CIVL4560 - Research Project - Lillian Singleton - 41739901

The Growth in Motor Vehicle Emissions
in Metropolitan Brisbane 2000 to 2011
Lillian SINGLETON
CIVL4560
Associate Professor Adam Pekol
25 October 2013

CIVL4560 Research Project Page 2 Lillian Singleton
LETTER OF SUBMISSION
TO: Professor José Torero
Head of School of Civil Engineering,
University of Queensland.
SUBJECT: Submission of Research Project Report
Dear Sir,
I take pleasure in submitting my report, on the research topic of “The Growth in Motor Vehicle
Emissions in Metropolitan Brisbane 2000 to 2011”.
This document has been prepared following work conducted for the subject CIVL4560: Research
Project at the St Lucia campus of the University of Queensland.
Acknowledgment is due to my supervisor - Associate Professor Adam Pekol, my course coordinator -
Dr Badin Gibbes, my research partner - Joellen Athanassiou, library staff, course resources provided
through them, as well as data provided by individual institutions and Government bodies (for use in
the research project).
DECLARATION OF ORIGINALITY:
I assert that this document is of my own creation and contains, as its main content, work which has
not previously been submitted for a degree at a tertiary institution excluding this course, CIVL4560,
at the University of Queensland. The work presented is my own interpretation of the literature and
research data.
Signed,
Lillian Singleton
25th
October 2013

TABLE OF CONTENTS
LETTER OF SUBMISSION...................................................................................................................................2
TABLE OF CONTENTS........................................................................................................................................3
NOMENCLATURE .............................................................................................................................................6
EXECUTIVE SUMMARY.....................................................................................................................................7
1 INTRODUCTION ......................................................................................................................................8
1.1 RESEARCH TOPIC...........................................................................................................................................8
1.2 SCOPE.........................................................................................................................................................8
1.2.1 Study Area.......................................................................................................................................9
1.2.2 Emissions.........................................................................................................................................9
1.3 REPORT STRUCTURE.......................................................................................................................................9
2 LITERATURE REVIEW.............................................................................................................................11
2.1 INFORMATION SOURCES ...............................................................................................................................11
2.2 MOTOR VEHILE EMISSIONS ...........................................................................................................................11
2.3 HEALTH.....................................................................................................................................................11
2.4 REGULATIONS.............................................................................................................................................12
2.5 TRAVEL DEMAND MODELS............................................................................................................................12
2.6 METHODS FOR ESTIMATING EMISSIONS ...........................................................................................................13
2.7 EXISTING MODELS........................................................................................................................................13
2.8 SUMMARY .................................................................................................................................................13
3 APPROACH ...........................................................................................................................................14
3.1 DATA COLLECTION.......................................................................................................................................14
3.1.1 Trip Generation.............................................................................................................................14
3.1.2 Trip Distribution ............................................................................................................................14
3.1.3 Trip Assignment ............................................................................................................................15
3.1.4 External Trips ................................................................................................................................15
3.1.5 Emission Forecasting.....................................................................................................................15
3.2 MODELLING ...............................................................................................................................................16
3.2.1 Road Network ...............................................................................................................................16
3.2.2 Zone System..................................................................................................................................17
3.2.3 Screenline Analysis........................................................................................................................18
3.3 EMISSION CALCULATIONS .............................................................................................................................19
3.4 TEMPORAL TRAFFIC DISTRIBUTION .................................................................................................................19
3.5 MAPPING ..................................................................................................................................................19
4 RESULTS................................................................................................................................................20
4.1 EMISSIONS.................................................................................................................................................20
4.1.1 Vehicle Type..................................................................................................................................20
4.1.2 Fuel Type.......................................................................................................................................22
4.1.3 Time of Day...................................................................................................................................23
4.1.4 Spatial Distribution .......................................................................................................................25
4.1.4.1 VOC ..........................................................................................................................................................25
4.1.4.2 CO2-e .........................................................................................................................................................28
4.2 VKT .........................................................................................................................................................29
4.3 SCREENLINE ANALYSIS...................................................................................................................................32

5 DISCUSSION..........................................................................................................................................34
5.1 EMISSIONS.................................................................................................................................................34
5.1.1 Vehicle Type..................................................................................................................................35
5.1.2 Fuel Type.......................................................................................................................................36
5.1.3 Vehicle Age ...................................................................................................................................36
5.1.4 Time of Day...................................................................................................................................37
5.1.5 Spatial Distribution .......................................................................................................................38
5.1.5.1 VOC ..........................................................................................................................................................38
5.1.5.2 CO2-e .........................................................................................................................................................38
5.2 VKT .........................................................................................................................................................38
5.3 SCREENLINE................................................................................................................................................39
6 CONCLUSIONS ......................................................................................................................................40
7 RECOMMENDATIONS ...........................................................................................................................41
ACKNOWLEDGMENTS....................................................................................................................................42
REFERENCES...................................................................................................................................................43
APPENDICES...................................................................................................................................................45
APPENDIX B – SUPPORTING RESULTS........................................................................................................................45
7.1.1 Emissions.......................................................................................................................................45
7.1.1.1 Time of Day ..............................................................................................................................................45
7.1.1.2 Vehicle Type.............................................................................................................................................46
7.1.1.3 Vehicle Age...............................................................................................................................................47
APPENDIX B – SPATIAL DISTRIBUTIONS FOR MODELLED EMISSIONS.................................................................................48

LIST OF FIGURES
Figure 1 – Study Area.......................................................................................................................................9
Figure 2 – Modelled Road Network................................................................................................................17
Figure 3 – Zone System ..................................................................................................................................18
Figure 4 – Increase in Emissions per Pollutant from 2000 to 2011..................................................................20
Figure 5 – Growth of CO2-e Production by Vehicle Type..................................................................................21
Figure 6 – 2011 Pollutant Production by Vehicle Type ...................................................................................22
Figure 7 – Growth in Number of Vehicles in Fleet by Fuel Type between 2000 and 2011...............................23
Figure 8 – Growth in Emissions by Time of Day between 2000 and 2011 .......................................................24
Figure 9 – Spatial Distribution of VOC for 2000..............................................................................................25
Figure 10 – Spatial Distribution of VOC for 2011 ............................................................................................26
Figure 11 – Difference in Spatial Distribution of VOC between 2000 and 2011 ..............................................27
Figure 12 – Difference in Spatial Distribution of CO2-e between 2000 and 2011 .............................................28
Figure 13 – Spatial Distribution of VKT for 2000.............................................................................................29
Figure 14 – Spatial Distribution of VKT for 2011.............................................................................................30
Figure 15 – Difference in Spatial Distribution of VKT between 2000 and 2011...............................................31
Figure 16 – Vehicle-Kilometres of Travel for 2000 and 2011 ..........................................................................32
Figure 17 – Growth between 2000 and 2011..................................................................................................34
Figure 18 – Composition of Fleet for 2001 and 2011 ......................................................................................35
Figure 19 – Vintage of Vehicles in Fleet for 2001 and 2011 ............................................................................37
Figure 20 – Difference in Spatial Distribution of CH4 between 2000 and 2011 ...............................................48
Figure 21 – Difference in Spatial Distribution of N20 between 2000 and 2011 ...............................................49
Figure 22 – Difference in Spatial Distribution of NOx between 2000 and 2011 ..............................................50
Figure 23 – Difference in Spatial Distribution of CO between 2000 and 2011.................................................51
Figure 24 – Difference in Spatial Distribution of PM10 between 2000 and 2011 .............................................52
Figure 25 – Difference in Spatial Distribution of SO2 between 2000 and 2011................................................53
Figure 26 – Difference in Spatial Distribution of CO2 between 2000 and 2011 ...............................................54
LIST OF TABLES
Table 1 – Growth in Emissions by Vehicle type from 2000 to 2011 ................................................................20
Table 2 – Growth in Number of Vehicles by Fuel Type ...................................................................................22
Table 3 – Growth in Emissions by Time of Day from 2000 to 2011 .................................................................23
Table 4 – Screenline Summary.......................................................................................................................33
Table 5 – 2000 Emissions by Time of Day.......................................................................................................45
Table 6 – 2011 Emissions by Time of Day.......................................................................................................45
Table 7 – 2000 Emissions by Vehicle Type......................................................................................................46
Table 8 – 2011 Emissions by Vehicle Type......................................................................................................46
Table 9 – 2011 Pollutant Production by Vehicle Type.....................................................................................46
Table 10 – Fleet Composition by Vehicle Vintage...........................................................................................47
LIST OF EQUATIONS
Equation 1 – Conversion of Carbon Monoxide, Nitrogen Oxides and Unburnt Hydrocarbons (Kašpar 2003)..12

NOMENCLATURE
TERM DEFINITION
ABS Australian Bureau of Statistics
AADT Annual Average Daily Traffic
BCC Brisbane City Council
DETE Department of Education, Training and Employment
DTMR Department of Traffic and Main Roads
EPA Environmental Protection Agency
SEQ South-East Queensland
VKT Vehicle-Kilometres of Travel

EXECUTIVE SUMMARY
Motor vehicle emissions across Metropolitan Brisbane continue to increase with population and car
ownership, despite recent investment in major new public transport infrastructure. The aim of this
project is to quantify and map the growth in vehicle emissions across the Brisbane Metropolitan
region between 2000 and 2011, based on a travel demand/vehicle emissions model developed for
the EPA in 2003. Output from the updated model for 2011 was compared to the equivalent 2000
estimates produced by the earlier study to quantify the change in vehicle emissions over the
intervening years.
This model was updated for the Brisbane Metropolitan region with 2011 data, with results garnered
showing the increase in both VKT and vehicle emission production.
To complete this task, after researching available literature on the topic, data was collected to be
used as input for the updated model. Data was collected in the categories of trip generation, trip
distribution and assignment, external trips, and emission estimation.
The spatial distribution of these emissions were concentrated around major road links and high
activity areas such as the Brisbane CBD. These emissions are influenced by many variables, with the
distribution across the network non-uniform and complex. The change in pollutant amount and
spatial distribution is a function of changes in technology, fleet composition, fleet vintage,
population growth, employment, socioeconomic factors and population movement around the
region.
While the model is not a complete update of the previous work, the results obtained by this study
are applicable to the year 2011, with a comparison of the work adding value and context.
Recommendations for continuation and improvement of the project include:
1. Establishing a plan to update the model.
2. Extending the boundary of the study area to match previous work.
3. Extending the model to include other motor vehicles in the network.
4. Extending the model to produce output for different emission production types.
5. Modifying the model to simulate different traffic demands.
6. Updating the model to reflect changes in technology and fuel.
7. Conducting research into the affect on motor vehicle usage due to improvements in
infrastructure, public transport and government initiatives.
8. Calculating emissions using the equations used by previous work.
9. Refining the model to reflect the vehicle fleet with a higher level of detail.
10. Continuing to investigate and document the improvements and changes to the air pollutant
inventory, to fill gaps in our understanding.

1 INTRODUCTION
1.1 RESEARCH TOPIC
The Environmental Protection Agency (EPA), in association with the Brisbane city council (BCC),
updated the air pollutant emissions inventory (including motor vehicle emissions) for the South-East
Queensland (SEQ) region for the year 2000. The study covered by this report, created in 2013,
relates to the update of this inventory for the Brisbane Metropolitan region for motor vehicle
emissions with 2011 data.
Motor vehicle ownership rates continue to increase in Metropolitan Brisbane (Australian Bureau of
Statistics 2013), as well as the corresponding emissions produced by such modes of transportation.
The emissions produced by vehicles on the Brisbane Metro road network can be quantified and
mapped using traffic demand modelling. Modelling and forecasting of motor vehicle emissions is an
important exercise, as governing bodies such as regional Councils rely on these forecasts to inform
policy and planning.
In this study, an existing model for SEQ, last updated by Adam Pekol Consulting, will be partially
updated for the Brisbane Metropolitan area. Results for the year 2011 compared with the output
produced in the earlier study for the year 2000.
1.2 SCOPE
The study is focused on the Brisbane Metropolitan area, discussed below, calculating Vehicle-
Kilometres of Travel (VKT) and emissions along the road network.
Trips between origins and destinations are modelled, using information collected in the categories of
trip generation, trip distribution and assignment, external trips, and emission forecasting.
Estimates of vehicle volumes are modelled, using a travel demand model, then VKT and emissions
calculated on the network using this amount of traffic for each link.
The work is completed for the following variables:
• Five different vehicle classes, including:
• Passenger cars
• Motorcycles
• Light commercial vehicles
• Rigid vehicles
• Articulated vehicles
• Six different road hierarchy categories
• Different fuel types
• Different vehicle ages/vintages
• Five time periods over an average weekday
• Five criteria pollutants
• Three greenhouse gases, as well as these gases in the form of a weighted equivalent

1.2.1 Study Area
The extent of the study area is presented below in Figure 1, which includes the Brisbane, Redland
and Logan districts, as well as parts of the Ipswich and Moreton bay region.
Figure 1 – Study Area
1.2.2 Emissions
Several different emissions are examined in this study, with five criteria pollutants and three
greenhouse gases, as well as CO2 equivalent (CO2-e) – a measure of all three greenhouse gases
studied as a comparable amount of CO2.
Methane (CH4), nitrogen dioxide( N20), carbon dioxide (CO2) are greenhouse gas emissions. Criteria
air pollutants studied include carbon monoxide (CO), mono-nitrogen oxides (NOx), volatile organic
compounds (VOC), particulate matter (PM10) and sulphur dioxide (SO2). These pollutants have
negative impacts on both health and the environment, with an effect noted in multiple studies for
both cardiac deaths and respiratory hospitalisations (Brunekreef 2002).
1.3 REPORT STRUCTURE
Following the definition of the project topic and the scope of the work, an introduction to the
subject being studied is presented in the form of a literature review. The literature review analyses

the information available on the topic, discussing both general information as well as information
specific to the undertaking of the work done for this project.
The methodology is then presented, detailing the steps taken to complete the task. These steps are
explained in relation to the theory researched by background material, and alternative approaches
evaluated where possible.
Results obtained from the modelling of volumes and calculation of VKT and emissions are offered,
via the use of graphs, tables and maps.
Subsequently, the discussion analyses the results, allowing explanation of the values, including
justification and analysis. This analysis uses knowledge background research as well as insights
gained while obtaining the results.
The work is concluded with a review of findings gained from this study, accompanied by
recommendations to guide and enhance future work on the topic.

2 LITERATURE REVIEW
2.1 INFORMATION SOURCES
To provide background and to highlight the importance of this work, books, journal articles are
reviewed in this document. Topics covered include reports covering the topics of vehicle emission
types and sources, technological advances and regulations that reduce emissions, as well as the
implications for human health.
Motor vehicle emission inventories are a valuable resource, such as the report associated with the
SEQ Queensland Model (Adam Pekol Consulting 2003), in addition to reports produced for Councils
around Australia and internationally.
2.2 MOTOR VEHILE EMISSIONS
The production rate of motor vehicle emissions is expected to rise, with forecasts predicting levels to
increase faster than population in SEQ (EPA Queensland 2004), while the Queensland population
itself is increasing faster than the national average (Queensland Treasury and Trade 2013).
Vehicle emissions can be categorised into different types depending on how they are produced.
These categories include resting loss, diurnal, hot-soak, running loss, cold start, hot start and hot
running (Adam Pekol Consulting 2003). This study only analyses running losses, which contribute a
significant amount to the total emissions produced by motor vehicles (Adam Pekol Consulting 2003).
Road vehicles are the main contributors to levels of volatile organic compounds (VOC) and mono-
nitrogen oxides (NOx) in urban airsheds (EPA Victoria 1997). Emissions from vehicles are generally
short-lived and impact regions as small as the street on which they are emitted (Bigazzi 2011).
Transport models like the one being updated in this project therefore have an appropriate scale
when plotting emissions, with regions split into cells using a 1km by 1km grid.
The emissions analysed in this study are split into two categories; greenhouse gas emissions and
criteria air pollutants. Methane (CH4), nitrogen dioxide( N20), carbon dioxide (CO2) are greenhouse
gas emissions, with CO2 equivalent (CO2-e) being a measure of all three of these as a comparable
amount of CO2 (Gohar 2007). Criteria air pollutants studied include carbon monoxide (CO), mono-
nitrogen oxides (NOx), volatile organic compounds (VOC), particulate matter (PM10) and sulphur
dioxide (SO2). These pollutants have negative impacts on both health and the environment, with an
effect noted in multiple studies for both cardiac deaths and respiratory hospitalisations (Brunekreef
2002).
2.3 HEALTH
Negative effects associated with motor vehicle emissions are well documented by the literature,
with articles citing the detriment to human health as early as 1955 (Hitchock 1955). Health
complications associated with motor vehicle emissions include a higher incidence of asthma
(particularly in children) (Gasana 2012), bronchial disorders (D'Amato et al 2005) and high-risk births
(Wilhelm 2004). Evidence also suggests a relationship between mortality and air quality in relation to
the total suspended particulates (Brindle 1999). Air quality in Queensland is currently monitored and
reported annually, with the 2011 report testing compliance with standards and analysing pollutant

distributions and trends (Department of Science, Information Technology, Innovation and the Arts
2012).
One of the emissions associated with toxic effects is VOC, which contributes to smog, with effects on
human health ranging from “carcinogenesis to neurotoxicity” (OECD 1995). Unlike CO2, which rises
into the atmosphere after production, VOCs remain in the area surrounding its origin (OECD 1995).
This has direct implications to human health, with large numbers of people residing in areas such as
cities, which have concentrated emission production.
2.4 REGULATIONS
Air quality standards are currently in use across Australia, with different pollutants and their health
impacts, and countermeasures to control motor vehicle emissions (Brindle 1999). Currently, the
Australian Design Rules are the national standards covering motor vehicle emissions, initially coming
into law in 1969 (Department of Infrastructure and Transport 2013).
Several policy decisions have been made to enforce improvements in technology, to reduce the
amount and effects of emissions produced by motor vehicles. Leaded petrol, which was accountable
for approximately 90 per cent of airborne lead in Australia's urban areas, was phased out between
1986 and 2002 (Department of the Environment and Heritage 2001). Catalytic converters where
introduced in 1986, able to remove up to 90 per cent of noxious gases present in car exhaust (Kašpar
2003). Looking toward the future, the increased use of natural gas vehicles has benefits for both the
environment and human health (Fark 2002).
Three-way catalytic converters aim to reduce CO by converting to CO2 (Equation 1), NOx to nitrogen
and oxygen, and unburnt hydrocarbons (HC) to carbon dioxide and water (Koltsakis 1997).
2CO + 2NO → 2CO2 + N2
2H2 + 2NO → 2H2O + N2
HC + NO → CO2 + H2O + N2
Equation 1 – Conversion of Carbon Monoxide, Nitrogen Oxides and Unburnt Hydrocarbons (Kašpar 2003)
These processes are most efficiently conducted when at a raised temperature (Farrauto 1999), with
pre-heated catalytic converters greatly minimising cold-start emissions (Ramanathan 2004).
2.5 TRAVEL DEMAND MODELS
Data such as traffic data, population and employment are used as input for travel demand models to
produce output such as traffic forecasts, estimating the future traffic demand on a road network.
Road networks are represented via a "node and link" model, with nodes representing road junctions
and links representing roads linking nodes. (Department of Environment and Conservation 2010)
Different model types exist, including four-step models and activity-based models. The four-step
model approach is the traditional approach and is widely used (Adam Pekol Consulting 2003,
Martens 2011), and is utilised in this project. The first step determines the frequency of trip
attractions (destinations) and productions (origins), using the statistic likelihood of occurrence

corresponding with the zone characteristics (e.g. economic factors, land use). The next step connects
origins with destinations, and then these trips are distributed between different modes of
transportation (e.g. car, freight). The final step allocates these trips to a route, for which different
approaches exist. One approach is to give a certain amount of priority to highways and freeways, to
try and mimic the behaviour of traffic on the road network, other approaches may include taking the
shortest path in terms of time or distance (Martens 2011).
2.6 METHODS FOR ESTIMATING EMISSIONS
In this project, the travel demand model (using the EMME/2 modelling suite) VKT to estimate
emissions for individual cells in the grid. These emissions are produced separately for different
vehicle classes, road hierarchies, fuel types, vehicle ages, various times of the day, pollutant types
and other factors (Adam Pekol Consulting 2003). Activity data can be taken from many sources,
including the Australian Bureau of Statistics, government departments or surveys can be
commissioned (Department of Environment and Climate Change NSW 2003).
2.7 EXISTING MODELS
Various studies have been commissioned by governing bodies to quantify the motor vehicle
emissions produced in their area of authority. In Australia, many regional Councils with jurisdiction
over metropolitan areas have developed or commissioned emission inventories to identify areas
which require action. Reports from across Australia use the findings from investigations to diminish
the amount and effect of pollutants via policy changes, as well as to indicate changes in emission
production due to changes in the system (e.g. demographic changes, new roads) (Department of
Environment and Conservation 2010).
Models from the states of Victoria, New South Wales, Western Australia and South Australia have all
been updated and/or extended with newer data within the last decade (in some cases, more than
once), but the Queensland inventory has not been reassessed since its last update in 2003.
2.8 SUMMARY
Understanding of the changes in the emissions due and the impacts is incomplete and untested
against new information. The method and importance of motor vehicle emission inventories has
been established, with implications for human health, policy and planning. This update is required to
continue to be a valuable resource for the transport engineering sector in the region and maintain
standards consistent with similar ventures across Australia. Upon completion of this update, the gap
left of the lack of update will be filled for the Brisbane Metropolitan region.

3 APPROACH
3.1 DATA COLLECTION
The objective of this study is to model the vehicle emissions produced in the Brisbane Metropolitan
region. To complete this task, after researching available literature on the topic, data was collected
to be used as input for the updated model. Figures were collected in the categories of trip
generation, trip distribution and assignment, external trips, and emission forecasting.
3.1.1 Trip Generation
Trip generation data pertains to the characteristics of a region, and was collected from various
sources. Data regarding the population, number of households, white/blue collar workers,
dependents, industry of employment and visitors on census night were collected using from the
Australian Bureau of Statistics (ABS). Information was gathered for the Brisbane Statistical Division
using the TableBuilder online tool.
Gathering and implementing this data provides the model with a way to simulate the geographic
variability of the study area in terms of socioeconomic conditions, population density and
movement, compared to the 2000 model.
By using the ABS 2011 Census data, the information used was the best available quality for the area,
with both excellent level of detail, spatial accuracy and quality, as well as a representation of
information from a significant proportion of the population.
Values for enrolments of students in preschool, primary, secondary and tertiary education, as well as
TAFE students were collected. For state schools in Queensland, number enrolments for 2011 were
obtained from the Department of Education, Training and Employment (DETE) website; private
school enrolment data was provided by DETE. Tertiary and TAFE enrolments that were not publically
available online were obtained by contacting each organisation. Locations for each campus was
subsequently ascertained online, assuming locations had not changed since 2011 and 2013, and the
subset of data that corresponded to the study area was used as input for the model. For those
institutions for which data could not be obtained, forecasts from the 2000 model were used.
Using the data provided by DETE as well as each individual institution, exact values for both the
study period and location were obtained. In the cases where forecasts had to be used, while not
ideal, these values are the good solution to the problem. By using forecasts based off the 2000
model, the 2011 work contains less sources of error than it would if other approximation methods
were employed.
3.1.2 Trip Distribution
Trip distribution involved the assessment of the current road network and extrapolating changes to
infrastructure between 2000 and 2011. These changes were primarily limited to network-level
modifications, such as the duplication of the Gateway Bridge (Sir Leo Hielsher Bridges) and the
creation of the Clem7 tunnel system. Due to the model not including pedestrian, cyclist or public
transport movements, changes in infrastructure such as the construction of the Eleanor Schonell,
Goodwill and Kurilpa Bridges were not investigated or modelled.

Figures for Annual Average Daily Traffic (AADT) were obtained for these from a variety of sources.
Brisbane City Council (BCC) provided data for all the bridges they operate, bar the Go Between
bridge, including the Story, Victoria, William Jolly (Grey Street), and Walter Taylor bridges. The
Clem7 tunnel network data was obtained via their website, with current and historic 28 day rolling
data available to the public. For bridges where data was not available or free to be released, the
DTMR Traffic Census for adjacent intersections was used to estimate travel.
Obtaining data from the operators of the roads was the optimal approach, however for the bridges
where no data was available to be released, estimating using the Department of Traffic and Main
Roads (DTMR) Traffic Census delivered reasonable estimates. In the absence of other data, these
estimates offered a realistic value, with many of the automated traffic counters residing where the
majority of traffic travelled directly to the bridge in question.
3.1.3 Trip Assignment
Trips were assigned between origins and destinations, with movements produced between zones
(interzonal), inside individual zones (intrazonal), as well as outside of the study area (external).
Observed average trip lengths were used to calculate the length of each trip, which were used in
later processes to calculate emissions produced by each of these trips. The assignment of trips
attempts to replicate the movements of traffic between productions and attractions, such as trips to
work or school, to the shops, and back home. Number of trips are estimated using the data obtained
above in trip generation and trip distribution. Trip generation is based on characteristics such as
economic status and level of employment in the population across the Brisbane Metropolitan
region. These trips are then assigned to links on the network, with routes with higher capacities
(such as freeways or highways) assigned a larger proportion of traffic, to mimic the behaviour of the
population.
Using the characteristics of an area to determine the amount and type of travel generated is the
best method of trip assignment, with data used to project these movements being far more accurate
than individuals recording their trips. Any trip data that isn’t modelled or extrapolated is likely to
experience bias that would severely underestimate the amount of trips, and incorrectly distribute
them spatially.
3.1.4 External Trips
External station data was obtained using the DTMR Traffic Census data, available to the public via
the website. Data for locations previously used in the 2000 model was obtained, as well as other
roads controlled by the department that crossed over the boundary of the study area.
By using the data available from the DTMR was by far the best option available, with up-to-date and
consistent data obtained directly. The data was retrieved in the same fashion as it was in the
previous study, minimising errors that could have been obtained by sourcing data from differing
sources in different ways.
3.1.5 Emission Forecasting
Calculating emissions produced by motor vehicles requires the composition of the fleet, in terms of
proportion of vehicles in the fleet of particular vintage and type. This data was obtained from the

ABS from the 2001 and 2011 Censuses, being very reliable and indicative of reality, with a minor
concession made for the 2000 fleet data being the Census data from the next year. The error
introduced by this is acceptable considering the alternative solutions providing less reliable data
than the ABS, and introducing a different source than the 2011 data. In all instances, data collection
aimed to be from the same source for both years to eliminate discrepancies due to differing
resources and methods of collection.
Emission factors, emission equations and consumption rates for the pollutants analysed were
derived from those available from COPERT. COPERT has specialised in estimating emissions for over
a decade, and as such, is the best resource for calculating emissions for this project (COPERT 2013).
3.2 MODELLING
The EMME/2 model was updated to reflect changes in the region between 2000 and 2011. This
involves inputting the work done in Section 3.1 above, including implementing changes in the road
network and population characteristics. Analysis was undertaken for 5 time periods to make up one
24 hour period, to model vehicle numbers more accurately. These time periods cover morning peak
period (7am to 9am), daytime (9am to 4pm), afternoon peak period (4m to 6pm), evening (6m to
10pm), and night-time hours (10pm to 7am).
EMME/2 was chosen for this exercise due to the previous 2000 model having been created using this
program, allowing adjustments to this model, as opposed to a complete rebuild using another
software package.
3.2.1 Road Network
Figure 2 below maps the road network modelled in the study area, including motorways, highways,
arterial, sub-arterial, and higher order collector/district roads. Roads are modelled with information
as to number of lanes and posted speed in order to more accurately reflect the influence of said
factors on route choice.

Figure 2 – Modelled Road Network
3.2.2 Zone System
Figure 3 displays the zone system used in the four-step model, of a similar nature to that which was
used for the 2000 model. The area is broken up into 253 suburb-based zones, used to group trip
origins and destinations. External zones to areas outside the area boundary were modelled, but are
not shown below.

Figure 3 – Zone System
3.2.3 Screenline Analysis
A screenline analysis is used to ascertain the usefulness and accuracy of the model. Traffic is counted
over a ‘line’, with a set number of crossing points, and compared with those produced by the model.
In this study, the geographical feature of the Brisbane River is a perfect example, as it intrudes a fair
way into the study area, cutting the busiest traffic areas into rough halves. Using the principle of
equilibrium, flows in equal flows out, traffic moving around the network is expected to be relatively
comparable moving each way across the river. If the model does not model does not produce a
reasonable count across the screenline; error checking or calibration would be required depending
on the magnitude of the discrepancy.
Using a screenline test is a relatively straightforward way to determine the models precision. While
other checks could be conducted, this test is an excellent indicator for the correctness of the model
across many points along an important structure in the study area.

3.3 EMISSION CALCULATIONS
Calculating emissions produced by motor vehicles was accomplished using Microsoft Excel using
data sources discussed in Section 3.1.5 above. Files were produced for each time period for each
year, with emissions of each pollutant calculated for each link in the network.
Unlike the previous model which involved a much more complicated analysis involving more
emission types, only running emissions were calculated, using equations that could be implemented
using Microsoft Excel. Utilising a common program well known by the undertakers of this task was
advantageous for the ease of creation, troubleshooting and checking of the calculations. Other
options would have completed the same task, but would hold no advantage considering the sunk
cost of time taken to learn the program.
3.4 TEMPORAL TRAFFIC DISTRIBUTION
Since the model produces average weekday data, to adjust for weekend traffic use, stationary traffic
counts with data for weekends and weekdays was required. Using this data, average weekend traffic
as a proportion of average weekday volumes were produced and applied to the results obtained.
3.5 MAPPING
For this step, the end goal was to achieve a geographically referenced map showing the emission
production distribution across the study area. Emissions and VKT for each link were calculated and
imported to Geographic Information System (GIS) software. The roads on which VKT and emissions
exist were split into 1km by 1km grid cells for producing spatial renditions of vehicle emissions as
predicted by the model.
While the GIS software ‘ArcGIS’ was used for this step (due to its use by industry and availability to
University of Queensland students) other GIS software packages could achieve the same result.

4 RESULTS
4.1 EMISSIONS
Figure 4 below displays the growth in emission obtained for each pollutant between 2000 and 2011.
Total CO2-e emissions calculated were 0.79 and 1.87 million tonnes per annum for 2000 and 2011
respectively. This is equivalent to a 138% growth in CO2-e emissions over the study period.
Figure 4 – Increase in Emissions per Pollutant from 2000 to 2011
4.1.1 Vehicle Type
The growth in emission production between 2000 and 2011 for each vehicle type are shown below
in Table 1.
Growth in Emissions (%)
Pollutant
Passenger
Cars
Motorcycles
Light
Commercial
Rigid Articulate
CH4 4 283 -11 129 233
N20 66 12415 123 164 366
NOX -23 242 34 304 483
CO 184 316 172 14951 608
VOC 198 338 173 441 1980
PM 171 297 101 416 638
SO2 96 127 43 557 1483
CO2 134 535 123 388 590
CO2-e 133 585 122 385 588
Table 1 – Growth in Emissions by Vehicle type from 2000 to 2011
CH4 N20 NOX CO VOC PM10 SO2 CO2 CO2-e
Growth (%) 6 77 -8 207 215 162 99 139 138
-50
-
50
100
150
200
250
Growth(%)
Pollutant
Increase inEmissions per Pollutant
between 2000 and2011

Figure 5 – Growth of CO2-e Production by Vehicle Type
Figure 6 below displays the composition of 2011 emissions produced by each vehicle type, for each
pollutant.
Values for each pollutant by vehicle type (as well as total emissions) for 2000 and 2011 can be found
in Table 7, Table 8, and Table 9 in Appendix A, Section 7.1.1.2.
Passenger
Cars
Motorcycles
Light
Commercial
Rigid Articulate
CO2-e 133 585 122 385 588
0
100
200
300
400
500
600
700Growth(%)
Vehicle Type
Growthof CO2-e Productionby Vehicle Type

Figure 6 – 2011 Pollutant Production by Vehicle Type
4.1.2 Fuel Type
Table 2 and Figure 7 below display the growth in vehicle numbers by fuel type between 2001 and
2011.
Growth in Number of Vehicles by Fuel Type (%)
Vehicle Type Unleaded Diesel LPG Hybrid Ethanol Blend Biodiesel
Passenger Cars 7 188 -4 22804 2621 0
Motorcycles 70 0 0 0 4229 0
Light Commercial -4 145 90 0 2212 0
Rigid -63 63 -83 0 2484 10698
Articulated 0 42 0 0 0 9589
Bus -1 11 -58 0 1809 6622
Other Trucks -72 187 -100 0 448 0
Table 2 – Growth in Number of Vehicles by Fuel Type
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
CH4 N20 NOX CO VOC PM SO2 CO2 CO2-e
PercentageofTotal
Pollutant
2011 Pollutant Productionby Vehicle Type
Articulate
Rigid
LightCommercial
Motorcycles
Passenger Cars

Figure 7 – Growth in Number of Vehicles in Fleet by Fuel Type between 2000 and 2011
4.1.3 Time of Day
Table 3 below displays the growth in emission obtained for each pollutant between 2000 and 2011.
Total CO2-e emissions calculated were 0.79 and 1.87 million tonnes per annum for 2000 and 2011
respectively. This is equivalent to a 138% growth in CO2-e emissions over the study period.
Values for each pollutant by time of day (as well as total emissions) for 2000 and 2011 can be found
in Table 5 and Table 6 in Appendix A, Section 7.1.1.1.
Displayed in the table below, is the percentage change in emissions between 2000 and 2011.
Growth in Emissions (%)
Pollutant
Morning
Peak
Daytime
Afternoon
Peak
Evening Night-time Total
CH4 6 5 -19 19 20 6
N20 81 74 34 98 103 77
NOX 8 -13 -35 -8 12 -8
CO 161 212 104 434 205 207
VOC 136 226 90 640 191 215
PM10 184 125 98 313 210 162
SO2 107 89 52 129 133 99
CO2 146 131 81 168 178 139
CO2-e 145 130 80 167 177 138
Table 3 – Growth in Emissions by Time of Day from 2000 to 2011
Unleaded Diesel LPG Hybrid
Ethanol
Blend
Biodiesel
2001 2,004,247 275,832 54,200 28 19,828 69
2011 2,151,475 631,456 66,700 6,413 539,300 5,833
% Growth 7 129 23 22,804 2,620 8,306
-
5,000
10,000
15,000
20,000
25,000
-
1
1
2
2
3
Growth(%)
NumberofVehicles(Millions)
Fuel Type
Growth inVehicle Numbers by Fuel Type
between2001 and 2011

The figure below presents the growth in emissions per pollutant between 2000 and 2011.
Figure 8 – Growth in Emissions by Time of Day between 2000 and 2011
-100
0
100
200
300
400
500
600
700
MorningPeak Daytime Afternoon Peak Evening Night-time
Growth(%)
Time ofDay
Vehicle Emissions by Time of Day
CH4
N20
NOX
CO
VOC
PM
SO2
CO2
CO2-e

4.1.4 Spatial Distribution
4.1.4.1 VOC
Presented below is the spatial distribution of VOC over the Brisbane Metropolitan region, for the
year 2000, in tonnes per annum.
Figure 9 – Spatial Distribution of VOC for 2000
Moreton IslandMoreton IslandMoreton IslandMoreton IslandMoreton IslandMoreton IslandMoreton IslandMoreton IslandMoreton Island
BeenleighBeenleighBeenleighBeenleighBeenleighBeenleighBeenleighBeenleighBeenleigh
Port ofPort ofPort ofPort ofPort ofPort ofPort ofPort ofPort of
BrisbaneBrisbaneBrisbaneBrisbaneBrisbaneBrisbaneBrisbaneBrisbaneBrisbane
North StradbrokeNorth StradbrokeNorth StradbrokeNorth StradbrokeNorth StradbrokeNorth StradbrokeNorth StradbrokeNorth StradbrokeNorth Stradbroke
IslandIslandIslandIslandIslandIslandIslandIslandIsland
CabooltureCabooltureCabooltureCabooltureCabooltureCabooltureCabooltureCabooltureCaboolture
Redclif f eRedclif f eRedclif f eRedclif f eRedclif f eRedclif f eRedclif f eRedclif f eRedclif f e
CBDCBDCBDCBDCBDCBDCBDCBDCBD
IpswichIpswichIpswichIpswichIpswichIpswichIpswichIpswichIpswich
2000 VOC (t / year)
7.5+
3.5 to 7.5
1.5 to 3.5
0.7 to 1.5
0.3 to 0.7
0 to 0.3

Presented below is the spatial distribution of VOC over the Brisbane Metropolitan region, for the
year 2011, in tonnes per annum.
Figure 10 – Spatial Distribution of VOC for 2011
2011 VOC (t / year)
7.5+
3.5 to 7.5
1.5 to 3.5
0.7 to 1.5
0.3 to 0.7
0 to 0.3

Presented below is the difference in spatial distribution of VOC between the years 2000 and 2011, in
tonnes per annum.
Figure 11 – Difference in Spatial Distribution of VOC between 2000 and 2011
2011-2000 VOC (t / year)
3.252+
0.851 to 3.252
0.263 to 0.851
0.104 to 0.263
0.048 to 0.104
0.022 to 0.048
-7.041 to 0.022

4.1.4.2 CO2-e
Presented below is the difference in spatial distribution of CO2-e between the years 2000 and 2011,
in tonnes per annum.
Figure 12 – Difference in Spatial Distribution of CO2-e between 2000 and 2011
Difference distributions for the other emissions modelled can be found in Appendix B.
2011-2000 CO2e (t / year)
4,000+
1,250 to 4,000
250 to 1,250
80 to 250
40 to 80
10 to 40
< 10

4.2 VKT
Presented below is the spatial distribution of VKT over the Brisbane Metropolitan region, for the
year 2000, in millions of Vehicle-Kilometres of Travel per annum.
Figure 13 – Spatial Distribution of VKT for 2000
2000 VKT (Mvkt / year)
13+
6 to 13
3 to 6
1 to 3
0.3 to 1
0 to 0.3

Presented below is the spatial distribution of VKT over the Brisbane Metropolitan region, for the
year 2011, in millions of Vehicle-Kilometres of Travel per annum.
Figure 14 – Spatial Distribution of VKT for 2011
2011 VKT (Mvkt / year)
13+
6 to 13
3 to 6
1 to 3
0.3 to 1
0 to 0.3

Presented below is the difference in spatial distribution of VKT between the years 2000 and 2011, in
tonnes per annum.
Figure 15 – Difference in Spatial Distribution of VKT between 2000 and 2011
2011-2000 VKT (Mvkt / year)
4.154+
1.188 to 4.154
0.193 to 1.188
0.053 to 0.193
0.026 to 0.053
0.007 to 0.026
< 0.007

The figure below displays the VKT for each time period over an average day, as well as the
percentage growth, between 2000 and 2011.
Figure 16 – Vehicle-Kilometres of Travel for 2000 and 2011
4.3 SCREENLINE ANALYSIS
Results of the screenline check of the 2011 model are shown below.
Morning
Peak
Daytime
Afternoon
Peak
Evening Night-time
2000 2,406,462 6,360,183 2,530,722 1,859,756 1,426,114
2011 7,036,186 16,130,378 7,505,797 3,840,198 4,600,873
Growth % 192 154 197 106 223
-
50
100
150
200
250
-
2
4
6
8
10
12
14
16
18
Growth(%)
Vehicle-KilometresofTravel
(Millionsofveh.km)
Time Period
Vehicle-Kilometresof Travel
for 2000 and2011

Crossing Point Observed Modelled
Gateway Bridge 115,300 146,100
Clem7 30,700 30,900
Story Bridge 96,700 84,900
Captain Cook Bridge 141,700 115,900
Victoria Bridge 16,300 28,400
William Jolly Bridge 36,100 54,700
Go Between Bridge 13,500 19,100
Walter Taylor Bridge 31,800 23,100
Centenary Bridge 86,100 60,000
Brisbane-Moggill Ferry Road 1,100 -
Mt Crosby Rd 10,200 14,600
Total 579,500 577,700
Absolute Difference -1,800
Relative Difference -0.3%
Table 4 – Screenline Summary
Average weekday volumes were produced by the model. To adjust for weekend loading, three 2010
traffic counts for weekends and weekdays were analysed. These counts are from static stations
located at highly trafficked river crossings in the study area. Average weekend traffic was found to
be 78% of the average weekday.
This result reduces the average production for each year by 23 days, as opposed to calculating
annual emission production with average weekday data.

5 DISCUSSION
5.1 EMISSIONS
The growth in emissions produced between the 2000 and 2011 study periods is displayed in Figure 4.
The overall trend for emissions is a significant growth, with CO and VOCs more than tripling over the
study period.
The growth between 2000 and 2011 in factors that may impact vehicle emissions are displayed in
Figure 15 below. CO2-e has been selected as an indicative pollutant, considering that it is an
amalgamation of greenhouse gases into equivalent CO2, which is the largest pollutant produced by
mass by a significant margin.
Figure 17 – Growth between 2000 and 2011
VKT is observed to have undergone the most growth of the variables displayed. This is an excellent
explanation of the growth in emissions observed, as emissions are calculated using VKT. The VKT in
this study can be thought of as “activity”, being a direct measurement of vehicle movement. This
increase in activity is supported by the increase in growth in other areas, with higher port
movements, airport passengers, greater population and labour force, generating more vehicle
travel.
For emissions which experienced more growth than VKT over the study period, this is partially
explained by the increase in congestion. With VKT and other factors growing at a faster rate than the
road network is developed, the demand is greater than the capacity, causing congestion. The
infrastructure development is reactive, where greater capacity in the road system is constructed
when it is required. Congestion is indicated in our results by greater emissions than VKT, that is,
CO2-e VKT
QLD
Fleet
QLD
GSP
Port
Move-
ments
Airport
Pass-
engers
Popul-
ation
Labour
Force
Growth (%) 138 168 45 44 47 54 27 7
-
20
40
60
80
100
120
140
160
180
Growth(%)
Pollutantor Variable
Growthbetween2000 and2011

there are more emissions produced over less distance travelled. This effect is magnified by the
number of vehicles in the fleet increasing at a faster rate than the population over the same period.
This is discussed in greater detail below.
The variables shown above apply to either the Brisbane (study) area, or the regional area. This
comparison of data points that apply to different areas introduces error. Using the metric of growth,
however, each term is essentially without units, being in terms of its own growth. This goes some
way to cancelling out some bias and inaccuracy caused by comparing these directly to one another.
5.1.1 Vehicle Type
In Figure 4, for every pollutant except NOx, greater amounts of pollutant were produced in 2011
than 2000. A significant portion of the cause of negative change in growth for NOx can be attributed
to the use of catalytic converters, which reduce the amounts of CO, NOx, and unburnt hydrocarbons
present in car exhaust.
This can be further explained by the different proportions of different pollutants produced by
different vehicle types. ABS motor vehicle census data for 2001 and 2011 is shown below in Figure
18, showing the proportional makeup of the fleet, as well as the overall 44.5% growth in the size of
the fleet. The increase in both the overall size of the fleet, and the disproportional growth of
different vehicle types will influence the growth of different pollutants produced between the two
study years. Additionally, this graph underpins the reasonability of the exclusion of buses from the
model, with their proportion of the fleet being overwhelmed by different modes of travel.
Figure 18 – Composition of Fleet for 2001 and 2011
0
0.5
1
1.5
2
2.5
3
3.5
4
2001 2011
NumberofVehicles(Millions)
Year
Compositionof Fleet for 2001 and 2011
Other Trucks
Bus
Articulated
Rigid
LightCommercial
Motorcycles
Passenger Cars

Figure 6 shows the breakdown of growth in pollutant production by vehicle type. The percentage of
emissions produced by vehicle type generally reflects the composition of the fleet. However, for NOx
and PM10 in particular, the production of these pollutants is disproportionate. The fuel type used by
different classes of vehicles is a possible explanatory variable, and is discussed below.
5.1.2 Fuel Type
As can be seen from Figure 7, the growth in number of vehicles for different fuel types has not been
uniform between the two years studied. Growth for hybrid, biodiesel and ethanol blend fuelled
vehicles is significantly large, due to the small number of vehicles using these fuel types in the year
2001.
The increase in the use of biodiesel and hybrid systems is concentrated to particular vehicle types.
Buses, rigid vehicles, and articulate vehicles account for the increase in the number of biodiesel
fuelled vehicles, and hybrid vehicle gains are all due to passenger cars. Ethanol blend growth is
spread across all vehicle types bar articulated trucks.
5.1.3 Vehicle Age
The overall age of the fleet did not stay the same between 2000 and 2011. Figure 19 below displays
the composition of the fleet by vintage for the study years. This graph displays both the overall
increase in volume of the fleet (by 44.5%, as mentioned above), as well as the difference in the
proportion of the fleet of different vintages. Supporting data for this figure can be found in Appendix
A, Section 7.1.1.3, Table 10.
0
50
100
150
200
250
300
1966
1968
1970
1972
1974
1976
1978
1980
1982
1984
1986
1988
1990
1992
1994
1996
1998
2000
2002
2004
2006
2008
2010
NumberofVehicles
(Thousands)
Vehicle Vintage
Vehicle Fleet by Vintage
2001
2011

Figure 19 – Vintage of Vehicles in Fleet for 2001 and 2011
Cars are subject to regulations regarding their emissions, and as technology improves, emissions
produced by vehicles are reduced. From the above graph, even though the overall fleet is larger, the
2011 fleet is more “current” than the 2000 fleet, resulting in less emissions per average vehicle being
produced.
The 2011 fleet shows a noticeable drop for both years around 1986, as well as after 2007 for the
2011 fleet. The first drop is partly due to the introduction of mandatory catalytic converters, with
issues regarding power and fuel economy affecting the purchase of newer cars. Therefore pre-1986
vehicles with no catalytic converters are disproportionately represented, with more impact on the
2000 fleet while more vehicles were in service. This directly affects the amount of pollutants
produced in the 2000 model. For the 2011 model, with less cars as part of the fleet of this vintage,
the effects will be minimised.
A factor affecting the smaller amount of new cars in the 2011 fleet is the Global Financial Crisis
(GFC). The GFC affected the disposable income available to people, as well as budgets available to
businesses and governments. It is conceivable that this influenced the amount of people buying
vehicles, as well as the age of vehicles being purchased and introduced to the fleet.
The fleet values examined above are for the Queensland fleet. While a significant portion of the fleet
resides in the south-east corner of the state, this data is skewed in both magnitude and age. The
study area will contain a smaller amount of vehicles, and since the area includes the capital city, with
urban road settings, the data used be skewed towards vehicles driven on rural roads. This bias is
detrimental to the study, with rural areas experiencing far greater fuel efficiency, with less emissions
produced compared to VKT. This bias also influences the study to be more conservative, with lower
emission vehicles such as hybrid cars existing in urban areas, and a likelihood of newer vehicles
present near an economic hub such as the capital city of Brisbane.
The discontinuity in growth across fuel type, fleet composition and vintage, as well as pollutant
production by different vehicle types, goes some way to explain the different rates of growth in
production of different pollutants between 2000 and 2011.
Using ABS motor vehicle census data, the information analysed was the best available quality for the
area, with both excellent level of detail, spatial accuracy and quality, as well as representing a
significant proportion of the fleet. Inaccuracies introduced by analysing data for the year 2001,
instead of the study year 2000, are outweighed by the quality of the data. By using later data than
the study year, the values will also be slightly more conservative than the actual figures for 2000, as
that years worth of growth is not reported in the data used.
5.1.4 Time of Day
In the analysis undertaken for vehicle emissions by time of day, the growth in emissions in percent is
displayed in Table 3 and Figure 8.
Using CO2-e as an indicative pollutant once again, as it accounts for the largest equivalent pollutants
in its calculations, the overall trend is of a growth in emissions between 2000 and 2011. For each
time period, growth appear relatively similar excepting the afternoon peak period, where growth is

approximately half that of the other periods of the day. The reason for this could be due to the
change in behaviour of the traffic in the study area, with peak periods being experiencing less
congestion than other time periods due to changes in the road network. Patterns of behaviour were
not modelled very differently in this study than for the 2000 case, and thus behavioural change to do
with intent are not included. What is included in the model is a preference for traffic flow to be
routed to roads operating at higher speeds, i.e. speeds not experiencing congestion. This would
favour new developments in the network, such as the Clem7 tunnel system and the duplication of
the Gateway bridge.
Using Figure 8, the emission growth by time of day is observed to not be uniform. A possible reason
for this is the dissimilar activity of different vehicle modes, i.e. rigid and articulated vehicles may
travel disproportionately more at night than the average vehicle. Improvements into understanding
the spatial and temporal distribution of each vehicle type may provide insights in future work.
5.1.5 Spatial Distribution
5.1.5.1 VOC
VOCs are important chemicals to track due to its effects on human health and the environment, as
well as the propensity of this pollutant to remain in the area in which it was produced. Figure 9,
Figure 10, and Figure 11 show the spatial distribution of VOCs for 2000, 2011, as well as the
difference between the two, respectively.
From the 2000 spatial distribution map, common trip routes, including travel from the Brisbane CBD
to Ipswich, or Beenleigh can be seen with a higher concentration of emissions displayed along the
routes. The 2011 map shows a marked increase in emissions along more routes. This is displayed
effectively in the difference plot, with trip routes more difficult to determine due to the overall
increase in emissions over most of the south-east region.
These results are not unexpected, and are supported by the literature, however they demonstrate
the need for more frequent modelling and monitoring of air quality in the study area. Much of the
area where high increases in VOCs are present are residential areas, with high VOC levels present in
a radius to the Brisbane CBD, where there exists high density living.
5.1.5.2 CO2-e
The findings for VOC are applicable to the results obtained for CO2-e, however impacts on human
health are not so direct for this compound. The increase in CO2-e production in the areas shown
contribute to larger region than that studied, with these greenhouse gases contributing to climate
change which affects the region on a global scale. Implications from these findings are still relevant
to the area, with CO2-e production an important factor for decision making and planning by
governing bodies.
5.2 VKT
The spatial distribution of VKT follows the same trends as those found for VOCs and CO2-e, with trip
routes being particularly subject to increases in VKT.

Figure 16 displays VKT production for 2000 and 2011, as well as the percentage growth between
these two years. All times of day experienced an increase in VKT, with morning peak, afternoon peak
and night time activity approximately tripling over the time period. Figure 17, displays other
measures of growth in the region, including the growth in the size of the Queensland fleet. These
two factors are partly responsible for the increase in VKT, with general “activity” in the area and
number of vehicles higher in 2011 than 2000.
5.3 SCREENLINE
The screenline analysis concluded the model had a relative difference to the observed traffic
volumes of -0.3%. This is well within the acceptable limits for a screenline test, and shows the model
more than adequately calculates vehicle movements in the study region. This adds to the confidence
in the results found by this research project.
Average weekend traffic of 78% of the average weekday is within reasonable limits, although is
higher than would be expected. It would expected the temporal distribution throughout the day, as
well as routes travelled would be different for an average weekend day compared to an average
week day. Weekend traffic would be expected to have less extreme peaks in traffic and congestion,
at later times in the day. Weekend analysis was not included in the scope of this work, and thus the
temporal traffic distribution was used to estimate the effects of weekend on total annual emissions
and VKT.
Using the estimations based on these points is reasonable, however could be improved by using
additional data from other areas within the study boundary. Further improvement could be made by
modelling the average weekend day as well as the average weekday. Additional error has been
introduced by using this data, as these traffic counts do not distinguish between vehicle types, and
thus include counts for buses which were not included in the model.
This result reduces the average production for each year by 23 days, as opposed to calculating
annual emission production with average weekday data. Implications of this finding
There are assumptions inherent in the use of average weekday and weekend days, as no special
events or circumstances, such as school holidays, are accounted for that may cause unusual traffic
conditions. Despite this, the results found using weighted average values give a practical result to
represent average traffic over a calendar year.

6 CONCLUSIONS
Motor vehicle emissions have become an important consideration for government bodies such as
the EPA and BCC. This project began with the 2003 update for the motor vehicle emissions model, as
part of the SEQ air pollutant emissions inventory. The increasing numbers of motor vehicle
ownership, as well as their associated emissions identify a need to update the model, to better
reflect the emissions produced in this region.
This model was updated for the Brisbane Metropolitan region with 2011 data, with results garnered
showing the increase in both VKT and vehicle emission production. The spatial distribution of these
emissions are concentrated around major road links and high activity areas such as the Brisbane
CBD. These emissions are influenced by many variables, with the distribution across the network
non-uniform and complex. The change in pollutant amount and spatial distribution is a function of
changes in technology, fleet composition, fleet vintage, population growth, employment,
socioeconomic factors and population movement around the region.
While the model is not a complete update of the previous work, the results obtained by this study
are applicable to the year 2011, with a comparison of the work adding value and context.

7 RECOMMENDATIONS
Although the best available information was used, possible continuation and improvements to the
project could be achieved by the methods listed below.
1. Establish a plan to update the model.
While the work done for this research project has produced a model for 2011, future updates are
vital to the continuing monitoring and understanding of motor vehicle emissions in the study area. It
is recommended that an update be conducted approximately every 10 years, to coincide with the
release of ABS census data.
2. Extend the boundary of the study area to match previous work.
The work conducted in this study applied only to the Brisbane Metropolitan region, whereas the
work conducted in 2003 for the 2000 model applied to a larger area of SEQ.
3. Extend the model to include other motor vehicles in the network.
This work did not model public transport, including buses. While the proportion of the fleet for
buses is small, the effects of the vehicle emissions produced would be of interest to regional
councils.
4. Extend the model to produce output for different emission production types.
This study only analyses running losses, which contribute a significant amount to the total emissions
produced by motor vehicles, however are less than the total emissions produced by vehicles. Future
work should endeavour to include emissions from resting loss, diurnal, hot-soak, running loss, cold
start, hot start and hot running.
5. Modify the model to simulate different traffic demands.
The average weekday was modelled in this study, however patterns for average weekend day
emissions would increase the accuracy of the calculations for vehicle emissions.
6. Update the model to reflect changes in technology and fuel.
As time moves forward, technology improves, minimising the effects of motor vehicle emissions
produced by more efficient vehicles. Changes to the fleet since the last model include a growth in
biodiesel fuelled and hybrid powered vehicles. Keeping track of these changes and implementing
them in the model will further aid the understanding of the contribution of motor vehicle emissions
on air pollutants.
7. Conduct research into the affect on motor vehicle usage due to improvements in
infrastructure, public transport and government initiatives.
Future infrastructure changes, improvements in the public transport network and additional
government initiatives (e.g. citycycle), will render the current work out of date. The effects on motor
vehicle usage due to these changes should be included in future continuations of this study, to
improve the accuracy of calculated motor vehicle emissions.
8. Calculate emissions using the equations used by previous work.
9. Refine model to reflect the vehicle fleet with a higher level of detail.
10. Continue to investigate and document the improvements and changes to the air pollutant
inventory, to fill gaps in our understanding.

ACKNOWLEDGMENTS
I hereby acknowledge the assistance and/or use of the University of Queensland library staff, library
and course resources, my course supervisor Dr Badin Gibbes in the making of this document, and
associated coursework throughout the semester.
Accredited also are the various Government bodies, education institutions, and road operators for
their assistance in providing data for this project.
Particular recognition is given to my project supervisor, Associate Professor Adam Pekol, who has
guided and informed the work undertaken for this project, as well as the creation of this document,
throughout the semester.
Finally, recognition goes to my work partner, Joellen Athanassiou, for her assistance on this research
project. While our reports and discussions of our findings remain our individual accomplishments,
the labour involved in achieving our results was shared between us, and for that, I extend my
deepest appreciation.

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APPENDICES
APPENDIX B – SUPPORTING RESULTS
7.1.1 Emissions
7.1.1.1 Time of Day
2000 Emissions (tonnes/year)
Pollutant
Morning
Peak
Daytime
Afternoon
Peak
CH4 32 70 27 19 35 183
N20 4 8 3 2 4 21
NOX 504 1,424 482 370 539 3,319
CO 1,406 2,451 857 630 1,514 6,859
VOC 127 193 64 45 135 563
PM10 5 14 3 2 5 29
SO2 19 45 15 11 20 111
CO2 132,881 305,662 110,141 81,034 145,021 774,739
CO2-e 134,686 309,714 111,641 82,135 146,996 785,172
Table 5 – 2000 Emissions by Time of Day
2011 Emissions (tonnes/year)
Pollutant
Morning
Peak
Daytime
Afternoon
Peak
CH4 34 74 22 23 42 195
N20 7 14 4 4 8 38
NOX 542 1,242 311 340 602 3,037
CO 3,668 7,653 1,752 3,366 4,619 21,058
VOC 300 628 121 330 394 1,774
PM10 14 31 6 10 16 77
SO2 39 86 23 26 48 222
CO2 326,584 707,519 198,954 217,566 403,824 1,854,447
CO2-e 329,349 713,547 200,668 219,420 407,228 1,870,212
Table 6 – 2011 Emissions by Time of Day

7.1.1.2 Vehicle Type
2000 Emissions by Vehicle Type (tonnes/year)
Pollutant
Passenger
Cars
Motorcycles
Light
Commercial
Rigid Articulate Total (g)
CH4 176 1 7 0 0 183
N20 20 0 1 0 0 21
NOX 2938 2 314 39 26 3319
CO 6308 73 469 8 1 6859
VOC 502 8 48 3 3 563
PM 19 0 9 1 0 29
SO2 101 0 10 1 0 111
CO2 729236 608 40268 4090 538 774739
CO2-e 739170 633 40696 4133 541 785172
Table 7 – 2000 Emissions by Vehicle Type
2011 Emissions by Vehicle Type (tonnes/year)
Pollutant
Passenger
Cars
Motorcycles
Light
Commercial
Rigid Articulate Total (g)
CH4 182 4 6 0 0 193
N20 33 1 2 0 0 37
NOX 2265 6 422 158 149 2999
CO 17898 303 1277 1247 5 20730
VOC 1497 37 130 15 61 1740
PM 51 0 19 4 1 76
SO2 198 0 14 6 1 219
CO2 1707723 3863 89628 19941 3712 1824866
CO2-e 1721925 4335 90398 20053 3727 1840438
Table 8 – 2011 Emissions by Vehicle Type
2011 Pollutant Production by Vehicle Type (%)
Pollutant
Passenger
Cars
Motorcycles
Light
Commercial
Rigid Articulate
CH4 94.7 2.0 3.0 0.2 0.0
N20 90.0 3.4 5.6 0.9 0.1
NOX 75.5 0.2 14.1 5.3 5.0
CO 86.3 1.5 6.2 6.0 0.0
VOC 86.0 2.1 7.5 0.8 3.5
PM 67.8 0.6 25.0 5.5 1.0
SO2 90.5 0.2 6.3 2.6 0.5
CO2 93.6 0.2 4.9 1.1 0.2
CO2-e 93.6 0.2 4.9 1.1 0.2
Table 9 – 2011 Pollutant Production by Vehicle Type

7.1.1.3 Vehicle Age
Vintage
Census Year
2001 2011
1966 16721 9778
1967 3576 1460
1968 4349 1591
1969 4858 1826
1970 7929 2752
1971 9334 2953
1972 8677 3620
1973 10155 3933
1974 14834 5012
1975 16460 4767
1976 20397 6213
1977 20976 5723
1978 27120 6651
1979 35711 7004
1980 42633 7913
1981 57101 10754
1982 68712 12038
1983 67589 12200
1984 86916 19147
1985 102567 26867
1986 72776 18776
1987 65266 16862
1988 89088 29722
1989 110099 44938
1990 114198 53926
1991 97518 54316
1992 104858 64382
1993 107906 72255
1994 120680 87231
1995 122074 93243
1996 118144 96276
1997 135590 117820
1998 154794 140943
1999 150258 141999
2000 143270 148072
2001 21219 142577
2002 163992
2003 190135
2004 209900
2005 230882
2006 224632
2007 247649
2008 239234
2009 212943
2010 204692
2011 2334
Table 10 – Fleet Composition by Vehicle Vintage

APPENDIX B – SPATIAL DISTRIBUTIONS FOR MODELLED EMISSIONS
Figure 20 – Difference in Spatial Distribution of CH4 between 2000 and 2011
2011-2000 CH4 (t / year)
0.080 +
0.009 to 0.080
0.003 to 0.009
0.001 to 0.003
0.000 to 0.001
-0.040 to 0.000
< -0.04

Figure 21 – Difference in Spatial Distribution of N20 between 2000 and 2011
2011-2000 N2O (t / year)
0.06+
0.01 to 0.06
0.002 to 0.01
0.0005 to 0.002
0.0002 to 0.0005
-0.0005 to 0.0002
< -0.0005

Figure 22 – Difference in Spatial Distribution of NOx between 2000 and 2011
RedcliffeRedcliffeRedcliffeRedcliffeRedcliffeRedcliffeRedcliffeRedcliffeRedcliffe
2011-2000 NOX (t / year)
0.3 +
0.050 to 0.300
0.015 to 0.050
-0.015 to 0.015
-0.150 to -0.015
-1.500 to -0.150
< -1.500

Figure 23 – Difference in Spatial Distribution of CO between 2000 and 2011
2011-2000 CO (t / year)
50 +
15 to 50
5 to 15
1 to 5
0.50 to 1
0.25 to 0.50
< 0.25

Figure 24 – Difference in Spatial Distribution of PM10 between 2000 and 2011
2011-2000 PM10 (t / year)
0.1500 +
0.0400 to 0.1500
0.0090 to 0.0400
0.0032 to 0.0090
0.0016 to 0.0032
0.0008 to 0.0016
< 0.0008

Figure 25 – Difference in Spatial Distribution of SO2 between 2000 and 2011
2011-2000 SO2 (t / year)
0.500 +
0.100 to 0.500
0.025 to 0.100
0.010 to 0.025
0.005 to 0.010
0.001 to 0.005
< 0.001

Figure 26 – Difference in Spatial Distribution of CO2 between 2000 and 2011
2011-2000 CO2 (t / year)
4,000+
1,250 to 4,000
250 to 1,250
80 to 250
40 to 80
10 to 40
< 10

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