Tracing the Origin of Groundwater Nitrate in an Area Affected by Acid Rain Using Dual Isotopic Composition of Nitrate
Bibliographic record
Abstract
Acid rain with a relatively high concentration of ammonium and nitrate can accelerate rock weathering. However, its impact on groundwater nitrate is uncertain. This study evaluated the dual isotopic composition of nitrate (δ15N-NO3- and δ18O-NO3-) from precipitation to groundwater in a rural mountainous area affected by acid rain. The average concentration for NH4+ is 1.25 mg/L and NO3- is 2.59 mg/L of acid rain. Groundwater NO3- concentrations ranged from <0.05 to 11.8 mg/L (baseline), and NH4+ concentrations ranged from 0.06 to 0.28 mg/L. The results show that groundwater δ18O-NO3- values (-4.7‰ to +4.2‰) were lower than the values of rainfall δ18O-NO3- (+24.9‰ to +67.3‰), suggesting that rainfall NO3- contributes little to groundwater NO3-. Groundwater δ15N-NO3- values (+0.1‰ to +7.5‰) were higher than the values of δ15N-NO3- derived from the nitrification of rainfall NH4+ (less than -4.7‰ in the study area), suggesting that nitrification of rainfall NH4+ also contributes little to groundwater NO3-. This implies that rainfall NO3- and NH4+ have been utilized. The dual isotopic composition of nitrate shows that baseline groundwater NO3- is derived mainly from nitrification of soil nitrogen. The denitrification process is limited in the groundwater system. This study shows that the rainfall NO3- and NH4+ contribute little to groundwater NO3-, improving the understanding of the nitrogen cycle in areas with a high concentration of NH4+ and NO3- in rainfall.
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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 | 0.000 | 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) | 0.000 | 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".