Long-term evaluation and calibration of three types of low-cost PM2.5 sensors at different air quality monitoring stations

To evaluate the performance of low-cost PM2.5 sensors and develop calibration models for cor-recting 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 R-2 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 mu g/m(3), 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%).

Automated summary

The study evaluated the performance of low-cost PM2.5 sensors and found that both multivariate linear regression and non-linear regression models can accurately calibrate the sensor data. The calibrated sensor data performed well at all monitoring locations and can be used for education, information, hotspot identification, personal exposure study, and supplementing air quality monitoring data.