On-field test and data calibration of a low-cost sensor for fine particles exposure assessment.
2021
Abstract Background Accurate individual exposure assessment is crucial for evaluating the health effects of particulate matter (PM). Various portable monitors built upon low-cost optical sensors have emerged. However, the main challenge for their application is to guarantee accuracy of measurements. Objective To assess the performance of a newly developed PM sensor, and to develop methods for post-hoc data calibration to optimize its data quality. Method We conducted a series of laboratory experiments and field evaluations to quantify the reproducibility within Plantower PM sensors 7003 (PMS 7003) and the consistency between sensors and two established PM2.5 measurement methods [tapered element oscillating microbalances (TEOM) and gravimetric method (GM)]. Post-hoc data calibration methods for sensors were based on a multiple linear regression model (MLRM) and a random forest model (RFM). Ratios of raw and calibrated readings over the data of reference methods were calculated to examine the improvement after calibration. Results Strong correlations (≥0.82) and relatively small relative standard deviations (16–21%) between sensors were found during the laboratory and the field sampling. Compared with the reference methods, moderate to strong coefficients of determination (0.56–0.83) were observed; however, significant deviations were presented. After calibration, the ratios of PMS measurements over that of two reference methods both became convergent. Conclusions Our study validated low-cost optical PM sensors under a wide range of PM2.5 concentrations (8–167 μg/m3). Our findings indicated potential applicability of PM sensors in PM2.5 exposure assessment, and confirmed a need of calibration. Linear calibration methods may be sufficient for ambient monitoring using TEOM as a reference, while nonlinear calibration methods may be more appropriate for indoor monitoring using GM as a reference.
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