Estimation of greenhouse gas emissions from a tracer gas study

dc.contributor.advisor Philip Dixon
dc.contributor.author Heilmann, Cory
dc.contributor.department Statistics
dc.date 2018-08-25T00:47:44.000
dc.date.accessioned 2020-06-30T07:59:22Z
dc.date.available 2020-06-30T07:59:22Z
dc.date.copyright Sat Jan 01 00:00:00 UTC 2005
dc.date.issued 2005-01-01
dc.description.abstract <p>This thesis models the emission of three greenhouse gases that exist in nature, CH4, CO2, and N2O, from a hoop structure, using the artificial introduction of a tracer gas, SF6, which exists in nature at levels below detection limit. Hoop structures are facilities used to house pigs before their slaughter. Many other studies of hoop structure emissions measure only one sample of the tracer gas and greenhouse gas at a time. However, the data sets in our study consist of 25 to 45 samples of each gas, taken on fixed grids of points. We will construct models to account for the relation between the greenhouse gases and SF6 as well as spatial relations in the data set. We will use these models along with the known emission rate of SF6 to estimate the relative rate of emission of the greenhouse gases;We fit a Bayesian hierarchical model to the data sets. In this model, we relate the pointwise concentrations of one greenhouse gas and SF6 and then analyze the posterior distribution of a parameter representing the relative rates of emission of the greenhouse gas and SF6. We assume lognormal measurement errors of the greenhouse gas and SF6 around the true concentration of each gas;We also fit geostatistical models to estimate the rates of emission of these gases. We consider block kriging, block co-kriging, and lognormal block kriging to estimate the concentration of each gas. An advantage of geostatistical models over the Bayesian hierarchical model is that we do not assume strict proportionality of the concentrations of the gases. These estimates can be related to the relative rate of emission of the gases. Due to the small size of these data sets, we take into consideration the uncertainty of the variogram parameters and how this uncertainty affects block kriging averages and variances;We use simulations from both geostatistical models and Bayesian hierarchical models to determine superiority of one set of models in terms of coverage probabilities, bias, and length of coverage sets or confidence intervals. We also address the concern of spatial design for the geostatistical models.</p>
dc.format.mimetype application/pdf
dc.identifier archive/lib.dr.iastate.edu/rtd/1736/
dc.identifier.articleid 2735
dc.identifier.contextkey 6105369
dc.identifier.doi https://doi.org/10.31274/rtd-180813-11257
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath rtd/1736
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/71164
dc.language.iso en
dc.source.bitstream archive/lib.dr.iastate.edu/rtd/1736/r_3200423.pdf|||Fri Jan 14 21:21:17 UTC 2022
dc.subject.disciplines Environmental Sciences
dc.subject.disciplines Statistics and Probability
dc.subject.keywords Statistics
dc.title Estimation of greenhouse gas emissions from a tracer gas study
dc.type article
dc.type.genre dissertation
dspace.entity.type Publication
relation.isOrgUnitOfPublication 264904d9-9e66-4169-8e11-034e537ddbca
thesis.degree.level dissertation
thesis.degree.name Doctor of Philosophy
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