An assessment of the uncertainties of methane generation in landfills.

Abstract

The accurate estimation of methane generation in landfills is crucial for effective greenhouse gas management and energy recovery, requiring site-specific assessments due to the inherent variability in waste composition and properties before and after disposal. This study investigates the uncertainties associated with methane generation predictions by employing a combination of stoichiometric methods, Biochemical Methane Potential (BMP) assays, and Bayesian inference. Fresh and aged (1-year-old and 5-year-old) samples collected in the tropical Saravan dump site in Gilan, Iran, were used to evaluate the waste's methane generation potential and degradation rate in the field. The average methane generation potential (L₀) for fresh samples by the stoichiometric simplified method was 83.4 m³ CH₄/Mg MSW, which decreased to 44.8 m³ CH₄/Mg MSW and 32.8 m³ CH₄/Mg MSW for 1-year-old and 5-year-old waste samples, respectively. The BMP tests led to similar results, further validating the decreasing trend of L₀ with waste age. The Bayesian approach combined with MCMC simulations revealed that uncertainty in methane estimation is highest in the early years and gradually declines as waste stabilizes, improving long-term prediction accuracy. By integrating sensitivity analysis with Bayesian inference, this study advances uncertainty quantification approaches, addressing limitations in existing landfill methane estimation models. This innovative framework identifies the most influential parameters, providing a robust foundation for refining predictive models. The decay rate constant (k) was determined to be 0.26 year^-1, aligned with the guidelines for humid areas. Notably, the highest standard deviation in methane estimation was observed during the initial post-disposal years, reaching 1,384,751.5 m³ CH₄/year using the BMP method and 2,266,762 m³ CH₄/year with the simplified method, highlighting how early-stage variability impacts overall methane predictions, emphasizing the critical need for site-specific data. These insights contribute to improved landfill gas management strategies and support decision-making for sustainable waste management practices.Implications: This research underscores the importance of integrating methodologies like stoichiometric analysis, BMP assays, and Bayesian inference to enhance methane generation estimates from landfills. A significant outcome is the recognition of the inherent uncertainty in key parameters, particularly ultimate methane potential and decay rate constant. By employing Bayesian inference and Monte Carlo simulation, we quantified the uncertainty associated with these parameters and analyzed its influence on methane production predictions. The findings reveal that different methodologies yield varying levels of uncertainty, highlighting the necessity for a comprehensive framework that utilizes site-specific data. This approach not only improves the reliability of methane estimates but also informs greenhouse gas management strategies, fostering more effective decision-making in waste management practices.