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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 NO 2 , which creates PM 2.5 by means of chemical reactions in the atmosphere, can be used to improve the calibration performance of low-cost PM 2.5 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 PM 2.5 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 PM 2.5 data. In the per-month analysis, three key features – namely temperature, relative humidity (RH), and NO 2 – were considered. The NO 2 , called collocated NO 2 , 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. NO 2 was shown to be a key factor in increasing the value of R2 in the months when moderate correlation based on only PM 2.5 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 NO 2 had a much larger effect than that of RH when both PM 2.5 and temperature were considered for calibration in both models. When NO 2 , temperature, and relative humidity were considered, the MLR method achieved similar calibration performance to the RF method. In addressing the feasibility of utilizing distant NO 2 measurements for calibration in lieu of collocated data, the study highlights the effectiveness of distant NO 2 when correlated strongly with collocated measurements. This finding offers a practical solution for situations where obtaining collocated NO 2 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 NO 2. 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 NO 2 in improving calibration for low-cost PM 2.5 sensors and presents insights into leveraging distant NO 2 measurements as a viable alternative for calibration in the absence of collocated data. [ABSTRACT FROM AUTHOR]

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, R 2 > 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 R 2 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. [ABSTRACT FROM AUTHOR]