摘要:

Samples of airborne particulate matter were collected over a continuous sequence of 1 week intervals at Alert, Canada beginning in 1980 and analyzed for a number of chemical species, It was found that the measured weekly average concentrations display strong, persistent seasonal variations. In another recent study, the measured concentration of 24 constituents were arranged into both 2-way and 3-way data arrays and bilinear and trilinear models were used to fit the data using a new mathematical technique, positive matrix factorization. Five factors were found to explain the data for both 2-way and 3-way modeling with each factor representing a likely particle source. In the 2-way modeling, the yearly cyclical seasonal variations were not directly retrieved since the whole 11 yr of data was regarded as a single mode in the fitting. In the 3-way analysis, assuming the week-to-week patterns of the source contributions recur from year to year imposed fixed seasonality on the solutions. The resulting fit becomes worse if the year-to-year pattern of variation is not identical for any given source. These results suggested that a mixed model containing both 2-way and 3-way components might provide the best representation of the data. The methodology to calculate such a mixed model has just been developed. The multilinear engine is introduced in this study to estimate a mixed 2-way/3-way model for the Alert aerosol data. Five 2-way and two S-way factors have been found to provide the best fit and interpretation of the data. Each factor represented probable source with a distinctive compositional profile and seasonal variations. The five 2-way factors are (i) winter Arctic haze dominated by SO42- including metallic species with highest concentrations from December to April, (ii) soil represented by Si, Al, Ca, (iii) sea salt, (iv) sulfate with high acidity peaking in late March and April and (v) iodine representing most of the observed I with two maximal one around September and October and another around March and April. The two 3-way factors are (i) bromine characterized by a maximum in the spring around March and April; and (ii) biogenic sulfur which includes sulfate and methanesulfonate with maxims in May and August. The acidic sulfate, bromine, and iodine factors have a common maximum around March/April, just after polar sunrise, suggesting the influence of increased photochemistry at that time of year. The strength of the year-to-year biogenic sulfur factor showed a moderate correlation (r(2) = 0.5) with the yearly average Northern Hemisphere Temperature Anomaly suggesting a relationship of temperature with biogenic sulfur production. The results obtained are consistent with those obtained in the previous study and agree with the current understanding of the Arctic aerosol. (C) 1999 Elsevier Science Ltd. All rights reserved.