Potential impact of the 2023 Lahaina wildfire on the marine environment: Modeling the transport of ash-laden benzo[a]pyrene and pentachlorophenol
Bibliographic record
Abstract
The Lahaina urban/wildland fire event is considered the deadliest wildfire in the past century of U.S. history. This fire resulted in over 2200 building structures destroyed or damaged, approximately 4000 automobiles were incinerated and between 450 and 878 ha of grassland burned in areas adjoining the town of Lahaina, Maui County, State of Hawaii, U.S.A. One of the most abundant contaminants of both wildland and urban fires is the incomplete combustion product, benzo[ a ]pyrene. Pentachlorophenol from burned and unburned utility poles/residential burn sites enter into navigable waters, thus posing a serious risk to the water quality of coastal waters. The Risk Quotient Plumes for benzo[ a ]pyrene and pentachlorophenol, mobilized from Lahaina into coastal waters were calculated based on a hydrodynamic analysis and an integrated ecological risk assessment. This plume was simulated using rainfall events in November 2022 as a proxy for the first major rainfall event expected in Lahaina in 2024. The models indicated that the estimated levels of benzo[ a ]pyrene and pentachlorophenol posed a risk to near shore habitants within 2 km of Lahaina. The levels of pentachlorophenol were more widespread than benzo[ a ]pyrene and were predicted to pose a hazard to marine life as far away as Molokini Shoal Marine Life Conservation District and ʻĀhihi-Kīnaʻu Natural Area Reserve. Fisheries species captured near these areas should be tested for consumption safety. • The Lahaina, HI fire event of 2023 is deadliest fire in the past century in U.S. history. • Ash runoff in the first monsoonal rains mobilize pollutants into the coastal habitats. • Modeled Risk Quotient plumes for benzo[ a ]pyrene and pentachlorophenol of coastline. • Pentachlorophenol may pollute habitats as far south as the coral reefs of Molokini.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".