Evaluation of calibration performance of a low-cost particulate matter sensor using collocated and distant NO₂

Abstract

Low-cost optical particle sensors have the potential to supplement existing particulate matter (PM) monitoring systems and to provide high spatial and temporal resolutions. 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 whether NO2, which creates PM2.5 by means of 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 and 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 2 years, since July 2017. Based on the operating periods of the PA-II units, we then chose the period of January 2018 to December 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 – namely temperature, relative humidity (RH), and NO2 – were considered. The NO2, called collocated NO2, was collected from the reliable instrument collocated with the PA-II unit. The per-month analysis showed that the PA-II unit had a good correlation (coefficient of determination R2>0.819) with the BAM-1020 during the months of November, December, and January in both 2018 and 2019, but their correlation intensity was moderate during other months, such as in July and September 2018 and August, September, and October 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. In addressing the feasibility of utilizing distant NO2 measurements for calibration in lieu of collocated data, the study highlights the effectiveness of distant NO2 when correlated strongly with collocated measurements. This finding offers a practical solution for situations where obtaining collocated NO2 data proves to be challenging or costly. We assessed the performance of different PA-II units to determine their efficacy. Our investigation reveals a significant enhancement in calibration performance across different PA-II units upon integrating NO2. Importantly, this improvement remains consistent even when employing models trained with different PA-II units within the same location. Overall, this investigation emphasizes the significance of NO2 in improving calibration for low-cost PM2.5 sensors and presents insights into leveraging distant NO2 measurements as a viable alternative for calibration in the absence of collocated data.