Articles | Volume 23, issue 22
https://doi.org/10.5194/acp-23-14505-2023
© Author(s) 2023. 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-23-14505-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
24 Nov 2023
Research article |
| 24 Nov 2023
| 24 Nov 2023
Dynamics-based estimates of decline trend with fine temporal variations in China's PM2.5 emissions
Zhen Peng
School of Atmospheric Sciences, Nanjing University, Nanjing 210093, China
Lili Lei
School of Atmospheric Sciences, Nanjing University, Nanjing 210093, China
Key Laboratory of Mesoscale Severe Weather, Ministry of Education, Nanjing University, Nanjing 210093, China
Zhe-Min Tan
CORRESPONDING AUTHOR
School of Atmospheric Sciences, Nanjing University, Nanjing 210093, China
Key Laboratory of Mesoscale Severe Weather, Ministry of Education, Nanjing University, Nanjing 210093, China
State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
Aijun Ding
School of Atmospheric Sciences, Nanjing University, Nanjing 210093, China
Xingxia Kou
Institute of Urban Meteorology, China Meteorological Administration, Beijing 100089, China
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Qi En Zhong, Chunlei Cheng, Zaihua Wang, Lei Li, Mei Li, Dafeng Ge, Lei Wang, Yuanyuan Li, Wei Nie, Xuguang Chi, Aijun Ding, Suxia Yang, Duohong Chen, and Zhen Zhou
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Yuliang Liu, Wei Nie, Yuanyuan Li, Dafeng Ge, Chong Liu, Zhengning Xu, Liangduo Chen, Tianyi Wang, Lei Wang, Peng Sun, Ximeng Qi, Jiaping Wang, Zheng Xu, Jian Yuan, Chao Yan, Yanjun Zhang, Dandan Huang, Zhe Wang, Neil M. Donahue, Douglas Worsnop, Xuguang Chi, Mikael Ehn, and Aijun Ding
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Markku Kulmala, Tom V. Kokkonen, Juha Pekkanen, Sami Paatero, Tuukka Petäjä, Veli-Matti Kerminen, and Aijun Ding
Atmos. Chem. Phys., 21, 8313–8322, https://doi.org/10.5194/acp-21-8313-2021, https://doi.org/10.5194/acp-21-8313-2021, 2021
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Shibao Wang, Yun Ma, Zhongrui Wang, Lei Wang, Xuguang Chi, Aijun Ding, Mingzhi Yao, Yunpeng Li, Qilin Li, Mengxian Wu, Ling Zhang, Yongle Xiao, and Yanxu Zhang
Atmos. Chem. Phys., 21, 7199–7215, https://doi.org/10.5194/acp-21-7199-2021, https://doi.org/10.5194/acp-21-7199-2021, 2021
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Yanxu Zhang, Xingpei Ye, Shibao Wang, Xiaojing He, Lingyao Dong, Ning Zhang, Haikun Wang, Zhongrui Wang, Yun Ma, Lei Wang, Xuguang Chi, Aijun Ding, Mingzhi Yao, Yunpeng Li, Qilin Li, Ling Zhang, and Yongle Xiao
Atmos. Chem. Phys., 21, 2917–2929, https://doi.org/10.5194/acp-21-2917-2021, https://doi.org/10.5194/acp-21-2917-2021, 2021
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Ying Jiang, Likun Xue, Rongrong Gu, Mengwei Jia, Yingnan Zhang, Liang Wen, Penggang Zheng, Tianshu Chen, Hongyong Li, Ye Shan, Yong Zhao, Zhaoxin Guo, Yujian Bi, Hengde Liu, Aijun Ding, Qingzhu Zhang, and Wenxing Wang
Atmos. Chem. Phys., 20, 12115–12131, https://doi.org/10.5194/acp-20-12115-2020, https://doi.org/10.5194/acp-20-12115-2020, 2020
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We analyzed the characteristics and sources of HONO in the upper boundary layer and lower free troposphere in the North China Plain, based on the field measurements at Mount Tai. Higher-than-expected levels and broad daytime peaks of HONO were observed. Without presence of ground surfaces, aerosol surface plays a key role in the heterogeneous HONO formation at high altitudes. Models without additional HONO sources largely
underestimatedthe oxidation processes in the elevation atmospheres.
Baozhu Ge, Syuichi Itahashi, Keiichi Sato, Danhui Xu, Junhua Wang, Fan Fan, Qixin Tan, Joshua S. Fu, Xuemei Wang, Kazuyo Yamaji, Tatsuya Nagashima, Jie Li, Mizuo Kajino, Hong Liao, Meigen Zhang, Zhe Wang, Meng Li, Jung-Hun Woo, Junichi Kurokawa, Yuepeng Pan, Qizhong Wu, Xuejun Liu, and Zifa Wang
Atmos. Chem. Phys., 20, 10587–10610, https://doi.org/10.5194/acp-20-10587-2020, https://doi.org/10.5194/acp-20-10587-2020, 2020
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Performances of the simulated deposition for different reduced N (Nr) species in China were conducted with the Model Inter-Comparison Study for Asia. Results showed that simulated wet deposition of oxidized N was overestimated in northeastern China and underestimated in south China, but Nr was underpredicted in all regions by all models. Oxidized N has larger uncertainties than Nr, indicating that the chemical reaction process is one of the most importance factors affecting model performance.
Xiao Han, Lingyun Zhu, Mingxu Liu, Yu Song, and Meigen Zhang
Atmos. Chem. Phys., 20, 9979–9996, https://doi.org/10.5194/acp-20-9979-2020, https://doi.org/10.5194/acp-20-9979-2020, 2020
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China is one of the largest agricultural countries in the world. Some of the major PM2.5 particles that cause the atmospheric haze and impact the climate change were converted from agricultural NH3 emission. This paper applied the numerical modeling system, coupled with a high-resolution agricultural NH3 emissions inventory, to investigate the contribution of agricultural NH3 to PM2.5 mass burden in China and obtained some interesting results.
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Short summary
Annual PM2.5 emissions in China consistently decreased by about 3% to 5% from 2017 to 2020 with spatial variations and seasonal dependencies. High-temporal-resolution and dynamics-based PM2.5 emission estimates provide quantitative diurnal variations for each season. Significant reductions in PM2.5 emissions in the North China Plain and northeast of China in 2020 were caused by COVID-19.
Annual PM2.5 emissions in China consistently decreased by about 3% to 5% from 2017 to 2020 with...
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