Fine particle mass monitoring with low-cost sensors: Corrections and long-term performance evaluation

Low-cost sensors for the measurement of fine particulate matter mass (PM_2.5) enable dense networks to increase the spatial resolution of air quality monitoring. However, these sensors are affected by environmental factors such as temperature and humidity and their effects on ambient aerosol, which must be accounted for to improve the in-field accuracy of these sensors. We conducted long-term tests of two low-cost PM_2.5 sensors: Met-One NPM and PurpleAir PA-II units. We found a high level of self-consistency within each sensor type after testing 25 NPM and 9 PurpleAir units. We developed two types of corrections for the low-cost sensor measurements to better match regulatory-grade data. The first correction accounts for aerosol hygroscopic growth using particle composition and corrects for particle mass below the optical sensor size cut-point by collocation with reference Beta Attenuation Monitors (BAM). A second, fully-empirical correction uses linear or quadratic functions of environmental variables based on the same collocation dataset. The two models yielded comparable improvements over raw measurements. Sensor performance was assessed for two use cases: improving community awareness of air quality with short-term semi-quantitative comparisons of sites and providing long-term reasonably quantitative information for health impact studies. For the short-term case, both sensors provided reasonably accurate concentration information (mean absolute error of ∼4 µg/m³) in near-real time. For the long-term case, tested using year-long collocations at one urban background and one near-source site, error in the annual average was reduced below 1 µg/m³. Hence, these sensors can supplement sparse networks of regulatory-grade instruments, perform high-density neighborhood-scale monitoring, and be used to better understand spatial patterns and temporal air quality trends across urban areas. Copyright © 2019 American Association for Aerosol Research用于细颗粒物质量（PM₂.₅）检测的低成本传感器，可搭建密集监测网络以提升空气质量监测的空间分辨率。然而，此类传感器易受温度、湿度等环境因素以及环境气溶胶的影响，若要提升其野外监测精度，必须对这些影响进行校正。我们对两款低成本PM₂.₅传感器——Met-One NPM与PurpleAir PA-II——开展了长期测试。在对25台NPM传感器与9台PurpleAir传感器进行测试后，我们发现同类型传感器具备极高的自一致性。我们针对低成本传感器的测量数据开发了两类校正方法，以使其结果更贴合监管级监测数据。第一种校正方法结合颗粒物组分，考虑气溶胶吸湿增长效应，并通过与参比β射线衰减监测仪（Beta Attenuation Monitors，BAM）同步布设的方式，校正光学传感器尺寸截点以下的颗粒物质量。第二种则为完全基于经验的校正方法，依托同一同步布设数据集，采用环境变量的线性或二次函数形式进行校正。两种校正模型均能较原始测量数据实现相当程度的精度提升。我们针对两类应用场景评估了传感器性能：一是通过站点间短期半定量对比，提升公众对空气质量的认知；二是为健康影响研究提供长期的可靠定量监测信息。在短期场景中，两款传感器均可提供近实时且精度较为可靠的浓度数据，平均绝对误差约为4 µg/m³。在长期场景中，我们在一处城市背景点与一处近源点开展了为期一年的同步布设测试，校正后年平均浓度误差被降至1 µg/m³以下。综上，此类传感器可弥补监管级仪器监测网络的稀疏性不足，实现高密度的街区尺度空气质量监测，助力更深入地解析城区空气质量的空间分布格局与时间变化趋势。本研究版权归2019年美国气溶胶研究协会（American Association for Aerosol Research）所有。