EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 360 (2022) E3S Web Conf., 360 (2022) 01027 References
Open Access
Issue
E3S Web Conf.
Volume 360, 2022
2022 8th International Symposium on Vehicle Emission Supervision and Environment Protection (VESEP2022)
Article Number 01027
Number of page(s) 11
DOI https://doi.org/10.1051/e3sconf/202236001027
Published online 23 November 2022
  1. China Daily. The population of pravite passenger cars has reached 200 million.(in Chinese) [EB/OL]. http://paper.people.com.cn/rmrb/html/2020-01/08/nw.D110000renmrb_20200108_5-04.htm [Google Scholar]
  2. Bureau of Transportation Statistics U S. Number of U.S Aircraft, Vehicles, Vessles, and Other Conveyances [M]. U.S. Bureau of Transportation Statistics. [Google Scholar]
  3. European Environment Agency E. Vehicle Ownership and Truck Intensity [M]. European Environment Agency. [Google Scholar]
  4. Uherek E., Halenka T., Borken-Kleefeld J., et al. Transport impacts on atmosphere and climate: Land transport [J]. Atmospheric Environment, 2010, 44(37): 4772-816. [CrossRef] [Google Scholar]
  5. Chian News. Nine city released the reaults of source apportionment. (in Chinese) [EB/OL]. http://www.chinanews.com/gn/2015/04-01/7176182.shtml [Google Scholar]
  6. Puntoni R., Ceppi M., Reggiardo G., et al. Occupational exposure to carbon black and risk of bladder cancer [J]. The Lancet, 2001, 358(9281): 562. [CrossRef] [Google Scholar]
  7. Jerrett M., Burnett R. T., Beckerman B., et al. Spatial Analysis of Air Pollution and Mortality in California [J]. American Journal of Respiratory and Critical Care Medicine, 2013, 188(5): 593-9. [CrossRef] [PubMed] [Google Scholar]
  8. Wu X., Wu Y., Zhang S., et al. Assessment of vehicle emission programs in China during 1998-2013: Achievement, challenges and implications [J]. Environ Pollut, 2016, 214:556-67. [CrossRef] [PubMed] [Google Scholar]
  9. Wu Y., Zhang S., Hao J., et al. On-road vehicle emissions and their control in China: A review and outlook [J]. Sci Total Environ, 2017, 574(332-49). [CrossRef] [PubMed] [Google Scholar]
  10. Zheng B., Zhang Q., Borken-Kleefeld J., et al. How will greenhouse gas emissions from motor vehicles be constrained in China around 2030? [J]. Applied Energy, 2015, 156(230-40). [CrossRef] [Google Scholar]
  11. Pan L., Yao E., Yang Y. Impact analysis of traffic-related air pollution based on real-time traffic and basic meteorological information [J]. Journal of Environmental Management, 2016, 183: 510-20. [CrossRef] [PubMed] [Google Scholar]
  12. Zheng B., Huo H., Zhang Q., et al. High-resolution mapping of vehicle emissions in China in 2008 [J]. Atmospheric Chemistry and Physics, 2014, 14(18): 9787-805. [CrossRef] [Google Scholar]
  13. Zheng J., Che W., Wang X., et al. Road-Network-Based Spatial Allocation of On-Road Mobile Source Emissions in the Pearl River Delta Region, China, and Comparisons with Population-Based Approach [J]. J Air Waste Manage Assoc, 2009, 59(12): 1405-16. [CrossRef] [PubMed] [Google Scholar]
  14. Gately C. K., Hutyra L. R., Wing I. S., et al. A bottom up approach to on-road CO2 emissions estimates: improved spatial accuracy and applications for regional planning [J]. Environ Sci Technol, 2013, 47(5): 2423-30. [CrossRef] [PubMed] [Google Scholar]
  15. Gately C. K., Hutyra L. R., Sue Wing I. Cities, traffic, and CO2: A multidecadal assessment of trends, drivers, and scaling relationships [J]. Proc Natl Acad Sci U S A., 2015, 112(16): 4999-5004. [CrossRef] [PubMed] [Google Scholar]
  16. C. M. B., C. M. Z., W. M. E., et al. High - resolution mapping of motor vehicle carbon dioxide emissions [J]. Journal of Geophysical Research: Atmospheres, 2014, 119(9): 5283-98. [CrossRef] [Google Scholar]
  17. Gately C. K., Hutyra L. R., Peterson S., et al. Urban emissions hotspots: Quantifying vehicle congestion and air pollution using mobile phone GPS data [J]. Environmental Pollution, 2017, 229(496-504). [CrossRef] [Google Scholar]
  18. Xie Y., Chowdhury M., Bhavsar P., et al. An integrated modeling approach for facilitating emission estimations of alternative fueled vehicles [J]. Transportation Research Part D: Transport and Environment, 2012, 17(1): 15-20. [CrossRef] [Google Scholar]
  19. Abou-Senna H., Radwan E., Westerlund K., et al. Using a traffic simulation model (VISSIM) with an emissions model (MOVES) to predict emissions from vehicles on a limited-access highway [J]. J Air Waste Manage Assoc, 2013, 63(7): 819-31. [CrossRef] [PubMed] [Google Scholar]
  20. Zhou Y., Wu Y., Yang L., et al. The impact of transportation control measures on emission reductions during the 2008 Olympic Games in Beijing, China [J]. Atmospheric Environment, 2010, 44(3): 285-93. [CrossRef] [Google Scholar]
  21. Jing B. Y., Wu L., Mao H. J., et al. Development of a high temporal-spatial resolution vehicle emission inventory based on NRT traffic data and its impact on air pollution in Beijing - Part 1: Development and evaluation of vehicle emission inventory [J]. Atmospheric Chemistry and Physics Discussions, 2015, 15(19): 26711-44. [Google Scholar]
  22. Yang D., Zhang S., Niu T., et al. High-resolution mapping of vehicle emissions of atmospheric pollutants based on large-scale, real-world traffic datasets [J]. Atmos Chem Phys, 2019, 19(13): 8831-43. [CrossRef] [Google Scholar]
  23. James G., Witten D., Hastie T., et al. An Introduction to Statistical Learning: With Applications in R [M]. 2013. [Google Scholar]
  24. Liaw A., Wiener M. Classification and Regression by RandomForest [J]. Forest, 2001, 23 [Google Scholar]
  25. Hoek G., Beelen R., De Hoogh K., et al. A review of land-use regression models to assess spatial variation of outdoor air pollution [J]. Atmospheric Environment, 2008, 42(33): 7561-78. [CrossRef] [Google Scholar]
  26. Brokamp C., Jandarov R., Rao M. B., et al. Exposure assessment models for elemental components of particulate matter in an urban environment: A comparison of regression and random forest approaches [J]. Atmospheric Environment, 2017, 151(1-11). [CrossRef] [Google Scholar]
  27. Brokamp C., Jandarov R., Hossain M., et al. Predicting Daily Urban Fine Particulate Matter Concentrations Using a Random Forest Model [J]. Environmental Science & Technology, 2018, 52(7): 4173-9. [CrossRef] [PubMed] [Google Scholar]
  28. Fu L., Rilett L. R. Estimation of time dependent, stochastic route travel times using artifical neural netwoks. [J]. Transportation Planning and Technology, 2000, 24(1): 2548. [Google Scholar]
  29. Ghanim M., Abulebdeh G. Real-Time Dynamic Transit Signal Priority Optimization for Coordinated Traffic Networks Using Genetic Algorithms and Artificial Neural Networks [J]. Journal of Intelligent Transportation Systems, 2015, 19(4): 327-38. [CrossRef] [Google Scholar]
  30. Yuan F., Cheu R. L. Incident detection using support vector machines [J]. Transportation Research Part C: Emerging Technologies, 2003, 11(3): 309-28. [CrossRef] [Google Scholar]
  31. Sun J., Sun J. Real-time crash prediction on urban expressways: identification of key variables and a hybrid support vector machine model [J]. Iet Intelligent Transport Systems, 2016, 10(5): 331-7. [CrossRef] [Google Scholar]
  32. Williams C. K. I., Rasmussen C. E. Gaussian Processes for Machine Learning [J]. 2005, 2 [Google Scholar]
  33. Xie Y., Zhao K., Sun Y., et al. Gaussian Processes for Short-Term Traffic Volume Forecasting [J]. Transportation Research Record, 2010, 2165(1): 69-78. [CrossRef] [Google Scholar]
  34. Liu S., Yue Y., Krishnan R. Adaptive Collective Routing Using Gaussian Process Dynamic Congestion Models [M]. 2013. [Google Scholar]
  35. Marshall J. D., Mckone T. E., Deakin E., et al. Inhalation of motor vehicle emissions: effects of urban population and land area [J]. Atmospheric Environment, 2005, 39(2): 283-95. [CrossRef] [Google Scholar]
  36. Evans J. S., Wolff S. K., Phonboon K., et al. Exposure efficiency: an idea whose time has come? [J]. Chemosphere, 2002, 49(9): 1075-91. [CrossRef] [PubMed] [Google Scholar]
  37. Carslaw D. C., Beevers S. D., Tate J. E., et al. Recent evidence concerning higher NOx emissions from passenger cars and light duty vehicles [J]. Atmospheric Environment, 2011, 45(39): 7053-63. [CrossRef] [Google Scholar]
  38. Zhang S., Wu Y., Huang R., et al. High-resolution simulation of link-level vehicle emissions and concentrations for air pollutants in a traffic-populated eastern Asian city [J]. Atmospheric Chemistry and Physics, 2016, 16(15): 9965-81. [CrossRef] [Google Scholar]
  39. Carslaw D. C., Beevers S. D. Estimations of road vehicle primary NO2 exhaust emission fractions using monitoring data in London [J]. Atmospheric Environment, 2005, 39(1): 167-77. [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.