fg a

j, bu

Article history:Received 21 November 2008Received in revised form18 December 2009Accepted 10 January 2010

physical (thermal) technologies that can be implemented either exsitu or in situ. Efciency and cleanup times may vary substantiallybetween remediation technologies as may associated costs andenvironmental impact of each method. While remediation ofa contaminated site reduces a local contamination problem, otherenvironmental impacts are also created on global, regional and

(1990) and further exemplied for subsurface remediation byMassmann et al. (1991). The objective function (Eq. (1)) describesthe net present value, 4, of a decision alternative as a function ofannual benet, B, annual cost, C, and a risk term, R, covering theexpected cost of failure. T is the time span of the analysis and r is thediscount rate (Freeze et al., 1990):

f XTt0

1

1 rthBt Ct Rt

i(1)* Corresponding author. Tel.: 45 4525 1595; fax: 45 4593 2850.

Contents lists availab

Journal of Environm

ls

Journal of Environmental Management 91 (2010) 11691182E-mail address: gil@env.dtu.dk (G. Lemming).1. Introduction

The management of contaminated sites as undertaken bymunicipal, regional or national authorities is not solely a matter ofwhether or not a site is contaminated and if a site should beremediated but also how the site should be remediated. Decision-makers are often faced with a broad range of different technicalapproaches for site cleanup, including biological, chemical and

local scales as a consequence of the energy andmaterials consumedby the remediation process itself. Such impacts can be quantiedusing life cycle assessment (LCA), which has been suggested asa valuable decision support tool for comparing the environmentalimpacts arising from remedial activities (Lemming et al., in press;Cadotte et al., 2007; Bayer and Finkel, 2006; Toffoletto et al., 2005).

The risk-based cost-benet approach to aid decision-makingwithin the eld of hydrogeology was described by Freeze et al.Available online 1 February 2010

Keywords:Decision supportRemediationContaminated sitesGroundwaterLife cycle assessmentChlorinated solventsHealth riskUncertainty modelingFirst- and second-order reliability methods0301-4797/$ see front matter 2010 Elsevier Ltd.doi:10.1016/j.jenvman.2010.01.011Remediation methods for contaminated sites cover a wide range of technical solutions with differentremedial efciencies and costs. Additionally, they may vary in their secondary impacts on the envi-ronment i.e. the potential impacts generated due to emissions and resource use caused by the reme-diation activities. More attention is increasingly being given to these secondary environmental impactswhen evaluating remediation options. This paper presents a methodology for an integrated economicdecision analysis which combines assessments of remediation costs, health risk costs and potentialenvironmental costs. The health risks costs are associated with the residual contamination left at the siteand its migration to groundwater used for drinking water. A probabilistic exposure model using rst- andsecond-order reliability methods (FORM/SORM) is used to estimate the contaminant concentrations ata downstream groundwater well. Potential environmental impacts on the local, regional and globalscales due to the site remediation activities are evaluated using life cycle assessments (LCA). Thepotential impacts on health and environment are converted to monetary units using a simplied costmodel.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) soilvapor extraction and (d) thermally enhanced soil vapor extraction by electrical heating of the soil.Ultimately, the developed methodology facilitates societal cost estimations of remediation scenarioswhich can be used for internal ranking of the analyzed options. Despite the inherent uncertainties ofplacing a value on health and environmental impacts, the presented methodology is believed to bevaluable in supporting decisions on remedial interventions.

2010 Elsevier Ltd. All rights reserved.a r t i c l e i n f o a b s t r a c tRisk-based economic decision analysis oa PCE-contaminated site

Gitte Lemming a,*, Peter Friis-Hansen b, Poul L. BjeraDepartment of Environmental Engineering, Technical University of Denmark, MiljoevebDet Norske Veritas (DNV), Veritasveien 1, 1322 Hvik, Norway

journal homepage: www.eAll rights reserved.remediation options at

ilding 113, DK-2800 Kgs. Lyngby, Denmark

le at ScienceDirect

ental Management

evier .com/locate/ jenvman

If the included benets, risks and costs reect the preferences ofthe decision maker, then the optimal decision is one which maxi-mizes the objective function. The risk term is dened as the prob-ability of failure, Pf, (or generally the frequency of failure)multiplied by the cost of failure Cf:

Rt Pf t$Cf t (2)The risk-based cost-benet framework has been used previously

to evaluate groundwater remediation strategies (e.g. Khadam andKaluarachchi, 2003; Rosen et al., 1998), however neglecting benetevaluation and simplied to a risk-cost minimization model:

f XT 1

1 rthCt Rt

i(3)

are not monetized and cannot directly be used in an economicdecision analysis.

This article presents a holistic methodology for combiningassessments of health risk and environmental impacts associatedwith remediation of a contaminated site. The method appliesa probabilistic risk assessment using rst- and second-order reli-ability methods (Ditlevsen and Madsen, 1996) to estimate thehuman health risk and cost associated with the residual contami-nation and the potential ingestion of contaminated drinking water.Environmental impacts caused by the remedial activities are eval-uated using life cycle assessments (LCA) of each remediationscenario. The human health risk cost is estimated using the LifeQuality Time Allocation Index (Ditlevsen and Friis-Hansen, 2007b)and the environmental cost is estimated using a preliminary

clayey till overlies a sand layer which is saturated from approxi-

G. Lemming et al. / Journal of Environmental Management 91 (2010) 116911821170t0

Rosen et al. (1998) used this framework to evaluate two alter-natives to reduce groundwater impacts from diffuse nitrate andaluminium sources andmonetized the groundwater contaminationrisk based on the water price. Khadam and Kaluarachchi (2003)used the risk-cost-benet approach to evaluate the optimalpumping period for a pump-and-treat system, and expressed thecost-effectiveness in terms of remediation costs per life saved.Other decision support frameworks for contaminated sitemanagement includemulti-criteria utility theory, as used by Scholzand Schnabel (2006), to calculate the overall utility of a decisionalternative. This is based on the sum of four partial utilities whichdescribe human health impacts, remediation costs, soil produc-tivity and market value of land after remediation. The utilities werenormalized to absolute values between zero and one, with a singleutility score of 1 representing the most favorable outcome, i.e. nohealth effects or a remediation cost of zero. In this way, the authorsovercome the difculties in monetizing the various impacts of theremediation alternatives. Nevertheless the method implies aninternal weighting between the four utilities, and the nal utilitysum can be difcult to interpret. The decision support system byNasiri et al. (2007) aimed to assess the compatibility of ground-water remediation technologies based on characteristics of thecontaminated zone. Fuzzy sets theory was used to include uncer-tainty of linguistic data in their multiple attribute decision analysis.

In brief, none of the reviewed decision frameworks includeexternal environmental costs in their analyses, such as externalitiesdue to air emissions. These external societal costs should ideally bea part of a societal analysis (Hardisty and Ozdemiroglu, 2005).Studies which apply LCA to evaluations of environmental impactsfrom remediation express these impacts in terms of an emission ofa reference substance (e.g. kg of CO2-equivalents for impacts onclimate change). The impacts may be normalized (e.g. expressed asperson equivalents, PE) and weighted to a single impact index butFig. 1. Overview of the framework for the integrated analysis of remediation cosmately 20 m below ground surface. A thin clayey till layer separatesthe upper aquifer from the regional chalk aquifer from wheredrinking water is abstracted. The protective clay layer diminishes inthe direction towards the water supply. The contaminated site issituated within the groundwater catchment of the largest Danishwell eld located approximately 2000 m down gradient from thesite. Groundwater is the sole source of drinking water in this areaand therefore represents a scarce and valuable resource. Aconceptual model of the site is presented in Fig. 2.environmental cost model. Together with the direct remediationcost of each method, the health cost and the environmental costscombines into a relative cost estimate of each remediation scenariothat can be used for an internal ranking of the assessed remediationtechnologies. Themethodology is illustrated in Fig.1. Human healthcosts estimated in the methodology are associated with thepotential ingestion of contaminated drinkingwater. Other exposureroutes such as exposure via indoor air or via soil are disregarded inthis analysis for simplicity purposes.

2. Materials and methods

2.1. Site description

The risk-based economic decision analysis presented in thisarticle is used to assess three alternatives for remediating a site inDenmark which is heavily contaminated with chlorinated ethenes.The contaminant source zone lies in an unsaturated and possiblyfractured clayey till which extends approximately 7.5 m belowground surface. The top 1 m of soil is regarded as unpolluted. Themass of chlorinated solvents is estimated at 10 tons and primarilyin the form of tetrachloroethene (PCE). A signicant part of thiscontamination is present as an immobile separate phase trapped asblobs within the soil pores (residual phase contamination). Thets, environmental costs and health costs of contaminated site remediation.

1. Excavation and off-site treatment2. Soil vapor extraction (SVE)3. Thermally enhanced soil vapor extraction by electrical heating

of the soil

The rst option involves the active removal of contaminated soilvolume by excavation. The soil is then transported to a soil treat-ment facility for ex situ aeration. The extracted PCE-contaminatedair is collected and treatedwith activated carbon. The excavation pitis backlled using gravel from a local gravel pit. In option 2, soilvapor extraction (SVE), the contamination is left in the ground andthe aim is to prevent contaminant migration to the groundwater

activated carbon before being released to the atmosphere.

mental Management 91 (2010) 11691182 11712.2. Calculating the probability of an event: rst- and second-orderreliability methods

First- and second-order reliability methods (FORM/SORM) areanalytical models developed in the eld of structural engineering tocalculate the probability of failure of engineered structures. Theyhave recently been applied to address uncertainty withincontaminant transport modeling by e.g. Hamed and El-Beshry(2006) and Baalousha and Kongeter (2006). Both methods involvedening a limit state function GX that divides the set of functionvalues into a failed set GX 0 and a safe set GX > 0. The failureprobability as dened in Eq. (4) is (Ditlevsen and Madsen, 1996):

PhGX 0

i

ZGX0

f xdx (4)

where f x is the multivariate density function of the randomvariable X of dimension n. Note that capital letters refer to randomvariables and that lower case letters refer to a specic outcome ofthe capital lettered random variable.

Standard numerical integration techniques are generally notfeasible to solve the high-dimensional integral in Eq. (4) (large n). Ingeneral, either Monte Carlo simulation (MCS) or analytically basedrst- and second-order reliability methods (FORM/SORM) musttherefore be used. It is straightforward to apply MCS to obtain thecumulative density function, FGXgx. However, it can be a draw-back that MCS may require a signicant amount of function calls toevaluate the possible complex function GX, especially for small

Fig. 2. Conceptual model of the contaminated site (not to scale).

G. Lemming et al. / Journal of Environprobability levels. FORM/SORM are analytical probability integra-tion methods and thus, when they do apply, are quite fast. It shouldbe noted that FORM and SORM apply only to random variable reli-ability problems where the set of basic variables are continuous.

An important advantage of the FORM/SORM method comparedto MCS is the importance factors (often called the a-vector), whichare by-products of the calculation. These describe how much theindividual random variables in the X-vector contributes to theuncertainty in the probability calculation. If the importance factor ofa variable is below 1014%, the variable may be changed to its meanvalue alonewithout any signicant loss in accuracy. This is known assensitivity omission (Madsen et al., 1986). The four steps in a proba-bility computation by FORM/SORM are presented in Appendix I.

3. Remediation methods and costs

Three main technologies are considered for remediating thecontaminated clayey till formation. These have been outlined ina sketch project by the consulting company Kruger:Table 1 summarizes the remediation costs for each of theconsidered remediation methods. Excavation is estimated to be themost expensive solution, and the budget for this method includesthe cost of expropriating two resident houses within the excavatedarea. Soil vapor extraction is estimated as the cheapest option; theremediation costs were estimated based on a 30-year operationperiod.

4. Health cost model

4.1. Probabilistic contaminant exposure model

The applied limit state function represents the difference ina certain target concentration level Ctarget and the 30-year averagecontaminant concentration Cd,av in drinking water produced at thedown gradient well eld:

GX Ctarget Cd;av Ctarget 1T

Z T0Cdtdt

Z T

0

Ctarget Cdt

fT tdt (5)

Table 1Remediation costs estimated by Kruger. All costs are in million Euro (MEuro, 2007prices). No discounting of costs is applied. Prices were converted from DKK 2005values by updating to 2007 prices using Denmark Consumer Price Index (OECD StatExtracts) and converted to Euro using Purchasing Power Parities (OECD StatExtracts).

Phase Excavation Soil vaporextraction

Electricalheating

Project design phase 0.04 0 0.16Establishment phase 3.2 0.06 1.2Operation and maintenance phase 0 1.8 1.1Dismantling phase 0 0.01 0.08aquifer by extracting soil vapor from the unsaturated sand belowthe source and pumping of groundwater from the upper shallowaquifer. An SVE system has already been established at the site asa temporary solution until a permanent solution is chosen. The SVEoption provides a very slow removal of contaminant mass from thesource due to a slow release from the low-permeability clay tilllayer. A minimum operating time of 30 years is anticipated in theproject outline. Finally in option 3, the extraction of soil vapor iscombined with electrical heating of the soil using heating elementssubmersed into steel cased wells (in situ thermal desorption). Byheating the soil to above 100 C, a fast volatilization of chlorinatedsolvents and thus a high removal rate is achieved. The in situtreatment period is thereby anticipated to be reduced to approxi-mately 10 months of heating. Extracted PCE vapors from the SVEsystems in options 2 and 3 will be cleaned on-site by adsorption toTotal remediation costs 3.2 1.9 2.5

T 30 years is the considered time horizon, and fT t is thedensity function for time, modeled as uniformly distributed in the

literature is however scarce. For the SVE method no compilation of

G. Lemming et al. / Journal of Environment1172interval 0; T1. The failure probability PGX 0 thus representsthe probability of exceeding a certain target concentration and iscalculated for a range of target concentration levels. Cd(t) is the timedependent contaminant concentration in the drinking watercalculated as described below.

A subsurface system consisting of a contaminant source locatedin an unsaturated soil layer is considered. The ux of contaminantmass, J(t) (g yr1), leaving the source zone is modeled as thedownward advectivemass ux following the approach in Troldborget al. (2008):

Jt Cwt$I$A (6)where I (m yr1) is the inltration rate, A (m2) is the source zonearea perpendicular to the ow and Cw(t) (g m

3) is the timedependent solute contaminant concentration in the source zone.Source mass is depleted with time due to dissolution in inltratingwater and the change in contaminant mass in the 3-phase system(air, soil and water). This is expressed as (Troldborg et al., 2008):

dMdt

Vqw Kdrb qaKHdCwdt

I$A$Cwt (7)

where qw and qa describe the fraction of the pore volume occupiedby water and air respectively and Kd (L kg

1) and KH (dimension-less) are the soil-water and the air-water partitioning coefcientsrespectively. rb (kg L

1) is the bulk density of the soil and V is thetotal soil volume (m3).

By solving Eq. (7), an exponentially declining solute sourceconcentration is obtained:

Cwt Cw$explt (8)where l IDqwKdrbqaKH

Cw (g m3) is the initial contaminant concentration and D (m) is

the depth of the contaminated zone. The time dependentcontaminant ux then becomes:

Jt I$A$Cw$explt (9)For simplication, it is assumed that no degradation occurs in

the source zone as the contaminant in focus, PCE, is persistent inthe aerobic environment within this specic case (see Sitedescription, Section 2.1).

For areas assessed to contain contamination as a separate orresidual phase, the initial solute concentration is assumed to beequal to the aqueous solubility of the contaminant, S (g m3). Thetotal mass ux is then calculated as the sum of the mass ux fromthe hotspot area (J2) and the ux from the surrounding lightercontaminated soil volume (J1):

Jtotalt J1t J2t I$A$explt$1 x$Cw1 x$Cw2

(10)

where x denotes the fraction of the total area occupied by residualphase contamination. For the no action scenario, Cw is the initialsolute concentration, whereas in the remediation scenarios Cw isreduced as a consequence of the remedial effort. The resultingsolute concentrations after remediation are estimated from theexpected efciencies (effi) of the remediation technologies toremove the source or prevent leaching of contaminant mass to thesaturated zone:

1 This modeling adjustment allows averaging out to be directly within the FORM/SORM analysis by extending it one additional random variable, fT(t).Cw;after Cw;initial1 effi (11)The remedial effect is assumed to occur instantaneously.Theworst case contaminant concentration in the down gradient

supplywell Cp(t) (gm3) is found by dividing the contaminantmass

ux, J(t) (g yr1) by the pumping rate, Qp, (m3 yr1) at the well eld(Einarson and Mackay, 2001). This concentration expresses thetheoretical maximum concentration in the abstracted waterassuming that the plume is fully captured by the well and that nodegradation occurs:

Cpt JtQp (12)

Conventional treatment of groundwater for drinking watersupply in Denmark involves aeration and sand ltration. Assumingthat a cascade aerator is used, it can be expected that approxi-mately 70% of the chlorinated solvents are stripped to the atmo-sphere (Arvin, 1992). Thus a stripping potential, Ps, of 0.7 isincluded in the analysis and the resulting drinking water concen-tration Cd(t) is:

Cdt 1 Ps$Cpt (13)

4.2. Input data and uncertainty modeling

The variability and uncertainty in input data is addressed in theFORM/SORM analysis by representing variables as statisticaldistributions instead of deterministic values. For this purposeprobability distributions of the beta type as well as the log-normaltype are used. The beta distribution is dened by its upper andlower boundaries (a, b) and the two shape parameters (r, s). This isadvantageous since it makes it possible to specify minimum andmaximum limits (a, b) for the distribution, whereas the log-normaldistribution is dened from zero (or a possible offset) to innity.The beta distribution was used for variables for which more infor-mation was available from the site characterization or for whicha physical upper and lower boundary exist e.g. fractions such asporosity and water content.

The beta shape parameters were determined by tting thedistribution to observation data or based on expert judgment ofmean, standard deviation and upper/lower limits. For a betadistribution with shape parameters (r, s) and minimum andmaximum limits (a, b) the corresponding mean, m, and standarddeviation, s, are (Johnson et al., 1995):

m a b a$ rr s

(14)

s b a$ rr s

$

s

r$r s 1r

(15)

Table 2 presents the site-specic input parameters used inuncertainty modeling of contaminant concentrations in drinkingwater. The initial pore water concentration in areas withoutresidual phase contamination is represented by a probabilitydistribution of the beta type with a minimum value of 0 anda maximum of 227 g/m3 (equal to the aqueous solubility of PCE at10 C). Mean and standard deviation of the distribution weredetermined by tting observation data to the beta distributionusing the method of least squares (see Fig. 3).

Table 3 presents the beta distributions applied for the uncer-tainty modeling of the remedial efciencies of the three remedia-tion techniques. Compilations of remedial efciency data in the

al Management 91 (2010) 11691182efciencies could be found in the literature. Instead the average

Table 2Uncertainty modeling of the site-specic variables. For the log-normal distributionthe mean and standard deviation are given in brackets [m,s]. For the beta distribu-tions the values in parentheses represent the two shape parameters and the upperand lower limit of the parameter respectively (r, s, a, b). The correspondingmean andstandard deviation of the beta distribution are given in brackets [m,s].

Variable Symbol Unit Distribution Parameters

Inltration I m yr1 Log-normal [0.15, 0.04]Contaminated area A m2 Log-normal [1150, 150]Fraction of area with

residual phasecontamination

x Decimalfraction

Beta (17.2, 40.1, 0, 1)[0.3, 0.06]

Depth of contaminatedzone

D m Beta (3, 2, 5, 7.5)[6.5, 0.5]

Initial solute sourceconcentration of PCE

Cw,initial g m3 Beta (0.24, 1.4, 0, 227)

[33, 49]Porosity in source zone n Vol/vol Beta (5.1, 5.1, 0.2, 0,5)

[0.4, 0,08]Water saturation in

unsaturated zoneSat Decimal

fractionBeta (55.5, 18.5, 0, 1)

[0.75, 0.05]Bulk density of soil rb kg L

1 Log-normal [1.6, 0.1]Fraction of organic carbon foc g kg

1 Log-normal [0.0053, 0.0024]Pumping rate Qp m

3 yr1 Fixed 6 106

Table 3Uncertainty modeling of remediation efciencies. For the beta distributions the

G. Lemming et al. / Journal of Environmental Management 91 (2010) 11691182 1173efciencies for the similar air sparging method from Bass et al.(2000) was assumed to represent a rough estimate for the meanefciency of the SVE method (91%). The average remediation ef-ciency of 97% found in McGuire et al. (2006) was used to representthe electrical heating method. This efciency is however based onthermal remediation using steam injection. The excavation methodmean efciency was assumed to be 98%. Compound-specicphysicalchemical parameters for PCE are presented in Table 4. Dueto the generally low variability in this type of data compared toother parameters, these were modeled as xed values.

The extent of residual phase contamination, x, is estimatedbased on the fraction of total samples (water or soil) that indicatespresence of residual phase contamination. For water samples, PCEconcentrations greater than 1% of the aqueous solubility areassumed to indicate the presence of residual/separate phasecontamination (US EPA, 1992). For soil samples with a totalcontaminant concentration of CT (mg/kg soil), residual phasecontamination is assumed to exist if the calculated 3-phaseFig. 3. Cumulative probability of observations of initial solute source concentrations,Cw,initial, and of the beta distribution tted to the observations. The probability densityfunction of the beta t is plotted on the secondary axis.equilibrium concentration in the water phase, Cw, results inconcentrations above the aqueous solubility of PCE:

Cw CTrbqw Kdrb qaKH

(16)

4.3. Contaminant exposure results from the FORM/SORM analysis

The 30-year average contaminant concentration in drinkingwater (equation (5)) was calculated for each scenario using FORM/SORM. The resulting cumulative probability distributions of thecontaminant concentrations are seen in Fig. 4. The curves weretted to a probability distribution of the gamma type using themethod of least squares, and the mean value and standard devia-tion of the distribution were determined. The mean contaminantconcentrations in drinking water range from 0.03 mg/L (excavationscenario) to 18 mg/L (no action scenario), see Table 5. All remedia-tion scenarios have mean values below the current DanishMaximum Concentration Limit for PCE in drinking water of 1 mg/L.The gamma distributions were used as input to the health costcalculation, see Section 4.4.

As earlier described, the importance factors are a very importantby-product of the FORM/SORM analysis. They dene how mucheach individual variable contributes to the total uncertainty of theresult. A low importance factor (approx. < 10%)2 implies that thevariable without signicant error may be considered to be deter-ministic. Importance factors of each variable are illustrated in Fig. 5as a function of the resulting 30-year average concentration. In theno action scenario and for small values of Cd,av, the inltration ratefollowed by the fraction of residual phase contamination and thesize of the contaminated area are the variables with highestimportance for the result. With increasing concentration levels, theinitial contaminant concentration becomes the most importantparameter. In all three remediation scenarios, the remediationefciency is by far the most dominating variable for the entirerange of values of the PCE concentration in drinking water. Theinitial concentration has some inuence at higher concentration

values in parentheses represent the two shape parameters and the upper and lowerlimit of the parameter respectively (r, s, a, b). The corresponding mean and standarddeviation are given in brackets [m,s].

Variable Symbol Unit Distribution Parameters

Remediation efciency excavation

effexc Decimalfraction

Beta (2.9, 0.03, 0, 1)[0.98, 0.04]

Remediation efciency soil vapor extraction

effSVE Decimalfraction

Beta (6.38, 0.61, 0, 1)[0.91, 0.1]

Remediation efciency electrical heating

effheat Decimalfraction

Beta (10.6, 0.33, 0, 1)[0.97, 0.05]levels. The remaining variables all have (summed) importancefactors below 10%, which implies that they all, without loss ofaccuracy of the cumulated density function of Cd,av, may be replacedby their deterministic mean value.

4.4. Probabilistic health cost model

4.4.1. Health risk modelThe average daily dose, ADD, (mg kg1 d1) of PCE from intake of

drinking water with an average contaminant concentration of Cav

2 Note that if several variables each have an importance factor of 10%, these ofcourse cannot all be removed.

Table 5Mean and standard deviation of the 30-year average PCE concentrations (Cd,av) asfound by tting the FORM/SORM results to a gamma distribution.

Mean PCE concentration(mg L1)

Standard deviation(mg L1)

No action 1.80 0.63Excavation 0.03 0.08Soil vapor extraction 0.12 0.20Electrical heating 0.04 0.11

Table 4Compound-specic parameters for PCE.

Variable Symbol Unit Distribution Parameter

Log octanol-water partioningcoefcient a

LogKow Dimensionless

Fixed 2.71

Henrys law constant (10 C)b KH Dimensionless

Fixed 0.32

Aqueous solubility (10 C)c S g m3 Fixed 227Stripping potential of PCE

during drinking wateraeration in waterworks

Ps Decimalfraction

Fixed 0.70

a Average value from literature study in Schaerlaekens et al. (1999).b Calculated for 10 C using regression formula from Gosset (1987).c The vapor pressure, p (kPa), at 10 C is estimated with Antoines equation for

PCE: log p AB/(t C), where t is temperature (C), A 6.1017, B 1386.9 andC 217.52 (Riddick et al., 1986). The aqueous solubility (mol L1) is calculated fromthe vapor pressure (atm) at 10 C and Henrys law constant as: S p/(KH R T), whereR is the gas constant (0.0821 L atm mol1 K1) and T is the temperature (K).

G. Lemming et al. / Journal of Environmental Management 91 (2010) 116911821174(mg L1) during the averaging time, AT (yr), is estimated as(Maxwell et al., 1998):

ADD Cav$IRBW

ED$EFAT

(17)

IR/BW (L kg1 d1) is the ingestion rate per body weight, ED isthe exposure duration (yr) and EF is the exposure frequency(d yr1). The health-risk-specic parameters used for estimatingthe average daily dosewere adopted fromMaxwell et al. (1998) andcan be seen in Table 6. The 30-year average exposure concentrationin each scenario was represented by gamma type distributions (cf.Section 4.3).

The incremental excess lifetime cancer risk IELCR due to expo-sure of an organic contaminant can be described with the one-hitequation (US EPA, 1989), expressing the probability of an individualdeveloping cancer:

IELCR 1 expADD$CPF (18)CPF is the chemical specic cancer potency factor (kg d mg1).

The model assumes that exposure to any amount of a carcinogenwill increase the risk of cancer, i.e. there is no safe or thresholddosage. For small cancer probabilities (P < 0.01) the linear versionof the equation is considered valid (US EPA, 1989):

IELCR ADD$CPF (19)Fig. 4. Cumulative probability distributions of the 30-year average PCE concentrationin drinking water calculated using FORM/SORM.

Fig. 5. Importance factors of variables for determining the 30-year average contami-nant concentration of drinking water of each scenario. The mean value and thecumulative density function (CDF) of the contaminant concentration have been plottedtogether with the importance factors.

ICAF 591 yr$33 340 Euro=yr1:81

10:9 MEuro

ICAI 48 yr$33 340 Euro=yr1:81

0:9 MEuro

These values are used to estimate health costs associated withincreased cancer probability in the population exposed tocontaminated drinking water. The model takes into account thata person diagnosed with cancer will either die of the disease orrecover after a certain illness period. The general probability of

then the Commission recommends adjusting for this by multi-

mental Management 91 (2010) 11691182 11754.4.2. Health cost estimationIt is beyond rational reasoning to place a value on a human life in

good health. However, rational principles may be formulated toguide this setting. Inspired by the so-called Life Quality Index,Ditlevsen and Friis-Hansen (2007a,b) formulated the Life QualityTime Allocation Index (LQTAI), which is a social indicator thatserves the purpose of allocating a reasonable part of a countrysGross Domestic Product (GDP) to life-saving initiatives. Human lifevalue is assessed by requiring LQTAI invariance, implying that anyactivity that reduces the life in good health must be balanced by anequivalent increase in societal productivity, such that life qualitydoes not decrease by the activity.

In risk and decision analysis the LQTAI can be used to obtainloss values associated with fatalities and injuries. The empiricalmodel was tted to a time series of working hours andproductivity for Denmark and 18 other OECD countries (Ditlev-sen and Friis-Hansen, 2007b), and model tted constants (c, pmin)representing the country-specic equilibrium between workingtime and leisure time were established based on these. The ttedcoefcients show limited variation the 18 evaluated OECDcountries.

According to the LQTAI model, the maximum productivity time,DTp, that a country can allocate to averting a fatality or an injury iscalculated using the empirical expressions in Eqs. (20) and (21)respectively (Ditlevsen and Friis-Hansen, 2007b):

DTpfatality r0c12

1 V2L

E$pmin (20)

DTpinjury 1c 1r0log

r0 cpminc

ET $pmin (21)

Table 6Probability density functions of health-risk-specic variables. For the log-normaldistribution the mean and standard deviation are given in brackets [m,s].

Variable Symbol Unit Distribution Parameters

Ingestion rate perbody weight

IR/BW L kg1 d1 Log-normal [3.3 102,1.3 102]

Exposure duration ED yr Fixed 30Averaging time AT d Fixed 25 550 (70 yr)Exposure

frequencyEF d yr1 Fixed 350

Cancer potencyfactora

CPF kg d mg1 Fixed 0.051

Exposed population N Number ofpeople

Log-normal [1.1 105,1 104]

a The provisional oral cancer slope factor calculated by US EPA (1988).

G. Lemming et al. / Journal of Environr0 denotes the fraction of life that can be converted to working timeand is set to 2/3 assuming that 1/3 of a persons life is occupied bysleep. c (0.085) and pmin (1.81) are model tted constants forDenmark. E is the life expectancy, which is set to 80 years here witha coefcient of variation VL of 0.2. E[T] denotes the expectedduration of an illness period i.e. the experienced lifetime loss ingood health for at a person that survives cancer. Such data could notbe located; however a 2-year-period was assumed as an averagevalue. Using these values, a productivity time allocation of 591years for averting a fatality and 48 years for averting an injury isobtained. The allocated time values can be converted to a country-specic monetary values by multiplying with the annual workbased salary, S. S is equal to the GDP divided by the equilibriumproductivity per working time, pmin. Hereby the incurred costs ofaverting a fatality (ICAF) and an injury (ICAI), respectively, arefound. With a GDP of 33 340 Euro per capita for Denmark thefollowing values are obtained:plying with a factor of 1.43. Table 7 presents the best estimate valueas well as the upper and lower range values calculated for a mortalcancer incidence based on these guidelines. The best estimate valuefrom the European Commission will be used in a sensitivityscenario.

4.5. Health cost results from the FORM/SORM analysis

Fig. 6 presents the baseline results of the health cost modelingusing a LQTAI value of 10.9 MEuro. By tting the results to gammatype distributions, mean values and standard deviations weredetermined and summarized in Table 8. The mean value of thehealth cost for the no action scenario is 0.94 MEuro, whereas thehealth costs of the remediation scenarios span from 0.016 MEuro(excavation) to 0.062 MEuro (soil vapor extraction). The resultingmean health costs when applying the lower ICAF value of 2.7MEuro are a factor of 3.5 lower.

The importance factor plots in Fig. 7 show that for the no actionscenario the uncertainty in the PCE exposure concentration andingestion rate per bodyweight contribute signicantly to the result,whereas in the remediation scenarios only the PCE concentration indrinking water has a high importance. For very high health coststhe importance factor for the ingestion rate per body weight in the

Table 7Recommended values by the European Commission (2001) for preventinga cancer-related fatality for an average age population.

2000 pricesa (MEuro) 2007 pricesb (MEuro)

Best estimate value 2.1 2.7Lower range value 1.4 1.8Upper range value 5.4 6.9

a Based on the values 1.5, 1.0 and 3.8 multiplied with a factor of 1.43 to adjust foran average age population.surviving cancer once diagnosed, psurv, has been set to 0.39, whichcorresponds to the number of cancer incidences per year divided bythe number of deaths due to cancer per year. These data are basedon Danish statistics for all cancer types except skin cancer for theperiod 19962000 (Danish Cancer Society, 2006). The health costassociated with fatalities and illness periods due to the increasedprobability of cancer is calculated based on the following expres-sion, where N denotes the size of the exposed population:

Health cost IELCR$NICAF$1 psurv ICAI$psurv (22)The European Commission (2001) recommends a best estimate

value of 1.0 MEuro (year 2000 prices) for preventing a fatality. Fordeath as a result of cancer, this value is increased to 1.5MEurowhenincluding the period of ill health. This value is applicable to deathsin a principally elderly population where the reduction in lifeexpectancy is likely to be short (less than a year). If a carcinogenicpollution affects a population characterized by a more average age,b The value is updated from 2000 to 2007 prices using the OECD-EuropeExcluding High Ination Consumer Price Index. (OECD Stat Extracts).

G. Lemming et al. / Journal of Environment1176SVE scenario increases, but still the uncertainty in the ingestion rateis of limited inuence.

5. Environmental cost model

5.1. Life cycle assessment modeling

The primary goal of the remediation efforts is to reduce a localcontamination problem, but secondary effects or externalities to theenvironment are costs that should be included in the overall costevaluation. Seen in a life cycle perspective, the remediation activ-ities consume energy and other non-renewable resources andcauses emissions on local, regional and global scales. In order toquantify and compare these impacts in a systematic and consistentway, a life cycle assessment (LCA) of each remediation method isconducted. The LCA result describes the aggregated potentialenvironmental impacts over the entire life cycle of each remedia-tion technology from extraction of raw materials to production ofcomponents and electricity to end-of-life disposal, reuse or recy-cling of utilized materials.

The life cycle assessment was modeled using the GaBi 4 LCASoftware and the EDIP unit process database combined withadditional data collection. The EDIP97 Impact Assessment meth-

Fig. 6. The cumulative probability distributions of the incurred health costs for eachscenario.odology was chosen as impact assessment method (EnvironmentalDesign of Industrial Products, Wenzel et al., 1997). As the LCA itselfis not the primary scope for this paper, the analysis is only brieypresented.

The goal of the LCA is to compare the environmental impacts ofthe three different approaches for site cleanup. The comparedservice (the functional unit in the LCA) is dened as the treatmentof the 7500 m3 of contaminated soil within a 30-year time frame.The functional unit does not dene a cleanup level for the reme-diation. This implies that different levels of residual contaminationwill be left in the ground depending on the evaluated method. The

Table 8Mean health cost values of each scenario. Standard deviations are given inparentheses.

Health costs (MEuro)ICAF of 10.9 MEuro

Health costs (MEuro)ICAF of 2.7 MEuro

No action 0.94 (0.49) 0.27 (0.14)Excavation 0.016 (0.043) 0.005 (0.012)Soil vapor extraction 0.062 (0.11) 0.018 (0.030)Electrical heating 0.022 (0.055) 0.006 (0.016)al Management 91 (2010) 11691182consequences of the residual contamination are dealt with specif-ically in the health cost part of this framework.

The main operation activities included in the different remedi-ation scenarios are presented in Table 9. Raw material extractionand production of materials (steel, activated carbon, plastic mate-rials, concrete etc.) and energy sources (electricity, diesel) are notmentioned specically in Table 9 but are also included. AverageDanish electricity is assumed for on-site operations and off-site soiltreatment. As a common limitation, the construction of vehiclessuch as excavators, drill rigs, trucks and cars are excluded as it isassumed that only a minor part of their service life is ascribed to

Fig. 7. Importance factors of variables for determining the health costs of eachscenario. The mean and the cumulative density function (CDF) of the health cost havebeen plotted together with the importance factors.

this remediation case. Laboratory analyses of soil, water and airsamples were furthermore excluded due to their expected negli-gible impact.

The EDIP97 impact assessment includes a number of impactcategories on the global scale (global warming and ozone deple-tion) as well as on regional and local scales (photochemical ozoneformation, acidication, nutrient enrichment, human and ecotoxicimpacts andwaste generation). In addition to this, the consumptionof nite resources is accounted for. Emissions and resourceconsumption are normalized to person equivalents (PE) by dividingwith the impacts/resource use from an average person. Thenormalized impacts are afterwards weighted based on politicallyset targets for the reference year 2004. Resource consumption isweighted according to the inverse supply horizon of each resourcetype, i.e. consumption of crude oil has a higher weighting factorthan coal since the supply horizon is shorter. The weighted impactsare expressed in the unit targeted person equivalents (PET) and the

Priority System (EPS) by Steen (1999). However, EPS is based on an

lowest impact load (1740 PET) and resource use (64 PR), whereasexcavation has the highest aggregated resource consumption

Euro/PET and for resources of 10 700 Euro/PR (cf. Table 10), theaggregated environmental costs sum to 1 MEuro for the excavation

Electrical heating

, pumps and activated carbon units0 y of soil ventilation, waterse ventilationse for water and air treatment

ials, equipment and people

On site: Drilling of wells Materials for wells Materials for ventilation layer, isolationand capping.

Electricity use for 8 months of heating Activated carbon use for treatmentof extracted air

Transport: Transport of materials, equipmentand people

Table 10Unit cost for environmental impacts and resource consumption. The Stern Reviewvalue is included for comparison only and is not used in the valuation.

Emission unit cost Euro/ton CO2 Euro/PETc

Stern (2006) 73 570Danish EPA (2007) a 18 140

Resource unit cost Euro/kg oil Euro/PRd

Crude oil priceb (2007 average) 0.434 10 700

All Currency conversion was done using Purchasing Power Parities from OECD StatExtracts.

a The value in Euro/ton CO2 represents the alternative costs of reducing CO2emissions as estimated at 180 Danish kroner per ton CO2 by Danish EPA (2007).

b 1 PET (Targeted Person Equivalent) is equal to the average emission of 8.7 tonsCO2-eq./cap/yr divided by the weighting factor for global warming of 1.12(Stranddorf et al., 2005).

c The value in Euro/kg of crude oil is based on the 2007 world average oil price of69.2 USD/barrel (EIA, 2008).

d 1 PR (person reserve) is equal to the average consumption of 588 kg oil/cap/yrdivided by a weighting factor of 0.024 (LCA Center, 2005).

G. Lemming et al. / Journal of Environmental Management 91 (2010) 11691182 1177endpoint impact assessment method and cannot be combined withthe midpoint LCA result obtained with the EDIP method. The EPSenvironmental load units for CO2 emission and crude oil depletion

Table 9Main activities included in the three life cycle assessments.

Excavation Soil Vapor Extraction

On site: Excavation and backlling Demolition of 2 houses

Off site: Construction of 2 houses Electricity for 1 y of soil aeration Materials for construction of treatment facilitya Activated carbon use for treatmentof extracted air

Extraction of soil for backllingOff site:

Transport of soil to treatment (80 km) and tonal disposal (40 km)

Transport of materials, equipmentand people

On site: Drilling of wells Materials for wells Electricity use for 3extraction and hou

Activated carbon uTransport:

Transport of materresource consumption is expressed in person reserves (PR).Appendix II lists all the applied normalization references andweighting factors.

5.2. Environmental cost estimation

Ideally each unit of weighted emissions (PET) should representthe same environmental cost since they are based on politically setweightings. Therefore an environmental cost is calculatedassuming the same unit cost per weighted impact. The SternReview (Stern, 2006) estimated a damage cost of 85 US Dollar (73Euro) per ton of CO2 emitted if no action is done to controlgreenhouse gas emissions. In this analysis a unit cost of 18 Euro perton of CO2 emission is used to estimate the environmental costs.This value represents the estimated average market price of CO2offsets (i.e. alternative cost of reducing CO2 emissions) from 2005onwards and is suggested by the Danish EPA to be used in cost-benet evaluations (Danish EPA, 2007). The environmental cost ofresource consumption is likewise estimated assuming that eachperson reserve represents the same economic value. The average2007 crude oil price is used as a basis for placing a value on theresource consumption. Table 10 summarizes the applied unit costs.It should be mentioned that life cycle impact assessment methodsincluding a monetary valuation step do exist e.g. Environmentala Only a part of the materials are ascribed to this remediation project based on the asequivalent to 70 PR.Converted to a cost using the unit costs for emissions of 140of 108 Euro/ton and 0.506 Euro/kg respectively can however becompared to the applied unit costs in this study.

5.3. Environmental cost results

The weighted results from the life cycle assessments are seen inFig. 8 (environmental impacts) and Fig. 9 (resource consumption).As expected, the environmental impacts and resource use prolesare very similar for soil vapor extraction and electrical heating,since these methods are technically similar and both very elec-tricity consuming. Conversely, the excavation method is highlydiesel consuming for excavation and transportation, which resultsin higher toxic impacts and consumption of more crude oil than theother methods. Soil for backlling of the excavation pit is the mainresource used in the excavation scenario.

Fig. 10 presents the summed weighted impact potential andresource use of the three remediation methods. Electrical heatinghas the highest aggregated impact load (2000 PET), but excavationis only slightly lower at 1930 PET. Soil vapor extraction has both thesumed treatment capacity and lifetime of the facility.

Fig. 8. Weighted environmental impact potentials quantied in the life cycle assessment (PET: Targeted Person Equivalent).

G. Lemming et al. / Journal of Environmental Management 91 (2010) 116911821178and electrical heating methods and to 0.9 MEuro for the SVEmethod (see Fig. 11).

6. Ranking of remedial alternatives

As remediation benets are not included in the evaluation, theriskcostbenet model is reduced to a riskcost minimizationmodel. The time frame of the analysis is set to 30 years. No dis-counting of costs and benets is carried out. However, it can beshown that a decision model with a nite time frame, T, and a dis-counting rate, r, of zero is decision theoretically equivalent toa decision model with an innite time frame and a discounting rateof 1/T, see Friis-Hansen and Ditlevsen (2003). Consequently, a 30-year time frame as used in the present analysis is essentially equalto applying an innite time frame and a discount rate of 1/30 3.3%, which is similar to currently recommended discountrates by Danish EPA (2007). The total cost associated with a reme-diation initiative, 4, is thus calculated as the sum of the remediationcosts and the risks in terms of environmental and health costs(equal to Eq. (3) without discounting):

f XTt0

hCt Rt

i(23)

Fig.12a illustrates the results of the total cost estimatesmade forthe no action scenario and each of the remediation scenarios. Thetotal societal costs of the three remediation scenarios range from2.9MEuro (soil vapor extraction) to 4.3 MEuro (excavation). AllFig. 9. Weighted resource consumption quantied inremediation scenarios have higher summed total societal costs thanthe no action scenario which has an expected health cost of 0.9MEuro. The remediation cost is in this case the most signicantdifference between the scenarios, because the health costs are lowand the environmental costs are of similarmagnitude (see Table 11).

Ranking the methods based on their total societal costs placessoil vapor extraction rst, followed by electrical heating, and exca-vation is placed last. In the sensitivity scenario, Fig.12b, the incurredcost of averting a fatalitywas reduced from10.9MEuro to 2.7MEuro.This however does not alter the internal ranking of the methodssince the health costs are insignicant for the total cost result.

7. Discussion

The presented methodology combines assessment of remedia-tion costs, health costs and environmental costs into an overallsocietal cost comparison of different technical solutions for sitecleanup. Results can be used to support decision-making regardingthe selection of remedial strategies for a contaminated site. Byincluding both impacts on health and environment in the overallcost estimate, the methodology enables a more complete compar-ison of remedial actions compares to the reviewed studies thatfocused only on one of these issues.

7.1. Model approach

The chosen rst ordermodel used to describe contaminantmassux to the aquifer over time is a relatively simple model ofthe life cycle assessment (PR: person reserve).

menta complex contaminant transport system. For a source zone in a lowpermeable and possibly fractured media such as clay till, morecomplex models could be applied to give a better description of themass input to the aquifer. Attenuation in the unsaturated zone andaquifer is disregarded, possibly overestimating concentrations indrinking water. Another simplication is that the remedial effect isassumed to occur instantly in the abstracted groundwater at thewell eld, when in fact, this effect is projected in time.

The conducted life cycle assessments constitute the backbone ofthe evaluation of externalities to the environment incurred by theremedial actions. Life cycle assessments are elaborate evaluationsthat quantify a wide spectrum of potential environmental impactsin a systematic and comparable way. However, they are also asso-ciated with uncertainty both stemming from the data input and theunderlying impact assessment models. The applied impactassessment method, EDIP97, was chosen as it has been a widely

Fig. 10. Sum of weighted impact potentials and sum of weighted resource consump-tion for each scenario.

G. Lemming et al. / Journal of Environapplied method. It should be noted, however, that more recentmethods with more advanced impact assessment models areavailable especially in regard to the modeling of human andecotoxic impacts e.g. the USEtox model (Rosenbaum et al., 2008).

In the present analysis a time frame of 30 years was chosen. Itshould be noted, however, that this time frame may favor the soilvapor extraction method, since the slow release of contaminantsfrom the low-permeability layer indicates that the treatment couldexceed 30 years. This would then result in higher environmentalcosts. A more likely scenario of 100 years of soil vapor extractioncauses a signicant increase in both remediation and environ-mental costs, as seen in Fig. 12c and d.

Finally, any inconvenience and disruption (e.g. noise, dust,esthetic deterioration of the landscape etc.) experienced by resi-dents at or near the contaminated site was not included in theanalysis, but may be a decisive factor in the regulators decision-making. Site excavation and in situ electrical heating will causea high level of disruption for a relatively short period. Conversely,the level of disruption experienced due to soil vapor extraction islower but will prevail for decades.

7.2. Importance of variables in the contaminant exposure model

The FORM/SORM procedure used to calculate the exposureconcentrations and expected health costs provides a fast andfunctional way of generating probabilistic model results. Uncertainvariables were represented as continuous probability distributionseither of the log-normal or the beta type. The log-normal distri-bution is dened from zero to innity, whereas the beta distribu-tion is bound by lower and upper values. Although somewhatcomplex, the modeling of bounded variables using the beta distri-bution is preferred in this study because of the distributions highdegree of exibility.

The importance factors, which are easily calculated in FORM/SORM, can be used for an easy identication of which uncertainvariables contribute the most to the uncertainty of the nal result.Thus the analysis identies which variables should be investigatedfurther in order to reduce the uncertainty of the overall outcome.In the present analysis it is found that the most important vari-ables depend on whether a no action scenario or a remediationscenario is considered. For the no action scenario, importantvariable uncertainty is associated especially with the character-ization of the source area represented by the source zonecontaminant concentration, the presence of residual phasecontamination and the size of the contaminated area. In addition,

Fig. 11. Environmental cost of each remediation scenario.

al Management 91 (2010) 11691182 1179the inltration rate to the aquifer is of high importance. In thethree remediation scenarios, the remediation efciency is by farthe single most dominating cause of uncertainty. Thus a betterdescription of remediation efciencies would be benecial forfuture use of this model.

7.3. Economic valuation

The results of the analysis showed that the environmental costsdue to externalities from remediation were indeed signicant andadded 3050% to the total cost estimates of the remediationscenarios. The applied valuation method for environmentalimpacts and resource consumption was rather simplied assuminga xed cost for each weighted unit of environmental impact (1 PET)and each weighted unit of resource use (1 PR) respectively. Theapplied unit cost for environmental impacts was based on theexpected CO2 offset market price. In comparison, damage estimatespresented in the Stern Review are a factor of 4 higher, whichsuggests that this cost may be underestimated. Regarding resourceuse, the market price of oil was used as a basis for the valuation of 1person reserve. This might underestimate the total socioeconomicvalue of a resource but serves as a best estimate. The signicantcontribution of the environmental costs to the total costs estimates

arioan ICe er

G. Lemming et al. / Journal of Environment1180Fig. 12. Remediation cost, environmental cost and health cost of each remediation scenMEuro. (b) Health costs based on an ICAF value of 2.7 MEuro. (c) Health costs based onbased on an ICAF value of 2.7 MEuro, 100 year time frame for soil vapor extraction. Thindicates that these are in fact important to include. Sensitivitystudies may show how sensitive the identied optimal solution isto the chosen values.

The incurred cost of averting a fatality (ICAF) represents thesocietal cost which can be justied for life-saving initiatives. Inthe baseline scenarios, a value of 10.9 MEuro is applied based onthe Life Quality Time Allocation Index principle by Ditlevsen andFriis-Hansen (2007a,b). This value in fact can be viewed as anupper boundary value since it is based on the maximalproduction time that a country can justify to allocate for theprevention of a fatality. The lower value of 2.7 MEuro, as rec-ommended by the European Commission (2001), representsa more typical value applied in cost-benet evaluations. Thehealth costs due to ingestion of contaminated groundwater werenegligible compared to the total cost estimates of all scenariosexcept for the no action scenario. Using the lower ICAF value didtherefore not affect the internal ranking of the three remediationscenarios.

The analysis showed that remediation gives a signicantreduction in the expected health costs associated with thecontaminated site. However, the total societal costs accrued in theremediation scenarios are all larger than that of the no action

Table 11Costs (MEuro) of remediation options. Values in parentheses are responding tohealth costs calculated using an ICAF value of 2.7 MEuro.

No action Excavation Soil vaporextraction

Electricalheating

Remediation cost 0 3.2 1.9 2.5Environmental cost 0 1.0 0.9 1.0Health cost 0.9 (0.3) 0.02 (0.005) 0.06 (0.018) 0.02 (0.006)

Total cost 0.9 4.3 2.9 3.5. All costs are in million Euro (MEuro). (a) Health costs based on an ICAF value of 10.9AF value of 10.9 MEuro, 100 year time frame for soil vapor extraction. (d). Health costsror bars mark the 5th and 95th percentile of the health cost distribution.al Management 91 (2010) 11691182scenario. Due to the limitations in the analysis, the results obtainedcannot be considered as complete cost-benet comparisons sincebenets were not included. It should furthermore be noted that forsimplicity purposes the health costs are only related to the inges-tion of drinking water. Health effects associated with the contam-inated soil e.g. via evaporation to indoor air and/or direct soilcontact could affect the cost analysis. Also the 30-year timeboundary used in the analysis favors the no action scenario sincethe groundwater contamination will most likely persist forcenturies.

In the future, benets such as increased land value and the valueof maintaining good quality groundwater should be included if theaim is to provide a full cost-benet evaluation for site cleanup. Thetotal economic value of an environmental good such as ground-water can be considered to consist of both a use value and a non-use value (NRC, 1997). Little research exists on the non-use valuesof groundwater but nonetheless indicate that thewillingness to payfor non-use benets is signicant. The fact that groundwater isclean resource that can be passed on to future generations isvaluable even if the resource is not used presently (Hardisty andOzdemiroglu, 2005).

8. Conclusion

The presented methodology includes an economic evaluationencompassing three aspects of site remediation: remedial costs,external costs to the environment and health costs associated withresidual contamination left after remediation. The method allowsfor an evaluation of the total societal costs associated witha cleanup strategy for a contaminated site within a drinking watercatchment which is also compared with a no action scenario. Thehealth costs were evaluated using a 2-step probabilistic FORM/SORM analysis in order to include variable uncertainty. This

analysis showed that the source characterization variables(contaminant concentrations, presence of residual phase contam-ination and size of contaminated area) were of high importance forthe contaminant concentration levels in the drinking water well inthe no action scenario, whereas the remedial efciencies were thesingle most important variable in the remediation scenarios. Thehealth costs associated with the ingestion of contaminateddrinking water were, however, only minor compared to the directremediation costs and the environmental costs. In the analyzedcase the most favorable remediation technique was soil vaporextraction when a time boundary of 30 years was used. This timeboundary, however, favors remediation options with long timeframes, since operation, health and environmental costs occurringafter this period are omitted. If a more realistic time frame is used,the soil vapor extraction method becomes less favorable and theelectrical heating method is the preferred option. The methodologybrings insight to the overall societal costs of a remedial strategy andcan facilitate more holistic decision-making that does not solely

space to u-space is exact. The one-to-one transformation, T, ofx-space on u-space maps the failure surface fxjGX 0g intoa corresponding surface fujgU 0g. Note thatgu GTx.

2. identication of the most likely failure point. This point isdened as the point of maximum density on the failure surfacein u-space and is obtained by solving the optimization problemfminjuj; s:t: gu 0g. The optimal solution is in the mostlikely failure point, u*, which is also called the design point.

3. approximation of the failure surface GX 0 in the u-space.Since the identied design point describes the point in the u-space of maximum contribution to the failure probability, itis natural to approximate the limit state around this point.When using FORM the failure surface is approximated bya tangent hyper plane through the u* and when using SORMthe failure surface is approximated by a second-order hypersphere.

4. computation of the failure probability corresponding to the

/yr/cap/yrp/yrp/yryryr

yr

G. Lemming et al. / Journal of Environmental Management 91 (2010) 11691182 1181focus on the direct cost of remediation but also incorporatesexternal costs to health and environment.

Acknowledgements

The authors wish to acknowledge E. Pfeilschifter, M.S. Mllerand E. Sgaard for their contribution to the data collection for theconducted life cycle assessments and S.I. Olesen, DTUManagement,for technical support with the LCA software. C.B. Jensen (CapitalRegion of Denmark) and J. Elkjr (Capital Region of Denmark, nowat Kbenhavns Energi) provided data for the case study and DTUfunded the Ph.D scholarship.

Appendix I

It is not the intention to give a comprehensive summary ofFORM/SORM here, but only to refer to the main principles. Refer-ence is left to Madsen et al. (1986) or Ditlevsen and Madsen (1996)for an in-depth treatment.

A probability computation by FORM/SORM consists of four mainsteps,

1. transformation of the random variables X into a standardnormal vector U by solving Fui Fxi, in which F$ is thestandard normal density function. This holds only for inde-pendent variables. It is worth emphasizing that FORM/SORMare full distributional methods and the transformation from x-

Table A1Normalization references and weighting factors for environmental impacts.

Impact category Normalization reference

Unit

Global warming 8700 kg CO2-eq./capOzone depletion 0.103 kg CFC-11-eq.Acidication 74 kg SO2-eq./capEutrophication 119 kg NO3

-eq./caPhotochemical oxidant potential 25 kg C2H4-eq./caEcotoxicity water chronic 352000 m3 water/cap/Ecotoxicity water acute 29100 m3 water/cap/Ecotoxicity soil chronic 964000 m3 soil/cap/yrHuman toxicity air 6.09 1010 m3 air/cap/yrHuman toxicity water 52200 m3 water/cap/Human toxicity soil 127 m3 soil/cap/yrBulk waste 1350 kg/cap/yrHazardous waste 20.7 kg/cap/yrRadioactive waste 0.035 kg/cap/yr

Slags and ashes 350 kg/cap/yrAppendix II

The normalization references and weighting factors applied inthe life cycle assessments are presented in Tables A1 and A2together with the associated reference region and year. Normali-zation references and weighting factors for environmental impactsupdated EDIP values are from Stranddorf et al. (2005) except for thewaste categories which are fromWenzel et al. (1997). Resource usewas normalized andweighted according to global values from 2004(LCA Center, 2005). Normalization and weighting of the soilresource is not included in the EDIP impact assessment method-ology. Therefore Danish normalization references and weightingfactors from ScanRail Consult et al. (2000) were used.

Weighting factor

Reference region (year) Reference region (year)

Global (1994) 1.12 Global (2004)/yr Global (1994) 63 Global (2004)

EU15 (1994) 1.27 EU15 (2004)EU15 (1994) 1.22 EU15 (2004)EU15 (1994) 1.33 EU15 (2004)EU15 (1994) 1.18 EU15 (2004)EU15 (1994) 1.11 EU15 (2004)EU15 (1994) 1 EU15 (2004)EU15 (1994) 1.4 EU15 (2004)EU15 (1994) 1.3 EU15 (2004)EU15 (1994) 1.23 EU15 (2004)DK (1991) 1.1 DK (2000)DK (1991) 1.1 DK (2000)S (1989) 1.1 DK (2000)approximating failure surfaces. When using FORM the failureprobability is simply calculated as pf Fb, where F isthe standard normal cumulative density function and b ju*jis the distance from the origin in the u-space to the designpoint. For SORM results the integration becomes moreinvolved, see Ditlevsen and Madsen (1996) p. 104 Eq. 6.49.

Since the optimization is performed directly in the u-spacewhere the search is for the shortest distance from the origin to thefailure surface, the calculation time becomes independent of thesought probability level. In general FORM gives good results for lowprobability estimations, whereas it is recommended to use SORMfor mid-range probabilities.DK (1991) 1.1 DK (2000)

Khadam, I., Kaluarachchi, J.J., 2003. Applicability of risk-based management and theneed for risk-based economic decision analysis at hazardous waste contami-nated sites. Environment International 29, 503519.

Lemming, G., Hauschild, M.Z., Bjerg, P.L., 2010. Life cycle assessment of soil andgroundwater remediation technologies: literature review. International Journalof Life Cycle Assessment 15, 115127.

Table A2Normalization references and weighting factors for nite resources.

Resource Normalization reference Weighting factor

Unit Reference region Reference region

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Risk-based economic decision analysis of remediation options at a PCE-contaminated siteIntroductionMaterials and methodsSite descriptionCalculating the probability of an event: first- and second-order reliability methods

Remediation methods and costsHealth cost modelProbabilistic contaminant exposure modelInput data and uncertainty modelingContaminant exposure results from the FORM/SORM analysisProbabilistic health cost modelHealth risk modelHealth cost estimation

Health cost results from the FORM/SORM analysis

Environmental cost modelLife cycle assessment modelingEnvironmental cost estimationEnvironmental cost results

Ranking of remedial alternativesDiscussionModel approachImportance of variables in the contaminant exposure modelEconomic valuation

ConclusionAcknowledgementsAppendix IAppendix IIReferences