A Surrogate‐Based Optimization Methodology for the Optimal Design of an Air Quality Monitoring Network
Bibliographic record
Abstract
A surrogate‐based optimization methodology was proposed for identifying and determining the optimal location and configuration of an air quality monitoring network (AQMN) in an industrial area for different pollutants such as sulfur dioxide (SO 2 ), nitrogen oxide (NO x ), and carbon monoxide (CO). Within the framework of the described methodology, an optimal AQMN design was proposed to assess the violation and pattern scores for each pollutant. For this purpose, a criterion for assessing the allocation of monitoring stations was developed by applying a utility function that could describe the spatial coverage of the network and its ability to detect violations of standards for multiple pollutants. An air dispersion model based on the multiple cell approach was used to create monthly spatial distributions for the concentrations of the pollutants emitted from different sources. The data was used to develop the surrogate models. The proposed methodology was applied to a network of existing refinery stacks, and the locations of monitoring stations and their area coverage percentage were obtained. Results clearly indicated that the proposed methodology was successful in designing AQMNs and could be used for as many stations as required.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".