ArcGIS (ESRI) was used to obtain geographic information system (GIS) information used in the air pollution exposure modeling.
Obviously, to complete the quantitative estimates of air pollution, meteorologic data will need to be acquired for the NCS location and segments from the NOAA-NCDC or from state or local studies and then "scaled down" to the neighborhood level in order to become appropriate for air pollution exposure modeling.
In contrast, no association was observed between long-term average air pollution exposure modeled from historical emission data and overall incidence of myocardial infarction in a recent case control study (Rosenlund et al. 2006).
We develop a concept for air pollution research based on trajectory mapping by using an empirical model that articulate the overall work process, system architecture, traffic-related air pollution and exposure modelling.
However, given that transport is a source of both noise and air pollutants and that noise and air pollution exposure models include the same inputs (such as traffic flows, traffic composition and traffic speed), potential for collinearity needs to be carefully considered.
A critical aspect of air pollution exposure models is the estimation of the air exchange rate (AER) of individual homes, where people spend most of their time.
Daily concentrations of PM2.5 and O3 at each participant's home address were predicted with a hybrid air quality exposure model.
The capability of the LBL, LBLX, and SF models could help reduce the AER uncertainty in air pollution exposure models used to develop exposure metrics for health studies.
To better understand the linkages between ambient concentrations and exposures, we are developing an air pollution exposure model for individuals (EMI) in health studies (2− 4).
This capability could help to reduce AER uncertainty in air pollution exposure models used to predict exposure metrics for epidemiological analysis in health studies.
