Field Evaluation and Calibration of Low-Cost Air Pollution Sensors for Environmental Exposure Research

• Published in: Sensors (Basel, Switzerland) Switzerland), 2022-03, Vol.22 (6), p.2381
• Main Authors: Huang, Jianwei, Kwan, Mei-Po, Cai, Jiannan, Song, Wanying, Yu, Changda, Kan, Zihan, Yim, Steve Hung-Lam
• Format: Article
• Language: English
• Subjects: Accuracy; Aerosols; Air Pollutants - analysis; Air pollution; Air Pollution - analysis; AirBeam2; Bias; Calibration; different aggregated temporal units; Environmental conditions; Environmental Exposure; Environmental Monitoring - methods; Humidity; Indoor environments; Linearity; Low cost; low-cost sensors; Mass transit; Medical research; Mobility; Neighborhoods; Office buildings; Particulate emissions; particulate matter; Precipitation; sensor calibration; Sensors; Subway stations; Supermarkets; urban environments
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s22062381

This paper seeks to evaluate and calibrate data collected by low-cost particulate matter (PM) sensors in different environments and using different aggregated temporal units (i.e., 5-s, 1-min, 10-min, 30 min intervals). We first collected PM concentrations (i.e., PM , PM , and PM ) data in five different environments (i.e., indoor and outdoor of an office building, a train platform and lobby of a subway station, and a seaside location) in Hong Kong, using five AirBeam2 sensors as the low-cost sensors and a TSI DustTrak DRX Aerosol Monitor 8533 as the reference sensor. By comparing the collected PM concentrations, we found high linearity and correlation between the data reported by the AirBeam2 sensors in different environments. Furthermore, the results suggest that the accuracy and bias of the PM data reported by the AirBeam2 sensors are affected by rainy weather and environments with high humidity and a high level of hygroscopic salts (i.e., a seaside location). In addition, increasing the aggregation level of the temporal units (i.e., from 5-s to 30 min intervals) increases the correlation between the PM concentrations obtained by the AirBeam2 sensors, while it does not significantly improve the accuracy and bias of the data. Lastly, our results indicate that using a machine learning model (i.e., random forest) for the calibration of PM concentrations collected on sunny days generates better results than those obtained with multiple linear models. These findings have important implications for researchers when designing environmental exposure studies based on low-cost PM sensors.