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. Crown Copyright (c) 2012 Published by Elsevier Ltd. All rights reserved.
Joint optimization of population pattern and end-of-pipe control is proposed for Lake Dianchi water-quality management with uncertainties, a highly eutrophic lake in the Yunnan-Guizhou Plateau, southwest China. The four crucial components were (1) to develop a risk-based population pattern and end-of-pipe control optimization model based on enhanced-interval linear programming for Lake Dianchi Watershed, where the objective function was to optimize the total urban and rural population and the constraints were accounted for including drinking water availability, land resources restriction, nutrient total maximum daily loads in domestic wastewater, and urban and rural non-point source pollution, (2) to analyze the differences between joint optimization and the optimization without changes in population pattern; (3) to identify the impacts of factors on optimal solutions of both population patterns and end-of-pipe control under uncertainty, and (4) to determine the optimal population transfer strategy to meet the requirement of lake water-quality management. The lower and upper bounds of the total population were 1.21 and 4.87 million inhabitants, respectively; the associated optimal TN and TP loadings were [5054, 6850] t and [1963, 3490] t, respectively. Although the projected populations in 2015 and 2020 were within the optimal range, population transfer would be also essential for increasing environmental carrying capacity. The population pattern was sensitive to the domestic wastewater collection rate, the best values of which were 84.1 and 85.4% in 2015 and 2020, respectively. Compared to traditional end-of-pipe control approach, joint optimization of population pattern and end-of-pipe control could achieve the optimal balance between human activities and lake water-quality management.