Air−Soil Exchange of Organochlorine Pesticides in Agricultural Soils. 2. Laboratory Measurements of the Soil−Air Partition Coefficient
Bibliographic record
Abstract
This is the second of two papers that investigate soil−air exchange of organochlorine (OC) pesticides. In the present paper, a fugacity meter was used to measure soil−air partition coefficients ( K SA ) under controlled laboratory conditions for OC pesticides in three different soils as a function of temperature and soil organic carbon content. A strong temperature dependence of K SA was observed for all three soils. A linear relationship between K SA and the octanol−air partition coefficient ( K OA ) was observed for all soils. The enthalpy of soil−air exchange (Δ H SA ) was calculated and shown to be slightly greater than the enthalpy of octanol−air exchange or the enthalpy of vaporization, possibly because of interactions between the pesticides and the organic matter in the soil. Δ H SA values were very similar for all soils, irrespective of organic matter content/type. An expression for predicting K SA values on the basis of K OA, fraction of organic carbon, and soil density (Karickhoff relationship) was tested against the chamber measurements and found to produce consistent values of K SA . A small offset was observed between field-derived values of Q SA (soil−air quotient, an approximation of K SA ) and chamber-derived values of K SA . This was attributed to several factors including possible incomplete equilibrium during the field measurements and fluctuation of meteorological parameters that govern soil−air exchange. Ultimately, the findings of this work and the previous field study of soil−air exchange indicate that the Karickhoff relationship is useful and valid from a modeling standpoint where it is necessary to compromise accuracy in exchange for simple relationships that utilize readily available input data.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".