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dc.contributor.advisorPierce, Jeffery
dc.contributor.authorLassman, William
dc.contributor.committeememberFischer, Emily
dc.contributor.committeememberSchumacher, Russ
dc.contributor.committeememberMagzamen, Sheryl
dc.contributor.committeememberPfister, Gabriele
dc.date.accessioned2017-01-04T22:59:13Z
dc.date.available2017-01-04T22:59:13Z
dc.date.issued2016
dc.description2016 Fall.
dc.descriptionIncludes bibliographical references.
dc.description.abstractIn the western US, emissions from wildfires and prescribed fire have been associated with degradation of regional air quality. Whereas atmospheric aerosol particles with aerodynamic diameters less than 2.5 μm (PM 2.5 ) have known impacts on human health, there is uncertainty in how particle composition, concentrations, and exposure duration impact the associated health response. Due to changes in climate and land-management, wildfires have increased in frequency and severity, and this trend is expected to continue. Consequently, wildfires are expected to become an increasingly important source of PM 2.5 in the western US. While composition and source of the aerosol is thought to be an important factor in the resulting human health-effects, this is currently not well-understood; therefore, there is a need to develop a quantitative understanding of wildfire-smoke-specific health effects. A necessary step in this process is to determine who was exposed to wildfire smoke, the concentration of the smoke during exposure, and the duration of the exposure. Three different tools are commonly used to assess exposure to wildfire smoke: in-situ measurements, satellite-based observations, and chemical-transport model (CTM) simulations, and each of these exposure-estimation tools have associated strengths and weakness. In this thesis, we investigate the utility of blending these tools together to produce highly accurate estimates of smoke exposure during the 2012 fire season in Washington for use in an epidemiological case study. For blending, we use a ridge regression model, as well as a geographically weighted ridge regression model. We evaluate the performance of the three individual exposure-estimate techniques and the two blended techniques using Leave-One-Out Cross-Validation. Due to the number of in-situ monitors present during this time period, we find that predictions based on in-situ monitors were more accurate for this particular fire season than the CTM simulations and satellite-based observations, so blending provided only marginal improvements above the in-situ observations. However, we show that in hypothetical cases with fewer surface monitors, the two blending techniques can produce substantial improvement over any of the individual tools.
dc.format.mediumborn digital
dc.format.mediummasters theses
dc.identifierLassman_colostate_0053N_13930.pdf
dc.identifier.urihttp://hdl.handle.net/10217/178887
dc.languageEnglish
dc.publisherColorado State University. Libraries
dc.relation.ispartof2000-2019 - CSU Theses and Dissertations
dc.rightsCopyright of the original work is retained by the author.
dc.subjectmodel
dc.subjectPM2.5
dc.subjectwildfire
dc.subjectobservations
dc.subjectexposure
dc.subjectsmoke
dc.titleBlending model output with satellite-based and in-situ observations to produce high-resolution estimates of population exposure to wildfire smoke
dc.typeText
dcterms.rights.dplaThe copyright and related rights status of this Item has not been evaluated (https://rightsstatements.org/vocab/CNE/1.0/). Please refer to the organization that has made the Item available for more information.
thesis.degree.disciplineAtmospheric Science
thesis.degree.grantorColorado State University
thesis.degree.levelMasters
thesis.degree.nameMaster of Science (M.S.)


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