Distribution of NO2 concentrations over shooting (400 µg/m3 per 1 hour)

calculated with POLAIR dispersion model using (2004) NO2 concentrations

from the national monitoring network at Gomohria site

1 Dr. Heba Adly

Annual mean NO2: observations versus predictions at some monitoring sites in Greater Cairo during 2004

0

10

20

30

40

50

60

70

80

90

Tabbin FumElKhalig Gomohria Qulaly Shoubra ElKemia

Monitoring Sites

NO

2 co

nce

ntr

atio

n(µ

g/m

3)

NO2 observations ((µg/m3)

NO2 predictions (µg/m3)

2 Dr. Heba Adly

• The model results appear to over estimate considerably the

majority of the observed NO2 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 NO2 is over predicted in industrial urban site (Shoubra

ElKhemia) by only 7%, NO2 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.3 Dr. Heba Adly

• 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%

4 Dr. Heba Adly

POLAIR dispersion model performance results for PM10 concentrations prediction using

national monitoring stations network in Greater

Cairo during 2004 Monitoring

sitesNo. of OBS.

Mean Calculated

(µg/m3)

Mean Observed (µg/m3)

Ratio of means

Calc/Obs

Mean error

(µg/m3)

Normalized mean error%

Fractional error%

Correlation coefficient, r2

Shoubra8759142.0169.60.832.7616.327.70.79

Abassyia865582.693.80.880.3811.2434.030.72

Qualaly8655194.0230.60.841.0415.8749.250.73

Gomohria336083.1133.10.621.4237.5623.130.57

Fum El Khalig

8698309.0202.91.521.352.2951.810.43

Maadi876457.767.60.850.2814.6431.600.65

Tabbin876478.8103.10.760.6923.5626.710.60

5 Dr. Heba Adly

Annual mean PM10: observations versus predictions at some monitoring sites in Greater

Cairo during 2004

0

200

400

600

800

1000

1200

1400

Tabbin Maadi FumElKhalig Gomohria Qulaly Abassyia ShoubraElKemia

Monitoring sites

PM

10

co

nc

en

tra

tio

n(µ

g/m

3

PM10 observations ((µg/m3)

PM10 predictions (µg/m3)

6 Dr. Heba Adly

• 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.

7 Dr. Heba Adly

POLAIR dispersion model performance results for CO concentrations prediction using national

monitoring stations network in Greater Cairo during 2004

Monitoring sitesNo. of OBS.

Mean Calculated

(mg/m3)

Mean Observed (mg/m3)

Ratio of means

Calc/Obs

Mean error

(mg/m3)

Normalized mean error

%

Fractional error%

Correlation coefficient, r2

Gomohria85795.176.30.8217.930.0919.700.71

Fum El Khalig

86985.997.00.8514.420.0831.10.76

8 Dr. Heba Adly

Distribution of CO concentrations over 8-hours in µg/m3 calculated with POLAIR dispersion model using (2004) CO concentrations from the national

monitoring network at Gomohria site

9 Dr. Heba Adly

Distribution of CO concentrations over shooting (10 mg/m3 per 8-hours) calculated with POLAIR dispersion model

using (2004) CO concentrations from the national monitoring network at Fum El Khalig site

10 Dr. Heba Adly

• 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%.

11 Dr. Heba Adly

• Model performed well for SO2, NO2 and PM10 predictions for distances far from the monitoring sites (200 m- 2000 m) with conformity of 76% for SO2, and an accuracy of 82% for NO2, and 85% for PM10.

12 Dr. Heba Adly

• 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 NO2 at Tabbin site, whereas the model

accuracy was 86% for PM10 and NO2 at Qulaly

sites.

13 Dr. Heba Adly

Model Evaluation

Comparison of Model performance evaluations conducted by other modeling groups

Parameter

Polair Model 2004

EPA Gaussian Modeling (EPA,2005c)

Non EPA- Gaussian Modeling Studies

Baylan,2004Morris et al.,2003

Tonnesen, 2003Zhang, 2003Seigneru, 2003

ME (µg/m3)

FE%

ME (µg/m3)

FE%

ME (µg/m3)

FE%

ME (µg/m3)

FE%

ME (µg/m3)

FE%

ME (µg/m3)

FE%

ME (µg/m3)

FE%

SO21.450.730.67442301.525147-18-47

NO20.1244.560.3642155--2.495-27-44

PM1013.138.156.9648.54550-501445-333.350

CO (mg/m3)1.0734.49-----35-85-881.230

14 Dr. Heba Adly

Conculsion & RecomendationConculsion & Recomendation

15 Dr. Heba Adly

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.

16 Dr. Heba Adly

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.

17 Dr. Heba Adly

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.

18 Dr. Heba Adly

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. 19 Dr. Heba Adly

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.

20 Dr. Heba Adly

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. 21 Dr. Heba Adly