Mining and modeling for a metropolitan Atlanta ozone pollution decision-making framework

• Published in: IIE transactions Vol. 39; no. 6; pp. 607 - 615
• Main Authors: Yang, Zehua, Chen, Victoria C. P., Chang, Michael E., Murphy, Terrence E., Tsai, Julia C. C.
• Format: Journal Article
• Language: English
• Published: Norcross Taylor & Francis Group 01.06.2007; Taylor & Francis Ltd
• Subjects: Atmospheric chemistry; Data mining; Decision making; Decision making models; Dynamic programming; Emissions control; metamodeling; Outdoor air quality; Ozone; ozone pollution; stochastic dynamic programming; Stochastic models; Studies; urban airshed model; Atlanta Georgia; United States > US
• ISSN: 0740-817X; 2472-5854; 1545-8830; 2472-5862
• DOI: 10.1080/07408170600899508

In this paper, we present a Decision-Making Framework (DMF) for reducing ozone pollution in the metropolitan Atlanta region. High ground-level concentrations of ozone continue to be a serious problem in several US cities, and Atlanta is one of the most serious of these cases. 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 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 US EPA's Urban Airshed Model for Atlanta, we use mining and metamodeling tools to develop a computationally efficient representation of the relevant ozone air chemistry. The proposed approach is able to effectively model changes in ozone concentrations over a 24-hour period.