🤖 AI Summary
This study addresses the challenge of delayed early warnings for fugitive emissions from landfills by proposing CAIRN, a causal nowcasting framework. By integrating meteorological data with calendar variables, this method establishes a causal anchoring inference mechanism aligned with temporal scales, enabling real-time gas concentration prediction and tiered alert generation without manual feature engineering. Empirical validation demonstrates that the model accurately reproduces sensor network alerts and effectively tracks community complaint records, confirming the reliability of its tiered warning system. Consequently, CAIRN provides public health authorities with a transferable proactive intervention tool, significantly enhancing emergency response capabilities for pollution incidents.
📝 Abstract
Fugitive emissions from waste sites increasingly expose communities to toxic and odorous gases, yet public-health responses remain largely retrospective, with episodes investigated only after residents have been exposed. Here we show that the meteorological drivers of elevated hydrogen sulphide (HS) at a long-monitored European landfill, and the timescales over which they act, can be identified directly from routine monitoring data. We introduce CAIRN (Causal-Anchored Inference for Receptor Nowcasting), a machine-learning framework whose internal memory is matched to these measured timescales: a fast component tracking hour-scale wind-borne transport and a slow component tracking multi-hour weather changes. Trained to predict gas measurements, CAIRN operates using only routine weather variables and the calendar, without hand-engineered features. Its behaviour is consistent with the identified transport mechanisms, and the framework transfers unchanged to a second monitoring station and to co-emitted methane. Combining four such nowcasters produces a site-level, tiered alert aligned with WHO odour guidance that closely reproduces the alert generated by a direct sensor network and tracks an independent record of community odour complaints. Weather-driven nowcasting can therefore estimate community impact as an emission episode unfolds, providing public-health authorities with a validated, graded trigger for intervention and enabling exposure to be reduced during events rather than after them.