You are here:
STOCHASTIC DESCRIPTION OF SUBGRID POLLUTANT VARIABILITY IN CMAQ
Citation:
Herwehe, J A., J.K S. Ching, AND J. SWALL. STOCHASTIC DESCRIPTION OF SUBGRID POLLUTANT VARIABILITY IN CMAQ. Presented at 2004 Models-3 Conference, Chapel Hill, NC, October 18 - 20, 2004.
Impact/Purpose:
The objective of this task is to develop and evaluate numerical and physical modeling tools for simulating ground-level concentrations of airborne substances in urban settings at spatial scales ranging from ~1-10 km. These tools will support client needs in the areas of air toxics and homeland security. The air toxics tools will benefit the National Air Toxics Assessment (NATA) program and human exposure modeling needs within EPA. The homeland security-related portion of this task will help in developing tools to assess the threat posed by the release of airborne agents. Both sets of tools will consider the effects induced by urban morphology on fine-scale concentration distributions.
Description:
This paper describes a tool for investigating and describing fine scale spatial variability in model concentration fields with the goal of improving the use of air quality models for driving exposure modeling to assess human risk to hazardous air pollutants or air toxics. Regional scale Eulerian air quality models are typically limited to relatively coarse grid resolutions when simulating mean pollutant concentrations for each grid cell volume, and subgrid pollutant extremes are not represented. Continual improvements in computing power and refinement of nested grid techniques have allowed the regional air quality models to simulate down to grid spacings on the order of one kilometer. Our approach uses exploratory data analysis (EDA) statistical techniques applied to available fine resolution gridded model results to produce stochastic descriptions representing pollutant subgrid variability applicable to the coarser grid resolutions, thereby somewhat bridging the gap between regional scale and neighborhood scale air quality models. Products include, but are not limited to, pollutant probability density functions (pdfs) for use in human exposure models and new parameterizations to represent subgrid pollutant variability in regional air quality prediction systems.
The research presented here was performed under the Memorandum of Understanding between the U.S. Environmental Protection Agency (EPA) and the U.S. Department of Commerce's National Oceanic and Atmospheric Administration (NOAA) and under agreement number DW13821548. Although is has been reviewed by EPA and NOAA and approved for publication. It does not necessarily reflect their policies or views.,