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Record W7009316857

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

2021· dissertation· en· W7009316857 on OpenAlexaboutno aff

Bibliographic record

VenueUWSpace (University of Waterloo) · 2021
Typedissertation
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsnot available
Fundersnot available
KeywordsWater qualityWater treatmentSurface runoffTurbidityResilience (materials science)Water sourceWater pollution
DOInot available

Abstract

fetched live from 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.004
GPT teacher head0.183
Teacher spread0.178 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2021
Admission routes1
Has abstractyes

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