Identification of crucial socioeconomic driving factors impacting pollution loads and their scenarios analysis in a typical watershed, southwestern China

Study region: The study area is the Tuojiang River watershed, located in the southwestern China. Study focus: Using the pollutant emission coefficient method, the Chemical Oxygen Demand (COD) and Total Nitrogen (TN) pollution loads were estimated in a typical watershed (Tuojiang River watershed) of southwestern China from 2007 to 2017. The spatial distribution and agglomeration characteristics for the same contaminants were analyzed using Exploratory Spatial Data Analysis (ESDA). Additionally, the extended Stochastic Impact by Regression on Population, Affluence and Technology (STIRPAT) and the Geographically Weighted Regression (GWR) models were applied to evaluate the global and local effects of the main socioeconomic driving factors on pollution loads. Finally, the STIRPAT model and scenarios analysis considering future growth for different driving factors were combined to predict the future trends of COD and TN pollution loads in each district (county). New hydrological insights: The COD and TN pollution loads increased from 324.46 × 103t and 64.01 × 103t in 2007–403.68 × 103t and 75.79 × 103t in 2017, respectively. The total population had the largest impact on the COD and TN pollution loads, while driving factors closely related to agricultural activity had smaller impacts on it. COD and TN pollution loads will increase from 2017 to 2030 under the four socioeconomic growth scenarios. The proposed research framework can provide information for managers to formulate water pollution reduction policies in the watershed.