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Frontiers in Environmental Science ·

Volume 14 - 2026 | https://doi.org/10.3389/fenvs.2026.1960703

Methane consumption by a landfill cover soil under variable soil moisture and temperature conditions

  • Christina Lam1
  • Mehdi Ramezanzadeh1
  • Stephanie Slowinski1
  • Olivia Penn1
  • Laura A. Hug2,3
  • Philippe Van Cappellen1,3
  • Fereidoun Rezanezhad1,3*corresponding author
  1. 1Ecohydrology Research Group, Department of Earth and Environmental Sciences, University of Waterloo, Waterloo, ON, Canada
  2. 2Department of Biology, University of Waterloo, Waterloo, ON, Canada
  3. 3Water Institute, University of Waterloo, Waterloo, ON, Canada
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Abstract

Landfills are one of the largest anthropogenic sources of methane (CH 4), and hot-spots of CH 4 emissions in landfill cover soils can enrich microbes that oxidize CH 4 to carbon dioxide (CO 2). CH 4 oxidation rates are modulated by multiple variables including soil moisture and temperature, although the interactive effects of these factors on CH 4 oxidation rates have not been well-studied. Here, we conducted closed-headspace batch incubations with cover soil from a former landfill in Ontario, Canada to measure the variations in CH 4 consumption and CO 2 efflux rates associated with combined variations in soil moisture and temperature. Soil samples were incubated under a factorial design of 5 soil moisture contents (20%, 40%, 60%, 80%, and 100% water-filled pore space; %WFPS), and 4 temperatures (1, 15, 25, and 35 °C). At each temperature and moisture combination, CH 4 was spiked into the headspace through multiple consecutive injections, and headspace CH 4 and CO 2 concentrations were measured to calculate CH 4 consumption and CO 2 efflux rates. The maximum CH 4 consumption rate was observed at the moderate soil moisture and temperature conditions (330 ± 12.3 nmol h -1 g DW -1 at 60% WFPS and 25 °C), while the maximum CO 2 efflux rate was observed at the maximal WFPS and temperature conditions of the experiment (652 ± 85.0 nmol h -1 g DW -1 at 100% WFPS and 35 °C). A diffusion-reaction model was fit to the observed data to represent the effects of temperature and soil moisture on the CH 4 consumption and CO 2 efflux rates. The model predicted similar optimal conditions as those observed for both the CH 4 consumption and the CO 2 efflux rates. The modeling and experimental results show that the dominant controls on the optimal soil moisture for CH 4 consumption are the interactive effects of moisture limitation of microbial activity and of gas (CH 4 and oxygen) diffusion, whereas for the CO 2 effluxes, the dominant controls were the interactive effects of moisture limitation of gas (oxygen) diffusion and solute mobility. These results provide insight into how seasonal changes in soil moisture and temperature could impact CH 4 oxidation rates, and therefore also net CH 4 emissions, in landfill cover soils and other environments.

Keywords

1 Introduction

Waste management makes up ∼18% of the global anthropogenic methane (CH4) emissions (Bogner et al., 2008; Scharff et al., 2024; Scheutz et al., 2009), in which municipal solid waste in landfills represent 60% of waste sector CH4 emissions (Gillespie et al., 2024; Mor et al., 2024). Landfills can be sources of CH4 through the anaerobic decomposition of organic waste, which can diffuse through the landfill cover soil surface to the atmosphere (Börjesson and Svensson, 1997; Gebert et al., 2022; Themelis and Ulloa, 2007). As a result, there can be localized sites of concentrated CH4 emissions, or “hot-spots”, which are important to consider due to their greater relative contribution to CH4 released into the atmosphere (Delgado et al., 2022; Gebert et al., 2022; Wu et al., 2023). These hot-spots can enrich methanotrophic communities capable of oxidizing CH4 to carbon dioxide (CO2), which is less potent as a greenhouse gas (Hanson and Hanson, 1996; Hilger and Humer, 2003; Scheutz et al., 2009). Microbial CH4 oxidation has been widely studied as a biological approach to reducing CH4 emissions in landfills, and is known to be affected by soil moisture, temperature, CH4 concentration, and other factors (Boeckx and Van Cleemput, 1996; Park et al., 2005; Reddy et al., 2019).

Previous studies often examined the impact of soil moisture and temperature on CH4 oxidation activity independently. Low to moderate soil moisture contents between 10% and 35% (Boeckx and Van Cleemput, 1996; Einola et al., 2007; Park et al., 2005) and temperatures between 30 °C and 36 °C (Boeckx and Van Cleemput, 1996; Hilger and Humer, 2003; Spokas and Bogner, 2011) have been reported as optimal conditions for CH4 oxidation in landfill cover soils. The temperature sensitivity of CH4 oxidation can also be assessed using a Q 10 value, which is a temperature coefficient that represents the increase in reaction rate given a 10 °C increase in temperature (Davidson and Janssens, 2006; Scheutz et al., 2009; Winkler et al., 1996). Biological processes of soils typically have Q 10 values of approximately 2–3 (Byun et al., 2021; Davidson and Janssens, 2006), and studies on temperature sensitivity of CH4 oxidation in landfill cover soils have reported Q 10 values from 1.7 to 8.4 (Boeckx and Van Cleemput, 1996; Börjesson et al., 2004; Einola et al., 2007; Park et al., 2005; Scheutz et al., 2009). However, the impact of soil moisture and temperature together on CH4 oxidation in landfill cover soils has been understudied. Einola et al. (2007) and Whalen and Reeburgh (1996) are a few studies that also assessed the impact of landfill soil moisture and temperature simultaneously, emphasizing that these factors are closely linked and it is necessary to understand their combined effects on CH4 oxidation.

In this study, the experimental conditions (temperature and soil moisture) were controlled in a series of soil incubations to mimic the variations in soil moisture and temperature that might result from seasonal dynamics and intensifying climate change. The consumption rate of CH4 and the efflux rate of CO2 were measured in batch experiments under variable soil moisture (20%, 40%, 60%, 80%, and 100% water-filled pore space; %WFPS) and temperature (1, 15, 25, and 35 °C) conditions. We assessed the effects of soil moisture and temperature variations on CH4 consumption rates using an approach whereby multiple injections of CH4 were supplied consecutively. The separate injections of CH4 were meant to progressively saturate the soil and assess the rate of CH4 consumption with each subsequent injection. Headspace gases were collected following injections of CH4 to estimate the rate of CH4 consumption, as well as CO2 efflux, across each soil moisture and temperature combination. In addition, Q 10 values were calculated to assess the temperature sensitivity of CH4 consumption and CO2 efflux rates, and a diffusion-reaction model was developed to represent the processes controlling CH4 consumption and CO2 production and the influence of temperature and moisture content on these processes. This study is unique given the simultaneous variation of soil moisture and temperature, in combination with the modelling simulations of CH4 consumption in a landfill soil.

2 Materials and methods

2.1 Soil collection, preparation, and characterization

Landfill cover soil was collected from a former municipal landfill in Southern Ontario, Canada, which is now used as a city park. The soil samples were collected from the soil surface to a depth of approximately 10 cm, air dried, sieved to less than 4 mm, and biomass was removed before homogenization to minimize any physical heterogeneities between samples (Fairbairn et al., 2023). Following the preparation, the soil was stored at 4 °C in an environmental chamber (Percival I-41NL XC9) prior to starting the incubations. Soil aliquots were used to determine initial moisture content (θ), porosity (ϕ), bulk density (ρ *b*), and solid-phase chemical composition using standard procedures. The porosity and bulk density of the soil samples were determined gravimetrically from the saturated mass, the oven-dried mass (24 h at 105 °C), the original sample volume, and an assumed particle density of 2.65 g cm-3, following the methods of Boelter (1972) and Gardner (1986). The bulk density and porosity were 1,230 kg m-3 and 0.32, respectively. The soil organic carbon (OC) and total nitrogen (TN) were measured on a CHNS Carbo Erba analyzer (method detection limit, MDL: 1% by mass). OC and TN were determined as 8.6% and <1%, respectively. The soil’s particle size distribution was analyzed using the pipette method (detection limit of 0.2% on a dry weight basis for sand, silt, and clay fractions) (Gee and Bauder, 1986), and was determined as 63.36% sand, 25.07% silt and 11.57% clay, indicating that the soil was a fine sandy loam.

2.2 Soil incubation

A series of incubation experiments were conducted by incubating soils of different imposed moisture contents (20%, 40%, 60%, 80%, and 100% water-filled pore space; %WFPS) at selected temperatures (1, 15, 25, and 35 °C). A glass bottle for each of the five soil moisture contents was prepared for incubation at the four temperatures, for a total of 20 experimental bottles to examine CH4 consumption rates at each soil moisture and temperature combination (Figure 1).

A schematic of the incubation at different temperatures **(A)**, CH<sub>4</sub> injection procedure **(B)**, and a sample graph showing the headspace concentrations of CH<sub>4</sub>, indicating consumption of the injected CH<sub>4</sub> during the incubation **(C)**.
Figure 1. A schematic of the incubation at different temperatures (A), CH4 injection procedure (B), and a sample graph showing the headspace concentrations of CH4, indicating consumption of the injected CH4 during the incubation (C).

Aliquots of 108 g of homogenized and air-dried soil were packed into 240 mL amber bottles (Fisherbrand Amber Boston Round Glass Bottles) with an open-top cap (Thermo Scientific I-Chem Open-Top Septa cap) and 22 mm Teflon septa (Chromatographic Specialties), leaving ∼120 mL of headspace gas volume in the experimental bottles. Moisture content was adjusted with an aqueous solution prepared to closely match the pH, electrical conductivity, and ionic composition of rainwater in the field site, by dissolving NaCl (4.5 μM), KCl (1.2 μM) (NH4)2SO4 (23 μM), Ca(NO3)2 (15 μM), and Mg(NO3)2 (5 μM) salts in ultrapure deionized water. This artificial rainwater was intended to minimize disturbances to the native microbial community upon large changes in porewater chemistry that can cause osmotic or pH shocks (Halverson et al., 2000; Killham, 1985). The soil moisture levels were achieved by adding 2.1, 9.6, 17.2, 24.9, and 32.5 mL of the artificial rainwater to the soil and homogenizing the sample. The corresponding moisture levels were approximately 20%, 40%, 60%, 80% and 100% WFPS (Figure 1). The moisture contents as the percentage WFPS were calculated from the gravimetric moisture, bulk density, porosity and the amounts of water added, plus the previously determined residual water remaining in the air-dried soil.

Two additional samples of each moisture and temperature condition were prepared for use as controls. One set of samples was used as a “soil, no CH4” control, in which the sieved soil was prepared and incubated in the same manner, then received injections of lab air in the same volumes in place of CH4 (see 2.3). The other set of samples was used as the “abiotic” control, in which sieved soil was autoclaved three times over 5 days at 121 °C and 15 psi for 1 h. The autoclaved soil was further oven-dried at 100 °C for 2 days, prepared and incubated in the same manner, then received injections of CH4 in the same manner as the experimental bottles (see 2.3). All bottles, solutions, and equipment for the abiotic controls were also autoclaved before use at 121 °C and 15 psi for 30 min.

The bottles of soil were prepared to the appropriate moisture content, then placed in an environmental chamber incubator (Percival I-41NL XC9) where air temperature was controlled at 1, 15, 25, and 35 °C. All bottles of soil were placed in the environmental chamber incubator approximately 3 days before CH4 injections and gas flux measurements to allow for equilibration of the soil to the temperature. The bottles were left open for this equilibration period to allow for the input of oxygen into the bottles, then capped prior to the gas sampling. The incubation period of the bottles with the CH4 (or lab air for the “soil, no CH4” control) in the closed headspace was 25 min.

2.3 CH4 injections

Caps were placed on the bottles in order to take gas samples from the headspace and estimate consumption of CH4 by the soil following CH4 injections. First, a “rate test” was performed by injecting a known amount of 100% CH4 into an additional bottle prepared and incubated in the same manner, to assess the rate of CH4 consumption at that temperature and moisture in 5 min. For the rate test, a 30 mL sample of the headspace was taken out of the bottle to account for pressure differences from injecting CH4 gas and taking gas samples. CH4 was injected into the headspace for a target starting concentration of 1,250 ppm, and a t 0 gas sample was taken from the headspace immediately following the injection. 5 min after the initial injection, a t 1 gas sample was taken (Figure 1). The concentration of CH4 consumed within this 5-min period was used as the target concentration for the subsequent 4 injections of CH4 into the same bottle of the “multi-injection test”.

The “multi-injection test” was performed by introducing multiple injections of CH4 into the same bottle. The multiple injections were conducted to continually saturate the system with CH4 and observe whether there was a difference in consumption rate with subsequent injections of CH4, by amending the CH4 concentration to approximately the same level with each injection (Supplementary Figure S1). For the multi-injection test, the same initial steps were carried out as described above for the rate test. From there, a subsequent injection of the pre-determined amount from the rate test was prepared and injected immediately following the previous t 1 sample, then the next t 0 sample was taken immediately following the injection. The t 1 sample was taken after another 5 min, and this cycle was repeated for a total of 5 injections (1 initial and 4 subsequent injections) (Figure 1). The rate test and multi-injection test were repeated for all combinations of temperature and moisture conditions.

2.4 Gas flux measurements and temperature sensitivity (Q10)

The CH4 and CO2 gas concentrations of all collected headspace gas samples were measured using a Shimadzu Gas Chromatograph (Model GC-2014) equipped with a flame ionization detector and methanizer. Gas fluxes (F g a s, nmol h-1 g DW-1; grams dry weight) were obtained from the observed changes in gas concentration between the t 0 and t 1 samples of each injection after closure, calculated by:where Δ C g a s is the change in gas (CH4 or CO2) concentration over the closed incubation period, determined by subtracting the gas concentration at t 0 (immediately following injection) from the gas concentration at t 1 (5 min) in the experimental bottles (nmol mol-1), V is the volume of the headspace (L), R is the gas constant (0.082057 L atm K−1 mol-1), T is the temperature (K), Δ t is the time between injections (h), and M s o i l is the mass of the soil sample (g). The F g a s values calculated using CH4 and CO2 concentrations are hereafter defined as the CH4 consumption rate and CO2 efflux rate, respectively.

F g a s = Δ C g a s V R T Δ t M s o i l (1)

Q 10 values were calculated as a measure of temperature sensitivity of the soil samples in the experiment, using the average CH4 consumption or CO2 efflux rate up to the optimal temperature at a given moisture. This was calculated using an exponential rate increase:where r 2 is the CH4 consumption or CO2 efflux rate (nmol h-1 g DW-1) at the optimal temperature (T 2 in °C) and r 1 is the rate at 1 °C (T 1).

Q 10 = r 2 r 1 10 T 2 − T 1 (2)

2.5 Porewater geochemistry

Porewater was extracted from the initial soil sample and selected experimental samples (80% WFPS at 25 °C and 100% WFPS at 25 °C) upon termination of the experiment. These samples were analyzed for pH, dissolved organic carbon (DOC) and dissolved inorganic carbon (DIC). To extract porewater, ultrapure deionized water was added to the soil samples up to saturation (100% WFPS) if required, as some samples (i.e., the initial soil) were too dry to extract sufficient porewater for analyses. The wet soil was mixed manually, transferred into a 50 mL Teflon centrifuge tube (Nalgene®), and centrifuged at 4,000 rpm for 30 min and the supernatant porewater was filtered through a 0.45 µm pore size membrane filter (polypropylene syringe filters, VWR) into 50 mL polypropylene centrifuge tubes. Unfiltered porewater was analyzed for pH using handheld meters (LAQUA Twin meters, model Horiba B-213). Porewater concentrations of DOC and DIC were measured in the filtered porewater using a total organic carbon analyzer (Shimadzu TOC-LCPH/CPN; MDL: 4.6, 2.9, and 2.8 μmol L-1, respectively). The samples for DOC analysis were acidified to pH < 3 with 20 μL 1 M hydrochloric acid (HCl).

2.6 Modeling simulations

To simulate the variations in experiment’s measured CH4 consumption rates as a function of the variations in soil moisture content and temperature, a diffusion-reaction model was developed to represent the processes removing and producing CH4 in the headspace, as well as the effects of temperature and soil moisture on these processes. The mass conservation equation for CH4 in a water saturated porous medium with the main controls on CH4 mass conservation being the CH4 oxidation chemical reaction kinetics and CH4 diffusion between the headspace and porewater in one dimension is:with the effective diffusion coefficients in the liquid and gas phases, D e l , i and D e g , i, respectively, calculated using Equations 4–6: where θ l and θ g are the volume fraction of liquid and gas phases, c i is the concentration [ML-3] of the specimen i, c G , i is the gas phase concentration [ML-3], k G , i is the volatilization rate constant [-], D e l , i and D e g , i are the effective diffusion coefficients [L2 T-1] in the aqueous phase and gas phase, respectively, r j is the rate of j th reaction in a reaction network, and ν i , j is the stoichiometric coefficient of specimen i in the reaction j. ϕ e is effective porosity, and D L , i and D G , i are the diffusion coefficients [L2 T-1] in water and air, respectively. In Equation 6, D 0 , i is the temperature independent pre-exponential constant for substance i, E A , i is the activation energy for diffusion, and R is the gas constant (8.314 J mol-1 K−1). f t e m p is a function representing the temperature dependency of the reactions as follows (Jin and Kirk, 2018; Rosso et al., 1993):where T min, T max, and T o p t (°C) are the minimum, maximum, and optimum temperature values for the reaction.

∂ θ l c i ∂ t + ∂ θ g c G , i ∂ t + ∇ . − D e l , i + D e g , i k G , i ∇ c i = ∑ j ν i , j r j f t e m p , j (3)

D e l , i = θ l 10 3 ϕ e 2 D L , i (4)

D e g , i = θ g 10 3 ϕ e 2 D G , i (5)

D L / G , i = D 0 , i exp ⁡ − E A , i R T (6)

f t e m p = T − T max T − T min 2 T o p t − T min T o p t − T min T − T o p t − T o p t − T max T o p t + T min − 2 T (7)

To investigate the impact of soil moisture and temperature on CH4 consumption rates in a landfill cover soil, the CH4 consumption was represented by assuming that CH4 oxidation was the dominant reaction controlling CH4 production and consumption kinetics. The following reaction rate expression was used to represent the rate of CH4 oxidation (i.e., consumption):where r max ⁡ , C H 4 is the rate constant for CH4 oxidation [m o l m 3 sec], C C H 4 is the concentration of CH4 [m o l m 3], K C H 4 and K O 2 are the half saturation constant for CH4 and O2 [m o l m 3], and C O 2 is the concentration of O2 [m o l m 3]. In the modeling simulations performed in this study, the transport and biodegradation processes in the soil domain were evaluated by solving Equation 3 and the rate of CH4 consumption was evaluated by Equation 8. These sets of equations were solved using the COMSOL Multiphysics® software (version 5.6).

r C H 4 = − r max ⁡ , C H 4 C C H 4 K C H 4 + C C H 4 C O 2 K O 2 + C O 2 (8)

To investigate the impact of temperature and moisture on biodegradation processes and CH4 consumption, a microbial reaction network of 3 microbially-catalyzed chemical reactions was implemented in the numerical simulations. The list of reactions in the reaction network and their reaction rate constants fitted by the model are presented in Table 1. It should be noted that the half saturation constants in these tables are not fitting parameters and are based on literature values (Amos, 2006; Ramezanzadeh et al., 2023). The initial concentrations of each chemical simulated in the reaction network are provided in Table 2. The parameters of flow and reactive transport processes used in numerical simulations are provided in Table 3. The diffusion coefficients, Henry’s law constants, and diffusion activation energies for different substances in Table 3 are based on literature values. However, r max, T min, T max, and T o p t were the fitting parameters that were fit to the data.

TABLE 1. List of reactions included in the microbial reaction network and their reaction rate constants, half saturation constants, and other parameters.

Reaction nameChemical reactionReaction parameters
Rate constant, r max ⁡ , i [m o l m 3 . ⁡ sec]Electron donor half saturation constant, KED [m o l m 3]Electron acceptor half saturation constant, KEA [m o l m 3]
Methane oxidationC H 4 + 2 O 2 → C O 2 + 2 H 2 O1.8e-91.0e-63.41e-6
Glucose aerobic oxidationC 6 H 12 O 6 + 6 O 2 → 6 C O 2 + 6 H 2 O1.5e-70.0010.025
Glucose fermentationC 6 H 12 O 6 + 0.857 H 2 O → 0.857 C O 2 + 2.57 C 2 H 3 O 2 − + 1.714 H 2 + 2.576 H +2.6e-80.004-

TABLE 2. Initial concentrations of each species modeled in the microbial reaction network.

SubstanceInitial concentrationUnits
C 6 H 12 O 61.82[mol m-3]
DOC10.93[mol m-3]
C O 2 (g)11.82[g]
O 2 (g)8.94[g]
C H 4 (g) (before injection)0[g]

TABLE 3. Parameters of flow and reactive transport processes used in the numerical simulation.

ParameterValueUnitsReferences
K C H 4 H s0.034[mol m-3]Linstrom and Mallard (2001)
K C O 2 H s0.83[mol m-3]Linstrom and Mallard (2001)
K O 2 H s0.032[mol m-3]Linstrom and Mallard (2001)
D C H 4 g1.9e-5[m2 sec-1]Langenberg et al. (2020)
D C H 4 a q1.6e-9[m2 sec-1]Cussler (2009)
D C O 2 g1.1e-5[m2 sec-1]Haynes (2016)
D C O 2 a q1.2e-9[m2 sec-1]Cussler (2009)
D O 2 g1.6e-5[m2 sec-1]Haynes (2016)
D O 2 a q2.1e-9[m2 sec-1]Cussler (2009)
D G l u c6.14e-10[m2 sec-1]Stein and Litman (2014)
E A , C H 412.8[kJ.mol-1]Maharajh and Walkley (1973)
E A , C O 217.9[kJ.mol-1]Tamimi et al. (1994)
E A , G l u c16.74[kJ.mol-1]Koirala et al. (2022)
T min, T o p t, T max for CH4 oxidation−7, 30, 40[°C]-
T min, T o p t, T max for glucose oxidation−2, 47, 65[°C]-
ϕ e0.34---
ρ b1,230kg m-3-

3 Results

3.1 CH4 consumption rates

The maximal average CH4 consumption rate was 330 ± 12.3 nmol h-1 g DW-1 (±SD; standard deviation), observed at moderate soil moisture and temperature conditions (60% WFPS and 25 °C; Figure 2). The rate of CH4 consumption was consistently highest at 60% WFPS across all temperatures and follows a similar moisture pattern across the temperatures (Figure 2A). CH4 consumption was generally highest at 25 °C for all moistures except 20% WFPS, and there was also comparable CH4 consumption at 15 °C, especially at the moderate soil moistures between 40% and 80% WFPS (Figure 2B). There was less variability (∼27% difference) in the rates at 20% WFPS across different temperatures, while the effect of the different temperatures was more pronounced (∼125% difference for 80% WFPS) at the higher WFPS conditions (Figure 2B). Overall, CH4 consumption rates increased up to the optimal soil moisture or temperature condition, then decreased after the optimal condition. The rate of CH4 consumption did not show clear patterns with subsequent injections (Supplementary Figure S2A). For example, 20% WFPS at 1 °C exhibited a higher rate for injection 1, then decreased but remained constant for injections 2 to 5 (108% difference; Supplementary Figure S2A, S20% WFPS), whereas rates for 60% WFPS at 1 °C were relatively constant for injections 1 to 5 (13% difference; Supplementary Figure S2A, S60% WFPS).

Average CH<sub>4</sub> consumption rates at each temperature and soil moisture condition versus temperature **(A)** or versus % WFPS**(B)**. Error bars are the standard deviation of the 5 injections at a given temperature and soil moisture.
Figure 2. Average CH4 consumption rates at each temperature and soil moisture condition versus temperature (A) or versus % WFPS(B). Error bars are the standard deviation of the 5 injections at a given temperature and soil moisture.

3.2 CO2 efflux rates

The maximal average CO2 efflux rate of 652 ± 85.0 nmol h-1 g DW-1 was observed at the greatest soil moisture and temperature conditions of the experiment (100% WFPS and 35 °C; Figure 3). CO2 fluxes were highest at 80% WFPS in the 15 °C and 25 °C incubations, at 40% WFPS in the 1 °C incubations, and at 100% WFPS in the 35 °C incubation (Figure 3A). CO2 efflux was typically highest at 35 °C for the higher WFPS conditions from 40% to 100%, or 15 °C for 20% WFPS (Figure 3B). For moistures other than 20% WFPS, the CO2 efflux gradually increased with temperature up to 35 °C. The CO2 efflux magnitudes were generally lower than the CH4 consumption rate magnitudes, except for at 100% WFPS and 35 °C (Figures 2, 3). The rate of CO2 efflux did not show much variation with subsequent injections, but similar to CH4 consumption rates, a higher rate for injection 1 was observed at 20% WFPS for all temperatures, and at 1 °C for most moistures (Supplementary Figure S2B).

Average CO<sub>2</sub> effluxes at each temperature and moisture conditions versus temperature **(A)** or versus % WFPS **(B)**. Error bars are the standard deviation of the 5 injections at a given temperature and soil moisture. Note the different *y*-axis scale compared to Figure 2 for CH<sub>4</sub> consumption.
Figure 3. Average CO2 effluxes at each temperature and moisture conditions versus temperature (A) or versus % WFPS (B). Error bars are the standard deviation of the 5 injections at a given temperature and soil moisture. Note the different y-axis scale compared to Figure 2 for CH4 consumption.

3.3 Model simulation results

The experimental and model simulation results showed a similar moisture pattern for CH4 consumption (Figure 4A), with a goodness of fit or R-squared value of 0.71. The model underestimated the rate of CH4 consumption at 1 °C, while the other temperatures were generally aligned (Figure 4B). The simulation results indicate that the maximum CH4 consumption rate occurs at 60% WFPS and 24 °C, which is aligned with the maximal rates observed in the experimental results under the tested conditions (60% WFPS and 25 °C). The experimental and modelled CO2 effluxes were also in good agreement, with an R-squared value of 0.59, or 0.83 excluding the highest moisture and temperature condition (100% WFPS, 35 °C) where the experimental fluxes were much higher than those predicted by the model (Figures 4C,D). Overall, most of the model results followed a similar pattern as the experimental results, deviating the most at the high temperature and high moisture conditions.

Modelling simulation results of CH<sub>4</sub> consumption rate versus temperature **(A)** and versus soil moisture as % WFPS **(B)**, and modelling simulation results for CO<sub>2</sub> efflux versus temperature **(C)** and versus soil moisture as % WFPS **(D)**. The points represent experimental data, while the lines represent the model simulation results.
Figure 4. Modelling simulation results of CH4 consumption rate versus temperature (A) and versus soil moisture as % WFPS (B), and modelling simulation results for CO2 efflux versus temperature (C) and versus soil moisture as % WFPS (D). The points represent experimental data, while the lines represent the model simulation results.

3.4 Temperature sensitivity results (Q10)

The highest Q 10 value for CH4 consumption was 2.57 for 80% WFPS, while the lowest was 1.17 for 20% WFPS (Table 4; Figure 5A). Some Q 10 values for CO2 efflux were slightly higher than those of CH4 consumption (Table 4; Figure 5B), where the greatest Q 10 value for CO2 efflux from this incubation was 2.58 for 100% WFPS, and the lowest was 1.11 for 20% WFPS. Most Q 10 values for CH4 consumption and CO2 efflux were relatively close to 2, aside from the lowest moisture (20% WFPS). For both CH4 consumption and CO2 efflux, the highest Q 10 value occurred at approximately the same moisture condition as the optimal condition of those rates.

TABLE 4. Temperature sensitivity (Q 10) values calculated for CH4 consumption and CO2 efflux using the optimal temperature (T 2 in °C) and the rate at 1 °C (T 1) at each % WFPS. The number of data points used in the calculation of each Q 10 value are presented in Figure 5.

% WFPSQ 10 value (CH4 consumption)Q 10 value (CO2 efflux)
201.171.11
401.661.66
601.542.05
802.572.44
1001.702.58
Plots for CH<sub>4</sub> consumption **(A)** and CO<sub>2</sub> efflux **(B)** for each soil moisture condition. The dashed line denotes the experimental data points which were used in the *Q* <sub>10</sub> values calculated in Table 4.
Figure 5. Plots for CH4 consumption (A) and CO2 efflux (B) for each soil moisture condition. The dashed line denotes the experimental data points which were used in the Q 10 values calculated in Table 4.

3.5 Porewater geochemistry and carbon mass balance

The mass of DIC in the soil porewater was higher in the end-of-experiment 80% WFPS at 25 °C and 100% WFPS at 25 °C treatments compared to the initial soil (12.2 and 11.0 μmol versus 9.8 μmol, respectively). In addition, the mass of DOC in the soil porewater was higher in the end-of-experiment 80% WFPS at 25 °C and 100% WFPS at 25 °C treatments compared to the initial soil (80.3 and 77.6 µmol versus 51.1 µmol, respectively). The porewater pH was 7.4 in the initial soil, and 7.3 and 7.5 in the end-of-experiment 80% WFPS at 25 °C and 100% WFPS at 25 °C treatments. To provide insight into the contribution of CH4 consumption to headspace CO2 plus porewater DIC production, we compared the change in the mass of DIC with the change in the mass of headspace CO2 and in the mass of headspace CH4 for the two treatments for which porewater DIC was measured. In both treatments, the sum of the headspace and aqueous inorganic carbon (CO2 plus DIC) produced was within 10% of or higher than the mass of CH4 consumed. Across all the treatments, for the cases where the CO2 efflux rate was less than the CH4 consumption rate, the ratio of the CO2 production rate to the CH4 consumption rate ranged from 0.21 to 0.90.

4 Discussion

4.1 Optimal soil moisture and temperature for CH4 consumption

It is evident that both soil moisture and temperature had an effect on the CH4 consumption rate due to the presence of an optimal condition identified in this study, with the highest average rate of CH4 consumption (330 ± 12.3 nmol h-1 g DW-1) observed at moderate soil moisture and temperature conditions (60% WFPS and 25 °C; Figure 2). The rapid reduction in CH4 concentration from the headspace within 5 min for each injection indicated an active microbial community that was capable of consuming CH4 once supplied to the soil, likely attributed to the initial soil being enriched with a well-established methanotrophic community from a former landfill CH4 hot-spot (Ferguson et al., 2026). This was also supported by the abiotic control samples, where minimal reduction in the CH4 concentration relative to the experimental samples was observed over the same incubation period (Supplementary Figure S3).

The optimal soil moisture observed in this experiment (60% WFPS) is higher than reported from previous studies, as many observed the maximal rates of CH4 oxidation at a range of 10%–35% soil moisture (Bender and Conrad, 1995; Park et al., 2005). Low soil moistures are reported to be ideal for CH4 oxidation, as there is a greater proportion of pore space for the diffusion of CH4 and O2, the two substrates of CH4 oxidation, via gas-filled pore spaces (Gebert et al., 2022; Scheutz et al., 2009). As gases, both O2 and CH4 can diffuse throughout the soil faster in less saturated soils due to their higher solubility in the gas versus aqueous phase (Einola et al., 2007; Gebert et al., 2022; Hilger and Humer, 2003; Park et al., 2005; Whalen et al., 1990). At higher soil moistures, such as 100% WFPS, gas diffusion transitions to aqueous molecular diffusion, slowing the transport of CH4 and O2 into the soil and limiting CH4 consumption. Soil moisture also affects the connectivity of the soil aggregates and pore spaces, and thus the ability of the microbes to access dissolved substrates, where the air spaces become discontinuous with increasing water (Borowik and Wyszkowska, 2016; Gebert et al., 2022; Reddy et al., 2014). Microbial processes in soil require some amount of water for basic cell function, as very low moisture content could result in water stress and desiccation, leading to a decline in microbial activity (Einola et al., 2007; Manzoni et al., 2016; Park et al., 2005; Scheutz et al., 2009).

While lower moistures are reported in the literature as being optimal for CH4 oxidation, 60% WFPS is consistently the moisture content that yielded the highest CH4 consumption rate for all temperatures of the experiments in this study, and this is also in agreement with the modelling simulation results (Figure 4). 60% WFPS is still a moderate moisture, and for this soil, was the optimal point where there is sufficient moisture content for microbial activity and a sufficient percent of unsaturated pore volumes for gas exchange. This difference from literature may be due to the varying ways that soil moisture is expressed in literature (i.e., gravimetrically or volumetrically), as well as the effect that soil properties could have on moisture content, and thus gas exchange and CH4 oxidation potential. Some studies suggest representing soil moisture as water holding capacity or soil moisture potential for more standard comparison (Einola et al., 2007; Spokas and Bogner, 2011).

The optimal temperature for CH4 consumption was 25 °C (Figure 2), and while on the lower end of the optimal temperature range observed in literature, this has also been observed by studies including Bender and Conrad (1995) in natural soils and Hanson and Hanson (1996) in peat soils. Higher source CH4 environments exert a greater enzymatic control on microbial CH4 oxidation, resulting in more temperature-sensitive CH4 oxidation rates (Whalen and Reeburgh, 1996). In landfill environments, the reported optimal temperature for CH4 oxidation is typically higher, ranging from 30 °C to 36 °C (Gebert et al., 2022; Hilger and Humer, 2003; Scheutz et al., 2009). However, there was still considerable CH4 consumption observed outside this temperature range in this study, with other studies observing CH4 consumption at temperatures ranging from 1 °C to 40 °C (Einola et al., 2007; Park et al., 2005). The detection of CH4 consumption even at the lowest temperature tested indicates potential changes to the soil vertical profile and CH4-oxidizing species composition in the soil (Einola et al., 2007). The proposed optimal range of 20 °C–35 °C would be expected for methanotrophs, which are typically mesophiles that thrive in moderate temperatures (Hanson and Hanson, 1996). Temperature is a key factor for the kinetics of microbial reactions and can also affect gas solubility in water (Scheutz et al., 2009; Velasco et al., 2018).

For temperature sensitivity, the highest observed Q 10 value for CH4 consumption was 2.57 for 80% WFPS (Table 4; Figure 5A). Overall, the Q 10 values for CH4 consumption from this experiment are generally lower than other Q 10 values reported in literature, though relatively close to the typical value of 2 reported for biological processes (Davidson and Janssens, 2006). Q 10 values for CH4 oxidation have been observed to range from 1.9 to 4.1 (Boeckx et al., 1996; Scheutz et al., 2009), with some studies reporting values greater than 8 (Christophersen et al., 2000; Einola et al., 2007) from landfill cover soils. The lower Q 10 values from our data may be due to our study site being a former landfill, where the inherent microbial community of this soil may have lower CH4 consumption capacity than an active landfill cover soil regularly exposed to high CH4 concentrations (Boeckx et al., 1996). The conditions of the experiment may also have an impact, considering the lower relative CH4 concentration supplied to the headspace. For comparison, Park et al. (2005) reported Q 10 values of 2.57 and 2.69 using an initial headspace CH4 concentration in batch incubations of 400,000 ppm, demonstrating that CH4 consumption via CH4 oxidation is also affected by the concentration of CH4 supplied in the incubation (Bender and Conrad, 1995; Bogner et al., 1997).

4.2 Optimal soil moisture and temperature for CO2 efflux

For the CO2 efflux, the maximal average rate observed was 652 ± 85.0 nmol h-1 g DW-1 at 100% WFPS and 35 °C (Figure 3). Typically, CO2 effluxes are expected to be optimal at moderate soil moistures with lower CO2 effluxes at higher soil moistures because of the limitation of O2 diffusion and, thus, of aerobic microbial metabolisms (Byun et al., 2021; Fairbairn et al., 2023). This was the case for the temperature conditions other than 35 °C, where CO2 effluxes decreased at 100% WFPS (Figure 3A), demonstrating the optimal rates at moderate conditions. The high CO2 efflux observed at 100% WFPS in the 35 °C incubation could be due to anaerobic degradation of soil organic matter, leading to the production of CO2 at high temperatures (Chen et al., 2021; Fairbairn et al., 2023).

The optimal temperature for CO2 effluxes was 35 °C, which was within the expected range for methanotrophs (Hanson and Hanson, 1996; Reddy et al., 2019). The Q 10 values for CO2 efflux were slightly higher than those of CH4 consumption, indicating that this process was more temperature sensitive for most moistures (Table 4; Figure 5B). We observed lower Q 10 values for CO2 efflux associated with CH4 consumption compared to previous studies, as the greatest Q10 value from this incubation was 2.58. Einola et al. (2007) reported a range of 5.7–5.8 for CO2 production from their CH4 assays, though their study showed some of the highest Q 10 values for CH4 oxidation and CO2 production among studies from landfill cover soils. The different response of the CO2 effluxes is likely driven by other microbial groups, outside of the methanotrophs of this study.

4.3 CH4 consumption to CO2 efflux ratio

Given the reaction stoichiometry for CH4 oxidation, the ratio of CH4 consumed to CO2 produced during CH4 oxidation is 1 mol CH4 consumed:1 mol CO2 produced (Le Mer and Roger, 2001; Scheutz et al., 2009). Although the relative rates measured in the headspace of the CO2 effluxes and CH4 consumption observed in this study did not reflect this ratio, with CH4 consumption exceeding CO2 efflux at most combinations of soil moisture and temperature conditions (16 of 20 combinations), we still interpret that the CH4 consumption observed in the experiments was the result of microbial CH4 oxidation. This interpretation is supported by the abiotic controls, where CH4 concentrations remained stable and accumulated with subsequent injections of CH4 over the course of the incubation as expected, with minor decreases in concentration likely due to the collection of gas samples from the headspace for analyses (Supplementary Figure S3). In addition, the CO2 headspace concentrations (and therefore the CO2 effluxes) in the “soil, no CH4” control were much lower than in the experimental bottles where CH4 was added, providing additional evidence that the CO2 effluxes produced in the experimental bottles was attributable to the CH4 consumption via CH4 oxidation (Supplementary Figure S3).

Higher CH4 consumption rates compared to CO2 efflux rates have also been observed in other studies, where it has been attributed to the assimilation of CO2 into the microbial biomass (Megraw and Knowles, 1987; Whalen et al., 1990), accumulation of CO2 in the soil (Einola et al., 2007; Megraw and Knowles, 1987), or the dissolution of CO2 as DIC in the porewater (Ferdush and Paul, 2021; Ramnarine et al., 2012). Soil moisture, playing a greater role in these mechanisms, generally dominates through diffusive control of atmospheric CH4 oxidation when assessing soil moisture-temperature interactions under field conditions (Whalen and Reeburgh, 1996). Because the incubations used in this study were short incubations lasting approximately 25 min, it is unlikely that there was substantial CO2 consumption via CO2 assimilation into biomass within this time frame. Therefore, the lower CO2 efflux relative to CH4 consumption is possibly due to the accumulation of CO2 in the pore spaces which do not reach the headspace, or its presence as bicarbonate (measured as DIC, Supplementary Figure S5) in the porewater. Possibly, because just the headspace gas was collected from the bottles, those samples may not have captured the gas accumulated in the pore spaces. In addition, the increase in the mass of DIC concentration from the initial soil porewater to the end-of-experiment porewater (0.25 and 0.05 mM for 80% and 100% WFPS at 25 °C, respectively), suggests that the CO2 present in the headspace may have been converted to DIC in the porewater (Supplementary Figure S5). Comparing the mass of headspace and aqueous inorganic carbon produced (the sum of the porewater aqueous DIC and the headspace CO2 produced) with the mass of CH4 consumed (Supplementary Table S1) for the two treatments where DIC was measured indicates that sum of the DIC and CO2 produced exceeded the mass of CH4 consumed. The higher mass of headspace plus aqueous inorganic carbon produced compared to the mass of headspace CH4 consumed indicates that the inorganic carbon was produced by a combination of oxidative CH4 consumption and organic carbon respiration. This is also in agreement with the model simulations, which incorporated organic carbon respiration reactions, alongside the oxidative CH4 consumption, to explain the CO2 effluxes. Although it is not explicitly represented and therefore not shown in the modeling results, the model does also account for the partitioning of CO2 between the aqueous phase (as DIC) and the headspace gas. The increase in soil porewater DOC with time (Supplementary Table S1) also supports the interpretation that respiration was contributing to DIC plus CO2 production, given that this DOC produced by particulate organic matter hydrolysis and dissolution can be then used for respiration reactions. The near-neutral pH of the porewater samples provided further evidence that a fraction of the inorganic carbon produced by respiration and/or CH4 consumption could be present as DIC, supporting the idea that dissolution of DIC was possible (Ferdush and Paul, 2021).

Accumulation of CO2 in the pore spaces may be more likely for lower moisture contents as less of the pore space is filled with water, while dissolution of CO2 to DIC in the porewater may be more likely for higher moisture contents. Some studies have also reported the molar ratio between moles of CH4 consumed and CO2 produced, where a ratio of CO2:CH4 less than one indicates less CO2 produced than CH4 consumed. Einola et al. (2007) reported ratios between 0.49 and 0.93, and Megraw and Knowles (1987) reported a ratio of 0.27, which is in line with the ratio observed in our study of 0.21–0.90 for cases where the CO2 efflux rate is less than the CH4 consumption rate (16 of 20 combinations).

4.4 Model limitations

Although the results of this study advance the process-based understanding of how soil moisture and temperature interact to control CH4 consumption and CO2 efflux in landfill cover soils, the diffusion-reaction model developed here has some limitations. The reaction network was built with a primary focus on CH4 oxidation, and its treatment of anaerobic processes is correspondingly simplified: anaerobic organic matter degradation is represented only through glucose fermentation, and does not extend to further anaerobic mineralization pathways, that may become increasingly active as the soil approaches full saturation and O2 availability declines. Because these pathways are also strongly temperature-dependent, their omission would be expected to matter most at high temperature, consistent with the good agreement between the model and the observed CO2 effluxes at 100% WFPS across the lower temperatures tested and the model’s underestimation of CO2 efflux specifically at 100% WFPS and 35 °C (R-squared of 0.59 overall, improving to 0.83 when this point is excluded). The model also represents gas and solute transport through a homogeneous, effective-porosity medium rather than explicitly resolving soil structural heterogeneity, and does not explicitly simulate CO2-DIC equilibrium partitioning; both simplifications could further contribute to the model’s underestimation of CO2 efflux at 100% WFPS and 35 °C. Future refinement of the reaction network to include additional anaerobic mineralization pathways and explicit CO2 phase partitioning, together with sensitivity analyses to evaluate parameter uncertainty, would help improve the model’s predictive performance under this combination of high moisture and high temperature conditions.

5 Conclusion

This study assessed the combined effects of variations in soil moisture and temperature on CH4 consumption rates in landfill cover soil collected from a hot-spot of CH4 emissions at a former landfill. Maximum rates of CH4 consumption were observed at moderate soil moisture and temperature conditions (330 ± 12.3 nmol h-1 g DW-1 at 60% WFPS and 25 °C), and CO2 efflux at higher soil moisture and temperature conditions (652 ± 85.0 nmol h-1 g DW-1 at 100% WFPS and 35 °C). Overall, these results highlight that soil moisture and temperature are two important factors modulating microbial CH4 consumption via CH4 oxidation, and the net CH4 consumption observed is a result of the interactions of the different transport and microbial reaction processes occurring in the soil. The different optimal conditions highlight that there are different microbial groups responsible for the various processes, resulting in the observed fluxes. The results were further supported by the temperature sensitivity analysis (Q 10) and the diffusion-reaction model. The experimental results, combined with the modelling simulations, provide insight into the optimal conditions for microbial CH4 oxidation. These results can be further used in larger-scale modelling of CH4 oxidation in landfill cover soils and/or in the practical design of landfill cover soils.

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Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fenvs.2026.1960703/full#supplementary-material

Supplementary material Supplementary material at the source

Footnotes

Edited by: Marco Contin, University of Udine, Italy

Reviewed by: Juris Burlakovs, Riga Technical University, Latvia

Suaidah Rahim, University of Technology Brunei, Brunei

Statements

Conflict of interest

The authors declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

The author FR declared that they were an editorial board member of Frontiers at the time of submission. This had no impact on the peer review process and the final decision.

Funding

The author(s) declared that financial support was received for this work and/or its publication. The Mitigation of methane emission hot-spots from municipal landfills project was undertaken with the financial support of the Government of Canada’s Climate Action and Awareness Fund. Ce projet a été réalisé avec l’appui financier du gouvernement du Canada. We also acknowledge funding from the Canada Excellence Research Chair (CERC) program in Ecohydrology and Global Water Futures Observatories (GWFO) program funded by Canada Foundation for Innovation-Major Science Initiatives (CFI-MSI). CL was further supported by an NSERC Alexander Graham Bell Canada Graduate Scholarship.

Data availability

The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://doi.org/10.20383/103.01243.

Author contributions

CL: Visualization, Writing – original draft, Formal Analysis, Data curation, Methodology, Conceptualization, Investigation, Validation, Writing – review and editing. MR: Writing – review and editing, Conceptualization, Software, Methodology, Investigation, Visualization, Formal Analysis, Validation. SS: Investigation, Writing – review and editing, Visualization, Formal Analysis, Methodology, Data curation, Validation, Conceptualization. OP: Data curation, Investigation, Conceptualization, Writing – review and editing, Methodology, Visualization. LH: Investigation, Writing – review and editing, Funding acquisition, Visualization, Methodology, Validation, Project administration, Conceptualization, Supervision. PV: Conceptualization, Visualization, Resources, Investigation, Writing – review and editing, Methodology, Validation, Supervision. FR: Supervision, Investigation, Writing – review and editing, Conceptualization, Resources, Visualization, Project administration, Funding acquisition, Methodology.

Acknowledgements

We thank the Regional Municipality of Waterloo Waste Management Division for access to the field site where the soil was collected. We thank Shuhuan Li, Marianne Vandergriendt, Tara Ferguson, Katie Miller, Ben DePetris, and Melissa LeBlonc for their assistance with laboratory experiments and analyses.

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Citation

Lam, C., et al. (2026). Methane consumption by a landfill cover soil under variable soil moisture and temperature conditions. Frontiers in Environmental Science, Volume 14. https://doi.org/
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14 - 2026

Copyright

Copyright © 2026 Lam, Ramezanzadeh, Slowinski, Penn, Hug, Van Cappellen and Rezanezhad.

License: https://creativecommons.org/licenses/by/4.0/

*Correspondence: Fereidoun Rezanezhad

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