Evaluation of Passive Reduction of Salts and Nutrients from Greenhouse Effluent using Vegetated Bioreactors
Bibliographic record
Abstract
The discharges from greenhouse operations (greenhouse effluent) in Ontario contain elevated concentrations of salts as well as nutrients such as nitrate and phosphate. The untreated release of this effluent can threaten the quality of our waters, while recycling it for repeated irrigation in greenhouses could reduce costs and save on water and nutrient resources. However, the crop damage caused by the accumulated salinity in the recirculation systems is a major drawback to this otherwise sustainable practice. In this study, engineered vegetated and unplanted gravel and wood-chip bioreactors, as well as a vertical-flow constructed wetland (CW) were operated over a long term and analysed for their nutrient and salt removal performances without seeding. The gravel-based reactors showed relatively low treatment efficiencies, while the wood-chip bioreactor planted with Typha angustifolia (narrowleaf cattail) was the first unit to achieve a near-complete denitrification, demonstrating average nitrate and phosphate removals of 92% and 26% respectively. The unplanted wood-chip bioreactor exhibited a similarly high denitrification efficiency at a later stage, while the other bioreactors removed up to 36% of the nitrate concentrations. Moreover, Schoenoplectus tabernaemontani (softstem bulrush) was selected as the species with the best phytodesalination performance. The microbial characterization on the interstitial water and biofilm samples showed a positive correlation between the abundance of the denitrifying genes nirS and nosZ with the denitrification performance in the T. angustifolia and unplanted units. A greater population of denitrifiers was found in these bioreactors, while the rhizosphere of T. angustifolia displayed the highest microbial activity. The contributions from the plant’s roots as well as the presence of wood-decomposing bacteria such as Bacillus spp. were suggested as the potentially important drivers of denitrification. The hybrid CW planted with the halophytic species Distichlis spicata (saltgrass) demonstrated average nitrate and phosphate removal efficiencies of 84% and 66%, as well as sodium and conductivity reductions of 15% and 25% respectively. Overall, the wood-based denitrifying systems showed promising results as an effective passive and sustainable technology for nutrient reduction for greenhouses, while more research on additional treatment was found necessary to achieve lower levels of salinity suitable for irrigation of sensitive crops using the recycled effluent.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 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".