Distribution, sources, and fate of nitrate in groundwater in agricultural areas of Southern Alberta, Canada
Bibliographic record
Abstract
Nitrate pollution frequently impacts groundwater quality, particularly in agricultural regions across the world, but identifying the sources of nitrate (NO 3 − ) pollution remains challenging. The extensive use of nitrogen-containing fertilizers, surpassing crop requirements, and livestock management practices associated with the spreading of manure can lead to the accumulation and transport of NO 3 − into groundwater, potentially affecting drinking water sources. We investigated the occurrence and distribution of NO 3 − in groundwater in Southern Alberta, Canada, a region characterized by intensive crop cultivation and livestock industry. Over 3500 samples from a provincial-scale groundwater quality database, collated from multiple projects and sources, involving domestic wells, monitoring wells, and springs, coupled with newly obtained samples from monitoring wells provided comprehensive geochemical insights into groundwater quality. While stable isotope compositions of NO 3 − (δ 15 N and δ 18 O) were exclusively available for groundwater samples obtained from monitoring wells, the stable isotope data were instrumental in constraining NO 3 − sources and transformation processes within the aquifers of the study region. Among all samples, 49% (n = 1746) were associated with NO 3 − concentrations below the detection limits. Ten percent (n = 369) of all groundwater samples, including samples with concentrations below detection limits, exceed the Canadian drinking water maximum acceptable concentration of 10 mg/L for nitrate as nitrogen (NO 3 − –N). Elevated NO 3 − concentrations (> 10 mg/L as NO 3 − –N) in groundwater were mainly detected at shallow depths (< 30 m) predominantly in aquifers in surficial sediments and less frequently in bedrock aquifers. Statistical correlations between aqueous geochemical parameters showed positive associations between concentrations of NO 3 − –N and both potassium (K + ) and chloride (Cl − ), indicating the influence of synthetic fertilizers on groundwater quality. In addition, isotope analyses of NO 3 − (δ 15 N and δ 18 O) revealed three NO 3 − sources in groundwater, including mineralization of soil organic nitrogen followed by nitrification in soils, nitrification of ammonium or urea-based synthetic fertilizers in soils, and manure. However, manure was identified as the dominant source of NO 3 − exceeding the maximum acceptable concentration in groundwater within agriculturally dominated areas. Additionally, this multifaceted approach helped identify denitrification in some groundwater samples, a process that plays a key role in reducing NO 3 − concentrations under favorable redox conditions in shallow aquifers. The methodological approach used in this study can be applied to other regions worldwide to identify NO 3 − sources and removal processes in contaminated aquifers, provided there are well networks in place to monitor groundwater quality and drinking water sources.
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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 | 0.000 |
| 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 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".