Pollutant dispersion simulation for low wind speed condition by the ILS method

• Published in: Atmospheric environment (1994) Vol. 39; no. 34; pp. 6282 - 6288
• Main Authors: Carvalho, Jonas C., de Vilhena, Marco Túllio M.B.
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 01.11.2005; Elsevier Science
• Subjects: Air pollution modeling; Applied sciences; Atmospheric pollution; Autocorrelation function; Exact sciences and technology; Lagrangian particle model; Low wind speed condition; Model evaluation; Picard iterative method; Pollutants physicochemistry study: properties, effects, reactions, transport and distribution; Pollution; USA, Idaho; Autocorrelation function; Model evaluation; Air pollution modeling; Picard iterative method; Low wind speed condition; Lagrangian particle model; Atmospheric condition; Pollutant behavior; Troposphere; Iterative method; Wind effect; Lagrange equation; Transport process; Three dimensional model; Langevin equation; Aerosols; Air pollution; Numerical simulation; Atmospheric dispersion; Stochastic equation
• ISSN: 1352-2310; 1873-2844
• DOI: 10.1016/j.atmosenv.2005.07.007

A semi-analytical Lagrangian particle model to simulate the pollutant dispersion during low wind speed conditions is presented and tested. The method relies to a stochastic integral equation whose solution is obtained using ILS method, which consists in the iterative solution of Langevin equation by the Picard's iteration method. To consider the low wind speed effect, the solution for the horizontal components of the turbulent velocity takes account the Eulerian autocorrelation function as suggested by Frenkiel [1953. Advances in Applied Mechanics 3, 61–107]. The model results are shown to agree very well with the field tracer data collected during stable conditions at Idaho National Engineering Laboratory (INEL) and during convective conditions from the series of field experiments at Indian Institute of Technology (IIT). A statistical analysis reveals that the model simulates very well the experimental data and presents results comparable or even better than ones obtained with other models used as comparison. The analytical feature of the ILS method and the inclusion of the Eulerian autocorrelation function suggested by Frenkiel (1953) allow generating more accurate results.