Investigation of pesticides in rivers and an on-farm mitigation strategy for reducing point-source pollution
Bibliographic record
Abstract
By screening for up to 172 pesticide compounds (primarily herbicides and insecticides), this research investigated the types and concentrations of pesticide compounds present in water-column and bottom-sediment samples collected from four rivers in the Province of Manitoba, Canada. A total of 34 unique compounds were detected in the water-column (n=202) with broadleaf herbicides among the most frequently detected (2,4-D, bentazone, clopyralid, MCPA), in addition to herbicides atrazine and metolachlor that are widely used in the United States Corn Belt. Herbicide triclopyr was only detected after the Red River entered urban landscapes in Manitoba but many other unique active ingredients were already detected in the first sampling station immediately after the Canadian-United States border. Only 6 unique compounds have set Canadian Water Quality Guidelines for the Protection of Aquatic Life, and their guidelines were never exceeded. A total of 32 unique compounds were detected in bottom sediments (n=94) of which 78% are current-use active ingredients in Manitoba. In addition to sediments, pesticides can be sorbed to other constituents present in rivers such as microplastics which are believed to be carriers of legacy insecticide DDT (dichlorodiphenyltrichloroethane). This study investigated the sorption of current-use herbicides (2,4 D, atrazine, glyphosate) by microplastics which was virtually negligible, except for glyphosate sorption by PVC (35%). In contrast, the sorption of DDT by these same microplastics was always >50% (of the initial DDT in solution). Finally, this study investigated the efficiency of single and dual-cell biobeds in the Prairies to minimize point-source pollution by allowing the capture and degradation of pesticide residues associated with sprayer rinsing. With a few exceptions (clopyralid, fluroxypyr and imazethapyr), biobeds always showed to be highly effective in reducing pesticides concentrations in rinsate. The PTI (Pesticide Toxicity Index) values determined for a range of indicator species were always much larger for influent than effluent samples, suggesting ecological benefits to the broad adoption of biobeds in Prairie municipalities and on-farms. Biobeds were least effective for current-use pesticides that have relatively high GUS values (> 2.8) suggesting that further improvements in biosystem design need to be made for optimizing the recycling of these pesticides.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".