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Comparison of human exposure model estimates of PM2.5 exposure variability using fine-scale CMAQ simulations from the Baltimore DISCOVER-AQ evaluation
Citation:
Xu, Y., J. Burke, W. Appel, AND S. Roselle. Comparison of human exposure model estimates of PM2.5 exposure variability using fine-scale CMAQ simulations from the Baltimore DISCOVER-AQ evaluation. 2016 CMAS Conference, Chapel Hill, NC, October 24 - 26, 2016.
Impact/Purpose:
The National Exposure Research Laboratory (NERL) Computational Exposure Division (CED) develops and evaluates data, decision-support tools, and models to be applied to media-specific or receptor-specific problem areas. CED uses modeling-based approaches to characterize exposures, evaluate fate and transport, and support environmental diagnostics/forensics with input from multiple data sources. It also develops media- and receptor-specific models, process models, and decision support tools for use both within and outside of EPA.
Description:
Human exposure models estimate population distributions of exposure to air pollutants by combining ambient (outdoor) concentration data with human activity patterns to account for the time people spend in different locations (e.g., outdoors, indoors, in vehicles) and the various factors influencing concentrations in those locations. Although measured concentrations at monitoring network sites are typically used as inputs, exposure model results may be improved by taking advantage of the greater spatial and/or temporal resolution of air pollutant concentration fields available from air quality model simulations.? An evaluation of simulations using the Community Multiscale Air Quality (CMAQ) model version 5.1 employing 12x12 km, 4x4 km and 1x1 km horizontal grid-cell resolution performed for the Baltimore DISCOVER-AQ study in July 2011 showed significant improvement in operational model performance for the 4-km vs. 12-km simulations compared to available measurements, but a relatively small change in model performance between the 4-km and 1-km simulations.? However, the spatial pattern of PM2.5 concentrations across the Baltimore-Washington DC area differed between the 4-km and 1-km simulations, with more defined areas of high PM2.5 concentrations for the 1-km simulation. The Stochastic Human Exposure and Dose Simulation for Particulate Matter (SHEDS-PM) model was applied using three different hourly PM2.5 concentrations for the Baltimore-Washington DC area (i.e., 4-km CMAQ, 1-km CMAQ, and available measurement data) to evaluate the impact of the different input concentrations on PM2.5 exposure variability.? Distributions of PM2.5 exposures from SHEDS-PM showed the influence of capturing the fine-scale spatial and temporal variability, as well as commuting patterns, on the distributions of PM2.5 exposures.
