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Quantifying Methane Emissions and Energy Recovery Potential From Landfill Sites: Insights From Statistical Machine Learning and Predictive Models.

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Title: Quantifying Methane Emissions and Energy Recovery Potential From Landfill Sites: Insights From Statistical Machine Learning and Predictive Models.
Authors: Bhuiya, Khaled Mohammad Shifullah1 (AUTHOR), Azad, A M Almas Shahriyar2 (AUTHOR) aaaarnab1234@gmail.com, Udoy, Sabbir Ahmed1 (AUTHOR), Islam, Ariful3 (AUTHOR), Das, Pronob1 (AUTHOR), Haque, Md. Ariful2 (AUTHOR), Hasan, Md. Hasibul1 (AUTHOR), Hasan, Mehedi1 (AUTHOR), Rana, Md. Sohel1 (AUTHOR), Al‐Fahim, Abdullah1 (AUTHOR), Shoshi, Sumaiya Rashid2 (AUTHOR)
Source: Environmental Quality Management. Fall2025, Vol. 35 Issue 1, p1-18. 18p.
Subject Terms: *Landfills, *Climate change, *Greenhouse gas mitigation, *Waste management, *Waste recycling, Prediction models, Statistical learning, Sensitivity analysis
Geographic Terms: Bangladesh
Company/Entity: Intergovernmental Panel on Climate Change
Abstract: In developing countries, open landfills are major contributors to methane emissions. This study assesses methane emissions and energy recovery potential at the Nawdapara Landfill Site, Bangladesh, using four predictive models: United States Environmental Protection Agency (USEPA) LandGEM (v3.02), IPCC zero‐order decay method (ZODM), Intergovernmental Panel on Climate Change (IPCC) first‐order decay method (FODM), and modified triangular method (MTM). ZODM shows the highest energy recovery potential at 1.54 MW/year, while MTM presents the lowest at 0.206 MW/year. Methane generation predictions range from 4755.9 Mg/year for ZODM to 632.5 Mg/year for MTM. Sensitivity analysis via Monte Carlo simulations (MCS) reveals that FODM and ZODM are highly sensitive to input variability, while LandGEM and MTM provide more stable estimates. The study also incorporates statistical machine learning (SML) techniques for ZODM and MTM, which explain methane generation variability based on rainfall, temperature, and solid waste data. SML models for FODM and LandGEM account for over 92% of the observed variability. This research highlights the complex relationship between methane generation and climatic factors, offering a novel approach to predicting methane emissions and energy potential from landfills, thereby filling a critical knowledge gap in sustainable waste prediction strategies. [ABSTRACT FROM AUTHOR]
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Database: GreenFILE
Description
Abstract:In developing countries, open landfills are major contributors to methane emissions. This study assesses methane emissions and energy recovery potential at the Nawdapara Landfill Site, Bangladesh, using four predictive models: United States Environmental Protection Agency (USEPA) LandGEM (v3.02), IPCC zero‐order decay method (ZODM), Intergovernmental Panel on Climate Change (IPCC) first‐order decay method (FODM), and modified triangular method (MTM). ZODM shows the highest energy recovery potential at 1.54 MW/year, while MTM presents the lowest at 0.206 MW/year. Methane generation predictions range from 4755.9 Mg/year for ZODM to 632.5 Mg/year for MTM. Sensitivity analysis via Monte Carlo simulations (MCS) reveals that FODM and ZODM are highly sensitive to input variability, while LandGEM and MTM provide more stable estimates. The study also incorporates statistical machine learning (SML) techniques for ZODM and MTM, which explain methane generation variability based on rainfall, temperature, and solid waste data. SML models for FODM and LandGEM account for over 92% of the observed variability. This research highlights the complex relationship between methane generation and climatic factors, offering a novel approach to predicting methane emissions and energy potential from landfills, thereby filling a critical knowledge gap in sustainable waste prediction strategies. [ABSTRACT FROM AUTHOR]
ISSN:10881913
DOI:10.1002/tqem.70179