The role of technology and policy in mitigating regional nitrogen pollution
Bibliographic record
Abstract
Human activity greatly influences nitrogen (N) pollution in urbanized and adjacent areas. We comprehensively studied the N cycling in an urban–rural complex system, the Greater Hangzhou Area (GHA) in southeastern China. Our results indicated that subsurface N accumulation doubled, riverine N export tripled and atmospheric N pollutants increased 2.5 times within the GHA from 1980–2004. Agriculture was the largest N pollution source to air and water before 2000, whereas industry and human living gradually became the primary N pollution sources with the socioeconomic development. Based on the sensitivity analysis, we developed a scenario analysis to quantify the effects of technology and policy on environmental N dynamics. The fertilization reduction scenario suggested that the groundwater N pollution could decrease by 17% with less than a 5% reduction in crop production; the N effluent standard revision scenario led to a surface water N pollution reduction of 45%; the constructed wetlands implementation scenario could reduce surface water pollution by 43%–64%. Lastly, the technological improvement scenario mitigated atmospheric N pollution by 65%. Technologies play a key role in atmospheric N pollution control, policies mainly contribute to groundwater N pollution control, while technology and policy both work on surface water N mitigation within an urban–rural complex.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".