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ANN modeling of ambient particulate ...

He, Yaming.

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ANN modeling of ambient particulate matter in Fort McKay, Alberta.

Record Type:	Electronic resources : Monograph/item
Title/Author:	ANN modeling of ambient particulate matter in Fort McKay, Alberta./
Author:	He, Yaming.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2004,
Description:	150 p.
Notes:	Source: Masters Abstracts International, Volume: 43-03, page: 9540.
Contained By:	Masters Abstracts International43-03.
Subject:	Environmental engineering. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=MQ95762
ISBN:	9780612957626

ANN modeling of ambient particulate matter in Fort McKay, Alberta.
He, Yaming.

ANN modeling of ambient particulate matter in Fort McKay, Alberta. - Ann Arbor : ProQuest Dissertations & Theses, 2004 - 150 p.

Source: Masters Abstracts International, Volume: 43-03, page: 9540.

Thesis (M.Sc.)--University of Alberta (Canada), 2004.

Particulate matter with a diameter smaller than 2.5 mum (PM2.5 ) is a key urban air pollutant. Modeling of ambient PM2.5 is considered important for understanding its contribution sources. However, conventional models employing mathematical formulae are difficult to use for modeling ambient PM2.5 because of complexities involved in its formation and behaviour in the atmosphere.

ISBN: 9780612957626Subjects--Topical Terms:

548583
Environmental engineering.

ANN modeling of ambient particulate matter in Fort McKay, Alberta.
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020 $a 9780612957626
035 $a (MiAaPQ)AAIMQ95762
035 $a AAIMQ95762
040 $a MiAaPQ $c MiAaPQ
100 1 $a He, Yaming. $3 3350482
245 1 0 $a ANN modeling of ambient particulate matter in Fort McKay, Alberta.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2004
300 $a 150 p.
500 $a Source: Masters Abstracts International, Volume: 43-03, page: 9540.
500 $a Adviser: Warren B. Kindzierski.
502 $a Thesis (M.Sc.)--University of Alberta (Canada), 2004.
520 $a Particulate matter with a diameter smaller than 2.5 mum (PM2.5 ) is a key urban air pollutant. Modeling of ambient PM2.5 is considered important for understanding its contribution sources. However, conventional models employing mathematical formulae are difficult to use for modeling ambient PM2.5 because of complexities involved in its formation and behaviour in the atmosphere.
520 $a Artificial neural network (ANN) is more suited to solving complex non-linear problems associated with formation and behaviour of PM2.5 in the atmosphere. ANN was used to model ambient PM2.5 concentrations at Fort McKay, Alberta in order to examine the potential influence of operations at the Syncrude Canada Ltd. Mildred Lake oilsand facility.
520 $a Results of ANN modeling were able to demonstrate that changes in ambient PM2.5 concentrations at Fort McKay, Alberta were, in part, due to changes in ambient PM2.5 concentrations observed at the Syncrude facility. ANN showed promise as a tool for predicting ambient PM2.5 concentrations.
590 $a School code: 0351.
650 4 $a Environmental engineering. $3 548583
650 4 $a Physics, Atmospheric Science. $3 1019431
690 $a 0775
690 $a 0608
710 2 $a University of Alberta (Canada). $3 626651
773 0 $t Masters Abstracts International $g 43-03.
790 $a 0351
791 $a M.Sc.
792 $a 2004
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=MQ95762