A Detailed Urban Road Traffic Emissions Inventory … · Vitor Gois - Raleigh, 16 May 2007 InventAr...

Vitor Gois - Raleigh, 16 May 2007

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A detailed urban road traffic emissions inventory model using aerial photography

and GPS surveys

Vitor Gois - H. Maciel, L. Nogueira, C. Almeida, P. Torres, S. Mesquita, F. Ferreira

InventAr, Estudos e Projectos Unip, Lda., Portugal.Departamento de Ciências e Engenharia do Ambiente, Universidade Nova de Lisboa, Portugal.Comissão de Coordenação e Desenvolvimento Regional de Lisboa e Vale do Tejo, Portugal.

[email protected]

EPA’s 16th Annual International Emission Inventory Conference


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• Scope• Case-study• Problem Definition• Principles• Methodology • Results and methodology

validation

Overview


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Scope• EU law (Directive 96/62/CE) defines zones, where air quality must be

assessed in detail, using:

– Monitoring stations– Periodic campaigns (e.g. Passive sampling)– Modelling & Air emission inventories

• Air quality problems have been detected in Lisbon area in most recent years

– Particulate matter (PM10): widespread– NO2: confined to traffic hotspots– Ozone in summer

• Particulate levels (PM10) in Lisbon show the highest values in Europe as consequence of:

– Importance of diesel vehicles– Specific meteorological conditions– Road re-suspension– Topographic conditions– Natural events (Saharan dust, forest fires)


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Lisbon: Identification of “Zones” of Air Quality Management


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Air Quality Problems in recent yearsLisbon area - PM10


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Air Quality Problems in recent yearsLisbon area - NO2


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Urban Transportation as major contributor to Air Quality

• Road Transport is the dominant factor of PM in Lisbon

– Weekly variation, with maximum values at weekdays especially on Fridays when traffic levels tend to be the highest

– Chemical analysis of particles collected in samplers (55 per cent of particulate matter are originated, directly or indirectly)

– Natural events (Saharan dust outbreaks and forest fires)

OLIVAIS monitoring station(μg/m3 , %) N = 103 days

3,410%

7,421%

13,838%

10,931%

crustal marine aerosol secondary PM road traffic

Av. LIBERDADE monitoring station(μg/m3 , %) N = 49 days

3,29%6,4

17%

14,238%

13,636%

crustal marine aerosol secondary PM road traffic


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Air Quality Management Tools under development in Lisbon Region

• Air Quality Management is under responsibility of the

Commission for Coordination and Regional Development of Lisbon and Tagus Valley (CCDR-LVT)

• Policies– Improvement of the monitoring survey system:

• Stationary stations• Extensive monitoring: period campaigns using Passive sampling

(Diffusion tubes and portable PM samplers)– Plans and Programs (June, 2005)

• Identification of measures and policies, mainly traffic related– Modelling tools

• Regional level (TAPM from CSIRO)• European level (Chimere, CAMx, REM-3 under CAFE program)


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Definition of the Inventory model• The Air Emission Inventory Model has to consider that:

– Urban road transport MUST be the major component – Air quality problems are very local– A high level of spatial detail is necessary

• Suitable for the scale used in modelling• Considering the scale at which policies and measures are defined

– Affordable costs and low investment• Relying in a small team• Using available data as much as possible• Unfeasible to extend the existing traffic monitoring system

• Main objectives were to gain knowledge:

– How many vehicles are moving at a given place and time– What type of vehicles exist (in movement)– How fast are vehicles moving (time of travel)– How are they moving (topography)


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Methodology: General Structure

Length(i)(km)

Car Counting(i)(number of vehicles)

Car Density(i)(vehicles/km)

Speed(i)(km/h)

GPS(i)(km/h)

Path Survey(i)(km/h) TMH(i)

(vehicles/h)

Temporal Correction Factor(i)

Gertrude(i)(traffic data)

TMA(i,f)(vehicles/year)

Vkm(i,k,f)vehic/km

Fleet(k)(%)

FC Factor (i,k,f)(g/km)

EF(i,k,p)(g/km)

Emissions (i,p)(kg/road link)

Road NetworkGIS

Fuel Sales (f)(ton)

Commuters (k,f)(vehic/day) Fuel Consumption

(f)(ton)

Speed(i)(km/h)

i - road linkk - vehicle classf - fuel typep - pollutant

Aerial Photography


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Definition of the Urban Structure in the area under study

• Definition of main roads and neighbourhoods should be done prior to data collection considering– Major road boundaries;– Road structure (density, width, intersections nb, slope)– Economic Activity

• Commerce - frequent stoping/parking 2nd line• Institutional• Residential

– Distance to city centre•


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Identification of moving vehicles

Source: Lisbon Municipality


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Density of moving vehicles

• 80 000 vehicles identified– 15 % total licenses (Insurance

data) in the area


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Speed:Method 1 - Predefined paths

• Path definition– Pre-defined objective points– 3 random paths at 3 different periods

• (Morning, noon, evening)

• Advantages– No special equipment needed– Possible to use in all conditions– Suitable for areas with low car density

• Problems– Low detail– Restricted knowledge of speed variations


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Speed: Method 1

1 - Largo do Rato2 - EPAL3 - Cemitério dos Prazeres4 - Cruzamento de Alcântara5 - Palácio das Necessidades6 - Basílica da Estrela

0

10

20

30

40

50

60

0-10

10-2

0

20-3

0

30-4

0

40-5

0

50-6

0

60-7

0

70-8

0

80-9

0

90-1

00

100-

110

110-

120

Spee

d Fr

eque

ncy

%


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Speed:Method 2 - GPS

• GPS in vehicle• Rules for test driver

– Keep with main flow– but copy driver behaviour -> objective oriented

travel• E.g. Service Stations, Museums

• Data acquisition problems in narrow roads with tall buildings


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Speed HistogramsUrban and Sub-urban areas

Cascais

0.0%

5.0%

10.0%

15.0%

20.0%

0 - 1

0

20 -

30

40 -

50

60 -

70

80 -

90

100

- 110

120

- 130

140

- 150

km/h

Lisboa

0.0%

10.0%

20.0%

30.0%

40.0%

50.0%0

- 10

20 -

30

40 -

50

60 -

70

80 -

90

100

- 110

120

- 130

140

- 150

km/h

Municipality Main roads Secondary roadsCascais 39.4 28.2Oeiras 49.2 28.3Lisboa 25.1 28.1Amadora 48.6 24.4Odivelas 33.9 27.5Loures 52.7 39.8


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Emission Factors

• Base - > EMEP/CORINAIR (EEA, 2002)– Based on extensive monitoring and database– Variables

• Vehicle type, age, fuel, technology, engine size• Vehicle speed per link• Road gradient• Vehicle wear• Non-exhaust emissions


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Characterization of the Fleet(Moving Vehicles)

• Methodology (Torres et al, 2006)– Survey/questionnaire in

• Traffic lights• Parking

– Questions• Age (license date)• Vehicle type: PC, LDV, HDV, Bus, 2w, mopeds• Fuel type: gasoline, diesel, LPG, Natural Gas• Engine size (c.c.)• Mileage (km)• Mobile Air Conditioner

• 17 800 results (5.6% vehicles registered in insurance companies)


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Fleet: Results

PassCar79%

LDV12% HDV

4% Bus2% Moped

1%Moto_4t

2%

0

20

40

60

80

100

120

0-1

1-2

2-3

3-4

4-5

5-6

6-7

7-8

8-9

9-10

10-1

1

11-1

2

12-1

3

13-1

4

14-1

5

15-1

6

16-1

7

17-1

8

18-1

9

19-2

0

20-2

1

21-2

2

22-2

3

23-2

4

24-2

5

>25

age

Per

cen

t of v

ehic

les

Light Vehicles Heavy Vehicles


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Emission Factors for a normalized vehicle

y = 0.0000000041991x6 - 0.0000017227623x 5 + 0.0002769847078x4 - 0.0220572694394x3 + 0.9163300293065x2 - 19.6391104069688x + 253.8422015941410

R 2 = 0.9912804495488

0.000

20.000

40.000

60.000

80.000

100.000

120.000

140.000

160.000

180.000

200.000

0 20 40 60 80 100 120 140

vel (km/h )

g/km

F C P oly. (FC)

y = 0.00000000000189745x6 - 0.00000000079325011x5 + 0.00000013102685701x 4 - 0.00001087088226117x 3 + 0. 00049295664148869x2 - 0.01275310043676920x + 0.20095104605219900

R 2 = 0.99866235527923600

0.000

0.020

0.040

0.060

0.080

0.100

0.120

0.140

0.160

0 20 40 60 80 100 120 140

vel (km/h)

g/km

PST Poly. (PST)


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Time Variation• tFAC - Hourly to daily traffic volume

– 11h-14 h -> Annual Daily Average– 10 representative GERTRUDE traffic monitoring

stations• Working Days + Weekends

– TFac = TMDA/TMDAw * TMDAw/TMD11h-14h– TFac = 0.88 * 16 = 14.6

0

2

4

6

8

10

12

14

16

00:0

0

02:0

0

04:0

0

06:0

0

08:0

0

10:0

0

12:0

0

14:0

0

16:0

0

18:0

0

20:0

0

22:0

0Hou

rly

Tra

ffic

as p

erce

ntag

e of

dai

ly fl

ow (%

)


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Top-down approachConsumption = Sales + Import in commuters * FC * Length

Consumption = Sales + (Vehicle Inflow - Vehicle Outflow) * FC * Length


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Results: NOx


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Evaluation: traffic• GERTRUDE

(Gestion Electronique de Règulation en Temps Réel pour L’Urbanisme, les Déplacements et l’Environnement)

– Lisbon Municipality– local groups: 10– 110 counters (2000)– Restricted to central/busy areas– Objective: Traffic Control

y = 0.5068x + 3867.6R2 = 0.3386

y = 1.3114x + 9989.5R2 = 0.3386

0

50 000

100 000

150 000

0 50 000 100 000 150 000

Veic/day

Veic

/day


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Model Validation Comparison with air quality surveys

2001 and 2002 average (ug/m3)

y = 0,6851x + 9,5829R2 = 0,6851

010203040506070

0 10 20 30 40 50 60 70

Measured

Mod

eled


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Final Results: Air Quality Mapping


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Conclusions• Conclusions

– Methodology is feasible– Comparison to air quality surveys shows good

possibilities– Relatively inexpensive

• Main costs are GPS survey and characterization of the fleet

– Appropriate for diverse media• Central urban areas and sub-urban areas

– Several potential uses beyond air emission inventories


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Thank you

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A Detailed Urban Road Traffic Emissions Inventory … · Vitor Gois - Raleigh, 16 May 2007 InventAr...

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