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NEIGHBORHOOD SCALE MODELING OF PM 2.5 AND AIR TOXICS CONCENTRATION DISTRIBUTIONS TO DRIVE HUMAN EXPOSURE MODELS
Citation:
Ching, J.K S., A. Lacser, J Herwehe, AND D W. Byun. NEIGHBORHOOD SCALE MODELING OF PM 2.5 AND AIR TOXICS CONCENTRATION DISTRIBUTIONS TO DRIVE HUMAN EXPOSURE MODELS. Presented at 12th Joint Conference on Air Pollution (AWMA), Norfolk, VA, May 20-24, 2002.
Impact/Purpose:
The objectives of this task are to continuously develop and improve the Community Multiscale Air Quality (CMAQ) modeling system, which is the science implementation within the Models-3 system framework for air quality simulation. CMAQ is a multiscale and multi-pollutant chemistry-transport model (CTM) that includes the necessary critical science process modules for atmospheric transport, deposition, cloud mixing, emissions, gas- and aqueous-phase chemical transformation processes, and aerosol dynamics and chemistry. It relies on Models-3 I/O API to support machine independent data access and maintains simple interfaces among science processor modules to provide a high-level of modularity.
Description:
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 2.5), its constituents and size and number distributions, as well as airborne toxic pollutants ("air toxics") 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. This modeling approach is meant to enhance and complement the more limited data from central site monitoring networks to provide concentration fields at high temporal and spatial resolutions. By providing further information on concentration variability at sub-grid scales, we complete the requirements needed for exposure assessments. The various elements of this modeling approach and some of their specific modeling issues are described.
The information in this manuscript as been prepared under funding by the United States Environmental Protection Agency. It has been subjected to Agency review and approved for publication. Mention of trade names or commercial products does not constitute endorsement or recommendation for use.