Surface water monitoring networks play an important role in the stream water quality management. Since a time series of data is obtained from the monitoring network, multivariate statistical techniques can be used to identify important factors or pollution sources of water system. Positive matrix factorization (PMF) is an improved factor analysis tool that has had limited application to water systems. The objective was to apply PMF to monitoring data to apportion water pollution sources in the Daliao River (DLR) basin. The DLR basin includes the Hun and Taizi River catchments in northeast China. This basin is densely populated and heavily industrialized. Fourteen monitoring stations located on the two rivers were used for monitoring 13 physical and chemical parameters from 1990 to 2002. Results show that five sources/processes in the Hun River and four in the Taizi River were identified by marker species and spatial-temporal variations of resolved factors, including point and nonpoint sources for both rivers. In addition, the industrial pollution source emission inventory data were used to compare with the resolved industrial sources. Results reveal that chemical transformations have influenced some chemical species. However, this influence is small compared with observed seasonal variations. Therefore, identification of pollution point and nonpoint sources by their seasonal variations is possible, which will also aid in water quality management. The spatial variation of the industrial pollutants typically corresponded with the urban industrial pollution source inventories.