The recent decline in sea ice cover in the Arctic Ocean could affect the regional radiative forcing via changes in sea ice-atmosphere exchange of dimethyl sulfide (DMS) and biogenic aerosols formed from its atmospheric oxidation, such as methanesulfonic acid (MSA). This study examines relationships between changes in total sea ice extent north of 70 degrees N and atmospheric MSA measurement at Alert, Nunavut, during 1980-2009; at Barrow, Alaska, during 1997-2008; and at Ny-Alesund, Svalbard, for 1991-2004. During the 1980-1989 and 1990-1997 periods, summer (July-August) and June MSA concentrations at Alert decreased. In general, MSA concentrations increased at all locations since 2000 with respect to 1990 values, specifically during June and summer at Alert and in summer at Barrow and Ny-Alesund. Our results show variability in MSA at all sites is related to changes in the source strengths of DMS, possibly linked to changes in sea ice extent as well as to changes in atmospheric transport patterns. Since 2000, a late spring increase in atmospheric MSA at the three sites coincides with the northward migration of the marginal ice edge zone where high DMS emissions from ocean to atmosphere have previously been reported. Significant negative correlations are found between sea ice extent and MSA concentrations at the three sites during the spring and June. These results suggest that a decrease in seasonal ice cover influencing other mechanisms of DMS production could lead to higher atmospheric MSA concentrations.
In this study, three classification techniques (self-organizing maps, hierarchical cluster analysis and discriminant analysis) were applied to identify spatial water pollution levels, temporal water quality response delay phenomena (WQRDP), source pollution types (point, urban non-point, or agricultural non-point). Two models (principal components analysis (PCA), and positive matrix factorization (PMF)) were used to do the further quantitative source apportionment studying. The 27 inflow rivers in spatial were divided into three pollution levels (A, high; B, medium; C, low). The primary pollution pattern in spatial Clusters A, B, and C were point, urban non-point and agricultural non-point separately, in consideration of simultaneous land use types. Source apportionment results identified five typical factors in spatial Cluster A and six typical factors in spatial Cluster B and C as responsible for the data structure, explaining 80%-90% of the total variance of the dataset. Crown Copyright (c) 2012 Published by Elsevier Ltd. All rights reserved.