Polycyclic Aromatic Hydrocarbons (PAHs) and Metals in Diverse Biochar Products: Effect of Feedstock Type and Pyrolysis Temperature
Why this work is in the frame
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Bibliographic record
Abstract
Biochar's agricultural and environmental benefits have been widely demonstrated; however, it may cause environmental contamination if it contains large amounts of pollutants such as polycyclic aromatic hydrocarbons (PAHs) and heavy metals (HMs). Therefore, this study aimed to assess the contents of PAHs and HM in a range of biochars generated from different sources and pyrolysis temperatures. A range of feedstock was converted to biochar, including sewage sludge (SS), olive mill pomace (OP), feather meal (FM), soft offal meal (CSM), chicken manure (CM), and date palm residues (DPR). Each feedstock was then pyrolyzed at three temperatures of 300, 500, or 700 °C, thereby producing a total of 18 types of biochar. These biochar products were analyzed for 16 PAHs and eight metals (Cr, Mn, Fe, Ni, Cu, Zn, Cd, and Pb). Benzo[b]fluoranthene, benzo[k]fluoranthene, and benzo(a)pyrene were significantly greater in the biochar produced at 700 °C than in that produced at 300 °C, especially for CM. The concentrations of dibenz(a,h)anthracene were significantly lower at 700 °C but greater at 500 °C and 300 °C in DPR. Increasing the pyrolysis temperature from 300 to 700 °C significantly increased the concentrations of metals, including Cr in SS and OP; Mn in CM; and Fe, Ni, Cu, and Zn in SS. However, the concentration of Cd was significantly lower in the SS when biochar was produced at 700 °C than at 500 or 300 °C. The type of feedstock used and the pyrolysis temperature are key factors influencing the contents of PAHs and HMs in biochar, both of which need to be considered during the production and use of biochar. Further investigations are recommended to establish the relationships between pyrolysis temperature and types of feedstock and the formation of PAH or the concentrations of metals. Monitoring the concentrations of PAHs and HMs before applying biochar to soil is also recommended.
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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) | 0.000 | 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 it