Gaseous Contaminant Emissions as Affected by Burning Scrap Tires in Cement Manufacturing
Bibliographic record
Abstract
We studied the environmental impact (gaseous emissions) of using scrap tires as a fuel substitute at a cement plant that produces one million tons of cement per year. Using a combination of tires and coal as opposed to only coal caused variations in the pollutant emission rate. The study recorded a 37% increase in the rate of emission for CO, a 24% increase for SO2, an 11% decrease for NOx, and a 48% increase for HCl when tires were included. The rate of emission for metals increased 61% for Fe, 33% for Al, 487% for Zn, 127% for Pb, 339% for Cr, 100% for Mn, and 74% for Cu, and decreased 22% for Hg. On the other hand, the emission rate of organic compounds dropped by 14% for polycyclic aromatic hydrocarbons, 8% in naphthalene, 37% in chlorobenzene, and 45% in dioxins and furans. We used a Gaussian model of atmospheric dispersion to calculate the average pollutant concentration (1-h, 24-h, and annual concentrations) in the ambient air at ground level with the help of the ISC-ST2 software program developed by the USEPA. When tires were used, we observed (i) a 12 to 24% increase in particulate matter, this range considering the concentration variation depending on the average used (1-h, 24-h, and annual basis), 31 to 52% in CO, 22 to 34% in SO2, 39 to 52% in HCl, 12 to 27% in Fe, -3 to 8% in Al, 30 to 37% in Zn, and 270 to 885% in Pb; (ii) a decrease of 8 to 13% in NOx, 9 to 13% in polycyclic aromatic hydrocarbons, 6 to 7% in naphthalene, 32 to 39% in chlorobenzene, and 32 to 45% in dioxins and furans. The results obtained showed that the maximum ground-level concentrations were well within the environmental standards (for operation with only coal as well as for operation with a combination of coal and tires).
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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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".