A decision making framework for assessing control strategies for ground level ozone

This paper presents a decision-making framework (DMF) for selecting ozone control strategies based on reductions of precursor emissions that are targeted by time and location. Conventional across-the-board reductions are uniform throughout the region and throughout the day. The DMF is comprised of four phases: (1) Initialization, (2) Experimental Design and Data Mining, (3) Metamodeling, and (4) Optimization. This paper details the first three and presents an example for selecting strategies in the optimization phase. Specifically, to enable an efficient optimization phase, metamodels for maximum 8-h averaged ozone are employed to represent the output from an advanced photochemical model, such as CAMx. The process for each phase is presented for a Dallas Fort Worth (DFW) 2009 future emission scenario, based on a 10-day meteorological episode from August 13–22, 1999. Over 600 emission variables were identified in three source categories viz. point, area (includes non-road) and line (on-road). The DFW application demonstrates how control measures can be efficiently explored in a targeted manner instead of using the conventional “trial and error” approach.

► We developed a decision-making framework (DMF) for reducing ozone.
► It uses statistical, data mining, and optimization methods to explore control measures.
► DMF identifies key sources, time periods, and ozone control strategies.
► DMF can be used to explore control strategies in a targeted manner.
► DMF is a valuable tool for policy makers to identify sensitive control strategies.