The large excess of solid waste generated in cities is a result of population growth and economic development. Properly managing this municipal solid waste (MSW) is a challenge, mainly in underdeveloped and developing countries where financial concerns are an added problem. From the environmental point of view, a major issue is properly disposing MSW taking into consideration a wide range of factors, and working with different spatial data. In this study, we used geographic information system (GIS) to perform multi-criteria decision analysis (MCDA) conducted by analytical hierarchy process (AHP). The development of the environmental impact susceptibility model (EISM) for municipal solid waste disposal sites (MSWDS) applied to the state of Sao Paulo, Brazil considered factors such as geology, pedology, geomorphology, water resources, and climate represented by fifteen associated sub-factors. The results indicated that more than 82% of Sao Paulo’s territory is situated in areas with very low, low, and medium environmental impact susceptibility categories. However, in the remaining 18% of the state land area, 85 landfills are located in areas with high and very high environmental impact susceptibility categories. These results are alarming because these 85 landfills receive approximately 17,886 tons of MSW on a daily basis, which corresponds to 46% of all municipal solid waste disposed in Sao Paulo state. Therefore, decision makers, urban planners and policymakers could use the findings of the EISM towards mitigating the environmental impacts caused by MSWDS.
Modeling Geographic Information System (GIS) Environmental Impact Municipal Solid Waste Landfills Multi-Criteria Decision Analysis (MCDA) Analytic Hierarchy Process (AHP)1. Introduction
The rapid world population growth and economic development are causing changes in terrestrial systems that can have serious and lasting consequences. The large amount of municipal solid waste (MSW) generated exceeds the capacity of the environment to decompose and recycle these wastes through natural processes [1] . The lack of proper waste management is a major environmental problem [2] . Sustainable management of MSW is required to achieve low environmental impact. One essential part in this process is to properly dispose waste, since disposal sites are permanent facilities that pose risks to the environment and population as they need to be monitored for extended periods of time [3] .
Among many methods to dispose MSW in underdeveloped countries, the most common are open dumps and landfills. Open dumps are uncontrolled facilities where waste is directly disposed in the ground without any control causing several impacts. In contrast, sanitary landfills use techniques and methods to better control environmental impacts and are commonly used around the world, particularly in developed countries [4] . Although the number of sanitary landfills is increasing in the last decades in Brazil [5] , the nation is through an inadequate MSW disposal scenario, with more than 60% of its cities still disposing MSW in open dumps [6] [7] .
It is important take into consideration the environmental impact caused by municipal solid waste disposal (MSWD) in São Paulo, Brazil due to several factors. First, São Paulo is the most populous state in America and Western Hemisphere. Second, São Paulo is the biggest producer of MSW among all Brazilian states. Third, the per capita waste generation rate in São Paulo state is the biggest rate in Brazil with 1.4 kg/habitant/day with a growing trend over the years [8] . Finally, various São Paulo cities still dispose MSW improperly [9] . Therefore, all these factors together lead to the occurrence of negative environmental impacts.
Among diverse kinds of environmental impacts caused by humans, the MSWD is one of the most impactful, because solid wastes are retained in the same place where they are deposited even though they may undergo chemical and physical transformations over the years [10] [11] [12] .
The improper MSWD locally cause environmental impacts, such as contamination of soil [13] [14] [15] [16] , water sources [13] [17] [18] [19] and health public impacts [20] [21] and also globally cause environmental impacts, such as increase of greenhouse gases due to methane emissions [22] [23] . Consequently, the improper MSWD causes global and local impacts on the environment on different scales.
For this reason, assessing the environmental impact caused by MSWD sites must consider different parameters, to avoid potential negative effects. Developing a model for assessing environmental impact susceptibility must take into consideration multiple issues, values, scales and degrees of uncertainty, as well as assist stakeholder engagement. In this process, the models are usually built to satisfy one or more of five main purposes: 1) prediction, 2) forecasting, 3) management and decision-making under uncertainty, 4) social learning, and 5) developing system understanding and experimentation [24] .
In this study to develop an environmental impact susceptibility model (EISM) for municipal solid waste disposal sites (MSWDS), we used a multi criteria decision analysis (MCDA) approach via an analytic hierarchic process (AHP) coupled with geographic information system (GIS). This paper is organized as follows: Section 2 discusses the literature review of GIS, MCDA and AHP applied to environmental studies. Section 3 describes the methods used to develop the EISM and describes the study area. Section 4 presents the model results for the state of São Paulo and the MSWDS assessment. Finally, the conclusions are presented in Section 5.
2. Background Literature Review
In this section, the literature review is divided into four parts: Section 2.1 includes the advantages of GIS in environmental studies, Section 2.2 demonstrates the importance of MCDA applied to municipal solid waste issues, and Section 2.3 explains the use of AHP.
2.1. Geographic Information System (GIS)
The use of geographic information system is one of the most promising approaches to investigate complex spatial phenomena, because GIS has the advantage of storing, retrieving and analyzing a considerable amount of disaggregated data from various sources and displaying the results spatially, which helps decision makers solve several problems [25] [26] .
GIS has been used for several purposes [27] , including environmental applications. Examples include estimating groundwater recharge [28] , assessing water pollution [29] , identifying forest fire susceptibility [30] , mapping landslide susceptibility [31] and flood susceptibility [32] , modeling erosion [33] , and evaluating ecological vulnerability of sites [34] .
GIS has also been used in numerous studies to improve municipal solid waste management (MSWM). Examples include predicting generation and composition patterns of MSW [35] [36] , improving MSW collection and transport [25] [37] [38] [39] [40] , selecting locations for MSW transfer stations [41] [42] , assessing groundwater vulnerability [43] [44] and impact [45] near a MSWDS, and identifying areas for siting landfills [46] - [60] .
2.2. Multi Criteria Decision Analysis (MCDA)
Multi criteria decision analysis is a method to structure a problem through the action concepts and intelligible criterion group to facilitate the communication in decision process, forming a conviction rather than determining an optimum [61] . Combining MCDA with spatial decision problems usually contains a large set of feasible alternatives and conflicts with an incommensurate evaluation criteria [62] .
The MCDA applied to environmental studies had a significant growth over the last decade [63] [64] . The integration of spatial analysis using GIS to MCDA has been used in different environmental studies. Examples include, analyzing the possibility to convert pastures to croplands in Brazil [65] , mapping the landslide susceptibility [31] , and identifying geotechnical land suitability [66] .
Spatial analyses associated with MCDA is considered one of the main application for GIS [67] , and have also been used in several studies related to municipal solid waste issues [26] [41] [46] [49] - [54] [57] [60] [68] - [75] . Because MSWM involves multiple factors such as environmental, economic, political and social [37] , combining MCDA with GIS increases the analysis effectiveness and accuracy [50] helping to understand the complexity of the problem, ensuring the robustness and reliability of the final decision.
2.3. Analytical Hierarchy Process (AHP)
In this study, we use the Analytical Hierarchy Process (AHP), which is a component of the Multi Criteria Decision Analysis method. The AHP was developed by Saaty in the 1970s [76] and consists of an assessment theory through pairwise comparison to help decision makers set priorities and choose the best decision [37] [59] . The AHP in combination with GIS has been widely used in the field of natural resources and environmental management [77] , first because the combined approaches are easy to implement using map algebra operations and cartographic models, and second because the approaches are intuitively appealing to decision makers [62] . The comparisons are made using a scale of absolute judgments ranging from one to nine, where one represents equal importance and nine represents the highest importance from one element to another (Table 1). In addition, a reciprocal value is used to express the inverse comparison [78] .
The process of AHP determination involves these subsequent steps: 1) compute sum of values in each column of pairwise matrix, 2) normalize the matrix by dividing each element by its column total and, 3) compute the mean of the elements in each row of the normalized matrix [60] .
Afterwards to determinate the consistency of the AHP judgment, a consistency index (CI) (Equation (1)) is determined [76] .
In this equation, is the principal judgement matrix value [76] [78] . Subsequent the determination of CI, a consistency ratio (CR) needs to be calculated (Equation (2)) [76] .
The comparison scale in AHP
Definition
Intensity of Importance
1
Equal importance
3
Weak importance
5
Essential or strong importance
7
Demonstrated importance
9
Absolute importance
2, 4, 6, 8
Intermediate values between adjacent judgments
In this equation, random index (RI) depends on the number of elements being compared [76] [78] . The CR is acceptable if its value is less than 10%. However, if this number is higher than 10%, the judgments may be inconsistent and should be re-evaluated [53] .
3. Methods
To develop the environmental impact susceptibility model for municipal solid waste disposal sites, we considered six major steps: 1) selection of environmental decision factors and sub-factors; 2) data acquisition and integration into a GIS database; 3) definition of classes and assignment of ratings; 4) data standardization to a common scale of measurement; 5) calculation of relative weights using the AHP technique; and 6) derivation of the final model map using weighted linear combination (WLC) aggregation method (Figure 1). Each step is described as follows.
3.1. Selection of Environmental Decision Factors and Sub-Factors
In this study, the selection of environmental factors and sub-factors was based on the literature that takes into account the environmental impact susceptibility associated with disposal of municipal solid waste e.g. [3] [26] [43] [44] [49] [50] [53] [54] [57] [58] [59] [60] [74] [75] [80] [81] [82] [83] . We also took into consideration guidelines, relevant legislation and regulations, experts’ opinions, and available data. Overall, a total of five factors including geology, pedology, geomorphology, water resources, and climate, with fifteen associated sub-factors were used in the model (Figure 2). This list is not exhaustive; we only considered what the literature included as the most important criteria to develop the environmental impact susceptibility model for municipal solid waste sites.
Flowchart of proposed methodology to develop the environmental impact susceptibility model
Factors and sub-factors used to develop the environmental impact susceptibility model for MSWDS
3.1.1. Geology
Geological features influence the environmental susceptibility of municipal solid waste disposal sites because they can cause land instability in an earthquake region [66] . They can also, influence water infiltration if the rock formations are porous or have faults [84] . For this reason, when municipal solid waste is disposed above susceptible rocks, the process of waste landslide and water contamination may occur. Some geological aspects are considered in previous studies, including [43] [53] [54] [83] [85] . However, these studies did not consider simultaneously the four geological sub-factors used in this model, which are 1) distance to faults, 2) porosity of rocks, 3) distance to seismic areas, and 4) distance to caves.
3.1.2. Pedology
Soil parameters, such as depth and physical characteristics, could interfere in environmental susceptibility related to siting municipal solid waste facilities, mainly for two reasons. First, strength characteristics of the soil are important to support the overlying load from the waste mass. Second, the soil permeability can interfere in infiltration process, which in turn can cause contamination of water bodies. Multiple studies in MSWDS issues included pedologic aspects in their assessments, e.g., [3] [46] [82] [83] . In particular, we used the pedology sub-factors of 1) type of soil and 2) infiltration rate.
3.1.3. Geomorphology
Geomorphology is mainly related to terrain features and the influence of these characteristics on the topography and runoff process. For example, flat areas influence leachate infiltration, while steep areas influence terrain instability. Therefore, both can cause environmental impacts. Many studies took into consideration topographical aspects, e.g., [26] [59] [60] [83] . In particular, we used the geomorphological sub-factors of 1) landslide risk and 2) slope.
3.1.4. Water Resources
Another aspect that affects environmental susceptibility is associated with surface and underground water resources. It is not appropriate to have MSWDS close to surface water sources or in areas where the water table level is shallow due to the higher contamination risk. Several studies took into consideration these aspects, e.g., [26] [43] [49] [53] [54] [58] [60] [74] [82] . In this study, we used surface water resources sub-factors of 1) distance to rivers and lakes and 2) flood risk, while, for underground water resources, we used the sub-factors of 1) distance to wells, 2) aquifer flow, and 3) aquifer vulnerability to pollution.
3.1.5. Climate
Climate factors need to be used in modeling the environmental impact susceptibility for municipal solid waste disposal, mainly because they can interfere in the decomposition process of solid waste and in the volume of leachate generated, due to the water balance as well as the amount of landfill gas generated. Climate aspects also were considered in previous investigations, e.g., [44] [57] [59] [82] . In this study, we used the climatic sub-factors of 1) precipitation and 2) temperature.
3.2. Study Area
São Paulo state, in Southeastern Brazil, is located between 19˚ and 25˚ South latitude and 44˚ and 53˚ West longitude. It borders the Minas Gerais state to the north, Rio de Janeiro state to the northeast, the Atlantic Ocean to the east, Paraná state to the south, and Mato Grosso do Sul state to the west (Figure 3). São Paulo is the most populous Brazilian state, with approximately 44.4 million inhabitants in 2015 living in 645 municipalities with a total area around of 248.2 million∙km2 [79] . São Paulo is also the biggest producer of municipal solid waste in Brazil, generating about 39 thousand tons per day, which are disposed in 420 official municipal solid waste disposal sites [9] .
3.3. Data Acquisition and Integration into a GIS Database
The spatial database used in the environmental impact susceptibility model for municipal solid waste disposal sites applied to the São Paulo state was created using a variety of sources including geologic, pedologic, geomorphologic, hydrologic and, climatologic data of different scales (Table 2). The successful use of GIS depends on the accessibility of data, as well as its quality, representing the real world conditions through diverse layers [56] .
In this study, all data layers were stored, manipulated, analyzed, and visualized using ArcGIS version 10.2 ModelBuilder as a starting point for a multi-criteria decision analysis. ModelBuilder is a GIS extension that encodes complex sequences of GIS operations into a simple graphic model from which the steps can be executed [86] . The data layers were georeferenced using the UTM System Datum SIRGAS 2000 (Zone 22 and 23 South).
3.4. Definition of Classes and Rating
Each of the fifteen sub-factors used in the environmental impact susceptibility
Map of the state of São Paulo, Brazil
model for municipal solid waste disposal sites was divided into classes. Each class was rated on a scale from one to ten, where one represents the lowest level of susceptibility and ten represents the highest level of susceptibility for environmental impact.
The rating intervals from one to ten was selected based on similar scales used by [43] [55] [87] [88] , as well as based on the experience and judgment of the authors. Furthermore, the importance for each class could vary based on the region of interest and characteristics of the specific area [56] . In this study the classes were assigned considering the relevant conditions in the state of São Paulo (Table 3).
3.5. Data Standardization to a Common Scale of Measurement
In order to overlay the spatial information to calculate the environmental impact susceptibility, it is necessary to standardize the data into a common measurement scale. Therefore, the fifteen sub-factors were converted into raster grid format consisting of 50 m × 50 m cells resulting in an image of 18,790 columns and 12,744 rows.
Spatial data used in the environmental susceptibility impact model for MSWDS in São Paulo state
Factors
Sub-factors
Sources
Information used to create layers
Format
Scale or Resolution
Date
Geology
Distance to Faults
Geology Report: [92]
Structures
Digital
1:700,000
2009
Porosity of Rocks
Geology Report: [92]
Primary porosity
Digital
1:700,000
2009
Distance to Seismic Areas
Geology Report: [92]
Geological/ Geotechnical Risks and Earthquakes
Digital
1:700,000
2009
Distance to Caves
Permanent Cave Protection Areas in the São Paulo State: [93]
Caves
Digital
1:50,000
2015
Pedology
Type of Soil
Pedology Report: [94]
Type of Soil
Digital
1:500,000
1999
Infiltration Rate
Pedology Report: [94]
Factor K
Digital
1:500,000
1999
Geomorphology
Landslide Risk
Landslide Hazard Index [95]
Landslide Hazard Classes
Digital
1:75,000
2014
Slope
Digital Elevation Model-DEM [96]
Calculated using DEM
Digital
1:50,000
2013
Water Resources-Surface
Distance to Rivers and Lakes
Hydrology Report: [92]
Hydrography Unifilar and Bifilar
Digital
1:700,000
2009
Flood Risk
Flood Hazard Index [95]
Flood Hazard Classes
Digital
1:75,000
2014
Water Resources-Underground
Distance to Wells
Hydrology Report: [92]
Representative Wells
Digital
1:700,000
2009
Aquifer Flow
Hydrology Report: [92]
Aquifer Flow Classes
Digital
1:700,000
2009
Aquifer Vulnerability
Natural vulnerability of aquifer to pollution [97]
Aquifer Vulnerability Classes
Digital
1:1,000,000
2013
Climate
Precipitation
Zoning bioenergy crops in São Paulo state report: [98]
Isohyet Lines
Digital
1:500,000
2008
Temperature
Zoning bioenergy crops in São Paulo state report: [98]
Isotherm
Digital
1:500,000
2008
3.6. Weight Assignment Using AHP
The construction of a comparison matrix and the derivation of weights in our study uses the analytical hierarchic process web-based tool developed by [89] . First, the AHP methodology was applied to the factors (Table 4) and sub-factors (Table 5). Then, by multiplying these two results, the global weighting for each sub-factors was obtained (Table 6).
3.7. Weight Linear Combination (WLC) Method
After checking the reliability of the pairwise comparisons for factors and sub-factors, the environmental impact susceptibility model for municipal solid waste disposal sites in the São Paulo state was built using a weighted linear combination method, following (Equation (3)).
In this equation, S is the EISM final score, is the sub-factor weight, and is the standardized class rating of factor i. As the sum of weight for factor i is a multiplication of and for each sub-factor, the is constrained to one, while varies from zero to ten, and the final combined estimate is presented on this scale.
Therefore, the EISM final score was obtained for each raster cell as a sum of
Rating classes
Factors
Sub-factors
Class
Rating
Geology
Distance to Faults
<500 m 500 - 1000 m 1000 - 1500 m 1500 - 2000 m 2000 - 2500 m 2500 - 3000 m 3000 - 3500 m 3500 - 4000 m 4000 - 4500 m >4500 m
<10,000 m 10,000 - 20,000 m 20,000 - 30,000 m 30,000 - 40,000 m 40,000 - 50,000 m 50,000 - 60,000 m 60,000 - 70,000 m 70,000 - 80,000 m 80,000 - 90,000 m >90,000 m
10 9 8 7 6 5 4 3 2 1
Distance to Caves
<500 m 500 - 1000 m 1000 - 1500 m 1500 - 2000 m 2000 - 2500 m 2500 - 3000 m 3000 - 3500 m 3500 - 4000 m 4000 - 4500 m >4500 m
Pairwise comparison matrix, ranking, and weights for factors
Factors (CR 2.1%)
[1]
[2]
[3]
[4]
[5]
[6]
Rank
Weight (%)
Geology
1
5
5.6
Pedology
3
1
2
17.9
Geomorphology
2
1/2
1
4
10.4
Surface Water Resources
4
2
2
1
1
26.0
Underground Water Resources
4
2
2
1
1
1
26.0
Climate
3
1/2
2
1/2
1/2
1
3
14.1
Pairwise comparison matrix, ranking, and weights for factors and sub-factors
Factors (CR %)
Sub-factors
[1]
[2]
[3]
[4]
Rank
Weight (%)
Geology CR (5.6%)
Distance to Faults
1
2
20.6
Porosity of Rocks
5
1
1
64.0
Distance to Seismic Areas
1/4
1/7
1
4
6.0
Distance to Caves
1/3
1/6
2
1
3
9.4
Pedology CR (0.0%)
Type of Soil
1
2
33.3
Infiltration Rate
2
1
1
66.7
Geomorphology CR (0.0%)
Landslide Risk
1
2
20.0
Slope
4
1
1
80.0
Water Resources
Surface CR (0.0%)
Distance to Rivers/Lakes
1
1
85.7
Flood Risk
1/6
1
2
14.3
Underground CR (1.0%)
Distance to Wells
1
3
16.3
Aquifer Flow
2
1
2
29.7
Aquifer Vulnerability
3
2
1
1
54.0
Climate CR (0.0%)
Precipitation
1
1
66.7
Temperature
1/2
1
2
33.3
the products of ratings assigned for each class (Table 3) and global weights obtained by AHP (Table 6) (Figure 4). The results were grouped into five categories
Global weighting for sub-factors
Factors
Sub-factors
Global Rank
Global Weight (%)
Geology
Distance to Faults
13
1.2
Porosity of Rocks
11
3.6
Distance to Seismic Areas
15
0.3
Distance to Caves
14
0.5
Pedology
Type of Soil
7
6.0
Infiltration Rate
3
11.9
Geomorphology
Landslide Risk
12
2.1
Slope
5
8.3
Water Resources
Surface
Distance to Rivers/Lakes
1
22.3
Flood Risk
10
3.7
Underground
Distance to Wells
9
4.2
Aquifer Flow
6
7.7
Aquifer Vulnerability
2
14.0
Climate
Precipitation
4
9.4
Temperature
8
4.7
of environmental impact susceptibility for municipal solid waste disposal sites: Very Low (S1), Low (S2), Medium (S3), High (S4) and Very High (S5) (Table 7).
4. Results and Discussion4.1. Environmental Impact Susceptibility Model for Municipal Solid Waste Disposal Sites
The results of the environmental impact susceptibility model for municipal solid waste disposal sites in the state of São Paulo are presented in (Figure 5). The area for each susceptibility category indicate that most part of São Paulo state, 77.3% have medium environmental impact susceptibility category (S3), 16.8% has high category (S4), 4.8% has low category (S2), 1.1% has very high category (S5) and there is no representative areas for the very low category (S1) (Table 8).
The high and very high categories (S4 and S5, respectively) in the state of São Paulo extend are located near the surface water resources, which is correlated to the EISM global weights that has the sub-factor distance to rivers and lakes as the most important contributor. There is also a concentration of the higher categories near the Atlantic Ocean mainly in the southeast of the state of São Paulo, which can be explained by a combination of geographical variables. For example, there is a mountain range in this area formed by the Serra do Mar and Serra da Mantiqueira, which has a concentration of steep areas. In addition, these mountain range stop the humidity that comes from the ocean to the continent, which makes the precipitation near the coast very high in comparison to the rest of the state of São Paulo.
The EISM for MSWDS in the state of São Paulo was progressed well due to availability and reliability of spatial data and the findings in this study provide
Maps for all selected sub-factors (color figure online).
Environmental impact susceptibility model categories for municipal solid waste disposal sites
Categories
Values
Very Low (S1)
0 - 2
Low (S2)
2 - 4
Medium (S3)
4 - 6
High (S4)
6 - 8
Very High (S5)
8 - 10
Environmental susceptibility categorization for MSWDS in the São Paulo state
Environmental impact susceptibility categorys
Area (km2)
Area Percentage
Very Low (S1)
0
0%
Low (S2)
12,054
4.8%
Medium (S3)
192,631
77.3%
High (S4)
41,764
16.8%
Very High (S5)
2677
1.1%
Total
249,126
100%
Environmental impact susceptibility for municipal solid waste disposal sites in São Paulo state (color figure online)
an advancement to previous models due to three main reasons: 1) a higher number of factors used, 2) a higher number of sub-factors used, and 3) a more extensive set of combined factors and sub-factors used. Therefore, the EISM results for MSWDS indicate a decent environmental impact susceptibility representation of the state of São Paulo.
4.2. Analysis of Susceptibility for MSW Disposal Sites in São Paulo State
In order to evaluate the environmental impact susceptibility for each municipal solid waste disposal site in São Paulo state we developed a spatial analysis (Figure 6) and statistical study (Table 9).
The geographical coordinates of municipal solid waste disposal sites for the 645 municipalities in São Paulo state were obtained from spreadsheets used to assess the waste quality index developed by the Environmental Company of São Paulo State (CETESB) [90] . Because some of São Paulo’s cities use consortia to dispose solid waste, there are currently 420 municipal solid waste disposal sites
Environmental impact susceptibility categorization of municipal solid waste disposal sites in São Paulo state (color figure online)
Municipal solid waste disposal sites in the state of São Paulo according to environmental susceptibility category
Environmental impact susceptibility category
Number of Ditch Landfills
Number of Sanitary Landfills
Ton of MSW/disposed per day
S1 + S2
6
0
38.57
S3
271
57
20,957.56
S4
54
31
16,430.81
S5
0
1
1454.82
Total
331
89
38,880.76
cataloged and evaluated in the annual Inventory of Solid Waste [9] . Furthermore, after visual assessment through RapidEye satellite images for the years of 2013 and 2014, provided by the Ministry of Environment (MMA), it was determined that some of the municipal solid waste disposal sites were mislocated in the spreadsheets, for that reason, the locations were corrected and additionally the MSWDS areas were defined.
Afterwards, a spatial analysis was performed by overlaying the results of the EISM and the locations of municipal solid waste disposal sites in the state of São Paulo. Thus, it was possible to identify the specific MSWDS and the amount of municipal solid waste disposed of in each environmental impact susceptibility categories. For cases where the MSWDS had more than one susceptibility category, the highest category was assigned.
In São Paulo state, two different kinds of municipal solid waste disposal approach are used: ditch landfills (331 units) and sanitary landfills (89 units). Ditch landfills are a disposal technique for municipal solid waste on the ground without compaction and consequently with fewer requirements for implementation than a sanitary landfill. This procedure allows small towns, with population under to 25,000 inhabitants and daily generation of MSW less than ten tons to have their waste disposed without the necessity to construct a sanitary landfill [91] . Even though the quantity of MSW disposed in ditch landfills are smaller than the quantity disposed in sanitary landfills, usually ditch landfills pose more environmental risks and cause environmental impacts.
São Paulo is one of the states in Brazil where almost all cities use landfills instead of open dumps, which represent an improvement to avoid negative environmental impacts caused by MSWDS. Nevertheless, disposal of MSW in sanitary landfills instead of ditch landfills does not eliminate all possible environmental impacts but reduces the probability of their occurrence.
In addition, the increasing population and MSW generation in the São Paulo state has caused pressure in the old MSWDS that are almost filled. This problem added to the lack of suitable areas for new sanitary landfills are some of the most critical problems faced by municipalities, especially near the metropolitan areas of São Paulo, Campinas, Baixada Santista and Vale do Paraíba, where there is a high concentration of population and consequently production of MSW.
The assessment for MSWDS in the state of São Paulo, indicates that the number of landfills located in each environmental impact susceptibility category has a positive correlation with the extent of each category in the state area. If sanitary and ditch landfills are added in the assessment, approximately, 1.6% of them are placed in very low, low and very high environmental impact susceptibility categories (S1, S2 and S5 respectively). Approximately 20.4% of the landfill sites are located in high category (S4) and, the great majority, approximately 78% are situated in medium susceptibility category (S3).
When a separate analysis was performed for sanitary and ditch landfills, a total of six ditch landfills were in the lower susceptibility categories (S1 and S2) and just one sanitary landfill was in the very high susceptibility category (S5) (Table 9). Even though only 54 ditch landfills and 32 sanitary landfills are in the (S4 and S5) categories, a large amount of municipal solid waste (17,886 tons) is disposed of at these MSWDS daily, which corresponds to approximately 46% of the total MSW disposed in the state of São Paulo.
Based on the total amount of municipal solid waste classified under the highest susceptibility categories (S4 and S5), 97.5% is disposed in sanitary landfills, and only 2.5% is disposed in ditch landfills. This is a positive finding, since if properly operated and monitored, sanitary landfills provide better environmental protection than ditch landfills. The list of municipalities and the quantity of municipal solid waste disposed in sanitary and ditch landfills located in the high susceptibility categories are provided in (Table 10).
5. Conclusions
Through the development of the environmental impact susceptibility model for municipal solid waste disposal sites using multi criteria decision analysis and analytical hierarchic processes coupled with geographic information system, it was possible to identify the most and least environmentally susceptible areas using five environmental factors associated with fifteen sub-factors. With the application of the EISM, it was also possible to assess the current susceptibility of municipal solid waste disposal sites in São Paulo state, Brazil.
In this study, the results of the environmental impact susceptibility model indicated that even though more than 82% of the land area in São Paulo state is situated in very low, low, and medium susceptibility categories, 85 of 420 landfills, were located in the high and very high susceptibility categories. In these landfills, approximately 17,886 tons of municipal solid waste are disposed on a daily basis, which indicated that 46% of all MSW of the state of São Paulo is disposed in environmentally susceptible areas. For that reason, municipal solid waste disposal sites in São Paulo state require more attention and control to prevent the occurrence of negative environmental impacts and reduce the economic as well as social consequences.
The development of this model took three main modeling purposes into consideration, including prediction, management decision-making under uncertainty, and developing system understanding and experimentation. This type of spatial analysis can help stakeholders promote the mitigation of environmental
Municipal solid waste disposed of in landfills in high and very high categories
Categories
Sanitary Landfills
MSW/day (tons)
Ditch Landfills
MSW/day (tons)
S5
Santos
1454.82
0
0
S4
Avaré Cachoeira Paulista Caieiras Cerquilho Dracena Embu Guatapará Jacareí Jales Jambeiro Jardinópolis Juquia Leme Limeira Mauá Mogi-Guaçu Onda Verde Pedreira Penópolis Pereira Barreto Peruíbe Piedade Porto Ferreira Presidente Prudente Quatá Rio Claro Santo André São Carlos São José dos Campos São Paulo* Tupã
Adamantina Alvarez Machado Americo de Campos Andradina Anhembi Apiaí Bálsamo Barra do Turvo Bernardino de Campos Bõa Esperança do Sul Cajati Campina do Monte Alegre Cassia dos Coqueiros Charqueada Corumbataí Divinolândia Dourado Estiva Gerbi Gália Garça Gastão Vidigal Guiaçara Guapiara Guareí Ibaté Iporanga Iracemopolis Itaoca Itápolis Itariri Junqueirópolis Marinópolis Nantes Óleo Ouro Verde Pacaembú Pedra Bela Pedrinhas Paulista Pedro de Toledo Piquete Pirangi Poloni Presidente Bernardes Ribeirão dos Indios Sabino Sales Sales de Oliveira Santa Maria da Serra São Francisco Severinia Tapiratiba Torre de Pedra Tupi Paulista Vargem
Source: [9] ; *Landfill located at Av. Sapopemba, n˚ 22,254-CTL.
impacts and assist in the process of identifying areas for new landfills.
This model can be applied to different areas, especially in developing countries, where most of the municipal solid waste is disposed directly in the ground, without control, resulting in adverse environmental impacts. Although the EISM was developed focusing in MSWDS, the authors consider that the model can also be used with some adaptations for other point source of environmental impact, such as, fuel stations, mines, and any type of solid waste disposal facilities such as industrial or hazardous wastes.
The main limitation in the development of the EISM is the accessibility of spatial data, as well as its quality. In addition, there is the subjectivity of class and rating definition of the sub-factors and the weight assignment using AHP, where variation in these values can cause a different result in the analysis. Furthermore, the importance for each class could vary based on the region of interest and characteristics of the specific area.
For future studies, to improve the environmental impact susceptibility assessment for MSWDS the authors suggest adding 1) forecasting, using different climate scenarios that influence leachate generation and emission of greenhouse gases, and 2) social learning, coupling a social model with the EISM, which could result in a greater understanding of global susceptibility.
Acknowledgements
The authors thank Fundação de Amparo a Pesquisa no Estado de São Paulo (FAPESP) for Victor Fernandez Nascimento doctoral fellowships (No. 13/09039-7), (No. 15/24344-6) and the Nitrogen cycling in Latin America: Drivers, Impacts and Vulnerabilities project (CRN 3005), the Earth System Science Center (CCST) at the National Institute for Space Research (INPE), and the Global Waste Research Institute at California Polytechnic State University, for the support provided during this research. The authors also thank Dr. Marie Rosenwasser for English review.
Cite this paper
Nascimento, V.F., Sobral, A.C., Andrade, P.R., Ometto, J.P.H.B. and Yesiller, N. (2017) Modeling Environmental Susceptibility of Municipal Solid Waste Disposal Sites: A Case Study in São Paulo State, Brazil. Journal of Geographic Information System, 9, 8-33. https://doi.org/10.4236/jgis.2017.91002
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