Session Presentations

Modeling the Contribution of Long-Range Transport to Nitrogen Deposition in U.S. Hydrological Regions

Sharmin Akter ^[1] , Michael Crowl ^[2], and Kristina Wagstrom ^[2]

The excessive deposition of atmospheric nitrogen containing species to aquatic systems and watersheds can lead to harmful algae growth and loss of biodiversity, particularly in coastal waterways. It increases the risk of acidification and hypoxia by reducing oxygen levels for living organisms in marine environments. It is important to determine the major species, source sectors, and source regions responsible for atmospheric nitrogen deposition to develop effective watershed management systems. We use the Comprehensive Air Quality Model with extensions (CAMx) version 6.0, along with the Particulate Matter Source Apportionment Technology (PSAT), to identify and separate source region contributions to atmospheric nitrogen deposition. We model the amount of atmospheric nitrogen deposition from each of the 18 Hydrologic Regions (2-digit Hydrological Unit Code) in the United States and southern Canada and northern Mexico. CAMx+PSAT provides the source-receptor relationships for atmospheric nitrogen concentrations and deposition. We use emissions, meteorology, boundary conditions, and ozone column inputs from the United States Environmental Protection Agency's 2011 Modeling Platform. We evaluate the dry and wet deposited mass of atmospheric nitrogen containing species against measurements. This information will aid environmental regulators in developing watershed management plans to protect the health of aquatic and terrestrial ecosystems. 

Investigating the influences of dust storms on precipitation in Iran using WRF-CHEM model

Mastooreh Ameri^a, Khosro Ashrafi^a, Sarmad Ghader^b, Mohammad Amin Mirrezaei^a

^a School of Environment, College of Engineering, University of Tehran, Tehran, Iran

^b Space Physics Department, Institute of Geophysics, University of Tehran, Tehran, Iran

Aerosols affect the climate system in various ways, including direct reflection, absorbing incoming or outgoing short or long-wave radiation, or affecting cloud albedo and precipitation processes. Sand and dust storms are common meteorological hazards in arid and semi-arid regions. In this study, the main focus is on the role of dust storms on Iran's precipitation patterns. Dust storms originating from the west and southwest of Iran have the ability to affect weather through the changes in atmospheric humidity. These impacts are addressed using the Weather Research and Forecasting model coupled with chemistry (WRF-Chem 3.6.1). First, by comparing the Air Quality Monitoring stations of 30 provinces of Iran, Synoptic data of the capital cities and satellite maps of Aqua/AIS and MEYYA-2 satellites, three four-day periods of dust storm events were selected which coincide with rainfall. Also, the extensive distribution of precipitation through the domain was considered in determining the spatial-temporal precipitation patterns. Moreover, WRF physics and precipitation parameters have been chosen based on the appropriate schemes for Iran. In order to improve the accuracy of the simulation, the latest modification of microphysics and Cumulus parametrization schemes that have great importance for precipitation predictions has been applied. Second, the simulation variables such as temperature, pressure, wind speed and direction were validated by station reports. There was a reasonable positive correlation between simulated and observed data. The results of WRF-CHEM simulations depicted that in most diagrams, there is often a decline in the amount of precipitation, especially at the end of dust storm periods, in comparison to the WRF run series. Considering the aerosol-cloud feedbacks, the mean cumulative precipitation for the dust episodes during March, April and May were changed respectively from 12.45 mm to 12.84 mm, 31 mm to 30.86 mm and 8.79 mm to 7.78 mm. Also, the highest anomaly in total cumulative precipitation values was 0.8 mm, which was observed in the last day of May episode. Besides, the predicted PM10 and PM2.5 values in western and southwestern regions such as Ahvaz, Zabol and Genaveh were less than observed values, which is acceptable, as these regions are exposed to high levels of dust storms. Moreover, at the early stages of the dust storm, there is a decline in values of precipitation caused by radiative forcing and humidity reduction and then, rainfall rises due to indirect effects including, CCN activation. Finally, the results revealed that aerosols play a pivotal role in precipitation patterns and the dominant impact is reducing the rainfall amounts, especially in the regions which are severely affected by dust storms.

TDep Measurement Model Fusion (MMF) method to fuse modeled and measured air quality data to estimate total deposition with Python geoprocessing

Shih Ying Chang¹, Nathan Pavlovic¹, Greg Beachley², Melissa Puchalski², and Christopher Rogers³

¹ Sonoma Technology, Inc., Petaluma, CA

² U.S. Environmental Protection Agency, Washington, D.C.

³ Wood Environment & Infrastructure Solutions, Inc., Jacksonville, FL.

To assess spatial and temporal trends in annual atmospheric deposition, the Total Deposition Science Committee (TDep) under the National Atmospheric Deposition Program (NADP) developed a measurement-model fusion (MMF) method to estimate fluxes of total, wet, and dry deposition of sulfur, nitrogen, base cations, and chloride. The TDep method provides interpolated maps of annual deposition from the year 2000 to the present, using a modeling approach that fuses the Community Multiscale Air Quality (CMAQ) model data with data from ambient air quality and wet deposition monitoring sites. Due to its large spatial coverage, fine spatial resolution (i.e. 4 x 4 km²), and multiple data sources, the TDep approach involves complex geoprocessing steps that require an organized and iterable implementation to support ongoing data production and future process enhancements. In an effort to modernize and streamline the method and facilitate the development of new features, a new modularized Python geoprocessing TDep application was developed. The TDep dry deposition estimates are constructed by combining measurement-adjusted, CMAQ-modeled dry deposition fluxes with "measured" dry deposition fluxes cast as the product of spatially resolved CMAQ-modeled deposition velocities and ambient pollutant concentrations measured in the Clean Air Status and Trends Network (CASTNET). The TDep wet deposition estimates are constructed by first fusing an annual modeled precipitation field (Parameter-elevation Regression on Independent Slopes Model; PRISM) with precipitation data measured by NADP networks (National Trends Network; NADP/NTN and Mercury Deposition Network). The fused precipitation grid is combined with annually aggregated NADP/NTN precipitation chemistry measurements to calculate the wet deposition. Wet and dry deposition grids are combined to estimate the total annual deposition of pollutants. In this presentation, we will focus on the methodology of the TDep MMF method and the development and structure of the updated Python geoprocessing application. The developed Python framework breaks down the TDep approach into six modules: (1) data ingestion, (2) data interpolation, (3) bias adjustment for modeled data, (4) data fusion, (5) data aggregation, and (6) product export. Through the modularized framework, the Python application has greatly reduced the complexity of the codebase and will support streamlined processing. The new TDep framework will facilitate future improvements to the TDep MMF method, such as inclusion of satellite data or CMAQ-modeled wet deposition to improve the accuracy of interpolation for locations without available monitoring sites.

Source apportionment of ozone under different synoptic patterns in the Pearl River Delta region
Wanying CHEN, Yiang CHEN, Xingcheng LU, Jimmy C.H. FUNG*

In recent years, with the effective control of fine particulate matter (PM2.5) by the government, the concentration of PM2.5 in the Pearl River Delta (PRD) region has decreased gradually. However, the pollution problem characterized by a high concentration of ozone is sequentially emerging. In the case of relatively stable emissions, the meteorological condition is an essential factor affecting ozone pollution. Therefore, exploring the contribution of various source regions and source categories to the O3 concentration under different synoptic patterns is an integral part of regional atmospheric environmental research. In this work, the Comprehensive Air Quality Model with extension (CAMx) modeling system with Ozone Source Apportionment Technology (OSAT) module was applied to analyze the influence of particular synoptic patterns, including sea high pressure, equalizing pressure field and subtropical high pressure, on ozone concentration and source contribution covering the PRD region. The results showed that the concentration of ozone increased under three meteorological conditions. Under distinctive synoptic patterns, the emissions outside PRD have the most significant contribution invariably. Preceded by the cross-boundary transport, mobile and biogenic sources attribute to the highest contribution under subtropical high pressure and sea high pressure, respectively. With the effect of the subtropical high pressure, the local contribution in the PRD region increased significantly, especially in Guangzhou (+7.9%) and Huizhou (+6.3%), compared with the monthly mean contribution. As the equalizing pressure field changed the direction of the southernly prevailing wind in April, the contribution of emissions outside the PRD increased by 6.3%, compared with the monthly contribution. Our results indicated that collaborative emission control measures should be strengthened with the surrounding area. Combined with the meteorological situation, controlling the endogenous emission in the PRD plays a pivotal role in preventing O3 pollution.

Implementing satellite NO2 data assimilation in CMAQ for identifying emissions biases and improving regional boundary conditions

James East, Barron Henderson, Shannon Koplitz, Sergey Napelenok, Fernando Garcia Menendez, Allen Lenzen, Brad Pierce

We adapt a composition assimilation framework to identify potential biases in NO_X emissions on the regional and hemispheric scale. The assimilation framework was developed as a part of NASA HAQAST and can be used to improve model agreement with satellite retrievals directly or to infer emissions updates. Assimilated results are useful for hemispheric-scale modeling as boundary conditions for free running simulations, while emissions inferences are valuable for emissions quality assurance and rapid emission updates at either scale. The results in this presentation will focus on inferring emissions updates, which can subsequently be used in free running simulations.

We apply a 3-D variational data assimilation technique in CMAQ over the continental US (CONUS) to fuse satellite-based NO₂ observations measured by OMI and TROPOMI with modeled NO₂. We relate the analysis increment (the difference between the assimilated and unassimilated total NO₂ vertical column) to emissions biases through a sensitivity parameter to infer spatially resolved monthly average emissions changes.

On the regional scale over the US, we find that modeled NO₂ columns have a small but ubiquitous low bias compared to satellite NO₂, but that much of this difference occurs in the upper troposphere. This leads to an iterative inference of lightning NO production rates first and then anthropogenic emissions. After accounting for lightning NO, a priori anthropogenic emissions generally produce modeled columns that match the satellite-infused column and do not require updating. However, over some urban areas and shipping channels, we infer small emissions increases on the order of 2-3%. There are larger widespread analysis increments in the northwestern US and southern Canada, which may be attributable to uncertainties in soil NO_X emissions.

Results suggest possible biases on natural and anthropogenic emissions inside the CONUS domain. In addition, these results lay the framework for assimilation on the hemispheric scale. The presentation will introduce initial hemispheric results of the analysis increment and top-down constraints on uncertain emissions outside the CONUS domain that are currently underway.

Estimating Societal Damages from Aviation Emissions in Canada

Tugce Erdem, Yasar Burak Oztaner, Marjan Soltanzadeh, Amir Hakami, and the Adjoint Development Team

Aviation emissions contribute to adverse health impacts mainly resulting from landing/take-off, from cruising emissions of primary particulate matter and precursors of secondary particulate matter and ozone. Ground service vehicles and airport-related traffic that are non-aircraft emissions also cause a significant rise in health impacts. This study applies the adjoint version of the U.S. Environmental Protection Agency's air quality model, the Community Multi-scale Air Quality (CMAQ) model to quantify location-specific aviation benefit-per-tons (BPTs), or the health-related benefits of reducing one additional unit mass of pollutants associated with aviation emissions for use in benefit-cost analysis and health policy assessment. The analyses are performed for a national scale (12 km) and include BPT estimates for cruising and landing/take-off phases of commercial flights. Our estimates show that aviation associated health benefits can be as high as $1,370,000 per ton of reduction of PM_2.5 and $53,000 per ton of reduction of NO_x emissions. The largest health benefit estimates are attributed to landing/takeoff that contributes to a quarter of the benefits.

On the Sensitivity of a Machine Learning-Based Model to Predict Chlorophyll- Using Multi-Media Modeling Environmental Predictors

Christina Feng Chang¹, Marina Astitha¹, Valerie Garcia², Chunling Tang², Penny Vlahos³, David Wanik⁴, Jesse Bash²

¹Department of Civil and Environmental Engineering, University of Connecticut, Storrs, CT 06269, USA

²National Exposure Research Laboratory, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC 27711, USA

³Deparment of Marine Sciences, University of Connecticut, Groton, CT 06340, USA

⁴Department of Operations and Information Management, Stamford, CT 06901, USA

In the past, we have introduced the concept of using multi-media modeling and machine learning (ML) to assess environmental variables (predictors) that affect water quality by using Lake Erie as a case study. Since then, we have improved our water quality model by utilizing outputs from updated versions of the numerical prediction models. This time, we will focus on the influence of each set of predictors (meteorological, air quality, hydrological and agricultural) by varying the usage of the ML-based model inputs through various sensitivity tests. In-situ chlorophyll- (chlor- ) measurements, which serve as a proxy for harmful algal blooms (HABs), are provided by the Lake Erie Committee Forage Task Group (LEC FTG) for the 2002-2012 period. Meteorological weather variables from WRF, hydrological variables from VIC, nitrogen deposition from CMAQ, and agricultural management practice variables from EPIC for the 11-year period are used to fit a random forest ML model to predict chlor- concentrations. We discuss the importance of explanatory variables that originate from these individual modeling systems, and analyze the contribution of each covariate in the model to better understand the occurrence of high chlor- concentrations. As HABs and hypoxia continue to threaten water bodies across the nation, lessons learned from developing and testing this ML-based approach can be used to tackle water quality problems in other lakes or coastal areas and inform policy decisions.

Four decades of United States on-road air pollution: spatial-temporal trends assessed by ground-based monitors, air quality models, and satellites

Lucas RF Henneman, Huizhong Shen, Armistead G Russell, Corwin M Zigler

On-road emissions sources have been strictly regulated over the past 40 years due to their propensity to increase traffic-related air pollution (TRAP) concentrations. Epidemiologists have linked increased exposure to TRAP with many adverse health impacts and inequities; often, these studies employ simple metrics such as road proximity or more complex modeled air pollutants to estimate exposure. We assess the ability of distance-based metrics, two long-term CMAQ modeling products, and multiple satellite records to reproduce observed spatial TRAP gradients and their evolution over time using a hierarchical linear regression. Overall, road proximity explained decreasing variability in observed spatial-temporal trends as time passed between 1980 and 2019. CMAQ simulated CO and EC concentrations are more sensitive to road proximity in space and time than other TRAP pollutants and the model largely captures observed NO2 trends from 2002-2010, suggesting that emissions estimates during that period were accurate, at least in a relative sense. We show that California's on-road vehicle regulations contributed to decreases in NO2, NOx, and EC concentrations in California. In the 11 other states that adopted California's light-duty automobile regulations, however, the near road decreases in TRAPs were statistically similar to the reductions in states that did not adopt their standards, though there is some indication that the CO and EC decline was greater near roads in states that adopted California's policies than other states.

Impacts of Differences in Land Use Characterization and Dry Deposition Schemes on AQMEII4 CMAQ Simulations

Christian Hogrefe, Jesse Bash, Jon Pleim, Donna Schwede, Rob Gilliam, Kristen Foley, Wyat Appel, and Rohit Mathur

Center for Environmental Measurement and Modeling, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC 27513, USA

Phase 4 of the Air Quality Model Evaluation International Initiative (AQMEII) is focused on the diagnostic intercomparison and evaluation of dry and wet deposition of trace gases and aerosols simulated by regional-scale air quality modeling systems. Using the common emissions and boundary conditions datasets developed for AQMEII4, CMAQv5.3.1 simulations for the years 2010 and 2016 were performed over a North American modeling domain with both the M3DRY and STAGE dry deposition schemes. Meteorological fields for these simulations were prepared with WRFv4.1.1 using the Pleim-Xiu land-surface model and MODIS land-use classification scheme. CMAQ sensitivity simulations with both M3DRY and STAGE were performed for 2016 in which the WRFv4.1.1 Pleim-Xiu land-surface model was configured to use the NLCD40 land-use classification scheme and satellite-derived leaf area index (LAI) and vegetation fractions. Fields of surface characteristics, meteorological and concentration variables, dry deposition velocities, and deposition fluxes were analyzed to quantify the impacts of different land use characterizations and dry deposition schemes on simulated air quality and to gain mechanistic insights into model behavior. Results show that variability in the estimated dry deposition sink results from both differences in representation of the underlying land use and model process formulation and that the magnitude of both effects was comparable in the simulations analyzed in this study. CMAQ driven by the WRF configuration using MODIS yields consistently lower O3 and higher PM2.5 than the WRF configuration using NLCD40 and satellite-derived LAI and vegetation fractions. Using M3DRY results in lower PM2.5 year-round and generally lower O3 during the summer and higher O3 during winter compared to using STAGE for both 2010 and 2016. Land-use specific dry deposition velocities calculated by M3DRY and STAGE will be intercompared using diagnostic information about different surface resistance pathways generated as part of the AQMEII4 CMAQ simulations.

Changes in Ozone Production Chemistry across the U.S. between 2007 and 2016: An Integrated Modeling Assessment

Shannon Koplitz, Heather Simon, Luke Valin, Andrew Whitehill, Barron Henderson, Ben Wells, Jenny Liljegren, Gail Tonnesen

US EPA

Anthropogenic nitrogen oxide (NOx) and VOC emissions in the U.S. have declined substantially over the last decade, altering the NOx-VOC chemistry and ozone (O3) production characteristics of many areas. In this work we use multiple air quality analysis tools to assess how these large reductions in NOx and VOC have affected ozone production regimes across the U.S. between 2007 and 2016. We first compare observed and modeled evolution of NOx-limited and VOC-limited O3 formation regimes using a day-of-week analysis. This comparison builds confidence in the model's ability to capture ozone changes due to chemistry and meteorology both within years and across periods of large emissions decreases. Day-of-week analysis, however, cannot definitively differentiate between emissions and meteorology impacts. We therefore supplement this analysis with sensitivity calculations from CAMx-HDDM to characterize modeled shifts in ozone formation chemistry between 2007 and 2016 in different regions of the U.S. This work provides insights into how O3 production chemistry has evolved in large population centers in the U.S.

Source Contributions to Ozone in Connecticut
Yukui Li, UConn

According to American Lung Association's "State of the Air 2020" report, every county in Connecticut continued to earn an F for ozone in spite of a slight decrease in the number of the most unhealthy days. There is an urgent need to lower ozone concentrations in Connecticut.

We model the point sources contributing to ozone concentrations in Connecticut in 2016 using both the Comprehensive Air Quality Model with Extensions (CAMx) and the Community Multiscale Air Quality Modeling System (CMAQ) and their respective source apportionment tools: Ozone Source Apportionment Tools (CAMx-OSAT) and Integrated Source Apportionment Method (CMAQ-ISAM). For consistency, we use emissions and meteorological inputs based on the US EPA's 2016beta platform for both models using a domain covering the full United States, southern Canada, and northern Mexico.

We model the contributions from nine major sources in the region with the goal of narrowing down the top contributing sources. These sources include Roseton Generating, Arthur Kill Generating Station, Bowline Generating Station, East River Generating Station, 74th Street Station, Astoria Gas Turbine Power, Astoria Generating Station, Ravenswood Generating Station, E F Barrett Power Station, Northport Generating Station, Holtsville Facility, and Bergen Generating Station (New Jersey). As a first estimate of the impact of these sources, instead of tracking the emissions specifically from the source, we track the contribution from all sources within the grid cell. This provides a quicker approach to narrow down the major contributors. We quantify the impact of these high emitting sources to average and peak ozone concentrations in Connecticut from April-October 2016. This will aid in the development of effective, targeted ozone policies to improve the air quality in Connecticut.

CAMx-OSAT shows that the boundary conditions and remaining sources (i.e. those not explicitly tracked) account for over 99% of the average ozone concentration between June and August of 2016. The nine selected source grids combined contribute less than one half a percent of the average ozone concentration.

Atmospheric emission reduction scenarios in Colombia: Assessing the sectoral contributions to aerosol, NOx, and Ozone population-weighted concentration

Alejandra Montejo-Barato, Sebastian Espitia-Cano, Ricardo Morales Betancourt, Jorge Bonilla, Claudia Aravena

Air quality modelling is a widely used tool to represent the complex meteorological and chemical processes that take place in the atmosphere and ultimately determine the air quality at a given location. These tools, however, have not been thoroughly utilized in Colombia, and have often been left out of the analysis and design of environmental public policy. In this work, we use the chemical transport model WRF-Chem version 3.9.1 to evaluate the impact of different economic sectors on air quality in Colombia and its capital, Bogota. Three nested domains located in northern South America were used with resolutions of 27 km, 9 km, and 3 km, and 120x120, 126x126 and 132x132 cells respectively with 41 vertical levels. Gas-phase chemistry is described by the RACM mechanism, and aerosols are described with the two-moment, modal aerosol scheme MADE-VBS. Anthropogenic emissions from the global emissions inventory EDGARv4.3.1 were merged with a local emissions inventory for the city of Bogota. Biogenic emissions are also included using MEGAN. Biomass burning emissions were included using the FINN emission inventory. A base case simulation was generated for the three domains. To attribute the sectoral contributions to PM2.5, NOx, and Ozone concentration, five scenarios were defined disaggregating anthropogenic emissions into Agricultural, Electric Power Generation, Industrial, Mobile, Commercial and other's sectors. Our simulations show that in Colombia, the sectors with the greatest contribution to PM2.5 concentrations are Mobile and Industrial with a mean contribution of 2.8 and 2.3 g/m ^3 respectively. the attribution of mobile sources in Colombia to the average concentration of O3 is 2.12 ppb and for NOX it is 2.12 ppb. For the city of Bogota, the local impacts of Industrial, Mobile, and Resuspended Particulate Matter were evaluated. It was found that the paving of unpaved roads and the heavy-duty vehicles renewal and can generate a maximum reduction of 8.0 and 2.0 g/m ^3of PM2.5 respectively. The O3 and NOX change were minimum in the tree scenarios evaluated and a maximum reduction of 2.2 ppb of SO2 due to technology change from coal to natural gas in the industry, was shown to benefit low- and middle-income communities. A first analysis of the impact of potential reductions on environmental inequality was also explored using the modeling results.

Ongoing Multi-model Evaluation of Operational Air Quality Forecasts over North America: 20172020

Moran, M.D., P.M. Manseau, S.J. Peng, I. Stajner, J. Flemming, J. McQueen, P. Lee, M. Razinger, and R. Pavlovic

Since 2017 three agencies that produce daily operational air quality forecasts for North America have been exchanging their forecasts daily, allowing a side-by-side comparison and ongoing evaluation of these forecasts. The three participating agencies are Environment and Climate Change Canada (ECCC), the U.S. National Oceanic and Atmospheric Administration (NOAA), and the European Centre for Medium-range Weather Forecasts (ECMWF). As well, ECCC has developed a multi-model verification system that receives and ingests the North American AQ forecasts from the ECCC regional AQ forecast system, the NOAA-NWS regional AQ forecast system, and the ECMWF-CAMS global AQ forecast system and then evaluates these forecasts using near-real-time North American hourly surface measurements of O3, NO2, and PM2.5. This new verification system, which holds daily forecasts from January 2017 onwards, automatically generates monthly multi-model performance statistics for North American daily maximum forecasts of O3, NO2, and PM2.5 at the end of every month. While the system computes a number of standard statistical metrics, it also computes a new, pollutant-specific Air Quality forecast Performance Index (AQPI), which combines unitless measures of model bias, error, and correlation, to track and compare overall monthly performance and trends for the three AQ forecast models and three species. By exchanging these forecast scores on a regular basis, this international collaboration provides useful information to each agency on multi-model performance, including peer performance, seasonal variations, and the impacts of model upgrades implemented by each agency.

Evaluation of CMAQ and CAMx ozone and PM2.5 using the 2016v1 emissions inventory

Heather Simon, Shannon Koplitz, Norm Possiel, Barron Henderson, Sharon Phillips, Chris Misenis, Alison Eyth, Jeff Vukovich, Caroline Farkas

US EPA Office of Air Quality Planning & Standards

Over the past year, EPA has coordinated with state and local air agencies and multi-jurisdictional organizations (MJOs) in a collaborative effort to compile the 2016v1 emissions modeling platform. We have recently applied this new emissions platform with CMAQv5.3.1 and CAMxv.7beta. Here we present a model performance evaluation for ozone and PM2.5 species using ambient network data across the US. We supplement surface comparisons with vertical profile sonde evaluations and vertically integrated satellite comparisons evaluations. Finally, we completed a series of sensitivity simulations testing the sensitivity of ozone and PM2.5 performance to various model inputs and configuration options. We highlight key findings from those sensitivity runs in terms of ozone and PM2.5 performance.

Estimating US background ozone levels using data fusion

T. Nash Skipper, Yongtao Hu, M. Talat Odman, Barron H. Henderson, Christian Hogrefe, Rohit Mathur, Armistead G. Russell

US background (US-B) ozone is the ozone that would be observed in the absence of US anthropogenic emissions. US-B ozone originates from noncontrollable sources (e.g., wildfires, stratosphere-troposphere exchange, non-domestic pollution) and can vary significantly by region, elevation, and season. Typically, US-B ozone is quantified using a chemical transport model (CTM), though results are uncertain due to potential errors in model process descriptions and inputs. There are also significant differences in various model estimates of US-B ozone. A method to fuse observed ozone with US-B ozone simulated by a regional CTM (CMAQ) has been developed. We apportion the model bias as a function of space and time to US-B and US anthropogenic (US-A) ozone. Trends in ozone bias are explored across different simulation years and varying model scales. The estimated bias differs by season and location, by model resolution, and by ozone origin (US-B vs. US-A). Exploration of these findings can help illuminate the timing and location of biases within the model and inform the planning of more targeted research to investigate specific causes of bias. With the application of our data fusion bias adjustment method, we estimate a significant improvement in the agreement of adjusted US-B ozone.

Enhancement and Testing of Hemispheric CAMx

Pradeepa Vennam, Chris Emery, Lynsey Parker, Jeremiah Johnson, Greg Yarwood

Air quality planning in the US is increasingly reliant on quantifying and assessing contributions from international transport of air pollution. This study follows from our 2019 work to enhance and further evaluate CAMx applications on the hemispheric scale (H-CAMx) in preparation for local and regional regulatory and policy use. The advantages and tangible benefits of linking H-CAMx with regional CAMx applications include chemical consistency, seamless integration of the CAMx Source Apportionment (SA) technology, and immediate availability of future CAMx enhancements for applications at all scales. Continuing with a 2016 hemispheric dataset developed by EPA for CMAQ, we developed and evaluated the use of satellite data to derive ozone top boundary conditions to characterize stratospheric ozone, and developed a set of monthly-averaged, spatially-variable hemispheric initial/boundary conditions from our global modeling archive. These monthly data are applicable for H-CAMx applications for a range of years , while improving representativeness over the use of default profiles and shortening model spin-up. We tested and evaluated H-CAMx sensitivity to vertical resolution and sub-grid convection by applying alternate meteorological model configurations. We will present comparisons among these H-CAMx sensitivity runs, and from 2016 runs of GEOS-Chem and H-CMAQ, to deep ozone profiles from 22 global ozonesonde launch sites as well as to surface-level ozone measurements from 67 global monitoring sites.