Industrial sludge containing pharmaceutical residues and explosives alters inherent toxic properties when co-digested with oat and post-treated in reed beds
Bibliographic record
Abstract
Methane production as biofuels is a fast and strong growing technique for renewable energy. Substrates like waste (e.g. food, sludge from waste water treatment plants (WWTP), industrial wastes) can be used as a suitable resource for methane gas production, but in some cases, with elevated toxicity in the digestion residue. Former investigations have shown that co-digesting of contaminated waste such as sludge together with other substrates can produce a less toxic residue. In addition, wetlands and reed beds demonstrated good results in dewatering and detoxifying of sludge. The aim of the present study was to investigate if the toxicity may alter in industrial sludge co-digested with oat and post-treatment in reed beds. In this study, digestion of sludge from Bjorkborn industrial area in Karlskoga (reactor D6) and co-digestion of the same sludge mixed with oat (reactor D5) and post-treatment in reed beds were investigated in parallel. Methane production as well as changes in cytotoxicity (Microtox(R); ISO 11348–3), genotoxicity (Umu-C assay; ISO/13829) and AhR-mediated toxicity (7-ethoxyresorufin-O-deethylase (EROD) assay using RTW cells) were measured. The result showed good methane production of industrial sludge (D6) although the digested residue was more toxic than the ingoing material measured using microtox 30min and Umu-C. Co-digestion of toxic industrial sludge and oat (D5) showed higher methane production and significantly less toxic sludge residue than reactor D6. Furthermore, dewatering and treatment in reed beds showed low and non-detectable toxicity in reed bed material and outgoing water as well as reduced nutrients. Co-digestion of sludge and oat followed by dewatering and treatment of sludge residue in reed beds can be a sustainable waste management and energy production. We recommend that future studies should involve co-digestion of decontaminated waste mixed with different non-toxic material to find a substrate mixture that produce the highest biogas yield and lowest toxicity within the sludge residue.
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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".