The importance of particularising the model to estimate landfill GHG emissions

Methane generation in landfills can be estimated using mathematical models. One of the most widespread estimation models is that developed by the Intergovernmental Panel on Climate Change (IPCC). Despite its popularity, the simplicity that characterises this model markedly limits the possibility of representing operation alternatives, which can strongly impact surface emissions and hinder the introduction of local data that are sometimes available. In this study, the IPCC model was applied to a case study from which field data on gas emissions were available. To fit the model to the studied landfill conditions, a series of modifications were made, including changes in Degradable Organic Carbon (DOC) and methane generation rate constant (k) values, and degradation times for some waste fractions, and by considering leachate carbon and the inclusion of gas lateral migration phenomena or changes in the methane oxidation factor. The model's Final Version improved the fit of its Initial Version to the experimentally estimated values in the case study by more than 65%. Some modifica-tions, such as considering the carbon dragged by leachate or the contour migration of gas, have a minor impact on the model's fit. However, changes in the degradation time of some fractions according to their particular pretreatment or the modification of parameter k in accordance with the moisture conditions in each landfill phase, strongly influence the model's results. This highlights the importance of particularising estimation models to achieve more accurate results, which allow better estimates of the efficiency of mitigation measures for landfill gas emissions in each facility.

Automated summary

This study applied the IPCC model to a case study on landfill gas emissions and made modifications to improve its fit to experimental values. The final version of the model showed a fit improvement of more than 65%. Some modifications had minor impacts on the model's fit, while personalized modifications based on waste treatment and moisture conditions strongly influenced the results. This highlights the importance of personalized estimation models for accurate results in mitigating landfill gas emissions.