1. Distribution of NO 2 concentrations over shooting (400 µg/m 3 per 1 hour) calculated with POLAIR dispersion model using (2004) NO 2 concentrations from the national monitoring network at Gomohria site 1Dr. Heba Adly

2. Annual mean NO 2 : observations versus predictions at some monitoring sites in Greater Cairo during 2004 2Dr. Heba Adly

3. The model results appear to over estimate considerably the majority of the observed NO 2 concentrations for all the sites marked as industrial urban and down town sites. The maximum over estimation is found in urban street canyons sites (Gomohria and Qulaly) by 20% and 12% respectively. Whereas NO 2 is over predicted in industrial urban site (Shoubra ElKhemia) by only 7%, NO 2 concentration is under predicted in the south of Greater Cairo sites described as industrial and road side (Tabbin and Fum ElKhalig) by 24% and 18% respectively. The results show that the model gives much better prediction in the northern area of Greater Cairo than the southern area and with least performance in downtown areas. 3Dr. Heba Adly

4. In general, NO2 concentration is over estimated by only 9% against the mean observed concentration. All the model comparisons for NO2 calculations indicate a satisfactory level of model performance and indicate that the model is able to describe the major features influencing the NO2 distribution across Greater Cairo. The model performance shows an accuracy of 90% 4Dr. Heba Adly

5. POLAIR dispersion model performance results for PM10 concentrations prediction using national monitoring stations network in Greater Cairo during 2004 Correlation coefficient, r 2 Fractional error % Normalized mean error % Mean error (µg/m 3 ) Ratio of means Calc/Obs Mean Observed (µg/m 3 ) Mean Calculated (µg/m 3 ) No. of OBS. Monitoring sites 0.7927.716.32.760.83169.6142.08759Shoubra 0.7234.0311.240.380.8893.882.68655Abassyia 0.7349.2515.871.040.84230.6194.08655Qualaly 0.5723.1337.561.420.62133.183.13360Gomohria 0.4351.8152.291.31.52202.9309.08698 Fum El Khalig 0.6531.6014.640.280.8567.657.78764Maadi 0.6026.7123.560.690.76103.178.88764Tabbin 5Dr. Heba Adly

6. Annual mean PM10: observations versus predictions at some monitoring sites in Greater Cairo during 2004 6Dr. Heba Adly

7. The model results appear to under estimate the majority of the observed PM10 concentration for all the examined sites. The performance annual statistics for PM10 concentrations at Greater Cairo sites indicate an overall under prediction by 17%. That means the model performance shows conformity of 83% for PM10 predictions. The under prediction of PM10 is clearly shown in mostly of Cairo sites. The north of Cairo site described as urban industrial site is under predicted by 17% (Shoubra ElKhemia) and between 15%-23% in south of Cairo industrial and residential sites (Tabbin and Maadi), while in Cairo center areas which is described as street canyon, the under prediction is 16%, 37%, 14% (Qualaly, Gomhria and Abassyia) respectively. 7Dr. Heba Adly

8. POLAIR dispersion model performance results for CO concentrations prediction using national monitoring stations network in Greater Cairo during 2004 Correlation coefficient, r 2 Fractional error % Normalized mean error % Mean error (mg/m 3 ) Ratio of means Calc/Obs Mean Observed (mg/m 3 ) Mean Calculated (mg/m 3 ) No. of OBS. Monitoring sites 0.7119.700.0917.930.826.35.178579Gomohria 0.7631.10.0814.420.857.05.998698 Fum El Khalig 8Dr. Heba Adly

9. Distribution of CO concentrations over 8-hours in µg/m 3 calculated with POLAIR dispersion model using (2004) CO concentrations from the national monitoring network at Gomohria site 9Dr. Heba Adly

10. Distribution of CO concentrations over shooting (10 mg/m 3 per 8-hours) calculated with POLAIR dispersion model using (2004) CO concentrations from the national monitoring network at Fum El Khalig site 10Dr. Heba Adly

11. The model shows an overall under prediction for CO concentration by 16% in the examined sites. The under prediction is shown in both of the measured sites described as street canyon and road side sites (Gomohria and Fum ElKhalig) by 18% and 14% respectively. The model performance for CO predictions shows an accuracy of 92%. 11Dr. Heba Adly

12. Model performed well for SO 2, NO 2 and PM10 predictions for distances far from the monitoring sites (200 m- 2000 m) with conformity of 76% for SO 2, and an accuracy of 82% for NO 2, and 85% for PM10. 12Dr. Heba Adly

13. The model performance appears to predict the pollutants concentrations at urban areas with an accuracy of 92% for PM10 at Maadi site, while the model performance was much less at industrial and down town areas with an accuracy of 77% for PM10 and NO 2 at Tabbin site, whereas the model accuracy was 86% for PM10 and NO 2 at Qulaly sites. 13Dr. Heba Adly

14. Model Evaluation Comparison of Model performance evaluations conducted by other modeling groups Non EPA- Gaussian Modeling Studies EPA Gaussian Modeling (EPA,2005c) Polair Model 2004 Parameter Seigneru, 2003Zhang, 2003Tonnesen, 2003 Morris et al.,2003 Baylan,2004 FE % ME (µg/m 3 ) FE % ME (µg/m 3 ) FE % ME (µg/m 3 ) FE % ME (µg/m 3 ) FE % ME (µg/m 3 ) FE % ME (µg/m 3 ) FE % ME (µg/m 3 ) 47-18-471251.5302440.6750.731.4SO 2 44-27-952.4--551420.3644.560.12NO 2 503.333-451450- 548.546.9638.1513.1PM 10 301.288-85-35-----34.491.07CO (mg/m 3 ) 14Dr. Heba Adly

15. Conculsion & Recomendation 15Dr. Heba Adly

16. 1. In conclusion, this study showed the importance of using air dispersion modeling in air quality management in Greater Cairo. As the model predictions performance results showed a great confidence for using dispersion modeling providing a scientifically credible approach for assessing air pollutants in Cairo air quality. 2. The modeling as a modern technological tool is useful in the development of air quality management system for an efficient approach for continuous improvement of air quality status. 16Dr. Heba Adly

17. 3. The advanced modeling capabilities of dispersion models are highly expected to be beneficial for environmentalist, planners and decision makers so that they can reliably generate a strategy for air pollution control that can be achieved within four phases. First phase includes monitoring, second modeling, third development of decision support tool and last phase is the execution. 17Dr. Heba Adly

18. 4. The study also showed the importance of using air dispersion modeling as an application of an interpolation procedure for the estimation of the concentration of an un-sampled location using values at sampled locations such as monitoring stations. 18Dr. Heba Adly

19. 5. Air pollutants concentration modeling is the assessment of potential patterns of exposure to specific events and episodes such as the atmospheric phenomena which greater Cairo area is always exposed to in autumm represented in wind stillness and the dropping of the thermal change layer level, induced by the obstruction of pollutants and their dispersion causing sever pollution episodes in the troposphere. 19Dr. Heba Adly

20. 6. The air dispersion modeling can highly help in determining the location of new monitoring station which can in turn help in providing a more accurate interpolated surface for future air quality assessments. 20Dr. Heba Adly

21. Still air dispersion modeling used for assessing air quality management facing some difficulties. There is a need for improved parameterizations in practical models which may be based on advanced building- resolving numerical models and measurements. There remains a basic in-compability between output derived from traffic counts and models, and the basic traffic input requirements of both emission and dispersion models. Emission and dispersion models still require disaggregated data not just by vehicle type (for example, passenger cars, vans, heavy goods vehicles) but by fuel type, engine size and most significantly by vehicle age. These parameters combined with vehicle speed that are used to characterize the emissions associated with individual vehicles. The translation of these counts into corresponding emission categories undoubtedly introduces additional certainty. 21Dr. Heba Adly