Microplastic pollution in India-Evidence of major health concern
Bibliographic record
Abstract
According to the new study published in Nature, India has secured the top spot as biggest plastic polluter in the world, releasing 9.3 million tonnes (Mt) annually. Plastic pollution remains a global challenge and this alarming trend of rising plastic waste in India has severe consequences for the environment, wildlife, and human health. The Indian government has launched initiatives like the Swachh Bharat Abhiyan to improve waste management, but more needs to be done to address the plight of waste pickers. Microplastics in water sources and food chains pose significant risks to human health, affecting the respiratory and reproductive systems and contributing to conditions like cancer. Studies link plastic pollution to an increased risk of cancer, male and female sterility, cardiovascular diseases, diabetes, and obesity. Microplastic has been found in food and beverages. Microplastic are also found in disposable plastic cups for drinking and single-use food containers for home delivery of tea, coffee, and hot beverages. Hence it is recommended that avoid drinking tea, coffee and hot beverages in the plastic cups. Since microplastics do not degrade, those particles which enter the human body through ingestion, inhalation or touch, but are not excreted, can be expected to accumulate in tissues of the human body. Tissue accumulation of microplastics has been demonstrated in marine organisms and mammals. Additives to plastic of major health concern include toxic metals, such as lead, cadmium, arsenic and chromium, bisphenol A (BPA). phthalates, brominated flame retardants (BFR) and endocrine- disrupting chemicals (EDCs). The three main methods for detecting and quantifying microplastic concentrations in water are FTIR Spectroscopy, py-GC/MS, and Raman Spectroscopy.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".