Integrated exposure modeling: a model using GIS and GLM

Traffic exhaust is a source of air contaminants that have adverse health effects. Quantification of traffic as an exposure variable is complicated by aerosol dispersion related to variation in layout of roads, traffic density, meteorology, and topography. A statistical model is presented that uses Geographic Information Systems (GIS) technology to incorporate variables into a generalized linear model that estimates distribution of traffic-related pollution. Exposure from a source is expressed as an integral of a function proportional to average daily traffic and a nonparametric dispersion function, which takes the form of a step, polynomial, or spline model. The method may be applied using standard regression techniques for fitting generalized linear models. Modifiers of pollutant dispersion such as wind direction, meteorology, and landscape features can also be included. Two examples are given to illustrate the method. The first employs data from a study in which NO(2) (a known pollutant from automobile exhaust) was monitored outside of 138 Connecticut homes, providing a model for estimating NO(2) exposure. In the second example, estimated levels of nitrogen dioxide (NO(2)) from the model, as well as a separate spatial model, were used to analyze traffic-related health effects in a study of 761 infants.