Modelling multitudes of pharmaceuticals in the global river system at high spatial resolution
Notice bibliographique
Résumé
Treated and untreated domestic wastewaters that are discharged into surface waters often contain a variety of chemical substances, including residuals of pharmaceuticals that are not fully metabolized by the human body. These substances may be harmful to the health of aquatic ecosystems and to humans who rely on them as a source of water supply. Despite growing concerns and their frequent detection in wastewaters and surface waters, the concentrations of pharmaceuticals are not regularly monitored in water bodies. As an alternative to comprehensive monitoring campaigns that tend to be very resource intensive, contaminant fate models may be used to provide information to support the development of targeted local monitoring schemes in regions of highest exposure to pharmaceuticals in the environment as well as the prioritization of substances for further investigation.In this work, a global contaminant fate model (called HydroFATE) was developed with the objective of estimating the concentration of contaminants of emerging concern (including pharmaceuticals) in the global river network at a high spatial resolution (500 m). The contaminant emission is calculated based on consumption per capita and population density. Then, the contaminant loads of treated or untreated wastewaters are reduced in the model either by centralized or decentralized wastewater treatment, by natural attenuation in soils and runoff, and/or by decay processes in rivers and lakes. HydroFATE’s structure is based on a vector routing structure, which besides its spatial precision being higher than in global pixel-based models, it is also fast to process. This key aspect allows for more complex analyses, including repeated execution of multiple substances and different scenarios in a short period of time, making HydroFATE a capable tool to inform on the prioritization of substances.The model’s performance was validated by comparing predicted concentrations in river reaches worldwide against literature reports of measured concentrations of 22 broadly consumed antibiotics for which at least sparsely monitored data existed. The sensitivity of the model’s predictions was tested by altering key model parameters. This validation process showed that HydroFATE is generally able to predict aquatic concentrations measured worldwide to within one order of magnitude, which is judged to be sufficient for the intended purposes of the model.Finally, HydroFATE was applied to estimate the concentrations of the 40 most widely used antibiotics in households worldwide and to compare these concentrations, both individually and cumulatively, to established no-effect thresholds of environmental exposure. It was estimated that a total of 8,500 tonnes of antibiotics per year are discharged into the river system. We found that 6.0 million km of rivers worldwide may have environmental exposure levels that exceed the no-effect concentration of antibiotic pollution during low streamflow conditions, with the largest extent of these rivers being in Southeast Asia, the most densely populated region in the world. The main contributors of exposure were found to be the widely and heavily used antibiotics amoxicillin, ceftriaxone, and cefixime.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».