Abstract
Abstract. There are limited measurements of the chemical composition, abundance and sources of atmospheric particles in the High Arctic To address this, we report 93 d of soot particle aerosol mass spectrometer (SP-AMS) data collected from 20 February to 23 May 2015 at Villum Research Station (VRS) in northern Greenland (81∘36′ N). During this period, we observed the Arctic haze phenomenon with elevated PM1 concentrations ranging from an average of 2.3, 2.3 and 3.3 µg m−3 in February, March and April, respectively, to 1.2 µg m−3 in May. Particulate sulfate (SO42-) accounted for 66 % of the non-refractory PM1 with the highest concentration until the end of April and decreasing in May. The second most abundant species was organic aerosol (OA) (24 %). Both OA and PM1, estimated from the sum of all collected species, showed a marked decrease throughout May in accordance with the polar front moving north, together with changes in aerosol removal processes. The highest refractory black carbon (rBC) concentrations were found in the first month of the campaign, averaging 0.2 µg m−3. In March and April, rBC averaged 0.1 µg m−3 while decreasing to 0.02 µg m−3 in May. Positive matrix factorization (PMF) of the OA mass spectra yielded three factors: (1) a hydrocarbon-like organic aerosol (HOA) factor, which was dominated by primary aerosols and accounted for 12 % of OA mass, (2) an Arctic haze organic aerosol (AOA) factor and (3) a more oxygenated marine organic aerosol (MOA) factor. AOA dominated until mid-April (64 %–81 % of OA), while being nearly absent from the end of May and correlated significantly with SO42-, suggesting the main part of that factor is secondary OA. The MOA emerged late at the end of March, where it increased with solar radiation and reduced sea ice extent and dominated OA for the rest of the campaign until the end of May (24 %–74 % of OA), while AOA was nearly absent. The highest O∕C ratio (0.95) and S∕C ratio (0.011) was found for MOA. Our data support the current understanding that Arctic aerosols are highly influenced by secondary aerosol formation and receives an important contribution from marine emissions during Arctic spring in remote High Arctic areas. In view of a changing Arctic climate with changing sea-ice extent, biogenic processes and corresponding source strengths, highly time-resolved data are needed in order to elucidate the components dominating aerosol concentrations and enhance the understanding of the processes taking place.
Highlights
Climate change driven by anthropogenic emission of greenhouse gases seriously impacts the Arctic, which has experienced average temperature increases of twice the global mean during the last 100 years (AMAP, 2015; IPCC, 2018)
Time-dependent organic aerosol (OA), SO24−, NO−3, NH+4, Cl− and refractory black carbon (rBC) concentrations [μg m−3] measured by the soot particle aerosol mass spectrometer (SP-aerosol mass spectrometry (AMS)) are presented in Fig. 1 together with temperature [◦C], mean wind speed [m s−1] and wind direction [◦] for the time period 21 February to 23 May 2015
As for the other species, OA showed a decrease in concentration from mid-April relative to February and March. rBC concentrations were found to be highest in the first month and decrease throughout the campaign with average concentrations of 0.2, 0.1, 0.1 and 0.02 μg m−3 in February, March, April and May, respectively
Summary
Climate change driven by anthropogenic emission of greenhouse gases seriously impacts the Arctic, which has experienced average temperature increases of twice the global mean during the last 100 years (AMAP, 2015; IPCC, 2018). It is well established that the aerosol concentration in the Arctic atmosphere is seasonally varying, resulting in higher loadings during winter and spring compared to summer and fall, often referred to as “Arctic haze” (Heidam et al, 1999, 2004; Tunved et al, 2013; Quinn et al, 2007; Barrie et al, 1981; Heidam, 1984). The objectives are to gain better insight into the processes influencing the chemical composition of High Arctic aerosols and to allocate potential sources and source types by use of positive matrix factorization (PMF)
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