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 distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 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 teacher head, 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".