A low-cost air quality station has been developed for real-time monitoring of main atmospheric pollutants. Sensors for CO, CO₂, NO₂, O₃, VOC, PM_2.5 and PM₁₀ were integrated on an Arduino Shield compatible board. As concerns PM_2.5 and PM₁₀ sensors, the station underwent a laboratory calibration and later a field validation. Laboratory calibration has been carried out at the headquarters of CNR-IBIMET in Florence (Italy) against a TSI DustTrak reference instrument. A MATLAB procedure, implementing advanced mathematical techniques to detect possible complex non-linear relationships between sensor signals and reference data, has been developed and implemented to accomplish the laboratory calibration. Field validation has been performed across a full “heating season” (1 November 2016 to 15 April 2017) by co-locating the station at a road site in Florence where an official fixed air quality station was in operation. Both calibration and validation processes returned fine scores, in most cases better than those achieved for similar systems in the literature. During field validation, in particular, for PM_2.5 and PM₁₀ mean biases of 0.036 and 0.598 µg/m³, RMSE of 4.056 and 6.084 µg/m³, and R² of 0.909 and 0.957 were achieved, respectively. Robustness of the developed station, seamless deployed through a five and a half month outdoor campaign without registering sensor failures or drifts, is a further key point.

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.

As the basis of livestock feeding and related performances, pastures evolution and dynamics need to be carefully monitored and assessed, particularly in the Alps where the effects of land abandonment are further amplified by climate change. As such, increases in temperature associated with changes in precipitation patterns and quantity are leading to modifications of grassland extent and composition with consequences on the pastoral systems. This study applied a machine learning approach (Random Forest) and GIS techniques to map the suitability of seven pasture macro types most representative of the Italian Alps and simulated the impact of climate change on their dynamics according to two future scenarios (RCP4.5, 8.5), two time-slices (2011–2040, 2041–2070), and three RCMs (Aladin, CMCC, ICTP). Results indicated that (i) the methodology was robust to map the current suitability of pasture macro types (mean accuracy classification = 98.7%), so as to predict the expected alterations due to climate change; (ii) future climate will likely reduce current extend of suitable pasture (−30% on average) and composition, especially for most niche ecosystems (i.e., pastures dominated by Carex firma and Festuca gr. Rubra); (iii) areas suited to hardier but less palatable pastures (i.e., dominated by Nardus stricta and xeric species) will expand over the Alps in the near future. These impacts will likely determine risks for biodiversity loss and decreases of pastoral values for livestock feeding, both pivotal aspects for maintaining the viability and profitability of the Alpine pastoral system as a whole.