Spatial variation and source apportionment of surface water pollution in the Tuo River, China, using multivariate statistical techniques

The increasingly serious water pollution of rivers has attracted wide attention from all countries in the world. Investigating spatial variations of water pollution and source apportionment is particularly important for the effective management of river quality. The water samples collected every two months at 31 sampling sites containing 12 water quality parameters during 2018 and 2019 were analyzed to investigate the spatial patterns and the apportionment of the pollutants in the Tuo River. Cluster analysis (CA), pollution index (PI), factor analysis (FA), principal component analysis (PCA), and absolute principal component score-multiple linear regression (APCS-MLR) were used in the current study. The PI found that the Tuo River was most severely polluted with phosphorus and nitrogen. Additionally, compared with that in 2018, the water quality in the Tuo River has significantly improved in 2019. The CA divided the sampling sites into three categories, which are defined as clean, low-polluted, and moderate-polluted areas, respectively. FA/PCA resulted in four latent pollution sources, explaining 74.09% of the total variance. The contributions of the identified pollution sources to pollutants were realized using APCS-MLR. Most variables were mainly affected by the pollution of agricultural runoff, industrial wastewater, domestic sewage, and soil weathering. According to the results, we can also find that agricultural runoff and industrial wastewater were dominating in the Tuo River. These results provide a scientific basis for formulating more reasonable and strict pollution control strategies for the Tuo River.