@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{hallar_brown_crosman_barsanti_cappa_faloona_fast_holmes_horel_lin_et al._2021, title={Coupled Air Quality and Boundary-Layer Meteorology in Western US Basins during Winter: Design and Rationale for a Comprehensive Study}, volume={102}, ISSN={["1520-0477"]}, DOI={10.1175/BAMS-D-20-0017.1}, abstractNote={Abstract}, number={10}, journal={BULLETIN OF THE AMERICAN METEOROLOGICAL SOCIETY}, author={Hallar, A. Gannet and Brown, Steven S. and Crosman, Erik and Barsanti, Kelley and Cappa, Christopher D. and Faloona, Ian and Fast, Jerome and Holmes, Heather A. and Horel, John and Lin, John and et al.}, year={2021}, month={Oct}, pages={E2012–E2033} }
 @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{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{bray_strum_simon_riddick_kosusko_menetrez_hays_rao_2019, title={An assessment of important SPECIATE profiles in the EPA emissions modeling platform and current data gaps}, volume={207}, ISSN={["1873-2844"]}, DOI={10.1016/j.atmosenv.2019.03.013}, abstractNote={The United States (US) Environmental Protection Agency (EPA)'s SPECIATE database contains speciated particulate matter (PM) and volatile organic compound (VOC) emissions profiles. Emissions profiles from anthropogenic combustion, industry, wildfires, and agricultural sources among others are key inputs for creating chemically-resolved emissions inventories for air quality modeling. While the database and its use for air quality modeling are routinely updated and evaluated, this work sets out to systematically prioritize future improvements and communicate speciation data needs to the research community. We first identify the most prominent profiles (PM and VOC) used in the EPA's 2014 emissions modeling platform based on PM mass and VOC mass and reactivity. It is important to note that the on-road profiles were excluded from this analysis since speciation for these profiles is computed internally in the MOVES model. We then investigate these profiles further for quality and to determine whether they were being appropriately matched to source types while also considering regional variability of speciated pollutants. We then applied a quantitative needs assessment ranking system which rates the profile based on age, appropriateness (i.e. is the profile being used appropriately), prevalence in the EPA modeling platform and the quality of the reference. Our analysis shows that the highest ranked profiles (e.g. profile assignments with the highest priority for updates) include PM2.5 profiles for fires (prescribed, agricultural and wild) and VOC profiles for crude oil storage tanks and residential wood combustion of pine wood. Top ranked profiles may indicate either that there are problems with the currently available source testing or that current mappings of profiles to source categories within EPA's modeling platform need improvement. Through this process, we have identified 29 emissions source categories that would benefit from updated mapping. Many of these mapping mismatches are due to lack of emissions testing for appropriate source categories. In addition, we conclude that new source emissions testing would be especially beneficial for residential wood combustion, nonroad gasoline exhaust and nonroad diesel equipment.}, journal={ATMOSPHERIC ENVIRONMENT}, author={Bray, Casey D. and Strum, Madeleine and Simon, Heather and Riddick, Lee and Kosusko, Mike and Menetrez, Marc and Hays, Michael D. and Rao, Venkatesh}, year={2019}, month={Jun}, pages={93–104} }
 @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={Abstract}, 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_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} }