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Predicting Air Quality Impacts Associated with Oil and Gas Development in the Uinta Basin Using EPA’s Photochemical Air Quality Model
Citation:
Matichuk, R., G. Tonnesen, R. Gilliam, D. Luecken, S. Napelenok, AND S. Roselle. Predicting Air Quality Impacts Associated with Oil and Gas Development in the Uinta Basin Using EPA’s Photochemical Air Quality Model. Western Air Quality Modeling Workshop, Boulder, CO, May 13 - 15, 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:
Rural areas with close proximity to oil and natural gas operations in Utah have experienced winter ozone levels that exceed EPA’s National Ambient Air Quality Standards (NAAQS). Through a collaborative effort, EPA Region 8 – Air Program, ORD, and OAQPS used the Community Multiscale Air Quality (CMAQ) model to understand processes leading to the formation and sustenance of winter ozone in the Uinta Basin. Our project compared CMAQ results to measurement data to identify any model performance issues during the 2013 Uinta Basin Winter Ozone Study1. Primary objectives included: (1) enhancing knowledge of how well CMAQ performs in areas with extensive oil and natural gas development and complex terrain during winter-time and meteorological conditions; (2) identifying specific improvements in model processes and input data.