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The identification of fine particulate matter (PM2.5) concentrations and its driving factors are crucial for air pollution prevention and control. The factors that influence PM2.5 in different regions exhibit significant spatial heterogeneity. Current research has quantified the spatial heterogeneity of single factors but fails to discuss the interactions between factors. In this study, we first divided the study area into subregions based on the spatial heterogeneity of factors in a multi-scale geographically weighted regression model. We then investigated the interactions between different factors in the subregions using the geographical detector model. The results indicate that there was significant spatial heterogeneity in the interactions between the driving factors of PM2.5. The interactions between natural factors have significant uncertainty, as do those between the normalized difference vegetation index (NDVI) and socioeconomic factors. The interactions between socioeconomic factors in the subregions were consistent with those in the whole region. Our findings are expected to deepen the understanding of the mechanisms at play among the aforementioned drivers and aid policymakers in adopting unique governance strategies across different regions.
Jin et al. (Tue,) studied this question.