Study of Dispersion of HCl from Waste-To-Energy Plant at Different Receptor and Chimney Height at Taman Beringin, Kuala Lumpur
Bibliographic record
Abstract
Combustion of solid waste from waste-to-energy (WtE) plant emits a variety of air pollutants that may be hazardous to the environment and health. In order to control these hazardous air pollutants, regulation and emission limits has been established. The emission of these air pollutants can be control but not eliminated by implementing air pollution control (APC) devices. The emitted air pollutants will then be dispersed base on local weather. In Malaysia, the Malaysia Ambient Air Quality Guideline (MAAQG) is referred to ensure that the concentration of pollutants does not exceeds limit. For parameters that are not included in the MAAQS, other standard from other nation such as the Alberta’s Ambient Air Quality Objectives and Guidelines (AAQOG) is referred. Ground level concentrations (GLC) are commonly reported. In a developing city where new high-rise building may be constructed in the future, it is important that the concentration of pollutants at the higher height to be reported as well. In this study, Taman Beringin, Kuala Lumpur, Malaysia is taken as a case study for studying the air pollutants concentration at different height for a 1,200 t capacity WtE plant. This study is performed using an air dispersion software preferred by the United State (US) Environmental Protection Agency (EPA) known as AERMOD atmospheric dispersion modelling system. Based on the local weather pattern of Taman Beringin and a chimney stack height of 60 m, the maximum height of a building at the location of N 3° 13’ 31.61” E 101° 39’ 36.59” is about 80 m.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".