Long-term evaluation and calibration of three types of low-cost PM_2.5 sensors at different air quality monitoring stations

To evaluate the performance of low-cost PM_2.5 sensors and develop calibration models for correcting for the PM_2.5 sensor data (PM_2.5,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 PM_2.5 sensors. Both multivariate linear regression (MLR) and non-linear regression (NLR) models using hourly RHs and original sensor PM_2.5 data as parameters were able to obtain accurate calibrated hourly PM_2.5 values with MNBs (mean normalized biases) less than about ±10% and MNEs (mean normalized errors) less than about 30% for all three types of PM_2.5 sensors at all monitoring locations. On the other hand, the MNB and MNE of the calibrated 24-hr average PM_2.5 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 PM_2.5 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 PM_2.5 data of the air quality monitoring stations (MNE<20%).