Rethink biosolids: Risks and opportunities in the circular economy
Bibliographic record
Abstract
• Biosolids are not suitable for land applications due to the embedded emerging pollutants. • Biosolids offer limited nutrient value in relation to society’s overall nutrient demand. • Innovative valorization strategies are worth exploring, including carbon capture and storage. • Holistic life-cycle assessments are needed for these novel biosolids management strategies. Biosolids are a byproduct of wastewater treatment with potential value because of their rich nutrient, organic matter, and mineral content. However, due to the environmental and public health risks posed by contaminants, such as per- and polyfluoroalkyl substances (PFAS), micro-/nano-plastics (MNPs), and antibiotic resistance genes (ARGs), the conventional practice of applying biosolids to land, especially on agricultural lands, can be unsustainable. These contaminants can accumulate in soil, disrupt ecosystems, and pose long-term risks to food safety and human health. Through the lens of a circular economy, biosolids should be valorized through holistic and sustainable approaches that prioritize both environmental protection and resource recovery. Advanced technologies, such as anaerobic membrane bioreactors, pyrolysis, and hydrothermal liquefaction, can extract valuable resources, such as energy and bioplastics, while also destroying harmful contaminants. In addition, biosolids and biosolids-derived biochar hold untapped potential for carbon capture and storage (CCS), offering a sustainable alternative to land applications while also helping mitigate climate change. Hence, a circular economy approach not only minimizes waste and reduces environmental impacts but also unlocks an important potential of biosolids as a renewable resource moving forward.
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.009 | 0.015 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".