Xiaowei Sun1, Shuiyuan Cheng 1,2, Jianbing Li3, Wei Wen4

  • 1 Key Laboratory of Beijing on Regional Air Pollution Control, Beijing University of Technology, Beijing 100124, China
  • 2 Collaborative Innovation Center of Electric Vehicles in Beijing, Beijing 100081, China
  • 3 Environmental Engineering Program, University of Northern British Columbia, Prince George, British Columbia V2N 4Z9, Canada
  • 4 Environmental Meteorology Forecast Center of Beijing-Tianjin-Hebei, China Meteorological Administration, Beijing 100089, China

Received: September 26, 2016
Revised: January 21, 2017
Accepted: February 20, 2017
Download Citation: ||https://doi.org/10.4209/aaqr.2016.09.0418 

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Cite this article:
Sun, X., Cheng, S., Li, J. and Wen, W. (2017). An Integrated Air Quality Model and Optimization Model for Regional Economic and Environmental Development: A Case Study of Tangshan, China. Aerosol Air Qual. Res. 17: 1592-1609. https://doi.org/10.4209/aaqr.2016.09.0418


HIGHLIGHTS

  • ILP method was applied.
  • Air quality Models and optimization models were coupled.
  • Industries with higher sensitivity coefficients should be controlled first.

 

ABSTRACT


An interval programming optimization model was formulated to develop effective and feasible regional economic structure adjustment plan using Tangshan Municipality in China as a case study. The optimization model was coupled with a WRF-CAMx-PSAT air quality simulation system through the estimated industrial emission sensitivity coefficients and equivalent coefficients for PM2.5 concentrations. Seven categories of industries were examined, and the results indicated that industries with higher emission sensitivity coefficients should be given priority for control. The effectiveness of the obtained optimal schemes was further assessed by the air quality simulation system. It indicated that PM2.5 concentrations in Tangshan would decrease by [33.5%, 39.3%] than those in 2013. This study provided an effective method framework for industries to maximize profits while meeting certain air quality constraints under uncertainty through the coupling of air quality simulation and optimization models.


Keywords: Air quality model; Emission sources; Interval programming; Optimization model; Sensitivity coefficient


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