Advertisement

Evaluating the Impact of Traffic Congestion on Mid-block Fine Particulate Matter Concentrations on an Urban Arterial

  • Xiaonian ShanEmail author
  • Changjiang Zheng
  • Xiaoli Zhang
Conference paper
  • 14 Downloads
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 617)

Abstract

The primary objective of this paper is to evaluate the impact of traffic congestion on mid-block fine particulate matter (PM2.5) concentrations on an urban arterial. Data of mid-block and background PM2.5 concentrations were collected second by second during peak and non-peak hours on an urban arterial. Then micro traffic conditions were extracted from videos at ten seconds intervals, including traffic volume, traffic flow speed and high-duty vehicle fraction. Results showed that traffic volume had significant influence on mid-block PM2.5 concentrations. Mid-block PM2.5 concentrations were not correlated with traffic level of service. Furthermore, a modified passenger car equivalent was calculated from the aspect of contribution on PM2.5 concentrations using multiple linear regressions model. Then a comprehensive model was established to model the impact of micro traffic conditions on PM2.5 concentrations. Results of the comprehensive model showed that PM2.5 concentrations increased with the increase of total volume or heavy-duty vehicle fraction. Besides, low traffic flow speed resulted in high PM2.5 emission factor, leading to the increase of PM2.5 concentrations. The findings of this study can help better understand traffic congestion and micro traffic conditions on PM2.5 concentrations.

Keywords

Traffic congestion PM2.5 concentrations Micro traffic conditions Multiple linear regressions model 

Notes

Foundation Item

Project supported by the Fundamental Research Funds for the Central Universities (No. 2018B08014) and by the National Science Foundation of China (No. 51608171).

References

  1. 1.
    Klemm RJ, Mason RM, Heilig CM, Neas LM, Dockery DW (2000) Is daily mortality associated specifically with fine particles? Data reconstruction and replication of analyses. J Air Waste Manage Assoc 50(7):1215–1222CrossRefGoogle Scholar
  2. 2.
    Ambient Air Quality Standards (2012) Ministry of Environmental Protection, P.R. ChinaGoogle Scholar
  3. 3.
    National Ambient Air Quality Standards (NAAQS) (2012) Environmental Protection Agency, United States of AmericaGoogle Scholar
  4. 4.
    Three Factors Result in the Haze of Beijing, Local Pollutant Emission is Top the List. Chinese Radio Network. http://news.cnr.cn/native/gd/201411/t20141104_516722367.shtml. Accessed 4 Nov 2014
  5. 5.
    Results of Atmospheric Particulates Source in Shanghai. Chinese Environment Network. http://www.cenews.com.cn/xwzx2013/hjyw/201501/t20150109_786238.html. Accessed 9 Jan 2015
  6. 6.
    Vehicle Emission of PM2.5 is about 24.6% in Nanjing. Yangzi Evening News Network. http://www.21cs.cn/details/?id=528126. Accessed 30 Apr 2015
  7. 7.
    Moore A, Figliozzi M, Bigazzi A (2014) Modeling impact of traffic conditions on variability of midblock roadside fine particulate matter case study of an urban arterial corridor. Transp Res Rec 2428:35–43CrossRefGoogle Scholar
  8. 8.
    Wang Z, He H, Lu F, Lu QC, Peng ZR (2015) A hybrid model for prediction of carbon monoxide and fine particulate matter concentrations near road intersection. Transp Res Rec 2503:29–38CrossRefGoogle Scholar
  9. 9.
    Cai M, Yin Y, Xie M (2009) Prediction of hourly air pollutant concentrations near urban arterials using artificial neural network approach. Transp Res Part D 14:32–41CrossRefGoogle Scholar
  10. 10.
    Wang Y, Li J, Cheng X, Lun X, Sun D, Wang X (2014) Estimation of PM10 in the traffic-related atmosphere for three road types in Beijing and Guangzhou, China. J Environ Sci 26:197–204CrossRefGoogle Scholar
  11. 11.
    He H, Lu WZ, Xue Y (2009) Prediction of PM10 concentrations at urban traffic intersections using semi-empirical box modelling with instantaneous velocity and acceleration. Atmos Environ 43:6336–6342CrossRefGoogle Scholar
  12. 12.
    Liu H, Chen X, Wang Y, Han S (2013) Vehicle emission and near-road air quality modeling for Shanghai, China: based on global positioning system data from taxis and revised moves emission inventory. Transp Res Rec 2340:38–48CrossRefGoogle Scholar
  13. 13.
    Su F, Roorda MJ, Miller EJ, Morrow E (2015) An integrated approach to estimate pedestrian exposure to roadside vehicle pollutants. In: TRB 94th annual meeting, Washington D.C.Google Scholar
  14. 14.
    Chai M, Wei H, Li Z, Lu M (2010) Modeling impact of traffic operation on carbon monoxide dispersion. In: ICCTP: integrated transportation systems-green intelligent reliable, pp 2890–2902Google Scholar
  15. 15.
    Venkatram A, Isakov V, Seila R, Baldauf R (2009) Modeling the impacts of traffic emissions on air toxics concentrations near roadways. Atmos Environ 43:3191–3199CrossRefGoogle Scholar
  16. 16.
    Pu Y, Yang C (2014) Estimating urban roadside emissions with an atmospheric dispersion model based on in-field measurements. Environ Pollut 192:300–307CrossRefGoogle Scholar
  17. 17.
    Code for Design of Urban Road Engineering (2012) Ministry of Housing and Urban-Rural Development, P.R. ChinaGoogle Scholar
  18. 18.
    Bigazzi AY, Figliozzi MA (2012) Congestion and emissions mitigation: a comparison of capacity, demand, and vehicle based strategies. Transp Res Part D 17:538–547CrossRefGoogle Scholar
  19. 19.
    Huang J, Liu Y, Cheng X, Li H, Lai D (2014) Vehicle emission characteristics of PM2.5 with COPERT IV model. Environ Sci Technol 37(1):43–47Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.College of Civil and Transportation EngineeringHohai UniversityNanjingPeople’s Republic of China

Personalised recommendations