Urban air quality is subject to the increasing pressure of urbanization, and, consequently. the potentia I impact of air quality changes must be addressed. A Bayesian hierarchical model was developed in this paper for urban air quality predication. Literature data on three pollutants and four external driving factors in Xiamen City, China, were studied. The air quality model structure and prior distributions of model parameters were determined by multivariate statistical methods, including correlation analysis, classification and regression trees (CART), hierarchical cluster analysis (CA), and discriminant analysis (DA). A multiple linear regression (MLR) equation was proposed to measure the relationship between pollutant concentrations and driving variables; and Bayesian hierarchical model was introduced for parameters estimation and uncertainty analysis. Model fit between the observed data and the modeled values was demonstrated, with mean and median values and two credible levels (2.5% and 97.5%). The average relative errors between the observed data and the mean values of SO(2), NO(x), and dust fall were 6.81%, 6.79%, and 3.52%, respectively. (c) 2008 Elsevier Ltd. All rights reserved.

An inexact chance-constrained linear programming (ICCLP) model for optimal water pollution management at the watershed scale was developed. We selected the net expenditures of the alternative strategies, including initial capital investment and operating costs, as the objectives of water pollution management. The total environmental capacity of the water bodies at different probability levels (q(i)) was considered a key constraint; other constraints included in the model were government minimum requirements on farmland area, land cover, treatment rate of domestic wastewater and rural wastes, and certain technical constraints. The ICCLP model was applied to Lake Qionghai watershed in China for water quality improvement with the goal of achieving a minimum total cost. Alternative strategies were incorporated following discussions with shareholders and experts. A three-period optimization was conducted based on the alternative strategies; the model parameters were based on field investigations. Five probability levels were considered in the model: q(i)=0.01, 0.25, 0.50. 0.90, and 0.99. The model results showed that the total optimized costs were between US\$[55,710.86,80,691.81] x 10(4) and US\$[72,151.39,101,338.6] x 10(4) under different probability levels. The model results indicate that soil erosion treatment, nonpoint source control measures, and rural waste treatment have much higher costs than other strategies, and our findings indicate that the ICCLP model can effectively deal with optimal water pollution management under uncertainty at the watershed scale.

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 paper presents an ecological-economic model for a lake and its watershed systems. We describe the linkage between the watershed system and the lake aquatic ecosystem and the modeling process. The take-watershed system was divided into six subsystems: social system, economic system, terrestrial ecosystem, lake water system, pollutant system, and lake aquatic ecosystem. The model equations were constructed based on five main assumptions. The Lake Qionghai watershed in southwestern China, which is undergoing rapid eutrophication, was used as a case study. The targeted goals for total phosphorus (TP) and chlorophyll a (Chl a) concentrations in the lake in 2015 are 0.025 and 10.0 mg m(-3), respectively. We present two scenarios from 2004 to 2015 based on the ecological-economic model. In both scenarios, the TP and Chl a concentrations in the lake are predicted to increase under the effects of watershed pressures and the targeted goals cannot be met. The application of techniques to reduce pollutants loading and the corresponding pollutants reductions are reflected again in the constructed model. The model predicts that TP and Chl a concentrations will decrease to 0.024 and 7.71 mg m(-3), respectively, which meet the targeted thresholds. The model results provide directions for local government management of watersheds and lake aquatic ecosystem restoration. (C) 2007 Elsevier GmbH. 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.