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

被引:15
|
作者
Huang, Jianwei [1 ]
Kwan, Mei-Po [1 ,2 ]
Cai, Jiannan [1 ]
Song, Wanying [1 ]
Yu, Changda [1 ]
Kan, Zihan [1 ]
Yim, Steve Hung-Lam [3 ,4 ,5 ]
机构
[1] Chinese Univ Hong Kong, Inst Space & Earth Informat Sci, Hong Kong, Peoples R China
[2] Chinese Univ Hong Kong, Dept Geog & Resource Management, Hong Kong, Peoples R China
[3] Nanyang Technol Univ, Asian Sch Environm, Singapore 639798, Singapore
[4] Nanyang Technol Univ, Lee Kong Chian Sch Med, Singapore 639798, Singapore
[5] Nanyang Technol Univ, Earth Observ Singapore, Singapore 639798, Singapore
关键词
particulate matter; AirBeam2; low-cost sensors; urban environments; different aggregated temporal units; sensor calibration; INDIVIDUAL EXPOSURE; REAL-TIME; PERFORMANCE; MOBILITY; MASS; VARIABILITY; HUMIDITY;
D O I
10.3390/s22062381
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
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., PM1, PM2.5, and PM10) 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.
引用
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页数:17
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