On-field test and data calibration of a low-cost sensor for fine particles exposure assessment

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 PM_2.5 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 PM_2.5 concentrations (8-167 μg/m³). Our findings indicated potential applicability of PM sensors in PM_2.5 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.

Keywords: Calibration; Exposure assessment; Fine particulate matter; Low-cost sensor; Random forest.