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Record W2062639723 · doi:10.5194/acp-11-2703-2011

Dry deposition of reactive nitrogen to European ecosystems: a comparison of inferential models across the NitroEurope network

2011· article· en· W2062639723 on OpenAlexaff
Christophe Fléchard, Eiko Nemitz, R. I. Smith, D. Fowler, Alex Vermeulen, Albert Bleeker, Jan Willem Erisman, David Simpson, Leiming Zhang, Y. Sim Tang, Mark A. Sutton

Bibliographic record

VenueAtmospheric chemistry and physics · 2011
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric chemistry and aerosols
Canadian institutionsEnvironment and Climate Change Canada
FundersInstitut National de la Recherche AgronomiqueEuropean CommissionAgentschap voor Natuur en Bos
KeywordsDeposition (geology)Reactive nitrogenEnvironmental scienceEcosystemSink (geography)Atmospheric sciencesNitrogenVegetation (pathology)Environmental chemistryTerrestrial ecosystemAerosolPollutantHydrology (agriculture)ChemistryEcologyMeteorologyGeographyGeologyBiology

Abstract

fetched live from OpenAlex

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−.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.579
Threshold uncertainty score0.591

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.021
GPT teacher head0.224
Teacher spread0.203 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations335
Published2011
Admission routes1
Has abstractyes

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