A chance-constrained model for regional air quality management

Abstract

Regional air pollution has been a major global problem due to its health-associated risks together with its economic and environmental impacts. Though there have been significant advances in air pollution control technologies, the implementation of these control strategies are costly. It is thus desirable for effective air quality planning and management to be undertaken to identify and implement cost-effective strategies, ensuring local air quality at safe levels. In this study, an inexact chance-constrained optimization model (ICCLP) was developed for air quality management under uncertainty. The ICCLP was formulated by integrating the inexact linear programming (ILP) and the chance-constrained programming model (CCP). The ICCLP allows the left-hand side (LHS) random variables to be expressed as interval numbers while letting the right-hand side (RHS) constraints to be expressed as probabilistic functions. In this way, the highly random RHS constraints will be satisfied at predetermined confidence levels, providing a more flexible and in-depth tradeoff analysis when applied to air quality management. To determine the applicability of the proposed ICCLP to regional air quality management, it was applied to a hypothetical case study, where the results were analyzed and compared with those from the ILP and CCP models. It was observed that the ICCLP could incorporate more uncertain information within its modeling framework. In addition, the method provides not only decision variable solutions presented as intervals but also the associated risk levels in violating the system constraints. It can therefore support an elaborate analysis of the tradeoff between system cost and system-failure risk. Hence, it is a useful tool for generating decision alternatives and thus helps policy makers identify desired policies under various environmental, economic, and system-reliability constraints.