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

Gung Hwa Hong, Thi Cuc Le, Jing Wei Tu, Chieh Wang, Shuenn Chin Chang, Jhih Yuan Yu, Guan Yu Lin, Shankar G. Aggarwal, Chuen-Jinn Tsai*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

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%).

Original languageEnglish
Article number105829
Pages (from-to)1-16
Number of pages16
JournalJournal of Aerosol Science
Volume157
DOIs
StatePublished - Sep 2021

Keywords

  • BAM-1020
  • Long-term field test
  • Low-cost PM sensor
  • Multivariate linear regression (MLR)
  • Non-linear regression (NLR)

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