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.     

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.     

A framework for integrated resource planning in the electric power sector

A framework is presented showing the process of integrated resource planning in the electric power sector. This takes into account the traditional utility planning process, and shows how the use of advanced decision analysis tools would facilitate the integration of demand-side management (DSM) and environmental factors. The concept of influence diagram is introduced in the utility planning arena. Using the interdependent data analysis (IDA) technique, a way is shown to provide the probability estimates necessary for the influence diagram. The IDA technique allows the use of expert opinions and intuitive judgements to develop the necessary probability estimates. A sample case study is presented where the issue of environmental impact from stack emissions is incorporated. A base case coal option is compared against a gas turbine combined cycle (GTCC) alternative. Energy costs from these two options are compared by taking into account the capital, licensing, fuel, operation and maintenance, and emissions related costs.     

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.     

Propagation of uncertainties in soil and pesticide properties to pesticide leaching

In the new Dutch decision tree for the evaluation of pesticide leaching to groundwater, spatially distributed soil data are used by the GeoPEARL model to calculate the 90th percentile of the spatial cumulative distribution function of the leaching concentration in the area of potential usage (SP90). Until now it was not known to what extent uncertainties in soil and pesticide properties propagate to spatially aggregated parameters like the SP90. A study was performed to quantify the uncertainties in soil and pesticide properties and to analyze their contribution to the uncertainty in SP90. First, uncertainties in the soil and pesticide properties were quantified. Next, a regular grid sample of points covering the whole of the agricultural area in the Netherlands was randomly selected. At the grid nodes, realizations from the probability distributions of the uncertain inputs were generated and used as input to a Monte Carlo uncertainty propagation analysis. The analysis showed that the uncertainty concerning the SP90 is 10 times smaller than the uncertainty about the leaching concentration at individual point locations. The parameters that contribute most to the uncertainty about the SP90 are, however, the same as the parameters that contribute most to uncertainty about the leaching concentration at individual point locations (e.g., the transformation half-life in soil and the coefficient of sorption on organic matter). Taking uncertainties in soil and pesticide properties into account further leads to a systematic increase of the predicted SP90. The important implication for pesticide regulation is that the leaching concentration is systematically underestimated when these uncertainties are ignored.     

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.     

A formal model for consensus and negotiation in environmental management

Environmental management decisions typically lie at the interface of science and public policy. Consequently, these decisions involve a number of stakeholders with competing agendas and vested interests in the ultimate decision. In such cases, it is appropriate to adopt formal methods for consensus building to ensure transparent and repeatable decisions. In this paper, we use an environmental management case study to demonstrate the utility of a mathematical consensus convergence model in aggregating values (or weights) across groups. Consensus models are applicable when all parties agree to negotiate in order to resolve conflict. The advantage of this method is that it does not require that all members of the group reach agreement, often an impossible task in group decision making. Instead, it uses philosophical foundations in consensus building to aggregate group members' values in a way that guarantees convergence towards a single consensual value that summarizes the group position. We highlight current problems with ad hoc consensus and negotiation methods, provide justification for the adoption of formal consensus convergence models and compare the consensus convergence model with currently used methods for aggregating values across a group in a decision making context. The model provides a simple and transparent decision support tool for group decision making that is straightforward to implement.     

Uncertainty modelling in multi-criteria analysis of water safety measures

Water utilities must assess risks and make decisions on safety measures in order to obtain a safe and sustainable drinking water supply. The World Health Organization emphasises preparation of water safety plans, in which risk ranking by means of risk matrices with discretised probability and consequence scales is commonly used. Risk ranking enables prioritisation of risks, but there is currently no common and structured way of performing uncertainty analysis and using risk ranking for evaluating and comparing water safety measures. To enable a proper prioritisation of safety measures and an efficient use of available resources for risk reduction, two alternative models linking risk ranking and multi-criteria decision analysis (MCDA) are presented and evaluated. The two models specifically enable uncertainty modelling in MCDA, and they differ in terms of how uncertainties in risk levels are considered. The need of formal handling of risk and uncertainty in MCDA is emphasised in the literature, and the suggested models provide innovations that are not dependent on the application domain. In the case study application presented here, possible safety measures are evaluated based on the benefit of estimated risk reduction, the cost of implementation and the probability of not achieving an acceptable risk level. Additional criteria such as environmental impact and consumer trust may also be included when applying the models. The case study shows how safety measures can be ranked based on preference scores or cost-effectiveness and how measures not reducing the risk enough can be identified and disqualified. Furthermore, the probability of each safety measure being ranked highest can be calculated. The two models provide a stepwise procedure for prioritising safety measures and enable a formalised handling of uncertainties in input data and results.     

中国土壤修复与治理的投融资政策最新进展与展望

中国目前的土壤污染修复资金主要来源于政府财政性投资,融资渠道较为单一。《土壤污染防治行动计划》实施后,如何满足实现土壤污染修复与治理目标的投资需求是一个迫切需要解决的问题,充分创新发挥市场力量,建立长效的融资政策机制是核心内容。本文评估了中国土壤污染修复与治理投融资政策现状,识别了投融资面临的关键问题和挑战,提出了"十三五"时期中国土壤修复与治理的投融资机制建设的政策建议,为"十三五"时期中国土壤修复投融资模式创新提供管理技术支撑。     

A Conceptual Model for Defining and Assessing Land Management Units Using a Fuzzy Modeling Approach in GIS Environment

Appropriate land management decisions are important for current and future use of the land to ensure its sustainability. This requires that land management units (LMUs) be specified to enable the identification of specific parameters employed in decision making processes. This paper presents the development of a conceptual model, within geographic information systems (GIS), for defining and assessing LMUs from available biophysical information. The model consists of two main components (sub-models): land quality-based suitability analysis and soil erosion estimation. Using a fuzzy set methodology, the first sub-model was constructed to derive a land suitability index (LSI) for a cropping land utilization type. The LSI thus highlights the suitability grades of every pixel in the study area on a continuous basis. A sub-model of soil erosion was established based on the Revised Universal Soil Loss Equation (RUSLE) utilising the same spatial data bases employed for structuring the LSI. Using a soil loss tolerance principle, a fuzzy membership function of average annual soil loss (called soil loss index, SLI) was established, leading to compatibility between LSI and SLI for data integration. LMUs were then derived from various combinations of LSI and SLI. The methodology developed shows the significance of the model for refining available land suitability evaluation systems, which take no account of expected land degradation (from erosion) due to a nominated land use. It also provides a valuable guideline for cost-effective GIS applications in the identification and assessment of homogeneous land units, using available spatial information sets, at a finer scale.     

Surfactant-soil interactions during surfactant-amended remediation of contaminated soils by hydrophobic organic compounds: a review

Surfactants are amphiphilic molecules that reduce aqueous surface tension and increase the solubility of hydrophobic organic compounds (HOCs). Surfactant-amended remediation of HOC-contaminated soils and aquifers has received significant attention as an effective treatment strategy - similar in concept to using soaps and detergents as washing agents to remove grease from soiled fabrics. The proposed mechanisms involved in surfactant-amended remediation include: lowering of interfacial tension, surfactant solubilization of HOCs, and the phase transfer of HOC from soil-sorbed to pseudo-aqueous phase. However, as with any proposed chemical countermeasures, there is a concern regarding the fate of the added surfactant. This review summarizes the current state of knowledge regarding nonionic micelle-forming surfactant sorption onto soil, and serves as an introduction to research on that topic. Surfactant sorption onto soil appears to increase with increasing surfactant concentration until the onset of micellization. Sorbed-phase surfactant may account for the majority of added surfactant in surfactant-amended remediation applications, and this may result in increased HOC partitioning onto soil until HOC solubilization by micellar phase surfactant successfully competes with increased HOC sorption on surfactant-modified soil. This review provides discussion of equilibrium partitioning theory to account for the distribution of HOCs between soil, aqueous phase, sorbed surfactant, and micellar surfactant phases, as well as recently developed models for surfactant sorption onto soil. HOC partitioning is characterized by apparent soil-water distribution coefficients in the presence of surfactant.     

USE OF FIRST-ORDER UNCERTAINTY ANALYSIS TO OPTIMIZE SUCCESSFUL STREAM WATER QUALITY SIMULATION1

ABSTRACT: A first-order uncertainty technique is developed to quantify the relationship between field data collection and a modeling exercise involving both calibration and subsequent verification. A simple statistic (LTOTAL) is used to quantify the total likelihood (probability) of successfully calibrating and verifying the model. Results from the first-order technique are compared with those from a traditional Monte Carlo simulation approach using a simple Streeter-Phelps dissolved oxygen model. The largest single difference is caused by the filtering or removal of unrealistic outcomes within the Monte Carlo framework. The amount of bias inherent in the first-order approach is also a function of the magnitude of input variability and sampling location. The minimum bias of the first-order technique is approximately 20 percent for a case involving relatively large uncertainties. However the bias is well behaved (consistent) so as to allow for correct decision making regarding the relative efficacy of various sampling strategies. The utility of the first-order technique is demonstrated by linking data collection costs with modeling performance. For a simple and inexpensive project, a wise and informed selection resulted in an LTOTAL value of 86 percent, while an uninformed selection could result in an LTOTAL value of only 55 percent.     

Upscaling of Environmental Information: Support of Land-Use Management Decisions by Spatio-Temporal Regionalization Approaches

This article presents several case studies in southwest Germany, which aimed to support land use management decisions by a process-oriented statistical upscaling of point-related environmental monitoring data to the landscape scale. When techniques of data subsetting were used in a sensible way and corresponding to the appropriate scale for the evaluation envisaged, multiple linear regression offered a data mining technique which was able to spatially predict relatively complex environmental patterns with parsimonious, interpretable and accurate models, whereby different evaluation scales were best represented by different DTM resolutions. Scenario models based upon the regression formulas were a valuable tool for visualizing management options and evaluating management impacts (tree species selection) on soil functions (carbon storage), which qualifies the presented methodology as a useful aid in decision making. Such upscaling techniques may be used for forecasting long-term effects of ecosystem management, but they provided no information on temporal dynamics. Therefore, time trends of point information on soil solution data were scaled by linking them to soil chemical data which was available in higher spatial resolution, using both statistical and process-oriented methods.     

Spatial assessment of the economic feasibility of short rotation coppice on radioactively contaminated land in Belarus, Ukraine, and Russia. I. Model description and scenario analysis

The economic feasibility of short rotation coppice (SRC) production and energy conversion in areas contaminated by Chernobyl-derived (137)Cs was evaluated taking the spatial variability of environmental conditions into account. Two sequential GIS-embedded submodels were developed for a spatial assessment, which allow for spatial variation in soil contamination, soil type, and land use. These models were applied for four SRC production and four energy conversion scenarios for the entire contaminated area of Ukraine, Belarus, and Russia and for a part of the Bragin district, Belarus. It was concluded that in general medium-scale SRC production using local machines is most profitable. The areas near Chernobyl are not suitable for SRC production since the contamination levels in SRC wood exceed the intervention limit. Large scale SRC production is not profitable in areas where dry and sandy soils predominate. If the soil contamination does not exceed the intervention limit and sufficient SRC wood is available, all energy conversion scenarios are profitable.     

Prioritizing groundwater remediation policies: a fuzzy compatibility analysis decision aid

The implementation of groundwater remediation strategies in contaminated areas includes not only a cost-benefit analysis and an environmental risk assessment but also another type of study called compatibility analysis. A compatibility analysis targets the interactions between remediation technologies and site characteristics, such as the types of active contaminants and their concentrations, soil composition and geological features, etc. The purpose of this analysis is to identify the most compatible remediation plan for the contaminated site. In this paper, we introduce a decision support system for the prioritization of remediation plans based on their estimated compatibility index. As this model receives data in terms of linguistic judgments and experts' opinions, we use fuzzy sets theory to deal with these uncertainties. First, we break down the concept of compatibility into the measurable factors. Then by using a multiple-attribute decision-making (MADM) outline, we compute a factorial, regional and overall compatibility indicator for each plan. Finally, by comparing these generated indicators, we rank the remediation policies.     

Statistical considerations of progressive value and risk in mineral exploration

Financial markets recognise maximisation of expected value (E), in an essentially risk-neutral context, as the main corporate financial objective of private enterprise. This may be valid for large, integrated mining companies. Yet, most junior and middle-size exploration companies behave in a risk-averse fashion when making decisions about progressively more expensive exploration programs. From their perspective, a potential increase in expected value from either an increase in target value or related probability of discovery, or both, may not be a sufficient incentive to embark in an exploration programme if the resultant increase in expected value is accompanied by a significant increase in possible maximum loss. Risk-averse explorers may be unwilling to bear larger, albeit less probable losses, when the cost of successive exploration programmes is taken into account. The paper provides a practical methodology for such explorers to optimise the decision whether to progress to the next stage of exploration or to farm out a risky project. It uses a decision-tree model incorporating the effectiveness of the proposed exploration programme, the explorer's risk tolerance and related utility values and the probability distribution of the possible value of the exploration target.     

Discussion of the paper "Cost effective policies for alternative distributions of stochastic water pollution" by Gren, Destouni and Tempone

This discussion addresses some aspects of a recent paper appearing in this journal which investigates cost effective coastal water management based on different assumptions of the probability distributions (normal and log-normal) of pollutant transport. We also suggest an alternative approach to overcome the technical problems of using the theoretical correct distribution for characterising environmental data (log-normal) within a probabilistic programming framework.     

Discussion of the paper “Cost effective policies for alternative distributions of stochastic water pollution” by Gren, Destouni and Tempone

This discussion addresses some aspects of a recent paper appearing in this journal which investigates cost effective coastal water management based on different assumptions of the probability distributions (normal and log-normal) of pollutant transport. We also suggest an alternative approach to overcome the technical problems of using the theoretical correct distribution for characterising environmental data (log-normal) within a probabilistic programming framework.     

Two-stage planning for sustainable water-quality management under uncertainty

In water-quality management problems, uncertainties may exist in a number of impact factors and pollution-related processes (e.g., the volume and strength of industrial wastewater and their variations can be presented as random events through identifying a statistical distribution for each source); moreover, nonlinear relationships may exist among many system components (e.g., cost parameters may be functions of wastewater-discharge levels). In this study, an inexact two-stage stochastic quadratic programming (ITQP) method is developed for water-quality management under uncertainty. It is a hybrid of inexact quadratic programming (IQP) and two-stage stochastic programming (TSP) methods. The developed ITQP can handle not only uncertainties expressed as probability distributions and interval values but also nonlinearities in the objective function. It can be used for analyzing various scenarios that are associated with different levels of economic penalties or opportunity losses caused by improper policies. The ITQP is applied to a case of water-quality management to deal with uncertainties presented in terms of probabilities and intervals and to reflect dynamic interactions between pollutant loading and water quality. Interactive and derivative algorithms are employed for solving the ITQP model. The solutions are presented as combinations of deterministic, interval and distributional information, and can thus facilitate communications for different forms of uncertainties. They are helpful for managers in not only making decisions regarding wastewater discharge but also gaining insight into the tradeoff between the system benefit and the environmental requirement.     

Towards sustainable airport development

Summary This paper considers the application of the principles of sustainable development to an airport situation. If sustainable development is to be enshrined as the global development pathway which reconciles economic and environmental issues then it must be applicable to a range of spatial scales. Airports will interact at a variety of levels with this multi-level decision making and enactment structure. The requirements imposed upon an airport may differ according to whether decisions are being taken by local, regional or national decision makers. Conflicts and uncertainties will inevitably result. Definitions of sustainable development are considered and policy guidance from the United Nations (UN), the European Union (EU) and the nation State level evaluated for its relevance to airport decision making. An attempt is made to reconcile definitions and interpretations before an idealized representation of a sustainable airport is presented. Mechanisms by which this may be actualized are then suggested. A need for improved theoretical knowledge is identified but research is also necessary on application and localization procedures for sustainable development.Professor James Longhurst is Head of Department of Environmental Health and Science at UWE, Bristol. Until recently he was the Director of the Atmospheric Research and Information Centre. David Gibbs is Professor of Human Geography at the University of Hull, UK. David Raper is Deputy Director and D.E. Conlan Environmental Projects Manager in the Atmospheric Research and Information Centre, Manchester Metropolitan University, UK.