Physicochemical characterization and surface reactivity of natural pyrogenic carbon and its role in metals and nutrients transport
Bibliographic record
Abstract
Wildfire-derived pyrogenic carbon (F-PyC) is produced in immense quantities, up to 385x109 kg, each year and yet it has studied far less than its commercially-produced counterpart, biochar (B-PyC). While B-PyC dominates the literature, its physicochemical properties and behaviour in the environmental differ significantly from F-PyC, raising concerns about its use as a proxy. This dissertation addresses critical gaps in the formation, reactivity, and nutrient and metals transport potential of F-PyC, particularly in fluvial systems, and evaluates whether B-PyC is an appropriate proxy as has been proposed in the past. Firstly, the "B-PyC proxy problem" is addressed through leaching experiments that reveal the unique role of F-PyC’s inorganic ash fraction in elemental cycling. The findings demonstrate that B-PyC and F-PyC have substantially different physicochemical properties, resulting in differing elemental transport potential, thus proving that slow-pyrolysis B-PyC is not a suitable proxy for F-PyC. Concluding this, F-PyC reactivity is then tested to determine how F-PyC participates in reactive transport processes in freshwater fluvial systems, with implications for contaminant mobility and nutrient cycling following wildfire events. Finally, the effect of pyrolysis intensity, quantified as the average maximum temperature (AMT), char intensity (CI), and peak derivative thermogravimetric temperature (DTG), on the physicochemical properties of F-PyC was tested. It was determined that while no single temperature-derived metric fully captures the F-PyC physicochemical properties, combining several analytical approaches promises to provide insight into its reactivity, elemental composition, and surface chemistry and thus resultant transport potential. These results underscore the need to incorporate F-PyC explicitly into geochemical and reactive transport models, especially in wildfire prone regions such as western Canada. Recommendations for future research include studies on marine systems, weathered and colloidal F-PyC, groundwater\ninteractions, and reconstructing wildfire intensity from F-PyC properties. As wildfire regimes shift with climate change, understanding the unique behavior of F-PyC is critical to predicting its role in elemental cycling and contaminant transport on a global scale. This research establishes a foundation for a new generation of F-PyC studies that reflect the complexities of real-world fire conditions, moving beyond the simplified B-PyC paradigm that now dominates the literature.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".