Assessing Tropical Cyclone-Driven Aerosol Transport – A High-Resolution WRF-Chem Analysis of Major Hurricane Matthew and Implications for Future Climate Scenarios
Bibliographic record
Abstract
Tropical cyclones (TCs) are traditionally viewed as atmospheric cleansers, removing pollutants through precipitation and winds. However, emerging evidence suggests that TCs also transport pollutants over long distances, with their full impact on pollutant dynamics still underexplored. In this study, aerosol transport and chemical transformation during Hurricane Matthew (2016) are analyzed using high-resolution WRF-Chem simulations. The Hurricane-Driven Aerosol Transport Analysis Framework (WHDA-TAF) is developed to assess aerosol behavior under both current and future climate scenarios. Simulations based on Pseudo Global Warming (PGW) projections indicate that aerosols, such as sea salt, particulate matter, and carbonaceous aerosols, are lifted to higher altitudes and dispersed far from the storm's core. Photochemical reactions within the cyclone generate secondary pollutants such as ozone and nitrates, which can worsen air quality even after the hurricane has dissipated. Copula-based analysis reveals that stronger hurricanes in high-emission scenarios (SSP585) are likely to increase the frequency and severity of extreme compound pollution events, even in regions distant from the storm's direct impact. These findings challenge the view of TCs as mere atmospheric cleansers, highlighting their significant role as carriers and reactors of pollutants. Enhancing model resolution is crucial to better capture such TC-aerosol interactions and address the risks of cyclone-driven aerosol transport under climate change.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".