Total Remediation Costs Research Articles

Indicator use in soil remediation investments: Views from policy, research and practice

Soil contamination poses a global problem. To address this a lot of scientific progress has been made. However, the development of the economic aspect has been lacking even though the total cost of soil remediation in Europe is estimated to be 119 billion euro. Frameworks and tools are available (for example SuRF-UK framework) to address economic aspects of soil remediation, but an established set of well-defined indicators is currently not available. The introduction of a basic set of indicators for the investment analysis of a soil remediation project, based on an international survey, aims to fill this gap. The basic indicator set contains those indicators that were considered most relevant for an investment analysis by the respondents and includes, among other things, cost description, potential land use after remediation and estimation of human risk. In addition to this basic indicator set, three other indicator sets are defined to address the different needs for information of three large stakeholder groups (research, policy and practice) within the soil remediation sector. An indicator set for practitioners that includes, complementary to the basic indicator set, additional indicators concerning the practical issues of soil remediation projects (e.g. a BATNEEC analysis). For people working in policy, site valuation turned out to be a significant additional theme for an investment analysis, including indicators such as future land value. The indicator set designed to address the preferences of researchers included additional indicators related to financial support, local residents and neighbourhood, and boundaries and uncertainties allowing for a broader but also more detailed overview of the soil remediation project.

A comprehensive mitigation strategy for heavy metal contamination of farmland around mining areas – Screening of low accumulated cultivars, soil remediation and risk assessment

A three-year field test was conducted in an area surrounding past mining activity (mining area) to investigate the value of a novel comprehensive remediation strategy for Cd and Pb contamination, which included screening of low accumulated vegetable cultivars that take up Cd and Pb less than normal cultivars, in situ soil remediation using different soil amendments, and health risk assessment that evaluates the possibility of safe consumption for the vegetables. Results showed that cultivar Huoqing 91-5C of which vegetable was selected as a low accumulator of Cd and Pb in a soil contaminated with 0.5 mg kg−1 and 8180 mg kg−1 total Cd and Pb concentrations, respectively. Addition of 20 t ha−1 of biochar with 2 t ha−1 of calcium superphosphate in 10 cm depth could decrease available Cd and Pb by 70% and 85% after 1 year, respectively. Following treatments, hazard quotients for adults and children were below 1, indicating that the vegetables grown were safe for human consumption. The total cost of remediation was $3885 ha−1, so the cost of the remediation of the combination of Cd and Pb was economic in this mining area.

On the effectiveness of site investigation in regard to project costs – a case study

AbstractGeotechnical and environmental investigations for the planning of remediation measures are often reduced to the minimum required by national standards and authorities – or even less. This paper will highlight the positive effects of increased investigation efforts on reduction of total remediation costs at a specific site. The subsoil of this site is partially contaminated with perchloroethene. The site investigation included two campaigns of Membrane Interface Probing (MIP). The results of the MIP investigation were validated by taking depth‐oriented soil and groundwater samples. This high resolution of site characterization aimed to identify the contaminated hot spots as prerequisite for successful site remediation. A conceptual site model containing the spatial distribution of the contamination and the lithology was generated for the area of interest and was continuously updated during the investigation period. Based on this model, the architecture, volume and size of the contamination source zone that has to be treated was estimated with increasing stringency.

Discontinuous permeable adsorptive barrier design and cost analysis: a methodological approach to optimisation.

The following paper presents a method to optimise a discontinuous permeable adsorptive barrier (PAB-D). This method is based on the comparison of different PAB-D configurations obtained by changing some of the main PAB-D design parameters. In particular, the well diameters, the distance between two consecutive passive wells and the distance between two consecutive well lines were varied, and a cost analysis for each configuration was carried out in order to define the best performing and most cost-effective PAB-D configuration. As a case study, a benzene-contaminated aquifer located in an urban area in the north of Naples (Italy) was considered. The PAB-D configuration with a well diameter of 0.8m resulted the best optimised layout in terms of performance and cost-effectiveness. Moreover, in order to identify the best configuration for the remediation of the aquifer studied, a comparison with a continuous permeable adsorptive barrier (PAB-C) was added. In particular, this showed a 40% reduction of the total remediation costs by using the optimised PAB-D.

Chance-constrained multi-objective optimization of groundwater remediation design at DNAPLs-contaminated sites using a multi-algorithm genetically adaptive method

In this paper, a multi-algorithm genetically adaptive multi-objective (AMALGAM) method is proposed as a multi-objective optimization solver. It was implemented in the multi-objective optimization of a groundwater remediation design at sites contaminated by dense non-aqueous phase liquids. In this study, there were two objectives: minimization of the total remediation cost, and minimization of the remediation time. A non-dominated sorting genetic algorithm II (NSGA-II) was adopted to compare with the proposed method. For efficiency, the time-consuming surfactant-enhanced aquifer remediation simulation model was replaced by a surrogate model constructed by a multi-gene genetic programming (MGGP) technique. Similarly, two other surrogate modeling methods—support vector regression (SVR) and Kriging (KRG)—were employed to make comparisons with MGGP. In addition, the surrogate-modeling uncertainty was incorporated in the optimization model by chance-constrained programming (CCP). The results showed that, for the problem considered in this study, (1) the solutions obtained by AMALGAM incurred less remediation cost and required less time than those of NSGA-II, indicating that AMALGAM outperformed NSGA-II. It was additionally shown that (2) the MGGP surrogate model was more accurate than SVR and KRG; and (3) the remediation cost and time increased with the confidence level, which can enable decision makers to make a suitable choice by considering the given budget, remediation time, and reliability.

Cost-effective optimal design of a pump-and-treat system for remediating groundwater contaminant at an industrial complex

Trichloroethylene (TCE) and other solvents are found as groundwater contaminants in industrial complexes. Pump-andtreat (PAT) systems are commonly selected for remediation, but have the disadvantages of long operation time and high cost. To reduce the remediation time and cost of PAT systems, a cost-effective optimal design was obtained with a simulation–optimization model. MODFLOW and MT3DMS were used for simulating groundwater flow and contaminant transport. A genetic algorithm was used for optimization, and a parallel computing technique was used for faster optimization. Cost-effective optimal design was achieved using two objective functions, which calculated the total remediation cost by means of installation, operational, and maintenance costs. Based on the restrictions in the study area, two remediation scenarios were examined. Scenario 1 involved the removal of TCE, and Scenario 2 involved the containment of TCE within the compliance line. The total remediation costs of scenario 2 were 77.4~90.4% of scenario 1. The remediation time decreased from 9.87 years in scenario 1 to 8.13 years in scenario 2. Sensitivity analysis determined the effects of installation, operational, and maintenance costs on the total remediation cost, showing that optimal total cost and remediation time were most affected by operational cost. A parallel computing technique using 40 networked slave processes increased optimization speed by 16.9 times. However, the efficiency of the parallel computing technique decreased as networking time increased.

Optimal design of groundwater remediation system using a probabilistic multi-objective fast harmony search algorithm under uncertainty

This study develops a new probabilistic multi-objective fast harmony search algorithm (PMOFHS) for optimal design of groundwater remediation systems under uncertainty associated with the hydraulic conductivity (K) of aquifers. The PMOFHS integrates the previously developed deterministic multi-objective optimization method, namely multi-objective fast harmony search algorithm (MOFHS) with a probabilistic sorting technique to search for Pareto-optimal solutions to multi-objective optimization problems in a noisy hydrogeological environment arising from insufficient K data. The PMOFHS is then coupled with the commonly used flow and transport codes, MODFLOW and MT3DMS, to identify the optimal design of groundwater remediation systems for a two-dimensional hypothetical test problem and a three-dimensional Indiana field application involving two objectives: (i) minimization of the total remediation cost through the engineering planning horizon, and (ii) minimization of the mass remaining in the aquifer at the end of the operational period, whereby the pump-and-treat (PAT) technology is used to clean up contaminated groundwater. Also, Monte Carlo (MC) analysis is employed to evaluate the effectiveness of the proposed methodology. Comprehensive analysis indicates that the proposed PMOFHS can find Pareto-optimal solutions with low variability and high reliability and is a potentially effective tool for optimizing multi-objective groundwater remediation problems under uncertainty.

A Multi-Objective Optimal Model for a Petroleum-Contaminated Aquifer Groundwater Remediation in Western Canada

During the groundwater has been suffered from varies degrees pollution. It needs an approach to help decision maker to determine remediation strategies. In this study, a groundwater multi-objective model that includes two objectives (i.e. total remediation cost and average concentration after remediation) is proposed and successfully applied to a petroleum-contaminated aquifer located in western Canada. It can determine a remediation strategy through optimal software MATLAB after convert multi-objective into a non-linear objective using α liner weighting method.

Optimal management design of a pump and treat system at the industrial complex in Wonju, Korea

The optimization of the management design for the remediation of contaminated groundwater, using a pump and treat system, was performed for an industrial complex site in Wonju, Korea. The groundwater in the study area was contaminated with trichloroethylene (TCE) and other solvents. The pump and treat system was selected as a remediation technique, with a genetic algorithm selected as the optimization technique. The groundwater flow and contaminant transport were simulated using MODFLOW, MT3D and RT3D. Three possible scenarios were considered to obtain a cost effective remediation strategy. The cost effectiveness was determined by the total cost, including the installation cost of pumping wells and the operational cost of the pump and treat system. Scenario 1 involved the removal of TCE from the entire contaminated area using pre-existing candidate pumping wells and additional candidate wells. The results with scenario 1 showed that additional candidate wells were required to reduce the total remediation cost. Scenario 2 involved the simultaneous removals of TCE and other solvents from the entire contaminated area. The total cost of scenario 2 was 180% that of scenario 1. Scenario 3 entailed containing the TCE within a compliance line, with another remediation technique applied to the rest of the contaminated area to reduce the total remediation cost. The total cost of scenario 3 was reduced to 37% that of scenario 1 under the same cleanup time constraint. If the cost of the other remediation technique does not exceed the difference between the total costs of the other two scenarios, the optimal management design of scenario 3 would be the most cost effective remediation strategy.

Multi-objective optimization for optimal groundwater remediation design and management systems

Groundwater remediation and management systems generally encompass multiple often conflicting objectives. This paper proposes a multi-objective groundwater remediation and management methodology based on pump-and-treat technology to determine optimal strategies for cleaning up the affected portion of a contaminated aquifer and at the same time removal of sufficient quantity of clean water from the unaffected portion of the same aquifer for supply to end users for drinking purposes. Two objective functions are incorporated into the proposed optimization model: (i) minimization of the total remediation cost, and (ii) maximization of clean water extraction rate. Pumping rates and well locations are the decision variables of the optimization model with imposed constraints on hydraulic heads and contaminant concentrations at several specified locations. This work employs a new and very efficient technique for interfacing C with FORTRAN programs to couple NSGA-II coded in C with FORTRAN programs MODFLOW and MT3DMS and use in this methodology to obtain a tradeoff between remediation cost and clean water extraction rate. The Pareto front thus obtained consists of several optimal solutions to the problem and is used to analyze the variation of remediation cost with the extraction rate of uncontaminated water. Sensitivity analyses on some important input parameters have been carried out to account for the effects of variability of these parameters on the model result. Main contributions of this paper are: (i) use of a novel technique of linking C programs with FORTRAN programs, and (ii) revelation and exploitation of insightful features of multi-objective optimization algorithms applicable to pump-and-treat groundwater remediation problems. Results are satisfactory and show great promise for wide applicability in the field of groundwater remediation.

Economic benefits of large-scale remediation of contaminated marine sediments—a literature review and an application to the Grenland fjords in Norway

As input to a cost-benefit analysis of large-scale remediation measures of contaminated sediments in the Grenland fjords in Norway, we conducted a contingent valuation (CV) survey of a representative sample of households from municipalities adjacent to these fjords. The CV method aimed at valuing the benefits perceived by households of removing dietary health advisories on seafood consumption currently in place through the fjords. Mean household willingness-to-pay (WTP) per year over a 10-year period was found to decrease with increased distance from the Grenland fjords and was somewhat higher than in a similar study conducted 10 years earlier than our study. Aggregating mean WTP over all households in neighbouring municipalities to the fjords resulted in total economic benefits of the same magnitude as the total remediation costs. The WTP results make the case in such a way that the high costs of remediation of contaminated marine sediments can be defended by the large economic benefits generated for households around the fjord. The research was financed by the local environmental authorities and the local industry that had caused the contaminated sediments. The WTP results were strongly contested by the industry upon completion of the study. The paper addresses the industry's critiques of this particular CV study and discusses how to better inform local stakeholders about the potential and limitations of the CV method and how to improve communication of economic valuation results.

A multi-objective optimization framework for surfactant-enhanced remediation of DNAPL contaminations

The occurrence of Dense Non-Aqueous Phase Liquid (DNAPL) contaminations in the subsurface is a threat for drinkwater resources in the western world. Surfactant-Enhanced Aquifer Remediation (SEAR) is widely considered as one of the most promising techniques to remediate DNAPL contaminations in-situ, be it with considerable additional costs compared to classical pump-and-treat remediations. A cost-effective design of the remediation set-up is therefore essential. In this work, a pilot SEAR test is executed at a DNAPL contaminated site in Belgium in order to collect data for the calibration of a multi-phase multi-component model. The calibrated model is used to assess a series of scenario-analyses for the full-scale remediation of the site. The remediation variables that were varied were the injection and extraction rate, the injection and extraction duration, and the surfactant injection concentrations. A constrained multi-objective optimization of the model was applied to obtain a Pareto set of optimal remediation strategies with different weights for the two objectives of the remediation: (i) the maximal removal of DNAPL and (ii) a total minimal cost. These Pareto curves can help decision makers to select an optimal remediation strategy in terms of cost and remediation efficiency. The Pareto front shows a considerable trade-off between the total remediation cost and the removed DNAPL mass.

Cost‐optimal contaminant plume management with a combination of pump‐and‐treat and physical barrier systems

AbstractThe economic potential of combining hydraulic barriers and pump‐and‐treat systems to manage contaminant plumes is investigated by means of a comparative cost analysis. The analysis is based on a preceding study on the hydraulic performance of barrier‐supported and conventional pump‐and‐treat systems, which revealed the efficiency of accurately positioned barriers in reducing pumping rates for contaminant plume capture. In the present paper, we examine whether the application of physical barriers like slurry walls or sheet piles also leads to economic benefits, i.e., whether the reduction of the operational costs outweighs the capital costs associated with the construction of the barriers. An economic model is presented that quantifies remediation costs by the use of cost functions relating costs to site‐specific and system‐specific parameters. The model is applied to a hypothetical remediation scenario. The results clearly demonstrate that physical barriers may yield significant savings in the total costs, particularly if unit costs for on‐site treatment are high. Furthermore, as barriers tend to reduce the uncertainty about the pumping rate required to capture a plume, they smooth the trade‐off curves between total remediation costs and system reliability.

Multi-Objective Optimization of the Setup of a Surfactant-Enhanced DNAPL Remediation

Surfactant-enhanced aquifer remediation (SEAR) is widely considered a promising technique to remediate dense nonaqueous phase liquid (DNAPL) contaminations in-situ. The costs of a SEAR remediation are important and depend mostly on the setup of the remediation. Costs can be associated with the installation of injection and extraction wells, the required time of the remediation (and thus labor costs, lease of installations, and energy), the extracted water volume (the purification of the extracted water), and the injected surfactant amount. A cost-effective design of the remediation setup allows an optimal use of resources. In this work, a SEAR remediation was simulated for a hypothetical typical DNAPL contamination. A constrained multi-objective optimization of the model was applied to obtain a Pareto set of optimal remediation strategies with different weights for the two objectives of the remediation: (i) the maximal removal of DNAPL mass (ii) with a minimal total cost. A relatively sharp Pareto front was found, showing a considerable tradeoff between DNAPL removal and total remediation costs. These Pareto curves can help decision makers select an optimal remediation strategy in terms of cost and remediation efficiency depending on external constraints such as the available budget and obligatory remediation goals.

A Proactive Approach Can Make Site Remediation Less Expensive

Without any incentive to clean up a contaminated site, remediation is often delayed until the site owner is compelled to act by regulatory agencies. In such a context, the selected technology is typically the one that will reach the remediation goals as quickly as possible. Unfortunately, this criterion is often met by overly expensive technologies, resulting in high and sometimes unaffordable total remediation costs, leading to a remediation with a negative net benefit. This study examines the effects of time constraint and benefit value on the optimal remediation strategy for a diesel-contaminated site. This strategy is developed using the technico-economic model METEORS, which takes into account the technology's effectiveness, the uncertainty of the level of contamination, and the possibility of reducing this uncertainty through either an additional characterization (before selecting and applying a technology) or the monitoring of the remediation technology (during its use). Results of simulations with both economic and temporal constraints support a proactive approach to site remediation.

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.

Cost‐effective strategy for installation and operation of a bioventing system to remediate jet fuel contamination beneath the former refueling apron at Griffiss Air Force Base, New York

AbstractA bioventing system was designed and installed beneath the former refueling apron, Apron 1, to enhance biodegradation of residual jet fuel‐contaminated soils at the former Griffiss Air Force Base (GAFB) located in Rome, New York. The innovative reuse of existing fuel supply lines for air injection has allowed for the continued use of the 29‐acre apron surface, while at the same time providing for aeration and remediation of approximately 60,000 cubic yards of petroleum‐contaminated soil beneath the apron. Additionally, use of the fuel supply lines eliminated the need for costly saw cutting and excavation of the 18‐inch concrete apron surface, or use of horizontal drilling techniques for installation of piping to the air injection wells, and resulted in a savings of approximately $150,000. Installation costs were further reduced by installing aboveground piping to air injection wells located at the former lateral control points in areas isolated from the biopile operations and maintenance activities. Total remediation costs for contaminated soil beneath the apron are approximately $10 per cubic yard of treated soil.The former refueling apron is currently used for the ex situ biopile treatment of contaminated soils collected from throughout the base. The arrangement of the existing biopiles, and the need for additional biopiles on the apron in the future, necessitated the use of underground piping to supply air to injection wells located at former refueling hydrants. In order to facilitate uninterrupted remediation of the biopiles, existing underground fuel supply pipelines that were cleaned and closed in place during the deactivation of the refueling apron in 1996 were utilized to supply air from the edge of the refueling apron to the air injection wells.This remediation strategy, which maximizes the use of available space by utilizing both in situ and ex situ treatment technologies, will significantly reduce the period of time required for remediation of contaminated soil at the former GAFB and provides a cost‐effective approach to attaining beneficial reuse of the site. © 2003 Wiley Periodicals, Inc.

Stochastic groundwater quality management: Role of spatial variability and conditioning

In this paper a methodology for the stochastic management of groundwater quality problems is presented. A stochastic algorithm to solve the coupled flow and mass transport inverse problem is combined with a stochastic management approach to design a reliable pump‐and‐treat scheme. Three main differences exist between this research and previously published research in the field of groundwater management. The first is the method of parameterization, where we have explicitly considered spatial variability of flow and mass transport parameters in our method. Second, we have used adjoint and/or sensitivity equations to calculate first and second derivatives of state variables with respect to hydraulic parameters. Finally, the third point is the nonlinear multiobjective formulation we adopt which enables us to explore the whole trade‐off curve between reliability and cost of remediation. A number of key results are listed here. The stochastic inverse algorithm used here, the representers method, makes it possible to estimate heterogeneity as reflected by the measurements of parameters and state variables. Meanwhile, using the adjoint operator to accurately estimate sensitivity of state variables with respect to the parameters has facilitated the use of the head and concentration measurements in the inverse algorithm. Moreover, stochastic management, which is conditioned on head and concentration measurements simultaneously are shown to provide more realistic and reliable remediation schemes. Finally, with the aid of the multiobjective formulation of the management problem and analysis of its results, it is possible to identify a significant remediation scheme. Such a scheme has the advantage of producing maximum possible reliability with the least possible increase in the total cost of remediation.

Accounting for measurement error in uncertainty modeling and decision‐making using indicator kriging andp‐field simulation: application to a dioxin contaminated site

AbstractIn many environmental studies spatial variability is viewed as the only source of uncertainty while measurement errors tend to be ignored. This article presents an indicator kriging‐based approach to account for measurement errors in the modeling of uncertainty prevailing at unsampled locations. Probability field simulation is then used to assess the probability that the average pollutant concentration within remediation units exceeds a regulatory threshold, and probability maps are used to identify hazardous units that need to be remediated. This approach is applied to two types of dioxin data (composite and single spoon samples) with different measurement errors which were collected at the Piazza Road dioxin site, an EPA Superfund site located in Missouri. A validation study shows that the proportion of contaminated soil cores provides a reasonable probability threshold to identify hazardous remediation units. When a lower probability threshold is chosen, the total remediation costs are unreasonably high while false negatives are unacceptably frequent for a higher probability threshold. The choice of this threshold becomes critical as the sampling density decreases. Copyright © 2002 John Wiley & Sons, Ltd.

An integrated global and local optimization approach for remediation system design

A global optimization (metaheuristic) method, tabu search, is integrated with linear programming to solve remediation design problems. This integrated approach takes advantage of the fact that the global optimization approach is most effective for optimizing discrete well location variables, while linear programming is much more efficient for optimizing continuous pumping rate variables. In addition, an efficient forward solution updating procedure is used to lessen the computational burden of the global optimization approach. With this procedure the new solution to a linear flow model perturbed by pumping is obtained as the sum of a nonperturbed base solution and the solution to the perturbed portion of the flow system, which can be derived directly without running the flow model. Numerical results, based on a two‐dimensional capture zone design problem, show that the computation time can be reduced to a small fraction of that required by the conventional approach, in which a forward simulation model is run each time the objective function needs to be evaluated. It is also demonstrated that the maximum number of wells allowed in a given design has a significant effect on the total remediation costs. (The total remediation costs are nearly doubled when only one well is allowed instead of the optimal number of six for the test problem.) A Monte Carlo analysis, based on 200 realizations of a lognormally distributed random hydraulic conductivity field (the variance of lnT = 1.0), further reveals that the total remediation costs determined for the heterogeneous aquifer have a large uncertainty (the ratio of standard derivation over mean is 0.4). The total remediation costs and associated uncertainty are also shown to increase with the uncertainty of the hydraulic conductivity field.