Impact of Domestic Wastewater on Lotic Surface Water Quality: A Literature Review
Bibliographic record
Abstract
The contamination of lotic surface waters, defined as freshwater bodies with continuous flow, such as rivers and streams, by domestic wastewater represents an urgent and growing threat to both human and environmental health. The objective was to analyze the scientific literature related to the impact of domestic wastewater on the quality of lotic surface waters, using a comparative approach by geographic region, to identify degradation factors, ecological impacts, and technological strategies applied for their treatment. The analysis focuses on the Americas, the area with the highest number of available studies, and is complemented by evidence from Africa, Europe, and Asia. Common patterns and regional differences are identified in the degradation factors, environmental impacts, and technological strategies used for wastewater treatment. The results demonstrate the priority of integrated approaches that consider technical, community-based and regulatory aspects. These approaches are crucial for addressing the critical factors, including untreated discharges, low sanitation coverage, and contamination by nutrients and pathogens. Documented impacts include eutrophication, biodiversity loss, and associated diseases. The technologies applied vary by region, ranging from conventional systems to natural and advanced solutions. Gaps persist in the integration of these approaches. This study contributes to a stronger global understanding of the impacts of domestic wastewater. It provides a scientific foundation for designing public policies, sanitation strategies, and community-based actions aimed at improving the sustainable quality of water.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.013 |
| 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) | 0.003 | 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".