Baker Street and Gloucester Place 2-way System

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Air Quality Assessment:

Baker Street and

Gloucester Place 2-way

System

March 2016

Baker Street and Gloucester Place 2-way System Air Quality Assessment

Air Quality Consultants Ltd 23 Coldharbour Road, Bristol BS6 7JT Tel: 0117 974 1086 12 Airedale Road, London SW12 8SF Tel: 0208 673 4313 [email protected]

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Client Westminster City Council Principal Contact Anju Banga

Report Prepared By: Dr Ben Marner

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Report No. Date Status Reviewed by

J2451/2/F1 2 March 2016 Final Stephen Moorcroft (Director)

This report has been prepared by Air Quality Consultants Ltd on behalf of the Client, taking into account the agreed scope of works.

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Job Number J2451

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J2451 1 of 55 March 2016

Executive Summary

The air quality impacts associated with the proposed 2-way system on Baker Street and

Gloucester Place in Westminster have been assessed.

Existing air quality conditions have been described using the results of monitoring carried out by

Defra and Westminster Council, and detailed baseline dispersion modelling. The operational

impacts have been assessed using detailed dispersion modelling. Concentrations of the key air

pollutants associated with road traffic, i.e. nitrogen dioxide and fine particulate matter (PM10 and

PM2.5), have been determined with and without the Scheme. The predicted concentrations have

been compared with air quality objectives set by the Government to protect human health.

Existing conditions within the study area show poor air quality, with nitrogen dioxide concentrations

well above the air quality objectives. The site lies within an Air Quality Management Area.

The annual mean nitrogen dioxide objective is predicted to be exceeded at all receptors without or

with the Scheme, but the Scheme will reduce the number of receptors where exceedences of the

1-hour mean nitrogen dioxide objective are predicted.

The Scheme will improve air quality in some locations but worsen it in others. The number of

receptors where benefits are predicted is almost twenty times the number where adverse impacts

are predicted. With specific regard to residential properties, a substantially greater number of

properties (up to 190 times more) will experience benefits than disbenefits as a result of the

Scheme.

Balancing the predicted benefits of the Scheme with the predicted disbenefits, it is considered that

the Scheme will have a significant beneficial air quality impact.


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Contents

1 Introduction ....................................................................................................... 4

2 Scope ................................................................................................................ 5

3 Policy Context and Assessment Criteria ............................................................ 7

4 Assessment Approach .................................................................................... 10

5 Baseline Conditions......................................................................................... 18

6 Impact Assessment ......................................................................................... 23

7 Conclusions .................................................................................................... 27

8 References ...................................................................................................... 28

9 Glossary .......................................................................................................... 29

10 Appendices ..................................................................................................... 31

A1 Extracts from the Mayor’s Air Quality Strategy, and Description of the Low Emission Zones (LEZs) ................................................................................... 32

A2 EPUK & IAQM Planning for Air Quality Guidance ............................................ 34

A3 Professional Experience .................................................................................. 37

A4 Modelling Methodology ................................................................................... 38

A5 Adjustment of Short-Term Data to Annual Mean ............................................. 48

A6 Predicted Concentrations ................................................................................ 49

Tables

Table 1: Air Quality Criteria for Nitrogen Dioxide, PM10 and PM2.5 ................................... 9

Table 2: Summary of Nitrogen Dioxide (NO2) Monitoring (2009-2014) a ........................ 18

Table 3: Summary of PM10 and PM2.5 Automatic Monitoring (2009-2014) a ................... 20

Table 4: Estimated Annual Mean Background Pollutant Concentrations in 2014 and 2018 ................................................................................................................ 21

Table 5: Number of Receptors Classified into Each of the IAQM Descriptors for Annual Mean Impacts.................................................................................................. 24

Table 6: Numbers of Residential Properties Predicted to be Affected by the Scheme a . 25

Table 7: Impacts of the Proposed Scheme on Annual Mean Concentrations at the Automatic Monitoring Sites (µg/m3) a ............................................................... 26

Table A2.1: Air Quality Impact Descriptors for Individual Receptors for All Pollutants a 35

Table A4.1: Summary of Adjustments Made to Emission Factor Toolkit ....................... 39

Table A5.1: Data used to Adjust Monitoring Data at the Oxford Street Monitor ............. 48

Table A6.1: Dispersion Model Results a ........................................................................ 49


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Figures

Figure 1: Roads Included in Dispersion Model ................................................................. 6

Figure 2: Locations of all Receptors to the North of Marylebone Road (Receptors 1 to 64) 12

Figure 3: Locations of all Receptors between Marylebone Road and George Street (Receptors 65 to 213) ...................................................................................... 13

Figure 4: Locations of all Receptors on and to the South of George Street (Receptors 214 to 334) ............................................................................................................. 14

Figure 5: Monitoring Locations ....................................................................................... 19

Figure A4.1: Excerpt from Modelled Road Network (See Figure 1 for full network). ...... 40

Figure A4.2: Urban Background Monitors used to Calibrate Defra’s Background Maps 41

Figure A4.3: Comparison of Measured and Estimated Background- Annual Mean Nitrogen Dioxide Concentrations at all Three Urban Background Automatic Monitoring Sites in 2014 (µg/m3) ..................................................................... 42

Figure A4.4: Comparison of Measured and Estimated Background Annual Mean PM10 Concentrations at all Three Urban Background Automatic Monitoring Sites in 2014 (µg/m3) ................................................................................................... 42

Figure A4.5: Comparison of Measured Road NOx to Unadjusted Modelled Road NOx Concentrations. The dashed lines show ± 25%. ............................................. 44

Figure A4.6: Comparison of Measured Total NO2 to Final Adjusted Modelled Total NO2 Concentrations. The dashed lines show ± 25%. ............................................. 45

Figure A4.7: Hourly Mean Nitrogen Oxides vs Nitrogen Dioxide at Marylebone Road and Oxford Street in 2014 (µg/m3) (also showing best-fit relationship) ................... 47


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1 Introduction

1.1 This report considers the air quality impacts associated with the proposed revision to highways on

and around Baker Street and Gloucester Place in Westminster (the ‘Scheme’). The assessment

has been carried out by Air Quality Consultants Ltd on behalf of Westminster City Council.

1.2 Baker Street and Gloucester Place currently carry 1-way traffic, with Baker Street being

southbound and Gloucester Place being northbound. The proposed Scheme involves allowing

traffic on both roads to travel in either direction. It will also involve numerous associated

amendments to junctions, new pedestrian footways, and public realm improvements.

1.3 This report describes the impacts of these changes on ambient air quality.


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2 Scope

2.1 The assessment covers the operational impacts of the proposed Scheme on ambient

concentrations of nitrogen dioxide, PM10 and PM2.5, as these are the pollutants of principal concern

in relation to road traffic emissions.

Spatial Scope

2.2 The study area for the assessment is that of the micro-simulation traffic model that was used to

assess the impacts of the Scheme. The roads included in the dispersion model are shown in

Figure 1.

Temporal Scope

2.3 The report describes existing, local air quality conditions (2014), and the predicted air quality in the

future assuming that the proposed development does, or does not proceed. The assessment

focuses on 2018, which is the first complete calendar year in which the Scheme could become

operational.


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Figure 1: Roads Included in Dispersion Model

Contains Ordnance Survey data © Crown copyright and database right 2016


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3 Policy Context and Assessment Criteria

The UK Air Quality Strategy

3.1 The Air Quality Strategy published by the Department for Environment, Food, and Rural Affairs

(Defra) provides the policy framework (Defra, 2007) for air quality management and assessment in

the UK. It provides air quality standards and objectives for key air pollutants, which are designed

to protect human health and the environment. It also sets out how the different sectors: industry,

transport and local government, can contribute to achieving the air quality objectives. Local

authorities are seen to play a particularly important role. The strategy describes the Local Air

Quality Management (LAQM) regime that has been established, whereby every authority has to

carry out regular reviews and assessments of air quality in its area to identify whether the

objectives have been, or will be, achieved at relevant locations, by the applicable date. If this is

not the case, the authority must declare an Air Quality Management Area (AQMA), and prepare an

action plan which identifies appropriate measures that will be introduced in pursuit of the

objectives.

The London Mayor’s Air Quality Strategy

3.2 The revised Mayor’s Air Quality Strategy (MAQS) was published in December 2010 (GLA, 2010).

The overarching aim of the Strategy is to reduce pollution concentrations in London to achieve

compliance with the EU limit values as soon as possible. The Strategy commits to the continuation

of measures identified in the 2002 MAQS, and sets out a series of additional measures. These

additional measures and the role of the Low Emission Zone are described in Appendix A1

Assessment Criteria

Health Criteria

3.3 The Government has established a set of air quality standards and objectives to protect human

health. The ‘standards’ are set as concentrations below which effects are unlikely even in

sensitive population groups, or below which risks to public health would be exceedingly small.

They are based purely upon the scientific and medical evidence of the effects of an individual

pollutant. The ‘objectives’ set out the extent to which the Government expects the standards to be

achieved by a certain date. They take account of economic efficiency, practicability, technical

feasibility and timescale. The objectives for use by local authorities are prescribed within the Air

Quality (England) Regulations, 2000, Statutory Instrument 928 (2000) and the Air Quality

(England) (Amendment) Regulations 2002, Statutory Instrument 3043 (2002).

3.4 The objectives for nitrogen dioxide and PM10 were to have been achieved by 2005 and 2004

respectively, and continue to apply in all future years thereafter. The PM2.5 objective is to be


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achieved by 2020. Measurements across the UK have shown that the 24-hour PM10 objective

could be exceeded where the annual mean concentration is above 32 g/m3 (Defra, 2009). The

predicted annual mean PM10 concentrations are thus used as a proxy to determine the likelihood of

an exceedence of the 24-hour mean PM10 objective. Where predicted annual mean

concentrations are below 32 µg/m3 it is unlikely that the 24-hour mean objective will be exceeded.

3.5 The objectives apply at locations where members of the public are likely to be regularly present

and are likely to be exposed over the averaging period of the objective. Defra explains where

these objectives will apply in its Local Air Quality Management Technical Guidance (Defra, 2009).

The annual mean objectives for nitrogen dioxide and PM10 are considered to apply at the façades

of residential properties, schools, hospitals etc.; they do not apply at hotels. The 24-hour objective

for PM10 is considered to apply at the same locations as the annual mean objective, as well as in

gardens of residential properties and at hotels. The 1-hour mean objective for nitrogen dioxide

applies wherever members of the public might regularly spend 1-hour or more, including outdoor

eating locations and pavements of busy shopping streets.

3.6 The 1-hour mean objective for nitrogen dioxide allows no more than 18 exceedences of 200 µg/m3

as a 1-hour mean concentration in a year. For a complete year of data, the 19th highest hour

equates to the 99.79th percentile. It is thus common practice to predict 99.79

th percentiles of 1-

hour mean nitrogen dioxide concentrations and compare these directly against the 200 µg/m3

standard.

3.7 The European Union has also set limit values for nitrogen dioxide, PM10 and PM2.5. The limit

values for nitrogen dioxide are the same numerical concentrations as the UK objectives, but

achievement of these values is a national obligation rather than a local one (Directive 2008/50/EC

of the European Parliament and of the Council, 2008). In the UK, only monitoring and modelling

carried out by UK Central Government meets the specification required to assess compliance with

the limit values. Central Government does not recognise local authority monitoring or local

modelling studies when determining the likelihood of the limit values being exceeded.

3.8 The relevant air quality criteria for this assessment are provided in Table 1.


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Table 1: Air Quality Criteria for Nitrogen Dioxide, PM10 and PM2.5

Pollutant Time Period Objective

Nitrogen Dioxide

1-hour Mean 200 g/m3 not to be exceeded more than 18 times a year

Annual Mean 40 g/m3

Fine Particles (PM10)

24-hour Mean 50 g/m3 not to be exceeded more than 35 times a year

Annual Mean 40 g/m3 a

Fine Particles (PM2.5)

b

Annual Mean 25 µg/m3

a A proxy value of 32 g/m

3 as an annual mean is used in this assessment to assess the likelihood of the

24-hour mean PM10 objective being exceeded. Measurements have shown that, above this

concentration, exceedences of the 24-hour mean PM10 objective are possible (Defra, 2009).

b The PM2.5 objective, which is to be met by 2020, is not in Regulations and there is no requirement for

local authorities to meet it.

Descriptors for Air Quality Impacts and Assessment of Significance

3.9 There is no official guidance in the UK in relation to development control on how to describe air

quality impacts, nor how to assess their significance. The approach developed jointly by

Environmental Protection UK (EPUK) and the Institute of Air Quality Management (IAQM)1 (EPUK

& IAQM, 2015) has therefore been used. This includes defining descriptors of the impacts at

individual receptors, which take account of the percentage change in concentrations relative to the

relevant air quality objective, rounded to the nearest whole number, and the absolute concentration

relative to the objective. The overall significance of the air quality impacts is determined using

professional judgement, taking account of the impact descriptors. Full details of the EPUK/IAQM

approach are provided in Appendix A2. The approach includes elements of professional

judgement, and the experience of the consultants preparing the report is set out in Appendix A3.

1 The IAQM is the professional body for air quality practitioners in the UK.


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4 Assessment Approach

Existing Conditions

4.1 Information on existing air quality has been obtained by collating the results of monitoring carried

out by Defra and Westminster Council. There are two monitoring sites within the study area; one

on Marylebone Road and one on Oxford Street. Three further monitoring sites which are outside

of the study area have been used to determine background concentrations away from roads. The

background concentrations in the study area have also been defined using the national pollution

maps published by (Defra, 2016). These cover the whole country on a 1x1 km grid. Further details

of the approach taken to deriving background concentrations are given in Appendix A4.

4.2 Current exceedences of the annual mean EU limit value for nitrogen dioxide have been identified

using the results from the Marylebone Road monitor, which operates to EU data quality standards,

and which Defra uses to report exceedences of the limit value to the European Commission.

Sensitive Locations

4.3 Concentrations have been predicted at 334 receptors, which represent the roadside façades of

those buildings where the highest concentrations are expected. When selecting these receptors,

particular attention has been paid to assessing impacts close to junctions. Receptors have all

been modelled at a height of 1.5 m. The receptors are shown in Figure 2, Figure 3, and Figure 4.

4.4 It should be recognised that the receptors do not specifically represent locations where either the

long-term or short-term objectives apply. At many of the receptors, there is no residential

exposure and the predicted concentrations will be higher than those at the nearest residential

property. Conversely, there may be relevant exposure to the 1-hour mean nitrogen dioxide

objective closer to roads than where the receptors are located2, and thus short-term impacts may

be under-predicted. The selected receptors, nevertheless, provide a reasonable indication of the

overall impacts of the Scheme on local air quality.

4.5 Concentrations have also been predicted at the monitoring sites on Marylebone Road and Oxford

Street. This has allowed the model to be verified (see Appendix A4) and for the impact of the

Scheme on the EU limit values to be determined.

4.6 Finally, concentrations have been predicted at 7,180 receptors which represent all residential

properties within the study area. These receptors were provided by Westminster Council from

electoral role information. They were positioned within each building footprint and not at the

roadside façades. Furthermore, all addresses were modelled at a height of 1.5 m regardless of the

2 The receptors are on the façades of buildings, but the 1-hour mean nitrogen dioxide objective can apply on

pavements and where there is outdoor seating.


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number of apartments within each building. The results from these receptors have been used to

compare the total number of addresses where benefits and disbenefits are predicted and any

inaccuracy in precise receptor positioning is only of minor concern.


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Figure 2: Locations of all Receptors to the North of Marylebone Road (Receptors 1 to 64) Contains Ordnance Survey data © Crown copyright and database right 2016. Additional data sourced from third parties, including public sector information licensed under the Open Government Licence v1.0.


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Figure 3: Locations of all Receptors between Marylebone Road and George Street (Receptors 65 to 213) Contains Ordnance Survey data © Crown copyright and database right 2016. Additional data sourced from third parties, including public sector information licensed under the Open Government Licence v1.0.


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Figure 4: Locations of all Receptors on and to the South of George Street (Receptors 214 to 334) Contains Ordnance Survey data © Crown copyright and database right 2016. Additional data sourced from third parties, including public sector information licensed under the Open Government Licence v1.0.


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Assessment Scenarios

4.7 Predicted concentrations of nitrogen dioxide, PM10 and PM2.5 have been carried out for 2018,

assuming both that the development does proceed (With Scheme), and does not proceed (Without

Scheme). In addition to the set of ‘official’ predictions, a sensitivity test has been carried out for

nitrogen dioxide that involves assuming much higher nitrogen oxides emissions from certain

vehicles than have been predicted by Defra. This is to address the potential under-performance of

emissions control technology on modern diesel vehicles (AQC, 2016a).

4.8 Concentrations at the monitoring sites have also been predicted for 2014 to allow the model to be

verified against measurements.

Modelling Methodology

4.9 Norman Rourke Pryme provided detailed information on traffic flows (by vehicle type) and speeds

for each of the key vehicle lanes and turning movements across the study area. These covered

the morning peak hour, the evening peak hour, and the inter-peak period. Norman Rourke Pryme

derived these data by combining the absolute flows predicted by the SATURN model with the

speeds and proportional turning movements predicted by the VISIM model. Norman Rourke

Pryme also provided a set of factors to predict off-peak flows and annual average flows. The latter

were used to calculate average weekend flows. The traffic data provided were for 2017 Without

Scheme and 2017 With Scheme. The 2017 Without Scheme data were used to represent

conditions both in 2014 and in 2018. The 2017 With Scheme data were used to represent

conditions in 2018.

4.10 The predicted hourly-mean flows and speeds for each vehicle class were entered into Defra’s

Emissions Factor Toolkit (EFT V6.02) to calculate hourly average vehicle-type-specific emissions

of nitrogen oxides, PM10 and PM2.5. These were then collated as a set of diurnal and weekly

emissions profiles. The same approach was then carried out using AQC’s emissions calculator

for the nitrogen oxides emissions sensitivity test as described in Paragraph A4.5 in Appendix A4.

4.11 Dispersion modelling was carried out using the ADMS-Roads model. Because more than 1,000

links, each with an individual set of diurnal emissions profiles were modelled, it was necessary to

split the network up across several separate model runs.

4.12 Further details of the modelling methodology are given in Appendix A4.

Uncertainty

4.13 There are many components that contribute to the uncertainty of modelling predictions. The

dispersion model used in this assessment is dependent upon the traffic data that have been input,


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which will have inherent uncertainties associated with them. There are then additional

uncertainties, as models are required to simplify real-world conditions into a series of algorithms.

4.14 An important stage in the process is model verification, which involves comparing the model output

with measured concentrations (see Appendix A4). The level of confidence in the verification

process is necessarily enhanced when data from an automatic analyser have been used, as has

been the case for this assessment (see Appendix A4). Because the model has been verified and

adjusted, there can be reasonable confidence in the prediction of current year (2014)

concentrations.

4.15 Predicting pollutant concentrations in a future year will always be subject to greater uncertainty.

For obvious reasons, the model cannot be verified in the future, and it is necessary to rely on a

series of projections provided by DfT and Defra as to what will happen to traffic volumes,

background pollutant concentrations and vehicle emissions.

4.16 Historically, large reductions in nitrogen oxides emissions have been projected, which has led to

significant reductions in nitrogen dioxide concentrations from one year to the next being predicted.

Over time, it was found that trends in measured concentrations did not reflect the rapid reductions

that Defra and DfT had predicted (Carslaw et al., 2011). This was evident across the UK, although

the effect appeared to be greatest in inner London; there was also considerable inter-site variation.

Emission projections over the 6 to 8 years prior to 2009 suggested that both annual mean nitrogen

oxides and nitrogen dioxide concentrations should have fallen by around 15-25%, whereas

monitoring data showed that concentrations remained relatively stable, or even showed a slight

increase. Analysis of more recent data for 23 roadside sites in London covering the period 2003 to

2012 showed a weak downward trend of around 5% over the ten years (Carslaw and Rhys-Tyler,

2013), but this still falls short of the improvements that had been predicted at the start of this

period.

4.17 The reason for the disparity between the expected concentrations and those measured relates to

the on-road performance of modern diesel vehicles. New vehicles registered in the UK have had

to meet progressively tighter European type approval emissions categories, referred to as "Euro"

standards. While the nitrogen oxides emissions from newer vehicles should be lower than those

from equivalent older vehicles, the on-road performance of some modern diesel vehicles has often

been no better than that of earlier models. This has been compounded by an increasing

proportion of nitrogen dioxide in the nitrogen oxides emissions, i.e. primary nitrogen dioxide, which

has a significant effect on roadside concentrations (Carslaw et al., 2011) (Carslaw and Rhys-Tyler,

2013).

4.18 A detailed analysis of emissions from modern diesel vehicles has been carried out (AQC, 2016a).

This shows that, where previous standards had limited on-road success, the ‘Euro VI’ and ‘Euro 6’


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standards that new vehicles have had to comply with from 2013/163 are delivering real on-road

improvements. A detailed comparison of the predictions in Defra’s latest Emission Factor Toolkit

(EFT v6.0.2) against the results from on-road emissions tests has shown that Defra’s latest

predictions still have the potential to under-predict emissions from some vehicles, albeit by less

than has historically been the case (AQC, 2016a). In order to account for this potential under-

prediction, a sensitivity test has been carried out in which the emissions from Euro IV, Euro V,

Euro VI, and Euro 6 vehicles have been uplifted as described in Paragraph A4.5 in Appendix A4.

The results from this sensitivity test are likely to over-predict emissions from vehicles in the future

(AQC, 2016a) and thus provide a reasonable worst-case upper-bound to the assessment.

3 Euro VI refers to heavy duty vehicles, while Euro 6 refers to light duty vehicles. The timings for meeting the

standards vary with vehicle type and whether the vehicle is a new model or existing model.


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5 Baseline Conditions

Air Quality Review and Assessment

5.1 The City of Westminster Council has investigated air quality within its area as part of its

responsibilities under the LAQM regime. In 1999 the Council declared an AQMA covering the

entire borough for exceedences of the nitrogen dioxide and PM10 objectives.

Local Air Quality Monitoring

5.2 There are two automatic air quality monitors within the study area, one at Marylebone Road, and

one at Oxford Street. Results for the years 2009 to 2014 are summarised in Table 2 and the

monitoring locations are shown in Figure 5.

Table 2: Summary of Nitrogen Dioxide (NO2) Monitoring (2009-2014) a

Site No. Location 2009 2010 2011 2012 2013 2014

Annual Mean (µg/m3)

MY1 Marylebone Road 107 98 97 94 85 94

WM6 Oxford Street - - - - - b 141

b,c

Objective 40

No. of Hours > 200 µg/m3

MY1 Marylebone Road 469 524 217 122 59 60

WM6 Oxford Street - - - - 1,502 d 1,532

d

Objective 18

a Data downloaded from the London Air website (King's College London, 2016). Exceedences of the

objectives are shown in bold

b Poor data capture.

c Data annualised as shown in Appendix A5.

d Poor data capture and the number of exceedences from a full year of data may have been higher.

5.3 Measured nitrogen dioxide concentrations in this area are very high and both the annual mean and

1-hour mean objectives have been exceeded by a considerable margin during all years of

measurement. The number of 1-hour mean exceedences has fallen significantly at the

Marylebone Road site over the last six years, but this is not reflected in the annual mean

concentration. The absence of any downward trend in the annual mean measurements contrasts

with the expected decline due to the progressive introduction of new vehicles operating to more

stringent standards (the implications of this are discussed in Section 4 of this report).


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Figure 5: Monitoring Locations



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5.4 The Marylebone Road and Oxford Street monitors also measure concentrations of PM10, although

data capture at Oxford Street has been poor over recent years. The Marylebone Road site also

measures PM2.5. Data for 2009-2014 are presented in Table 3. Concentrations have been below

the level of the objectives in each year except 2009 and 2011 at Marylebone Road, when the 24-

hour mean objective was exceeded. Measured PM2.5 concentrations have been below the level of

the objective each year.

Table 3: Summary of PM10 and PM2.5 Automatic Monitoring (2009-2014) a

Site No. Location 2009 2010 2011 2012 2013 2014

PM10 Annual Mean (µg/m3)

MY1 Marylebone Road 37 32 38 - 29 26

WM6 Oxford Street - - 30 - - -

Objective 40

PM10 No. Days >50 µg/m3

MY1 Marylebone Road 43 23 57 - 21 14

WM6 Oxford Street - - 23 - - -

Objective 35

PM2.5 Annual Mean (µg/m3)

MY1 Marylebone Road 21 - 16 21 20 18

Objective 25

a All data from FDMS-TEOM monitors. PM10 data downloaded from the London Air website (King's

College London, 2016). PM2.5 data downloaded from Defra’s UK-Air website (Defra, 2016). Objective

exceedences shown in bold.

Exceedences of EU Limit Value

5.5 The Marylebone Road monitor is part of Defra’s AURN network, the results of which are used to

report exceedences of the limit values to the EC. It is one of several sites across the Greater

London Urban Area that has measured exceedences of the nitrogen dioxide limit values. The

Greater London Urban Area has thus been reported to the EC as exceeding the limit value for

annual mean nitrogen dioxide concentrations. Defra has not measured or predicted any recent

exceedences of the PM10 or PM2.5 limit values anywhere in London.

Background Concentrations

5.6 In addition to these locally measured concentrations, estimated background concentrations in the

study area have been determined for 2014 and 2018. Table 4 shows the range of predicted

background concentrations across the study area. The background concentrations have been

derived as described in Appendix A4. The background concentrations for PM10 and PM2.5 are all


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below the objectives. Some of the background concentrations for nitrogen dioxide are below the

objective while others are above the objective.

Table 4: Estimated Annual Mean Background Pollutant Concentrations in 2014 and 2018

Year NO2 PM10 PM2.5

2014 38.7 – 50.8 16.6 – 18.6 11.4 – 12.6

2018 a 33.3 – 43.6 15.9 – 17.7 10.7 – 11.8

2018 Worst-case Sensitivity Test b 34.7 – 45.5 - -

Objectives 40 40 25 b

n/a = not applicable. The range of values is for the different 1x1 km grid squares covering the study area.

a In line with Defra’s forecasts

b Assuming higher emissions from modern diesel vehicles as described in Appendix A4.

c The PM2.5 objective, which is to be met by 2020, is not in Regulations and there is no requirement for

local authorities to meet it.

Baseline Dispersion Model Results

5.7 Baseline concentrations of nitrogen dioxide, PM10 and PM2.5 have been modelled at each receptor

(see Figure 2 to Figure 4). The results for all 334 receptors in 2018 without the Scheme are set

out in Table A6.1 in Appendix A6. The predictions for nitrogen dioxide include a sensitivity test

which accounts for the potential under-performance of emissions control technology on modern

diesel vehicles. In addition, the modelled road components of nitrogen oxides have been

increased from those predicted by the model based on a comparison with local measurements

(see Appendix A4).

5.8 Annual mean nitrogen dioxide concentrations in 2018 are predicted to range from 45 µg/m3

to 103 µg/m3. All of the predicted concentrations thus exceed the 40 µg/m

3 objective. The lowest

predicted concentration is at Receptor 34 (Linhope Street), while the highest is at Receptor 57

(junction of Marylebone Road and Gloucester Place). The 99.79th percentiles of 1-hour mean

nitrogen dioxide concentrations range from 100 µg/m3 to 270 µg/m

3. The lowest predicted

concentration is at Receptor 280 (Manchester Square), while the highest is at Receptor 67 (corner

of Marylebone Road and Baker Street)4. In total, the 1-hour objective of 200 µg/m3 (as the 99.79

th

percentile) is predicted to be exceeded at 48 out of the 334 receptors, without the Scheme. All of

these predicted concentrations represent roadside receptors. At locations further from roads,

4 The reason why the maxima and minima of annual mean and 1-hour mean concentrations are not co-located

relates to differences in the diurnal emissions profiles on different roads, and differences in the background concentration fields (spatial variation in background concentrations across the study area has been included in the annual mean modelling but not in the 1-hour mean modelling – see Appendix A4).


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concentrations will be lower. Well away from roads, concentrations will approach the background

levels set out in Table 4.

5.9 Concentrations of PM10 are predicted to range from 17 to 24 µg/m3, all below the 40 µg/m

3

objective. Furthermore, all of the predicted annual mean PM10 concentrations are below 32 µg/m3,

making it unlikely that the 24-hour mean PM10 objective was exceeded. The predicted PM2.5

concentrations range from 11 µg/m3 to 16 µg/m

3, all below the 25 µg/m

3 objective.

Worst-case Sensitivity Test for Nitrogen Dioxide

5.10 The results from the upper-bound sensitivity test show slightly higher levels, with annual mean

concentrations of nitrogen dioxide predicted to range from 47 µg/m3 to 109 µg/m

3, while the 99.79

th

percentiles of 1-hour mean nitrogen dioxide concentrations are predicted to range from 103 µg/m3

to 287 µg/m3.


J2451 23 of 55 March 2016

6 Impact Assessment

6.1 Predicted concentrations of nitrogen dioxide, PM10 and PM2.5 for all receptors are set out in

Table A6.1 in Appendix A6. These tables also describe the annual mean impacts at each receptor

using the impact descriptors given in Appendix A2. For nitrogen dioxide, results are presented for

two scenarios to account for the potential under-performance of emissions control technology on

modern diesel vehicles.

Nitrogen Dioxide

6.2 The annual mean nitrogen dioxide objective is predicted to be exceeded at all receptors, with or

without the Scheme. Changes range from a 16% reduction to a 14% increase in annual mean

concentrations. The impacts have been described following the approach recommended by

EPUK/IAQM, and these results are summarised in Table 5. Substantial beneficial or moderate

beneficial impacts are predicted at 307 receptors. Substantial adverse or moderate adverse

impacts are predicted at just 16 receptors.

6.3 The EPUKIAQM guidance does not provide descriptors for road-traffic impacts on total 1-hour

mean nitrogen dioxide concentrations, but the Scheme is predicted to increase the 99.79th

percentiles of 1-hour mean concentrations at 37 receptors and reduce them at 297 receptors.

Furthermore, the Scheme is predicted to reduce the number of receptors where the 1-hour mean

nitrogen dioxide objective is exceeded from 48 receptors to 39 receptors.

Worst-case Sensitivity Test

6.4 The worst-case sensitivity test for nitrogen dioxide predicts slightly higher concentrations at most

receptors, both without and with the Scheme. In this case, the number of receptors where

moderate or substantial beneficial impacts are predicted increases to 313 (from 307) while the

number of receptors with moderate or substantial adverse impacts increases to 17 (from 16)

(Table 5).

6.5 In terms of 1-hour mean nitrogen dioxide concentrations, 288 receptors are predicted to

experience benefits, while 46 receptors are predicted to experience disbenefits. The numbers of

receptors where exceedences of the 1-hour mean objective are predicted are 62 Without Scheme

and 49 With Scheme5.

5 i.e. the benefits of the Scheme are more pronounced in the worst-case emissions sensitivity test in which total

predicted concentrations are higher.


J2451 24 of 55 March 2016

PM10 and PM2.5

6.6 The predicted concentrations of PM10 and PM2.5 are below the annual mean objectives at all

receptors, with or without the Scheme. Furthermore, as the annual mean PM10 concentrations are

below 32 µg/m3, it is unlikely that the 24-hour mean PM10 objective will be exceeded at any of the

receptors. Table 5 shows that all of the impacts are described as negligible, with the exception of

two receptors where slight adverse impacts are predicted. These are Receptors 105 and 106

(which are both on the corner of Baker Street and Crawford Street).

Table 5: Number of Receptors Classified into Each of the IAQM Descriptors for Annual Mean Impacts

Impact Descriptor

‘Official’ Nitrogen Dioxide

Predictions a

Worst-case Sensitivity

Test for Nitrogen Dioxide

b

PM10 PM2.5

Substantial Beneficial 291 297 0 0

Moderate Beneficial 16 16 0 0

Slight Beneficial 0 0 0 0

Negligible 11 4 332 332

Slight Adverse 0 0 2 2

Moderate Adverse 4 4 0 0

Substantial Adverse 12 13 0 0

a In line with Defra’s forecasts.

b Assuming higher emissions from modern diesel vehicles as described in Paragraph A4.5.

Overall Local Air Quality Impacts

6.7 As explained in Paragraph 4.6, annual mean concentrations have also been predicted for all 7,180

residential properties within the study area. The results are summarised in Table 6. It should be

noted that all predicted changes have been counted, regardless of how small they are. In terms of

nitrogen dioxide, more than 7,000 properties will benefit from the Scheme while fewer than 100

properties will experience adverse impacts. For PM10 and PM2.5, almost 7,000 properties will

benefit while fewer than 500 properties will experience adverse impacts. The objectives for PM10

and PM2.5 will continue to be achieved across the study area either with or without the Scheme.


J2451 25 of 55 March 2016

Table 6: Numbers of Residential Properties Predicted to be Affected by the Scheme a

Pollutant Number of Residential

Properties Where Benefits are Predicted

Number of Residential Properties Where

Disbenefits are Predicted

‘Official’ Nitrogen Dioxide Predictions

b 7,143 37

Worst-case Sensitivity Test for Nitrogen Dioxide

c 7,091 89

Annual Mean PM10 6,902 278

Annual Mean PM2.5 6,775 405

a It should be noted that all predicted changes have been counted no matter how small.

b In line with Defra’s forecasts.

c Assuming higher emissions from modern diesel vehicles as described in Paragraph A4.5.

Assessment against the EU Limit Values

6.8 The Marylebone Road monitor is one of the sites used to report exceedences of the limit values to

the EC. Table 7 sets out the predicted impacts of the Scheme on concentrations at both the

Marylebone Road and Oxford Street monitors. Concentrations at the Oxford Street site are not

currently reported to the EC as limit value exceedences, but the results are shown for

completeness.

6.9 The Scheme is predicted to reduce concentrations of all pollutants at both monitoring sites. The

reduction at the Marylebone Road site is predicted to be more than 5 µg/m3. In all recent years,

the Marylebone Road site has measured higher annual mean nitrogen dioxide concentrations than

any of the other sites used by Defra to report exceedences of the limit values to the EC. A

reduction in concentrations at this site thus has the potential to bring forward compliance of the UK

with the Limit Values.


J2451 26 of 55 March 2016

Table 7: Impacts of the Proposed Scheme on Annual Mean Concentrations at the Automatic Monitoring Sites (µg/m

3) a

Pollutant Scenario Marylebone Road Oxford Street Limit Value

‘Official’ Annual Mean NO2

Without Scheme 98.1 87.9 40

With Scheme 92.8 81.4

Change -5.3 -6.5 -

Sensitivity Test Annual Mean NO2



Change -5.1 -7.9 -

Annual Mean PM10



Change -1.7 -0.8 -

Annual Mean PM2.5



Change -1.4 -0.7 -

a exceedences of the limit values shown in bold.


J2451 27 of 55 March 2016

7 Conclusions

7.1 The operational impacts of changed traffic flows and speeds and altered lane positions associated

with the Scheme have been assessed. In the case of nitrogen dioxide, a sensitivity test has also

been carried out which considers the potential under-performance of emissions control technology

on modern diesel vehicles.

7.2 It is concluded that concentrations of PM10 and PM2.5 will remain below the objectives in 2018

whether the Scheme is developed or not. The predicted PM10 and PM2.5 impacts are all negligible

except for at three receptors where slight adverse impacts are predicted.

7.3 Annual mean concentrations of nitrogen dioxide will remain above the objective at all receptors

whether the Scheme is developed or not. Using impact descriptors developed by the IAQM, the

impacts of the Scheme range from substantial beneficial to substantial adverse, but the number of

receptors where benefits are predicted is almost twenty times the number where adverse impacts

are predicted. The Scheme will also reduce the number of receptors where exceedences of the 1-

hour mean nitrogen dioxide objective are predicted.

7.4 Comparing the numbers of residential properties where air quality is predicted to improve with the

number where air quality is predicted to worsen shows that many more (up to 190 times more)

properties will experience benefits than disbenefits as a result of the Scheme.

7.5 The Scheme is predicted to reduce annual mean nitrogen dioxide concentrations at both the

Marylebone Road and Oxford Street air quality monitoring stations by more than 5 µg/m3. This is

important because results from the Marylebone Road monitor are used by Defra when it reports

assessment against the European Limit Values to the European Commission. The Scheme thus

has the potential to bring forward compliance of the UK with the Limit Values.

7.6 Balancing the predicted benefits of the Scheme with the predicted disbenefits, it is considered that,

overall, the Scheme will have a significant beneficial air quality impact.

7.7 The Mayor of London has committed to the implementation of an Ultra-Low Emission Zone (ULEZ)

by September 2020 at the latest. Parts of the study area will fall within this ULEZ. The effects of

the ULEZ in reducing pollutant concentrations in central London has not been accounted for in this

assessment.


J2451 28 of 55 March 2016

8 References

AQC (2016a) Emissions of Nitrogen Oxides from Modern Diesel Vehicles , [Online],

Available: http://www.aqconsultants.co.uk/Resources/Download-Reports.aspx.

AQC (2016b) Deriving Background Concentrations of NOx and NO2, [Online], Available:

http://www.aqconsultants.co.uk/Resources/Download-Reports.aspx.

Carslaw, D., Beevers, S., Westmoreland, E. and Williams, M. (2011) Trends in NOx and

NO2 emissions and ambient measurements in the UK, [Online], Available: uk-

air.defra.gov.uk/reports/cat05/1108251149_110718_AQ0724_Final_report.pdf.

Carslaw, D. and Rhys-Tyler, G. (2013) Remote sensing of NO2 exhaust emissions from

road vehicles, July, [Online], Available: http://uk-

air.defra.gov.uk/assets/documents/reports/cat05/1307161149_130715_DefraRemoteSensi

ngReport_Final.pdf.

Defra (2007) The Air Quality Strategy for England, Scotland, Wales and Northern Ireland,

Defra.

Defra (2009) Review & Assessment: Technical Guidance LAQM.TG(09), Defra.

Defra (2016) Defra Air Quality Website, [Online], Available: http://laqm.defra.gov.uk/.

Directive 2008/50/EC of the European Parliament and of the Council (2008).

EPUK & IAQM (2015) Land-Use Planning & Development Control: Planning For Air

Quality, IAQM.

GLA (2010) Mayor's Air Quality Strategy: Cleaning the Air.

King's College London (2016) London Air.

The Air Quality (England) (Amendment) Regulations, 2002, Statutory Instrument 3043

(2002), HMSO.

The Air Quality (England) Regulations, 2000, Statutory Instrument 928 (2000), HMSO.

http://www.aqconsultants.co.uk/Resources/Download-Reports.aspx

http://www.aqconsultants.co.uk/Resources/Download-Reports.aspx

http://uk-air.defra.gov.uk/assets/documents/reports/cat05/1307161149_130715_DefraRemoteSensingReport_Final.pdf



http://laqm.defra.gov.uk/


J2451 29 of 55 March 2016

9 Glossary

ADMS-Roads Atmospheric Dispersion Modelling System model for Roads

AQC Air Quality Consultants

AQMA Air Quality Management Area

AURN Automatic Urban and Rural Network

DCLG Department for Communities and Local Government

Defra Department for Environment, Food and Rural Affairs

DfT Department for Transport

EFT Emission Factor Toolkit

EPUK Environmental Protection UK

Exceedence A period of time when the concentration of a pollutant is greater than the

appropriate air quality objective. This applies to specified locations with relevant

exposure

FDMS Filter Dynamics Measurement System

HDV Heavy Duty Vehicles (> 3.5 tonnes)

HGV Heavy Goods Vehicle

IAQM Institute of Air Quality Management

LAQM Local Air Quality Management

LDF Local Development Framework

LDV Light Duty Vehicles (<3.5 tonnes)

LEZ Low Emission Zone

μg/m3 Microgrammes per cubic metre

MAQS Mayor’s Air Quality Strategy

NO Nitric oxide

NO2 Nitrogen dioxide

NOx Nitrogen oxides (taken to be NO2 + NO)

NPPF National Planning Policy Framework

Objectives A nationally defined set of health-based concentrations for nine pollutants, seven of

which are incorporated in Regulations, setting out the extent to which the


J2451 30 of 55 March 2016

standards should be achieved by a defined date. There are also vegetation-based

objectives for sulphur dioxide and nitrogen oxides

PHV Private Hire Vehicle

PM10 Small airborne particles, more specifically particulate matter less than 10

micrometres in aerodynamic diameter

PM2.5 Small airborne particles less than 2.5 micrometres in aerodynamic diameter

Standards A nationally defined set of concentrations for nine pollutants below which health

effects do not occur or are minimal

TEOM Tapered Element Oscillating Microbalance


J2451 31 of 55 March 2016

10 Appendices

A1 Extracts from the Mayor’s Air Quality Strategy, and Description of the Low Emission Zones (LEZs) ................................................................................... 32

A2 EPUK & IAQM Planning for Air Quality Guidance ............................................ 34

A3 Professional Experience .................................................................................. 37

A4 Modelling Methodology ................................................................................... 38

A5 Adjustment of Short-Term Data to Annual Mean ............................................. 48

A6 Predicted Concentrations ................................................................................ 49


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A1 Extracts from the Mayor’s Air Quality Strategy, and Description of the Low Emission Zones (LEZs)

The Mayor’s Air Quality Strategy

A1.1 The Mayor’s Air Quality Strategy commits to the continuation of measures identified in the 2002

MAQS, and sets out a series of additional measures, including:

Policy 1 – Encouraging smarter choices and sustainable travel;

Measures to reduce emissions from idling vehicles focusing on buses, taxis, coaches, taxis, PHVs

and delivery vehicles;

Using spatial planning powers to support a shift to public transport;

Supporting car free developments.

Policy 2 – Promoting technological change and cleaner vehicles:

Supporting the uptake of cleaner vehicles.

Policy 4 – Reducing emissions from public transport:

Introducing age limits for taxis and PHVs.

Policy 5 – Schemes that control emissions to air:

Implementing Phases 3 and 4 of the LEZ from January 2012

Introducing a NOx emissions standard (Euro IV) into the LEZ for Heavy Goods Vehicles (HGVs),

buses and coaches, from 2015.

Policy 7 – Using the planning process to improve air quality:

Minimising increased exposure to poor air quality, particularly within AQMAs or where a

development is likely to be used by a large number of people who are particularly vulnerable to air

quality;

Ensuring air quality benefits are realised through planning conditions and section 106 agreements

and Community Infrastructure Levy.

Policy 8 – Creating opportunities between low to zero carbon energy supply for London and air

quality impacts:

Applying emissions limits for biomass boilers across London;

Requiring an emissions assessment to be included at the planning application stage.


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Low Emission Zone (LEZ)

A1.2 A key measure to improve air quality in Greater London is the Low Emission Zone (LEZ). This

entails charges for vehicles entering Greater London not meeting certain emissions criteria, and

affects older, diesel-engined lorries, buses, coaches, large vans, minibuses and other specialist

vehicles derived from lorries and vans. The LEZ was introduced on 4th February 2008, and was

phased in through to January 2012. From January 2012 a standard of Euro IV was implemented

for lorries and other specialist diesel vehicles over 3.5 tonnes, and buses and coaches over 5

tonnes. Cars and lighter Light Goods Vehicles (LGVs) are excluded. The third phase of the LEZ,

which applies to larger vans, minibuses and other specialist diesel vehicles, was also implemented

in January 2012. As set out in the 2010 MAQS, a NOx emissions standard (Euro IV) is included in

the LEZ for HGVs, buses and coaches, from 2015.

Ultra Low Emission Zone (ULEZ)

A1.3 The Mayor has confirmed the introduction of the Ultra Low Emission Zone (ULEZ) in the Capital on

7 September 2020. The ULEZ will operate 24 hours a day, 7 days a week in the same area as the

current Congestion Charging zone. All cars, motorcycles, vans, minibuses and Heavy Goods

Vehicles will need to meet exhaust emission standards (ULEZ standards) or pay an additional daily

charge to travel within the zone. The ULEZ standards are Euro 3 for motorcycles; Euro 4 for petrol

cars, vans and minibuses; Euro 6 for diesel cars, vans and minibuses; and Euro VI for HGVs,

buses and coaches.


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A2 EPUK & IAQM Planning for Air Quality Guidance

A2.1 The guidance issued by EPUK and IAQM6 (EPUK & IAQM, 2015) is comprehensive in its

explanation of the place of air quality in the planning regime. Key sections of the guidance not

already mentioned above are set out below.

Air Quality as a Material Consideration

“Any air quality issue that relates to land use and its development is capable of being a material

planning consideration. The weight, however, given to air quality in making a planning application

decision, in addition to the policies in the local plan, will depend on such factors as:

the severity of the impacts on air quality;

the air quality in the area surrounding the proposed development;

the likely use of the development, i.e. the length of time people are likely to be exposed at that

location; and

the positive benefits provided through other material considerations”.

Impact Descriptors and Assessment of Significance

A2.2 There is no official guidance in the UK in relation to development control on how to describe the

nature of air quality impacts, nor how to assess their significance. The approach developed by

EPUK and IAQM (EPUK & IAQM, 2015) has therefore been used. This approach involves a two

stage process:

a qualitative or quantitative description of the impacts on local air quality arising from the

development; and

a judgement on the overall significance of the effects of any impacts.

Impact Descriptors

A2.3 Impact description involves expressing the magnitude of incremental change as a proportion of a

relevant assessment level and then examining this change in the context of the new total

concentration and its relationship with the assessment criterion. Table A2.1 sets out the method

for determining the impact descriptor for annual mean concentrations at individual receptors,

having been adapted from the table presented in the guidance document. For the assessment

criterion the term Air Quality Assessment Level or AQAL has been adopted, as it covers all

pollutants, i.e. those with and without formal standards. Typically, as is the case for this

6 The IAQM is the professional body for air quality practitioners in the UK.


J2451 35 of 55 March 2016

assessment, the AQAL will be the air quality objective value. Note that impacts may be adverse or

beneficial, depending on whether the change in concentration is positive or negative.

Table A2.1: Air Quality Impact Descriptors for Individual Receptors for All Pollutants a

Long-Term Average Concentration At

Receptor In Assessment Year

b

Change in concentration relative to AQAL c

0% 1% 2-5% 6-10% >10%

75% or less of AQAL Negligible Negligible Negligible Slight Moderate

76-94% of AQAL Negligible Negligible Slight Moderate Moderate

95-102% of AQAL Negligible Slight Moderate Moderate Substantial

103-109% of AQAL Negligible Moderate Moderate Substantial Substantial

110% or more of AQAL Negligible Moderate Substantial Substantial Substantial

a Values are rounded to the nearest whole number.

b This is the ‘without scheme’ concentration where there is a decrease in pollutant concentration and the

‘with scheme’ concentration where there is an increase.

c AQAL = Air Quality Assessment Level, which may be an air quality objective, EU limit or target value, or

an Environment Agency ‘Environmental Assessment Level (EAL)’.

Assessment of Significance

A2.4 The IAQM guidance (EPUK & IAQM, 2015) is that the assessment of significance should be based

on professional judgement, with the overall air quality impact of the scheme described as either

significant or not significant. In drawing this conclusion, the following factors should be taken into

account:

the existing and future air quality in the absence of the development;

the extent of current and future population exposure to the impacts;

the influence and validity of any assumptions adopted when undertaking the prediction of

impacts;

the potential for cumulative impacts and, in such circumstances, several impacts that are

described as ‘slight’ individually could, taken together, be regarded as having a significant

effect for the purposes of air quality management in an area, especially where it is proving

difficult to reduce concentrations of a pollutant. Conversely, a ‘moderate’ or ‘substantial’

impact may not have a significant effect if it is confined to a very small area and where it is

not obviously the cause of harm to human health; and

the judgement on significance relates to the consequences of the impacts; will they have

an effect on human health that could be considered as significant? In the majority of

cases, the impacts from an individual development will be insufficiently large to result in


J2451 36 of 55 March 2016

measurable changes in health outcomes that could be regarded as significant by health

care professionals.

A2.5 The guidance is clear that other factors may be relevant in individual cases. It also states that the

effect on the residents of any new development where the air quality is such that an air quality

objective is not met will be judged as significant. For people working at new developments in this

situation, the same will not be true as occupational exposure standards are different, although any

assessment may wish to draw attention to the undesirability of the exposure.

A2.6 A judgement of the significance should be made by a competent professional who is suitably

qualified. A summary of the professional experience of the staff contributing to this assessment is

provided in Appendix A3.


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A3 Professional Experience

Stephen Moorcroft, BSc (Hons) MSc DIC MIEnvSc MIAQM CEnv

Mr Moorcroft is a Director of Air Quality Consultants, and has worked for the company since 2004.

He has over thirty-five years’ postgraduate experience in environmental sciences. Prior to joining

Air Quality Consultants, he was the Managing Director of Casella Stanger, with responsibility for a

business employing over 100 staff and a turnover of £12 million. He also acted as the Business

Director for Air Quality services, with direct responsibility for a number of major Government

projects. He has considerable project management experience associated with Environmental

Assessments in relation to a variety of development projects, including power stations, incinerators,

road developments and airports, with particular experience related to air quality assessment,

monitoring and analysis. He has contributed to the development of air quality management in the

UK, and has been closely involved with the LAQM process since its inception. He has given expert

evidence to numerous public inquiries, and is frequently invited to present to conferences and

seminars. He is a Member of the Institute of Air Quality Management.

Dr Ben Marner, BSc (Hons) PhD CSci MIEnvSc MIAQM

Dr Marner is a Technical Director with AQC, and has seventeen years’ experience in the field of air

quality. He has been responsible for air quality and greenhouse gas assessments of road

schemes, rail schemes, airports, power stations, waste incinerators, commercial developments and

residential developments in the UK and abroad. He has been an expert witness at several public

inquiries, where he has presented evidence on health-related air quality impacts, the impacts of air

quality on sensitive ecosystems, and greenhouse gas impacts. He has extensive experience of

using detailed dispersion models, as well as contributing to the development of modelling best

practices. Dr Marner has arranged and overseen air quality monitoring surveys, as well as

contributing to Defra guidance on harmonising monitoring methods. He has been responsible for

air quality review and assessments on behalf of numerous local authorities. He has also

developed methods to predict nitrogen deposition fluxes on behalf of the Environment Agency,

provided support and advice to the UK Government’s air quality review and assessment helpdesk,

Transport Scotland, Transport for London, and numerous local authorities. He is a Member of the

Institute of Air Quality Management and a Chartered Scientist.

Full CVs are available at www.aqconsultants.co.uk.

http://www.aqconsultants.co.uk/


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A4 Modelling Methodology

Model Inputs

Traffic Data and Emissions Calculations

A4.1 As explained in Section 4, Norman Rourke Pryme provided detailed information on traffic flows (by

vehicle type) and speeds for each of the key vehicle lanes and turning movements across the

study area. These covered the morning peak, evening peak, and interpeak periods. Norman

Rourke Pryme also provided a set of factors to use to predict off-peak flows and annual average

flows. The latter was used to calculate average weekend flows; such that the average daily flow

across a week equalled the predicted annual average daily flow. Speeds during the off-peak

periods and weekend were assumed to be equal to those predicted for the interpeak.

A4.2 While the traffic data provided included all of the key turning movements in the network, they also

included information on lane changes and other ‘events’ on the network that could not feasibly be

included in the dispersion modelling. It was thus necessary to edit the traffic dataset to ensure a

consistent coverage across the network and to remove instances of double counting. In some

cases, it was also necessary to calculate flows on additional links within junctions.

A4.3 The predicted hourly-mean flows and speeds for each vehicle class were entered into Defra’s

Emissions Factor Toolkit (EFT V6.02) (Defra, 2016) to calculate hourly average vehicle-type-

specific emissions of nitrogen oxides, PM10 and PM2.5. In order to do this, it was necessary to set

the speed for all movements with a predicted hourly average speed less than 5 kph to equal 5 kph.

The EFT was used to generate emissions specific to ‘inner London’.

A4.4 The hourly-average emissions from the EFT were collated as a set of diurnal and weekly

emissions profiles (i.e. with constant emissions during each period and constant emissions over

the weekends). Each link and each turning movement had its own set of emissions profiles.

Sensitivity Test for Nitrogen Oxides and Nitrogen Dioxide

A4.5 As explained in Section 4, AQC has carried out a detailed analysis which showed that, where

previous standards had limited on-road success in reducing nitrogen oxides emissions from diesel

vehicles, the ‘Euro VI’ and ‘Euro 6’ standards are delivering real on-road improvements (AQC,

2016a). Furthermore, these improvements are expected to increase as the Euro 6 standard is fully

implemented. Despite this, the detailed analysis suggested that, in addition to modelling using the

EFT, a sensitivity test using elevated nitrogen oxides emissions from certain diesel should be

carried out (AQC, 2016a). A worst-case sensitivity test has thus been carried out by applying the


J2451 39 of 55 March 2016

adjustments set out in Table A4.1 to the emission factors used within the EFT7. The justifications

for these adjustments are given in AQC (2016a). Results are thus presented for two scenarios:

first the ‘official prediction’, which uses the EFT with no adjustment, and second the ‘worst-case

sensitivity test’, which applies the adjustments set out in Table A4.1. The results from this

sensitivity test are likely to over-predict emissions from vehicles in the future and thus provide a

reasonable worst-case upper-bound to the assessment.

Table A4.1: Summary of Adjustments Made to Emission Factor Toolkit

Vehicle Type Adjustment Applied to Emission Factors

All Petrol Vehicles No adjustment

Light Duty Diesel Vehicles

Euro 5 and earlier No adjustment

Euro 6 Increased by 60%

Heavy Duty Diesel Vehicles

Euro III and earlier No adjustment

Euro IV and V Set to equal Euro III values

Euro VI Set to equal 20% of Euro III emissions a

a Taking account of the speed-emission curves for different Euro classes as explained in AQC (2016a).

Dispersion Modelling

A4.1 Predictions have been carried out using the ADMS-Roads dispersion model (v3.4). Each of the

roads/movements described above was entered as a separate source, and the diurnal emissions

profiles were entered as ‘.fac’ files. Each road/movement was assigned a width based on the

number of lanes involved. Because the network was modelled as a large number of sources, it

was not practical to use the ADMS canyon module. In practice, since virtually all of the roads in

the study area can be described as street canyons, and because the model has been verified

against measurements made in two of these street canyons, the canyon effect has been implicitly

accounted for through the model verification and adjustment.

A4.2 As an example of how the roads were entered into ADMS, Figure A4.1 shows a small excerpt from

the modelled network. The extent of the full modelled network is shown in full in Figure 1.

A4.3 The model has been run using the full year of meteorological data that corresponds to the most

recent set of nitrogen dioxide monitoring data (2014). The meteorological data has been taken

from the monitoring station located at Heathrow Airport, which is considered suitable for this area.

7 All adjustments were applied to the COPERT functions. Fleet compositions etc. were applied following the same

methodology as used within the EFT.


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Figure A4.1: Excerpt from Modelled Road Network (See Figure 1 for full network).

Background Concentrations

Annual Mean Concentrations

A4.4 The background pollutant concentrations across the study area have been defined using the

national pollution maps published by Defra (2016). These cover the whole country on a 1x1 km

grid and are published for each year from 2011 until 2030.

A4.5 The mapped background nitrogen dioxide concentrations for 2014 have been verified against

concurrent measurements made at the urban background automatic monitoring sites at Russell

Square Gardens in Bloomsbury (‘London Bloomsbury’), Horseferry Road in Westminster

(‘Horseferry Road’), and St Charles Square in Kensington and Chelsea (‘North Ken’). These sites

are shown in Figure A4.2. The sites were chosen since they are the closest urban background

automatic monitoring sites to the study area. A further site (Westminster Victoria) was discounted

from this analysis, since it was not considered to ideally represent background concentrations due

to the proximity of emission sources. The verification is shown in Figure A4.3 and Figure A4.4. It

is concluded that Defra’s maps currently under-predict annual mean nitrogen dioxide

concentrations in the area by around 5%, but that they over-predict concentrations of PM10. All


J2451 41 of 55 March 2016

mapped background nitrogen dioxide and PM10 concentrations have thus been calibrated by

applying factors of 1.0512 and 0.716 respectively.

A4.6 A similar comparison was not possible for PM2.5. The background PM2.5 maps have thus been

calibrated using the factor for PM10 (0.716).

Figure A4.2: Urban Background Monitors used to Calibrate Defra’s Background Maps



J2451 42 of 55 March 2016

Figure A4.3: Comparison of Measured and Estimated Background- Annual Mean Nitrogen Dioxide Concentrations at all Three Urban Background Automatic Monitoring Sites in 2014 (µg/m

3)

Figure A4.4: Comparison of Measured and Estimated Background Annual Mean PM10 Concentrations at all Three Urban Background Automatic Monitoring Sites in 2014 (µg/m

3)


J2451 43 of 55 March 2016

Background Annual Mean NO2 Concentrations for Sensitivity Test

A4.7 The road-traffic components of nitrogen dioxide in the background maps have been uplifted in

order to derive future year background nitrogen dioxide concentrations for use in the sensitivity

test. Details of the approach are provided in the report prepared by AQC (2016b).

Hour-by-hour NO2 Concentrations

A4.8 In order to calculate the 99.79th percentiles of 1-hour mean nitrogen dioxide concentrations, hour-

by-hour background nitrogen oxides concentrations are required. These were taken from the 2014

dataset from the London Bloomsbury site.

Model Verification

A4.9 In order to ensure that ADMS-Roads accurately predicts local concentrations, it is necessary to

verify the model against local measurements.

Nitrogen Dioxide

A4.10 Most nitrogen dioxide (NO2) is produced in the atmosphere by reaction of nitric oxide (NO) with

ozone. It is therefore most appropriate to verify the model in terms of primary pollutant emissions

of nitrogen oxides (NOx = NO + NO2). The model has been run to predict the annual mean NOx

concentrations during 2014 at the Marylebone Road and Oxford Street monitors.

A4.11 The model output of road-NOx (i.e. the component of total NOx coming from road traffic) has been

compared with the ‘measured’ road-NOx. Measured road-NOx has been calculated from the

measured NO2 concentrations and the predicted background NO2 concentration using the NOx

from NO2 calculator (Version 4.1) available on the Defra LAQM Support website (Defra, 2016).

A4.12 An adjustment factor has been determined as the slope of the best-fit line between the ‘measured’

road contribution and the model derived road contribution, forced through zero (Figure A4.5). The

calculated adjustment factor of 3.334 has been applied to the modelled road-NOx concentration for

each receptor to provide adjusted modelled road-NOx concentrations.

A4.13 The total nitrogen dioxide concentrations have then been determined by combining the adjusted

modelled road-NOx concentrations with the predicted background NO2 concentration within the

NOx to NO2 calculator. Figure A4.6 compares final adjusted modelled total NO2 at each of the

monitoring sites to measured total NO2, and shows a close agreement.

A4.14 The model has over-predicted concentrations at Marylebone Road and under-predicted

concentrations at Oxford Street. The most likely explanations for this are:

Oxford Street is likely to experience a greater street canyon effect than Marylebone Road, and

it was not possible to account for this in the model; and


J2451 44 of 55 March 2016

having to round very slow (<5 kph) hourly speeds up to 5 kph may have resulted in emissions

along this section of Oxford Street being slightly under-predicted.

A4.15 The results imply that the model has under predicted the road-NOx contribution. This is a common

experience with this and most other models. The adjustment factor is slightly higher than might

usually be expected because it is being used to account for the effects of street canyons. These

model adjustment factors have been applied to both annual mean and 1-hour mean model outputs.

Figure A4.5: Comparison of Measured Road NOx to Unadjusted Modelled Road NOx Concentrations. The dashed lines show ± 25%.


J2451 45 of 55 March 2016

Figure A4.6: Comparison of Measured Total NO2 to Final Adjusted Modelled Total NO2 Concentrations. The dashed lines show ± 25%.

PM10 and PM2.5

A4.16 The model has been run to predict annual mean road-PM concentrations during 2014 at the

Marylebone Road monitor. A similar process to calculate the road-NOx adjustment factor has

been followed.

A4.17 The measured road-PM10 and modelled road-PM10 concentrations are compared to provide the

factor for PM10. The data used to calculate the adjustment factor are provided below:

Measured PM10: 26.3 μg/m3

‘Measured’ road-PM10 (measured – background at monitor): 26.3 –17.4 = 8.8 μg/m3

Modelled road-PM10 = 4.6 μg/m3

Road-PM10 adjustment factor: 8.8 / 4.6 = 1.908 (calculated from unrounded numbers)

A4.18 The measured road-PM2.5 and modelled road-PM2.5 concentrations have also been compared to

provide a factor for PM2.5. The data used to calculate the adjustment factor are provided below:

Measured PM2.5: 18.3 μg/m3

‘Measured’ road-PM2.5 (measured – background at monitor): 18.3 – 11.9 = 6.4 μg/m3


J2451 46 of 55 March 2016

Modelled road-PM2.5 = 3.5 μg/m3

Road-PM2.5 adjustment factor: 6.4 / 3.5 = 1.817 (calculated from unrounded numbers).

Model Verification for NOx and NO2 Sensitivity Test

A4.19 The approach set out above has been repeated using the predicted road-NOx specific to the

sensitivity test. This has resulted in an adjustment factor of 2.783, which has been applied to all

modelled road-NOx concentrations within the sensitivity test.

Model Post-processing

The model predicts road-NOx concentrations at each receptor location. These concentrations

have then been adjusted using the adjustment factor, which, along with the background NO2, has

been processed through the NOx to NO2 calculator available on the Defra LAQM Support website

(Defra, 2016). The traffic mix within the calculator has been set to “All London traffic”, which is

considered suitable for the study area. The calculator predicts the component of NO2 based on the

adjusted road-NOx and the background NO2.

1-hour Mean Nitrogen Oxides and Nitrogen Dioxide

A4.20 In order to predict 1-hour mean nitrogen dioxide concentrations, the model was run to predict hour-

by-hour concentrations for every hour of the year (i.e. 8760 values per receptor). These hourly-

mean nitrogen oxides results were multiplied by the primary adjustment factors given in

Paragraphs A4.12 and A4.19 and then added to the concurrent hourly nitrogen oxides

measurement from the Russell Square Gardens (Bloomsbury) background monitor.

A4.21 In order to derive a nitrogen oxides to nitrogen dioxide relationship for the study area, the

measurements from the Marylebone Road and Oxford Street monitors were plotted (Figure A4.7).

The relationship between hourly nitrogen oxides and nitrogen dioxide concentrations was

determined to be as shown by the line and equation in Figure A4.7.

A4.22 The 99.79th percentiles of predicted hourly-mean nitrogen oxides concentrations were multiplied by

the relationship shown in Figure A4.7 to give the equivalent predicted nitrogen dioxide

concentration.


J2451 47 of 55 March 2016

Figure A4.7: Hourly Mean Nitrogen Oxides vs Nitrogen Dioxide at Marylebone Road and Oxford Street in 2014 (µg/m

3) (also showing best-fit relationship)


J2451 48 of 55 March 2016

A5 Adjustment of Short-Term Data to Annual Mean

A5.1 Data capture for nitrogen dioxide at the Oxford Street monitor during 2014 was only 74%. The

data have been adjusted to an annual mean, based on the ratio of concentrations during the

available monitoring period to those over the calendar year at the Marylebone Road site. This is

considered to be more appropriate than focusing on trends at background sites, which will be quite

different from those at roadside sites.

A5.2 The annual mean nitrogen dioxide concentrations and the period mean for at the Marylebone Road

monitor from which adjustment factor has been calculated are presented in Table A5.1.

Table A5.1: Data used to Adjust Monitoring Data at the Oxford Street Monitor

Period Marylebone Road

Periods when Oxford Street produced valid data 94.6 µg/m3

Full calendar year 93.6 µg/m3

Adjustment Factor 0.99


J2451 49 of 55 March 2016

A6 Predicted Concentrations

A6.1 Predicted concentrations at all of the receptors are set out in Table A6.1, which uses the following nomenclature:

DM = without Scheme

DS = with Scheme

Ch1 = absolute change

Ch2 = percentage change

Ch3 = change as percentage of the objective rounded to the nearest whole number. For PM10, while the annual mean objective is 40 µg/m3, 32 µg/m

3 is the annual mean concentration above which an exceedence of the 24-hour

mean PM10 concentration is possible, as outlined in LAQM.TG(09) (Defra, 2009). A value of 32 µg/m3 is thus used as a proxy to determine the likelihood of exceedence of the 24-hour mean PM10 objective, as recommended

in EPUK & IAQM guidance (EPUK & IAQM, 2015).

D = IAQM Impact Descriptor (see Appendix A2)

SA = Substantial Adverse (see Appendix A2)

MA = Moderate Adverse (see Appendix A2)

SlA = Slight Adverse (see Appendix A2)

N = Negligible (see Appendix A2)

MB = Moderate Beneficial (see Appendix A2)

SB = Substantial Beneficial (see Appendix A2)

Table A6.1: Dispersion Model Results a

‘Official’ Annual Mean NO2 Official 99.8th

%iles of 1h NO2 Sensitivity Test Annual Mean NO2 Sensitivity 99.8h %iles 1h NO2 Annual Mean PM10 Annual Mean PM2.5

R DM DS Ch1 Ch2 Ch3 D DM DS Ch1 Ch2 Ch3 DM DS Ch1 Ch2 Ch3 D DM DS Ch1 Ch2 Ch3 DM DS Ch1 Ch2 Ch3 D DM DS Ch1 Ch2 Ch3 D

1 46.0 43.9 -2.0 -4.4 -5 SB 103 99 -4 -4 -2 48.3 46.1 -2.2 -4.6 -6 SB 106 102 -4 -4 -2 17.2 17.1 -0.1 -0.7 0 N 11.5 11.4 -0.1 -0.6 0 N

2 46.0 43.8 -2.2 -4.9 -6 SB 104 100 -4 -4 -2 48.3 45.9 -2.5 -5.1 -6 SB 108 102 -6 -6 -3 17.2 17.1 -0.1 -0.8 0 N 11.5 11.4 -0.1 -0.7 0 N

3 46.6 44.4 -2.1 -4.6 -5 SB 106 101 -4 -4 -2 48.9 46.6 -2.3 -4.7 -6 SB 109 105 -4 -4 -2 17.2 17.1 -0.1 -0.8 0 N 11.5 11.4 -0.1 -0.6 0 N

4 46.6 44.7 -1.9 -4.1 -5 SB 105 101 -4 -4 -2 49.0 46.9 -2.1 -4.2 -5 SB 109 105 -4 -3 -2 17.3 17.1 -0.1 -0.7 0 N 11.5 11.4 -0.1 -0.6 0 N

5 55.7 55.8 0.1 0.2 0 N 142 147 5 4 3 58.4 58.7 0.2 0.4 1 MA 150 153 3 2 2 18.0 18.0 0.0 -0.2 0 N 12.0 11.9 0.0 -0.1 0 N

6 56.7 56.2 -0.5 -0.9 -1 MB 148 147 -1 0 0 59.5 59.0 -0.4 -0.7 -1 MB 154 154 0 0 0 18.1 18.0 -0.1 -0.6 0 N 12.0 12.0 -0.1 -0.4 0 N

7 58.2 58.5 0.2 0.4 1 MA 120 121 1 1 1 61.1 61.4 0.3 0.6 1 MA 124 125 1 0 0 18.3 18.3 0.0 0.0 0 N 12.1 12.1 0.0 0.1 0 N

8 58.8 58.4 -0.4 -0.7 -1 MB 122 122 -1 -1 0 61.6 61.3 -0.4 -0.6 -1 MB 126 127 1 1 0 18.3 18.3 -0.1 -0.5 0 N 12.1 12.1 0.0 -0.3 0 N

9 55.0 53.5 -1.5 -2.8 -4 SB 125 122 -3 -2 -1 58.5 57.3 -1.2 -2.0 -3 SB 132 132 0 0 0 17.8 17.6 -0.2 -1.0 -1 N 11.8 11.7 -0.1 -0.8 0 N

10 52.8 51.9 -0.8 -1.6 -2 SB 117 116 -1 -1 -1 55.8 55.3 -0.5 -0.9 -1 MB 123 123 0 0 0 17.7 17.6 -0.1 -0.6 0 N 11.8 11.7 -0.1 -0.5 0 N

11 55.7 54.5 -1.2 -2.2 -3 SB 127 128 1 1 0 59.2 58.5 -0.7 -1.3 -2 SB 135 137 2 1 1 17.9 17.7 -0.2 -0.9 -1 N 11.9 11.8 -0.1 -0.9 0 N

12 63.0 61.1 -2.0 -3.1 -5 SB 142 129 -14 -10 -7 66.9 65.0 -2.0 -3.0 -5 SB 150 135 -15 -10 -8 18.5 18.4 -0.1 -0.3 0 N 12.2 12.2 0.0 0.0 0 N

13 63.1 57.1 -6.1 -9.6 -15 SB 178 148 -30 -17 -15 67.5 60.7 -6.8 -10.1 -17 SB 194 158 -36 -18 -18 18.4 18.0 -0.3 -1.8 -1 N 12.2 12.0 -0.2 -1.5 -1 N

14 63.8 61.7 -2.1 -3.2 -5 SB 138 129 -9 -7 -5 68.1 66.5 -1.6 -2.4 -4 SB 146 136 -10 -7 -5 18.4 18.3 -0.1 -0.6 0 N 12.2 12.2 0.0 0.0 0 N

15 54.2 51.5 -2.8 -5.1 -7 SB 130 121 -9 -7 -4 57.4 54.6 -2.8 -4.9 -7 SB 138 128 -9 -7 -5 17.7 17.6 -0.2 -0.8 0 N 11.8 11.7 -0.1 -0.5 0 N

16 60.3 57.5 -2.8 -4.6 -7 SB 153 145 -8 -5 -4 63.9 61.2 -2.7 -4.2 -7 SB 162 153 -8 -5 -4 18.3 18.1 -0.2 -0.8 0 N 12.1 12.0 0.0 -0.4 0 N

17 62.8 60.5 -2.3 -3.6 -6 SB 135 129 -6 -5 -3 66.8 64.1 -2.7 -4.1 -7 SB 144 135 -9 -6 -4 18.4 18.3 -0.1 -0.4 0 N 12.1 12.1 0.0 -0.1 0 N

18 56.2 52.9 -3.3 -5.8 -8 SB 141 130 -11 -8 -6 59.4 55.8 -3.6 -6.1 -9 SB 149 135 -14 -9 -7 17.9 17.7 -0.2 -1.1 -1 N 11.9 11.8 -0.1 -0.8 0 N

19 62.4 58.7 -3.7 -5.9 -9 SB 140 129 -11 -8 -6 66.6 63.2 -3.4 -5.1 -9 SB 150 139 -10 -7 -5 18.3 18.0 -0.3 -1.7 -1 N 12.2 12.0 -0.2 -1.4 -1 N

20 65.3 64.1 -1.1 -1.7 -3 SB 139 139 0 0 0 70.3 70.2 -0.1 -0.1 0 N 149 155 6 4 3 18.6 18.2 -0.4 -2.1 -1 N 12.4 12.1 -0.2 -2.0 -1 N

21 66.4 61.4 -5.0 -7.5 -12 SB 139 128 -11 -8 -5 71.4 66.6 -4.8 -6.7 -12 SB 150 140 -10 -7 -5 18.6 18.1 -0.5 -2.7 -2 N 12.3 12.1 -0.3 -2.4 -1 N


J2451 50 of 55 March 2016




22 64.6 59.0 -5.6 -8.7 -14 SB 161 138 -23 -14 -11 68.9 63.7 -5.2 -7.5 -13 SB 171 150 -21 -12 -11 18.6 17.9 -0.7 -3.6 -2 N 12.3 11.9 -0.4 -3.3 -2 N

23 63.8 60.4 -3.4 -5.3 -8 SB 134 128 -6 -4 -3 68.6 64.9 -3.8 -5.5 -9 SB 143 135 -9 -6 -4 18.4 18.1 -0.3 -1.5 -1 N 12.2 12.0 -0.2 -1.4 -1 N

24 70.5 62.2 -8.3 -11.8 -21 SB 183 156 -27 -15 -13 75.3 67.0 -8.3 -11.0 -21 SB 197 170 -28 -14 -14 19.4 18.2 -1.2 -6.4 -4 N 13.0 12.1 -0.9 -6.9 -4 N

25 72.8 64.5 -8.3 -11.5 -21 SB 189 164 -25 -13 -13 77.5 69.5 -8.0 -10.3 -20 SB 204 178 -26 -13 -13 20.0 18.4 -1.6 -7.8 -5 N 13.4 12.2 -1.2 -9.1 -5 N

26 55.7 52.8 -2.9 -5.1 -7 SB 121 115 -7 -6 -3 58.6 55.7 -2.9 -4.9 -7 SB 128 120 -8 -6 -4 18.0 17.7 -0.3 -1.6 -1 N 12.0 11.8 -0.2 -1.5 -1 N

27 55.5 52.5 -3.0 -5.3 -7 SB 119 114 -5 -4 -2 58.3 55.3 -3.0 -5.1 -7 SB 126 119 -7 -6 -4 18.0 17.7 -0.3 -1.6 -1 N 12.0 11.8 -0.2 -1.4 -1 N

28 47.3 45.4 -1.9 -4.1 -5 SB 109 104 -5 -4 -2 49.8 47.8 -2.1 -4.1 -5 SB 115 107 -8 -7 -4 17.3 17.2 -0.1 -0.7 0 N 11.5 11.5 -0.1 -0.6 0 N

29 57.8 55.0 -2.8 -4.8 -7 SB 125 118 -7 -6 -4 60.8 58.1 -2.7 -4.5 -7 SB 134 125 -9 -7 -4 18.2 17.9 -0.3 -1.6 -1 N 12.1 11.9 -0.2 -1.5 -1 N

30 48.5 46.4 -2.1 -4.3 -5 SB 114 106 -8 -7 -4 51.1 48.9 -2.2 -4.3 -6 SB 118 110 -8 -7 -4 17.4 17.2 -0.1 -0.7 0 N 11.6 11.5 -0.1 -0.6 0 N

31 57.6 54.7 -2.9 -5.1 -7 SB 126 119 -7 -6 -4 60.5 57.6 -2.9 -4.9 -7 SB 133 126 -7 -5 -3 18.2 17.9 -0.3 -1.6 -1 N 12.1 11.9 -0.2 -1.5 -1 N

32 55.0 51.2 -3.8 -6.9 -9 SB 115 106 -9 -8 -5 58.0 53.9 -4.1 -7.0 -10 SB 123 111 -12 -10 -6 17.9 17.6 -0.3 -1.6 -1 N 11.9 11.7 -0.2 -1.5 -1 N

33 70.5 62.2 -8.3 -11.8 -21 SB 153 128 -24 -16 -12 74.1 65.4 -8.8 -11.8 -22 SB 162 133 -29 -18 -14 19.3 18.6 -0.7 -3.7 -2 N 12.8 12.4 -0.4 -3.5 -2 N

34 45.0 44.1 -0.9 -2.0 -2 SB 102 100 -2 -2 -1 47.1 46.2 -0.9 -2.0 -2 SB 104 102 -1 -1 -1 17.2 17.1 -0.1 -0.3 0 N 11.4 11.4 0.0 -0.3 0 N

35 70.0 63.7 -6.3 -9.0 -16 SB 148 127 -21 -14 -10 73.8 67.1 -6.6 -9.0 -17 SB 154 135 -19 -12 -9 19.2 18.7 -0.5 -2.7 -2 N 12.8 12.4 -0.3 -2.5 -1 N

36 45.7 44.8 -0.9 -1.9 -2 SB 103 101 -2 -2 -1 47.8 46.9 -0.9 -1.9 -2 SB 106 104 -2 -2 -1 17.2 17.1 -0.1 -0.3 0 N 11.5 11.4 0.0 -0.3 0 N

37 49.7 48.2 -1.5 -3.0 -4 SB 105 101 -3 -3 -2 52.3 50.9 -1.4 -2.7 -4 SB 109 106 -3 -3 -2 17.5 17.4 -0.1 -0.8 0 N 11.7 11.6 -0.1 -0.7 0 N

38 58.9 58.2 -0.6 -1.1 -2 SB 109 106 -4 -3 -2 61.5 60.7 -0.8 -1.3 -2 SB 113 109 -4 -3 -2 19.0 19.0 0.0 -0.2 0 N 12.6 12.6 0.0 -0.2 0 N

39 50.7 48.9 -1.9 -3.7 -5 SB 109 102 -7 -7 -4 53.4 51.6 -1.8 -3.4 -5 SB 113 107 -6 -5 -3 17.6 17.4 -0.2 -1.0 -1 N 11.7 11.6 -0.1 -0.9 0 N

40 56.8 55.9 -1.0 -1.7 -2 SB 112 111 -1 -1 -1 59.3 58.2 -1.1 -1.8 -3 SB 116 115 -1 -1 0 18.8 18.8 -0.1 -0.4 0 N 12.5 12.5 0.0 -0.4 0 N

41 59.6 56.3 -3.2 -5.4 -8 SB 122 116 -6 -5 -3 62.4 59.0 -3.3 -5.3 -8 SB 127 118 -9 -7 -5 18.4 18.1 -0.3 -1.6 -1 N 12.2 12.0 -0.2 -1.4 -1 N

42 55.4 54.3 -1.1 -2.1 -3 SB 141 135 -6 -4 -3 58.0 56.8 -1.2 -2.1 -3 SB 145 140 -5 -3 -3 18.0 17.9 -0.1 -0.5 0 N 12.0 11.9 -0.1 -0.4 0 N

43 58.0 57.9 -0.1 -0.1 0 N 123 123 -1 -1 0 61.0 61.0 0.0 0.0 0 N 128 126 -2 -1 -1 18.2 18.2 0.0 0.0 0 N 12.0 12.1 0.0 0.2 0 N

44 55.6 54.9 -0.7 -1.3 -2 SB 141 141 0 0 0 58.5 57.8 -0.6 -1.1 -2 SB 147 148 1 1 1 18.0 17.9 -0.1 -0.4 0 N 11.9 11.9 0.0 -0.2 0 N

45 75.1 71.7 -3.4 -4.6 -9 SB 163 154 -9 -6 -4 80.9 77.4 -3.5 -4.3 -9 SB 176 166 -10 -6 -5 19.6 19.1 -0.5 -2.4 -1 N 13.0 12.7 -0.3 -2.6 -1 N

46 77.6 72.2 -5.4 -7.0 -14 SB 200 187 -12 -6 -6 83.4 77.6 -5.8 -7.0 -15 SB 217 200 -17 -8 -8 19.8 19.2 -0.6 -3.2 -2 N 13.2 12.7 -0.4 -3.2 -2 N

47 100.7 94.5 -6.2 -6.1 -15 SB 244 229 -15 -6 -8 107.9 100.9 -7.0 -6.5 -18 SB 258 239 -20 -8 -10 23.0 22.3 -0.7 -3.2 -2 N 15.1 14.6 -0.5 -3.2 -2 N

48 97.4 92.9 -4.5 -4.7 -11 SB 207 195 -12 -6 -6 105.0 99.7 -5.2 -5.0 -13 SB 224 206 -17 -8 -9 22.4 21.9 -0.5 -2.1 -1 N 14.7 14.4 -0.3 -2.0 -1 N

49 66.5 64.0 -2.5 -3.7 -6 SB 142 130 -12 -8 -6 69.5 67.1 -2.4 -3.5 -6 SB 149 135 -14 -9 -7 19.7 19.4 -0.3 -1.3 -1 N 13.0 12.9 -0.2 -1.2 -1 N

50 56.7 52.9 -3.8 -6.6 -9 SB 138 123 -14 -10 -7 59.4 55.2 -4.2 -7.0 -10 SB 145 130 -15 -10 -7 18.1 17.9 -0.3 -1.4 -1 N 12.1 11.9 -0.2 -1.3 -1 N

51 101.9 90.9 -11.0 -10.8 -28 SB 231 212 -19 -8 -9 108.7 96.4 -12.2 -11.3 -31 SB 244 224 -21 -8 -10 23.3 22.1 -1.2 -5.0 -4 N 15.2 14.5 -0.7 -4.8 -3 N

52 75.0 64.1 -10.9 -14.5 -27 SB 207 170 -37 -18 -18 79.2 67.4 -11.8 -14.9 -29 SB 220 180 -41 -18 -20 19.7 18.8 -0.9 -4.6 -3 N 13.0 12.5 -0.6 -4.4 -2 N

53 98.7 90.6 -8.1 -8.2 -20 SB 239 214 -25 -10 -12 104.8 95.9 -8.9 -8.5 -22 SB 250 225 -25 -10 -12 23.0 22.2 -0.8 -3.6 -3 N 15.1 14.6 -0.5 -3.5 -2 N

54 90.3 87.5 -2.7 -3.0 -7 SB 216 210 -6 -3 -3 94.1 91.7 -2.3 -2.5 -6 SB 224 217 -7 -3 -4 22.4 22.3 -0.1 -0.3 0 N 14.7 14.8 0.1 0.5 0 N

55 85.8 85.4 -0.4 -0.5 -1 MB 195 195 -1 0 0 89.3 88.7 -0.5 -0.6 -1 MB 199 200 1 1 1 21.9 22.0 0.1 0.3 0 N 14.5 14.5 0.1 0.5 0 N

56 81.1 79.3 -1.9 -2.3 -5 SB 191 182 -9 -4 -4 84.5 82.8 -1.7 -2.0 -4 SB 198 192 -7 -3 -3 21.3 21.3 0.0 -0.2 0 N 14.0 14.1 0.0 0.3 0 N

57 102.9 94.6 -8.3 -8.1 -21 SB 258 235 -22 -9 -11 107.8 99.9 -7.9 -7.3 -20 SB 271 246 -24 -9 -12 23.8 22.7 -1.1 -4.4 -3 N 15.6 14.9 -0.7 -4.7 -3 N

58 85.7 83.8 -1.9 -2.2 -5 SB 201 197 -5 -2 -2 89.0 87.0 -2.0 -2.2 -5 SB 204 200 -4 -2 -2 21.9 21.7 -0.2 -0.9 -1 N 14.5 14.4 -0.1 -0.8 0 N

59 98.2 94.0 -4.2 -4.3 -11 SB 222 214 -8 -4 -4 103.4 99.3 -4.1 -4.0 -10 SB 234 224 -10 -4 -5 23.1 22.7 -0.4 -1.6 -1 N 15.2 14.9 -0.2 -1.6 -1 N

60 94.4 88.1 -6.3 -6.7 -16 SB 231 208 -23 -10 -11 99.0 92.5 -6.5 -6.6 -16 SB 242 217 -24 -10 -12 23.0 22.3 -0.7 -3.1 -2 N 15.2 14.7 -0.4 -2.9 -2 N

61 81.6 73.4 -8.2 -10.1 -21 SB 201 170 -31 -15 -15 86.5 77.4 -9.1 -10.5 -23 SB 212 178 -34 -16 -17 20.7 19.8 -0.9 -4.5 -3 N 13.7 13.1 -0.6 -4.3 -2 N

62 93.6 88.1 -5.4 -5.8 -14 SB 240 220 -19 -8 -10 98.0 92.4 -5.5 -5.7 -14 SB 246 232 -14 -6 -7 22.9 22.3 -0.6 -2.5 -2 N 15.1 14.8 -0.3 -2.3 -1 N

63 89.9 82.7 -7.2 -8.0 -18 SB 212 200 -12 -5 -6 94.9 86.9 -7.9 -8.3 -20 SB 223 209 -14 -6 -7 21.9 20.9 -1.0 -4.6 -3 N 14.5 13.8 -0.7 -4.9 -3 N

64 77.3 76.2 -1.2 -1.5 -3 SB 181 179 -2 -1 -1 81.0 79.7 -1.3 -1.6 -3 SB 188 188 0 0 0 20.5 20.3 -0.1 -0.7 0 N 13.6 13.5 -0.1 -0.8 0 N

65 89.1 88.1 -1.0 -1.1 -2 SB 228 223 -5 -2 -2 92.0 91.1 -1.0 -1.1 -2 SB 229 223 -5 -2 -3 22.6 22.5 -0.1 -0.5 0 N 15.0 14.9 -0.1 -0.4 0 N

66 91.0 89.5 -1.6 -1.7 -4 SB 224 221 -3 -1 -2 94.2 92.5 -1.6 -1.7 -4 SB 229 221 -8 -3 -4 22.8 22.6 -0.2 -0.8 -1 N 15.1 15.0 -0.1 -0.7 0 N

67 98.7 93.8 -5.0 -5.0 -12 SB 270 243 -26 -10 -13 105.7 99.7 -6.0 -5.7 -15 SB 287 257 -30 -11 -15 22.7 22.3 -0.4 -1.9 -1 N 14.9 14.7 -0.2 -1.6 -1 N

68 101.2 97.2 -4.0 -3.9 -10 SB 245 232 -12 -5 -6 108.9 103.7 -5.1 -4.7 -13 SB 259 242 -17 -7 -8 22.8 22.6 -0.2 -0.8 -1 N 14.9 14.9 0.0 -0.1 0 N

69 101.5 94.6 -6.9 -6.8 -17 SB 245 224 -21 -9 -10 107.7 99.7 -8.1 -7.5 -20 SB 249 229 -20 -8 -10 23.4 23.0 -0.4 -1.5 -1 N 15.5 15.3 -0.2 -1.5 -1 N

70 88.0 87.3 -0.7 -0.8 -2 SB 222 219 -3 -1 -1 91.3 90.5 -0.8 -0.9 -2 SB 224 224 0 0 0 22.3 22.4 0.1 0.3 0 N 14.7 14.8 0.1 0.6 0 N

71 98.4 90.7 -7.7 -7.8 -19 SB 269 248 -21 -8 -10 103.8 95.3 -8.5 -8.2 -21 SB 281 259 -22 -8 -11 23.1 22.5 -0.6 -2.8 -2 N 15.3 14.9 -0.4 -2.8 -2 N

72 87.6 86.8 -0.7 -0.8 -2 SB 217 215 -2 -1 -1 90.9 90.0 -0.9 -1.0 -2 SB 219 220 1 0 0 22.2 22.3 0.1 0.4 0 N 14.7 14.8 0.1 0.9 1 N

73 87.2 86.3 -0.9 -1.1 -2 SB 219 215 -4 -2 -2 90.7 89.8 -0.9 -1.0 -2 SB 225 222 -4 -2 -2 22.2 22.2 0.0 0.1 0 N 14.6 14.7 0.1 0.5 0 N

74 85.2 80.2 -5.0 -5.9 -13 SB 203 186 -17 -8 -9 89.3 83.9 -5.4 -6.0 -13 SB 209 191 -18 -9 -9 21.7 21.0 -0.8 -3.5 -2 N 14.5 13.9 -0.6 -3.8 -2 N

75 86.2 85.1 -1.2 -1.3 -3 SB 214 208 -6 -3 -3 89.7 88.6 -1.1 -1.2 -3 SB 220 213 -7 -3 -4 22.0 22.0 0.0 -0.1 0 N 14.5 14.5 0.0 0.3 0 N

76 87.0 83.0 -4.0 -4.6 -10 SB 223 207 -16 -7 -8 90.7 86.4 -4.3 -4.7 -11 SB 229 213 -16 -7 -8 22.2 21.9 -0.3 -1.3 -1 N 14.5 14.4 -0.1 -0.9 -1 N

77 88.3 83.6 -4.7 -5.3 -12 SB 228 210 -18 -8 -9 92.1 87.1 -5.0 -5.4 -12 SB 235 217 -18 -8 -9 22.3 22.0 -0.4 -1.6 -1 N 14.6 14.4 -0.2 -1.4 -1 N

78 93.1 87.0 -6.1 -6.5 -15 SB 254 228 -25 -10 -13 97.0 90.4 -6.6 -6.8 -16 SB 262 234 -28 -11 -14 23.0 22.4 -0.6 -2.6 -2 N 15.1 14.7 -0.4 -2.7 -2 N

79 96.4 87.1 -9.3 -9.7 -23 SB 245 218 -27 -11 -14 100.4 90.4 -10.0 -10.0 -25 SB 253 223 -30 -12 -15 23.4 22.3 -1.1 -4.8 -3 N 15.4 14.7 -0.8 -4.9 -3 N

80 90.6 83.8 -6.8 -7.5 -17 SB 220 201 -19 -9 -10 94.3 88.0 -6.3 -6.7 -16 SB 226 207 -19 -8 -9 22.1 21.3 -0.8 -3.5 -2 N 14.7 14.2 -0.5 -3.5 -2 N

81 100.6 94.6 -6.0 -5.9 -15 SB 257 229 -28 -11 -14 105.0 99.7 -5.3 -5.0 -13 SB 262 238 -24 -9 -12 23.4 23.0 -0.4 -1.8 -1 N 15.5 15.2 -0.3 -1.7 -1 N

82 87.0 83.1 -3.8 -4.4 -10 SB 226 214 -12 -5 -6 91.0 87.0 -4.0 -4.4 -10 SB 231 220 -11 -5 -6 22.0 21.1 -0.9 -4.0 -3 N 14.7 14.0 -0.7 -4.7 -3 N


J2451 51 of 55 March 2016




83 77.6 74.2 -3.4 -4.4 -8 SB 196 186 -10 -5 -5 81.1 77.7 -3.4 -4.2 -9 SB 200 190 -10 -5 -5 20.8 20.1 -0.7 -3.6 -2 N 14.0 13.4 -0.6 -4.3 -2 N

84 86.5 82.8 -3.7 -4.2 -9 SB 225 216 -9 -4 -4 90.6 86.5 -4.1 -4.5 -10 SB 231 221 -11 -5 -5 21.8 21.1 -0.7 -3.2 -2 N 14.5 14.0 -0.5 -3.7 -2 N

85 85.5 81.6 -3.9 -4.5 -10 SB 222 211 -11 -5 -5 89.5 85.2 -4.3 -4.8 -11 SB 229 216 -12 -5 -6 21.7 20.9 -0.7 -3.5 -2 N 14.5 13.9 -0.6 -4.1 -2 N

86 84.4 82.6 -1.8 -2.1 -4 SB 220 214 -6 -3 -3 88.3 86.4 -1.9 -2.2 -5 SB 225 219 -6 -3 -3 21.3 21.0 -0.3 -1.4 -1 N 14.2 13.9 -0.2 -1.7 -1 N

87 69.5 66.0 -3.4 -5.0 -9 SB 147 135 -12 -8 -6 72.7 69.2 -3.5 -4.8 -9 SB 153 140 -13 -8 -6 19.7 19.3 -0.3 -1.7 -1 N 13.1 12.9 -0.2 -1.6 -1 N

88 67.5 63.5 -4.0 -6.0 -10 SB 142 132 -10 -7 -5 70.5 66.5 -4.0 -5.7 -10 SB 146 135 -11 -8 -6 19.5 19.1 -0.4 -2.1 -1 N 13.0 12.7 -0.3 -2.0 -1 N

89 68.0 63.3 -4.7 -6.9 -12 SB 140 130 -11 -8 -5 70.9 66.3 -4.6 -6.5 -12 SB 145 135 -10 -7 -5 19.6 19.1 -0.5 -2.6 -2 N 13.0 12.7 -0.3 -2.4 -1 N

90 68.9 62.9 -5.9 -8.6 -15 SB 125 115 -10 -8 -5 71.4 65.3 -6.2 -8.6 -15 SB 128 119 -9 -7 -4 19.8 19.2 -0.5 -2.6 -2 N 13.2 12.9 -0.3 -2.5 -1 N

91 68.0 61.5 -6.5 -9.6 -16 SB 125 113 -11 -9 -6 70.5 63.7 -6.8 -9.6 -17 SB 128 117 -11 -9 -6 19.7 19.1 -0.6 -3.0 -2 N 13.1 12.8 -0.4 -3.0 -2 N

92 63.3 59.3 -4.0 -6.4 -10 SB 124 114 -10 -8 -5 66.0 62.1 -3.9 -5.9 -10 SB 128 119 -9 -7 -4 19.1 18.7 -0.4 -2.1 -1 N 12.8 12.5 -0.3 -2.0 -1 N

93 63.5 58.5 -5.0 -7.9 -13 SB 126 113 -14 -11 -7 66.0 61.2 -4.8 -7.3 -12 SB 130 116 -14 -10 -7 19.2 18.7 -0.5 -2.7 -2 N 12.8 12.5 -0.3 -2.5 -1 N

94 60.4 57.4 -3.0 -5.0 -8 SB 117 106 -11 -9 -5 62.7 59.4 -3.2 -5.2 -8 SB 119 110 -8 -7 -4 18.9 18.6 -0.3 -1.4 -1 N 12.6 12.4 -0.2 -1.4 -1 N

95 60.4 58.0 -2.4 -4.0 -6 SB 116 107 -9 -8 -4 62.7 60.1 -2.6 -4.2 -7 SB 117 113 -4 -3 -2 18.9 18.7 -0.2 -1.0 -1 N 12.6 12.5 -0.1 -1.1 -1 N

96 59.3 58.5 -0.8 -1.4 -2 SB 116 117 1 1 1 61.9 60.5 -1.4 -2.2 -3 SB 119 120 1 1 0 18.7 18.7 0.0 0.0 0 N 12.5 12.5 0.0 -0.2 0 N

97 59.3 57.7 -1.7 -2.8 -4 SB 118 116 -2 -2 -1 61.7 59.5 -2.1 -3.4 -5 SB 119 118 -1 -1 -1 18.8 18.7 -0.1 -0.5 0 N 12.5 12.4 -0.1 -0.7 0 N

98 55.5 54.4 -1.1 -1.9 -3 SB 105 106 1 1 1 57.9 56.6 -1.3 -2.2 -3 SB 108 108 0 0 0 18.4 18.4 0.0 0.0 0 N 12.3 12.3 0.0 -0.2 0 N

99 68.7 72.5 3.8 5.6 10 SA 143 152 9 7 5 72.9 77.1 4.3 5.9 11 SA 150 161 11 7 6 19.3 19.7 0.4 2.3 1 N 12.9 13.2 0.3 2.1 1 N

100 65.3 68.3 3.0 4.6 8 SA 134 157 23 17 11 69.0 72.6 3.6 5.2 9 SA 140 166 26 18 13 19.1 19.4 0.3 1.7 1 N 12.7 13.0 0.2 1.8 1 N

101 64.5 65.2 0.7 1.1 2 SA 129 147 18 14 9 68.2 69.1 0.9 1.3 2 SA 136 155 18 14 9 19.0 19.1 0.1 0.6 0 N 12.7 12.7 0.1 0.5 0 N

102 70.1 69.9 -0.2 -0.3 -1 MB 132 148 16 12 8 74.5 73.9 -0.6 -0.8 -1 MB 137 156 19 14 9 19.4 19.5 0.1 0.6 0 N 12.9 13.0 0.0 0.2 0 N

103 75.2 82.1 6.9 9.2 17 SA 142 157 15 10 7 78.7 85.7 7.0 8.9 17 SA 148 162 14 10 7 20.5 21.8 1.4 6.7 4 N 13.7 14.7 1.1 7.9 4 N

104 73.7 78.2 4.5 6.0 11 SA 148 173 26 17 13 77.3 81.3 4.0 5.2 10 SA 155 179 24 15 12 20.3 21.6 1.2 6.1 4 N 13.6 14.6 1.0 7.6 4 N

105 74.8 84.0 9.2 12.3 23 SA 169 205 36 21 18 77.9 86.8 8.9 11.4 22 SA 172 210 38 22 19 20.5 22.8 2.3 11.3 7 SlA 13.7 15.6 1.9 14.0 8 SlA

106 76.4 87.2 10.8 14.1 27 SA 162 200 38 23 19 79.6 90.6 11.0 13.8 28 SA 165 202 37 23 19 20.7 22.9 2.3 10.9 7 SlA 13.8 15.6 1.8 13.1 7 SlA

107 74.4 69.8 -4.5 -6.1 -11 SB 172 157 -15 -9 -7 79.0 74.3 -4.7 -6.0 -12 SB 182 167 -15 -8 -7 20.1 19.7 -0.5 -2.3 -1 N 13.4 13.1 -0.3 -2.3 -1 N

108 73.3 69.6 -3.7 -5.1 -9 SB 169 158 -11 -7 -6 78.0 74.2 -3.8 -4.9 -10 SB 181 168 -13 -7 -7 20.1 19.7 -0.4 -2.1 -1 N 13.4 13.1 -0.3 -2.2 -1 N

109 77.4 75.5 -1.9 -2.5 -5 SB 157 157 0 0 0 82.7 81.0 -1.6 -2.0 -4 SB 166 166 0 0 0 20.4 20.1 -0.4 -1.7 -1 N 13.6 13.4 -0.3 -1.9 -1 N

110 78.3 77.4 -0.9 -1.1 -2 SB 162 165 4 2 2 82.9 82.3 -0.6 -0.8 -2 SB 168 173 6 3 3 20.6 20.4 -0.1 -0.7 0 N 13.6 13.5 -0.1 -0.5 0 N

111 76.8 77.3 0.5 0.6 1 MA 179 190 11 6 5 81.3 82.5 1.2 1.4 3 SA 190 203 12 7 6 20.5 20.4 -0.1 -0.5 0 N 13.5 13.5 0.0 -0.3 0 N

112 74.3 74.0 -0.3 -0.4 -1 MB 172 181 9 5 5 78.6 78.8 0.2 0.3 1 MA 182 193 10 6 5 20.2 20.1 -0.1 -0.7 0 N 13.4 13.3 -0.1 -0.5 0 N

113 77.9 75.9 -2.0 -2.6 -5 SB 158 169 11 7 6 82.6 80.6 -2.0 -2.5 -5 SB 165 177 12 7 6 20.5 20.3 -0.2 -1.0 -1 N 13.5 13.4 -0.1 -0.8 0 N

114 70.4 67.9 -2.4 -3.5 -6 SB 150 144 -5 -4 -3 74.2 71.5 -2.7 -3.6 -7 SB 157 152 -6 -4 -3 19.9 19.8 -0.2 -0.9 -1 N 13.2 13.1 -0.1 -0.9 0 N

115 67.5 65.2 -2.4 -3.5 -6 SB 140 135 -5 -4 -3 71.2 68.5 -2.7 -3.8 -7 SB 146 141 -5 -3 -2 19.6 19.5 -0.2 -0.8 0 N 13.0 12.9 -0.1 -0.8 0 N

116 84.7 79.8 -4.9 -5.8 -12 SB 176 171 -5 -3 -3 90.7 84.2 -6.5 -7.1 -16 SB 186 177 -9 -5 -4 21.2 21.0 -0.2 -0.8 -1 N 14.0 13.9 -0.1 -0.9 0 N

117 83.8 80.2 -3.6 -4.3 -9 SB 182 172 -10 -6 -5 89.5 84.6 -4.9 -5.5 -12 SB 190 178 -12 -6 -6 21.2 21.1 -0.1 -0.5 0 N 14.1 14.0 -0.1 -0.5 0 N

118 82.2 78.1 -4.1 -5.0 -10 SB 220 207 -13 -6 -6 87.7 82.4 -5.3 -6.0 -13 SB 236 220 -16 -7 -8 21.0 20.8 -0.2 -0.9 -1 N 14.0 13.8 -0.1 -0.8 0 N

119 83.5 76.3 -7.2 -8.6 -18 SB 170 155 -15 -9 -8 89.5 79.9 -9.6 -10.7 -24 SB 179 160 -19 -11 -9 20.9 20.7 -0.2 -1.0 -1 N 13.9 13.7 -0.1 -1.1 -1 N

120 77.1 74.3 -2.8 -3.7 -7 SB 199 183 -16 -8 -8 82.1 77.6 -4.5 -5.4 -11 SB 212 192 -21 -10 -10 20.3 20.5 0.2 1.0 1 N 13.5 13.6 0.1 1.0 1 N

121 76.0 74.6 -1.3 -1.7 -3 SB 194 193 0 0 0 80.8 78.3 -2.6 -3.2 -6 SB 209 203 -6 -3 -3 20.2 20.5 0.3 1.5 1 N 13.4 13.6 0.2 1.7 1 N

122 82.0 76.4 -5.6 -6.8 -14 SB 168 166 -2 -1 -1 87.9 80.4 -7.5 -8.6 -19 SB 178 169 -9 -5 -5 20.7 20.6 -0.1 -0.4 0 N 13.7 13.7 0.0 -0.2 0 N

123 66.0 62.6 -3.4 -5.2 -8 SB 129 125 -4 -3 -2 68.4 64.8 -3.6 -5.3 -9 SB 135 128 -7 -5 -3 19.7 19.5 -0.2 -1.2 -1 N 13.1 13.0 -0.1 -0.8 0 N

124 63.8 61.3 -2.6 -4.0 -6 SB 142 133 -9 -6 -4 66.2 63.5 -2.7 -4.1 -7 SB 147 137 -10 -7 -5 19.5 19.3 -0.2 -0.9 -1 N 12.9 12.9 -0.1 -0.6 0 N

125 64.2 61.1 -3.1 -4.9 -8 SB 145 140 -5 -3 -2 66.6 63.3 -3.3 -5.0 -8 SB 149 143 -6 -4 -3 19.6 19.3 -0.2 -1.1 -1 N 13.0 12.9 -0.1 -0.8 0 N

126 66.0 62.8 -3.2 -4.8 -8 SB 130 133 3 3 2 68.3 64.9 -3.5 -5.1 -9 SB 135 136 1 1 1 19.7 19.5 -0.2 -1.0 -1 N 13.1 13.0 -0.1 -0.6 0 N

127 88.5 85.1 -3.4 -3.9 -9 SB 219 209 -10 -5 -5 93.3 89.6 -3.7 -4.0 -9 SB 228 217 -11 -5 -6 22.1 21.7 -0.4 -1.9 -1 N 14.6 14.3 -0.3 -1.9 -1 N

128 64.9 62.0 -2.9 -4.5 -7 SB 124 120 -4 -3 -2 68.3 65.2 -3.2 -4.6 -8 SB 130 125 -5 -4 -2 19.4 19.2 -0.2 -0.9 -1 N 12.9 12.8 -0.1 -0.8 0 N

129 64.8 61.5 -3.3 -5.0 -8 SB 127 120 -7 -5 -3 68.2 64.6 -3.6 -5.3 -9 SB 132 125 -7 -6 -4 19.3 19.2 -0.2 -1.0 -1 N 12.9 12.7 -0.1 -0.9 0 N

130 64.5 61.3 -3.2 -5.0 -8 SB 126 121 -5 -4 -2 67.9 64.4 -3.5 -5.1 -9 SB 132 127 -4 -3 -2 19.3 19.1 -0.2 -1.0 -1 N 12.8 12.7 -0.1 -0.9 0 N

131 70.3 65.6 -4.8 -6.8 -12 SB 152 142 -10 -7 -5 73.1 68.2 -4.8 -6.6 -12 SB 158 146 -12 -8 -6 20.2 19.7 -0.5 -2.3 -1 N 13.4 13.1 -0.3 -2.1 -1 N

132 67.5 64.5 -3.0 -4.4 -7 SB 164 150 -14 -9 -7 70.3 67.2 -3.1 -4.4 -8 SB 168 154 -14 -8 -7 19.9 19.6 -0.2 -1.2 -1 N 13.2 13.0 -0.1 -1.0 -1 N

133 63.6 63.1 -0.5 -0.9 -1 MB 139 134 -5 -3 -2 66.7 65.5 -1.2 -1.8 -3 SB 146 140 -7 -5 -3 19.3 19.5 0.2 0.9 1 N 12.9 13.0 0.1 1.1 1 N

134 63.3 63.4 0.1 0.2 0 N 139 143 4 3 2 66.3 65.7 -0.6 -0.9 -2 SB 146 147 0 0 0 19.3 19.6 0.2 1.2 1 N 12.8 13.0 0.2 1.5 1 N

135 64.8 61.8 -3.0 -4.6 -7 SB 147 137 -11 -7 -5 67.7 64.5 -3.3 -4.8 -8 SB 153 140 -13 -8 -6 19.5 19.3 -0.2 -1.0 -1 N 12.9 12.8 -0.1 -0.9 0 N

136 64.1 61.5 -2.5 -4.0 -6 SB 147 135 -13 -9 -6 66.9 64.2 -2.8 -4.1 -7 SB 151 139 -12 -8 -6 19.5 19.3 -0.2 -0.8 -1 N 12.9 12.8 -0.1 -0.7 0 N

137 61.1 58.4 -2.7 -4.5 -7 SB 128 121 -7 -5 -3 63.7 60.6 -3.0 -4.8 -8 SB 133 124 -8 -6 -4 19.2 19.0 -0.2 -0.9 -1 N 12.7 12.7 -0.1 -0.7 0 N

138 60.4 58.0 -2.4 -3.9 -6 SB 127 119 -9 -7 -4 62.9 60.2 -2.6 -4.2 -7 SB 133 122 -12 -9 -6 19.1 19.0 -0.1 -0.7 0 N 12.7 12.6 -0.1 -0.5 0 N

139 63.0 61.3 -1.6 -2.6 -4 SB 140 132 -8 -5 -4 65.7 63.9 -1.7 -2.6 -4 SB 145 137 -8 -6 -4 19.4 19.3 -0.1 -0.5 0 N 12.9 12.8 0.0 -0.4 0 N

140 62.8 61.4 -1.4 -2.3 -4 SB 139 132 -6 -5 -3 65.5 63.9 -1.5 -2.3 -4 SB 144 137 -7 -5 -3 19.4 19.3 -0.1 -0.4 0 N 12.8 12.8 0.0 -0.3 0 N

141 59.0 57.8 -1.2 -2.1 -3 SB 122 117 -6 -5 -3 61.4 60.0 -1.4 -2.3 -4 SB 126 119 -7 -6 -4 19.0 19.0 0.0 -0.2 0 N 12.6 12.6 0.0 -0.1 0 N

142 59.0 58.0 -0.9 -1.6 -2 SB 121 116 -5 -4 -2 61.2 60.1 -1.1 -1.8 -3 SB 125 120 -5 -4 -3 19.0 19.0 0.0 -0.1 0 N 12.6 12.7 0.0 0.1 0 N

143 63.5 62.3 -1.2 -2.0 -3 SB 138 133 -5 -4 -2 66.2 64.7 -1.5 -2.2 -4 SB 141 137 -5 -3 -2 19.5 19.5 0.0 0.0 0 N 12.9 13.0 0.0 0.3 0 N


J2451 52 of 55 March 2016




144 62.0 60.9 -1.1 -1.7 -3 SB 136 133 -3 -2 -2 64.5 63.3 -1.2 -1.8 -3 SB 140 136 -5 -3 -2 19.3 19.3 0.0 -0.2 0 N 12.8 12.8 0.0 0.0 0 N

145 59.2 58.4 -0.8 -1.4 -2 SB 120 115 -4 -4 -2 61.5 60.3 -1.1 -1.8 -3 SB 123 119 -4 -3 -2 19.1 19.1 0.0 0.2 0 N 12.7 12.7 0.1 0.4 0 N

146 59.1 58.3 -0.8 -1.3 -2 SB 122 119 -3 -2 -1 61.3 60.3 -1.1 -1.7 -3 SB 126 121 -5 -4 -2 19.1 19.1 0.0 0.2 0 N 12.7 12.7 0.1 0.4 0 N

147 60.8 59.9 -0.9 -1.4 -2 SB 119 117 -2 -1 -1 62.5 61.7 -0.8 -1.3 -2 SB 121 118 -3 -2 -1 19.4 19.3 -0.1 -0.3 0 N 12.9 12.9 0.0 -0.2 0 N

148 58.3 57.5 -0.8 -1.4 -2 SB 122 119 -3 -3 -2 60.4 59.5 -1.0 -1.6 -2 SB 124 121 -3 -2 -1 19.0 19.0 0.0 0.0 0 N 12.6 12.6 0.0 0.1 0 N

149 58.8 57.3 -1.6 -2.6 -4 SB 123 119 -4 -3 -2 61.1 59.4 -1.7 -2.8 -4 SB 128 121 -7 -6 -4 19.0 18.9 -0.1 -0.4 0 N 12.6 12.6 0.0 -0.3 0 N

150 60.6 57.8 -2.8 -4.6 -7 SB 130 123 -6 -5 -3 62.9 60.0 -3.0 -4.7 -7 SB 134 126 -8 -6 -4 19.2 19.0 -0.2 -1.0 -1 N 12.7 12.6 -0.1 -0.8 0 N

151 67.3 62.7 -4.6 -6.9 -12 SB 122 122 0 0 0 70.2 65.4 -4.8 -6.8 -12 SB 126 126 0 0 0 19.7 19.4 -0.3 -1.7 -1 N 13.1 12.9 -0.2 -1.4 -1 N

152 66.6 63.2 -3.4 -5.1 -8 SB 135 130 -6 -4 -3 69.4 65.8 -3.6 -5.2 -9 SB 138 133 -5 -4 -3 19.7 19.5 -0.2 -1.1 -1 N 13.1 13.0 -0.1 -0.7 0 N

153 66.8 62.3 -4.5 -6.7 -11 SB 135 126 -10 -7 -5 69.6 65.0 -4.6 -6.6 -12 SB 137 128 -10 -7 -5 19.7 19.3 -0.4 -1.8 -1 N 13.1 12.9 -0.2 -1.5 -1 N

154 67.6 62.2 -5.4 -8.0 -14 SB 123 116 -7 -6 -3 70.5 65.0 -5.6 -7.9 -14 SB 128 121 -7 -5 -4 19.7 19.3 -0.4 -2.2 -1 N 13.1 12.8 -0.3 -2.0 -1 N

155 87.3 74.5 -12.8 -14.7 -32 SB 169 141 -28 -17 -14 91.8 78.4 -13.4 -14.6 -34 SB 174 146 -29 -16 -14 21.2 20.4 -0.8 -3.7 -2 N 14.1 13.6 -0.5 -3.8 -2 N

156 88.0 75.2 -12.9 -14.6 -32 SB 189 150 -39 -21 -19 92.7 79.2 -13.5 -14.6 -34 SB 193 154 -39 -20 -19 21.6 20.5 -1.1 -5.1 -3 N 14.4 13.6 -0.8 -5.4 -3 N

157 80.0 73.6 -6.4 -8.0 -16 SB 203 179 -24 -12 -12 83.8 77.5 -6.2 -7.4 -16 SB 209 186 -22 -11 -11 20.8 20.3 -0.4 -2.2 -1 N 13.8 13.5 -0.3 -2.2 -1 N

158 80.5 72.1 -8.3 -10.4 -21 SB 200 169 -31 -16 -16 84.4 75.9 -8.6 -10.1 -21 SB 205 176 -30 -14 -15 20.7 20.2 -0.5 -2.4 -2 N 13.8 13.4 -0.3 -2.4 -1 N

159 67.4 62.4 -4.9 -7.3 -12 SB 142 126 -17 -12 -8 69.9 64.8 -5.0 -7.2 -13 SB 147 131 -16 -11 -8 19.8 19.4 -0.4 -1.8 -1 N 13.2 12.9 -0.2 -1.6 -1 N

160 67.5 62.7 -4.8 -7.1 -12 SB 155 140 -15 -10 -8 69.9 65.0 -4.8 -6.9 -12 SB 158 144 -14 -9 -7 19.8 19.5 -0.4 -1.8 -1 N 13.2 13.0 -0.2 -1.6 -1 N

161 69.5 63.2 -6.3 -9.1 -16 SB 132 120 -12 -9 -6 71.6 65.2 -6.4 -8.9 -16 SB 136 123 -13 -9 -6 20.1 19.6 -0.6 -2.8 -2 N 13.4 13.0 -0.3 -2.4 -1 N

162 69.3 62.8 -6.5 -9.4 -16 SB 147 132 -15 -10 -7 71.4 64.8 -6.6 -9.2 -16 SB 149 135 -14 -9 -7 20.2 19.6 -0.6 -3.0 -2 N 13.4 13.0 -0.3 -2.6 -1 N

163 66.4 61.9 -4.5 -6.8 -11 SB 156 139 -18 -11 -9 68.5 63.9 -4.6 -6.8 -12 SB 159 142 -17 -11 -8 19.8 19.5 -0.4 -1.8 -1 N 13.2 13.0 -0.2 -1.5 -1 N

164 66.8 62.5 -4.3 -6.4 -11 SB 146 129 -17 -12 -9 68.9 64.4 -4.4 -6.4 -11 SB 149 132 -17 -12 -9 19.8 19.5 -0.3 -1.6 -1 N 13.2 13.0 -0.2 -1.3 -1 N

165 61.9 58.8 -3.1 -5.0 -8 SB 123 114 -10 -8 -5 63.8 60.7 -3.1 -4.9 -8 SB 126 117 -9 -7 -5 19.4 19.2 -0.2 -1.2 -1 N 12.9 12.8 -0.1 -0.9 0 N

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J2451 53 of 55 March 2016




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Baker Street and Gloucester Place 2-way System

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Transcript of Baker Street and Gloucester Place 2-way System