preliminary environmental information report...by cambridge environmental research consultants...
TRANSCRIPT
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Heathrow Expansion PRELIMINARY ENVIRONMENTAL INFORMATION REPORT
© Heathrow Airport Limited 2019
Volume 3, Chapter 7: Air quality and odour
Appendices
Appendix 7.1 Technical appendix on dispersion modelling
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Heathrow Expansion PRELIMINARY ENVIRONMENTAL INFORMATION REPORT: Chapter 7: Appendix 7.1
Appendix 7.1 © Heathrow Airport Limited 2019
Appendix 7.1 Technical appendix on dispersion modelling
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Heathrow Expansion PRELIMINARY ENVIRONMENTAL INFORMATION REPORT: Chapter 7: Appendix 7.1
Appendix 7.1 © Heathrow Airport Limited 2019
Contents
1. Introduction 1
2. Meteorological parameters 2
3. Aircraft emissions 3
3.1 General procedure 3
3.2 The dispersion model 4
3.3 Emissions sources: Aircraft emissions 5 Modes of runway operation 5 Aircraft activity 7 TAAM modelling 12 Reduced engine taxi 12 Main engine emissions: Emission rates 13 Main engine emissions: Future emission factors 17 Main engine emissions: Times in mode 17 Main engine emissions: Thrust settings 21 Auxiliary power units (APU) emissions 22 Brake and tyre wear emissions 24 Aircraft emissions: Spatial disaggregation 24 Aircraft emissions: Runway assignments and temporal variation 29
3.4 Emissions sources: On-airport, non-aircraft emissions 30 Ground support equipment (GSE) 30 Fire training 32
4. Approach to modelling emissions from road traffic 33
4.1 Model domain 33
4.2 Traffic data 34
4.3 Calculating road traffic emissions 35
4.4 Sensitivity test 36
4.5 Additional features 36
5. Approach to modelling car park emissions 38
6. Approach to modelling Lakeside Waste Management Facility emissions 39
7. Approach to predicting background concentrations 40
7.2 Background NO2 and NOX concentrations for sensitivity test 42
7.3 Modelling the background concentration field for traffic emissions 42
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Heathrow Expansion PRELIMINARY ENVIRONMENTAL INFORMATION REPORT: Chapter 7: Appendix 7.1
Appendix 7.1 © Heathrow Airport Limited 2019
8. Calculation of total NO2 concentrations 46
8.2 EU limit value compliance 47
9. Model verification methodology 48
9.2 NOX and NO2 48
9.3 PM10 and PM2.5 57
10. Effect on EU limit value compliance methodology 63
11. CURED sensitivity test results 65
11.1 Community area results 65
11.2 Summary results across the CAQOAA 73
11.3 Effect on EU limit value compliance results 75
12. Bibliography 80
TABLE OF TABLES
Table 3.1: Movements per year by aircraft type: baseline scenarios 8 Table 3.2: Movements per year by aircraft type: ‘Future Baseline’ scenarios 9 Table 3.3: Movements per year by aircraft type: ‘DCO Project’ scenarios 10 Table 3.4: Aircraft data 14 Table 3.5: Approach and landing roll times by aircraft type 18 Table 3.6: Take-off roll and climb times 20 Table 3.7: Take-off thrusts for future scenarios 22 Table 3.8: APU data 22 Table 6.1: Emission data for the existing facility 39 Table 7.1: Sectors included in the background maps 41 Table 9.1: Sites used in the model verification 48 Table 9.2: Calculated factors for each year 49 Table 9.3: Statistical performance of each of the models 50 Table 11.1: CURED sensitivity test NO2 results at selected representative receptors within each community area 66 Table 11.2: Count of properties by impact descriptor 73 Table 11.3: Count of properties by impact descriptor (CURED sensitivity test) 73 Table 11.4: Count of properties by magnitude of change in concentration 74 Table 11.5: Count of properties by magnitude of change in concentration (CURED sensitivity test)74 Table 11.6: Local air quality receptors informing DCO Project significance 74 Table 11.7: Local air quality receptors informing DCO Project significance (CURED sensitivity test)75 Table 11.8: NO2 annual mean EU limit value compliance in 2022 76 Table 11.9: NO2 annual mean EU limit value compliance in 2027 77 Table 11.10: NO2 annual mean EU limit value compliance in 2030 78
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Heathrow Expansion PRELIMINARY ENVIRONMENTAL INFORMATION REPORT: Chapter 7: Appendix 7.1
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TABLE OF GRAPHICS
Graphic 3.1: Approach NOX emissions density plot 25 Graphic 3.2: Landing roll NOX emissions density plot 26 Graphic 3.3: Taxi-in NOX emissions density plot 26 Graphic 3.4: Taxi-out NOX emissions density plot 27 Graphic 3.5: Take-off roll NOX emissions density plot 27 Graphic 3.6: Initial climb NOX emissions density plot 28 Graphic 3.7: APU NOX emissions density plot 28 Graphic 3.8: Brake PM10 emissions density plot 29 Graphic 3.9: Tyre PM10 emissions density plot 29 Graphic 3.10: GSE NOX emissions density plot 31 Graphic 4.1: 1km x 1km receptor cell and corresponding explicitly modelled roads 34 Graphic 7.1: Air quality core assessment area and HHASAM Fully Modelled Area 44 Graphic 7.2: Receptor grid example 45 Graphic 9.1: 2015 Model performance – simple verification approach 50 Graphic 9.2: 2015 Model performance – LOOCV verification approach 51 Graphic 9.3: 2016 Model performance – simple verification approach 51 Graphic 9.4: 2016 model performance – LOOCV verification approach 52 Graphic 9.5: 2017 Model performance – simple verification approach 52 Graphic 9.6: 2017 Model performance – LOOCV verification approach 53 Graphic 9.7: LOOCV primary verification factors 54 Graphic 9.8: Modelled vs measured NO2 at diffusion tube sites close to busy junctions 55 Graphic 9.9: 2015 Modelled vs measured NO2 at diffusion tube sites 56 Graphic 9.10: 2016 Modelled vs measured NO2 at diffusion tube sites 56 Graphic 9.11: 2017 Modelled vs measured NO2 at diffusion tube sites 57 Graphic 9.12: 2015 model performance – PM10 58 Graphic 9.13: 2015 model performance – PM2.5 58 Graphic 9.14: 2016 model performance – PM10 59 Graphic 9.15: 2016 model performance – PM2.5 59 Graphic 9.16: 2017 model performance – PM10 60 Graphic 9.17: 2017 model performance – PM2.5 60
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Heathrow Expansion PRELIMINARY ENVIRONMENTAL INFORMATION REPORT: Chapter 7: Appendix 7.1
Appendix 7.1-1 © Heathrow Airport Limited 2019
1. INTRODUCTION
1.1.1 This Appendix describes in detail the methodology applied in the dispersion
modelling carried out, as discussed in Chapter 7: Air quality and odour. The
assessment has focussed on the prediction of total pollutant concentrations under
a variety of scenarios, both with and without the DCO Project, at receptors
throughout the illustrative ‘Core Air Quality Objective Assessment Area’
(CAQOAA), a defined 12 x 11km primarily rectangular area centred on the Airport.
The CAQOAA has been extended slightly to the east to enable the prediction of
concentrations at all residential properties within the Heston Community Area (to
inform the Health Impact Assessment work) and is shown in Graphic 7.1.
Dispersion modelling has been used to predict pollutant concentrations in Windsor
and Old Windsor, as the tiered assessment using traffic data outside of the
CAQOAA indicated that impacts in this area may be greater than negligible.
1.1.2 In order to determine total pollutant concentrations, a number of individual
contributions have been determined, and combined. The assessment has
combined the contributions of several sources. The approach taken to determining
these contributions is set out in detail for each source in this Appendix. The
sources are:
1. The emissions from aircraft and all airside sources (including airside vehicles,
machinery and energy plant), modelled using the ADMS-Airport model
2. Road traffic emissions modelled using models from the ADMS-Urban suite
3. Emissions from vehicles using car parks at Heathrow Airport, including
additional cold start emissions
4. The contribution of the Lakeside Waste Management Facility in baseline and
future baseline scenarios
5. The contribution from all other sources (i.e. background concentrations).
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2. METEOROLOGICAL PARAMETERS
2.1.1 Meteorological data from the monitoring station at Heathrow has been purchased
from the Met Office for use in the assessment. The surface roughness for the area
has been set in the model at 0.5m, with the minimum Monin-Obukhov length set at
30m. Given that the meteorological monitoring station is located in the centre of
the model domain, in a setting similar to much of the study area, these parameters
have been applied for both the dispersion site and meteorological measurement
site.
2.1.2 Wherever possible, the urban canopy flow module has been utilised to better
represent the effects of buildings on the flow of air throughout the model domain.
This module cannot be used when modelling certain sources (e.g. jet sources), but
has been used when modelling road sources, car park sources, the Lakeside
Waste Management Facility (in the baseline and future baseline scenarios) and
some airside sources. For baseline and future baseline scenarios, the input data
used for the urban canopy flow module was the 1km resolution dataset published
by Cambridge Environmental Research Consultants (CERC, 2016), which
developed the ADMS models. For the scenarios with the DCO Project, this input
file has been edited to reflect the changes to building coverage, average building
height and cross-wind vertical area of buildings associated with the DCO Project.
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3. AIRCRAFT EMISSIONS
3.1 General procedure
3.1.1 There are two principal sets of recommendations for carrying out an airport air
quality study. The first arises from the Project for the Sustainable Development of
Heathrow (PSDH) (DfT, 2006). The objective of PSDH was to develop the best
practical methodology for assessing the air quality impacts of a third runway at
Heathrow. This came up with a number of specific recommendations but contains
some areas where the best approach depends on data availability. For example,
PSDH does not make any recommendations about how to determine how long
aircraft spend operating in various modes as there are various potential data
sources, and it is left to the analyst to use their judgement as to the best way of
extracting suitable operating durations.
3.1.2 The PSDH methodology was implemented by Heathrow Airport for its 2008/9
emissions inventory (Underwood et al, 2010a), modelling study (Underwood et al,
2010b) and model evaluation study (Underwood et al, 2010c). The reports give a
detailed description of the methodology used and forms a useful reference. The
model evaluation found that modelled concentrations generally agreed well with
the extensive monitoring data around Heathrow and formed a suitable basis for
evaluating the impacts of future airport developments. Subsequent inventories
produced for Heathrow have used essentially the same methodology, with some
updates where new airport-specific data has become available (e.g. for taxiing
times).
3.1.3 The second methodology was published by the International Civil Aviation
Organization (ICAO) in 2011. This document considers production of emission
inventories for historic years, with very little attention paid to how inventories for
future years might be produced. As such it is less directly relevant to the present
work for the DCO Project.
3.1.4 The ICAO methodology offers different levels of assessment, described as
‘simple’, ‘advanced’ and ‘sophisticated’, each requiring increasingly detailed data.
The sophisticated approach generally requires detailed data on times, engine
settings and so forth for each individual aircraft movement, so it is unsuitable for
modelling future scenarios where assumptions on future operation must be made.
The advanced approach is similar to the PSDH recommendations in terms of data
requirements and can generally be adapted to future scenarios given suitable
forecast data. Much of the detail of the methodology is the same or similar
between PSDH and ICAO.
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3.1.5 A third ‘standard’ is the Aviation Environmental Design Tool (AEDT), published by
the US Federal Aviation Administration (FAA) for airport air quality inventories and
noise studies. Detailed documentation of the methodology used by the tool is not
readily available.
3.1.6 While various research groups have suggested ways in which parts of the
inventory calculation can be improved, few of these have been generally
incorporated into received methodologies. One notable exception is the FOA 3a
method (Kinsey and Wayson, 2009) for calculating PM10 emissions from smoke
number emissions.
3.1.7 Defra issues technical guidance on Local Air Quality Management (LAQM) (Defra,
2016), which is an important source of guidance on approaching common sources
of air pollution. However, other than providing a screening threshold of 10 million
passengers per annum or 1 million tonnes of freight, it does not provide
recommendations on the technical issues of modelling air quality around large
airports.
3.1.8 The methodology used in this assessment follows the general approach of the
ICAO advanced and PSDH approaches, and implements many of their specific
recommendations, with decisions about the best approach being led by the
availability of data.
3.2 The dispersion model
3.2.1 The PSDH carried out a model inter-comparison study to compare the use of
various dispersion modelling tools for airport air quality modelling. As a result, the
PSDH endorsed the use of ADMS-Airport, a version of the long-established
dispersion modelling tool ADMS adapted to account for the momentum and
buoyancy fluxes from jet engines. However, the use of the regular version of
ADMS with suitable initial dispersion characteristics was also found to be
acceptable.
3.2.2 AEDT uses AERMOD for the dispersion modelling. AERMOD was developed in
the United States by the American Meteorological Society (AMS)/United States
Environmental Protection Agency (USEPA) Regulatory Model Improvement
Committee (AERMIC). ADMS was developed in the UK by CERC in collaboration
with the Meteorological Office, National Power and the University of Surrey. Both
AERMOD and ADMS are termed ‘new generation’ models, parameterising stability
and turbulence in the planetary boundary layer by the Monin-Obukhov length and
the boundary layer depth. This approach allows the vertical structure of the
planetary boundary layer to be more accurately defined than by the stability
classification methods of earlier dispersion models such as the R91 Gaussian
plume model or Industrial Source Complex (ISC).
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3.2.3 Numerous model inter-comparison studies have demonstrated little difference
between the output of ADMS and AERMOD, except in certain complex terrain
scenarios. The principal difference between ADMS and ADMS-Airport is the jet
engine module, which tends to reduce modelled ground-level concentrations from
aircraft engines, especially at high thrust settings, as a result of the heat of the
plume.
3.2.4 Taking the jet engine module into consideration, ADMS-Airport (Version 4.1.1) has
been selected as the most appropriate model to use for the purposes of this
particular study.
3.3 Emissions sources: Aircraft emissions
Modes of runway operation
3.3.1 Each of the runways at the Airport can be used in two directions, with aircraft
moving along it either eastwards or westwards. This means there are two distinct
and independent aspects to the way that the runways are used for departures,
landings or both (‘mixed’):
1. Whether aircraft take off and land facing in a westerly direction or in an easterly
direction
2. Which physical runways are used for departures and which for landings.
3.3.2 Whether the Airport operates in westerly or easterly mode at any given time
depends on the weather. It is safer and more efficient for aircraft to take off and
land facing into the wind, although at Heathrow there is a preference to use
westerly operations as long as the tailwind is only slight. Since the wind direction
also affects the dispersion of pollutants, it is essential to ensure that runway
assignments are aligned with the met data used for the dispersion modelling. The
Airport changes between easterly and westerly operations at unpredictable times,
since it depends on the weather. At Heathrow, westerly operations are more
common than easterlies, by a ratio of about 70:30.
3.3.3 The choice of which runway is used for departures and which for landings is called
the runway operating mode. These modes change regularly, in what is called a
runway alternation pattern, in order to provide noise respite to residents near the
Airport. There are three alternation patterns relevant to this assessment, as
follows.
3.3.4 The alternation pattern currently operated and used for the assessment of
baseline years (2015–2017), is as follows. In westerly operations (i.e. when aircraft
are landing and departing facing in a westerly direction), there are two modes of
operation, identified by a two-letter abbreviation:
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1. DL: Departures on the northern runway (27R), landings on the southern
runway (27L)
2. LD: Landings on the northern runway (27R), departures on the southern
runway (27L).
3.3.5 The Airport alternates between these two modes at regularly scheduled times, so
that they are used equally often. The result is that over the course of a year, there
is an equal number of departures on each runway, and an equal number of
landings, for westerly operations.
3.3.6 In easterly operations, there is a single mode of operation: Landings on the
northern runway (09L), departures on the southern runway (09R) (Some landings
also take place on the southern runway early in the morning when there are a
large number of arrivals).
3.3.7 A new alternation pattern will be introduced for 2R operations for all future years.
In this pattern, westerly operations alternate as at present, but in easterly
operations a pattern of alternation similar to that for westerlies will be introduced,
alternating equally between two modes of operation:
1. DL: Departures on the northern runway (09L), landings on the southern runway
(09R)
2. LD: Landings on the northern runway (09L), departures on the southern
runway (09R).
3.3.8 The effect of this is that whether operating in easterlies or westerlies, each runway
will be used for an equal number of departures and an equal number of landings.
3.3.9 Finally, 3R operations will require a new alternation pattern. This will have four
modes of operation, regardless of westerly or easterly operation. These are
identified by a three-letter abbreviation:
1. MDL: Mixed (both landings and departures) on the (new) northern runway
(09L/27R), departures on the centre (currently northern) runway (09C/27C),
and landings on the southern runway (09R/27L)
2. MLD: Mixed on the northern runway, landings on the centre runway, and
departures on the southern runway
3. DLM: Departures on the northern runway, landings on the centre runway, and
mixed on the southern runway
4. LDM: Landings on the northern runway, departures on the centre runway, and
mixed on the southern runway.
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3.3.10 The alternation pattern will use each of these four modes equally. This means that
each of the runways will have equal number of departures and landings over the
course of four weeks. However, when any given mode is in operation, each
landing or departure will have two possible runways available, and this choice will
be based on operational parameters such as aircraft size, stand and (for
departures) the airspace routing. This means that the three physical runways will
not necessarily have the same number of movements.
3.3.11 For future scenarios, it is not possible to know which mode will be in operation
when. Therefore, for each scenario, each relevant mode has been modelled for
the full assessment year and the average of the two or four modes taken. This
provides the best estimate of the long-term average emissions.
Aircraft activity
3.3.12 For the baseline modelling, aircraft activity has been taken from movement
records extracted by Heathrow from their Business Objective Search System
(BOSS) database. For each movement in the year, this provides:
1. Hour of the year
2. Whether arrival or departure
3. Aircraft type
4. Stand
5. Runway (including direction).
3.3.13 For the future scenarios, the details of the aircraft movements are taken from the
forecast schedule. This gives a list of movements for each cargo and passenger
aircraft type for a typical busy summer day, for each of the modelled scenarios
(Future Baseline and DCO Project, for each assessment year). For each
movement in the day, this provides:
1. Hour of the day
2. Whether arrival or departure
3. Aircraft type
4. Terminal
5. Preferred physical runway (separate runway assignments were given for each
of the four possible alternation modes, DLM, LDM, MDL and MLD).
3.3.14 For the future scenarios, the same fleet is assumed to operate every day of the
year, but each movement is adjusted by a factor so that the total number of
movements in the year agrees with the number forecast for that year. For
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example, in the 2035 3R scenario, there are 2,153 movements in the busy day
schedule, and the total number of movements over the year is 740,000, so each
movement is adjusted by a factor of 740,000 / (2,153 × 365) = 0.942.
3.3.15 It is assumed that there are the same number of movements every day of the
year. Historic data shows that there are slightly more movements per day in
summer than in winter, but the difference is small, and sensitivity modelling
undertaken as part of previous air quality modelling work for Heathrow has shown
this assumption to be conservative.
3.3.16 These movements are summarised in Table 3.1 to Table 3.3. In these tables,
aircraft types that are not currently on the drawing board and are not expected to
enter into service before 2035 are described in terms of their current analogues
with a ‘G+2’ identifier; more details of this and the Generation column for the future
fleets are given in paragraph 3.3.31.
Table 3.1: Movements per year by aircraft type: baseline scenarios
Aircraft
code
Aircraft description 2015 2016 2017
318 Airbus A318 750 862 1,424
319 Airbus A319ceo 83,532 80,282 79,871
320 Airbus A320ceo 138,680 114,518 110,920
32A Airbus A320neo 2,301 25,785 30,421
321 Airbus A321ceo 42,768 40,496 36,386
32B Airbus A321neo 255 2,544 2,159
332 Airbus A330-200 6,403 7,085 4,998
333 Airbus A330-300 11,447 11,582 10,349
343 Airbus A340-300 1,982 2,224 1,142
346 Airbus A340-600 6,818 5,418 4,577
359 Airbus A350-900 58 714 2,809
388 Airbus A380-800 14,784 18,265 18,483
734 Boeing 737-400 1,534 1,390 702
736 Boeing 737-600 2,418 1,988 866
73G Boeing 737-700 4,903 7,205 4,794
738 Boeing 737-800 8,035 7,551 8,026
7M8 Boeing 737 MAX 8 0 0 102
73J Boeing 737-900 54 412 656
744 Boeing 747-400 24,900 19,967 19,650
74Y Boeing 747-400 Freighter 560 520 458
74H Boeing 747-8 10 7 242
752 Boeing 757-200 4,654 3,586 3,465
763 Boeing 767-300 25,296 22,961 23,092
764 Boeing 767-400 2,942 2,976 647
772 Boeing 777-200 31,346 29,718 28,177
77L Boeing 777-200LR 778 1,386 494
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Aircraft
code
Aircraft description 2015 2016 2017
77X Boeing 777-200 Freighter 200 206 326
773 Boeing 777-300 392 0 4
77W Boeing 777-300ER 29,867 29,931 32,304
788 Boeing 787-8 12,148 13,992 16,050
789 Boeing 787-9 3,453 13,599 20,435
AB4 Airbus A300 1,013 0 0
ABY Airbus A600-600 Freighter 126 1,581 1,850
AR1 Avro RJ100 690 496 56
CR9 Canadair Regional Jet 900 710 546 180
CS1 Airbus A220-100 0 6 1,258
DH4 Bombardier Dash 8 400Q 42 50 3,442
E90 Embraer E190 1,892 1,454 2,284
E95 Embraer E195 462 670 332
F70 Fokker 70 2,174 1,646 356
Other Other 3,717 1,359 2,128
Total 474,094 474,978 475,915
Table 3.2: Movements per year by aircraft type: ‘Future Baseline’ scenarios
Aircraft
code
Aircraft description Genera
tion
2R 2022
480k
2R 2027
480k
2R 2030
480k
2R 2035
480k
318 Airbus A318 G0 689 692 0 0
319 Airbus A319ceo G0 67,145 25,263 10,382 0
319N Airbus A319neo G+1 0 0 2,076 2,076
320 Airbus A320ceo G0 101,234 90,324 44,989 14,881
32H Airbus A320ceo G0 15,495 15,573 15,573 8,306
320N Airbus A320neo G+1 28,235 82,019 149,849 197,606
320X Airbus A320 G+2 G+2 0 0 0 0
321 Airbus A321ceo G0 40,287 39,798 20,072 3,461
321N Airbus A321neo G+1 14,806 15,573 36,683 53,295
32B Airbus A321neo G0 3,443 3,461 2,076 2,076
321X Airbus A321 G+2 G+2 0 0 0 0
332 Airbus A330-200 G0 6,198 4,845 2,076 692
333 Airbus A330-300 G0 14,118 12,112 7,267 1,384
339 Airbus A330-900 G+1 0 2,769 6,229 6,229
343 Airbus A340-300 G0 0 0 0 0
346 Airbus A340-600 G0 4,132 0 0 0
359 Airbus A350-900 G+1 3,443 9,690 9,690 13,151
359N Airbus A350-900 G+2 G+2 0 0 0 0
351 Airbus A350-1000 G+1 5,854 21,110 27,686 28,378
351N Airbus A350-1000 G+2 G+2 0 0 0 0
388 Airbus A380-800 G0 15,151 15,227 15,227 12,459
73W Boeing 737-700 G0 3,443 3,461 0 0
7M7 Boeing 737 MAX 7 G+1 0 0 4,845 4,845
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Heathrow Expansion PRELIMINARY ENVIRONMENTAL INFORMATION REPORT: Chapter 7: Appendix 7.1
Appendix 7.1-10 © Heathrow Airport Limited 2019
Aircraft
code
Aircraft description Genera
tion
2R 2022
480k
2R 2027
480k
2R 2030
480k
2R 2035
480k
738 Boeing 737-800 G0 11,019 9,690 1,384 0
73H Boeing 737-800 G0 2,755 2,769 0 0
7M8 Boeing 737 MAX 8 G+1 689 2,076 4,845 5,537
7X8 Boeing 737 MAX 8 G+2 G+2 0 0 0 0
73J Boeing 737-900 G0 689 692 0 0
7M9 Boeing 737 MAX 9 G+1 689 1,384 1,384 1,384
7X9 Boeing 737 MAX 9 G+2 G+2 0 0 0 0
744 Boeing 747-400 G0 11,707 0 0 0
74Y Boeing 747-400
Freighter
G0 689 346 346 346
74H Boeing 747-8 G0 689 692 692 692
763 Boeing 767-300 G0 0 0 0 0
76W Boeing 767-300 G0 3,443 0 0 0
772 Boeing 777-200 G0 26,514 7,267 0 0
77X Boeing 777-200
Freighter
G0 689 0 0 0
773 Boeing 777-300 G0 9,641 9,690 8,998 6,921
77W Boeing 777-300ER G0 25,825 25,955 25,263 16,265
779 Boeing 777-9 G+1 0 0 2,076 11,766
779N Boeing 777-9 G+2 G+2 0 0 0 0
788 Boeing 787-8 G+1 26,514 26,647 26,647 27,340
788N Boeing 787-8 G+2 G+2 0 0 0 0
789 Boeing 787-9 G+1 24,792 42,221 46,373 53,641
789N Boeing 787-9 G+2 G+2 0 0 0 0
781 Boeing 787-10 G+1 2,410 2,422 2,422 2,422
781N Boeing 787-10 G+2 G+2 0 0 0 0
ABY Airbus A600-600
Freighter
G0 1,377 0 0 0
DH4 Bombardier Dash 8
400Q
G0 4,821 0 0 0
E90 Embraer E190 G0 1,377 1,384 0 0
E95 Embraer E195 G0 0 4,845 4,845 0
E95-2 Embraer E195 G+1 G+1 0 0 0 4,845
Total 480,000 480,000 480,000 480,000
Table 3.3: Movements per year by aircraft type: ‘DCO Project’ scenarios
Aircraft
code
Aircraft description Generation 2R 2022
485K
3R 2027
567K
3R 2030
665K
3R 2035
740K
318 Airbus A318 G0 681 690 0 0
319 Airbus A319ceo G0 66,415 25,883 11,037 0
319N Airbus A319neo G+1 0 0 2,070 3,448
320 Airbus A320ceo G0 100,133 90,071 46,219 16,207
32H Airbus A320ceo G0 15,327 15,530 15,521 8,276
320N Airbus A320neo G+1 32,697 139,766 248,685 326,207
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Heathrow Expansion PRELIMINARY ENVIRONMENTAL INFORMATION REPORT: Chapter 7: Appendix 7.1
Appendix 7.1-11 © Heathrow Airport Limited 2019
Aircraft
code
Aircraft description Generation 2R 2022
485K
3R 2027
567K
3R 2030
665K
3R 2035
740K
320X Airbus A320 G+2 G+2 0 0 0 0
321 Airbus A321ceo G0 39,849 39,687 20,695 4,138
321N Airbus A321neo G+1 14,645 16,220 38,631 60,000
32B Airbus A321neo G0 3,406 3,451 2,070 2,069
321X Airbus A321 G+2 G+2 0 0 0 0
332 Airbus A330-200 G0 7,493 6,212 4,139 2,069
333 Airbus A330-300 G0 14,645 14,149 11,382 5,172
339 Airbus A330-900 G+1 0 2,761 6,209 6,897
343 Airbus A340-300 G0 681 0 0 0
346 Airbus A340-600 G0 4,087 0 0 0
359 Airbus A350-900 G+1 3,406 11,733 15,866 26,207
359N Airbus A350-900 G+2 G+2 0 0 0 0
351 Airbus A350-1000 G+1 5,790 22,432 35,871 42,759
351N Airbus A350-1000
G+2
G+2 0 0 0 0
388 Airbus A380-800 G0 14,986 15,875 17,246 15,862
73W Boeing 737-700 G0 3,406 3,451 0 0
7M7 Boeing 737 MAX 7 G+1 0 0 4,829 6,897
738 Boeing 737-800 G0 10,899 10,353 3,449 690
73H Boeing 737-800 G0 2,725 2,761 0 0
7M8 Boeing 737 MAX 8 G+1 681 2,071 8,968 13,103
7X8 Boeing 737 MAX 8
G+2
G+2 0 0 0 0
73J Boeing 737-900 G0 681 690 0 0
7M9 Boeing 737 MAX 9 G+1 681 1,380 2,070 2,069
7X9 Boeing 737 MAX 9
G+2
G+2 0 0 0 0
744 Boeing 747-400 G0 11,580 0 0 0
74Y Boeing 747-400
Freighter
G0 681 345 345 345
74H Boeing 747-8 G0 681 690 690 1,379
763 Boeing 767-300 G0 0 690 690 0
76W Boeing 767-300 G0 3,406 0 0 0
772 Boeing 777-200 G0 28,269 11,388 0 0
77X Boeing 777-200
Freighter
G0 681 0 0 0
773 Boeing 777-300 G0 9,537 11,043 11,727 8,966
77W Boeing 777-300ER G0 25,544 27,263 30,008 19,655
779 Boeing 777-9 G+1 0 0 2,759 14,483
779N Boeing 777-9 G+2 G+2 0 0 0 0
788 Boeing 787-8 G+1 26,225 28,298 33,112 36,207
788N Boeing 787-8 G+2 G+2 0 0 0 0
789 Boeing 787-9 G+1 25,204 52,110 79,676 102,414
789N Boeing 787-9 G+2 G+2 0 0 0 0
781 Boeing 787-10 G+1 2,384 2,416 4,139 5,517
781N Boeing 787-10 G+2 G+2 0 0 0 0
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Heathrow Expansion PRELIMINARY ENVIRONMENTAL INFORMATION REPORT: Chapter 7: Appendix 7.1
Appendix 7.1-12 © Heathrow Airport Limited 2019
Aircraft
code
Aircraft description Generation 2R 2022
485K
3R 2027
567K
3R 2030
665K
3R 2035
740K
ABY Airbus A600-600
Freighter
G0 1,362 0 0 0
DH4 Bombardier Dash 8
400Q
G0 4,768 0 0 0
E90 Embraer E190 G0 1,362 1,380 0 0
E95 Embraer E195 G0 0 6,212 6,898 2,069
E95-2 Embraer E195 G+1 G+1 0 0 0 6,897
Total 485,000 567,000 665,000 740,000
TAAM modelling
3.3.17 Additional information for the future scenarios comes from Total Airspace and
Airport Modeler (TAAM) simulations. TAAM is a software tool which simulates the
movements of every aircraft on the ground and in the air over the course of the
day, taking account of interactions between aircraft (e.g. waiting for another
aircraft to clear a runway or taxiway). The primary purpose of the simulations is to
ensure that the airfield layout and schedule can function properly without
excessive delays.
3.3.18 The TAAM model takes the forecast schedule as input, along with a mode of
operation (easterly or westerly; MDL, MLD, DLM or LDM). Part of the TAAM output
is the time at which each movement passes various gates (exits runway, arrives at
stand, starts pushback, starts taxi, enters hold zone, starts take-off roll, etc.),
which allows various times in mode to be extracted. In addition, it assigns each
movement to a stand (based on the availability of suitably-sized stands at the
required terminal when the inbound flight arrives) and to a runway (which is
sometimes different from the preferred runway given in the forecast schedule) to
optimise taxiway and runway usage within the constraints of the operating mode.
3.3.19 NATS carried out TAAM simulations using the preliminary airfield layout and 740K
ATM schedule, for all four modes of westerly operation (MDL, MLD, DLM and
LDM), and for LDM in easterlies. For the remaining three easterly modes, NATS
provided data in a similar movement-by-movement format but based on judgement
rather than running the TAAM model. These have since been modelled in TAAM
and the new data will be used in the assessment for the ES.
Reduced engine taxi
3.3.20 Although traditional practice is to have all of an aircraft’s engines running during
taxi-out and taxi-in, it is increasingly common to use reduced engine taxi (RET) for
at least part of the taxi-out or taxi-in stages. In RET, one or more engines is
switched off for part of the taxi, the remaining engine or engines being sufficient to
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Appendix 7.1-13 © Heathrow Airport Limited 2019
propel the aircraft. RET reduces emissions and reduces fuel burn, but there are
various operational considerations which constrain its use.
3.3.21 During RET, it is normal to have the Auxiliary Power Unit (APU) operating, to
ensure that there is a redundant power source. Use of RET has been modelled by:
1. Reducing the number of engines operating during periods of RET, from 2 to 1
for twin-engine aircraft and from 4 to 2 for 4 engine aircraft
2. Assuming that the APU is operating at ‘normal running’ load during periods of
RET.
3.3.22 Heathrow Airport’s BOSS system records whether RET is used for departures. For
baseline scenarios, this record is used to determine whether a departure uses
RET. BOSS does not record whether RET is used for arrivals; since RET is used
more commonly for arrivals than departures, the assumption has been made that
RET is used for arrivals corresponding to departures that use RET.
3.3.23 In addition, a survey of airlines has been carried out to find which airlines use
RET, and on which aircraft types. The results of this survey have been used for
future scenarios; it is assumed that if an airline uses RET for a given aircraft type,
then all arrivals and departures of that airline and aircraft type use RET. Due to
their design, Boeing 787 aircraft do not use RET.
3.3.24 For movements that use RET, it is assumed that the final 2 minutes 30 seconds of
taxi-out before start of roll, and the first 2 minutes 30 seconds of taxi-in, operate
with all engines running (and the APU is not running during these periods).
Main engine emissions: Emission rates
3.3.25 For the baseline modelling, engine assignments were taken from the Heathrow
AUWR (All-Up Weight Return) database. These are collected by Heathrow for the
purpose of emissions charging. Engine assignments are provided in the form of
the engine UID, a unique identifier used in the ICAO databank of emissions
certification data; this allows each aircraft’s engines to be indexed directly in the
databank. Assignments from AUWR have been cross-checked against other
published sources (including BuchAir’s JP Airline Fleets product (Flightglobal,
2013)) and found to have good reliability.
3.3.26 Emission factors for jet engines are taken from the ICAO databank, version 24
(ICAO, 2018). The databank provides emission indices for nitrogen oxides (NOX),
carbon monoxide (CO) and hydrocarbons (HC), fuel flow rates and smoke
numbers; each of these is given at four power settings (100%, 85%, 30% and 7%
of rated thrust). Emission indices (in g of pollutant per kg of fuel burned) are
multiplied by fuel flow rates (in kg s−1) to obtain an emission factor in g s−1.
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Appendix 7.1-14 © Heathrow Airport Limited 2019
3.3.27 The ICAO databank gives smoke numbers which need to be converted to
emission indices. This is done using the FOA 3a method (Kinsey and Wayson,
2009), with the amendment that the factor of (1 – bypass ratio) in equation 7a is
only applied to mixed turbofan engines (Underwood et al, 2010a). For some
engines, smoke number data points at certain thrust settings are missing, so an
approach originally developed by Qinetiq for PSDH has been used in which factors
are applied to the maximum smoke number.
3.3.28 For turboprop engines, emission factors are taken from the internationally
recognised Swedish FOI (Swedish Defence Research Agency) database (Swedish
FOI, no date).
3.3.29 For the future scenarios, for those aircraft types whose engines have been
certified and are in the ICAO databank, it has been assumed that the same mix of
engines as in the current Heathrow fleet continues into the future. In other words,
for aircraft types that are currently in service, no improvement in engine emissions
is assumed. This is a conservative assumption, since there are likely to be
incremental improvements in combustor technology over the lifetimes of some of
these aircraft types.
3.3.30 The only turboprop aircraft movements in the forecast schedules relate to a small
number (about 1% of total movements) of Bombardier Dash 8 400Q aircraft in
2022 (only). For these aircraft the current engine assignments from AUWR have
been used.
3.3.31 For other aircraft types, namely the Boeing 777-9 and those which are not
currently on the drawing board (i.e. those described as G+2), a single engine was
assigned for each aircraft type, with emission indices and fuel flow rates based on
typical current engines and projected forward based on forecast technology
improvement rates (see paragraph 3.3.35).
3.3.32 The aircraft engine assignments are summarised in Table 3.4. The UID is the
engine identifier used in the ICAO emissions databank. MTOW is maximum take-
off weight, used in the calculation of brake and tyre wear.
Table 3.4: Aircraft data
Aircraft type Aircraft description MTOW (kg) Number of
engines
Most common UIDs
318 Airbus A318 61679 2 7CM048, 8CM060
319 Airbus A319 68323 2 3CM027, 3CM028,
3IA006, 8CM057
319N Airbus A319 NEO 75500 2 18PW119
320 Airbus A320 74717 2 3CM026, 1IA003,
8CM055
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Heathrow Expansion PRELIMINARY ENVIRONMENTAL INFORMATION REPORT: Chapter 7: Appendix 7.1
Appendix 7.1-15 © Heathrow Airport Limited 2019
Aircraft type Aircraft description MTOW (kg) Number of
engines
Most common UIDs
320N Airbus A320 NEO 79000 2 17CM082, 18PW122
320X Airbus A320 Replacement 79000 2 NE33, NE34
321 Airbus A321 86243 2 3IA008, 3CM023,
1IA005
321N Airbus A321 NEO 97000 2 17CM083, 18PW126
321X Airbus A321 Replacement 97000 2 NE33, NE34
32B Airbus A321 (Sharklets) 89324 2 3IA008, 10IA017,
8CM054
32H Airbus A320 (Sharklets) 75157 2 8CM055, 10IA013,
1IA003, 17CM082
332 Airbus A330-200 234455 2 3RR030, 4GE080
333 Airbus A330-300 234332 2 3RR030, 4GE080,
4PW067
339 Airbus A330-900 NEO 251000 2 19RR098
33X Airbus A330-1000 NEO 251000 2 3RR030, 2RR023
343 Airbus A340-300 269162 2 2CM015, 7CM047,
1CM011
346 Airbus A340-600 370421 2 6RR041, 8RR045
351 Airbus A350-1000 311000 2 18RR080
351N Airbus A350-1000 Replacement 311000 2 NE31
359 Airbus A350-900 271754 2 14RR075
359N Airbus A350-900 Replacement 271754 2 NE31
388 Airbus A380-800 553367 4 9EA001, 8RR046
738 Boeing 737-800 Passenger 78411 2 8CM051, 8CM065,
11CM072
73H Boeing 737-800 (Winglets) 77453 2 11CM072, 8CM051,
8CM065
73J Boeing 737-900 (Winglets) 82966 2 11CM081, 8CM051
73W Boeing 737-700 (Winglets) 66905 2 3CM031, 8CM063,
3CM032
744 Boeing 747-400 384244 4 4RR036, 2GE045,
1PW055, 1RR010
74H Boeing 747-8 Passenger 447306 4 13GE157
74Y Boeing 747-400 Freighter 402578 4 1PW042, 3GE057,
12PW102, 1PW059
763 Boeing 767-300 181834 2 1RR011, 1GE029,
1GE030, 12PW102,
1PW043
76W Boeing 767-300 (Winglets) 183947 2 1GE029, 12PW101,
1PW043, 1GE030
772 Boeing 777-200 282026 2 10PW099, 2RR027,
5RR040
773 Boeing 777-300 299370 2 2RR027
779 Boeing 777-900 351534 2 NE30
779N Boeing 777-900 Replacement 351534 2 NE31
77W Boeing 777-300ER 347006 2 7GE099
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Appendix 7.1-16 © Heathrow Airport Limited 2019
Aircraft type Aircraft description MTOW (kg) Number of
engines
Most common UIDs
77X Boeing 777-200 Freighter 347626 2 7GE097
781 Boeing 787-10 254011 2 17GE177, 19RR096
781N Boeing 787-10 Replacement 254011 2 NE32
788 Boeing 787-8 227930 2 13GE162, 13GE161,
11GE137
788N Boeing 787-8 Replacement 227930 2 NE32
789 Boeing 787-9 251543 2 13GE160, 12RR067,
12RR068
789N Boeing 787-9 Replacement 251543 2 NE32
7M7 Boeing 737 MAX 7 (Winglets) 80286 2 18CM087
7M8 Boeing 737 MAX 8 (Winglets) 81498 2 18CM086, 18CM087
7M9 Boeing 737 MAX 9 (Winglets) 88314 2 18CM085
7X8 Boeing 737 MAX 8 (Winglets)
Replacement
81498 2 NE33, NE34
7X9 Boeing 737 MAX 9 (Winglets)
Replacement
88314 2 NE33, NE34
ABY Airbus A300-600 Freighter 157296 2 1PW048
DH4 De Havilland DHC-8-400 29343 2 TUR096
E90 Embraer 190 47821 2 10GE129, 11GE142,
8GE115, 10GE130
E95 Embraer 195 50790 2 10GE130, 8GE115,
8GE117
E95-2 Embraer 195 E2 61500 2 18PW117
3.3.33 The PSDH recommended a procedure for taking into account changes in ambient
temperature, pressure and humidity on aircraft engine emissions, which it found
changed overall aircraft NOX emissions by about 2 or 3% (DfT, 2006). The PSDH
also recommended a methodology for take-off roll, accounting for non-uniform
acceleration, effects of the forward speed on the engine thrust, etc. It found that
these made a difference of between 2 and 7% on average to NOX emissions from
the take-off roll phase. Unfortunately, the engine-specific data that underlie these
methodologies were not published and remain proprietary; moreover, they do not
cover engines introduced since about 2007. Therefore, new factors were derived
using the same approach as used for the PSDH, and have been applied in the
same way as recommended by PSDH.
3.3.34 ICAO databank emission factors are based on new production engines, so in-
service engines are likely to have suffered deterioration which may affect their
emissions. PSDH recommended correction factors to account for this, namely a
4.3% increase in fuel flow and a 4.5% increase in NOX emission rate (the product
of emission index and fuel flow rate). PSDH did not have sufficient data to resolve
these factors into individual engine types, ages or thrust setting, so they have
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been applied uniformly across the engine fleet for all phases of the Landing and
Take-Off (LTO) cycle.
Main engine emissions: Future emission factors
3.3.35 As noted in paragraph 3.3.31, emission factors for the Boeing 777-9 and those
which are not currently on the drawing board have been estimated based on
typical current engines and projected forward based on forecast technology
improvement rates. Improvements in emissions of NOx and PM have been
modelled, along with improvements in fuel consumption.
3.3.36 To develop these projections, interviews of industry experts and other secondary
research were carried out in order to identify the potential technologies that can be
adopted to reduce emissions. Potential technologies are identified for the two main
types of combustor design, namely the Lean combustor and the Rich Burn–Quick
Quench–Lean Burn (RQL) combustor. Examples include staged combustion,
advanced premixing nozzles and novel hot zone casing materials.
3.3.37 These are then correlated against historic improvements, both incremental
improvements within an engine generation and step changes between
generations, to estimate the typical emissions reductions due to each potential
technology. This takes into account the trend for increasing operational pressure
ratio. These potential improvements are then adjusted to take into account
technological and commercial feasibility, industry expectations and regulatory
trends.
Main engine emissions: Times in mode
3.3.38 Approach times for the baseline modelling were derived from data from the
Heathrow Noise and Track-Keeping (NTK) system for a sample of historic
movements. The NTK system provides radar squawks for arriving and departing
aircraft giving position, height and elapsed time, but only while aircraft are more
than a few hundred feet above the ground. Approach times for future cases were
derived from data supplied from the Heathrow Operational Planning and
Scheduling (OPAS) database for a sample of historic movements covering five
non-contiguous days in June and September. The OPAS system provides similar
data to NTK but with a higher time resolution and includes data for aircraft on the
ground.
3.3.39 Overall, OPAS data is preferred to NTK data, because of its higher precision and
its availability for aircraft on the ground, which provides greater consistency across
parts of the LTO cycle. However, preparing the OPAS data into a usable form
requires a significant amount of manual work, which is why data is only available
for a relatively small sample of time. The OPAS data was not available until the
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Appendix 7.1-18 © Heathrow Airport Limited 2019
modelling of the baseline scenarios was completed, which is why NTK data was
used for these scenarios. It is intended that modelling for the ES will use OPAS
data.
3.3.40 Approach times do not depend significantly on runway, so a single approach time
was used for all runways, for both historic and future 2R and 3R scenarios.
Approach is divided into two phases: Phase 1 is from 3000 feet (914 m) to
2000 feet (610 m) at a constant speed of 160 knots (82 m s−1), and Phase 2 is
from 2000 feet to the runway threshold, at uniform deceleration to landing speed
(which depends on aircraft type). The resulting approach times are summarised in
Table 3.5.
3.3.41 Landing roll times were derived from data supplied from the OPAS database for a
sample of historic movements. These have been extracted as a function of aircraft
type (Table 3.5) and also by wake vortex category. The former is used where the
aircraft type is in the sample data and the latter where the aircraft type is not in the
sample data. Landing roll times are assumed not to depend on runway or on
runway exit taxiway.
Table 3.5: Approach and landing roll times by aircraft type
Aircraft type Approach Phase 1 time
(s)
Approach Phase 2 time
(s)
Landing roll time (s)
319 78 166 54
320 78 160 54
321 78 156 55
32A 78 161 54
32B 81 157 52
32N 76 166 53
32Q 90 167 56
332 79 158 63
333 77 157 64
346 75 151 59
359 96 160 60
388 83 161 65
738 73 157 51
73H 84 153 53
73W 77 159 58
744 74 150 62
75W 75 168 57
764 73 154 55
76W 73 155 58
772 72 157 63
77W 79 154 60
788 79 156 62
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Aircraft type Approach Phase 1 time
(s)
Approach Phase 2 time
(s)
Landing roll time (s)
789 76 152 62
7M8 72 154 53
ABY 100 170 60
CS3 93 164 54
DH4 79 168 53
E90 87 165 48
3.3.42 Taxi-in and pushback/taxi-out/hold times for the baseline scenarios are derived
from Heathrow Electronic Flight Processing Strip (EFPS) data. For each
movement of the assessed years, this gives the times at which the aircraft passes
various gates (exits runway, arrives at stand, starts pushback, starts take-off roll).
For the baseline scenarios, it is possible to associate most movements with their
EFPS data and so obtain movement-specific times. Average times by terminal and
runway end are used for movements which cannot be matched with an EFPS
movement. Times are assumed to be independent of aircraft type. The distribution
along the taxi route (including time spent stationary during pushback and hold)
was derived from OPAS data for each taxi route.
3.3.43 Taxi-in and pushback/taxi-out/hold times for the future scenarios are derived from
TAAM simulation data supplied by NATS. For the 2035 DCO Project scenario, it
was possible to match TAAM output with individual movements in the schedule, so
times were applied on a movement-by-movement basis. For other 3R scenarios,
times are calculated from 2035 TAAM modelling as the average taxi time for the
terminal/runway end combination. For 2R scenarios in westerlies and in easterly
LD mode, times are calculated from 2R TAAM model outputs as the average taxi
time for the terminal/runway end combination. For 2R scenarios in easterly DL
mode, taxi times were taken from averages from the 3R TAAM simulations. These
times are assumed to be independent of aircraft type.
3.3.44 The distribution along the taxi route (including time spent stationary during
pushback and hold) was also derived from the TAAM data for each taxi route. For
the 2R configurations, although the airfield layout and activity levels will be similar
to the baseline scenarios, TAAM data is preferred over EFPS partly for
consistency with the DCO Project scenarios, and partly because the introduction
of full runway alternation in easterly operations introduces a new mode of
operation for which EFPS data is not available.
3.3.45 Take-off roll times were derived from the OPAS dataset. A distribution of take-off
times was derived for each aircraft, runway and runway access taxiway
combination. Fall-back distributions were derived for aircraft types which were not
present in sufficient numbers in the sample data, based on weight vortex category.
For future scenarios, times were also averaged across runways. Take-off times
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are apportioned into five-second intervals, and each distribution consists of the
fraction of movements in the source data for which the take-off time falls within
each bin. Average take-off roll times are summarised in Table 3.6.
Table 3.6: Take-off roll and climb times
Aircraft type Take-off roll
time (s)
Initial climb to
1000 ft time (s)
Initial climb to
1500 ft time (s)
Climb-out
from 1000 ft
time
Climb-out
from 1500 ft
time
318 38 20 33 72 59
319 37 22 36 68 53
320 34 19 33 75 61
321 33 18 30 70 59
32A 34 19 32 72 59
32B 35 18 29 72 61
332 39 18 30 65 53
333 41 19 33 79 64
33X 33 18 32 74 60
343 46 34 54 96 76
346 40 34 60 95 69
359 36 18 33 69 54
388 46 39 63 92 68
734 32 38 47 43 34
736 39 21 36 70 55
738 40 20 34 61 47
73G 38 20 37 59 43
73H 37 20 33 61 48
73J 39 22 35 77 63
73W 39 22 35 63 49
744 41 34 55 64 43
74H 36 26 43 68 51
74N 35 33 54 64 43
74Y 37 18 28 50 39
752 37 17 28 50 39
75F 30 17 28 50 39
75W 36 16 27 49 39
763 37 22 36 62 48
76W 34 18 29 47 36
772 36 21 35 77 63
77L 38 18 28 48 37
77W 35 19 31 62 51
77X 34 17 27 45 35
788 38 24 37 62 48
789 39 26 40 72 58
AB6 41 14 23 38 30
ABY 27 14 22 38 29
CS3 36 19 30 60 49
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Aircraft type Take-off roll
time (s)
Initial climb to
1000 ft time (s)
Initial climb to
1500 ft time (s)
Climb-out
from 1000 ft
time
Climb-out
from 1500 ft
time
DH4 32 20 36 67 51
E90 38 19 29 59 49
E95 35 19 29 59 48
3.3.46 Initial climb and climb-out times were derived from NTK data. These do not
depend significantly on runway, so a single time was used for all runways, for both
baseline and future baseline and with DCO Project scenarios. These times have
only a weak dependence on aircraft type (aircraft are designed to take off at
similar speeds to ensure that separation distances are maintained), but this was
retained since data were available (Table 3.6).
Main engine emissions: Thrust settings
3.3.47 Approach thrusts are assumed to be 15% of maximum rated thrust between
3000 feet and 2000 feet, and 30% from 2000 feet to touchdown. (Heights are
relative to runway level.) This is based on PSDH recommendations.
3.3.48 For taxi, PSDH recommendations are again followed for movements that do not
use RET. For these, the fuel flow rate is assumed to be 17.5% or 32.5% lower
than the fuel flow rate at 7% thrust, for non-Rolls Royce and Rolls Royce engines
respectively. The emission indices are set to those for the 7% thrust setting. For
aircraft movements that use RET, the fuel flow rate and emissions indices are
those for 7% thrust.
3.3.49 Aircraft sometimes use reverse thrust on landing, usually where the runway is
short and/or when weather conditions are poor (e.g. wet or icy). It has not been
possible to obtain robust quantitative data on reverse thrust usage at Heathrow.
Advice from the Airline Working Group was that use of reverse thrust above idle
was uncommon. Therefore, it has been assumed that all aircraft use a thrust
setting of 7%, corresponding to idle, during the landing roll.
3.3.50 It is common for aircraft to take-off at less than 100% thrust, sometimes as low as
75%, primarily to reduce wear on the engines. This is possible because engines
are overpowered for routine take-offs since aircraft need to be able to complete
the manoeuvre safely with the loss of one engine. For baseline modelling, take-off
thrust settings were based on survey data compiled before the 2008/9 Heathrow
inventory. For future scenarios, a simpler set of take-off thrust settings were
assumed (Table 3.7), which were intended to ensure the assumptions were
conservative.
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Table 3.7: Take-off thrusts for future scenarios
Aircraft type Reduced thrust setting (%) Flights using 100% thrust (%)
Narrow-body, twin engine 80 6
Wide-body, twin engine 80 6
Wide-body, four engine 84 14
Auxiliary power units (APU) emissions
3.3.51 As well as their main engines, many aircraft have APUs, which are small gas
turbines used to generate electrical power for purposes such as starting the main
engines, powering air conditioning and other services.
3.3.52 Emission factors for NOX and Particulate Matter (PM) were taken from work
carried out for the PSDH, which provided representative emission factors for
various groups of aircraft types, including future types. Emission factors for fuel,
SO2 and HC are taken from the FAA Emissions and Dispersion Modeling System
(EDMS), the forerunner of AEDT.
Table 3.8: APU data
Aircraft type APU class for NOX APU class for PM APU model name
318 c A 36-300
319 c A 36-300
319N c A 36-300
320 c A 36-300
320N c A 36-300
320X c A 36-300
321 c A 36-300
321N c A 36-300
321X c A 36-300
32B c A 36-300
32H c A 36-300
332 e A 331-350
333 e A 331-350
339 e A 331-350
33X e A 331-350
343 e A 331-350
346 e A 331-350
351 f A 331-500
351N f A 331-500
359 f A 331-500
359N f A 331-500
388 g A A388
738 b A 131-9
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Aircraft type APU class for NOX APU class for PM APU model name
73H b A 131-9
73J b A 131-9
73W b A 131-9
744 e A PW901A
74H e A PW901A
74Y e A PW901A
763 c C 331-200ER
76W c C 331-200ER
772 f A 331-500
773 f A 331-500
779 f A 331-500
779N f A 331-500
77W f A 331-500
77X f A 331-500
781 c C 331-200ER
781N c C 331-200ER
788 c C 331-200ER
788N c C 331-200ER
789 c C 331-200ER
789N c C 331-200ER
7M7 b A 131-9
7M8 b A 131-9
7M9 b A 131-9
7X8 b A 131-9
7X9 b A 131-9
ABY d C 331-200ER
DH4 a B 36-150[ ]
E90 a B 36-150[ ]
E95 a B 36-150[ ]
E95-2 a B 36-150[ ]
3.3.53 Running times for APUs on stand for the baseline modelling are derived from
monitoring undertaken to ensure compliance with Heathrow’s Operational Safety
Instructions (OSIs).
3.3.54 For future scenarios, times are taken from the OSIs. The OSIs specify the
following maximum periods for which APUs may be operated:
1. Narrow-bodied aircraft: 10 minutes after arrival on stand and 15 minutes prior
to departure
2. Wide-bodied aircraft: 10 minutes after arrival on stand and 50 minutes prior to
departure; and
3. A380: 15 minutes after arrival on stand and 90 minutes prior to departure.
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Brake and tyre wear emissions
3.3.55 Emissions of PM from brake and tyre wear are calculated using the PSDH
methodology (ICAO omits this source). Brake wear emissions, in g PM10 per
arrival, are calculated as 2.53 × 10–4 × MTOW, where MTOW is the maximum
take-off weight in kg. Tyre wear emissions, in g PM10 per arrival, are calculated as
2.23 × 10–4 × MTOW – 8.74 for aircraft with an MTOW > 50,000 kg, and 2.41 ×
MTOW / 50,000 for smaller aircraft.
3.3.56 PM2.5 emissions are calculated by multiplying the PM10 emission by 0.4 for brake
wear and 0.7 for tyre wear.
3.3.57 For the baseline modelling, MTOW was taken from the AUWR database. For the
future scenarios, MTOW was taken from the AUWR database for the majority of
aircraft types. For G+2 aircraft types, MTOW has been assumed to be the same
as the analogous current aircraft type (with no account taken of likely reductions in
weight due to improvements in design and materials).
Aircraft emissions: Spatial disaggregation
3.3.58 Emissions from approach, landing roll, taxi-in, taxi-out, hold, take-off roll, initial
climb, climb-out and APU during taxi-in and taxi-out are modelled in ADMS-Airport
as jet sources, spread along a series of straight line segments. Landing roll
emissions are assumed to decelerate at a constant rate from 130 knots (67 m s−1)
at touch-down to 15 knots (8 m s−1) when exiting the runway. Take-off roll is
assumed to accelerate in accordance with a speed-emission curve, depending on
aircraft type, using parameters from PSDH.
3.3.59 Emissions from taxi-in and taxi-out are assigned to a set of straight-line segments
making up each of the taxi routes from stand group to runway. Aircraft do not
travel at uniform speed along the taxi routes; for example, during taxi-out there are
commonly delays for pushback and in the hold zone, as well as waiting for other
aircraft to push back and at taxiway crossings. To take this into account, data from
OPAS (for baseline modelling) and TAAM (for future scenarios) has been
analysed to determine average occupancy times for each segment of each taxi
route, and emissions are distributed along the taxi routes in proportion to the
occupancy times.
3.3.60 Emissions from APU usage on stand are modelled as volume sources, of
dimensions 50 m × 50 m horizontally and with vertical extent 12 m, centred on the
respective stands.
3.3.61 Emissions from tyre wear are modelled as volume sources, of length 300 m, width
50 m and vertical extent 15 m, centred on the touchdown point of the respective
runways.
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3.3.62 Emissions from brake wear are modelled as volume sources, of width 50 m and
vertical extent 15 m, extending from the touchdown point to the most common exit
taxiway of the respective runways.
3.3.63 For baseline modelling, the stand is known for each movement. For future
scenarios, the forecast schedules provide terminal assignments for each
movement. For the DCO Project 2035 scenario, the TAAM modelling assigns each
movement to an individual stand, and this stand was used for the air quality
modelling. For other future scenarios, movements were assigned probabilistically
to each of the stands on the specified terminal of a suitable size for the aircraft
type.
3.3.64 Example plots of the emissions are shown in Graphic 3.1 to Graphic 3.9. These
show the emissions for the DCO Project 2035 scenario within 100 m squares
aligned with the National Grid. (Note that the runways at Heathrow are aligned
slightly off the National Grid east-west axis, giving rise to ‘steps’ in the plotted
runway emissions.)
Graphic 3.1: Approach NOX emissions density plot
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Graphic 3.2: Landing roll NOX emissions density plot
Graphic 3.3: Taxi-in NOX emissions density plot
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Graphic 3.4: Taxi-out NOX emissions density plot
Graphic 3.5: Take-off roll NOX emissions density plot
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Graphic 3.6: Initial climb NOX emissions density plot
Graphic 3.7: APU NOX emissions density plot
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Graphic 3.8: Brake PM10 emissions density plot
Graphic 3.9: Tyre PM10 emissions density plot
Aircraft emissions: Runway assignments and temporal variation
3.3.65 For modelling, each aircraft movement needs to be assigned to a runway. For
baseline modelling, the runway actually used is known, but for future scenarios,
runways are assigned probabilistically. These probabilities need to align with the
met data used for the dispersion modelling. Because meteorological data is only
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available on an hourly basis, it is sufficient to determine runway probabilities for
each hour of the year.
3.3.66 The met year used for the dispersion modelling corresponds to a historic year for
which actual runway usage is available. This historic year is used to obtain the
relative frequency of easterly and westerly operations in each hour of the year.
3.3.67 The probability of using each physical runway in any given hour of the year is
determined by assuming that each of the four operational modes is equally likely
(or each of the two operational modes for 2R scenarios). For the DCO Project
2035 scenario, for each operational mode, the runway for each movement is
determined from TAAM data. For the other DCO Project scenarios, for each
operational mode, the runway for each movement is determined from the forecast
schedule. For 2R scenarios, for each operational mode, there is only one runway
available for each movement.
3.3.68 In addition, the number of aircraft movements varies with hour of the day and the
time of year (although at Heathrow there is little variation from month to month).
Since the weather also varies systematically between hours of the day, and
between seasons of the year, it is therefore desirable for the model to take this
temporal variation in emissions into account.
3.3.69 The hour of day is known for each movement, in the baseline, future baseline and
With DCO Project scenarios. Emissions were calculated for each hour of the year,
taking into account the movements in that hour and the weather conditions (which
affect emissions through temperature, pressure and humidity effects, as described
in paragraph 3.3.33, as well as the runway direction), for each mode of runway
operation. These were used to create an hour-by-hour time-varying emissions
weighting (‘hfc’) file for each emission source. The emissions were fed into the
dispersion model for each mode of runway operation. The resulting concentrations
were then averaged across mode of runway operation to obtain the final modelled
concentrations. This process was carried out for each of the three met years
2015–2017.
3.4 Emissions sources: On-airport, non-aircraft emissions
Ground support equipment (GSE)
3.4.1 GSE emissions are calculated using the equipment fleet mix from the 2017
Heathrow inventory update (Ricardo, 2018), which is assumed to represent the
mix of equipment types and ages in each future scenario. A bottom-up calculation
of fuel consumption is adjusted to fit actual measured fuel consumption, provided
by the Airside Operations team, and emissions of air pollutants are adjusted
accordingly.
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3.4.2 Emission factors for road vehicles are taken from COPERT V (Emisia, no date),
which is the dataset used by Defra’s Emission Factors Toolkit version 8 (Defra,
2017a). Emission factors for non-road mobile machinery (NRMM) are assumed to
be equal to the limits in the EU directive on NRMM (European Parliament, 2004).
In each case, emission factors appropriate to the age of the vehicle, and therefore
the assumed emission control standard, are use, based on the age mix from the
2017 inventory.
3.4.3 Emissions are scaled to future scenarios in proportion to forecast aircraft
movements. Emissions are also adjusted to account for projected penetration of
electric equipment into the fleet, provided by the Airside Operations team. Electric
vehicles are assumed to have zero tail-pipe emissions, with only fugitive emissions
of PM10 and PM2.5 from brake and tyre wear.
3.4.4 Emissions are assumed to occur on aircraft stands, and are modelled as 50 m ×
50 m × 3 m volume sources. Total emissions are calculated and then distributed
between stands and between hours of the year in proportion to the sum of the
MTOWs of the aircraft using that stand during that hour.
3.4.5 An example plot of the emissions is shown in Graphic 3.10. This shows the
emissions per 100 m square for the DCO Project 2035 scenario.
Graphic 3.10: GSE NOX emissions density plot
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Heating plant
3.4.6 Emissions from heating plant are included in all scenarios. Data is based on the
2008–9 inventory (Underwood et al, 2010a), but with updates including the closure
of the Thames Valley Power combined heat and power unit, the introduction of a
new energy centre with biomass-powered boilers; and associated changes to the
assumed usage of the Building 448, Terminal 5 and BA Cargo units.
Fire training
3.4.7 Emissions due to fire training were taken from the 2008–9 emissions inventory
(Underwood et al, 2010a). It is not expected that emissions from this source will
increase with the DCO Project. This source is in any case very small.
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4. APPROACH TO MODELLING EMISSIONS FROM ROAD TRAFFIC
4.1 Model domain
4.1.1 A receptor file has been created that covers the CAQOAA, incorporating specific
sensitive receptors as well as a fine grid of receptors to enable contour plots to be
produced1. This receptor file has been subdivided into 1km x 1km squares.
4.1.2 When predicting concentrations at receptors, all roads within a 2km x 2km square
(which overlaps the 1km x 1km receptor square by 500 m in each cardinal
direction) have been modelled explicitly. Graphic 4.1 shows a 1km x 1km
selection of receptors and the 2km x 2km extent of the road traffic network
modelled explicitly for that selection of receptors.
4.1.3 In this way, the explicitly-modelled road network has been re-defined for each 1km
x 1km grid of receptors, but the number of roads modelled explicitly for each
receptor has been minimised, allowing significantly quicker model run times than if
all roads were modelled explicitly (which would have been prohibitively slow).
4.1.4 Emissions from road traffic outside of each 2km x 2km square have not been
ignored; their incorporation into background concentrations is described in
Section 7.
1 Receptors have been modelled over Cartesian grids at 10 m intervals within 100 m of modelled roads, at 20 m intervals between 100 m and 200 m from modelled roads, and at 50 m intervals beyond 200 m from modelled roads.
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Heathrow Expansion PRELIMINARY ENVIRONMENTAL INFORMATION REPORT: Chapter 7: Appendix 7.1
Appendix 7.1-34 © Heathrow Airport Limited 2019
Graphic 4.1: 1km x 1km receptor cell and corresponding explicitly modelled roads
Contains Ordnance Survey data © Crown copyright and database right 2018. Ordnance Survey licence
number 100046099.
4.2 Traffic data
4.2.1 Traffic data for the assessment have been derived from the Heathrow Highway
Assignment and Surface Access Model (HHASAM), with the outputs processed to
give Annual Average Daily Traffic (AADT) flows for each link, along with the fleet
composition (proportion Cars, LGVs, HGVs and Buses/Coaches) and an average
speed.
4.2.2 HHASAM does not model motorcycle flows, therefore appropriate motorcycle
numbers have been manually added to the flows for each link using road type-
specific proportions provided by the surface access modelling team.
4.2.3 Where traffic will typically be free-flowing (i.e. away from junctions or other
features that will slow traffic on a specific short section of a road), the modelled
average speeds have been used to determine average emissions for sections of
road. Where there are junctions or other features that will slow traffic on a specific
short section of a road, speeds have been reduced using professional judgement
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to account for increased emissions in these locations as a result of slow-moving
and/or queuing traffic. Speeds on the remainder of such links have not been
increased to account for this reduction on some sections, as the slow sections are
typically short and unlikely to lead to substantial changes in speed along the
remainder of the link, and, as a result, unlikely to significantly affect modelled
concentrations.
4.2.4 Diurnal traffic flow profiles have been provided by the surface access modelling
team for the DCO Project. Monthly flow profiles have been derived from the
national profiles published by DfT (DfT, 2017).
4.3 Calculating road traffic emissions
4.3.1 The Defra Emission Factor Toolkit (EFT) v8.0.1 has been used to calculate vehicle
emissions. This tool requires that the user enter one of seven Road Types:
1. Urban (not London)
2. Rural (not London)
3. Motorway (not London)
4. London Centre
5. London Inner
6. London Outer
7. London Motorway
4.3.2 It is important to note that these categories describe the vehicle fleet composition
rather than the precise physical location of the road. The fleet composition does
not, for example, change as a road passes from an urban to a rural area in the
absence of any intervening junctions. The Notes for Users section which
accompanies EFT V8.0.1 explains that:
‘The urban categorisation relates to the DfT definition of an urban area with a population of
10,000 or more.
The London road types are consistent with the area categories defined in the London
Atmospheric Emissions Inventory (LAEI). 'Central' corresponds to the Ultra Low Emission
Zone (ULEZ) area, whilst 'Motorway' denotes the M25 - other motorways in London should
be defined as 'London - Inner' or 'London - Outer' as appropriate’.
4.3.3 Rigidly applying these definitions would artificially introduce step changes in
vehicle fleet compositions part-way along links. It would also run the risk of
artificially changing the fleet composition on any links which are realigned as part
of the DCO Project, thus introducing error into the comparative analysis.
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4.3.4 The following approach has, therefore, been followed when using the EFT to
calculate vehicle emissions:
1. All sections of the M25 (including all non-M4 slip-roads) have been assigned
Road Type 7
2. Sections of motorway and slip-roads (other than the M25) outside of the Outer
London area have been assigned Road Type 3
3. All other roads have been assigned Road Type 6.
4.3.5 Applying this approach ensures minimal step changes in emission factors, and
consistency throughout the CAQOAA, where emissions are likely to generally be
very similar to the Outer London fleet.
4.3.6 The changes to the Low Emission Zone (LEZ) and Ultra-Low Emission Zone
(ULEZ) announced by the Mayor of London in June 2018 will change the fleet
composition within London, especially within the north and south circular roads,
although these are outside of the CAQOAA. It has not been possible to take
account of these changes; as such, any predictions for years beyond 2020 that are
made using EFT v8.0.1 can be expected to over-estimate nitrogen dioxide
concentrations, and will thus be worst-case.
4.4 Sensitivity test
4.4.1 Air Quality Consultants Ltd. (AQC) has carried out a detailed, peer-recognised
analysis which showed that, whereas previous standards had 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, 2016).
Defra’s EFT v8.0.1 takes account of these observed improvements, but also
makes additional assumptions regarding the performance of diesel cars and vans
that will be produced in the future. In particular, it assumes that diesel cars and
vans registered for type approval after 2020 will, on average, emit significantly less
NOX than earlier models. A sensitivity test has been carried out using AQC’s
CURED v3A model (AQC, 2017), which assumes that this post-2020 technology
does not deliver any benefits (as a worst-case assumption). Further details of
CURED v3A are provided in the supporting report prepared by AQC (AQC,
2018a).
4.5 Additional features
4.5.1 There are a number of road tunnels within the model domain, and the DCO Project
will introduce some new road tunnels. To ensure a robust assessment, these have
been modelled as tunnels using the ADMS Tunnels module. Some of the modelled
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tunnel links are long, and would extend beyond the boundary of some of the 2km x
2km model grid areas (see Paragraph 4.1.2). In order to avoid introducing artificial
tunnel ends where this occurs, tunnels have been removed from the primary
modelled road network and modelled explicitly with a separate receptor file. This
receptor file has incorporated all of the specific receptors within the 1km x 1km
grid squares within which the tunnel links are located and within 500m of the
edges of these areas, then a grid of receptors at 100m resolution further afield, to
align with the approach taken elsewhere in the assessment.
4.5.2 Noise barriers have also been incorporated into the model, where currently
present. The design of the DCO Project is not sufficiently advanced as to
incorporate any potential noise barriers along proposed new roads to be built as
part of the DCO Project, thus noise barriers in the with DCO Project scenarios
have only been included along roads that are to be retained and which currently
have noise barriers present.
4.5.3 Where relevant, elevated roads have been modelled at height. Only sections of
road longer than 50m and which have clear air underneath the carriageway have
been modelled at height. This is because the option to give road sources an
elevation is really intended for true bridges, with the initial mixing of the emissions
given an extra downward component to account for the passage of air beneath the
source. As such, it would not be appropriate to model roads without clear air
underneath them as being at height.
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5. APPROACH TO MODELLING CAR PARK EMISSIONS
5.1.1 Emissions from vehicles using car parks operated by Heathrow have been
modelled using the following approach:
1. Volume sources of 3 m depth have been defined covering the area of each car
park (sensitivity testing has identified a volume source of 3 m depth at ground
level as best representing ground level vehicle emissions, when compared to
modelling as a line source), with multi-storey car park volume sources given a
total depth assuming each storey to be 3 m deep
2. An average trip length through the car park has been defined based on
mapping data
3. Emissions have been calculated using the EFT and CURED for every vehicle
using the car park (using usage figures provided by the surface access team)
on the assumption that they travel this average distance at 5 kph, the speed
associated with the highest emissions in the EFT
4. The total emission rate for each car park volume source has been calculated
and modelled using ADMS-5, assuming a constant diurnal and seasonal profile
of emissions
5. Cold-start emissions have been defined using the National Atmospheric
Emissions Inventory (NAEI) cold start emission rates (which are derived from
COPERT). 50% of the total cold start emission for each car park has been
applied to the modelled volume source, with the other 50% averaged over 200
m long line sources representing the most likely routes of traffic exiting from the
car parks.
5.1.2 This is considered to represent a suitably robust approach to modelling emissions
from vehicles using car parks, which represent a very small proportion of total
concentrations. In reality, there is likely to be some diurnal and seasonal profile to
these emissions, but these will be different to typical road traffic profiles on the
highway network, thus it would not be appropriate to apply a default highway
network profile. It is considered more appropriate to simply assume a constant
profile, in the knowledge that this is unlikely to lead to significant uncertainty in
annual mean contributions.
5.1.3 Concentrations have been predicted at all of the gridded and discrete receptors
within 200m of a car park or likely route of traffic exiting a car park, and at 100m
resolution beyond this distance.
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6. APPROACH TO MODELLING LAKESIDE WASTE MANAGEMENT FACILITY EMISSIONS
6.1.1 Emissions from the Lakeside Waste Management Facility in the baseline and
future baseline scenarios have been modelled using the ADMS-5 model. The
urban canopy module has been used in order for the modelling to align with the
bulk of the other modelling carried out. This has resulted in buildings not being
incorporated into the model, but the urban canopy flow module should itself
account for some of the effects of the presence of the process building, and the
stack at the existing facility is considerably higher than the highest point of the
process building (75 m as opposed to 42 m), thus building effects will be relatively
limited. As such, the approach is deemed appropriate.
6.1.2 Emissions data for the existing facility have been derived from published datasets,
which include average pollutant emissions monitoring data from 2010 to 2016, and
are summarised in Table 6.1, generally to two significant figures. The facility has
three lines of plant, one clinical waste incinerator (CWI) and two energy from
waste (EfW).
Table 6.1: Emission data for the existing facility
Parameter CWI EfW (per line)
Stack Location (x,y) 503900,177341
Temperature 140 145
Exhaust Volume Flow Rate
(Nm3/s)
4.1 66
Exit velocity 15 19
NOX (mg/Nm3) 200 166
NOX (g/s) 0.82 6.6
PM10 (mg/Nm3) 10 1.2
PM10 (g/s) 0.041 0.047
6.1.3 Concentrations have been predicted at each of the monitoring sites used in the
model verification, across a 100 m resolution Cartesian grid of receptors covering
the CAQOAA, and across a coarser nested grid of receptors at 2km and 4km
resolution covering the extent of the HHASAM Fully Modelled Area (FMA). The
FMA is the area in which all trip movements are included in the model. These
concentrations have been added to the total concentrations at receptors in the
future baseline scenarios using the approach described in Section 8. The with
DCO Project scenarios have assumed no Lakeside Waste Management Facility to
be in place.
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7. APPROACH TO PREDICTING BACKGROUND CONCENTRATIONS
7.1.1 Defra’s background maps have been used to provide the concentrations
associated with pollutant sources which are not being explicitly modelled.
7.1.2 In order to avoid double-counting, emissions from sources that have been
explicitly modelled (e.g. airport and road traffic) have been removed from Defra’s
maps. Defra provides a method to do this, but this functionality is limited to
removing pre-defined sectors and to removing all ‘in-square’ emissions and/or all
‘out-square’ emissions2. It would, in theory, be possible to configure the dispersion
model in such a way that only ‘in-square’ emissions were being explicitly modelled
for any one receptor. This would allow all ‘out-square’ background components to
be retained. The main problems with such an approach are:
1. it would introduce inconsistency and step-changes in the outputs
2. it would ignore the effect that the change in traffic caused by the DCO Project
over the wider network will have on the ‘background’ concentration field.
7.1.3 The method set out below (in paragraph 7.3.1 onwards) seeks to predict a
spatially-consistent background concentration field which takes account of wider-
scale changes in traffic emissions caused by the DCO Project.
7.1.4 Emissions attributed to Heathrow Airport fall within several sectors, some obvious
(e.g. ‘Aircraft’) and some less so (e.g. ‘Other’). In order to ensure that no double-
counting of Heathrow emissions occurs, Ricardo Energy & Environment were
commissioned, with Defra’s approval, to determine the explicit contribution of
Heathrow Airport to all non-road sectors of the background maps, so that this can
be removed from the mapped backgrounds.
7.1.5 For completeness, the sectors included in the background maps are detailed in
Table 7.1.
2 i.e. all said emissions originating inside or outside of a specific cell of the 1km x 1km grid over which the maps are provided.
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Table 7.1: Sectors included in the background maps
7.1.6 The following road traffic sources have also been removed from Defra’s
background maps:
1. Motorway
2. Trunk A Rd
3. Primary A Rd
4. Brake+Tyre Wear (PM Only)
5. Road Abrasion (PM Only)
7.1.7 The contribution of the Minor Rd+Cold Start sector has been retained, given that
most minor roads will not be modelled explicitly due to not being incorporated in
the HHASAM network, and to allow for the contribution of cold start emissions
across the study area. This will result in some double-counting of emissions from
minor roads, and cold start emissions from Heathrow’s car parks, but this will be
very small. Accounting for the additional emissions from the development-
generated traffic across the HHASAM FMA is considered more important than
avoiding a small amount of double-counting of the baseline component.
7.1.8 Cold start emissions for trips associated with the DCO Project originating at the
airport have been modelled explicitly for all Heathrow-controlled car parks. Cold
start emissions for trips associated with the DCO Project originating elsewhere will
not be counted, other than through the inclusion of the contribution of the Minor
Rd+Cold Start sector from the background maps.
Sector
Motorway
Trunk A Rd
Primary A Rd
Minor Rd+Cold Start
Brake+Tyre Wear (PM Only)
Road Abrasion (PM Only)
Industry
Domestic
Rail
Other
PM secondary (PM Only)
Residual+Salt (PM Only)
Point Sources
Rural (NOX Only)
Aircraft (NOX Only)
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7.1.9 In determining appropriate mapped background NOX and nitrogen dioxide (NO2)
concentrations, Defra’s sector removal tool has been used to remove the relevant
road and non-road contributions from the background maps. The end result is a
set of background concentrations that do not include the contribution of major
roads, sources within Heathrow Airport or the Lakeside Waste Management
Facility.
7.1.10 Defra’s background maps use 2015 as a base year, and this is widely
acknowledged to have been a low-pollution year. As a result, the maps may tend
to under-predict future year background concentrations, due to having been
validated against 2015. In order to offset any such under-prediction when using
2016 or 2017 as a base year, the residual mapped background concentrations of
NOX and NO2 have been adjusted upward using the factors recommended in
AQC’s report on Calibrating Defra’s 2015-based Background NOX and NO2 Maps
against 2016 and 2017 Measurements (AQC, 2018b).
7.2 Background NO2 and NOX concentrations for sensitivity test
7.2.1 The road-traffic components of nitrogen oxides and nitrogen dioxide in the
background maps (which are limited to Minor Rd+Cold Start, as the other road
sectors have been removed) have been uplifted in order to derive future year
background nitrogen dioxide and nitrogen oxides concentrations for use in the
sensitivity test. Details of the approach are provided in the report prepared by AQC
(AQC, 2018c).
7.3 Modelling the background concentration field for traffic emissions
7.3.1 While the bulk of the road traffic emissions in the background maps have been
removed, the explicit modelling of road sources only extends to at least 500m from
any given receptor, thus it is necessary to re-introduce the contribution of roads
not explicitly modelled as road sources to the background concentration at each
receptor.
7.3.2 Graphic 7.1 shows the CAQOAA, which is the area over which total
concentrations have been predicted. It also shows the FMA considered in
HHASAM. The extent of the FMA is the boundary beyond which not all trip
movements are included in the model and there is only skeletal coverage of the
road network.
7.3.3 Emissions from all roads within the FMA have been used to predict the road
component of the background concentration field, using the following approach:
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1. Step 1) A single 100 m x 100 m volume source3 has been modelled in ADMS
with a unitary emission rate4, with concentrations predicted for a large 100 m x
100 m Cartesian receptor grid.
2. Step 2) The FMA has been divided into 100 m x 100 m grid cells, and those
grid cells which contain roads have been identified. For each traffic model
scenario, link-specific traffic emissions have been calculated and assigned to
the respective grid cells (based on the length of road within each cell).
3. Step 3) The outputs from Step 1 and Step 2 have been combined for each
source-cell, and summed for each receptor on the Cartesian grid, to give the
total concentration at each gridded receptor. This has been done in such a way
that only those roads which are not being modelled explicitly (as described in
Section 4, and including relevant tunnels modelled explicitly) are included.
3 Sensitivity testing by AQC has shown that using a volume source more accurately reproduces the concentrations predicted when modelling road traffic emissions as line sources, when compared to using an area source. This sensitivity testing has identified an optimal volume source of 3 m depth at ground level. 4 A unitary emission rate is a default assumed value, e.g. 1 g/m3/s. Its use allows the actual emission rate to be applied in post-processing, by multiplying the output concentration by the actual emission rate divided by the unitary emission rate (a value of one is used to remove the requirement for this division). The relationship between primary pollutant model inputs and model outputs is linear, and rounding will not introduce significant error, thus this approach results in largely identical modelled concentrations when compared to modelling the actual emission rate.
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Graphic 7.1: Air quality core assessment area and HHASAM Fully Modelled Area
Imagery © Google
7.3.4 Graphic 7.2 provides an example subset of the 100m x 100m Cartesian receptor
grid within a defined 1km x 1km square. It also presents the nearest of the
surrounding 100m x 100m volume sources (located outside of the area within
which roads are being modelled explicitly for that 1km x 1km square), whose
contribution to background concentrations at this subsect of receptors will be
calculated following the approach set out above.
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Graphic 7.2: Receptor grid example
Contains Ordnance Survey data © Crown copyright and database right 2018. Ordnance
Survey licence number 100046099.
7.3.5 This approach ensures that changes in traffic emissions as a result of the DCO
Project throughout the FMA are incorporated into the total concentrations
predicted. It does result in some minor double-counting of traffic emissions, as
traffic emissions greater than 16km away are effectively included in the ‘Rural’
sector of the background maps. However, these contributions are small, and it is
considered more important that the change as a result of the DCO Project is
accounted for than this minor double-counting is avoided.
7.3.6 The spatial variation in NOX concentrations predicted using this methodology is
limited, as the concentrations primarily relate to distant sources. As such, a
nearest-neighbour approach has been taken to determining the concentration at
discrete receptors, with the road traffic background contribution taken to be that of
the nearest 100m x 100m grid point.
7.3.7 The output NOX concentrations have been adjusted by applying the primary
adjustment factor for road traffic emissions (see Section 9), in order to avoid step
changes in concentrations at the edges of the 1km x 1km grid squares, and to
ensure that background concentrations are not under-predicted.
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8. CALCULATION OF TOTAL NO2 CONCENTRATIONS
8.1.1 NOX is emitted as a mixture of nitric oxide (NO) and NO2 (primary NO2), and
reactions in the atmosphere convert NO to NO2, and vice versa. Concentrations of
NOX are conserved5, so are straightforward to calculate through dispersion
modelling. Concentrations of NO2 at receptors are, however, a complex function of
emissions of NO and primary NO2, concentrations of oxidants (principally NO2 and
O3) in the air, the magnitude of incoming solar radiation and travel time. Modellers,
therefore, require a procedure for calculating NO2 concentrations from the NOX
concentrations calculated by dispersion modelling.
8.1.2 The NOX to NO2 Calculator available from the LAQM website
(https://laqm.defra.gov.uk/review-and-assessment/tools/background-
maps.html#NOXNO2calc) has been used to predict total NO2 concentrations. It
takes a semi-empirical approach (Abbott, J, 2005) which ‘uses a one-dimensional
finite difference model of the reactions and mixing of NO, NO2 and O3 in the
surface stress layer of the atmospheric boundary layer.’
8.1.3 The NOX to NO2 calculator requires the user to define a specific local planning
authority area, which is used to estimate regional concentrations of O3, NOX and
NO2 above the surface layer. Hillingdon has been used throughout this
assessment, as it is considered to appropriately represent conditions in the vicinity
of Heathrow Airport, and using a varied selection would result in unrealistic step
changes in concentrations at local planning authority boundaries.
8.1.4 The user is also required to define the traffic mix, which is used to define the
appropriate fNO2 value for road traffic emissions. ‘All London traffic’ has been used
throughout this assessment, as it is considered the most representative option for
the area of interest while ensuring no step changes in concentrations.
8.1.5 In order to determine total pollutant concentrations, all of the individual
contributions have been combined. The process for this is described below.
8.1.6 The first step has been to determine a receptor-specific background NO2
concentration. The mapped background concentrations with the contribution of
Heathrow Airport and major road sources removed (see Section 7) have been
interpolated to provide receptor specific Total NOX, Road NOX, Non-Road NOX
and Total NO2 concentrations. These have been input to Defra’s sector removal
tool, and the adjusted modelled road traffic background contribution added to them
5 Small losses through deposition are ignored for the purposes of modelling air concentrations, giving rise to a small degree of double counting of the deposited NOx.
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by entering a negative value in column K, to give a final ‘background’ annual mean
NO2 concentration for every receptor.
8.1.7 The modelled road NOX (including the tunnels contribution) has then also been
adjusted by applying the primary adjustment factor for road traffic emissions (see
Section 9), with the interpolated modelled car park and airport contributions then
added to give a total modelled NOX concentration for every receptor. Receptor-
specific fNO2 values have then been calculated by multiplying each constituent
part by its source-specific fNO2 value, to determine an appropriate overall fNO2
value.
8.1.8 The total modelled NOX, total background NO2 and fNO2 values for each receptor
have then been run through Defra’s NOX to NO2 calculator to define a total
modelled NO2 concentration, to which the secondary adjustment factor has been
applied to give a final total NO2 concentration at every receptor. The application of
a secondary adjustment factor in order to bring the modelled NO2 concentrations
into alignment with the local monitoring is considered appropriate, as the bulk of
the total concentration has been modelled, either explicitly or through a kernel
approach. The need for such a factor is thought to relate to the over-estimation of
road vehicle fNO2 in the NOX to NO2 calculator.
8.2 EU limit value compliance
8.2.1 It should be noted that the approach to the conversion of NOX to NO2 adopted
when assessing EU limit value compliance was different to that described above,
utilising the Jenkin approach set out in Defra’s SL_PCM concentration tool
spreadsheets (Defra, 2017b). This is in order to ensure that the conversion aligns
with the PCM modelling carried out by Defra, which is necessary when considering
limit value compliance. The approach to assessing limit value compliance is
discussed further in Section Error! Reference source not found. of this note.
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9. MODEL VERIFICATION METHODOLOGY
9.1.1 The model output concentrations have been verified against measured
concentrations from suitable automatic monitoring sites within the CAQOAA. The
sites used in the model verification are listed in Table 9.1.
Table 9.1: Sites used in the model verification
9.2 NOX and NO2
9.2.1 Most NO2 is produced in the atmosphere by reaction of NO with ozone (O3). It is
therefore most appropriate to verify the model in terms of primary pollutant
emissions of NOX (NOx = NO + NO2).
9.2.2 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 by subtracting the following components from the measured
NOx concentration at each monitor:
1. background NOx
2. airside NOx (including emissions from aircraft, ground support equipment and
heating and cooling plant)
3. the modelled NOx contribution of Heathrow’s car parks
4. the modelled NOx contribution of the Lakeside Waste Management Facility.
Site Name Years Used Pollutants Measured
London Hillingdon 2015, 2016, 2017 NOX, NO2
London Harlington 2015, 2016, 2017 NOX, NO2, PM10, PM2.5
LHR2 2015, 2016, 2017 NOX, NO2, PM10, PM2.5
Oaks Road 2015, 2016, 2017 NOX, NO2, PM10, PM2.5
Green Gates 2015, 2016, 2017 NOX, NO2, PM10, PM2.5
Oxford Avenue 2015, 2016, 2017 NOX, NO2, PM10 (2016 and 2017 only)
Harmondsworth 2015, 2016, 2017 NOX, NO2, PM10
Hayes 2015, 2016, 2017 NOX, NO2, PM10
Feltham 2015, 2016, 2017 NOX, NO2, PM10
Heston Road 2015, 2016, 2017 NOX, NO2, PM10
Southall 2015 NOX, NO2
Sipson 2015, 2016, 2017 NOX, NO2
Cranford 2015, 2016, 2017 NOX, NO2, PM10
Hatton Cross 2015, 2016, 2017 NOX, NO2, PM10
SLH 3 & SLH6 2015, 2016, 2017 NOX, NO2, PM10
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9.2.3 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. The total nitrogen dioxide concentrations have then been
determined by combining the adjusted total NOx concentrations with the predicted
background NO2 concentration within the NOx to NO2 calculator. A secondary
adjustment factor has then been calculated as the slope of the best-fit line applied
to the adjusted total NO2 concentrations and forced through zero.
9.2.4 Model verification factors for NOX and NO2 have been determined for the years
2015, 2016 and 2017 using two approaches, a ‘Simple’ calculation of the factors
when using data from all of the automatic monitoring sites included in the model
verification, and alternative factors calculated using the Leave One Out Cross
Validation (LOOCV) approach. The modelled Road NOX has been adjusted,
followed by total NO2. There is no strong justification for adjusting any other
contributions. The calculated factors for each year are presented in Table 9.2.
Table 9.2: Calculated factors for each year
Year Method Primary Road NOX
Adjustment Factor
Secondary Total NO2
Adjustment Factor
2015 Simple 3.2585 0.9779
LOOCV 3.2668 0.9773
2016 Simple 3.4487 0.8935
LOOCV 3.4615 0.8928
2017 Simple 3.5843 0.9235
LOOCV 3.5915 0.9230
9.2.5 The factors calculated using the two approaches are very similar in each year. The
primary adjustment factors increase year-on-year; this is likely due to projected
emissions reductions in the EFT not having been realised in terms of an obvious
reduction in measured concentrations. 2015 having been a ‘low-pollution year’ in
terms of measured concentrations UK-wide will also have influenced the lower
primary adjustment factor for this year, as well as the secondary adjustment factor,
which is much closer to one.
9.2.6 The secondary adjustment factors calculated demonstrate that secondary
adjustment is necessary to avoid significant over-estimation of annual mean
nitrogen dioxide concentrations. This is thought to be at least partially due to the
over-estimation of primary NO2 from road traffic in Defra’s NOX to NO2 calculator.
Further sensitivity testing around this is proposed for inclusion in the ES.
9.2.7 The statistical performance of each of the models is presented in Table 9.3. LAQM
TG.16 advises that ‘ideally an RMSE within 10% of the air quality objective would
be derived, which equates to 4µg/m3 for the annual average NO2 objective’.
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However, it is only recommended that model inputs and verification should be
revisited if RMSE values are higher than ±25% of the objective.
Table 9.3: Statistical performance of each of the models
Year Method Correlation
Coefficient
Root Mean Square Error
Fractional Bias
2015 Simple 0.88 4.15 0.01
LOOCV 0.84 4.93 0.01
2016 Simple 0.80 5.06 0.02
LOOCV 0.75 5.93 0.01
2017 Simple 0.87 4.40 0.01
LOOCV 0.83 5.03 0.01
‘Ideal’ Value 1 0 0
9.2.8 Graphic 9.1 to Graphic 9.6 plot the final modelled annual mean nitrogen dioxide
concentrations against the measured concentration for each verification scenario.
Graphic 9.1: 2015 Model performance – simple verification approach
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Graphic 9.2: 2015 Model performance – LOOCV verification approach
Graphic 9.3: 2016 Model performance – simple verification approach
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Graphic 9.4: 2016 model performance – LOOCV verification approach
Graphic 9.5: 2017 Model performance – simple verification approach
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Graphic 9.6: 2017 Model performance – LOOCV verification approach
9.2.9 The graphs generally show good agreement. There is one specific outlier, the
Heston automatic monitor in Hounslow, which falls outside of the 25% lines in
some of the graphs. Graphic 9.7 presents the primary adjustment factors
calculated for each site using the data from all of the other sites in the LOOCV
analyses, and highlights that this monitor is a clear outlier, with the factors at the
other sites all being very consistent.
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Graphic 9.7: LOOCV primary verification factors
9.2.10 The Heston automatic monitor is located adjacent to the traffic-light controlled
junction of Great West Road (A4) and Heston Road (A3005), while also being
some 150 m west of the traffic-light controlled junction of Great West Road (A4)
and Jersey Road. The A4 here has three lanes in each direction, carries a large
volume of traffic (> daily vehicles), and will see regular idling traffic and quick
acceleration, due to the presence of the traffic lights. The average speeds
assumed in the modelling for the roads at these junctions are low (generally
around 20 kph) and are considered appropriate bearing in mind the above. It is
unsurprising, therefore, that the model is predicting a relatively large road-NOX
contribution.
9.2.11 Further consideration has been given to model predictions at busy junctions, to
determine whether there is any systematic over-prediction of road-NOX. A
comparison of total modelled annual mean NO2 against measured NO2 at 19
diffusion tube monitoring sites close to busy junctions is shown in Graphic 9.8.
This shows a range of over-and under-prediction of concentrations in each year,
which is to be expected and demonstrates that there is no systematic issue with
the model’s performance close to busy junctions. Heston is simply an outlier, a
location where concentrations could reasonably be expected to be higher than
measured, and thus not one that invalidates the overall model performance in any
way.
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Graphic 9.8: Modelled vs measured NO2 at diffusion tube sites close to busy junctions
9.2.12 While the removal of the Heston monitor from the verification would reduce the
RMSE to below 10% of the air quality objective for all three years, doing so would
result in a higher primary adjustment factor being applied, and higher total
concentrations being predicted. Graphic 9.9 to Graphic 9.11 plots demonstrate
the model performance against diffusion tube monitoring sites within the
CAQOAA. They show a relatively large scatter, which is to be expected when
comparing against diffusion tube data, which itself has a relatively large
measurement error margin. Overall, the plots suggest that the model is performing
well on average and, if anything, is slightly over-predicting total concentrations,
which is worst-case.
9.2.13 Several of the diffusion tube monitoring sites at which the model is tending to over-
predict, when compared to measured concentrations, are located close to the M25
motorway. Without these sites included, the comparison would be much closer to
a 1:1 relationship. This suggests that, in general, the model is performing well, but
that over-predictions close to the M25 are likely. This is somewhat unsurprising,
given that the model adjustment factors calculated have been applied to all road
traffic contributions, including motorways, when it is common knowledge among
air quality professionals that models tend to perform better when it comes to
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motorway emissions, requiring less or no upward adjustment. In considering the
results of the air quality modelling it should be borne in mind that concentrations
close to the M25 are likely to have been over-predicted, and are thus worst-case,
especially in the case of the CURED sensitivity test predictions.
Graphic 9.9: 2015 Modelled vs measured NO2 at diffusion tube sites
Graphic 9.10: 2016 Modelled vs measured NO2 at diffusion tube sites
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Graphic 9.11: 2017 Modelled vs measured NO2 at diffusion tube sites
9.2.14 The comparison to diffusion tube monitoring has demonstrated that the model is
already tending towards over-predicting concentrations, and this over-prediction
would be made worse without Heston incorporated in the verification. As such, it is
considered that there is no robust justification for removing the Heston monitor
from the verification.
9.2.15 Bearing in mind this tendency to over-predict, the adjustment factors calculated
using the ‘Simple’ approach have been applied, rather than the averages from the
LOOCV scenario. The LOOCV scenarios provide valuable insight into the model
performance, and produce factors very similar to the ‘Simple’ approach, albeit
ones that would produce very slightly higher total concentrations. It has been
judged best to apply the factors derived using all of the monitoring sites in each
year.
9.3 PM10 and PM2.5
9.3.1 The model performance has been tested against monitoring data for PM10 and
PM2.5. Graphic 9.12 to Graphic 9.17 present graphs of the modelled annual mean
concentrations plotted against the measured concentrations at all of the
appropriate monitoring sites in the CAQOAA.
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Graphic 9.12: 2015 model performance – PM10
Graphic 9.13: 2015 model performance – PM2.5
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Graphic 9.14: 2016 model performance – PM10
Graphic 9.15: 2016 model performance – PM2.5
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Graphic 9.16: 2017 model performance – PM10
Graphic 9.17: 2017 model performance – PM2.5
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9.3.2 The graphs for PM2.5 show a very good alignment with the monitoring data. In
reality, concentrations of this pollutant are dominated by the background
concentration; the graphs imply that the background concentrations used are
reasonable, and that the model is doing an adequate job of predicting the road
traffic and airport contributions, without the need for adjustment.
9.3.3 The graphs for PM10 do not present such a strong alignment, suggesting that,
while the model is performing well at some sites, concentrations of this pollutant
are being under-predicted at a number of sites. The locations of these sites have
been carefully considered to establish whether there is any reason why the model
might systematically under-predict concentrations at them, but in reality they share
little in common with one another with regards to the key modelled sources (i.e.
road traffic and airport emissions). This analysis has, however, identified that there
may well be specific local sources affecting these sites. The following have been
identified:
1. The Hayes automatic monitor is located adjacent to the junction of North Hyde
Road and North Hyde Gardens, with the latter being an access road to a
number of industrial premises, some of which are known to handle aggregates
(e.g. Conway Asphalt Plant). Historic photography of the junction shows that
the roads are routinely very dusty, a localised issue likely to lead to higher
measured concentrations that might reasonably be expected, and not one that
can readily be allowed for in the modelling
2. Historic photography of the junction adjacent to the Heston automatic monitor
also routinely shows very dusty roads. There are no obvious local sources for
this dust, but its presence on the roads will lead to higher particulate
concentrations than might otherwise be expected. The fact that the Hayes and
Heston monitors measured considerably higher concentrations in every year
when compared to the other sites suggests that these persistently dirty roads
are likely to be driving the high concentrations
3. The Harmondsworth automatic monitor is located adjacent to a quiet, dead-end
road, and the high measured concentrations are unlikely to relate to road traffic
emissions. There are no obvious local sources that might be causing the high
concentrations measured here. It is possible that agricultural practices on the
adjacent fields might be leading to dust generation, or the industrial sites on the
opposite side of the M25 (upwind under prevailing wind conditions) might
represent a significant source, but this is merely speculation. Airport-related
sources will not be contributing significantly to the measured concentrations,
which are clearly above the wider background levels, and there is no
justification for adjusting any of the modelled contributions
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4. Roads close to the Oxford Avenue monitor do not appear to routinely be dusty,
and there are no other obvious sources of PM nearby. The higher
concentrations measured here are unlikely to be airport-related, given how
much lower concentrations measured at nearby LHR2 are. It is possible that
they relate to road traffic sources, but the modelled road traffic contribution
would have to be increased by a factor of around 15 to match the measured
values. Such an increase seems unrealistic, and does not inspire confidence
that adjustment is appropriate
5. The other sites mostly fall within the 25% error margins, but those closer to the
upper 25% bound are likely to also be influenced by more minor, local sources,
although these are not obvious. One example is Slough’s Colnbrook monitor,
located in Poyle. Poyle is surrounded by industrial estates to the north, east
and south, and these are likely the reason why measured concentrations are
above wider background levels, although the measured concentrations
probably do represent an appropriate background concentration for the village
of Poyle. Given that the background maps represent 1km square averages,
they will inevitably slightly over or under-predict concentrations at some
locations within each square.
9.3.4 It is considered most likely that local sources are leading to the high measured
concentrations at some sites, and these are not sources that could readily be
incorporated into the model. As such, it would not be appropriate to adjust the
model outputs, which are judged to represent an appropriate prediction of PM10
concentrations across the CAQOAA in the absence of specific local sources of
particulate matter.
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10. EFFECT ON EU LIMIT VALUE COMPLIANCE METHODOLOGY
10.1.1 In order to assess the impact of the DCO Project on compliance with the NO2
annual mean EU limit value, emissions associated with the additional traffic
generated by the DCO Project have been modelled on a selection of the worst-
case roads for potential limit value impacts within the CAQOAA. These are those
roads where the DCO Project causes the largest changes in traffic volumes or
fleet composition, and those with the highest predicted concentrations in future-
year PCM mapping.
10.1.2 Concentrations have been predicted at 4 m from the kerb on stretches of road that
are representative of 100 m of road and not within 25 m of major junction, in line
with historical PCM modelling.
10.1.3 A NOX increment associated with the DCO Project has been calculated at each of
these PCM receptors using the same approach as described above for the
emissions modelling, by subtracting the total NOX concentration in the with DCO
Project scenarios from those in the future baseline scenarios. This increment has
incorporated the additional emissions from road traffic changes throughout the
FMA and changes in aircraft and other airside source emissions.
10.1.4 The NOX increment calculated has been added to the NOX concentration in
Column Y of the ‘Calcs_Scenario’ tab of the SL_PCM concentration tool
spreadsheets available from the Defra website (Defra, 2017b). The local oxidant
value in column AA has been adjusted to account for these additional
contributions, with the link-specific fNO2 value from the spreadsheet applied for
road-NOX and an appropriate receptor-specific fNO2 value applied for airside NOX,
derived based on the relative contribution of the sources and their relative fNO2
values. The spreadsheet has then derived a new total NO2 concentration (in
Column AC).
10.1.5 The change in NO2 as a result of the DCO Project has been determined by
comparing this new total NO2 concentration to the PCM baseline, and compliance
with the limit value considered. It must be noted that this analysis utilises Defra’s
2015 reference year modelling, which is the only modelling for which SL_PCM
concentration tool spreadsheets are available. In order to consider the impacts
against Defra’s recently published 2017 reference year modelling (Defra, 2019),
the change in concentrations calculated using the 2015 reference year
spreadsheets has simply been added to the 2017 reference year baseline
concentrations. While this method will not have resulted in exactly the right
conversion ratio of NOx to NO2 being applied, the baseline concentrations vary
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relatively little between the two scenarios, thus any inconsistency will be extremely
small, and highly unlikely to affect the conclusions drawn. It is also the best
possible solution without 2017 reference year SL_PCM concentration tool
spreadsheets, which are not available.
10.1.6 Outside of the CAQOAA, the key limit value compliance locations of concern are
those in Central London; impacts here have been considered on the basis of traffic
data within the FMA and qualitatively using demand forecast information from the
surface access modelling.
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11. CURED SENSITIVITY TEST RESULTS
11.1 Community area results
11.1.1 The NO2 results produced for each community area using the CURED sensitivity
test emission factors are presented in Table 11.1.
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Table 11.1: CURED sensitivity test NO2 results at selected representative receptors within each community area
Year Maximum
Concentration
(µg/m3)
Magnitude (µg/m3) Impact Descriptors at Selected Representative Receptors
Future
Baseline
With
DCO
Project
Maximu
m
decrease
or
minimu
m
increase
Maximu
m
Increase
Subs.
Ben.
Mod.
Ben.
Slight
Ben.
Neg. Slight
Adv.
Mod.
Adv.
Subs.
Adv.
Harmondsworth
2022 35.6 36.7 0.0 1.2 0 0 0 28 7 0 0
2027 30.0 29.4 -4.1 5.0 0 0 2 24 6 3 0
2030 28.1 26.3 -3.2 5.4 0 0 2 24 5 4 0
2035 28.6 25.8 -5.2 5.9 0 1 8 17 4 5 0
West Drayton
2022 46.8 47.2 0.1 1.6 0 0 0 15 6 5 0
2027 39.6 41.1 0.3 2.4 0 0 0 10 14 2 0
2030 37.3 38.2 0.3 1.9 0 0 0 16 9 1 0
2035 37.8 38.7 0.2 2.5 0 0 0 15 5 6 0
Sipson
2022 29.3 29.7 0.1 0.6 0 0 0 31 0 0 0
2027 24.8 26.3 0.9 5.0 0 0 0 8 19 4 0
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Year Maximum
Concentration
(µg/m3)
Magnitude (µg/m3) Impact Descriptors at Selected Representative Receptors
Future
Baseline
With
DCO
Project
Maximu
m
decrease
or
minimu
m
increase
Maximu
m
Increase
Subs.
Ben.
Mod.
Ben.
Slight
Ben.
Neg. Slight
Adv.
Mod.
Adv.
Subs.
Adv.
2030 23.3 24.4 0.8 4.8 0 0 0 8 20 3 0
2035 23.7 25.2 1.0 4.9 0 0 0 7 19 5 0
Harlington
2022 44.5 45.1 0.2 1.0 0 0 0 14 5 0 1
2027 36.9 36.9 -0.4 5.0 0 0 0 17 1 2 0
2030 34.2 34.4 0.1 4.4 0 0 0 17 2 1 0
2035 34.6 34.5 -0.1 4.1 0 0 0 14 6 0 0
Hayes
2022 44.3 44.9 0.2 0.6 0 0 0 7 3 5 0
2027 36.7 36.5 -0.1 0.6 0 0 0 15 0 0 0
2030 34.0 34.1 -0.1 0.3 0 0 0 15 0 0 0
2035 34.4 34.2 -0.1 0.4 0 0 0 15 0 0 0
Cranford Cross
2022 29.1 29.5 0.2 0.4 0 0 0 13 0 0 0
2027 24.3 23.7 1.4 1.4 0 0 0 13 0 0 0
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Heathrow Expansion PRELIMINARY ENVIRONMENTAL INFORMATION REPORT: Chapter 7: Appendix 7.1
Appendix 7.1-68 © Heathrow Airport Limited 2019
Year Maximum
Concentration
(µg/m3)
Magnitude (µg/m3) Impact Descriptors at Selected Representative Receptors
Future
Baseline
With
DCO
Project
Maximu
m
decrease
or
minimu
m
increase
Maximu
m
Increase
Subs.
Ben.
Mod.
Ben.
Slight
Ben.
Neg. Slight
Adv.
Mod.
Adv.
Subs.
Adv.
2030 22.9 22.6 1.7 1.7 0 0 0 13 0 0 0
2035 22.8 23.1 2.2 2.2 0 0 0 13 0 0 0
Cranford
2022 39.4 39.6 0.3 0.4 0 0 0 13 1 0 0
2027 32.8 33.7 0.2 0.9 0 0 0 12 2 0 0
2030 30.6 31.0 0.3 0.4 0 0 0 14 0 0 0
2035 30.9 31.3 0.3 0.4 0 0 0 14 0 0 0
Heston
2022 45.9 46.3 0.2 0.5 0 0 0 9 6 2 0
2027 37.7 37.7 0.2 0.4 0 0 0 16 0 0 0
2030 34.9 35.0 0.3 0.3 0 0 0 17 0 0 0
2035 35.2 35.5 0.3 0.3 0 0 0 17 0 0 0
Hounslow (Central and South)
2022 36.0 36.3 0.0 0.2 0 0 0 6 0 0 0
2027 29.7 30.7 0.0 1.0 0 0 0 5 1 0 0
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Heathrow Expansion PRELIMINARY ENVIRONMENTAL INFORMATION REPORT: Chapter 7: Appendix 7.1
Appendix 7.1-69 © Heathrow Airport Limited 2019
Year Maximum
Concentration
(µg/m3)
Magnitude (µg/m3) Impact Descriptors at Selected Representative Receptors
Future
Baseline
With
DCO
Project
Maximu
m
decrease
or
minimu
m
increase
Maximu
m
Increase
Subs.
Ben.
Mod.
Ben.
Slight
Ben.
Neg. Slight
Adv.
Mod.
Adv.
Subs.
Adv.
2030 27.9 28.9 0.0 1.0 0 0 0 6 0 0 0
2035 28.3 28.7 0.0 0.4 0 0 0 6 0 0 0
Hounslow (West and Heath)
2022 38.5 38.7 0.0 0.7 0 0 0 22 2 0 0
2027 32.3 33.5 0.0 1.2 0 0 0 21 3 0 0
2030 30.3 31.0 0.0 0.7 0 0 0 23 1 0 0
2035 30.6 31.3 0.0 0.7 0 0 0 23 1 0 0
Feltham North
2022 38.0 38.3 0.3 0.5 0 0 0 11 1 0 0
2027 31.8 32.3 -0.1 0.7 0 0 0 11 1 0 0
2030 29.6 29.9 -0.1 0.4 0 0 0 12 0 0 0
2035 29.9 30.6 0.6 0.7 0 0 0 11 1 0 0
Bedfont
2022 37.2 37.5 0.2 0.4 0 0 0 12 0 0 0
2027 31.2 32.1 -0.6 1.3 0 0 0 10 2 0 0
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Heathrow Expansion PRELIMINARY ENVIRONMENTAL INFORMATION REPORT: Chapter 7: Appendix 7.1
Appendix 7.1-70 © Heathrow Airport Limited 2019
Year Maximum
Concentration
(µg/m3)
Magnitude (µg/m3) Impact Descriptors at Selected Representative Receptors
Future
Baseline
With
DCO
Project
Maximu
m
decrease
or
minimu
m
increase
Maximu
m
Increase
Subs.
Ben.
Mod.
Ben.
Slight
Ben.
Neg. Slight
Adv.
Mod.
Adv.
Subs.
Adv.
2030 29.1 30.0 -0.4 1.2 0 0 0 12 0 0 0
2035 29.4 30.6 0.6 1.5 0 0 0 11 1 0 0
Stanwell
2022 32.9 33.3 0.3 0.5 0 0 0 18 0 0 0
2027 27.2 28.2 0.0 1.0 0 0 0 18 0 0 0
2030 25.4 27.4 0.2 2.1 0 0 0 18 0 0 0
2035 25.6 27.6 1.1 2.3 0 0 0 17 1 0 0
Stanwell Moor
2022 31.2 31.7 0.4 1.1 0 0 0 12 0 0 0
2027 26.3 26.1 -0.2 2.2 0 0 0 12 0 0 0
2030 24.7 26.2 -0.5 4.3 0 0 0 7 4 1 0
2035 24.8 26.6 -0.1 4.6 0 0 0 7 3 2 0
Poyle
2022 30.4 30.7 0.2 0.3 0 0 0 12 0 0 0
2027 25.8 25.6 -0.1 2.4 0 0 0 11 1 0 0
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Heathrow Expansion PRELIMINARY ENVIRONMENTAL INFORMATION REPORT: Chapter 7: Appendix 7.1
Appendix 7.1-71 © Heathrow Airport Limited 2019
Year Maximum
Concentration
(µg/m3)
Magnitude (µg/m3) Impact Descriptors at Selected Representative Receptors
Future
Baseline
With
DCO
Project
Maximu
m
decrease
or
minimu
m
increase
Maximu
m
Increase
Subs.
Ben.
Mod.
Ben.
Slight
Ben.
Neg. Slight
Adv.
Mod.
Adv.
Subs.
Adv.
2030 24.2 24.8 0.1 2.3 0 0 0 11 1 0 0
2035 24.3 25.4 0.3 2.4 0 0 0 10 2 0 0
Colnbrook
2022 32.3 32.8 0.1 0.5 0 0 0 12 0 0 0
2027 26.7 26.8 0.4 1.3 0 0 0 12 0 0 0
2030 24.6 24.8 0.4 1.1 0 0 0 12 0 0 0
2035 24.7 25.2 0.4 1.2 0 0 0 12 0 0 0
Brands Hill
2022 45.8 47.4 0.3 3.0 0 0 0 2 2 4 8
2027 39.1 39.3 -1.2 0.6 0 1 4 9 2 0 0
2030 36.5 36.6 -1.4 0.6 0 0 4 12 0 0 0
2035 37.1 37.0 -1.7 0.9 0 0 4 11 1 0 0
Iver and Richings Park
2022 52.2 52.9 0.3 0.7 0 0 0 12 0 0 2
2027 44.7 44.9 0.5 0.8 0 0 0 13 1 0 0
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Heathrow Expansion PRELIMINARY ENVIRONMENTAL INFORMATION REPORT: Chapter 7: Appendix 7.1
Appendix 7.1-72 © Heathrow Airport Limited 2019
Year Maximum
Concentration
(µg/m3)
Magnitude (µg/m3) Impact Descriptors at Selected Representative Receptors
Future
Baseline
With
DCO
Project
Maximu
m
decrease
or
minimu
m
increase
Maximu
m
Increase
Subs.
Ben.
Mod.
Ben.
Slight
Ben.
Neg. Slight
Adv.
Mod.
Adv.
Subs.
Adv.
2030 41.8 41.8 0.6 0.6 0 0 0 14 0 0 0
2035 42.3 42.3 0.6 0.6 0 0 0 14 0 0 0
Notes:
Impacts described as beneficial or adverse and substantial, moderate, slight or negligible.
The maximum pollutant concentrations show the maximum at a receptor in a community area for a particular scenario. The maximum concentration
does not necessarily occur at the same receptor in the Future Baseline and With DCO Project scenario in each year.
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Heathrow Expansion PRELIMINARY ENVIRONMENTAL INFORMATION REPORT: Chapter 7: Appendix 7.1
Appendix 7.1-73 © Heathrow Airport Limited 2019
11.2 Summary results across the CAQOAA
11.2.1 The property counts for NO2 concentrations produced using Defra EFT road traffic
emissions factors and CURED sensitivity test emission factors are presented in
Table 11.2 to Table 11.7.
Table 11.2: Count of properties by impact descriptor
Impact Descriptor Scenario
2022 2027 2030 2035
Substantial Adverse 1 0 0 0
Moderate Adverse 106 14 27 70
Slight Adverse 315 327 378 371
Negligible 145030 145105 145047 145011
Slight Beneficial 0 6 0 0
Moderate Beneficial 0 0 0 0
Substantial Beneficial 0 0 0 0
Table 11.3: Count of properties by impact descriptor (CURED sensitivity test)
Impact Descriptor Scenario
2022 2027 2030 2035
Substantial Adverse 19 0 0 0
Moderate Adverse 186 18 16 51
Slight Adverse 387 483 438 437
Negligible 144860 144918 144961 144925
Slight Beneficial 0 30 35 39
Moderate Beneficial 0 3 2 0
Substantial Beneficial 0 0 0 0
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Heathrow Expansion PRELIMINARY ENVIRONMENTAL INFORMATION REPORT: Chapter 7: Appendix 7.1
Appendix 7.1-74 © Heathrow Airport Limited 2019
Table 11.4: Count of properties by magnitude of change in concentration
Magnitude of Change in Annual Mean NO2 (µg/m³)
Scenario
2022 2027 2030 2035
>4.0 0 29 51 93
2.0 – 4.0 3 404 512 759
0.4 – 2.0 1701 11033 18157 43330
0.0 – 0.4 135638 132264 126318 100847
-0.4 – 0.0 8110 1552 376 367
-2.0 – -0.4 0 170 38 56
-4.0 – -2.0 0 0 0 0
<-4.0 0 0 0 0
Table 11.5: Count of properties by magnitude of change in concentration (CURED sensitivity test)
Magnitude of Change in Annual Mean NO2 (µg/m³)
Scenario
2022 2027 2030 2035
>4.0 0 15 19 53
2.0 – 4.0 3 403 546 804
0.4 – 2.0 2017 12580 17802 39264
0.0 – 0.4 135795 130101 125918 104050
-0.4 – 0.0 7637 2125 975 988
-2.0 – -0.4 0 224 191 280
-4.0 – -2.0 0 4 1 13
<-4.0 0 0 0 0
Table 11.6: Local air quality receptors informing DCO Project significance
Magnitude of Change in Annual Mean NO2 (µg/m³)
Total Number of Receptors
2022 2027 2030 2035
A6 B7 A B A B A B
Large (>4.0) 0 0 0 0 0 0 0 0
Medium (2.0 – 4.0) 0 0 0 0 0 0 0 0
Small (0.4 – 2.0) 33 0 0 0 0 0 0 0
6 Worsening of air quality objective already above objective or creation of a new exceedance. 7 Improvement of an air quality objective already above objective or the removal of an existing exceedance.
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Heathrow Expansion PRELIMINARY ENVIRONMENTAL INFORMATION REPORT: Chapter 7: Appendix 7.1
Appendix 7.1-75 © Heathrow Airport Limited 2019
Table 11.7: Local air quality receptors informing DCO Project significance (CURED sensitivity test)
Magnitude of Change in Annual Mean NO2 (µg/m³)
Total Number of Receptors
2022 2027 2030 2035
A B A B A B A B
Large (>4.0) 0 0 0 0 0 0 0 0
Medium (2.0 – 4.0) 0 0 0 0 0 0 0 0
Small (0.4 – 2.0) 104 0 3 0 1 2 0 0
11.3 Effect on EU limit value compliance results
11.3.1 The maximum predicted total annual mean NO2 concentrations along the key PCM
links within the CAQOAA, using both the EFT and the CURED sensitivity test, are
presented in Table 11.8 to Table 11.10.
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Heathrow Expansion PRELIMINARY ENVIRONMENTAL INFORMATION REPORT: Chapter 7: Appendix 7.1
Appendix 7.1-76 © Heathrow Airport Limited 2019
Table 11.8: NO2 annual mean EU limit value compliance in 2022
Census ID
Road Annual Mean NO2 Concentration (µg/m3)
Future Baseline With DCO Project
EFT Emissions CURED Emissions
PCM151 PCM172 Increment PCM15 PCM17
Increment
PCM15 PCM17
6123 A4 Bath Road East of Waggoners Roundabout 32.7 32.4 0.3 33.0 32.7 0.3 33.0 32.8
16112 A4 Bath Road (Heathrow Boulevard to Nobel Drive)
37.4 37.8 0.9 38.3 38.7 0.9 38.3 38.7
18727 A312 between M4 and Hayes Road 44.1 44.5 0.7 44.8 45.2 0.8 44.8 45.3
26914 A312 between High Street and M4 41.1 41.0 0.2 41.3 41.2 0.2 41.3 41.2
36309 A30 past Hatton Cross 37.0 39.3 1.2 38.3 40.6 1.2 38.3 40.6
48810 A312 between Pump Lane and A4020 45.6 44.5 0.5 46.1 45.0 0.5 46.1 45.1
49028 A312 North of A4020 40.9 40.0 0.5 41.4 40.5 0.5 41.4 40.5
56686 A312 between A30 and High Street 39.2 39.7 0.2 39.4 39.9 0.2 39.4 39.9
59008 A4 near Henlys Roundabout 39.5 38.7 0.5 39.9 39.2 0.5 40.0 39.2
73633 A30 West of A312 34.4 36.4 0.0 34.4 36.4 0.0 34.4 36.4
73636 A312 between Hayes Road and Pump Lane 45.5 44.5 0.6 46.1 45.1 0.6 46.2 45.1
36013 M4 Spur 32.5 36.2 2.1 34.6 38.4 2.1 34.6 38.4
75071 A4 Great West Road East of Jersey Road 37.1 37.7 0.1 37.2 37.9 0.1 37.2 37.9
78344 A4 Brands Hill 30.3 30.4 2.4 32.7 32.8 2.5 32.9 32.9
18487 M4 Near J3 34.6 36.2 1.1 35.7 37.2 1.2 35.8 37.3
26012 M4 East of Heston Road 36.4 41.5 0.9 37.4 42.4 1.0 37.4 42.5
6013 M4 East of J4 34.2 35.5 1.2 35.4 36.7 1.3 35.5 36.8
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Heathrow Expansion PRELIMINARY ENVIRONMENTAL INFORMATION REPORT: Chapter 7: Appendix 7.1
Appendix 7.1-77 © Heathrow Airport Limited 2019
73446 A4020 The Broadway 33.3 31.3 0.2 33.5 31.5 0.2 33.6 31.5 1 This is the predicted baseline concentration from Defra’s 2017 NO2 projections data (2015 reference year) 2 This is the predicted baseline concentration from Defra’s 2019 NO2 projections data (2017 reference year)
Table 11.9: NO2 annual mean EU limit value compliance in 2027
Census ID Road Annual Mean NO2 Concentration (µg/m3)
Future Baseline With DCO Project
EFT Emissions CURED Emissions
PCM15 PCM17 Increment PCM15 PCM17 Increment PCM15 PCM17
6123 A4 Bath Road East of Waggoners Roundabout 25.1 25.5 0.2 25.3 25.7 0.2 25.3 25.6
16112 A4 Bath Road (Heathrow Boulevard to Nobel Drive)
35.0 35.2 -3.4 31.6 31.8 -3.7 31.3 31.5
18727 A312 between M4 and Hayes Road 33.1 33.3 0.3 33.4 33.5 0.5 33.6 33.7
26914 A312 between High Street and M4 31.4 31.5 0.8 32.3 32.3 1.1 32.5 32.6
36309 A30 past Hatton Cross 28.9 30.6 1.5 30.4 32.1 2.1 31.0 32.7
48810 A312 between Pump Lane and A4020 34.7 33.7 0.6 35.3 34.3 0.8 35.5 34.5
49028 A312 North of A4020 30.5 29.9 0.3 30.8 30.2 0.4 30.9 30.3
56686 A312 between A30 and High Street 30.7 31.3 1.1 31.8 32.4 1.4 32.1 32.8
59008 A4 near Henlys Roundabout 29.7 29.7 0.2 29.9 29.9 0.2 29.9 29.9
73633 A30 West of A312 27.1 28.7 0.9 28.0 29.6 1.1 28.3 29.9
73636 A312 between Hayes Road and Pump Lane 34.7 33.7 0.3 35.0 34.0 0.5 35.1 34.1
36013 M4 Spur 26.3 28.5 5.3 31.6 33.7 6.6 32.9 35.1
75071 A4 Great West Road East of Jersey Road 27.7 28.6 0.3 28.1 28.9 0.4 28.1 29.0
78344 A4 Brands Hill 23.9 23.9 -0.7 23.2 23.2 -0.9 23.0 23.0
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Heathrow Expansion PRELIMINARY ENVIRONMENTAL INFORMATION REPORT: Chapter 7: Appendix 7.1
Appendix 7.1-78 © Heathrow Airport Limited 2019
Census ID Road Annual Mean NO2 Concentration (µg/m3)
Future Baseline With DCO Project
EFT Emissions CURED Emissions
PCM15 PCM17 Increment PCM15 PCM17 Increment PCM15 PCM17
18487 M4 Near J3 26.9 27.7 -0.1 26.8 27.6 -0.2 26.7 27.5
26012 M4 East of Heston Road 27.5 30.6 -0.3 27.2 30.3 -0.4 27.1 30.2
6013 M4 East of J4 26.8 27.3 0.9 27.7 28.2 1.1 27.9 28.3
73446 A4020 The Broadway 25.1 24.4 0.4 25.6 24.9 0.5 25.6 24.9
Table 11.10: NO2 annual mean EU limit value compliance in 2030
Census ID
Road Annual Mean NO2 Concentration (µg/m3)
Future Baseline With DCO Project
EFT Emissions CURED Emissions
PCM15 PCM17 Increment PCM15 PCM17 Increment PCM15 PCM17
6123 A4 Bath Road East of Waggoners Roundabout 22.3 22.9 0.2 22.5 23.1 0.1 22.4 23.0
16112 A4 Bath Road (Heathrow Boulevard to Nobel Drive)
33.9 34.0 -4.4 29.5 29.6 -5.1 28.9 28.9
18727 A312 between M4 and Hayes Road 29.0 29.1 0.5 29.5 29.6 0.7 29.8 29.8
26914 A312 between High Street and M4 27.7 27.8 0.6 28.3 28.5 0.8 28.4 28.6
36309 A30 past Hatton Cross 25.8 27.4 1.0 26.8 28.3 1.5 27.3 28.8
48810 A312 between Pump Lane and A4020 30.6 29.7 0.6 31.3 30.4 0.8 31.5 30.6
49028 A312 North of A4020 26.5 26.1 0.4 26.9 26.5 0.5 27.0 26.6
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Heathrow Expansion PRELIMINARY ENVIRONMENTAL INFORMATION REPORT: Chapter 7: Appendix 7.1
Appendix 7.1-79 © Heathrow Airport Limited 2019
Census ID
Road Annual Mean NO2 Concentration (µg/m3)
Future Baseline With DCO Project
EFT Emissions CURED Emissions
PCM15 PCM17 Increment PCM15 PCM17 Increment PCM15 PCM17
56686 A312 between A30 and High Street 27.5 28.1 0.8 28.3 28.9 1.0 28.5 29.1
59008 A4 near Henlys Roundabout 26.0 26.4 0.3 26.3 26.7 0.2 26.3 26.6
73633 A30 West of A312 24.3 25.8 0.6 24.9 26.4 0.6 25.0 26.5
73636 A312 between Hayes Road and Pump Lane 30.6 29.7 0.4 31.0 30.1 0.5 31.1 30.2
36013 M4 Spur 23.8 25.3 3.5 27.3 28.9 4.5 28.3 29.8
75071 A4 Great West Road East of Jersey Road 24.2 25.2 0.4 24.6 25.6 0.5 24.7 25.7
78344 A4 Brands Hill 21.6 21.5 -0.8 20.8 20.7 -1.1 20.4 20.3
18487 M4 Near J3 23.7 24.2 0.2 23.9 24.5 0.1 23.8 24.3
26012 M4 East of Heston Road 23.6 26.0 0.0 23.5 26.0 -0.2 23.4 25.9
6013 M4 East of J4 23.7 23.9 1.1 24.7 25.0 1.3 24.9 25.2
73446 A4020 The Broadway 22.0 22.0 0.4 22.4 22.4 0.4 22.5 22.4
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Heathrow Expansion PRELIMINARY ENVIRONMENTAL INFORMATION REPORT: Chapter 7: Appendix 7.1
Appendix 7.1-80 © Heathrow Airport Limited 2019
12. BIBLIOGRAPHY
Full text reference In-text reference
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analysis of monitoring data. Netcen/ED48208120/R/1925/Issue 1.
Abbott, 2005
Air Quality Consultants. (2016). Emissions of Nitrogen Oxides from
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210116.pdf.aspx [Accessed 17 May 2019].
AQC, 2016
Air Quality Consultants. (2017). CURED v3A. [online]. Available at:
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[Accessed 17 May 2019].
AQC, 2017
Air Quality Consultants. (2018). Adjusting Background NO2 Maps for
CURED v3A. [online]. Available at:
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[Accessed 17 May 2019].
AQC, 2018c
Air Quality Consultants. (2018)., Calibrating Defra’s 2015-based
Background NOX and NO2 Maps against 2016 and 2017 Measurements.
[online]. Available at:
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Calibration-News-F2.pdf [Accessed 17 May 2019].
AQC, 2018b
Air Quality Consultants. (2018a)., Development of the CURED v3A
Emissions Model. [online] Available at:
http://www.aqconsultants.co.uk/Resources/Download-Reports.aspx
[Accessed 17 May 2019].
AQC, 2018a
CERC. (2016). London Urban Canopy Data. [online]. Available at:
http://www.cerc.co.uk/IJARSG2016 [Accessed 17 May 2019].
CERC, 2016
Defra. (2016). Local Air Quality Management Technical Guidance (TG16).
[online]. Available at: https://laqm.defra.gov.uk/technical-guidance/
[Accessed 17 May 2019].
Defra, 2016
Defra. (2017). 2017 NO2 projections data (2015 reference year). [online]
Available at:https://uk-air.defra.gov.uk/library/no2ten/2017-no2-projections-
from-2015-data [Accessed 17 May 2019].
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