Water quality assessment is an important method for understanding the spatial-temporal variation characteristics of water quality. Therefore, the present study has been performed to evaluate the water quality for a typical plain river network area in Changzhou City, Jiangsu, China, where the river system is characterized by reciprocal flow and diverse pollution sources. The water quality samples from 2017 to 2021 were comprehensively assessed using comprehensive methods that combine the single-factor pollution index (SFPI) method with multivariate statistical analysis. Initially, statistical analyses were conducted to evaluate water quality exceedances and correlations and the SFPI method was applied to classify water quality categories. Furthermore, principal component analysis (PCA) and cluster analysis (CA) were employed to reduce the dimensionality of water quality indicators and group monitoring sections with similar characteristics. The results indicate that the overall water quality in Changzhou City is lightly polluted with a trend of improvement. The primary pollutants identified are total phosphorus (TP) and ammonia nitrogen (NH₃-N). This study highlights that organic pollution, self-purification capacity, and eutrophication of river water bodies are the most significant factors affecting water quality. The sampling sites were classified into three groups (good, moderate, and poor). The water quality assessment results of this study provide a theoretical reference for water environment management and ecological protection in plain river network areas.

Green infrastructure (GI) has been implemented globally to mitigate the negative effects of urbanization. GI also regulates the urban runoff process and reduces non-point source pollution by intercepting initial runoff pollution and stormwater storage. In this paper, the impacts on GI were quantified and analyzed, considering eight designed storms with a 24 h duration and eight others with a 2 h duration with the combination of two characteristics (return period and peak time). The runoff process and reduction effect of pollutants were simulated for GI combinations (green roofs, vegetative swale, bio-retention units, and permeable pavement) using the Storm Water Management Model, taking the Dongshan campus of Shanxi University as an example case study. The results show that the GI combination can reduce runoff, suspended solids (SS), and chemical oxygen demand (COD). For short- and long-duration rainstorms, the average reduction rates of runoff, SS, and COD were 39.7%, 38.8%, and 39.6%, and 36.5%, 31.7%, and 32%, respectively, indicating its better effectiveness for short-duration storms. The GI’s effect was more sensitive during the short-duration storms owing to the greater absolute value of the 2 h elastic coefficients versus that of the 24 h, and the best reduction effect was observed with a rainfall peak coefficient of 0.1. These results provide a scientific reference for GI planning and implementation under a changing climate in the future.