Structural control of the non-ionic surfactant alcohol ethoxylates (AEOs) on transport in natural soils
Bibliographic record
Abstract
Surfactants, after use, enter the environment through diffuse and point sources such as irrigation with treated and non-treated waste water and urban and industrial wastewater discharges. For the group of non-ionic synthetic surfactant alcohol ethoxylates (AEOs), most of the available information is restricted to the levels and fate in aquatic systems, whereas current knowledge of their behavior in soils is very limited. Here we characterize the behavior of different homologs (C12–C18) and ethoxymers (EO3, EO6, and EO8) of the AEOs through batch experiments and under unsaturated flow conditions during infiltration experiments. Experiments used two different agricultural soils from a region irrigated with reclaimed water (Guadalete River basin, SW Spain). In parallel, water flow and chemical transport were modelled using the HYDRUS-1D software package, calibrated using the infiltration experimental data. Estimates of water flow and reactive transport of all surfactants were in good agreement between infiltration experiments and simulations. The sorption process followed a Freundlich isotherm for most of the target compounds. A systematic comparison between sorption data obtained from batch and infiltration experiments revealed that the sorption coefficient (Kd) was generally lower in infiltration experiments, performed under environmental flow conditions, than in batch experiments in the absence of flow, whereas the exponent (β) did not show significant differences. For the low clay and organic carbon content of the soils used, no clear dependence of Kd on them was observed. Our work thus highlights the need to use reactive transport parameterization inferred under realistic conditions to assess the risk associated with alcohol ethoxylates in subsurface environments.
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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 | 0.000 | 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".