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A Decision-Making Framework for Ozone Pollution Control

Abbott Laboratories, Irving, Texas 75038
Department of Industrial and Manufacturing Systems Engineering, The University of Texas at Arlington, Arlington, Texas 76019
School of Earth and Atmospheric Sciences, Georgia Institute of Technology, Atlanta, Georgia 30332
Department of Civil Engineering, The University of Texas at Arlington, Arlington, Texas 76019
PROS Revenue Management, Houston, Texas 77002
zehua.yang{at}abbott.com
vchen{at}uta.edu
chang{at}eas.gatech.edu
msattler{at}uta.edu
awen{at}prosrm.com

In this paper, an intelligent decision-making framework (DMF) is developed to help decision makers identify cost-effective ozone control policies. High concentrations of ozone at the ground level continue to be a serious problem in numerous U.S. cities. Our DMF searches for dynamic and targeted control policies that require a lower total reduction of emissions than current control strategies based on the "trial and error" approach typically employed by state government decision makers. Our DMF utilizes a rigorous stochastic dynamic programming (SDP) formulation and incorporates an atmospheric chemistry module to model how ozone concentrations change over time. Within the atmospheric chemistry module, methods from design and analysis of computer experiments are employed to create SDP state transition equation metamodels, and critical dimensionality reduction is conducted to reduce the state-space dimension in solving our SDP problem. Results are presented from a prototype DMF for the Atlanta metropolitan region.

Subject classifications: environment; dynamic programming; applications; statistics; data analysis.
History: Received December 2005; revision received February 2008; accepted February 2008.