THE USE OF ARTIFICIAL WETLANDS TO TREAT GREENHOUSE EFFLUENTS
Bibliographic record
Abstract
Untreated greenhouse effluents or leak solution constitute a major environmental burden because their nitrate and phosphate concentrations may induce eutrophication. Artificial wetlands may offer a low cost alternative treatment of greenhouse effluents and consequently improve the sustainability of greenhouse growing systems. The objectives of this study were to 1) characterize the efficiency of different types of wetland to reduce ion content of greenhouse tomato effluent, and 2) improve the wetland efficiency by adding carbon of 0-800 mg L-1 sucrose. Experiments were conducted at Laval University where 30 pilot scale horizontal subsurface flow artificial wetlands (0.81 m3) were built. Two types of aquatic macrophytes, Pragmites australis and Typha latifolia, and a control group without plants were tested. The hydraulic retention time was 10 days. During the study, EC of the greenhouse effluent ranged between 1.5 to 5.5 mS cm-1, while 0 to 800 mg L-1 of sucrose was provided to improve the biological activity of the wetland. The macro- and micro-elements, the greenhouse gases (CH4, CO2, N2O) and the population of bacteria were measured for each unit. At commercial scale, two vertical subsurface wetlands (43.2 m3) were installed at Ste-Sophie Québec, on the production site of Les Serres Nouvelles Cultures (Sagami). According to our results, 50-90% of nitrate (NO3-) and 40-100% of phosphate (PO43-) were removed from the effluent. At Laval University, artificial wetlands with Typha latifolia were more efficient than wetlands with Phragmites australis or without plants. Addition of sucrose increased wetlands’ microbial population and consequently reduced the mineral content of the wastewater, but increased significantly the emission of greenhouse gases. Results will further be discussed in terms of the best wetland design to treat greenhouse effluents, but also in terms of the environmental impact.
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".