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    Identification of Source Nature and Seasonal Variations of Arctic Aerosol byPositive Matrix Factorization

    Source: Journal of the Atmospheric Sciences:;1999:;Volume( 056 ):;issue: 002::page 249
    Author:
    Xie, Yu-Long
    ,
    Hopke, Philip K.
    ,
    Paatero, Pentti
    ,
    Barrie, Leonard A.
    ,
    Li, Shao-Meng
    DOI: 10.1175/1520-0469(1999)056<0249:IOSNAS>2.0.CO;2
    Publisher: American Meteorological Society
    Abstract: Week-long samples of airborne particulate matter were obtained at Alert, Northwest Territories, Canada, between 1980 and 1991. The concentrations of 24 particulate constituents have some strong, persistent seasonal variations that depend on the transport from their sources. In order to explore the nature of the cyclical variation of the different processes that give rise to the measured concentrations, the observations were arranged into both a two-way matrix and a three-way data array. For the latter, the three modes consist of chemical constituents, weeks within a year, and years. The two-way bilinear model and a three-way trilinear model were used to fit the data and a new data analysis technique, positive matrix factorization (PMF), has been used to obtain the solutions. PMF utilizes the error estimates of the observations to provide an optimal pointwise scaling data array for weighting, which enables it to handle missing data, a common occurrence in environmental measurements. It can also apply nonnegative constraints to the factors. Five factors have been obtained that reproduce the data quite well for both two-way and three-way analyses. Each factor represents a probable source with a compositional profile and distinctive seasonal variations. Specifically, there are (i) an acid photochemical factor typified by Br?, H+, and SO2?4 and characterized by a concentration maximum around April, or shortly after polar sunrise;(ii) a soil factor representing by Si, Al, and Ca and having its main seasonal maximum in September and October;(iii) an anthropogenic factor dominated by SO2?4 together with metallic species like Pb, Zn, V, As, Sb, Se, In, etc., peaking from December to April; (iv) a sea salt factor consisting mainly of Cl, Na, and K with maximum concentrations during the period from October to April; and (v) a biogenic factor characterized by methanesulfonate and having a primary maximum at May and a secondary maximum in August. The results obtained by both two-way and three-way PMF analyses are generally consistent with one another. However, there are differences because of additional constraints on the solution imposed by the three-way analysis. The results also help to confirm the hypotheses regarding the origins of the Arctic aerosol.
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      Identification of Source Nature and Seasonal Variations of Arctic Aerosol byPositive Matrix Factorization

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4158680
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    • Journal of the Atmospheric Sciences

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    contributor authorXie, Yu-Long
    contributor authorHopke, Philip K.
    contributor authorPaatero, Pentti
    contributor authorBarrie, Leonard A.
    contributor authorLi, Shao-Meng
    date accessioned2017-06-09T14:35:13Z
    date available2017-06-09T14:35:13Z
    date copyright1999/01/01
    date issued1999
    identifier issn0022-4928
    identifier otherams-22250.pdf
    identifier urihttp://onlinelibrary.yabesh.ir/handle/yetl/4158680
    description abstractWeek-long samples of airborne particulate matter were obtained at Alert, Northwest Territories, Canada, between 1980 and 1991. The concentrations of 24 particulate constituents have some strong, persistent seasonal variations that depend on the transport from their sources. In order to explore the nature of the cyclical variation of the different processes that give rise to the measured concentrations, the observations were arranged into both a two-way matrix and a three-way data array. For the latter, the three modes consist of chemical constituents, weeks within a year, and years. The two-way bilinear model and a three-way trilinear model were used to fit the data and a new data analysis technique, positive matrix factorization (PMF), has been used to obtain the solutions. PMF utilizes the error estimates of the observations to provide an optimal pointwise scaling data array for weighting, which enables it to handle missing data, a common occurrence in environmental measurements. It can also apply nonnegative constraints to the factors. Five factors have been obtained that reproduce the data quite well for both two-way and three-way analyses. Each factor represents a probable source with a compositional profile and distinctive seasonal variations. Specifically, there are (i) an acid photochemical factor typified by Br?, H+, and SO2?4 and characterized by a concentration maximum around April, or shortly after polar sunrise;(ii) a soil factor representing by Si, Al, and Ca and having its main seasonal maximum in September and October;(iii) an anthropogenic factor dominated by SO2?4 together with metallic species like Pb, Zn, V, As, Sb, Se, In, etc., peaking from December to April; (iv) a sea salt factor consisting mainly of Cl, Na, and K with maximum concentrations during the period from October to April; and (v) a biogenic factor characterized by methanesulfonate and having a primary maximum at May and a secondary maximum in August. The results obtained by both two-way and three-way PMF analyses are generally consistent with one another. However, there are differences because of additional constraints on the solution imposed by the three-way analysis. The results also help to confirm the hypotheses regarding the origins of the Arctic aerosol.
    publisherAmerican Meteorological Society
    titleIdentification of Source Nature and Seasonal Variations of Arctic Aerosol byPositive Matrix Factorization
    typeJournal Paper
    journal volume56
    journal issue2
    journal titleJournal of the Atmospheric Sciences
    identifier doi10.1175/1520-0469(1999)056<0249:IOSNAS>2.0.CO;2
    journal fristpage249
    journal lastpage260
    treeJournal of the Atmospheric Sciences:;1999:;Volume( 056 ):;issue: 002
    contenttypeFulltext
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