Local Emissions Drive Summer PM<sub>2.5</sub> Pollution Under Adverse Meteorological Conditions: A Quantitative Case Study in Suzhou, Yangtze River Delta
Accurately identifying the sources of fine particulate matter (PM<sub>2.5</sub>) pollution is crucial for pollution control and public health protection. Taking the PM<sub>2.5</sub> pollution event that occurred in Suzhou in June 2023 as a typical case, this study analyzed th...
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2025-07-01
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| author | Minyan Wu Ningning Cai Jiong Fang Ling Huang Xurong Shi Yezheng Wu Li Li Hongbing Qin |
| author_facet | Minyan Wu Ningning Cai Jiong Fang Ling Huang Xurong Shi Yezheng Wu Li Li Hongbing Qin |
| author_sort | Minyan Wu |
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| description | Accurately identifying the sources of fine particulate matter (PM<sub>2.5</sub>) pollution is crucial for pollution control and public health protection. Taking the PM<sub>2.5</sub> pollution event that occurred in Suzhou in June 2023 as a typical case, this study analyzed the characteristics and components of PM<sub>2.5</sub>, and quantified the contributions of meteorological conditions, regional transport, and local emissions to the summertime PM<sub>2.5</sub> surge in a typical Yangtze River Delta (YRD) city. Chemical composition analysis highlighted a sharp increase in nitrate ions (NO<sub>3</sub><sup>−</sup>, contributing up to 49% during peak pollution), with calcium ion (Ca<sup>2+</sup>) and sulfate ion (SO<sub>4</sub><sup>2−</sup>) concentrations rising to 2 times and 7.5 times those of clean periods, respectively. Results from the random forest model demonstrated that emission sources (74%) dominated this pollution episode, significantly surpassing the meteorological contribution (26%). The Weather Research and Forecasting model combined with the Community Multiscale Air Quality model (WRF–CMAQ) further revealed that local emissions contributed the most to PM<sub>2.5</sub> concentrations in Suzhou (46.3%), while external transport primarily originated from upwind cities such as Shanghai and Jiaxing. The findings indicate synergistic effects from dust sources, industrial emissions, and mobile sources. Validation using electricity consumption and key enterprise emission data confirmed that intensive local industrial activities exacerbated PM<sub>2.5</sub> accumulation. Recommendations include strengthening regulations on local industrial and mobile source emissions, and enhancing regional joint prevention and control mechanisms to mitigate cross-boundary transport impacts. |
| format | Article |
| id | doaj-art-63bea52ad25746debb6b7521abb0c2d0 |
| institution | DOAJ |
| issn | 2073-4433 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
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| series | Atmosphere |
| spelling | doaj-art-63bea52ad25746debb6b7521abb0c2d02025-08-20T02:45:37ZengMDPI AGAtmosphere2073-44332025-07-0116786710.3390/atmos16070867Local Emissions Drive Summer PM<sub>2.5</sub> Pollution Under Adverse Meteorological Conditions: A Quantitative Case Study in Suzhou, Yangtze River DeltaMinyan Wu0Ningning Cai1Jiong Fang2Ling Huang3Xurong Shi4Yezheng Wu5Li Li6Hongbing Qin7Suzhou Environmental Monitoring Station, Suzhou 215000, ChinaSuzhou Environmental Monitoring Station, Suzhou 215000, ChinaSchool of Environmental and Chemical Engineering, Shanghai University, Shanghai 200444, ChinaSchool of Environmental and Chemical Engineering, Shanghai University, Shanghai 200444, ChinaCenter of Air Quality Simulation and System Analysis, Chinese Academy of Environmental Planning, Beijing 100041, ChinaSuzhou Environmental Monitoring Station, Suzhou 215000, ChinaSchool of Environmental and Chemical Engineering, Shanghai University, Shanghai 200444, ChinaSchool of Environmental Science and Engineering, Suzhou University of Science and Technology, Suzhou 215009, ChinaAccurately identifying the sources of fine particulate matter (PM<sub>2.5</sub>) pollution is crucial for pollution control and public health protection. Taking the PM<sub>2.5</sub> pollution event that occurred in Suzhou in June 2023 as a typical case, this study analyzed the characteristics and components of PM<sub>2.5</sub>, and quantified the contributions of meteorological conditions, regional transport, and local emissions to the summertime PM<sub>2.5</sub> surge in a typical Yangtze River Delta (YRD) city. Chemical composition analysis highlighted a sharp increase in nitrate ions (NO<sub>3</sub><sup>−</sup>, contributing up to 49% during peak pollution), with calcium ion (Ca<sup>2+</sup>) and sulfate ion (SO<sub>4</sub><sup>2−</sup>) concentrations rising to 2 times and 7.5 times those of clean periods, respectively. Results from the random forest model demonstrated that emission sources (74%) dominated this pollution episode, significantly surpassing the meteorological contribution (26%). The Weather Research and Forecasting model combined with the Community Multiscale Air Quality model (WRF–CMAQ) further revealed that local emissions contributed the most to PM<sub>2.5</sub> concentrations in Suzhou (46.3%), while external transport primarily originated from upwind cities such as Shanghai and Jiaxing. The findings indicate synergistic effects from dust sources, industrial emissions, and mobile sources. Validation using electricity consumption and key enterprise emission data confirmed that intensive local industrial activities exacerbated PM<sub>2.5</sub> accumulation. Recommendations include strengthening regulations on local industrial and mobile source emissions, and enhancing regional joint prevention and control mechanisms to mitigate cross-boundary transport impacts.https://www.mdpi.com/2073-4433/16/7/867PM<sub>2.5</sub>air pollutiontypical citiescause analysismachine learningchemical models |
| spellingShingle | Minyan Wu Ningning Cai Jiong Fang Ling Huang Xurong Shi Yezheng Wu Li Li Hongbing Qin Local Emissions Drive Summer PM<sub>2.5</sub> Pollution Under Adverse Meteorological Conditions: A Quantitative Case Study in Suzhou, Yangtze River Delta Atmosphere PM<sub>2.5</sub> air pollution typical cities cause analysis machine learning chemical models |
| title | Local Emissions Drive Summer PM<sub>2.5</sub> Pollution Under Adverse Meteorological Conditions: A Quantitative Case Study in Suzhou, Yangtze River Delta |
| title_full | Local Emissions Drive Summer PM<sub>2.5</sub> Pollution Under Adverse Meteorological Conditions: A Quantitative Case Study in Suzhou, Yangtze River Delta |
| title_fullStr | Local Emissions Drive Summer PM<sub>2.5</sub> Pollution Under Adverse Meteorological Conditions: A Quantitative Case Study in Suzhou, Yangtze River Delta |
| title_full_unstemmed | Local Emissions Drive Summer PM<sub>2.5</sub> Pollution Under Adverse Meteorological Conditions: A Quantitative Case Study in Suzhou, Yangtze River Delta |
| title_short | Local Emissions Drive Summer PM<sub>2.5</sub> Pollution Under Adverse Meteorological Conditions: A Quantitative Case Study in Suzhou, Yangtze River Delta |
| title_sort | local emissions drive summer pm sub 2 5 sub pollution under adverse meteorological conditions a quantitative case study in suzhou yangtze river delta |
| topic | PM<sub>2.5</sub> air pollution typical cities cause analysis machine learning chemical models |
| url | https://www.mdpi.com/2073-4433/16/7/867 |
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