Machine Learning Modeling Reveals Divergent Air Pollutant Responses to Stringent Emission Controls in the Yangtze River Delta Region
Ozone (O<sub>3</sub>) and fine particulate matter (PM<sub>2.5</sub>) are critical atmospheric pollutants whose complex chemical coupling presents significant challenges for multi-pollutant control strategies. This study investigated the spatiotemporal variations and driving m...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-06-01
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| Online Access: | https://www.mdpi.com/2073-4433/16/6/710 |
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| author | Qiufang Yao Linhao Wang Wenjing Qiu Yutong Shi Qi Xu Yanping Xiao Jiacheng Zhou Shilong Li Haobin Zhong Jinsong Liu |
| author_facet | Qiufang Yao Linhao Wang Wenjing Qiu Yutong Shi Qi Xu Yanping Xiao Jiacheng Zhou Shilong Li Haobin Zhong Jinsong Liu |
| author_sort | Qiufang Yao |
| collection | DOAJ |
| description | Ozone (O<sub>3</sub>) and fine particulate matter (PM<sub>2.5</sub>) are critical atmospheric pollutants whose complex chemical coupling presents significant challenges for multi-pollutant control strategies. This study investigated the spatiotemporal variations and driving mechanisms of O<sub>3</sub> and PM<sub>2.5</sub> in Jiaxing, China, during different COVID-19 lockdown periods from November 2019 to January 2024. Using high-resolution monitoring data, random forest modeling, and HYSPLIT backward trajectory analysis, we quantified the relative contributions of anthropogenic emissions, meteorological conditions, and regional transport to the formation and variation of O<sub>3</sub> and PM<sub>2.5</sub> concentrations. The results revealed a distinct inverse relationship between O<sub>3</sub> and PM<sub>2.5</sub>, with meteorologically normalized PM<sub>2.5</sub> decreasing significantly (−5.0 μg/m<sup>3</sup> compared to the pre-lockdown baseline of 0.6 μg/m<sup>3</sup>), while O<sub>3</sub> increased substantially (15.2 μg/m<sup>3</sup> compared to the baseline of 5.3 μg/m<sup>3</sup>). Partial dependency analysis revealed that PM<sub>2.5</sub>-O<sub>3</sub> relationships evolved from linear to non-linear patterns across lockdown periods, while NO<sub>2</sub>-O<sub>3</sub> interactions indicated shifts from VOC-limited to NO<sub>x</sub>-limited regimes. Regional transport patterns exhibited significant temporal variations, with source regions shifting from predominantly northern areas pre-lockdown to more diverse directional contributions afterward. Notably, the partial lockdown period demonstrated the most balanced pollution control outcomes, maintaining reduced PM<sub>2.5</sub> levels while avoiding O<sub>3</sub> increases. These findings provide critical insights for developing targeted multi-pollutant control strategies in the Yangtze River Delta region and similar urban environments. |
| format | Article |
| id | doaj-art-fbbb374c119a435e84cd5849c25ea202 |
| institution | Kabale University |
| issn | 2073-4433 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Atmosphere |
| spelling | doaj-art-fbbb374c119a435e84cd5849c25ea2022025-08-20T03:26:49ZengMDPI AGAtmosphere2073-44332025-06-0116671010.3390/atmos16060710Machine Learning Modeling Reveals Divergent Air Pollutant Responses to Stringent Emission Controls in the Yangtze River Delta RegionQiufang Yao0Linhao Wang1Wenjing Qiu2Yutong Shi3Qi Xu4Yanping Xiao5Jiacheng Zhou6Shilong Li7Haobin Zhong8Jinsong Liu9School of Advanced Materials Engineering, Jiaxing Nanhu University, Jiaxing 314001, ChinaSchool of Advanced Materials Engineering, Jiaxing Nanhu University, Jiaxing 314001, ChinaSchool of Advanced Materials Engineering, Jiaxing Nanhu University, Jiaxing 314001, ChinaSchool of Advanced Materials Engineering, Jiaxing Nanhu University, Jiaxing 314001, ChinaSchool of Advanced Materials Engineering, Jiaxing Nanhu University, Jiaxing 314001, ChinaSchool of Advanced Materials Engineering, Jiaxing Nanhu University, Jiaxing 314001, ChinaSchool of Advanced Materials Engineering, Jiaxing Nanhu University, Jiaxing 314001, ChinaSchool of Advanced Materials Engineering, Jiaxing Nanhu University, Jiaxing 314001, ChinaSchool of Advanced Materials Engineering, Jiaxing Nanhu University, Jiaxing 314001, ChinaSchool of Advanced Materials Engineering, Jiaxing Nanhu University, Jiaxing 314001, ChinaOzone (O<sub>3</sub>) and fine particulate matter (PM<sub>2.5</sub>) are critical atmospheric pollutants whose complex chemical coupling presents significant challenges for multi-pollutant control strategies. This study investigated the spatiotemporal variations and driving mechanisms of O<sub>3</sub> and PM<sub>2.5</sub> in Jiaxing, China, during different COVID-19 lockdown periods from November 2019 to January 2024. Using high-resolution monitoring data, random forest modeling, and HYSPLIT backward trajectory analysis, we quantified the relative contributions of anthropogenic emissions, meteorological conditions, and regional transport to the formation and variation of O<sub>3</sub> and PM<sub>2.5</sub> concentrations. The results revealed a distinct inverse relationship between O<sub>3</sub> and PM<sub>2.5</sub>, with meteorologically normalized PM<sub>2.5</sub> decreasing significantly (−5.0 μg/m<sup>3</sup> compared to the pre-lockdown baseline of 0.6 μg/m<sup>3</sup>), while O<sub>3</sub> increased substantially (15.2 μg/m<sup>3</sup> compared to the baseline of 5.3 μg/m<sup>3</sup>). Partial dependency analysis revealed that PM<sub>2.5</sub>-O<sub>3</sub> relationships evolved from linear to non-linear patterns across lockdown periods, while NO<sub>2</sub>-O<sub>3</sub> interactions indicated shifts from VOC-limited to NO<sub>x</sub>-limited regimes. Regional transport patterns exhibited significant temporal variations, with source regions shifting from predominantly northern areas pre-lockdown to more diverse directional contributions afterward. Notably, the partial lockdown period demonstrated the most balanced pollution control outcomes, maintaining reduced PM<sub>2.5</sub> levels while avoiding O<sub>3</sub> increases. These findings provide critical insights for developing targeted multi-pollutant control strategies in the Yangtze River Delta region and similar urban environments.https://www.mdpi.com/2073-4433/16/6/710machine learningair pollution controlpartial dependency analysisregional transportYangtze River Delta region |
| spellingShingle | Qiufang Yao Linhao Wang Wenjing Qiu Yutong Shi Qi Xu Yanping Xiao Jiacheng Zhou Shilong Li Haobin Zhong Jinsong Liu Machine Learning Modeling Reveals Divergent Air Pollutant Responses to Stringent Emission Controls in the Yangtze River Delta Region Atmosphere machine learning air pollution control partial dependency analysis regional transport Yangtze River Delta region |
| title | Machine Learning Modeling Reveals Divergent Air Pollutant Responses to Stringent Emission Controls in the Yangtze River Delta Region |
| title_full | Machine Learning Modeling Reveals Divergent Air Pollutant Responses to Stringent Emission Controls in the Yangtze River Delta Region |
| title_fullStr | Machine Learning Modeling Reveals Divergent Air Pollutant Responses to Stringent Emission Controls in the Yangtze River Delta Region |
| title_full_unstemmed | Machine Learning Modeling Reveals Divergent Air Pollutant Responses to Stringent Emission Controls in the Yangtze River Delta Region |
| title_short | Machine Learning Modeling Reveals Divergent Air Pollutant Responses to Stringent Emission Controls in the Yangtze River Delta Region |
| title_sort | machine learning modeling reveals divergent air pollutant responses to stringent emission controls in the yangtze river delta region |
| topic | machine learning air pollution control partial dependency analysis regional transport Yangtze River Delta region |
| url | https://www.mdpi.com/2073-4433/16/6/710 |
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