| 天津市2017年移动源高时空分辨率排放清单 |
| 摘要点击 2005 全文点击 655 投稿时间：2020-03-21 修订日期：2020-04-27 |
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| 中文关键词 移动源 排放清单 排放因子 空间分布 天津 |
| 英文关键词 mobile source emission inventory emission factor spatial distribution Tianjin |
| 作者 | 单位 | E-mail | | 刘庚 | 南开大学环境科学与工程学院, 天津市城市交通污染防治研究重点实验室, 天津 300071 | 1325946113@qq.com | | 孙世达 | 南开大学环境科学与工程学院, 天津市城市交通污染防治研究重点实验室, 天津 300071 | | | 孙露娜 | 南开大学环境科学与工程学院, 天津市城市交通污染防治研究重点实验室, 天津 300071 | | | 金嘉欣 | 南开大学环境科学与工程学院, 天津市城市交通污染防治研究重点实验室, 天津 300071 | | | 房键旭 | 南开大学环境科学与工程学院, 天津市城市交通污染防治研究重点实验室, 天津 300071 | | | 宋鹏飞 | 南开大学环境科学与工程学院, 天津市城市交通污染防治研究重点实验室, 天津 300071 | | | 王婷 | 南开大学环境科学与工程学院, 天津市城市交通污染防治研究重点实验室, 天津 300071 | | | 吴琳 | 南开大学环境科学与工程学院, 天津市城市交通污染防治研究重点实验室, 天津 300071 | | | 毛洪钧 | 南开大学环境科学与工程学院, 天津市城市交通污染防治研究重点实验室, 天津 300071 | hongjun_mao@hotmail.com |
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| 中文摘要 |
| 移动源已成为城市地区大气污染的主要贡献源.已有研究多关注道路移动源（机动车）或非道路移动源（工程机械、农业机械、船舶、铁路内燃机车和民航飞机）中单一源类的排放，欠缺对移动源总体排放特征的把握.本研究提出了移动源高时空分辨率排放清单的构建方法，据此建立了天津市2017年移动源排放清单，并分析其排放构成与时空特征.结果表明，天津市移动源CO、VOCs、NOx和PM10的排放量分别为18.30、6.42、14.99和0.84万t.道路移动源是CO和VOCs的主要贡献源，占比分别为85.38%和86.60%.非道路移动源是NOx和PM10的主要贡献源，占比分别为57.32%和66.95%.从时间变化来看，移动源所有污染物排放在2月均为最低，CO和VOCs在10月排放最高，而NOx和PM10则在8月排放最高.节假日（如春节和国庆节等）对移动源排放的时间变化影响显著.从空间分布来看，CO和VOCs排放主要集中于城区和车流量大的公路（高速路和国道）上，NOx和PM10在城区与港区均具有较高排放强度.污染物的空间分布差异是由其主要贡献源的空间位置决定的.本研究可为天津市大气污染的精细化管控和空气质量模拟提供数据支撑，同时可为其他地区移动源排放清单的建立提供方法参考. |
| 英文摘要 |
| 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, NOx, and PM10 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 NOx and PM10 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 NOx and PM10. 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 NOx and PM10 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. |
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