Abstract
In this work an alternative numerical approach to solve the three-dimensional stochastic Langevin equation applied to air pollution is proposed and tested. The method leads to a first-order differential equation whose solution is obtained through the method of successive approximations or Picard’s iteration method. Langevin models for Gaussian and non-Gaussian turbulence are obtained, considering Gaussian and bi-Gaussian probability density functions (PDF) of the turbulent velocity, respectively. The proposed approach is evaluated through the comparison with experimental data and results from three different models. A statistical analysis shows a good agreement between the comparisons.
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