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Synchronic interval Gaussian mixed-integer programming for air quality management.

Guanhui Cheng1, Guohe Gordon Huang2, Cong Dong2

  • 1Faculty of Engineering and Applied Science, University of Regina, Regina, SK S4S 0A2, Canada.

The Science of the Total Environment
|September 15, 2015
PubMed
Summary
This summary is machine-generated.

This study introduces a novel approach for air quality management (AQM) systems, addressing uncertainties and tradeoffs. The synchronic interval Gaussian mixed-integer programming (SIGMIP) model enhances decision-making for complex environmental challenges.

Keywords:
Air quality managementConstraint violationDispersion processGaussian modelGlobal optimalityInterval linear programmingMixed-integerSynchronism

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Area of Science:

  • Environmental Science
  • Operations Research
  • Mathematical Modeling

Background:

  • Air quality management (AQM) systems face complex challenges including interval uncertainties, tradeoffs between optimality and security, discrete expansion options, uncertain pollutant dispersion, and seasonal wind variations.
  • Existing models often struggle to simultaneously address these interconnected factors, limiting their effectiveness in providing robust and reliable AQM strategies.

Purpose of the Study:

  • To propose a synchronic interval Gaussian mixed-integer programming (SIGMIP) approach to effectively manage air quality under various uncertainties and tradeoffs.
  • To develop a robust interval Gaussian dispersion model that accounts for pollutant dispersion under interval uncertainties and seasonal wind patterns.

Main Methods:

  • Developed a robust interval Gaussian dispersion model to simulate pollutant dispersion under interval uncertainties and seasonal variations.
  • Introduced interval functions to capture the synchronic effects of interval uncertainties on the programming objective.
  • Defined constraint violation degrees to quantify the tradeoff between system optimality and constraint violation.
  • Utilized the cutting-plane method to solve integer variables within the SIGMIP model.

Main Results:

  • The proposed SIGMIP model successfully identified an optimal scheme for air quality management in a case study region.
  • The model demonstrated enhanced robustness, increasing the reliability of suggested AQM schemes amidst complexities.
  • Effectively balanced interrelated tradeoffs among control measures, emission sources, economic, and environmental goals, coordinating stakeholder interests.

Conclusions:

  • The SIGMIP approach provides an effective algorithm for air quality management, enabling harmony between economic development and environmental control.
  • Constraint violation degrees are valuable for decision-makers to assess risks and mitigate potential issues like exceeding air quality standards or funding shortfalls.
  • The study highlights the importance of integrated approaches for robust and reliable air quality decision-making in complex environments.