Methane (CH4) is the 2nd most important greenhouse gas (GHG), accounting for about 20% of positive radiative forcing (Myhre et al., 2013). However, considering indirect effects associated with increased atmospheric ozone and water vapor, CH4 is responsible for about 40% of positive forcing. Even though annual emissions of 500-600 Tg are well-constrained by atmospheric measurements, their allocation to various natural (e.g., wetlands, termites, caribou) and anthropogenic sources (e.g., oil/gas production & transport, domesticated ruminants, rice production, coalbed leakages, wastewater, landfills) remains highly uncertain. Landfills are currently considered to be the 3rd largest source of atmospheric CH4 in California (Deshpande, 2014) as well as the US, estimated at 18% of the total US methane emissions by source (USEPA, 2014, USEPA, 2015). However, recent field measurements for the city of Indianapolis, for example, have demonstrated that landfills may account for >30% [33 ± 10%] of the total urban CH4 source (Cambaliza et al., 2015). Regional- and urban-scale CH4 inventories guide local mitigation strategies; thereby, we need the best estimates for individual sources including landfill CH4.
In the U.S., the first “sanitary” landfills during the 1950’s and 1960’s were operated under minimal regulatory guidance (some states and municipalities) with minimal engineering (e.g. soil cover on top of buried waste to reduce animal vectors, blowing waste and nuisance odors). Beginning in the 1970’s and accelerating in the 1980’s -1990’s under the U.S. EPA Subtitle D landfill regulations and Clean Air Act amendments, municipal solid waste landfills are now highly engineering and monitored facilities. Currently, routine practices include control of inputs, compaction of waste, “cell” construction with engineered synthetic liner systems and collection/management of landfill leachate [liquids], onsite or offsite leachate treatment, engineered structures for collection and management of runoff to minimize infiltration and leachate generation, internal and external monitoring of leachate and biogas, engineered cover systems, and engineered systems for collection and utilization of biogas. Some typical landfill cover types and thicknesses are shown in the Supplemental Information (Table S1- Cover Types). Individual landfill sites can have multiple daily, intermediate, and final covers at a particular site. This greatly complicates both the monitoring and modeling of emissions because of varying source strengths, wind directions, topography, and CH4 concentration gradients affecting diffusive flux through each individual cover type. The cover soils provide a major barrier to gaseous emissions, while concurrently promoting internal anaerobic conditions in the buried waste for methanogenesis. In addition, the interaction of seasonal climate with the different cover soils, resulting in soil moisture and temperature changes with depth through an annual cycle. These alterations can result in large temporal variations in both soil gas transport and microbial methane oxidation rates.
The biodegradable fractions of landfilled waste (paper, food, garden waste) decompose anaerobically via a complex collection of microbial reactions mediated by hydrolytic, fermentative, acidogenic, acetogenic, and methanogenic microorganisms. The first observations of methane production from organic matter decomposition were made by the Italian physicist Alessandro Volta in 1776, after reading of the presence of a “flammable gas” from the research of Benjamin Franklin in the US (Theresa, 2012). Ever since these initial observations, the major assumption has been that waste decomposition and biogas formation is related to the amount of degrading organic material. The early biogas generation models were empirical in nature and possessed a variety of mathematical forms (i.e., single component/multiple component kinetic models; lag time/no lag time). However, all of these models shared the common goal to predict future biogas generation and potential recovery rates from past landfill performance. The initial biogas model development in the US occurred in California about 4 decades ago, following the first project to commercially recover landfill gas during the U.S. “energy crisis” of the 1970’s at the Palos Verdes Landfill. Model validation consisted of a comparison between predicted and actual annual biogas recovery over a period of a few years, to derive the empirical constants to optimize the model fit. For some examples of the first applications of these equaions see EMCON (1980) and Halvadakis et al. (1983), which correlated landfill biogas production to the total landfilled waste.
During the 1980’s, the use of these predictive models for biogas projects diminished. It was recognized that a multiplicity of operational and engineering factors (e.g. waste type, compaction, moisture aviability) control both the quantity and quality of recoverable landfill biogas (Klink and Ham, 1982). One could not know, a priori, whether a particular model was accurate and predicted biogas recovery; moreover, utilization hardware purchased solely on the basis of empirical modeling had resulted in expensive mistakes. Installation of biogas control and collection systems is becoming routine as part of more optimized landfill design and management practices. For commercial biogas projects, a preferred strategy often consisted of installing gas collection infrastructure, evaluating gas quantity & quality, and committing to gas utilization hardware based on site-specific economics for a preferred utilization option. Although a few sites have historically upgraded the gas to pipeline quality during periods of high natural gas prices, the majority of the >600 current U.S. projects focus on electrical generation for sale to the local grid or direct gas use in industrial/commercial boilers [see http://www.epa.lmop.gov]. At individual sites, the gas recovery infrastructure is expanded in a timely manner concurrent with landfill expansions, often including both horizontal collectors and vertical wells.
In the late 1980’s and early 1990’s, there was a revival of interest in 1st order models to estimate biogas generation as the starting point for emission estimates for three major applications:
Historically, the first field studies to quantity landfill CH4 emissions [for example Boeckx et al. (1997)] were being conducted at the same time that the first IPCC (1996) national GHG inventory guidelines (see Smith and Bogner, 1997). Similar to the early landfill biogas projects, “field validation” for the IPCC emissions consisted of comparing modeled biogas generation to limited measured biogas recovery data, primarily for 9 full-scale Dutch landfills (Oonk, 2010; Van Zanten and Scheepers, 1995).
Are these empirical models accurate? Realistically, one might argue that landfills fall somewhere between engineered digesters and anaerobic ecosystems in more open environmental settings (e.g., wetlands) (Bogner et al., 2000). In general, when applied to specific sites, these models can yield very large underestimates or overestimates for predicted vs. actual gas recovery, their original application (Thompson et al., 2009). For example, we note that landfill biogas CDM projects have consistently underperformed relative to baseline predictions (Couth et al., 2011), while well-operated California sites can recover 2-3 times the “predicted” biogas generation (Spokas et al., 2011). Thus, even for gas recovery predictions, the models have had difficulties in accurately predicting rates (Thompson et al., 2009). With regard to emissions, both the LANDGEM and IPCC FOD models were developed prior to a critical mass of field data on actual emission rates and mechanisms, and neither model was field-validated for emissions (Scheutz et al., 2009). During the last decade, field measurements have consistently indicated that unlike gas generation, landfill CH4 emissions are not related to the biogas generation rate, but on: (1) the physical properties of site-specific cover materials to retard gaseous emissions; (2) presence of a biogas recovery system; and (3) methanotrophic CH4 oxidation in site-specific cover soils related to seasonal soil microclimate conditions (Spokas and Bogner, 2011).
Seasonality and soil microclimate differences impact the CH4 budget for wetlands (Morin et al., 2014) and other ecosystems (Cicerone et al., 1983; Sass et al., 1990). Not surprisingly, the same dependency exists for landfills; seasonal oxidation can vary from negligible to more than 100% (uptake of atmospheric CH4) (Bogner et al., 1997; Sadasivam and Reddy, 2014; Scheutz et al., 2003; Yang et al., 2014). However, in addition to the current IPCC (2006) methodology not being independently field-validated for emissions (as discussed above), this methodology only allows a constant 10% annual oxidation at well managed sites, based on the first study to model annual oxidation at a small landfill in New Hampshire, USA (Czepiel et al., 1996a,1996b). Published literature has confirmed that CH4 emissions from landfill cover soils, similar to other soil sources of atmospheric CH4, have high spatial and temporal variability due to soil texture and microclimate-dependencies for gaseous transport and methanotrophic oxidation (Albanna et al., 2007; Bogner et al., 1997; Chanton and Liptay, 2000; Chiemchaisri et al., 2011; Czepiel et al., 1996a, 1996b; Goldsmith Jr. et al., 2012; Harborth et al., 2013; Lee et al., 2009; Pawłowska et al., 2003; Pratt et al., 2013; Rachor et al., 2013). Moreover, unique to landfill soils, emissions are also dependent on site-specific engineering and management factors, including the cover thickness & texture; areal extent of daily, intermediate, and final cover soils; and the direct effect of biogas extraction systems on soil gas CH4 concentration gradients which control diffusive flux (Abichou et al., 2006a; Bogner et al., 2011; Perdikea et al., 2008). With small-scale rates (static chambers) ranging over 6-7 orders of magnitude for individual cover materials (<0.001 to >1000 g CH4 m-2 d-1) (Li et al., 2004; Park and Shin, 2001) and large-scale “whole landfill” rates (e.g., aircraft-based mass balance techniques) significantly higher, but still ranging over 2-3 orders of magnitude (<160 to >1600 g CH4 s-1) (Peischl et al., 2013), it is clear that a significant challenge remains to quantify and model site-specific CH4 emissions. Moreover, one must also consider the uncertainties associated with diverse field techniques (e.g., diffusion accumulation chambers, tracer techniques, micrometeorological techniques, aircraft mass balance) (Lai et al., 2012; Levy et al., 2011; Mann and Lenschow, 1994). Finally, since each field campaign represents a snapshot in time, a robust modeling framework is needed to integrate diel and seasonal rates over a typical annual cycle for each cover design at a specific site.
Herein, we challenge the adequacy of current inventory empirical models for landfill CH4 emissions. Unlike the theoretical models which address the seasonality of GHG fluxes in other managed and natural ecosystems (Bond-Lamberty et al., 2007; Davi et al., 2006; Li et al., 2004; Parton, 1996), the current landfill methodology does not consider major climate and soil-microclimate drivers for CH4 emissions from landfill cover soils with variable thickness, soil textures, and seasonal- and climate-dependent oxidation rates. All of these factors critically influence CH4 emission rates through landfill cover soils (Park and Shin, 2001; Scheutz et al., 2009). California Landfill Methane Emissions Model (CALMIM) is an evolving site-specific, field-validated, process-based model originally developed for California in 2007–2010 (Bogner et al., 2011; Spokas et al., 2011; Spokas and Bogner, 2011) (CALMIM available at http://www.ars.usda.gov/services/software/download.htm?softwareid=300). Through a finite-difference solution to soil gas diffusion transport, CALMIM theoretically predicts a typical annual cycle for landfill CH4 emissions based on the average site-specific climate and user inputted cover soils (Spokas et al., 2011). Using this predicted soil microclimate, soil CH4 oxidation is estimated by empirical models correlated to soil moisture and temperature characteristics (Spokas and Bogner, 2011).
It is also important to examine the current status of “top down” emissions estimates inclusive of landfill CH4 and other waste sector emissions in addition to the “bottom up” models. The most recent global estimates are included in the EDGAR-HTAP dataset, which is a harmonized 0.1° x 0.1° gridded air pollution database (Janssens-Maenhout et al., 2012). For landfill CH4, EDGAR-HTAP uses country-level inventory data using IPCC (2006) for the developed countries. For developing and middle income countries not required to report annually, in addition to issues associated with IPCC (2006) as discussed above, there can be large disparities between the quality and quantity of temporally-varying national waste data, the basis for inventory calculations using IPCC (2006). For EDGAR-HTAP, the country-level data are dispersed on a 0.1° X 0.1° global grid according to population density. Thus, these data have the added convenience of 0.1° X 0.1° gridding but, as these estimates are based on IPCC (2006), they do not consider any of the major drivers for landfill CH4 emissions now known from literature and field measurements as discussed above. Moreover, for both developed and developing countries, landfill sites are becoming increasingly dissociated from dense urban population centers as older landfills are filled and closed with new remote sites developed outside of urban corridors (El Baba et al., 2014).
Making use of a new large California landfill dataset (Walker, 2012), the field-validated process-based model (CALMIM), and existing data for measured California landfill emissions from existing studies (Bogner et al., 2011; Goldsmith et al., 2012; Jeong et al., 2013; Peischl et al., 2013; Spokas et al., 2011; see Table S4), we focus on:
In late 2012, a comprehensive dataset for permitted California landfills was developed by the California Dept. of Resources Recovery and Recycling [CalRecycle] (Figure S1). The complete electronic database (Walker et al., 2012) is provided in the supplemental files (Spreadsheet S2: CA-LANDFILLDATABASE.xlsx). From this collection, we initially used the 2010 data for 129 California sites with LFG recovery data to examine relationships between normalized landfill recovery [Nm3 LFG h-1 MgWIP-1], landfill age, size, operating status (open or closed), and local climate. These 129 sites with engineered biogas recovery represent 89.3% of the total 2010 WIP in permitted California landfills (Table S3). Both the waste in place (WIP) and biogas recovery data were independently reported by individual site operators to CalRecycle. General climate data [mean annual temperature (MAT) and precipitation] were derived from existing interpolated resources (Lawrimore et al., 2011; Legates and Willmott, 1990; Peterson and Vose, 1997).
We used the 2010 California GHG Inventory (Deshpande et al., 2014) as a reference point for current estimated landfill CH4 emissions using the IPCC (2006) methodology. Previous publications provided measured field data for 10 California landfill sites (Bogner et al., 2011; Goldsmith et al., 2012; Jeong et al., 2013; Peischl et al., 2013; Shan et al., 2013; Spokas et al., 2011).
We utilized the data given in Walker et al. (2012) for data on WIP, waste footprint, cover materials, biogas recovery, and CH4 content. Therefore, consistent with recent literature emphasizing strong seasonal dependencies for CH4 transport, oxidation, and emissions in other managed and pristine soil ecosystems (Cao et al., 1995; Wille et al., 2008), CALMIM modeling was utilized to generate an estimate of site emissions and these results were compared to the existing 2010 California inventory (Deshpande et al., 2014) relying on IPCC methodology (IPCC, 2006; Supplemental Information). The major research questions were:
There was a relatively robust linear relationship [Fig. 1; R2 = 0.82] observed between waste WIP (tons) and average biogas recovery rate for the landfills in the California dataset [n=128 (dropped Puente Hills)]. From this, we can estimate a normalized LFG recovery rate of 126 x 10-6 Nm3 CH4 hr-1 Mgwaste-1. It is interesting to note that almost 90% of the waste in permitted California landfills has engineered gas extraction (Table S2). Figure 1 suggests that a relatively constant rate of gas generation and recovery can be maintained over long time periods for a wide variety of small to large, open and closed sites across diverse climatic regions of California. In addition, this simple relationship is further supported when examined against values from other US and international landfills (Figure S2), with improved predictability of closed landfills in the USEPA landfill methane outreach program database, with only 2% of sites falling outside of the 95% confidence intervals of this relationship (Figure S2c).
To address whether biogas recovery rates are related to climate and landfill operational factors (e.g., landfill age, open or closed status), we initially screened the California data for correlations (Figure S4) and step-wise regressions (Table S3). The only statistically significant correlation for the entire dataset was between biogas flow and WIP (Figure 1). The step-wise regression analysis indicated that WIP was a dominant factor controlling biogas recovery rate (P= 2 x 10-16); disposal starting year was also statistically significant (P=0.01), but with a much lower coefficient (17.1 ± 6.7; Table S3). Notably, none of the climate variables (air temperature or precipitation) were statistically significant in this regression analysis, which suggests the lack of climate dependency on the biogas production rate.
Using CALMIM, the 2010 CH4 emissions were estimated at 337,430 Mg CH4 yr-1 compared to the CARB inventory value of 301,748 Mg CH4 yr-1 (Figure S3). Despite this numerical similarity, the spatial distribution for these predictions is drastically different (Figure 2). The similarity of the totals suggests that, for selected sites, there may also be a serendipitous similarity for some sites between the measured emissions and current CARB inventory values. The top ten emitting landfill sites differ between the new CALMIM (Fig. 2A) and the 2010 CARB inventory (Fig. 2B). Using CALMIM, the highest-emitting sites are in the desert areas, Central Valley, and higher elevation mountain sites with low annual oxidation due to lack of favorable conditions for CH4 oxidation. Focusing on the intermediate cover, which is 47% of the total reported landfill area but accounts for 96% of the estimated landfill emissions, there is a very strong relationship with precipitation (Figure 3A). Notably, for sites receiving >500 mm of precipitation, the predicted intermediate cover emissions were less than 15 g CH4 m-2 d-1. Moreover, for sites receiving <500 mm of precipitation, there is an exponential increase in the emission rate with decreasing precipitation, which is attributed to the lack of adequate soil moisture at these locations to support soil CH4 oxidation activity (Figure S3) (Boeckx et al., 1997; Spokas and Bogner, 2011). For mean annual air temperature (MAT; Figure 3B), the relationship is less robust, likely confounded by corresponding precipitation differences. However, there is the suggestion of an optimum MAT of 11°C associated with the lowest emissions and highest rates of soil CH4 oxidation. This temperature is, of course, below optimum temperatures for methanotrophic oxidation in controlled laboratory studies (typically 30–40 °C) (Börjesson and Svensson, 1997; Spokas and Bogner, 2011), since it integrates annual temperature and precipitation cycles. In particular, both desert areas of California [high MAT, low precipitation] and high elevation areas [lower MAT] are associated with higher emissions and lower soil oxidation capacities.
From CALMIM modeling, Table 1 shows that 2010 monthly CH4 emissions for California vary about 17-fold with minimum rates in April [5,183 Mg] and maximum rates in October [89,611 Mg], which agrees with the seasonal pattern observed in prior California field assessments (e.g., Goldsmith et al., 2012; Park and Shin, 2001; Yazdani and Imhoff, 2010). Lower emissions are typically observed during periods of higher precipitation events (wet season: Aug–Mar) and then elevated surface emissions during the summer (June–Sept). This large differential in rates is attributed to variable CH4 oxidation rates in cover soils coupled to fluctuating soil moisture and temperature conditions. Without soil oxidation, the seasonal difference is only predicted to be 2-fold by the model due to the lower impact of temperature changes on soil gas diffusion rates (Table 1). For the entire state, monthly totals of CH4 oxidation range from 151,000 to 217,000 Mg, or an annual total of 2,273,758 Mg CH4 oxidized for the entire state in one year. This amounts to an average statewide landfill CH4 oxidation flux density of 62 g CH4 m-2 d-1, accounting for the total area of Californian landfills.
|Total estimated emissions with soil oxidation (Mg/month)||Total emissions without oxidation (Mg/month)||CH4 oxidized (Mg/month)||% oxidation prediction|
|Annual Totals (Mg/yr)||337,430||2,611,187||2,273,758||87%|
CALMIM modeled results for landfill CH4 emissions at 10 California landfill sites were compared to published field measurements, including seasonal data where possible (Bogner et al., 2011; Goldsmith Jr et al., 2012; Peischl et al., 2013; Shan et al., 2013). Figure 4 compares site-specific CALMIM inventory estimates for the 10 sites (Figure S8) to field measurements using multiple methods taken at various times and various dates during 2005–2014. All of the total site emissions, where available, were normalized on an area basis (g CH4 m-2 d-1) for this comparison using the Walker (2012) database for 2010 footprint areas. For all of the sites, the field measurements and CALMIM inventory estimates are within the same order of magnitude (Table 2).
|California Solid Waste Information System Identifier||CARB (g CH4/m2/day)||CARB 2010 emissions (MT CH4/yr)||2010 Waste-In-Place (tons)||Int (gCH4/m2/day)||Daily (gCH4/m2/day)||Final Cover (gCH4/m2/day)||Site Calculated CALMIM Emissions (g CH4/m2/day)||CALMIM Total (Mt CH4/yr)||Range of field measurements (g CH4/m2/day)||Percentage of Intermediate Cover at Landfill|
|01-AA-0008||16.03||2723||10103797||14.11||7.65||0.00||13.55||2,301||5.6 – 7.1||91%|
|01-AA-0009||36.38||12627||44281078||29.41||7.96||0.00||23.99||8,328||0.7 – 12.8||80%|
|19-AA-0053||33.33||29537||124963317||40.10||8.60||0.00||7.09||6,287||0.88 (final cover) 38.4 (plane whole site) 8.5 (chambers)||17%|
|19-AA-0056||8.57||5265||23441895||45.14||8.65||0.00||44.27||27,202||0.05 (final cover)||98%|
|27-AA-0010||4.35||2025||8388784||24.47||8.12||0.00||23.95||11,143||56.4 (intermediate cover)||97%|
|30-AB-0035||21.12||13105||52017040||36.78||8.17||0.00||36.10||22,397||4.3 – 20 (intermediate cover)||98%|
|43-AN-0008||10.17||2403||7312751||22.19||7.72||0.00||21.28||5,030||0.07 – 20.9 (intermediate cover)||94%|
One must also keep in mind that a field measurement campaign only represents a “snapshot” in time without any information regarding the temporal variability in emissions or oxidation over the annual cycle. To a large extent, this figure also illustrates the difficulty of site-specific emissions comparisons to CALMIM modeling in the absence of site-specific data for the major drivers for oxidation and emissions (soil moisture, soil temperature). The site-specific differences between measured and modeled values may be due to the variability in the physical characteristics of site-specific cover soils (e.g., texture, thickness) and annual soil microclimate (i.e., soil moisture, temperature). Figure 4 illustrates the range of field measurements (shown in the colored points for the month the measurements were conducted) compared to CALMIM-modeled CH4 emission ranges for each site (upper blue line represents no soil oxidation, black line oxidized flux prediction, and shaded region for the range between the oxidized and non-oxidized emission estimate). As each field campaign represents only a snapshot in time, it is important to put the measurements into the context of expected emissions variability over a typical annual cycle (Figure 5; Figure S9). The main observation from the new CALMIM inventory and the field measurements is the lack of any significant relationship between these two estimates and the WIP (Figure S10).
Based on the correlation between WIP and average biogas recovery rates in the 2010 California dataset, we can estimate a normalized LFG recovery rate of 126 x 10-6 Nm3 CH4 hr-1 Mgwaste-1 (Figure 1), which appears very robust with the existing data from other studies (Figure S2). Unlike previous estimates based on small datasets or laboratory studies (Gioannis et al., 2009), this is the first time that a large internally-consistent database of full-scale sites has been available for this analysis. It is important to note that these data include older landfill sites (>50 years old), the first U.S. engineered landfills [1960s], and the first biogas recovery projects [1970s]. The average recovered CH4 concentration was 36.5±11% CH4 (v/v), which is lower than the typical range for produced biogas [50% CH4]. This could be due to mixing with air, since many California recovery systems tolerate lower CH4 concentrations to comply with strict air quality regulations [including quarterly surface scans for elevated CH4 concentrations at ground level] and to minimize nuisance odors. We normalized the biogas recovery data to 50% CH4 to remove this variable effect.
Coupled with local climate, there is a strong seasonal imprint on CALMIM’s prediction of the site’s emission profile (Table 1). In a California study, Park and Shin (2001) documented temporally variable CH4 emissions, including maximum fluxes temporally corresponding with maximum surface temperatures above optimum for CH4 oxidation. For California studies, Yazdani and Imhoff (2010) observed lower CH4 oxidation rates in the Fall (Oct) than the Spring (March), and Bogner et al. (2011) measured lower wet season (March) and higher dry season (August) CH4 fluxes. Park and Shin (2001) documented temporally variable CH4 emissions, including minimum fluxes corresponding with minimum surface temperatures (cooler, wet season; March) and maximum fluxes corresponding with maximum temperatures (above optimum for CH4 oxidation). This dependency has been observed ever since the first field and laboratory study for annual oxidation in landfill soils, which led to the current 10% default in IPCC (2006) for annual soil methane oxidation, based on one site in New Hampshire, USA (Czepiel et al., 1996a). However, this temporal variability, which takes into account local soils and climate, has not been previously embedded in an inventory methodology. In a recent review of field studies using stable carbon isotopic methods, average oxidation has generally been 30–40% across a variety of sites (Chanton et al., 2009).
To improve inventory estimates for landfill CH4 emissions, it is clear that the seasonality of soil oxidation, consistent with site-specific cover soils and climate, need to be considered. Previous literature has described process-based models which rigorously address the seasonality of gaseous carbon and nitrogen fluxes in other managed and natural ecosystems [e.g., CENTURY (Parton, 1996); CASTANEA (Davi et al., 2006); and LPJmL (Müller et al., 2006)], but similar seasonal models have not been developed for landfills. There have also been a number of recent studies attempting to improve the mathematical prediction of landfill CH4 emissions inclusive of spatial and temporal variability (Chiemchaisri et al., 2011; Goldsmith Jr et al., 2012; Harborth et al., 2013; Rachor et al., 2013) and consideration of major controls on soil methanotrophy (Albanna et al., 2007; Bogner et al., 1997; Chanton and Liptay, 2000; Czepiel et al., 1996a; Lee et al., 2009; Pawłowska et al., 2003; Pratt et al., 2013). However, to date, the universal default method for estimating landfill CH4 emissions has retained reliance on empirical models for biogas generation; indeed, recent proposals have suggested additional modifications including further revisions for k values assumed to be related to climate (Amini et al., 2012; Garg et al., 2006; Karanjekar, 2012; Sormunen et al., 2013). Concurrently, there have also been more mechanistic models developed to simulate gas diffusion and/or advection processes in landfill cover soils (Abichou et al., 2006a, 2011, 2006b; De Visscher and Van Cleemput, 2003); however, these detailed modeling efforts have complex requirements for site-specific input parameters with uncertain variability which cannot be readily translated to a known precision for regional inventory purposes. Finally, some recent studies have also proposed the use of artificial neural networks (ANN) to account for overall soil complexity in the absence of robust mechanistic models addressing interrelated factors (Young et al., 2001). As an example, Abushammala et al.(2013a) utilized an ANN to predict the percentage of oxidation for a particular landfill, which they assumed could account for a variety of climatic and soil properties at a particular site, then proposed inserting this improved percentage in the IPCC guidelines in place of the current 10% default value (Abushammala et al., 2013b). However, ANN models would require separate training (calibration) for different soil textures, climates, and cover geometries, greatly complicating their application.
CALMIM, like all models, is an abstraction from reality and represents a simplification of complex soil processes. By simplifying the emissions process to 1-D diffusion inclusive of seasonal oxidation at a particular site, this model represents a first step toward accounting for the site-specific seasonality of landfill CH4 emissions neglected by current inventory methods. As whole site measurements of landfill emissions become more common, there are implications that the homogenous source assumption has on the ultimate validity of the estimation methods (Tratt et al., 2014).
Using California as a test case, with homage to the California origins of the 1st order kinetic framework for the IPCC (1996, 2006) inventory methodology for landfill CH4 emissions, we used field data from 128 currently-permitted landfill sites to develop a simple empirical relationship for biogas generation & recovery from the waste mass. Importantly, this direct relationship circumvents issues with selection of kinetic constants and “recovery efficiency” assumptions made with no field data support, which has been much discussed in previous literature (Di Bella et al., 2011; Oonk, 2012; Xue and Liu, 2013). The strong correlation (Fig. 1) indicates a universal biogas production-recovery rate per unit mass waste that is statistically robust across California landfills of different sizes, geometries, ages of waste, and climatic regions. This relationship also holds at other non-California sites (Figure S2). Since landfill covers are designed to limit precipitation/infiltration entry with designated regulatory cover designs (Coccia et al., 2013; Hanson et al., 2010), this also provides thermal insulation to preserve the self-heating effect of the anaerobic microbial decomposition reactions.
Previously, only a limited number of sites or test cells were typically used for the development of kinetic models for biogas generation requiring individual site “calibration” (Amini et al., 2013; Emcon, 1980; Faour et al., 2007; McBean, 2011), including the Dutch studies underpinning the current IPCC model based on degradable organic carbon (Oonk, 2010; Oonk and Boom, 1995; Van Zanten and Scheepers, 1995).
Using CALMIM, which was previously developed and field-validated for California, we developed a new 2010 statewide inventory for landfill CH4 emissions and compared the results to field measurements. The highest-emitting sites shifted significantly from the CARB inventory, from the sites with the largest mass of waste (CARB) to the sites with low annual oxidation and large areas of thinner intermediate cover soils (CALMIM). For the entire state, based on cover types, CH4 emissions averaged 10.6 (daily), 325.3 (intermediate), and 1.5 (final) g CH4 m-2 yr-1, respectively, resulting in >95% of the total emissions originating from intermediate cover areas. This shift from sites with the largest waste mass (CARB) has profound implications for developing improved local and regional inventories consistent with a growing database of whole landfill measurements (e.g. Peischl et al., 2013; Cambaliza et al., 2015) and will result in greatly-improved CH4 inventories inclusive of landfill sources.
In comparisons with data from field campaigns at 10 sites, CALMIM model results show good agreement with field data and are consistent with literature indicating elevated emissions from thinner intermediate cover soils (Abichou et al., 2006a). From the CALMIM results, the ten highest-emitting landfill sites are characterized by >70% of the waste footprint being covered by intermediate cover soils. Conversely, the CARB results indicate the highest emissions consistently occurring at sites with the largest amount of waste, despite the fact that some of these sites also have large areas of final cover (Table 2). This association of high CH4 emissions with large areas of final cover is inconsistent with literature indicating lower emissions from thicker final cover soils (Abichou et al., 2006a; Goldsmith Jr et al., 2012; Park et al., 2001).
We recognize that we are proposing a new methodology for GHG inventory calculations for landfill CH4 emissions that differs significantly from historic methods based on estimated generation with climate dependencies and subsequent allocation of a fraction of the estimated generation to surface emissions. However, as field and laboratory studies over the last two decades have emphasized the soil- and climate-related dependencies for emissions, and as herein demonstrated for California, it is time to reconsider the historic methodology which is misleading with respect to average annual emissions at specific sites, the regional [spatial] distribution of emissions, and the seasonal [temporal] variability of emissions. For ultimately reducing landfill CH4 emissions in California, thicker intermediate covers could be installed, as is already practiced at some sites (see Figure S8). Some remaining uncertainties, requiring further study, include:
To conclude, in order to achieve a better science-based quantification of landfill CH4 emissions there is the need to replace the current GHG inventory methodology with a more robust approach based on the correct drivers, including site-specific cover soils and climate-based estimation of seasonal oxidation in landfill cover soils.
All data is included in the supplemental information along with the CALMIM model at http://www.ars.usda.gov/services/software/download.htm?softwareid=300.
© 2015 Spokas et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Contributed to conception and design: JB, KS, MF, SW
Contributed to acquisition of data: JB, KS, SW, MC
Contributed to analysis and interpretation of data: KS, JB, MF, SW
Drafted and/or revised the article: KS, JB, MF, SW
Approved the submitted version for publication: JB, KS, MF, SW
The authors have declared that no competing interests exist.
The project team gratefully acknowledges the financial support and encouragement of the Environmental Research & Education Foundation (Dr. Bryan Staley, President) during 2011–2013 under UIC grant #G5534-555200 and UIC subcontract agreement #2010-03400-01-00 with U.S. Dept. of Agriculture, Agricultural Research Service.
We wish to recognize and acknowledge informal collaborations with many organizations and individuals since the beginning of the CALMIM project (2007) who generously shared their time, resources, and data: California Dept. of Resources Recovery & Recycling (CALRECYCLE), especially S. Walker; California Air Resources Board (ARB), especially L. Hunsaker; Los Angeles County Sanitation Districts, especially F. Capone and D. Kong; Monterey Bay Regional Waste Management Authority, especially W. Merry; USDA field and laboratory personnel, including C. Rollofson, M. duSaire, D. Peterson; UIC students, including P. Pilosi, P. Roots, and T. Badger.
Cooperation and discussions with: Florida State University, Tallahassee, Florida, especially J. Chanton; Waste Management, Inc., especially R. Green and G. Hater; Veolia Environmental Services Solid Waste, Inc. (US), now Advanced Disposal, Inc. (after November, 2012); Veolia Environment (FR); University of Agricultural Sciences/Vienna, especially M. Huber-Humer; University of Hamburg (DE); University of the Witwatersrand (SA); Danish Technical University; Melbourne University, especially S. Yuen, D. Chen, J. Sun, and M. Asadi; Purdue University, especially M. Cambaliza, P. Shepson.
Initial support for the CALMIM project was provided by the California Energy Commission PIER (Public Interest Energy Research) Program during 2007–2010 (G. Franco).
Abichou T , Chanton J , Powelson D , Fleiger J , Escoriaza S , et al. 2006a. Methane flux and oxidation at two types of intermediate landfill covers. Waste Manage 26(11): 1305–1312.
Abichou T, Mahieu K, Chanton J, Romdhane M, Mansouri I. 2011. Scaling methane oxidation: From laboratory incubation experiments to landfill cover field conditions. Waste Manage 31(5): 978–986.
Abichou T, Powelson D, Chanton J, Escoriaza S, Stern J. 2006b. Characterization of methane flux and oxidation at a solid waste landfill. J Environ Eng 132(2): 220–228.
Abushammala MFM, Basri NEA, Elfithri R, Younes MK, Irwan D. 2013a. Modeling of CH4 oxidation in landfill cover soil using an artificial neural network. J Air Waste Manage Assoc : 150–159.
Abushammala MM, Basri N, Basri H, Kadhum A, El-Shafie A. 2013b. Empirical gas emission and oxidation measurement at cover soil of dumping site: example from Malaysia. Environ Monit Assess 185(6): 4919–4932.
Albanna M, Fernandes L, Warith M. 2007. Methane oxidation in landfill cover soil; the combined effects of moisture content, nutrient addition, and cover thickness. J Environ Eng Sci 6(2): 191–200.
Amini HR, Reinhart DR, Mackie KR. 2012. Determination of first-order landfill gas modeling parameters and uncertainties. Waste Manage 32(2): 305–316.
Amini HR, Reinhart DR, Niskanen A. 2013. Comparison of first-order-decay modeled and actual field measured municipal solid waste landfill methane data. Waste Manage 33(12): 2720–2728.
Blanco G , Gerlagh R , Suh S , Barrett J , de Coninck HC , et al. 2014. Drivers, Trends and Mitigation, in EdenhoferO , Pichs-MadrugaR , SokonaY , FarahaniE , KadnerS , et al., eds., Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change . Cambridge, United Kingdom and New York, NY: Cambridge University Press.
Boeckx P, Van Cleemput O, Villaralvo I. 1997. Methane oxidation in soils with different textures and land use. Nutr Cycl Agroecosys 49(1–3): 91–95.
Bogner JE, Sass RL, Walter BP. 2000. Model comparisons of methane oxidation across a management gradient: Wetlands, rice production systems, and landfill. Global Biogeochem Cy 14(4): 1021–1033.
Bogner JE, Spokas KA, Burton EA. 1997. Kinetics of methane oxidation in a landfill cover soil: temporal variations, a whole-landfill oxidation experiment, and modeling of net CH4 emissions. Environ Sci Technol 31(9): 2504–2514.
Bogner JE, Spokas KA, Chanton JP. 2011. Seasonal greenhouse gas emissions (methane, carbon dioxide, nitrous oxide) from engineered landfills: Daily, intermediate, and final California cover soils. J Environ Qual 40(3): 1010–1020.
Bond-Lamberty B, Gower ST, Ahl DE. 2007. Improved simulation of poorly drained forests using Biome-BGC. Tree Physiol 27(5): 703–715.
Börjesson G, Svensson BH. 1997. Seasonal and diurnal methane emissions from a landfill and their regulation by methane oxidation. Waste Manage Res 15(1): 33–54.
Cambaliza MOL , Shepson P , Bogner J , Caulton DR , Stirm B , et al. 2015. Quantification and source apportionment of the methane emission flux from the city of Indianapolis. Elem Sci Anth 3(1): 000037.
Cao M, Dent JB, Heal OW. 1995. Modeling methane emissions from rice paddies. Global Biogeochem Cy 9(2): 183–195.
Chanton J, Liptay K. 2000. Seasonal variation in methane oxidation in a landfill cover soil as determined by an in situ stable isotope technique. Global Biogeochem Cy 14(1): 51–60.
Chanton JP, Powelson DK, Green RB. 2009. Methane oxidation in landfill cover soils, is a 10% default value reasonable? J Environ Qual 38(2): 654–663.
Chiemchaisri C, Chiemchaisri W, Kumar S, Wicramarachchi PN. 2011. Reduction of methane emission from landfill through microbial activities in cover soil: A brief review. Crit Rev Environ Sci Technol 42(4): 412–434.
Cicerone RJ, Shetter JD, Delwiche CC. 1983. Seasonal variation of methane flux from a California rice paddy. J Geophys Res-Oceans (1978–2012) 88(C15): 11022–11024.
Coccia CJR, Gupta R, Morris J, McCartney JS. 2013. Municipal solid waste landfills as geothermal heat sources. Renew Sust Energ Rev 19(0): 463–474.
Couth R , Trois C , Parkin J , Strachan LJ , Gilder A , et al. 2011. Delivery and viability of landfill gas CDM projects in Africa—A South African experience. Renew Sust Energ Rev 15(1): 392–403.
Czepiel P, Mosher B, Crill P, Harriss R. 1996a. Quantifying the effect of oxidation on landfill methane emissions. J Geophys Res-Atmos (1984–2012) 101(D11): 16721–16729.
Czepiel P , Mosher B , Harriss R , Shorter J , McManus J , et al.. 1996b. Landfill methane emissions measured by enclosure and atmospheric tracer methods. J Geophys Res-Atmos (1984–2012) 101(D11): 16711–16719.
Davi H , Bouriaud O , Dufrêne E , Soudani K , Pontailler J , et al. 2006. Effect of aggregating spatial parameters on modelling forest carbon and water fluxes. Agr Forest Meteorol 139(3): 269–287.
De Visscher A, Van Cleemput O. 2003. Simulation model for gas diffusion and methane oxidation in landfill cover soils. Waste Manage 23(7): 581–591.
Deshpande B , Hunsaker L , Vayssières M , Lutter K , Eslinger K , et al. 2014. California’s 2000–2011: Greenhouse Gas Emissions Inventory. California Air Resources Board . Sacramento, CA: State of California. Current CARB Inventory available at: http://www.arb.ca.gov/cc/inventory/inventory.htm. Accessed last on 3/24/2015.
Di Bella G, Di Trapani D, Viviani G. 2011. Evaluation of methane emissions from Palermo municipal landfill: Comparison between field measurements and models. Waste Manage 31(8): 1820–1826.
El Baba M, Kayastha P, De Smedt F. 2014. Landfill site selection using multi-criteria evaluation in the GIS interface: A case study from the Gaza Strip, Palestine. Arabian J Geosci : 1–15.
EMCON. 1980. Methane Generation and Recovery from Landfills . Ann Arbor, MI, USA: Ann Arbor Science.
Faour AA, Reinhart DR, You H. 2007. First-order kinetic gas generation model parameters for wet landfills. Waste Manage 27(7): 946–953.
Garg A, Achari G, Joshi RC. 2006. A model to estimate the methane generation rate constant in sanitary landfills using fuzzy synthetic evaluation. Waste Manage Res 24(4): 363–375.
Gioannis GD, Muntoni A, Cappai G, Milia S. 2009. Landfill gas generation after mechanical biological treatment of municipal solid waste. Estimation of gas generation rate constants. Waste Manage 29(3): 1026–1034.
Goldsmith Jr CD , Chanton J , Abichou T , Swan N , Green R , et al. 2012. Methane emissions from 20 landfills across the United States using vertical radial plume mapping. J Air Waste Manage Assoc 62(2): 183–197.
Halvadakis CP, Robertson AP, Leckie J, Gas P. 1983. Landfill Methanogenesis: Literature Review and Critique: Final Summary Report. Technical Report 271. Department of Civil Engineering, Stanford University, Stanford, CA.
Hanson J, Yeşiller N, Oettle N. 2010. Spatial and temporal temperature distributions in municipal solid waste landfills. J Environ Eng 136(8): 804–814.
Harborth P, Fuß R, Münnich K, Flessa H, Fricke K. 2013. Spatial variability of nitrous oxide and methane emissions from an MBT landfill in operation: Strong N2O hotspots at the working face. Waste Manage 33(10): 2099–2107.
Henneberger R , Chiri E , Bodelier PE , Frenzel P , Lüke C , et al.. 2015. Field-scale tracking of active methane-oxidizing communities in a landfill cover soil reveals spatial and seasonal variability. Environ Microbiol : in press.in press. doi: 10.1111/1462-2920.12617.
IPCC. 1996. (Intergovernmental Panel on Climate Change) Climate Change 1995-The Science Of Climate Change. Cambridge, UK: Cambridge University Press.
IPCC, 2006. 2006 IPCC Guidelines for National Greenhouse Gas Inventories. EgglestonHS, BuendiaL, MiwaK eds. , NgaraT, TanabeK (eds). Hayama, Japan: IGES.
Janssens-Maenhout G , Dentener F , van Aardenne J , Monni S , Pagliari V , et al. 2012. EDGAR-HTAP: A harmonized gridded air pollution emission dataset based on national inventories. European Commission Joint Research Centre Institute for Environment and Sustainability. JRC 68434 UR 25229 EUR 25229, ISBN 978-92-79-23123-0 . doi:10.2788/14102. Luxembourg: Publications Office of the European Union. http://ies.jrc.ec.europa.eu/ and http://www.jrc.ec.europa.eu/.
Jeong S , Hsu Y-K , Andrews AE , Bianco L , Vaca P , et al. 2013. A multitower measurement network estimate of California’s methane emissions. J Geophys Res-Atmos 118(19): 2013JD019820.
Karanjekar RV. 2012. An improved model for predicting methane emissions from landfills based on rainfall, ambient temperature and waste composition. PhD Dissertation . University of Texas at Arlington. 293 pp.
Klink RE, Ham RK. 1982. Effects of moisture movement on methane production in solid waste landfill samples. Resour Conserv 8(1): 29–41.
Lai D, Roulet N, Humphreys E, Moore T, Dalva M. 2012. The effect of atmospheric turbulence and chamber deployment period on autochamber CO2 and CH4 flux measurements in an Ombrotrophic peatland. Biogeosciences 9(8): 3305–3322.
Lawrimore JH , Menne MJ , Gleason BE , Williams CN , Wuertz DB , et al. 2011. An overview of the global historical climatology network monthly mean temperature data set, version 3. J Geophys Res-Atmos 116(D19): D19121.
Lee S-W , Im J , DiSpirito A , Bodrossy L , Barcelona M , et al. 2009. Effect of nutrient and selective inhibitor amendments on methane oxidation, nitrous oxide production, and key gene presence and expression in landfill cover soils: characterization of the role of methanotrophs, nitrifiers, and denitrifiers. Appl Microbiol Biot 85(2): 389–403.
Legates DR, Willmott CJ. 1990. Mean seasonal and spatial variability in global surface air temperature. Theor Appl Climatol 41(1–2): 11–21.
Levy P , Gray A , Leeson S , Gaiawyn J , Kelly M , et al. 2011. Quantification of uncertainty in trace gas fluxes measured by the static chamber method. Eur J Soil Sci 62(6): 811–821.
Li C , Mosier A , Wassmann R , Cai Z , Zheng X , et al. 2004. Modeling greenhouse gas emissions from rice-based production systems: Sensitivity and upscaling. Global Biogeochem Cy 18(1): GB1043.
Mann J, Lenschow DH. 1994. Errors in airborne flux measurements. J Geophys Res-Atmos (1984–2012) 99(D7): 14519–14526.
McBean EA. 2011. In-situ estimation of the methane generation rate for a wet and highly organic solid waste landfill. Int J Environ Waste Manage 8(1): 123–132.
Monni S, Pipatti R, Lehtilla A, Savolainen I, Syri S. 2006. Global Climate Change Mitigation Scenarios for Solid Waste Management. Espoo: Technical Research Centre of Finland VTT Publications. Available at: http://www.vtt.fi/inf/pdf/publications/2006/P603.pdf.
Morin TH , Bohrer G , Naor-Azrieli L , Mesi S , Kenny WT , et al. 2014. The seasonal and diurnal dynamics of methane flux at a created urban wetland. Ecol Eng 72(0): 74–83.
Mou Z, Scheutz C, Kjeldsen P. 2014. Evaluating the biochemical methane potential (BMP) of low-organic waste at Danish landfills. Waste Manage 34(11): 2251–2259.
Müller C, Bondeau A, Lotze-Campen H, Cramer W, Lucht W. 2006. Comparative impact of climatic and nonclimatic factors on global terrestrial carbon and water cycles. Global Biogeochem Cy 20(4).
Myhre, G , Shindell, D , Bréon, F , Collins, W , Fuglestvedt, J , et al., eds. 2013. Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change . Cambridge, UK and New York, US: Cambridge University Press: p. 658–740.
Oonk H. 2010. Literature Review: Methane From Landfills Methods To Quantify Generation, Oxidation, and Emission. Report for the Sustainable Landfill Foundation . Assendelft, Netherlands.
Oonk H. 2012. Efficiency of landfill gas collection for methane emission reduction. Greenhouse Gas Meas Manage 2(2–3): 129–145.
Oonk H, Boom T. 1995. Validation of landfill gas formation models. Studies in Environmental Science 65: 597–602.
Park J-W, Shin H-C. 2001. Surface emission of landfill gas from solid waste landfill. Atmos Environ 35(20): 3445–3451.
Parton W. 1996. The CENTURY model, in, PowlsonD, SmothP eds. , SmithJU, eds., Evaluation of Soil Organic Matter Models Using Using Existing Long-Term Datasets . Springer Publishers: p. 283–291.
Pawłowska M, Stępniewski W, Czerwiński J. 2003. The effect of texture on methane oxidation capacity in a sand layer — a model laboratory study, in, PawłowskiL, DudzińskaM eds. , PawłowskiA, eds., Environmental Engineering Studies . United States: Springer. p. 339–354.
Peischl J , Ryerson TB , Brioude J , Aikin KC , Andrews AE , et al. 2013. Quantifying sources of methane using light alkanes in the Los Angeles basin, California. J Geophys Res-Atmos 118(10): 4974–4990.
Perdikea K, Mehrotra AK, Hettiaratchi JPA. 2008. Study of thin biocovers (TBC) for oxidizing uncaptured methane emissions in bioreactor landfills. Waste Manage 28(8): 1364–1374.
Peterson TC, Vose RS. 1997. An overview of the global historical climatology network temperature database. B Am Meteorol Soc 78(12): 2837–2849.
Pratt C, Walcroft AS, Deslippe J, Tate KR. 2013. CH4/CO2 ratios indicate highly efficient methane oxidation by a pumice landfill cover-soil. Waste Manage 33(2): 412–419.
Rachor I, Gebert J, Gröngröft A, Pfeiffer EM. 2013. Variability of methane emissions from an old landfill over different time-scales. European J Soil Sci 64(1): 16–26.
Sadasivam B, Reddy K. 2014. Landfill methane oxidation in soil and bio-based cover systems: A review. Rev Environ Sci Bio/Technol 13(1): 79–107.
Sass RL, Fisher FM, Harcombe PA, Turner FT. 1990. Methane production and emission in a Texas rice field. Global Biogeochem Cy 4(1): 47–68.
Scheutz C , Kjeldsen P , Bogner JE , De Visscher A , Gebert J , et al. 2009. Microbial methane oxidation processes and technologies for mitigation of landfill gas emissions. Waste Manage Res 27(5): 409–455.
Shan J, Iacoboni M, Ferrante R. 2013. Estimating greenhouse gas emissions from three Southern California landfill sites. Proceedings of SWANA’s 2013 Landfill Gas Symposium . Silver Springs, MD.
Smith K, Bogner J. 1997. Joint North American-European Workshop on Measurement and Modeling of Landfill Methane Emissions from Landfills. Final Report Sponsored by the International Global Atmospheric Chemistry [IGAC] Project-International Geosphere Biosphere Project ) [IGBP] and U.S. EPA. Held Argonne National Laboratory, 1996 . Cambridge, MA: IGAC Project Office.
Sormunen K, Laurila T, Rintala J. 2013. Determination of waste decay rate for a large Finnish landfill by calibrating methane generation models on the basis of methane recovery and emissions. Waste Manage Res 31(10): 979–985.
Spokas K, Bogner J, Chanton J. 2011. A process-based inventory model for landfill CH4 emissions inclusive of seasonal soil microclimate and CH4 oxidation. J Geophys Res 116(G4): G04017.
Spokas K, Bogner J, Chanton J, Franco G. 2009. Developing a new field-validated methodology for landfill methane emissions in California. Proc Sardinia .
Spokas K , Bogner J , Chanton JP , Morcet M , Aran C , et al. 2006. Methane mass balance at three landfill sites: What is the efficiency of capture by gas collection systems? Waste Manage 26(5): 516–525.
Spokas KA, Bogner JE. 2011. Limits and dynamics of methane oxidation in landfill cover soils. Waste Manage 31(5): 823–832.
Theresa D. 2012. Benjamin Franklin-Unabridged Guide . Newstead, Australia: Emereo Publishing.
Thompson S, Sawyer J, Bonam R, Valdivia JE. 2009. Building a better methane generation model: Validating models with methane recovery rates from 35 Canadian landfills. Waste Manage 29(7): 2085–2091.
Tratt DM , Buckland KN , Hall JL , Johnson PD , Keim ER , et al. 2014. Airborne visualization and quantification of discrete methane sources in the environment. Remote Sens Environ 154(0): 74–88.
USEPA. 2014. Municipal Solid Waste in the United States: 2012 Facts and Figures. Washington, D.C: Office of Solid Waste and Emergency Response, U.S. Environmental Protection Agency. Available online at http://epa.gov/epawaste/nonhaz/municipal/pubs/2012_msw_dat_tbls.pdf. Accessed 4-May-2015.
USEPA. 2015. Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990–2013. Washington, DC: U.S. Environmental Protection Agency. Available at http://epa.gov/climatechange/Downloads/ghgemissions/US-GHG-Inventory-2015-Main-Text.pdf Accessed 4-May-2015. 564pp.
Van Zanten B, Scheepers MJJ. 1995. Modeling of landfill gas potentials. Proceedings of SWANA 18th Annual Landfill Gas Symposium . New Orleans, LA.
Walker S. 2012. Landfill Data Compilation. With the assistance of W. Gin (Senior Eng.; deceased), M. Holmes, and H. Hansra, in, CALRecycle, editor: CalRecycle Engineering Support Branch . (See supplemental information CA-LANDFILLDATABASE.xlsx)
Wille C, Kutzbach L, Sachs T, Wagner D, Pfeiffer E-M. 2008. Methane emission from Siberian arctic polygonal tundra: eddy covariance measurements and modeling. Glob Change Biol 14(6): 1395–1408.
Xu L, Lin X, Amen J, Welding K, McDermitt D. 2014. Impact of changes in barometric pressure on landfill methane emission. Global Biogeochem Cy 28(7): 679–695.
Xue Q, Liu L. 2013. Study on optimizing evaluation and recovery efficiency for landfill gas energy collection. Environ Prog Sustainable Energy 33(3): 972–977.
Yang T, Yue DB, Han B, Sun Y. 2014. Field methane oxidation efficiency at municipal solid waste landfills located in the north of China. Advanced Materials Research 878: 812–820.
Yazdani R, Imhoff P. 2010. Biocovers at landfills for methane emissions reduction demonstration. Report to California Department of Resources Recycling and Recovery . Sacramento, CA.
Young I, Crawford J, Rappoldt C. 2001. New methods and models for characterising structural heterogeneity of soil. Soil Till Res 61(1): 33–45.