Fate of Pesticides in the Arid Subtropics, Botswana, Southern Africa
Bibliographic record
Abstract
Despite a history of pesticide usage, few data exist on their concentrations in air and soil of Southern Africa. To add to the understanding of the processes controlling the fate of organic contaminants in arid regions, the levels, spatial trends, and seasonal variability of pesticides were studied in air and soil from Botswana. XAD resin-based passive air samplers (PAS) were deployed at 15 sites across the country from May 2006 to May 2007. Soil samples were collected from the vicinity of nine of the PAS sampling sites. In addition, 27 24-h high-volume air samples were collected in Maun, at the southeastern edge of the Okavango Delta, every two weeks for one year. Levels of pesticides in PAS were low, with α-endosulfan and lindane being most abundant. Concentrations in soils were extremely low and only soils with high organic carbon contained notable amounts of dieldrin and traces of other pesticides. In particular, air and soil from the Okavango Delta had very low levels even though the area had repeatedly been sprayed with DDT and endosulfan in the past. Air samples from Eastern Botswana, where the majority of the population lives, contained higher levels. Higher air concentrations of α-endosulfan occurred during summer and higher HCB levels occurred in winter. This seasonality was related with neither minor seasonal changes in temperature nor hydrological seasonal events such as the rainy season or the flooding of the Okavango Delta. Thus, the observed spatial and seasonal patterns are more likely related to pesticide usage pattern than to environmental factors or historical use. High temperature and low organic matter content limit the uptake capacity of most subtropical soils for pesticides. No evidence was found that sorption to dry mineral matter plays a major role. Arid soils in subtropical regions are therefore neither a major reservoir of organic contaminants nor do they constitute a significant long-term source of pesticides to the atmosphere.
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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) | 0.000 | 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 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".