A Review on Emerging Pollutants in the Water Environment: Existences, Health Effects and Treatment Processes
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Emerging pollutants (EPs), also known as micropollutants, have been a major issue for the global population in recent years as a result of the potential threats they bring to the environment and human health. Pharmaceuticals and personal care products (PPCPs), antibiotics, and hormones that are used in great demand for health and cosmetic purposes have rapidly culminated in the emergence of environmental pollutants. EPs impact the environment in a variety of ways. EPs originate from animal or human sources, either directly discharged into waterbodies or slowly leached via soils. As a result, water quality will deteriorate, drinking water sources will be contaminated, and health issues will arise. Since drinking water treatment plants rely on water resources, the prevalence of this contamination in aquatic environments, particularly surface water, is a severe problem. The review looks into several related issues on EPs in water environment, including methods in removing EPs. Despite its benefits and downsides, the EPs treatment processes comprise several approaches such as physico-chemical, biological, and advanced oxidation processes. Nonetheless, one of the membrane-based filtration methods, ultrafiltration, is considered as one of the technologies that promises the best micropollutant removal in water. With interesting properties including a moderate operating manner and great selectivity, this treatment approach is more popular than conventional ones. This study presents a comprehensive summary of EP’s existence in the environment, its toxicological consequences on health, and potential removal and treatment strategies.
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.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
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 it