Ehsan Mohammad Hassani, Reza Rafei, Mazaher Moeinaddini, Niki Aghapour,
Volume 17, Issue 1 (6-2024)
Background and Objective: One of the largest sources of methane emissions is landfills, and various models have been developed to predict landfill methane production and emissions. The main goal of this research is to utilize the inverse Gaussian model to estimate g methane greenhouse gas emissions and model it using field data. This study introduces a simple method to estimate the amount of methane emissions based on ambient methane concentrations.
Materials and Methods: In this research, the methane emission rates from landfill were estimated for warm (July) and cold (February) seasons using a sampling campaign from 27 stations and standard inverse Gaussian dispersion equations. Monte Carlo simulation was also employed. To determine the model, an optimization-based method, along with inverse scattering modeling, was utilized to process surface emission monitoring data.
Results: The model results indicated during the cold (February) and warm (July) seasons, the methane emission rates were estimated at 1696.99 and 16.53 g/s, respectively. These findings confirm that the methane production and emission during the cold season were lower than in the warm season, likely due to reduced temperature and bacterial activity.
Conclusion: The method used in this study, the inverse Gaussian dispersion model, can be applied to estimate methane gas emission rates from other landfills. However, it necessitates the permanent recording of data and the use of daily or weekly averages in calculations to mitigate potential errors and enhance the accuracy of modeling.
