Post-Horse River Wildfire Surface Water Quality Monitoring Using the Water Cytotoxicity Test
Bibliographic record
Abstract
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).
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".