Utilizing Raman Microscopy to Identify Sources of PM10 Downwind of Agricultural Operations

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dc.contributor.advisorTorrents, Albaen_US
dc.contributor.advisorMcConnell, Laura Len_US
dc.contributor.authorHuang, Qiangen_US
dc.contributor.departmentCivil Engineeringen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.date.accessioned2012-02-17T07:14:17Z
dc.date.available2012-02-17T07:14:17Z
dc.date.issued2011en_US
dc.description.abstractEmission of particulate matter (PM) from animal feeding operations (AFOs) poses a potential threat to the health of human and livestock. Current efforts to characterize PM emissions from AFOs generally examine variations in mass concentration and particle size distributions over time and space, but these methods do not provide information on the sources of the PM. Raman microscopy was employed in an innovative manner to quantify the contributions of source materials to PM10 emitted from a large cattle feedlot. Raman spectra from potential source materials were first compiled to create a spectral library. Multivariate statistical analysis model was developed to identify the source of particles collected on PM10 sample filters. Cross validation of the model resulted in 99.76% correct classification of the model spectra. Source characterization results from samples collected over a two-day period indicate that pen surface dominated in PM10 emission with unpaved road as the second biggest contributor.en_US
dc.identifier.urihttp://hdl.handle.net/1903/12398
dc.subject.pqcontrolledEnvironmental engineeringen_US
dc.subject.pqcontrolledEnvironmental studiesen_US
dc.subject.pqcontrolledEnvironmental scienceen_US
dc.subject.pquncontrolledagricultureen_US
dc.subject.pquncontrolledmultivariate statistical analysisen_US
dc.subject.pquncontrolledParticulate matteren_US
dc.subject.pquncontrolledRaman microscopyen_US
dc.subject.pquncontrolledsource apportionmenten_US
dc.titleUtilizing Raman Microscopy to Identify Sources of PM10 Downwind of Agricultural Operationsen_US
dc.typeThesisen_US

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