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

Transcription

1 International Journal of Civil Engineering and Technology (IJCIET) Volume 7, Issue 1, Jan-Feb 2016, pp , Article ID: IJCIET_07_01_006 Available online at Journal Impact Factor (2016): (Calculated by GISI) ISSN Print: and ISSN Online: IAEME Publication 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 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 editor@iaeme.com

2 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 INTRODUCTION The World Health Organization reports that the Air Pollution in Downtown Cairo is 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 editor@iaeme.com

3 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 editor@iaeme.com

4 Calibration of Vehicle Emissions-Speed Relationships For The Greater Cairo Roads 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 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(E pm (t))= *10-2 v(t)-7.45*10-4 v(t) *10-6 v(t) a(t)-4.65*10-2 D d (t)+7.09*10-2 D i (t)-0.188d ~-2.5 (t)-3.91*10-2 D -2.5~0.5 (t)+0.133d 0.5~2.5 (t)+0.401d 2.5~ (t)..(1) Where (E pm (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; D d (t): dummy variable (1 or 0) = 1 for deceleration speed and 0 otherwise at time t; D i (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; D 0.5~2.5 (t): dummy variable (1 or 0) taken 1 when a slope is between 0.5% and 2.5% and 0 otherwise at time t; D 2.5~ (t): dummy variable (1 or 0) = 1when a slope is more than 2.5% and 0 otherwise at time t; 77 editor@iaeme.com

5 Ibrahim M. I. Ramadan and Naglaa kamal Rashwan 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 = X X X X 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 = [ ( tan (sin )) ] , CO = [ ( tan (sin )) ] , HC = ( tan (sin )) (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 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 The average value 78 editor@iaeme.com

6 Calibration of Vehicle Emissions-Speed Relationships For The Greater Cairo Roads 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 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 = x x x (3) R² = 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 R 2 is accepted 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 relationship seems logic because at value of 60 km per hour the car was in acceleration. The best nonlinear regression model between CO2 emission and speed has been plotted and found as follows: y = x x (4) R² = editor@iaeme.com

7 Ibrahim M. I. Ramadan and Naglaa kamal Rashwan 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 R 2 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 relationship seems logic because at value of 60 km per hour the car was in acceleration. The best nonlinear regression model between NOx emission and speed has been plotted and found as follows: y = x x (5) R² = 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 R 2 is accepted. Figure (6) Speed to NOx emission relationship 80 editor@iaeme.com

8 Calibration of Vehicle Emissions-Speed Relationships For The Greater Cairo Roads 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 = e x..(6) R² = It is clear from equation (6) that the best relationship between speed and HC emission is an exponential function. The value of R 2 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 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 editor@iaeme.com

9 Ibrahim M. I. Ramadan and Naglaa kamal Rashwan REFERENCES [1] Hassanein, Salah, 2015: "Air Pollution in Cairo - The Cost". Arab world books. [2] Hala abu ali, 2010: Regional Workshop on Economic Incentives and Environmental Regulation in the MENA Region Beirut, June, 2010 [3] World Bank, 2014: Cairo traffic congestion study, World bank [4] D. Stead, 1999: Relationships between transport emissions and travel patterns in Britain, Transport Policy 6 (1999) , Pergamon, UK. [5] N. Johnstone, K. Karousakis, 1999: Economic incentives to reduce pollution from road transport: the case for vehicle characteristics taxes, Transport Policy 6 (1999) [6] Hesham Rakha, and Yonglian Ding, 2003: Impact of stops on vehicle fuel consumption and emissions, Journal of transportation engineering, ASCE, USA. [7] Xiugang Li1, Lei Yu2, and Wei Wang, 2003: Derivation of Emission Factors for Nanjing, China Using MOBILE5, TRB 2003 Annual Meeting CD-ROM [8] Tetsuo YAI, et at, 2004: Integrated Modeling system of traffic and air quality for wide area network using microscopic simulation, TRB 2004 Annual Meeting CD-ROM [9] K. Kishore Kumar, M.siva Krishna, D.RAVITEJ and D.Bhavana, Design of Automatic Guided Vehicles, International Journal of Mechanical Engineering and Technology, 3(1), 2012, pp [10] Prof. Dr. Z. Łukasik Phd Waldemar Nowakowski and Phd Aldona Kuśmińska- Fijałkowsk, Asynchronous Drive Control of A Traction Vehicle Using TCMS System, International Journal of Mechanical Engineering and Technology, 3(1), 2012, pp [11] Dr. S.P. Mahendra, 2010: Air Quality Deterioration Due To Road Traffic, TRB 2010 Annual Meeting CD-ROM [12] Han Xue, 1 Shan Jiang, 2 and Bin Liang3, 2013: "A Study on the Model of Traffic Flow and Vehicle Exhaust Emission", Hindawi Publishing Corporation, Mathematical Problems in Engineering Volume 2013, Article ID editor@iaeme.com