Globally-significant arsenic release by wildfires in a mining-impacted boreal landscape
Bibliographic record
Abstract
Abstract Metal mining and smelting activities are one of the largest anthropogenic sources of arsenic pollution to the environment, with pervasive consequences to human and environmental health. Several decades of metal processing activities near Yellowknife, NT, Canada have resulted in widespread accumulation of arsenic in biomass, soils, and sediments, exceeding environmental and human health limits. The landscape surrounding Yellowknife is frequently disturbed by wildfire, most recently in 2023, when 2500 km 2 burned. While wildfire-mediated release of stored arsenic around Yellowknife likely represents an incipient threat to human and ecosystem health, a quantification of the potential magnitude of arsenic remobilization from wildfires is absent. Here we combine publicly available soil and biomass arsenic concentrations and land cover datasets with the current best estimates of pyrogenic arsenic speciation and release in upland and wetland ecosystems to estimate the potential range of arsenic remobilization due to wildfires in the region surrounding Yellowknife from 1972 to 2023. Since 1972, wildfires have potentially led to the release of 141–562 Mg of arsenic, with 61–381 Mg emitted to the atmosphere and 39–109 Mg mobilized as water-soluble species. The large range in potential atmospheric emissions was due to the range in peat emission efficiency (5%–84%) that resulted in more arsenic being released from wetlands than the uplands. In 2023 alone, our estimated atmospheric release from just four wildfires was between 15%–59% of global annual arsenic wildfire emissions and likely represented between 2 and 9% of total global arsenic emissions from all natural sources. Given that climate change has and will continue to increase both annual area burned and soil burn severity, we emphasize that future increased wildfire activity closer to Yellowknife will place legacy soil arsenic stores at risk of an even larger catastrophic and unprecedented release, especially as wetlands become drier.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.002 | 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; both teacher heads agree on what is shown here.
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".