Unexpected Mobility and Bioaccumulation of Effluent- and Biosolids-Borne PBDEs and Chlorinated Paraffins in an Organic-Rich Soil System
Bibliographic record
Abstract
Land application of wastewater residuals introduces hydrophobic contaminants into soils, where high organic matter (OM) is widely assumed to ensure contaminant immobilization. We tested this assumption in greenhouse mesocosms using an organic-rich soil (50% OM, 50% sand) receiving selected polybrominated diphenyl ethers (PBDEs) and chlorinated paraffins (CPs) from biosolids or effluent. The soil’s composition allowed us to test the conflict between high potential for contaminant sorption (to OM) and high hydraulic conductivity (from sand). Despite high soil retention of contaminants (>70%), response curves indicated significant leaching from both sources with up to 11.5% of a short-chain CP (SCCP) being mobilized. While phytoaccumulation was minimal (BAF dw < 1), earthworms accumulated high loads (BAF dw up to 23), suggesting a potent trophic transfer pathway. Both mobility and bioaccumulation were source-dependent. For instance, effluent-irrigated soils showed more than 6-fold greater medium-chain CPs (MCCPs) leaching (6.7% vs ∼1%) and higher bioavailability than biosolids-amended soils. These findings suggest hydraulic flux can override the sequestration capacity of organic-rich soils, highlighting the limitations of OM’s role as an effective sink under dynamic, nonequilibrium conditions. The study provides critical data urging a re-evaluation of risk assessment models for land-applied wastes.
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 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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".