Understanding the drivers of PM2.5 is critical for the establishment of PM2.5 prediction models and the prevention and control of regional air pollution. In this study, the Yangtze River Delta is taken as the research object. Spatial cluster and outlier method was used to analyze the temporal and spatial distribution and variation of surface PM2.5 in the Yangtze River Delta from 2015 to 2020, and Random Forest was utilized to analyze the drivers of PM2.5 in this area. The results indicated that (1) based on the spatial cluster distribution of PM2.5, the northwest and north of Yangtze River Delta region were mostly highly concentrated and surrounded by high concentrations of PM2.5, while lowly concentrated and surrounded by low concentrations areas were distributed in the southern; (2) the relationship between PM2.5 concentrations and drivers in the Yangtze River Delta was modeled well and the explanatory rate of drivers to PM2.5 were more than 0.9; (3) temperature, precipitation, and wind speed were the main driving forces of PM2.5 emission in the Yangtze River Delta. It should be noted that the repercussion of wildfire on PM2.5 was gradually prominent. When formulating air pollution control measures, the local government normally considers the impact of weather and traffic conditions. In order to reduce PM2.5 pollution caused by biomass combustion, the influence of wildfire should also be taken into account, especially in the fire season. Meanwhile, high leaf area was conducive to improving air quality, and the increasing green area will help reduce air pollutants.
Keywords: PM2.5 drivers; Random Forest; Spatial autocorrelation; Spatial distribution; Yangtze River Delta.
© 2022. The Author(s), under exclusive licence to Springer Nature Switzerland AG.