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

Up to 200% underestimation of cumulative methane emission from individual landfills uncovers tens of Tg of overlooked annual emission worldwide | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Up to 200% underestimation of cumulative methane emission from individual landfills uncovers tens of Tg of overlooked annual emission worldwide Yao Wang, Ke Yin, Mingliang Fang, Yuliang Guo, Xiaoqing Pi, Yijie Wang, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3423080/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Landfill methane (CH 4 ) emissions account for ~ 10% of all anthropogenic CH 4 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 CH 4 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 CH 4 emission globally. Our findings highlight the importance of prioritizing landfill CH 4 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 Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Main Methane (CH 4 ) is a potent greenhouse gas (GHG) with significantly greater heat-trapping capacity than carbon dioxide (CO 2 ). Landfill gas (LFG), generated by anaerobic degradation of organic waste in municipal solid waste (MSW), consists mainly of equimolar CH 4 and CO 2 . Landfills, which encompass both uncontrolled dumpsites and sanitary landfills, represent a substantial source of anthropogenic CH 4 emissions (Cai et al., 2018 ; Zhu et al., 2023 ). The global annual CH 4 emissions from landfills are estimated to be on the order of several tens of Tg (Fig. 1 a), accounting for ~ 20% of all annual anthropogenic CH 4 emissions (Saunois et al., 2020 ). In certain megacities, the proportions of CH 4 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 CH 4 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 CH 4 leakage. Nonetheless, landfill CH 4 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 CH 4 annually, but requires 10 ~ 100 USD/t CO 2 -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. 1 a). A more accurate assessment of landfill CH 4 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 CH 4 emissions by 2030, as pledged in the 2021 United Nations Climate Change Conference (COP26). The current estimation of global landfill CH 4 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 CH 4 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 CH 4 generation rate (kg CH 4 /year), W is the waste disposal amount (dry or wet mass, kg), t is the time since waste disposal (year), and t 0 is the lag time between waste disposal and the initiation of CH 4 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 (L 0 , L CH 4 /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 L 0 are subject to significant variability due to waste heterogeneity and landfill conditions. The value of L 0 , 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 CH 4 emission inventories employing IPCC’s default k values, thereby underlining the significant magnitude of potential CH 4 emission misestimations and importance of k value refinement. Besides IPCC’s inventory method (bottom-up) using a generalized model to calculate landfill CH 4 emissions, remote sensing of CH 4 concentration and atmospheric inversion modeling (top-down) provides direct and independent quantifications of CH 4 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 CH 4 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. 1 b). 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 CH 4 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 CH 4 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 CH 4 emission trends uncovered by the k CMT predictions and potential mitigation strategies (Fig. 1 c and 1 d). 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. 2 a and 2 b). 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 CH 4 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 CH 4 emissions from landfills. Field and laboratory k r values complement each other to improve CH 4 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 CH 4 generation (He and Fei, 2020 ). Conversely, field k r measurements are the most valuable first-hand data to quantify landfill CH 4 emissions. Nonetheless, field measurements encompass considerably higher uncertainties than laboratory tests and time series data of CH 4 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. 2 b) (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 ℃ (p = 0.03, Fig. 2 c), but statistically insignificant variation between annual precipitation of 1,000 mm (p = 0.13, Fig. 2 d). 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. 2 e), 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 CH 4 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; k 0 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. k 0 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. 4 a), while 104 landfills show δ( k IPCC ) < 50% (Fig. 4 b). 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. 4 c). 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 CH 4 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 CH 4 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 CH 4 emissions from single landfills (Maasakkers et al., 2022 ; Tu et al., 2022 ). We gather 32 landfills (Fig. 2 a) with available inversed atmospheric CH 4 fluxes ( Q r , Gg/y) and sufficient operating information as benchmarks against the corresponding CH 4 emissions estimated using k CMT and k IPCC . For each landfill, we estimate the annual CH 4 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 CH 4 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. 4 i), 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 L 0 (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 CH 4 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. 4 b). As the annual CH 4 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. 4 a, 4 c, and 4 d). 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. 4 c). 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. 4 f). The other landfills (Fig. 4 b, 4 e, 4 g, and 4 h,) 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 CH 4 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 CH 4 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 CH 4 emissions since the operation to 2030 using k IPCC ( ΣQ IPCC , Gg CH 4 ) and k CMT ( ΣQ CMT , Gg CH 4 ) (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 CH 4 emissions in 2030 amounts to 10 ~ 20 Tg/yr, which is already commensurate with the total annual CH 4 emission from the wastewater sector. The overlooked CH 4 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 CH 4 emission mitigation measures in landfills may be even lower than the current expectations of 0–10 USD/t CO 2 -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 CH 4 emission compared to reality. Overall, the newly introduced k CMT uncovers new mitigation opportunities obscured by the current k IPCC . Discussion In this study, we inspect the status quo IPCC’s inventory results and atmospheric inversions of global landfill CH 4 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 CH 4 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 CH 4 emissions uncovered in this study on the order of tens of Tg/yr. In fact, the feasibilities of CH 4 mitigation measures are highly sensitive to their prices. Reducing landfill CH 4 emissions often yields net negative or minimal marginal abatement costs (< 1 USD/t CO 2 -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 CH 4 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 CH 4 emissions in this study are counted, zero- or low-cost CH 4 mitigation measures in landfills could contribute optimistically up to 60 Tg/year of CH 4 emission reduction in 2030, which represents one of the most cost-effective mitigation options (IPCC, 2023 ). Therefore, prioritizing the reduction of landfill CH 4 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 CH 4 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 CH 4 to 300 billion kWh/year of electricity, which is equivalent to saving ~ 50 billion USD/year in energy costs (assume CH 4 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 CH 4 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 CH 4 oxidation factor (OX), CH 4 correction factor (MCF) for landfill management conditions, and CH 4 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. Method 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 CH 4 emissions in landfills, either directly reporting k r or CH 4 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 CH 4 generation data with time. Screenings were conducted first to ensure data quality (Fig. S2). The k values were recalculated if CH 4 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 CH 4 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 CH 4 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 CH 4 emissions in each landfill were evaluated to calibrate the parameters in the FOD model, such as CH 4 correction factor for landfill management (MCF), oxidation factor (OX), and CH 4 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 CH 4 fluxes (Q r ) In this study, we compared landfill CH 4 emission inventory with the inversion results to validate the CMT method. Therefore, we selectively included CH 4 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 CH 4 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. 6 a). We also illustrate an example of utilizing the CMT method to calculate k CMT and Q CMT for Lakhodair landfill in Lahore, Pakistan (Fig. 6 b). 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 CH 4 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. 6 c). This discretization approach enables easy identification of the corresponding k C value through Fig. 6 c, once the waste composition is known. Prediction of landfill CH 4 emissions The annual CH 4 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 CH 4 emissions occurring in year y from a landfill (Gg/year), R y stands for the fraction of CH 4 capture at the landfill and then flared, combusted, or used in another manner that prevents CH 4 emissions in year y, OX y stands for the fraction of CH 4 oxidized in the soil or other materials covering the waste in year y, 16/12 stands for the molecular weight ratio of CH 4 /C, F stands for the volume fraction of CH 4 in LFG, DOC y stands for the weight fraction of degradable organic carbon (DOC) that degrades in year y, MCF y stands for the CH 4 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. 6 c). 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 CH 4 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 CH 4 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. Declarations 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3423080","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":238727927,"identity":"c7c72913-0223-4a3b-8216-2bc81b8bef09","order_by":0,"name":"Yao Wang","email":"","orcid":"https://orcid.org/0000-0003-2373-0201","institution":"Nanyang Technological University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yao","middleName":"","lastName":"Wang","suffix":""},{"id":238727928,"identity":"19a3234d-6f67-4216-aa33-b93780290af7","order_by":1,"name":"Ke Yin","email":"","orcid":"","institution":"Nanjing Forest University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ke","middleName":"","lastName":"Yin","suffix":""},{"id":238727929,"identity":"fced716a-4682-4ab5-85d3-b50876f9f875","order_by":2,"name":"Mingliang Fang","email":"","orcid":"","institution":"Fudan University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Mingliang","middleName":"","lastName":"Fang","suffix":""},{"id":238727930,"identity":"328f7c2c-7f85-40ef-abad-aca494db0d3a","order_by":3,"name":"Yuliang Guo","email":"","orcid":"","institution":"Nanyang Technological University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yuliang","middleName":"","lastName":"Guo","suffix":""},{"id":238727931,"identity":"3aab1868-e346-4374-ac11-3f83d6f9dbdb","order_by":4,"name":"Xiaoqing Pi","email":"","orcid":"","institution":"Nanyang Technological University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xiaoqing","middleName":"","lastName":"Pi","suffix":""},{"id":238727932,"identity":"56bb5371-cc0b-4ab9-9294-95e961f1d847","order_by":5,"name":"Yijie Wang","email":"","orcid":"","institution":"Nanyang Technological University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yijie","middleName":"","lastName":"Wang","suffix":""},{"id":238727933,"identity":"6f21da73-24ee-4f8a-8812-5d353788c060","order_by":6,"name":"Hongping He","email":"","orcid":"","institution":"Shenzhen University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Hongping","middleName":"","lastName":"He","suffix":""},{"id":238727934,"identity":"cbfc7dbb-ae43-41a3-bf1e-e749ea27aab7","order_by":7,"name":"Ziyang Lou","email":"","orcid":"","institution":"Shanghai Jiao Tong University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ziyang","middleName":"","lastName":"Lou","suffix":""},{"id":238727935,"identity":"39523427-70e3-4ef4-a1d1-879997344a00","order_by":8,"name":"Xunchang Fei","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxElEQVRIiWNgGAWjYFACxgcMCRUQpgSRWpgNGBLOQFSToIWxjRQtBscPMz54OO9OncEB5oO3eRjq7BoIaZHsSWY2SNz2TMLgAFuyNQ/D4WSCWvgl+I9JJG47DNTCYybNw3AgmaDD2CSY2X8kzgFp4f8G1FJHWAu/BDMbQ2ID2BY2oBZmO4JaQH6RSDh2WHLmYTZjyzkGhxMIagGF2McfNYf5+Y43P7zxpqLOnqAWBGAGmwB0JAl6IIAUW0bBKBgFo2CEAABTEDVSO2qufwAAAABJRU5ErkJggg==","orcid":"","institution":"Nanyang Technological University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Xunchang","middleName":"","lastName":"Fei","suffix":""}],"badges":[],"createdAt":"2023-10-09 07:09:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3423080/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3423080/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":44323330,"identity":"5745190f-dc01-4937-9440-c9d2b13bebfa","added_by":"auto","created_at":"2023-10-09 21:58:45","extension":"tiff","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":295626,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Global CH\u003csub\u003e4\u003c/sub\u003e emissions in waste and wastewater sector as estimated by different inventory databases. EDGAR (Crippa et al., 2021), CAIT (ClimateWatch, 2022), and PRIMAP-hist (Gütschow et al., 2016) provide global total CH\u003csub\u003e4\u003c/sub\u003e emissions from the waste sector, but the uncertainties of their estimations range between 10~20 Tg/year. UNFCCC (UNFCCC, 2022) has detailed subsector emission data for Annex I parties with lower uncertainty, including all OECD (Organization for Economic Co-operation and Development) countries and several EIT (economy in transition) countries. (b) Diagram showing the working flow of this study, including data collection and analysis, method development and verification, and method application. (c) Current estimation method for landfill CH\u003csub\u003e\u003cdel\u003e4\u003c/del\u003e\u003c/sub\u003e emissions using IPCC’s default \u003cem\u003ek\u003c/em\u003e (\u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003eIPCC\u003c/em\u003e\u003c/sub\u003e) may lead to biased results and missed mitigation opportunities, thereby exacerbating climate change. (d) This study demonstrates that the corrected \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003eCMT\u003c/em\u003e\u003c/sub\u003e is verified by atmospheric inversion results and uncovers significant amounts of landfill CH\u003csub\u003e\u003cdel\u003e4\u003c/del\u003e\u003c/sub\u003e emissions that were previously overlooked, indicating new mitigation opportunities to curb climate change.\u003c/p\u003e","description":"","filename":"Figures1.tiff","url":"https://assets-eu.researchsquare.com/files/rs-3423080/v1/2917a339b274c658c60e20c0.tiff"},{"id":44323335,"identity":"2ec0be5a-cfa6-4153-8d6b-e4731ebb2702","added_by":"auto","created_at":"2023-10-09 21:58:45","extension":"tiff","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":3720727,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Locations of field measured \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003er\u003c/em\u003e\u003c/sub\u003e (N=196), laboratory tested \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003er\u003c/em\u003e\u003c/sub\u003e (N=80), and reported CH\u003csub\u003e4\u003c/sub\u003e fluxes by atmospheric inversions (N=32) overlying the global map with Köppen–Geiger climate zones. (A: tropical, Bs: Arid-Steppe, Bw: Arid-Desert, Cf: Temperate-Without dry season, Cs: Temperate-Dry summer, Cw: Temperate-Dry winter, Df: Cold-Without dry season, Ds: Cold-Dry summer, Dw: Cold-Dry Winter, E: Polar.) The \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003er\u003c/em\u003e\u003c/sub\u003e values are compared between (b) field measurements and laboratory tests, (c) two levels of annual temperature, (d) two levels of annual precipitation, and (e) Annex I and Non-Annex I countries.\u003c/p\u003e","description":"","filename":"Figures2.tiff","url":"https://assets-eu.researchsquare.com/files/rs-3423080/v1/85aadfddc8df927bdb7eb519.tiff"},{"id":44323329,"identity":"1a829f41-98e0-4f10-ade5-d16e4a0d26e9","added_by":"auto","created_at":"2023-10-09 21:58:45","extension":"tiff","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":63127,"visible":true,"origin":"","legend":"\u003cp\u003eError analysis of \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003eIPCC\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003eCMT\u003c/em\u003e\u003c/sub\u003e compared with \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003er\u003c/em\u003e\u003c/sub\u003e. (a) Comparison between \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003er\u003c/em\u003e\u003c/sub\u003e from 196 measurements and the corresponding estimated \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003eCMT\u003c/em\u003e\u003c/sub\u003e. (b) Comparison between \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003er\u003c/em\u003e\u003c/sub\u003e from 196 field measurements and the corresponding estimated\u003cem\u003e k\u003c/em\u003e\u003csub\u003e\u003cem\u003eIPCC\u003c/em\u003e\u003c/sub\u003e. (c) Comparison between the relative errors of \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003eIPCC\u003c/em\u003e\u003c/sub\u003e (δ(\u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003eIPCC\u003c/em\u003e\u003c/sub\u003e)) and \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003eCMT\u003c/em\u003e\u003c/sub\u003e (δ(\u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003eCMT\u003c/em\u003e\u003c/sub\u003e)) as compared to the corresponding \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003er\u003c/em\u003e\u003c/sub\u003e. Considering the average values and overall distributions of the δ(\u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003eCMT\u003c/em\u003e\u003c/sub\u003e) and δ(\u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003eIPCC\u003c/em\u003e\u003c/sub\u003e), we use a difference of 0.1 between δ(\u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003eCMT\u003c/em\u003e\u003c/sub\u003e) and δ(\u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003eIPCC\u003c/em\u003e\u003c/sub\u003e) as the threshold to distinguish the performance of \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003eCMT\u003c/em\u003e\u003c/sub\u003e into three levels: the “superior” range indicates that δ(\u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003eIPCC\u003c/em\u003e\u003c/sub\u003e) – δ(\u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003eCMT\u003c/em\u003e\u003c/sub\u003e) \u0026gt; 0.1; the “no significant difference” range represents |δ(\u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003eIPCC\u003c/em\u003e\u003c/sub\u003e) – δ(\u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003eCMT\u003c/em\u003e\u003c/sub\u003e)| ≤ 0.1; and the “inferior” range indicates that δ(\u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003eCMT\u003c/em\u003e\u003c/sub\u003e) – δ(\u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003eIPCC\u003c/em\u003e\u003c/sub\u003e) \u0026gt; 0.1.\u003c/p\u003e","description":"","filename":"Figures3.tiff","url":"https://assets-eu.researchsquare.com/files/rs-3423080/v1/c040014aba1ab6b47e2b6269.tiff"},{"id":44324822,"identity":"39567997-d16e-4f53-bad1-b87e554995ae","added_by":"auto","created_at":"2023-10-09 22:06:45","extension":"tiff","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":114572,"visible":true,"origin":"","legend":"\u003cp\u003eEstimations of annual CH\u003csub\u003e4\u003c/sub\u003e emissions (Gg/y) using \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003eCMT\u003c/em\u003e\u003c/sub\u003e (\u003cem\u003eQ\u003c/em\u003e\u003csub\u003e\u003cem\u003eCMT\u003c/em\u003e\u003c/sub\u003e) and \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003eIPCC\u003c/em\u003e\u003c/sub\u003e (\u003cem\u003eQ\u003c/em\u003e\u003csub\u003e\u003cem\u003eIPCC\u003c/em\u003e\u003c/sub\u003e) for the landfills with reported inversed atmospheric CH\u003csub\u003e4\u003c/sub\u003e fluxes (Qr) in (a) Buenos Aires, Argentina, (b) Lahore, Pakistan, (c) Delhi, India, (d) Mumbai, India, (e) Sloughhouse, California, U.S., (f) San Diego, California, U.S., (g) Sylmar, California, U.S., and (h) Vasco, California, U.S. (i) Comparison between the relative errors of CH\u003csub\u003e4\u003c/sub\u003e emission estimations using \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003eCMT\u003c/em\u003e\u003c/sub\u003e (δ(\u003cem\u003eQ\u003c/em\u003e\u003csub\u003e\u003cem\u003eCMT\u003c/em\u003e\u003c/sub\u003e)) and \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003eIPCC\u003c/em\u003e\u003c/sub\u003e (δ(\u003cem\u003eQ\u003c/em\u003e\u003csub\u003e\u003cem\u003eIPCC\u003c/em\u003e\u003c/sub\u003e)) as compared to the corresponding Q\u003csub\u003er\u003c/sub\u003e. We distinguish the performance of \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003eCMT\u003c/em\u003e\u003c/sub\u003e into two levels: “superior” means δ(\u003cem\u003eQ\u003c/em\u003e\u003csub\u003e\u003cem\u003eIPCC\u003c/em\u003e\u003c/sub\u003e) ≥ δ(\u003cem\u003eQ\u003c/em\u003e\u003csub\u003e\u003cem\u003eCMT\u003c/em\u003e\u003c/sub\u003e); “inferior” means δ(\u003cem\u003eQ\u003c/em\u003e\u003csub\u003e\u003cem\u003eIPCC\u003c/em\u003e\u003c/sub\u003e) \u0026lt; δ(\u003cem\u003eQ\u003c/em\u003e\u003csub\u003e\u003cem\u003eIPCC\u003c/em\u003e\u003c/sub\u003e). *The inflection points in (g) and (f) are due to the change in regulation by U.S. Environmental Protection Agency for landfill managers to report annual landfill volume in 2010.\u003c/p\u003e","description":"","filename":"Figures4.tiff","url":"https://assets-eu.researchsquare.com/files/rs-3423080/v1/ffcd28db9d91194bf7275f37.tiff"},{"id":44323332,"identity":"17276f69-1a87-48b8-be13-e95961d2ac18","added_by":"auto","created_at":"2023-10-09 21:58:45","extension":"tiff","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1072197,"visible":true,"origin":"","legend":"\u003cp\u003eThe predicted cumulative CH\u003csub\u003e4\u003c/sub\u003e emissions since operation to 2030 using \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003eIPCC\u003c/em\u003e\u003c/sub\u003e (\u003cem\u003eΣQ\u003c/em\u003e\u003csub\u003e\u003cem\u003eIPCC\u003c/em\u003e\u003c/sub\u003e, Gg) and \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003eCMT\u003c/em\u003e\u003c/sub\u003e (\u003cem\u003eΣQ\u003c/em\u003e\u003csub\u003e\u003cem\u003eCMT\u003c/em\u003e\u003c/sub\u003e, Gg) of 12 major sanitary landfills and open dumpsites worldwide.\u003c/p\u003e","description":"","filename":"Figures5.tiff","url":"https://assets-eu.researchsquare.com/files/rs-3423080/v1/b66ddf734050cadc9b9eeca1.tiff"},{"id":44324824,"identity":"6047b289-a4fc-41a7-8c3a-4c9602c34acd","added_by":"auto","created_at":"2023-10-09 22:06:45","extension":"tiff","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":137872,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Working flow of the CMT method by inputting waste composition, precipitation, temperature, and landfill operating conditions. (b) An example of utilizing the CMT method to calculate \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003eCMT\u003c/em\u003e\u003c/sub\u003e and estimate \u003cem\u003eQ\u003c/em\u003e\u003csub\u003e\u003cem\u003eCMT\u003c/em\u003e\u003c/sub\u003e for Lakhodair landfill in Lahore, Pakistan. (c) Discrete ternary plot of \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003eC\u003c/em\u003e\u003c/sub\u003e with respect to waste composition (B\u003csub\u003es\u003c/sub\u003e, B\u003csub\u003ef\u003c/sub\u003e, and N\u003csub\u003eb\u003c/sub\u003e) overlayed by the waste composition in typical countries (United States, China, German, South Africa, Brazil, and India). The \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003eC\u003c/em\u003e\u003c/sub\u003e values are categorized into four classes, each with different recommended values and uncertainties. Once the waste composition is known, the \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003eC\u003c/em\u003e\u003c/sub\u003e can be identified using this ternary plot.\u003c/p\u003e","description":"","filename":"Figures6.tiff","url":"https://assets-eu.researchsquare.com/files/rs-3423080/v1/b229269efa05945145950362.tiff"},{"id":44326530,"identity":"d02351ea-a30a-4c0a-8f01-e9ea4d2d2806","added_by":"auto","created_at":"2023-10-09 22:14:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":9195374,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3423080/v1/0eb70159-077b-41af-a750-827f89a14aad.pdf"},{"id":44323334,"identity":"e92d655a-164d-425c-9f1d-c8107109901c","added_by":"auto","created_at":"2023-10-09 21:58:45","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":4668092,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary Information\u003c/p\u003e","description":"","filename":"NCCSI.docx","url":"https://assets-eu.researchsquare.com/files/rs-3423080/v1/89b5b17414cbfff2eb9085f8.docx"}],"financialInterests":"","formattedTitle":"\u003cp\u003e\u003cstrong\u003eUp to 200% underestimation of cumulative methane emission from individual landfills uncovers tens of Tg of overlooked annual emission worldwide\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Main","content":"\u003cp\u003eMethane (CH\u003csub\u003e4\u003c/sub\u003e) is a potent greenhouse gas (GHG) with significantly greater heat-trapping capacity than carbon dioxide (CO\u003csub\u003e2\u003c/sub\u003e). Landfill gas (LFG), generated by anaerobic degradation of organic waste in municipal solid waste (MSW), consists mainly of equimolar CH\u003csub\u003e4\u003c/sub\u003e and CO\u003csub\u003e2\u003c/sub\u003e. Landfills, which encompass both uncontrolled dumpsites and sanitary landfills, represent a substantial source of anthropogenic CH\u003csub\u003e4\u003c/sub\u003e emissions (Cai et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Zhu et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The global annual CH\u003csub\u003e4\u003c/sub\u003e emissions from landfills are estimated to be on the order of several tens of Tg (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea), accounting for ~\u0026thinsp;20% of all annual anthropogenic CH\u003csub\u003e4\u003c/sub\u003e emissions (Saunois et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In certain megacities, the proportions of CH\u003csub\u003e4\u003c/sub\u003e emissions from landfills even exceed 50% (Maasakkers et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThere are approximately 300,000-500,000 existing landfills worldwide, receiving\u0026thinsp;\u0026gt;\u0026thinsp;1.5\u0026nbsp;billion tonnes of MSW annually (Kaza et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Some huge landfills emit CH\u003csub\u003e4\u003c/sub\u003e at rates comparable to those of \u0026ldquo;ultra-emitters\u0026rdquo; in oil and gas industry (Lauvaux et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), not to mention the vast number of small landfills with continuous CH\u003csub\u003e4\u003c/sub\u003e leakage. Nonetheless, landfill CH\u003csub\u003e4\u003c/sub\u003e 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 CH\u003csub\u003e4\u003c/sub\u003e annually, but requires 10\u0026thinsp;~\u0026thinsp;100 USD/t CO\u003csub\u003e2\u003c/sub\u003e-eq more mitigation cost than the waste sector (EPA, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Furthermore, landfill emissions have an estimated uncertainty range of 10\u0026thinsp;~\u0026thinsp;20 Tg/year, the value of which already exceeds the total emission of wastewater treatment that received considerable attention recently (Du et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Song et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). A more accurate assessment of landfill CH\u003csub\u003e4\u003c/sub\u003e emissions is essential to formulate effective waste management strategies and emission mitigation strategies (Fei et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) to realize the targeted 30% reduction in global annual CH\u003csub\u003e4\u003c/sub\u003e emissions by 2030, as pledged in the 2021 United Nations Climate Change Conference (COP26).\u003c/p\u003e \u003cp\u003eThe current estimation of global landfill CH\u003csub\u003e4\u003c/sub\u003e 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.\u0026nbsp;1) to calculate site- or region-specific annual landfill CH\u003csub\u003e4\u003c/sub\u003e emissions based on waste disposal amount and landfill operating time (IPCC, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2006\u003c/span\u003e).\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\begin{array}{c}{r}_{CH4}={L}_{0}\\times W\\times k\\times {e}^{-k\\times (t-{t}_{0})}\\#\\left(1\\right)\\end{array}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere r\u003csub\u003eCH4\u003c/sub\u003e is the gravimetric CH\u003csub\u003e4\u003c/sub\u003e generation rate (kg CH\u003csub\u003e4\u003c/sub\u003e/year), W is the waste disposal amount (dry or wet mass, kg), t is the time since waste disposal (year), and t\u003csub\u003e0\u003c/sub\u003e is the lag time between waste disposal and the initiation of CH\u003csub\u003e4\u003c/sub\u003e generation (year).\u003c/p\u003e \u003cp\u003eThe FOD model (Eq.\u0026nbsp;1) necessitates another two critical input parameters, namely first-order decay constant (also known as waste decay rate, \u003cem\u003ek\u003c/em\u003e, year\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) and methane generation potential (L\u003csub\u003e0\u003c/sub\u003e, L CH\u003csub\u003e4\u003c/sub\u003e/kg dry waste) (IPCC, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). The model was initially adopted by IPCC in 2006 and remained largely unchanged thereafter, except for a minor refinement in 2019 (IPCC, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The values of \u003cem\u003ek\u003c/em\u003e and L\u003csub\u003e0\u003c/sub\u003e are subject to significant variability due to waste heterogeneity and landfill conditions. The value of L\u003csub\u003e0\u003c/sub\u003e, an inherent property of waste components, has been comprehensively reviewed by previous research (Krause et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), and can be accurately determined based on waste composition (IPCC, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Krause et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). However, the IPCC\u0026rsquo;s guideline only provides four default \u003cem\u003ek\u003c/em\u003e 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 \u003cem\u003ek\u003c/em\u003e estimations. However, 198 countries and regions under UNFCCC framework report their landfill CH\u003csub\u003e4\u003c/sub\u003e emission inventories employing IPCC\u0026rsquo;s default \u003cem\u003ek\u003c/em\u003e values, thereby underlining the significant magnitude of potential CH\u003csub\u003e4\u003c/sub\u003e emission misestimations and importance of \u003cem\u003ek\u003c/em\u003e value refinement.\u003c/p\u003e \u003cp\u003eBesides IPCC\u0026rsquo;s inventory method (bottom-up) using a generalized model to calculate landfill CH\u003csub\u003e4\u003c/sub\u003e emissions, remote sensing of CH\u003csub\u003e4\u003c/sub\u003e concentration and atmospheric inversion modeling (top-down) provides direct and independent quantifications of CH\u003csub\u003e4\u003c/sub\u003e fluxes from landfills (Deng et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Erland et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The biases of the conventional \u0026ldquo;bottom-up\u0026rdquo; inventory estimations have been acknowledged by previous studies (Spokas et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) and further corroborated by the recent \u0026ldquo;top-down\u0026rdquo; inversions leveraging state-of-the-art satellites (Maasakkers et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and aircrafts (Duren et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), which reveal up to 2.6 times more CH\u003csub\u003e4\u003c/sub\u003e emissions from certain landfills compared to the inventory estimations.\u003c/p\u003e \u003cp\u003eNevertheless, 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\u0026rsquo;s inventory method is widely accessible and user-friendly, we seek to improve it by revising the \u003cem\u003ek\u003c/em\u003e estimation method for the FOD model. Here, contrasting the previous studies relying on the \u003cem\u003ek\u003c/em\u003e values measured from a single landfill (De la Cruz et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Delgado et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) or a small number of laboratory tests (Karanjekar et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), we survey and analyze exhaustively reported \u003cem\u003ek\u003c/em\u003e (\u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003er\u003c/em\u003e\u003c/sub\u003e) values obtained from landfill measurements (N\u0026thinsp;=\u0026thinsp;196 in 18 countries) and laboratory tests (N\u0026thinsp;=\u0026thinsp;80 in 12 countries) in the literature (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). The operating and environmental conditions of each landfill are also collected to establish a comprehensive landfill database.\u003c/p\u003e \u003cp\u003eThereafter, a new \u003cem\u003ek\u003c/em\u003e 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 \u003cem\u003ek\u003c/em\u003e (\u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003eCMT\u003c/em\u003e\u003c/sub\u003e) with the IPCC\u0026rsquo;s default \u003cem\u003ek\u003c/em\u003e (\u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003eIPCC\u003c/em\u003e\u003c/sub\u003e) following a two-stage approach. In the first stage, we compare the \u003cem\u003ek\u003c/em\u003e estimation error between CMT method and IPCC\u0026rsquo;s method by referencing the reported \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003er\u003c/em\u003e\u003c/sub\u003e values. In the second stage, we contrast the CH\u003csub\u003e4\u003c/sub\u003e prediction results based on both \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003eCMT\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003eIPCC\u003c/em\u003e\u003c/sub\u003e, utilizing the atmospheric inversion results as independent benchmarks. Subsequently, we apply the \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003eCMT\u003c/em\u003e\u003c/sub\u003e to predict the cumulative CH\u003csub\u003e4\u003c/sub\u003e 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 \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003eCMT\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003eIPCC\u003c/em\u003e\u003c/sub\u003e are gauged. Lastly, we explore the novel insights on landfill CH\u003csub\u003e4\u003c/sub\u003e emission trends uncovered by the \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003eCMT\u003c/em\u003e\u003c/sub\u003e predictions and potential mitigation strategies (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec and \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eSurvey of reported waste decay rates worldwide\u003c/h3\u003e\n\u003cp\u003eThis study conducts a comprehensive survey and analysis of measured and reported waste decay rates (\u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003er\u003c/em\u003e\u003c/sub\u003e) on a global scale. We exhaustively compile available \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003er\u003c/em\u003e\u003c/sub\u003e values and distinguish them by the sources into field measurements and laboratory tests (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). Compared to \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003eIPCC\u003c/em\u003e\u003c/sub\u003e, the \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003er\u003c/em\u003e\u003c/sub\u003e 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\u0026thinsp;~\u0026thinsp;28.1 ℃ and annual average precipitation of 79\u0026thinsp;~\u0026thinsp;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 CH\u003csub\u003e4\u003c/sub\u003e emission data from landfills, while no reliable data exists for African countries. In developing countries, where field measurements or laboratory experiments are not practicable, \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003eIPCC\u003c/em\u003e\u003c/sub\u003e are extensively utilized, with over 100 studies employing \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003eIPCC\u003c/em\u003e\u003c/sub\u003e to estimate site- or city-level CH\u003csub\u003e4\u003c/sub\u003e emissions from landfills.\u003c/p\u003e \u003cp\u003eField and laboratory \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003er\u003c/em\u003e\u003c/sub\u003e values complement each other to improve CH\u003csub\u003e4\u003c/sub\u003e emission estimation and understanding of influential factors. Laboratory tests typically create controlled and ideal environments for MSW degradation, yielding high \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003er\u003c/em\u003e\u003c/sub\u003e and nearly complete time series data of CH\u003csub\u003e4\u003c/sub\u003e generation (He and Fei, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Conversely, field \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003er\u003c/em\u003e\u003c/sub\u003e measurements are the most valuable first-hand data to quantify landfill CH\u003csub\u003e4\u003c/sub\u003e emissions. Nonetheless, field measurements encompass considerably higher uncertainties than laboratory tests and time series data of CH\u003csub\u003e4\u003c/sub\u003e generation data is often incomplete. 92% of the field \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003er\u003c/em\u003e\u003c/sub\u003e are between 0.01\u0026thinsp;~\u0026thinsp;0.3, with a few outliers resulting from unique operating conditions, e.g., \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003er\u003c/em\u003e\u003c/sub\u003e = 0.87 for a pilot-scale bioreactor test cell (Yazdani et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The laboratory \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003er\u003c/em\u003e\u003c/sub\u003e values are more dispersed between 0.8\u0026thinsp;~\u0026thinsp;25.6. This is attributed to the widely varied operating conditions of laboratory tests, e.g., \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003er\u003c/em\u003e\u003c/sub\u003e = 25.6 for a small test column with intensive leachate recirculation (Pezzolla et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003er\u003c/em\u003e\u003c/sub\u003e = 0.8 for a larger column with a lower recirculation frequency (Huang et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The average \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003er\u003c/em\u003e\u003c/sub\u003e of the laboratory tests (5.0) substantially surpasses that of field measurements (0.12) due mainly to scale effect and degradation-promoting conditions (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb) (Fei et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Factors contributing to the scale effect between field and laboratory \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003er\u003c/em\u003e\u003c/sub\u003e 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 \u003cem\u003ek\u003c/em\u003e estimation are considered in this study.\u003c/p\u003e \u003cp\u003eThe \u003cem\u003ek\u003c/em\u003e value is influenced by factors such as moisture, temperature, waste and leachate properties, and landfill operations, among which the IPCC\u0026rsquo;s guideline identifies moisture, temperature, and waste composition as the three primary factors. Our survey reveals that the \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003er\u003c/em\u003e\u003c/sub\u003e values exhibit substantial variation between annual temperature of \u0026lt;\u0026thinsp;20 and \u0026gt;\u0026thinsp;20 ℃ (p\u0026thinsp;=\u0026thinsp;0.03, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec), but statistically insignificant variation between annual precipitation of \u0026lt;\u0026thinsp;1,000 and \u0026gt;\u0026thinsp;1,000 mm (p\u0026thinsp;=\u0026thinsp;0.13, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed). This does not imply that precipitation has a negligible impact on \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003er\u003c/em\u003e\u003c/sub\u003e values; rather, it suggests that the dichotomous classification adopted by the IPCC\u0026rsquo;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 \u003cem\u003ek\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eMoreover, significant disparities in \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003er\u003c/em\u003e\u003c/sub\u003e values are observed among the countries (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ee), 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\u003csub\u003ef\u003c/sub\u003e in %, including food and yard waste) than the Non-Annex I countries (IPCC, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In many European countries that advocate waste incineration, such as Germany and Denmark, B\u003csub\u003ef\u003c/sub\u003e even approaches 0% in landfiled waste (Kaza et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Conversely, B\u003csub\u003ef\u003c/sub\u003e constitutes substantial portions of disposed waste in many developing countries, e.g., B\u003csub\u003ef\u003c/sub\u003e = 59% in China and 53% in India (IPCC, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). A gap remains in quantifying the influence of waste composition on estimated \u003cem\u003ek\u003c/em\u003e, 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 \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003er\u003c/em\u003e\u003c/sub\u003e and estimated \u003cem\u003ek\u003c/em\u003e. Overall, the significant discrepancy between the \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003er\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003eIPCC\u003c/em\u003e\u003c/sub\u003e highlights the urgent need for improving \u003cem\u003ek\u003c/em\u003e estimation to be used in the IPCC\u0026rsquo;s method.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eImproved\u003c/b\u003e \u003cb\u003ek\u003c/b\u003e\u003csub\u003e\u003cb\u003eCMT\u003c/b\u003e\u003c/sub\u003e \u003cb\u003eestimations are frequently closer to\u003c/b\u003e \u003cb\u003ek\u003c/b\u003e\u003csub\u003e\u003cb\u003er\u003c/b\u003e\u003c/sub\u003e \u003cb\u003ethan\u003c/b\u003e \u003cb\u003ek\u003c/b\u003e\u003csub\u003e\u003cb\u003eIPCC\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo improve the estimation accuracy of \u003cem\u003ek\u003c/em\u003e and CH\u003csub\u003e4\u003c/sub\u003e emission and reduce the potential bias induced by \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003eIPCC\u003c/em\u003e\u003c/sub\u003e, we develop a CMT correction method based on the analysis of available \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003er\u003c/em\u003e\u003c/sub\u003e values worldwide. The CMT method quantifies the influences of moisture, temperature, and waste composition on \u003cem\u003ek\u003c/em\u003e (Eq.\u0026nbsp;2), rather than categorizing \u003cem\u003ek\u003c/em\u003e value dichotomously as has been done in IPCC\u0026rsquo;s method. The details of the development and application procedures of the CMT method are provided in the Methods.\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\begin{array}{c}{k}_{CMT}={k}_{0}{\\times f}_{C}\\times {f}_{T}{\\times f}_{M}\\#\\left(2\\right)\\end{array}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003eCMT\u003c/em\u003e\u003c/sub\u003e refers to \u003cem\u003ek\u003c/em\u003e after CMT corrections; \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003e0\u003c/em\u003e\u003c/sub\u003e refers to the \u003cem\u003ek\u003c/em\u003e under baseline moisture, temperature, and waste composition; f\u003csub\u003eC\u003c/sub\u003e, f\u003csub\u003eM\u003c/sub\u003e, and f\u003csub\u003eT\u003c/sub\u003e refer to the correction factors for waste composition (C), moisture (M), and temperature (T), respectively. \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003e0\u003c/em\u003e\u003c/sub\u003e represents the maximum \u003cem\u003ek\u003c/em\u003e value under optimum conditions where f\u003csub\u003eC\u003c/sub\u003e = f\u003csub\u003eM\u003c/sub\u003e = f\u003csub\u003eT\u003c/sub\u003e = 1.\u003c/p\u003e \u003cp\u003eWe first determine the \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003eCMT\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003eIPCC\u003c/em\u003e\u003c/sub\u003e for the 196 landfills with reported \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003er\u003c/em\u003e\u003c/sub\u003e as described in the previous section. The relative errors of \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003eIPCC\u003c/em\u003e\u003c/sub\u003e (δ(\u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003eIPCC\u003c/em\u003e\u003c/sub\u003e)) and \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003eCMT\u003c/em\u003e\u003c/sub\u003e (δ(\u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003eCMT\u003c/em\u003e\u003c/sub\u003e)) as compared to the corresponding \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003er\u003c/em\u003e\u003c/sub\u003e are calculated. The \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003er\u003c/em\u003e\u003c/sub\u003e 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 \u003cem\u003ek\u003c/em\u003e and the actual \u003cem\u003ek\u003c/em\u003e value. A higher relative error denotes a diminished accuracy of the \u003cem\u003ek\u003c/em\u003e estimation method. The average δ(\u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003eCMT\u003c/em\u003e\u003c/sub\u003e) is 56.2%, which is significantly lower than the average δ(\u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003eIPCC\u003c/em\u003e\u003c/sub\u003e) of 78.7% (p\u0026thinsp;=\u0026thinsp;0.05). The δ(\u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003eCMT\u003c/em\u003e\u003c/sub\u003e) values are \u0026lt;\u0026thinsp;50% (taken as the acceptable error) in 134 out of the 196 landfills (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea), while 104 landfills show δ(\u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003eIPCC\u003c/em\u003e\u003c/sub\u003e)\u0026thinsp;\u0026lt;\u0026thinsp;50% (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb). The error analysis reveals that the \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003eCMT\u003c/em\u003e\u003c/sub\u003e values are superior in 105 out of the 196 landfills and equivalent in 45 out of the 196 landfills as compared to the \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003eIPCC\u003c/em\u003e\u003c/sub\u003e values, giving \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003eCMT\u003c/em\u003e\u003c/sub\u003e a 77% likelihood of being equally or more accurate than \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003eIPCC\u003c/em\u003e\u003c/sub\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec). While retaining the essence of \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003eIPCC\u003c/em\u003e\u003c/sub\u003e, that is simplicity and \u003cem\u003ea priori\u003c/em\u003e estimation, \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003eCMT\u003c/em\u003e\u003c/sub\u003e improves significantly the accuracy of \u003cem\u003ek\u003c/em\u003e estimation.\u003c/p\u003e \u003cp\u003eWe recognize that high deviations between \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003eCMT\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003er\u003c/em\u003e\u003c/sub\u003e still exist, e.g., \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003eCMT\u003c/em\u003e\u003c/sub\u003e = 0.09 and \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003er\u003c/em\u003e\u003c/sub\u003e = 0.01 for a landfill in Maryland, U.S. (Jain et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). One explanation for the high δ(\u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003eCMT\u003c/em\u003e\u003c/sub\u003e) could be the uncertainties in determining f\u003csub\u003eT\u003c/sub\u003e, f\u003csub\u003eM\u003c/sub\u003e, and f\u003csub\u003eC\u003c/sub\u003e for \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003eCMT\u003c/em\u003e\u003c/sub\u003e 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(\u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003eCMT\u003c/em\u003e\u003c/sub\u003e). The accuracy of \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003eCMT\u003c/em\u003e\u003c/sub\u003e can be improved if more landfills with complete site information and time-dependent CH\u003csub\u003e4\u003c/sub\u003e generation data are included in the analysis, whereas the accuracy of \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003eIPCC\u003c/em\u003e\u003c/sub\u003e is unaffected per its standard calculation procedure. The generally lower δ(\u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003eCMT\u003c/em\u003e\u003c/sub\u003e) than δ(\u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003eIPCC\u003c/em\u003e\u003c/sub\u003e) highlights that more accurate CH\u003csub\u003e4\u003c/sub\u003e emission estimations can be made by using \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003eCMT\u003c/em\u003e\u003c/sub\u003e than \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003eIPCC\u003c/em\u003e\u003c/sub\u003e, as demonstrated in the next section.\u003c/p\u003e \u003cp\u003e \u003cb\u003eInversed atmospheric CH\u003c/b\u003e \u003csub\u003e \u003cb\u003e4\u003c/b\u003e \u003c/sub\u003e \u003cb\u003efluxes are better estimated by\u003c/b\u003e \u003cb\u003eQ\u003c/b\u003e\u003csub\u003e\u003cb\u003eCMT\u003c/b\u003e\u003c/sub\u003e \u003cb\u003ethan\u003c/b\u003e \u003cb\u003eQ\u003c/b\u003e\u003csub\u003e\u003cb\u003eIPCC\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eRecent advancements in high-resolution remote sensing technology using satellites and unmanned aerial vehicles have enabled atmospheric inversion as a powerful tool for measuring CH\u003csub\u003e4\u003c/sub\u003e emissions from single landfills (Maasakkers et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Tu et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). We gather 32 landfills (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea) with available inversed atmospheric CH\u003csub\u003e4\u003c/sub\u003e fluxes (\u003cem\u003eQ\u003c/em\u003e\u003csub\u003e\u003cem\u003er\u003c/em\u003e\u003c/sub\u003e, Gg/y) and sufficient operating information as benchmarks against the corresponding CH\u003csub\u003e4\u003c/sub\u003e emissions estimated using \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003eCMT\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003eIPCC\u003c/em\u003e\u003c/sub\u003e. For each landfill, we estimate the annual CH\u003csub\u003e4\u003c/sub\u003e emissions using both \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003eCMT\u003c/em\u003e\u003c/sub\u003e (\u003cem\u003eQ\u003c/em\u003e\u003csub\u003e\u003cem\u003eCMT\u003c/em\u003e\u003c/sub\u003e, Gg/y) and \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003eIPCC\u003c/em\u003e\u003c/sub\u003e (\u003cem\u003eQ\u003c/em\u003e\u003csub\u003e\u003cem\u003eIPCC\u003c/em\u003e\u003c/sub\u003e, Gg/y), and compared these results with the corresponding \u003cem\u003eQ\u003c/em\u003e\u003csub\u003e\u003cem\u003er\u003c/em\u003e\u003c/sub\u003e to yield the respective errors δ(\u003cem\u003eQ\u003c/em\u003e\u003csub\u003e\u003cem\u003eCMT\u003c/em\u003e\u003c/sub\u003e) and δ(\u003cem\u003eQ\u003c/em\u003e\u003csub\u003e\u003cem\u003eIPCC\u003c/em\u003e\u003c/sub\u003e). 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 CH\u003csub\u003e4\u003c/sub\u003e underestimated by the current IPCC\u0026rsquo;s method. Apart from one landfill with a \u003cem\u003eQ\u003c/em\u003e\u003csub\u003e\u003cem\u003er\u003c/em\u003e\u003c/sub\u003e lower than the \u003cem\u003eQ\u003c/em\u003e\u003csub\u003e\u003cem\u003eIPCC\u003c/em\u003e\u003c/sub\u003e (Victorville, U.S., Fig. S1), all the other \u003cem\u003eQ\u003c/em\u003e\u003csub\u003e\u003cem\u003er\u003c/em\u003e\u003c/sub\u003e exceed \u003cem\u003eQ\u003c/em\u003e\u003csub\u003e\u003cem\u003eIPCC\u003c/em\u003e\u003c/sub\u003e by 4-737%. The average δ(\u003cem\u003eQ\u003c/em\u003e\u003csub\u003e\u003cem\u003eCMT\u003c/em\u003e\u003c/sub\u003e) and root mean square error (RMSE) of \u003cem\u003eQ\u003c/em\u003e\u003csub\u003e\u003cem\u003eCMT\u003c/em\u003e\u003c/sub\u003e are 36% and 7.3 for the 32 landfills, while the δ(\u003cem\u003eQ\u003c/em\u003e\u003csub\u003e\u003cem\u003eIPCC\u003c/em\u003e\u003c/sub\u003e) and RMSE of \u003cem\u003eQ\u003c/em\u003e\u003csub\u003e\u003cem\u003eIPCC\u003c/em\u003e\u003c/sub\u003e are 45% and 14.5. The \u003cem\u003eQ\u003c/em\u003e\u003csub\u003e\u003cem\u003eCMT\u003c/em\u003e\u003c/sub\u003e values are closer to the \u003cem\u003eQ\u003c/em\u003e\u003csub\u003e\u003cem\u003er\u003c/em\u003e\u003c/sub\u003e than \u003cem\u003eQ\u003c/em\u003e\u003csub\u003e\u003cem\u003eIPCC\u003c/em\u003e\u003c/sub\u003e in 26 out of the 32 landfills (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ei), further demonstrating that adopting \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003eCMT\u003c/em\u003e\u003c/sub\u003e generates more accurate estimations than using \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003eIPCC\u003c/em\u003e\u003c/sub\u003e. It is noteworthy that \u003cem\u003ek\u003c/em\u003e only controls the annual emission rate, but not the cumulative emission amount, which is instead governed by L\u003csub\u003e0\u003c/sub\u003e (Krause et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAlthough the \u003cem\u003eQ\u003c/em\u003e\u003csub\u003e\u003cem\u003eCMT\u003c/em\u003e\u003c/sub\u003e is more accurate than the \u003cem\u003eQ\u003c/em\u003e\u003csub\u003e\u003cem\u003eIPCC\u003c/em\u003e\u003c/sub\u003e for most of the landfills, the improvements by \u003cem\u003eQ\u003c/em\u003e\u003csub\u003e\u003cem\u003eCMT\u003c/em\u003e\u003c/sub\u003e as compared to the \u003cem\u003eQ\u003c/em\u003e\u003csub\u003e\u003cem\u003eIPCC\u003c/em\u003e\u003c/sub\u003e ((\u003cem\u003eQ\u003c/em\u003e\u003csub\u003e\u003cem\u003eCMT\u003c/em\u003e\u003c/sub\u003e-\u003cem\u003eQ\u003c/em\u003e\u003csub\u003e\u003cem\u003eIPCC\u003c/em\u003e\u003c/sub\u003e)/\u003cem\u003eQ\u003c/em\u003e\u003csub\u003e\u003cem\u003eIPCC\u003c/em\u003e\u003c/sub\u003e\u0026times;100%) vary between 9\u0026thinsp;~\u0026thinsp;177%. The difference is mainly attributed to landfill age and climate conditions. The CH\u003csub\u003e4\u003c/sub\u003e generation and emission rates of a new landfill increase exponentially according to the FOD model, thus the difference between \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003eCMT\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003eIPCC\u003c/em\u003e\u003c/sub\u003e leads to an increasingly widened gap between the \u003cem\u003eQ\u003c/em\u003e\u003csub\u003e\u003cem\u003eIPCC\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003eQ\u003c/em\u003e\u003csub\u003e\u003cem\u003eCMT\u003c/em\u003e\u003c/sub\u003e (e.g., Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb). As the annual CH\u003csub\u003e4\u003c/sub\u003e emission reaches a peak while assuming a relatively stable annual waste disposal rate, the \u003cem\u003eQ\u003c/em\u003e\u003csub\u003e\u003cem\u003eCMT\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003eQ\u003c/em\u003e\u003csub\u003e\u003cem\u003eIPCC\u003c/em\u003e\u003c/sub\u003e values tend to intersect (e.g., Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea, \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec, and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed). Considering the climate conditions, the landfills in tropical wet zones have comparable \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003eIPCC\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003eCMT\u003c/em\u003e\u003c/sub\u003e values. Thus, the \u003cem\u003eQ\u003c/em\u003e\u003csub\u003e\u003cem\u003eIPCC\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003eQ\u003c/em\u003e\u003csub\u003e\u003cem\u003eCMT\u003c/em\u003e\u003c/sub\u003e are also comparable. For instance, Kanjurmarg landfill in Mumbai, India, has \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003eIPCC\u003c/em\u003e\u003c/sub\u003e = 0.17 year\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and a waste decay half-life (t\u003csub\u003e1/2,IPCC\u003c/sub\u003e) of 4.1 years. In contrast, the \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003eCMT\u003c/em\u003e\u003c/sub\u003e = 0.3 year\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and t\u003csub\u003e1/2,CMT\u003c/sub\u003e = 2.3 years for this landfill. With an increase of 0.13 year\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e from \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003eIPCC\u003c/em\u003e\u003c/sub\u003e to \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003eCMT\u003c/em\u003e\u003c/sub\u003e, the t\u003csub\u003e1/2\u003c/sub\u003e is shortened by only 1.8 years, and the \u003cem\u003eQ\u003c/em\u003e\u003csub\u003e\u003cem\u003eCMT\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003eQ\u003c/em\u003e\u003csub\u003e\u003cem\u003eIPCC\u003c/em\u003e\u003c/sub\u003e in 2021 differ by just 25% (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec). Conversely, the West Miramar landfill in San Diego, U.S., in temperate dry zone has \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003eIPCC\u003c/em\u003e\u003c/sub\u003e = 0.02 year\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003eCMT\u003c/em\u003e\u003c/sub\u003e = 0.06 year\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, which is a three-time difference. This leads to the t\u003csub\u003e1/2,CMT\u003c/sub\u003e to be 23.1 years lower than the t\u003csub\u003e1/2,IPCC\u003c/sub\u003e and 70% higher \u003cem\u003eQ\u003c/em\u003e\u003csub\u003e\u003cem\u003eCMT\u003c/em\u003e\u003c/sub\u003e than \u003cem\u003eQ\u003c/em\u003e\u003csub\u003e\u003cem\u003eIPCC\u003c/em\u003e\u003c/sub\u003e in 2017 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ef). The other landfills (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb, \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ee, \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eg, and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eh,) have a similar trend as in this case.\u003c/p\u003e \u003cp\u003eOverall, the differences between \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003eCMT\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003eIPCC\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003eQ\u003c/em\u003e\u003csub\u003e\u003cem\u003eCMT\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003eQ\u003c/em\u003e\u003csub\u003e\u003cem\u003eIPCC\u003c/em\u003e\u003c/sub\u003e 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 \u003cem\u003eQ\u003c/em\u003e values to the accuracy of \u003cem\u003ek\u003c/em\u003e values. In fact, the biased \u003cem\u003eQ\u003c/em\u003e\u003csub\u003e\u003cem\u003eIPCC\u003c/em\u003e\u003c/sub\u003e are not confined to these 32 landfills, but most landfills worldwide. We apply the \u003cem\u003ek\u003c/em\u003e correction method to additional landfills worldwide and discuss new perspectives and mitigation strategies for landfill CH\u003csub\u003e4\u003c/sub\u003e emissions in the next section.\u003c/p\u003e \u003cp\u003e \u003cb\u003eΣQ\u003c/b\u003e \u003csub\u003e \u003cb\u003eCMT\u003c/b\u003e \u003c/sub\u003e \u003cb\u003epredictions are equal to or much higher than\u003c/b\u003e \u003cb\u003eΣQ\u003c/b\u003e\u003csub\u003e\u003cb\u003eIPCC\u003c/b\u003e\u003c/sub\u003e \u003cb\u003eby 2030\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnderestimation (\u003cem\u003eQ\u003c/em\u003e\u003csub\u003e\u003cem\u003ediff\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e%\u003c/em\u003e)\u003csup\u003ea\u003c/sup\u003e of CH\u003csub\u003e4\u003c/sub\u003e 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.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClimate zone\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA: tropical\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eB: arid\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC: temperate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eD: continental\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eE: polar\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnnual MSW generation in 2018 (tons/year)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e546,099,396\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e677,757,999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e629,788,663\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e218,461,487\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e275,270\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShare of global annual MSW generation in 2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eQ\u003c/em\u003e\u003csub\u003e\u003cem\u003ediff\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e%\u003c/em\u003e for 12 major landfills\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBrasilia, Brazil (1%);\u003c/p\u003e \u003cp\u003eMumbai, India (2%);\u003c/p\u003e \u003cp\u003eGuangzhou, China (11%);\u003c/p\u003e \u003cp\u003eJakarta, Indonesia (12%);\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePietermartzburg, South Africa (40%);\u003c/p\u003e \u003cp\u003eArequipa, Peru (49%);\u003c/p\u003e \u003cp\u003eBennett, U. S. (209%);\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eShanghai, China (22%);\u003c/p\u003e \u003cp\u003eRandleman, U.S. (30%);\u003c/p\u003e \u003cp\u003eLondon, U.K. (47%);\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBelgrade, Serbia (25%);\u003c/p\u003e \u003cp\u003eMorrisville, U.S. (182%);\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eN.A.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003csup\u003ea\u003c/sup\u003e \u003cem\u003eQ\u003c/em\u003e\u003csub\u003e\u003cem\u003ediff\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e%\u003c/em\u003e = (\u003cem\u003eΣQ\u003c/em\u003e\u003csub\u003e\u003cem\u003eCMT\u003c/em\u003e\u003c/sub\u003e - \u003cem\u003eΣQ\u003c/em\u003e\u003csub\u003e\u003cem\u003eIPCC\u003c/em\u003e\u003c/sub\u003e) / \u003cem\u003eΣQ\u003c/em\u003e\u003csub\u003e\u003cem\u003eIPCC\u003c/em\u003e\u003c/sub\u003e \u0026times; 100% (by year 2030)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003csup\u003eb\u003c/sup\u003e Estimated based on the annual MSW generation rate (Kaza et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) and primary climate zone of each country, except for differentiating different provinces in China and different states in the U.S. (Kottek et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2006\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe higher accuracies of \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003eCMT\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003eQ\u003c/em\u003e\u003csub\u003e\u003cem\u003eCMT\u003c/em\u003e\u003c/sub\u003e compared to \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003eIPCC\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003eQ\u003c/em\u003e\u003csub\u003e\u003cem\u003eIPCC\u003c/em\u003e\u003c/sub\u003e establish a foundation for extending the CMT correction method and \u003cem\u003eQ\u003c/em\u003e\u003csub\u003e\u003cem\u003eCMT\u003c/em\u003e\u003c/sub\u003e 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 CH\u003csub\u003e4\u003c/sub\u003e emissions since the operation to 2030 using \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003eIPCC\u003c/em\u003e\u003c/sub\u003e (\u003cem\u003eΣQ\u003c/em\u003e\u003csub\u003e\u003cem\u003eIPCC\u003c/em\u003e\u003c/sub\u003e, Gg CH\u003csub\u003e4\u003c/sub\u003e) and \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003eCMT\u003c/em\u003e\u003c/sub\u003e (\u003cem\u003eΣQ\u003c/em\u003e\u003csub\u003e\u003cem\u003eCMT\u003c/em\u003e\u003c/sub\u003e, Gg CH\u003csub\u003e4\u003c/sub\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). These chosen landfills are among the largest sanitary landfills and uncontrolled dumpsites worldwide (Fei et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), 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).\u003c/p\u003e \u003cp\u003eThe \u003cem\u003eΣQ\u003c/em\u003e\u003csub\u003e\u003cem\u003eCMT\u003c/em\u003e\u003c/sub\u003e values of the 12 landfills are on average 52% higher than the ΣQ\u003csub\u003eIPCC\u003c/sub\u003e values. The landfills in arid, temperate, and continental climate zones have higher differences between \u003cem\u003eΣQ\u003c/em\u003e\u003csub\u003e\u003cem\u003eIPCC\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003eΣQ\u003c/em\u003e\u003csub\u003e\u003cem\u003eCMT\u003c/em\u003e\u003c/sub\u003e (\u003cem\u003eQ\u003c/em\u003e\u003csub\u003e\u003cem\u003ediff\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e%\u003c/em\u003e) than those in the tropical zone. The four landfills in the tropical zone show only 1\u0026ndash;12% of \u003cem\u003eQ\u003c/em\u003e\u003csub\u003e\u003cem\u003ediff\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e%\u003c/em\u003e, the reason for which has been discussed in the previous section. The eight landfills in arid, temperate, and continental zones reveal considerable underestimations by \u003cem\u003eΣQ\u003c/em\u003e\u003csub\u003e\u003cem\u003eIPCC\u003c/em\u003e\u003c/sub\u003e as compared to \u003cem\u003eΣQ\u003c/em\u003e\u003csub\u003e\u003cem\u003eCMT\u003c/em\u003e\u003c/sub\u003e. In the arid zone, where 33% of global MSW is generated annually, the \u003cem\u003eQ\u003c/em\u003e\u003csub\u003e\u003cem\u003ediff\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e%\u003c/em\u003e reaches 209% in a 6-year-old landfill in Bennete, U.S. In temperate zone, where 30% of global MSW is generated annually, the \u003cem\u003eQ\u003c/em\u003e\u003csub\u003e\u003cem\u003ediff\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e%\u003c/em\u003e reaches 47% in a landfill in London, the United Kingdom (U.K.). In the continental zone with 11% of global annual MSW generation, the \u003cem\u003eQ\u003c/em\u003e\u003csub\u003e\u003cem\u003ediff\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e%\u003c/em\u003e reaches 182% in a 6-year-old landfill in Morrisville, U.S. (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). If we extrapolate such level of underestimation to all landfills in arid, temperate, and continental climate zones, the potentially overlooked annual CH\u003csub\u003e4\u003c/sub\u003e emissions in 2030 amounts to 10\u0026thinsp;~\u0026thinsp;20 Tg/yr, which is already commensurate with the total annual CH\u003csub\u003e4\u003c/sub\u003e emission from the wastewater sector.\u003c/p\u003e \u003cp\u003eThe overlooked CH\u003csub\u003e4\u003c/sub\u003e emissions from landfills are informative to the operations and monitoring throughout their lifespans. A considerable portion of underestimated \u003cem\u003eΣQ\u003c/em\u003e 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 \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003eCMT\u003c/em\u003e\u003c/sub\u003e values instead of the \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003eIPCC\u003c/em\u003e\u003c/sub\u003e values or a fixed delay time. Moreover, the faster waste degradation rates as represented by the higher \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003eCMT\u003c/em\u003e\u003c/sub\u003e values than the \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003eIPCC\u003c/em\u003e\u003c/sub\u003e 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, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Once the post-closure care period is over, the landfill can be redeveloped to alleviate land scarcity. The higher \u003cem\u003eQ\u003c/em\u003e\u003csub\u003e\u003cem\u003eCMT\u003c/em\u003e\u003c/sub\u003e than \u003cem\u003eQ\u003c/em\u003e\u003csub\u003e\u003cem\u003eIPCC\u003c/em\u003e\u003c/sub\u003e of most landfills indicates that the unit costs of implementing CH\u003csub\u003e4\u003c/sub\u003e emission mitigation measures in landfills may be even lower than the current expectations of 0\u0026ndash;10 USD/t CO\u003csub\u003e2\u003c/sub\u003e-eq (IPCC, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). 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 \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003eIPCC\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003eQ\u003c/em\u003e\u003csub\u003e\u003cem\u003eIPCC\u003c/em\u003e\u003c/sub\u003e of a landfill, which underestimate its CH\u003csub\u003e4\u003c/sub\u003e emission compared to reality. Overall, the newly introduced \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003eCMT\u003c/em\u003e\u003c/sub\u003e uncovers new mitigation opportunities obscured by the current \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003eIPCC\u003c/em\u003e\u003c/sub\u003e.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we inspect the \u003cem\u003estatus quo\u003c/em\u003e IPCC\u0026rsquo;s inventory results and atmospheric inversions of global landfill CH\u003csub\u003e4\u003c/sub\u003e emissions, highlighting the issues with the widely accepted \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003eIPCC\u003c/em\u003e\u003c/sub\u003e. Notably, there is significant discrepancy between the IPCC\u0026rsquo;s inventory estimations and reported atmospheric inversions for studied landfills (N\u0026thinsp;=\u0026thinsp;32). After analyzing an exhaustive global landfill database gathered as part of this study, we propose a correction method to convert \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003eIPCC\u003c/em\u003e\u003c/sub\u003e to \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003eCMT\u003c/em\u003e\u003c/sub\u003e by considering the impacts of waste composition, moisture, and temperature on \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003eIPCC\u003c/em\u003e\u003c/sub\u003e. The \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003eCMT\u003c/em\u003e\u003c/sub\u003e method requires similar inputs as the \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003eIPCC\u003c/em\u003e\u003c/sub\u003e, thus is readily integrated into the IPCC\u0026rsquo;s method and maintains its user-friendliness and simplicity. The \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003eCMT\u003c/em\u003e\u003c/sub\u003e 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 CH\u003csub\u003e4\u003c/sub\u003e emissions from landfills.\u003c/p\u003e \u003cp\u003eThe 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\u0026deg;C or even 2\u0026deg;C within the 21st century (IPCC, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), which can be further exacerbated by the underestimated landfill CH\u003csub\u003e4\u003c/sub\u003e emissions uncovered in this study on the order of tens of Tg/yr. In fact, the feasibilities of CH\u003csub\u003e4\u003c/sub\u003e mitigation measures are highly sensitive to their prices. Reducing landfill CH\u003csub\u003e4\u003c/sub\u003e emissions often yields net negative or minimal marginal abatement costs (\u0026lt;\u0026thinsp;1 USD/t CO\u003csub\u003e2\u003c/sub\u003e-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\u0026thinsp;~\u0026thinsp;40 Tg/year of CH\u003csub\u003e4\u003c/sub\u003e 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\u0026thinsp;~\u0026thinsp;50 Tg/year). (EPA, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; H\u0026ouml;glund-Isaksson et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Moreover, if the underestimated CH\u003csub\u003e4\u003c/sub\u003e emissions in this study are counted, zero- or low-cost CH\u003csub\u003e4\u003c/sub\u003e mitigation measures in landfills could contribute optimistically up to 60 Tg/year of CH\u003csub\u003e4\u003c/sub\u003e emission reduction in 2030, which represents one of the most cost-effective mitigation options (IPCC, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Therefore, prioritizing the reduction of landfill CH\u003csub\u003e4\u003c/sub\u003e emissions, especially in developing countries facing economic and technical constraints, can effectively bridge the gap between current emissions and future mitigation targets.\u003c/p\u003e \u003cp\u003eFeasible approaches to reduce landfill CH\u003csub\u003e4\u003c/sub\u003e 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, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2005\u003c/span\u003e), converting up to 50 Tg/yr of CH\u003csub\u003e4\u003c/sub\u003e to 300\u0026nbsp;billion kWh/year of electricity, which is equivalent to saving\u0026thinsp;~\u0026thinsp;50\u0026nbsp;billion USD/year in energy costs (assume CH\u003csub\u003e4\u003c/sub\u003e calorific value is 55,530 kJ/kg, gas engine efficiency is 40% (Johari et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2012\u003c/span\u003e)d average electricity price is 0.15 USD/kWh in 2022). The synergistic effect of timely LFG collection should be greatly promoted.\u003c/p\u003e \u003cp\u003eWe use the \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003eCMT\u003c/em\u003e\u003c/sub\u003e to quantify underestimations of landfill CH\u003csub\u003e4\u003c/sub\u003e emissions, which are the prerequisite for devising appropriate countermeasures. In addition to \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003eCMT\u003c/em\u003e\u003c/sub\u003e, we suggest re-assessing some other factors in the IPCC\u0026rsquo;s method, such as CH\u003csub\u003e4\u003c/sub\u003e oxidation factor (OX), CH\u003csub\u003e4\u003c/sub\u003e correction factor (MCF) for landfill management conditions, and CH\u003csub\u003e4\u003c/sub\u003e recovery rate (R), as their default values are currently considered as obsolete (Spokas et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Spokas et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). More thorough and regular measurements in landfills facilitate the derivation of these site-specific factors (Powell et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). 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, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) Although the verification of \u0026ldquo;bottom-up\u0026rdquo; inventory estimation against \u0026ldquo;top-down\u0026rdquo; 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., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This approach could be initially promoted among Annex I countries to standardize the methodology and subsequently expand to other parts of the world.\u003c/p\u003e"},{"header":"Method","content":"\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eData acquisition\u003c/h2\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003eGlobal waste decay rates (k)\u003c/h2\u003e \u003cp\u003eThe reported values of \u003cem\u003ek\u003c/em\u003e (\u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003er\u003c/em\u003e\u003c/sub\u003e), 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 CH\u003csub\u003e4\u003c/sub\u003e emissions in landfills, either directly reporting \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003er\u003c/em\u003e\u003c/sub\u003e or CH\u003csub\u003e4\u003c/sub\u003e emission data with time. The laboratory tests simulate waste degradation in controlled large-volume reaction vessels (\u0026gt;\u0026thinsp;2 L), and either directly reporting \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003er\u003c/em\u003e\u003c/sub\u003e or CH\u003csub\u003e4\u003c/sub\u003e generation data with time. Screenings were conducted first to ensure data quality (Fig. S2). The \u003cem\u003ek\u003c/em\u003e values were recalculated if CH\u003csub\u003e4\u003c/sub\u003e generation or emission data with time were provided for data consistency, using the regression method described in our previous study (Fei et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) (N\u0026thinsp;=\u0026thinsp;69 for laboratory tests and N\u0026thinsp;=\u0026thinsp;22 for field measurements). If CH\u003csub\u003e4\u003c/sub\u003e generation data was not available in the corresponding literature, directly reported \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003er\u003c/em\u003e\u003c/sub\u003e values were adopted (N\u0026thinsp;=\u0026thinsp;11 for laboratory tests and N\u0026thinsp;=\u0026thinsp;174 for field measurements). A detailed list of references can be found in Supplementary Data Table.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eAnnual waste disposal data\u003c/h2\u003e \u003cp\u003eThe annual waste disposal quantity, also known as landfill activity data in IPCC\u0026rsquo;s guidelines, is the essential data required for calculating annual CH\u003csub\u003e4\u003c/sub\u003e emissions. In the U.S., all landfills are under the framework of the Landfill Methane Outreach Program (LMOP) (EPA, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) 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., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Kaza et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) and assumed that the annual waste disposal quantity is relatively stable from the operation started time.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eWaste composition information\u003c/h2\u003e \u003cp\u003eThe waste composition originally reported in the corresponding literature was recorded (N\u0026thinsp;=\u0026thinsp;77 for laboratory tests and N\u0026thinsp;=\u0026thinsp;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\u0026thinsp;=\u0026thinsp;3 for laboratory tests and N\u0026thinsp;=\u0026thinsp;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, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) (N\u0026thinsp;=\u0026thinsp;19 for field measurements).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eTemperature and moisture conditions\u003c/h2\u003e \u003cp\u003eFor 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., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), to determine the moisture correction factor (f\u003csub\u003eM\u003c/sub\u003e) in the CMT method. For field measurements, ambient temperature and precipitation data were obtained from Climate Data for Cities Worldwide (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://en.climate-data.org/\u003c/span\u003e\u003cspan address=\"https://en.climate-data.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). 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., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). 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., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), which is commonly 5\u0026thinsp;~\u0026thinsp;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).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eLandfill operating conditions\u003c/h2\u003e \u003cp\u003eLandfill operations that could affect CH\u003csub\u003e4\u003c/sub\u003e emissions in each landfill were evaluated to calibrate the parameters in the FOD model, such as CH\u003csub\u003e4\u003c/sub\u003e correction factor for landfill management (MCF), oxidation factor (OX), and CH\u003csub\u003e4\u003c/sub\u003e recovery rate (R). Since we only focused on \u003cem\u003ek\u003c/em\u003e refinement in this study, the recommended values of these parameters by IPCC\u0026rsquo;s guidelines were utilized, based on landfill management, cover type, gas collection systems, and country income level (IPCC, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eAtmospheric inversions of CH\u003csub\u003e4\u003c/sub\u003e fluxes (Q\u003csub\u003er\u003c/sub\u003e)\u003c/h2\u003e \u003cp\u003eIn this study, we compared landfill CH\u003csub\u003e4\u003c/sub\u003e emission inventory with the inversion results to validate the CMT method. Therefore, we selectively included CH\u003csub\u003e4\u003c/sub\u003e fluxes calculated from atmospheric inversions at the level of individual landfills (Cai et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Cambaliza et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Duren et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Lavoie et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Maasakkers et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Each reported CH\u003csub\u003e4\u003c/sub\u003e flux should cover the entire landfill area to ensure comparability with the IPCC\u0026rsquo;s inventory emission. In addition, the selected landfills should have sufficient information supporting emission inventory calculation using IPCC\u0026rsquo;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.\u003c/p\u003e \u003cp\u003e \u003cb\u003ek\u003c/b\u003e \u003cb\u003ecalculation using CMT method\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe CMT method was developed based on the quantitative relationships between \u003cem\u003ek\u003c/em\u003e and waste composition, moisture, and temperature, respectively (Eq.\u0026nbsp;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.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea). We also illustrate an example of utilizing the CMT method to calculate \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003eCMT\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003eQ\u003c/em\u003e\u003csub\u003e\u003cem\u003eCMT\u003c/em\u003e\u003c/sub\u003e for Lakhodair landfill in Lahore, Pakistan (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSimilar to the IPCC\u0026rsquo;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\u003csub\u003eC\u003c/sub\u003e), moisture correction factor (f\u003csub\u003eM\u003c/sub\u003e), and temperature correction factor (f\u003csub\u003eT\u003c/sub\u003e) can be calculated. Subsequently, the \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003eCMT\u003c/em\u003e\u003c/sub\u003e value can be calculated using Eq.\u0026nbsp;1.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eTemperature correction factor\u003c/h2\u003e \u003cp\u003eThe effect of temperature on waste degradation and methanogenesis has been elucidated in previous studies (Rosso et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e1993\u003c/span\u003e; Schupp et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Sun et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The temperature correction factor f\u003csub\u003eT\u003c/sub\u003e can be expressed by a cardinal temperature model with inflection (CTMI) (Eq.\u0026nbsp;3) (Rosso et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e1993\u003c/span\u003e).\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\begin{array}{c}{f}_{T}=\\frac{\\left(T-{T}_{max}\\right){\\times \\left(T-{T}_{min}\\right)}^{2}}{\\left({T}_{opt}-{T}_{min}\\right)\\times \\left[({T}_{opt}-{T}_{min})\\times \\left(T-{T}_{opt}\\right)-\\left({T}_{opt}-{T}_{max}\\right)\\times \\left({T}_{opt}+{T}_{min}-2T\\right)\\right]}\\#\\left(3\\right)\\end{array}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere T\u003csub\u003emin\u003c/sub\u003e, T\u003csub\u003emax\u003c/sub\u003e, and T\u003csub\u003eopt\u003c/sub\u003e are three cardinal temperatures. T\u003csub\u003emin\u003c/sub\u003e refers to the minimum temperature required for the growth of microorganisms, below which no observable microbial activity can occur. T\u003csub\u003emax\u003c/sub\u003e is the maximum temperature at which microbial growth can be sustained, above which growth is not possible. T\u003csub\u003eopt\u003c/sub\u003e 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.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eMoisture correction factor\u003c/h2\u003e \u003cp\u003ePrevious studies (Sun et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Xiao et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) have utilized a sigmoid curve (Eq.\u0026nbsp;4) to describe the effect of moisture on waste degradation. It should be noted that both moisture level and movement can impact CH\u003csub\u003e4\u003c/sub\u003e generation in landfills (Fei et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Hartz and Ham, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e1983\u003c/span\u003e). 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.\u003cdiv id=\"Equd\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equd\" name=\"EquationSource\"\u003e\n$$\\begin{array}{c}{f}_{M}=\\frac{1.2}{{1+e}^{-0.95\\times MC+3.1}}\\#\\left(4\\right)\\end{array}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eComposition correction factor\u003c/h2\u003e \u003cp\u003eThere is currently a lack of a universally accepted method for quantifying the impact of waste composition on \u003cem\u003ek\u003c/em\u003e. Previous studies have reported correlations between \u003cem\u003ek\u003c/em\u003e and certain individual waste constituents, such as paper waste (Qu et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) and food waste (Dearman and Bentham, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). 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., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). 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\u003csub\u003es\u003c/sub\u003e), which includes paper, cardboard, wood, and textile waste; fast-degradable waste (B\u003csub\u003ef\u003c/sub\u003e), which includes food waste and yard waste; and non-biodegradable waste (N\u003csub\u003eb\u003c/sub\u003e), which includes metals, plastics, and other materials that do not degrade.\u003c/p\u003e \u003cp\u003eIn order to quantify the influence of waste composition, the impact of temperature and moisture on \u003cem\u003ek\u003c/em\u003e values should be initially compensated for using Eq.\u0026nbsp;5.\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\begin{array}{c}{k}_{C}=\\frac{{k}_{CMT}}{{{f}_{T}\\times f}_{M}}={k}_{0}\\times {f}_{C}\\#\\left(5\\right)\\end{array}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003eHere, \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003eC\u003c/em\u003e\u003c/sub\u003e denotes the \u003cem\u003ek\u003c/em\u003e value at a given waste composition, while the temperature and moisture are adjusted to optimum baseline conditions, where f\u003csub\u003eT\u003c/sub\u003e = f\u003csub\u003eM\u003c/sub\u003e = 1. The \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003eC\u003c/em\u003e\u003c/sub\u003e values from the laboratory tests and field measurements were subsequently plotted on two ternary diagrams with respect to B\u003csub\u003es\u003c/sub\u003e, B\u003csub\u003ef\u003c/sub\u003e, and N\u003csub\u003eb\u003c/sub\u003e, 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 \u003cem\u003ek\u003c/em\u003e-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 \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003eC\u003c/em\u003e\u003c/sub\u003e values within their ranges (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ec). This discretization approach enables easy identification of the corresponding \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003eC\u003c/em\u003e\u003c/sub\u003e value through Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ec, once the waste composition is known.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003ePrediction of landfill CH\u003csub\u003e4\u003c/sub\u003e emissions\u003c/h2\u003e \u003cp\u003eThe annual CH\u003csub\u003e4\u003c/sub\u003e emissions from landfills (\u003cem\u003eQ\u003c/em\u003e) were calculated using the FOD model following IPCC\u0026rsquo;s guidelines (IPCC, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2006\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) as required by UNFCCC. The \u003cem\u003ek\u003c/em\u003e values used in the improved estimations were \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003eCMT\u003c/em\u003e\u003c/sub\u003e, while the \u003cem\u003ek\u003c/em\u003e values used in the conventional inventory estimations were \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003eIPCC\u003c/em\u003e\u003c/sub\u003e. The values of other parameters were determined in compliance with the IPCC\u0026rsquo;s recommendations.\u003cdiv id=\"Eque\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Eque\" name=\"EquationSource\"\u003e\n$$\\begin{array}{c}{Q}_{y}=\\left(1-{R}_{y}\\right)\\times \\left(1-O{X}_{y}\\right)\\times \\frac{16}{12}\\times F\\times DO{C}_{y}\\\\ \\times MC{F}_{y}\\times \\sum _{x=1}^{y}{[W}_{x}\\times DOC\\times {e}^{-k\\times (y-x)}\\times (1-{e}^{-k})]\\#\\left(6\\right)\\end{array}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cem\u003eQ\u003c/em\u003e\u003csub\u003e\u003cem\u003ey\u003c/em\u003e\u003c/sub\u003e stands for CH\u003csub\u003e4\u003c/sub\u003e emissions occurring in year y from a landfill (Gg/year), R\u003csub\u003ey\u003c/sub\u003e stands for the fraction of CH\u003csub\u003e4\u003c/sub\u003e capture at the landfill and then flared, combusted, or used in another manner that prevents CH\u003csub\u003e4\u003c/sub\u003e emissions in year y, OX\u003csub\u003ey\u003c/sub\u003e stands for the fraction of CH\u003csub\u003e4\u003c/sub\u003e oxidized in the soil or other materials covering the waste in year y, 16/12 stands for the molecular weight ratio of CH\u003csub\u003e4\u003c/sub\u003e/C, F stands for the volume fraction of CH\u003csub\u003e4\u003c/sub\u003e in LFG, DOC\u003csub\u003ey\u003c/sub\u003e stands for the weight fraction of degradable organic carbon (DOC) that degrades in year y, MCF\u003csub\u003ey\u003c/sub\u003e stands for the CH\u003csub\u003e4\u003c/sub\u003e correction factor in year y, W\u003csub\u003ex\u003c/sub\u003e stands for the amount of waste disposed of in the landfill in year x (from 1 to y), and \u003cem\u003ek\u003c/em\u003e stands for the waste decay rate that is either \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003eCMT\u003c/em\u003e\u003c/sub\u003e or \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003eIPCC\u003c/em\u003e\u003c/sub\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eUncertainties and limitations\u003c/h2\u003e \u003cp\u003eThe uncertainty of the CMT method is embodied mainly in the f\u003csub\u003eT\u003c/sub\u003e, f\u003csub\u003eM\u003c/sub\u003e, and \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003eC\u003c/em\u003e\u003c/sub\u003e (Eq.\u0026nbsp;1 and Eq.\u0026nbsp;5). The uncertainty of \u003cem\u003ef\u003c/em\u003e\u003csub\u003e\u003cem\u003eT\u003c/em\u003e\u003c/sub\u003e primarily stems from the determination of the three cardinal temperature, T\u003csub\u003emin\u003c/sub\u003e, T\u003csub\u003emax\u003c/sub\u003e, and T\u003csub\u003eopt\u003c/sub\u003e (Schupp et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Additionally, the variation of annual average temperature may also contribute to the uncertainty of f\u003csub\u003eT\u003c/sub\u003e. The value of f\u003csub\u003eM\u003c/sub\u003e 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\u003csub\u003eM\u003c/sub\u003e by one MC level. The uncertainty of \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003eC\u003c/em\u003e\u003c/sub\u003e primarily arises from the standard deviation of \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003eC\u003c/em\u003e\u003c/sub\u003e within the same small triangle in the discretized ternary diagram (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ec). Due to the individual uncertainty of each predicted \u003cem\u003ek\u003c/em\u003e value, it is impossible to establish a universal uncertainty range for \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003eCMT\u003c/em\u003e\u003c/sub\u003e. However, since confidence intervals for \u003cem\u003ef\u003c/em\u003e\u003csub\u003e\u003cem\u003eT\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003ef\u003c/em\u003e\u003csub\u003e\u003cem\u003eM\u003c/em\u003e\u003c/sub\u003e, and \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003eC\u003c/em\u003e\u003c/sub\u003e are provided, the uncertainty of each \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003eCMT\u003c/em\u003e\u003c/sub\u003e value can be obtained using Monte Carlo simulation. The details of the uncertainty assessment are provided in Supplementary Information Section 4.1.\u003c/p\u003e \u003cp\u003eThe CH\u003csub\u003e4\u003c/sub\u003e emission estimations were based on the FOD model stipulated by UNFCCC, thus carrying uncertainties as described in the IPCC\u0026rsquo;s guidelines (IPCC, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) and UNFCCC\u0026rsquo;s methodologies (UNFCCC, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The main sources of uncertainties in CH\u003csub\u003e4\u003c/sub\u003e emission estimation originate from the input parameters in Eq.\u0026nbsp;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, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). 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.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe 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.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contribution Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eY.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.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interest Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data and results are presented in this paper and supplementary information.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe custom computer code or algorithms used to generate the results in this study can be made available to researchers upon request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCai, B., Lou, Z., Wang, J., Geng, Y., Sarkis, J., Liu, J., and Gao, Q. (2018). 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Nature Food, 1\u0026ndash;10.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Nanyang Technological University","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-3423080/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3423080/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eLandfill methane (CH\u003csub\u003e4\u003c/sub\u003e) emissions account for ~\u0026thinsp;10% of all anthropogenic CH\u003csub\u003e4\u003c/sub\u003e emissions globally, amounting to ~\u0026thinsp;50 Tg/year. Contrasted by \u0026ldquo;top-down\u0026rdquo; atmospheric inversion results, the mainstream \u0026ldquo;bottom-up\u0026rdquo; emission inventories, which use the Intergovernmental Panel on Climate Change\u0026rsquo;s (IPCC) model, exhibit significant bias due to inaccurate \u003cem\u003ea priori\u003c/em\u003e decay constant (\u003cem\u003ek\u003c/em\u003e) estimations. We improved the \u003cem\u003ek\u003c/em\u003e estimation method by incorporating composition- and environment-specific corrections, which are readily integrated into the IPCC\u0026rsquo;s model. We demonstrate that the accuracies of CH\u003csub\u003e4\u003c/sub\u003e emission predictions are significantly improved by using the corrected \u003cem\u003ek\u003c/em\u003e 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 CH\u003csub\u003e4\u003c/sub\u003e emission globally. Our findings highlight the importance of prioritizing landfill CH\u003csub\u003e4\u003c/sub\u003e emission monitoring and reduction as one of the most cost-effective mitigation options to achieve current climate goals.\u003c/p\u003e","manuscriptTitle":"Up to 200% underestimation of cumulative methane emission from individual landfills uncovers tens of Tg of overlooked annual emission worldwide","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-10-09 21:58:40","doi":"10.21203/rs.3.rs-3423080/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"060a4f71-2224-4d69-b786-e1d9b054c0fb","owner":[],"postedDate":"October 9th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":25248339,"name":"Climate Analysis and Modeling"},{"id":25248340,"name":"Environmental Engineering"},{"id":25248341,"name":"Civil Engineering"}],"tags":[],"updatedAt":"2023-10-09T21:58:40+00:00","versionOfRecord":[],"versionCreatedAt":"2023-10-09 21:58:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3423080","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3423080","identity":"rs-3423080","version":["v1"]},"buildId":"ehx78VzkSd0WSzXnipQa-","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}