评估小时级空气污染预报对个人出行决策的实用价值
Are Hourly PM2.5 Forecasts Sufficiently Accurate to Plan Your Day? Individual Decision Making in the Face of Increasing Wildfire Smoke
- 对比六种模型在2023年美国野火季的PM2.5预报表现
- 发现现有预报对户外活动时间选择帮助有限，存在明显改进空间
- 提出新评估指标，适合关注空气质量与健康防护的研究者
随着气候变化导致野火频发，空气污染带来的健康风险日益严重。人们常依据小时天气预报安排户外活动，类似地，可靠的小时级空气质量预报可能帮助个体减少污染暴露。本文评估了2023年美国本土期间六种地面细颗粒物（PM2.5）预报模型的表现，涵盖物理模拟、集成方法和人工智能模型。研究聚焦于个人决策场景，如：是否在高PM2.5日外出，或选择何时外出以降低暴露。通过特定地点的小时预报可视化及任务相关指标分析，引入新的外出时间决策评估指标。结果表明，当前预报在支持个人健康决策方面仍有显著提升空间，可通过改进物理模型、融合更多数据源及应用人工智能实现优化。
原文摘要 · Abstract (English)
Wildfire frequency is increasing as the climate changes, and the resulting air pollution poses health risks. Just as people routinely use hourly weather forecasts to plan their day's activities around precipitation, reliable hourly air quality forecasts could help individuals reduce their exposure to air pollution. In the present work, we evaluate six existing forecasts of ground-level fine particulate matter (PM2.5) within the continental United States during the 2023 fire season. We include forecasts using physical simulation, ensembling, and artificial intelligence. We focus our evaluation on individual decisions, such as (1) whether to go outside on a day with potentially high PM2.5 or (2) when to go outside for the lowest PM2.5 exposure. Our evaluation consists of both visualizations of hourly PM2.5 forecasts in particular locations as well as metrics summarizing forecast skill for the two tasks above. As part of our analysis, we introduce a new evaluation metric for the task of deciding when to go outside. We find meaningful room for improvement in PM2.5 forecasting, which might be realized by improving physical models, incorporating more data sources, and using artificial intelligence tools.
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