This paper aims to gain a better understanding of urban river pollution through evaluation of water quality. Data for 10 parameters at eight sites of the Tongzhou Section of the Beiyun River (TSBR) are analyzed. Hierarchical cluster analysis, fuzzy comprehensive assessment, discriminant analysis and Spearman's correlation analysis were used to estimate the water situation of each cluster and analyze its spatial-temporal variations. Principal component analysis/factor analysis were applied to extract and recognize the sources responsible for water-quality variations. The results showed that temporal variation is greater than spatial and sewage discharge is the dominant factor of the seasonal distribution. Moreover, during the rapid-flow period, water quality is polluted by a combination of organic matter, phosphorus, bio-chemical pollutants and nitrogen; during the gentle-flow period, water quality is influenced by domestic and industrial waste, the activities of algae, aquatic plants and phosphorus pollution. In regard to future improvement of water quality in TSBR, the control of reclaimed wastewater from adjacent factories should first be put in place, as well as other techniques, for example, an increase of the impervious area, low-impact development, and integrated management practices should also be proposed in managing storm water runoff.hydrological and hydro-chemical characteristic when compared to natural river courses. Studies on pollution status and distribution in urban river courses may serve as a guidance for water quality improvement and water safety insurance in urban areas.Spatial and temporal variations of river water condition have always been a hotspot for research on water environments. While most studies focus on natural river courses or lakes, there is still lack of research on highly artificialized urban river courses, which always occur on a small scale. V. Simeonov et al. focus on a three-year survey conducted in the major river systems (Aliakmon, Axios, Gallikos, Loudias and Strymon) as well as streams, tributaries and ditches in Northern Greece, using a multivariate receptor model to estimate the contribution of identified sources to the concentration of the physicochemical parameters [6]. Research on marine water quality in Eastern Hong Kong found that water quality in 12 months could be grouped into two groups, June-September and the remaining months, and the entire area could be divided into two parts, representing different pollution levels. Pattern recognition provided better information for Hong Kong offshore water-quality monitoring and function division, in the meantime allowed the identification of possible factors that influenced the water systems [10]. Pejman et al. [11] used multivariate statistical techniques to evaluate spatial and seasonal variations of water quality in the Haraz River Basin, providing a representative and reliable estimation to get more information about water quality and design monitoring network.Multivariate mathematical and statistical techniques ser...