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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]
Copyright of Environmental Quality Management is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Quantifying Methane Emissions and Energy Recovery Potential From Landfill Sites: Insights From Statistical Machine Learning and Predictive Models.
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  Data: <searchLink fieldCode="AR" term="%22Bhuiya%2C+Khaled+Mohammad+Shifullah%22">Bhuiya, Khaled Mohammad Shifullah</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Azad%2C+A+M+Almas+Shahriyar%22">Azad, A M Almas Shahriyar</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> aaaarnab1234@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Udoy%2C+Sabbir+Ahmed%22">Udoy, Sabbir Ahmed</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Islam%2C+Ariful%22">Islam, Ariful</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Das%2C+Pronob%22">Das, Pronob</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Haque%2C+Md%2E+Ariful%22">Haque, Md. Ariful</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Hasan%2C+Md%2E+Hasibul%22">Hasan, Md. Hasibul</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Hasan%2C+Mehedi%22">Hasan, Mehedi</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Rana%2C+Md%2E+Sohel%22">Rana, Md. Sohel</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Al‐Fahim%2C+Abdullah%22">Al‐Fahim, Abdullah</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Shoshi%2C+Sumaiya+Rashid%22">Shoshi, Sumaiya Rashid</searchLink><relatesTo>2</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Environmental+Quality+Management%22">Environmental Quality Management</searchLink>. Fall2025, Vol. 35 Issue 1, p1-18. 18p.
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  Data: *<searchLink fieldCode="DE" term="%22Landfills%22">Landfills</searchLink><br />*<searchLink fieldCode="DE" term="%22Climate+change%22">Climate change</searchLink><br />*<searchLink fieldCode="DE" term="%22Greenhouse+gas+mitigation%22">Greenhouse gas mitigation</searchLink><br />*<searchLink fieldCode="DE" term="%22Waste+management%22">Waste management</searchLink><br />*<searchLink fieldCode="DE" term="%22Waste+recycling%22">Waste recycling</searchLink><br /><searchLink fieldCode="DE" term="%22Prediction+models%22">Prediction models</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+learning%22">Statistical learning</searchLink><br /><searchLink fieldCode="DE" term="%22Sensitivity+analysis%22">Sensitivity analysis</searchLink>
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  Data: <searchLink fieldCode="DE" term="%22Bangladesh%22">Bangladesh</searchLink>
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  Data: <searchLink fieldCode="DE" term="%22Intergovernmental+Panel+on+Climate+Change%22">Intergovernmental Panel on Climate Change</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: 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]
– Name: AbstractSuppliedCopyright
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  Data: <i>Copyright of Environmental Quality Management is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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    Identifiers:
      – Type: doi
        Value: 10.1002/tqem.70179
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 18
        StartPage: 1
    Subjects:
      – SubjectFull: Landfills
        Type: general
      – SubjectFull: Climate change
        Type: general
      – SubjectFull: Greenhouse gas mitigation
        Type: general
      – SubjectFull: Waste management
        Type: general
      – SubjectFull: Waste recycling
        Type: general
      – SubjectFull: Prediction models
        Type: general
      – SubjectFull: Statistical learning
        Type: general
      – SubjectFull: Sensitivity analysis
        Type: general
      – SubjectFull: Bangladesh
        Type: general
      – SubjectFull: Intergovernmental Panel on Climate Change
        Type: general
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      – TitleFull: Quantifying Methane Emissions and Energy Recovery Potential From Landfill Sites: Insights From Statistical Machine Learning and Predictive Models.
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            NameFull: Bhuiya, Khaled Mohammad Shifullah
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            – D: 01
              M: 09
              Text: Fall2025
              Type: published
              Y: 2025
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