Department of Environmental Engineering and Earth Sciences, Clemson University, Clemson, South Carolina, USA.
Department of Forestry and Environmental Conservation, Clemson University, Clemson, South Carolina, USA.
J Air Waste Manag Assoc. 2022 Nov;72(11):1219-1230. doi: 10.1080/10962247.2022.2093293.
Many low-cost particle sensors are available for routine air quality monitoring of PM, but there are concerns about the accuracy and precision of the reported data, particularly in humid conditions. The objectives of this study are to evaluate the Sensirion SPS30 particulate matter (PM) sensor against regulatory methods for measurement of real-time particulate matter concentrations and to evaluate the effectiveness of the Intelligent Air sensor pack for remote deployment and monitoring. To achieve this, we co-located the Intelligent Air sensor pack, developed at Clemson University and built around the Sensirion SPS30, to collect data from July 29, 2019, to December 12, 2019, at a regulatory site in Columbia, South Carolina. When compared to the Federal Equivalent Methods, the SPS30 showed an average bias adjusted R = 0.75, mean bias error of -1.59, and a root mean square error of 2.10 for 24-hour average trimmed measurements over 93 days, and R = 0.57, mean bias error of -1.61, and a root mean square error of 3.029, for 1-hr average trimmed measurements over 2300 hours when the central 99% of data was retained with a data completeness of 75% or greater. The Intelligent Air sensor pack is designed to promote long-term deployment and includes a solar panel and battery backup, protection from the elements, and the ability to upload data via a cellular network. Overall, we conclude that the SPS30 PM sensor and the Intelligent Air sensor pack have the potential for greatly increasing the spatial density of particulate matter measurements, but more work is needed to understand and calibrate sensor measurements. This work adds to the growing body of research that indicates that low-cost sensors of particulate matter (PM) for air quality monitoring has a promising future, and yet much work is left to be done. This work shows that the level of data processing and filtering effects how the low-cost sensors compare to existing federal reference and equivalence methods: more data filtering at low PM levels worsens the data comparison, while longer time averaging improves the measurement comparisons. Improvements must be made to how we handle, calibrate, and correct PM data from low-cost sensors before the data can be reliably used for air quality monitoring and attainment.
许多低成本的粒子传感器可用于 PM 的常规空气质量监测，但人们对报告数据的准确性和精密度存在担忧，特别是在潮湿条件下。本研究的目的是评估 Sensirion SPS30 颗粒物（PM）传感器与实时颗粒物浓度测量的法规方法的对比，并评估智能空气传感器套件用于远程部署和监测的效果。为此，我们将克莱姆森大学开发的、以 Sensirion SPS30 为核心的智能空气传感器套件与位于南卡罗来纳州哥伦比亚的一个监管站点一起进行数据收集，时间为 2019 年 7 月 29 日至 2019 年 12 月 12 日。与联邦等效方法相比，SPS30 在 93 天的 24 小时平均修剪测量中，平均偏差调整 R 为 0.75，平均偏差误差为-1.59，均方根误差为 2.10，在 2300 小时的 1 小时平均修剪测量中，当保留数据完整性为 75%或更高的中央 99%数据时，R 为 0.57，平均偏差误差为-1.61，均方根误差为 3.029。智能空气传感器套件旨在促进长期部署，包括太阳能电池板和备用电池、免受自然因素影响的保护，以及通过蜂窝网络上传数据的能力。总的来说，我们得出结论，SPS30 PM 传感器和智能空气传感器套件有可能大大提高颗粒物测量的空间密度，但需要做更多的工作来理解和校准传感器测量。这项工作增加了越来越多的研究，这些研究表明，用于空气质量监测的低成本颗粒物（PM）传感器具有广阔的前景，但仍有许多工作要做。这项工作表明，数据处理和过滤水平会影响低成本传感器与现有联邦参考和等效方法的比较：在低 PM 水平下进行更多的数据过滤会使数据比较恶化，而较长时间的平均处理则会改善测量比较。在将低成本传感器的数据用于空气质量监测和达标之前，必须改进我们处理、校准和修正 PM 数据的方式。
