Estimating Exposure by Loose-Coupling an Air Dispersion Model and a Geospatial Information System

Abstract

The regulation of air quality is important for ensuring the health of a population. Current air quality decision support systems are very useful if the user possesses sufficient data to operate them and the necessary expertise to interpret their results. In general, these systems suffer as a result of their excessive complexity. The present study describes the development of a scalable air quality decision support system using the CALPUFF air dispersion model and a Geospatial Information System (GIS). This system uses receptor level exposure modeling and outputs from CALPUFF to estimate the relative impacts on human populations from multiple air pollution sources by calculating intake, defined as the amount of pollution that is inhaled by a population and intake fraction, defined as the fraction of pollutant emitted by a pollution source that is inhaled by a population. Unlike ground level pollution concentration, intake and intake fraction consider receptors and offer a more valuable estimate of pollution exposure, especially when faced with limited input data. The system also leverages the inherent strength of GIS to improve accessibility of geospatial data by generating maps of ground level pollutant concentration, intake, and intake fraction using graduated color schemes. This enables any user to identify potentially hazardous pollution sources and prioritize decisions such as development, maintenance, and decommission.