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THE COMPUTATIONAL INVESTIGATION OF THE INFLUENCE OF WEATHER CONDITIONS ON OZONE FORMATION IN URBAN ATMOSPHERE Dmitry A. Belikov, Alexander V. Starchenko.

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Presentation on theme: "THE COMPUTATIONAL INVESTIGATION OF THE INFLUENCE OF WEATHER CONDITIONS ON OZONE FORMATION IN URBAN ATMOSPHERE Dmitry A. Belikov, Alexander V. Starchenko."— Presentation transcript:

1 THE COMPUTATIONAL INVESTIGATION OF THE INFLUENCE OF WEATHER CONDITIONS ON OZONE FORMATION IN URBAN ATMOSPHERE Dmitry A. Belikov, Alexander V. Starchenko Tomsk State University, Russia, Tomsk Institute of Atmospheric Optics of the SB RAS, Russia, Tomsk

2 Photochemical smog in world towns The annual American Lung Association study says about 159 million Americans percent of the population -- reside in counties where the air is heavily polluted with ozone. For many large cities in the world (e.g., Los Angeles) the presence in the daylight hours in the atmospheric surface layer of high concentrations of ozone, one of most widely spread pollutants, is an inevitable reality.

3 Simplified scheme of photochemical ozone formation Tropospheric ozone is formed by photochemical reactions involving volatile organic compounds (VOC) and the oxides of nitrogen. It has been proved that the major source of ozone precursors (nitrogen oxide and dioxide, hydrocarbon radicals) is automobile exhausts. According to the researches by Mage, performed in 1996, for the megapolises with the population of more than ten million, automobiles are the primary source of atmospheric pollution, and for a half of them are the only one. A significant contribution of emissions from industrial and power plants should also be noted. By having been transferred from coal to gas, they have substantially reduced emissions of sulfur, dust, and soot, but increased those of nitrogen and carbon

4 Model formulation – is concentration of the i-th admixture component; In this work, calculation of admixture concentration, taking into account chemical reactions between components, is realized on the basis of the model, represented in the photochemical Hurleys scheme (Hurley P.J. The Air Pollution Model (TAPM) Version 1: Technical Description and Examples //CSIRO Atmospheric Research Technical Paper No.43. Aspendale: CSIRO p.): – are turbulent correlations of concentration with wind velocity constituents; – stands for pollution discharge into the atmosphere or its settling onto the underlying surface; – refers to chemical reactions between admixture components;

5 Reactions and reaction rates of Harleys model

6 – are introduced into the system in order to provide effectiveness. APM include SNGOC, SNGN, SNGS as a result, the terms of R i of equation (1) for 8 pollutants will have the form: Reactions and reaction rates of Harleys model 1.smog reactivity R smog,2.nitric oxide NO, 3.radical pool RP,4.nitrogen dioxide NO 2, 5.hydrogen peroxide H 2 O 2,6.ozone O 3, 7.sulfur dioxide SO 2, 8.stable non-gaseous organic carbon SGOC, 9. stable gaseous nitrogenated substances SGN, 10. stable non-gaseous nitrogenated substances SNGN, 11. stable non-gaseous sulfurous substances SNGS, 12. airborne particulate matter (APM) that includes secondary particulate concentrations consisting of SNGOC, SNGN, SNGS

7 Species of Seinfelds model In model of Seinfeld 12 equation with 11 species are considered: ozone O 3, nitric oxide NO, nitrogen dioxide NO 2, atomic oxygen O, hydrocarbon RH, hydroxyl radical OH, aldehydes RO 2, peroxide radical HO 2, hydrocarbon substances RCHO, RCHOO 2, RCHOO 2 NO 2

8 Reactions and reaction rates of Seinfelds model

9 One dimensional model of atmospheric boundary layer K-l model of turbulence: K-l model of turbulence:

10 Initial and boundary conditions The boundary conditions for model of atmospheric boundary layer are: eight distribution 1.The results of height distribution obtained due to weather balloon are used as initial conditions for wind velocity, air temperature and moisture and also for correction of this variables. 2.For turbulence characterizations the initial profiles are generated on the basis of above model at fast averaged dynamic and temperature parameters of atmosphere. 3.At upper boundary wind velocity equals to the velocity of geostrophic wind which are calculated on the basis of atmosphere pressure (data of ); 3.At upper boundary wind velocity equals to the velocity of geostrophic wind which are calculated on the basis of atmosphere pressure (data of

11 Aria of investigation Conditions of calculations 1.Spatial time-dependent equations (1) were solved numerically for a parallelepiped with 119 linear (roads), 12 point and area 338 emission sources; 2.The typical diurnal evaluation of the traffic ratio are used;

12 Method of computation 1.In the investigated area that covers a town and its neighborhood, the finite-difference grid was built with the cells having constant horizontal sizes and variable vertical sizes, the latter decreasing when approaching the surface: 50x50x100 for meteorology;12x12x100 for pollution; 2.Approximation of differential operators in Eqs.(1) was accomplished with the second accuracy order over coordinates and with the first accuracy order over time; 3.Advective terms of transport equations (1) are approximated using the monotoned antistreaming Van Leer scheme that does not allow appearance of non-physical values of the concentration; 4.In the computations high-performance calculation resources were used, namely, multiprocessors of Tomsk State University (http://cluster.tsu.ru) and Institute of Atmospheric Optics SB RAS (http://cluster.iao.ru);http://cluster.tsu.ru)http://cluster.iao.ru 5.Parallelization of numerical method of solution of Eqs.(1) was executed using geometric principle, i.e. data decomposition.

13 Fig. 1. Results of model calculation over two days ( ) and measurement data. Negative values on the time scale correspond to the first day, positive values refer to the second day. Dots indicate measurement data, curves are calculation results (red line – 1d (Hurley) calculation, blue line – 3d (Hurley), firm line indicates the district of Akademgorodok, dotted line refers to the center of Tomsk). Results of model calculation

14 Fig. 2. Results of model calculation over two days ( ) and measurement data. Negative values on the time scale correspond to the first day, positive values refer to the second day. Dots indicate measurement data, curves are calculation results (red line – 1d (Hurley) calculation, blue line – 3d (Hurley), firm line indicates the district of Akademgorodok, dotted line refers to the center of Tomsk). Results of model calculation

15 Fig. 3. Results of model calculation over two days ( ) and measurement data. Negative values on the time scale correspond to the first day, positive values refer to the second day. Dots indicate measurement data, curves are calculation results (red line – 1d (Hurley) calculation, blue line – 1d (Seinfeld), firm line indicates the district of Akademgorodok, dotted line refers to the center of Tomsk). Results of model calculation

16 Fig. 3. Results of model calculation over two days ( ) and measurement data. Negative values on the time scale correspond to the first day, positive values refer to the second day. Dots indicate measurement data, curves are calculation results (red line – 1d (Hurley) calculation, blue line – 1d (Seinfeld), firm line indicates the district of Akademgorodok, dotted line refers to the center of Tomsk). Results of model calculation

17 Conclusion At this work: 1.The model of spreading pollution taking into account chemical reactions between substances at the atmosphere surface layer was used for investigation of ozone formation and transformation; 2.The result of calculation with observation data were compared. Well agreement was received; 3.The comparison with 3d meteorological and Seinfelds photochemical models are conducted; 4.Some features of generation and spreading ozone in Tomsk area for different weather conditions were revealed;

18 The research was founded by grant RFBR and grant of RF Ministry of Education. Thank you for attention!


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