project deliverables: d12.5. scenario analysis: lisbon ... · scenario analysis: lisbon city report...

Project Deliverables: D12.5.

Scenario Analysis: Lisbon City Report

Programme name: Energy, Environment and Sustainable Development

Research Programme: 1.1.4. - 4.4.1, 4.1.1

Project acronym: SUTRA

Contract number: EVK4-CT-1999-00013

Project title: Sustainable Urban Transportation

Project Deliverable: D12.5.

Related Work Package: WP 12 Scenario Analysis (all cities)

Type of Deliverable: RE Technical Report

Dissemination level: RES Restricted

Document Author: University of Aveiro, UAV

Edited by: University of Aveiro, UAV

Reviewed by:

Document Version: 1

Revision history:

First Availability: 2003 - 05

Final Due Date:

Last Modification:

Hardcopy delivered to: Eric Ponthieu DG XII-DI.4 (SDME 4/73) Rue de la Loi, 200 B-1049 Brussels, Belgium

SUTRA EVK4-CT-1999-00013 D12.5 Scenario analysis: Lisbon

D12.5 – LISBON CITY REPORT

1. Introduction

This document contains a detail description of the methodology for the scenarios implementation and the model cascade results for the Lisbon City Case. In this way, the objectives of this deliverable are:

• The analysis of the set of scenarios defined in WP10 for Lisbon.

• The evaluation of the scenarios in terms of the indicators of sustainable urban transportation defined in work package WP08.

2. City-case description

2.1 City Location, Urban Structure and Land Use

Lisbon, the westernmost city in continental Europe, is located in 9ºW longitude and 39ºN latitude in the Southwest of Portugal, on the north bank of the Tagus Estuary, on the European Atlantic coast. The city has approximately 600.000 inhabitants. However, accounting with the various satellite towns, the population of Greater Lisbon raises to approximately 2,1 million people, in an area of 1.000 km2. Due to the Atlantic Ocean influence, this region is characterised by complex sea breeze circulations, with a significant impact on the pattern of transport and dispersion of air pollutants. A particular effect on local air quality has also the complex system of hills (Lisbon is known as “the town of the seven hills”) and the flat terrain close to river estuary. Lisbon Metropolitan Area (Figure 1) can be sub-divided in various crowns: crowns A to D represent Lisbon City, crowns A to E account for the Great Lisbon Area, and crowns A to F the Lisbon


University of Aveiro

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Metropolitan Area. The land use in Lisbon can be divided in 5 classes through the ratio jobs/population for each crown: residential areas, mixed areas with residential dominance, mixed areas with jobs dominance, job and dense job areas. Concerning this classification, the Lisbon City Centre (Crowns A and B) is a dense job area, C and D crowns are mixed areas with job dominance, and crowns E and F are residential areas. Therefore the land use patterns show the large travelling needs in Lisbon Metropolitan Area: public transport, which includes buses, taxis, trams, trains, metropolitan and fluvial transport, was responsible for the transportation of about 520 millions of passengers in 1998; every day, the total number of private vehicles in circulation in the city is of about 1 million, considering the 40% that proceed from surrounding areas.

Figure 1. Lisbon Metropolitan Area (division by crowns).

In the last years an unprecedented road-building process has been under way in Portugal: from 1990 to 1994, the total length of motorway increased in about 84%. This trend is expected to continue and emissions from the transport sector are predicted to increase 46% between 1990 and 2000 and 78% by 2010. Actually, traffic emission of NOx and CO represent about 97% and 92% respectively of total anthropogenic emissions in Lisbon municipality. The impact of road traffic on Lisbon air quality at regional scale was analysed on previous works confirming the importance of the development and application of an efficient air pollution abatement strategy in this area.



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The resident population in Lisbon in 1998 was 2 570 000 inhabitants, there were 1 163 000 working places and the total trips realized during the year were 4 860 000.

2570000

1163000

4860000

010000002000000300000040000005000000

ResidentPopulation

Jobs Total trips

Figure 2. Number of resident population, jobs and total trips.

3. Reference situation: definition and model cascade application

3.1 VISUM

3.1.1 Domain Definition

The VISUM domain for Lisbon City case includes, besides the city of Lisbon (53 parishes), 29 municipalities around the city (Figure 3).

Lisbon Lisbon Network Network Lisbon Lisbon Network Network

Figure 3. VISUM domain for Lisbon City Case.



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The resident population of Lisbon do every day 1,9 trips. From these, 1,4 trips are motorised and 0,5 by foot. The number of trips realized daily by the resident population of the metropolitan area of Lisbon is about 4,86 millions: 1,17 million are made on foot, 2,1 million in private transports, 1,3 million in public transports, 0,08 million in a combination of private and public transports and 0,18 million in another mean of transport.

Figure 4 shows the distribution of trips by motives, in the metropolitan area of Lisbon.

12.1%

13.9%18.6%

41.9%

8.2%

4.1%

1.1%

0.0% 10.0% 20.0% 30.0% 40.0% 50.0%

Percentage

Leasure

Shopping/Services/HealthSchool

Work

Visiting relativesOn Service

Others

Motiv

e

Trips by purposes

Figure 4. Distribution of the trips by motives in the metropolitan area of Lisbon.

The population with age of 25-64 years is responsible for 61,8% of all trips. On foot trips, the ages between 25-64 represents 52,7%, and the motorized trips represent 67,9% of all the trips.

Modal trips by ages

0.0%10.0%20.0%30.0%40.0%50.0%

0 a 14 15 a 24 25 a 44 45 a 64 64+

Ages

On footMotorizedTotal Trips

Figure 5. Modal distribution of trips by population age groups.



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Every day, many people come to Lisbon mainly to work, and as it can be seen in Figure 6, the major population is coming by private transports, which causes daily many problems of traffic congestion. .

Modal distribution of total trips

24%

43% 27%

2%4% On foot

PuTPrTOthersPrT+PuT

Figure 6. Modal distribution of total trips.

Modal distribution of work trips

17%

29%47%

4% 3% On footPuTPrTOthersPrT+PuT

Figure 7. Modal distribution of work trips

Modal distribution in peak hour

15%

30%48%

4% 3%

On footPuTPrTOthersPrT+PuT

Figure 8. Modal distribution of trips in the morning peak hour.

3.1.2 Input Data Description



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For VISUM, information about public and private transports is need, as well as an OD Matrix, for each transport system:

- Private transport

• Category and capacity of roads

• Maximum velocity allowed

• Connection to the public transports network

The fundamental roads include arterial and principal ways. These guarantee the travelling connections of the arterial way to the diverse urban sectors and integrate the main avenues and urban roads. The main routes establish the connections between the city sectors. The secondary way has the role of distributing and collecting the traffic from the local to the fundamental network. The local network assures predominantly the local access to the urban functions and activities, which include streets with distinct utility and that mix vehicles and pedestrians.

In the Lisbon network there are 5 link types. Each one has a unique set of private transport capacity and maximum velocity allowed:

Type Cap-Prt V0-Prt 12 78 750 110 22 50 000 100 32 12 250 80 65 16 000 50 67 7 875 30

- Public transport

The public transport consists on 50 lines of Buses (26), Trains (10), Trams (5), Subway (4) and Boats (5).

The input information for the network related to the public transports was:

• All public transports networks, their timetables and stops

• Connection between the different network

- OD Matrix



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The OD Matrix is calculated taking into account the different motives (work, school, health, leisure, visit relatives, etc.) that lead people to dislocate them selves, daily, form one zone to another. For Lisbon city case exists 3 daily OD matrixes (public, private and good vehicles). It can be seen in figure 9 the public transport OD matrix for the baseline scenario, through the MUULI tool.

Figure 9. OD Matrix for Lisbon City Case.

3.1.3 Result analysis

VISUM gives the flux of vehicles, for private transports and passengers, for public transports, as shown in Figures 10 and 11.

Figure 10. Results for private transports: Number of vehicles/link per day.



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Figure 11. Results for public transports: Number of passengers/link per day.

After running the assignment and PcomTest.exe, the results obtained for lisboa15.ver are:

Table 1. Results of Baseline Scenario using PcomTest.exe Indicators By Mode By Transport System

B S F P L T Z NPrT PuT PuT PuT PuTWalk PrT PrT PuT PuT PuT

Vehicle-km 17791482 73622 32748 14437 0 16781990 1009505 5297 19415 1725Vehicle-hr 497765 3287 1920 559 0 434995 62770 289 426 94Additional vehicle-hr d 258229 0 0 0 217548 40682 0 0 0Vehicle-hr in jam 84521 0 0 0 64726 19795 0 0 0Passenger-km 12189434 3010553 2277088 0 0 0 87665 5697935 1116184Passenger-hr 416290 131394 86270 0 0 0 3284 140755 54588Passenger-hr in overcrowded veh 370896 105066 71337 0 0 0 2 139904 54588

Where, B=Bus, S=Subway, P=Car, L=Heavy vehicles, T=Tram, Z=Train and N=Boat. The results of VISUM for private transport were tested against measured data from traffic counters located in strategic roads sections. Direct comparison between simulations and measures together with elaboration of statistics, (VISUM has a proper module for that) indicates that the model, reasonably, represents the real Lisbon situation for what concerns private traffic.

Comparing the public transport results with the values of literature (no information is available for public counters in Lisbon) it can be seen that there is a good concordance if we look for the Passenger-Km indicator, but not for the vehicle-km. For example, for Bus, there is a difference of 7,1% in Passenger-km and a difference of 73,3% in Vehicle-km.



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3.1.4 Indicators

Pressure indicators:

private transport: 6 493 890 930

Passenger transport demand (pkm per year)

public transport: 444 914 310

State indicators:

Crowding (hours in an overcrowded public transport): 370 896

Traffic jams (hours spent in traffic jams): 84 521

3.2. TREM

3.2.1 Domain Definition

TREM model has been applied to the Lisbon Metropolitan Area (AML). A definition of the study area is based on availability of traffic volume data required for emission estimation, and for this reason the TREM application is coincide with the domain selected for VISUM transportation model.

3.2.2 Input Data Description

The main inputs required by TREM are: - Traffic volume; - Vehicle speed; - Fleet composition.

- Traffic volume Traffic volumes for the study domain with link resolution are provided by VISUM. Before using for the emission quantification, the data were checked against the traffic counts and statistical indicators. For this purpose, several measurements point located within the urban area were selected. The mean daily traffic volume measured during two consecutive weeks of May 2000 was compared with VISUM outputs. An overall, the modelling results underestimate traffic volume on 28%. This difference may be caused by incomplete network, and also by the distinct simulation year (O/D matrix for 1998) compared against 2000 measurement data. An indicator used for validation of public transport is Passenger x km.



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The model estimations, assumed to be representative for a typical day, were multiplied by 365 and compared with annual statistics in 1998 [INE]. For bus, the difference between the Passenger x km estimated by the transport model and survey correspond to 20% and for tram this indicator represent 29% difference. - Vehicle speed Vehicle speed is estimated from VISUM outputs considering road length and impedance for each road segment. Based on the estimations, an average vehicle speed for the Lisbon urban area correspond to 23 km.h-1, while outside the city this parameter is 73 km.h-1. A frequency distribution of average vehicle speed for a typical day in the Lisbon Region (including the city) is presented on Figure 12.

0

100

200

300

400

0 10 20 30 40 50 60 70 80 90 100

110

120

130

speed, km.h-1

frequ

ency

Figure 12. Frequency distribution of road-link vehicle speed for a typical day in the Lisbon Region

- Fleet composition In order to prepare the inputs to the emission model, statistical data on vehicle fleet composition provided by different institutions were processed. The data for 1998, considered as a reference year, were selected always when possible. Otherwise, the year more close to the reference year is used.

Vehicle park registered in AML in 1998 (Figure 13) is characterised by important contribution of passenger cars (79.7%) reflecting the national trend but presenting significantly higher weight than Portuguese average value (63.7%). The Lisbon municipality identifies even more accentuated influence of passenger cars on fleet composition with 85.06% fraction (Figure 14). The role of motorcycles in Lisbon is also verysignificant. As a general trend, LDV reveal to have not very important part in AML rounding about 6% compared with national values (19.4%).



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Cas

cais

Lisb

oa

Lour

es

Oei

ras

Sin

tra

Vila

Fra

nca

de X

ira

Am

ador

a

Maf

ra

Aza

mbu

ja

Alc

olhe

te

Alm

ada

Bar

reiro

Moi

ta

Mon

tijo

Pal

mel

a

Sei

xal

Ses

imbr

a

Set

ubal

0

20

40

60

80

100

Passenger carsMotorcyclesLDVHDVBus

Figure 13. Vehicle Park Registered in AML (source: ISP, 1998)

Lisbon municipality

3.59

85.06

0.85

3.620.69

0.28

5.91

Passenger cars LDVHDV < 20 tonHDV > 20 tonBusMotorcycles <50ccMotorcycles > 50cc

Figure 14. Fleet composition of Lisbon Municipality

Vehicle age is one of the most important characteristics for the emission estimation. The statistic data provided by ACAP summarise the age distribution of vehicles in circulation in Portugal in 1999 (Table 2). This information is used to calculate relative contribution of each vehicle class in the fleet composition assuming a correspondence between vehicle age and emission standards.

Table 2. Age distribution of vehicles in circulation in Portugal on 31-12-99 (From ACAP).



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Age (Years)

Passenger Cars Jeeps LDV HDV Bus Motorcycles

>50cc

<1 282 882 24 895 102 201 7 066 648 22 551 1-2 259 524 18 985 101 270 5 879 714 18 413 2-3 226 618 13 368 90 778 5 180 970 15 036 3-4 234 056 10 507 75 414 4 229 976 10 100 4-5 220 288 8 059 59 613 6 292 934 8 441

5-10 1 270 691 34 254 343 243 41 984 3 733 39935 10-15 678 171 7 792 129 675 32 040 2 113 9271 15-20 173 440 1 114 17 197 20 321 2 456 1253* >20 4 329 26 608 10 008 1 256 -

Average age 7,0 4,2 5,7 10,2 13,0 4,5 * > 15 years

3.2.3 Result analysis

The TREM model was applied based on the information described above in order to quantify different pollutants induced by traffic. The total traffic emissions for the study domain, their spatial distribution and some indicators were estimated and analysed (Figure 15, Table 3).

daily emission CO [kg/km]01 - 55 - 5050- 500> 500

Figure 15. Spatial distribution of daily traffic emission in Lisbon.

Table 3. Daily traffic emissions estimated for AML and Lisbon Municipality

Daily emission in 1998 CO CO2 NOx PM* SO2 VOC

1. Total emission [kg]



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AML Lisbon

100171 74610

3608360 2190596

19101 8844

590 345

483 290

13199 11233

2. Emission per capita [g]

AML Lisbon

37,34 130,20

1345,06 3821,70

7,12 15,40

0,22 0,60

0,19 0,51

4,92 19,6

3. Emission per area [kg.km-2]

AML Lisbon

42,0 909,9

1477,6 26714,6

7,3 107,9

0,26 4,21

0,21 3,53

6,6 137,0

4. Average emission rate [g.VKT-1]

AML Lisbon

7,11 9,70

250,00 284,73

1,24 1,15

0,044 0,045

0,035 0,038

1,11 1,46

* Only exhaust emission VKT – vehicle*km travelled As could be seen from the results, emissions induced by traffic in Lisbon contribute significantly to the total AML emissions. Pollutants related primary to fuel consumption, such as CO2 and SO2, represent about half of the daily total values. However, for CO, NOx and VOC this relation is more complex and depends on average speed and proportion of vehicles with catalyst. It is also important to recognise that total emission of NOx in AML are higher than VOC, while in the urban area NOx emission is under VOC levels. This fact may be important for future modelling of photochemical pollution. As could be expected, emission rates per capita and per area are always higher for Lisbon in comparison with average AML values. The quantitative information presented in Table 4 stress once again the importance of traffic as emission source in urban areas. Cold-start emission, analysed in this study, demonstrates to be extremely important for the total results with only exception for NOx. The average contribution of cold-start emissions to the total is varying between 14,5 – 31,4 % depending on pollutant (Table 4). At the same time, their maximum contribution can achieve 82%. Table 4. Contribution of cold-start to the total emissions (%).

CO CO2 NOx VOC Average 25,6 14,5 0,4 31,4

Maximum 51,1 27,1 1,5 81,6



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In order to guarantee the consistency of the quantified emissions, the data completeness of Lisbon emission estimations were analysed in this study. The analysis is based on fuel consumption data. TREM model applied for emission quantification was also used to calculate fuel consumption. Afterwards, these data were compared with fuel consumption statistics available with high spatial resolution. A significant difference between the estimated and statistical values was found for diesel consumption, since the industrial consumption of diesel is also contemplated in statistical report. For gasoline, the model represents overestimation within Lisbon City. The 51% difference is mainly related to the daily income of vehicles from neighbourhoods to this area that is not reflected in fuel consumption survey. It could be expected that Lisbon District statistics represent better the situation and gasoline is sailed and consumed within the same administrative unit. For the Lisbon District the model slightly underestimate gasoline consumption and reveal only 9% difference with statistical data. However, for the entire AML the underestimation correspond to 24% revealing some problems on transport activity data primary in South AML.

3.3 VADIS

3.3.1 Domain definition

The domain defined in Lisbon city centre is located in the city downtown near Tagus river (see Figure 16), between Terreiro do Paço and Rossio, spots of special touristic interest.

Figure 16. Lisbon city road map (scale 1:56133), available on site http://dmpel.ambisig.com/civ2/ .



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As represented on Figure 17, a cluster of rectilinear buildings and perpendicular streets characterise this area, as a heritage of the modern architectural style that characterised the 1755 earthquake reconstruction plan. This residential and commercial area comprehends several one-way roads with an intense flux of traffic and a pedestrian zone, where local hot-spot air pollutants levels are expected to occur. The study domain covers an area of 450 m x 450 m, and the height considered for calculation was of 100 m.

Figure 17. Lisbon downtown cartographic map (scale 1:7016). The simulation domain is represented by

the black rectangle.

3.3.2 Input data description

The buildings and emission sources coordinates definition, the meteorological conditions description and the emissions characterization related to CO, NOx and PM10 pollutants, constitute the main VADIS input data.

Buildings Volumetry Downtown buildings form an homogeneous block of 26 rectangular buildings, all with the height of 15 m, as shown on the 3D perspective on Figure 18. To simplify the obstacles definition process, and as all of the 26 buildings have a 344º orientation, the cartographic grid was rotated according with this alignment.



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Figure 18. Simulation domain 3D perspective.

Meteorological Data VADIS model simulations were performed for the 8th of July of the year 1997, a typical summer day chosen using a statistical meteorological approach. The wind direction during the simulation day was mainly from Northwest, with velocities varying between 1 m.s-1 and 6 m.s-1 (Figure 19).

0

1

2

3

4

5

6

0 2 4 6 8 10 12 14 16 18 20 22 24

Time (h)

Velo

city

(m.s

-1)

0

90

180

270

360

Wind velocity Wind direction

N

S

E

W

S

Figure 19. Wind velocity and direction hourly variation for the 8th of July.

As a result of the 344º domain orientation, the 282º wind direction had to be corrected and the “real” wind direction was, thought, defined as 298º.



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Emissions Data Three roads had to be considered inside the study domain: Ouro, Betesga and Prata, as shown on Figure 20.

Prata street

Ouro street

Betesga street

Figure 20. Lisbon simulation domain (scale 1:4592), represented by the red rectangle, and traffic-

counting stations marked with black dots. The hourly traffic emissions data were estimated using TREM model. The numerical system TREM/VADIS was developed towards the estimation of the atmospheric pollution induced by road traffic in urban areas. The VISUM model gave the needed traffic volume per road segment used by TREM for the estimation of traffic emissions. The hourly variation of CO emissions for the simulation day estimated by TREM model is presented on Figure 21. From this figure, it is possible to notice that the evolution of CO emissions with time is very similar for both streets. On the other hand, this behaviour agrees with typical traffic flux, with smaller values occurring during night and with the increasing of emissions during diurnal hours without clearly defined peaks. The emission sources were introduced as parallelepipeds with a height of 3 m, as a way to implicitly consider vehicle-induced turbulence. VADIS was applied using the CO, NOx and PM10 emissions resulting from road traffic.



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0

5000

10000

15000

20000

25000

0 2 4 6 8 10 12 14 16 18 20 22 24

Time (h)

CO

(g.k

m-1

)

Ouro street Prata street Betesga street

Figure 21. Hourly variation of CO emissions for Prata and Ouro streets estimated by TREM Model

3.3.3. Results Analysis

The wind and CO dispersion fields were simulated with VADIS for the 6 p.m. of July the 8th, as presented in Figure 22.

0 50 100 150 200 250 300 350 400 450

West/East (m)

0

50

100

150

200

250

300

350

400

450

Sou

th/N

orth

(m)

50

150

300

500

1000

2000

3000

4000

7000

CO(µg.m-3)

Our

o St

reet

Prat

a St

reet

Betesga Street

Figure 22. Wind and CO dispersion fields for 6 p.m. of July the 8th (▲- location of the air quality station).



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From 6 p.m. simulation results presented on figure 22 it is possible to notice higher CO concentration values located in Prata street and adjacent pedestrian streets. This fact agrees with the main winds from Northwest. A quantitative analysis to determine modelling uncertainties has been applied to the estimated CO concentration values. The analysis was based on the maximum deviation of the measured and calculated levels within the considered period. To be compared with modelling quality objectives established by the Directive (2000/69/EC), 8-hour average CO values were evaluated. Based on this approach, the average uncertainty of the model prediction for this study corresponds to 24%, achieving 52% as a maximum for the last simulation hours. This value slightly exceeds the 50% acceptability limit defined by the Directive.

3.3.4. Indicators

State indicators:

CO peak concentration (µg.m-3): 17830

NOx peak concentration (µg.m-3): 1194,1

PM10 peak concentration (µg.m-3): 55,6

3.4. OFIS

3.4.1. Domain definition

The chosen domain for the application of the OFIS model is presented in Figure 23.

Lisbon

150 km2

150 km2

LisbonLisbon

150 km2

150 km2

Figure 23. OFIS Domain for Lisbon city case (150x150Km2).



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3.4.2. Description of the input data

Input requirements:

Emissions: The hourly non-urban, suburban and urban emission rates are provided in kg/(km2*h) for surface emissions and elevated emissions. It is appropriate that such data are organised in an emission inventory. Meteorology: Meteorological data such as daily average wind speed and direction as well as surface temperature and temperature lapse rate above the mixing layer, thermophysical data and surface roughness depending on the land use are also required. Boundary conditions: Daily average regional background concentrations of NO, NO2, O3 and all other species included in the chemical reaction mechanism. Urban scale background boundary layer concentrations are computed with the three-layer box model embedded in OFIS. Other input requirements: A file with control run data and rural land use.

3.4.3. Results Analysis

Output data

Concentrations of chemically reacting pollutants for each grid location, within the urban plume, as well as average concentration over the whole domain are the outputs of the OFIS model. Annual average concentrations; percentiles and exceedance probabilities of threshold values are also possible OFIS outputs. The analysis of the input data for Lisbon baseline scenario reveals that there are some basic background concentrations missing. In comparison to the GEA results, emissions are lower. Overall, no exceedances are observed and therefore the final reference scenario for Lisbon should be considered as one with zero exceedances (this is why no figure is provided here). This results supports previous findings, that state that for the year 1999, available ozone concentration level information suggest no exceedances above the EU 1-hour threshold and no 8-hour exceedances of the WHO guideline and EU thresholds. Nevertheless, the same reference also stated that “preliminary analysis indicates that daily

exposures in 1999 may have contributed up to 1.6% of all deaths (± 350 cases) and 1.9% of respiratory



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hospital admissions in Lisbon”, thus revealing the complex relationship between exposure limit values and actual health related impacts.

Table 5. Indicators for the baseline scenario (AOT 60 and number of days exceeding 120 µg.m-3)

AOT(max), AOT(ave), AOT(sub), AOT(town) AOT60 0 0 0 0

E120(domain) E120(sub) E120(town) DAYS 0 0 0

Table 6. «New Statistics»for Ozone

ST ave_ofis aot_ofis ind_ofis 1 5.03 0 0 2 4.55 0 0 3 5.12 0 0 4 4.8 0 0 5 1.45 0 0 6 4.81 0 0 7 5.11 0 0 8 4.54 0 0 9 5.04 0 0

3.5. MARKAL

3.5.1 Domain definition

The chosen domain for the application of the Markal-Lite model was Lisbon Municipality (Figure 24).

Figure 24. MARKAL Lisbon Municipality simulation domain.



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Lisbon Municipality has a population of 556 797 inhabitants, in an area of 82 km2. In 1996 the energy consumption was:

- 1260675 TOE.year-1; - 2.264 TOE.person-1. year-1;

- 15374 TOE.km-2.year-1. 3.5.2. Description of the input data

The reference energy system (RES) for the Lisbon aplication domain is mainly dominated by the tranport sector. The Lisbon municipality has no internal energy production and the industry sector has reduced importance when compared with the transport sector (in terms of energy comsumption and emissions). Hence, the biggest efforts in the characterization of the Lisbon RES were made in the transport sector.

In order to run a MARKAL model, several types of data are needed.

Imported energy prices Demand data Residual Capacities Techno-economic data Input/output coefficients, availability and lifetime of technologies Pollutants emissions associated with technologies.

3.5.3. Result Analysis

The MARKAL model has complex input data not always easy to estimate accurately. By that reason, the model input data continues being improved in order to correctly represent reality in the Lisbon city case. The presented results concern the transport sector due to its importance among other sectors in the Lisbon city case. The modeled transport technologies are listed below.

Table 7. Cars technology description Car Description TEH1 Hydrogen car TEM Methanol car TEN Compressed natural gaz TES Electric small/medium T1Q Fuel Cell Hydrogen T1R Fuel Cell Ethanol T1S Fuel Cell Gasoline T1T Fuel Cell Natural Gas



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TE8 GPL Car TEE1CD Euro-1 Diesel Car TEE1CG Euro-1 Gasoline Car TEE2CD Euro-2 Diesel Car TEE2CG Euro-2 Gasoline Car TEE3CD Euro-3 Diesel Car TEE3CG Euro-3 Gasoline Car TEE4CG Euro-4 Diesel Car TEE4CD Euro-4 Gasoline Car

Table 8. Delivery vehicles technology description

DV Description TLE1 D.V. Euro 1 TLE2 D.V. Euro 2 TLE3 D.V. Euro 3 TLE4 D.V. Euro 4 T2B D.V. Fuel Cell Hydrogen T2C D.V. Fuel Cell Ethanol T2D D.V. Fuel Cell NaturalGas

Table 9. Trucks technology description

Truck Description THE1 Truck Euro 1 THE2 Truck Euro 2 THE3 Truck Euro 3 THE4 Truck Euro 4 THE5 Truck Euro 5 THM Truck Methanol / Dst T38 Truck Fuel Cell Hydrogen T39 Truck Fuel Cell Ethanol T3A Truck Fuel Cell NaturalGas

Table 10. Public transport technology description

DV Description TA1 Autobus Diesel TA2 Trolleybus TA3 Autobus Gecam TA4 Autobus IBRIDI T4F Bus Fuel Cell Hydrogen T4G Bus Fuel Cell Natural Gas T4H Bus Fuel Cell Ethanol TAH Bus Liquid Hydrogen TAM1 Bus Methanol 0.9 TAM2 Bus Methanol 1 TB1 Tramway TC1 Train



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TS1 Metro TT1 Boat

baseline - private transport

0

1000

2000

3000

4000

1 2 3 4 5 6 7 8 9

Period

veh.

km.d

ay-1

TLE1THE1TESTEMTEH1TEE3CGTEE2CGTEE1CGTEE1CDTE8T39T2B

Figure 25. Installed capacity in the private transport Sector.

baseline - public transport

0

20

40

60

80

1 2 3 4 5 6 7 8 9

Period

veh.

km.d

ay-1

TT1

TS1

TC1

TB1

TAM2

TA1

T4G

Figure 26. Installed capacity in the public transport sector.

3.5.4. Indicators

Table 11. Pressure indicators for the baseline scenario (year 2000).

Consumption of fossil fuels (PJ/year) Data provider baseline Markal domain

Transports MARKAL 59,6 Nox -Air emissions by mode

[tons per year] Data provider baseline

Markal domain



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Other sources (as modeled in MARKAL) 1655,8

4. City scenarios

All models were run for a baseline situation (2000) and results have been described in the above sections. However as the aim of the SUTRA was to investigate and plan a “City of Tomorrow”, each city ran every model under conditions highlighted in a set of “common scenarios”. Four scenarios were chosen which depicted four variation of change in four parameters: demography, economy, land-use and technology, for the year 2030. Each city ran exactly the same scenario so that comparisons could be made between cities experiencing identical changes by 2030.

4.1 VISUM

In order to implement the four chosen common scenarios it was applied a spreadsheed (CommonScenarios.xls), developed by a working group including: PTV, Municipality of Genoa, FEEM and ARPAL. Using this file it was possible both to modify the O/D matrices, according to the scenarios, and to obtain some indicators about the traffic assignements. In the following pictures, it can be seen the specific input and output values: Table12. Lisbon input data for the common scenario implementation. InputPopulation data 2000

total 535,740Age groups 0-17 65+

sector 3/office sector 3/tele sector 2 unemployedpopulation shares 15% 50% 3% 8% 2% 22%mobility rates 1.5 2.3 0.93 2.3 0.93 0.93

Car occupancy rate 1.4

Transport means Ped/Bic Pub PriMode shares 25% 32% 43%

Land Use : distance changesSensitivity Factor [a] [10 ... 0 ... -1] -0.66 9.00 -0.66 9.00Form Factor [b] [0.1 ... 1 ... 4] 0.11 2.13 0.11 2.13

18-64



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The values of the Sensitivity Factor and Form Factor have been found out through an iterative process in order to achieve the desired distance changes, according to the chosen scenario. The obtained results reflect the characteristic of the Lisbon Traffic network; in fact strong changes in land use likely influence both long distance trips and short ones Sensivity Factor: (1) Densification : 0 ... -1

-0.1 small reduction of long distance trips -0.9 strong reduction of long distance trips

(2) Sprawl : 0 ... 10 1 moderate increase of long distance trips 10 strong increase of long distance trips

Form Factor: >1: strong changes only for long distances 1: linear change <1: strong changes also for short distances

Table 13. Lisbon output data from the commonscenarios.xls

OutputIndicator Analysis 1 2 3 4Population 535740 837405 837405 396287 396287Average Trip Rate 1.80 1.71 1.71 1.64 1.64PuT Share 0.32 0.47 0.32 0.47 0.32PrT Share 0.43 0.28 0.43 0.28 0.43Car Occupancy Rate 1.400 1.470 1.386 1.470 1.386PuT Matrix Sum 1308923 2850171 1940542 1296477 882708PrT Matrix Sum 2075924 1908625 3108745 868190 1414097Average Distance PuT 9.30 7.80 10.84 7.80 10.84Average Distance PrT 8.15 6.52 9.78 6.52 9.78Distance Change PuT -16.1% 16.6% -16.1% 16.6%Distance Change PrT -20.0% 20.0% -20.0% 20.0%

Young and

Virtuous

Old and Vicious

Young and

Vicious

Old and Virtuous

After running the CommonScenarios.xls file, 8 new O/D matrixes were developed for each of the 4 scenarios. In the case of Lisbon, at this time exists one O/D matrix for public transport and another O/D matrix for the private transport, for each of the 4 scenarios. Besides these 8 new O/D matrixes, we also have a matrix for Good Vehicles, for the baseline and for the common scenarios. Next it will be described the steps followed to implement the 4 common scenarios:

1. Add the new O/D matrixes to the 4 new networks (lisboa15_SC1.ver, lisboa15_SC2.ver, lisboa15_SC3.ver, lisboa15_SC4.ver);



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2. Modify the link capacities, according to the scenario definition (SC1, SC3: 10%; SC2, SC4: 5%); 3. Do the assignment for each network; 4. Run the PcomTest.exe to achieve lisboa15_SC1.csv, lisboa15_SC2.csv, lisboa15_SC3.csv and

lisboa15_SC4.csv; 5. After the firsts results another indicator has been added: Congestion rate (passenger-

hr/passenger-hr in overcrowded vehicle). So, inside the VISUM network, by an iterative process, it was changed the Total Capacity of the PuT Vehicle Types;

6. Do the assignment again; 7. Run Pcomtest.exe; 8. Analyse the indicator Congestion rate until it achieves the values as the scenario definition (0%

in the virtuous city scenarios and 20% in the vicious city scenarios).

4.1.1 SC1 – Young and Virtuous

Figure 27. Lisboa15_SC1.ver network, on the left private transport (Volume-PrT[Veh]) and on the right public transport (Volume-PuT[Pers]).

4.1.2 SC2 – Young and Vicious



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4.1.3 SC3 – Old and Virtuous


4.1.4 SC4 – Old and Vicious




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4.1.5 Results analysis

In the following tables are synthesized the main results of the four assignments:

Table 14. Definition of Common Scenarios: SC1 SC2 SC3 SC4 Demographic Changes + + - - Economic Structural Changes + + - - Technological Changes + - + - Land Use Changes + - + -

Table 15. Results for the 4 common scenarios assignments, for the private transport. PrT Baseline SC1 SC2 SC3 SC4 Vehicle-km 17 791 482 13 370 390 30 876 152 6 578 702 14 561 217 Vehicle-hr 497 765 315 080 1 101 457 130 563 320 450 Additional vehicle-hr due to congestion 258 229 133 451 696 980 38 526 130 518 Vehicle-hr in jam 84 521 29 993 285 152 4 474 24 507

Table 16. Results for the 4 common scenarios assignments, for the public transport. PuT Baseline SC1 SC2 SC3 SC4 Vehicle-km 73 622 73 622 73 622 73 622 7 3622 Vehicle-hr 3 287 3 287 3 287 3 287 3 287 Passenger-km 12 189 434 22 865 838 19 953 448 10 401 135 9 076 620 Passenger-hr 416 290 787 796 675 029 358 351 307 067 Passenger-hr in overcrowded vehicles 370 896 0 158 104 0 66 819 Congestion rate 89,0% 0,0% 23,4% 0,0% 21,8% Some remarks:

The Vehicle-Km and Vehicle-hr of the public transport don’t change because we haven’t modified the public lines characteristics, except for the vehicles capacity;

Table 17. Comparison of the results of the common scenarios with the baseline scenario. PrT SC1 SC2 SC3 SC4 Vehicle-km -24,8% 73,5% -63,0% -18,2% Vehicle-hr -36,7% 121,3% -73,8% -35,6% Additional vehicle-hr due to congestion -48,3% 169,9% -85,1% -49,5% Vehicle-hr in jam -64,5% 237,4% -94,7% -71,0% PuT SC1 SC2 SC3 SC4 Vehicle-km 0,0% 0,0% 0,0% 0,0% Vehicle-hr 0,0% 0,0% 0,0% 0,0% Passenger-km 87,6% 63,7% -14,7% -25,5% Passenger-hr 89,2% 62,2% -13,9% -26,2% Passenger-hr in overcrowded vehicles -100,0% -57,4% -100,0% -82,0%



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According to scenario definition the best “solution” is the CS1, with decreasing of the private traffic flow and increasing of the public one. For what concerns the value of the “passenger-hr in overcrowded vehicles”, it is due to the considerable increase of the capacity of the public transport vehicles. Regarding the CS3, it could seem better than the first one, but it is necessary to stress that the considerable decreasing of the indicators, in this case, is linked to a shrinking and getting older city.

4.2 TREM

TREM input data for the scenarios consider several technological indicators: penetration rate of new vehicle technologies, vehicle fleet changes and fuel properties. Additionally to the technological indicators, TREM inputs concern different vehicle volumes and speed as provided by VISUM for each of the scenarios.


For the scenario SC1, the following input data were considered: 1) 27% penetration rate of New vehicle technologies in passenger cars distributed

between Hybrid Electric vehicles (13%), Electric Vehicles (7%) and Fuel Cell Electric Vehicles (7%);

2) Vehicle fleet composition based on the MEET data for Portugal in 2020 (Figure 31)

05

10152025303540

k 10 20 30 40 50 60 70 85 96

vehicle classes

%

reference

scenario 1,3

Passenger cars LDV HDV Bus MotoNew

technologies

Figure 31. Fleet composition considered for the reference situation and Scenario 1 and 3. 3) Fuel properties in accordance with European legislation:

Sulphur contents in gasoline: 50 (ppm)



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Sulphur contents in diesel: 50 (ppm)

4) Transport activity data provided by VISUM Private transport: -24% vehicle-km in comparison with the reference situation Public transport: +87.6% passenger-km in comparison with the reference situation

4.2.2. SC2 – Young and Vicious

For the scenario SC2, the following input data were considered: 1) 14% penetration rate of New vehicle technologies in passenger cars distributed between Hybrid Electric vehicles (7%), Electric Vehicles (4%) and Fuel Cell Electric Vehicles (3%); 2) Vehicle fleet composition based on the MEET data for Portugal in 2020 3) Fuel properties in accordance with European legislation:

Sulphur contents in gasoline: 50 (ppm) Sulphur contents in diesel: 50 (ppm)

4) Transport activity data provided by VISUM

Private transport: +73% vehicle-km in comparison with the reference situation Public transport: +67.3% passenger-km in comparison with the reference situation

4.2.3. SC3 – Old and Virtuous

For the scenario SC3, the following input data were considered:

1) 27% penetration rate of New vehicle technologies in passenger cars distributed between Hybrid Electric vehicles (13%), Electric Vehicles (7%) and Fuel Cell Electric Vehicles (7%); 2) Vehicle fleet composition based on the MEET data for Portugal in 2020 3) Fuel properties in accordance with European legislation:




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4) Transport activity data provided by VISUM Private transport: -63% vehicle-km in comparison with the reference situation Public transport: -14.7% passenger-km in comparison with the reference situation 4.2.4. SC4 – Old and Vicious

For the scenario SC4, the following input data were considered:

1) 14% penetration rate of New vehicle technologies in passenger cars distributed between Hybrid Electric vehicles (7%), Electric Vehicles (4%) and Fuel Cell Electric Vehicles (3%); 2) Vehicle fleet composition based on the MEET data for Portugal in 2020 3) Fuel properties in accordance with European legislation:


4) Transport activity data provided by VISUM

Private transport: -18.2% vehicle-km in comparison with the reference situation Public transport: -25.5% passenger-km in comparison with the reference situation

4.2.5 Results Analysis Emissions estimated by TREM for the four common scenarios reflect non-linear relation between total amount of the pollutants emitted by transport and the mobility increase. Thus, only CO2 and PM emissions for the scenario 2 represent values above the reference situation. All other pollutants are expected to decrease within scenarios time interval, primary due to emission reduction technologies implemented in new vehicles and low sulphur levels in gasoline and diesel (Table 18, Figure 32).

Table 18. Emission increase for the scenarios relatively to the reference situation

[%] Scenario1 Scenario2 Scenario3 Scenario4 Total emission CO -83.2 -55.4 -90.6 -80.7 Total emission CO2 -27.0 +75.2 -59.1 -20.5 Total emission NOx -66.8 -38.9 -78.3 -66.6 Total emission PM -12.1 61.4 -40.3 -6.4 Total emission SO2 -85.1 -62.6 -91.5 -83.0



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Total emission VOC -67.6 -38.3 -79.8 -67.2

Figure 32. Comparison of total traffic emissions for the common scenarios with the reference situation (reference situation=100%) Tthe analysis of emission per capita demonstrates that highest values also correspond to the reference situation and in the near future this indicator is decreasing for all the scenarios. The “yang and virtuous” scenario (scenario 1) represents lower emission values (Table 19).

Table 19. Emissions per capita

[g.day-1] Reference Scenario1 Scenario2 Scenario3 Scenario4 CO per capita 37,34 4,01 10,65 4,76 9,76 CO2 per capita 1345,06 628,21 1508,03 743,87 1444,96 NOx per capita 7,12 1,51 2,78 2,09 3,22 PM per capita 0,22 0,12 0,23 0,18 0,28 SO2 per capita 0,19 0,02 0,05 0,02 0,04 VOC per capita 4,92 1,02 1,94 1,34 2,18

Also, contribution of public transport to the total emissions levels were analysed for the four scenarios. As could be seen from the Figure 33, the scenario 1 represent highest values for this indicator due to increase of public transport share. The analysis of the data highlights elevated contribution of NOx emissions comparatively to other pollutants induced by public transport.

-150 -100 -50 0 50 100 150 %

cs4cs3cs2cs1ref

VOC

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PM

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CO2

CO



36

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%refcs1cs2cs3cs4

Figure 33. Contribution of public transport (%) to the total emissions estimated for the reference situation and for the four common scenarios.

4.3 VADIS

Considering the city scenarios, the local scale model VADIS was applied to the same simulation domain of the baseline scenario and with identical meteorological conditions. The traffic emissions resulting from the VISUM/TREM cascade for city scenarios application was used in order to perform the air quality simulations.




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Figure 34. Wind and CO dispersion fields for Scenario 1. (▲- location of the air quality station)


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Figure 36. Wind and CO dispersion fields for Scenario 3.

(▲- location of the air quality station)


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4.3.5 Results Analysis

The analysis of the state indicators for VADIS model for the reference situation and city scenarios in Lisbon application shows an increase of CO, NOx and PM10 concentrations for scenario 2 relatively to the baseline scenario. This situation can be related to the raise of the number of trips and the consequently increase of traffic emissions in a young and vicious city. In scenarios 1 and 3 the peak concentration determined in the simulation domain for each pollutant is lower than the one verified for the reference situation. The scenario 4 presents a similar behavior to the one verified in the actual situation.

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nd P

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Figure 38. State indicators of the reference situation and city scenarios for the Lisbon application.

State indicators:

Table 20. State indicators for City scenarios application. SC1 SC2 SC3 SC4

CO peak concentration (µg/m3) 7831,19 39305 6294,77 15161 NOx peak concentration (µg/m3) 470,4 2640 564 1058,6

PM10 peak concentration (µg/m3) 33,3 140,05 23,95 52,51

4.4 MARKAL



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Due to the difficulty found in using the VISUM outputs for the Markal scenarios input, mainly due to the differences in the domains another strategy was used in order to calculate Markal inputs for the 4 scenarios. This strategy is only an exercise and it doesn’t intend to replace the MARKAL runs with the VISUM outputs. The effort to adapt VISUM outputs to MARKAL inputs is still present in our work. Strategy description Four main issues were identified to the development of scenarios: demographic, economic structure, technological and land use. The analysis of these driving forces gave rise to four different scenarios, presented in Table 21, that reflect the general response of the Lisbon transport system to “extreme variations” due to general policy strategies.

Table 21. Scenarios and main driving forces. Scenario Description Demographic/

Economic Structural

Technological/ Land Use

Transport Service Demand

Share Public Transport

SC1 Young and virtuous

Increasing Increasing + 30% + 15%

SC2 Young and vicious

Increasing Decreasing + 30% + 1%

SC3 Old and virtuous Decreasing Increasing + 10% + 15%

SC4 Old and vicious Decreasing Decreasing + 10% + 1%

Scenarios 1 and 2 reflect a young society (population growth and stable youth share), with high tele-work level and high mobility rates. Scenario 1, as well as scenario 3, is a virtuous scenario. It reflects increases in public transport share, in car occupancy rate and decrease of average car trip length due to a mixing land use. It also has high penetration rates of clean transport technologies (fuel cell, electric and hybrid vehicles). In contrast, the vicious scenarios reflect stability in the public transport share, decrease of the car occupancy rate and increase of average car trip length. The rates of penetration in these scenarios of clean transport technologies are low. Scenarios 3 and 4 outline an old society with negative demographic rates decrease in youth share, low telework level and small increase in mobility rate. The variations on indicators that characterize the four main driving forces are reflected in MARKAL by change on transport service demand and share of public transports (Table 21). Some minimum rates of



41

new technologies penetration were also considered depending on scenarios. The rest of the energy system was left unchanged.

Environmental objectives Two environmental objectives that were translated into emission constraints (E.C.) in the model were considered: (i) It was assumed that the policy in this urban region consists first in reducing the emissions of ozone precursors (NO2 and VOCs mainly); (ii) The second possible environmental objective is to reduce the emissions of CO2 in conformity with Kyoto/Marrakech agreements. Ozone pollution is related to episodes and may be translated through the use of indicators that are built from the prevalence of critical weather conditions over a typical year. When only the precursors due to transportation are considered, it has been found possible to represent in a single linear expression the relationship between the precursor emissions and the O3 pollution indicators. The imposed constraints on the total NO2 emissions due to the transportation sector are indicated in Table 22. The second environmental constraint, imposed on the energy system, concerns the emissions of CO2, the major greenhouse gas derived from combustion. The schedule of emissions bound shown in Table 22 corresponds to an objective of limiting to 6.4 MT.year-1 the CO2 emissions from 2010 onward. This objective is consistent with the Kyoto protocol commitment of Portugal. Table 22. Upper bounds on NO2 and CO2 emissions considered in Lisbon MARKAL-Lite simulations.

Five-year period 1 2 3 4 5 6 7 8 9 NO2 (ton.year-1) 750 750 750 750 700 600 500 400 300

CO2 (Mton.year-1) 4,5 4,5 5,4 6,2 6,3 6,3 6,3 6,3 6,4




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TLE1TLE3THE3THE1TEH1TEE4CGTEE4CDTEE3CDTEE1CGTEE1CDTE8T39T38T2CT2B

Figure 39. Installed capacity in the private transport sector.

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Figure 41. Emissions from the transport sector.




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Figure 44. Emissions from transport sector.



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Figure 46. Installed Capacity in the public transport sector.

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Figure 49. Installed Capacity in the public transport sector.

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Table 23. Comparison of scenarios.

Baseline SC1 SC2 SC3 SC4

Installed capacity – private transport

(veh.km.day-1) 3700 4000 5000 3500 4000

Installed capacity – public transport

(veh.km.day-1) 70 900 50 800 40

Fossil fuel consumption (PJ.year-1)

60,0 94,3 20,9 77,4 19,0

Total Cost (M€) 39811 52500 39851 49326 38990

When comparing the installed capacity for private transport it can be seen that only for scenario 3 the installed capacity decreases. For all the other scenarios it increases achieving a maximum of 5000 veh.km.day-1 in scenario 2.

For the installed capacity for public transport SC1 and SC3 very high values are achieved. This values were not expected but are explained by the increase of the share of the public transport in these two scenarios. When the share for the public transport is increased in 15% in vehicle-km, the increase in passanger-km increases much more. In the future this increase in the share has to be changed in order to reflect the increase in passenger-km and not in vehicle-km. The increase in the consumption of fossil fuels in these two scenarios is associated with the increase of public transport.

Scenarios 1 and 3, the virtuous scenarios, are the ones that have higher total costs. This fact can be explained by the introduction of new and more expensive technologies.

Table 24. Pressure indicators for common scenarios (year 2030).

Consumption of fossil fuels (PJ per year) Data provider SC1 SC2 SC3 SC4

Markal domain Transports MARKAL 94,3 20,9 77,4 19,04

Nox -Air emissions by mode [tons per year]

Data provider SC1 SC2 SC3 SC4



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Markal domain Other sources (as modelled in MARKAL) 19,1 19,1 18,7 18,66

4.5 OFIS

The OFIS emissions for the 4 scenarios were estimated according to the output of VISUM and TREM models. For each scenario were estimated evolution factors relative to the baseline scenario emissions. Since the OFIS domain is not the same of the VISUM model, it was necessary to do some assumptions.

In the following table are presented the estimated emissions evolution factors used for each the 4 scenarios.

Table 25. Emission evolution factors for each scenario

Rural area Urban and suburban area

NOx CO SO2 VOC NOx CO SO2 VOC

Scenario1 0,38 0,42 1,2 0,53 0,28 0,43 0,94 0,53

Scenario2 1,78 1,79 6,30 1,51 0,88 1,10 3,22 0,95

Scenario3 0,13 0,17 0,41 0,26 0,09 0,17 0,29 0,30

Scenario4 0,50 0,53 1,68 0,59 0,30 0,46 1,08 0,51

As it was expected, only the SC2 presents emissions evolution factors greater than 1.00, which means that the emissions considered in the OFIS simulation will be bigger than the used in the baseline scenario.


The indicators resulting for this scenario are as follows (due to the zero values, no figure is provided)

Table 26. Indicators for the scenario 1 (AOT 60 and number of days exceeding 120 µg.m-3) AOT(max), AOT(ave), AOT(sub), AOT(town)

AOT60 1,01 0,11 0,14 0,18 E120(domain) E120(sub) E120(town)

DAYS 0 0 0


ST ave_ofis aot_ofis ind_ofis 1 4,16 0,01 0 2 3,89 0 0



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3 4,28 0,11 1 4 4,28 0,09 1 5 2,88 0,22 1 6 4,49 0,43 1 7 4,24 0 0 8 3,9 0 0 9 4,37 0,13 1


The indicators resulting for this scenario are as follows (due to the zero values, no figure is provided)


AOT60 0 0 0 0 E120(domain) E120(sub) E120(town)

DAYS 0 0 0


ST ave_ofis aot_ofis ind_ofis 1 5,67 0 0 2 5,07 0 0 3 5,77 0 0 4 5,41 0 0 5 1,56 0 0 6 5,41 0 0 7 5,78 0 0 8 5,06 0 0 9 5,69 0 0


The indicators resulting for this scenario are as follows


AOT60 371,64 51,88 92,18 128,83 E120(domain) E120(sub) E120(town)

DAYS 5 9 11


ST ave_ofis aot_ofis ind_ofis 1 5,61 5,58 3 2 8,11 17,7 2 3 11,75 31,82 2



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4 25,93 83,87 5 5 55,25 162,07 10 6 37,87 127,81 13 7 4,83 3,76 2 8 9,31 22,42 2 9 32,43 115,37 11


The indicators resulting for this scenario are as follows (due to the zero values, no figure is provided) Table 32. Indicators for the scenario 4 (AOT 60 and number of days exceeding 120 µg.m-3)

AOT(max), AOT(ave), AOT(sub), AOT(town) AOT60 0,01 0 0 0

E120(domain) E120(sub) E120(town) DAYS 0 0 0


ST ave_ofis aot_ofis ind_ofis 1 4,32 0 0 2 3,99 0 0 3 4,4 0 0 4 4,3 0 0 5 2,42 0 0 6 4,4 0 0 7 4,41 0 0 8 3,99 0 0 9 4,45 0 0


The next figures provides with an overall summary of the indicators vs scenarios for Lisbon, indicating that the scenario performance is identical, with the pronounced exception of scenario S3. Figure 51 compares the indicators for the scenario baseline and the 4 scenarios concerning the OFIS application results. Scenario 3 produces the most interesting (non zero) results, as it correlated to the lower ozone consumption pollutants, accompanied by scenario 1. It should be notes that the two scenarios result in similar indicators for some of the receptors (like int_ofis for receptor 3), while they correspond to very different indicator values for other receptors, thus pronouncing the influence of the meteorology over emission and concentration patterns.



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Figure 52 shows the same comparison but for each kind of indicator.

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Figure 51. Indicators for the all the scenarios (AOT 60 and number of days exceeding 120 µg.m-3)

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Figure 52. Receptors (x axis) and indicator values (y axis) for the Lisbon scenarios.

project deliverables: d12.5. scenario analysis: lisbon ... · scenario analysis: lisbon city report...

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