Evaluation of Calibration Performance of a Low-cost Particulate Matter Sensor Using Colocated and Distant NO2
Abstract. Low-cost optical particle sensors have the potential to supplement existing particulate matter (PM) monitoring systems to provide high spatial and temporal resolution. However, low-cost PM sensors have often shown questionable performance under various ambient conditions. Temperature, relative humidity (RH), and particle composition have been identified as factors that directly affect the performance of low-cost PM sensors. This study investigated if NO2, which creates PM2.5 by chemical reactions in the atmosphere, can be used to improve the calibration performance of low-cost PM2.5 sensors. To this end, we evaluated the PurpleAir PA-II, called PA-II, a popular air monitoring system that utilizes two low-cost PM sensors that is frequently deployed near air quality monitoring sites of the Environmental Protection Agency (EPA). We selected a single location where 14 PA-II units have operated for more than two years since July 2017. Based on the operating periods of the PA-II units, we then chose the period of Jan. 2018 to Dec. 2019 for study. Among the 14 units, a single unit containing more than 23 months of measurement data with a high correlation between the unit's two PMS sensors was selected for analysis. Daily and hourly PM2.5 measurement data from the PA-II unit and a BAM 1020 instrument, respectively, were compared using the federal reference method (FRM), and a per-month analysis was conducted against the BAM-1020 using hourly PM2.5 data. In the per-month analysis, three key features, temperature, relative humidity (RH), and NO2, were considered. The NO2, called colocated NO2, was collected from the reliable instrument colocated with the PA-II unit. The per-month analysis showed the PA-II unit had a good correlation (coefficient of determination, R2 > 0.819) with the BAM-1020 during the months of Nov., Dec., and Jan. in both 2018 and 2019, but their correlation intensity was moderate during other months, such as July and Sep. 2018, and Aug., Sep., and Oct. 2019. NO2 was shown to be a key factor in increasing the value of R2 in the months when moderate correlation based on only PM2.5 was achieved. This study calibrated a PA-II unit using multiple linear regression (MLR) and random forest (RF) methods based on the same three features used in the analysis studies as well as their multiplicative terms. The addition of NO2 had a much larger effect than that of RH when both PM2.5 and temperature were considered for calibration in both models. When NO2, temperature, and relative humidity were considered, the MLR method achieved similar calibration performance to the RF method. Since it is practically infeasible to colocate a reliable NO2 instrument colocation with high accuracy at low-cost PM sensors, we investigated the effectiveness of using NO2 data (which we call distant NO2), collected from monitoring sites deployed at locations far from the considered low-cost PM sensor for calibration performance enhancement. It was shown that the use of distant NO2 enhances the calibration performance compared to calibration without NO2 when it is highly correlated with colocated NO2. Overall, PA-II units have good agreement with PM2.5 monitoring systems of high quality. Moreover, the calibration performance can be improved by using machine learning algorithms and by considering temperature, RH, and especially NO2.
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