Mining and Modeling for a Metropolitan Atlanta Ozone Pollution Decision-Making Framework

In this paper, we present a decision-making framework (DMF) for reducing ozone pollution in metropolitan Atlanta. High concentrations of ozone at the ground-level continues to be a serious problem in several U.S. cities, and Atlanta is one of the most serious. In contrast to the “trial and error” approach utilized by state government decision makers, our DMF searches for dynamic and focused control strategies that require a lower total reduction of emissions than current control strategies. Our DMF utilizes a rigorous stochastic dynamic programming (SDP) formulation and includes an Atmospheric Chemistry Module to represent how ozone concentrations change over time. This paper focuses on the procedures within the Atmospheric Chemistry Module. Using the U.S. EPA’s Urban Airshed Model for Atlanta, we employed mining and metamodeling tools to develop a computationally-efficient representation of the relevant ozone air chemistry. These effectively model the transitions over time within the SDP optimization.