Water quality assessment and pollution source apportionment using multivariate statistical and PMF receptor modeling techniques in a sub- watershed of the upper Yangtze River, Southwest China

Abstract

Abstract The rapid industrial and agricultural development as well as urbanization significantly affect the water environment, especially in sub-watersheds where the contaminants/constituents present in the pollution sources are complex and the flow is unstable. Water quality assessment and quantitative identification of pollution sources are the primary prerequisites for the improvement of water management and quality. In this work, 168 water samples were collected from seven stations throughout 2018–2019 along the Laixi River, which is an important pollution control unit in the upper reaches of the Yangtze River. Multivariate statistics and positive matrix factorization (PMF) receptor modeling techniques were used to evaluate the characteristics of the river-water quality and reveal the pollution sources. Principal component analysis (PCA) was used to screen the crucial parameters and establish an optimized water quality assessment procedure to reduce the analysis cost and improve the assessment efficiency. Cluster analysis (CA) further illustrates the spatiotemporal distribution characteristics of river water quality. Results indicated that high-pollution areas are concentrated in the tributaries, and the high-pollution periods are the spring and winter, which verifies the reliability of the evaluation system. The PMF model identified five and six potential pollution sources in the cold and warm seasons, respectively. Among them, pollution from agricultural activities and domestic wastewater shows the highest contributions (33.2% and 30.3%, respectively) during the cold and warm seasons, respectively. The results of this study can provide corresponding theoretical support for pollutant control and water quality improvement, and avoid the ecological and health risks caused by the deterioration of water quality.