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Yunsoo Choi
Department of Earth and Atmospheric Sciences University of Houston Houston TX USA

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Research letter
Published: 28 July 2021 in Geophysical Research Letters
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Satellite-derived aerosol optical depth (AOD) is negatively impacted by cloud cover and surface reflectivity. As these issues lead to biases, they need to be discarded, which significantly increases the amount of missing data within an image. This paper presents a unique application of the partial convolutional neural network (PCNN) for imputing missing data from the Geostationary Ocean Color Imager (GOCI) by training the PCNN model with the Community Multiscale Air Quality model simulated AOD. The PCNN model outperforms various models and algorithms for imputing GOCI images with a significant amount of missing data (45% of the data set has at least 80% missing pixels) and distance to the nearest known pixel within the GOCI image. Once trained, the model requires significantly less processing time and fewer resources than the other models and methods. The model allows the accurate imputation of remote sensing images within significant amounts of missing data.

ACS Style

Yannic Lops; Arman Pouyaei; Yunsoo Choi; Jia Jung; Ahmed Khan Salman; Alqamah Sayeed. Application of a Partial Convolutional Neural Network for Estimating Geostationary Aerosol Optical Depth Data. Geophysical Research Letters 2021, 48, 1 .

AMA Style

Yannic Lops, Arman Pouyaei, Yunsoo Choi, Jia Jung, Ahmed Khan Salman, Alqamah Sayeed. Application of a Partial Convolutional Neural Network for Estimating Geostationary Aerosol Optical Depth Data. Geophysical Research Letters. 2021; 48 (15):1.

Chicago/Turabian Style

Yannic Lops; Arman Pouyaei; Yunsoo Choi; Jia Jung; Ahmed Khan Salman; Alqamah Sayeed. 2021. "Application of a Partial Convolutional Neural Network for Estimating Geostationary Aerosol Optical Depth Data." Geophysical Research Letters 48, no. 15: 1.

Journal article
Published: 02 July 2021 in Atmospheric Research
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This study investigates the chemical properties of high-concentration sulfate (SO42−) that appeared on the surface of the Yellow Sea during the KORUS-AQ period (April–June 2016). For quantitative analysis, we carry out numerical simulation using the Community Multi-scale Air Quality (CMAQ) model for the KORUS-AQ period (the BASE case) and another simulation of ocean emissions (the OCEAN case) that determines the effect of including ocean emissions on the results of the analysis. CMAQ-simulated (the BASE case) sulfate (SO42−), nitrate (NO3−), ammonium (NH4+), elemental carbon (EC), and organic carbon (OC) show high concentrations of these constituents centering around eastern China, the Liaodong Peninsula, and the western inland area of the Korean Peninsula. SO42−, unlike the other particulate matter constituents, shows high concentrations (up to 14.44 μg/m3) at the surface of the Yellow Sea. Results of the Integrated Process Rate (IPR) analysis show that the chemical production of SO42− over the Yellow Sea can primarily be attributed to the “aerosol process”, which is mainly dependent on weather conditions (e.g., temperature and wind speed) and concentrations of precursors such as SO2 and OH. The results of the analysis of the mechanism of SO42− formation using the Sulfur Tracking Model (STM) show that most chemical SO42− production (79.12%) on the surface of the Yellow Sea is the result of the aqueous-phase chemical reactions following the SO2 oxidation reaction (OH + SO2 → H2SO4 + HO2). Comparing the results of the OCEAN and BASE cases, we find that the primary mechanism of SO42− formation over the Yellow Sea shows no significant change with regard to ocean emissions. These results also confirm that increases in SO42− concentrations (up to 4.79 μg/m3) on the surface of the sea are not proportional to the distribution and amounts of ocean emissions, and in some areas, concentrations could decrease (up to −2.65 μg/m3) as a result of complex non-linear chemical reactions.

ACS Style

Wonbae Jeon; Jaehyeong Park; Yunsoo Choi; JeongHyeok Mun; Dongjin Kim; Cheol-Hee Kim; Hyo-Jung Lee; Juseon Bak; Hyun-Young Jo. The mechanism of the formation of high sulfate concentrations over the Yellow Sea during the KORUS-AQ period: The effect of transport/atmospheric chemistry and ocean emissions. Atmospheric Research 2021, 261, 105756 .

AMA Style

Wonbae Jeon, Jaehyeong Park, Yunsoo Choi, JeongHyeok Mun, Dongjin Kim, Cheol-Hee Kim, Hyo-Jung Lee, Juseon Bak, Hyun-Young Jo. The mechanism of the formation of high sulfate concentrations over the Yellow Sea during the KORUS-AQ period: The effect of transport/atmospheric chemistry and ocean emissions. Atmospheric Research. 2021; 261 ():105756.

Chicago/Turabian Style

Wonbae Jeon; Jaehyeong Park; Yunsoo Choi; JeongHyeok Mun; Dongjin Kim; Cheol-Hee Kim; Hyo-Jung Lee; Juseon Bak; Hyun-Young Jo. 2021. "The mechanism of the formation of high sulfate concentrations over the Yellow Sea during the KORUS-AQ period: The effect of transport/atmospheric chemistry and ocean emissions." Atmospheric Research 261, no. : 105756.

Journal article
Published: 16 June 2021 in Journal of Advances in Modeling Earth Systems
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To improve the representation of convective mixing of atmospheric pollutants in the presence of clouds, we developed a convection module based on Kain and Fritsch (KF) method and implemented it in the CMAQ (Community Multiscale Air Quality) model. The KF-convection method is a mass-flux-based model that accounts for updraft flux, downdraft flux, entrainment, detrainment, and the subsidence effect. The method is consistent with the convection parametrization of the meteorology model. We apply the KF-convection model to an idealized case and to a reference setup prepared for East Asia during the KORUS-AQ campaign period to investigate its impact on carbon monoxide (CO) concentration at various atmospheric altitudes. We investigate the impact of KF-convection on the horizontal distribution of CO concentrations by comparing it to aircraft measurements and the MOPITT CO column. We further discuss two types of impacts of KF-convection: the direct impact caused by vertical movement of CO concentrations by updraft or downdraft and the indirect impact caused by transport of lifted CO concentrations to another region. May 12 saw a high indirect impact originating from the Shanghai region at higher altitudes and a high direct impact of updraft fluxes at 1 km altitude. However, May 26 revealed an immense updraft increasing higher altitude concentrations (up to 40 ppbv) and diverse indirect impacts over the region of the study (±50 ppbv). The overall comparison shows a strong connection between differences in the amount of concentration caused by the direct impact at each altitude with the presence of an updraft at that altitude. The developed model can be employed in large domains (i.e., East Asia, Europe, North America, and Northern Hemisphere) with sub-grid scale cloud modeling to include the impacts of convection.

ACS Style

Arman Pouyaei; Bavand Sadeghi; Yunsoo Choi; Jia Jung; Amir H. Souri; Chun Zhao; Chul Han Song. Development and Implementation of a Physics‐Based Convective Mixing Scheme in the Community Multiscale Air Quality Modeling Framework. Journal of Advances in Modeling Earth Systems 2021, 13, 1 .

AMA Style

Arman Pouyaei, Bavand Sadeghi, Yunsoo Choi, Jia Jung, Amir H. Souri, Chun Zhao, Chul Han Song. Development and Implementation of a Physics‐Based Convective Mixing Scheme in the Community Multiscale Air Quality Modeling Framework. Journal of Advances in Modeling Earth Systems. 2021; 13 (6):1.

Chicago/Turabian Style

Arman Pouyaei; Bavand Sadeghi; Yunsoo Choi; Jia Jung; Amir H. Souri; Chun Zhao; Chul Han Song. 2021. "Development and Implementation of a Physics‐Based Convective Mixing Scheme in the Community Multiscale Air Quality Modeling Framework." Journal of Advances in Modeling Earth Systems 13, no. 6: 1.

Journal article
Published: 26 March 2021 in Atmospheric Research
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Despite the considerable reductions in primary and secondary air pollutants in China, surface ozone levels have increased in recent years. We report a trend of 3.3 ± 4.7 μg.m−3 year−1 in the annual mean maximum daily average ozone over an 8-h period (MDA8 ozone) across China between 2015 and 2019. Leveraging the Kolmogorov–Zurbenko filter method, we find that meteorology enhanced the ozone levels in Beijing–Tianjin–Hebei (BTH), the Yangtze River Delta (YRD), and the Pearl River Delta (PRD) while the reduction of solar radiation and the planetary boundary layer height accelerated ozone decreases in the Sichuan Basin (SCB) after 2017. Solar radiation and temperature increases, together with the reduction in sea level pressure, were the main contributors to enhance ozone in the YRD. They also contributed to 32% of ozone increases in BTH. Weaker meridional wind, lower relative humidity, and higher temperature escalated ozone enhancement in the PRD between 2016 and 2018. Regarding precursor emissions, NO2 long-term components showed a noticeable decline in all regions after 2017, partially due to the introduction of the most current action plan to reduce air pollutants over China in 2018. In contrast, the satellite-retrieved data suggest that VOC concentrations did not change substantially in target regions during the study period. After 2017, however, VOCs slightly increased in BTH, the YRD, and the PRD, which might be driven by temperature enhancements. Overall, the impact of meteorology on ozone variations was dominant in the YRD, the PRD, and the SCB from 2015 to 2019. Precursor emissions, however, played a leading role in ozone enhancement over the BTH. We also found that BTH and the YRD were in a transitional ozone formation regime while the PRD and the SCB tended to be more NOx-sensitive.

ACS Style

SeyedAli Mousavinezhad; Yunsoo Choi; Arman Pouyaei; Masoud Ghahremanloo; Delaney L. Nelson. A comprehensive investigation of surface ozone pollution in China, 2015–2019: Separating the contributions from meteorology and precursor emissions. Atmospheric Research 2021, 257, 105599 .

AMA Style

SeyedAli Mousavinezhad, Yunsoo Choi, Arman Pouyaei, Masoud Ghahremanloo, Delaney L. Nelson. A comprehensive investigation of surface ozone pollution in China, 2015–2019: Separating the contributions from meteorology and precursor emissions. Atmospheric Research. 2021; 257 ():105599.

Chicago/Turabian Style

SeyedAli Mousavinezhad; Yunsoo Choi; Arman Pouyaei; Masoud Ghahremanloo; Delaney L. Nelson. 2021. "A comprehensive investigation of surface ozone pollution in China, 2015–2019: Separating the contributions from meteorology and precursor emissions." Atmospheric Research 257, no. : 105599.

Journal article
Published: 02 March 2021 in Journal of Geophysical Research: Atmospheres
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In this study, we investigate the impact of sea fog over the Yellow Sea on air quality with the direct effect of aerosols for the entire year of 2016. Using the WRF‐CMAQ two‐way coupled model, we perform four model simulations with the up‐to‐date emission inventory over East Asia and dynamic chemical boundary conditions provided by hemispheric model simulations. During the spring of 2016, prevailing west‐southwesterly winds and anticyclones caused the formation of a temperature inversion over the Yellow Sea, providing favorable conditions for the formation of fog. The inclusion of the direct effect of aerosols enhanced its strength. On foggy days, we find dominant changes of aerosols at an altitude of 150‐200 m over the Yellow Sea resulted by the production through aqueous chemistry (∼12.36% and ∼3.08% increases in sulfate and ammonium) and loss via the wet deposition process (∼‐2.94% decrease in nitrate); we also find stronger wet deposition of all species occurring in PBL. Stagnant conditions associated with reduced air temperature caused by the direct effect of aerosols enhanced aerosol chemistry, especially in coastal regions, and it exceeded the loss of nitrate. The transport of air pollutants affected by sea fog extended to a much broader region. Our findings show that the Yellow Sea acts as not only a path of long‐range transport but also as a sink and source of air pollutants. Further study should investigate changes in the impact of sea fog on air quality in conjunction with changes in the concentrations of aerosols and the climate. This article is protected by copyright. All rights reserved.

ACS Style

Jia Jung; Yunsoo Choi; David C. Wong; Delaney Nelson; Sojin Lee. Role of Sea Fog Over the Yellow Sea on Air Quality With the Direct Effect of Aerosols. Journal of Geophysical Research: Atmospheres 2021, 126, 1 .

AMA Style

Jia Jung, Yunsoo Choi, David C. Wong, Delaney Nelson, Sojin Lee. Role of Sea Fog Over the Yellow Sea on Air Quality With the Direct Effect of Aerosols. Journal of Geophysical Research: Atmospheres. 2021; 126 (5):1.

Chicago/Turabian Style

Jia Jung; Yunsoo Choi; David C. Wong; Delaney Nelson; Sojin Lee. 2021. "Role of Sea Fog Over the Yellow Sea on Air Quality With the Direct Effect of Aerosols." Journal of Geophysical Research: Atmospheres 126, no. 5: 1.

Journal article
Published: 12 January 2021 in Atmospheric Environment
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PM2.5 is an important atmospheric constituent associated to human health. Therefore, the capability of estimating PM2.5 concentrations at high spatiotemporal resolutions, particularly in places with no ground stations, would be invaluable. Although several studies have involved the estimation of PM2.5, few have estimated PM2.5 concentrations at high spatial resolutions. In this study, we leverage the aerosol optical depth (AOD) and random forest (RF) algorithm to estimate daily 1-km PM2.5 concentrations over Texas from 2014 to 2018. For this purpose, we use collection 6 Multi-Angle Implementation of Atmospheric Correction (MAIAC) AOD products from the Moderate Resolution Imaging Spectroradiometer (MODIS). To address the different sources and speciation of PM2.5 over Texas, we use several important control parameters. As a result, the accuracy of RF remains consistent throughout the study area. After estimating ground-level PM2.5 levels, we apply a ten-fold cross-validation approach to obtain a correlation coefficient (R) of 0.83–0.90 and a mean absolute bias (MAB) of 1.47–1.77 μg/m3. Our results show that RF is highly capable of estimating ground-level PM2.5 concentrations. In addition to the RF model, we also compare the capability of commonly used models, including multiple linear regression (MLR) and mixed effects model (MEM), for estimating the PM2.5 concentrations of global regions. Results indicate that RF, compared to the other models, has the highest accuracy, MEM the second-highest, and MLR the third. We also leverage the USEPA Environmental Benefits Mapping and Analysis Program Community Edition (BenMAP-CE) to estimate the impact of changes in PM2.5 levels on the number of respiratory-related premature mortalities in Texas in 2014–2018. Considering 2014 as the baseline year, the BenMAP analyses reveal that PM2.5 reductions could have prevented a large number of premature mortalities, particularly among adults aged 25–99, from 2014 to 2018 in Texas.

ACS Style

Masoud Ghahremanloo; Yunsoo Choi; Alqamah Sayeed; Ahmed Khan Salman; Shuai Pan; Meisam Amani. Estimating daily high-resolution PM2.5 concentrations over Texas: Machine Learning approach. Atmospheric Environment 2021, 247, 118209 .

AMA Style

Masoud Ghahremanloo, Yunsoo Choi, Alqamah Sayeed, Ahmed Khan Salman, Shuai Pan, Meisam Amani. Estimating daily high-resolution PM2.5 concentrations over Texas: Machine Learning approach. Atmospheric Environment. 2021; 247 ():118209.

Chicago/Turabian Style

Masoud Ghahremanloo; Yunsoo Choi; Alqamah Sayeed; Ahmed Khan Salman; Shuai Pan; Meisam Amani. 2021. "Estimating daily high-resolution PM2.5 concentrations over Texas: Machine Learning approach." Atmospheric Environment 247, no. : 118209.

Model evaluation paper
Published: 09 December 2020 in Geoscientific Model Development
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As the deep learning algorithm has become a popular data analysis technique, atmospheric scientists should have a balanced perception of its strengths and limitations so that they can provide a powerful analysis of complex data with well-established procedures. Despite the enormous success of the algorithm in numerous applications, certain issues related to its applications in air quality forecasting (AQF) require further analysis and discussion. This study addresses significant limitations of an advanced deep learning algorithm, the convolutional neural network (CNN), in two common applications: (i) a real-time AQF model and (ii) a post-processing tool in a dynamical AQF model, the Community Multi-scale Air Quality Model (CMAQ). In both cases, the CNN model shows promising accuracy for ozone prediction 24 h in advance in both the United States of America and South Korea (with an overall index of agreement exceeding 0.8). For the first case, we use the wavelet transform to determine the reasons behind the poor performance of CNN during the nighttime, cold months, and high-ozone episodes. We find that when fine wavelet modes (hourly and daily) are relatively weak or when coarse wavelet modes (weekly) are strong, the CNN model produces less accurate forecasts. For the second case, we use the dynamic time warping (DTW) distance analysis to compare post-processed results with their CMAQ counterparts (as a base model). For CMAQ results that show a consistent DTW distance from the observation, the post-processing approach properly addresses the modeling bias with predicted indexes of agreement exceeding 0.85. When the DTW distance of CMAQ versus observation is irregular, the post-processing approach is unlikely to perform satisfactorily. Awareness of the limitations in CNN models will enable scientists to develop more accurate regional or local air quality forecasting systems by identifying the affecting factors in high-concentration episodes.

ACS Style

Ebrahim Eslami; Yunsoo Choi; Yannic Lops; Alqamah Sayeed; Ahmed Khan Salman. Using wavelet transform and dynamic time warping to identify the limitations of the CNN model as an air quality forecasting system. Geoscientific Model Development 2020, 13, 6237 -6251.

AMA Style

Ebrahim Eslami, Yunsoo Choi, Yannic Lops, Alqamah Sayeed, Ahmed Khan Salman. Using wavelet transform and dynamic time warping to identify the limitations of the CNN model as an air quality forecasting system. Geoscientific Model Development. 2020; 13 (12):6237-6251.

Chicago/Turabian Style

Ebrahim Eslami; Yunsoo Choi; Yannic Lops; Alqamah Sayeed; Ahmed Khan Salman. 2020. "Using wavelet transform and dynamic time warping to identify the limitations of the CNN model as an air quality forecasting system." Geoscientific Model Development 13, no. 12: 6237-6251.

Journal article
Published: 07 September 2020 in Science of The Total Environment
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This study leverages satellite remote sensing to investigate the impact of the coronavirus outbreak and the resulting lockdown of public venues on air pollution levels in East Asia. We analyze data from the Sentinel-5P and the Himawari-8 satellites to examine concentrations of NO2, HCHO, SO2, and CO, and the aerosol optical depth (AOD) over the BTH, Wuhan, Seoul, and Tokyo regions in February 2019 and February 2020. Results show that most of the concentrations of pollutants are lower than those of February 2019. Compared to other pollutants, NO2 experienced the most significant reductions by almost 54%, 83%, 33%, and 19% decrease in BTH, Wuhan, Seoul, and Tokyo, respectively. The greatest reductions in pollutants occurred in Wuhan, with a decrease of almost 83%, 11%, 71%, and 4% in the column densities of NO2, HCHO, SO2, and CO, respectively, and a decrease of about 62% in the AOD. Although NO2, CO, and formaldehyde concentrations decreased in the Seoul and Tokyo metropolitan areas compared to the previous year, concentrations of SO2 showed an increase in these two regions due to the effect of transport from polluted upwind regions. We also show that meteorological factors were not the main reason for the dramatic reductions of pollutants in the atmosphere. Moreover, an investigation of the HCHO/NO2 ratio shows that in many regions of East China, particularly in Wuhan, ozone production in February 2020 is less NOX saturated during the daytime than it was in February 2019. With large reductions in the concentrations of NO2 during lockdown situations, we find that significant increases in surface ozone in East China from February 2019 to February 2020 are likely the result of less reaction of NO and O3 caused by significantly reduced NOX concentrations and less NOX saturation in East China during the daytime.

ACS Style

Masoud Ghahremanloo; Yannic Lops; Yunsoo Choi; SeyedAli Mousavinezhad. Impact of the COVID-19 outbreak on air pollution levels in East Asia. Science of The Total Environment 2020, 754, 142226 -142226.

AMA Style

Masoud Ghahremanloo, Yannic Lops, Yunsoo Choi, SeyedAli Mousavinezhad. Impact of the COVID-19 outbreak on air pollution levels in East Asia. Science of The Total Environment. 2020; 754 ():142226-142226.

Chicago/Turabian Style

Masoud Ghahremanloo; Yannic Lops; Yunsoo Choi; SeyedAli Mousavinezhad. 2020. "Impact of the COVID-19 outbreak on air pollution levels in East Asia." Science of The Total Environment 754, no. : 142226-142226.

Communication
Published: 30 August 2020 in Sustainability
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The COVID-19 pandemic has significantly affected human health and the economy. The implementation of social distancing practices to combat the virus spread, however, has led to a notable improvement in air quality. This study compared the surface air quality monitoring data from the United States Environmental Protection Agency (U.S. EPA)’s AirNow network during the period 20 March–5 May in 2020 to those in 2015–2019 from the Air Quality System (AQS) network over the state of California. The results indicated changes in fine particulate matter (PM2.5) of −2.04 ± 1.57 μg m−3 and ozone of −3.07 ± 2.86 ppb. If the air quality improvements persist over a year, it could potentially lead to 3970–8900 prevented premature deaths annually (note: the estimates of prevented premature deaths have large uncertainties). Public transit demand showed dramatic declines (~80%). The pandemic provides an opportunity to exhibit how substantially human behavior could impact on air quality. To address both the pandemic and climate change issues, better strategies are needed to affect behavior, such as ensuring safer shared mobility, the higher adoption of telecommuting, automation in the freight sector, and cleaner energy transition.

ACS Style

Shuai Pan; Jia Jung; Zitian Li; Xuewei Hou; Anirban Roy; Yunsoo Choi; H. Gao. Air Quality Implications of COVID-19 in California. Sustainability 2020, 12, 7067 .

AMA Style

Shuai Pan, Jia Jung, Zitian Li, Xuewei Hou, Anirban Roy, Yunsoo Choi, H. Gao. Air Quality Implications of COVID-19 in California. Sustainability. 2020; 12 (17):7067.

Chicago/Turabian Style

Shuai Pan; Jia Jung; Zitian Li; Xuewei Hou; Anirban Roy; Yunsoo Choi; H. Gao. 2020. "Air Quality Implications of COVID-19 in California." Sustainability 12, no. 17: 7067.

Model evaluation paper
Published: 05 August 2020 in Geoscientific Model Development
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This paper introduces a novel Lagrangian model (Concentration Trajectory Route of Air pollution with an Integrated Lagrangian model, C-TRAIL version 1.0) output from a Eulerian 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 (Community Multiscale Air Quality) Eulerian 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.

ACS Style

Arman Pouyaei; Yunsoo Choi; Jia Jung; Bavand Sadeghi; Chul Han Song. Concentration Trajectory Route of Air pollution with an Integrated Lagrangian model (C-TRAIL Model v1.0) derived from the Community Multiscale Air Quality Model (CMAQ Model v5.2). Geoscientific Model Development 2020, 13, 3489 -3505.

AMA Style

Arman Pouyaei, Yunsoo Choi, Jia Jung, Bavand Sadeghi, Chul Han Song. Concentration Trajectory Route of Air pollution with an Integrated Lagrangian model (C-TRAIL Model v1.0) derived from the Community Multiscale Air Quality Model (CMAQ Model v5.2). Geoscientific Model Development. 2020; 13 (8):3489-3505.

Chicago/Turabian Style

Arman Pouyaei; Yunsoo Choi; Jia Jung; Bavand Sadeghi; Chul Han Song. 2020. "Concentration Trajectory Route of Air pollution with an Integrated Lagrangian model (C-TRAIL Model v1.0) derived from the Community Multiscale Air Quality Model (CMAQ Model v5.2)." Geoscientific Model Development 13, no. 8: 3489-3505.

Journal article
Published: 09 March 2020 in Environmental Pollution
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The interpretation of large air pollution datasets involves a great deal of complexity. To gain a better understanding of the complicated relationships and patterns within datasets, we perform factor analysis. Between December 2015 and December 2017, fine particulate matter (PM2.5) samples were collected at a suburban site northeast of the Houston metropolitan area, TX. A total of 233 filter samples were analyzed for chemical composition. The average of all PM2.5 samples consisted of 38.1% inorganic ions, 28.9% elements, 29.1% organic carbon, and 3.7% elemental carbon and other organic materials. Principal component analysis and positive matrix factorization were utilized to identify eight factors: regional aerosols, biomass burning, gasoline combustion, industry, crustal material, incineration, marine dust, and fireworks. The first three contributed more than 70% of the total PM2.5 mass. The receptor models also captured the impact of fireworks and classified it as a source of PM2.5 over Houston. To identify the origins of air masses transporting PM2.5 to the site, we applied the NOAA hybrid single-particle Lagrangian integrated trajectory model and performed a cluster analysis of back trajectories and determined six cluster source regions: the Gulf of Mexico, the Southeast, two midwestern clusters, the Pacific Northwest, and the Southwest. The results of our analysis show that during the summer months, marine and crustal sources were often associated with an onshore flow from the Gulf of Mexico and that four clusters covering 38% of the West Liberty area were strongly influenced by trajectories originating from biomass burning. The results of this study represented a variety of sources that affect the PM2.5 over the Houston metropolitan area. The quantified contributions of these sources could provide policymakers with useful information for developing more efficient control systems and making more effective decisions to cope with the harmful effects of ambient air pollution.

ACS Style

Bavand Sadeghi; Yunsoo Choi; Subin Yoon; James Flynn; Alexander Kotsakis; Sojin Lee. The characterization of fine particulate matter downwind of Houston: Using integrated factor analysis to identify anthropogenic and natural sources. Environmental Pollution 2020, 262, 114345 .

AMA Style

Bavand Sadeghi, Yunsoo Choi, Subin Yoon, James Flynn, Alexander Kotsakis, Sojin Lee. The characterization of fine particulate matter downwind of Houston: Using integrated factor analysis to identify anthropogenic and natural sources. Environmental Pollution. 2020; 262 ():114345.

Chicago/Turabian Style

Bavand Sadeghi; Yunsoo Choi; Subin Yoon; James Flynn; Alexander Kotsakis; Sojin Lee. 2020. "The characterization of fine particulate matter downwind of Houston: Using integrated factor analysis to identify anthropogenic and natural sources." Environmental Pollution 262, no. : 114345.

Original article
Published: 12 December 2019 in Neural Computing and Applications
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Several studies have used regression analyses to forecast pollen concentrations, yet few have applied a deep neural network in their research. This study implements a deep convolutional neural network with the great potential to recognize patterns of pollen phenomena that enable the prediction of pollen concentrations. We train the model using data from 2009 to 2015 from multiple meteorological datasets, satellite data and processed data reflecting pollen flux as input for the model. The model forecasts pollen counts 1–7 days ahead for the entire year of 2016. Comparison of daily forecasts to observations, the algorithm obtains a relatively high index of agreement and Pearson correlation coefficient of up to 0.90 and 0.88, respectively. An evaluation of categorical statistics based on defined threshold levels shows satisfactory results. Critical success index of the model forecasts is as high as 0.887 for weed pollen, 0.646 for tree pollen, and 0.294 for grass pollen. Forecasts of grass pollen exhibit the largest decrease in accuracy because of the strong variance in annual pollen concentrations. Forecasts of weed pollen exhibit the greatest consistency, with a 7-day forecast correlation and index of agreement of 0.82 and 0.77, respectively, during the peak season. This correlates with the consistency of annual and seasonal trends of weed pollen within the study area. Compared to the conventional modeling approaches, convolutional neural network shows a promising ability to predict pollen for multiple days to allow individuals with allergies to take proper precautions during high pollen days.

ACS Style

Yannic Lops; Yunsoo Choi; Ebrahim Eslami; Alqamah Sayeed. Real-time 7-day forecast of pollen counts using a deep convolutional neural network. Neural Computing and Applications 2019, 32, 11827 -11836.

AMA Style

Yannic Lops, Yunsoo Choi, Ebrahim Eslami, Alqamah Sayeed. Real-time 7-day forecast of pollen counts using a deep convolutional neural network. Neural Computing and Applications. 2019; 32 (15):11827-11836.

Chicago/Turabian Style

Yannic Lops; Yunsoo Choi; Ebrahim Eslami; Alqamah Sayeed. 2019. "Real-time 7-day forecast of pollen counts using a deep convolutional neural network." Neural Computing and Applications 32, no. 15: 11827-11836.

Journal article
Published: 01 December 2019 in Journal of Applied Meteorology and Climatology
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In this study, we classify wind patterns that impacted PM10 concentrations in the Seoul Metropolitan Area (SMA), South Korea, from 2012 to 2016 and analyze their contributions to annual variability in particulate matter smaller than 10 μm in diameter (PM10). Using a k-means clustering analysis, we identify major wind patterns affecting PM10 concentrations from 2002 to 2016. We confirm that the impact of wind pattern changes on PM10 variability in the SMA from 2012 to 2016 was relatively greater than the impact from 2002 to 2011. We find that PM10 from 2012 to 2016 was mainly affected by wind patterns that were 1) associated with the transport of foreign emissions (our clusters H2, H4, and H5) and 2) favorable for ventilation (our clusters L1 and L2). This finding shows that PM10 variability was determined by overall variations in the respective wind patterns particularly associated with high (over 80 μg m−3) and low (below 30 μg m−3) PM10 concentrations. The results from 2012 to 2016 CMAQ simulations indicate that the effects of meteorological conditions (e.g., wind, temperature, humidity, and so on) on PM10 vary from year to year. The calculated PM10 anomalies from 2012 to 2016 were −4.97, 3.55, 1.73, 0.15, and −0.46 μg m−3, suggesting that the wind patterns in 2012 produced the least PM10 and those in 2013 produced the most.

ACS Style

Wonbae Jeon; Hwa Woon Lee; Tae-Jin Lee; Jung-Woo Yoo; JeongHyeok Mun; Soon-Hwan Lee; Yunsoo Choi. Impact of Varying Wind Patterns on PM10 Concentrations in the Seoul Metropolitan Area in South Korea from 2012 to 2016. Journal of Applied Meteorology and Climatology 2019, 58, 2743 -2754.

AMA Style

Wonbae Jeon, Hwa Woon Lee, Tae-Jin Lee, Jung-Woo Yoo, JeongHyeok Mun, Soon-Hwan Lee, Yunsoo Choi. Impact of Varying Wind Patterns on PM10 Concentrations in the Seoul Metropolitan Area in South Korea from 2012 to 2016. Journal of Applied Meteorology and Climatology. 2019; 58 (12):2743-2754.

Chicago/Turabian Style

Wonbae Jeon; Hwa Woon Lee; Tae-Jin Lee; Jung-Woo Yoo; JeongHyeok Mun; Soon-Hwan Lee; Yunsoo Choi. 2019. "Impact of Varying Wind Patterns on PM10 Concentrations in the Seoul Metropolitan Area in South Korea from 2012 to 2016." Journal of Applied Meteorology and Climatology 58, no. 12: 2743-2754.

Journal article
Published: 22 July 2019 in Journal of Geophysical Research: Atmospheres
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ACS Style

Jia Jung; Amir H. Souri; David C. Wong; Sojin Lee; Wonbae Jeon; Jhoon Kim; Yunsoo Choi. The Impact of the Direct Effect of Aerosols on Meteorology and Air Quality Using Aerosol Optical Depth Assimilation During the KORUS‐AQ Campaign. Journal of Geophysical Research: Atmospheres 2019, 124, 8303 -8319.

AMA Style

Jia Jung, Amir H. Souri, David C. Wong, Sojin Lee, Wonbae Jeon, Jhoon Kim, Yunsoo Choi. The Impact of the Direct Effect of Aerosols on Meteorology and Air Quality Using Aerosol Optical Depth Assimilation During the KORUS‐AQ Campaign. Journal of Geophysical Research: Atmospheres. 2019; 124 (14):8303-8319.

Chicago/Turabian Style

Jia Jung; Amir H. Souri; David C. Wong; Sojin Lee; Wonbae Jeon; Jhoon Kim; Yunsoo Choi. 2019. "The Impact of the Direct Effect of Aerosols on Meteorology and Air Quality Using Aerosol Optical Depth Assimilation During the KORUS‐AQ Campaign." Journal of Geophysical Research: Atmospheres 124, no. 14: 8303-8319.

Original article
Published: 10 June 2019 in Neural Computing and Applications
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Six generalized machine learning (ML) ensemble models were developed to predict the real-time hourly ozone concentration of the following day. These models were used to forecast hourly ozone concentrations of the following day for all of 2017 in the city of Seoul, South Korea. To prepare the training dataset, it was referred to observed meteorology and air pollution parameters of the 2014–2016 period. The ensemble models fuse two regression models: a low-ozone peak model and a high-ozone model. For both, extremely randomized trees and deep neural networks were used. A regularization approach was also adopted that adjusts the model toward capturing higher ozone peaks by resampling the training dataset based on the peaks. Adopting the proposed ML ensemble forecasting method over single-model ML techniques as a part of mainstream practice for air quality forecasting will be beneficial for several reasons. For one, the proposed method, which captures daily maximum ozone concentrations during the high-ozone season (April–September), reduces the ozone peak prediction error by 5 to 30 ppb. In addition, compared to station-specific (independent) ML models with more frequent low-ozone values, models are trained with a uniformly distributed dataset, so they are more generalizable in nature. As a result, unlike station-specific models, they retain their accuracy (yearly IOA = 0.84–0.89) in all stations with an IOA increment. Proposed models also make predictions several times faster, requiring only one-time training for predicting an entire station network. Based on a categorical analysis of the training dataset, an algorithm was proposed for selecting the most suitable model for each month. The “best” model further improves the accuracy of both the ML ensemble and individual models by up to 2.4%. This study shows that the ML ensemble modeling approach is a fast, reliable, and robust technique that can benefit environmental decision-makers in urban regions.

ACS Style

Ebrahim Eslami; Ahmed Salman; Yunsoo Choi; Alqamah Sayeed; Yannic Lops. A data ensemble approach for real-time air quality forecasting using extremely randomized trees and deep neural networks. Neural Computing and Applications 2019, 32, 7563 -7579.

AMA Style

Ebrahim Eslami, Ahmed Salman, Yunsoo Choi, Alqamah Sayeed, Yannic Lops. A data ensemble approach for real-time air quality forecasting using extremely randomized trees and deep neural networks. Neural Computing and Applications. 2019; 32 (11):7563-7579.

Chicago/Turabian Style

Ebrahim Eslami; Ahmed Salman; Yunsoo Choi; Alqamah Sayeed; Yannic Lops. 2019. "A data ensemble approach for real-time air quality forecasting using extremely randomized trees and deep neural networks." Neural Computing and Applications 32, no. 11: 7563-7579.

Journal article
Published: 01 April 2019 in Journal of Applied Meteorology and Climatology
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This study analyzes wind patterns in the Dallas–Fort Worth (DFW) area to gain a clearer understanding of meteorological patterns that have historically led to ozone exceedances in this region. Using a clustering algorithm called “self-organizing maps,” we analyzed five notable characteristic regional wind patterns that occurred between April and October in 2000–14. A regional-scale high pressure system, cluster 2, produced weak southeast winds over DFW and accounted for 35.2% of ozone exceedances. Clusters 1 and 5, characterized by southwesterly winds over the DFW area, were together associated with one-third of total ozone exceedances and show quantifiable impacts of the Barnett Shale region on downwind ozone production. Cluster 3, associated with Bermuda-high conditions, had relatively lower ozone in DFW (45.3 ppbv) resulting from transport of lower background ozone from the Gulf of Mexico. For clusters that produce southeasterly or southwesterly winds over Houston, ozone values in DFW were always larger than those in Houston. Further, to determine the potential impact of Houston pollution on DFW ozone, a sensitivity simulation with no Houston emissions and a base simulation were performed. The difference between the simulations revealed ozone enhancements of 1–2 ppbv and coincident enhancements in NOy under south-southeasterly wind conditions. From these results, we conclude that downwind pollution from Houston and the Barnett Shale area exacerbates DFW ozone concentrations, underscoring the impacts of specific wind patterns on air quality in DFW.

ACS Style

Alexander Kotsakis; Yunsoo Choi; Amir H. Souri; Wonbae Jeon; James Flynn. Characterization of Regional Wind Patterns Using Self-Organizing Maps: Impact on Dallas–Fort Worth Long-Term Ozone Trends. Journal of Applied Meteorology and Climatology 2019, 58, 757 -772.

AMA Style

Alexander Kotsakis, Yunsoo Choi, Amir H. Souri, Wonbae Jeon, James Flynn. Characterization of Regional Wind Patterns Using Self-Organizing Maps: Impact on Dallas–Fort Worth Long-Term Ozone Trends. Journal of Applied Meteorology and Climatology. 2019; 58 (4):757-772.

Chicago/Turabian Style

Alexander Kotsakis; Yunsoo Choi; Amir H. Souri; Wonbae Jeon; James Flynn. 2019. "Characterization of Regional Wind Patterns Using Self-Organizing Maps: Impact on Dallas–Fort Worth Long-Term Ozone Trends." Journal of Applied Meteorology and Climatology 58, no. 4: 757-772.

Journal article
Published: 24 March 2019 in Atmospheric Environment
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Significant emissions from transportation contribute to the formation of O3 and fine particulate matter (PM2.5), causing poor air quality and health. In this study, four scenarios were developed to understand how future fleet electrification and turnover of both gasoline and diesel vehicles affect air quality and health in the Houston Metropolitan area. These scenarios considered increased vehicle activity and various configurations of emissions controls. Comparing to a base year of 2013, model predictions for 2040 indicated a ∼50% emissions increase in the Business As Usual (BAU) case, and ∼50%, ∼75%, and ∼95% reductions in the three distinct emissions control cases, the Moderate Electrification (ME), Aggressive Electrification (AE), and Complete Turnover (CT) cases, respectively. Each modeling scenario was conducted using a high-resolution (1 km) WRF-SMOKE-CMAQ-BenMAP air quality and health modeling framework, which helped capture urban features in higher detail. The emissions control cases resulted in 1–4 ppb maximum 8 h O3 increase along highways and reductions both in the regions enclosed by the highways and those downwind. Simulated PM2.5 concentrations decreased between 0.5 and 2 μg m−3. Health impact results suggest that increased O3 and PM2.5 concentrations from the BAU case will lead to 122 additional premature deaths with respect to 2013. However, reduced emissions for the control cases (ME, AE, CT) will prevent 114–246 premature deaths. Additionally, about 7,500 asthma exacerbation and 5,500 school loss days will be prevented in the ME case, benefiting younger individuals. The economic benefits generally followed the same trends as health impacts. The analysis framework developed in this study can be applied to other metropolitan areas. The effects of motor vehicle electrification on power plant emissions were estimated using the Argonne National Laboratory's Autonomie data, and indicated the electrification load to be negligible as opposed to projected electricity generation.

ACS Style

Shuai Pan; Anirban Roy; Yunsoo Choi; Ebrahim Eslami; Stephanie Thomas; Xiangyu Jiang; H. Oliver Gao. Potential impacts of electric vehicles on air quality and health endpoints in the Greater Houston Area in 2040. Atmospheric Environment 2019, 207, 38 -51.

AMA Style

Shuai Pan, Anirban Roy, Yunsoo Choi, Ebrahim Eslami, Stephanie Thomas, Xiangyu Jiang, H. Oliver Gao. Potential impacts of electric vehicles on air quality and health endpoints in the Greater Houston Area in 2040. Atmospheric Environment. 2019; 207 ():38-51.

Chicago/Turabian Style

Shuai Pan; Anirban Roy; Yunsoo Choi; Ebrahim Eslami; Stephanie Thomas; Xiangyu Jiang; H. Oliver Gao. 2019. "Potential impacts of electric vehicles on air quality and health endpoints in the Greater Houston Area in 2040." Atmospheric Environment 207, no. : 38-51.

Article
Published: 25 March 2018 in Journal of Geophysical Research: Atmospheres
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A number of satellite-based instruments have become an essential part of monitoring emissions. Despite sound theoretical inversion techniques, the insufficient samples, and the footprint size of current observations have introduced an obstacle to narrow the inversion window for regional models. These key limitations can be partially resolved by a set of modest high-quality measurements from airborne remote sensing. This study illustrates the feasibility of nitrogen dioxide (NO2) columns from the Geostationary Coastal and Air Pollution Events Airborne Simulator (GCAS) to constrain anthropogenic NOx emissions in the Houston-Galveston-Brazoria area. We convert slant column densities to vertical columns using a radiative transfer model with i) NO2 profiles from a high-resolution regional model (1×1 km2) constrained by P-3B aircraft measurements, ii) the consideration of aerosol optical thickness impacts on radiance at NO2 absorption line, and iii) high-resolution surface albedo constrained by ground-based spectrometers. We characterize errors in the GCAS NO2 columns by comparing them to Pandora measurements and find a striking correlation (r> 0.75) with an uncertainty of 3.5×1015 molec.cm-2. On nine of ten total days, the constrained anthropogenic emissions by a Kalman filter yield an overall 2-50% reduction in polluted areas, partly counterbalancing the well-documented positive bias of the model. The inversion, however, boosts emissions by 94% in the same areas on a day when an unprecedented local emissions event potentially occurred, significantly mitigating the bias of the model. The capability of GCAS at detecting such an event ensures the significance of forthcoming geostationary satellites for timely estimates of top-down emissions.

ACS Style

Amir H. Souri; Yunsoo Choi; Shuai Pan; Gabriele Curci; Caroline R. Nowlan; Scott J. Janz; Matthew G. Kowalewski; Junjie Liu; Jay R. Herman; Andrew J. Weinheimer. First Top‐Down Estimates of Anthropogenic NO x Emissions Using High‐Resolution Airborne Remote Sensing Observations. Journal of Geophysical Research: Atmospheres 2018, 123, 3269 -3284.

AMA Style

Amir H. Souri, Yunsoo Choi, Shuai Pan, Gabriele Curci, Caroline R. Nowlan, Scott J. Janz, Matthew G. Kowalewski, Junjie Liu, Jay R. Herman, Andrew J. Weinheimer. First Top‐Down Estimates of Anthropogenic NO x Emissions Using High‐Resolution Airborne Remote Sensing Observations. Journal of Geophysical Research: Atmospheres. 2018; 123 (6):3269-3284.

Chicago/Turabian Style

Amir H. Souri; Yunsoo Choi; Shuai Pan; Gabriele Curci; Caroline R. Nowlan; Scott J. Janz; Matthew G. Kowalewski; Junjie Liu; Jay R. Herman; Andrew J. Weinheimer. 2018. "First Top‐Down Estimates of Anthropogenic NO x Emissions Using High‐Resolution Airborne Remote Sensing Observations." Journal of Geophysical Research: Atmospheres 123, no. 6: 3269-3284.

Journal article
Published: 01 February 2018 in Science of The Total Environment
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This study investigates a significant biomass burning (BB) event occurred in Colorado of the United States in 2012 using the Community Multi-scale Air Quality (CMAQ) model. The simulation reasonably reproduced the significantly high upper tropospheric O3 concentrations (up to 145ppb) caused by BB emissions. We find the BB-induced O3 was primarily affected by chemical reactions and dispersion during its transport. In the early period of transport, high NOx and VOCs emissions caused O3 production due to reactions with the peroxide and hydroxyl radicals, HO2 and OH. Here, NOx played a key role in O3 formation in the BB plume. The results indicated that HO2 in the BB plume primarily came from formaldehyde (HCHO+hv=2HO2+CO), a secondary alkoxy radical (ROR=HO2). CO played an important role in the production of recycled HO2 (OH+CO=HO2) because of its abundance in the BB plume. The chemically produced HO2 was largely converted to OH by the reactions with NO (HO2+NO=OH+NO2) from BB emissions. This is in contrast to the surface, where HO2 and OH are strongly affected by VOC and HONO, respectively. In the late stages of transport, the O3 concentration was primarily controlled by dispersion. It stayed longer in the upper troposphere compared to the surface due to sustained depletion of NOx. Sensitivity analysis results support that O3 in the BB plume is significantly more sensitive to NOx than VOCs.

ACS Style

Wonbae Jeon; Yunsoo Choi; Amir Hossein Souri; Anirban Roy; Lijun Diao; Shuai Pan; Hwa Woon Lee; Soon-Hwan Lee. Identification of chemical fingerprints in long-range transport of burning induced upper tropospheric ozone from Colorado to the North Atlantic Ocean. Science of The Total Environment 2018, 613-614, 820 -828.

AMA Style

Wonbae Jeon, Yunsoo Choi, Amir Hossein Souri, Anirban Roy, Lijun Diao, Shuai Pan, Hwa Woon Lee, Soon-Hwan Lee. Identification of chemical fingerprints in long-range transport of burning induced upper tropospheric ozone from Colorado to the North Atlantic Ocean. Science of The Total Environment. 2018; 613-614 ():820-828.

Chicago/Turabian Style

Wonbae Jeon; Yunsoo Choi; Amir Hossein Souri; Anirban Roy; Lijun Diao; Shuai Pan; Hwa Woon Lee; Soon-Hwan Lee. 2018. "Identification of chemical fingerprints in long-range transport of burning induced upper tropospheric ozone from Colorado to the North Atlantic Ocean." Science of The Total Environment 613-614, no. : 820-828.

Article
Published: 11 November 2017 in Journal of Geophysical Research: Atmospheres
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The primary sources for inorganic aerosols from biomass burning are rather negligible, but they are predominantly formed chemically following emission of their precursors (e.g., SO2, NH3, HOx, and NOx). The biomass burning contributions to some of the precursors can be considerable. Accordingly, we quantify the impact of the emissions on major inorganic aerosols in April–October 2012–2014 using a regional model simulation verified by extensive surface observations throughout the U.S. Simulated CO enhancements on an hourly basis are used to classify the U.S. into weak-moderate (5 < COBiomass-COBase < 20 ppbv) and strongly impacted periods (COBiomass-COBase > 20 ppbv). This separation not only facilitates the identification of the spatial frequency of the impact but also helps to filter out nonimpacted periods, enabling us to focus on long-term contributions. Despite the nonlinear responses of several trace gases to emissions, we observe increases (weak-moderate and strong) in daily surface SO42− (1.16 ± 0.32 and 6.57 ± 4.65 nmol/m3), NO3− (0.36 ± 0.63, 4.70 ± 7.05 nmol/m3), and NH4+ (2.70 ± 0.92 and 17.82 ± 15.17 nmol/m3) on a national scale. These primarily resulted from (i) increases in daily surface SO2 (0.02 ± 0.01 and 0.10 ± 0.07 ppbv), afternoon OH (1.28 ± 4.24 and 12.82 ± 23.76 ppqv), and H2O2 (0.06 ± 0.02 and 0.10 ± 0.08 ppbv), which may have accelerated the conversion of S(IV) to S(VI), and (ii) increases in daily surface NH3 (1.08 ± 0.73 and 8.61 ± 7.73 nmol/m3) and HNO3 (1.44 ± 0.48 and 7.15 ± 4.25 nmol/m3), which could have produced more particle-phase NH4NO3. In the West, where atmospheric moisture is limited, enhanced SO42− leaves less available water for NH4NO3 to become ions. Our results suggest that the major inorganic aerosol enhancement (mass) can reach to 23% of that of the carbonaceous aerosols.

ACS Style

Amir H. Souri; Yunsoo Choi; Wonbae Jeon; Adam K. Kochanski; Lijun Diao; Jan Mandel; Prakash V. Bhave; Shuai Pan. Quantifying the Impact of Biomass Burning Emissions on Major Inorganic Aerosols and Their Precursors in the U.S. Journal of Geophysical Research: Atmospheres 2017, 122, 12,020 -12,041.

AMA Style

Amir H. Souri, Yunsoo Choi, Wonbae Jeon, Adam K. Kochanski, Lijun Diao, Jan Mandel, Prakash V. Bhave, Shuai Pan. Quantifying the Impact of Biomass Burning Emissions on Major Inorganic Aerosols and Their Precursors in the U.S. Journal of Geophysical Research: Atmospheres. 2017; 122 (21):12,020-12,041.

Chicago/Turabian Style

Amir H. Souri; Yunsoo Choi; Wonbae Jeon; Adam K. Kochanski; Lijun Diao; Jan Mandel; Prakash V. Bhave; Shuai Pan. 2017. "Quantifying the Impact of Biomass Burning Emissions on Major Inorganic Aerosols and Their Precursors in the U.S." Journal of Geophysical Research: Atmospheres 122, no. 21: 12,020-12,041.