Spatiotemporal variation patterns of water pollution drivers: The case of China's south-north water transfer project

Abstract

Water pollution is subject to the effects of various drivers and exhibits significant spatiotemporal effects. This paper reviews the spatiotemporal variation pattern of water pollution drivers within the extended framework of the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) that considers spatiotemporal effects. Taking China's South-North Water Transfer Project (SNWTP) as an example, this paper examines the spatial spillover and spatiotemporal heterogeneity of water pollution drivers in the SNWTP's water receiving cities by constructing spatial autocorrelation regression and geographical and temporal weighted regression models. The findings demonstrate that water pollution drivers have interactions between adjacent economic cities, making it more challenging to assess water pollution in specific cities accurately. Moreover, there are significant spatiotemporal heterogeneities in the drivers'impact on water pollution. First, the spatial effect of the population driver is more prominent than the temporal effect. Second, the economic driver is positively correlated with pollution and fluctuates with time. Finally, the technology driver also presents significant spatiotemporal heterogeneity. These spatiotemporal heterogeneities make water pollution control more complicated.