Development of a Sequential Decision-Making Model for Controlling Multiple Air Pollutants Under Stochastic Uncertainty |
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Authors: | Ying Lv Guohe Huang Yongping Li Zhifeng Yang Wei Sun |
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Institution: | (1) MOE Key Laboratory of Regional Energy Systems Optimization, S-C Energy and Environmental Research Academy, North China Electric Power University, Beijing, 102206, China;(2) School of Environment, Beijing Normal University, Beijing, 100875, China;(3) Faculty of Engineering, University of Regina, Regina, Sask, S4S 0A2, Canada |
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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. |
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