Selection and Aggregation of Low-cost Particle Sensors for Outdoor Particulate Matter Measurement

A growing number of low-cost sensors (LCS) have been used to monitor air pollution in outdoor air. The benefit of utilizing LCS lies in its ability to offer increased spatial coverage, which provides real-time measurements at a reduced cost. The selection and combination of low-cost sensors represent the primary challenge in conducting observations using such sensors. This paper employs a sensor quality ranking strategy, utilizing random forest (RF) for aggregating the selected LCS combination, followed by evaluating the correction results using various model evaluation metrics. The LCS used in this study, regardless of their quality grades, achieves a coefficient of determination of 0.93 or higher after model calibration, indicating the effectiveness of employing RF for aggregation. It is found that using a pair of top and averaged LCS can significantly enhance the measurement quality by 25% in RMSE. Using RF to calibrate a single LCS increases the measurement performance at least two times in terms of MSE, RMSE, and MAE. Using paired LCS with RF aggregation for measuring PM2.5, the aggregated observation significantly approximates the reference measurement with
$R^{2}=0.986$
.