TY - JOUR
T1 - Impact on air quality of measures to reduce CO 2 emissions from road traffic in Basel, Rotterdam, Xi'an and Suzhou
AU - Keuken, M. P.
AU - Jonkers, S.
AU - Verhagen, H. L.M.
AU - Perez, L.
AU - Trüeb, S.
AU - Okkerse, W. J.
AU - Liu, J.
AU - Pan, X. C.
AU - Zheng, L.
AU - Wang, H.
AU - Xu, R.
AU - Sabel, C. E.
N1 - Funding Information:
This work was supported by the 7th European Framework Project: Urban Reduction of GHG Emissions in China and Europe (URGENCHE: Grant Agreement No. 265114 ).
Publisher Copyright:
© 2014 Elsevier Ltd.
PY - 2014/9/9
Y1 - 2014/9/9
N2 - Two traffic scenarios to reduce CO 2 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 NO 2 , PM 2.5 , PM 10 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 CO 2 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 CO 2 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 CO 2 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 PM 2.5 and PM 10 , and decrease for NO 2 . 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 CO 2 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 NO 2 , PM 2.5 , PM 10 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 CO 2 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 CO 2 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 CO 2 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 PM 2.5 and PM 10 , and decrease for NO 2 . 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 - CO emission reduction
KW - Elemental carbon
KW - Particulate matter
KW - Traffic measures
UR - http://www.scopus.com/inward/record.url?scp=84907065909&partnerID=8YFLogxK
U2 - 10.1016/j.atmosenv.2014.09.024
DO - 10.1016/j.atmosenv.2014.09.024
M3 - Article
AN - SCOPUS:84907065909
SN - 1352-2310
VL - 98
SP - 434
EP - 441
JO - Atmospheric Environment
JF - Atmospheric Environment
ER -
