A Dissertation
Entitled
Characterization of Vehicular Exhaust Emissions and Indoor Air Quality of Public
Transport Buses Operating on Alternative Diesel Fuels
By
Abhilash Vijayan
Submitted as partial fulfillment of the requirements for
the Doctor of Philosophy in Engineering
Advisor: Dr. Ashok Kumar
College of Graduate Studies
The University of Toledo
December 2007 The University of Toledo
College of Engineering
I HEREBY RECOMMEND THAT THE DISSERTATION PREPARED UNDER MY
SUPERVISION BY Abhilash Vijayan
ENTITLED Characterization of Vehicular Exhaust Emissions and Indoor Air Quality of
Public Transport Buses Operating on Alternative Diesel Fuels
BE ACCEPTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR
THE DEGREE OF DOCTOR OF PHILOSOPHY IN ENGINEERING
______Dissertation Advisor: Dr. Ashok Kumar
Recommendation concurred by
______Dr. Martin Abraham Committee ______Dr. Brian Randolph On ______Dr. Defne Apul Final Examination ______Dr. Qin Shao
______
Dean, College of Engineering An Abstract of
Characterization of Vehicular Exhaust Emissions and Indoor Air Quality of Public
Transport Buses Operating on Alternative Diesel Fuels
Abhilash Vijayan
Submitted as partial fulfillment of the requirements for
the Doctor of Philosophy in Engineering
The University of Toledo
December, 2007
Over the years, vehicular traffic has increased multifold causing an associated increase in the total emissions from transportation sources. These vehicular emissions affect two human occupied environments most significantly: human occupied regions near and around the roads including residences, offices, schools, hospitals etc., and indoor vehicle compartments that act as individual microenvironments trapped inside the high concentration zone. A detailed literature review indicated five major gaps in the knowledge base related to pollutant characteristics from the vehicular exhaust and pollutant behavior inside buses which are addressed in this research.
• Characterization of indoor air pollutant behavior: The literature review did not
yield any elaborate study results on indoor pollutant behavior trends and/or
models for public transport bus compartments. Comparative studies on the
iii behavior of indoor-outdoor pollutant relationships and concentration variation at
different locations inside the bus were also limited in scope.
• Indoor air quality in buses and factors influencing the indoor air quality (IAQ):
No comprehensive study has been reported in the literature in which simultaneous
measurement of multiple gaseous contaminants and particulate matter (PM) inside
buses have been carried out that have focused on the identification of important
explanatory variables of indoor air quality.
• Measurement of fine particulates: The current work reported in the literature is
limited to particulate measurements for sizes more than 2.5 micrometer (µm) and
most of the studies focus on PM10 (particle matter less than 10 µm in diameter)
concentrations. Very few studies have measured the number of particles/unit
volume and compared the associated abundance of particles to their respective
mass concentrations.
• Characterization of exhaust emission behavior: Most of the work reported has a
limitation on the number of vehicles studied and fails to elaborate the interaction
between influencing variables. Very few studies have reported emission
comparisons for biodiesel and ultra-low sulfur diesel (ULSD) operated fleets and
limited models are available to study fleet emission behavior and instantaneous
emission concentrations with respect to engine performance and fuel.
• Impacts of alternative fuels on indoor air quality have not been studied
extensively.
This research has tried to lessen these knowledge gaps in the field and is the first to
attempt an extensive real time monitoring and measurement of numerous operational and
iv traffic variables that could have an effect on the emissions and the air quality of public
transport buses. A comprehensive emission testing protocol was developed for
characterizing emission characteristics from Toledo Area Regional Transportation
Authority (TARTA) public transport buses, and over 120 buses were tested in engine
idling and on-road operation modes. This research was also the first to attempt a
comprehensive indoor air quality study spanning thirteen months of data collection
involving the monitoring and measurement of multiple indoor gaseous pollutants and
ultra-fine particulate number and mass concentrations.
The emission protocol identified important influencing factors that affect vehicular
emissions during real-world operating conditions. Emission comparisons for TARTA
buses showed that although B20 biodiesel in comparison to ULSD fuel emitted higher
concentrations of nitric oxide (NO) and nitrogen dioxide (NO2) for 300 series (Bluebird)
fleet, and lower carbon monoxide (CO) concentration for both 300 and 500 series
(Thomas) fleets, other factors such as engines’ operating conditions, preventative maintenance history, vehicle operation at different engine loads and engine operating temperatures had a larger influence on emission behavior. Regular engine idling mode and higher engine temperatures were found to reduce vehicular emissions most
significantly (up to 30-42%) while performing preventative maintenance reduced
emission concentrations by 15-20%. Emission models for seven TARTA fleets were
developed for six gases – oxygen (O2), carbon monoxide, carbon dioxide (CO2), nitric
oxide, nitrogen dioxide and sulfur dioxide (SO2). These models explained an average of
90% of the emission data for each pollutant. Instantaneous models also developed for the six pollutants based on real-time on-road test data explained an average of over 80% the
v variability in the pollutant emissions. Engine temperatures, exhaust temperature, accelerator pedal position, % engine load, and engine rpm were the most important variables affecting the concentrations of the pollutants studied (90% of the models had p<0.001 for each of the variables).
The indoor concentrations of carbon dioxide, carbon monoxide, sulfur dioxide and nitrogen oxides (NOx) were found to be independent of the fuel used in the bus.
Variation of all the pollutants studied were dependant on the route traveled, and additionally carbon dioxide was also found to be greatly affected by passenger ridership.
Higher concentrations for all the pollutants were observed during the morning pullout and in periods of heavy traffic (around 9 am). The indoor concentration of fine particulates was found to be identical in both the B20 and ULSD buses suggesting minimal effect of fuel on the particulate concentrations. The effect of the fuel used in the bus was observed only during large periods of idling with the doors and windows opened, but during an average run, TARTA buses do not continuously idle for long periods with the windows opened. The indoor fine particulate levels were primarily a result of just-outside
(roadside) concentrations and passenger activity. Over 95% of the indoor particulates have diameter less than 1 µm. PM1.0 mass was determined to be comprised of over 40% particles less than 0.40 µm, 25% particles between 0.40-0.50 µm and 35% particles between 0.50 and 1.0 µm in diameter. These pose the highest risk to humans as they can travel deep inside the lungs. The regression models developed using a combination of vehicular, traffic, ambient meteorology and in-vehicle comfort parameters, and ambient concentration explained approximately 72-81% of the hourly indoor mass and number concentrations of fine and ultra-fine particulates.
vi In conclusion, this dissertation demonstrates a feasible strategy for developing a comprehensive emission inventory and indoor air quality database to study the indoor and
ambient impacts of public transport buses. The dissertation also presents a new analysis
procedure for identifying the potential impact of each influencing variable from a
comprehensive multi-variable environmental database developed from an experimental
procedure. This research is also the first to attempt a comprehensive study to characterize
the ultra-fine particulate (PM<1.0 µm) behavior inside public transport buses and to develop predictive models for multiple indoor pollutants including fine and ultra-fine
particulate mass and number concentrations. The research was also able to characterize
the emission and indoor air pollutant behavior for public transport fleets and identify the
important influencing variables affecting the overall air quality.
vii ACKNOWLEDGEMENT
This dissertation is an outcome of the support and well wishes of many individuals. I
would like to take this opportunity to acknowledge and thank all of them.
To Dr. Ashok Kumar, for your guidance and support and for giving me the opportunity to
work with you. Throughout the course of my graduate studies, you have guided me with inspiration.
To my PhD dissertation committee - Dr. Brian Randolph, Dr. Martin Abraham, Dr. Defne
Apul, and Dr. Qin Shao, for your time and valuable inputs to make this work a success.
To TARTA management and employees for their continued interest and involvement in this work. Special thanks to Mr. David Burnham and Mr. Kevin Russell for their swift management in helping me with the project related issues at TARTA.
To Dave, for having so much patience to listen to my ideas, and to always say “Adapt,
Overcome, Improvise” even during the most testing phases of the project.
To my team at the Air Pollution Research Group - Ravi, Akhil and Vinay for watching the computer screen for hours counting the passengers inside the bus.
To Charanya, for working with me long hours and for listening to my thoughts and giving suggestions.
To my family, for their constant love and support, and for their continued confidence in me.
And above all, to God…
Thank you.
viii Table of Contents
Page No.
ABSTRACT iii
ACKNOWLEDGEMENTS viii
TABLE OF CONTENTS ix
LIST OF FIGURES xi
LIST OF TABLES xiv
Chapter 1 INTRODUCTION 1- 8
Chapter 2 LITERATURE REVIEW 9-18
2.1 Studies on Vehicular Emission 9
2.2 Studies on Vehicular Indoor Air Quality 14
2.3 Summary from Literature Review 17
Chapter 3 METHODOLOGY 19-45
3.1 Exhaust Emission Study 19
3.1.1 Experimental Design 22
3.1.2 Testing Procedures / Study Design 24
3.1.3 Data Collected 28
3.2 Indoor Air Quality Study 28
3.2.1 Experimental Design 28
3.2.2 Data Collection 33
3.2.3 Data Quality 34
3.2.4 Experimental Procedure: Data Collected 35
3.2.5 Data Used for Development of Indoor Air Quality 39 Models
ix Page No.
3.2.6 Data Used for Indoor/Outdoor and Front/Back 40 Comparisons
3.3 Analysis Procedures 42
3.3.1 Data Processing and Classification: Development of 42 Emission Inventories
3.3.2 Statistical Analysis 43
Chapter 4 RESULTS AND DISCUSSION 46-133
4.1 Vehicular Exhaust Emissions 46
4.1.1 Characterizing the Effects of Operational Variables 46 on Emission Behavior
4.1.2 Fleet Emission Trend Characteristics 72
4.1.3 Instantaneous Emission Models: On-road Test Mode 80
4.1.4 Fleet Emission Models: Idling Mode – 15 Minute 90 Averages
4.2 Indoor Air Quality Characterization 101
4.2.1 Characterizing Pollutant Behavior 101 4.2.2 Identifying Important Variables Affecting Indoor Air 113 Quality 4.2.3 Development of Indoor Pollutant Concentration 127 Models
Chapter 5 CONCLUSION AND RECOMMENDATIONS FOR 134-139 FUTURE RESEARCH
5.1 Recommendations for Future Research 138
REFERENCES 140-146
APPENDIX A – Emission Inventory 147-154
APPENDIX B – Central Business District (CBD) Cycle 155-156
x List of Figures
Page No.
Figure 2.1 Factors Affecting Emission Rates 10
Testo 350XL Unit Setup Interfaced with the Figure 3.1 22 Computer
Designed Aluminum Shield for Attaching Figure 3.2 24 Probes to the Tailpipe
Analyzer Probes Inserted into the Exhaust Pipe Figure 3.3 24 Using the Attachment
Figure 3.4 Idling Test Setup 25
Figure 3.5 Run 1 and Run 2 of the UT-TARTA Test Cycle 27
Map Showing TARTA Route 11 (Source: Figure 3.6 29 TARTA website, 2007)
Map Showing TARTA Route 20 (Source: Figure 3.7 30 TARTA website, 2007)
Figure 3.8 IAQ Instruments Installed in the GPS Cage 32
Figure 3.9 Indoor Air Quality Testing: Experimental Setup 33
Figure 3.10 PM1.0 Indoor/Outdoor Comparison Setup 41
Figure 4.1 Emission Behavior during Run 1 48
Figure 4.2 (a): Engine Performance during Run 1 49
Figure 4.2 (b): Engine Performance during Run 1 50
Emission Comparisons for Idling and On-road Figure 4.3 53 Testing Modes
Pollutant Concentration Comparison: Hot vs. Figure 4.4 55 Cold Start
Vehicular Emissions vs. Engine Operating Figure 4.5 (a-d): 56 Temperature/Starting Condition (CBD)
Figure 4.6 CO Concentration Comparison: Effect of PMI 58
xi Page No.
Figure 4.7 CO2 Concentration Comparison: Effect of PMI 59
Figure 4.8 NO Concentration Comparison: Effect of PMI 59
Figure 4.9 SO2 Concentration Comparison: Effect of PMI 60
Figure 4.10 NO2 Concentration Comparison: Effect of PMI 60
Pollutant Concentration Comparison for Figure 4.11 62 Normal and Fast Idling
Figure 4.12 (a-d) The Effect of Fuel on Pollutant Concentrations 64 - 65
Pollutant Concentration Comparison: B20 vs. Figure 4.13 66 ULSD
Fuel Rate Comparison of B20 and ULSD Bus Figure 4.14 72 Fleets
Figure 4.15 CO Concentration Trends: Fleet Comparison 74
Figure 4.16 NO Concentration Trends: Fleet Comparison 74
Figure 4.17 SO2 Concentration Trends: Fleet Comparison 75
Figure 4.18 NO2 Concentration Trends: Fleet Comparison 76
Figure 4.19 CO2 Concentration Trends: Fleet Comparison 77
Comparison on Fleet Emissions in Different Figure 4.20 79 Operation Modes
Figure 4.21 Weekly CO2 Concentration Trend 102
Figure 4.22 Daily CO2 Concentration Trend 102
Figure 4.23 Weekly CO Concentration Trend 104
Figure 4.24 Daily CO Concentration Trend 104
Figure 4.25 Weekly SO2 Concentration Trend 105
Figure 4.26 Daily SO2 Concentration Trend 105
Figure 4.27 Weekly NO Concentration Trend 106
xii Page No. Avg. Variation of PM Components (Mass Figure 4.28 1.0 108 Concentration) with Time of Day Avg. Variation of PM Components (Particle Figure 4.29 1.0 108 Counts) with Time of Day Figure 4.30 PM Size Fraction Ratios 109 PM Concentration Comparison: Inside vs. Figure 4.31 1.0 110 Outside the Bus CO Concentration: Indoor vs. Outdoor Figure 4.32 2 111 Comparison CO Concentration: Indoor vs. Outdoor Figure 4.33 112 Comparison PM Concentration Comparison: Front vs. Figure 4.34 1.0 113 Back of the bus Comparison of Weekly Indoor PM 1.0 Conc. for Figure 4.35 (a-b) B20 and ULSD Buses 115 - 116
Comparison of Indoor Fine PM with Fuel: Figure 4.36 117 ULSD vs. B20 Impact of Fuel on Indoor Pollutant Figure 4.37 (a-c) 119 – 120 Concentrations Weekly Concentration with Route: CO Figure 4.38 (a) 2 121 Concentration Concentration Range Comparison with Route: Figure 4.38 (b) CO Concentration 122
Concentration Range Comparison with Route: Figure 4.38 (c) CO Concentration 122
Weekly Concentration with Route: SO Figure 4.38 (d) 2 123 Concentration Effects of Various Factors on Particulate Figure 4.39 Number Concentration for Fine Particles 124 Between 0.30 and 0.40 µm in Diameter Main Effects Plot for PM vs. Bus Operation & Figure 4.40 (a-d) 126 Door Status Figure B1 CBD Driving Cycle 156
xiii List of Tables
Page No.
Table 1.1 Pollutants from Vehicular Exhaust 4
Table 3.1 TARTA Fleet Details 20
Table 3.2 TARTA Fleets 21
Table 3.3 Properties of Bio-fuel and USLD 21
Table 3.4 Sensor Calibration Details 35
Idling Test: Average Exhaust Emissions from Table 4.1 66 Different Fuels
Table 4.2 Effect of Fuel on Pollutant Concentrations 71
Regression Results for Pollutant O : Instantaneous Table 4.3 2 84 Models
Regression Results for Pollutant CO: Instantaneous Table 4.4 85 Models
Regression Results for Pollutant NO: Instantaneous Table 4.5 86 Models
Regression Results for Pollutant SO : Instantaneous Table 4.6 2 87 Models
Regression Results for Pollutant NO : Table 4.7 2 88 Instantaneous Models
Regression Results for Pollutant CO : Table 4.8 2 89 Instantaneous Models
Fleet Emission Model Coefficients and Statistics: Table 4.9 97 Fleet 200 and 500
Fleet Emission Model Coefficients and Statistics: Table 4.10 98 Fleet 900 and 400
Fleet Emission Model Coefficients and Statistics: Table 4.11 99 Fleet 300
Fleet Emission Model Coefficients and Statistics: Table 4.12 100 Fleet 600 and 700
xiv Page No.
Table 4.13 PM1.0 Mass-Number Relationship 107 Regression Results: Identification of Significant Table 4.14 127 Variables Affecting PM Emission Inventory – TARTA Fleets: On-road and Table A1 148 Idling Modes Table A2 Emission Inventory – TARTA Fleets: Idling Mode 151
xv Chapter One
Introduction
Urban populations spend almost 7% of their daily time commuting, most of which is to travel between their workplace and place of residence (Jenkins et al., 1992). Almost half of this commute time is spent stuck in traffic (CARB Report, 2003). This could be attributed to the quadrupled increase in the number of cars on the road as compared to
1960, in addition to the increase in the per capita driving over 2.5 times to 10,000 miles
(U.S. Environmental Protection Agency, 2000). This increase in the number of vehicles on the roads and their total operation time has resulted in a tremendous increase in vehicular pollution.
All over the world, mobile sources stand out as the largest contributors of a number of air pollutants. The emission potential of gasoline-powered vehicles especially for the production of fine particles, carbon monoxide, oxides of nitrogen and volatile organic compounds, including aromatic volatile organic compounds has been well documented in the literature (Schauer et al., 2002; Huang et al., 1994). In the recent past, much focused interest has been directed towards diesel-powered vehicles and their potential to emit fine particles, NOx, sulfur compounds, semi-volatile organic compounds and elemental carbon (EC) some of which are mutagenic and/or carcinogenic.
Carbon monoxide, which forms due to the incomplete combustion of carbon in fuel from vehicular traffic, is an important criteria pollutant that reduces the oxygen carrying capacity of the blood. Unlike many other air pollutants, carbon monoxide levels 1 in the outside air typically peak during colder months. Volatile organic compounds
(VOC) are comprised of organic acids, hydrocarbons, aldehydes, and ketones. Some
VOCs such as benzene are carcinogenic and have a significant human threat. Most VOC
emissions result from incomplete fuel combustion and from fuel evaporation and are precursors to ground level ozone that causes health problems such as difficulty in
breathing, lung damage, and reduced cardiovascular functioning. Nitrogen oxides form
when fuel burns at high temperatures, such as in motor vehicle engines. Mobile sources
are responsible for more than half of all nitrogen oxide emissions in the United States.
Nitrogen oxides can travel long distances and aid in secondary air pollutant formation such as ground level ozone, smog, and particulate matter in locations far from their emission sources due to atmospheric reactions and transformations. The health effects of
NOx on humans include nose and eye irritation, pulmonary edema, and bronchitis (US
Environmental Protection Agency, (EPA)). Various emissions from vehicles such as
nitrogen oxides, sulfur dioxide, and hydrocarbons in addition to being definite threats to
the human health and environment have been identified as being precursors to ozone and
secondary particulate formation (Chan et al., 2002; Chan and Chung, 2003) that pose a
severe global air pollution problem.
Diesel emissions being very high in primary particulate matter emissions need to
be controlled. Particulate matter (PM) is the term for solid or liquid particles found in the air. Mobile source particulate emissions consist mainly of fine particulates (PM2.5) that are released directly and those that are products of secondary formation. Any abundance in the particulate matter concentration is caused by the increase in the number of particles which range in size from coarse to ultra-fine. Considering a spherical shape and similar
2 matter density (mass/volume), one particle with a 20 micron diameter is equivalent to
8000 particles of 1 micrometer diameter, or 8 million particles of 0.1 micrometer
diameter. Fine particulate matter is a health concern because very fine particles can reach
the deepest regions of the lungs. Health effects include asthma, difficult or painful
breathing, and chronic bronchitis, especially in the sensitive population. Fine particulate
matter associated with diesel exhaust is considered a potential carcinogen and is therefore
listed as a mobile source air toxic (U.S. EPA, 2002a; Health Effects Institute, 2002). In
recent years, numerous studies worldwide have linked particulate matter to a wide range
of adverse health effects including deaths (Chow et al. 2006, Biswas et al. 2005, Kaiser
2000, Ostro et al. 1996). There is evidence that exposure to particulate matter from traffic
and other combustion sources have more severe impacts on human health than PM from
other sources, inferring that the health effects associated with PM would be related
mostly to anthropogenic emission sources (Ku¨nzli et al., 2000). Kaiser (2000) estimated
that the exposure to high levels of particulate matter is alone responsible for more than
60,000 deaths each year in the U.S. Fine particulate matter can also be dispersed to longer
distances from their place of origin and is a major cause of haze, which reduces visibility.
Table 1.1 summarizes the different pollutants emitted from vehicular exhaust, their
formation and health effects.
Several Environmental Protection Agency (U.S. EPA) emission control strategies have been implemented to reduce vehicular emissions over the past years, the most recent one being implemented on January 1, 2007. The current regulation requires a 97 percent reduction in the sulfur content of highway diesel fuel from 500 parts per million (ppm) in low sulfur diesel (LSD) to 15 ppm in ultra-low sulfur diesel.
3 Table 1.1 Pollutants from Vehicular Exhaust
Total annual Pollutants Environmental significance / Health Formation contribution emitted risks (EPA) Incomplete combustion of carbon in fuel. Carbon monoxide Highest CO levels occur when the weather is very Reduces the oxygen carrying capacity o PC: 575 lb (CO) cold or at high elevations where there is less oxygen of the blood o LT: 854 lb in the air to burn the fuel Carbon dioxide PC: 11450 lbs Complete combustion product Global warming and climate change o (CO2) o LT: 16035 lbs Volatile organic Potential carcinogens; react with compounds nitrogen oxides in the presence of For HC Unburned or partially burned fuel is emitted from (VOCs) - Mainly sunlight to form ozone; ozone PC : 77.1 lbs the engine as exhaust; leaks and evaporation o Hydrocarbons component of smog, lung damage and o LT: 108 lbs (HC) respiratory problems Formed when the oxygen and nitrogen in the air Eye, nose, Throat irritation, Central Nitrogen oxides react with each other during combustion at high nervous system damage, chronic o PC: 38.2 lbs (NOx) temperature and excess oxygen (more than what is respiratory problems. Precursors to o LT: 55.8 lbs needed to burn the fuel) secondary pollutant formation Particulate matter from mobile sources is primarily Asthma; chronic respiratory problems; Particulate matter PM2.5. potential carcinogens; (PM) Direct emissions and secondary formation in Visibility issues and haze atmosphere (Source: Compiled from the United States Environmental Protection Agency (USEPA)) PC - Passenger cars LT - Light Trucks
4 Many such avenues are available for decision makers in the transportation industry to
move towards a cleaner sustainable operation.
Yet, with the growing fleet of vehicles and escalating amount of time spent in
traffic by the drivers and passengers, the continuous exposure to urban pollutants during
commuting has become a concern to the general public. Understanding the human
exposure due to the traffic pollutants in buses and the indoor–outdoor air quality
relationship is thus very important in understanding our total lifetime health risk.
A detailed literature review conducted on the environmental impacts of mobile
source pollution clearly identified five major knowledge gaps in the field of
transportation related air quality issues. Elaborate studies on characterization of multiple
indoor air pollutant behavior, the identification of influencing and explanatory variables
for the emissions and the characterization of indoor air quality are quite few and limited
in scope. Studies involving the measurement and analysis of fine particulate number
concentrations are few; still fewer are the studies with ultra fine particulates.
These gaps in the knowledge base formed the framework for an advanced research on environmental impact assessment of alternative diesel fuels on the Toledo
Area Regional Transit Authority public transport buses as a part of a US Department of
Transportation funded grant. In Toledo, Ohio, TARTA has been the "Ride of Toledo" since 1971 and has over 40 routes in the metropolitan area, serving nine communities and carrying almost 5 million passengers every year. The annual passenger mileage for
TARTA buses is around 22 million miles (TARTA website, 2007). TARTA buses cater to Northwest Ohio’s entire public transportation needs and operate from five in the morning until midnight everyday. With over 180 buses running throughout the day,
5 TARTA buses contribute to a significant percentage of the total emissions in the region.
Understanding the importance of a cleaner environment, TARTA started adopting the use
of alternative diesels including B20 and ULSD fuels in June 2006.
To understand the scope and magnitude of the pollution potential of the public transportation buses, the research was divided into two sections: (i) study of emission behavior affecting the ambient air quality and the public, and (ii) study of the bus indoor air quality affecting the passengers. The exhaust emission study focuses on the different parameters that affect vehicular emissions, and the behavior of vehicular emissions under various operating conditions. Data collection was carried out for both idling conditions
(over 60% of the buses) and true operating conditions using 1) a selected TARTA route and, 2) a standard load simulation on a dynamometer (performed on samples from each fleet). The results from these tests were compared to evaluate the emissions under different operating conditions. The emission data were classified according to the recorded engine performance data including mileage, fuel, load and operation characteristics to understand the emission behavior of diesel buses and to identify the important influencing variables affecting the emissions. The profiling information was also used to study the impacts of using alternative diesel technology on ambient pollution concentrations and to identify the least polluting and most fuel efficient option for operation.
The indoor air quality study was aimed at identifying important variables that affect the concentrations of important air pollutants that affect passengers during everyday commutes. This study involved daily continuous real-time monitoring of five pollutants: carbon dioxide, carbon monoxide, sulfur dioxide, nitrogen dioxide, and nitric
6 oxide; particulate matter distribution from PM0.30 to PM20 (mass and number concentrations); and two important indoor air quality parameters: temperature and humidity. Outdoor concentration data were also collected frequently to study the indoor pollutant behavior as a function of the road-side air concentrations. The collected data were used to model pollutant behaviors inside the bus as a function of vehicle operating conditions, fuel, traffic and passenger activity, ambient concentrations and ambient meteorology.
The research objectives of this thesis are presented below:
1. Characterizing the effects of vehicle operation variables on exhaust emission
behavior and identification of the most important factors influencing pollutant
emissions.
2. Development of fleet emission behavior models and instantaneous real-world
emission models for different fleets to predict real-time emission behavior as a
function of vehicular operation variables, engine maintenance, and ambient
meteorology.
3. Characterizing daily and weekly indoor air quality trends and pollutant behavior
at different locations inside public transport buses and their relationship with
outdoor and ambient concentrations.
4. Identifying important influencing variables affecting indoor air quality and
developing indoor air concentration models for pollutant gases and particulate
matter (mass and number concentration) inside public transport buses.
7 This research applies a new procedure to analyze multivariate environmental data
from vehicle sources to identify the true influence of different variables taking into
consideration a combined effect of multiple variables on the pollutant concentrations.
Such a detailed analysis for traffic and air quality data involving a comprehensive database could not be found in the literature. The study also attempts to provide new insights into the behavior of various pollutants including fine and ultra-fine particulate matter inside public transport buses and their relationships with bus operation conditions, ambient meteorology and on-road pollutant levels. This dissertation also presents different pollutant specific models developed for predicting indoor air concentrations and exhaust emission behavior for public transport buses. The research aims to use this information to study the impacts of using alternative diesel technology on ambient pollution concentrations and to identify the least polluting and the most fuel efficient option for operation of the TARTA buses.
The following dissertation document is organized into 4 chapters: Chapter 2 gives an exhaustive review of the vehicular emissions and vehicular indoor air quality studies.
Chapter 3 discusses in detail the experimental design for data collection and the methodology adopted to analyze the gathered information. Results from the experimental and statistical analyses are discussed in Chapter 4. Chapter 5 concludes the dissertation with the key findings from the work and offers a list of recommended avenues for future work.
8 Chapter Two
Literature Review
2.1 Studies on Vehicular Emission
Vehicular emissions are mainly the by-products of combustion of fuels within the vehicles engine combustion chamber and are released into the atmosphere through the tail pipe or by the fugitive evaporative release of hydrocarbons escaping from the fuel storage/ delivery system. A number of parameters that affect the emission rate of pollutants are presented in Figure 2.1 (compiled from the USEPA and Oak Ridge
National Laboratory, 2006).
A detailed literature review was conducted to understand the magnitude of work done in the area of vehicular pollution behavior analysis. Tao et al. (2006) commented that despite their relatively small numbers, heavy-duty diesel vehicles are disproportionate contributors to the emission inventory for oxides of nitrogen and particulate matter due to their high individual vehicle emissions rates, lack of engine after-treatment, and high vehicle miles traveled. Shah et al. (2006) suggested that changes in engine technology, operating mode, fuel properties, vehicle speed, and ambient conditions can have significant effects on these excessive emissions.
In a recent study, Chen et al. (2007) observed that vehicles show different vehicle specific power (VSP) profiles due to different average speed, vehicle mass, and
9 Factors Affecting Emission Rates
Vehicle conditions Vehicle Route Operating conditions
Model year Start mode: Hot / Cold
Type: Highway, Residential, Industrial Accrued vehicle mileage Average vehicle speed
Pavement condition Fuel delivery system Load
Passenger count Emission control system Driver behavior Traffic conditions Inspection and maintenance
Vehicle operating environment: Vehicle class and size Altitude, Humidity, Temperature
Fuel type
Fuel content: Oxygen, Benzene, Metal content
Figure 2.1 Factors Affecting Emission Rates
distribution of speed and acceleration, thus resulting in different emission factors. These were similar to the results seen by Clark et al. (2002) who showed that NOx emissions could vary by a factor of three when measured using different chassis dynamometer test schedules. These observations also enforce the importance of scheduling a test cycle that accurately mimics real-world operations. The data comparisons showed that injection timing variances could increase NOx emissions by a factor of 2 depending on operating
10 conditions. Supporting suggestions were also made by Lim et al. (2007) who indicated
that the engine operating conditions strongly influence the emission rate of pollutants,
and an increase in fuel consumption occurs when a vehicle is operated at higher power
which leads to increase in the emission of the pollutants.
Chen et al. (2007) also noted that vehicle emission rates were largely distributed
and varied significantly with factors such as speed and acceleration. Even under the same
acceleration, the emission rates of CO, total hydrocarbons (THC), and NOx were found to
be different, but showed a closer relationship with vehicle driving cycle and fuel
combustion. Their measurements showed that low-speed conditions with frequent
acceleration and deceleration, particularly in congestion conditions, were the main factors
that aggravated vehicle emissions and caused high emissions of CO and THC. They
further explained that when a vehicle starts to accelerate, the gas mixture accumulates
quickly and the combustion situation deteriorates, thus, resulting in a high THC
concentration, followed by a peak in the CO concentration. With the improvement of
combustion conditions, the concentrations of THC and CO slowly decrease, while the
NOx concentration increases due to the higher temperature in the exhaust gas.
Another important factor that has recently been compared with vehicular
emissions is the inspection and maintenance history. In a few recent studies, Kuhns et al.
(2004) and Chan et al. (2004) commented that older vehicles without good maintenance
usually emitted more pollutants than newer ones. Chen et al. (2007) also observed that
the older trucks had higher CO emission factors but lower NOx emission factors due to poor engine combustion associated with their high usage rates and limited maintenance.
They recommended that further studies be conducted with a wider sample size to better
11 understand the relationship between vehicle emission behavior and their maintenance
history.
Many alternative diesel fuels are being investigated to understand their social,
economic and environmental impacts on the society. A quantifiable relationship has also
been found to exist between diesel fuel features, composition of emissions and their
biological effects. Coburn et al. (1998) and Gambino et al. (1990) investigated the use of
alternative fuels to reduce the environmental impact of diesel emissions. They found
biodiesel, an ester-based fuel obtained from vegetable oil, to be a priority choice as it was
free from sulfur and aromatic compounds, and could be used in diesel engines without
modifications. Biodiesel is also a non polluting, biodegradable, and renewable energy
source. Turrio-Baldassarri et al. (2003) also commented that the most common form of
biodiesel used in Europe was the rape seed oil-based methyl esters (RME), while a soy
bean ester is widely used in the US.
In their comparative study on diesel and biodiesel (B20), Turrio-Baldassarri et al.
(2003), found no significant differences for pollutants such as THC, CO, NOx, and PM, but observed a 3% increase in fuel consumption of B20. Particulate matter for both the fuels had the same chemical and physical composition, with most of the particles in the
0.06–0.3 µm range. The study found that even though the mass emission of particulate matter in the two fuels was the same, a small variation in the number of fine particles induced a larger variation in the number of ultra-fine particles, suggesting the comparative study of particle size distribution of different fuel grades to understand their potential risk to humans. In all the B20 modes, the researchers observed an increase of the ultra-fine and a decrease of the fine fractions in comparison to diesel. Formaldehyde
12 showed a significant increase with biodiesel blend (Turrio-Baldassarri et al., 2003;
Howes et al., 1988; Krahl et al., 1996). In a comparison of conventional diesel fuel with diesel–biodiesel blends, Nabi et al. (2006) observed lower CO, and smoke emissions but higher NOx emission.
In another study, Durbin et al. (2000) found that the B20 blend resulted in lower
THC and CO emissions, and comparable NOx emissions for most of the vehicles
operating on reference diesel fuel. Significant differences in PM emission rates from
vehicle to vehicle were also observed. However in a later study, they also found that
polyaromatic hydrocarbon emissions from three of the five vehicles fueled with the
biodiesel blends were comparable or lower than the reference diesel fuel. THC, CO, PM
and NOx emissions were generally comparable for all the vehicles tested, with variation
within the experimental error (Durbin et al., 2001).
Several studies have also confirmed that the use of neat biodiesel (B100) resulted
in reduced emission of CO, particulate matter, and THC and an increased emission of
NOx (Wang et al., 2000; Bagley et al., 1998; Sharp et al., 2000b; Howes et al., 1988;
Krahl et al., 1996). Sharp et al. (2000a) reported a substantial reduction in carbonyl
emissions with neat biodiesel and a smaller reduction with B20 blend, ranging from none
to as much as 30% depending on the engine used.
Comparing the emissions due to fuel change from low sulfur diesel to the ultra-
low sulfur diesel, Lanni (2003), showed that switching to ULSD resulted in overall
reductions of 76% THC, 29% CO, and 29% PM. The ultra low sulfur content of the fuel
(30 ppm) caused more than 90% reduction in SO2 emission and sulfate formation, but
had little effect on the particle size distribution. The researcher could not clearly identify
13 the reason for the slight change in the number of particles smaller than 30 nm and hence
attributed it to either the reduced sulfur level of the fuel or the engine speed and load.
2.2 Studies on Vehicular Indoor Air Quality
This section discusses the results from a comprehensive literature review on the
reported studies and findings on indoor air quality inside vehicular microenvironments.
A number of studies have found that the concentrations of CO, NOx, and fuel-
related VOCs are significantly higher inside the vehicles than in the ambient air (Shikiya
et al., 1989; Ptak and Fallon, 1994; Lawryk and Weisel, 1995; Rodes et al., 1998; Jo and
Park, 1999; Alm et al., 1999; Solomon et al., 2001; Wargo et al., 2002). Shikiya et al.
(1989) observed that the in-vehicle concentrations of emitted criteria pollutants such as
CO and NOx could be two to four times those measured at fixed site monitors. Several
studies also observed high concentrations of toxic pollutants such as benzene and other aromatic VOCs within the vehicle microenvironments and estimated these pollutants to contribute 10 to 60 percent of a nonsmoker’s total exposure (Chan et al., 1991 a, b;
Weisel et al., 1992; Lawryk and Weisel, 1995; Fruin et al., 2001). Chan (2003) in his study commented that the CO2 level inside a fully occupied air-conditioned bus could
reach up to 10 times the outside concentration. The examiners also observed that better air exchange with the outdoor air resulted in lowered CO and CO2 levels for a non-air-
conditioned bus. The study also determined a strong dependence of the exposure level of
CO2 inside an air-conditioned vehicle to the number of passengers and not the driving environment.
14 Rodes et al. (1998), in their study found four to ten times higher than ambient air
VOC and CO concentrations inside or just outside the vehicles. However, PM2.5
concentrations though higher than the ambient air concentrations measured at a
neighboring monitoring station, were found to be lower inside the vehicle than just
outside the bus. The composition of these particles was investigated by Diaz et al. (2003)
who found that the fine and ultra fine particles in diesel particulate matter (DPM) were
composed principally of elemental carbon with adsorbed compounds such as VOCs, sulfate, nitrate, ammonia, metals, and other trace elements.
A study by Solomon et al. (2001) concluded that the black carbon level in the back of a closed window school bus could be up to four times higher than in a passenger car ahead of the bus. In a similar study, Wargo et al. (2002), found black carbon and
PM2.5 concentrations inside commuting school buses to be 5-10 times higher than rural
background concentrations. In a study by Rodes et al. (1998), the investigators analyzed
several factors that influenced the indoor concentrations and concluded that factors such
as ventilation settings and vehicle type had little influence on the pollutant concentrations
while, factors such as driving lane (e.g. carpool lane versus right lane), roadway type,
congestion level, and time of day had significant influence. The study also concluded that
the pollutant concentration within the vehicle varies geographically (higher in Los
Angeles than in Sacramento) and the occupant’s pollutant exposure greatly depended on
the exhaust from vehicles directly ahead. In a similar study, Fruin (2003), identified
exhaust location of the vehicle being followed, the following distance, and road type as
primary factors for concentrations inside a vehicle following a diesel automobile. Both
Fruin (2003) and Rodes et al. (1998), found that the indoor black carbon and fine
15 particulate counts were significantly higher when following a diesel vehicle while the
total diesel particulate matter concentrations had weak or no association when following
a gasoline-powered passenger vehicle or alternate fuel bus.
Solomon et al. (2001), in their study on the pollutant concentrations from diesel
exhaust inside school buses in Los Angeles revealed higher level of black carbon in the
back of the buses compared to the front. The study involved measuring concentrations in
open and closed window positions and during idling. While the black carbon
concentration increased when all windows were in closed positions, no change in its level
was noticed during idling. In a study by Wargo et al. (2002), window ventilation, bus
idling behavior, and outdoor concentrations on bus routes were all found to influence the fine particulate concentration. The study results showed that the mean concentrations of both black carbon and particulates increased in idling buses with open windows than moving buses; whereas no significant variation in the concentration level could be found between the front and rear of the bus. Both Solomon et al. (2001) and Wargo et al. (2002) suggested various measures such as prohibiting bus idling, retrofiting buses with particle traps and catalytic converters, using ultra-low sulfur fuels, allocating the cleanest buses to the longest route, limiting ride duration, purchasing alternative fuel school buses, and keeping windows open on school buses to effectively reduce children’s exposure to
DPM.
Praml and Schierl (2000), found that particulate exposure in buses and trams in
Munich depended on external sources and factors like out-vehicle concentration and road traffic. In a study on the determinants of PM2.5 exposure in London, UK, by Adams et al.,
(2001), wind speed and route were found to be significant factors, while transport mode
16 was not significant. Other meteorological variables such as wind direction, precipitation,
temperature, atmospheric pressure and relative humidity were also included in the study.
In a study conducted by Kinney et al. (2000), PM2.5 concentration showed little
association with the proximity to local diesel traffic, while elemental carbon
concentration exhibited a strong spatial gradient across sites in Harlem, New York. The
average elemental carbon concentration ranged from 1.5 µg/m3 to 6.2 µg/m3 and
indicated a four-fold difference between two sites with the largest contrast in diesel
traffic counts.
2.3 Summary from Literature Review
Despite a substantial number of comparative studies conducted on buses running
on different fuels, there is a significant discord in the reported results that could be attributed to a small test fleet and limited observations made. Even the examination performed do not account for varied permutations of the influencing variables.
Quantitative comparisons are also not abundantly available between regular, bio-diesel and ultra-low sulfur diesel for the same test fleet. Chen et al. (2007) also recommended that further studies need to be conducted with a wider sample size to better understand
the relationship between vehicle emission behavior and their maintenance history.
Studies have also confirmed that the variation of emissions with the use of
biodiesel found in the literature depends on several variables, such as the type of engine,
transient or steady-state of the engine, and the type of fuel used (Turrio-Baldassarri et al.,
2003). Yet an exhaustive quantitative analysis is not presented for a complete fleet based
on each influencing variable and the resulting vehicular emissions. Durbin and Norbeck,
(2002) concluded that “further research may be needed to better understand the effects of
17 different fuel compositions on emissions under different operation conditions and for different vehicle types”.
The literature also indicates that a relationship exists between the in-vehicle concentration of the pollutants and a number of variables such as classification of vehicles in the proximity, operating locations and vehicular conditions. Yet, no single study has attempted to quantitatively analyze and characterize the indoor air quality as a function of multiple influencing variables through simultaneous measurements. The literature does not report any study that has incorporated more than two or three factors and studied their interaction with each other. Also, there are many pollutants emitted by vehicles that are dispersed on the roadways that eventually reach inside the bus microenvironment, but most studies have measured and reported only one or two pollutants over a long period.
18 Chapter Three
Methodology
The vehicular emission and indoor air quality study required handling of
extensive amount of data. In order to accomplish the objectives of the research, a protocol
was developed that can be broadly classified into four sections:
• Project planning / Experimental design,
• Experimental data collection,
• Data classification, and
• Data analysis.
The following section details the methodology and the study design for the exhaust
emissions and indoor air quality studies.
3.1 Exhaust Emission Study
The initial stages of the exhaust emission study required extensive planning and preparations. The first step was to understand the fleet operation. The details on the different TARTA fleets and the type of fuel used are presented below.
Fleet: The Toledo Area Regional Transit Authority runs over 180 buses on over 40 routes daily and caters to nine communities in and around Toledo. In order to provide this
19 service, TARTA runs seven different full-duty fleets for their regular runs. Table 3.1 presents the different fleet information including engine and chassis manufacturers, year of manufacture, vehicle classification and gross vehicle weight rating (GVWR) for each of the fleets.
Table 3.1: TARTA Fleet Details
Fleet Year of Vehicle GVWR Engine Chassis Number Manufacture Class (lbs)
6V92 Detroit 200 RTS Dec, 1990 Heavy 36900 Diesel
300 ISB 275 Cummins Bluebird Nov, 2005 Medium 29841
Series 50 Detroit 400 TMC Sep, 1994 Heavy 39500 Diesel
MBE900 500 Thomas Mar, 2003 Medium 28580 Mercedes Benz
Series 40 600 International Gillig Sep, 1998 Heavy 39600 Navistar
Series 40 700 International Gillig Dec, 1999 Heavy 39600 Navistar
6V92 Detroit 900 Flxible Mar, 1993 Heavy 38300 Diesel
20 Fuels: TARTA buses use two types of fuels: ultra-low sulfur diesel containing less than
15 ppm sulfur content, and B20 grade biodiesel (20% methyl ester bio-fuel + 80%
ULSD). The distribution of fuels used in the different TARTA fleets is given below in
Table 3.2. Table 3.3 gives the fuel properties of bio-fuel and ULSD.
Table 3.2: TARTA Fleets
ULSD Only Fleets B20 Only Fleets
1) Series 400: 20 Buses 1) Series 200: 2 Buses
2) Series 600: 13 Buses 2) Series 300: 5 Buses
3) Series 700: 20 Buses 3) Series 500: 17 Buses
4) Series 900: 20 Buses
5) Series 200: 41 Buses
6) Series 300: 5 Buses
7) Series 500: 17 Buses
Table 3.3: Properties of Bio-fuel and ULSD
Fuel Property B100 Bio-fuel ULSD
Methyl Soyate, Ultra Low Sulfur No. 2 Synonyms Rapeseed Methyl Ester (RME), Amoco Oil Methyl Tallowate
Cetane Number 53.6 40
Summer - +20 oF Cloud Point Winter 31.1 oF +15 oF
Total Sulfur (ppm, max.) 0.00008 15
Moisture/Water 0.045 0.05 (vol. %, max.)
B20 biodiesel was obtained by mixing 20% of B100 bio-fuel and 80% ULSD
21 3.1.1 Experimental Design
This section discusses in detail the instrumentation and other important additional accessories used in the study.
Instrumentation: Exhaust emissions were measured using a standard Testo 350XL unit with six gas sensors: O2, CO2, CO, SO2, NO and NO2 with calculated NOx, and a temperature sensor. The instrument was setup to provide emission analysis output on a 1- second interval. In order to facilitate efficient datalogging, the instrument was connected to the computer and data download was performed on a real-time basis. Figure 3.1 shows the Testo 350XL unit inside the TARTA bus connected to the laptop for downloading the measured pollutant concentrations.
Figure 3.1: Testo 350XL Unit Setup Interfaced with the Computer
22 On-Board Diagnostic (OBD) Software: During the test procedures, an On-Board
Diagnostics unit was connected to the engine computer module (ECM) of the buses to get a digital readout of the engine performance including rpm, speed, and engine power. The
OBD also gives details regarding total vehicle and engine mileage, total operation time of the vehicle, total time at idling, fuel and engine temperature, which are all used in the
data analysis. The OBD unit was interfaced with the computer using the software
purchased from Detroit Diesel (for series 200, 400, 500, 900) and Cummins (for series
300). TARTA did not have the manufacturer supplied OBD software for series 600 and
700 buses and therefore the engine performance data was obtained from a handheld OBD monitor at the beginning and end of the study.
Exhaust pipe attachment: An aluminum attachment was designed by the researcher to
ensure a constant immersion depth for the Testo 350 XL analyzer probes inside the
vehicular exhaust pipes and to also serve as a heat shield for the probes. The attachment
had two identically spaced holes on each face which were just wide enough to allow the
probes to pass through. A vice grip attached at the top enabled clamping the attachment to the end of the exhaust pipe (Figures 3.2 and 3.3).
23
Figure 3.2: Designed Aluminum Shield for Attaching Probes to the Tailpipe
Figure 3.3: Analyzer Probes Inserted into the Exhaust Pipe Using the Attachment
3.1.2 Testing Procedures / Study Design
The emission data were collected for three types of test protocols:
i. Idle testing: Idle testing refers to the emission testing cycle in which the bus in the
idling mode. Idle test of buses involved setting up the engine diagnostic software in
24 the computer and connecting it to the ECM of the bus through the OBD interface. A list of important engine parameters to be monitored were then generated and prepared for data-logging. Emission monitoring instrumentation was connected to the sampling probes that were setup at the tailpipe/exhaust. Both sets of instruments were started simultaneously for easy comparison of the data. The bus was then started in the desired idling mode (fast or normal idle) and the engine and emission characteristics were monitored for a 15 minute period. Figure 3.4 shows a TARTA bus in the garage being tested in the idling mode.
Figure 3.4: Idling Test Setup
25 ii. Dynamometer testing: A sample set of four buses from the 500 series were
subjected to the Central Business District (CBD) cycle (refer Appendix C) to
simulate a standard operating condition of public transport buses. Emission testing
was carried out during the cycle load simulation to obtain real-time emission
behavior of the buses during operation. This data was used for temperature
comparison (hot vs. cold start) and instantaneous emission modeling. iii. UT-TARTA test cycle: A new test protocol was developed by the researcher to
standardize the testing procedure for all TARTA buses. The development of a new
site specific protocol for TARTA buses was essential for characterizing the
emission behavior of the different fleets as studies have already established that
accurate measurements can only be made by simulating the location specific real
world operation (Clark et al., 2002). This cycle simulated an actual operational run
of TARTA buses (Route #20) and gave specific details about the emission behavior
of buses in the City of Toledo. All buses were tested after midnight on a weekend as
it ensured minimal or no traffic to disrupt the standard run. The buses were run on
TARTA Route # 20 (which runs between TARTA garage and Meijer on the Central
Avenue strip, shown as red stars in Figure 3.5) that has a distance of approximately
7.5 miles each way. The bus is scheduled to make five stops (blue stars shown in
Figure 3.5) in each direction which last 10 seconds each to simulate passenger
pickup/drop-off. During Run 1 (TARTA garage to Meijer), the buses were
generally started in ‘cold start’, and during Run 2 (Meijer to TARTA garage), the
engines warm up and demonstrate ‘hot start’ characteristics. The total run time for
each test stage was always between 20 minutes and 15 seconds to 21 minutes.
26 Run 1
Run 2
Figure 3.5: Run 1 and Run 2 of the UT-TARTA Test Cycle
27 3.1.3 Data Collected
Emission monitoring instrumentation was ordered in December 2006 and the full shipment was received in February 2007. The emission monitoring for series 300 buses were started and completed in March 2007. The Detroit Diesel fleets could not be tested till May 2007 as the OBD software was not available with the distributor. The emission data collection for these fleets was conducted between May 5 and June 8, 2007. In order to collect maximum data in this short span and to avoid interference with TARTA bus schedules, all of the emission testing and data collection were conducted between 11 pm and 5 am on weekdays and 9 am to 5 am during weekends. As all the data collected were measured within a one month period, and because the instrument was bought new in factory calibrated condition, data quality was expected to be good. Therefore all the data collected was used for statistical analyses and model development.
3.2 Indoor Air Quality Study
3.2.1 Experimental Design
Study Design: The indoor air quality study design involved bus and route selection.
During the initial stages of the study, the test buses were scheduled on two routes each month: Route 11 (refer Figure 3.6) for the first two weeks of the month, and Route 20
(refer Figure 3.7) for the third and fourth weeks of the month (week starts on Mondays).
This schedule helped to study the impacts of operation route and location on the indoor air quality inside the buses. For the purpose of model development, the data collection was conducted inside the same two vehicles that were run on the same route (Route 20) throughout the selected test period for model development. In order to compare the
28 indoor concentrations inside the bus compartment, the two selected test buses were operated on the same route with a short 12-minute time lag between each run. This ensured comparability of vehicular and passenger traffic.
Figure 3.6: Map Showing TARTA Route 11 (Source: TARTA website, 2007)
29
Figure 3.7: Map Showing TARTA Route 20 (Source: TARTA website, 2007)
30 Selected Bus Fleet: The fleet used for this study was the 500 series Thomas built buses
(acquired by Detroit Diesel) of the TARTA line up, with a Mercedes Benz MBE 900
engine. The buses were built in 2001 and have a total seating capacity of 32. The test fleet consisted of 37 buses with 19 buses running on B20 and 18 buses running on ULSD.
Instrumentation: Indoor air quality data were monitored continuously for two selected
buses from the 500 series operating on ULSD and B20 grade biodiesel. Each bus was set
up with a complete set of IAQ monitoring instruments consisting of each of the
following:
1. Yes ‘Plus’ IAQ monitor with seven gas sensors (CO2, CO, NO, NO2, SO2, VOC
and formaldehyde (HCHO)), and a temperature and relative humidity sensors.
VOC and HCHO sensors had a large sensitivity to shock and the measured values
were erroneous and were therefore not calibrated. Hence this data was not used
for any analysis.
2. Grimm Dustmonitor 1.108 for 15 grades of particulate matter mass and number
concentration measurement.
Initially, two TSI DustTrak units (Model #8520) were used for PM1.0 and PM2.5 particulate mass concentration monitoring. These monitors were replaced by the Grimm units for IAQ monitoring as the Grimm unit could simultaneously measure multiple grades of particulate matter as compared to one grade from DustTrak models. The
DustTrak units were thereafter used for other important analyses such as comparison of indoor and outdoor concentrations, variation in front and back end concentrations etc.
DustTrak units were ideal for these analyses as they had a higher protection against
31 shock, vibration, and road dust.
Instrument Setup: The instruments were installed in a wire mesh cage/box and were held using Velcro attachments at the bottom (Figure 3.8). The box was placed over the enclosure built for GPS systems inside the bus and was secured using a locking mechanism. The box was located at the front end (passenger seating area) of the bus on the left side from the front windshield at a distance of 3 meters (Figure 3.9). A glass panel divided the instrument cage from the front door. The instrument was placed at a height of the breathing level of the sitting passengers. The location selection of the setup was based on easy power availability and instrument safety. The instrument drew power from AC adapters connected to an inverter inside the bus. This ensured continuous and uninterrupted power supply.
Figure 3.8: IAQ Instruments Installed in the GPS Cage in the Front of the Bus
32
Figure 3.9: Indoor Air Quality Testing: Experimental Setup
3.2.2 Data Collection
Indoor air quality data collection was carried out between March 2006 and June
2007. The IAQ monitors conducted real-time monitoring of the various pollutants on a
24x7 operation on a 1-second interval and provided output as 1-minute averages. The
collected data were stored in the in-built memory of the instrument and data download
was performed frequently in order to ensure proper working of the instruments.
Meteorological Data: Meteorological data was downloaded from the National Climatic
Data Center website (NCDC, 2007). The weather data provided hourly details of wind speed, wind direction, temperature, relative humidity, cloud cover, precipitation etc. This information was used to analyze the influence of meteorology on indoor air quality inside
33 the buses. The indoor temperature and humidity monitored using the Yes ‘Plus’ unit was
also used to understand the build-up of indoor pollutants.
Traffic and Ridership Data: The vehicle’s operation factors (such as door position status
(open/closed), bus operating mode (idling/running)) and traffic conditions (passenger numbers; buses and cars ahead of the bus) were monitored every minute from the output of closed-circuit cameras installed inside the buses. All the 500 series buses were factory installed with 5 cameras (Figure 3.10): one facing the road through the front windshield,
two facing the doors and two facing indoors. The road facing camera was used to count
buses, trucks and cars in front of the bus. The indoor facing cameras were used for
counting passengers. The door facing cameras along with the road facing camera were
used for checking the vehicle idling and door opening status. All the camera recordings
were checked every minute for getting the traffic, passenger and bus status information,
which were then tabulated for analysis and model development.
3.2.3 Data Quality
In order to maintain data quality, the Yes ‘Plus’ monitors were calibrated each
week (Sunday nights) since May 2007 for CO2, CO, NO2, NO and SO2 sensors using
calibration gases supplied by Calgaz. As the instrument had a low draw rate, a 0.5 liter
per minute (lpm) fixed flow regulator was used and a factory supplied inlet fixture was
connected to the instrument to flow the gases. CO2 and NO were zero calibrated using
99.9% nitrogen (N2) gas and CO, SO2 and NO2 were zero calibrated using Zero Air. Span
calibration gas concentrations for the sensors are given below (Table 3.4):
34 Table 3.4: Sensor Calibration Details
Max Sensor Detection Span calibration: Gas Sensor Limit concentration
CO2 5000 ppm 2000 ppm
CO 50 ppm 25 ppm
NO 100 ppm 50 ppm
NO2 20 ppm 5 ppm
SO2 20 ppm 5 ppm
The Grimm Dustmonitor 1.108 and Testo 350XL instruments were regularly cleaned with canned air and the particulate filters were frequently replaced. As both the instruments were still under factory calibration period, data quality was assured. Two
Grimm Dustmonitor units were used simultaneously in order to check for comparative precision of the instruments as well.
3.2.4 Experimental Procedure: Data Collected
Indoor air quality
Indoor air quality data collection was started in March 2006. Datasets used for the analysis procedures were required to be collected from a particular bus on a pre-selected route.
Problems encountered: IAQ study
IAQ data collection was affected by several problems during and outside operation. Many of the problems were related to the bus operation and maintenance. The author does understand that these problems should be expected from any public transport
35 bus system, and that the first priority of the Transit Authority is to ensure that the required bus demand is met everyday, and especially with a limited fleet like TARTA, this requirement is ever so heightened. But as part of an experimental work, it is important to document all the problems faced so that they can be reduced in future tests.
Some of the key issues that were encountered are therefore presented here.
Bus routing: A detailed plan for the selected UT test buses and their requested routes was noted on a calendar and kept with the TARTA Dispatch/Scheduling department. On several occasions, the test buses did not go on the requested routes and in many cases were not even sent out on a run. These test days were excluded from all analyses.
Bus breakdown: 500 series buses are designed for medium-duty traffic and passenger activity with good road conditions (designed for Britain). Toledo roads had a very negative impact on these buses and they broke down regularly. This reduced the ‘on road’ period for the buses by several days at a stretch. The researcher tried to change the test bus as early as was notified by the maintenance department.
Instrument power disconnection: The IAQ instruments are capable of running for a maximum of 15-20 hours on their in-built batteries. Therefore a detailed power conversion plan was laid out inside the bus to draw power from the bus batteries (DC) and transform it to 110V AC so that all the instrument adapters could draw power from the bus continuously using a power strip. On many occasions, the power strip or the adapters were found to be disconnected which resulted in instruments running till zero power and eventual shutdowns. Yes ‘Plus’ monitor was found to be highly sensitive to these shutdowns and had to be sent back to Canada for warranty repairs after every such incident. This reduced the data bank significantly. To combat this problem, the adapters
36 were wire-tied to the power strip and the power box was locked which reduced some of
the problems.
Camera error: On most of the 500 series buses, one or more of the indoor cameras do not
work. This led to a very small bus bank for selecting a test bus for instrument setup and scheduling. Also, as TARTA did not have spare cameras, the faulty camera problems could not be fixed in many cases for a long time. The days without proper video output were removed from all analysis.
Hard-disk problems: All the camera outputs were recorded on an in-built hard-drive.
Every 10 days, these hard-drives were taken out and analyzed to extract traffic and passenger activities and bus operation data. Many times, the hard-drives were removed by the Transportation division for their monitoring (for which it was actually designed) and replaced in another bus without any record keeping. This also significantly reduced the completeness of data.
Video monitoring: The hard-disks collecting the video output inside the buses had a capacity to store data for 10 days continuously and therefore were replaced by another set for data extraction every 10 days. As the video monitoring was a data intensive job, it was assigned to a team of graduate students in order to train them with the project. On a number of occasions, the students took more than 10 days for analyzing a hard disk and that led to loss of video data. Also, it is suggested that the team handling the data be as small as possible as different people with different data perception (e.g. counting the number of vehicles ahead of the bus) would alter the data behavior.
Passenger/driver interactions: Passengers and drivers were seen to put objects inside the cage from time to time. A driver was once seen to blow smoke inside the cage even
37 though it was a non-smoking coach. These interactions are bound to alter the expected concentration trends.
Internal filter clogging and instrumentation servicing: Due to heavy particulate dust inside the compartment and the instrumentation running for a 24x7 operation, the internal filters for Yes ‘Plus’ monitors were clogged initially on four occasions in 2006. Since the manufacturer was not expecting this initially, there was no precautionary measure suggested by them. Since September 2006, the instruments were attached with external filters to combat this problem which were replaced every week when the instrument gave the “Low pump” error message. Grimm Dustmonitor units also started demonstrating an internal filter clog in February 2007 and therefore were cleaned more frequently using pressurized air (suggested by manufacturer). In May 2007, one instrument stopped working and had to be taken out for servicing. In addition, as the Yes ‘Plus’ monitor was introduced in a selective research based market; many other servicing issues were also encountered where internal circuitry had to be examined and therefore sent to Canada for
2-week periods. Most of these problems occurred at different time periods and although allowed the research to go on, restricted the amount of data for comparing ULSD and
B20 fleet buses.
Instrument sensitivity to shock and dust was also an issue as sometimes a gutter impact would set the instruments to reset. The researcher made an attempt to reduce this problem by providing adequate padding to the instrument box/cage.
Many other unforeseen factors such as delay in ordering instrumentation (the
Purchase Order for calibration equipment was delayed at the Purchasing Department for over 20 days) and development of instrumentation (in the case of Yes ‘Plus’, the
38 instrument was ordered in June 2005 and the first unit was supplied in February 2006 and
the second unit only arrived around June 2006) were also some of the problems that
affected quality data collection.
All the data sets that were not affected by any of these problems were classified as
good data and were used for trend analysis.
3.2.5 Data Used for Development of Indoor Air Quality Models
The IAQ monitors provided real-time data on indoor concentrations of CO2, CO,
NO2, NO, and SO2. In order to reduce the short-term variation and to manage the data
properly, the instrumentation was set to provide data output in 1-minute average format.
Indoor air quality models required additional information regarding traffic
conditions, passenger activity, vehicle door status (open/closed), engine operating mode
(idling/running) etc. These parameters were not monitored till January 2007 as necessary
equipment was not available. TARTA ordered a new set of equipment for interfacing to
the special hard drives in January and traffic and vehicle operation data was monitored by
a team of students. Initially, the video output was studied on a 5 minute interval until
January 15, 2007. Once the students became comfortable with video monitoring and data
extraction, the interval was reduced to 1 minute.
Initial model development was performed using data from January 2007 until
April 2007. The gaseous measurements showed a consistent drop in concentrations after
2 weeks of factory calibration. This trend was noticed in March 2007 and additional
calibration instrumentation was ordered which was received in May 2007. Yes ‘Plus’
monitors were calibrated every Sunday for CO2, CO, SO2, NO2 and NO sensors in May
39 and June 2007 and the instruments were found to be working efficiently at the end of the
week using a Span calibration check (exposing the sensor to a known concentration and
checking the output). Final models were developed using the IAQ data monitored for
these five pollutants inside the test buses operating on both fuel types in the following
weeks (indicated in the first brackets):
1. January 2007 – 1 week (4) after factory calibration (ULSD)
2. February 2007 – 1 week (2) following the last calibration (ULSD)
3. May 2007 – 3 weeks (2-4) with weekly on-site calibration performed (B20)
4. May 2007 – 1 week with weekly on-site calibration performed (ULSD)
5. June 2007 – 3 weeks (1, 3, 4) with weekly on-site calibration performed (B20)
6. June 2007 – 1 week with weekly on-site calibration performed (ULSD)
The Grimm PM monitors were cleaned every three weeks or whenever it gave an
error message using canned (pressurized) air and new filters were installed. The old
filters were time stamped and stored in zip-lock bags for future analyses. As the data quality was checked frequently, most of the good data collected in 2007 was usable for model development. As the above datasets (traffic, meteorology and indoor comfort parameters such as temperature and humidity) used for indoor gaseous modeling were
cleaned and processed and formed 70% of the usable data period for PM, they were also
used for indoor PM model development.
3.2.6 Data used for Indoor/Outdoor and Front/Back Comparisons
Comparison of PM1.0 concentration inside and outside the bus compartment was
performed using the two TSI DustTrak units. Both units were calibrated, time corrected
40 and started at the same time. One of the instruments was attached with a 2 ft plastic tube
to draw air from outside the compartment. The instruments were installed inside the wire
mesh box at the front of the bus to avoid passenger interference. The plastic tube was
taped to the body of the bus and fixed to draw air from the outside (Figure 3.10). The
total data collection for this analysis was continued for three weeks between February
and March 2007.
Front/Back comparison
Comparison of PM1.0 concentration at the front and the back end of the bus was
performed using the two TSI DustTrak units. Both units were calibrated, time corrected and started at the same time. They were attached with a six inch plastic tube at the nozzle
Figure 3.10: PM1.0 Indoor/Outdoor Comparison Setup
to draw air. The instruments were inconspicuously hidden inside the bus chambers to
avoid passenger interference. Figure 3.9 shows the position of the instruments at the front
41 and the back of the bus for this analysis. The total data collection for this analysis was continued for five weeks between March and April 2007.
Comparison of pollutant gas concentrations at the front and back end of the bus was not conducted as the instrument was highly sensible to vibration and dust.
3.3 Analysis Procedures
Different procedures were used to process, understand, select, and model the
collected data. Tools such as Microsoft Excel, Microsoft Access, and Minitab 15 were
chosen to develop inventories and analyze the data.
3.3.1 Data Processing and Classification: Development of Emission Inventories
Following data collection, the entire test data was classified using the following
categories (refer Appendix A on emission inventories):
a. Testing protocol (Dynamometer / Idle / On-road test cycle)
b. Vehicle classification: Vehicle category/manufacturer. TARTA currently has
seven different fleets of regular operation buses.
c. Vehicle mileage
d. Fuel: B20 grade bio-diesel and ultra-low sulfur diesel (ULSD)
e. Operating conditions: Hot / cold start
f. Ambient and exhaust temperatures
g. Engine & fuel temperatures
h. Load / engine speed
42 This classification helped in understanding the behavior of vehicular emissions
with changes in vehicular and operating conditions. For example, the idle test data were
compared with the test data from the on-road emission test in order to understand the variation in the emission characteristics of the buses during idling and running conditions. Similar important comparisons were made with respect to operating fuel, engine temperature, vehicle type and age, mileage and load conditions.
3.3.2 Statistical Analysis
Best subset regression is a technique that helps in identifying the best set of significant variables that satisfactorily explain the variation in the dependant variables and need to be included in multiple regression modeling. All possible combinations of the predictors are modeled, i.e., models with one predictor (independent variable), models using combinations of two predictors or three predictors and so on, until all possible combinations of the predictor variables are considered. Minitab presents the final two best models from each of the combination subset based on the coefficient of multiple
2 determinations (R ). The best model from these best subset combinations is selected
2 2 based on the R , adjusted R , the standard deviation and the Mallows coefficient (Cp).
2 2 The model that best fits the data will have a high R and adjusted R with a low standard
deviation and Mallows’ coefficient Cp close to or below ‘p+1’ where ‘p’ is the number of
variables considered in the model.
2 R provides information on the percentage of variation explained by the model
2 2 and the independent variables taken together. Adjusted R , is a modified R adjusted for
2 the number of predictors in the model as R might overestimate the strength of
43 association between the response and the multiple predictors. Cp measures the differences of the fitted regression model from the true model. A regression model differing from the true model by only the random error, will have a Cp of (p+1), where p is the number of parameters. Thus, using these statistics, best subset regression technique allows for the easy identification a simple model with the least number of the most influencing predictor variables that can then be used for the model development using multiple regression technique.
Multiple linear regression investigates and models the linear relationship between a response (C) and two or more predictors (x). It helps to determine the variation in the response variable with the change in predictor variables. The regression models also help to predict the value of the response variable for different combinations of the predictor variables. The true regression model has the form
C = a * x + a *x +.... + a *x + d + ε………….. (3.1) 1 1 2 2 n n
where, C is the dependent variable, a , a … a are the coefficients of the variables x , x 1 2 n 1 2
…x , d is the constant (value C is predicted to have when all the independent variables n are equal to zero) and ε is the random error.
As the number of predictors used in this study were large best subsets model- selection technique was used to screen the predictors that were not highly correlated with the response before fitting a regression model with all the significant predictors. In this study, C is representative of pollutant concentrations and x represents the different
variables studied.
While analyzing certain continuous data series, short term variation in the data significantly affected the statistical analysis. In order to minimize this effect, moving
44 averages were calculated for all the variables before attempting the regression modeling.
The analyses containing this procedure have been identified in their respective discussions.
This research proposes the use of a combination of best subset and multiple regression techniques and tests the effectiveness of the method in traffic emission and air quality related studies.
45 Chapter Four
Results and Discussions
This research involved extensive real time measurement of many operational and traffic variables that could have an effect on the emissions and the indoor air quality of public transport buses. The data collected on emissions and indoor air quality were used to understand the public transport bus emissions and characterize the bus microenvironment pollutants. This chapter elucidates the results of the emission and air quality analyses.
4.1 Vehicular Exhaust Emissions
This section discusses the experimental and statistical results obtained from the vehicular emissions study.
4.1.1 Characterizing the Effects of Operational Variables on Emission Behavior
As shown in Figure 2.1, the factors affecting vehicular emissions are classified broadly under three major categories: vehicular condition, vehicular operation condition and vehicle route. This section presents the results and discussion of the effect of vehicular condition and vehicular operation condition on emissions.
a. Operation Mode
Mode of operation (idling / acceleration / deceleration / cruise) significantly affects the vehicular exhaust emissions as the vehicle’s operating conditions (speed, engine load, fuel rate etc.) change under different modes. A sample set from each fleet
46 was tested on the UT-TARTA on-road test cycle. The resulting emissions are reported in
Table A.1 (refer to Appendix A). Table A.1 also lists the idling emissions for comparison to understand the emission behaviors in different modes.
Figure 4.1 shows the emission and vehicle operation behavior of a sample set of buses during Run 1. Note that the emissions of all the pollutants increase at the exact intervals during which the vehicle acceleration, speed and fuel rate increase. It should also be noted that these emissions are an effect of the combination of multiple factors, i.e., different factors may contribute to two different concentration spikes occurring at different time periods for a pollutant. Although this behavior has been represented in many studies, detailed analysis of this behavior has never been reported for multiple pollutants in a study. Therefore, additional statistical analysis for analyzing this behavior was conducted and is presented in section 4.1.3.
47 Instantaneous Emission Behavior During Run 1
O2 (%) CO (ppm) NO (ppm) 400 800 20.0
300 600 17.5
15.0 200 400
12.5 100 200
ation 10.0 r 0 0 SO2 (ppm) NO2 (ppm) CO2 (%) 60 10.0 80 Concent 45 7.5 60 30 5.0 40
20 15 2.5
0 0 0.0 1 240 480 720 960 1 240 480 720 960 1 240 480 720 960
Time (seconds)
Figure 4.1: Emission Behavior During Run 1
48 Instantaneous Engine Performance During Run 1 (Part 1)
Engine Speed(rpm) Fuel Rate(gal/hr) 15 2400
1800 10
1200 5 600
0 0 Percent Engine Load(%) Vehicle Speed(mph) 100 40
75 30
50 20
25 10
0 0 1 240 480 720 960 1 240 480 720 960 Time (seconds)
Figure 4.2 (a): Engine Performance During Run 1
49 Instantaneous Engine Performance During Run 1 (Part 2)
Accelerator Pedal Pos(%) Engine Coolant Temp(°F) 100 160
75 120
50 80
25 40
0 0 Output Torque(ft.lb) Boost Pressure(psi)
600 20
450 15
300 10 150 5 0 0 1 240 480 720 960 1200 1 240 480 720 960 1200 Time (seconds)
Figure 4.2 (b): Engine Performance During Run 1
50 On-road emissions (Run 1, Run 2) were also compared with engine idling
emissions in order to understand the emission behavior in different modes. Series 500
buses were selected for a detailed study of the effects of engine operating mode on
vehicular emissions. It should be noted that during Run 2, the driver was forced to stop
the bus at more traffic stops than during Run 1. This in turn decreased the continuous
driving time during Run 2 as compared to Run 1, and therefore shows a higher emission temperatures and higher fuel rates during Run 1.
Oxygen concentration for idling mode (in cold start) was always higher than the on-road test mode which shows that buses run cleaner in the idling mode. It was also observed that idling and Run 2 modes produced higher average O2 concentrations than
Run 1 due to longer continuous driving periods in Run 1. For bus 532, Run 1 got many more red lights on traffic intersections than usual and therefore showed higher than usual
O2 concentration (Figure 4.3). On the other hand, CO2 followed the exact opposite trend
as compared to O2. It was observed that as O2 increased, CO2 decreased and vice-versa.
CO2 concentrations for idling mode (in cold start) were always lower than the on-
road test modes. Run 1 always emitted higher CO2 concentration than during Run 2 due
to longer continuous driving (acceleration) periods. Run 1 for bus #532 shows an
anomalous behavior due to the reasons explained earlier (Figure 4.3). Carbon monoxide,
nitric oxide, nitrogen dioxide and sulfur dioxide concentrations for idling mode (in cold
start) were always higher than the on-road test mode. Run 1 also emitted higher NO and
CO concentrations as compared to Run 2 due to longer continuous driving (acceleration)
periods, whereas the differences in NO2 and SO2 concentrations between the two runs
were not as significant (Figure 4.3).
51 It was observed that for the same amount of time in operation, vehicles in idling mode produced higher average concentrations of CO, NO, NO2 and SO2. This is an
important finding as reducing the idling time or shutting down the vehicle during long
expected durations of idling could substantially reduce the total vehicular impact on air
quality.
52 Emission Comparisons for Idling, Run1 and Run2 Modes
O2 (%) CO (ppm) NO (ppm)
16 600 200 12 450 150
8 300 100
4 50 150 Mode Idling 0 0 0 Run1 SO2 (ppm) NO2 (ppm) CO2 (%) Run2 4.8 100 60 Concentration
75 45 3.6
50 30 2.4
25 15 1.2
0 0 0.0 BUS501 509 527 532 501 509 527 532 501 509 527 532
Figure 4.3: Emission Comparisons for Idling and On-road Testing Modes
53 b. Engine Temperature
Engine temperature is an important variable affecting the vehicular emission behavior as it affects the combustion temperature of the fuel. This section presents detailed results on the effects of engine starting mode (hot start/cold start) and engine operating temperature on its emissions.
Idling Test - Series 500: Effect of Engine Starting Temperature (Hot Start vs. Cold Start) on Exhaust Emissions
A comparison of exhaust emissions during cold start and hot start is given in
Figure 4.4. Two 500 series buses were tested for ‘cold start’ and ‘hot start’ modes on the same day. It was noted that the starting engine temperatures during the ‘hot start’ were almost double those of the ‘cold start’. The results show that the CO emissions are not statistically different during the two start modes. It was also observed that during cold start, the buses emitted higher pollutant concentration than during hot start for NO (up to
45%), NO2 (up to 25%), SO2 (up to 30%) and CO2 (up to 25%).
54 450
400
350
300
250
200 Concentration
150
100
50
0 Engine CO (ppm) NO (ppm) SO2 (ppm) NO2 (ppm) CO2 ( X 100 Temperature ppm) (oF) 500 Cold 500 Hot 503 Cold 503 Hot
Figure 4.4: Pollutant Concentration Comparison: Hot vs. Cold Start (Data collected for 15-minute intervals on the same day)
Dynamometer Test - CBD Cycle: Emissions vs. Temperature Analysis
Emission testing for Bus #515 (biodiesel B20) was carried out in hot start (HS) and ‘cold start’ (CS) phases in the CBD cycle. The observations from the tests are discussed below.
• CO Concentrations (Figure 4.5 (a)): Initial spike in CO concentrations for ‘cold
start’ engine (430 ppm) was found to be about twice as high as the ‘hot start’
engine (225 ppm) during startup. After startup stage, the concentration for ‘cold
start’ engine dropped significantly (about 1/4th of the concentration observed
during startup) and the concentration fell below the emissions from the hotter
engine (HS).
55 CO Emissions vs. Engine Operating Temperature Figure (a) NO Emissions vs. Engine Operating Temperature Figure (b)
450 1400
400 1200
350
1000 300
800 250 HS HS CS CS 200 600 Concentration in ppm in Concentration ppm in Concentration 150 400
100
200 50
0 0 1 31 61 91 121 151 181 211 241 271 301 331 361 391 421 451 481 511 541 571 1 31 61 91 121 151 181 211 241 271 301 331 361 391 421 451 481 511 541 571 601 Time in seconds Time in seconds
NO2 Emissions vs. Engine Operating Temperature Figure (c) CO2 Emissions vs. Engine Operating Temperature Figure (d)
100.0 8.00
90.0 7.00
80.0 6.00 70.0
5.00 60.0
HS HS 50.0 4.00 CS CS
40.0
Concentration in % 3.00 Concentration in ppm in Concentration
30.0 2.00 20.0
1.00 10.0
0.0 0.00 1 31 61 91 121 151 181 211 241 271 301 331 361 391 421 451 481 511 541 571 601 1 31 61 91 121 151 181 211 241 271 301 331 361 391 421 451 481 511 541 571 Time in seconds Time in seconds
Figure 4.5 (a-d): Vehicular Emissions vs. Engine Operating Temperature/Starting Condition (CBD) 56 • NO Concentrations (Figure 4.5 (b)): NO concentrations were higher for ‘cold
start’ engines at startup by up to 100% and remained higher throughout the run
period. In the ‘cold start’, as the engine temperature increased, the NO
concentrations decreased and after 15 minutes, the emission characteristics were
similar to a ‘hot start’ engine.
• NO2 Concentrations (Figure 4.5 (c)): NO2 concentrations were higher for ‘cold
start’ engines throughout the test run. An initial spike during startup was much
more prominent for ‘cold start’ engines (up to 50% higher than ‘hot start’).
• CO2 Concentrations (Figure 4.5 (d)): CO2 concentrations were higher for ‘cold
start’ engines at startup by up to 40% and remained higher than ‘hot start’ run
emissions throughout the run period. c. Inspection and Maintenance (I/M)
Preventative Maintenance Inspection (PMI) is an industry set term for testing and maintaining vehicular performance by inspection and correction. A PMI improves the engine performance and therefore, was expected to improve the emission characteristics as well. Old vehicles without proper regular maintenance are likely to emit more compared to new and well maintained vehicles.
The primary variable of interest for this study was the air filter as it facilitated easier and cleaner air intake. Fuel filter age was also an important variable as it facilitates debris removal from the fuel stream and also gives an idea of how long ago the engine performance was last checked.
For this analysis, a 500 series bus scheduled for a category C PMI (air filter, fuel filter, oil filter and coolant filter change) was selected and emission testing was
57 performed the day before the scheduled PMI. The emission testing was performed again a day after the PMI and the results were compared. The engine temperature (cold start) and idling modes (fast idle) were kept the same in both cases for an accurate comparison. It was observed that although all the average pollutant emissions reduced by close to 15-
20% after the PMI, CO and CO2 emission spikes during startup were significantly higher for the after-PMI test by up to 20% (refer to Figures 4.6 – 4.10). The concentration spikes could not be explained satisfactorily, requiring further research into the effect of PMI on emissions.
Effect of PMI: CO Concentration
600
500
400
After 300 Before
Concentration (ppm) Concentration 200
100
0 1 61 121 181 241 301 361 421 481 541 601 661 721 781 841 901 Time (seconds)
Figure 4.6: CO Concentration Comparison: Effect of PMI
58 Effect of PMI: CO2 Concentration
6.00
5.00
4.00
After 3.00 Before Concentration (%) 2.00
1.00
0.00 1 61 121 181 241 301 361 421 481 541 601 661 721 781 841 901 Time (seconds)
Figure 4.7: CO2 Concentration Comparison: Effect of PMI
Effect of PMI: NO Concentration
600
500
400
After 300 Before
Concentration (ppm) Concentration 200
100
0 1 61 121 181 241 301 361 421 481 541 601 661 721 781 841 901 Time (seconds)
Figure 4.8: NO Concentration Comparison: Effect of PMI
59 Effect of PMI: SO2 Concentration
100
90
80
70
60
After 50 Before
40 Concentration (ppm) Concentration 30
20
10
0 1 61 121 181 241 301 361 421 481 541 601 661 721 781 841 901 Time (seconds)
Figure 4.9: SO2 Concentration Comparison: Effect of PMI
Effect of PMI: NO2 Concentration
70.0
60.0
50.0
40.0 After Before 30.0 Concentration (ppm) (ppm) Concentration 20.0
10.0
0.0 1 61 121 181 241 301 361 421 481 541 601 661 721 781 841 901 Time (seconds)
Figure 4.10: NO2 Concentration Comparison: Effect of PMI
60 d. Engine Load (%)
Engine load is one of the key variables affecting vehicular emissions. To support a greater engine load, the engine has to produce higher work done requiring higher fueling rate and thereby producing higher emissions. A lower engine load is therefore expected to generate lower emissions than at a higher load. Lim et al. (2007) also suggested a similar line of thought stating that when a vehicle is operated in higher power, there is an increase in fuel consumption resulting in higher emissions.
For this analysis, a 500 series bus was selected and emission testing was performed at a higher engine load (fast idle enabled, 1200 rpm, 15 minute period).
Emission testing was performed again the next day at a lower engine load (normal idle / fast idle disabled, 1000 rpm, and auto shutdown after 5 minutes) and the results were compared. The testing was performed at the same engine temperatures (cold start) in both cases for an accurate comparison. It was observed that for each of the pollutants, the emission trends were similar for both the operation modes. The scale of increase during startup for ‘fast idle’ mode in comparison to regular idle mode was most significant for
CO (over 40%), CO2 (30%), NO (33%) and NO2 (20%). At 5 minutes from startup, CO2 and NO concentration for ‘fast idle’ mode reduced to the ‘normal idle’ concentrations.
For CO and NO2, the differences in emission concentration were maintained throughout
the test period (Figures 4.11).
61 Comparison of Emission Concentrations for Fast Idle vs. Normal Idle
Comparison of Fast Idle vs. Normal Idle: CO (ppm) Comparison of Fast Idle vs. Normal Idle: NO (ppm)
300 1200
900 200
Variable Variable CO (ppm): Fast 600 NO (ppm): F a st CO (ppm) : Normal NO (ppm): Norma l 100 300 Concentration (ppm) Concentration (ppm)
0 0 1 1 60 60 120 180 240 300 360 420 480 540 600 660 720 780 840 900 960 120 180 240 300 360 420 480 540 600 660 720 780 840 900 960 1020 1080 1140 1020 1080 1140 Time (seconds) Time (seconds)
Comparison of Fast Idle vs. Normal Idle: NO2 (ppm) Time Series Plot of O2 (%): Fast, O2 (%): Normal
80 20
60 18 Variable Variable NO 2 (ppm): F a st O2 (%): Fast 40 NO 2 (ppm): Norma l O2 (%): Normal 16 Concentration (%)
Concentration (ppm) 20
14
0 1 1 60 60 120 180 240 300 360 420 480 540 600 660 720 780 840 900 960 120 180 240 300 360 420 480 540 600 660 720 780 840 900 960 1020 1080 1140 1020 1080 1140 Time (seconds) Time (seconds)
Figure 4.11: Pollutant Concentration Comparison for Normal and Fast Idling
62 e. Fuel: ULSD vs. B20
Idling Test - Series 300: Fuel Comparison
Emission testing for all ten 300 series Bluebird buses (five B20 + five ULSD) was done in fast idle mode. It was observed that the emission concentrations for each of the pollutants were highly distributed with respect to fuel type and no single fuel could be
identified to be a better, cleaner option. The average results from this idling study showed
a 55% increase in CO concentrations and 25% increase in NO concentrations, but a 14% 2
decrease in NO concentrations and 3% decrease in CO concentrations for the series 300 2
buses using biodiesel (Figure 4.12 (a-d), Table 4.1).
Idling emissions were also monitored for the first 15-minutes for all the 37
Thomas buses from the 500 series (19 B20 + 18 ULSD) in fast idle mode. Comparison of
idling emission concentrations show that on an average, B20 grade biodiesel emitted
lower concentrations of all the monitored pollutants except for CO which showed a 2.6% 2
increase for B20 as compared to ULSD fueled buses (Figure 4.13). NO concentrations
were similar in both cases with B20 fuel emitting 1.17% less than ULSD buses. CO
concentration showed a marked 15% reduction for B20 fuel as compared to ULSD fuel.
SO and NO for B20 fuel showed nearly 5% and 6% reductions respectively. There was 2 2 less than 1% difference in the average exhaust gas temperatures for the two fueled buses, and therefore the datasets were deemed comparable.
63 CO Concentrations vs. Fuel
700
600
500 300 301 302 400 303 304 305 306 300 307
Concentration in ppm in ppm Concentration 308 309 200
100
0 1 36 71 106 141 176 211 246 281 316 351 386 421 456 491 526 561 596 631 666 701 736 771 806 841 876 911 946 981 Time in seconds
Figure 4.12(a): The Effect of Fuel on Pollutant Concentrations -CO
NO Concentrations vs. Fuel
700
600
500 300 301 302 400 303 304 305 300 306 307
Concentration in ppm in ppm Concentration 308 309 200
100
0 1 36 71 106 141 176 211 246 281 316 351 386 421 456 491 526 561 596 631 666 701 736 771 806 841 876 911 946 981 Time in seconds
Figure 4.12(b): The Effect of Fuel on Pollutant Concentrations-NO
64 NO2 Concentrations vs. Fuel
100.0
90.0
80.0
70.0 300 301 60.0 302 303 304 50.0 305 306 40.0 307
Concentration in ppm 308 30.0 309
20.0
10.0
0.0 1 36 71 106 141 176 211 246 281 316 351 386 421 456 491 526 561 596 631 666 701 736 771 806 841 876 911 946 981 Time in seconds
Figure 4.12(c): The Effect of Fuel on Pollutant Concentrations- NO2
CO2 Concentrations vs. Fuel
7.00
6.00
5.00 300 301 302 4.00 303 304 305 3.00 306 307 Concentration in % in Concentration 308 309 2.00
1.00
0.00 1 36 71 106 141 176 211 246 281 316 351 386 421 456 491 526 561 596 631 666 701 736 771 806 841 876 911 946 981 Time in seconds
Figure 4.12(d): The Effect of Fuel on Pollutant Concentrations - CO2
65 Table 4.1: Idling Test: Average Exhaust Emissions from Different Fuels NO Fuel O (%) CO (ppm) NO (ppm) CO (%) 2 (ppm) 2 2 Bio-Diesel (B20) 16.21 235.82 258.08 48.81 3.61 ULSD 15.97 152.02 301.49 38.96 3.72
% Decrease by -1.49% -55.12% 14.40% -25.28% 2.97% using Biodiesel
Series 500 - Emission Concentration Comparison
600.00
500.00
400.00
B20 300.00 ULSD Concentration
200.00
100.00
0.00 % O2 ppm CO ppm NO ppm SO2 ppm NO2 ppm NOx % CO2i
Figure 4.13: Pollutant Concentration Comparison: B20 vs. ULSD
Closer evaluation of results from 300 and 500 series fuel analysis indicated that the emissions were affected by other operating conditions that could not be straightforwardly identified. For instance, lower ambient temperatures and dissimilar operation schedules for all the buses resulted in varied engine temperatures. As engine temperature is an important factor affecting the emission behavior which is primarily
66 controlled by the above mentioned factors, this variation could strongly differentiate the emission behavior for different buses. Therefore, to further understand the effect of the different variables on emissions, additional statistical analysis was conducted to appropriately analyze the emission variation with fuel.
In order to model the emission behavior of diesel operated buses, a detailed set of plausible engine performance and operating variables were selected and a multiple regression analysis was conducted. The selected variables are discussed below. The notations provided here are used throughout this document.
• Accelerator pedal position (A): It represents the relative position of the accelerator
pedal with the reference 100% indicating full acceleration.
• Air filter age in days (AF): Air filter restricts any debris in the air from entering the
combustion chamber. It starts affecting the emission process when there is substantial
collection of particulates and dust on the surface of the filter that can affect the air
draw rate. The air filters are generally changed by TARTA maintenance after visual
inspection. Using a light source (flashlight), the mechanics try to see if the light is
visible through the medium. If the light is not visible, the filter is changed.
• Ambient temperature (Ta): Temperature (oF) of the ambient air which represents the
temperature of the intake air being drawn inside the combustion chamber. Krause et
al. (1973) found that intake air temperature substantially affected the HC, CO, and
smoke emissions of some engines while humidity did not have any significant effect
on emissions.
• Boost pressure (BP): Boost pressure is the pressure in excess of the atmospheric
pressure produced inside the intake manifold by any supercharger and is measured in
67 psi, inches of mercury, or bars. Increasing the inlet pressure increases the engine
specific power. Podevin et al. (2007) found no significant influence of boost pressure
on emissions except for NOx where a slight positive relation was observed for engine
speed between 2000-2500 rpm.
• Engine coolant temperature (CT): Temperature of the engine coolant which is
representative of the engine temperature. The starting engine coolant temperature
(SCT) differentiates hot-start from cold-start while the average engine coolant
temperature (ACT) gives the average temperature for the test period (15-minute
period for idling analysis in this study).
• Engine load (L): Load is a measure of the work done by an engine. The load is
considered minimal when a vehicle is going downhill whereas a vehicle going uphill
or pulling a heavy trailer is considered to have high engine load. The load is measured
in percent.
• Engine output torque (T): It is the amount of rotational force produced by the engine
that is transferred as tractive force to the wheels. A vehicle will have the maximum
acceleration at a peak torque in any gear. The effect of torque can be felt by the driver
while accelerating.
• Engine speed (ES): The number of revolutions per minute (rpm) at which the engine
crankshaft turns. This is irrespective of the movement of the vehicle (stationary or in
motion).
• Exhaust gas temperature (Tf): Temperature (oF) of the air emitted after the
combustion cycle which is exhausted through the tailpipe. It represents the
combustion temperature.
68 • Fuel filter age in days (FF): The fuel filter restricts fuel based debris from clogging
fuel injectors. FF age represents the last time the preventative maintenance was
performed and therefore represents adequate engine performance. The fuel filter is
changed during every PMI which is conducted every three months for each bus.
• Fuel category (F): The type of fuel used affects the emission behavior as the
composition and properties of different fuels affects emissions differently. In this
study B20 (1) and ULSD (0) emissions are compared. Table 3.3 gives the fuel
properties of the two fuels.
• Fuel rate (FR): It is the amount of fuel used in the combustion process. The fuel rate
represents the air/fuel ratio, a lean mix (less fuel, more air) or rich mix (more fuel,
less air).
• Vehicle speed (S): Vehicle speed is the distance traveled by the vehicle in unit time
(mph). The acceleration and deceleration of the vehicle affects the emission behavior.
For analyzing the effect of fuel, biodiesel buses were given a fuel category identifier of ‘1’ and ULSD buses were identified as ‘0’. From the statistical analysis, it was observed that fuel was a statistically significant variable affecting the emission concentrations of CO, NO and NO for 300 series buses. Examination of the coefficient 2 scale and ‘p’ value for “fuel category” for each of the pollutant showed that the use of
B20 grade fuel resulted in 159.74 ppm lower CO (p = 0.043), 138.3 ppm higher NO
(p=0.016) and 8.01 ppm higher NO (p = 0.076) concentrations as compared to ULSD 2 fuel (Table 4.2).
Statistical analysis was also performed to accurately assess the fuel-emission relationships for the 500 series buses. From the regression analysis, it was observed that
69 for 500 series buses, fuel was a statistically significant variable affecting the emission
concentrations for only CO pollutant. Examination of the coefficient scale and ‘p’ value
for “fuel category” for the pollutant shows that use of B20 grade fuel will result in 28.4 ppm lower CO (p = 0.001) concentrations and 2.59 ppm lower NO2 (p = 0.003) as
compared to ULSD. NO did not show any significant relationship with the type of fuel used (Table 4.2)
.
70 Table 4.2: Effect of Fuel on Pollutant Concentrations
Effect of fuel on CO concentrations
Air Fuel Starting Average Exhaust Ambient Engine Fuel % Output Boost Fuel R2 Filter Filter Coolant Coolant Temp. Temp. Speed Rate Engine Torque Pressure (B20=1, Constant Adj. Age Age Temp. Temp (0F) (0F) (rpm) (gal/hr) Load (ft-lb) (psi) ULSD=0) R2 (days) (days) (0F) (0F)
- - - 94.4% 300 -12.783 1.2622 381.58 -159.74 3051.7 3.0245 34.997 187.83 95.5%
- - - 102.33 83.4% 500 - 28.4 0.543 28.4 0.489 0.000 81.1% Effect of fuel on NO concentrations
- - 100% 300 13.83 -13.90 31.44 1.173 138.3 861.7 0.6681 2.2884 100% 90.7% 500 1.11 - 3.75 161 - 2.59 682 88.9%
Effect of fuel on NO2 concentrations - 79.9% 300 8.01 85.6 0.691 73.2% - - - 95.0% 500 3.79 - 2.59 127 0.0846 0.292 0.208 93.7%
71 Fuel Consumption
Figure 4.14 shows the average idling fuel consumption of B20 and ULSD buses from the 300 and 500 series. The graph shows that the 15 minute average idling fuel rate
for B20 buses in comparison with ULSD buses was 5.82% and 13.68% higher for 300
and 500 series B20 buses respectively. This is a new finding as the literature does not report any findings on comparison of ULSD and B20. The literature on diesel and B20
comparison shows a 3% increase in fuel consumption for biodiesel vehicles (Turio-
Baldassarri et al., 2003).
Fuel Rate Comparison of B20 and ULSD Bus Fleets 4.0
3.5
3.09 3.0 2.92
2.5
2.0 Fuel Rate (gph) Rate Fuel 1.5
1.14 1.0 0.99
Fuel B20 ULSD B20 ULSD Series 300 500
Figure 4.14: Fuel Rate Comparison of B20 and ULSD Bus Fleets
4.1.2 Fleet Emission Trend Characteristics
At the time of reporting, TARTA operated seven medium and heavy duty fleets for regular daily operational runs. All the buses that were purchased together were classified in one fleet category together. The emission characteristics of a sample bus
72 from each fleet exhibiting the average emission characteristics of the fleet are discussed below. All the fleets were tested with ‘fast idle’ switch engaged. All fleets were capable to operate the ‘fast idle’ mode at startup except for the 300 series. In series 300 buses, the engine operated in ‘ramp mode’ at startup for 5 minutes after which it engaged the ‘fast idle’ mode.
CO concentration trends: Series 400 had the highest startup spike for CO concentration followed by series 300 and series 900. Series 300 exhibited higher concentration during the ‘ramp mode’ but once the ‘fast idle’ was engaged, the emission concentration reduced to the lowest in the group. All other fleets had relatively higher emission spikes during startup right after which they reduced to the average emission behavior (less than 200 ppm). In the long run, the emission concentrations seemed to increase with engine temperature for 500 and 900 series, while all other fleets showed a reduction. (Figure
4.15).
NO concentration trends: Series 400 had the highest startup spike in NO concentration followed by a sustained reduction over the next ten minutes. Series 500 had the second highest emission concentration for NO. While series 300 exhibited lower concentration during the ‘ramp mode’, once the ‘fast idle’ was engaged, the emission concentration increased by over 100%. All other fleets had a relatively sustained reduction in emissions in the first 5 minutes after which they maintained the average emission behavior (less than 400 ppm). Series 200 seemed to emit the least NO concentrations (Figure 4.16).
73 CO Concentration Trends: Idling Mode
800
700
600
500 200 300 400 400 500 600 702 300 900 Concentration (ppm) Concentration
200
100
0 1 31 61 91 121 151 181 211 241 271 301 331 361 391 421 451 481 511 541 571 601 631 661 691 721 751 781 811 Time (Seconds)
Figure 4.15: CO Concentration Trends: Fleet Comparison
NO Concentration Trends: Idling Mode
1600
1400
1200
1000 200 300 400 800 500 600 702 600 900 Concentration (ppm)
400
200
0 1 31 61 91 121 151 181 211 241 271 301 331 361 391 421 451 481 511 541 571 601 631 661 691 721 751 781 811 Time (Seconds) .
Figure 4.16: NO Concentration Trends: Fleet Comparison
74 SO2 concentration trends: Series 400 had the highest startup spike in SO2 concentration.
Series 600 and 700 had the second highest emission concentration for SO2 out of all the
fleets. Series 400 and 500 had a high spike at startup and followed a sustained reduction
pattern over the course of the test period. All other fleets had a steady increase in
emission concentration in the first 3 minutes after which they maintained the average
emission behavior (between 50-100 ppm). Series 200 and 900 seemed to emit the least
SO2 concentrations (Figure 4.17).
SO2 Concentration Trends: Idling Mode
300
250
200 200 300 400 150 500 600 702 900
Concentration (ppm) Concentration 100
50
0 1 31 61 91 121 151 181 211 241 271 301 331 361 391 421 451 481 511 541 571 601 631 661 691 721 751 781 811 Time (Seconds)
Figure 4.17: SO2 Concentration Trends: Fleet Comparison
NO2 concentration trends: All the fleets had a high spike at startup and followed a
sustained reduction pattern over the course of the test period. At the end of the test
period, all fleets seemed to maintain the average emission behavior (between 40-80 ppm).
75 Series 300 had a higher emission during the ‘ramp mode’ but reduced significantly to the lowest in the group once the ‘fast idle’ was engaged (Figure 4.18).
CO2 concentration trends: All the fleets had a high spike at startup and followed a
sustained reduction pattern over the course of the test period. At the end of the test
period, all fleets seemed to maintain the average emission behavior (3%). Series 300 had
a higher emission during the ‘ramp mode’ but reduced significantly once the ‘fast idle’
was engaged (Figure 4.19).
NO2 Concentration Trends: Idling Mode
120.0
100.0
80.0 200 300 400 60.0 500 600 702 900
Concentration (ppm) Concentration 40.0
20.0
0.0 1 31 61 91 121 151 181 211 241 271 301 331 361 391 421 451 481 511 541 571 601 631 661 691 721 751 781 811 Time (Seconds)
Figure 4.18: NO2 Concentration Trends: Fleet Comparison
76 CO2 Concentration Trends: Idling Mode
8.00
7.00
6.00
5.00 200 300 400 4.00 500 600 702 3.00 900 Concentration (%)
2.00
1.00
0.00 1 31 61 91 121 151 181 211 241 271 301 331 361 391 421 451 481 511 541 571 601 631 661 691 721 751 781 811 Time (Seconds)
Figure 4.19: CO2 Concentration Trends: Fleet Comparison
Even though there was a considerable difference in the age of the different fleets, there was no clear indication that older vehicles emitted higher amount of pollutants as compared to newer models. It can therefore be inferred that engine technology and operation are more significant influencing factors affecting emission behavior as compared to the age of the vehicle. Similar observations were also made by Tao et al.
(2003) and Yao et al. (2006) who reported that engine technology strongly affected emission behavior.
Comparing the average mode concentrations in Run 1 and Run 2, it was observed that they follow same trends as were seen in the idling and on-road operation comparison
(Figure 4.20). NO and CO2 concentrations were clearly higher for Run 1 as compared to
Run 2, while NO2 concentrations exhibited the exact opposite behavior. This could be
77 attributed to the fact that Run 1 had a larger continuous run time (less stops encountered at traffic lights) and therefore a higher temperature and a higher fuel rate. CO and SO2 concentrations did not show clear trends as were observed in the earlier comparison of idling with the on-road operation modes. It was interesting to note that as compared to the idling emissions, series 200 exhibited an entirely different behavior for CO emissions.
The average CO emissions during idling mode for series 200 was around 150 ppm (which was comparable with the CO emissions of the other fleets) whereas during on-road testing, it rose to over 1000 ppm which was four times the emissions compared to any other fleet. This could be attributed to vehicle age and technology as the fleet was manufactured in 1990 and was due for retirement in 2002. Series 400, 600 and 700 emitted the highest amount of all the other pollutants which was also seen from the idling behavior as well. However, the scale of difference varied for most pollutants indicating that engine technology was the most important variable affecting the emission behavior of a vehicle. It is apparent that to accurately characterize the emission potential of a vehicle series, a combination of idling and on-road emission simulations need to be studied. It is also important to study the behavior for every socio-economic region separately through the simulation of load cycles that represent the location’s driving conditions. For instance, a simulation of New York Bus (NYBus) cycle will not be able to represent the driving conditions in the Toledo region.
78 Comparison of Fleets in Different Run Modes
O2 (%) CO (ppm) NO (ppm) 16 1000 600 12 750 450 8 500 300
4 250 150 Mode 0 0 0 Run1 SO2 (ppm) NO2 (ppm) CO2 (%) 6.0 Run2 80 40 Concentration 4.5 60 30 3.0 40 20
1.5 20 10
0 0 0.0 Bus215 300 402 501 600 708 915 215 300 402 501 600 708 915 215 300 402 501 600 708 915
Figure 4.20: Comparison on Fleet Emissions in Different Operation Modes
79 4.1.3 Instantaneous Emission Models: On-road Test Mode
Instantaneous emission modeling was carried out on the data obtained from the
on-road tests for each fleet capable of giving a 1-second OBD output. Each test leg (Run
1 or Run 2) consisted of a 21 minute (7.5 miles) run on the UT-TARTA test cycle each way. This gave enough data points (2500 data points) for a reasonable analysis. Emission and vehicle performance data from a continuous 1-second operation contained a lot of short term variation which affected the regression modeling output. Therefore, in order to reduce the short term variation, a 60-second moving average was used to process the entire data set. Best subset and multiple regression analyses were then carried out on the processed data set to identify the significant variables forming the best regression models for each pollutant of every fleet. Multiple-regression modeling technique was used to develop the regression models using the identified variables from the best subset regression. Tables 1-6 show the coefficients of the modeled variables in the developed models. The tables also list the R-squared and adjusted R-squared values along with the T
2 and p value. R explains the variability explained by the model and shows its predictive
capability and performance of the model (ideal 100%), while T and p values provide
information on the significance of the variables. As the T-value gets higher and p-value
(p < 0.05) gets lower, the variable is considered more significant.
These models were developed to understand the on-road emission behavior of the
different TARTA buses in real-world settings. The models also help in identifying how a
variable would affect the on-road emissions of pollutants. One bus from each fleet was
selected for this study. All the models showed good predictive ability but as the vehicular
characteristics were different, the emission behavior and emission-performance
80 relationships were sometimes different for different fleets. A total of 18 models were developed, 3 for each pollutant (refer to Tables 4.3-4.8). A set of the models explaining each pollutant is given below.
O2 (%) = 27.87 -0.00271 Tf -0.137 Ta + 0.0143 A +0.00656 CT + 0.00196 ES -0.2020 L + 0.00494 T + 0.0814 BP ...... …………. (4.1) R2 = 99.03, Adj. R2 =99.03
CO (ppm) = 484.7 - 1.152 Tf + 7.6 Ta - 2.30 A + 0.63 CT -0.523 ES + 163.8 FR + 13.7 L -3.43 T + 24.2 BP ...... …………. (4.2) R2 = 86.41, Adj. R2 =86.30
CO2 (%) = - 0.37999 + 0.00699 Tf + 0.0251 A - 0.01463 CT - 0.367 FR + 0.1101 L + 0.00297 T - 0.229 BP ...... …………. (4.3) R2 = 93.52, Adj. R2 =93.47
NO (ppm) = - 1071.7 + 0.940 Tf + 20.58 Ta - 8.87 A - 4.316 CT - 84.0 FR + 24.19 L - 1.93 S - 0.180 T ...... …………. (4.4) R2 = 96.08, Adj. R2 =96.05
NO2 (ppm) = -87.31 - 0.1003 Tf + 1.955 Ta + 0.141 A -0.0708 CT - 0.01542 ES + 0.0094 T + 3.68 BP ...... …………. (4.5) R2 = 97.69, Adj. R2 =97.69
SO2 (ppm) = -205.3 - 0.0619 Tf + 4.523 Ta - 0.307 A - 0.553 CT - 0.0065 ES -14.83 FR + 3.151 L - 0.324 S - 0.1844 T + 3.14 BP ...... …………. (4.6) R2 = 95.36, Adj. R2 =95.32
81 O concentrations were modeled to understand the efficiency of combustion 2
during different combinations of engine performance. O concentrations showed a very 2
consistent negative correlation for exhaust temperature and engine load and a consistent
positive correlation with engine coolant temperature and boost pressure. Engine load was
determined to be the most significant influencing variable for predicting O 2
concentrations (T statistic between -20 and – 70). The results indicated that an increase in
engine load and exhaust gas temperature would always decrease the O concentrations, 2
and increasing engine coolant temperature or boost pressure will result in an increase in
O emissions from the exhaust (refer to Table 4.3). 2
CO concentrations showed a consistent negative correlation with exhaust temperature and engine speed, and a positive correlation with fuel rate for all the fleets
studied. These observations were expected though not reported, as an increase in the fuel
rate decreases the air/fuel ratio making it a rich mixture and would result in incomplete combustion of fuel, and therefore result in higher CO concentrations. Engine coolant temperature was significant for two fleets; whereas all the other variables showed a very discordant behavior for different bus fleets (refer to Table 4.4).
Modeling NO behavior was difficult using the selected variables as no clear trend could be delineated. Exhaust temperature and Ta and load positively affected the NO concentrations for series 200 and 500, but did not seem to affect the 900 series concentration. Increase in fuel rate and accelerator pedal position decreased the concentration, but these trends were observed only for two series. These results show that
NO concentrations are affected by a combination of factors and there exist no single
variable that will always affect NO concentrations in a specific direction (positive or
82 negative). It is interesting to note though, that when Fuel rate is negative, boost pressure
always is positively affecting the response (refer to Table 4.5).
From Table 4.6, it can be seen that ‘accelerator pedal position’ showed a clear
negative correlation for all three series. Exhaust temperature and fuel rate showed a
negative relation to SO while boost pressure showed a positive relation to SO for series 2 2
500 and 900. A similar relation could not be achieved for series 200. A consistent effect of a variable could not be established suggesting the substantial effect of engine technology on emissions.
NO2 concentrations show a very clear negative correlation with exhaust temperature
and distinctive positive correlations with Ta and boost pressure. The most significant
variables identified from the analysis were exhaust temperature (T statistic between 26-44)
and ambient temperature (T statistic between 9-40). Engine temperature showed a
negative correlation when Ta had a stronger influence and positive correlation when Ta
had a smaller significance showing that temperature difference between ambient and flue
gas temperature (representing engine temperature) was the most important influencing variable affecting NO2 concentration. Also, an increase in boost pressur,e thereby
increasing the quantity of air inside the engine chamber increased the NO2 production in
the combustion process (refer to Table 4.7).
CO2 concentrations show a clear negative correlation with fuel rate and a
distinctive positive correlation with % engine load. Also, engine load was the most
significant (T > 20) of all the variables selected for the study. All other variables show a contradictory behavior in comparison within different fleets, indicating that their effect was specific to engine technology (refer to Table 4.8)
83
Table 4.3: Regression Results for Pollutant O2: Instantaneous Models
Exhaust Ambient Acceleration Engine Engine Fuel % Vehicle Output Boost 2 Temp., Temp., Pedal Coolant Speed, Rate, Engine Speed, R O2 Torque, Pressure, Constant 2 Tf Ta Position, A Temp., ES FR Load, S Adj. R 0 0 0 T (ft-lb) BP (psi) ( F) ( F) (%) CT ( F) (rpm) (gal/hr) L (mph) - Coeff. -0.166 0.0215 0.01194 0.00087 -0.251 0.0144 0.180 30.89 0.00379 96.23% 200 T-value 96.215 -6.07 -7.52 5.24 8.26 3.36 -20.83 7.79 2.94 p-value 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
- - Coeff. -0.1370 0.0143 0.00656 0.00196 0.00494 0.0814 27.87 0.00271 0.2020 99.03% 500 T-value 99.0% -8.54 -26.08 3.83 8.52 18.11 -69.15 5.80 6.40 p-value 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
- - Coeff. 0.0613 -0.02628 0.01725 0.183 0.223 15.61 0.00748 0.1280 97.41% 900 T-value 97.39% -20.68 9.48 -27.41 24.19 5.36 -53.90 13.41 p-value 0.000 0.000 0.000 0.000 0.000 0.000 0.000
84
Table 4.4: Regression Results for Pollutant CO: Instantaneous Models
Exhaust Ambient Acceleration Engine Engine Fuel % Vehicle Output Boost 2 Temp., Temp., Pedal Coolant Speed, Rate, Engine Speed, R CO Torque, Pressure, Constant 2 Tf Ta Position, A Temp., ES FR Load, S Adj. R 0 0 0 T (ft-lb) BP (psi) ( F) ( F) (%) CT ( F) (rpm) (gal/hr) L (mph) - Coeff. -13.91 91 25.4 21.5 -4.22 1405 18.8 -2066 232.9 74.65% 200 - T-value -18.13 3.08 5.16 11.28 -13.65 14.87 7.71 74.48% 10.84 0.000 0.002 0.000 0.000 0.000 0.000 0.000 p-value 0.000 - Coeff. -0.380 -5.58 2.73 59.0 -3.43 1.209 -20.04 725.4 0.0931 80.51% 500 8.33 T-value 80.36% -24.21 -12.05 7.55 -18.77 15.87 -9.82 0.000 -11.64 p-value 0.000 0.000 0.000 0.000 0.000 0.000 0.000
Coeff. -1.152 7.6 -2.30 0.63 -0.523 163.8 13.7 -3.43 24.2 484.7 86.41% 900 T-value 86.30% -9.41 3.72 -3.29 2.77 -12.35 12.48 10.37 -17.70 2.92 p-value 0.000 0.000 0.001 0.006 0.000 0.000 0.000 0.000 0.004
85
Table 4.5: Regression Results for Pollutant NO: Instantaneous Models
Exhaust Ambient Acceleration Engine Engine Fuel % Vehicle Output Boost 2 Temp., Temp., Pedal Coolant Speed, Rate, Engine Speed, R NO Torque, Pressure, Constant 2 Tf Ta Position, A Temp., ES FR Load, S Adj. R 0 0 0 T (ft-lb) BP (psi) ( F) ( F) (%) CT ( F) (rpm) (gal/hr) L (mph) - - Coeff. 1.005 18.8 -0.62 8.84 -88.0 0.084 1702.53 81.18% 200 T-value 81.09% 16.19 8.40 -4.11 -6.27 46.72 -17.76 p-value 0.000 0.000 0.000 0.000 0.000 0.000
Coeff. 0.940 20.58 -8.87 -4.316 -84.0 24.19 -1.93 -0.180 -1071.7 96.08% 500 - T-value 24.87 31.68 -23.76 -48.39 64.11 -9.68 -3.20 96.05% 33.95 0.000 0.000 0.000 0.000 0.000 0.000 0.001 p-value 0.000
Coeff. -7.17 1.652 0.460 -234 4.87 20.0 -900.0 79.26% 900 - T-value -11.94 19.38 11.26 45.38 2.63 79.14% 18.98 0.000 0.000 0.000 0.000 0.000 p-value 0.000
86
Table 4.6: Regression Results for Pollutant SO2: Instantaneous Models
Exhaust Ambient Acceleration Engine Engine Fuel % Vehicle Output Boost 2 SO2 Temp., Temp., Pedal Coolant Speed, Rate, Engine Speed, R Torque, Pressure, Constant 2 Tf Ta Position, A Temp., ES FR Load, S Adj. R 0 0 0 T (ft-lb) BP (psi) ( F) ( F) (%) CT ( F) (rpm) (gal/hr) L (mph) - - Coeff. 1.767 -0.1611 0.338 0.0419 -111.5 0.1170 90.96% 200 T-value -5.87 90.92% 23.43 -30.03 -46.04 6.61 p-value 0.000 0.000 0.000 0.000 0.000 - - - - Coeff. 4.523 -0.307 -0.553 -14.83 3.151 3.14 -205.3 0.0619 0.0065 0.324 0.1844 95.36% 500 - T-value -10.39 43.21 -3.31 -38.43 -3.23 -18.36 37.67 -11.30 7.90 95.32% 10.31 0.000 0.000 0.001 0.000 0.001 0.000 0.000 0.000 0.000 p-value 0.000 -0.156 Coeff. -0.828 0.362 0.0812 -35.4 0.620 3.5 -94.83 75.80% 900 T-value 75.64% -11.45 -10.03 12.43 14.10 -20.76 41.65 3.21 p-value 0.000 0.000 0.000 0.000 0.000 0.000 0.001
87
Table 4.7: Regression Results for Pollutant NO2: Instantaneous Models
Exhaust Ambient Acceleration Engine Engine Fuel % Vehicle Output Boost NO Temp., Temp., Pedal Coolant Speed, Rate, Engine Speed, R2 2 Torque, Pressure, Constant Tf Ta Position, A Temp., ES FR Load, S Adj. R2 T (ft-lb) BP (psi) (0F) (0F) (%) CT (0F) (rpm) (gal/hr) L (mph) - - - Coeff. 1.955 0.141 0.0094 3.68 -87.31 0.1003 0.0708 0.01542 97.69 200 T-value 97.67 -44.89 24.71 9.48 -13.74 -16.43 3.67 16.85 p-value 0.000 0.000 0.000 0.000 0.000 0.000 0.000
- - - - Coeff. 2.915 0.298 -0.0061 -5.35 1.341 1.41 -117.5 0.1068 0.2012 0.140 0.1369 93.52 500 T-value 93.46 -26.24 40.80 4.71 -20.48 -4.45 -9.69 23.48 -6.52 -12.29 5.19 p-value 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
- - Coeff. 0.98 0.240 0.0045 0.609 0.0962 4.52 -43.89 0.1813 13.17 82.44 900 - T-value -30.12 9.64 20.83 3.73 10.92 12.66 12.97 82.31 22.92 0.000 0.000 0.000 0.000 0.000 0.000 0.000 p-value 0.000
88
Table 4.8: Regression Results for Pollutant CO2: Instantaneous Models
Exhaust Ambient Acceleration Engine Engine Fuel Vehicle % Output Boost Temp., Temp., Pedal Coolant Speed, Rate, Speed, R2 CO Engine Torque, Pressure, Constant 2 Tf Ta Position, A Temp., ES FR S Adj. R2 0 0 0 Load, L T (ft-lb) BP (psi) ( F) ( F) (%) CT ( F) (rpm) (gal/hr) (mph)
- Coeff. -0.075 -0.0149 0.00280 0.32175 -0.0262 7.079 0.274 91.08 200 T-value -3.84 -6.30 4.33 -7.95 23.76 -14.30 91.04
p-value 0.000 0.000 0.000 0.000 0.000 0.000 - - Coeff. 0.0785 -0.0743 0.1590 0.0096 0.00982 0.067 -3.525 0.00841 0.581 96.28 500 -14.16 T-value 9.35 -13.07 -9.08 23.64 3.72 14.50 2.24 96.26 0.000 p-value 0.000 0.000 0.000 0.000 0.000 0.000 0.026
0.0069 - - 0.1101 - 900 Coeff. 0.0251 0.00297 -0.229 9 0.01463 0.367 0.37999 93.52
T-value 17.72 15.47 -17.16 -8.47 21.36 3.94 -10.68 93.47
p-value 0.000 0.000 0.000 0.000 0.000 0.000 0.000
89 4.1.4 Fleet Emission Models: Idling Mode – 15 Minute Averages
Fleet emission models were developed to estimate the emissions from different
buses in a fleet. These models help to explain the variation in emissions of different buses in the fleet under different settings or operating conditions such as: cold/hot weather, varied maintenance etc., or changes in the engine parameters such as fuel rate, boost pressure, engine temperature etc. Emission models were developed for each fleet specifically as variation in engine technology could not be accounted for in statistical analysis.
Best subset regression was run with the selected variables. The model with the
2 highest adjusted R and lowest Mallows coefficient Cp were identified and Multiple
Linear Regression technique was used to obtain the models. The ‘p’ and ‘T’ statistics for
each variable were analyzed to ascertain the significance of each variable identified in the
model. It is important to note that a variable is only selected by the model (in regression)
if it has a significant variation in value and if there is a significant contribution of that
variation to the change in emissions. Therefore, if a variable (e.g. ‘fuel rate’) is similar
for all the buses in a fleet, it would be not be included by the procedure as it will not
explain any variation in the output. Also, even if a variable’s value changes, but the
change does not explain the variation in the emission behavior, its p statistic will be non-
significant. The inclusion of the variable then will increase the error (Cp or S) and
2 2 therefore a lower R and adjusted R .
A total of six pollutant specific models were developed for each of the fleets.
Tables 4.9-4.12 discuss the coefficients of the modeled variables in the developed models
to identify the significance of each variable for explaining overall emission behavior. The
90 tables also list the R-squared and adjusted R-squared values which represent the predictive capability and performance of the model (ideal 100%). The best fleet emission model for each of the pollutant is given below:
Fleet Emission Model: O2
O2 (%) = 8.20 + 0.0387 Tf + 0.0353 SCT + 0.0289 Ta - 1.28 FR – 0.104 L -19 BP ...... …………. (4.7) R2 = 100.0%, Adjusted R2 = 100.0%
Fleet Emission Model: CO CO (ppm) = 1587 - 5.19 Tf + 3.84 SCT - 1.26 AF - 6.20 ACT + 263 FR ...... …………. (4.8) R2 = 97.7%, Adjusted R2 = 94.7%
Fleet Emission Model: NO NO (ppm) = 548 - 232 Fuel + 7.55 Tf + 8.52 Ta - 25.3 SCT - 270 FR - 23.6 L + 310 BP ...... …………. (4.9) R2 = 99.2%, Adjusted R2 = 93.7%
Fleet Emission Model: SO2
SO2 (ppm) = 270 - 0.574 AF + 80.0 BP - 0.918 ACT - 80.0 FR + 5.81 L ...... …………. (4.10) R2 = 98.4%, Adjusted R2 = 96.3%
Fleet Emission Model: NO2
NO2 (ppm) = 430 + 20.2 T - 116 L + 1017 BP - 747 FR - 0.615 ES ...... …………. (4.11) R2 = 97.0%, Adjusted R2 = 91.9%
Fleet Emission Model: CO2
CO2 (%) = - 309 + 0.0257 Tf + 10.2 BP - 0.002 AF + 0.00242 FF + 0.509 ES + 0.0117 T ...... …………. (4.12) R2 = 97.7%, Adjusted R2 = 95.9%
91 All the models show good predictive ability but as the vehicular characteristics
were different, the emission behavior and emission-performance relationships were
sometimes different for different fleets. Tables 4.9-4.12 provide the regression results for
all the pollutants from each of the fleet. The discussion below reflects the interpretation
of the results from the table.
Series 200: This fleet consisted of 46 buses of which 44 were operated on ULSD fuel and
the other two used B20 fuel. At the time of testing, eight buses were not operational.
Therefore 18 buses were selected for emission testing including the two operating on
B20. Fuel filter, ambient temperature and exhaust gas temperatures were found to be the
most important variables explaining the behavior of most of the pollutants emitted from
this fleet. Fuel category results showed considerable impact on CO; NO2 and SO2 concentrations (refer Table 4.9).
Series 500: This fleet consisted of 37 buses, half of which were operated on ULSD fuel and the other half used B20 fuel. All the buses were selected for emission testing in idle mode in order to analyze the effect of fuel on the emission behavior. Exhaust gas
temperature, engine temperatures (SCT and ACT) and fuel rate were the most influencing
variables for the different pollutants emitted from the 500 series buses. Boost pressure,
fuel category and output torque were the other variables that affected the pollutant
concentrations from this fleet of buses. All the models showed very high model statistics
2 (adjusted R >89%, P=0.000). The good predictions could be attributed to a larger dataset
which also allowed for a larger number of variables to be studied together (refer to Table
4.9).
92 Series 900: This fleet consisted of 20 buses all of which were operated on ULSD fuel.
Therefore ten of the buses were selected for emission testing in idle mode. Fuel rate, boost pressure, engine load, and engine speed were the most significant variables affecting most of the pollutants emitted from 900 series buses. These buses were tested in
fast idle and regular idle modes and therefore engine load and engine speed showed came
out as significant variables for exhaust emission concentration estimations for this fleet.
All models except for CO could explain over 90% of the variance in the pollutant
concentrations (refer to Table 4.10).
Series 400: This fleet consisted of 20 buses, all operated on ULSD fuel. Ten of these
buses were selected for emission testing in idle mode. Fuel rate was found to be the most
significant influencing factor for series 400 buses followed by engine load, AF and SCT.
Other variables showed an influence only for a few of the pollutants. All the models had
an adjusted R2 value close to 90% showing good predictive ability. FF, engine speed and
output torque were not at all significant for any pollutant from this fleet. Since all the buses were operated on the same fuel (ULSD), fuel category was not an influencing variable (refer to Table 4.10).
Series 300: This fleet consisted of ten buses of which half were operated on B20 and the
other half on ULSD fuels. Therefore, all the ten buses were tested in order to understand
the effect of fuel on emission behavior. Fuel was the most important variable that
influenced the emissions of most of the pollutants, while FF, engine speed, ACT and
torque did not show any effect on the emission of any of the pollutants. The adjusted R2 for most of the pollutants was close to 70% indicating the need for further analysis and
93 inclusion of more explanatory variables. As all the buses were tested for this fleet, more data could not be obtained (refer to Table 4.11).
Series 600: This fleet had only 13 buses and seven of them were tested for emissions.
Limited number of data points and non-availability of OBD software affected the data analysis for this fleet. Engine performance data collection from the OBD was limited to engine coolant temperature, fuel temperature, ambient temperature, fuel rate, engine load and engine rpm at the beginning and end of the testing cycle. Tf, fuel rate, engine load and ACT were the only monitored variables that had any effect on the pollutants emitted from this fleet. All the adjusted R2 were quite high for these buses (all over 90%), indicating that the selected variables were pertinent for the analysis (refer to Table 4.12).
Series 700: This fleet consisted of 20 buses, all operating on ULSD fuel out of which ten buses were selected for emission testing in the idling mode. The data collection for this fleet was also affected by the non-availability of OBD software. Engine performance data collection from the OBD was limited to engine coolant temperature, fuel temperature, ambient temperature, fuel rate, engine load and engine rpm at the beginning and end of the testing cycle. Tf, Ta, fuel rate and engine load were all significant for explaining the behavior of all the monitored pollutants. Since output torque and boost pressure were not monitored through the handheld OBD and since all the buses were operated on the same fuel, these variables were not used in analysis. All the models had very high predictive ability (adjusted R2 over 96%) indicating that the selected variables were pertinent for the analysis (refer to 4.12).
Some of the important results from this fleet emission models are as follows:
94 • Fuel rate was the most significant variable for predicting O2 concentrations and
had a strong negative correlation which was similar to the results observed for
instantaneous models indicating a decrease in O2 concentrations with the increase
in fuel rate.
• For all the pollutants which had engine temperatures (ACT and SCT) as a
significant influencing variable (except oxygen), an increase in engine
temperature decreased the total emissions. This behavior was also seen from the
experimental analysis.
• Higher boost pressure also seemed to increase the production of carbon dioxide,
sulfur dioxide and nitrogen dioxide establishing that higher oxygen availability
increased the production of completely combusted by-products.
• Fuel category (B20 = 1, ULSD = 0) had a negative correlation with CO for series
200, 300, and 500 series buses. NO2 concentrations showed a positive correlation
for series 300 but a negative correlation for series 200 and 500 buses. The results
show that CO concentration reduced by using B20, but NO2 concentrations were
affected more by engine technology and fuel had inconclusive effects on their
emissions.
• Tf, engine load and fuel rate were the most significant variables affecting the
emission behavior of most of the pollutants.
• A combination of engine temperature at the beginning and average value over the
test period also showed significant influence on most of the pollutants. This
finding is different from the observation made by Chou and David (1997) who
stated that temperature played a minor role in exhaust emission modeling. This
95 could be attributed to the fact that all the buses tested in other studies were in ‘hot
start’ whereas this research ensured that emission concentration monitoring was
done on a proportional mix of hot and cold buses. While effect of temperature
would be minimal for ‘hot starts’, it should still be considered to accurately
characterize the real-world emission.
• This analysis was affected by non-uniform record keeping regarding the
maintenance history of the buses at TARTA. Some of the vehicles showed that
the air filters and fuel filters were changed over 500 days ago. Availability of
better data would improve the results.
• These results show that multiple regression technique could be effectively applied
to understand the behavior of vehicular emissions, given that an adequate number
of pertinent variables are available.
96 Table 4.9: Fleet Emission Model Coefficients and Statistics: Fleet 200 and 500
Series 200 Exhaust Ambient Fuel Starting Engine Fuel % Average Boost Air Filter Output Fuel, F Temp., Temp., Filter Coolant Speed, Rate, Engine Coolant Pressure, R2 Age, AF Torque, (B20=1, Constant 2 Tf Ta Age, FF Temp., ES FR Load, Temp., BP Adj. R (days) T (ft-lb) ULSD=0) (0F) (0F) (days) SCT (0F) (rpm) (gal/hr) L ACT (0F) (psi) - - - - 98.7% O 0.0364 0.00303 0.0555 0.708 -9.81 21.5 2 0.0225 0.00351 0.0684 0.0931 96.3%
- 94.6% CO - 15.5 - 0.357 0.577 - 205 - 13.2 - 101 126800 33.1 89.2% - 97.7% CO 0.0257 0.00242 0.509 0.0117 10.2 - 309 2 0.00200 95.9% - - 97.0% NO 2.49 89.9 115 5.39 - 54063 0.0801 2586 94.8% - 95.5% NO - 0.695 - 1.52 0.0384 - 0.604 809 - 8.81 116 2 32.9 91.0% 95.1% SO - 0.744 - 1.60 0.0255 - 0.300 336 - 1.75 - 12.5 227 2 90.3%
Series 500
- - 93.6% O 0.00820 20.7 2 0.0114 2.60 92.7% - 102.33 83.4% CO - 0.543 - 28.4 0.489 0.000 81.1% - 92.7% CO 0.00905 1.89 0.166 2 0.00634 91.6% 90.7% NO 1.11 - 3.75 161 - 2.59 682 88.9% - 95.0% NO - 0.292 - 0.208 3.79 - 2.59 127 2 0.0846 93.7% 94.8% SO - 0.835 - 0.448 8.80 202 2 94.1%
97 Table 4.10: Fleet Emission Model Coefficients and Statistics: Fleet 900 and 400
Series 900
Exhaust Ambient Air Fuel Starting Engine Fuel % Average Boost Output Fuel, F Temp., Temp., Filter Filter Coolant Speed, Rate, Engine Coolant Pressure, R2 Torque, (B20=1, Constant 2 Tf Ta Age, AF Age, FF Temp., ES FR Load, Temp., BP Adj. R T (ft-lb) ULSD=0) (0F) (0F) (days) (days) SCT (0F) (rpm) (gal/hr) L ACT (0F) (psi) - - 100.0% O 0.0387 0.0289 0.0353 - 19.0 8.20 2 1.28 0.104 100.0% - 96.9% CO - 11.1 6.47 - 49.8 - 10.3 68.9 5833 4783 1257 75.5% - - 97.9% CO 0.0173 9.52 2 0.0109 0.0221 96.6% 99.2% NO 4.77 3.01 1764 276 - 1.09 - 47.6 - 3790 - 1754 93.7% - 97.0% NO - 747 - 116 20.2 1017 430 2 0.615 91.9% - 92.3% SO - 273 7.15 92.8 2 41.4 87.6%
Series 400
- - - - 99.9% O 0.0146 2.03 24.2 2 0.0254 0.0169 0.143 0.0110 99.8% 97.7% CO - 5.19 - 1.26 3.84 263 - 6.20 1587 94.7% - - 99.9% CO 0.0185 0.00832 0.108 0.00861 - 2.24 2 0.0106 1.45 99.8% 99.8% NO 2.33 - 1.38 - 2.21 - 295 24.1 188 382 99.5% - - 92.0% NO 3.69 78.7 2 0.131 45.9 88.0% - - 98.4% SO 5.81 - 0.918 80.0 270 2 0.574 80.0 96.3%
98 Table 4.11: Fleet Emission Model Coefficients and Statistics: Fleet 300
Series 300 Fuel Average Exhaust Ambient Starting Engine Fuel % Boost Air Filter Filter Coolant Output Fuel, F R2 Temp., Temp., Coolant Speed, Rate, Engine Pressure, Age, AF Age, Temp., Torque, (B20=1, Constant Adj. Tf Ta Temp., ES FR Load, BP (days) FF ACT T (ft-lb) ULSD=0) R2 (0F) (0F) SCT (0F) (rpm) (gal/hr) L (psi) (days) (0F)
0.00257 - 89.67 O 17.43 2 0.101 86.23
- - - 94.4% CO -12.783 1.2622 381.58 -159.74 3051.7 3.0245 34.997 187.83 95.5% - 79.0% CO - 0.435 7.77 2 0.0485 72.0% - - 100% NO 13.83 -13.90 31.44 1.173 138.3 861.7 0.6681 2.2884 100% - 79.9% NO 8.01 85.6 2 0.691 73.2%
99
Table 4.12: Fleet Emission Model Coefficients and Statistics: Fleet 600 and 700
Series 600 Ambient Starting Engine Fuel Average Boost Exhaust Air Filter Fuel Filter % Output Fuel, F Temp., Coolant Speed, Rate, Coolant Pressure, R2 Temp., Tf Age, AF Age, FF Engine Torque, (B20=1, Constant 2 Ta Temp., SCT ES FR Temp., BP Adj. R (0F) (days) (days) Load, L T (ft-lb) ULSD=0) (0F) (0F) (rpm) (gal/hr) ACT (0F) (psi) - 99.9% O 0.00438 - 6.72 0.200 2 0.00580 99.6% 99.9% CO - 4.01 - 277 27.3 0.272 99.4% - 99.9% CO 3.77 - 0.112 0.00334 2 0.00262 99.4% 99.7% NO - 0.509 935 - 34.0 0.705 98.4% 98.0% NO - 0.454 96.9 - 4.15 0.0222 2 90.2% 99.0% SO - 0.464 182 - 7.98 - 0.0904 2 94.8%
Series 700 - - 100.0% O 0.00574 0.00104 - 0.00594 0.00359 - 1.84 0.00237 2 0.00545 0.0695 100.0% 99.2% CO - 1.08 - 1.76 - 0.493 - 2.26 51.1 - 3.94 2.17 96.4% - - - - 100.0% CO 0.00319 0.0393 0.00363 1.03 2 0.00316 0.000767 0.00199 0.00094 100.0% 99.6% NO 2.23 1.19 0.095 5.67 - 388 26.0 1.934 98.4% - 99.5% NO - 0.418 1.86 - 0.203 35.4 - 0.977 0.613 2 0.0421 97.9% 99.7% SO - 0.179 1.45 0.0069 - 0.0983 0.550 - 0.181 - 13.1 1.99 2 97.2%
100 4.2 Indoor Air Quality Characterization Pollutant concentrations inside public transport buses were monitored from May
2006 till June 2007. This section presents the trends and behavior of the pollutants in the
TARTA bus compartment and also discusses the developed models for characterizing indoor air quality.
4.2.1 Characterizing Pollutant Behavior a. Indoor Pollutant Concentrations: Daily and Weekly Trends
To study the pollutant trends and for the purpose of this comparison, only the air quality datasets monitored for Route 20 (Meijer on Central Avenue - TARTA garage) during the data collection period are presented.
CO2 concentrations: Figures 4.21 and 4.22 represent two sample time series plots
showing the variation of CO2 with time (6 am – 11 pm) for a test week. The average
concentration trend (Figure 4.21) shows an exponential increase during the first three
hours which can be attributed to the office/school going passengers and heavy traffic on
the route. After 8 am, the concentration decreases significantly till around 1 pm when the
passengers and traffic subside. Between 2-6 pm, the CO2 concentration remains high
reaching the second highest peak for the day at 5 pm, very similar to the trend seen in
passenger and traffic activity. After 6 pm, the concentrations steadily decreased until 11
pm barring a spike between 8-9 pm when there is a momentary increase in passenger
traffic. Detailed studies of daily trends (Figure 4.22) show that the CO2 concentration is a
highly correlated function of passenger activity. Although the selected TARTA bus route
had an identical daily ridership pattern and followed the same concentration trend, any
change in ridership would have an effect on the indoor CO2 concentrations.
101 Time Series Plot of CO2(PPM)
1300 Day Fri 1200 Mon Thu 1100 Tue Wed 1000
900
800 CO2(PPM) 700
600
500
400 Mon Tue Wed Thu Fri Day (Each set represents hourly conecentrations between 6 am - 11 pm)
Figure 4.21: Weekly CO2 Concentration Trend
Figure 4.22: Daily CO2 Concentration Trend
102 The figures also showed that during periods of high ridership, the in-vehicle CO2 concentration regularly reached 1500 ppm and reached as high as 2500 ppm without proper air exchange/ventilation. Similar observations were made by Chan (2003) who commented that during periods of high passenger numbers, indoor concentrations reached up to 10 times the out-vehicle concentration (up to 3900 ppm) which is close to the concentration of concern set by the World Health Organization.
CO concentrations: Figure 4.23 represents a sample time series plot showing the variation
of CO with time (6 am – 11 pm) for a test week. The concentration trend is almost
identical for the whole week. A sample one-day concentration profile is shown in Figure
4.24 and discussed. The concentrations showed an exponential increase during the first
three hours which could be attributed to the office/school going traffic on the route. After
the 8 am rush, the concentrations decrease till evening and as the evening traffic builds
up, the CO concentrations increased as well. During the test week, there was a marked
increase in the traffic especially for trucks and heavy vehicles on Wednesday and
Thursday evening which was seen directly in the CO concentration spikes. The indoor
CO concentrations reached up to 40 ppm consistently during morning pullouts and could
reach as high as 50 ppm during heavy traffic. These concentration levels were
significantly higher than ambient concentrations and regularly exceeded the 1-hr National
Ambient Air Quality Standard (NAAQS) for CO (35 ppm).
SO2 concentrations: Sulfur dioxide concentrations also showed a marked correlation with
the heavy truck traffic on the road. The concentrations increased during the 8-9 am traffic
and remained higher than the secondary NAAQS standard for SO2 (0.5 ppm for 3-hr time
103 average) throughout the run. During the evening hours (after 8 pm), the concentration starts to decrease and follows the trend until the bus returns to the garage.
Time Series Plot of CO(PPM)
30 Day Fri Mon 25 Thu Tue Wed 20
15 CO(PPM) 10
5
0
Mon Tue Wed Thu Fri Day (Each set represents hourly conecentrations between 6 am - 11 pm)
Figure 4.23: Weekly CO Concentration Trend
CO Concentrations: Daily Trend
40
35
30
25
20
15 Concentration (ppm) 10
5
0 5:00 6:00 7:00 8:00 9:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00 18:00 19:00 20:00 21:00 22:00 23:00 Time CO(PPM) Figure 4.24: Daily CO Concentration Trend
104
Time Series Plot of SO2(PPM)
1.0 Day Fri Mon Thu 0.9 Tue Wed
0.8 SO2(PPM)
0.7
0.6
Mon Tue Wed Thu Fri Day (Each set represents hourly conecentrations between 6 am - 11 pm)
Figure 4.25: Weekly SO2 Concentration Trend
SO2 Concentrations: Daily Trend
1.2
1
0.8
0.6
0.4 Concentration (ppm) Concentration
0.2
0 5:00 6:00 7:00 8:00 9:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00 18:00 19:00 20:00 21:00 22:00 23:00 Time SO2(PPM) 60 per. Mov. Avg. (SO2(PPM))
Figure 4.26: Daily SO2 Concentration Trend
105 The SO2 concentration had significant short-term variation as compared to the other pollutants and maintained a see-saw trend throughout the day. SO2 concentration also
follows the same overall trend throughout the week (Figures 4.25 and 4.26).
NO concentrations: Nitric oxide concentrations (Figure 4.27) revealed an increasing trend
starting at 8 am and showed high concentrations until 11 pm. The concentrations showed
a marked decline every day around noon, increasing again during the evening hours
between 4 pm and 8 pm. These phases also correlated well with high traffic periods
during the day.
Time Series Plot of NO(PPM)
0.8 Day Fri Mon 0.7 Thu Tue Wed 0.6
0.5 NO( PPM) 0.4
0.3
0.2 Mon Tue Wed Thu Fri Day (Each set represents hourly conecentrations between 6 am - 11 pm)
Figure 4.27: Weekly NO Concentration Trend
PM1.0 concentrations: Indoor PM concentrations were found to be greatly affected by
ambient PM concentrations. As the ambient levels do not follow a daily trend, it was
106 difficult to characterize a repetitive trend for indoor particulate concentrations. This
section presents the most observed trends for PM1.0 concentrations inside the buses.
The average variation of PM1.0 with the time of day showed that the concentration
of PM1.0 was highest during the morning pullout from the garage (6-7 am). As the bus moves out of the garage, the concentration constantly decreased until 7.30 am. The concentration increased at 7.30 am and remained high until 12 pm. This 4.5 hour period corresponded to the highest commute during the day both for school going children and other vehicular traffic. The concentration dropped from 12-1.30 pm when there was negligible ridership on the bus. A slight increase in concentration was also observed between 1.30-2.30 pm, and the concentration again drops continuously until 8.30 pm.
Following a sudden increase at 8.30 pm, the concentration decreased until 11.20 pm when the bus run ended (refer to Figures 4.28 and 4.29).
From Figures 4.28 and 4.29 it can also be concluded that all components of PM1.0 contribute considerably to any peak in PM1.0 mass concentration. The plots also showed
that PM1.0 mass comprised of over 40% of particles less than 0.40 µm but these particles contributed close to 65-70% of total measured particle count. Particles with aerodynamic diameter between 0.40-0.50 µm contributed approximately 25% to PM1.0 mass and count
concentration. Particles between 0.50 and 1.0 µm in diameter collectively contributed
only 6-10% of total number of particles but their contribution to mass is almost 35%
(Table 4.13).
Table 4.13: PM1.0 Mass-Number Relationship
PM 1.0 Component Mass Contribution % Number Contribution
PM < 0.40 um 40% 65-70%
PM 0.40 – 0.50 um 25% 25%
107 PM 0.50 – 1.0 um 35% 6-10%
Time Series Plot of Hourly PM1.0 Components
25 Variable PM 0.3-0.4 PM 0.4-0.5 PM 0.5-0.65 20 PM 0.65-0.8 PM 0.8-1.0
15
10 Concentration (ug/m3) 5
0 Hour6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
Figure 4.28: Avg. Variation of PM1.0 Components (Mass Concentration) with Time of Day
Time Series Plot of PM1.0 Number Concentration
180000 Variable PM 0.30-0.40 um 160000 0.40-0.50 um 0.50-0.65 um 140000 0.65-0.80 um 0.80-1.0 um
120000
100000
80000
60000
40000 Concentration (particles/litre) 20000
0 Hour6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
Figure 4.29: Avg. Variation of PM1.0 Components (Particle Counts) with Time of Day
Figure 4.30 shows the PM2.5/PM10 and PM1.0/PM2.5 size fractions for indoor PM during daily bus operation. It can be seen that PM2.5/PM10 ratio reduced to 0.6 when in
operation (6 am – 11pm) and was generally higher while parked in the garage (buses
idling). The PM1.0/PM2.5 ratio also followed a similar trend and exhibited an average
108 value of 0.9. The PM2.5/PM10 ratio has generally been reported between 0.5 and 0.7 in the
literature. These are important as any increase in these ratios indicates a corresponding
increase in the ultra-fine particles that can reach deep inside the human lungs.
PM Ratios for Bio-Diesel Buses
1.2
1
0.8
PM2.5 / PM10 0.6 PM1.0 / PM2.5 Fraction
0.4 Literature suggests that ambient PM2.5/PM10 ratio in US and Europe generally ranges from 0.5 to 0.7, with an average of 0.6 0.2
0 2:00 6:00 10:00 14:00 18:00 22:00 2:00 6:00 10:00 14:00 18:00 22:00 2:00 6:00 10:00 14:00 18:00 22:00 Time
Figure 4.30: PM Size Fraction Ratios
b. Concentration Comparison: Indoor/Outdoor
PM concentrations: For comparing the PM concentration behavior inside and just-
outside the test bus cabin, and to analyze their relationship, two TSI Dust Trak 8520
monitors fitted with 1 μm nozzles were used. One unit drew air from inside the cabin and the other unit was connected to a 2-ft long pipe that drew air from outside the cabin.
It was observed that the indoor concentration trends were similar to the concentration trends just outside the bus and were found to be affected by the just-outside concentrations (similar to Wargo et al., 2002), but were generally lower than ‘just- outside’ concentrations (similar to Rodes et al., 1998) by a margin of 30-70% during test
109 runs (refer to Figure 4.31). It was also noted that even though outdoor concentrations were affecting the fine particulate concentrations inside the bus, any peak in concentration inside the bus was a result of passenger activity and not the ambient/ surrounding concentrations, resulting in a much higher increase (up to 4 times) in concentrations than the outdoor air. It was also observed that the concentration was highest (sometimes ten times higher) when the test bus was inside the garage with all buses idling (before the morning pullout).
PM1.0 Concentrations: Indoor vs. Outdoor
1.4 Bus inside Bus on the road Bus inside garage garage, 1.2 All buses idling
1
0.8
mg/m3 0.6 Concentration Concentration 0.4
0.2
0 2:00 3:00 4:00 5:00 6:00 7:00 8:00 9:00 0:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00 18:00 19:00 20:00 21:00 22:00 23:00 Time
Inside Outside
Figure 4.31: PM1.0 Concentration Comparison: Inside vs. Outside the Bus
Gaseous pollutant - CO2 and CO: CO2 concentrations inside and just outside the bus were compared using two calibrated Yes ‘Plus’ instruments to study the behavior of the pollutant. Analyzing for CO2 concentrations indoor and just-outside the bus gave an understanding of indoor-outdoor relationship and could represent the behavior of other
110 pollutant gases that also have a potential of being generated within the bus compartment
(Figure 4.32).
From Figure 4.32, it can be seen that indoor concentration always replicated
outdoor concentration peaks suggesting the effect of outdoor concentrations on indoor
concentrations. A substantial variation in the outdoor concentrations can also be seen, which could be attributed to different traffic zones in which the bus was being operated.
The indoor concentration variation clearly followed the outdoor trend which was probably an interactive effect of door opening and operating route/zone. However for indoor CO2 concentrations, it was found that the passenger number (indoor) was a more
important influencing factor than traffic activity (outdoor).
CO2 Concentration: Indoor vs. Outdoor 1400
1200
) 1000
800 Outsi de Inside 600
400 Concentration (ppm
200
0 3:00 5:00 7:00 9:00 1:00 3:00 5:00 7:00 9:00 11:00 13:00 15:00 17:00 19:00 21:00 23:00 11:00 13:00 15:00 17:00 19:00 21:00 23:00 Time
Figure 4.32: CO2 Concentration: Indoor vs. Outdoor Comparison
CO concentrations were also compared inside and just outside the vehicle using the same setup as explained for CO2. As the CO sensors had a maximum detection limit
111 of 50 ppm, any concentration levels beyond that limit could not be measured. Although
the trends could not be compared accurately to study the contribution of just outside
concentrations on the indoor CO levels, it was observed that indoor CO levels were
almost always lower than the outside concentrations. It was also seen that the on-road
concentration of CO was dangerously high throughout, and opening the bus door may
result in higher concentrations indoor (Figure 4.33).
CO Concentration: Indoor vs. Outdoor
60
50
40
Outside 30 Inside
Concentration (ppm) Concentration 20
10
0 3:00 5:00 7:00 9:00 11:00 13:00 15:00 17:00 19:00 21:00 23:00 Time
Figure 4.33: CO Concentration: Indoor vs. Outdoor Comparison c. Concentration Comparison: Front of the Bus vs. Back of the Bus
The Dust Trak instruments were also used for comparing the particulate concentration variation between the front and the back of the bus. A marked difference in the concentration build-up during the operational run and when the bus was parked in the garage was observed (Figure 4.34). When the bus was parked in the garage after the runs,
112 the indoor and outdoor concentrations were similar throughout the three days of
observations. During the runs, the concentration at the back of the bus was consistently higher by 2-7 times the front end concentration which is similar to that observed by
Solomon et al. (2001).
PM 1.0 Concentrations in 2 Locations inside a Bus
0.3
0.25
0.2 m
0.15 Concentration (mg/ Concentration 0.1
0.05
0 01 03 05 07 09 11 13 15 17 19 21 23 01 03 05 07 09 11 13 15 17 19 21 23 01 03 05 07 09 11 13 15 17 19 21 23 01 Time (Hour)
Front Back Bus on the Run Bus inside Garage
Figure 4.34: PM1.0 Concentration Comparison: Front vs. Back of the Bus
4.2.2 Identifying Important Variables Affecting Indoor Air Quality
a. Effect of Fuel: Comparison of Indoor PM concentrations in B-20 and ULSD buses:
Comparison of indoor fine particulate concentrations inside B20 and ULSD buses was conducted using an 11-day hourly dataset between December 21 and December 31,
2006. Barring some episodes of high concentrations, the concentration of PM1.0 remained identical in both B20 and ULSD buses. In order to analyze the difference in the fine
113 particulate data statistically, a ‘paired t-test’ was performed. For particles ranging from
0.30-0.40 µm, the paired t-test for mean difference = 0 gave a p-value (0.241) > 0.05.
This showed that there was insufficient evidence to suggest a difference in the two datasets. Similar results were observed for particles ranging between 0.80-1.0 µm.
These test results proved that in both the cases, the fine particulate concentrations inside bio-diesel and ULSD fueled buses are statistically similar (Figure 4.36). This showed that the indoor concentrations in both the buses were affected by a single source of pollutant which is expected to be the ambient concentrations.
Additional analysis was conducted using weekly data sets for the two buses operating on the same route (Figure 4.35 (a-b)). It can be observed from Figure 4.35 that indoor PM1.0 trends for both the buses followed the same trend. Closer evaluation of the data showed that the indoor concentrations followed the ambient PM1.0 trends and were
always replicated inside the buses. Multiple peaks occurring inside the bus that were not
seen in the ambient PM data were also observed suggesting either accumulation or the
presence of indoor sources. Indoor PM1.0 concentration was almost always higher than
ambient PM2.5 levels (2-10 times) during morning pullouts (staggered pullout starting at 5
am; buses continue to leave the garage until 7 am everyday) from the TARTA garage.
This behavior was expected as TARTA buses idle every morning from 4 am in order to
identify any operational oddity in their systems. This contributes significantly to the fine
particle concentrations. Indoor concentrations were also found to be higher than ambient
levels during increased traffic on the road.
The PM1.0 concentration peaks were found to be higher for the B20 bus during
most morning pullouts, which could be due to a later pullout from the garage which
114 increases the stay time inside the idling exhaust polluting zone. The ULSD bus concentration peaks were sometimes higher than B20 buses during afternoon operation, but overall the concentration trends were similar. This clearly showed that even though the indoor PM1.0 concentration was more affected by the ambient concentrations, a significant contribution to the indoor PM1.0 levels was also from a mix of vehicular traffic and passenger related activities that are variable with the operating time periods for the two buses. As there was no single bus exhibiting a consistent higher concentration trend, it can be inferred that the type of fuel used in the bus is not an influencing variable affecting indoor PM1.0 concentrations.
PM 1.0 Concentrations Inside Biodiesel (B20) and ULSD Buses
30
25
20
BD PM1.0 15 UL PM1.0
Concentration (ug/m3) Concentration 10
5
0 3:00 0:00 7:00 4:00 1:00 8:00 5:00 2:00 9:00 6:00 3:00 0:00 10:00 17:00 14:00 21:00 11:00 18:00 15:00 22:00 12:00 19:00 16:00 23:00 13:00 20:00 10:00 17:00 Time
Figure 4.35 (a): Comparison of Weekly Indoor PM 1.0 Conc. for B20 and ULSD Buses
115 PM 1.0 Concentrations Inside Biodiesel (B20) and ULSD Buses
120
100
80
UL 60 BD Ambient Number of particles 40
20
0 3:00 9:00 3:00 9:00 3:00 9:00 3:00 9:00 3:00 9:00 3:00 9:00 3:00 9:00 3:00 9:00 15:00 21:00 15:00 21:00 15:00 21:00 15:00 21:00 15:00 21:00 15:00 21:00 15:00 21:00 15:00 21:00 Time
Figure 4.35 (b): Comparison of Weekly Indoor PM 1.0 Conc. for B20 and ULSD Buses
116
Figure 4.36: Comparison of Indoor Fine PM with Fuel: ULSD vs. B20 Paired T-Test: T-Test of mean difference = 0 (vs not = 0):
T-Value = -1.18, P-Value = 0.241 T-Value = 0.37, P-Value = 0.714
Time Series Plot of 0.80-1.0 um Sized Particles inside TARTA Buses Time Series Plot of 0.30-0.40 um Sized Particles inside TARTA Buses 16000 Variable 800000 Variable BD BD 14000 UL 700000 ULSD
12000 600000
10000 500000
8000 400000 Data Data 6000 300000
4000 200000
2000 100000
0 0
1 28 56 84 112 140 168 196 224 252 280 1 28 56 84 112 140 168 196 224 252 280 Hour Index (Dec 21-Dec 31) Hour Index (Dec 21 - Dec 31)
117 b. Effect of vehicle fuel on indoor air quality
Figures 4.37 (a-c) show the indoor pollutant concentration levels inside B20 and
ULSD buses collected simultaneously on the same day. The buses were operated on different runs of the same route with a time lag of less than 12 minutes. This ensured similar overall outdoor air quality encountered by the buses. The graphs were drawn with error bars to indicate the sensor sensitivity and the maximum expected error in recording.
For any of the three pollutants, it can be seen that no single bus indicated a higher concentration throughout the run. CO2 concentrations were considerably higher for the
ULSD bus, but this difference could be attributed to it being the first bus on the run and
also having a higher ridership. CO concentrations were higher for the ULSD bus in the
first half of the day, but for the second half, the B20 bus showed a higher concentration
level. SO2 concentrations were very low as compared to health standards and each bus’s
concentration level was easily within the other’s percentage error range. This clearly indicated that a vehicle’s self-pollution is not a significant influencing variable affecting indoor air quality inside air conditioned public transport buses, and the vehicles fuel had little impact on its own indoor air quality. The effect of fuel will only be felt during periods of long idling with doors or windows open. TARTA buses in general do not idle for long duration with their doors or windows open.
118 Impact of Fuel on IAQ: CO2 Concentrations with Error Bars
1600
1400
1200
1000
B20 800 ULSD
600 Concentration (ppm)
400
200
0 7:00 8:00 9:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00 18:00 Time
Figure 4.37 (a): Impact of Fuel on Indoor CO2 Concentrations
Impact of Fuel on IAQ: CO Concentrations with Error Bars
60
50
40
B20 30 ULSD
Concentration (ppm) Concentration 20
10
0 7:00 8:00 9:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00 18:00 Time
Figure 4.37 (b): Impact of Fuel on Indoor CO Concentrations
119 Impact of Fuel on IAQ: SO2 Concentrations with Error Bars
1.2
1
0.8
B20 0.6 ULSD
Concentration (ppm) 0.4
0.2
0 7:00 8:00 9:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00 18:00 Time
Figure 4.37(c): Impact of Fuel on Indoor SO2 Concentrations b. Effect of bus operation route on indoor air quality
This analysis was conducted using four weeks of indoor air quality data collected inside a biodiesel bus in the months of October and November 2006. The bus was scheduled to run on Route 11 during the first two weeks and Route 20 during the last two weeks for both the months. Route 20 was predominantly an east-west run and Route 11 was primarily a north-south run. The instruments were used right after factory calibration for both the months and data quality was expected to be good. Figures 4.38 (a-d) show the effect of route on CO2 and CO concentrations.
The CO2 trend comparison of the two routes indicated that the general trend
throughout the week for each of the routes remained the same. In addition, it was also
observed that even though both the routes face an exponential increase around 8 am and
again around 1 pm, there is a considerable difference in the magnitude of the
120 concentration variation for the two routes and in the pollutant trend for the rest of the day.
The CO2 trend showed clear differences for different routes traveled. It should be noted
that CO2 concentrations are a strongly correlated function of passenger ridership and even for two buses operating on the same route simultaneously; there could be considerable difference in the indoor concentration levels (Figures 4.22 and 4.37 (a)).
Figures 4.38 (c-d) show the concentration range of CO concentration for Route 11 and 20 respectively during the test period. CO concentration variation for the two routes showed a very dissimilar distribution throughout the week for both the routes.
Concentration was prominent between 10 am and 11 pm for Route 11 and 7.30 am and 8 pm for Route 20. The magnitude of concentrations observed for the two routes were also quite different with Route 20 showing a higher concentration than Route 11 throughout the day by an average of 30%.
Weekly Concentration Variation with Route: CO2 Concetration
1800
1600
1400
1200
1000 Route 20: Oct Route 11: Oct 800
Concentration (ppm) 600
400
200
0 4:48 7:12 9:36 0:00 12:00 14:24 16:48 19:12 21:36 Time
Figure 4.38(a): Weekly Concentration with Route: CO2
121 Interval Plot of CO (PPM) for Route 11 95% CI for the Mean 25
20
15
10 CO (PPM)CO
5
0 5:00 6:00 7:00 8:00 9:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00 18:00 19:00 20:00 21:00 22:00 23:00 Time
Figure 4.38(b): Concentration Range Comparison with Route: CO
Interval Plot of CO (PPM) for Route 20 95% CI for the Mean 25
20
15
10 CO (PPM) CO
5
0 5:00 6:00 7:00 8:00 9:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00 18:00 19:00 20:00 21:00 22:00 23:00 Time
Figure 4.38(c): Concentration Range Comparison with Route: CO
122 Weekly Concentration Variation with Route: SO2 Concetration
1.4
1.2
1
0.8 Route 20: Oct Route 11: Oct 0.6 Concentration (ppm) Concentration 0.4
0.2
0 4:48 7:12 9:36 0:00 12:00 14:24 16:48 19:12 21:36 Time
Figure 4.38(d): Weekly Concentration with Route: SO2 Concentration
SO2 concentrations showed a very prominent short term variation and did not show any distinguishable long term variation throughout the test period for both the routes. Even though the concentrations seemed to exhibit a steady range throughout the day for both routes, the Route 20 concentrations were consistently higher than Route 11 concentrations. c. Effect of operational and traffic conditions on PM concentrations
Main effects plots (ANOVA) were drawn for particles using Minitab 15 to qualitatively understand the influence of the various operational and traffic conditions on particulate concentrations. The software plots the influencing factors against the mean concentration obtained at each interval of the variable for the desired dataset and connects the means for ready comparison. It can be seen that concentrations of ultra-fine particles were clearly affected by the bus operation status and door status (similar to
123 Wargo et al., 2002). Higher concentrations were observed both during bus idling, and
open door conditions. These observations are in agreement with the results presented by
Hammond (2007) who suggested that vehicle idling and window/door position would
impact indoor PM concentrations. Presence of a diesel operated bus/truck ahead of the vehicle also increased the fine particulate concentration and had an almost linear trend
with the number of buses/trucks. This was expected as the major source of ultra-fine
particles are diesel operated trucks and buses. Total passengers and presence of gasoline
operated cars did not show a clear correlation with the ultra-fine particulate
concentrations (see Figure 4.39). These results agree with the findings of Fruin et al.
(2003) and Rodes et al. (1998).
Main Effects Plot for PM 0.30-0.40 um Data Means
Total Passengers Cars Bus/Truck 80000
70000
60000
50000 1 2 3 4 5 6 7 8 9 11 13 15 0 1 2 3 4 0 1 2
Mean Bus Door 80000
70000
60000
50000 IDLE RUNNING CLOSED OPEN
Figure 4.39: Effects of Various Factors on Particulate Number Concentration for Fine Particles between 0.30 and 0.40 µm in Diameter Note: Y axis - the number concentration of the particulate X axis – Number of passengers in the bus, cars ahead, bus/trucks ahead, idling or running condition, and closed or open doors
124
Main effects plots were also drawn for analyzing the effects of bus operation
mode and door status on different PM size fractions. The following behavior can be clearly noticed from the plots (Figures 4.40 (a-d)):
• Ultra-fine particle concentration was higher during idling (bus status = 1) and
when the doors were open (door status = 1).
• Coarse particle concentration was higher during running mode and when the
doors were closed.
• Finer particles were affected by outside traffic and coarse particles were affected
by indoor passenger activity.
It can also be seen that there was a strong influence of bus operating status (idling
(1) / running (0)) and door position status (open (1) / closed (0)) on particle size distribution. Vehicle idling and open door status facilitated a significantly higher concentration of smaller particles (diameter between 0.30 and 0.40 µm). As the size of particles increased, their presence inside the cabin was influenced by closed door and vehicle moving conditions (Figures 4.40 (a-d) and Table 4.14). Closer examination showed that the presence of finer particles inside the cabin was enhanced when the door was open, as the finer particles are generated from vehicle exhaust and the open door facilitates air movement from outside to inside of the cabin. Coarse particle concentration inside the cabin increased when the occupancy increased. These were expected to be originating from human dander and their presence inside the cabin was facilitated when the door was closed and there wasn’t any significant air exchange from the outdoors.
125
Main Effects Plot for PM 0.30-0.40 um 4.40(a) Main Effects Plot for 0.65-0.80 um 4.40(b) Data Means Data Means Bus Status Door Status Bus Status Door Status 68000 1050
67000 1000 66000 Mean Mean 950 65000
64000 900
63000 850 0 1 0 1 0 1 0 1
Main Effects Plot for 7.5-10.0 um 4.40(c) Main Effects Plot for >20.0 um 4.40(d) Data Means Data Means
Bus Status Door Status Bus Status Door Status 0.07 2.8 0.06
2.6 0.05
2.4 0.04 Mean Mean 0.03 2.2 0.02 2.0 0.01
1.8 0.00
0 1 0 1 0 1 0 1
Figure 4.40 (a-d): Main effects plot for PM vs. Bus Operation & Door Status
126 Table 4.14: Regression Results: Identification of Significant Variables Affecting PM
Particle Size (μm) Regression Equation Constants
Predictor Coefficient SE Coeff T P Pr Constant 48541 1041 46.62 0.000 Total Occupancy -188.0 163.2 -1.15 0.250 Cars -320.1 226.1 -1.42 0.157 0.001 0.30 – 0.40 Bus -2091 1005 -2.08 0.038 Bus Status (0=Run, 1=Idle) 314 1087 0.29 0.773 Door Status (0=Closed, 1=Open) 4774 1388 3.44 0.001 Constant 469.43 39.31 11.94 0.000 Total Occupancy -1.383 6.161 -0.22 0.823 Cars 16.241 8.536 1.90 0.058 0.003 0.80 – 1.0 Bus -130.39 37.93 -3.44 0.001 Bus Status (0=Run, 1=Idle) 55.02 41.03 1.34 0.180 Door Status (0=Closed, 1=Open) -7.84 52.42 -0.15 0.881 Constant 86.78 13.96 6.22 0.000 Total Occupancy 4.883 2.187 2.23 0.026 Cars 3.020 3.031 1.00 0.319 0.000 2.0 - 3.0 Bus -59.22 13.47 -4.40 0.000 Bus Status (0=Run, 1=Idle) -3.66 14.57 -0.25 0.802 Door Status (0=Closed, 1=Open) -11.63 18.61 -0.62 0.532 Constant 3.8782 0.6799 5.70 0.000 Total Occupancy 0.2967 0.1066 2.78 0.006 Cars -0.0889 0.1477 -0.60 0.548 0.000 7.5 - 10 Bus -2.8824 0.6561 -4.39 0.000 Bus Status (0=Run, 1=Idle) -0.1055 0.7097 -0.15 0.882 Door Status (0=Closed, 1=Open) -0.8309 0.9066 -0.92 0.360 Constant 1.3237 0.2238 5.91 0.000 Total Occupancy 0.07579 0.03508 2.16 0.031 Cars -0.04154 0.04860 -0.85 0.393 0.002 10 - 15 Bus -0.6306 0.2160 -2.92 0.004 Bus Status (0=Run, 1=Idle) -0.1987 0.2336 -0.85 0.395 Door Status (0=Closed, 1=Open) 0.0031 0.2984 0.01 0.992
4.2.3 Development of Indoor Pollutant Concentration Models
The pollutant concentrations obtained from the instruments were presented in 1-
minute intervals. For the purpose of modeling, both the concentration and operational
data (including traffic, and passengers) were processed to obtain 1-hour averages and
were combined with the ambient meteorological data (obtained from the NCDC website
in 1-hr average format). Best subset regression technique was applied for identifying the
pollutant concentration behavior inside the bus using eleven plausible influencing
variables including passenger count, number of cars and buses/trucks ahead of the test bus, test bus operation status (idling (1) / running (0)), door position status (open (1) / closed (0)), in-vehicle temperature (oF), in-vehicle relative humidity (%), ambient
127 temperature (oF), wind speed (miles per hour), and relative humidity (%).
The best model variables were selected and used in multiple regression to
develop the regression models. A total of 8 pollutant specific models were developed that
explain the behavior of the pollutant inside the bus compartment for CO2, CO, SO2, PM1.0 mass concentration, PM2.0 mass concentration, PM0.30-0.40 µm number concentration,
PM0.40-0.50 µm number concentration, PM0.50-0.65 µm number concentration. NO2, and NO models exhibited poor modeling statistics and are not considered in the present study.
More analysis using additional variables would be required to better predict those pollutants. The developed equations are discussed below. The different variable notations used in the modeling are listed below:
TP is the Total passengers
BT is the number of buses or trucks ahead of the test vehicle
CR is the total number of cars in front of the test vehicle
ID is indicator variable for idling, (idling=1, running=0)
DO is the indicator variable for door status (Door open=1, door closed=0)
TR is the indicator variable for heavy traffic (Heavy traffic=1, else ‘0’)
Ti is the average in-vehicle temperature in oF
Ta is the average ambient temperature in oF
RHi is the in-vehicle humidity in %
RH is the ambient relative humidity in %
WS is the average ambient wind speed in mph
CO2: Indoor Concentration Model:
The developed regression model for CO2 is of the form
128 CO2 (ppm) = 443 + 16.7 TP + 170 TR
...... …………. (4.13)
R2 = 81.9%, Adjusted R2 = 81.5%
2 The CO2 model showed a high adjusted R value (81.5%) and the concentrations
were highly influenced by the total passenger numbers and heavy traffic around the bus.
The results are in complete agreement with the experimental analysis as the CO2 concentration tends to increase with number of passengers onboard and with heavy traffic on road. The model using only two variables for prediction also corresponds to the properties of an ideal model of adequacy and parsimony. The model statistics are also very high for a continuous data set corresponding to a non-stationary source. These results are similar to the observations made by Chan (2003) who commented that in vehicle CO2 concentrations are almost solely dependent on the number of passengers traveling in the vehicle.
SO2: Indoor Concentration Model
The SO2 model showed dependence on passenger count, traffic numbers and
traffic indicator values, bus idling status, and indoor and ambient meteorological data.
2 The developed model has a reasonably high adjusted R value of 67.8%. The SO2
concentrations tend to be inversely dependent on idling and are directly correlated to the
number of buses or trucks operating ahead of the vehicle. The model developed for SO2 is of the form
SO2 (ppm) = 0.181 + 0.00820 TP + 0.0633 BT - 0.0441 ID - 0.0958 TR + 0.00514 Ti + 0.00272 Ta + 0.00148 RHi - 0.00143 WS ...... …………. (4.14)
R2 = 70.7%, Adjusted R2 = 67.8%
129
CO: Indoor Concentration Model
CO concentrations showed dependence on heavy traffic around the bus, bus
engine idling, door open status and ambient meteorological conditions. The CO
concentrations tend to be positively affected by the combination of door opening and
presence of heavy traffic near the bus (refer equation 4.15).
CO (ppm) = 2.16 - 14.5 ID + 15.4 DO + 1.92 TR + 0.605 Ti - 1.96 Ta + 0.0973 RH + 1.03 WS ...... …………. (4.15)
R2 = 73.4%, Adjusted R2 = 72.2%
CO concentrations outside the bus compartment are generally higher than in-bus
concentrations and opening the door seems to contribute considerably to the indoor
concentration levels. Similar results were obtained by Esber et al. (2007) whose study showed that in-vehicle CO levels were moderately correlated to out-vehicle CO levels for ventilation settings that allowed for outdoor air intake. Their study also noted a weak correlation between indoor CO concentrations with outdoor levels for a tightly closed cabin which had no outdoor air recirculation. Chan and Chung (2003) and Chan and Liu
(2001) also observed that in-vehicle CO concentrations were greatly affected by the outside concentrations. Longer idling periods usually occurred near the final destinations of the bus routes which are generally associated with low traffic. This leads to lower CO and SO2 concentrations and hence a negative correlation with the variable.
PM1.0: Indoor Concentration Model
Best subset regression was applied for identifying PM1.0 mass and sub-1 μm
130 particle number concentration relationship inside the bus using the ambient PM2.5
concentrations (μg/m3) and ambient meteorological data for visibility (0-10) in addition
to other used in the indoor pollutant gas modeling.
The analysis identified five models with an adjusted R2 value ranging between 72-
81%. The best regression model for the PM1.0 and PM2.0mass concentration consisted of eight influencing variables – passenger count, bus operation status, visibility, ambient
temperature, humidity, visibility, indoor temperature, indoor humidity, door status,
traffic, and ambient PM2.5 concentration. The set of PM mass and number models are
given below. From the equations it can be clearly seen that the position of the door is
very significant for the ultra-fine particles (0.30-0.40 and 0.40-0.50 μm) that are
generally produced outdoors from traffic emissions and reach the bus microenvironment
through the open doors. Also, with an increase in ambient dry bulb temperature and
ambient PM2.5 concentration, there was a subsequent increase in the concentration of
indoor PM mass and number concentrations. These observations are in accordance with the results presented by Hammond et al. (2007) who indicated that particle number concentrations were noticeably affected by ambient background pollution levels. These results also agree with numerous ambient PM studies which showed PM concentration increases with ambient temperature (refer Varadarajan, 2004 for a detailed list).
PM1.0 Mass Concentration Model: 3 PM1.0 (μg/m ) = 11.7 + 0.327 PM2.5A - 0.762 DO - 0.141 Ti - 0.244 RHi + 0.0399 Vis + 0.216 Ta - 0.0129 RH ...... …………. (4.16) R2= 82.2%, Adjusted R2 =80.8%
PM2.0 Mass Concentration Model
3 PM2.0 (μg/m ) = 17.4 + 0.359 PM2.5A + 0.358 TR - 0.205 Ti - 0.484 RHi + 0.100 Vis
131 + 0.302 Ta ...... …………. (4.17) R2=74.1%, Adjusted R2 =72.4%
PM0.30-0.40 µm Number Concentration Model:
PM0.30-0.40 µm = 44676 + 1979 PM2.5A + 2448 DO - 741 Ti - 943 RHi + 167 Vis + 1301 Ta + 32 RH ...... …………. (4.18) R2=82.5%, Adjusted R2 =81.2%
PM0.40-0.50 µm Number Concentration Model:
PM0.40-0.50 µm = 16714 + 611 PM2.5A + 105 DO - 204 Ti - 224 RHi - 38.1 Vis + 312 Ta - 40.8 RH ...... …………. (4.16) R2=77.3%, Adjusted R2 =75.6%
PM0.50-0.65 µm Number Concentration Model:
PM0.50-0.65 µm = 5703 + 178 PM2.5A - 560 DO - 60.4 Ti - 109 RHi + 15.6 Vis + 86.2 Ta - 10.7 RH ...... …………. (4.17) R2=76.2%, Adjusted R2 =74.4% where,
3 PM2.5 A is the ambient PM2.5 concentration in μg/m
Vis is the outdoor visibility which is a measure of ambient PM concentrations
These developed indoor air quality models correspond to a non-stationary receptor location which is affected by multiple and varying sources. The experimental data collection encountered many setbacks including bus breakdowns, battery discharge, power cutoffs by drivers and passengers and other uncontrollable factors. Certain bus operating conditions such as passengers/drivers opening the windows, variable air exchange rates, driver smoking etc. could also not be controlled by the researcher.
132 Considering these and many other limitations, the obtained models provide a desirable predictive/analytical capability. The model statistics could be improved by the addition of ambient pollutant concentrations which currently are not measured by the USEPA network for the Toledo area. Addition of real-time traffic data surrounding the bus (not just ahead of the bus) in different lanes and at intersections, and incorporation of route classification (residential/commercial/industrial) and location of the bus would also improve the model statistics.
133 Chapter Five
Conclusions and Recommendations for Future Research
This thesis successfully studied the environmental impacts of vehicular sources on air quality both inside the bus compartment and in the ambient air. In order to conduct a comprehensive assessment, the research was divided into two sections: (a) study of vehicular exhaust emissions and, (b) indoor air quality inside the public transport bus compartments.
The vehicular emission study analyzed the impacts of the different parameters from the two major influencing categories, vehicle conditions and vehicle operation conditions (Figure 2.1.) on the emission characteristics for different public transport bus fleets for Toledo, Ohio. The indoor air quality study focused on characterizing the pollutant behavior inside the bus compartment and analyzed the concentration trends against important operating conditions such as traffic, passenger counts and activity, bus operation status, in-vehicle comfort parameters and ambient meteorology to study the influence of each variable on the pollutants. This research employed the use of extensive experimental data collection and multiple statistical procedures to study different pollutant behaviors and to analyze the effects of alternative diesel fuels on vehicular emissions and indoor air quality in the buses.
A comprehensive emission testing protocol was developed for characterizing
emission behavior of public transport buses, and over 120 buses were tested in engine
134 idling and real world on-road operation modes. Some key results are presented below.
• Emission comparison for TARTA buses showed that although B20 biodiesel from
rapeseed (20% methyl ester bio-fuel + 80% ULSD) in comparison to ULSD fuel
emitted higher concentrations of nitric oxide (NO) and nitrogen dioxide (NO2) for
300 series, and lower carbon monoxide (CO) concentration for both 300 and 500
series fleets, other factors such as engine rpm, maintenance history, engine
temperatures, and engine technology influence the emissions to a greater degree.
• Regular engine idling mode and higher engine temperatures were found to reduce
vehicular emissions most significantly (up to 30-42%), while performing
preventative maintenance reduced emission concentrations by 15-20%.
• Emission models for seven TARTA fleets were developed for six pollutant gases
– oxygen, carbon monoxide, carbon dioxide, nitric oxide, nitrogen dioxide and
sulfur dioxide which explained an average of 90% of the emission data for each
pollutant. Instantaneous emission models were also developed for the six
pollutants based on real-time on-road test data that explained over 80% of the
variability in the pollutant emissions.
• Fuel rate, engine load, engine temperatures, exhaust temperature, accelerator
pedal position, and engine rpm were found to be the most important variables
affecting the concentrations of the pollutants in the real world operating scenarios.
• It was observed that for the same amount of time in operation, vehicles in idling
mode produced higher average concentrations of CO, NO, NO2 and SO2. This is
an important finding as reducing the idling time or shutting down the vehicle
during long expected durations of idling could substantially reduce the total
135 vehicular impact on the air quality.
This thesis also characterized the indoor air pollutant behavior inside public
transport buses during daily operational runs using continuous data collection of indoor
pollutant concentrations inside the bus microenvironment spanning over 13 months. The study concluded the following:
• Comparisons of indoor and outdoor concentrations showed that outdoor
concentrations strongly influence the indoor concentrations and were
consistently higher than the indoor concentrations during the runs.
• Particulate matter concentration at the rear end of the bus was 2-7 times
higher than the front end concentration.
• Over 95% of the indoor particulates had diameter less than 1 micron. These
particles pose the highest risk to humans as they can travel deep inside the
human lungs and settle there.
• PM1.0 mass comprised of over 40% particles less than 0.40 µm, 25% particles
between 0.40-0.50 µm and 35% particles between 0.50 and 1.0 µm in
diameter.
• Three hourly concentration models were developed for predicting indoor
gaseous air pollutant concentrations using the most significant variables.
Regression models developed for particulate matter using eleven variables
(total passenger counts, cars and bus/trucks ahead, traffic, bus status
(idle/running), door status (open/closed), ambient PM2.5 concentrations,
visibility (as a measure of outdoor particulate highs), temperature, relative
humidity and wind speed) explained approximately 72-81% of the hourly
136 indoor mass and number concentrations of fine and ultra-fine particulates.
• The indoor concentrations of CO2, CO, SO2 and NOx were similar for ultra
low sulfur diesel and biodiesel buses. This could be attributed to a very small
percentage of vehicle’s self-exhaust contamination inside the compartment.
• The effect of the fuel used in the vehicle on indoor concentrations of the
pollutants was observed only during large periods of idling when the bus is
stationary and the doors/windows are open. During an average run, TARTA
buses do not continuously idle for long periods with the doors/windows open.
• PM concentrations also were not dependant on the type of fuel used. The
indoor concentrations were primarily a result of just-outside (roadside)
concentrations and passenger activity.
• The big spikes in indoor concentration of particulates are a result of passenger
activity, and not the outdoor concentrations.
• Variation of all the indoor pollutant levels studied (CO2, CO, SO2) were
dependant on the bus route and location.
• Most of the concentration spikes for all the pollutants were seen during the
morning pullouts and in periods of heavy traffic (around 9 am).
• As public transport buses load and unload the riders, constant opening and
closing of the doors prevents concentration buildup of CO inside the bus, but 2
could lead to higher levels of other air pollutants.
In conclusion, this research was able to identify a new procedure to collect and analyze multi-variable environmental data. This study contributes to the area of
alternative diesel research in the form of identification of important influencing variables
137 affecting pollutant emissions and concentrations. A comprehensive set of models
developed for emissions and in-vehicle air quality add to the knowledge base. This
research is also the first to take a significant step to understand the ultra-fine particulate
behavior inside public transport buses.
5.1 Recommendations for Future Research
The following recommendations are an outcome of the knowledge gained in the course of
conducting the present research. The identified objectives may improve and add further to the knowledge base and fill in the information gaps currently in the field of impacts of
vehicular pollution.
Emission Analysis
• Exhaust particulate analysis needs to be performed to compare the PM
morphology and speciation for different fuels.
• Study of PM emission behavior during different operation modes may lead to
insights into the identification of variables affecting PM emissions.
• Studies should also focus on the application of particulate traps and other
diesel control technology at the tailpipe to study the reduction in pollution.
• The effect of using B50 and B100 fuels on emission behavior is another
important avenue for research.
This study analyzed the effects of different vehicle conditions and vehicle operation conditions on the vehicular emissions. Due to time limitations, the third emission influencing category, ‘vehicle routing’ was not studied in detail. Future work should
identify the impact of route on vehicular emissions.
138 Indoor Air Quality Analysis
• Indoor air quality models can be improved by the incorporation of real-time traffic
condition around the bus (not just in front of it).
• In-vehicle PM species should be studied in detail to characterize their source and
origin.
• Advanced risk assessment analysis needs to be conducted to study the health
impacts from the use of alternative fuels.
• Modeling the seasonal effects on IAQ and incorporation of seasonal behavior may
improve the model prediction.
• Advanced tracer gas analysis needs to be looked into to study the degree of
vehicles’ self pollution.
139
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146
Appendix A
Emission Inventory
147 Table A1: Emission Inventory – TARTA fleets: On-road and Idling Modes
CO NO NO2 NOx CO2 Fuel Bus Mode O2 (%) Tf (°F) Ta (°F) (ppm) (ppm) (ppm) (ppm) (%)
Run1 13.59 811.80 205.92 4.71 210.61 547.93 76.68 5.19 201 Run2 14.27 971.76 252.17 2.77 254.94 542.36 73.33 4.82
Idling 15.99 280.45 180.59 50.03 230.63 189.81 72.63 3.67 ULSD 215 Run1 13.94 962.73 257.61 12.27 269.85 466.23 80.73 5.06 Run2 14.63 1051.47 246.56 13.71 260.24 446.75 81.52 4.53
Series Idling 16.65 186.21 195.78 50.41 246.18 180.65 73.98 3.25 Average
CO NO NO2 NOx CO2 Fuel Bus Mode O2 (%) Tf (°F) Ta (°F) (ppm) (ppm) (ppm) (ppm) (%)
Idling 16.38 291.46 240.83 47.13 287.99 211.37 70.03 3.50 300 Run1 13.52 156.79 202.03 23.62 225.64 421.88 66.32 5.61 Run2 13.82 172.22 217.07 29.10 246.14 431.19 66.52 5.37
B20 Idling 15.33 165.25 480.47 52.29 532.70 211.96 63.47 4.22 304 Run1 13.01 171.26 266.20 12.44 286.87 449.04 66.93 Run2 13.43 178.58 247.74 19.83 274.56 428.43 66.92
Idling 16.16 157.58 258.21 33.27 291.45 213.44 72.29 3.61 305 Run1 13.62 195.25 211.13 17.21 228.31 428.55 65.27 5.42 Run2 14.38 189.15 190.52 23.04 213.57 413.94 65.25 4.93
ULSD Idling 15.95 129.28 298.46 34.78 333.23 222.74 72.00 3.70 307 Run1 13.60 193.79 219.79 18.48 238.27 425.70 66.61 5.58 Run2 13.94 212.78 200.53 23.74 224.26 427.14 68.66 5.43
Series Idling 16.01 186.68 301.25 44.15 345.39 212.82 67.44 3.72 Average
148
CO NO SO2 NO2 NOx CO2 Fuel Bus Mode O2 (%) Tf (°F) Ta (°F) (ppm) (ppm) (ppm) (ppm) (ppm) (%)
402 Run1 14.11 183.93 576.60 29.76 18.91 595.52 498.80 79.35 4.93 Run2 14.52 180.49 526.90 28.41 21.65 548.57 484.27 77.47 4.66
ULSD 419 Run1 13.94 206.46 592.38 22.72 15.79 608.18 514.96 78.59 5.16 Run2 14.68 176.49 532.80 27.32 23.37 556.18 475.87 75.51 4.76
Series Idling 15.49 188.20 1028.83 208.00 103.79 1132.62 263.95 70.60 4.09 Average
O2 CO NO SO2 NO2 NOx CO2 Fuel Bus Mode Tf (°F) Ta (°F) (%) (ppm) (ppm) (ppm) (ppm) (ppm) (%)
Idling 15.39 137.16 424.70 62.65 46.14 470.85 247.53 81.03 4.26 501 Run1 14.26 108.08 424.45 42.98 27.71 452.15 436.08 72.63 5.14 Run2 14.61 98.40 403.01 44.41 30.37 433.39 425.26 74.78 4.86
B20 Idling 15.82 144.77 597.60 103.52 64.36 661.98 236.36 71.25 3.89 509 Run1 14.76 113.30 436.89 36.66 29.22 466.12 435.88 71.38 4.65 Run2 15.38 103.26 365.95 31.58 29.07 395.01 423.59 74.77 4.17
Idling 16.56 232.95 473.13 90.41 64.30 537.40 211.82 72.69 3.32 527 Run1 14.20 168.30 424.13 41.88 26.77 450.91 439.91 71.13 5.02 Run2 15.10 146.06 365.10 39.86 27.93 393.07 430.24 72.88 4.42
ULSD Idling 15.45 159.85 633.17 110.55 68.59 701.70 222.77 71.46 4.13 532 Run1 15.14 124.64 383.59 40.29 34.76 418.36 373.96 67.16 4.29 Run2 15.12 108.84 366.93 38.52 34.43 401.31 377.72 72.23 4.30
Series Idling 16.18 175.41 458.82 77.26 55.12 513.94 226.12 75.41 3.61 Average
149
CO NO SO2 NO2 NOx CO2 Fuel Bus Mode O2 (%) Tf (°F) Ta (°F) (ppm) (ppm) (ppm) (ppm) (ppm) (%)
Idling 17.00 245.02 342.34 94.96 81.03 423.37 234.79 77.46 3.02 600 Run1 14.76 202.02 687.58 86.45 42.94 730.54 452.46 72.94 4.32 Run2 15.22 233.74 649.33 84.51 45.10 694.42 445.69 71.61 4.27
ULSD 611 Run1 14.50 240.16 706.46 87.75 44.10 750.54 464.45 73.21 4.68 Run2 15.09 237.08 631.54 83.23 45.94 677.52 449.03 70.54 4.38
Series Idling 16.57 223.02 387.86 98.70 79.38 467.24 251.21 76.02 3.31 Average
CO NO SO2 NO2 NOx CO2 Fuel Bus Mode O2 (%) Tf (°F) Ta (°F) (ppm) (ppm) (ppm) (ppm) (ppm) (%)
708 Run1 14.76 158.84 567.17 58.43 27.98 595.15 426.79 79.09 4.37 Run2 15.18 102.31 487.25 51.40 26.85 514.11 429.24 77.13 4.21
715 Run1 14.40 126.14 665.16 51.09 13.61 678.76 514.62 78.46 4.90 ULSD Run2 15.21 176.52 516.77 34.13 12.25 529.02 475.56 76.67 4.15
Series Idling 16.36 94.60 527.50 92.64 60.64 588.15 248.64 77.22 3.47 Average
CO NO SO2 NO2 NOx CO2 Fuel Bus Mode O2 (%) Tf (°F) Ta (°F) (ppm) (ppm) (ppm) (ppm) (ppm) (%)
Idling 17.07 200.24 243.55 62.18 52.50 296.02 156.76 70.96 2.95 910 Run1 15.93 204.38 304.12 26.44 20.02 324.13 438.36 82.30 3.69
Run1 15.86 221.81 301.58 25.60 19.19 320.77 453.33 77.41 3.72 915 ULSD Run2 16.23 173.31 274.63 30.83 23.88 298.50 436.72 76.45 3.36
Series Idling 15.89 193.85 259.31 60.12 55.44 314.76 180.97 71.70 3.82 Average
150
Table A2: Emission Inventory – TARTA fleets: Idling Mode
CO NO SO NO NO Bus Fuel Date O (%) 2 2 x Tf (°F) Ta (°F) CO (%) 2 (ppm) (ppm) (ppm) (ppm) (ppm) 2 205 ULSD 5/20/2007 16.59 152.93 222.73 55.46 53.82 276.50 176.74 73.98 3.30 208 ULSD 5/20/2007 16.54 307.10 163.81 61.12 62.27 226.10 184.30 73.09 3.24 215 ULSD 5/19/2007 15.99 280.45 180.59 52.26 50.03 230.63 189.81 72.63 3.67 216 B20 5/22/2007 16.73 135.85 214.90 52.10 46.85 261.74 170.57 67.71 3.22 219 ULSD 5/20/2007 16.41 159.62 210.22 44.63 46.02 256.28 186.94 76.60 3.46 221 ULSD 5/20/2007 15.92 255.48 168.44 48.30 51.73 220.16 201.02 71.16 3.75 223 ULSD 5/19/2007 17.12 196.73 170.87 42.69 43.75 214.61 178.98 80.42 2.92 224 B20 5/22/2007 16.77 163.91 154.39 44.23 41.92 196.27 184.84 67.40 3.17 226 ULSD 5/22/2007 16.77 163.91 154.39 44.23 41.92 196.27 184.84 67.40 3.17 230 ULSD 5/19/2007 16.53 176.99 172.49 42.34 43.38 215.91 188.77 78.32 3.38 233 ULSD 5/19/2007 16.76 113.85 236.32 47.80 43.42 279.75 175.82 77.50 3.18 235 ULSD 5/19/2007 17.06 100.45 214.56 40.62 37.69 252.17 174.19 82.14 2.97 236 ULSD 5/19/2007 16.89 137.73 215.97 60.71 52.97 268.91 182.81 76.90 3.05 239 ULSD 5/20/2007 16.43 128.18 222.40 45.34 45.07 267.47 175.71 76.28 3.44 240 ULSD 5/22/2007 17.54 167.98 188.77 63.78 57.22 245.95 177.09 66.61 2.61 241 ULSD 5/20/2007 16.36 315.96 175.28 71.85 74.67 249.99 171.02 76.64 3.46
400 ULSD 5/13/2007 15.58 140.31 982.39 190.16 93.89 1076.26 261.06 69.97 4.02 404 ULSD 5/17/2007 15.83 294.80 999.22 217.59 115.74 1114.97 232.51 67.07 3.84 405 ULSD 5/19/2007 15.11 174.57 1064.24 206.11 99.79 1164.03 270.53 76.35 4.35 406 ULSD 5/19/2007 15.16 172.29 1081.66 215.49 104.30 1185.96 272.57 76.75 4.32 408 ULSD 5/17/2007 16.48 202.22 829.09 162.06 88.34 917.45 257.13 69.61 3.38 411 ULSD 5/13/2007 14.53 146.26 1191.25 237.63 110.23 1301.48 300.20 67.00 4.80 412 ULSD 5/13/2007 14.79 151.91 1161.91 230.70 107.14 1269.05 284.63 71.64 4.63 413 ULSD 5/17/2007 16.34 210.16 885.76 181.28 97.11 982.90 246.56 70.52 3.45 414 ULSD 5/13/2007 15.00 176.80 1158.16 235.97 111.60 1269.73 275.80 69.65 4.47 415 ULSD 5/17/2007 17.66 249.60 274.30 60.61 50.01 324.33 189.18 67.86 2.53 417 ULSD 5/17/2007 16.06 212.68 934.58 203.05 109.78 1044.34 238.50 67.42 3.66
151
500 B20 5/23/2007 16.69 196.35 421.94 76.93 54.92 476.86 206.13 71.40 3.19 501 B20 5/16/2007 15.39 137.16 424.70 62.65 46.14 470.85 247.53 81.03 4.26 502 B20 5/10/2007 15.18 127.33 520.70 72.21 54.37 575.07 241.90 76.62 4.47 503 B20 5/22/2007 16.70 214.10 393.34 73.25 55.49 448.82 195.92 60.72 3.20 504 B20 5/17/2007 16.71 195.52 452.30 85.03 60.02 512.32 199.41 71.11 3.19 505 B20 5/17/2007 16.85 204.48 360.25 64.55 49.81 410.06 200.92 68.89 3.13 506 B20 5/19/2007 16.32 166.12 540.93 90.45 59.50 600.44 209.22 76.33 3.51 507 B20 5/9/2007 15.41 138.97 412.95 55.68 44.73 457.69 245.53 83.13 4.12 508 B20 5/13/2007 15.91 156.35 594.10 107.98 67.96 662.03 223.07 70.41 3.81 509 B20 5/13/2007 15.82 144.77 597.60 103.52 64.36 661.98 236.36 71.25 3.89 510 B20 5/16/2007 15.88 144.80 368.11 59.00 44.45 412.59 265.18 79.11 3.84 511 B20 5/19/2007 16.51 121.94 464.63 74.91 49.11 513.75 210.84 74.45 3.34 512 B20 5/23/2007 15.63 144.51 455.67 71.04 49.72 505.39 243.16 73.24 4.01 513 B20 5/23/2007 16.00 151.11 403.27 61.27 45.26 448.52 255.61 77.38 3.75 514 B20 5/16/2007 15.80 145.00 459.39 67.04 47.13 506.53 248.32 80.57 3.97 515 B20 5/19/2007 16.41 157.38 492.46 80.10 55.20 547.68 205.42 83.09 3.42 516 B20 5/16/2007 15.94 163.94 503.69 81.12 56.55 560.25 235.48 79.61 3.86 517 B20 5/9/2007 15.48 135.09 402.33 54.93 46.57 448.88 262.80 89.17 4.06 518 ULSD 5/13/2007 16.25 182.20 618.32 115.47 72.24 690.56 196.46 67.24 3.59 519 ULSD 5/16/2007 16.06 167.78 432.61 66.79 49.31 481.92 250.06 75.84 3.74 520 ULSD 5/9/2007 15.15 164.82 434.81 60.62 51.62 486.43 245.42 87.62 4.34 521 ULSD 5/19/2007 16.52 186.30 459.69 80.09 56.22 515.87 210.58 73.75 3.33 522 ULSD 5/16/2007 15.28 171.11 503.06 77.65 54.05 557.13 252.24 81.48 4.32 523 ULSD 5/19/2007 17.11 163.53 377.49 67.85 48.42 425.90 198.25 71.61 2.85 524 ULSD 5/19/2007 17.15 205.93 404.29 72.46 54.21 458.52 207.01 76.31 2.86 525 ULSD 5/13/2007 16.83 196.39 463.98 89.42 62.44 526.45 199.84 72.88 3.12 526 ULSD 5/10/2007 15.98 180.44 425.60 64.03 52.62 478.24 244.23 75.18 3.81 527 ULSD 5/13/2007 16.56 232.95 473.13 90.41 64.30 537.40 211.82 72.69 3.32 528 ULSD 5/13/2007 17.01 237.59 482.71 96.15 68.15 550.88 201.44 70.76 3.01 529 ULSD 5/13/2007 16.92 224.06 449.20 87.24 62.31 511.47 199.88 70.35 3.08 530 ULSD 5/16/2007 15.28 166.54 455.90 62.27 44.53 500.45 281.38 79.09 4.33
152 531 ULSD 5/16/2007 15.69 165.63 467.44 70.85 50.52 517.96 266.07 81.49 4.05 532 ULSD 5/13/2007 15.45 159.85 633.17 110.55 68.59 701.70 222.77 71.46 4.13 533 ULSD 5/9/2007 15.20 173.75 456.49 64.74 52.40 508.89 252.34 85.53 4.22 534 ULSD 5/19/2007 15.43 135.46 557.44 87.42 56.52 613.97 232.95 80.93 4.14 535 ULSD 5/13/2007 16.89 247.76 475.30 96.71 67.84 543.12 209.85 71.27 3.04 536 ULSD 5/13/2007 16.31 203.16 566.43 107.11 69.84 636.28 204.21 69.19 3.51 500H B20 5/23/2007 17.57 195.76 290.12 56.80 46.23 336.35 213.72 71.86 2.59 503H B20 5/22/2007 16.94 211.79 327.70 64.43 51.79 379.49 212.91 61.68 3.04 503PM B20 5/23/2007 17.06 198.74 329.58 59.53 49.36 378.93 198.38 80.54 3.00
600 ULSD 5/15/2007 17.00 245.02 342.34 94.96 81.03 423.37 234.79 77.46 3.02 602 ULSD 5/15/2007 16.14 173.95 464.94 108.98 82.24 547.18 264.04 79.99 3.63 603 ULSD 5/18/2007 17.22 284.05 324.42 102.98 85.32 409.75 233.22 71.37 2.75 604 ULSD 5/15/2007 15.76 200.14 459.50 100.61 78.04 537.55 266.41 75.70 3.93 608 ULSD 5/21/2007 16.92 246.30 343.84 97.47 79.45 423.29 240.05 72.01 3.05 609 ULSD 5/15/2007 16.37 188.64 392.11 87.20 70.19 462.28 268.78 79.61 3.48
702 ULSD 5/15/2007 16.81 98.44 560.88 105.54 68.63 629.49 244.33 79.78 3.16 704 ULSD 5/23/2007 15.73 86.47 625.14 104.15 63.41 688.57 259.77 74.81 3.95 709 ULSD 5/19/2007 17.45 126.86 381.35 85.67 63.77 445.17 214.47 70.55 2.66 710 ULSD 5/15/2007 15.97 82.06 522.07 89.59 58.69 580.77 267.15 79.35 3.69 712 ULSD 5/19/2007 17.05 131.50 416.71 85.54 62.16 478.86 222.23 71.56 2.94 713 ULSD 5/23/2007 15.65 72.65 548.65 85.32 54.46 603.10 274.76 77.91 4.01 716 ULSD 5/15/2007 16.66 81.56 515.41 86.95 56.96 572.39 250.95 78.75 3.28 717 ULSD 5/15/2007 15.80 76.40 629.74 100.45 59.41 689.12 258.80 80.28 3.90 718 ULSD 5/23/2007 16.93 138.09 442.00 87.48 64.36 506.38 222.05 79.15 3.07 719 ULSD 5/15/2007 15.57 52.00 633.08 95.66 54.56 687.67 271.94 80.09 4.08
900 ULSD 5/20/2007 15.19 173.53 287.41 47.61 46.76 334.18 219.42 73.88 4.35 901 ULSD 5/20/2007 15.47 336.26 247.03 77.73 78.50 325.55 168.59 74.35 4.13 902 ULSD 5/20/2007 17.77 144.66 196.09 63.32 53.93 249.99 166.02 70.67 2.44 906 ULSD 5/20/2007 13.99 134.13 309.54 31.79 32.61 342.15 193.76 74.63 5.23
153 910 ULSD 5/20/2007 17.07 200.24 243.55 62.18 52.50 296.02 156.76 70.96 2.95 911 ULSD 5/20/2007 16.13 174.53 296.37 69.26 63.93 360.34 180.30 75.68 3.68 912 ULSD 5/20/2007 15.97 130.04 280.80 59.15 48.36 329.23 191.28 64.89 3.70 914 ULSD 5/20/2007 15.50 201.74 250.73 59.44 59.15 309.81 173.29 75.34 4.11 916 ULSD 5/20/2007 15.90 249.49 222.30 70.63 63.26 285.54 179.34 64.88 3.81
CO NO NO NO Bus Fuel Date O (%) 2 x Tf (°F) Ta (°F) CO (%) 2 (ppm) (ppm) (ppm) (ppm) 2 300 B20 3/14/2007 16.38 291.46 240.83 47.13 287.99 211.37 70.03 3.50 301 B20 3/14/2007 16.25 245.11 202.18 56.21 258.37 181.04 71.70 3.62 302 B20 3/14/2007 15.94 193.27 294.02 40.14 334.16 218.22 67.91 3.86 303 B20 3/15/2007 16.29 212.97 293.45 51.45 344.87 208.24 63.83 3.45 304 B20 3/14/2007 15.33 165.25 480.47 52.29 532.70 211.96 63.47 4.22 305 ULSD 3/14/2007 16.16 157.58 258.21 33.27 291.45 213.44 72.29 3.61 306 ULSD 3/15/2007 15.75 155.38 321.60 41.28 362.88 240.81 70.10 3.86 307 ULSD 3/13/2007 15.95 129.28 298.46 34.78 333.23 222.74 72.00 3.70 308 ULSD 3/14/2007 15.86 148.52 299.50 36.72 336.20 215.41 71.02 3.86 309 ULSD 3/25/2007 16.20 168.03 323.80 48.26 372.08 204.93 52.09 3.54
154
Appendix B
Central Business District (CBD) Cycle
155 Central Business District (CBD) Cycle: A chassis dynamometer testing procedure for heavy-duty vehicles (SAE J1376). The CBD cycle represents a “sawtooth” driving pattern, which includes 14 repetitions of a basic cycle composed of idle, acceleration, cruise, and deceleration modes. The following are characteristic parameters of the cycle:
• Duration: 560 s
• Average speed: 20.23 km/h
• Maximum speed: 32.18 km/h (20 mph)
• Driving distance: 3.22 km
• Average acceleration: 0.89 m/s2
• Maximum acceleration: 1.79 m/s2
Vehicle speed over the duration of the CBD cycle is shown in Figure B1.
Figure B1: CBD Driving Cycle
(Source: http://www.dieselnet.com/standards/cycles/cbd.html)
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