Assessing Dynamic Exposure to Air Pollution - Space-Time Integration in Geography and GIScience - page 281

when concentrations were not sampled can be very crude. Finally, when individual
exposures are assessed based on these crude air pollution data without consideration
of people's activity patterns (i.e. time spent at home, work, and school), the actual
levels of individual exposure can be significantly underestimated.
The third method for air pollution assessment is land use regression modeling,
which utilizes land use attributes and traffic characteristics as the basis for making
pollution estimates (e.g., Briggs et al. 1997 ). More specifically, these models predict
the level of pollution concentrations for unsampled locations in a study area using
data on traffic volume, land cover, and topography. The output from a land use
regression model is a pollution surface created based on the data collected at the
monitor sites. Similar to the previous two methods, land use regression analysis
requires measurement data for the target pollutants. Once again, the availability
of a dense monitoring network plays a crucial role in the reliability of land
use regression pollution estimations, where an increase in monitor density helps
to reduce prediction errors. Studies have shown that data derived from a single
monitoring location can only represent a small area surrounding a particular site
(e.g., Briggs et al. 1997 ). Reliance on a sparse monitoring network for estimating
exposure levels can thus lead to considerable uncertainty and error.
The fourth approach to modeling spatial variations of air pollution concentrations
is the utilization of dispersion models. These models conceptualize pollution disper-
sion in terms of deterministic processes, which are implemented through Gaussian
plume equations (e.g., Gualtieri and Tartaglia 1998 ; Clench-Aas et al. 1999b ;
Bellander et al. 2001 ). The core components that formulate these processes include
data on emissions, meteorological conditions, and topography. Data on emissions,
also referred to as background concentrations, are usually obtained from govern-
ment monitoring stations and serve as a model calibrator. Data on wind speed, wind
direction, outside surface temperature, solar radiation, and atmospheric stability
comprise the meteorological component. Furthermore, topographical information
relating to the elevation of the study area is essential along with emission data
that can be subdivided into two main categories: stationary and mobile. These three
broad types of factors are used in dispersion models to estimate spatial variations
of air pollution concentrations across a specified region. Once all parameters are
obtained, dispersion calculations are performed to represent hourly variations or
yearly averages in pollution concentrations.
Air pollution exposure studies that utilize dispersion models illustrate the advan-
tages of integrating both spatial and temporal disparities of pollutant levels across a
given region (e.g., Bartonova et al. 1999 ; Clench-Aas et al. 1999a ). This method
overcomes the need for extensive monitoring networks that are common with
previously discussed techniques such as proximity-based assessment, geostatistical
interpolation, and land use regression. Further, external parameters which diffuse
air pollutants such as wind, atmospheric stability, topography, and traffic flows are
accounted for in dispersion frameworks (Jerrett et al. 2005 ). Dispersion models can
be applied to various spatial and temporal scales, such as short term air pollution
episodes in urban areas or pollution transfer for large regions over space and time.