Identification of odor emission sources in urban areas using machine learning-based classification models
Bibliographic record
Abstract
Odor-causing substances are generated by various emission sources in urban areas. Recently, urbanization has greatly increased the density of odor emission facilities, implying the identification of odorants emission source is challenging. Identifying emission source is multifactorial, and a machine learning approach is considered useful for these complicated matters. The objectives of this study were to propose a method using machine learning-based classification models to identify odor sources in urban areas. We collected 34,539 data points regarding quantitative data of 22 compounds emitting from 11 types of facilities in urban areas (i.e., automobile industry, bio factory, wastewater treatment plant, landfill, construction site, farm industrial complex area, restaurant, gas station, roadside, park) and odor intensity of these 11 facilities. Decision tree (DT) and random forest (RF) algorithms were used as classification models for identifying odor sources with 23 variables (22 compounds + odor intensity). The DT model identified 7 out of 11 emission sources with 87.15% accuracy. The RF model identified all 11 emission sources with 99.23% accuracy. When including 6 important variables only (i.e., hydrogen sulfide, ammonia, trimethylamine, methyl mercaptan, acetaldehyde, odor intensity) in the RF model, accuracy (99.15%) was almost same with that (99.23%) obtained from all 23 variables included as variables in the model. Our findings imply that a machine learning approach can help to identify odor emission sources with high accuracy and we can save time and cost in the identification of odor emission sources by including the 6 important variables only.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".