Long-term time trend of lead exposure in young German adults – Evaluation of more than 35 Years of data of the German Environmental Specimen Bank
Bibliographic record
Abstract
Lead is a ubiquitous pollutant with well-known effects on human health. As there is no lower toxicological threshold for lead in blood and since data gaps on lead exposure still exist in many European countries, HBM data on lead is of high importance. To address this, the European Human Biomonitoring Initiative HBM4EU classified lead as a priority substance. The German Environmental Specimen Bank (German ESB) has monitored lead exposure since more than 35 years. Using data from the early 1980s to 2019 we reveal and discuss long-term trends in blood lead levels (BLLs) and current internal exposure of young adults in Germany. BLLs in young adults decreased substantially in the investigated period. As results from the ESB sampling site Muenster demonstrate, the geometric mean of BLLs of young adults decreased from 1981 (78,7 μg/L) to 2019 (10.4 μg/L) by about 87%. Trends in human exposure closely correlate with air lead levels (ALLs) provided by the European Monitoring and Evaluation Programme (EMEP). Hence, the decrease of BLLs largely reflects the drop in air lead pollution. Known associations of sex, smoking, alcohol consumption, and housing situation with BLLs are confirmed with data of the German ESB. Although internal lead exposure in Germany decreased substantially, the situation might be different in other European countries. Since 2010, BLLs of young adults in Germany levelled out at approximately 10 μg/L. The toxicity of lead even at low levels is known to cause adverse health effects especially in children following exposure of the child or the mother during pregnancy. To identify current exposure sources and to minimize future lead exposure, continuous monitoring of lead intake and exposure levels is needed.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".