COMPUTER AIDED AMBIENT AIR QUALITY MODELLING IN HIGH SPEED DECISION TAKING SYSTEM

Abstract

During the last two decades there have been a simultaneous growth in awareness of the quality of environment, in the ability to measure its chemical constituents with greater accuracy and precision, and in the widespread use of mathematical models for estimating the transport and dispersion of air pollutant. In recant years, special attention has been given to the problem resulting from large-range-transport of air pollutant over a large regional/continental scale area. In many of air pollution dispersion modelling studies, theoretical models have been developed and applied to simulate the transport, diffusloyt , tranformation and removal of gaseous pollulant (SO2) in the atmosphere from the multitude of sources located within a given region. The development of suitable model for this application is a difficult task, considering the very large geographical area involved. This is because of the need to strike an approprite compromise between two equally desirable but conflicting model characterstic4- accuracy and practicality. The achievement of additional accuracy in model requires additional sophistication in physical formulations, as well as additional details in controlling input variables. These in turn required additional computer time for running the model and additional effort for preparing the input data, which together results in a less economical and thus less practical model. iv The diagnosis of air pollution episode involving regional scale has been aided by .Gaussian dispersion model. This analysis involves the use of Gaussian probability distribution function for calculating the concentration of gaseous pollutant at any point in space coordinate. In the Pasquill - Gifford approach, who too had adopted the some function, o•y and o-z, were taken only the function of distance from stack and empirical stability condition cofficients but the atmospheric turbulence, heat flux, time from sunrise, natural and man made terrain roughness, temperature gradient and wind frequency, solar flux, and stack' charactersticithad not been taken in to consideration. Since these factors affect the ay and T parameters very much, so consideration of above mentioned factors will lead to more closer correlation' to the prevailing environment. Now, .the advance mathematical tools and fast computations with the utilization of appropriate computer code (C) and optimum input date-procedure (input through Geographical input information system, GIS:) has facilitated the economical and efficient prediction of pollutant concentration with desired accuracy. When the pollutant concentration at any point is known with respect to all above mentioned parameters, the point of maximum concentration and amount of maximum concentration can be calculated by differentiating the concentration equation & equating it to zero. Keeping above variables in view, during this investigation the atmospheric stability has been classified into seven classes viz, A,B,C,D,E,F,G, and accordingly for every class the "Stack height v/s Ground concentration of pollutant", Ground concentration of polutant v/s Ground distance for various stack height from 25 mts. to 125 mts.", Maximum Ground concentration point v/s Distance for 30 mts. Stack height" plots have been drawn. These plots and tables can directly be utilized in calculation of required stack height under given environmental condition without going deep. into numerical analysis. A comparisle'n of "presented model" with "Obseved data and two other models" has been done 'to show the prospective of presented model.