Abstract
The study used multiple linear regression models and the inverse distance weight interpolation method to construct a regression analysis model of PM2.5 concentration influencing factors based on the measured data of 9 PM2.5 concentration monitoring points in Nanjing from 2012 to 2019, in order to clarify the spatial and temporal distribution characteristics of PM2.5 pollution in the main urban area of Nanjing. The results show that industrial land area, arable land area, and the density of food and beverage outlets all contribute to PM2.5 concentration, while forest land area has a certain inhibitory effect on the increase of PM2.5 concentration; simulation results show that PM2.5 concentration in Nanjing’s main urban area is decreasing year by year, and the spatial distribution of PM2.5 is primarily influenced by the density of food and beverage outlets. The modeling findings reveal that PM2.5 concentrations in Nanjing’s main urban region are decreasing year by year, with a “low north and high south” pattern dominating the spatial distribution.
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