Understudied BVOC emissions in Europe and their potential atmospheric impacts
Bibliographic record
Abstract
Europe is one of the most studied areas related to biogenic volatile organic compound (BVOC) emissions. However, our knowledge of these atmospheric reactive compounds is still quite limited even there. Total hydroxyl radical (OH) reactivity studies indicate that half of the atmospheric reactive compounds are still unknown especially in the forested areas (Yang et al. 2016) and OH and ozone reactivity studies of our group have shown high fractions of reactivity from biogenic emissions (Praplan et al. 2020 and Thomas et al. 2023).Globally, isoprene is the primary emitted BVOC. While boreal forests in Northern Europe are mainly considered as monoterpene emitters, Central Europe is expected to be dominated by isoprene (e.g. Messina et al. 2016). However, our results from a campaign at 17 stations over Europe in summer 2022 indicated that BVOC mixing ratios are highly variable and some areas also in Central Europe may be dominated by monoterpenes.Sesquiterpenes and diterpenes have very high potential for secondary organic aerosol formation, but much less is known on their emissions and atmospheric concentrations. Our studies show that birches and spruces may be strong sesquiterpene emitters. We have also found that some urban trees in Montreal and wetlands in Lapland known as isoprene emitters may also release significant amounts of sesquiterpenes. Additionally, forest floor represents a potential source of sesquiterpenes.Compared to terrestrial sources very little is known on the marine emissions of BVOCs. There are studies on dimethyl sulphide, but our recent results on an island in Baltic Sea suggest that other sulphuric compounds, like methanethiol, may be important too and could have strong impacts on SO2 production and therefore also on new particle and cloud formation. Furthermore, our recent campaign at the coast of Baltic Sea indicates that phytoplankton and macrophytes could be a source of isoprene and monoterpenes (Thakur et al., 2024 publication under prep).Compounds classified as BVOCs (e.g. monoterpenes) can also be emitted from anthropogenic sources, such as construction sites (e.g. from wooden material), as well as cleaning and personal care products. Our studies in a street canyon in Helsinki in 2022 indicates that they strongly impact local atmospheric chemistry even in wintertime. Messina, P., Lathière, J., Sindelarova, K., Vuichard, N., Granier, C., Ghattas, J., Cozic, A., and Hauglustaine, D. A.: Global biogenic volatile organic compound emissions in the ORCHIDEE and MEGAN models and sensitivity to key parameters, Atmos. Chem. Phys., 16, 14169–14202, https://doi.org/10.5194/acp-16-14169-2016, 2016Praplan, A. P., Tykkä, T., Schallhart, S., Tarvainen, V., Bäck, J., and Hellén, H.: OH reactivity from the emissions of different tree species: investigating the missing reactivity in a boreal forest, Biogeosciences, 17, 4681–4705, https://doi.org/10.5194/bg-17-4681-2020, 2020.Thomas, S. J., Tykkä, T., Hellén, H., Bianchi, F., and Praplan, A. P.: Undetected biogenic volatile organic compounds from Norway spruce drive total ozone reactivity measurements, Atmos. Chem. Phys., 23, 14627–14642, https://doi.org/10.5194/acp-23-14627-2023, 2023.Yang, Y., Shao, M., Wang, X., Nölscher, A. C., Kessel, S., Guenther, A., and Williams, J.: Towards a quantitative understanding of total OH reactivity: A review, Atmos. Environ., 134, 147–161, https://doi.org/10.1016/j.atmosenv.2016.03.010, 2016.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".