Understanding the spatial and temporal variations and source apportionment of water pollution is important for efficient water environment management. The non-negative matrix factorization (NMF) method, which is naturally well suited for non-negative data of high dimension, was used to identify the latent factors and apportion the contributions from identified pollution sources to each water quality parameter. We obtained a data matrix with 11 water quality variables collected from 2013 to 2016 in the Luanhe River Basin in northern China. The results highlight the substantial contribution of industrial and livestock wastewater. All land-use types have a slightly weaker impact on surface water pollution during the dry season than during the rainy season. The aim of this study is to illustrate the practicability of multivariate statistical analysis, especially the application of NMF, which has major potential for source separation and the apportionment of water pollution.

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