Environ Sci Pollut Res (2015) 22:7754–7765
DOI 10.1007/s11356-015-4182-7

RESEARCH ARTICLE

Optimizing landfill site selection by using land classification maps
M. Eskandari & M. Homaee & S. Mahmoodi & E. Pazira &M. Th. Van Genuchten

Received: 19 July 2014 / Accepted: 28 January 2015 / Published online: 11 February 2015
# Springer-Verlag Berlin Heidelberg 2015

Abstract Municipal solid waste disposal is a major environmental concern throughout the world. Proper landfill siting
involves many environmental, economic, technical, and sociocultural challenges. In this study, a new quantitative method for landfill siting that reduces the number of evaluation
criteria, simplifies siting procedures, and enhances the utilityof available land evaluation maps was proposed. The method
is demonstrated by selecting a suitable landfill site near thecity of Marvdasht in Iran. The approach involves two separate
stages. First, necessary criteria for preliminary landfill siting
using four constraints and eight factors were obtained from aland classification map initially prepared for irrigation purposes. Thereafter, the criteria were standardized using a rating
approach and then weighted to obtain a suitability map for
landfill siting, with ratings in a 0–1 domain and divided into
five suitability classes. Results were almost identical to thoseobtained with a more traditional environmental landfill siting
Responsible editor: Michael Matthies
M. Eskandari
Young Researchers and Elites Club, Science and Research Branch,
Islamic Azad University, Tehran, Iranapproach. Because of far fewer evaluation criteria, the proposed weighting method was much easier to implement while
producing a more convincing database for landfill siting. The
classification map also considered land productivity. In thesecond stage, the six best alternative sites were evaluated forfinal landfill siting using four additional criteria. Sensitivity
analyses were furthermore conducted to assess the stability ofthe obtained ranking. Results indicate that the method provides a precise siting procedure that should convince all pertinent stakeholders.
Keywords Landfill siting . Land classification maps .
Municipal solid waste
Abbreviations
AHP
Analytical hierarchy process
ESS
Environmental site selection
GIS
Geographic information system
LCM
Land classification map
MCDA
Multi-criteria decision analysis
MSW
Municipal solid waste
SAW
Simple additive weighting
SMCDA Spatial multi-criteria decision analysis

M. Homaee (*)
Department of Soil Science, Tarbiat Modares University,
Tehran 14115-336, Irane-mail: mhomaee@modares.ac.ir
S. Mahmoodi
Department of Soil Science, Faculty of Agriculture, Tehran
University, Karaj, Iran
E. Pazira
Department of Soil Science, Science and Research Branch, Islamic
Azad University, Tehran, Iran
M. T. Van Genuchten
Department of Mechanical Engineering, COPPE/LTTC, Federal
University of Rio de Janeiro, Rio de Janeiro, RJ, Brazil

Introduction
Solid waste management remains an important worldwide
environmental task. According to the U.S. Environmental
Protection Agency (USEPA 2010), integrated waste management consists of a hierarchy of four components involving, inorder of preference, source reduction, recycling, combustion
with energy recovery, and disposal through landfilling. Even
with increased emphasis on waste reduction at the source asEnviron Sci Pollut Res (2015) 22:7754–7765

well as on recovery and recycling, disposal of solid waste bylandfilling remains to be the most common method (Sumathi
et al. 2008; Kim and Owens 2010; Eskandari et al. 2012).
Ideally, a well-designed landfill uses an engineered methodof disposal of solid waste on land in a manner that minimizes
environmental hazards by spreading the solid waste in thinlayers, compacting the solid waste to the smallest practicalvolume, and applying a cover at the end of each operatingday (Sumathi et al. 2008).
A large number of studies have been conducted in the past
several decades on how landfills may impact on the environment, such as through gas emission, olfactory nuisance, andglobal greenhouse effects (e.g., Molins et al. 2008; Chanton
et al. 2009; Lou and Nair 2009; Bogner et al. 2011), landfill
leachate generation (Lee et al. 2002; MacDonald et al. 2008;
Renou et al. 2008; Santos et al. 2013), pollution control andhealth risk assessment (Chrysikou et al. 2008; Mari et al.
2009), and landfill phytoremediation and bioremediation(Bennett et al. 2003; Kim and Owens 2010; Justin et al.
2010; Davari et al. 2015). Other studies have focused on optimal siting of waste treatment facilities (Aragonés-Beltrán
et al. 2010; Tavares et al. 2011) and on landfill site selection(Ekmekcioglu et al. 2010; Gorsevski et al. 2012; Eskandari
et al. 2013).
Siting a sanitary landfill requires a reliable evaluation process that should identify the best possible disposal location.
This location must comply with governmental regulations
while, at the same time, minimizing economic, environmental,
health, and social costs (Sumathi et al. 2008; Chang et al.
2008; Nas et al. 2010). Therefore, many siting factors or
criteria could potentially be applied. As a consequence, landfill siting is increasingly becoming a difficult, complex, andoften extremely protracted process (Chang et al. 2008). To
make the task more convenient, some have used multicriteria decision analysis (MCDA) methods which could account for many specific but often conflicting criteria(Eskandari et al. 2012).
Geographical information systems (GISs) have recently emerged as an important tool for land use suitability
analyses. A geographical information system can beequally useful for landfill siting. GIS-based approaches
for siting will not only reduce the time and cost of site
selection but will also provide a digital data bank forlong-term monitoring of the site (Sumathi et al. 2008).
The combination of GIS features and MCDA techniques
produces an approach for spatial multi-criteria decision
analysis (SMCDA) that is very convenient for optimal
landfill siting (Eskandari et al. 2012). The method has
been widely employed in various landfill site selection
studies (e.g., Kontos et al. 2005; Sener et al. 2006, 2011;
Buenrostro Delgado et al. 2008; Sumathi et al. 2008;
Wang et al. 2009; Nas et al. 2010; Geneletti 2010;
Gorsevski et al. 2012; Eskandari et al. 2012).

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Like in many other countries, two main factors have contributed to the rapid increase in solid waste being generated in
Iran: accelerated population growth and changes in lifestyle.
Most of the municipal solid waste (MSW) in Iran is disposedof in open pits, thus causing at times severe effects on theenvironment and public health. Constructing sanitary landfills
that meet the environmental legislation and reduce their undesired impact is now a major priority for MSW management in
Iran (Eskandari et al. 2012). For this reason, several studies
have recently addressed optimal landfill siting using the
SMCDA methodology.
While existing research addresses several landfill siting
issues and their solutions, few of any models exist for
selecting an optimal landfill site with only a minimum ofavailable data. Our literature review indicates that the numberof factors or evaluation criteria used for landfill siting is steadily increasing. Consequently, this points that the acquisition of
the required amount of information is becoming a real challenge, especially for developing countries that may lack adequately trained staff in local governments. Methods that produce reliable results with a minimum of data are for thesereasons indispensable.
Although the capabilities of GIS systems in landfill siting
procedures have been recognized in terms of reducing timeand cost, their use requires the construction of a relative large
database. Developing such a database is a tedious, costly, andtime-consuming endeavor. By comparison, soil scientists andothers over the years have prepared many soil survey or land
classification maps which now contain much useful information, including from many developing countries. These maps
are in great demand as tools for practical land planning andmanagement (Brady and Weil 2008). For example, since1954, preliminary land selection for irrigated agriculture in
Iran is based on Iran’s land classification method for irrigation.
Soil maps of Iran’s plains now exist in realistic detail (commonly 1/50,000) and are well suited for many other applications, including landfill siting.
The objective of this study was to introduce a new quantitative landfill site selection method based on available soil
classification maps. The method would reduce the numberof evaluation criteria, simplify the site selection process, andenhance multiple applicabilities of the soil maps. To test the
proposed method, a case study was performed for Marvdasht
City in the center of Fars Province, Iran. The proposed method
is compared with a more commonly used environmental landfill siting method in Iran.

Methods and materials
To provide a context on how the two methods is applied andcompared, we first give brief description of the Marvdasht
study area.

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The study area
Marvdasht is located in Fars Province, Iran, between 29° 15′
and 30° 59′ N latitudes and 51° 44′ and 53° 30′ E longitudes.
The area covers 4040 km2, of which 1837 km2 is mountainous
and the remaining 2203 km2 consists of a fertile agricultural
plain. Its average temperature is about 17.7 °C, and the average rainfall 390 mm, mostly falling in the winter months.
According to the Iranian Statistics Center (2006), Marvdasht,
after the capital Shiraz, is the second most populated city ofFars Province, having a population of about 125,000. Geologically, the study area is dominated by thick Quaternary alluvium deposits with occasional deposits of salts. The dominantland use in the region is agriculture. Historically, Marvdasht
and its suburbs are important in Iran because of several internationally registered ancient sites like the Persepolis palace
complex, the Naghsh-e-Rajab, Naghsh-e-Rostam
archeological sites, the Khan and Band-e-Amir bridges, andthe ancient city of Estakhr which collectively attract millionsof visitors every year.
Recent estimates indicate that Marvdasht generates about
87 tons of solid wastes per day or an average per capita ofalmost 0.7 kg day−1 (Eskandari et al. 2012). The wastes contain relatively high amounts of organic matter (about 71 % of
dry weight). There is neither waste segregation nor any gas orleachate utilization (Eskandari et al. 2012). The current
Marvdasht landfill is located at 29° 52′28″ N latitude and52° 53′1″ E longitude, at an elevation of 1737 m above sea
level. The landfill will be moved to another more secure location due to existing environmental hazards. The new landfillsite must cover an area of at least 100 ha for future development, such as the construction of a compost plant and otherstructures (Eskandari et al. 2012).
We next describe the two landfill site selection methods
used in this study, one based on a relatively standard environmental site selection approach that has been used several times
previously (Kontos et al. 2005; Sener et al. 2006; Sumathi
et al. 2008; Wang et al. 2009; Nas et al. 2010; Eskandari
et al. 2012), further referred to as the environmental site selection (ESS) method, and our proposed alternative approachbased on information deduced from land classification maps,
further referred to as the land classification map (LCM) method. Results obtained with the two methods are compared
subsequently.

Environ Sci Pollut Res (2015) 22:7754–7765

selected based on opinion sampling and the results of questionnaires completed by 15 environmental experts familiar
with the local situation. The factors were divided into environmental, economical, and sociocultural criteria as shown by
the hierarchical site selection scheme shown in Fig. 1.
Consistent with the invoked selection criteria, a number ofthe digitized maps were constructed. These included a geology map (1:100,000) showing the location of faults; a digital
topography map (1:25,000) defining elevation contour lines;
maps of residential areas, rivers, and roads; and various agricultural land evaluation and soil maps (1:50,000) specifying
for each map unit the subsurface hydraulic conductivity, thesoil depth, the suitability class for irrigation, and the frequencyof flooding. The required information on springs, wells,
qanats, and groundwater quality and depth was mapped in a
GIS domain, while the locations of historical sites were
mapped using GIS software. The dominant wind direction

Environmental landfill site selection
Selection criteria
The ESS method uses two types of criteria: constraints andfactors. A total of 13 spatial constraints were selected firstbased on Iran’s environmental protection regulations for
MSW landfill siting (IEPO 2010). Thereafter, 16 factors were

Fig. 1 Hierarchical structure used for the ESS environmental landfill
siting method and values of the weights of the various factors

Environ Sci Pollut Res (2015) 22:7754–7765(mostly westward) in the study area towards the historical andresidential areas was also mapped in a GIS domain. All information was included in a comprehensive GIS database formunicipal landfill siting. Version 3.3 of the ILWIS software(ITC-ILWIS 2005) was used later for data analysis and performance of the multi-criteria decision analysis.
Data standardization
Because different evaluation criteria operate at different
scales, the raster maps obtained in the previous section werestandardized in several ways. The constraints (Fig. 1) werestandardized using Boolean methods to limit their values toeither 0 or 1 (Malczewski 1999).
Map standardizations for distances from historical sites,
wells, residential areas, springs, rivers, qanats, and groundwater depth were accomplished by using concave value functions
considered as benefit criteria. Distances from roads were standardized using a convex value function considered as the costcriterion. In these functions, the X-axis and Y-axis for eachcriterion represent the distance and the standardized value inthe 0–1 range, respectively. A symmetric sigmoidal function
was used to standardize the distance from the waste generation
source. This function stipulates that any increase in the distance increases the standardized value up to some optimal
distance, after which the standardized value decreases becauseof economic factors. Based on input from the municipality,
waste transportation to the landfill was only allowed up to a
distance of 30 km from the city, with no economic justification
to use locations beyond that point. To facilitate different typesof standardization functions, the SMCE module of the ILWIS
software was used.
Some criteria such as groundwater quality, soil depth, landuse, wind direction, and soil permeability were standardized
by allocating grading values between 0 and 1 to each criterion.
For instance, the raster map of groundwater quality was provided in three classes based on University of California Committee of Consultation Guidelines (1974). They were allocatedwith grading values of 0 to 1 and used as standardized values
in the suitability site selection analysis. Table 1 shows thegrading values of these criteria. Transformation of the criteria
maps into numerical maps finalized the data standardization
process.
Criteria weighting
After completing the standardization process, weights wereassigned to the various criteria. The weights were definedupon consultation of 15 environmental science experts.
Criteria were weighted using a rank ordering (rank sum)scheme. This method places all factors in a certain order, fromwhich the numerical weights are calculated. Features of the7757
Table 1 Grading values used for the class criteria in the environmental
landfill siting (ESS) method
Class criteria

Grading values allocated to each class

Soil permeability (cm/h)

Rapid (0.2), medium (0.5), slow (0.8),
very slow (1)High (0.2), moderate (0.7), low (1)Groundwater quality
class (dS/m)Land use (agriculturalland units)
Soil depth (cm)Wind direction

Class 3 (0.4), class 4 (0.7), class 5 (0.9),
class 6 (1)
10–25 (0.1), 25–50 (0.3), 50–80 (0.5),
80–120 (0.7), >120 (1)West (0.1), southeast (0.3), southwest (0.4),
east (0.6), northwest (0.7), northeast (0.8),
south (0.9), north (1)ILWIS software were again utilized to assign weights to eachcriterion.
Criteria aggregation
The composite index value for each pixel was calculated using
simple additive weighting (SAW) as follows (Kontos et al.
2005):Vi ¼

n
X

W jV i jð1Þ

j¼1

where Vi is the suitability index for area i, Wj is the weight ofcriterion j, Vij is the standardized value of area i under criterionj, and n is the total number of criteria.
Preliminary landfill siting using land classification maps
The land classification map (LCM) method uses two separate
steps to select the most optimal MSW landfill site. In the firststep, preliminary landfill siting is performed based on a land
classification map. This requires the identification of the mostsuitable land units. In the second step, discussed hereafter,
candidate sites within the most suitable land units are identified and then evaluated in more detail to identify the final
landfill site. We first explain here the various steps comprising
the preliminary site selection process, staring with a list of the
invoked selection criteria.
Selection criteria
Iranian land classification maps for irrigation are based on 18
different soil and land characteristics. These characteristics arearranged in four groups to indicate their limitations or hazards
for irrigation purposes. The four groups consist of soil

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Environ Sci Pollut Res (2015) 22:7754–7765

limitation (S), salinity and alkalinity limitations (A), topographic limitations (T), and drainage limitations (W). All of
the properties of each land unit are included in a composite
equation called the limitation formula. Consequently, basedon land use limitations, the land units are classified into sixclasses. Classes I, II, and III are considered suitable for irrigation (from the best to relatively good), class IV is not irrigable
except under especial conditions, class V has an undeterminedsuitability for irrigation, and class VI is non-irrigable (Syset al. 1991). This formula has been popular in many land
classification studies and is also used in Iran for preliminary selection of lands for irrigation. Some of the properties can also be used for landfill site selection as explained in Table 2.

constraints. Eight criteria consisting of topsoil texture, subsoil
stoniness, subsoil permeability, soil depth, soil salinity, soil
alkalinity, overall slope, and erosion status are considered tobe the factors as delineated in Fig. 2.

Data standardization
The four constraints (Fig. 2) were standardized using theBoolean method and assigned values of 0 or 1. The eightfactors were standardized using a grading method, with suitable values within the 0–1 domain assigned to each class ofcriteria based on the suitability of the site to function as landfill. Table 2 shows the grading values used for the various
criteria. A few details of the adopted grading values are given
below.

Decision algorithm
After selecting suitable criteria from the land classification
map, a decision tree was designed. The decision algorithm ispresented in Fig. 2, which shows that the evaluation criteria in
our study are classified into one constraint and a factor group.
Four criteria involving prime agricultural land (classes I and II
based on the soil classification map), groundwater depth (allland units classified as W), flooding hazards (all units classified as F), and miscellaneous areas were considered as
Table 2Topsoil texture Landfilling should not be considered as the
final land use of a certain tract of land, but rather as one stage
in some changing land use sequence in time. When a landfill
in an area has reached its planned limit, the land should berestored to a productive and esthetically pleasing state(Loughry 1973). For this reason, the local topsoil should be
used in landfilling operations to serve as the final cover for thevegetation. The better the topsoil for plant growth, the moresuitable this soil will be as a landfill cover.

The properties in limitation formula used for (LCM) landfill site selection and the grading values used for the class criteria

Criteria

Classification

Grading values used

Topsoil texture (texture of the upper 20 cmof the soil profile)

Very coarse textured (VC), coarse textured (C),medium textured (M), fine textured (F), veryfine textured (VF)g (15–40 % coarse fragments), G (40–75 % coarse
fragments), Z (more than 75 % coarse fragments)
BVery rapid^ (more than 12), Brapid^ (6–12),
Bmoderate^ (0.5–6), Bslow^ (0.1–0.5),
Bvery slow^ (less than 0.1)
BVery deep^ (more than 120), Bdeep^ (80–120),
Bmoderately deep^ (50–80), Bshallow^ (25–50),
Bvery shallow^ (10–25)
S0 (less than 4), S1 (4–8), S2 (8–16), S3 (16–32),
S4 (more than 32)
A0 (ESP<10), A1 (10–15), A2 (15–30), A3 (30–50),
A4 (more than 50)

VC (0.3), C (0.5), M (1),F (0.9), VF (0.7)

Subsoil stoniness (the average percentage (byvolume) of coarse fragments in the subsoil)
Subsoil permeability (cm/h)

Soil depth (thickness of the soil profile above some
limiting or low-permeability layer (if any)) (in cm)
Soil salinity (electrical conductivity of thesoil saturation extract (dS/m))Soil alkalinity (maximum exchangeable sodium
percentage (ESP) within the first 75 cm of the soil)
Overall slope (the longest slope in a mapping unit)

Present erosion status
Groundwater depth (measured to a depth of 5 m)
Flooding hazard
Miscellaneous areas

A (0–2 %), B (2–5 %), C (5–8 %), D (8–12 %),E (12–25 %), F (25–40 %), G (40–70 %),H (more than 70 %)E0 (no apparent erosion), E1 (slight erosion),
E2 (moderate erosion), E3 (sever erosion)
W0, W1, W2, W3
F1 (slight flooding hazard), F2 (moderate flooding
hazard), F3 (severe flooding hazard)
RW (gravelly river bed), U (residential area),
M (marsh land), R (rocky hill), E(land destroyed by gully erosion)g (0.3), G (0.1), Z (0)VR (0), R (0.2), M (0.5),
S (0.8), VS (1)>120 (1), 80–120 (0.8), 50–80(0.6), 25–50 (0.4), 10–25 (0.1)
S0 (0.1), S1 (0.3), S2 (0.5),
S3 (0.7), S4 (1)A0 (0.1), A1 (0.3), A2 (0.5),
A3 (0.8), A4 (1)A and B (1), C (0.8), D (0.6),
E (0.4), F (0.2), G and H (0)E0 (0.1), E1 (0.3), E2 (0.7), E3 (1)W0 (0), W1 (0), W2 (0), W3 (0)F1 (0), F2 (0) F3 (0)RW (0), U (0), M (0), R (0), E (0)Environ Sci Pollut Res (2015) 22:7754–7765

Subsoil stoniness Subsoil stoniness is not a desired characteristic because of the risk for environmental contamination due to enhanced percolation and, hence, the volume of leachate that can be generated. For this reason,
higher subsoil stoniness implies a less suitable condition
for landfill construction.
Subsoil permeability Since lower recharge rates will protect groundwater resources more from contamination,
locations having subsoils with a low permeability provide a more suitable condition for landfill construction.
Soil depth Soil depth constitutes an important economic criterion in landfill siting in that it can reduce the cost of landfill
operation substantially. Consequently, the thicker the soil, themore suitable the area is for landfill construction.
Soil salinity The suitability of a soil for plant production
decreases when salinity increases. In many areas, thesalinity is so high that there are no economic or practical justifications for saline soil reclamation. Hence, it isattractive to use these areas for some other land uses
such as solid waste landfilling. Consequently, the moresaline the soil, the more suitable the soil is for landfill
siting.
Soil alkalinity Alkalinity has similar effects on plant production as salinity. As a result, the more alkaline the soil, the moresuitable the soil is for landfill construction.

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Overall slope Sites with steep slopes are not suited technicallyfor landfill construction. Accordingly, the flatter the area, thegreater is its suitability to serve as a landfill site (Kontos et al.
2005).
Present erosion status Land units affected by soil erosion
generally have lower plant productivity due to the removalof fertile top soil. Consequently, land units with more soil
erosion are more suitable for land uses other than agriculture,such as landfilling. However, lands with gully erosion must be
assessed more carefully because of their vulnerable geology.
Groundwater depth According to the Iranian Environmental
Protection Organization (IEPO 2010), any area with a watertable less than 5 m from the soil surface is not suitable for
landfill construction. Therefore, all mapping units associatedwith a W class on the classification map are not suitable for
landfill siting and must be avoided.
Flooding hazard All areas with a flooding hazard are unsuitable for landfill sites, and hence, any mapping unit characterized by an F must be eliminated from further considerations.
Miscellaneous areas According to IEPO, all the miscellaneous areas listed in Table 2 are unsuitable for landfill construction. Therefore, all land units associated with these classes must not be considered for landfill siting. Prime agricultural lands were eliminated also for possible landfill siting. As
discussed earlier, land units in the classification method weredivided into six classes based on their limitations for irrigatedagriculture. In this study, classes I and II were considered as
prime agricultural lands.
For performing multi-criteria analyses, the thematic mapsof all designated standardized criteria were produced from theland classification map using 100-m cell size raster maps.
Criteria weighting
After standardization, weights were again assigned to eachfactor using the expected value rank ordering method. Accordingly, the numerical weights were calculated using
X

nþ1−k

Wk ¼

Fig. 2 Hierarchical structure used for the LCM landfill siting method andweights of the various factors

1
n
ð
n
þ
1−iÞ
i¼1

ð2Þ

where Wk is the weight of criterion k, n is the number ofcriteria, and k is the rank of each criterion (ITC-ILWIS 2005).
The weighting method assigns more weight to criteria thatwere placed at a higher (first-order) rank. In our study, weplaced both soil permeability and soil salinity at the first rank,
meaning that land units with a saline soil have a higher priority

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to have a landfill. Thereafter, soil alkalinity was also considered to be an important criterion for site selection. The normalized weights were assigned to the factor criteria based on
their relative importance in landfill site selection, and the conditions of the study area, as shown in Fig. 2.
Criteria aggregation
Following determination of the various criterion scores andtheir weights, a composite index value for each pixel was
calculated, again using the simple additive weighting (SAW)
method.
Final landfill siting using land classification maps
After completing the preliminary landfill siting step, the second stage of MSW site selection was performed. For this step,
we interviewed several stakeholders of the Marvdasht Municipality to identify possible candidate sites (each having an areaof at least 100 ha) within the most suitable land units as identified in the preliminary stage. After this, a second set of factors was used to compare and rank the potential sites. The
factors and their weights were identified through interviews
with seven experts familiar with local conditions. An alternative ranking was concluded using the analytical hierarchy process (AHP) method. The AHP is a widely accepted decisionmaking method based on pairwise comparisons (Kontos et al.
2005; Chang et al. 2008; Wang et al. 2009; Sener et al. 2010).
The method utilizes the relative importance of the criteria in a
specific decision-making problem. This includes an index
called consistency ratio (CR) that indicates the overall consistency of the pairwise comparison matrix. The CR should havea value of less than 10 % to indicate a consistent method(Kontos et al. 2005). The AHP allows group decision-making,where group members can use their experience, values, andknowledge to break down a problem into a hierarchy, solving
it using the AHP steps (Chang et al. 2008). Finally, sensitivity
analyses are used to assess the stability of the obtained siteranking. All analyses in the second stage were carried out
using the Expert Choice software (Expert Choice 1997).

Results and discussion
Values of the weights obtained for the various factors andcriteria in the environmental (ESS) method are shown in
Fig. 1. The weights listed in this figure were derived fromcompleted questionnaires by 15 environmental experts. Theseexperts indicated that the environmental group of criteria
should have the highest priority in the landfill site selection
process. The priority groups thereafter are the socioculturaland economic criteria.

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Using these results, a landfill suitability map could be constructed using simple additive weighting (SAW). Five levelsof suitability were identified as shown in Fig. 3. Areas withsuitability indexes of 0.0–0.2, 0.2–0.4, 0.4–0.6, 0.6–0.8, and
0.8–1 were designated to be unsuitable, poorly suitable, moderately suitable, suitable, and most suitable, respectively. As
shown in Fig. 3, the most suitable locations for landfill construction comprise an area of about 7900 ha, mostly southeastof Marvdasht City. These locations satisfy the minimum requirements of a MSW landfill site based on Iran’s EPO guidelines and also consider equally important criteria based on theopinion of environmental experts. The areas contain relatively
deep soil profiles and are located quite far from flooding areas,
rivers, and historical sites (at least 19 km from the closest site).
The average groundwater level of the most suitable areas is
deeper than 10 m. The final site for construction of the
Marvdasht landfill should be selected within these areas, presumably after considering additional criteria and detailed site
visits.
Results of the first (preliminary) stage based on the LCM
are presented in Fig. 4. Land units in this figure having composite suitability indices of 0.0–0.2, 0.2–0.4, 0.4–0.6, 0.6–0.8,
and 0.8–1 were designated as unsuitable, poorly suitable,
moderately suitable, suitable, and most suitable, respectively.
As shown in this figure, almost all of the most suitable areas
for landfill construction are located southeast of Marvdasht
City. The most suitable piece of land occupies 8338 ha andconsists of highly saline (class S4) and sodic (class A2) soil
with poor land use as well as lacking any residential population. Soil erosion in these areas is medium, and there are norivers, active wells, and qanats in the entire land class designated as most suitable. The natural vegetation in the area is
halophytic, which resulted in class VI according to the land
classification map. Based on stakeholder opinions, the required area for landfill construction in the study area is almost
100 ha, which could be selected after supplementary fieldvisits and assessment of more detailed criteria to obtain the
final most suitable locations.
The precision of the proposed LCM method was examinedby comparing the identified Bmost suitable^ lands with thoseobtained using the environmental (ESS) method. Comparing
Fig. 3 with Fig. 4 shows that the two approaches produce
almost the same location considered to be most suitable forthe landfill. The most suitable areas obtained with the LCM
and ESS methods occupy 4.9 and 4.4 % of the entire study
area, respectively. These results suggest that the two methods
produce almost the same results. The main difference is theamount of information that was needed to produce the maps in
Figs. 3 and 4. The environmental (ESS) approach used 13
constraints and 16 factors that were digitized from at least
eight different maps. In contrast, the LCM-based results in
Fig. 4 were obtained using four constraints and eight factors
from only one land classification map.

Environ Sci Pollut Res (2015) 22:7754–7765

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Fig. 3 Landfill suitability classes
based on the ESS environmental
methodIn spatial multi-criteria decision analyses, reducing thenumber of evaluation criteria can help to minimize the costs
and time needed to digitize suitable maps for constructing a
database as well as for simplifying the weighting method. As
Fig. 4 Landfill suitability classes
based on the land classificationmap (LCM) method

pointed out in several other studies, having many evaluation
criteria in one problem can make the weighting approach more
complicated, such as in the case with the analytic hierarchy
process used by Malczewski (1999) and Sener et al. (2006). It

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Fig. 5 Overlay of the most suitable areas overlain with a SPOT satellite
image for the environmental ESS method (a) and the proposed land
classification LCM method (b)Fig. 6 Location of the selected
six candidate alternatives for final
landfill site selection based on the
LCM method

Environ Sci Pollut Res (2015) 22:7754–7765

may also reduce the precision in some methods such as therank order approach (Malczewski 1999). Our results show thatweighting of the designated factors in the proposed LCM
method is much simpler than the environmental ESS method,
yet gives comparable results.
Still, we note that several potentially important criteria in
the proposed LCM landfill siting process, such as distancefrom rivers, wells, qanats, and residential areas, were not considered, even though the selected most suitable landfill locations have enough distance from these points. By eliminating
prime agricultural lands and giving more priority to unproductive soils, many of the criteria related to soil quality wereindirectly included in the proposed method. In other words,
when a higher priority is given to unproductive saline andsodic soils, the sites acquire more distance from residential
areas. If there is no population within the region, then otherrelated infrastructures may not exist also.
Figure 5 shows overlays of the obtained maps with a SPOT
satellite image of the study area. Limits of the most suitableareas are indicated with a yellow color, while the blue arrows
point to agricultural lands. Figure 5a shows that some of theselected most suitable landfill locations using the ESS method
are located within the agricultural units. By contrast, the mostsuitable areas for the landfill obtained with the land classification map are located in areas without any land use.

Environ Sci Pollut Res (2015) 22:7754–7765

7763

Fig. 7 Results of pairwise comparisons of different alternatives with respect to distance from faults (a), distance from Marvdasht City (b), distance fromresidential areas (c), groundwater depth (d), relative importance of weights for each factor (e), and overall rank value of the sites (f)Considering the benefits of agricultural lands, one may conclude that the proposed LCM method has more utility than the
traditional environmental method, in particular when unproductive lands exist in the region. Even though the land classification map we used was not specifically designed for landfill
siting (e.g., having much soil surface soil information andrelatively few subsurface properties), results of this study

confirm that landfill siting can be performed well based on
soil classification maps, at least in a preliminary stage for
eliminating the most unsuitable lands and designating mostsuitable areas.
The above results using the land classification map approach were based on the preliminary or first stage of the
LCM landfill siting process. In the second stage, six

Fig. 8 Sensitivity of site ranking with respect to changes in the weights assigned to distance from faults (a), groundwater depth (b), distance from
Marvdasht City (c), and distance from residential areas (d)7764

alternative locations within the most suitable land units wereidentified by stakeholders from Marvdasht City. The potentialsites, shown in Fig. 6, have very similar composite suitability
indices as derived from the first siting stage. Additional information is hence needed to select from these the most appropriate location for the landfill.
For the second set of factors, we selected distance from
Marvdasht City, distance from faults, distance from residential
areas, and groundwater depth. The results of pairwise comparisons of the different factors are shown in Fig. 7(a–d). The
relative weights of importance computed for each factor are
shown in Fig. 7(e). Using this information, the overall rank
values of the six sites were computed and are listed inFig. 7(f). Results show that site 4 should be the most suitable
place for constructing the Marvdasht MSW landfill, followedby sites 5 and 6. Figure 7 also lists the inconsistency indices ofthe pairwise comparison matrixes.
We also carried out an analysis of the sensitivity of theresults to changes in the weights of the various criteria. The
results, summarized in Fig. 8, show how the site rankingschange in response to weights assigned to distance fromfaults, groundwater depth, distance from Marvdasht City,and distance from residential areas. The vertical line in eachcase represents the original weight, while the slanting lines
show in a linear manner how the rankings are affected by
the weights of each factor. The important things to begained from such a gradient sensitivity are the points at
which the various lines cross each other. These are the
Btradeoff points^ where the preferred alternative with respect to the selected criterion changes (Eskandari et al.
2013). Figure 8 shows that, for all four factors, the reversal points between the different sites occur at more than
±30 % from the original weights (i.e., along the horizontalaxis). The sensitivity analysis hence indicates that the obtained ranking is stable and that site 4 is the preferred
location for constructing the landfill.
Results from the second siting stage using the LCM
method confirm earlier findings by Eskandari et al.
(2012) in that the landfill of Marvdasht City should belocated based on a broad range of environmental, economic, and sociocultural considerations.

Conclusions
Different land evaluation criteria have been introduced inmany countries based on very site-specific conditions andconsiderations. Common to all of these methods is the elaborated construction and use of multiple maps for evaluating
various land and soil characteristics. Fast-growing residential
areas in recent years are increasingly pressing governments tomake decisions for land use planning surrounding the cities.
Consequently, the need for data required for optimal land use

Environ Sci Pollut Res (2015) 22:7754–7765

planning has been increasing substantially. Getting these data
is a serious challenge in most developing countries. One possible way to overcome this problem is the use of land evaluation maps and extending their applicability to other purposes.
Consequently, we proposed in this study a new method for
landfill siting in which all required data can be obtained from asingle agricultural land classification map (LCM). Construction of the preliminary database needed for the proposed LCM
method is considerably time and cost saving as compared tomore conventional environmental landfill siting approaches(ESS). Since our method requires only a limited number of
evaluation criteria, the weighting method is relatively simple
and compatible with existing weighting methods such as AHPor the use of rank order schemes. Moreover, the method can befollowed by most or all soil or environmental scientists orpractitioners having a hard copy of the map in hand without
using GIS features. The method seems particularly attractive
for developing countries often lacking adequate expertise andreliable data. Since the productivity of lands is well addressedin the proposed method, many or most related economical,
environmental, and social issues are also implemented. Our
results confirm the precision of the proposed method for landfill siting during the preliminary site selection stage. Thereafter, by considering site-specific situations within a given region and with stakeholder opinions, the most optimal site canbe selected with only a few additional criteria.

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