Improved PM2.5 concentration estimates from low-cost sensors using calibration models categorized by relative humidity

Abstract

Low-Cost Sensors (LCS) of fine particulate matter (PM2.5) have been widely used to supplement regular air quality monitoring stations. However, the sensor output is impacted by environmental factors, especially relative humidity, and must be calibrated to yield estimates of concentrations. In this study, we evaluate the performance of a linear model and a generalized additive model (GAM) to calibrate the output of LCS PM2.5 measurements in terms of different relative humidity levels. The method is based on co-located measurements from an LCS and a conventional reference monitor at two sites in an urban area in northern China. A stepwise variable selection of air pollutant concentrations and meteorological observations is used to select inputs to the model in order to improve the calibration ability. The results show that when relative humidity is below 75%, linear calibration of LCS PM2.5 observations can output PM2.5 mass concentrations close to the reference method with a correlation coefficient (R2) of 0.86. When relative humidity is above 75%, the GAM calibration model significantly outperforms the linear model, with an R2 of approximately 0.83. Overall, the linear model exhibits good fitness in dry conditions, while the GAM captures PM2.5 variations best in humid conditions. We conclude that low-cost PM2.5 sensors are sensitive to relative humidity and that therefore condition-specific calibration methods need to be used to improve the quality of the data as well as to improve the match with reference measurements.Copyright © 2021 American Association for Aerosol Research