Drinking Water Treatment Technology Resilience for Management of Severely Deteriorated Water after Wildfire
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".