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  9. Air Pollution Modelling And Control
  11. An Assessment Of The Uncertainties Of Methane Generation In Landfills

An assessment of the uncertainties of methane generation in landfills

Mohammad Ali Rasouli1, Mehran Karimpour-Fard1, Sandro Lemos Machado2

  • 1Department of Civil Engineering, Iran University of Science and Technology, Teharn, Iran.

Journal of the Air & Waste Management Association (1995)
|March 12, 2025

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View abstract on PubMed

Summary
This summary is machine-generated.

Accurate landfill methane estimation requires site-specific data, as waste age significantly impacts methane potential. Bayesian inference combined with assays reveals early-stage uncertainty, improving long-term predictions for greenhouse gas management.

Area of Science:

  • Environmental Science
  • Chemical Engineering
  • Waste Management

Background:

  • Accurate methane generation estimation is vital for landfill greenhouse gas management and energy recovery.
  • Landfill waste composition and properties vary significantly, necessitating site-specific assessments.
  • Existing models often lack robust uncertainty quantification for methane predictions.

Purpose of the Study:

  • To investigate uncertainties in methane generation predictions using stoichiometric methods, Biochemical Methane Potential (BMP) assays, and Bayesian inference.
  • To evaluate the methane generation potential and degradation rate of fresh and aged landfill waste.
  • To advance uncertainty quantification approaches for landfill methane estimation models.

Main Methods:

  • Collection and analysis of fresh, 1-year-old, and 5-year-old waste samples from Saravan dump site, Iran.

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  • Application of stoichiometric methods and Biochemical Methane Potential (BMP) assays.
  • Utilizing Bayesian inference with Markov Chain Monte Carlo (MCMC) simulations for uncertainty quantification and sensitivity analysis.

    • Main Results:

    • Methane generation potential (L0) decreased with waste age: 83.4 m³ CH₄/Mg MSW (fresh) to 32.8 m³ CH₄/Mg MSW (5-year-old).
    • Bayesian inference showed highest uncertainty in early years, declining with waste stabilization, improving long-term accuracy.
    • Decay rate constant (k) was determined as 0.26 year⁻¹, consistent with humid area guidelines.

    Conclusions:

    • Integrating stoichiometric analysis, BMP assays, and Bayesian inference enhances landfill methane generation estimates.
    • Uncertainty in ultimate methane potential and decay rate is significant, particularly in early waste stages.
    • A comprehensive, site-specific framework improves methane prediction reliability, informing greenhouse gas management and waste practices.