Modèles pour l'estimation de la variabilité régionale présente et future de la présence des trihalométhanes dans l'eau potable

Cette thèse a comme objectif d’estimer la variabilité régionale présente et future des trihalométhanes (THM) dans les systèmes d’approvisionnement en eau potable (SAEP) desservis en eau de surface à l’aide de modèles multiniveau. La variabilité régionale des THM a été étudiée à l’aide d’une importante base de données développée dans le cadre de cette thèse et concernant les résultats réglementaires de la qualité de l’eau potable, les infrastructures de traitement, la qualité de l’eau de surface et plusieurs paramètres spatio-temporels pouvant influencer la qualité de la source d’eau (Chapitre 2). Les modèles de régression multiniveaux permettent de considérer la structure hiérarchique des données et ainsi d’étudier la variabilité des échantillons de THM tout en contrôlant pour le regroupement des concentrations de THM entre les SAEP et entre les divisions écologiques du territoire. Dans un premier temps, des modèles de régression linéaire multiniveaux ont été développés pour estimer la variabilité régionale et temporelle d’un indicateur des précurseurs des THM, soit le carbone organique dissous (COD) (Chapitre 3). En utilisant les connaissances acquises lors de cette étude et à l’aide de modèles de régression logistique multiniveaux, nous avons ensuite développé un modèle permettant d’estimer la variabilité régionale de la probabilité que les concentrations de THM dans l’eau potable dépassent un seuil spécifique (Chapitre 4). Ce modèle de régression fut ensuite utilisé afin d’estimer l’impact possible des changements climatiques sur la variabilité régionale future de la probabilité que les concentrations de THM dans l’eau potable dépassent ce même seuil (Chapitre 5). Les modèles de régression multiniveaux ont été très peu utilisés dans le domaine de la modélisation de la qualité de l’eau potable. Or, les résultats de cette thèse ont démontré qu’ils s’avèrent un outil très efficace pour considérer la hiérarchie naturelle des variables permettant de modéliser la qualité de l’eau potable à l’échelle régionale. La base de données développée et l’exploration méthodologique des modèles de régression multiniveaux effectuée dans les différents chapitres de cette thèse offrent une plateforme unique pour les futures études de modélisation concernant la variabilité régionale de la qualité de l’eau potable. Mots clés : eau potable, eau de surface, modèle de régression multiniveau, cadre écologique du territoire, trihalométhanes, sous-produits de la désinfection, carbone organique dissous, changements climatiquesThe objective of this thesis is to estimate the present and the future regional variability of trihalomethane (THM) occurrence in drinking water utilities (DWUs) supplied by surface water using multilevel models. The regional variability of THMs was investigated using a large database developed as part of this thesis that takes into account information about regulatory drinking water quality analyses, treatment infrastructures, surface water quality, and many other spatiotemporal parameters that may influence source water quality (Chapter 2). The multilevel models allow for the consideration of the hierarchical structure of the data and the study of variability among THM samples, while controlling for the grouping of THM concentrations within DWUs and at the regional level. At first, multilevel linear regression models were developed to estimate the regional and temporal variability of an indicator of THM precursors (i.e., dissolved organic carbon – DOC) (Chapter 3). Then, using the knowledge acquired in this study and based on multilevel logistic regression models, we developed a model which allows us to estimate the regional variability in the probability of THM concentrations exceeding a specific threshold in drinking water (Chapter 4). Then, this model was used to estimate the possible impact of climate change on the future regional variability in the probability of THM concentrations exceeding the threshold (Chapter 5). Multilevel models have only rarely been used in the field of drinking water quality modelling. And yet results from this thesis demonstrate that they are quite useful in considering the natural hierarchy of variables allowing for the modelling of drinking water quality on a regional basis. The database we developed and the methodological exploration of multilevel regression models that is carried out through the chapters of this thesis offer a useful framework for future modelling studies which examine the regional variability of drinking water quality. Keywords: drinking water, surface water, multilevel regression models, regional ecological framework, trihalomethanes, disinfection by-products, dissolved organic carbon, climate chang

Psychological distress among hospital caregivers during and after the first wave of COVID-19: Individual factors involved in the severity of symptoms expression

Coronavirus disease 2019 has spread rapidly over the globe and has put an unprecedent psychological pressure on health care workers (HCWs). The present study aimed at quantifying the psychological consequences of the COVID-19 pandemic on HCWs during and after the first wave and identify sociodemographic, situational, and psychological risk/protective factors for symptoms severity. An online survey was sent by e-mail to all nurses and physicians employed by a teaching hospital in Brussels, Belgium. 542 (20,62%) completed the survey. 47%, 55%, 32% and 52% of participants reported posttraumatic stress, anxiety, depression and insomnia symptoms, respectively, during the peak. Two to three months later, posttraumatic symptoms emerged de novo in 54% of HCWs. It persisted in 89% of those presenting severe symptoms initially. Neuroticism was the strongest predictor of posttraumatic stress, anxiety, and insomnia. Work overload was the strongest predictor of depression and second predictor of posttraumatic stress, anxiety, and insomnia. Other significant predictors included being a nurse, the number of past traumatic experiences, avoidant coping style, and expressive suppression of emotion

Modelling the regional variability of the probability of high trihalomethane occurrence in municipal drinking water

The regional variability of the probability of occurrence of high total trihalomethane (TTHM) levels was assessed using multilevel logistic regression models that incorporate environmental and infrastructure characteristics. The models were structured in a three-level hierarchical configuration: samples (first level), drinking water utilities (DWUs, second level) and natural regions, an ecological hierarchical division from the Quebec ecological framework of reference (third level). They considered six independent variables: precipitation, temperature, source type, seasons, treatment type and pH. The average probability of TTHM concentrations exceeding the targeted threshold was 18.1 %. The probability was influenced by seasons, treatment type, precipitations and temperature. The variance at all levels was significant, showing that the probability of TTHM concentrations exceeding the threshold is most likely to be similar if located within the same DWU and within the same natural region. However, most of the variance initially attributed to natural regions was explained by treatment types and clarified by spatial aggregation on treatment types. Nevertheless, even after controlling for treatment type, there was still significant regional variability of the probability of TTHM concentrations exceeding the threshold. Regional variability was particularly important for DWUs using chlorination alone since they lack the appropriate treatment required to reduce the amount of natural organic matter (NOM) in source water prior to disinfection. Results presented herein could be of interest to authorities in identifying regions with specific needs regarding drinking water quality and for epidemiological studies identifying geographical variations in population exposure to disinfection by-products (DBPs)

Impact of catchment geophysical characteristics and climate on the regional variability of dissolved organic carbon (DOC) in surface water

Dissolved organic carbon (DOC) is a recognized indicator of natural organic matter (NOM) in surface waters. The aim of this paper is twofold: to evaluate the impact of geophysical characteristics, climate and ecological zones on DOC concentrations in surface waters and, to develop a statistical model to estimate the regional variability of these concentrations. In this study, multilevel statistical analysis was used to achieve three specific objectives: (1) evaluate the influence of climate and geophysical characteristics on DOC concentrations in surface waters; (2) compare the influence of geophysical characteristics and ecological zones on DOC concentrations in surface waters; and (3) develop a model to estimate the most accurate DOC concentrations in surface waters. The case study involved 115 catchments from surface waters in the Province of Quebec, Canada. Results showed that mean temperatures recorded 60 days prior to sampling, total precipitation 10 days prior to sampling and percentages of wetlands, coniferous forests and mixed forests have a significant positive influence on DOC concentrations in surface waters. The catchment mean slope had a significant negative influence on DOC concentrations in surface waters. Water type (lake or river) and deciduous forest variables were not significant. The ecological zones had a significant influence on DOC concentrations. However, geophysical characteristics (wetlands, forests and slope) estimated DOC concentrations more accurately. A model describing the variability of DOC concentrations was developed and can be used, in future research, for estimating DBPs in drinking water as well evaluating the impact of climate change on the quality of surface waters and drinking water

Presentation_1_Case study: Developing a strategy combining human and empirical interventions to support the resilience of healthcare workers exposed to a pandemic in an academic hospital.PDF

The COVID-19 pandemic has put healthcare workers under important psychological pressure. Concerns have been raised regarding the mental health and psychological status of healthcare workers and have underlined the need for institutions to develop long-term interventions to support their resilience. The current case study presents the way a large university hospital in Brussels, Belgium, has evolved to deal with this health crisis and support its workers. Initiatives were multiple and complementary, as it was decided to combine different forms of clinical interventions that were developed by psychologists, psychiatrists, and human resources, to an empirical approach including a large survey that permitted to reach a much larger audience (the results of the study have been published previously). We describe the initially proposed measures of psychological support, including the creation of a telephone hotline, the presence of psychologists among teams of dedicated COVID-19 units, discussion groups, and individualized follow-ups, and their consequences on healthcare workers. Second, we address how these initial measures of support were modified to tailor in the best way possible the needs of healthcare workers, using a research action project that used a survey to measure and address the psychological distress of healthcare workers. We explain how, through different objectives (screening of distress, adaptation of initial measures based on reported needs, active reinforcement of individual and collective resilience, reminder of availability of help, and normalization of distress), a research action project can be a form of support and is an effective way for an institution to show its pre-occupation for the mental health of its teams. The current case study highlights how an institution can provide support and the importance of the use of a combined strategy to limit the consequences of a major health crisis on the mental health of its healthcare workers. Improving the resilience of healthcare workers both in the short and long term is of the essence to maintain optimal care of patients.</p