@article{
 title = {Advanced factor analysis for multiple time resolution aerosol composition data},
 type = {article},
 year = {2004},
 pages = {4909-4920},
 volume = {38},
 id = {4c855644-f619-3b45-a0fc-d627b28a600f},
 created = {2014-10-08T16:28:18.000Z},
 file_attached = {false},
 profile_id = {363623ef-1990-38f1-b354-f5cdaa6548b2},
 group_id = {02267cec-5558-3876-9cfc-78d056bad5b9},
 last_modified = {2017-03-14T17:32:24.802Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {Zhou:AE:2004a},
 source_type = {article},
 private_publication = {false},
 abstract = {New monitoring technologies have now permitted the
measurement of a variety of chemical species in airborne
particulate matter with time resolution as high as 10 min to 1 h.
There are still species that are measured with longer integration
periods such as several hours to a day. These data from different
measurement methods produce a data set of mixed time resolution.
Traditional eigenvalue-based methods used in solving multivariate
receptor models are unable to analyze this kind of data set since
these data cannot form a simple matrix. Averaging the high time
resolution data or interpolating the low time resolution data to
produce data on the same time schedule is not acceptable. The
former method loses valuable temporal information and the latter
produces unreliable high resolution series because of the invalid
assumption of temporal smoothness. In the present work, a solution
to the problem of multiple sampling time intervals has been
developed and tested. Each data value is used in its original time
schedule without averaging or interpolation and the source
contributions are averaged to the corresponding sampling interval.
For data with the highest time resolution, the contributions are
not actually averaged. The contribution series are smoothed by
regularization auxillary equations especially for sources
containing very little high resolution species. This new model will
be explored using data from the Pittsburgh supersite. (C) 2004
Elsevier Ltd. All rights reserved. C1 Clarkson Univ, Ctr Air
Resources Engn & Sci, Potsdam, NY 13699 USA. Clarkson Univ, Dept
Chem Engn, Potsdam, NY 13699 USA. Univ Helsinki, Dept Phys Sci,
Helsinki, Finland.
Univ Maryland, Dept Chem & Biochem, College Pk, MD 20742 USA.
Carnegie Mellon Univ, Dept Civil & Environm Engn, Pittsburgh, PA
15213 USA.},
 bibtype = {article},
 author = {Zhou, L M and Hopke, P K and Paatero, P and Ondov, J M and Pancras, J P and Pekney, N J and Davidson, C I},
 journal = {Atmos. Environ.}
}

{"_id":{"_str":"5411bfbb480d3bb058002abf"},"__v":0,"authorIDs":[],"author_short":["Zhou, L., M.","Hopke, P., K.","Paatero, P.","Ondov, J., M.","Pancras, J., P.","Pekney, N., J.","Davidson, C., I."],"bibbaseid":"zhou-hopke-paatero-ondov-pancras-pekney-davidson-advancedfactoranalysisformultipletimeresolutionaerosolcompositiondata-2004","bibdata":{"title":"Advanced factor analysis for multiple time resolution aerosol composition data","type":"article","year":"2004","pages":"4909-4920","volume":"38","id":"4c855644-f619-3b45-a0fc-d627b28a600f","created":"2014-10-08T16:28:18.000Z","file_attached":false,"profile_id":"363623ef-1990-38f1-b354-f5cdaa6548b2","group_id":"02267cec-5558-3876-9cfc-78d056bad5b9","last_modified":"2017-03-14T17:32:24.802Z","read":false,"starred":false,"authored":false,"confirmed":"true","hidden":false,"citation_key":"Zhou:AE:2004a","source_type":"article","private_publication":false,"abstract":"New monitoring technologies have now permitted the\nmeasurement of a variety of chemical species in airborne\nparticulate matter with time resolution as high as 10 min to 1 h.\nThere are still species that are measured with longer integration\nperiods such as several hours to a day. These data from different\nmeasurement methods produce a data set of mixed time resolution.\nTraditional eigenvalue-based methods used in solving multivariate\nreceptor models are unable to analyze this kind of data set since\nthese data cannot form a simple matrix. Averaging the high time\nresolution data or interpolating the low time resolution data to\nproduce data on the same time schedule is not acceptable. The\nformer method loses valuable temporal information and the latter\nproduces unreliable high resolution series because of the invalid\nassumption of temporal smoothness. In the present work, a solution\nto the problem of multiple sampling time intervals has been\ndeveloped and tested. Each data value is used in its original time\nschedule without averaging or interpolation and the source\ncontributions are averaged to the corresponding sampling interval.\nFor data with the highest time resolution, the contributions are\nnot actually averaged. The contribution series are smoothed by\nregularization auxillary equations especially for sources\ncontaining very little high resolution species. This new model will\nbe explored using data from the Pittsburgh supersite. (C) 2004\nElsevier Ltd. All rights reserved. C1 Clarkson Univ, Ctr Air\nResources Engn & Sci, Potsdam, NY 13699 USA. Clarkson Univ, Dept\nChem Engn, Potsdam, NY 13699 USA. Univ Helsinki, Dept Phys Sci,\nHelsinki, Finland.\nUniv Maryland, Dept Chem & Biochem, College Pk, MD 20742 USA.\nCarnegie Mellon Univ, Dept Civil & Environm Engn, Pittsburgh, PA\n15213 USA.","bibtype":"article","author":"Zhou, L M and Hopke, P K and Paatero, P and Ondov, J M and Pancras, J P and Pekney, N J and Davidson, C I","journal":"Atmos. Environ.","bibtex":"@article{\n title = {Advanced factor analysis for multiple time resolution aerosol composition data},\n type = {article},\n year = {2004},\n pages = {4909-4920},\n volume = {38},\n id = {4c855644-f619-3b45-a0fc-d627b28a600f},\n created = {2014-10-08T16:28:18.000Z},\n file_attached = {false},\n profile_id = {363623ef-1990-38f1-b354-f5cdaa6548b2},\n group_id = {02267cec-5558-3876-9cfc-78d056bad5b9},\n last_modified = {2017-03-14T17:32:24.802Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n citation_key = {Zhou:AE:2004a},\n source_type = {article},\n private_publication = {false},\n abstract = {New monitoring technologies have now permitted the\nmeasurement of a variety of chemical species in airborne\nparticulate matter with time resolution as high as 10 min to 1 h.\nThere are still species that are measured with longer integration\nperiods such as several hours to a day. These data from different\nmeasurement methods produce a data set of mixed time resolution.\nTraditional eigenvalue-based methods used in solving multivariate\nreceptor models are unable to analyze this kind of data set since\nthese data cannot form a simple matrix. Averaging the high time\nresolution data or interpolating the low time resolution data to\nproduce data on the same time schedule is not acceptable. The\nformer method loses valuable temporal information and the latter\nproduces unreliable high resolution series because of the invalid\nassumption of temporal smoothness. In the present work, a solution\nto the problem of multiple sampling time intervals has been\ndeveloped and tested. Each data value is used in its original time\nschedule without averaging or interpolation and the source\ncontributions are averaged to the corresponding sampling interval.\nFor data with the highest time resolution, the contributions are\nnot actually averaged. The contribution series are smoothed by\nregularization auxillary equations especially for sources\ncontaining very little high resolution species. This new model will\nbe explored using data from the Pittsburgh supersite. (C) 2004\nElsevier Ltd. All rights reserved. C1 Clarkson Univ, Ctr Air\nResources Engn & Sci, Potsdam, NY 13699 USA. Clarkson Univ, Dept\nChem Engn, Potsdam, NY 13699 USA. Univ Helsinki, Dept Phys Sci,\nHelsinki, Finland.\nUniv Maryland, Dept Chem & Biochem, College Pk, MD 20742 USA.\nCarnegie Mellon Univ, Dept Civil & Environm Engn, Pittsburgh, PA\n15213 USA.},\n bibtype = {article},\n author = {Zhou, L M and Hopke, P K and Paatero, P and Ondov, J M and Pancras, J P and Pekney, N J and Davidson, C I},\n journal = {Atmos. Environ.}\n}","author_short":["Zhou, L., M.","Hopke, P., K.","Paatero, P.","Ondov, J., M.","Pancras, J., P.","Pekney, N., J.","Davidson, C., I."],"bibbaseid":"zhou-hopke-paatero-ondov-pancras-pekney-davidson-advancedfactoranalysisformultipletimeresolutionaerosolcompositiondata-2004","role":"author","urls":{},"downloads":0},"bibtype":"article","biburl":null,"creationDate":"2014-09-11T15:28:59.315Z","downloads":0,"keywords":[],"search_terms":["advanced","factor","analysis","multiple","time","resolution","aerosol","composition","data","zhou","hopke","paatero","ondov","pancras","pekney","davidson"],"title":"Advanced factor analysis for multiple time resolution aerosol composition data","year":2004}