An Integrated Air Quality Model and Optimization Model for Regional Economic and Environmental Development: A Case Study of Tangshan, China

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 PM 2.5 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 PM 2.5 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.

Considerations for evaluating green infrastructure impacts in microscale and macroscale air pollution dispersion models

“…Hybrid models describe combinations of two or more models which are either used in series to generate desirable outputs (Karamchandani et al, 2009;Korek et al, 2016;Sharma et al, 2013;Sun et al, 2017) or complementarily to target specific problems such as the development of modified flow fields due to complex terrain, the estimation of dry deposition because of GI, or the generation of spatial meteorological data etc. within domain (Fallahshorshani et al, 2012).…”

An Integration Method for Regional PM2.5 Pollution Control Optimization Based on Meta-Analysis and Systematic Review

“…Environmental engineering research uses optimization techniques to address the mitigation of PM 2.5 pollution. This research often focuses on designing a suitable combination of pollution control measures by establishing the quantitative relationship between the emission of harmful substances and the atmosphere and cost and benefit [ 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 ]. For example, Carnevale et al [ 38 ] established a nonlinear optimization model and applied it to the study of O 3 pollution control in large cities in northern Italy; the model focused on the decontamination costs and improvement of the air quality index.…”