Population and Environment

, Volume 27, Issue 1, pp 63–87 | Cite as

Estimating Traffic Emissions Using Demographic and Socio-Economic Variables in 18 Chilean Urban Areas

  • Roberto M. Corvalán
  • Mauricio Osses
  • Cristian M. Urrutia
  • Patricio A. González


A macro-scale methodology for vehicle emissions estimation is described. The methodology is based on both correlations between activity level and PM, CO, THC and NO x vehicle emissions and relationships between demographic and socio-economic variables and transportation activity level. First, pollutant emissions were correlated with transportation activity, expressed as vehicle-km/year, using existing data collected from mobile sources emission inventories in nine urban cities of Chile. Second, demographic and socio-economic variables were pre-selected from those that could intuitively be correlated with vehicle activity level and considering the data availability. Using the individual R 2 correlation coefficient as variable selection criterion, population, the number of vehicles, fuel consumption, gross domestic product, average family incomes and road kilometers were finally chosen. A different set of explicative variables was considered for different vehicle categories, based on the selection criterion above mentioned. Then, correlation functions between these variables and transport activity were obtained by non-linear Gauss–Newton least square method. This methodology was applied to eighteen provinces of the country obtaining total annual emission for mobile sources, divided into six main vehicles categories.

Key words

traffic emissions demography and environment socio-economic factors and environment atmospheric pollutants 



The authors wish to acknowledge the support provided by the National Environmental Commission, Metropolitan Region Unit, CONAMA-RM, the governmental agency that funded the studies whose results were used in this investigation. Acknowledgment are also extended to the Governmental Transport Planning Bureau, SECTRA, for preliminary data concerning emission and activity level modeled for the four cities considered in the verification of the methodology presented on this paper.


  1. Cardelino C., (1998). Daily variability of motor vehicle emissions derived from traffic counter dataJournal of the Air & Waste Management Association 48:637–645Google Scholar
  2. CENMA (2000). Mejoramiento del Inventario de Emisiones de la Región Metropolitana. Informe Final. Centro Nacional del Medio Ambiente, CENMA, para la Comisión Nacional del Medio Ambiente, CONAMA. http/ Scholar
  3. CENMA (2002). 2a Fase Estudio de la Calidad de Aire en Regiones Urbano Industriales. Informe Final. Centro Nacional del Medio Ambiente, CENMA, para la Comisión Nacional del Medio Ambiente, CONAMA. http/ Scholar
  4. Cole, M. A., & Neumayer, E. (2004). Examining de impact of demographic factors on air pollution. Population & Environment, 26(1), 5–21; 23(5), 479–494Google Scholar
  5. Corvalán R. M., Urrutia C. M., (2000). Emission factors for gasoline light-duty vehicles: Experimental program in Santiago, Chile Journal of the Air & Waste Management Association 50:2102–2111Google Scholar
  6. Corvalán R. M., Osses M., (2002). Hot emission model for mobile sources: Application to the Metropolitan Region of the City of Santiago, Chile Journal of the Air & Waste Management Association 52:167–174Google Scholar
  7. De Cea J., Fernádez J. E., (1993). Transit Assigment Congested Public System An Equilibrium Model. Transport Sci. 27(2):133–147Google Scholar
  8. De Haan, P., & Keller, M. (1999). Emission factors for passenger cars: Application to instantaneous emission modeling. 8th International Symposium of Transport and Air Pollution, Graz, Austria Google Scholar
  9. Dietz, T., & Rosa, E. A. (1997). Effects of population and affluence on CO2 emissions. Procedings of the National Academy of Science, 94(1), 175–179Google Scholar
  10. Fernádez, J. E., & De Cea, J. (1990). An application of equilibrium modeling to urban transport planning in developing countries. The case of Santiago of Chile. Operational Research 90. Pergamon, Elmsford, NYGoogle Scholar
  11. Goyal P., Rama Krishna T. V., (1998). Various methods of emission estimation of vehicular traffic in Delhi Transportation Research Part D 5:309–317CrossRefGoogle Scholar
  12. Kim D., Kim J., (2000). Development of a speciated, hourly and gridded air pollutants emission modeling system. A case study on the precursors o photochemical smog in Seoul Metropolitan area, Korea Journal of the Air & Waste Management Association 50:340–347Google Scholar
  13. Lyons T. J., Kenworthy J. R., Moy C., Dos Santos F., (2003). An international Urban air pollution model for the transportation sector Transportation Research Part D 8:159167Google Scholar
  14. Ntziachristos, L., Samaras, Z., Eggleston, S., Goriβen, N., Hassel, D., Hickman, J., Joumard, R., Rijkeboer, R., & Zierock, H. (1999). Computer programme to calculate emissions from road transport, COPERT III. Methodology and emission factors. European Environmental Agency. European Topic Centre on Air EmissionGoogle Scholar
  15. Reynolds A. W., Broderick B. M., (2000). Development of an emission inventory model for mobile sources Transportation Research Part D 5:77–101CrossRefGoogle Scholar
  16. Riley K., (2002). Motor vehicles in China: The impact of demographic and economics changes Population & Environment 23(5):479–494CrossRefGoogle Scholar
  17. Sharma P., Khare M., (2001). Modelling of vehicular exhaust – A review Transportation Research Part D 6:179–198CrossRefGoogle Scholar
  18. Sturm P. J., Almbauer R., Sudy C., Pucher K., (1997). Application of coputational methods for the determination of traffic emissions Journal of the Air & Waste Management Association 47:1204–1210Google Scholar
  19. US EPA (1991). Compilation of Air Pollutant Emission Factors. Vol II. Mobile Sources. AP-42, Suppl. A, U.S. Environmental Protection Agency Google Scholar
  20. York R., Rosa E. A., Dietz T., (2003). STIRPAT, IPAT and ImPACT: Analytical tools for unpacking the driving forces of environmental impacts Ecological Economics 46(3): 351–365CrossRefGoogle Scholar
  21. Zachariadis T., Zamaras Z., (1999). An integrated modeling system for estimation of motor vehicle emissionsJournal of the Air & Waste Management Association 49:1010–1026Google Scholar

Copyright information

© Springer Science+Business Media, Inc. 2006

Authors and Affiliations

  • Roberto M. Corvalán
    • 1
  • Mauricio Osses
    • 1
  • Cristian M. Urrutia
    • 1
  • Patricio A. González
    • 1
  1. 1.Mechanical Engineering DepartmentUniversity of ChileSantiagoChile

Personalised recommendations