Assessing Dynamic Exposure to Air Pollution - Space-Time Integration in Geography and GIScience - page 279

the study area and determining which portion of the population is at greatest risk.
They seek to address four important questions: (a) What are the primary sources
of specific pollutants? (b) What environmental factors influence the dispersion
of air borne particles? (c) How can one accurately portray relevant pollution
concentrations across a particular study area? (d) How can one assess the exposure
to air pollution of different individuals who live and undertake daily activities in
different parts of the study area?

16.2.1 Estimating the Spatial and Temporal Patterns of Air

pollution

In efforts to understand people's exposure to air pollution, past studies have used
different methods to estimate the spatial and temporal distribution of pollution
concentrations relative to the geographic distribution of the affected population. Six
broad categories of methods have been used to date: proximity-based assessments,
geostatistical interpolation, land use regression models, dispersion models, inte-
grated models, and hybrid models (Jerrett et al. 2005 ). Each method entails varying
levels of complexity, data requirement, sophistication, and time commitment. Sev-
eral researchers have conducted health-related studies using various combinations of
these six air pollution assessment methods. A brief critical overview of proximity-
based assessments, geostatistical interpolation, land use regression models, and
dispersion models is provided as follows.
Proximity-based assessments establish relationships between air pollution and
exposure by assuming that individuals face greater risk if they are located near
major emission sources (Jerrett et al. 2005 ). A common approach of this method
is to relate distance to heavily traveled roadways, traffic density, and surrounding
populations. Venn et al. ( 2001 ), for instance, adopted this approach and applied a
buffer of 150 m around major roadways that intersected the study area to capture
school children's exposure to traffic-related air pollution. Janssen et al. ( 2001 ), on
the other hand, gathered actual measurements of traffic-related air pollutants outside
and inside of schools located 400 m from major highways. The study found an
association between PM 2.5 concentrations for both indoor and outdoor air with an
increase in truck traffic density and also a decrease with increasing distance from
the roadways.
Proximity assessment offers a simple approach for exposure analysis. The
method, however, has several limitations. First, the dynamic nature of human
activities and travel is ignored. As English et al. ( 1999 ) argued, assessment of
potential exposure in this approach focuses narrowly on people's residence, work,
and/or school locations while other daily activities are overlooked. Clearly, human
activities are not limited to two or three isolated localities throughout a typical 24-h
day (Kwan 2009 , 2012a , b ). As Gulliver and Briggs ( 2005 ) highlighted, although
most people spend a relatively small amount of their time traveling, journey-time