Moss Bags as Biomonitors of Atmospheric Microplastic Deposition in Urban Environments
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Microplastics (plastic particles <5 mm) were first identified in the environment during the 1970s and have since become ubiquitous across every environmental compartment. However, few studies have focused on atmospheric microplastics, and even fewer have used biological monitoring to assess their atmospheric deposition. Here, we assess the efficacy of moss bags as an active biomonitoring technique for atmospheric microplastic deposition. Moss (Pleurozium schreberi) bags were exposed in duplicate at nine deployment sites across a gradient of urban intensity in southern Ontario, Canada. A total of 186 microplastics (mp) were detected in the moss bags, resulting in a mean accumulation of 7.9 mp g−1 dry weight moss across all sites during the exposure period (45 days). The median microplastic length was 0.56 mm (range 0.03–4.51 mm), and the dominant microplastic type was fibres (47%), followed by fragments (39%). Microplastic accumulation significantly increased with urban intensity, ranging from 3.7 mp g−1 in low-density suburban areas to 10.7 mp g−1 in densely populated and trafficked urban areas. In contrast, microfibres by proportion dominated in suburban (62%) compared with urban areas (33%). Microplastic deposition was estimated to range from 21 to 60 mp m−2 day−1 across the nine deployment sites. The results suggest that moss bags may be a suitable technique for the active biomonitoring of atmospheric microplastic deposition in urban environments.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.001 | 0.002 |
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 it