Environmental Modeling & Assessment

, Volume 13, Issue 3, pp 383–392 | Cite as

Emission Factors Determination of Euro III 1,200- to 1,400-cc Petrol Passenger Cars with a PLS Multivariate Regression Model

  • M. Rapone
  • M. V. Prati
  • M. A. Costagliola
  • L. Della Ragione
  • G. Meccariello


This paper presents emission factors of a class of passenger cars obtained by applying a statistical model developed to evaluate average emission factors based on driving cycle emission measurements. A multivariate regression method based on principal components, namely, the partial least squares (PLS) method, is applied to calculate the model. The method was applied to emission data from a sample of petrol Euro III 1,200- to 1,400-cc passenger cars taken from the ARTEMIS database. A vehicle effect analysis showed that vehicle effect is considerable, in some cases comparable to or greater than the driving cycle effect. Determination of emission factors is obviously affected by these aspects. Thus, the CO2 PLS model fit results are good, CO, HC and NOX more or less sufficient. PLS-predicted quantities were compared with corresponding quantities estimated by a multiple regression model (GLM) based on a quadratic polynomial equation of sub-cycle overall mean speed. GLM goodness of fit was poorer than PLS ones. A validation effort of models is in progress, which is considering the ARTEMIS database extended with tests performed within other national or international projects. In this way, an extended population of combinations of vehicles and driving cycles will provide a better calculation of models and emission factors.


Emission factors ARTEMIS Hierarchical statistical model Partial least squares (PLS) 



Reported research activity was funded by EU Artemis project and by the Italian Ministry of the Environment


  1. 1.
    André, M. (2004). Real-world driving cycles for measuring cars pollutant emissions – Part A: The Artemis European driving cycles. INRETS report, Bron, France, no. LTE 0411, p. 97.Google Scholar
  2. 2.
    André, M., Rapone, M., Adra, N., Pollàk, I., Keller, M., & Mccrae, I. (2006). Traffic characteristics for the estimation of the pollutant emissions from road transport - ARTEMIS WP1000 project, Report INRETS-LTE 0606.Google Scholar
  3. 3.
    Barth, M. J., Younglove, T., Malcolm, C., & Scora, G. (2002). Mobile source emissions new generation model: using a hybrid database prediction technique, Final report to US EPA under Award 68-C-01-169.Google Scholar
  4. 4.
    Duan, N. (1983). Smearing Estimate, A Nonparametric retransformation Method. Journal of American Statistical Association, 78(383), 605–610, September.CrossRefGoogle Scholar
  5. 5.
    EMFAC. (2000). Calculating emission inventories for vehicles in California. User’s Guide (http://www.arb.ca.gov).
  6. 6.
    EPA United States Environmental Protection Agency Air and Radiation, User’s Guide to MOBILE6.1 and MOBILE6.2. Mobile Source Emission Factor Model, EPA420-R-03-010, 2003.Google Scholar
  7. 7.
    Hickman, A. J., & McCrae, I. S. (2003). Revised technical annex ARTEMIS, Assessment and reliability of transport emission models and inventory systems, Project funded by the European Commission within the 5th Framework Research Programme. DG TREN Contract No 1999-RD.10429, ARTEMIS website – http://www.trl.co.uk/artemis/.
  8. 8.
    Joumard, R., Jost, P., & Hickman, J. (1995). Influence of instantaneous speed and acceleration on hot passenger car emissions and fuel consumption, SAE paper 950928.Google Scholar
  9. 9.
    Kim, B. (2000). Development of a modal emissions model using data from the cooperative industry/government exhaust emissions test program, 93rd Annual Conference on Air and Waste Management, Salt Lake City, UT.Google Scholar
  10. 10.
    Lee, C., & Miller, E. J. (2001). A microsimulation model of CO2 emissions from passenger cars: model framework and applications” Paper No 01-2231, Transportation Research Board 80th Annual Meeting, Washington DC.Google Scholar
  11. 11.
    Luping, T., & Schouenborg, B. (2000). Methodology of Inter-comparison tests and Statistical Analysis of Test Results, Nordetest project No. 1483-99, SP Swedish, National Testing and Research Institute, SP Report 2000:35, Building Technology Boras, Sweden.Google Scholar
  12. 12.
    Ntziachristos, L., & Samaras, Z. (2000). COPERT III, Computer programme to calculate emissions from road transport. Methodology and emission factors (Version 2.1), ETC/AEM, European Environment Agency Technical report No 49.Google Scholar
  13. 13.
    TEAM – TNO Emission Assessment Model. Retrieved June 2006 from http://www.tno.nl
  14. 14.
    Tenenhaus, M. (1998). La regression PLS Theorie et Pratique, Editions Technip Paris.Google Scholar
  15. 15.
    Van Garderen, K., & Shah, J. (2002, October). The interpretation of dummy variables in semilogarithmic equations in the presence of estimation uncertainty, UVA Econometrics, Discussion Paper, Retrieved June 2006 from http://www.fee.uva.nl/ke/UvA-Econometrics.
  16. 16.
    Westerhuis, J. A., Kourti, T., Macgregor, J. F. (1998). Analysis of Multiblock and Hierarchical PCA and PLS Models. Journal of Chemometrics, 12, 301–321.CrossRefGoogle Scholar
  17. 17.
    Wold, S., Sjostrom, M., Eriksson, L. (2001). PLS-regression: a basic tool of chemometrics. Journal of Chemometrics and Intelligent Laboratory Systems, 58, 109–130.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2007

Authors and Affiliations

  • M. Rapone
    • 1
  • M. V. Prati
    • 1
  • M. A. Costagliola
    • 1
  • L. Della Ragione
    • 1
  • G. Meccariello
    • 1
  1. 1.CNR Istituto Motori – Via MarconiNapoliItaly

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