Atmospheric nitrogen deposition to forest and estuary environments in the Pearl River Delta region, southern China
Bibliographic record
Abstract
Due to its significant ecological and climate consequences, atmospheric nitrogen (N) deposition is a growing global concern, especially in the severely N-polluted regions such as the Pearl River Delta (PRD) region of southern China. One-year measurements of reactive N species, including ammonium nitrogen (NH4+-N), nitrate nitrogen (NO3--N) and total organic nitrogen (ON) in dry and wet deposition, were conducted using an automated wet–dry sampler incorporated with a DDAS (dry deposition on aqueous surface) sampling device at Dinghushan (DHS), a natural forest site in the northwest of PRD and at Hengmen (HM), an estuary site in the south of PRD during 2006–2007. Total deposition fluxes of N at DHS and HM were up to 48.2 and 37.8 kg ha−1 yr−1, respectively, with most of the deposition occurring in the rainy season. Wet deposition was the dominant form, contributing 65–70% to the total deposition.NH4+-N was the largest contributor to the total N deposition at DHS (47%) due to significant influence of agriculture emissions. ON was the most important N component at HM (41%), which is probably attributed to the marine sources. However,NO3--N deposition is increasing rapidly recently and is expected to be more important in the near future. The current N deposition level in PRD is much higher than those in Europe and North America. Great challenges exist in reducing reactive N emission in this region. Thus, a scenario of rising N deposition in PRD in the near future cannot be ruled out. The environmental consequences due to elevated N deposition should therefore be paid more attention in the future.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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 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".