Risk-based multi-objective optimization for the control of mobile source air pollution: A framework methodology for analyzing risk transferral among exposure, emissions, and economic costs

This thesis develops a new framework methodology for planning air pollution control strategies. A multi-objective optimization model is constructed as a decision-analysis tool, which facilitates explicit evaluation of the trade-offs between human exposure and economic costs in the choice of policy options. Application of the model is demonstrated in the context of a case study to evaluate options for the control of mobile-source air pollution in metro Manila, the Philippines.;Traditional policy analysis models have generally only considered exposure after the policy choice has been made on an economic basis. Therefore, they do not properly value the economic benefits to be gained by considering exposure reduction as one of the objective functions in decision-making. These limitations are of increasing concern, particularly in developing countries, as human exposure to air pollution from various sources is not necessarily directly proportional to overall source contributions.;The recent development of new technologies for personal exposure monitoring, which measure air pollution concentrations in the breathing zone, has introduced the potential for improved assessment of the exposure impacts of policy options. The incorporation of a microenvironment modeling approach in a policy analysis framework here represents an important shift in the traditional policy analysis paradigm, in which policies may have improved ambient air quality, without reducing human exposure to air pollution, by overlooking risk transferal among pollutants.;The E-CUBE model developed here represents a truly inter-disciplinary approach to deciding policy and planning issues. It facilitates the integrated analysis of three objective functions, represented by the "three-E's"--Exposure, Emissions, and Expenditure--associated with policies for the control of air pollution. After the decision-maker specifies the objective function, the model minimizes emissions, exposure, or expenditure. A range of policy options are considered in the model, including fuel quality improvements, technological retro-fits, traffic and demand management strategies. Application of the model in a case-study in Manila, the Philippines, demonstrates its usefulness in structuring complex decisions, which require the balancing of the conflicting objectives of minimizing human health risks posed by air pollution, and minimizing expenditure for control.