Heat Waves and Their Impacts
on Human Health in Urban
Areas of Central Oklahoma
Kyle Thiem, Jessica Voveris, & Emma Fagan
University of Oklahoma, School of Meteorology
Heather Basara
Assistant Professor, University of Oklahoma
Department of Geography
Jeffrey Basara
Associate Professor, University of Oklahoma
School of Meteorology
Background
 Heat waves are common occurrences
 Especially dangerous within urban
environments
 Higher air pollutant concentrations
 UHI phenomenon
 Chicago (1995), Europe (2003), Russia (2010)
Basara et al. 2008
Societal Impacts
 Increased heat related and pollution related illnesses
during extreme heat events
 Increased urbanization in the future
 Heat waves are projected to increase in frequency,
longevity, and intensity
 Better mitigation techniques are needed
European Heat Wave
of 2003
Research Goals
 Expand the current knowledge of the relationships between
heat waves, urban environments, and human health
 Determine which populations are most at risk during
extreme heat events within Oklahoma City at a
neighborhood scale
What We’ve Done
 Determined our event
 July 30 – August 6,
2008
 Sorted through and put
all of the data from the
Micronet and Census
Tracts into GIS for
further analysis
 3 types of data:
Demographic, Pollutant,
Temperature
Basara et al. 2011
Demographic
 Decided to use Clusters (defined by statistical
similarities of demographic data) to group the
Census Tracts (Hall and Basara)
Hall and Basara
Pollutant
 Studies have shown that as stagnant air over a city
due to a heat wave can cause an increse in the
amounts of pollutants in the air.
 Result: Length of heat wave in this event was too
short to see an increase in Ozone or PM data
Temperature
 Higher temperatures in urban areas apparent
(approx. 1-2 C on average), especially at night.
 1ºC increase in temperature above a
comfortability threshold correlates to a 1-3%
increase in mortality.
What’s Left To Do
 Determine climatological average maximum
and minimum temperatures to establish
comfortability thresholds
 Assign vulnerability levels to each Census
Tract based on maximum and minimum
temperature, population density, and
demographic factors.
 Combine risk assessment of all factors in GIS
to determine which neighborhoods are
considered most at risk in final analysis.
Summary
 Heat waves are common occurrences, and
especially dangerous in urban environments.
 Our goal is to determine which populations
are most at risk during extreme heat events
within Oklahoma City at a neighborhood scale
 Determined demographics and temperature
exposure to be main risks.
 Currently working on last step: defining the
vulnerability levels of each census tract.
References
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J. Schroeder, and K. C. Crawford, 2011: The Oklahoma City Micronet. Metr. Appl., 18, 252-261.
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