<<

A Thesis

entitled

Real-Time Exhaust Emission Analysis on Public Buses Equipped with

Different Exhaust Control Systems.

by

Ravi Shankar Viyyuri

Submitted to the Graduate Faculty as partial fulfillment of the requirements for the

Master of Science Degree in Civil Engineering

______Dr. Ashok Kumar, Committee Chair

______Dr. Dong Shik Kim, Committee Co-Chair

______Dr. Liangbo Hu, Committee Member

______Dr. Amanda Bryant-Friedrich, Dean College of Graduate Studies

The University of Toledo

May 2018

Copyright 2018, Ravi Shankar Viyyuri

This document is copyrighted material. Under copyright law, no parts of this document may be reproduced without the expressed permission of the author. An Abstract of

Real-Time Emission Analysis on Public Transport buses Equipped with Different Exhaust Control Systems.

by

Ravi Shankar Viyyuri

Submitted to the Graduate Faculty as partial fulfillment of the requirements for the Master of Science Degree in Degree in Civil Engineering

The University of Toledo May 2018

The main objective of this experimental thesis was to present a comprehensive analysis of exhaust emissions from transit buses during daily routine operations. The monitored in this study are Particulate Matter (PM), NOx, CO2, and HC released from three different buses with different exhaust control systems such as NON-EGR bus,

EGR- bus with EGR+DPF+DOC and hybrid bus with EGR+DPF+DOC+SCR. All these buses were tested on the same route each day. To further categorize and elaborate our findings, the runtime was divided into both and running conditions.

With a specific end goal to accomplish extensive outcomes, the idle condition was additionally divided into two distinct cases, i.e., cold idle and hot idle conditions. The running conditions were also divided into acceleration, deceleration, variable speed, and intersections. The NOx, CO2 and HC emission were gathered and analyzed for every one of the conditions and modes depicted above. The particulate emission was collected and analyzed in idle conditions.

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In idle condition NOx, CO2 and HC decrease with time and stay constant after they reach 15 minutes of idle time. The cold idle emissions are observed to be very high when compared to the hot idle condition, this is because the hot idle emissions are collected after the bus gets back to the garage from its daily route with a hot and this delivers the appropriate amount of into the engine for complete . Whereas cold idle mode does not run at its optimum temperature that leads to incomplete combustion and increases in emission formation. The NOx and HC emissions decreased from NON-EGR to EGR to the hybrid bus because of the emission control systems: SCR, DOC, and EGR,

Whereas CO2 emissions, increase by using the same emissions control systems from NON-

EGR to EGR to the hybrid bus.

The study shows that hybrid bus emits less amount of NOx when compared to EGR and NON- EGR buses, this is because of the exhaust control system SCR. SCR was used to minimize NOx emissions in the exhaust gas by using (NH3) as the reductant.

This exhaust/ammonia mixture passes through the SCR catalyst, where the oxides of are turned into nitrogen and water. Moreover, CO2 emissions were high in the hybrid bus then compared to EGR and NON-EGR buses. This was because of chemical reaction that takes place in SCR and DOC releasing CO2 as a product. Whereas, the amount of HC emitted from EGR and hybrid buses were almost in the same range, but very low when compared to the NON-EGR bus. This study also shows that NOx, CO2, and HC are directly proportional to speed (RPM) of the bus.

The collected NOx, CO2, and HC are analyzed to develop a statistical regression model using Lasso and Extra Tree Regression techniques that can predict the concentrations of the exhaust emissions, with respect to different running modes and idle iv

modes for transit buses equipped with EGR+DPF+DOC, EGR+SCR+ systems, and a NON-EGR bus. This regression analysis identified the relation and impact of engine variables on pollutant levels. In this study, Extra Tree Regression and Lasso Regression techniques were used to testify the data. The parameters used for prediction of CO2, HC, and NOx emissions in the idle mode were engine temperature and time. Whereas, in running mode rpm was used to predict exhaust emissions. Therefore, Extra Tree

Regression was proved as the best regression technique because of its accuracy and low

RMSE values when compared to Lasso.

Particulate Matter analysis was conducted on all three buses using a catch can instrument and quartz filter papers. Energy Dispersive Spectroscopy (EDS) and

Inductively Coupled Plasma Mass Spectroscopy (ICP-MS) was performed on the particulate matter. In EDS analysis, it was observed that the NON-EGR bus samples seem to have a high carbon content detected when compared to EGR bus and Hybrid bus. This was expected because of lack of EGR and DPF systems on NON-EGR bus. Moreover,

EGR and Hybrid buses recorded literally the same amount of carbon content, this could be because of having the same exhaust control systems: EGR and DPF.

This study and analysis of exhaust emissions for different exhaust control system equipped buses, will assist the manufacturers and regulators of air in selecting the appropriate exhaust control system equipped bus for emission control strategies in their area.

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I dedicate this work to my family and my wife Anupama Viyyuri.

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Acknowledgments

First and foremost, I would like to express my deep gratitude to my advisor and thesis committee chairman, Dr. Ashok Kumar for all the kind and enthusiastic motivation, support and guidance that he has provided me with over the course of my masters.

I would like to offer my special thanks to my co-advisor, Dr. Dong Shik Kim for his valuable and constructive suggestions during the period of my research work. His willingness to give his time so generously has been very much appreciated.

I thank Dr. Liangbo Hu for being my committee member and for his benevolent and gracious guidance.

I would like to thank TARTA authority for the alternate fuel grant that funded this research. I would also like to express my deepest gratitude to David Palmar, Deputy Director of Maintenance at TARTA for helping me with the experimental procedure, and his great knowledge on diesel helped to learn deep about emissions. I am indebted to Isha

Muthreja for helping me throughout my thesis documentation and analysis, and Manideep

Yarlagadda for guiding me through my experimental and analysis procedure. I thank Sai

Kameshwar and Ruthwik Junuthula for helping me in developing the Python code. I also thank

Rahul Harsha Thati, Suman Sapkota, Sudheer Kumar, Hamid, Thilak Therala, Anil Penumatsa,

Abhiram Bandreddy and Venkatesh Chalasani for helping me with the Documentation. Most importantly, I would like to thank my brother Kiran Chand Viyyuri and my family for their continuous love and support. vii

Table of Contents

Abstract ...... iii

Acknowledgments...... vi

Table of Contents ...... viii

List of Tables ...... x

List of Figures ...... xi

List of Abbreviations ...... xii

1 Introduction….…………………………………………...... …………………….1

2 Literature Review...... 10

2.1 Health Effects Caused by Vehicle Emissions ...... 10

2.2 Diesel Vehicle Emission Regulations ...... 13

2.2.1 National Ambient Air Quality Standards (NAAQS) ...... 13

2.2.2 Fuel Standards ...... 14

2.2.3 Control Systems ...... 16

2.2.3.1 Diesel Oxidation Catalyst (DOC) ...... 16

2.2.3.2 Exhaust Gas Recirculation (EGR) ...... 17

2.2.3.3 Selective Catalytic Reduction (SCR) ...... 18

2.2.3.4 Diesel Particulate Filter (DPF)...... 19

2.3 Background on Emission Studies ...... 19

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2.4 Summary ...... 29

2.5 Findings and Objectives ...... 31

3 Methodology ...... 33

3.1 Transit bus Fleet Characteristics ...... 33

3.2 Test Fuel ...... 34

3.3 Test Route ...... 36

3.4 Instrumentation ...... 37

3.5 Experimental Study Design ...... 40

3.6 Data Analysis ...... 43

4 Results and Discussion ...... 48

4.1 Analysis of NOx, CO2, HC Emission data ...... 48

4.1.1 Idle Conditions ...... 49

4.1.1.1 Cold Idle...... 54

4.1.1.2 Hot Idle ...... 56

4.1.2 Running Conditions ...... 58

4.1.2.1 Acceleration Mode ...... 60

4.1.2.2 Variable Speed Mode ...... 65

4.1.2.3 Deceleration ...... 68

4.1.2.4 Intersections ...... 73

4.1.3 Analysis of NOx, CO2 and HC Emissions using Regression

Method ...... 77

4.1.3.1 Cold Idle Mode Analysis ...... 79

4.1.3.2 Hot Idle Mode Analysis ...... 80 ix

4.1.3.3 Acceleration Mode Analysis ...... 83

4.1.3.4 Variable Speed Mode Analysis ...... 86

4.1.3.5 Deceleration Mode Analysis ...... 88

4.1.3.6 Intersection Analysis ...... 91

4.1.4 NOx, CO2 and HC emission prediction model ...... 95

4.2 Elemental analysis of Particulate Matter Samples ...... 107

4.2.1 EDS/SEM Analysis Results ...... 112

5 Conclusion………………………………………………….…………… ...... 119

5.1 Future Works ...... 123

References ...... 124

A Regression Analysis on 802-EGR bus in Cold Idle Mode ...... 131

B Regression Analysis on 610 NON- EGR bus in Cold Idle Mode ...... 134

C Regression Analysis on 802-EGR bus in Hot Idle Mode ...... 137

D Regression Analysis on 610 NON- EGR bus in Hot Idle Mode...... 140

E Regression Analysis on 802-EGR bus in Acceleration Mode ...... 143

F Regression Analysis on 610 NON- EGR bus in Acceleration Mode ...... 146

G Regression Analysis on 802-EGR bus in Variable Speed Mode ...... 149

H Regression Analysis on 610 NON- EGR bus in Variable Speed Mode ...... 152

I Regression Analysis on 802-EGR bus in Deceleration Mode ...... 155

J Regression Analysis on 610 NON- EGR bus in Deceleration Mode ...... 158

K Regression Analysis on 802-EGR bus in at Intersections ...... 161

L Regression Analysis on 610 NON- EGR bus at Intersections ...... 164

x

List of Tables

2.1 Environmental and health effects caused by the exhaust emissions ...... 12

2.2 HD emissions standards ...... 14

2.3 NAAQS Table ...... 15

2.4 Environmental Impacts of Alternative ...... 22

2.5 Factors Influencing Vehicle Emissions ...... 24

2.6 Percentage share of literature report ...... 25

3.1 Engine specifications of the transit buses tested ...... 34

3.2 Properties of test Fuel ...... 35

3.3 Specifications of MEXA 584L Portable Emission Analyzer ...... 40

4.1 CO2, HC, and NOx emission prediction errors for all the cases of the bus fleet ...93

4.2 ICP-MS results of the composition of blank quartz filter paper ...... 108

4.3 ICP-MS results of PM samples (610 bus)...... 110

4.4 ICP-MS results of PM samples (802 bus)...... 110

4.5 ICP-MS results of PM samples (826 bus)...... 111

4.6 Limit of detection for the ICP-MS analysis ...... 111

4.7 EDS results of different elements for all cases of PM samples ...... 117

xi

List of Figures

1-1 History of diesel engines emission standards ...... 3

1-2 on pollution emission from and diesel sources in 2010 ...... 4

1-3 PM vehicle emission statistics ...... 5

1-4 CO vehicle emission statistics ...... 6

1-5 NOx vehicle emission statistics ...... 7

1-6 VOC vehicle emission statistics...... 8

1-7 Pollution from new buses versus existing buses ...... 9

2-1 Diesel oxidation catalyst...... 17

2-2 Exhaust gas recirculation ...... 18

3-1 Route map of the test run ...... 37

3-2 Instrument setup inside the bus ...... 38

3-3 Aluminum design sheet to hold the probe in place ...... 38

3-4 Shows the board diagnostic unit connected to a 9-pin plug connector ...... 39

3-5 Show the Catch Can used to collect PM samples ...... 40

3-6 Schematic diagram of MLP ANN with 3 layers ...... 44

4-1 Cold idle values of NOx emissions from EGR, NON-EGR, and hybrid buses .....50

4-2 Hot idle values of NOx emissions from EGR, NON-EGR, and hybrid buses...... 51

4-3 Cold idle values of CO2 emissions from EGR, NON-EGR, and hybrid buses ...... 52

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4-4 Hot idle values of CO2 emissions from EGR, NON-EGR, and hybrid buses ...... 52

4-5 Cold idle values of HC emissions from EGR, NON-EGR, and hybrid buses ...... 53

4-6 Hot-idle values of HC emissions from EGR, NON-EGR and hybrid buses ...... 53

4-7 NOx emission in cold idle for all three different buses ...... 55

4-8 CO2 emission in cold idle for all three different buses ...... 55

4-9 HC emission in cold idle for all three different buses ...... 56

4-10 NOx emission in hot idle for all three different buses ...... 57

4-11 CO2 emission in hot idle for all three different buses ...... 57

4-12 HC emission in hot idle for all three different buses ...... 58

4-13 HC emissions in acceleration mode for all the three buses ...... 61

4-14 Highest points of HC in acceleration mode ...... 62

4-15 CO2 emissions in acceleration mode for all the three buses ...... 63

4-16 Highest points of CO2 in acceleration mode ...... 63

4-17 NOx emissions in acceleration mode for all the three buses ...... 64

4-18 Highest points of NOx in acceleration mode ...... 64

4-19 NOx emission data for all the three buses in variable speed mode ...... 65

4-20 Highest points of NOx in variable speed mode ...... 64

4-20 CO2 emission data for all the three buses in variable speed mode ...... 66

4-21 Highest points of CO2 in variable speed mode ...... 67

4-22 HC emission data for all the three buses in variable speed mode ...... 68

4-23 Highest points of HC in variable speed mode ...... 68

4-24 NOx emission data for all the three buses in deceleration mode ...... 69

4-25 Highest points of NOx in deceleration mode ...... 69 xiii

4-26 HC emission data for all the three buses in deceleration mode ...... 70

4-27 Highest points of HC in deceleration mode ...... 71

4-28 CO2 emission data for all the three buses in deceleration mode ...... 72

4-29 Highest Points of CO2 in deceleration mode ...... 72

4-30 NOx emissions at intersections for all the three buses ...... 74

4-31 Highest Points of NOx at intersections ...... 74

4-32 CO2 emissions at Intersections for all the three buses ...... 75

4-33 Highest Points of CO2 at intersections ...... 75

4-34 HC emissions at intersections for all the three buses ...... 76

4-35 Highest Points of HC at Intersections ...... 76

4-36 NOx emission prediction model using ET for 826-hybrid bus in Cold Idle ...... 78

4-37 NOx emission prediction model using Lasso for 826-hybrid bus in Cold Idle .....78

4-38 HC emission prediction model using ET for 826-hybrid bus in Cold Idle ...... 79

4-39 HC emission prediction model using Lasso for 826-hybrid bus in Cold Idle ...... 79

4-40 CO2 emission prediction model using ET for 826-hybrid bus in Cold Idle...... 79

4-41 CO2 emission prediction model using Lasso for 826-hybrid bus in Cold Idle ...... 80

4-42 CO2 emission prediction model using ET for hybrid bus in the hot idle mode .....81

4-43 CO2 emission prediction model using Lasso for hybrid bus in the hot idle ...... 81

4-44 HC emission prediction model using ET for hybrid bus in the hot idle mode ...... 81

4-45 HC emission prediction model using Lasso for hybrid bus in the hot idle ...... 82

4-46 NOx emission prediction model using ET for hybrid bus in the hot idle mode ....82

4-47 NOx emission prediction model using Lasso for hybrid bus in the hot idle mode 82

4-48 NOx emission prediction model using ET for hybrid bus in acceleration mode ...82 xiv

4-49 NOx emission prediction model using Lasso for hybrid bus in acceleration ...... 84

4-50 HC emission prediction model using ET for hybrid bus in acceleration mode .....84

4-51 HC emission prediction model using Lasso for hybrid bus in acceleration ...... 83

4-52 CO2 emission prediction model using ET for hybrid bus in acceleration...... 85

4-53 CO2 emission prediction model using Lasso for hybrid bus in acceleration ...... 85

4-54 CO2 emission prediction model using ET for hybrid bus in variable speed ...... 86

4-55 CO2 emission prediction model using Lasso for hybrid bus in variable speed .....86

4-56 HC emission prediction model using ET for hybrid bus in variable speed ...... 87

4-57 HC emission prediction model using Lasso for hybrid bus in variable speed ...... 87

4-58 NOx emission prediction model using ET for hybrid bus in variable speed ...... 87

4-59 NOx emission prediction model using Lasso for hybrid bus in variable ...... 88

4-60 CO2 emission prediction model using ET for hybrid bus in variable speed ...... 89

4-61 CO2 emission prediction model using Lasso for hybrid bus in variable speed .....89

4-62 HC emission prediction model using ET for hybrid bus in variable speed ...... 89

4-63 HC emission prediction model using Lasso for hybrid bus in variable speed ...... 90

4-64 NOx emission prediction model using ET for hybrid bus in variable speed ...... 90

4-63 NOx emission prediction model using Lasso for hybrid bus in variable speed .....90

4-66 CO2 emission prediction model using ET for hybrid bus in variable speed ...... 91

4-67 CO2 emission prediction model using Lasso for hybrid bus in variable speed .....92

4-68 HC emission prediction model using ET for hybrid bus in variable speed ...... 92

4-69 HC emission prediction model using Lasso for hybrid bus in variable speed ...... 92

4-70 NOx emission prediction model using ET for hybrid bus in variable speed ...... 93

4-71 HC emission prediction model using Lasso for hybrid bus in variable speed ...... 93 xv

4-72 Image of blank filter paper showing membrane fibers without deposits ...... 113

4-73 SEM image of 610 Cold idle sample filter paper showing membrane ...... 113

4-74 SEM image of 610 hot idle sample filter paper showing membrane ...... 114

4-75 SEM image of 802 cold idle sample filter paper showing membrane ...... 114

4-76 SEM image of 802 hot idle sample filter paper showing membrane ...... 115

4-77 SEM image of 826 cold idle sample filter paper showing membrane ...... 115

4-78 SEM image of 826 hot idle sample filter paper showing membrane ...... 116

xvi

List of Abbreviations

ANN ...... Artificial Neural Networks

CNG ...... Compressed CO ...... CO2 ...... CRB...... California Air Resources Board

DOC ...... Diesel Oxidation Catalyst DPF ...... Diesel Particulate Filter

EDS ...... Energy Dispersive X-Ray Spectroscopy EGR...... Exhaust Gas Recirculation ET ...... Extra Tree

FTA ...... Federal Transit Administration

GHGs ...... Green House

HC ......

ICP ...... MS- Inductively Coupled Plasma- Mass Spectrometry

LNG ...... Liquefied Natural Gas

NAAQS ...... National Ambient Air Quality Standards NOx ...... Nitrogen Oxide NOx ...... Nitrogen Oxides

PM ...... Particulate Matter PPM...... Parts Per Million

SCR ...... Selective Catalytic Reduction SO2 ......

ULSD ...... Ultra- Low Sulphur Diesel xvii

USEPA ...... U.S. Environmental Protection Agency

VOCs...... Volatile Organic Compounds

xviii

Chapter 1

Introduction

During the 1920s an efficient diesel engine came into the picture. Soon after that, the diesel engines gained enormous popularity because of its less use of volatile inflammable fuel [11]. After World War II there were plenty of changes in financial development, population development, running toward urban development, and prompted to more dependence on individual vehicles for transportation by the closing of some major transit systems. The number of and trucks in the United States expanded significantly.

One consequence of the quick increment of engine vehicles was that lead to damage of public health and environment [1].

Interestingly since 1979, surface transportation system has the highest amount of carbon pollution released into the atmosphere in about a span of 12-months than any other sector. Roughly 85 percent of these emissions are identified with the surface transportation systems in federal highways, public , and so forth [2]. This happens because of overreliance on the single-occupant vehicle rather than public transportation, carbon emission from single occupant vehicle surpassed any other mode of transportation. A single

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passenger vehicle per mile produces 50 percent more carbon dioxide than buses. From a report by ICF International says that using public transportation not only save 4800 pounds of carbon dioxide, but it also saves a $6,251 annually which is an average two-persons spent on households [2].

According to the Federal Transit Administration (FTA), Americans take 10 billion trips on public transportation each year [3]. And from a survey conducted by American public transport association, the number of trips taken on U.S. Public Transportation in

2014 increased to a record level of 10.8 billion and this was the highest annual ridership in

58 years [4]. Now a new history is created by drastically adopting to public transit buses, this brings up new challenges to control pollution other than carbon dioxide (CO2) and hydrocarbons (HC) from gasoline engines. These diesel engine buses are now the dominant source of nitrogen oxide (NOx) and particulate matter (PM) emissions. History of diesel engine emission standards are shown in Figure 1-1 [5].

Automotive exhaust emissions are the major source of air pollution [6].

Automobiles are the culprits for the major amount of air pollution in urban areas was identified in California during the 1950s. Prof. Hazen Smith for the first time identified the cause of famous Los Angeles ‘Photochemical ’ which was due to the vehicular emission of HC and NOx that formed a brown above the ground level in the Los

Angeles area [7].

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Figure 1-1: History of diesel engines emission standards

After an increase in commuters using public transit buses, all the attention is on the air pollution caused by these buses. “In 2004, almost a quarter of carbon dioxide emission is from transport vehicles. Whereas three- quarters of transport-related emissions are from road traffic” [8]. The pollution caused by conventional road transport are reported high in cities which face several traffic congestions. The major pollutants emitted into the atmosphere by these public transport buses are particulate matter of different size fractions

(PM10 and PM25), carbon monoxide (CO), nitrogen oxide, carbon dioxide and sulfur dioxide (SO2). There is no guaranty that all combustion engines produce all the above- listed pollutants, but some engines in combustion mode react with other particles in the air and caused a massive drawback of human health [9].

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Figure 1-2: on road Pollution Emission from Gasoline and Diesel Sources in 2010 [10]

During combustion in vehicles, a complex mixture of gases and fine particles are produced into the atmosphere. Some of the primary pollutants emitted from diesel engines are:

1. Particulate Matter (PM)

2. Carbon Monoxide (CO)

3. Nitrogen Oxides (NOx)

4. Hydrocarbons (HC)

5. Volatile Organic Compounds (VOCs) [12]

Health studies demonstrate the effects of diesel exhaust on a human being and found out it damages the and worsens , lung function, , and

4

allergies. There were proofs that diesel exhaust can expand the danger of heart issues, premature death, and [12].

Particulate matter is basically known as solid or liquid particles. A few particles are large or sufficiently dark to be viewed as or smoke, yet most are fine particulate matter.

The fine particulate matter is made of small particles found in , smoke, air, dirt, and liquid droplets. More than 90 percent of diesel exhaust are fine particles matter- more commonly identified as PM2.5 (less than 2.5 microns in diameter). Particulate matter causes health effects by traveling deep into the lungs leading to aggravating asthma, chronic bronchitis, emphysema, and other lung conditions. Bigger particles get pumped out during the respiration process, whereas the smaller particles settle in the lungs and the smallest apparently get into the blood [12]. Figure 1-3, shows PM emissions from a different type of vehicles.

Figure 1-3: PM Vehicle Emission Statistics [15]

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Carbon monoxide results in incomplete combustion of vehicle fuel [13]. The carbon monoxide so formed reacts with other pollutants in the air to produce which is harmful at the ground level. Figure 1-4, shows CO emissions from a different type of vehicles.

Figure 1-4: CO Vehicle Emission Statistics [15]

Carbon monoxide is also called as a common type of poisoning, it can cause dizziness, headache, and nausea by cutting down the amount of flowing to the cells and in high concentration results in unconsciousness and death [9].

A group of highly reactive gases is characterized as nitrogen oxides or oxides of nitrogen, all of which contain oxygen and nitrogen in different proportions. Nitrogen oxides are formed during the combustion process when fuel is burned at high temperature

[12]. Nitrogen oxide specifically nitrogen dioxide affects the respiration system and increase the existing cases of pneumonia or bronchitis, while nitrogen oxide in higher

6

concentration can drastically damage lung tissues [9]. Figure 1-5, shows NOx emissions from a different type of vehicles.

Figure 1-5: NOx Vehicle Emission Statistics [15]

Hydrocarbons are chemical compounds with a combination of carbon and . Most of the engines and other motor vehicles use -based fuel either diesel or gasoline. Emission of hydrocarbons from vehicle exhaust is due to unburned or partially burned fuel and direct evaporation of fuel emits hydrocarbons into the atmosphere. While in presence of sunlight hydrocarbons react with nitrogen oxide which results in the formation of ozone and leads to smog effect. Ozone and smog cause respiratory disorder and lung , whereas some toxics present along with hydrocarbons might lead to cancer [14].

Most parts of volatile organic compounds are hydrocarbons which are emitted from vehicle exhausts due to partial or no burning of fuel during combustion. These are highly

7

toxic in nature and can cause serious health effect along with the environment. Volatile organic compounds in the atmosphere lead to , flame production and cause serious damage to eyes and lungs. Volatile organic compounds at their early stage are not that harmful but turn out to be dangerous when they react with other gases and compounds forming one of the major air pollutants.

Figure 1-6: VOC Vehicle Emission Statistics [15]

According to a study conducted in British Columbia, pollution emitted from new buses are comparatively less than the existing buses [16]. The Figure 1-7 gives us a graphical comparison of pollutants like nitrogen oxides, volatile organic compounds, particulate matter and Green House Gases (GHGs) from a new state-of-the-art diesel transit bus equipped with diesel particulate filter to emissions from typical diesel transit bus presently in use. This show that new buses emit very less VOCs and NOx pollutants and comparatively fewer greenhouse gases than the pre-existing buses [16].

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Figure 1-7: Pollution from new buses versus existing buses [16]

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Chapter 2

Literature Review

2.1 Health Effects Caused by Vehicle Emissions

Diesel engines have generally been more adaptive and less expensive to keep running than gasoline engines. But, the exhaust emissions from the diesel engine contains substances that can be a health hazard [17]. In 1998, a comprehensive health assessment of diesel exhaust was completed by the California Environmental Protection Agency's Office of Environmental Health Hazard Assessment (OEHHA). Based on the above-conducted assessment California Air Resources Board (CRB) has identified the main culprit as the diesel exhaust emissions. Whereas it is the primary cause of the damage to human health because diesel engine vehicles produced air toxins, diesel exhaust particles account a huge rate of the particles discharged in numerous towns and urban areas. [17]. Due to incomplete combustion of in motor vehicles produce various gases, liquids, and solid particles. Thought it produces less carbon monoxide compared to a gasoline engine, but give rise to a greater amount of nitrogen oxides and aldehydes [18]. These exhausts can cause morbidity such as irritation of the upper respiratory tract. It also emits submicron particles which cause soiling and poor visibility [19].

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Keeping in mind the recent health effects caused by diesel engines a recent study was conducted on ultrafine particles (diameter <0.05–0.10 μm). These ultrafine particles have been found abundant in an urban environment where the emission rates were high. They enter the bloodstream penetrating through the epithelium and vascular walls. It has been theorized that ultrafine particles represent the systemic impacts of diesel exhaust particles, for example, increases carcinogenicity, potentiation of autoimmune disorders, changes in blood coagulability and increase in cardiovascular issues [20-23].

These emissions also have profound impacts on the environment, production of smog, acid rains, hydrocarbons and air toxics. These can affect plants, animals, and water resources

[24]. Table 2.1 summarizes the environmental and health effects caused by the exhaust emissions from diesel engines.

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Table 2.1, Environmental and health effects caused by the exhaust emissions (Source: U.S. EPA, Factsheet: USACHPPM

Acute Chronic Effects Environmental Possible Effects Effects Improvements

Irritation of eyes, Lung Cancer Ozone formation Use of Cleaner nose, and throat. burning and renewable fuels (natural gas, propane, electricity)

cough, Bronchitis Acid rain Retro-fitting of nausea headache, and reduce lung dizziness function existing engines within children particle filters

Heartburn, chest, Respiratory and heart Global Climate Use of new diesel tightness, diseases Change engines of advances wheezing, technologies that vomiting and produce 90% lesser increase the particle emissions frequency of asthma attacks

Damage to Pneumonia Visibility issues and respiratory and haze central nervous system

When exposed Emphysema, & to higher Premature deaths levels: coma, convulsions, and death

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2.2 Diesel Vehicle Emission Regulations

2.2.1 National Ambient Air Quality Standards

(NAAQS)

To shield environment and to protect human beings from air toxins according to the Clean Air Act, corrected in 1963. The U.S. Environmental Protection Agency (USEPA) is required to set National Ambient Air Quality Standards (NAAQS). The clean air act has distinguished two sorts of air quality principles to be primary and secondary standards.

Guidelines required to keep up public health protection are known as primary standards and norms required for maintenance and giving public welfare security are known as secondary standards [25].

The EPA has set national ambient air quality standards for six foremost pollutants, which are called "criteria" air toxics. From time to time, the standards are reviewed and updated.

The current updated standards are recorded underneath in Table 2.2. Units of measure for the standards are parts per million (ppm) by volume, parts per billion (ppb) by volume, and micrograms per cubic meter of air (µg/m3) [25].

During recent years, diesel emissions measures have turned out to be progressively stringent. This is shown in Tables 2.2, which give the criteria discharge standards for heavy-duty diesel motors in the U.S. Later in background studies, we have discussed the

13

importance of sophisticated exhaust after-treatment systems and their role in reducing the emissions [39]

Table 2.2, HD diesel engine emissions standards — U.S.

Year US emission levels, g/bhp-hr

HC CO NOX PM 1988 1.30 15.50 10.70 0.60 1990 1.30 15.50 6.00 0.60 1991 1.30 15.50 5.00 0.25 1994 1.30 15.50 5.00 0.10 1998 1.30 15.50 4.00 0.10 2004 0.50 15.50 2.50 0.10 2007 0.14 15.50 1.20 0.01 2010 0.14 15.50 0.20 0.01 2015 0.14 15.50 0.20 0.01

2.2.2 Fuel Standards

Due to the limited supply of petroleum products and its negative effect on the environment, numerous nations over the world are currently inclined toward and some of them are (CNG), Liquefied Natural Gas (LNG),

Ultra- Low Sulphur Diesel (ULSD) and [26]. Whereas CNG and LNG fueled vehicles emit very less amount of regulated pollutants including PM, CO2, HC, and NOx, even when compared with Diesel or gasoline vehicles. Furthermore, CNG vehicles can

14

emit less carbon dioxide and other greenhouse gasses. U.S. environmental protection agency guidelines require a significant decrease in the sulfur content of diesel fuel and emission levels from diesel vehicles. In order to meet the benchmarks of EPA oil industries came up with the ULSD fuel, the sulfur content in this fuel is around 15ppm to 30ppm which is 94 percent less sulfur when compared to regular diesel fuel [27]. Table 2.3 shows the National Ambient Air Quality Standards (NAAQS).

Table 2.3, NAAQS Table (μg/m3)

Standard Pollutant Averaging Time Standard Type Level Primary Carbon monoxide 8-hour 9ppm

Primary Carbon monoxide 1-hour 35ppm

Primary & Nitrogen Dioxide Annual 53ppb Secondary Primary & Ozone 8-hour 0.070 ppm Secondary (O3) Primary Particulate Matter 1 year 12.0 µ g/m3 (PM2.5) Primary & Particulate Matter 24-hour 35 µ g/m3 Secondary (PM2.5) Primary & Lead 3-month 0.l5 µ g/m3 Secondary Primary Sulfur Dioxide 1-hour 75 ppb (SO2) Secondary Sulfur Dioxide 3-hour 0.5 ppm (SO2)

Biodiesel has gained a lot of interest as a substitute fuel which is being used significantly in most of the vehicles. Biodiesel is produced from vegetable oils, animal fats, or waste oil. Regularly, biodiesel is produced using soybean oil or waste fats left finished from cooking. Biodiesel is also used to increase the lubricity of the ultra-low sulfur diesel 15

with a good quality of blending easily with petroleum. It produces less number of hydrocarbons, carbon monoxide, and particulate matter but a significant increase in NOx

[27]. However, biodiesel could be viewed as an option because it is non-fossil, biodegradable, CO2 neutral and its combustion is Sulphur oxide free.

2.2.3 Diesel Exhaust Control Systems

There are different types of exhaust control systems used in diesel engine buses, equipped to reduce exhaust emissions. The highly equipped exhaust control systems on diesel buses are described below.

2.2.3.1 Diesel Oxidation Catalyst (DOC)

The most typically employed is the Diesel Oxidation Catalyst

(DOC) in diesel engines. DOC contain palladium, platinum, and aluminum, together act as catalysts to oxidize the hydrocarbons and carbon monoxide with oxygen to form carbon dioxide and water.

DOC oxidize NO into NO2 in combination with SCR catalyst (Eq.2.3). There are three main reactions which occur in DOCs [57].

CO +1/2 O2→ CO2CO +1/2 O2→ CO2 Eq.2.1

C3H6+9/2 O2→ 3 CO2+ 3H2OC3H6+9/2 O2→ 3 CO2+ 3H2O Eq.2.2

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NO +1/2 O2→ NO2NO +1/2 O2→ N O2 Eq.2.3

In the above equations, CO and HC are oxidized to form CO2 and H2O [Eqn. 2.1,

2.2] in Figure 2-1. A range of 2% to 17 % by volume of diesel exhaust gases usually contain O2 which does not react with the fuel in the combustion chamber. This O2 is steadily expanded in DOC [56].

Figure 2-1, Diesel oxidation catalyst (DOC)

These converters reduce soot particulate matter and eliminate diesel odor by functioning at 90 percent efficiency. DOC does not help in reduction of NOx and these catalysts work efficiently at high temperatures around 650 degrees F. None of the conversion take plays when the catalyst is cold [58].

2.2.3.2 Exhaust Gas Recirculation (EGR)

EGR is exhaust gas recirculation technique equipped with combustion diesel engines to reduce NOx emissions. EGR works by recirculating a portion of an engine exhaust gas back to the engine cylinders to diminish the combustion chamber temperature.

Usually, NOx is produced when the combustion chamber temperature goes higher than 17

2,370 degrees Fahrenheit. Reintroducing the exhaust gas back into the cylinders using EGR technique reduces the chamber temperature. As EGR has no oxygen in it, the temperature remains underneath the NOx production threshold, this helps to reduce the major portion of NOx concentration in diesel exhaust emissions [59]. Figure 2.2 Shows the EGR valve of a diesel engine.

Figure 2-2, Exhaust Gas Recirculation (EGR)

2.2.3.3 Selective Catalytic Reduction (SCR)

SCR is an exhaust after-treatment system that significantly reduces the number of oxides of nitrogen emitted from the exhaust. The bus carries a tank of , a liquid that is injected into the decomposition chamber by the dosing module. When the diesel exhaust fluid mixes with the hot exhaust, ammonia is released. This exhaust/ammonia mixture passes through the SCR catalyst, where the oxides of nitrogen are turned into nitrogen and water.

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The overall reduction of NOx by urea is:

2 (NH2) 2CO + 4 NO + O2 → 4 N2 + 4 H2O + 2 CO2 and Eq.2.4

1 2 (NH2) 2CO + 3 NO2 → 3 ⁄2 N2 + 4 H2O + 2 CO2 Eq.2.5

2.2.3.4 Diesel Particulate Filter (DPF)

Diesel exhaust generally contains an abnormal amount of soot particulate matter with a huge majority of elemental carbon. Catalytic converters can clean up to 90 percent of the soluble substances, but cannot eliminate elemental carbon. So, the soot particulate matter is trapped using diesel particulate filter or soot trap. During the process of collecting soot particulate matter, a back pressure is generated in the exhaust system, this is due to the increase of soot trapped on DPF. Periodic regenerations are required to initiate combustion of the trapped soot and thereby reducing the exhaust back pressure.

Regeneration is a process of cleaning accumulated soot by burning at high temperature

(around 600°c) to leave only a residue of ash.

2.3 Background of Exhaust Emission Studies

In the course of recent years, United States has taken the challenging task to develop reformulated and alternate diesel fuel. One of the essential thought processes in this development is to reduce diesel emissions. On the other hand, this alternate diesel fuel helps to stand independently from petroleum sources [28]. As per the code of federal regulation mentioned by the EPA defined test procedure, diesel engines were regulated for

19

CO, THC, Particulate Matter less than 10μm (PM10) and NOx. Moreover, the major alternative fuels that are low in emission are:

1. Biodiesel

2. Compressed natural gas

3. Liquefied Petroleum Gas (LPG)

4. Alcohol fuels (methanol, )

5. Hydrogen

In Table 2.4 different alternate fuels are represented by their environmental effects and sources. All the fuel types mentioned in the table helps in reduction of at least one regulated pollutant [29]. CNG is basically collected from underground reserves and plays a highly effective role in controlling vehicle emissions besides hydrocarbons. The significant trouble with compressed natural gas is that it is not easily obtained or accessible. CNG vehicles are more expensive when compared to diesel vehicles that are $8,000 - $11,000 high in price. Moreover, ethanol can be derived from starch crops, corn, wheat and agricultural waste. When compared to petrol- diesel ethanol lessen ozone-forming emissions by 25%. On the other side, there is a significant disadvantage of utilizing ethanol as a fuel because of its destructive nature, which will in the end prompt lower working life of the engine. Hydrogen is also being utilized as a substitute fuel and can be obtained from gaseous petrol and electrolysis. Additionally, it has no significant diminishment in emissions when contrasted with gasoline. Whereas, LPG is utilized as a substitute fuel and is produced as a result of petroleum refining. It decreases CO2 discharge. However, it is high in NOx discharge [31]. 20

Among all the above alternate fuels it is inferred that biodiesel has gained its position on to the top by having comparable properties as that of conventional diesel and also being derived from renewable sources like sunflowers, soybeans, vegetable oil or animal fat.

Biodiesel is essentially mono-alkyl esters of medium to long-chain fatty acids produced by trans esterifying organic oils to a viscosity close to that of oil diesel. It is biodegradable, helps to clean carbon depositions inside a combustion chamber and most importantly, it can be substituted with traditional diesel without any engine modification [29-31]. Another considerable aspect of biodiesel is its price. Its cost relies upon few factors such as: the vegetable cultivated, agricultural techniques, processes used to produce the refined vegetable oil, soil and climate conditions, and taxes. In few countries, the price of biodiesel is 2 – 3 times higher than the traditional diesel this is due to high prices of ingredients like soybean and corn [32]. Additionally, Congress has passed strict regulations to Federal and

State fleet managers to meet the EPAct’s which requires a minimum of 20% blend of biodiesel mixed to conventional diesel [28].

Despite the fact that there is an agreement on the overall reduction of emissions using biodiesel, the particular level of reduction differs from study to study. In 2005, from a study conducted by Szybist et al., [37] it was observed that the Particulate Matter decreased by

25% and NOx increased by 3% in all the engines. In 2017, Can et al., [38] used canola biodiesel blends and observed high NOx and CO2 emissions whereas low smoke, CO and

THC emissions. However, in 2007, Knothe et al., [29] reported a 10% decrease in PM, a 21 % decrease in hydrocarbons, an 11% decrease in CO, and a 2% increase in NOx.

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Table 2.4, Environmental Impacts of Alternative Fuels

Fuel Biodiesel Ethanol Compressed Liquefied Hydrogen Petroleu (E85) Natural Gas m Gas (LPG) (CNG)

Sunflowers, Starch Underground A by- Natural soybeans, crops, corn, reserves product of gas, animal fats or wheat, petroleum electrolysis agricultural recycled refining or and other waste Source cooking oil. natural energy gas sources processing (gasoline, diesel, methanol etc)

Reduces PM 1) Reduces 1) Reduces Zero and GHG GHG CO2 regulated emissions. emissions emissions by emissions by 20-80%. 25% and PM Environmen NOx for fuel by 95%, tal impacts emissions cells compared to of burning may be operating gasoline. fuel (in increased on pure H2 comparison CO and 2 to gasoline NOx and diesel) 2) Reduces emissions 2)Reduces NOx equivalent ozone- emissions by to gasoline, forming 80% and PM for internal emissions by 95% combustion (CO and compared to engines NOx) by Diesel. HC 25% emissions may be increased.

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Source: http://www.eere.energy.gov

Though 100 % biodiesel or neat biodiesel produces fewer pollutants, it may also lead to exhaust control system breakdown by clogging the filters. In 2012, Chauhan et al.,

[36] utilized a non-eatable Jatropha oil-based biodiesel to study exhaust emissions, combustion characteristics, and performance of biodiesel and diesel fuel. In this study, it was proved that HC, CO, and CO2 emissions were reduced by using Jathropa biodiesel, but there was an increase in NOx emissions.

Table 2.5. Represents the parameters such as a number of the vehicle, fuel, operations and environmental variables that are found to impact the exhaust emissions [33].

Tailpipe emissions rely on road conditions and drivers’ behaviors (speed, acceleration and speed time acceleration) which are some of the vital factors influencing the exhaust emissions [34].

Vijay and Kumar [40] developed an emission model that recognizes the elements influencing the emission from transit buses and clarifies the relationship between exhaust emission pollutants. The model identifies some of the vital parameters that affect the concentration of pollutants such as fuel rate, engine load, engine type, and exhaust temperature and engine rpm. From the study, it was presumed that effect on air quality can be reduced to a substantial degree by reducing the idle time during the long duration of vehicle idle. In 2014 Shandilya and Kumar [35] together conducted a literature review of heavy vehicle exhaust emissions. A synopsis of the majority of studies led by previous researchers on biodiesel found an increase and decrease in pollutants like NOx, CO, and

THC.

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Table 2.5, Factors Influencing Vehicle Emissions

Vehicle Parameters Fuel Operating Environmental Parameters conditions parameters

Vehicle Manufacturing Type of Fuel Average Vehicle Altitude year, type, technology, speed and class

Fuel properties Load on the engine such as oxygen due to heavy load content, sulfur on the vehicle, content, accessory loading volatility, due to the usage of Fuel delivery system content, air conditioners Humidity lead and metal etc. content, aromatic hydrocarbon content etc. Air conditioning and Traffic patterns and Diurnal other vehicle Driver behavior temperature appurtenances. changes

Emission control system Cold or hot start Ambient temperature

Fuel Quality Vehicle miles Emissions traveled (VMT) standards in effect

Inspection/maintenance Type of road and and anti-tampering road grade program

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Whereas few studies show no change in concentration of NOx, CO, and THC. All these study outlines are tabulated below in Table 2.6.

Table2.6, the percentage share of literature reporting increase, same or decrease in

emissions over regular diesel with the use of biodiesel

Percentage of total studies

Emissions Increase Same Decrease

NOx 87 9 4

PM 2 2 96

THC 2 2 96

CO 2 3 95

McCormich et al., [41] identified that the effects of biodiesel blend (B20) on NOx emission were due to a high chance of reliance on the test vehicle and the driving cycle conditions.

Whereas, a study conducted on Cummins heavy-duty engine confirmed that the NOx impact of biodiesel has changed significantly with load [43]. In a study conducted by

Hoekman et al., [39] point out that injection time, ignition delay, adiabatic flame temperature, radiative heat loss, and other combustion phenomena played a major role in the reduction of NOx emissions.

Choi et al., [44] performed a study on slow and split injection technique using biodiesel decreases the NOx emissions in a heavy-duty caterpillar engine. The split

25

injection technique hypothesis gained attention once again by Kim et al., [45] it was found that NOx emissions decrease using common rail injection system in a single-cylinder engine. Later, Kegl et al., [46] proved the hypothesis correct by conducting an experiment on biodiesel using a 6-barrel MAN transport engine and concluded that biodiesel could produce lower NOx than conventional diesel fuel if the injection time was hindered to accomplish maximum torque.

In 2012, Hoekman et al., [39] compared conventional diesel fuel with biodiesel and found that using biodiesel reduced emissions of hydrocarbons (HC), carbon monoxide, and particulate matter whereas increased NOx emissions. Engine gas recirculation is an engine modification technique that reduces exhaust gas emissions, this technique is basically used to control NOx emissions. Moreover, EGR is used to decrease cylinder temperature by reintroducing a portion of exhaust gas of high specific heat (containing substantial levels of H2O and CO2) back into the engine. This process brings down the cylinder temperature and helps to reduce NOx formation. Furthermore, few other studies have examined the details regarding the advantages of EGR in reducing NOx emissions by using biodiesel in research laboratory test engines. EGR was generally used in conventional diesel engine and biodiesel engines, so in order to have edge over conventional diesel engines, a greater degree of EGR was used [39]. As observed by Muncrief et al., [47], the use of EGR together with a diesel particulate filter was a powerful approach to decrease NOx and PM emission.

However, using selective catalytic reduction in combination with EGR, DPF and diesel oxidation catalyst produced much low NOx emissions.

EGR technology was used to reduce NOx and it was known for it. In a study

Pierpont et al., [48] conducted an experiment to look at the possible reduction of particulate 26

matter along with NOx by using both EGR and multiple injection techniques. As the decrease in PM emissions were already noticed by using multiple injection techniques.

Now, in addition to it, they found out that multiple injections along with EGR technique reduced particulate matter and NOx simultaneously.

In a study conducted by Lopez et al., [50], two exhaust after-treatment systems such as diesel particulate filter and an exhaust gas recirculation system (DPF + EGR) were compared with a selective catalytic reduction for B20, B100, and neat diesel fuels.

Therefore, it was observed that DPF + EGR helped to reduce CO and PM emissions.

Whereas, CO2 and NOx emissions were reduced by SCR. However, there was no significant change in HC and PM emission levels when observed as a comparison among traditional diesel, B20, and B100 fuels.

In 2015, a study conducted by Ozcelik et al., [49], it was observed that hydrocarbons formed from unburnt fuel differ with different biodiesel blends. It was observed that hydrocarbon emissions for B100 bend were 68.8% and for B7 fuel was

37.5% less compared to conventional diesel fuel. Whereas CO2 formed as a result of combustion were tested for both B7 and B100 blends. The results revealed that the CO2 emissions from diesel fuel were on an average 20% less compared to CO2 emissions of B7 and B100 fuels. Moreover, biodiesel contains oxygen and in order to have complete combustion more amount of oxygen is taken into the chamber, So CO2 emission that occurred as a consequence of this process also increased [49]. Finally, NOx emissions for

B7 and B100 were found 17.6% and 58.8% high compared to diesel fuel.

In 2015 a study was conducted to examine tailpipe emissions using waste animal fat based -bioethanol-diesel fuel blends in DI diesel engine. As per the results 27

from brake-specific emissions, it was observed that biodiesel fuel emits a higher amount of carbon dioxide and oxides of nitrogen when compared to conventional diesel. On the other side emits low carbon monoxide. Moreover, the THC emissions increased and CO2 emissions decreased slightly for bioethanol fuel blend when compared to B20 fuel blend

[52].

It was observed from a study conducted by Kumar et al., [51], that vehicle emissions are basically subject to engine characteristics and concentration of biodiesel in the base fuel. Whereas the impact of biodiesel on vehicle emissions differ from pollutant to pollutant. The observations show that, during on-road mode CO, CO2, and SO2 emissions increased with increase in the percentage of biodiesel in the base fuel. On the other hand, in the idle mode except for CO2, all other emissions increased with increase in the percentage of biodiesel in the base fuel. Moreover, CO2 emissions were recorded high in hot and cold idle modes.

It has been proved that traffic conditions, driver’s operation, road topography, and conditions affected engine performance and increased the formation of NOx and

PM [53]. In 2011 a study was conducted on real-time dynamic urban traffic conditions to examine NOx, CO2, CO, and HC emissions in real traffic conditions and found that emissions were irregular in nature. Higher emissions were observed in acceleration mode, whereas emissions were found to be low in idle mode. Moreover, during delay events,

NOx, HC, CO, and CO2 emissions were found remarkably higher than during non-delay events [54]. Research discoveries confirm that the level of oxygen, the degree of unsaturation, and the size of the fatty acids in biodiesel are major factors that govern the amounts and formation of NOx and PM emissions [55]. NOx, CO2 and HC formation 28

during biodiesel combustion is related to various factors, such as the properties of biodiesel and ignition conditions. Whereas load percentage and engine speed were considered to be reliable for NOx, CO2, and HC formation.

2.4 Summary

The above literature review gives us a detail information on exhaust emissions studies. It has been concluded that NOx, CO2, and HC vehicle exhaust emission levels and their effect depend on different parameters such as engine load percent, engine speed, properties of biodiesel, type of engine, ignition conditions and engine exhaust control systems. It has also been proved that traffic conditions, driver’s operation, road topography, weather conditions and so on affect NOx, CO2, HC, PM and engine performance.

Experimental analysis of exhaust emissions from transit buses fueled with biodiesel proved that CO and CO2 emissions increased with increase in the percentage of biodiesel base fuel during the running mode.

Previous studies proved that PM decreased by using multiple injection techniques.

Therefore, by using EGR and multiple injection techniques, it was observed that particulate

Matter and NOx emissions decreased simultaneously. A greater degree of EGR was used in the engine cylinder in order to reduce NOx emissions.

It was also mentioned that studies were necessary because there were very few commercial vehicles equipped with EGR, DPF, DOC and SCR exhaust control systems.

Another study shows that slow and split injection technique when using biodiesel as fuel

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helped to reduce NOx formation in Heavy-Duty engines. Moreover, there were very few studies conducted on running buses, this could be due to the lack of data collecting instruments, weather conditions, unavailability of vehicles, the discrepancy in the bus schedules etc.

It was observed that there were very few studies comparing NOx emissions and

PM emissions in both EGR and NON-EGR buses with real-time data collection. Moreover, there were no studies recorded comparing the NOx, CO2, PM and HC emissions from buses equipped with EGR+DPF+DOC and hybrid buses with EGR+DPF+DOC+SCR exhaust control systems. And studying the emissions behavior of buses with different exhaust control systems in running and idle modes will assist the manufacturers and regulators of air pollution in selecting the appropriate exhaust control system equipped bus for emission control strategies in urban areas.

The above literature review showed us the truth behind this innovative machine and their impact on the environment in the form of harmful exhaust gas pollutants. The knowledge gaps associated in this area are the objectives of this study.

• Studies on characterization of real-world on-road exhaust emissions from urban

transit buses fueled with biodiesel blends are limited.

• There is a gap in comparing exhaust emission data in idle condition for different

exhaust control systems such as NON-EGR bus, EGR bus with (DPF+DOC) and

hybrid bus with (EGR+DOC+SCR+UREA).

• There are very few studies on exhaust emission data in running condition of transit

buses.

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• There is a gap between comparison study on exhaust emission data in running

condition for different exhaust gas after-treatment technologies such as NON-EGR

bus, EGR bus with (DPF+DOC) and hybrid bus with (EGR+SCR+UREA).

• There are limited studies on diesel engine emission tests conducted in different

running modes such as acceleration, deceleration, variable speed modes, and at

intersections.

• There is a gap studying the effect of exhaust control system in reducing NOx, CO2

and HC emissions during acceleration, deceleration, variable speed and

intersections modes with respect to engine parameter rpm.

• There is a gap in the study of hybrid buses equipped with (ERG+SCR+UREA) in

different driving modes with respect to RPM and there is a gap in the study of PM

emissions from hybrid buses.

• There is a gap in the study of NOx, CO2 and HC emissions at intersections for all

three buses equipped with different exhaust control systems.

2.5 Objectives

These gaps became the primary objectives of this research as listed below.

• Identify the factors influencing exhaust emissions from public transport buses and

their behavior at various operating conditions using biodiesel fuel.

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• Analyze NOx, CO2 and HC emissions from public transit buses and their

relationship with engine data in idle modes for all the three different buses,

equipped with NON-EGR, EGR+DPF+DOC, and EGR+SCR+UREA exhaust

control systems.

• Quantify and compare the real-world tailpipe emissions data of public transit buses

during acceleration, deceleration, cruising mode and at intersections. And identify

the effect of NON-EGR, EGR, DPF, DOC and SCR+UREA exhaust control

systems on tailpipe emissions.

• Identify the influence of exhaust control systems in order to reduce particulate

matter emissions and compare the rate of emissions among Non- EGR, EGR and

hybrid buses.

• Develop a statistical regression model using Lasso and Extra Tree Regression

techniques that can predict the NOx, CO2 and HC exhaust emission concentrations

in relation to engine data as input during different running modes and idle modes

for transit buses equipped with exhaust control systems.

• Develop equations for all the three buses for NOX, CO2 and HC emissions. These

equations can be used to predict the emissions from a new set of buses with at most

accuracy for the mentioned engine inputs

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Chapter 3

Methodology

The objective of this thesis was to identify the difference in the concentration of

NOx, CO2, and HC emitted from diesel exhaust buses equipped with different combinations of emission control systems during running and idle conditions. This comparison is based on engine data which is used to calculate the NOx, CO2, and HC during acceleration, deceleration, variable speed, intersection, along with PM during hot

Idle and cold idle. To achieve these objectives, a plan was developed and executed to finish this research and the following sections below give a detailed understanding of the methodology.

3.1 Transit Bus Fleet Characteristics

Tailpipe emissions depend on vehicle characteristics like vehicle make, vehicle model, year, engine size and type. The tests are performed on TARTA (Toledo Area

Regional Transit Authority) buses. TARTA serves nine communities with over 250 buses categorized into ten different fleets.

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All the experiments in different driving modes were carried out on 610-bus (NON-

EGR), 802 (EGR with DOC, DPF) bus and 826 (EGR with SCR, UREA) hybrid bus. Table

3.1 summarizes the engine specifications of the transit buses tested.

Table 3.1, Engine specifications of the transit buses tested

bus Series/ Number 610 802 826

Engine Cummins Cummins ISL-07 Cummins ISB

ISL6LTAA 6.7L(hybrid)

Chassis Gillig Eldorado National Gillig BRT

Year of Manufacture 2003 2009 2012

Gross Vehicle Weight 39,000 42,760 39,600

(lbs.)

Engine Capacity 8.9L 8.9L 6.7L

Maximum Power 289HP @ 2000 289HP @ 2200 289HP @ 2200

RPM RPM RPM

Maximum Torque 900 ft.lbs. @ 900 ft.lbs. @ 900 ft.lbs. @

1300 RPM 1300 RPM 1300 RPM

Emission Certificate 2002 2007 NA

3.2 Test Fuel

Keeping environmental and health effects in mind TARTA took a step forward and adapted alternate fuel including B20 and ULSD since June 2006. Due to some availability 34

issues, TARTA has moved from B20 to B5 grade biodiesel in the last couple of years. The two fuels used are obtained from certified producers, Peter Cremer and Amoco fuels.

Some of the TARTA fleets run on NO. 2 Amoco Ultra Low Sulfur Diesel (ULSD).

It meets EPA on-road requirements for sulfur content and conforms to ASTM D-975 diesel fuel specifications. Whereas, Peter Cremer supplies 99.9% biodiesel and 5% of this 99.9% biodiesel is used by TARTA as B5, by mixing it with 95% of ULSD as base fuel. All the buses tested in this research used B5 grade biodiesel and it meets the EPA requirements and qualifies for ASTM D-6751 (EPA-4627) fuel specification.

Table 3.2, Properties of test Fuel

Properties Fuel

Biodiesel (B99.9%) ULSD

Cetane number 47 40 Cloud point (summer) (°F) - 20 Cloud point (winter) (°F) 42.8 15 Flash point (°F) >320 125 Sulfur (ppm) <1 15 Water & sediment (moisture) <0.005 0.05 (Vol. %) Kinematic Viscosity, 40°C 4 1.9-3.4 (mm2/sec)

35

3.3 Test Route

Selecting a test route is one of the major tasks in this experiment. To obtain real- time emission data, the selected route should have a decent flow of passengers and commute through the primary intersections in the city of Toledo. Real-time emission data was collected when the selected bus goes out on its regular specified route. Route number

22F was found perfect with all the above situations involved and also convenient for the test protocol. All the three buses were driven in this same route for better comparison. The bus numbers allocated for the test were 620,802 and 826.

The NOx, CO2 and HC emission data was collected for this route during acceleration, deceleration, variable speed, and intersections. The route starts from Tarta

Garage to Franklin Park Mall and head back to Jefferson Street in downtown Toledo and finally ends the trip at Tarta garage. Testing time was approximately 80 minutes. There were 25 traffic signals and several TARTA bus stops. All the three buses were driven in this route to collect exhaust emissions during different driving modes and data collection for cold idle was done before the bus leaves on the testing route and hot idle readings are taken after the bus returns from a trip. All the idle condition readings are conducted outside the TARTA garage to avoid any influence of outside particles. A pictorial view of the test run route is shown in the Figure 3-1

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Figure 3-1: Route Map of the test run

3.4 Instrumentation

NOx, CO2 and HC gases emitted from tailpipe were tested using a portable emission analyzer Horiba MEXA 584L. This is one of the highly accurate emission analyzers in the marker, with the light compact body along with a user-friendly interface.

Moreover, with a clear LCD, effortless operation and A Horiba software to store all the readings to a laptop, simplify the test process with utmost accuracy in data collection. This equipment simultaneously measures NOx, CO2 and HC concentrations from diesel and lean- burn engines. During the run, the instrument is connected to a drain separator along with a sampling tube and a pre-filter unit. The pre-filter unit is attached to the probe on the other end, which is inserted into the tailpipe at a uniform depth. An aluminum design sheet is clamped to the tailpipe in order to hold the probe in place. The experimental setup of the instrument is shown in Figure 3-2 and 3.3

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Figure 3-2: Instrument setup inside the bus

Figure 3-3: Aluminum design Sheet to Hold the Probe in Place

The Specifications of the Horiba MEXA 584L Portable Emission Analyzer are listed in

Table 3.3.

In order to record different engine parameters such as rpm, engine temperature, exhaust gas pressure, EGR Temperature etc. Cummins Insite 6 software with a 9-pin plug connector was used to connect the laptop to the board diagnostic unit for 802 and 826

38

buses. Whereas, A 6 pin plug connector was used for 610 buses. Figure 3-4, shows the board diagnostic unit connected to a 9-pin plug connector.

The particulate matter is collected using a Catch Can for both hot and cold idle. A

12 cm in diameter quartz filter paper was placed inside the Catch Can to collect PM. Figure

3-5, Shows the picture of a Catch Can used to collect PM samples. PALL flextissuquartz

2500 membrane quartz filters were used to collect PM in Catch Can, which has a retention rate of 99.9% for PM of size 0.3 (micro) meters.

Figure 3-4: shows the board diagnostic unit connected to a 9-pin plug connector.

Before conducting the test quartz filter papers were stored in a vacuum desiccator for 24 to

72 hours to equilibrate the filters and then weighed using a gravimetric mass balance. After the collection of PM, filter papers were equilibrated again and then gravimetrically weighed. The difference between the weight of the filter paper before and after collecting the PM gives the amount of PM collected.

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Figure 3-5: Shows the Catch Can used to Collect PM Samples

Table 3.3, Specifications of MEXA 584L Portable Emission Analyzer

Components NOx CO2 HC

Range 0-5000ppmvol 0.00- 20.00%vol 0- 10000ppmvol

Accuracy ±30ppm ±0.5% ±12ppm

Make Horiba, Japan

Warm-up time Approximately 5 minutes

Conditions Measures gas temperature from 4 to 900C

Ambient storage Ambient storage shall be within -30C to 60C

Humidity Relative humidity shall be stable and less than 90% (without dew)

Temperature Environmental temperature shall be stable and within the range of 0°C to 45°C (Temperature variation shall be within ±5°C)

3.5 Experimental Study Design

Preparing for field data collection involves few major tasks, 40

1. The most challenging part was to coordinate with the garage officer and the bus

driver for available bus schedules, timings, fuel type need to be used, duty cycle

performance and driving back the bus to a specific location every day after the bus

comes to garage, in order to find it easily in the morning instead of searching for it

in the fleet.

2. Coordination with the division of TARTA management regarding scheduling of

the test and access to the buses.

3. Calibrating the instrument before the test and verifying all necessary accessories

4. Changing the MEXA 584L portable emission analyzer filters every day before the

test.

5. Completing data collection and rearranging all the above-mentioned steps for next

day’s test.

There are three major stages of testing namely cold idle mode, running mode and hot idle mode. The test day starts with PM test around 5:30 am in the morning before the bus leaves the garage. In this phase Catch Can with a quartz filter paper in it is used to collect PM data.

Later after PM test begins the cold idle test. During this process engine data was also monitored and saved simultaneously. In this condition, the acceleration of the bus was zero, and the load was almost negligible. The bus starts at 7:30 am in the morning and the instrument was set up already to collect cold idle mode reading. It took around 30 minutes to calibrate and set up the instrument every day and took around 10 minutes to remove the setup off the bus after the run. Great care was taken during the setup and removal of the

41

instrument from the bus. Moreover, the running condition was divided into four different modes namely acceleration, deceleration, variable speed, and intersections.

The deceleration mode was the time taken from the time it starts decelerating to the time it reaches the idle position. Acceleration mode was the time taken for the buses to start from an idle position to the time it reaches a constant speed due to the busy traffic, the buses stopped. Also, due to various other road conditions, the buses moved with variable speeds accordingly and this data was considered the variable speed mode. The idle times attained at the traffic signals or intersections was considered as an intersection. Soon after the ride hot idle readings were collected, in this condition, the acceleration of the bus was zero, and the load was almost negligible.

Lastly, PM test was performed after the bus arrived at the garage finishing its route at 10:30 pm in the night. PM reading was collected making use of Catch Can.

The above-mentioned protocol was performed every day during the data collection process. In the running bus condition, the experiments were conducted for 8 days each on the 610,826 and the 802 buses. Whereas, the PM was conducted for 8 days on each bus respectively. The challenging part was to schedule the same bus on a particular route for a certain number of days was very difficult and that was one reason which delayed the experiment process. Data collected during any interruptions, for instance, the breakdown of the bus, slipping of instruments due to road conditions, sudden braking, sudden route change, EGR failure, DPF cleanup indicator on and sudden roadblock by police, was not considered and the test was determined as a fail test. In idle mode, all the emissions NOx,

CO2, and HC are plotted against time and in running condition, all the three emissions are plotted individually against rpm for all the modes i.e., acceleration, deceleration, Variable 42

speed, and intersections. And comparisons of NOx, CO2 and HC pollutants trend between exhaust gas after-treatment equipped buses.

3.6 Data Analysis

The collected NOx, CO2, and HC are analyzed to develop a statistical regression model that can predict the pollutant concentrations of the exhaust emissions with respect to different engine parameters in running modes and idle modes for transit buses equipped with NON-EGR, EGR+DPF+DOC, and EGR+SCR+UREA technologies. In this study,

Extra Tree Regression and Lasso techniques are used to compare the efficiency of the data.

ANN techniques are the type of artificial intelligence, which have developed as a powerful tool. ANN is capable of modeling non-linear data effortlessness and simulates the manner in which the neurons work in the human brain, and once trained perfectly it can make predictions and generalizations. For supervised ANN, the network factors such as weights and biases are updated using different algorithms. These algorithms make use of error signals to differentiate between the desired output and the current output. Multi-Layer

Perceptron (MLP) networks are most popularly used ANNs. MLP consists of three different layers namely input layer, hidden layer, and output layer all these layers are interconnected with neurons of other layers using weighted links [60-61]. Refer Figure

3-6, for a schematic diagram of MLP ANN with 3 layers.

43

Figure 3-6: Schematic Diagram of MLP ANN with 3 Layers

Additionally, data representation plays a major role in the performance of

Neural Network, particularly in the application of real-world problems. Min Max normalization was used in this study to present the data. In normalization of data, the higher esteemed values may tend to lessen the impact of smaller values. To keep away from this, the raw data was normalized using Min-Max normalization for better and exact output results. The selection of network structure was done based on the trial and error method by selecting the appropriate number of hidden nodes in such a way that the best performance of the network was ensured. During the network training the basic data was normalized, this normalized data was fed to the network to learn the input and output relationship.

Finally, the developed network was tested for the real-time data and the results and performances were presented by using an algorithm [62]. Most of the ANN studies related to vehicle emission performance [63-65] evaluated the validity of their models against statistical error measures. A commonly used statistical analysis is the root mean squared error (RMSE).

44

A simple ANN structure consists of three layers namely input layer, an output layer, and hidden layers as shown in the figure 3-6. Operating parameters of the engine were fed into the input layer, and NOx, CO2, and HC emissions were the outputs of the network. The main advantages of the Least Absolute Shrinkage and Selection Operator

(LASSO) constraint are the capability to control generalization just as the norm constraint does but with an automatic weight selection procedure. For multi-layer perceptron (MLP) neural networks as long as the topology is larger than the unknown optimal number of hidden nodes, the LASSO approach can reduce or eliminate some weights and network inputs on the final solution. This may result in a reduced topology and improved network performance. Moreover, this information can be used as a data underlying function complexity measure [73]. On the other hand, Extra Tree Regression model was used to train and test the data to find the prediction values. Therefore, the above two techniques were used to find the error between the actual and predicted outputs with respect to their fit.

Extremely Randomized Trees (Extra Tree) method is similar to the Random Forests algorithm in the sense that it is based on selecting at each node a random subset of K features to decide on the split. Unlike in the Random Forests method, each tree is built from the complete learning sample (no bootstrap copying) and, most importantly, for each of the features (randomly selected at each interior node) a discretization threshold (cut- point) is selected at random to define a split, instead of choosing the best cut-point based on the local sample (as in Tree Bagging or in the Random Forests method). As a consequence, when K is fixed to one, the resulting tree structure is actually selected independently of the output labels of the training set. In practice, the algorithm only 45

depends on a single main parameter, K. Good default values of K have been found empirically to be K = √p for classification problems and K = p for regression problems, where p is the number of input features [72].

In order to examine the elemental composition of the soot samples collected from all the three buses two elemental analysis where conducted namely ICP-MS and

EDS/SEM. In this analysis EDS/SEM was conducted to obtain the surface analysis whereas, ICP-MS was used for bulk analysis of the soot samples. Furthermore, ICP-MS results give the trace metal concentrations (Na, Mg, Al, P, K, Ca, Fe, Cu, Zn, and Mo) present in the soot samples. On the other hand, EDS/SEM was used to obtain C, O, Si and

Cu concentrations. Therefore, all the above-mentioned analysis gave a better understanding of the concentration of elements which were expected to be present in the samples. The instrument used for ICP-MS was the Xseries 2 (Thermo Scientific, MA, USA). The procedure for ICP-MS analysis was as follows: approximately 200 mg of each sample was digested in HNO3 using a CEM Mars microwave. For digestion, the conditions were as follows: power (100%), ramp (15 min), pressure (800 psi), temperature (210_C), and holding time (15 minutes). After digestion, samples were filtered to remove particulate material. The filtrate was then diluted to 3.5% HNO3 for analysis. For quantitative analysis, standards and internal standards were prepared by using the certified ICP-MS standards from inorganic ventures. Correlation coefficients for calibration curves were above 0.999.

Trace metals determined in the solutions were reported in micrograms per gram (µg/g) of the sample.

The particulate matter emission collection was carried out with quartz filter paper and a Catch Can instrument. An EDS (Energy Dispersive X-Ray Spectroscopy) and ICP- 46

MS (Inductively Coupled Plasma- Mass Spectrometry) was used to analyze the soot particles collected on the quartz filter paper. The instrument used for EDS analysis was

Oxford Instruments EDS X-Max 50mm2 / FEI Quanta 3D FEG Dual Beam Electron

Microscope. The following conditions were used spectrum range (0-10 keV) and detector

(X-Max). The samples were prepared as follows: resin flakes were placed on double-sided carbon tape then attached to aluminum stubs. The samples were then coated with gold using the sputter coater unit (Cressington 108 auto) for 30 seconds. Subsequently, sample stubs were loaded in the SEM and examined in the EDS. In this study, EDS/SEM analysis was used to give a surface analysis of all the samples tested for PM. Moreover, EDS was basically used to identify the concentration of carbon and oxygen present in the samples which could not be found using ICP-MS.

47

Chapter 4

Results in Discussions

4.1 Analysis of NOx, CO2, HC Emission Data

This research deals with the emissions from public transportation buses fueled with biodiesel and the effect of exhaust control systems in controlling NOx, CO2, HC and PM emissions with respect to various engine parameters. The measured vehicle emissions have been analyzed in detail and the analysis was divided into two sections, real-world on-road emission characterization, and idle-engine emission characterization. Three different buses equipped with different exhaust control system namely 610-NON-EGR bus, 802-EGR bus

(DPF+DOC) and 826-hybrid bus (EGR+SCR+UREA) were tested.

Field data presentation and regression model analysis of NOx, CO2, and HC emission data are presented in the first half of the chapter. Moreover, it includes the results of NOx, CO2, and HC emissions obtained from idle/acceleration/variable speed/deceleration and intersections conditions with respect to various engine parameters.

These results were tabulated and compiled for the 610-NON-EGR bus, 802-EGR bus 826- hybrid bus. The second part of results are from PM samples. The analysis used for studying

48

PM samples were ICP-MS, energy dispersive X-ray spectroscopy (EDS) and scanning electron microscopy (SEM).

4.1.1 Idle Condition

In this mode, vehicle engine keeps running when the vehicle is not in motion.

Regularly in the U.S., a large number of cars, buses, and trucks sit idle unnecessarily, occasionally they are left idle for hours and an idle vehicle can discharge as much contamination as a moving vehicle. This commonly occurs when vehicles are stopped at a red light, waiting while parked outside a business or residence, or otherwise stationary with the engine running. One cannot avoid idle when stopped at a traffic signal or stuck in slow- moving traffic. But other times idle is unnecessary. Quite a few years back, automobile manufacturers suggested warming up the engine for a few minutes to guarantee engine performance. Despite the fact that the fleet vehicle manufacturing process has experienced a quick change, but this practice has lingered on among vehicle operators and unknowingly affecting the engine performance and releasing harmful pollutants into the atmosphere. It is believed that restarting the engine uses more gas than idle. Reality: an engine restart uses fuel approximately equal to 10 seconds of idle. Therefore, in order to understand the pollutant concentration emitted in idle mode, it was further divided into Cold Idle mode and Hot Idle mode. Hot Idle emissions were collected during night time when the bus comes back to the garage from its regular route and the cold idle emissions were recorded in the morning prior the bus leaves on its specified route.

49

The values were collected for the 610-NON-EGR bus, 802-EGR bus, and 826- hybrid bus. These buses were tested in both cold idle and hot idle conditions for NOx emissions. The cold idle values of NOx for a hybrid bus were found to be 57% lower compared to the EGR bus and 73% lower compared to the NON-EGR bus as shown in

Figure 4-1.

The hot idle values for an 826-hybrid bus were noted to be 60-64% lower than an

802-EGR bus, and 68% lower than the 610-NON-EGR bus as shown in Figure 4-2. As mentioned earlier, both buses run on the same type of fuel were tested on the same routes

NOX Cold Idle emissions Max. 800 651 600 410 400

NOx PPM 175 200

0 Hybrid (EGR+SCR+Urea) 802 (EGR+DPF+DOC) 610 (NON-EGR) bus Type

Figure 4-1: Cold idle values of NOx emissions from EGR, NON-EGR, and hybrid buses

and in the same driving conditions with negligible changes in load. This clearly shows the effect of (EGR+SCR+UREA) system equipped on the hybrid in reducing the NOx emissions.

50

NOX Hot Idle emissions Max. 400 290 300 250 200 90

NOx PPM 100 0 Hybrid (EGR+SCR+Urea) 802 (EGR+DPF+DOC) 610 (NON-EGR) bus Type

Figure 4-2: Hot idle values of NOx emissions from EGR, NON-EGR, and hybrid buses.

The cold idle CO2 emissions for the 826-hybrid bus were found to be 19% higher as compared to the EGR bus, and 29% higher than the NON-EGR bus as shown in Figure 4-

3.

The hot idle CO2 emissions for a hybrid bus were noted to be 30% higher than EGR bus and 40% higher than NON-EGR equipped bus as shown in Figure 4-4. In this case study, hybrid bus emitted high CO2 emissions into the atmosphere than compared to EGR and

NON-EGR equipped buses.

Moreover, CO2 emissions are high in the 826-hybrid bus as compared to 802-

EGR and 826 NON-EGR buses. This is because of chemical reaction that takes place in

SCR and DOC as shown in Eq. 3.1 to 3.5 releasing CO2 as a product.

51

CO2 Cold Idle emissions Max.

15 12.39 10.03 10 7.1

5 CO2 CO2 %Vol.

0 Hybrid (EGR+SCR+Urea) 802 (EGR+DPF+DOC) 610 (NON-EGR) bus Type

Figure 4-3: Cold idle values of CO2 emissions from EGR, NON-EGR, and hybrid buses

CO2 Hot Idle emissions Max. 10 8.6 8 6 6 5.14 4

CO2 CO2 %Vol. 2 0 Hybrid (EGR+SCR+Urea) 802 (EGR+DPF+DOC) 610 (NON-EGR) bus Type

Figure 4-4: Hot idle values of CO2 emissions from EGR, NON-EGR, and hybrid buses.

And the cold idle values of HC for a hybrid (EGR+SCR+UREA) equipped bus were found to be 36% lower compared to the 802-EGR equipped bus and 92% lower as compared to the 610-NON-EGR bus as shown in Figure 4-5.

The hot idle values of HC for a hybrid bus are noted to be 28% lower than the 802-

EGR equipped bus and 88% lower than 610-NON-EGR equipped bus as shown in Figure

4-6. This clearly shows the effect of exhaust control systems (EGR+SCR+UREA) in the hybrid bus and (DPF+DOC) in an 802-EGR bus in reducing HC emissions.

52

HC Cold Idle emissions Max. 100 90 80 60

40 HCPPM 20 7 11 0 Hybrid 802 (EGR+DPF+DOC) 610 (NON-EGR) (EGR+SCR+Urea) bus Type

Figure 4-5: Cold idle values of HC emissions from EGR, NON-EGR, and hybrid buses

HC Hot Idle emissions Max. 50 45 40 30 20

HCPPM 7 10 5 0 Hybrid 802 (EGR+DPF+DOC) 610 (NON-EGR) (EGR+SCR+Urea) bus Type

Figure 4-6: Hot idle values of HC emissions from EGR, NON-EGR, and hybrid buses.

In this Study, HC emitted from 802-EGR and 826-hybrid bus are almost the same, but very low when compared with the 610-NON-EGR bus. This is because of the DOC emission control system equipped in the 826-hybrid bus and 802-EGR bus. DOC contain palladium, platinum, and aluminum, all of which serve as catalysts to oxidize the hydrocarbons and carbon monoxide with oxygen to form carbon dioxide and water.

53

4.1.1.1 Cold Idle

The NOx, CO2 and HC emissions were collected from Horiba MEXA 584L portable emission analyzer. Fundamentally, the data collected reached an optimum value in cold idle approximately around 15 minutes. Whereas, it took around 12 minutes to reach the optimum value during hot idle mode and these two-hypothesis match with the results of Kumar et al.,[65] having approximately the same time frame to reach optimum values in both hot and cold idle. As stated in the methodology, the data was collected for 1100 seconds for all three buses over a period of 8 days and the average was taken. This process is known as average time concentration and all the idle datasets were obtained and presented in this method.

The NOx emissions from the cold idle engine were comparatively higher and there were 55% to 75% higher emissions for the 610-NON-EGR bus, 802-EGR bus when compared to the 826-hybrid bus as shown in Figure 4-7. Whereas, the CO2 emissions for

610 NON-EGR, 802-EGR buses were 19% to 30 % lower than the 802-hybrid bus as shown in the Figure 4-8 and HC emissions for the 610-NON-EGR bus and 802-EGR bus were 36% to 92 % higher than 826-hybrid bus as shown in Figure 4-9. As shown in Figure

4-7 to 4-9, the NOx, CO2 and HC emissions for all the three buses tend to decrease and reach a constant value in 15 minutes. NOx, CO2 and HC emissions tend to future decrease beyond that point. But, the rate of change of decrease in emissions with respect to time was very low.

54

700 NOx Cold Idle 600 Hybrid (EGR+SCR+Urea) 802 (EGR+DPF+DOC) 500 610 (NON-EGR) 400

300 NOx ppm 200 100 0 0 200 400 600 800 1000 1200 Time in seconds

Figure 4.7: NOx emission in cold idle for all three different buses

14 CO2 Cold Idle 12

10 8

6 CO2 CO2 %Vol. 4 2 Hybrid (EGR+SCR+Urea) 802 (EGR+DPF+DOC) 0 0 200 400 600 800 1000 1200 Time in seconds

Figure 4.8: CO2 emission in cold idle for all three different buses

55

100 HC Cold Idle Hybrid 80 (EGR+SCR+Urea) 802 (EGR+DPF+DOC)

60 HC 40

20

0 0 200 400 600 800 1000 1200 Time in seconds

Figure 4.9: HC emission in cold idle for all three different buses

4.1.1.2 Hot Idle

The NOx emissions were 60% to 68% high for the 610-NON-EGR bus, 802-EGR bus when compared to the 826-hybrid bus as shown in Figure 4-10, Whereas, the CO2 emissions for 610-NON-EGR bus, 802-EGR bus were 27% to 40 % lower than 826-hybrid bus as shown in the Figure 4-11 and HC emissions for 610-NON-EGR bus, 802-EGR bus are 25% to 88 % higher than 826-hybrid bus as shown in Figure 4-12. Moreover, as shown in Figure 4-10 to 4-12, the NOx, CO2 and HC emissions for all the three buses tend to decrease and reach a constant value in 12 minutes. NOx, CO2 and HC emissions tend to future decrease beyond that point. But, the rate of change of decrease in emissions with respect to time was very low.

The cold idle emissions are observed to be very high when compared to the hot idle condition, this was because the hot idle emissions were collected after the bus came from

56

its daily route with a hot engine and this injects the appropriate amount of fuel to air into the engine for complete combustion. Whereas cold engine, during cold idle mode does not run at its optimum temperature that leads to incomplete combustion.

NOx Hot Idle 350

300 Hybrid (EGR+SCR+Urea) 802 (EGR+DPF+DOC) 250 610 (NON-EGR) 200

150 NOx ppm 100

50

0 0 200 400 600 800 1000 1200 Time in seconds

Figure 4.10: NOx emission in hot idle for all three different buses

10 CO2 Hot Idle

8

6

4 CO2 CO2 %Vol.

2 Hybrid (EGR+SCR+Urea) 610 (NON-EGR) 0 0 200 400 600 800 1000 1200 Time in seconds

Figure 4.11: CO2 emission in hot idle for all three different buses

57

50 HC Hot Idle Hybrid 40 (EGR+SCR+Urea) 802

30 (EGR+DPF+DOC) HC 20

10

0 0 200 400 600 800 1000 1200 Time in seconds Figure 4.12: HC emission in hot idle for all three different buses

4.1.2 Running Conditions

In running condition, many factors affect emission formation in diesel engines such as load, acceleration, deceleration, road traffic, traffic signals, nature of road and driver’s behavior. During running mode emission testing was conducted for acceleration, deceleration, variable speed and intersection modes. The deceleration condition is also known as breaking condition. During deceleration, no extra amount of load is applied to the engine in order to bring the bus to a halt. So, while decelerating the fuel consumption and the NOx, CO2 and HC emissions reduce. Whereas during acceleration mode, a large amount of fuel is burnt and an extra load is applied to the engine as a result, NOx, CO2 and

HC emissions increase. Moreover, when the bus moves at different speeds, different levels of the load is applied to the engine. In this situation, the bus moving at variable speed emits a different amount of NOx, CO2 and HC emissions into the atmosphere [65]. In a study

CO2 and NOx emissions decrease with horsepower in the deceleration mode, increase with

58

horsepower in acceleration and decrease at different levels with different variable speeds

[70-71]. A graphical representation of NOx, CO2 and HC emissions versus engine speed in a decelerating, accelerating, variable speed and intersections are presented in Figure:4-

13 to Figure: 4-33.

The NOx and HC emissions decreased from NON-EGR to EGR to the hybrid bus because of the emission control systems like SCR, DOC, and EGR. Whereas CO2 emissions increased by using the same emissions control systems from NON-EGR to EGR to hybrid bus.

In this Study, HC emitted from EGR and hybrid buses were almost the same, but very low when compared with the NON-EGR bus. This is because of the DOC emission control system Equipped with the hybrid bus and EGR bus. DOC contain palladium, platinum, and aluminum, all of these serve as catalysts to oxidize the hydrocarbons and carbon monoxide with oxygen to form carbon dioxide and water.

DOC may also be used in conjunction with SCR catalysts to oxidize NO into NO2

(Eq.3.3). There are three main reactions which occur in DOC [57].

CO +1/2 O2 → CO2CO +1/2 O2 → CO2 Eq.3.1

C3H6 + 9/2 O2→ 3 CO2 + 3 H2OC3H6 + 9/2 O2 → 3 CO2 + 3 H2O Eq.3.2

NO +1/2 O2→ NO2NO +1/2 O2→ NO2 Eq.3.3

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The study shows that 826-hybrid bus emits less amount of NOx when compared to

802-EGR and 610 NON- EGR bus, this was because of the exhaust treatment system SCR.

Selective Catalytic Reduction is an exhaust after-treatment system that significantly reduces the number of oxides of nitrogen emitted from the exhaust. The bus carries a tank of diesel exhaust fluid, a liquid that is injected into the decomposition chamber by the dosing module. When the diesel exhaust fluid mixes with the hot exhaust, ammonia is released. This exhaust/ammonia mixture passes through the SCR catalyst, where the oxides of nitrogen are turned into nitrogen and water. Whereas, 802-EGR bus makes use of EGR to recirculate a portion of exhaust gas back into the engine. Therefore, this regeneration process controls the formation of NOx. In specific, NOx reduces subsequently by a decrease in combustion temperature which is a result of the endothermic separation of H20

[65].

The overall reduction of NOx by urea is:

2 (NH2)2CO + 4 NO + O2 → 4 N2 + 4 H2O + 2 CO2 and Eq.3.4

1 2 (NH2)2CO + 3 NO2 → 3 ⁄2 N2 + 4 H2O + 2 CO2 Eq.3.5

Moreover, CO2 emissions are high in the 826-hybrid bus then compared to 802-

EGR and 610-NON-EGR buses. This is because of chemical reaction that takes place in

SCR and DOC as shown in Eq. 3.1 to 3.5 releasing CO2 as a product.

4.1.2.1 Acceleration Mode

In the acceleration mode, the highest amount of HC, CO2, and NOx emissions were observed. In all the three buses the average time period for acceleration was 10 to 20

60

seconds. In Figure 4-14, the difference between HC emissions was 92% for 826-hybrid and

610 Non- EGR buses, and a 33% difference of HC emissions between 826-hybrid and 802-

EGR buses. From Figure 4-13, it can be noticed that HC emissions decreased with rpm and vice versa. Moreover, it can be understood that HC emissions for 610 Non-EGR bus were high when compared to 802-EGR and 826-hybrid buses. This was because of the DOC emission control system Equipped with the hybrid bus and EGR bus. The HC emissions for 826-hybrid and 802-EGR buses were almost in the same range with minor differences, this was because of having almost the same exhaust control system on board. Whereas,

610-NON-EGR bus with no emission control systems on board raked high in emitting HC.

100 HC Acceleration

80

60 Hybrid

HCPPM (EGR+SCR+U 40 rea)

20

0 1550 1650 1750 1850 1950 2050 2150 2250 2350 rpm

Figure 4-13: HC emissions in acceleration mode for all the three buses.

61

HC Acceleration emissions Max. 120 98 100 80 60

HCPPM 40

20 8 12 0 Hybrid (EGR+SCR+Urea) 802 (EGR+DPF+DOC) 610 (NON-EGR) bus Type

Figure:4-14: Highest points of HC in acceleration mode

In Figure 4-16, the difference between CO2 emissions was 36% for 610 Non- EGR and 826-hybrid buses and a 26% difference of CO2 emissions between 610 Non- EGR and

802-EGR buses. From Figure 4-15, it can be noticed that CO2 emissions decreased with rpm and vice versa. Moreover, it can be understood that CO2 emissions for 610 Non-EGR bus were low when compared to 802-EGR and 826-hybrid buses. This is because of the chemical reaction that takes place in SCR and DOC exhaust control systems as shown in

Eq. 3.1 to 3.5 releasing CO2 as a product for 802-EGR and 826-hybrid buses.

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CO2 ACCELERATION 14

12

10 CO2%V 8 Hybrid (EGR+SCR+Urea)

6 802 (EGR+DPF+DOC) 610 (NON-EGR) 4 1550 1650 1750 1850 1950 2050 2150 2250 2350 RPM

Figure 4-15: CO2 emissions in acceleration mode for all the three buses.

CO2 Acceleration emissions Max. 16 13.91 14 11.98 12

10 8.92

8

CO2 CO2 Vol. % 6

4

2

0 Hybrid (EGR+SCR+Urea) 802 (EGR+DPF+DOC) 610 (NON-EGR) bus Type

Figure 4-16: Highest points of CO2 in acceleration mode

In Figure 4-18, the difference between NOx emissions was 47% for 826-hybrid and

610 Non- EGR buses, and a 30% difference of NOx emissions between 826-hybrid and

802-EGR buses. From Figure 4-17, it can be noticed that NOx emissions decreased with rpm and vice versa. Moreover, it can be understood that NOx emissions for 610 Non-EGR

63

bus were high when compared to 802-EGR and 826-hybrid buses. This was due to

EGR+SCR+UREA exhaust control systems on 826-hybrid bus and EGR on 8802-EGR bus. Whereas, 610-NON-EGR bus with no emission control systems on board raked high in emitting NOx.

700 NOX ACCELERATION 600

500

400 Hybrid

NOX PPM NOX (EGR+SCR+Urea) 300 802 (EGR+DPF+DOC) 610 (NON-EGR) 200

100 1550 1650 1750 1850 1950RPM 2050 2150 2250 2350

Figure 4-17: NOx emissions in acceleration mode for all the three buses.

NOX Acceleration emissions Max. 800 672 700 600 470 500 400 357

300 NOXPPM 200 100 0 Hybrid (EGR+SCR+Urea) 802 (EGR+DPF+DOC) 610 (NON-EGR) bus type

Figure 4-18: Highest points of NOx in acceleration mode

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4.1.2.2 Variable Speed Mode

The emission values recorded in variable speed mode are low when compared to acceleration mode. In Figure 4-20, the difference between NOx emissions were 46% for

826-hybrid and 610 Non- EGR buses, and a 24% difference of NOx emissions between

826-hybrid and 802-EGR buses. From Figure 4-19, it can be noticed that NOx emissions decreased with rpm and vice versa. Moreover, it can be understood that NOx emissions for

610 Non-EGR bus were high when compared to 802-EGR and 826-hybrid buses. This was due to EGR+SCR+UREA exhaust control systems on 826-hybrid bus and EGR on 810 bus. Whereas, 610-NON-EGR bus with no emission control systems on board raked high in emitting NOx.

700

600 NOX VARIABLE SPEED Hybrid 500 (EGR+SCR+Urea) 802 400 (EGR+DPF+DOC)

300 610 (NON-EGR) NOX PPM NOX

200

100

0 1100 1200 1300 1400 1500 1600 RPM

Figure 4-19: NOx emission data for all the three buses in variable speed mode.

65

NOX Variable speed emissions Max. 400 350 350 300 267 250 189 200

150 NOXPPM 100 50 0 Hybrid (EGR+SCR+Urea) 802 (EGR+DPF+DOC) 610 (NON-EGR) bus type

Figure 4-20: Highest points of NOx in variable speed mode

In Figure 4-21, the difference between CO2 emissions was 39% for 610 Non- EGR and 826-hybrid buses, and a 20% difference of CO2 emissions between 610 Non- EGR and

802-EGR buses. From Figure 4-20, it can be noticed that CO2 emissions decreased with rpm and vice versa. Moreover, it can be understood that CO2 emissions for 610 Non-EGR were low when compared to 802-EGR and 826-hybrid buses. This was because of chemical reaction that took place in SCR and DOC exhaust control systems as shown in

Eq. 3.1 to Eq. 3.5 releasing CO2 as a product for 802-EGR and 826-hybrid buses.

15 CO2 VARIABLE SPEED Hybrid (EGR+SCR+Urea)

10 CO2%V 5

0 1100 1200 1300 1400 1500 1600 RPM

Figure 4-20: CO2 emission data for all the three buses in variable speed mode, 66

CO Variable speed emissions Max. 12 2 9.82 10 7.83 8 5.98 6

CO2 CO2 Vol. % 4

2

0 Hybrid (EGR+SCR+Urea) 802 (EGR+DPF+DOC)bus Type 610 (NON-EGR)

Figure 4-21: Highest points of CO2 in variable speed mode

In Figure 4-23, the difference between HC emissions were 91% for 826-hybrid and

610 Non- EGR buses, and an 86% difference of HC emissions between 826-hybrid and

802-EGR buses. From Figure 4-22, it can be noticed that HC emissions decreased with rpm and vice versa. Moreover, it can be understood that HC emissions for 610 Non-EGR bus were high when compared to 802-EGR and 826-hybrid buses. As mentioned earlier, this was because of the DOC emission control system Equipped with the hybrid bus and

EGR bus. The HC emissions for 826-hybrid and 802-EGR buses were almost in the same range with minor differences, this is because of having almost the same exhaust control system on board. Whereas, 610-NON-EGR bus with no emission control systems on board raked high in emitting HC.

67

60 HC Acceleration

50

40 Hybrid (EGR+SCR+Urea) 30

802 (EGR+DPF+DOC) HCPPM 20 610 (NON-EGR)

10

0 1100 1200 1300 1400 1500 1600 rpm Figure4-22: HC emission data for all the three buses in variable speed mode

HC Variable speed emissions Max.

70 60 56 50 40 30 HCPPM 20 8 10 5 0 Hybrid (EGR+SCR+Urea) 802 (EGR+DPF+DOC) 610 (NON-EGR) bus Type

Figure 4-23: Highest points of HC in variable speed mode

4.1.2.3 Deceleration

The emission values recorded in deceleration mode were the lowest compared to all other modes. In Figure 4-25, the difference between NOx emissions was 40% for 826- hybrid and 610 Non- EGR buses, and a 24% difference of NOx emissions between 826- hybrid and 802-EGR buses. From Figure 4-24, it can be noticed that NOx emissions 68

decreased with rpm and vice versa. Moreover, it can be understood that NOx emissions for

610 Non-EGR bus were high when compared to 802-EGR and 826-hybrid buses. This was due to EGR+SCR+UREA exhaust control systems on 826-hybrid bus and EGR on 810- bus. Whereas, 610-bus with no emission control systems on board raked high in emitting

NOx.

100 NOX DECELERATION 90 Hybrid (EGR+SCR+Urea) 80 802 70 (EGR+DPF+DOC) 610 (NON-EGR) 60 50

40PPM NOX 30 20 10 0 700 800 900 1000 1100 1200 RPM

Figure 4-24: NOx emission data for all the three buses in deceleration mode.

NOX Deceleration emissions Max. 50 45

40 34 30 27

20 NOX PPM NOX 10

0 Hybrid (EGR+SCR+Urea) 802 (EGR+DPF+DOC) 610 (NON-EGR) bus type

Figure 4-25: Highest points of NOx in deceleration mode

69

In Figure 4-27, the difference between HC emissions was 89% for 826-hybrid and

610 Non- EGR buses, and an 86% difference of HC emissions between 826-hybrid and

802-EGR buses. From Figure 4-26, it can be noticed that HC emissions decreased with rpm and vice versa. Moreover, it can be understood that HC emissions for 610 Non-EGR bus were high when compared to 802-EGR and 826-hybrid buses. As mentioned earlier, this is because of the DOC emission control system Equipped with the hybrid bus and

EGR bus. The HC emissions for 826-hybrid and 802-EGR buses are almost in the same range with minor differences, this is because of having almost the same exhaust control system on board. Whereas, 610-bus with no emission control systems on board raked high in emitting HC’s.

40 35 HC Deceleration 30 25 20 Hybrid

HCPPM 15 (EGR+SCR+U rea) 10 5 0 700 800 900 1000 1100 1200 1300 rpm

Figure4-26: HC emission data for all the three buses in deceleration mode.

70

HC Deceleration emissions Max. 35 28 30 25 20 15 HCPPM 10 3 4 5 0 Hybrid (EGR+SCR+Urea) 802 (EGR+DPF+DOC) 610 (NON-EGR) bus Type

Figure 4-27: Highest points of HC in deceleration mode

In Figure 4-29, the difference between CO2 emissions was 44% for 610 Non- EGR and 826-hybrid buses and a 24% difference of CO2 emissions between 610 Non- EGR and 802-EGR buses. From Figure 4-28, it can be noticed that CO2 emissions decreased with rpm and vice versa. Moreover, it can be understood that CO2 emissions for 610 Non-

EGR were low when compared to 802-EGR and 826-hybrid buses. This is because of chemical reaction that takes place in SCR and DOC exhaust control systems as shown in

Eq. 3.1 to Eq. 3.5 releasing CO2 as a product for 802-EGR and 826-hybrid buses.

71

CO2 DECELERATION 14 Hybrid 12 (EGR+SCR+Urea) 802 10 (EGR+DPF+DOC) 8

CO2%V 6

4

2

0 700 800 900 1000 1100 1200 1300 RPM .

Figure4-28: CO2 emission data for all the three buses in deceleration mode.

6 CO2 Deceleration emissions Max. 5.29 5 3.91 4 2.97 3

CO2 CO2 Vol. % 2

1

0 Hybrid (EGR+SCR+Urea) 802 (EGR+DPF+DOC)bus Type 610 (NON-EGR)

Figure 4-29: Highest Points of CO2 in deceleration mode

72

4.1.2.4 Intersections

The emission values recorded at intersections are presented in this mode, and the idle time at intersections lasted about 20 to 30 seconds. From the Figure 4-30, 4-32, and 4-

34 we can see a sudden increase in emissions till a certain point and then decrease gradually. The possible reason could be because of the combustion temperature, In the process of stopping at an intersection, the bus decelerates from acceleration or variable speed mode. Therefore, a large amount of load was applied on the engine to bring the bus to a halt and during this process, the increased engine temperature during acceleration was carried out through the deceleration mode and later decreases at some point at an intersection making the NOx, CO2, and HC emissions fall gradually.

In Figure 4-31, the difference between NOx emissions were 61 % for 826-hybrid and 610 Non- EGR buses, and a 27% difference of NOx emissions between 826-hybrid and 802-EGR buses. From Figure 4-30, it can be understood that NOx emissions for 610

Non-EGR bus were high when compared to 802-EGR and 826-hybrid buses. This was due to EGR+SCR+UREA exhaust control systems on 826-hybrid bus and EGR on 810-bus.

Whereas, 610-bus with no emission control systems on board raked high in emitting NOx.

73

451 NOX INTERSECTION 401 351 301 251

201PPM NOX 151 Hybrid 101 (EGR+SCR+Ure 51 a) 1 0 5 10 15 RPM20 25 30 35 40

Figure 4-30: NOx emissions at intersections for all the three buses.

NOX Intersection emissions Max. 600 500 463 400 340 300 179

NOXPPM 200 100 0 Hybrid (EGR+SCR+Urea) 802 (EGR+DPF+DOC) 610 (NON-EGR) bus type

Figure 4-31: Highest Points of NOx at intersections

In Figure 4-33, the difference between CO2 emissions was 38% for 610 Non- EGR and 826-hybrid buses, and a 20% difference of CO2 emissions between 610 Non- EGR and

802-EGR buses. From Figure 4-32 Moreover, it can be understood that CO2 emissions for

610 Non-EGR were low when compared to 802-EGR and 826-hybrid buses. This is because of chemical reaction that takes place in SCR and DOC exhaust control systems

74

as shown in Eq. 3.1 to Eq. 3.5 releasing CO2 as a product for 802-EGR and 826-hybrid buses.

15 CO2 INTERSECTION Hybrid (EGR+SCR+Urea) 10 802 (EGR+DPF+DOC)

610 (NON-EGR) CO2%V 5

0 0 5 10 15 20 25 30 35 RPM Figure 4-32: CO2 emissions at Intersections for all the three buses.

12 CO2 Intersection emissions Max.

10 9.31

8 7.42 5.81 6

CO2 CO2 Vol. % 4

2

0 Hybrid (EGR+SCR+Urea) 802 (EGR+DPF+DOC)bus Type 610 (NON-EGR) Figure 4-33: Highest Points of CO2 at intersections

In Figure 4-35, the difference between HC emissions was 91% for 826-hybrid and

610 Non- EGR buses, and an 85% difference of HC emissions between 826-hybrid and

802-EGR buses. From Figure 4-34 Moreover, it can be understood that HC emissions for 75

610 Non-EGR bus were high when compared to 802-EGR and 826-hybrid buses. As mentioned earlier, this is because of the DOC emission control system Equipped with the hybrid bus and EGR bus. The HC emissions for 826-hybrid and 802-EGR buses are almost in the same range with minor differences, this is because of having almost the same exhaust control system on board. Whereas, 610-bus with no emission control systems on board raked high in emitting HC.

70 60 HC Intersection 50 Hybrid 40 (EGR+SCR+U rea)

30 HCPPM 20 802 (EGR+DPF+D 10 OC) 0 0 5 10 Time15 in Seconds20 25 30 35

Figure 4-34: HC emissions at intersections for all the three buses.

HC Intersection emissions Max.

80 67 60 40

HCPPM 20 6 10 0 Hybrid (EGR+SCR+Urea) 802 (EGR+DPF+DOC) 610 (NON-EGR) bus Type

Figure 4-35: Highest Points of HC at Intersections

76

4.1.3 Analysis of NOx, CO2 and HC Emissions using

Regression Methods

Ganesan et al., Ghobadian et al., Najafi et al., and Kumar et al., [62-65] used different regression techniques in artificial neural networks and created a model to find the rate of error between their actual and predicted emission data. By making use of artificial neural networks the above studies proved it was evident that ANN models are a reliable tool to predict the performance and exhaust emissions of diesel engines. Moreover, Kumar et al.,

[65] was also among the studies that conducted field experiments on biodiesel-fueled urban transit buses and created an ANN model to predict NOx emission values.

In order to analyze the field data from vehicular emission in this study, two different regression techniques were employed for prediction of NOx, CO2, and HC emission values.

Lasso and ET regression techniques were employed to predict NOx, CO2 and HC emission values. These two techniques were applied to the field data using supervised machine learning and determined the most efficient and powerful model for the prediction of NOx,

CO2, and HC emission values.

The field data for each mode of the bus was split into 70% training data and 30% validation data. Results of all six conditions are explained individually in the following sections Figs. 4-36 to 4-61 are the outputs of the Lasso and ET regression models, which were generated using Python 3.6. In all the graphs below, the red line indicates the data predicted by the model whereas the blue line indicates the actual field data.

4.1.3.1 Cold Idle Mode Analysis 77

The Extra Tree regression technique was proved as the best regression technique over Lasso for the 826-hybrid bus and has error values of 0.024 for CO2, 0.255 for HC and

0.651for NOx. The graphs obtained from the NOx, CO2, and HC emission prediction models for the cold idle condition using the Lasso and ET regression for 826-hybrid bus are shown in Figs. 4.43 - 4.48 respectively.

200 Predicted NOx 150 Actual NOx

100 NOx ppm 50

0 0 200 400 600 800 1000 Time Sec. Figure 4-36: NOx emission prediction model using ET for 826-hybrid bus in Cold Idle

200 Predicted NOx 150 Actual NOx

100 NOx ppm 50

0 0 200 400 Time Sec. 600 800 1000 Figure 4-37: NOx emission prediction model using Lasso for 826-hybrid bus in Cold Idle

78

8 Predicted HC 6 Actual HC

4 HCppm

2

0 0 200 400 Time Sec. 600 800 1000 Figure 4-38: HC emission prediction model using ET for 826-hybrid bus in Cold Idle

8 7 6 5 4 Actual HCppm 3 CO2 2 Predicted CO2 1 0 0 200 400 600 800 1000 Time in Sec. Figure 4-39: HC emission prediction model using Lasso for 826-hybrid bus in Cold Idle

13

12 Predicted CO2

11 Actual CO2 10

CO2 CO2 % 9

8

7

6 0 200 400 Time Sec. 600 800 1000

Figure 4-40: CO2 emission prediction model using ET for 826-hybrid bus in Cold Idle

79

14 12 10 8 Actual CO2

CO2 CO2 % 6 4 Predicted CO2 2 0 0 200 400 Time Sec. 600 800 1000

Figure 4-41: CO2 emission prediction model using Lasso for 826-hybrid bus in Cold Idle

Moreover, for 802-EGR bus and 610-NON-EGR bus Extra Tree regression technique was proved as the best regression technique over Lasso. Therefore, the error values of 802-EGR bus for Extra Tree regression are 0.033 for CO2, 0.388 for HC and

0.880 for NOx, and the error values for Extra Tree regression of 610 NON- EGR bus are

0.034 for CO2, 1.093 for HC and 5.909 for NOx. The graphs obtained from the NOx, CO2, and HC emission prediction models for the cold idle condition using the Lasso and ET regression for 802-EGR bus and 610-NON-EGR bus are shown in Appendix A and B respectively.

4.1.3.2 Hot Idle Mode Analysis

The Extra Tree regression technique was proved as the best regression technique over Lasso for the 826-hybrid bus and has error values of 0.023 for CO2, 0.252 for HC and

0.477 for NOx. The graphs obtained from the NOx, CO2, and HC emission prediction

80

models for the cold idle condition using the Lasso and ET regression for 826-hybrid bus are shown in Figs. 4.42 - 4.47 respectively.

10 Predicted 8 CO2 Actual CO2

6 CO2 CO2 %

4

2 0 200 400 Time Sec. 600 800 1000

Figure 4.42: CO2 emission prediction model using ET for hybrid bus in the hot idle mode

10 Actual CO2 8 Predicted

6 CO2 CO2 CO2 % 4

2 0 200 400 600 800 1000 Time Sec.

Figure 4.43: CO2 emission prediction model using Lasso for hybrid bus in the hot idle mode

6 Predicte 5 d HC 4 Actual HC 3

HCppm 2 1 0 0 200 400 600 800 1000 Time Sec.

Figure 4.44: HC emission prediction model using ET for hybrid bus in the hot idle mode

81

6

5 Predicted HC 4

3

HCppm 2

1

0 0 200 400 Time Sec. 600 800 1000

Figure 4.45: HC emission prediction model using Lasso for hybrid bus in the hot idle

mode

100

80 Predicted NOx 60

40 NOx ppm 20

0 0 200 400 Time Sec. 600 800 1000

Figure 4.46: NOx emission prediction model using ET for hybrid bus in the hot idle mode

100

80 Predicted NOx Actual NOx 60

40 NOx ppm 20

0 0 200 400 Time Sec. 600 800 1000

Figure 4.47: NOx emission prediction model using Lasso for hybrid bus in the hot idle

mode

82

Moreover, for 802-EGR bus and 610-NON-EGR bus Extra Tree regression technique was proved as the best regression technique over Lasso. Therefore, the error values of 802-EGR bus for Extra Tree regression are 0.014 for CO2, 0.214 for HC and

0.748 for NOx, and the error values of 610 NON- EGR bus for Extra Tree regression are

0.047 for CO2, 0.627 for HC and 2.083 for NOx. The graphs obtained from the NOx, CO2, and HC emission prediction models for the cold idle condition using the Lasso and ET regression for 802-EGR bus and 610-NON-EGR bus are shown in Appendix C and D respectively.

4.1.3.3 Acceleration Mode Analysis

The Extra Tree regression technique was proved as the best regression technique over Lasso for the 826-hybrid bus and has error values of 0.253 for CO2, 0.708 for HC and

8.633 for NOx. The graphs obtained from the NOx, CO2, and HC emission prediction models for the acceleration condition using the Lasso and ET regression for 826-hybrid bus are shown in Figs. 4.48 – 4.53 respectively.

.

400

350

300

250 Predicted

NOx ppm NOx 200

150 1550 1650 1750 1850 1950 2050 2150 RPM 83

Figure 4.48: NOx emission prediction model using ET for hybrid bus in acceleration

mode

400

350

300 Predicte d NOx 250 Actual NOx ppm NOx 200

150 1550 1650 1750 RPM1850 1950 2050 2150 Figure 4.49: NOx emission prediction model using Lasso for hybrid bus in acceleration

mode

9

8

7

HCppm 6 Predicted HC 5 Actual HC

4 1550 1650 1750 RPM1850 1950 2050 2150

Figure 4.50: HC emission prediction model using ET for hybrid bus in acceleration mode

9

8

7

6 Predicted HCppm HC 5

4 1550 1650 1750 1850 1950 2050 2150 RPM 84

Figure 4.51: HC emission prediction model using Lasso for hybrid bus in acceleration

mode

15 14 13 12

Predicte CO2 CO2 % 11 d CO2 10 9 1550 1650 1750 RPM1850 1950 2050 2150

Figure 4.52: CO2 emission prediction model using ET for hybrid bus in acceleration

mode

15

14

13

12

Actual CO2 CO2 % 11 CO2

10

9 1550 1650 1750 1850 1950 2050 2150 RPM

Figure 4.53: CO2 emission prediction model using Lasso for hybrid bus in acceleration

mode

Moreover, for 802-EGR bus and 610-NON-EGR bus Extra Tree regression technique was proved as the best regression technique over Lasso. Therefore, the error values of 802-EGR bus are 0.486 for CO2, 0.777 for HC and 7.116 for NOx, and the error values of 610 NON- EGR bus are 0.112 for CO2, 2.716 for HC and 7.419 for NOx. The graphs obtained from the NOx, CO2, and HC emission prediction models for the cold idle

85

condition using the Lasso and ET regression for 802-EGR bus and 610-NON-EGR bus are shown in Appendix E and F respectively.

4.1.3.4 Variable Speed Mode Analysis

The Extra Tree regression technique was proved as the best regression technique over Lasso for the 826-hybrid bus and has error values of 0.406 for CO2, 0.551 for HC and

5.002 for NOx. The graphs obtained from the NOx, CO2, and HC emission prediction models for the cold idle condition using the Lasso and ET regression for 826-hybrid bus are shown in Figs. 4.54 - 4.49 respectively

12

10

8 CO2 CO2 % Predicted 6 CO2 Actual CO2 4 1100 1200 1300 RPM 1400 1500 1600

Figure 4.54: CO2 emission prediction model using ET for hybrid bus in variable speed

12

10

8

CO2 CO2 % Predicted CO2 6 Actual CO2

4 1100 1200 1300 RPM 1400 1500 1600

Figure 4.55: CO2 emission prediction model using Lasso for hybrid bus in variable speed 86

5.5

5

4.5

4

Predicted HCppm 3.5 HC 3

2.5 1100 1200 1300 RPM 1400 1500 1600

Figure 4.56: HC emission prediction model using ET for hybrid bus in variable speed

5.5

5

4.5

4

HCppm 3.5

3 Predicted HC 2.5 1100 1200 1300 RPM 1400 1500 1600

Figure 4.57: HC emission prediction model using Lasso for hybrid bus in variable speed

200

180

160

140 Predicted NOx NOx ppm 120

100

80 1100 1200 1300 RPM 1400 1500 1600

Figure 4.58: NOx emission prediction model using ET for hybrid bus in variable speed

87

200

180

160

140 Predicted NOx

NOx ppm 120

100

80 1100 1200 1300 RPM 1400 1500 1600

Figure 4.59: NOx emission prediction model using Lasso for hybrid bus in variable speed

Moreover, for 802-EGR bus and 610-NON-EGR bus Extra Tree regression technique was proved as the best regression technique over Lasso. Therefore, the error values of 802-EGR bus for Extra Tree regression are 0.638 for CO2, 0.711 for HC and

8.157 for NOx, and the error values of 610 NON- EGR bus for Extra Tree regression are

0.145 for CO2, 1.828 for HC and 17.927 for NOx. The graphs obtained from the NOx, CO2, and HC emission prediction models for the cold idle condition using the Lasso and ET regression for 802-EGR bus and 610-NON-EGR bus are shown in Appendix G and H respectively.

4.1.3.5 Deceleration Mode Analysis

The Extra Tree regression technique was proved as the best regression technique over Lasso for the 826-hybrid bus, and has error values of 0.555 for CO2, 0.620 for HC and

3.817 for NOx. The graphs obtained from the NOx, CO2, and HC emission prediction

88

models for the cold idle condition using the Lasso and ET regression for 826-hybrid bus are shown in Figs. 4.60 - 4.65 respectively.

6

5

4

3 Actual CO2 CO2 CO2 % 2

1

0 700 800 900 RPM 1000 1100 1200 1300

Figure 4.60: CO2 emission prediction model using ET for hybrid bus in variable speed

6

5

4

3 Actual CO2 CO2 CO2 % 2 Predicted CO2 1

0 700 800 900 RPM 1000 1100 1200 1300

Figure 4.61: CO2 emission prediction model using Lasso for hybrid bus in variable speed

3.5 3 2.5 2 Actual HC

1.5 HCppm Predicted HC 1 0.5 0 700 800 900 RPM 1000 1100 1200 1300

Figure 4.62: HC emission prediction model using ET for hybrid bus in variable speed

89

3.5 3 2.5 2 Actual HC 1.5 HCppm Predicted HC 1 0.5 RPM 0 700 800 900 1000 1100 1200 1300

Figure 4.63: HC emission prediction model using Lasso for hybrid bus in variable speed

30

25

20

15 Actual NOx NOx ppm 10 Predicted NOx 5

0 700 800 900 RPM 1000 1100 1200

Figure 4.64: NOx emission prediction model using ET for hybrid bus in variable speed

30

25

20

15 Actual NOx NOx ppm 10

5

0 700 800 900 RPM 1000 1100 1200

Figure 4.65: NOx emission prediction model using Lasso for hybrid bus in variable speed

90

Moreover, for 802-EGR bus and 610-NON-EGR bus Extra Tree regression technique was proved as the best regression technique over Lasso. Therefore, the error values of 802-EGR bus for Extra Tree regression are 0.357 for CO2, 0.850 for HC and

2.987 for NOx, and the error values of 610 NON- EGR bus for Extra Tree regression are

0.326 for CO2, 2.879 for HC and 2.772 for NOx. The graphs obtained from the NOx, CO2, and HC emission prediction models for the cold idle condition using the Lasso and ET regression for 802-EGR bus and 610-NON-EGR bus are shown in Appendix I and J respectively.

4.1.3.6 Intersection Analysis

The Extra Tree regression technique was proved as the best technique over Lasso for the 826-hybrid bus and has error values of 0.729 for CO2, 0.736 for HC and 2.435 for

NOx. The graphs obtained from the NOx, CO2, and HC emission prediction models for the cold idle condition using the Lasso and ET regression for 826-hybrid bus are shown in

Figs. 4.66 - 4.71 respectively.

10

8

6

CO2 CO2 % 4 Actual CO2

2

0 0 5 10 15 20 25 30 35 Time Sec.

Figure 4.66: CO2 emission prediction model using ET for hybrid bus in variable speed

91

12

10

8

6 CO2 CO2 % 4 Actual CO2 2

0 0 5 10 15Time Sec.20 25 30 35

Figure 4.67: CO2 emission prediction model using Lasso for hybrid bus in variable speed

8

6

4

Predicted HC HCppm Actual HC 2

0 0 5 10 15Time Sec.20 25 30 35

Figure 4.68: HC emission prediction model using ET for hybrid bus in variable speed

8

6

4 Predicted HCppm HC 2

0 0 5 10 15Time Sec.20 25 30 35

Figure 4.69: HC emission prediction model using Lasso for hybrid bus in variable speed

92

200

150

100 Predicted NOx NOx

Actual NOx 50

0 0 5 10 15Time Sec.20 25 30 35

Figure 4.70: NOx emission prediction model using ET for hybrid bus in variable speed

200

150

100 NOx Predicted NOx 50

0 0 5 10 15Time Sec.20 25 30 35

Figure 4-71: NOx emission prediction model using Lasso for hybrid bus in variable speed

Moreover, for 802-EGR bus and 610-NON-EGR bus Extra Tree regression technique was proved as the best regression technique over Lasso. Therefore, the error values of 802-EGR bus for Extra Tree regression are 0.312 for CO2, 0.608 for HC and

6.138 for NOx, and the error values of 610 NON- EGR bus for Extra Tree regression are

0.338 for CO2, 3.157 for HC and 6.702 for NOx.

Table 4.1: CO2, HC, and NOx emission prediction errors for all the cases of the bus fleet

93

bus/Condition CO2 CO2 HC HC NOx NOx

ET Lasso ET Lasso ET Lasso

610/Acceleration 0.112 0.243 2.716 4.713 7.419 17.006

802/Acceleration 0.486 0.708 0.777 0.863 7.116 11.382

826/Acceleration 0.253 0.601 0.708 0.733 8.633 10.878

610/Variable speed 0.145 0.856 1.828 3.900 17.927 25.825

802/Variable speed 0.638 0.797 0.711 0.886 8.157 14.088

826/Variable speed 0.406 0.717 0.551 0.603 5.002 5.230

610/Deceleration 0.326 0.585 2.879 3.493 2.772 4.812

802/ Deceleration 0.357 0.666 0.850 0.958 2.987 5.050

826/ Deceleration 0.555 0.705 0.620 0.666 3.817 5.536

610/Intersections 0.338 0.785 3.157 7.590 6.702 14.139

802/Intersections 0.312 0.763 0.608 1.528 6.138 8.790

826/Intersections 0.729 1.817 0.736 1.212 2.435 6.650

610/Cold Idle 0.034 0.136 1.093 5.357 5.909 24.730

802/Cold Idle 0.033 1.216 0.388 1.208 0.880 25.621

826/Cold Idle 0.024 1.228 0.255 1.263 0.651 9.661

610/Hot Idle 0.047 0.601 0.627 4.641 2.083 5.823

802/Hot Idle 0.014 0.606 0.214 1.081 0.748 6.69

826/Hot Idle 0.023 0.662 0.252 0.339 0.477 1.875

94

The graphs obtained from the NOx, CO2, and HC emission prediction models for the cold idle condition using the ET and Lasso algorithms for 802-EGR bus and 610-NON-

EGR bus are shown in Appendix K and L respectively. The error for all three different buses in six different modes are tabulated in Table 4.1

4.1.4 NOx, CO2, and HC emission prediction model

The above analysis regression techniques were used to predict CO2, HC and NOx emissions under different driving conditions. Moreover, it was difficult to predict equation out of it. Therefore, curve fitting tool was used in MATLAB to develop equations which could simplify the use of this analysis for future studies and to streamline research problems. The developed equations predict NOx, CO2 and HC emission values for different running conditions of the buses and the set of equations were polynomial. The degree of the polynomial equation changes according to the perfect fit for that particular data set.

Therefore, great care was taken to fit the curve for a set of data without causing any underfitting or overfitting. General Fourier model and Gauss model were used as an alternative to polynomial model in few cases by considering the best fit. This is to show that, all the three-model work flawlessly in developing prediction equations for NOx, CO2 and HC emissions.

Cold Idle Mode

826-hybrid bus: In the idle condition of the hybrid bus, the parameters used for the prediction of CO2, HC, and NOx emission values are cold idle time denoted by ‘t’ and EGR

95

temperature denoted by ‘T’. The model for 826-hybrid bus has been approximated by a set of linear polynomial equations. The root mean square error for CO2, HC and NOx are

0.1835, 0.3702, and 1.873 respectively. The values for the constant in generalized model form are represented by C (t, T).

Linear Polynomial model for CO2 emissions from 826-hybrid bus

퐶(푡, 푇) = 69.4 + 0.172 푡 − 1.73 T + 1.265 × 10−4푡2 − 2.609 × 10−3푡푇

+ 1.301 × 10−2푇2

Linear Polynomial model for HC emissions from 826-hybrid bus

퐶(푡, 푇) = 21.17 − 0.003317 푡 − 0.2567 푇 − 2.312 × 10−5푡2 + 0.0001586 t T + 0.0008584 푇2 Linear Polynomial model for NOx emissions from 826-hybrid bus

퐶(푡, 푇) = 106.3 + 6.507 푡 − 41.58 푇 + 136.6 푡2 − 305.1 t T + 173.7 푇2

802-EGR bus: In the idle condition of EGR bus, the parameters used for the prediction of

CO2, HC, and NOx emission values are cold idle time denoted by ‘t’ and EGR temperature denoted by ‘T’. The model for 802-EGR bus has been approximated by a set of linear polynomial equations. The root mean square error for CO2, HC and NOx are 0.0719,

0.4252, and 4.213 respectively. The values for the constant in generalized model form are represented by C (t, T).

Linear Polynomial model for CO2 emissions from 802-EGR bus

퐶(푡, 푇) = 1.357 × 104 + 26.22 푡 − 308.9 푇 + 0.01368 푡2 − 0.3116 푡 푇 + 1.807 푇2

Linear Polynomial model for HC emissions from 802-EGR bus

96

퐶(푡, 푇) = 171.3 + 0.1418 푡 − 2.921 푇 + 1.247 × 10−5푡2 + 0.001132 푡 푇 + 0.0127 푇2 Linear Polynomial model for NOx emissions from 802-EGR bus

퐶(푡, 푇) = 1.357 × 104 + 26.22 푡 − 308.9 푇 + 0.01368 푡2 − 0.3116 푡 푇 + 1.807 푇2

610-NON-EGR bus: In the idle condition of the NON-EGR bus, the parameters used for the prediction of CO2, HC, and NOx emission values are cold idle time denoted by ‘t’ and engine temperature denoted by ‘T’. The model for 610-NON-EGR bus has been approximated by a set of linear polynomial equations. The root mean square error for CO2,

HC and NOx are 0.066, 1.253 and 7.855 respectively. The values for the constant in generalized model form are represented by C (t, T).

Linear Polynomial model for CO2 emissions from 610-NON-EGR bus

퐶(푡, 푇) = −251.4 − 0.3635 푡 + 3.813 푇 + 1.258 × 10−4 푡2 − 0.002661 푡 푇 − 0.01405 푇2 Linear Polynomial model for HC emissions from 610-NON-EGR bus

퐶(푡, 푇) = 8838 + 12.16 푡 − 128.8 푇 + 4.259 × 10−3 푡2 − 0.08994 푡 푇 + 0.474 푇2

Linear Polynomial model for NOx emissions from 610-NON-EGR bus

퐶(푡, 푇) = 9220 + 7.749 푡 − 118 푇 + 2.196 × 10−3 푡2 − 0.05729 푡 푇 + 0.4058푇2

Hot Idle

826-hybrid bus: In the idle condition of the hybrid bus, the parameters used for the prediction of CO2, HC, and NOx emission values are hot idle time denoted by ‘t’ and EGR temperature denoted by ‘y’. The model for 826-hybrid bus has been approximated by a set of linear polynomial equations. The root mean square error for CO2, HC and NOx are

97

0.0863, 0.3462, and 0.999 respectively. The values for the constant in generalized model form are represented by C (t, T).

Linear Polynomial model for CO2 emissions from 826-hybrid bus

퐶(푡, 푇) = 280.5 + 0.1262 푡 − 5.917 푇 + 1.589 × 10−5 푡2 − 0.001419 t T + 0.03204 푇2 Linear Polynomial model for HC emissions from 826-hybrid bus

퐶(푡, 푇) = 1833 + 1.161 푡 − 42.68 푇 + 1.852 × 10−4 푡2 − 0.0136 t T + 0.2492 푇2

Linear Polynomial model for NOx emissions from 826-hybrid bus

퐶(푡, 푇) = −4.568 × 10−7푡3 + 0.145푇3 + 0.001206 푡2 − 0.447푇2 − 0.9974 푡

− 0.11푇 + 271.4

802-EGR bus: In the idle condition of EGR bus, the parameters used for the prediction of

CO2, HC, and NOx emission values are hot idle time denoted by‘t’ and EGR temperature denoted by ‘T’. The model for 802-EGR bus has been approximated by a set of linear polynomial equations. The root mean square error for CO2, HC and NOx are 0.0462, 0.363, and 2.011 respectively. The values for the constant in generalized model form are represented by C (t, T).

Linear Polynomial model for CO2 emissions from 802-EGR bus

퐶(푡, 푇) = 845.9 + 0.5151 푡 − 21.16 푇 + 8.094 × 10−5 푡2 − 0.006551 푡 푇 + 0.1333 푇2

Linear Polynomial model for HC emissions from 802-EGR bus

퐶(푡, 푇) = −7951 − 5.01 푡 + 202 푇 − 0.0007848 푡2 + 0.06351 푡 푇 − 1.282 푇2

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Linear Polynomial model for NOx emissions from 802-EGR bus

퐶(푡, 푇) = −2141 − 1.264 푡 + 54.62 푇 + 6.7 × 10−6 푇2 + 0.01118 푡 푇 − 0.3084 푇2

610-NON-EGR bus: In the idle condition of the NON-EGR bus, the parameters used for the prediction of CO2, HC and NOx emission values are hot idle time denoted by ‘t’ and engine temperature denoted by ‘T’. The model for 610-NON-EGR bus has been approximated by a set of linear polynomial equations. The root mean square error for CO2,

HC and NOx are 0.0042, 0.5503, and 1.101 respectively. The values for the constant in generalized model form are represented by C (t, T).

Linear Polynomial model for CO2 emissions from 610-NON-EGR bus

퐶(푡, 푇) = 1413 + 0.3406 푡 − 11.57 푇 + 2.05 × 10−5 푡2 − 0.00141 푡 푇 + 0.02378푇2

Linear Polynomial model for HC emissions from 610-NON-EGR bus

퐶(푡, 푇) = 2.596 × 104 + 6.797 푡 − 214.3 푇 + 4.363 × 10−4 푡2 − 0.02818 푡 푇 + 0.443푇2

Linear Polynomial model for NOx emissions from 610-NON-EGR bus

퐶(푡, 푇) = −1.325 × 104 − 1.745 푡 + 108.7 푇 + 1.356 × 10−4 푡2 + 0.005857 푡 푇 − 0.2178푇2

Acceleration Mode

826-hybrid bus: In acceleration condition of the hybrid bus, the parameter used for the prediction of CO2, HC, and NOx emission values is rpm denoted by x. The root mean

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square error for CO2, HC and NOx are 0.2499, 0.590, and 8.029 respectively. The values for the constant in generalized model form are represented by C(x).

Linear Polynomial model for CO2 emissions from 826-hybrid bus

퐶(푥) = 7.842 × 10−9푥3 − 4.002 × 10−5 푥2 − 0.07435푥 + 38.5 General Fourier model for HC emissions from 826-hybrid bus

퐶(푥) = 9.203 × 10−7

+ 9.203

× 10−7 cos(9.203 × 10−7 푥) + 9.203 × 10−7 sin(9.203 × 10−7 푥)

Linear Polynomial model for NOx emissions from 826-hybrid bus

퐶(푥) = −7.717 × 1011푥5 + 7.124 × 10−7푥4 − 2.623 × 10−3 푥3 + 4.817 푥2

− 4.411 푥 + 1.612 × 106

802-EGR bus in acceleration condition of 802-EGR bus, the parameter used for the prediction of CO2, HC, and NOx emission values is rpm denoted by x. The root mean square error for CO2, HC and NOx are 0.4492, 0.6699, and 8.849 respectively. The values for the constant in generalized model form are represented by C(x).

Linear Polynomial model for CO2 emissions from 802-EGR bus

퐶(x) = −4.265 × 10−10 푥4 + 3.235 × 10−6 푥3 − 0.00917 푥2 + 11.52 푥 − 5405 General Fourier model for HC emissions from 802-EGR bus

퐶(푥) = 10.05 + 0.08554 cos(0.006662 푥) + 1.446 sin(0.006662 푥)

Linear Polynomial model for NOx emissions from 802-EGR bus

퐶(x) = −2.321 × 10−13 푥6 + 2.637 × 10−9 푥5 − 1.246 × 10−5 푥4 + 0.03133 푥3

− 44.24 푥2 + 3.325 × 104 푥 − 1.039 × 107

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610-NON-EGR bus: In acceleration condition of 610 NON- EGR bus, the parameter used for the prediction of CO2, HC, and NOx emission values is rpm denoted by x. The root mean square error for CO2, HC and NOx are 0.113, 2.580, and 8.338 respectively. The values for the constant in generalized model form are represented by C(x).

Linear Polynomial model for CO2 emissions from 610-NON-EGR bus

퐶(x) = 1.375 × 10−12 푥5 − 1.332 × 10−8 푥4 + 5.14 × 10−5 푥3 − 0.09888 푥2

+ 94.8 푥 − 3.624 × 104

General Gauss model for HC emissions from 610-NON-EGR bus

−(푥−5.008 ×104) −(푥−1816) ( )2 ( )2 퐶(푥) = 2.117 × 1015 8629 + 15.76 190.8

Linear Polynomial model for NOx emissions from 610-NON-EGR bus

퐶(x) = −6.915 × 10−11 푥5 + 6.924 × 10−7 푥4 − 0.002763 푥3 + 5.493 푥2 − 5442 푥 + 2.149 × 106

Variable Speed Mode

826-hybrid bus: In variable speed condition of the hybrid bus, the parameter used for the prediction of CO2, HC, and NOx emission values is rpm denoted by x. The root mean square error for CO2, HC and NOx are 0.4311, 0.5124, and 5.337 respectively. The values for the constant in generalized model form are represented by C(x).

General Gauss model for CO2 emissions from 826-hybrid bus

−(푥−1526) −(푥−1573) ( )2 ( )2 퐶(푥) = 1.017 22.83 + 8.775 638.2

Linear Polynomial model for HC emissions from 826-hybrid bus

퐶(푥) = 0.004615 푥 − 1.996 Linear Polynomial model for NOx emissions from 826-hybrid bus

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퐶(푥) = −7.167 × 10−13푥6 + 5.551 × 10−9푥5 − 1.784 × 10−5 푥4 + 0.03047푥3

− 29.15푥2 + 1.482 × 104푥 − 3.125 × 106

802-EGR bus: In variable speed condition of 802-EGR bus, the parameter used for the prediction of CO2, HC and NOx emission values is rpm denoted by x. The root mean square error for CO2, HC and NOx are 0.5236, 0.626, and 12.980 respectively. The values for the constant in generalized model form are represented by C(x).

General Gauss model for CO2 emissions from 802-EGR bus

−(푥−1972) ( )2 퐶(푥) = 9.034 902.5

General Gauss model for HC emissions from 802-EGR bus

−(푥−2302) ( )2 퐶(푥) = 13.16 1035

General Fourier model for NOx emissions from 802-EGR bus

퐶(푥) = 177.6 − 67.97 cos(0.009983 푥)

+ 37.8 sin(0.009983 푥)

+ 9.267 cos(2 × 0.009983 푥) + 18.37 sin(2 × 0.009983 푥)

610-NON-EGR bus: In variable speed condition of 610 NON- EGR bus, the parameter used for the prediction of CO2, HC, and NOx emission values is rpm denoted by x. The root mean square error for CO2, HC and NOx are 0.1614, 1.617, and 17.17 respectively.

The values for the constant in generalized model form are represented by C(x).

Linear Polynomial model for CO2 emissions from 610-NON-EGR bus

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퐶(x) = −2.269 × 10−12 푥5 + 1.519 × 10−8 푥4 − 4.054 푥3 + 0.05391 푥2 − 35.72 푥 + 9431

Linear Polynomial model for HC emissions from 610-NON-EGR bus

퐶(x) = −7.145 × 10−12 푥5 + 4.781 × 10−8 푥4 − 0.0001274 푥3 + 0.1689 푥2 − 111.4 푥 + 2.924 × 104

General Gauss model for NOx emissions from 610-NON-EGR bus

−(푥−4572) −(푥−1195) ( )2 ( )2 퐶(푥) = 4.312 × 105 1127 + 42.32 168.3

Deceleration Mode

826-hybrid bus: In deceleration condition of the hybrid bus, the parameter used for the prediction of CO2, HC, and NOx emission values is rpm denoted by x. The root mean square error for CO2, HC and NOx are 0.6171, 0.5373, and 2.929 respectively. The values for the constant in generalized model form are represented by C(x).

Linear Polynomial model for CO2 emissions from 826-hybrid bus

퐶(푥) = −4.277 × 1012푥5 + 2.081 × 10−8푥4 − 4.021 × 10−5 푥3 + 0.03858 푥2

− 18.36 푥 + 3465

Linear Polynomial model for HC emissions from 826-hybrid bus

퐶(푥) = 0.004079 푥 − 1.44 Linear Polynomial model for NOx emissions from 826-hybrid bus

퐶(푥) = −4.568 × 10−7푥3 + 0.001206 푥2 − 0.9974 푥 + 271.4

802-EGR bus: In deceleration condition of 802-EGR bus, the parameter used for the prediction of CO2, HC, and NOx emission values is rpm denoted by x. The root mean

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square error for CO2, HC and NOx are 0.3419, 0.679, and 3.353 respectively. The values for the constant in generalized model form are represented by C(x).

Linear Polynomial model for CO2 emissions from 802-EGR bus

퐶(x) = −1.609 × 10−9 푥4 + 6.002 × 10−6 푥3 − 0.008361 푥2 + 5.167 푥 − 1196

Linear Polynomial model for HC emissions from 802-EGR bus

퐶(x) = −6.112 × 10−6 푥2 + 0.01868 푥 − 9.221 General Gauss model for NOx emissions from 802-EGR bus

−(푥−1061) −(푥−1099) ( )2 ( )2 퐶(푥) = 34.07 220.4 + 1.858 × 107 3.987

610-NON-EGR bus: In deceleration condition of 610 NON- EGR bus, the parameter used for the prediction of CO2, HC, and NOx emission values is rpm denoted by x. The root mean square error for CO2, HC and NOx are 0.2829, 2.517, and 2.344 respectively. The values for the constant in generalized model form are represented by C(x).

General Fourier model for CO2 emissions from 610-NON-EGR bus

퐶(푥) = 0.6775 − 1.747 cos(0.008891 푥)

+ 0.6204 sin(0.008891 푥)

+ 0.2925 cos(2 × 0.008891 푥) + 0.5464 sin(2 × 0.008891 푥)

General Fourier model for HC emissions from 610-NON-EGR bus

퐶(푥) = 15.34 − 9.723 cos(0.008988 푥) − 0.957 sin(0.008988 푥)

General Fourier model for NOx emissions from 610-NON-EGR bus

퐶(푥) = 31.29 + 2.674 cos(0.00974 푥) − 14.43 sin(0.00974 푥)

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Intersections

826-hybrid bus: At the Intersections of the hybrid bus, the parameters used for the prediction of CO2, HC, and NOx emission values are cold idle time denoted by ‘t’ and EGR temperature denoted by ‘T’. The model for 826-hybrid bus has been approximated by a set of linear polynomial equations. The root mean square error for CO2, HC and NOx are

0.8641, 0.7188, and 8.932 respectively. The values for the constant in generalized model form are represented by C (t, T).

Linear Polynomial model for CO2 emissions from 826-hybrid bus

퐶(푡, 푇) = −2.669 × 106 + 1016 푡 + 3.91 × 104 푇 + 5.368 푡2 − 10.97 t T

− 190.9 푇2 − 0.001325 푡3 − 0.02628 푡2푇 + 0.02939푡푇2 + 0.3016 푇3

Linear Polynomial model for HC emissions from 826-hybrid bus

퐶(푡, 푇) = −102.8 + 0.09514 푡 + 0.5173 푇

Linear Polynomial model for NOx emissions from 826-hybrid bus

퐶(푡, 푇) = −1.001 × 107 + 5.617 × 104 푡 + 1.49 × 105 푇 + 33.55 푡2 − 558.2 t T − 650.3 푇2 + 0.003454 푡3 − 0.1681 푡2푇 + 1.387푡푇2 + 1.003 푇3

802-EGR bus: At the Intersections of EGR bus, the parameters used for the prediction of

CO2, HC, and NOx emission values are cold idle time denoted by ‘t’ and EGR temperature denoted by ‘T’. The model for 802-EGR bus has been approximated by a set of linear polynomial equations. The root mean square error for CO2, HC and NOx are 0.8195,

0.6967, and 12.93 respectively. The values for the constant in generalized model form are represented by C (t, T).

Linear Polynomial model for CO2 emissions from 802-EGR bus

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퐶(푡, 푇) = −71.71 + 0.2409 푡 + 0.3379 푇

Linear Polynomial model for HC emissions from 802-EGR bus

퐶(푡, 푇) = −8.978 × 105 − 4078 푡 + 1.318 × 104 푇 − 6.299 푡2 + 39.7 t T − 64.5 푇2 − 0.00351 푇3 + 0.03056 푡2푇 − 0.09658푡푇2 + 0.1051 푇3 Linear Polynomial model for NOx emissions from 802-EGR bus

퐶(푡, 푇) = −3.152 × 105 − 88.75 푡 + 2984 푇 − 0.6079 푇2 + 0.5567푡 푇 − 7.063 푇2

610-NON-EGR bus: At the Intersections of the NON-EGR bus, the parameters used for the prediction of CO2, HC, and NOx emission values are cold idle time denoted by ‘t’ and engine temperature denoted by ‘T’. The model for 610-NON-EGR bus has been approximated by a set of linear polynomial equations. The root mean square error for CO2,

HC and NOx are 0.4789, 5.201 and 29.28 respectively. The values for the constant in generalized model form are represented by C (t, T).

Linear Polynomial model for CO2 emissions from 610-NON-EGR bus

퐶(푡, 푇) = −9326 − 15.09 푡 + 91.45 푇 − 0.007039 푡2 + 0.0747푡 푇 − 0.2241 푇2 Linear Polynomial model for HC emissions from 610-NON-EGR bus

퐶(푡, 푇) = −5.3 × 104 − 205.8 푡 − 489.8 푇 − 0.07807푡2 + 1.014 푡 푇 + 1.129 푇2

Linear Polynomial model for NOx emissions from 610-NON-EGR bus

퐶(푡, 푇) = −5.706 × 105 − 390.6 푡 + 5546 푇 − 0.6974 푡2 + 2.049푡 푇 − 13.47푇2

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4.2 Elemental analysis of Particulate Matter Samples

Elemental analysis of particulate matter was performed on 610, 802 and 826 buses, of which 610 is a traditional diesel bus with no exhaust control systems. Whereas 802 has

DPF, EGR, DOC and 826 has DPF, EGR, DOC, and SCR-UREA as exhaust control systems. In order to examine the elemental composition of the soot samples collected from all the three buses two elemental analysis were conducted namely ICP-MS and EDS/SEM.

In this analysis EDS/SEM was conducted to obtain the surface analysis whereas, ICP-MS was used for bulk analysis of the soot samples. Furthermore, ICP-MS results give the trace metal concentrations (Na, Mg, Al, P, K, Ca, Fe, Cu, Zn, and Mo) present in the soot samples. On the other hand, EDS/SEM was used to obtain C, O, Si and Cu concentrations.

Therefore, all the above-mentioned analysis gives us a better understanding of the concentration of elements which were expected to be present in the samples. Examining

Tables 4.2, to 4.5 together gives us an elaborated picture.

The table shows the concentration of elements detected in ICP-MS analysis: Na,

Mg, Al, P, K, Ca, Fe, Cu, Zn, and Mo. Some studies explained that sources of these elements were most likely from fuel, lubricants, fuel additives, lubricants, and wears from engine parts like piston rings, bearings, bushings, etc. [66-68]. In this study the abundant metals in exhaust were Ca, Fe, and Mo. These metals were also concluded by few other studies as high concentration elements when compared to Mg, Al, P, K, Cu, Zn [66-69].

The concentration values for 610, 802 and 826 buses were adjusted by subtracting the concentrations of blank sample and are mentioned as adjusted in the tables. ND indicates

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No Detection that means the concentration of that particular element was below detection level. The detection levels for each element were tabulated below in Table 4.6

Table 4.2: ICP-MS results of the composition of blank quartz filter paper

Elements Blank Paper Avg. (µg/g) Na 51.1

Mg 21.25

Al 23.73

P 14.77

K ND

Ca 74.38

Fe ND

Cu ND

Zn 11.02

Mo 45.62

As shown in Table 4.3 to Table 4.5, the concentration of elements in 610-bus was high than the 810 and 826 buses, this was due to the presence of DPF in both the 800 series buses that could have helped to reduce the soot formation. On the other hand, 810 and 826 buses when compared did not show much difference in concentration of elements present in PM because of the presence of DPF in both the buses. 826 bus has an extra SCR- Urea system along with all the exhaust control systems present in 802 bus, which did not show any additional impact on reducing the PM emissions.

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Calcium and Iron were the most abundant elements found in all the three buses such as 802, 610 and 826 buses after Molybdenum. 610 bus has higher concentrations of

Calcium and Iron than the other two buses in both hot and cold idle conditions, this was majorly due to lack of EGR and DPF systems. Moreover, increase in temperature did not show any decrease in concentration of Calcium and Iron for all the three buses. Calcium and Iron are detected in high concentration in hot idle mode. The higher values of Ca may be attributed to various sources like wear in bushings, injection shields, coolant core tubes, piston rings, bearings, sleeves, bearing cages, or even dust. Whereas, the possible sources for iron are rust and wear of machine parts, such as engine blocks, cylinders, gears, cylinder liners, value gauges, wrist pines, camshaft, and oil pumps [66].

Aluminum and Molybdenum were detected in all the three buses. Molybdenum was the most abundant element found in all the three buses, both in hot and cold idle condition.

Molybdenum concentration was high in cold idle for 610 bus when compared to hot idle, this shows there has been no substantial reduction in the concentration of elements during high temperatures. But 802 and 826 buses have a significant increase in the concentration of Molybdenum during hot idle, higher engine temperature is thought to be the reason for releasing more Molybdenum in hot idle. Moreover, Aluminum concentration decreased when the engine temperature was high, for all the three buses. And it can be seen that hot and cold idle of 610 bus has high Aluminum and Molybdenum concentrations than 802 and 826 buses.

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Table 4.3: ICP-MS results of PM samples (610 bus)

Elements Avg. 610 Cold Adjusted Avg. 610 Hot (µg/g) Adjusted (µg/g) Na 55.69 4.18 71.01 19.49 Mg 23.63 2.38 32.71 11.46 Al 39.30 15.57 37.63 13.91 P 13.66 ND 17.78 3.01 K 10.22 10.22 8.02 8.02 Ca 102.13 27.76 122.32 47.95 Fe 25.57 25.57 54.89 54.89 Cu 1.44 1.44 20.59 20.59 Zn 10.14 ND 12.25 1.24 Mo 235.24 189.62 209.81 164.19

Table 4.4: ICP-MS results of PM samples (802 bus)

Elements Avg. 802 Cold (µg/g) Adjusted Avg. 802 Hot (µg/g) Adjusted Na 64.98 13.46 53.44 1.93 Mg 31.02 9.77 30.97 9.72 Al 35.56 11.83 29.93 6.20 P 16.79 2.02 13.49 ND K ND ND ND ND Ca 88.52 14.15 104.90 30.53 Fe 18.65 18.65 48.52 48.52 Cu ND ND ND ND Zn 2.71 ND 8.73 ND Mo 160.84 115.22 185.12 139.50

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Table 4.5: ICP-MS results of PM samples (826 bus)

Elements Avg. 826 Cold (µg/g) Adjusted Avg. 826 Hot (µg/g) Adjusted Na 59.27 7.76 55.00 3.49 Mg 31.76 10.51 25.95 4.70 Al 32.67 8.95 29.57 5.84 P 16.12 1.35 14.64 ND K ND ND ND ND Ca 85.14 10.77 105.45 31.08 Fe 13.26 13.26 32.68 32.68 Cu ND ND ND ND Zn 4.75 ND 8.40 ND Mo 92.39 46.77 190.21 144.59

Table 4.6: Limit of detection for the ICP-MS analysis

Elements Limit of detection (µg/g)

Na 3.8841786

Mg 0.5538254

Al 0.340194

P 6.7858528

K 3.9682401

Ca 24.35749

Fe 7.1297256

Cu 3.5243389

Zn 0.961447

Mo 1.3252997

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This could be because of exhaust control system equipped with 802 and 826 buses.

Therefore, the Aluminum and Molybdenum could be from the wear and tear of engine parts.

Potassium has low detection levels in hot and cold idle of 802 and 826 buses. Where for 610 bus, potassium concentration was relatively high in cold idle when compared to hot idle. This could be because of the high engine temperature. Whereas, Phosphorous was detected in 802 cold, 826 cold and 610 hot.

Sodium and Magnesium concentration of 802 and 826 buses were significant when compared to 610 bus. In this situation, it could be said that EGR and DPF systems did not make a meaningful effect in cold idle to reduce the Sodium and Magnesium concentration.

Whereas, it could be said that, high engine temperatures in hot idle improved the EGR and

DPF systems performance to reduce the concentration of Sodium and Magnesium in 802 and 826 buses. The possible source of sodium could be specific additives in lubricant oil and Magnesium was thought to come from engine parts and fuel additives [66]. Lastly,

Copper and Zinc were detected in 610 cold and 610 hot.

EDS/SEM Analysis Results

In this study, EDS/SEM analysis was used to give a surface analysis of all the samples tested for PM. Moreover, EDS was basically used to identify the concentration of

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carbon and oxygen present in the samples which could not be found using ICP-MS. Figure

4-72 to 4-78 shows the results of SEM analysis.

Figure 4-72: Image of blank filter paper showing membrane fibers without deposits

Figure 4-73: SEM image of 610 Cold idle sample filter paper showing membrane fibers with deposits

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Figure 4-74: SEM image of 610 hot idle sample filter paper showing membrane Fibers with deposits

Figure 4-75: SEM image of 802 cold idle sample filter paper showing membrane Fibers with deposits

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Figure 4-76: SEM image of 802 hot idle sample filter paper showing membrane Fibers with deposits

Figure 4-77: SEM image of 826 cold idle sample filter paper showing membrane Fibers with deposits

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Figure 4-78: SEM image of 826 hot idle sample filter paper showing membrane Fibers with deposits

The above SEM image of an empty sample filter paper shows the strands of a filter membrane without any deposits. Whereas, SEM images when observed for 802 and 826 buses there was an indication of soot particles deposited on the strands of the filter membrane. The 610 bus samples seem to have a large concentration of PM deposits on the membrane fibers when compared to 802-bus and 826-bus. This was expected because of lack of EGR and DPF systems on 610 buses. Moreover, 802 and 826 buses have literally the same amount of soot particles deposited on membrane fibers, this could be because of having the same exhaust control systems such as EGR and DPF. Also, all the three buses show a large amount of PM deposited in the hot idle samples. Referring to previous studies, it was understood that a large amount of PM was high in hot idle because of the increase in the formation of methane at higher temperatures could be an originator for soot formation. However, it should be said that making speculation about the PM concentration just from the images lead to misinterpretation of results. Therefore, joining the results along 116

with the EDS results could be used to draw a quantitative comparison of the concentrations of C, O, Si, and Pd. Table 4.7, shows the EDS results of different elements for all cases of

PM samples

Table 4.7: EDS results of different elements for all cases of PM samples

Element Blank 610Cold 610 Hot 802 Cold 802 Hot 826 826

(%) (%) (%) (%) (%) Cold Hot

(%) (%)

C 5.12 10.72 15.91 6.45 9.75 7.12 9.84

O 65.58 64.29 64.98 65.61 67.04 60.86 64.49

Si 29.78 27.45 26.51 26.06 25.22 37.97 27.63

Pd 0.62 0.51 0.6 0.88 0.61 0.54 0.59

The above EDS results observed that, the 610 bus samples seem to have a high carbon content detected when compared to 802-bus and-826 bus. This was expected because of lack of EGR and DPF systems on 610 bus. Moreover, 802 and 826 buses have literally the same amount of carbon content, this could be because of having the same exhaust control systems such as EGR and DPF. Also, all the three buses show high carbon content detected in hot idle samples. Referring to previous studies, it was understood that concentration of carbon was high in hot idle because of the increase in the formation of methane at higher temperatures could be an originator for carbon formation. When comparing the oxygen, silicon and palladium content between the blank sample and soot samples, it was found that the O, Si, and Pd presence in the soot samples was negligible.

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Chapter 5

Conclusion

A quick glance at the literature review gives a brief idea of tailpipe emissions that were conducted on different vehicles and with different fuel combinations. From previous studies, it was clear that there were very few experimental studies carried out on heavy- duty diesel engines and moreover, real-time day to day emission testing in different running conditions are very limited. Therefore, this successful research was carried out to study tailpipe emissions from Toledo transportation buses in real day to day life. In this study, three different buses with different exhaust control systems such as 610-NON-EGR bus,

802-EGR bus with DPF, DOC and 826-hybrid bus with DPF, DOC, and SCR-UREA as exhaust control systems were evaluated under different driving conditions. The CO2, HC and NOx emissions study was carried out in cold and hot idle, acceleration and deceleration, variable speed mode and at intersections. Regression techniques and curve fitting were used to analyze the data by creating a prediction model and equations.

Moreover, PM analysis was conducted on all three buses using a catch can instrument and quartz filter papers. Energy dispersive spectroscopy (EDS) and inductively coupled plasma

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mass spectroscopy (ICP-MS) was performed on the particulate matter data collected. Some of the key findings from this study are detailed below.

1. In idle mode, hotter engines produce less NOx, HC, and CO2 emissions compared

to cold engines. This behavior was found to be uniform among all the three buses.

2. NOx, CO2 and HC emissions recorded in the idle mode were found to be dependent

on engine parameters such as exhaust gas pressure, fuel flow rate, engine

temperature, diesel oxidation catalyst intake temperature, diesel particulate filter

intake temperature and DPF outlet temperature. From this study, it was found that

in idle mode, an increase in idle time leads to increase in engine temperature, which

has a great effect on reducing NOx, CO2 and HC emissions.

3. It was observed that in idle mode and running mode NOx and HC emissions from

610-NON-EGR bus were higher when compared to 802-EGR and 826-hybrid

buses. Whereas, NOx and HC emissions from 826-hybrid bus were lowest. This

was due to the fact that 610-NON-EGR bus has no exhaust control system to cut

down the emissions. Moreover, 802-EGR and 826-hybrid buses are equipped with

DOC to reduce HC emissions and EGR along with SCR – UREA to reduce NOx

emissions.

4. HC emissions were found out to be in the same range for 802-EGR and 826-hybrid

buses, because of having the same exhaust control system on board (DOC) for both

the buses. On the other hand, HC emission from 826-hybrid bus was recorded at

10% low rate compared to 802-EGR bus. This was because the bus runs on a

parallel hybrid technology in which electric motor and combustion engine work

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together to power the bus. Therefore, parallel hybrid technology has little effect in

reducing HC emissions in both idle and running conditions.

5. It was observed that in idle mode and running mode CO2 emissions were high for

826-hybrid bus when compared to 802-EGR and 610-NON-EGR buses. This was

because of chemical reaction that takes place in DOC for 802-EGR and SCR

UREA, DOC for 826-hybrid buses gives out CO2 as a product. Whereas 610-

NON-EGR emits the lowest amount of CO2 emissions among the fleet of buses,

this is due to the absence of exhaust control systems onboard.

6. It was inferred that maximum NOx, CO2 and HC emissions were found high in

acceleration condition, followed by variable speed and least values were found in a

deceleration condition.

7. It was observed that NOx, CO2 and HC emissions increase with speed (rpm) and

vice versa. Therefore, NOx, CO2 and HC emissions in running condition are

directly proposal to rpm.

8. From the study it was understood, at intersections NOx, CO2 and HC emissions

increase for about 20 seconds and then gradually start to decent. This was due to

increased engine temperature during acceleration was carried out through the

deceleration mode and later decreases at some point at the intersection making the

NOx, CO2, and HC emissions fall gradually.

9. Regression techniques were used to analyze and predict NOx, CO2, and HC

emission values. Extra Tree regression and Lasso regression techniques were used

in this study. The ET regression was proved to be the best method to predict

emission values over Lasso for all the three buses. 120

10. It was perceived that replacing old 610 and 802 buses with 826-hybrid buses would

reduce NOx, CO2 and HC emission by around 25- 50%.

PM analysis was conducted using EDS and ICP-MS tests, these analysis methods gave us a good understanding of how PM values vary for different buses. All of them are presented below.

1. The concentration of elements detected in ICP-MS analysis: Na, Mg, Al, P, K,

Ca, Fe, Cu, Zn, Mo were found to be high in 610-NON-EGR when compared

with 802-EGR and 826-hybrid buses, this was due to lack of exhaust control

systems in 610 NON-EGR. Whereas, element concentration in 802-EGR and

826-hybrid buses were found to be in the same range which is due to the

presence of DPF, common in both the buses.

2. In SEM analysis all the three buses show a large amount of PM deposited in the

hot idle samples. Referring to previous studies, it was understood that a large

amount of PM was high in hot idle because of the increase in the formation of

methane at higher temperatures could be an originator for soot formation.

3. In EDS analysis, it was observed that the 610 bus samples seem to have a high

carbon content detected when compared to 802-bus and 826-bus. This was

expected because of lack of EGR and DPF systems on 610-bus. Moreover, 802

and 826 buses have literally the same amount of carbon content, this could be

because of having the same exhaust control systems such as EGR and DPF.

121

5.1 Future Work

According to the findings of this study, a few fields of further improvements are recommended, mainly:

1. Study of different biodiesel blends such as B40, B50 and more on hybrid buses

with different exhaust control systems.

2. Experimental study of PM during different driving modes by using an advance

PM instrument.

3. Study related to the relationship between internal combustion and exhaust

control systems effect on controlling exhaust emissions.

4. Comparison study of Parallel Hybrid buses with Series Hybrid buses equipped

with different exhaust control systems such as EGR, SCR-UREA, DPF and

DOC.

122

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129

Appendix A

Regression Analysis on 802-EGR Bus in Cold Idle Mode

11

Actual CO2 Predicted CO2 9

CO2 CO2 % 7

5 0 100 200 300 400Time Sec500 600 700 800 900

Figure A1: CO2 emission prediction model using ET for 802-EGR bus in cold idle

11

10 Actual CO2 Predicted CO2

9

8 CO2 CO2 % 7

6

5 Time Sec 0 100 200 300 400 500 600 700 800 900

Figure A2: CO2 emission prediction model using Lasso for 802-EGR bus in cold idle

130

11.5 Actual HC 9.5 Predicted HC

7.5 HC ppm HC 5.5

3.5 0 100 200 300 400Time Sec.500 600 700 800 900 Figure A3: HC emission prediction model using ET for 802-EGR bus in cold idle

12

10 Actual HC Predicted HC 8

6 HCppm

4

2 0 200 400 Time Sec. 600 800 1000

Figure A4: HC emission prediction model using Lasso for 802-EGR bus in cold idle

11

10 Actual CO2 Predicted CO2 9

8 CO2 CO2 % 7

6

5 0 100 200 300 400Time Sec500 600 700 800 900

Figure A5: NOx emission prediction model using ET for 802-EGR bus in cold idle

131

11

Actual CO2

9 Predicted CO2 CO2 CO2 % 7

5 0 100 200 300 400Time Sec500 600 700 800 900

Figure A6: NOx emission prediction model using Lasso for 802-EGR bus in cold idle

132

Appendix B

Regression Analysis on 610 NON- EGR Bus in Cold Idle

7.5

7 Predicted 6.5 CO2 6

5.5 CO2 % 5 4.5 4 0 100 200 300 400TIme Sec.500 600 700 800 900

Figure B1: CO2 emission prediction model using ET for NON- EGR bus in cold idle

7.5

7 Predicted 6.5 CO2

6

5.5 CO2 %

5

4.5

4 0 100 200 300 400 500 600 700 800 900 Time Sec.

Figure B2: CO2 emission prediction model using Lasso for NON- EGR bus in cold idle

133

100 Predicted HC 80 Actual HC

60

HCPPM 40

20

0 0 200 400 600 800 1000 Time Sec. Figure B3: HC emission prediction model using ET for NON- EGR bus in cold idle

100 Predicted 80 HC Actual HC 60

HC ppm HC 40

20

0 0 200 400 600 800 1000 Time Sec

Figure B4: HC emission prediction model using Lasso for NON- EGR bus in cold idle

700 600 Actual NOx Predicted NOx 500 400

NOx ppm 300 200 100 0 0 200 400 600 800 1000 Time in Sec

Figure B5: NOx emission prediction model using ET for NON- EGR bus in cold idle

134

700 600 Acutal NOx Predicted Nox 500 400

300 NOx ppm 200 100 0 0 200 400 600 800 1000 Time in Sec

Figure B6: NOx emission prediction model using Lasso for NON- EGR bus in cold idle

135

Appendix C

Regression Analysis on 802-EGR Bus in Hot Idle Mode

7

6 Predicted CO2 Actual CO2 5

4

CO2 CO2 % 3

2

1

0 0 200 400 600 800 1000 Time Sec.

Figure C1: CO2 emission prediction model using ET for 802-EGR bus in hot idle

7 Predicted CO2 6 Actual CO2 5

4

CO2 CO2 % 3

2

1

0 0 200 400 600 800 1000 Time Sec.

Figure C2: CO2 emission prediction model using Lasso for 802-EGR bus in hot idle 136

8 Predicted HC 7 Actual HC 6 5 4

HCppm 3 2 1 0 0 200 400 600 800 1000 Time Sec. Figure C3: HC emission prediction model using ET for 802-EGR bus in hot idle

8 Predicted HC 7 Actual HC 6 5 4

HCppm 3 2 1 0 0 200 400 Time Sec. 600 800 1000

Figure C4: HC emission prediction model using Lasso for 802-EGR bus in hot idle

300 Predicted NOx 250 Actual Nox

200

150

NOx ppm 100

50

0 0 200 400 Time Sec. 600 800 1000

Figure C5: NOx emission prediction model using ET for 802-EGR bus in hot idle 137

300 Predicted NOx 250 Actual Nox

200

150

NOx ppm 100

50

0 0 200 400 Time Sec. 600 800 1000

Figure C6: NOx emission prediction model using Lasso for 802-EGR bus in hot idle

138

Appendix D

Regression Analysis on 610 NON- EGR Bus in Hot Idle

6 Predicted CO2 5 Actual CO2

4

3 CO2 CO2 % 2

1

0 -100 100 300 500 700 900 Time Sec.

Figure D1: CO2 emission prediction model using ET for NON- EGR bus in hot idle

6 Predicted CO2 Actual CO2 5

4

3 CO2 CO2 % 2

1

0 -100 100 300 500 700 900 Time Sec.

Figure D2: CO2 emission prediction model using Lasso for NON- EGR bus in hot idle

139

Chart Title 50 Predicted HC 40 Actual HC

30

20 HCppm

10

0 0 200 400 600 800 1000 Time Sec. Figure D3: HC emission prediction model using ET for NON- EGR bus in hot idle

Chart Title 50 Predicted HC 40 Actual HC

30

HCppm 20

10

0 0 200 400 600 800 1000 Time Sec.

Figure D4: HC emission prediction model using Lasso for NON- EGR bus in hot idle

350 Predicted NOx 300 Actual NOx 250

200

150 NOxppm 100

50

0 -100 100 300 500 700 900 Time Sec.

Figure D5: NOx emission prediction model using ET for NON- EGR bus in hot idle

140

310 Predicted NOx 290 Actual NOx 270 250 230

NOxppm 210 190 170 150 -100 100 300 Time Sec. 500 700 900

Figure D6: NOx emission prediction model using Lasso for NON- EGR bus in hot idle

141

Appendix E

Regression Analysis on 802-EGR Bus in Acceleration

13

12

11

10

CO2 9

8 Predicted CO2 7 Actual CO2 6 1600 1700 1800 1900 2000 2100 2200 RPM

Figure E1: CO2 emission prediction model using ET for 802-EGR bus in acceleration

13

12

11

10

CO2 9

8

7 Predicted CO2 Actual CO2 6 1600 1700 1800 1900 2000 2100 2200 RPM

Figure E2: CO2 emission prediction model using Lasso for 802-EGR bus in acceleration 142

13 12 11 10 9

HCppm 8 7 Predicted HC 6 Actual HC 5 1500 1600 1700 1800 1900 2000 2100 2200 2300 RPM Figure E3: HC emission prediction model using ET for 802-EGR bus in acceleration

13 12 11 10 9

HCppm 8 7 Predicted HC 6 Actual HC 5 1500 1600 1700 1800 1900 2000 2100 2200 2300 RPM

Figure E4: HC emission prediction model using Lasso for 802-EGR bus in acceleration

500

450

400 NOx ppm 350 Predicted NOx Actual NOx 300 1600 1700 1800 1900 2000 2100 2200 RPM

Figure E5: NOx emission prediction model using ET for 802-EGR bus in acceleration

143

500 450 400 350 300

NOx ppm 250 200 Predicted NOx 150 Actual NOx 100 1600 1700 1800 1900 2000 2100 2200 RPM

Figure E6: NOx emission prediction model using Lasso for 802-EGR bus in acceleration

144

Appendix F

Regression Analysis on 610 NON- EGR Bus in Acceleration Mode

9.5 9 8.5 8 7.5 CO2 CO2 % 7 6.5 Predicted CO2 6 Actual CO2 5.5 1700 1800 1900 2000 2100 2200 2300 RPM

Figure F1: CO2 emission prediction model using ET for NON- EGR bus in acceleration

9.5 9 8.5 8 7.5

CO2 CO2 % 7 6.5 Actual CO2 6 Predicted CO2 5.5 1700 1800 1900 2000 2100 2200 2300 RPM

Figure F2: CO2 emission prediction model using Lasso for NON- EGR bus in acceleration 145

100

90

80

HCppm 70

60 Prediction HC Actual HC 50 1700 1800 1900 2000 2100 2200 2300 RPM Figure F3: HC emission prediction model using ET for NON- EGR bus in acceleration

100

90

80

HCppm 70

60 Actual HC Prediction HC 50 1700 1800 1900 2000 2100 2200 2300 RPM

Figure F4: HC emission prediction model using Lasso for NON- EGR bus in acceleration

750 Extra Tree 610 bus during acceleration

650

550

NOx (ppm) 450 Predicted NOx 350 1700 1800 1900 2000 2100 2200 2300 RPM

Figure F5: NOx emission prediction model using ET for NON- EGR bus in acceleration

146

Lasso 610 bus during acceleration 700 650 600 550

500 NOx ppm 450 Predicted NOx 400 Actual NOx 350 1700 1800 1900 2000 2100 2200 2300 RPM

Figure F6: NOx emission prediction model using Lasso for NON- EGR bus in acceleration

147

Appendix G

Regression Analysis on 802-EGR Bus in Variable Speed

9

8

7

6 CO2 CO2 % 5

4 Predicted CO2 Actual CO2 3 1100 1200 1300 1400 1500 1600 RPM

Figure G1: CO2 emission prediction model using ET for 802-EGR bus in variable speed

9

8

7

6 CO2 CO2 % 5

4 Predicted CO2 Actual CO2 3 1100 1200 1300 1400 1500 1600 RPM

Figure G2: CO2 emission prediction model using Lasso for 802-EGR bus in variable speed

148

10

8

6

HCppm 4

2 Predicted HC Actual HC 0 1100 1200 1300 1400 1500 1600 RPM Figure G3: HC emission prediction model using ET for 802-EGR bus in variable speed

9 8 7 6 5

4 HCppm 3 2 Predicted HC 1 Actual HC 0 1100 1200 1300 1400 1500 1600 RPM

Figure G4: HC emission prediction model using Lasso for 802-EGR bus in variable speed

300

250

200

150

NOx ppm 100

50 Predicted NOx Actual NOx 0 1100 1200 1300 1400 1500 1600 RPM

Figure G5: NOx emission prediction model using ET for 802-EGR bus in variable speed 149

300

250

200

150

NOx ppm 100

50 Predicted NOx Actual NOx 0 1100 1200 1300 1400 1500 1600 RPM

Figure G6: NOx emission prediction model using Lasso for 802-EGR bus in variable speed

150

Appendix H

Regression Analysis on 610 NON- EGR Bus in Variable Speed Mode

6.5

5.5

4.5 CO2 CO2 %

3.5 Predicted CO2 Actual CO2 2.5 1100 1200 1300 1400 1500 1600 RPM

Figure H1: CO2 emission prediction model using ET for NON- EGR bus in variable speed

6.5

5.5

4.5 CO2 CO2 %

3.5 Predicted CO2 2.5 1100 1200 1300 RPM 1400 1500 1600

Figure H2: CO2 emission prediction model using Lasso for NON- EGR bus in variable speed 151

60

55

50

45

HCppm 40

35 Predicted HC Actual HC 30 1100 1200 1300 1400 1500 1600 PPM Figure H3: HC emission prediction model using ET for NON- EGR bus in variable speed

60

55

50

45 HCppm 40

35 Predicted HC Actual HC 30 1100 1200 1300 1400 1500 1600 PPM

Figure H4: HC emission prediction model using Lasso for NON- EGR bus in variable speed

400

300

200 NOx ppm 100 Predicted NOx Actual NOx 0 1100 1200 1300 RPM 1400 1500 1600

Figure H5: NOx emission prediction model using ET for NON- EGR bus in variable speed 152

400 350 300 250 200

NOx ppm 150 100 50 Predicted NOx Actual NOx 0 1100 1200 1300 1400 1500 1600 RPM

Figure H6: NOx emission prediction model using Lasso for NON- EGR bus in variable speed

153

Appendix I

Regression Analysis on 802-EGR Bus in Deceleration

5

4

3

CO2 CO2 % 2

1 Actual CO2 Predicted CO2 0 750 800 850 900 950 1000 1050 1100 RPM

Figure I1: CO2 emission prediction model using ET for 802-EGR bus in deceleration

5

4

3

CO2 CO2 % 2

1 Actual CO2 Predicted CO2 0 750 800 850 900 950 1000 1050 1100 RPM

Figure I2: CO2 emission prediction model using Lasso for 802-EGR bus in deceleration

154

5

4

3

HCppm 2

1 Predicted HC Actual HC 0 750 800 850 900 RPM 950 1000 1050 1100 Figure I3: HC emission prediction model using ET for 802-EGR bus in deceleration

5

4

3

HCppm 2

1 Predicted HC Actual HC 0 750 800 850 900 950 1000 1050 1100 RPM

Figure I4: HC emission prediction model using Lasso for 802-EGR bus in deceleration

40 35 30 25 20

NOx ppm 15 10 Predicted NOx 5 Actual NOx 0 770 820 870 920 970 1020 1070 1120 RPM

Figure I5: NOx emission prediction model using ET for 802-EGR bus in deceleration

155

40 35 30 25 20

NOx ppm 15 10 Predicted NOx 5 Actual NOx 0 770 820 870 920 970 1020 1070 1120 RPM

Figure I6: NOx emission prediction model using Lasso for 802-EGR bus in deceleration

156

Appendix J

Regression Analysis on 610 NON- EGR Bus in Deceleration Mode

3.5 3 2.5 2

CO2 CO2 % 1.5 1 0.5 Predicted CO2 Actual CO2 0 750 800 850 900 950 1000 1050 1100 1150 RPM

Figure J1: CO2 emission prediction model using ET for NON- EGR bus in deceleration

3.5

3

2.5

2

CO2 CO2 % 1.5

1

0.5 Actual CO2 Predicted CO2 0 750 800 850 900 950 1000 1050 1100 1150 RPM

Figure J2: CO2 emission prediction model using Lasso for NON- EGR bus in deceleration 157

30

25

20

15

HCppm 10

5 Predicted HC Actual HC 0 750 800 850 900 950 1000 1050 1100 1150 RPM Figure J3: HC emission prediction model using ET for NON- EGR bus in deceleration

30

25

20

15 HCppm 10

5 Predicted HC Actual HC 0 750 800 850 900 950 1000 1050 1100 1150 RPM

Figure J4: HC emission prediction model using Lasso for NON- EGR bus in deceleration

50 45 40 35 30

NOx ppm 25 20 Predicted NOx 15 Actual NOx 10 750 800 850 900 950 1000 1050 1100 1150 RPM

Figure J5: NOx emission prediction model using ET for NON- EGR bus in deceleration 158

50 45 40 35 30

NOx ppm 25 20 Predicted NOx 15 Actual NOx 10 750 800 850 900 950 1000 1050 1100 1150 RPM

Figure J6: NOx emission prediction model using Lasso for NON- EGR bus in deceleration

159

Appendix K

Regression Analysis on 802-EGR Bus in at Intersections

8 7 6 5 4

CO2 CO2 % 3 2 1 Actual CO2 Predicted CO2 0 0 5 10 15 20 25 30 35 Time Sec.

Figure K1: CO2 emission prediction model using ET for 802-EGR bus at intersections

8 7 6 5 4

CO2 CO2 % 3 2 Actual CO2 1 Predicted CO2 0 0 5 10 15 20 25 30 35 Time Sec.

Figure K2: CO2 emission prediction model using Lasso for 802-EGR bus at intersections

160

12

10

8

6 HCppm 4

2 Predicted HC Actual HC 0 0 5 10 15 20 25 30 35 Time Sec. Figure K3: HC emission prediction model using ET for 802-EGR bus at intersections

12

10

8

6 HCppm 4

2 Predicted HC Actual HC 0 0 5 10 15 20 25 30 35 Time Sec.

Figure K4: HC emission prediction model using Lasso for 802-EGR bus at intersections

400 350 300 250 200

NOx ppm 150 100 Predicted NOx 50 Actual NOx 0 0 5 10 15Time Sec.20 25 30 35

Figure K5: NOx emission prediction model using ET for 802-EGR bus at intersections

161

400 350 300 250 200

NOx ppm 150 100 50 Predicted NOx Actual NOx 0 0 5 10 15 20 25 30 35 Time Sec.

Figure K6: NOx emission prediction model using Lasso for 802-EGR bus at intersections

162

Appendix L

Regression Analysis on 610 NON- EGR Bus at Intersections

7

6

5

4

CO2 CO2 % 3

2

1 Actual CO2 Predicted CO2 0 0 5 10 15 20 25 30 35 Time Sec.

Figure L1: CO2 emission prediction model using ET for NON- EGR bus at intersections

7

6

5

4

CO2 CO2 % 3

2

1 Actual CO2 Predicted CO2 0 0 5 10 15 20 25 30 35 Time Sec.

Figure L2: CO2 emission prediction model using Lasso for NON- EGR bus at intersections 163

80 70 60 50 40

HCppm 30 20 Predicted HC 10 Actual HC 0 0 5 10 15 20 25 30 35 Time Sec. Figure L3: HC emission prediction model using ET for NON- EGR bus at intersections

80 70 60 50 40

HCppm 30 20 Actual HC 10 Predicted HC 0 0 5 10 15 20 25 30 35 Time Sec.

Figure L4: HC emission prediction model using Lasso for NON- EGR bus at intersections

500

400

300

200 NOx ppm

100 Predicted NOx Actual NOx 0 0 5 10 15 20 25 30 Time Sec

Figure L5: NOx emission prediction model using ET for NON- EGR bus at intersections 164

500

400

300

200 Predicted NOx NOx ppm Actual NOx 100

0 0 5 10 15 20 25 30 Time Sec

Figure L6: NOx emission prediction model using Lasso for NON- EGR bus at intersections

165