Various chemometric methods were used to analyze data sets of marine water quality for 19 parameters measured at 16 different sites of southern Hong Kong from 2000 to 2004 (18,240 observations), to determine temporal and spatial variations in marine water quality and identify pollution sources. Hierarchical cluster analysis (CA) grouped the 12 months into three periods (January-April, May-August and September-December) and the 16 sampling sites into two groups (A and 13) based on similarities in marine water-quality characteristics. Discriminant analysis (DA) was important in data reduction because it used only eight parameters (TEMP, TURB, Si, NO3–N, NH4+-N, NO2–N, DO, and Chl-a) to correctly assign about 86% of the cases, and five parameters (SD, NH4+-N, TP, 3 4 2 4 NO2- -N, and BOD5) to correctly assign > 81.15% of the cases. In addition, principal component analysis (PCA) identified four latent pollution sources for groups A and B: organic/eutrophication pollution, natural pollution, mineral pollution, and nutrient/fecal pollution. Furthermore, during the second and third periods, all sites received more organic/eutrophication pollution and natural pollution than in the first period. SM5, SM6, SM17, SM10, SM11, SM12, and SM13 (second period) were affected by organic and eutrophication pollution, whereas SM3 (third period) and SM9 (second period) were influenced by natural pollution. However, differences between mineral pollution and nutrient/fecal pollution were not significant among the three periods. SM17 and SM10 were affected by mineral pollution, whereas SM4 and SM9 were highly polluted by nitrogenous nutrient/fecal pollution. (C) 2007 Elsevier Ltd; All rights reserved.

In conjunction with socioeconomic development in watersheds, increasingly challenging problems, such as scarcity of water resources and environmental deterioration, have arisen. Watershed management is a useful tool for dealing with these issues and maintaining sustainable development at the watershed scale. The complex and uncertain characteristics of watershed systems have a great impact on decisions about countermeasures and other techniques that will be applied in the future. An optimization method based on scenario analysis is proposed in this paper as a means of handling watershed management under uncertainty. This method integrates system analysis, forecast methods, and scenario analysis, as well as the contributions of stakeholders and experts, into a comprehensive framework. The proposed method comprises four steps: system analyses, a listing of potential engineering techniques and countermeasures, scenario analyses, and the optimal selection of countermeasures and engineering techniques. The proposed method was applied to the case of the Lake Qionghai watershed in southwestern China, and the results are reported in this paper. This case study demonstrates that the proposed method can be used to deal efficiently with uncertainties at the watershed level. Moreover, this method takes into consideration the interests of different groups, which is crucial for successful watershed management. In particular, social, economic, environmental, and resource systems are all considered in order to improve the applicability of the method. In short, the optimization method based on scenario analysis proposed here is a valuable tool for watershed management.

Comprehensive and joint applications of GIS and chemometric approach were applied in identification and spatial patterns of coastal water pollution sources with a large data set (5 years (2000-2004), 17 parameters) obtained through coastal water monitoring of Southern Water Control Zone in Hong Kong. According to cluster analysis the pollution degree was significantly different between September-next May (the 1st period) and June-August (the 2nd period). Based on these results, four potential pollution sources, such as organic/eutrophication pollution, natural pollution, mineral/anthropic pollution and fecal pollution were identified by factor analysis/principal component analysis. Then the factor scores of each monitoring site were analyzed using inverse distance weighting method, and the results indicated degree of the influence by various potential pollution sources differed among the monitoring sites. This study indicated that hybrid approach was useful and effective for identification of coastal water pollution source and spatial patterns.

Lake areas in Chinese urban fringes are under increasing pressure of urbanization. Consequently, the conflict between rapid urban sprawl and the maintenance of water bodies in such areas urgently needs to be addressed. An integrated GIS-based analysis system (IGAS) for supporting land-use management of lake areas in urban fringes was developed in this paper. The IGAS consists of modules of land-use suitability assessment and change/demand analysis, and land evaluation and allocation. Multicriteria analysis and system dynamics techniques are used to assess land-use suitability and forecast potential land-use variation, respectively. Cost approximation and hypothetical development methods are used to evaluate land resource and market values, respectively. A case study implementing the system was performed on the Hanyang Lake area in the urban fringe of Wuhan City, central China, which is under significant urbanization pressure. Five categories of suitability were investigated by analyzing 11 criteria and related GIS data. Two scenarios for potential land-use changes from 2006 to 2020 were predicted, based on a systematic analysis and system dynamics modeling, and a hierarchical land-use structure was designed for the conservation of aquatic ecosystems. The IGAS may help local authorities better understand and address the complex land-use system, and develop improved land-use management strategies that better balance urban expansion and ecological conservation. (c) 2007 Elsevier B.V. All rights reserved.

Multivariate statistical methods, i.e., cluster analysis (CA) and discriminant analysis (DA), were used to assess temporal and spatial variations in the water quality of the watercourses in the Northwestern New Territories, Hong Kong, over a period of five years (2000-2004) using 23 parameters at 23 different sites (31,740 observations). Hierarchical CA grouped the 12 months into two periods (the first and second periods) and classified the 23 monitoring sites into three groups (group A, group B, and group C) based on similarities of water quality characteristics. DA provided better results with great discriminatory ability for both temporal and spatial analysis. DA also provided an important data reduction because it only used six parameters (pH, temperature, five-day biochemical oxygen demand, fecal coliforms, Fe, and Ni) for temporal analysis, affording about 84% correct assignations, and seven parameters (pH, ammonia-nitrogen, nitrate nitrogen, fecal coliforms, Fe, Ni, and Zn) for spatial analysis, affording more than 90% correct assignations. Therefore, DA allowed a reduction in the dimensionality of the large data set and indicated a few significant parameters that were responsible for most of the variations in water quality. Thus, this study demonstrated that the multivariate statistical methods are useful for interpreting complex data sets in the analysis of temporal and spatial variations in water quality and the optimization of regional water quality monitoring network.