Herbicide Contamination in Ground Water and Their Probable Remediation
Bibliographic record
Abstract
Herbicides become integral components in modern agriculture as it offers low cost solution to manage weed flora both crop and non-crop field. Currently, promotion of conservation agriculture (CA)-based crop cultivation practices solely depends on herbicides to counter weeds. Herbicides were dominant agricultural chemicals used for crop protection purpose and also used in higher quantities compared to others. There is large volume of research articles on persistence of herbicides in soil had been done published so far. But information on herbicide residues in ground water is scanty and majority of the studies were reported from USA, Canada, and European countries. Major citations on the presence of herbicide residues dominated by triazine (atrazine, simezine, and metribuzin), chlor acetanilides (alachlor, metalochlor, and acetachlor), phenoxy alkalonic acid (2,4-D), and organo 112 phosphate (glyphosate) group of herbicides and along with their degraded products. Spatial and temporal variation of herbicide residue content in ground water was well documented by many researchers. It was observed that herbicide residues present in ground water in ranged below or more than maximum acceptable concentration (MAC) set by European Union (0.1 µg L −1 ). The presence of herbicide residues more than MAC level in drinking water may cause health hazard also reported from many countries like Peru, Argentina, and Sri Lanka, etc. There is necessity of strong regulatory body for monitoring of herbicide residues in ground water in each and every country. Thus, future research should be focused to minimize herbicide residue load in soil, water, and ecosystem. In this chapter, we have highlighted on the presence of herbicide residues in ground water reported from across the world, factors involved and way out pathways. Among the different methods of remediation of herbicide residue in ground water, preventive measures such as selection of appropriate herbicide, proper mixing & application procedure, prevention of runoff, and irrigation management are of sustainable approach. Phytoremediation is of widely used method amid different ground water remediation techniques. Bacterial ( Comamonadaceae and Sphingomonadales ) and algal ( Scenedesmus species) degradation were also found effective for treatment of herbicidal ground water contamination. Apart from this, a new nano-remediation technique was found effective for remediation of atrazine and bromacil in ground water.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".