Articles | Volume 14, issue 7
https://doi.org/10.5194/gmd-14-4641-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-14-4641-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
28 Jul 2021
Model description paper |
| 28 Jul 2021
| 28 Jul 2021
Exploring deep learning for air pollutant emission estimation
Lin Huang
Microsoft Research Lab – Asia, Beijing, China
Song Liu
State Key Joint Laboratory of Environmental Simulation and Pollution
Control, School of Environment, Tsinghua University, Beijing, China
State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing, China
Zeyuan Yang
School of Economics and Management, Tsinghua University, Beijing,
China
Jia Xing
CORRESPONDING AUTHOR
State Key Joint Laboratory of Environmental Simulation and Pollution
Control, School of Environment, Tsinghua University, Beijing, China
State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing, China
Jia Zhang
CORRESPONDING AUTHOR
Microsoft Research Lab – Asia, Beijing, China
Jiang Bian
Microsoft Research Lab – Asia, Beijing, China
Siwei Li
School of Remote Sensing and Information Engineering, Wuhan
University, Wuhan, China
State Key Laboratory of Information Engineering in Surveying, Mapping
and Remote Sensing, Wuhan University, Wuhan, China
Shovan Kumar Sahu
State Key Joint Laboratory of Environmental Simulation and Pollution
Control, School of Environment, Tsinghua University, Beijing, China
State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing, China
Shuxiao Wang
State Key Joint Laboratory of Environmental Simulation and Pollution
Control, School of Environment, Tsinghua University, Beijing, China
State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing, China
Tie-Yan Liu
Microsoft Research Lab – Asia, Beijing, China
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Yuying Cui, Qingru Wu, Shuxiao Wang, Kaiyun Liu, Shengyue Li, Zhezhe Shi, Daiwei Ouyang, Zhongyan Li, Qinqin Chen, Changwei Lü, Fei Xie, Yi Tang, Yan Wang, and Jiming Hao
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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
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Xiao He, Xuan Zheng, Shaojun Zhang, Xuan Wang, Ting Chen, Xiao Zhang, Guanghan Huang, Yihuan Cao, Liqiang He, Xubing Cao, Yuan Cheng, Shuxiao Wang, and Ye Wu
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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
Atmos. Chem. Phys., 22, 11845–11866, https://doi.org/10.5194/acp-22-11845-2022, https://doi.org/10.5194/acp-22-11845-2022, 2022
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This study constructed an emission inventory of condensable particulate matter (CPM) in China with a focus on organic aerosols (OAs), based on collected CPM emission information. The results show that OA emissions are enhanced twofold for the years 2014 and 2017 after the inclusion of CPM in the new inventory. Sensitivity cases demonstrated the significant contributions of CPM emissions from stationary combustion and mobile sources to primary, secondary, and total OA concentrations.
Shansi Wang, Siwei Li, Jia Xing, Yu Ding, Senlin Hu, Shuchang Liu, Yu Qin, Zhaoxin Dong, Jiaxin Dong, Ge Song, and Lechao Dong
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2022-368, https://doi.org/10.5194/acp-2022-368, 2022
Preprint withdrawn
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Future warming meteorological conditions may enhance the influence of regional transport on PM2.5 pollution. Our results prove that climate-friendly policy could lead to considerable co-benefits in mitigating the regional transport of PM2.5 in future. Meanwhile, climate change will exert larger impacts on across-regional (long-distance) transport than inner (neighboring provinces) regional transport, highlighting the significance of multi-regional cooperation in the future.
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
Atmos. Chem. Phys., 22, 5147–5156, https://doi.org/10.5194/acp-22-5147-2022, https://doi.org/10.5194/acp-22-5147-2022, 2022
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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.
Yuqiang Zhang, Drew Shindell, Karl Seltzer, Lu Shen, Jean-Francois Lamarque, Qiang Zhang, Bo Zheng, Jia Xing, Zhe Jiang, and Lei Zhang
Atmos. Chem. Phys., 21, 16051–16065, https://doi.org/10.5194/acp-21-16051-2021, https://doi.org/10.5194/acp-21-16051-2021, 2021
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In this study, we use a global chemical transport model to simulate the effects on global air quality and human health due to emission changes in China from 2010 to 2017. By performing sensitivity analysis, we found that the air pollution control policies not only decrease the air pollutant concentration but also bring significant co-benefits in air quality to downwind regions. The benefits for the improved air pollution are dominated by PM2.5.
Shuping Zhang, Golam Sarwar, Jia Xing, Biwu Chu, Chaoyang Xue, Arunachalam Sarav, Dian Ding, Haotian Zheng, Yujing Mu, Fengkui Duan, Tao Ma, and Hong He
Atmos. Chem. Phys., 21, 15809–15826, https://doi.org/10.5194/acp-21-15809-2021, https://doi.org/10.5194/acp-21-15809-2021, 2021
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Six heterogeneous HONO chemistry updates in CMAQ significantly improve HONO concentration. HONO production is primarily controlled by the heterogeneous reactions on ground and aerosol surfaces during haze. Additional HONO chemistry updates increase OH and production of secondary aerosols: sulfate, nitrate, and SOA.
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.
Xiaodan Ma, Jianping Huang, Tianliang Zhao, Cheng Liu, Kaihui Zhao, Jia Xing, and Wei Xiao
Atmos. Chem. Phys., 21, 1–16, https://doi.org/10.5194/acp-21-1-2021, https://doi.org/10.5194/acp-21-1-2021, 2021
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The present work aims at identifying and quantifying the relative contributions of the key factors in driving a rapid increase in summertime surface O3 over the North China Plain during 2013–2019. In addition to anthropogenic emission reduction and meteorological variabilities, our study highlights the importance of inclusion of aerosol absorption and scattering properties rather than aerosol abundance only in accurate assessment of aerosol radiative effect on surface O3 formation and change.
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
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.
Accurate estimation of emissions is a prerequisite for effectively controlling air pollution,...
