Decision Support–Oriented Selection of RemediationTechnologies to Rehabilitate Contaminated SitesAndrea Critto,� Lisa Cantarella,` Claudio Carlon,` Silvio Giove,§ Gianniantonio Petruzzelli,jj and AntonioMarcomini*�

�Department of Environmental Sciences, University of Venice, Calle Larga S. Marta 2137, I-30123 Venice, Italy`Consorzio Venezia Ricerche, Via della Liberta 5-12, I-30175 Marghera, Venice, Italy§Department of Applied Mathematics, University of Venice, Dorsoduro 3825/E, Venice, ItalyjjNational Research Council, Institute for the Study of Ecosystems, Soil Chemistry Section, Via Moruzzi 1, I-56124 Pisa, Italy

(Received 8 April 2005; Accepted 6 November 2005)

ABSTRACTA methodology for selecting remediation technologies is presented as part of a decision support system for the

rehabilitation of contaminated sites. It includes 2 steps: In the 1st step, a pool of suitable technologies is selected within a

technologies database according to their applicability to site-specific conditions; in the 2nd step, the selected technologies

are ranked according to a multicriteria decision analysis (MCDA) approach. The MCDA was applied to allow for a transparent

procedure and for the integration of expert analyses. The methodology was implemented in a previously developed

georeferenced information system–based decision support system for the rehabilitation of contaminated sites and then

applied to a case study (Porto Marghera, Venice, Italy). On the basis of the obtained results, the proposed methodology

appeared suitable to select remediation technologies according to both technical features and requirements of available

technologies, as well as site-specific environmental conditions of the site of concern, such as chemical contamination levels

and remediation objectives.

Keywords: Remediation technologies comparison Multicriteria decision analysis Contaminated sites Decision support

system

INTRODUCTIONA large number of contaminated sites are disseminated in

over the postindustrialized countries (estimated Europeancontaminated sites vary from 300,000 to 1.5 million [EC2002]; on average, 5 contaminated sites per 1,000 inhab-itants) with expected high financial consequences (e.g.,overall costs for the remediation of European contaminatedsites range between 59 and 109 billion Euros, according to theEuropean Environmental Agency [EC 1999]). Criteria andmethodologies for the rehabilitation of contaminated sites areurgently needed.

In recent years, new regulatory frameworks have beenproposed (Ferguson and Kasamas 1999) that highlight thefundamental role played by the environmental risk assessmentand call for the implementation of procedures allowing thecomparison of remediation technologies within the decisionprocess leading to the rehabilitation of a contaminated site(Ferguson et al. 1998; Vik and Bardos 2003). The bestavailable technology at sustainable cost is a well-establishedcriterion for the selection of reclamation technologies. Thechoice of best available technologies, however, should not bebased just on scientific and technical considerations becauseeconomic, social, and political aspects need to be taken intoaccount. The socioeconomic implications behind site requa-lification, according to well-defined land end uses, heavilyinfluence the identification of remediation objectives and,therefore, the choice of most suitable technologies.

The integration of environmental risk assessment and bestavailable technologies according to land end uses is a difficulttask, especially in the case of so-called megasites, areas of

notable extension (hundreds or thousands of ha) in whichseveral different sources of contamination are presentsimultaneously. Rehabilitation of megasites requires a set oftechnologies to be identified, taking into consideration spatial,temporal, and logistic aspects. Moreover, megasites usuallyoccupy strategic portions of land, triggering a multidiscipli-nary approach in which risk analysis, socioeconomic analysis,and the comparison of remediation technologies need to beintegrated according to a transparent framework.

This work will focus on the issues behind the selection andcomparison of remediation technologies. The main purpose isto define a site-specific methodology for selecting a set ofremediation technologies applicable to a contaminated siteand to develop a comparative system for these technologies. A‘‘set of technologies’’ is regarded as a combination oftechnologies, possibly organized in train technologies andextended over space and time, able to achieve the remedialgoals defined for the site under examination. Multicriteriadecision analysis (MCDA) is applied to rank remediationtechnologies and to develop alternative remediation scenariosto be offered to decision makers (i.e., stakeholders).

The proposed methodology is included in the decisionsupport system ‘‘decision support system for rehabilitation ofcontaminated sites’’ (DESYRE), software designed to assistexperts in the rehabilitation of large contaminated sites (i.e.,megasites; Carlon et al. 2003). A decision support system isdesigned to assist the decision maker involved with contami-nated lands in carrying out specialized analyses and ensuringreproducible and transparent processes that consider all thevariables involved (Bardos et al. 2003).

Very few decision support systems exist in the field ofremediation technologies selection. Among them, the Cost–Benefit Analysis for Remediation of Land Contamination, by

� To whom correspondence may be addressed: marcom@unive.it

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the UK Environment Agency (UKEA), guides in the selectionof a short list of potential remediation techniques throughMCDA and cost–benefit analysis (UKEA 1999).

All the other available resources are mainly searchabledatabases, such as the US Environmental Protection AgencyReachIT (USEPA 2004) or the Remediation TechnologiesScreening Matrix by the US Federal Remediation Technolo-gies Roundtable (FRTR 2002).

BACKGROUND

The DESYRE system

The structure of DESYRE (Carlon et al. 2003) consists of 5modules: 1) characterization, 2) socioeconomic analysis, 3)remediation technologies comparison, 4) risk analysis (RA),and 5) decision making.

The characterization module, developed according to therequirement of environmental risk assessment procedures(ASTM 1998), allows users to define the conceptual model ofthe site and to provide information regarding contaminantsdistribution and transport through the different environ-mental media, with georeferenced information system toolsfor handling spatial data. The socioeconomic module supportsthe selection of the optimal future use of the site. Socio-economic parameters are inputs of a fuzzy expert system thatgenerates a composite indicator of suitability for alternativeland uses. The remediation technologies comparison and riskanalysis modules integrate with each other to produce aneffective framework for the selection of cleanup techniques.The stepwise structure of this framework (Figure 1) allowsthe definition of different ‘‘remediation scenarios’’: Eachscenario refers to a suitable solution for the rehabilitation ofthe contaminated site, encompassing the final land use, thesocioeconomic benefits, a technologies set (with reference tocosts, intervention time, and environmental impacts), and theassociated residual risk.

The final decision-making module provides the descriptionof alternative remediation scenarios. Technological, environ-mental, and socioeconomic aspects are described by macro-indexes that are derived directly from the technological, riskassessment, and socioeconomic modules, respectively (Carlonet al. 2003).

Review of remedial technologies

The database created by the FRTR (2002) is universallyrecognized (Vik and Bardos 2003) as an exhaustive and up-to-date source of remedial technologies. It collects informationfrom studies and remediation actions within the SuperfundProgram, and it is continuously updated. Currently, the FRTRmatrix includes 64 technologies divided according to thetreated environmental matrix: solid (soil, sediment, bedrock,and sludge), liquid (groundwater, surface water, and leachate),or gaseous (air emission or off-gas treatment). Moreover, thetechniques are considered according to where the operationstake place—in situ or ex situ (i.e., ex situ treatments requirethe removal of contaminated matrix, in situ treatments donot)—and the adopted containment systems—solutionsmeant to prevent or reduce the migration of contaminantsthrough the ground or subsurface water.

Finally, the FRTR matrix groups the technologies accordingto their target pollutants, which are split into 6 categories ofcontaminants: Nonhalogenated volatile organic compounds,halogenated volatile organic compounds, nonhalogenated

semivolatile organic compounds, halogenated semivolatileorganic compounds, fuels, and inorganics.

Review of criteria for the comparison of remediationtechnologies

The comparison of remediation technologies was con-ducted by selecting internationally recognized evaluationcriteria proposed by the FRTR (2002), the Organization ofthe United Nations (UN 1997), and UKEA (1999).

The set of criteria proposed by FRTR and related to thetechnologies evaluation screening matrix introduced in theprevious paragraph includes a series of parameters that allowa detailed description of the different techniques withoutselection or ranking intent.

The UN study indicates criteria similar to those of FRTR(e.g., overall cost, cleanup time, system reliability andmaintainability), and provides a ranking of the technologies,which is based on a fixed, non–site-specific scoring system.

Finally, the UKEA defines a selection procedure based on acost–benefits analysis approach. It includes analysis of all theaspects that characterize remediation before and after theintervention actions. Each selected aspect is scored as a resultof expert judgment on site-specific conditions.

Review of MCDA methods

The MCDA approach is normally used for problems inwhich a decision maker classifies or chooses among somealternatives that are measured by a finite number of criteria orattributes (Norris and Marshall 1995). For cases in which nosingle alternative exhibits the most preferred available valuefor all attributes, MCDA is especially useful (Saaty 1980;Norris and Marshall 1995). This method corresponds to whathappens in the planning phase of a remediation interventionwhen the experts have to select 1 or few remediation optionsamong several cleanup technologies.

The MCDA method can be classified as a single- ormultiple-person decision process. The latter involves a groupof experts or decision makers and is considered a member ofgroup decision theory. In this case, the MCDA algorithmshave to include suitable consensus measures that show howmuch the group of decision makers agree or disagree aboutthe alternative ranking (Carlsson et al. 1992).

In the DESYRE tool, the comparison of remediationtechnologies was designed as a single-person activity, involv-ing the presence of only 1 expert. In fact, this choice wassuggested by the most probable usage of the software tool, inwhich only 1 expert or user at a time has an active role in thedecision process; if more than 1 participates, unanimousagreement with all judgments is expected. Thus, hereafter,only single MCDA algorithms will be considered.

According to the specialized literature (Vincke 1992),single MCDA tools are classified as multiattribute utilitytheory (MAUT), outranking and interactive. However, asdiscussed in a previous paper (Giove et al. 2006) and reportedby Linkov et al. (2004), the applications of MCDA toenvironmental problems are mainly based on MAUT, theanalytic hierarchic process (AHP), and some outrankingmethodology (in particular, the Elimination et Choix Tradui-sant la Realite [ELECTRE] method).

In MAUT methods, the attribute values of each alternativeare aggregated by means of a suitable ‘‘utility’’ function (or‘‘value’’ functions) to obtain the score of the investigatedalternatives. This approach is based on the hypothesis of a

274 Integr Environ Assess Manag 2, 2006—A Critto et al.

rational and consistent decision maker (Bridges et al. 2004) and

implies the existence of both the value functions and a suitable

aggregation operator. Many methods exist to define the value

functions (Keeney 1976), but the description of those method-

ologies is beyond the aim of this paper. Even the aggregation

operator needs to be carefully selected, and as discussed in a

previous paper (Giove et al. 2006), the simplest and most

widely used aggregation function in the MAUT context is given

by the weighted averaging operator. However, it should be

stressed that the choice of a particular aggregation operator

depends on several elements, such as the kind of the problem,

available information, and subjective reasoning of the expert.

As far as the AHP is concerned, it was 1st developed by

Saaty (1980) and widely reviewed and applied in the

literature (Saaty 2000; Ramanathan 2001). The AHP

aggregation algorithm simply computes a linear combination

of the criteria values as weighted averaging operators, making

use of pairwise comparison of alternatives (i.e., for each pair

of attributes, the expert specifies a judgment of ‘‘how much

more important’’ 1 attribute is compared with another),

Figure 1. Framework of the remediation technologies (RT) comparison and risk assessment (RA) module of the DESYRE decision support system.

Selection of Remediation Technologies—Integr Environ Assess Manag 2, 2006 275

allowing a numerical value to be obtained even fromintangible criteria. The pairwise comparison is a robustmethod, giving local and global weights that, as verified inmany applications, respect the hidden preference structure ofthe user. A main characteristic of the consistency property isthat it implies that a user is coherent if the pairwisecomparisons satisfy the transitivity property (Saaty 1980,2000). However, given the uncertain characteristics of humanthinking, a limited amount of inconsistency can be accepted.

As discussed in a previous paper (Giove et al. 2006),despite the great popularity and the large number of AHPapplications, some criticisms are warranted (Barzilai 2001):primarily, the rank reversal phenomenon and the greatnumber of comparisons required as the number of alter-natives increase. Methods are available to solve thesecriticisms, including the supermatrix approach and the AHPnetwork, originally suggested by Saaty (1980), and theadoption of the absolute mode (Ramanathan 2001). In theabsolute mode, the pairwise comparison is limited only to the1st level of the hierarchy to obtain the global weights,whereas for the lower levels, a direct judgment is required bythe user. In so doing, the amount of information requireddecreases by 1 order of magnitude (i.e., from N2 to N, whereN is the number of data points). Moreover, rank reversal isavoided because deleting an alternative, or inserting a newone, does not change the scoring of those remaining (becausethey were assigned from a direct judgment, unchangeable bythe insertion or deletion); thus, the ranking will not change.This method will be described in the methodology sectionwith particular reference for application to remediationtechnologies comparison.

Study area

The proposed methodology was applied to the PortoMarghera industrial zone, a contaminated megasite borderinga complex and fragile ecological–naturalistic and hydrogeo-logical transitional environment such as the Venice lagoon.

The Porto Marghera site covers a surface of 3,595 ha, out ofwhich 479 ha are occupied by canals. The site was originally amudflat (so-called barene) that has been raised up ;2 mabove sea level by fill, with material from the dredging oflagoon canals (Gatto and Carbognin 1981) and wasteproduction residues, including industrial toxic waste. Thearea started to operate at the beginning of the last century.

As for hydrogeology, the site is located over a coastalmultiple-aquifer system subject to tidal effects. By assemblingpreviously available information (Carbognin et al. 1972;Critto et al. 2004) implemented by further experimentalinvestigations, the hydrogeological conceptual model of thesite is shown in Figure 2.

The topsoil is covered by a layer of fill materialcontaminated by industrial waste, including red bauxiticmud, black organic sludge, or both. Below the topsoil, a 1stimpermeable layer is found, consisting of Holocene depositsof lagoon mudflats and an overconsolidated silt clay layercalled caranto. The 1st impermeable layer is followed by asemiconfined aquifer, delimited at the bottom by clayeyPleistocene sediments that constitute the deepest imperme-able layer (Carbognin et al. 1972).

Chemical contamination was characterized extensively on a100-m sampling grid. In the topsoil (i.e., the fill materiallayer), several classes of pollutants were found: amines,chlorobenzenes, chloronitrobenzenes, chlorophenols, dioxins,aliphatic hydrocarbons, polynuclear aromatic hydrocarbons,metals, metalloids, and inorganic anions (Venice City Council2001). In the semiconfined aquifer, the same classes ofpollutants were found.

Metals and metalloids showed the highest concentrationlevels and the widest spread of contamination. The soilsamples displayed high concentrations of arsenic (hot spots of900 mg/kg dry wt at 0–1 m depth), chromium (hot spots of4,200 mg/kg dry wt at 0–4 m depth), cadmium (hot spots of900 mg/kg dry wt at 3–4 m depth), copper (hot spots of3,000 mg/kg dry wt at 3–4 m depth), mercury (hot spots of130 mg/kg dry wt at 1–2 m depth), and lead (hot spots of26,000 mg/kg at 3–4 m depth). Moreover, on the basis of thenumber of samples that exceeded Italian law (Law Decree471/99 1999) for acceptable concentration limit (ACL) forindustrial use, widespread contamination was found, espe-cially for arsenic (117 of 1,392 samples exceeding limits; ACL¼ 20 mg/kg dry wt), cadmium (127 of 1,389; ACL¼ 2 mg/kgdry wt), and mercury (105 of 1,389; ACL¼ 1 mg/kg dry wt).Lead contamination was less widespread (39 of 1,393 samplesexceeding regulatory limits; ACL ¼ 100 mg/kg dry wt);finally, for copper (15 of 1,384; ACL¼120 mg/kg dry wt) andchromium (4 of 1,354; ACL ¼ 150 mg/kg dry wt), thecontamination appeared to be confined to only 1 hot spot.

Figure 2. Hydrogeological model of the Porto Marghera (Venice, Italy) contaminated megasite. a.s.l. ¼ above sea level.

276 Integr Environ Assess Manag 2, 2006—A Critto et al.

PROPOSED METHODOLOGY FOR THE SELECTION OFREMEDIATION TECHNOLOGIES

The proposed methodology consists of 3 main steps: 1)selecting the remediation technologies, 2) setting comparativecriteria, and 3) ranking the selected remediation technologieswith a comparative procedure.

The 1st step provides a pool of remediation technologiessuitable to the case study; the 2nd step assesses the previouslyselected techniques according to several evaluation criteria.The last step develops a ranking algorithm by MCDA thatallows a comparison and classification of the selectedtechnologies.

Selection of remediation technologies

To select the remediation technologies, 2 subsequentselection filters were applied to the input database oftechnologies provided by the FRTR matrix (Figure 3). The1st filter was based on 2 basic parameters, commercialavailability of the considered technology and the technologytarget contaminants overlapping those found in the site understudy. The 2nd filter was concerned with site-specificparameters (i.e., hydrogeological and physicochemical char-acteristics of the investigated environmental matrix) affectingthe feasibility of remediation technologies.

At the end of the selection, a pool of remediationtechnologies was chosen, including all technologies applicablesimultaneously or subsequently to the study site.

This selection procedure was implemented into theDESYRE software as a hypertext database document devel-oped in Microsoftt Word through Visual Basic Macro. It

included 3 interactive tables (A, B, and C) and a character-ization database, which drive the potential expert in theaforementioned selection pathway.

The A table (Figure 4) includes the input database oftechnologies and groups the cleanup technologies accordingto the treated contaminated matrix (i.e., soil, surface water, orgroundwater, emitted off-gas).

Each technology is characterized by target contaminants,commercial availability, general site characteristics required forapplicability, main benefits and disadvantages or drawbacks,and the target pollutants found at the site. Moreover, the lastcolumn of the table contains a synthetic judgment or evaluation

Figure 3. Steps applied to the selection of a suitable site-specific technologiespool.

Figure 4. Structures of the A, B, and C tables used sequentially in the selection of remediation technologies.

Selection of Remediation Technologies—Integr Environ Assess Manag 2, 2006 277

made by the expert (FA¼the technology is fully applicable andthe remediation action does not imply any impediment; AR¼applicable with reserve [i.e., the cleanup actions have notabletroubles in 1 of the listed parameters]; NA ¼ not applicable[i.e., the technology shows a specific impediment]).

The B table (Figure 4) includes only the technologiesselected by the expert in the previous table A afterapplication of the 1st filter (i.e., the technologies markedFA or AR). Table B is only descriptive and provides thecharacterization of the selected technologies according to theadditional criteria of remediation strategy (i.e., extraction,removal and retrieval, biodegradation, immobilization, de-struction, and chemical transformation), specific cleanupeffectiveness for the considered contaminant classes, andcapability to be included in train technology treatments.

The C table (Figure 4) lists site-specific parameters thataffect the applicability of the selected technologies: eitherrelated to the treated matrix (e.g., pH, total organic carbon,hydraulic conductivity, and soil cation exchange capacity) orrelated to the target contaminants (e.g., vapor pressure,solubility). The applicability range values of these parametersfor each technology were obtained by published technicaldocuments (Los Alamos National Laboratory 1996; NATO/CCMS 2001; Vik and Bardos 2003).

Finally, the characterization database (not reported here)includes all the information concerning the geotechnical (e.g.,soil granulometry) and physicochemical (e.g., soil organiccarbon content) characteristics of investigated environmentalmedia obtained during the characterization process.

With the use of the characterization database and table C,the expert can apply the 2nd site-specific filter by identifyingthe fit between hydrogeological and physicochemical charac-teristics of the site, summarized in the characterizationdatabase, with the aforementioned applicability range valuessummarized in the 2nd column of table C (labeled ‘‘Site-specific criteria that affect the applicability’’). The software

then prompts the expert for a final applicability judgment orassessment for all technologies, which is inserted in the 3rdcolumn of table C (labeled ‘‘Expert applicability judgment/assessment’’). Only the technology marked ‘‘Applicable’’ willbe selected, which leads to a pool of remediation technologiesactually applicable to the case study.

Setting of comparative criteria set: Macrocriteria andevaluation matrix

To compare the pool of selected remediation technologies,the following 6 comparative macrocriteria were defined:reliability, course of action (i.e., intervention condition),‘‘hazardousness,’’ community acceptability/impacts, effective-ness, and cost (Figure 5). Each macrocriterion is able todescribe a specific aspect of a cleanup action.

The reliability macrocriterion considers the maintenanceaspects and results obtained by the technology application toother case studies. The course of action (i.e., interventioncondition) macrocriterion identifies the logistic and technicalaspects related to a remediation action by differentiatingbetween in situ, ex situ, and off-site technologies and byconsidering the possibility of creating a train technology. Themacrocriterion measuring hazard allows assessment of thepotential effects for human health resulting from thetechnology application (e.g., effects related to the use ofhazardous reagents or the emission of dust and volatilesubstances). With the use of the community acceptability/impacts macrocriteria, the negative effects on the environ-ment, as well as the main factors on which depend publicjudgment of a particular remediation technology, are eval-uated. The effectiveness macrocriterion helps the expert toassess technology performance, which depends on cleanuptime and removal rates. Finally, the cost macrocriterion pointsout the parameters on which depend the actual or real costs ofa remediation action (e.g., time, installation and maintenancecost, need of waste disposal).

Figure 5. Macrocriteria and associated evaluative criteria used for the comparison of remediation technologies.

278 Integr Environ Assess Manag 2, 2006—A Critto et al.

Figure 5 reports the evaluation criteria associated with eachmacrocriterion. These evaluation criteria were identified fromthe review of international approaches (UN 1997; UKEA1999; FRTR 2002), and each can contribute to the definitionof more than 1 macrocriterion (e.g., cleanup time influencesboth effectiveness and cost), as shown in Figure 5. Some ofthe selected evaluation criteria are strictly correlated withtechnical aspects (i.e., costs, cleanup time, performance,reliability and maintenance, technology development status,cleanup operation locations, train technology, hazardousreagents use, contaminated matrix removal, and residualsproduction), whereas other criteria refer to the potentialeffects on human health and the environment (i.e., dust andvolatile substances emission, effects on water, and consequen-ces to soil and community acceptability).

Performance, cost, and cleanup time are the most impor-tant criteria in the description of a remediation technologyaccording to cost–benefit analysis. Performance is the removalratio, expressed as the ratio between the residual (i.e., aftertreatment) pollutant concentration in a given matrix and theinitial concentration in the same matrix. Cost gives informa-tion on the overall cost per treated matrix unit (US$/t of soilmatrix wet weight and US$/1,000 L of water matrix), and it isstrictly related to the removal rates. The costs applied to thestudy case were obtained from reviewing case studies withfeatures similar to those of the analyzed site, so that site-specific criteria could be introduced (Los Alamos NationalLaboratory 1996; NATO/CCMS 2001; OCETA 2001;USEPA 2001a, 2001b; Vik and Bardos 2003). Finally, cleanup

time is the average time required to clean a site with a specifictechnology. According to the USEPA (2001a) approach, allcleanup times were estimated by referring to standardconditions, namely 20,000 t of soil and 3,785,000 L of water.

For each evaluation criterion, a qualitative or quantitativerating was defined as shown in Table 1. This rating schemeallowed the so-called evaluation matrix to be obtained—1 foreach contaminant class found in the site analyzed (see Table 2,in which the evaluation matrix for inorganics is presented). Inthis matrix, evaluation criteria were reported in the columns,and the selected technologies suitable to treat specificpollutants were in the rows. The evaluation matrix providesthe input data for the technologies ranking step, described inthe following paragraph.

Comparative procedure and ranking of the selectedremediation technologies

The 3rd step of the proposed methodology, the compara-tive procedure, aims at obtaining a ranking of remediationtechnologies according to the macrocriteria previously pre-sented. The final classification will allow the experts to createremediation technologies sets that act as possible remediationsolutions.

The comparative process is based on a ranking algorithmthat was developed by single-person MCDA tools. Specifi-cally, the selected MCDA method uses a decision matrix asthe database (Table 7) that summarizes the available raw datafor the decision maker. Each row of the matrix corresponds toa specific alternative (in the current case, a specific

Table 1. Qualitative and quantitative rating of the evaluation criteria selected for comparing the remediation technologies

EVALUATION CRITERIA RATING

Technology development status Consolidated (C) Innovative (I)

Reliability/maintenance

Better(high reliability andlow maintenance)

Average(average reliabilityand maintenance)

Worse(low reliability,

high maintenance)

Residuals production No Yes

Train technology Yes No

Cleanup operation location In situ Ex situ Off site

Contaminated matrix removal No Yes

Community acceptability High Medium Low

Hazardous reagent use No Yes

Dust and volatilesubstance emission

No Yes

Effect on waters No Yes

Effect on soil No Yes

Performance (removal rate, %) 0–100

Overall cost $/ton for soil $/1,000 L for groundwater

Worse Average Better

Cleanup time (years) Soil in situ .3 3–1 ,1

Soil ex situ .1 0.5–1 ,0.5

Groundwater .10 3–10 ,3

Selection of Remediation Technologies—Integr Environ Assess Manag 2, 2006 279

remediation technology), whereas each column correspondsto a proposed macrocriterion. Accordingly, each element ij ofthe matrix represents the judgment of the alternative i withrespect to the macrocriterion j (i.e., the jth attribute value forthe alternative i) and can be expressed in different ways (i.e.,symbolic, numerical, Boolean).

Moreover, according to the discussion in the Backgroundsection, the selected MCDA method is based on the weightedaveraging operator associated with the absolute AHP tostructure the problem into a suitable hierarchy and todetermine the criterion weights. This choice was motivatedfrom the aforementioned characteristics, including easyinterpretability of its linear form, user-friendly capability,probability of low or null interaction among the criteria, andlimited amount of information required.

According to the standard AHP, the absolute AHP is basedon 3 fundamental steps: 1) Structuring the problem withhierarchy, which allows a complex problem to be divided into aseries of levels of analysis so that each attribute is a member of asmall set of attributes on the same level, all attributes arerelated to a single attribute on the level immediately abovethem, and the last level is formed by the available alternatives;2) comparison of judgments, which allows the relativeimportance of the variables (attributes or alternatives) belong-ing to the same level and relative to each of the associatedvariables belonging to the upper level to be calculated with theuse of a pairwise comparison method (i.e., for each pair ofattributes, the expert specifies a judgment of ‘‘how muchmore important’’ 1 attribute is compared with another) witha predefined (and limited) scale, usually the natural scale of1, 2, . . . , 9 points; 3) analysis of priorities, which leads,through suitable aggregation tools, to a final ranking of thealternatives (Saaty 1980; Norris and Marshall 1995).

Moreover, for specific application in the remediationtechnologies comparison, in the absolute AHP mode, therelative importance (i.e., the weight) was calculated with thepairwise comparison only for the hierarchic level in which themacrocriteria were included, not for all the levels, as therelative AHP provides for (i.e., overall goal concerning theenvironmental requalification, macrocriteria, evaluation cri-teria, and technological alternatives). Therefore, for theoverall goal, the relative importance is intended to be theimportance of each macrocriterion relative to the 1sthierarchic level (i.e., obtaining a ranking of the selectedremediation technologies that facilitates the definition of aremediation plan for the study case).

In accordance with the general considerations for AHPreported in the Background section, the absolute AHP wasadopted to avoid both the great number of required compar-isons of the alternatives and the undesired rank reversalphenomenon. Thus, the values of the macroattributes for eachalternative were directly assigned by the expert. This processallows the expert knowledge to be captured and implementedin a numeric form. In this way, the proposed process can beconsidered, at the same time, a support and check tool for theexperts who have to assess the several selected technologies toobtain different technologies sets.

The overall defined comparative process finally comprised1) structuring the problem with hierarchy, 2) computingmacrocriterion weights, 3) assessing the technologies with theuse of a judgment matrix, and 4) computing the final score foreach technology.

Structuring the problem in hierarchic levels—The proposedstructure include 4 hierarchic levels, as shown in Figure 6. The1st level consists of the final goal (i.e., obtaining a ranking ofthe selected remediation technologies to define a remediation

Table 2. Evaluation matrix for inorganic contaminants and the remediation technologies resulting from the selection step.For legend, see Table 1a

Soiltreatment Technology

Cleanuptime

Overallcost($/t or

$/1,000 L)

Performance (% removal)Cleanupoperationlocation

TraintechnologyAs Cr Cu Cd Hg Ni Pb Zn

In situ Phytoremediation Average 25 NAV 38 NAV NAV 42 NAV NAV 98 In situ Yes

Electrokineticseparation

Average 62 88 90 85 93 NAV 90 89 73 In situ Yes

Solidification/stabilization

Better 90 NAV NAV NAV 60 NAV NAV 74 60 In situ No

Ex situ Separation Better NAV NA NA NA NA NA NA NA NA Ex situ Yes

Soil washing Better 200 96 93 87 NAV NAV 92 91 98 Ex situ Yes

Solidification/stabilization

Better 140 99 99 97 98 98 97 98 97 Off-site No

Other Landfill cap Worse NA NA NA NA NA NA NA NA NA In situ No

Landfill capenhancement

Worse NA NA NA NA NA NA NA NA NA In situ No

Excavation,retrieval and

off-site disposal

Better NA NA NA NA NA NA NA NA NA Off-site No

a As¼ arsenic; Cr¼ chromium; Cu¼ copper; Hg¼mercury; Ni¼ nickel; Pb ¼ lead; Zn¼ zinc; NA ¼ not applicable; NAV¼ not available.b I¼ innovative; C¼ consolidated.

280 Integr Environ Assess Manag 2, 2006—A Critto et al.

Table 3. Stepwise selection of remedial technologies for inorganic contaminants

Technologies for inorganics fromtechnologies’ database (Table A)

Technologies resultingfrom the 1st selection

Pool of technologies applicable to thestudy case for inorganics category

Treatments for soil sediments and sludge in situ

1 Phytoremediation 1 Phytoremediation 1 Phytoremediation

2 Electrokinetic separation 2 Electrokinetic separation 2 Electrokinetic separation

3 Fracturing 3 Fracturing

4 Soil flushing 4 Soil flushing

5 Solidification/stabilization 5 Solidification/stabilization 3 Solidification/stabilization

6 Vitrification

Treatments for soil sediments and sludge ex situ

7 Chemical extraction

8 Oxidation/reduction 6 Oxidation/reduction

9 Separation 7 Separation 4 Separation

10 Soil washing 8 Soil washing 5 Soil washing

11 Solidification/stabilization 9 Solidification/stabilization 6 Solidification/stabilization

Containment and other treatments

12 Landfill cap 10 Landfill cap 7 Landfill cap

13 Landfill cap alternatives 11 Landfill cap alternatives 8 Landfill cap alternatives

14 Excavation, retrieval, andoff-site disposal

12 Excavation, retrieval, andoff-site disposal

9 Excavation, retrieval, andoff-site disposal

Table 2. Extended

Effects onwaters

Effects onsoils

Dust andvolatile

substancesemissions

Contami-natedmatrixremoval

Residualsproduction

Communityacceptability

Hazardousreagents

useReliability/

maintenance

Technologydevelopment

statusb

Yes No No No Yes High No Worse I

No Yes Yes No Yes High No Average C

Yes Yes Yes No No Average No Better C

No Yes Yes Yes Yes Average No Better C

No Yes Yes Yes Yes Low Yes Better C

No Yes Yes Yes Yes Low Yes Better C

Yes Yes Yes No Yes Low No Better C

Yes Yes Yes No Yes Low No Better C

Yes Yes Yes Yes NA Low No Better C

Selection of Remediation Technologies—Integr Environ Assess Manag 2, 2006 281

plan for the study case); the 2nd level consists of the proposedmacrocriteria, the 3rd level concerns the selected evaluationcriteria, and the 4th level concerns the different availablealternatives (i.e., the selected remediation technologies).

Computing macrocriteria weights—The 2nd step of thecomparison process is the computing of macrocriteriaweights. According to the AHP method (Saaty 1980), thisis obtained with a pairwise comparison matrix (i.e., Table 5),in which rows and columns list the proposed macrocriteria.For each pair of macrocriteria, the expert specifies a judgmentof ‘‘how much more important’’ 1 macrocriterion is thananother. To specify the pairwise comparison judgments, anumerical approach was adopted (i.e., the expert answerseach question with a number, as in ‘‘Attribute A is 3 times asimportant as Attribute B’’) on the basis of the Saaty numericscale (Saaty 1980) reported in Table 4. With the use of thisscale, the macrocriteria weights were successively estimatedby applying the eigenvector method (Saaty 1980). Moreover,the developed procedure allows both ordinal (maximum–minimum transitivity) and cardinal consistency analysis to beapplied, with the aim to avoid intransitive cycles (Kwiesie-lewicz and Van Uden 2004) and too-low cardinal consistency(Saaty 1980, 2000).

Evaluating remediation technologies with the judgment ma-trix—This step evaluates the selected remediation technolo-gies (i.e., the available alternatives) through the so-calledjudgment matrix (i.e., the aforementioned decision matrix).A specific judgment matrix has to be developed for eachcontaminant class. In this matrix (Table 7), the rows

correspond to the selected remediation technologies that areable to treat a particular contaminant class, whereas thecolumns correspond to the proposed macrocriteria, whoseweights were previously calculated. Each element ij of thematrix represents the score (i.e., expert judgment) of thetechnology i with respect to the macrocriterion j. The expertassigns this score by assessing the rating that the evaluationcriteria, correlated to a specific macrocriteria, assumes for aselected remediation technology in the aforementionedevaluation matrix (see Table 2 for inorganics). These judg-ments (i.e., scores) are expressed according to a numericalscale (1 ¼ sufficient, 2 ¼ rather good, 3 ¼ satisfactory, 4 ¼good, and 5¼ excellent) that allows several judgment levels tobe explained, while avoiding too-heavy computational efforts.Also in this step, a consistency check is performed: if analternative is dominated by any others, the criteria judgmenthas to be coherent, otherwise an alarm is sent to the user.

Ranking algorithm—The last step of the comparativeprocedure concerns the definition of the ranking algorithm,which estimates the final score for each selected remediationtechnology. The proposed algorithm is the sum of the productsof the numerical judgment (expressed for each macrocriterionthrough the judgment matrix) with the corresponding macro-criterion weight according to the Equation 1,

Pi ¼XL

j¼1

b0jvij ð1Þ

where Pi is the total scoring associated with the ithremediation technology, b

0

j is the normalized weight associ-ated with the jth macrocriterion, vij is the numericaljudgment assigned to the ith alternative (technology) and

Table 4. Saaty’s (1980) numerical scale applied in the matrixof pairwise comparison (reported in Table 5)

Intensity ofimportance

Definition

1 Equally as important

3 Moderately more important

5 Strongly more important

7 Very strongly more important

9 Extremely more important

2, 4, 6, 8 Intermediate values between de-fined rankings

Table 5. Matrix of pairwise comparison applied to the calculation of macrocriteria weights

EffectivenessCommunity

acceptability/impacts ReliabilityInterventionconditions Hazardousness Cost

Effectiveness 1 7 5 7 5 5

Community acceptability/impacts 1/7 1 1/3 1/3 1/5 1/7

Reliability 1/5 3 1 3 1/5 1/7

Intervention conditions 1/7 3 1/3 1 1/5 1/5

Hazardousness 1/5 5 5 5 1 1/3

Cost 1/5 7 7 5 3 1

Table 6. Macrocriteria weights calculated by the eigenvectormethod (Saaty 1980)

Macrocriterion Weights

Effectiveness b1 ¼ 0.4750

Community acceptability/impacts b2 ¼ 0.0288

Reliability b3 ¼ 0.0644

Intervention conditions b4 ¼ 0.0434

Hazardousness b5 ¼ 0.1472

Cost b6 ¼ 0.2412

282 Integr Environ Assess Manag 2, 2006—A Critto et al.

correlated with the jth macrocriterion, and L is the number of

macrocriteria considered.

APPLICATION OF THE PROPOSED METHODOLOGYAND DISCUSSION

The proposed methodology was applied to the contami-nated megasite of Porto Marghera for the selection andcomparison of treatment technologies for the removal of

inorganic contaminants present in the surface fill materiallayer (Figure 2).

Remediation technologies selection

To obtain a pool of remediation technologies suitable forthe case study, the characterization database was used, andthe A, B, and C table were applied (Figure 4).

The 1st selection filter, following the application of table Ato the 14 remedial technologies proposed by the FRTR matrix

for soil matrix and inorganic pollutants (Table 3, 1st column),allowed the 12 technologies reported in Table 3, 2nd column,to be selected. Vitrification was eliminated because it couldnot assure any homogeneity of the intervention over thewhole study area; chemical extraction was discarded becauseof the high cost of chemical reagents.

The 12 selected technologies were characterized by theadditional criteria reported in table B (Figure 4) and thenwere submitted to the 2nd selection filter resulting from theapplication of table C (Figure 4). By the matching of site-specific hydrogeological and physicochemical characteristicsof the surface soil, summarized in the characterizationdatabase, with the applicability range values presented inthe 2nd column of table C (Figure 4), a final remediationtechnologies pool was selected, which included 9 technologiesapplicable to the case study (Table 3, 3rd column). Theselected pool consisted of both ex situ treatments (i.e.,separation, soil washing, and solidification/stabilization) andin situ treatments (e.g., electrokinetic separation, solidifica-tion/stabilization). Moreover, it included the technologiessuitable for areas with widespread, near-surface, and low-levelcontamination (i.e., phytoremediation). Finally, containmentsystems (i.e., landfill cap, landfill cap alternatives andexcavation, retrieval, and off-site disposal) were also includedin the pool because they can be applied whenever thecontamination levels, volumes, or both are so high that noother remediation actions are feasible.

Comparative criteria set: Evaluation matrix

The selected remediation technologies were next subjectedto comparison steps. For the comparative procedure andranking algorithm, the qualitative or quantitative ratingsdefined for each evaluation criterion (Table 1) were used toimplement the evaluation matrix for the inorganic pollutants(Table 2) through a specific and suitable software interface. Allthe matrix data were inserted according to the mostconservative option (i.e., the worst case, for example, theaverage of the highest costs and the lowest performanceobtained by the reviewed study cases with features similar to

Table 7. Judgment matrix for inorganic contaminants and the remediation technologies selected in the stepwiseprocedurea

EffectivenessCommunity

acceptability/impacts ReliabilityInterventionconditions Hazardousness Cost

Electrokinetic separation 4 3 4 5 3 3

Solidification/stabilizationin situ 4 1 5 4 3 3

Phytoremediation 1 4 1 5 5 4

Separation 4 2 5 4 2 2

Soil washing 5 2 5 4 3 2

Solidification/stabilizationex situ 4 1 5 1 2 2

Landfill cap 1 1 5 3 4 2

Landfill cap alternatives 1 1 4 3 4 2

Excavation, retrieval andoff-site disposal 1 1 4 1 1 2

a 1 ¼ sufficient; 2 ¼ rather good; 3 ¼ satisfactory; 4 ¼ good; 5 ¼ excellent.

Figure 6. Hierarchic structure applied in the proposed absolute AHP that wasadopted for the comparison procedure. Gi¼macrocriteria, bi¼macrocriteriaweight, Ci ¼ evaluative criteria, Ti ¼ selected remediation technologies.

Selection of Remediation Technologies—Integr Environ Assess Manag 2, 2006 283

those of the analyzed site; Los Alamos National Laboratory1996; NATO/CCMS 2001; OCETA 2001; USEPA 2001a,2001b; Vik and Bardos 2003).

Comparative procedure and ranking

As previously illustrated in the Methods section, thecomparative procedure consisted of several steps. The resultsobtained by the application of each step to all selectedtechnologies are reported in the following paragraphs.

Computing macrocriteria weights—The pairwise comparisonmatrix was filled in (Table 5) and the macrocriteria weightswere estimated (Table 6) with the use of the Saaty numericalscale (Table 4) and a specific software interface included inthe DESYRE decision support system. According to Tables 5and 6, the macrocriteria ‘‘cost’’ and ‘‘effectiveness’’ obtainedthe highest preference judgments and weights, followed bythe macrocriterion ‘‘hazardousness.’’ This highlights therelative importance of technical, economic, and risk consid-erations within the process for the definition of remedialscenarios. The macrocriterion ‘‘community acceptability/impacts’’ obtained the lowest preference judgment andweight because of the low relevance given by the expert tothe environmental and social consequences that the remedia-tion process could cause to the already strongly compromisedcontext of the industrial area of Porto Marghera.

Technologies evaluation with the judgment matrix—On thebasis of the evaluation criteria rating (Table 1), the evaluationmatrix (Table 2), and the judgment matrix (Table 7), theselected technologies were evaluated. The assigned scores(i.e., expert judgments) for each technology and for eachmacrocriterion (i.e., attribute) are reported in Table 7. Forinstance, soil washing obtained an excellent judgment foreffectiveness and reliability because of the high performanceand reliability and the low maintenance and cleanup time.However the rather good judgment obtained by soil washingfor community acceptability/impacts and cost reflects therather low community acceptability (normally correlatedwith ex situ treatment) and the high cost.

Ranking algorithm—Using the macrocriteria weights re-ported in Table 6 and the assigned scores reported in Table 7,Equation 1 (in the Methods section) was applied and a finalscoring for each technology was estimated, thus obtaining theremediation technologies ranking (Table 8).

Soil washing was the technology with the highest ranking: Itis an effective solution to treat soil contaminated by metals

and metalloids (even for high concentration levels), and it isable to treat large amounts of soil. The 2nd option, electro-kinetic separation, is a suitable in situ solution for hot spots(with metal concentrations from a few parts per million totens of thousands of parts per million). The lowest score,assigned to landfill cap alternatives and excavation, retrieval,and off-site disposal, reflects the common features of theseremedial options: no reduction, only a containment, ofcontaminated soil is obtained. In addition, they present a highhazardousness level because of possible percolation intogroundwater or release of volatile compounds into theatmosphere. They usually are adopted when no otherremediation technologies are technically or economicallyfeasible.

CONCLUSIONSA methodology for selecting and comparing a set of

remediation technologies and for supporting the definitionof suitable site-specific remediation scenarios was developed.

The proposed selection and comparative procedure is user-friendly (i.e., organized on specific tables and matricesintegrated into a suitable software interface, which interactswith the experts), transparent (i.e., has stepwise traceabilityof any selection and comparison), and interdisciplinary (i.e.,the framework integrates technological, environmental, andsocioeconomic knowledge). The integration of guidelines andcriteria shared by the international scientific community givessolidity and reliability to the entire process.

Finally, the proposed procedure, through the integration ofsoftware modules, expert knowledge, and professional judg-ment, plays a key role within the DESYRE softwareapplication.

The integration of technologies selection with other aspectsof the remediation process in a single tool is the distinctivefeature of DESYRE compared with other available tools. Infact, the other few decision support systems and existingdatabases provide the user with the selection of suitableremediation technologies, but DESYRE includes this func-tionality in a more general framework for remediation of largecontaminated sites while considering socioeconomic, risk, andenvironmental impact aspects.

Moreover, DESYRE technologies selection through MCDAmethodologies is based on a wide variety of criteria,encompassing not only cleanup time and cost, but alsocommunity acceptability/impacts, performance, reliability/

Table 8. Ranking of the remediation technologies selected for soil matrix and inorganic contaminants

Rank Technology Scoring

1 Soil washing P 1 ¼ 3.85

2 Electrokinetics separation P 2 ¼ 3.64

3 Solidification/stabilization in situ P 3 ¼ 3.58

4 Separation P 4 ¼ 3.23

5 Solidification/stabilization ex situ P 5 ¼ 3.07

6 Phytoremediation P 6 ¼ 2.57

7 Landfill cap P 7 ¼ 2.02

8 Landfill cap alternatives P 8 ¼ 1.96

9 Excavation, retrieval, and off-site disposal P9 ¼ 1.43

284 Integr Environ Assess Manag 2, 2006—A Critto et al.

maintenance, location of cleanup operations, and effects onwaters and soils. In this respect, DESYRE supports a fullyintegrated and complete decision process for site remediation.

Acknowledgment—We thank Manuela Samiolo and StefanoSilvoni for assistance in analyzing data. This work and theDESYRE project were funded by the Italian Ministry forUniversity and Scientific Research.

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