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

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 PM_2.5 and PM₁₀ (≈1 µg m^-3 for PM_2.5 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.