Toxic trajectories: Modeling heavy metal-laden phosphate dust dispersion and multi-receptor health risks near Kpémé’s industrial zone
Bibliographic record
Abstract
Industrial emissions in developing regions pose catastrophic yet unquantified health-ecological threats, exemplified by Togo’s Kpémé phosphate plant. Current approaches fail to resolve atmospheric dispersion dynamics of toxic metal-laden TSP (e.g., Cd, Hg) or contextualize exposure risks for vulnerable receptors, leaving critical data gaps in meteorology and region-specific standards. We pioneer an integrated framework to establish receptor-resolved health risks by unveiling dispersion pathways and proposing Africa’s first harmonized air standards. Our novel methodology overcomes data poverty via synthetic meteorology validation and adapts regulations to local climatology. AERMOD View dispersion modeling leveraged MERRA-2/ERA5 meteorological data (2018–2022), validated by a Performance Score (PS=0.81), and 100 receptor sites. We introduced Togo-specific coefficients (e.g., K Togo =1.2) to adapt Québec air standards and developed new risk indices quantifying exposure, neurotoxic hazard quotients (HQ), and metal-specific carcinogenic risks (CR). Results demonstrate extreme TSP exceedances: 31.97 times daily standards (120 µg/m³) under normal conditions and 122.84 times during extreme events. Schools emerged as critical hotspots, with Keta Abate Kopé reaching 287 µg/m³ annually. Health impacts proved catastrophic: children’s HQ for neurotoxic metals (Pb and Hg) hit 356 times thresholds, while CR for Cr(VI) reached 12.46—exceeding safety limits (>0.0001) by orders of magnitude. Vulnerability analysis revealed clinics/schools endured triple the exposure of residential zones. This work establishes that contextual standardization and receptor-specific risk mapping are non-negotiable for Global South pollution governance. Fusing dispersion modeling with adaptive standards redefines industrial accountability, demanding urgent stack filtration and child safety buffers for climate-resilient policies in aerosol-exposed zones.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".