Global modeling of fungal spores with the EMAC chemistryclimate model: uncertainties in emission parametrizations and observations
Bibliographic record
Abstract
Abstract. Primary biological aerosol particles (PBAPs) may impact human health and aerosol-cloud-climate interactions. The role of PBAPs in the earth system is associated with large uncertainties, for example of source estimates and the atmospheric lifetime. We used a chemistry-climate model to simulate PBAPs in the atmosphere including bacteria and fungal spores. Three fungal spore emission parameterizations have been evaluated against an updated set of spore counts synthesized from observations reported in the literature. The comparison indicates an optimal fit for the emission parameterization proposed by Heald and Spracklen (2009) and adapted by Hoose et al. (2010) for particle sizes of 5 µm or 3 µm, although the model still overpredicts PBAP concentrations in some locations. The correlations between the spore count observations and meteorological parameters simulated by the model show a strong dependence on the leaf area index in non-urban areas and the specific humidity in urban areas. Additional evaluation was performed by comparing our combined bacteria and fungal spore simulations to a global dataset of fluorescent biological aerosol particle (FBAP) concentrations. The model predicts the total sum of measured PBAP concentrations relatively well, typically within a factor of two of FBAP. Further, the modeled fungal spore results deviate from the FBAP concentrations when used as a rough proxy for spores, depending on the particle size used in the parametrization. Uncertainties related to technical aspects of the FBAP and direct-counting spore measurements challenge the ability to further refine quantitative comparison on this scale. Additional long-term data of better quality are needed to improve emission parameterizations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".