2017
DOI: 10.1016/j.scitotenv.2016.09.040
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On-road vehicle emissions and their control in China: A review and outlook

Abstract: The large (26-fold over the past 25years) increase in the on-road vehicle fleet in China has raised sustainability concerns regarding air pollution prevention, energy conservation, and climate change mitigation. China has established integrated emission control policies and measures since the 1990s, including implementation of emission standards for new vehicles, inspection and maintenance programs for in-use vehicles, improvement in fuel quality, promotion of sustainable transportation and alternative fuel ve… Show more

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Cited by 498 publications
(250 citation statements)
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References 79 publications
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“…Consistent with previous studies, our analysis also shows that the percentage improvements by the CART-LM-KF-AN method are generally larger in relatively cleaner regions (e.g., the Pearl River Delta in South china, Northeast China, and other remote regions) than in heavily polluted regions (e.g., the North China Plain and the Yangtze River Delta in East China) (Figure 8), suggesting that there might be important factors missing in the trained relationship between model biases and predictor variables over polluted regions. One such factor is the fast-changing emissions in both magnitude and distribution in regions such as the North China Plain and the Yangtze River Delta during the modeled three years [48][49][50], a result of increasingly more strict emission control enforcements and/or economic fluctuations. The significant change of emission rates in these regions between the training years (2014)(2015) and the prediction year (2016) could confound the trained bias correction relationships.…”
Section: Discussionmentioning
confidence: 99%
“…Consistent with previous studies, our analysis also shows that the percentage improvements by the CART-LM-KF-AN method are generally larger in relatively cleaner regions (e.g., the Pearl River Delta in South china, Northeast China, and other remote regions) than in heavily polluted regions (e.g., the North China Plain and the Yangtze River Delta in East China) (Figure 8), suggesting that there might be important factors missing in the trained relationship between model biases and predictor variables over polluted regions. One such factor is the fast-changing emissions in both magnitude and distribution in regions such as the North China Plain and the Yangtze River Delta during the modeled three years [48][49][50], a result of increasingly more strict emission control enforcements and/or economic fluctuations. The significant change of emission rates in these regions between the training years (2014)(2015) and the prediction year (2016) could confound the trained bias correction relationships.…”
Section: Discussionmentioning
confidence: 99%
“…Urbanization growth has increased the vehicle population, which further increases NO X emission in China [53][54][55]. For example, the nitrogen pattern is highly attributed to urbanization in Shanghai [56].…”
Section: Urban Development Disparity and No X Emissionmentioning
confidence: 99%
“…To measure vehicle population, we consider the number of urban vehicles (passenger vehicles, trucks, and other vehicles) which have a registered license. Gasoline consumption by vehicles has dramatically increased in proportion to the growth of vehicle population, which results in more NO X emission [55,63]. The China Vehicle Emission Control Annual Report [64] announced the gasoline-fueled vehicles accounted for 81.3% of motor vehicles and were responsible for 39% of NO X emission by the end of 2009.…”
Section: Urban Development Disparity and No X Emissionmentioning
confidence: 99%
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“…They vary in several factors such as the vehicle type under study, urban structure, testing conditions and technologies, and the time of study, which correspond to the emission standards in place. Focusing on vehicle emissions, number of vehicles and emission standards, some studies also provide future trends of on-road vehicle emissions in China (Hao, Liu, Zhao, Li, & Hang, 2015;Wang, Fu, & Bi, 2011;Wu et al, 2017;Zhang, Wu, Wu et al, 2014).…”
mentioning
confidence: 99%