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 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 | 0.001 |
| 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".