Novel Sustainable In-Situ Geotextile Filtration Method for Eco-remediation of Eutrophic Lake Waters
Bibliographic record
Abstract
Climate change and human-made actions are synergically increasing eutrophication cases on inland waters. These augmentation circumstances are not only in places where contamination is higher (i.e., with increased nutrient input) but worldwide due to climate change disruptions. Those excessive nutrients scenarios are augmenting faster trophic status changes to inland waters. Diverse invasive, drastic, intricate, and expensive technologies are applied currently worldwide for eutrophic water remediation, adversely affecting the aquatic biota and reducing its water volume. In order to counterpart this issue, a novel approach method is under study and application, an effective, environmentally safe, and economic eco-remediation technique using a floating filtration system, a silt curtain, and geotextiles (woven and non-woven) as filter media. An in-situ water remediation methodology for the minimally invasive removal of suspended solids and particulate nutrients. A sustainable remediation method supporting the own waterbody’s restoration and directly following three of the 17 Sustainable Developments Goals (SDGs), proposed by United Nations (UN) to be reached before 2030: SDG 6 clean water and sanitation, SDG 12 responsible consumption, and production, and SDG 14 life below water. This pilot in-situ experiment was deployed at Lake Caron, a shallow eutrophic lake located in the Sainte-Anne-des-Lacs municipality in Quebec from summer until mid-fall for two consecutive years (i.e., 2019 and 2020). Lake water quality monitoring were performed using the following parameters: particle size analysis (PSA), total suspended solids (TSS), total phosphorus (TP), total nitrogen (TN), nitrate (NO3-), chemical oxygen demand (COD), pH, dissolved oxygen (DO), temperature (Temp.), oxidation-reduction potential (ORP), conductivity, turbidity, total dissolved solids (TDS), chlorophyll a (Chl. a) and blue-green algae-phycocyanin (BGA-PC). Turbidity, total suspended solids (TSS), total phosphorus (TP), blue-green-algae-phycocyanin (BGA-PC), and chlorophyll-a statistically significant average removal efficiencies were 49%, 53%, 22%, 56%, and 57%, respectively in the first-year study and 17%, 36%, 18%, 34% and 32%, respectively in the second year study. Those removal trends prevented primary productivity, in both years. This has demonstrated the hypothesis of sustainable lake water remediation by the method presented. A strong statistically positive correlation was also found, in the second year study, between TSS and turbidity, and with TSS and variables that could represent particles (i.e., total phosphorus, turbidity, chlorophyll-a) same behavior were found with turbidity. Additionally, to comply and strengthen sustainability principles within the project, waste management practices were investigated, based on potential reuse strategies (i.e., for used geotextiles and captured suspended solids) following circular economy principles. After proper washing, the geotextiles exhibited hydraulic proprieties close to a value of the unused ones (related to the flow rate and permittivity) characterizing its possible reuse. Also, liquid waste produced with the captured suspended solids may be classified for future reuse with the high phosphorus content where additional investigation is required. Using this surface water management technique in combination with the proper waste management route as presented, could present this remediation as a promising technique not only for shallow lakes but also for ponds, river sections, coastal regions, bays, and other water types, to ensure proper cleaner water for future generations.
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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 | 0.000 | 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) | 0.001 | 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".