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Exploring the Benefits of 3D City Models in the Field of Urban Particles Distribution Modelling—A Comparison of Model Results

  • Yahya GhassounEmail author
  • Marc-O. Löwner
  • Stephan Weber
Chapter
Part of the Lecture Notes in Geoinformation and Cartography book series (LNGC)

Abstract

We present a comparison of a particles distribution model using 3D parameters derived from a CityGML-based 3D city model with an already advanced but 2D-based Land Use Regression model. Particles, especially ultrafine particles have significant influence on the health status of the urban population. Next to emission by cars and others, its distribution is tightly coupled to the local wind field and, therefore, to urban morphology influencing this wind field. However, 3D city models, especially CityGML have been almost ignored when modelling urban particles distribution. We introduce 3D parameters derived from a CityGML-based 3D city model in an already tested Land Use Regression model and explore the benefits of 3D city models in the field of particles distribution modelling, especially, by minimizing the number of parameters entered to the model and the good results that it has shown and explore the enhancement by combining both models.

Keywords

Land use regression CityGML Ultrafine particle 3D city model Geostatistical model 

Notes

Acknowledgments

The authors would like to thank Matthias Ruths who conducted the mobile measurements of particle and pollutant concentrations.

References

  1. Arain AM, Blair R, Finkelstein N, Brook RJ, Sahsuvaroglu T, Beckerman B, Zhang L, Jerrett M (2007) The use of the wind fields in a land use regression model to predict air pollution concentrations for health exposure studies. Atmos Environ 41:3453–3464CrossRefGoogle Scholar
  2. Brand L, Löwner M-O (2014) Parametrisierung und Identifikation urbaner Straßenkreuzungen im Kontext der Feinstaubmodellierung. "Parameterization and identification of street crossings in the context of fine dust modelling". Gemeinsame Jahrestagung 2014 der DGfK, der DGPF, der GfGI und des GiN (DGPF Tagungsband 23, 2014)Google Scholar
  3. Brauer M, Hoek G, van Vliet P, Meliefste K, Fischer P, Gehring U, Heinrich J, Cyrys J, Bellander T, Lewne M, Brunekreef B (2003) Estimating long-term average particulate air pollution concentrations: application of traffic indicators and geographic information systems. Epidemiology 14:228–239Google Scholar
  4. Briggs DJ, de Hough C, Gulliver J, Wills J, Elliott P, Kingham S, Smallbone K (2000) A regression-based method for mapping traffic-related air pollution: application and testing in four contrasting urban environments. Sci Total Environ 253:151–167CrossRefGoogle Scholar
  5. Brook RD, Rajagopalan S, Pope CA 3rd, Brook JR, Bhatnagar A, Diez-Roux AV, Holguin F, Hong Y, Luepker RV, Mittleman MA, Peters A, Siscovick D, Smith SC, Whitsel L, Kaufman JD (2010) American heart association council on epidemiology and prevention, council on the kidney in cardiovascular disease, and council on nutrition, physical activity and metabolism. particulate matter air pollution and cardiovascular disease: an update to the scientific statement from the American heart association. Circulation. 121:2331–2378Google Scholar
  6. Eeftens M, Beekhuien J, Beelen R, Wang M, Vermeulen R, Brunekreef B, Huss A, Hoek G (2013) Quantifying urban street configuration for improvements in air pollution models. Atmos Environ 72:1–9Google Scholar
  7. Geiser M, Rothen-Rutishauser B, Kapp N, Schürch S, Kreyling W, Schulz H, Semmler M, Im Hof V, Heyder J, Gehr P (2005) Ultrafine particles cross cellular membranes by non-phagocytic mechanisms in lungs and in cultured cells. Environ Health Perspect 113:1555–1560CrossRefGoogle Scholar
  8. Gilbert LN, Goldberg SM, Beckerman B, Brook RJ, Jerrett M (2005) Assessing spatial variability of ambient nitrogen dioxide in Montreal, Canada, with a land-use regression model. Air waste Manag Assoc 55:1059–1063CrossRefGoogle Scholar
  9. Gröger G, Kolbe TH, Nagel C, Häfele K-H (2012) OGC city geography markup language (CityGML) encoding standard, version 2.0, OGC doc no. 12-019. Open Geospatial ConsortiumGoogle Scholar
  10. Henderson S, Beckerman B, Jerrett M, Brauer M (2007) Application of land use regression to estimate long-term concentrations of traffic-related nitrogen oxides and fine particulate matter. Environ Sci Technol. 41(7):2422–8Google Scholar
  11. Hoffmann B, Moebus S, Möhlenkamp S, Stang A, Lehmann N, Dragano N, Schmermund A, Memmesheimer M, Mann K, Erbel R, Jöckel K-H (2006) Residential exposure to traffic is associated with coronary atherosclerosis. Am Heart Assoc Circ 116:489–496Google Scholar
  12. Jerrett M (2011) Spatiotemporal analysis of air pollution and mortality in California based on the American cancer society cohort. Final reportGoogle Scholar
  13. Jerrett M, Beckerman B, Brook J, Finkelstein M, Gilbert N (2006) A land use regression model for predicting ambient concentrations of nitrogen dioxide in Hamilton, Festbrennstoffe, Biokraftstoffe, BiogasGoogle Scholar
  14. Löwner M-O, Casper E, Becker T, Benner J, Gröger G, Gruber U, Häfele K-H, Kaden R, Schlüter S (2013a) CityGML 2.0—ein internationaler Standard für 3D-Stadtmodelle, Teil 2: CityGML in der Praxis. Zeitschrift für Geodäsie, Geoinformation und Landmanagement, 2, 2013, pp 131–143Google Scholar
  15. Löwner M-O, Benner J, Gröger G, Häfele K-H (2013b) New concepts for structuring 3d city models—an extended level of detail concept for CityGML buildings. In: Murgante B, Misra S, Carlini M, Torre CM, Nguyen H-Q, Taniar D, Apduhan BO, Gervasi O, (eds) ICCSA 2013, Part III, LNCS 7973, Springer, Heidelberg, pp 466–480Google Scholar
  16. Mercer LD, Szpiro AA, Sheppard L, Lindström J, Adar S, Allen R, Avol E, Oron A, Larson T, Liu LJ, Kaufman JD (2011) Comparing universal kriging and land-use regression for predicting concentrations of gaseous oxides of nitrogen (NOx) for the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air). Atmos Environ pp 4412–4420Google Scholar
  17. Morawska L, Ristovski Z, Jayaratne ER, Keogh DU, Ling X (2008) Ambient nano and ultrafine particles from motor vehicle emissions: characteristics, ambient processing and implications on human exposure. Atmos Environ 42:8113–8138CrossRefGoogle Scholar
  18. Ott WR, Steinemann AC, Wallace LA (eds) (2007) Exposure analysis. CRC Press, Taylor & Francis Group, NW, 553 ppGoogle Scholar
  19. Ruths M, von Bismarck-Osten C, Weber S (2014) Measuring and modelling the local-scale spatio-temporal variation of urban particle number size distributions and black carbon. Atmos EnvironGoogle Scholar
  20. Saraswat A, Apte SJ, Kandlikar M, Brauer M, Henderson BS, Marshall DJ (2013) Spatiotemporal land use regression models of fine, ultrafine, and black carbon particulate matter in New Delhi. Environ Sci Technol 42:12903–12911Google Scholar
  21. Tang R, Blangiardo M, Gulliver J (2013) Using building heights and street configuration to enhance intraurban PM10, NOx, and NO2 Land use regression models. Environ Sci Technol 47:11643–11650Google Scholar
  22. Vardoulakis S, Gonzalez-Flesca N, Fisher BEA (2002) Assessment of traffic-related air pollution in two street canyons in Paris: implications for exposure studies. Atmos Environ 36:1025–1039CrossRefGoogle Scholar
  23. Weber S, Kordowski K, Kuttler W (2013) Variability of particle number concentration and particle size dynamics in an urban street canyon under different meteorological conditions. Sci Total Environ 449:102–114CrossRefGoogle Scholar
  24. WHO Panel (2013) Review of evidence on health aspects of air pollution—REVIHAAP Project. WHO Regional Office for Europe, Copenhagen, Denmark. http://www.euro.who.int/pubrequest. Accessed on 30, March 2014

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Yahya Ghassoun
    • 1
    Email author
  • Marc-O. Löwner
    • 1
  • Stephan Weber
    • 2
  1. 1.Institute for Geodesy and PhotogrammetryTechnische Universität BranschweigBrunswickGermany
  2. 2.Institute of GeoecologyTechnische Universität BranschweigBrunswickGermany

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