Emerging diversity of volatile organic compounds from freshwater and marine ecosystems
Bibliographic record
Abstract
The chemical diversity of volatile organic compound (VOC) emissions from terrestrial vegetation is relatively well understood, while research on VOC emissions from freshwater and marine systems has largely focused on dimethyl sulfide (DMS) and isoprene. Through VOC concentration measurements in water samples, and VOC flux measurements using floating chambers and the direct eddy covariance (EC) technique we aim to evaluate aquatic ecosystems as sources of VOCs. Here, we present selected case studies that demonstrate the need to consider other VOCs beyond DMS and isoprene when assessing aquatic sources of atmospheric VOCs.A survey of depth-specific VOC concentrations in water from four Alpine lakes in France showed that VOC concentrations were highest either at the deep chlorophyll maximum or at the surface. The VOC composition profiles differed between depths and lakes. In another study, we assessed net emissions of VOCs from three ponds in a rewetted peatland forest in Denmark. Again, the three ponds showed differences in the quantity and diversity of their emission profiles. Over 100 chemical species were detected, including acetone, acetaldehyde, isoprene, other terpenoids, and hydrocarbons. The most eutrophic and acidic pond had highest emission rates but lower VOC diversity compared to the alkaline ponds.The VOC emission rates and compositions also vary over time, depending on the balance between VOC production, consumption, and emission rates, driven by both biotic and abiotic factors. Our EC flux measurements on Utö Island in the Baltic Sea show strong seasonal variation in marine VOC emissions, which can be coupled to the biomass and phenology of the phytoplankton as well as to environmental factors.We highlight the emerging diversity of VOC emissions from aquatic ecosystems. These emissions need to be better quantified to assess their atmospheric fate and implications.
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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 | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".