Levels and Seasonal Variability of Pesticides in the Rural Atmosphere of Southern Ontario
Bibliographic record
Abstract
Air samples were collected continuously in Egbert, Ontario, which is in a rural agricultural area north of Toronto, between March 2006 and September 2007 and analyzed for pesticides of both current and historic use. The fungicide chlorothalonil was present in highest abundance with levels exceeding 2000 pg x m(-3) in the summer. Almost as abundant, with summer time concentrations around 400 to 600 pg x m(-3), were the herbicides atrazine, alachlor, and metolachlor. Other pesticides in current use, such as trifluralin, pendimethalin, chlorpyrifos, endosulfan, and disulfoton were consistently present at levels approximately 1 order of magnitude lower. Concentrations of banned pesticides (chlordanes and hexachlorocyclohexane) were generally below 10 pg x m(-3), except for hexachlorobenzene, which was present at the global average of approximately 50 pg x m(-3). These levels and the fact that they are generally lower than what has been reported for the area previously are in agreement with pesticide usage data for Ontario. Only the concentrations of chlorothalonil, chlorpyrifos, and HCB were correlated with air mass origin, as determined by back trajectory analysis. All pesticides had higher levels during the growing season compared to those in winter, but the ratio of concentrations during the different seasons is much higher for the pesticides in current use. That ratio may aid in distinguishing seasonal variability caused by pesticide application during the growing season from that caused by temperature-driven revolatilization. Higher concentrations of the banned pesticides during 2007 compared to those in 2006 may be due to higher volatilization rates caused by higher surface temperatures consistent with the El Nino Southern Oscillation.
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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".