Source apportionment of surface water pollution in North Anhui Plain, Eastern China, using APCS-MLR model combined with GIS approach and socioeconomic parameters

Abstract

• APCS-MLR model combined with GIS approach and socioeconomic parameters were used in this study. • Northern and western parts of the North Anhui Plain were the most polluted. • The contributions of river pollution sources in the North Anhui Plain were quantified for the first time. • Consideration of socioeconomic data with hydrochemical data improved the identification of pollution source. Various human activities have led to the deterioration of river water quality in many plain areas, which threatens human health and restricts social development. Identifying the spatio-temporal distribution of river pollutants and the objective apportionment of pollution sources is important for effectively protecting water resources. In this study, a geographic information system (GIS approach), principal component analysis/factor analysis (PCA/FA), and the absolute principal component score-multiple liner regression (APCS-MLR) receptor model were applied to evaluate the water quality and identify pollution sources in rivers in the North Anhui Plain, Eastern China. River water quality data for 43 months (January 2017 to July 2020), comprising 10 physico-chemical parameters from 43 monitoring sites across the study area, were used. The average concentrations of total nitrogen (TN), chemical oxygen demand (COD Cr ), permanganate index (COD Mn ), fluoride (F − ), biochemical oxygen demand (BOD), total phosphorous (TP), and NH 4 + –N in 66.9 %, 59.7 %, 41.9 %, 41.25, 30 %, 27.1 %, and 14.7 % of the samples, respectively, exceeded the Chinese surface water standards. BOD, NH 4 + –N, TN, and TP showed a decreasing annual trend, and the northern and western parts of the study area were the most polluted. According to the PCA/FA and the spatial distribution characteristics of pollutants and socio-economic parameters, industrial wastewater and municipal effluents, agricultural cultivation and domestic sewage, geogenic processes and meteorological factors were identified as being responsible for the degradation of the river water quality. The mean contributions of these source were 28.6 %, 5.5 %, 16.1 % and 25.1 %, respectively. This study shows the objectivity and reliability of using a combined approach of GIS with multivariate statistics to evaluate the river water quality status, which can help decision makers to develop more effective strategies for water resource protection.