Up to 200% underestimation of cumulative methane emission from individual landfills uncovers tens of Tg of overlooked annual emission worldwide

doi:10.21203/rs.3.rs-3423080/v1

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Up to 200% underestimation of cumulative methane emission from individual landfills uncovers tens of Tg of overlooked annual emission worldwide

https://doi.org/10.21203/rs.3.rs-3423080/v1

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Landfill methane (CH₄) emissions account for ~ 10% of all anthropogenic CH₄ emissions globally, amounting to ~ 50 Tg/year. Contrasted by “top-down” atmospheric inversion results, the mainstream “bottom-up” emission inventories, which use the Intergovernmental Panel on Climate Change’s (IPCC) model, exhibit significant bias due to inaccurate a priori decay constant (k) estimations. We improved the k estimation method by incorporating composition- and environment-specific corrections, which are readily integrated into the IPCC’s model. We demonstrate that the accuracies of CH₄ emission predictions are significantly improved by using the corrected k values, which are benchmarked against the atmospheric inversion results. We extend the emission estimations to landfills worldwide and reveal up to 209% underestimation in individual landfills and several tens of Tg/year of potentially overlooked CH₄ emission globally. Our findings highlight the importance of prioritizing landfill CH₄ emission monitoring and reduction as one of the most cost-effective mitigation options to achieve current climate goals.

Climate Analysis and Modeling

Environmental Engineering

Civil Engineering

Methane (CH₄) is a potent greenhouse gas (GHG) with significantly greater heat-trapping capacity than carbon dioxide (CO₂). Landfill gas (LFG), generated by anaerobic degradation of organic waste in municipal solid waste (MSW), consists mainly of equimolar CH₄ and CO₂. Landfills, which encompass both uncontrolled dumpsites and sanitary landfills, represent a substantial source of anthropogenic CH₄ emissions (Cai et al., 2018; Zhu et al., 2023). The global annual CH₄ emissions from landfills are estimated to be on the order of several tens of Tg (Fig. 1a), accounting for ~ 20% of all annual anthropogenic CH₄ emissions (Saunois et al., 2020). In certain megacities, the proportions of CH₄ emissions from landfills even exceed 50% (Maasakkers et al., 2022).

There are approximately 300,000-500,000 existing landfills worldwide, receiving > 1.5 billion tonnes of MSW annually (Kaza et al., 2018). Some huge landfills emit CH₄ at rates comparable to those of “ultra-emitters” in oil and gas industry (Lauvaux et al., 2022), not to mention the vast number of small landfills with continuous CH₄ leakage. Nonetheless, landfill CH₄ emissions receive disproportionally low attention in research and mitigation compared to other sources with similar emission levels. For example, the energy sector emits a comparable amount of CH₄ annually, but requires 10 ~ 100 USD/t CO₂-eq more mitigation cost than the waste sector (EPA, 2019). Furthermore, landfill emissions have an estimated uncertainty range of 10 ~ 20 Tg/year, the value of which already exceeds the total emission of wastewater treatment that received considerable attention recently (Du et al., 2023; Song et al., 2023) (Fig. 1a). A more accurate assessment of landfill CH₄ emissions is essential to formulate effective waste management strategies and emission mitigation strategies (Fei et al., 2021) to realize the targeted 30% reduction in global annual CH₄ emissions by 2030, as pledged in the 2021 United Nations Climate Change Conference (COP26).

The current estimation of global landfill CH₄ emissions relies on the reporting of national greenhouse gas inventories (NGHGI) to the United Nations Framework Convention on Climate Change (UNFCCC), which follows the guideline set by the Intergovernmental Panel on Climate Change (IPCC). Landfill managers and government officers most often use a first-order decay (FOD) model (Eq. 1) to calculate site- or region-specific annual landfill CH₄ emissions based on waste disposal amount and landfill operating time (IPCC, 2006).

$$\begin{array}{c}{r}_{CH4}={L}_{0}\times W\times k\times {e}^{-k\times (t-{t}_{0})}\#\left(1\right)\end{array}$$

where r_CH4 is the gravimetric CH₄ generation rate (kg CH₄/year), W is the waste disposal amount (dry or wet mass, kg), t is the time since waste disposal (year), and t₀ is the lag time between waste disposal and the initiation of CH₄ generation (year).

The FOD model (Eq. 1) necessitates another two critical input parameters, namely first-order decay constant (also known as waste decay rate, k, year^− 1) and methane generation potential (L₀, L CH₄/kg dry waste) (IPCC, 2006). The model was initially adopted by IPCC in 2006 and remained largely unchanged thereafter, except for a minor refinement in 2019 (IPCC, 2019). The values of k and L₀ are subject to significant variability due to waste heterogeneity and landfill conditions. The value of L₀, an inherent property of waste components, has been comprehensively reviewed by previous research (Krause et al., 2016), and can be accurately determined based on waste composition (IPCC, 2006; Krause et al., 2016). However, the IPCC’s guideline only provides four default k values based on different temperature and precipitation conditions. This fails to consider the diversity of landfills with different geographical locations, waste compositions, and potential climate change impacts, resulting in substantially biased k estimations. However, 198 countries and regions under UNFCCC framework report their landfill CH₄ emission inventories employing IPCC’s default k values, thereby underlining the significant magnitude of potential CH₄ emission misestimations and importance of k value refinement.

Besides IPCC’s inventory method (bottom-up) using a generalized model to calculate landfill CH₄ emissions, remote sensing of CH₄ concentration and atmospheric inversion modeling (top-down) provides direct and independent quantifications of CH₄ fluxes from landfills (Deng et al., 2022; Erland et al., 2022). The biases of the conventional “bottom-up” inventory estimations have been acknowledged by previous studies (Spokas et al., 2015; Wang et al., 2015) and further corroborated by the recent “top-down” inversions leveraging state-of-the-art satellites (Maasakkers et al., 2022) and aircrafts (Duren et al., 2019), which reveal up to 2.6 times more CH₄ emissions from certain landfills compared to the inventory estimations.

Nevertheless, the extensive data processing requirement, high data acquisition cost, and high personnel expertise threshold of atmospheric inversion limit its applicability to a large number of landfills. In contrast, as the IPCC’s inventory method is widely accessible and user-friendly, we seek to improve it by revising the k estimation method for the FOD model. Here, contrasting the previous studies relying on the k values measured from a single landfill (De la Cruz et al., 2016; Delgado et al., 2023) or a small number of laboratory tests (Karanjekar et al., 2015), we survey and analyze exhaustively reported k (k_r) values obtained from landfill measurements (N = 196 in 18 countries) and laboratory tests (N = 80 in 12 countries) in the literature (Fig. 1b). The operating and environmental conditions of each landfill are also collected to establish a comprehensive landfill database.

Thereafter, a new k estimation method based on the three most influential factors, site-specific waste composition, moisture, and temperature corrections (CMT method) is developed. The superior performance of the CMT method is showcased by comparing the corrected k (k_CMT) with the IPCC’s default k (k_IPCC) following a two-stage approach. In the first stage, we compare the k estimation error between CMT method and IPCC’s method by referencing the reported k_r values. In the second stage, we contrast the CH₄ prediction results based on both k_CMT and k_IPCC, utilizing the atmospheric inversion results as independent benchmarks. Subsequently, we apply the k_CMT to predict the cumulative CH₄ emissions between 2010 and 2030 from 12 major landfills in different climate zones and income-level countries worldwide. The biases between the predictions made using k_CMT and k_IPCC are gauged. Lastly, we explore the novel insights on landfill CH₄ emission trends uncovered by the k_CMT predictions and potential mitigation strategies (Fig. 1c and 1d).

Survey of reported waste decay rates worldwide

This study conducts a comprehensive survey and analysis of measured and reported waste decay rates (k_r) on a global scale. We exhaustively compile available k_r values and distinguish them by the sources into field measurements and laboratory tests (Fig. 2a and 2b). Compared to k_IPCC, the k_r values are considered to be more accurate for individual landfills, which distribute broadly across all major climate zones (except the polar zone) with an annual average temperature of 3.1 ~ 28.1 ℃ and annual average precipitation of 79 ~ 2,344 mm. Most field measurements and laboratory tests have been conducted in the United States (U.S.), Europe, and China. Brazil represents the sole South American country with trackable CH₄ emission data from landfills, while no reliable data exists for African countries. In developing countries, where field measurements or laboratory experiments are not practicable, k_IPCC are extensively utilized, with over 100 studies employing k_IPCC to estimate site- or city-level CH₄ emissions from landfills.

Field and laboratory k_r values complement each other to improve CH₄ emission estimation and understanding of influential factors. Laboratory tests typically create controlled and ideal environments for MSW degradation, yielding high k_r and nearly complete time series data of CH₄ generation (He and Fei, 2020). Conversely, field k_r measurements are the most valuable first-hand data to quantify landfill CH₄ emissions. Nonetheless, field measurements encompass considerably higher uncertainties than laboratory tests and time series data of CH₄ generation data is often incomplete. 92% of the field k_r are between 0.01 ~ 0.3, with a few outliers resulting from unique operating conditions, e.g., k_r = 0.87 for a pilot-scale bioreactor test cell (Yazdani et al., 2012). The laboratory k_r values are more dispersed between 0.8 ~ 25.6. This is attributed to the widely varied operating conditions of laboratory tests, e.g., k_r = 25.6 for a small test column with intensive leachate recirculation (Pezzolla et al., 2017) and k_r = 0.8 for a larger column with a lower recirculation frequency (Huang et al., 2012). The average k_r of the laboratory tests (5.0) substantially surpasses that of field measurements (0.12) due mainly to scale effect and degradation-promoting conditions (Fig. 2b) (Fei et al., 2016). Factors contributing to the scale effect between field and laboratory k_r include operating conditions (moisture, temperature, inoculation, vertical stress, oxygen availability, measurement accuracy, etc.) and waste properties (size of interest, heterogeneity, etc.). Both the scale effect and degradation-promoting conditions on k estimation are considered in this study.

The k value is influenced by factors such as moisture, temperature, waste and leachate properties, and landfill operations, among which the IPCC’s guideline identifies moisture, temperature, and waste composition as the three primary factors. Our survey reveals that the k_r values exhibit substantial variation between annual temperature of < 20 and > 20 ℃ (p = 0.03, Fig. 2c), but statistically insignificant variation between annual precipitation of < 1,000 and > 1,000 mm (p = 0.13, Fig. 2d). This does not imply that precipitation has a negligible impact on k_r values; rather, it suggests that the dichotomous classification adopted by the IPCC’s guideline (precipitation: wet or dry, temperature: tropical or temperate) cannot reflect quantitatively the impacts of the influencing factors. Consequently, it is crucial to conduct a quantitative analysis to distinctly characterize the impacts of temperature and moisture on k.

Moreover, significant disparities in k_r values are observed among the countries (Fig. 2e), primarily due to pronounced differences in waste composition among them. The difference arises from distinct income levels, lifestyles, product availabilities, and waste management strategies. The Annex I countries of UNFCCC, predominantly Organisation for Economic Co-operation and Development (OECD) countries, typically generate lower proportions of fast degradable waste (B_f in %, including food and yard waste) than the Non-Annex I countries (IPCC, 2019). In many European countries that advocate waste incineration, such as Germany and Denmark, B_f even approaches 0% in landfiled waste (Kaza et al., 2018). Conversely, B_f constitutes substantial portions of disposed waste in many developing countries, e.g., B_f = 59% in China and 53% in India (IPCC, 2019). A gap remains in quantifying the influence of waste composition on estimated k, which we aim to address in this study. Additionally, LFG recovery efficiency and type and placement time of landfill cover vary across countries, which also impact k_r and estimated k. Overall, the significant discrepancy between the k_r and k_IPCC highlights the urgent need for improving k estimation to be used in the IPCC’s method.

Improved k_CMT estimations are frequently closer to k_r than k_IPCC

To improve the estimation accuracy of k and CH₄ emission and reduce the potential bias induced by k_IPCC, we develop a CMT correction method based on the analysis of available k_r values worldwide. The CMT method quantifies the influences of moisture, temperature, and waste composition on k (Eq. 2), rather than categorizing k value dichotomously as has been done in IPCC’s method. The details of the development and application procedures of the CMT method are provided in the Methods.

$$\begin{array}{c}{k}_{CMT}={k}_{0}{\times f}_{C}\times {f}_{T}{\times f}_{M}\#\left(2\right)\end{array}$$

where k_CMT refers to k after CMT corrections; k₀ refers to the k under baseline moisture, temperature, and waste composition; f_C, f_M, and f_T refer to the correction factors for waste composition (C), moisture (M), and temperature (T), respectively. k₀ represents the maximum k value under optimum conditions where f_C = f_M = f_T = 1.

We first determine the k_CMT and k_IPCC for the 196 landfills with reported k_r as described in the previous section. The relative errors of k_IPCC (δ(k_IPCC)) and k_CMT (δ(k_CMT)) as compared to the corresponding k_r are calculated. The k_r values are considered to be the most accurate available data for the landfills, while the relative error serves as an indicator of the disparity between the estimated k and the actual k value. A higher relative error denotes a diminished accuracy of the k estimation method. The average δ(k_CMT) is 56.2%, which is significantly lower than the average δ(k_IPCC) of 78.7% (p = 0.05). The δ(k_CMT) values are < 50% (taken as the acceptable error) in 134 out of the 196 landfills (Fig. 4a), while 104 landfills show δ(k_IPCC) < 50% (Fig. 4b). The error analysis reveals that the k_CMT values are superior in 105 out of the 196 landfills and equivalent in 45 out of the 196 landfills as compared to the k_IPCC values, giving k_CMT a 77% likelihood of being equally or more accurate than k_IPCC (Fig. 4c). While retaining the essence of k_IPCC, that is simplicity and a priori estimation, k_CMT improves significantly the accuracy of k estimation.

We recognize that high deviations between k_CMT and k_r still exist, e.g., k_CMT = 0.09 and k_r = 0.01 for a landfill in Maryland, U.S. (Jain et al., 2021). One explanation for the high δ(k_CMT) could be the uncertainties in determining f_T, f_M, and f_C for k_CMT calculation. In addition, due to limited data availability, some landfills reported the waste composition during collection instead of disposal, which becomes a contributor to δ(k_CMT). The accuracy of k_CMT can be improved if more landfills with complete site information and time-dependent CH₄ generation data are included in the analysis, whereas the accuracy of k_IPCC is unaffected per its standard calculation procedure. The generally lower δ(k_CMT) than δ(k_IPCC) highlights that more accurate CH₄ emission estimations can be made by using k_CMT than k_IPCC, as demonstrated in the next section.

Inversed atmospheric CH ₄ fluxes are better estimated by Q_CMT than Q_IPCC

Recent advancements in high-resolution remote sensing technology using satellites and unmanned aerial vehicles have enabled atmospheric inversion as a powerful tool for measuring CH₄ emissions from single landfills (Maasakkers et al., 2022; Tu et al., 2022). We gather 32 landfills (Fig. 2a) with available inversed atmospheric CH₄ fluxes (Q_r, Gg/y) and sufficient operating information as benchmarks against the corresponding CH₄ emissions estimated using k_CMT and k_IPCC. For each landfill, we estimate the annual CH₄ emissions using both k_CMT (Q_CMT, Gg/y) and k_IPCC (Q_IPCC, Gg/y), and compared these results with the corresponding Q_r to yield the respective errors δ(Q_CMT) and δ(Q_IPCC). These landfills are not purposely chosen but represent the limited number of sites meeting the selection criteria (Fig. S2c). The comparisons underscore the significant amount of CH₄ underestimated by the current IPCC’s method. Apart from one landfill with a Q_r lower than the Q_IPCC (Victorville, U.S., Fig. S1), all the other Q_r exceed Q_IPCC by 4-737%. The average δ(Q_CMT) and root mean square error (RMSE) of Q_CMT are 36% and 7.3 for the 32 landfills, while the δ(Q_IPCC) and RMSE of Q_IPCC are 45% and 14.5. The Q_CMT values are closer to the Q_r than Q_IPCC in 26 out of the 32 landfills (Fig. 4i), further demonstrating that adopting k_CMT generates more accurate estimations than using k_IPCC. It is noteworthy that k only controls the annual emission rate, but not the cumulative emission amount, which is instead governed by L₀ (Krause et al., 2016).

Although the Q_CMT is more accurate than the Q_IPCC for most of the landfills, the improvements by Q_CMT as compared to the Q_IPCC ((Q_CMT-Q_IPCC)/Q_IPCC×100%) vary between 9 ~ 177%. The difference is mainly attributed to landfill age and climate conditions. The CH₄ generation and emission rates of a new landfill increase exponentially according to the FOD model, thus the difference between k_CMT and k_IPCC leads to an increasingly widened gap between the Q_IPCC and Q_CMT (e.g., Fig. 4b). As the annual CH₄ emission reaches a peak while assuming a relatively stable annual waste disposal rate, the Q_CMT and Q_IPCC values tend to intersect (e.g., Fig. 4a, 4c, and 4d). Considering the climate conditions, the landfills in tropical wet zones have comparable k_IPCC and k_CMT values. Thus, the Q_IPCC and Q_CMT are also comparable. For instance, Kanjurmarg landfill in Mumbai, India, has k_IPCC = 0.17 year^− 1 and a waste decay half-life (t_1/2,IPCC) of 4.1 years. In contrast, the k_CMT = 0.3 year^− 1 and t_1/2,CMT = 2.3 years for this landfill. With an increase of 0.13 year^− 1 from k_IPCC to k_CMT, the t_1/2 is shortened by only 1.8 years, and the Q_CMT and Q_IPCC in 2021 differ by just 25% (Fig. 4c). Conversely, the West Miramar landfill in San Diego, U.S., in temperate dry zone has k_IPCC = 0.02 year^− 1 and k_CMT = 0.06 year^− 1, which is a three-time difference. This leads to the t_1/2,CMT to be 23.1 years lower than the t_1/2,IPCC and 70% higher Q_CMT than Q_IPCC in 2017 (Fig. 4f). The other landfills (Fig. 4b, 4e, 4g, and 4h,) have a similar trend as in this case.

Overall, the differences between k_CMT and k_IPCC and Q_CMT and Q_IPCC are particularly evident in new landfills in arid, temperate, and continental climate zones. This is a major finding that has been overlooked so far, as it highlights the sensitivity of time-dependent Q values to the accuracy of k values. In fact, the biased Q_IPCC are not confined to these 32 landfills, but most landfills worldwide. We apply the k correction method to additional landfills worldwide and discuss new perspectives and mitigation strategies for landfill CH₄ emissions in the next section.

ΣQ _CMT predictions are equal to or much higher than ΣQ_IPCC by 2030

Table 1

Underestimation (Q_diff%)^a of CH₄ emissions by current inventories in 12 typical landfills situated in different climate zones and the annual MSW generation amount and share in each climate zone.
Climate zone	A: tropical	B: arid	C: temperate	D: continental	E: polar
Annual MSW generation in 2018 (tons/year)	546,099,396	677,757,999	629,788,663	218,461,487	275,270
Share of global annual MSW generation in 2018	26%	33%	30%	11%	0.0%
Q_diff% for 12 major landfills	Brasilia, Brazil (1%); Mumbai, India (2%); Guangzhou, China (11%); Jakarta, Indonesia (12%);	Pietermartzburg, South Africa (40%); Arequipa, Peru (49%); Bennett, U. S. (209%);	Shanghai, China (22%); Randleman, U.S. (30%); London, U.K. (47%);	Belgrade, Serbia (25%); Morrisville, U.S. (182%);	N.A.
^a Q_diff% = (ΣQ_CMT - ΣQ_IPCC) / ΣQ_IPCC × 100% (by year 2030)

^b Estimated based on the annual MSW generation rate (Kaza et al., 2018) and primary climate zone of each country, except for differentiating different provinces in China and different states in the U.S. (Kottek et al., 2006).

The higher accuracies of k_CMT and Q_CMT compared to k_IPCC and Q_IPCC establish a foundation for extending the CMT correction method and Q_CMT to different landfills and countries globally. Given the unique site conditions of each landfill, it is overwhelming and unfeasible to reassess all landfills worldwide using the CMT method in a single study. Alternatively, we select 12 representative major landfills in various climate zones and countries to predict their cumulative CH₄ emissions since the operation to 2030 using k_IPCC (ΣQ_IPCC, Gg CH₄) and k_CMT (ΣQ_CMT, Gg CH₄) (Fig. 5). These chosen landfills are among the largest sanitary landfills and uncontrolled dumpsites worldwide (Fei et al., 2022), situating across all major geographical regions (East Asia, South Asia, Southeast Asia, East Europe, West Europe, Africa, South America, and North America) and climate zones (A: tropical, B: arid, C: temperate, D: continental, and E: polar).

The ΣQ_CMT values of the 12 landfills are on average 52% higher than the ΣQ_IPCC values. The landfills in arid, temperate, and continental climate zones have higher differences between ΣQ_IPCC and ΣQ_CMT (Q_diff%) than those in the tropical zone. The four landfills in the tropical zone show only 1–12% of Q_diff%, the reason for which has been discussed in the previous section. The eight landfills in arid, temperate, and continental zones reveal considerable underestimations by ΣQ_IPCC as compared to ΣQ_CMT. In the arid zone, where 33% of global MSW is generated annually, the Q_diff% reaches 209% in a 6-year-old landfill in Bennete, U.S. In temperate zone, where 30% of global MSW is generated annually, the Q_diff% reaches 47% in a landfill in London, the United Kingdom (U.K.). In the continental zone with 11% of global annual MSW generation, the Q_diff% reaches 182% in a 6-year-old landfill in Morrisville, U.S. (Table 1). If we extrapolate such level of underestimation to all landfills in arid, temperate, and continental climate zones, the potentially overlooked annual CH₄ emissions in 2030 amounts to 10 ~ 20 Tg/yr, which is already commensurate with the total annual CH₄ emission from the wastewater sector.

The overlooked CH₄ emissions from landfills are informative to the operations and monitoring throughout their lifespans. A considerable portion of underestimated ΣQ occurs during early landfilling stage, when LFG collection is not implemented. To achieve the best economic and environmental benefits, landfill managers should determine flexibly the installation and operation time of LFG collection systems based on the k_CMT values instead of the k_IPCC values or a fixed delay time. Moreover, the faster waste degradation rates as represented by the higher k_CMT values than the k_IPCC values indicate shorter waste stabilization time after closure, which suggest that the recommended post-closure care periods of at least 30 years may be shortened on site-specific cases (EPA, 2016). Once the post-closure care period is over, the landfill can be redeveloped to alleviate land scarcity. The higher Q_CMT than Q_IPCC of most landfills indicates that the unit costs of implementing CH₄ emission mitigation measures in landfills may be even lower than the current expectations of 0–10 USD/t CO₂-eq (IPCC, 2023). On the other hand, the benefit of shifting waste management from disposal to recycling and incineration is also underestimated, as the current cost-benefit analysis of waste management is based on the k_IPCC and Q_IPCC of a landfill, which underestimate its CH₄ emission compared to reality. Overall, the newly introduced k_CMT uncovers new mitigation opportunities obscured by the current k_IPCC.

In this study, we inspect the status quo IPCC’s inventory results and atmospheric inversions of global landfill CH₄ emissions, highlighting the issues with the widely accepted k_IPCC. Notably, there is significant discrepancy between the IPCC’s inventory estimations and reported atmospheric inversions for studied landfills (N = 32). After analyzing an exhaustive global landfill database gathered as part of this study, we propose a correction method to convert k_IPCC to k_CMT by considering the impacts of waste composition, moisture, and temperature on k_IPCC. The k_CMT method requires similar inputs as the k_IPCC, thus is readily integrated into the IPCC’s method and maintains its user-friendliness and simplicity. The k_CMT method empowers managers and policymakers, particularly in developing countries with economic and technical constraints for long-term landfill monitoring, to gain more accurate and comprehensive understanding of CH₄ emissions from landfills.

The latest IPCC synthesis report (AR6) underscores that the mitigation and adaptation measures and policies at the current pace are insufficient to control the global temperature increase to be below 1.5°C or even 2°C within the 21st century (IPCC, 2023), which can be further exacerbated by the underestimated landfill CH₄ emissions uncovered in this study on the order of tens of Tg/yr. In fact, the feasibilities of CH₄ mitigation measures are highly sensitive to their prices. Reducing landfill CH₄ emissions often yields net negative or minimal marginal abatement costs (< 1 USD/t CO₂-eq), especially in developing countries with fast-growing populations and waste generations but poor waste management, including Southeast Asia, Latin America, India, and China. Based on current inventory, the whole world can achieve up to 30 ~ 40 Tg/year of CH₄ emission reduction in the waste sector with net negative marginal abatement costs by 2030, which is already equivalent to the possible reduction in the oil and gas sector at same cost (30 ~ 50 Tg/year). (EPA, 2019; Höglund-Isaksson et al., 2020). Moreover, if the underestimated CH₄ emissions in this study are counted, zero- or low-cost CH₄ mitigation measures in landfills could contribute optimistically up to 60 Tg/year of CH₄ emission reduction in 2030, which represents one of the most cost-effective mitigation options (IPCC, 2023). Therefore, prioritizing the reduction of landfill CH₄ emissions, especially in developing countries facing economic and technical constraints, can effectively bridge the gap between current emissions and future mitigation targets.

Feasible approaches to reduce landfill CH₄ emissions include improving cover material, improving LFG collection system and planning, and transitioning from open dumping to sanitary landfilling. Notably, promoting power generation from LFG brings tremendous economic benefits (Jaramillo and Matthews, 2005), converting up to 50 Tg/yr of CH₄ to 300 billion kWh/year of electricity, which is equivalent to saving ~ 50 billion USD/year in energy costs (assume CH₄ calorific value is 55,530 kJ/kg, gas engine efficiency is 40% (Johari et al., 2012)d average electricity price is 0.15 USD/kWh in 2022). The synergistic effect of timely LFG collection should be greatly promoted.

We use the k_CMT to quantify underestimations of landfill CH₄ emissions, which are the prerequisite for devising appropriate countermeasures. In addition to k_CMT, we suggest re-assessing some other factors in the IPCC’s method, such as CH₄ oxidation factor (OX), CH₄ correction factor (MCF) for landfill management conditions, and CH₄ recovery rate (R), as their default values are currently considered as obsolete (Spokas et al., 2021; Spokas et al., 2015). More thorough and regular measurements in landfills facilitate the derivation of these site-specific factors (Powell et al., 2016). Other useful site-specific information includes landfill age and dimensions, and specifications of drainage, cover, and LFG collection systems. In fact, there is a global need to improve the quality of landfill information reporting and to establish a standard information recording mechanism, such as the Landfill Methane Outreach Program (LMOP) in the U.S. (EPA, 2023) Although the verification of “bottom-up” inventory estimation against “top-down” atmospheric inversion is not mandatory under the framework of UNFCCC, a few countries have embraced inversions as a consistency check for their NGHGI, e.g., Switzerland, the U.K., and New Zealand (Deng et al., 2022). This approach could be initially promoted among Annex I countries to standardize the methodology and subsequently expand to other parts of the world.

Data acquisition

Global waste decay rates (k)

The reported values of k (k_r), used to develop the CMT method, were obtained from the 196 field measurements and 80 laboratory tests documented in the existing literature. The field measurements refer to on-site measurements of CH₄ emissions in landfills, either directly reporting k_r or CH₄ emission data with time. The laboratory tests simulate waste degradation in controlled large-volume reaction vessels (> 2 L), and either directly reporting k_r or CH₄ generation data with time. Screenings were conducted first to ensure data quality (Fig. S2). The k values were recalculated if CH₄ generation or emission data with time were provided for data consistency, using the regression method described in our previous study (Fei et al., 2016) (N = 69 for laboratory tests and N = 22 for field measurements). If CH₄ generation data was not available in the corresponding literature, directly reported k_r values were adopted (N = 11 for laboratory tests and N = 174 for field measurements). A detailed list of references can be found in Supplementary Data Table.

Annual waste disposal data

The annual waste disposal quantity, also known as landfill activity data in IPCC’s guidelines, is the essential data required for calculating annual CH₄ emissions. In the U.S., all landfills are under the framework of the Landfill Methane Outreach Program (LMOP) (EPA, 2023) and are required to report their annual amount of waste disposed regularly. For other countries, there is currently no authoritative official statistical data available. Therefore, we have adopted the annual waste generation data reported in (Fei et al., 2022; Kaza et al., 2018) and assumed that the annual waste disposal quantity is relatively stable from the operation started time.

Waste composition information

The waste composition originally reported in the corresponding literature was recorded (N = 77 for laboratory tests and N = 15 for field measurements). In cases where the original data was not available in OECD countries, the waste composition was approximated based on survey data of country-level MSW generation or state-level for U.S. (N = 3 for laboratory tests and N = 162 for field measurements). For instances where neither waste composition nor MSW generation survey data were available in non-OECD countries, the recommended values by IPCC were adopted (IPCC, 2006) (N = 19 for field measurements).

Temperature and moisture conditions

For laboratory tests, the operating temperature reported in the corresponding literature was recorded. The moisture conditions were categorized into five levels, as described in our previous study (Fei et al., 2016), to determine the moisture correction factor (f_M) in the CMT method. For field measurements, ambient temperature and precipitation data were obtained from Climate Data for Cities Worldwide (https://en.climate-data.org/). Notably, the temperature within landfills is typically higher than the ambient temperature, although the quantitative relationship between the two is currently uncertain due to factors such as landfill design, waste age, and waste composition (Hanson et al., 2010). In this study, we assumed a uniform 10 ℃ higher temperature in the waste as compared to the ambient temperature based on the available reported data (Hanson et al., 2010; Zhang et al., 2019), which is commonly 5 ~ 20 ℃ higher than the ambient temperature. Similar to laboratory tests, the moisture conditions for field measurements were classified into five levels based on precipitation, but with necessary adjustments considering bioreactor cells, leachate recirculation, and other liquid addition (Supplementary Information Section 3.2).

Landfill operating conditions

Landfill operations that could affect CH₄ emissions in each landfill were evaluated to calibrate the parameters in the FOD model, such as CH₄ correction factor for landfill management (MCF), oxidation factor (OX), and CH₄ recovery rate (R). Since we only focused on k refinement in this study, the recommended values of these parameters by IPCC’s guidelines were utilized, based on landfill management, cover type, gas collection systems, and country income level (IPCC, 2019).

Atmospheric inversions of CH₄ fluxes (Q_r)

In this study, we compared landfill CH₄ emission inventory with the inversion results to validate the CMT method. Therefore, we selectively included CH₄ fluxes calculated from atmospheric inversions at the level of individual landfills (Cai et al., 2020; Cambaliza et al., 2017; Duren et al., 2019; Lavoie et al., 2015; Maasakkers et al., 2022). Each reported CH₄ flux should cover the entire landfill area to ensure comparability with the IPCC’s inventory emission. In addition, the selected landfills should have sufficient information supporting emission inventory calculation using IPCC’s model, including start time of operation and annual waste disposal amount. Refer to Fig. S2c for the detailed selection procedure and Supplementary Data Table for detailed information of selected landfill sites.

k calculation using CMT method

The CMT method was developed based on the quantitative relationships between k and waste composition, moisture, and temperature, respectively (Eq. 1). Here, we provide an overview of the process involved in developing the CMT method, as well as the workflow of the CMT method (Fig. 6a). We also illustrate an example of utilizing the CMT method to calculate k_CMT and Q_CMT for Lakhodair landfill in Lahore, Pakistan (Fig. 6b).

Similar to the IPCC’s recommended method, the CMT method requires inputs of waste composition (C), average precipitation (M), and average temperature (T) of a landfill, as well as other operating condition information such as leachate recirculation and cover type. Thereafter, the composition correction factor (f_C), moisture correction factor (f_M), and temperature correction factor (f_T) can be calculated. Subsequently, the k_CMT value can be calculated using Eq. 1.

Temperature correction factor

The effect of temperature on waste degradation and methanogenesis has been elucidated in previous studies (Rosso et al., 1993; Schupp et al., 2020; Sun et al., 2023). The temperature correction factor f_T can be expressed by a cardinal temperature model with inflection (CTMI) (Eq. 3) (Rosso et al., 1993).

$$\begin{array}{c}{f}_{T}=\frac{\left(T-{T}_{max}\right){\times \left(T-{T}_{min}\right)}^{2}}{\left({T}_{opt}-{T}_{min}\right)\times \left[({T}_{opt}-{T}_{min})\times \left(T-{T}_{opt}\right)-\left({T}_{opt}-{T}_{max}\right)\times \left({T}_{opt}+{T}_{min}-2T\right)\right]}\#\left(3\right)\end{array}$$

where T_min, T_max, and T_opt are three cardinal temperatures. T_min refers to the minimum temperature required for the growth of microorganisms, below which no observable microbial activity can occur. T_max is the maximum temperature at which microbial growth can be sustained, above which growth is not possible. T_opt represents the optimal temperature range for microbial growth, where microbial activities are most vigorous. The recommended values of the three cardinal temperatures are presented in Supplementary Information Section 3.1.

Moisture correction factor

Previous studies (Sun et al., 2023; Xiao et al., 2021) have utilized a sigmoid curve (Eq. 4) to describe the effect of moisture on waste degradation. It should be noted that both moisture level and movement can impact CH₄ generation in landfills (Fei et al., 2016; Hartz and Ham, 1983). Therefore, the effect of moisture is evaluated based on categorized moisture conditions (MC), which takes into account both precipitation and operating conditions of a landfill, as described in the Supplementary Information Section 3.2.

$$\begin{array}{c}{f}_{M}=\frac{1.2}{{1+e}^{-0.95\times MC+3.1}}\#\left(4\right)\end{array}$$

Composition correction factor

There is currently a lack of a universally accepted method for quantifying the impact of waste composition on k. Previous studies have reported correlations between k and certain individual waste constituents, such as paper waste (Qu et al., 2009) and food waste (Dearman and Bentham, 2007). Other studies have stressed the importance of considering the synergistic effects of all constituents, including paper, food, yard, textile, and inorganic waste (Karanjekar et al., 2015). To account for the impact of multiple waste constituents and simplify the analysis, we classified all waste constituents into three categories: slow-degradable waste (B_s), which includes paper, cardboard, wood, and textile waste; fast-degradable waste (B_f), which includes food waste and yard waste; and non-biodegradable waste (N_b), which includes metals, plastics, and other materials that do not degrade.

In order to quantify the influence of waste composition, the impact of temperature and moisture on k values should be initially compensated for using Eq. 5.$\begin{array}{c}{k}_{C}=\frac{{k}_{CMT}}{{{f}_{T}\times f}_{M}}={k}_{0}\times {f}_{C}\#\left(5\right)\end{array}$

Here, k_C denotes the k value at a given waste composition, while the temperature and moisture are adjusted to optimum baseline conditions, where f_T = f_M = 1. The k_C values from the laboratory tests and field measurements were subsequently plotted on two ternary diagrams with respect to B_s, B_f, and N_b, respectively, and combined to one ternary diagram after standardization (Fig. S3 and Supplementary Information Section 3.3). We acknowledge that a precise analytical equation is not suitable to describe this intricate k-composition relationship. Therefore, discretization offers a simple and practical approach to quantifying it. We divided the ternary plot into 16 equilateral triangles, in which each small triangle represents a specific area with a 25% variation of waste composition. The 16 equilateral triangles are classified into four levels based on the corresponding average k_C values within their ranges (Fig. 6c). This discretization approach enables easy identification of the corresponding k_C value through Fig. 6c, once the waste composition is known.

Prediction of landfill CH₄ emissions

The annual CH₄ emissions from landfills (Q) were calculated using the FOD model following IPCC’s guidelines (IPCC, 2006, 2019) as required by UNFCCC. The k values used in the improved estimations were k_CMT, while the k values used in the conventional inventory estimations were k_IPCC. The values of other parameters were determined in compliance with the IPCC’s recommendations.

$$\begin{array}{c}{Q}_{y}=\left(1-{R}_{y}\right)\times \left(1-O{X}_{y}\right)\times \frac{16}{12}\times F\times DO{C}_{y}\\ \times MC{F}_{y}\times \sum _{x=1}^{y}{[W}_{x}\times DOC\times {e}^{-k\times (y-x)}\times (1-{e}^{-k})]\#\left(6\right)\end{array}$$

where Q_y stands for CH₄ emissions occurring in year y from a landfill (Gg/year), R_y stands for the fraction of CH₄ capture at the landfill and then flared, combusted, or used in another manner that prevents CH₄ emissions in year y, OX_y stands for the fraction of CH₄ oxidized in the soil or other materials covering the waste in year y, 16/12 stands for the molecular weight ratio of CH₄/C, F stands for the volume fraction of CH₄ in LFG, DOC_y stands for the weight fraction of degradable organic carbon (DOC) that degrades in year y, MCF_y stands for the CH₄ correction factor in year y, W_x stands for the amount of waste disposed of in the landfill in year x (from 1 to y), and k stands for the waste decay rate that is either k_CMT or k_IPCC.

Uncertainties and limitations

The uncertainty of the CMT method is embodied mainly in the f_T, f_M, and k_C (Eq. 1 and Eq. 5). The uncertainty of f_T primarily stems from the determination of the three cardinal temperature, T_min, T_max, and T_opt (Schupp et al., 2020; Wang et al., 2012). Additionally, the variation of annual average temperature may also contribute to the uncertainty of f_T. The value of f_M was classified into four MC levels with detailed and comprehensive classification criteria provided (Table S1). However, it is still possible that obscured descriptions of a landfill in the original data source and the fluctuations in annual average precipitation result in a deviation of f_M by one MC level. The uncertainty of k_C primarily arises from the standard deviation of k_C within the same small triangle in the discretized ternary diagram (Fig. 6c). Due to the individual uncertainty of each predicted k value, it is impossible to establish a universal uncertainty range for k_CMT. However, since confidence intervals for f_T, f_M, and k_C are provided, the uncertainty of each k_CMT value can be obtained using Monte Carlo simulation. The details of the uncertainty assessment are provided in Supplementary Information Section 4.1.

The CH₄ emission estimations were based on the FOD model stipulated by UNFCCC, thus carrying uncertainties as described in the IPCC’s guidelines (IPCC, 2006) and UNFCCC’s methodologies (UNFCCC, 2017). The main sources of uncertainties in CH₄ emission estimation originate from the input parameters in Eq. 6. The detailed uncertainty assessments for the parameters are provided in Table S3. In comparison to landfills in developed countries, the uncertainties are particularly higher for landfills in developing countries with poor waste management and limited landfill information. For example, the recommended uncertainty for MCF is 0% for managed landfills, but 50% for unmanaged landfills (UNFCCC, 2017). Additionally, the lack of reliable annual waste disposal amount of a landfill necessitates an approximation based on its historical data, which can deviate significantly from the actual amount.

Acknowledgement

The authors would like to acknowledge Nanyang Technological University, Singapore, for providing research scholarships for this study. The authors thank the supports from Debris of the Anthropocene to Resources (DotA2) Lab at NTU.

Author Contribution Statement

Y.W. and X.F. conceptualized the article. Y.W, K.Y., M.F., Y.G., X.P., and Y.J.W collected, analyzed, and illustrated data. The manuscript was written by Y.W. with revisions from all the authors.

Competing Interest Statement

The authors declare no competing interest.

Data Availability Statement

All data and results are presented in this paper and supplementary information.

Code Availability Statement

The custom computer code or algorithms used to generate the results in this study can be made available to researchers upon request.

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Up to 200% underestimation of cumulative methane emission from individual landfills uncovers tens of Tg of overlooked annual emission worldwide

Status:

Version 1

Abstract

Figures

Main

Survey of reported waste decay rates worldwide

Discussion

Method

Data acquisition

Global waste decay rates (k)

Annual waste disposal data

Waste composition information

Temperature and moisture conditions

Landfill operating conditions

Atmospheric inversions of CH₄ fluxes (Q_r)

Temperature correction factor

Moisture correction factor

Composition correction factor

Prediction of landfill CH₄ emissions

Uncertainties and limitations

Declarations

References

Supplementary Files

Status:

Version 1

Up to 200% underestimation of cumulative methane emission from individual landfills uncovers tens of Tg of overlooked annual emission worldwide

Status:

Version 1

Abstract

Figures

Main

Survey of reported waste decay rates worldwide

Discussion

Method

Data acquisition

Global waste decay rates (k)

Annual waste disposal data

Waste composition information

Temperature and moisture conditions

Landfill operating conditions

Atmospheric inversions of CH4 fluxes (Qr)

Temperature correction factor

Moisture correction factor

Composition correction factor

Prediction of landfill CH4 emissions

Uncertainties and limitations

Declarations

References

Supplementary Files

Status:

Version 1

Atmospheric inversions of CH₄ fluxes (Q_r)

Prediction of landfill CH₄ emissions