A Two-Phase Optimization Model Based on Inexact Air Dispersion Simulation for Regional Air Quality Control

This study proposes a two-phase optimization model for regional air pollution control. To predict the pollutant concentrations at receptor zones, an interval Gaussian plume model is advanced to facilitate the generation of optimal pollution control policies. Results from the case study indicate satisfactory performance of the proposed model in handling uncertainties in parameters expressed as intervals and in stipulations associated with pollutant emission and ambient air quality. Compared with conventional models, it has advantages of generating compromised management strategies according to decision makers’ preference. This would be useful when the guarantee of satisfying all constraints is inapplicable or too costly. The proposed model is capable of identifying key factors and/or input conditions that may intensely affect system outputs and thus facilitating decision makers in adjusting current system status to benefit future management. The results also reveal a significantly enhanced satisfactory level would be obtained compared with conventional “single-phase”-based optimization models.