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Long-Term Performance Assessment of Low-Cost Atmospheric Sensors in the Arctic Environment

Authors :
Federico Carotenuto
Lorenzo Brilli
Beniamino Gioli
Giovanni Gualtieri
Carolina Vagnoli
Mauro Mazzola
Angelo Pietro Viola
Vito Vitale
Mirko Severi
Rita Traversi
Alessandro Zaldei
Source :
Sensors, Vol 20, Iss 7, p 1919 (2020)
Publication Year :
2020
Publisher :
MDPI AG, 2020.

Abstract

The Arctic is an important natural laboratory that is extremely sensitive to climatic changes and its monitoring is, therefore, of great importance. Due to the environmental extremes it is often hard to deploy sensors and observations are limited to a few sparse observation points limiting the spatial and temporal coverage of the Arctic measurement. Given these constraints the possibility of deploying a rugged network of low-cost sensors remains an interesting and convenient option. The present work validates for the first time a low-cost sensor array (AIRQino) for monitoring basic meteorological parameters and atmospheric composition in the Arctic (air temperature, relative humidity, particulate matter, and CO2). AIRQino was deployed for one year in the Svalbard archipelago and its outputs compared with reference sensors. Results show good agreement with the reference meteorological parameters (air temperature (T) and relative humidity (RH)) with correlation coefficients above 0.8 and small absolute errors (≈1 °C for temperature and ≈6% for RH). Particulate matter (PM) low-cost sensors show a good linearity (r2 ≈ 0.8) and small absolute errors for both PM2.5 and PM10 (≈1 µg m−3 for PM2.5 and ≈3 µg m−3 for PM10), while overall accuracy is impacted both by the unknown composition of the local aerosol, and by high humidity conditions likely generating hygroscopic effects. CO2 exhibits a satisfying agreement with r2 around 0.70 and an absolute error of ≈23 mg m−3. Overall these results, coupled with an excellent data coverage and scarce need of maintenance make the AIRQino or similar devices integrations an interesting tool for future extended sensor networks also in the Arctic environment.

Details

Language :
English
ISSN :
14248220
Volume :
20
Issue :
7
Database :
Directory of Open Access Journals
Journal :
Sensors
Publication Type :
Academic Journal
Accession number :
edsdoj.b22cf37d695492789e63979105a0c58
Document Type :
article
Full Text :
https://doi.org/10.3390/s20071919