Post-Horse River Wildfire Surface Water Quality Monitoring Using the Water Cytotoxicity Test
Notice bibliographique
Résumé
The 2016 Horse River wildfire had a significant environmental impact on the Regional Municipality of Wood Buffalo (RMWB) in Northern Alberta, with a burn area exceeding 580,000 hectares. To understand the impact of this unprecedented event on water quality in the RMWB, water samples were collected from surface waters, drinking water treatment plants, wastewater treatment plants, and taps over the three-year period immediately proceeding the wildfire, beginning in May 2017. Samples were collected from sites directly impacted by the Horse River wildfire (Fort McMurray), as well as sites upstream (Athabasca) and downstream (Fort McKay, Fort Chipewyan) from the impacted area. Each water sample was tested using the cell-based water cytotoxicity assay, an in-house developed bioassay with quality control criteria and previous application to environmental testing. The underlying technology of the assay allows for non-invasive and continuous monitoring of human HepG2 cells, providing more human health relevant toxicity information than traditional assays with non-mammalian targets. Comparative toxicity values incorporating both concentration and temporal cellular response data were determined for each sample, allowing for the identification of trends across geographic location, source (surface, treatment plant, tap), and time. Complementary chemical analysis, including routine water chemistry and trace element analysis, was also performed to evaluate chemical components that may have influenced the measured cellular response and to observe trends in contaminant concentrations across time and sampling location. The surface water samples with the highest measured cytotoxicity were collected in 2017 from Fort McMurray, which was directly impacted by the Horse River wildfire. Sites located further downstream in Fort McKay and Fort Chipewyan were less cytotoxic, indicating dilution may have impacted the distribution of the wildfire contaminants. Trace chemical analysis revealed elevated concentrations of sixteen trace elements in these samples, which were highest in samples from the impacted area and lower in sites downstream. A second spike in toxicity was observed in many surface waters and drinking water source locations in 2019, which may reflect other wildfire events that occurred in the province that year. Among the wastewater treatment plants evaluated, all four facilities demonstrated effective treatment across their treatment streams, but the effluent from Fort McMurray, the only facility to utilize clarifiers, was the least cytotoxic. Exceedances of Canadian drinking water guideline values were evaluated in all sample types. Linear regression analysis found positive correlations between 1) concentrations of routine testing parameters and cytotoxicity in wastewater influent and 2) concentrations of trace elements and cytotoxicity in surface and source waters. Many samples containing trace amounts of targeted chemicals still presented with high cytotoxicity, indicating that untargeted substances or mixture effects impacted the cellular response and bioassay testing can complement traditional chemical analysis approaches for environmental monitoring. The water cytotoxicity assay provides numerous advantages, including limited sample preparation, small sample volume requirements (< 10 mL), and simple testing procedures. The testing method is also data rich, providing quantitative information that can be used to compare samples exhibiting low or high cytotoxicity. The results of this three-year investigation indicate that the water cytotoxicity assay has strong potential for application to routine environmental monitoring (to complement chemical-based monitoring programs), and to identify high toxicity samples that require further assessment/remediation as part of the investigation of emergency situations (e.g. an industrial spill).
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) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| 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 ».