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In this study, we present atmospheric ice-nucleating particle (INP) concentrations from the Gruvebadet (GVB) observatory in Ny-Ålesund (Svalbard). All aerosol particle sampling activities were conducted in April–August 2018. Ambient INP concentrations (n INP) were measured for aerosol particles collected on filter samples by means of two offline instruments: the Dynamic Filter Processing Chamber (DFPC) and the West Texas Cryogenic Refrigerator Applied to Freezing Test system (WT-CRAFT) to assess condensation and immersion freezing, respectively. DFPC measured n INPs for a set of filters collected through two size-segregated inlets: one for transmitting particulate matter of less than 1 µm (PM 1), the other for particles with an aerodynamic diameter of less than 10 µm aerodynamic diameter (PM 10). Overall, n INP PM10 measured by DFPC at a water saturation ratio of 1.02 ranged from 3 to 185 m -3 at temperatures (T s) of -15 to -22 ∘ C. On average, the super-micrometer INP (n INP PM10-n INP PM1) accounted for approximately 20 %–30 % of n INP PM10 in spring, increasing in summer to 45 % at -22 ∘ C and 65 % at -15 ∘ C. This increase in super-micrometer INP fraction towards summer suggests that super-micrometer aerosol particles play an important role as the source of INPs in the Arctic. For the same T range, WT-CRAFT measured 1 to 199 m -3. Although the two n INP datasets were in general agreement, a notable n INP offset was observed, particularly at -15 ∘ C. Interestingly, the results of both DFPC and WT-CRAFT measurements did not show a sharp increase in n INP from spring to summer. While an increase was observed in a subset of our data (WT-CRAFT, between -18 and -21 ∘ C), the spring-to-summer n INP enhancement ratios never exceeded a factor of 3. More evident seasonal variability was found, however, in our activated fraction (AF) data, calculated by scaling the measured n INP to the total aerosol particle concentration. In 2018, AF increased from spring to summer. This seasonal AF trend corresponds to the overall decrease in aerosol concentration towards summer and a concomitant increase in the contribution of super-micrometer particles. Indeed, the AF of coarse particles resulted markedly higher than that of sub-micrometer ones (2 orders of magnitude). Analysis of low-traveling back-trajectories and meteorological conditions at GVB matched to our INP data suggests that the summertime INP population is influenced by both terrestrial (snow-free land) and marine sources. Our spatiotemporal analyses of satellite-retrieved chlorophyll a , as well as spatial source attribution, indicate that the maritime INPs at GVB may come from the seawaters surrounding the Svalbard archipelago and/or in proximity to Greenland and Iceland during the observation period. Nevertheless, further analyses, performed on larger datasets, would be necessary to reach firmer and more general conclusions. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]