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Open AccessArticle

Long-Term Evaluation and Calibration of Low-Cost Particulate Matter (PM) Sensor

1
Department of Mechanical Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Korea
2
Air Quality Analysis and Control Center, Seoul Metropolitan Research Institute of Public Health and Environment, 30, Janggunmaeul 3-gil, Gwacheon-si, Gyeonggi-do, Seoul 08826, Korea
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(13), 3617; https://doi.org/10.3390/s20133617
Received: 28 April 2020 / Revised: 15 June 2020 / Accepted: 24 June 2020 / Published: 27 June 2020
(This article belongs to the Special Issue Air Quality and Sensor Networks)
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 μ g/m 3 ) and increases the correlation (e.g., R 2 : 0.41 → 0.89), this calibration model can be transferred to all sensor nodes through the sensor network. View Full-Text
Keywords: particulate matter (PM); low-cost sensor; calibration; multivariate linear regression (MLR); multilayer perceptron (MLP); segmented model and residual treatment (SMART) calibration particulate matter (PM); low-cost sensor; calibration; multivariate linear regression (MLR); multilayer perceptron (MLP); segmented model and residual treatment (SMART) calibration
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Lee, H.; Kang, J.; Kim, S.; Im, Y.; Yoo, S.; Lee, D. Long-Term Evaluation and Calibration of Low-Cost Particulate Matter (PM) Sensor. Sensors 2020, 20, 3617.

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