Estimation of fugitive landfill methane emissions using surface emission monitoring and Genetic Algorithms optimization

摘要

Highlights•Genetic Algorithms based optimization combined with standard Gaussian dispersion model are employed to identify locations and emission rates.•Results showed that the outcomes of the two methods are comparable confirming the effectiveness of the methodology.•The proposed methodology is a good step toward assisting landfill operators to reasonably estimate and locate major methane emissions.AbstractAs municipal solid waste (MSW) landfills can generate significant amounts of methane, there is considerable interest in quantifying fugitive methane emissions at such facilities. A variety of methods exist for the estimation of methane emissions from landfills. These methods are either based on analytical emission models or on measurements. This paper presents a method to estimate methane emissions using ambient air methane measurements obtained on the surface of a landfill. Genetic Algorithms based optimization combined with the standard Gaussian dispersion model is employed to identify locations as well as emission rates of potential emission sources throughout a municipal solid waste landfill. Four case studies are employed in order to evaluate the performance of the proposed methodology. It is shown that the proposed approach enables estimation of landfill methane emissions and localization of major emission hotspots in the studied landfills. The proposed source-locating-scheme could be seen as a cost effective method assisting landfill operators to reasonably estimate and locate major methane emissions.