Suivi et modélisation du potentiel hydrique du sol dans un contexte de stress climatiques : le cas d'une érablière à bouleau jaune à la marge nordique de sa distribution.

Dans un contexte de changements climatiques globaux, une question a guidé notre étude à l’interface entre la science des données et les sciences de l’environnement : quelle serait l’évolution temporelle de notre variable d’intérêt - le potentiel hydrique du sol - dans une future nouvelle normalité climatique des érablières à bouleau jaune à la marge nordique de leur distribution ? Notre questionnement a eu pour cadre expérimental le projet pancanadien SmartForests, et en particulier le site de la Station de biologie des Laurentides (SBL) qui a permis de cibler trois peuplements : une sapinière à érable (MW), une érablière à bouleau (HW) et une érablière à hêtre (HB). En réfléchissant à ce glissement vers une nouvelle normalité climatique, nous avons explicité la notion de tendance et d’événements extrêmes en identifiant également comment ce basculement vers un nouvel état de fond pouvait être caractérisé. Ceci nous a alors amené à clarifier la notion de résilience écologique, i.e. la capacité à résister aux changements. Ainsi, dans un contexte de sécheresse à déficit de précipitations nous nous sommes demandés si certains peuplements étaient plus résilients que d’autres. En exploitant la temporalité des millions de données qui ont été collectées à la SBL entre 2017 et 2020, nous avons développé et testé un modèle prédictif du potentiel hydrique du sol de type autorégressif à moyenne mobile qui intègre les variables météorologiques (ARIMAX). De plus, pour les trois peuplements d’intérêt, nous avons analysé les changements de régime de leur potentiel hydrique en exploitant les chaînes de Markov à temps discret. À l’issue de ce projet, nous proposons donc un modèle novateur qui permet de prédire l’évolution du potentiel hydrique du sol avec une très bonne exactitude sur un horizon de taille maximale de 60 jours, grâce à la connaissance des variables météorologiques antérieures. Ce modèle a été adapté à l’étude des sécheresse-flash durant lesquelles les arbres sont soumis à des stress hydriques. Pendant ces périodes d’assèchement, nous mettons en évidence le comportement spécifique de l’érablière à hêtre qui montre une meilleure régulation de la température de son sol et un maintient de son potentiel hydrique autour de valeurs moins élevées que celles observées pour la sapinière et l’érablière à bouleau. Enfin, nos analyses suggèrent la tendance d’un rapprochement plus imminent de la sapinière à érable vers un changement de régime hydrique. L’utilisation originale des séries temporelles pour développer des modèles prédictifs en écologie forestière s’avère ainsi prometteuse

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

Dynamics of soil water potential as a function of stand types in a temperate forest: Emphasis on flash droughts

In the context of a changing climate and the increasing occurrences of extreme events, including droughts, field evidence, and models suggest that cases of forest decline and migration of tree species to more suitable climates will augment in the 21st century. In northeastern North America, an expansion of American beech at the expense of maples has been observed since the 1970s and has been associated to several causes. Through an analysis of time series leveraging thousands of data collected in a temperate forest in southern Quebec, Canada, dynamics of soil water potential were analyzed in interaction with soil temperature, meteorological variables and forest types, including hardwoods (mostly maple) with a large presence of beech trees (hardwood-beech stands), hardwoods (maple and birch) and mixedwoods (maple and fir). During flash drought events with a net precipitation deficit and water stress, the presence of beech led to a decrease in soil temperature and favored the maintenance of low soil water potential and faster restoration of water reserves compared to mixedwoods. Using machine learning-based approaches, distinct critical soil temperature thresholds in regard to water potential were identified for the various forest types, and the temporality in soil water regime changes was more favorable under hardwood-beech stands. The presence of beech appears to render greater resilience in regard to water stress in this forest. A greater capacity of beech to preserve and restore soil water not only offers an additional explanation for its establishment in hardwoods in the last decades, but greater water conservation in the presence of beech, assuming it remains in the landscape, could also help local plant species adapt to climate change and to the predicted increased water deficits, as well as species migrating northward to find more suitable environmental envelopes

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