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dc.contributor.authorAgranovski, Igoren_US
dc.contributor.authorAgranovski, V.en_US
dc.contributor.authorGrinshpun, S.en_US
dc.contributor.authorReponen, T.en_US
dc.contributor.authorWilleke, K.en_US
dc.contributor.editorProf P.Hopkeen_US
dc.date.accessioned2017-05-03T13:07:29Z
dc.date.available2017-05-03T13:07:29Z
dc.date.issued2002en_US
dc.identifier.issn13522310en_US
dc.identifier.doi10.1080/027868202753571322en_US
dc.identifier.urihttp://hdl.handle.net/10072/6704
dc.description.abstractThe concentrations of air pollutants varied inherently with meteorological conditions and pollutant emission level. From the statistical properties (probability density) of air pollutants, it is easy to estimate how many times the exceedance compared with air quality standards occurs. In this paper, three distributions (lognormal, Weibull and type V Pearson distribution) were utilized to simulate the PM10 concentration distribution in Taiwan areas. Air quality data of three monitoring stations, Hsin-Chu, Sha-Lu and Gian-Jin, were taken to compare the characters of PM10 concentrations during a five-year period (1995-1999). Two parametric estimating methods, method of moments and method of least squares, were used to estimate the parameters of these three theoretic distributions. Therefore, the exceedance frequency of air pollutant concentration and emission source reduction can be predicted from these theoretic distributions. These results show that the lognormal is the best distribution to represent the PM10 daily average concentration. Between these two parametric estimation methods, the method of least squares has more accurate results than the moments method. The PM10 concentration distributions of Hsin-Chu and Sha-Lu stations are all unimodal distributions, but the distribution of Gian-Jin is a bimodal distribution. The measured PM10 concentrations of Gian-Jin station were divided into two seasons, and the parameters were computed individually. The reproduced bimodal distribution, which combined with the two unimodal distributions, agrees well with measured data. This result shows that the distribution type of PM10 concentration varied greatly in different areas, and could be influenced by local meteorological conditions in different seasons. In addition, the probabilities exceeding the air quality standard (PM10>125 姠m-3) and emission sources reduction of PM10 concentration to meet the air quality standard for Hsin-Chu, Sha-Lu and Gian-Jin stations are predicted successfully.en_US
dc.description.peerreviewedYesen_US
dc.description.publicationstatusYesen_US
dc.languageEnglishen_US
dc.language.isoen_US
dc.publisherElsevieren_US
dc.publisher.placeNetherlandsen_US
dc.relation.ispartofpagefrom502en_US
dc.relation.ispartofpageto509en_US
dc.relation.ispartofjournalAerosol Science and Technologyen_US
dc.relation.ispartofvolume36en_US
dc.subject.fieldofresearchcode291103en_US
dc.titleCollection of Airborne Microorganisms into Liquid by Bubbling Through Porous Medium.en_US
dc.typeJournal articleen_US
dc.type.descriptionC1 - Peer Reviewed (HERDC)en_US
dc.type.codeC - Journal Articlesen_US
gro.facultyGriffith Sciences, Griffith School of Engineeringen_US
gro.rights.copyright.en_US
gro.date.issued2015-02-03T03:03:49Z
gro.hasfulltextNo Full Text


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