Watershed-Scale Shallow Groundwater Anthropogenic Nitrate Source, Loading, and Contamination Assessment in a Typical Wheat Production Region: Case Study in Yiluo River Watershed, Middle of China
Bibliographic record
Abstract
Nitrate pollution in groundwater has become a global concern for agriculture and regional ecology. However, tracing the spatiotemporal groundwater nitrate pollution sources, calculating the total nitrogen loading, and assessing contamination at the watershed scale have not been well documented. In this study, 20 groundwater samplings from 2020 to 2021 (in dry and wet seasons) on the Yiluo River watershed in middle China were collected. Tracing groundwater nitrate pollution sources, calculating total nitrogen loading, and assessing contamination using dual isotopes (18ONO3 and 15NNO3), conservation of mass, and the nitrate pollution index (NPI), respectively. The results indicated that there were three nitrate sources in groundwater: (1) manure and sewage waste input (MSWI), (2) sediment nitrogen input (SNI), and (3) agriculture chemical fertilizer input (ACFI) in the Yiluo River watershed. ACFI and SNI were the main groundwater nitrogen pollution sources. The average nitrogen loading percentages of ACFI, SNI, and MSWI in the whole watershed were 94.7%, 4.34%, and 0.96%, respectively. The total nitrogen loading in the Yiluo River watershed was 7,256,835.99 kg/year, 4,084,870.09 kg/year in downstream areas, 2,121,938.93 kg/year in midstream areas, and 1,050,026.95 kg/year in upstream areas. Sixty percent of groundwater in the Yiluo River watershed has been polluted by nitrate. Nitrate pollution in midstream areas is more severe. Nitrite pollution was more serious in the wet season than in the dry season. The results of this study can provide useful information for watershed-scale groundwater nitrogen pollution control and treatment.
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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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".