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Record W4392580149 · doi:10.5194/egusphere-egu24-11118

Enhancing Nitrogen Removal in European River Basins: The Crucial Role of Wetland Conservation and Restoration as Nature-Based Solutions

2024· preprint· en· W4392580149 on OpenAlexaff
Leonardo Bertassello, N. B. Basu, Luc Feyen

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicPeatlands and Wetlands Ecology
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsWetlandEnvironmental scienceNitrogenConservationWater resource managementWetland conservationHydrology (agriculture)Environmental protectionEnvironmental resource managementAgroforestryEcologyGeologyChemistryBiologyGeotechnical engineering

Abstract

fetched live from OpenAlex

Over the past century, the nitrogen pollution problem has grown concomitantly with population growth, intensified agricultural practices, and a warming climate. In Europe, the presence of excess nitrogen in the environment has already exceeded safe planetary boundaries, posing a threat to Earth’s water supply and biodiversity.While considerable efforts are now focused toward mitigating this problem through increased regulatory measures on wastewater treatment plants and implementation of better agricultural management practices, there is a growing interest in the use of wetlands as nature-based solutions (NBS) to improve water quality and, in particular, to reduce nitrogen loading to downstream water.Despite these benefits, wetlands are among the most degraded ecosystem in Europe, having experienced significant shrinkage over the past centuries, now constituting only one-third of their 1700 extent. This decline is largely attributed to agricultural expansion on drained productive wetland soils, while also contributing to increased nitrogen pollution from excess use of fertilizers.To address these issues, starting from nitrogen surplus data and current wetland extent at European scale we estimate with a physically-based model that current removal potential of wetlands is about 1113 ± 101 kt of N per year (~6.5% of total N surplus in European soil). The significance of wetlands in water quality remediation is underscored by the fact that this nitrogen would otherwise enter the river network and, subsequently, the sea. Given that the current riverine loads in EU watersheds amount to about 2730 kt N per year, the loss of current wetlands would increase this figure by over 40%, with detrimental consequences for the status of surface waters and the eutrophication of coastal areas.We propose a set of restoration scenarios, along with the associated costs, for the restoration of wetlands that have been drained for agricultural purposes. Our analysis aligns with the objective of the Nature Restoration Law, requiring EU member states to implement effective restoration measures to cover at least 20% of the EU’s land and sea areas by 2030. We show that by restoring 2.6% of EU land (equivalent to 20% of historical wetlands), we could nearly double the current nitrogen uptake (2108 ± 187 kt of N per year), and significantly improve riverine water quality by reducing more than 30% of their loads to the sea. In addition, wetland restoration will offer a wide array of ecosystem co-benefits from flood prevention and carbon sequestration to provision of critical habitat for specialized flora and fauna.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.045
Threshold uncertainty score0.089

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.009
GPT teacher head0.227
Teacher spread0.218 · 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 source (direct Gemma or distilled Codex), 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

Citations1
Published2024
Admission routes1
Has abstractyes

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