University of Aveiro

1. University of Aveiro City Case Overview Department of Environment and Planning University of Aveiro (Portugal) Ana Isabel Miranda and Carlos Borrego

2. € • Economic competitiveness • Noise • .... An introduction ... The cities involved in SUTRA differ widely in terms of culture, environmental conditions , size, economic structure, social composition and demography. But ... they face common challenges in their transportation systems: • Air quality • Traffic congestion • Employment

3. An introduction ... A common methodology and a set of tools were used by SUTRA cities to generate directly comparable results for the overall evaluation. Indicators Models Scenarios

4. City case description • Models cascade application: • Baseline • Common scenarios • Main conclusions TOPICS TO BE COVERED • City case description • Models cascade application - baseline - common scenarios • Final comments

5. City case description • location, urban structure and land-use • demography • meteo conditions and air pollution • input indicators Gdansk Geneva Genoa Thessaloniki • Models cascade application: • Baseline • Common scenarios Lisbon TelAviv • Main conclusions The SUTRA cities

6. City case description • location, urban structure and land-use • demography • meteo conditions and air pollution • input indicators GENOA • mild Mediterranean climate • topographic and orographic peculiarity: sea and mountains  affect pollutants dispersion LISBON • complex sea breeze circulations • traffic NOx emissions represents 97% of total anthropogenic NOx emissions. • Models cascade application: • Baseline • Common scenarios THESSALONIKI • 45% of days characterized by stagnant conditions • high insolation • Main conclusions Meteorological conditions and air pollution problems

7. City case description • location, urban structure and land-use • demography • meteo conditions and air pollution • input indicators Population size Age structure Population size evolution • Models cascade application: • Baseline • Common scenarios Population size for VISUM and OFIS domains • Main conclusions Demographic indicators Demographic changes are crucial to determine traffic demand

8. City case description • location, urban structure and land-use • demography • meteo conditions and air pollution • input indicators GDP per capita % of employment in services over total employment • Models cascade application: • Baseline • Common scenarios • Main conclusions Economic indicators

9. City case description • location, urban structure and land-use • demography • meteo conditions and air pollution • input indicators passenger car peak occupancy rate % of public transport over total passenger transport • Models cascade application: • Baseline • Common scenarios • Main conclusions Technological indicators

10. City case description • Models cascade application: • Baseline • Common scenarios • VISUM • TREM • VADIS • OFIS • MARKAL A baseline scenario (specific of each city) Common scenarios of development • Main conclusions Which scenarios? Which models? VISUM VADIS TREM MARKAL OFIS

11. City case description Economic srtructure Technology Land use Demography • Models cascade application: • Baseline • Common scenarios • VISUM • TREM • VADIS • OFIS • MARKAL € € € Young and virtuous Young and vicious € € € Old and virtuous Old and vicious • Main conclusions Common scenarios ...definition

12. AREA: 241 km2 • POPULATION: 635 201 • N. NODES: 936 • N. LINKS: 888 • AREA: 2793 km2 • POPULATION: 2 682 676 • N. NODES: 1124 • N. LINKS: 2940 LISBON • City case description GENOA • Models cascade application: • Baseline • Common scenarios • VISUM • TREM • VADIS • OFIS • MARKAL TEL AVIV THESSALONIKI GENEVA • AREA: 1100 km2 • POPULATION: 894 435 • N. NODES: 1386 • N. LINKS: 2034 • AREA: 1447 km2 • POPULATION: 2 611 500 • N. NODES: 3144 • N. LINKS: 11850 • AREA: 282 km2 • POPULATION: 413 585 • N. NODES: 936 • N. LINKS: 2900 • Main conclusions VISUM transportation model...domain LISBON GENOA GENEVA

13. City case description • Models cascade application: • Baseline • Common scenarios • VISUM • TREM • VADIS • OFIS • MARKAL Trips by purposes OD matrixes + • road category and capacity • maximum velocity allowed • Main conclusions VISUM transportation model ...input data

14. City case description • Models cascade application: • Baseline • Common scenarios • VISUM • TREM • VADIS • OFIS • MARKAL • Main conclusions VISUM transportation model ...results Results example: Lisbon public network

15. Pressure indicator: passenger transport demand (pkm/year) • City case description • Models cascade application: • Baseline • Common scenarios • VISUM • TREM • VADIS • OFIS • MARKAL State indicator: time spent in crowding and traffic jams (hours) • Main conclusions VISUM transportation model ...indicators

16. City case description • Models cascade application: • Baseline • Common scenarios • VISUM • TREM • VADIS • OFIS • MARKAL A spreadsheet (CommonScenarios.xls) was developed to modify the O/D matrixes according to the scenarios. The obtained results reflect the characteristics of each traffic network. Ex. Genoa input data from the common scenario implementation • Main conclusions Ex. Lisbon output data from the common scenario spreadsheet VISUMtransportation model ...methodology

17. VISUM transportation model ...results and analysis Ex. Lisbon private network CS1 –Young and virtuous CS2 – Young and vicious • City case description • Models cascade application: • Baseline • Common scenarios • VISUM • TREM • VADIS • OFIS • MARKAL CS3 – Old and virtuous CS4 – Old and vicious • Main conclusions

18. City case description • Models cascade application: • Baseline • Common scenarios • VISUM • TREM • VADIS • OFIS • MARKAL Ex. Comparison of scenarios results for private transport in Geneva • Main conclusions VISUM transportation model ...results and analysis

19. TREM emissions model ...domain and input data For every city-case TREM domain coincides with VISUM domain. • City case description • Models cascade application: • Baseline • Common scenarios • VISUM • TREM • VADIS • OFIS • MARKAL Main inputs required by TREM: • Main conclusions ex. Gdansk • traffic volume and vehicle speed (provided by VISUM) • distribution of vehicles by categories • distribution of vehicles by classes

20. City case description Hourly variation of CO emissions for Lisbon (Prata and Ouro streets) • Models cascade application: • Baseline • Common scenarios • VISUM • TREM • VADIS • OFIS • MARKAL CO emission for Genoa domain • Main conclusions TREM emissions model ...results

21. City case description • Models cascade application: • Baseline • Common scenarios • VISUM • TREM • VADIS • OFIS • MARKAL • Main conclusions TREM emissions model ...indicators

22. TREM emissions model ...scenarios input data • City case description • Models cascade application: • Baseline • Common scenarios • VISUM • TREM • VADIS • OFIS • MARKAL Main inputs in TREM scenario application: • Main conclusions Ex. Fleet composition for different scenarios • technological indicators • new technologies penetration rates • vehicle fleet changes • fuel properties • VISUM outputs • vehicle volume • vehicle speed

23. TREM emissions model ...results and analysis CS1 –Young and virtuous CS2 – Young and vicious • City case description • Models cascade application: • Baseline • Common scenarios • VISUM • TREM • VADIS • OFIS • MARKAL CS3 – Old and virtuous CS4 – Old and vicious • Main conclusions Ex. Genoa CO emissions

24. TREM emissions model ...results and analysis Ex. Thessaloniki results • City case description • Models cascade application: • Baseline • Common scenarios • VISUM • TREM • VADIS • OFIS • MARKAL • Main conclusions Only CO2 emissions for Scenario 2 represent values above the reference situation. All other pollutants are expected to decrease primary due to emission reduction technologies and low sulphur levels in gasoline and diesel.

25. City case description • Models cascade application: • Baseline • Common scenarios • VISUM • TREM • VADIS • OFIS • MARKAL • Main conclusions VADIS local scale model ...domain

26. Emission data (provided by TREM) Buildings volumetry Meteorological data • City case description • Models cascade application: • Baseline • Common scenarios • VISUM • TREM • VADIS • OFIS • MARKAL Geneva Genoa Lisbon • Main conclusions Wind velocity and direction for Lisbon simulation VADIS local scale model ...input data

27. City case description • Models cascade application: • Baseline • Common scenarios • VISUM • TREM • VADIS • OFIS • MARKAL Wind and CO dispersion simulation for Thessaloniki for 7 a.m. of 9 September 1998 Wind and CO dispersion fields for Lisbon for 6p.m. of 8th of July 1997 • Main conclusions VADIS local scale model ...results

28. VADIS local scale model ...results and analysis Ex. Lisbon CO dispersion fields CS1 –Young and virtuous CS2 – Young and vicious • City case description • Models cascade application: • Baseline • Common scenarios • VISUM • TREM • VADIS • OFIS • MARKAL CS3 – Old and virtuous CS4 – Old and vicious • Main conclusions

29. Ex. Lisbon results VADIS local scale ..results and analysis • City case description • Models cascade application: • Baseline • Common scenarios • VISUM • TREM • VADIS • OFIS • MARKAL • Main conclusions European Legislation CO - 10000 µg.m-3 (8-hours average limit value)

30. Ex. Lisbon results VADISlocal scale model ..results and analysis • City case description • Models cascade application: • Baseline • Common scenarios • VISUM • TREM • VADIS • OFIS • MARKAL • Main conclusions European Legislation NO2 - 200µg.m-3 (not exceeding more than 18 times in a year) Hourly value for the protection of human health

31. VADIS local scale model ..results and analysis • City case description • Models cascade application: • Baseline • Common scenarios • VISUM • TREM • VADIS • OFIS • MARKAL • Main conclusions Ex. Lisbon results European Legislation PM10 - 50µg.m-3 (not exceeding more than 35 times in a year) Daily maximum value for the protection of human health

32. TEL AVIV GDANSK • City case description GENEVA • Models cascade application: • Baseline • Common scenarios • VISUM • TREM • VADIS • OFIS • MARKAL GENOA • Main conclusions THESSALONIKI • AREA: 150 km x 150 km OFIS photochemical model ...domain

33. OFIS photochemical model ...input data • City case description • Models cascade application: • Baseline • Common scenarios • VISUM • TREM • VADIS • OFIS • MARKAL Main inputs required by OFIS: • Main conclusions • emissions: hourly non-urban , suburban and urban emissions rates • meteorological data: daily average wind speed and direction, temperature and temperature lapse rate above the mixing layer • boundary conditions: daily average regional background concentrations (NO, NO2, O3 and other species)

34. Number of days with maximum 8 hour running average ozone concentration exceeding 120 g.m-3 (IND120). • City case description • Models cascade application: • Baseline • Common scenarios • VISUM • TREM • VADIS • OFIS • MARKAL • Main conclusions OFIS photochemical model ...results

35. City case description • Models cascade application: • Baseline • Common scenarios • VISUM • TREM • VADIS • OFIS • MARKAL AOT60 (maximum and average) AOT60 (suburbs and town), • Main conclusions OFIS photochemical model ...indicators

36. OFIS photochemical model ...results and analysis • City case description • Models cascade application: • Baseline • Common scenarios • VISUM • TREM • VADIS • OFIS • MARKAL • Main conclusions CS1 –Young and virtuous CS2 –Young and vicious GENEVA Ozone AOT60 CS3 –Old and virtuous CS4 –Old and vicious

37. OFISphotochemical model ...results and analysis Lisbon Indicators • Only Scenario 3 presents different results • Scenario 3 indicators are higher due to lower ozone consumption pollutants • City case description • Models cascade application: • Baseline • Common scenarios • VISUM • TREM • VADIS • OFIS • MARKAL • Main conclusions Thessaloniki Indicators • Significant differences between scenarios • Scenario 1 presents the highest results

38. MARKAL ...domaintechno-economic energy model LISBON • City case description • Lisbon municipality • 82 km2 • Models cascade application: • Baseline • Common scenarios • VISUM • TREM • VADIS • OFIS • MARKAL THESSALONIKI • Thessaloniki municipality + 18 municipalities and 2 communes • 1100 km2 • Main conclusions

39. MARKAL ...input datatechno-economic energy model • City case description • Models cascade application: • Baseline • Common scenarios • VISUM • TREM • VADIS • OFIS • MARKAL Main inputs required by MARKAL: • Main conclusions Ex. html file for input data • imported energy prices • demand data • residual capacities • techno-economic data • input/output coeficients • pollutants emissions associated with technologies

40. MARKAL ...resultstechno-economic energy model Lisbon Installed capacity in the private transport sector • City case description • Models cascade application: • Baseline • Common scenarios • VISUM • TREM • VADIS • OFIS • MARKAL Lisbon Installed capacity in the public transport sector • Main conclusions

41. MARKAL ...methodologytechno-economic energy model • City case description • Models cascade application: • Baseline • Scenarios • VISUM • TREM • VADIS • OFIS • MARKAL Two environmental constraints were considered: • reduction of ozone precursors • reduction of CO2 emissions in conformity with Kyoto/Marrakech agreements • Main conclusions Lisbon application: • The inclusion of MARKAL in the models cascade is in progress, trying to use VISUM outputs as MARKAL scenarios input. • A simple exercise was carried out, with a new strategy in order to calculate MARKAL inputs for different scenarios

42. MARKAL ...results and analysis Lisbon results for private transport CS1 –Young and virtuous CS2 – Young and vicious • City case description • Models cascade application: • Baseline • Scenarios • VISUM • TREM • VADIS • OFIS • MARKAL CS3 – Old and virtuous CS4 – Old and vicious • Main conclusions • When comparing installed capacity, only for the Scenario 3 this parameter decreases • Clean technologies (fuel cell and hydrogen) are significant only for CS1

43. City case description • Models cascade application: • Baseline • Common scenarios • Main conclusions Some final comments • The Old and Virtuous City (Scenario 3) seems to be the best choice, but it is linked to a shrinking and getting older citty. • The produced indicators allow a easy and practical analysis of the scenario results • MARKAL was used to generate baseline and also common scenarios allowing to test the technological, economical and energetic approach • This complex approach wasnot completely feasible for all the cities, due to several constraints (data availability, model specificity, ...) • Genoa,Lisbon and Thessalonikiare successful city-cases: the model cascade was applied for the baseline and for the 4 common scenarios