Prioritization of Volatile Organic Compound Reduction in the Tire Manufacturing Industry through Speciation of Volatile Organic Compounds Emitted at the Fenceline
Bibliographic record
Abstract
Volatile organic compounds (VOCs), with their ubiquitous presence across numerous global industries, pose multifaceted challenges, influencing air pollution and health outcomes. In response, countries such as the United States and Canada have implemented fenceline monitoring systems, enabling real-time tracking of organic solvents, including benzene. Initially, this focus was predominantly placed on the petroleum refining industry, but it has gradually been broadening. This investigation seeks to identify and analyze the specific VOCs produced in the tire manufacturing sector by utilizing both active and passive monitoring methodologies. The findings of the present study aim to recommend prioritized reduction strategies for specific VOCs. Percentage means the ratio of VOCs detected at the research site. At research target facility A, active monitoring demonstrated the presence of Methylene chloride (20.7%) and Carbon tetrachloride (15.3%), whereas passive monitoring identified Carbon tetrachloride (43.4%) and m,p-Xylene (20.8%). After converting these substances to their equivalent concentrations, we found a noteworthy correlation between the active and passive methodologies. At research target facility B, active monitoring detected n-Pentane (45.5%) and Isoprene (11.4%), while passive monitoring revealed Toluene (21.3%) and iso-Hexane (15.8%). Interestingly, even at sites like warehouses and test tracks where VOC concentrations were projected to be low, we observed VOC levels comparable to those in process areas. This underlines the fact that the dispersal of VOCs is considerably influenced by wind direction and speed. Specifically, in the tire manufacturing industry, emissions of Xylene and 3-Methylhexane, both having high photochemical ozone creation potential (POCP), contribute significantly to air pollution. However, the overall detection concentration in the tire manufacturing industry was detected at a low concentration of less than 2 μg/m3. This is less than 9 μg/m3, which is the standard for benzene, which has strong carcinogenicity regulations. This suggests that additional research is needed on synthetic rubber manufacturing rather than tire manufacturing.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".