LAUSR

The determination of the spatiotemporal patterns and driving factors of PM2.5 is of great interest to the atmospheric and climate science community, who aim to understand and better control the atmospheric linkage indicators. However, most previous studies have been conducted on pollution-sensitive cities, and there is a lack of large-scale and long-term systematic analyses. In this study, we investigated the spatiotemporal evolution of PM2.5 and its influencing factors by using an exploratory spatiotemporal data analysis (ESTDA) technique and spatial econometric model based on remote sensing imagery inversion data of the Yangtze River Economic Belt (YREB), China, between 2000 and 2016. The results showed that 1) the annual value of PM2.5 was in the range of 23.49-37.67 μg/m3 with an inverted U-shaped change trend, and the PM2.5 distribution presented distinct spatial heterogeneity; 2) there was a strong local spatial dependence and dynamic PM2.5 growth process, and the spatial agglomeration of PM2.5 exhibited higher path-dependence and spatial locking characteristics; and 3) the endogenous interaction effect of PM2.5 was significant, where each 1% increase in the neighbouring PM2.5 levels caused the local PM2.5 to increase by at least 0.4%. Natural and anthropogenic factors directly and indirectly influenced the PM2.5 levels. Our results provide spatial decision references for coordinated trans-regional air pollution governance as well as support for further studies which can inform sustainable development strategies in the YREB.