Analyzing the effects of socioeconomic, natural and landscape factors on PM_2.5 concentrations from a spatial perspective

PM_2.5, as a major air pollutant, remains unclear as to what factors influence it and the magnitude of the influence. Ten influencing factors, including socioeconomic, natural and landscape indicators, were chosen, and the effects of these factors on PM_2.5 concentration was examined through Pearson correlation analysis and the boosted regression tree model. The findings indicate that PM_2.5 concentration was most affected by GDP, NDVI and precipitation. The GDP imposed the most notable positive effect in China. The temperature imposed the greatest negative effect in East China. Northeast, North and Northwest China were the most negatively affected by the NDVI. Southwest and South-Central China were the most negatively affected by the relative humidity. More than half of the areas were affected by the main positive effects of GDP and more than a third of the areas were affected by the main negative effects of RH. This study systematically studied the correlations between PM_2.5 concentrations and their influencing factors from a spatial perspective over a long time series. The findings could contribute to a more comprehensive understanding of the factors influencing PM_2.5 and offer a theoretical basis for zonal PM_2.5 pollution management.