Estimating airborne heavy metal concentrations in Dunkerque (northern France)
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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.
KeywordsHarbour 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.
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