气象驱动的垃圾填埋场逸散排放因果临近预报技术可实现主动公共卫生响应

中文摘要 AI 辅助

垃圾填埋场的逸散排放正日益使周边社区暴露于有毒且有异味的气体中，但目前公共卫生响应仍以回溯性为主，仅在居民暴露后才对相关事件开展调查。本研究表明，可直接从常规监测数据中识别欧洲某长期监测垃圾填埋场硫化氢（H₂S）浓度升高的气象驱动因素及其作用时间尺度。研究人员提出CAIRN（因果锚定受体临近推断框架，Causal-Anchored Inference for Receptor Nowcasting），这一机器学习框架的内部记忆与测得的时间尺度匹配：快速分量追踪小时尺度的风传输送，慢速分量追踪多小时的天气变化。该框架仅利用常规气象变量和日历信息进行训练以预测气体测量值，无需人工设计特征；其行为与已识别的输送机制一致，且可原封不动地迁移至第二个监测站及共排放的甲烷。将四个此类临近预报器组合后，可生成符合世界卫生组织（WHO）异味指南的站点分级警报，其与直接传感器网络生成的警报高度吻合，且能跟踪独立的社区异味投诉记录。因此，气象驱动的临近预报可在排放事件发生时估算其对社区的影响，为公共卫生部门提供经验证的分级干预触发条件，从而在事件期间而非之后减少居民暴露。

英文摘要

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$_2$S) 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.

发表机构

• UKHSA（英国卫生安全局）

机构由 AI 辅助整理，请以论文原文为准。