Effect of composting and amendment with biochar and woodchips on the fate and leachability of pharmaceuticals in biosolids destined for land application
Bibliographic record
Abstract
Land application of biosolids can improve soil fertility and enhance crop production. However, the occurrence and persistence of pharmaceutical compounds in the biosolids may result in leaching of these contaminants to surface water and groundwater, causing environmental contamination. This study evaluated the effectiveness of two organic amendments [biochar (BC) and woodchips (WC)] for reducing the concentration and leachability (mobility) of four pharmaceuticals in biosolids derived from wastewater treatment plants in southern Ontario, Canada. The effect of 360-d composting on fate and leachabilities of target pharmaceuticals in biosolid mixtures was also investigated. Composting decreased total and leachable concentrations of pharmaceuticals in unamended and BC- and WC-amended biosolids to various degrees, from 10% up to 99% depending on the compound. Blending BC or WC into the biosolids greatly increased the removal rates of the target pharmaceuticals, while simultaneously decreasing their half-lives (t0.5), compared to unamended biosolids. The t0.5 of contaminants in this study followed the order: carbamazepine (304–3053 d) > gemfibrozil (42.3–92.4 d) > naproxen (15.3–104 d) > ibuprofen (12.5–19.0 d). Amendment with BC and(or) WC significantly reduced the leachability of carbamazepine, ibuprofen, and gemfibrozil to variable extents, but significantly enhanced the leachability of naproxen, compared to unamended biosolids (P < 0.05). Biochar and WC exhibited different (positive or negative) effects on the leachability of individual pharmaceuticals. Significantly lower concentrations of total and(or) leachable (mobile) pharmaceuticals were observed in amended biosolids than unamended biosolids (P < 0.05). Biochar and WC are effective amendments that can reduce the environmental impact of biosolid land applications with respect to pharmaceutical contamination.
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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) | 0.001 | 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".