Physical and practical constraints on atmospheric methane removal technologies
Bibliographic record
Abstract
Abstract Despite their apparent utility in mitigating climate change, technologies for removing methane from air are in early stages of development. Here we evaluate the limiting physical constraints, for three types of systems: two- and three-dimensional infrastructure and atmospheric oxidation enhancement, focusing on removing low ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:msub> <mml:mi>x</mml:mi> <mml:mtext>CH4</mml:mtext> </mml:msub> <mml:mo><</mml:mo> </mml:mrow> </mml:math> 1000 ppm) and ambient ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:msub> <mml:mi>x</mml:mi> <mml:mtext>CH4</mml:mtext> </mml:msub> <mml:mo><</mml:mo> </mml:mrow> </mml:math> 2 ppm) methane from air. With the space velocities and removal efficiencies of current three-dimensional technologies, volumes of 7–350 km 3 are required to remove 1 Tg CH 4 yr −1 . Two-dimensional solutions are limited by the transport rate of methane to a surface. If every molecule of methane that collides with the surface is removed, an area of 1130 km 2 is needed to remove 1 Tg CH 4 yr −1 at ambient concentration. However, research shows that per-collision reaction probabilities are <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:mo><</mml:mo> </mml:mrow> </mml:math> 10 −8 requiring a surface area of 10 10 –10 15 km 2 . Finally, we examine atmospheric oxidation enhancement, where 4.8 Tg yr −1 of Cl <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:msup> <mml:mrow/> <mml:mo>⋅</mml:mo> </mml:msup> </mml:mrow> </mml:math> or 8.8 Tg yr −1 of OH <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:msup> <mml:mrow/> <mml:mo>⋅</mml:mo> </mml:msup> </mml:mrow> </mml:math> is required to remove 1 Tg CH 4 yr −1 , with precursors such as H 2 O 2 or O 3 . However, limitations arise concerning multiple environmental impacts. We conclude that the physical and practical constraints are considerable, and identify the main barriers that must be addressed.
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 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 | 0.001 | 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 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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; both teacher heads agree on what is shown here.
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".