@article{lewis_battye_aneja_kim_bell_2023, title={Modeling and Analysis of Air Pollution and Environmental Justice: The Case for North Carolina's Hog Concentrated Animal Feeding Operations}, volume={131}, ISSN={["1552-9924"]}, DOI={10.1289/EHP11344}, abstractNote={Background: Concentrated animal feeding operations (CAFOs) emit pollutants that can cause negative impacts on human health. The concentration of hog production in North Carolina raises concerns regarding the disproportionate exposure of vulnerable communities to air pollution from CAFOs. Objectives: We investigated whether exposure to gaseous ammonia (NH3) and hydrogen sulfide (H2S) (in 2019) differs between subpopulations by examining demographics, including race/ethnicity, age, educational attainment, language proficiency, and socioeconomic status. Methods: We used an Air Monitoring Station (AMS)/Environmental Protection Agency (EPA) Regulatory Model (AERMOD)–based Human Exposure Model (version 3) to estimate ambient concentrations of NH3 and H2S from hog farms in Duplin County and its surrounding counties in North Carolina and estimate subsequent exposures of communities within 50km of Duplin County, North Carolina, or the Duplin County Region. We combined estimated exposures with 2016 American Community Summary Census data, at the block group level, using spatial analysis to investigate whether exposures to these pollutants differ by race and ethnicity, age, income, education, and language proficiency. Based on these estimations, we assessed associated exposure risks to the impacted communities and used multivariable regression modeling to evaluate the relationship between average ammonia exposures from Duplin regional hog farms and the presence of vulnerable populations. Results: The average [±standard deviation (SD)] annual estimated concentration of NH3 and H2S in the Duplin County Region is 1.75±2.81 μg/m3 and 0.0087±0.014 μg/m3, respectively. The maximum average annual ambient concentrations are estimated at 54.27±4.12 μg/m3 and 0.54±0.041 μg/m3 for NH3 and H2S, respectively. Our descriptive analysis reveals that people of low income, people of color, people with low educational attainment, and the linguistically isolated in the Duplin Region are disproportionately exposed to higher levels of pollutants than the average exposure for residents. Alternatively, our statistical results suggests that after adjusting for covariates, communities of color are associated with 1.70% (95% CI: −3.79, 0.44) lower NH3 concentrations per 1-SD increase. One-standard deviation increases in the adults with low educational attainment and children <19 years of age is associated with 1.26% (95% CI: −0.77, 3.33) and 1.20% (95% CI: −0.62, 3.05) higher NH3 exposure per 1-SD increase, respectively. Discussion: Exposures to NH3 and H2S differed by race and ethnicity, educational attainment, language proficiency, and socioeconomic status. The observed associations between exposure to CAFO-generated pollutants and sociodemographic indicators differed among demographics. The disproportionate distribution of hog facilities and resulting pollutant exposures among communities may have adverse environmental and human health impacts, raising environmental justice concerns. https://doi.org/10.1289/EHP11344}, number={8}, journal={ENVIRONMENTAL HEALTH PERSPECTIVES}, author={Lewis, Brandon M. and Battye, William H. and Aneja, Viney P. and Kim, Honghyok and Bell, Michelle L.}, year={2023}, month={Aug} }
 @article{akdemir_battye_myers_aneja_2022, title={Estimating NH3 and PM2.5 emissions from the Australia mega wildfires and the impact of plume transport on air quality in Australia and New Zealand (Jun, 10.1039/d1ea00100k, 2022)}, volume={6}, ISSN={["2634-3606"]}, DOI={10.1039/d2ea90015g}, abstractNote={Correction for ‘Estimating NH3 and PM2.5 emissions from the Australia mega wildfires and the impact of plume transport on air quality in Australia and New Zealand’ by Ece Ari Akdemir et al., Environ. Sci.: Atmos., 2022, https://doi.org/10.1039/d1ea00100k.}, journal={ENVIRONMENTAL SCIENCE-ATMOSPHERES}, author={Akdemir, Ece Ari and Battye, William H. and Myers, Casey Bray and Aneja, Viney P.}, year={2022}, month={Jun} }
 @article{wiegand_battye_myers_aneja_2022, title={Particulate Matter and Ammonia Pollution in the Animal Agricultural-Producing Regions of North Carolina: Integrated Ground-Based Measurements and Satellite Analysis}, volume={13}, ISSN={["2073-4433"]}, DOI={10.3390/atmos13050821}, abstractNote={Intensive animal agriculture is an important part of the US and North Carolina’s (NC’s) economy. Large emissions of ammonia (NH3) gas emanate from the handling of animal wastes at these operations contributing to the formation of fine particulate matter (PM2.5) around the state causing a variety of human health and environmental effects. The objective of this research is to provide the relationship between ammonia, aerosol optical depth and meteorology and its effect on PM2.5 concentrations using satellite observations (column ammonia and aerosol optical depth (AOD)) and ground-based meteorological observations. An observational-based multiple linear regression model was derived to predict ground-level PM2.5 during the summer months (JJA) from 2008–2017 in New Hanover County, Catawba County and Sampson County. A combination of the Cumberland and Johnston County models for the summer was chosen and validated for Duplin County, NC, then used to predict Sampson County, NC, PM2.5 concentrations. The model predicted a total of six 24 h exceedances over the nine-year period. This indicates that there are rural areas of the state that may have air quality issues that are not captured for a lack of measurements. Moreover, PM2.5 chemical composition analysis suggests that ammonium is a major component of the PM2.5 aerosol.}, number={5}, journal={ATMOSPHERE}, author={Wiegand, Rebecca and Battye, William H. and Myers, Casey Bray and Aneja, Viney P.}, year={2022}, month={May} }
 @article{bray_nahas_battye_aneja_2021, title={Impact of lockdown during the COVID-19 outbreak on multi-scale air quality}, volume={254}, ISSN={["1873-2844"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85104410171&partnerID=MN8TOARS}, DOI={10.1016/j.atmosenv.2021.118386}, abstractNote={One of the multi-facet impacts of lockdowns during the unprecedented COVID-19 pandemic was restricted economic and transport activities. This has resulted in the reduction of air pollution concentrations observed globally. This study is aimed at examining the concentration changes in air pollutants (i.e., carbon monoxide (CO), nitrogen dioxide (NO2), sulfur dioxide (SO2), ozone (O3), and particulate matters (PM2.5 and PM10) during the period March-April 2020. Data from both satellite observations (for NO2) and ground-based measurements (for all other pollutants) were utilized to analyze the changes when compared against the same months between 2015 and 2019. Globally, space borne NO2 column observations observed by satellite (OMI on Aura) were reduced by approximately 9.19% and 9.57%, in March and April 2020, respectively because of public health measures enforced to contain the coronavirus disease outbreak (COVID-19). On a regional scale and after accounting for the effects of meteorological variability, most monitoring sites in Europe, USA, China, and India showed declines in CO, NO2, SO2, PM2.5, and PM10 concentrations during the period of analysis. An increase in O3 concentrations occurred during the same period. Meanwhile, four major cities case studies i.e. in New York City (USA), Milan (Italy), Wuhan (China), and New Delhi (India) have also shown a similar reduction trends as observed on the regional scale, and an increase in ozone concentration. This study highlights that the reductions in air pollutant concentrations have overall improved global air quality likely driven in part by economic slowdowns resulting from the global pandemic.}, journal={ATMOSPHERIC ENVIRONMENT}, author={Bray, Casey D. and Nahas, Alberth and Battye, William H. and Aneja, Viney P.}, year={2021}, month={Jun} }
 @article{aneja_schlesinger_li_nahas_battye_2020, title={Characterization of the Global Sources of Atmospheric Ammonia from Agricultural Soils}, volume={125}, ISSN={["2169-8996"]}, url={https://doi.org/10.1029/2019JD031684}, DOI={10.1029/2019JD031684}, abstractNote={AbstractGlobal ammonia (NH3) emissions to the atmosphere are projected to increase in the coming years with the increased use of synthetic nitrogen fertilizers and cultivation of nitrogen‐fixing crops. A statistical model (NH3_STAT) is developed for characterizing atmospheric NH3 emissions from agricultural soils and compared to the performance of other global and regional NH3 models (e.g., Emission Database for Global Atmospheric Research, Magnitude and Seasonality of Agricultural Emissions, MIX, and U.S. Environmental Protection Agency). The statistical model was developed from a multiple linear regression between NH3 emission and the physicochemical variables. The model was evaluated for 2012 NH3 emissions. The results indicate that, in comparison to other data sets, the model provides a lower global NH3 estimate by 58%, (NH3_STAT: 13.9 Tg N yr−1; Emission Database for Global Atmospheric Research: 33.0 Tg N yr−1). We also performed a region‐based analysis (United States, India, and China) using the NH3_STAT model. For the United States, our model produces an estimate that is a ~1.4 times higher in comparison to the Environmental Protection Agency. Meanwhile, the NH3_STAT estimate for India shows NH3 emissions between 0.8 and 1.4 times lower when compared to other data sets. A lower estimate is also seen for China, where the model estimates NH3 emissions 0.4 to 5 times lower than other data sets. The difference in the global estimates is attributed to the lower estimates in major agricultural countries like China and India. The statistical model captures the spatial distribution of global NH3 emissions by utilizing a simplified approach compared to other readily available data sets. Moreover, the NH3_STAT model provides an opportunity to predict future NH3 emissions in a changing world.}, number={3}, journal={JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES}, publisher={American Geophysical Union (AGU)}, author={Aneja, Viney P. and Schlesinger, William H. and Li, Qi and Nahas, Alberth and Battye, William H.}, year={2020}, month={Feb} }
 @article{bray_battye_aneja_schlesinger_2021, title={Global emissions of NH3, NOx, and N2O from biomass burning and the impact of climate change}, volume={71}, ISSN={["2162-2906"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85097371854&partnerID=MN8TOARS}, DOI={10.1080/10962247.2020.1842822}, abstractNote={ABSTRACT Emissions of ammonia (NH3), oxides of nitrogen (NOx; NO +NO2), and nitrous oxide (N2O) from biomass burning were quantified on a global scale for 2001 to 2015. On average biomass burning emissions at a global scale over the period were as follows: 4.53 ± 0.51 Tg NH3 year−1, 14.65 ± 1.60 Tg NOx year−1, and 0.97 ± 0.11 Tg N2O year−1. Emissions were comparable to other emissions databases. Statistical regression models were developed to project NH3, NOx, and N2O emissions from biomass burning as a function of burn area. Two future climate scenarios (RCP 4.5 and RCP 8.5) were analyzed for 2050–2055 (“mid-century”) and 2090–2095 (“end of century”). Under the assumptions made in this study, the results indicate emissions of all species are projected to increase under both the RCP 4.5 and RCP 8.5 climate scenarios. Implications: This manuscript quantifies emissions of NH3, NOx, and N2O on a global scale from biomass burning from 2001–2015 then creates regression models to predict emissions based on climate change. Because reactive nitrogen emissions have such an important role in the global nitrogen cycle, changes in these emissions could lead to a number of health and environmental impacts.}, number={1}, journal={JOURNAL OF THE AIR & WASTE MANAGEMENT ASSOCIATION}, author={Bray, Casey D. and Battye, William H. and Aneja, Viney P. and Schlesinger, William H.}, year={2021}, month={Jan}, pages={102–114} }
 @article{baker_battye_robarge_arya_aneja_2020, title={Modeling and measurements of ammonia from poultry operations: Their emissions, transport, and deposition in the Chesapeake Bay}, volume={706}, ISSN={["1879-1026"]}, DOI={10.1016/j.scitotenv.2019.1115290}, journal={SCIENCE OF THE TOTAL ENVIRONMENT}, author={Baker, Jordan and Battye, William H. and Robarge, Wayne and Arya, S. Pal and Aneja, Viney P.}, year={2020}, month={Mar} }
 @article{aneja_schlesinger_li_nahas_battye_2019, title={Characterization of atmospheric nitrous oxide emissions from global agricultural soils}, volume={1}, ISBN={2523-3971}, url={https://doi.org/10.1007/s42452-019-1688-5}, DOI={10.1007/s42452-019-1688-5}, abstractNote={Nitrous oxide (N2O) is a potent greenhouse gas with an atmospheric lifetime of ~ 114 years. Agriculture activities are the main sources for N2O emission into the atmosphere by human activities. Global N2O emissions into the atmosphere are projected to increase in the coming years as demand for food, fibre and energy increases owing to increasing global population. Here, a statistical model (N2O_STAT) is developed for characterizing atmospheric N2O emissions from agricultural sources. We obtained N2O emissions and physicochemical variables (i.e. air temperature, soil temperature, soil moisture, soil pH, and N input to the soil) from published journal articles since 2000. A statistical model was developed by expressing a multiple linear regression equation between N2O emission and the physicochemical variables. The model was evaluated for 2012 N2O emissions. Results of the model are compared with other global and regional N2O models (e.g. EDGAR, EPA/USGS, and FAOSTAT). In comparison with other data sets, the model generates a lower global N2O estimate by 9–20% (N2O_STAT: 3.75 Tg N yr−1; EDGAR: 4.49 Tg N yr−1; FAO: 4.07 Tg N yr−1), but is ~ 25% higher when compared to Bouwman et al. (Glob Biogeochem Cycles 16:1–9. https://doi.org/10.1029/2001gb001812 , 2002) (2.80 Tg N yr−1). We also performed a region-based analysis (USA, India, and China) using the N2O_STAT model. For the USA, our model produces an estimate that ranges from − 13 to + 32% in comparison with other published data sets. Meanwhile, the N2O_STAT model estimate for India shows N2O emissions between − 56 and + 14% when compared to other data sets. A much lower estimate is seen for China, where the model estimates N2O emissions 38–177% lower than other data sets. The N2O_STAT model provides an opportunity to predict future N2O emissions in a changing world.}, number={12}, journal={SN APPLIED SCIENCES}, publisher={Springer Science and Business Media LLC}, author={Aneja, Viney P. and Schlesinger, William H. and Li, Qi and Nahas, Alberth and Battye, William H.}, year={2019}, month={Dec} }
 @article{battye_bray_aneja_tong_lee_tang_2019, title={Evaluating Ammonia (NH3) Predictions in the NOAA NAQFC for Eastern North Carolina Using Ground Level and Satellite Measurements}, volume={124}, ISSN={["2169-8996"]}, url={https://doi.org/10.1029/2018JD029990}, DOI={10.1029/2018JD029990}, abstractNote={AbstractAmmonia (NH3) in the atmosphere contributes to the formation of airborne fine particulate matter (PM2.5), which is associated with adverse human health effects. The emission, transport, reactions, and deposition of NH3 in the atmosphere are modeled using the Community Multiscale Air Quality (CMAQ) model, within the U.S. National Air Quality Forecast Capability (NAQFC). The purpose of this current work is to evaluate the capability of the NAQFC CMAQ model and to identify potential improvements to NH3 emissions estimates and prediction methods. This study focuses on CMAQ predictions of atmospheric NH3 in North Carolina, including a region with intensive animal production and enhanced NH3 emissions. The CMAQ model is run for July 2011 using a version of the 2011 National Emissions Inventory in which agricultural NH3 emissions were adjusted to reflect the lower end of the range of estimates from the current process‐based emissions model. The NAQFC CMAQ model overpredicted atmospheric NH3 at a continuous monitor in Clinton, NC, within the region of intensive animal production. The average concentration measured by the monitor was 6.6 ppbv, while the average predicted by the model was 10.5 ppbv, a 60% overprediction. Outside of the region of intensive animal production, both measured and modeled NH3 concentrations were low, 1.3 ppbv or less. The model underpredicted wet deposition of NH4+ and dry deposition of NH3. It is believed that the overestimation of NH3 at Clinton is attributable at least in part to the underestimation of wet and dry deposition in North Carolina.}, number={14}, journal={JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES}, publisher={American Geophysical Union (AGU)}, author={Battye, William H. and Bray, Casey D. and Aneja, Viney P. and Tong, Daniel and Lee, Pius and Tang, Youhua}, year={2019}, month={Jul}, pages={8242–8259} }
 @article{bray_battye_aneja_2019, title={The role of biomass burning agricultural emissions in the Indo-Gangetic Plains on the air quality in New Delhi, India}, volume={218}, ISSN={["1873-2844"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85072624596&partnerID=MN8TOARS}, DOI={10.1016/j.atmosenv.2019.116983}, abstractNote={Agricultural residue burning in the Indo-Gangetic Plains (IGP) releases large amounts of reactive nitrogen, among other pollutants, into the atmosphere each year. This study focuses on rice paddy residue burning and wheat residue burning during October–November and April–May, respectively, in 2016 and 2017. Emissions of reactive nitrogen species (ammonia (NH3), nitrous oxide (N2O) and oxides of nitrogen (NOx = NO + NO2)) were estimated for the study period using a suite of satellite products from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor on the National Aeronautics and Space Administration (NASA) Aqua and Terra satellites. Emissions were compared against ambient concentrations of fine particulate matter (PM2.5) in New Delhi, India, to help determine the impact that these agricultural burns have on PM2.5, which is known to have numerous health and environmental impacts associated with prolonged exposure to elevated concentrations. Daily average measured concentrations of PM2.5 in New Delhi range from 22.43 μg m−3 to 718.94 μg m−3 (average 127.15 μg m−3 ± 95.23 μg m−3), with the daily average PM2.5 concentration exceeding the national ambient air quality standard of 60 μg m−3 approximately 75% of the time. Concentrations of PM2.5 were found to peak during October–November, which corresponds with rice paddy residue burning in the IGP. In addition to this, statistical regression models were created to predict average daily PM2.5 concentrations in New Delhi, India, based on emissions of NH3 and organic carbon (OC) in the IGP as well as meteorological conditions. The regression model predicted ambient PM2.5 concentrations ranging from 35 to 719 μg m−3. The average modeled concentrations of PM2.5 in New Delhi, India, were 111 μg m−3 (standard deviation: ± 23 μg m−3) during April/May and 207 ± 87 μg m−3 during October/November. Both regression models (for wheat residue burning and for rice paddy residue burning) were comparable to the average observations (normalized mean bias less than 0.1%).}, journal={ATMOSPHERIC ENVIRONMENT}, author={Bray, Casey D. and Battye, William H. and Aneja, Viney P.}, year={2019}, month={Dec} }
 @article{bray_battye_uttamang_pillai_aneja_2017, title={Characterization of Particulate Matter (PM2.5 and PM10) Relating to a Coal Power Plant in the Boroughs of Springdale and Cheswick, PA}, volume={8}, ISSN={["2073-4433"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85030543881&partnerID=MN8TOARS}, DOI={10.3390/atmos8100186}, abstractNote={Ambient concentrations of both fine particulate matter (PM2.5) and particulate matter with an aerodynamic diameter less than 10 micron (PM10) were measured from 10 June 2015 to 13 July 2015 at three locations surrounding the Cheswick Power Plant, which is located between the boroughs of Springdale and Cheswick, Pennsylvania. The average concentrations of PM10 observed during the periods were 20.5 ± 10.2 μg m−3 (Station 1), 16.1 ± 4.9 μg m−3 (Station 2) and 16.5 ± 7.1 μg m−3 (Station 3). The average concentrations of PM2.5 observed at the stations were 9.1 ± 5.1 μg m−3 (Station 1), 0.2 ± 0.4 μg m−3 (Station 2) and 11.6 ± 4.8 μg m−3 (Station 3). In addition, concentrations of PM2.5 measured by four Pennsylvania Department of Environmental Protection air quality monitors (all within a radius of 40 miles) were also analyzed. The observed average concentrations at these sites were 12.7 ± 6.9 μg m−3 (Beaver Falls), 11.2 ± 4.7 μg m−3 (Florence), 12.2 ± 5.3 μg m−3 (Greensburg) and 12.2 ± 5.5 μg m−3 (Washington). Elemental analysis for samples (blank – corrected) revealed the presence of metals that are present in coal (i.e., antimony, arsenic, beryllium, cadmium, chromium, cobalt, lead, manganese, mercury, nickel and selenium).}, number={10}, journal={ATMOSPHERE}, author={Bray, Casey D. and Battye, William and Uttamang, Pornpan and Pillai, Priya and Aneja, Viney P.}, year={2017}, month={Oct} }
 @article{bray_battye_aneja_tong_lee_tang_nowak_2017, title={Evaluating ammonia (NH3) predictions in the NOAA National Air Quality Forecast Capability (NAQFC) using in-situ aircraft and satellite measurements from the CalNex2010 campaign}, volume={163}, ISSN={["1873-2844"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85019951530&partnerID=MN8TOARS}, DOI={10.1016/j.atmosenv.2017.05.032}, abstractNote={Atmospheric ammonia (NH3) is not only a major precursor gas for fine particulate matter (PM2.5), but it also negatively impacts the environment through eutrophication and acidification. As the need for agriculture, the largest contributing source of NH3, increases, NH3 emissions will also increase. Therefore, it is crucial to accurately predict ammonia concentrations. The objective of this study is to determine how well the U.S. National Oceanic and Atmospheric Administration (NOAA) National Air Quality Forecast Capability (NAQFC) system predicts ammonia concentrations using their Community Multiscale Air Quality (CMAQ) model (v4.6). Model predictions of atmospheric ammonia are compared against measurements taken during the NOAA California Nexus (CalNex) field campaign that took place between May and July of 2010. Additionally, the model predictions were also compared against ammonia measurements obtained from the Tropospheric Emission Spectrometer (TES) on the Aura satellite. The results of this study showed that the CMAQ model tended to under predict concentrations of NH3. When comparing the CMAQ model with the CalNex measurements, the model under predicted NH3 by a factor of 2.4 (NMB = −58%). However, the ratio of the median measured NH3 concentration to the median of the modeled NH3 concentration was 0.8. When compared with the TES measurements, the model under predicted concentrations of NH3 by a factor of 4.5 (NMB = −77%), with a ratio of the median retrieved NH3 concentration to the median of the modeled NH3 concentration of 3.1. Because the model was the least accurate over agricultural regions, it is likely that the major source of error lies within the agricultural emissions in the National Emissions Inventory. In addition to this, the lack of the use of bidirectional exchange of NH3 in the model could also contribute to the observed bias.}, journal={ATMOSPHERIC ENVIRONMENT}, author={Bray, Casey D. and Battye, William and Aneja, Viney P. and Tong, Daniel and Lee, Pius and Tang, Youhua and Nowak, John B.}, year={2017}, month={Aug}, pages={65–76} }
 @article{battye_aneja_schlesinger_2017, title={Is nitrogen the next carbon?}, volume={5}, ISSN={["2328-4277"]}, url={https://doi.org/10.1002/2017EF000592}, DOI={10.1002/2017ef000592}, abstractNote={Key Points

The rapid growth of anthropogenic reactive nitrogen production now makes it unquestionably dominant relative to the total of natural sources
Anthropogenic production of reactive nitrogen has increased almost five‐fold in the last 60 years.
This anthropogenic activity is a massive perturbation of a global geochemical cycle in a relatively short period of time.

}, number={9}, journal={EARTHS FUTURE}, publisher={Wiley-Blackwell}, author={Battye, William and Aneja, Viney P. and Schlesinger, William H.}, year={2017}, month={Sep}, pages={894–904} }
 @article{battye_bray_aneja_tong_lee_tang_2016, title={Evaluating ammonia (NH3) predictions in the NOAA National Air Quality Forecast Capability (NAQFC) using in situ aircraft, ground-level, and satellite measurements from the DISCOVER-AQ Colorado campaign}, volume={140}, ISSN={["1873-2844"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84973922839&partnerID=MN8TOARS}, DOI={10.1016/j.atmosenv.2016.06.021}, abstractNote={The U.S. National Oceanic and Atmospheric Administration (NOAA) is responsible for forecasting elevated levels of air pollution within the National Air Quality Forecast Capability (NAQFC). The current research uses measurements gathered in the DISCOVER-AQ Colorado field campaign and the concurrent Front Range Air Pollution and Photochemistry Experiment (FRAPPE) to test performance of the NAQFC CMAQ modeling framework for predicting NH3. The DISCOVER-AQ and FRAPPE field campaigns were carried out in July and August 2014 in Northeast Colorado. Model predictions are compared with measurements of NH3 gas concentrations and the NH4+ component of fine particulate matter concentrations measured directly by the aircraft in flight. We also compare CMAQ predictions with NH3 measurements from ground-based monitors within the DISCOVER-AQ Colorado geographic domain, and from the Tropospheric Emission Spectrometer (TES) on the Aura satellite. In situ aircraft measurements carried out in July and August of 2014 suggest that the NAQFC CMAQ model underestimated the NH3 concentration in Northeastern Colorado by a factor of ∼2.7 (NMB = −63%). Ground-level monitors also produced a similar result. Average satellite-retrieved NH3 levels also exceeded model predictions by a factor of 1.5–4.2 (NMB = −33 to −76%). The underestimation of NH3 was not accompanied by an underestimation of particulate NH4+, which is further controlled by factors including acid availability, removal rate, and gas-particle partition. The average measured concentration of NH4+ was close to the average predication (NMB = +18%). Seasonal patterns measured at an AMoN site in the region suggest that the underestimation of NH3 is not due to the seasonal allocation of emissions, but to the overall annual emissions estimate. The underestimation of NH3 varied across the study domain, with the largest differences occurring in a region of intensive agriculture near Greeley, Colorado, and in the vicinity of Denver. The NAQFC modeling framework did not include a recently developed bidirectional flux algorithm for NH3, which has shown to considerably improve NH3 modeling in agricultural regions. The bidirectional flux algorithm, however, is not expected to obtain the magnitude of this increase sufficient to overcome the underestimation of NH3 found in this study. Our results suggest that further improvement of the emission inventories and modeling approaches are required to reduce the bias in NAQFC NH3 modeling predictions.}, journal={ATMOSPHERIC ENVIRONMENT}, author={Battye, William H. and Bray, Casey D. and Aneja, Viney P. and Tong, Daniel and Lee, Pius and Tang, Youhua}, year={2016}, month={Sep}, pages={342–351} }