An integrated SOM-based multivariate approach for spatio-temporal patterns identification and source apportionment of pollution in complex river network
- 作者：Yonghui Yang ; Cuiyu Wang ; Huaicheng Guo ; guohc@pku.edu.cn ; Hu Sheng ; Feng Zhou
- 关键词：Self-organizing maps ; Multivariate statistical analysis ; Source apportionment ; River network ; Eutrophication control
- 刊名：Environmental Pollution
- 出版年：2012
- 期刊代码：28_02697491
- 类别：env
- 出版时间：September, 2012
- 卷：168
- 期：Complete
- 页码：71-79
- 文件大小：1519 K
摘要
In this study, three classification techniques (self-organizing maps, hierarchical cluster analysis and discriminant analysis) were applied to identify spatial water pollution levels, temporal water quality response delay phenomena (WQRDP), source pollution types (point, urban non-point, or agricultural non-point). Two models (principal components analysis (PCA), and positive matrix factorization (PMF)) were used to do the further quantitative source apportionment studying. The 27 inflow rivers in spatial were divided into three pollution levels (A, high; B, medium; C, low). The primary pollution pattern in spatial Clusters A, B, and C were point, urban non-point and agricultural non-point separately, in consideration of simultaneous land use types. Source apportionment results identified five typical factors in spatial Cluster A and six typical factors in spatial Cluster B and C as responsible for the data structure, explaining 80%-90%of the total variance of the dataset.