Potential for Increased Human Foodborne Exposure to PCDD/F When Recycling Sewage Sludge on Agricultural Land
Bibliographic record
Abstract
Sewage sludge from municipal wastewater treatment is used in agriculture as a nutrient source and to aid in moisture retention. To examine the potential impact of sludge-amended soil on exposures to polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs) from plant and animal foods, we conducted a review of published empirical data from international sources. Levels of PCDD/F in municipal sewage sludge ranged from 0.0005 to 8300 pg toxic equivalents (TEQ)/g. Background levels in soil ranged from 0.003 to 186 pg TEQ/g. In sludge-amended soils, levels of PCDD/F ranged from 1.4 to 15 pg TEQ/g. Studies that measured levels before and after sludge treatment showed an increase in soil concentration after treatment. Relationships between PCDD/F levels in soil and resulting concentrations in plants were very weakly positive for unpeeled root crops, leafy vegetables, tree fruits, hay, and herbs. Somewhat stronger relationships were observed for plants of the cucumber family. In all cases, large increases in soil concentration were required to achieve a measurable increase in plant contamination. A considerably stronger positive relationship was observed between PCDD/F in feed and resulting levels in cattle tissue, suggesting bioaccumulation. Although PCDD/Fs are excreted in milk, no association was found between feed contamination and levels of PCDD/Fs measured in milk. There is a paucity of realistic data describing the potential for entry of PCDD/Fs into the food supply via sewage sludge. Currently available data suggest that sewage sludge application to land used for most crops would not increase human exposure. However, the use of sludge on land used to graze animals appears likely to result in increased human exposure to PCDD/F.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".