Using toxicokinetic modeling approach to determine biological reference values (BRVs) and to assess human exposure to pesticides

Exposure to various pesticides has been characterized in workers and the general population, but interpretation and assessment of biomonitoring data from a health risk perspective remains an issue. For workers, a Biological Exposure Index (BEI®) has been proposed for some substances, but most BEIs are based on urinary biomarker concentrations at Threshold Limit Value - Time Weighted Average (TLV-TWA) airborne exposure while occupational exposure can potentially occurs through multiple routes, particularly by skin contact (i.e.captan, chlorpyrifos, malathion). Similarly, several biomonitoring studies have been conducted to assess environmental exposure to pesticides in different populations, but dose estimates or health risks related to these environmental exposures (mainly through the diet), were rarely characterized. Recently, biological reference values (BRVs) in the form of urinary pesticide metabolites have been proposed for both occupationally exposed workers and children. These BRVs were established using toxicokinetic models developed for each substance, and correspond to safe levels of absorption in humans, regardless of the exposure scenario. The purpose of this chapter is to present a review of a toxicokinetic modeling approach used to determine biological reference values. These are then used to facilitate health risk assessments and decision-making on occupational and environmental pesticide exposures. Such models have the ability to link absorbed dose of the parent compound to exposure biomarkers and critical biological effects. To obtain the safest BRVs for the studied population, simulations of exposure scenarios were performed using a conservative reference dose such as a no-observed-effect level (NOEL). The various examples discussed in this chapter show the importance of knowledge on urine collections (i.e. spot samples and complete 8-h, 12-h or 24-h collections), sampling strategies, metabolism, relative proportions of the different metabolites in urine, absorption fraction, route of exposure and background contribution of prior exposures. They also show that relying on urinary measurements of specific metabolites appears more accurate when applying this approach to the case of occupational exposures. Conversely, relying on semi-specific metabolites (metabolites common to a category of pesticides) appears more accurate for the health risk assessment of environmental exposures given that the precise pesticides to which subjects are exposed are often unknown. In conclusion, the modeling approach to define BRVs for the relevant pesticides may be useful for public health authorities for managing issues related to health risks resulting from environmental and occupational exposures to pesticides.

Hygiène des mains et utilisation de protection respiratoire : attention aux risques de contamination microbiologique imprévue!

Une mauvaise hygiène des mains peut représenter une perte économique importante pour les industries alimentaires ou les industries pharmaceutiques et constitue un gros problème de santé publique dans les établissements de soins. En effet, le risque de transmission manuportée de pathogènes d'un patient à l'autre via le personnel soignant est un problème récurrent dans tous les hôpitaux. C'est pourquoi, une hygiène irréprochable des mains du personnel soignant est une composante fondamentale de lutte contre les infections nosocomiales. En comparaison avec les autres techniques de séchage des mains, l'utilisation d'une serviette en papier à usage unique semble être la méthode la plus efficace pour éliminer les bactéries. En effet, les autres méthodes de séchage, utilisant des appareils à air chaud ou froid, peuvent générer un air contaminé ou remettre en suspension les germes restés sur la peau, facilitant ainsi leur dispersion et leur inhalation. C'est pourquoi, l'OMS a émis des recommandations déconseillant les sèche-mains électriques en milieu hospitalier au profit des serviettes en papiers. Cependant, ces serviettes à usage unique ne sont pas stériles et peuvent elles-mêmes déposer des germes, généralement non pathogènes, sur les mains lors du séchage. Le but du premier article analysé est d'évaluer le niveau de contamination de ces serviettes, ainsi que la possibilité de dépôt de ces bactéries présentes sur les serviettes, sur les mains. À côté du risque de propagation de pathogènes par contact direct, la contamination microbiologique par voie aérienne via les aérosols existe aussi. Pour éviter d'être contaminé par des personnes excrétrices de micro-organismes transmissibles par aérosols (tuberculose, grippe, grippe aviaire SRAS...), il faut porter des protections respiratoires de type FFP2 (1) (norme européenne équivalente à la norme anglosaxonne N95). Ces protections respiratoires ou masques filtrants (en formes de bec de canard) peuvent être portées théoriquement pendant 4 heures. Le but de l'étude du second article est d'évaluer le risque de remise en suspension dans l'air de particules virales présentes sur la face extérieure d'un masque de protection respiratoire lors de simulation d'épisodes de toux provenant du porteur de cette protection.

Risk Assessement of Herbicide Mixtures

Lake Geneva is one of the largest European lakes with a surface area of 580 km2. Its catchment area covers 7400 km2, of which approximately 20% is arable land. Monitoring campaigns have been carried out in 2004 and 2005 to determine the contamination of the lake by pesticides. The results highlight the widespread presence of herbicides in water, the measured concentrations for most substances remaining constant in 2004 and 2005. However, for some individual herbicides the concentrations increased drastically (e.g., the herbicide foramsulfuron). We assessed the environmental risk of the herbicides detected in the lake using water quality criteria recently determined for the Swiss environmental protection agency. Furthermore, we assessed the risk of herbicide mixtures, grouped based upon their mode of action. Generally, the risk estimated for all single substances is low, except for some sulfonylurea compounds. For these substances, the measured concentrations are higher than the predicted no-effect concentration. Impact on the flora of the lake can therefore not be excluded. When mixtures of pesticides with similar mode of action are taken into account, the risk remains lower than the mixture water quality criteria for all groups, but can reach as high as one third of this quality criteria. A further step would therefore be to assess the risk of the total pesticide mixture, including similar and dissimilar modes of action.

Exposition des travailleurs agricoles aux pesticides : revue systématique de la littérature scientifique disponible sur les expositions aux pesticides des travailleurs agricoles en France

Le présent rapport rend compte d'une partie du travail déjà réalisé sur l'inventaire des données disponibles dans la littérature scientifique sur l'exposition des travailleurs agricoles aux pesticides en France. La possibilité de tirer parti de la littérature produite sur d'autres pays que la France est également explorée dans deux études de cas (réentrée en arboriculture et insecticides en élevages ovins). Les résultats seront exposés par ailleurs car le recueil et l'analyse de données produites par ces études sur les situations d'exposition des personnes travaillant en milieu agricole dans des pays étrangers posent des problèmes méthodologiques spécifiques si on veut pouvoir en tirer des enseignements pour la France. En matière de revue de la littérature scientifique, l'idéal de l'exhaustivité est généralement impossible à atteindre pour des raisons qui tiennent à la croissance exponentielle du nombre de publications scientifiques, à la pluralité des langues de publication, à la fragmentation et à l'accessibilité limitée des bases documentaires ... Nous pensons avoir recueilli tous les documents accessibles, mais peut-être certains nous ont-il échappé. C'est pourquoi le présent rapport est à la fois un exposé de résultats et un appel à contributions complémentaires. Il explicite les détails de la démarche adoptée (critères de sélection des articles, traçabilité des étapes de l'analyse ...) sous une forme qui permet à chacun d'avoir prise sur les résultats mais aussi de proposer des compléments au recensement réalisé. Il vise à être la première brique d'une base de connaissances partagée qui permette de capitaliser les données disponibles et qui puisse être mise à jour régulièrement.

Robust Nonlinear Mapping of Soil Contamination Using Support Regression

Spatial data analysis mapping and visualization is of great importance in various fields: environment, pollution, natural hazards and risks, epidemiology, spatial econometrics, etc. A basic task of spatial mapping is to make predictions based on some empirical data (measurements). A number of state-of-the-art methods can be used for the task: deterministic interpolations, methods of geostatistics: the family of kriging estimators (Deutsch and Journel, 1997), machine learning algorithms such as artificial neural networks (ANN) of different architectures, hybrid ANN-geostatistics models (Kanevski and Maignan, 2004; Kanevski et al., 1996), etc. All the methods mentioned above can be used for solving the problem of spatial data mapping. Environmental empirical data are always contaminated/corrupted by noise, and often with noise of unknown nature. That's one of the reasons why deterministic models can be inconsistent, since they treat the measurements as values of some unknown function that should be interpolated. Kriging estimators treat the measurements as the realization of some spatial randomn process. To obtain the estimation with kriging one has to model the spatial structure of the data: spatial correlation function or (semi-)variogram. This task can be complicated if there is not sufficient number of measurements and variogram is sensitive to outliers and extremes. ANN is a powerful tool, but it also suffers from the number of reasons. of a special type ? multiplayer perceptrons ? are often used as a detrending tool in hybrid (ANN+geostatistics) models (Kanevski and Maignank, 2004). Therefore, development and adaptation of the method that would be nonlinear and robust to noise in measurements, would deal with the small empirical datasets and which has solid mathematical background is of great importance. The present paper deals with such model, based on Statistical Learning Theory (SLT) - Support Vector Regression. SLT is a general mathematical framework devoted to the problem of estimation of the dependencies from empirical data (Hastie et al, 2004; Vapnik, 1998). SLT models for classification - Support Vector Machines - have shown good results on different machine learning tasks. The results of SVM classification of spatial data are also promising (Kanevski et al, 2002). The properties of SVM for regression - Support Vector Regression (SVR) are less studied. First results of the application of SVR for spatial mapping of physical quantities were obtained by the authorsin for mapping of medium porosity (Kanevski et al, 1999), and for mapping of radioactively contaminated territories (Kanevski and Canu, 2000). The present paper is devoted to further understanding of the properties of SVR model for spatial data analysis and mapping. Detailed description of the SVR theory can be found in (Cristianini and Shawe-Taylor, 2000; Smola, 1996) and basic equations for the nonlinear modeling are given in section 2. Section 3 discusses the application of SVR for spatial data mapping on the real case study - soil pollution by Cs137 radionuclide. Section 4 discusses the properties of the modelapplied to noised data or data with outliers.