Passive monitoring techniques to evaluate environmental pesticide exposure: Results from the Infant's Environmental Health study (ISA)
Bibliographic record
Abstract
Pesticides used in agriculture may expose populations living nearby. Costa Rica is a major banana-exporting country, its production depends on extensive pesticide use. To evaluate environmental pesticide exposure, we measured levels of current-use pesticides in air and dust from 12 schools in Matina County, Costa Rica, with passive sampling methods. We selected ten proximal and two non-proximal schools and placed polyurethane foam passive air samplers outdoors at each school, during four consecutive periods. At three of these schools, we also placed an active air sampler during the first 24 h of each sampling period. We collected passive dust samples by placing a glass Petri Dish at the inside of each school. We subsequently performed a chemical analysis of 18 pesticides, using gas chromatography with mass detector. With passive air samplers we detected ten different pesticides: two insecticides, two nematicides, and six fungicides, of which nine reported to be used on banana plantations. More than half of the samples contained at least five pesticides. Chlorpyrifos was detected most-frequently, in 98% of samples, followed by the nematicides etoprophos and the fungicide pyrimethanil that were both detected in 81% of samples. Chlorpyrifos concentrations were five times higher in proximal as compared to non-proximal schools: mean = 18.2 ng/m3 (range = 6.1–36.1) and mean = 3.5 ng/m3 (range= <0.5–11.4) and varied more between schools than in time (intra-class correlation coefficient = 0.80). In general, results from passive and active samplers showed similar exposure patterns; yet median concentrations tended to be higher in passive samplers. In dust samples, mostly fungicides were detected; chlorothalonil was detected most frequently, in 50% of samples. Passive air sampling is a promising technique to characterize environmental exposure to current-use pesticides; more studies are needed to characterize the sampling rates, reproducibility and optimum sampling times for passive samplers. School environments near banana plantations are contaminated with multiple pesticides that include insecticides, nematicides, and fungicides, which is of concern.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".