GIS Based Spatial and Temporal Investigation of Groundwater and Soil Quality along Noyyal River, Tiruppur, India
Bibliographic record
Abstract
Abstract Soil and water quality crisis owing to industrialization is massive at present due to the illegal discharge of wastewater to the environment. Textile is one such industry, discharges untreated wastewater into nearby environment and poses major threats worldwide. Similar situation was observed for the last three decades (1980-2013) along Noyyal river in Tiruppur, India. Since 2013, zero liquid discharge (ZLD) has been adopted by the textile industries in Tiruppur to reduce further deterioration of environment. The groundwater from open wells and soil from agricultural land was examined continuously for three years (2015, 2016 and 2017) in order to assess the existing environment status along Noyyal river basin in Tiruppur. The GIS study reveals that 71% of groundwater remains unsuitable for drinking and also 54% are unfit for irrigation use. The findings further reveal that 61% of surface and 20% of subsurface soils are not suitable for agriculture. The detailed investigations established that the open wells located in the downstream of textile industries and near to Orathupalayam reservoir are highly contaminated with organic and inorganic contaminants associated with textile processing activities. The temporal variation of groundwater in these open wells indicated that the dilution by rainwater is very slow. It is also identified that soils near to the contaminated open wells are extremely affected and soil of 15 cm depth is extremely contaminated. Thus, implementation of ZLD somewhat reduced discharge of wastewater into the basin; however the recovery of groundwater to potable quality and soils for agriculture production needs immediate remediation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".