Volatile Organic Compounds Are Easily Produced by Nature Photochemical Processes To

ISBN 978-1-84626-xxx-x Proceedings of 2012 International Conference on Environmental Engineering and Applications (CCEA 2010) Thailand, 1-2 September, 2012, pp. xxx-xxx

Air Dispersion Modeling for Prediction Accidental Emission in the Atmosphere along Northern Coast of Egypt

M. O. Nafd(1), W. B. Ibrahim(2) and Medhat A.E. Moustafa(3) (1) Central laboratory of technical measurement, environmental air laboratory, EEAA, Phone (+203) (302-20691); Fax (+203) (302-4477); e-mail: [email protected] (2) Faculty of Engineering, Alexandria University, Phone (+2033933232, Fax +2035857571 e-mail: [email protected]. (3) Head of sanitary Engineering Department, Faculty of Engineering, Alexandria University, Phone (+2033933232, Fax +2035857571 e-mail: [email protected].

Abstract. Modeling of air pollutants from the accidental release is performed for quantifying the impact of industrial facilities into the ambient air. The mathematical methods are requiring for the prediction of the accidental scenario in probability of failure-safe mode and analysis consequences to quantify the environmental damage upon human health. The initial statement of mitigation plan is supporting implementation during production and maintenance periods. In a number of mathematical methods, the flow rate at which gaseous and liquid pollutants might be accidentally released is determined from various types in term of point, line and area sources. These emissions are integrated meteorological conditions in simplified stability parameters to compare dispersion coefficients from non-continuous air pollution plumes. The differences are reflected in concentrations levels and greenhouse effect to transport the parcel load in both urban and rural areas. This research reveals that the elevation effect nearby buildings with other structure is higher 5 times more than open terrains. These results are agreed with Sutton suggestion for dispersion coefficients in different stability classes. Key words: Air pollutants, dispersion modeling, GIS, health effect, urban planning

1. Introduction The organic emissions have adverse potential in contamination of ambient air with airborne materials spread into atmospheric layers. These emissions are constituent dioxin-like compounds (e.g. de-chlorination of polychlorinated biphenyls) which influencing the air soluble properties for performing radical reaction in replacement process through the atmosphere. The kinetics and mechanisms of airborne material are related to atmospheric conditions and air parcel trajectories for influencing strategic spread in environment (Hanna et al, 1982)(1). This gradual degradation of aromatic pollutant in suspension volumes is reflecting the photochemical reaction for deposition on surface ground. This deposition is measured across varying perception in urban or 1. rural areas. The long-pass loads are reluctant legislation to impose limits for regional setting across boundaries for controlling initial operations. In normal operations, pollutants monitoring has a great influence in predicting the gaseous behavior to reform complex compounds in the atmosphere. However, laboratories have investigated pollutants from insertion systems into industrial areas in defined methods to profile incidents. These methods are applied for certain pollutants emitted directly to air and forming other compounds disperses in air according to chemical and physical properties of the gaseous reactions. Other substances are emitted through formal exhaust for interaction with natural environment in the atmosphere for displacement certain composition to affect layer stability (i.e., carbon monoxide and ozone in constituent of photochemical smog). However, the natural saturation into air is controlling biological decomposition in long pass transport. The pollutants are dispersed along wind direction in dynamic variations grasp emission to particulate matters, aerosols and liquid droplet for decomposition in various distances (Briggs, 1972)(2). The plume dispersal model analyzes these refused substances and describes behavior reflected by biological contamination in stable conditions as described in Figure (1) (Beychok, 2005)(3).

ORGANIC COMPOUNDS IN ORGANIC COMPOUNDS IN DIFFERENT ATMOSPHERIC DIFFERENT ATMOSPHERIC CONDITIONS CONDITIONS OUTPUTOUTPUT INPUT INPUT PHOTODECOMPOSITION DECOMPOSITION COMBUSTION Dry Gaseous Natural Dry Particulate Anthropogenic Wet VOLATILIZATION LONG – TERM TRANSPORT Soil Vegetation Other Surfaces

Figure (1): The behavior of organic compounds in ambient air (Beychok, 2005)(3)

Several pollutants sources are distinguished by subsidiary systems in replacement communities' element further to interaction administration with engineering judgment in management decisions

2. The Major Pollutants in Ambient Air The fuel composition is influencing emissions constituents at air combustion Furness. It releases airborne material and dust holding vapor to constituent compounds in adsorption layer spread over partial effect for deposition on ground soil. The degradation rates relay upon conservative flux and several accumulation parameters such as geographic conditions, technology distribution with chemical and physical properties of deposition. In post processing, physical and chemical properties are controlling emission effects in transformation into the atmosphere (e.g. wet and dry

2. deposition, degradation and derivatization) (Finlayson-Pitts, 1986)(4). The manufacturing process starts from initial setting of extraction materials and extend to integration production processing with distribution through social services. The atmospheric stability categories are reflecting this dispersion contribution in ambient air of closed cycle as described in Figure (2) (Bosanquet, 1936)(5).

Ambient Air Stability Ambient Air Stability Deposition Incineration Refinery

Water Extraction Water Manufacture Soil

Distribution Export Process Recycle Application

Residues

Landfil Landfill Leaching Treatment l

Figure (2): The interaction sources of airborne materials (Bosanquet, 1936)(5)

The atmospheric conditions are reflecting distribution process from extraction materials to manipulation products in society. In stable conditions, a direct deposition dictated to physical, chemical and biological interaction (for both dry and wet) while volatilization, and photolytic process are affecting air loads transport in the environment. In diffusion behavior, sediment of organic compounds has a large area with unstable conditions to sink flux source. The flux concentrations address compounds productivity in air and sediment fraction in landfill leaching. These emission sources are transferring partial pollutant in either primary or secondary terms. In general mean, substances emitted from natural environment are primary pollutants, while secondary pollutant has indirect means to air when primary pollutants react or interact when displacement occur in atmospheric layers. These grasp different pollutants to particulate matter in a life time permit to constituent compounds. The particulate matter smaller than 10 microns in diameter (PM10) is a target of semi- volatile and volatile organic compounds (e.g. PAHs and VOCs) in ambient air to retain fugitive emission for deposition in varying distances Zoras, et al., (2006)(6).

3. 3. Gaseous Emission Measurement Methods The natural saturation of dynamic effect requires different methods in determination of biological contamination dispersion in ambient air. These methods and techniques are describing chemical transport through air emission till removal from air by deposition or diffusion in atmospheric layers. In contrast to measurement, spatial solutions are relevant to mitigation measure for determining concentration variation along wind direction. These aspects are modeling dispersion compounds from air pollutant in levels of concentration and predict different solutions in urban and industrial areas. In a complementary manner to monitoring systems, model parameters have influenced spatial design of coverage in variation elements where: 1. Levels are estimated with spatial variation of the quantity from existing measurements in the zone, or in similar situations elsewhere, or from emission inventories and model calculations. 2. Measurements are being used as basis criteria of the preliminary assessment not only to identify the location of maximum concentration, but also to determine how it is extent in vacant area that exceeds the limits. This basic strategy is measuring elements for assessing pollutants distribution according to design criteria of area background and density. These data are applied in frequent methods over a period of time to assess air quality considering uncertainty of the pollutants variability into space and time. In case of uniform terrain, Gaussian model provides reliable results for long term transport in relative inert values pollutants such as SO2, NOx, COx and other gaseous. In complex meteorological and topographical conditions, transport process is simulated numerically according to atmospheric diffusion equation in Eulerian approach or as described fluid elements that follow the instantaneous flow of Lagrangian approach. Lagrangian dispersion models have become increasingly more feasible, due to the advances in computer technology (e.g., Janicke et al., 1994(7); Oettl et al. (2001(a)(8)). Both Eulerian and Lagrangian models are less limited by topographical and meteorological conditions compared to Gaussian plume models, (e.g. Oettl et al. (2001(b)(9)). Both are embedded in prognostic models. These forward decisions to substitute agent with direct substances discovered in social manipulation in the environment (Sodemann, 2006)(10).

4. Plume Dispersion versus Stability The plume bouncy affects air parcel movement and consequently, dispersion coefficients which are varying according to the classification of atmospheric stability. In recent stability categories, methods are reviewed fragment classes into categories spread to specify air conditions during day and night measurements. The air temperature and wind speed are indicated stability classes, while local parameters and interconnections activities are rolling compounds behavior during indirect peak analysis in fluctuations time. These are arranged in a wide range of possible conditions for adjustment during principal means of wind speed, relative humidity, and temperature gradient. Pasquil-Gifford classified atmospheric stability as a function of the vertical temperature gradient (dT/dZ) and wind speed (). The temperature gradient is a function of the rate of solar heating (insolation), windspeed, and weather in the day or night (Pasquill, 1961)(11). These atmospheric stability methods are incorporating both mechanical and buoyant turbulence as proposed by Pasquill (1961). In this classification, it is assumed that stability in the layers near the ground depends on net radiation as a convective indication of mechanical turbulence and wind speed at 10 m height. Net radiation is determined based on insolation (incoming solar radiation) and cloud cover at day or night time separately. The primary advantages of this classification are its simplicity and its requirement of only routinely available 4. information from surface meteorological stations, such as the near-surface (10 m) wind speed, solar radiation and cloudiness.

Table (1-(a)): Pasquill's stability categories according to temperature gradient(11).

Category Description Vertical temperature gradient (dT/dZ) A Extermely unstable <−1.9 B Moderatly unstable −1.8 C Slightly unstable −1.6 D Neutral −1.0 E Slightly stable +1.0 F Moderatly stable −1.5 − +4.0 G Highly stable > +4.0

Table (1-(b)): Pasquill's stability categories according to wind speed(11). Surface Insolation Night Wind speed Strong Moderate Slight >4/8 cloud <3/8 cloud  (m/s) <2 A A−B B − − 2−3 A−B B C E F 3−5 B B−C C D E 5−6 C C−D D D D >6 C D D D D

The most stable conditions occur when the vertical temperature gradient is positive on clear night and commonly called temperature inversion. Conversely, the least stable conditions arise on hot days with only a slight breeze and strong winds promote neutral stability at all times. Other air stability schemes are classified to indicate inversion levels reflected in gaseous concentration at surface ground (Barrat, 2001)(12). Richardson number, Monin-Obukhov length, and Pasquill-Turner stability classes are some of common schemes. The mechanical and physical parameters are specified air conditions during emissions as adopted in Table (2) (Muhan and Siddique, 1998)(13).

Table (2): The stability interprets different schemes of atmospheric category classes (13)

Stability condition Richardson Obukhov -Monin Pasquill- Gifford PTM

5. Extremely unstable A 1 Rn < -0.04 -100 < Ln < 0 Unstable B 2

Slightly unstable -0.03 < Rn < 0 -105 ≤ Ln ≤ -100 C 3

Neutral Rn = 0 │Ln│ > 105 D 4

Slightly stable 0 < Rn < 0.25 10 ≤ Ln ≤ 105 E 5

Stable F 6 Rn > 0.25 0 < Ln < 10 Extremely stable G 7

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Different methods are used for determination stability with varying degrees of complexity. Most of these methods are based on the relative effects of solar radiation in mechanical turbulence of atmospheric motions. The Richardson number (Rn) is a turbulence indicator and also an index of stability as defined in (Pal Arya, 1999)(14)

2 g      du    (1) Rn      T   z   dz  where, (g) = the gravity acceleration,

(ΔΘ / Δz) = the potential temperature gradient,

(T) = the temperature and

(du l dz) = the wind speed gradient

In this equation, parameter of g (ΔΘ /Δz) / T is indicator of convection while (du/ dz )2, is pointer of mechanical turbulence due to mechanical shear forces.

The other key stability parameter is the Monin-Obukhov length, (Ln), which treats 3 atmospheric stability proportional to third power of friction velocity, (u *), divided by the surface turbulent (or sensible) heat flux from the ground surface, (Hs) Monin- Obukhov length is defined as in (Pal Arya, 1999)(14): 3  u*       k  Ln  (2)  g  H   s     C p  T 

Where, (u*) = friction velocity,

(g) = the gravity acceleration,

(Cp) = the specific heat of air at constant pressure,

6. (ρ) = the air density,

(T) = the air temperature, and

(k) = von- Karman constant taken to be 0.40.

(Hs) = height from ground surface, which is positive in daytime

and negative at nighttime.

The Pasquill–Turner Method (PTM) is based upon the work of Pasquill, that has been revised by Turner (1964)(16) for introducing incoming solar radiation in terms of solar elevation angle, cloud amount and cloud height. It classifies atmospheric stability into seven distinguishable categories. The importance of this method lies in the relation to atmospheric dispersion coefficients and classified stability for mechanically and thermally generated boundary-layer turbulence with Net Radiation Index (NRI). This modified Pasquill's algorithm to determine radiation into classes related to solar altitude, cloud cover and height to be as general indicator of insolation intensity so electronic computer can be used to compute stability. The NRI in PTM is determined in reference to the following procedure (Schenelle et al., 2000)(15): o 0, when the total cloud cover is 8/8 and the ceiling height of cloud base is less than 7000 ft (low clouds). o -2, during the night if the total cloud cover is ≤3/8, and if it is >3/8, then -1 o 4, (high radiation levels) ranging to 1 (low radiation levels) during daytime and depending on the solar altitude (Table (3)). The NRI corrections during daytime are following variations in parameters when: a. If the total cloud cover is ≤4/8, then the indices are used as from Table (3). b. If the cloud cover is >4/8 two cases are distinguished: o (b-1) ceiling <7000 ft, then 2 is subtracted and o (b-2) ceiling ≥7000 ft and <16000 ft then 1 in subtracted. c. If the total cloud cover is 8/8 and the ceiling height is ≥7000 ft, then 1 is subtracted. d. If the corrected value is less than 1, then it is considered equal to 1

Table (3): Insolation as a function of solar attitude Solar Attitude Insolation Insolation Class Number h(*) 60

7. and nights. Finally, the creation of inversions during nights with clear sky indicates stable atmosphere (Schenelle and Partha, 2000)(15). Therefore, using PTM for urban areas, unlike this study, is possible to combine stability categories 6 and 7 into one category. The atmospheric stability in term of pollution levels have appropriate correlation demonstrated in ground concentrations and warming temperature. The stability class in PTM is determined according to NRI and the wind speed, as given in Table (4) (Schenelle et al., 2000)(15)

Table (4): The stability class as a function of (NRI) and wind speed

Wind Speed Net Radiation Index (NRI) (knots) 4 3 2 1 0 -1 -2

0-1 1 1 2 3 4 6 7 2 -3 1 2 2 3 4 6 7 4-5 1 2 3 4 4 5 6 6 2 2 3 4 4 5 6 7 2 2 3 4 4 4 5 8-9 2 3 3 4 4 4 5 10 3 3 4 4 4 4 5 11 3 3 4 4 4 4 4 ≥12 3 4 4 4 4 4 4

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5. Plume Dispersal Modeling The model is used to predict ambient concentrations levels and compare dispersion coefficient of different methods with future emission scenarios. The dispersion coefficients are commonly applied to estimate air quality impact from existing emission source in different scenarios for various air management issues. The model is governing the dispersal of gas clouds, vapor, or small particles in the differential equation for diffusion with the axes x, y and z referring to down wind, cross wind and vertical direction in coordinate system. The bouncy conditions are a function of the atmospheric stability and types of source emission as modeled in Gaussian equation (3) and traced in concentration contours lines according to (Turner, 1994)(16)

Q  1  y2 z 2  R2    C( x, y, z)  exp   2u  2  2  2  (3) y z   y z  where,

QR2 = the rate of emission C = the concentraton of receptor u = the wind speed (m/s)

t z = the dispersion coefficient across and along wind y , z = the distance and hight form emission source In linear analysis, the dispersion coefficients are determined with correlation to Passquil atmospheric stability classes (from A to G) and the natural of terrain, e.g. urban, pasture, or frost. Sutton (1947)(17) proposed the following expressions:

8.  y 1 n / 2 (4)  hy x x 2

 z 1 n / 2 (5)  hz x x 2

Where n is a function of stability category and hy and hz are function of source height and atmospheric eddy velocity. For ground level release under neutral conditions and open terrain, Sutton suggests hy = 0.21, hz = 0.12 and n =0.25 this yields 0.875 y = 0.1485 x (6)

0.875 z= 0.0848 x (7)

In the same specified conditions, for a 50 metre elevated source, Sutton suggests hy = hz = 0.10 . Hence, in this instance

0.875 y = z = 0.0707 x (8)

The variation of vertical dispersion coefficients z with down wind distance (x) which is confirmed in Hosker graphs (1974)(18). The Gaussian model developed based on prartical application of current status dispersion in Hosker curves (Hosker, 1980)(19) and geospatial datasets which include annual means of wind speed and relative humidity to illustrate the terrain effects in urban as well as rural areas (Grynine, 1987)(20). The annual data on the frequency of wind speed and direction in different geographical location are available in monitoring station located in the latitude and longitude for monitoring meteorological parameters provided at the internet websites http://www.tutiempo.net/en/Climate/Egypt/EG.html.

Table (1): The weather stations along the northern coast of Egypt Altitude of Ground Weather Station Latitude Longitude From Sea Level (m) El Nouzha 31 12 29 57 (-02) El Dekheila 30 08 29 08 (+03) Borg El Arab 30 55 29 41 (+54) El Dabaa 30 56 28 28 (+17) Ras El Hekma 31 04 27 50 (+91) Marsa Matrouh 31 20 27 13 (+28) Sidi Barrani 31 38 25 58 (+21) El Salloum 31 32 25 11 (+04) Source is adopted from @http://www.tutiempo.net/en/Climate/Egypt/EG.html, accessed on Monday 24 March, 2008.

The concentration predicts emission distribution over the facility plant. Figure (3) overlay the dilution of pollutant from emission source in relative average concentration levels according to Sutton dispersal coefficients. Illustrative results of various meteorological conditions, mass of sources, wind velocity and position of the object are presented in Figure (3). In case of accidental gaseous releases, the area of hazards exposure is greater than 2.4 km2 downwind and crosswind direction. The widest plume dispersion is at distance 1 km downwind and about 800 m wide in wind velocity 2 m/sec and neutral meteorological conditions 9. 0.6

-0.6 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 Distance in km

Figure (3): Plume dispersion downwind direction in concentration contours lines

6. Elevation Effect on Dispersion The topographic feature effects are determined based on GIS datasets when critical loads included in spatial patterns to assess adsorption levels of pollution. The model determined pollutant boundaries along downwind direction for transmission in declared cumulative areas located in residential and/or industrial sectors. The plume is blocked by radiant heat and vertical gradient inversion of temperature. During daytime when the sun over the horizon, the solar radiation is warning the surface ground with other objects to affect gases dilution in the atmosphere and consequently concentrations around the facility site. The level of concentration is reflecting the sunlight radiation effects in summer higher than winter and varying transition distance during day time emission is longer than night time. This energy of solar radiation elevated surface temperature which influences dispersion coefficients and hence concentration levels in linear analysis of Gaussian plume model. This illustrated the variation crosswind with vertical dispersion coefficient along downwind distance x under varying atmospheric conditions for rural as well as urban terrain. The concentration levels in urban area may be up to 5 times greater than urban area to reflect greenhouse effect as traced in contour lines Figure (4) In rural areas: where open community nearby plant and the solar radiation has smaller effect on ground to prompts diffusion in air. The ground reflection effect is varying in urban side, the residential blocks elevated surface temperature where other building emit thermal radiation reflect warming obstacle pollutant dilution from distribution operations in atmospheric layers. These emissions affect plume diffusion over dominant lands to confirm greenhouse effect in urban area with other meteorology parameters. The flue gas emissions absorb some solar radiation at different wavelength and reflect changes in ozone layer of the atmosphere. Figure (4) compares the concentration levels in different land use patterns, the wider areas at slower density dispersion and higher elevation in urban side. The effect of terrain is reflecting temperature elevation in dispersion downwind direction. Spatial contours are denser in urban area with higher 10. limits than rural area as nomination of temperature gradient effect on dispersion coefficients of Sutton suggestion.

0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 0.6

-0.4 Urban Area Rural Area -0.6 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2

Figure (4): The concentration arc-line compares elevation effects downwind direction on urban and rural area

11. 7. Conclusion and Recommendation  In urban areas, during the day, surfaces are more reflective and become hotter and thus, producing more convective eddies. As a result, convective turbulence in an urban area is more significant than it is in the rural areas and rarely stable. These might reschedule manufacture activities over air stability conditions downwind direction of urban areas.  In the common application, technology has various methods to verify stability in physical properties of gaseous and characteristics chemical reactivity parameters. These elements have diverse regime to profile atmospheric conditions in adjustment relations to social and culture activities in community. The diversity of human ecosystems is reflected in correlation atmospheric stability by means of air pollution status factors that have general environment variables related to: o The environment in terms of climate, soil, and topographic feature, o Resources from the local soil and vegetation indeces, o Technology determined in the “extractive efficiency”, and o Architect design of buffering area for mitigation measures.  In regular societies, a uniform organization has a direct determinism in correlation population discernible levels of environmental resources analysis in a linear assembly. These variations have diverse categories in hyper action reveal a nonlinear assembly of historic culture with complex relation to population density and physical assertion of environmental resources. The supplement technology adjustment material transfer in quantitative assessment methods for balance actions. It has revised material and methods for engineering critical operation in standard model guidelines. However, partial process of different methods is defined air stability for analysis plume interaction in the environment. The linear algorithm is based on measurements methods of moderate elective theme which replaced in either peak or weak emission points. These have referred researcher to apply procedures controlling dispersion related with population exposure in general sources as o The quality of raw materials has to be improved in production process with specification to reduce residuals and increase the product life time o The monitoring with adequate frequency for area with adequate use of organic compounds in industry and agriculture. o The organic compounds applied in customer production have to be controlled from source and reduced to minimum. These support management of occupational exposure to gaseous emission in a schedule to minimize exposure-time and use advanced control system to mount mechanic operation with man-shield protective area. Other occupation of technical materials is handled in sealed container and control loading operation mechanically to reduce spillage or leakage applications in area. Finally, the technical monitoring of specific point and area sources emissions are reflecting the accuracy of estimated parameters of contaminants properties and contributing to reduce health hazards and predict precautions for health effect form toxic exposure limits.

12. 8. References for Further Reading

1) Hanna, S. R., G. A. Briggs and R. P. Hosker jr., " Handbook on Atmospheric Diffusion", U. S. Department of Energy, Technical Information Center, Springfield, VA, 1982. 2) Briggs G.A., "Discussion: chimney plumes in neutral and stable surroundings", Atmos. Envir., 6:507-510, 1972 3) Beychok, Milton R.. "Fundamentals of Stack Gas Dispersion", 4th Edition, author- published. ISBN 0-9644588-0-2, www.air-dispersion.com., 2005. 4) Finlayson-Pitts, B.J. and Pitts, Jr., J.N., "Atmospheric Chemistry: Fundamentals and Experimental Techniques", John Wiley & Sons, New York, 1098 p., 1986. 5) Bosanquet, C.H. and Pearson, J.L., "The spread of smoke and gases from chimneys", Trans. Faraday Soc., 32:1249, 1936. 6) 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. 7) Janicke L., Kost, W.D., and Röckle, R., "Modelling of Motor Vehicle Immissions in a Street System by Combination of Lagrange models and Surface Wind Field Simulation in Complex City Structures", Meteorol. Zeitschrift, N.F.3, pp. 172- 175, 1994. 8) Oetll, D., Almbauer, R.A., Sturm, P.J., "A New Method to Estimate Diffusion in Low Wind, Stable Conditions", J. Appl. Met. 40, pp. 259-268. 2001a. 9) Oettl, D., Kukkonen, J., Almbauer, R.A., Sturm, P.J., Pohjola, M., Härkönen, J., "Evaluation of A Gaussian and a Lagrangian Model Against A Roadside Dataset, with Focus on Low Wind Speed Conditions", Atmospheric Environment 35, pp. 2123-2132. 2001b. 10) Sodemann, H."Tropospheric transport of water vapour: Lagrangian and Eulerian perspectives", Diss. ETH No. 16623, 2006 11) Pasquill F., "The estimation of the dispersion of windborne material", The Meteorological Magazine, vol 90, No. 1063, pp 33-49, 1961. 12) Barrat, Rod, "Atmospheric Dispersion Modelling", (1st Edition ed.), Earthscan Publications. ISBN 1-85383-642-7, 2001. 13) Muhan M., Siddiqui T.A., “Analysis of Various Schemes for the Estimation of Atmospheric Stability Classification”, Atmospheric Environment, Vol. 32, pp. 3775-3781, 1998. 14) Pal Arya, “Air Pollution Meteorology and Dispersion”. Oxford University Press, Oxford. 1999. 15) Schenelle Karl B. and Dey Partha R., "Atmospheric Dispersion Modeling Compliance Guide", McGraw-Hill, ISBN 0-07-058059-6, 2000. 16) Turner D.B., "Workbook of Atmospheric Dispersion Estimates: An Introduction to Dispersion Modeling", 2nd Edition, CRC Press. ISBN 1-56670-023-X, www.crcpress.com., 1994 17) Sutton O.G., "The problem of diffusion in the lower atmosphere", QJRMS, 73:257, 1947 and "The theoretical distribution of airborne pollution from factory chimneys", QJRMS, 73:426, 1947

13. 18) Hosker R. P., IAEA-SA-181-19, p. 291-309, Vienna, 1974 19) Hosker, R. P., Practical applications of air pollutant deposition models – current status, data requirements and research needs. Proc. Internat. Conf. on Air Pollutants and their Effects on the Terrestrial Ecosystem, S. V. Krupa and A. H. Legge, eds. John Wiley and Sons, New York, ATDL Contribution, 80/8, NOAA, Oak Ridge, TN, 71 pp; 1980 20) Gryning, S.E., Holtslag, A.A.M., Irwin, J.S. and Sievertsen, B., "Applied Dispersion Modelling Based on Meteorological Scaling Parameters", Atmospheric Environment 21, pp. 79-89, 1987.

Moustafa Osman Nafd is environmental researcher at the EEAA, since graduation he realized the importance of integrating civil engineering with environmental issues for sustainable development. He has involved in several scientific projects at the UNEP and MEDPOL for management pollution and emission control. He has learned much about degradation prevention during operation stage. His research interests in modeling especially simulation air pollution dispersion with integration to GIS. These experiences provide a dynamic stability to switch work between objectives in different environment conditions and improve synthetic operations in system technology.

Waled B. Ibrahim is a professor in sanitation department, Faculty of civil Engineering at Alexandria University, He involved in different projects related to environmental rehabilitation of treatment plant. He devoted his research in biological treatment of wastewater for medium and small cities. He integrated modeling with technology for predicting environmental deterioration of wastewater disposal. His applications extend to layer control system in automation and replace real time data with periodical monitoring systems Medhat A. E. Moustafa is the Head of Sanitary Engineering Department, Faculty of Engineering, Alexandria University. He has involved in industrial pollution control system of wastewater plants. He applied a vast technique in integration mechanical and electrical equipment in treatment plant. His application extends to air emission control and monitoring of hot spot of large cities. The integration of operational and design stages increase his capabilities of control different parameters related to industrial pollution.