对流边界层中公路线源扩散的Monte-Carlo法模拟

谷德军; 刘乙敏; 江奕光

对流边界层中公路线源扩散的Monte-Carlo法模拟

1.
广州热带海洋气象研究所, 广东, 广州, 510080
2.
广东省环境保护监测中心站, 广东, 广州, 510035

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出版历程
- 收稿日期: 1998-11-23
- 修回日期: 1999-03-10

MONTE-CARLO ATMOSPHERIC DIFFUSIVE MODEL AND ITS SIMULATION OF LINE SOURCE IN CBL

1.
Guangzhou Institute of Tropical and Oceanic Metoerology, Guangzhou 510080, China
2.
Guangdong Province Environmental Protection Monitoring Centre, Guangzhou 510035, China

摘要

摘要: 开发了一个Monte-Carlo大气扩散模式且引入了一种检验随机数质量的填充二维点格的方法。通过与水槽试验资料对比,说明该模式具有较好的模拟效果。用该模式模拟公路线源的大气扩散,结果表明,高架路和一般公路的污染在地面浓度上差异较大,而在垂直方向上差异较小。高架路引起的地面浓度值和扩散范围都较一般公路的大。垂直方向上尤其是20米以上二者基本没有差异,浓度较大值都在50～100米以下。结合箱模式的思想,导出了一个适用于城市汽车尾气污染估计的实用公式。
- Monte-Carlo扩散模式 /
- 随机数 /
- 线源 /
- 汽车尾气
Abstract: A Monte-Carlo diffusive model is developed and the random number quantity is tested by loading two-dimensional dot grids. Comparing the simulative result against the convective box shows the good simulation effect of the model. The atmospheric diffusion of the line source is simulated using this model, showing that the ground concentration is much different and the vertical concentration difference is small between the flyover and the highway. The ground concentration and the diffusive range caused by the flyover are larger than those caused by the highway. There is almost no difference in vertical direction, especially above 20m and larger concentration is positioned below 50~100m. Based on the idea of box model, a practical formula is derived that is applicable for estimating tail gas from urban vehicles.
- Monte-Carlo diffusion model /
- random number /
- line source /
- tail gas from vehicles

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参考文献(13)

[1]	SAWFORD B L.Lagrangian statistical simulation of concentration mean and fluctuation fields[J].J Climate.Appl.Meteor.,1985.24:1152～ 1166.
[2]	SAWFORD B L.Recent developments in the Lagrangian stochastic theory of turbulent dispersion[J].Boundary layer meteor.,1993.62:197 ～215.
[3]	BINDER K.Heermann D W统计物理学中的蒙特卡罗模拟方法(秦克诚译)[M].北京:北京大学出版社,1993,95～95,21～21.
[4]	HANNA R S.Some statistics of Lagrangian and Eulerian wind fluctuation[J].J.Appl.Met.,1979,18:518～525.
[5]	LUHAR A K.Hibberd M F,Hurley P J.Comparison of closure schemes used to specify the velocity PDF in Lagrangian stochastic dispersion models for convective conditions[J].Atmospheric Environment.1996,30:1407～ 1418.
[6]	BAETENTSEN J H,Berkowicz R.Monte-Carlo simulation of plume dispersion in the convective boundary layer[J].Atmospheric Environment,1984,18:701 ～712.
[7]	WElL J C.A diagnosis of the asymmetry in top-down and bottom-up diffusion using a Lagrangian stochastic model[J] ,J.Atmos.Sci.,1990,47:501 ～515.
[8]	DU S,Wilson J D,Yee E.Probability density functions for velocity in the convective boundary layer and implied trajectory models[J].Atmospheric Environment,1994,28:1211 ～ 1217.
[9]	杨华中,汪蕙.数值计算方法与c语言工程函数库[M].北京:科学出版社,1996,292～318
[10]	STULL R B.边界层气象学导论(杨长新译)[M].北京:气象出版社,1991,380～396
[11]	赵鸣,苗曼倩,王彦昌.边界层气象学教程[M].北京:气象出版社,1991,230～233.
[12]	HANNA R S.Lagrangian and Eulerian time-scale relations in daytime boundary layer[J].J.Appl.Meteor.,1981,20:242～249.
[13]	WILLIS G E.Deardorff J W.A laboratory model of diffusion into the convective planetary boundary layer[J].Q.J.R.Met.Soc.,1976,102:427～445.