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Estimating the early-life exposure to perfluorinated compounds using PBPK modeling and biomarker measurements
No full textInternational audienceReverse dosimetry aims at reconstructing the external exposure using measured biomarkers, a physiologically based pharmacokinetic (PBPK) model accounting for the processes that the chemical undergoes in the human body, and individual characteristics. Such approaches are valuable to simulate exposure between biomarker measurement time points. In this work, we propose to estimate the early-life exposure of children to perfluorooctanesulfonic acid (PFOS) and perfluorooctanoic acid (PFOA). Our study involved 1239 mother-child pairs of the HELIX cohort from 6 European countries. The compounds were measured in maternal plasma at the time of pregnancy, and in child plasma at the age of 6-9 years old. A PBPK model including childhood, pregnancy, and lactation periods was parameterized for each woman and child based on their individual characteristics. The PBPK model was run for mothers to provide exposure estimates for the child during the pregnancy and breastfeeding period. These estimates were then used as inputs to the PBPK model for the child together with the biomarkers to reconstruct his/her early-life exposure (i.e., daily intakes). Finally the internal exposure of children was simulated. Our results showed that similar levels at birth and during childhood can correspond to very different exposure scenarios. A 3-factor in the diet exposure leads to a difference of 6 in Cmax. The main determinants of the child exposure were the levels at birth (correlated with the mother’s biomarker), the duration of breastfeeding, and the measured biomarker itself. Actual measurements during pregnancy and at the age of 6 to 9 do not correlate well with the predicted internal exposure during the first years of life (birth to 4 years old). The indicators depend most on the information collected through the questionnaires. Neglecting inter-individual differences and actual exposures can lead to large exposure misclassification problems, reducing the power of subsequent dose-response or association analyses
Adaptation requirements for the use of measured BCF for a realistic risk assessment of organic chemicals
No full textInternational audienceOne of the main factor in the secondary poisoning risk assessment is the bioavailability of potentially hazardous organic chemicals, especially in the case of soil contaminated with persistent organic pollutants. In the context of the TROPHé project, the transfer of PCBs and PCDD/Fs to plants and invertebrates has been studied: BCF in several plants and in earthworm had been measured and different models to calculate predator exposition have been used. One of the conclusions drawn is that there is no match between available guidelines to produce measured BCF in terrestrial organisms and the BCF needed with the REACH regulation guidance for ecological risk assessment. This guidance states that the exposure concentration for terrestrial predators can be calculated in taking in account the quantity of soil contained in the earthworms guts and the contaminant fraction bioaccumulated in its flesh. This fraction is calculated as the product of the contaminant concentration in interstitial water and the BCF. But this BCF, relatable to interstitial water, is not comparable with BCF measured with available guideline as OECD 317 – Bioaccumulation in Terrestrial Oligochaetes, relatable to total concentration in soil. Data obtained in the context of the TROPHé project allow for the comparison between PCB-PCDD/F BCFearthworm measured with the OECD 317 guideline and PCB-PCDD/F BCFearthworm extrapolated from the Kow of the substance. It was also possible to illustrate the impact of these differences on the results of the secondary poisoning exposure modeled concentrations. A screening on the ECHA registration site also provides an approximation of the number of registered substances that have a BCFearthworm measured with guideline relatable to total concentration in soil and therefore unusable as such in the recommended methodology according to REACH
Incendie, explosion : attention au phénomène d'auto-échauffement des solides divisés
No full textInternational audienc
Decision support tool for establishing an action plan aiming to decrease the discharge of micro pollutants into sewage system networks
No full textInternational audienceThe innovative decision support tool assists in ranking pollution sources and different urban watersheds, based on potential local emissions and the sensibility of the receiving water bodies. The potential local emissions are calculated by coupling characteristics of pollution sources to data bases containing potential emission coefficients of micro pollutants:, 1) Industrial/artisanal activities: APE (principal activity code) - emission (kg/year) by substance and by APE (mean values from national and local data bases): 2) Stormwater runoff: surface type (from national data bases and interpretation of satellite pictures) - emission (kg/year) by substance and by type of surface (literature data) and dependent on typical local rainfall: 3) Domestic: number of habitants - emission (kg/year) by substance and by habitant (literature data). The level of (eco) toxicity of each substance allows to transform potential emissions in potential pressures (PP). The hydraulic model of the sewage system network allows to affect parts of the local potential pressures to the water body. For each water body, a sensibility index (SI) is calculated based on its physico-chemical characteristics and its functions. The ratio PP/SI allows for ranking pollution sources. The coupling to a data base describing solutions for emission reduction for different pollution sources (substitution, treatment, education,..) will allow elaborating an action plan associated to a socio-economic evaluation
Compréhension et prédiction des effets de bisphénol A sur la dynamique de population de l'épinoche trois épines en mésocosme
No full textInternational audienceEn écotoxicologie, la compréhension des effets directs et indirects des contaminants à des niveaux d'organisations biologiques tels que les populations et les écosystèmes est d'un enjeu important pour l'évaluation des risques environnementaux. Dans ce contexte, des modèles individucentrés (IBM) couplés à des modèles bioénergétiques comme le modèle DEB (Dynamic Energy Budget) sont des outils pertinents pour extrapoler les effets des substances chimiques sur l'organisme aux effets sur les populations. De plus, l'utilisation de modèle DEB-IBM en appui aux résultats d'expériences en rivières artificielles (mésocosmes) pourrait permettre une meilleure compréhension des effets d'un stress chimique sur la dynamique de population dans ces mésocosmes. L'objectif de cette étude est donc de développer un modèle DEB-IBM pour prédire la dynamique de population de l'épinoche à trois épines (Gasterosteus aculeatus) en rivières artificielles (mésocosmes) en condition normale et exposée à un perturbateur endocrinien, le bisphenol A (O, 1, 10 et 100 ug/L en trois réplicas). Pour cela, le développement du modèle DEB-IBM a été réalisé en prenant en compte les processus biologiques de l'épinoche à trois épines décrits dans la littérature et observés en mésocosmes. Des méthodes d'analyses de sensibilité ont permis de hiérarchiser les paramètres en fonction de leur niveau de sensibilité pour les sorties du modèle et ainsi de mettre en évidence ceux à calibrer en priorité. La calibration a ensuite été effectuée à partir de deux jeux de données indépendants et provenant de deux expériences en mésocosmes sans exposition à un toxique. Ce modèle en condition témoin a finalement été validé sur les données témoins provenant de l'expérience en mésocosmes testant le bisphenol A. De plus, afin d'obtenir un modèle prédictif des effets toxiques, les effets du bisphenol A observés au niveau individuel et en laboratoire sur les processus physiologiques des poissons ont été implémentés dans le modèle. Les effets indirects du bisphenol A ont également pu être appréhendés en intégrant les données observées de proies des épinoches provenant des mésocosmes contaminés. Les courbes dose-réponses du bisphenol A sur les variables descriptives de la population extrapolées à partir du modèle prédictif ont pu être validées avec les observations des mésocosmes contaminés. Ainsi, le modèle DEB-IBM a permis une meilleure compréhension des effets directs et indirects du bisphenol A au niveau populationnel dans les mésocosmes. Le modèle en condition témoin pourra être ensuite utilisé pour prédire les effets sur la dynamique de population de l'épinoche à trois épines de d'autres substances chimiques testées en mésocosmes
Applying a global sensitivity analysis workflow to improve the computational efficiencies in physiologically-based pharmacokinetic model
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Mixture effects in samples of multiple contaminants - An inter-laboratory study with manifold bioassays
No full textInternational audienceChemicals in the environment occur in mixtures rather than as individual entities. Environmental quality monitoring thus faces the challenge to comprehensively assess a multitude of contaminants and potential adverse effects. Effect-based methods have been suggested as complements to chemical analytical characterisation of complex pollution patterns. The regularly observed discrepancy between chemical and biological assessments of adverse effects due to contaminants in the field may be either due to unidentified contaminants or result from interactions of compounds in mixtures. Here, we present an interlaboratory study where individual compounds and their mixtures were investigated by extensive concentration-effect analysis using 19 different bioassays. The assay panel consisted of 5 whole organism assays measuring apical effects and 14 cell- and organism-based bioassays with more specific effect observations. Twelve organic water pollutants of diverse structure and unique known modes of action were studied individually and as mixtures mirroring exposure scenarios in freshwaters. We compared the observed mixture effects against component-based mixture effect predictions derived from additivity expectations (assumption of non-interaction). Most of the assays detected the mixture response of the active components as predicted even against a background of other inactive contaminants. When none of the mixture components showed any activity by themselves then the mixture also was without effects. The mixture effects observed using apical endpoints fell in the middle of a prediction window defined by the additivity predictions for concentration addition and independent action, reflecting well the diversity of the anticipated modes of action. In one case, an unexpectedly reduced solubility of one of the mixture components led to mixture responses that fell short of the predictions of both additivity mixture models. The majority of the specific cell- and organism-based endpoints produced mixture responses in agreement with the additivity expectation of concentration addition. Exceptionally, expected (additive) mixture response did not occur due to masking effects such as general toxicity from other compounds. Generally, deviations from an additivity expectation could be explained due to experimental factors, specific limitations of the effect endpoint or masking side effects such as cytotoxicity in in vitro assays. The majority of bioassays were able to quantitatively detect the predicted non-interactive, additive combined effect of the specifically bioactive compounds against a background of complex mixture of other chemicals in the sample. This supports the use of a combination of chemical and bioanalytical monitoring tools for the identification of chemicals that drive a specific mixture effect. Furthermore, we demonstrated that a panel of bioassays can provide a diverse profile of effect responses to a complex contaminated sample. This could be extended towards representing mixture adverse outcome pathways. Our findings support the ongoing development of bioanalytical tools for (i) compiling comprehensive effect-based batteries for water quality assessment, (ii) designing tailored surveillance methods to safeguard specific water uses, and (iii) devising strategies for effect-based diagnosis of complex contamination
Speciation of organic fraction does matter for source apportionment. Part 1 : A one-year campaign in Grenoble (France)
No full textInternational audiencePM10 source apportionment was performed by positive matrix factorization (PMF) using specific primary and secondary organic molecular markers on samples collected over a one year period (2013) at an urban station in Grenoble (France). The results provided a 9-factor optimum solution, including sources rarely apportioned in the literature, such as two types of primary biogenic organic aerosols (fungal spores and plant debris), as well as specific biogenic and anthropogenic secondary organic aerosols (SOA). These sources were identified thanks to the use of key organic markers, namely, polyols, odd number higher alkanes, and several SOA markers related to the oxidation of isoprene, α-pinene, toluene and polycyclic aromatic hydrocarbons (PAHs). Primary and secondary biogenic contributions together accounted for at least 68% of the total organic carbon (OC) in the summer, while anthropogenic primary and secondary sources represented at least 71% of OC during wintertime. A very significant contribution of anthropogenic SOA was estimated in the winter during an intense PM pollution event (PM10 > 50 μg m− 3 for several days; 18% of PM10 and 42% of OC). Specific meteorological conditions with a stagnation of pollutants over 10 days and possibly Fenton-like chemistry and self-amplification cycle of SOA formation could explain such high anthropogenic SOA concentrations during this period. Finally, PMF outputs were also used to investigate the origins of humic-like substances (HuLiS), which represented 16% of OC on an annual average basis. The results indicated that HuLiS were mainly associated with biomass burning (22%), secondary inorganic (22%), mineral dust (15%) and biogenic SOA (14%) factors. This study is probably the first to state that HuLiS are significantly associated with mineral dust
Regulatory identification of BPA as an endocrine disruptor : Context and methodology
No full textInternational audienceBPA is one of the most investigated substances for its endocrine disruptor (ED) properties and it is at the same time in the center of many ED-related controversies, the analysis on how BPA fits to the regulatory identification as an ED is a challenge in terms of methodology. It is also a great opportunity to test the regulatory framework with a uniquely data-rich substance and learn valuable lessons for future cases. From this extensive database, it was considered important to engage in a detailed analysis so as to provide specific and strong evidences of ED while reflecting accurately the complexity of the response as well the multiplicity of adverse effects. An appropriate delineation of the scope of the analysis was therefore critical. Four effects namely, alterations of estrous cyclicity, mammary gland development, brain development and memory function, and metabolism, were considered to provide solid evidence of ED-mediated effects of BPA
Further insight into the gas flame acceleration mechanisms in pipes. Part II : numerical work
No full textInternational audienceThis paper is the second part of a global work investigating the physics of premixed flame propagation in several kinds of long pipes. It focuses on the potential of CFD for modelling such cases and on the key issues for being able to address generic cases. Four tests among the database detailed in the first part are selected. In each case, the pipe is straight, open at one end and closed at the other where ignition is triggered. The pipe is filled with a stoichiometric methane/air mixture at rest. Varied parameters are the inner pipe diameter and the pipe material. CFD computations, based on a URANS framework were carried out and enabled to recover several physical trends, such as the role of acoustics and boundary layer turbulence on the flame dynamics. Although most overpressure peaks orders of magnitude of the measured overpressure signals can be predicted numerically, the computed flames are quicker than the measured ones. It could be explained by the chosen turbulent model, the k-ω SST model, known to be adapted for wall-bounded flows but producing too much turbulence for accelerating flows. The criterion for the near wall cells (y+<200) might be too loose as well. Keywords: premixed flame, methane, pipe, CF