Recent Advancements in Urban Wastewater Management—Recommendations and Future Directions
Bibliographic record
Abstract
The expansion of human population and anthropogenic activities include mining, release of industrial waste, smelting of as-ore, incineration of fossil fuel, particularly coal, utilization of as-loaded water for irrigation, as-based pesticides, herbicides, and fertilizers have affected the availability of water resources for human consumption. Urban wastewater includes the presence of contaminants which are toxic to micro and macro-organisms and thus requires treatment. Microalgae are studied extensively for their efficiency to remediate the contaminants present in industrial and domestic effluents. Scientists focus on microalgae-based remediation of wastewater because of the ability of microalgae to grow and survive under diversified harsh environmental conditions. Carbon dioxide mitigation is a major role played by microalgae indicating an added benefit to the environment. Introduction of microalgae in remediation is much involved due to their contribution to the circular bio-economy approach which includes generation of integral bio-products during the traditional wastewater process. This systematic review focusses on the application of microalgae in urban wastewater treatment with discussion on remediation strategies and mechanisms followed for pollutants present in the wastewater. Additionally, the paper dwells on the future prospect of microalgae in incorporation of circular bioeconomy along with the wastewater remediation.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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".