Conjunctive Use of Models to Design Cost-Effective Ozone Control Strategies

Abstract

The management of tropospheric ozone (O 3 ) is particularly difficult. The formulation of emission control strategies requires considerable information including: (1) emission inventories, (2) available control technologies, (3) meteorological data for critical design episodes, and (4) computer models that simulate atmospheric transport and chemistry. The simultaneous consideration of this information during control strategy design can be exceedingly difficult for a decision-maker. Traditional management approaches do not explicitly address cost minimization. This study presents a new approach for designing air quality management strategies; a simple air quality model is used conjunctively with a complex air quality model to obtain low-cost management strategies. A simple air quality model is used to identify potentially good solutions, and two heuristic methods are used to identify cost-effective control strategies using only a small number of simple air quality model simulations. Subsequently, the resulting strategies are verified and refined using a complex air quality model. The use of this approach may greatly reduce the number of complex air quality model runs that are required. An important component of this heuristic design framework is the use of the simple air quality model as a screening and exploratory tool. To achieve similar results with the simple and complex air quality models, it may be necessary to "tweak" or calibrate the simple model. A genetic algorithm-based optimization procedure is used to automate this tweaking process. These methods are demonstrated to be computationally practical using two realistic case studies, which are based on data from a metropolitan region in the United States.

Nonlinear response of ozone to precursor emission changes in China: a modeling study using response surface methodology

“…Previous studies of O 3 control analyses explored the overall impacts of two factors (total NO x and total VOC emission) on ozone that may be successfully derived from statistical interpolation of dozens of training samples (Milford et al, 1989;Shih et al, 1998;Fu et al, 2006). The interpolation is much more complicated when the precursor emissions are separated by pollutants, sectors and regions (Wang and Milford, 2001).…”

“…RSM can be applied to design the optimal (e.g., least-cost) control targets (Wang and Milford et al, 2001;Fu et al, 2006). When available NO x /VOC control technologies are applied, the cumulative control cost functions can be estimated for each emission source categories.…”

Optimization of multipollutant air quality management strategies: A case study for five cities in the United States

“…In air quality management, several mathematical programming methods, including deterministic and stochastic linear programming (LP) and nonlinear programming (NLP), have been pursued in order to identify optimal emission controls in sources for pollution mitigations in receptors and meet prescribed standards (Cooper et al, 1997;Greenberg, 1995). Previous studies mainly focused on emission controls to reduce concentrations of single air pollutants at single locations (Fu et al, 2006;Loughlin et al, 2000;Yang et al, 2009). In this study, we develop an optimization-based model, OPERA, formulated as an NLP, to identify least-cost control strategies for attaining prescribed multipollutant air quality targets at multiple locations simultaneously.…”

The Detailed Emissions Scaling, Isolation, and Diagnostic (DESID) module in the Community Multiscale Air Quality (CMAQ) Modeling System version 5.3

“…To facilitate the optimization of emission control strategies over many possible cases (Huang et al, 2020;Fu et al, 2006;Cohan et al, 2006), response-surface models (RSMs) have been developed by fitting statistical models to the output of many CMAQ simulations (Xing et al, 2011(Xing et al, , 2017. Although deep learning methods may reduce the computational burden of RSM development (Xing et al, 2020), dozens of CMAQ simulations are still needed to sample the emission control space in developing RSMs for typical air quality management applications.…”