‘RS’ and ‘GIS’ based air quality atlas with integrated land use and land cover change analysis in India
Bibliographic record
Abstract
In this paper, the effect of land use and land cover and the impact of urbanisation on respirable particulate matter (RSPM), sulfur oxide (SO x ) and nitrogen oxide (NO x ) of the Hubli-Dharwad, a Tier II city in India, are correlated based on the trends in air quality observed from 2006 to 2013, population from 1990 to 2010, the number of vehicles between the periods of 2004 and 2013 and urbanisation between the periods of 1975 and 2009. It has been found that urbanisation has increased threefold from 92 km2 in 1975 to 271 km2 in 2009 and the corresponding decrease in agricultural area was from 368.22 to 123.43 km2. The RSPM in the study region is increasing at a rate of 8.9% per year. The study shows that vehicular pollutants are the major cause of air pollution, followed by industries, with the highest RSPM value of 128 μg/m3 at traffic junctions in the Hubli-Dharwad region in 2013. Based on the trend analysis, the air quality atlas predicted for 2030 shows that the RSPM level in the air will reach 150 μg/m3, well above the national ambient air quality standards, and will have serious consequences on human health if proper strategies are not undertaken. Owing to the unique geographical setting of Hubli-Dharwad, its future urbanisation will be in a narrow area, which may lead to a severe air pollution problem that needs immediate attention to provide a safe environment.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".