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Record W4413644641 · doi:10.5194/egusphere-2025-3801

Atmospheric nitrogen deposition fluxes into coastal wetlands and their impacts on ecosystem carbon sequestration in East Asia

2025· preprint· en· W4413644641 on OpenAlexaff
Jia Liu, Yan Zhang, Shenglan Jiang, Yuqi Xiong, Chenji Jin, Yu Qi, Weichun Ma

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicPeatlands and Wetlands Ecology
Canadian institutionsInstitute on Governance
FundersFudan UniversityNational Natural Science Foundation of ChinaNatural Science Foundation of Shanghai
KeywordsCarbon sequestrationWetlandEnvironmental scienceEcosystemDeposition (geology)East AsiaNitrogenEarth scienceEnvironmental protectionGeographyChinaEcologyGeologySedimentChemistry

Abstract

fetched live from OpenAlex

Abstract. Coastal wetlands serve as critical sinks for both carbon and nitrogen within regional ecosystems, playing an essential role in mitigating atmospheric greenhouse gases and nutrient enrichment. This study integrates high-resolution wetland type data, AIS-based ship emission inventories, and regional nitrogen deposition simulations to quantify nitrogen inputs to East Asian coastal wetlands from the perspective of source–sink coupling. Firstly, the atmospheric nitrogen deposition fluxes in coastal wetland areas of East Asia were simulated and evaluated with WRF-CMAQ. Nitrogen deposition fluxes were spatially coupled with classified wetland maps in ArcGIS. Net primary productivity (NPP) was estimated using a modified CASA light-use efficiency model, incorporating solar radiation and FPAR from remote sensing. Carbon sequestration and oxygen release were then quantified using stoichiometric relationships based on NPP. The results indicate that total nitrogen deposition across East Asian coastal wetlands follows a general gradient of “high in the south, low in the north” and “strong in urban-industrial clusters, weak in remote coastal zones.” On average, ship emissions contribute 10.13 % and 15.22 % to NO3--N and NH4+-N deposition, respectively, while their contribution to gaseous NH3-N is negligible. Among wetland types, salt marshes receive the highest nitrogen input per unit area (654.99 mg NO3--N·m-2·yr-1), although tidal flats dominate total regional nitrogen input due to their extensive spatial coverage. Dry and wet deposition exhibit significant seasonal variation: wet deposition consistently prevails during the spring and summer months due to frequent rainfall, while dry deposition becomes increasingly prominent in autumn and winter. For instance, in the Korean Peninsula, the wet-dry gap in nitrate deposition reaches 0.17 g N·m-2·yr-1, while the Yangtze River Delta exhibits relatively balanced ammonium inputs (dry-wet difference of only 0.05 g N·m-2·yr-1). Carbon sequestration capacity shows strong spatial and temporal coupling with nitrogen deposition. Mangrove forests exhibit the highest annual NPP (~776.16 g C·m-2·yr-1 in summer), supported by high FPAR and solar radiation (1749.29 MJ·m-2), followed by salt marshes and tidal flats. Seasonal patterns reveal a summer peak in carbon uptake across all wetland types, with mangrove NPP in summer being two times higher than winter values. Nitrogen deposition primarily enhances carbon sequestration during warm seasons; for instance, in the mangroves of the Pearl River Delta, nitrogen inputs increase summer CO2 fixation by 6.85 g C·m-2, while the effect is negligible in winter (<0.06 %) or in nitrogen-saturated regions. These findings provide a scientific foundation for understanding how coastal ecosystems respond to anthropogenic activities and long-range nitrogen transport. Furthermore, the results serve as an important reference for wetland conservation, nitrogen cycle management, and the development of regional carbon neutrality strategies.

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)0.000
metaresearch head score (Gemma)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.050
Threshold uncertainty score0.100

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.007
GPT teacher head0.217
Teacher spread0.210 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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