||5R21CA094723-03 Interpret this number
||Impact Assessment, Inc.
||Historic Traffic Exposure Maps for Cancer Studies
DESCRIPTION (provided by applicant):
Exposure to traffic exhaust is common in urban areas and its components have
been found to be associated with lung cancer and leukemia in epidemiological
studies. Determining whether or not these associations are causal in nature
has been especially limited by the lack of historical individual-level
exposure data. Recently, there have been calls for the development of exposure
maps showing modeled concentrations of ambient air pollution using geographic
information systems (GIS) to investigate long-term health effects. A study
subject's residence can be a good predictor of exposure if suitably modeled
levels of air pollution are achieved at a proper spatial scale. Typically,
modeling of air pollution has been done either at a regional (air basin) or
localized level. The regional approach results in coarse exposure maps, which
lack the spatial resolution needed for epidemiological studies and can result
in exposure misclassification. The localized models achieve a proper spatial
scale but require complex data inputs and are too data intensive for mapping
exposure over wide areas. We have previously developed GIS traffic exposure
maps at an appropriate scale for epidemiological studies that account for
properties of wind and dispersal behavior of specific pollutants. In this
study, we propose to further develop this model by accounting for the surface
texture of the landscape using GIS land use layers. We also propose to
evaluate and validate this and two other in-house developed models and four
external traffic exhaust exposure models in one California county with NO2
field measurements using passive diffusion tubes. We will compare the
predicted exposure level from each model to actual NO2 concentrations at each
monitored location. We will assess the amount of bias due to exposure
misclassification for each model by geocoding the addresses of lung cancer
cases from the California Cancer Registry and a random control series and
comparing the predicted and observed NO2 measurements. Finally, we will
develop a series of historic traffic exhaust exposure maps using the best
evaluated traffic model using retrospective data on traffic counts, land use,
meteorology, point sources, and ambient air monitoring data. These resulting
exposure maps could be applied to existing cohorts of study subjects to assign
previous exposures which would dramatically reduce the time and cost of
expensive prospective studies of traffic exhaust exposure and cancer risk.
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