Estimating airborne heavy metal concentrations in Dunkerque (northern France)

  • Germán Santos
  • Ignacio Fernández-Olmo
  • Ángel Irabien
  • Frédéric Ledoux
  • Dominique Courcot
Original Paper
Part of the following topical collections:
  1. DUST


This work aims to estimate the levels of lead (Pb), nickel (Ni), manganese (Mn), vanadium (V) and chromium (Cr) corresponding to a 3-month PM10 sampling campaign conducted in 2008 in the city of Dunkerque (northern France) by means of statistical models based on partial least squares regression (PLSR), artificial neural networks (ANNs) and principal component analysis (PCA) coupled with ANN. According to the European Air Quality Directives, because the levels of these pollutants are sufficiently below the European Union (EU) limit/target values and other air quality guidelines, they may be used for air quality assessment purposes as an alternative to experimental measurements. An external validation of the models has been conducted, and the results indicate that PLSR and ANNs, with comparable performance, provide adequate mean concentration estimations for Pb, Ni, Mn and V, fulfilling the EU uncertainty requirements for objective estimation techniques, although ANNs seem to present better generalization ability. However, in accordance with the European regulation, both techniques can be considered acceptable air quality assessment tools for heavy metals in the studied area. Furthermore, the application of factor analysis prior to ANNs did not yield any improvements in the performance of the ANNs.


Harbour town Immission levels PM10 Heavy metals Statistical models (PLSR, ANN) 



This work was supported by the Spanish Ministry of Economy and Competitiveness (MINECO) through the Projects CTM2010-16068/CTM2013-43904R and the FPI short stay EEBB-I-13-07691. Germán Santos would also like to thank the Unité de Chimie Environnementale et Interactions sur le Vivant (UCEIV) at La Maison de la Recherche en Environnement Industriel for welcoming him as a guest PhD student in their facilities.


  1. Abdi H (2010) Partial least squares regression and projection on latent structure regression (PLS regression). Wiley Interdiscip Rev Comput Stat 2:97–106CrossRefGoogle Scholar
  2. Aschner M, Erikson KM, Dorman DC (2005) Manganese dosimetry: species differences and implications for neurotoxicity. Crit Rev Toxicol 35(1):1–32CrossRefGoogle Scholar
  3. Borrego C, Monteiro A, Ferreira J, Miranda AI, Costa AM, Carvalho AC, Lopes M (2008) Procedures for estimation of modelling uncertainty in air quality assessment. Environ Int 34:613–620CrossRefGoogle Scholar
  4. Chelani AB, Gajghate DG, Hasan MZ (2002) Prediction of ambient PM10 and toxic metals using artificial neural networks. J Air Waste Manag Assoc 52:805–810CrossRefGoogle Scholar
  5. Chen LC, Lippmann M (2009) Effects of metals within ambient air particulate matter (PM) on human health. Inhal Toxicol 21:1–31CrossRefGoogle Scholar
  6. Denby B (2010) Guidance on the use of models for the European Air Quality Directive: a working document of the Forum for air quality modelling in Europe (FAIRMODE). ETC./ACC report version 6.2Google Scholar
  7. EC (2004) Council Directive 2004/107/EC Directive of the European Parliament and of the Council of 15 December 2004 relating to arsenic, cadmium, mercury, nickel and polycyclic aromatic hydrocarbons in ambient air. The European Parliament and the Council of the European Union. Off J L23:3–16Google Scholar
  8. EC (2008) Council Directive 2008/50/EC Directive of the European Parliament and of the Council of 21 May 2008 on ambient air quality and cleaner air for Europe. The European Parliament and the Council of the European Union. Off J L152:1–44Google Scholar
  9. Finkelstein MM, Jerrett M (2007) A study of the relationships between Parkinson’s disease and markers of traffic-derived and environmental manganese air pollution in two Canadian cities. Environ Res 104(3):420–432CrossRefGoogle Scholar
  10. Gardner MW, Dorling SR (1999) Neural network modelling and prediction of hourly NOx and NO2 concentrations in urban air in London. Atmos Environ 33:709–719CrossRefGoogle Scholar
  11. Hernández-Bonilla D, Schilmann A, Montes S, Rodríguez-Agudelo Y, Rodríguez-Dozal S, Solís-Vivanco R et al (2011) Environmental exposure to manganese and motor function of children in Mexico. Neurotoxicology 32(5):615–621CrossRefGoogle Scholar
  12. Hleis D (2010) Evaluation de la contribution d’émissions sidérurgiques à la teneur en particules en suspension dans l’atmosphère à une échelle locale. Dissertation, Université du Littoral Côte d’OpaleGoogle Scholar
  13. Karar K, Gupta AK (2006) Seasonal variations and chemical characterization of ambient PM10 at residential and industrial sites of an urban region of Kolkata. Atmos Res 81:36–53CrossRefGoogle Scholar
  14. Kfoury A (2013) Origin and physicochemical behaviour of atmospheric PM2.5 in cities located in the littoral area of the Nord-Pas-de-Calais region, France. Dissertation, Université du Littoral Côte d’OpaleGoogle Scholar
  15. Kukkonen J, Partanen L, Karppinen A, Ruuskanen J, Junninen H, Kolehmainen M, Niska H, Dorling S, Chatterton T, Foxall R, Cawley G (2003) Extensive evaluation of neural network models for the prediction of NO2 and PM10 concentrations, compared with a deterministic modelling system and measurements in central Helsinki. Atmos Environ 37:4539–4550CrossRefGoogle Scholar
  16. Kumar A, Luo J, Bennett G (1993) Statistical evaluation of lower flammability distance (LFD) using four hazardous release models. Process Saf Prog 12:1–11CrossRefGoogle Scholar
  17. Lu WZ, Wang WJ, Wang XK, Xu ZB, Leung AYT (2003) Using improved neural network model to analyse RSP, NOX and NO2 levels in urban air in Mong Kok, Hong Kong. Environ Monit Assess 87:235–254CrossRefGoogle Scholar
  18. Mergler D, Baldwin M, Belanger S, Larribe F, Beuter A, Bowler R et al (1999) Manganese neurotoxicity, a continuum of dysfunction: results from a community based study. Neurotoxicology 20(2–3):327–342Google Scholar
  19. Perez P, Reyes J (2002) Prediction of maximum of 24-h average of PM10 concentrations 30 h in advance in Santiago, Chile. Atmos Environ 36:4555–4561CrossRefGoogle Scholar
  20. Pires JCM, Martins FG, Sousa SIV, Alvim-Ferraz MCM, Pereira MC (2008) Prediction of the daily mean PM10 concentrations using linear models. Am J Environ Sci 4:445–453CrossRefGoogle Scholar
  21. Polat K, Durduran SS (2012) Usage of output-dependent data scaling in modelling and prediction of air pollution daily concentration values (PM10) in the city of Konya. Neural Comput Appl 21:2153–2162CrossRefGoogle Scholar
  22. Singh KP, Gupta S, Kumar A, Shukla SP (2012) Linear and nonlinear modelling approaches for urban air quality prediction. Sci Total Environ 426:244–255CrossRefGoogle Scholar
  23. Sousa SIV, Martins FG, Alvim-Ferraz MCM, Pereira MC (2007) Multiple linear regression and artificial neural networks based on principal components to predict ozone concentrations. Environ Model Softw 22:97–103CrossRefGoogle Scholar
  24. Sumanta KG, Rumpa S, Bidyut S (2015) Toxicity of inorganic vanadium compounds. Res Chem Intermed 41:4873–4897CrossRefGoogle Scholar
  25. Thorpe A, Harrison RM (2008) Sources and properties of non-exhaust particulate matter from road traffic: a review. Sci Total Environ 400:270–282CrossRefGoogle Scholar
  26. Ul-Saufie AZ, Yahaya AS, Ramli NA, Rosaida N, Hamid HA (2013) Future daily PM10 concentrations prediction by combining regression models and feedforward backpropagation models with principle component analysis (PCA). Atmos Environ 77:621–630CrossRefGoogle Scholar
  27. WHO (2000) Air quality guidelines for Europe, 2nd edn. World Health Organisation, GenevaGoogle Scholar
  28. Wold S, Sjöström M, Eriksson L (2001) PLS-regression: a basic tool of chemometrics. Chemom Intell Lab Syst 58:109–130CrossRefGoogle Scholar

Copyright information

© Saudi Society for Geosciences 2016

Authors and Affiliations

  • Germán Santos
    • 1
  • Ignacio Fernández-Olmo
    • 1
  • Ángel Irabien
    • 1
  • Frédéric Ledoux
    • 2
  • Dominique Courcot
    • 2
  1. 1.Departamento de Ingenierías Química y BiomolecularUniversidad de CantabriaSantanderSpain
  2. 2.Unité de Chimie Environnementale et Interactions sur le Vivant (UCEIV) EA 4492Université du Littoral Côte d’OpaleDunkerqueFrance

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