Preprints
https://doi.org/10.5194/amt-2022-140
https://doi.org/10.5194/amt-2022-140
 
16 May 2022
16 May 2022
Status: this preprint was under review for the journal AMT but the revision was not accepted.

Low-Cost Air Quality Sensor Evaluation and Calibration in Contrasting Aerosol Environments

Pawan Gupta1,2, Prakash Doraiswamy3, Jashwanth Reddy4,1, Palak Balyan5,6, Sagnik Dey5, Ryan Chartier3, Adeel Khan7, Karmann Riter3, Brandon Feenstra8, Robert C. Levy9, Nhu Nguyen Minh Tran4, Olga Pikelnaya8, Kurinji Selvaraj8, Tanushree Ganguly7, and Karthik Ganesan7 Pawan Gupta et al.
  • 1STI-Universities Space Research Associations, Huntsville, AL, USA
  • 2NASA Marshall Space Flight Center, Huntsville, AL, USA
  • 3RTI International, Research Triangle Park, NC, USA
  • 4The University of Alabama in Huntsville, AL, USA
  • 5Indian Institute of Technology, New Delhi, India
  • 6Health Effects Institute (HEI), Boston, U.S.A.
  • 7Council on Energy, Environment, and Water (CEEW), New Delhi, India
  • 8South Coast Air Quality Management District, CA, USA
  • 9NASA Goddard Space Flight Center, Huntsville, AL, USA

Abstract. The use of low-cost sensors (LCS) in air quality monitoring has been gaining interest across all walks of society, including community and citizen scientists, academic research groups, environmental agencies, and the private sector. Traditional air monitoring, performed by regulatory agencies, involves expensive regulatory-grade equipment and requires ongoing maintenance and quality control checks. The low-price tag, minimal operating cost, ease of use, and open data access are the primary driving factors behind the popularity of LCS. This study discusses the role and associated challenges of PM2.5 sensors in monitoring air quality. We present the results of evaluations of the PurpleAir (PA.) PA-II LCS against regulatory-grade PM2.5 federal equivalent methods (FEM) and the development of sensor calibration algorithms. The LCS calibration was performed for 2 to 4 weeks during December 2019–January 2020 in Raleigh, NC, and Delhi, India, to evaluate the data quality under different aerosols loadings and environmental conditions. This exercise aims to develop a robust calibration model that uses PA measured parameters (i.e., PM2.5, temperature, relative humidity) as input and provides bias-corrected PM2.5 output at an hourly scale. Thus, the calibration model relies on simultaneous measurements of PM2.5 by FEM as target output during the calibration model development process. We applied various statistical and machine learning methods to achieve a regional calibration model. The results from our study indicate that, with proper calibration, we can achieve bias-corrected PM2.5 data using PA sensors within 12 % percentage mean absolute bias at hourly and within 6 % for a daily average. Our study also suggests that pre-deployment calibrations developed at local or regional scales should be performed for the PA sensors to correct data from the field for scientific data analysis.

Pawan Gupta et al.

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on AMT-2022-140', Anonymous Referee #1, 05 Jul 2022
    • AC1: 'Reply on RC1', Pawan Gupta, 28 Sep 2022
  • RC2: 'Comment on amt-2022-140', Anonymous Referee #2, 10 Aug 2022
    • AC2: 'Reply on RC2', Pawan Gupta, 28 Sep 2022

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on AMT-2022-140', Anonymous Referee #1, 05 Jul 2022
    • AC1: 'Reply on RC1', Pawan Gupta, 28 Sep 2022
  • RC2: 'Comment on amt-2022-140', Anonymous Referee #2, 10 Aug 2022
    • AC2: 'Reply on RC2', Pawan Gupta, 28 Sep 2022

Pawan Gupta et al.

Pawan Gupta et al.

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Short summary
The use of low-cost sensors in air quality monitoring has been gaining interest across all walks of society. We present the results of evaluations of the PurpleAir against regulatory-grade PM2.5. The results indicate that with proper calibration, we can achieve bias-corrected PM2.5 data using PA sensors. Our study also suggests that pre-deployment calibrations developed at local or regional scales are required for the PA sensors to correct data from the field for scientific data analysis.