Science Inventory

Measurement and Modeling of Near-Road & Near-Port Air Quality

Citation:

Deshmukh, P., A. Sarva, R. Baldauf, T. Barzyk, G. Hagler, V. Isakov, AND Sue Kimbrough. Measurement and Modeling of Near-Road & Near-Port Air Quality. A&WMA Conference, Raleigh, NC, June 22 - 25, 2015.

Impact/Purpose:

The National Exposure Research Laboratory’s Atmospheric Modeling Division (AMAD) conducts research in support of EPA’s mission to protect human health and the environment. AMAD’s research program is engaged in developing and evaluating predictive atmospheric models on all spatial and temporal scales for forecasting the Nation’s air quality and for assessing changes in air quality and air pollutant exposures, as affected by changes in ecosystem management and regulatory decisions. AMAD is responsible for providing a sound scientific and technical basis for regulatory policies based on air quality models to improve ambient air quality. The models developed by AMAD are being used by EPA, NOAA, and the air pollution community in understanding and forecasting not only the magnitude of the air pollution problem, but also in developing emission control policies and regulations for air quality improvements.

Description:

This presentation provides a summary of mobile monitoring assessment studies conducted on major interstates in Detroit, Michigan and Phoenix, Arizona along with a near-port assessment focusing on the Port of Charleston in South Carolina, USA. We will also present our mobile measurement methodology and results on the spatial patterns of mobile source emitted gas-and particle- phase pollutants. The presentation will also highlight how this data were useful in the development and evaluation of local-scale air dispersion models assessing the impacts of roadway and port emission sources.

URLs/Downloads:

http://ace2015.awma.org/   Exit EPA's Web Site

Record Details:

Record Type:DOCUMENT( PRESENTATION/ POSTER)
Product Published Date:06/25/2015
Record Last Revised:06/03/2016
OMB Category:Other
Record ID: 317170