Field Testing Passive Air Samplers for Current Use Pesticides in a Tropical Environment
Bibliographic record
Abstract
Air was sampled for one year in the central valley of Costa Rica using an active high-volume sampler as well as passive samplers (PAS) based on polyurethane foam (PUF) disks and XAD-resin filled mesh cylinders. Extracts were analyzed for pesticides that are either banned or currently used in Costa Rican agriculture. Sampling rates for PUF-based passive air samplers, determined from the loss of depuration compounds spiked on the disks prior to deployment averaged 5.9 +/- 0.9 m3 x d(-1) and were higher during the windier dry season than during the rainy season. Sampling rates for the XAD-based passive sampler were determined from the slopes of linear relationships that were observed between the amount of pesticide sequestered in the resin and the length of deployment, which varied from 4 months to 1 year. Those sampling rates increased with decreasing molecular size of a pesticide, and their average of 2.1 +/- 1.5 m3 x d(-1) is higher than rates previously reported for temperate and polar sampling sites. Even though the trends of the sampling rate with molecular size and temperature are consistent with the hypothesis that molecular diffusion controls uptake in passive samplers, the trends are much more pronounced than a direct proportionality between sampling rate and molecular diffusivity would suggest. Air concentrations derived by the three sampling methods are within a factor of 2 of each other, suggesting that properly calibrated PAS can be effective tools for monitoring levels of pesticides in the tropical atmosphere. In particular, HiVol samplers, PUF-disk samplers, and XAD-based passive samplers are suitable for obtaining information on air concentration variability on the time scale of days, seasons and years, respectively. This study represents the first calibration study for the uptake of current use pesticides by passive air samplers.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".