Real-Time Emission Factors of Motor Vehicle Air Pollutants Based on Tunnel Experiments
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摘要:
机动车大气污染物动态排放因子是制约实时排放清单精度提升的一个关键参数.本研究选取某城市隧道，结合在线设备和监控摄像头开展7种大气污染物浓度和车流量的实时监测，采用YOLOv8l深度学习目标检测模型和SORT目标跟踪算法，获取车辆类型和速度；采用排放强度比值，推算车队整体和分车型的大气污染物排放因子.CO、NO、NO2、NOx、SO2、BC和PM2.5的平均排放因子分别为（1 064.9±479.8）、（496.5±209.3）、（55.5±30.4）、（578.6±267.6）、（6.3±2.2）、（3.3±1.5）和（37.7±19.2）mg/（km·辆）；车队整体排放因子分别为（634.7±477.2）、（266.0±142.9）、（26.4±13.5）、（302.3±159.5）、（3.5±1.9）、（2.0±1.10）和（19.8±12.3）g/km.隧道内周末的日车流量为工作日的88.6%，工作日除PM2.5外的污染物排放因子是周末的1.00~1.48倍. 在逐小时排放因子情境下，凌晨的柴油车流量占比是其余时间的1.6倍，各污染物的凌晨高值分别是其余时间平均值的2.0~3.5倍；车队排放呈现出早晚（7：00~9：00；17：00~19：00）双峰特征，为全天平均值的1.8~3.3倍.本研究可为区域高精度动态机动车排放清单构建和机动车排放污染物的精准管控提供基础数据和科学依据.
Abstract:Dynamic emission factors of motor vehicle air pollutants are a key parameter limiting the improvement of real-time emission inventory accuracy. In this study, a tunnel in a metropolitan area was selected as an observation site. Real-time monitoring of the concentrations of seven air pollutants (CO, NO, NO2, NOx, SO2, BC, and PM2.5) and traffic flow was conducted using online monitoring instruments and surveillance cameras. Vehicle type and speed were obtained by applying the YOLOv8l deep learning object detection model in combination with the SORT tracking algorithm. Based on the emission intensity ratio method, the average emission factors for the entire fleet and for different vehicle categories were estimated to be (1 064.9±479.8), (496.5±209.3), (55.5±30.4), (578.6±267.6), (6.3±2.2), (3.3±1.5), and (37.7±19.2) mg/(km·veh) for CO, NO, NO2, NOx, SO2, BC, and PM2.5, respectively. The overall fleet emission factors were (634.7±477.2), (266.0±142.9), (26.4±13.5), (302.3±159.5), (3.5±1.9), (2.0±1.1), and (19.8±12.3) g/km for these pollutants, respectively. During the observation period, the average daily traffic volume on weekends was 88.6% of that on weekdays. Except for PM2.5, weekday emission factors for all pollutants were 1.0-1.48 times higher than those on weekends. Hourly analysis showed that the proportion of diesel vehicles during the early morning was 1.6 times that of other periods, with pollutant emission peaks 2.0-3.5 times the daily average. Fleet emissions exhibited a bimodal diurnal pattern, peaking during morning (07:00-09:00) and evening (17:00-19:00) rush hours at 1.8-3.3 times the daily average. The findings provide essential data and scientific support for constructing high-resolution dynamic motor vehicle emission inventories and implementing refined control strategies for vehicular pollutant emissions.
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图 2 车辆识别界面和流程
Fig. 2. Interface and flowchart for vehicle detection and classification
图 3 观测期间隧道内车流量和车速日变化特征
a、b. 周末和工作日车速与车流量变化；c、d. 周末和工作日车速与车流量相关性
Fig. 3. Diurnal variation of traffic volume and vehicle speed inside the tunnel during the observation period
图 4 各污染物单车平均排放因子
Fig. 4. Per-vehicle average emission factors of pollutants from motor vehicles
图 5 我国近30年隧道实验排放因子变化趋势
1 据邓顺熙和董小林（2000）；2 据邓顺熙等（2000a）；3 据邓顺熙等（2000b）；4 据王伯光（2001）；5 据Cheng et al.（2006）；6 据Chiang et al.（2007）；7 据Song et al.（2018b）；8 据Zhang et al.（2015）；9 据Deng et al.（2015）；10 据Zhang et al.（2020）；11 据Wang et al.（2021b）；12 据Huang et al.（2017a）；13 据Song et al.（2018a）；14 据Luo et al.（2020）；15 据宋爱楠等（2023）；16 据本研究
Fig. 5. Trends in tunnel-based vehicle emission factors in China over the past 30 years
图 8 各污染物单车逐时排放因子
Fig. 8. Hourly dynamics of per-vehicle emission factors for pollutants
图 9 各污染物车队整体逐时排放因子
Fig. 9. Hourly dynamics of fleet-averaged emission factors for pollutants
图 10 车流量与排放因子的Pearson相关性
a.车队整体排放因子；b.单车平均排放因子
Fig. 10. Pearson correlation between traffic volume and emission factors
表 1 污染物排放因子比值参考
Table 1. Reference for pollutant emission factors
车型 CO a NO a NO2 a NOx a SO2 a BC b PM2.5 a GV 0.85 0.05 0.05 0.05 0.02 0.006 0.001 5 DV 0.45 0.3 0.3 0.3 0.03 0.022 5 0.03 R 0.53 6 6 6 1.5 3.75 20 注：其中，a. COPERT模型据 Gkatzoflias et al. (2007) ；b. MOVES模型据Koupal et al. (2003) .
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表 2 不同地区隧道实验得到的机动车大气污染物单车排放因子对比
Table 2. Comparison of emission factors from different tunnel studies
地点 年份 CO NO NO2 NOx SO2 BC PM2.5 参考文献 甘肃 1995 41.86 - - 3.88 - - - 邓顺熙和董小林，2000 西安 1996 33.28 - - 4.60 - - - 邓顺熙等, 2000a 成都 1996 28.73 - - 4.65 - - - 邓顺熙等, 2000b 广州 1999 15.40 - - 1.38 0.14 - - 王伯光, 2001 香港 2004 1.84 - - 0.88 - - 0.13 Cheng et al., 2006 台湾 2005 1.89 - - 0.73 0.02 - - Chiang et al., 2007 天津 2010 0.28 0.062 0.020 0.084 - - 0.009 2 Song et al., 2018b Braga（Portugal） 2013 4.09 0.61 0.29 1.18 - 0.005 0.133 Alves et al., 2015 广州 2014 3.10 - - 1.29 0.020 7 - 0.082 7 Zhang et al., 2015 香港 2015 1.80 1.33 0.24 1.58 - - 0.025 Wang et al., 2021b 上海 2016 1.84 - - 0.4 - - 0.034 Huang et al., 2017a 天津 2017 0.28 0.062 0.020 0.084 - - 0.009 2 Song et al., 2018b 西安‒汉中 2017 3.88 4.31 1.56 9.37 0.09 - - Luo et al., 2020 郑州 2019 1.49 0.051 0.006 7 0.086 0.00 - - 王雯楠等, 2024 天津 2019 0.41 - - 0.080 - - 0.009 3 宋爱楠等, 2023 Mumbai（India） 2019 1.60 - 0.147 - - 0.012 0.044 Raparthi et al., 2021 宜昌 2020 1.06 0.48 0.053 0.55 0.006 3 0.003 4 0.038 本研究 注：“-”表示无数据；单位：g/（km∙辆）.
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表 3 工作日和周末的平均排放因子
Table 3. Average emission factors on weekdays and weekends
时间情景 排放因子 单位 CO NO NO2 NOx SO2 BC PM2.5 工作日 单车 mg/(km∙辆) 1409.5 508.1 60.5 581.2 6.7 3.3 35.8 车队 g/km 757.8 252.0 28.6 288.6 3.9 2.0 17.9 周末 单车 mg/(km∙辆) 720.2 484.9 50.5 576.0 5.8 3.2 39.6 车队 g/km 512.9 146.4 13.9 159.9 1.6 1.1 13.6
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