Wednesday, 13 January 2016: 10:30 AM
Room 243 ( New Orleans Ernest N. Morial Convention Center)
John C. Lin, University of Utah, Salt Lake City, UT; and L. Mitchell,
D. Mendoza, R. Bares, B. Fasoli, D. Bowling, E. Crosman, J. Horel, R. Patarasuk, K. Gurney, M. Buchert, A. K. Kochanski, D. V. Mallia, D. Pataki, B. Eng, D. Catharine,
C. Strong, and J. R. Ehleringer
The University of Utah is leading efforts to understand the spatiotemporal patterns in both emissions and concentrations of greenhouse gases (GHG) and criteria pollutants within urban systems. The urbanized corridor in northern Utah along the Wasatch Front, anchored by Salt Lake City, is undergoing rapid population growth that is projected to double in the next few decades. The Wasatch Front offers multiple advantages as an unique “urban laboratory”: urban regions in multiple valleys spanning numerous orders of magnitude in population, each with unique airsheds, well-defined boundary conditions along deserts and tall mountains, strong signals during cold air pool events, seasonal contrasts in pollution, and a legacy of productive partnerships with local stakeholders and governments.
We will show results from measurements in the Salt Lake Valley, including one of the longest running continuous CO2 records in urban areas. Complementing this record are comprehensive meteorological observations and GHG/pollutant concentrations on mobile platforms: light rail, helicopter, and research vans.
Variations in the GHG and pollutant observations illustrate human behavior and the resulting “urban metabolism” taking place on hourly, weekly, and seasonal cycles, resulting in a coupling between GHG and criteria pollutants. Moreover, these observations illustrate systematic spatial gradients in GHG and pollutant distributions between and within urban areas, traced to underlying gradients in population, energy use, terrain, land use, as well as regional scale transport of pollution from wildfires.
Using atmospheric models, we further link concentrations of GHG and air quality-relevant pollutants to underlying emissions from the neighborhood to regional scales. These atmospheric models include a sophisticated time-reversed stochastic particle model that incorporates non-linear chemical reactions (STILT-Chem).
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