Traffic-related microplastic particles, metals, and organic pollutants in an urban area under reconstruction
Bibliographic record
Abstract
In urban environments, particularly areas under reconstruction, metals, organic pollutants (OP), and microplastics (MP), are released in large amounts due to heavy traffic. Road runoff, a major transport route for urban pollutants, contributes significantly to a deteriorated water quality in receiving waters. This study was conducted in Gothenburg, Sweden, and is unique because it simultaneously investigates the occurrence of OP, metals, and MP on roads and in stormwater from an urban area under reconstruction. Correlations between the various pollutants were also explored. The study was carried out by collecting washwater and sweepsand generated from street sweeping, road surface sampling, and flow-proportional stormwater sampling on several occasions. The liquid and solid samples were analyzed for metals, polycyclic aromatic hydrocarbons (PAH), oxy-PAH, aliphatics, aromatics, phthalates, and MP. The occurrence of OP was also analyzed with a non-target screening method of selected samples. Microplastics, i.e. plastic fragments/fibers, paint fragments, tire wear particles (TWP) and bitumen, were analyzed with a method based on density separation with sodium iodide and identification with a stereo microscope, melt-tests, and tactile identification. MP concentrations amounted to 1500 particles/L in stormwater, 51,000 particles/L in washwater, and 2.6 × 106 particles/kg dw in sweepsand. In stormwater, washwater and sweepsand, MP ≥20 μm were found to be dominated by TWP (38%, 83% and 78%, respectively). The results confirm traffic as an important source to MP, OP, and metal emissions. Concentrations exceeding water and sediment quality guidelines for metals (e.g. Cu and Zn), PAH, phthalates, and aliphatic hydrocarbons in the C16–C35 fraction were found in most samples. The results show that the street sweeper collects large amounts of polluted materials and thereby prevents further spread of the pollutants to the receiving stormwater.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".