رویکرد بهینه سازی غیر خطی چند هدفه به طراحی سیاست های کنترل کیفیت موثر هوا

This paper presents the implementation of a two-objective optimization methodology to select effective tropospheric ozone pollution control strategies on a mesoscale domain. The objectives considered are (a) the emission reduction cost and (b) the Air Quality Index. The control variables are the precursor emission reductions due to available technologies. The nonlinear relationship linking air quality objective and precursor emissions is described by artificial neural networks, identified by processing deterministic Chemical Transport Modeling system simulations. Pareto optimal solutions are calculated with the Weighted Sum Strategy. The two-objective problem has been applied to a complex domain in Northern Italy, including the Milan metropolitan area, a region characterized by frequent and persistent ozone episodes.

Tropospheric ozone originates, through nonlinear reactions, from precursor emissions (mainly VOC — volatile organic compounds and View the MathML sourceNOx — nitrogen oxides) and high solar radiation. Decision Makers are interested in developing air quality plans, acting in terms of precursor emission reductions. Due to nonlinearities bringing to formation and accumulation of ozone, it is very challenging to develop sound air quality policies. This task is even more difficult when considering at the same time air quality improvement and policy cost implementation. In the literature the following methodologies, based on Integrated Assessment Modeling, are available to evaluate alternative emission reductions: (a) scenario analysis (Thunis et al., 2007), (b) cost-benefit analysis (Rabl et al., 2005 and Reis et al., 2005) (c) cost-effectiveness analysis (Carslon et al., 2004 and Shih et al., 1998) and (d) multi-objective analysis (Carnevale et al., 2007 and Guariso et al., 2004). Scenario analysis is performed by evaluating the effect of an emission reduction scenario on air quality, using modeling simulations. Cost-benefit analysis monetizes all costs and benefits associated to an emission scenario in a target function, searching for a solution that maximizes the objective function. Due to the fact that quantifying costs and benefits of non-material issues is strongly affected by uncertainties, the cost-effective approach has been introduced. It searches the best solution considering non-monetizable objectives as constraints (non-internalizing them in the optimization procedure). Multi-objective analysis selects the efficient solutions, considering all the targets regarded in the problem in an objective function, and stressing possible conflicts among them. The multi-objective analysis has rarely appeared in the literature, due to the difficulties to include the nonlinear dynamics involved in ozone formation in the optimization problem. The pollution-precursor relationship can be simulated by deterministic 3D modeling systems, describing chemical and physical phenomena generating tropospheric ozone. Such models, due to their complexity, require high computational time and are not implementable in an optimization problem. The identification of simplified models synthesizing the relationship between the precursor emissions and ozone concentrations, therefore, is required. In the literature, source-receptor relationships have been described using ozone isopleths (Shih et al., 1998), or with reduced form models such as (a) simplified photochemical models, adopting semi-empirical relations calibrated with experimental data (Venkatram, Karamchandani, Pai, & Goldstein, 1994), and (b) statistical models, identified on the results of complex 3D Chemical Transport Models (Friedrich and Reis, 2000, Guariso et al., 2004 and Ryoke et al., 2000). In this paper, an integrated assessment methodology is proposed. It is focused on the mesoscale to better interpret the specific features of the area, the local meteorological and chemical conditions, the contribution of regional and local precursor emissions. It solves a two-objective (air quality and cost) optimization to select effective abatement strategies. The nonlinear relations between control variables (precursor emissions reduction) and Air Quality Index, defining the air quality objective, are described by artificial neural networks, identified processing long-term 3D deterministic multi-phase modeling system simulations. Emission reduction costs are described by deriving polynomial functions from a detailed technology dataset compiled by IIASA (Amann et al., 2004a). This paper is organized as follows. In Section 2 the methodology is proposed, focusing at first on the control variables used in the problem, then describing the air quality objective and the cost objective. Section 3 presents the applications of this approach on a case study, focusing on the Pareto boundary calculation and results. The conclusions (Section 4) stress the benefits of this kind of approach.

Tropospheric ozone long-term exposure is an increasing problem, due to the dangerous effect that this contaminant has on human health and ecosystems. An important task of Decision Makers is to develop plans to reduce such pollutant exposure. These plans are implemented in terms of precursor emission reductions, acting on VOC and View the MathML sourceNOx. This paper is focused on multi-objective optimization, a technique rarely described in the literature due to the difficulties to include in the procedure the nonlinear dynamics that brings to ozone formation and accumulation. The proposed approach suggests the use of artificial neural networks, identified on the basis of complex deterministic modeling system results, to describe the nonlinear relation that bring to ozone formation and accumulation. The multi-objective problem solutions draw the non-dominated points curve. The Pareto boundary suggests to the Decision Maker the optimal policies in terms of air quality and emission reduction costs, supporting control actions in priority emission sectors. The proposed methodology shows that it is possible to strongly reduce ozone exposure in Northern Italy with a low percentage of maximum possible cost.