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Environ Int

Title:		Modeling particulate nitrate in China: Current findings and future directions
Author(s):		Xie X; Hu J; Qin M; Guo S; Hu M; Wang H; Lou S; Li J; Sun J; Li X; Sheng L; Zhu J; Chen G; Yin J; Fu W; Huang C; Zhang Y;
Address:		"Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, School of Environmental Science and Engineering, Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology, Nanjing 210044, China. Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, School of Environmental Science and Engineering, Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology, Nanjing 210044, China. Electronic address: jianlinhu@nuist.edu.cn. State Key Joint Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China. State Environmental Protection Key Laboratory of Formation and Prevention of Urban Air Pollution Complex, Shanghai Academy of Environmental Sciences, Shanghai 200233, China. State Key Joint Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China; CAS Center for Excellence in Regional Atmospheric Environment, Chinese Academy of Science, Xiamen 361021, China. Electronic address: yhzhang@pku.edu.cn"
Journal Title:		Environ Int
Year:		2022
Volume:		20220622
Issue:
Page Number:		107369 -
DOI:		10.1016/j.envint.2022.107369
ISSN/ISBN:		1873-6750 (Electronic) 0160-4120 (Linking)
Abstract:		"Particulate nitrate (pNO(3)) is now becoming the principal component of PM(2.5) during severe winter haze episodes in many cities of China. To gain a comprehensive understanding of the key factors controlling pNO(3) formation and driving its trends, we reviewed the recent pNO(3) modeling studies which mainly focused on the formation mechanism and recent trends of pNO(3) as well as its responses to emission controls in China. The results indicate that although recent chemical transport models (CTMs) can reasonably capture the spatial-temporal variations of pNO(3), model-observation biases still exist due to large uncertainties in the parameterization of dinitrogen pentoxide (N(2)O(5)) uptake and ammonia (NH(3)) emissions, insufficient heterogeneous reaction mechanism, and the predicted low sulfate concentrations in current CTMs. The heterogeneous hydrolysis of N(2)O(5) dominates nocturnal pNO(3) formation, however, the contribution to total pNO(3) varies among studies, ranging from 21.0% to 51.6%. Moreover, the continuously increasing PM(2.5) pNO(3) fraction in recent years is mainly due to the decreased sulfur dioxide emissions, the enhanced atmospheric oxidation capacity (AOC), and the weakened nitrate deposition. Reducing NH(3) emissions is found to be the most effective control strategy for mitigating pNO(3) pollution in China. This review suggests that more field measurements are needed to constrain the parameterization of heterogeneous N(2)O(5) and nitrogen dioxide (NO(2)) uptake. Future studies are also needed to quantify the relationships of pNO(3) to AOC, O(3), NOx, and volatile organic compounds (VOCs) in different regions of China under different meteorological conditions. Research on multiple-pollutant control strategies involving NH(3), NO(X,) and VOCs is required to mitigate pNO(3) pollution, especially during severe winter haze events"
Keywords:		Chemical transport model China Control strategy Formation pathway Particulate nitrate Trends;
Notes:		"PublisherXie, Xiaodong Hu, Jianlin Qin, Momei Guo, Song Hu, Min Wang, Hongli Lou, Shengrong Li, Jingyi Sun, Jinjin Li, Xun Sheng, Li Zhu, Jianlan Chen, Ganyu Yin, Junjie Fu, Wenxing Huang, Cheng Zhang, Yuanhang eng Review Netherlands 2022/07/01 Environ Int. 2022 Jun 22; 166:107369. doi: 10.1016/j.envint.2022.107369"