Chemical Transport and Reduced-Form Models for Assessing Air Quality Impacts of Current and Future Energy Scenarios

Abstract:

Though essential for informed decision-making, it is challenging to estimate the air quality and public health impacts associated with current and future energy generation scenarios because the analysis must address the complicated atmospheric processes that air pollutants undergo: emissions, dispersion, chemistry, and removal. Employing a chemical transport model (CTM) is the most rigorous way to address these atmospheric processes. However, CTMs are expensive from a computational standpoint and, therefore, beyond the reach of policy analysis for many types of problems. On the other hand, previously available reduced-form models used for policy analysis fall short of the rigor of CTMs and may lead to biased results. To address this gap, we developed the Estimating Air pollution Social Impacts Using Regression (EASIUR) method, which builds parameterizations that predict per-tonne social costs and intake fractions for pollutants emitted from any location in the United States. Derived from a large database of tagged CTM simulations, the EASIUR method predicts social costs almost indistinguishable from a full CTM but with negligible computational requirements. We found that the average mortality-related social costs from inorganic PM_2.5 and its precursors in the United States are $150,000-180,000/t EC, $21,000-34,000/t SO₂, $4,200-15,000/t NO_x, and $29,000-85,000/t NH₃. This talk will demonstrate examples of using both CTMs and reduced-form models for assessing air quality impacts associated with current energy production activities as well as a future deployment of carbon capture and sequestration.