PM2.5扩散模型及预测研究

以武汉为例,以高斯扩散模型为基础研究PM2.5的扩散与衰减规律,充分考虑影响PM2.5扩散的因素,分析地面与建筑物边界反射、干沉积、雨洗湿沉积及湿度的影响,逐步改进高斯扩散模型,并引入时间t,计算当点源持续污染情况下,污染源上风和下风L公里处的浓度.通过数值仿真,得到距污染源下风向距离一定条件下污染扩散浓度的分布规律,预估突发情形下PM2.5的扩散距离及安全区域,结合三维图及平面图分析危险区及安全区.最后,结合小波理论及神经网络理论,提出小波神经网络的结构及算法,并通过Matlab实现了对PM2.5值的预测,并取得较高的预测拟合度.

Study on the Diffusion Model and Forecast of PM2.5 Pollution

This paper researched the diffusion and attenuation rules of PM2.5 based on the Gauss diffusion model and take Wuhan as an example.We considered the factors that influence the diffusion of PM2.5 fully and analysis the boundary reflections between buildings and ground,dry deposition,wet deposition and humidity,and then improved the Gauss diffusion model gradually.Then introduced the time variable t,calculated the concentration of pollution sources upwind and downwind L kilometers when the point source pollution continue.Through numerical simulation,got the distribution of concentration diffusion when the pollution source downwind under certain conditions.Estimating the diffusion distance and safety area under the sudden circumstances,and analysis the danger zones and security zones combined with 3D maps and plans.Finally,combining the wavelet theory and neural network theory got the structure and algorithm of wavelet neural network,and achieved the predictive value of PM2.5 through Matlab,and achieved a good forecasting effect.