1 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), 28 th May 2014 Includes additional amendments to Appendices made post-19 th May 2013
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Newcastle/Gateshead Low-Emissions Zone Feasibility Study – Air Quality Report May 28, 2014
1
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
Newcastle/Gateshead Low-Emissions Zone Feasibility Study – Air Quality Report May 28, 2014
2
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
0.6 10/05/2013 Paul Goodman, Fabio Galatioto, Anil Namdeo
Anil Namdeo (part) LEZ Feasibility Study Steering Group
1.0 19/05/2013 Paul Goodman, Fabio Galatioto, Anil Namdeo
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]
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).
Newcastle/Gateshead Low-Emissions Zone Feasibility Study – Air Quality Report May 28, 2014
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Table of Contents
Version Control ....................................................................................................................................... 2
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
Newcastle/Gateshead Low-Emissions Zone Feasibility Study – Air Quality Report May 28, 2014
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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
Newcastle/Gateshead Low-Emissions Zone Feasibility Study – Air Quality Report May 28, 2014
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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.
Crown Copyright all rights reserved Newcastle City
Council 100019569 2012
Newcastle/Gateshead Low-Emissions Zone Feasibility Study – Air Quality Report May 28, 2014
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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;
Crown Copyright all rights reserved Newcastle City
Council 100019569 2012
Newcastle/Gateshead Low-Emissions Zone Feasibility Study – Air Quality Report May 28, 2014
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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.
Newcastle/Gateshead Low-Emissions Zone Feasibility Study – Air Quality Report May 28, 2014
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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
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).
Newcastle/Gateshead Low-Emissions Zone Feasibility Study – Air Quality Report May 28, 2014
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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.
Newcastle/Gateshead Low-Emissions Zone Feasibility Study – Air Quality Report May 28, 2014
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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
Newcastle/Gateshead Low-Emissions Zone Feasibility Study – Air Quality Report May 28, 2014
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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.
Newcastle/Gateshead Low-Emissions Zone Feasibility Study – Air Quality Report May 28, 2014
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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.
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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:
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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.
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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.
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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).
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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;
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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
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%)
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 -
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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.
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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.
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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.
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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.
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Buses:
Figure 3.19: Euro class splits for buses in 2010 (DfT and NAEI data) as well as operator and NEXUS data for 2012
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Figure 3.20: Resultant fleet-weighted emissions curves for NOx (left) and PM10 (right) for buses (for clarity PM2.5 curves not shown).
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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.
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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
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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
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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.
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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.
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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
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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.
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Figure 3.25: Finalised modelling framework used in the LEZ Feasibility Study
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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
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
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%
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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Council 100019569
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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
= 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 5.10: 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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Cars LGVs
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
contour = 35 μg/m3] and proportion of total NOx contribution from vehicle classes (Right).
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Council 100019569
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Cars LGVs
HGVs Buses
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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
contour = 35 μg/m3] and proportion of total NOx contribution from vehicle classes (Right).
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Council 100019569
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Cars LGVs
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
= 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 5.14: LEZ 2021 Scenario 6, All cars are 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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Council 100019569
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Cars LGVs
HGVs Buses
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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).
Crown Copyright all
rights reserved
Newcastle City
Council 100019569
2012
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).
Crown Copyright all
rights reserved
Newcastle City
Council 100019569
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Cars LGVs
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
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
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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
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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).
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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
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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
A_B_TOID2 TOID2 Qc Ql Qh v2 C 0.5Qb2
A_B_TOID3 TOID3 Qc Ql Qh v3 C 0.5Qb3
Split link 1 Split link 2 Split Link3
A_B_TOID1 A_B_TOID2 A_B_TOID3
A B
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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.
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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)
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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].
.
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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)
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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)
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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)
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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
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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
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.
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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.
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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.
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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
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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).
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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).
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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).
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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).
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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 2 2021 All vehicles E6 H 106 35.57 -4.39 35.53 22.20 - 61.61 7.55
LEZ Scn 3 2021 All goods E5 H 106 39.92 -0.04 37.86 23.12 - 75.55 11.00
LEZ Scn 4 2021 All goods E6 H 106 39.19 -0.78 37.39 22.94 - 71.88 10.35
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