Community-Based Measurements Reveal Unseen Differences during Air Pollution Episodes
Community-Based Measurements Reveal Unseen Differences during Air Pollution Episodes
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DOI：
10.1021/acs.est.0c02341
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发表时间：
2021-01-05
影响因子：
11.4
通讯作者：
Whitaker, Ross T.
中科院分区：
文献类型：
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作者：
Kelly, Kerry E.;Xing, Wei W.;Whitaker, Ross T.
短期暴露于细颗粒物（PM2.5）污染与许多不利的健康影响有关。野火等污染事件可能导致PM2.5水平大幅上升。然而，稀疏的监管措施提供了一个不完整的了解污染梯度。在这里，我们展示了一个基础设施，该基础设施将低成本PM2.5传感器网络的基于社区的测量与严格的校准和高斯过程模型相结合，以了解三次污染事件（2018年7月4日，烟花; 2018年7月5日和6日，野火; 2019年1月3日至7日，持续冷空气池，PCAP）期间的社区规模PM2.5浓度。烟花/野火事件包括84个地点的118个传感器，而PCAP事件包括138个地点的218个传感器。模型结果准确地预测参考测量期间的烟花（n：16，每小时均方根误差，RMSE，12.3-21.5 μ g/m（3），n（归一化）RMSE：14.9-24%），野火（n：46，RMSE：2.6-4.0 μ g/m3; nRMSE：13.1-22.9%）和PCAP（n：96，RMSE：4.9-5.7 μ g/m3; nRMSE：20.2-21.3%）。他们还揭示了PM2.5浓度的巨大地理空间差异，这些差异在仅考虑政府测量或查看美国环境保护局的AirNow可视化时并不明显。高度分辨率的不确定性地图补充了PM2.5估计和可视化。总之，这些结果说明了低成本传感器网络的潜力，结合数据融合算法和适当的校准和训练，可以动态地和更高的精度估计污染事件期间的PM2.5浓度。这些高分辨率的不确定性估计可以提供一种急需的策略来向最终用户传达不确定性。
Short-term exposure to fine particulate matter (PM2.5) pollution is linked to numerous adverse health effects. Pollution episodes, such as wildfires, can lead to substantial increases in PM2.5 levels. However, sparse regulatory measurements provide an incomplete understanding of pollution gradients. Here, we demonstrate an infrastructure that integrates community-based measurements from a network of low-cost PM2.5 sensors with rigorous calibration and a Gaussian process model to understand neighborhood-scale PM2.5 concentrations during three pollution episodes (July 4, 2018, fireworks; July 5 and 6, 2018, wildfire; Jan 3-7, 2019, persistent cold air pool, PCAP). The firework/wildfire events included 118 sensors in 84 locations, while the PCAP event included 218 sensors in 138 locations. The model results accurately predict reference measurements during the fireworks (n: 16, hourly root-mean-square error, RMSE, 12.3-21.5 mu g/m(3), n(normalized)RMSE: 14.9-24%), the wildfire (n: 46, RMSE: 2.6-4.0 mu g/m(3); nRMSE: 13.1-22.9%), and the PCAP (n: 96, RMSE: 4.9-5.7 mu g/m(3); nRMSE: 20.2-21.3%). They also revealed dramatic geospatial differences in PM2.5 concentrations that are not apparent when only considering government measurements or viewing the US Environmental Protection Agency's AirNow visualizations. Complementing the PM2.5 estimates and visualizations are highly resolved uncertainty maps. Together, these results illustrate the potential for low-cost sensor networks that combined with a data-fusion algorithm and appropriate calibration and training can dynamically and with improved accuracy estimate PM2.5 concentrations during pollution episodes. These highly resolved uncertainty estimates can provide a much-needed strategy to communicate uncertainty to end users.