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Record W2796452566 · doi:10.1002/2018ea000370

Production of Sulfates Onboard an Aircraft: Implications for the Cost and Feasibility of Stratospheric Solar Geoengineering

2018· article· en· W2796452566 on OpenAlexfundno aff
Jordan P. Smith, J. A. Dykema, David W. Keith

Bibliographic record

VenueEarth and Space Science · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate Change and Geoengineering
Canadian institutionsnot available
FundersPratt and Whitney CanadaNational Aeronautics and Space Administration
KeywordsStratosphereEnvironmental scienceGeoengineeringSulfateRadiative forcingSulfate aerosolSulfurAerosolAtmospheric sciencesMeteorologyChemistryClimate changePhysics

Abstract

fetched live from OpenAlex

Abstract Injection of sulfate aerosols into the stratosphere, a form of solar geoengineering, has been proposed as a means to reduce some climatic changes by decreasing net anthropogenic radiative forcing. The cost and technical feasibility of forming aerosols with the appropriate size distribution are uncertain. We examine the possibility of producing the relevant sulfur species, SO2 or SO3, by in situ conversion from elemental sulfur onboard an aircraft. We provide a first‐order engineering analysis of an open cycle chemical plant for in situ sulfur to sulfate conversion using a Brayton cycle combustor and a catalytic converter. We find that such a plant could have sufficiently low mass that the overall requirement for mass transport to the lower stratosphere may be reduced by roughly a factor of 2. All else equal, this suggests that—for a given radiative forcing—the cost of delivering sulfate aerosols may be nearly halved. Beyond reducing cost, the use of elemental sulfur reduces operational health and safety risks and should therefore reduce environmental side effects associated with delivery. Reduction in cost is not necessarily beneficial as it reduces practical barriers to deployment, increasing the urgency of questions concerning the efficacy, risks, and governance of solar geoengineering.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.032
GPT teacher head0.269
Teacher spread0.237 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations39
Published2018
Admission routes1
Has abstractyes

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