Modeling spatial variation in area source emissions

Urban and regional air quality needs to be analyzed at various geographical scales. Area source emission inventories usually estimate total emissions for various industrial and commercial activities at the county or larger scales. Consequently, information on spatial variation of emissions within a county, a critical requirement for urban airshed modeling, is largely unavailable. This paper proposes a Poisson regression approach that enables us to model small-area variation in source activities and to allocate county or region wide emission estimates to subcounty units. The new approach is used to model the spatial distribution of automobile refinishing activities in the Sacramento modeling region in California. The paper addresses the problem of overdispersion of variance in Poisson regression and evaluates the effect of grid location on modeling results. The usefulness of geographical information systems in spatial statistical analysis is demonstrated.