Date of Award

Winter 2-2015

Embargo Period

2-3-2016

Degree Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Engineering and Public Policy

Advisor(s)

Peter Adams

Abstract

Though essential for informed decision-making, it is challenging to estimate the public health impacts of air quality because it must address the complicated atmospheric processes of air pollutants: emissions, dispersion, chemistry, and removal. Employing a chemical transport model (CTM) is the most rigorous way to address these atmospheric processes. The first part of this thesis analyzed the potential risk of ammonia emissions from post-combustion carbon capture and storage (CCS) technology using a CTM. It was found that, if not controlled properly, CCS ammonia may create a serious public health problem, substantially compromising the benefit of reducing carbon dioxide. The results will guide the level of appropriate control for a wide range of future scenarios. CTMs are expensive from a computational standpoint and, therefore, beyond the reach of policy analysis for many types of problems. On the other hand, current tools 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 cost and intake fraction at any location in the United States like a CTM with negligible computational costs. With tagged CTM simulations, the EASIUR method builds a dataset of air quality impacts for a large number of representative emissions sources in the United States and then derives parameterizations for those results. We used an “average plume,” a generic PM2.5 plume generated from CTM results, to describe the exposed population over large receptor areas around an emissions source. The parameterizations have intuitive functional forms with population and common atmospheric variables; their coefficients explain key underlying mechanisms. Out-of-sample evaluations meet the ‘excellent’ criteria of a common air quality model performance metric in most cases, with some exceptions meeting the ‘good’ criteria. We found that the average seasonal per-tonne social costs in the United States are $150,000-180,000/t EC, $21,000-34,000/t SO2, $4,200-15,000/t NOx, and $29,000-85,000/t NH3. It is hoped that the EASIUR model will be of great use in policy research that involves changes in air quality.

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