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Innovations in projecting emissions for air quality modeling
Citation:
Loughlin, Dan. Innovations in projecting emissions for air quality modeling. Presented at 15th Annual CMAS Conference, Chapel Hill, NC, October 23 - 26, 2016.
Impact/Purpose:
Dr. Loughlin will be serving as a panelist for a forum on the topic of emerging approaches for improving emission projections. Panelists have been asked to make a short presentation to stimulate the discussion. These slides were prepared for this purpose. Most of the slides have been used in prior presentations.
Description:
Air quality modeling is used in setting air quality standards and in evaluating their costs and benefits. Historically, modeling applications have projected emissions and the resulting air quality only 5 to 10 years into the future. Recognition that the choice of air quality management strategy has climate change implications is encouraging longer modeling time horizons. However, for multi-decadal time horizons, many questions about future conditions arise. For example, will current population, economic, and land use trends continue, or will we see shifts that may alter the spatial and temporal pattern of emissions? Similarly, will technologies such as building-integrated solar photovoltaics, battery storage, electric vehicles, and CO2 capture emerge as disruptive technologies - shifting how we produce and use energy - or will these technologies achieve only niche markets and have little impact? These are some of the questions that are being evaluated by researchers within the U.S. EPA’s Office of Research and Development. In this presentation, Dr. Loughlin will describe a range of analytical approaches that are being explored. These include: (i) the development of alternative scenarios of the future that can be used to evaluate candidate management strategies over wide-ranging conditions, (ii) the application of energy system models to project emissions decades into the future and to assess the environmental implications of new technologies, (iii) and methodological improvements that allow emissions to be spatially and temporally re-allocated to take into account factors such as population growth and migration and the changing role of fossil energy in electricity production.