Groundwater phosphorus concentrations: global trends and links with agricultural and oil and gas activities
Bibliographic record
Abstract
Abstract Although observations show that anthropogenic phosphorus (P) can reach groundwater supplies, there has been no comprehensive evaluation of P in groundwater at the global scale and P contamination from sources such as agriculture and oil and gas activities are poorly understood. We compile and analyze 161 321 groundwater P measurements in 12 different countries to determine the extent of P contamination at the global scale. We find that all 12 countries report groundwater samples with concentrations >0.1 mg P l−1, a concentration at which the risk for eutrophication of surface waters is high. In Canada and the United States (US), we perform an analysis of 1529 dissolved oxygen (DO) concentrations to determine the degree of association between DO and groundwater P measurements. For P concentrations <0.1 mg P l−1, we find a strong inverse relationship between DO and P. However, for P concentrations >0.1 mg P l−1, we find a weak inverse correlation, which suggests anthropogenic sources are responsible for elevated P concentrations in groundwater. To identify anthropogenic sources of P, we conduct an analysis on land use data and the 24 146 P concentrations in Canada and the US. Although we find that 12% (2899) of all P concentrations are >0.1 mg P l−1, 33% of P concentrations from P monitoring sites located on pastureland (managed grassland) report concentrations >0.1 mg P l−1. In Alberta and Ontario, we analyze P measurements with respect to their proximity to oil and gas wells and find the relationship to be inconclusive. Overall, we find a positive correlation with agriculture and elevated groundwater P concentrations, but additional data are needed to identify a relationship between oil and gas wells and elevated groundwater P concentrations. Characterizing the role of agriculture and oil and gas wells on groundwater P contamination can help regulators develop effective strategies to protect water quality and ecosystem health.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| 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 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".