CALIBRATION OF VEHICLE EMISSIONS-SPEED RELATIONSHIPS FOR THE GREATER CAIRO ROADS

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International Journal of Civil Engineering and Technology (IJCIET)
Volume 7, Issue 1, Jan-Feb 2016, pp. 74-82, Article ID: IJCIET_07_01_006
Available online at
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ISSN Print: 0976-6308 and ISSN Online: 0976-6316
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CALIBRATION OF VEHICLE EMISSIONS-
SPEED RELATIONSHIPS FOR THE
GREATER CAIRO ROADS
Ibrahim M. I. Ramadan, PhD
Civil Engineering Department, Faculty of Engineering at Shoubra,
Banha University, Cairo, Egypt
Naglaa kamal Rashwan, PhD
Civil Engineering. Department, Industrial Education College,
Beni-Suef, EGYPT
ABSTRACT
The air pollution in Cairo is a matter of serious concern. The air pollution
in greater Cairo is more than 10 to 100 times of acceptable world standards.
There is a wide range of speed variation in Cairo. Consequentially, there is a
wide range of emission rates. This research explains the relationship between
vehicle speed and emissions for small cars using field tests. The representative
car in this research is the Daewoo Lanus model 2000. This car is a
representative for most small modern cars in Egypt. The mobile emission
detector has been fixed on the car emission source. Tests have been
implemented in two roads: Salah Salem road and Auto strad road. More than
1000 readings have been taken from the detector at various speeds. The speed
varied between 0 and 85 km per hour and the relationships between speed and
four types of emissions have been studied. These emissions include Carbon
Mono oxide (CO), Carbon Dioxide (CO2), Nitrogen Oxide (NOx), and
hydrocarbons (HC).The above data has been transferred from the device
format into Excel sheet format for analysis.
In this research, the best relationship between speed and CO emission was
found to be a multinomial function with third degree. Similarly, the same
conclusion was found for relationship between speed and both CO2 and NOx.
In contrast, the best relationship between speed and HC is an exponential
function.
Authors recommend extending these experimental studies to include all
types of vehicles and to include all factors that affect emissions. These factors
include car model, ambient temperature, motor temperature, machine load,
road grade, and other factors.

Calibration of Vehicle Emissions-Speed Relationships For The Greater Cairo Roads
Key words: Vehicle Speed, Vehicle Emissions, Emission Modeling.
Cite this Article: Ibrahim M. I. Ramadan and Naglaa kamal Rashwan,
Calibration of Vehicle Emissions-Speed Relationships for the Greater Cairo
Roads, International Journal of Civil Engineering and Technology, 7(1),
2016, pp. 74-82.
http://www.iaeme.com/IJCIET/issues.asp?JType=IJCIET&VType=7&IType=1
1. INTRODUCTION
The World Health Organization reports that the Air Pollution in Downtown Cairo is
10-100 times what is considered a safe limit. Cairo is in the company of other Cities
like Mexico City, Bangkok, San Paulo, Delhi, Tokyo which are among the worst
Cities in the World in terms of air pollution. (Hassanein, Salah, 2015).
In Egypt, Traffic is responsible for 26% of the total emission of particulate matter
(PM10), 90% of carbon monoxide (CO), and 50% of nitrogen oxides (Hala Abu Ali,
2010).
The overall health impacts of air pollution in Greater Cairo, puts the costs
associated to air pollution at about 0.8 to 1 percent of Egypt’s GDP, and identifies
congestion as the main source of air pollution coming from transport (WB, 2014).
Travel speeds in Greater Cairo on corridors are in the range of 50 to 60 percent of
free flow speeds, while on local streets they could reach 20 to 30 percent (WB, 2014).
A key gap in our understanding of these emissions is the effect of changes in
vehicle speed and engine load on average emission rates for the on-road vehicle fleet.
Therefore, it is the objective of this paper which is the identification of the emission
rates at various speeds according to the operating condition in Egypt. In addition,
Models for various emissions will be estimated for the Egyptian conditions. This will
help transportation economist while estimating the benefits of suggested transport
projects.
The scope of this paper is the calculation of the emission rate in Greater Cairo
region especially through Salah Salem road and Auto strad road using Daewoo Lanus
2000 car at various speeds.
Therefore, this research composed of four parts in addition to this introduction.
Part two is a review of all the available researches that handled the relations between
speed and emissions. Part three explains the data collection program. Part four data
analysis has been introduced .Part five introduced summary, conclusion and
recommendations.
2 LITERATURE REVIEW
2.1. The effects of vehicle speed on emissions
Vehicle emissions and energy consumption are dependent on journey characteristics
(such as distance) and a number of different operating conditions such as the vehicle
type, occupancy, vehicle age, fuel type, engine temperature, travel speed and engine
size. In what follow a discussion on how speed can affect emissions and energy
consumption (Dr. S.P. Mahendra, 2010).
Hesham Rakha, and Yonglian Ding, 2003, confirmed that the HC emission rate
followed a convex function with unequal sides. i.e. It is higher for high speeds.
Specifically, the minimum HC emission rate was attained at a cruise speed of 55

Ibrahim M. I. Ramadan and Naglaa kamal Rashwan
km/hr, while the highest emission rate occurred at a cruise speed of 120 km/hr. The
CO emission rate was achieved at a speed of 20 km/hr, while the maximum rate was
reached at 120 km/hr. Similarly, NOx emission rate demonstrated a trend that was
consistent with CO emissions (Hesham Rakha, and Yonglian Ding, 2003).
Xiugang Li1, Lei Yu2, and Wei Wang, 2003, introduced figures that can explain
the relation between the average speed and the various emission items. Figure (1)
shows the relation between the vehicle speed and HC. It is clear that as the vehicle
speed increases, the HC will decrease for all types of vehicles.
Figure (1) The relation of computed HC emission factor with average speed (source:
Xiugang Li1, Lei Yu2, and Wei Wang, 2003).
Figure (2) explains the relation between the vehicle speed and the CO emissions.
It clear also that as the vehicle speed increases, the CO emission will decrease for all
types of vehicles.
Figure (2) The relation of computed CO emission factor with average speed (source:
Xiugang Li1, Lei Yu2, and Wei Wang, 2003).
The last figure introduced by Xiugang Li1, Lei Yu2, and Wei Wang, 2003, is the
relationship shown in figure (3) between the average speed and the NOx emissions. It
is clear from that relation that the effect of speed is not clear on all types of vehicles
except the heavy diesel vehicle. For that type of vehicles which operated by diesel
fuel, the NOx emissions are high at low speed. As the speed increases, the NOx
emission will decrease up till the certain value of speed (optimum speed), the
emissions begin to increase with the increasing of the vehicle speed.

Figure (3) The relation of computed NOX emission factor with average speed
(source: Xiugang Li1, Lei Yu2, and Wei Wang, 2003).
From the above review of previous researches, it can be concluded that there is no
clear relationship between speed and various emission. It differs from country to
another and from vehicle to other and so on.
2.2. Emission modeling
In order to facilitate investigations analyzing this situation, local authorities in
environmental protection and urban planning agencies are interested in performing
emission and air pollution simulation as well as scenario analysis by means of model
based simulation systems
Tetsuo YAI, et at, 2004, derived a model that relates the instantaneous speed with
the pm emissions in Tokyo taking into considerations the effect of road slop on the
PM emission value, as follows:
Ln(Epm(t))=-2.09+4.96*10-2
v(t)-7.45*10-4
v(t)2
+5.21*10-6
v(t)3
+0.116a(t)-4.65*10-
2
Dd(t)+7.09*10-2
Di(t)-0.188D~-2.5(t)-3.91*10-2
D-2.5~0.5(t)+0.133D0.5~2.5(t)+0.401D2.5~(t)
……..(1)
Where
(Epm(t)): The instantaneous particulate matter (PM2.5) discharge (g/min) at time t;
v(t): instantaneous speed (km/h) at time t;
a(t): instantaneous acceleration (km/h/s) at time t;
Dd(t): dummy variable (1 or 0) = 1 for deceleration speed and 0 otherwise at time t;
Di(t): dummy variable (1 or 0) = 1for idling and 0 otherwise at time t;
D~-2.5(t): dummy variable (1 or 0) = 1 when a slope is less than –2.5% and 0 otherwise
at time t;
D-2.5~0.5(t): dummy variable (1 or 0) = 1 when a slope is between –2.5% and -0.5 and 0
otherwise at time t;
D0.5~2.5(t): dummy variable (1 or 0) taken 1 when a slope is between 0.5% and 2.5%
and 0 otherwise at time t;
D2.5~(t): dummy variable (1 or 0) = 1when a slope is more than 2.5% and 0 otherwise
at time t;

Dr. S.P. Mahendra, 2010, has estimated a multiple regression equations developed
for the concentration of CO with various combinations of traffic and meteorological
parameters are as follows:
Y = 2.821 + 0.0020 X1- 0.086 X2- 0.304X3+ 0.087 X4+ 0.0027 X5 (2)
Where Y = Carbon monoxide concentration (mg/m3)
X1= Petrol driven vehicles
X2= Weighted spot speed of vehicles (kmph)
X3= Wind speed (m/sec)
X4= Air temperature (0C)
X5= Relative humidity (% age)
Han Xue,2013, the vehicle specific power (VSP) is one of the parameters most
close to the actual conditions, and it has been one of the core parameters of the next
generation mobile emission model. VPS denotes the ratio of the motor vehicle
output power and its quality (in kW/t). VPS combines parameters such as speed,
acceleration, slope, and wind resistance, so it can greatly improve the accuracy of the
fitting. denotes speed, denotes acceleration, and denotes a road gradient
expressed in radians, and the following functions are obtained:
NO = 2.0164 + [1.1 + 9.81 ( tan (sin )) − 0.0142] + 0.0001 2 + 0. 00000053 3,
CO = 167.154 + [1.1 + 9.81 ( tan (sin )) − 5.2911] + 0.0662 2 + 0.0003 3,
HC = 68.7252 1.1 +9.81( tan (sin )) −0.7760 (3)
Thus, the basic functional relationship between exhaust emission and speed has
been obtained. In order to more accurately reflect the actual urban road conditions of
Beijing,
The authors calculated the mean value of VSP form any types of motor vehicles.
If the accurate VSP of a particular type of vehicle is to be obtained, more samples are
needed to correct the specific parameters.
3. DATA COLLECTION
Data collection has been executed using mobile vehicle emission detector that has
been fixed on the car emission source. The test car used was a Daewoo Lanus Model
2000. This car is a representative for most small modern cars in Egypt. The car
emission has been measured while the car was running in two roads; Salah Salem
road and Auto strad road. The driver was driving the car with the average vehicles
speeds around the test car. The car speed has been ranged between 0 and 85 km per
hour which is almost the maximum speed in these roads. More than 1000 readings
have been taken between car speed and various emissions. These emissions include
Carbon Mono oxide (CO), Carbon Dioxide (CO2), Nitrogen Oxides (NOx), and
hydrocarbons (HC).
The above data has been transferred from the device format into Excel sheet
format for analysis.
4. DATA ANALYSIS
The collected data has been classified into groups in term of speed. The group length
is 5 km per hour. The first group is 0-5 and the last group is 80-85. The average value

of each emission type in each group has been calculated. For example, the average
value of CO emission for all speeds between 0 and 5 km per hour has been calculated
and recorded as the value of CO emission for speed group between 0-5 km per hour.
This has been repeated for all emission types. The resulted data has been utilized in
the analysis. In what follows, result of the analysis for each emission type is
presented.
4.1. Carbon monoxide (CO)
Figure (4) shows the plot of the carbon monoxide emission verses speed. It can be
concluded from this figure that the relation between CO emission and speed form a
convex function with unequal sides. It shows higher emission rate with higher speeds.
The maximum emission occurs at a value of speed about 40 km per hour. This
relationship seems logic because at value of 40 km per hour the car was in
acceleration.
Figure (4) Speed to CO emission relationship
The best nonlinear regression model between CO emission and speed has been
plotted and found as follows:
y = 0.0008x3
- 0.1477x2
+ 7.2387x + 0.6971……….(3)
R² = 0.7345
It is clear from equation (3) that the best relationship between speed and CO
emission is a multinomial function with third degree. The value of R2
is accepted.
4.2. Carbon dioxide (CO2)
Figure (5) shows the plot of the carbon dioxide emission verses speed. It can be
concluded from this figure that the relation between CO2 emission and speed form a
convex function with unequal sides. It shows higher emission with higher speeds. The
maximum CO2 emission occurs at a value of speed about 60 km per hour. This
acceleration.
The best nonlinear regression model between CO2 emission and speed has been
y = -0.0005x2
+ 0.0554x + 0.3017………..(4)
R² = 0.7477

It is clear from equation (4) that the best relationship between speed and CO2
emission is a multinomial function with second degree. The value of R2
is accepted.
Figure (5) Speed to CO2 emission relationship
4.3. Nitrogen oxides (NOx)
Figure (6) shows the plot of the Nitrogen oxides emission verses speed. It can be
concluded from this figure that the relation between NOx emission and speed form a
convex function with unequal sides. It shows higher emission with higher speeds. The
maximum CO2 emission occurs at a value of speed about 60 km per hour. This
acceleration.
The best nonlinear regression model between NOx emission and speed has been
y = -0.0046x2
+ 0.6223x - 1.3741………….(5)
R² = 0.7249
It is clear from equation (5) that the best relationship between speed and NOx
emission is a multinomial function with second degree. The value of R2
is accepted.
Figure (6) Speed to NOx emission relationship

4.4. Hydrocarbons (HC)
Figure (7) shows the plot of the Hydrocarbon emission verses speed. It can be
concluded from this figure that the HC emission increase with increasing speed. It is
clear that, HC emission increase with the upsurge of speed. The best nonlinear
regression model between HC emission and speed has been plotted and found as
follows:
y = 1.8541e0.0184x
……………..(6)
R² = 0.6346
It is clear from equation (6) that the best relationship between speed and HC
emission is an exponential function. The value of R2
is accepted.
Figure (7) Speed to HC emission relationship
5. SUMMARY, CONCLUSION, AND RECOMMENDATION
This research explains the relationship between vehicle speed and emissions for small
car using field tests. The representative car in this research is the Daewoo Lanus
model 2000. A mobile emission detector has been fixed on the car emission source.
Tests have been implemented in two roads: Salah Salem road and Auto strad road.
More than 1000 readings have been taken from the detector at various speeds. Speed
varied between 0 and 85 km per hour. The relationships between speed and four types
of emissions have been studied. These emissions are Carbon monoxide, Carbon
dioxide, Nitrogen oxides, and Hydrocarbons.
Literature proved that there is no fixed relation between speed and various
emissions. In this research, the best relationship between speed and CO emission was
found to be a multinomial function with third degree. Similarly, the same conclusion
was found for relationship between speed and both CO2 and NOx. In contrast, the
best relationship between speed and HC is an exponential function. Furthermore, the
following can be concluded:
 The maximum CO emission rate for the test car occurs at speed 40 km per hour,
 The maximum CO2 emission rate for the test car occurs at speed 60 km per hour.
 The maximum NOx emission rate for the test car occurs at speed 60 km per hour.
 HC emissions increase as the speed increases.
Authors recommend extending these experimental studies to include all types of
vehicles and to include all factors that affect emissions. These factors include car
model, ambient temperature, motor temperature, machine load, road grade, and other
factors.

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