A PREDICTIVE MODEL FOR OZONE UPLIFTING IN OBSTRUCTION PRONE ENVIRONMENT

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1 International Journal of Civil Engineering and Technology (IJCIET) Volume 7, Issue 1, Jan-Feb 216, pp , Article ID: IJCIET_7_1_28 Available online at Journal Impact Factor (216): (Calculated by GISI) ISSN Print: and ISSN Online: IAEME Publication A PREDICTIVE MODEL FOR OZONE UPLIFTING IN OBSTRUCTION PRONE ENVIRONMENT Terry Henshaw Africa Center of Excellence, University of Port Harcourt, Rivers state, Nigeria Ify L. Nwaogazie Department of Civil and Environmental Engineering, University of Port Harcourt, Rivers State, Nigeria Vincent Weli Department of Geography and Environmental Science, University of Port Harcourt, Rivers State, Nigeria ABSTRACT A model for predicting uplifting of ozone gas in obstruction prone areas is developed. The model is dependent on ground level temperature, four metre height temperature, wind speed and solar radiationand the obstruction used in this research is an existing four metre fence wall. With data points established both inside and outside the fence wall, and four metre height above the ground level of the inside and outside positions, data were collected for five days at two hour intervals. The Buckingham s π-method of dimensional analysis was adopted to develop this model and collated field measurements were used to calibrate the model through regression. Results show that the model developed for Ozone uplifting attained a correlation coefficient of.996.verification of the model showed a correlation coefficient of.95 and a mean square error (MSE) of.7 between the predicted and observed ozone concentration. Detailed statistical sensitivity analysis carried out showed temperature as the most important meteorological parameter and solar radiation as the least important in case of pollutant uplifting. Verification of the modified model without the solar radiation term showed a correlation coefficient of.95 and a MSE of.7 between the predicted and observed ozone concentrations and this confirmed solar radiation as the least important meteorological parameter in obstruction environment.no 2, SO 2 and TSP showed poor correlation coefficients of.3,.45 and 6.2, respectively when uplifting models were calibrated and verified for them, they also showed statistically that ground level temperature is the most significant editor@iaeme.com

2 Terry Henshaw, Ify L. Nwaogazie and Vincent Weli meteorological parameter for pollutant uplifting in obstruction prone environment. Key words: Uplifting, Obstruction, Ozone, Air, Pollutant, Predictive model Cite this Article: Terry Henshaw, Ify L. Nwaogazie and Vincent Weli, A Predictive Model For Ozone Uplifting In Obstruction Prone Environment, International Journal of Civil Engineering and Technology, 7(1), 216, pp INTRODUCTION Air pollution models have generally failed to accurately predict concentration of pollutants (Lolymer, 211). Most of these failures have been attributed to environmental conditions for which these models were not developed to handle. Literature has recorded different models which have been developed for different environmental configuration. Some of these models have shown improvement when compared to Gaussian models (Henricheen, 1986; Stull, 1988). Most of the existing air pollution models can be classified as follows :(i)those that have incorporated wind speed and vertical eddy diffusivity as a power function of vertical height (Seinfeld, 1986; Lin and Hildemann, 1996); (ii) Those that have incorporated wind speed as a function of height and eddy diffusivity as a function of downwind distance (Sharan and modani, 26); (iii) Models that have incorporated wind speed as a function of vertical height and vertical eddy diffusivity as a function of both vertical height and downwind distance from the source (Sharan and Kumar, 29); and (iv) Models that have incorporated low wind and unbounded region (Anikender and Goyal, 213).Obstructions from artificial and natural facilities have also been attributed to failures of dispersion models in predicting pollutant concentration. These obstructions impede the flow from the direction of the source. Works have been carried out on different aspects of obstruction to pollutant dispersion. Works of Zhao-Lin and others (211) considered street canyons as the obstruction and concentrated on the effect on uneven heights of buildings given that earlier works had considered even heights. Works of Yucong and others (214) also considered street canyons but tried to use different street canyon configurations. Flow pattern inside the street canyon has been studied to be controlled by flow wind velocity, building shapes, atmospheric instability and height of building (Xie and others, 25; Niachou and others, 28; Hang and others, 21; Baik and others, 2; Ahmad and others, 25). As a result of these obstructions poor air quality has been recorded at pedestrian levels. Air recirculation has been seen to be the major reason why pollutants do not move in the wind ward direction (Depaul and Sheih, 1986; Oke,1988).Many methods have been used to investigate obstruction of pollutants dispersion where mostly street canyons have been used as the obstruction. These widely used methods are the in-situ measurements (Depaul and Sheih, 1986; Kumar and others, 29; Li and others, 27) and the computational fluid dynamic (CFD) simulations (Baik and others, 2; Yang and shao, 28; Murena and others, 29; Gu and others, 21; Balczo and others, 29).Works of Harisankar and Paruthuraj (21) used a hill slope as the obstruction to flow and results also showed pollutant recirculation but at the summit of the hill and this grows intense with steeper slopes editor@iaeme.com

3 A Predictive Model For Ozone Uplifting In Obstruction Prone Environment Left out of literature is a model to predict the concentration of pollutant at a height above ground level in obstruction prone areas and a sensitivity analysis to see which meteorological parameter plays the major role in vertical uplifting of pollutants in obstruction environments as it is obvious that wind speed is the major meteorological parameter responsible fordispersing air pollutants in non-obstruction environments (Andrej and others, 215; Seinfeld, 1986). Xie and others (25) observed the effect of solar radiation on pollutant dispersion in street canyons. They observed that the heating of the earth s surface causes some sort of buoyancy force which helps to disperse pollutants by reducing pollutant concentration in street canyons. Works of Henshaw and others (215) have observed surface solar radiation as high as 122 W/m 2 which is capable of heating the earth s surface to as high as 39 C in the southern part of Nigeria and this can be used to an advantage in case of obstruction to wind direction. The pollutant used to demonstrate this effect of uplifting is the ozone gas. Ground level Ozone is a secondary pollutant formed from nitrogen oxides and VOC s in the presence of sunlight, it is colorless and the impact to health on exposure is that it affects the human respiratory system especially the lungs. The World Bank group in 1998 has generalized short term concentrations to be within 3 8 µg/m 3 in urban regions. This work addresses the problem by using a 4 metre wall within a high activity area as the obstruction in the windward direction and considering meteorological parameters like wind speed, solar radiation and temperatures using the dimensional analysis approach. The use of the Buckingham s π-method in Dimensional Analysis has been recorded in literature as a means of deriving empirical models wherein calibrations are done with experimental data or field measurements via the linear or multiple regression analysis (Afangideh, 28). This work is aimed at developing a model which is capable of predicting ozone concentration above ground level in obstruction prone areas where the obstruction is in the direction of the wind. The obstruction used in this work is a four metre fence wall within a high activity area. The proposed model is dependent of ground level temperature, four metre height temperature, solar radiation and wind speed. 2. MATERIALS AND METHOD 2.1. Study Area The study area for this work is the section of Choba Park, one of the three campuses of the University of Port Harcourt, Nigeria. The exact location of Choba Park where measurements were made is within the small exit gate of the park. This section of Choba Park comprises of high commercial activities with respect to small kiosk for sale of snacks and business centers that do printing/photocopying jobs and sales of stationeries, etc. With the insufficiency in power supply these business own generators which are always left on when the central power supply is unavailable. Mostly students and lecturers do business in these areas and most of these business centers have been observed to run till 8. pm. Outside the Choba park premises is the Choba junction which is one of the busiest junctions in Port Harcourt because it serves as an exit towards the western region of Nigeria. Figures 1 and 2 represent the study area; Choba park (Figure 1) and enlarged position of pollutant measurement (Figure 2) editor@iaeme.com

4 Terry Henshaw, Ify L. Nwaogazie and Vincent Weli Figure 1 Study area showing observation point and weather station point Figure 2 Study area showing details of the observation point 2.2. Measuring Equipment The Equipment used for this work are as listed in Table 1 Table1 List of Equipment used for Ozone uplifting modeling S/N Equipment Number Purpose 1 Military compass 1 To determine the direction of the poles 2 Weather station 1 To measure meteorological parameters 3 Solar radiation meter 1 To measure solar radiation 4 Aeroqual gas monitor 4 To measure pollutant gases 2.3. Procedure A total of four locations were established for observation of ozone gas. Two of these locations are inside the Choba park premises by the fence and the other two outside the park premises by the fence. Both inside and outside locations comprise of ground and a four metre height point. The weather station was installed at 1 metres from the ground level. The observation of ozone was carried out for five days with a time interval of two hours. The solar radiation was measured hourly from sun rise at about 6. am to 9. pm for the 5-day duration editor@iaeme.com

5 A Predictive Model For Ozone Uplifting In Obstruction Prone Environment 2.4. Model Formulation and Development The technique used for model development is the Buckingham s π-method and the various parameters considered are presented in Table 2 with their corresponding symbols and dimensions. Table 2 Proposed model parameters for Ozone uplifting modeling S/N Variable Symbol Dimensions LMT Ѳ 1 Height H L 2 Wind speed v LT -1 3 Solar radiation I MT -3 4 Temperature at ground level T GL Ѳ 5 Temperature at four metre height above T H Ѳ ground 6 Pollutant concentration at ground level C GL ML -3 7 Pollutant concentration at four metre height C H ML -3 above ground Let the pollutant concentration, C H be a function of the six parameters listed in Table 2, viz C H = f (H, v, I, T H, T GL, C GL )..Equation (1) Where number of variables n = 7; m = number of standard units=4; and Number of π s according to Buckingham theory = n-m(7-4=3) Thus, Equation (1) can be rewritten as: F ( ) =.Equation (2) Selecting the repeating variables as H, v, I, and C GL, the π- models become: = (H, v, I,C GL,C H )...Equation (3) = (H, v, I,C GL,C GL ).. Equation (4) = (H, v, I,T GL,T H )...Equation (5) As a typical example, is evaluated by substituting the applicable dimensions (from Table 1) to Equation (3), to obtain Equation (6): =.Equation (6) By relating the constants a, b, and c to Length, L; Mass, M; Time, T and Temperature, Ѳ the resulting three simultaneous Equations were solved with the following results: a=,b=3,c=-1 and d=, respectively. Thus, Equation (6) becomes: =.. Equation (7) Adopting similar procedure we obtain the following results for Equations (4) and (5), respectively = Equation (8) =...Equation (9) Thus, Equation (2) can be rewritten as editor@iaeme.com

6 Terry Henshaw, Ify L. Nwaogazie and Vincent Weli =.Equation (1) or Let = Equation (11) Y= ; x 1 = and x 2 = ; That is, Y= or Ln Y = a Ln + b Ln + K.. Equation (12) 2.5. Model Calibration Using multiply regression software on Microsoft excel, the model (Equation 12) was developed for prediction of O 3 at four metre height above ground level. Table 3 presents field data from the observation of Ozone concentrations and estimation of x 1, x 2 and Y as in Equation (12). Table 4 presents a modified version of Table 3 by striking out all zeros, Table 5 shows the regression analysis summary and the calibrated model for the prediction of O 3 pollutant at 4 metre above ground height as per Equation (12) editor@iaeme.com

7 A Predictive Model For Ozone Uplifting In Obstruction Prone Environment S/N DAY GL 4m Table 3 Input Data Table SR WS CGL C4M x 1 x 2 Y Lnx 1 Lnx 2 LnY 1 MON #DIV/! 1.16 #DIV/! #DIV/! #DIV/! #NUM! #NUM! #NUM! #NUM! #NUM! #DIV/! #DIV/! #DIV/! #DIV/! #DIV/! 1 #DIV/! #DIV/! #DIV/! 1 TUE #DIV/! 1.16 #DIV/! #DIV/! #DIV/! #DIV/! #DIV/! #DIV/! #DIV/! #DIV/! 1 #DIV/! #DIV/! #DIV/! 19 WED #DIV/! 1 #DIV/! #DIV/! #DIV/! #DIV/! 1 #DIV/! #DIV/! #DIV/! #DIV/! #DIV/! #DIV/! #DIV/! 28 THUR #DIV/!.96 #DIV/! #DIV/! -.48 #DIV/! #NUM! #NUM! #NUM! #NUM! #DIV/! 1 #DIV/! #DIV/! #DIV/! #DIV/! #DIV/! #DIV/! #DIV/! 37 FRI #DIV/! #DIV/! #DIV/! #DIV/! #NUM! #NUM! #NUM! #NUM! SR- Solar Radiation; WS-Wind Speed; CGL Pollutant Concentration At Ground Level; C4M- Pollutant Concentration At Four Metre Height Level editor@iaeme.com

8 Terry Henshaw, Ify L. Nwaogazie and Vincent Weli Table 4 Modified Input Data Table S/N GL 4m SR WS PGL P4M X1 X2 Y LnX1 LnX2 LnY SUMMARY OUTPUT Table 5 Results from Multiple Regression analysis Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations 22 ANOVA df SS MS F Significance F Regression E-2 Residual Total Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.% Upper 95.% Intercept LnX E LnX editor@iaeme.com

9 A Predictive Model For Ozone Uplifting In Obstruction Prone Environment From Table 5, the parameters of Equation (12) are thus, substituted to yield Equation (13) as: OR Equation (13a) Equation (13b) Substituting the terms of Equation (11) into Equation (13) yields Equation (14) as: OR Equation (14a).. Equation (14b) OR Equation (14c) Equation (15) 2.5. Model Verification The model was verified by plotting the observed against predicted ozones (See Figure 3) and the correlation coefficient was obtained together with the mean square error (MSE). Table 6 shows observed and predicted Ozones with differences in error. Table 6 Observed and predicted Ozone concentrations S/N OBSERVED PREDICTED DIFF IN PERCENTAGE ERROR ERROR (%) E E E E E E E E editor@iaeme.com

10 predicted concentration. Terry Henshaw, Ify L. Nwaogazie and Vincent Weli S/N OBSERVED PREDICTED DIFF IN PERCENTAGE ERROR ERROR (%) E Estimating the Mean Square Error (MSE) The Computation of MSE value is obtained via Equation (15), viz: MSE =. Equation (16) where N is number of observations MSE = observed concentration. y =.9436x +.25 R² =.959 Linear (PREDICTED) Figure 3 Plot of observed against predicted Ozone concentration 2.7. Sensitivity Analysis A sensitivity analysis was carried out to estimate the significance of each meteorological parameter to the model developed as Equation (15). Table 7 presents wind speed, ground level temperature, four metre height temperature and solar radiation as they relate to ground level ozone and four metre height ozone concentrations. Tables 8,9,1 and 11 present regression summaries of the significance of each of the meteorological parameters related to pollutant uplifting. Table 7 Field meteorological parameters with ozone concentrations TEMP TEMP S/N WS SR PGL 4M editor@iaeme.com

11 A Predictive Model For Ozone Uplifting In Obstruction Prone Environment SUMMARY OUTPUT S/N WS SR TEMP 4M PGL P4M Table 8 Regression summary of significance of wind speed to Ozone uplifting Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations 43 ANOVA df SS MS F Significance F Regression E-5 Residual Total Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.% Upper 95.% Intercept WS PGL E editor@iaeme.com

12 Terry Henshaw, Ify L. Nwaogazie and Vincent Weli SUMMARY OUTPUT Table 9 Regression summary of significance of solar radiation to Ozone uplifting Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations 43 ANOVA df SS MS F Significance F Regression Residual Total Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.% Upper 95.% Intercept E SR 2.441E E E E-5-5E E-5 PGL E Table 1 Regression summary of significance of wind speed to Ozone uplifting SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations 43 ANOVA df SS MS F Significance F Regression E-5 Residual Total Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.% Upper 95.% Intercept GL PGL E editor@iaeme.com

13 A Predictive Model For Ozone Uplifting In Obstruction Prone Environment Table 11 Regression summary of significance of temperature at four metre height to Ozone uplifting 8 Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations 43 ANOVA df SS MS F Significance F Regression E-5 Residual Total Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.% Upper 95.% Intercept m PGL E Modified Equation and Verification Equation (15) is rewritten by removing the solar radiation parameter (See Equation 17). Table 12 shows new values of observed and predicted ozone values and these are used to estimate correlation coefficient by plotting observed against predicted ozone concentrations. Equation (17) Table 12 Observed and predicted Ozone concentrations POINTS OBSERVED PREDICTED DIFF IN ERROR PERCENTAGE ERROR (%) E E E E E E E E E E E editor@iaeme.com

14 predicted Terry Henshaw, Ify L. Nwaogazie and Vincent Weli POINTS OBSERVED PREDICTED DIFF IN ERROR PERCENTAGE ERROR (%) E E observed y =.9436x +.25 R² =.959 Linear (PREDICTED) Figure 4 Plot of observed against predicted Ozone concentration 3. DISCUSSION OF RESULTS The development of a model to predict ozone concentration in obstruction prone areas is presented as Equation (15). The model developed is obtained via dimensional analysis and the multiple linear regression was employed to calibrate it. This model achieves a correlation coefficient of.996 and shows a very high significance in the x 1 term made of wind speed, ground level ozone concentration and solar radiation. Verification of the model shows a correlation coefficient of.95 and MSE of.7 when the observed concentrations were plotted against the predicted. The maximum ozone gas measured from the Choba study area was.45mg/m and this lies within the range for urban areas (.3mg/m 3.8mg/m 3 ) as stated by the World Bank group in Detailed sensitivity analysis on individual meteorological parameters was carried out to evaluate the significance of each parameter on the uplifting of ozone. A summary of the results of significance is presented on Table 13. Because of the sensitivity in variation of air pollution concentrations 2% level of significance was selected for critical value of t-statistic. Temperature at ground level is the most significant meteorological parameter in pollutant uplifting. Though it has been established that thermal effects result mainly from the variation of solar heating of the ground (Xie, 25), surprisingly solar radiation shows no significance in pollutant uplifting. This is because the process of heating takes time and so the time a high solar radiation is measured would be different from the time pollutants start showing significant uplifting (See Figure 5) editor@iaeme.com

15 ozone conct. wind speed OZONE CONCENTRATION SOLAR RADIATION A Predictive Model For Ozone Uplifting In Obstruction Prone Environment Table 13 Results of Significance. S/N Parameter t-statistic t-critical (t.8 ) comment 1 Wind speed Significant 2 Solar radiation Non-Significant 3 Temperature at ground level Significant 4 Temperature at 4 m height Significant SAMPLE DAY RECORD SIGNIFICAN GROUND LEVEL OZONE FOUR METRE OZONE SOLAR RADIATION NUMBER OF OBSERVATIONS Figure 6 Plot showing solar radiation against ground level and four metre height ozone With the information obtained from Table 13, Equation (15) is rewritten with solar radiation removed and the verification of the modified model shows no reduction in the correlation coefficient which further confirms the irrelevance of solar radiation in the developed model. Figures 6 and 7 show a plot of the concentrations of ozone at ground level and four metre height measured on the field with corresponding wind speed and ground level temperatures. In agreement with the modeling carried out, peak values of wind speed and temperatures are associated with ground level uplifting (the case where the 4 metre height ozone concentrations are greater than the ground level concentrations) OZONE CONC. GL OZONE 4M WIND SPEED sampling number Figure 6 Field observations of average wind speed, ground and four metre height ozone concentrations editor@iaeme.com

16 PREDICTEDTSP ozone conct. ground level temp. Terry Henshaw, Ify L. Nwaogazie and Vincent Weli sampling number OZONE CONC. GL OZONE 4M GL Figure 7 Field observations of ground level temperature, ground and four metre height Ozone concentrations High temperatures and wind speed have been seen to facilitate vertical uplifting of ozone and this agrees with works of Xie (215), which considered heating of the earth s surfaces with small velocities of 1 & 2 m/s. In this work higher wind velocities and temperatures have shown increased pollutant uplifting (See to Figures 6 & 7). Other measured pollutants also confirmed temperature as the most important meteorological variable in pollutant uplifting when their significance are considered in relation to pollutant uplifting (See Table 14). Table 14 T-statistic for other pollutants on relating the significance of pollutant uplifting S/N VARIABLES NO CO TSP t-critical (t.8 ) t-statistic values 1 ground level Lastly, efforts to calibrate the general model (Equation 11) for other pollutants like NO 2, CO and TSP measured from the study area failed to produce high correlation coefficients (See Figures 8-1 and Table 15). This may be taken as an unexplained variation which can be attributed to the fact that ozone is a secondary pollutant and can be formed hundreds of kilometers from the source of emission(world Bank group, 1998) and primary pollutants do not have this property. The other measured pollutants still show trends of pollutant uplifting during periods of high temperature and wind speed (See Figures 11-16). OBSERVED VERSUS PREDICTED TSP 8 6 y =.566x R² = OBSERVED TSP Figure 8 Comparison of observed and predicted TSP editor@iaeme.com

17 TEMP/CONCT CONCT. PREDICTED NO2 PREDICTED CO A Predictive Model For Ozone Uplifting In Obstruction Prone Environment OBSERVED VERSUS PREDICTED CO y = x R² = OBSERVED CO Figure 9 Comparison of observed and predicted CO OBSERVED VERSUS PREDICTED NO y =.6825x R² = OBSERVED NO2 Figure 1 Comparison of observed and predicted NO GL TEMP 4 M CO POLLUTANT GL CO POLLUTANT SAMPLE NUMBER Figure 11 Field observations of ground level temperature, ground and four metre height CO concentrations editor@iaeme.com

18 TEMP NO2 CONCT. WS NO2 CONCT. CO CONCT/ WS CO CONCT. Terry Henshaw, Ify L. Nwaogazie and Vincent Weli SAMPLE NUMBER WS 4 M CO CONCT GL CO CONCT Figure 12 Field observations of average wind speed, ground and four metre height CO concentrations WS GL NO2 CONCT. 4 M NO2 CONCT SAMPLE NUMBER Figure 13 Field observations of average wind speed, ground and four metre height NO2 concentrations GL TEMP. GL NO2 CONCT M NO2 CONCT SAMPLE NUMBER Figure 14 Field observations of ground level temperature, ground and four metre height NO2 concentrations editor@iaeme.com

19 TSP CONCT. GL TEMP. TSP CONCT. WS A Predictive Model For Ozone Uplifting In Obstruction Prone Environment GL TSP CONCT. 4 M TSP CONCT. WS SAMPLE NUMBER Figure 15 Field observations of average wind speed, ground and four meter height TSP concentrations GL TSP 4 M TSP CONCT. GL TEMP SAMPLE NUMBER Figure 16 Field observations of ground level temperature, ground and four metre height TSP concentrations 4. CONCLUSION From the research carried out, the following conclusions can be drawn; 1. A model has been developed and calibrated for predicting ozone uplifting from ground level to a maximum of four metre height. The model yielded a correlation coefficient of The process of pollutant uplifting is generally facilitated by ground level temperature and wind speed velocity. 3. The most important meteorological parameter during ozone uplifting has been established statistically through test of significance to be ground level Temperature editor@iaeme.com

20 Terry Henshaw, Ify L. Nwaogazie and Vincent Weli 4. NO 2, CO and TSP also show similar trend in uplifting as temperature remains the most important variable. 5. Solar radiation though responsible for heating the earth s surface is found to have a negligible effect in the developed model. 6. The maximum amount of ozone measured from the study area (.45 mg/m 3 ) lies within the WHO range for urban ozone which is.3.8 mg/m The property of ozone that makes it form hundreds of kilometers away from any source is suspected to be the reason why it is the most responsive pollutant in calibrating the uplifting model. REFERENCES [1] Afangideh, C. (28): Comparative Study of constant Vacum Pressure Dimensional Filtration Equation for Compressible Sludge cakes. PhD thesis, Department of Civil Engineering, University of Nigeria, NSUKKA. [2] Ahmad, K., Khare, M., Chaudhry, K.(25): wind tunnel simulation studies on dispersion at urban street canyons and intersections- A review. Journal of Wind Engineering and industrial aerodynamics.93 (9): [3] Andrej, S., Radovan, S., Rffaele, A., Simon, B., Giuseppe, C.,Doron, E., Nedeljko, L., Marko, D., and Davorin, K. (215). Integrated air pollution dispersion simulation models and GIS for urban air pollution emergency management. 7 th Vienna conference on mathematical modeling, Vienna University of Technology, Austria. [4] Anikender, K. and Goyal, P.(213). Air Quality prediction of PM through an analytical dispersion model for Delhi. Taiwan association of aerosol research. ISSN: print/ online. [5] Balczo, M., Gromke, C. and Ruck, B. (29): Numerical modeling of flow and pollutant dispersion in street canyons with tree planting. Meteorological Zeitschrift 29; 18(2): [6] Baik, J., Park,R., Chun, H., and Kim, J. (2):A laboratory model of urban street canyon flows. Journal of Applied Meteorology. 39(9): pp [7] Dapaul, F., Sheih, C.,(1986): Measurement of wind velocities in a street canyon.atmospheric Environment:2(3): [8] Gu, Z.,Zhang, Y., and Lei, K.(21):Large eddy simulation of flow in a street canyon with tree planting under various atmospheric instability conditions. SCIENCE CHINA Technological Science 21; 53 (7): [9] Hang, J., Li, Y.,Sanberg, M., and Claesson, L.(21):wind conditions and ventilations in high rise long street models. Building and Environment. 45(6):pp [1] Harishankar, M. and Pruthvira, U. (21): Pollutant Dispersion in the Wake of a Hill: A Numerical Analysis. Journal of Environmental research and development. 21; 5(2): [11] Henshaw, T. Nwaogazie, L. and Weli, V. (215): Modelling surface solar radiation using a cloud depth factor. Int J Recent Sci Res. 6(1), pp [12] Hinrichsen, K. (1986). Comparison of four Analytical Dispersion models for near-surface releases above a grass surface. Atmos. Environ. 2:29-4. [13] Kumar, P., Garmory, A., Ketzel, M., Berkowicz, R. and Britter, R. (29): Comparative study of measured and modelled number concentration nanoparticles in an Urban street Canyon. Atmospheric Environment: 43 (4): editor@iaeme.com

21 A Predictive Model For Ozone Uplifting In Obstruction Prone Environment [14] Li, X., Wang, J., Tu, X.,Liu, W. and Huang, Z.(27):vertical variation of particle number concentration and size distribution in a street canyon in Shanghai, China. Science of the Total Environment: 378(3): [15] Lin, J. and Hildemann, L. (1996). Analytical solutions of the Atmospheric Diffusion Equation with multiple sources and height dependent wind speed and eddy diffusivity. Atmos. Environ. 3: [16] Lohmeyer, A. (21). Comparison of the procedures of different modelers for air pollutant concentrations prediction in a street canyon- the podbielski street exercise, [17] Murena, F.,Favale, G., Vardoulakis, S. and Solazzo, E. (29): Modeling dispersion of traffic pollution in a deep street Canyon: Application of CFD and operational models. Atmospheric Environment 29; 43(14): [18] Niachou, K., Livada, I., Santamouris, M. (28): Experimental study of Temperature and airflow distribution inside an urban street canyon during hot summer weather conditions. Part II: air flow analysis. Building and Environment. 43(8): [19] Oke, T. (1988): Design of urban canopy layer climate. Energy and Building: 11: [2] Stull, R.B. (1988). An Introduction to Boundary layer Meteorology, Kluwer Academic Publishers [21] Seinfeld, J. (1986). Atmospheric chemistry and physics of air pollution, Wileyinterscience, New York. [22] Sharan, M. and Modani, M. (26): A two-dimensional analytical model for the dispersion of air pollution in the atmosphere with a capping inversion. Atmos Environ. 4: [23] Sharan, M. and Kumar, P. (29): An analytical model for crosswind integrated concentration released from a continuous source in a finite Atmospheric boundary layer. Atmos Environ. 43: [24] The World Bank Group (1998) [25] Xie, X., Huang, Z., Wang, J., and Xie, Z. (25): The impact of solar radiation and street layout on pollutant dispersion in street canyon. Science direct. 4 (25) [26] Yang, Y. and Shao, Y. (28): Numerical simulations of flow and pollutant dispersion in urban atmospheric boundary layers. Environmental modeling and assessment: 23: [27] Yucong, M, Shuhua, L, Yijia, Z., Shu, W., and Yuan, Li. (214): Numerical study of Traffic pollutant dispersion within different street canyon configurations. Advances in meteorology. Volume 214 [28] Zhao-Lin, G., Yun-Wei, Z., Yan, C., and Shun-Cheng, L. (211): Effect of uneven building layout on air flow and pollutant dispersion in non-uniform street canyons. Building and Environment. 46 (211) editor@iaeme.com