Pesticide residues in groundwater and soil of a prairie province in Canada
Bibliographic record
Abstract
The study was conducted to monitor pesticide residues in groundwater of two agriculturally intensive regions of Alberta, to determine the occurrence of pesticide residues in soil and groundwater of an experimental plot in southern Alberta, and to investigate the influence of land management factors and soil depth on pesticide residues in soil. A total of 440 groundwater samples were collected in three years (2013-2015) and analyzed for the presence of 142 pesticides using Liquid-liquid Extraction (LLE) in combination with Gas Chromatography- Mass Selective Detector (GC-MSD) and Gas Chromatography - Tandem Mass Spectrometry (GC-MS/MS). Ten herbicides, seven insecticides, and six fungicides were detected. Herbicides 2,4-D, MCPA, and clopyralid were the most frequently detected, and the only pesticides consistently detected every year in both regions. Pesticides were detected more frequently in summer and fall compared to spring. 4.5% of the samples were contaminated with more than one pesticides. No pesticides exceeded the Guidelines for Canadian Drinking Water Quality. Similarly, a total of 213 groundwater samples (December 2014-November 2015) and 61 soil samples (in December 2014) in duplicates were analysed from the experimental plots in Lethbridge. Soil samples were collected at various depths (0-75 cm) and analyzed for the presence of 130 pesticides. A linear mixed effect model was fitted to determine the effect of soil depth, cropping systems and manure amendments on pesticide concentration in soil. In groundwater, bentazone, 2,4-D, and MCPA occurred most frequently and pesticide detection frequency varied seasonally with greater detections in July and September compared to other months. 37 different pesticide compounds were detected, and DDT isomers, 2,4-D, difenoconazole, MCPA and trans-heptachlor epoxide accounted for 85% of all detections. p,p’-DDE, 2,4-D, p,p-DDT, difenoconazole and MCPA occurred throughout the soil column and all of these except p,p-DDT were detected in underlying groundwater. Bentazone was found in groundwater throughout the year but not in soil. A greater number and higher concentrations of pesticides were found in soil. However, not all of them leached to groundwater. Soil depth and cropping system were significantly associated with total pesticide and total DDT concentration in the soil. Manure amendments had no affect pesticide concentrations.
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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 | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".