4.6 Article

Research on the Vehicle Emission Characteristics and Its Prevention and Control Strategy in the Central Plains Urban Agglomeration, China

期刊

SUSTAINABILITY
卷 13, 期 3, 页码 -

出版社

MDPI
DOI: 10.3390/su13031119

关键词

vehicle emissions; prevention and control scenarios; the CPUA

资金

  1. Scientific and Technological Innovation Programs of Higher Education Institutions in Shanxi (STIP) [2019L0491, 2020L0251]
  2. Philosophy and social science planning project of Shanxi Province in 2019 [2019B173]

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Vehicle emissions in the Central Plains Urban Agglomeration in China have significantly increased from 1999 to 2015, with different types of vehicles contributing to various pollutants. The spatial distribution of emissions tends to concentrate in urban centers. Comprehensive integrated scenarios are more effective in controlling emissions compared to individual scenarios.
With rapid economic development and urbanization in China, vehicle emissions are increasingly becoming one of the major factors affecting air quality. The Central Plains Urban Agglomeration (CPUA), which has undergone a fast increase in vehicle population and has an advantageous geographical location, was selected as the study area. Vehicle emissions estimated based on the COPERT IV model in this area changed greatly between 1999 and 2015, during which time the emissions of NOx, CO2, and NH3 increased markedly. Passenger cars and light-duty vehicles were the main contributors to pollutants CO and non-methane volatile organic compounds (NMVOC) emissions. Heavy-duty trucks and buses were the important contributors to pollutants NOx, PM2.5, and PM10. Passenger cars were the major contributors to CO2, CH4, N2O, NH3, and SO2. The city with the most emissions is Zhengzhou, followed by Luoyang, Shangqiu, and Zhoukou. The spatial distribution of vehicle emissions has formed around or tended to concentrate in urban centers. Then, this study also predicts the vehicle emissions from 2015 to 2025 and designs ten policy scenarios for the prevention and control of emissions to evaluate the emission reduction effect. The radical integrated scenario was most effective for controlling CO, NMVOC, NOx, PM2.5, PM10, CO2, N2O, and NH3 emissions than any one scenario by itself.

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