CIVL4560 - Research Project - Lillian Singleton - 41739901

The Growth in Motor Vehicle Emissionsin 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

Gro

wth

(%

)

Pollutant

Increase in Emissions per Pollutant

between 2000 and 2011


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

CarsMotorcycles

Light

CommercialRigid Articulate

CO2-e 133 585 122 385 588

0

100

200

300

400

500

600

700G

row

th (

%)

Vehicle Type

Growth of CO2-e Production by 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

Pe

rce

nta

ge

of

To

tal

Pollutant

2011 Pollutant Production by Vehicle Type

Articulate

Rigid

Light Commercial

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 HybridEthanol

BlendBiodiesel

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

Gro

wth

(%

)

Nu

mb

er

of

Ve

hic

les

(Mil

lio

ns)

Fuel Type

Growth in Vehicle Numbers by Fuel Type

between 2001 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

Morning Peak Daytime Afternoon Peak Evening Night-time

Gro

wth

(%

)

Time of Day

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

PeakDaytime

Afternoon

PeakEvening 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

Gro

wth

(%

)

Ve

hic

le-K

ilo

me

tre

s o

f T

rav

el

(Mil

lio

ns

of

ve

h.k

m)

Time Period

Vehicle-Kilometres of Travel

for 2000 and 2011


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 VKTQLD

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

Gro

wth

(%

)

Pollutant or Variable

Growth between 2000 and 2011


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

Nu

mb

er

of

Ve

hic

les

(Mil

lio

ns)

Year

Composition of Fleet for 2001 and 2011

Other Trucks

Bus

Articulated

Rigid

Light Commercial

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

19

66

19

68

19

70

19

72

19

74

19

76

19

78

19

80

19

82

19

84

19

86

19

88

19

90

19

92

19

94

19

96

19

98

20

00

20

02

20

04

20

06

20

08

20

10

Nu

mb

er

of

Ve

hic

les

(Th

ou

san

ds)

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.


REFERENCES

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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


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


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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