The development and application of a multilevel decision analysis model for the remediation of contaminated groundwater under uncertainty

A study was initiated which combined elements of stochastic hydrology, risk assessment, simulation modeling, cost analysis and decision making to define the optimum remediation choice(s) for a Superfund site in the southern United States. The effort focused upon the premise that groundwater remediation is inherently complex due to uncertainties in the geological matrix as well as in contaminant concentrations at points of compliance and/or exposure. The technical analyst should supply the decision maker with estimates of these uncertainties as well as the cost penalties required to reduce them to manageable levels. Monte Carlo transport modeling was employed to define the probability of contaminant excursions from the site, while geostatistical simulation identified a joint plume configuration and its attendant probability. Bayesian modeling was used to define the worth of additional data. These individual components were combined within a Decision Model to identify optimum remediation configurations for a given levels of risk tolerance which could be supplied by the decision maker or affected community. Sensitivity analyses were conducted to define ranges over which the decision would not be affected by variation in the respective decision parameter.     

THE USE OF A SIMPLE MODEL AND UNCERTAINTY ANALYSIS IN LAKE MANAGEMENT1

ABSTRACT: A simple, black-box lake model was developed for phosphorus, using nonlinear regression analysis on a data base of north temperate lakes. The uncertainty associated with the model was then combined with the parameter uncertainty and the independent variable uncertainty to provide an estimate of the confidence limits associated with a predicted value. The prediction uncertainty is often neglected, yet it is an important measure of the usefulness of a model. Prediction uncertainty reflects the modeler's confidence in the model, and it should be used by a decision maker as a weight indicating the value of the model prediction. A procedure is outlined that combined lake modeling and uncertainty analysis for use in lake quality assessment and lake management. An example is provided illustrating the use of this procedure in nutrient budget sampling design, data analysis, and the evaluation of lake management strategies for a 208 program in New Hampshire.     

Risk-based economic decision analysis of remediation options at a PCE-contaminated site

Remediation methods for contaminated sites cover a wide range of technical solutions with different remedial efficiencies and costs. Additionally, they may vary in their secondary impacts on the environment i.e. the potential impacts generated due to emissions and resource use caused by the remediation activities. More attention is increasingly being given to these secondary environmental impacts when evaluating remediation options. This paper presents a methodology for an integrated economic decision analysis which combines assessments of remediation costs, health risk costs and potential environmental costs. The health risks costs are associated with the residual contamination left at the site and its migration to groundwater used for drinking water. A probabilistic exposure model using first- and second-order reliability methods (FORM/SORM) is used to estimate the contaminant concentrations at a downstream groundwater well. Potential environmental impacts on the local, regional and global scales due to the site remediation activities are evaluated using life cycle assessments (LCA). The potential impacts on health and environment are converted to monetary units using a simplified cost model.A case study based upon the developed methodology is presented in which the following remediation scenarios are analyzed and compared: (a) no action, (b) excavation and off-site treatment of soil, (c) soil vapor extraction and (d) thermally enhanced soil vapor extraction by electrical heating of the soil. Ultimately, the developed methodology facilitates societal cost estimations of remediation scenarios which can be used for internal ranking of the analyzed options. Despite the inherent uncertainties of placing a value on health and environmental impacts, the presented methodology is believed to be valuable in supporting decisions on remedial interventions.     

Applying value of information methods to prioritize elements for water quality management with an example of linear alkylbenzene sulfonate in the Yodo River,Japan

Narrowing the decision space is crucial in water quality management at the meso-scale for developing countries, where a lack of data and financial budgets prevent the development of appropriate management plans and result in serious water quality degradation in many rivers. In this study, a framework for handling this task is proposed, comprising a lumped water quality model, with sensitivity and uncertainty analyses, and a management domain, including loss estimation and value of information analysis. Through a case study with linear alkylbenzene sulfonate (LAS) in the Yodo River, it is found that non-point sources and flow rate are factors that influence LAS concentration at the hot spot location. By considering the entire process of water quality management planning, we identify that the definition of the cost function of LAS treatment determines the appropriate estimation for the expected loss in reducing LAS under uncertain water quality conditions. The value of information analysis with “expected value of including uncertainty” and “expected value of perfect information” further helps estimate the benefit of including uncertainty in decision-making and the financial cost for obtaining more information regarding inputs that have been previously prioritized.     

A technicoeconomic approach for the selection of a site remediation strategy--part A: theory

A technicoeconomic model is developed to select an optimal strategy for the remediation of a contaminated site and to determine the value of this remediation strategy. The model is an extension of actual cost-benefit analysis, with consideration of "irreversible" remediation technology choices, technology effectiveness, and uncertainty on the site's level of contamination. The model considers the possibility of reducing uncertainty by both acquiring more and better information on the level of contamination and by offering the decision-maker the opportunity to reevaluate his decision and switch to a more appropriate technology. It is believed that this model will help decision-makers in the selection of a remediation strategy by presenting all potentially feasible strategies, and how uncertainty on the site's level of contamination affects these strategies.     

A Hydraulic MultiModel Ensemble Framework for Visualizing Flood Inundation Uncertainty

While deterministic forecasts provide a single realization of potential inundation, the inherent uncertainty associated with forecasts also needs to be conveyed for improved decision support. The objective of this study was to develop an ensemble framework for the quantification and visualization of uncertainty associated with flood inundation forecast maps. An 11‐member ensemble streamflow forecast at lead times from 0 to 48 hr was used to force two hydraulic models to produce a multimodel ensemble. The hydraulic models used are (1) the International River Interface Cooperative along with Flow and Sediment Transport with Morphological Evolution of Channels solver and (2) the two‐dimensional Hydrologic Engineering Center‐River Analysis System. Uncertainty was quantified and augmented onto flood inundation maps by calculating statistical spread among the ensemble members. For visualization, a series of probability flood maps conveying the uncertainty in forecasted water extent, water depth, and flow velocity was disseminated through a web‐based decision support tool. The results from this study offer a framework for quantifying and visualizing model uncertainty in forecasted flood inundation maps.     

A Technicoeconomic Approach for the Selection of a Site Remediation Strategy—Part A: Theory

A technicoeconomic model is developed to select an optimal strategy for the remediation of a contaminated site and to determine the value of this remediation strategy. The model is an extension of actual cost–benefit analysis, with consideration of “irreversible” remediation technology choices, technology effectiveness, and uncertainty on the site's level of contamination. The model considers the possibility of reducing uncertainty by both acquiring more and better information on the level of contamination and by offering the decision-maker the opportunity to reevaluate his decision and switch to a more appropriate technology. It is believed that this model will help decision-makers in the selection of a remediation strategy by presenting all potentially feasible strategies, and how uncertainty on the site's level of contamination affects these strategies.     

Buying price of event information in two-action decision problems

Being able to determine in advance whether certain events occur or not enables a decision maker to reduce the uncertainty of a two-action lottery, although the exact outcome of the lottery may still not be known with certainty. This paper studies the á priori value of information in such a decision making environment. Of interest to the decision maker is to compare the value of information about different events in advance of gathering the information. Using buying price as value of information, we show that when information about the occurrence of two different events are offered to a risk neutral decision maker, the event with a greater contribution in absolute value to the expected value of the lottery has higher value in terms of its buying price. For risk averse decision makers, a preference condition needs to be imposed on the set difference of two events to obtain a generic conclusion. We provide several examples that demonstrate the usefulness of these results.     

WATER QUALITY SAMPLING SCHEMES FOR VARIABLE FLOW CANALS AT REMOTE SITES1

ABSTRACT: Growing interest in water quality has resulted in the development of monitoring networks and intensive sampling for various constituents. Common purposes are regulatory, source and sink understanding, and trend observations. Water quality monitoring involves monitoring system design; sampling site instrumentation; and sampling, analysis, quality control, and assurance. Sampling is a process to gather information with the least cost and least error. Various water quality sampling schemes have been applied for different sampling objectives and time frames. In this study, a flow proportional composite sampling scheme is applied to variable flow remote canals where the flow rate is not known a priori. In this scheme, historical weekly flow data are analyzed to develop high flow and low flow sampling trigger volumes for auto‐samplers. The median flow is used to estimate low flow sampling trigger volume and the five percent exceedence probability flow is used for high flow sampling trigger volume. A computer simulation of high resolution sampling is used to demonstrate the comparative bias in load estimation and operational cost among four sampling schemes. Weekly flow proportional composite auto‐sampling resulted in the least bias in load estimation with competitive operational cost compared to daily grab, weekly grab sampling and time proportional auto‐sampling.     

A Technicoeconomic Approach for theSelection of a Site Remediation Strategy—Part B: Model Application

Different tools, such as a screening matrix or decision framework, are available to select a remediation technology to treat a contaminated site. However, unless these methods can point out the appropriate technology in regards to the decision-maker's knowledge about the contaminated site, they are less useful to evaluate both the technical effectiveness and the cost of the remediation, and to assess different remediation strategies from either future data acquisition or the use of an irreversible remediation technology. A model developed to allow such evaluations has been used to simulate the remediation of a virtual contaminated site. From this, four remediation recommendations have been made. These recommendations are guidelines for the build up of a remediation strategy that would both maximize the effectiveness of the decontamination and minimize its total cost.     

A technicoeconomic approach for the selection of a site remediation strategy--part B: model application

Different tools, such as a screening matrix or decision framework, are available to select a remediation technology to treat a contaminated site. However, unless these methods can point out the appropriate technology in regards to the decision-maker's knowledge about the contaminated site, they are less useful to evaluate both the technical effectiveness and the cost of the remediation, and to assess different remediation strategies from either future data acquisition or the use of an irreversible remediation technology. A model developed to allow such evaluations has been used to simulate the remediation of a virtual contaminated site. From this, four remediation recommendations have been made. These recommendations are guidelines for the build up of a remediation strategy that would both maximize the effectiveness of the decontamination and minimize its total cost.     

In situ bioremediation through mulching of soil polluted by a copper-nickel smelter

Bioremediation of a heavy metal-polluted soil was investigated in a 3-yr field experiment by adding mulch to a polluted forest floor. The mulch consisted of a mixture of compost and woodchips. The remediation treatment decreased the toxicity of the soil solution to bacteria as determined by the [3H]-thymidine incorporation technique, that is, by measuring the growth rate of soil bacteria extracted from unpolluted humus after exposing them to soil solution containing heavy metals from the experimental plots. Canonical correlation analysis was performed in order to identify the chemical and microbiological changes in the soil. The pH of the mulched organic layer increased by one unit. The concentration of complexed Cu increased and that of free Cu2+ decreased in the soil solution from the mulch treatment. According to basal respiration and litter decomposition, microbial activity increased during the 3 yr following the remediation treatment. The [3H]-thymidine incorporation technique was also used to study the growth rate and tolerance of bacteria to Cu. The bacterial growth rate increased and the Cu tolerance decreased on the treated plots. The structure of the microbial community, as determined by phospholipid fatty acid (PLFA) analysis, remained unchanged. The results indicate that remediation of the polluted soil had occurred, and that adding a mulch to the forest floor is a suitable method for remediating heavy metal-polluted soil.     

Decision making under uncertainty in case of soil remediation

Decisions on soil remediation are one of the most difficult management issues of municipal and state agencies. The assessment of contamination is uncertain, the costs of remediation are high, and the impacts on the environment are multiple. This paper presents a general, transparent, and consistent method for decision making among the remediation alternatives. Soil washing, phytoremediation, and no remediation are exemplarily considered. Multi-criteria utility functions including (a) the cost of remediation (b) the impact on human health and agricultural productivity, and (c) the economic gain after remediation are constructed using probability density functions representing contamination for all site coordinates. Herewith, the probability of different types of (i) correct decisions such as a hit or a true rejection and (ii) erroneous decisions such as a false alarm or miss are examined. The decision theoretic model is applied to a case study on heavy metal contaminated soil. This case study reveals the non-linear structure of multi-criteria-decision making. The case study shows that the geostatistical uncertainties of the log-normal distributed soil contamination must be taken into account: When uncertainties are not considered and the utilities are assessed according to the estimated value for a spatial unit, only few (N=26) spatial units result where the utility score of the alternative soil washing are higher than the utility score to the no remediation alternative. However, when taking into account geostatistical uncertainties of the log-normal soil distribution this number is about ten times greater (N=237). Furthermore, the use of 'maximizing expected utility' as decision rule is critical in that it may lead to a high probability of misses.     

规模化农田土壤重金属污染修复的中试试验研究——以某镇土壤污染治理为例

农用地重金属污染长期以来一直是生态环境的热点和难点问题。2017年,对某镇农田土壤重金属污染开展详查,单项污染指数评价结果表明,镉超标率为67.60%(Pi>1),基于内梅罗污染指数法的评价结果显示,57.30%的点位受到不同程度的污染(PN≥1.0);2018年,分别选取钝化修复和植物修复进行中试试验,经钝化修复后土壤有效镉和总镉含量分别平均降低32.73%和5.64%。经超富集植物籽粒笕种植一季修复后,土壤总镉含量降低15%以上。本次中试试验修复效果良好,能为下一阶段的土壤镉污染修复方案的制定和优化提供科学的依据,为全国农用地重金属污染修复提供了有价值的借鉴意义。     

规模化农田土壤重金属污染修复的中试试验研究——以某镇土壤污染治理为例

农用地重金属污染长期以来一直是生态环境的热点和难点问题。2017年,对某镇农田土壤重金属污染开展详查,单项污染指数评价结果表明,镉超标率为67.60%(Pi>1),基于内梅罗污染指数法的评价结果显示,57.30%的点位受到不同程度的污染(PN≥1.0);2018年,分别选取钝化修复和植物修复进行中试试验,经钝化修复后土壤有效镉和总镉含量分别平均降低32.73%和5.64%。经超富集植物籽粒笕种植一季修复后,土壤总镉含量降低15%以上。本次中试试验修复效果良好,能为下一阶段的土壤镉污染修复方案的制定和优化提供科学的依据,为全国农用地重金属污染修复提供了有价值的借鉴意义。     

Uncertainty Assessment for Management of Soil Contaminants with Sparse Data

In order for soil resources to be sustainably managed, it is necessary to have reliable, valid data on the spatial distribution of their environmental impact. However, in practice, one often has to cope with spatial interpolation achieved from few data that show a skewed distribution and uncertain information about soil contamination. We present a case study with 76 soil samples taken from a site of 15 square km in order to assess the usability of information gleaned from sparse data. The soil was contaminated with cadmium predominantly as a result of airborne emissions from a metal smelter. The spatial interpolation applies lognormal anisotropic kriging and conditional simulation for log-transformed data. The uncertainty of cadmium concentration acquired through data sampling, sample preparation, analytical measurement, and interpolation is factor 2 within 68.3 % confidence. Uncertainty predominantly results from the spatial interpolation necessitated by low sampling density and spatial heterogeneity. The interpolation data are shown in maps presenting likelihoods of exceeding threshold values as a result of a lognormal probability distribution. Although the results are not deterministic, this procedure yields a quantified and transparent estimation of the contamination, which can be used to delineate areas for soil improvement, remediation, or restricted area use, based on the decision-makers probability safety requirement.     

重金属污染土壤的修复方法及其在几类典型土壤修复中的应用

文中首先阐述了重金属污染土壤的途径与特点。介绍了当前几类主要的重金属污染土壤修复方法:物理化学方法、植物修复法、微生物修复法、动物修复法等的技术要点,详述了各自的技术特点及机理,并对它们的修复效能、环境友好性等进行了比较。从土壤的特性出发,区分出了3种典型的遭受重金属污染的土壤:农业土壤、城市土壤、矿区土壤,并从国内外最新的重金属污染修复实践中选取了适合各自土壤类型的修复方法进行了重金属污染土壤的治理探讨。最后,对土壤重金属污染修复技术的发展进行了展望。     

Remediation of contaminated lands: a decision methodology for site owners

Deciding how to remediate and redevelop contaminated lands should involve more than just selecting remediation techniques to clean a site to meet regulations for a predetermined site use. Owners and their consultants also need to understand aspects such as alternative site uses and liability, and how issues such as uncertainty can affect them. A methodology has been developed that provides a framework for current site owners when making decisions. It clarifies the above issues and details the type of information that is needed. It offers a step-by-step approach to improve decision making when contemplating remediation of contaminated sites by identifying the site use and remedial action combination that maximizes the current owner's net benefits. It examines various factors in decision making--with special emphasis on the timely issues of liability and uncertainty--and how expert opinion can be used to address diverse or incomplete data. Future research should include developing a complementary methodology that incorporates community and ecological objectives, resulting in a unified decision framework.     

Resolution of an uncertain closed-loop logistics model: An application to fuzzy linear programs with risk analysis

With the urgency of global warming, green supply chain management, logistics in particular, has drawn the attention of researchers. Although there are closed-loop green logistics models in the literature, most of them do not consider the uncertain environment in general terms. In this study, a generalized model is proposed where the uncertainty is expressed by fuzzy numbers. An interval programming model is proposed by the defined means and mean square imprecision index obtained from the integrated information of all the level cuts of fuzzy numbers. The resolution for interval programming is based on the decision maker (DM)’s preference. The resulting solution provides useful information on the expected solutions under a confidence level containing a degree of risk. The results suggest that the more optimistic the DM is, the better is the resulting solution. However, a higher risk of violation of the resource constraints is also present. By defining this probable risk, a solution procedure was developed with numerical illustrations. This provides a DM trade-off mechanism between logistic cost and the risk.     

表土剥离对土地整治项目收益的影响研究

为了揭示表土剥离对土地整治项目收益的不同影响，作者采用采样分析、调查问卷、地统计分析、空间分析等方法对这种影响进行了分析。研究表明，表土剥离是土地整治工程必不可少的一步。通过土壤质量空间分布和作物投入一产出值比较分析，土地整治可有敬减少农业生产成本，而表土剥离可最大限度地保护土壤表层，通过追肥和田间管理，在2—3年时间可基本恢复耕地肥力。