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Spatial and temporal estimates of population exposure to wildfire smoke during the Washington State 2012 wildfire season using blended model, satellite, and in-situ data: process data and code archive

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dc.contributor.authorLassman, William
dc.coverage.temporal2014-08-2016-09
dc.date.accessioned2017-02-22T17:43:40Z
dc.date.available2017-02-22T17:43:40Z
dc.date.issued2017
dc.descriptionIncludes 4 datafiles in the 'Data' subdirectory: 1) sites.npz-in-situ measurements made by the Washington Department of Ecology, USA EPA (from the AQS network), and Canadian surface measurements provided by Kathleen McClean and Dr. Sarah Henderson at University of British Columbia. All files were downloaded and processed by Dr. Bonne Ford, and William Lassman; 2) PyKrige.npz-kriged estimates of PM2.5, produced by William Lassman; 3) MODIS_regrid.npz: Downloaded from the NASA MODIS website (https://lpdaac.usgs.gov/) by Dr. Bonne Ford, and post-processed by William Lassman; 4) WRF-Chem.npz: Produced by Dr. Bonne Ford and William Lassman under the guidance of Dr. Gabi Pfister; There are also a number of python scripts (in the 'Code' subdirectory; All python codes were written by William Lassman under the guidance of Dr. Jeff Pierce.
dc.descriptionDepartment of Atmospheric Science
dc.description.abstractIn the western U.S., smoke from wild and prescribed fires can severely degrade air quality. 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 air pollutants in the western U.S. Hence, 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 have been used in past studies to assess exposure to wildfire smoke: in-situ measurements, satellite-based observations, and chemical-transport model (CTM) simulations. Each of these exposure-estimation tools have associated strengths and weakness. We investigate the utility of blending these tools together to produce estimates of PM2.5 exposure from wildfire smoke during the Washington 2012 fire season. For blending, we use a ridge-regression model and 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. We find that predictions based on in-situ monitors are more accurate for this particular fire season than the CTM simulations and satellite-based observations because of the large number of monitors present; therefore, blending provides 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.description.sponsorshipFunded in part by NASA Applied Sciences, grant number NNX15AG35G.
dc.description.sponsorshipFunded in part by Join Fire Science Program, grant number JFSP 13-1-1-4.
dc.description.sponsorshipCoauthor works at NCAR, which is operated by the University Corporation of Atmospheric Research under sponsorship by the National Science Foundation.
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dc.identifier.urihttp://hdl.handle.net/10217/179811
dc.identifier.urihttp://dx.doi.org/10.25675/10217/179811
dc.languageEnglish
dc.language.isoeng
dc.publisherColorado State University. Librariesen_US
dc.relation.ispartofResearch Data
dc.titleSpatial and temporal estimates of population exposure to wildfire smoke during the Washington State 2012 wildfire season using blended model, satellite, and in-situ data: process data and code archiveen_US
dc.typeDataseten_US

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