Dry deposition of reactive nitrogen to European ecosystems: a comparison of inferential models across the NitroEurope network
Bibliographic record
Abstract
Abstract. Inferential models have long been used to determine pollutant dry deposition to ecosystems from measurements of air concentrations and as part of national and regional atmospheric chemistry and transport models, and yet models still suffer very large uncertainties. An inferential network of 55 sites throughout Europe for atmospheric reactive nitrogen (Nr) was established in 2007, providing ambient concentrations of gaseous NH3, NO2, HNO3 and HONO and aerosol NH4+ and NO3− as part of the NitroEurope Integrated Project. Network results providing modelled inorganic Nr dry deposition to the 55 monitoring sites are presented, using four existing dry deposition routines, revealing inter-model differences and providing ensemble average deposition estimates. Dry deposition is generally largest over forests in regions with large ambient NH3 concentrations, exceeding 30–40 kg N ha−1 yr−1 over parts of the Netherlands and Belgium, while some remote forests in Scandinavia receive less than 2 kg N ha−1 yr−1. Turbulent Nr deposition to short vegetation ecosystems is generally smaller than to forests due to reduced turbulent exchange, but also because NH3 inputs to fertilised, agricultural systems are limited by the presence of a substantial NH3 source in the vegetation, leading to periods of emission as well as deposition. Differences between models reach a factor 2–3 and are often greater than differences between monitoring sites. For soluble Nr gases such as NH3 and HNO3, the non-stomatal pathways are responsible for most of the annual uptake over many surfaces, especially the non-agricultural land uses, but parameterisations of the sink strength vary considerably among models. For aerosol NH4+ and NO3− discrepancies between theoretical models and field flux measurements lead to much uncertainty in dry deposition rates for fine particles (0.1–0.5 μm). The validation of inferential models at the ecosystem scale is best achieved by comparison with direct long-term micrometeorological Nr flux measurements, but too few such datasets are available, especially for HNO3 and aerosol NH4+ and NO3−.
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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 | 0.000 | 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) | 0.000 | 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".