Modeling the environmental susceptibility of landfill sites in California

Abstract

​​The proper management of solid waste (SW) is a global environmental challenge. A major issue is the proper disposal of SW while balancing a wide range of criteria and working with different spatial data. In this study, we used geographic information system as a tool to perform multi-criteria decision analysis with an analytical hierarchy process to develop an environmental impact susceptibility model (EISM) for landfills. The model was applied to the state of California, USA and results are presented herein. In particular, the EISM considers factors such as geology, pedology, geomorphology, water resources, and climate as represented by 13 associated environmental indicators. The results of the EISM indicate that more than 75% of California’s territory is situated in areas with very low, low, and medium environmental impact susceptibility categories. However, in the remaining 25% of the state’s land, 61 landfills are located in the high and very high categories. These results are alarming because during the period from 2000 to 2015, these 61 landfills received approximately 308 million tons of SW, which corresponds to more than 57% of all SW disposed in California. The model results can be used toward mitigating the environmental impacts of these facilities.

Keywords:

• modeling
• geographic information system (GIS)
• environmental impact
• solid waste management
• landfill
• multi-criteria decision analysis (MCDA)

Introduction

Anthropogenic activities are usually associated with the generation of solid waste (SW) (Adamović et al. 2016). Even if the capacity of different means of waste management approaches and facilities such as recycling, composting, and incineration is increased, landfills are still necessary and remain a common method for SW disposal in many regions of the world, including the USA (OECD 2015; Townsend et al. 2015; USEPA 2015). Data from 2013 indicate that 64.7 million tons of municipal SW was recovered through recycling and 22 million tons were recovered through composting in the USA. Subtracting out what was recycled and composted, the remaining 167 million tons of waste were placed in landfills (USEPA 2015). For the near future, landfilling is expected to be the dominant method to manage SW in the USA (Qian, Koerner, and Gray 2002; Powell, Townsend, and Zimmerman 2015; USEPA 2015).

The most significant potential environmental threats associated with landfill disposal are release of gases and leachate (Tchobanoglous, Theisen, and Vigil 1993). The main landfill gases are methane and carbon dioxide, which are greenhouse gases. In addition, many trace gas components in the landfill gas are also ozone-depleting substances (USEPA 2017). Furthermore, migration of leachate to surface or underground water has potential for contamination of these water resources. Geotechnical instability and failure of the waste mass or containment system components also can pose significant environmental risks due to the uncontrolled release of landfilling by-products of gas and leachate as well as the waste materials (Koerner and Soong 2000; Qian, Koerner, and Gray 2002). The scale of the environmental impact depends on the composition and quantity of SW involved, the quality of the landfill’s operation and management, and the physical characteristics of the local area.

The lack of more sustainable waste management is a major environmental problem (Mishra et al. 2016). Sustainable management of SW is required to achieve low environmental impact (Adamović et al. 2016), and an essential part in this process is the proper disposal of waste. Disposal sites are usually permanent facilities that pose risks to the environment and population, and need to be monitored for extended periods of time (Leão, Bishop, and Evans 2004).

For these reasons, the environmental impact of landfills needs to be considered to avoid potential negative effects. In order to develop a comprehensive model for assessing the environmental impact susceptibility of landfills, multiple issues must be taken into consideration: value, scale, and uncertainty. In this process, the models are usually built to satisfy one or more of five main purposes: (i) prediction; (ii) forecasting; (iii) management and decision-making under uncertainty; (iv) social learning; and (v) developing system understanding and enabling experimentation (Letcher et al. 2013).

The overall objective of this study is to spatially assess the environmental susceptibility of landfill sites. An improvement in understanding how physical factors such as geology, pedology, geomorphology, climate, and surface and underground water resources connect and influence the environmental susceptibility is essential to avoid any negative impacts of SW disposal sites. In this study, we developed an environmental impact susceptibility model (EISM) for landfill sites considering at least three of the five main modeling purposes of prediction, management decision-making under uncertainty, and developing system understanding and enabling experimentation. To accomplish this task, we used multi-criteria decision analysis (MCDA) with the analytical hierarchy process (AHP) method coupled with geographic information system (GIS) to apply the model to the state of California, USA

Study area

The state of California, on the west coast of the US, is located between 32° and 42° north latitude and 114° and 124° west longitude. It borders the states of Oregon to the north, Nevada to the northeast, Arizona to the southeast, Baja California, Mexico to the south, and the Pacific Ocean to the west (Figure 1). California is the most populous state in the USA with a population of approximately 39.2 million in July 2016 (CALIFORNIA 2016). Moreover, the state is also one of the biggest producers of SW in the USA, generating approximately 31.3 million tons of SW in 2014 (CalRecycle 2015).

Figure 1. Map of the state of California, USA (Color figure online).

California is also the third largest state by land area in the USA, with an area approximately of 408,000 km², and a diverse geography from forests to deserts and several large cities, including the nation’s second largest, Los Angeles. Furthermore, the state is considered to be the most crowded subnational entity in the Western Hemisphere and the Americas, with a population second only to that of São Paulo State in Brazil. Generally, densely populated areas are subject to more concerns associated with SW disposal that is widely practiced through the landfill method in both developed and developing countries (Ahmad et al. 2016). For these reasons, we chose the state of California to assess the environmental impact susceptibility and to analyze the susceptibility of the existing and new landfill sites in its territory.

Methods

To build the EISM for landfill sites, six major steps were considered: (1) selection of environmental decision criteria; (2) data acquisition and integration into a GIS database; (3) definition of classes and assignment of ratings; (4) standardization of environmental indicators to a common scale of measurement; (5) calculation of relative criteria weights using the AHP technique; and (6) derivation of the final model map using the weighted linear combination (WLC) aggregation method. Each step is described as follows.

Step 1: selection of environmental decision criteria

In this study, the environmental factors and indicators selected were based on literature that took into account environmental impact susceptibility associated with landfill sites (Leão, Bishop, and Evans 2004; Butt, Lockley, and Oduyemi 2008; Eskandari, Homaee, and Mahmodi 2012; Gbanie et al. 2013; Butt et al. 2014; Khan and Samadder 2015; Maanifar and Fataei 2015; Motlagh and Sayadi 2015; Eskandari et al. 2015; Bahrani et al. 2016; Chabuk et al. 2016; Chonattu, Prabhakar, and Harikumar 2016a; Demesouka, Vavatsikos, and Anagnostopoulos 2016; Chonattu, Prabhakar, and Harikumar 2016a and 2016b​​; Kharat et al. 2016; Eskandari, Homaee, and Falamaki 2016; Yıldırım and Güler 2016; Zhang et al. 2016). In addition, existing guidelines, relevant legislation and regulations, experts’ opinions obtained in informal meetings, and available relevant data were considered in the development of the EISM. Overall, a total of six environmental factors – geology, pedology, geomorphology, climate, surface, and underground water resources – as 13 associated environmental indicators were transformed into layers and used in the model (Figure 2). This list is not exhaustive; the most important criteria as indicated in previous studies and deemed significant by the authors were used to develop the EISM for landfill sites.

Figure 2. Factors and environmental indicators used to develop the environmental impact susceptibility model for landfill sites.

Geology

Geological features influence the environmental susceptibility of landfill sites because they can cause land instability in an earthquake region (Qian, Koerner, and Gray 2002; Akyol, Kaya, and Alkan 2016). The geological features can also influence water infiltration if the rock formations are porous or include faults (Deepa et al. 2016). For this reason, when SW is disposed of above susceptible geological formations, instability of the containment systems and the waste mass may occur resulting in potential contamination of the surrounding surface and subsurface soils and water. Geological criteria were considered in previous studies that investigated environmental impacts caused by SW in landfills (Aydi et al. 2016; Bahrani et al. 2016; Chonattu, Prabhakar, and Harikumar 2016a; Yıldırım and Güler 2016; Zhang et al. 2016). However, these studies did not consider simultaneously the three geological environmental indicators used in the current study, which were (i) distance to faults; (ii) seismic hazard zones; and (iii) distance to karstic areas. Karst areas are limestone landforms subject to rapid ground erosion by dissolution, with potential release of wastes and leachates into the subsurface water bodies.

Pedology

Soil parameters, such as depth and physical characteristics, also affect the environmental susceptibility related to siting of landfills, because permeability of the soil underlying and overall surrounding a landfill facility can influence the infiltration process, which in turn can cause contamination of groundwater (Sharma and Reddy 2004). Multiple studies on landfilling issues have included pedologic aspects in their assessments (Leão, Bishop, and Evans 2004; Maanifar and Fataei 2015; Motlagh and Sayadi 2015; Zhang et al. 2016). Of great importance is the rate of soil water infiltration, indeed often landfills are lined with clays that saturate quickly and prevent water percolation (Talalaj and Biedka 2016). In particular, we used (i) drainage class and (ii) hydrologic soil group as pedologic environmental indicators in this study.

Geomorphology

Geomorphology is related to terrain features and the influence of these characteristics on the topography. For example, the increase of slope steepness also increases the terrain instability (Qian, Koerner, and Gray 2002; Sharma and Reddy 2004). In addition, landfills should not be located in areas susceptible to stream erosion or landslides. Many previous studies have taken topographical aspects into consideration (Gbanie et al. 2013; Khan and Samadder 2015; Kharat et al. 2016; Zhang et al. 2016). In particular, we used the geomorphological environmental indicators of (i) landslide risk and (ii) slope.

Water resources

Another significant aspect that affects environmental susceptibility is associated with surface and underground water resources. It is not appropriate to have landfills close to surface water sources or in areas where the water table level is shallow due to the higher contamination risk (Qian, Koerner, and Gray 2002; Sharma and Reddy 2004). Several studies have taken into consideration these aspects (Gbanie et al. 2013; Eskandari et al. 2015; Khan and Samadder 2015; Maanifar and Fataei 2015; Motlagh and Sayadi 2015; Bahrani et al. 2016; Chabuk et al. 2016; Chonattu, Prabhakar, and Harikumar 2016a; Yıldırım and Güler 2016). In this study, we used the surface water resource environmental indicators of (i) distance to rivers and lakes and (ii) flood frequency, while, for underground water resources we used the environmental indicators of (i) distance to wells and (ii) depth to water table.

Climate

Climate factors, which are the average and succession of meteorological conditions, also need to be considered in modeling the environmental impact susceptibility for landfills. Climatic conditions significantly influence SW decomposition and degradation process, which control the generation of the three main by-products of SW landfilling which are leachate, gas, and heat (Tchobanoglous, Theisen, and Vigil 1993; Yesiller, Hanson, and Liu 2005). Climatic aspects were also considered in previous investigations (Maanifar and Fataei 2015; Chonattu, Prabhakar, and Harikumar 2016a and 2016b​​; Eskandari, Homaee, and Falamaki 2016; Kharat et al. 2016). In this study, we used the most significant climatic environmental indicators of (i) precipitation and (ii) temperature. Data for the 30-year period from 1981 to 2010 were used in the analysis.

Step 2: data acquisition and integration into a GIS database

The use of GIS is one of the most promising approaches for investigating 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 on maps (Gbanie et al. 2013; Kallel, Serbaji, and Zairi 2016). MCDA techniques combined with GIS have been successfully used in environmental studies (Huang, Keisler, and Linkov 2011; Guarnieri 2015) including investigations related to landfill suitability analysis (Demesouka, Vavatsikos, and Anagnostopoulos 2014). Coupling MCDA with GIS is crucial for spatial analysis for two reasons, first because the coupled analysis informs the spatial decision (Carver 1991), and second because the analysis can support the participation of experts and decision makers improving the confidence in the results (Malczewski 2006).

The spatial database used in the EISM for landfill sites applied to the state of California was created using a variety of sources including geologic, pedologic, geomorphologic, hydrologic, and climate data at different scales (Table 1). The table provides the data used to create the layers. All data used were available in digital format and dated between the years 2001 and 2016. Furthermore, all data used to develop the EISM are available for downloading online with specific references to the relevant websites provided under the references.

Table 1. Spatial data used in the environmental impact susceptibility model for landfills in the state of California.

Factors	Environmental indicators	Sources	Information used to create layers	Format	Scale or resolution	Date
Geology	Distance to faults	State Geologic Maps (USGS 2005)	Structures	Digital	1:750,000	2005
	Seismic areas	Rukstales (2012)	Seismic areas	Digital	1:2,000,000	2012
	Distance to karstic areas	Tobin and Weary (2005)	Location of karstic areas	Digital	1:7,500,000	2005
Pedology	Drainage class	Digital General Soil Map of U.S. (USDA/NRCS 2006)	Drainage class	Digital	1:250,000	2006
	Infiltration rate	Digital General Soil Map of U.S. (USDA/NRCS 2006)	Hydrologic soil group class	Digital	1:250,000	2006
Geomorphology	Landslide risk	Godt (2001)	Incidence and susceptibility for landslides	Digital	1:4,000,000	2001
	Slope	Digital Elevation Model – DEM (USDA/NRCS 2015)	Calculated using DEM	Digital	30 m	2015
Water resources – surface	Distance to rivers and lakes	(USDA/NRCS 2016)	Rivers, streams, and lakes	Digital	1:24,000	2016
	Flood frequency class	Digital General Soil Map of U.S. (USDA/NRCS 2006)	Flood frequency class	Digital	1:250,000	2006
Water resources – underground	Distance to wells	California Department of Conservation Division of Oil, Gas and Geothermal Sources (2016)	Representative wells	Digital	1:100,000	2016
	Depth of water table	Digital General Soil Map of U.S. (USDA/NRCS 2006)	Depth of water table	Digital	1:250,000	2006
Climate	Precipitation	Climate Data (USDA/NRCS 2012)	Isohyetal lines (1981–2010)	Digital	30 m	2012
	Temperature	Climate Data (USDA/NRCS 2012)	Isotherm lines (1981–2010)	Digital	30 m	2012

References used in the table:

California Department of Conservation Division of Oil, Gas, and Geothermal Resources. 2016. “Wells California.” http://www.conservation.ca.gov/dog.

Godt, Jonathan W. 2001. “Landslide Incidence and Susceptibility in the Conterminous United States.” https://catalog.data.gov/dataset/landslide-incidence-and-susceptibility-in-the-conterminous-united-states-direct-download.

Rukstales, K. S. 2012. “Seismic Hazard Map for the United States.” https://geo.nyu.edu/catalog/stanford-rm034qp5477.

Tobin, B.D., and D.J. Weary. 2005. “Engineering Aspects of Karst.” http://catalog.data.gov/dataset/engineering-aspects-of-karst-direct-download.

USDA/NRCS. 2006. “Digital General Soil Map of U.S.” http://websoilsurvey.nrcs.usda.gov.

USDA/NRCS. 2012. “Climate Data (1981–2010).” https://gdg.sc.egov.usda.gov/.

USDA/NRCS. 2015. “National Elevation Data 30 m.” http://nationalmap.gov/elevation.html.

USDA/NRCS. 2016. “National Hydrography Dataset (NHD) – 24k.” http://nhd.usgs.gov/.

USGS. 2005. “National-Scale Geologic Map to Support National and Regional Level Projects, Including Mineral Resource and Geo-Environmental Assessments.” http://mrdata.usgs.gov/geology/state/.

The spatial data used to develop the EISM were the most recent and highest resolution data available for the study area. In some cases, better resolution or more recent data were identified for a specific municipality or county, but not for the entire state of California. In such cases, the data available for the entire state were used to prevent bias that may have resulted from using different resolutions in the analysis.

All the data layers were collected, stored, projected, manipulated, analyzed, and visualized using ArcGIS version 10.2 ModelBuilder as a starting point for the MCDA. ModelBuilder is a GIS extension that encodes complex sequences of GIS operations into a simple graphic model from which the steps can be executed (Allen 2011). The data were georeferenced using the Universal Transverse Mercator (UTM)​​ system and the North American Datum of 1983 (NAD83) (Zones 10 and 11 North).

Step 3: definition of class and rating

Each of the 13 environmental indicators used in the EISM for landfill sites was divided into classes. Each class was rated on a scale from 1 to 10, where 1 represents the lowest level of susceptibility and 10 represents the highest level of susceptibility for environmental impact (Table 2). The rating interval from 1 to 10 was selected based on similar scales used by comparable studies (Alavi et al. 2013; Zakerian, Mokhtari, and Almodaresi 2015; Chonattu, Prabhakar, and Harikumar 2016a​​; Rahmat et al. 2016) and based on the experience and judgment of the authors. The specific classes in Table 2 were assigned considering the relevant conditions in California. Other classes could be used in applications to different study areas.

Table 2. Environmental impact susceptibility model classes and associated ratings.

Factors	Environmental indicators	Class	Rating
Geology	Distance to faults	<500 m	10
		500–1000 m	9
		1000–1500 m	8
		1500–2000 m	7
		2000–2500 m	6
		2500–3000 m	5
		3000–3500 m	4
		3500–4000 m	3
		4000–4500 m	2
		>4500 m	1
	Seismic hazard zones	>100 pG	10
		100–80 pG	9
		80–60 pG	8
		60–40 pG	7
		40–30 pG	6
		30–25 pG	5
		25–20 pG	4
		20–15 pG	3
		15–10 pG	2
		<10 pG	1
	Distance to karstic areas	<1000 m	10
		1000–2000 m	9
		2000–3000 m	8
		3000–4000 m	7
		4000–5000 m	6
		5000–6000 m	5
		6000–7000 m	4
		7000–8000 m	3
		8000–9000 m	2
		>9000 m	1
Pedology	Drainage class	Excessively drained	10
		Somewhat excessively drained	9
		Well drained	7
		Moderately well drained	6
		Poorly drained	5
		Somewhat poorly drained	4
		Very poorly drained	3
	Hydrologic group	Group A	10
		Group B	8
		Group C	6
		Group D	4
Geomorphology	Landslide risk	High	10
		Combo-high	8
		Susceptibility-high	6
		Moderate	4
		Susceptibility moderate	2
		Low	1
	Slope	>40%	10
		40–35%	9
		35–30%	8
		30–25%	7
		25–20%	6
		20–15%	5
		15–10%	4
		10–5%	3
		<5%	2
Water resources – surface	Distance to rivers and lakes	<500 m	10
		500–1000 m	9
		1000–1500 m	8
		1500–2000 m	7
		2000–2500 m	6
		2500–3000 m	5
		3000–3500 m	4
		3500–4000 m	3
		4000–4500 m	2
		>4500 m	1
	Flood frequency	Frequent	10
		Occasional	8
		Rare	6
		None	5
Water resources – underground	Distance to wells	<500 m	10
		500–1000 m	9
		1000–1500 m	8
		1500–2000 m	7
		2000–2500 m	6
		2500–3000 m	5
		3000–3500 m	4
		3500–4000 m	3
		4000–4500 m	2
		>4500 m	1
	Depth to water table	<20 m	10
		20–40 m	9
		40–60 m	8
		60–80 m	7
		80–100 m	6
		100–120 m	5
		120–140 m	4
		140–160 m	3
		160–180 m	2
		>180 m	1
Climate	Precipitation	>100 in	10
		100–90 in	9
		90–80 in	8
		80–70 in	7
		70–60 in	6
		60–50 in	5
		50–40 in	4
		40–30 in	3
		30–20 in	2
		<20 in	1
	Temperature	>90°F	10
		90–85°F	9
		85–80°F	8
		80–75°F	7
		75–70°F	6
		70–65°F	5
		65–60°F	4
		60–55°F	3
		55–50°F	2
		<50°F	1

Step 4: standardization of environmental indicators to a common scale of measurement

In order to overlay the spatial information to calculate the environmental impact susceptibility, it was necessary to standardize the data to a common measurement scale. Therefore, the 13 environmental indicators data were converted into raster grid format consisting of 50 m cells resulting in an image of 19,311 columns and 21,769 rows.

Step 5: criteria weight assignment using AHP

In this study, we used the analytical hierarchy process (AHP), which is a component of the MCDA method. The AHP was developed by Saaty in the 1970s and consists of an assessment theory through pairwise comparison to help decision makers set priorities and choose the best decision (Kharat et al. 2016). The AHP in combination with GIS has been widely used in the field of natural resources and environmental management (Jothibasu and Anbazhagan 2016), first because it is easy to implement using map algebra operations and cartographic models, and second because the approaches are intuitively appealing to decision makers (Malczewski 2006). The comparisons in the AHP were made using a scale of absolute judgments ranging from 1–9, where 1 represents equal importance and 9 represents the highest importance from one element to another. In addition, a reciprocal value is used to express the inverse comparison (Saaty 2008).

The process of AHP determination involves these sequential steps: (i) compute the sum of values in each column of a pairwise comparison matrix, (ii) normalize the matrix by dividing each element by its column total, and (iii) compute the mean of the elements in each row of the normalized matrix (Khan and Samadder 2015).

Subsequent to determination of the AHP, a consistency ratio (CR) needs to be calculated. 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 reevaluated or excluded (Yıldırım and Güler 2016).

The construction of a comparison matrix and the derivation of weights were conducted using the web-based tool developed by Goepel (2013). First, the AHP methodology was applied to the factors (Table 3) and environmental indicators (Table 4). Then, by multiplying the factor weights by the environmental indicator weights, the global weighting for each criterion was obtained (Table 5).

Table 3. Pairwise comparison matrix, ranking, and weights for factors.

Factors (CR 2.8%)	[1]	[2]	[3]	[4]	[5]	[6]	Rank	Weight (%)
Geology	1						1	24.8
Pedology	1/3	1					6	5.8
Geomorphology	1/3	2	1				4	14.5
Surface water resources	1/2	3	1	1			5	12.7
Underground water resources	1	4	1	2	1		2	21.1
Climate	1	4	1	2	1	1	2	21.1

Table 4. Pairwise comparison matrix, ranking, and weights for environmental indicators.

Factors (CR %)		Environmental indicators	[1]	[2]	[3]	Rank	Weight (%)
Geology CR (0.0%)		Distance to faults	1			1	45.5
		Seismic hazard zones	1	1		1	45.5
		Distance to karstic areas	1/5	1/5	1	3	9.0
Pedology CR (0.0%)		Drainage class	1			1	50.0
		Hydrologic group	1	1		1	50.0
Geomorphology CR (0.0%)		Landslide risk	1			1	66.7
		Slope	1/2	1		2	33.3
Water resources	Surface CR (0.0%)	Distance to rivers/lakes	1			2	33.3
		Flood frequency	2	1		1	66.7
	Underground CR (0.0%)	Distance to wells	1			2	25.0
		Depth to water table	3	1		1	75.0
Climate CR (0.0%)		Precipitation	1			1	80.0
		Temperature	1/4	1		2	20.0

Table 5. Global weighting for the criteria.

Factors		Environmental indicators	Global rank	Global weight (%)
Geology		Distance to faults	3	11.3
		Seismic hazard zone	3	11.3
		Distance to karstic areas	10	2.4
Pedology		Drainage class	9	2.9
		Hydrologic Group	9	2.9
Geomorphology		Landslide risk	4	9.6
		Slope	7	4.8
Water resources	Surface	Distance to rivers/lakes	8	4.2
		Flood frequency	5	8.4
	Underground	Distance to wells	6	5.3
		Depth to water table	2	15.8
Climate		Precipitation	1	16.9
		Temperature	8	4.2

Step 6: weight linear combination method

After checking the reliability of the pairwise comparisons for factors and indicators, the EISM for landfill sites in California was built using a WLC method. The final score was obtained for each raster cell as a sum of the products of ratings assigned to each of the classes (Table 2) and the global weights obtained by AHP (Table 5) (Figure 3). The results varied from 1.93 to 8.01, the minimum and maximum values, respectively. This interval was classified by the natural breaks method (Jenks and Caspall 1971) and then grouped into five categories of environmental impact susceptibility for landfill sites: very low (S1), low (S2), medium (S3), high (S4), and very high (S5).

Figure 3. Maps for all selected environmental indicators in the state of California (Color figure online).

Results and discussion

Through the development of the EISM for landfill sites using MCDA and the AHP coupled with GIS, it was possible to identify the most and least environmentally susceptible areas. With the application of the model, it was also possible to assess the current susceptibility of landfill sites in the state of California, USA.

The geographical coordinates of 207 landfill sites in the state of California were obtained from spreadsheets used for the landfill tonnage reports between the years of 2000 and 2015. Of these 207 landfills, 133 were classified as active, 10 as inactive, 8 as closing, 1 as planned, and 55 as closed (CalRecycle 2016).

A spatial analysis was performed by overlaying the results of the EISM and the locations of SW landfill sites in the state of California. This analysis allowed identification of the specific landfills included in each of the environmental impact susceptibility categories and the amount of SW disposed of during the period of 2000–2015 in each class of susceptibility.

Environmental impact susceptibility model for landfills

The results of applying the EISM to landfill sites in California are presented in Figure 4. The area for each susceptibility category indicated that almost half of California’s land, 47.4%, is classified under very low (S1) and low (S2) environmental impact susceptibility categories, 28.3% is under medium (S3) category, and 24.3% is under the high (S4) and very high (S5) categories (Table 6).

Table 6. Environmental susceptibility for landfills in California.

Environmental impact susceptibility category	Area (km^2)	Area percentage
Very low (S1)	78,233	19.2%
Low (S2)	115,184	28.2%
Medium (S3)	115,787	28.3%
High (S4)	68,793	16.8%
Very high (S5)	30,492	7.5%
Total	408,489	100%

Figure 4. Environmental impact susceptibility for landfill sites in California (Color figure online).

The very high class alone has an area of 30,492 km², which represents 7.5% of the land area of California. This area is located mainly less than 100 km from the ocean, on the North Coast, in the San Francisco Bay Area and near the city of Los Angeles. This can be explained by a combination of geographical variables. On the North Coast, the highest rate of precipitation was observed, which is the environmental indicator with the highest level of importance in the EISM. The very high environmental impact susceptibility observed near the San Francisco Bay Area and near the city of Los Angeles, resulted mainly from the geologic environmental indicators of seismic hazard zones and distance to faults, the third and fourth most important environmental indicators in this study.

The development of EISM for landfills in California proceeded well due to the availability and reliability of spatial data for the state. Overall, the 6 factors – geology, pedology, geomorphology, climate, surface, and underground water resources – with 13 corresponding environmental indicators selected in this study provided an improvement over previous models for 3 main reasons: (i) a higher number of factors used, (ii) more environmental indicators used, and (iii) a more extensive set of combined factors and environmental indicators used. Therefore, the framework of this model can provide an effective tool to policymakers and landfill designers and operators as well as urban planners to avoid the environmental impacts caused by susceptible landfill sites.

Analysis of susceptibility for landfill sites in California

In order to evaluate the environmental impact susceptibility for each landfill site in California, a spatial analysis (Figure 5) and a statistical summary (Table 7) were developed. The number of landfills included in each environmental impact susceptibility category has a positive correlation with the extent of each category in the state of California. Approximately 70% of the landfills are located in very low, low, and medium environmental impact susceptibility categories (S1, S2, and S3, respectively). Even though only 30% of the landfills are located in the high and very high categories (S4 and S5, respectively), a large amount of SW (307,991,749 tons) was disposed of in these facilities over the last 15 years (Table 7). The list with the information about the landfills and the quantity of SW disposed in the two highest susceptibility categories is provided in Table 8.

Table 7. Environmental susceptibility category for landfill sites in the state of California.

Environmental impact susceptibility category	Number of landfills	Tons of solid waste disposed during the period from 2000 to 2015
S1	39	70,915,928
S2	49	84,296,137
S3	58	74,782,438
S4	48	165,868,838
S5	13	142,122,911
Total	207	537,986,252

Table 8. Solid waste landfills in high and very high categories (S4 and S5).

Category	Landfill codes	Location (county/city)	Classification	Tons of solid waste disposed over the period 2000–2015
S5	01-AA-0010	Alameda/Livermore	Active	5,803,172
	12-AA-0029	Humboldt/Arcata	Inactive	0
	19-AA-0052	Los Angeles/Castaic	Active	20,482,418
	19-AA-0053	Los Angeles/Whittier	Closing	43,253,356
	21-AA-0001	Marin/Novato	Active	4,836,102
	21-AA-0002	Marin/Point Reyes Station	Closed	0
	28-AA-0003	Napa/Napa	Closed	0
	30-AB-0019	Orange/Santa Ana	Active	9,071,686
	30-AB-0035	Orange/Santa Ana	Active	29,373,407
	47-AA-0027	Siskiyou/Yreka	Closed	633
	56-AA-0005	Ventura/Ventura	Active	5,668,447
	56-AA-0007	Ventura/Simi Valley	Active	11,457,885
	19-AA-2000	Los Angeles/Sylmar	Active	12,175,805
S4	01-AA-0009	Alameda/Oakland	Active	19,677,794
	06-AA-0002	Colusa/Colusa	Active	433
	07-AA-0001	Contra Costa/Richmond	Closed	2,231,245
	07-AA-0002	Contra Costa/Martinez	Active	270,085
	07-AA-0032	Contra Costa/Pittsburg	Active	11,965,716
	08-AA-0006	Del Norte/Crescent City	Closed	103,552
	12-AA-0005	Humboldt/Eureka	Closing	11,747
	13-AA-0004	Imperial/El Centro	Active	28,196
	13-AA-0010	Imperial/El Centro	Active	2,511
	13-AA-0019	Imperial/Imperial	Active	2,185,886
	13-AA-0022	Imperial/Calipatria	Active	1,086,760
	15-AA-0044	Kem/Bakersfield	Closing	0
	15-AA-0105	Kem/McKittrick	Active	1,275,857
	17-AA-0001	Lake/Lakeport	Active	787,086
	19-AA-0012	Los Angeles/Whittier	Active	5,260,922
	19-AA-0015	Los Angeles/Whittier	Closed	127,089
	19-AA-0056	Los Angeles/Whittier	Active	5,539,475
	19-AA-0057	Los Angeles/Saugus	Inactive	0
	19-AA-5624	Los Angeles/Palmdale	Active	2,600,059
	19-AF-0001	Los Angeles/West Covina	Closed	0
	19-AH-0001	Los Angeles/Whittier	Active	1,321,848
	23-AA-0007	Mendocino/Branscomb	Closed	34,586
	23-AA-0018	Mendocino/Ukiah	Inactive	2,456
	30-AB-0018	Orange/Santa Ana	Closed	0
	30-AB-0360	Orange/Santa Ana	Active	30,092,453
	32-AA-0007	Plumas/Portola	Closed	2,349
	32-AA-0008	Plumas/Quincy	Closed	200
	33-AA-0006	Riverside/Moreno Valley	Active	9,222,599
	33-AA-0011	Riverside/Moreno Valley	Closed	2,256,622
	33-AA-0217	Riverside/Corona	Active	31,011,179
	36-AA-0056	San Bernardino/San Bernardino	Closed	69,425
	36-AA-0087	San Bernardino/San Bernardino	Active	2,639,749
	37-AA-0902	San Diego/Camp Pendleton	Active	13,670
	39-AA-0010	San Joaquin/Costa Mesa	Closed	0
	40-AA-0008	San Luis Obispo/Templeton	Active	1,116,915
	41-AA-0002	San Mateo/Half Moon Bay	Active	9,971,118
	41-AA-0008	San Mateo/Colma	Closed	341,014
	42-AA-0015	Santa Barbara/Santa Barbara	Active	3,097,695
	43-AA-0004	Santa Clara/Gilroy	Closed	513,949
	43-AM-0001	Santa Clara/Palo Alto	Closed	246,007
	43-AN-0001	Santa Clara/San Jose	Active	298,833
	43-AN-0003	Santa Clara/Milpitas	Active	9,316,879
	43-AN-0007	Santa Clara/San Jose	Active	128,764
	43-AN-0008	Santa Clara/Morgan Hill	Active	3,709,806
	43-AN-0015	Santa Clara/San Jose	Active	3,083,076
	44-AA-0001	Santa Clara/Santa Cruz	Active	827,500
	49-AA-0001	Sonoma/Petaluma	Active	3,395,733
	53-AA-0035	Trinity/Hayfork	Closed	0

Source:[1], [2]

References used in the table:

[1]CalRecycle, “Annual California Solid Waste Disposal,” 2016.

[2]CalRecycle, “Solid Waste Information System (SWIS).” 2016.

Figure 5. Categorization of environmental impact susceptibility for landfill sites in California (Color figure online).

From the 13 landfills located in the very high category, 3 did not receive SW during the period 2000–2015. Among the remaining 10 landfills, 8 are active, 1 is closing, and 1 is closed. These landfills received in total 142,122,911 tons, which represent more than 26% of the total SW disposed of in the state of California.

From 48 landfills located in the high environmental impact susceptibility category, 6 did not receive SW during the period of 2000–2015. Among the remaining 42 landfills, 29 are active, 1 is inactive, 1 is closing, and 11 are closed. These landfills received in total 165,868,838 tons of SW, which represented more than 30% of the total SW disposed of in the state of California.

When the amount of SW in the 12 largest landfills located in the high and very high environmental impact susceptibility categories are added, the total SW disposed is more than 238 million tons, which represents more than 77% of the total SW disposed in landfills located in these higher categories.

Conclusions

The results obtained using the EISM indicated that even though more than 75% of California’s land is situated in very low, low, and medium susceptibility categories, 29% (61 of 207) of the landfills in the state are located in the high and very high susceptibility categories. In these 61 landfills, approximately 308 million tons of SW was disposed of during 2000–2015, which indicates that more than 57% of SW disposed of in California was placed in environmentally susceptible areas. The results of this study can help decision makers, landfill managers, and local governments develop control and mitigation measures against the occurrence of negative environmental impacts from these landfills.

The development of this model included three main modeling purposes, prediction, management decision-making under uncertainty, and developing system understanding and enabling experimentation. This type of spatial analysis can help decision makers mitigate environmental impacts and also assist in the process of identifying areas for new landfills. For future studies, the authors suggest adding: (i) forecasting, using different climate scenarios that influence leachate generation and emission of greenhouse gases, which can improve the assessment of the environmental impact susceptibility, and (ii) social learning, coupling a social model with the EISM, which could result in a greater understanding of global susceptibility for the siting and management of landfills.

Acknowledgments

The authors thank Fundação de Amparo a Pesquisa no Estado de São Paulo (FAPESP) for Victor Fernandez Nascimento’s doctoral fellowships (No. 13/09039-7) and (No. 15/24344-6), the Earth System Science Center (CCST), 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.

Disclosure statement

No potential conflict of interest was reported by the authors.​​

Additional information

Funding

​The authors thank Fundação de Amparo a Pesquisa no Estado de São Paulo (FAPESP) for Victor Fernandez Nascimento’s doctoral fellowships (No. 13/09039-7) and (No. 15/24344-6).