Water quality assessment and source apportionment of Huangpu River water pollution in Shanghai City, eastern China using APCS-MLR

Abstract

Abstract As a result of increasing anthropogenic disturbance, the degradation of the surface water environment has become a key concern for water resource management. Controlling possible pollution sources is necessary for protecting water resources. In this study, water quality data from online monitoring national control stations were analyzed in terms of pH, water temperature (WT), electrical conductivity (EC), turbidity (NTU), dissolved oxygen (DO), and concentrations of permanganate index (CODMn), ammonium nitrogen (NH3+–N), total nitrogen (TN), total phosphorus (TP). Principal component analysis/factor analysis (PCA/FA) were employed to qualitatively figure out the potential sources of river water pollution of Huangpu River in Shanghai City, eastern China. An absolute principal component score-multiple linear regression (APCS-MLR) receptor model was used to analyze each source's contribution to the variables affecting water quality quantitatively. The results showed that all observed water quality indices met the quality criteria specified in the Chinese surface water standards, except for TN. Five sources of river water pollution were identified, and their contribution ratios in a descending order were as follows: the meteorological process (26%) > agricultural activities (14%) > industrial sewage (10%) > natural environmental sources (4%) = domestic sewage (4%). Therefore, recommendations for enhancing the quality of surface water resources in this area involve decreasing agricultural pollution and improving the sewage system.