Articles | Volume 26, issue 9
https://doi.org/10.5194/acp-26-6117-2026
https://doi.org/10.5194/acp-26-6117-2026
Research article
 | 
08 May 2026
Research article |  | 08 May 2026

Multi-machine-learning approaches to modeling small-scale source attribution of ozone formation

Zheng Xiao, Yifeng Lu, and Guangli Xiu

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Cited articles

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Cao, X., Yi, J., Li, Y., Zhao, M., Duan, Y., Zhang, F., and Duan, L.: Characteristics and Source Apportionment of Volatile Organic Compounds in an Industrial Area at the Zhejiang–Shanghai Boundary, China, Atmosphere, 15, 237, https://doi.org/10.3390/atmos15020237, 2024. 
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This study innovates air pollution tracking in industry by combining AI with traditional methods. By analyzing 3 years of data from a chemical park in Shanghai, we identified sources of ozone pollution and seasonal variations, revealing chemical solvents and fuel vapor as key factors. Our approach identifies sources of pollution faster and more accurately, helping to make better air quality decisions in rapidly developing areas.
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