The current IPCC landfill methane (CH4) methodology excludes critical process drivers now known to control emissions. These include site-specific (1) operational factors (i.e., thickness and composition of various cover soils; physical extent of engineered biogas recovery) and (2) temporal climate effects on soil moisture/temperature profiles in each cover which, in turn, drive gaseous transport, microbial methanotrophic oxidation, and temporally variable “net” CH4 emissions over an annual cycle. Herein, we address the international field validation and application of a process-based model CAlifornia Landfill Methane Inventory Model (CALMIM) which encompasses site-specific climate, cover soils, engineered biogas recovery, and other site-specific strategies. Using embedded soil microclimate models with (a) default 30-year climate data, (b) site-specific annual weather data, or (c) future climate predictions (i.e., CMIP5), the transient soil moisture and temperature effects on bidirectional diffusive CH4/oxygen transport and microbial oxidation can be estimated for any cover soil at any global location. We focus on site-specific field data comparisons to CALMIM-predicted annual and monthly CH4 emissions both without and without methanotrophic oxidation. Overall, 74% of 168 individual surface CH4 emission measurements across 34 international sites were consistent with CALMIM-modeled annual predictions with oxidation (+ or – SD). Notably, the model overpredicted 30 comparisons and underpredicted 13 comparisons. In addition to improving site-specific landfill CH4 inventories, we address how this freely available tool can be used to (a) recommend site-specific cover soil modifications to minimize emissions; (b) systematically compare the spatial and temporal variability of emissions for diverse global locations, latitudinal gradients, extreme climates, and future climate scenarios; (c) assist scheduling of field campaigns to capture seasonal variability; and (d) provide a 12-month annual framework with average monthly CH4 emission statistics for comparison to periodic temporal results from diverse bottom-up and top-down field techniques with variable uncertainties. Importantly, CALMIM does not require intensive site-specific model calibrations.

Robust source-specific, site-specific, and field-validated measurement and modeling tools are needed to improve quantification of anthropogenic methane (CH4) emissions for urban- to regional-scale greenhouse gas (GHG) inventories and verifiable mitigation strategies. CH4 is the second most important GHG after carbon dioxide with atmospheric CH4 mixing ratios increasing 150% since the beginning of the industrial age (c 1750) to current levels of 1,870 ppb (v/v), which is unprecedented over the last 800,000 years (Dlugokency, 2020). Landfill CH4 is currently considered to be the third largest U.S. source of anthropogenic CH4 after ruminant animals and natural gas sources (NASEM, 2018). Addressing improved CH4 inventories for the United States, NASEM (2018) recommended (a) the development of an annual U.S. gridded (0.1° × 0.1°) CH4 inventory for improved linkage between field measurements and inventory estimates and (b) periodic updates for inventory methodologies consistent with current scientific understanding. Herein, we begin to address these recommendations with respect to landfill CH4: The first requires credible downscaled estimates for specific landfill sites, whereas the second requires new approaches that contrast sharply with highly simplistic estimation strategies developed several decades ago (IPCC, 1996, 2006, 2019) for annual national landfill CH4 emission accounting. See also NASEM (2018) for discussion of current shortcomings.

Briefly, the current national GHG inventory methodology for landfill CH4 relies on a first-order kinetic equation for biogas generation from the assumed degradable organic C content of buried waste (IPCC, 1996, 2006, 2019). Biogas generation from an annual mass of waste is assumed to peak in the year following disposal and decline exponentially thereafter. Via the scaling of the kinetic constant, this method assumes a generalized climate effect (wet/dry, hot/cold) on CH4 generation rates (Jain et al., 2021) but neglects the physical and biological mitigating effects of the cover soil on emissions. The modeled annual CH4 generation is then partitioned into engineered recovery (where it exists, often assumed to be 75% of generation at engineered sites) and cover soil oxidation (typically 10% based on the first published study for annual oxidation in the literature; Czepiel et al., 1996): The remainder is assumed to equal surface CH4 emissions. It should be noted that the IPCC methodology was initially “validated” during the 1990s, prior to widespread field campaigns quantifying site-specific emissions, via comparisons of modeled generation to measured biogas recovery, not to measured CH4 emissions (Oonk et al., 1994; Oonk and Boom, 1995a, 1995b; Boerboom et al., 2010; Oonk, 2010). Notably, as currently formulated, this methodology yields CH4 emission estimates that are proportional to site-specific waste in place (WIP)—that is, the largest landfills are the largest emitters, which is not systematically correct (see Text S1 and discussion in Spokas et al., 2015). IPCC (2006) also forms the basis for newer (2010 and later) landfill CH4 emissions estimates under the USEPA Greenhouse Gas Reporting Program (GHGRP HH-) inclusive of larger U.S. landfills; therein, some additional choices for scaling factors and defaults have been added for oxidation and emissions calculations. However, those factors were derived from general ranges observed in literature and never comprehensively field-validated for site-specific applications.

Herein, we focus on improvements, international field validation, and applications for CAlifornia Landfill Methane Inventory Model (CALMIM), a freely available, user-friendly 1-D process-level model for site-specific landfill CH4 emission estimates for individual cover materials over an annual cycle with 10-min time steps and 2.5 cm depth increments. CALMIM was originally developed to address the shortcomings of IPCC (2006) for the California landfill CH4 inventory (Bogner et al., 2011; Spokas et al., 2011; Spokas and Bogner, 2011). CALMIM was field-validated for seasonal emissions at several California sites and subsequently applied to a revised 2010 site-specific landfill CH4 inventory for the entire state (Spokas et al., 2015). Within this article, we focus on refinements, detailed international field validations, and other scientific applications for CALMIM. Background information on CALMIM components, theoretical underpinnings, structure, inputs/outputs, graphics, default values, supporting laboratory studies, field validation, and previous applications are given in the Supporting Information (Text S1 and S2; Table S1; Figures S1–S5). Also, Text S3 in the SI provides an overview of engineered landfill practices and current CH4 emissions issues; these are also addressed in Meyer-Dombard et al. (2020). CALMIM focuses on the major drivers for emissions known from literature, namely, site-specific climate and operational factors, including (a) the composition and thickness of individual layered daily, intermediate, and final cover soils; (b) the extent of engineered gas recovery associated with each cover area; (c) climate-driven bidirectional CH4 and O2 diffusional transport (from the atmospheric and landfill gas concentration boundary conditions); and (d) methanotrophic oxidation rates in each cover soil as a function of transient soil temperature and moisture potential. In addition to the California studies addressed above and in Text S1, CALMIM modeling has also been compared to aircraft mass balance measurements at diverse Indiana landfills (Cambaliza et al., 2015) and to a wide variety of field techniques (see Figure S3) at one Indiana landfill (Cambaliza et al., 2017). Moreover, unlike recent California site-specific inventory results using IPCC (2006) in which estimated annual CH4 emissions are linearly related to annual site-specific WIP (Spokas et al., 2015; see Figure S5), CALMIM-modeled emissions for California sites were greater at sites with a) large areas of thinner intermediate cover and/or b) reduced CH4 oxidation rates due to non-optimum seasonal soil moisture and temperature in the cover materials (Spokas et al., 2015). From extensive supporting laboratory studies (Spokas and Bogner, 2011), the optimum landfill soil oxidation temperature is around 35°C (Figure S1) with optimal soil moisture potential (SMP) near the water-holding capacity (–33 kPa; Figure S2).

As discussed in a recent study addressing California landfills (Cusworth et al., 2020), landfill sites can also have fugitive CH4 emissions from colocated waste-related operations (i.e., windrow composting of green wastes becoming anaerobic during wet weather; leakages from wastewater treatment or anaerobic digestion facilities). These sources are not addressed by CALMIM nor does CALMIM attempt to address adjacent non-waste sources of CH4 (natural gas/petroleum; enteric fermentation; natural wetland/marine sources). Furthermore, CALMIM excludes fugitive landfill “super emitter” events (Duren et al., 2019; Cusworth et al., 2020) resulting from leaky wellheads and biogas recovery infrastructure, leachate collection systems, or fractured cover soils. Such transient events can lead to significant fugitive CH4 emissions, some of which are addressed under current U.S. landfill regulations which require a quarterly gridded “walkover” survey using a portable CH4 analyzer, followed by timely remediation. Given rapidly evolving sensor (Martinez et al., 2020) and drone technology (Daugėla et al., 2020; Kim et al., 2021), it is likely that the surface detection methods can be significantly expanded, especially from localized areas that may not be currently included (i.e., steep slopes). Finally, of particular importance at U.S. landfills is the relatively common practice of removing an existing intermediate cover prior to vertical expansions for new cell development—this exposes fully methanogenic older waste resulting in very high CH4 emissions per unit area from a small footprint (Cambaliza et al, 2017; Figure S4).

Concerning more intensive site-specific monitoring of landfill CH4 emissions, despite recent improvements in multiple techniques (Taylor et al., 2016; Taylor et al., 2018; Fjelsted et al., 2019; Matacchiera et al., 2019; Mønster et al., 2019; Scheutz and Kjeldsen, 2019), current methodologies do not typically yield continuous measurements over monthly or annual time frames and are prohibitively expensive to deploy multiple times per year at large numbers of sites. Also, all field techniques deployed at a specific time and place have innate weaknesses/uncertainties with respect to cover material variability, terrain constraints, time of day (i.e., atmospheric boundary layer development), transient weather conditions (wind speed/ direction, barometric pressure/temperature), and other campaign-specific uncertainties (Mønster et al., 2019). Therefore, field-validated models such as CALMIM are needed to estimate individual cover soil emissions with corresponding confidence intervals over a typical annual cycle as controlled primarily by (a) site-specific engineering, operational, and management strategies and (b) climate feedbacks, especially soil moisture and temperature effects on cover-specific CH4 transport and oxidation rates (Spokas et al., 2011; Spokas et al., 2015; Bian et al., 2021).

Herein, we discuss improvements to CALMIM, various CALMIM utilization strategies, and greatly extend the previous model validation to include 34 international sites on 6 continents (Figure S6). We provide recommendations for CALMIM applications to (a) site-specific annual GHG inventories, (b) improved understanding of emissions in extreme climates as well as expected regional emissions (i.e., latitudinal gradient), (c) strategies for minimizing emissions by maximizing seasonal methanotrophic oxidation, (d) capturing seasonal variability within site-specific field campaigns, and (e) predicting future landfill emissions using climate change scenarios (e.g., IPCC CMIP5/Coupled Model Intercomparison Project).

2.1. International field validation

Measured emissions from 34 landfill sites in N. & S. America, Europe, Asia, Africa, and Australia were compared to CALMIM modeled emissions, relying both on published literature and collaborations with U.S. and international research groups (Table S2; Figure S6). In many cases, in addition to published literature, we contacted individual research groups who generously provided additional project details and background information (see Acknowledgments and Data accessibility statement). In general, measured landfill CH4 emissions include a diverse range of field measurement techniques (tracer dispersion, chamber, vertical radial plume mapping [VRPM], micrometeorological methods, aircraft mass balance) as no single field technique is universally appropriate, depending on the research questions and purpose of a specific campaign, the spatial/temporal scale of the measurements, and technique-specific constraints and uncertainties (Monster et al., 2019). For the current validation, because of the necessity for cover-specific measurements for direct comparison to CALMIM estimates, we focused on historic chamber and VRPM data. In general, labor-intensive static chambers remain a good choice for small-scale process-based studies of specific covers at the m2 scale, but the combination of chambers with larger scale techniques can yield both a snapshot of whole site emissions as well as the partitioning of emissions from various subareas (e.g., differing cover materials, construction practices, topography, active versus inactive areas, and gas management strategies).

The CALMIM model was run using site-specific latitude and longitude, respective default cover type (daily, intermediate, final), the site-specific layered cover soil descriptions, and CALMIM default climate data (e.g., 30-year average daily min/max temperature and daily precipitation with 0.5 × 0.5° resolution). This methodology is consistent with the recommended use of CALMIM as a “Tier 3” inventory method for annual GHG inventory reporting (Spokas et al., 2011), including the use of default soil gas CH4 and O2 concentrations for the base of cover and upper [atmospheric] boundaries (see Table S1). When field measurement dates were available, due to the temporally variable climatic dependency of the predicted CALMIM emissions, individual field-measured values within a given month were compared to the 2 modeled monthly means for the same month (both with and without soil oxidation “turned on”). If specific field measurement dates were not available, comparisons were made between the average field results and CALMIM annual means with and without oxidation. More succinct comparisons (e.g., daily comparisons) were not possible due to insufficient site-specific weather data, especially the timing of precipitation events relative to field measurements. In short, CALMIM average predictions with and without soil oxidation were compared to the mean of various field measurements to determine whether the model correctly estimated the range of field measurements, or over- or underpredicted observed emissions. Deviations between measured results and CALMIM predictions both with and without soil CH4 oxidation were also calculated.

2.2. Additional applications for CALMIM

In addition to the routine use of CALMIM as a higher-tier annual inventory tool, we have tested additional applications encompassing both broader research questions and site-specific operational issues. These include the following:

2.2.1. Variability of annual landfill CH4 emissions along a latitudinal gradient

We selected 61 global sites ranging from 70°N to –50°S latitude, comprising a latitudinal gradient from southern Argentina to northern North America (Figure S7), then applied CALMIM to estimate seasonal CH4 emissions with and without oxidation over a typical annual cycle for hypothetical landfills along this broad N–S gradient. We focused on selected final cover designs of 1 m clay, 1 m silt, and 1 m loam; no vegetation, and no engineered gas recovery. Final covers were selected because (1) these are in place for many decades and are particularly important for long-term temporal considerations, and (2) many existing international studies have focused on final covers. Although the locations included a mix of developed and developing countries, a 1 m final cover without biogas recovery can be considered a global minimum for an engineered landfill site. Following all CALMIM runs for each location and the various covers, data were assembled to enable analysis and graphing in R (R Core Team, 2017).

2.2.2. Projected landfill CH4 emissions under extreme climate conditions

Using current embedded CALMIM weather data (30-year averages for daily precipitation and min/max temperature with 0.5 × 0.5°. interpolation), we focused on global locations with the most extreme climates (Table 1), assuming a hypothetical landfill with a 1 m final cover over a range of soil textures (clay, silt, and loam). The sites were: Lut Desert, Iran (hottest); Mawsynram, India (wettest); Oymakon, Russia/Siberia (coldest); and Quillague, Chile (driest). These comparisons are useful to understand the upper and lower limits for landfill CH4 emissions with and without oxidation under the most extreme weather conditions on our planet.

Table 1.

Global extreme climate conditions. DOI: https://doi.org/10.1525/elementa.2020.00050.t1

LocationLatitudeLongitudeAverage Annual Temperature (°C)aAverage Annual Precipitation (mm)a
Quillague, Chile Driest -21.66 -69.53 17.10 9.9 
Oymakon, Siberia Coldest 63.46 142.77 -15.92 104.7 
Lut Desert, Iran Hottest 30.61 59.07 25.53 117.7 
Mawsynram, India Wettest 25.29 91.58 20.70 2281.5 
LocationLatitudeLongitudeAverage Annual Temperature (°C)aAverage Annual Precipitation (mm)a
Quillague, Chile Driest -21.66 -69.53 17.10 9.9 
Oymakon, Siberia Coldest 63.46 142.77 -15.92 104.7 
Lut Desert, Iran Hottest 30.61 59.07 25.53 117.7 
Mawsynram, India Wettest 25.29 91.58 20.70 2281.5 

aThese values were taken directly from the 30-year average climate model within CALMIM and may differ from annual climate records.

2.2.3. Projected landfill CH4 emissions under future climate change scenarios

CALMIM is the first process-based model capable of estimating future landfill CH4 emissions for diverse cover soils at specific global locations. In a previous study, we used older SRES scenarios compiled for the IPCC fourth Assessment Report (IPCC Special Report on Emissions Scenarios) for Lulea, Sweden (high northern latitude, temperate: 65.6°N, 22.2°E); Cairo, Egypt (mid-northern latitude, dry, 30.1°N, 22.2°E); Macapa, Brazil (equatorial, tropical: 0.03°N, –31.2°W); and Cape Town, South Africa (mid-southern latitude, coastal: –33.6°S, 18.3°E; see discussion in Bogner et al., 2014). Herein, we focus on 2 California locations with contrasting future climate scenarios (Site 1: Eureka and Site 2: Lancaster) to examine projected climate impacts on future landfill emissions (Figure S8). These locations have divergent projected precipitation patterns: positive (wetter) for Eureka and negative (drier) for Lancaster, as derived from the average of four CMIP5 models for a future high emission scenario (RCP 8.5: emissions rising through 2050 and plateauing in 2100; Pierce et al., 2018). We specifically focused on projections to the end of the current century (2070–2099) from the following 4 models: HadGEM2-ES (Met Office Hadley Centre for the CMIP5 centennial simulations; Warm/Drier), CNRM-CM5 (ESM developed jointly by CNRM-GAME [Météo-France/CNRS] and CERFACS; Cooler/Wetter), CanESM2 (second generation Canadian Earth System Model; Average), and MIROC5 (Model for Interdisciplinary Research On Climate-The University of Tokyo Center). See model details in Pierce et al. (2018). As there was generally good agreement across climatic predictions from all 4 of these models, particularly for air temperature (see Figure S9), we then focused specifically on the CNRM-CM5 RCP 8.5 output.

For the 2 California locations, daily maximum/minimum temperature and daily precipitation forecasts from the CNRM-CM5 RCP 8.5 model were downloaded for individual future years (https://cal-adapt.org/data/download). These were implemented in multiple CALMIM runs for every fifth year between 2020 and 2100 using the CALMIM “site-specific” weather functionality. Simulations were conducted for a landfill final cover 1 m thick over a wide variety of soil textures (clay, silt, loam, sandy clay, loamy sand, sandy clay loam, sandy loam, silty loam, and sandy clay). Model output was subsequently evaluated for emission correlations to temperature and precipitation through Pearson correlation coefficients. Likewise, the influence of soil texture was evaluated via curve fitting of emission trends for diverse textural classes.

Additionally, via the selection of 3 different annual precipitation patterns (low, medium, and high), finer simulations were conducted to scrutinize the influence of precipitation and temperature as driving forces for CH4 emissions. Each precipitation sequence was held constant over a range of predicted air temperatures to evaluate the combined influence of air temperature and precipitation. The % oxidation was estimated from the following relationship:

%Oxidation=(Emission without oxidationEmission with oxidation)Emission without oxidation×100%.
1

Expressed as a percentage of the total potential diffusive flux without oxidation, this estimate was assumed to represent the oxidation “capacity” of each cover. Concurrently, of course, gaseous diffusion rates vary temporally due to the temporal variability of soil gas concentration gradients.

CALMIM modeling for the hypothetical Lancaster, CA landfill site (1 m final cover with and without CH4 oxidation) with predicted 2058 climate data was also used to examine the influence of variable soil texture in a future warmer climate. Via selection of the default lower boundary condition (55% v/v soil gas CH4 at base of cover), these simulations assumed no biogas recovery.

2.2.4. Examination of cover soil texture and thickness dependencies for CH4 emissions along Western hemisphere latitudinal gradient

Using CALMIM modeling, we addressed the optimum final cover thickness capable of minimizing emissions for 5 hypothetical landfills located along a north–south latitudinal gradient from E. Canada to southern South America (53°S, 72°W; 17°S 72°W; 0°N, 96°W; 45°N, 94°W; 60°N, 96°W). Other assumptions included a loam-textured final cover of variable thickness (15, 45, 60, 100, 120, 200, and 240 cm), default climatic data, and no biogas recovery (55% v/v soil gas CH4 at base of waste).

3.1. Comprehensive comparisons across all sites

In this section, we directly compare field measurements to CALMIM-modeled results at the 34 international sites, both with and without inclusion of soil CH4 oxidation. The individual comparisons are summarized in Table S2, including CALMIM results with and without oxidation; overall comparisons by cover type are given in Table 2 and Figure 1. Note that chamber data dominate the results as these provided cover-specific field data. Figure S10 compares site-specific annual average CALMIM emissions projections (with and without oxidation) to field measurements by cover type. For shorter time frames, Figure S11 illustrates downscaled monthly comparisons for a subset of 12 sites with various covers where such comparisons were possible; these focused on field observations in specific months versus CALMIM-modeled monthly means and standard deviations. Examining the mean error (yCALMIMyfield) of simulations in Table S2, the average error from the CALMIM modeling with oxidation (the more realistic scenario) was an underprediction of 2.3 g CH4 m–2 d–1. If CH4 oxidation is turned off, the overprediction was 64 g CH4 m–2 d–1 averaged across all comparisons (Table S2). This clearly demonstrates the attenuating effect of cover soil methanotrophic activity. Also, these direct comparisons relied on CALMIM 30-year average weather, not concurrent site-specific weather data, which could improve the direct field comparisons. From this validation, CALMIM credibly simulates emissions compared to field-measured values spanning several orders of magnitude.

Table 2.

Averages by cover type for field-measured and CALMIM-modeled emissions (g CH4 m–2 d–1) for all field comparisons conducted in this study. DOI: https://doi.org/10.1525/elementa.2020.00050.t2

Field MeasurementsCALMIM Modeling Results
Cover TypenMeannWith CH4 OxidationnWithout CH4 Oxidation
  g CH4 m–2 d–1 g CH4 m–2 d–1 
BC (biocover) 13 8.4 (6.6) 24 9.46 (26.7) 24 130 (41) 
Daily 14 8.9 (9.1) 24 8.7 (2.7) 24 11 (3.7) 
Final 44 40 (48) 96 21 (45) 96 95 (60) 
Final-HDPE 18 2.8 (4.9) 24 0.000017 (0.000048) 24 0.021 (0.0084) 
Intermediate 140 (110) 24 67 (69) 24 190 (85) 
Field MeasurementsCALMIM Modeling Results
Cover TypenMeannWith CH4 OxidationnWithout CH4 Oxidation
  g CH4 m–2 d–1 g CH4 m–2 d–1 
BC (biocover) 13 8.4 (6.6) 24 9.46 (26.7) 24 130 (41) 
Daily 14 8.9 (9.1) 24 8.7 (2.7) 24 11 (3.7) 
Final 44 40 (48) 96 21 (45) 96 95 (60) 
Final-HDPE 18 2.8 (4.9) 24 0.000017 (0.000048) 24 0.021 (0.0084) 
Intermediate 140 (110) 24 67 (69) 24 190 (85) 

Values are given as mean (standard deviation).

Figure 1.

Graphical comparison of CALMIM-modeled emissions compared to field-measured emissions by cover type. All values g CH4 m–2 d–1. Cover types include final cover, final cover with HDPE geomembrane, intermediate (Int), daily, and engineered biocover (BC; includes all engineered landfill cover systems to enhance CH4 oxidation). See also Table 2. Panel A represents the CALMIM prediction without oxidation (worst case) compared to the field measurements. Panel B is the more realistic comparison of the CALMIM results with oxidation compared to the field measurements. For both panels, the 1:1 line represents modeled output equal to the field measurement. DOI: https://doi.org/10.1525/elementa.2020.00050.f1

Figure 1.

Graphical comparison of CALMIM-modeled emissions compared to field-measured emissions by cover type. All values g CH4 m–2 d–1. Cover types include final cover, final cover with HDPE geomembrane, intermediate (Int), daily, and engineered biocover (BC; includes all engineered landfill cover systems to enhance CH4 oxidation). See also Table 2. Panel A represents the CALMIM prediction without oxidation (worst case) compared to the field measurements. Panel B is the more realistic comparison of the CALMIM results with oxidation compared to the field measurements. For both panels, the 1:1 line represents modeled output equal to the field measurement. DOI: https://doi.org/10.1525/elementa.2020.00050.f1

Table 2 summarizes CALMIM-modeled comparisons to field data by cover type. The averages for measured values across all the field studies were 8.4 ± 6.6, 8.9 ± 9.1, 40 ± 48, 2.8 ± 4.9, and 140 ± 110 g CH4 m–2 d–1 for the biocover, daily, final, final-HDPE, and intermediate covers, respectively. Except for the final-HDPE cover, CALMIM-modeled predictions are statistically equivalent to field measurements when examined by cover type across all locations (Table 2). It seems likely that edge leakages and other construction issues may be affecting the site-specific performance of some HDPE final covers. In general, by directly comparing CALMIM modeled results with CH4 oxidation to the measured emissions, the model accurately predicted the measured surface emissions in 74.4% (125 out of 168 comparisons) while underpredicting 13 comparisons (7.7%) and overpredicting 30 comparisons (17.9%). When assessing the accuracy of the model by cover type as illustrated in Figure 1 and Table 2, one can see a systematic under-prediction by the model for high-density polyethylene (HDPE) final cover scenarios without CH4 oxidation (e.g., when turned off for CALMIM modeling). This suggests that the modeling within CALMIM for HDPE geomembranes could benefit from improvements. However, CALMIM was developed as a soil transport model and this was not unexpected. Overpredictions for the final and daily cover comparisons without soil CH4 oxidation could be due to site-specific deviations from assumed default soil gas boundaries at the base of cover (55% CH4 assumed for all final cover scenarios and 0.3% CH4 for daily cover; Table S1)—however, site-specific soil gas profile data were not available to improve these estimates. Previous literature has demonstrated that soil gas CH4 at the base of the cover can be 50%–60% in the absence of engineered biogas recovery but can drop into the ppm (v/v) range under efficient biogas extraction, in some cases resulting in negative CH4 emissions (uptake of atmospheric CH4 by landfill cover soils with high oxidation capacities; Bogner et al., 1997; Klusman and Dick, 2000). Also, please note that CALMIM does not currently accommodate “negative” CH4 emissions. In general, CH4 emissions from landfills normalized by area (g CH4 m–2 d–1) can vary by many orders of magnitude and may also be influenced secondarily by intermittent meteorological events (e.g., Rees-White et al., 2019). In practice, sites with engineered gas recovery already adjust field vacuums to respond to such events; therefore, we suggest that a broader systematic examination of cover-specific field data for soil gas CH4 at the base of cover over a variety of recovery system vacuums and meteorological conditions (e.g., barometric pressure) at numerous field sites could be used to further improve simulations.

Overall, the field results match well with CALMIM predictions as seen in previous studies (Figure S3). However, we note that some chamber-based studies targeted “hot-spots” (with visual surface cracks) for chamber placement (i.e., Li et al., 2020). Chamber methods target diffusive flux and should not be deployed where advective flux could dominate gaseous transport (Gebert and Groengroeft, 2006).

It should also be noted that if no data on cover thickness were available for specific sites, we assumed a minimum of 30 cm daily, 60 cm intermediate, and a 1.5 m final cover thicknesses. Although U.S. regulatory agencies typically specify a 30 cm daily cover, a 60–90 cm intermediate cover, and a 100 cm final soil cover or geomembrane composite cover, site-specific covers can frequently be thicker than permitted minima to reduce offsite odors, completely cover bulky waste, and (for intermediate covers) provide temporary bulk storage for stockpiled cover soils. Literature reports intermediate covers of 60–90 cm (Peyton and Schroeder, 1988), 1–5 m variability for a California intermediate cover (Zornberg et al., 2003), and 1–2 m variability for a New Hampshire final cover (Czepiel et al., 1996). California and other western U.S. states routinely use thick “water balance” or monolithic final covers (>1.5 m). Moreover, landfill sites that top out at high elevations (e.g., southern California canyon fills) can routinely use final cover thicknesses >2–3 m for better odor control, especially at sites near residential developments.

These comparisons indicate that CALMIM provides an acceptable order-of-magnitude estimate for typical monthly and annual emissions for site-specific landfill cover materials. Comparisons can be improved using site-specific soil gas CH4 and O2 at the base of each cover and annual weather, where such data exist. Importantly, using 30-year average climate data, CALMIM replicates the typical annual variability of emissions for the combination of gaseous transport and temperature/moisture-dependent CH4 oxidation rates. CALMIM thus provides an improved estimate of annual emissions based on the major processes that directly control CH4 emission rates—namely surface areas, thickness and physical properties of each cover material, the presence of engineered gas extraction, and seasonally variable CH4 oxidation rates in each cover. CALMIM simulations also yield spatially explicit information regarding the magnitude and temporal variability of emissions from specific sites and are thus suitable for GHG inventory estimates.

Some major conclusions from the CALMIM field validation are as follows:

  • CALMIM performed well with respect to order-of-magnitude emissions (Table 2; Figure 1). Even though the annual 30-year average climate data did a very good job of matching measured values, this could have been further improved by using site-specific annual weather data and site-specific soil gas CH4 and O2 data at the base of each cover (e.g., the concentration gradient directly drives diffusive transport).

  • The comparisons also illustrate, given documented temporal variability, the difficulty with direct comparisons between modeled values (e.g., CALMIM monthly averages reflecting 30-year climate data) and site-specific measurements reflecting both localized weather (daily precipitation/temperature data) and a wide variety of field techniques with variable duration and uncertainty. One might suggest that longer term climate data are more representative of multiyear trends for GHG inventory purposes. However, as discussed below, emissions variability over decadal time frames are becoming increasingly important with respect to mitigation strategies; thus, site-specific annual weather data are highly recommended for CALMIM input.

  • In general, CALMIM represents a significant step forward over IPCC methodologies as a rigorous process-based model for the estimation of site-specific emissions without onerous site-specific calibration requirements (see discussion in NASEM, 2018). We also note that, within the newer USEPA GHGRP (Greenhouse Gas Reporting Program, HH- designation), some empirical “scaling” factors for oxidation and emissions were introduced as “add-ons” to the historic IPCC (2006) methodology, ostensibly to improve site-specific estimates. However, these factors are empirically and linearly scaled to averages/ranges in the published literature and were never independently field-validated with respect to the numerical values proposed, nor their suitability for inventory purposes within the GHGRP.

  • In IPCC (2006), the largest sites (in terms of WIP) are the highest emitters as shown in Spokas et al. (2015). This is not consistent with field measurements and clearly excludes the consideration of major drivers (e.g., site operational characteristics including cover soils and biogas recovery; site climate).

  • In general, we conclude that direct comparisons between modeled and measured emissions for 34 global sites confirm CALMIM’s applicability for improving site-specific CH4 emission estimates via the inclusion of known drivers and proven mitigation measures (soil cover thickness, landfill gas recovery). A major deficiency of the IPCC methodologies is that it is not possible to directly quantify the effect of major mitigation strategies on the resulting CH4 emissions (e.g., thicker covers or increased density of biogas recovery).

3.2. Results for global latitudinal study

Table S3 summarizes annual climate data for each site (mean annual temperature [MAT]; mean annual precipitation [MAP]; average daily solar irradiance), and Figure S7 illustrates latitudinal distribution. Figure S12 compares CALMIM-modeled annual CH4 emissions with oxidation (final cover; no biogas recovery; three soil textures) to MAT and MAP. Figure S12A indicates a strong relationship for CALMIM-modeled predicted emissions to MAP (P < 2 × 10–16), with the overall relationship across all soil textures for a 1 m thick cover given by:

Site Emissions with CH4Oxidation (g CH4m2d1)= 350[48 * ln (MAP)].
2

The MAP is expressed in mm. This relationship suggests that precipitation is a primary driver: when precipitation is low (<500 mm), the predicted emissions are significantly higher across all temperatures (Figure S12). When MAP is >1,500 mm yr–1, emissions are negligible (Equation 2). Typically, there are lower emissions in equatorial locations, with predicted emissions increasing toward both poles. This trend correlates to some extent with MAT (Figure S12B). However, the geographical significance of MAT as a driving force is reduced by the dominant relationship with MAP (Equation 2; Figure S12A). We previously demonstrated a similar MAP relationship with emissions for California landfills, demonstrating substantially reduced emissions when MAP was >500 mm (n = 372; Spokas et al., 2015). Collectively, these results emphasize the critical importance of soil moisture and soil temperature on CH4 transport, oxidation, and net emission rates.

3. 3. Timing of field measurement campaigns to capture maximum emission events

Figure 2 illustrates another latitudinal sequence for 9 western hemisphere sites [60°N to –52.16°S], focusing on CALMIM-predicted emissions for a 1 m loam final cover: See caption for latitude/longitude coordinates. When timing field campaigns, it is important to capture the expected seasonal variability for the site. In general, for northern hemisphere landfill sites with strong seasonality (harsh winter conditions), many summer campaigns but few winter campaigns have been historically conducted. As shown in Figure 2, such winter campaigns would be expected to document higher CH4 emissions due to low CH4 oxidation rates under cold temperature conditions. Also note the expected seasonal reversal between northern and southern hemisphere sites, typically indicating minimal summer emissions due to optimum CH4 oxidation for sites with robust seasonality. However, for equatorial/tropical locations, the climate supports CH4 oxidation and lower “net” emissions during the full year due to favorable soil moisture and temperature conditions. Other major latitudinal trends are not immediately apparent due to individual site differences in the timing of seasonal precipitation and temperature, thus reinforcing the primary importance of localized climatic trends to drive site-specific responses.

Figure 2.

Variability in the predicted timing and magnitude of CALMIM-modeled surface emissions as a function of latitude, ranging from North to South (60°N, 50°N, 40°N, 9.95°N, 4.6°N, 0.7°N, –5.18°S, –21.66°S, and –52.16°S). Simulations assume 1 m loam final cover. The blue line indicates emissions with oxidation; the red line indicates emissions without oxidation. Note the overall decline in predicted emissions with methane oxidation (blue) as a function of latitude with equatorial locations supporting methane oxidation during the entire annual cycle. At high northern latitudes, winter emissions increased due to cold, dry winter conditions (Days 0–100 & 250–365); similarly, emissions increased during the southern hemisphere winter (Days 100–250). All values g CH4 m–2 d–1. DOI: https://doi.org/10.1525/elementa.2020.00050.f2

Figure 2.

Variability in the predicted timing and magnitude of CALMIM-modeled surface emissions as a function of latitude, ranging from North to South (60°N, 50°N, 40°N, 9.95°N, 4.6°N, 0.7°N, –5.18°S, –21.66°S, and –52.16°S). Simulations assume 1 m loam final cover. The blue line indicates emissions with oxidation; the red line indicates emissions without oxidation. Note the overall decline in predicted emissions with methane oxidation (blue) as a function of latitude with equatorial locations supporting methane oxidation during the entire annual cycle. At high northern latitudes, winter emissions increased due to cold, dry winter conditions (Days 0–100 & 250–365); similarly, emissions increased during the southern hemisphere winter (Days 100–250). All values g CH4 m–2 d–1. DOI: https://doi.org/10.1525/elementa.2020.00050.f2

One possible strategy going forward to minimize landfill CH4 emissions might be seasonally timed and metered surface irrigation at drier locations (or mulching) to promote higher soil moisture, preservation of surface vegetation, and higher CH4 oxidation rates. Also, although most landfill cover designs specify compacted fine-grained soils to retard infiltration and subsequent leachate generation, we focused in Figure 2 on what might be, from an engineering perspective, a “worst-case” condition (1 m loam soil). However, it is also important to consider the shape of the SMP curve: “Available” soil moisture for both plants and microorganisms occurs at lower volumetric moisture contents in soils with higher sand content. Correspondingly, a typical “sandy soil” curve is characterized by a rapid increase in SMP over a small range of volumetric moisture content (Hillel, 1998). Therefore, an important consideration, especially for semiarid locations, is that sandy cover soils can support viable soil methanotrophic populations at lower volumetric water contents than is typical for fine-grained soils.

3.4. Projected landfill CH4 emissions under extreme climate conditions

To examine the impact of extreme climate conditions on landfill CH4 emissions, CALMIM simulations were completed for 1 m clay, loam, and silt-textured cover materials for hypothetical landfills in the Lut Desert (Iran, hottest), Mawsynram (India, wettest), Omykan (Siberia, coldest excluding Antarctica), and the Atacama Desert (Quillague, Chile, driest excluding Antarctica). Figure 3 summarizes area-normalized CALMIM-modeled CH4 emissions (g CH4 m–2 d–1) with (blue) and without (red) oxidation over a typical annual cycle for the 3 soil texture scenarios: 1 m clay, 1 m loam, and 1 m silt. These were chosen to encompass typically permitted textures: the textural-triangle end-members clay and silt, as well as mixed-texture loam. The figure illustrates expected daily variability in emissions, highlighting especially the importance of soil moisture and moderate soil temperatures for oxidized emissions through a typical annual cycle. The wettest site (Mawsynram) demonstrated the highest percent oxidation of 89%–99.6% (Figure 3) and, hence, the lowest CH4 emissions. On the other hand, the other climate extremes (cold, dry, and hot) had substantially lower % oxidation, including virtually no soil methanotrophic activity in the driest location (<10–5% total oxidation; Figure 3; see Figure S13 for additional depth-based plots for soil oxidation activity in the clay, silt, and loam final covers). Several conclusions are noteworthy:

  • There is high variability in site-specific daily emissions due solely to climate. This refutes regulatory approaches that assign emission or oxidation values based generally on cover materials or climate “regions” (e.g., USEPA GHGRP) instead favoring a process-based site-specific approach such as CALMIM. As discussed above, NASEM (2018) recommended the development of 0.1 × 0.1° gridded CH4 inventories for specific sources, including landfills. At present, CALMIM uses 0.5 × 0.5° 30-year average climate data in the default scenario but is also capable of using finer scaled site-specific annual weather data or future climate projections such as the CMIP5 scenarios (see Section 3.5).

  • There are several orders of magnitude variability in emissions (g CH4 m–2 d–1) across the climate extremes for the 3 selected cover soils. This variability reflects the major drivers for emissions (climate; site operational factors such as cover soils; and, in this case, lack of biogas recovery), refuting the major driver for emissions in the current IPCC methodologies, namely mass of WIP (Spokas et al., 2015). Note that the Atacama Desert site had negligible oxidation rates (Figure 3) although limited lower soil profile oxidation activity during the entire annual cycle. The Lut Desert site had notable early spring (shallow) oxidation, whereas the Siberia site had some late summer oxidation activity (Figures 3 and S13). Only the wettest Mawsynram site in India had significant oxidation activity throughout the cover soil profile during the entire annual cycle and, hence, the lowest CH4 emissions (Figures 3 and S13).

  • The soil texture dependence on soil oxidation is not as critical as the microenvironment (soil temperature and moisture), as seen in the similarity of the “percentage of time” oxidizing profiles for the different textures (Figure S13). At the driest and coldest sites (Quillague and Omykan, respectively) as well as the hottest site (Lut Desert), the main zone of methane oxidation activity is found deeper in the soil profile (>30 cm) since this is where conditions would support methanotrophic activity. At the wettest site (Mawsynram), the highest CH4 oxidation activity occurs in the shallow 0–25 cm depth interval. It should also be mentioned that CALMIM realistically assumes a saturated water vapor phase within the landfilled waste.

  • Soil moisture is critical to achieve optimum oxidation rates. Note that the wettest scenario had consistently higher oxidation rates and hence lower emissions during the annual cycle. This reinforces previous literature (i.e., Spokas and Bogner, 2011; Bian et al., 2021, Figure S2) that attention to fluctuating weather and cover soil moisture (potential) can significantly improve landfill CH4 emissions estimates.

Figure 3.

CALMIM-modeled CH4 emissions for hypothetical landfills in global extreme climate locations (Lut Desert [hottest], Mawsynram [wettest], Oymakon [coldest], and Quillague [driest]; see Table 1 for climate specifics) for 1 m clay, loam, and silt final covers, no vegetation, no biogas recovery, and default 30-year average weather. Modeled emissions (g CH4 m–2 d–1) with oxidation (blue) and without oxidation (red) over a typical annual cycle. Also shown at the top of each panel are the average annual emission rates (blue with oxidation; red without oxidation) and the cumulative total annual % CH4 oxidation (green). All values g CH4 m–2 d–1. DOI: https://doi.org/10.1525/elementa.2020.00050.f3

Figure 3.

CALMIM-modeled CH4 emissions for hypothetical landfills in global extreme climate locations (Lut Desert [hottest], Mawsynram [wettest], Oymakon [coldest], and Quillague [driest]; see Table 1 for climate specifics) for 1 m clay, loam, and silt final covers, no vegetation, no biogas recovery, and default 30-year average weather. Modeled emissions (g CH4 m–2 d–1) with oxidation (blue) and without oxidation (red) over a typical annual cycle. Also shown at the top of each panel are the average annual emission rates (blue with oxidation; red without oxidation) and the cumulative total annual % CH4 oxidation (green). All values g CH4 m–2 d–1. DOI: https://doi.org/10.1525/elementa.2020.00050.f3

3.5. Using CALMIM to predict future landfill CH4 emissions using CMIP5 climate change scenarios

Projected landfill emissions to 2099 are presented in Figure 4 for the selected hypothetical California sites with opposite projected weather trends under a high warming scenario (RCP 8.5): Site 1 (Eureka, wetter) and Site 2 (Lancaster, drier). For both sites, there were no statistically significant correlations between air temperature and surface emissions “without oxidation” at either site (Figures S14 and S15). However, for Eureka (Site 1), there were statistically significant negative correlations between air temperature (daily max/min) and CH4 emissions with oxidation for 2 textures, clay (R = –.82) and silt (R = –.86; see Site 1; Table S4). On the other hand, there were no statistically significant correlations between air temperature and surface emissions with or without oxidation at the Lancaster site (see Site 2; Table S4). There were also statistically significant negative correlations for emissions with precipitation across both sites (Figures S14 and S15), with a slightly stronger relationship at Lancaster (Site 2) (R range: –.82 to –.93] compared to Eureka (Site 1; R range: –.65 to –.73]. Generally, the simulation runs for emissions without oxidation across all soil textures demonstrated stronger correlations with precipitation than the corresponding runs with methane oxidation (Table S4).

Figure 4.

Temporal trends for site-specific California air temperature and precipitation to 2099 (CMIP5 CNRM-CM5 RCP8.5) paired with CALMIM-modeled CH4 emissions with and without oxidation (g CH4 m–2 d–1) for hypothetical landfill sites: Site 1 (Eureka, CA) and Site 2 (Lancaster, CA). Average annual air temperature (°C), cumulative annual precipitation (mm), and predicted CALMIM CH4 emissions with (blue) and without soil CH4 oxidation (red) are shown in separate panels versus year. These simulations assumed 1 m final cover (no vegetation or biogas recovery for clay, loam, and silt textures) and default soil gas concentration boundary conditions (Table S1). DOI: https://doi.org/10.1525/elementa.2020.00050.f4

Figure 4.

Temporal trends for site-specific California air temperature and precipitation to 2099 (CMIP5 CNRM-CM5 RCP8.5) paired with CALMIM-modeled CH4 emissions with and without oxidation (g CH4 m–2 d–1) for hypothetical landfill sites: Site 1 (Eureka, CA) and Site 2 (Lancaster, CA). Average annual air temperature (°C), cumulative annual precipitation (mm), and predicted CALMIM CH4 emissions with (blue) and without soil CH4 oxidation (red) are shown in separate panels versus year. These simulations assumed 1 m final cover (no vegetation or biogas recovery for clay, loam, and silt textures) and default soil gas concentration boundary conditions (Table S1). DOI: https://doi.org/10.1525/elementa.2020.00050.f4

Figure 5 illustrates modeled emission predictions for a series of scenarios where, first, the simulated precipitation for each year was used, followed by 3 scenarios where the simulated precipitation patterns for 3 specific years were fixed for each of the changing air temperature simulations for a 1 m clay final cover at Lancaster (Site 2). These results demonstrate that the precipitation variability is a significant contributing factor to alterations in CALMIM-predicted surface emissions, for example, there was no significant trend when the same precipitation sequence was designated across varying surface temperatures (|R| < .60, across all soil textures). There was also a significant decrease in the variability of the surface emissions when a fixed precipitation pattern was used. For predicted emissions for a clay cover soil without oxidation, the variability decreased from 14% with actual precipitation predictions to less than 1% with the fixed precipitation simulations (Figure 5). However, the variability for this cover increases drastically with soil oxidation “turned on” in CALMIM: Then the resulting trends indicate a relative 65% difference compared with <10% for the fixed precipitation simulations (Figure 5). Due to the reduced variability, there is no significant correlation with increasing temperature if precipitation is held constant over the time series. This suggests that projected air temperature increases for future climate change scenarios may not always result in statistically significant increases or decreases in landfill emissions. Conversely, for the Lancaster site, the model results indicate that precipitation variability will largely control future emissions using site-specific climate projections.

Figure 5.

Modeled annual CH4 emission trends to 2099 for the hypothetical Lancaster, CA site only, assuming a 1 m clay final cover. Far left panels labeled “Actual” show predicted maximum and minimum temperatures (CMIP5 CNRM-CM5 RCP8.5); predicted precipitation to 2099 (CMIP5 CNRM-CM5 RCP8.5); and CALMIM-predicted CH4 emissions (g CH4 m–2 d–1) with oxidation in blue and without oxidation in red. Panels to the right show comparative simulations using the same predicted air temperatures but with precipitation fixed to selected future years (2043, 2060, and 2096). DOI: https://doi.org/10.1525/elementa.2020.00050.f5

Figure 5.

Modeled annual CH4 emission trends to 2099 for the hypothetical Lancaster, CA site only, assuming a 1 m clay final cover. Far left panels labeled “Actual” show predicted maximum and minimum temperatures (CMIP5 CNRM-CM5 RCP8.5); predicted precipitation to 2099 (CMIP5 CNRM-CM5 RCP8.5); and CALMIM-predicted CH4 emissions (g CH4 m–2 d–1) with oxidation in blue and without oxidation in red. Panels to the right show comparative simulations using the same predicted air temperatures but with precipitation fixed to selected future years (2043, 2060, and 2096). DOI: https://doi.org/10.1525/elementa.2020.00050.f5

Our modeling to date indicates that the alteration in precipitation is the major driving force affecting landfill CH4 emission in future climate scenarios. This finding is in contrast to projected climate change impacts on plant productivity (Lobell and Burke, 2008) as well as wetland emissions (Zhang et al., 2017), where temperature dependency is hypothesized as the primary driving force for future emissions projections under climate change scenarios. However, our projections do agree with previous studies focusing on forest soil CH4 uptake due to climate change, which cite soil moisture as the driving variable (i.e., Liu et al., 2019). Thus, there is still an urgent need to improve and expand process-based models to further elucidate the impact of climate change on specific soil microbial ecosystems (Cavicchioli et al., 2019; Jansson and Hofmockel, 2020). Similar to projected plant growth responses to climate change (Saleem et al., 2019), future alterations in landfill CH4 emissions are also directly tied to soil microbial system responses via methanotrophic oxidation.

3.6. Impact of soil texture on future landfill CH4 emissions

Figure 6 (top) illustrates trends in the year 2058 for surface CH4 emission with and without oxidation at the Lancaster site as a function of sand content, illustrating dependency on soil texture (Table S5). As clearly seen in Figure 6, there are drastic differences in predicted emissions when microbial methane oxidation is considered in the simulation. Predicted emissions without methane oxidation average 140 ± 39 g CH4 m–2 d–1 across all textures; these significantly reduce to 31 ± 23 g CH4 m–2 d–1 across all soil textures when CH4 oxidation is considered. This represents an average methane oxidation potential of 77.5% if averaged across all soil textures. Notably, this value is significantly higher than the 10% currently allotted for landfill CH4 oxidation in most national GHG inventory protocols (IPCC, 2006, 2019), itself derived from the first field-based study addressing annual landfill CH4 oxidation at a New Hampshire (US) landfill (Czepiel et al., 1996).

Figure 6.

Impact of soil texture on CALMIM-predicted future (year = 2058) CH4 emissions (g CH4 m–2 d–1) for the hypothetical Lancaster, CA site. Figure relies on simulated climate from high future emissions scenario (CMIP5 CRM-CM5 RCP8.5). Panel A plots CALMIM-modeled 2058 CH4 emissions with (blue) and without (red) oxidation vs. percent sand in the cover soil. Panel B plots % CH4 oxidation (e.g., % of total “unoxidized” emissions; Equation 1) versus percentage of clay in cover soil for the range of soil textures simulated. DOI: https://doi.org/10.1525/elementa.2020.00050.f6

Figure 6.

Impact of soil texture on CALMIM-predicted future (year = 2058) CH4 emissions (g CH4 m–2 d–1) for the hypothetical Lancaster, CA site. Figure relies on simulated climate from high future emissions scenario (CMIP5 CRM-CM5 RCP8.5). Panel A plots CALMIM-modeled 2058 CH4 emissions with (blue) and without (red) oxidation vs. percent sand in the cover soil. Panel B plots % CH4 oxidation (e.g., % of total “unoxidized” emissions; Equation 1) versus percentage of clay in cover soil for the range of soil textures simulated. DOI: https://doi.org/10.1525/elementa.2020.00050.f6

Finally, there is also a significant maximum for CH4 emissions, corresponding to a minimum oxidation potential, across textures containing 30%–60% sand (Figure 6). There are higher oxidation percentages and lower emissions on each end of the % sand plot (Figure 6). If we ignore the “pure” clay texture soil, the remaining textures clearly show a statistically significant linear decrease in the percent oxidation as clay content increases (Figure 6, lower graph). The exact reason behind this relationship is not further investigated here, but it is likely due to textural limitations for atmospheric O2 diffusion into the cover soil to promote methane oxidation activity. Overall, these model results agree with other field (Boeckx et al., 1997), laboratory column (Gebert et al., 2011), and mathematical simulations (Sihi et al., 2020) on the impact of soil texture on soil methanotrophic activity.

3.7. Impact of cover thickness to minimize methane emissions

In general, across the many global soil types, contrasting climate regions, and management scenarios, an intermediate or final long-term cover should be a minimum of 1 m thick to achieve emissions at or below about 10 g CH4 m–2 d–1 (see Figure 7 for loam cover). However, for hot/dry and cold climate sites, for most soil types, 1 m is insufficiently thick to achieve such low emissions. As shown in Figure 7, CALMIM simulations indicate that considering a specific global location, there is an “optimum” cover thickness associated with minimum cover-specific emissions (<5 g CH4 m–2 d–1). That is, the specific cover soil must be sufficiently thick to promote seasonal oxidation throughout the cover profile (Yilmaz et al., 2021). As seen in Figure 7, for the more extreme northern and southern locations (60°N, 53°S), there are still predicted emissions >5 g CH4 m–2 d–1 (inclusive of oxidation) for 2.4 m thick cover soils (14 and 8 g CH4 m–2 d–1, respectively). However, at more equatorial position, there are negligible emissions for the same cover thickness (<1 g CH4 m–2 d–1). Thus, at 45°N, a cover thickness of almost 2 m is required to achieve emissions below 1 g CH4 m–2 d–1, whereas for equatorial locations (0°N), the required thickness to achieve similar annual emissions would be approximately 1.2 m.

Figure 7.

Comparison of CALMIM-predicted surface emissions (g m–2 d–1) for LOAM final cover soil with variable thickness (15–250 cm). BLUE: emissions with oxidation. RED: emissions without oxidation. Plot assumes hypothetical landfills at 60°N (–96.3°W); 45°N (–94.04°W); 17°S (–93.52°W), and 53°S (–72°W) using CALMIM default 30-year average climate data. For each panel, the green text summarizes average annual CH4 emissions without oxidation, with oxidation, and predicted total annual % oxidation. DOI: https://doi.org/10.1525/elementa.2020.00050.f7

Figure 7.

Comparison of CALMIM-predicted surface emissions (g m–2 d–1) for LOAM final cover soil with variable thickness (15–250 cm). BLUE: emissions with oxidation. RED: emissions without oxidation. Plot assumes hypothetical landfills at 60°N (–96.3°W); 45°N (–94.04°W); 17°S (–93.52°W), and 53°S (–72°W) using CALMIM default 30-year average climate data. For each panel, the green text summarizes average annual CH4 emissions without oxidation, with oxidation, and predicted total annual % oxidation. DOI: https://doi.org/10.1525/elementa.2020.00050.f7

What lessons can be learned from the CALMIM global validation and comparative simulations discussed herein? In general, CALMIM is the first comprehensive modeling tool that addresses temporal variations in cover-specific emissions resulting from the combined effects of soil texture, thickness, temperature, and moisture on CH4 transport, oxidation, and emission rates. The climate change simulations emphasize the importance of considering site-specific combinations of cover soils and thicknesses interacting with climate-driven annual variability in soil moisture and temperature. Because future emissions depend both on cover soil properties and future climate at a specific global location, there is no single or simple universal answer regarding how climate change will affect future landfill CH4 emissions. A thicker clay soil is not necessarily optimum: For some locations, coarser-grained sandy loam or sand soils may promote higher rates of oxidation due to their ability to retain microorganism-available moisture at very low volumetric water contents, as well as promote O2 diffusion to greater depth due to higher gas-filled porosity. Quite reasonably, for high Northern latitudes, if we apply CALMIM to future climate scenarios at specific sites, projected increases in soil oxidation rates under warmer climate change scenarios are expected to result in decreased landfill emissions. Conversely, increased emissions could occur at equatorial locations due to increasing temperatures above known oxidation optima. To date, CALMIM simulations have suggested that low annual emissions are generally associated with >500 mm precipitation yr–1 (this article and Spokas et al., 2015). Where natural precipitation now and in the future is insufficient to achieve high rates of oxidation, some potential management strategies might include limited moisture additions (ideally using recycled water sources) or surface mulches with higher water-holding capacity than existing cover soils, thus simultaneously promoting seasonal vegetation and reducing emissions.

Other site-specific strategies to reduce emissions include the voluntary use of thicker cover materials (greater than regulatory minimum requirements), climate-specific “biocovers” to optimize oxidation, early biogas recovery (including use of horizontal gas collection systems concurrent with filling), increased use of CH4 surface scans beyond current U.S. quarterly requirements (i.e., monitoring steep slopes using evolving drone technology and innovative CH4 sensors), and minimizing the active filling area, especially where older exhumed waste has been daylighted after removal of a previous intermediate cover for vertical expansion to a new cell (Figure S4; Cambaliza et al., 2017).

More broadly, we propose that science-based GHG inventory methodologies such as CALMIM are needed for site-specific, process-based, field-validated modeling approaches to more robustly quantify landfill CH4 emissions, as well as directly credit engineering and operational strategies known to mitigate emissions. As discussed herein, the shortcomings of historic landfill CH4 inventory approaches (e.g., IPCC, 1996, 2006, 2019; U.S. GHGI, GHGRP) include outdated assumptions/methods and lack of comprehensive field validation which have, to some extent, been justified for >2–3 decades by the need to “backcast” estimated emissions to 1990’s baselines. In order to realistically address current and future climate scenarios, updated modeling is required to focus more directly on emissions inclusive of soil oxidation, as opposed to reliance on a CH4 generation model applied to all global landfills. Moreover, considering the high temporal variability of oxidation rates in individual cover soil profiles, use of a single estimated “% oxidation” routinely applied to many sites is not recommended. Also, the routine use of actual CH4 recovery data should replace the use of a hypothetical “% CH4 collection efficiency.”

CALMIM can assist site-specific engineering decisions now and in the future, as well provide a field-validated process-based inventory tool which focuses on known engineering and climatic drivers to better quantify landfill CH4 emissions. Our independent comparisons to field data, latitudinal simulations, and climate change projections confirm CALMIM’s international applicability for realistic CH4 emissions estimates inclusive of seasonal oxidation. Importantly, emissions for a specific site are intimately related to important climate indicators (daily precipitation, daily min/max temperatures) regarding gaseous transport and oxidation in specific cover soils at global locations. Although CALMIM can readily be used with existing site-specific information and embedded default values for “conservative” GHG inventories, we recommend increased reliance on site-specific cover properties and annual weather data which drive temporal site-specific CH4 emissions. Currently, landfill covers are designed to comply with permitted “minimum thickness” and maximum hydraulic conductivity/aqueous permeability values. As cover thicknesses are typically greater than permitted minima (discussed above) and maximum permitted permeability does not infer specific textural information, neither measure adequately addresses the optimization of methanotrophy in specific cover soils to minimize emissions.

Indeed, for the future, considering our changing climate, broadly discussed and scientifically updated national and international approaches are critically needed for multiple anthropogenic CH4 emissions sources to improve inventories and underpin reporting at multiple spatial/temporal scales (see discussion in NASEM, 2018). For landfill emissions, CALMIM can assist site-specific engineering decisions now and in the future by providing a field-validated process-based inventory tool which focuses on known engineering and climatic drivers to better quantify landfill CH4 emissions. Our independent comparisons to field data, latitudinal simulations, and climate change projections confirm CALMIM’s international applicability for realistic CH4 emissions estimates inclusive of seasonal oxidation. Importantly, emissions for a specific site are intimately related to important climate indicators (daily precipitation, daily min/max temperatures) regarding gaseous transport and oxidation in specific cover soils at specific global locations. Finally, although CALMIM can readily be used with existing site-specific information and embedded default values for “conservative” GHG inventories, we recommend reliance on site-specific cover properties and annual weather data which drive temporal site-specific CH4 emissions. In light of recent field studies indicating that landfills can be significant contributors to diverse urban-scale CH4 emissions (Peischl et al., 2012; Peischl et al., 2013; Cambaliza et al., 2015), CALMIM can assist with reducing current uncertainties for site-specific landfill emissions via inclusion of temporal climate and operational strategies.

The CALMIM model is available at https://data.nal.usda.gov/dataset/calmim. Please note that detailed descriptions of landfill cover materials, soil gas data, and supporting literature for the individual U.S. and international sites used for this validation can be found in Appendix D (pp. 266–390) of the EREF report (https://erefdn.org/wp-content/uploads/2015/12/IPCC_Final_Report.pdf) cited as Bogner et al. (2014) in Table S2. The California Energy Commission final report is publicly available at https://ww2.energy.ca.gov/2013publications/CEC-500-2013-080/CEC-500-2013-080.pdf (validated 5 July 2020).

The CNRM-CM5 RCP 8.5 output data were downloaded from the Cal-Adapt Data Server API (https://cal-adapt.org/data/download/). The WCRP CMIP5 multi-model datasets are available at https://esgf-node.llnl.gov/projects/cmip5/.

All relevant data are within the paper and its Supporting Information files.

The supplemental files for this article can be found as follows:

Text S1–S4; Figures S1–S15; and Tables S1–S5 in supplemental Word DOCX file.

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 Department 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, and D. Peterson.

  • UIC students M. Corcoran, P. Pilosi, P. Roots, and T. Badger.

  • Cooperation and discussions with:

    • Florida State University, especially J. Chanton and T. Abichou.

    • Waste Management, Inc., especially R. Green and G. Hater.

    • Advanced Disposal, Inc., formerly Veolia Environmental Services/Solid Waste, Inc. (US).

    • Veolia Environnement (FR), especially Y. Moreau, T. Lagier, M. Morcet, C. Aran, and A. Babillote.

    • University of Agricultural Sciences/Vienna, especially M. Huber-Humer.

    • University of Hamburg (DE), especially J. Gebert.

    • University of the Witwatersrand (SA), especially J. Morris (now Geosyntec).

    • Danish Technical University, especially C. Scheutz and P. Kjeldsen.

    • Melbourne University, especially S. Yuen, D. Chen, J. Sun, and M. Asadi.

    • Purdue University, especially M. Cambaliza and P. Shepson.

We further acknowledge the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and the WCRP’s Working Group on Coupled Modelling (WGCM) for making available the WCRP CMIP5 multi-model dataset used for the future climate projections in this article. Support of this dataset is provided by the Office of Science, U.S. Department of Energy. The USDA is an equal opportunity employer. Mention of trade names or commercial products in this publication is solely for the purpose of providing specific information and does not imply recommendation or endorsement by the U.S. Department of Agriculture.

We gratefully acknowledge the financial support of the Environmental Research & Education Foundation (EREF) under UIC grant #G5534-555200 and UIC subcontract agreement #2010-03400-01-00 with the U.S. Department of Agriculture, Agricultural Research Service. This research was supported in part by the U.S. Department of Agriculture, Agricultural Research Service. The findings and conclusions in this publication are those of the author(s) and should not be construed to represent any official USDA or U.S. Government determination or policy. Some of the sites were the subject of advanced degree programs at diverse institutions and we benefitted from dissertations, peer-reviewed publications, and direct contact with researchers. Initial support for the CALMIM project was provided by the California Energy Commission (CEC) Public Interest Energy Research (PIER) Program during 2007–2010 (G. Franco).

The authors declare that they have no competing interests.

  • Contributed to conception and design: KS, JB, MC.

  • Contributed to acquisition of data: KS, JB, MC.

  • Contributed to analysis and interpretation of data: KS, JB, MC.

  • Drafted and/or revised the article: KS, JB, MC.

  • Approved the submitted version for publication: KS, JB, MC.

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How to cite this article: Spokas, KA, Bogner, J, Corcoran, M. 2021. Modeling landfill CH4 emissions: CALMIM international field validation, using CALMIM to simulate management strategies, current and future climate scenarios. Elementa: Science of the Anthropocene 9(1). DOI: https://doi.org/10.1525/elementa.2020.00050

Domain Editor-in-Chief: Steven Allison, University of California, Irvine, CA, USA

Associate Editor: Asmeret Asefaw Berhe, Department of Geological Sciences, University of California, Merced, CA, USA

Knowledge Domain: Ecology and Earth Systems

This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License (CC-BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. See http://creativecommons.org/licenses/by/4.0/.

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