Quantification and characterization of nanoparticles in environmental matrices: release from surface coatings and distributions in natural waters
Bibliographic record
Abstract
Nanoparticle (NP) emissions to the environment are increasing as a result of anthropogenic activities, prompting concerns with respect to the ecosystem and human health. In order to evaluate the risk of NPs, it is necessary to know their concentrations in various environmental compartments, on regional and global scales; however, these data have remained elusive, mainly due to the analytical difficulties of measuring NPs in complex natural matrices. The fundamental aim of this work was thus to develop an improved analytical strategy for the high-throughput detection, quantification, and characterization of NPs in complex natural waters. To this end, inductively coupled plasma mass spectrometry (ICP-MS) was optimized for single particle analysis. Enhanced sensitivities that enable the determinations of smallest NPs and multi-element analysis that can provide potential insight into NP origins on a particle-by-particle basis were an important focal point of the project. Using state-of-the-art techniques, the methods of single particle analysis were applied for the monitoring of NPs in two important environmental contexts: (i) understanding the emission patterns of engineered TiO2 NPs from a prevalently used nano-enabled product, i.e. surface coatings, under natural weathering scenarios, and (ii) examining the presence and distribution of three major NPs (Ag-, Ce- and Ti-NPs), including engineered ones, in global natural waters. Measurements were performed with respect to particle sizes (distributions), mass/number concentrations and compositions/purities. Surface-leaching experiments were designed to elicit the role of seasonal changes, weathering variables, surface exposure scenarios, and coating matrix properties in NP release patterns. The data clearly showed the strong impact of weathering on NP release trends, with wet and freeze-thaw (i.e. rainfall and slushy snow) conditions notably favouring NP leaching. While the release quantities did not surpass 10-200 µg-Ti m-2-coating, even after several months of weathering, increasing emissions may occur given the ageing and degradation of coatings over time. Natural waters were monitored globally, which entailed a sampling campaign from 46 sites in 13 countries and was aimed to link NP occurrences and distributions to particle type, size, origin, and sampling location. The results demonstrated the ubiquitous presence of NPs in the environment, including the remotest landscapes, such as Northern Canada and within Iceland glaciers. The original multiplexed data presented in this body of research is much needed for reliable parametrization and validation of NP exposure models and for NP risk monitoring. The work also lays a foundation for broader systematic analysis of NP release and distribution patterns in the ecosystem
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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 | 0.000 | 0.000 |
| 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".