Kullback-leibler NMF under linear equality constraints. Application to pollution source apportionment

International audienceNon negative matrix factorisation (NMF) coupled to divergence measure has been investigated in the frame of an application to polluant source identification. It relies on receptor modelling which considers the data matrix as the result of cumulative effects of p sources. NMF aims at finding a contribution matrix G and a profile matrix F by minimizing a specific cost function. The focus is made here on the Kullback-Leibler divergence (KL) cost function. Linear equality constraints are incorporated into parts of the decomposition and general mu-tiplicative like expressions, which take into account these constraints, are derived. This method is applied in the frame of source apportion-ment of particulate matter

Estimating airborne heavy metal concentrations in Dunkerque (northern France)

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.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

Chemical characterization and in vitro toxicity on human bronchial epithelial cells BEAS-2B of PM2.5_{2.5} from an urban site under industrial emission influence

Particulate Matter (PM) is one of the most relevant environment-related health issues all over the world. In 2013, the International Agency for Research on Cancer (IARC) has classified air pollution and PM as a carcinogen for humans [1]. However, the mechanisms involved in the toxicity of these particles remains poorly understood, mainly because PM are uniquely complex owing to their physicochemical characteristics. In this study, fine particles were collected in the city center of Dunkirk, northern France using a 5 stages high volume cascade impactor (Staplex® 235, 68m3/h) and a Digitel DA80 high volume sampler (30m3/h).Samples were extensively characterized for their physico-chemical properties, including trace metals, water-soluble ions and organic species. Normal human bronchial epithelial cells (BEAS-2B) were used as cell model for toxicological analysis. Cytotoxicity, PAHs-metabolizing enzymes gene expression and genotoxic alterations were evaluated after 24, 48 or 72 h of exposure considering increasing concentrations of PM$_{2.5-0.3}$, organic extracts (OE) and water-soluble fraction (WF) of PM$_{2.5-0.3}$ and PM$_{2.5}$. Several sources such as road traffic, industrial activities mainly related to steelmaking, marine emissions including sea-salts and shipping, as well as soil resuspension were found to contribute to the PM$_{2.5}$ composition. Cytotoxicity assessment results showed time and dose dependent responses, with effects mainly related to PAH compounds in PM$_{2.5}$ OE in which their content were 12 times higher than in PM$_{2.5-0.3}$ one [2]. Differences in the induction of CYP1A1, CYP1B1 and NQO1 genes expression involved in the metabolic activation of organic compounds, as well as genotoxic effects (oxidative DNA adducts, H2A.X phosphorylation) were also evidenced after cells exposure to OE and PM$_{2.5-0.3}$ [3]. These results confirm the major effect of organic compounds on toxic effects, but also the potential contribution of the inorganic fraction of the PM which maintains longer the effects in exposed cells

PLSR and ANN estimation models for PM10-bound heavy metals in Dunkerque (Northern France)

The aim of this work is to develop statistical estimation models of some EU regulated heavy metal levels (Pb, Ni) and some non-regulated heavy metal levels (Mn, V and Cr) in the ambient air of the city of Dunkerque (Northern France) so that they might be used for air quality assessment as an alternative to experimental measurements, since these levels are relatively low compared to the EU limit/target values and other air quality guidelines. Three different approaches were considered: Partial Least Squares Regression (PLSR), Artificial Neural Networks (ANN) and Principal Component Analysis (PCA) coupled with ANN. External validation results evidence that PLSR and ANN-based statistical models for regulated metals and for Mn and V provide adequate mean values estimations while fulfill the EU uncertainty requirements.This work was supported by the Spanish Ministry of Economy and Competitiveness (MINECO) through the Project CTM2010-16068 and the FPI short stay EEBB-I-13-07691

Toxicity of fine and quasi-ultrafine particles: focus on the effects of extractable and non-extractable matter fractions

Air pollution represents today one of the major risk factors for human health. An important part of this threat is due to the presence in the atmosphere of fine particulate matter (PM$_{2.5}$). PM$_{2.5}$ forms a heterogeneous mixture of inorganic pollutants (metals, ions…), organic pollutants (volatile organic compounds (VOC), polycyclic aromatic hydrocarbons (PAHs), dioxins, polychlorobiphenyls (PCBs)…), and biological contaminants (pollen, bacteria, fungi…). To date many studies have demonstrated the toxicity of PAHs and some metals, but so far, no study has been able to clearly attribute the toxicological effects observed to a class of pollutants. Therefore, this study aims to determine the physicochemical characteristics of PM$_{2.5-0.3}$ and PM$_{0,3}$ and to compare the toxicity of native PM$_{2.5-0.3}$, organic fractions of fine (EOM$_{2.5-0.3}$) and quasi ultrafine particles (OEM$_{0.3}$), and PM$_{2.5-0.3}$ freed from this organic fraction (dPM$_{2.5-0.3}$) on BEAS-2B cells in culture. Fine and quasi-ultrafine particles were sampled in the southern suburb of Beirut, Lebanon. Chemical characterization showed that quasi-ultrafine particles were about 40 times more concentrated in PAHs than fines one suggesting a significant influence of anthropogenic activities and combustion sources (industries, road traffic and electric generators) on the emission of quasi-ultrafine particles. The influence of combustion sources was confirmed by investigation of PAHs diagnostic ratios. In addition, BEAS-2B cells exposed to PM$_{2.5-0.3}$, dPM$_{2.5-0.3}$, EOM$_{2.5-0.3}$ and EOM$_{0.3}$ lead to different results concerning metabolic activation of PAHs pathway and proteins expression of biomarkers implicated in the pathway of genotoxicity. Globally, EOM$_{0.3}$ was the most inducer for phase I and phase II enzymes implicated in the metabolic activation of PAHs (AhR, AhRR, ARNT, Cyp1A1, Cyp1B1, EPHX-1, GSTA-4) and EOM$_{0.3}$ induced DNA damage, felt by ATR and followed by a cascade of protein phosphorylations contributing to the cell cycle arrest (P21 and P53 induction)