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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

Abstract. There is growing interest in developing spatially resolved methane (CH4) isotopic source signatures to aid in geographic source attribution of CH4 emissions. CH4 hydrogen isotope measurements (δ2H–CH4) have the potential to be a powerful tool for geographic differentiation of CH4 emissions from freshwater environments, as well as other microbial sources. This is because microbial δ2H–CH4 values are partially dependent on the δ2H of environmental water (δ2H–H2O), which exhibits large and well-characterized spatial variability globally. We have refined the existing global relationship between δ2H–CH4 and δ2H–H2O by compiling a more extensive global dataset of δ2H–CH4 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 δ2H–H2O, as well as δ13C-CH4 and δ13C–CO2 measurements. We found that estimates of δ2H–H2O explain approximately 42 % of the observed variation in δ2H–CH4, with a flatter slope than observed in previous studies. The inferred global δ2H–CH4 vs. δ2H–H2O regression relationship is not sensitive to using either modelled precipitation δ2H or measured δ2H–H2O as the predictor variable. The slope of the global freshwater relationship between δ2H–CH4 and δ2H–H2O 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 δ2H of acetate, controlled by environmental δ2H–H2O, is important in determining variation in δ2H–CH4. The relationship between δ2H–CH4 and δ2H–H2O leads to significant differences in the distribution of freshwater δ2H–CH4 between the northern high latitudes (60–90∘ N), relative to other global regions. We estimate a flux-weighted global freshwater δ2H–CH4 of −310 ± 15 ‰, which is higher than most previous estimates. Comparison with δ13C measurements of both CH4 and CO2 implies that residual δ2H–CH4 variation is the result of complex interactions between CH4 oxidation, variation in the dominant pathway of methanogenesis, and potentially other biogeochemical variables. We observe a significantly greater distribution of δ2H–CH4 values, corrected for δ2H–H2O, in inland waters relative to wetlands, and suggest this difference is caused by more prevalent CH4 oxidation in inland waters. We used the expanded freshwater CH4 isotopic dataset to calculate a bottom-up estimate of global source δ2H–CH4 and δ13C-CH4 that includes spatially resolved isotopic signatures for freshwater CH4 sources. Our bottom-up global source δ2H–CH4 estimate (−278 ± 15 ‰) is higher than a previous estimate using a similar approach, as a result of the more enriched global freshwater δ2H–CH4 signature derived from our dataset. However, it is in agreement with top-down estimates of global source δ2H–CH4 based on atmospheric measurements and estimated atmospheric sink fractionations. In contrast our bottom-up global source δ13C-CH4 estimate is lower than top-down estimates, partly as a result of a lack of δ13C-CH4 data from C4-plant-dominated ecosystems. In general, we find there is a particular need for more data to constrain isotopic signatures for low-latitude microbial CH4 sources.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.019
Threshold uncertainty score0.440

Codex and Gemma teacher scores by category

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

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 teacher head, 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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