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The availability of 5-year time series of velocity and temperature data from two sonic anemometers installed at Jang Bogo Station, Antarctica, allowed a systematic investigation of the turbulence features in a stable layer affected by submeso motions and characterized by the vertical divergence of some second-order moments for a large fraction of time (quite a non-ideal surface layer). The investigation of the effect of the averaging time interval on the statistics of the second-order moments showed that this is greater for the variances of the velocity components with respect to that for the vertical fluxes. This corresponds to a greater contribution from low-frequency motions. The turbulence statistics were investigated and compared with current literature results in terms of vertical structure, share of energy between horizontal and vertical components, skewness of the vertical velocity and turbulent velocities. As a general result, all the normalized second-order moments show a clear change passing from neutral to stable conditions, passing through the range of bulk Richardson number equal to 0.1–1.

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.