Title:
A Descriptive Analysis of Temporal Patterns of Air Pollution in Atlanta, GA and an Assessment of Measurement Error in Air Pollution Monitoring Networks in Atlanta, GA

dc.contributor.advisor Mulholland, James A.
dc.contributor.author Wade, Katherine Signs en_US
dc.contributor.committeeMember Russell, Armistead
dc.contributor.committeeMember Tolbert, Paige
dc.contributor.department Civil and Environmental Engineering en_US
dc.date.accessioned 2006-01-18T22:14:30Z
dc.date.available 2006-01-18T22:14:30Z
dc.date.issued 2005-08-26 en_US
dc.description.abstract This research is intended to serve as an in-depth analysis of air pollution patterns and monitoring networks in the Atlanta area. A ten year database of carbon monoxide (CO), sulfur dioxide (SO2), nitrogen oxides (NOx), ozone (O3), and particulate matter (PM2.5 and PM10) measurements at 17 monitoring stations across the Atlanta area was developed for use in this research. Temporal profiles of air pollutants are analyzed and described. Several factors are identified that impact these profiles, including changes in emissions, meteorology, and photochemistry. Most sites exhibited decreasing annual average concentrations during the study period, with the exception of O3 and NOx, both of which initially increased and then decreased. CO, NOx, and SO2 all have the lowest concentrations in the summer months, while O3 and PM2.5 are highest in the summer months. CO, NOx, and SO2 are also slightly lower on the weekends. CO and NOx have peak daily concentrations at rush hour, while O3 and SO2 peak in the afternoon hours. Instrument error was evaluated through audit and calibration data and collocated data. Collocated data is assumed to be a more accurate representation of instrument error; the percent error calculated using collocated data is much higher than that calculated using audit data. Percent errors were similar for all pollutants using audit and calibration data (2-4%) and were similar for all concentration ranges. Percent errors using collocated data were several times larger. Semivariogram plots are developed to quantify spatial variation of air pollutants. These plots can be interpreted to give the fraction of temporal variation in a pollutant that is actually due to spatial variation. As expected, primary pollutants have higher spatial variation than secondary pollutants. Population weighted averages of the semivariogram function are developed to give a level of uncertainty for a pollutant across the study area. Pollution rose plots are developed to qualitatively examine local sources that are impacting the monitoring sites used in this research. Point sources are easily identified in SO2 plots, as are mobile sources in CO and NOx plots. Pollution roses are also corrected for time of day and season to eliminate false sources. en_US
dc.description.degree M.S. en_US
dc.format.extent 4396887 bytes
dc.format.mimetype application/pdf
dc.identifier.uri http://hdl.handle.net/1853/7477
dc.language.iso en_US
dc.publisher Georgia Institute of Technology en_US
dc.subject Spatial variability en_US
dc.subject Measurement error
dc.subject Environment
dc.subject Air Pollution
dc.subject Atmosphere
dc.subject Criteria pollutants
dc.subject.lcsh Air Pollution Georgia Atlanta en_US
dc.subject.lcsh Air Pollution Georgia Atlanta Measurement en_US
dc.title A Descriptive Analysis of Temporal Patterns of Air Pollution in Atlanta, GA and an Assessment of Measurement Error in Air Pollution Monitoring Networks in Atlanta, GA en_US
dc.type Text
dc.type.genre Thesis
dspace.entity.type Publication
local.contributor.advisor Mulholland, James A.
local.contributor.corporatename School of Civil and Environmental Engineering
local.contributor.corporatename College of Engineering
relation.isAdvisorOfPublication 5a9d5952-ce03-49f6-81f0-b0681cc8ffa0
relation.isOrgUnitOfPublication 88639fad-d3ae-4867-9e7a-7c9e6d2ecc7c
relation.isOrgUnitOfPublication 7c022d60-21d5-497c-b552-95e489a06569
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