Assessment of exposure and mortality attributable to air pollution is one of the most important tasks to inform strategies for mitigation and prevention of the negative effects of degraded air quality. While the vast majority of studies rely on either satellite data or sparse (and expensive) monitoring networks and focus on global- or regional-scale assessments, emerging low-cost citizen science networks are a new source of information that can be used to provide more detailed estimates at urban scale, thereby refining previous estimates and providing spatially resolved information at census tract resolution. In this work, we demonstrate how a citizen science network, validated with a reference air pollution station, provides sensibly different (and spatially resolved) estimates of air pollution levels and associated exposure/mortality than assuming constant air pollution across the analyzed urban area. Specifically we focus on the extreme pollution episode of the 2020 California wildfires that significantly impacted the city of San Francisco. We estimate that during the wildfires, degraded air quality was responsible for an increase between 2 and 4% of the baseline mortality in the same city over the time period of study (August 19, 2020 to September 30, 2020).

评估空气污染造成的暴露和死亡率是最重要的任务之一，可以为缓解和预防空气质量下降的负面影响的战略提供信息。虽然绝大多数研究依赖于卫星数据或稀疏（且昂贵）的监测网络，并侧重于全球或区域规模的评估，但新兴的低成本公民科学网络是一种新的信息来源，可用于提供更多信息。在城市尺度上进行详细估计，从而改进先前的估计并提供人口普查区域分辨率的空间解析信息。在这项工作中，我们展示了公民科学网络如何通过参考空气污染站进行验证，与假设所分析的城市地区的空气污染持续存在相比，它提供了对空气污染水平和相关暴露/死亡率明显不同（和空间解析）的估计。具体而言，我们关注 2020 年加利福尼亚野火的极端污染事件，该事件严重影响了旧金山市。我们估计，在野火期间，空气质量下降导致同一城市在研究期间（2020 年 8 月 19 日至 2020 年 9 月 30 日）的基线死亡率增加了 2% 至 4%。