Various multivariate methods were used to analyze datasets of river water quality for 11 variables measured at 20 different sites surrounding Lake Taihu from 2006 to 2010 (13,200 observations), to determine temporal and spatial variations in river water quality and to identify potential pollution sources. Hierarchical cluster analysis (CA) grouped the 12 months into two periods (May to November, December to the next April) and the 20 sampling sites into two groups (A and B) based on similarities in river water quality characteristics. Discriminant analysis (DA) was important in data reduction because it used only three variables (water temperature, dissolved oxygen (DO) and five-day biochemical oxygen demand (BOD₅)) to correctly assign about 94% of the cases and five variables (petroleum, volatile phenol, dissolved oxygen, ammonium nitrogen and total phosphorus) to correctly assign >88.6% of the cases. In addition, principal component analysis (PCA) identified four potential pollution sources for Clusters A and B: industrial source (chemical-related, petroleum-related or N-related), domestic source, combination of point and non-point sources and natural source. The Cluster A area received more industrial and domestic pollution-related agricultural runoff, whereas Cluster B was mainly influenced by the combination of point and non-point sources. The results imply that comprehensive analysis by using multiple methods could be more effective for facilitating effective management for the Lake Taihu Watershed in the future.

The numerical source-apportionment model is an efficient and useful method for analyzing water-quality responses to nutrient loading in rivers and lakes. In this study, the Environmental Fluid Dynamic Code (EFDC) and numerical source-apportionment model were applied to Lake Bali in Jiujiang City, China to predict the contributions of various pollution sources to the lake at any time and position. We calibrated and validated the model by comparing its predictions with observed hydrodynamic and water-quality parameters from 2014 to 2015. Application of the calibrated model to simulate water-quality responses to a pollution source showed that the contribution of a pollution source to water quality in the lake has strong spatial heterogeneity. The results provide useful information for the optimization of pollution load reduction in Lake Bali and can be used to determine the most effective implementation of its pollution-control plan. The model built in this study can also be used for pollution source-apportionment in other urban lakes and is superior to other traditional source-apportionment models.

The determination of the total amount of water pollutant emission in different regions is a difficult problem faced by managers and researchers. Previous studies mostly focused on operability and fairness with little attention paid to local water quality. In order to make total emission pollutant control (TEPC) truly serve the improvement of water quality, a water total emission pollutant allocation model was built based on water environmental carrying capacity (WECC) in this paper. This model was used to construct a water pollutant emission control allocation scheme for 28 cities in Henan Province, China. The results showed that the chemical oxygen demand (COD) reduction rates for these cities ranged from 16.8 to 38.6% and ammonia-nitrogen (NH₃-N) reduction rates ranged from 5.7 to 43.5% in 2020, which were different from the previous targets for these cities without considering their current status of water quality. The largest COD reduction rates for different types of point sources (industrial, urban, and large-scale livestock sources) were 35.4%, 39.0%, and 38.0%, respectively, and the largest NH₃-N reduction rates were 62.2%, 42.5%, and 43.5%, respectively. This study solves the problem of long-term disconnection between TEPC and water quality improvement in China. The results can also be applied to implement the TEPC to improve water quality in other regions with similar problem.