Articles | Volume 22, issue 13
https://doi.org/10.5194/acp-22-8597-2022
© 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-22-8597-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Dramatic changes in atmospheric pollution source contributions for a coastal megacity in northern China from 2011 to 2020
Baoshuang Liu
State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, Tianjin Key Laboratory of Urban Transport Emission Research, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China
CMA-NKU Cooperative Laboratory for Atmospheric Environment–Health Research, Tianjin 300350, China
Yanyang Wang
State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, Tianjin Key Laboratory of Urban Transport Emission Research, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China
CMA-NKU Cooperative Laboratory for Atmospheric Environment–Health Research, Tianjin 300350, China
He Meng
Qingdao Eco-environment Monitoring Center of Shandong Province, Qingdao 266003, China
State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, Tianjin Key Laboratory of Urban Transport Emission Research, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China
CMA-NKU Cooperative Laboratory for Atmospheric Environment–Health Research, Tianjin 300350, China
Liuli Diao
State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, Tianjin Key Laboratory of Urban Transport Emission Research, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China
CMA-NKU Cooperative Laboratory for Atmospheric Environment–Health Research, Tianjin 300350, China
Jianhui Wu
State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, Tianjin Key Laboratory of Urban Transport Emission Research, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China
CMA-NKU Cooperative Laboratory for Atmospheric Environment–Health Research, Tianjin 300350, China
Laiyuan Shi
Qingdao Eco-environment Monitoring Center of Shandong Province, Qingdao 266003, China
Jing Wang
Qingdao Eco-environment Monitoring Center of Shandong Province, Qingdao 266003, China
Yufen Zhang
CORRESPONDING AUTHOR
State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, Tianjin Key Laboratory of Urban Transport Emission Research, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China
CMA-NKU Cooperative Laboratory for Atmospheric Environment–Health Research, Tianjin 300350, China
State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, Tianjin Key Laboratory of Urban Transport Emission Research, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China
CMA-NKU Cooperative Laboratory for Atmospheric Environment–Health Research, Tianjin 300350, China
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Short summary
Understanding effectiveness of air pollution regulatory measures is critical for control policy. Machine learning and dispersion-normalized approaches were applied to decouple meteorologically deduced variations in Qingdao, China. Most pollutant concentrations decreased substantially after the Clean Air Action Plan. The largest emission reduction was from coal combustion and steel-related smelting. Qingdao is at risk of increased emissions from increased vehicular population and ozone pollution.
Understanding effectiveness of air pollution regulatory measures is critical for control policy....
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