A 2–D meteorological pre–processor for real–time 3–D ATD models. (4th August 2014)

• Record Type: Journal Article
• Authors: Finardi, S; Morselli, M.G.; Brusasca, G.; Tinarelli, G.
• Is Part Of: International journal of environment and pollution. Volume 8:Number 3-6(1997)
• Page Start: 478
• Page End: 488
• Publication Date: 2014-08-04
• Subjects: atmospheric dispersion models -- land use -- 2D meteorological pre–processors -- modelling -- control networks -- non–uniformity -- particle models -- turbulence scaling -- air pollution -- environmental pollution -- thermal power plants -- Italy -- air quality -- wind fields; Environmental policy -- Periodicals; Environmental engineering -- Periodicals; Environmental sciences -- Periodicals; Pollution -- Periodicals; 363.73
• Languages: English
• ISSNs: 0957-4352

Abstract

Horizontal non–uniformity has important effects on the structure of the atmospheric boundary layer and, consequently, on pollutant transport and dispersion. These effects can be dominant in coastal sites or in the presence of complex terrain. Advanced 3–D atmospheric dispersion models can be used to calculate short–term pollutant concentrations in such complex situations. The use of new generation models (puff, Eulerian or particle models) in the frame of pollution control networks requires a real–time, fast and reliable evaluation of surface layer similarity scale variables, able to take account of the horizontal non–homogeneity of the computed parameters. A meteorological pre–processor has been built to calculate atmospheric turbulence scale parameters (u*, L, H0, zi, w* ) over a 2–D domain, covering land–use variations, local meteorological data (surface data, SODAR, RASS), and results from diagnostic wind field models. The code implements updated methods to calculate similarity scale parameters, both on shore and over sea (lake) surfaces. The pre–processor is installed in the atmospheric pollution control network of a thermal power plant in Northern Italy, where a system consisting of a mass–consistent wind field model and a Lagrangian particle model gives real–time estimates of pollutant concentrations on an hourly base, using the local air quality control network data.