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Stochastic Environmental Research and Risk Assessment

, Volume 32, Issue 9, pp 2527–2536 | Cite as

An environmental indicator: particulate characteristics on pedestrian pathway along integrated urban thoroughfare in Metropolis

  • Wei Pan
  • Hong-Di He
  • Yu Xue
  • Wei-Zhen Lu
Original Paper
  • 52 Downloads

Abstract

Particulate matter (PM) originated by road transport constitutes an urgent task for megacities and pedestrians are supposed to be the first batch of innocent victims that exposed to and inhaled the polluted air. Footbridges have become a promising resolution to land tension, the location and design of them should be more considered in order to provide a more desirable walking system to pedestrians. In this study, three groups of PM [i.e., 0.3–0.9 μm (sub-fine), 0.9–2.5 μm (fine) and 2.5–10 μm (coarse)] were measured at different traffic scenario related footbridges (i.e., upstream of the on-ramp, downstream of the on-ramp, and signalized intersection) along an urban artery in Hong Kong, and their traffic volume composition, multifractality and cross-correlation behavior were investigated thereafter. Multifractal detrended fluctuation analysis and multifractal detrended fluctuation cross-correlation analysis were used simultaneously to quantify the persistency of different PM groups and interaction between them. The results indicate that although the particle concentration at intersection above footbridges presents the lowest, it has the highest emission rate and the strongest multifractality and cross-correlation behavior, especially the finer ones. Hence, it is suggest that the nature ventilation style of footbridges should avoid to be built above the signalized intersection due to the long persistency of particles and active interaction between different particle groups.

Keywords

Footbridges On-ramp Intersection Particulate matter Multifractality Cross-correlation 

Notes

Acknowledgements

The work was partially supported by Strategic Research Grant, City University of Hong Kong Grant (SRG-7004637 and 7004867), Innovation and Technology Fund (ITF) (CityU-9678142), and National Natural Science Foundation of China (Nos. 11302125 and 11672176) and Innovation Foundation of Shanghai Municipal Ministry of Education (No. 13YZ085). Authors would like to thank the Hong Kong Observatory for providing the weather data. And the comments provided by anonymous referees are also appreciated.

References

  1. Brantley HL, Hagler GS, Deshmukh PJ, Baldauf RW (2014) Field assessment of the effects of roadside vegetation on near-road black carbon and particulate matter. Sci Total Environ 468:120–129CrossRefGoogle Scholar
  2. Broday DM (2010) Studying the time scale dependence of environmental variables predictability using fractal analysis. Environ Sci Technol 44(12):4629–4634CrossRefGoogle Scholar
  3. Chow TT, Lin Z, Bai W (2006) Assessment of alternative ventilation schemes at public transport interchange. Transp Res Part D Transp Environ 11(6):447–458CrossRefGoogle Scholar
  4. Colls JJ, Micallef A (1999) Measured and modelled concentrations and vertical profiles of airborne particulate matter within the boundary layer of a street canyon. Sci Total Environ 235(1):221–233CrossRefGoogle Scholar
  5. Dong Q, Wang Y, Li P (2017) Multifractal behavior of an air pollutant time series and the relevance to the predictability. Environ Pollut 222:444–457CrossRefGoogle Scholar
  6. Farah W, Nakhlé MM, Abboud M, Annesi-Maesano I, Zaarour R, Saliba N, Germanos G, Gerard J (2014) Time series analysis of air pollutants in Beirut, Lebanon. Environ Monit Assess 186(12):8203–8213CrossRefGoogle Scholar
  7. Fuzzi S, Baltensperger U, Carslaw K, Decesari S, Denier Van Der Gon H, Facchini MC, Fowler D, Koren I, Langford B, Lohmann U, Nemitz E, Pandis S, Riipinen I, Rudich Y, Schaap M, Slowik JG, Spracklen DV, Vignati E, Wild M, Williams M, Gilardoni S (2015) Particulate matter, air quality and climate: lessons learned and future needs. Atmos Chem Phys 15(14):8217–8299CrossRefGoogle Scholar
  8. Goel A, Kumar P (2015) Zone of influence for particle number concentrations at signalised traffic intersections. Atmos Environ 123:25–38CrossRefGoogle Scholar
  9. Gokhale S, Raokhande N (2008) Performance evaluation of air quality models for predicting PM 10 and PM 2.5 concentrations at urban traffic intersection during winter period. Sci Total Environ 394(1):9–24CrossRefGoogle Scholar
  10. Habilomatis G, Chaloulakou A (2015) A CFD modeling study in an urban street canyon for ultrafine particles and population exposure: the intake fraction approach. Sci Total Environ 530:227–232CrossRefGoogle Scholar
  11. Hagler GS, Lin MY, Khlystov A, Baldauf RW, Isakov V, Faircloth J, Jackson LE (2012) Field investigation of roadside vegetative and structural barrier impact on near-road ultrafine particle concentrations under a variety of wind conditions. Sci Total Environ 419:7–15CrossRefGoogle Scholar
  12. Harris SJ, Maricq MM (2001) Signature size distributions for diesel and gasoline engine exhaust particulate matter. J Aerosol Sci 32(6):749–764CrossRefGoogle Scholar
  13. He HD (2017) Multifractal analysis of interactive patterns between meteorological factors and pollutants in urban and rural areas. Atmos Environ 149:47–54CrossRefGoogle Scholar
  14. He HD, Pan W, Lu WZ, Xue Y, Peng GH (2016) Multifractal property and long-range cross-correlation behavior of particulate matters at urban traffic intersection in Shanghai. Stoch Env Res Risk Assess 30(5):1515–1525CrossRefGoogle Scholar
  15. Hess DB, Ray PD, Stinson AE, Park J (2010) Determinants of exposure to fine particulate matter (PM 2.5) for waiting passengers at bus stops. Atmos Environ 44(39):5174–5182CrossRefGoogle Scholar
  16. Hirabayashi T, Ito K, Yoshii T (1992) Multifractal analysis of earthquakes. Pure Appl Geophys 138(4):591–610CrossRefGoogle Scholar
  17. Kai S, Chun-qiong L, Nan-shan A, Xiao-hong Z (2008) Using three methods to investigate time-scaling properties in air pollution indexes time series. Nonlinear Anal Real World Appl 9(2):693–707CrossRefGoogle Scholar
  18. Kantelhardt JW, Zschiegner SA, Koscielny-Bunde E, Havlin S, Bunde A, Stanley HE (2002) Multifractal detrended fluctuation analysis of nonstationary time series. Physica A 316(1):87–114CrossRefGoogle Scholar
  19. Kaur S, Nieuwenhuijsen MJ, Colvile RN (2007) Fine particulate matter and carbon monoxide exposure concentrations in urban street transport microenvironments. Atmos Environ 41(23):4781–4810CrossRefGoogle Scholar
  20. Liao CM, Hsieh NH, Chio CP (2011) Fluctuation analysis-based risk assessment for respiratory virus activity and air pollution associated asthma incidence. Sci Total Environ 409(18):3325–3333CrossRefGoogle Scholar
  21. Liu Z, Xu J, Chen Z, Nie Q, Wei C (2014) Multifractal and long memory of humidity process in the Tarim River Basin. Stoch Environ Res Risk Assess 28(6):1383–1400CrossRefGoogle Scholar
  22. Lu WZ, Xue Y, He DH (2014) Detrended fluctuation analysis of particle number concentrations on roadsides in Hong Kong. Build Environ 82:580–587CrossRefGoogle Scholar
  23. Milner JT, ApSimon HM, Croxford B (2006) Spatial variation of CO concentrations within an office building and outdoor influences. Atmos Environ 40(33):6338–6348CrossRefGoogle Scholar
  24. Moore A, Figliozzi M, Monsere C (2012) Air quality at bus stops: empirical analysis of exposure to particulate matter at bus stop shelters. Transp Res Record J Transp Res Board 2270:76–86CrossRefGoogle Scholar
  25. Oświȩcimka P, Kwapień J, Drożdż S (2006) Wavelet versus detrended fluctuation analysis of multifractal structures. Phys Rev E 74(1):016103CrossRefGoogle Scholar
  26. Pan W, Xue Y, He HD, Lu WZ (2017) Traffic control oriented impact on the persistence of urban air pollutants: a causeway bay revelation during emergency period. Transp Res Part D Transp Environ 51:304–313CrossRefGoogle Scholar
  27. Panis LI, De Geus B, Vandenbulcke G, Willems H, Degraeuwe B, Bleux N, Mishraa V, Thomasd I, Meeusen R (2010) Exposure to particulate matter in traffic: a comparison of cyclists and car passengers. Atmos Environ 44(19):2263–2270CrossRefGoogle Scholar
  28. Peng CK, Buldyrev SV, Havlin S, Simons M, Stanley HE, Goldberger AL (1994) Mosaic organization of DNA nucleotides. Phys Rev E 49(2):1685CrossRefGoogle Scholar
  29. Podobnik B, Stanley HE (2008) Detrended cross-correlation analysis: a new method for analyzing two nonstationary time series. Phys Rev Lett 100(8):084102CrossRefGoogle Scholar
  30. Stanley HE, Amaral LN, Goldberger AL, Havlin S, Ivanov PC, Peng CK (1999) Statistical physics and physiology: monofractal and multifractal approaches. Physica A 270(1):309–324CrossRefGoogle Scholar
  31. Tang TQ, Yu Q, Yang SC, Ding C (2015a) Impacts of the vehicle’s fuel consumption and exhaust emissions on the trip cost allowing late arrival under car-following model. Physica A 431:52–62CrossRefGoogle Scholar
  32. Tang TQ, Huang HJ, Shang HY (2015b) Influences of the driver’s bounded rationality on micro driving behavior, fuel consumption and emissions. Transp Res Part D Trans Environ 41:423–432CrossRefGoogle Scholar
  33. Thai A, McKendry I, Brauer M (2008) Particulate matter exposure along designated bicycle routes in Vancouver, British Columbia. Sci Total Environ 405(1):26–35CrossRefGoogle Scholar
  34. Wang GJ, Xie C (2013) Cross-correlations between the CSI 300 spot and futures markets. Nonlinear Dyn 73(3):1687–1696CrossRefGoogle Scholar
  35. Wang J, Shang P, Ge W (2012) Multifractal cross-correlation analysis based on statistical moments. Fractals 20(03n04):271–279CrossRefGoogle Scholar
  36. Wang Z, Lu F, Lu QC, He HD, Wang D, Peng ZR (2015) Fine-scale estimation of carbon monoxide and fine particulate matter concentrations in proximity to a road intersection by using wavelet neural network with genetic algorithm. Atmos Environ 104:264–272CrossRefGoogle Scholar
  37. Xue C, Shang P, Jing W (2012) Multifractal detrended cross-correlation analysis of BVP model time series. Nonlinear Dyn 69(1):263–273CrossRefGoogle Scholar
  38. Xue Y, Pan W, Lu WZ, He HD (2015) Multifractal nature of particulate matters (PMs) in Hong Kong urban air. Sci Total Environ 532:744–751CrossRefGoogle Scholar
  39. Yu HL, Lin YC, Sivakumar B, Kuo YM (2013) A study of the temporal dynamics of ambient particulate matter using stochastic and chaotic techniques. Atmos Environ 69:37–45CrossRefGoogle Scholar
  40. Yu HL, Lin YC, Kuo YM (2015) A time series analysis of multiple ambient pollutants to investigate the underlying air pollution dynamics and interactions. Chemosphere 134:571–580CrossRefGoogle Scholar
  41. Zhou WX (2008) Multifractal detrended cross-correlation analysis for two nonstationary signals. Phys Rev E 77(6):066211CrossRefGoogle Scholar
  42. Zhuang X, Wei Y, Zhang B (2014) Multifractal detrended cross-correlation analysis of carbon and crude oil markets. Physica A 399:113–125CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Architecture and Civil EngineeringCity University of Hong KongKowloonHong Kong
  2. 2.Logistics Research CenterShanghai Maritime UniversityShanghaiChina
  3. 3.Institute of Physical Science and EngineeringGuangxi UniversityNanningChina

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