THE USE OF ARTIFICIAL WETLANDS TO TREAT GREENHOUSE EFFLUENTS
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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.
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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 | 0.000 | 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 it