Forecasting the number of soil samples required to reduce remediation cost uncertainty

Sampling scheme design is an important step in the management of polluted sites. It largely controls the accuracy of remediation cost estimates. In practice, however, sampling is seldom designed to comply with a given level of remediation cost uncertainty. In this paper, we present a new technique that allows one to estimate of the number of samples that should be taken at a given stage of investigation to reach a forecasted level of accuracy. The uncertainty is expressed both in terms of volume of polluted soil and overall cost of remediation. This technique provides a flexible tool for decision makers to define the amount of investigation worth conducting from an environmental and financial perspective. The technique is based on nonlinear geostatistics (conditional simulations) to estimate the volume of soil that requires remediation and excavation and on a function allowing estimation of the total cost of remediation (including investigations). The geostatistical estimation accounts for support effect, information effect, and sampling errors. The cost calculation includes mainly investigation, excavation, remediation, and transportation. The application of the technique on a former smelting work site (lead pollution) demonstrates how the tool can be used. In this example, the forecasted volumetric uncertainty decreases rapidly for a relatively small number of samples (20-50) and then reaches a plateau (after 100 samples). The uncertainty related to the total remediation cost decreases while the expected total cost increases. Based on these forecasts, we show how a risk-prone decision maker would probably decide to take 50 additional samples while a risk-averse decision maker would take 100 samples.     

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.     

An integrated fuzzy-stochastic modeling approach for risk assessment of groundwater contamination

An integrated fuzzy-stochastic risk assessment (IFSRA) approach was developed in this study to systematically quantify both probabilistic and fuzzy uncertainties associated with site conditions, environmental guidelines, and health impact criteria. The contaminant concentrations in groundwater predicted from a numerical model were associated with probabilistic uncertainties due to the randomness in modeling input parameters, while the consequences of contaminant concentrations violating relevant environmental quality guidelines and health evaluation criteria were linked with fuzzy uncertainties. The contaminant of interest in this study was xylene. The environmental quality guideline was divided into three different strictness categories: "loose", "medium" and "strict". The environmental-guideline-based risk (ER) and health risk (HR) due to xylene ingestion were systematically examined to obtain the general risk levels through a fuzzy rule base. The ER and HR risk levels were divided into five categories of "low", "low-to-medium", "medium", "medium-to-high" and "high", respectively. The general risk levels included six categories ranging from "low" to "very high". The fuzzy membership functions of the related fuzzy events and the fuzzy rule base were established based on a questionnaire survey. Thus the IFSRA integrated fuzzy logic, expert involvement, and stochastic simulation within a general framework. The robustness of the modeling processes was enhanced through the effective reflection of the two types of uncertainties as compared with the conventional risk assessment approaches. The developed IFSRA was applied to a petroleum-contaminated groundwater system in western Canada. Three scenarios with different environmental quality guidelines were analyzed, and reasonable results were obtained. The risk assessment approach developed in this study offers a unique tool for systematically quantifying various uncertainties in contaminated site management, and it also provides more realistic support for remediation-related decisions.     

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.     

GROUND WATER SOLUTE TRANSPORT,OPTIMAL REMEDIATION PLANNING,AND DECISION MAKING UNDER UNCERTAINTY1

ABSTRACT: A groundwater quality modeling advisory system has been developed for the U.S. Air Force for use in investigating remediation alternatives for the cleanup of subsurface contamination. The system is capable of accounting for uncertainty, not only in the prediction of solute transport but also in the optimization of the remediation scheme through chance constraints. The system guides users in the selection of appropriate transport models through an algorithm independently tested with machine learning codes. An application to Hill Air Force Base, Utah, is presented for which different pump-and-treat strategies are considered: the results are evaluated in terms of the cumulative distribution of the contaminant concentration for each case and the tradeoff relationship between the cost of remediation and the probability that the remediation strategy exceeds an established maximum allowable contaminant concentration.     

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.     

Simulation and optimization technologies for petroleum waste management and remediation process control

Leakage and spill of petroleum hydrocarbons from underground storage tanks and pipelines have posed significant threats to groundwater resources across many petroleum-contaminated sites. Remediation of these sites is essential for protecting the soil and groundwater resources and reducing risks to local communities. Although many efforts have been made, effective design and management of various remediation systems are still challenging to practitioners. In recent years, the subsurface simulation model has been combined with techniques of optimization to address important problems of contaminated site management. The combined simulation-optimization system accounts for the complex behavior of the subsurface system and identifies the best management strategy under consideration of the management objectives and constraints. During the past decades, a large number of studies were conducted to simulate contaminant flow and transport in the subsurface and seek cost-effective remediation designs. This paper gives a comprehensive review on recent developments, advancements, challenges, and barriers associated with simulation and optimization techniques in supporting process control of petroleum waste management and site remediation. A number of related methodologies and applications were examined. Perspectives of effective site management were investigated, demonstrating many demanding areas for enhanced research efforts, which include issues of data availability and reliability, concerns in uncertainty, necessity of post-modeling analysis, and usefulness of development of process control techniques.     

OPTIMIZATION OF INTERMITTENT PUMPING SCHEDULES FOR AQUIFER REMEDIATION USING A GENETIC ALGORITHM1

ABSTRACT: Using a genetic algorithm (GA), optimal intermittent pumping schedules were established to simulate pump‐and‐treat remediation of a contaminated aquifer with known hydraulic limitations and a water miscible contaminant, located within the Duke Forest in Durham, North Carolina. The objectives of the optimization model were to minimize total costs, minimize health risks, and maximize the amount of contaminant removed from the aquifer. Stochastic ground water and contaminant transport models were required to provide estimates of contaminant concentrations at pumping wells. Optimization model simulations defined a tradeoff curve between the pumping cost and the amount of contaminant extracted from the aquifer. For this specific aquifer/miscible contaminant combination, the model simulations indicated that pump‐and‐treat remediation using intermittent pumping schedules for each pumping well produced significant reductions in predicted contaminant concentrations and associated health risks at a reasonable cost, after a remediation time of two years.     

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.     

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.     

Eco-efficiency in contaminated land management in Finland – Barriers and development needs

In Finland the number of potentially contaminated sites totals ca. 20?000. The annual costs of remediation are 60–70 million euros. Excavation combined with disposal or off-site treatment is the most common soil remediation method. To define which factors make contaminated land management (CLM) eco-efficient and to study whether eco-efficiency has been considered in CLM decisions we carried out a literature survey, two stakeholder seminars, thematic interviews and a questionnaire study on economic instruments. Generally speaking, eco-efficiency means gaining environmental benefits with fewer resources. To assess its realization in CLM, it is necessary to have a more specific definition. In our study, we arrived at a list of several qualifications for eco-efficiency. It was also shown that eco-efficiency has hardly been a real issue in the selection of remediation techniques or generally, in the decision-making concerning contaminated sites. The existing policy instruments seem to be insufficient to promote eco-efficiency in CLM. Several concrete barriers to eco-efficiency also came up, urgency and lack of money being the most important. The scarcity of the use of in situ remediation methods and the difficulties involved in recycling slightly contaminated or treated soil were considered to be major problems. Insufficient site studies, inadequate or unsuitable methods for risk assessment and cost evaluation, and deficient and mistimed risk communication can also hinder the realization of eco-efficiency. Hence, there is a need to promote the use of more eco-efficient remediation techniques and to develop CLM policy instruments, guidelines, and participatory processes and methods to assess the eco-efficiency of CLM options.     

A response surface methodology to address uncertainties in cap rock failure assessment for CO2 geological storage in deep aquifers

Cap rock failure assessment, either tensile fracturing or shear slip reactivation of pre-existing fault, is a key issue for preventing CO₂ leakage from deep aquifer reservoirs up to the surface. For an appropriate use in risk management, the uncertainties associated with such studies should be investigated. Nevertheless, uncertainty analysis requires multiple simulations and a direct use of conventional numerical approaches might be too computer time consuming. An alternative is to use conventional analytical models, but their assumptions appear to be too conservative. An intermediate approach is then proposed based on the response surface methodology, consisting in estimating the effective stress state after CO₂ injection as a linear combination of the most influential site properties based on a limited number of numerical simulations. The decision maker is provided with three levels of information: (1) the identification of the most important site properties; (2) an analytical model for a quick assessment of the maximal sustainable overpressure and (3) a simplified model to be used in a computationally intensive uncertainty analysis framework. This generic methodology is illustrated with the Paris Basin case using a large-scale hydromechanical model to assess cap rock failure in the injector zone.     

Determination of the effect of operating cost uncertainty on mining project evaluation

Mining projects are complex businesses that demand constant risk assessment. This is because several kinds of uncertainties influence the value of a mine project, typically. These uncertainties may be classified as exploration uncertainties, economic uncertainties and engineering uncertainties. The evaluation of a mine project under these uncertainties is a complicated job, which may lead to making a wrong decision by managers and stockholders. Therefore, at first, the engineers must recognize the mining uncertainties before carrying out the project evaluation. The economic uncertainties are the most important factors, which may affect the project evaluation. Among the mentioned uncertainties, the operating cost uncertainty is an important and effective factor, which is ignored to a certain extent.     

Microwave-assisted stripping of oil contaminated drill cuttings

The application of microwave heating technology to conventional gas stripping processes has been investigated in the remediation of contaminated drill cuttings. The technical feasibility and limitations of nitrogen and steam stripping processes are demonstrated, and it is shown that the combination of microwave heating with the stripping process offers a step change in performance. Order of magnitude improvements in processing time are shown for the microwave-assisted processes, as well as greatly improved levels of remediation. The mechanisms of contaminant removal are discussed, along with the phenomena which occur with microwave heating processes. The energy requirements of each of pure gas and microwave-assisted processes are also discussed, and the potential applications of each technology are highlighted relative to the overall remediation requirements.     

RESERVOIR MANAGEMENT RULES DERWED WITH THE AID OF MULTIPLE OBJECTIVE DECISION MAKING TECHNIQUES1

ABSTRACT: The determination of optimum reservoir operating rules for reservoirs with multiple conflicting objectives is still a difficult task - despite many publications in this field. In this paper a three-step Multi Objective Decision Making (MODM) method is presented, the emphasis of which is placed on the necessity to make the work easy for the decision maker, which many MODM techniques fail to achieve. The method is applied to the development of a compromise optimum operating rule for a multi-purpose reservoir. In the first step of the method stochastic DP is chosen which is combined with the “weighting method” allowing combination of various objectives into one objective function. By systematically varying the weights for the objectives a large number of pareto optimum reservoir operating rules is generated. In the second step of the method the performance of all these operating rules is tested with the aid of a model simulating reservoir operation. The results are statistically analyzed and the reliabilities for attaining the various objectives are computed. The third step of the model applies another MODM technique which allows the decision maker - in a computer dialog - to select his optimum reservoir operating rule from the large number of pareto optimum solutions generated in step 1. Here he can specify his preferences for the various objectives. For this purpose two alternative MODM techniques are offered: Compromise Programming and the SEMOPS method. Their performance is shown along with the generation and selection of operating rules for the multi-objective Wupper reservoir system in Germany.     

Environmental portfolio analysis: An integrated strategic approach to environmental management

As businesses strive to reduce costs and become more competitive, environmental costs and potential future liability issues continue to raise overhead expenses. The decision process is further challenged by the various interpretations of existing laws and the uncertainty of future applicable regulations and their interpretation. To make more informed business decisions and bridge the gap between the environmental and business perspective, organizations need to be able to see the overall environmental picture and how it affects the current and future business operation. This article presents a systematic approach to developing an organization's integrated baseline “environmental portfolio” with various business risk levels and expected costs. Utilizing computer simulation, sensitivity iterations are performed to show the results of different scenarios. These scenarios can include various probabilities of cost levels, permitting strategies, and litigation, as well as the success of new technologies. Management can then focus attention on the main driving factors and avoid spending too much attention on lesser items. An additional benefit to this process is that communication between the various segments of an organization are enhanced since their perspectives are clearly articulated as part of the analysis. Sensitivity analysis also provides the framework for a sanity check of the process and results. Are projected levels of success reasonable? What levels would be required to change the decision, and how likely are they to occur? What level of overall business risk associated with environmental issues is prudent? In addition this article shows how computer modeling and simulation can bring a valuable perspective to the decision-making process.     

Two-stage vertex analysis method for planning electric power systems with greenhouse gas abatement consideration

This study presents a two-stage vertex analysis (TSVA) method for the planning of electric power systems (EPS) under uncertainty. TSVA has advantages in comparison to other optimization techniques. Firstly, TSVA can incorporate greenhouse gas (GHG) abatement policies directly into its optimization process, and, secondly, it can readily integrate inherent system uncertainties expressed as fuzzy sets and probability distributions directly into its modeling formulation and solution procedure. The TSVA method is applied to a case study of planning EPS and it is demonstrated how the TSVA efficiently identify optimal electricity-generation schemes that could help to minimize system cost under different GHG-abatement considerations. Different combinative considerations on the uncertain inputs lead to varied system costs and GHG emissions. Results reveal that the total electricity supply will rise up along with the time period due to the increasing demand and, at the same time, more non-fossil fuels should be used to satisfy the increasing requirement for GHG mitigation. Moreover, uncertainties in connection with complexities in terms of information quality (e.g., capacity, efficiency, and demand) result in changed electricity-generation patterns, GHG-abatement amounts, as well as system costs. Minimax regret (MMR) analysis technique is employed to identify desired alternative that reflects compromises between system cost and system-failure risk.     

Optimal Land Use Management for Soil Erosion Control by Using an Interval-Parameter Fuzzy Two-Stage Stochastic Programming Approach

Soil erosion is one of the most serious environmental and public health problems, and such land degradation can be effectively mitigated through performing land use transitions across a watershed. Optimal land use management can thus provide a way to reduce soil erosion while achieving the maximum net benefit. However, optimized land use allocation schemes are not always successful since uncertainties pertaining to soil erosion control are not well presented. This study applied an interval-parameter fuzzy two-stage stochastic programming approach to generate optimal land use planning strategies for soil erosion control based on an inexact optimization framework, in which various uncertainties were reflected. The modeling approach can incorporate predefined soil erosion control policies, and address inherent system uncertainties expressed as discrete intervals, fuzzy sets, and probability distributions. The developed model was demonstrated through a case study in the Xiangxi River watershed, China’s Three Gorges Reservoir region. Land use transformations were employed as decision variables, and based on these, the land use change dynamics were yielded for a 15-year planning horizon. Finally, the maximum net economic benefit with an interval value of [1.197, 6.311] × 10⁹ $ was obtained as well as corresponding land use allocations in the three planning periods. Also, the resulting soil erosion amount was found to be decreased and controlled at a tolerable level over the watershed. Thus, results confirm that the developed model is a useful tool for implementing land use management as not only does it allow local decision makers to optimize land use allocation, but can also help to answer how to accomplish land use changes.     

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.