servers. Such a cloud interface for GFS-driven CMAQ applications is not currently available, and this capability is advantageous to the air quality modeling community and beyond. The main project tasks and progress described in our presentation include the following: 1) cloud environment setup and GFSv16 data transfer/storage to the cloud, 2) migration of NACC software to cloud-High Performance Computing (HPC) platform. 3) development and demo of the NACC-in-the-Cloud web-based user interface for the CMAQ community. 4) availability of a “Google Form” for the CMAS community to gauge interests in using NACC-in-the-Cloud for GFS-driven CMAQ applications globally, and 5) future work and challenges. All of the NACC-in-the-Cloud development, tests, and prototypes are based on the Amazon Web Services (AWS)-HPC platform (specifically, AWS ParallelCluster 3.0). We envision that the GFSv16 and NACC-in-the-Cloud product will have implications for NOAA and the weather and air quality modeling research community.

CMAQv5.3-hyd: a novel model to compute numerically exact first- and second-order sensitivities in CMAQ

Jiachen Liu, Eric Chen, Ryan P. Russell, Shannon L. Capps

Sensitivity analysis methods in chemical transport models help researchers understand how output variables will change with respect to input parameters. They can also help determine the robustness and uncertainties in chemical transport models. Traditional methods of calculating forward sensitivities involve the finite-difference method (FDM) and the decoupled direct method (DDM). The finite-difference approach suffers from truncation and subtractive cancellation error. The direct decoupled method requires developers to write new sensitivity equations when there is an update to the original model. Here, we demonstrate the development of the hyperdual-step method to a column model version of the Community Multiscale Air Quality Model (CMAQ) version 5.3.2 to develop CMAQv5.3.2-hyd. CMAQv5.3.2-hyd can compute numerically exact first- and second-order sensitivities of outputs with input parameters with a single run. We illustrate the model with the newly implemented gas-phase chemistry and biogenic aerosol formation mechanism (CB6R3_AERO7) in CMAQ version 5.3.2. The first- and second-order sensitivities calculated with CMAQv5.3.2-hyd are compared with those computed using FDM and a hybrid FDM-hyd method. We also demonstrate the importance of computing the second-order sensitivities by showing how they affect our understanding of specific processes. Future work will focus on reducing the computational cost and dealing with the asymptotic behavior of calculated sensitivities.

Open-innovation and Open-development Framework for the Unified Forecastsystem - An EPIC Approach
Maoyi Huang
EPIC Program Manager, Weather Program Office, National Oceanic and Atmospheric Administration, Silver Spring, MD 20910

The National Oceanic and Atmospheric Administration (NOAA) established the Earth Prediction Innovation Center (EPIC) to be the catalyst for community research and modeling focused on informing and accelerating advances in our nation's operational NWP forecast modeling systems. Initially, EPIC focuses on the Unified Forecast System (UFS). The Unified Forecast System (UFS) is a community-based, coupled, comprehensive Earth modeling system. The UFS numerical applications span local to global domains and predictive time scales from sub-hourly analyses to seasonal predictions. It is designed to support the Weather Enterprise and to be the source system for NOAA's operational numerical weather prediction applications, including the Rapid-Refresh Forecast System (RRFS) - Multiscale Air Quality (CMAQ) model system to enhance the high resolution forecasting capability of the National Air Quality Forecast Capability (NAQFC). EPIC applies an open-innovation and open-development framework that embraces open-source code repositories integrated with automated Continuous Integration/Continuous Deployment (CI/CD) pipelines on cloud and on-prem HPCs. EPIC also supports UFS public releases, tutorials and training opportunities (e.g., student workshops, hackathons, and codesprints), and advanced user support via a virtual community portal (epic.noaa.gov). This framework allows community developers to track the status of their contributions, and facilitate rapid incorporation of innovation by implementing consistent and transparent, standardized and community-driven validation and verification tests. In this presentation, I will demonstrate capabilities in the EPIC framework that could benefit the air quality modeling community.

Spray aerosol emissions parameterizations in the CMAQ modeling system

Charbel Harb; Xinyue Huang; Hosein Foroutan

Spray aerosols, specifically sea and lake spray aerosols (SSAs and LSAs) emitted from saltwater and freshwater breaking waves, respectively, can potentially impact radiative properties, chemical compositions, and biodiversity of the atmosphere in regional-to-global scales. Depending on their size, morphology, and physiochemical properties, these particles may serve as cloud condensation nuclei (CCN) and ice nucleating particles (INPs), provide a medium for heterogeneous reactions of oxidants and radicals, and transfer microbes and viruses to the atmosphere. Using a consistent methodology coupling measurements of foam area and size-resolved spray aerosol emission flux in a wave breaking analogue tank, we developed SSA and LSA emissions parameterizations suitable for regional-to-global atmospheric chemical transport models. The developed SSA emissions model was compared to a collection of common parameterizations from literature as well as field measurements and equal or improved performance was observed. Not surprisingly, LSA emissions were one to two orders of magnitude lower than those of SSA. The newly developed emission parameterizations were then implemented in the Community Multiscale Air Quality (CMAQ v5.3) modeling system to simulate SSA and LSA emissions in a domain encompassing the conterminous United States. Simulation results were compared to the default CMAQ model as well as the observations from air quality network sites. Simulated fine SSAs in the updated CMAQ were significantly lower than those simulated by the default model. Furthermore, incorporating LSA emissions in the model led to a significant, as high as 100%, increase in PM10 concentrations over and around the Great Lakes. LSA particles can get transported hundreds of kilometers inland although concentrations rapidly decrease with distance from the source.

Use of Isotopic Analysis and CMAQ Modeling to Categorize Dust Emission Sources in Utah

Zachary Lawless, Micah Marcy, Bradley Adams, Greg Carling

Categorization of dust emission sources can improve understanding of local air quality impacts and guide land use planning. Both detailed dust modeling and dust sample measurements have limitations that can affect accuracy and/or completeness. Combining these techniques provides the confidence of real dust measurements with the completeness of modeling. This paper illustrates this combined source categorization approach for Utah dust events in 2009 and 2020. Geochemical measurements of dust samples based on Sr isotope ratios (87Sr/86Sr) have been used to “fingerprint” major dust sources in Utah. An archived 24-hr PM10 filter sample collected on August 7, 2009 in Salt Lake City was analyzed and found to contain a significant mass of dust, suggesting a dust event may have occurred on this day. Analysis also showed a relatively high 87Sr/86Sr ratio of 0.7122, suggesting a notable fraction of the dust originated from the dry lakebed of the Great Salt Lake (GSL). A WRF-CMAQ dust simulation showed a strong storm front moving west to east through the GSL area on August 7, 2009. Predictions for this period showed elevated dust concentrations originating west of the GSL and being transported over the Salt Lake City site. A second simulation of a September 8, 2020 dust event showed that dust initially originated in the Sevier Dry Lake area and was carried northwest to a dust sample site at BYU in Provo, Utah. During this period, storm conditions shifted winds to south-southeast and changed emissions to the GSL desert, and transported the dust south and east, including lower concentrations over BYU. Analysis of a multi-day dust collection filter at BYU suggested dust samples originated from both Sevier Dry Lake and GSL sources, consistent with model predictions. These examples illustrate the benefits of using isotopic measurements to confirm more detailed model predictions and categorize dust sources.

Development and Evaluation of an Advanced Aerosol Dry Deposition Model

Bin Cheng¹, Kiran Alapaty², Qian Shu², and Saravanan Arunachalam¹

¹Institute for the Environment, The University of North Carolina at Chapel Hill, Chapel Hill, NC

²U.S. Environmental Protection Agency, Research Triangle Park, NC

Magnitude of atmospheric turbulence, a key driver of several processes that contribute to aerosol (i.e., particle) deposition, is typically underrepresented in current models. Various formulations have been developed to model particle dry deposition; all these formulations mostly rely on friction velocity and some use additional ad hoc factors to represent enhanced impacts of turbulence. However, none were formally linked with the three-dimensional (3-D) turbulence. Here, we propose a set of 3-D turbulence-dependent resistance formulations for particle dry deposition simulation and intercompare the performance of the new resistance formulations with that obtained from using the existing formulations and measured dry deposition velocity. Turbulence parameters such as turbulence velocity scale, turbulence factor, intensity of turbulence, effective sedimentation velocity, and effective Stokes number are newly introduced into two different particle deposition schemes to improve turbulence representation. For an assumed particle size distribution, the newly proposed schemes predict stronger diurnal variation of particle dry deposition velocity and are comparable to corresponding measurements while existing formulations indicate large underpredictions. We also find that the incorporation of new turbulence parameters either introduced or added stronger diurnal variability to sedimentation velocity and collection efficiencies values, resulting in higher deposition values during daytime and nighttime predicted by the new schemes when compared to existing schemes. The findings from this research may help improve the capability of dry deposition schemes and help foster the development of a community dry deposition modeling system for use in regional and global models.