Preprints
https://doi.org/10.5194/amt-2020-489
https://doi.org/10.5194/amt-2020-489

  22 Feb 2021

22 Feb 2021

Review status: this preprint is currently under review for the journal AMT.

Unraveling a black box: An open-source methodology for the field calibration of small air quality sensors

Seán Schmitz1, Sherry Towers1, Guillermo Villena2, Alexandre Caseiro1, Robert Wegener3, Dieter Klemp3, Ines Langer4, Fred Meier5, and Erika von Schneidemesser1 Seán Schmitz et al.
  • 1Institute for Advanced Sustainability Studies e. V. (IASS), Berliner Strasse 130, 14467 Potsdam, Germany
  • 2Bergische Universität Wuppertal, Physikalische und Theoretische Chemie/FK4, Gaussstrasse 20, 42119 Wuppertal, Germany
  • 3Forschungszentrum Jülich GmbH, Institute of Energy and Climate Research, IEK8: Troposphere, 52425 Jülich, Germany
  • 4Freie Universität Berlin, Institut für Meteorologie, Carl-Heinrich-Becker Weg 6-10, 12165 Berlin, Germany
  • 5Chair of Climatology, Institute of Ecology, Technische Universität Berlin, Rothenburgstraße 12, D-12165 Berlin, Germany

Abstract. The last two decades have seen substantial technological advances in the development of low-cost air pollution instruments using small sensors. While their use continues to spread across the field of atmospheric chemistry, the air quality monitoring community, as well as for commercial and private use, challenges remain in ensuring data quality and comparability of calibration methods. This study introduces a seven-step methodology for the field calibration of low-cost sensors using reference instrumentation with user-friendly guidelines, open access code, and a discussion of common barriers to such an approach. The methodology has been developed and is applicable for gas-phase pollutants, such as for the measurement of nitrogen dioxide (NO2) or ozone (O3). A full example of the application of this methodology to a case study in an urban environment using both Multiple Linear Regression (MLR) and the Random Forest (RF) machine-learning technique is presented with relevant R code provided, including error estimation. In this case, we have applied it to the calibration of metal oxide gas-phase sensors (MOS). Results reiterate previous findings that MLR and RF are similarly accurate, though with differing limitations. The methodology presented here goes a step further than most studies by including explicit, transparent steps for addressing model selection, validation, and tuning, as well as addressing the common issues of autocorrelation and multicollinearity. We also highlight the need for standardized reporting of methods for data cleaning and flagging, model selection and tuning, and model metrics. In the absence of a standardized methodology for the calibration of low-cost sensors, we suggest a number of best practices for future studies using low-cost sensors to ensure greater comparability of research.

Seán Schmitz et al.

Status: open (extended)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on amt-2020-489', Anonymous Referee #3, 31 May 2021 reply

Seán Schmitz et al.

Seán Schmitz et al.

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Short summary
The last two decades have seen substantial technological advances in the development of low-cost air pollution instruments. This study introduces a 7-step methodology for the field calibration of low-cost sensors with user-friendly guidelines, open access code, and a discussion of common barriers. Our goal with this work is to push for standardized reporting of methods, to make critical data processing steps clear for users, and encourage responsible use in the scientific community and beyond.