Newcastle/Gateshead Low-Emissions Zone Feasibility Study – Air ...

Newcastle/Gateshead Low-Emissions Zone Feasibility Study – Air Quality Report May 28, 2014

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Newcastle/Gateshead Low-Emission Zone Feasibility Study:

Vehicle Emissions and Air Quality

Modelling

Newcastle University Transport Operations Research Group

(TORG)

Dr Paul Goodman, Dr Fabio Galatioto, Dr Anil Namdeo,

Professor Margaret C. Bell

Version 1.2 (Final), 28th May 2014

Includes additional amendments to Appendices made post-19th May 2013


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Version Control

Version Number

Date Author(s) Reviewed By Circulation

0.1 (Outline) 10/01/2013 Paul Goodman

N/A LEZ Feasibility Study Steering Group

0.2 28/02/2013 Paul Goodman

Anil Namdeo Internal

0.3 04/03/2013 Paul Goodman, Fabio Galatioto

N/A LEZ Feasibility Study Steering Group

0.4 18/03/2013 Paul Goodman, Fabio Galatioto

N/A Internal

0.5 19/03/2013 Paul Goodman, Fabio Galatioto, Anil Namdeo

Anil Namdeo (part) LEZ Feasibility Study Steering Group


Anil Namdeo (part) LEZ Feasibility Study Steering Group


N/A LEZ Feasibility Study Group

(1.1) 23/05/2014 Paul Goodman, Fabio Galatioto

N/A Ed Foster, Caroline Shield, NU Internal

(1.2) 28/05/2014 Paul Goodman, Fabio Galatioto

N/A Ed Foster, Caroline Shield, NU Internal

File location: e:\lez\newcastle\LEZ_Final_Report_280514.docx

Contacts For further information on the Newcastle/Gateshead LEZ Feasibility Study, please contact:

Mr Edwin Foster, Team Manager (Environment and Safety), Regulatory Services and Public Protection, Environment and Regeneration Directorate, Newcastle City Council, Newcastle upon Tyne, NE1 8PB Tel: +44 (0)191 211 6132 Email: [email protected] For enquiries to Gateshead City Council, please contact

Caroline Shield Team Leader (Transport Policy and Research), Transport Strategy Service, Development and Enterprise Group, Gateshead Council, Civic Centre, Regent Street, Gateshead, NE8 1HH Tel: +44 (0)191 433 3084 Email: [email protected]

For further information about this document or its contents, please contact: Dr Paul Goodman, Research Associate, School of Civil Engineering and Geosciences, Room 2.22, Cassie Building, Newcastle University, Newcastle upon Tyne, NE1 7RU Tel: +44 (0)191 222 5945 Email: [email protected]

mailto:[email protected]




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Acknowledgements The authors would like to thank the following people who have been instrumental in the preparation

of this report: LEZ Feasibility Study Steering group members, DEFRA, Staff at Newcastle and

Gateshead Councils, especially Trevor Arkless, NU Staff/Students (Graeme Hill, Patrizia Franco, Ayan

Chakravatty, Glyn Rhys-Tyler, James O’Brien, Justin Ashley-Cairns, Lindsey Allan), NEXUS, Regional

bus operators (GO North East, Arriva and Stagecoach), Yvonne Brown (Bureau Veritas), Beth Conlan

(Ricardo-AEA), Daryl Lloyd (DfT), Richard Crowther and David Cherry (Leeds City Council).


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Table of Contents

Version Control ....................................................................................................................................... 2

Contacts .................................................................................................................................................. 2

Acknowledgements ................................................................................................................................. 3

Executive Summary ................................................................................................................................. 8

1. Introduction .................................................................................................................................. 10

1.1 Study Aims and Objectives .......................................................................................................... 10

1.2 Document Structure.................................................................................................................... 11

2. Transport and Urban Air Quality ................................................................................................... 12

2.1 Current Legislation and Key Pollutants ....................................................................................... 12

2.2 Transport and Air-Quality in Newcastle and Gateshead ............................................................ 14

2.2.1 Declared Air-Quality Management Areas (AQMAs) ............................................................ 14

2.2.2 The NewcastleGateshead Urban Core Area ......................................................................... 16

2.3 Low-Emissions Zones .................................................................................................................. 18

2.3.1 Source Emissions Reduction – The ‘EURO’ standards .......................................................... 19

2.3.2 Present and Future NOx Emissions ....................................................................................... 22

2.3.3 Retrofitting Vehicles ............................................................................................................. 24

2.3.4 Alternative LEZ Compliance Criteria ..................................................................................... 27

2.3.5 Alternative and Complimentary Policies to Low Emission Zones ......................................... 27

2.3.6 Effectiveness of Low Emission Zones .................................................................................... 28

3. Modelling Framework Development ............................................................................................ 31

3.1 Proposed Methodology .............................................................................................................. 31

3.1.1 Tyne and Wear Transport Planning Model .......................................................................... 32

3.1.2 PITHEM Emissions Model ..................................................................................................... 33

3.1.3 ADMS-Urban Air Quality Dispersion Model ......................................................................... 34

3.1.4 ArcGIS Platform .................................................................................................................... 34

3.2 Pilot Framework Development ................................................................................................... 35

3.2.1 Pilot Model Assumptions ...................................................................................................... 35

3.2.2 Pilot Model Results ............................................................................................................... 37

3.2.3 Pilot Model Discussion ......................................................................................................... 37

3.3 Developments over the Pilot Model ........................................................................................... 39

3.3.1 Selection of Base Year and LEZ Target Year ......................................................................... 39

3.3.2 Selection of Spatial Domain ................................................................................................. 40


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3.3.3 Modifications to Traffic Modelling ....................................................................................... 43

3.3.3.1 Update of TPM base year to 2010 .................................................................................... 43

3.3.3.2 Development of Public Transport (Bus) Model ................................................................. 47

3.3.3.3. Linking TPM and Bus Model information to OS Master Map Layers ............................... 48

3.3.3.4 Utilisation of Traffic Master Speed Data:.......................................................................... 49

3.3.4 Modifications to Emissions Modelling ................................................................................. 50

3.3.5 Additional Data Requirements for Dispersion Modelling .................................................... 62

3.4 Post-Framework Development ................................................................................................... 64

3.4.1 Source Apportionment of Concentrations ............................................................................ 64

3.4.2 Concentration Modelling Issues ........................................................................................... 65

3.5 Finalised Modelling Framework .................................................................................................. 66

4 Base Year Modelling ........................................................................................................................... 68

4.1 Emission Results .......................................................................................................................... 68

4.1.1 Emissions totals .................................................................................................................... 68

4.1.2. Source apportionment for sub-areas .................................................................................. 69

4.2 Base Year Validation against AURN data .................................................................................... 72

4.1.1 Total NOx and NO2 concentrations ....................................................................................... 72

4.1.2 Total PM10 and PM2.5 Concentrations ................................................................................... 74

4.1.3 Source Apportionment ......................................................................................................... 74

4.3 Concentration Results ................................................................................................................. 77

4.3.1 Total NOx (as NO2) Concentrations ...................................................................................... 77

4.3.2 Nitrogen Dioxide (NO2) ........................................................................................................ 79

4.3.3 Particulate Matter (PM10 and PM2.5) .................................................................................... 80

4.3.4 Sensitivity and Uncertainty .................................................................................................. 81

4.4 Summary and Discussion ............................................................................................................ 83

4.4.1 Initial Findings ...................................................................................................................... 83

4.4.2 Modelling Limitations .......................................................................................................... 84

4.4.3 Implications for LEZ Design .................................................................................................. 85

5 LEZ Scenario Modelling ...................................................................................................................... 87

5.1 Changes to Transport Modelling ................................................................................................. 87

5.1.1 Network changes ................................................................................................................. 87

5.1.2 Traffic growth ....................................................................................................................... 87

5.2 Changes to Emissions Modelling ................................................................................................. 89

5.2.1 Future 2021 and LEZ Fleets .................................................................................................. 89


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5.3 Tested LEZ Scenarios ................................................................................................................... 93

5.4 Emission Results .......................................................................................................................... 93

5.4.1 Total NOx (as NO2) ................................................................................................................ 93

5.4.2 Primary NO2 ......................................................................................................................... 96

5.4.3 Particulate Matter (PM10 and PM2.5) .................................................................................... 97

5.4.4 Carbon Dioxide ..................................................................................................................... 98

5.5 Changes to Local Air Quality Modelling ...................................................................................... 99

5.5.1 Spatial domain ..................................................................................................................... 99

5.5.2 Meteorological data ............................................................................................................. 99

5.5.3 Background Concentration Data .......................................................................................... 99

5.6 Concentration Results: .............................................................................................................. 100

5.6.1 Nitrogen Dioxide (NO2) ....................................................................................................... 100

5.6.2 Sensitivity and Uncertainty ................................................................................................ 113

5.7 Summary and Discussion .......................................................................................................... 114

5.7.1 Modelling Limitations ........................................................................................................ 114

5.7.2 Analysis of Future Scenarios ............................................................................................... 114

5.7.3 Implications for LEZ Design ................................................................................................ 115

6. Concluding Remarks ........................................................................................................................ 117

References: ......................................................................................................................................... 118

Appendices: ......................................................................................................................................... 128

Appendix A: The Low Emission Zone Steering Group ..................................................................... 128

Appendix B: Technical notes on links between TPM and PITHEM ................................................. 129

Appendix C: Methodology for updating TPM using detector data ................................................. 130

Appendix D: Processing of Cordon and Count Information ............................................................ 136

D.1 Newcastle Cordons: .............................................................................................................. 136

D.2 Gateshead Cordons: ............................................................................................................. 138

Appendix E: Bus Network Modelling .............................................................................................. 140

Appendix F: Linking TPM to OS MasterMap via PITHEM ................................................................ 142

Appendix G: Applying TrafficMaster Speed Data to TPM/Bus Models: ......................................... 143

Appendix H: Changes in Emissions Factors: .................................................................................... 146

H.1 Oxides of Nitrogen (NOx as NO2) .......................................................................................... 146

H.2 Primary Nitrogen Dioxide (pNO2) ......................................................................................... 147

H.3 Particulate Matter (PM10) ..................................................................................................... 148

H.4 Particulate Matter (PM2.5) .................................................................................................... 149


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H.5 Total Emissions across Sub-Domains (NOx, NO2, PM10 and PM2.5) ....................................... 150

Appendix I: Vehicle Licensing Statistics obtained from DFT ........................................................... 151

Appendix J: Diurnal Profile Scaling Factors ..................................................................................... 155

Appendix K: Meteorological Data for 2010 .................................................................................... 156

Appendix L: Street Canyon and Topographical Data ...................................................................... 157

L.1 Canyons in the Gosforth AQMA: ........................................................................................... 157

L.2 Canyons in the Newcastle City Centre/Coast Road AQMA: .................................................. 158

L.3 Canyons in the Gateshead AQMA: ........................................................................................ 161

L.4 Additional Topographical Concerns: ..................................................................................... 161

Appendix M: Background Maps ...................................................................................................... 165

M.1 Oxides of Nitrogen (NOx as NO2) ......................................................................................... 165

M.2 Nitrogen Dioxide (NO2) ........................................................................................................ 166

M.3 Particulate Matter (PM10) .................................................................................................... 167

M.4 Particulate Matter (PM2.5) ................................................................................................... 167

Appendix N: Newcastle AURN Sites ................................................................................................ 169

Appendix O: Future-Year Traffic Growth ........................................................................................ 170

Appendix P: Future-Year Traffic Speeds ......................................................................................... 172

Appendix Q: Future-Year Fleets for non-NOx Pollutants ................................................................ 173

Appendix R: Apportionment of Emissions in AQMAS and Urban Cores ......................................... 174

R.1 Oxides of Nitrogen (NOx as NO2) ........................................................................................... 174

R.2 Primary Nitrogen Dioxide (pNO2) .......................................................................................... 175

R.3 Particulate Matter (PM10) ..................................................................................................... 176

R.3 Particulate Matter (PM2.5) ..................................................................................................... 177

Appendix S: Pollutant Concentrations in AQMAs and Cores .......................................................... 178

S.1 Oxides of Nitrogen (NOx as NO2) ........................................................................................... 178

S.2 Nitrogen Dioxide (NO2) .......................................................................................................... 181

S.2 Sensitivity of LEZ changes in NO2 to background NOx levels, and f-NO2 ratios ..................... 186


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Executive Summary This document presents a summary of the work done by the Transport Operations Research Group

(TORG) at Newcastle University, as part of the Newcastle/Gateshead Low Emission Zone Feasibility

Study. Within the document the development of a modelling framework is presented. This

framework leverages existing transport, emissions and air-quality modelling and data assets across

involved local authority, university and consultant partners, to produce a system capable of

calculating pollutant changes arising from Low Emission Zone (LEZ), or other transport policy

interventions.

The focus of the document is on an extensive technical summary, both of work done on the study,

and on complimentary projects at the University. It is divided into several main sections: a literature

review to provide context for LEZ development, developments of an initial modelling chain,

subsequent revisions to that chain in the light of initial results, calculation of emissions and

concentrations for the base year of 2010 as a validation exercise, through to final examination of LEZ

scenario options. Assessment of the scenarios was primarily based on their performance in reducing

total Oxides of Nitrogen (NOx), and Nitrogen Dioxide (NO2) levels, as these are the primary pollutants

of concern to the Local Authorities.

The following scenarios were tested, based on the assumption of implementation of an LEZ,

stretching across the entirety of the Newcastle and Gateshead Metropolitan Borough areas, coming

into effect in the year 2021:

1. A baseline, ‘Business and Usual’ scenario – using UK National Atmospheric Emissions

Inventory (NAEI) 2021 fleets for all vehicle classes;

2. A LEZ scenario where all vehicle classes are assumed Euro 5/V compliant;

3. As 2, but all vehicle classes are assumed Euro 6/VI compliant;

4. A LEZ scenario where only goods vehicles are assumed Euro 5 compliant;

5. As 4, but all goods vehicles are assumed Euro 6 compliant;

6 A LEZ where all buses are assumed Euro VI compliant, and;

7. A LEZ all passenger cars are assumed Euro 6 compliant.

All of the scenarios above make a key assumption regarding the effectiveness of incoming ‘Euro 6’

(light duty vehicles) and ‘Euro VI’ (heavy duty vehicles) regulations. Two further, pessimistic

scenarios have also been examined, exploring LEZ effectiveness under the assumption that the real-

world performance of the new regulations does not exceed that of existing vehicles (i.e. new Euro

5/V vehicle performance).

For scenarios where the Euro 6/VI regulations were assumed effective it was found that general fleet

turnover and renewal over the 2010 to 2021 period, coupled with emissions improvements in other

sectors, lead to an approximate 45% reduction (≈10 – 15 µg/m3) in mean NO2 concentrations for

receptor points in Newcastle City and Gateshead Air Quality Management Areas (AQMAs). This

reduction by itself is sufficient to significantly reduce the chances of exceedence of the National Air

Quality Standard for annual mean NO2 (currently 40 µg/m3) in those areas, though the potential for

‘hot-spot areas’ with excessively high concentrations may remain. Model output resolution was not

sufficient to examine these further.


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The LEZ options as tested gave up to a further 2 µg/m3 mean reduction in receptor point

concentrations over the 2021 BAU scenario, though reductions at specific points in the AQMAs could

be considerably higher, depending on the targeted vehicle type.

In terms of the effectiveness of proposed LEZ Measures in reducing NOx emissions and NO2

concentrations, over the 2021 BAU scenario, the following general rank order was found:

All goods vehicles Euro 5/V;

All vehicle classes Euro 5/V;

All goods vehicles Euro VI;

All cars Euro 6;

All buses Euro VI;

All vehicles Euro 6/VI.

It was noted that design of LEZ options based on applying the Euro V criteria for heavy goods

vehicles could be at best, relatively ineffective, or at worst, counter-productive. For the scenarios

where the Euro 6/VI regulations were considered ineffective, mean concentrations in the AQMAs

remained at around 75% of the 2010 levels, with evidence of exceedences of the NO2 air-quality

standard still present within AQMAs.

Least Effective

Most Effective


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1. Introduction This document presents a summary of the work done by the Transport Operations Research Group

(TORG) at Newcastle University, as part of the Newcastle/Gateshead Low Emission Zone Feasibility

Study, funded by a UK DEFRA (Department for Environment, Food and Rural Affairs) Air Quality

Grant. The work was undertaken on behalf of Newcastle City Council, over the period from January

2012 to February 2013.

1.1 Study Aims and Objectives The aim of this document is to provide supporting information for the feasibility and potential of

implementation of one, or more, Low Emissions Zones (LEZs), in order to address already identified

air quality issues within the Newcastle City and Gateshead Metropolitan Borough areas. The full

brief of the feasibility study includes the ‘implementation, operation, air-quality impact, viability,

costs, benefits and public acceptability of LEZ’.

Newcastle University was initially contracted (NCC, 2011a) to produce the following elements

towards the overall goal of the feasibility study:

1) A traffic emission inventory, by vehicle type and fleet age for nitrogen dioxide,

particulate matter and carbon dioxide, for Newcastle and Gateshead Air Quality

Management Areas (AQMAs);

2) A baseline assessment of existing air quality across the Newcastle and Gateshead

AQMAs, for nitrogen dioxide (NO2) and particulate matter (PM);

3) A baseline source apportionment analysis of emissions within the AQMA areas;

4) The remodelling of air-quality to show the effectiveness of the proposed LEZ for two

time periods (2 years and 5 years after implementation). Remodelling to include the

effects of road impacts and potential displacement of vehicles;

However, this document presents final work that differs slightly to the original outline as envisaged

in the study proposal, whilst retaining its spirit. The primary difference being that, rather than

remodelling two future time periods of the selected LEZ option, multiple LEZ options are presented

for a single, future year period (2021). Additionally, the potential for displacement of vehicles from

the LEZ areas has not been fully addressed in the modelling work to date.

The reason for these changes primarily comes down to timescale pressures arising from:

issues in the initial development of the baseline assessment model;

major changes to the emissions factors used during the study period, leading to subsequent

re-development of all original modelling;

The assessment procedure outlined above falls within the scope of the Screening and Intermediate

Assessment guidance for Emissions and Air Quality Impact Assessment outlined in sections 3.2.1 and

3.2.2 of DEFRA’s ‘Local Air Quality Management Practical Guidance 2: Practical Guidance to Local

Authorities on Low Emissions Zones’ (DEFRA, 2009). At all times the work was guided by the LEZ

Steering Group (see Appendix A) with changes to the work programme agreed at scheduled

meetings of this group. Also note that this document confines itself to those issues surrounding

emissions and air-quality only – no attention is given to public acceptability, travel times, operator

cost, regulator cost, enforcement issues or environmental issues other than carbon and local air


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quality, except on the broadest possible basis, to give context and inform discussion, where

appropriate.

1.2 Document Structure This report is structured into six main sections:

1) This introduction;

2) An introduction to transport and air quality in general, the situation in Newcastle and

Gateshead in particular, followed by a discussion of low emission zones, concentrating

on the primary mechanism for their implementation – the Euro standards. Attention is

focused on the potential of current and future technologies for reducing emissions of

oxides of nitrogen (de-NOx technologies), as well as previous studies of the effectiveness

of LEZs;

3) The development of the emissions and air-quality modelling framework for

Newcastle/Gateshead for baseline and future scenario assessment is outlined.

4) Using the framework described in Section 3, initial verification results, as well as the

baseline scenario results for both source apportionment and pollution concentrations

are presented;

5) Again, using the framework presented in section 3, a number of future LEZ scenario

options are presented, both from source apportionment and pollutant concentration

perspectives;

6) Final discussions, conclusions and recommendations for future detailed work, should the

proposal for an LEZ proceed beyond the initial feasibility study stage;


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2. Transport and Urban Air Quality This section provides background information on current air quality legislation within the UK, before

briefly discussing local transport and air quality issues in Newcastle and Gateshead, to provide

context for LEZ design. A literature review concerning the impact of the existing Euro 5 and incoming

Euro 6 standards to NOx emission reduction from vehicles forms the bulk of the section as this is

seen as key to the success of any proposed LEZ in the next decade. Information is also provided on

the retrofitting of older vehicles, development of emission zone on criteria alternate to Euro

standards and the effectiveness of other, already active, schemes.

2.1 Current Legislation and Key Pollutants The Statutory Instrument ‘Environmental Protection: The Air Quality Standards Regulations SI 2010’

presents legally binding limit and exceedence values for a number of key, ‘scheduled’ pollutants. The

SI codifies the legally binding targets set down in EU Directive 2008/50/EC for the United Kingdom.

Table 2.1 shows the current scheduled pollutants from the Air Quality Standards Regulations

associated with road transport, the limit values associated with those pollutants and the averaging

periods used in assessment.

Table 2.1: Key transport-related pollutants and relevant limit values (from Environmental Protection:

The Air Quality Standards Regulations SI 2010 No. 1001, Schedule 2, Regulation 17(1) and (2))

Pollutant Averaging Period Limit Value Nitrogen Dioxide (NO2) 1-hour

Calendar Year

200 μg/m3 not to be exceeded more

than 18 times a calendar year 40 μg/m

3

Particulate Matter (aerodynamic diameter <10μm) (PM10)

One day

Calendar Year

50 μg/m3 not to be exceeded more than

35 times a calendar year 40 μg/m

3

Particulate Matter (aerodynamic diameter <2.5μm) (PM2.5)

Calendar Year 25 μg/m3 (target for 1

st January 2005.

Tolerance limit of 20% of this value commences 11

th June 2008, and

decreases the next 1st

January and every 12 months thereafter by equal annual percentages to reach 0% by 1

st January

2015)

Carbon Monoxide (CO) Maximum 8-hour daily mean 10 mg/m3

Sulphur Dioxide (SO2) One hour 350 μg/m3 not to be exceeded more

than 24 times a calendar year 125 μg/m

3 not to be exceeded more

than 3 times a calendar year

Benzene (C6H6) Calendar Year 5 μg/m3

Lead (Pb) Calendar Year 0.5 μg/m3

Given the specific contributions of road transport to local concentrations of individual pollutants,

and changes in both vehicle fuel and emission control technologies since the list of scheduled


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pollutants was drawn up, generally only three pollutants are of direct concern: Nitrogen Dioxide

(NO2), Particulate matter with aerodynamic diameter under 10 microns (PM10 = ‘coarse fraction’

particles) and Particulate Matter under 2.5 microns (PM2.5 =‘fine fraction’ particles).

Nitrogen Dioxide is of concern due to its impact on the human respiratory system. High

concentrations of NO2 cause inflammation of the airways, whilst long-term exposure may affect

overall lung function (DEFRA, 2007a). NO2 is both emitted directly from combustion sources (so

called ‘primary NO2’), or is formed by photochemistry. During daylight hours the ratio of NO2 to

another compound of nitrogen, Nitric Oxide (NO) is governed by available sunlight and Ozone (O3)

concentrations. The combination of both NO and NO2 is termed NOx or total Oxides of Nitrogen. As

NO and NO2 have different molecular masses, in is usual to convert and report a mass of NOx as an

equivalent NO2 value (NOx as NO2) (CERC, 2011, Appendix B). Oxides of Nitrogen are formed in

combustion engines when oxygen reacts with nitrogen at high temperatures. NO2 levels are

expected to remain high, and exceed limit values in many European City Centres for some time to

come (Carslaw, Beevers and Bell. 2007, Grice et al. 2009).

Particulate matter may arise from many sources (e.g. remnants of combustion, secondary particles

from atmospheric chemistry, residues from brake or tyre wear, re-suspended dust, salt from sea-

spray etc.) Particles under 10 microns size ‘are likely to be inhaled into the thoracic region of the

respiratory tract’ (DEFRA, 2007a) and there is evidence to suggest that both PM10 and PM2.5 are

associated with a variety of health effects, with stronger correlations associated with PM2.5. Indeed,

at the time of writing there is no threshold concentration for fine particles under which they may be

considered to have no effect on human populations (DEFRA, 2007a) – hence the AQ standards (see

Table 1) adopt a policy of continual improvement based on ‘exposure reduction’.

The ‘Environment Act’ of 1995 paved the way for the introduction of the ‘National Air Quality

Strategy’ (NAQS). This document provides an overview of UK Government (and the Devolved

Administration’s) policy towards achieving the ambient air quality standards. Volume 1 (DEFRA,

2007a) of the strategy outlines policy, whilst volume 2 presents the evidence base to support those

policies (DEFRA, 2007b). Within the strategy, whilst it is recognised that national and international

efforts are required to reduce pollution, many local air quality issues are caused by transport,

especially road transport, and Local Authorities (LAs) have a major role to play in there amelioration.

Part IV of the Environment Act places a statutory duty on Local Authorities within England to

manage local air quality within their areas, through a regime of regular monitoring and assessment

against the air quality objectives. Where it is considered likely that a particular objective will not be

met, the LA should declare by order an ‘Air Quality Management Area’ (AQMA). The LA should

subsequently proceed to develop and implement an ‘Air Quality Action Plan’ to achieve compliance

in that area. Each AQMA is both defined by its geographic extent, and the pollutants for which

exceedences are expected to occur. ‘Policy Measure G’, outlined within NAQS, specifically addressed

the suggested implementation of low-emissions zones in London (now implemented, albeit in a

different form to that originally envisaged in the NAQS) and seven other urban areas in the UK –

including Newcastle (DEFRA, 2007c).


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2.2 Transport and Air-Quality in Newcastle and Gateshead

2.2.1 Declared Air-Quality Management Areas (AQMAs)

By way of introduction, the feasibility study project brief, received by Newcastle University from

Newcastle City Council in December 2011 states:

“The main sources of air pollution in Newcastle and Gateshead are attributable to road traffic

emissions due to traffic flows and congestion on key areas within the local road network throughout

the city. Hotspot areas have been identified as exceeding the NO2 annual mean objective for NO2 and

city centre AQMAs have been declared. Bus and HGV emissions contribute a substantial majority of

the emissions within the AQMAs. Although 80% of the bus fleet in Newcastle and Gateshead is Euro

IV compliant this has not resulted in lower concentrations of NO2. Gateshead town centre has also

been declared as an AQMA due to road traffic emissions, so the proposal is to investigate LEZ for both

districts.” (Foster, 2011).

Historically a number of AQMAs have been declared by Newcastle City Council. These have included:

the City Centre (NCC, 2004), Quayside (NCC, 2005a), adjacent to the A1058 Jesmond

Road/Cradlewell (NCC, 2005b), Blue House Roundabout (NCC, 2005c) and parts of the A189 and

B1318 Gosforth High Street (NCC, 2008). The three former, and the two latter AQMAs now currently

form two larger AQMAs, both declared for exceedence of the Nitrogen Dioxide annual mean

standard (i.e. 40 μg/m3 from Table 1). Within this study, the two areas are colloquially referred to as

the Newcastle City Centre and Gosforth AQMAs.

Within Gateshead there are two AQMAs currently declared (GC, 2005), Gateshead Town Centre (GC,

2005) and an area adjacent to services on the A1M at Birtley (GC, 2008) in the south of the region

(declared 01/04/2008). As with Newcastle, both of these areas were declared for exceedence of the

Nitrogen Dioxide annual mean standard. Within this study the two areas are colloquially referred to

as the Gateshead and Birtley AQMAs.

The location of the AQMAs within the larger Tyne and Wear region is shown in Figure 2.1, whilst

Figure 2.2 presents the AQMAs in the context of the urban centre of Newcastle/Gateshead and the

area of Gosforth.


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Figure 2.1: Location of Declared Newcastle and Gateshead Air Quality Management Areas (AQMAs)

within Tyne and Wear Region. Major motorways, A-roads and B-roads are also shown.

In support of the declared Air Quality Management Areas, and subsequent Air Quality Action Plans,

two air quality monitoring stations are run by Newcastle City Council – one sited in the City Centre,

adjacent to the council offices at Newcastle Civic Centre, and one to the east of the city centre at

Cradlewell. Both of these monitoring stations form part of the UK’s Automatic Urban and Rural

Network (AURN) for air-quality (DEFRA, 2012a). More information on the AURN sites may be found

in Section 4.2. Data from these sites has been used in support of the modelling work undertaken in

this study.

Both councils also possess and operate a number of non-AURN monitors for various pollutants, and

undertake regular assessments through the use of local diffusion tube monitoring of Nitrogen

Dioxide. Data from non-fixed sites has been made available to Newcastle University, by both

Gateshead and Newcastle City Councils, though it has not been used directly in this study.

Birtley

Crown Copyright all rights reserved Newcastle City

Council 100019569 2012


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Figure 2.2: Declared Air Quality Management Areas (AQMAs) in Newcastle and Gateshead with all

roads and locations of Automatic and Rural Network monitoring (AURN) sites shown

2.2.2 The NewcastleGateshead Urban Core Area

Both Newcastle and Gateshead Councils recognise the inter-relationship and co-dependence of their

two areas, their economic importance to the North East of England as a whole, and the present need

for sustained economic growth. A coherent and combined approach to local development planning

is given in the joint ‘NewcastleGateshead1 One Core Strategy’ (GC&NCC, 2011a). The urban core of

NewcastleGateshead is recognised as possessing ‘high levels of accessibility and sustainability’,

focused on the ‘government, higher education, business, shopping, leisure and tourism’ sectors. In

order to focus development of the ‘One Core Strategy’ a key ‘Urban Core Area’, encompassing both

Newcastle and Gateshead’s historic centres, has been identified. This Core Area, shown in Figure 2.3,

1 Whilst the term ‘NewcastleGateshead’ is the name used for the combined areas as considered in the One

Core Strategy, the nomenclature ‘Newcastle/Gateshead’ has been used within this report to refer generally to the two boroughs.


Council 100019569 2012


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has been adopted in the Council’s joint ‘Urban Core Action Plan’ (UCAP) (GC&NCC, 2011b), and

shows a high degree of overlap with the Newcastle City Centre and Gateshead AQMAs .

Figure 2.3: Newcastle and Gateshead Urban Core Area

Given the overlap between the core area, and the central AQMAs, the ‘Urban Core Action Plan’ puts

forward a number of Objectives and Policy Options that would potentially impact or influence the

design of any Low Emission Zone (LEZ) options. Transport-related Objectives and Options include:

‘Objective 6’: The adoption of a general prioritised hierarchy of travel modes within the Core Area

(in order: Walking, Cycling, Public Transport (including taxis), service vehicles and general traffic).

This objective influences subsequent policy options, including;

‘Policy Option 7: Pedestrians and Cycling’, including:

o Greater prioritisation of pedestrians and cycling infrastructure at the expense of

general car traffic;

‘Policy Option 8: Public Transport’, including sub-headings for:

o Greater priority to buses over cars, freeing up road space for buses;

o Utilising Urban Traffic Management and Control (UTMC) systems to improve bus

services;

o Working with bus operators to reduce carbon and other emissions;

o Rationalising movements of vehicles around Newcastle Central Station;

o Exploring relocation of Newcastle Coach Station to Central Station to form a

transportation hub and interchange;


Council 100019569 2012


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o Improving facilities for taxis;

o Introducing ‘layover’ facilities for coaches attending city centre events;

‘Policy Option 9: General Traffic, Parking and Servicing’, including:

o Introduction of ‘freight consolidation’ methods for the City Centre;

o Prioritisation of freight traffic over general car traffic, and reducing car traffic to a

‘more sustainable level’;

o Focussing traffic entering the centre onto strategic routes along the A189, A167 and

A184;

o Utilising Urban Traffic Management and Control (UTMC) systems to improve general

traffic routing;

o Development of a comprehensive parking strategy, including long-stay and peak-

demand, park-and-ride options out of the Core Area, accommodation short and

medium stay parking off-street within the Core Area and a general reduction of

private, non-residential parking for commuters.

The original LEZ feasibility study brief suggests that the following, example measures are within the

general scope of a low emission strategy for Newcastle and Gateshead:

Demand management actions;

Bus priority lanes;

Bus quality partnerships;

Freight quality partnerships;

Electric Vehicle charging points (Foster, 2011).

Hence, whilst these issues are not directly covered in this document, the detailed design and

assessment of any LEZ options affecting the NewcastleGateshead Urban Core Area must be

considerate of the LPT and UCAP proposals, and ideally, complimentary to them.

2.3 Low-Emissions Zones A Low Emission Zone or LEZ may be defined as a pollution control scheme, where certain vehicles

are forbidden to enter, or charged to enter a particular area. It aims to accelerate the uptake of low

emission vehicles (Foster, 2011) which will affect both the zone itself, and the wider fleet. As the aim

of an LEZ is to reduce concentrations of air-pollutants within its boundaries, generally those vehicles

with the largest gross contribution to emissions are targeted initially.

Many early LEZ (pre-2005) were aimed solely at reducing particulate matter from heavy duty

vehicles, as this was the most-cost effective way of implementation (DEFRA, 2009a), and particulates

were a primary health concern. However, the improved availability of de-NOx technologies across all

vehicle sectors (see next section) have enabled more recent proposals to cover both PM and NOx.

Given that the AQMAs in Newcastle/Gateshead are declared for NO2, the focus of this study has

been on LEZ options that aim to reduce NOx, whilst being mindful of the ‘exposure reduction’ policy

for particulate matter. Indirectly, measures introduced to combat NOx and NO2 emissions will also

have an effect on Ozone (O3) concentrations, due to complex photochemical reactions between

these pollutants.


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2.3.1 Source Emissions Reduction – The ‘EURO’ standards

Within the European Union, vehicle emissions are controlled at source through the application of

the ‘Euro Standards’, which proscribe set limits by pollutant on tailpipe emissions, over a particular

test ‘drive cycle’. Meting these limits is required for type approval of new vehicles being sold within

the Union. The standards themselves derive from amendments to the 1970 EU Directive 70/220/EEC,

though the initial Euro I standard was adopted across Europe in the early 1990s. Successive

iterations have been implemented approximately every 4-5 years since then. Initially the standards

covered only Carbon Monoxide (CO), Hydrocarbons and NOx (HC + NOx) and Particulate Matter, but

have subsequently been expanded to cover Total Hydrocarbons (THC), Non-Methane Hydrocarbons

(NMHC), Total NOx, and particulate number and/or smoke2.

At the time of writing, the Euro 6 standard for type approval of new Light Duty Vehicles (LDVs: cars

and light commercial vans) is due for implementation in the 2014-15 timeframe (type approval is

one year before first registration of vehicles), whilst Euro VI for Heavy Duty Vehicles (HDVs: heavy

commercial vans, rigid and articulated goods vehicles, buses and coaches) will come into effect

during 2013-143. DEFRA guidance on LEZs (DEFRA, 2009a) recommends that LEZs implemented from

2010 and 2012 should consider higher standards than Euro 3/III as a minimum, though ‘local source

apportionment’ should be used to identify target vehicles.

Whilst the implementation of the standards has been instrumental in reducing urban pollution via

driving abatement technologies forward, there has been concern in recent years that ambient NOx

and NO2 concentrations adjacent to roads have not reduced in commensurate fashion with the NOx

emissions standards (AQEG, 2007; Carslaw et al. 2007; Carslaw et al., 2011), nor have previously

modelled air-quality benefits materialised. The ‘Science for Environmental Policy’ bulletin of the

European Commission DG Environment, recently stated that ‘the most recent Euro 5 standard,

adopted in 2009… did not produce the desired reduction in on-road emissions’ (SEP, 2013).

Reported discrepancies between expected and observed emissions and concentrations of NOx and

NO2 have been explained by three, principal factors:

1. Total NOx emissions of vehicles when in use are generally higher than anticipated for all

vehicle types, and;

2. Whilst total NOx emissions may have reduced for passenger cars and light goods vehicles

over the period of the standards, evidence suggests that the fraction of NOx emitted as NO2

(called primary NO2 or f-NO2) directly at the tail-pipe may have increased in modern (Euro

3+) diesel vehicles, especially when exhaust-after treatment systems are employed (AQEG,

2007);

2 As well as tailpipe emissions, individual standards may also proscribe evaporative and crankcase emissions, as well as in-

service testing and acceptable deterioration after a certain mileage. Emissions referred to in this document are generally ‘tailpipe’ emissions, unless otherwise stated.

3 The LDV standards use Arabic numerals and define tailpipe emissions limits in terms of mass per distance (g/km), whilst

Roman numerals refer to HDV standards, which are defined in mass per energy output (g/kWh)]. A brief, but comprehensive, summary of the standards may be found in Delphi (2013a) and Delphi (2013b) for light duty vehicles, and heavy duty vehicles respectively.


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3. Given the global economic downturn from 2007 onwards, the renewal rate of the vehicle

fleet has been lower than anticipated as older vehicles have been retained in order to save

costs.

Regarding the first point, there is a body of research suggesting that on-road NOx emissions tend to

exceed emissions levels established through laboratory testing for type approval (Weiss et al., 2012).

This may arise from on-road behaviour being considerably different from Type Approval testing.

Rexeis and Hausberger (2009) note that the urban element of ‘New European Drive Cycle (NEDC)’

used for vehicle Type Approval for passenger cars requires relatively low engine loading, leading to

low NOx emissions. There is also evidence of vehicles being tuned by manufacturers to produce

specific, optimal performance over that test cycle, with ‘off cycle’ emissions being greater (so called

‘cycle-beating’).

Carslaw et al. made the following detailed observations, based on comparison of on-street Remote

Sensing Data (RSD) to NOx emissions calculated using the UK emissions factors (UKEF) and National

Atmospheric Emissions Inventory (NAEI) fleet information:

NOx factors for older petrol cars are higher than expected – possibly due to under-estimation

of the effect of deterioration of catalytic converters on these vehicles;

For diesel cars and vans, the data suggested little change in NOx emissions for perhaps the

past 20 years;

NOx emissions from HGVs appeared static over time, until introduction of Euro IV, where

emissions decreased by a factor of 1/3rd. For buses, NOx emissions appeared to be either

static or increasing slightly over the past 10-15 years;

For modern diesel cars (EURO III+) under high engine load conditions, an “increasing trend of

NOx emissions is observed … that is not apparent in older vehicles”).

In order to meet current Euro 5/V, and future Euro 6/VI NOx limits, two key de-NOx technologies are

applied to diesel engines, for both light and heavy duty vehicles. These technologies are Selective

Catalytic Reduction (SCR) and Exhaust Gas Recirculation (EGR). In SCR systems, oxides of nitrogen are

converted into nitrogen (N2) and water (H2O), via introduction of a reducing agent, typically

ammonia (NH3). In EGR systems, captured exhaust gases are passed back, via a control valve, to the

intake of the engine cylinders. This reduces oxygen in fuel/air mix, which in turn lowers combustion

temperature and greatly reduces NOx formation. Lean NOx traps (LNTs), in which zeolites

(microporous aluminosilicates) are used to absorb up to 90% of NO and NO2 in exhaust gasses also

offer a potential way forward, (Parks, Ferguson and Storey, 2012).

The problems of application of de-NOx technologies to diesel engines are compounded by the need

to meet standards for particulate matter, for which vehicles are fitted with some form of diesel

particulate filter (DPF) technology – typically Continually Regenerating Trap (CRT) systems, where

accumulated soot is burnt off in an NO2 rich atmosphere. Such technology is mature, having been

refined over the previous decade, and considered capable of reducing PM emissions by between 30-

95% (90% is the value currently assumed in the UK EFTv5.1.3 for DPF fitted vehicles, DEFRA, 2012d).

Therefore, an understanding of how de-NOx systems interact with DPF, CRT and other catalyst

systems, such Diesel Oxidation Catalysts (DOC) used to reduce particulate matter and other

emissions (i.e. HC + CO), is required. As highlighted by the Air Quality Expert Group (AQEG, 2004),


21

complex interactions between systems may give reduction in total NOx that potentially comes at the

expense of reduced engine efficiency, increased CO2 emissions, increased PM emissions, increased

primary NO2 emissions (Heeb et al., 2011; Hu et al., 2012), increased emissions of other pollutants

(e.g. ammonia, NH3) or greenhouse gases (e.g. CO2 is produced if urea is used as the ammonia

source in SCR, or Nitrous Oxide (N2O) may be produced from SCR chemistry (AQEG, 2004). ‘Ammonia

slip’ where low-temperatures lead to unreacted ammonia in the exhaust is also an issue in SCR

systems.

The Euro 6 regulations (see: EC 715/2007, EC 692/2008 and (EU) 566/2011) for diesel LDVs

mandates a 55% reduction in NOx emissions over the Euro 5 standard - from 0.18 g/km to 0.08 g/km

for passenger cars and N1-I light vans, from 0.235 to 0.105 g/km for N1-II vans and 0.280 g/km to

0.125 g/km for N1-III vans. There are no relevant changes to NOx limits between Euro 5 and Euro 6

for petrol vehicles.

Recent research by Weiss et al. (2012), based on Portable Emissions Monitoring System (PEMS)

measurements on a fleet of Euro 4 and 5 diesel passenger cars, plus a single Euro 6 compliant vehicle

using SCR, suggested that the NOx performance of the Euro 6 car was indeed better than the earlier

vehicles (average Euro 6 NOx = 0.21 ± 0.09g/km vs. 0.76 ± 0.12g/km for Euro 4 and 0.71 ± 0.30g/km

for Euro 5), over a wide range of driving conditions. However, on road emissions of all vehicles

substantially exceeded the relevant emissions standard by 260 ± 130%. Whilst it may not advisable

to draw general conclusions from a single vehicle, other data on the on-road performance of

modern diesel cars is extremely limited (Rhys-Tyler, Legassick and Bell, 2011) and Euro 6 diesel car

and light commercial van performance is a fundamental question to the effectiveness of any future

low emission zone.

It has been suggested (Ligertink et al., 2009; Rexeis and Hausberger, 2009; Verbeek et al., 2010) that

SCR systems in heavy duty vehicles may not be effective under urban driving conditions. Operating

temperature plays a large role in SCR efficiency, and urban operations may result in an engine never

reaching the high temperatures required for optimum SCR performance. Based on PEMS analysis of

trucks fitted with SCR systems Ligertink et al. (2009) reported on-road emissions in urban conditions

which were three times higher than the Euro V standard to which the trucks supposedly conformed,

whilst under motorway conditions the SCR system performed well. A follow up study by Verbeek et

al. (2010) suggested high variability in the urban NOx emission performance of Euro V vehicles, by up

to a factor of six between the lowest and highest emitters. Again average urban NOx emissions were

found to be ‘a factor of two to three higher than the expected level based on until recently used

emissions factors’, with SCR performing adequately at speeds over 70km/h. The three-fold

discrepancies in heavy duty NOx emissions carried over into a doubling of total road length expected

to exceed NO2 limit values in the Netherlands (Velders, Geilenkerchen and de Lange, 2011).

Ligertink et al. (2009) reported that an EGR heavy vehicle achieved better NOx performance in urban

conditions, compared to any comparable SCR vehicles tested, a finding backed up work by Rexeis

and Hausberger (2009) who reported greater consistency between urban and motorway conditions

for EGR heavy vehicles. This implies that knowledge of the distribution of the technologies is

important in predicting both current, and in the short-term, future urban air quality, based on

adoption of Euro V vehicles. Carslaw et al. (2011) suggested a ratio of 80%:20% SCR:EGR systems,


22

based on limited knowledge of the heavy duty market at the time of writing, whilst the current NAEI

Euro V fleet (Venfield and Pang, 2012) assumes a 75%:25% split by default.

The Euro VI regulations (see: Reg EC No. 595/2009, EU 582/2011, EU 64/2012) for HDVs mandates a

75-80% reduction in NOx emissions over Euro V - from 2.0g/kWh to 0.46 g/kWh for transient testing

of positive ignition vehicles, and from 2.0 g/kWh to 0.4 g/kWh for steady state testing of

compression ignition engines. Rexeis and Hausberger (2009) note that these changes:

“… are (from a technological point of view) the single most drastic step ever performed within the

changeover of an emissions standard. From today's point of view, the applied engine concept is

uncertain, since several combinations of combustion concepts and exhaust after treatment seem

possible. Therefore, the prognosis on the real world emission levels of Euro-VI vehicles has to be

considered to be rather uncertain, especially for NOx.” (Rexeis and Hausberger, 2009).

Future HDVs will require active electronic engine management and variable geometry turbocharging

(VGT), high levels of EGR, further oxidation catalyst use, active particle filtering, all alongside SCR

treatment to reach the Euro VI standard, whilst aiming to keep fuel consumption and CO2 emissions

at comparable levels to existing Euro V vehicles (Baker et al., 2009; DAF, 2013; Delphi, 2013b;

ECOpoint, 2013). Additional technology costs are expected to drive up the average price of an Euro

VI goods vehicle by approximately 5% over a similar Euro V vehicle, whilst fuel consumption may

increase by 0-3% (TTR, 2009).

In order to ensure compliance with the standard under real world conditions the testing regime

(Regulation (EU) No 582/2011, OJEC, 2011) for Euro VI includes new ‘off-cycle’ and ‘in-use’

conditions, as defined by UNECE (United Nations Economic Commission for Europe) global technical

regulation No.4 (UNECE, 2007).

The issue of primary NO2 has also become of increasing importance over the past few years

(Murrells, 2011); given the trend for UK fleet-operators and consumers to purchase diesel cars on

the basis of their ‘green credentials’ in terms of fuel consumption and CO2 emissions. The diesel car

market surpassed the petrol car market in terms of sales in 2011 (SMMT, 2012). AQEG (2007) noted

that the general assumption that the fraction of primary NO2 (f-NO2) in vehicle exhaust emissions

was of the order of 5-10%, based on ‘engine out’ measurements from McCrae et al., 2002). With

‘dieselification’ of the UK fleet, and after engine exhaust treatment technologies, the actual 2009

value of f-NO2 was estimated to be in the order of 15-16% (Carslaw et al., 2011), and may reach

approximately 25% in the short term, before decreasing again (DEFRA, 2012b; DEFRA, 2012c).

2.3.2 Present and Future NOx Emissions

At the time of Carslaw et al.’s discussion, the UK vehicle speed-related emissions factors for NOx

were based on the findings of a series of reports (Boulter, 2009; Boulter and Latham, 2009; Boulter,

Barlow and McCrae, 2009; Boulter et al., 2009a; Boulter et al., 2009b) produced by the Transport

Research Laboratory (TRL), on behalf of the Department for Transport (DfT). These factors

incorporated into DEFRA’s Emissions Factor Toolkit (EFT) version 4 (DEFRA, 2010). The NOx emissions

factors in EFTv4 have subsequently been replaced by the COPERT4 (Emisia, 2009) emissions factors

incorporated into the latest toolkit, to address the shortcomings expressed by Carslaw et al. (2011).

Likewise, the NAEI vehicle fleet assumptions, discussed by Carslaw et al., have been superseded by


23

new information, taking into account economic conditions (Venfield and Pang, 2012). As of January,

2013, the most current tool kit is version 5.2c (DEFRA, 2013).

Figure 2.4 presents the NOx emissions at 50km/h under urban conditions for the default 2013 non-

London, urban vehicle fleet, calculated using the latest EFT v5.2c. Absolute NOx emissions rates

range from 0.11 g/km for petrol cars, to 5.7 g/km for coaches, with the fleet-weighted average

(based on vehicle kilometres travelled, VKM) of 0.45g/km. The green line shows the percentage

contribution of that vehicle category to the fleet weighted total (‘source apportioned’ emission),

diesel cars being the greatest contributor (36%), followed by diesel LGVs (19%), rigid HGVs (14%) and

buses (9%).

Figure 2.4: Comparative NOx emission rates at 50km/h for vehicles in the English Urban Fleet for 2013

(using EFTv5.2c, DEFRA, 2013)

Figure 2.5 presents the same information, on the same axes scales, but calculated for the default

NAEI 2020 fleet, where 47-94% of VKM travelled, are by vehicles that meet the EURO 6/VI standard

(depending on vehicle type petrol LGVs lowest percentage, articulated HGVs highest),. Absolute

emissions rates fall to a range between 0.06 g/km for petrol cars (47% decrease over 2013) and 2.2

g/km for coaches (62% decrease), with the most marked decrease being for articulated HGVs (86%

decrease), with the fleet-weighted average dropping to 0.22 g/km, a 51% decrease overall. Looking

at relative contributions to the fleet-weighted total, diesel cars are predicted as contributing over 50%

of the total NOx emissions, followed by diesel LGVs (22%), rigid HGVs (6%) and buses (5%). In effect,

the predicted NAEI fleet turnover for the vast majority of heavy duty vehicles to conform to the

EURO VI standard by 2020 increases the relative contribution expected from Light Duty Vehicles.

Figure 2.6 presents sample NOx emissions rates for the 3.5-7.5t rigid lorry sub-category of the NAEI

fleet. These vehicles make up the single largest proportion of the heavy goods vehicle component of

the UK NAEI fleet in urban areas. It is interesting to note that:


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The curve for Euro II vehicles is marginally higher than that for Euro I vehicles;

the Euro V SCR curve is comparable to that of earlier Euro III vehicles at speeds below

30km/h (which would be found in congested urban conditions). At speeds below 20km/h,

the EURO I, III and V SCR curves are almost indistinguishable. In modelling UK-wide NOx

emission Oxley et al.(2012) commented that this ‘effectively equates to a failure of the Euro

standard (in urban areas)’.

Figure 2.5: Comparative NOx emission rates at 50km/h for vehicles in the English Urban Fleet for 2020

(using EFTv5.2c, DEFRA, 2013)

Figure 2.6: NOx emission rates with speed for Rigid Lorries (7.5-12 tonne weight category) of differing

Euro class (using EFTv5.2c, DEFRA, 2013)

2.3.3 Retrofitting Vehicles

In addition to the application of emission control technologies as standard to new vehicles, existing

vehicle fleets may be ‘retrofitted’ with DPF or SCR exhaust technologies. This offers a cheaper


25

alternative to the outright purchase of a new vehicle, or replacement of the engine in an existing

vehicle, though it is considered possible that the cost of retrofit may be higher (£4,000-14,000

capital cost for SCR and SCR+DPF systems) than the residual value of the vehicle, especially pre-Euro

IV vehicles (AMEC, 2011). There is also the on-going cost to operators of urea consumption

(estimated at £400-£1,500 per vehicle per annum) plus fitting and servicing costs. If a DPF has

already been previously fitted to a vehicle, there may be an issue with lack of space to fit a further

SCR system (AMEC, 2011).

For LEZ modelling, there is a need to understand how such retrofitted technologies impact emissions

from older vehicles. The latest EFTv5.2c (DEFRA, 2013) incorporates emissions factors that suggest

better NOx performance from retrofitted Euro II, III and IV buses, than for new Euro V SCR buses at

speeds below 35km/h, see Figure 2.7. Data for retrofitting for heavy vehicles other than buses is

limited (AMEC, 2011), and these are not considered in the latest EFT (DEFRA, 2013).

Figure 2.7: NOx Emission rates for Euro V and VI buses (15-18 tonne weight category) compared to

earlier class + SCR Retrofit (SCRRF) buses (using EFTv5.2c, DEFRA, 2013)

[Note that the emissions factors presented in Figure 3, and similar factors for other bus and coach weight classes only

became available in January 2013 – after the bulk of modelling work for this study was completed].

Retrofitting also leads to the potential of a vehicle meeting one set of standards for a particular

pollutant, but not for others (e.g. a retrofitted DPF may mean that a bus meets Euro III or IV for PM,

but not for NOx). This has implications for LEZ design if a ‘split’ approach is taken regarding different

pollutants (e.g. regarding the initial London LEZ proposals, the bus-operator Arriva suggested that

proposed standards for PM could be met via DPF retrofit to their existing London fleet, but

application of additional NOx standards within a short timeframe, would lead to prohibitive vehicle

or engine replacement expenses (TfL, 2006).

Examples of retrofitting for NOx reduction to meet LEZ criteria include Phase 5 of the London LEZ

(implementation in 2015). As of 13th February 2013, this has been amended to only apply to TfL


26

operated buses in 2015, rather than heavy duty vehicles, but will involve the retrofitting of 900

EURO III buses with SCR systems, replacement of another 900 EURO III buses with EURO VI

equivalents and the introduction of 600 hybrid buses by 2015 (with a further 600 introduced in

2016). It is estimated that the revised scheme will still ‘deliver 75% of the NOx reductions of the

original scheme’ (TfL, 2013). Coyle (2012), reporting on the London Bus Euro III SCR retrofit,

presented the following:

Whilst engine out levels of f-NO2 from baseline London Euro III fleet buses tended to be

around 10%, use of an oxidation catalyst potentially increased f-NO2 levels;

The retrofit standard was based on requirements that:

o A 70% reduction in NOx levels relative to the baseline was achieved (given the

temperature dependency of SCR efficiency, London had developed its own London

Bus Test Cycle for assessment);

o That CO2e (CO2 equivalent) emissions should not increase by more than 5% of total

CO2 emissions over the test cycle. CO2 itself should not increase by more than 1%

(within test repeatability);

o That there should be a 50% reduction in NO2, post-any NOx abatement equipment in

the exhaust, over engine out NO2 levels;

o That ammonia slip from the SCR would also be limited and controlled.

Arrowsmith et al. (date unknown) report that a trial on several buses using and Eminox combined

SCR and CRT (Continuously Regenerating Trap particle filter system) produced 87% NOx reductions in

chassis dynamometer testing (12,000 miles of drive cycle testing), and 77% reductions in-service (6hr

short-term measurements). Reductions in other pollutants (HC, CO, PM) testing were all over 85%,

based on the dynamometer testing. Coyle (2012) reported similar results for NOx from the

dynamometer test of a single bus, alongside a 55% reduction of NO2, at the expense of a 3% increase

in CO2 emissions, and large increases in releases of nitrous oxide (N2O) and ammonia (NH3).

The current LEZ in operation in Norwich since 2008 has restricted access in the Castle Meadow area

of the city to buses achieving Euro III standard or better. Implementing the LEZ on a phased-

approach of retrofitting an increasing percentage year-on-year of Euro II buses operated within the

city with SCR equipment. Grants of up to 65% of retrofit costs were offered by the council to

operators (Watt, 2011). The SCR catalysts retrofitted to the Euro II buses are claimed to deliver NOx

reductions of ‘up to 64%’ (Eminox, date unknown). Original values stated in the case for support

(DEFRA, date unknown) of the LEZ cite potential NOx reductions of 30-70% for SCR, and 40-50% for

EGR, though it is not clear as to which vehicles these values apply.

The pending Oxford LEZ allows retrofitting of buses in order to reach Euro V NOx compliance by 1st

Jan 2014 (LEEZEN, 2008), and has been anecdotally cited as promoting ‘healthy competition’

between the cities two main bus operators (OG, 2012).

AMEC (2011) discarded analysis on the costs of retrofitting EGR to heavy duty vehicles on the basis

that EGR retrofit requires ‘extensive engine rebuilds’ and trials have led to ‘higher fuel consumption,

increased PM emissions and a reduction in performance, whilst in some cases failing to reach a

target NOx reduction of 50%’. Likewise AMEC (2011) reported ‘no evidence for LNTs applied as

retrofit solutions for heavy duty engines has been found’, and discounted analysis of the technology.


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2.3.4 Alternative LEZ Compliance Criteria

Aside from using compliance with the EURO standards to control vehicle access, an alternate

strategy is that of limiting vehicles simply by age – for example the LEZs in the Swedish cities of

Gothenburg, Helsingborg and Lund limit access to vehicles under 6-8 years old (LEEZEN, 2008). The

expected lifespan of a fleet-operated heavy-duty vehicle is typically of this order (TfL, 2006; TTR,

2009), with DfT statistics giving the annual number of years since first registration of HGVs in the UK

as 7.35 years in 2011 (DfT, 2012a), with just under 3% of annual UK HGV registrations being in the

North East region (DfT, 2012b). For buses the DfT Public Vehicle Survey (DfT, 2012c) gives the

average age of fleets in English metropolitan areas as 7.9 years in 2011. The economic situation has

also lead to the current car fleet being the oldest in 14 years (BCA,2012), with DfT statistics giving

the average age since first registration as 7.54 years (DfT, 2012d). A similar situation exists with light

commercial vehicles, where the average age since first registration is 7.57 years (DfT, 2012e). The

age of vehicles within the North East region, and Newcastle/Gateshead in particular is considered

further in section 3.3.4.2.

DEFRA guidance (DEFRA, 2009a) also suggests consideration of LEZs based on Vehicle Excise Duty

(VED). This would have the effect of altering the fleet profile for cars depending on engine size and

CO2 emissions, giving a trend towards smaller engines and lower emissions in the LEZ. Consideration

of the handling of alternate fuel vehicles would need to be given. For heavy goods vehicles, using

VED would be the equivalent of a weight restriction, again potentially leading to lower emissions.

For light goods vehicles VED is partially based on engine size or Euro standard already, depending on

the tax class of the vehicle (DVLA, 2012). Unless changes to the basis to VED are made for the car

and HGV classes, it is difficult to see how NOx/NO2 issues could be directly tackled through its use as

an LEZ criteria.

2.3.5 Alternative and Complimentary Policies to Low Emission Zones

Other policy options to reduce urban air pollution may be considered to compliment the option for a

‘pure’ LEZ. Some options already considered for Newcastle and Gateshead have been suggested in

Section 2.2.2. Indeed, such options may be necessary where a proposed LEZ alone will not have the

impact desired. For example, other measures included in the Norwich LEZ package of options were:

converting a small number of Euro VI+ taxis (5) to LPG (Liquid Petroleum Gas) fuel, restricting bus

idling and the ‘softer’ measure of offering free training to fleet operators in ‘eco-driving’ techniques

(Watt, 2011). Watt presented evidence of a 16% reduction in fuel consumption based on bus

operator data as one of the benefits of the eco-driving training.

Wolff and Perry (2010) list other policy measures used in Germany to impact urban air quality. These

include:

Expanding public transportation;

Utilising ring roads to bypass central areas;

Improving and smoothing flow via Urban Traffic Management and Control (UTMC).

Wolff and Perry (2010) also note that ‘direct price incentive’ policy instruments, such as road pricing

and congestion charging (e.g. Singapore, London and Stockholm) may have the same benefits as Low

Emission Zones – though the London experience with the Congestion Charging Scheme (CCS)

seemed to suggest limited air quality benefits have been realised, probably due to the small area


28

covered by the scheme in relation to the larger urban area around it (Kelly et al., 2011), non-

transport emissions and countervailing trends in vehicle NOx and f-NO2, (TfL, 2007; TfL, 2008).

Other alternate policies include:

License Plate Programs (LPPs) where driving is prohibited on certain days, based on digits

within license plate (popular in South American Cities, and recently introduced to Beijing,

China). Anecdotally, such programs may encourage the purchasing of second vehicles to

circumvent the restriction;

Partial or Total bans on traffic within an area. These may be either time-based bans for

certain vehicle types (e.g. HGVs restricted to off-peak loading and unloading only), or

threshold-based bans when air quality is predicted to exceed limits (with the drawback that

this requires dynamic forecasting and monitoring of air-quality);

Pedestrianisation of city centre areas;

Reallocation of road space to favour walking and cycling over car traffic;

Utilising ‘traffic cell architecture’ where traffic can travel freely within a given cell, but must

either use defined roads to travel between cells, or is prohibited from moving between

certain cells. The idea dates back to the 1960s and was used in Bremen, Germany and

Gothenburg, Sweden (Vuchic, 1999).

Note that none of the above policies are under consideration in this study; they are mentioned for

the sake of completeness.

2.3.6 Effectiveness of Low Emission Zones

Wolff and Perry (2010) state that there are 152 cities in nine EU countries which have implemented

LEZs, with Germany being at the forefront of their creation. The LEEZEN website currently lists

almost 350 applied LEZ controls across the EU, dating back to 2002. However, the vast majority of

these controls apply to Euro 4/IV or previous vehicles, or consider implementation for particulate

matter only, with any NOx/NO2 reductions viewed as an ‘added bonus’. It is therefore an open

question as to how effective a future LEZs targeting compliance for EURO 5/V and 6/VI vehicles will

be.

Likewise, whilst there is an existing body of literature (e.g. Cloke et al., 2000; Carslaw and Beevers,

2002; TfL, 2008; Boogaard et al., 2012) that has examined the effectiveness of LEZs, many studies

have utilised pre-implementation emissions modelling, rather than post hoc analysis of monitored

air-quality data. There appears to be some evidence that earlier LEZ feasibility studies may have

been optimistic, with expected emissions benefits on paper not necessarily materialising in

concentration reductions in the real world. Kelly et al. (2011), in examining the impacts of the

London LEZ (introduced in phases from 2008 – starting with EURO III compliance for heavy vehicles,

followed by EURO 3 compliance for LGVs in 2010, and EURO 4/IV compliance for HGVs in 2012, TfL,

2013), estimated PM10 emission reductions of 6.6%, and NOx emission reductions of 7.3% by 2012,

but also concluded that predicted changes in concentrations were ‘generally small… and would be

difficult to detect in actual monitoring data’.

Boogard et al. (2012), looking at roadside and sub-urban monitoring data from five Dutch cities,

from one year before and two years after policy implementation, concluded that ‘local traffic

policies including LEZ were too modest to produce significant decreases in traffic-related air


29

pollution’. However, in one urban street (Stille Veerkade, The Hague) where multiple measures, in

addition to the LEZ were implemented, leading to a reduction in traffic intensity of over 50%, NOx

and NO2 levels fell by 39.5% and 13.4% respectively (with NO2 levels falling from 54.1 µg/m3 to 40.7

µg/m3). This is compared to changes across the other sites studied of between +5.2 - -21.6% for NOx,

and +2.5 - -8.4% for NO2. Boogard et al. (2012) also noted that changes between sites tended to

diminish when meteorological variations are taken into consideration. Carslaw and Beevers (2002)

point out that the non-linear relationship of NO2 and NOx concentrations do not necessarily mean

that a reduction in total NOx leads to the same relative reduction in NO2, locations with the highest

NOx levels show the smallest reduction in NO2 as NOx emissions reduce.

As mentioned in the previous section, regional UK LEZs formed the heart of ‘Policy Measure G3’,

reported by the DEFRA Interdepartmental Group on Costs and Benefits (IGCB) (DEFRA, 2007c) as

part of the NAQS. This policy considered the benefits and costs associated with the introduction of

LEZ in the central area of Newcastle (amongst 7 other cities), hypothetically during the year 2010,

affecting HGVs, Buses and Coaches, bringing those vehicles to at least Euro II + RCP (Reduced

Pollution Certificate) standard.

The IGCP noted that LEZ policy option G3 for the regional cities would result in ‘significantly reduced’

benefits over the London scheme due to delayed opening of the LEZs in the regions, in turn leading

to ‘less high-polluting, older vehicles relative to the baseline’. It was also noted that there were

lower benefits per tonne of emission produced in the regional cities due to lower population

densities in comparison to London and hence reduced damage costs (DEFRA, 2007c). Both

statements will hold true for implementation of a theoretical LEZ in Newcastle, the longer the delay

in implementation, the more like the baseline an LEZ fleet will become, with a commensurate

reduction in potential benefits. Likewise, the population density the NewcastleGateshead area

remains far lower than the capital (Gateshead Metropolitan Area: 1410 persons/km2, Newcastle

Metropolitan Area: 1965 persons/km2, Inner London: 10160 persons/km2 (ONS, 2012). The DEFRA

guidance on LEZs (DEFRA, 2009a) also makes the point that setting the earliest possible compliance

date yields ‘more local air quality and emissions benefits, but usually at higher costs’.

The IGCP concluded that LEZ options would benefit roadside concentrations in central urban areas

(reducing exceedences in terms of km of urban roads by 0-33% depending on pollutant), potentially

reduce noise levels (albeit only a minor reduction associated with introduction of newer, quieter

vehicles), and possibly have a positive social justice aspect (benefitting deprived areas adjacent to

city centres). However, it was also noted that impacts on human health and on urban ecosystems

(based on critical load assessment) were negligible or not readily quantifiable. Additionally, as

studied, LEZ options were also thought to have a ‘potential negative impact’ on competition, with

‘possible disproportionate effects on small businesses’ (though more detailed assessment of specific

implementation options was recommended to quantify any impacts). It was noted that LEZ options

disproportionally affect fleet operators ‘predominantly or solely‘ operating in covered areas, and

those operators requiring specialist vehicles (usually having longer operating and replacement cycles

than regular vehicles). LEZ operation in turn could distort the second-hand market for vehicles by

reducing re-sale values of older vehicles, affecting operators and leasing companies (DEFRA, 2007c,

Ch4, Para 77).


30

Carslaw and Beevers (2002) note that ‘even ambitious LEZ scenarios in central London produce

concentrations of nitrogen oxides that are achieved through a do nothing scenario only five years

later’, given assumptions on the performance and turnover of Pre-Euro to Euro 3 vehicles in the

capital. The DEFRA guidance (DEFRA, 2009a) states that LEZ recommendations should ‘produce

three to four years’ benefits. A similar situation is likely to exist with the introduction of Euro 5 and 6

vehicles in the context of a Newcastle/Gateshead LEZ.

Therefore, based on the literature, key compounding factors in the assessment of the effectiveness

LEZ measures for Newcastle/Gateshead will include:

Local fleet considerations (i.e. existing base fleet and future turnover rate), as well as

network operating conditions, and concentration on particulate matter, lead to limited

transferability of results from pre-existing studies;

Import of pollution from outside of the LEZ area that may not be successfully accounted for,

leading to overestimation in modelled benefits (e.g. Kelly et. al., 2012);

Meteorological effects, leading to general changes in pollution that are greater than

observable LEZ effects (Boogard et al., 2012) - e.g. the unusually cold year of 2010, leading

to elevated NOx concentrations (DEFRA, 2012e);

Displacement of traffic to non-considered areas (e.g. Carslaw and Beevers, 2002);

Real-world effectiveness of Euro standards under urban driving conditions (Carslaw et al.,

2011), especially for Euro V heavy duty vehicles;

The lack of hard data on the performance of Euro 6/VI vehicles of all types.

Ideally, the methodological approach used to assess the Newcastle/Gateshead LEZ options should

attempt to address these compounding factors in its structure and implementation.

Based on the four conclusions of the DEFRA guidance document (DEFRA, 2009a), paraphrased below,

the following recommendations may be drawn:

1. Appropriate emissions standards for the LEZ must be set to achieve objectives, bearing in

mind costs to operators. Higher standards yield bigger potential reductions. For the case of

Newcastle and Gateshead (or the rest of the UK) this will generally mean application of

either the Euro 5/V or 6/VI standards;

2. When setting a base year for implementation of an LEZ, ‘earlier is better’ in terms of

emissions and local air quality outcomes, at potential greater expense. The question of base

year is an open one, though given the current economic climate ‘later rather than sooner’ is

expected. This issue is discussed further in sections 3.3.1 and 4;

3. That after initial introduction of the LEZ, subsequent, more rigorous phases be considered,

‘otherwise the benefits of the policies will be eroded by natural vehicle replacement rates’.

More rigorous phases after initial implementation have not been directly considered in this

study, though changes in standards across vehicle types are discussed in Section 5;

4. Emission standards and implementation year need to be balanced against costs, including

‘the level of action required to achieve the air quality objectives of the AQMA’. Whilst no

consideration of costs is given in this document, the level of action require in AQMAs is

partially addressed in Section 5.


31

3. Modelling Framework Development This section outlines the development of the air-quality modelling framework for the LEZ study. The

pilot framework initially created as a ‘proof of concept’, is presented alongside results. This pilot

informed number developments for both traffic and emissions modelling. These are described along

with their calibration and validation, before the final modelling frame work is presented. The use of

the framework to model current air quality and future LEZ scenarios, are presented in Sections 4 and

5 respectively.

3.1 Proposed Methodology The initial methodology proposed by Newcastle University was to develop a modelling chain based

on combining pre-existing data and components, from within the University and the respective

councils, to ensure a rapid, cost-effective approach.

Network and traffic intensity data from the Tyne and Wear Transport Planning Model (TPM) would

be processed by Newcastle University’s own PITHEM (Platform for Integrated Traffic, Health and

Emissions Modelling) software (Namdeo and Goodman, 2012), which would subsequently produce

daily and annual emissions estimates. Emissions estimates from PITHEM would be directly allocated

to sources for either vehicle- or link-based apportionment, as well as passed on further to air quality

modelling software in order to calculate pollutant concentrations. The software chosen for the latter

element was ADMS-Urban (CERC, 2011). All output elements (i.e. from TPM, from PITHEM and from

ADMS Urban) would be linked within GIS (Geographical Information System) for subsequent analysis

and display. The chosen GIS platform was ArcMAP, part of the ArcGIS geospatial processing suite

(ESRI, 2012). Figure 3.1 outlines the general workflow, and linkages within, the proposed

methodology.

Figure 3.1 Proposed Modelling Methodology for the Newcastle/Gateshead LEZ Feasibility Study


32

The system components themselves and the reasons for their selection are briefly outlined in

sections 3.1.1 to 3.1.4 below.

3.1.1 Tyne and Wear Transport Planning Model

The Transport Planning Model (TPM) is a large scale, strategic, multi-modal transport model,

covering all five metropolitan boroughs (Newcastle, Gateshead, Sunderland, North Tyneside and

South Tyneside) in Tyne and Wear. Jacobs Consultancy undertook initial development in 2006 with a

remit to provide ‘a system capable of realistically representing and accurately assessing most travel

behavioural responses to transport policy in order to appraise future transport scenarios and

packages in Tyne and Wear’ and is ‘broadly based on the principles and guidance included in DfT’s

WebTAG’ (Jacobs, 2008a). The trip distribution, modal split and trip assignment elements of the TPM

are built around the CITILABS CUBE/TRIPS package (CITILABS, 2013).

The TPM model was selected as appropriate for this study as it:

Was considered a ready source of transport information for Newcastle and Gateshead (i.e.

the data covered in the Technical Notes submitted by Jacobs Consultancy to the Tyne and

Wear Joint Transport Working Group, Jacobs, 2008a; 2008b; 2008c; 2008d);

Has sufficient coverage to model either the region as a whole, as well as the

Newcastle/Gateshead urban areas in sufficient detail, bearing in mind its purpose as a

strategic tool;

Has previously been recognised as ‘fit for purpose’ by the DfT and the Highways Agency;

Has previously been used to support the various council’s LTPs, TIF (Transport Infrastructure

Fund) bids and as part of the previous regional DaSTS (Delivering a Sustainable Transport

System) programme (Jacobs, 2010);

Is currently in use by Newcastle University staff in support of the EPSRC (Engineering and

Physical Sciences Research Council) funded SECURE (SElf-Conserving Urban Environments)

project (SECURE Consortium, 2013);

The baseline version of the TPM used in the initial stages of this study was Version 3.1, with O-D

matrix and network data for a base year of 2005. The calibration and validation of this version is

reported in Jacobs (2010). Later stages of the study have used developments of TPM 3.1, modified

by Newcastle University, and are outlined from Section 3.3 onwards.

Figure 3.2 shows the baseline TPM v3.1 network for the Tyne and Wear region. Links are coloured by

the defined capacity of roads in terms of Passenger Car Units (PCUs) per hour.


33

Figure 3.2: Tyne and Wear Transport Planning Model (v3.1, 2005) links classified by road capacity (in

PCUs/hour).

3.1.2 PITHEM Emissions Model

The PITHEM model (Namdeo and Goodman, 2012) provides link-based emissions estimates from

transport, based on the GIS-centric approach taken by Namdeo, Mitchell and Dixon (2002). PITHEM

takes period output from a suitable transport model, applies speed-based, factor curves (e.g. see

Figures 2.6 and 2.7) to vehicle kilometre travelled data to produce emissions estimates for those

periods. The software then scales period data to account for diurnal, weekly and annual variation,

and then outputs that data in a form that may be analysed by GIS, or used as input to a suitable

dispersion model.

The baseline version of PITHEM (version 1.0.0.350) used during the initial phases of this study

implemented the emissions factors presented in Boulter, Barlow and McCrae (2009), with emission

factor tables verified against the Emissions Factor Toolkit version 4.2.2 (DEFRA, 2010). Results

presented in this document were produced using versions of PITHEM 1.0.3.500 and above, with

emissions factor tables verified against EFT 5.1.3 (DEFRA, 2012d)4 – see Section 3.3.4.1 and appendix

4 The development and verification of both the baseline (v1.0.1.471) and current versions (v1.0.3.500+) of

PITHEM have relied on ‘unlocked’ versions of the Emissions Factor Toolkits, provided by Bureau Veritas (Brown, 2012) and NAEI fleet information provided by AEA (Murrells and Li, 2010), with the kind permission of DEFRA, as part of the Local Air Quality Management (LAQM) Helpdesk services. This has allowed: a) analysis of the macro code within the EFT to identify discrepancies with the independently coded implementation developed using C++ in PITHEM, b) extraction of emission factor coefficients and fleet parameters for direct use within PITHEM, and c) separate verification of the individual stages in the fleet-weighted emissions calculation.


34

G for more information. For compatibility with the various EFT versions, vehicle fleet information in

PITHEM is based on the hierarchical data structure presented in Figure 2 of Boulter, Barlow and

McCrae (2009), combined with NAEI fleet-proportion VKM information (originally Murrells and Li,

2009; superseded by Venfield and Pang, 2012).

PITHEM was selected as appropriate for this study as it:

Supports emissions calculations using the same methodology as the DEFRA Emissions Factor

Toolkit, for the required pollutants of NOx, PM10, PM2.5 and CO2;

Was developed internally by Newcastle University, and therefore could be customised

directly to interface with TPM and to ADMS-Urban;

Allows the direct mapping of a vehicle user class in TPM to a sub-section of the NAEI fleet

for calculation of bespoke emissions tables, and for source-apportionment of emissions;

Allows manipulation of the NAEI hierarchical data tables to implement changes in fleet

proportions (e.g. to produce spatially and temporally specific vehicle fleets, and to allow

early introduction of Euro classes for specific vehicle types to simulate introduction of LEZ

restrictions);

Outputs emissions data in a format compatible with both ADMS-Urban and ArcGIS.

3.1.3 ADMS-Urban Air Quality Dispersion Model

The ADMS (Atmospheric Dispersion Modelling System) model, from Cambridge Environmental

Research Consultants (CERC) allows the calculation of pollutant concentrations at specified receptor

points in complex urban topography, using a ‘Gaussian-type’ dispersion model. It is ‘used by, or on

behalf of, over 70 UK local authorities for Review and Assessment’ purposes (CERC, 2011). The

software combines a user interface to develop emissions, inventories and databases, as well as to

set up dispersion modelling runs.

ADMS-Urban was selected as appropriate for this study as it:

Has a long pedigree of being used within the UK for urban Air Quality Management;

Directly supports an integrated chemistry model for NO2, NOx and O3 reactions;

Directly supports urban street canyon modelling where appropriate;

Has previously been used as part of both Newcastle and Gateshead’s Air Quality Review and

Assessment processes (e.g. Laxen, Wilson and Marner, 2005a; 2005b; 2005c; 2005d; Laxen

et al. 2005);

Outputs concentration data that may be imported into ArcGIS.

Given that Newcastle City Council has a history of using ADMS as part of its AQ Review and

Assessments, and in support of its planning function, a number of ADMS-compatible emissions

databases and a sizable amount of meteorological data (see Section 3.3.5.1 and Appendix K) was

made available to Newcastle University from the inception of this study.

3.1.4 ArcGIS Platform

The ArcGIS platform (ESRI, 2012) was selected as the geospatial data manipulation tool for this study

primarily because:


35

All partners (clients and contractors) use ESRI shapefiles (ESRI, 1998) for geospatial data as a

common standard, and have access to ESRI software;

ADMS-Urban includes a direct link to import data to ArcGIS;

All conceivable spatial and temporal operations on input or output data (e.g. calculation of

sub-totals of emissions by area, plotting of results for display etc.) were supported in ArcGIS.

3.2 Pilot Framework Development Early in the study an initial pilot framework was developed for presentation to the LEZ steering

group to identify deficiencies in the proposed approach. This initial model was developed using the

baseline TPM v3.1, interfaced with PITHEM, using EFT v4.2.2 factors, to produce both the spatial

distribution of emissions and emissions totals for the Newcastle/Gateshead region for the year 2005.

The pilot model was based on ongoing carried out under the SECURE project to predict regional CO2

emissions.

3.2.1 Pilot Model Assumptions

The pilot model made a number of assumptions regarding data within and produced by TPM, based

on information within the model technical documents (Jacobs, 2008a; 2008b; 2008c; 2008d), direct

communication with Jacobs consultancy, and analysis of outputs. These assumptions involved the

scope of the model domain, time periods modelled, and network topographical information, link

type identifier information and vehicle user class information. Specific information is presented in

Appendix B, derived from information in Goodman (2012a).

3.2.1.1. Model Spatial Domain

The spatial scope of the pilot model was the entirety of Tyne and Wear, as shown in Figure 3.3. This

represented the same area as the ‘core area’ of TPM, but excluding links in the broader ‘travel-to-

work’ catchment area, and abstracted links to the UK as a whole (Jacobs, 2008a, Figure 1: ‘Area

Definitions’).

3.2.1.2. Time Periods and Scaling

The TPM produces outputs for a typical weekday, with flows in terms of average PCUs per hour, for

three time periods:

1. AM-Peak (3hrs, 07:00-10:00);

2. Inter-peak (6hrs, 10:00-16:00), and;

3. PM-Peak (3hrs, 16:00-19:00).

The conversion factor to scale the calculated 12-hour weekday total to a 24-hour weekday total was

1.24 – i.e. weekday 24h flow = 1.24*[(AM*3)+(IP*6)+(PM*3)] (Mahmud, 2011). Hence an additional

overnight period using inter-peak flows, scaled by a factor 0.24, was added to complete the diurnal

profile in PITHEM. The further conversion factor to scale 24h weekday to 24h weekend flow was

0.77. These scaling values were previously used to support carbon calculations from TPM in support

of DaSTS (Jacobs, 2010). PITHEM uses the calendar for the modelled year to calculate the proportion

of weekdays to weekends. In calculating annual totals, no monthly variation was assumed, other

than the number of days in each month.


36

3.2.1.3 Network Topography

The spatial positioning of the links within TPM was assumed to be fundamentally correct (i.e. links

were approximately in the correct location, with coordinates given as 6-figure OS grid coordinates,

without need for any further transformation or scaling). Therefore, link length could be

approximately calculated using the coordinates of the start and end nodes.

3.2.1.3 Network Flows

The baseline for the network flows, related to the base year of 2005, are related to the version of

the TPM 3.1, which has been produced by Jacobs (2010).

3.2.1.4 Network Identifiers

Outputs from the three time periods were checked to ensure that the use of star and end node (A-B

node) identifiers was consistent across the network. Based on direct discussion with Jacobs, links

representing connections for centroids, parking, non-motorised transport and the Tyne and Wear

Metro LRT network were filtered and removed (Filtering was based on the TPM ‘LINK_TYPE’ field, as

discussed in Appendix B).

3.2.1.5 User Class Information

Output from the assignment phase of TPM uses a vehicle segmentation based on six categories:

1. Passenger car (Non-work long-term stay in car-park);

2. Passenger car (In-work short-term stay in car park);

3. Passenger car (Non-work short-term stay in car-par);

4. Light Goods Vehicles;

5. Other Goods Vehicle (i.e. OGV 1+2);

6. Preload vehicles (i.e. Buses pre-loaded using the TPM public transport model).

In mapping these categories to emissions segments in PITHEM it was assumed that:

TPM Segments 1-3 could be combined into a single PITHEM user class, called ‘cars’, based on

the combined NAEI Level 2 ‘Cars <2.5t’ and ‘Cars >2.5t’ sub-categories;

TPM segment 4, applied directly to the PITHEM user class called ‘LGVs’, based on the NAEI

Level 2 ‘LGV’ sub-category;

TPM Segment 5 mapped on to a PITHEM user class called ‘HGVs’, based on the combined

NAEI Level 2 ‘Rigid HGV’ and ‘Articulated HGV’ sub-categories;

TPM Segment 6 mapped on to a PITHEM user class called ‘Buses’ the combined NAEI Level 2

‘Bus’ and ‘Coach’ sub-categories.

The PCU factor values, used to convert TPM flows to vehicle flows for the four PITHEM categories

were initially defined as: cars, 1.0; LGVs, 1.0; HGVs, 1.89; Buses, 2.0. All roads were modelled as

‘urban roads’ for calculating VKM travelled within PITHEM for lower levels (i.e. NAEI Levels 3+) of the

fleet hierarchy. All cars and light goods vehicles were assumed to be fuelled by either petrol or

diesel, given the assumed (<1%) low penetration of alternate fuelled vehicles in the fleet, as per

Murrells and Li (2009). Emission contributions from Hackney Carriages and Powered Two Wheel

(PTW) vehicles were also ignored, again given their relatively small presence in the vehicle fleet as a

whole, as well as not being a vehicle segment explicitly modelled in TPM.


37

3.2.1.6 Network Speeds

Within the modelled TPM time periods (i.e. 7am – 7pm) the network speeds predicted by TPM were

used. Outside of this time period network free-flow speeds, based on the speed/capacity curves by

link type, were substituted for the TPM calculated values.

3.2.2 Pilot Model Results

The pilot model produced link-based results for:

Total annual flow, average speed and VKM travelled, broken down by user class;

Mass-based emissions totals for NOx, PM10, PM2.5, uCO2 and primary NO2, in tonnes per

annum, also broken down by user class5.

The link-base values were subsequently processed using ArcGIS to produce the regional totals given

in Table 3.1.

Table 3.1: Results obtained from the pilot model for 2005

Area VKM, b.km

CO2, kTonnes

NOx, tonnes

pNO2, tonnes

PM10, tonnes

PM2.5, tonnes

Tyne and Wear

5.40 1090 4725 519 266 196

Newcastle + Gateshead

2.71 557 2460 273 137 101

As can be seen from Table 3.1, as modelled traffic in Newcastle and Gateshead combined accounted

for approximately 50% of the total emissions within the whole of Tyne and Wear.

3.2.3 Pilot Model Discussion

The pilot model accomplished the goal of linking the PITHEM software to outputs from TPM, in order

to produce emissions estimates for the Tyne and Wear area. However, the following observations

were made by LEZ steering committee members:

The 2005 base year was considered outdated for practical use;

Given the relative prosperity of the North East compared to the UK as a whole, it was felt

that the NAEI UK average fleet (Murrells and Li, 2009) may not be representative of the area;

Annual VKM values for car and freight traffic were lower for Newcastle and Gateshead

(>25%) than the values given in DfT statistics (DfT, 2012f; DfT, 2012g), though direct

comparison between total values is problematic, due to differing road coverage and

methodologies (see below);

Following from the above, the CO2 estimates for car and freight transport for Newcastle and

Gateshead were grossly lower (>35%) than the revised 2005 CO2 estimates published by

5 Note that the CO2 emissions value produced by PITHEM is the ‘ultimate CO2 value (uCO2)’ arising when all

tailpipe emissions are considered oxidised to CO2 – it is not a simple tailpipe CO2 or equivalent CO2 (CO2e) value - see discussion in Ropkins (2009) and Boulter, Barlow and McCrae (2009). The emission mass value for primary-NO2 is calculated by PITHEM using the total NOx emission value scaled by the COPERT4 vehicle and technology-specific percentage f-NO2 values presented in Boulter, Barlow and McCrae (2009). For Euro 5 and 6 cars and LGVs, based on private communication with Carslaw (2010) and Tate (2010), PITHEM assumes a value of 40%. This calculation may only be viewed as a crude measure of primary NO2, given that its simplistic nature ignores many factors, such as retro-fitted exhaust treatments and engine loading (Beebe, 2013).


38

DECC (AEA, 2012) (though again differences in coverage and methodology make direct

comparison problematic);

The 51%:49% ratio between CO2 emissions in Newcastle/Gateshead and the rest of the

region was approximately equal to the 49%:51% ratio reported in DECC statistics (AEA, 2012);

The estimated values for both VKM and CO2 for bus operations were considered

exceptionally low;

Concern was expressed that the effects of congestion in the network were not adequately

represented by the pilot model;

The number of heavy goods vehicles using the Central Motorway in Newcastle was

considered high, especially during the Inter-peak and PM-peak periods. This issue was also

identified in the preparation of TPM for use in DaSTS (Jacobs, 2010);

Concern was expressed over the lack of a specific ‘taxi’ user class in the TPM, as it was felt

that the council could exert some control via licensing of private hire and Hackney carriage

vehicles in any proposed LEZ.

The first two observations may be partially explained by the fact that TPM, as a strategic model,

does not cover every single minor road within the two boroughs. The CO2 estimates produced by the

pilot fall between the published DECC totals for ‘major roads + motorways’ and ‘all roads’, though

are closer to the former. The discrepancy between coverage strategic routes in TPM versus minor

roads is assumed to be worse in suburban areas (based solely on visual inspection), with coverage of

major emitters in the urban core, and to the periphery of the boroughs being considered adequate.

The assumption that all roads are urban in the pilot is also incorrect. The areas to the south of

Gateshead, and north-west of Newcastle contains many stretches of rural roads. The south of

Gateshead also possesses appreciable lengths of motorway (the A1(M) and A194(M)). Both Rural

and Motorway sections have elevated CO2 emission levels due to changes in the assumed VKM ratio

of articulated to rigid heavy goods vehicles in NAEI, only partially compensated by assumptions on

the VKM ratio of diesel to petrol cars (i.e. articulated HGVs and diesel cars are more prevalent on

roads associated with long distance journeys).

Minor discrepancies due to road positioning, use of ‘crow-fly’ distances and inappropriate clipping

on GIS of roads on the boundary of the borough areas, were also consider to contribute to under-

prediction of VKM and emission totals.

3.2.3.1 Recommendations from the Pilot

Based on the above, it was decided that the pilot model would be overhauled to meet the

requirements of the feasibility study. This would involve the following steps:

Updating of the Newcastle University copy of TPM to reflect a more relevant baseline year;

Focusing the modelling domain for emissions and concentrations to an area surrounding

central Newcastle and Gateshead, rather than Tyne and Wear in general, whilst retaining

those transport model portions required for key routing for heavy goods vehicles to the east

of the Urban Core Area, via the Tyne Tunnels;

Updating the underlying network geometry to better reflect the position and length of roads;


39

Revisiting and amending the diurnal traffic profiles to hourly values to more accurately

reflect daily trends in emissions, rather than using ‘scaled blocks’ of averages values over

several hours;

Retaining passenger car and freight flow information from the TPM, whilst also leveraging

additional information held by Newcastle City Council and NEXUS, to provide a new model of

bus services for the city centre areas – see Appendix ;

Including time-based speed data collected by TrafficMaster, held by Newcastle City Council,

to attempt to address localised emissions associated with congestion.

In addition to the above, the following steps were identified as necessary in the development of the

methodological approach to produce source apportioned emissions, and pollutant concentrations:

For source apportionment, emissions values would be broken down by defined sub-areas, to

provide more detailed information in evaluating target criteria for LEZ development;

For modelling of concentrations, several steps were considered necessary:

o the interface between PITHEM and ADMS-Urban would be developed further, to

allow handling of emissions rates on a per vehicle class basis, rather than on a link

total basis;

o a methodology for handling conversion of NOx to NO2 concentrations would be

developed, based on guidance from DEFRA and the latest conversion tool (DEFRA,

2012b; 2012c);

o a methodology for handling background concentrations, and concentrations arising

from sources other than road traffic would be developed, based on guidance in

LAQM.TG09 (DEFRA, 2009b), and using the latest DEFRA background maps and

background selector tool (DEFRA, 2012e; 2012f);

o further meteorological and other supporting information, for the base and future

years would be appropriately sourced, as necessary.

3.3 Developments over the Pilot Model This section outlines the improvements made over the pilot model, to produce the final

methodology subsequently used to analyse the base year (Section 4), as well as LEZ scenarios

(Section 5). It was agreed between the University and Newcastle City Council that some of the

developments to the pilot model would be undertaken using EPSRC funding from the SECURE

project (SECURE Consortium, 2013), as direct developments to TPM were not originally anticipated

within the remit of LEZ feasibility study. It was anticipated that such developments would in the

long-term benefit both the University and the Local Authorities.

3.3.1 Selection of Base Year and LEZ Target Year

Based on discussions within the LEZ steering group, the availability of the most recent complete year

of traffic information held within the Tyne and Wear Accident Data Unit (TADU, 2011) at the onset of

development, and the requirement to calculate annual mean values (and potentially exceedence

values) for comparison to air quality standards, the base year for subsequent modelling was set to

be 2010. However, as noted by DEFRA (2012f), 2010 represents a possibly atypical ‘high’ year for

NOx levels across the UK – this should be borne in mind when analysing results.


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Initial discussions within the LEZ steering group considered the possibility of introduction of an LEZ

for the end of 2016. However, based on advice from Newcastle City Council and consideration that

this timeframe was within that already considered by LTP3, this was discounted. An alternate future

implementation year of 2021 was proposed and adopted.

3.3.2 Selection of Spatial Domain

The initial spatial domain of the study was considered to be the entirety of the Newcastle and

Gateshead areas, as defined by the NUTS4 (Nomenclature of Units for Territorial Statistics –

Observatory District and Unitary level) boundaries. This area is used within the definition of the ‘One

Core Strategy’, and covers approximately 255km2, with an estimated 2010 population of 483,900

(GC & NCC, 2009).

In order to ensure that the study region would a) include possible required emissions contributions

from roads on the periphery of the NUTS4 boundaries (identified as an issue in the pilot model), and

b) correspond to any 1km grid data required from the UK National Atmospheric Emissions Inventory

(NAEI), e.g. to calculate Background levels (DEFRA, 2012e; 2012f), an additional 1km buffer was

added to the region and then expanded to encompass all intersecting OS kilometre grid squares. This

procedure gave the final spatial domain shown as the red region in Figure 3.3, which includes areas

of Durham and Northumbria, as well as other boroughs in Tyne and Wear. All subsequent

geographical information used in the study has been clipped to this domain.

Figure 3.3: Initial study spatial domain, including buffer region (red) consisting of

Newcastle/Gateshead NUTS4 boundary, plus 1km buffer region, clipped to 1km OS grid

Note that this spatial domain is far larger than the declared AQMAs within Newcastle and Gateshead

(see Figures 2.1 and 2.2), but was retained as far as possible within the study to allow both the

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maximum possible flexibility in the spatial design of LEZ options, as well as the potential to analyse

impacts over a wider area than just the AQMAs.

Within the buffered domain (i.e. the red area in Figure 3.3) several spatial sub-domains were

generated. These were:

The Newcastle Centre, Gateshead and Gosforth AQMAs, see Figure 3.4;

The Urban Core Area, subdivided into Newcastle and Gateshead Sections, see Figure 3.5;

The boundaries of the cordons used for assessing traffic entering the city centres. For

Newcastle three cordon areas were defined based on the councils Central, Inner and Outer

cordons. These regions were developed ‘by-eye’ from a raster images provided by Newcastle

Council (NCC, 2011b) in GIS as shape files were not available. For Gateshead one cordon

around the centre was produced, based on fitting a convex hull in GIS to point locations of

count sites obtained from TADU, see Figure 3.6.

In similar fashion to the development of the buffer for the complete modelling areas, the shape file

boundaries for each of the above elements were expanded to include all intersecting squares on a

200m grid, nested within the main 1km grid in Figure 3.3, working outwards from the centre. In this

way each region would contain both its own links, and links on the periphery of the region6.

Figure 3.4: Gridded AQMAs: Central Newcastle, Gateshead and Gosforth

6 This methodology leads to a slight issue as seen in figure 3.5. In giving smaller/inner areas precedence over

larger areas when generating buffer zones, the Gateshead urban core becomes slightly larger as the borough boundaries are pushed northwards, leading to emissions on bridges crossing the Tyne to be included in the Gateshead totals – rather than being split equally between the two areas.

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Figure 3.5: Gridded Urban Core Area, divided into Newcastle and Gateshead sub-areas

Figure 3.6: Gridded Traffic Cordon Areas

The sub-areas in Figure 3.6 have been used to both calibrate the traffic model for the 2010 base year

(see section 3.3.3.1) and to provide emission totals for source apportionment (see Sections 4.1.2 and

5.4). Those in Figures 3.4 and 3.5 have only been used in source apportionment and dispersion

modelling results analysis.

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3.3.3 Modifications to Traffic Modelling

Based on the findings presented in Section 3.2.3.1, the following modifications were made to the

traffic modelling process:

General traffic flows in the TPM model were updated to reflect a new 2010 base year;

A separate model for bus transport was produced, based on public transport information

(NaPTAN, ATCO-CIF/TransXChange route and timetable data) held by Newcastle City Council

(Arkless, 2012);

The geometry of both models was linked to the Ordnance Survey’s MasterMap Integrated

Transport Network map layer (OS, 2013a);

Hourly speed values from council held TrafficMaster link-speed dataset were assigned to

both models via OS TOID attributes – see later in Section 3 and Appendices F and G.

3.3.3.1 Update of TPM base year to 2010

Updating the 2005 TPM model to the base year of 2010 primarily involved changes to overall traffic

flow levels, changes to reflect the general trend of reduced numbers of heavy goods vehicles in

Newcastle centre over the period 2005-2010, followed by a limited validation of the updated model

based on observed flow patterns.

3.3.3.1.1 Network Changes

After examination of the relevant modelling documents, primarily supporting technical information

from other, post-2008 developments of TPM (e.g. Jacobs, 2010), and discussion with the LEZ

steering group, the network for the revised 2010 model was assumed to be the same as the network

for 2005.

3.3.3.1.2 Traffic Flows within AM, IP and PM periods

The general traffic (cars and freight transport) flows within TPM were updated using automatic

traffic monitoring data received from TADU across the Tyne and Wear, Northumbria and Durham

regions. Flow data for both the TPM original base year of 2005, and the updated base year of 2010

were received.

Sites common to both years were identified (initially 860 sites in total). The individual hourly data

was then processed to give number of records, daily flow totals, and average hourly diurnal flow

profiles, and period averages (AM, IP, PM) throughout the year for weekday and weekends

separately. Detectors with less than 2 months of data within the year, detectors associated with

cycle lanes, and detectors for which no credible TPM link could be found, were subsequently

removed (approximately 300 in total). Detector locations were then matched to individual TPM links

and assigned screen line identifiers, via a semi-manual process, and assigned. Finally a proportion of

flow was removed from each detector site to account for bus traffic that was to be handled

separately in the bus model, to prevent double counting. Figure 3.7 shows the final distribution of

detector sites used. Links within the modelling buffer region (Figure 3.6) are shown in blue, whilst

general TPM links are shown in grey.


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Figure 3.7: Automatic traffic detector sites across the Tyne and Wear region used in TPM model

update

In order to update the network flows of the year 2005 to the chosen base case, year 2010, a matrix

update process (ME2) was implemented. The 2010 flows related to the common count sites

identified for both years were used. In total 585 count sites have been considered. The ME2 process

which requires screen lines flows as input file was tested under two options, individual flow

associated to single counts (EST10_S) and bi-directional flows associated to each screen line

(EST10_B). A full discussion of the methodology used for processing detector information and

updating the TPM model may be found in Appendix C.

3.3.3.1.3 Vehicle Types within Cordon Areas

In addition to the alteration of overall flow levels to reflect the 2010 base year using detector data, it

was also considered necessary to adjust fleet proportions in Central Newcastle and Gateshead to

reflect trends towards the presence of lighter goods vehicles, over HGVs (NCC, 2011b). To this end,

classified cordon count information, covering surveys from 2009, 10 and 11, was received from

TADU. These data were processed in GIS to a) link survey points to the TPM network and b) merge

data from different hours together to form AM, IP and PM period information c) merge together

vehicle categories and turning movements to form two-way counts for cars, LGVs and HGVs on links

crossing the gridded cordon (Figure 3.6) boundaries. From the counts the relative proportions of

private and freight vehicles were calculated via GIS. Likewise, proportions from TPM were calculated

from links crossing the boundaries.

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The resulting values were then used to iteratively adjust TPM total vehicles matrices for the three

time periods and the three different classes of cars, LGVs and HGVs using the MATRIX module in

CUBE. The process was performed identifying the O/D pairs related to the Central Newcastle and

Gateshead, with the constraint of maintaining the overall estimated total number of vehicles and for

each matrix leaving the proportion of trips between the different O/D pairs for each zone. By an

iterative process the percentages of the three different classes for each time period were adjusted.

3.3.3.1.4 Validation of Revised TPM Model

Following the ME2 process, assignment of flows into the 2010 network was performed in order to

assess the validity of the updated matrices generated using the two different approaches. By

comparing the results of the two different approaches the first approach (EST10_S) although was

performing better in term of correlation between measured and modelled flows, the second one

(EST10_B) presented slope much closer to 1 and satisfactory good correlation coefficient above 0.92

(Figure 3.8).

In terms of GEH performance (Figure 3.9) for both PM and IP modelled periods 86% and 85% of the

links respectively were compliant with WebTAG 3.19 guidelines, with GEH of 6 or below, while for

AM period 80% of links were with GEH of 6 or below, to note that this slightly lower performance of

the AM period is consistent with the results in TPM 3.1.

Figure 3.8: Correlation of assigned and counted flows for both of the approaches using TPM model

Figure 3.9: GEH distribution for the 3 modelled periods using EST10_B assigned flow


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Table 3.1 and Figure 3.10 present the proportions of each vehicle class crossing the boundaries of

the three Newcastle Cordons (NCC, 2011b), from the processed count information, from both the

final, revised TPM used in this study, and the original 2005 version. Note that even after several

iterations of calibration/validation, the proportions of both light and heavy goods vehicles in the

revised model are higher, for all time periods than observed data (a similar observation was made by

Jacobs (2010), for the IP and PM periods in the DaSTS document).

Table 3.1: Observed 2010 versus modelled base 2005 and revised 2010 percentages of vehicles

crossing cordon boundaries (all values in percentages)

Cordon/ Period

Count Car

Count LGV

Count HGV

2005 Base Car

2005 Base LGV

2005 Base HGV

2010 Revised

Car

2010 Revised

LGV

2010 Revised

HGV Central AM 82.42 14.51 3.07 66.26 21.67 12.07 73.77 20.98 5.24

Central IP 82.24 15.35 2.41 77.01 12.86 10.13 77.54 17.51 4.95

Central PM 91.34 8.09 0.57 84.06 10.52 5.41 89.02 9.75 1.23

Inner AM 83.62 13.67 2.71 79.69 12.38 7.93 85.31 11.06 3.63

Inner IP 79.42 16.86 3.72 78.53 12.50 8.98 78.57 16.71 4.73

Inner PM 90.62 8.55 0.83 87.84 8.97 3.19 91.20 7.80 1.00

Outer AM 83.63 13.21 3.16 75.29 15.10 9.61 81.28 13.03 5.69

Outer IP 81.11 14.85 4.04 79.16 11.11 9.73 79.83 13.53 6.64

Outer PM 90.58 8.37 1.05 85.47 10.01 4.52 88.47 9.01 2.52

[NB: Values may not add up to 100% due to rounding.]

Figure 3.10: Observed versus modelled proportion of vehicles crossing cordon boundaries. Cordon 1 =

Newcastle Central Cordon, Cordon 2 = Newcastle Inner Cordon, Cordon 3 = Newcastle Outer Cordon

Results for the Gateshead central area may be found in Appendix D (Table D.1).


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Based on the above, the following observations were made:

Across all time periods and cordons, the proportion of HGVs appears to be overestimated,

by an average of 160% in the AM and Inter-peak periods, and 190% in the PM period. Care is

therefore required in interpreting source apportioned emissions and concentration data

for heavy goods vehicles;

For the Central cordon the proportion of LGVs crossing the cordon is over-estimated by 44%

in the AM peak, 14% in the IP and 20% in the PM peak. For other time periods and cordons

there is a 0% - 20% under-estimation of LGV proportion. It is suggested that matrices with

origins and destinations outside of the central areas could all some further HGV traffic to be

converted to the LGVs, across all periods.

Time constraints prevented any further calibration and validation of the revised TPM for 2010

beyond this and the decision was taken to proceed with the general traffic model on the above basis

for the feasibility study. It is strongly recommended that the cause of the overestimation of HGVs in

all time periods in be investigated and rectified before any further detailed assessment is carried

out. One possible source of error is the digitisation of the cordon areas by hand (Newcastle) or by

hull fitting (Gateshead) which possibly allows inclusion of links not in the cordon areas to skew the

count and proportion totals. Further investigation is certainly warranted.

3.3.3.2 Development of Public Transport (Bus) Model

Data on weekday bus flows was received from Newcastle City Council. The data had been pre-

processed by the council to link timetabled bus stop information to OS MasterMap links. A routing

algorithm was then used to assign individual bus route flows to links between successive stops.

Finally hourly bus flow totals were calculated from summation of contributions from all routes

(Arkless, 2012). Speed information was then added to the bus network, as outlined in section 3.3.3.4.

The development of a separate bus model to that already existent in TPM v3.1 was considered

necessary after analysis of the pilot results. One downside of the use of a separate model to TPM is

that the bus flows are no longer related to the PT demand and mode choice elements of TPM, and

are hence ‘static’, and not easily updated to reflect PT policy decisions affecting routing or patronage.

However, this was not considered an issue in the initial development of LEZ scenarios. It was

considered of greater importance that the bus model accurately represented on-street flows, routes

and vehicle kilometres.

Another issue with the use of the bus model was that the coverage of roads differs slightly to TPM

(i.e. the bus model contains flows on minor roads that do not exist in the strategic TPM model). Two

options existed here: 1) filter the bus model so that only links present in the TPM are present, or 2)

retain the additional information, with the proviso that the emissions, source apportionment and

concentration results in certain areas (especially suburban and peripheral areas) may be biased

towards contributions from buses. In the end, given time constrains, option 2. was adopted, after it

was noted that network coverage between the two models in the AQMA areas was ostensibly

similar.

Figure 3.11 shows a snapshot of bus routes, colour coded by flow from the completed model for

8am on a weekday. The highest intensity of flow occurs around Gateshead Interchange, though high


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flows also appear throughout Newcastle City Centre, and along the Great North Road/Gosforth

corridor.

Figure 3.11: Public Transport (Bus) routes and Transport Planning Model (grey) links mapped on to

OS MasterMap Integrated Transport Network (ITN) layer

A final issue with the current bus model is that the summation of flows from route, in order to give

an hourly flow values, abstracts the original data by removing the link to the individual bus services.

It is therefore not possible to assign a portion of the flow to an individual operator, and hence to the

specific fleet (and associated Euro standards) on the link. Such allocation for a detailed LEZ

assessment would be technically feasible, given adequate resources. It is also recommended that a

more rigorous validation of the bus model be carried out before any detailed modelling of LEZ

options is undertaken. Further information on the Bus Model in general may be found in Appendix E.

3.3.3.3. Linking TPM and Bus Model information to OS Master Map Layers

Link geometry, for all elements in the modelling methodology was moved to that provided by the

Ordnance Survey Master Map ITN layer data (OS,2013a). This was done via creating either a

mapping between TPM links (identified by ‘A’ and ‘B’ node numbers) and ITN links (identified by

‘TOID’ - TOpological IDentifier, a unique 16-digit code given to every OS map object in the UK). The

link between TPM A-B ID and TOID was assumed to produce either a ‘1-to-1’ or ‘1-to-N’ mapping.

Actual linking of data was done in PITHEM, via a module written for the SECURE project, which

provides a graphical interface for the mapping, see Appendix F.

Once complete, the mapping allowed data exchange, via the common TOID identifier, to add (e.g.

apply speeds) or subtract (e.g. remove bus flows) across the individual model boundaries, as long as

the TOIDs are retained in input or output layers.

The TPM/OS mapping itself was a relatively time-intensive, manual process that requires some

technical judgement on the allocation links, especially in instances of complex junctions, such as

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those along the A1/A1(M), or for circulating flows on roundabouts. It is recommended that this

manual process be revisited at some point in the future to determine if a more automatic process

could be used to reduce potential human error in allocation.

3.3.3.4 Utilisation of Traffic Master Speed Data:

In an attempt to address perceived issues with using period based values from TPM, hourly average,

unidirectional speed data on OS ITN links, was provided by Newcastle City Council for use in this

study. These data covered the majority of the road network for weekday hours from 6am to 10pm.

Matching of speed data to TPM links was achieved via the TOID. For those links without speed data

available on the TrafficMaster (TM) dataset TPM speed data was retained. For hours outside of the

data range (i.e. 11pm to 5am), a suitable free-flow speed was substituted (see Appendix G).

As noted in Section 3.3.3.3, at a 1-to-N mapping between TPM links and ITN links was found possible.

Therefore two possible methods of applying the TM speed data to TPM links were developed:

1. Calculating the spatially-averaged speed for each TPM link from its component ITN links. This

has the advantage of retaining the original number of TPM links as input to the PITHEM or

ADMS models, but reducing the spatial resolution of the speed data as values are ‘smeared’

along the length of the TPM link, and hence emissions associated with heavily congested

short link sections will be ‘lost’;

2. Breaking the TPM links down into the component ITN parts and applying the individual

speeds from the TM data to each part. This retains the spatial resolution of the speed data,

and hence emission data, at the expense of increasing the number of links passed to the

other models.

During the study, the former approach has been referred to as producing the ‘merged’ traffic model

(as in TM speed data is merged along the length of the link), whilst the latter has been referred to as

the ‘split’ model (as the TPM links become split to accommodate the raw speed data). Differences in

emissions calculated between the two methodologies are discussed in Appendix G. Figure 3.12

provides an example of the TPM model with merged speed data, for 8am on a weekday. Links with

very low speeds (<10km/h) are highlighted in blue, links with very high speeds (>100km/h) are

highlighted in red.


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Figure 3.12: Network speeds for weekday 8am, merged with revised TPM geometry

For the bus model, the original dataset comprised of TOID plus hourly, two-way bus flow. Hence for

the final bus model, flows where necessary were split across each side of the road on a 50%:50%

basis, and the unidirectional TM speeds were added directly. Hence the behaviour of the final bus

model is equivalent to that of the split model outlined above.

3.3.4 Modifications to Emissions Modelling

Three major modifications in emissions modelling were made over the pilot model. These being:

1. Changing the emissions factors used EFT version 4.2.2 (DEFRA, 2010) to those used in

EFT version 5.1.3 (DEFRA, 2012d);

2. Developing and testing new, Tyne and Wear specific, fleets for the baseline 2010 model;

3. Developing 24-hour emissions profiles for ADMS-Urban from the transport model data.

3.3.4.1: Emissions Factor Changes

As noted in Section 2.3, there have been concerns raised over the issue of discrepancies between

observed and modelled air quality data, partially arising from limitations in the then-current NOx

emissions factors (Carslaw et al., 2011). Later Emissions Factor Toolkits (versions 5.0 and greater)

have substantially changed NOx emissions factors, especially for lighter vehicle classes. Given that

the latest version of the EFT represents the standard that should be used for UK modelling, and to

improve the credibility of this study, the decision was taken by the LEZ steering committee in June

2012, to move all emissions modelling to the (then) latest EFT version (version 5.1.3. DEFRA , 2012d).

This was done, though a significant amount of modelling work had been completed using EFT v4.2.2

(DEFRA, 2010). As noted in section 2.3.2, the latest version of the EFT is Version 5.2c (DEFRA, 2013),

though after brief, non-exhaustive testing this produces comparable results to v5.1.3.Changes in NOx

emissions between the two EFT versions, implemented in PITHEM, may be summarised as follows:

Emissions polynomial functions for all vehicle types were altered from the original TRL

functions (Boulter, Barlow and McCrae, 2009) to the COPERT4 equivalents;


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NOx emissions degradation functions with mileage for cars and LGVs were altered from the

original TRL functions (Boulter, 2009) to the COPERT4 equivalents;

Technology adoption and catalyst failure behaviour was altered to allow for more complex

patterns (e.g. a SCR NOx system on vehicle could be considered to fail separately to a DPF

PM system), clarified in discussion with Bureau Veritas (Brown, 2012);

Overall fleet changes were made to reflect the current economic situation (Venfield and

Pang, 2012).

Appendix H demonstrates the effect of changes between EFT v4.2.2 and EFT v5.1.3 (as implemented

in PITHEM) for NOx emissions with speed.

3.3.4.2 Tyne and Wear-specific Fleet Development

An open question at the start of the study was that of how similar the fleet in the Tyne and Wear

region to that presented in the NAEI fleet hierarchy. It was felt by the LEZ steering group that the

economic situation in the North East, relative to the rest of England, would mean an older, on road

fleet was a possibility. Generally, fleet information may be determined by several means, including

analysis of vehicle licensing and registration data, personal travel surveys and on-street manual or

automatic surveys. Automatic surveys make use of Automatic Number Plate Recognition (ANPR)

data (Pang, Tsagatakis and Murrells (2012); Murrells, 2012).

In the absence of suitable ANPR data7 a request was made to DVLA to access a variety of vehicle

licensing and registration statistics for the region, broken down by the individual local authority

areas, for the base year of 2010. The data requested and received from the DVLA (Lloyd, 2012) is

summarised in Table 3.2. Based on the discussion in Section 2.3.4. Table 3.3 summarises the average

age of vehicles since the time of first registration for vehicles registered in Great Britain, the North

East, Tyne and Wear and Newcastle and Gateshead respectively. Table 3.4 provides the number of

vehicle registered in each region.

Table 3.2: Vehicle data received from the Department for Transport for the North East of England

DfT Table ID Table Title and Description VEH0203 Licensed cars by fuel type as at 31st December 2010

VEH0205a Licensed cars by engine size as at 31st December 2010

VEH206 Licensed cars by CO2 band as at 31st December 2010 (based on full CO2 bands - i.e. what band each pre-2006 car would be in if it were new now)

VEH207 Cars by age

VEH306 Motorcycles by engine size

VEH307 Motorcycles by age

VEH403 Licensed LGVs by fuel type as at 31st December 2010

VEH407 LGVs by age

VEH506 HGVs by weight

VEH507 HGVs by age

VEH522 Rigid goods vehicles by gross weight and body type

VEH607 Buses and coaches by age

7 During the course of the study, though after the development of the fleet information presented here ANPR data for

Newcastle and Gateshead was kindly made available to Newcastle University by Gateshead Council – unfortunately due to time and resource pressures this was not analysed and incorporated into this study – though remains a detailed data source that requires further investigation in any, more detailed LEZ design. It is also noted that the presentation of Murrells (2012) contains the location of ANPR system used to produce NAEI fleet information – 4 locations out of 184 appear to be in Tyne and Wear (with a further 3 in the Durham area).


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Table 3.3: Average age of vehicles from year of first registration, by vehicle class in 2010

Vehicle class Great Britain North East Tyne and Wear Newc/Gates. Cars 7.33 6.61 6.57 6.57

Light Goods Vehicles 7.40 6.59 6.22 5.57

Heavy Goods Vehicles 7.21 7.26 6.75 6.18

Buses 7.71 8.81 8.38 7.69

PTWs 11.5 11.56 10.92 11.31 1 The value for buses is given for Great Britain biased downwards by a large number of relatively new buses in London (5.9 years). The

average age for English Metropolitan Areas (exc. London) is 7.9 years. Figures for buses are available to 1d.p. only.

Table 3.4: Number of vehicles registered, in thousands, by vehicle class at end 2010

Vehicle class Great Britain North East Tyne and Wear Newc/Gates. Cars 28,420.9 (84.8%) 1037.2 (85.9%) 394.3 (86.5%) 157.2 (84.5%)

Light Goods Vehicles 3,207.8 (9.6%) 107.8 (8.9%) 39.7 (8.7%) 20.2 (10.9%)

Heavy Goods Vehicles 470.1 (1.4%) 15.2 (1.3%) 5.3 (1.2%) 2.9 (1.6%)

Buses 171.2 (0.5%) 6.8 (0.6%) 2.9 (0.6%) 0.9 (0.5%)

PTWs 1234.4 (3.7%) 40.6 (3.4%) 13.5 (3.0%) 4.8 (2.6%)

From Table 3.3 it is interesting to note that for all vehicle classes and regions, except buses and HGVs

when considering the North East as a whole, the ages calculated are newer than the GB statistics as

a whole. The age of light goods vehicles is especially interesting, as values are skewed by the

presence of a large number (5.2 thousand) of light vans less than 3 years old registered in Gateshead.

The steering group suggested that this may be partially due to the presence of several van hire

operators in the Team Valley area. The value for Newcastle and Gateshead buses is in line with the

value for buses registered in English metropolitan areas.

Assuming age of first registration correlated with Euro standard, an Excel (Microsoft, 2013)

spreadsheet model was built to assign information from Tyne and Wear totals from the DfT stats to

the NAEI Fleet hierarchy levels (weight, fuel, engine-size and Euro class as appropriate). Further

information on the DfT data summarised in Tables 3.2 to 3.4 and the spreadsheet model is provided

in Appendix I.

For buses, fleet data a fleet data was also acquired from two additional sources:

1. From on-street license plate and operator service surveys undertaken by NEXUS (NEXUS,

2012);

2. Directly from the major bus operators in the region: Arriva, Go North East and

Stagecoach – data was received by email in a variety of forms, from simple summary

tables, to individual bus chassis, maintenance history and applicable route information.

The data received from both NEXUS and the operators was fleet split information for vehicles used

either in Newcastle/Gateshead, or throughout Tyne and Wear, already allocated to Euro class. The

data was based on most recent bus fleets at the time (early-mid, 2012). NEXUS also provided

estimated bus numbers for minor operators. From the NEXUS and bus operator data, approximately

2-3% of buses in Newcastle/Gateshead use hybrid diesel engines. After discussion with both the LEZ

steering group and NEXUS these have also been excluded, with diesel bus proportions re-weighted

for the analysis. Taxis (Hackney Cabs) and PTWs were excluded from the analysis, given lack of

further information, after discussion at the LEZ steering group. As mentioned previously (Section

3.2.1.5), neither Taxis nor PTWs are explicitly modelled in TPM. Alternate fuelled, electric and hybrid


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vehicles were also ignored. From the DfT data the proportions of non-petrol or diesel fuelled

vehicles registered in Tyne and Wear were 0.3% and 0.4% for cars and LGVs respectively.

Using available licensing and operator information, the revised fleet splits shown in Figures 3.13,

3.15, 3.17 and 3.19 for cars, LGVs, HGVs and buses respectively were produced. Figures 3.14, 3.16,

3.18 and 3.20 show the speed-emission curves for NOx, PM10 and PM2.5 produced by PITHEM for

each vehicle class. Fleet data originates from the following sources, as labelled: DfT tables and the

spreadsheet model (‘DfT_2010’), the latest NAEI fleet (Venfield and Pang, 2012 – labelled here as

‘NAEI_2011’ based on an internal NAEI reference in the data), the original NAEI data available at the

start of the project (Murrells and Li, 2009 – labelled as ‘NAE_2009’), NEXUS data (‘NEXUS_2012’) or

bus operator data (‘FO_2012’). Table 3.5 presents sample emissions rates for all considered

pollutants at 50km/h, for each vehicle class, when calculated using the non-NAEI fleet information.

Also presented are the ranges of percentage differences found between using the most recent NAEI

fleet, and the fleet derived from DfT data.

From the analysis of the fleet data, the following broad observations were made:

The ‘DfT_2010’ and ‘NAEI_2011’ fleets are in greater agreement with each other than the

earlier ‘NAEI_2009’ fleet, used at the start of the study;

For cars, the ‘DfT_2010’ fleet has a higher proportion of smaller-engine petrol vehicles than

the ‘NAEI_2011’ fleet;

The ‘DfT_2010’ fleet generally has a higher proportion of Euro 1/I/2/II and a lower

proportion of Euro 4/5/IV/V vehicles than the ‘NAEI_2011’ fleet;

Large discrepancies exist between the HGV weight data. These are thought at least partially

due to the method of distributing proportions of the DfT weight bands to the differing NAEI

weight bands in the spreadsheet model. The NAEI splits were retained in calculating the

emissions rates presented in the Figures and Table 3.5;

From the bus data, whilst there is a higher proportion of Euro II buses reported in the non-

NAEI data, and fewer Euro IV buses, Euro V bus proportions in the NEXUS and operator data

are in line with ‘NAEI_2011’ – implying that operators may have ‘skipped a generation’,

going directly from Euro II and III to Euro V buses. The spreadsheet model appears to

perform poorly in predicting bus technology splits, with under-predictions of Euro IV and V

proportions.

Considering the emissions data, for NOx and PM emissions using the spreadsheet model ‘DfT_2010’

fleet elevates emissions by single to low-double digit percentages over the latest NAEI fleet. The

HGV category affected most by assumptions in the spreadsheet model. For primary NO2 emissions

and ‘DfT_2010’ the PITHEM model calculates lower f-NO2 fractions due to the elevated numbers of

small engine petrol, and Euro 2 and 3 diesel vehicles in the fleet. Regarding buses, interestingly, even

though the ‘DfT_2010’ and ‘NEXUS_2012’ fleets are different, the resulting emissions for NOx and

PM are within 2% of each other, across the entire speed range.

There are a number of issues with using the DfT registration statistics with the NAEI fleet hierarchy.

The most fundamental one being that emissions rates should be calculated based on vehicle

kilometres travelled. It is known that certain categories of vehicles are driven further than others -


54

e.g. diesel cars do more mileage than petrol vehicles, newer vehicles generally do more mileage than

older vehicles etc. (Pang, Tsagatakis and Murrells, 2012). Such assumptions have been built into the

various NAEI fleet spreadsheets and Emissions Factor Toolkits. However, available public information

from NAEI, and the unlocked EFT toolkit information (Brown, 2012) did not contain any further

information on how DfT registration statistics (or other data, such as ANPR) may be converted into

VKM values. Hence, the spreadsheet model as built treats all vehicle categories as equally

weighted, with VKM proportion values for each fuel, weight, engine size and technology category

being based on their frequency of occurrence in the DfT registration data. Re-weighting of the

spreadsheet model, if used, should be considered of primary importance in any further LEZ (or

other air quality) work.


55

Cars:

Figure 3.13: Fuel type splits (left), Engine size splits(middle) and Euro class (Emissions standard) splits for cars in 2010 (NB: Engine size and Euro category

show weighted average for both petrol and diesel cars)

Figure 3.14: Resultant fleet-weighted emissions curves for NOx (left) and PM10 and PM2.5 (right) for cars in 2010.


56

Light Goods Vehicles:

Figure 3.15: Fuel type splits (left) and Euro class splits (right) for LGVs in 2010 (NB: Euro category shows weighted average for both fuels)

Figure 3.16: Resultant fleet-weighted emissions curves for NOx (left) and PM10 and PM2.5 (right) for LGVs in 2010.


57

Heavy Goods Vehicles:

Figure 3.17: Weight splits (left) and Euro class splits (right) for HGVs in 2010 (NB: Both figures show weighted average of rigid and articulated HGVs).

Figure 3.18: Resultant fleet-weighted emissions curves for NOx (left) and PM10 and PM2.5 (right) for HGVs in 2010.


58

Buses:

Figure 3.19: Euro class splits for buses in 2010 (DfT and NAEI data) as well as operator and NEXUS data for 2012


59

Figure 3.20: Resultant fleet-weighted emissions curves for NOx (left) and PM10 (right) for buses (for clarity PM2.5 curves not shown).


60

Table 3.5: Summary of Emissions Rates for 2010 modelling, using different fleet data sources

Vehicle Type

Data source Pollutant Value at 50km/h Relative to NAEI 2011[1]

Car DfT, 2010 uCO2 148.7 g/km -3% - 0%

NOx 0.320 g/km +1% - +8%

pNO2 (0.05 g/km)[2]

-30% - -24%

PM10 0.036 g/km 0% - +5%

PM2.5 0.023 g/km 0% - +8%

LGV DfT, 2010 uCO2 187.9 g/km -1% - +1%

NOx 0.791 g/km +3% - +7%

pNO2 (0.22 g/km) [2]

-8% - -9%

PM10 0.076 g/km +7% - 10%

PM2.5 0.054 g/km +7% + 13%

HGV DfT, 2010 uCO2 655.2 g/km 0%

NOx 4.51 g/km +4% - +18%

pNO2 (0.59 g/km) [2]

+11% - +15%

PM10 0.184 g/km +15% - +18%

PM2.5 0.125 g/km +4% - +21%

Bus DfT, 2010 uCO2 618.3 g/km -4% - 0%

NOx 5.64 g/km +2% - +14%

pNO2 (0.63 g/km) [2]

0% - +11%

PM10 0.203 g/km +7% - +14%

PM2.5 0.144 g/km +10% +16%

Bus NEXUS, 2012 uCO2 618.5 g/km -4% - 0%

NOx 5.59 g/km +4% - +13%

pNO2 (0.65 g/km) [2]

0% - +9%

PM10 0.203 g/km +7% - +13%

PM2.5 0.144 g/km +10% +15%

Bus Fleet Operator, 2012 uCO2 615.3 g/km -3% - 0%

NOx 4.97 g/km -2% - 0%

pNO2 (0.59 g/km) [2]

-5% - -3%

PM10 0.182 g/km -6% - -2%

PM2.5 0.126 g/km -6% - -2% [1]

This column shows the percentage difference between the emissions rate calculated using EFT5.1.3. factors and the

listed ‘data source’ fleet, and the rate calculated using the EFT5.1.3 factors and the 2011 update fleet of the fleet from

Venfield and Pang (2012). A range is given due to the speed dependency of emissions. [2]

Estimate based on NOx emission

values multiplied by fleet-weighted COPERT4 f-NO2 factor given in Boulter, P.G., Barlow T. J. and McCrae, I. S. (2009).

Another issue is that the spreadsheet model is based on vehicles registered within the Tyne and

Wear regions - there will be vehicles present on the roads of Newcastle and Gateshead, from outside

this boundary. Fleet operators with national scope may potentially move vehicles all around the

country to meet demands, whilst commuters will travel from a wider area to the two cities. It is

suggested that, given additional resources, an improvement to the spreadsheet model would be a

re-weighting of vehicle based on commuting patterns found in the ‘travel-to-work’ area defined in

TPM (Jacobs, 2008a) and freight movement patterns. As previously mentioned, the possibility of

modelling bus routes individually by operator could also be examined.

As a final comment on heavy duty vehicles, linking first registration data (or ANPR data) to Euro class,

may also be problematic as a number of vehicles will have been re-engined (and possibly emission

control retrofitted) since their registration. The age of the chassis does not ‘match’ the Euro class

implied (Crowther, 2012). It is assumed that the number of affected vehicles would be small, though

further information on operators on rates would be desirable.


61

3.3.4.3 Development of 24 hour Emissions Profiles

The detector data, acquired from TADU, and processed as outlined in Section 3.3.3.2, was also used

to produce a diurnal traffic scaling profile for PITHEM at hourly resolution. In the pilot model only

four periods were used (Section 3.2.1.2) to produce the diurnal profile. In the final framework, after

the three networks from TPM, covering the AM, IP, PM periods, were merged with the hourly speed

data for the period (Section 3.3.3.5), 24 individually scaled networks covering the day were

produced. The actual factors used, and there derivation, is discussed in Appendix J, the resulting

traffic scaling factors, compared to those calculated using DfT statistics data for Great Britain in 2010

(DfT, 2012h), are given in Figure 3.21. Note that these are the scaling factors applied to the TPM

model outputs covering specific periods, not the normalised diurnal flow profile.

Using the scaling profiles, the hourly link emissions totals for NOx as calculated by PITHEM, were

averaged and normalised against average annual hourly data, to produce the emissions scaling

profile required for ADMS-Urban (see CERC, 2012: Section 4.1.1). This profile therefore accounts not

only for traffic flow variations, but also fleet composition and speed changes and their effect on

emissions throughout the day. Due to time constraints, a single weekday profile was developed and

applied in ADMS-Urban, to all links, for all vehicle classes, for both NOx and PM calculations.

Saturdays and Sundays profiles were based on scaled weekday data.

Figure 3.22 presents the finalised diurnal NOx emission profile. The AM-peak period is noticeable in

the data, with NOx emissions in weekdays being a factor of over 200% higher than the average

annual hourly emissions rate.

Figure 3.21: Daily scaling factor profiles, applied to flows in TPM period outputs, calculated using

either TADU detector information, or DfT transport statistics data


62

Figure 3.22: Finalised emissions scaling profile for NOx, applied in ADMS-Urban

The use of a single emissions profile for all links is a limitation in the current model, though one

thought acceptable for the purpose of calculating guideline annual mean concentrations across the

whole network area. However, for more detailed LEZ design it is suggested that further, link-specific

and vehicle class specific profiles be developed. The current version of PITHEM has the facility to do

this, but it was not applied due to time constraints. Likewise the version of ADMS-Urban used

(version 3.1.0) could support up to 500 individual profiles. The application of a sing profile to both

PM and NOx calculations appears to be a limitation with the ADMS software – one that could be

rectified by using separate modelling runs, with specific profiles for each pollutant, at the cost of

additional runtime.

3.3.4.4 Calculation of Emissions Rates

The original version of PITHEM outputted period-based emission totals only for road links in units of

either kilograms or tonnes. For use with ADMS-Urban, outputs were changed to produce emissions

rates in terms of grams per kilometre per second (g/km/s). Additionally, results were formatted as

plain text files, rather than as ESRI shapefiles, for conversion into ADMS-Urban Emissions Inventory

databases (see CERC, 2012: Section 7.1).

3.3.5 Additional Data Requirements for Dispersion Modelling

Aside from the modifications to the pilot framework outlined in the previous sections, dispersion

modelling also requires additional information relating to the topography and meteorology of the

site. In addition, as the methodology considered so far only accounts for localised road traffic

emissions, consideration was also given to background levels of pollutants, and emissions from other

sources. These additional requirements are briefly discussed below.

3.3.5.1 Meteorological Information

Meteorological data for 2010, in a format suitable for use with ADMS-Urban (.met file format) was

provided by Newcastle City Council for this study. The wind rose in Figure 3.23, plotted ADMS-Urban,

clearly shows that the predominant wind direction was just north of due west (280°) over the year.

Hence higher pollutant concentrations will generally occur to the east of road sources. The mean

temperature for the year, 8.04°C, falls just outside of the expected range for the annual average

mean for the North East of England of 8.5°C-9.5°C (Met Office, 2013).

Hour Beginning


63

Figure 3.23: Wind rose plotted from Newcastle Meteorological data for 2010

Further information about the data provided, and meteorological conditions for 2010 may be found

in Appendix K.

3.3.5.2 Background Pollution and Pollution from non-Transport Sources

Given time constraints it was considered impractical to either build up a completely new emissions

inventory, or to modify existing inventories held by the respective councils, to cover non-traffic

emissions sources within the timeframe of the study. Hence, information on background pollutant

levels, and non-transport sources was taken directly from the latest DEFRA source-apportioned

background maps (DEFRA, 2012e; 2012f).

The 1km grid square maps for Gateshead, Newcastle, North Tyneside and South Tyneside for 2010,

for NOx, NO2, PM10 and PM2.5 were downloaded separately from the DEFRA LAQM website and

merged together using ArcGIS. Raster grids of background concentrations were then created at

200m resolution using nearest-neighbour interpolation in ArcGIS. This was done to smooth the maps,

to prevent excessive concentration fluctuations at grid boundaries, though it is recognised that the

choice of interpolation alters the underlying information, and the 1km2 grid size of the original maps

limits the spatial resolution for calculating local concentrations.

For NOx, non-road, minor road and major road sources were handled separately, for use with the

DEFRA background source selector tool (DEFRA, 2012e). This was done to study the effects of

inclusion or exclusion of minor roads from the calculation of concentrations, given the incomplete

coverage of the TPM network. The effect of background level changes, including minor road

backgrounds in calculations, and the impact on modelled concentrations, is discussed at greater

length in Sections 4 and 5. Maps of the background levels used in the study may be found in

Appendix M. As ArcGIS was used to combine data layers, and the GRS model in ADMS was not used,

background levels in ADMS were set to 0µg/m3.


64

3.3.5.3 Terrain

Due to time constraints in running the dispersion model, all flat terrain was assumed throughout the

model domain. This represents an obvious limitation to the modelling, especially for areas on the

banks of the Tyne. It is suggested that a suitable Digital Terrain Model (DTM) be sourced for future

modelling. The latitude of the site was set to 54.9° north.

3.3.5.4 Street Canyon Geometries

The LEZ Steering Group was asked by Newcastle University to provide a list of streets considered to

be canyons within the Urban Core and AQMA areas. In the absence of more detailed building height

and street width information generally canyons within the centre were assumed to be 12-20m high

(approximately 3-5 storeys), and 12-20m wide, based on average values form previous modelling in

Leeds and Leicester, and visual inspection of OS Master Map Topographical Layers (OS, 2013b) in Arc

GIS. Canyon information was added, as required, as a post-processing operation on PITHEM

emissions outputs, prior to conversion to ADMS-Urban Emissions Inventory Databases. A list of

street canyons, and the values applied is given in Appendix L. These should be revisited if any further,

or more detailed modelling is undertaken.

3.3.5.5 Elevated Sections of Road

In the absence of detailed information, and due to time constraints, all sections of road were

assumed to be at ground level. This represents an obvious limitation in modelling, especially for

areas surrounding the Central Motorway in Newcastle (sections of the road are elevated, whilst

other sections are in covered cuttings), the crossings over the Tyne, the Team Valley area to the east

of the A1, and areas along radial routes (e.g. Jesmond Tunnel on the A1058, sections of the A167 in

Gateshead). It is recommended that for any subsequent modelling, that further attention is paid to

such areas, to address these issues.

3.3.5.6 Modelling of Secondary NO2 Formation

ADMS-Urban allows the direct modelling of the interaction of organic compounds with O3, NO and

NO2 in the presence of sunlight by using the Generic Reaction Set (Venkatram et al., 1994) semi-

empirical model. However, given time constraints this model was not used. Rather it was decided to

model NOx only and use appropriate background concentrations (Section 3.3.5.2), combined road

source concentrations from ADMS run results and appropriate f-NO2 factors within the latest LAQM

DEFRA NOx to NO2 spreadsheet tool (DEFRA, 2012b; 2012c) to produce final NO2 concentrations. The

modelling of NO2 concentrations is discussed further throughout Sections 4 and 5.

3.3.5.7 ADMS-Dispersion Parameters

The ADMS surface roughness parameter was set to 1.0m, the default recommended value for city

areas (CERC, 2012). Likewise surface albedo and the minimum Monin-Obukov mixing length

parameters were kept at the default values of 0.23 and 30 respectively (CERC, 2012).

3.4 Post-Framework Development

3.4.1 Source Apportionment of Concentrations

In order to attempt apportioning of concentrations, as well as emissions, it was decided that the

individual vehicle classes were modelled separately in ADMS-Urban, with gridded results combined

in ArcGIS. This added an additional computational burden in terms of ADMS-runtimes to the study,

see below.


65

3.4.2 Concentration Modelling Issues

An advantage of ADMS-Urban is that the software runs on a standard Windows machine, using a

very limited memory footprint. However, initial trial runs of the framework, using a 50m output grid

resolution, and full resolution ITN network geometry gave unacceptably long estimated run times

(i.e. several months for a single scenario run on a dual-core desktop machine).

In order to reduce runtimes it was proposed that:

The ITN network geometry supplied to ADMS would be simplified to reduce the number of

link sections required. This was achieved by further pre-processing of network geometry, via

application of a variant of the Douglas-Peuker algorithm (Douglas and Peuker, 1973);

Further modelling of concentrations would be limited to an area surrounding the

Newcastle/Gateshead urban core area, bounded by a lower-left OS coordinate of (422200,

428600) and an upper-right coordinate of (558200, 569600);

In order to provide a ‘broad brush’ assessment of concentrations output grid size would be

lowered to 200m. This is considered quite a low resolution, on the boundary of what could

be considered acceptable, given the size of the Urban Core Area and AQMAs;

The bus model would be further split into overlapping Newcastle and Gateshead

components, with a rough boundary along the river Tyne. Within the overlapping area,

results would be based on the maximum concentration reported by either model;

All dispersion model elements would be run on a twelve-core server machine. This proved

capable of running two and a half scenarios simultaneously, with a turnaround time of

approximately 3 days.

These proposals were agreed by the LEZ steering committee, and were included in the basis of the

dispersion modelling presented in following sections. The extent of the central area modelled, the

receptor points used, as well as the area of overlap between the two bus model areas is shown in

Figure 3.24.

Figure 3.24: Dispersion model domains and receptor points for general traffic (left) and buses (right).

Overlap between the two bus model areas is shown in green

The 200m resolution of grid points was considered the lowest practical limit of grid size, given that

NO2/NOx chemistry and fall-off to background generally occur within several hundred meters of the


66

roadside in open conditions. A version of the framework was setup with ADMS-Urban using the

‘intelligent’ or ‘source oriented’ gridding option (CERC, 2012, section 3.5.2), where more points are

used close to roads, to enhance output definition. This was subsequently abandoned for two reasons:

1. The ADMS grid pre-processor failed to produce receptor points for the TPM networks. This

was thought probably due to the short length of some link sections in the TPM model when

combined with ITN data, and;

2. Work with the consultants ARUP on another network (Tiwary and Goodman, 2013) showed

that ADMS with the ‘source oriented’ grid option activated was doubling receptor points on

either side of unidirectional links in the transport model, leading to increased runtimes.

3.5 Finalised Modelling Framework Figure 3.25 presents the finalised modelling framework for NOx and NO2, developed from the

methodology proposed at the start of this section. The additional complexity of the framework

reflects the key changes and lessons learn from the pilot, as outlined in Section 3.2.3.1. The

framework for PM10 and PM2.5 is similar, though less complex - not requiring consideration of

chemistry to calculate concentrations. Note that where ‘general traffic’ networks and grids are

mentioned as outputs from PITHEM and Adms-Urban in Figure 3.25, these refer to layers retaining

information on emissions and concentrations cars, LGVs and HGVs respectively.


67

Figure 3.25: Finalised modelling framework used in the LEZ Feasibility Study


68

4 Base Year Modelling This section presents the results of the emissions and air quality modelling undertaken, using the

framework outlined in the previous section. Emissions totals and source apportionment for the sub-

domains outlined in Section 3.3.2 are presented, before the section proceeds to examine pollutant

concentrations. Regarding concentrations, an initial validation study for the two AURN (Automatic

Urban and Rural Network) monitoring sites (DEFRA, 2012a) in Newcastle is described, before outputs

across the central area of Newcastle and Gateshead are presented. The section finishes with a brief

discussion of sensitivity and uncertainty in the modelling framework, and the implications of the

results for LEZ implementation.

4.1 Emission Results Emissions modelling was undertaken using the fleets derived from both the spreadsheet model and

DFT data (Section 3.3.4.2) as well as the baseline NAEI fleet for 2010. The emission results for cars,

LGVs and HGVs are based on those from the speed merging process as outlined in Section 3.3.3.5

and Appendix G. Up till mid-2012, all modelling was based on the EFT v4.2.2. emissions factors,

before being changed to use EFT v5.1.3. For interest, Appendix H retains details of the effects of this

change, though all subsequent results in this study are based on EFT v5.1.3.

4.1.1 Emissions totals

Table 4.1 presents the emissions totals for the entirety of the Newcastle/Gateshead model domain,

including the surrounding buffer region (hence the values in the table are not directly comparable to

those presented for the pilot in Table 3.1). VKM travelled by the vehicle classes is also shown.

Table 4.1: Vehicle kilometres (VKM) travelled and emissions totals for the entire model domain

VKM uCO2 NOx PM10 PM2.5 pNO2[1] f-NO2

[2] All All NAEI DfT NAEI DfT NAEI DfT NAEI DfT NAEI DfT NAEI DfT

Unit b.km %-age kT kT T T T T T T T T %-age %-age

Car 2.79 77% 464 452 946 988 104 106 65 67 198 147 20.9% 14.9%

LGV 0.53 15% 116 117 447 467 41 45 29 33 140 128 31.2% 27.5%

HGV 0.23 6% 163 163 1034 1200 44 50 31 36 134 153 13.0% 12.7%

Bus 0.06 2% 61 61 518 554 17 18 12 14 63 66 12.2% 11.9%

Total 3.62 100% 804 793 2945 3209 206 219 137 149 535 494 18.2%[3] 15.4%[3]

[NB: Values may not add up to 100% due to rounding.] [1]

Estimate based on NOx emission values multiplied by fleet-

weighted COPERT4 factor (Boulter, Barlow and McCrae, 2009). [2]

f-NO2 = pNO2 / NOx. [3]

c.f. value given for non-London

Urban traffic in 2010 (DEFRA, 2012a) is 19.6%.

As with the pilot model, values for VKM and uCO2 for private car traffic in Table 4.1 remain

significant underestimates (>35%) compared to published data for the region from DfT and DECC –

which will translate into other emissions totals too. These underestimates were still thought due to

the lack of coverage of sub-urban and rural roads in outlying areas, with more limited impact on

central areas and AQMAs. The choice of DfT/spreadsheet fleet, over the NAEI base fleet, leads to an

approximate 10% increase in emissions for all pollutants, except primary NO2 from cars and LGVs.

NB: At the time of writing primary NO2 apportionment remains a ‘guideline-only’ function of the

PITHEM software, not an official output of the EFT. Scaling for weekend versus weekday values

reduces emissions totals by approximately 7% over assuming all weekday values.


69

4.1.2. Source apportionment for sub-areas

Figures 4.1 and 4.2 present the source apportioned emissions for each sub-domain, 4.1 presents the

apportioned totals, whilst 4.2 presents the percentage contributions. Results presented are based

on the use of the DfT/spreadsheet model fleets – in order to provide conservative estimates of

emissions. Note that for the traffic cordon areas (see Figure 3.6), emissions values are ‘nested’ and

include contributions from sub-domains within their own domain (e.g. ‘Gateshead Whole’ in the

Figures includes the data for the ‘Gateshead Tadu’ cordon.

As Figure 4.1 presents totals, it is not unexpected that increases occur in correlation with the size (or

rather total VKM) of sub-domain considered. The figure is indicative of the disparity between

emissions in the central areas, or the AQMAs, compared to emissions across the region. If air quality

issues are associated with localised problems only, then the relative fleet proportions will be the

primary issue in LEZ design, whilst if the AQMAs themselves are quite small, or linear in nature, then

it will be imported emissions from outside the area that may dominate design of the size of the low

emission zone. From the Figure, it can be seen that the NOx emissions within the AQMAs, the UCAs

and the Newcastle central cordon are an order of magnitude smaller than emissions the areas they

are nested in. Emissions totals and apportionments for the Newcastle City Centre AQMA, and the

Newcastle UCA, and the Gateshead AQMA and UCA respectively, are similar. This is unsurprising

given the high degree of overlap between the areas (see Figures 3.4 and 3.5). The emissions totals

for central Gateshead are approximately half of those for Newcastle City Centre, whilst totals for the

Metropolitan Boroughs as a whole are comparable. Figure 4.2 presents percentage values

normalised to the totals, and shows the increasing influence of buses’ contributions towards the

urban cores, whilst influence of HGVs, and (to a lesser extent LGVs) decreases. In central Newcastle,

over 70% of total road emissions for NOx are modelled as arising from buses, whilst in the AQMAs

the NOx emission apportioned to buses are in the range of 37-47%.

Whilst the primary NO2 apportionment should be viewed with some caution, given the simplicity of

the model compared to the complexity of issue, it does show the reduced impact of buses and HGVs

in primary NO2 emissions as these are modelled as having a low f-NO2 ratio. Conversely, the elevated

f-NO2 ratio for light duty diesel engine vehicles increases their relative importance. Within the

central traffic cordon 67% of pNO2 emissions are apportioned to buses, 12% to cars, 11% to LGVs,

and 10% to HGVs. For the AQMAs, the contribution from buses ranges from 28% in Gosforth to 38%

in Newcastle Centre, with values for cars ranging from 25% to 40%, LGVs 20 to 22% and HGVs 10 to

17%. For particulate matter, in all except the Newcastle central traffic cordon, cars account for

approximately 50% of emissions, with LGVs producing approximately a further 20%. As the area of

regard is expanded towards the region as a whole, the influence of HGVs on all emissions increases

drastically, through the inclusion of the A1(M) and A194(M) and other radials in the totals.

The results of the source apportionment should also be viewed in the light of the discussion of

shortcomings in the traffic modelling (see Sections 3.2, 3.3 and 4.1.1). Towards the edges of the

network it is believed that the current framework under-predicts the contributions from cars by up

to 30%, whilst over predicting the contributions from HGVs, in all areas, possibly by as much as 60-

90%. The use of a separate model for buses also may be slightly prejudicial. Though this effect is

thought to be limited in the central areas, and the AQMA, the values for buses in outer areas may be

more heavily biased. Whilst work was started on gridded source apportionment over the region, to

attempt to assess some of these issues, that work took place prior to mid-2012, and used EFT v4.2.2

emissions. Due to time constraints, it was not repeated with EFT v5.1.3 emissions.


70

Figure 4.1: Source apportioned emissions totals by sub-domain areas: NOx (top left), pNO2 (top right), PM10 (bottom left), PM2.5 (bottom right)


71

Figure 4.2 Normalised source apportioned emissions by sub-domain areas: NOx (top left), pNO2 (top right), PM10 (bottom left), PM2.5 (bottom right


72

4.2 Base Year Validation against AURN data In order to validate the framework within the Newcastle Centre AQMA, ratified data for 2010 was

obtained for the two AURN sites in Newcastle, Newcastle Centre (B1307 St Mary’s Place) and

Newcastle Cradlewell (A1058 Jesmond Road) (see Appendix N). These data was compared to output

for the same two locations from the modelling framework, in order to assess performance under

two main assumptions:

1. Using background levels where major roads been removed, using the sectoral information in

the DEFRA data. This is referred to as ‘back_1’ in subsequent tables and figures, and;

2. Using background levels where ALL roads had been removed referred to as ‘back_2’.

For calculation of NO2 levels using the DEFRA LAQM NOx to NO2 converter tool (DEFRA, 2012b;

2012c), further combinations were tested, based on the guidelines for cross checking model validity

with both pollutants in box 6.3 of LAQM.TG(09) (DEFRA, 2009b). These were:

1. Using road increment NOx (modelled) + background NOx (‘back_1’ or ‘back_2’) + DEFRA f-

NO2 (from NOx to NO2 tool) to give final NO2;

2. Using road increment NOx (modelled) + background NOx (‘back_1’ or ‘back_2’) + f-NO2 (from

PITHEM – see Section 3.3.4.5.) to give final NO2;

3. Using road increment NOx (modelled) + background NO2 (‘back_1’ or ‘back_2’) + DEFRA f-

NO2 to give final NO2. Background NO2 is calculated using the DEFRA sector removal tool.

This gives six combinations of model assumptions in total. Note that calculated background NO2

levels (point 3 above) using the DEFRA Background sector removal tool (DEFRA, 2012e) also

possesses its own internal assumptions about f-NO2 from roads. Whilst these are presumably the

same as the ones in the NOx to NO2 converter tool, unlike that tool the f-NO2 assumptions cannot be

changed manually and hence the 4th possible combination - of using PITHEM f-NO2 values, with

background NO2 levels, was not tested.

In all cases, background contributions from transport sectors other than road transport, as well as

natural, domestic and industrial sources were retained. Effects on NOx, NO2, PM10 and PM2.5 were

examined. As with section 4.1 emissions were based on the DfT/spreadsheet model and are

considered conservative. More information on the two AURN sites, as well as links to the data used,

may be found in Appendix N, alongside the values used in the DEFRA conversion spreadsheet itself.

4.1.1 Total NOx and NO2 concentrations

Table 4.2 presents the annual mean NOx and NO2 concentrations from the AURN sites and from the

various combinations of assumptions. Based on EU guidelines for modelling studies, predicted

annual NOx/NO2 levels should be within 30% of observed values. The ratio between the modelled

and observed concentrations is given in the last column, and colour coded depending on value.

From the table it can be seen that the use of background containing contributions from minor roads

‘back_1’ led to over-prediction of NO2 levels by greater than 20% at the Newcastle Centre (urban

background) site, and greater than 30% at the Newcastle Cradlewell (roadside) site. For NOx

including minor roads, leads to an over-prediction of 34% at Central, but only 1% at Cradlewell.

Using the background with all road contributions removed ‘back_2’ leads to better predictions (all

within 6% of observed for Newcastle Centre), but a marginal under-prediction of NOx at Cradlewell.


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Using ‘back_2’ at the Cradlewell site results in a 15-20% over-prediction in NO2. Using the PITHEM

calculated f-NO2 value (15.4%) using the DfT/spreadsheet model calculated fleet (section 3.3.4.2),

rather than the DEFRA Value (19.6%), reduces the annual mean NO2 by 0.6 µg/m3 at the Central site,

and by around 2 µg/m3. In comparison, changes in the background NO2 level give rise to

concentration differences of ≈6.9 µg/m3 and ≈7.5 µg/m3 respectively. The effect of the original

interpolation method used in producing the background maps, over using the raw 1km2 values has

not been evaluated.

Table 4.2: Observed versed modelled concentrations of total NOx (as NO2) and NO2 for the Newcastle

AURN sites under differing background and f-NO2 assumptions

Site Pollutant Modelling Assumptions Observed Annual Mean

Modelled Annual Mean

Ratio (Modelled: Observed)

Newcastle Centre

NOx Back_1_NOx 53.77 72.30 1.34

NOx Back_2_NOx 53.77 56.33 1.05

NO2 Back_1_NOx + PITHEM_f-NO2 30.95 39.71 1.28

NO2 Back_2_NOx + PITHEM_f-NO2 30.95 32.71 1.06

NO2 Back_1_NOx + DEFRA_f-NO2 30.95 39.04 1.26

NO2 Back_2_NOx + DEFRA_f-NO2 30.95 32.10 1.04

NO2 Back_1_NO2 + DEFRA_f-NO2 30.95 38.19 1.23


Cradlewell NOx Back_1 100.21 101.68 1.01

NOx Back_2 100.21 86.87 0.87

NO2 Back_1_PITHEM_f-NO2 36.04 49.44 1.37

NO2 Back_2_PITHEM_f-NO2 36.04 43.42 1.20

NO2 Back_1_DEFRA_f-NO2 36.04 47.49 1.32

NO2 Back_2_DEFRA_f-NO2 36.04 41.54 1.15



Observations from the two AURN sites show neither site indicated exceedences based on the hourly

average annual mean criteria for NO2 (40 µg/m3 – see table 2.1), whilst the modelled data shows

values close to the limit if ‘back_1’ is used at Newcastle Centre, and exceedence at Cradlewell,

irrespective of the background level used.

Table 4.3 presents the ratios of annual mean NO2 to NOx as NO2, in order to give an indication of the

performance of the NOx to NO2 conversion at the sites. Performance appears acceptable for the

Newcastle Centre site, but at the roadside Cradlewell site the modelled NO2:NOx ratio is 12-15%

higher than observed. These results could potentially be improved by using a more complex

chemistry model (e.g. the GRS (Venkatram et al., 1994) in ADMS) on the individual hourly data,

rather than a single value correction to produce an annual mean.

Table 4.3 Modelled versus observed NO2:NOx ratios for the Newcastle AURN sites

Site Observed Back_1_NOx

+ DEFRA_f-NO2

Back_2_NOx

+ DEFRA_f-NO2

Back_1_NOx + PITHEM_f-

NO2

Back_2_NOx + PITHEM_f-

NO2

Back_1_NO2 + DEFRA_f-

NO2

Back_2_NO2 + DEFRA_f-

NO2

Newcastle Centre

0.58 0.55 0.58 0.54 0.57 0.53 0.54

Cradlewell 0.36 0.49 0.50 0.47 0.48 0.48 0.48


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4.1.2 Total PM10 and PM2.5 Concentrations

Table 4.4 presents the same analysis as in section 4.1.1, for PM10 and PM2.5 concentrations, from the

Newcastle Centre site. Only two sets of results are presented, as the PM calculations vary only by

the inclusion or exclusion of the background levels for minor roads.

Table 4.4: Observed versed modelled concentrations of total NOx (as NO2) and NO2 for the Newcastle

AURN sites under differing background and f-NO2 assumptions

Site Pollutant Modelling Assumptions Observed Annual Mean

Modelled Annual Mean

Ratio (Modelled: Observed)

Newcastle

Centre PM10 Back_1 14.47 16.01 1.11

PM10 Back_1 14.47 15.01 1.04

PM2.5 Back_2 9.48 10.95 1.16

PM2.5 Back_2 9.48 9.42 0.99

As with the NOx/NO2 results, using the background without inclusion of minor roads provides a

better fit to the data for Newcastle Centre. The values recorded for both PM10 and PM2.5 are less

than half the limit value allowed or proposed (see table 2.1).

4.1.3 Source Apportionment

Figure 4.2 presents the source apportionment of concentrations for the two sites, for all pollutants.

For NO2 the apportionment presented represents the use of the ‘Back_1_NOx + DEFRA_f-NO2’ and

‘Back_1_NOx + DEFRA_f-NO2’ assumptions from Table 4.2.

The source apportionment of NO2 was based on the contribution of each vehicle class to the total

road NOx concentration, then scaled by the total NO2 contribution from traffic. This is similar to the

approach given in LAQM TG(09) Annex 3, Box A3.1, or the approach previously using in Newcastle

(Laxen et al., 2005). Though it should always be borne in mind that the non-linear nature and

complexity of the chemistry involved makes any such apportionment somewhat approximate (e.g. it

does not take into account the differing f-NO2 rates of the vehicles, see Table 4.1, nor that vehicles

may operate at different times of day, with different levels of sunlight present, HGV deliveries at

night for example).

It is quite clear that the selected background plays a critical part of modelling the concentrations at

the two sites. The following observations are made:

For the Newcastle Centre AURN:

o For NOx the background is modelled to contribute 49-62% of the total, followed by

buses(26-35%), cars (6-7%), HGVs (4-6%), with LGVs making the least contribution

(2-3%);

o For NO2 the background contributes 60-69% of the total, followed by buses (20-26%),

cars (4-6%), HGVs (3-4%) and LGVs (2%);

o For PM10 and PM2.5, over 90% of the modelled concentration is coming from the

background concentration. Indeed, the background levels from the interpolated

DEFRA maps are higher than the observed concentrations, even after road sector

removal.


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Figure 4.2: Source apportionment for NOx and NO2 concentrations at both sites (top row), and PM for Newcastle Centre only (bottom row)


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For the Cradlewell Roadside AURN:

o As a roadside site the ambient background makes less of a contribution to final

concentrations than at the urban background Newcastle Centre site;

o For NOx the background is modelled to contribute 49-62% of the total, followed by

buses (26-35%), cars (6-7%), HGVs (4-6%), with LGVs making the least contribution

(2-3%);

o For NO2 the background contributes 24-35% of the total, followed by buses (24-28%),

cars (20-23%), HGVs (13-15%) and LGVs (7-9%);

Whilst it is impossible to extrapolate performance of the whole modelling framework from just two

sites, the results do show that the choice of ambient background concentration makes an enormous

difference to the resulting concentrations. It is therefore recommended that the modelling

framework, as presented in this document be considered suitable for indicative guideline purposes

only, and that more detailed verification, development of correcting factors if necessary, followed

by calibration and validation is required before the framework may be considered ‘fit for purpose’

for calculating concentrations at specific receptor locations. As a priority, additional information

within the AQMAs (e.g. Gosforth High Street, Percy Street in Newcastle and information for

Gateshead locations) should be sourced.

Additional data on observed concentrations, both through passive monitoring (i.e. NOx diffusion

tube data) has been made available to Newcastle University, by both Gateshead and Newcastle City

Councils for the purpose of the above, but due to time constraints, further investigation was

considered outside of the scope of this study.

In order to attempt to assess the effect of background choice in a more general and practical fashion,

difference maps between the two background grids for each pollutants have been plotted. Figure

4.3 gives a sample difference map for NO2 backgrounds for 2010, whilst maps for other pollutants

may be found in Appendix M.

Figure 4.3: Background concentrations (µg/m3) of NO2 in 2010, assuming minor roads included (left)

and all roads excluded (middle). The difference map between the two is on the right.

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Using the difference maps a first approximation may be gained of the effect of choice of background

on the final concentration – though it must be stressed that they only offer an approximation of

changes given limitations of the interpolation method and changes in ratio between background and

road contributions. The two AURN positions are shown on Figure 4.3, and both fall in the 7.5 - 9

µg/m3 difference band, for comparison the actual values given in section 4.1.1 were 6.9 µg/m3 and

7.5 µg/m3. As plotted, the worst discrepancy between the two background levels (9.2 µg/m3) occurs

in the vicinity of Prince Consort Road in Gateshead. Also notable is that even towards the edges of

the map differences of 3 – 4.5 µg/m3 could be expected, a lower absolute value than towards the

urban centres, but a higher relative value of overall annual concentration

4.3 Concentration Results Initial ADMS-Urban runs were made using the background levels including the contributions from

minor roads and the DfT/spreadsheet model fleet. As with the emissions totals presented in

section 4.1 the logic for doing this was to present a conservative estimate (essentially ‘worst-case’)

of air-quality within the Newcastle/Gateshead area, and to see if the framework would highlight

the existing AQMAs as problem areas for investigation as a first ‘sanity check’ of performance.

However the model will also be biased towards the background to a certain extent, and hence less

responsive to changes in emissions from the roads.

As noted in Section 3.5 each vehicle class was run separately, then the resulting grids of

concentrations combined using the ‘map algebra’ tools within ArcGIS Spatial Analyst, to produce a

‘road total contribution’ to concentrations. This ‘road contribution’ was then added to the ‘ambient

background’ contribution (see previous section and Appendix M) to produce final concentrations.

Additionally, the proportion of each vehicle class’ contribution to the ‘road total contribution’ was

also calculated in ArcGIS, to provide source-apportionment information on the relative contribution

of vehicles in particular areas.

Baseline results for annual means of each pollutant are presented in the following sections. ADMS-

Urban was also set up to provide ‘comprehensive output’ files, for examination of the number of

short-term exceedences, though due to time constraints, these were subsequently not used in this

study.

4.3.1 Total NOx (as NO2) Concentrations

Figure 4.4 presents the output for NOx (as NO2). A pattern of elevated concentrations in the central

areas of both Newcastle and Gateshead is clearly visible. The highest concentrations occur adjacent

the A1 to the south west of Gateshead centre, with Levels around the Team Valley Trading estate to

the east of the A1 being comparable to those in the centre. Statistical summary results for selected

model sub-domains (the AQMAs and the Urban Cores) are given in Table 4.5.

Given the low resolution of the output grid, further analysis is somewhat problematic. The maximum

levels recorded for the city centres, and the region around the A1 are recorded in those cells where

the central point falls close to (i.e. within 10m or so) the centre line of the road. For example, in

central Newcastle a maximum of 168.2µg/m3 is recorded near the junction of the Central Motorway

A167M, and the Great North Road, where a receptor point falls on the motorway carriageway.

Another ‘hot-spot’ (150µg/m3) occurs near the ’55 Degrees North’ apartment complex, where the

receptor point falls between the two motorway carriageways. A third occurs on the A189 Gallowgate


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(156µg/m3 ). For Gateshead centre, the highest levels (119.0 and 116.7µg/m3) are recorded where

receptor points fall on the carriageway of A184. Several similar points occur along the A184 outside

of the centre. The maximum level recorded across the entire domain is 268µg/m3 where a receptor

point falls next to the northbound A1. A further three cells along the A1 record values above

170µg/m3. Also notable are points on the carriageway of the A1058 Coast Road at 120+µg/m3, along

Gosforth High Street (91µg/m3) and at the A1 Junction with Kingston Park Road (139µg/m3) – again

where the receptor point falls between the main carriageways.

Figure 4.4: Modelled annual mean total NOx concentrations in μg/m3

Table 4.5 Summary NOx statistics for selected model sub-domains

Sub-Domain Count Mean (µg/m3) Range (µg/m

3) Std. Dev. (µg/m

3)

Newc. Central AQMA 106 73.51 39.53 - 168.19 27.75

Gosforth AQMA 48 42.23 33.12 – 90.96 12.53

Gateshead AQMA 51 70.38 45.24 – 119.01 17.51

Newcastle UCA 193 64.85 35.44 – 168.19 22.17

Gateshead UCA 79 67.66 48.34 – 119.01 15.51

Newcastle/Gateshead 1914 46.74 18.8 – 268.36 19.23

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4.3.2 Nitrogen Dioxide (NO2)

Figure 4.5 and Table 3.6 present the results for NO2. A contour representing the 40µg/m3 limit value

has been added to the map. However given the limited resolution of the model, the contour should

be interpreted as indicative of a potential problem area existing adjacent to the road in question –

not as the absolute boundary of an area of exceedence. Hence, based on Figure 4.5, it may be said

that problem areas exist in:

Newcastle Centre and areas east of the Central Motorway;

Crossings over the Tyne (Tyne Bridge/Quayside area);

Byker Bridge/Shields Road area;

Gateshead Centre adjacent to the A184;

Along the A1 generally, but specifically including the Team Valley Trading Estate;

Along the A1058 Coast Road;

Along the A184 Newcastle Road,

Within Gosforth High Street, B1318;

On the A167 approaching Ponteland Road;

Along the A695 Scotswood Road.

Whilst some of these locations are outside of the declared AQMAs, it must also be borne in mind

that the model is expected to be very conservative. The issue of grid resolution and how locations on

the list above match with existing AQMA boundaries is discussed further in Section 4.3.4.

From Table 4.6, the average concentration of receptor points within both Newcastle Centre AQMA

and Gateshead AQMAs are modelled as very slightly below the exceedence threshold (by 0.1 and 0.9

µg/m3 respectively). However, both areas show exceedence in terms of the maximum values

recorded (by approximately +30 and +17 µg/m3 respectively). The Gosforth AQMA receptor points

give a mean well below the threshold (≈13.0 µg/m3 under), but the maximum value recorded also

shows a possible exceedence (+6.3 µg/m3).

Table 4.6 Summary NO2 statistics for selected model sub domains

Sub-Domain Count Mean (µg/m3) Range (µg/m

3) Std. Dev. (µg/m

3)

Newc. Central AQMA 106 39.90 25.69 – 70.35 9.83

Gosforth AQMA 48 26.71 22.60 – 46.27 5.27

Gateshead AQMA 51 39.13 28.41 – 56.67 6.82

Newcastle UCA 193 36.62 23.24 – 70.35 8.21

Gateshead UCA 79 38.03 29.86 – 56.67 6.03


As with the NOx results, the ‘hot-spots’ are associated with receptor points falling near carriageways,

with a maximum level recorded adjacent to the A1 of 92.8 µg/m3 – a clear exceedence, even with

the elevated background level, but in an unpopulated area involving a Highways Agency Road.

Based on use of the difference maps presented in Appendix M, the reduced background excluding all

roads, would result in the mean concentrations for Newcastle and Gateshead Central AQMAs

dropping to values between 31.4 and 33.1 µg/m3, though exceedence in both areas would still be

present. This guideline analysis should not be considered a replacement for a more rigorous analysis

at higher grid resolution however.


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Figure 4.5: Modelled annual mean NO2 concentrations in μg/m3. Red contour shows boundary of

areas with levels over 40 μg/m3 exceedence limit

4.3.3 Particulate Matter (PM10 and PM2.5)

Figure 4.6, as well as Tables 4.7 and 4.8, present the spatial results and summary statistics for PM10

and PM2.5 respectively. All annual average values are well below the exceedence limits, reinforcing

both the original decision for declaration of AQMAs in the area for NO2 only, and the subsequent

need for any LEZ to be based on NOx Euro Class Criteria.

Table 4.7: Summary PM10 statistics for selected model sub domains

Sub-Domain Count Mean (µg/m3) Range (µg/m3) Std. Dev. (µg/m3) Newc. Central AQMA 106 16.3 13.9 – 25.1 1.9

Gosforth AQMA 48 13.6 12.9 – 16.3 0.7

Gateshead AQMA 51 16.3 14.4 – 20.1 1.3

Newcastle UCA 193 15.8 13.4 – 25.1 1.5

Gateshead UCA 79 16.2 14.7 – 20.1 1.2



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As with the NOx/NO2 concentrations, the highest levels of particles recorded are at the already noted

receptor ‘hot-spots’ along the A1.

Figure 4.6: Modelled Annual Hourly Mean PM10 (left) and PM2.5 (right) Concentrations in μg/m3

Table 4.8: Summary PM2.5 statistics for selected model sub domains

Sub-Domain Count Mean (µg/m3) Range (µg/m3) Std. Dev. (µg/m3) Newc. Central AQMA 106 11.6 9.0 – 17.0 1.3

Gosforth AQMA 48 9.6 9.1 – 11.5 0.5

Gateshead AQMA 51 11.5 10.1 – 14.0 0.9

Newcastle UCA 193 11.1 9.3 – 17.0 1.1

Gateshead UCA 79 11.4 10.3 – 14.0 0.8


Based on the above results and analysis, showing little potential for any exceedence of limit values,

whilst particulate matter was included in subsequent LEZ scenario modelling, not effort has been

made to analyse the resultant data.

4.3.4 Sensitivity and Uncertainty

Addressing uncertainty in a complex modelling framework is a difficult issue – given time constraints

no direct attention has been paid to overall model uncertainty in this study. Each element of the

framework has its own range of assumptions, systematic limitations and potential biases. The

framework as a whole may perform better in some locations or over some timeframes, than in

others. Whilst it is possible for the effects of uncertainty in individual model outputs to ‘cancel each

other out’ along the modelling chain, it is also possible for uncertainties to propagate and grow

along the model chain. Model verification followed by calibration and validation, seeks to limit these

effects. Whilst efforts have been made throughout the study to perform these operations, and

identify issues, in individual model undoubtedly, problems remain, and may be compounded in the

whole.

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4.3.4.1 Sensitivity of Concentration Outputs

Some limited work on analysing the sensitivity of the framework was done to:

1. Investigate how identified potential air quality problem areas would change with enhanced

resolution of modelling, and;

2. Investigate how using the reduced background ‘back_2’ would affect identified ‘hot-spots’.

Figure 4.7 shows the results of the first exercise. The left figure shows baseline results using a 200m

grid, with AQMAs added as shaded regions, whilst the right shows the results of using a grid of

approximately 100m resolution (NB: ‘approximately’ is used for the right image as it uses down-

sampling from a 50m grid for the bus model layers). The blue contour represents the 40μg/m3

contour (i.e. potential problem areas) and the green 30 μg/m3 (‘high’ level zones).

Figure 4.7: Sample NO2 Concentrations for 2010, based on using 200m resolution grid (left) and

≈100m resolution grid (right)

From the figure it can be seen that, whilst potential problem areas broadly match when using the

differing model resolutions, contour boundaries shift, and there is the tendency for issues along

radial routes, and the A1 to become more clearly defined longitudinally as individual ‘hot-spots’

merge, but reduce laterally, as the higher resolution grid more accurately reflects dispersion and

chemistry away from the road. Also of note, in using down sampling and interpolation on the bus

grid to match the general traffic network, some problem areas disappear (e.g. Gosforth High Street

to the south of the A191), whilst others appear (e.g. Blue House Roundabout and Gosforth High

Street to the north of the A191).

Figure 4.8 shows the results of the second exercise. It is clear that the choice of inclusion or

exclusion of minor roads makes a major impact on the final concentrations especially in the urban

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core areas. For example both mean and maximum NO2 values recorded by receptor points in the

Newcastle City Centre AQMA fall by around 6 µg/m3, representing a significant change in

concentration (approximately 15% in the overall mean concentration), though slightly smaller than

the value the background difference maps in Appendix M suggest (≈7.5 µg/m3).

Figure 4.7: NO2 ‘Hot-spot’ areas, based on using background NO2 levels including minor roads

(‘back_1’ left) and excluding minor roads (‘back_2’ right)

What is also apparent, from Figures 4.5, 4.7 and Table 4.6 is that, as modelled the receptors in the

centres of Newcastle and Gateshead are right at the limit of the exceedence threshold, suggesting

that a relatively small change in concentration, such as that produced through and LEZ, could be all

that is required to reduce levels below the limit for large areas of the centre. Given the low

resolution of the output grid used in the current modelling, quantification of the areas affected is

not possible. Likewise more work is needed to verify, calibrate and validate background levels

appropriately.

4.4 Summary and Discussion This section concludes with a brief summary of findings from the initial modelling work, a round-up

of the limitations of the framework and the implications of both for LEZ design.

4.4.1 Initial Findings

For the 2010 base year, in the Newcastle Central Cordon area buses appear to be the

dominant source of NOx, accounting for over 70% of emissions. In the AQMA areas the

contribution from buses appears to be around 37-45%. NOx emissions in the Newcastle and

Newcastle and Gateshead AQMAs are more dependent on the bus contribution than for the

Gosforth AQMA;

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The influence of buses on NOx emissions decreases away from the urban core areas, whilst

the contribution from LGVs and HGVs increases. Around the A1/A1M HGVs contribute

towards very high (>150 µg/m3) annual NOx concentrations.

There are clearly modelled exceedences of NO2 in all of the defined AQMAs, as well as at

other locations, e.g. further along the Coast Road than the current AQMA limit, though the

use of the high NOx background should be kept in mind;

Particulate matter (either PM10 or PM2.5) does not appear to be a particular issue in

Newcastle or Gateshead, though the policy of continual exposure reduction is noted. The

modelled PM concentrations are more dependent on the choice of background level than

for NOx or NO2;

Cars play a more important role in production of PM, than for NOx, accounting for

approximately 40 – 50% of total emissions;

4.4.2 Modelling Limitations

Based on both Sections 3 and 4 the following limitations in the current modelling framework are

highlighted.

4.4.2.1 Transport Modelling

The base year for all modelling is 2010, which is now three years ago. Whilst VKM travelled

appears to be somewhat static between then and now, the fleet changes in the period are

not fully known, hence the current results are potentially limited in application to the

present situation;

The use of ME2 matrix estimation to calibrate link flows for the urban cores is not ideal, as

this distorts the original underlying OD matrices. In the case of any study relating to

transport then any uncertainties in the initial demand matrices will be propagated on to the

assignment of flows on network. Even in a calibrated strategic transport model, which

performs well for the network as a whole, there may be large (50%+) discrepancies in flows

on individual links, which will propagate into the emissions and dispersion modelling. The

pollution ‘picture’ as a whole may be broadly correct, but inaccurate adjacent to specific

roads;

Classified Cordon flows were checked across multiple cordons in Newcastle, but only one

cordon in Gateshead. Further validation work may be required for Gateshead;

The use of separate bus and private/fleet transport networks and models, which are then

merged with observed speed data, produces a further problems and complexities in

ensuring data is applied consistently. Further work is required to improve integration. For

example, under the current framework it is not possible to examine the effects of changes

on implementing non-car lanes, as these would generally have the same TOID (and hence be

assigned the same data) as their ‘parent’ road;

It has been assumed in the study that the bus and speed information provided by Newcastle

City Council is correct, further investigation of the ground-truth and underlying assumptions

of these data is warranted;

4.4.2.2 Emissions Modelling

For emissions modelling, using the current framework, total emissions are derived from the

number of vehicle kilometres travelled by user class. Therefore, for NOx/NO2 emissions

modelling in the recent past and at present, the routes chosen by heavy duty vehicles (or in


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the case of buses, the routes allocated to) are of concern, as these vehicles at present have a

disproportionate effect on emissions (e.g. see Figure 2.4). For NOx/NO2 emissions in the near

future, the apparent underestimation VKM for diesel cars and LGVs becomes more

important (e.g. see Figure 2.5);

The use of average speed based emissions curves, and merging of speed information with

the traffic model also reduces both the spatial resolution and overall totals produced. An

alternative for detailed modelling would be the use of a traffic micro-simulation model,

coupled with an instantaneous emissions model, at the expense of increased modelling

resources due to the stochastic nature of these models;

The effects of motorcycles, taxis and coaches as separate vehicle classes in emissions

modelling have not been considered. This was primarily due to lack of information on these

classes in the transport model, and their assumed small presence in the overall fleet.

However it is noted that:

o Licensing of private hire vehicles may be a mechanism for LEZ compliance;

o Flows of coaches may be appreciable along key routes such as the A1;

o From the DfT data presented in 3.3.4.2 (and Appendix I) motorcycles comprise over

2% of the registered vehicle fleet, if not the VKM travelled.

No attempt has been made to investigate either the uncertainties in the emissions curves

themselves, nor in the fleet inventories build using them, other than the brief analysis in

Section 3.3.4.2.

4.4.2.3 Dispersion Modelling

The low resolution of the output grid used makes accurate exceedence/LEZ boundary

definitions unfeasible. A broad indication of problem areas can be gained from the

concentration maps only. Presence of pollution hotspots is currently biased towards

receptor points falling on, or adjacent to roads, where other problem areas may be missed.

As a priority, increasing the grid resolution, and fixing issues with the current networks and

the ‘intelligent gridding’ option in ADMS-Urban should be investigated;

The choice of appropriate background level is critical to the calculation of concentrations.

Whilst the decision was taken to continue using the high background level to provide

conservative estimates of concentrations for the rest of the study, this will incorporate an

element of double counting of emissions, as well as an element of the background that is

insensitive to LEZ and f-NO2 changes;

For modelling historic and current concentrations the model is more sensitive to choice of

background level than to the f-NO2 levels selected for NOx to NO2 conversion. This probably

holds true for future year scenarios as well.

4.4.3 Implications for LEZ Design

Based on the above it is suggested that:

Outputs from the current framework are limited. Precise definition of LEZ boundaries for

detailed appraisal requires a higher resolution of output. Likewise interpreting absolute

values of concentrations at receptor points will distorted by receptor location and

background level selection;


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Given the high apportionment of NOx emissions to buses in the AQMA areas, and the large

potential NOx benefits of Euro 6 (or retrofit) technologies (section 2.3.2 and 2.3.3), it would

seem logical to investigate an LEZ (or other mechanism) to encourage uptake of such;

As there is a relatively large volume of traffic (especially LGV and HGV traffic) modelled as

using the A167M Central Motorway in Newcastle, and the A167/A184 rout in Gateshead, an

LEZ targeting these roads may be effective. However, this could have a detrimental effect on

businesses within the urban cores, as well as potentially shifting additional heavy traffic onto

the A1 and A19 north-south corridors.


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5 LEZ Scenario Modelling This section deals with the modelling of various scenarios for the chosen LEZ implementation year of

2021. The section begins with an outline of the changes made to the modelling framework,

documenting alterations to both TPM and bus models to cover the intervening years. Discussion is

then made of changes to the vehicle fleets used in emissions modelling, followed by presentation of

the LEZ scenarios. The revised framework is then applied to the scenarios, to calculate source

apportioned emissions and pollutant concentrations.

5.1 Changes to Transport Modelling As Section 3.3 outlined changes required from the pilot (2005) framework to the finalised (2010)

framework, further changes were required to move the 2010 framework to the 2021

implementation year. These were:

Network changes;

Changes to traffic flow volumes due to growth, and;

Impacts of increased flows on network speeds.

Specific changes are outlined in the following sections.

5.1.1 Network changes

Several network coding changes were made to the Newcastle University revised TPM model to

reflect road alterations and capacity changes anticipated over the 2010 to 2021 time period. Many

of these changes are outlined in Jacobs (2010), with further information obtained from the current

consultants responsible for updating the council-held TPM model over the 2021 to 2031 timeframe,

JMP (ref?). Analysis using GIS shows that the length of the modelled road network increases by

6.2km over the period (new roads, excluding additional length of carriageway added by widening, or

reduced by PT priority schemes).

5.1.2 Traffic growth

An appropriate methodology was required to grow traffic flows over the 2010 to 2021 periods.

Whilst a future year 2021 TPM model had already been developed by Jacobs Consultancy (Jacobs,

2008d), and supplied to Newcastle University, this was based on extrapolation from the 2005 TPM

v3.1, and predated the global financial crisis’ impact on the economy. Additional information was

therefore sought both from official sources, e.g. utilising NTM (National Transport Model, DfT, 2012h;

2012i), WebTAG (DfT, 2012j) and NTEM/TEMPro 6.2 (National Trip End Model, DfT, 2013), as well as

discussions with both Jacobs and JMP over previous and future TPM developments.

Table 5.8 summarises the expected changes in the number of trips for driver car travel, and public

transport and coach travel over the 2010-2021 time period from TEMPro6.2 (DfT, 2013):

Table 5.8: Period growth factors for cars and buses from 2010 to 2021 from TEMPRO6.2 for Tyne and Wear

Period Car (Drivers) All Trips

Buses and coaches

AM-Peak +9.07% -2.43%

Inter-Peak +10.94% -3.26%

PM-Peak +9.19% -4.52%


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For growth in goods vehicle traffic two methodologies were examined, recalculating growth based

on either using re-scaled information from TPM v3.1 documents (Jacobs, 2008), or by calculating

growth factors based on the long-term regional projections found in NTM results (DfT, 2012h). Table

5.9 summarises the growth factors from both methodologies, whilst more information on the

calculation of factors using NTM may be found Appendix O. The ‘NE Large Urban’ and ‘NE All’

parameters are different sub-sets of the DfT VKM prediction data (DfT, 2012i) used in predictions.

Note that the growth factors calculated from the NTM data are higher than those from the TPM

documentation (Jacobs, 2008e), even though the version of TPM predates the global financial crisis,

and assumes GDP growth rates in the order of 2.5%. Given inaccuracies of calculation using the NTM

methodology, the TPM LGV and HGV values from Table 5.9, combined with the TEMPro car values

from Table 5.8 were used in final modelling. For buses, after discussion with the LEZ steering group,

no overall change in the level of service was assumed for 2021.

Table 5.9: Period growth factors for 2010 to 2021 from TPMv3.1 (Jacobs, 2008e) and examination of NTM (DfT, 2012h) data

Parameter Cars LGVs HGVs1 Buses TPMv3.1

Predicted change % N/A 23.1% 12.9% N/A

NE Large Urban

Predicted change % 10.5%-11.9% 30.9-34.3% 0.00-23.1% 0.00%

NE All

Predicted change % 11.0-11.2% 32.1-38.6% 8.9-15.0% 0.00%

The final growth factors were applied to the 2010 matrices in the Newcastle University revised TPM

model, and the network assignment re-run to give general car and freight vehicle flows for 2021. It is

noted that the application of growth factors to HGVs may be compounding the apparent

overestimation noted in Section 3.3.3.

5.1.3 Network speed changes

The Traffic Master speeds in both the TPM and bus models, as summarised in Section 3.3.3.4, are

static, observed values that cover the 2010 base year only. Therefore a methodology was devised to

utilise the Volume to Capacity (VtoC or V/C) ratios output from the TPM model, with the default

speed-capacity curves from the model to give percentage change in speed from 2010 to 2021 on

each link. These percentile changes were then applied to the Traffic Master 2010 data to produce

revised speeds for each hour of the day. The revised speeds were then applied to the TPM and bus

models based on the use of the TOID identifier assigned to each link (see Section 3.3.3.3) and the

‘merge’ methodology. Those links for which no TPM V/C was available were kept at the same speed

as in 2010. No speed changes were applied to links with a V/C ratio in both periods of <0.15.

Figure 5.1 shows the impact of the V/C changes between the 2010 and 2021 networks on calculated

network-average speeds. As would be expected the largest differences in speed occur in the AM and

PM peak periods. These differences are in the region of >2km/h. Differences during the inter-peak

period are generally of the order of 1.7km/h. Differences in modelled speed are negligible in the

overnight period from 23:00 to 06:00, in the evening the average difference is 0.58km/h.

Whilst Figure 5.1 and the above report average network values, speed changes on individual links

are much greater. Under the methodology, the greatest changes in absolute speed occur for those


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links whose initial or final V/C ratio falls between 0.45 or 0.95, or above 1.45 due to a discontinuity in

the TPM speed-capacity curves at that high level of congestion (though this only affects around 5

links, totalling 1.1km of road, being slip roads, junction elements adjacent to major road sections

outside of the urban centres).

Figure 5.1 Modelled network-wide speed changes between 2010 and 2021

For more information on the calculation of speeds on links in the future year scenario networks, see

Appendix P.

5.2 Changes to Emissions Modelling The changes made to emission modelling, over the finalised framework presented in Section 3,

were the development and introduction of future year and LEZ fleets within the PITHEM software.

5.2.1 Future 2021 and LEZ Fleets

All of the fleets used to model the 2021 scenarios were based directly of those presented in the NAEI

fleet (Venfield and Pang, 2011). The fleet data for 2021 was manipulated using Microsoft Excel

(Microsoft, 2013) to produce the LEZ fleets by elevating all vehicles of lower Euro class into the class

stipulated by the desired LEZ criteria. In one sense, this potentially gives a conservative estimate for

the vehicle fleet (and emissions changes). In reality aging vehicles would be replaced with a mix of

both second-hand and the latest vehicles. The methodology assumes that 100% of vehicles will

comply with the LEZ criteria, whilst in reality the actual value of compliance will depend heavily on

enforcement policy. Sensitivity of LEZ effectiveness to the proportion of compliant vehicles has not

been explicitly addressed in this study.

The decision to base all scenarios off the NAEI fleets, rather than extrapolating forward from the

fleets developed from the DfT/spreadsheet model used in Section 4, was based on time constrains

and lack of available information on fleet turnover (new purchases versus scrappage rates). This area


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should be revisited for more detailed LEZ design, based on further consultation with relevant parties

such as DfT/DVLA, DEFRA, SMMT (Society of Motor Manufacturers and Traders), NEXUS, SMEs

(Small and Medium Enterprises), local fleet operators etc.

Tables 5.1 to 5.7 present the vehicle fleet changes used in this study. Note that tables are given for

the fleets used to calculate NOx emissions only. Separate data used to calculate carbon dioxide and

particulate emissions are given in Appendix Q. Note that certain Euro classes have two entries: ‘OK’

and ‘Fail’, these refer to the percentage of vehicles fully operational, versus those whose fitted

catalyst (three-way catalyst for petrol vehicles, SCR catalyst for diesel vehicles) is non-functional.

(Likewise the fleets for particulate matter deal with presence or absence of DPF systems). Catalyst

failure rates for the LEZ fleets have been calculated by taking the base NAEI ratios of failed catalysts

for 2021 and applying those as scaling factors for the revised fleet proportions. The tables

themselves show reductions in proportions for LEZ fleets from the Future 2021 fleet in red, and

increases in green – these colours refer to the changes only, and do not imply ‘good’ or ‘bad’

changes in emissions.

As well as the baseline fleet, two LEZ fleets are given for Euro 5/V compliance (‘Euro 5/V LEZ 2021’),

and Euro 6/VI (‘Euro 6/VI LEZ 2021’) compliance. After initial pollutant concentration modelling (see

Section 5.x) using these fleets, and discussion amongst the LEZ steering group, based on the

literature presented in Section 2.3.1 and 2.3.2, two further options were developed: ‘E6/VI Fail 2021’

and ‘E6/VIF E5 LEZ 2021’. These represent ‘worst case’ contingency scenarios if Euro 6/VI

technologies fail to deliver any benefit over Euro 5/VI. The former being the baseline 2021 fleet, but

with no Euro 6/VI vehicles present, the latter being the same, but under a Euro 5/V LEZ compliance.

As can be seen from the tables, the baseline fleet changes for LEZ compliance are relatively minor

for Heavy Duty Vehicles (where approximately 90%+ vehicles were Euro VI compliant already) and

diesel LGVs, larger for passenger cars and buses, and largest for petrol LGVs. Conversely, the ‘Euro 6

failure’ fleets impact HDVs most, and petrol LGVs the least.

It is also acknowledged that bus and fleet operators tend to purchase vehicles in cycles, and that

given an operational lifespan for a vehicle of 5 to 8 years it is possible for a particular fleet to

completely ‘skip’ a Euro class. As Bryan (2013) notes:

“A worst case scenario would be to assume the current fleet composition represents the

most recent “upgrade” and the next fleet replacement is not due until 2020. As Euro VI

would be the standard for all new buses being built, by default all the operators would

upgrade and adhere to the proposed 2021 scenario.”

This scenario would lead to lower Euro 6 proportion values than suggested by the NAEI values in the

tables below, but has not been investigated further due to lack of time and supporting information

on turnover rates.

Likewise, due to the unavailability of suitable emissions factors until late in the study, options for

retrofitting heavy duty vehicles with de-NOx equipment have not been investigated.


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Table 5.1: Petrol Car fleet for Base 2021 and tested LEZ scenarios [1]

Pre-Euro Euro 1 Euro 2 Euro 3 Euro 4 Euro 5 Euro 6 Cat Status N/A OK Fail OK Fail OK Fail OK Fail OK Fail OK Fail

Base 2021 0.00% 0.00% 0.00% 0.00% 0.00% 1.94% 0.02% 9.60% 0.12% 22.93% 0.29% 64.37% 0.73%

Euro 5 LEZ 2021 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 34.47% 0.43% 64.37% 0.73%

Euro 6 LEZ 2021 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 98.87% 1.13%

E6 Fail 2021 0.00% 0.00% 0.00% 0.00% 0.00% 1.94% 0.02% 9.60% 0.12% 87.30% 1.01% 0.00% 0.00%

E6F E5 LEZ 2021 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 98.77% 1.23% 0.00% 0.00% [1] Percentages may not sum to 100% due to rounding.

Table 5.2: Diesel Car fleet for Base 2021 and tested LEZ scenarios [1][2]

Pre-Euro Euro 1 Euro 2 Euro 3 Euro 4 Euro 5 Euro 6 SCR Status N/A N/A N/A N/A N/A N/A OK Fail

Base 2021 0.00% 0.00% 0.00% 1.05% 8.12% 26.88% 63.22% 0.72%

Euro 5 LEZ 2021 0.00% 0.00% 0.00% 0.00% 0.00% 36.06% 63.22% 0.72%

Euro 6 LEZ 2021 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 98.87% 1.13%

E6 Fail 2021 0.00% 0.00% 0.00% 1.05% 8.12% 90.83% 0.00% 0.00%

E6F E5 LEZ 2021 0.00% 0.00% 0.00% 0.00% 0.00% 100.00% 0.00% 0.00% [1] Percentages may not sum to 100% due to rounding. [2] This fleet is used for NOx calculations only, separate fleets exist for other pollutants.

Table 5.3: Petrol LGV fleet for Base 2021 and tested LEZ scenarios[1]

Pre-Euro Euro 1 Euro 2 Euro 3 Euro 4 Euro 5 Euro 6 Cat Status N/A OK Fail OK Fail OK Fail OK Fail OK Fail OK Fail

Base 2021 0.00% 0.00% 0.00% 0.57% 0.12% 2.66% 0.56% 9.21% 1.88% 21.24% 3.86% 55.13% 4.79%

Euro 5 LEZ 2021 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 34.47% 5.61% 55.13% 4.79%

Euro 6 LEZ 2021 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 91.32% 8.68%

E6 Fail 2021 0.00% 0.00% 0.00% 0.57% 0.12% 2.66% 0.56% 9.21% 1.88% 76.36% 8.64% 0.00% 0.00% E6F E5 LEZ 2021 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 84.62% 15.38% 0.00% 0.00%

[1] Percentages may not sum to 100% due to rounding.

Table 5.4: Diesel LGV fleet for Base 2021 and tested LEZ scenarios [1][2]

Pre-Euro Euro 1 Euro 2 Euro 3 Euro 4 Euro 5 Euro 6 Cat Status N/A N/A N/A N/A N/A N/A OK Fail

Base 2021 0.00% 0.00% 0.14% 0.40% 4.23% 19.63% 74.78% 0.81%

Euro 5 LEZ 2021 0.00% 0.00% 0.00% 0.00% 0.00% 24.41% 74.78% 0.81%

Euro 6 LEZ 2021 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 98.90% 1.10%

E6 Fail 2021 0.00% 0.00% 0.14% 0.40% 4.23% 95.22% 0.00% 0.00%

E6F E5LEZ 0.00% 0.00% 0.00% 0.00% 0.00% 100.00% 0.00% 0.00% [1] Percentages may not sum to 100% due to rounding. [2] This fleet is used for NOx calculations only, separate fleets exist for other pollutants


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Table 5.5: Rigid HGV fleet for Base 2021 and tested LEZ scenarios[1]

Pre-Euro Euro 1 Euro 2 Euro 3 Euro 4 Euro 5 Euro 6 Cat Status N/A N/A N/A N/A N/A SCR EGR N/A

Base 2021 0.00% 0.00% 0.00% 0.00% 1.74% 6.89% 2.30% 89.08%

Euro V LEZ 2021 0.00% 0.00% 0.00% 0.00% 0.00% 8.19% 2.73% 89.08%

Euro VI LEZ 2021 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 100.0%

EVI Fail 2021 0.00% 0.00% 0.00% 0.00% 1.74% 73.70% 24.57% 0.00%

EVI Fail EV LEZ 2021 0.00% 0.00% 0.00% 0.00% 0.00% 75.00% 25.00% 0.00% [1] Percentages may not sum to 100% due to rounding.

Table 5.6: Articulated HGV fleet for Base 2021 and tested LEZ scenarios[1]


Base 2021 0.00% 0.00% 0.00% 0.00% 0.19% 2.23% 0.74% 96.84%

Euro V LEZ 2021 0.00% 0.00% 0.00% 0.00% 0.00% 2.37% 0.79% 96.84%

Euro VI LEZ 2021 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 100.0%

EVI Fail 2021 0.00% 0.00% 0.00% 0.00% 0.19% 74.86% 24.95% 0.00%


Table 5.7: Bus fleet for Base 2021 and tested LEZ scenarios[1]


Base 2021 0.00% 0.00% 0.34% 5.08% 3.93% 13.79% 4.60% 72.26%

Euro V LEZ 2021 0.00% 0.00% 0.00% 0.00% 0.00% 20.80% 6.93% 72.26%

Euro VI LEZ 2021 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 100.0%

EVI Fail 2021 0.00% 0.00% 0.34% 5.08% 3.93% 67.99% 22.66% 0.00%



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5.3 Tested LEZ Scenarios Based on discussions between LEZ steering group members, Newcastle University was presented

with a range of LEZ scenarios to model by Capita Symonds (Bryan, 2012). These scenarios were

based on combinations of the vehicle fleet tables (Tables 5.1-5.7 and Appendix O) and are listed

below:

1. Future year 2021 ‘Business and Usual’ scenario – using the NAEI 2021 fleets for all vehicle

classes;

2. LEZ scenario 1 – all vehicle classes are assumed Euro 5/V compliant;

3. LEZ scenario 2 – all vehicle classes are assumed Euro 6/VI compliant;

4. LEZ scenario 3 – all goods vehicles (i.e. petrol LGVs, diesel LGVs, rigid HGVs, articulated HGVs)

are assumed Euro 5 compliant;

5. LEZ scenario 4 – as above, but all goods vehicles are assumed Euro 6 compliant;

6. LEZ scenario 5 – all buses are assumed Euro VI compliant;

7. LEZ scenario 6 – all passenger cars (petrol car, diesel car) are assumed Euro 6 compliant.

Note that, given the uncertainties in modelling the proportions of light and heavy goods vehicles on

the roads, presented in Section 3.3.3, the decision was made to treat these as a single category for

LEZ compliance. Based on the initial results from modelling, as noted in the previous section, two

further ‘what if?’ scenarios were developed to assess air quality under the possibility of Euro 6/VI

failing to deliver benefits over Euro 5/V:

8. Future year 2021 BAU scenario 2 - Euro 6 failure – all vehicles that were Euro 6/VI compliant

in scenario 1 above are assumed to be 5/V only;

9. LEZ scenario 7 - As 8 above, but all vehicles comply with a minimum of Euro 5/V.

These latter two scenarios are extreme, and are not anticipated to materialise in reality, given the

additional stipulations for off-cycle and in-service testing in Euro 6/VI regulations. They are however

presented for both for interest, and as a cautionary note on the potential impacts if technology fails

to deliver.

5.4 Emission Results This section presents the emission modelling results from the nine scenario options, based on the

traffic modelling and fleet changes outline above. Whilst modelling was undertaken for carbon

dioxide and particulate matter too, and limited results are given for these, the focus of this section is

on NOx and NO2 modelling. Results from the 2010 baseline modelling are included in the analysis for

comparative purposes. Additionally, for brevity, the emission modelling only considers the three

AQMA sub-domains. This is due to the Urban Core sub-domains being found to behave in very

similar fashion to the Newcastle City Centre and Gateshead, see Section 4.1.1. Full results for the

AQMA and Urban Core sub-domains may be found in Appendix R.

5.4.1 Total NOx (as NO2)

Figure 5.2 presents the modelling NOx modelling results for the Newcastle City Centre, Gosforth and

Gateshead AQMAs respectively. The scenarios are ranked in the figures by overall performance in

NOx emissions – highest to lowest. Total values in tonnes of emission per vehicle type are also shown.


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Figure 5.2: Source-apportioned NOx emissions for Newcastle City Centre (top), Gosforth (middle) and

Gateshead (bottom) AQMAs, under the various base year (2010), future year (2021) and LEZ

scenarios


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All LEZ options, assuming Euro 6/VI achieves its objectives, represent an improvement on the

baseline 2021 scenarios. As would be expected, the best LEZ option is to force all vehicles to be Euro

6/VI compliant, with total emissions only ≈25% of the 2010 baseline, and ≈60% of the 2021 future

year baseline. Also, somewhat predictably, given the relative proportions of vehicles in the AQMAs,

the order of the least to most effective LEZ option is almost constant, as shown below:

LEZ scenario 3 – all goods vehicles Euro 5/V;

LEZ scenario 1 – all vehicle classes Euro 5/V;

LEZ scenario 4 – all goods vehicles Euro VI;

LEZ scenario 6 - all cars Euro 6;

LEZ scenario 5 – all buses Euro VI;

LEZ scenario 2 – all vehicles Euro 6/VI.

What is immediately apparent from Figure 5.2 is the important role that the Euro 6/VI standard

plays in emissions reduction, especially for the heavy duty vehicle classes. The extreme Euro VI

failure results are within ≈85% of the 2010 emissions totals, whereas even the baseline future 2021

scenario sees emissions drop to ≈40% of the 2010 totals. The figures also show that the relative

importance of cars (i.e. especially diesel cars) to NOx emissions increases markedly between 2010

and 2021 (as discussed in Section 2.3.2). The LEZ based on all goods Euro 5/V is (scenario 3) appears

particularly ineffective. Assumed benefits to light goods vehicle compliance are partially cancelled

out by increased emissions of NOx from SCR-equipped heavy goods vehicles. Likewise the Euro 6

failure scenarios also show increases in heavy goods NOx emissions, even over the 2010 baseline for

the category.

Table 5.8 summarises the range of NOx emissions changes over the three AQMA and areas, relative

to both the 2010 base year and 2021 BAU scenarios.

Table 5.8: Percentage NOx emissions changes associated with the LEZ scenarios, based on sum of

total emissions within the three AQMA areas

Scenario LEZ Compliance Relative to 2010 base

Relative to 2021 BAU

Base 2010 N/A N/A +146.6%

2021 BAU N/A -59.5% N/A

LEZ Scn 1 2021 All Euro 5 -61.1% -4.2%

LEZ Scn 2 2021 All Euro 6 -75.8% -40.5%

LEZ Scn 3 2021 All Goods Euro V -59.6% -0.4%

LEZ Scn 4 2021 All Goods Euro VI -62.2% -7.0%

LEZ Scn 5 2021 All Buses Euro VI -67.9% -21.0%

LEZ Scn 6 2021 All Cars Euro 6 -64.5% -12.5%

2021 BAU Scn 2 N/A – Euro 6/VI fail -13.2% +114.0%

LEZ Scn 7 2021 E6/VI fail, All Euro 5 -14.6% +110.5%

As modelled, the most effective LEZ option targeting a single vehicle class is that for Euro VI applied

to buses. However, note for the Gosforth area, the effectiveness of scenarios 6 (cars meet Euro 6)

and 5 (buses meet Euro VI) are reversed. Additionally, the caveats given in Section 3.3.3.2, namely

possible bias towards bus network coverage and use of un-validated bus flows in the council data set

must be borne in mind.

Least Effective

Most Effective


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5.4.2 Primary NO2

Figure 5.3 presents the results of the emissions total and source apportionment analysis for primary

NO2 emissions for the Newcastle Centre AQMA. Again these results should not be considered as

anything more than illustrative, given the limitations of the primary NO2 calculation methodology in

the PITHEM software (i.e. scaling NOx emissions by the COPERT4 primary NO2 factors). For

comparative purposes, the order of results from the NOx figures has been retained.

Figure 5.3: Source-apportioned pNO2 emissions for Newcastle City Centre AQMA under the various

base year (2010), future year (2021) and LEZ scenarios

Whilst Figure 5.3 presents a similar picture to those in Figure 5.2, in that the general order of

effectiveness remains relatively unchanged, with the exception that an LEZ controlling emissions

from cars to Euro 6, becomes more effective than the buses meeting Euro VI option. This reflects the

general distortion of the values by late-Euro light duty engines having higher (>25%) primary NO2

emissions than late-Euro heavy duty engines (≈10%) in the COPERT4 factors. Indeed, for all of the

2021 options, the primary NO2 contribution from HGVs is modelled as fairly negligible compared to

contributions from the other vehicle classes.

Generally the primary NO2 emissions for all of the 2021 scenarios that assume that the Euro 6/VI

standard delivers the expected on-road emissions benefits are under 50% of the 2010 total. Under

the Euro 6 failure scenarios, primary NO2 emissions remain comparable to the 2010 levels.

However, the range of potential f-NO2 values (5%-70%) for light duty vehicles presented in the

COPERT4 data (Boulter, Barlow and McCrae, 2009) must be borne in mind. The blue and red

elements in Figure 5.3 (and in Appendix R) could be almost 8 times shorter, or twice as long! More

measurements and discussion in the area of primary NO2 emissions is expected in the near future in

work being undertaken by King’s College London and Newcastle University, on behalf of the London

boroughs and DEFRA (Rhys-Tyler, 2013). Ideally, modelling of primary NO2 in Newcastle and

Gateshead AQMAs should be revisited in future before detailed LEZ options are evaluated. Further

primary NO2 results from PITHEM are given in Appendix R for the Gosforth and Gateshead AQMAs.


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5.4.3 Particulate Matter (PM10 and PM2.5)

Figure 5.4 presents the results of the emissions total and source apportionment analysis for PM10

emissions for the Newcastle Centre AQMA. As the focus of the Euro 6/VI regulation is on NOx

reduction, rather than on particulate matter (which was more the goal of earlier Euro standards),

the emissions reductions achieved are less impressive than for NOx or primary NO2. Also changes in

Euro class affect only tailpipe PM emissions, not those associated with brake and tyre wear, or from

abrasion of the road surface itself.

Figure 5.4: Source-apportioned PM10 emissions for Newcastle City Centre AQMA under the various


Table 5.9 summarises the changes in PM10 and PM2.5 emissions with each scenario. Note that the

changes associated with PM2.5 are greater than those for PM10. Generally all changes for PM10

relative to 2010 are in the order of 30%, whilst for PM2.5 changes are over 40%. The LEZ options

generally make single digit percentage improvements over the ‘2021 BAU’ scenario. As before, the

LEZ with all vehicles Euro 6/VI compliant is the most effective option.

Table 5.9: Percentage PM10 and PM2.5 emissions changes associated with the LEZ scenarios, based on

sum of total emissions within the three AQMA areas

Scenario Name PM10 Relative to 2010

PM10 Relative to 2021

PM2.5 Relative to 2010

PM2.5 Relative to 2021

Base 2010 Baseline N/A +41.3% N/A +72.4%

2021 BAU Baseline Future Scn 1 -29.2% N/A -41.9% N/A

LEZ Scn 1 2021 All Euro 5 -31.8% -3.6% -46.0% -7.0%

LEZ Scn 2 2021 All Euro 6 -33.3% -5.7% -48.0% -10.2%

LEZ Scn 3 2021 All Goods Euro V -29.7% -0.7% -42.6% -1.0%

LEZ Scn 4 2021 All Goods Euro VI -30.0% -1.0% -42.9% -1.6%

LEZ Scn 5 2021 All Buses Euro VI -31.1% -2.6% -45.0% -5.3%

LEZ Scn 6 2021 All Cars Euro 6 -30.7% -2.1% -43.9% -3.4%

Future 2021 Baseline Future Scn 2 -23.7% +7.8% -34.8% +12.4%

LEZ Scn 7 2021 E6/VI fail, All Euro 5 -26.6% +3.8% -39.2% +4.8%

Further particulate matter results for the other AQMAs and UCBs are given in Appendix R.


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5.4.4 Carbon Dioxide

While not the primary focus of this study, Newcastle University was also asked to by the LEZ Steering

group to provide some feedback on the impact of the LEZs on greenhouse gas emissions,

represented here by ultimate CO2 values. Figure 5.5 presents the CO2 results for the Newcastle City

Centre AQMA.

Figure 5.5: Source-apportioned CO2 emissions for Newcastle City Centre AQMA under the various


All of the 2021 scenarios show an increase in CO2 emissions. The downward trend in car and LGV

emissions with improved technology is more than cancelled out over the period by the increase in

VKM travelled, compounded with the increase in fuel consumption from heavy duty vehicles with

emissions control technology fitted. The ‘All vehicles Euro 6 compliant’ and ‘Cars Euro 6 compliant’

options perform best. The ‘All goods Euro 5’, ‘All goods Euro 6’ and ‘All buses Euro 6’ all produce

results within 300 tonnes of the ‘2021 BAU’ case. Table 5.10 summarises the uCO2 changes from

each scenario option.

Table 5.10: Percentage uCO2 changes associate with the LEZ options, based on sum of total

emissions within the three AQMA areas

Scenario Name Relative to 2010 Relative to 2021

Base 2010 Baseline N/A -7.3%

2021 BAU Baseline Future Scn 1 +7.9% N/A

LEZ Scn 1 2021 All Euro 5 +7.2% -0.6%

LEZ Scn 2 2021 All Euro 6 +5.4% -2.2%

LEZ Scn 3 2021 All Goods Euro V +7.9% 0%

LEZ Scn 4 2021 All Goods Euro VI +7.9% 0%

LEZ Scn 5 2021 All Buses Euro VI +7.9% 0%

LEZ Scn 6 2021 All Cars Euro 6 +5.4% -2.2%

Future 2021 Scn 2 E6/VI Fail +11.1% +3.0%

LEZ Scn 7 2021 E6/VI Fail, All Euro 5 +10.5% +2.4%


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The ‘Euro 6 Failure’ options increase emissions over the ‘Baseline Future 2021’ scenario, primarily

due to the assumption of no fuel consumption improvements in cars and LGVs over Euro 5 levels. As

with the other pollutants, further CO2 results are given in Appendix R.

5.5 Changes to Local Air Quality Modelling No fundamental changes were made to the process of modelling local air quality in ADMS-Urban for

the 2021 scenarios. However, assumptions used in the modelling are outlined below.

5.5.1 Spatial domain

The issue of spatial scope of LEZ options was discussed at some length by the LEZ Steering

Committee, with debate on whether the AQMA areas (or similar areas based on the Urban Core

Boundaries) should be used as constraints to the modelling of pollutant concentrations arising from

LEZ options. It was the considered opinion of the group that any LEZ option would likely include all

traffic entering the respective city centres, and therefore effects and benefits would extend to radial

routes as well.

Given the above, and time constraints in modelling, it was therefore proposed that the idea of

modelling LEZ compliant vehicles as separate user classes in the TPM, with distinct costs attached to

entering LEZ areas, in order to provide a measure of the amount of rerouting due to LEZ proposals

would be dropped from the initial study. Rather the air quality modelling would focus on providing

an envelope to the maximum changes in concentrations that could be expected of an LEZ

functioning over all of the Metropolitan Borough areas, bearing in mind the approach of being

conservative with regard to changes (i.e. favouring modelling processes that produce the highest

concentrations to give an indication of any possibility of exceedences). Hence emissions changes

from the scenarios outlined in Section 5.3 were applied globally across the entire region.

5.5.2 Meteorological data

The meteorological data used for the 2021 scenarios was the same as that used for the 2010

baseline modelling. This was done to ensure that changes in the air quality modelling were due to

changes in traffic patterns and emission technologies only. Therefore the concentrations produced

do not include any assumed changes in climate over the intervening decade.

5.5.3 Background Concentration Data

As noted in Sections 4.2 and 4.3 the choice of inclusion or exclusion of the contributions from minor

roads makes a major difference to the final concentrations, influencing interpretation as to whether

exceedences are present or not within the AQMA areas. As with the Baseline 2010 modelling,

background maps for 2021 were sourced from the DEFRA LAQM site (DEFRA, 2012f) and processed

in the same manner as Sections 3.3.5.2 and 4.1.3.

For comparison with Figure 4.3, Figure 5.6 presents the background NO2 concentration maps, and

difference map used for the 2021 scenarios. Note that for 2021 the differences between the two

maps including or excluding minor road totals are reduced over the 2010 case, due to assumptions

in reduction of road NOx emissions over the period included in the map. In order to be consistent

with the modelling in Section 4.3, to present conservative estimates of concentrations, the higher

background levels, including minor roads, were again used to produce final results.


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Figure 5.6: Background concentrations (µg/m3) of NO2 in 2021, assuming minor roads included (left)

and all roads excluded (middle). The difference map between the two is on the right.

However, the inclusion of minor road background therefore leads to a further compounding issue;

that the background levels for the LEZ scenario options should also be affected by changes to the

fleets on minor roads. Likewise for the ‘Euro 6 failure’ scenarios, the background levels would be

closer to the baseline 2010 map, rather than the 2021 maps. Due to time constraints these issues

have not been investigated further in this study. Maps for other pollutants are given in Appendix M.

5.6 Concentration Results: This section presents the results for NO2 concentration modelling of the future year baseline and LEZ

scenarios. Only NO2 results have been included in the interest of brevity, however for comparative

purposes the 2010 results from Section 4.3 are also included. Appendix S also provides results for

NOx (as NO2).

5.6.1 Nitrogen Dioxide (NO2)

Figures 5.7 through 5.16 present the results from NO2 concentration modelling at 200m grid

resolution, alongside spatial apportionment of NOx emissions per vehicle type. Tables 5.11, 5.12 and

5.13 provide statistical summaries of the concentrations modelled at receptor points falling within

the AQMA sub-domains, for each of the tested scenarios. The tables present the mean value

recorded by receptors falling in the sub-domain, as well as the difference from the baseline 2021

scenario, the median, range and standard deviation of values. Where maximum or mean levels

exceed the 40µg/m3 limit table entries have been coloured red, whilst levels within 5µg/m3 of the

threshold have been coloured amber. Given limitations in the calculation methodology, and the low

resolution of the output grid, the tables are provided for comparative performance between the

options, and should not be interpreted as precise predictions of concentrations within the areas.

Given the non-normality of the underlying distribution of pollutant levels (i.e. many levels near the

background value, but a few high levels adjacent to road sources) the mean values themselves

should be treated with some caution, the median is more likely representative of typical

concentrations across the area.

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Table 5.11: Descriptive NO2 statistics for receptor points in the Newcastle Central AQMA

Scenario Name N. Mean, µg/m

3

Reduction on 2021, µg/m

3

Median, µg/m

3

Range, µg/m

3

Std.Dev., µg/m

3

Base 2010 - 106 39.90 +15.90 37.81 25.69 - 70.35 9.83

2021 BAU - 106 24.01 N/A 23.19 15.76 - 39.82 5.07

LEZ Scn 1 2021 All vehicles E5 106 23.72 -0.28 23.04 15.69 - 39.04 4.87


LEZ Scn 3 2021 All goods E5 106 23.99 -0.02 23.17 15.75 - 39.74 5.06


LEZ Scn 5 2021 All buses E6 106 22.94 -1.07 22.74 15.58 - 38.58 4.43

LEZ Scn 6 2021 All cars E6 106 23.40 -0.60 22.84 15.56 - 36.95 4.62

Future 2021 (2) Euro 6 Fail 106 30.39 +6.38 28.06 17.55 - 58.67 9.26

LEZ Scn 7 2021 AllE5, E6 Fail 106 30.21 +6.21 27.94 17.49 - 58.06 9.15

Table 5.12: Descriptive NO2 statistics for receptor points in the Newcastle Gosforth AQMA


3


3

Median, µg/m

3

Range, µg/m

3

Std.Dev., µg/m

3

Base 2010 - 48 26.71 +10.72 24.58 22.60 - 46.27 5.27

2021 BAU - 48 15.99 N/A 15.12 13.86 - 24.57 2.24






LEZ Scn 6 2021 All cars E6 48 15.61 -0.37 14.91 13.70 - 22.98 1.91

Future 2021 Euro 6 Fail 48 18.63 +2.65 16.77 15.22 - 36.13 4.56


Table 5.13: Descriptive NO2 statistics for receptor points in the Gateshead Central AQMA


3


3

Median, µg/m

3

Range, µg/m

3

Std.Dev., µg/m

3

Base 2010 - 51 39.13 +15.68 37.38 28.41 - 56.67 6.82

2021 BAU - 51 23.45 N/A 22.47 17.57 - 34.26 3.89






LEZ Scn 6 2021 All cars E6 51 22.81 -0.63 22.01 17.36 - 32.44 3.53

Future 2021 Euro 6 Fail 51 29.84 +6.39 27.39 19.57 - 52.47 7.45


The results in the tables reinforce the findings of the emissions modelling in the previous section

that over the defined period the introduction of technology changes through general fleet renewal

makes a greater impact than any of the studied LEZ options. However, all LEZ options offer

additional benefits over the general fleet changes.

The rank order of effectiveness of the LEZ options is unsurprising and almost identical to the list

presented in Section 5.4.1, in that the least effective option is ‘All Goods Euro 5 compliant’, whilst

the most is ‘All vehicles Euro 6’. However it is noted that the two Euro 5 options produce changes in

mean levels by under 0.5µg/m3. The Euro 6 options produce larger changes, but still typically under

1µg/m3. The ‘All vehicles Euro 6’ option produces reductions of around 2µg/m3 in both Newcastle

and Gateshead centres, but only 0.9 µg/m3 for the Gosforth AQMA, given its lower overall levels.


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Figure 5.7: Base 2010 Scenario: Annual Hourly Mean NO2 Concentrations (Left) [All concentrations in μg/m3. Red contour = 40 μg/m3, Blue contour = 35

μg/m3] and proportion of total NOx contribution from vehicle classes (Right).

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Cars LGVs

Buses HGVs


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Figure 5.8: 2021 ‘Business as Usual’ Scenario, NAEI/EFT5.1.3 Fleet: Annual Hourly Mean NO2 Concentrations (Left) [All concentrations in μg/m3, Blue contour

= 35 μg/m3] and proportion of total NOx contribution from vehicle classes (Right).

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Cars LGVs

HGVs Buses


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Figure 5.9: LEZ 2021 Scenario 1, All Vehicles EURO 5/V Compliant: Annual Hourly Mean NO2 Concentrations (Left) [All concentrations in μg/m3, Blue contour


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HGVs Buses


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Figure 5.10: LEZ 2021 Scenario 2, All Vehicles EURO 6/VI Compliant: Annual Hourly Mean NO2 Concentrations (Left) [All concentrations in μg/m3, Blue

contour = 35 μg/m3] and proportion of total NOx contribution from vehicle classes (Right).

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HGVs Buses


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Figure 5.11: LEZ 2021 Scenario 3, All Goods Vehicles EURO 5/V Compliant: Annual Hourly Mean NO2 Concentrations (Left) [All concentrations in μg/m3, Blue


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Figure 5.12: LEZ 2021 Scenario 4, All Goods Vehicles EURO 6/VI Compliant: Annual Hourly Mean NO2 Concentrations (Left) [All concentrations in μg/m3, Blue


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HGVs Buses


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Figure 5.13: LEZ 2021 Scenario 5, Buses are EURO 6/VI Compliant: Annual Hourly Mean NO2 Concentrations (Left) [All concentrations in μg/m3, Blue contour


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Figure 5.14: LEZ 2021 Scenario 6, All cars are EURO 6/VI Compliant: Annual Hourly Mean NO2 Concentrations (Left) [All concentrations in μg/m3, Blue


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Figure 5.15: 2021 ’Business as Usual’, Scenario 2, ‘EURO 6/VI Failure’: Annual Hourly Mean NO2 Concentrations (Left) [All concentrations in μg/m3. Red

contour = 40 μg/m3, Blue contour = 35 μg/m3] and proportion of total NOx contribution from vehicle classes (Right).

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Cars LGVs

HGVs Buses


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Figure 5.16: LEZ 2021 Scenario 7, All vehicles EURO 5/V Compliant, EURO 6/VI Failure: Annual Hourly Mean NO2 Concentrations (Left) [All concentrations in

μg/m3. Red contour = 40 μg/m3, Blue contour = 35 μg/m3] and proportion of total NOx contribution from vehicle classes (Right).

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HGVs Buses


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Generally, in each of the three AQMAs, mean NO2 concentrations for 2021, under the assumption

the Euro 6/VI regulations fulfil their ambition, are modelled as being approximately 55% of those in

2010. For the two ‘Euro 6/VI failure’ scenarios, NO2 levels are approximately 60-70% of the 2010

values.

There is similarity in effectiveness between the ‘All cars Euro 6’, ‘All goods Euro 6’ and ‘All buses Euro

6’ options, with the ‘All cars Euro 6’ option perhaps being less effective than would be implied by the

NOx emission results in Figure 5.2. This is partially due to the spatial distribution of the points in

related to the traffic on the road networks. The ‘All cars Euro 6’ option reduces concentrations

across the sub-domain, whilst the ‘Buses Euro 6’ option reduces concentrations at City Centre points

to the south and west of the A167 in both Newcastle and Gateshead. The ‘Goods Euro 6’ option

reduces concentrations at points associated with the A167 and along the Coast Road in Newcastle, to

the North East of the City Centre. This effect can be seen in the difference maps for the central area

presented in Figure 5.15.

Figure 5.17 Difference maps for NO2 concentrations between the ‘All goods Euro 6’ (left), ‘All Buses

Euro 6’ (centre) and ‘All cars Euro 6’ (right) and the baseline 2021 scenario.

Whilst, Figure 5.17 highlights the low resolution of the underlying output grid, it also does show that

the Euro 6 LEZ options targeting different vehicle types affect different areas of the City Centres

within the defined AQMAs, and therefore possibly could be used to target specific areas of concern.

The definition of precise LEZ boundaries from the current low resolution grid is not considered

feasible.

In terms of exceedences of the annual mean limit:

For the Newcastle City Centre AQMA all scenarios, bar ‘All vehicles Euro 6’ and ‘All cars Euro

6’ show evidence of possible exceedences. These are related to receptor points falling near

to the A167/Great North Road junction, and near Swan House Roundabout.

For Gosforth, there does not appear to be any evidence of an air-quality problem regarding

NO2 in 2021, under any scenario. Modelled levels are well within the current limit threshold,

including those where the Euro 6 standard was presumed to not be effective;


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For the Gateshead AQMA, modelled air quality is generally better than that in the Newcastle

City Centre AQMA. The maximum values recorded at receptor points all fall below the NO2

threshold, except for receptor points near the A184 in Central Gateshead and along the A167

in the ‘Euro 6 ineffective’ scenarios.

The NOx source apportionment figures adjacent to the concentration maps reinforce the general

dominance of cars (i.e. diesel cars) in NOx emissions production under the Euro 6 scenarios across

most of the model domain – especially in more rural areas to the north of Newcastle. For the

Newcastle City AQMA areas buses remain a significant contributor. The patches of high bus

contribution values outside of the city centre, most notably to the south west of Gateshead are likely

artefacts from the separate bus and road networks.

In LEZ scenarios where buses are not targeted (i.e. All goods or cars comply to Euro 6/VI) they still

may account for 60-70% of road NOx emissions. The contribution from LGVs is generally second or

third to either cars or buses, though in the vicinity of the A1 HGVs remain a significant contributor.

The Euro 6 failure scenarios show a more ‘balanced’ pattern of apportionment with vehicle class –

with no one vehicle dominating contribution across large areas of the map. Actual contributions are

more localised and less biased towards cars.

5.6.2 Sensitivity and Uncertainty

The sensitivity of the effectiveness of the options to changes in background concentrations, and to

assumed f-NO2 levels have also been tested. Table 5.14 presents the results for the Newcastle City

Centre AQMA.

Table 5.14: Sensitivity of LEZ options to variations in background levels and varying f-NO2 ratio for the

Newcastle City Centre AQMA. Difference from 2021 BAU scenario given in µg/m3

Scenario Back_1 (High) +

default f-NO2

Back_2 (Low) +

default f-NO2

Back_1 (High) +

PITHEM f-NO2

Back_2 (Low) +

PITHEM f-NO2

Default f-NO2

(DEFRA, 2012b)

PITHEM f-NO2

[1]

LEZ Scn 1 2021 -0.28 -0.29 -0.31 -0.32 0.216 0.327

LEZ Scn 2 2021 -2.04 -2.10 -2.34 -2.37 0.216 0.311

LEZ Scn 3 2021 -0.02 -0.02 -0.02 -0.02 0.216 0.325

LEZ Scn 4 2021 -0.35 -0.36 -0.40 -0.41 0.216 0.325

LEZ Scn 5 2021 -1.07 -1.09 -1.20 -1.22 0.216 0.325

LEZ Scn 6 2021 -0.60 -0.62 -0.74 -0.75 0.216 0.311

Future 2021 E6 Fail +6.38 +6.54 +7.67 +7.81 0.216 0.340

LEZ Scn 7 2021 +6.21 +6.37 +7.49 +7.62 0.216 0.342 [1] The PITHEM f-NO2 value is calculated from VKM-weighted emissions values for each scenario, using the COPERT4

factors. The primary reason for the difference from the DEFRA f-NO2 value is the assumption of high a high f-NO2 value

(40%) for Euro 3, 4 and 5 diesel cars and LGVs. It is recognised that this value may be too high based on work on-going at

King’s College London and Newcastle University on remote sensing of NO/NO2 ratios (Rhys-Tyler, 2013).

Note that whilst the choice of background level makes a large difference to the absolute values of

modelled concentrations, the performance relative to the 2021 future-year baseline scenario is

practically unaffected, with changes across those scenarios where Euro 6 is assumed effective being

less than 0.02 µg/m3. The assumption of a higher f-NO2 value in PITHEM for Euro 5 light duty vehicles

makes a larger relative impact, though also does not impact on the rank order of LEZ effectiveness.

Full results may be seen in Appendix S.


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Ideally further sensitivity testing to the growth factors applied to vehicle flow values would be tested

further to provide low, central and high scenarios, each having its own impact on network speeds.

Due to time constraints these have not been produced at present.

5.7 Summary and Discussion Based on the modelling presented in this, and previous, sections the following key points are made

regarding the limitations of the model, the LEZ scenarios and their implications for LEZ design.

5.7.1 Modelling Limitations

The same limitations as outlined in section 4.4.2 exist in the LEZ scenario modelling,

compounded by the additional uncertainties in:

o traffic growth across the four vehicle categories;

o the influence of growth on network speeds;

o the assumption that the Newcastle/Gateshead fleets of 2021 will be the same as the

NAEI English Urban fleet. Cyclical fleet renewal or retrofitting of HDVs has not been

investigated;

The emissions and concentration changes associated with the Euro 6 LEZ scenarios are

considered to represent the upper bound of what LEZ implementation could achieve. This is

due in part to the fleet considerations above, but additionally due to:

o LEZ emissions changes were globally applied across the entire spatial domain of the

model. Smaller LEZs potentially would have reduced impact due to import of

pollutants from outside the area (e.g. see Kelly et al., 2011a; 2011b).

o Assumption of perfect compliance with the LEZ criteria

o Assumption of no rerouting effects of vehicles to avoid LEZs completely;

The scenarios dealing with failure of Euro 6 to deliver on its promised NOx reductions are

even more uncertain, more research is generally needed on the real-world performance of

Euro 6, compared to earlier standards. These are considered worst case scenarios.

Based on forthcoming work, the primary NO2 emissions and f-NO2 ratios, as calculated by the

PITHEM software, may be too high for LDVs (Rhys-Tyler, 2013).

5.7.2 Analysis of Future Scenarios

Noting the limitations above, results from the scenarios suggest that:

General improvements in emissions across all non-transport sectors, plus the NAEI

assumptions about fleet turnover and Euro 6 effectiveness in reducing NOx emissions lead to

city centre concentrations for the 2021 ‘Business as Usual’ scenario are modelled as

averaging just over half of those in the 2010 base case, an average reduction at AQMA

receptor points of 10-15 µg/m3.

There is no evidence of NO2 air-quality problems in the AQMAs in the 2021 BAU scenario –

though given the low resolution of modelling, ‘hot-spots’ are likely to remain near

congested locations.

Against this background of overall low levels of NO2 the LEZ options may make up to a

further 2 µg/m3 reduction, if all vehicle types are considered to comply with Euro 6. Other

tested LEZ options offer smaller performance benefits. All tested LEZ options offer

improvements over the ‘2021 Business as usual’ scenario.


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In each of the AQMA sub-domains the ‘order of preference’ for LEZ options in terms of NOx

emissions and concentration reductions appears to be fairly static, and is given below (from

best to worst):

o All vehicles Euro 6/VI;

o All buses Euro VI;

o All cars Euro 6;

o All goods vehicles Euro 6/VI;

o All vehicles Euro 5;

o All goods vehicles Euro 5/V.

The order given above may change if primary-NO2 emissions are not tackled successfully in

LDVs – with the Euro 6 LEZ option for cars possibly becoming more attractive than that for

buses. As above, it is noted that the PITHEM calculated f-NO2 ratios may be too high, and

over emphasise LDV emissions;

Irrespective of the PITHEM pNO2 calculations, in future years, as Euro 6 HDVs become more

commonplace, NOx/NO2 air-quality issues may become more associated with cars and LGVs;

The above benefits are highly dependent on the ability of the Euro 6/VI regulations to deliver

the expected NOx improvements at mid-to-low speeds on urban roads. If the regulations fails

to deliver, and emissions remain similar to those considered from Euro 5/V vehicles, then

average NO2 concentrations may remain within 75% of 2010 levels, and potential for

exceedences of the air quality standards will remain in the central AQMA areas;

Following from the above, given the current NAEI emissions factors, their underlying

assumptions on SCR catalyst numbers, and de-NOx performance, any LEZ based on the Euro

V standard for HGVs may actually compound NOx problems. The ‘All goods Euro 5/V LEZ’

option only shows NOx improvements due to the inclusion of LGVs into the design. Likewise,

under Euro 6 failure, the ‘All vehicles Euro 5 LEZ’ option shows improvements due to

inclusion of the other vehicle classes;

If air quality issues do remain in the urban core areas in future, depending on the location of

problems, LEZ targeting cars or goods vehicles using the Central Motorway, Coast Road and

other radial routes would be most effective to the east of the core areas, whilst targeting

buses would be most effective within the centres themselves;

All of the tested LEZ scenarios had little impact on particulate matter within AQMAs or the

urban cores. Carbon emissions in all 2021 scenarios increase over the 2010 base case,

primarily due to the increase in VKM by all types of vehicle, except buses, though the impact

of additional particulate trap and de-NOx technologies are also slightly carbon negative.

5.7.3 Implications for LEZ Design

Given the scenarios as presented it is recommended that:

If at all feasible, the year of LEZ implementation should be brought forward from 2021 to

enhance the potential benefits;

Any LEZ option using Euro V compliance criteria for Heavy Vehicles be examined closely for

alternatives, or even rejected, due to issues with potential ineffectiveness or exacerbation of

NOx emissions at low to medium speeds. Retrofitting of existing vehicles may be a better

option.


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Ideally any LEZ (or other emissions reduction option) should target moving as many buses as

feasible towards complying with the Euro VI regulations as soon as possible. This would

reduce NO2 issues in the urban centres;

Ensuring or enabling Euro 6 compliance for cars and light goods vehicles would bring benefits

distributed across the region, not just to roads near bus-routes or primary freight corridors.

However, ‘selling’ the need for this to the public and SMEs, against the perception (based on

earlier LEZ implementations where particulate matter from heavy duty vehicles was the

primary concern), would be an issue;

As noted previously, the resolution of output from the current framework is not considered

adequate for detailed option design; it can, however, provide guidelines as to effects of fleet

changes, broad areas of potential effect, the magnitude of changes in those areas.

Likewise, prediction of future absolute concentrations using the framework is problematic

without further work on choice of background concentrations. However the currently

modelled concentrations appear plausible.


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6. Concluding Remarks Whilst the tested LEZ scenarios are limited to ‘simple’ global changes applied to the NAEI fleet as a

whole, rather than to those fleets entering defined LEZ regions. They are therefore considered

indicative of the potential envelope of the magnitude of changes that could be associated with large

scale LEZ implementation, beginning in 2021 in the Newcastle/Gateshead region. All emissions and

concentration reductions associated with LEZ options are however considerably smaller that the

changes reflected through general fleet renewal and introduction of Euro 6/VI vehicles onto the

roads.

It must be kept in mind that, almost all of the predicted air-quality benefits are due to the assumed

effectiveness of the Euro 6/VI regulations in reducing NOx emissions for new vehicles. If these

benefits do not materialise (along with expected improvements in other sectors) then the potential

exists for the air-quality situation in the Newcastle/Gateshead region to be only marginally improved

on that of today. It is also noted that modelled LEZ options based on Euro V heavy duty vehicles may

exacerbate existing problems, though all LEZ options test overall improve air-quality regarding NO2.

Finally, it is recognised that the length of this report exceeds that required to simply present a

screening or scoping assessment of proposed LEZ options. This reflects the amount of development

time and effort that was spent on alterations to the Tyne and Wear Transport Planning Model, and

the incorporation of separate public transport and speed information, over the initial study

proposals. These are of benefit not only to this study, but to other major projects running

concurrently at Newcastle University. It is also hoped, both by the University and the LEZ Steering

Group, that some of the experience and alterations to TPM may be of use, not only to Newcastle and

Gateshead, but also to other Local Authorities in the region.

If the decision to proceed with LEZ development in the Newcastle/Gateshead area is made, the

following recommendations apply for any subsequent detailed LEZ design undertaken using the

framework developed in this study:

The potential over-prediction of VKM travelled for heavy goods vehicles and under

prediction of VMK travelled by cars be addressed;

Further work is undertaken to reconcile the public transport model to the general TPM

model – including examination of bespoke operator fleets on given routes within the model;

The issue of background concentrations from local road sources, applied through ADMS-

Urban, be investigated further – this may be done alongside extension of framework

verification and calibration against monitored concentrations;

That potentially fleet uptake rates be revised considerably, to reflect the cyclical nature of

freight and public transport operators, and that de-NOx retrofitting options are examined;

That NOx and primary NO2 emissions from cars and light goods vehicles be examined further,

in the light of new research data, as this appears.


118

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Zone Steering Group. A report by AEA, King’s College London, TRL, TTR, University of Westminster and Acona to

the Greater London Authority and Transport for London. Online resource:

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Quality Research Group, University of West of England. Online resource:

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AQEG (2007). Trends in Nitrogen Dioxide in the United Kingdom. Report by the Air Quality Expert Group for the Department

of Environment, Food and Rural Affairs; the Scottish Executive; the Welsh Assembly Government and the

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http://archive.defra.gov.uk/environment/quality/air/airquality/publications/primaryno2-

trends/documents/primary-no-trends.pdf [Accessed: 19/2/13].

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Appendices:

Appendix A: The Low Emission Zone Steering Group The Newcastle and Gateshead Low Emissions Zone Feasibility Study Steering Group (LEZ steering

group) consisted of members of both the client authorities (Newcastle City Council and Gateshead

Metropolitan Borough Council) and the consultants (Capita Symonds and Newcastle University). The

group met approximately once every two to three months throughout 2012 and early 2013.

Primary members of the group were:

Ed Foster (Chair) – Newcastle City Council;

Caroline Shield – Gateshead City Council;

Stuart Clarke – Capita Symonds;

Nicholas Bryan – Capita Symonds;

Professor Margaret C. Bell, CBE – Newcastle University;

Dr. Anil Namdeo – Newcastle University;

Dr Fabio Galatioto – Newcastle University;

Dr. Paul Goodman – Newcastle University;

LEZ Steering group meetings were also attended by other interested parties, particularly

representatives of the other Tyne and Wear Local Authorities, and the Passenger Transport Executive,

NEXUS.


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Appendix B: Technical notes on links between TPM and PITHEM This appendix summarises information regarding the linking of the University’s PITHEM software to

the TPM. Information in this Appendix is mostly taken from information in the first ‘LEZ feasibility

study technical note’ by Newcastle University (Goodman, 2012a), dated 9th February 2012, with

additional data added to reflect subsequent changes. Table B.1 shows the TPM output fields used by

PITHEM.

Table B.1: TPM output fields used in PITHEM

TPM Output Field TPM Notes Use in PITHEM Anode A-node (link start node) identifier Mapped to PITHEM ‘A_ID’

Bnode B-node (link end node) identifier Mapped to PITHEM ‘B_ID’

Link Type Link type identifier Used by PITJEM to filter unwanted links, see Table B.3.

CapIdx Link-based Speed vs. Volume to Capacity curve identifier.

Later used for updating network speeds, see Appendix P.

VL1: NWLT Non-work long-term stay in car-park) Not directly used.

VL2: IWST In-work short-term stay in car-park) Not directly used.

VL3: NWST Non-work short-term stay in car-park) Not directly used.

VL4: LGV Light Goods Vehicles – i.e. vans Mapped to PITHEM User Class 2

VL5: OGV Rigid and Articulated HGVs Mapped to PITHEM User class 3.

VL6: Preload Bus pre-load flows Initially mapped to PITHEM User Class 4. Later not used.

VL7: Total Flow i.e. V1+V2+V3+V4+V5+V6 Not directly used

VL8: Cars i.e. V1+V2+V3 Mapped to PITHEM user class 1.

VL9: V/C Ratio Volume to capacity ratio Later used for updating network speeds, see Appendix P.

VL10: Speed (kph) V10: Speed (kph) Initially mapped to PITHEM speeds for UCs 1, 2, 3 and 4. Later not used.

Table B.2 shows the Passenger Car Unit (PCU) Conversion factors originally used by PITHEM in the

development of the pilot model.

Table B.2: TPM output fields used in PITHEM

TPM Output Field PCU Conversion Factor

V1: NWLT 1.0

V2: IWST 1.0

V3: NWST 1.0

V4: LGV 1.0

V5: OGV 1.89

V6: Preload (Bus) 2.50

V6: Cars 1.0

Table B.3 summarised the TPM link types used by PITHEM, versus those that were filtered out from

the model during data import.

Table B.B: TPM link types not used in PITHEM

TPM Link Type TPM Notes

1 Centroid connectors

17, 18, 19 Walking, Metro and Rail and Ferry Links

20, 21, 22, 23, 24, 25 Parking Links


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Appendix C: Methodology for updating TPM using detector data This appendix summarises how traffic count information (primarily inductive loop information) was

processed for use in calibrating and validating the revised Transport Planning Model.

1. Traffic flow data was received from Newcastle City Council for the years 2005 and 2010,

from reports generated by TADU (Tyne and Wear Traffic and Accident Data Unit, run by

Gateshead Council – see: http://www.gateshead.gov.uk/TADU/home.aspx ). These data was

received in the form of approximately 2000+ individual Microsoft Excel spreadsheets. The

main ‘body’ of each spreadsheet provided rows of daily data for one loop location in Tyne

and Wear for the year. Columns within the spreadsheet provided hourly totals on a given

day. At the top of each spreadsheet additional ‘header’ information on the detector

identifier (ID number), location (easting and northing), real-world location (description,

direction and positional information), and the types of vehicle identified (typically pedal

cycle vs. general traffic) were also provided.

2. An ‘R’ script was created and run to strip the body information from the Excel spreadsheets

and save the raw data in a plain-text format (actually comma-separated variable ‘.csv’

format). Once .csv file was produced for each detector (i.e. ~1650 in total). Each file was

given a filename in the format ‘ID_Exxxxxx_Nxxxxxx.csv’, where ‘ID’ was the detector

identifier and ‘Exxxxxx’ and ‘Nxxxxxx’ were the OS 6-digit grid coordinates respectively (NB:

most grid coordinates were rounded in the original excel files to the nearest 10 metres).

Figure C.1 shows a sample .csv file after R processing.

Figure C.1: Sample detector .csv file for detector 1/101 processed from TADU Excel spreadsheets

3. A further ‘R’ script was produced to collate all of the information from both 2005 and 2010

within the individual ‘.csv’ files into a single, ‘master’ csv file. As well as collation of both

years and individual detectors, the script also calculated the following parameters for each

detector:

http://www.gateshead.gov.uk/TADU/home.aspx


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Number of valid records for each hour of the day, based on day of week;

Hourly averages for each hour of the day, based on day of week;

Average and total daily flows for each day of week;

Averages and totals for the TPM model periods (i.e. AM, IP and PM periods);

AADT and AAWT flows. The detector ID, Easting and Northing were retained as the

first 3 fields of information in the final collated .csv file. The final file size was just

over 1 MB of data for all 2010.

4. The collated .csv file was imported into ArcGIS and the Easting and Northing data used to

produce a ‘point events’ dataset of detector locations. This was saved as an ESRI Shapefile

(.shp). The TPM 2010 road network was then also loaded into ArcGIS, to allow for further

spatial and temporal processing of combined detector and link information.

5. A pass on the data was performed to remove:

Detectors with incorrect locational information (e.g. one detector pair gave

coordinates in the North Sea);

Any detectors with very low flows (<200veh/d);

Any detector associated with detection of pedal cycles, rather than motor traffic

(usually identified through the word ‘cycle’ being present in the detector header

information, with the detector not being readily identifiable with any particular road,

or with the flow being low – see first point;

Any detector with less than 2 months contiguous data available for the year;

Any detector not associated with a TPM link;

Any detector not falling within the Tyne and Wear boundary (in the 2010 data there

were a clusters of points associated with Durham, to the south of Tyne and Wear,

and in Northumbria, to the north and west of Tyne and Wear);

6. A spatial process was then manually performed on the remaining detector points of moving

the locations directly on to the relevant TPM links (‘snapping’ detectors to TPM links).

7. For model calibration and validation it is necessary to allocate individual detectors at a given

site to specific TPM links. As unidirectional TPM links in opposing directions tend to lie on the

same, central line when plotted spatially, the initial ‘snapped’ detector locations may be

associated with multiple TPM links. Therefore further analysis was used to assign individual

detector loops to specific TPM links. This involved:

An initial manual allocation that examined detector location and its proximity to

TPM links;

Addition of cardinal directionality flags (N, S, E, W) to TPM links based on the bearing

between their start and end nodes in PITHEM, then linking this to any directionality

mentioned in the description of the detector (e.g. matching a link with an ‘N’ flag to

a detector description mentioning ‘N’, ‘North’ or ‘Northbound’);

Where the above failed, a spatial search was performed on the local region to

identify all possible link and detector combinations. For each detector/link

combination three ‘pseudo-GEH’ statistics covering the AM, IP and PM periods,

based on the TPM 2005 data (modelled) versus the 2005 observed data. Detectors

were then assigned to links based on rank-ordering of the combined error in the GEH

scores (i.e. the lowest combined GEH score gives an estimate of the most probable

‘link-detector’ pair). [As well as GEH ordering was also tried using ordering on


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combined period Root Mean Square (RMS) errors of absolute flow values with

similar results].

For each of the above step an additional numeric code was added to the detector

information to identify which mechanism had been used to allocate the detector.

Figure C.2 shows initial detector locations in Tyne and Wear (brown points) to final used

points (green points), against TPM links (purple lines). After matching detectors present in

both 2005 and 2010 datasets, filtering and removal of unwanted locations, and directional

matching, the final number of detectors was reduced from 1653 to 639 – a 61% reduction.

The majority of this reduction (792 detectors/49%) was due to detectors for which records

existed in 2010, but not 2005.

Figure C.2: TPM Links, Initial detector locations and final detector locations used in

calibration and validation of the revised TPM model

8. Finally, detectors were allocated a ‘screen-line’ number based on geographic proximity to

other detectors (i.e. to identify spatial clusters of detectors).

9. In Figure C.3 the schematic process used to update the 2005 matrices (AM, IP, PM) to 2010,

using the CUBE module “Analyst” is illustrated.


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Figure C.3: Process in block modules to update 2005 TPM matrices to 2010 using Analyst and two

approaches single and bi-directional “combi” detector locations.

The matrix update process used the flow (in veh/h) of the detector identified in point 8. To

generate the trip end file the confidence level was calculated based on the available days of

collection of data for each detector (es. 365 days = 100% confidence level, while 150 days =

150/365 = 41%) , the confidence level is a weight factor that enables the Analyst module to treat

in a different way the OD pairs contribution to the link flow, so that major adjustment will be

carried out for those contributing to link flow with confidence level of 100% and minor to those

with lower confidence level.

10. In Figure C4.a illustrates the CUBE module used to adjust the 2010 estimated matrices (AM, IP,

PM) to reflect the measured fleet composition of cars, LGVs and HGVs. Figure C4.b is presents

the script used to update the fleet composition. The data used for this adjustments comes from

the cordon information supplied by Gateshead council, and outlined in Appendix D.

Figure C.4a: Process used to update the fleet composition

for LGV and HGV vehicle classes


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Figure C.4b: Script used to update the fleet composition for LGV and HGV vehicle classes

11. Figure C.5 illustrates the schematic process used to validate, using the GEH statistics, the

updated matrices for year 2010. (NB: Validation covered all periods, though only the AM

schematic is shown in the Figure. Results of the validation have been included in the main

report in section 3.3.3.1.4, pag.45)

Figure C.5: Process in block modules to validate the updated matrices for year 2010


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Appendix D: Processing of Cordon and Count Information Cordon and Traffic Count Information, from both Newcastle City Council, and Gateshead City Council), were

provided to Newcastle University for the LEZ project. Data for Gateshead was received after the initial

calibration of classified volumes, outlined in section D.1 below. Given this, and the fact that data provided by

Gateshead was in differing format to that of Newcastle, analyses of the two data sets was separate and only

the calibration and validation of vehicle classifications based on the Newcastle data contributed to the final

emissions model. Section 3.3.3.1.4 in the main report document presents the Newcastle results. The results

for Gateshead, post calibration and validation on the Newcastle data are presented in this appendix in

section D.2.

D.1 Newcastle Cordons:

For the Newcastle City Centre Area:

1. An initial map of the Newcastle traffic cordon areas was provided by Edwin Foster of Newcastle City

Council. This map was in raster .tif format, and is shown in Figure D.1. In order to produce usable

cordon boundaries this map was imported to ArcGIS and digitised. The resulting shapefiles of the

cordon boundaries were used to produce the gridded traffic cordon areas for Newcastle shown in

Figure 3.6 in the main report. NB: Whilst the original tiff file was of relatively high resolution (4857 x

3403 pixels), the need for ortho-rectification in GIS means that some error was expected in the

positioning of the digitised cordon boundaries. Additionally, the presence of the legend and graphs

on the tiff obscured some of the boundaries of the Outer Cordon, meaning that, in some areas the

cordon boundary was extrapolated to form a complete shape;

Figure D.1: Newcastle Cordon Boundary Map (Source: Newcastle City Council) NB: This image has been resized

from the original

2. Manual turning movements and two-way classified flows at cordon locations were obtained from the

City Council as Excel spreadsheet files. These were converted to ArcGIS shapefiles using coordinate


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information within the spreadsheets to create point event files. NB: For the calibration and

validation exercise, only the two-way flow counts were used;

3. The two way flow count file contained information for sites outside of the Newcastle/Gateshead

boundary (i.e. North and South Tyneside and Sunderland). These were stripped from the file by

clipping the locations to the overall Newcastle/Gateshead domain boundary (see Figure 3.3 in the

main report);

4. The remaining count sites were allocated to a specific cordon based (i.e. Central, Inner or Outer) on

using the ‘identity’ tool in ArcGIS. Points for which the identity operation failed (i.e. those points

falling outside of any cordon due to digitising errors in step 1. above) were allocated manually. Figure

D.2 shows the site locations and cordon allocations;

Figure D.2: Count Sites in Newcastle allocated within cordon boundaries from Figure D.1

5. It was noted that the count data included specific hours of the day. Using these information SQL

queries were run to separate data into the three periods used by the TPM (i.e. AM-peak, Inter-peak

and PM-peak). It was also noted that the complete dataset spanned data collected on individual days

at different cordon locations, with the collection period spread over three years (2009, 2010 and

2011). NB: For the calibration and validation exercise, it was assumed that data for all days, over

the three years would be applied as if it had been collected in 2010;

6. Count data was based on a 10-vehicle classification scheme, including pedal cycles. The pedal cycle

and bus categories were stripped from the data, and the remaining classifications merged into the

three vehicle classification scheme used by the general traffic model within TPM (i.e. car, LGV and

HGV categories);

7. The total number of vehicles at each count location was then calculated for each time period, by

summating data for each site and each individual hour;

8. The output TPM links for the relevant period were then loaded into ArcGIS, and those links that

straddled the cordon boundaries with a site present, or collocated with a site location, were

identified;


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9. For the identified links the total bi-directional flow at the cordon was calculated for the three vehicle

categories;

10. The totals for each vehicle category from step 7. and step 9. were converted to percentages of overall

flow and examined in Microsoft Excel. The relative proportions of vehicle types were then used to re

adjust the fleet weightings in TPM, via the methodology and scripts outlined in Appendix C. step 10.

11. Steps 9. and 10. were repeated iteratively, in order to achieve the results presented in Section

3.3.3.1.4. of the main document.

D.2 Gateshead Cordons:

For the Gateshead Central Area:

1. Count information from was received from Ian Abernethy (Gateshead City Council). This information

was received in the form of .csv files containing data from individual count sites (identifiable through

site ‘CP’ number). Figure D.3 presents a sample of the count information received;

Figure D.3: Sample count information received from Gateshead City Council

2. Through the CP number the count site was linked to a specific geographic location, and then turned

into a point file in GIS through OS Map Coordinates;

3. As with the Newcastle data above, the count locations spanned a larger area than just that

of the core area of Gateshead (see yellow area of Figure 3.6 in the main report); 4. As with the Newcastle data, the information was hourly based, and was therefore split and

summated via SQL queries in ArcGIS to provide classified flow information covering the three TPM

periods. Unlike the Newcastle data, the collection periods of the data were all days during 2009.

Therefore, for the calibration and validation exercise, the 2009 data was assumed applicable to the

2010 situation. 5. As with Newcastle data, the information spanned 10 vehicle classes (excluding additional

summary fields of certain classes) – therefore pedal cycle and bus information was removed; 6. Steps 7. to 10., outlined for the Newcastle data above, were repeated to compare the TPM

model totals to the cordon totals.


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Table D.1 presents the results for the Gateshead cordon area, for the three time periods, based

on the TPM model, post-calibration and validation using the methodology for the Newcastle

Cordon data, as presented in section D.1.

Table D.1: Vehicle fleet percentages for each time period within the Gateshead Cordon Area

Period Class Observed Modelled Relative %age

AM Cars 80.54% 81.36% 101%

LGV 16.45% 14.82% 90%

HGV 3.01% 3.82% 126%

IP Cars 79.67% 73.6% 92%

LGV 16.88% 20.53% 122%

HGV 3.45% 5.85% 169%

PM Cars 87.22% 88.86% 101%

LGV 11.73% 9.44% 80%

HGV 1.05% 1.70% 162%

As noted in section 3.3.3.1.4, similar to the results from Newcastle, even after validation there

appears to be a substantial overestimation of the percentage of HGV traffic on the roads in

each of the time periods, but especially the Inter-Peak and PM-Peak periods. LGV traffic for all

periods appears to be under-predicted. This overestimation will also carry over into the possible

overestimation of emissions from such vehicles. The percentage of HGVs and LGVs for

Gateshead appears slightly higher than those for Newcastle City Centre, presumably through

the relative contribution of sites major road locations in the count data (i.e. locations on the

A184 and A167).


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Appendix E: Bus Network Modelling Bus network information was received from Newcastle City Council. This appendix is based on

personal communication with Trevor Arkless of the City Council as to how the data was produced.

1. Bus stop positional information from public transport information file (ATCO-CIF .cif) files

was linked to OS MasterMap ITN data in GIS by determining the closest ITN link line to

individual bus stop coordinate point;

2. Bus routes, consisting of chains of bus-stops, were extracted from the .cif files;

3. In GIS, a shortest path algorithm was used to determine the exact links between successive

pairs of bus stops that the bus would be assumed to take;

4. The timetabled information in the .cif files was then used to allocate buses from a particular

service onto the shortest paths;

5. As buses from different services were allocated to links, a tally was kept on the number of

buses expected in each hour of the day;

6. The collated file of bus information was saved as a .csv file. The file contained a list of

individual links (with OS TOID) and hourly weekday bus flows from 04:00 to 00:00.

Figure E.1 presents a sample of the bus information as received from the City Council.

Figure E.1: Bus information .csv file received from Newcastle City Council

7. The information from the .csv file was re-joined in ArcGIS to the OS MasterMap ITN

geometry information at Newcastle University. When plotted in ArcGIS, ‘gaps’ along routes

were noticed in a number of locations where the shortest path algorithm from point 3 above,

has failed to provide an appropriate route. These were ‘patched’ manually in ArcGIS to

ensure routes were as complete as possible. NB: some gaps may still exist within the


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network, and that future iterations of the model be more thoroughly checked for such

anomalies;

8. Spurious ‘spurs’ on routes (locations where the route left a main road to travel as short

distance down a side street, before returning back to the main road) were also removed.

After discussion with the City Council, it was believed that these were caused by the ‘point-

link’ matching algorithm in point 1. above choosing the minor road as the closest link to the

bus stop, rather than the more distant major road. NB: some spurs and spurious routes may

still exist within the network, and that future iterations of the model be more thoroughly

checked for such anomalies;

9. The initial bus network (including gaps and spurs) is presented in Figure E.2. The finalised

network is discussed in Section 3.3.3.2 of the main report.

Figure E.2: Bus Network Used in Emissions Modelling


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Appendix F: Linking TPM to OS MasterMap via PITHEM Linking TPM network data to OS MasterMap Integrated Transport Network (ITN) layer data was done as a

manual process, assisted via a bespoke interface developed in the PITHEM software for the EPSRC ReVISIONS

and SECURE projects. This interface allowed visual selection of TPM links by A and B node, followed by entry of

corresponding nodes in the ITN layer via ‘mouse clicking’ along desired routes.

Figure F.1 shows a sample screenshot from PITHEM during this process. The upper portion of the screenshot

shows the graphical interface used to link the two sets of network data, whilst the lower portion displays

information from the link database. Each TPM road link may correspond to a chain of ITN links and nodes of

variable length.

Figure F.1: Screenshot from PITHEM showing linkages between TPM links (green) and OS MasterMap

ITN data (red). Unassigned ITN links are shown in orange, and TPM centroid connectors and non-

motorised transport network are grey

The PITHEM interface and software allows automatic identification of routes along the ITN link vector chain,

based on user-defined distance criteria. Typically the user wishes the TPM link to be mapped onto the shortest

path along ITN links. However, there are cases (e.g. dual carriageways, slip roads, roundabouts, traffic islands

etc.) where this is not the case – so the PITHEM tool also allows selection on the median or longest paths as

well. Links may also be disabled via the interface (e.g. to remove centroid connectors), retained with their

default geometric information, or links on the other side of the carriageway automatically added. Where the

geometry of ITN vector chains run counter to the direction of the TPM link, these are automatically reversed by

the software.

The final database may be exported from PITHEM as an XML (eXtensible Markup Language) file, or used

directly to update a TPM network with ITN geometry, an speeds from the TrafficMaster dataset, see Appendix

G. Changes across multiple TPM networks may also be applied simultaneously, providing that those networks

share common link identifiers (i.e. A and B node Ids).


143

Appendix G: Applying TrafficMaster Speed Data to TPM/Bus Models: Based on the allocation of OS MasterMap TOID to TPM link identifiers, outlined in Appendix F, as the

TOID forms a unique identifier, it becomes possible to merge and link information together from:

Flow and speed information, by user class, from the TPM;

Ordnance Survey MasterMap Integrated Transport Network (ITN) Layer data;

Link-based average hourly speed information, based on data from TrafficMaster, and held

by Newcastle City Council;

Hourly bus flow information(see Appendix E);

However, the nature of the TPM network leads to several issues issue in mapping data to and from

TPM outputs to data from the other sources, including:

1. There is not necessarily a ‘1-to-1’ correspondence between a link in TPM and a link in OS ITN,

rather either an ‘N-to-1’ (i.e. multiple TPM links are represented by a single ITS link), or a ‘1-

to-N’ mapping (i.e. a single TPM link spans multiple ITN links) may exist. Of the possibilities,

the ‘1-to-N’ mapping is the most likely – see below for discussion on handling mapping

between network links;

2. Daily bus information spanned a 20-hour period from 04:00 to 24:00, whilst MasterMap

speed information spanned a 16-hour period from 06:00 to 21:00. Outside of these periods

bus flows were assumed to be zero, whilst speeds were set to either the TPM link-type

default speed, or the assumed speed limit for the road. Missing values in the TrafficMaster

data, within the 06:00 to 21:00 period were replaced with the network average speed, in the

final network data supplied to PITHEM;

3. Bus information, as provided, gave a single hourly flow value for a particular OS ITN link (see

Appendix E). Therefore, this value was divided by a factor of 2 when applied to uni-

directional TPM links – i.e. it was assumed that 50% of the allocated bus flow on a TPM link

was in each direction of the link) in the final network data supplied to PITHEM;

4. TrafficMaster speed information,as provided, was directional, with either the code ‘A’ or ‘B’

being appended to the TOID, to indicate whether the speed applied to the direction along

the OS ITN vector chain (‘A’) or against it (‘B’). This directionality was retained in the final

network data supplied to PITHEM;

5. Routes through complex junctions and roundabouts, represented in the TPM data by single

nodes, were expanded to match specific ITN links. However this expansion was, at times a

difficult process. Such routes were assigned manually using ‘engineering judgement’,

assumed behaviour based on ‘rules of the road’ and analysis of road markings in

satellite/ground-level photography in Google Earth – rather than through a more automated

process, hence human errors most likely exist at junctions in the network.

Returning to point 1 above, two approaches of linking TPM data to TM speed and bus network data

were trialled during initial deve3lopment of the detailed air-quality models. These approaches were

christened the ‘split’ and ‘merge’ approaches, and have been summarised below:

When ‘splitting’ link flow and capacity data from an initial TPM link was copied into as many

component OS ITN links as required. The ITN link geometry was retained for each individual

component. Bus and speed data came directly from the relevant source information. An


144

abstract example is given in figure G1, where a single TPM link has been split into 3 smaller

OS ITN links (with bus flows assumed to be two-way). Splitting a link retains better spatial

resolution of data from the TrafficMaster speed source, which could potentially adversely

affect emissions from slow moving vehicles near junctions and on congested road sections;

Figure G.1: Splitting TPM link data into a number of OS ITN based links

When ‘merging’ speed and bus data spanning a number of OS ITN links was spatially

averaged (i.e. average values calculated by the weighted sum of OS ITN link lengths) to

produce single data values to be appended to the TPM link information. The geometry of the

link comes from the merging of all OS ITN vector chains. Merging link data reduces overall

volume and complexity of data, at the sacrifice of some spatial resolution (e.g. emissions

associated with small sections of queuing traffic close to junctions would be less pronounced,

with effects ‘smeared’ along the length of the link). Link merging is shown in figure G.2

(again the example assumes the original bus flow are two-way flows).

The flow data for general private traffic (i.e. Qc, Ql and Qh) produced by the ‘split’ or ‘merge’

operations spanned a full 24-hour period, with period values from values scaled using the flow

profile given in Appendix J.

The actual process of merging or splitting was coded in C++ as part of the PITHEM network-linking

interface outlined in Appendix F. Note that both the finalised split and merge links retained encoded

information as to their original data sources (as a supplemental XML file – see appendix F), so

TPM Link A-B : Car Flow Qc, LGV flow Ql, HGV flow Qh, Capacity: C

ITN Link TOID1 ITN Link TOID2 ITN Link TOID3

Length: l1 Length: l2 Length: l3

TM Speed: v1 TM Speed: v2 TM Speed: v3

Bus flow: Qb1 Bus flow: Qb2 Bus flow: Qb3

Split link ID TOID ID Car Flow LGV Flow HGV Flow Speed Capacity Bus flow

A_B_TOID1 TOID1 Qc Ql Qh v1 C 0.5Qb1



Split link 1 Split link 2 Split Link3

A_B_TOID1 A_B_TOID2 A_B_TOID3

A B


145

generation of data in the other direction is also feasible (e.g. to give base loadings of bus flows on an

initial network for use in network reassignments in CUBE).

Figure G.1: Merging OS ITN links to create a single link containing averaged link information

Table G.1 summarises the differences between the two approaches in terms of the number of links

present in the network in Tyne and Wear, the average network speeds at select hours of the day,

and differences in NOx emissions totals (excluding buses) for the 2010 base case weekday at those

hours.

Table G.1: Summary of differences between ‘split’ and ‘merge’ network methodologies

Network # of Links Avg. speed (08:00)

Avg. speed (12:00)

Avg. speed (17:00)

Total NOx (08:00)

Total NOx (12:00)

Total NOx

(17:00)

Split 13268 34.7 km/h 36.2 km/h 35.1 km/h 583.1 kg 432.3 kg 451.1 kg

Merge 2887 34.5 km/h 37.2 km/h 34.9 km/h 574.8 kg 458.0 kg 424.0 kg

In the finalised model, based on discussion of emissions results within the advisory group, and the

estimated length of time required to process ‘split’ network data in ADMS-Urban, only the ‘merge’

networks data was used in PITHEM. The bulk of the analysis within this report is therefore based on

the ‘merge’ network data.


146

Appendix H: Changes in Emissions Factors: This Appendix presents samples of the different speed-emissions curves used during the study, and their impact on total emissions in the model sub-domains. Changes

arose from the implementation of fleet changes, switching from TRL to COPERT factors for NOx, and addition of road abrasion factors for particulate matter in EFTv5.

H.1 Oxides of Nitrogen (NOx as NO2)

Figure H.1: Changes in NOx Emissions between EFTv4.1.2 and EFTv5.1.3, as implemented in PITHEM software, Cars (top left), LGVs (top right), HGVs (bottom

left), and Buses (bottom right)


147

H.2 Primary Nitrogen Dioxide (pNO2)

Figure H.2: Changes in primary NO2 Emissions due to changes in EFTv4.1.2 and EFTv5.1.3 NOx emissions, as implemented in PITHEM software, Cars (top left),

LGVs (top right), HGVs (bottom left), and Buses (bottom right) [NB: pNO2 emissions are not officially part of EFT].

.


148

H.3 Particulate Matter (PM10)

Figure H.3: Changes in PM10 Emissions between EFTv4.1.2 and EFTv5.1.3, as implemented in PITHEM software, Cars (top left), LGVs (top right), HGVs (bottom

left), and Buses (bottom right)


149

H.4 Particulate Matter (PM2.5)

Figure H.4: Changes in PM2.5 Emissions between EFTv4.1.2 and EFTv5.1.3, as implemented in PITHEM software, Cars (top left), LGVs (top right), HGVs

(bottom left), and Buses (bottom right)


150

H.5 Total Emissions across Sub-Domains (NOx, NO2, PM10 and PM2.5)

Note that the total emissions shown below date from 20th

September 2012 and are not the final values presented elsewhere in this study. The figures are for indicative

purposes only. Over the sub-domains, the switch from EFTv4 to EFTv5 increased sub-domain NOx totals by between 4-21%, pNO2 totals by 6%-24%, and both PM10 and

PM2.5 totals by 18-25%. uCO2 totals were relatively unchanged, as the underlying factors were not altered between EFTv4 and EFTv5, though the fleets do differ.

Figure H.5: Changes in Total Emissions between EFTv4.1.2 and EFTv5.1.3, as implemented in PITHEM software, NOx (top left), pNO2 (top right, PM10 (bottom

left), PM2.5 (bottom right)


151

Appendix I: Vehicle Licensing Statistics obtained from DFT Data in the following tables was provided by Dr Daryl Lloyd, Vehicle Licensing Statistics, DfT for use within the

LEZ feasibility study. Data covers the NE region, broken down by Local Authority area. Note that the actual

breakdown of statistics within the tables doesn’t necessarily match the categories or the parameters in the

NAEI fleet hierarchy, so further manipulation of the table data is required if they are to be used to develop

emissions inventories. Most notably the tables contain raw vehicle numbers, rather than vehicle kilometres

travelled.

VEH0203: Licensed cars by fuel type as at 31st December 2010 ,000s

LA Petrol Diesel Gas / gas bi-fuel Electric Hybrid Others All cars

Darlington UA 30.706 13.807 0.074 0 0.078 0 44.665

Durham UA 144.796 74.401 0.357 0.002 0.258 0.001 219.815

Gateshead 50.876 20.641 0.098 0 0.078 0.001 71.694

Hartlepool UA 24.512 10.263 0.054 0 0.044 0 34.873

Middlesbrough UA 36.547 12.941 0.056 0 0.055 0 49.599

Newcastle upon Tyne 61.093 24.119 0.136 0 0.144 0 85.492

North Tyneside 58.74 21.725 0.111 0 0.108 0.003 80.687

Northumberland UA 97.303 50.77 0.256 0.001 0.178 0.003 148.511

NULL 0.286 0.08 0 0 0.001 0 0.367

Redcar and Cleveland UA 43.08 16.627 0.09 0 0.059 0.001 59.857

South Tyneside 39.944 13.403 0.083 0 0.059 0 53.489

Stockton-on-Tees UA 60.417 24.471 0.142 0 0.151 0 85.181

Sunderland 75.492 27.226 0.139 0.003 0.113 0.002 102.975

VEH0205a: Licensed cars by engine size as at 31st December 2010 ,000s

LA 1-1000 cc 1001 - 1550 cc 1551 - 2000 cc 2001-2500 cc 2501-3000 cc >3000 cc Unknown All cars

Darlington UA 2.208 15.983 21.632 2.579 1.517 0.745 0.001 44.665

Durham UA 11.78 81.207 103.571 12.707 7.152 3.394 0.004 219.815

Gateshead 4.287 27.034 33.673 3.62 2.067 1.013 0 71.694

Hartlepool UA 2.136 12.278 16.812 1.917 1.192 0.538 0 34.873

Middlesbrough UA 2.888 18.883 23.462 2.246 1.446 0.674 0 49.599

Newcastle upon Tyne 4.916 30.831 40.993 4.431 2.82 1.501 0 85.492

North Tyneside 4.973 28.963 39.03 4.21 2.348 1.162 0.001 80.687

Northumberland UA 7.826 50.948 70.967 9.982 5.819 2.966 0.003 148.511

NULL 0.037 0.125 0.141 0.03 0.018 0.016 0 0.367

Redcar and Cleveland UA 3.38 21.967 28.538 3.265 1.853 0.854 0 59.857

South Tyneside 3.548 20.275 24.652 2.556 1.671 0.787 0 53.489

Stockton-on-Tees UA 4.751 30.493 40.699 4.771 3.039 1.428 0 85.181

Sunderland 7.024 39.778 46.761 5.036 2.925 1.447 0.004 102.975


152

VEH0207: Cars by age ,000s

0-1 years 1-2 years 2-3 years 3-4 years 4-6 years 6-13 years 13 years + Unknown1 Total

NULL 0.021 0.022 0.013 0.013 0.036 0.1 0.156 0.006 0.367

Darlington UA 3.009 3.159 3.446 3.928 7.993 19.871 2.577 0.682 44.665

Durham UA 14.717 16.016 17.354 18.853 39.327 98.096 12.081 3.371 219.815

Gateshead 4.855 5.191 5.563 6.021 12.458 32.833 3.701 1.072 71.694

Hartlepool UA 1.981 2.246 2.539 2.85 5.966 16.788 1.973 0.53 34.873

Middlesbrough UA 2.797 3.185 3.345 4.125 8.632 24.104 2.732 0.679 49.599

Newcastle upon Tyne 5.908 5.911 6.393 7.088 14.653 39.881 4.274 1.384 85.492

North Tyneside 5.572 6.043 6.309 6.898 14.063 36.76 3.965 1.077 80.687

Northumberland UA 10.511 11.781 12.492 13.75 26.313 63.386 7.983 2.295 148.511

Redcar and Cleveland UA 3.377 3.816 3.966 4.744 9.862 29.052 4.163 0.877 59.857

South Tyneside 3.265 3.904 3.962 4.217 8.947 25.719 2.756 0.719 53.489

Stockton-on-Tees UA 5.74 6.223 6.683 7.376 14.88 38.427 4.653 1.199 85.181

Sunderland 6.874 7.181 7.553 8.342 17.386 48.906 5.31 1.423 102.975

VEH0403: Licensed LGVs by fuel type ,000s

LA Petrol Diesel Gas / gas bi-fuel Electric Others All vans

NULL 0.023 0.085 0.001 0.004 0 0.113

Darlington UA 0.181 5.173 0.013 0.001 0.001 5.369

Durham UA 0.886 20.588 0.075 0.033 0.005 21.587

Gateshead 0.257 12.685 0.041 0.018 0.002 13.003

Hartlepool UA 0.105 2.885 0.005 0.001 0.002 2.998

Middlesbrough UA 0.149 3.692 0.015 0.001 0 3.857

Newcastle upon Tyne 0.238 6.954 0.044 0.007 0 7.243

North Tyneside 0.236 7.225 0.028 0.001 0.001 7.491

Northumberland UA 0.592 14.004 0.049 0.013 0.004 14.662

Redcar and Cleveland UA 0.26 4.602 0.019 0 0 4.881

South Tyneside 0.14 3.749 0.015 0.004 0.001 3.909

Stockton-on-Tees UA 0.228 14.393 0.022 0.006 0 14.649

Sunderland 0.29 7.617 0.023 0.077 0.005 8.012

VEH0407: LGVs by age ,000s


NULL 0.001 0.003 0.005 0.006 0.015 0.024 0.057 0.002 0.113

Darlington UA 0.53 0.397 0.582 0.581 0.95 1.899 0.339 0.091 5.369

Durham UA 0.983 0.948 1.197 1.771 4.441 9.779 1.828 0.64 21.587

Gateshead 1.801 1.355 2.024 1.755 2.207 3.23 0.464 0.167 13.003

Hartlepool UA 0.065 0.127 0.14 0.263 0.656 1.455 0.233 0.059 2.998



North Tyneside 0.452 0.561 0.744 0.926 1.564 2.749 0.365 0.13 7.491



South Tyneside 0.215 0.306 0.26 0.302 0.718 1.733 0.277 0.098 3.909


Sunderland 0.302 0.312 0.415 0.59 1.616 4.016 0.595 0.166 8.012


153

VEH0506: HGVs by weight ,000s

3.5 to 7 t over 7 to 8 t over 8 to 18 t over 18 to 31 t over 31 to 41 t over 41 t Total

NULL 0 0.007 0.005 0.004 0.003 0.001 0.02

Darlington UA 0.074 0.152 0.091 0.068 0.075 0.123 0.583

Durham UA 0.473 0.878 0.893 0.686 0.595 0.89 4.415

Gateshead 0.18 0.503 0.425 0.395 0.149 0.252 1.904

Hartlepool UA 0.064 0.116 0.063 0.048 0.041 0.055 0.387

Middlesbrough UA 0.064 0.157 0.098 0.07 0.018 0.12 0.527

Newcastle upon Tyne 0.214 0.304 0.277 0.101 0.056 0.037 0.989

North Tyneside 0.113 0.238 0.156 0.123 0.107 0.196 0.933

Northumberland UA 0.238 0.645 0.283 0.216 0.221 0.472 2.075

Redcar and Cleveland UA 0.072 0.157 0.08 0.067 0.024 0.373 0.773

South Tyneside 0.074 0.128 0.099 0.058 0.032 0.056 0.447

Stockton-on-Tees UA 0.12 0.25 0.142 0.121 0.089 0.423 1.145

Sunderland 0.112 0.296 0.225 0.119 0.11 0.172 1.034

VEH0507: HGVs by age

,000s


NULL 0 0 0 0.001 0.007 0.007 0.004 0.001 0.02

Darlington UA 0.028 0.03 0.047 0.038 0.125 0.218 0.085 0.012 0.583

Durham UA 0.182 0.218 0.381 0.235 1.023 1.854 0.466 0.056 4.415

Gateshead 0.151 0.138 0.262 0.168 0.467 0.578 0.125 0.015 1.904

Hartlepool UA 0.014 0.016 0.017 0.018 0.069 0.18 0.067 0.006 0.387



North Tyneside 0.057 0.088 0.112 0.064 0.181 0.338 0.073 0.02 0.933



South Tyneside 0.03 0.021 0.024 0.024 0.077 0.211 0.056 0.004 0.447


Sunderland 0.037 0.018 0.058 0.064 0.206 0.503 0.141 0.007 1.034

VEH0507: HGVs by age ,000s


NULL 0 0 0 0.001 0.007 0.007 0.004 0.001 0.02

Darlington UA 0.028 0.03 0.047 0.038 0.125 0.218 0.085 0.012 0.583

Durham UA 0.182 0.218 0.381 0.235 1.023 1.854 0.466 0.056 4.415

Gateshead 0.151 0.138 0.262 0.168 0.467 0.578 0.125 0.015 1.904

Hartlepool UA 0.014 0.016 0.017 0.018 0.069 0.18 0.067 0.006 0.387



North Tyneside 0.057 0.088 0.112 0.064 0.181 0.338 0.073 0.02 0.933



South Tyneside 0.03 0.021 0.024 0.024 0.077 0.211 0.056 0.004 0.447


Sunderland 0.037 0.018 0.058 0.064 0.206 0.503 0.141 0.007 1.034


154

VEH0607: Buses and coaches by age ,000s


NULL 0.005 0 0 0 0.002 0.003 0.003 0 0.013

Darlington UA 0.014 0.017 0.046 0.031 0.024 0.054 0.024 0.007 0.217

Durham UA 0.082 0.075 0.111 0.129 0.218 0.936 0.288 0.042 1.881

Gateshead 0.022 0.021 0.038 0.04 0.06 0.143 0.05 0.008 0.382

Hartlepool UA 0.003 0.008 0.006 0.017 0.018 0.07 0.026 0.002 0.15



North Tyneside 0.011 0.05 0.008 0.008 0.057 0.15 0.053 0.009 0.346



South Tyneside 0.002 0.007 0.014 0.012 0.03 0.08 0.024 0.01 0.179


Sunderland 0.057 0.13 0.124 0.08 0.154 0.677 0.259 0.014 1.495

VEH0306: Motorcycles by engine size ,000s

1 - 50cc 51 - 150cc 151 - 400cc 401 - 600cc 601 - 800cc 801 - 1,000cc 1,000cc + Unknown Total

NULL 0.03 0.025 0.058 0.034 0.019 0.008 0.005 0 0.179

Darlington UA 0.165 0.384 0.266 0.487 0.309 0.29 0.283 0.003 2.187

Durham UA 0.664 1.563 0.991 1.897 1.253 1.397 1.372 0.022 9.159

Gateshead 0.196 0.458 0.252 0.56 0.369 0.344 0.324 0.003 2.506

Hartlepool UA 0.133 0.264 0.129 0.258 0.162 0.166 0.225 0.001 1.338



North Tyneside 0.205 0.548 0.239 0.622 0.395 0.43 0.424 0 2.863



South Tyneside 0.157 0.477 0.173 0.432 0.306 0.321 0.323 0.002 2.191


Sunderland 0.283 0.817 0.354 0.733 0.444 0.459 0.584 0.003 3.677

VEH0307: Motorcycles by age ,000s


NULL 0.001 0.001 0.001 0.001 0.001 0.001 0.167 0.006 0.179

Darlington UA 0.123 0.142 0.144 0.115 0.221 0.677 0.522 0.243 2.187

Durham UA 0.486 0.562 0.681 0.642 1.013 3.057 1.932 0.786 9.159

Gateshead 0.147 0.153 0.151 0.177 0.298 0.895 0.471 0.214 2.506

Hartlepool UA 0.072 0.103 0.086 0.09 0.15 0.461 0.264 0.112 1.338



North Tyneside 0.192 0.195 0.225 0.209 0.338 0.979 0.512 0.213 2.863



South Tyneside 0.132 0.153 0.193 0.149 0.25 0.735 0.425 0.154 2.191


Sunderland 0.186 0.249 0.293 0.237 0.459 1.266 0.689 0.298 3.677


155

Appendix J: Diurnal Profile Scaling Factors Table J.1 presents the scaling factors used in PITHEM to calculate time-varying flows throughout a typical

weekday. These data comes from the analysis of normalised data from all valid detectors, as presented in

Appendix C. The scaling factor percentage is divided by 100 and then applied to each link in the specified

network for the particular hour (e.g. flow for 08:00-09:00 is 1.129*AM-peak average flow). Hours using the

inter-peak TPM network are shown in black (daytime) and blue (night-time) respectively, whilst hours using

the AM-peak are red, and PM-peak in amber.

Table J.1: Diurnal Profile Scaling Factors used in PITHEM

Start Hour End Hour Scaling Factor (%)

TPM Network

00:00:00 01:00:00 13.67 Inter-Peak

01:00:00 02:00:00 8.36 Inter-Peak

02:00:00 03:00:00 6.41 Inter-Peak

03:00:00 04:00:00 4.38 Inter-Peak

04:00:00 05:00:00 4.60 Inter-Peak

05:00:00 06:00:00 10.55 Inter-Peak

06:00:00 07:00:00 31.48 Inter-Peak

07:00:00 08:00:00 83.91 AM-Peak

08:00:00 09:00:00 112.90 AM-Peak

09:00:00 10:00:00 103.19 AM-Peak

10:00:00 11:00:00 86.92 Inter-Peak

11:00:00 12:00:00 95.64 Inter-Peak

12:00:00 13:00:00 102.46 Inter-Peak

13:00:00 14:00:00 103.26 Inter-Peak

14:00:00 15:00:00 101.89 Inter-Peak

15:00:00 16:00:00 109.93 Inter-Peak

16:00:00 17:00:00 109.29 PM-Peak

17:00:00 18:00:00 106.01 PM-Peak

18:00:00 19:00:00 84.70 PM-Peak

19:00:00 20:00:00 69.81 Inter-Peak

20:00:00 21:00:00 48.45 Inter-Peak

21:00:00 22:00:00 35.88 Inter-Peak

22:00:00 23:00:00 26.81 Inter-Peak

23:00:00 00:00:00 20.13 Inter-Peak

Scaling factors in table J.1 were calculated to obey the following constraints:

The average scaling factors over the AM (07:00 – 10:00), IP (10:00 – 16:00) and PM (16:00 –

19:00) peaks should be 100%;

That 23.6% of the inter-peak flow occurs during the night (19:00 – 07:00) period, to match

the average overnight flows obtained from the 2010 detector data;

The hourly scaling factors within all four periods should match the relative average-hourly

profiles obtained from the detector data.


156

Appendix K: Meteorological Data for 2010 Table K.1 provides a summary of the meteorological data fields found in the 2010 ADMS-Urban .met files

received by Newcastle University from Newcastle City Council. The data was obtained from the meteorological

mast and station co-located with the Cradlewell AURN site in Newcastle (OS Grid Coords: 425992, 565831). The

minimum, maximum, mean and median of parameter values are also provided, where appropriate.

Table K.1: Summary of Hourly Meteorological Data provided by Newcastle City Council

Parameter Name Data Format Comments

Station DCCN Integer Station Identifier (not used by ADMS)

Year Integer Year (i.e. 2010)

TDay Integer Julian Day within the Year (range 1-365)

THour Integer Hour of the day (Start time within range 0-23)

T0C Integer Temperature in °C (min = -11°C, max = 25°C,

mean = 8.04°C, median = 8°C)

U Decimal (1d.p.) Wind speed in m/s (min = 0 m/s, max = 18.5 m/s,

mean = 3.18 m/s, median = 2.6 m/s)

PHI Integer (10° resolution)

Wind direction in ° (Predominant direction = 280°)

P Decimal (2d.p.) Precipitation in mm (min = 0mm, max = 1.84mm,

mean = 0.08mm, median = 0mm)

CL Integer Cloud Cover in Oktas

RHUM Decimal (1d.p.) Relative Humidity in % (min = 29.1%, max = 100%,

mean = 82.95%, median = 86.7%)

Table K.2 provides a summary of the validity of the meteorological data, based on output from ADMS-Urban’s

meteorological pre-processor. Note that dispersion calculations for approximately 17% of the year use ‘calm’

conditions due to a lack of wind data, and for a further 7% no calculation was possible at all.

Table K2: Summary of Validity of Data (ADMS meteorological pre-processor output)

Total Hours Hours of assumed ‘calm’ conditions (Inadequate data)

Hours ignored (Invalid or no

data)

8760 (i.e. 365 x 24) 1473 (16.8%) 635 (7.2%)


157

Appendix L: Street Canyon and Topographical Data Due to time constraints processing of detailed street canyon information was limited to the AQMA

areas. Outside of these areas the effect of canyons on concentrations was not considered.

Street canyon processing created a series of cross-mapping tables between street-canyon

information, as required for ADMS-Urban, TPM Links and Bus Route Links. Processing was done

manually through the following steps:

1. TPM Link and bus route information was loaded into ArcGIS, and clipped to shapefiles

representing the main AQMAs (Gosforth, Newcastle City Centre/Coast Road and Gateshead

respectively);

2. Building footprint and height information from the ‘Cities Revealed’ dataset for Tyne and

Wear was loaded into ArcGIS;

3. Canyons were then identified and inspected visually. The average building height was

calculated to be the average of all building heights adjacent to the TPM/Bus route links in

question and applied directly to each link. Canyon widths were calculated using the ArcGIS

‘measure’ tool. Where canyon widths varied ;

4. Where there was a major discrepancy (i.e. >10m) between the height of buildings on either

side of the road, this was noted;

5. The resulting data appended to the shapefiles (as information in the .dbf data files), was

then exported via Microsoft Excel as a plain text (.csv) file;

6. Information from the .csv files was then used to ‘patch’ final outputs from PITHEM (i.e.

ADMS-Urban database inputs) with the correct canyon information, based on the A_B node

ID (for TPM links) or the TOID ID (for Bus routes) respectively.

As would be expected, the manual definition process leads to a rather subjective interpretation of

what exactly constituted a canyon. This is considered a weakness of the model presented in this

report, an could be further improved through a more automated process of collection canyon

information (e.g. ray-tracing perpendicular to road links), and a more rigorous definition of canyons.

It would also have been beneficial to further subdivide TPM/Bus links based on canyon geometry

(albeit at the expense of increased run times in ADMS).

L.1 Canyons in the Gosforth AQMA:

Table L.1 presents the street canyons identified in the Gosforth AQMA. The table provides the ‘A’

and ‘B’ nodes of the canyon links, as well as the average building height ‘H’ applied (in metres) and

the average canyon width ‘W’ (in metres). Any notes about the canyon geometry are also provided

in the table.

Table L.1 Street Canyon information for Gosforth AQMA A B H W Notes

6314 9285 14 25 Gosforth High Street

9285 6314 14 25 "

6315 9145 14 16 "

9145 6315 14 16 "

6315 6316 12 17 Gosforth High Street, Broken Canyon

6316 6315 12 17 "


158

L.2 Canyons in the Newcastle City Centre/Coast Road AQMA:

Table L.2 presents the street canyons identified in the Newcastle CC AQMA.

Table L.2 Street Canyon information for Newcastle City Centre AQMA A B H W Notes

5636 5789 14 50 Coast Road, Wide canyon

5789 5636 14 50 "

9011 5636 10 23 Portland Terrace

5636 9011 10 23 "

9011 5637 10 23 "

5637 9011 10 23 "

5686 9630 22 30 John Dobson Street, Broken Canyon

9630 5686 22 30 "

9361 9362 22 30 "

5686 9360 22 30 "

9362 9361 22 30 "

5685 5686 22 30 "

9364 9363 22 30 "

5686 5685 22 30 "

9363 9364 22 30 "

5689 5742 17 21 College Street, Broken Canyon

5742 5689 17 21 "

9365 9366 26 18 "

9366 9365 26 18 "

9369 5679 24 11 "

5679 9369 24 11 "

5699 5739 40 29 Queen Victoria Road, Broken Canyon

5739 5699 40 29 "

5698 5739 23 27 "

5739 5698 23 27 "

5692 5801 17 17 St Thomas Street

5801 5692 17 17 "

5694 5695 25 11 Leazes Park Road

5696 5695 17 11 "

5695 5696 17 11 "

5693 5800 25 25 B1307, Newgate Street, Broken Canyon

5800 5693 25 25 "

9328 9329 25 25 "

9329 9328 25 25 "

9330 9331 25 25 "

9332 9333 25 25 "

9331 9330 25 25 "

9333 9332 25 25 "

5723 5704 25 25 "


159

5704 5705 25 25 "

5705 5704 25 25 "

9453 9454 25 25 Newgate Street

9454 9453 25 25 "

9455 9456 25 25 "

9456 9455 25 25 "

9003 5715 25 10 Stowell Street, L-shaped canyon

9334 9335 25 16 Gallowgate

9335 9334 25 16 "

9336 9337 24 16 "

9337 9336 24 16 "

5707 5710 23 18 Clayton Street

5710 5707 23 18 "

5710 5712 23 20 Clayton Street, L-shaped canyon

5712 5710 23 20 "

5713 5711 21 12 Low Friar Street

5711 5706 21 12 "

9450 9449 21 13 A186, Westgate Road, Partial Canyon

9451 9452 21 13 "

5714 5713 23 12 "

9445 9446 25 13 "

9446 9445 25 13 "

5724 5721 27 23 Neville Street, Broken Canyon

5721 5720 27 23 "

5709 5713 22 16 Clayton Street West

5720 5709 22 16 "

5718 5720 17 20 A695, Centre for Life, Un-even Canyon

5720 5718 17 20 "

5718 5719 19 24 Westmorland Road

5719 5718 19 24 "

5714 5748 25 18 Waterloo Street

5748 5714 25 18 "

5728 5804 29 17 Scotswood Road, Un-even Canyon

5804 5728 29 17 "

5675 5723 25 17 Westgate Road

5723 5674 25 16 Mosley Street

5670 5744 21 16 "

5744 5670 21 16 "

5646 5670 20 21 "

5670 5646 20 21 "

5670 5745 17 18 Dean Street

5745 5670 17 18 "

5666 5670 23 23 Grey Street


160

5670 5666 23 23 "

5666 5678 23 21 "

5707 5674 25 26 B1307/Bigg Market

5647 5676 23 19 Pilgrim Street

5676 5647 23 19 "

5676 5677 23 25 "

5677 5676 23 25 "

5680 9377 25 25 "

8376 9378 25 25 "

9377 5680 25 25 "

9378 9376 25 25 "

9372 9373 25 18 Market Street

9373 9372 25 18 "

9374 9375 25 18 "

9375 9374 25 18 "

5678 5677 22 20 "

9004 5666 22 13 Shakespere Street

5678 5708 25 20 Market Street

5708 5678 25 20 "

5707 5708 22 18 "

5708 5707 22 18 "

5707 5710 23 18 Grainger Street

5710 5707 23 18 "

???? ???? 22 22 Blackett Street (Bus Model Only), TOID 400000000775148

???? ???? 24 81 Eldon Square (Bus Model Only), TOID 4000000007750326 & 7750180

5693 5800 22 27 Percy Street

5800 5693 22 27 "

9328 9329 22 27 "

9329 9328 22 27 "

9326 9327 15 32 Percy Street, Haymarket

9327 9326 15 32 "

9321 9322 15 32 "

9322 9321 15 32 "

9323 9324 15 32 "


161

L.3 Canyons in the Gateshead AQMA:

Table L.3 presents the street canyons identified in Gateshead AQMA.

Table L.3: Street Canyons within the Gateshead AQMA A B H W Notes

5897 5896 17 12 Jackson Street

5855 9472 14 21 High Street

9472 5855 14 21 "

5855 5897 15 21 "

5897 5855 15 21 "

5897 5943 13 22 "

5943 5897 13 22 "

5836 5943 22 26 High Street, unequal L-shaped canyon

5943 5836 22 26 "

5856 5896 17 21 High W. Street

5896 5856 17 21 "

5855 9472 13 22 High Street

9472 5855 13 22 "

9473 9474 14 19 High Street, unequal L-shaped canyon

9474 9473 14 19 "

9220 9221 14 31 Durham Road

9221 9220 14 31

???? ???? 25 19 Bus route only (TOID: 4000000007751633)

???? ???? 20 12 Bus route only (TOID: 4000000007750590)

???? ???? 19 16 Bus route only (TOID: 4000000007880515), Un-even canyon

???? ???? 19 16 Bus route only (TOID: 4000000008000346), Un-even canyon

L.4 Additional Topographical Concerns:

In addition to the identification of street canyons, a number of other topographical concerns within the

AQMAs were identified in relation to the TPM and bus networks. These concerns included:

Areas where roads were in tunnels, under-passes or cuttings (e.g. Cradlewell/Jesmond Tunnel);

Areas where roads are on elevated sections or flyovers (e.g. the A167(M) Central Motorway to the

north of Newcastle City Centre and the A167, Gateshead Highway);

Areas with very complex road geometry, coupled with dense and complex building geometry (e.g.

the area around the Pilgrim Street (Swan House) Roundabout in central Newcastle, where the

A167(M) actually passes under the ‘55 Degrees North’ Complex;

Areas with substantial gradients – notably the quayside areas on both sides of the River Tyne;

Bridges across the river Tyne, at considerable elevation above the surrounding terrain (e.g. The

Historic Tyne Bridge, The High Level Bridge and Redheugh Bridge).

Due to time constrains, no attempt was made to model these issues in detail. Associated TPM road links are

listed here as indicators of areas of potential concern and improvement for future iterations of any modelling.


162

Table L.4a lists TPM links associated with the Cradlewell/Jesmond Tunnel, whilst Table L.4b lists those links on

the Central Motorway that are underneath the Durant Road (B1309)/New Bridge Street (A193) Roundabout.

Table L4a: TPM Links associated with Cradlewell/Jesmond Tunnel A B

6454 9183

9183 6454

Table L4b: TPM llinks associated with Central Motorway (A167(M)) Underpass

A B

5632 5763

5623 5761

Table L.4c lists TPM links associated with elevated sections and flyovers in Newcastle City Centre (A167(M)),

whilst Table L.4d lists TPM links associated with elevated sections of the Gateshead Highway (A167).

Table L4c: TPM Links associated with Central Motorway (A167(M)) Elevated Sections and Flyovers A B

5609 5757

5610 5609

5611 5608

5611 5754

5611 5760

5612 5615

5612 5758

5613 5611

5614 5613

5614 5758

5615 5623

5616 5614

5639 5765

5749 9393

5756 5609

5757 5612

5759 5613

5759 5615

5763 5616

5765 5614

5765 5616

5787 5757

9291 5786

9294 9297

9393 5749

9395 9397

9397 9394


163

Table L.4d: TPM Links associated with Gateshead Highway (A167) Elevated Sections and Flyovers A B

5843 5845

5845 5844

5861 5844

5861 5850

5843 5870

5851 5870

5842 5843

5844 5945

Table L.4e lists links around Swan House Roundabout/55 Degrees North.

Table L.4e: TPM Links within the Swan House Roundabout Area A B

5619 5761

5623 5761

5632 5628

5632 5763

5633 5632

5642 5643

5643 5644

5643 5667

5644 5645

5644 5649

5645 5646

5646 5647

5647 5648

5648 5633

5648 5642

5667 5643

5676 5647

5761 5762

5762 5634

5762 5642

5766 5633

5766 5645

Table L.4f lists links associated with bridges over the Tyne between Newcastle And Gateshead City Centres,

that are in the AQMAs of either council.

Not table exists for roads with substantial gradients (at the time of writing). All terrain was assumed to be flat

in the final models, as presented in this report.


164

Table L.4e: TPM Links associated with City Centre bridges over the Tyne A B

5634 5817

5816 5766

5817 5822

5826 5816

5671 5815

5815 5671

5814 5908

5908 5814

5673 5814

5814 5673


165

Appendix M: Background Maps The following maps were produced by merging 1km

2 grid data downloaded from the DEFRA LAQM website for

the Gateshead, Newcastle, North Tyneside and South Tyneside Areas. The original ‘comma separated variable

(csv)’ files were imported into ArcGIS, converted to point feature shape files, merged together and then

Nearest-Neighbour interpolation was applied to produce raster layers at 200m resolution. Two layers for each

pollutant (NOx, PM10, PM2.5) were produced: the first containing contributions from major roads, primary roads

and motorways, both into and out of grid cells removed, the second with contributions from ALL roads

removed. The figures below display the minor roads removed background to the left, the all roads removed

background in the centre, and a difference map between the two layers to the right. All concentrations are in

units of µg/m3, with consistent colour scales and the point locations of the two Newcastle AURN sites shown.

M.1 Oxides of Nitrogen (NOx as NO2)

Figure M.1a: Background Concentrations of Total NOx for 2010 - Including minor roads (left),

excluding all roads (middle), and difference between the two (right).

Figure M.1b: As M.1a above, but for Total NOx for 2021

Crown Copyright all

rights reserved

Newcastle City

Council 100019569

2012

Crown Copyright all

rights reserved

Newcastle City

Council 100019569

2012


166

M.2 Nitrogen Dioxide (NO2)

Figure M.2a: Background Concentrations of NO2 for 2010 - Including minor roads (left), excluding all

roads (middle), and difference between the two (right).

Figure M.2b: Background Concentrations of NO2 for 2021 - Including minor roads (left), excluding all

roads (middle), and difference between the two (right).

Crown Copyright all

rights reserved

Newcastle City

Council 100019569

2012

Crown Copyright all

rights reserved

Newcastle City

Council 100019569

2012


167

M.3 Particulate Matter (PM10)

Figure M.3a: Background Concentrations of Particulate Matter (PM10) for 2010: Including minor

roads (left), excluding all roads (middle) and difference between the two (right).

Data for background concentrations of PM10 in 2021 has been analysed, but not plotted. PM10 was not

considered a priority for 2021 modelling.

M.4 Particulate Matter (PM2.5)

Figure M.4a: Background Concentrations of Particulate Matter (PM2.5) for 2010: Including minor

roads (left), excluding all roads (middle) and difference between the two (right).

Data for background concentrations of PM2.5 in 2021 has been analysed, but not plotted here. PM2.5 was not

considered a priority for 2021 modelling.

The annual mean values for 2010 for estimated regional concentrations of pollutants above the surface

boundary layer, used in the DEFRA NOx to NO2 conversion tool, and based on the Newcastle Metropolitan

Borough (Gateshead in brackets), are given below:

Crown Copyright all

rights reserved

Newcastle City

Council 100019569

2012

Crown Copyright all

rights reserved

Newcastle City

Council 100019569

2012


168

Ozone = 57.2 µg/m3

(Gateshead = 58.1 µg/m3)

Oxides of Nitrogen = 18.2 µg/m3

(NOx as NO2) (Gateshead = 16.8 µg/m3

(NOx as NO2))

Nitrogen Dioxide = 14.6 µg/m3


Regional f-NO2 = 19.63%

(Gateshead = 19.63%)

For 2021 the values are as follows:

Ozone = 61.5 µg/m3


Oxides of Nitrogen = 11.4 µg/m3

(NOx as NO2) (Gateshead = 10.6 µg/m3

(NOx as NO2))

Nitrogen Dioxide = 9.6 µg/m3


Regional f-NO2 = 21.58%

(Gateshead = 21.58%)

As the Gateshead Metropolitan Borough is larger and more rural than Newcastle, it is expected that Ozone

concentrations would be higher, and NOx lower.

Source for the regional ‘above boundary surface concentrations’ and ‘f-NO2’ values is: DEFRA. (2012b). NOx to

NO2 Calculator. Version 3.2. September 2012. Department of Environment, Food and Rural Affairs. Online resource:

http://laqm.defra.gov.uk/documents/NOx-NO2-Calculator-v3.2.xls [Accessed: 10/1/13].

Source for all background data is: DEFRA. (2012f). 2010-Based Background Maps for NOx, NO2, PM10 and PM2.5.

Department of Environment, Food and Rural Affairs. Online resource: http://laqm.defra.gov.uk/maps/maps2010.html


http://laqm.defra.gov.uk/documents/NOx-NO2-Calculator-v3.2.xls

http://laqm.defra.gov.uk/maps/maps2010.html


169

Appendix N: Newcastle AURN Sites Table N.1 provides information on the two AURN sites used to validate the modelling framework for Newcastle

City Centre.

Table N.1: Newcastle AURN Site Information

Site Name Newcastle City Centre Newcastle Cradlewell OS Coordinates 425026, 564918 425992, 565831

Altitude (metres) 45 42

Sample height (metres) 3 3

Site type Urban Background Urban traffic (Roadside)

Parameters monitored CO, Temperature, Wind Direction, Wind Speed, NO, NO2, Total NOx (as NO2), Non-Volatile PM10, Non-Volatile PM2.5, Ozone, PM10, PM2.5, SO2, Volatile PM10, Volatile PM2.5

Temperature, Wind Direction, Wind Speed, NO, NO2, Total NOx (as NO2)

Data for 2010? Yes (NOx, NO2, PM10, PM2.5) Partial (NOx, NO2)

URL on UK Air Website http://uk-air.defra.gov.uk/networks/aurn-site-info?site_id=NEWC

http://uk-air.defra.gov.uk/networks/aurn-site-info?site_id=NCA3

Site Picture

All data courtesy of the Department for Environment, Food and Rural Affairs and Bureau Veritas Solutions

Table N.2 is copied directly from the DEFRA NOx to NO2 conversion tool, used to calculate NO2 levels at the

AURN sites. The background levels were extracted using the point coordinates of the AURN sites, and the

background maps presented in Appendix M. The f-NO2 value used (15.4%) was calculated by PITHEM using the

‘DfT_2010’ fleets outlined in Section 3.3.4.2 in the main document. The ‘Notes’ column contains the total NOx

value (i.e. ‘Road Increment NOx + Background NOx’).

Table N.2: Data from DEFRA NOx to NO2 Conversion Tool for the AURN Sites.

Local Authority: Newcastle Year: 2010

Traffic Mix:

All other urban UK

traffic

Receptor ID Easting,m Northing, m

Road increment

NOx Background g m

-3

Fraction emitted as NO2 Total NO2 Road NO2 Notes

g m-3 NOx NO2 g m

-3 g m

-3

Centre_Base 425026 564918 28.72 43.58 39.71 12.23 72.3

Cradlewell_Base 425992 565831 66.8 34.88 49.44 26.08 101.68

Centre_Reduced_Background 425026 564918 28.72 27.61 32.71 12.88 56.33

Cradlewell_Reduced_Background 425992 565831 66.8 20.07 43.42 27.32 86.87

Centre_Base 425026 564918 28.72 43.58 0.154 39.04 11.56 72.3

Cradlewell_Base 425992 565831 66.8 34.88 0.154 47.49 24.13 101.68

Centre_Reduced_Background 425026 564918 28.72 27.61 0.154 32.1 12.26 56.33

Cradlewell_Reduced_Background 425992 565831 66.8 20.07 0.154 41.54 25.44 86.87

Centre_Base 425026 564918 28.72 25.82 38.19 12.37 72.3

Cradlewell_Base 425992 565831 66.8 22.12 48.41 26.29 101.68

Centre_Reduced_Background 425026 564918 28.72 17.61 30.67 13.06 56.33

Cradlewell_Reduced_Background 425992 565831 66.8 13.8 41.52 27.72 86.87

http://uk-air.defra.gov.uk/networks/aurn-site-info?site_id=NEWC

http://uk-air.defra.gov.uk/networks/aurn-site-info?site_id=NEWC




170

Appendix O: Future-Year Traffic Growth Table O.1 presents growth factors for light and heavy vehicles, as calculated from TPM v3.1 (Jacobs, 2008e).

Table O.1: Estimated traffic growth in TPM3.1 (base year 2001 = 100)

Year Car LGV HGV

2001 100 (base) 100 (base) 100 (base)

2010 (?) 118.18 110.63

2021 (?) 145.45 125.00

Growth 2010-2021 (?) 23.1% 12.9%

An alternate methodology was examined using published DfT statistics (DfT 2021h and DfT, 2012i), from the National Transport Model (NTM). This involved applying the compound growth factor equation below to VKM data from the model, over various time horizons.

(

)

Where ‘g’ is the long-term growth factor (i.e. compound rate of change of VKM), ‘VKMf’ is the VKM

value in the future year (e.g. 2035) ‘VKMb’ is the VKM value in the base year (e.g. 2010) and ‘n’ is the

number of periods (e.g. 25).

Unfortunately, the accuracy of the above VKM growth method is somewhat limited, due to the

limited resolution of the values in the DfT spreadsheet (billion km to 1d.p.). This, in turn, means that

calculated factors for individual user classes vary considerably depending on the selection of time

period, area and/or road type. Table O.2 gives a sample of the range of increases over the 2010 to

2021 period, calculated using either the North East (Full) or North East (Large Urban) datasets, with

growth factors taken using the periods 2010-2020, 2010-2025 and 2010-2035.

Table O.2: Estimated traffic growth in the North East using English regional traffic growth forecasts

Parameter Cars LGVs HGVs1 Buses

NE Large Urban Data

VKM (2010) 8.4 b.km 1.3 b.km 0.3 b.km 0.2 b.km

VKM (Predicted 2021) 9.28-9.41 b.km 1.70-1.75 b.km 0.30-0.37 b.km 0.2 b.km

Predicted change % 10.5%-11.9% 30.9-34.3% 0.00-23.1% 0.00%

NE All Data

VKM (2010) 16.0 b.km 2.6 b.km 0.8 b.km 0.3 b.km

VKM (Predicted 2021) 17.77-17.80 b.km 3.44-3.61 b.km 0.87-0.92 b.km 0.3 b.km

Predicted change % 11.0-11.2% 32.1-38.6% 8.9-15.0% 0.00%

Finally, Table O.3 gives growth factors extracted from the latest National Trip End Model (NTEM) using the DfT Tempro (DfT, 2013) software, for cars and buses over the 2021 period.

Table O.3: Period growth factors for 2010 to 2021 from TEMPRO6.2 for Tyne and Wear Period Car (Drivers) All Trips Buses and coaches

AM-Peak 9.07% -2.43%

Inter-Peak 10.94% -3.26%

PM-Peak 9.19% -4.52%

Note that, using TEMPRO, there was the suggestion that the number of trips would actually decline

slightly over the period, however to be more conservative it was decided to leave the number of bus

trips of 2021 the same of year 2010.


171

Therefore, in the final 2021 model, a combination of data from tables O.1, and O.3, with zero change in bus

VKM was applied to growth for the future year scenarios.

Figure O.1 presents the block diagram process that has been used to update year 2010 matrices (AM,

IP, and PM) and the relative proportion of cars, LGVs, and HGVs.

Figure O.1: Block module process to update the 2010 matrices (AM, IP, PM) and vehicle classes to year 2021.


172

Appendix P: Future-Year Traffic Speeds This appendix briefly outlines the process for changing mean traffic speeds between the 2010 base year, and

2021 future year scenarios. All links within TPM are assigned a ‘CAPIDX’ value, which corresponds to a

particular set of table entries relating speed to the ‘Volume to Capacity’ (VtoC or V/C) ratio of the link. Precise

details of the speed to V/C curves, and how they relate to published DfT/COBA curves, recommended for UK

traffic models may be found in Jacobs, 2008c. The speed versus V/C curves themselves are plotted in Figure

P.1. Curves 2 through 10 are applied to rural roads, 11-16 suburban roads, and 17-25 urban roads. Note the

large discontinuity above V/C ratios of 1.45 is present in the underlying methodology in the TPM

model for highly congested roads, but affects only a small fraction (≈5 links) of the total number of

links in the model.

Figure P.1: Speed vs. V/C ratio curves by TPM link capacity index (CAPIDX) field

In order to calculate the change in speed between the base and future-year network networks, the

criteria in Table P.1 were applied to each link in the future year network.

Table P.1: Speed-change criteria applied for future year scenarios

Link presence Calculated Base Year VtoC and Future (2021) VtoC ratio

% Speed change applied to TrafficMaster speed

Link in 2010 network but not 2021 N/A Link ignored

Link in both 2010 and 2021 network Both below 0.15 0%

Link in both 2010 and 2021 network Either V/C between 0.15 and 1.45 (

)

Link in both 2010 and 2021 network Both above 1.45 0%

Link in 2021 network but not 2010 N/A N/A - Use TPM 2021 speed

Note ‘VF’ is the future year (2021) speed calculated using the V/C curves in Figure P.1. Likewise ‘VB’ is the base

year (2010) speed calculated using the same curves.


173

Appendix Q: Future-Year Fleets for non-NOx Pollutants These fleet tables are supplemental to those in the main body of the report, and arise from the fact that EFTv5.1.3 (and hence PITHEM) treat light vehicles equipped with diesel particulate filters separately from those equipped with de-NOx equipment. Generally the NOx fleets are only used for COPERT NOx/NO2 calculations, whilst the PM fleets also affect emissions of Hydrocarbons and ultimate CO2.

Table Q.1: Diesel Car fleet for Base 2021 and tested LEZ scenarios (and uCO2 Calculations)

Pre-Euro

Euro 1 Euro 2 Euro 3 Euro 4 Euro 5 Euro 6

DPF Status N/A N/A N/A Without With Without With OK Fail OK Fail

Base 2021 0.00% 0.00% 0.00% 0.86% 0.19% 6.50% 1.62% 26.55% 0.33% 63.22% 0.72%

Euro V LEZ 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 35.60% 0.46% 63.22% 0.72%

E6 Fail 2021 0.00% 0.00% 0.00% 0.86% 0.19% 6.50% 1.62% 89.71% 1.12% 0.00% 0.00%

E6F E5LEZ 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 98.77% 1.23% 0.00% 0.00%

Table Q.2: Diesel LGV fleet for Base 2021 and tested LEZ scenarios (PM and uCO2 Calculations)

Pre-Euro

Euro 1 Euro 2 Euro 3 Euro 4 Euro 5 Euro 6

DPF Status N/A N/A N/A N/A N/A OK Fail OK Fail

Base 2021 0.00% 0.00% 0.14% 0.40% 4.23% 19.39% 0.24% 74.78% 0.81%

Euro V LEZ 0.00% 0.00% 0.00% 0.00% 0.00% 24.10% 0.31% 74.78% 0.81%

Euro VI LEZ 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 98.90% 1.10%

E6 Fail 2021 0.00% 0.00% 0.14% 0.40% 4.23% 93.36% 1.86% 0.00% 0.00%

E6F E5LEZ 0.00% 0.00% 0.00% 0.00% 0.00% 98.77% 1.23% 0.00% 0.00%


174

Appendix R: Apportionment of Emissions in AQMAS and Urban Cores

R.1 Oxides of Nitrogen (NOx as NO2)

Figure R.1: Source Apportioned Emissions of Oxides of Nitrogen for Newcastle City AQMA (top left), Gosforth AQMA (top centre), Gateshead AQMA (top right), Newcastle Urban Core Area (Bottom left) and Gateshead Urban Core Area (bottom centre).


175

R.2 Primary Nitrogen Dioxide (pNO2)

Figure R.2: Source Apportioned Emissions of primary Nitrogen Dioxide for Newcastle City AQMA (top left), Gosforth AQMA (top centre), Gateshead AQMA (top right), Newcastle Urban Core Area (Bottom left) and Gateshead Urban Core Area (bottom centre).


176

R.3 Particulate Matter (PM10)

Figure R.3: Source Apportioned Emissions of PM10 for Newcastle City AQMA (top left), Gosforth AQMA (top centre), Gateshead AQMA (top right), Newcastle Urban Core Area (Bottom left) and Gateshead Urban Core Area (bottom centre).


177

R.3 Particulate Matter (PM2.5)

Figure R.4: Source Apportioned Emissions of PM2.5 for Newcastle City AQMA (top left), Gosforth AQMA (top centre), Gateshead AQMA (top right), Newcastle Urban Core Area (Bottom left) and Gateshead Urban Core Area (bottom centre).


178

Appendix S: Pollutant Concentrations in AQMAs and Cores This appendix presents spatial statistics for receptor points within the various model AQMA and Urban Core

sub-domains, for total NOx (as NO2) and Nitrogen dioxide (NO2).

S.1 Oxides of Nitrogen (NOx as NO2)

Table S.1a: Descriptive NOx statistics for receptor points in the Newcastle City AQMA

Scenario Name Back[1]

N. Mean, µg/m

3

Reduction on 2021,

µg/m3

Median, µg/m

3

Range, µg/m

3

Std.Dev., µg/m

3

Base 2010 - H 106 73.51 +33.54 66.46 39.53 - 168.19 27.75

2021 BAU - H 106 39.96 N/A 37.90 23.13 - 75.74 11.03

LEZ Scn 1 2021 All vehicles E5 H 106 39.35 -0.62 37.60 22.99 - 73.77 10.52


LEZ Scn 3 2021 All goods E5 H 106 39.92 -0.04 37.86 23.12 - 75.55 11.00


LEZ Scn 5 2021 All buses E6 H 106 37.68 -2.29 36.99 22.77 - 72.62 9.55

LEZ Scn 6 2021 All cars E6 H 106 38.64 -1.33 37.19 22.74 - 68.58 9.84

2021 BAU Scn 2 Euro 6 Fails H 106 54.90 +14.93 48.22 26.66 - 131.69 23.31

LEZ Scn 7 2021 AllE5, E6 Fails H 106 54.46 +14.50 47.96 26.55 - 129.33 22.91

Base 2010 - L 106 54.92 +23.78 48.53 25.17 - 138.02 25.14

2021 BAU - L 106 31.14 N/A 29.30 16.05 - 66.18 10.72

LEZ Scn 1 2021 All vehicles E5 L 106 30.52 -0.62 28.96 15.91 - 64.22 10.21


LEZ Scn 3 2021 All goods E5 L 106 31.10 -0.04 29.28 16.04 - 65.99 10.69


LEZ Scn 5 2021 All buses E6 L 106 28.85 -2.29 27.93 15.69 - 63.90 9.30

LEZ Scn 6 2021 All cars E6 L 106 29.81 -1.33 28.59 15.66 - 59.03 9.48

2021 BAU Scn 2 Euro 6 Fails L 106 46.07 +14.93 39.03 19.58 - 124.72 23.08

LEZ Scn 7 2021 AllE5, E6 Fails L 106 45.64 +14.50 38.77 19.47 - 122.36 22.68

[1] Code: ‘H‘ = High NOx background including minor roads, ‘L‘ = Low NOx background excluding minor roads.

Table S.1b: Descriptive NOx statistics for receptor points in the Gosforth AQMA


N. Mean, µg/m

3

Reduction on 2021,

µg/m3

Median, µg/m

3

Range, µg/m

3

Std.Dev., µg/m

3

Base 2010 - H 48 42.23 +18.58 37.20 33.12 - 90.96 12.53

2021 BAU - H 48 23.65 N/A 21.88 19.45 - 41.38 4.55









Base 2010 - L 48 28.96 +11.62 24.54 20.73 - 71.73 11.24

2021 BAU - L 48 17.34 N/A 15.45 13.62 - 34.62 4.49











179

Table S.1c: Descriptive NOx statistics for receptor points in the Gateshead AQMA


N. Mean, µg/m

3

Reduction on 2021,

µg/m3

Median,

µg/m3

Range, µg/m3 Std.Dev.,

µg/m3

Base 2010 - H 51 70.38 +31.82 65.23 45.24 - 119.01 17.51

2021 BAU - H 51 38.56 N/A 36.47 26.67 - 62.42 8.19









Base 2010 - L 51 51.57 +21.23 45.80 29.26 - 95.32 15.72

2021 BAU - L 51 30.34 N/A 28.29 19.08 - 54.33 8.12










Table S.1d: Descriptive NOx statistics for receptor points in the Newcastle Urban Core Area


N. Mean, µg/m

3

Reduction on 2021,

µg/m3

Median,

µg/m3


µg/m3

Base 2010 - H 193 64.85 +28.23 60.25 34.44 - 168.19 22.17

2021 BAU - H 193 36.61 N/A 35.53 20.43 - 75.74 9.10









Base 2010 - L 193 47.71 +19.33 43.36 21.96 - 138.02 19.49

2021 BAU - L 193 28.38 N/A 27.27 14.20 - 66.18 8.51











180

Table S.1e: Descriptive NOx statistics for receptor points in the Gateshead Urban Core Area


N. Mean, µg/m

3

Reduction on 2021,

µg/m3

Median,

µg/m3


µg/m3

Base 2010 - H 79 67.66 +29.81 60.62 48.34 - 119.01 15.51

2021 BAU - H 79 37.85 N/A 35.60 28.56 - 62.42 6.86









Base 2010 - L 79 49.58 +19.76 44.44 32.57 - 95.32 13.74

2021 BAU - L 79 29.82 N/A 28.12 21.21 - 54.33 6.78











181

S.2 Nitrogen Dioxide (NO2)

Note that in the tables below NO2 concentration values above the exceedence threshold of 40 µg/m3 are shown

in red, whilst levels within 5 µg/m3

of the limit are shown in amber.

Table S.2a: Descriptive NO2 statistics for receptor points in the Newcastle AQMA

Scenario Name Back &

fNO2[1]

N. Mean, µg/m

3

Reduction on 2021,

µg/m3

Median, µg/m

3

Range, µg/m

3

Std.Dev., µg/m

3

Base 2010 - H- 106 39.90 15.90 37.81 25.69 - 70.35 9.83

Future 2021 - H- 106 24.01 N/A 23.19 15.76 - 39.82 5.07

LEZ Scn 1 2021 All vehicles E5 H- 106 23.72 -0.28 23.04 15.69 - 39.04 4.87


LEZ Scn 3 2021 All goods E5 H- 106 23.99 -0.02 23.17 15.75 - 39.74 5.06


LEZ Scn 5 2021 All buses E6 H- 106 22.94 -1.07 22.74 15.58 - 38.58 4.43

LEZ Scn 6 2021 All cars E6 H- 106 23.40 -0.60 22.84 15.56 - 36.95 4.62

Future 2021 Euro 6 Fails H- 106 30.39 6.38 28.06 17.55 - 58.67 9.26

LEZ Scn 7 2021 AllE5, E6 Fails H- 106 30.21 6.21 27.94 17.49 - 58.06 9.15

Base 2010 - L- 106 33.13 13.51 30.73 19.30 - 64.60 10.11

Future 2021 - L- 106 19.62 N/A 18.89 12.09 - 35.50 5.03

LEZ Scn 1 2021 All vehicles E5 L- 106 19.34 -0.29 18.72 12.02 - 34.71 4.82


LEZ Scn 3 2021 All goods E5 L- 106 19.60 -0.02 18.87 12.08 - 35.43 5.02


LEZ Scn 5 2021 All buses E6 L- 106 18.53 -1.09 18.21 11.90 - 34.24 4.40

LEZ Scn 6 2021 All cars E6 L- 106 19.01 -0.62 18.54 11.89 - 32.55 4.54

Future 2021 Euro 6 Fails L- 106 26.17 6.54 23.56 13.91 - 54.81 9.35

LEZ Scn 7 2021 AllE5, E6 Fails L- 106 25.99 6.37 23.44 13.85 - 54.19 9.23

Base 2010 - HP 106 38.84 14.30 37.15 25.54 - 64.93 8.65

Future 2021 - HP 106 24.54 N/A 23.43 15.84 - 42.56 5.66

LEZ Scn 1 2021 All vehicles E5 HP 106 24.23 -0.31 23.26 15.76 - 41.67 5.42


LEZ Scn 3 2021 All goods E5 HP 106 24.52 -0.02 23.41 15.83 - 42.47 5.65


LEZ Scn 5 2021 All buses E6 HP 106 23.34 -1.20 22.93 15.64 - 41.15 4.92

LEZ Scn 6 2021 All cars E6 HP 106 23.80 -0.74 23.01 15.62 - 38.83 5.03

Future 2021 Euro 6 Fails HP 106 32.22 7.67 29.01 17.80 - 68.14 11.32

LEZ Scn 7 2021 AllE5, E6 Fails HP 106 32.03 7.49 28.90 17.74 - 67.32 11.19

Base 2010 - LP 106 32.13 12.02 30.12 19.19 - 59.26 8.94

Future 2021 - LP 106 20.12 N/A 19.12 12.14 - 38.16 5.62

LEZ Scn 1 2021 All vehicles E5 LP 106 19.80 -0.32 18.93 12.06 - 37.24 5.37


LEZ Scn 3 2021 All goods E5 LP 106 20.10 -0.02 19.10 12.13 - 38.06 5.60


LEZ Scn 5 2021 All buses E6 LP 106 18.90 -1.22 18.34 11.94 - 37.18 4.89

LEZ Scn 6 2021 All cars E6 LP 106 19.37 -0.75 18.70 11.92 - 34.37 4.95

Future 2021 Euro 6 Fails LP 106 27.93 7.81 24.46 14.12 - 65.20 11.40

LEZ Scn 7 2021 AllE5, E6 Fails LP 106 27.74 7.62 24.33 14.06 - 64.37 11.27

[1] Code: ‘H‘ = High NOx background including minor roads, ‘L‘ = Low NOx background excluding minor roads, ‘-‘ = Default

fNO2 ratio (DEFRA, 2012a), ‘P’ = fNO2 ratio calculated from PITHEM using COPERT4 data (Boulter, Barlow and McCrae,

2009).


182

Table S.2a: Descriptive NO2 statistics for receptor points in the Gosforth AQMA


fNO2[1]

N. Mean, µg/m

3

Reduction on 2021,

µg/m3

Median, µg/m

3

Range, µg/m

3

Std.Dev., µg/m

3

Base 2010 - H- 48 26.71 10.72 24.58 22.60 - 46.27 5.27

Future 2021 - H- 48 15.99 N/A 15.12 13.86 - 24.57 2.24









Base 2010 - L- 48 20.83 8.11 18.77 16.37 - 40.56 5.35

Future 2021 - L- 48 12.73 N/A 11.77 10.81 - 21.23 2.25









Base 2010 - HP 48 26.38 10.22 24.45 22.50 - 44.00 4.81

Future 2021 - HP 48 16.16 N/A 15.20 13.91 - 25.75 2.48









Base 2010 - LP 48 20.54 7.67 18.66 16.32 - 38.35 4.91

Future 2021 - LP 48 12.87 N/A 11.82 10.84 - 22.36 2.48











2009).


183

Table S.3a: Descriptive NO2 statistics for receptor points in the Gateshead AQMA


fNO2[1]

N. Mean, µg/m

3

Reduction on 2021,

µg/m3

Median, µg/m

3

Range, µg/m

3

Std.Dev., µg/m

3

Base 2010 - H- 51 39.13 15.68 37.38 28.41 - 56.67 6.82

Future 2021 - H- 51 23.45 N/A 22.47 17.57 - 34.26 3.89









Base 2010 - L- 51 32.27 12.60 29.95 21.31 - 50.83 7.18

Future 2021 - L- 51 19.67 N/A 18.50 14.17 - 30.48 3.88









Base 2010 - HP 51 38.23 14.29 36.74 28.23 - 53.69 6.15

Future 2021 - HP 51 23.94 N/A 22.83 17.66 - 36.16 4.28









Base 2010 - LP 51 31.43 11.62 29.27 21.18 - 47.94 6.53

Future 2021 - LP 51 19.81 N/A 18.57 13.74 - 32.31 4.33











2009).


184

Table S.4a: Descriptive NO2 statistics for receptor points in the Newcastle Urban Core Area


fNO2[1]

N. Mean, µg/m

3

Reduction on 2021,

µg/m3

Median, µg/m

3

Range, µg/m

3

Std.Dev., µg/m

3

Base 2010 - H- 193 36.62 14.18 35.20 23.24 - 70.35 8.21

Future 2021 - H- 193 22.44 N/A 22.02 14.37 - 39.82 4.25









Base 2010 - L- 193 30.07 11.75 28.33 17.24 - 64.60 8.22

Future 2021 - L- 193 18.32 N/A 17.85 11.12 - 35.50 4.05









Base 2010 - HP 193 35.90 13.09 34.68 23.14 - 64.93 7.36

Future 2021 - HP 193 22.80 N/A 22.20 14.42 - 42.56 4.68









Base 2010 - LP 193 29.40 10.75 27.94 17.16 - 59.26 7.37

Future 2021 - LP 193 18.65 N/A 18.02 11.15 - 38.16 4.47











2009).


185

Table S.5a: Descriptive NO2 statistics for receptor points in the Gateshead Urban Core Area


fNO2[1]

N. Mean, µg/m

3

Reduction on 2021,

µg/m3

Median, µg/m

3

Range, µg/m

3

Std.Dev., µg/m

3

Base 2010 - H- 79 38.03 14.95 35.28 29.86 - 56.67 6.03

Future 2021 - H- 79 23.07 N/A 22.05 18.53 - 34.26 3.24









Base 2010 - L- 79 31.32 12.25 28.99 22.99 - 50.83 6.30

Future 2021 - L- 79 19.06 N/A 18.30 14.77 - 30.48 3.28









Base 2010 - HP 79 37.27 13.78 34.91 29.67 - 53.69 5.43

Future 2021 - HP 79 23.48 N/A 22.21 18.62 - 36.16 3.59









Base 2010 - LP 79 30.61 11.17 28.61 22.84 - 47.94 5.71

Future 2021 - LP 79 19.44 N/A 18.43 14.85 - 32.31 3.62











2009).


186

S.2 Sensitivity of LEZ changes in NO2 to background NOx levels, and f-NO2 ratios

Table S.3a: Sensitivity of LEZ options to variations in background levels and varying f-NO2 ratio for the

Newcastle City Centre AQMA. Difference from Base Future 2021 scenario given in µg/m3


default f-NO2

Back_2 (Low) + default f-NO2

Back_1 (High) + PITHEM f-NO2

Back_2 (Low) + PITHEM f-

NO2

Default f-NO2

(DEFRA, 2012b)

PITHEM f-NO2

[1]

LEZ Scn 1 2021 -0.28 -0.29 -0.31 -0.32 0.216 0.327

LEZ Scn 2 2021 -2.04 -2.10 -2.34 -2.37 0.216 0.311

LEZ Scn 3 2021 -0.02 -0.02 -0.02 -0.02 0.216 0.325

LEZ Scn 4 2021 -0.35 -0.36 -0.40 -0.41 0.216 0.325

LEZ Scn 5 2021 -1.07 -1.09 -1.20 -1.22 0.216 0.325

LEZ Scn 6 2021 -0.60 -0.62 -0.74 -0.75 0.216 0.311

Future 2021 E6 Fail +6.38 +6.54 +7.67 +7.81 0.216 0.340

LEZ Scn 7 2021 +6.21 +6.37 +7.49 +7.62 0.216 0.342

Table S.3b: Sensitivity of LEZ options to variations in background levels and varying f-NO2 ratio for the

Gosforth AQMA. Difference from Base Future 2021 scenario given in µg/m3


default f-NO2




NO2

Default f-NO2

(DEFRA, 2012b)

PITHEM f-NO2

[1]

LEZ Scn 1 2021 -0.14 -0.14 -0.15 -0.15 0.216 0.327

LEZ Scn 2 2021 -0.91 -0.92 -1.00 -1.01 0.216 0.311

LEZ Scn 3 2021 -0.01 -0.01 -0.01 -0.01 0.216 0.325

LEZ Scn 4 2021 -0.14 -0.14 -0.15 -0.15 0.216 0.325

LEZ Scn 5 2021 -0.39 -0.40 -0.43 -0.44 0.216 0.325

LEZ Scn 6 2021 -0.37 -0.38 -0.42 -0.43 0.216 0.311

Future 2021 E6 Fail +2.65 +2.69 +3.02 +3.05 0.216 0.340

LEZ Scn 7 2021 +2.55 +2.60 +2.92 +2.95 0.216 0.342

Table S.3c: Sensitivity of LEZ options to variations in background levels and varying f-NO2 ratio for the

Gateshead AQMA. Difference from Base Future 2021 scenario given in µg/m3


default f-NO2




NO2

Default f-NO2

(DEFRA, 2012b)

PITHEM f-NO2

[1]

LEZ Scn 1 2021 -0.27 -0.60 -0.30 -0.30 0.216 0.327

LEZ Scn 2 2021 -1.96 -2.32 -2.22 -2.25 0.216 0.311

LEZ Scn 3 2021 -0.02 -0.34 -0.02 -0.02 0.216 0.325

LEZ Scn 4 2021 -0.38 -0.70 -0.42 -0.43 0.216 0.325

LEZ Scn 5 2021 -0.93 -1.27 -1.04 -1.06 0.216 0.325

LEZ Scn 6 2021 -0.63 -0.96 -0.76 -0.77 0.216 0.311

Future 2021 E6 Fail +6.39 +6.23 +7.59 +7.72 0.216 0.340

LEZ Scn 7 2021 +6.21 +6.04 +7.40 +7.52 0.216 0.342


187

Table S.3d: Sensitivity of LEZ options to variations in background levels and varying f-NO2 ratio for the

Newcastle Urban Core Area. Difference from Base Future 2021 scenario given in µg/m3


default f-NO2




NO2

Default f-NO2

(DEFRA, 2012b)

PITHEM f-NO2

[1]

LEZ Scn 1 2021 -0.20 -0.21 -0.22 -0.23 0.216 0.327

LEZ Scn 2 2021 -1.48 -1.51 -1.67 -1.70 0.216 0.311

LEZ Scn 3 2021 -0.02 -0.02 -0.02 -0.02 0.216 0.325

LEZ Scn 4 2021 -0.28 -0.29 -0.32 -0.32 0.216 0.325

LEZ Scn 5 2021 -0.72 -0.74 -0.81 -0.82 0.216 0.325

LEZ Scn 6 2021 -0.46 -0.47 -0.56 -0.56 0.216 0.311

Future 2021 E6 Fail +4.89 +5.01 +5.76 +5.86 0.216 0.340

LEZ Scn 7 2021 +4.75 +4.87 +5.62 +5.71 0.216 0.342

Table S.3e: Sensitivity of LEZ options to variations in background levels and varying f-NO2 ratio for the

Gateshead Urban Core Area. Difference from Base Future 2021 scenario given in µg/m3


default f-NO2




NO2

Default f-NO2

(DEFRA, 2012b)

PITHEM f-NO2

[1]

LEZ Scn 1 2021 -0.23 -0.23 -0.25 -0.25 0.216 0.327

LEZ Scn 2 2021 -1.64 -1.68 -1.86 -1.88 0.216 0.311

LEZ Scn 3 2021 -0.02 -0.02 -0.02 -0.02 0.216 0.325

LEZ Scn 4 2021 -0.34 -0.34 -0.38 -0.38 0.216 0.325

LEZ Scn 5 2021 -0.74 -0.76 -0.83 -0.84 0.216 0.325

LEZ Scn 6 2021 -0.55 -0.56 -0.66 -0.67 0.216 0.311

Future 2021 E6 Fail +5.55 +5.68 +6.54 +6.64 0.216 0.340

LEZ Scn 7 2021 +5.39 +5.51 +6.36 +6.46 0.216 0.342

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