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Inter- versus Intracity Variations in the Performance and Calibration of Low-Cost PM2.5 Sensors: A Multicity Assessment in India

  • Sreekanth V
    Sreekanth V
    Center for Study of Science, Technology and Policy, Bengaluru560094, India
    More by Sreekanth V
  • Ajay Bhargav R
    Ajay Bhargav R
    ILK Labs, Bengaluru560046, India
  • Padmavati Kulkarni
    Padmavati Kulkarni
    Center for Study of Science, Technology and Policy, Bengaluru560094, India
  • Naveen Puttaswamy
    Naveen Puttaswamy
    Department of Environmental Health Engineering, Faculty of Public Health, Sri Ramachandra Institute of Higher Education and Research, Chennai600116, India
  • Vignesh Prabhu
    Vignesh Prabhu
    Center for Study of Science, Technology and Policy, Bengaluru560094, India
  • Pratyush Agrawal
    Pratyush Agrawal
    Center for Study of Science, Technology and Policy, Bengaluru560094, India
  • Adithi R. Upadhya
    Adithi R. Upadhya
    ILK Labs, Bengaluru560046, India
  • Sofiya Rao
    Sofiya Rao
    Centre for Atmospheric Sciences, Indian Institute of Technology-Delhi, New Delhi110016, India
    More by Sofiya Rao
  • Ronak Sutaria
    Ronak Sutaria
    Respirer Living Sciences, Pune411007, India
  • Suman Mor
    Suman Mor
    Department of Environment Studies, Panjab University, Chandigarh160014, India
    More by Suman Mor
  • Sagnik Dey
    Sagnik Dey
    Centre for Atmospheric Sciences, Indian Institute of Technology-Delhi, New Delhi110016, India
    More by Sagnik Dey
  • Ravindra Khaiwal
    Ravindra Khaiwal
    Department of Community Medicine and School of Public Health, Post Graduate Institute of Medical Education & Research, Chandigarh160012, India
  • Kalpana Balakrishnan
    Kalpana Balakrishnan
    Department of Environmental Health Engineering, Faculty of Public Health, Sri Ramachandra Institute of Higher Education and Research, Chennai600116, India
  • Sachchida Nand Tripathi
    Sachchida Nand Tripathi
    Department of Civil Engineering, Indian Institute of Technology-Kanpur, Kanpur208016, India
  • , and 
  • Pratima Singh*
    Pratima Singh
    Center for Study of Science, Technology and Policy, Bengaluru560094, India
    *Email: [email protected]
Cite this: ACS Earth Space Chem. 2022, 6, 12, 3007–3016
Publication Date (Web):November 21, 2022
https://doi.org/10.1021/acsearthspacechem.2c00257
Copyright © 2022 American Chemical Society

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    Abstract

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    Low-cost sensors (LCSs) have revolutionized the air pollution monitoring landscape. However, the sensitivities of particulate matter (PM) LCS measurements to various particle microphysical properties and meteorological aspects warrant an accuracy investigation. We investigated the inter- and intracity variations in the accuracy of LCS-measured PM2.5 across geographically and demographically distinct Indian cities. The collocation data of PM2.5 (collected during March–April 2022) from an LCS (Atmos) and a reference-grade instrument (β attenuation monitor) from nine sites (across five cities) were analyzed. The root-mean-square error (RMSE) in the hourly mean raw (uncorrected) Atmos PM2.5 measurements varied significantly across the cities. The Atmos PM2.5 overestimated the reference-grade PM2.5 values in cities located in the Indo-Gangetic Plain (Chandigarh and New Delhi) but considerably underestimated the values in the city located in western India (Mumbai). In south Indian cities (Bengaluru and Chennai), the Atmos PM2.5 measurements were relatively close to the reference-grade PM2.5 measurements. Among various statistical calibration models trained to correct the Atmos PM2.5 measurements for most locations, a generalized additive model performed better than other models. The performance of the calibration models was investigated using the holdout cross-validation method. The correction models improved the accuracy of the Atmos PM2.5 measurements by up to 70%. The bias range of the intracity (Mumbai) raw Atmos PM2.5 measurements was approximately comparable to the intercity bias range. Across the study locations, the generalized additive model performed the best in correcting the raw LCS PM2.5 measurements. We also demonstrated that the application of the location-specific calibration model to correct Atmos PM2.5 measurements improved the accuracy of the LCS PM2.5 measurements compared with the application of a single-location calibration model for city-wide data.

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    The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acsearthspacechem.2c00257.

    • Time series of PM2.5 from collocated LCSs in different cities (Figure S1); city-wise time series of hourly BAM and Atmos PM2.5 (Figure S2); scatter plots between BAM and Atmos PM2.5, data segregated in three RH bins (Figure S3); comparison of temperature measured by meteorological station and collocated Atmos (Figure S4); comparison of RH measured by meteorological station and collocated Atmos (Figure S5); city-wise diurnal variations of BAM and Atmos PM2.5 (Figure S6); geographical locations of five collocation sites within Mumbai city (Figure S7); time series comparison of BAM and Atmos PM2.5 in various sites within Mumbai city (Figure S8); demographic and geographical characteristics and major PM2.5 sources of the study cities (Table S1); city-wise summary statistics of BAM PM2.5 (Table S2); city-wise performance statistics of uncorrected Atmos PM2.5 (Table S3); city-wise regression coefficients of linear fits made to scatter plots between BAM and Atmos PM2.5, data segregated by RH (Table S4); model-wise regression coefficients of various city-wise statistical models (Table S5); and performance statistics of uncorrected PM2.5 across sites in Mumbai city (Table S6) (PDF)

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