TY - JOUR
T1 - Long-term evaluation and calibration of three types of low-cost PM2.5 sensors at different air quality monitoring stations
AU - Hong, Gung Hwa
AU - Le, Thi Cuc
AU - Tu, Jing Wei
AU - Wang, Chieh
AU - Chang, Shuenn Chin
AU - Yu, Jhih Yuan
AU - Lin, Guan Yu
AU - Aggarwal, Shankar G.
AU - Tsai, Chuen-Jinn
N1 - Publisher Copyright:
© 2021
PY - 2021/9
Y1 - 2021/9
N2 - To evaluate the performance of low-cost PM2.5 sensors and develop calibration models for correcting for the PM2.5 sensor data (PM2.5,S), field comparison tests were conducted based on Met One BAM-1020 data at various locations using long-term (≥one year) data of Plantower PMS5003, Sensirion SPS30, and Honeywell HPMA115S0 PM2.5 sensors. Both multivariate linear regression (MLR) and non-linear regression (NLR) models using hourly RHs and original sensor PM2.5 data as parameters were able to obtain accurate calibrated hourly PM2.5 values with MNBs (mean normalized biases) less than about ±10% and MNEs (mean normalized errors) less than about 30% for all three types of PM2.5 sensors at all monitoring locations. On the other hand, the MNB and MNE of the calibrated 24-hr average PM2.5 data for the two models were less than ±13% and 20%, respectively. Moreover, the slope, intercept, and R2 of the linear regression line of the calibrated 24-hr average PM2.5 and BAM-1020 data were as good as 1.0 ± 0.1, 0.0 ± 2.0 μg/m3, and ≥0.88, respectively. Therefore, these well-calibrated sensors can well be served for education and information (MNE<50%), hotspot identification and characterization (MNE<30%), and personal exposure study (MNE<30%) purposes, and even supplement the existing daily PM2.5 data of the air quality monitoring stations (MNE<20%).
AB - To evaluate the performance of low-cost PM2.5 sensors and develop calibration models for correcting for the PM2.5 sensor data (PM2.5,S), field comparison tests were conducted based on Met One BAM-1020 data at various locations using long-term (≥one year) data of Plantower PMS5003, Sensirion SPS30, and Honeywell HPMA115S0 PM2.5 sensors. Both multivariate linear regression (MLR) and non-linear regression (NLR) models using hourly RHs and original sensor PM2.5 data as parameters were able to obtain accurate calibrated hourly PM2.5 values with MNBs (mean normalized biases) less than about ±10% and MNEs (mean normalized errors) less than about 30% for all three types of PM2.5 sensors at all monitoring locations. On the other hand, the MNB and MNE of the calibrated 24-hr average PM2.5 data for the two models were less than ±13% and 20%, respectively. Moreover, the slope, intercept, and R2 of the linear regression line of the calibrated 24-hr average PM2.5 and BAM-1020 data were as good as 1.0 ± 0.1, 0.0 ± 2.0 μg/m3, and ≥0.88, respectively. Therefore, these well-calibrated sensors can well be served for education and information (MNE<50%), hotspot identification and characterization (MNE<30%), and personal exposure study (MNE<30%) purposes, and even supplement the existing daily PM2.5 data of the air quality monitoring stations (MNE<20%).
KW - BAM-1020
KW - Long-term field test
KW - Low-cost PM sensor
KW - Multivariate linear regression (MLR)
KW - Non-linear regression (NLR)
UR - http://www.scopus.com/inward/record.url?scp=85109397498&partnerID=8YFLogxK
U2 - 10.1016/j.jaerosci.2021.105829
DO - 10.1016/j.jaerosci.2021.105829
M3 - Article
AN - SCOPUS:85109397498
SN - 0021-8502
VL - 157
SP - 1
EP - 16
JO - Journal of Aerosol Science
JF - Journal of Aerosol Science
M1 - 105829
ER -