为了探究不同车辆排放对道路空气污染的贡献，以深圳市为例，基于道路运输排放因子手册（Handbook on Emission Factors for Road Transport, HBEFA）求取深圳本地化排放因子，结合深圳市典型道路实测交通流数据，计算该路段每小时CO, NOx平均排放因子和排放强度，以及不同车型机动车对路段CO, NOx的贡献率，并利用加利福尼亚线源扩散模型（California Line Sources Dispersion Model, CALINE4）对道路交通的污染排放进行模拟验证。结果表明：路段CO, NOx每小时单车排放因子分别为（1.04±0.71）g/km 和（2.95±2.41）g/km，排放强度分别为（2664.27±1626.20）g/（km?h）和（7017.85±3382.99）g/（km?h），NOx排放强度日夜变化显著；大型货车约占总交通量的41%，其CO, NOx排放因子分别是小型客车的3～9倍和17～24倍，CO, NOx的路段贡献率分别是77.3%和92.9%；大型货车CO, NOx排放标准每提高10%或大型货车占比减少10%，该道路的CO, NOx排放分别减少约7.7%和9.3%。因此，提高大型货车排放标准、降低大型货车比例或是减少道路交通污染的有效途径。
In order to explore the contribution of different vehicle emissions to road air pollution, taking Shenzhen as an example, the local emission factors of Shenzhen were calculated by using the handbook on emission factors for road transport(HBEFA). Combined with the measured traffic flow data of one typical road in Shenzhen, the hourly average emission factors and emission intensity of CO and NOx, as well as the contribution rate of different motor vehicles to the road section were calculated, and the road traffic pollution emission was simulated and verified by California line sources dispersion model (CALINE 4). The results show that: the hourly emission factors of CO and NOx are (1.04±0.71)g/km and (2.95±2.41)g/km respectively, and the emission intensity are (2664.27±1626.20)g/(km?h) and (7017.85±3382.99)g/(km?h) respectively; the emission intensity of NOx changes significantly day and night; large trucks account for 41% of the total traffic flow, and the emission factors of CO and NOx are 3～9 times and 17～24 times of those of small buses, and the contribution rates of CO and NOx of large trucks are 77.3% and 92.9% respectively. When the CO and NOx emission standards of large trucks are increased by 10% or the number of large trucks is reduced by 10%, the CO and NOx emissions of the road will be reduced by 7.7% and 9.3% respectively. Therefore, it could be an effective way to reduce road traffic pollution by improving the emission standard of large trucks or reducing the proportion of large trucks.
[1] 生态环境部. 中国移动源环境管理年报（2020）[R]. 北京：生态环境部，2020.
[2] European Environment Agency. COPERT 4: Computer programme to calculate emissions from road transport user manual[Z]. Copenhagen: EuroEPA, 2012.
[3] US Environmental Protection Agency, MOVES-2014a User Manual[Z]. Washington DC: EPA, 2015.
[4] KENDRICK C M, KOONCE P, GEORGE L A. Diurnal and seasonal variations of NO, NO2 and PM2.5 mass as a function of traffic volumes alongside an urban arterial[J]. Atmospheric Environment, 2015, 122(12): 133-141.
[5] CARSLAW D C, FARREN N J, VAUGHAN A R, et al. The diminishing importance of nitrogen dioxide emissions from road vehicle exhaust[J]. Atmospheric Environment: X, 2019, 1: 1-6.
[6] KARNER A A, EISINGER D S, NIEMEIER D A. Near-roadway air quality: Synthesizing the findings from real-world data[J]. Environmental Science & Technology, 2010, 44(14): 5334-5344.
[7] WANG Y J, NGUYEN M T, STEFFENS J T, et al. Modeling multi-scale aerosol dynamics and micro-environmental air quality near a large highway intersection using the CTAG model[J]. Science of the Total Environment, 2013, 443(3): 375-386.
[8] NAGPURE A S, GURJAR B R, KUMAR V, et al. Estimation of exhaust and non-exhaust gaseous, particulate matter and air toxics emissions from on-road vehicles in Delhi[J]. Atmospheric Environment, 2016, 127(2): 118-124.
[9] 段仲渊. 城市交通排放监测平台建设与应用[J]. 交通与运输，2019，32（Z1）：154-159.
[10] 何巍楠，刘莹，孙胜阳，等. 基于HBEFA的城市交通温室气体排放模型——以北京本地化建模为例[J]. 交通运输系统工程与信息，2014，14（4）：222-229.
[11] 卢俊宇. 城市系统温室气体排放核算框架构建及实证研究[D]. 南京：南京大学，2013.
[12] 吴纯靓. 高速公路养护工作区车辆尾气排放特性及设置技术研究[D]. 南京：东南大学，2017.
[13] 原安妮. 哈尔滨市高分辨机动车尾气排放清单研究[D]. 哈尔滨：哈尔滨工业大学，2018.
[14] 亓浩雲，樊守彬，王凯. 北京市不同功能区机动车排放特征研究[J]. 环境污染与防治，2019，41（9）：1056-1063，1069.
[15] 李贝睿，刘湛，尤翔宇，等. 长株潭区域机动车尾气排放清单及特征分析[J]. 环境科学与技术，2016，39（11）：167-173.
[16] 张磊. 西安市近几年机动车尾气污染物排放特征及对策[D]. 西安：西安工程大学，2018.
[17] 吕改艳. 重庆市主城区机动车尾气污染物排放特征及减排情景研究[D]. 重庆：重庆大学，2019.
[18] 刘永乐，仝纪龙，谢南洪，等. 兰州市交通限行措施机动车尾气减排量核算及空间分布[J]. 环境工程，2018，36（12）：199-204.
[19] 王莹，李成名，赵占杰，等. 城市机动车尾气扩散过程三维动态可视化研究[J]. 测绘科学，2020，45（5）：112-118.
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