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Projecting Changes in Climate & Air Quality for the Southeastern U.S.
Citation:
Nolte, Chris, T. Spero, R. Bullock, J. Herwehe, M. Mallard, J. Bowden, AND Kiran Alapaty. Projecting Changes in Climate & Air Quality for the Southeastern U.S. Carolinas Climate Resilience Conference, Charlotte, NC, April 28 - 29, 2014.
Impact/Purpose:
The National Exposure Research Laboratory (NERL) Atmospheric Modeling and Analysis Division (AMAD) conducts research in support of EPA mission to protect human health and the environment. AMAD research program is engaged in developing and evaluating predictive atmospheric models on all spatial and temporal scales for forecasting the 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:
Recent improvements in air quality in the United States have been due to significant reductions in emissions of precursors of ozone and particulate matter (PM), and these downward emissions trends are expected to continue in the next few decades. To ensure that planned air quality regulations are robust under a range of possible future climates and to evaluate possible policy actions to mitigate climate change, it is important to characterize and understand the effects of climate change on air quality. Recent work by several research groups using global and regional models has suggested that there is a "climate penalty," in which climate change leads to increases in surface ozone levels in polluted continental regions. One approach to simulating future air quality at the regional scale is via dynamical downscaling, in which fields from a global climate model are used as input for a regional climate model, and these regional climate data are subsequently used for chemical transport modeling. In our group, outputs from Intergovernmental Panel on Climate Change global climate models are being downscaled and used as input for the Community Multiscale Air Quality (CMAQ) model. We will show changes in simulated concentrations of ozone and PM around 2030, with particular focus over the Carolinas, and discuss implications of these climate change and air quality interactions for human health and air quality management.