Long-Term Evaluation and Calibration of Low-Cost Particulate Matter (PM) Sensor
Long-Term Evaluation and Calibration of Low-Cost Particulate Matter (PM) Sensor
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DOI：
10.3390/s20133617
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发表时间：
2020-07-01
期刊：
影响因子：
3.9
通讯作者：
Lee, Dongjun
中科院分区：
文献类型：
--
作者：
Lee, Hoochang;Kang, Jiseock;Lee, Dongjun
为了克服政府运营的β衰减监测器（BAM）时空分辨率低的局限性，低成本的光散射颗粒物（PM）传感器已被广泛研究和部署。然而，低成本传感器的准确性受到质疑，从而阻碍了它们在实践中的广泛采用。为了评估低成本PM传感器在现场的准确性，开发了一个多传感器平台，并于2019年1月15日至2019年9月4日与韩国首尔洞雀区的BAM合作。在本文中，低成本传感器的样本变化进行了分析，同时使用三个商业低成本PM传感器。还描述了环境条件（例如湿度、温度和环境光）对PM传感器的影响。基于这些信息，我们开发了一种新的组合校准算法，该算法选择性地应用多个校准模型并在统计上减少残差，同时使用预构建的参数查找表，其中每个单元记录当前输入参数下每个校准模型的统计参数。由于我们提出的框架显着提高了低成本PM传感器的准确性（例如，RMSE：23.94 -> 4.70 μ g/m3）并增加相关性（例如，R2：0.41 -> 0.89），该校准模型可以通过传感器网络传输到所有传感器节点。
Low-cost light scattering particulate matter (PM) sensors have been widely researched and deployed in order to overcome the limitations of low spatio-temporal resolution of government-operated beta attenuation monitor (BAM). However, the accuracy of low-cost sensors has been questioned, thus impeding their wide adoption in practice. To evaluate the accuracy of low-cost PM sensors in the field, a multi-sensor platform has been developed and co-located with BAM in Dongjak-gu, Seoul, Korea from 15 January 2019 to 4 September 2019. In this paper, a sample variation of low-cost sensors has been analyzed while using three commercial low-cost PM sensors. Influences on PM sensor by environmental conditions, such as humidity, temperature, and ambient light, have also been described. Based on this information, we developed a novel combined calibration algorithm, which selectively applies multiple calibration models and statistically reduces residuals, while using a prebuilt parameter lookup table where each cell records statistical parameters of each calibration model at current input parameters. As our proposed framework significantly improves the accuracy of the low-cost PM sensors (e.g., RMSE: 23.94 -> 4.70 mu g/m3) and increases the correlation (e.g., R2: 0.41 -> 0.89), this calibration model can be transferred to all sensor nodes through the sensor network.