Preprints
https://doi.org/10.5194/amt-2019-299
https://doi.org/10.5194/amt-2019-299

  19 Aug 2019

19 Aug 2019

Review status: this preprint was under review for the journal AMT but the revision was not accepted.

Field Calibration of Low-Cost Air Pollution Sensors

Andres Gonzalez1, Adam Boies2, Jacob Swason3, and David Kittelson4 Andres Gonzalez et al.
  • 1Department of Civil, Environmental, and Geo-Engineering, University of Minnesota, Minneapolis, MN 55455, USA
  • 2Department of Engineering, University of Cambridge, Cambridge, CB2 1PZ, UK
  • 3Department of Integrated Engineering, Minnesota State University, Mankato, MN 56001, USA
  • 4Department of Mechanical Engineering, University of Minnesota, Minneapolis, MN 55455, USA

Abstract. To implement effective policies and strategies to control air pollution, it is crucial to obtain accurate air quality data. Stationary air monitoring stations (AMSs) help local authorities and environmental agencies in achieving these goals; however, these measurements have limitations. AMSs provide detailed temporal data on air quality, but only at discrete locations at relatively high cost. An alternative method, low-cost mobile air quality monitoring (LCMAQM) sensors, complement AMSs. LCMAQM sensors can cover larger areas and the cost of typical sensors for LCMAQM are $150–200 each. We have developed a wireless Mobile Autonomous Air Quality Sensor box (MAAQSbox) to measure air pollution. The MAAQSbox contains LCMAQM sensors (gas and particle) and a wireless broadcasting system, which enables autonomous field operation for varied mobile applications. Nitrogen dioxide (NO2), nitric oxide (NO), carbon monoxide (CO), and ozone (O3) gases are measured by B4 sensors. Particulate matter (PM2.5) is measured by OPC-N2. A field calibration has been performed by making side by side measurements with the MAAQSbox and Minnesota Pollution Agency AMS. The calibrations of LCMAQM sensors were determined by multivariate linear regressions (MLR). MLR results for all sensors were improved by including the temperature and relative humidity as independent variables. The R2 of CO, NO, NO2, and O3 gas sensors are 0.96, 0.97, 0.81, and 0.95 respectively, while the R2 of PM2.5 particle sensor is 0.6. B4 sensors are sensitive to ambient conditions such as temperature and relative humidity. The results with OPC-N2 differs from the AMS indicating further developments are needed to enable more accurate PM2.5 measurements.

Andres Gonzalez et al.

 
Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement

Andres Gonzalez et al.

Andres Gonzalez et al.

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Short summary
To implement effective policies and strategies to control air pollution, it is crucial to obtain accurate air quality data. Stationary air monitoring stations (AMSs) help local authorities and environmental agencies in achieving these goals; however, these measurements have limitations. AMSs provide detailed temporal data on air quality, but only at discrete locations at relatively high cost. An alternative method, low-cost mobile air quality monitoring (LCMAQM) sensors, complement AMSs.