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Low-cost sensors (LCS) provide opportunities for neighborhood-level air pollution data collection, yet significant knowledge gaps remain regarding the accurate application and interpretation of LCS. In this study, we present an in-field calibration of a network of 20 low-cost ambient particulate matter sensors (LCS) in greater Kolkata, India, operating between October 2018-April 2019. In order to understand LCS performance in relation to local reference-grade PM2.5 monitors (RGMs), three of these LCS were co-located with RGMs operated by the West Bengal Pollution Control Board at Rabindra Bharati University (RBU), Victoria Memorial (VICTORIA), and Padmapukur (Howrah, PDM). Data from the co-locations were used to calibrate the LCS network using random forest regression and multiple linear regression approaches. Measured relative humidity and temperature were significant model features. Agreement between the LCS and RGM for 24-h averaged PM2.5 measurements was strongest at RBU, with an uncalibrated root mean squared error (RMSE) of 27.1 μg m-3, followed by PDM (32.6 μg m-3) and VICTORIA (50.7 μg m-3). Multiple linear regression was used to derive calibration models. Cross-calibration between co-located LCS-RGM pairs was tested. The LCS data after cross-calibration correctly identified days as being in or out of attainment with the 24h National Ambient Air Quality Standard of 60 μg m-3 91% of the time. The corrected data accurately identifies days with an India scale Air Quality Index of “poor” or worse 94% of the time. This suggests that LCS can be a useful supplement to RGM networks for air quality management.

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Low-cost sensors (LCS) provide opportunities for neighborhood-level air pollution data collection, yet significant knowledge gaps remain regarding the accurate application and interpretation of LCS. In this study, we present an in-field calibration of a network of 20 low-cost ambient particulate matter sensors (LCS) in greater Kolkata, India, operating between October 2018-April 2019. In order to understand LCS performance in relation to local reference-grade PM2.5 monitors (RGMs), three of these LCS were co-located with RGMs operated by the West Bengal Pollution Control Board at Rabindra Bharati University (RBU), Victoria Memorial (VICTORIA), and Padmapukur (Howrah, PDM). Data from the co-locations were used to calibrate the LCS network using random forest regression and multiple linear regression approaches. Measured relative humidity and temperature were significant model features. Agreement between the LCS and RGM for 24-h averaged PM2.5 measurements was strongest at RBU, with an uncalibrated root mean squared error (RMSE) of 27.1 μg m-3, followed by PDM (32.6 μg m-3) and VICTORIA (50.7 μg m-3). Multiple linear regression was used to derive calibration models. Cross-calibration between co-located LCS-RGM pairs was tested. The LCS data after cross-calibration correctly identified days as being in or out of attainment with the 24h National Ambient Air Quality Standard of 60 μg m-3 91% of the time. The corrected data accurately identifies days with an India scale Air Quality Index of “poor” or worse 94% of the time. This suggests that LCS can be a useful supplement to RGM networks for air quality management. Diurnal trends and a high level of correlation across the hybrid LCS-RGM network suggest regional and secondary sources of PM2.5 are important in Kolkata.