Life cycle comparison of biochar and activated carbon adsorbents for PFOS removal: Impacts and disposal pathways
Bibliographic record
Abstract
This study carries out a comparative life cycle assessment (LCA) to quantify the environmental impacts associated with different options for the treatment of perfluorooctane sulfonates (PFOS). Four carbonaceous materials are analyzed: Metal oxide sawdust biochar (MOSB), Corn straw biochar (CSB), Bamboo activated carbon (BAC), and Commercial granular activated carbon (GAC). The key impact categories included Human Carcinogenic Toxicity, Marine Ecotoxicity, Freshwater Ecotoxicity, and Freshwater Eutrophication. Results showed GAC with the lowest environmental impacts (≤ 1 % of impacts across categories), followed by BAC (1–2 %), CSB (5–7 %) and MOSB with the highest environmental impacts (> 90 % across categories). Electricity, nitrogen, and water are the main contributors to over 90 % of the environmental impacts of the adsorbents. In terms of energy, MOSB uses the most energy (0.72 MJ), mostly from non-renewable sources, while GAC is the most energy-efficient, using only 0.0023 MJ. Regarding the final waste scenario, landfilling presented lower environmental impacts than incineration for MOSB, CSB, BAC, and GAC in general, except for land use and mineral resource scarcity. The treatment of hazardous waste causes over 90 % of the total impacts, while transportation has minimal effects. Sensitivity analyses on yield, electricity mix, water source, and ReCiPe time-horizon assumptions confirmed that the relative ranking of adsorbents remains stable, even though absolute impact levels vary. The findings of this study indicate the use of GAC as a promising method for producing high-quality sorbents that can effectively remove PFOS from water, with the lowest environmental impacts associated.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".