Development of a Sequential Decision-Making Model for Controlling Multiple Air Pollutants Under Stochastic Uncertainty

Abstract

Most of previous programming methods for air-quality management merely considered single pollutant from point sources. However, air pollution control is characterized by multiple pollutants from various sources. Meanwhile, uncertain information in the decision-making process cannot be neglected in the real-world cases. Thus, an inexact multistage stochastic programming model with joint chance constraints based on the air quality index (air-quality management model with joint chance constraints (AQM-JCC)) is developed for controlling multiple pollutants deriving from point and mobile sources and applied to a regional air-quality management system. In the model, integrated air quality associated with the joint probability existing in terms of environmental constraints is evaluated; uncertainties expressed as probability distributions and interval values are addressed; risks of violating the overall air-quality target under joint chance constraints are examined; and dynamics of system uncertainties and decision processes under a complete set of scenarios within a multistage context are reflected. The results indicate that useful solutions for air quality management practices in sequential stochastic decision environments have been generated, which can help decision makers to identify cost-effective control strategies for overall air quality improvement under uncertainties.