Improving small urban wastewater treatment plants with a nature-based reactor for tertiary treatment
Bibliographic record
Abstract
Urban wastewater treatment has significantly improved in recent decades, reducing the environmental impacts of their effluents and improving the chemical and ecological status of receiving water bodies. However, specific treatments, focused on nitrogen and phosphorus removal, have been implemented principally in large urban wastewater treatment plants (UWWTPs) serving over 10,000 population equivalents (P.E). In contrast, small UWWTPs (<10,000 P.E.) are generally not required to meet nutrient discharge limits despite the revised Urban Wastewater Treatment Directive. Nature-Based Solutions (NBS) offer cost-effective alternatives for small facilities as potential tertiary treatments. This research evaluated a pond-stream system, based on the biological activity of benthic (biofilms) and planktonic microbial communities (biofilm-plankton reactor, BPR), as an additional treatment step for activated sludge UWWTPs. The BPR achieved removal efficiencies for nitrogen (67.4 ± 11.1 %) and Escherichia coli (75.4 ± 37.3 %), while phosphorus, carbon, and targeted contaminants of emerging concern were highly variable and were not consistently removed. Microbial communities' structure and functions were assessed through algal biomass, stoichiometry, and extracellular enzymatic activities, providing a distinctive perspective into the BPR's microbial ecological dynamics related to removal efficiencies. Shotgun metagenomics identified a broad range of nitrogen functional genes, mainly involved in biodegradation and biosynthesis processes. This next-generation sequencing approach complemented conventional E. coli count methods, offering a deeper understanding of potential pathogen hotspots in treated effluents. Overall, the BPR system demonstrated a promising NBS for nitrogen and microbiological contaminant removal in small UWWTPs, whereas further investigation is needed to optimise the removal of other important water quality parameters.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".