Volume 40 Issue 2
Apr.  2022
Turn off MathJax
Article Contents
Article Navigation >  Journal of Transport Information and Safety  > 2022  >  40(2): 108-115
ZHU Caihua, ZHANG Xinyu, ZENG Mingzhe, HAN Fei, LI Yan. Assessment of Level of Concentration and Intake Quantity of Particulate Matter Within the Space of Various Travel Modes[J]. Journal of Transport Information and Safety, 2022, 40(2): 108-115. doi: 10.3963/j.jssn.1674-4861.2022.02.013
Citation: ZHU Caihua, ZHANG Xinyu, ZENG Mingzhe, HAN Fei, LI Yan. Assessment of Level of Concentration and Intake Quantity of Particulate Matter Within the Space of Various Travel Modes[J]. Journal of Transport Information and Safety, 2022, 40(2): 108-115. doi: 10.3963/j.jssn.1674-4861.2022.02.013
shu

Assessment of Level of Concentration and Intake Quantity of Particulate Matter Within the Space of Various Travel Modes

doi: 10.3963/j.jssn.1674-4861.2022.02.013
  • 1.

    College of Transportation Engineering, Chang'an University, Xi'an 710064, China

  • 2.

    Shenzhen Transportation Design & Research Institute CO., LTD., Shenzhen 518003, Guangdong, China

  • Received Date: 2022-02-23
    Available Online: 2022-05-18
    Abstract
  • In order to improve travel health of residents, an assessment system is established to evaluate travelers'intake quantity of particulate matter (PM) when traveling with different travel modes. A multiple linear regression model is developed to study the factors that have an impact onto the PM concentration using data collected at the space used by different travel modes (i.e., the surroundings of travelers when traveling using different travel modes, such as carriage, platform, or sidewalk) through PM detectors. A air intake model is developed considering the changes of heart rate index of travelers. Intake quantity of PM2.5 and PM10 over a single trip for a given traveler can be estimated based on air intake per unit time, travel time and PM concentration. Analysis results based on experimental data from Xi'an show that compared to data from environmental monitoring stations (background environment), there is a significant difference for the PM concentration detected in taxi, bus carriage and subway carriage, while the difference is not significant for the PM concentration detected in sidewalk, non-motorized lane, taxi stop, bus stop, subway station hall and subway platform. In addition, study results also show that the PM concentration and humidity in background environment have a positive influence on the increase of PM concentration in the space used by various travel modes, while temperature and wind speed have negative impacts. For this specific test route, travelers taking bicycle and subway show the lowest PM intake quantity among non-active and active travel modes, respectively. Walking-travelers have lower air intake per unit time but longer exposure time to traffic space, bicycle-travelers have higher air intake per unit time but shorter exposure time to traffic space. Waiting for the bus on the platform and buses'frequent stops contribute to the travelers'PM intake quantity which using bus service for travel. The results of the study can be used to predict the travelers'PM intake quantity in completed trips and based on those provide suggestions for travelers to choose healthier travel mode.

     

    • FullText(HTML)
  • loading
    • References(21)
  • [1]
    JOHANNESSON S, ANDERSSON E, STOCKFELT L, et al. Urban air pollution and effects on biomarkers of systemic inflammation and coagulation: A panel study in healthy adults[J]. Inhalation toxicology, 2014, 26(2): 84-94. doi: 10.3109/08958378.2013.856968
    [2]
    ZHU C H, FU Z K, LIU L J, et al. Health risk assessment of PM2.5 on walking trips[J]. Scientific Reports, 2021, 11(1): 19249. doi: 10.1038/s41598-021-98844-6
    [3]
    LI Y, ZHOU W H, NAN S R, et al. Redesign of the cross-section of bicycle lanes considering electric bicycles[J]. Proceedings of the Institution of Civil Engineers, 2017, 170(tr5): 255-266.
    [4]
    SHI L, ZANOBETTI A, KLOOG I, et al. Low-concentration PM2.5 and mortality: Estimating acute and chronic effects in a population-based study[J]. Environmental Health Perspectives, 2016, 124(1): 46-52. doi: 10.1289/ehp.1409111
    [5]
    PATRICK A, PIERRE S, ALESSANDRA D M, et al. Long-term exposure to ambient PM2.5 and impacts on health in rome, Italy[J]. Clinical Epidemiology & Global Health, 2020, 8(2): 531-545.
    [6]
    李嘉琛, 王裕, 王童, 等. 不同出行方式PM2.5个体暴露水平的比较研究[J]. 环境与健康杂志, 2016, 33(8): 659-662. https://www.cnki.com.cn/Article/CJFDTOTAL-HJYJ201608001.htm

    LI J C, WANG Y, WANG T, et al. Comparison of personal exposure to PM2.5 by different commuting modes[J]. Journal of Environment and Health, 2016, 33(8): 659-662. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-HJYJ201608001.htm
    [7]
    MANOJKUMAR N, MONISHRAJ M, SRIMURUGANANDAM B. Commuter exposure concentrations and inhalation doses in traffic and residential routes of Vellore city, India[J]. Atmospheric Pollution Research, 2021, 12(1): 219-230. doi: 10.1016/j.apr.2020.09.002
    [8]
    QIU Z W, SONG J H, XU X Q, et al. Commuter exposure to particulate matter for different transportation modes in Xi'an, China[J]. Atmospheric Pollution Research, 2017, 8(5): 940-948. doi: 10.1016/j.apr.2017.03.005
    [9]
    ZALUSKA M, GLADYSZEWSKA F K. Regression model of PM2.5 concentration in a single-family house[J]. Sustainability, 2020, 12(15): 5952. doi: 10.3390/su12155952
    [10]
    SHIN H H, GOGNA P, MAQUILING A, et al. Comparison of hospitalization and mortality associated with short-term exposure to ambient ozone and PM2.5 in Canada[J]. Chemosphere, 2021(265): 128683.
    [11]
    刘小宇, 盛萍, 马晓凤, 等. 城市高密集人群区域机动车污染物时空分布及健康影响[J]. 交通信息与安全, 2018, 36 (1): 119-128. doi: 10.3963/j.issn.1674-4861.2018.01.016

    LIU X Y, SHENG P, MA X F, et al. Temporal and spatial distribution and health risks of vehicle pollution in high-populated urban areas[J]. Journal of Transport Information and Safety, 2018, 36(1): 119-128. (in Chinese) doi: 10.3963/j.issn.1674-4861.2018.01.016
    [12]
    赵淑婷, 王丽涛, 齐孟姚, 等. 邯郸市PM2.5-O3复合污染特征及相互影响研究[J]. 环境科学学报, 2021, 41(6): 2250-2261. https://www.cnki.com.cn/Article/CJFDTOTAL-HJXX202106020.htm

    ZHAO S T, WANG L T, QI M Y, et al. Study on the characteristics and mutual influence of PM2.5-O3 complex pollution in Handan[J]. Acta Scientiae Circumstantiae, 2021, 41(6): 2250-2261. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-HJXX202106020.htm
    [13]
    苏志华, 王建华. 贵阳市大气颗粒物的污染特征及其影响因素分析[J]. 中山大学学报(自然科学版), 2015, 54(5): 77-84. https://www.cnki.com.cn/Article/CJFDTOTAL-ZSDZ201505016.htm

    SU Z H, WANG J H. Pollution characteristics and determinants of atmospheric particulate matter and its determinants in Guiyang[J]. Acta Scientiarum Naturalium Universitatis Sunyatseni, 2015, 54(5): 77-84. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZSDZ201505016.htm
    [14]
    GUO H G, LI W F, YAO F, et al. Who are more exposed to PM2.5 pollution: A mobile phone data approach[J]. Environment International, 2020(143): 105821.
    [15]
    WANG Z C, CHEN L R, DING Z N, et al. An enhanced interval PM2.5 concentration forecasting model based on BEMD and MLPI with influencing factors[J]. Atmospheric Environment, 2020(223): 117200.
    [16]
    ARIF M, PARVEEN S. Carcinogenic effects of indoor black carbon and particulate matters (PM2.5 and PM10) in rural households of India[J]. Environmental Science and Pollution Research, 2021, 28(2): 2082-2096. doi: 10.1007/s11356-020-10668-5
    [17]
    贺宇千, 王杰, 黄合来. 基于相对危险暴露量的驾驶人事故风险分析[J]. 交通信息与安全, 2019, 37(5): 40-45. doi: 10.3963/j.issn.1674-4861.2019.05.006

    HE Y Q, WANG J, HUANG H L. An analysis of crash risk of drivers based on quasi-induced exposure[J]. Journal of Transport Information and Safety, 2019, 37(5): 40-45. (in Chinese) doi: 10.3963/j.issn.1674-4861.2019.05.006
    [18]
    UBOLSAKKA J C, TONGDEE P, JONES D A. The effects of slow loaded breathing training on exercise blood pressure in isolated systolic hypertension[J]. Physiotherapy Research International, 2019, 24(4): e1785.
    [19]
    朱才华, 孙晓黎, 李岩. 站点分类下的城市公共自行车交通需求预测[J]. 吉林大学学报(工学版), 2021, 51(2): 531-540. https://www.cnki.com.cn/Article/CJFDTOTAL-JLGY202102017.htm

    ZHU C H, SUN X L, LI Y. Forecast of urban public bicycle traffic demand by station classification[J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51 (2): 531-540. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JLGY202102017.htm
    [20]
    ZUURBIER M, HOEK G, OLDENWENING M, et al. Commuters'exposure to particulate matter air pollution is affected by mode of transport, fuel type, and route[J]. Environmental Health Perspectives, 2010, 118(6): 783-789. doi: 10.1289/ehp.0901622
    [21]
    陈艳, 李永波, 沈讷敏, 等. 口罩对大气PM2.5中主要污染物的阻隔效果研究[J]. 环境与健康杂志, 2019, 36(1): 34-37. https://www.cnki.com.cn/Article/CJFDTOTAL-HJYJ201901013.htm

    CHEN Y, LI Y B, SHEN N M, et al. Obstruction effect of masks to major pollutants in PM2.5[J]. Journal of Environment and Health, 2019, 36(1): 34-37. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-HJYJ201901013.htm
    • Relative Articles
    • Supplements(0)
    • Cited By
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(3)  / Tables(5)

    Get Citation
    PDF
    XML

    Article Metrics

    Article views (1022) PDF downloads(36) Cited by()
    Proportional views
    Related

    Copyright Editorial Office of Transport Information and Safety鄂ICP备05003336号-2

    Address：No. 1178，Heping Avenue, Wuchang, Wuhan, CHINA (430063)

    Tel：027-86580355 Email：jtjsj@vip.163.com

    Supported by: Beijing Renhe Information Technology Co. Ltd  

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return