A Model to Determine Atmospheric Stability and its Correlation with CO Concentration

Authors: Kh. Ashrafi, Gh. A. Hoshyaripour

Abstract:

Atmospheric stability plays the most important role in the transport and dispersion of air pollutants. Different methods are used for stability determination with varying degrees of complexity. Most of these methods are based on the relative magnitude of convective and mechanical turbulence in atmospheric motions. Richardson number, Monin-Obukhov length, Pasquill-Gifford stability classification and Pasquill–Turner stability classification, are the most common parameters and methods. The Pasquill–Turner Method (PTM), which is employed in this study, makes use of observations of wind speed, insolation and the time of day to classify atmospheric stability with distinguishable indices. In this study, a model is presented to determination of atmospheric stability conditions using PTM. As a case study, meteorological data of Mehrabad station in Tehran from 2000 to 2005 is applied to model. Here, three different categories are considered to deduce the pattern of stability conditions. First, the total pattern of stability classification is obtained and results show that atmosphere is 38.77%, 27.26%, 33.97%, at stable, neutral and unstable condition, respectively. It is also observed that days are mostly unstable (66.50%) while nights are mostly stable (72.55%). Second, monthly and seasonal patterns are derived and results indicate that relative frequency of stable conditions decrease during January to June and increase during June to December, while results for unstable conditions are exactly in opposite manner. Autumn is the most stable season with relative frequency of 50.69% for stable condition, whilst, it is 42.79%, 34.38% and 27.08% for winter, summer and spring, respectively. Hourly stability pattern is the third category that points out that unstable condition is dominant from approximately 03-15 GTM and 04-12 GTM for warm and cold seasons, respectively. Finally, correlation between atmospheric stability and CO concentration is achieved.

Keywords: Atmospheric stability, Pasquill-Turner classification, convective turbulence, mechanical turbulence, Tehran.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1071954

References:

[1] Zoras, S., Triantafyllou, A.G, Deligiorgi, D., "Atmospheric stability and PM10 concentrations at far distance from elevated point sources in complex terrain: Worst-case episode study". J. of Environmental Management, 80, pp. 295-302, 2006.
[2] Pal Arya, "Air pollution meteorology and dispersion". Oxford University Press, Oxford. 1999.
[3] Wark, K., Warner, C.F., Davis, W.T., "Air pollution: Its Origin and Control". Addison-Wesley, 1998.
[4] Muhan M., Siddiqui T.A., "Analysis of various schemes for the estimation of atmospheric stability classification". Atmospheric Environment, Vol. 32, pp. 3775-3781, 1998.
[5] Schenelle, K.B, Dey, P.R., "Atmospheric dispersion modeling compliance guide". McGraw-Hill companies, 2000.
[6] Ludwig, F.L., "Comparison of two practical atmospheric stability classification schemes in an urban application", J. Appl. Meteor., 15, pp. 1172-1176, 1976.
[7] Turner, D.B., "A diffusion model for an urban area". J. Appl. Meteor., 3, pp. 83-91, 1964.
[8] Pasquill, F., "The estimation of the dispersion of windborne material". Meteorological Magazine, 90, pp. 33-49, 1961.
[9] Seinfeld.J.H, Pandis.S.N, "Atmospheric chemistry and physics: from air pollution to climate change", 1998.
[10] Jacobson, M.Z.," Fundamentals of atmospheric modeling", Cambridge University Press, 2005.
[11] Iqbal, M., "An Introduction to Solar Radiation". Academic Press, Toronto. 1983.
[12] Perez-Roa, R., Castro, J., Jorquera, H., Perez-Correa, J.R., Vesovic, V., 2006. "Air-pollution modelling in an urban area: Correlating turbulent diffusion coefficients by means of an artificial neural network approach". Atmospheric Environment, 40, pp. 109-125, 2006.