WP-Traffic

“develop GIS method and support cities to assess the impact of traffic-related CO2 emission reduction scenarios

on air quality and noise in 2020”

Laura Perez/Stephan Trueeb (STI-Basel); Arja Asikainen/Marjo Niittynen (THL-Kuopio); Willem-Jan

Okkerse (DCMR-Rotterdam)/Menno Keuken (TNO); Sandra Torras Ortiz (Uni Stuttgart)/Manfred

Wacker (Uni Stuttgart-Stuttgart); Denis Sarigiannis/Periklis Kontoroupis/Spyros Karakitsi (CERTH-

Thessaloniki); Junhan Liu/Hongshuang Lau/Xiaochuan Pan (Uni Peking-Xi’an City) and Haikun Wang

(Uni Nanjing-Suzhou City)

ObjectiveWhat is the effect of traffic scenarios to reduce CO2 emissions

on well being and health in 2020?

• traffic scenarios: 1. 10% less traffic/ 50% electric cars: Rotterdam, Basel, Xi’an and Suzhou2. Bundle of measures: Stuttgart3. Biofuels: Kuopio4. Extra metro: Xi’an and Thessaloniki

• well-being: noise (Lden > 55 dB): highly annoyed

• health: AQ: annual average: NO2, PM2.5, PM10 and EC noise: (Lnight > 50 dB): sleep disturbed

• exposure population: streets: > 10 000/24-h; < 100 m motorways; rest population

Method

1. Input: GIS road infrastructure; GIS traffic; emission factors; AQ background

concentrations; meteorology

2. Dispersion modelling: 2010, 2020, 2020+CO2 reduction scenarios

3. Output: AQ and noise data to WP-Health

4. Innovation:

EC (“soot”) as traffic/health indicator

Spatial resolved health effects: streets, motorways and “rest”

Dispersion modelling in Chinese cities

Results (1)

CO2 emissions from road traffic

CO2 emission road traffic (Mt/year)

2010 2020 2020-I 2020-II

Basel 0.2 0.2 0.2 0.1

Rotterdam 1.1 1.1 1.0 0.8

Xi’an 17 23 21 19

Suzhou 11 16 15 12

Results (2)EC (µg/m3) in Kuopio

Cleaner vehicles: Euro-6!

2010 2020

Results (3)

EC (µg/m3) in Suzhou

2010 2020

Lower regional background: fly-ash removal coal plants!

Results (4)EC (µg/m3) in Rotterdam: 2020 & 2020-50% electric

Results (4)EC (µg/m3) in Rotterdam: 2020 & 2020-50% electric

Results (5)EC (µg/m3) in busy streets and urban background in Basel,

Rotterdam, Xi’an and Suzhou

Results (6)Health impact assessment – WP-Health

Impact of change in air quality on all-cause mortality in Life Years Lost:

•~ one year per 10 µg m-3 PM2.5

•~ one year per 1 µg m-3 EC

Results (7)Peer-reviewed papers

• “Impact of measures to reduce CO2 emissions from road traffic

on air quality in Basel, Rotterdam, Xi’an and Suzhou”

• “Health impact assessment for noise and air quality related to transport policies in Rotterdam”

• “Health impact assessment of transport policies in Basel”

• …..

Lessons learned?

1.Urban traffic CO2 emissions in 2020 versus 2010?

30-50% increase in China and stabilize in Europe

2. Effect urban traffic measures on CO2 emissions? 5% (I) and 25% (II) reduction from urban road traffic

3. Effect urban traffic measures on AQ and noise? limited on AQ and noise (even) near busy street canyons

4. AQ assessment in China and Europe? similar approach is applicable with “open source data” in China but more validation with observations is required: # vehicles, fleet composition, emission factors, AQ data and source apportionment

Policy implications?

Most effective to reduce CO2 emissions from urban road traffic is more stringent CO2 emission standards (clean technology:

all electric)

Most effective local climate policy is support/facilitate/stimulate electric vehicles:

maximum reduction 50% CO2 emissions from urban road traffic

Most effective local integrated policy is supporting/facilitating physical/public

transport to improve air quality, health and well-being