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Record W3106379752 · doi:10.5194/bg-18-3505-2021

Geographic variability in freshwater methane hydrogen isotope ratios and its implications for global isotopic source signatures

2021· article· en· W3106379752 on OpenAlexafffund
Peter Douglas, Emerald Stratigopoulos, Sanga Park, Dawson Phan

Bibliographic record

VenueBiogeosciences · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaMcGill University
KeywordsMethaneEnvironmental scienceWetlandδ13CStable isotope ratioSpatial variabilityIsotopic signaturePrecipitationMethanogenesisAtmospheric sciencesIsotopeIsotopes of carbonEcologyGeologyBiologyTotal organic carbonGeographyMeteorologyPhysics

Abstract

fetched live from OpenAlex

There is growing interest in developing spatially resolved methane (CH 4 ) isotopic source signatures to aid in geographic source attribution of CH 4 emissions. CH 4 hydrogen isotope measurements ( δ 2 H–CH 4 ) have the potential to be a powerful tool for geographic differentiation of CH 4 emissions from freshwater environments, as well as other microbial sources. This is because microbial δ 2 H–CH 4 values are partially dependent on the δ 2 H of environmental water ( δ 2 H–H 2 O), which exhibits large and well-characterized spatial variability globally. We have refined the existing global relationship between δ 2 H–CH 4 and δ 2 H–H 2 O by compiling a more extensive global dataset of δ 2 H–CH 4 from freshwater environments, including wetlands, inland waters, and rice paddies, comprising a total of 129 different sites, and compared these with measurements and estimates of δ 2 H–H 2 O, as well as δ 13 C-CH 4 and δ 13 C–CO 2 measurements. We found that estimates of δ 2 H–H 2 O explain approximately 42 % of the observed variation in δ 2 H–CH 4 , with a flatter slope than observed in previous studies. The inferred global δ 2 H–CH 4 vs. δ 2 H–H 2 O regression relationship is not sensitive to using either modelled precipitation δ 2 H or measured δ 2 H–H 2 O as the predictor variable. The slope of the global freshwater relationship between δ 2 H–CH 4 and δ 2 H–H 2 O is similar to observations from incubation experiments but is different from pure culture experiments. This result is consistent with previous suggestions that variation in the δ 2 H of acetate, controlled by environmental δ 2 H–H 2 O, is important in determining variation in δ 2 H–CH 4 . The relationship between δ 2 H–CH 4 and δ 2 H–H 2 O leads to significant differences in the distribution of freshwater δ 2 H–CH 4 between the northern high latitudes (60–90 ∘ N), relative to other global regions. We estimate a flux-weighted global freshwater δ 2 H–CH 4 of −310 ± 15 ‰, which is higher than most previous estimates. Comparison with δ 13 C measurements of both CH 4 and CO 2 implies that residual δ 2 H–CH 4 variation is the result of complex interactions between CH 4 oxidation, variation in the dominant pathway of methanogenesis, and potentially other biogeochemical variables. We observe a significantly greater distribution of δ 2 H–CH 4 values, corrected for δ 2 H–H 2 O, in inland waters relative to wetlands, and suggest this difference is caused by more prevalent CH 4 oxidation in inland waters. We used the expanded freshwater CH 4 isotopic dataset to calculate a bottom-up estimate of global source δ 2 H–CH 4 and δ 13 C-CH 4 that includes spatially resolved isotopic signatures for freshwater CH 4 sources. Our bottom-up global source δ 2 H–CH 4 estimate (−278 ± 15 ‰) is higher than a previous estimate using a similar approach, as a result of the more enriched global freshwater δ 2 H–CH 4 signature derived from our dataset. However, it is in agreement with top-down estimates of global source δ 2 H–CH 4 based on atmospheric measurements and estimated atmospheric sink fractionations. In contrast our bottom-up global source δ 13 C-CH 4 estimate is lower than top-down estimates, partly as a result of a lack of δ 13 C-CH 4 data from C 4 -plant-dominated ecosystems. In general, we find there is a particular need for more data to constrain isotopic signatures for low-latitude microbial CH

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.009
GPT teacher head0.230
Teacher spread0.221 · 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 designObservational
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

Citations26
Published2021
Admission routes2
Has abstractyes

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