Machine Learning Analysis of Particulate Matter Driving Contributions in the Southern Sichuan Basin
Abstract
The southern Sichuan Basin in China is frequently plagued by particulate matter (PM 2.5 ) pollution events in winter, owing to its unique topographical and meteorological conditions. This study combined the machine learning method with the receptor model to reveal the significance of driving factors and their impacts on PM 2.5 concentrations. The results indicated that three primary wintertime severe PM 2.5 pollution episodes in the southern Sichuan Basin were driven by the combination of high temperatures (>283 K), high atmospheric pressure (P) (>980 hPa), high relative humidity (RH) (>80%), weak wind speeds (WS) (
Fields of Study & Subjects
Atmospheric chemistry and aerosols
Air Quality Monitoring and Forecasting
Air Quality and Health Impacts
Publication details
- Journal / source
- Johnson Matthey Technology Review
- Volume
- 70
- Issue
- 4
- Publication date
- 27 January 2026
- Language
- EN