Comparison of Four Active and Passive Sampling Techniques for Pesticides in Air
Bibliographic record
Abstract
Four sampling systems were evaluated for their ability to determine the concentrations of pesticides in the atmosphere of rural southern Ontario. Two active air samplers (AAS, high-volume and low-volume pumps) and two passive air samplers (PAS, polyurethane foam disks and XAD-resin) were deployed between March 2006 and September 2007 using different sampling frequencies (biweekly to annually) and durations (24 h to 1 yr). Concentrations of nine pesticides in air determined by the different systems were compared at time scales of two weeks, two months, and one year. Agreement in the average concentrations obtained by different techniques improved with increasing length of the comparison period, especially for pesticides with high short-term temporal concentration variability. Such variability was high for the most volatile and reactive pesticides (trifluralin and pendimethalin). Except for these two pesticides, the annually averaged air concentrations determined by the different systems are within a factor of 2.5 for all pesticides and are not statistically different. Even though the PUF-PAS may have approached equilibrium with the atmosphere during deployment, the air concentrations are not statistically significantly different from those determined by AAS when averaged over longer time scales. Two month XAD-PAS deployments during the second summer resulted in sufficient sampling volumes to reliably establish air concentrations. If the sole purpose of collecting air samples is the assessment of long-term air concentration trends, this can be achieved most cost-effectively, i.e., with the least number of samples with year-long XAD-PAS.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.000 | 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 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".