Evaluating the Performance of Low-Cost PM2.5 Sensors in Mobile Settings.
Evaluating the Performance of Low-Cost PM2.5 Sensors in Mobile Settings.
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DOI：
10.1021/acs.est.3c04843
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发表时间：
2023-01
影响因子：
11.4
通讯作者：
P. deSouza;An Wang;Yukihiro Machida;T. Duhl;Simone Mora;Prashant Kumar;R. Kahn;C. Ratti;J. Durant;N. Hudda
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文献类型：
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作者：
P. deSouza;An Wang;Yukihiro Machida;T. Duhl;Simone Mora;Prashant Kumar;R. Kahn;C. Ratti;J. Durant;N. Hudda
用于测量空气污染的低成本传感器（LCS）越来越多地部署在移动应用中，但有关测量质量的问题仍未得到解答。例如，在移动环境中纠正 LCS 数据的最佳方法是什么？哪些因素对移动 LCS 数据与更高质量仪器的数据之间的差异影响最大？来自濒海战斗舰的数据能否用于识别热点并生成可概括的污染物浓度图？为了帮助解决这些问题，我们在美国马萨诸塞州波士顿的移动实验室部署了低成本 PM2.5 传感器 (Alphasense OPC-N3) 和研究级仪器 (TSI DustTrak)。我们首先将这些仪器与附近监管站点的固定 PM2.5 参考监测仪 (Teledyne T640) 搭配使用。接下来，使用参考测量结果，我们开发了不同的模型来校正 OPC-N3 和 DustTrak 测量结果，然后将校正结果传输到移动设置。我们观察到，在静止环境中，更复杂的校正模型似乎比更简单的模型表现更好；然而，当转移到移动设置时，校正后的 OPC-N3 测量结果与校正后的 DustTrak 数据的一致性较差。一般来说，使用分钟级配置测量得出的校正比使用每小时平均数据得出的校正更好地转移到移动设置。移动实验室速度、相对于行进方向的 OPC-N3 方向、日期、一天中的时间和道路等级共同解释了移动部署期间校正的 OPC-N3 和 DustTrak 测量之间微小但显着的变化。 OPC-N3 识别的持续热点与 DustTrak 识别的一致。同样，由移动校正 OPC-N3 和 DustTrak 测量生成的 PM2.5 分布图也非常吻合。这些结果表明，识别热点和开发 PM2.5 的通用地图是移动 LCS 数据的适当用例。
Low-cost sensors (LCSs) for measuring air pollution are increasingly being deployed in mobile applications, but questions concerning the quality of the measurements remain unanswered. For example, what is the best way to correct LCS data in a mobile setting? Which factors most significantly contribute to differences between mobile LCS data and those of higher-quality instruments? Can data from LCSs be used to identify hotspots and generate generalizable pollutant concentration maps? To help address these questions, we deployed low-cost PM2.5 sensors (Alphasense OPC-N3) and a research-grade instrument (TSI DustTrak) in a mobile laboratory in Boston, MA, USA. We first collocated these instruments with stationary PM2.5 reference monitors (Teledyne T640) at nearby regulatory sites. Next, using the reference measurements, we developed different models to correct the OPC-N3 and DustTrak measurements and then transferred the corrections to the mobile setting. We observed that more complex correction models appeared to perform better than simpler models in the stationary setting; however, when transferred to the mobile setting, corrected OPC-N3 measurements agreed less well with the corrected DustTrak data. In general, corrections developed by using minute-level collocation measurements transferred better to the mobile setting than corrections developed using hourly-averaged data. Mobile laboratory speed, OPC-N3 orientation relative to the direction of travel, date, hour-of-the-day, and road class together explain a small but significant amount of variation between corrected OPC-N3 and DustTrak measurements during the mobile deployment. Persistent hotspots identified by the OPC-N3s agreed with those identified by the DustTrak. Similarly, maps of PM2.5 distribution produced from the mobile corrected OPC-N3 and DustTrak measurements agreed well. These results suggest that identifying hotspots and developing generalizable maps of PM2.5 are appropriate use-cases for mobile LCS data.