A multicriteria-based methodology for site ...

Environment International 35 (2009) 920–930

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Environment International j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / e n v i n t

A multicriteria-based methodology for site prioritisation in sediment management Manuel Alvarez-Guerra a,b, Javier R. Viguri b, Nikolaos Voulvoulis a,⁎ a b

Centre for Environmental Policy, Imperial College London, London, SW7 2AZ, United Kingdom Department of Chemical Engineering and Inorganic Chemistry, ETSIIT, University of Cantabria, Avda. de los Castros s/n 39005, Santander, Spain

a r t i c l e

i n f o

Article history: Received 6 February 2009 Accepted 30 March 2009 Available online 14 May 2009 Keywords: Sediment Management Multicriteria Analysis Site prioritisation

a b s t r a c t Decision-making for sediment management is a complex task that incorporates the selections of areas for remediation and the assessment of options for any mitigation required. The application of Multicriteria Analysis (MCA) to rank different areas, according to their need for sediment management, provides a great opportunity for prioritisation, a first step in an integrated methodology that finally aims to assess and select suitable alternatives for managing the identified priority sites. This paper develops a methodology that starts with the delimitation of management units within areas of study, followed by the application of MCA methods that allows ranking of these management units, according to their need for remediation. This proposed process considers not only scientific evidence on sediment quality, but also other relevant aspects such as social and economic criteria associated with such decisions. This methodology is illustrated with its application to the case study area of the Bay of Santander, in northern Spain, highlighting some of the implications of utilising different MCA methods in the process. It also uses site-specific data to assess the subjectivity in the decision-making process, mainly reflected through the assignment of the criteria weights and uncertainties in the criteria scores. Analysis of the sensitivity of the results to these factors is used as a way to assess the stability and robustness of the ranking as a first step of the sediment management decisionmaking process. © 2009 Elsevier Ltd. All rights reserved.

1. Introduction Decision-making in the field of sediment management is a difficult task (Apitz et al., 2005a), challenged by the prohibitive costs of some remediation options and general lack of availability of resources. For example, in this area, cost effectiveness is normally referred to as optimised resource allocation for maximum remediation benefits (Apitz and White, 2003). For this reason, when establishing a possible management project, an important first decision is to determine where to start with management actions, as zones with different sediment qualities, derived from different degrees of anthropogenic pressure, would require different levels of intervention in terms of urgency and severity of problems. The identification of areas where a great and urgent need for intervention is required is a decisionmaking process relying on many criteria. Sediments are complex matrices, often contaminated with a plethora of chemicals, requiring integrated assessment of quality characteristics, associated with measuring and interpreting multiple lines of scientific evidence (Simpson et al., 2005). Moreover, in addition to sediment quality, other aspects such as economic or social parameters can have an influence on the prioritisation of areas to be managed (Babut et al., 2007).

⁎ Corresponding author. Tel.: +44 20 7594 7459; fax: +44 20 7594 9334. E-mail address: [email protected] (N. Voulvoulis). 0160-4120/$ – see front matter © 2009 Elsevier Ltd. All rights reserved. doi:10.1016/j.envint.2009.03.012

Following this first step of selecting areas for management, the next issue that arises is the selection of options for the integrated management of those sediments, the assessment and development of cost effective and sustainable remediation strategies. This is a very complex task that relies on a variety of techniques and treatments, which incorporate multiple technical, social, economic and environmental criteria required for any informed decisions (Alvarez-Guerra et al., 2008a). As a result, decision-making needs to be carried out in consideration of different and sometimes conflicting criteria, thereby providing an opportunity for the use of Multicriteria Analysis (MCA) in the process. MCA provides a good framework for procedures that rank alternatives, based on their assessment across selected criteria, and such methods have been widely applied in different environmental areas quite effectively in the past (Balasubramaniam and Voulvoulis, 2005; Kiker et al., 2005; Linkov et al., 2006a). Very recent applications of MCA can be found, for instance, in water management (Chowdhury and Rahman, 2008; Menezes and Heller, 2008) or in the field of land remediation (Balasubramaniam et al., 2007; Harbottle et al., 2008). MCA has also been utilised as a tool that supports the decision-making system to select measurement endpoints for site-specific risk assessment of contaminated soil (Critto et al., 2007; Semenzin et al., 2007) or to obtain integrated effect indices of impairment on terrestrial ecosystems (Semenzin et al., 2008). Although MCA has rarely been applied to sediment management (Linkov et al., 2006b), there is some evidence in the literature to demonstrate how MCA has

M. Alvarez-Guerra et al. / Environment International 35 (2009) 920–930

been used to derive numerical scores on integrated information to assess the risks to the ecosystem in sediments of the delta of the rivers Rhine and Meuse (Den Besten et al., 1995), or as a tool that selects the best potential action for carrying out dredging operations (Abriak et al., 2006; Junqua et al., 2006). Moreover, frameworks that use MCA when choosing best management options to remediate contaminated sediments of a certain site (Linkov et al., 2006a; Linkov et al., 2005; Kiker et al., 2007), together with their application in two case studies in the USA (Yatsalo et al., 2007; Linkov et al., 2007; Kiker et al., 2008), have recently been reported in the literature. There is enough evidence to suggest the suitability of MCA tools for dealing with environmental problems in general and for their application in the area of sediment management in particular. Following this theme, our work aimed to develop a methodology based on the application of MCA for site prioritisation, taking into account not only scientific evidence, but also other relevant aspects such as social or economic criteria. This approach is intended to be the first step of an integral multicriteria-based methodology for decisionmaking in sediment management, in which a second step would consist of selecting the most suitable management alternative for the areas needing such intervention. Fig. 1 shows a schematic representation of this two-phase MCA-based methodology for sediment management. This paper however, covers only the proposed methodological steps for the prioritisation of sites for management (Phase 1 in Fig. 1), illustrating their applicability using real data for the case study of sediment management in the area of the Bay of Santander, northern Spain. The second part of the proposed methodology (Phase 2 in Fig. 1), is the self-contained task that concerns the selection of the best management alternative for the areas identified in this phase. This will be the subject of a separate paper. 2. Site prioritisation methodology The site prioritisation methodology for sediment management includes the following steps, as illustrated in Fig. 1. 2.1. Definition of management units in the area under study The first step of the proposed method is to assess how many areas the zone under study could be divided into, in order to define areas or

Fig. 1. Proposed methodology for decision-making in sediment management.

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“management units”, which share similar sediment quality problems and therefore will require similar interventions. This step is of major importance in terms of subsequent analysis, as the definition of these management units will define boundaries for the assessed options, influence costs and management options. The delimitation of these areas will follow a sound methodology and will need to meet other management drivers and constraints, mainly reflected in the European Union through the application of the Water Framework Directive (WFD) (EC, 2000). For example, the WFD already defines “water bodies”, which represent classification and management units (COAST, 2003). The Directive requires identification and characterisation of surface water body types, using a system of typology with a range of descriptors included in its Annex II. According to the definition of the WFD, water bodies must be “discrete and significant” so it may be necessary to divide an area, which is one type further, into different water bodies (COAST, 2003). After analysing the distribution of pressures and impacts in the zone under study, the water bodies or management units will be those areas that are subject to common significant pressures and impacts and thus can be considered as homogeneous units with regard to sediment management. 2.2. Definition and evaluation of criteria to rank areas The second step involves the identification and evaluation of criteria that influence the ranking of the management units, according to their greater need or cost effectiveness, in terms of sediment management. Assessment of sediment quality and potential risks associated with contaminated sediments are undoubtedly the fundamental drivers of such prioritisation. The use of multiple Lines of Evidence (LOE) is currently the best way of assessing sediment quality (Simpson et al., 2005; Apitz et al., 2005b) and of conducting an effective scientific risk assessment (Chapman, 2007). Although different frameworks have been proposed (Burton et al., 2002a), the Sediment Quality Triad (Long and Chapman, 1985; Chapman, 1990; Chapman, 1996) is one of the most commonly used approaches when developing several LOEs (Apitz et al., 2005a; Chapman et al., 2002; Burton et al., 2002b). The Triad approach requires evidence in three separate components (Apitz et al., 2005b): sediment chemistry reflecting on contamination, laboratory toxicity covering effects under standardised conditions and resident community alteration (generally the benthic in fauna) which is measured at field conditions (Chapman, 1996). Each of these LOEs needs to be considered as criteria in the MCA. However, the assessment of each LOE is carried out by multiple analyses or tests, e.g. measurement of concentrations of different chemicals for assessing sediment chemistry; several bioassays using different species for assessing toxicity; or measurement of different parameters, based on taxonomic identifications and community descriptive statistics, to determine benthic alteration. The data from the different tests have to be integrated to obtain representative and holistic results for each LOE. Different approaches to data combining within each LOE have been proposed and summarised by various authors (Babut et al., 2007; Burton et al., 2002a; Chapman et al., 2002; Grapentine et al., 2002). For example, the use of indices or a ratio to guideline values has been commonly practised when combining chemistry data (Babut et al., 2007). Results of toxicity tests have been integrated within that LOE by means of scoring systems and statistical summarisation methods (Chapman et al., 2002). A wide variety of biotic indices, as shown in the review of Pinto et al. (2009), have been developed and utilised for summarising information on benthic conditions. Apart from these three basic LOEs of the Triad approach, the inclusion of other LOEs in the MCA, such as measurements of biomagnification, biomarkers, in situ bioassays, or toxicity identification evaluation (Chapman, 2007; Chapman and Hollert, 2006), could also be required, depending on the specific case under study. Although

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these detailed assessments are normally used less, due to their higher costs in time and money, they can sometimes be necessary to obtain a more exhaustive assessment of the risks associated with sediment quality in the selected areas. In any case, there is no single multiple LOE approach for sediment quality assessment. Studies frequently need to be custom designed and LOEs chosen to suit the site-specific circumstances (Simpson et al., 2005), according to the application of tiered assessment frameworks in sequential steps (Chapman and Anderson, 2005; Chapman, 2007). In addition to the sediment quality characteristics, the proposed methodology incorporates other types of criteria beyond the scientific LOEs, to account for practicalities and socio-economic objectives (Apitz and White, 2003). Table 1 shows some of the possible additional factors that may influence the prioritisation of the need to manage the different areas. One of these criteria accounts for the economic relevance that sediment management could have for the management zone; for example, the larger the number and importance of economic activities that are directly affected by sediment quality in the area (e.g. fisheries, tourism, recreation activities), the higher the priority that should be given to the area. Another important aspect to consider is the likelihood of recontamination in each of the possible areas to be managed (Apitz and White, 2003). As financial resources will presumably be limited, it seems logical that investments should be allocated to sites where sediment management will probably be effective, rather than in those whose sediments, due to proximity of the water body to urban and heavily industrialised areas, are likely to be subject to recontamination (Apitz et al., 2005a). The evaluation of this “likelihood” could be carried out by analysing the sources that are expected to continue providing inputs for contaminants, establishing the need for alternative strategies such as source management instead of sediment remediation. The number of people living around each of the management units in this process could also be taken into account in the selection process. Since an environmental problem that directly affects a lot of people may be of higher priority (Al-Rashdan et al., 1999), this could also be used to influence the selection. The sensitivity of the environment is of great importance and could be reflected through the ecological value of the areas to be managed, using drivers such as the Habitats Directive, which covers sites that merit exceptional and priority protection, not covered by other criteria (Apitz and White, 2003). Finally, when considering areas governed by different administrations, the degree of support and cooperation that these are likely to offer (Tzeng et al., 2002) should influence the decision-making process. Although this could turn out to be a very subjective criterion, it may be evaluated by analysing the historical degree of involvement that each administration has shown in the past (e.g. number of environmental remediation projects completed, economic resources invested, etc.).

Table 1 Additional criteria to Lines of Evidence (LOEs) that can be considered in an MCA to prioritise areas for sediment management. Criterion

Evaluation

Economic relevance

Existence of economic activities directly affected by sediment quality in the area Level of sources of contamination that are expected to continue after remediation Population density of the surrounding area

Likelihood of recontamination Social: number of people living around the area Sensitivity of the environment Cooperation and support of administration

Degree of ecological value of the area Degree of involvement of the administration in previous environmental projects

In general, the list of criteria suggested in Table 1 is not exhaustive or finite; it should be adapted to the specific characteristics of the areas to be compared in this prioritisation stage. 2.3. Elicitation of weights The relative importance of the criteria selected and used should also be assessed, in order to reduce the subjectivity of the process. Equality among the different Triad components can be assumed since relative weighting among the LOEs can be considered as “of questionable value” (Chapman, 1996). Although recent works on Weight of Evidence (WOE) sediment quality assessments have given more weight to the toxicity and benthos LOE than to the sediment chemistry LOE (Chapman, 2007), or have assigned the most weight to the benthic community LOE (McPherson et al., 2008; Chapman and Anderson, 2005), the case-specific context can assist with the need for differential weighting. Assigning weights for the LOEs in relation to the additional aspects considered, could be more problematic. The opinion of stakeholders has been shown to assist with this challenge, mainly through the use of surveys, questionnaires and meetings (Balasubramaniam and Voulvoulis, 2005). Stakeholder participation has been considered of concern in the past, especially when selecting sediment remediation options for particular sites (Linkov et al., 2006b, 2005). However, since the aim of this methodology is not to choose a management option for a particular site but to prioritise different areas under study, the opinions of the stakeholders could be less useful. Stakeholders from each zone would probably give more importance to the criteria that they think are more relevant for their particular area. On the other hand, the use of a single set of criteria weights, elicited from expert opinion, might also be questioned as an option that incorporates a degree of subjectivity to the analysis. A common way to resolve such issues is to use several scenarios of weights with different distributions between the percentage of weights assigned to LOEs and the percentage given to the rest of criteria, in order to assess the sensitivity of the selected option to these changing weights. 2.4. Application of MCA The final step of this prioritisation phase involves calculations on the multicriteria comparison of the performance of the different management units, with regard to the selected weighted criteria, resulting in a ranking order of sites to be managed first. Numerous techniques for solving an MCA problem have been developed, growing rapidly over recent decades (Hajkowicz and Higgins, 2008). Comprehensive overviews of many different theories, methodologies and techniques of MCA can be found, for example, in Belton and Stewart (2001) or Figueira et al. (2005). Briefly, some of the best known methodologies are methods from within Multiple Attribute Utility Theory (MAUT) (Keeney and Raiffa, 1976), which tries to assign a utility value to each criterion and aggregates them to represent the desirability of each decision option; the Analytical Hierarchy Process (AHP) (Saaty, 1980), which has in its implementation many similarities with the MAUT approach, but makes different assumptions; or outranking approaches (Roy, 1996), such as the ELECTRE and PROMETHEE methods, which focus on pair wise comparison of alternatives. Since any ranking of alternatives based on the application of just one of the multitude of available MCA tools may be questioned (Yatsalo et al., 2007), it is advisable to use more than one MCA technique to determine the robustness of the options (Balasubramaniam et al., 2007). Likewise, uncertainty in criteria values can have a significant affect on rankings and decisions (Kiker et al., 2008). Therefore, apart from performing sensitivity analyses of weights, it can be useful to explore


how results may change when considering uncertainty in scores. If results are quite stable, decision makers may surmise that rankings are strongly held (Linkov et al., 2006b). 3. Case study The above methodology for site prioritisation was applied to a case study of sediment management in the Bay of Santander, which is an estuarine area of 24.4 km2 located in the Cantabrian Sea along the northern coast of Spain. Sediments in different areas of the Bay of Santander are affected to different degrees by urban, industrial and port activities, and have different impacts on economic interests, associated with sediment quality (e.g. tourism or fishing). Thus, this case study provides an interesting example for the proposed methodology. 3.1. Definition of management units The studies carried out to implement the WFD in the region of Cantabria, Spain, following the regulations of the Directive, were used to define three water bodies in the Bay of Santander (RMEC, 2005) as management units to be compared during the prioritisation phase. This was based on existing detailed analyses of the distribution of

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pressures and impacts, which revealed important differences among the three areas of the Bay (Fig. 2): Area S1: The water body defined as S1 is situated on the western side of the Bay and includes two commercial ports, the fishing port and an important commercial port, in which the transport of goods exceeded 6,200,000 tons in 2007. This area has a population of more than 200,000 inhabitants and has high levels of tourism. The development of the port activity involves the need to dredge periodically in order to maintain the depth of the navigation channel, and most of the natural limits have been changed by anchoring structures. Because of these physical alterations, this area has substantially changed in character so, according to the definitions of the WFD, it should be considered as a “heavily modified water body”. Area S2: The central part of the Bay includes the estuaries of Boo, Solía and San Salvador, and is also subject to urban pressure. However, this area has less severe physical alterations than S1 and is close to the most heavily industrialised areas of the Bay, with mainly metallurgical and chemical industries located along the shoreline. These characteristics make this area remarkably different from the rest of the Bay, so it was considered as another water body (S2). Area S3: The rest of the Bay includes the intertidal moors of the eastern side of the Bay and the estuary of Cubas. These rural areas

Fig. 2. Management units defined in the case study of the Bay of Santander, Spain.

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share similar environmental characteristics and low degrees of anthropogenic pressure, and therefore the whole portion is defined as water body S3. 3.2. Definition and evaluation of criteria The criteria defined in this case study comprised the three most common LOEs of a Sediment Quality Triad (i.e. sediment chemistry, toxicity and benthic community alteration), together with four of the additional criteria suggested in Table 1. The criterion “cooperation and support of administration”, suggested in Table 1, was not included in this case study since the three management units to be ranked are situated in the same region and are governed by the same administrative bodies. The seven criteria considered were evaluated for each of the management units defined in the Bay, and the results of this evaluation were summarised in Table 3. 3.2.1. Evaluation of LOEs Data from chemical analyses and toxicity tests originated from previous studies of sediment quality in the Bay of Santander (Viguri et al., 2007; Alvarez-Guerra et al., 2008b; DelValls et al., in preparation). The concentrations of metals (As, Cr, Cu, Ni, Pb and Zn) and polycyclic aromatic hydrocarbons (PAHs) (low molecular weight PAHs (2–3 aromatic rings) and high molecular weight PAHs (4–6 aromatic rings), were used to calculate mean Sediment Quality Guidelines quotients (mSQGs) (Long et al., 2006) for each management unit. This approach can be a useful way of integrating the multiple results of chemical analyses, since mSQGs values provide a single, easily understood, effects-based numerical index of the relative degree of chemical contamination of sediments (Alvarez-Guerra et al., 2007). The widely applied ERM guidelines of Long et al. (1995) were used to derive mean ERM quotients (mERMq) for each of the areas as the arithmetic mean of quotients obtained by dividing the concentration of chemicals by their respective ERM values. Sediment toxicity was evaluated by means of three different bioassays: 10-day amphipod mortality test with Ampelisca brevicornis, 7-day polychaeta mortality assay with Arenicola marina and Microtox Basic Solid Phase Test (BSPT) bioassay. In order to synthesise the three toxicity results into a single measure, a scoring system of 1 to 6 (Table 2) was developed, based on the decision criteria for integrating individual toxicity data recently proposed by McPherson et al. (2008). The results of the amphipod and polychaeta bioassays were assessed, using the percentage reduction in survival (i.e. whether the reduction is N20% or N50%), giving more weight to toxicity data that were statistically significant (p b 0.05) compared to the negative control than to data that did not show a statistically significant difference (McPherson et al., 2008). Regarding Microtox BSPT, the criteria proposed in Spain to consider a sediment sample as toxic (EC50 b 750 mg/L d.w. = 0.075%) (Morales-Caselles et al., 2007) were used within the rating system as a reference threshold for assessing Microtox results. Benthic community data were obtained from studies developed in the Bay of Santander in the context of WFD implementation (RMEC, 2005), where the quality of benthic invertebrate communities in each of the water bodies was assessed, using three different measures:

Table 3 Criteria values for each of the management units. Criterion

Measure

S1

S2

S3

LOE-chemistry LOE-toxicity LOE-benthos Social: people living around the area Sensitivity of the environment Economic relevance Likelihood of recontamination

mERMq Scores of 1 to 6a M-AMBI Inhabitants/km2 Qualitative (+/+++) Qualitative (+/+++) Qualitative (+/+++)

0.68 1 0.78 2978 + +++ ++

1.09 4 0.75 589 ++ ++ ++

0.15 1 1.00 143 +++ +++ +

a

See Table 2.

richness (number of species), Shannon–Wiener diversity index (Shannon and Weaver, 1949) and the AMBI index. The AMBI (AZTI's Marine Biotic Index) (Borja et al., 2000) offers a “pollution or disturbance classification of a particular site, representing a good overview of the benthic community health” (Borja and Muxika, 2005). It is the most commonly used biotic index within the WFD (Bigot et al., 2008). The M-AMBI (Multivariate AMBI) method (Borja et al., 2004; Muxika et al., 2007) was applied to aggregate these three measures into one global result of benthic quality. The M-AMBI combines AMBI with richness and Shannon's diversity through the use of statistical multivariate tools, Factor Analysis and Discriminant Analysis techniques (Borja et al., 2008). The value of M-AMBI for each management unit was computed using the “AMBI software version 4.1” (freely available online at http://www.azti.es), following the recommendations of its developers (Borja and Mader, 2008). 3.2.2. Evaluation of other criteria A thorough compilation of information about each of the management units was carried out to determine the values for the other criteria. The density of population around each area was calculated, using data from the latest Municipal Register of Inhabitants (for 2007) that were obtained from the Spanish National Statistics Institution (INE, 2008). The sum of the number of people living in the municipalities that border on each of the management units was divided by their surfaces to derive the value of population density summarised in Table 3. The rest of the criteria considered in this study were evaluated on a qualitative scale (+, ++, +++), based on the analysis of the information gathered about each of the zones, as explained briefly below. The sensitivity of the environment of unit S3 was assessed as high, since it is a zone of significant ecological value, including an area of 6.11 km2 (called “Dunas del Puntal y Estuario del Miera”) which has been defined as a Site of Community Importance (SCI), according to the European Habitats Directive 92/43/ECC (ICANE, 2008). This area has important botanical and ornithological interest, constituting unique scenery of high conservation value. On the contrary, and as mentioned before, S1 is a “heavily modified water body” in which the 99.6% of its perimeter has been altered (i.e. its natural margins have been replaced by anchoring structures) (RMEC, 2005). The sensitivity of the environment in S2 is between S3 and S1; its shoreline has been modified by mining and naval activities but the alterations have not been as severe as in unit S1. Although S2 does not contain areas of such significance as the SCI in S3, it includes some sites of relative ecological

Table 2 Score system used for integrating individual toxicity test results into a sediment toxicity rating (based on McPherson et al., 2008). Score (1–6)

Observed pattern in toxicity data

6

Greater than a 50% reduction in at least one acute endpoint (i.e. survival) Greater than a 20% reduction in two acute endpoints (i.e. survival) and the differences are statistically significant Greater than a 20% reduction in survival in at least one acute endpoint (i.e. survival) with an statistically significant difference, and Microtox EC50 is b 0.075% Greater than a 20% reduction in survival in at least one acute endpoint (i.e. survival) with an statistically significant difference, but Microtox EC50 is N 0.075% Greater than a 20% reduction both in amphipod and polychaeta survival, but the differences are not statistically significant; Microtox EC50 is b0.075% Greater than a 20% reduction both in amphipod and polychaeta survival, but the differences are not statistically significant; Microtox EC50 is N0.075% Greater than a 20% reduction only in survival in one acute endpoint (i.e. survival) but the differences are not statistically significant Less than a 20% reduction both in amphipod and polychaeta survival but Microtox EC50 b 0.075% Less than a 20% reduction both in amphipod and polychaeta survival, and Microtox EC50 N 0.075%

5 4 3 2 1


value, such as the wetlands “Marismas Blancas”. These have a surface area of 0.2 km2 and have been designated as a bird sanctuary because they provide shelter and nesting sites for many aquatic birds. Regarding the importance of economic activities that could be directly affected by sediment quality, in general the Bay of Santander is a tourist zone where fishing and shell fishing take place. However, after analysing the information compiled, some differences were found among the units. S1 is the main area for tourism, as revealed by the 7298 accommodation places officially available there, compared with 3567 places in S3 and only 1281 places in S2. The number of accommodation places includes hotels, inns, hostels, apartments, rural lodgings and campsites, which existed in 2007 in the municipalities that surround each unit, according to the data from the Statistics Institute of Cantabria (ICANE, 2008). Despite having fewer accommodation places than S1, unit S3 is an area with important tourist interests and, with its 3.8 km or more of beaches, is a focus for recreational activities. In addition, most of S3 has been established during the last two years as a zone for commercial fishing and shell fishing by the regional government (BOC, 2007, 2008). Although unit S2 is not a tourist spot like the other two areas, the commercial shell fishing in some of its estuaries (BOC, 2007, 2008), as well as its zones for mollusc production (BOE, 2005), means that S2 scored as medium (i.e. ++) for “economic relevance“. The criterion “likelihood of recontamination” was evaluated as low in S3 and medium in S1 and S2. The problem of the direct discharge without treatment of urban wastewater, which has been identified in previous studies as one of the sources of historical sediment contamination in the Bay of Santander (González-Piñuela, 2007), has been progressively solved since 1999, due to the implementation, in several stages, of sanitation systems in the perimeter of the Bay. The analysis of pressures, developed to implement the WFD in Cantabria, identified several villages in the area of S3 that discharged their sewage into this water body in an uncontrolled way (RMEC, 2005). There are works in progress to connect these villages to the rest of the sanitary system so, in the short term, the whole Bay will have an integral sanitary scheme. Bearing in mind that S3 has only low urban and industrial pressure, the likelihood of its recontamination is low. The same cannot be said for the other two areas, where the probability of recontamination after management is higher. In S1, the diffuse pollution sources from the urban front areas, as well as several point sources due to port activities identified in the analysis of pressures within the WFD (RMEC, 2005) would be active after a hypothetical remediation. In S2, the elimination of wastewater discharges from industries surrounding this water body (mainly metallurgical, mineral and industrial park activities) (RMEC, 2005) is not foreseen either. Moreover, both in S1 and S2, the port and industrial character of its activities means that the probability of direct pollutant accidental spills is much higher than in S3. 3.3. Elicitation of weights Seven different scenarios of weights were considered, with the aim of performing a sensitivity analysis to explore how rank ordering of management units might change when using different criteria weights. In each of these scenarios, weights were equally distributed among each of the two types of criteria but in different percentages, as explained below: - In one scenario (“scenario 1”), the same weight is given to the three LOEs and to the other criteria. - In other scenarios, more weight is given to one of the two great types of criteria, with different distributions of percentages between the scientific LOEs and the other criteria: 75% LOEs–25% others, 60–40, 40–60 and 25–75 (“scenarios 2 to 5” respectively). - Two extreme scenarios were also considered, in order to analyse what happened when the decision was based solely on the LOEs (“scenario 6”) or solely on the other criteria included (“scenario 7”).

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3.4. Application of MCA The multicriteria analyses were performed using the software package DEFINITE 3.0 (Janssen et al., 2003). Four different MCA methods were applied in order to analyse the effect that the choice of the MCA technique could have on the results: Weighted Summation, ELECTRE II, Evamix and Regime. Detailed descriptions of these methods and of their theoretical foundations are presented in Nijkamp et al. (1990), Janssen (1992) or Janssen et al. (2003). Briefly, Weighted Summation is considered to be a very simple technique of the MAUT family of methodologies, but extremely popular in practice (Balasubramaniam et al., 2007), and is based on the simple process of multiplying criteria scores by their corresponding weights and summing the weighted scores of all criteria to attain an appraisal score for each alternative. ELECTRE II is an outranking approach, in which the ranking of the alternatives is achieved by a systematic elimination in pair wise comparisons, based on the concepts of concordance index (the degree to which one alternative is preferred to another) and discordance index (the amount in which one alternative is worse than another) (Belton and Stewart, 2001). Evamix is another outranking approach, with the difference that separate dominance indices are constructed for the qualitative and quantitative criteria (Figueira et al., 2005). Finally, the Regime method can be seen as an ordinal generalisation of pair wise comparison methods (Figueira et al., 2005) or as an analytic hierarchy process that relies on pair wise comparison of the scores for all alternatives and for each criterion (Voulvoulis et al., 2002). The results of the rankings of the sediment management units obtained, using Weighted Summation and Evamix for the seven scenarios of quantitative weights considered, are summarised graphically in Fig. 3, together with the final appraisal scores of each alternative. Fig. 3 also shows the results obtained with ELECTRE 2, although this method does not provide final evaluation scores and, as can be seen in the figure, did not provide a complete ranking in any of the scenarios (i.e. two management units were always ranked equal). The prioritisation of areas for sediment management, obtained within each given scenario of weights, was very similar whichever of the three MCA methods was applied. In fact, differences in the management unit ranked as first were only found in scenario 4 (40% of weights for LOEs, 60% for other criteria), where both Weighted Summation and ELECTRE 2 ranked unit S2 as the first option, but for Evamix, S1 was ranked first. Nevertheless, analysing the final appraisal scores with Weighted Summation and Evamix for scenario 4 (Fig. 3), it can be observed that the differences among the scores for the three alternatives in each of these methods were negligible so the ranking orders might not be conclusive. Apart from scenario 4, and although the differences in the final scores of the units varied among scenarios, the rankings derived from them could be considered as relevant to the rest of the quantitative distributions of weights. The evolution of the ranking order of each management unit, obtained with the different MCA methods and distributions of weights, is represented in Fig. 4, to analyse the sensitivity of the results easily to the criteria weights. In the situation where only scientific LOEs were included in the MCA (scenario 6), the area needing more management was S2. In the opposite situation, with the exclusion of LOEs from the analysis and only the consideration of other criteria (scenario 7), the higher priority area turned out to be unit S3. Fig. 4 clearly shows the evolution of the results between these two extreme situations: unit S2 appeared as the first option, when weights of up to 40% were distributed among the LOEs, but when LOEs were only assigned 25% of the weights or were not considered, the area ranked first was S3. However, all the scenarios in which at least 50% of the weights were distributed among the LOEs (scenarios 1, 2, 3 and 6) led to the same ranking order of the management units: 1st S2, 2nd S1 and 3rd S3, or in the case of ELECTRE 2 (which, as mentioned before, was not able to provide complete rankings), both S1 and S3 ranked equal as the second option.

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Fig. 3. Results of ranking of management units (S1, S2 and S3) in the different weight scenarios and with different MCA methods.


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Fig. 4. Sensitivity analysis of the ranking of management units (S1, S2 and S3) to the criteria weightings with different MCA methods. An intermediate rank number was assigned when two alternatives ranked equal. For Weighted Summation, ELECTRE 2 and Evamix, the scenarios of quantitative weights are arranged in decreasing order of importance assigned to the LOEs. For Regime, the qualitative scenarios are described in the box below the figure.

The results obtained with Regime (also shown in Fig. 4), a method that uses only ordinal information on weights, confirmed the results found in the sensitivity analysis carried out with quantitative weights.

In applying the Regime method, only the order of importance of the criteria is required; since it does not allow the user to specify quantitative weights, the seven scenarios could not be used and

Fig. 5. Influence of criteria scores uncertainty in the ranking of management units with different MCA methods. The size of the circles is proportional to the probability that each management unit receives a certain position in the ordering.

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qualitative possibilities were analysed. As shown in Fig. 4, the evolution of the results was similar to those obtained using the other three MCA methods. When the scientific LOEs were given qualitatively higher or similar importance than the additional criteria, Regime also ranked S2 as the priority area to be managed. Only when the decision was made without including the LOEs, Regime gave a ranking where S3 was the first option. Finally, sensitivity analyses to assess the effect of uncertainties in the criteria scores were performed, as a final study to check the stability of the results. Scenario 1, a balanced scenario where weights were equally divided between LOEs and the additional criteria, was chosen as a reference to test the influence of scores' uncertainty in the rankings, using a procedure included in the software package DEFINITE. This procedure calculates the ranking of the alternatives many times, using a random generator that creates different drawings by changing the scores within the specified uncertainty percentage. The probability of an alternative ranking in a certain position is calculated by dividing the number of times the alternative ranks in that position, by the total number of drawings (Janssen et al., 2003). Fig. 5 shows graphically the results obtained, considering uncertainties in scores of 10, 25 and 50% for all criteria and using the same three MCA methods as in the sensitivity analysis of weights. In the case of 10% uncertainty, the large-sized circles on the main diagonal of the graphs indicate that, despite scores deviating from the values assigned up to 10%, the ranking of areas hardly varied. As could be expected, when uncertainties were increased to 25% and 50%, the probabilities of obtaining different rankings were higher, particularly with the ELECTRE 2 method, which again only gave incomplete rankings. However, with Weighted Summation and especially Evamix, even with 50% of uncertainty, the results remained very stable. Therefore, these analyses helped to confirm the robustness of the rankings obtained, and reiterated the decision to identify unit S2 as the most convenient area of the Bay of Santander for sediment management. 4. Discussion An integrated sediment management strategy should be based on the inclusion of technical, economic, social and environmental criteria for the assessment of the needs of the areas to be managed, as well as in the selection of the best management option. In this sense, the MCA-based methodology for prioritisation of areas, described in this paper, can be a useful tool that allows decisions to be made on multiple and sometimes conflicting criteria, with the transparency and objectivity required for tackling environmental management issues in general, and sediment management in particular. The methodology can be applied both to prioritise sites within a certain basin (e.g. within a fluvial area, an estuary or a marine area) or to prioritise areas in different basins. However, it should be emphasised that the proposed approach is not meant to be rigid or prescriptive but is intended to be a flexible methodology, in which the adaptation of its steps to the peculiarities of the case under study is of crucial importance. Thus, although we believe that the use of the definition of “water bodies” considered in the WFD is a very sound method for delimitating significant management units, the specific characteristics of the zones under consideration may favour using other kinds of divisions. For example, it may be more effective to sub-divide “water bodies” into smaller management units, or previous studies or projects in the zone may offer a background knowledge that already advises its division in certain management units. Similarly, flexibility is also necessary to define the criteria to be included in the MCA. The different criteria beyond the scientific LOEs discussed in this methodology, as well as the means of evaluating them, should only be seen as possible suggestions for considering social, economic, ecological or practical aspects that may influence the

decisions. These criteria must be adapted (maybe with replacement or additional criteria) to suit the particular circumstances of the areas to be prioritised. Likewise, the design of the multiple LOE approach to assess the quality of the sediments should take into account the sitespecific conditions of each of the management units, together with possible constraints in time or costs. Usually, the triad of results of chemical analyses, toxicity tests and benthic community structure analyses will be necessary. However, the application of tiered frameworks in sequential steps to the case under study (Chapman and Anderson, 2005; Chapman, 2007) may reveal that additional or more extensive studies (such as in situ bioassays, biomarkers or biomagnification) are required to assess accurately the sediment quality of that area. If, after applying a tiered assessment framework, the result is that the sediments of some of the management units are not polluted or pose a negligible environmental risk, these units could be dismissed from consideration for the subsequent steps of the methodology. However, since on certain occasions the convenience of management can be motivated by the need for achieving socioeconomic objectives (Apitz and White, 2003), it could be sometimes interesting to consider for the MCA areas that do not show important levels of pollution. For example, this was the situation of unit S3 in the case study of the Bay of Santander: in spite of its low pollution levels, the great ecological relevance of this area and the high socio-economic interest in its conservation led us to maintain it in the MCA as a possible unit to be managed. It is important to clarify that, in these situations, the possible management of these low polluted areas would not focus on the remediation of the sediments but on the performance of measures of pollution prevention and minimisation. One of the strong points of this methodology is that, since it considers other socio-economic or ecological aspects apart from LOEs, it can identify areas that may not require active remediation. They may need priority attention by means of urgent pollution monitoring and prevention to improve, or at least ensure, that the quality of the sediments does not get worse and thus protect the resources on which economic and social activities depend. The results of the case study in the Bay of Santander highlight the importance of applying different MCA methods and of analysing the sensitivity of the results to criteria weights and uncertainties in criteria scores to determine the stability and robustness of the rankings of units. Due to the quantity of MCA tools available, decisions made using only one method may be open to question so the use of different MCA methods is strongly advisable. If several MCA tools produce similar rankings of areas, the prioritisations will become much more sound and defendable but, if results vary with different methods, it may not be possible to establish priorities clearly among the areas and there may be compromises. One of the possible limitations of a methodology based on MCA could be the difficulty of weighting the relative importance of the different criteria. For that reason, regardless of the technique used to elicit the weights (e.g. surveys, definition of scenarios, etc.), it is important not to make the decision, using a single set of weights. Instead, we recommend analysis of the sensitivity of the results using different distributions of weights to take into account a wide range of viewpoints. The sensitivity analysis in the case study of Santander is an example of the difference in order of priority of the areas, depending on the importance of social, economic or ecological aspects weighted against the scientific LOE. When basing a decision on scientific LOEs only, the central area of the Bay S2 would need more urgent sediment remediation. If prioritisation was based only on social, economic or ecological criteria, S3 would be the area whose management would be more appropriate. Though it could be important to include these other criteria in the MCA, the information on sediment quality derived from LOEs should not be disregarded or reduced to a minor role, when eliciting the criteria weights. Although it is interesting to study the robustness of the MCA results, using a wide range of scenarios of weights, realistically decisions should be


based mainly on scenarios that give at least the same importance to the LOEs as to the other criteria. Another limitation of the methodology is the possible uncertainty in scoring the criteria, especially those whose evaluation is more difficult, due to lack of data or to their qualitative nature. The use of software tools to explore how results may change, when considering uncertainty in criteria values (such as that applied in the case study), can be extremely useful to assess the strength and robustness of the prioritisation of areas obtained. Finally, it is worth noting that the application of this methodology of prioritisation of areas should be considered only as the first part of an integrated approach for sediment management. Once the areas of concern have been identified, it is necessary to tackle the challenging task of assessing and selecting the best alternative for the sustainable management of those sediments. Acknowledgment This research project was supported by the financial help of the Spanish Ministry of Science and Innovation Project CTM 2005-07282C03-03. Manuel Alvarez-Guerra was funded by the Spanish Ministry of Science and Innovation by means of an F.P.U. fellowship. References Abriak NE, Junqua G, Dubois V, Gregoire P, Mac Farlane F, Damidot D. Methodology of management of dredging operations. I. Conceptual developments. Environ Technol 2006;27(4):411–29. Al-Rashdan D, Al-Kloub B, Dean A, Al-Shemmeri T. Environmental impact assessment and ranking the environmental projects in Jordan. Eur J Oper Res 1999;118(1):30–45. Alvarez-Guerra M, Viguri JR, Casado-Martínez MC, DelValls TA. Sediment quality assessment and dredged material management in Spain: part I, application of sediment quality guidelines in the Bay of Santander. Integr Environ Assess Manag 2007;3(4):529–38. Alvarez-Guerra M, Alvarez-Guerra E, Alonso-Santurde R, Andrés A, Coz A, Soto J, et al. Sustainable management options and beneficial uses for contaminated sediments and dredged material. Fresenius Environ Bull 2008a;17(10A):1539–53. Alvarez-Guerra M, González-Piñuela C, Andrés A, Galán B, Viguri JR. Assessment of selforganizing map artificial neural networks for the classification of sediment quality. Environ Int 2008b;34(6):782–90. Apitz S, White S. A conceptual framework for river-basin-scale sediment management. J Soils Sediments 2003;3(3):132–8. Apitz SE, Davis JW, Finkelstein K, Hohreiter DW, Hoke R, Jensen RH, et al. Assessing and managing contaminated sediments: part I, developing an effective investigation and risk evaluation strategy. Integr Environ Assess Manag 2005a;1(1):2–8. Apitz SE, Davis JW, Finkelstein K, Hohreiter DW, Hoke R, Jensen RH, et al. Assessing and managing contaminated sediments: part II, evaluating risk and monitoring sediment remedy effectiveness. Integr Environ Assess Manag 2005b;1(1):e1–e14. Babut M, Oen A, Hollert H, Apitz SE, Heise S, White S. Prioritisation at river basin scale, risk assessment at site-specific scale: suggested approaches. In: Heise S, editor. Sustainable management of sediment resources. Sediment Risk Management and CommunicationAmsterdam, The Netherlands: Elsevier; 2007. p. 107–51. Balasubramaniam A, Voulvoulis N. The appropriateness of multicriteria analysis in environmental decision-making problems. Environ Technol 2005;26(9):951–62. Balasubramaniam A, Boyle AR, Voulvoulis N. Improving petroleum contaminated land remediation decision-making through the MCA weighting process. Chemosphere 2007;66(5):791–8. Belton V, Stewart TJ. Multiple criteria decision analysis: an integrated approach. Dordrecht, The Netherlands: Kluwer Academic Publishers; 2001. Bigot L, Grémare A, Amouroux J, Frouin P, Maire O, Gaertner JC. Assessment of the ecological quality status of soft-bottoms in Reunion Island (tropical Southwest Indian Ocean) using AZTI marine biotic indices. Mar Pollut Bull 2008;56(4):704–22. [BOC] Boletín Oficial de Cantabria/ Official Gazette of Cantabria. 2007. Order GAN/35/ 2007 of 6 June. BOC Num 115: 8612–8616. [BOC] Boletín Oficial de Cantabria/ Official Gazette of Cantabria. 2008. Order DES/33/ 2008 of 29 April. BOC Num 93: 6666–6671. [BOE] Boletín Oficial del Estado/ Official Spanish Gazette. 2005. Order APA/3228/2005. BOE 249 of 18/10/2005 Sec3: 34100–34117. Borja A, Mader J. Instructions for the use of the AMBI index software (version 4.1). AZTITecnalia; 2008. 13 pp. Borja A, Muxika I. Guidelines for the use of AMBI (AZTI's marine biotic index) in the assessment of the benthic ecological quality. Mar Pollut Bull 2005;50(7):787–9. Borja A, Franco J, Pérez V. A marine biotic index to establish the ecological quality of soft-bottom benthos within European estuarine and coastal environments. Mar Pollut Bull 2000;40(12):1100–14. Borja A, Franco J, Valencia V, Bald J, Muxika I, Belzunce MJ, et al. Implementation of the European water framework directive from the Basque country (northern Spain): a methodological approach. Mar Pollut Bull 2004;48(3–4):209–18.

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