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GCAM-USA and the GLIMPSE decision support system for air quality management
Citation:
Loughlin, Dan, Y. Ou, S. Smith, AND Chris Nolte. GCAM-USA and the GLIMPSE decision support system for air quality management. PM and Related Pollutants in a Changing World, RTP, NC, November 28 - 29, 2018.
Impact/Purpose:
GLIMPSE is a prototype decision support tool. It is aimed at providing national-, regional-, and state-level air quality managers (i) understand the efficacy of existing rules and regulations, (ii) anticipate how various uncertain factors may affect that efficacy (e.g., population growth and migration, economic growth and transformation, technology change, and energy and environmental policies), and (iii) devise robust, cost-effective management strategies. This presentation will be given to an audience of air quality and health impact modelers. Members of the audience could potentially use GLIMPSE to support their research and policy analysis objectives. The presentation will both provide a background and demonstrate the use of GLIMPSE on a real world problem. Feedback from the audience will be useful in understanding how they could use the system and what new features would more fully support their efforts.
Description:
The GCAM-based Long-term Interactive Multi-Pollutant Scenario Evaluator (GLIMPSE) is a prototype decision support tool that is intended to inform national, regional, and state-level air quality management decisions. The “computational engine” for GLIMPSE is the Global Change Assessment Model, or GCAM, which has been developed by the Department of Energy’s Pacific Northwest National Laboratory (PNNL). GCAM is a human-earth systems model that includes representations of the energy system, agriculture, land use, and the atmosphere and simulates the evolution of these interconnected systems through 2100. Users can explore wide-ranging scenarios, including those with alternative assumptions regarding population growth and migration, economic growth and transformation, technology costs, and environmental and energy policy. From an air quality management perspective, key outputs of GCAM include emissions of criteria pollutants, estimates of the health effects associated with emissions, and water demands from energy and agriculture. The base or “core” GCAM model has global coverage, broken into 32 geographic regions. Two variants of GCAM have been developed that have additional spatial resolution. GCAM-USA represents the 32 global regions, but the U.S. region has been disaggregated into 50 states and the District of Columbia. Similarly, GCAM-China disaggregates the representation of China to China’s 31 provinces. Thus, GCAM-USA and GCAM-China can support the analysis of state- and province-level air quality management strategies within the context of a global scenario. While GCAM is very flexible and has the potential to positively affect air quality decision making, the complexity of the model and associated data system has limited its use to a small community of developers and “power users.” However, the model is well suited to be integrated into a decision support tool that automates complicated functions (e.g., setting up input files, aggregating outputs over regions, and examining differences from one scenario to another), making the model accessible to a much broader set of users. Developing such a decision support tool is the primary goal of the GLIMPSE project. Over the past several years, we have implemented a GLIMPSE prototype and are engaged with beta testers. In this presentation, we will provide an overview of GLIMPSE. We will then demonstrate its use for a real-world problem: assessing the potential air pollutant emissions implications of expanding the Regional Greenhouse Gas Initiative region to include Virginia, New Jersey, and Pennsylvania. Results to be explored include the state-level air pollutant co-benefits for the RGGI states.