Environmental Policy and Governance of Emerging Contaminants in Drinking Water: A Comparative Analysis of Global Regulations and Remediation Strategies
Bibliographic record
Abstract
Emerging contaminants (ECs) in drinking water — such as pharmaceuticals, personal care products, endocrine-disrupting chemicals, and microplastics — pose growing challenges to environmental health and water governance. Despite increasing scientific attention to their occurrence and potential health risks, regulatory frameworks remain inconsistent across countries, with significant disparities in detection limits, priority substances, and remediation strategies. This review comprehensively analyses environmental policies and governance approaches addressing ECs in drinking water across major global regions. Drawing from peer-reviewed literature and international regulatory documents, we compare how entities such as the United States Environmental Protection Agency (EPA), the European Union, Canada, China, Australia, and several developing nations approach risk assessment, monitoring, and remediation of ECs. We also evaluate the effectiveness of current strategies, identify policy gaps, and examine the influence of socioeconomic, political, and technological factors on regulatory development. Furthermore, we explore adaptive governance models, public engagement, and cross-border cooperation as essential for advancing policy effectiveness. The review concludes with recommendations for harmonising global policy efforts and strengthening local governance structures to ensure safer drinking water systems in the face of evolving chemical threats.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".