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Record W1911135842 · doi:10.5194/bg-12-6823-2015

Photomineralization and photomethanification of dissolved organic matter in Saguenay River surface water

2015· article· en· W1911135842 on OpenAlexafffundabout
Yong Zhang, Huixiang Xie

Bibliographic record

VenueBiogeosciences · 2015
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal ecosystems
Canadian institutionsUniversité du Québec à Rimouski
FundersNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of China
KeywordsColored dissolved organic matterDissolved organic carbonPhotobleachingEnvironmental chemistrySaturation (graph theory)ChemistryAbsorbanceSurface waterWater columnIrradianceOrganic matterEnvironmental scienceOceanographyGeologyPhytoplanktonEnvironmental engineeringFluorescence

Abstract

fetched live from OpenAlex

Abstract. Rates and apparent quantum yields of photomineralization (AQYDOC) and photomethanification (AQYCH4) of chromophoric dissolved organic matter (CDOM) in Saguenay River surface water were determined at three widely differing dissolved oxygen concentrations ([O2]) (suboxic, air saturation, and oxygenated) using simulated-solar radiation. Photomineralization increased linearly with CDOM absorbance photobleaching for all three O2 treatments. Whereas the rate of photochemical dissolved organic carbon (DOC) loss increased with increasing [O2], the ratio of fractional DOC loss to fractional absorbance loss showed an inverse trend. CDOM photodegradation led to a higher degree of mineralization under suboxic conditions than under oxic conditions. AQYDOC determined under oxygenated, suboxic, and air-saturated conditions increased, decreased, and remained largely constant with photobleaching, respectively; AQYDOC obtained under air saturation with short-term irradiations could thus be applied to longer exposures. AQYDOC decreased successively from ultraviolet B (UVB) to ultraviolet A (UVA) to visible (VIS), which, alongside the solar irradiance spectrum, points to VIS and UVA being the primary drivers for photomineralization in the water column. The photomineralization rate in the Saguenay River was estimated to be 2.31 × 108 mol C yr−1, accounting for only 1 % of the annual DOC input into this system. Photoproduction of CH4 occurred under both suboxic and oxic conditions and increased with decreasing [O2], with the rate under suboxic conditions ~ 7–8 times that under oxic conditions. Photoproduction of CH4 under oxic conditions increased linearly with photomineralization and photobleaching. Under air saturation, 0.00057 % of the photochemical DOC loss was diverted to CH4, giving a photochemical CH4 production rate of 4.36 × 10−6 mol m−2 yr−1 in the Saguenay River and, by extrapolation, of (1.9–8.1) × 108 mol yr−1 in the global ocean. AQYCH4 changed little with photobleaching under air saturation but increased exponentially under suboxic conditions. Spectrally, AQYCH4 decreased sequentially from UVB to UVA to VIS, with UVB being more efficient under suboxic conditions than under oxic conditions. On a depth-integrated basis, VIS prevailed over UVB in controlling CH4 photoproduction under air saturation while the opposite held true under O2-deficiency. An addition of micromolar levels of dissolved dimethyl sulfide (DMS) substantially increased CH4 photoproduction, particularly under O2-deficiency; DMS at nanomolar ambient concentrations in surface oceans is, however, unlikely a significant CH4 precursor. Results from this study suggest that CDOM-based CH4 photoproduction only marginally contributes to the CH4 supersaturation in modern surface oceans and to both the modern and Archean atmospheric CH4 budgets, but that the photochemical term can be comparable to microbial CH4 oxidation in modern oxic oceans. Our results also suggest that anoxic microniches in particulate organic matter and phytoplankton cells containing elevated concentrations of precursors of the methyl radical such as DMS may provide potential hotspots for CH4 photoproduction.

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 score0.020
Threshold uncertainty score0.978

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)0.0010.000

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 head0.018
GPT teacher head0.207
Teacher spread0.189 · 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

Citations50
Published2015
Admission routes3
Has abstractyes

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