Spatiotemporal assessment of lead (Pb) and cadmium (Cd) contamination in urban tree rings near an industrial smelter: high intraspecific variability but limited spatial differentiation
Bibliographic record
Abstract
Assessing lead (Pb) and cadmium (Cd) concentration in tree rings may provide historical records of environmental contamination, capturing temporal changes and spatial distribution. This study examined heavy metal (HM) bioaccumulation in tree rings to assess the impact of smelting activities using urban trees. Tree rings from urban conifers were collected within a 5 km radius of the smelter to estimate spatiotemporal trends of Pb and Cd concentrations, evaluating the outcomes of contamination reduction measures and interspecies bioaccumulation patterns. Pb isotopic ratios ( 206 Pb/ 207 Pb, 208 Pb/ 206 Pb) were also measured to evaluate the Pb origin. Results showed no clear spatial pattern in relation to distance to the smelter, which may be due to the small sampling area. However, Cd bioaccumulation in urban pine was 4 to 7 times higher than in the distant site, 80 km away, indicating a local impact of industrial contamination in the urban area. Temporal analysis for pine showed a decrease of 0.27 mg/kg Cd (47%) between 1990 and 2020, reflecting the potential influence of contamination-reducing measures, while Pb concentrations in pine were 2.7 times higher (increased by 0.06 mg/kg) for the same period. Pine bioaccumulated more Cd than spruce, while spruce accumulated higher levels of Pb compared to pine. Isotope measurement confirmed that the copper smelter is the primary source of Pb. These findings underscore the complex nature of HM uptake in urban trees and suggest that further research is needed to understand the spatiotemporal effects on HM bioaccumulation patterns and which species are best suited for phytoremediation.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".