Liu Geng, Sun Shi-da, Sun Lu-Na, Jin Jia-Xin, Fang Jian-Xu, Song Peng-Fei, Wang Ting, Wu Lin, Mao Hong-Jun
Tianjin Key Laboratory of Urban Transport Emission Research, College of Environmental Science and Engineering, Nankai University, Tianjin 300071, China.
Huan Jing Ke Xue. 2020 Oct 8;41(10):4470-4481. doi: 10.13227/j.hjkx.202003215.
Mobile source emissions have become a major contributor to air pollution in urban areas. Most of the previous studies focus on the emissions from a single source such as on-road mobile source (vehicles) or non-road mobile source (construction machinery, agricultural machinery, ships, railway diesel locomotives, aircraft), but few studies investigate the mobile source emissions as a whole. In this study, we introduced a method for developing mobile source emission inventory with high spatiotemporal resolution, and applied this method in Tianjin in 2017 to analyze the emission compositions and spatiotemporal characteristics there. The results showed that the CO, VOCs, NO, and PM emissions from the mobile sources were 183.03, 64.18, 149.85, and 8.36 thousand tons, respectively. The on-road mobile source was the main contributor to CO and VOCs emissions, accounting for 85.38% and 86.60%, respectively. The non-road mobile source was the main contributor to NO and PM emissions, accounting for 57.32% and 66.95%, respectively. According to the temporal distributions, the mobile source emissions were lowest in February for all pollutants. Moreover, they were highest in October for CO and VOCs and in August for NO and PM. Holidays (such as Spring Festival and National Day) have a significant impact on the temporal distribution of the mobile source emissions. According to the spatial distributions, the CO and VOCs emissions were concentrated in urban areas and roads with heavy traffic flow (highways and national highways), and the NO and PM were concentrated in urban areas and port areas. The spatial distributions of different pollutants were determined by the location of their major contributors. This study can provide the required data for fine air pollution control and air quality simulation in Tianjin. Moreover, this method can be applied to the other areas where a mobile source emission inventory needs to be developed.
移动源排放已成为城市地区空气污染的主要来源。以往的大多数研究都集中在单一来源的排放上，如道路移动源（车辆）或非道路移动源（建筑机械、农业机械、船舶、铁路柴油机车、飞机），但很少有研究将移动源排放作为一个整体来研究。在本研究中，我们介绍了一种开发高时空分辨率移动源排放清单的方法，并于2017年在天津应用该方法分析了当地的排放成分及时空特征。结果表明，移动源的CO、VOCs、NO和PM排放量分别为183.03、64.18、149.85和8.36千吨。道路移动源是CO和VOCs排放的主要贡献者，分别占85.38%和86.60%。非道路移动源是NO和PM排放的主要贡献者，分别占57.32%和66.95%。根据时间分布，所有污染物的移动源排放在2月份最低。此外，CO和VOCs排放在10月份最高，NO和PM排放在8月份最高。节假日（如春节和国庆节）对移动源排放的时间分布有显著影响。根据空间分布，CO和VOCs排放集中在城市地区和交通流量大的道路（高速公路和国道），而NO和PM集中在城市地区和港口地区。不同污染物的空间分布由其主要贡献者位置决定。本研究可为天津的精细空气污染控制和空气质量模拟提供所需数据。此外，该方法可应用于其他需要编制移动源排放清单的地区。
