Efficiency of Azolla pinnata in Purifying Treated Wastewater in Lebanon via Phytoremediation as a Nature-Based Solution
Bibliographic record
Abstract
Irresponsible wastewater management has caused water pollution to increase to alarming levels in Lebanon. This is compounded by the economic hardships that have forced several wastewater treatment plants (WWTPs) to either shut down their operations or be inconsistent with the treatment level. Given the unsustainable performance of centralized WWTPs andtheir vulnerability to economic shocks, integrating nature-based solutions such as phytoremediation has become essential. As such, this study evaluates the potential of growing Azolla pinnata, a floating fern (macrophyte), for the purification of primary,secondary and tertiary TWW through phytoremediation. Two seasons of experiments were conducted to study the temporal variation in the physicochemical properties of water, nutrient removal efficiency, sediment composition, biomass composition and economic feasibility. All nutrients that were considered in this study were reduced in the presence of A. pinnata in TWW, except for nitrates and sodium. The highest nutrient removal efficiencies were observed in the primary TWW, with an average of 97% for ammonium, 88% for orthophosphates and 90% for potassium. Additionally, chemical oxygen demand (COD)decreased between 66-86% in the three TWW types. This reduction has been negatively correlated with dissolved oxygen (R= -0.683, p-value=0.000). The results of the phosphorus (P) mass balance have shown that 74% of the P was fixed by Azolla in primary TWW, out of 84% P removal efficiency. In contrast, only an average of 60% and 64% P was absorbed by Azolla in STWW and TTWW out of 100% and 95% P removal efficiency, respectively. Although Azolla has a rich nutritional value, the economic assessment has shown little economic savings from its use in animal feed. Further studies on the expansion of thistechnique, microbial and heavy metals contamination in Azolla, palatability of Azolla by different animals, disposal of sediments and the utilization of the Azolla biomass are needed.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".