The role of land use on the local climate and air quality during calm inter-monsoon in a tropical city
Bibliographic record
Abstract
The modification of land use is known to be a major climate change driver to the local warming and air quality in cities. Despite the reduction of NOx over the years, the Selangor state has captured a higher level of O3 in year 2011. The measurement result has shown that the surge in O3 level was attributed to the reduction of NOx/NMHC ratio. This paper hence attempted to identify the role of land use change from 1999 to 2011 on the ground ozone air quality in the tropical urban conurbation, Greater Kuala Lumpur (GKL), Selangor, Malaysia. With the state-of-the-art chemical weather prediction tool, WRF-Chem, the external synoptic factors and emission inventory were controlled when comparing the chronological land use changes. The results showed that the urban-induced temperature and wind bias in the tropical region has induced stronger wind to disperse the NOx and carries the TVOC from the suburban to the downwind urban region. The reduction of NOx/TVOC has gradually shifted towards the optimum O3 formation regime in 2011. The formation of highly concentrated ozone becomes more sensitive to the increment of TVOC as the NOx level reduces in the urban. This highlights the essential involvement of TVOC in the ozone formation in lieu of the NOx reduction in the tropical city, a region with growing emitter of reactive biogenic ozone precursors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".