By B. O. Akkoyunlu, M. Tayanç (auth.), Dimitrios Melas, Dimiter Syrakov (eds.)
An realizing of long-range shipping of air toxins within the surroundings calls for a data of the suitable atmospheric dynamic and chemical methods energetic on the local scale in addition to the temporal and spatial distribution of emissions. Numerical modeling is the best solution to be certain the atmospheric shipping, photochemistry and deposition pathways. The booklet for this reason discusses the actual and chemical tactics that make sure nearby pollution and offers the suitable modeling thoughts to explain the several atmospheric tactics which are lively at that scale.
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Extra info for Air Pollution Processes in Regional Scale
Both errors are increasing, because the errors committed in each time -step accumulate in time. Ver y interestingly, the splitting problem does not reflect this behavior in Fig . 2: its tota l error is decreasing after a fast increase at the beginning of the time interval under consideration. , the origi nal problem provides a better solution, can be seen in Fig . 3. 1O ·' r - - - , - - - - - , - - - - - , - - - , . e· Figure I . Relative erro rs for matrix A (AI and A 2 commute) by applying Runge-Kutta method and SWS splitt ing.
Farago, 1. and Havasi, A. , submitted. Dekk er, K. and Verwer, J. G. (1984) Stability of Runge -Kutta methods for stiff non-linear di fferenti al equat ions, CWI Monograph s 2, North-Holland Publishing Co. , Amsterdam. Dimov , 1. , Faragov L, Havasi, A and Zlatev, Z. (2002 ) L-commutativity of ope rators for splitting methods in air pollu tion models, Annal. Univ. Sci. Sec. Math. 44, pp. 127-14 8. Havasi, A, Barthol y, J. and Farago, 1. (200 I ) Splitting method and its application in air pollution modelin g, Idojaras 105, pp.
The test of comparison of Standard Errors shows that the lognormal distribution results more appropriate. It is well known that air pollution data have skew distribution, usually closer to lognormal than to normal distribution . However, the normal distribution can still be used in the case of our data since normal probability plot produced lie roughly along a straight line. The ease with which the characteristic of interest can be altered is a considerable advantage over direct method of analysis, where any change in the variable being considered involves a return to the raw data.