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Analyzing the association between fine particulate matter (PM 2.5 ) pollution and socio-economic factors has become a major concern in public health. Since traditional analysis methods (such as correlation analysis and geographically weighted regression) cannot provide a full assessment of this relationship, the quantile regression method was applied to overcome such a limitation at different spatial scales in this study. The results indicated that merely 3% of the population and 2% of the Gross Domestic Product (GDP) occurred under an annually mean value of 35 μg/m³ in mainland China, and the highest population exposure to PM 2.5 was located in a lesser-known city named Dazhou in 2014. The analysis results at three spatial scales (grid-level, county-level, and city-level) demonstrated that the grid-level was the optimal spatial scale for analysis of socio-economic effects on exposure due to its tiny uncertainty, and the population exposure to PM 2.5 was positively related to GDP. An apparent upward trend of population exposure to PM 2.5 emerged at the 80th percentile GDP. For a 10 thousand yuan rise in GDP, population exposure to PM 2.5 increases by 1.05 person/km² at the 80th percentile, and 1.88 person/km 2 at the 95th percentile, respectively.

Quantitative Assessment of Relationship between Population Exposure to PM2.5 and Socio-Economic Factors at Multiple Spatial Scales over Mainland China