Treatment of Severely-Deteriorated Post-Fire Runoff: A Comparison of Conventional and High-Rate Clarification to Demonstrate Key Drinking Water Treatment Capabilities and Challenges
Bibliographic record
Abstract
The ability to treat worst-case scenario, “black water” resulting from wildfire ash transport directly from hillslopes to source waters was investigated—this has not been reported previously. The treatment response capabilities of conventional chemical pre-treatment and high rate clarification processes were evaluated at bench scale; these included: sand-ballasted flocculation (SBF), SBF with enhanced coagulation, and SBF with powdered activated carbon (PAC). Fresh ash was collected from the Thuya Lake Road (TLR) wildfire (+51.4098 latitude, -120.2435 longitude; burn area 556 ha), which was part of the Little Fort Fire Complex that burned in July 2017, near Little Fort, British Columbia, Canada. The ash was used to prepare a severely-deteriorated source water matrix. It was added to high quality river water (Elbow River, Calgary, Alberta) to reflect post-fire water quality conditions when ash is mobilized off the landscape to receiving waters during a major runoff event. Prior to mixing, ash was sieved through a 1 mm screen to remove any large debris and conifer needles that typically would not be found in water treatment plant influent streams. Three concentrations of ash in river water were prepared (2.0, 10.0, and 20.0 g×L -1 of ash; five replicates of each) by adding ash to 1000 mL of Elbow River water in 2-L plastic square beakers, and mixed using a jar test apparatus (Phipps & Bird, PB-900 Series Programmable 6-Paddle Jar Tester, Richmond, VA) at 120 RPM for 2 minutes. Turbidity and dissolved organic carbon (DOC) concentrations consistent with or slightly higher than the levels that have been reported following severe wildfire (i.e., >1000 NTU and >15mg×L -1 , respectively) were targeted. These water matrices were black-colored, in a manner consistent with previous reports of severely-deteriorated water conditions after wildfire. Standard methods were used to evaluate turbidity (Method 2130B; Hach 2100 N turbidimeter, Loveland, CO), pH (4500-H + B Electrometric method; Orion 720A pH meter, Thermo Fisher Scientific, Waltham, MA), DOC concentration (filtration through pre-rinsed 0.45 µm Nylaflo membranes, Pall, Port Washington, NY; Method 5310C; Shimadzu TOC-V WP analyzer, Kyoto, Japan), and UVA 254 (Method 5910B; 1 cm quartz cell; Hach DR 5000 Spectrophotometer, Loveland, CO). Specific ultraviolet absorbance at 254 nm (SUVA) was calculated by dividing UVA 254 absorbance by the DOC concentration.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".