论文标题
确定PM2.5空气质量传感器最佳放置的新方法：连续美国的案例研究
A new approach for determining optimal placement of PM2.5 air quality sensors: case study for the contiguous United States
论文作者
论文摘要
分配了可观的财务资源来衡量美国的环境空气污染，但是这些监控地点的位置可能无法优化以捕获当前污染可变性的全部范围。先前研究用于监测细颗粒物（PM2.5）污染的最佳传感器位置的研究很少：大多数研究的范围不超过中型城市或检查时间范围的时间超过一周。在这里，我们提出了一项使用多解决模态分解（MRDMD）的试点研究，以确定2000 - 2016年在连续美国的PM2.5传感器的最佳位置。这种新颖的方法结合了PM2.5的变化，从一天到十多年的时间尺度上，以捕获空气污染的可变性。我们发现，MRDMD算法确定了美国西部的高优先级传感器位置，但传感器密度明显低于东部海岸的预期，那里的EPA PM2.5监测器目前居住。具体而言，MRDMD优化的传感器位置中有53％位于第100子午线的西部，而当前EPA网络中只有32％。 MRDMD传感器位置可以从野火和高污染事件中捕获PM2.5，西方特别高技能。这些结果表明，在加利福尼亚州北加州圣华金山谷以及太平洋西北地区（爱达荷州，华盛顿东部和俄勒冈州）的当前EPA监测网络中有很大的差距。我们的框架诊断将空气质量传感器放置在哪里，以便它们可以最好地监测野火中的烟雾。我们的框架也可以应用于城市地区，以公平地放置PM2.5监视器。
Considerable financial resources are allocated for measuring ambient air pollution in the United States, yet the locations for these monitoring sites may not be optimized to capture the full extent of current pollution variability. Prior research on best sensor placement for monitoring fine particulate matter (PM2.5) pollution is scarce: most studies do not span areas larger than a medium-sized city or examine timescales longer than one week. Here we present a pilot study using multiresolution modal decomposition (mrDMD) to identify the optimal placement of PM2.5 sensors from 2000-2016 over the contiguous United States. This novel approach incorporates the variation of PM2.5 on timescales ranging from one day to over a decade to capture air pollution variability. We find that the mrDMD algorithm identifies high-priority sensor locations in the western United States, but a significantly lower density of sensors than expected along the eastern coast, where a large number of EPA PM2.5 monitors currently reside. Specifically, 53% of mrDMD optimized sensor locations are west of the 100th meridian, compared to only 32% in the current EPA network. The mrDMD sensor locations can capture PM2.5 from wildfires and high pollution events, with particularly high skill in the West. These results suggest significant gaps in the current EPA monitoring network in the San Joaquin Valley in California, northern California, and in the Pacific Northwest (Idaho, and Eastern Washington and Oregon). Our framework diagnoses where to place air quality sensors so that they can best monitor smoke from wildfires. Our framework may also be applied to urban areas for equitable placement of PM2.5 monitors.
