ABSTRACT

Smoke impacts from large wildfires are mounting, and the projection is for more such events in the future as the one experienced October 2017 in Northern California, and subsequently in 2018 and 2020. Further, the evidence is growing about the health impacts from these events which are also difficult to simulate. Therefore, we simulated air quality conditions using a suite of remotely-sensed data, surface observational data, chemical transport modeling with WRF-CMAQ, one data fusion, and three machine learning methods to arrive at datasets useful to air quality and health impact analyses. To demonstrate these analyses, we estimated the health impacts from smoke impacts during wildfires in October 8–20, 2017, in Northern California, when over 7 million people were exposed to Unhealthy to Very Unhealthy air quality conditions. We investigated using the 5-min available GOES-16 fire detection data to simulate timing of fire activity to allocate emissions hourly for the WRF-CMAQ system. Interestingly, this approach did not necessarily improve overall results, however it was key to simulating the initial 12-hr explosive fire activity and smoke impacts. To improve these results, we applied one data fusion and three machine learning algorithms. We also had a unique opportunity to evaluate results with temporary monitors deployed specifically for wildfires, and performance was markedly different. For example, at the permanent monitoring locations, the WRF-CMAQ simulations had a Pearson correlation of 0.65, and the data fusion approach improved this (Pearson correlation = 0.95), while at the temporary monitor locations across all cases, the best Pearson correlation was 0.5. Overall, WRF-CMAQ simulations were biased high and the geostatistical methods were biased low. Finally, we applied the optimized PM_2.5 exposure estimate in an exposure-response function. Estimated mortality attributable to PM_2.5 exposure during the smoke episode was 83 (95% CI: 0, 196) with 47% attributable to wildland fire smoke.

Implications: Large wildfires in the United States and in particular California are becoming increasingly common. Associated with these large wildfires are air quality and health impact to millions of people from the smoke. We simulated air quality conditions using a suite of remotely-sensed data, surface observational data, chemical transport modeling, one data fusion, and three machine learning methods to arrive at datasets useful to air quality and health impact analyses from the October 2017 Northern California wildfires. Temporary monitors deployed for the wildfires provided an important model evaluation dataset. Total estimated regional mortality attributable to PM_2.5 exposure during the smoke episode was 83 (95% confidence interval: 0, 196) with 47% of these deaths attributable to the wildland fire smoke. This illustrates the profound effect that even a 12-day exposure to wildland fire smoke can have on human health.

摘要

大型野火造成的烟雾影响正在加剧，预计未来会发生更多此类事件，如 2017 年 10 月在北加州发生的那场事件，以及随后在 2018 年和 2020 年发生的事件。此外，越来越多的证据表明这些事件对健康的影响也很难模拟。因此，我们使用一套遥感数据、表面观测数据、WRF-CMAQ 化学传输模型、一个数据融合和三种机器学习方法来模拟空气质量状况，以获得可用于空气质量和健康影响分析的数据集。为了证明这些分析，我们估计了 2017 年 10 月 8 日至 20 日北加州野火期间烟雾对健康的影响，当时超过 700 万人暴露在不健康到非常不健康的空气质量条件下。我们使用 5 分钟可用的 GOES-16 火灾探测数据来模拟火灾活动的时间，以便为 WRF-CMAQ 系统分配每小时的排放量。有趣的是，这种方法不一定能改善整体结果，但它是模拟最初 12 小时爆炸性火灾活动和烟雾影响的关键。为了改善这些结果，我们应用了一种数据融合和三种机器学习算法。我们还有一个独特的机会来评估专门为野火部署的临时监视器的结果，并且性能明显不同。例如，在永久监测位置，WRF-CMAQ 模拟的皮尔逊相关性为 0.65，数据融合方法改进了这一点（皮尔逊相关性 = 0.95），而在所有情况下的临时监测位置，最佳皮尔逊相关性为0.5。总体而言，WRF-CMAQ 模拟的偏差较高，而地统计方法的偏差较低。最后，我们在暴露响应函数中应用了优化的 PM _{2.5暴露估计。}烟雾事件期间因接触PM _{2.5造成的估计死亡率为 83 人 (95% CI: 0, 196)，其中 47% 因野火烟雾造成。}

影响：美国，尤其是加利福尼亚州的大型野火变得越来越常见。与这些大规模野火相关的是烟雾对空气质量和数百万人健康的影响。我们使用一套遥感数据、表面观测数据、化学传输模型、一种数据融合和三种机器学习方法来模拟空气质量状况，以获得对 2017 年 10 月北加州野火的空气质量和健康影响分析有用的数据集。为野火部署的临时监测器提供了重要的模型评估数据集。烟雾事件期间暴露于 PM _{2.5 的}区域死亡率估计总数为 83 例（95% 置信区间：0, 196），其中 47% 的死亡归因于荒地火灾烟雾。这说明即使暴露在野外火灾烟雾中 12 天也会对人类健康产生深远影响。