Articles | Volume 17, issue 7
https://doi.org/10.5194/essd-17-3315-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-17-3315-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
10 Jul 2025
Data description paper |
| 10 Jul 2025
| 10 Jul 2025
Integrating point sources to map anthropogenic atmospheric mercury emissions in China, 1978–2021
Yuying Cui
State Key Laboratory of Regional Environment and Sustainability, School of Environment, Tsinghua University, Beijing 100084, China
State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
Qingru Wu
CORRESPONDING AUTHOR
State Key Laboratory of Regional Environment and Sustainability, School of Environment, Tsinghua University, Beijing 100084, China
State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
Shuxiao Wang
State Key Laboratory of Regional Environment and Sustainability, School of Environment, Tsinghua University, Beijing 100084, China
State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
Kaiyun Liu
College of Environmental Science and Engineering, North China Electric Power University, Beijing 102206, China
Shengyue Li
State Key Laboratory of Regional Environment and Sustainability, School of Environment, Tsinghua University, Beijing 100084, China
State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
Zhezhe Shi
State Key Laboratory of Regional Environment and Sustainability, School of Environment, Tsinghua University, Beijing 100084, China
State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
Daiwei Ouyang
State Key Laboratory of Regional Environment and Sustainability, School of Environment, Tsinghua University, Beijing 100084, China
State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
Zhongyan Li
Weiyang College, Tsinghua University, Beijing 100084, China
Qinqin Chen
State Key Laboratory of Regional Environment and Sustainability, School of Environment, Tsinghua University, Beijing 100084, China
State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
Changwei Lü
School of Ecology and Environment, Inner Mongolia University, Hohhot 010021, China
Institute of Environmental Geology, Inner Mongolia University, Hohhot 010021, China
School of Ecology and Environment, Inner Mongolia University, Hohhot 010021, China
Institute of Environmental Geology, Inner Mongolia University, Hohhot 010021, China
Yi Tang
State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
Yan Wang
College of Electromechanical Engineering, Qingdao University of Science and Technology, Qingdao 266061, China
Jiming Hao
State Key Laboratory of Regional Environment and Sustainability, School of Environment, Tsinghua University, Beijing 100084, China
State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
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Earth Syst. Sci. Data, 17, 5113–5135, https://doi.org/10.5194/essd-17-5113-2025, https://doi.org/10.5194/essd-17-5113-2025, 2025
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Ashu Dastoor, Hélène Angot, Johannes Bieser, Flora Brocza, Brock Edwards, Aryeh Feinberg, Xinbin Feng, Benjamin Geyman, Charikleia Gournia, Yipeng He, Ian M. Hedgecock, Ilia Ilyin, Jane Kirk, Che-Jen Lin, Igor Lehnherr, Robert Mason, David McLagan, Marilena Muntean, Peter Rafaj, Eric M. Roy, Andrei Ryjkov, Noelle E. Selin, Francesco De Simone, Anne L. Soerensen, Frits Steenhuisen, Oleg Travnikov, Shuxiao Wang, Xun Wang, Simon Wilson, Rosa Wu, Qingru Wu, Yanxu Zhang, Jun Zhou, Wei Zhu, and Scott Zolkos
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Qianqian Zhang, K. Folkert Boersma, Chiel van der Laan, Alba Mols, Bin Zhao, Shengyue Li, and Yuepeng Pan
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Yuzhi Jin, Jiandong Wang, Chao Liu, David C. Wong, Golam Sarwar, Kathleen M. Fahey, Shang Wu, Jiaping Wang, Jing Cai, Zeyuan Tian, Zhouyang Zhang, Jia Xing, Aijun Ding, and Shuxiao Wang
Atmos. Chem. Phys., 25, 2613–2630, https://doi.org/10.5194/acp-25-2613-2025, https://doi.org/10.5194/acp-25-2613-2025, 2025
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Atmos. Chem. Phys., 25, 1869–1881, https://doi.org/10.5194/acp-25-1869-2025, https://doi.org/10.5194/acp-25-1869-2025, 2025
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Black carbon (BC) exerts notable warming effects. We use a particle-resolved model to investigate the long-term behavior of the BC mixing state, revealing its compositions, coating thickness distribution, and optical properties all stabilize with a characteristic time of less than 1 d. This study can effectively simplify the description of the BC mixing state, which facilitates the precise assessment of the optical properties of BC aerosols in global and chemical transport models.
Jiewen Shen, Bin Zhao, Shuxiao Wang, An Ning, Yuyang Li, Runlong Cai, Da Gao, Biwu Chu, Yang Gao, Manish Shrivastava, Jingkun Jiang, Xiuhui Zhang, and Hong He
Atmos. Chem. Phys., 24, 10261–10278, https://doi.org/10.5194/acp-24-10261-2024, https://doi.org/10.5194/acp-24-10261-2024, 2024
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We extensively compare various cluster-dynamics-based parameterizations for sulfuric acid–dimethylamine nucleation and identify a newly developed parameterization derived from Atmospheric Cluster Dynamic Code (ACDC) simulations as being the most reliable one. This study offers a valuable reference for developing parameterizations of other nucleation systems and is meaningful for the accurate quantification of the environmental and climate impacts of new particle formation.
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Atmos. Chem. Phys., 23, 14359–14373, https://doi.org/10.5194/acp-23-14359-2023, https://doi.org/10.5194/acp-23-14359-2023, 2023
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Zeqi Li, Shuxiao Wang, Shengyue Li, Xiaochun Wang, Guanghan Huang, Xing Chang, Lyuyin Huang, Chengrui Liang, Yun Zhu, Haotian Zheng, Qian Song, Qingru Wu, Fenfen Zhang, and Bin Zhao
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Chupeng Zhang, Shangfei Hai, Yang Gao, Yuhang Wang, Shaoqing Zhang, Lifang Sheng, Bin Zhao, Shuxiao Wang, Jingkun Jiang, Xin Huang, Xiaojing Shen, Junying Sun, Aura Lupascu, Manish Shrivastava, Jerome D. Fast, Wenxuan Cheng, Xiuwen Guo, Ming Chu, Nan Ma, Juan Hong, Qiaoqiao Wang, Xiaohong Yao, and Huiwang Gao
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Atmos. Chem. Phys., 23, 4271–4281, https://doi.org/10.5194/acp-23-4271-2023, https://doi.org/10.5194/acp-23-4271-2023, 2023
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Fei Xie, Yue Su, Yongli Tian, Yanju Shi, Xingjun Zhou, Peng Wang, Ruihong Yu, Wei Wang, Jiang He, Jinyuan Xin, and Changwei Lü
Atmos. Chem. Phys., 23, 2365–2378, https://doi.org/10.5194/acp-23-2365-2023, https://doi.org/10.5194/acp-23-2365-2023, 2023
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Lulu Cui, Di Wu, Shuxiao Wang, Qingcheng Xu, Ruolan Hu, and Jiming Hao
Atmos. Chem. Phys., 22, 11931–11944, https://doi.org/10.5194/acp-22-11931-2022, https://doi.org/10.5194/acp-22-11931-2022, 2022
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A 1-year campaign was conducted to characterize VOCs at a Beijing urban site during different episodes. VOCs from fuel evaporation and diesel exhaust, particularly toluene, xylenes, trans-2-butene, acrolein, methyl methacrylate, vinyl acetate, 1-butene, and 1-hexene, were the main contributors. VOCs from diesel exhaust as well as coal and biomass combustion were found to be the dominant contributors for SOAFP, particularly the VOC species toluene, 1-hexene, xylenes, ethylbenzene, and styrene.
Mengying Li, Shaocai Yu, Xue Chen, Zhen Li, Yibo Zhang, Zhe Song, Weiping Liu, Pengfei Li, Xiaoye Zhang, Meigen Zhang, Yele Sun, Zirui Liu, Caiping Sun, Jingkun Jiang, Shuxiao Wang, Benjamin N. Murphy, Kiran Alapaty, Rohit Mathur, Daniel Rosenfeld, and John H. Seinfeld
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Jiandong Wang, Jia Xing, Shuxiao Wang, Rohit Mathur, Jiaping Wang, Yuqiang Zhang, Chao Liu, Jonathan Pleim, Dian Ding, Xing Chang, Jingkun Jiang, Peng Zhao, Shovan Kumar Sahu, Yuzhi Jin, David C. Wong, and Jiming Hao
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Aerosols reduce surface solar radiation and change the photolysis rate and planetary boundary layer stability. In this study, the online coupled meteorological and chemistry model was used to explore the detailed pathway of how aerosol direct effects affect secondary inorganic aerosol. The effects through the dynamics pathway act as an equally or even more important route compared with the photolysis pathway in affecting secondary aerosol concentration in both summer and winter.
Xiaomeng Wu, Daoyuan Yang, Ruoxi Wu, Jiajun Gu, Yifan Wen, Shaojun Zhang, Rui Wu, Renjie Wang, Honglei Xu, K. Max Zhang, Ye Wu, and Jiming Hao
Atmos. Chem. Phys., 22, 1939–1950, https://doi.org/10.5194/acp-22-1939-2022, https://doi.org/10.5194/acp-22-1939-2022, 2022
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Our work pioneered land-use machine learning methods for developing link-level emission inventories, utilizing hourly traffic profiles, including volume, speed, and fleet mix, obtained from the governmental intercity highway monitoring network in the "capital circles" of China. This research provides a platform to realize the near-real-time process of establishing high-resolution vehicle emission inventories for policy makers to engage in sophisticated traffic management.
Lin Huang, Song Liu, Zeyuan Yang, Jia Xing, Jia Zhang, Jiang Bian, Siwei Li, Shovan Kumar Sahu, Shuxiao Wang, and Tie-Yan Liu
Geosci. Model Dev., 14, 4641–4654, https://doi.org/10.5194/gmd-14-4641-2021, https://doi.org/10.5194/gmd-14-4641-2021, 2021
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Accurate estimation of emissions is a prerequisite for effectively controlling air pollution, but current methods lack either sufficient data or a representation of nonlinearity. Here, we proposed a novel deep learning method to model the dual relationship between emissions and pollutant concentrations. Emissions can be updated by back-propagating the gradient of the loss function measuring the deviation between simulations and observations, resulting in better model performance.
Sunling Gong, Hongli Liu, Bihui Zhang, Jianjun He, Hengde Zhang, Yaqiang Wang, Shuxiao Wang, Lei Zhang, and Jie Wang
Atmos. Chem. Phys., 21, 2999–3013, https://doi.org/10.5194/acp-21-2999-2021, https://doi.org/10.5194/acp-21-2999-2021, 2021
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Surface concentrations of PM2.5 in China have had a declining trend since 2013 across the country. This research found that the control measures of emission reduction are the dominant factors in the PM2.5 declining trends in various regions. The contribution by the meteorology to the surface PM2.5 concentrations from 2013 to 2019 was not found to show a consistent trend, fluctuating positively or negatively by about 5% on the annual average and 10–20% for the fall–winter heavy-pollution seasons.
Runlong Cai, Chao Yan, Dongsen Yang, Rujing Yin, Yiqun Lu, Chenjuan Deng, Yueyun Fu, Jiaxin Ruan, Xiaoxiao Li, Jenni Kontkanen, Qiang Zhang, Juha Kangasluoma, Yan Ma, Jiming Hao, Douglas R. Worsnop, Federico Bianchi, Pauli Paasonen, Veli-Matti Kerminen, Yongchun Liu, Lin Wang, Jun Zheng, Markku Kulmala, and Jingkun Jiang
Atmos. Chem. Phys., 21, 2457–2468, https://doi.org/10.5194/acp-21-2457-2021, https://doi.org/10.5194/acp-21-2457-2021, 2021
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Based on long-term measurements, we discovered that the collision of H2SO4–amine clusters is the governing mechanism that initializes fast new particle formation in the polluted atmospheric environment of urban Beijing. The mechanism and the governing factors for H2SO4–amine nucleation in the polluted atmosphere are quantitatively investigated in this study.
Jia Xing, Siwei Li, Yueqi Jiang, Shuxiao Wang, Dian Ding, Zhaoxin Dong, Yun Zhu, and Jiming Hao
Atmos. Chem. Phys., 20, 14347–14359, https://doi.org/10.5194/acp-20-14347-2020, https://doi.org/10.5194/acp-20-14347-2020, 2020
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Quantifying emission changes is a prerequisite for assessment of control effectiveness in improving air quality. However, traditional bottom-up methods usually take months to perform and limit timely assessments. A novel method was developed by using a response model that provides real-time estimation of emission changes based on air quality observations. It was successfully applied to quantify emission changes on the North China Plain due to the COVID-19 pandemic shutdown.
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Short summary
We develop P-CAME, a long-term gridded emission inventory for China spanning from 1978 to 2021. P-CAME enhances the accuracy of emissions mapping, identifies potential pollution hotspots, and aligns with observed Hg0 concentration trends. With its improved spatial resolution and reliable long-term trends, P-CAME offers valuable support for global emissions modeling, legacy impact studies, and evaluations of the Minamata Convention.
We develop P-CAME, a long-term gridded emission inventory for China spanning from 1978 to 2021....
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