Meteorology-driven Causal Nowcasting of Fugitive Landfill Emissions from Measured Coupling Timescales
Abstract
Which meteorological processes control exposure to fugitive gases downwind of a source, and on what timescales, have largely been inferred from dispersion theory and partial field evidence. Here we show that the meteorological drivers of elevated hydrogen sulphide (H2S) exposure at a long-monitored European landfill, and the timescales over which each acts, can be identified directly from monitoring data. Wind direction, wind speed and atmospheric pressure form the causal core, with the share of directed information carried by pressure increasing with aggregation scale. The recovered timescales are consistent with those expected from the underlying atmospheric processes. We use these driver timescales to initialise CAIRN (Causal-Anchored Inference for Receptor Nowcasting), a machine-learning nowcaster with fast and slow memory components. Trained on past exceedances of WHO guideline levels, CAIRN nowcasts them from surface weather measurements and the calendar alone, without hand-engineered features. Combining four such nowcasters produces a site-level, tiered alert that agrees substantially with that generated by a direct sensor network and tracks an independent record of community odour reports. Meteorological variables can therefore serve as an inference-time proxy for exposure relative to WHO guideline levels, and they link atmospheric dynamics to community impact as an episode unfolds.
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BibTeX
@misc{pearce2026meteorologydriven,
title = {{Meteorology-driven Causal Nowcasting of Fugitive Landfill Emissions from Measured Coupling Timescales}},
author = {Timothy C. Pearce and David J. T. Smith and Alec Dobney and Alessia Freddo},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2608.14254},
eprint = {2608.14254},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2608.14254},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Meteorology-driven Causal Nowcasting of Fugitive Landfill Emissions from Measured Coupling Timescales
AU - Timothy C. Pearce
AU - David J. T. Smith
AU - Alec Dobney
AU - Alessia Freddo
PY - 2026
DA - 2026/10/01
PB - arXiv
DO - 10.48550/arxiv.2608.14254
UR - https://arxiv.org/abs/2608.14254
AB - Which meteorological processes control exposure to fugitive gases downwind of a source, and on what timescales, have largely been inferred from dispersion theory and partial field evidence. Here we show that the meteorological drivers of elevated hydrogen sulphide (H_2S) exposure at a long-monitored European landfill, and the timescales over which each acts, can be identified directly from monitoring data. Wind direction, wind speed and atmospheric pressure form the causal core, with the share of directed information carried by pressure increasing with aggregation scale. The recovered timescales are consistent with those expected from the underlying atmospheric processes. We use these driver timescales to initialise CAIRN (Causal-Anchored Inference for Receptor Nowcasting), a machine-learning nowcaster with fast and slow memory components. Trained on past exceedances of WHO guideline levels, CAIRN nowcasts them from surface weather measurements and the calendar alone, without hand-engineered features. Combining four such nowcasters produces a site-level, tiered alert that agrees substantially with that generated by a direct sensor network and tracks an independent record of community odour reports. Meteorological variables can therefore serve as an inference-time proxy for exposure relative to WHO guideline levels, and they link atmospheric dynamics to community impact as an episode unfolds.
ER -