确定PM2 .5空气质量传感器最佳位置的新方法：美国邻近地区的案例研究
A new approach for determining optimal placement of PM2.5 air quality sensors: case study for the contiguous United States
摘要：
美国为测量环境空气污染分配了相当多的财政资源，但这些监测点的位置可能没有得到优化，以捕捉当前污染变化的全部范围。关于监测细颗粒物（PM2.5）污染的最佳传感器位置的先前研究很少：大多数研究的范围不超过中等城市，或者研究的时间尺度不超过一周。在这里，我们提出了一个试点研究，使用多分辨率模态分解（mrDMD），以确定PM2.5传感器的最佳位置，从2000年至2016年在美国本土。这种新方法结合了PM2.5在一天到十多年的时间尺度上的变化，以捕捉空气污染的变化。我们发现，mrDMD算法识别高优先级的传感器位置在美国西部，但一个显着低于预期的传感器密度沿东海岸，其中大量的EPA PM2.5监测器目前居住。具体而言，53%的mrDMD优化传感器位置位于第100子午线以西，而目前EPA网络中只有32%。mrDMD传感器的位置可以从野火和高污染事件中捕获PM2.5，在西方尤其具有高技能。这些结果表明，目前美国环保署在加利福尼亚州圣华金河谷，加利福尼亚州北部和太平洋西北部（爱达荷州，华盛顿州东部和俄勒冈州）的监测网络存在重大差距。我们的框架可以诊断空气质量传感器的位置，以便它们能够最好地监测野火产生的烟雾。我们的框架也可以适用于城市地区的PM2.5监测器的公平放置。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
