An evaluation of metal transport from shoulder highway sections into roadside soils due to atmospheric and runoff processes
Bibliographic record
Abstract
A comprehensive study of the migration pathways that contribute to the dispersal, accumulation and mobility of metals (Cu, Fe, Pb, Mn, and Zn) in roadside soils was performed at two highway sites with similar design, but different environmental, traffic and land use characteristics. Samples were collected from multiple media, which included: road sediment, atmospheric dustfall, atmospheric suspended particulates, stormwater runoff and roadside soils. Total metal concentrations, as well as the relative metal partitioning in different fractions, were evaluated to provide an estimate of their mobility and potential bioavailability across different environmental media. Metals showed an increasing degree of bioavailability with decreasing particle size in all sampled media at the two highway monitoring sites. Thus, metals showed low bioavailability in roaddust and roadside soils (except in highly contaminated spots in the case of roadside soil), intermediate metal bioavailability was found in dustfall, whereas metals in atmospheric suspended particulates and runoff were the most potentially bioavailable. These results stressed the importance of the contribution of atmospheric and runoff processes, particularly on surface water bodies, where a significant percentage of metal from deposited atmospheric particulates or incoming runoff may be readily available. Lead, was found to occur at the lowest metal concentration of the five metals measured in runoff and atmospheric samples. However, significant amounts of Pb remained in the roadside soils sampled. Most of the Pb contaminated soils exhibited greater amounts of labile metal and a distinct decrease in the proportion of the tightly bound "residual" extraction component. This pattern was also observed for metals Cu, Mn, and Zn at suspected anthropogenic metal input locations. A forensic investigation of the process of roadside soil contamination was achieved with the aid of Pb isotopic analyses and linked the accumulation of this metal with Cu and Zn. Additionally, a predictive methodology was proposed, which covered the coupled atmospheric and runoff metal loading processes and the main geochemical metal-roadside soil interactions. The methodology has applicability for identifying sensitive areas in highways systems and can be used as a predictive tool aiding in risk assessment or risk management activities when it is coupled with receptor toxicological data.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".