The conflict of water environment protection and economic development has brought severe water pollution and restricted the sustainable development in the watershed. A risk explicit interval linear programming (REILP) method was used to solve integrated watershed environmental-economic optimization problem. Interval linear programming (ILP) and REILP models for uncertainty-based environmental economic optimization at the watershed scale were developed for the management of Lake Fuxian watershed, China. Scenario analysis was introduced into model solution process to ensure the practicality and operability of optimization schemes. Decision makers' preferences for risk levels can be expressed through inputting different discrete aspiration level values into the REILP model in three periods under two scenarios. Through balancing the optimal system returns and corresponding system risks, decision makers can develop an efficient industrial restructuring scheme based directly on the window of ``low risk and high return efficiency'' in the trade-off curve. The representative schemes at the turning points of two scenarios were interpreted and compared to identify a preferable planning alternative, which has the relatively low risks and nearly maximum benefits. This study provides new insights and proposes a tool, which was REILP, for decision makers to develop an effectively environmental economic optimization scheme in integrated watershed management.

Blue-green algae (BGA) bloom is a typical phenomenon in eutrophied lakes. However, up to now, no environmental mechanism has been commonly accepted. Systematic and complete data sets of BGA blooms and environmental factors without any missing data are rare, which seriously affected previous studies. In this study, a bootstrapping based multiple imputation algorithm (EMB) was first applied to reconstruct a complete data set from the available data set with missing data, hence forming a basis for quantitatively relating BGA bloom to contributing factors. Then, the probability of RCA bloom outbreak was simulated using a binomial (or binary) logistic regression model, which is an effective tool for recognizing key contributing factors. The results suggest that 1) the outbreak frequency or probability of BGA bloom tends to first increase and then decrease with a turning point between June and September each year: 2) air temperature, relative humidity, and precipitation were significant positive factors correlated with outbreak frequency, whereas wind speed and the number of sunshine hours were negative factors: 3) water temperature had a strong positive effect on the probability of BGA bloom outbreak, whereas other water quality factors, such as concentrations of organics and nutrients, were not so significant. However, water quality factors, such as NO3-N, SD, pH, NH4-N. COD and DO, still need to be concerned, which had a potential to aggravate the outbreak of BGA bloom in Dianchi Lake, if they were out of control. (c) 2012 Elsevier B.V. All rights reserved.

Eight molybdenite samples were selected from the Yaojiagou molybdenum deposit. The Re-Os isotopic model ages ranging from 166. 1 +/- 2. 3Ma to 169. 1 +/- 2. 5Ma, yielded an isochron age of 168.8 +/- 3. 9Ma (MSWD =1. 12), which was interpreted to be the ore-forming age of the deposit. Combined with existing geochronologic data of Yaojiagou granit pluton, we assume that there were multistage of intrusions in Yaojiagou area and the Yaojiagou molybdenum deposit was related to the magma intrusion activities in 168. 8 +/- 3. 9Ma. In combination with metallogenic geological background, we infer that the Yaojiagou molybdenum deposit developed from Early to Middle Jurassic, influenced by magma and fluid function of post-collision between North China Craton and Siberia Craton.

Lake Fuxian is the largest deep freshwater lake in China. Although its average water quality meets Class I of the China National Water Quality Standard (CNWQS), i.e., GB3838-2002, monitoring data indicate that the water quality approaches the Class II threshold in some areas. Thus it is urgent to reduce the watershed load through the total maximum daily load (TMDL) program. A three-dimensional hydrodynamic and water quality model was developed for Lake Fuxian, simulating flow circulation and pollutant fate and transport. The model development process consists of several steps, including grid generation, initial and boundary condition configurations, and model calibration processes. The model accurately reproduced the observed water surface elevation, spatiotemporal variations in temperature, and total nitrogen (TN), total phosphorus (TP), and chemical oxygen demand (COD) concentrations, suggesting a reasonable numerical representation of the prototype system for further TMDL analyses. The TMDL was calculated using two interpretations of the water quality standards for Class I of the CNWQS based on the maximum instantaneous surface and annual average surface water concentrations. Analysis of the first scenario indicated that the TN, TP and COD loads should be reduced by 66%, 68% and 57%, respectively. Water quality was the highest priority; however, local economic development and cost feasibility for load reduction can pose significant issues. In the second interpretation, the model results showed that, under the existing conditions, the average water quality meets the Class I standard and therefore load reduction is unnecessary. Future studies are needed to conduct risk and cost assessments for realistic decision-making.

Previous optimization-based watershed decision making approaches suffer two major limitations. First of all, these approaches generally do not provide a systematic way to prioritize the implementation schemes with consideration of uncertainties in the watershed systems and the optimization models. Furthermore, with adaptive management, both the decision environment and the uncertainty space evolve (1) during the implementation processes and (2) as new data become available. No efficient method exists to guide optimal adaptive decision making, particularly at a watershed scale. This paper presents a guided adaptive optimal (GAO) decision making approach to overcome the limitations of the previous methods for more efficient and reliable decision making at the watershed scale. The GAO approach is built upon a modeling framework that explicitly addresses system optimality and uncertainty in a time variable manner, hence mimicking the real-world decision environment where information availability and uncertainty evolve with time. The GAO approach consists of multiple components, including the risk explicit interval linear programming (REILP) modeling framework, the systematic method for prioritizing implementation schemes, and an iterative process for adapting the core optimization model for updated optimal solutions. The proposed approach was illustrated through a case study dealing with the uncertainty based optimal adaptive environmental management of the Lake Qionghai Watershed in China. The results demonstrated that the proposed GAO approach is able to (1) efficiently incorporate uncertainty into the formulation and solution of the optimization model, and (2) prioritize implementation schemes based on the risk and return tradeoff. As a result the GAO produces more reliable and efficient management outcomes than traditional non-adaptive optimization approaches. (C) 2011 Elsevier Ltd. All rights reserved.

Water quality management is subject to large uncertainties due to inherent randomness in the natural system and vagueness in the decision-making process. For water quality management optimization models, this means that some model coefficients can be represented by probability distributions, while others can be expressed only by ranges. Interval linear programming (ILP) and risk explicit interval linear programming (REILP) models for optimal load reduction at the watershed scale are developed for the management of Lake Qionghai Watershed, China. The optimal solution space of an ILP model is represented using intervals corresponding to the lower and upper bounds of each decision variable. The REILP model extends the ILP model through introducing a risk function and aspiration levels (lambda(pre)) into the model formulation. The REILP model is able to generate practical solutions and trade-offs through solving a series of submodels, minimizing the risk function under different aspiration levels. This is illustrated in the present study by solving 11 submodels corresponding to different aspiration levels. The results show that the ILP model suffers severe limitations in practical decision support, while the REILP model can generate solutions explicitly relating system performance to risk level. Weighing the optimal solutions and corresponding risk factors, decision makers can develop an efficient and practical implementation plan based directly on the REILP solution.

Hypoxia is a long-standing threat to the integrity of the Chesapeake Bay ecosystem. In this study, we introduce a Bayesian framework that aims to guide the parameter estimation of a Streeter-Phelps model when only hypoxic volume data are available. We present a modeling exercise that addresses a hypothetical scenario under which the only data available are hypoxic volume estimates. To address the identification problem of the model, we formulated informative priors based on available literature information and previous knowledge from the system. Our analysis shows that the use of hypoxic volume data results in reasonable predictive uncertainty, although the variances of the marginal posterior parameter distributions are usually greater than those obtained from fitting the model to dissolved oxygen (DO) profiles. Numerical experiments of joint parameter estimation were also used to facilitate the selection of more parsimonious models that effectively balance between complexity and performance. Parameters with relatively stable posterior means over time and narrow uncertainty bounds were considered as temporally constant, while those with time varying posterior patterns were used to accommodate the interannual variability by assigning year-specific values. Finally, our study offers prescriptive guidelines on how this model can be used to address the hypoxia forecasting in the Chesapeake Bay area.

Recent studies of Chesapeake Bay hypoxia suggest higher susceptibility to hypoxia in years after the 1980s. We used two simple mechanistic models and Bayesian estimation of their parameters and prediction uncertainty to explore the nature of this regime shift. Model estimates show increasing nutrient conversion efficiency since the 1980s, with lower DO concentrations and large hypoxic volumes as a result. In earlier work, we suggested a 35% reduction from the average 1980-1990 total nitrogen load would restore the Bay to hypoxic volumes of the 1950s-1970s. With Bayesian inference, our model indicates that, if the physical and biogeochemical processes prior to the 1980s resume, the 35% reduction would result in hypoxic volume averaging 2.7 km(3) in a typical year, below the average hypoxic volume of 1950s-1970s. However, if the post-1980 processes persist the 35% reduction would result in much higher hypoxic volume averaging 6.0 km(3). Load reductions recommended in the 2003 agreement will likely meet dissolved oxygen attainment goals if the Bay functions as it did prior to the 1980s; however, it may not reach those goals if current processes prevail.

The civil and environmental decision-making processes are plagued with uncertain, vague, and incomplete information. Interval linear programming (ILP) is a widely applied mathematical programming method in assisting civil and environmental decision making under uncertainty. However, the existing ILP decision approach is found to be ineffective in generating operational schemes for practical decision support due to a lack of linkage between decision risk and system return. In addition, the interpretation of the ILP solutions represented as the lower and upper bounds of decision variables can cause problems of infeasibility and nonoptimality in the resulted implementation schemes. This study proposed a risk explicit ILP (REILP) approach to overcome the limitations of existing ILP approaches. The REILP explicitly reflects the tradeoffs between risk and system return for a decision-making problem under an interval-type uncertainty environment. A risk function was defined to enable finding solutions which maximize system return while minimizing system risk, hence leading to crisp solutions that are feasible and optimal for practical decision making. A numerical experiment on land-use decision making under total maximum daily load was conducted to illustrate the REILP approach. The model results show that the REILP approach is able to efficiently explore the interval uncertainty space and generate an optimal decision front that directly reflects the tradeoff between decision risks and system return, allowing decision makers to make effective decision based on the risk-reward information generated by the REILP modeling analysis.

A multivariate statistical approach integrating the absolute principal components score (APCS) and multivariate linear regression (APCS-MLR), along with structural equation modeling (SEM), was used to model the influence of water chemistry variables on chlorophyll a (Chl a) in Lake Qilu, a severely polluted lake in southwestern China. Water quality was surveyed monthly from 2000 to 2005. APCS-MLR was used to identify key water chemistry variables, mine data for SEM, and predict Chl a. Seven principal components (PCs) were determined as eigenvalues > 1, which explained 68.67% of the original variance. Four PCs were selected to predict Chl a using APCS-MLR. The results showed a good fit between the observed data and modeled values. with R(2) = 0.80. For SEM, Chl a and eight variables were used: NH(4)-N (ammonia-nitrogen), total phosphorus (TP), Secchi disc depth (SD), cyanide (CN), arsenic (As), cadmium (Cd), fluoride (F), and temperature (T). A conceptual model was established to describe the relationships among the water chemistry variables and Chl a. Four latent variables were also introduced: physical factors, nutrients, toxic substances, and phytoplankton. In general, the SEM demonstrated good agreement between the sample covariance matrix of observed variables and the model-implied covariance matrix. Among the water chemistry factors, T and TP had the greatest positive influence on Chl a, whereas SD had the largest negative influence. These results will help researchers and decision-makers to better understand the influence of water chemistry on phytoplankton and to manage eutrophication adaptively in Lake Qilu. (C) 2009 Elsevier B.V. All rights reserved.

Applications using simulation-optimization approaches are often limited in practice because of the high computational cost associated with executing the simulation-optimization analysis. This research proposes a nonlinearity interval mapping scheme (NIMS) to overcome the computational barrier of applying the simulation-optimization approach for a waste load allocation analysis. Unlike the traditional response surface methods that use response surface functions to approximate the functional form of the original simulation model, the NIMS approach involves mapping the nonlinear input-output response relationship of a simulation model into an interval matrix, thereby converting the original simulation-optimization model into an interval linear programming model. By using the risk explicit interval linear programming algorithm and an inverse mapping scheme to implicitly resolve nonlinearity in the interval linear programming model, the NIMS approach efficiently obtained near-optimal solutions of the original simulation-optimization problem. The NIMS approach was applied to a case study on Wissahickon Creek in Pennsylvania, with the objective of finding optimal carbonaceous biological oxygen demand and ammonia (NH4) point source waste load allocations, subject to daily average and minimum dissolved oxygen compliance constraints at multiple points along the stream. First, a simulation-optimization model was formulated for this case study. Next, a genetic algorithm was used to solve the problem to produce reference optimal solutions. Finally, the simulation-optimization model was solved using the proposed NIMS, and the obtained solutions were compared with the reference solutions to demonstrate the superior computational efficiency and solution quality of the NIMS.

Hypoxia is a critical issue in the Gulf of Mexico that has challenged management efforts in recent years by an increase in hypoxia sensitivity to nitrogen loads. Several mechanisms have been proposed to explain the recent increase in sensitivity. Two commonly cited mechanisms are bottom-water reducing conditions preventing nitrification and thus denitrification, leading to more N recycling and production of oxygen-consuming organic matter, and carryover of organic matter from previous years increasing oxygen demand, making the system more sensitive. We use models informed by these mechanisms and fit with Bayesian inference to explore changes in Gulf of Mexico hypoxia sensitivity. We show that a model including an annually fit parameter representing variation in the fraction of nutrient loading and recycling contributing to bottom water oxygen demand provides a good fit to observations and is not improved by explicit inclusion of organic matter carryover to subsequent years. Both models support two stepwise increases in system sensitivity during the period of record. This change in sensitivity has greatly increased the nutrient reduction needed to achieve the established hypoxia goal. If the Gulf remains at the current state of sensitivity, our analysis suggests a roughly 70% reduction of spring TN loads from the 1988-1996 average of 6083 ton/day may be required.

Conserving fish in lakes requires the systematic analysis and assessment of fish species. The issues, conservation needs and fish assemblage changes are described for Lake Qionghai (China). The annual fishery production (AFP) was analysed from 1949 to 2003, which indicated a high disturbance of the aquatic ecosystem in Lake Qionghai caused especially by fisheries. The continuous increase in AFP and the introduction of economically important fish species have changed the fish species structure and diversity of the lake. Only five of 20 native fish species remained extant in 2003. The alien species accounted for 83.58% of the total fish production in 2003. Scoring criteria for 10 indexes of biotic integrity (IBI metrics) were selected for Lake Qionghai. The overall IBI score decreased from 40 in the 1940s to 26 in the 1980s to 20 in 2003. Changes in biotic condition were mainly caused by the destruction of fish physical habitat, pollution, bycatch and the invasion of alien species. Based on the IBI analysis, an ecosystem approach was developed for fish conservation in Lake Qionghai, including conservation at the watershed scale, habitat improvement and restoration, rebuilding of aquatic ecosystems and adaptive ecosystem-based fishery management.

The susceptibility of estuaries to nutrient loading is an important issue that cuts across a range of management needs. We used a theory-driven but data-tested simple model to assist classifying estuaries according to their susceptibility to nutrients. This simple nutrient-driven phytoplankton model is based on fundamental principles of mass balance and empirical response functions for a wide variety of estuaries in the United States. Phytoplankton production was assumed to be stoichiometrically proportional to nitrogen load and an introduced ``efficiency factor'' intended to capture the myriad processes involved in converting nitrogen load to algal production. A Markov Chain Monte Carlo algorithm of Bayesian inference was then employed for parameter estimation. The model performed remarkably well for chlorophyll estimates, and the predicted estimates of primary production, grazing, and sinking losses are consistent with measurements reported in the literature from a wide array of systems. Analysis of the efficiency factor suggests that estuaries with the ratio of river inflow to estuarine volume (Q/V) greater than 2.0 per year are less susceptible to nutrient loads, and those with Q/V between 0.3 and 2.0 per year are moderately susceptible. This simple model analysis provides a first-order screening tool for estuarine susceptibility classification.

The water supply to Chinese cities is increasingly degrading from pollution due to watershed activities. Consequently, water source protection requires urgent action using optimal land-use management efforts. An inexact linear programming model for optimal land-use management of surface water source area was developed. The model was proposed to balance the economic benefits of land-use development and water source protection. The maximum net economic benefit (NEB) was chosen as the objective of land-use management. The total environmental capacity (TEC) of rivers and the minimum water supply (MWS) were considered key constraints. Other constraints included forest coverage, government requirements concerning the proportions of various land-use types, soil loss, slope lands, and technical constraints. A case study was conducted for the Songhuaba Watershed, a reservoir supplying water to Kunming City, the third largest city in southwestern China. A 15-year (2006 to 2020) optimal model for land-use management was developed to better protect this water source and to gain maximum benefits from development. Ten constraints were involved in the optimal model, and results indicated that NEB ranged between 893 and 1,459 million US\$. The proposed model will allow local authorities to better understand and address complex land-use systems and to develop optimal land-use management strategies for balancing source water protection and local economic development.

Biological contact oxidation technology was used to remediate the polluted river water in Hanyang, China. The purification effect of the polluted river water in three simulated rivers was investigated using two different carriers, i.e., AquaMats made in USA and semi flexible supports made in China. The results showed that when the hydraulic loading rate increased, purification effect in terms of COD(Mn) decreased. Moreover, the removal efficiency of total nitrogen (TN) increased when AquaMats support was used, however it decreased when semi flexible support was used, in comparison with the control experiment. The removal ability of total phosphorus (TP) was strengthened by using different carriers.

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.

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.

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.