Wildfire Threats to Groundwater Supplies: Implications for Pathogen and Particulate Contaminant Transport in Porous Media
Bibliographic record
Abstract
Climate change-associated wildfires are increasing in frequency and severity, causing increasingly variable or deteriorated water quality, and challenging in-plant treatment processes beyond design and operational response capacities, to the point of service disruptions. Recent work has shown that the wildfire impacts on drinking water treatability can extend far downstream and be long-lasting. Notably, very little information regarding the impacts of severe wildfire on groundwater supplies is currently available.Wildfire transforms fuels (i.e. biomass, soil organic matter). Pyrogenic carbonaceous material formed after wildfire includes particulate ash and biochar, which often contains toxic polyaromatic hydrocarbons, dioxins and furans, as well as some heavy metals. These mobile materials may be incorporated into soil profiles (change the soil properties, e.g., hydrophobicity, pH), redistributed, or removed from a burned site by wind and water erosion to source water. While surface water treatment technologies may have some capacity to remove these contaminants from surface water, the subsurface fate and mobility of these toxic particles has not been documented and is not understood. Moreover, the implications of potential changes in dissolved organic carbon on pathogen transport in these systems has not been documented. Because groundwater-based drinking water supplies do not typically require treatment beyond disinfection, it is possible that contaminated particles could enter drinking water wells after wildfire. Moreover, NOM-associated changes in water quality may increase the risk of pathogen transport through the subsurface.Here, the impacts of wildfire on the transport E. coli and Cryptosporidium parvum oocysts in various porous media environments (e.g., particle properties, solution chemistry, organic matter character) were evaluated. Column tests were conducted using laboratory prepared wildfire ash-impacted water and wildfire impacted surface water collected after the 2017 Kenow Wildfire in Waterton, Alberta, Canada. These investigation demonstrate that under certain conditions potential post-fire shifts in water quality can substantially enhance particle/microbe transport in porous media, thereby underscoring the need to evaluate microbial risks to groundwater supplies after severe wildfire.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".