GIS-based multicriteria municipal solid waste landfill suitability analysis: a review of the methodologies performed and criteria implemented.

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GIS-based multicriteria municipal solid waste landfill suitability analysis: A review of the methodologies performed and criteria implemented OE Demesouka, AP Vavatsikos and KP Anagnostopoulos Waste Manag Res published online 13 March 2014 DOI: 10.1177/0734242X14526632 The online version of this article can be found at: http://wmr.sagepub.com/content/early/2014/03/13/0734242X14526632

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WMR0010.1177/0734242X14526632Waste Management & ResearchDemesouka et al.

Review Article

GIS-based multicriteria municipal solid waste landfill suitability analysis: A review of the methodologies performed and criteria implemented

Waste Management & Research 1–27 © The Author(s) 2014 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav DOI: 10.1177/0734242X14526632 wmr.sagepub.com

OE Demesouka, AP Vavatsikos and KP Anagnostopoulos

Abstract Multicriteria spatial decision support systems (MC-SDSS) have emerged as an integration of the geographical information systems (GIS) and multiple criteria decision analysis (MCDA) methods. GIS-based MCDA allows the incorporation of conflicting objectives and decision maker (DM) preferences into spatial decision models. During recent decades, a variety of research articles have been published regarding the implementation of methods and/or tools in a variety of real-world case studies. The article discusses, in detail, the criteria and methods that are implemented in GIS-based landfill siting suitability analysis and especially the exclusionary and non-exclusionary criteria that can be considered when selecting sites for municipal solid waste (MSW) landfills. This paper reviews 36 seminal articles in which the evaluation of candidate landfill sites is conducted using MCDA methods. After a brief description of the main components of a MC-SDSS and the applied decision rules, the review focuses on the criteria incorporated into the decision models. The review provides a comprehensive guide to the landfill siting analysis criteria, providing details regarding the utilization methods, their decision or exclusionary nature and their monotonicity. Keywords Geographical information systems, multicriteria analysis, landfill siting, suitability analysis, site selection criteria

Introduction The rapidly growing world population, the increasing complexity of products, the use of environmentally problematic substances in consumer goods or the enormous material consumption of highly developed economies, as well as the degree of urbanization, people’s ethics, behaviours and attitudes (Kollikathara et al., 2009) are only some of the factors that have generated the need to develop efficient waste management systems. Although modern waste management is a combination of different treatment technologies aiming to increase recycling and deposit ‘useless’ and more or less harmless materials, municipal solid waste (MSW) landfills are the most common used method of waste treatment due to their simplicity in application. However, the MSW landfills that are already in use cannot fulfil regional landfilling needs due to unsuitability or lack of adequate infrastructure. The site selection process is considered one of the most critical tasks related to MSW management systems because of the consequences for the natural environment and the social opposition that landfill siting may evoke (Tchobanoglous et al., 1993). Urbanization and the lack of appropriate infrastructure related to waste management raise severe issues concerning public health and the ecology of the residential area. In addition to social acquiescence, the construction of new waste disposal sites must

comply with legal and institutional constraints to prevent air and water contamination, therefore prohibiting landfill siting in environmentally sensitive areas. However, the quest for appropriate sites cannot be approached as a single-criterion decision-making problem that aims only to address environmental awareness issues because numerous factors are involved in the decision-making process. In particular, people raising opposition to the implementation of landfills near residential areas, due to the negative effects (e.g. social, economic) to the surrounding areas, are responsible for major delays during the site selection process. These delays frequently lead to exceeding in the available budget due to increments of the construction costs (Erkut and Moran, 1991; Lober and Green, 1994). Widely known as the NIMBY (‘not in my back yard’) syndrome, this opposition is the main reason for the increasing difficulty in

Department of Production Engineering and Management, Democritus University of Thrace, Xanthi, Greece Corresponding author: AP Vavatsikos, Department of Production Engineering and Management, Democritus University of Thrace, Vas. Sofias 12, 67100 Xanthi, Greece Email: [email protected]


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Waste Management & Research

finding appropriate sites for landfills, or locally unacceptable land uses (LULUs), especially in densely populated areas (Noble, 1992). Land-use suitability mapping and analysis aims to identify appropriate spatial patterns for future land use according to specific constraints, preferences or predictions of some activity (Collins et al., 2001). Therefore, there is an urgent need for approaches that can assure rational decision-making, minimize the environmental impacts and, at the same time, increase the possibility of avoiding public opposition. In that direction, geographical information systems (GIS)-based suitability analysis expands the traditional approaches to the consideration of both attributes and spatial data. During the last three decades, a significant number of studies have been conducted related to solid and hazardous waste landfill siting (e.g. Gebhardt and Jankowski, 1987) or even more to the siting of recycling facilities (e.g. Hokkanen and Salminen, 1997; Lukasheh et al., 2001). A common practice among these approaches is that the evaluation of alternatives occurs in an aspatial decision context where alternatives’ performance on the analysis criteria is considered already known. However, the spatial nature of the MSW landfill site selection problem requires the use of GIS. Based on McHarg’s idea of map layering (McHarg, 1992), the procedures supported for managing, elaborating and performance of spatial information enhance their role in siting analysis. Furthermore, their synergy with multiple criteria decision analysis (MCDA) methods results in the development of multicriteria spatial decision support systems (MC-SDSS). This synergy aims to rank acceptable locations according to their importance in satisfying analysis objectives. MC-SDSS research focuses on the development of methods, rules and software packages that enable the mapping of decision maker (DM) preferences and the evaluation of the candidate sites based on suitability index estimations. The publication of a variety of scientific articles (Malczewski, 2006), illustrates the advantages derived by the synergy of GIS and MCDA methods in site selection feasibility studies. A common practice in these articles is the development of approaches that aim to identify the most suitable sites for locating MSW landfills. In this paper, a presentation of the applied GIS-based MCDA methods and an extensive survey of the exclusion and non-exclusion criteria that have been applied over the last three decades to select suitable sites for MSW landfill siting are attempted. These approaches focus on expanding the standard GIS overlay procedures, which are used to satisfy constraints, mainly derived from the legislative framework (e.g. European Council Directive 1999/31/EC), to the consideration of DMs preferences. The procedure aims to reach consensus among the different interested groups that interact with in the decision making process and in the same time to provide tradeoffs against conflicting objectives. The aim of this paper is to review the methodological frameworks and criteria performed regarding MSW landfill suitability analysis. This paper presents the applied GIS-based MCDA

Figure 1. Decision making process in spatial systems. Source: Anagnostopoulos and Vavatsikos (2010).

methods that have been performed to combine the per-criterion impact scores and the implemented criteria. To the best of our knowledge, this is the first time that utilization procedures, preference functions monotonicity and the threshold values of the performed buffer zones of the constraints (either imposed by the relative legislative framework or for safety reasons by DMs) implemented in GIS-based MSW landfill suitability analysis are discussed in detail. In that manner, both analysts and researchers can obtain the performed methodologies and criteria implemented that has been considered up to now to obtain locations candidate for MSW landfills siting. Moreover, they can be informed about the recent developments regarding the proposed and/or adopted practices that have been considered aiming to achieve consensus during the site selection process.

Decision making using MC-SDSS The combined use of GIS-based tools and decision analysis methods broadens the capabilities of the research area in a complementary way. This approach provides a consistent framework for handling conflicting objectives and structured or semistructured problems, and it allows the analysis to consider the preferences of stakeholders. Recent developments in both fields expand the abilities of the Boolean overlay procedures, which are supported by the commercial GIS software packages, to consider decision criteria as well (Malczewski, 1999). MC-SDSS maximizes the efficiency of the entire analysis by allowing the ranking of acceptable solutions according to their relative importance in satisfying the analysis objectives (Carver, 1991; Demesouka et al., 2013b). Fuzzy extensions of the synergies mentioned above are capable of sufficiently handling the vagueness of the DMs (Anagnostopoulos and Vavatsikos, 2011; Vavatsikos, 2008). Rational decision making is characterized by a coherent sequence of actions that protects DMs from dubious results in the final outcome. As a procedure, decision making consists of a set of activities that aim to decompose decision problems into their constituent parts. The methodological framework, as stated by Simon (1960), can be distinguished by three phases (Figure 1). In the intelligence phase, decision space is well stated by


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Demesouka et al. identifying objective trees and determining constraint criterion maps. In the design phase, feasible alternative scenarios are determined by performing Boolean overlays among the constraint criterion maps. After the formation of the analysis decision table, the relative importance of the criterion maps is estimated, and utilization/standardization procedures on the geographical data are performed. In the choice phase, suitability index maps are derived as the result of synthesis of the criterion utilities under a certain decision rule. The latter not only allows the classification of the alternatives but also affects the final result, as it depicts the interaction between the criteria and the alternatives for the satisfaction of the analysis objectives. Finally, a sensitivity analysis is performed to determine the robustness of the final solution when the relative importance of the analysis criteria varies. When conflicts arise, a compromise solution for implementation in the study area is identified (Anagnostopoulos and Vavatsikos, 2007; Malczewski, 1999; Turban, 1993). Subsequently, in the implementation phase, the credibility of the selected alternative is assessed and an effort is made to estimate the public opposition. If the chosen alternative does not achieve the expectations of the DMs, the previous phases are repeated. The procedure ends when an alternative that ensures public acceptance and minimizes environmental consequences is chosen. The major advantage derived from the implementation of GIS-based MCDA is demonstrated by the potential to help build consensus and reduce conflicts during the siting process (Higgs, 2006).

Landfill siting using MC-SDSS The indisputable benefits derived from the synergy of GIS with the MCDA methods have resulted in the publication of numerous articles over the last three decades (Malczewski, 2006; Vavatsikos, 2008). A large number of the articles consider the application of MC-SDSS to the landfill siting problem (e.g. Gemitzi et al., 2007; MacDonald, 1996; Sharifi and Retsios, 2004). This is owing to the need for developing methodologies whereby such criteria can be evaluated through potentially consensus-based tools. In addition, the increased promotion of participative tools for rational decision making, as well as the rapid evolution in computer science, which enabled the development of user-friendly commercial GIS packages, significantly contributed to the expansion of MC-SDSS application. An extensive search in the electronic databases of the scientific journals publishers revealed a well-established body of literature regarding the implementation of GIS-based MCDA approaches relative to the MSW landfill siting problem over the years. The search for the identification of the most relevant articles was performed in the electronic libraries of the following scientific journal publishers: ASCE (http://www.asce.org/), ScienceDirect (http://www.sciencedirect.com), SpringerLink (http://link.springer.com), Sage journals (http://online.sagepub.com) and Taylor and Francis (http://www.tandf.co.uk/ journals), while the Scopus (http://www.scopus.com) database

has also been considered. The search was made by using the following terms: GIS and multicriteria, GIS and suitability analysis, GIS and MSW, landfill siting and multicriteria. Thereafter, all the collected articles were limited to those in English language referring to GIS-based (both raster- and vector-driven analyses) landfill siting using MCDA methods. In addition, their references sections and authors were searched separately for the identification of other relevant articles in the area of GIS-based MCDA MSW landfill siting. In particular, 36 articles have been identified (Table 1), almost two-thirds of them (61%) being released after 2005, reflecting the growing interest in this research area (Figure 2). The evaluation of candidate alternative locations for siting sanitary landfills using the multicriteria GIS-based site screening process is characterized by the predominance of the raster over vector analysis by a percentage of 81%. Vector-driven GIS-based suitability analysis allows the evaluation of a finite set of discrete alternative locations that are stored in the geodatabase developed to support the analysis goals. In that sense, vector-driven analyses presuppose that either the alternatives have been identified by a preliminary analysis in a previous stage, or they are identified directly by the DMs. Conversely, raster-driven analysis considers that all the locations (pixels) that satisfy the analysis constraints form that alternative locations set. In that manner, new locations can be identified as possible alternative solutions. Indisputably, the predominance of raster-based analysis has been supported due to recent advances in computer science and especially in the remote sensing and digital image processing research areas, which allowed the efficient manipulation of raster data.

Decision rules The term ‘decision rule’ is used in MCDA to describe the process that allows the synthesis of the per-criterion impact scores to enable the ranking of alternatives. The weighted linear combination (WLC) method is the most popular approach for suitability index estimations (83%) due to the simplicity of the additive weight model (Table 1). As a decision rule, WLC focuses on alternative evaluation according to the contribution of these alternatives in achieving the overall analysis goal (Malczewski, 2004; Yoon and Hwang, 1995). The suitability index Si of the ith alternative is obtained as the sum of the per-criterion satisfaction xijs that is achieved according to its performance xij over the attribute j (Eq. 1). WLC allows full compensation between analysis criteria because a low performance on one criterion can be compensated by a high performance on another. Technically, WLC in the GIS environment is implemented by overlaying weighted standardized criterion maps. When utility functions (UF) are used for standardization, the method is referred to as spatial MAUT (multiattribute utility theory; Keeney and Raiffa, 1976; Pereira and Duckstein, 1993). This approach has been applied in four studies for identifying potential solid waste disposal sites in Italy (Geneletti, 2010), Colombia (Sharifi and Retsios, 2004) and Brazil (Leao et al., 2001, 2004).


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Table 1. Decision rules, criteria weights elicitation methods (Demesouka et al. 2013a). Decision rule

Criterion weights elicitation methods

Reference

WLC MAUT Compromise programming OWA

N/A Equal importance

Kao and Lin (1996) Lane and McDonald (1983) Lane and McDonald (1983) Baban and Flannagan (1998) Dikshit et al. (2000) Halvadakis (1993) Sadek et al. (2006) Delgado et al. (2008) Zamorano et al. (2008) Nas et al. (2010) Siddiqui et al. (1996) Charnpratheep et al. (1997) Kontos et al. (2003) Kontos et al. (2005) Mahini and Gholamalifard (2006) Sener et al. (2006) Sumathi et al. (2008) Wang et al. (2009) Moeinaddini et al. (2010) Sener et al. (2010) Sener et al. (2011) Eskandari et al. (2012) Yildirim (2012) Chang et al. (2008) Ouma et al. (2011) Isalou et al. (2013) Leao et al. (2001) Leao et al. (2004) Sharifi and Retsios (2004) Geneletti (2010) Vatalis and Manoliadis (2002) Demesouka et al. (2013a) Melo et al. (2006) Gemitzi et al. (2007) Gorsevski et al. (2012) Gbanie et al. (2013)

Ratio

AHP

FAHP ANP Ratio

Equal importance AHP AHP

WLC, weighted linear combination; AHP, analytic hierarchy process; FAHP, fuzzy analytic hierarchy process; ANP, analytic network process; MAUT, multiattribute utility theory; OWA, ordered weighted average.

WLC:

m

Si = ∑ w j × xijs (1) j =1

A recently developed approach, the ordered weighted average (OWA; Jiang and Eastman, 2000), is cited in four articles aiming to perform site screening for MSW disposal sites (Gbanie et al., 2013; Gemitzi et al., 2007; Gorsevski et al., 2012; Melo et al., 2006) in Sierra Leone, Greece, FYROM and Brazil, respectively. Finally, though their methodological framework is well stated in the MCDA literature, compromise programming methods (Hwang et al., 1993; Zeleny, 1982) have been implemented only twice. Vatalis and Manoliadis (2002) evaluated eight candidate sites in Northwestern Greece with respect to the distance from the ideal solution, using vector-based GIS analysis, while

Demesouka et al. (2013a) combined a variety of distance metrics to perform raster-based suitability analysis in the Thrace region, Northeastern Greece. Although WLC is implemented arbitrarily without good knowledge of the assumptions of the additive model (Malczewski, 2000), it remains the most popular decision rule among the researchers. The latter is mainly because WLC can be easily implemented in both raster and vector decision environments. In particular, GIS software packages facilitate weighted overlay operators or calculators that enable the performance of the addition operation in a cell-by-cell basis, while in a vector decision environment the performance of weighted summation of attributes row data can be easily obtained. Moreover, WLC provides a flexible procedure that can be easily combined with the total of utilization and criterion elicitation approaches, thus providing a


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Demesouka et al. Vector Based

Cumulave No. of Aricles

11 10 9 8 7 6 5 4 3 2 1 0

40 35 30 25 20 15 10 5

Cumulave No. of Arcles

No. of arcles

Raster Based

0

Publicaon Period

Figure 2. Research area’s development over the years (Demesouka et al., 2013a).

variety of modifications that can meet DMs’ preferences. OWA seemed to gain attention among researchers during the last decade, providing an extension principle of the Boolean overlays and weighted summation approaches. The fact that its computational part is supported by IDRISI (Eastman, 2003) anticipates that OWA will be implemented more frequently in the future. Compromise programming methods are enabled with procedures that allow manipulation of the compensation level among the alternatives performances to the analysis criteria. However, these approaches cannot be implemented in a straightforward manner in a GIS environment, which justifies the relatively small citation rate. Finally, outranking relations methods such as ELECTRE (Figueira et al., 2005) and PROMETHEE (Brans and Marechal, 2005) are not cited in the literature of MSW landfill suitability analysis mainly because their implementation in raster-driven analysis faces computational limitations, given that pairwise comparisons (PC), among the alternative attributes, are demanded (Marinoni, 2006).

Criterion weights elicitation Weights interpret the degree of dominance among the evaluation criteria, and their estimation is thus essential to an analysis. Under the standard assumptions underlying MAUT, weights are scaling constants that reflect the impact on the final outcome when moving each attribute from its worst to its best level (Anagnostopoulos et al., 2010; Jia et al., 1998). A variety of methods for obtaining attribute weights, including ratio, swing and tradeoff weighting, have been proposed in the literature (Edwards, 1977; Keeney and Raiffa, 1976). However, equal weighting, ratio scale weighting and the analytic hierarchy process (AHP; Saaty, 1995) monopolize the literature of GIS-MCDA MSW landfill siting (Table 1). In particular, AHP is cited in 17 of the 36 articles (47%). Siddiqui et al. (1996) were the first to introduce AHP implementation in landfill site screening analysis according to the methodological framework of spatial-AHP, as proposed in the seminal work by Banai-Kashani

(1989). Since its release by Saaty (1977), AHP has gained a great amount of attention due to its simplicity. As a procedure, AHP enables weight estimations of both quantitative and intangible criteria, using PC matrices to build utilities functions. Recently, an AHP extension to fuzzy logic (FAHP: fuzzy analytic hierarchy process) has been applied by Chang et al. (2008) to rank seven candidate locations in the USA–Mexico borderlands and by Ouma et al. (2011) to support landfill siting at the municipality level in Kenya. In the same manner, the generalization of AHP to the consideration of interdependencies and feedback among the decision analysis criteria known as the analytic network process (ANP; Saaty, 1996) has been implemented for the MSW landfill site selection problem by Isalou et al. (2013). Ratio scale weighting methods are second, with a percentage of 25% (nine of 36). In those approaches, weights are obtained in a straightforward manner according to a rationale similar to the allocation of a fixed amount of money among a number of budget categories. Practically, the allocation is achieved by assigning the highest grade of a predefined scale (i.e. 1–10, 1–100) to the most important criterion and the lowest grade to the least important criterion, where the remaining criteria are given intermediate grades. Ultimately, attribute weights are obtained through normalization, according to Eq. 2. The use of ratio weights has been introduced by Halvadakis (1993) relative to the sanitary landfill site screening in Chania, Greece.

wi =

Ri m

∑ Rk

i = 1, 2,..., m

(2)

k =1

In several studies (Baban and Flannagan, 1998; Dikshit et al., 2000; Jensen and Christensen, 1986; Lane and McDonald, 1983; Vatalis and Manoliadis, 2002), decision criteria contribute equally to the overall analysis goal. According to Kao and Lin’s (1996) analysis, the criterion map weights were heuristically assigned without applying any decision-making procedure. In general, the selection of the most suitable method for the estimation of criterion weights is in accordance with the


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available information provided by the DMs. An equal weighting approach demands the minimum level information since weights are established automatically as soon as DMs identify that the analysis criteria equally contribute to the analysis. Ratio weighting approaches enable straightforward estimation of the criteria relative importance through the normalization of the provided information. On the other hand, AHP, FAHP and ANP are more exacting procedures since pairwise comparison among the analysis objectives is demanded. The main advantage of the fuzzy extensions of AHP is considered their ability to handle efficiently DMs vagueness using fuzzy numbers instead of the crisp values that correspond to the fundamental scale of preferences provided by the typical AHP. ANP is a generalization of the AHP to the consideration of interdependencies and feedback among the decision analysis parameters. Technically, this is achieved using networks instead of hierarchies in order to represent the decision problem. The implementation of ANP demands far more judgments to be provided by the DMs and this is a major drawback when real-world case studies are implemented. However, ANP provides a consistent way of dealing with the criterion and utility independence biases that underlay behind the additive utility norm (Keeney and Raiffa, 1976).

Criterion map standardization The utilization or standardization process aims to represent the per-criterion value system of stakeholders and, at the same time, to enable attribute measures transformation in terms of utilities providing a common scale of measurement (Demesouka et al., 2013a; Malczewski, 1999). As this process is derived from Table 1, the weighted additive model is by far the most common approach for combining attribute data in landfill siting analysis. However, the additive model demands the transformation of the attribute map layers to comparable units using a single unidimensional scale of measurement. Over the years, a variety of standardization approaches have been proposed in the MC-SDSS literature (Malczewski, 1999; Voogd, 1983). Regarding the MSW landfill siting problem, the ratings (R) option is by far the most common approach because it can be found in 29 of the 36 articles in our review. As a procedure, the R option allows the classification of criterion map row data on the levels of a constructed scale of preference intensities (usually from 1 to 5). Ten articles use fuzzy membership functions (FMF) for modelling imprecision of spatial linguistic terms that are used extensively by DMs when performing evaluations (Charnpratheep et al., 1997; Robinson, 2003). UF and linear (L) transformations that convert criterion map row data to standardized criterion scores ranging from 0 to 1 are used in four articles. The PC mode is performed in three studies to obtain utilities from classifications of the attribute row data. Finally, the fuzzy ratings (FR) option has a single citation in the literature. Table 2 summarizes the number of standardization processes that have been performed to utilize the decision attributes for each of the literature review articles.

Suitability criteria – site selection criteria Although efficient landfill siting depends principally on the adequacy of the site selection criteria, in the whole process, a large number of factors are involved (e.g. stakeholders, DMs, legislation). DMs have made extensive efforts to come up with factors that meet the requirements of government regulations and minimize the economic, environmental, health and social costs in conjunction with the characteristics of the study area (McBean et al., 1995; Noble, 1992; Siddiqui et al., 1996). According to their role in the decision making process, these factors are differentiated as exclusionary criteria (C: constraints), nonexclusionary criteria (DC: decision criteria) and decision criteria that consider the formation of constraint zones (DCC). All of the constraints are implemented during a preliminary screening process of the examined area to identify all potentially feasible sites, thereby prohibiting landfill siting in areas that do not comply with national and international legislation. In that sense, feasible solutions are highlighted using standard Boolean procedures. The decision criteria are used to rank the remaining locations according to preferences of DMs with respect to their suitability. Under the heading ‘evaluation criteria’, Table 2 summarizes the number of the C, DC and DCC that have been used to obtain and then evaluate feasible solutions using GIS-based MCDA methods. An extensive examination of the literature revealed that 37 criteria have been implemented up to now to support landfill site selection/screening processes. To provide a coherent discussion regarding the implemented criteria they have been grouped into five principal categories. These categories express more generic considerations that should be fulfilled during the site selection process. In the next stage, an in-depth survey of the criteria is conducted that presents the performed constraints and utilization procedures for each one of the implemented criteria. To obtain a measure of the criterion importance its appearance rate is calculated. However, a low participation rate does not necessarily mean that a criterion is not important, as other reasons may be responsible for its limited use (e.g. data availability, absence of natural phenomena and/or infrastructure). Aiming to describe their role in the decision making process, these criteria have been grouped into 17 sub-objectives that provide further definitions for the five principal objectives that should be accomplished. Most authors recognize these five objectives, i.e. environmental, area availability, design considerations, prospects of development, insurance and social considerations, as the sub-goals that ensure coherent results in the alternative evaluation. Figure 3 provides the taxonomy of the decision analysis objectives and sub-objectives proposed here, thus forming a three-level decision tree. In the next sections, the role and the preferential system (e.g. CD: criterion description, TV: threshold values, SM: standardization method, CT: criterion type) that have been adopted for each of the 37 decision criteria are discussed in detail.


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Demesouka et al. Table 2. Decision criteria and their standardization methods. Reference

Evaluation criteria

Total

Lane and McDonald (1983)a Lane and McDonald (1983)b Lane and McDonald (1983) Halvadakis (1993) Kao and Lin (1996) Siddiqui et al. (1996) Charnpratheep et al. (1997) Baban and Flannagan (1998) Dikshit et al. (2000) Leao et al. (2001) Vatalis and Manoliadis (2002) Kontos et al. (2003) Leao et al. (2004) Sharifi and Retsios (2004) Kontos et al. (2005) Mahini and Gholamalifard (2006) Melo et al. (2006) Sadek et al. (2006) Sener et al. (2006) Gemitzi et al. (2007) Yildirim (2012) Delgado et al. (2008) Sumathi et al. (2008) Zamorano et al. (2008) Nas et al. (2010) Wang et al. (2009) Geneletti (2010) Moeinaddini et al. (2010) Sener et al. (2010) Ouma et al. (2011) Sener et al. (2011) Eskandari et al. (2012) Gorsevski et al. (2012) Yildirim (2012) Demesouka et al. (2013a) Gbanie et al. (2013) Isalou et al. (2013)

7 6 7 15 11 14 11 9 11 6 17 21 9 9 11 7 10 18 13 18 10 17 12 12 9 10 16 20 10 11 9 14 11 16 27 12 10

C

4 3 7

Standardization DC 7 6 1 7 1 6 1

1 7 2 7 4 1 1

5 9 7 1 1 1 7

17 8 3 3 5 1 4 1 1 4 1 6 12 5 4 5

5 3 2 12 1

8 4 6 1 9 6 6

DCC

FR

6 4 7 1 10 8 4 4 6 2 5 6 5 6 18 7 8 6 9 5 8 4 5 20 5 6 1 7 5 13 6 5 4

FMF

L

PC

R

UF

7 6 7 11 8

3 4 6

7 11 8 3 4 17 14 1 4 11 1

1

1

5 10

18 8 1

8 10

10 11 12 8 9 5

4 20 9

1

6 9 8 11 6 4

1

5 2 14 4 5 6

C, constraint; DC, decision criteria; DCC, decision criteria that consider the formation of constraint zones; FR, fuzzy ratings; FMF, fuzzy membership function; L, linear; PC, pairwise comparison; R, ratings; UF, utility function. aCase Study 1. bCase Study 2.

Environmental criteria Issues related to the protection of the environment are embodied in the analysis using several factors, i.e. biodiversity, hydrographic network and hydrogeological criteria. The role of these factors is crucial, given that the selected locations for landfill siting should not affect the biophysical environment and the surrounding ecology (Erkut and Moran, 1991; Kontos et al., 2003; Nas et al., 2010; Siddiqui et al., 1996). Environmental criteria aim to prevent the further contamination of the natural environment and to protect against water resource pollution. Given the importance of the environmental criteria, the adoption of strict regulations by national and international legislation directives is

easily explained. Furthermore, regarding environmental awareness issues, environmental protection criteria are vital for ensuring the necessary public acquiescence for the landfill siting process. Biodiversity Protection of environmentally sensitive areas. This criterion notes the necessary distance a landfill must have from environmentally protected or sensitive areas to ensure the protection of these areas. The definition of protected/sensitive areas includes areas that portray exceptional ecological interest for nature conservation (endangered or threatened species or plants), including areas that are protected by national laws or


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Figure 3. Landfill suitability analysis objectives and sub-objectives decision tree. Table 3. Environmentally sensitive areas protection criterion. Reference

CD

TV

SM

CT

Lane and McDonald (1983) Halvadakis (1993) Kao and Lin (1996) Siddiqui et al. (1996) Charnpratheep et al. (1997) Baban and Flannagan (1998) Dikshit et al. (2000) Leao et al. (2001) Vatalis and Manoliadis (2002) Kontos et al. (2003) Leao et al. (2004) Kontos et al. (2005) Sadek et al. (2006) Gemitzi et al. (2007) Yildirim (2012) Delgado et al. (2008) Geneletti (2010) Moeinaddini et al. (2010) Sener et al. (2010) Yildirim (2012) Demesouka et al. (2013a)

DCC C DCC C DCC DCC C C DC

0.16 km Protected areas exclusion 0.7 km 1.6 km 0.05 km Protected areas exclusion Protected areas exclusion 0.3 km

R

Qualitative Ascending Ascending Qualitative Ascending

DCC C DCC DCC C DC C DCC DCC DCC DCC DCC

0.5 km 0.3 km Protected areas exclusion 1 km 0.5 km

R FMF R

R R R R FR 3 km Protected areas exclusion Sensitive ecosystems exclusion 0.15 km 1 km 0.5 km

R FMF PC R FMF

Ascending Ascending Ascending Qualitative Qualitative Ascending Ascending Ascending Ascending

Abbreviations as in Table 2. CD, criterion description; TV, threshold values; SM, standardization method; CT, criterion type.

international conventions (i.e. European Network NATURA 2000; Ramsar Convention, 1971). The risk of environmental degradation is why some of the articles not only exclude these areas from the study area but also form exclusion zones to prevent the encroachment of any development into their vicinity (Baban and Flannagan, 1998). This criterion is referred to 21 articles in the literature (58%). Even though it is not enforced by national/international regulations, buffer zones are implemented in 13 of them denoting that area ecology protection was main concern of the DMs. In Kontos et al. (2005), wetlands and human-made surface water bodies (water dams and reservoirs) are excluded from further consideration in addition to the constraints derived by the legislation. Although landfill siting demands only the exclusion

of environmental protected areas in 13 of the 21 articles, DMs form buffer zones, which range from 0.16 to 3 km, around the environmentally protected areas in an effort to avoid raising public obstructions due to the potential degradation of the area. The mean buffer zone, resulting from Table 3, of the necessary distance around protected areas is 0.751 km, with areas within this range considered unsuitable for landfill siting. Hydrographic network Water supplies protection. This criterion’s goal is to minimize the risk of drinking water contamination by placing the landfill a sufficient distance from water supply intakes, according to


9

Demesouka et al. Table 4. Water supplies protection criterion. Reference

CD

TV

SM

Kao and Lin (1996) Siddiqui et al. (1996) Charnpratheep et al. (1997) Dikshit et al. (2000) Vatalis and Manoliadis (2002) Kontos et al. (2003) Kontos et al. (2005) Sadek et al. (2006) Gemitzi et al. (2007) Yildirim (2012) Delgado et al. (2008) Sumathi et al. (2008) Nas et al. (2010) Geneletti (2010) Moeinaddini et al. (2010) Ouma et al. (2011) Eskandari et al. (2012) Gorsevski et al. (2012) Isalou et al. (2013)

C C DCC C DC C DCC DCC C DCC C C DCC C DCC C DCC DCC DCC

Water resources protection areas exclusion 3.2 km from wells-1.6 km from water resources supply 0.2 km 0.05 km from ponds and tanks that provide drinking water

FMF R

0.5 km from Water-Supply Well and Springs 0.5 km from wells and springs 0.3 km from public water supply intake 0.5 km from springs and wells 0.05 km from wells 1 km from drinking water recharge areas 0.5 km from Public water supply intake 0.3 km from wells Springs, wells protection areas exclusion Springs, wells and qanats exclusion 0.3 km from drinking water sources, 0.5 km from wells 1 km drinking wells, 0.4 km from wells, qanats, springs 0.5 km from springs 0.3 km from wells and springs

R R FR

R FMF UF FMF FMF

CT Ascending Ascending Ascending Ascending Ascending Ascending Ascending Ascending Ascending Ascending

Abbreviations as in Tables 2 and 3.

the EU directive (European Council Directive 1999/31/EC), and thus to diminish any possibility of leaching by derivatives of the landfill (Gemitzi et al., 2007). These derivatives are the liquid formations (leachate) containing a variety of chemical constituents derived from the solubilization of the disposed materials and the products of the chemical and biochemical reactions occurring in the landfill (Tchobanoglous and Kreith, 2002). As a result, necessary distances and buffer zones from wells, springs and water supply intakes must be considered during the landfill siting process by DMs for protecting water supply sources from potential leachate pollutants. In that manner, Siddiqui et al. (1996), Ouma et al. (2011) and Eskandari et al. (2012) assigned a variety of buffer zone radii from wells and water supply intakes to distinguish the pollution risk with respect to the water source type (Table 4). Kontos et al. (2003) proposed the consideration of a 0.5-km buffer zone around irrigation and water supply intakes based on the assumption that a 50–60-day period is required for the inactivation of the pathogens in accordance with the average linear velocity (1–10 m day−1) of the groundwater (Kallergis, 1986). Another approach for candidate site assessment is by measuring the flow direction azimuth of the groundwater (Kontos et al., 2005). The significance of the criterion lies in the health impacts that can arise from the direct or indirect contamination of drinking water. Given the significance of drinking water contamination, the criterion is cited in 19 of the 36 articles (53%). As shown in Table 4, in nine articles the contamination of drinking water is considered an exclusionary criterion, whereas in nine studies, the contamination of drinking water is implemented as a decision criterion with the consideration of exclusion zones. Protection of surface water. This criterion considers the necessary distance from any type of surface water concentrations (e.g. lakes, rivers, streams, wetlands, ponds, reservoirs) that DMs should implement. As the European directive (European Council

Directive 1999/31/EC) on the landfill of waste does not impose any distance from the surface water, various exclusion zones are maintained in most of the case studies. Where no legislation framework exists, the decision is made according to the DM perceptions by forming protective distances from surface water bodies based on the watershed size (Chang et al., 2008). As shown in Table 5, many researchers consider a differentiation of the implemented buffer zones based on such distinguishing water bodies as rivers, streams, lakes and wetlands (e.g. Charnpratheep et al., 1997; Ouma et al., 2011; Sadek et al., 2006). The fact that 94% of the articles include this criterion in their studies outlines its importance for the viability of an adequate landfill siting process. The criterion is most frequently (28 of the 34) implemented as a decision criterion with the consideration of buffer zones (DCC). This criterion forms an ascending criterion type, reflecting DMs’ preferences in locating landfills as far as possible from areas of surface water concentration. Hydrogeology Protection of groundwater resources. This criterion refers to groundwater protection against the leakage of landfill leachate. Alternative site performance for this criterion is related to the depth of the water table and the vulnerability of aquifers. All of these attributes are considered of great importance and should be considered by DMs. As the European directive (European Council Directive 1999/31/EC) provides specific guidelines concerning permeability and thickness requirements (e.g. for nonhazardous waste landfills: k≤10−9 m s−1, thickness ≥1 m), DMs should guarantee a minimal risk of groundwater contamination using either natural or technical means. Consequently, areas with a deep underground water table level and low vulnerability for aquifers are considered the most appropriate for landfill siting because the risk of aquifer contamination is almost negligible. This criterion has a percentage of 56% in the MSW landfill site


10


Table 5. Distance from surface water criterion. Reference

CD

Lane and McDonald (1983)a Lane and McDonald (1983)b Lane and McDonald (1983) Halvadakis (1993) Kao and Lin (1996) Siddiqui et al. (1996) Charnpratheep et al. (1997)

DC DC DCC DC DCC C DCC

Baban and Flannagan (1998) Dikshit et al. (2000) Leao et al. (2001) Kontos et al. (2003) Leao et al. (2004)

DCC C DCC DCC DCC

Kontos et al. (2005) Mahini and Gholamalifard (2006) Melo et al. (2006) Sadek et al. (2006)

DCC DCC DCC DCC

Sener et al. (2006) Gemitzi et al. (2007) Yildirim (2012)

DCC DCC DCC

Delgado et al. (2008) Sumathi et al. (2008) Zamorano et al. (2008) Nas et al. (2010) Wang et al. (2009) Geneletti (2010) Moeinaddini et al. (2010) Sener et al. (2010) Ouma et al. (2011)

DCC DCC DC C DCC DCC DCC DCC DCC

Sener et al. (2011) Eskandari et al. (2012) Gorsevski et al. (2012) Yildirim (2012)

DCC DCC DCC DCC

Demesouka et al. (2013a)

DCC

Gbanie et al. (2013) Isalou et al. (2013)

DCC DCC

TV

SM

CT

0.16 km from wetlands

R R R R R

Qualitative Ascending Qualitative Descending Ascending Ascending

0.18 km from surface water bodies 0.8 km, Wetlands exclusion 0.3 km from perennial streams, 0.1 km intermittent streams, 0.3 km ponds 0.5 km from surface water bodies 0.2 km from streams, Wetlands exclusion 0.2 km from permanent water bodies 0.5 km from surface water bodies 0.3 km from permanent water bodies, 0.3 km from wetlands Permanent water bodies and wetlands exclusion 0.2 km from rivers 0.2 km from any water collection or water course 0.5 km from main rivers, 0.25 km temporary rivers, 0.15 km streams, 1 km from lakes Wetlands exclusion 0.5 km from surface water bodies 1 km from rivers and streams, 0.075 km from wetlands 1 km from surface water bodies 0.2 km from ponds, 0.1 km from rivers Streams, lakes, rives and wetlands exclusion 0.5 km surface water bodies 0.15 km from rivers, lakes and wetlands Main and secondary streams exclusion 0.5 km surface water bodies 0.3 km from lakes, ponds, reservoirs, rivers streams, 0.1 km from wetlands, swampy areas 1 km from urface water 1 km from rivers 0.5 km from rivers and lakes 0.5 km from streams, 1 km from dams, lakes, wetlands exclusion 0.5 km from rivers, lakes, Streams and wetlands exclusion 0.15 km from streams 0.6 km from rivers

FMF R R R UF

Ascending Ascending Ascending Ascending

R FMF FMF R


R FMF FR

Qualitative Ascending Ascending

R R R R L FMF PC L

Ascending Ascending Ascending Ascending Descending Ascending Ascending Ascending

PC UF FMF R


FMF

Ascending

FMF FMF

Ascending Ascending

Abbreviations as in Tables 2 and 3. aCase Study 1. bCase Study 2.

selection literature (Table 6). Deep distances to the water table and unsaturated zones are preferred (ascending criterion type) because the possibility of landfill by-products reaching the underground water is thus eliminated, thereby avoiding groundwater contamination. A constraint criterion was implemented by Delgado et al. (2008) and Ouma et al. (2011) by excluding areas where the depth to the water table is less than 0.010 and 0.015 km, respectively. In 18 articles, the criterion is implemented as a decision criterion (10 articles) or as a decision criterion with the consideration of buffer zones. When the criterion is utilized in the analysis using the distance to the groundwater table, this criterion forms an ascending criterion type. When the evaluation of the groundwater contamination risk is utilized with the vulnerability

risk of aquifers, qualitative evaluations are performed. An exception to the above rule consists of the criterion implementation by Yildirim (2012), where the distance from significant aquifers provides the measure of evaluation of candidate sites. With respect to the evaluation of aquifers, Demesouka et al. (2013a), Gemitzi et al. (2007), Sener et al. (2011) and Wang et al. (2009) applied a new scale for estimating groundwater vulnerability by forming discrete classes according to intrinsic aquifer parameters (type of aquifer, depth to water table, hydraulic conductivity). Sener et al. (2011) and Sumanthi et al. (2008) performed site screening processes by combining the depth to the groundwater table and the vulnerability of aquifer criteria.


11

Demesouka et al. Table 6. Groundwater resources protection. Reference

CD

Lane and McDonald (1983)a Lane and McDonald (1983)b Halvadakis (1993) Siddiqui et al. (1996) Baban and Flannagan (1998) Vatalis and Manoliadis (2002) Mahini and Gholamalifard (2006) Sadek et al. (2006) Sener et al. (2006) Gemitzi et al. (2007) Delgado et al. (2008) Sumathi et al. (2008) Zamorano et al. (2008) Wang et al. (2009) Moeinaddini et al. (2010) Ouma et al. (2011) Sener et al. (2011) Eskandari et al. (2012) Yildirim (2012) Demesouka et al. (2013a) Gbanie et al. (2013)

DC DC DCC DC DCC DC DCC DCC DCC DCC C DC DC DC DCC C DC DC DCC DC DC

TV

0.5 km for porous strata, 8 km from karstic strata Areas of high groundwater vulnerability 0.01 km to ground water table 0.03 km to ground water table Major aquifers exclusion Areas of high/very high aquifers vulnerability 0.01 km to ground water table

0.015 km to ground water table 0.015 km to ground water table

1 km from significant aquifers

SM

CT

R R R PC R R FMF R R FMF

Qualitative Ascending Descending Ascending Qualitative Qualitative Ascending Ascending Qualitative Qualitative Ascending–Qualitative Qualitative Qualitative Ascending Ascending–Qualitative Qualitative Ascending Descending Ascending

R R R FMF PC R R R FMF

Abbreviations as in Tables 2 and 3. aCase Study 1. bCase Study 2.

Permeability of soils. Even though modern landfills are equipped with bottom liner systems to collect leachate and minimize leakage to the subsurface, an inappropriate underlying soil type minimizes the risk of possible groundwater contamination through percolation of the landfill leachate through the underlying strata (Peavy et al., 1985). As a result, the site selection process should provide DMs with tools that eliminate such risks. Soils permeability is a precautionary measure to prevent against possible construction failures and public opposition. Soil permeability constitutes an exclusively descending criterion type because areas with low soil permeability are thought to be most suitable for landfill placement, as they do not present a high risk of contamination of groundwater aquifers. According to Kallergis (2000), the most appropriate areas for locating waste disposal sites are those with low water vulnerability (k

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