Machine Learning Framework Predicts Landfill Gas Emissions Using Weather Data

A new machine learning framework, CAIRN (Causal-Anchored Inference for Receptor Nowcasting), has been developed to predict fugitive landfill emissions, specifically hydrogen sulphide (HS), using routine weather variables and calendar data. The research, published on arXiv (2608.14254), demonstrates that meteorological drivers of elevated HS at a long-monitored European landfill can be identified directly from monitoring data. CAIRN's internal memory is designed to match measured timescales: a fast component tracks hour-scale wind-borne transport, while a slow component tracks multi-hour weather changes. The framework operates without hand-engineered features and its behavior is consistent with identified transport mechanisms. This approach enables proactive public health responses, shifting from retrospective investigations to real-time predictions, potentially reducing community exposure to toxic and odorous gases. The study highlights the potential of machine learning in environmental monitoring and public health protection.