A fluorescence approach for an online measurement technique of atmospheric microplastics
Bibliographic record
Abstract
Microplastic particles in the atmosphere are regularly detected in urban areas as well as in very remote locations. Yet the sources, chemical transformation, transport, and abundance of airborne microplastics still remains largely unexplained. Therefore, their impact on health, weather and climate related processes lacks comprehensive understanding. Single particle detection presents a substantial challenge due to its time-consuming process and is conducted solely offline. To get more information about the distribution, fluxes and sources of microplastics in the atmosphere, a reliable and fast online measurement technique is of utmost importance. Here we demonstrate the use of autofluorescence of microplastic particles for their online detection with a high sensitivity towards different widely used polymers. We deploy online single particle fluorescence spectroscopy with a Wideband Integrated Bioaerosol Sensor WIBS 5/NEO (Droplet Measurement Technologies), which enables single particle fluorescence measurements at two excitation wavelengths (280 nm and 370 nm) and in two emission windows (310 – 400 nm and 420 – 650 nm). We investigated shredded (<100 μm) everyday plastic products (drinking bottles, yogurt cups) and purchased pure powders of polyethylene terephthalate (PET), polyethylene (PE) and polypropylene (PP). For that broad range of typical plastic products analyzed, we detected fluorescence on a single particle level using the WIBS. The online detection is possible even for particles smaller than 2 μm, with a remarkable detection efficiency of microplastic particles from a PET bottle as small as 1.2 μm with 95% effectivity. Comparison with biological aerosol reveals that microplastics can be distinguished from two abundant pollen species and investigation of the complete fluorescence excitation emission maps of all samples show that online identification of microplastics might be possible with fluorescence techniques if multiple channels are available.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.001 | 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 teacher head, 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".