Articles | Volume 22, issue 18
https://doi.org/10.5194/acp-22-12629-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-22-12629-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
28 Sep 2022
Research article |
| 28 Sep 2022
| 28 Sep 2022
Seasonal modeling analysis of nitrate formation pathways in Yangtze River Delta region, China
Jinjin Sun
Jiangsu Key Laboratory of Atmospheric Environment Monitoring and
Pollution Control, Collaborative Innovation Center of Atmospheric
Environment and Equipment Technology, Nanjing University of Information
Science & Technology, Nanjing 210044, China
Key Laboratory of Meteorological Disaster, Ministry of Education,
Joint International Research Laboratory of Climate and Environment Change,
Collaborative Innovation Center on Forecast and Evaluation of Meteorological
Disasters, Key Laboratory for Aerosol-Cloud-Precipitation of China
Meteorological Administration, Nanjing University of Information Science and
Technology, Nanjing 210044, China
Jiangsu Key Laboratory of Atmospheric Environment Monitoring and
Pollution Control, Collaborative Innovation Center of Atmospheric
Environment and Equipment Technology, Nanjing University of Information
Science & Technology, Nanjing 210044, China
Xiaodong Xie
Jiangsu Key Laboratory of Atmospheric Environment Monitoring and
Pollution Control, Collaborative Innovation Center of Atmospheric
Environment and Equipment Technology, Nanjing University of Information
Science & Technology, Nanjing 210044, China
Wenxing Fu
Jiangsu Key Laboratory of Atmospheric Environment Monitoring and
Pollution Control, Collaborative Innovation Center of Atmospheric
Environment and Equipment Technology, Nanjing University of Information
Science & Technology, Nanjing 210044, China
Yang Qin
Jiangsu Key Laboratory of Atmospheric Environment Monitoring and
Pollution Control, Collaborative Innovation Center of Atmospheric
Environment and Equipment Technology, Nanjing University of Information
Science & Technology, Nanjing 210044, China
Key Laboratory of Meteorological Disaster, Ministry of Education,
Joint International Research Laboratory of Climate and Environment Change,
Collaborative Innovation Center on Forecast and Evaluation of Meteorological
Disasters, Key Laboratory for Aerosol-Cloud-Precipitation of China
Meteorological Administration, Nanjing University of Information Science and
Technology, Nanjing 210044, China
Li Sheng
Jiangsu Key Laboratory of Atmospheric Environment Monitoring and
Pollution Control, Collaborative Innovation Center of Atmospheric
Environment and Equipment Technology, Nanjing University of Information
Science & Technology, Nanjing 210044, China
Lin Li
Jiangsu Key Laboratory of Atmospheric Environment Monitoring and
Pollution Control, Collaborative Innovation Center of Atmospheric
Environment and Equipment Technology, Nanjing University of Information
Science & Technology, Nanjing 210044, China
Jingyi Li
Jiangsu Key Laboratory of Atmospheric Environment Monitoring and
Pollution Control, Collaborative Innovation Center of Atmospheric
Environment and Equipment Technology, Nanjing University of Information
Science & Technology, Nanjing 210044, China
Ishaq Dimeji Sulaymon
Jiangsu Key Laboratory of Atmospheric Environment Monitoring and
Pollution Control, Collaborative Innovation Center of Atmospheric
Environment and Equipment Technology, Nanjing University of Information
Science & Technology, Nanjing 210044, China
Lei Jiang
Jiangsu Key Laboratory of Atmospheric Environment Monitoring and
Pollution Control, Collaborative Innovation Center of Atmospheric
Environment and Equipment Technology, Nanjing University of Information
Science & Technology, Nanjing 210044, China
Lin Huang
Jiangsu Key Laboratory of Atmospheric Environment Monitoring and
Pollution Control, Collaborative Innovation Center of Atmospheric
Environment and Equipment Technology, Nanjing University of Information
Science & Technology, Nanjing 210044, China
Xingna Yu
Key Laboratory of Meteorological Disaster, Ministry of Education,
Joint International Research Laboratory of Climate and Environment Change,
Collaborative Innovation Center on Forecast and Evaluation of Meteorological
Disasters, Key Laboratory for Aerosol-Cloud-Precipitation of China
Meteorological Administration, Nanjing University of Information Science and
Technology, Nanjing 210044, China
Jianlin Hu
CORRESPONDING AUTHOR
Jiangsu Key Laboratory of Atmospheric Environment Monitoring and
Pollution Control, Collaborative Innovation Center of Atmospheric
Environment and Equipment Technology, Nanjing University of Information
Science & Technology, Nanjing 210044, China
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Xun Li, Momei Qin, Lin Li, Kangjia Gong, Huizhong Shen, Jingyi Li, and Jianlin Hu
Atmos. Chem. Phys., 22, 14799–14811, https://doi.org/10.5194/acp-22-14799-2022, https://doi.org/10.5194/acp-22-14799-2022, 2022
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Photochemical indicators have been widely used to predict O3–NOx–VOC sensitivity with given thresholds. Here we assessed the effectiveness of four indicators with a case study in the Yangtze River Delta, China. The overall performance was good, while some indicators showed inconsistencies with the O3 isopleths. The methodology used to determine the thresholds may produce uncertainties. These results would improve our understanding of the use of photochemical indicators in policy implications.
Elyse A. Pennington, Karl M. Seltzer, Benjamin N. Murphy, Momei Qin, John H. Seinfeld, and Havala O. T. Pye
Atmos. Chem. Phys., 21, 18247–18261, https://doi.org/10.5194/acp-21-18247-2021, https://doi.org/10.5194/acp-21-18247-2021, 2021
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Volatile chemical products (VCPs) are commonly used consumer and industrial items that contribute to the formation of atmospheric aerosol. We implemented the emissions and chemistry of VCPs in a regional-scale model and compared predictions with measurements made in Los Angeles. Our results reduced model bias and suggest that VCPs may contribute up to half of anthropogenic secondary organic aerosol in Los Angeles and are an important source of human-influenced particular matter in urban areas.
Ruqian Miao, Qi Chen, Manish Shrivastava, Youfan Chen, Lin Zhang, Jianlin Hu, Yan Zheng, and Keren Liao
Atmos. Chem. Phys., 21, 16183–16201, https://doi.org/10.5194/acp-21-16183-2021, https://doi.org/10.5194/acp-21-16183-2021, 2021
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We apply process-based and observation-constrained schemes to simulate organic aerosol in China and conduct comprehensive model–observation comparisons. The results show that anthropogenic semivolatile and intermediate-volatility organic compounds (SVOCs and IVOCs) are the main sources of secondary organic aerosol (SOA) in polluted regions, for which the residential sector is perhaps the predominant contributor. The hydroxyl radical level is also important for SOA modeling in polluted regions.
Meng Gao, Yang Yang, Hong Liao, Bin Zhu, Yuxuan Zhang, Zirui Liu, Xiao Lu, Chen Wang, Qiming Zhou, Yuesi Wang, Qiang Zhang, Gregory R. Carmichael, and Jianlin Hu
Atmos. Chem. Phys., 21, 11405–11421, https://doi.org/10.5194/acp-21-11405-2021, https://doi.org/10.5194/acp-21-11405-2021, 2021
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Light absorption and radiative forcing of black carbon (BC) is influenced by both BC itself and its interactions with other aerosol chemical compositions. In this study, we used the online coupled WRF-Chem model to examine how emission control measures during the Asian-Pacific Economic Cooperation (APEC) conference affect the mixing state and light absorption of BC and the associated implications for BC-PBL interactions.
Zhihao Shi, Lin Huang, Jingyi Li, Qi Ying, Hongliang Zhang, and Jianlin Hu
Atmos. Chem. Phys., 20, 13455–13466, https://doi.org/10.5194/acp-20-13455-2020, https://doi.org/10.5194/acp-20-13455-2020, 2020
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Meteorological conditions play important roles in the formation of O3 and PM2.5 pollution in China. O3 is most sensitive to temperature and the sensitivity is dependent on the O3 chemistry formation or loss regime. PM2.5 is negatively sensitive to temperature, wind speed, and planetary boundary layer height and positively sensitive to humidity. The results imply that air quality in certain regions of China is sensitive to climate changes.
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Short summary
NO3- has become the dominant and the least reduced chemical component of fine particulate matter in China. NO3- formation is mostly in the NH3-rich regime in the Yangtze River Delta (YRD). OH + NO2 contributes 60 %–83 % of the TNO3 production rates, and the N2O5 heterogeneous pathway contributes 10 %–36 %. The N2O5 heterogeneous pathway becomes more important in cold seasons. Local emissions and regional transportation contribute 50 %–62 % and 38 %–50 % to YRD NO3- concentrations, respectively.
NO3- has become the dominant and the least reduced chemical component of fine particulate matter...
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