Wakefield Modelling of Action Plan Measures Vehicle ... · Improved vehicle emission assessments A...

Project Report:

Wakefield Modelling of Action Plan Measures

- Vehicle Emission Modelling

Version 1.2: 20th October 2015

Dr James Tate

WAKEFIELD MODELLING OF ACTION PLAN MEASURES

INSTITUTE FOR TRANSPORT STUDIES

York LEZ Feasibility Study – Vehicle Emission Modelling

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Principal Author(s)Dr James Tatea

Author’s Affiliationsa Institute for Transport Studies, Faculty of the Environment, University of Leeds,Leeds, LS2 9JT, UK. Email: [email protected], Tel: +44 (0) 113 343 6608.


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EXECUTIVE SUMMARY

Improved vehicle emission assessments

A detailed, integrated traffic-vehicle emission modelling framework has been used to evaluatethe environmental benefits of a range of fleet renewal policies, network alterations (junction re-designs) and forecast emission contributions in 2018 and 2020 within the Wakefield Air QualityManagement Areas (AQMAs): Ackworth, Castleford, Featherstone and Hemsworth. Themodelling couples traffic microsimulations (www.aimsun.com), which model the movement ofindividual vehicles through the well specified study area road networks, with an instantaneousvehicle emission model (PHEM11) that provides second-by second fuel consumption and tail-pipe emission predictions. The emission assessments are considered to be more reliable thanthose from alternative UK emission prediction methods as:

The local, operational vehicle fleet was accurately specified in the model. Vehicleregistration numbers observed in the AQMAs were cross-referenced with databases thatcontain information on individual vehicles so the share of cars, taxis, vans, buses andcommercial vehicles; and their respective fuel type, engine size, weight and Euro standardproportions were known and accounted for. Alternative UK vehicle emission modelstypically assume default vehicle type/ fuel and Euro standard proportions;

The movements of cars, vans, buses and commercial vehicles in the AQMAs have beensimulated at a detailed/ microscopic level as they negotiate traffic junctions, signals andinteract with each other. This detailed (traffic microsimulation) modelling captures manyof the vehicle dynamics that effect tail-pipe emissions: critically whether a vehicle is idling,accelerating, cruising at a desired speed or decelerating;

As the traffic microsimulations consider all the vehicle movements across the study areasecond-by-second (speed, acceleration/ braking etc), unlike alternative less detailedapproaches, they can be used to study congestion effects and stop-start driving conditions;

The emission factors (grams per kilometre travelled) for each of the vehicle sub-types (e.g.vehicle and fuel type, Euro status) are in agreement with those derived from recent, on-roadvehicle emission Remote Sensing measurements (Tate, 2013). This is not the case foralternative UK/ EU vehicle emission estimate approaches, which are now considered to beoverly optimistic and unreliable for light-duty (cars and vans) and heavy-duty (Buses,Coaches and HGVs) diesel vehicles;

The ebb and flow of traffic demand through the 24-hours of a typical weekday has beensimulated. The impact of queues during AM and PM peak periods and how the fleet mixevolves through the day are considered. Scheduled Bus services for example do not operatein night-time hours. This approach provides a more robust assessment of emissions througha typical day, which can then be scaled-up to an annual total; and

The modelling approach is a step towards a second-by-second “virtual” representation ofthe “real” traffic network. It naturally encapsulates many events and processes that effectvehicle emissions. Urban Buses for example have to make additional stops-and-starts topick up passengers on their scheduled routes. As Buses are large, heavy-duty diesel vehiclesthese stop-start motions have a significant fuel and emission penalty. The detailedmodelling approach demonstrated in this study does simulate/ consider this behaviour,more aggregate approaches do not.

As the microscopic traffic model attempt to replicate the day-to-day variability in flowconditions (stochastic), multiple (five) replications of each 24-hour period were simulated. Thetotal distance of simulated journeys for the four networks and scenarios is 3.4 millionkilometres.


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Emission contributions

The improved traffic-vehicle emission assessments indicate that whilst Buses only completeda very small proportion (≈1%) of the total vehicle kilometres over a typical weekday in each of the AQMAs, they were predicted to contribute a disproportionate amount (≈11%) to the total NOX emitted across the network. Diesel light-duty vehicles (cars and vans) also makesignificant contributions (≈55%) to the total NOX emitted over a typical weekday, and an evengreater share of the primary NO2 contribution (≈90%). This is important as the emission of primary NO2 will add directly to roadside concentrations and background levels, rather thanNO2 forming through secondary reactions in the atmosphere that can be limited by theavailability of ground-level ozone. In roadside, air quality hotspot locations such as in theAckworth, Castleford, Featherstone and Hemsworth AQMAs, this direct contribution to NO2

levels is especially important in stable atmospheric conditions i.e. low wind speeds.

The lack of improvement in the NOX emission performance of light-duty diesels and to a lesserextent heavy-duty diesels in urban driving conditions, has important implications for the likelyfuture trends in ambient NOX and NO2 concentrations, and consequently European memberstates ability to meet the annual mean NO2 limit value of 40μg•m3. Vehicle fleet turnover takesmany years, so the Euro 4/ 5 diesels with poor NOX and (primary) NO2 performance are likelyto still be emitting and contributing to emission totals in 10 years’ time. It is critical that thenext generation of Euro 6 emission standard diesel vehicles now entering the operational fleetdo emit less NOX and NO2 in urban driving conditions if European air quality limits are to bemet in the next 5-10 years. Whilst heavy-duty NOX emission controls on Euro VI vehicles arenow considered to be effective (approximately 10-fold reduction) in urban driving conditions(Spreen et al, 2014); light-duty diesels are still emitting NOX at a high rate, roughly half that ofEuro V cars and vans.

Outlook

By 2018 traffic demand in the Ackworth, Castleford, Featherstone and Hemsworth AQMAs isexpected to grow by 3.3%, 28.9%, 7.7% and 6.6% respectively. The forecast accounts forunderlying increase in demand and planned developments on the networks. The Castlefordnetwork is expected to experience the highest rate of increase in vehicle activity (kilometrestravelled). This rate of increase in traffic demand and distance travelled through a typical day(24-hours) is expected to continue through to 2020 with kilometres travelled in the Ackworth,Castleford and Featherstone AQMAs 5.1%, 38.9% and 15.3% respectively. In addition topredicting the flow, congestion and vehicle emission contributions in the 2018 and 2020 futureyears; scenarios assessing the impact of demand management measures and a highway scheme(a series of junction re-designs) on the Castleford network were assessed. It is expectedadditional measures will be needed on the Castleford network so the high rate of forecast trafficdemand and ensuing congestion does not unduly impact on air quality levels.

There is a compelling need to develop a sustainable, low carbon public transport system thatdelivers substantial reductions in emissions that are harmful to both the global (carbon) andlocal environments. The Bus fleet within the Wakefield District is therefore a priority vehiclesector. The Council also has a greater influence over these vehicles than other light- and heavy-duty vehicle types, through the Bus Quality partnership, Local Transport Plan and Bus operatorengagements etc. As Buses make repeated scheduled journeys each day there is also a greateropportunity to reduce emissions in a sensitive area by renewing a small number of vehicles withcleaner technology than with any other category of vehicle. The next generation of Euro 6 Busesare observed to be emitting significantly less NOX emissions than its predecessors(approximately 10-fold reduction). Although Buses are relatively high emitters of NOX as theyare large, heavy-duty diesel vehicles, that have to perform additional stops and starts to pick uppassengers, the scenario tested replacing all scheduled Bus services with cleaner Euro VIvehicles was only predicted to lower NOX and tail-pipe PM10 emissions in the AQMAs by 3-4%. This is because the share of kilometres travelled by scheduled Bus services is relativelylow (less than 2%).


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The evolution and natural renewal of the vehicle fleet is forecast, finally to have a significantimpact on emissions of air quality pollutants. NOX emissions from Euro VI diesel cars areapproximately half that of previous generations (Euro I, II, III, IV, V). Petrol cars emissions ofNOX are at a low level. NOX emission controls on Euro VI heavy-duty vehicles (HGVs andBuses) do now control tail-pipe emissions well, with emissions significantly lower than EuroV and older. Therefore NOX emissions across are expected to fall from 2014 levels over thenext four years (2018): Ackworth -38%, Castleford -19% (includes development traffic),Featherstone -32% and Hemsworth -25%. By 2020 further reductions are expected on theAckworth (-48%) network. The degrading traffic flow levels on the Castleford and Featherstoneby 2020 are forecast to broadly offset the emission improvements from the year on year renewalof vehicles (2019 and 2020) that achieve the latest Euro VI emission standards.

If the natural renewal of the vehicle fleet for the future design years is not considered i.e. theobserved Base (2014) fleet is maintained, emissions of all pollutants increase incrementally in-line with the expected traffic growth. On the networks where traffic flow conditions degrade, afurther emissions penalty from the congested flow conditions (stop-start driving) is predicted.

Finally diesel vehicle emission control technology is delivering significant reductions in NOX

Real Driving Emissions (RDE) emissions. The modelling reported is considered to be the mostrobust forecast of future years emissions due to the quality of input information and (validated)tools applied. By 2020 the expected halving in NOX emissions in the Ackworth AQMA areexpected to lower NO2 concentrations to levels below the 40 µg.m3 annual mean standard.Annual mean NO2 levels in the Featherstone AQMA are higher at 50 µg.m3. The forecast 32%reduction in NOX emissions in this AQMA by 2020 is calculated to be at the level needed tomeet the 40 µg.m3 NO2 annual mean standard. This result is marginal and there remainuncertainties with the calculations due to the complexities of atmospheric chemistry, underlyingvariations in background levels and the differential changes in total NOX and primary NO2

emissions may not be fully considered at ‘hotspot’ locations. The predicted 20% reduction inNOX emissions in the Castleford AQMA is not expected to be sufficient to reduce the observed60 µg.m3 annual NO2 mean below the 40 µg.m3 standard (Wakefield Council, 2008).Accounting for background levels and atmospheric chemistry, a 52% reduction in NOX

emissions is expected to be needed to improve air quality in Castleford to meet the NO2 airquality standards (Wakefield Council, 2008).

With concentrations NO2 forecast to be at or just above the 40 µg.m3 NO2 annual mean standardby 2020 in the Wakefield Council AQMAs, a holistic approach and range of sustainabletransport policies are still needed to manage demand levels and accelerate the up-take of trulylow emission vehicle technologies. Finally clean vehicle technologies are commercially viable:

Battery Electric (BEV) Buses are commercially viable on some routes; Petrol-hybrid cars are well suited to the urban, stop-start nature of taxi operations. 3-4

year old cars commonly bought through company car schemes (with significant taxincentives for low CO2 vehicles) are now available on the second-hand market. A‘green taxi’ fleet could be a rare “win-win” where profitability is increased foroperators through lower fuel bills, whilst emissions of local air quality pollutants (NOX,NO2 and PM) are reduced to a nominal level;

Euro VI heavy-duty emission controls are proving to effectively control emissions ofNOX and PM10. Compressed Natural Gas and (CNG) and Compressed Bio-Gas (CBG)fuelled heavy-duty vehicles also promise significant environmental benefits, reportedto emit ≈10 times less NOX than comparable standard Euro 5 diesel vehicles. CNG orCBG engines also do not need to rely on overly complex exhaust after treatmentsystems to control tailpipe emissions and achieve EEV status (EnhancedEnvironmentally friendly Vehicle); and

Plug-in Hybrid drive systems could be used and configured to remove tail-pipeemissions from sensitive areas i.e. operate as Full Electric Vehicles (FEV) in theWakefield AQMAs using ‘geo-fencing’ technology.

TABLE OF CONTENTS

EXECUTIVE SUMMARY iii

1. BACKGROUND 1

2. MODELLING 3

2.1 The vehicle fleet 2

2.2 Traffic microsimulations: AIMSUN 4

2.3 Instantaneous emission model: PHEM 6

3. RESULTS 58

3.1 Emission factors – Passenger cars 59

3.2 Emission factors – Light-Goods Vehicles (vans) 62

3.3 Emission factors – Heavy-Goods Vehicles (HGVs) 63

3.4 Emission factors – Buses 64

3.5 Emission contributions 65

3.6 Scenarios 58

4. RECOMMENDATIONS FOR FUTURE WORK 59

5. ACKNOWLEDGEMENTS 60

6. REFERENCES 61

7. APPENDICES 63


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TABLE OF FIGURES

FIGURE 1. THE COUPLED TRAFFIC-EMISSION MODELLING FRAMEWORK ..................................... 3FIGURE 2. THE WAKEFIELD AIMSUN NETWORKS: (A) ACKWORTH (TOP-LEFT); (B)

CASTLEFORD (TOP-RIGHT); (C) FEATHERSTONE (BOTTOM-LEFT); (D) HEMSWORTH(BOTTOM-RIGHT)...................................................................................................................................... 1

FIGURE 3. ANPR SURVEY LOCATIONS ....................................................................................................... 1TABLE 1. MODELLING SCENARIOS .............................................................................................................. 2TABLE 2. FUEL TYPE AND EURO STANDARD SPLIT (%) OF PASSENGER CARS IN THE

WAKEFIELD STUDY AREAS (OCTOBER 2014), PLUS PROPORTIONS OF TAXIS, LGVS,BUSES AND HGVS. .................................................................................................................................. 3

FIGURE 5. PASSENGER CAR FLEET PROPORTIONS.............................................................................. 3FIGURE 6. PASSENGER CAR FLEET (A) AVERAGE KERB WEIGHT, (B) AVERAGE ENGINE

POWER........................................................................................................................................................ 4FIGURE 7. DISTANCE TRAVELLED (MODEL) IN EACH SIMULATION HOUR ...................................... 5FIGURE 8. AVERAGE SPEED (MODELLED) IN EACH SIMULATION HOUR ......................................... 5FIGURE 9. AVERAGE SPEED (MODELLED) IN EACH SIMULATION HOUR ......................................... 7TABLE 3. THE LONDON DRIVE CYCLE STATISTICS. ............................................................................... 7TABLE 4. PASSENGER CAR SPECIFICATIONS TESTED OVER THE LDC. ......................................... 8FIGURE 10. ILLUSTRATIVE TIME-SERIES PLOT OF A SECTION OF THE AM PEAK MOTORWAY

SECTION OF THE LONDON DRIVE CYCLE DRIVEN BY A EURO IV DIESEL MPV (A) SPEED;(B) CO2; AND (C) NOX. .............................................................................................................................. 8

FIGURE 11. SCATTER PLOTS COMPARING MODELLED (PHEM) AND OBSERVED VALUES FORALL SECTIONS (URBAN, SUBURBAN AND MOTORWAY) OF THE LDC. .................................... 1

FIGURE 12. THE DISTRIBUTION OF ROAD LINK GRADIENTS IN EACH OF THE WAKEFIELDNETWORKS ................................................................................................................................................ 1

FIGURE 13. TIME SERIES PLOT OF PHEM RESULTS FOR A SAMPLE SIMULATED EURO 5DIESEL CAR OPERATING IN THE CASTLEFORD AQMA IN THE AM (BASE) PEAK: (A)SPEED, (B) ROAD GRADIENT, (C) CO2, (D) NOX AND NO2, (E) PARTICLE MASS (PM). .......... 2

TABLE 5. MODELLED NO2/ NOX FRACTIONS ............................................................................................ 59FIGURE 14. PASSENGER CAR NOX EMISSION FACTORS FOR (A) DIESEL PASSENGER CARS,

(B) PETROL PASSENGER CARS......................................................................................................... 60FIGURE 15. PASSENGER CAR PM10 EMISSION FACTORS FOR (A) DIESEL PASSENGER CARS,

(B) PETROL PASSENGER CARS......................................................................................................... 61FIGURE 16. PASSENGER CAR CO2 EMISSION FACTORS FOR (A) DIESEL PASSENGER CARS,

(B) PETROL PASSENGER CARS......................................................................................................... 61FIGURE 17. LIGHT-DUTY NOX EMISSION FACTORS FOR SIZE (A) N1 VANS, (B) N2 VANS, (C) N3

VANS. ......................................................................................................................................................... 62FIGURE 18. LIGHT-DUTY PM10 EMISSION FACTORS FOR SIZE (A) N1 VANS, (B) N2 VANS, (C)

N3 VANS.................................................................................................................................................... 62FIGURE 19. HEAVY-DUTY NOX EMISSION FACTORS (A) ARTICULATED HGVS, (B) RIGID HGVS.

..................................................................................................................................................................... 63FIGURE 20. HEAVY-DUTY PM10 EMISSION FACTORS (A) ARTICULATED HGVS, (B) RIGID HGVS.

..................................................................................................................................................................... 63FIGURE 21. BUS NOX EMISSION FACTORS FOR (A) DOUBLE-DECKERS, (B) SINGLE-DECKER 64FIGURE 22. BUS PM10 EMISSION FACTORS FOR SIZE (A) ARTICULATED HGVS, (B) RIGID

HGVS.......................................................................................................................................................... 64FIGURE 23. TOTAL NOX EMISSION CONTRIBUTIONS FROM EACH VEHICLE TYPE FOR THE

WAKEFIELD AIMSUN NETWORKS: (A) ACKWORTH (TOP-LEFT); (B) CASTLEFORD (TOP-RIGHT); (C) FEATHERSTONE (BOTTOM-LEFT); (D) HEMSWORTH (BOTTOM-RIGHT)......... 58

FIGURE 24. TOTAL NO2 EMISSION CONTRIBUTIONS FROM EACH VEHICLE TYPE FOR THEWAKEFIELD AIMSUN NETWORKS: (A) ACKWORTH (TOP-LEFT); (B) CASTLEFORD (TOP-RIGHT); (C) FEATHERSTONE (BOTTOM-LEFT); (D) HEMSWORTH (BOTTOM-RIGHT)......... 59

FIGURE 25. TOTAL PM EMISSION CONTRIBUTIONS FROM EACH VEHICLE TYPE FOR THEWAKEFIELD AIMSUN NETWORKS: (A) ACKWORTH (TOP-LEFT); (B) CASTLEFORD (TOP-RIGHT); (C) FEATHERSTONE (BOTTOM-LEFT); (D) HEMSWORTH (BOTTOM-RIGHT)......... 60


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TABLE 6. SUMMARY EMISSION CONTRIBUTIONS FROM EACH VEHICLE TYPE .......................... 58TABLE 7. THE EVOLUTION OF THE ACKWORTH PASSENGER CAR FLEET: FUEL TYPE AND

EURO STANDARD SHARES (%) FOR THE BASE (2014), 2018 AND 2020 FUTURE YEARS. 58APPENDIX A. THE OBSERVED AND MODELLED FLEET BREAKDOWN FOR THE BASE (2014)

AND FUTURE YEARS (2018 AND 2020) ON THE ACKWORTH, CASTLEFORD,FEATHERSTONE AND HEMSWORTH NETWORKS. ...................................................................... 64

APPENDIX B. THE OBSERVED (AND MODELLED, PHEM) VEHICLE SPECIFICATIONS................ 65

1. BACKGROUNDThe 60th anniversary of the Great Smog of London in 1952 has passed, which resulted in 4,000 deathsacross the capital. The same number of people still die each year in London from air pollution. The totalnumber of UK deaths in 2008 due to poor air quality has been put at 30,000. Although the health impactsand costs, estimated to be £8-20 billion per annum are almost twice those of physical inactivity, it failsto receive the same level of attention. Under closer scrutiny the health evidence is strengthening, withthe World Health Organization classifying diesel engine exhaust as carcinogenic to humans in June2012 (WHO, 2012).

Road transport is the main source of the pollution in UK urban areas. The ever more stringent EUvehicle emission standards were perceived to deliver cleaner air, but levels of a key pollutant in ourbusy streets haven’t been falling. As concentrations of the pollutant in question, Nitrogen Dioxide(NO2), are often above EC air quality standards (limit values) in European urban areas, nations areexposed to potential infraction fines for non-compliance with EU law. Wakefield, like most UK andEU urbanised areas, is exceeding the nitrogen dioxide (NO2) annual average 40μg/m3 air qualitystandard (limit value) at a number of heavily trafficked locations across the City. These areas are termedTechnical Breach Areas (TBAs). So why hasn’t air quality been improving, when traffic levels havebeen relatively stable in the central areas of UK Cities since the 1990’s?

Modern diesel vehicle emission controls underperform in urban driving conditions when exhaust gasesfrom the engine are relatively cool, inhibiting the operation of catalysts and filters. Such stop-start trafficmotions are common place in the streets of our towns and cities, but aren’t adequately represented inthe legislated vehicle emission standard test conditions. Recent UK research that surveyed the emissionperformance of large numbers of vehicles on the road has highlighted the deficiencies in diesel vehicleemission controls in urban driving conditions (Carslaw et al, 2011). The oxides of nitrogen emissionperformance of diesel cars and vans in urban driving conditions have been shown to have changed littlein the past 15 years. So a brand new diesel car and one that has been driven for over 10 years, in urbandriving conditions, emit similar amounts of a critical pollutant. Worryingly from a local air qualityperspective diesel cars are more popular than ever. In 2010 sales of diesel cars overtook those withpetrol engines for the first time (SMMT, 2011).

European Commission and UK policies are encouraging the purchase of new diesel cars over theirpetrol-driven counter-parts, due to their lower like-for-like carbon dioxide (CO2) emission ratings.Whilst this shift in purchasing behaviour is helping motor manufacturers meet their initial average carCO2 rating targets (gCO2/km) in-place from 2012 onwards, the trade-off has been the halt in urban airquality improvements since 2000-2004. Motor manufacturers face a similar trade-off between CO2 andemissions of local air quality pollutants, but at a vehicle level as they optimize the operation of theengine and emission controls. Motor manufacturers are complying with the emissions legislation bydeveloping exhaust after-treatment technologies and configuring their operation for the test conditions.The accelerations in the artificial 1970’s test cycle still in use are however slight in comparison withthose of “normal” driving. Real-world driving with prompter accelerations demands more power fromthe engine. At higher power demands more emphasis is given to vehicle performance than the emissionsof local air quality pollutants. As the complexity and sophistication of engine management and exhaustafter-treatment systems has increased, so has the potential for motor manufacturers to optimize theiroperation for the legislated test conditions to a greater degree, at the disregard of “normal” on-roadoperations. There are also concerns about how the performance of the complex, multi-component dieselemission control systems will degrade with time and usage.

Frustratingly although the technology and capability exists, the combination of weak outdated Europeanregulations and an industry intent on working to legislation, but not in the spirit of the laws, has resultedin air quality not improving as it should. The substantial health and environmental implications, alongwith the threat of potentially unlimited fines from the European Commission for not achieving airquality targets, has raised the importance of air pollution once again. The problem is not the UK’s notalone; most Member States in the EU are not complying with the air quality standards. The motorindustry is going to have to do much more to produce cleaner more efficient vehicles as the easy, low-cost options of encouraging a shift to diesel and reducing vehicle weight by losing the spare tyre havebeen taken.


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UK and EU road transport emission inventories have historically considered flow volume and emissionrates for different vehicle categories and sub-types (i.e. car, fuel type, engine size, Euro standard). Theemission rates for a road section (or link) have also been based on the road type or the average speed.The data under-pinning such inventories (e.g. www.naei.org.uk, HBEFA 2011) are mostly derived fromlaboratory dynamometer (or “rolling-road”) tests, where vehicles are driven over defined (artificial)speed-profiles (or drive-cycles) and their exhaust emissions analysed. Whether these drive-cycles arerepresentative of on-road traffic flow conditions and behaviour is often debated. Indeed, there ismounting concern that such approaches do not adequately reflect emissions from congested urban andinter-urban networks. The research methods and techniques demonstrated in this project attempt toaddress the recognised deficiencies with the commonly applied traffic-emission modelling approaches.The detailed approach used in this study, coupling traffic microsimulations and an instantaneousemission model, allows the influence of vehicle accelerations, road gradient, vehicle and engine load tobe considered in the emission assessments. This is clearly highly desirable when attempting tounderstand vehicle emissions across congested networks, evaluate environmental traffic managementand vehicle fleet renewal policies.

Clearly such higher resolution approaches are both information hungry and computationally intensive.Resources such as ANPR (Automatic Number Plate Recognition) data cross-referenced to the UKMotor Vehicle Registration Information database mean it is now feasible to develop a detailedunderstanding of the local vehicle fleet composition i.e. broken down by vehicle type (car, van, bus,coach, rigid- and articulated- HGV), fuel type, Euro standard, weight, etc. It is straight-forward for thecoupled traffic-emission simulations to use this information. As individual vehicles enter the trafficmicrosimulation network, they are allocated a vehicle type according to specified proportions. In thecase of scheduled Bus services, these operate to set timetables and routes. At the emission modellingstage vehicles are assigned a fuel type and Euro standard. This ‘virtual’ traffic-emission modellingenvironment is therefore not based on an average vehicle fleet; rather being a much closer representationof reality with individual vehicles having set attributes e.g. a Euro 4 diesel passenger car. Advances indesktop computing mean it is also now feasible to micro-simulate traffic movements at a detailed levelas they negotiate traffic junctions, signals and interact with each other for complete City networks.

With the modelling framework delivering vehicle emission predictions that better reflect Real DrivingEmissions (RDE) (Tate, 2014), it is able to deliver more realistic assessments of emissions acrossnetworks. As it can account for changing traffic flow conditions e.g. congested stop-start driving, it isalso well suited to evaluate the impact of road improvement schemes, such as junction re-designs.Policies to replace older, more polluting types of vehicles with cleaner powertrains such as hybrids canalso evaluated. With recent knowledge of the emission performance of the latest generation of Euro VIvehicles now known, the emission contribution of future vehicle fleets can now be predicted withconfidence. Diesel Euro VI light-duty (passenger cars and vans) do emit approximately half the oxidesof nitrogen (NOX) per kilometre driven than their predecessors. The exhaust after-treatment systems onEuro VI Heavy-duty (Bus, Coach and Heavy-Goods Vehicles) diesel can now mitigate the majority ofNOX emissions. Road side concentrations of NO2 and NOX are therefore finally expected to fall. Themodelling in this report assesses the current emission generated by the current Wakefield fleet (2015)and predictions the contribution in the future years 2018 and 2020.


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2. MODELLING

The modelling approach couples traffic microsimulations, which model the movement of individualvehicles through the well specified study area road network, with an instantaneous vehicle emissionmodel that provides second-by second fuel consumption and tail-pipe emission predictions. Theschematic in Figure 1 presents the modelling framework and flow of information. In this project theAimsun (http://www.aimsun.com/) traffic micro-simulator has been coupled with the instantaneousemission model PHEM (Passenger car and Heavy Emission Model – Hausberger et al, 2011). Themodelling framework has been developed and evaluated by Dr James Tate (Institute of TransportStudies, University of Leeds) in collaboration with the Institute of Internal Combustion Engines andThermodynamics at the Technical University of Graz, Austria (Zallinger et al, 2008). The models areable to provide results for the complete network, selected road links or individual vehicles.

Figure 1. The coupled traffic-emission modelling framework

Aimsun traffic microsimulation networks were constructed and validated by Fore Consulting Limited(Fore) for the four Wakefield study areas: Ackworth (Fore 2014a), Castleford (Fore 2014b),Featherstone (Fore 2014c) and Hemsworth (Fore 2014d).

Figure 2. The Wakefield Aimsun networks: (a) Ackworth (top-left); (b) Castleford (top-right); (c) Featherstone (bottom-left); (d) Hemsworth(bottom-right)

The operational vehicle fleet composition was well-known and specified in the models. Data collectionincluded 24-hour ANPR traffic surveys conducted by http://www.axiomtraffic.co.uk/ in each of thestudy areas over a typical weekday in October 2014. Automatic Number Plate Recognition (ANPR)camera systems were installed to survey the bi-directional flows. The list of registration plates (orVehicle Registration Mark – VRM) was passed to CarwebUK (http://www.carweb.co.uk/) to obtaindetailed information for every observed vehicle. The analysis of the local vehicle fleet proportionsanalysis is presented in the following section (2.1).

Figure 3. ANPR survey locations

The data from the ANPR surveys, supplemented with additional traffic flow observation in the area,traffic signal control coded to match on-street timings provided by Wakefield Council and PublicTransport routes/ frequencies (http://www.wymetro.com/BusTravel/). The models were setup torepresent three average week-day time periods, plus a 24-hour so the generation of emissions indifferent traffic conditions throughout the day (24-hours) could be assessed.

AM peak period (07:30 to 09:30hrs); Inter-peak (IP) period (11:00 to 13:00hrs); PM peak period (16:00 to 18:00hrs); and a 24-hour period (00:00 to 24:00hrs).

This is important so not only do the diurnal variation in traffic flow and congestion can be assessed, butalso the composition of the vehicle fleet. In the majority of traffic modelling and emission modellingstudies only AM and PM peak periods are assessed, with other time periods simply factored from thepeak models. This approach cannot account for congestion effects, nor how the fleet mix changesthrough the day. The configuration, calibration and validation of the traffic microsimulations aredescribed in the Fore Model Validation Reports (MVR) 2014a, 2014b, 2014c and 2014d.

The Wakefield Air Quality Action measures assessed for the BASE (2014 demand and fleet) and futureyears (2018 and 2020) included: replacing scheduled City Bus services with Euro VI vehicles, andimplementing travel plans to reduce development traffic by 10%. The scenarios modelled on each


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network are summarised in Table 1. Networks with increased traffic demand for future years andchanges to the physical network were also simulated with the BASE 2014 fleet, so changes due to thenetwork/ demand and natural fleet renewal could be identified. The renewal of the Bus fleet with EuroVI vehicles was not simulated for the Ackworth network as the volume of Bus traffic is low (less than1% of vehicle kilometres driven – see Table 2).

Table 1. Modelling scenarios

SCENARIOSNETWORK

Ackworth Castleford Featherstone Hemsworth

BASE√ √ √ √

BASE + Euro 6 City-Buses√ √ √

2018√ √ √ √

2018 demand2014 fleet 2014 fleet 2014 fleet 2014 fleet

2018 + travel plans[development traffic reduced by 10%]

√

2020√ √ √ √

2020 demand[2020 demand + 2014 fleet] 2014 fleet 2014 fleet 2014 fleet 2014 fleet2020 + travel plans

[development traffic reduced by 10%]√

The observed vehicle fleet is presented in section 2.1. Further details on the Aimsun simulations, withsummary results presented for the BASE (2014) networks are presented in section 2.2. Theinstantaneous emission modelling is outlined in section 2.3. The traffic movements, fuel consumptionand emissions in the Wakefield study areas are considered to have been simulated using the bestavailable information and tools.

2.1 The vehicle fleet

The detailed local vehicle fleet information was analysed to firstly determine the vehicle typeproportions (car, taxis, van, rigid- and articulated- HGV, bus and coach) in each study network. At theemission modelling stage, the vehicle type share was augmented with details of the fuel type and Eurostandard distribution for each vehicle type using the ANPR data (October, 2014) cross-referenced to aUK vehicle fleet database. The ANPR surveys identified 43,611 valid number plates (VRMs). The taxifleet was also identified as a separate subset of the light-duty fleet, as the list of licensed taxis wasprovided by Wakefield Council. This approach allows the local vehicle fleet to be characterised intofuel type (passenger cars) and Euro status sub-categories. Table 2 documents the fuel type and Europroportions for the light-duty vehicle types, along with the proportions of the taxis, Light-GoodsVehicles (LGVs), Buses (Single-decker, Double-decker, Coach) and Heavy-Goods Vehicles (HGVs,articulated and rigid). The complete vehicle fleet proportions are documented in Appendix A.

As the emission characteristics of Euro 4 and 5 heavy-duty vehicles are dependent on the exhaust after-treatment system employed, best endeavours were made to quantify the split of heavy-duty vehiclesequipped with Exhaust Gas Recirculation (EGR) or Selective Catalytic Reduction (SCR) systems(Rexeis, 2011).


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Table 2. Fuel type and Euro standard split (%) of passenger cars in the Wakefield studyareas (October 2014), plus proportions of taxis, LGVs, Buses and HGVs.

Note: Diesel fraction is the % of diesel passenger cars in each car Euro category

Vehicletype

Fueltype

Eurostandard Ackworth Castleford Featherstone Hemsworth

% fleetDieselfraction % fleet

Dieselfraction % fleet

DieselFraction % fleet

Dieselfraction

CAR Petrol Euro 00.11 0.16 0.05 0.06

Euro 10.17 0.21 0.13 0.07

Euro 23.22 3.09 3.23 3.29

Euro 312.66 13.68 13.94 14.24

Euro 416.70 16.21 16.43 18.01

Euro 511.42 11.54 10.30 12.30

Euro 60.15 0.13 0.12 0.09

Hybrid Petrol Euro 40.03 0.03 0.07 0.02

Euro 50.39 0.16 0.17 0.13

Diesel Euro 00.09 43.5 0.03 14.3 0.03 40.0 0.13 70.0

Euro 10.11 39.4 0.12 37.5 0.12 48.4 0.15 66.7

Euro 20.78 19.6 0.49 13.8 0.66 17.1 0.89 21.3

Euro 36.45 33.8 5.94 30.3 6.72 32.5 6.98 32.9

Euro 413.36 44.4 13.09 44.6 12.58 43.3 13.73 43.2

Euro 516.44 58.2 14.77 55.8 14.22 57.6 15.22 55.1

Euro 60.29 66.7 0.23 63.5 0.17 57.1 0.06 37.5

Full Electric Vehicle (FEV) 0.00 0.00 0.01 0.00

Hybrid DieselEuro 5

0.02 0.01 0.00 0.02

TOTAL 82.39 79.88 78.96 85.39

TAXIS 0.77 2.22 1.52 1.40

LGVs 13.56 13.34 14.78 11.03

BUS 0.80 1.63 1.42 0.86

HGV 2.47 2.93 3.33 1.32

Figure 5. Passenger car fleet proportions

0

5

10

15

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25

30

35

40

Euro 0 Euro 1 Euro 2 Euro 3 Euro 4 Euro 5 Euro 6

Pas

sen

ger

car

fle

et

pro

po

rtio

n(%

)

EURO STANDARD

DIESEL-HYBRID

DIESEL

PETROL-HYBRID

PETROL


4

Figure 5 illustrates the distribution of petrol and diesel passenger cars in each Euro category. The on-road passenger car fleet in the Wakefield study areas is dominated by Euro 4 classification or newervehicles, which comprise > 70% of the passenger car fleet. As observed at other sites in the UK (Carslawet al, 2011), the share of diesel fuelled passenger cars has increased with time. For the Euro 5 standard,more diesel passenger cars were observed than those with petrol engines. In 2011 sales of dieselpassengers overtook petrol fuelled vehicles for the first time in UK (SMMT, 2011).

The weight and frontal area (aerodynamic drag) of vehicles also influences fuel consumption andexhaust emissions. Figure 6 illustrates the changes in the average observed passenger cars (diesel andpetrol) for each Euro standard (a) kerb weight (un-laden) and (b) frontal area (m2). The weight and sizeof diesel cars is rising, with the average Euro 5 diesel car being ≈30% heavier than its petrol counterpart. These and the changes to (rated) engine power in the vehicle fleet are specified in the emission modelthrough the Euro standards, for different vehicle and fuel types (documented in Appendix B).

Figure 6. Passenger car fleet (a) Average kerb weight, (b) Average frontal area.

The PHEM11 City (scheduled) Bus categories were extended to differentiate single-decker, double-decker and coaches, each with different frontal areas (aerodynamic drag), engine power (rated), weightand loading (also documented in Appendix B).

2.2 Traffic microsimulations: Aimsun

Fore Consulting Limited (Fore) constructed the Aimsun micro-simulation models of the study areas.To account for the variability (stochastic modelling) in microsimulation processes multiple simulationsof the 24-hour periods were run. In-line with ‘best practice’ (Dowling et al, 2004), ten simulations ofeach time period/ scenario were run with different initiating ‘number seeds’. The speed trajectories (ineach simulation time-step, 0.5sec) of all vehicles were harvested from the Aimsun simulations of eachtime period using an API (Advanced Programming Interface) written by ITS, Leeds.

The total distance travelled (vehicle kilometres) in each hour of the day are illustrated in Figure 7. Theassociated average vehicle speeds are presented in Figure 8. The profiles demonstrate how themodelling is replicating the changes in the traffic demand and activity of each vehicle type, across eachnetwork. Castleford is the largest network (see Figure 2) with the highest vehicle kilometres travelled.Ackworth has the highest average speed, with the small Hemsworth network having the slowest movingtraffic. The increase in demand and vehicle activity during the AM (0800-0900) and PM (1700-1800)peak hours is evident (Figure 7). The higher demand leads to traffic congestion levels worsening withlower average network speeds (Figure 8).

800

1000

1200

1400

1600

1800


KER

BW

EIG

HT

(kg)

EURO STANDARD

PETROL

DIESEL

2

2.2

2.4

2.6

2.8

3


FRO

NTA

LA

REA

(m2)

EURO STANDARD

PETROL

DIESEL


5

Figure 7. Distance travelled (model) in each simulation hour

Figure 8. Average speed (modelled) in each simulation hour

Hour

To

tald

ista

nce

(km

-p

erre

plic

atio

n)

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5 10 15 20

BASE

Hour

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eed

(km

.h1)

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BASE


6

2.3 Instantaneous emission model: PHEM

PHEM is a comprehensive power-instantaneous emission model that is able to simulate fuelconsumption and NOX, NO2, HCs, Particulate Mass (PM10), Particle Number (PN), Carbon Monoxide(CO) and Hydrocarbons (HC) tail-pipe emissions of the whole vehicle fleet of Euro 0 to Euro 6, petrol/diesel/ CNG and bio-gas fuels, heavy-duty vehicles, passenger cars and light commercial vehiclessecond-by-second. PHEM has been developed at the Technical University of Graz (TUG, AU) since2000 in the research projects ARTEMIS, COST346 and HBEFA. The model is based on light- andheavy-duty vehicle engine speed – power emission maps established from engine and chassisdynamometer measurements (Rexeis et al, 2007, Zallinger et al. 2005). The supporting model data-setcurrently includes emission measurements from 61 passenger cars, 20 Light-Commercial and 118Heavy-Duty Vehicles. PHEM modelling was conducted using version 11 released in January 2012.PHEM is considered to the leading model of its kind in Europe (Boulter et al, 2007).

PHEM has a time alignment and correction sub-model to relate engine speed – power events topredicted engine-out emissions. Emissions recorded in chassis dynamometer, exhaust emissionsampling and analysis facilities have been delayed and engine-out peaks smoothed during transportthrough the exhaust, sampling and analysis systems. Dynamic correction and time alignment functionshave been developed through experimental investigation and CFD-simulations of the exhaust gastransport (Zallinger et al, 2005). It is the time corrected emission measurements that are used to populatethe engine speed – power emission maps. These methods make PHEM capable of simulating theinstantaneous fuel consumption and emissions for any speed profile or driving cycle.

The IEM PHEM has been validated with second-by-second Transport for London (TfL) chassisdynamometer (Millbrook) measured data. A sample of Euro 4 Passenger Cars have been tested over adrive-cycle (speed profile) intended to represent the broad range of London (real-world) drivingconditions. ITS completed the comparison of observed and modelled second-by-second as part of DrJames Tate’s part-time secondment into TfL as their Road Transport Emissions Advisor. The ‘LondonDrive Cycle’ for light-duty vehicles has been developed by TfL as part of an on-going Vehicle EmissionStudy. The drive cycle was developed in association with Millbrook, who were commissioned to tracka car (VBox GPS and CAN Bus link) making repeated circuits of a set route in the North-East of Londonat different times of day: AM peak, Inter-peak and in Free-flow conditions. The route contained sectionsof (urban) motorway, suburban and urban (central London) driving conditions. The speed profile (time-series) of the London Drive Cycle is illustrated in Figure 9. The drive-cycle is considered to representtypical driving style/ behaviour in the UK. The drive-cycle doesn’t consider fluctuations in roadgradient. Importantly the distribution of engine power demands broadly reflects the WLTP (Worldwideharmonized Light vehicles Test Procedures). Summary statistics for the different elements of the drivecycle (road type and time period) are documented in Table 3.


7

Figure 9. Average speed (modelled) in each simulation hour

Table 3. The London Drive Cycle statistics.

RoadType

TimePeriod

Duration(seconds)

Distance(km)

AverageSpeed

(km.h-1)

MaximumAcceleration

(m.s-2)

SectionID

Urban Free-flow 1202 8.92 26.73 2.67 7

Urban AM peak 2048 8.93 15.69 1.97 8

Urban Inter-Peak 2311 8.93 13.91 2.48 9

Suburban Free-flow 1036 13.33 46.31 2.4 10

Suburban AM peak 1894 13.33 25.33 2.67 11

Suburban Inter-Peak 1591 13.33 30.16 2.31 12

Motorway Free-flow 1023 24.61 86.60 1.62 13

Motorway AM peak 1884 24.61 47.03 1.69 14

Motorway Inter-Peak 1030 24.61 86.02 2.46 15

The agreement of a sample of TfL second-by-second vehicle emission measurements over the LDCwith IEM predictions from PHEM (version 11.4) for four Euro IV Passenger cars has been evaluated.The specification of the four vehicles tested and simulated is documented in Table 4. The PHEM vehiclespecifications were adjusted to match the test vehicles. The PHEM engine power and emission mapsare an average (normalised) of several vehicles of that category. The fuel efficiency and emissionperformance of broadly comparable (size, weight and engine power) makes and models are variable,

Time (seconds)

Sp

ee

d(k

m/h

)

0

20

40

60

80

100

120motorway

0

20

40

60

80

100

120suburban

0

20

40

60

80

100

120

0 500 1000 1500 2000

urban


8

particularly for air quality pollutants. Fuel efficiency and emission performance of two identical cars(make, model and year) can also vary by ≈ ± 10%, so it is not expected the observed and modelled will be in close agreement, rather that the predictions will replicate the trends and dynamics of themeasurements.

Table 4. Passenger Car specifications tested over the LDC.

Fuel Type Market

Segment

Euro

Standard

Make &Model

Engine Rated enginepower (kW)

GVW(kg)

Frontalarea (m2)

Petrol Compact IV Peugeot107

1.0 L 50 900 2.4

Small Family IV Ford Focus 1.6L (Zetec) 75 1370 2.75

Diesel Mini IV Ford Fiesta 1.4L TDCi 66 1223 2.92

MPV IV FordGalaxy

2.0L TDCi(Zetec)

105 1890 3.74

PHEM produces emission predictions for a vehicle trip, totals for road sections (links, segments) andsecond-by-second results. It is these second-by-second emission predictions that have been comparedwith the transient measurements from the chassis dynamometer. The chassis dynamometer (ConstantVolume Sampling – CVS) measurements have been adjusted to account for time delays and shifts inemission peaks, to make them comparable with instantaneous tail-pipe emission predictions. Figure 10presents a time-series of the ‘raw’ second-by-second speed, tail-pipe CO2 and NOX emissions for thespeed profile (top-panel). The elevated emissions during acceleration phases, particularly at higherspeeds i.e. 2000 – 2200 seconds, are evident. The observed and modelled data track each other closely.

Figure 10. Illustrative time-series plot of a section of the AM peak motorway section of theLondon Drive Cycle driven by a Euro IV diesel MPV (a) speed; (b) CO2; and (c) NOX.

2000 2200 2400 2600 2800

020

40

60

80

100

Speed

(km

.h1)

2000 2200 2400 2600 2800

05

10

15

20

CO

2(g

.sec1)

2000 2200 2400 2600 2800

050

100

150

Time (seconds)

NO

X(m

g.s

ec1)


9

The comparison of the observed and modelled values for the entire LDC, for the four passenger carsare presented in the scatterplots in Figure 11. The frequency of data points in a hexagonal bin isillustrated on a colour-scale, so both the range in values and where the core of the data lies are visualised.

The second-by-second observed and modelled CO2 data are in close agreement, demonstrating PHEMis replicating the dynamics and magnitude of measured values well, for all four test cars. The CO2 modelperformance visualised in Figure 10 and 11 is extremely good for all test cars and driving settings(urban, suburban and motorway). The only slight deviation between observed and modelled values isfor higher emission rates from petrol cars in urban driving. This is suggested to be due to minordifferences in the fuel/ emission map of the average petrol cars in PHEM being slightly better than thetest cars during transient acceleration events, when higher power is demanded from the engine.

Predicting tail-pipe emission of air quality pollutants is more challenging as engine/ emission maps aremore variable and the impact of exhaust after treatment systems also needs to be considered. Petrol carNOX emissions are at a low-level and not illustrated. The scatterplots for oxides of nitrogen (NOX)demonstrate that PHEM is also reliably predicting the dynamics and magnitude of NOX emissions wellfor diesel cars. The main discrepancy is for the smaller ‘Mini’ diesel car at higher emission rates ( >0.04 grams.sec-1). The model is predicting slightly lower emissions during these more polluting periodsthat have higher engine power demands. The optimisation of the engine/ emission map of the observedcar may be slightly worse than the average medium sized diesel car in the model. These differences areto be expected between specific vehicles and fleet averages.

The NOX model performance visualised in Figure 10 and 11 is also good for the diesel test cars, withthe petrol vehicles emitting NOX at a low-level. Again the model is under-predicting the higher emissionrate emissions on the motorway, particularly for the ‘Mini’ but the differences are considered acceptableand expected when the known variations in NOX performance of makes/ models and vehicles sizes areconsidered.

For a given ‘Real Driving’ or ‘micro-simulated’ speed profile (drive-cycle) the PHEM model isconsidered to reliably predict the second-by-second tail-pipe emissions of CO2 and NOX for the EuroIV passenger car test data. The PHEM model is considered to be an appropriate tool to couple to ‘RealDriving’ vehicle trajectories to predict CO2 and NOX tail-pipe emissions, and evaluate the impact ofpolicies that adapt the distribution of vehicle speeds and accelerations.

A full PHEM validation report is being prepared by Dr James Tate over a more complete range of EuroIV, V and VI light- and heavy-duty vehicles. TfL is collating a dataset of Euro VI measurements overthe same drive-cycle, but this data is not available until the ‘Ultra-Low Emission Zone’ consultation(2015) is complete.

It is important to differentiate PHEM from more basic instantaneous models that predict tail-pipeemissions using speed/ acceleration emission maps. The first generation of instantaneous emissionmodels such as MODEM (Jost et al, 1992) derived the speed/ acceleration/ fuel consumption andemission maps (or tables) from continuous (second-by-second) laboratory measurements. The absenceof dynamic time alignment and correction functions limited the prediction accuracy of such models,especially for more modern (Euro 2 and newer) vehicles equipped with exhaust after-treatment systems(Zallinger et al, 2005). More recently the AIRE (Analysis of Instantaneous Road Emissions- TransportScotland, June 2011) has been launched which uses instantaneous emission modelling tables derivedsecond-hand from PHEM modelled outputs. SIAS Limited in collaboration with TRL (TransportResearch Laboratory) developed AIRE using a version of PHEM released in 2005 (Hausberger, 2011),so is not able to predict emissions from modern Euro 4 and 5 vehicles that now dominate the fleet. Fewdetails are available of the speed profiles (drive-cycles) supplied to PHEM, to in turn derive the speed/acceleration emission tables. If these speed profiles (drive-cycles) are not representative of the driverbehaviour and traffic flow conditions of a study network, then model prediction uncertainties willincrease. The use of AIRE is not recommended until further details of the model development andmodel accuracy are made available.

Pollutant

PETROL DIESEL

Compact Small Family Mini MPV

CO2

NOX

Figure 11. Scatter plots comparing Modelled (PHEM) and Observed values for all sections (urban, suburban and motorway) of the LDC.(Top-row) CO2 (a) Petrol compact car (left); (b) Petrol small family car (middle-left); (c) Diesel mini car (middle-right); (d) Diesel MPV (right);(Bottom-row) NOX (e) Diesel mini car (middle-right); (f) Diesel MPV (right). NOTE: Petrol car NOX emissions at a low-level and not illustrated

Modelled_CO2

Ob

serv

ed_

CO

2

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0 2 4 6

Counts

12346

101625396298156247390618978

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Modelled_CO2

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CO

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12346

101625396298155244386611966

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Modelled_CO2

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112358

111726385786129194290435653

Modelled_NOx

Ob

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x

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Counts

12347

1219315082134219357582950

15502530

Modelled_NOx

Ob

serv

ed_

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x

0.00

0.05

0.10

0.00 0.02 0.04 0.06 0.08

Counts

122469

1421325077119184285440680

1050

As input, PHEM requires 1Hz speed data, road gradient and the vehicle specification including: fueltype, Euro emission standard, engine size, exhaust after-treatment systems, vehicle weight, frontal area,drag coefficients and transmission ratios. Although the PHEM model has default EU average valuesavailable, as this study had a detailed understanding of the local vehicle fleet (see section 2.1), theknown distribution of fuel types, Euro standard proportions (as documented in Table 2), vehicle weightand engine power information was used to parameterise the emission model (documented in AppendixB).

The influence of road gradient was also considered. Road (link) gradients were established from theGoogle Earth (2015) terrain model and combined with the speed trajectories from the Aimsunsimulations.

All the PHEM simulation results assumed vehicles were in a ‘hot-running’ condition.

Figure 12. The distribution of road link gradients in each of the Wakefield networks

The time series plots in Figure 13 illustrate PHEM’s capability to predict the tail-pipe emissions of anAIMSUN micro-simulated trajectory (speed profile) second-by-second. The sample vehicle is asimulated Euro 5 diesel car travelling through Castleford AQMA network in the AM (base) peak.

0

1000

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-11

to-9

-9to

-7

-7to

-5

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-3to

-1

-1to

1

1to

3

3to

5

5to

7

7to

9

9to

11

DIS

TAN

CE

(met

res)

GRADIENT RANGE (%)

Ackworth [11.5km]

Castleford [11.9km]

Featherston [3km]

Hemsworth [2.7km]

Figure 13. Time series plot of PHEM results for a sample simulated Euro 5 diesel car operating in the Castleford AQMA in the AM (base) peak:(a) Speed, (b) Road gradient, (c) CO2, (d) NOX and NO2, (e) Particle Mass (PM).

0 50 100 150 200

020

40

60

Sp

ee

d(k

m.h1)

0 50 100 150 200

-6-2

24

6

Gra

die

nt(%

)

0 50 100 150 200

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812

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2(g

.se

c1)

0 50 100 150 200

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X(m

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ec

1)

NOxNO2

0 50 100 150 200

0.0

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6

Time (seconds)

PM

(mg

.se

c1)

3. RESULTS

The traffic-emission NOX, NO2 and PM modelling results are analysed to:

illustrate the emission performance of the modelled vehicles per kilometre travelled; predict the contribution of each vehicle sub-category to the emission totals; and evaluate the impact of the proposed interventions (renewing the Bus fleet with cleaner

Euro VI vehicles, and lower volumes of development traffic due to travel plansimplemented).

The modelled NOX, PM10 and CO2 emissions per kilometre travelled for each vehicle sub-category and network are presented (sections 3.1 to 3.4).

Although the DEFRA (2009) source apportionment methods do not currently consider theprimary (direct) NO2 emissions explicitly, the PHEM modelling results for NO2 have beenanalysed. The assumptions embedded with PHEM regarding the proportion of NO2/ NOX invehicle exhausts for different vehicle types are documented in Table 5. These NO2/ NOX

fractions (f-NO2) are compared with values from Grice et al. (2009) and Jerksjö et al. (2008).For current petrol car technologies the f-NO2 is low. The addition of Diesel OxidisationCatalysts (DOCs) on light-duty diesel vehicles from Euro 3 onwards has led to a dramaticincrease in emissions of NO2 directly from vehicles (Carslaw, 2005). The peak NO2 fraction isfor Euro 4 light-duty diesels. The f-NO2 values used by PHEM are slightly lower than proposedby Grice et al. (2009) and Jerksjö et al. (2008). The Jerksjö et al. (2008) approach is based onRSD measurements. It is suggested that once exiting the tail-pipe fast NOX chemistry is able totake place in the exhaust plume trailing (turbulent mixing) the vehicle, leading to higher valuesof f-NO2. The PHEM f-NO2 for Euro 5 light-duty diesels is lower than that of Euro 4 vehicles.This is because Euro 5 DOCs have a higher palladium content that are known to have better f-NO2 characteristics (ERMES, 2011). Heavy diesel f-NO2 are significantly lower than thosereported for light-duty diesels.

As increases in primary NO2 emissions are influencing ambient concentrations of NO2,particularly in heavily trafficked urban street environments (close to the source), it will beimportant to research the degradation of diesel emission control technologies further. As DOCsdecay, it is suggested their f-NO2 will decrease. This fall however may well be offset by anincrease in the total amount of NOX they emit per kilometre.


59

Table 5. Modelled NO2/ NOX fractions

Vehicle class Euro class PHEM% NO2 (by volume)

% NO2 (by volume)Grice et al. (2009)

% NO2 (by volume)Jerksjö et al. (2008)

Petrol carsAll 5 3 ≈ 1

Diesel carsEuro I and earlierEuro IIEuro IIIEuro IVEuro V

81135

42.636.3

1111305555

14 – 2014 – 2030 – 4755 – 60

VansEuro I and earlierEuro IIEuro IIIEuro IVEuro V

811354535

1111305555

14 – 2014 – 2030 – 4755 – 60

HGVsEuro I and earlierEuro IIEuro IIIEuro IVEuro V

1.72.76.811.07.4

1111141010

7791313

BusesEuro I and earlierEuro IIEuro IIIEuro IVEuro V

2.83.26.68.57

1111143510

101030

25 - 5248

As the traffic-emission modelling framework naturally combines the emission characteristicsof the different vehicle sub-types with their usage (i.e. detailed vehicle activity and proportions,see Table 2), the contribution to total emissions from each vehicle sub-category is predicted.These emission contribution results for the current conditions are documented and discussed insection 3.5. Finally the simulated impact of the fleet renewal policies, future vehicle fleets,levels of traffic demand and impact of development traffic travel plans are evaluated in section3.6.

3.1 Emission factors – Passenger cars

The average emissions per kilometre travelled for each vehicle sub-type have been calculatedfrom each of the network’s traffic-emission modelling outputs of the current (‘Base’ 2014)situation. As well as generating the large volumes of second-by-second predictions of fuelconsumption and exhaust emissions information (see Figure 13), the PHEM model providesaggregated data for each vehicle journey, namely: total emissions (grams), distance travelled(kms) and duration (secs). The average emission factors for each vehicle sub-type were derivedfrom the sum of the modelled emission estimates (grams) and distance travelled (kms) from theten simulations (replications) of the 24-hour period (a daily average). Figure 14 presents themodelled NOX emission factors for passenger cars. The results for each passenger car sub-vehicle type (passenger car diesel, passenger car petrol) are presented in a separate panel.


60

Figure 14. Passenger car NOX emission factors for (a) diesel passenger cars, (b)petrol passenger cars.

The modelled emission factors for diesel passenger cars (left panel, Figure 14) are broadlysimilar through all Euro generations, until Euro VI which are now known to emit approximatelyhalf NOX of previous generations of diesel cars. The modelled emission factors for petrolpassenger cars (right panel, Figure 14) decrease substantially through the Euro standards. Olderpre-catalyst (Euro 0) petrol vehicles are modelled as having high NOX emissions, similar to thelevels from diesel passenger cars. The modelled emission factors for petrol cars suggest Eurostandards have successful delivered improvements in the NOX emission controls.

The predicted emission factors for the Featherstone and Hemsworth networks are higher thanAckworth and Castleford. This is because the Featherstone and Hemsworth networks haveproportionally more polluting stop-start traffic movements as the smaller networks and loweraverage network speeds (see Figure 8).

Diesel passenger cars are an increasingly important vehicle category with respect to NOX

emissions as their:

vehicle fleet share continues to increase (see Table 2 and Figure 5); and NOX emission controls under-perform in urban driving conditions. Recent evidence

and research is suggesting that diesel exhaust NOX after-treatment technologies are in-effective in urban driving conditions, with their lower power demands, exhaust flowrates, exhaust gas temperatures and consequently catalyst temperatures. This isreflected in these modelling results of urban driving across the Wakefield networks.

The emission factor estimates for petrol passenger cars (Figure 8, right panel) show NOX

emissions from each successive Euro generation have fallen.

The modelled PM10 emission factors for diesel passenger cars (left panel, Figure 15) illustratediesel passenger car emission controls (diesel particle filters (DPFs)) have improved, with tail-pipe emissions of PM10 at a low-level for modern (Euro V and VI) diesel cars. PM10 emissionsfrom petrol cars are low for all generations (Euro standards).

Euro standard

NO

X(g

.km

1)

0.0

0.5

1.0

1.5


DIESEL


PETROL


61

Figure 15. Passenger car PM10 emission factors for (a) diesel passenger cars, (b)petrol passenger cars.

The modelled CO2 (proportional to fuel consumption) emission factors presented in Figure 16for diesel and petrol passenger cars (left and right panels respectively) illustrate in urban drivingthe fuel efficiency of diesel cars has remained broadly stable through the Euro generations ofvehicles. Improvements in engine and powertrain technology has been offset by the burden ofheavier, larger vehicles (see Figure 6). The amount of CO2 emitted per kilometre driven iscomparable between diesel and petrol cars. Whilst diesel engines offer better like for like fueland CO2 efficiencies, diesel cars are on average ≈ 30% heavier than their petrol counter-parts,which especially in stop-start urban driving conditions is a significant penalty.

Figure 16. Passenger car CO2 emission factors for (a) diesel passenger cars, (b)petrol passenger cars.

Euro standard

PM

10

(g.k

m

1)

0.00

0.05

0.10

0.15


DIESEL


PETROL

Euro standard

CO

2(g

.km

1)

100

200

300

400


DIESEL


PETROL


62

3.2 Emission factors – Light-Goods Vehicles (vans)

The average emissions per kilometre travelled for the three size classes (N1, N2 and N3) vanshave been established from each of the network’s traffic-emission modelling outputs of thecurrent (‘Base’ 2014) situation. Figure 17 presents the modelled NOX emission factors for vanssize N1 (left-panel), N2 (middle-panel) and N3 (right-panel). Petrol van emission factors arenot resented as they only comprise a small proportion of vehicle activity (0.5% vehiclekilometres BASE). In-line with the diesel car emission factors, the Hemsworth network has thehighest amount of NOX emitted per kilometre, followed by Featherstone, Castleford andAckworth. The increasing size of vans through the N2 and N3 sub-categories clearly raises theamount of NOX emitted per kilometre, particularly for the older Euro categories. As Euro 6 (VI)vans are not yet on the market (2015), they do not comprise part of the fleet in the BASEnetworks, so no emission factors are calculated/ illustrated.

Figure 17. Light-duty NOX emission factors for Size (a) N1 vans, (b) N2 vans, (c) N3vans.

The modelled PM10 emission factors for diesel vans are presented in Figure 18. These broadlytrack the reported PM10 emission factors of passenger cars (Figure 15), albeit at a higher level,particularly for the larger Size categories.

Figure 18. Light-duty PM10 emission factors for Size (a) N1 vans, (b) N2 vans, (c) N3vans.

Euro standard

NO

X(g

.km

1)

1.0

1.5

2.0

2.5

Euro 0 Euro 1 Euro 2 Euro 3 Euro 4 Euro 5

Size N1


Size N2


Size N3

Euro standard

PM

10

(g.k

m1)

0.00

0.05

0.10

0.15

0.20

0.25

0.30


Size N1


Size N2


Size N3


63

3.3 Emission factors – Heavy-Goods Vehicles (HGVs)

The average NOX and PM10 emissions per kilometre travelled for the rigid and articulated HGVcategories are illustrated in Figures 19 and 20 respectively. The relative magnitude of emissionfactors across the networks reflects that of the light-duty categories (Hemsworth highest,followed by Featherstone, Castleford then Ackworth). As would be expected, the larger, heavierHGVs with high power output diesel engines emit significantly more NOX and PM10 perkilometre travelled than their light-duty counter-parts. Emissions of air quality pollutants fromthe latest generation of Euro 6 (VI) HGVs is comparatively low, at a similar level to theemissions from light-duty Euro 6 diesel vehicles. This is in-line with the latest measurementsand research e.g. Spreen et al, 2014; Kadijk et al, 2015. This is because the sophisticated exhaustafter-treatment systems fitted to HGVs do now better control engine-out emissions of NOX andPM10 even in stop-start urban driving conditions. Previously these operating conditions hadprevented catalysts i.e. SCR - Selective Catalytic Reduction, from reaching their operationaltemperatures.

Figure 19. Heavy-duty NOX emission factors (a) Articulated HGVs, (b) Rigid HGVs.

Figure 20. Heavy-duty PM10 emission factors (a) Articulated HGVs, (b) Rigid HGVs.

Euro standard

NO

X(g

.km

1)

0

5

10

15

20

25


Articulated


Rigid

Euro standard

PM

10

(g.k

m1)

0.0

0.5

1.0


Articulated


Rigid


64

3.4 Emission factors – Buses

Public transport activity was observed to be low in the Ackworth and Hemsworth networks(less than 1% of vehicle kilometres, see Table 2), largely limited to occasional services such asschool Buses, typically run by older vehicles. Bus activity is higher on the Castleford (1.6% ofvehicle kilometres) and the Featherstone (1.4% of vehicle kilometres) networks, but still at arelatively low level. Due to the observed age distribution/ Euro standard, not all Euro standardsare observed/ represented in the networks and therefore result graphs (Figures 21 and 22) donot contain Emission Factor results for all Bus types (Single- and Double-decker, Euro 0 – 6).E.g. Hemsworth Euro 4 and 5 Double-decker emission factors are not reported.

Urban Bus services have to make additional stops-and-starts to pick up passengers at stops ontheir scheduled routes. As Buses are large, heavy-duty diesel vehicles these stop-start motionshave a significant fuel and emission penalty. The detailed modelling approach in this study doessimulate/ consider this behaviour, more aggregate approaches do not. The emission factors forBuses are therefore high in comparison to light-duty categories, and at a similar level to HGVs,even though the HGVs are heavier vehicles.

Figure 21. Bus NOX emission factors for (a) Double-deckers, (b) Single-decker

Figure 22. Bus PM10 emission factors for Size (a) Articulated HGVs, (b) Rigid HGVs.

Euro standard

NO

X(g

.km

1)

5

10

15

20

25


Double-Decker (DD)


Single-Decker (SD)

Euro standard

PM

10

(g.k

m1)

0.0

0.2

0.4

0.6

0.8

1.0


Double-Decker (DD)


Single-Decker (SD)


65

3.5 Emission contributions

In this section the NOX, NO2 and PM emission contributions over the course of the averageweekday (24-hours) simulated are considered. The Figure 23 bar chart therefore presents thepredicted NOX emission contributions from each vehicle type. The breadth of each bar isproportional to the share of total (simulated) vehicle kilometres completed by that vehicle sub-type in the Wakefield AQMA networks. The emission contributions for each network are alsodocumented in Table 6.

Although petrol passenger cars undertake nearly half of the vehicle kilometres driven, theymake a relatively small contribution to air quality, emitting approximately 5% and 1% of thetotal NOX and NO2 respectively.

Nearly half of the road transport NOX emitted on all four networks is from light-duty dieselvehicles i.e. cars and vans. HGVs also make a significant contribution to total NOX emissions,as the older Euro standard vehicles observed and simulated are known to have poor NOX

performance (high emission factors, see Figure 19) in urban driving conditions. Buses make asmaller contribution than HGVs, partly as their fraction of vehicle kilometres driven is low,particularly on the Ackworth and Hemsworth networks.

The dominant source of primary NO2 emissions are the light-duty diesel vehicles (cars andvans) as the fraction of NOX emitted as NO2 is higher than other petrol and Heavy-duty vehicles(see Table 5, Figure 24). On all four networks the light-duty diesel vehicles are predicted toemit close to 90% of the primary NO2. This is significant, particularly at heavily trafficked NO2

‘hotspots’ where the primary contribution adds to the short-term and long-term totalconcentrations.

The source of PM emissions in the four AQMAs is mainly generated by the stock of light- andheavy-duty diesel vehicles (Figure 25). The heavy-duty diesels make a disproportionatecontribution. Whilst only completing a modest share of the kilometres driven in the networkse.g. Castleford ≈5%, they contribute ≈30% of tail-pipe PM10. Being larger vehicles, they willalso re-suspend comparatively more PM10 as they travel on the networks.

Ackworth Castleford

Featherstone Hemsworth

Figure 23. Total NOX emission contributions from each vehicle type for the Wakefield AIMSUN networks: (a) Ackworth (top-left); (b) Castleford(top-right); (c) Featherstone (bottom-left); (d) Hemsworth (bottom-right)

CAR-petrol CAR-diesel VAN BUS

NO

X(%

)

01

02

03

04

05

0

HGVCAR-petrol CAR-diesel VAN BUS

NO

X(%

)

01

02

03

04

05

0

HGV


NO

X(%

)

01

02

03

04

05

0


NO

X(%

)

01

02

03

04

05

0

HGV


Ackworth Castleford


Figure 24. Total NO2 emission contributions from each vehicle type for the Wakefield AIMSUN networks: (a) Ackworth (top-left); (b) Castleford(top-right); (c) Featherstone (bottom-left); (d) Hemsworth (bottom-right)


NO

2(%

)

02

04

06

08

0

HGV CAR-petrol CAR-diesel VAN BUS

NO

2(%

)

02

04

06

08

0

HGV


NO

2(%

)

02

04

06

08

0


NO

2(%

)

02

04

06

08

0

HGV


Ackworth Castleford


Figure 25. Total PM emission contributions from each vehicle type for the Wakefield AIMSUN networks: (a) Ackworth (top-left); (b) Castleford(top-right); (c) Featherstone (bottom-left); (d) Hemsworth (bottom-right)


PM

10

(%)

01

02

03

04

05

0


PM

10

(%)

01

02

03

04

05

0

HGV


PM

10

(%)

01

02

03

04

05

0


PM

10

(%)

01

02

03

04

05

0

HGV

Table 6. Summary emission contributions from each vehicle type

NETWORKVEHICLETYPE

% traveltime

% vehiclekms % NOX % NO2 % PM10

Ackworth CAR petrol 46.14 46.29 5.43 1.15 5.27

CAR diesel 37.04 37.35 41.06 62.42 46.79

VAN 12.78 12.85 18.61 27.37 26.49

HGV 3.31 3.12 30.70 7.98 18.86

BUS 0.73 0.39 4.20 1.08 2.59

Castleford CAR petrol 45.59 46.44 4.80 1.12 6.60

CAR diesel 35.54 36.39 37.42 61.26 42.21

VAN 11.75 11.89 16.34 26.03 22.58

HGV 4.34 4.09 30.33 8.87 20.87

BUS 2.78 1.19 11.10 2.72 7.74

Featherstone CAR petrol 45.92 46.42 4.48 1.06 7.38

CAR diesel 35.33 35.85 37.01 62.36 43.09

VAN 12.84 12.84 15.02 25.19 24.03

HGV 3.85 3.82 32.35 8.22 19.16

BUS 2.07 1.07 11.14 3.17 6.33

Hemsworth CAR petrol 48.22 48.61 4.46 1.08 8.21

CAR diesel 36.46 37.19 40.00 66.74 46.71

VAN 9.54 9.61 13.19 21.65 19.66

HGV 3.20 3.16 29.50 6.77 18.32

BUS 2.58 1.43 12.84 3.76 7.10

3.6 Scenarios

The Wakefield Air Quality Action measures assessed (listed in Table 1) from the BASEnetworks (2014 demand and fleet) and future years (2018 and 2020) included (also listed inTable 1):

Accounting for the forecast increase in traffic demand, including the additional trafficgenerated from expected developments. The increase in demand is therefore notconsistent across the four networks;

Reflecting the natural turn-over/ renewal of the vehicle fleet for the future years 2018and 2020. The age profile of the observed (ANPR) fleet in each study area was assumedto remain the same for each vehicle type, but rolled forward 4 and 6 years for the 2018and 2020 design years. This is illustrated in Figure 26, which presents the age profileof observed passenger cars, alongside the assumed future year distributions. Table 7also documents the proportion (%) of passenger cars in each Euro standard for the Base(2014), 2018 and 2020 years at the Ackworth site;

For comparison, the future year networks (2018 and 2020) were also simulated withthe Base (2014) fleet, so changes due to the network/ demand, not blurred by the naturalfleet renewal could be identified;

Replacing scheduled Bus services with the latest Euro 6 vehicles, which emit local airquality pollutants such as NOX and PM10 at a relatively low-level. Note this scenariowas not modelled at the Ackworth site as the share of Buses was low (less than 1% ofvehicle kilometres); and

The Castleford network with a significant amount of additional traffic demand due todevelopments in future years, also had a scenario where travel planning measures wereimplemented to reduce development traffic by 10%.

PASSENGER CARS

PETROL DIESEL

Figure 26. Observed and forecast (2018 and 2020) Passenger car fleet age profiles

1990

1995

2000

2005

2010

2015

2020

2025

0.00

0.02

0.04

0.06

0.08

0.10

0.12

Year of first Registration

Density

1990

1995

2000

2005

2010

2015

2020

2025

0.00

0.02

0.04

0.06

0.08

0.10

0.12

Year of first RegistrationD

ensi

ty

Table 7. The evolution of the Ackworth Passenger Car fleet: Fuel type and Eurostandard shares (%) for the Base (2014), 2018 and 2020 future years

Fueltype

Eurostandard

BASE(2014)

2018 2020

Petrol Euro 0 0.13 0.06 0.02

Euro 1 0.21 0.05 0.04

Euro 2 3.87 0.20 0.10

Euro 3 15.23 3.52 1.01

Euro 4 20.13 18.48 12.71

Euro 5 14.21 19.29 20.76

Euro 6 0.18 12.35 19.30

Diesel Euro 0 0.11 0.03 0.02

Euro 1 0.13 0.02 0.03

Euro 2 0.94 0.08 0.03

Euro 3 7.86 1.11 0.47

Euro 4 16.76 9.36 5.02

Euro 5 19.89 17.52 14.73

Euro 6 0.35 17.92 25.74

The summary results from the scenario modelling are documented in Table 7. The impact ofthe policies for the different years (2014, 2018 and 2020) on total distance travelled on therespective networks, average network speed, total tail-pipe emissions (NOX, NO2 and PM10) arevisualised in Figures 26, 27, 28 and 29 respectively.

ACKWORTH

The Ackworth network is the least congested with the highest average speed for the Base (2014)situation of 43.5 km.h-1. Traffic growth is expected to be low, with only a 3.3% and 5.1%increase in kilometres travelled for the 2018 and 2020 design years respectively. The networkalso has the (spare) capacity to service this increase demand without undue additional delay.Under the slight increase in demand the average network speed was only forecast to fall by afraction of a km.h-1 in 2018 and 2020.

The evolution of the vehicle fleet is forecast to have a significant impact on emissions of airquality pollutants. NOX emissions from Euro VI diesel cars are approximately half that ofprevious generations (Euro I, II, III, IV, V). Petrol cars emissions of NOX are at a low level.NOX emission controls on Euro VI heavy-duty vehicles (HGVs and Buses) do now control tail-pipe emissions well, with emissions significantly lower than Euro V and older (see Figure 19).NOX emissions across the Ackworth network are expected to fall from 2014 levels by 38% overthe next four years (2018) and nearly half in six years’ time (2020). The impact on primary NO2

emissions over the network is also significant at -24% (2018) and -33% (2020). The differenceis less pronounced than for NOX as the fraction emitted as primary NO2 is still high for Euro VIlight-duty diesels. Tail-pipe emissions of PM10 are also expected to fall dramatically (-58% by2018, -72% by 2020) as the fleet of diesel vehicles become dominated by those equipped witheffective diesel particle filters (DPFs). If the natural renewal of the vehicle fleet for the futuredesign years is not considered i.e. the observed Base (2014) fleet is maintained, emissions of


59

all pollutants stay at broadly the same level, increasingly incrementally in-line with the low-level of expected traffic growth.

It is recommended these reductions are considered alongside observed roadside and backgroundconcentrations, to identify when annual mean NO2 air quality levels will fall below the 40 µg.m3

standard across the Ackworth AQMA.

CASTLEFORD

The Castleford network is expected to experience the greatest increase in demand levels. Thereis therefore a greater need to manage demand through sustainable transport policies and demandmanagement, but also explore whether physical changes to the network are needed.

The share of Buses observed in the study area and embedded within the Castleford networksimulations, like the other Wakefield networks studied is relatively low at 1.6% of vehiclekilometres travelled. Although Buses are relatively high emitters as they are large, heavy-dutydiesel vehicles that perform additional stops and starts to pick up passengers (considered in thisprojects simulations, not accounted for in more aggregated approaches), the policy tested ofreplacing all scheduled Bus services with cleaner Euro VI vehicles was only predicted to lowerNOX and tail-pipe PM10 emissions by 3% and 2% respectively.

The forecast volume of vehicle kilometres travelled increases by ~29% between 2014 and 2018due to impact of planned developments and underlying traffic growth assumptions. Delay perkilometre travelled also increased with the additional demand lowering the average networkspeed by 6%. CO2 emissions increased broadly in-line with demand. Any slight improvementsin the fuel economy of newer vehicles through engine/ powertrain development and light-weighting are offset by the degradation in flow conditions i.e. congested stop-start driving. Theassumed renewal of operational fleet with cleaner Euro VI vehicles was forecast to reduce NOX

emissions by 19% and tail-pipe PM10 by 45%. Against these significant improvements theprimary NO2 contribution remained relatively unchanged between 2014 and 2018. This isbecause the fleet is forecast to turnover a larger share of older, petrol cars that have a lowNO2/NOX fraction (Table 5), which are replaced with a higher proportion of diesel cars thathave a higher NO2/NOX fraction. There is also the underlying increase in vehicle activity. Thepredicted 20% reduction in NOX emissions is not expected to be sufficient to reduce theobserved 60 µg.m3 annual NO2 mean below the 40 µg.m3 standard (Wakefield Council, 2008).Accounting for background levels and atmospheric chemistry, a 52% reduction in NOX

emissions is expected to be needed to improve air quality in Castleford to meet the NO2 airquality standards (Wakefield Council, 2008).

By 2020 the planned developments are expected to generate significantly more traffic demand,with vehicle kilometres driven 40% higher than in 2014. Delays and congestion on the networkrise markedly, with total travel time increasing by close to 70% with the average network speedfalling from 35 to 29 km.h-1 (-18%). The worsening traffic flow conditions and delay offsetsthe gains in emission reductions due to natural fleet turnover to 2020. NOX emissions are onlyexpected to fall an additional 2% from 2018 levels. This is not expected to be sufficient to meetair quality standards in the study area.

The demand management and highway scheme designs were simulated with the 2014 fleetbreakdown. Demand management was assumed to reduce the traffic generated by the planneddevelopments by 10%. The slight reduction in demand and kilometres driven (-3% 2018 levels,-4% in 2020) were predicted to slightly improve flow conditions (average network speed up 0.3km.h-1 in 2018 and 1.7 km.h-1 in 2020). Without the renewal of the vehicle fleet, emissions areforecast to rise, broadly in-line with the increases in traffic activity. Even if fleet renewal wasconsidered, the modest demand management considered would not be sufficient to meet airquality standards. There are however uncertainties predicting future traffic demand, with theadditional delays expected to discourage drivers from making trips or changing mode of


60

transport (public transport, walk or cycle), particularly in peak periods (peak spreading). Thehigh level of traffic growth expected may, or may not be realised.

The highway scheme modelled was simulated not to improve or worsen traffic flow on thenetwork over 2018 and 2020 levels. Little expected change in emissions from the do-nothing2018 and 2020 simulations are expected.

FEATHERSTONE

The average speed in the Featherstone network is low at 20 km.h-1 as it is centred on a signalisedintersection. A proportion of traffic will therefore be held up by the cycle of the traffic signals,lowering the average network speed.

The policy of operating the scheduled Bus services with Euro VI vehicles was predicted toreduce NOX emissions over the network by close to 4%. This % is greater than the Bus fleetshare of 1.4%. As only the fleet specification changed, the simulated traffic flow was identicalto the Base (2014) situation. Euro VI Heavy-duty vehicle exhaust after-treatment technologiesnow employed in Buses are considered to effective in reducing emissions of particles and NOX,even in urban driving conditions. They therefore emit considerably less NOX per kilometre thantheir predecessors, with emission factors falling from ≈ 20 grams.km-1 (Figure 21) for a Euro Vdouble-decker Bus to ≈ 1 grams.km-1 for a Euro VI specification vehicle (driven over the same,simulated drive-cycle/ speed profile; Spreen et al, 2014).

Traffic growth of ~8% by 2018 was predicted to lower the average network speeds by ~10%.The network is considered to be operating at, or above capacity at times, with the additionaldemand inducing delay and congestion. Although the additional traffic and congestion meantfuel consumption/ CO2 emissions increased in the 2018 conditions, NOX and tail-pipe PM10

emissions fell by 32% and 54% respectively as the Euro VI diesel vehicles that are forecast tostart to dominate the local, operational fleet in future years do emit less air quality pollutantsthan their predecessors.

By 2020 the traffic flow on the network is forecast to have degraded further to an average speedof 14.2 km.h-1. Increased queuing traffic, with repeated stop-start driving motions mean fuelconsumption/ CO2 emissions on the 2020 network increase by ~32% from the 2014 conditions.The reduction in NOX emissions due to fleet renewal is curtailed by the degradation in the trafficflow conditions, with 2020 emission levels forecast to remain at, or close to those of 2018.Tailpipe PM10 emissions are forecast to continue to fall towards 2020, with levels ~62% lessthan in 2014. This is because particle (mass) emission controls and filters are predicted to workefficiently in all conditions including stop-start driving.

The ~32% reduction in NOX emissions by 2018, with 2018 levels maintained through to 2020,are at the necessary reduction levels specified by Wakefield Council (2008). This reduction isforecast to lower an NO2 annual mean of 50 µg.m3 to less than 40 µg.m3. The necessary NOX

reduction was estimated using an appropriate atmospheric nitrogen oxides reactions model(Wakefield Council, 2008, pg 24).

HEMSWORTH

The Hemsworth network, like Featherstone is centred around a signalised intersection. Averagenetwork speeds are again relatively low at 17.4 km.h-1. Like on the Featherstone network therewas a low proportion of activity by Buses (0.9% vehicle kilometres). This meant the Euro VIscheduled Bus policy simulations only resulted in a modest reduction in NOX emissions (4%)over the network.

The results for the design year 2018 are similar to those from the Featherstone network, withthe increase in traffic demand (6.6%) leading to more delay and a reduction in average networkspeed. The natural renewal of the fleet with a higher proportion of cleaner Euro VI light- and


61

heavy-duty vehicles was forecast to reduce NOX by 25% and tail-pipe PM10 emissions by 50%.Again these reductions, need to be compared against trends in background concentrations,observed and modelled levels to judge whether this is expected to be sufficient to prevent anyfuture exceedance of annual average NO2 concentrations in this AQMA.

Figure 27. Total distance travelled on the Wakefield Aimsun networks for scenariosand years (Base 2014, 2018 and 2020) (a) Fleet renewal (left); (b) Demand

management (middle); and (c) Highway scheme (right).

Figure 28. Average Network Speed on the Wakefield Aimsun networks for scenariosand years (Base 2014, 2018 and 2020) (a) Fleet renewal (left); (b) Demand


Year

To

talD

ista

nce

Tra

ve

lled

(ve

hic

le.k

m)

2e+04

4e+04

6e+04

8e+04

2014 2016 2018 2020

Fleet renewal

2014 2016 2018 2020

Scheme - Demand management

2014 2016 2018 2020

Scheme - Highway

Year

Sp

ee

d(k

m.h1)

15

20

25

30

35

40

2014 2016 2018 2020

Fleet renewal

2014 2016 2018 2020


2014 2016 2018 2020

Scheme - Highway

Table 7. Modelling summary scenarios results for the Ackworth, Featherstone and Hemsworth networks (24-hour totals and averages).

NETWORK SCENARIOTotal TravelTime (hours)

Total DistTravelled (km)

Average networkSpeed (km.h-1) CO2 (kg) NOX (kg) NO2 (kg) PM10 (kg)

Ackworth 2014 200.5 8715.66 43.5 1821.78 4.616 1.076 0.154

2018 208.08 9002.38 43.3 1916.82 2.848 0.82 0.066

% change (2014) 3.78 3.29 -0.47 5.22 -38.33 -23.87 -57.77

2020 212.88 9160.22 43 1957.78 2.386 0.72 0.044

% change (2014) 6.18 5.1 -1.01 7.47 -48.3 -33.06 -72.08

Featherstone 2014 107.94 2211.42 20.5 732.4 2.128 0.45 0.06

Euro VI Bus As above As above As above 732.18 2.046 0.446 0.0594

2018 128.46 2380.76 18.5 817.34 1.448 0.37 0.028

% change (2014) 19.02 7.66 -9.55 11.6 -31.97 -17.59 -53.7

2020 179.48 2550.44 14.2 963.16 1.466 0.388 0.022

% change (2014) 66.28 15.33 -30.64 31.51 -31.1 -13.46 -62.28

Hemsworth 2014 141.54 2464.72 17.4 919.04 2.678 0.564 0.074


2018 176.02 2627.62 14.9 1018.5 2.022 0.482 0.036

% change (2014) 24.36 6.61 -14.27 10.82 -24.53 -14.4 -51.35


Table 8. Modelling scenarios results for the Castleford network (24-hour totals and averages).

NETWORK SCENARIOTotal TravelTime (hours)

Total DistTravelled (km)

Average networkSpeed (km.h-1) CO2 (kg) NOX (kg) NO2 (kg) PM10 (kg)

Castleford 2014 1933.06 67693.35 35.02 19428.02 53.97 11.64 1.62


2018 2644.78 87256.67 32.99 25571.76 43.71 11.59 0.89

% change (2014) 36.82 28.90 -5.79 31.62 -19.00 -0.48 -45.24

2018 + Demand managementfor Developments (2014 fleet) 2561.43 85259.33 33.29 24433.98 65.59 14.96 1.97

% change (2014) 32.51 25.95 -4.95 25.77 21.53 28.50 21.68

2018 with Highway scheme(2014 fleet) 2644.78 87081.10 32.93 25078.62 67.11 15.35 2.02

% change (2014) 36.82 28.64 -5.98 29.08 24.35 31.80 24.46

2020 3276.19 94293.76 28.78 28761.65 41.40 11.63 0.65

% change (2014) 69.48 39.30 -17.81 48.04 -23.28 -0.13 -59.64

2020 + Demand managementfor Developments (2014 fleet) 3003.73 91570.15 30.49 27075.80 71.68 16.65 2.15

% change (2014) 55.39 35.27 -12.95 39.36 32.82 43.04 32.54

2020 with Highway scheme(2014 fleet) 3276.19 94032.42 28.70 28363.83 74.75 17.49 2.24

% change (2014) 69.48 38.91 -18.04 45.99 38.51 50.22 38.04

Figure 29. Total NOX emissions on the Wakefield Aimsunnetworks for scenarios andyears (Base 2014, 2018 and 2020) (a) Fleet renewal (left); (b) Demand management

(middle); and (c) Highway scheme (right).

Figure 30. Total NO2 emissions on the Wakefield Aimsun networks for scenarios andyears (Base 2014, 2018 and 2020) (a) Fleet renewal (left); (b) Demand management

(middle); and (c) Highway scheme (right).

Figure 31. Total PM10 emissions on the Wakefield Aimsun networks for scenariosand years (Base 2014, 2018 and 2020) (a) Fleet renewal (left); (b) Demand


Year

NO

X(k

g)

20

40

60

80

2014 2016 2018 2020

Fleet renewal

2014 2016 2018 2020


2014 2016 2018 2020

Scheme - Highway

Year

NO

2(k

g)

5

10

15

20

2014 2016 2018 2020

Fleet renewal

2014 2016 2018 2020


2014 2016 2018 2020

Scheme - Highway

Year

PM

10

(kg

)

0.0

0.5

1.0

1.5

2.0

2.5

2014 2016 2018 2020

Fleet renewal

2014 2016 2018 2020


2014 2016 2018 2020

Scheme - Highway


59

4. RECOMMENDATIONS FOR FUTURE WORK

This project is considered to have demonstrated a step-change in the resolution, capability andvalidity of traffic-vehicle emission assessments. The step to a more detailed modelling scalehowever inherently demands higher specification input information. Every effort was made tosource and use the best available data. The breadth and quality of information (e.g. detailedlocal vehicle specification, vehicle performance/ dynamics set to match driver behaviour/vehicle tracking data, expert assessment of the share of exhaust after-treatment technology(proportion of EGR/ SCR on Euro4 and 5’s)), used to setup and calibrate the traffic-emissionmodel are considered to surpass those used in other studies reported to date. There are howeverseveral opportunities to further enhance the modelling and its specification:

A Hybrid Vehicle module for the instantaneous emission model (PHEM) is expectedto be available in 2016. This would allow Hybrid petrol- or diesel-electric powertrainsto be modelled explicitly;

Further research on-road driver behaviour to enhance the specification of the variabilityin vehicle performance (acceleration and deceleration rates) and “desired” cruisingspeed in the traffic microsimulations. Efforts to fuse empirical evidence with trafficmicrosimulations could fulfil this aim. ‘BIG’ transport telematics data is now becomingavailable in the UK, but the methods to fuse such empirical evidence within simulationsneeds to be developed;

Investigate the “real-world” loading of cars, vans, Buses, Coaches, rigid- andarticulated HGVs. Weigh-in-motion (WIM) systems for example can determine theloading of each passing axle, with measurements linked to ANPR;

Keep reviewing the emission factors embedded with the coupled traffic andinstantaneous emission modelling reflect those on-the-road. By scanning through theexhaust plume trailing a vehicle as they drive through a remote sensing measurementstation, and knowing each vehicle’s specification (e.g. 2012, 2.0 litre diesel car, AudiA4) from its number plate, the Real Driving Emission performance of groups, types orthose equipped with a specific type of diesel engine for example can be assessed. Thisis valuable evidence to verify road transport emission models. Importantly thisapproach can also review and assess the degradation of engines and their emissioncontrols, for example tracking the effectiveness of diesel particle filters as they age;

Evaluate the impact of a broader range of sustainable transport and cleaner fleet policiese.g. incentives for petrol-hybrid taxis; geo-fencing for hybrid and plug-in hybridvehicles to operate in full electric mode in sensitive areas i.e. AQMAs; and public/business uptake of Full Electric Vehicles (FEVs);

Work to identify and evaluate local, National and European policies and interventionsto accelerate the up-take and use of cleaner vehicles e.g. priority ‘green’ (petrol-hybridor better) taxi ranks at key locations such as railway stations, changing the fuel, VEDand company car tax landscape to discourage purchase of polluting diesel cars;

Improve the visualisation of results, for example by displaying the spatial variation inemissions at a high resolution (e.g. 10m increments) using digital mapping resourcessuch as Google Earth (www.earth.google.co.uk/); and

Integrating the aggregated hour-by-hour link emission rate predictions within anappropriate air pollution dispersion model, would allow the impact on local air qualityto be assessed. As well as forecasting when concentrations would meet air qualitystandards, the spatial mapping of concentrations linked to population density and dose-response functions, would allow the expected health impacts to be evaluated andmonetised. Extending to health impacts and costs is necessary to strengthen theeconomic case for investing in cleaner vehicle technology, sustainable transportsolutions and clean air policies.


60

5. ACKNOWLEDGEMENTS

The Technical University of Graz (TUG, Austria) are thanked for providing access and up-dates to their instantaneous emission model PHEM.

ITS PhD student Arwa Sayegh is thanks for her assistance on the project.


61

6. REFERENCES

Boulter, P, McCrae, I., Barlow, T. 2007. A Review of Instantaneous Emission models for Roadvehicles. TRL Published Project Report (PPR 267). [Accessed 12th October 2011]http://www.highways.gov.uk/knowledge_compendium/assets/documents/Portfolio/A_Review_of_Instantaneous_Emission_Models_for_Road_Vehicles_-_936.pdf

Carslaw, D. 2005. Evidence of an increasing NO2/NOX emissions ratio from road traffic emissions.Atmospheric Env. 39, pp. 4793 – 4802.

Carslaw, D., Beevers, S. Westmoreland, E. Williams, M. Tate, J. Murrells, T. Stedman, J. Li, Y., Grice,S., Kent, A. and I. Tsagatakis (2011). Trends in NOX and NO2 emissions and ambient.[Accessed 9th September 2011] http://uk-air.defra.gov.uk/reports/cat05/1108251149_110718_AQ0724_Final_report.pdf

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7. APPENDICES


64

Appendix A. The observed and modelled fleet breakdown for the Base (2014) and futureyears (2018 and 2020) on the Ackworth, Castleford, Featherstone and Hemsworth networks.

Vehicle Fuel Euro ACKWORTH CASTLEFORD FEATHERSTONE HEMSWORTHType type standard Number 2014 2018 2020 Number 2014 2018 2020 Number 2014 2018 2020 Number 2014 2018 2020

CAR Petrol Euro 0 13 0.0013 0.001 0.001 24 0.002 0 0 6 0.0006 0 0 3 0.0006 0 0

Euro 1 20 0.0021 0.0005 0 30 0.0025 0.001 0 16 0.0017 0 0 4 0.0008 0 0

Euro 2 374 0.0387 0.002 0.001 450 0.0381 0.002 0.001 389 0.0402 0.002 0.001 177 0.038 0 0

Euro 3 1471 0.1523 0.035 0.01 1992 0.167 0.04 0.012 1681 0.1737 0.037 0.011 765 0.1641 0.039 0.008

Euro 4 1940 0.2013 0.143 0.085 2361 0.198 0.153 0.095 1982 0.2057 0.163 0.104 968 0.2079 0.153 0.097

Euro 5 1327 0.1421 0.199 0.21 1681 0.1428 0.196 0.211 1242 0.1306 0.204 0.215 661 0.1433 0.203 0.218

Euro 6 17 0.0018 0.159 0.233 19 0.0016 0.16 0.232 15 0.0015 0.147 0.224 5 0.0011 0.162 0.232

Diesel Euro 0 10 0.0011 0.0003 0 4 0.0003 0 0 4 0.0004 0 0 7 0.0015 0 0

Euro 1 13 0.0013 0.0002 0 18 0.0015 0 0 15 0.0015 0 0 8 0.0017 0 0

Euro 2 91 0.0094 0.001 0 72 0.006 0.001 0 80 0.0082 0.001 0 48 0.0103 0.001 0

Euro 3 750 0.0786 0.011 0.005 865 0.0765 0.007 0.002 810 0.0855 0.012 0.004 375 0.0828 0.012 0.005

Euro 4 1553 0.1676 0.067 0.03 1907 0.1787 0.067 0.029 1517 0.1688 0.074 0.033 738 0.1684 0.071 0.034

Euro 5 1910 0.1989 0.16 0.132 2152 0.1822 0.176 0.148 1715 0.1795 0.166 0.14 818 0.1789 0.165 0.141

Euro 6 34 0.0035 0.221 0.293 33 0.0028 0.197 0.27 20 0.0021 0.194 0.268 3 0.0006 0.194 0.265

Hybrid-Diesel Euro 5 2 N/a N/a N/a 1 N/a N/a N/a 0 N/a N/a N/a 1 N/a N/a N/a

Hybrid-Petrol Euro 4 4 N/a N/a N/a 4 N/a N/a N/a 8 N/a N/a N/a 1 N/a N/a N/a

Euro 5 45 N/a N/a N/a 23 N/a N/a N/a 21 N/a N/a N/a 7 N/a N/a N/a

TOTAL 9574 11636 9521 4589

HGV Articulated Euro 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

(Diesel) Euro 1 0 0 0 0 1 0.0023 0 0 1 0.0025 0 0 0 0 0 0

Euro 2 1 0.0035 0 0 1 0.0023 0 0 1 0.0025 0 0 0 0 0 0

Euro 3 22 0.0767 0 0 21 0.0492 0.005 0.002 22 0.0549 0 0 3 0.0422 0 0

Euro 4 11 0.0383 0.014 0.011 49 0.1148 0.005 0.002 11 0.0274 0.002 0.002 3 0.0422 0 0

Euro 5 80 0.2787 0.08 0.028 78 0.1827 0.124 0.056 57 0.1421 0.065 0.035 2 0.0282 0.085 0.028

Euro 6 6 0.0209 0.324 0.38 9 0.0211 0.239 0.31 4 0.01 0.173 0.202 0 0 0.028 0.085

Rigid Euro 0 1 0.0035 0.0035 0.003 0 0 0 0 0 0 0 0 0 0 0 0

(Diesel) Euro 1 1 0.0035 0 0 1 0.0023 0 0 0 0 0 0 1 0.0141 0 0

Euro 2 25 0.0871 0 0 14 0.0328 0 0 47 0.1172 0 0 8 0.1127 0 0

Euro 3 59 0.2056 0.0314 0.017 90 0.2108 0.021 0.007 76 0.1895 0.025 0.005 24 0.338 0.042 0.028

Euro 4 31 0.108 0.0661 0.024 59 0.1382 0.035 0.014 62 0.1546 0.05 0.04 21 0.2958 0.042 0.014

Euro 5 50 0.1742 0.244 0.206 103 0.2412 0.274 0.2 115 0.2868 0.259 0.217 9 0.1268 0.592 0.38

Euro 6 0 0 0.237 0.331 1 0.0023 0.297 0.409 5 0.0125 0.426 0.499 0 0 0.211 0.465

TOTAL 287 427 401 71

BUS Single-Decker Euro 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

Euro 1 1 0.0111 0 0 2 0.0086 0 0 5 0.0298 0 0 1 0.0227 0 0

Euro 2 11 0.1222 0.011 0 29 0.1244 0 0 2 0.0119 0.03 0.012 0 0 0.023 0

Euro 3 20 0.2222 0.122 0.04 5 0.0214 0.1331 0.009 14 0.0833 0.012 0.024 0 0 0 0.023

Euro 4 16 0.1778 0.078 0.09 23 0.0987 0.009 0.133 31 0.1845 0.208 0.006 13 0.2954 0 0

Euro 5 33 0.3667 0.256 0.16 76 0.3262 0.094 0.077 56 0.3333 0.393 0.125 21 0.4773 0.159 0.136

Euro 6 0 0 0.433 0.61 0 0 0.3431 0.361 0 0 0 0.476 0 0 0.614 0.636

Double-Decker Euro 0 0 0 0 0 0 0 0 0 1 0.006 0 0 0 0 0 0

Euro 1 0 0 0 0 1 0.0043 0 0 0 0 0.006 0.006 2 0.0455 0 0

Euro 2 5 0.0556 0 0 53 0.2275 0.0043 0 17 0.1012 0 0 3 0.0682 0.045 0

Euro 3 4 0.0444 0.056 0.022 30 0.1288 0.2275 0.009 37 0.2202 0.166 0.19 4 0.0909 0.068 0.045

Euro 4 0 0 0.044 0.034 14 0.0601 0.129 0.223 0 0 0.155 0.131 0 0 0.091 0.068

Euro 5 0 0 0 0.044 0 0 0.06 0.128 5 0.0298 0 0 0 0 0 0.092

Euro 6 0 0 0 0 0 0 0 0.06 0 0 0.03 0.03 0 0 0 0

TOTAL 90 233 168 44

Vehicle Fuel Euro ACKWORTH CASTLEFORD FEATHERSTONE HEMSWORTHType type standard Number 2014 2018 2020 Number 2014 2018 2020 Number 2014 2018 2020 Number 2014 2018 2020

LCV N1-Petrol Euro 0 0 0 0 0 1 0.0005 0 0 2 0.0011 0.001 0.001 0 0 0 0

Euro 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0.0017 0 0

Euro 2 3 0.0019 0 0 1 0.0005 0 0 2 0.0011 0 0 0 0 0 0

Euro 3 1 0.0006 0.001 0 1 0.0005 0.001 0 4 0.0022 0 0 0 0 0.002 0.002

Euro 4 4 0.0025 0.001 0.001 1 0.0005 0.001 0.001 0 0 0.001 0 2 0.0033 0 0

Euro 5 0 0 0.003 0.003 2 0.001 0.001 0.002 0 0 0.002 0.003 0 0 0.003 0.003

Euro 6 0 0 0 0.001 0 0 0.001 0.001 0 0 0 0 0 0 0 0

N2-Petrol Euro 0 1 0.0006 0.001 0.001 1 0.0005 0.001 0.001 1 0.0006 0.001 0 0 0 0 0

Euro 1 1 0.0006 0 0 0 0 0 0 0 0 0 0.001 0 0 0 0

Euro 2 0 0 0 0 2 0.001 0 0 0 0 0 0 0 0 0 0

Euro 3 0 0.001 0 0 0.001 0.001 0 0 0 0 0 0

Euro 4 0 0 0 0.001 1 0.0005 0 0 0 0 0 0 0 0 0 0

Euro 5 0 0 0 0 0 0 0.0005 0 0 0 0 0 0 0 0 0

Euro 6 0 0 0 0 0 0 0 0.001 0 0 0 0 0 0 0 0

N3-Petrol Euro 0 1 0.0006 0 0 0 0 0 0 0 0 0 0 0 0 0 0

Euro 1 0 0 0.001 0.001 0 0 0 0 0 0 0 0 0 0 0 0

Euro 2 1 0.0006 0 0 0 0 0 0 0 0 0 0 0 0 0 0

Euro 3 0 0 0.001 0 2 0.001 0 0 1 0.0006 0 0 1 0.0017 0 0

Euro 4 1 0.0006 0 0.001 1 0.0005 0.001 0.001 1 0.0006 0.001 0.001 0 0 0.002 0

Euro 5 0 0 0.001 0 0 0 0.0005 0.001 0 0 0.001 0 0 0 0 0.002

Euro 6 0 0 0 0.0006 0 0 0 0 0 0 0 0.001 0 0 0 0

N1-Diesel Euro 0 0 0 0 0 0 0 0 0 2 0.0011 0 0 0 0 0 0

Euro 1 0 0 0 0 0 0 0 0 0 0 0.001 0.001 1 0.0017 0 0

Euro 2 19 0.012 0 0 27 0.0138 0 0 18 0.01 0 0 2 0.0033 0 0

Euro 3 98 0.062 0.008 0.0025 106 0.0542 0.007 0.006 96 0.0533 0.009 0.003 28 0.047 0.005 0.005

Euro 4 100 0.0632 0.043 0.022 130 0.0665 0.047 0.031 103 0.0572 0.031 0.022 41 0.0689 0.037 0.012

Euro 5 26 0.0164 0.061 0.066 50 0.0256 0.056 0.056 36 0.02 0.051 0.05 16 0.0269 0.054 0.055

Euro 6 0 0 0.04 0.0625 0 0 0.05 0.067 0 0 0.049 0.066 0 0 0.052 0.076

N2-Diesel Euro 0 1 0.0006 0.001 0.0006 1 0.0005 0 0 1 0.0005 0 0 3 0.005 0 0

Euro 1 6 0.0038 0 0 5 0.0026 0.0005 0 4 0.0022 0 0 1 0.0017 0.003 0.003

Euro 2 9 0.0057 0 0 15 0.0077 0.0005 0.001 3 0.0017 0.001 0.001 4 0.0067 0 0

Euro 3 61 0.0386 0.01 0.005 109 0.0557 0.012 0.005 47 0.0261 0.003 0.001 23 0.0387 0.008 0.005

Euro 4 105 0.0677 0.03 0.014 155 0.0793 0.043 0.018 137 0.0761 0.025 0.009 46 0.079 0.042 0.018

Euro 5 116 0.0733 0.061 0.0505 137 0.07 0.077 0.072 114 0.0633 0.067 0.059 45 0.0756 0.071 0.059

Euro 6 0 0 0.088 0.119 0 0 0.082 0.12 0 0 0.074 0.099 0 0 0.082 0.121

N3-Diesel Euro 0 3 0.0019 0.001 0.00106 2 0.001 0 0 1 0.0005 0 0 1 0.0017 0.002 0.002

Euro 1 11 0.007 0 0 15 0.0077 0 0 16 0.0089 0 0 12 0.0202 0 0

Euro 2 48 0.0303 0.002 0.001 58 0.0297 0 0 44 0.0255 0.001 0 12 0.0202 0 0

Euro 3 203 0.129 0.026 0.0056 242 0.1247 0.025 0.005 234 0.1355 0.031 0.011 84 0.1429 0.044 0.007

Euro 4 388 0.2472 0.11 0.065 469 0.2454 0.113 0.062 534 0.2998 0.11 0.057 145 0.2437 0.108 0.091

Euro 5 369 0.2333 0.22 0.1966 409 0.2091 0.226 0.218 382 0.2121 0.265 0.214 125 0.2101 0.22 0.171

Euro 6 0 0 0.289 0.379 0 0 0.253 0.33 0 0 0.275 0.4 0 0 0.265 0.368

TOTAL 1582 1956 1801 595


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Appendix B. The observed (and modelled, PHEM) vehicle specifications.

Vehicle Fuel Euro Rated engine Frontal Kerbweight Loading

type type Standard power (kW) area (m2) (kg) (kg)

Bus Double-Decker Diesel E0 137.4 10.84 11300 3000

E1 137.4 10.84 11300 3000

E2 137.4 10.84 11300 3000

E3 151.0 10.84 11287 3000

E4 180.0 10.84 12000 3000

E5 184.0 10.84 12000 3000

E6 184.0 10.84 12000 3000

Bus Single-Decker Diesel E1 99.6 7.65 7667 1875

E2 99.6 7.65 7667 1875

E3 160.5 7.65 9064 1875

E4 125.7 7.65 1818 1041

E5 167.0 7.65 12228 1875

E6 167.0 7.65 12228 1875

Car Diesel E0 118.7 2.53 1155 201

E1 71.7 3.24 1582 313

E2 71.9 2.74 1297 292

E3 83.1 2.84 1384 278

E4 92.6 2.84 1432 261

E5 121.8 2.85 1647 265

E6 129.8 2.88 1576 287

Petrol E0 77.2 2.40 1050 205

E1 93.8 2.49 1189 233

E2 70.7 2.61 1062 264

E3 75.5 2.65 1131 253

E4 95.5 2.72 1377 177

E5 85.3 2.68 1285 203

E6 120.4 2.57 1211 238

HGV_artic Diesel E1 270.3 9.00 18400 13800

E2 294.0 9.00 18800 14100

E3 324.7 9.00 18041 13531

E4 326.8 9.00 17235 12926

E5 332.6 8.36 10091 7569

E6 329.3 9.00 9907 7430

HCV_rigid Diesel E0 106.6 6.87 4933 1284

E1 106.6 6.87 4933 1284

E2 158.3 6.87 6893 4040

E3 156.1 6.87 7492 4113

E4 155.8 6.87 8385 4667

E5 183.8 6.87 7882 4602

E6 263.3 6.87 11333 7667

LCV_NI Diesel E0 50.0 2.72 1070 290

E1 50.0 2.72 1105 280

E2 46.4 2.85 1050 348

E3 53.4 2.95 1160 308

E4 59.2 2.99 1202 291

E5 57.9 3.03 1152 293

E6 N/a N/a N/a N/a

Petrol E0 33.0 2.48 730 220

E1 33.0 2.48 760 325

E2 54.5 2.85 890 293

E3 52.6 2.50 898 309

E4 62.9 3.32 1201 221

E5 54.0 2..90 1070 305

E6 N/a N/a N/a N/a

LCV_NII Diesel E0 50.0 3.21 1443 536

E1 57.9 4.21 1477 488

E2 55.8 3.23 1323 466

E3 60.3 3.79 1393 410

E4 66.3 3.50 1443 387

E5 65.9 3.48 1398 353

E6 65.9 3.48 1398 353

Petrol E0 44.0 4.22 1505 535

E1 62.0 4.22 1505 535

E2 78.0 3.37 1299 325

E4 78.0 3.37 1299 325

E5 N/a N/a N/a N/a

E6 N/a N/a N/a N/a

LCV_NIII Diesel E0 77.0 2.57 1820 840

E1 59.9 2.57 1618 864

E2 73.2 3.90 1727 610

E3 75.8 3.78 1771 604

E4 84.8 4.29 1831 664

E5 90.9 4.57 1936 626

E6

Petrol E0 85.0 4.22 1293 544

E2 85.0 4.22 1630 585

E3 104.5 4.72 1589 746

E4 107.0 4.72 1589 746

E5 N/a N/a N/a N/a

E6 N/a N/a N/a N/a

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