Long-term exposure to atmospheric metals assessed by mosses and mortality in France
Bibliographic record
Abstract
BACKGROUND: Long-term exposure to air pollution affects health, but little is known about exposure to atmospheric metals. Estimating exposure to atmospheric metals across large spatial areas remains challenging. Metal concentrations in mosses could constitute a useful proxy. Here, we linked moss biomonitoring and epidemiological data to investigate the associations between long-term exposure to metals and mortality. METHODS: We modelled and mapped 13 atmospheric metals from a 20-year national moss biomonitoring program to derive exposure estimates across France. In the population-based Gazel cohort, we included 11,382 participants from low to intermediate population density areas and assigned modelled metals to their residential addresses. We distinguished between airborne metals that are primarily of natural origin and those primarily of anthropogenic origin. Associations were estimated between exposure to metals and mortality (natural-cause, cardiovascular and respiratory), using Cox models, with confounder adjustment at individual level. FINDINGS: Between 1996 and 2017, there were 1313 deaths in the cohort (including 181 cardiovascular and 33 respiratory). Exposure to the anthropogenic metals was associated with an increased risk of natural-cause mortality (hazard ratio of 1.16 [1.08-1.24] per interquartile range of exposure), while metals from natural sources were not. INTERPRETATION: Some atmospheric anthropogenic metals may be associated with excess mortality - even in areas with relatively low levels of exposure to air pollution. Consistent with the previous literature, our findings support the use of moss biomonitoring as a tool to assess health effects of air pollution exposure at individual level.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".