[Spatio-temporal Variation and Multi-dimensional Detection of Driving Mechanism of PM2.5 Concentration in the Chengdu-Chongqing Urban Agglomeration from 2000 to 2021].

ABSTRACT

Studies on the spatio-temporal variation and driving mechanism of PM2.5 concentration in the Chengdu-Chongqing urban agglomeration are of great significance for regional atmospheric environment protection and national economic sustainable development. Based on PM2.5 remote sensing data, DEM data, in situ meteorological data, MODIS NDVI data, population density data, nighttime lighting data, road network data, and land use type data, a series of mathematical methods such as Theil-Sen Medium analysis and Mann-Kendall significance test, combined with the Geo-detector model were used to analyze the spatio-temporal variation and multi-dimensional detection of the driving mechanism of PM2.5 concentration in the Chengdu-Chongqing urban agglomeration. The results showed that the overall PM2.5 concentration showed a fluctuating downward trend in the Chengdu-Chongqing urban agglomeration from 2000 to 2021, and the PM2.5 pollution was the most prominent in winter. PM2.5 concentration exhibited obvious spatial heterogeneity with "high in the middle and low in the surrounding areas." The high-PM2.5 concentration areas were mainly concentrated in Zigong, Neijiang, Ziyang, and Guang'an, and the areas with a PM2.5 concentration decrease were mainly concentrated in the west of Chongqing. Influencing detection results showed that the spatial heterogeneity of PM2.5 concentration in the Chengdu-Chongqing urban agglomeration was influenced by the combined effects of climate factors, topographic factors, vegetation cover, and anthropogenic factors. Furthermore, elevation, slope, and road network density were regarded as the dominant factors influencing the spatial heterogeneity of PM2.5 concentration in the study area. Topographic factors and climate factors showed the highest and lowest contribution rate to the spatial heterogeneity of PM2.5 concentration, respectively. The contribution rate of topographic factors and anthropogenic factors had gradually increased, and the contribution rate of climate factors and vegetation cover had gradually decreased in the study area from 2000 to 2021. Interaction detection results showed that the spatial heterogeneity of PM2.5 concentration in the Chengdu-Chongqing urban agglomeration was mostly affected by the interaction effects of elevation and road network density, slope, precipitation, sunshine duration, and land use type. The interaction detection results exhibited obvious regional differences on the city level. For instance, the spatial heterogeneity of PM2.5 concentration in Chengdu, Deyang, and Leshan was mostly affected by the interaction between different influencing types, and the spatial heterogeneity of PM2.5 concentration in Dazhou, Meishan, Ya'an, Ziyang, Neijiang, and Zigong was mostly affected by the interaction within a single influencing type.