Application of aquatic plant-based green technology as phytoindicator and phytoremediation and for heavy metals contaminat in freshwater bodies
Bibliographic record
Abstract
The aim of the research is to study the potential of aquatic spesies to be used as indicator for unhealthy environment such as polluted freshwater bodies. Aquatic plants growing in or near water are emergent, submerge, or floating. Aquatic plants are beneficial to lakes because they provide shelter for fish and substrate for aquatic invertebrates. They also produce oxygen, which assists with overall lake functioning, and provide food for some fish and other wildlife. A lack of aquatic plants in system where they are expected to occur may suggest a reduced population of sport and wildlife. In addition, the absence of aquatic plants may also indicate water quality problems as a result of excessive nutrients, herbicides or heavy metals and may interfere with lake functioning, recreational activities, and detract from the aesthetic appeal of the system. Aquatic plants s are excellent indicator for unhealthy freshwater bodies because they respond to nutrients, light, toxic, contaminants, metals, herbicides, turbidity, water level change, and salt. Heavy metal and other toxic compounds discharged into the environment from industrial, agricultural, and domestic sources have contaminated a substantial portion of the hydrosphere and other parts of the biosphere. Their effects on aquatic ecosystems are a result of their low solubility in water, chemical stability in sediments, and accumulation through biological concentration and magnification in food-webs. Thus, aquatic plant-based green technology offers a simple, cheap and energy efficient method of treating polluted water and wastewater including heavy metal. In this study, thirteen samples of water were collected from different places where nine different aquatic plant species were abundance and dominant. All the water samples were analyzed for six types of heavy metals which are iron (Fe), lead (Pb), copper (Cu), zinc (Zn), nickel (Ni) and manganese (Mn) . Results showed that the abundance of certain aquatic species indicate high concentration of certain heavy metal in that particular environment (Table 3). Some of the concentration of heavy metal are exceeded the limits of recommended range by Canadian standard (CCME)-protection of aquatic life. The concentrations of metals in aquatic plants can be more than 100 000 times greater than in the associated water (Albers and Camardese, 1993). Therefore, macrophyte is effective in responding heavy metal in low level environmental contamination that might otherwise be difficult to detect.
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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 | 0.001 | 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".