Wastewater-based epidemiology to assess human exposure to personal care and household products – A review of biomarkers, analytical methods, and applications
Bibliographic record
Abstract
Humans are nowadays exposed to numerous chemicals in our day-to-day life, including parabens, UV filters, phosphorous flame retardants/plasticizers, bisphenols, phthalates and alternative plasticizers, which can have different adverse effects to human health. Estimating human's exposure to these potentially harmful substances is, therefore, of paramount importance. Human biomonitoring (HBM) is the existing approach to assess exposure to environmental contaminants, which relies on the analysis of specific human biomarkers (parent compounds and/or their metabolic products) in biological matrices from individuals. The main drawback is its implementation, which involves complex cohort studies. A novel approach, wastewater-based epidemiology (WBE), involves estimating exposure from the analysis of biomarkers in sewage (a pooled urine and feces sample of an entire population). One of the key challenges of WBE is the selection of biomarkers which are specific to human metabolism, excreted in sufficient amounts, and stable in sewage. So far, literature data on potential biomarkers for estimating exposure to these chemicals are scattered over numerous pharmacokinetic and HBM studies. Hence, this review provides a list of potential biomarkers of exposure to more than 30 widely used chemicals and report on their urinary excretion rates. Furthermore, the potential and challenges of WBE in this particular field is discussed through the review of pioneer WBE studies, which for the first time explored applicability of this novel approach to assess human exposure to environmental contaminants. In the future, WBE could be potentially applied as an "early warning system", which could promptly identify communities with the highest exposure to environmental contaminants.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".