Optimizing the management of quarry fines for on-site carbon removal: Implications of grain size and mineralogy on CO2 mineralization
Bibliographic record
Abstract
• Basaltic fines from UK quarries are suitable feedstocks for carbon dioxide removal. • Particle size and surface area have the greatest impact on carbonation of basaltic fines. • Bulk fines can sequester between 360 and 460 t CO 2 /yr if spread over 0.6–0.8 km 2 . • Sieved fines (<100 μm) can sequester between 1100 and 1480 t CO 2 /yr if spread over 0.8–1.1 km 2 . • Crushing rock to <100 μm costs more than carbon could be sold for at current UK prices. Weathering of basaltic quarry fines can enable quarries to remove CO 2 by optimizing the management of underutilized rock fines. In this study, basaltic fines from two quarries in Scotland are used as potential feedstocks for ERW. Using column experiments, fines from both sites were placed into columns as layers with varying thicknesses (1 cm and 5 cm) and grain sizes (bulk and <100 μm). Fines were saturated (≈60 % pore water) and exposed to ambient UK conditions (10 °C, 0.04 % CO 2 ) and accelerated carbonation conditions (50 °C, 20 % CO 2 ). Quarry site 1 experienced negligible increases in TIC within bulk fines under ambient conditions, yet fines <100 μm experienced carbonation equivalent to 440 g CO 2 /m 2 /yr. However, the total inorganic carbon content (TIC) nearly doubled in the bulk fines from quarry site 2 (5 cm) under ambient conditions, equivalent to 570 g CO 2 /m 2 /yr. In the sieved fines from the same site the TIC content nearly tripled, equivalent to 1330 g CO 2 /m 2 /yr. At site 2, if the bulk fines could be deposited over 0.8 km 2 of land in 5 cm thicknesses, approximately 460 t CO 2 /yr could be sequestered with minimal management practices in place. Using fresh fines that have not previously weathered in stockpiles is important for maximizing the carbon dioxide removal potential. Despite higher carbon offsets within the sieved material, the energy and cost required to crush rock from bulk to <100 μm is not economically feasible, as it exceeds the value of carbon which it could be sold for.
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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 | 0.000 |
| 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 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".