A Bayesian approach was applied to river water quality modeling (WQM) for load and parameter estimation. A distributed-source model (DSM) was used as the basic model to support load reduction and effective water quality management in the Hun-Taizi River system, northeastern China. Water quality was surveyed at 18 sites weekly from 1995 to 2004; biological oxygen demand (BOD) and ammonia (NH4+) were selected as WQM variables. The first-order decay rate (k(i)) and load (L-i) of the 16 river segments were estimated using the Bayesian approach. The maximum pollutant loading (L-m) of NH4+ and BOD for each river segment was determined based on DSM and the estimated parameters of k(i). The results showed that for most river segments, the historical loading was beyond the L-m threshold; thus, reduction for organic matter and nitrogen is necessary to meet water quality goals. Then the effects of inflow pollutant concentration (Ci-1) and water velocity (v(i)) on water quality standard compliance were used to demonstrate how the proposed model can be applied to water quality management. The results enable decision makers to decide load reductions and allocations among river segments under different Ci-1 and v(i) scenarios. (c) 2008 Elsevier Ltd. All rights reserved.

This article presents the application of an integrated method that estimates the dispersion of polycyclic aromatic hydrocarbons (PAHs) in air, and assesses the human health risk associated with PAHs inhalation. An uncertainty analysis method consisting of three components were applied in this study, where the three components include a bootstrapping method for analyzing the whole process associated uncertainty, an inhalation rate (IR) representation for evaluating the total PAH inhalation risk for human health, and a normally distributed absorption fraction (AF) ranging from 0% to 100% to represent the absorption capability of PAHs in human body. Using this method, an integrated process was employed to assess the health risk of the residents in Beijing, China, from inhaling PAHs in the air. The results indicate that the ambient air PAHs in Beijing is an important contributor to human health impairment, although over 68% of residents seem to be safe from daily PAH carcinogenic inhalation. In general, the accumulated daily inhalation amount is relatively higher for male and children at 10 years old of age than for female and children at 6 years old. In 1997, about 1.73% cancer sufferers in Beijing were more or less related to ambient air PAHs inhalation. At 95% confidence interval, approximately 272-309 individual cancer incidences can be attributed to PAHs pollution in the air. The probability of greater than 500 cancer occurrence is 15.3%. While the inhalation of ambient air PAHs was shown to be an important factor responsible for higher cancer occurrence in Beijing, while the contribution might not be the most significant one.

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.

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.

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.

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.

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.

Integrated watershed management is required to ensure the reasonable use of resources and reconcile interactions among natural and human systems. In the present study, an interval fuzzy multiobjective programming (IFMOP) method was used to solve an integrated watershed management problem. Based on system analysis, an IFMOP model suitable for a lake watershed system İFMOPLWS\ was developed and applied to the Lake Qionghai watershed in China. Scenario analysis and an interactive approach were used in the solution process. In this manner, various system components were incorporated into one framework for holistic consideration and optimization. Integrality and uncertainty, as well as the multiobjective and dynamic characteristics of the watershed system, were well addressed. Using two scenarios, two planning schemes were generated. Agriculture, tourism, macroeconomics, cropland use, water supply, forest coverage, soil erosion, and water pollution were fully interpreted and compared to identify a preferable planning alternative for local agencies. This study showed that the IFMOPLWS is a powerful tool for integrated watershed management planning and can provide a solid base for sustainable watershed management.}

Lake eutrophication caused by excess phosphorus (P) loading from point sources (PS) and nonpoint sources (NPS) is a persistent and serious ecological problem in China. A phosphorus budget, based on material flow analysis(MFA) and system dynamic (SD), is proposed and applied for the agriculture-dominated Qionghai Lake watershed located in southwestern China. The MFA-SD approach will not only cover the transporting process of P in the lake-watershed ecosystems, but also can deal with the changes of P budget due to the dynamics of watershed. P inflows include the fertilizer for agricultural croplands, soil losses, domestic sewage discharges, and the atmospheric disposition such as precipitation and dust sinking. Outflows are consisted of hydrologic export, water resources development, fishery and aquatic plants harvesting. The internal P recycling processes are also considered in this paper. From 1988 to 2015, the total P inflows for Lake Qionghai are in a rapid increase from 35.65 to 78.73 t/a, which results in the rising of P concentration in the lake. Among the total P load 2015, agricultural loss and domestic sewage account for 70.60% and 17.27% respectively, directly related to the rapid social-economic development and the swift urbanization. Future management programs designed to reduce P inputs must be put into practices in the coming years to ensure the ecosystem health in the watershed.

Ecological demonstration area (EDA) is an authorized nomination, which should be assessed from several aspects, including ecological, social, environmental, economic ones and so on. It is difficult to advance an exact developing level index of EDA due to its indicator system's complexity and disequilibrium. In this paper, a framework of indicators was set to evaluate, monitor and examine the comprehensive level of ecological demonstration area (EDA). Fuzzy logic method was used to develop the fuzzy comprehensive evaluation model (FCEM), which could quantitatively reveal the developing degree of EDA. Huiji District of Zhengzhou, Henan Province, one of the 9th group of national EDAs, was taken as a study case. The framework of FCEM for the integrated system included six subsystems, which were social, economic, ecological, rural, urban and accessorial description ones. The research would be valuable in the comprehensive quantitative evaluation of EDA and would work as a guide in the construction practices of Huiji ecological demonstration area.