TY - JOUR
T1 - Impact on air quality of measures to reduce CO2 emissions from road traffic in Basel, Rotterdam, Xi'an and Suzhou
AU - Keuken, Menno
AU - Jonkers, Sander
AU - Verhagen, Henk
AU - Perez, Laura
AU - Trueb, Stephan
AU - Okkerse, Willem-Jan
AU - Liu, Junhan
AU - Pan, X
AU - Zheng, Lijuan
AU - Wang, Haikun
AU - Xu, R
AU - Sabel, Clive E
PY - 2014
Y1 - 2014
N2 - Two traffic scenarios to reduce CO2 emissions from road traffic in two European cities (Basel and Rotterdam) and two Chinese cities (Xi'an and Suzhou) were evaluated in terms of their impact on air quality. The two scenarios, one modelling a reduction of private vehicle kilometres driven by 10% on urban streets and the other modelling the introduction of 50% electric-powered private vehicle kilometres on urban streets, were both compared to a scenario following “business-as-usual”: 2020-BAU. The annual average concentrations of NO2, PM2.5, PM10 and elemental carbon (EC) were modelled separately in busy street canyons, near urban motorways and in the remainder of the urban area. It was concluded that traffic-related CO2 emissions in 2020-BAU could be expected to remain at the levels of 2010 in Basel and Rotterdam, while in Xi'an and Suzhou to increase 30-50% due to growth in the traffic volume. Traffic related CO2 emissions may be reduced by up to 5% and 25%, respectively using the first and second scenarios. Air pollution in the Chinese cities is a factor 3 to 5 higher than in the European cities in 2010 and 2020-BAU. The impact of both CO2 reduction scenarios on air quality in 2020-BAU is limited. In Europe, due to implementation of stringent emission standards in all sectors, air quality is expected to improve at both the urban background and near busy road traffic. In China, the regional background is expected to improve for EC, stabilize for PM2.5 and PM10, and decrease for NO2. The urban background follows this regional trend, while near busy road traffic, air pollution will remain elevated due to the considerable growth in traffic volume. A major constraint for modelling air quality in China is access to the input data required and lack of measurements at ground level for validation.
AB - Two traffic scenarios to reduce CO2 emissions from road traffic in two European cities (Basel and Rotterdam) and two Chinese cities (Xi'an and Suzhou) were evaluated in terms of their impact on air quality. The two scenarios, one modelling a reduction of private vehicle kilometres driven by 10% on urban streets and the other modelling the introduction of 50% electric-powered private vehicle kilometres on urban streets, were both compared to a scenario following “business-as-usual”: 2020-BAU. The annual average concentrations of NO2, PM2.5, PM10 and elemental carbon (EC) were modelled separately in busy street canyons, near urban motorways and in the remainder of the urban area. It was concluded that traffic-related CO2 emissions in 2020-BAU could be expected to remain at the levels of 2010 in Basel and Rotterdam, while in Xi'an and Suzhou to increase 30-50% due to growth in the traffic volume. Traffic related CO2 emissions may be reduced by up to 5% and 25%, respectively using the first and second scenarios. Air pollution in the Chinese cities is a factor 3 to 5 higher than in the European cities in 2010 and 2020-BAU. The impact of both CO2 reduction scenarios on air quality in 2020-BAU is limited. In Europe, due to implementation of stringent emission standards in all sectors, air quality is expected to improve at both the urban background and near busy road traffic. In China, the regional background is expected to improve for EC, stabilize for PM2.5 and PM10, and decrease for NO2. The urban background follows this regional trend, while near busy road traffic, air pollution will remain elevated due to the considerable growth in traffic volume. A major constraint for modelling air quality in China is access to the input data required and lack of measurements at ground level for validation.
KW - Air Pollution
KW - China
KW - road traffic
KW - health impact
U2 - 10.1016/j.atmosenv.2014.09.024
DO - 10.1016/j.atmosenv.2014.09.024
M3 - Article (Academic Journal)
SN - 1352-2310
VL - 98
SP - 434
EP - 441
JO - Atmospheric Environment
JF - Atmospheric Environment
ER -