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Understanding the mechanisms of various factors that affect PM2.5 can assist in the development of scientific measures to improve air quality. Nevertheless, existing research has concentrated on exploring local effect mechanisms, while structural effect mechanisms at regional or national scales have scarcely been analysed. Consequently, this study presents an analytical framework for elucidating the structural effect mechanisms of associated factors on PM2.5. Geographically and temporally weighted regression was used to explore the local effect mechanisms. This was followed by spatial clustering analysis to reveal these mechanisms by detecting their aggregation patterns. In the analysis, datasets for annual mean PM2.5 and socio-economic factors in China from 1999 to 2016 were employed. Urban population, gross industrial output, and sulphur dioxide emissions were identified as factors affecting changes in PM2.5 concentrations. These three factors had both negative and positive effects, while the gross industrial output had the largest coefficient variation degree. Three geographically related factors exhibited different impacts on PM2.5 concentrations in most of mainland China. These factors were the urban population roughly west of the Heihe-Tengchong line, gross industrial output primarily in southwestern China, and sulphur dioxide emissions primarily in southern China.

A Clustering Framework to Reveal the Structural Effect Mechanisms of Natural and Social Factors on PM2.5 Concentrations in China