Abstract
Abstract. Particle sensing technology has shown great potential for monitoring particulate matter (PM) with very few temporal and spatial restrictions because of its low cost, compact size, and easy operation. However, the performance of low-cost sensors for PM monitoring in ambient conditions has not been thoroughly evaluated. Monitoring results by low-cost sensors are often questionable. In this study, a low-cost fine particle monitor (Plantower PMS 5003) was colocated with a reference instrument, the Synchronized Hybrid Ambient Real-time Particulate (SHARP) monitor, at the Calgary Varsity air monitoring station from December 2018 to April 2019. The study evaluated the performance of this low-cost PM sensor in ambient conditions and calibrated its readings using simple linear regression (SLR), multiple linear regression (MLR), and two more powerful machine-learning algorithms using random search techniques for the best model architectures. The two machine-learning algorithms are XGBoost and a feedforward neural network (NN). Field evaluation showed that the Pearson correlation (r) between the low-cost sensor and the SHARP instrument was 0.78. The Fligner and Killeen (F–K) test indicated a statistically significant difference between the variances of the PM2.5 values by the low-cost sensor and the SHARP instrument. Large overestimations by the low-cost sensor before calibration were observed in the field and were believed to be caused by the variation of ambient relative humidity. The root mean square error (RMSE) was 9.93 when comparing the low-cost sensor with the SHARP instrument. The calibration by the feedforward NN had the smallest RMSE of 3.91 in the test dataset compared to the calibrations by SLR (4.91), MLR (4.65), and XGBoost (4.19). After calibrations, the F–K test using the test dataset showed that the variances of the PM2.5 values by the NN, XGBoost, and the reference method were not statistically significantly different. From this study, we conclude that a feedforward NN is a promising method to address the poor performance of low-cost sensors for PM2.5 monitoring. In addition, the random search method for hyperparameters was demonstrated to be an efficient approach for selecting the best model structure.
Highlights
Particulate matter (PM), whether it is natural or anthropogenic, has pronounced effects on human health, visibility, and global climate (Charlson et al, 1992; Seinfeld and Pandis, 1998)
The objectives of this study are (1) to evaluate the performance of the low-cost PM sensor in a range of outdoor environmental conditions by comparing its PM2.5 readings with those obtained from the Synchronized Hybrid Ambient Real-time Particulate (SHARP) instrument and (2) to assess four calibration methods: (a) an simple linear regression (SLR) or univariate linear regression based on the low-cost sensor values; (b) a multiple linear regression (MLR) using the PM2.5, relative humidity (RH), and temperature measured by the low-cost sensor as predictors; (c) a decision-tree-based ensemble algorithm, called XGBoost or Extreme Gradient Boosting; and (d) a feedforward neural network (NN) architecture with a back-propagation algorithm
The p value from the Fligner and Killeen (F–K) test was less than 2.2 × 10−16, indicating that the variance of the PM2.5 values measured by the low-cost sensor was statistically significantly different from the variance of the PM2.5 values measured by the SHARP instrument
Summary
Particulate matter (PM), whether it is natural or anthropogenic, has pronounced effects on human health, visibility, and global climate (Charlson et al, 1992; Seinfeld and Pandis, 1998). To minimize the harmful effects of PM pollution, the Government of Canada launched the National Air Pollution Surveillance (NAPS) program in 1969 to monitor and regulate PM and other criteria air pollutants in populated regions, including ozone (O3), sulfur dioxide (SO2), carbon monoxide (CO), and nitrogen dioxide (NO2). Many of the monitoring stations use a beta attenuation monitor (BAM), which is based on the adsorption of beta radiation, or a tapered element oscillating microbalance (TEOM) instrument, Published by Copernicus Publications on behalf of the European Geosciences Union. M. Si et al.: Evaluation and calibration of a low-cost particle sensor in ambient conditions which is a mass-based technology to measure PM concentrations. An instrument that combines two or more technologies, such as the Synchronized Hybrid Ambient Real-time Particulate (SHARP) monitor, is used in some monitoring stations. The SHARP instrument combines light scattering with beta attenuation technologies to determine PM concentrations
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