title: Synchronic interval Gaussian mixed-integer programming for air quality management.
abstract: To reveal the synchronism of interval uncertainties, the tradeoff between system optimality and security, the discreteness of facility-expansion options, the uncertainty of pollutant dispersion processes, and the seasonality of wind features in air quality management (AQM) systems, a synchronic interval Gaussian mixed-integer programming (SIGMIP) approach is proposed in this study. A robust interval Gaussian dispersion model is developed for approaching the pollutant dispersion process under interval uncertainties and seasonal variations. The reflection of synchronic effects of interval uncertainties in the programming objective is enabled through introducing interval functions. The proposition of constraint violation degrees helps quantify the tradeoff between system optimality and constraint violation under interval uncertainties. The overall optimality of system profits of an SIGMIP model is achieved based on the definition of an integrally optimal solution. Integer variables in the SIGMIP model are resolved by the existing cutting-plane method. Combining these efforts leads to an effective algorithm for the SIGMIP model. An application to an AQM problem in a region in Shandong Province, China, reveals that the proposed SIGMIP model can facilitate identifying the desired scheme for AQM. The enhancement of the robustness of optimization exercises may be helpful for increasing the reliability of suggested schemes for AQM under these complexities. The interrelated tradeoffs among control measures, emission sources, flow processes, receptors, influencing factors, and economic and environmental goals are effectively balanced. Interests of many stakeholders are reasonably coordinated. The harmony between economic development and air quality control is enabled. Results also indicate that the constraint violation degree is effective at reflecting the compromise relationship between constraint-violation risks and system optimality under interval uncertainties. This can help decision makers mitigate potential risks, e.g. insufficiency of pollutant treatment capabilities, exceedance of air quality standards, deficiency of pollution control fund, or imbalance of economic or environmental stress, in the process of guiding AQM.
authors.affiliation: Faculty of Engineering and Applied Science, University of Regina, Regina, SK S4S 0A2, Canada.
authors.forename: Guanhui
authors.initials: G
authors.lastname: Cheng
authors.affiliation: Institute for Energy, Environment and Sustainability Research, University of Regina, Regina, SK S4S 0A2, Canada. Electronic address: gordon.huang.submission@gmail.com.
authors.forename: Guohe Gordon
authors.initials: GG
authors.lastname: Huang
authors.affiliation: Institute for Energy, Environment and Sustainability Research, University of Regina, Regina, SK S4S 0A2, Canada.
authors.forename: Cong
authors.initials: C
authors.lastname: Dong
mesh_terms: D000393:Air Pollutants / Q000032:analysis*; D000397:Air Pollution / Q000706:statistics & numerical data*; D000465:Algorithms; D002681:China; D004784:Environmental Monitoring / Q000379:methods*; D017143:Fuzzy Logic; D015233:Models, Statistical; D016011:Normal Distribution; D015203:Reproducibility of Results; D035501:Uncertainty
chemical_list: D000393:Air Pollutants
keywords: Air quality management; Constraint violation; Dispersion process; Gaussian model; Global optimality; Interval linear programming; Mixed-integer; Synchronism