Quantifying meteorological impacts on local landfill methane emission by using field measurements and machine learning

Abstract. Landfills are a major anthropogenic source of methane (CH₄), contributing up to 20 % of global CH₄ emissions. Although CH₄ emissions from landfills are highly sensitive to meteorological conditions, their response to climate variations remains poorly understood, leading to substantial uncertainty in emission projections under climate change. This study evaluated the impact of meteorological factors on landfill CH₄ generation, using a site-specific machine-learning-based model optimized for temperature and precipitation. The model optimized for meteorological conditions performed better than conventional models such as LandGEM and the IPCC model, with a root mean squared error (RMSE) of 6.57 million m³ CH₄, a mean absolute error (MAE) of 4.91 million m³ CH₄, and Pearson correlation coefficients of 0.89, when compared with field measurements. CH₄ generation exhibited a linear correlation with increasing temperature, and a parabolic response to increasing precipitation. Quantification of the contributions of the meteorological variables, revealed that temperature accounted for 5.96±3.06 %, and precipitation for 7.38±0.58 % of the total modeled CH₄ generation. These results highlight the high importance of incorporating meteorological variability into landfill CH₄estimation to improve predictive accuracy, and emphasize the need of stronger and faster CH₄ mitigation efforts under climate change.