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.
Research article
| 
05 Jul 2022
Research article |
| 05 Jul 2022
| 05 Jul 2022
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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Cited
13 citations as recorded by crossref.
- Comparison of Visibility Impairment Due to the Change in PM2.5 Chemical Compositions in Daejeon between 2014 and 2020 J. Moon et al. 10.5572/KOSAE.2022.38.5.702
- Measuring the Emission Changes and Meteorological Dependence of Source‐Specific BC Aerosol Using Factor Analysis Coupled With Machine Learning T. Dai et al. 10.1029/2023JD038696
- Characterization and sources of carbonaceous aerosol in ambient PM1 in Qingdao, a coastal megacity of northern China from 2017 to 2022 J. Du et al. 10.1016/j.atmosenv.2024.120666
- Impact of China’s carbon emissions trading scheme on urban air quality: a time-varying DID model H. Sun & D. Cao 10.1007/s11356-023-29465-x
- Deep Sequence Learning for Prediction of Daily NO2 Concentration in Coastal Cities of Northern China X. Jia et al. 10.3390/atmos14030467
- Gaseous elemental mercury and its evasion fluxes in the marine boundary layer of the marginal seas of the northwestern Pacific: Results from two cruises in September–December 2019 V. Kalinchuk 10.1016/j.scitotenv.2022.159711
- Estimating visibility and understanding factors influencing its variations at Bangkok airport using machine learning and a game theory–based approach N. Aman et al. 10.1007/s11356-024-34548-4
- Heterogeneous variations in wintertime PM2.5 sources, compositions and exposure risks at urban/suburban rural/remote rural areas in the post COVID-19/Clean-Heating period Z. Li et al. 10.1016/j.atmosenv.2024.120463
- Quantifying vehicle restriction related PM2.5 reduction using field observations in an isolated urban basin Y. Guo et al. 10.1088/1748-9326/ad2238
- Trends of source apportioned PM2.5 in Tianjin over 2013–2019: Impacts of Clean Air Actions Q. Dai et al. 10.1016/j.envpol.2023.121344
- Tracking changes in atmospheric particulate matter at a semi-urban site in Central France over the past decade Y. Jiao et al. 10.1016/j.scitotenv.2023.163807
- Impact of Clean Air Policy on Criteria Air Pollutants and Health Risks Across China During 2013–2021 R. Li et al. 10.1029/2023JD038939
- Quantifying the pollution changes and meteorological dependence of airborne trace elements coupling source apportionment and machine learning H. Wang et al. 10.1016/j.scitotenv.2024.174452
13 citations as recorded by crossref.
- Comparison of Visibility Impairment Due to the Change in PM2.5 Chemical Compositions in Daejeon between 2014 and 2020 J. Moon et al. 10.5572/KOSAE.2022.38.5.702
- Measuring the Emission Changes and Meteorological Dependence of Source‐Specific BC Aerosol Using Factor Analysis Coupled With Machine Learning T. Dai et al. 10.1029/2023JD038696
- Characterization and sources of carbonaceous aerosol in ambient PM1 in Qingdao, a coastal megacity of northern China from 2017 to 2022 J. Du et al. 10.1016/j.atmosenv.2024.120666
- Impact of China’s carbon emissions trading scheme on urban air quality: a time-varying DID model H. Sun & D. Cao 10.1007/s11356-023-29465-x
- Deep Sequence Learning for Prediction of Daily NO2 Concentration in Coastal Cities of Northern China X. Jia et al. 10.3390/atmos14030467
- Gaseous elemental mercury and its evasion fluxes in the marine boundary layer of the marginal seas of the northwestern Pacific: Results from two cruises in September–December 2019 V. Kalinchuk 10.1016/j.scitotenv.2022.159711
- Estimating visibility and understanding factors influencing its variations at Bangkok airport using machine learning and a game theory–based approach N. Aman et al. 10.1007/s11356-024-34548-4
- Heterogeneous variations in wintertime PM2.5 sources, compositions and exposure risks at urban/suburban rural/remote rural areas in the post COVID-19/Clean-Heating period Z. Li et al. 10.1016/j.atmosenv.2024.120463
- Quantifying vehicle restriction related PM2.5 reduction using field observations in an isolated urban basin Y. Guo et al. 10.1088/1748-9326/ad2238
- Trends of source apportioned PM2.5 in Tianjin over 2013–2019: Impacts of Clean Air Actions Q. Dai et al. 10.1016/j.envpol.2023.121344
- Tracking changes in atmospheric particulate matter at a semi-urban site in Central France over the past decade Y. Jiao et al. 10.1016/j.scitotenv.2023.163807
- Impact of Clean Air Policy on Criteria Air Pollutants and Health Risks Across China During 2013–2021 R. Li et al. 10.1029/2023JD038939
- Quantifying the pollution changes and meteorological dependence of airborne trace elements coupling source apportionment and machine learning H. Wang et al. 10.1016/j.scitotenv.2024.174452
Latest update: 21 Nov 2024
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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