Dimitris Margaritis1,2, Christos Keramydas3, Ioannis Papachristos3, Dimitra Lambropoulou This email address is being protected from spambots. You need JavaScript enabled to view it.1,4 

1 Aristotle University of Thessaloniki / School of Chemistry, University Campus, 54124, Thessaloniki, Greece
2 Centre for Research and Technology Hellas (CERTH) / Hellenic Institute of Transport (HIT), 57001 Thermi, Thessaloniki, Greece
3 Department of Supply Chain Management, International Hellenic University, 60100, Katerini, Greece
4 Center for Interdisciplinary Research and Innovation (CIRI-AUTH), Balkan Center, Thessaloniki, GR-57001, Greece


Received: April 16, 2021
Revised: July 31, 2021
Accepted: September 10, 2021

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.


Download Citation: ||https://doi.org/10.4209/aaqr.210073  

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Cite this article:

Margaritis, D., Keramydas, C., Papachristos, I., Lambropoulou, D. (2021). Calibration of Low-cost Gas Sensors for Air Quality Monitoring. Aerosol Air Qual. Res. https://doi.org/10.4209/aaqr.210073


HIGHLIGHTS

  • Low-cost sensors proved to be an alternative solution in monitoring air quality.
  • The pods performance in following the trend of the reference data is satisfactory.
  • The NO2 measurements appear to be the most accurate among the studied pollutants.
  • Calibration results derived from simple linear regression can be quite reliable.
 

ABSTRACT


Mobile monitoring devices equipped with low-cost gas sensors in fixed stations are an emerging solution to enhance the spatial coverage of air quality monitoring networks. We estimated the measurement accuracy of two AQMesh devices, evaluated their agreement, and examined the related calibration characteristics. Three widely used calibration approaches were investigated, namely uni- and multi-variate linear regression analysis and the random forest algorithm. Two identical commercial AQMesh platforms (monitoring NO, NO2, O3, and SO2) were installed on a fixed municipal station for 4 consecutive weeks. Widely used statistical indexes were employed to evaluate device performance and calibration outcomes. The devices exhibited favorable performance in following the pattern of the station’s reference time series in a 10-min average resolution. Nevertheless, their performance was lower, with respect to the reference values, in terms of the average error and overall bias. The calibration improved the agreement between the device and reference measurements. The emission time series of each device was consistent with the other (pre- and post-calibration) in terms of measurement patterns and point-by-point deviations. The three alternative methodologies had similar calibration performance overall. The random forest algorithm appeared to have an advantage in several cases, mostly in terms of following the pattern in the O3 and SO2 time series, but also in terms of the average error and bias for all pollutants.


Keywords: Electrochemical sensors, Gas emissions, Statistical analysis, Calibration models




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