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<p>The use of low-cost sensors (LCS) for air quality monitoring has grown rapidly across a wide range of groups, including community and citizen scientists, academic researchers, environmental agencies, and the private sector. Traditional air monitoring conducted by regulatory agencies relies on expensive, regulatory-grade instruments that require frequent maintenance and rigorous quality control procedures. In contrast, the low purchase price, minimal operating costs, user-friendly design, and open data accessibility have significantly contributed to the widespread adoption of LCS. Over the past decade, hundreds of studies have proposed diverse calibration strategies to tailor LCS performance to specific project needs. This study examines the role of PM<sub>2.5</sub> sensors in monitoring air quality across contrasting environments and highlights the importance of inter-sensor consistency. We evaluate PurpleAir (PA) PA-II sensors against regulatory-grade Federal Equivalent Method (FEM) PM<sub>2.5</sub> instruments and develop calibration algorithms to improve data accuracy. Calibration deployments were conducted for 2–4 weeks in Raleigh, North Carolina, and Delhi, India, to assess sensor behavior under different aerosol loadings and environmental conditions. The goal of this effort is to create a robust calibration model that uses PA-measured parameters, PM<sub>2.5</sub>, temperature, and relative humidity as inputs to generate bias-corrected hourly PM<sub>2.5</sub> values. The model relies on concurrent FEM PM<sub>2.5</sub> measurements as the reference data during calibration development. Multiple statistical and machine-learning approaches were applied to produce a regional calibration model. Our results show that, with proper calibration, PA sensors can provide bias-corrected PM<sub>2.5</sub> estimates within 12% mean absolute bias at hourly resolution and within 6% for daily averages. The findings also indicate that pre-deployment calibrations for a specific location or region are essential for ensuring that PA sensor data are suitable for scientific analysis and interpretation.</p> Graphical Abstract <p></p>

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Evaluating and Calibrating Low-Cost Air Quality Sensors in Contrasting Aerosol Regimes

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月21日

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  • Pawan Gupta,
  • Prakash Doraiswamy,
  • Alqamah Sayeed,
  • Palak Baliyan,
  • Sagnik Dey,
  • Ryan Chartier,
  • Robert C. Levy

摘要

The use of low-cost sensors (LCS) for air quality monitoring has grown rapidly across a wide range of groups, including community and citizen scientists, academic researchers, environmental agencies, and the private sector. Traditional air monitoring conducted by regulatory agencies relies on expensive, regulatory-grade instruments that require frequent maintenance and rigorous quality control procedures. In contrast, the low purchase price, minimal operating costs, user-friendly design, and open data accessibility have significantly contributed to the widespread adoption of LCS. Over the past decade, hundreds of studies have proposed diverse calibration strategies to tailor LCS performance to specific project needs. This study examines the role of PM2.5 sensors in monitoring air quality across contrasting environments and highlights the importance of inter-sensor consistency. We evaluate PurpleAir (PA) PA-II sensors against regulatory-grade Federal Equivalent Method (FEM) PM2.5 instruments and develop calibration algorithms to improve data accuracy. Calibration deployments were conducted for 2–4 weeks in Raleigh, North Carolina, and Delhi, India, to assess sensor behavior under different aerosol loadings and environmental conditions. The goal of this effort is to create a robust calibration model that uses PA-measured parameters, PM2.5, temperature, and relative humidity as inputs to generate bias-corrected hourly PM2.5 values. The model relies on concurrent FEM PM2.5 measurements as the reference data during calibration development. Multiple statistical and machine-learning approaches were applied to produce a regional calibration model. Our results show that, with proper calibration, PA sensors can provide bias-corrected PM2.5 estimates within 12% mean absolute bias at hourly resolution and within 6% for daily averages. The findings also indicate that pre-deployment calibrations for a specific location or region are essential for ensuring that PA sensor data are suitable for scientific analysis and interpretation.

Graphical Abstract