EXPLORING LAND COVER EFFECTS ON URBAN AIR QUALITY: A CASE OF 659 DISTRICTS IN INDIA
Bibliographic record
Abstract
Abstract. Land use and land cover changes (LUCC) affects the atmospheric environment directly or indirectly. Therefore, understanding the atmospheric response to LUCC is of great significance to maintain and improve the ecological environment. In this study, based on fine particulate matter (PM2.5) and LC products, we first compared the differences of PM2.5 between urban and surrounding areas, and then further investigated the variations of PM2.5 in different and land cover (LC) using Mann-Kendall (MK) test and Sen’s trend analysis approach at the district-level in India during 1998–2015. The results showed that the numbers of districts where the differences of PM2.5 (DPM2.5) between urban and the surrounding areas were greater than zero were increasing during 1998–2015. There is an upward trendency of annual mean PM2.5. The annual mean PM2.5 was higher than 40 μg/m3 in 58% of India’s areas where there were mainly located in the Ganges plains of northern India with cropland (L01) and urban areas (L07). The annual mean PM2.5 was less than 10 μg/m3 were mainly found in north-western India with permanent ice and snow (L10), accounting for 10% of India’s area. There are significant positive trends of PM2.5 concentration in 90% of cropland (L01) and 88% of urban area (L07) and the average slope were 0.83 μg/m3 and 0.82 μg/m3 respectively, which were higher than those in the rest of LC. This research serves as the basis of reference for the equitable allocation of land resources and restructuring of land use and land cover patterns in urban areas of India that severely affected by air pollution.
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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 | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| 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 | 0.000 |
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".