Atmospheric particulate matter from an industrial area as a source of metal nanoparticle contamination in aquatic ecosystems
Bibliographic record
Abstract
Air pollution legislation and control worldwide is based on the size of particulate matter (PM) to evaluate the effects on environmental and human health, in which the small diameter particles are considered more dangerous than larger sizes. This study investigates the composition, stability, size and dispersion of atmospheric settleable particulate matter (SePM) in an aqueous system. We aimed to interrogate the changes in the physical properties and characteristics that can contribute to increased metal uptake by aquatic biota. Samples collected in an area influenced by the steel and iron industry were separated into 8 fractions (425 to ≤10 μm) and analysed physically and chemically. Results from ICP-MS and X-ray showed that the PM composition was mainly hematite with 80% of Fe, followed by Al, Mn and Ti. Among 27 elements analysed we found 19 metals, showing emerging metallic contaminants such as Y, Zr, Sn, La, Ba and Bi. Scanning electron microscopy (SEM) showed that SePM fractions are formed by an agglomeration of nanoparticles. Furthermore, dynamic light scattering (DLS), zeta potential and nanoparticle tracking analysis (NTA) demonstrated that SPM were dissociated in water, forming nanoparticles smaller than 200 nm, which can also contribute to water pollution. This study highlights that SePM contamination may be substantially higher than expected under that allowed in atmospheric regulatory frameworks, thereby extending their negative effect to water bodies upon settling, which is an underexplored area of our knowledge. We therefore provide important insights for future investigations on safety regulations involving SePM in the environment, indicating the need to revise the role of SePM, not solely associated with air pollution but also considering their deleterious effects on water resources.
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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 | 0.001 | 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".