A review of per- and polyfluoroalkyl substances in biosolids: geographical distribution and regulations
Bibliographic record
Abstract
Applying sewage sludge and biosolids to agricultural lands has become an increasingly essential aspect of sustainable waste management and circular economy as it contributes positively to nutrient recycling, soil fertility and environmental health. Due to the widespread presence of per and poly-fluoroalkyl substances (PFAS) globally, wastewater treatment plants have become a sink for PFAS. PFAS resist degradation by conventional wastewater treatment processes and are usually adsorbed to sewage sludge and biosolids. However, there have been significant concerns that land application of sewage sludge and biosolids could become a probable pathway for PFAS to enter the food chain. This article assessed the global sewage sludge/biosolids generation and country-to-country management methods through a systematic review. The global occurrence, distribution and prevalence of different classes of PFAS were assessed. We also evaluate the factors influencing PFAS contamination in sewage sludge/biosolids and the existing regulations on the upper limit of PFAS in biosolids before their disposal or application to farmland (or other usages). Additionally, most reports revealed high PFAS concentrations in influent, effluent, sewage sludge and biosolids generated worldwide. Overall, recorded PFAS concentration on a global scale varied from 2.2 to 2,156 ng/L (influents), 1.9–4,800 ng/L (effluents) and 2.1–500,000 ng/g (biosolids). While most studies focused on legacy PFAS detection, recent studies have revealed the prevalence of diPAPs in high concentrations in sewage sludge and biosolids, contributing from 40% to 95% of the total PFAS concentration. Across all PFAS classes, PFAAs and diPAPs were the dominant groups exhibiting elevated detection rates (35%–95%). Due to documented PFAS contamination in agricultural lands, rigorous regulations need to be instituted to govern the application of these biowastes on agricultural lands. However, several countries lack data on the level of PFAS in the sewage sludges they generate, and there are currently few or no regulations guiding their application to farmlands. Notably, the diPAPs class of PFAS was shown to be present in biosolids and sewage sludge; their inclusion in the list of PFAS required in standardized analytical methods and risk assessment becomes imperative.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".