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Integrated exposure modeling: a model using GIS and GLM.

Theodore R Holford¹, Keita Ebisu, Lisa A McKay

¹Division of Biostatistics, Department of Epidemiology and Public Health, Yale School of Medicine, New Haven, CT 06520, USA. theodore.holford@yale.edu

Statistics in Medicine

|October 14, 2009

Summary

This study presents a statistical model using Geographic Information Systems (GIS) to estimate traffic-related air pollution distribution. The model helps quantify exposure to contaminants like nitrogen dioxide (NO2) for health impact assessments.

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Area of Science:

Environmental Science
Public Health
Geographic Information Systems

Background:

Traffic exhaust is a significant source of air pollutants with adverse health effects.
Quantifying traffic exposure is challenging due to complex factors like road layout, traffic density, meteorology, and topography.
Existing methods struggle to accurately model the dispersion of traffic-related contaminants.

Purpose of the Study:

To develop and present a statistical model integrating Geographic Information Systems (GIS) technology for estimating traffic-related pollution distribution.
To provide a framework for quantifying exposure to traffic-related air contaminants.
To demonstrate the model's application in real-world scenarios for health studies.

Main Methods:

A generalized linear model (GLM) was developed, incorporating GIS data to estimate pollution distribution.
Exposure was modeled as an integral of average daily traffic and a nonparametric dispersion function (step, polynomial, or spline).
The model accommodates modifiers of pollutant dispersion, including wind direction, meteorology, and landscape features.

Main Results:

The model successfully estimated nitrogen dioxide (NO2) exposure in a study of 138 Connecticut homes.
Estimated NO2 levels from the model were used to analyze traffic-related health effects in a cohort of 761 infants.
The GIS-based statistical model proved effective in quantifying traffic-related pollution exposure.

Conclusions:

The developed statistical model provides a robust method for estimating traffic-related air pollution distribution.
This approach enhances the quantification of exposure variables in environmental health research.
The model facilitates a better understanding of the health impacts associated with traffic-related air contaminants.