Interval-Parameter Conditional Value-at-Risk Two-Stage Stochastic Programming Model for Management of End-of-Life Vehicles

The management of end-of-life vehicles conserves natural resources, provides economic benefits, and reduces water, air, and soil pollution. Sound management of end-of-life vehicles is vitally important worldwide thus requiring sophisticated decision-making tools for optimizing its efficiency and reducing system risk. This paper proposes an interval-parameter conditional value-at-risk two-stage stochastic programming model for management of end-of-life vehicles. A case study is conducted in order to demonstrate the usefulness of the developed model. The model is able to provide the trade-offs between the expected profit and system risk. It can effectively control risk at extremely disadvantageous availability levels of end-of-life vehicles. The formulated model can produce optimal solutions under predetermined decision-making risk preferences and confidence levels. It can simultaneously determine the optimal long-term allocation targets of end-of-life vehicles and reusable parts as well as capital investment, production planning, and logistics management decisions within a multi-period planning horizon. The proposed model can efficiently handle uncertainties expressed as interval values and probability distributions. It is able to provide valuable insights into the effects of uncertainties. Compared to the available models, the resulting solutions are far more robust.

     

Design of River Water Quality Monitoring Networks: A Case Study

Karoon River, from Gotvand Dam to Persian Gulf with more than 450 km in length and an annual discharge of 11,891 million cubic meters, is the largest river in Iran. Increasing water withdrawal from and wastewater discharge to the river has endangered the aquatic life of this important ecosystem. Furthermore, the drinking and in-stream water quality standards have been violated in many instances. In this paper, a river water quality monitoring network is designed, including determination of sampling frequencies as well as location of water quality monitoring stations. In this regard, two models are developed. The first model is a Genetic Algorithm-based optimization model and the second one is a combination of Kriging method and Analytical Hierarchy Process. The temporal variation of the concentration of water quality variables along Karoon and Dez Rivers are evaluated and the main water quality indicators are selected. Then, thirty five stations are selected and the application of Entropy Theory in calculating the sampling frequency is demonstrated. The results show the significant value of the proposed methodology in the design of monitoring network.     

Development of Dissolved Oxygen Model for a Highly Variable Flow River: A Case Study of Ravi River in Pakistan

A framework for dissolved oxygen (DO) modeling of the Ravi River has been developed based on a combination of laboratory measurements and field and monitoring data. Both the classical Streeter-Phelps (CSP) and the modified Streeter-Phelps (MSP) models are used for DO simulations. The MSP model considers the carbonaceous biochemical oxygen demand (CBOD) and nitrogenous biochemical oxygen demand (NBOD) separately, whereas the CSP model is evaluated considering only the CBOD and NBOD is incorporated in the overall BOD utilization rate. CBOD, NBOD and BOD rates have been determined through long-term BOD analysis of five main wastewater outfalls and two surface drains discharging into the Ravi River over a 98 km stretch. Analysis by Thomas Method manifests strong coefficient of determination “R²” between 0.72 and 0.98 for all the three types of BOD rates. Sensitivity analyses have also been carried out to find out a suitable reaeration rate formula for highly variable flows in the Ravi River. The CSP model results based on classical approach of considering only CBOD show significant difference between the model predictions and field measurements suggesting that NBOD needs to be incorporated for the model development. The dissolved oxygen values calculated using the MSP model and the CSP model based on overall BOD rate are in close agreement with field measurements and are thus suitable to model DO levels in the Ravi River.     

Correlated Parameters Uncertainty Propagation in a Rainfall-Runoff Model,Considering 2-Copula; Case Study: Karoon III River Basin

Hydrological models are widely used to investigate practical issues of water resources. Parametric uncertainty is considered as one of the most important sources of uncertainty in environmental researches. Generally, it is assumed that the parameters are independent mutually, but correlation within the parameter space is an important factor having the potential to cause uncertainty. The objective and innovation of this study was to address the effects of parameters correlation on a continuous hydrological model uncertainty. HEC-HMS with soil moisture accounting (SMA) infiltration method was used to model daily flows and simulate certainty bounds for Karoon III basin, southwest of IRAN, in two scenarios, independent and correlated parameters using 2-copula. The parameters were represented by probability distributions, and the effect on prediction error were evaluated using Latin hypercube sampling (LHS) on Monte Carlo simulation (MCS). Saturated hydraulic conductivity (K), Clark storage-coefficient (R), and time of concentration (tc) were chosen for investigation, based on observed sensitivity analysis of simulated peak over threshold (POT). One hundred runs were randomly generated from 100 parameter sets captured from LHS of parameters distributions in each sub-basin. Using generated parameter sets, 100 continuous hydrographs were simulated and values of certainty bounds calculated. Results showed that when 2-copula correlated R and tc, with 0.656 Kendall’s Tau and 0.818 Spearman’s Rho coefficients, were propagated, decreasing of outputs’ sharpness was more than when considering K and R (K-R), with 0.166 and 0.262; therefore, incorporation of correlations in the MCS is important, especially when the correlation coefficients exceed 0.65. The model was evaluated at the outlet of the basin using daily stream flow data. Model reliability was better for above-normal flows than normal and below-normal. Reliability increases of simulated flow when considering correlated R-tc was more than K-R because of the correlation values. Incorporation of copula for K-tc not only did not improve the model reliability but also decreased it. Results showed improvement of model reliability, by decreasing predicted error of hydrologic modeling, when dealing with correlated parameters in the system.     

Hydrodynamic Simulation of River Yamuna for Riverbed Assessment: A Case Study of Delhi Region

A well known river hydrodynamic model RiverCAD has been used to simulate and visualize flood scenarios for different designated flood flows under complex riverbed geometry with several man made structures like bridges and barrages. The model applied successfully for the stretch of 23 km in the Yamuna floodplain of Delhi region from Wazirabad barrage in the upstream to Okhla barrage. Flood flows for various return periods namely once in 10, 25, 50 and 100 years were estimated based on recorded flow data for the period of 1963 to 2003 using standard flood frequency analysis techniques. The simulation results were compared and the model was calibrated with water surface elevation records of the previous floods at various barrage and bridge locations. Simulation results enabled prediction of maximum water levels, submergence scenarios and land availability under different designated flood flows for riverbed assessment, development and management.     

Predicting Water Quality Impaired Stream Segments using Landscape-Scale Data and a Regional Geostatistical Model: A Case Study in Maryland

In the United States, probability-based water quality surveys are typically used to meet the requirements of Section 305(b) of the Clean Water Act. The survey design allows an inference to be generated concerning regional stream condition, but it cannot be used to identify water quality impaired stream segments. Therefore, a rapid and cost-efficient method is needed to locate potentially impaired stream segments throughout large areas. We fit a set of geostatistical models to 312 samples of dissolved organic carbon (DOC) collected in 1996 for the Maryland Biological Stream Survey using coarse-scale watershed characteristics. The models were developed using two distance measures, straight-line distance (SLD) and weighted asymmetric hydrologic distance (WAHD). We used the Corrected Spatial Akaike Information Criterion and the mean square prediction error to compare models. The SLD models predicted more variability in DOC than models based on WAHD for every autocovariance model except the spherical model. The SLD model based on the Mariah autocovariance model showed the best fit (r² = 0.72). DOC demonstrated a positive relationship with the watershed attributes percent water, percent wetlands, and mean minimum temperature, but was negatively correlated to percent felsic rock type. We used universal kriging to generate predictions and prediction variances for 3083 stream segments throughout Maryland. The model predicted that 90.2% of stream kilometers had DOC values less than 5 mg/l, 6.7% were between 5 and 8 mg/l, and 3.1% of streams produced values greater than 8 mg/l. The geostatistical model generated more accurate DOC predictions than previous models, but did not fit the data equally well throughout the state. Consequently, it may be necessary to develop more than one geostatistical model to predict stream DOC throughout Maryland. Our methodology is an improvement over previous methods because additional field sampling is not necessary, inferences about regional stream condition can be made, and it can be used to locate potentially impaired stream segments. Further, the model results can be displayed visually, which allows results to be presented to a wide variety of audiences easily.     

决策树模型在水环境监测网络中选取代表性样点的应用

地表水体中的硝酸盐污染已经成为全球关注的热点环境问题之一。现今,国内外均建立了相关的监测网络对地表水体的水质实施长期监测,但是却导致大量的监测数据累积,给后续的科学研究工作带来了不便,尤其是在庞大的监测网络中如何选取有代表性样点的研究点则成为急需解决的问题之一。以比利时弗拉芒地区地表水的长期监测物理化学指标为例,利用决策树模型评估地表水样点的硝酸盐污染来源专家分类的有效性,为点位优化提供理论依据。原有监测点位的污染源专家分类和模型输出的可匹配率为80%,优化后监测点位从原有47个点降低到30个点,提高了监测工作效率。     

无污染农产品对土壤铅环境质量标准要求的变化——个案研究

研究了江苏南通某乡镇土壤铅的污染以及水稻对土壤铅的吸收。结果表明,研究区土壤与稻米铅污染严重。通过盆栽实验建立了水稻对铅的吸收积累及评估模型,提出了符合该地区大米安全生产的土壤铅临界含量值,为土壤铅环境质量标准的研究提供了参考实例。同时结合国内外环境质量标准的对比,提出无污染农产品对土壤铅环境质量标准要求的变化,建议制/修订土壤铅环境质量标准值。土壤环境二级标准值由现行的250～350mg/kg改为80mg/kg。     

DMSP-IEES: A Stochastic Programming Model Based on Dual-Interval and Multi-Stage Scenarios Modeling Approaches for Energy Systems Management and GHG Emissions Control

Energy-related activities contribute a major portion of anthropogenic greenhouse gas (GHG) emissions into the atmosphere. In this study, a dual-interval multi-stage stochastic programming model for the planning of integrated energy-environment systems (DMSP-IEES) model is developed for integrated energy-environment systems management, in which issues of GHG-emission mitigation can be reflected throughout the process of energy systems planning. By integrating methodologies of interval linear programming (when numbers are described as interval values without distribution information), dual-interval programming (when lower and upper bounds of interval values are not available as deterministic values but as discrete intervals), and multi-stage stochastic programming, the DMSP-IEES model is capable of dealing with uncertainties expressed as discrete intervals, dual intervals, and probability distributions within a multi-stage context. Decision alternatives can also be generated through analysis of the single- and dual-interval solutions according to projected applicable conditions. A case study is provided for demonstrating the applicability of the developed methodology. The results indicate that the developed model can tackle the dual uncertainties and the dynamic complexities in the energy-environment management systems through a multi-layer scenario tree. In addition, it can reflect the interactions among multiple system components and the associated trade-offs.     

A Water Quality Monitoring Network Design Methodology for the Selection of Critical Sampling Points: Part I

The principal instrument to temporally and spatially manage water resources is a water quality monitoring network. However, to date in most cases, there is a clear absence of a concise strategy or methodology for designing monitoring networks, especially when deciding upon the placement of sampling stations. Since water quality monitoring networks can be quite costly, it is very important to properly design the monitoring network so that maximum information extraction can be accomplished, which in turn is vital when informing decision-makers. This paper presents the development of a methodology for identifying the critical sampling locations within a watershed. Hence, it embodies the spatial component in the design of a water quality monitoring network by designating the critical stream locations that should ideally be sampled. For illustration purposes, the methodology focuses on a single contaminant, namely total phosphorus, and is applicable to small, upland, predominantly agricultural-forested watersheds. It takes a number of hydrologic, topographic, soils, vegetative, and land use factors into account. In addition, it includes an economic as well as logistical component in order to approximate the number of sampling points required for a given budget and to only consider the logistically accessible stream reaches in the analysis, respectively. The methodology utilizes a geographic information system (GIS), hydrologic simulation model, and fuzzy logic.     

Development of a Local-Scale Spatially Refined Multimedia Fate Model (LSRMFM) for Urban-Scale Risk Assessment: Model Formulation, GIS-Based Preprocessing, and Case Study

A local-scale spatially refined multimedia fate model (LSRMFM) was developed to evaluate in detail the multimedia transport of organic compounds at a spatial level. The model was derived using a combination of an advection?Cdispersion?Creaction partial differential equation, a steady-state multimedia fugacity model, and a geographical information system. The model was applied to predicting four major volatile organic compounds that are produced as emissions (benzene, toluene, xylene, and styrene) in an urban and industrial area (the 50?×?50-km area was divided into 0.5?×?0.5-km segments) in Korea. To test the accuracy of the model, the LSRMFM was used to predict the extent of dispersion and the data compared with actual measured concentrations and the results of a generic multimedia fate model (GMFM). The results indicated that the method developed herein is appropriate for predicting long-term multimedia pollution. However, the comparison study also illustrated that the developed model has some limitations (e.g., steady-state assumption) in terms of explaining all the observed concentrations, and additional verification and study (e.g., validation using a large observed data set, integration with a more accurate runoff model) would be desirable. In comparing LSRMFM and GMFM, discrepancies between the LSRMFM and GMFM outputs were found, as the result of geographical effects, even though the environmental parameters were identical. The geographical variation for LSRMFM output indicated the existence of considerable local human and ecological risks, whereas the GMFM output indicated less average risk. These results demonstrate that the model has the potential for improving the management of pollutant levels under these refined spatial conditions.     

A Novel Formulation for Designing a Monitoring Strategy: Application to the Design of a River Quality Monitoring System

In this work, we propose a technique to automatically optimize the monitoring of any distributed indicator (concentration of a substance along a river, blood pressure of a patient over time, etc.) for which a reliable estimate is previously available. From a mathematical point of view, the problem is based on obtaining a reliable estimate of the chosen indicator (e.g., by numerical simulation), and then solving a multi-objective optimization problem (with mixed real and integer variables) whose solution must provide an efficient and satisfactory monitoring strategy. As an illustrative case, we show the steps to follow in order to implement that strategy when designing a system for monitoring water quality in a river. Finally, we present and analyze the results when applying the proposed technique to study a real case in the Neuse River (North Carolina, USA).     

陆源排海污水生物毒性控制限值研究——以秦皇岛市为例

在枯水期和丰水期,对秦皇岛市15个典型入海排污口污水进行生物效应监测,选用发光细菌(费歇尔弧菌)、藻类(中肋骨条藻)、甲壳类(卤虫)、鱼类(海水青鳉幼鱼) 4种不同的生物毒性测试生物对污水进行短期急性毒性测试。结果表明:排海污水的毒性大小易受雨水稀释影响;海水青鳉幼鱼对污水最为敏感,其次为卤虫和中肋骨条藻,费歇尔弧菌敏感性最低。对排海污水毒性评估结果与理化指标的相关性分析进一步表明,仅仅依靠理化指标来评估污水的环境影响具有局限性。     

基于浮游植物生物完整性的北部湾生态健康评价

应用生物完整性理论和方法,研究基于浮游植物生物完整性指数(Phytoplanktonic Index of Biotic Integrity,P-IBI)在海湾生态评价中的方法构建,通过生境区域划分、评价因子选择、阈值确定等构建适合海湾的P-IBI生态健康评价指标体系,并以北部湾为例开展应用与验证。基于P-IBI的北部湾生态健康评价结果显示:3个水期中枯水期相对较好,丰水期次之,平水期最差,钦州市茅尾海海域内4个采样点在3个水期评价结果大部分为"差"和"较差",其余大部分采样点都在"一般"及以上。Spearman相关性分析显示,P-IBI均与水质类别正相关。研究结果表明P-IBI指数在北部湾生态健康评价应用与验证结果基本符合实际,应用P-IBI能够较好地开展海湾的生态健康评价,可因地制宜应用于近岸海域生态评价。     

Optimal Policy Mechanism Design for Cross-Sector and Multi-Stage Pollution Control with a Bilevel Model: Application to SO<Subscript>2</Subscript> Emission in China

In this paper, we construct a multi-stage coordinated programming model under tax system to control SO₂ emission. The model is based on an explicitly formulated SO₂ abatement cost function created under Chinese condition. Analysis of the effectiveness and impact on the economy of the model is carried out with consideration of game theory. By solving the model, theoretical results show that the volume-based multi-stage SO₂ tax system has two properties: effectiveness and equal-rate. Based on these theoretical results, empirical study is also performed using Chinese historical data. Compared with yearly single-stage programming model, the tax rate generated by the coordinated multi-stage programming model is time-invariant and rather moderate in scale. The total abatement cost among planning years in our model is 21.03 % less than the actual number and 6.68 % less than that in the single-stage situation. The tax payment suggested by our model is 10.62 % less than by the single-stage model. In general, a coordinated multi-stage programming model helps reduce the overall costs of environmental protection while achieving the same emission control target with less burden added to the economy.     

An Integrated Approach Towards Assessing the Value of Water: A Case Study on the Zambezi Basin

The aim of this paper is to develop a methodology for assessing the value of water in the different stages in the water cycle. It is hypothesised that if a cubic metre of water provides some benefit in some spot at a certain moment, this cubic metre of water has a certain value not only at that point in space and time, but in its previous stages within the water cycle as well. This means that, while water particles flow from upstream to downstream, water values ‘flow’ in exactly the opposite direction. The value of water in a certain place is equal to its value in situ plus an accumulated value derived from downstream. This value-flow concept is elaborated for the Zambezi basin. It is found that water produces the smallest direct economic benefits in the upper part of the Zambezi basin. However, water flows in this part of the basin − due to their upstream location − have the highest indirect values. Return flows from the water-using sectors are particularly valuable in the upstream sub-basins. The analysis shows that the value per unit of river water increases if we go from downstream to upstream. Another finding of the study is that percolation of rainwater is generally more valuable than surface runoff. Finally, a plan to export water from the river Zambezi to South Africa is evaluated in terms of its opportunity costs. The results of this study show that the value-flow concept offers the possibility of accounting for the cyclic nature of water when estimating its value. It is stressed, however, that for the current study many crude assumptions had to be made, so that the exact numbers presented should be regarded with extreme caution. Further research is necessary to provide more precise and validated estimates. This revised version was published online in July 2006 with corrections to the Cover Date.     

Artificial Neural Networks for Risk Decision Support in Natural Hazards: A Case Study of Assessing the Probability of House Survival from Bushfires

Risk decision-making in natural hazards encompasses a plethora of environmental, socio-economic and management-related factors, and benefits greatly from exploring possible patterns and relations among these multivariate factors. Artificial neural networks, capable of general pattern classifications, are potentially well suited for risk decision support in natural hazards. This paper reports an example that assesses the risk patterns or probabilities of house survival from bushfires using artificial neural networks, with a simulation data set based on the empirical study by Wilson and Ferguson (Predicting the probability of house survival during bushfires, Journal of Environmental Management 23 (1986) 259–270). The aim of this study was to re-model and predict the relationship between risk patterns of house survival and a series of independent variables. Various configurations for input and output variables were tested using neural networks. An approach for converting linguistic terms into crisp numbers was used to incorporate linguistic variables into the quantitative neural network analysis. After a series of tests, results show that neural networks are capable of predicting risk patterns under all tested configurations of input and output variables, with a great deal of flexibility. Risk-based mathematical functions, be they linear or non-linear, can be re-modelled using neural networks. Finally, the paper concludes that the artificial neural networks serve as a promising risk decision support tool in natural hazards.     

Artificial Neural Networks for Risk Decision Support in Natural Hazards: A Case Study of Assessing the Probability of House Survival from Bushfires

Risk decision-making in natural hazards encompasses a plethora of environmental, socio-economic and management-related factors, and benefits greatly from exploring possible patterns and relations among these multivariate factors. Artificial neural networks, capable of general pattern classifications, are potentially well suited for risk decision support in natural hazards. This paper reports an example that assesses the risk patterns or probabilities of house survival from bushfires using artificial neural networks, with a simulation data set based on the empirical study by Wilson and Ferguson (Predicting the probability of house survival during bushfires, Journal of Environmental Management 23 (1986) 259–270). The aim of this study was to re-model and predict the relationship between risk patterns of house survival and a series of independent variables. Various configurations for input and output variables were tested using neural networks. An approach for converting linguistic terms into crisp numbers was used to incorporate linguistic variables into the quantitative neural network analysis. After a series of tests, results show that neural networks are capable of predicting risk patterns under all tested configurations of input and output variables, with a great deal of flexibility. Risk-based mathematical functions, be they linear or non-linear, can be re-modelled using neural networks. Finally, the paper concludes that the artificial neural networks serve as a promising risk decision support tool in natural hazards.     

An Environmentally Sound Method for Disposal of Both Ash and Sludge Wastes by Mixing with Soil: A Case Study of Bangkok Plain

This study reported the test done on ash-sludge mixture foramendment of soil in pot experiments. Ash-sludge mixture ratiostudies revealed that 1:5 fly ash-sludge mixture and 1:10 bottom ash-sludge mixture were the optimum mixture ratio thatminimized toxic element and provided sufficient nutrients. Experiments indicated that ash-sludge mixtures is more suitablefor amendment of acid soil than neutral soil which can increasesoil pH and reduce available heavy metal toxicity. The maximumheavy metal adsorption occurred in a pH range of 4 to 6 for allsoil studied. The finding also revealed that fly ash applicationseemed more effective than bottom ash, due to its higher loadingrate and metal contents. Heavy metal toxicity was monitored usingseed germination test. Marigold and tomato seeds were the two crops selected for this test. Seed germination test result showsthat percentage of seed germination increased in pot experimentswith sludge only and ash-sludge mixtures. In addition, higherpercentages of seed germination were observed to vary with longer incubation time (1–8 weeks). After week 12 of the incubation period, percentage of seed germination began to decline, as a result of reduced soil pH and release of toxic heavy metals.