Glyphosate, glufosinate ammonium, and AMPA occurrences and sources in groundwater of hilly vineyards
Bibliographic record
Abstract
Glyphosate [N-(phosphonomethyl) glycine] and glufosinate ammonium [ammonium dl-homoalanin-4-(methyl) phosphinate] are broad-spectrum, nonselective, post-emergence herbicides extensively used in various applications for weed control in both agricultural and non-crop areas. Aminomethylphosphonic acid (AMPA) is the major degradation product of glyphosate found in plants, water, and soil. Due to glyphosate's presumed low mobility, its monitoring in European water was limited. Recently both glyphosate and AMPA have been detected in several groundwater samples in Europe, U.S, Canada, Argentina, and China. Understanding the sources of these substances in water, especially in groundwater used for drinking, becomes a priority. In the present work the occurrences and the main drives of glyphosate, AMPA, and glufosinate ammonium in the groundwater of hilly vineyards located in the North-West of Italy were evaluated. Groundwater monitoring results showed frequent detection and concentrations above EQSGW for glyphosate and AMPA, while glufosinate ammonium was never detected. More frequent occurrences and higher concentrations were detected in the samples collected from wells located in the farmyards, most of them being used for irrigation and/or preparation of PPPs mixtures. Indeed, AMPA was the only compound detected in one groundwater well used for drinking, at values bellow EQSGw/DWQS. Such monitoring results were not expected as the modelling estimations under local pedoclimatic conditions indicated no risk of leaching to groundwater. However, the modelling performance and output may have been influenced by the non-consideration of important specific processes. Integrating monitoring and modelling results with information concerning the agricultural practices adopted and the wells use and location, possible contamination drivers were identified. These include the non-agricultural use of glyphosate in the farmyard, the point source contamination of wells and the possible transport with the subsurface lateral inflow of water from up-hill vineyard. This study strengthens the position of SETAC EMAG-Pest GW group concerning the necessity of spatial and temporal contextualisation of groundwater monitoring for a better understanding of its contamination drivers by PPPs.
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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 | 0.001 | 0.005 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.003 |
| 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".