MétaCan
Menu
← Retour à la cohorte
Enregistrement W7009316857

Drinking Water Treatment Technology Resilience for Management of Severely Deteriorated Water after Wildfire

2021· dissertation· en· W7009316857 sur OpenAlexaboutno aff

Notice bibliographique

RevueUWSpace (University of Waterloo) · 2021
Typedissertation
Langueen
DomaineEnvironmental Science
ThématiqueFire effects on ecosystems
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésWater qualityWater treatmentSurface runoffTurbidityResilience (materials science)Water sourceWater pollution
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Severe wildland fire is occurring with increased frequency in many regions and can be potentially catastrophic for the provision of safe drinking water due to increasingly variable or deteriorated source quality. Deteriorated water quality can have negative implications for drinking water treatment by challenging in-plant treatment technologies beyond design and operational response capacity as a result of elevated levels of carbon, turbidity and nutrients. While it is increasingly recognized that severe wildland fire can have long-lasting (i.e., decades or longer) impacts on source water quality, it is the short-lived, episodic impacts following major runoff events that can most challenge drinking water treatability. Although case studies are increasingly available regarding wildfire impacts on drinking water treatment, they typically provide limited or no information about treatment technology capabilities. While the presence of anthropogenic contaminants from the urbanized landscape may require specialized treatment after wildfire, this research strives to address the common belief that commonly available technologies are insufficient for treating severely deteriorated source water after wildland fire. \nTo date, the treatment of severely deteriorated, wildfire ash-impacted water representative of worst-case scenario “black water” that sometimes results from wildfire ash transport directly from hillslopes to source waters has not been investigated. Recognizing that the ability to treat the most severely deteriorated water that might be observed post-fire implies that less deteriorated water could also be treated, protocols were developed herein to collect fresh post-fire ash and prepare wildfire ash-impacted source water with high levels of turbidity and dissolved organic carbon (DOC) consistent with the most severely deteriorated levels that have been reported to date. These wildfire ash-impacted source waters were then used to evaluate the capacity to treat severely deteriorated, wildfire ash-impacted source water using commonly available water treatment technologies (e.g., conventional, high-rate treatment) at bench-scale. While turbidity was effectively reduced in all cases, reductions in DOC concentrations were often limited; however, powdered activated carbon (PAC) demonstrated potential for reducing DOC concentrations and disinfection by-product formation potential. \nPilot-scale investigations of focused on evaluating the capacity to treat severely deteriorated, wildfire ash-impacted source water was subsequently conducted using sand-ballasted flocculation (SBF) because this type of high rate clarification is designed to respond to rapid and large fluctuations in source water quality. The studies were conducted in two distinct ecozones with markedly different source water quality. The Montane Cordillera study in Calgary, Canada involved high quality source water (low turbidity and DOC) while the Boreal Plains study in Fort McMurray, Canada involved more deteriorated (higher turbidity and DOC) source water. SBF was investigated alone and in combination with enhanced coagulation or PAC for additional DOC removal and DBP management. These studies were critical to informing operational challenges associated with the various technologies. Consistent with the bench-scale outcomes, the studies indicated that while turbidity was again effectively reduced in all cases, reductions in DOC concentrations were often limited. PAC demonstrated potential for reducing DOC concentrations and disinfection by-product formation potential if needed, and without the challenges that were associated with fluctuating source water pH resulting from wildfire ash dissolution, which challenged the pH control required for effective enhanced coagulation at the conditions investigated. \nThe impacts of different vegetation types in different physiographic regions and wildfire burn severity on drinking water treatability after wildland fire were assessed to provide insight regarding the transferability of knowledge regarding wildfire impacts on drinking water treatment in different regions and after different types of wildfires. This work is novel in demonstrating that burn severity can significantly impact water treatability. In contrast, although the magnitudes of impacts on source water quality differed regionally when normalized to ash mass, similar treatment performance was achieved at consistent burn severity, regardless of regional differences between vegetation in wildland regions. Regardless of differences in wildland fire severity and setting, the key aspects of water quality that govern chemical pre-treatment efficacy were also those that were consistently impacted by ash delivery to source water, namely: turbidity, DOC concentration and character, alkalinity and pH. \nThe novel insights described above were synthesized in conjunction with a literature review to develop a framework to detail watershed- and plant-scale operational response actions for describing and mitigating the potential impacts of wildfire on drinking water treatment (Chapter 6). Thus collectively, this dissertation (1) provides an in-depth analysis of the treatment challenges and potential solutions that may be implemented to effectively treat severely deteriorated, wildfire ash-impacted source water following wildfire and (2) contributes to advancing risk management, operational resilience, and climate change adaptation planning for drinking water utilities in fire-prone regions.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,001
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,006
Score d'incertitude au seuil0,012

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,000
Études des sciences et des technologies0,0010,000
Communication savante0,0010,001
Science ouverte0,0010,001
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0030,001

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.

Tête enseignante Opus0,004
Tête enseignante GPT0,183
Écart entre enseignants0,178 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeExpérimental (laboratoire)
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1
Publié2021
Routes d'admission1
Résumé présentoui

Explorer davantage

Même revueUWSpace (University of Waterloo)→Même sujetFire effects on ecosystems→Travaux en français237 207→