Air quality (AQ) simulation models provide a basis for implementing the National Ambient Air Quality Standards (NAAQS) and are a tool for performing risk-based assessments and for developing environmental management strategies. Fine particulate matter (PM (sub)2,5), its constituents and size and number distribution, as well as airborne toxic pollutants (air toxins) have characteristically different degrees of spatial and temporal variability especially in urban areas and in different geographical-climatic regimes. In this study, we explore the specific role of AQ models as a means to drive human exposure models (Burke et al. 2001) and to address situations in which pollutants exhibit high spatial and temporal variability. We seek a capability to capture the resolved-scale concentration fields and to provide measures of sub-grid-scale variability in concentration distributions that impact human exposures.