A feasible experimental framework for field calibration of portable light-scattering aerosol monitors: Case of TSI DustTrak. (December 2019) - Li, Zhiyuan; Che, Wenwei; Lau, Alexis K.H.; Fung, Jimmy C.H.; Lin, Changqing; Lu, Xingcheng
Abstract: Portable light-scattering aerosol monitors (PLSAMs) can supplement existing air quality monitoring networks through measuring air pollutant exposure concentrations at high spatiotemporal resolution. However, data collected by PLSAMs are often subject to the simplicity of measurement principle which may lead to errors compared to the regulatory data observed at fixed-site air quality monitoring stations. The main objective of this study was to develop a feasible experimental framework to assess the influence of key factors (e.g., relative humidity (RH)) on the performance of PLSAMs in the real-world conditions. Following the proposed framework, the accuracy and precision of the TSI DustTrak aerosol monitor were evaluated through side-by-side comparison with the stationary reference instruments (SRIs) while taking characteristics of particles, RH, and the concentration range into consideration. DustTrak generally demonstrated low accuracy but high precision in measuring PM2.5 concentrations at the two selected stations. Three calibration models between DustTrak and the SRIs were used to bias correct the DustTrak PM2.5 measurements. The RH-adjusted linear regression calibration method led to better calibration results than the simple linear regression method and the RH-adjusted empirical method, with CV R 2 values higher than 0.97, root mean square error less than 1.0 μg/m 3, and accuracy values at 3% for two DustTraks. The proposed experimental framework can be extended to field calibration of various types of PLSAMs, and the obtained calibration results can promote a more accurate investigation of particle air pollution using these PLSAMs. Graphical abstract: Image 1 Highlights: DustTrak overestimated PM2.5 concentrations by 2–4 times. PM2.5 measurements by DustTrak monitors can be bias corrected. The RH-adjusted calibration models outperform the linear regression model. The 10-fold cross-validation method improves the validation of calibration models.