Session Presentations

Ross Beardsley and Greg Yarwood

The first version of CB6 was completed in 2010 and the first revision to be widely used was revision 2 (CB6r2) in 2013 which introduced heterogeneous reaction of organic nitrates (ONs) and updated reactions of isoprene and aromatics. The current version of CMAQ has CB6r3 (introducing ON improvements for winter ozone) and CAMx has CB6r4 (adding iodine and dimethyl sulfide for marine environments). However, the CB6 core inorganic reactions were last updated in 2010. For CB6r5, we performed a literature review that considered 152 of the 233 reactions in CB6r4 with focus on inorganic and simpler organic reactions. We revised reaction rates for 47 reactions and added one new reaction. CB6r5 tends to predict higher ozone concentrations than CB6r4 over land although CB6r5 has lower ozone over portions of the Gulf of Mexico. Chemically, the ozone changes due to CB6r5 updates are associated with small changes (generally increases) in nitrogen dioxide (NO₂) in regions where ozone production is NOx-limited. Quantitative performance evaluation for 8-hour average ozone in Texas during June 2012 found that CB6r5 performs similarly to CB6r4 with statistical metrics for both mechanisms meeting recommended criteria. The ozone changes associated with CB6r5 updates are too small for ozone model performance evaluation to assess their validity when taking into consideration that models have uncertainties other than the chemistry including emissions, boundary concentrations, deposition and meteorology. We recommend additional testing and evaluation to understand how CB6r5 mechanism updates influence CAMx and CMAQ model performance for ozone and other pollutants.

Bryan Berman¹, Bryan Cummings¹, Anita Avery², Shannon Capps¹, Peter DeCarlo³, Michael Waring¹

¹Drexel University, Philadelphia, Pennsylvania, USA

²Aerodyne Research Inc., Billerica, Massachusetts, USA

³Johns Hopkins University, Baltimore, Maryland, USA

Many indoor aerosols originate from the outdoor environment. However, certain aerosol components may be physically or chemically processed upon transport from outdoors to indoors. For instance, temperature and relative humidity (RH) gradients between the indoors and the outdoors may influence the repartitioning of certain aerosol components. Cummings and Waring (2019) developed a model that simulates indoor organic aerosol (OA) concentration, composition, partitioning behavior, and secondary formation. We expand this model to predict inorganic aerosol (IA) repartitioning by integrating the thermodynamic model, ISOYYOPIA, which predicts concentrations of various inorganic species in the aerosol and gas phases at chemical equilibrium. To our knowledge, this is the first instance of applying ISOYYOPIA in an indoor model to simulate indoor IA thermodynamics. Specifically, we modeled inorganic concentrations and compared them to indoor aerosol concentration measurements from aerosol mass spectrometer (AMS) data obtained by Avery et al. (2019). To evaluate the model, sulfate normalized indoor-to-outdoor concentration ratios, which may be used to distinguish repartitioning losses from physical loss mechanisms, were computed for inorganic nitrate (NO₃^-) and ammonium (NH₄⁺) ([I/O]_NO3/SO4 and [I/O]_NH4/SO4) across the simulation set. Our simulated [I/O]_NO3/SO4 and [I/O]_NH4/SO4 were then compared to observed data from Avery et al. (2019). Both exhibited qualitatively similar exponentially decaying curves with respect to the indoor-outdoor temperature differences, T as well as outdoor-indoor RH difference, RH in the summer. Additionally, the exponential trends were in agreement when sufficient indoor ammonia sources were modelled, from both human occupants and surface reservoirs. This model evaluation serves as a proof of concept towards modeling chemical processes of inorganic aerosols and gases at key points in a heating, ventilating, and air-conditioning (HVAC) system, in the summer and winter, with ISOYYOPIA. Therefore, future work will involve exploring how HVAC systems influence indoor aerosol composition and chemical processing, with a focus on indoor aerosols of outdoor origin.

Patrick C. Campbell^1,2, Youhua Tang^1,2, Pius Lee¹, Barry Baker^1,2, Daniel Tong^1,2, Rick Saylor¹, Ariel Stein¹, Jianping Huang^3,4, Ho-Chun Huang^3,4, Edward Strobach^3,4, Jeff McQueen³, Ivanka Stajner³, Dorothy Koch⁵, Jose Tirado-Delgado^5,6, and Youngsun Jung⁵

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. I.M. Systems Group Inc., Rockville, MD

5. NOAA NWS/STI

6. Eastern Research Group, Inc (ERG)

There is an ongoing transition at NOAA to use a new dynamical core in their Global Forecast System (GFS) and Limited Area Models (LAMs) known as the Finite Volume Cubed-Sphere (FV3), developed at both NASA and NOAA's Geophysical Fluid Dynamics Laboratory over the past few decades. There are also efforts at NOAA to upgrade FV3-GFS to version 16, which includes a number of significant developmental advances to the model configuration, data assimilation, and underlying model physics, particularly for atmospheric composition to weather feedback. Simultaneous to the GFSv16 upgrade, we are coupling the GFSv16 with the Community Multiscale Air Quality (CMAQ) model to form a "next-generation" National Air Quality Forecasting Capability (NAQFC) that will continue to protect human and ecosystem health in the U.S. In Part I (of II) for this work, we describe the vision and development of the FV3-GFSv16 coupling with the "state-of-science" CMAQ model version 5.3.1. The direct GFS-CMAQ coupling is based on the seminal version of the NOAA-ARL Atmosphere-Chemistry Coupler (NACC), which will ultimately form the next NAQFC system (i.e., NACC-CMAQ) and include numerous scientific advancements that will be highlighted. Such advancements include satellite-based data acquisition technology to improve land cover and soil characteristics, and wildfire smoke and dust predictions that are vital to predictions of PM2.5 concentrations during hazardous events impacting society, ecosystems, and health. We also demonstrate the capability of the NACC-CMAQ system as an additional community research model/tool for atmosphere-chemistry coupling, as well as other ways that NOAA-ARL can facilitate NACC-CMAQ community usage for air quality modeling applications. Part II of this work will provide more details into the science advancements and evaluation of NACC-CMAQ.

Xiaoyang Chen, Yang Zhang, Kai Wang, Daniel Tong, Pius Lee, Youhua Tang, Jianping Huang, Patrick C. Campbell, Jeff Mcqueen, Havala O.T. Pye, Benjamin N. Murphy, and Daiwen Kang

The next-generation of the operational National Air Quality Forecast Capability (NAQFC) consists of the Finite Volume Cube-Sphere dynamic core (FV3)-based Global Forecast System (GFS) and the Community Multiscale Air Quality (CMAQ) modelling system. In this study, the forecast skill of offline-coupled GFSv15-CMAQv5.0.2 is comprehensively evaluated for the year 2019. The forecast system shows good overall annual-mean performance but larger seasonal and monthly biases for temperature and relative humidity at 2-m and wind speed at 10-m, and moderate-to-large biases for hourly precipitation. It shows an overall good forecast skill in predicting annual and seasonal mean maximum daily average 8-h ozone (O₃), despite a significant overprediction near the Gulf Coast during O₃ season. While the forecast system performs well in forecasting fine particles (PM_2.5) for warm months (May-September), it significantly overpredicts for other months, particularly in the U.S. EPA designated regions 5 and 7 (mostly in the Midwest), and the states of Oregon and Washington, due mainly to the high predicted concentrations of fine fugitive, coarse-mode, and nitrate components. Underpredictions in the southeastern U.S. and California during summer are attributed to missing sources and mechanisms of secondary organic aerosol formation from biogenic volatile organic compounds (VOCs) and semi- or intermediate-VOCs. Categorical evaluation indicates that GFSv15-CMAQv5.0.2 can predict well the exceedance of "moderate" classification but not well for the "unhealthy for sensitive groups" in the U.S. air quality index system, due to the aforementioned PM_2.5 overprediction. The region-specific, time-specific, and categorical evaluations in this work can provide a scientific basis for the further development of NAQFC, in particular in improving the emissions and model chemical representations, as well as the development of the science-based bias correction method to improve forecasting skill for O₃ and PM_2.5.

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

This presentation covers thread parallel performance results for CMAQ 5.3 for a 101-day simulation and extends the results in [1]. Here attention is focused on the Gear, Rosenbrock, and EBI solvers in the Chemistry Transport Model (CTM), for both FSparse [1], and the legacy JSparse [2] algorithms. The former implements OpenMP thread parallelism for all three solvers in the CTM. The results include execution performance and numerical precision for the first quarter of the 2016 annual CONUS scenario 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 version on a heterogeneous cluster of 14 nodes with a total of 192 cores. The implementation of thread parallelism in the horizontal advection science procedures (HADV) will also be discussed since these dominate the fraction of total wall clock time with increased number of 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 input data and resolving implementation issues.

Chris Emery, Gary Wilson, Greg Yarwood

We summarize several updates and improvements available in version 7.0 of the Comprehensive Air quality Model with extensions (CAMx; www.camx.com). A major update includes enhancements to the way large datasets are provided to the model, including: (1) support for netCDF v3/4 file formats for large gridded fields (emissions, meteorology, and initial/boundary conditions); (2) a new 3-D gridded emissions file in netCDF for certain non-point source sectors that are emitted above the surface and have traditionally been input to the model as elevated point sources (fires, lightning NOx, aircraft); (3) support for multiple point, 2-D and 3-D gridded input emission files (removing the requirement to merge emission sectors into single files); and (4) enhanced I/O for Probing Tools. CAMx v7 also includes a bi-directional ammonia algorithm, updates to secondary organic chemistry, chemistry for oceanic dimethyl sulfide (DMS), the addition of eight primary PM_2.5 elemental species (Fe, Mg, Mn, Ca, K, Al, Si, Ti), several enhancements to the Decoupled Direct Method (DDM) tool, updates to support source apportionment for 1-way nested applications between hemispheric and regional scales, and an increased capacity for larger Probing Tool applications.

Siqi Ma1,2, Daniel Tong2,3,*, Lok Lamsal4,5, Julian Wang6, Youhua Tang3,6, Tianfeng Chai4, Pius Lee6, Patrick Campbell3,6, Barry Baker3,6, Rick Saylor6

1. National Research Council, hosted by the National Oceanic and Atmospheric Administration Air Resources Lab, College Park, MD 22030 USA 2. Department of Atmospheric, Oceanic and Earth Sciences, George Mason University, Fairfax, VA 22030 USA 3. Center for Spatial Information Science and Systems, George Mason University, Fairfax, VA 22030 USA 4. Atmospheric Chemistry and Dynamics Laboratory, NASA Goddard Space Flight Center, Greenbelt, Maryland, USA 5. Universities Space Research Association, Columbia, MD 21046 USA 6. National Oceanic and Atmospheric Administration Air Resources Laboratory, College Park, MD 22030 USA

Although Air quality in the United States has improved remarkably in the past decades, ground-level ozone continues to rise to exceed the national ambient air quality standards in many nonattainment areas, including the Long Island Sounds (LIS) and its surrounding areas. Accurate prediction of high ozone episodes is highly desirable to assist air quality and health agencies in mitigating the harmful effects of air pollution. Here we employ a suite of forecasting techniques, including rapid emission refresh and chemical data assimilation, to assess the effectiveness of different techniques on improving forecasting performance for ozone (O3) and nitrogen dioxide (NO2) with a high-resolution (3 km) Community Multi-scale Air Quality (CMAQ) modeling system over the LIS region. All simulations were conducted for a high ozone episode (August 25~31, 2018) during the 2018 Long Island Sound Tropospheric Ozone Study (LISTOS), which provides abundant observations for evaluating model performance. The results show that each of these forecasting techniques employed in this study is able to enhance the capability of the forecasting model. Individually, the most significant improvement comes from the dynamic boundary conditions derived from NOAA National Air Quality Forecast Capability (NAQFC), which increases the correlation coefficient (R) of O3 from 0.81 to 0.93 and reduces the Root Mean Square Error (RMSE) from 14.97 ppb to 8.22 ppbv, compared to that without the NAQFC boundary conditions. This is due in part to the accuracy in NAQFC prediction and the relatively small model domain used in this study that is more susceptible to the influence of regional transport. The results that applied multi-method adjustments with both dynamic BCs and data assimilation (optimal interpolation) presented the best simulating ability on surface activities, while it resulted in significant overestimations when reproducing the vertical profiles of O3 and NO2. Therefore, the decrease of NOx emission adjustment, which decreased the emission levels, showed a positive influence on vertical simulation. This study demonstrates a high-resolution O3 forecasting system that has high performance on both surface and vertical simulation, and this system has already been applied for the daily predictions of O3, NO2 and PM2.5.

Gavendra Pandey¹, Chowdhury Moniruzzaman¹, Akula Venkatram², and Saravanan Arunachalam¹

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

²University of California at Riverside

The impact of airport operations on air quality is a key public health concern for the population surrounding an airport. Air pollution regulations require the assessment of this impact using dispersion models. Modeling dispersion of aircraft-related sources poses challenges because of the large number and variety of airport sources, which include aircraft, ground operation vehicles, and traffic in and out of the airport, most of which are mobile. Emissions from these sources are transient, buoyant, and occur at different heights from the ground. Quantifying these emissions as well as modeling the governing processes is challenging. An added complexity occurs when the airport is situated near a shoreline where meteorological conditions are far from being spatially uniform. These features that characterize the dispersion of airport emissions are being incorporated into the model described in this paper. The relative importance of each of these features is being evaluated by comparing model estimates of NOx and SO2 with corresponding measurements made during a field study conducted at the Los Angeles International Airport (LAX) during February and March of 2012 as part of the LAX Air Quality Source Apportionment Study (AQSAS). We focused on SO2 measurements, a useful tracer of aircraft operations, made at four locations named AQ, CN, CE, and CS. This paper focuses on the impact of shoreline meteorology on dispersion. In the current phase of the project, we used components of AERMOD and AERMET as the building blocks of the new model. We modified results from AERMET to account for the formation of the internal boundary layer that is formed when stable air from the ocean flows onto the warmer land surface of the airport. Simulations with unmodified AERMET yielded concentrations that were substantially higher than the concentrations at AQ and CS and much lower than those at CN and CE. Model performance, characterized with Q-Q plots, improved substantially by replacing meteorological parameters such as friction velocity and Monin-Obukhov (M-O) length during stable conditions with those corresponding to neutral conditions. The fraction of model estimates within a factor of two of the observations improved from 34 to 51% at the CN site and at the CS site, by up to 25%. The ratio of medians of the observed to the modeled concentrations improved from 4.2 to 2.8 at the CS site and showed little change at the CN site. The correlation coefficients between the monthly averaged diurnal profiles improved from -0.08, -0.28, 0.53, and -0.37 to -0.04, 0.39, 0.60, and 0.31 for AQ, CN, CS, and CE, respectively.

Elyse Pennington, Karl Seltzer, Havala Pye, Melissa Venecek, John Seinfeld

Volatile chemical products (VCPs) have important implications for air quality but are not well characterized. Field studies have demonstrated the prevalence of gas-phase VCPs in urban environments, but few chamber studies have been performed to quantify their ability to form secondary organic aerosol (SOA). Quantitative structure-activity-relationships (SAR) models describe the properties and activities of compounds based on their chemical structure and are useful in the absence of empirical data. We use existing SAR models to estimate reactivity against OH and other oxidants, volatility, partitioning and heterogeneous uptake coefficients, and other parameters relevant to SOA formation. Oxidation products and SOA yields are estimated using multigenerational oxidation schemes and compared to published yields. We compile the results of these simulations to present a broad level understanding of the ability of VCPs to form SOA. A binned volatility model predicts SOA formation from VCPs that are currently unspeciated in air quality models. This model improves our understanding of the environmental fate of VCPs, specifically the impact on SOA mass and speciation. This model can be implemented in CMAQ to better represent anthropogenic SOA formation.

Arman Pouyaei, Yunsoo Choi, Jia Jung, Bavand Sadeghi, Chul Han Song

We introduce a novel Lagrangian model (Concentration Trajectory Route of Air pollution with an Integrated Lagrangian model, C-TRAIL version 1.0) output from CMAQ Eulerian-based air quality model for validating the source-receptor direct link by following polluted air masses. To investigate the concentrations and trajectories of air masses simultaneously, we implement the trajectory-grid (TG) Lagrangian advection scheme in the CMAQ model version 5.2. The TG algorithm follows the concentrations of representative air "packets" of species along trajectories determined by the wind field. The diagnostic output from C-TRAIL accurately identifies the origins of pollutants. For validation, we analyze the results of C-TRAIL during the KORUS-AQ campaign over South Korea. Initially, we implement C-TRAIL in a simulation of CO concentrations with an emphasis on the long- and short-range transport effects. The output from C-TRAIL reveals that local trajectories were responsible for CO concentrations over Seoul during the stagnant period (17-22 May 2016) and during the extreme pollution period (25-28 May 2016), highly polluted air masses from China were distinguished as sources of CO transported to the Seoul Metropolitan Area (SMA). We conclude that during the study period, long-range transport played a crucial role in high CO concentrations over the receptor area. Furthermore, for May 2016, we find that the potential sources of CO over the SMA were the result of either local transport or long-range transport from the Shandong Peninsula and, in some cases, from regions north of the SMA. By identifying the trajectories of CO concentrations, one can use the results from C-TRAIL to directly link strong potential sources of pollutants to a receptor in specific regions during various time frames.

Youhua Tang^1,2, Patrick C. Campbell^1,2, Pius Lee¹, Barry Baker^1,2, Daniel Tong^1,2, Rick Saylor¹, Ariel Stein¹, Jianping Huang^3,4, Ho-Chun Huang^3,4, Edward Strobach^3,4, Jeff McQueen³, Ivanka Stajner³, Dorothy Koch⁵, Jose Tirado-Delgado^5,6, and Youngsun Jung⁵

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. I.M. Systems Group Inc., Rockville, MD 5. NOAA NWS/STI 6. Eastern Research Group, Inc (ERG)

he existing NOAA National Air Quality Forecasting Capability (NAQFC) is using CMAQ 5.0.2 with cb05 chemical mechanism driven by the North American Mesoscale Forecast System (NAM). As one of our efforts to upgrade this system, the Part I described the overview of the next-generation upgrade that uses the latest CMAQv5.3.1 CB6r3-Aero7 model, driven by the NOAA operational forecast based on the Finite Volume Cubed-Sphere (FV3)-Global Forecast System (GFS), version 16. The meteorological preprocessor for CMAQ was also overhauled based on the seminal version of the NOAA-ARL Atmosphere-Chemistry Coupler (NACC), i.e., NACC-CMAQ. Differing from the normal WRF-ARW/CMAQ system, the interpolation-based coupler can use various meteorological output to drive CMAQ even they are in different grids. Here in Part II, we evaluate NACC-CMAQ against numerous observation networks (e.g., U.S. EPA AirNow) for the effect of different scientific configurations and NOAA-ARL advancements on the surface PM2.5/ozone predictions over the contiguous United States (CONUS) in summer 2019. The NACC-CMAQ prediction interpolated by FV3GFSv16 meteorology showed better results than that driven by the corresponding WRF downscaling meteorology. We tested the NACC-CMAQ for the impacts of its existing scientific packages, including the dry deposition schemes of Surface Tiled Aerosol and Gaseous Exchange (STAGE) versus M3Dry, and the runs with and without bidirectional NH3 exchange, compared to AIRNow network for summer 2019. Besides these standard NACC-CMAQ features, we also tested the modules developed in NOAA-ARL, such as the inline FENGSHA dust module with updated land/soil datasets. The fire emission based on Global Biomass Burning Emissions Product eXtended (GBBEPx) can be available in near real-time, and using that emission improved the model's correlation coefficient. An updated global lateral boundary condition based on Tang et al. (2020, https://doi.org/10.5194/acp-2020-587) improved the surface ozone prediction over CONUS. This tuning effort and evaluation will help not only improve the NAQFC's prediction, but also serve as the testbed for the NOAA future inline regional air quality modeling system.

Jose Tirado-Delgado^1,9, Dorothy Koch¹, Youngsun Jung¹, Ivanka Stajner², Jeff McQueen², Pius Lee³, Jianping Huang^{2, 5}, Ho-Chun Huang^{2, 5}, Edward Strobach^{2, 5}, Youhua Tang^3,6, Daniel Tong^{3, 6}, Patrick Campbell^{3, 6}, Ariel Stein^3, James Wilczak⁴, Irina Djalalova^4,8, Phil Dickerson⁷

(1) NOAA NWS/Office of Science and Technology Integration (2) NOAA NWS/National Center for Environmental Prediction (3) NOAA OAR/Air Resources Laboratory (4) NOAA Physical Sciences Laboratory (5) I. M. Systems Group Inc. (6) Cooperative Institute for Satellite Earth System Studies, George Mason University (7) US Environmental Protection Agency (8) Cooperative Institute for Research in Environmental Sciences, University of Colorado (9) Eastern Research Group Inc. 

The National Weather Service (NWS) National Air Quality Forecast Capability (NAQFC) provides operational Air Quality predictions of ozone, fine Particulate Matter (PM2.5), smoke and dust for the continental United States (CONUS), Alaska, and Hawaii. The forecast guidance is available to the general public at airquality.weather.gov and as GIS webservice at https://idpgis.ncep.noaa.gov/arcgis/rest/services/NWS_Forecasts_Guidance_Warnings. The current operational model is an implementation of the Community Multiscale Air Quality (CMAQ) V5.0.2 model with NEI 2014v2 linked with the NOAA National Centers for Environmental Prediction (NCEP) North American Mesoscale (NAM) regional weather prediction model at 12 km to 48 hours for 06 and 12 UTC cycles. This capability includes a bias corrected product using the Kalman Filter Analog (KFAN) technique to reduce prediction errors for both ozone and PM2.5. This presentation provides an overview of the current operational model performance and experimental updates planned for future possible operational implementation. Future updates include the development of a new chemistry meteorology coupler, the NOAA-ARL Atmosphere-Chemistry Coupler (NACC), to link the most recent version of CMAQ (V5.3.1) with NOAA's next-generation operational Global Forecast System (GFSv16) that uses the NOAA Geophysical Fluid Dynamics Laboratory Finite Volume Cubed-Sphere (FV3) dynamical core. This upgrade will allow for several improvements including the extension of our forecast guidance from 48 to 72 hours with bias correction and improved emissions processing of aerosols. Wildfire smoke improvements will use emissions estimates based on NOAA-NESDIS Blended Global Biomass Burning Emissions Product extended (GBBEPx) and dust emissions processing and estimates will benefit from an improved inline FENGSHA dust module. We will also use one member of the operational Global Ensemble Forecast System (GEFS) at 25 km that will include aerosols for improving chemical lateral boundary conditions including influxes of dust and smoke aerosols. We will present results of these recent updates and future plans to include inline chemistry for the FV3 LAM.

Kai Wu, Christopher D. Cappa, Shupeng Zhu

Atmospheric ammonia (NH3) can affect nitrogen deposition, particle acidity, gas-particle partitioning, and, potentially, aerosol uptake process. One aspect of the atmospheric chemistry of ammonia insufficiently considered to date is the impact of interactions between gas-phase ammonia and secondary organic aerosol (SOA) on air quality. Laboratory studies indicate that NH3 can react with SOA, converting gas-phase NH3 into particulate organic matter, with consequent impacts on particulate matter composition and properties. In this study, we use a modified version of the CMAQ model to simulate the potential importance of the SOA-ammonia uptake mechanism on air quality over China in summer and winter 2017, considering a range of assumed NH3 uptake coefficients (10-3-10-5). Our results show that uptake of NH3 by SOA leads to a decrease in gas-phase NH3 concentration, by as much as 27.5% and 19.0% for the highest uptake coefficient scenario of 10-3 in summer and winter, respectively. The largest reduction of ammonia occurs over the Sichuan Basin and North China Plain. The reduction of gas-phase NH3 engenders a decrease of ammonium nitrate, by up to 30%, but has little impact on the ammonium sulfate concentration. Uptake of NH3 does not significantly affect SOA concentrations owing to overall moderate changes in aerosol acidity, and thus small effects on SOA formation from isoprene (which is sensitive to pH). Altogether, NH3 uptake led to a reduction in the average PM2.5 concentration up to 8.9% and 8.7% for the highest uptake coefficient (10-3) in summer and winter, respectively. These results indicate the need for better constraints on the NH3-SOA uptake coefficient.

Shuping Zhang, Golam Sarwar, Jia Xing, Biwu Chu, Chaoyang Xue, Arunachalam Sarav, Tao Ma, Dian Ding, Haotian Zheng, Yujing Mu, Fengkui Duan, Hong He

Nitrous acid (HONO) plays important roles in atmospheric chemistry since it undergoes photolysis during the day and produces hydroxyl radical (OH) which reacts with organic and inorganic compounds and alters atmospheric composition. In this study, we apply the Community Multiscale Air Quality (CMAQv5.3) model to simulate air quality in China for December 2015 and compare model predictions with observed data in Beijing. The CMAQv5.3 model severely underestimates observed HONO concentration (Normal Mean Bias = -95%). We revise the original HONO formation reactions and also implement several additional HONO formation reactions. The revised chemistry substantially enhances HONO prediction and improves the model performance (Normal Mean Bias = -5%). Model results suggest that the heterogeneous reaction on the ground surface is the most significant reaction contributing ~80% of the predicted night-time and ~60% of the day-time surface HONO concentration. Enhanced HONO production increases OH concentrations. Predicted OH concentration without the updated HONO chemistry is substantially lower than the observed data. However, the model with the updated HONO chemistry successfully reproduces the observed OH concentration. The updated model also enhances ozone, inorganic aerosols, and secondary organic aerosols in Beijing winter. The presentation will include additional analysis and a comparison of model predictions with available observed data in China.