Peng He1, Bohong Zheng1, Jian Zheng 2


School of Architecture and Art, Central South University, Changsha 410075, China
School of Architecture, South China University of Technology, Guangzhou 510640, China



Received: June 30, 2017
Revised: April 7, 2018
Accepted: July 15, 2018
Download Citation: ||https://doi.org/10.4209/aaqr.2017.06.0223  

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Cite this article:
He, P., Zheng, B. and Zheng, J. (2018). Urban PM2.5 Diffusion Analysis Based on the Improved Gaussian Smoke Plume Model and Support Vector Machine. Aerosol Air Qual. Res. 18: 3177-3186. https://doi.org/10.4209/aaqr.2017.06.0223


HIGHLIGHTS

  • Excessive urbanization in China creates haze, which is a health hazard.
  • A new Gaussian smoke plume model on diffusion and evolution of PM2.5 was proposed.
  • The algorithm obtained much precise simulation results with lower error.
  • The results will help in government’s strategies of reducing air pollution.

ABSTRACT


With the acceleration of urbanization in China, haze has become a growing threat to human health. However, comprehensive research on the diffusion and evolution of PM2.5 is still lacking. Therefore, this study proposed an improved Gaussian smoke plume model that considered the influence of multiple factors, such as rain wash, gravity sedimentation, and surface rebound, on PM2.5. Additionally, the evolution of PM2.5 was predicted by selecting 9 factors with a large influence. In the prediction, a support vector machine (SVM) and radial basis function kernel were adopted to construct classifiers and obtain the maximum distinction degree, respectively. Finally, the diffusion simulation and experimental evolution prediction were verified using data obtained from nine PM2.5 monitoring stations in Wuhan. The experimental results showed that the algorithm could obtain considerably accurate simulation results of the PM2.5 diffusion with low error for measured values. Therefore, this model may be useful in government plans for formulating strategies that control and reduce environmental pollution.


Keywords: PM2.5; Diffusion simulation; Evolution prediction; Gaussian smoke plume model; SVM.

 



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