Practicalities of Mainstreaming Biomarker Use – A Canadian Perspective
Notice bibliographique
Résumé
Abstract This paper discusses the use of biomarkers within the environmental protection framework that has evolved since the Canadian Nuclear Safety Commission (CNSC) was given a broad mandate for the protection of the environment under the Nuclear Safety and Control Act ( NSCA ) in 2000. Unique insights have been obtained through environmental assessments for major nuclear projects conducted under the Canadian Environmental Assessment Act , and through the Environmental Effects Monitoring (EEM) requirements at uranium mines and mills ( Metal Mining Effluent Regulations under the Fisheries Act ( FA )). Altogether, the Canadian nuclear sector now has 17 years of experience in applying biological evidence in decision-making. Key examples are discussed where improved effluent controls were implemented at uranium mines for three substances (U, Mo, Se) based on risk assessments and supporting biological evidence. In the case of U, potential for localized harm from the chemical toxicity rather than radiological toxicity of U was identified at three older mines through environmental risk assessment. Evidence of potential harm in the field was also obtained from a community ecology bioindicator (benthic invertebrate biodiversity). This led to the improvement of effluent controls for U that were straightforward to implement under the NSCA . In the case of Mo, the weight of evidence for potential health effects on moose and other riparian wildlife from ecological risk assessments (supported by field evidence from Sweden) prompted improved controls based on precaution and pollution prevention. A technological solution was readily available for reducing Mo in effluent and was therefore implemented. In the case of Se, population level effects in fish and individual level effects in waterfowl in the USA led to selenium risks being evaluated downstream of uranium mines. Biomarkers (larval teratogenic deformities) played a pivotal role in attributing observed effects (harm) to the probable cause (selenium accumulation in the environment). However, as technological solutions were not straightforward, effort was required to build a consensus on achievable effluent control targets in a multi-stakeholder and multi-jurisdictional context. Through site-specific research and the latest scientific literature, criteria for selenium risk evaluation and water treatment system improvements were agreed upon and implemented. Within the EEM program, similar issues have arisen in managing a robust and defensible regulatory framework for controls on multiple hazardous substances across many mining sectors. However, an initial review of biomarkers resulted in the selection of only ecologically-relevant parameters (fish health and population indicators, benthic invertebrate biodiversity) as triggers for regulatory action. Altogether, these and other parallel experiences are discussed in terms of the desirable attributes of biological effects monitoring in a Canadian regulatory context.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,003 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 tête enseignante, 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 ».