Biochar mitigates methane emissions from organic mulching in urban soils: Evidence from a long-term mesocosm experiment
Bibliographic record
Abstract
Methane (CH₄), a potent greenhouse gas (GHG) with high global warming potential, significantly contributes to urban GHG emissions. Organic mulching, commonly practiced in urban forestry, may promote CH₄ emissions via anaerobic decomposition; yet its impact on the urban carbon budget has largely been unexamined. Biochar has shown promise in mitigating CH₄ emissions in agricultural soils, but its effectiveness in urban mulched systems remains unknown. This study employed a mesocosm experiment to investigate the effects of organic mulches (woodchips and bark) and biochar amendments (50 t/ha), applied either on the surface (top-dressed) or incorporated (mixed), on fluxes of CH₄, CO₂, and H₂O. Fluxes were measured using an off-axis integrated cavity output spectroscopy analyzer. Results indicate that mulched soils emitted CH₄ at 1.0–1.5 nmol m⁻ 2. s⁻ 1 , whereas biochar amendments promoted CH₄ uptake, in the case of both woodchips (−1.65 ± 1.03 nmol m⁻ 2. s⁻ 1 ) and bark mulch (−0.49 ± 0.16 nmol m⁻ 2. s⁻ 1 ) by the second year. Mixed treatments showed greater CH₄ uptake; for instance, incorporating biochar into bark mulch led to a mean CH₄ uptake (−2.02 ± 1.02 nmol m⁻ 2. s⁻ 1 ), nearly fivefold greater than controls. While mulch additions reduced water loss and increased soil organic carbon—factors contributing to CH₄ emissions—biochar amendments increased CO₂ emissions by 26.7%–121.1%. Biochar-mediated CH₄ uptake correlated with substrate pH, bulk density, and C:N ratio, suggesting enhanced microbial activity and increased CO₂ release. Overall, findings indicate that biochar, combined with organic mulching, can serve as an effective GHG mitigation strategy, informing climate-smart soil management in urban landscapes. • Mulch increased methane emissions, while biochar-amended mulch enhanced methane uptake. • Biochar boosted methane uptake through lower pH, higher C:N ratios, and enhanced microbial activity. • Mulch, with or without biochar, significantly reduced water loss compared to bare soil. • Biochar offer potential for improving urban soil management, reducing CH 4 emission, and supporting climate-smart planning.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".