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Nature Geoscience

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Online ISSN: 1752-0908

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Print ISSN: 1752-0894

Disciplines: Earth sciences; Sciences de la terre

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Contrasting sulfur speciation at ambient and entrapment temperatures under reducing and oxidizing conditions
a–d, Raman spectra of fluids trapped as ~20-µm-diameter SFIs under reducing (a,b) and oxidizing (c,d) conditions. The raw Raman spectra presented here are vertically offset for better readability, except for those in the insets. Insets show detailed spectral features in areas marked with dashed rectangles.
Source data
Sulfur speciation in an aqueous supercritical fluid phase at 2 kbar and 875 °C
a–c, Sulfur speciation as determined experimentally in this study (a), calculated using the experimentally derived equilibrium constants of Binder and Keppler⁷ (b) and calculated using the thermochemical data reported in Supplementary Table 5 with the S3⁻ radical included in the calculation (c). Note that only those sulfur species that reached a concentration of at least 1% of total sulfur at any of the considered fO2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$f_{{\mathrm{O}}_2}$$\end{document} values are visualized. In a, the horizontal error bars correspond to the estimated 2σ error in fO2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$f_{{\mathrm{O}}_2}$$\end{document} (that is, 0.3logfO2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$0.3 \log f_{{\mathrm{O}}_2}$$\end{document} units²⁸) and the vertical error bars correspond to the estimated 1σ error on sulfur speciation that arises from the peak fitting of one Raman spectrum per experiment.
Source data
Raman spectra showing the contrasting detection of S2⁻ and S3⁻ with different excitation wavelengths
a,b, Spectra of S2⁻ (a) and S3⁻ (b) were collected under temperature and fO2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$f_{{\mathrm{O}}_2}$$\end{document} conditions at which the respective species showed the strongest signals in the resonant Raman spectra of fluids containing 1 mol NaCl per kg H2O. S2⁻ remained below the detection limit in its non-resonant Raman spectrum collected at 532 nm excitation (a), and S3⁻ remained below the detection limit in its non-resonant Raman spectrum collected at 405 nm excitation (b). The raw Raman spectra presented here were vertically offset for better readability. Insets show detailed spectral features in areas marked with dashed rectangles.
Source data
Sulfur speciation and equilibria in a calc-alkaline magmatic system related to arcs
aThis work. bJugo et al.³⁸. cBénard et al.⁴¹. dDing et al.⁵¹. eBinder and Keppler⁷. fDrummond⁴³. gNi and Keppler¹⁸. hWallace and Edmonds²⁰. ⁱRoberts et al.⁴⁷. (aq), aqueous species; (g), gaseous species; (m), species in silicate melt; (s), solid species; Me, divalent metal (for example, Ca²⁺).
Gold solubility at 2 kbar and 875 °C
a,b, Total gold solubility (a) and the corresponding fraction of reduced sulfur species (b) in our experimental fluids. In a, the drop in gold solubility with increasing fO2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$f_{{\mathrm{O}}_2}$$\end{document} corresponds to the drop in HS⁻ + H2S in the fluid. No maximum in gold solubility is observed at the centre of the sulfur redox transition (~NNO + 0.2) where the concentration of sulfur radical ions should be the highest (Fig. 2c). In a and b, the horizontal error bars correspond to the estimated 2σ error in fO2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$f_{{\mathrm{O}}_2}$$\end{document} (that is, 0.3logfO2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$0.3 \log f_{{\mathrm{O}}_2}$$\end{document} units²⁸); in a, the vertical error bars correspond to the estimated 1σ error around the arithmetic mean of the data shown as filled symbols, and in b they correspond to the estimated 1σ error on sulfur speciation that arises from the peak fitting of one Raman spectrum per experiment. c, Gold solubility and speciation predicted by thermodynamic model calculations. Note that the model severely underestimates the gold solubility and the concentration of HS⁻ and Au–HS– species in the fluid. Moreover, the Au–HS– species under reducing conditions are dominated by Au(HS)2⁻, and Au–OH complexes are not shown as their predicted concentration remained below 1 µg g⁻¹ under all fO2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$f_{{\mathrm{O}}_2}$$\end{document} conditions. Note that the sulfur redox transition occurs at about 0.5logfO2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$0.5 \log f_{{\mathrm{O}}_2}$$\end{document} units higher in the model calculation (Fig. 2), and the predicted gold solubility pattern is offset accordingly compared with the experimental data.
Source data
Sulfur species and gold transport in arc magmatic fluids

December 2024

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Nature Geoscience is a journal dedicated to publishing high-quality original research across all areas of the geosciences, encompassing field work, modelling and theoretical studies. The journal is committed to publishing significant, high-quality research in the Earth Sciences through a fair, rapid and rigorous peer review process that is overseen by a team of full-time professional editors. In 2023, over 50% of the articles published were related to one or more of the Sustainable Development Goals (SDGs).

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Mars H, C, O chemistry including ground sinks and escape processes
Cartoon is split into two epochs: warm, wet (with crustal hydration and oxidation releasing H2) and cold, dry (with oxidants lost to oxidizing reduced iron near the surface). In all epochs, CO2 and H2O photolysis (energy from photons represented in the cartoon as hv) drives the photochemistry, and escape of H, C and O is considered. At Mars today, dissociative recombination (DR) is the main process for oxygen escape.
Surface temperatures and steady-state chemistry in cases with crustal hydration
a–f, Mixing ratio (dimensionless) profiles of H2 (solid), O2 (dots) and CO (dashed) in five atmospheres: 200 mbar 90% CO2 (a), 500 mbar 90% CO2 (b), 800 mbar 62% CO2 (c), 1 bar 90% CO2 (d), 1 bar 75% CO2 (e) and 1.3 bar 75% CO2 (f). The complementary component of the atmosphere is N2. We assume the 200 mbar atmosphere would correspond to the Hesperian period; therefore, we consider only a lower range of sinks in this case. Surface temperatures (K) are shown in insets at the top of each panel following the same colour scheme. Models shown are run to steady state (>10⁸ years, after which there is negligible variation in species concentration over time). In the text, mixing ratio is described in units of percentage (where 100% is a mixing ratio of 1).
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Mixing ratio outputs from cold early Mars cases
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Forecasting the onset, evolution and end of volcanic eruptions relies on interpretation of monitoring data—particularly seismic signals, such as persistent volcanic tremor—in relation to causative magmatic processes. Petrology helps establish such links retrospectively but typically lacks the required temporal resolution to directly relate to geophysical data. Here we report major and volatile element compositions of glass from volcanic ash continuously sampled throughout the 2021 Tajogaite eruption of Cumbre Vieja volcano, La Palma, Canary Islands. The data reveal the evolving chemistry of melts supplied from depth at a daily temporal resolution. Erupted melt compositions become progressively more primitive until the tenth week of activity, but a sharp reversal of this trend then marks the decline of mantle magma supply and a precursory signal to the eruption end. We find that melt SiO2 content is positively correlated with the amplitude of narrow-band volcanic tremor. Tremor characteristics, inferences from simulations and model calculations point to melt viscosity-controlled degassing dynamics generating variations in tremor amplitude. Our results show promise for a monitoring and forecasting tool capable of quickly identifying rejuvenated and waning phases of volcanic eruptions and illustrate how subtle changes in melt composition may translate to large shifts in geophysical signals.


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There is considerable uncertainty regarding the impact of irrigation on heat stress, partly stemming from the choice of heat stress index. Moreover, existing simulations are at scales that cannot appropriately resolve population centres or clouds and thus the potential for human impacts. Using multi-year convection-permitting and urban-resolving regional climate simulations, we demonstrate that irrigation alleviates summertime heat stress across more than 1,600 urban clusters in North America. This holds true for most physiologically relevant heat stress indices. The impact of irrigation varies by climate zone, with more notable irrigation signals seen for arid urban clusters that are situated near heavily irrigated fields. Through a component attribution framework, we show that irrigation-induced changes in wet-bulb temperature, often used as a moist heat stress proxy in the geosciences, exhibit an opposite sign to the corresponding changes in wet bulb globe temperature—a more complete index for assessing both indoor and outdoor heat risk—across climate zones. In contrast, the local changes in both wet-bulb and wet bulb globe temperature due to urbanization have the same sign. Our results demonstrate a complex relationship between irrigation and heat stress, highlighting the importance of using appropriate heat stress indices when assessing the potential for population-scale human impacts.



Late Palaeozoic proxy records of climate and seawater chemistry
a–d, Time-calibrated (GTS 2020) compilation of published and new carbon isotopes (δ¹³C) (a), oxygen isotopes (δ¹⁸O) (b), radiogenic strontium isotopes (⁸⁷Sr/86Sr) (c) and boron isotopes (δ¹¹B) (d) from preserved brachiopod calcite interpolated with Gaussian process smoothing (showing mean values and 16% and 84% as well as 5% and 95% confidence levels). The error bars show analytical uncertainty (2 s.d.); 0.2‰ in this study and, thus, smaller than symbol size. Previously published carbonate δ¹⁸O compilation from ref. ⁴⁷ and brachiopod δ¹¹B (with 2 s.d.) from refs. 23,48 are shown for comparison. e, Our boron-derived CO2 plotted alongside previously published CO2 estimates from soil carbonate- and fossil leaf-based proxies shown with 16% and 84% confidence intervals (ref. ¹³ provides new terrestrial CO2 as well as updated values for large part of data compiled in ref. ¹ for this time interval). The solid line and blue shading show CO2 evolution that does not consider the effect of temperature on CO2 calculation, and the dashed line and red shading show CO2 evolution considering ~9 °C global warming in the Early Permian (‘Temp.’, temperature scenario). f, The number of documented glacial deposits (from ref. ⁵). g, The extent of tropical coal forests (10³ km²; from ref. ³⁰). h, Documented LIPs (following refs. 38, 39, 40, 41–42) at their approximate latitudes, with major eruptive phases indicated by black bars. Note that the distribution of glacial deposits and coal forests should be taken as approximate; the paucity of the sedimentary record and the nature of the data make their spatiotemporal distribution and uncertainty difficult to constrain. Stratigraphic column according to International Chronostratigraphic Chart 2023/9 (https://stratigraphy.org/chart). SCLIP, Skagerrak-Centred LIP; Guad., Guadalupian.
Carboniferous–Permian evolution of boron and carbon system chemistry
a, The boron isotope composition of aqueous borate (δ¹¹B4 from δ¹¹Bbrachiopod). b–f, The boron isotope composition of seawater (δ¹¹Bsw) (b), ocean pH (c), DIC concentration (d), ocean saturation state (Ω) (e) and atmospheric CO2 (f); reconstructed using our main scenario (Main), and a scenario considering an ~9 °C warming in the Early Permian (‘Temp.’, temperature scenario). Each panel shows mean values and 16% and 84% as well as 5% and 95% confidence levels. C–P, Carboniferous–Permian boundary; A–S, Asselian–Sakmarian boundary.
Palaeozoic CO2 from different proxies
Boron: this study and ref. ¹⁷; palaeosols: refs. 1,13,25,49; phytoplankton: ref. ⁵⁰; plants: ref. ⁵¹; stomata: refs. 1,13,25 (and references therein). The error bars show 16% and 84% confidence intervals. LOME, Late Ordovician Mass Extinction; LDME, Late Devonian Mass Extinction; PTME, Permian–Triassic Mass Extinction. C., Cambrian; T., Triassic; Mes., Mesozoic.
Climate states of the Late Palaeozoic
a,b, Paired brachiopod carbon isotopes (a) and oxygen isotopes (b) versus atmospheric CO2. The transition from Carboniferous LPIA to Early Permian warmhouse was driven by a rapid CO2 release around the Asselian–Sakmarian boundary (about 294 ± 1 Ma), which leads to a modified trajectory in δ¹³C-CO2 (a) and δ¹⁸O-CO2 (b) space. The CO2 release event coincides with the emplacement of Skagerrak-Centred LIP (SCLIP); the location of other Early Permian LIPs (Tarim, Panjal and Zaduo) that have been hypothesized to have influenced Permian climate is illustrated for comparison. Palaeogeographic map following ref. ²⁷.
The end of the LPIA and the dawn of the Early Permian warmth
A relatively rapid rise in atmospheric CO2 approximately 294 Ma released the Earth from its penultimate icehouse (left) and transitioned the world to a warmer and drier climate of the Early Permian (right). Palaeo-artistic rendering based on findings of this study and previously published literature (Supplementary Information Section 7).
Rapid rise in atmospheric CO2 marked the end of the Late Palaeozoic Ice Age

January 2025

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522 Reads

Atmospheric CO2 is thought to play a fundamental role in Earth’s climate regulation. Yet, for much of Earth’s geological past, atmospheric CO2 has been poorly constrained, hindering our understanding of transitions between cool and warm climates. Beginning ~370 million years ago in the Late Devonian and ending ~260 million years ago in the Permian, the Late Palaeozoic Ice Age was the last major glaciation preceding the current Late Cenozoic Ice Age and possibly the most intense glaciation witnessed by complex lifeforms. From the onset of the main phase of the Late Palaeozoic Ice Age in the mid-Mississippian ~330 million years ago, the Earth is thought to have sustained glacial conditions, with continental ice accumulating in high to mid-latitudes. Here we present an 80-million-year-long boron isotope record within a proxy framework for robust quantification of CO2. Our record reveals that the main phase of the Late Palaeozoic Ice Age glaciation was maintained by prolonged low CO2, unprecedented in Earth’s history. About 294 million years ago, atmospheric CO2 rose abruptly (4-fold), releasing the Earth from its penultimate ice age and transforming the Early Permian into a warmer world.


Seasonal productivity of the equatorial Atlantic shaped by distinct wind-driven processes

January 2025

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88 Reads

The eastern equatorial Atlantic hosts a productive marine ecosystem that depends on upward supply of nitrate, the primary limiting nutrient in this region. The annual productivity peak, indicated by elevated surface chlorophyll levels, occurs in the Northern Hemisphere summer, roughly coinciding with strengthened easterly winds. For enhanced productivity in the equatorial Atlantic, nitrate-rich water must rise into the turbulent layer above the Equatorial Undercurrent. Using data from two trans-Atlantic equatorial surveys, along with extended time series from equatorial moorings, we demonstrate how three independent wind-driven processes shape the seasonality of equatorial Atlantic productivity: (1) the nitracline shoals in response to intensifying easterly winds; (2) the depth of the Equatorial Undercurrent core, defined by maximum eastward velocity, is controlled by an annual oscillation of basin-scale standing equatorial waves; and (3) mixing intensity in the shear zone above the Equatorial Undercurrent core is governed by local and instantaneous winds. The interplay of these three mechanisms shapes a unique seasonal cycle of nutrient supply and productivity in the equatorial Atlantic, with a productivity minimum in April due to a shallow Equatorial Undercurrent and a productivity maximum in July resulting from a shallow nitracline coupled with enhanced mixing.


Contrast in outcomes for a potential Charon-capturing collision when implementing strength
a,b, Cross-sections after 60 h of two identical low-velocity collisions without (a) and with (b) strength. The colours represent the composition (yellow and green for ice mantles, blue and purple for rocky cores) of target and impactor. The impact angle is θcoll = 45°, and the impact velocity is vcoll/vesc = 1.1. The fluid collision (a) results in a merger, consistent with prior studies. The collision with strength (b) ends up capturing the projectile as a mostly intact satellite. Tidal filaments in a are suppressed in b owing to strength. The white circles correspond to the centre of mass for each body.
Time series of a potential Charon-capturing collision at 45°
a–h, The simulation from Fig. 1b, shown at t = 0.5, 2.5, 5, 10, 15, 25, 35 and 60 h. The colours are as in Fig. 1; see Supplementary Information for the corresponding video. Proto-Charon initially contacts proto-Pluto (a) before briefly separating (b) to recollide and co-rotate with proto-Pluto (c), during which it loses a small fraction of its mass (d). The bulk of Charon, outside of co-rotation, is unable to orbit as fast as Pluto rotates, so the bodies begin to decouple (e), remaining intact as they complete their separation (f). Pluto torques Charon (g) into a close, relatively low-eccentricity (e = 0.2–0.3) orbit from which it begins to migrate further (h). Finer time resolution of the initial kiss is shown in Fig. 3b. The full video for this simulation can be viewed in Supplementary Video 1.
Orbital and thermal evolution of Pluto and Charon during and after kiss-and-capture
The graphs are based on the simulation shown in Fig. 2. a, The evolution of the semimajor axis (cyan) and periapsis (red) of Charon compared with the Pluto–Charon co-rotation radius (lavender), with respect to Pluto’s radius. b, Charon’s eccentricity (yellow), trending towards circularization. Subframes show snapshots from t = 0.5, 3, 5 and 20 h (Fig. 2 and Supplementary Videos 1 and 2). c, Charon’s orbital period (cyan), Pluto’s orbital period (lavender) and Charon’s spin period, in comparison with the ratio of Charon’s orbit to its spin (red), which oscillates around 1 (grey region), indicating tidal lock. d, The total angular momentum of the system compared with the actual Pluto–Charon value (red), and angular momentum contributions from Pluto and Charon’s orbital momentum (cyan) and from the spin of each of the bodies (lavender and green). The sharp change at ~15 h corresponds to the escape of the ‘tail’ of material seen in Fig. 2d, ~10²⁰ kg of material. Lost material is categorized as debris and removed from the angular momentum calculation. e, The change in temperature in Pluto’s core (cyan), Pluto’s core–mantle boundary (CMB, lavender) and Charon’s core (pink).
Time series and orbital evolution of a potential Charon-capturing collision with more Pluto- and Charon-like initial bodies
The impact angle is θcoll = 45°, the impact velocity is vcoll/vesc = 1.2 and the impactor is 55% rock by mass. Shown at t = 1, 2, 20 and 48 h. The colours are the same as in Fig. 1. a–d, Proto-Charon briefly contacts proto-Pluto (a) before separating briefly (b) to recollide and co-rotate (c) with proto-Pluto before decoupling (d) and stabilizing into a bound orbit. e,f, Orbital evolution includes evolution of the semimajor axis and periapsis of Charon compared with the Pluto–Charon co-rotation radius with respect to Pluto’s radius (e), the total angular momentum of the system compared with the actual Pluto–Charon value and angular momentum contributions from Pluto and Charon’s orbital momentum and from the spin of each body (f), as in Fig. 3. The drop in semimajor axis and periapsis at ~15 h corresponds to Charon’s recollision with Pluto and the start of co-rotation. The sharp change at ~25 h corresponds to the ‘tail’ in Fig. 4c, ~5 × 10²⁰ kg of debris. The full video for this simulation can be viewed in Supplementary Video 2.
Capture of an ancient Charon around Pluto

Pluto and Charon are the largest binary system in the known population of trans-Neptunian objects in the outer Solar System. Their shared external orbital axis suggests a linked evolutionary history and collisional origin. Their radii, ~1,200 km and ~600 km, respectively, and Charon’s wide circular orbit of about 16 Pluto radii require a formation mechanism that places a large mass fraction into orbit, with sufficient angular momentum to drive tidal orbital expansion. Here we numerically model the collisional capture of Charon by Pluto using simulations that include material strength. In our simulations, friction distributes impact momentum, leading Charon and Pluto to become temporarily connected, instead of merging, for impacts aligned with the target’s rotation. In this ‘kiss-and-capture’ regime, coalescence of the bodies is prevented by strength. For a prograde target rotation consistent with the system angular momentum, Charon is then tidally decoupled and raised into a near-circular orbit from which it migrates outwards to distances consistent with its present orbit. Charon is captured relatively intact in this scenario, retaining its core and most of its mantle, which implies that Charon could be as ancient as Pluto.


Location of ice cores and marine sediment cores used in the manuscript
The white circles indicate marine sediment (Ocean Drilling Program (ODP), Polarstern (PS)) and ice core positions and the black arrows illustrate the typical mean 5 day air mass back-trajectory to EDML for all trajectories (dashed line) and trajectories connected to snow fall at EDML (solid line, arrows adapted from ref. ⁴⁶). Dashed lines illustrate the positions of the Polar Front (PF), Subantarctic Front (SAF) and the Subtropical Front (STF). Map adapted from the original version provided by Bethan Davies (www.AntarcticGlaciers.org).
Measured EDML aerosol and temperature tracer data
a, The total Na⁺ (Na⁺tot) and ssNa. b, The total Ca²⁺ (Ca²⁺tot) and nssCa (total Ca²⁺ data are nearly indistinguishable from the nssCa data). c, The surface temperature (Tsurf) derived from δ¹⁸O (ref. ³⁶) (note that the surface temperature is significantly colder than the condensation temperature at the top of the inversion layer depicted in Fig. 4). d, The Δ³³SO4²⁻ (with excursions indicative for mass-independent fractionation of volcanic sulfate in the stratosphere indicated by ‘v’). e, The δ³⁴SO4²⁻ (with samples indicative of volcanic horizons of stratospheric or tropospheric input indicated by ‘v’). f, The total measured SO4²⁻. The red dots in f refer to values of the discrete samples taken for SO4²⁻ isotope analysis and the black line shows the high-resolution SO4²⁻ data from continuous melt analysis. All values are provided on the extended AICC2023 age scale⁴⁷. The orange bar indicates the LIG and the blue bar indicates the PGM with termination II (TII) in between.
Contributions from different sources to the total SO4²⁻ budget at EDML
Top: the relative contributions of sea-salt (light blue), mineral dust (brown), biogenic (green) and volcanic (purple) emissions to total SO4²⁻. a, Sea-salt SO4²⁻ (SO4,sea). b, Biogenic SO4²⁻ (SO4,bio). c, Terrestrial SO4²⁻ (SO4,dust). d, Volcanic SO4²⁻ (SO4,volc). e, Total measured SO4²⁻ (SO4,tot). The filled symbols and their error bars (1 s.d.) were derived using Monte Carlo error propagation of analytical uncertainties for fixed (nssSO4²⁻/nssCa)dust = 0.7 (Supplementary Information). The open circles represent alternative decompositions for (nssSO4²⁻/nssCa)dust = 1.0 and (nssSO4²⁻/nssCa)dust = 0.18. Note that these are not discernible from the standard run for biogenic SO4²⁻. The magenta peak in volcanic SO4²⁻ indicates one sample, where a negative volcanic δ³⁴SO4²⁻ signature needs to be assumed to close the budget. The light-green dot in biogenic SO4²⁻ (with its volcanic contribution as indicated in pink) represents an outlier that would require a much lighter isotopic signature of biogenic SO4²⁻ to obtain values that are similar to the neighbouring samples. As we have no objective reason to reject this outlier, we included this sample in our dataset, but refrain from interpreting this value as being indicative of an even higher biogenic SO4²⁻ emission at that time. All values are provided on the extended AICC2023 age scale⁴⁷, the orange bar indicates the LIG and the blue bar indicates the PGM with termination II (TII) in between.
Modelled atmospheric concentration of biogenic SO4²⁻
a, Tsource (grey circles). b, The atmospheric biogenic SO4²⁻aerosol concentration at the source (SO4,bio source). c, The atmospheric biogenic SO4²⁻ aerosol concentration on the ice sheet (SO4,bio site). d, The ice core concentration of biogenic SO4²⁻ (SO4,bio ice). The filled symbols and their error bars (1 s.d.) in b and c were derived using Monte Carlo error propagation of the transport model for our standard decomposition (nssSO4²⁻/nssCa)dust = 0.7. The orange-/red-coloured dot represents an outlier and suggests that the source decomposition is biased for this data point. As we have no objective reason to reject this outlier, we included this sample in our dataset, but refrain from interpreting this value as being indicative of an even higher biogenic SO4²⁻ emission at that time. e, The condensation temperature at the top of the inversion layer over the ice sheet, Tsite (black circles). All values are provided on the extended AICC2023 age scale⁴⁷, the orange bar indicates the LIG and the blue bar indicates the PGM with termination II (TII) in between.
Bioproductivity in the Atlantic Sector-Southern Ocean north and south of the mAPF
a, The alkenone concentration (C) at ODP site ODP1090 north of the mAPF⁴⁸. b, The deep ocean oxygenation tracer authigenic uranium at ODP1094 south of the mAPF⁴³. c, Biogenic Ba flux at ODP1094 (ref. ⁸). d, Atmospheric biogenic SO4²⁻ aerosol concentration at the source. The orange-coloured dot in d represents an outlier and suggests that the source decomposition is biased for this data point. We refrain from interpreting this value as being indicative of an even higher biogenic SO4²⁻emission at that time. e–g, Biogenic Ba flux south of the mAPF at the Antarctic continental margin at PS1575 (e), PS1648 (f) and PS1821 (g), respectively (see Fig. 1 for their location)⁴⁹. Note that all records are provided on their individual age scale (AICC2013 in case of EDML), which can largely differ from each other. The orange bar indicates the LIG and the blue bar indicates the PGM with termination II (TII) in between.
Limited decrease of Southern Ocean sulfur productivity across the penultimate termination

Productivity in the Pleistocene glacial Southern Ocean was probably enhanced owing to iron fertilization by aeolian dust. Marine sediments indicate such an increase north of the modern Antarctic Polar Front but reduced biogenic activity south of it. However, quantitative estimates for the integrated net effect are difficult to obtain. Here we use the SO4²⁻ isotopic composition and other geochemical ice core records from the Atlantic sector of the Southern Ocean to reconstruct net changes in integrated biogenic sulfur productivity in the surface ocean over the penultimate glacial termination. We show that biogenic SO4²⁻ aerosol contributes 58% and 85% to the sulfate budget in Dronning Maud Land during glacial and interglacial times, respectively, and that biogenic sulfate is derived predominately from the seasonal sea ice zone. Using our quantitative reconstruction of biogenic aerosol production in the Southern Ocean source region, we show that the average biogenic sulfate production integrated over the Atlantic sector was 16% higher in the penultimate glacial 137,000–153,000 years ago compared with the later Last Interglacial 120,000–125,000 years ago. An intermittent decrease in productivity observed during early peak interglacial warming suggests that a reduction in the seasonal sea ice zone may disrupt Southern Ocean ecosystems.



Conceptual model for DOC cycling in sediments
The proposed conceptual model incorporates mechanisms of geopolymerization, equilibrium adsorption and kinetic sorption and a modified concept of hydrolysis that follows DOC cycling in the water column²⁰. Model schematic nomenclature includes POC, GPS, lrDOC, various mineral-sorbed DOC ((semi)labile DOC-MOC, GPS-MOC and lrDOC-MOC), DIC and MW. All DOC, GPS and lrDOC pools can interact with minerals through equilibrium adsorption and kinetic sorption. In general, POC pools that originate from the water column can be hydrolysed at any depth in the sediment or remain unhydrolysed. Their transport in the sediment is similar to the sediment solid minerals. MOC pools are transported similarly to sediment solid minerals and POC, but they originate from the net sorption of DOC, GPS and lrDOC to minerals and are further assumed to be unreactive unless the carbon is desorbed from minerals. Part of the POC, which is not hydrolysed at a given depth, and part of the MOC, which is not desorbed at that depth, are considered to be permanently buried.
Comparing model-generated PE with literature data
a,b, Data generated for 1,450 model runs in a Monte Carlo approach (transparent black dots) for the conventional approach to calculate PE of POC that considers only POC (a) and for the newly defined PE that considers both POC and MOC (b). These are compared with field data from previous studies12,36. The spread of model data is derived from a normal distribution of the net sediment accumulation rate data observed in the global grid datasets (Supplementary Figs. 1 and 2). The envelope line (dashed line) represents the general boundaries of the spread of data identified in previous studies13,41. Low BW O2 stands for low bottom water oxygen concentration.
The relative importance of different processes
a,b, The relative importance (%) of six processes to PE when MOC is considered in addition to POC (a) and to preservation rates for MOC (b). The six processes are DOC hydrolysis, DOC remineralization, mixing, equilibrium adsorption, kinetic sorption and geopolymerization. The newly defined PE is given by equation (7). The preservation rates for MOC are shown as the rate of MOC formation, which is the sum of net kinetic sorption rates integrated at the depth of 1 m (µmol cm⁻² yr⁻¹) for DOC, GPS and lrDOC. The importance of each process is obtained on the basis of the maximum sensitivity of the parameters categorized for each process. The categorization is presented in Supplementary Table 9. Each bar is the mean of 1,000 executions of the process importance analysis, and the error bars represent the 95% confidence interval. Details of sampling in the Monte Carlo method for process importance analysis are provided in Supplementary Sections 1.3–1.5 and in previous studies³⁵.
Depth profiles obtained from Monte Carlo modelling
a, The ratio of the kinetic sorption rates averaged for 1,450 runs of the Monte Carlo modelling to the desorption rates also averaged for 1,450 model runs at different depths. b, The percentage contribution of the final GPS pool to the total lrDOC production (Fig. 1) at different depths. These contributions are averaged for 1,450 runs of the Monte Carlo modelling. The mathematical equations used to produce these plots are presented in Supplementary Section 3. c, The PE (%) for only POC and for both POC and MOC together, demonstrating that MOC increases calculated PE by a factor of 2.7 at 1 m depth compared with the conventional approach considering only POC. d, POC fluxes and MOC preservation rate. MOC content exceeds POC at a depth of ~50 cm and still continues to rise below this depth. All values have been averaged for 1,450 Monte Carlo model runs. The shaded areas of the curves represent 95% confidence intervals obtained from Monte Carlo model runs. The light-yellow-shaded region represents the mixed layer depth (top 10 cm).
Preservation of organic carbon in marine sediments sustained by sorption and transformation processes

January 2025

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293 Reads

Controls on organic carbon preservation in marine sediments remain controversial but crucial for understanding past and future climate dynamics. Here we develop a conceptual-mathematical model to determine the key processes for the preservation of organic carbon. The model considers the major processes involved in the breakdown of organic carbon, including dissolved organic carbon hydrolysis, mixing, remineralization, mineral sorption and molecular transformation. This allows redefining of burial efficiency as preservation efficiency, which considers both particulate organic carbon and mineral-phase organic carbon. We show that preservation efficiency is almost three times higher than the conventionally defined burial efficiency and reconciles predictions with global field data. Kinetic sorption and transformation are the dominant controls on organic carbon preservation. We conclude that a synergistic effect between kinetic sorption and molecular transformation (geopolymerization) creates a mineral shuttle in which mineral-phase organic carbon is protected from remineralization in the surface sediment and released at depth. The results explain why transformed organic carbon persists over long timescales and increases with depth.


Cumulative C-CO2, cumulative C-CH4 and total C-CO2e produced by day ~365 of the incubations
a–d, Aerobic incubations at 4 °C (a), anaerobic incubations at 4 °C (b), aerobic incubations at 4 °C, 10 °C and 20 °C (c), and anaerobic incubations at 4 °C, 10 °C and 20 °C (d). Each value represents an average of the analytical replicates across the final three time points (n = 9), normalized by initial quantity of SOC. Error bars show s.e.m. Reference data can be found in Extended Data Table 2.
Q10 temperature sensitivities by incubation depth at early, middle and end periods of the incubations (30, 150 and 365 days, respectively)
a,b, Aerobic incubations (a) and anaerobic incubations (b). Q10 coefficients were computed as a linear regression of cumulative production versus temperature at depths where three incubation temperatures were analysed; data included three time points that bracketed each incubation time period (for example, three time points around the 30-day period), three analytical replicates per time point and three temperatures per time point (n = 27). Significant (P < 0.05) Q10 values are displayed as solid points, whereas insignificant values are open points (insignificance was largely driven by negligible or variable C-CH4 contributions).
GSL annual sediment column production potentials for total carbon [C-CO2 + C-CH4]
Each bar represents a whole core production potential. Depth-integrated production was calculated for all analytical replicates within a given depth increment, temperature and headspace treatment, across the final three incubation time points (n = variable by depth–temperature combination; see Table 2). Reference data can be found in Extended Data Table 4.
Substantial and overlooked greenhouse gas emissions from deep Arctic lake sediment

January 2025

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43 Reads

Thermokarst lakes cause abrupt and sustained permafrost degradation and have the potential to release large quantities of ancient carbon to the atmosphere. Despite concerns about how lakes will affect the permafrost carbon feedback, the magnitude of carbon dioxide and methane emissions from deep permafrost soils remains poorly understood. Here we incubated a very deep sediment core (20 m) to constrain the potential productivity of thawed Yedoma and underlying Quaternary sand and gravel deposits. Through radiocarbon dating, sediment incubations and sediment facies classifications, we show that extensive permafrost thaw can occur beneath lakes on timescales of decades to centuries. Although it has been assumed that shallow, aerobic carbon dioxide production will dominate the climate impact of permafrost thaw, we found that anaerobic carbon dioxide and methane production from deep sediments was commensurate with aerobic production on a per gram carbon basis, and had double the global warming potential at warmer temperatures. Carbon release from deep Arctic sediments may thus have a more substantial impact on a changing climate than currently anticipated. These environments are presently overlooked in estimates of the permafrost carbon feedback.


O3 concentration, VOC ERs and OH reactivity versus temperature during the summers of 2018–2020 in Shanghai
a, Observed MDA8 O3 level versus temperature at the sites supervised by the China National Environmental Monitoring Centre (CNEMC) in Shanghai. b, Observed daytime (10:00–18:00 lt) and night-time (22:00–06:00 lt) ERs of VOC groups versus temperature, with the top (bottom) row showing temperature-dependent (temperature-independent) VOCs, based on the measurements on weekdays at the Shanghai Academy of Environmental Sciences (SAES) site. In a and b, daily O3 and ERs (with the sample size shown in the lower left corner of each panel) were split evenly into ten bins according to the paired temperatures. The average values for O3, daytime ER and night-time ER in each bin are represented by green (in a), yellow and blue (in b) open circles, respectively, with the standard deviations (±1σ) represented by whiskers. The grey circles in a represent daily O3 levels, with a linear least-squares regression line and light grey shading indicating the 95% confidence interval. The definitions of individual VOCs using the SAPRC07tic chemical mechanism in b are shown in Supplementary Table 3. c, OH reactivity contributed by temperature-dependent VOCs (red) and temperature-independent VOCs (blue) according to the measured VOCs in b. ALK1–5, alkanes; ARO1 and ARO2, aromatics that are not explicitly represented; BDE13, 1,3-butadiene; BENZ, benzene; B124, 1,2,4-trimethylbenzene; ETHE, ethene; OLE1 and OLE2, alkenes that are not explicitly represented; PRPE, propene; TOLU, toluene; XYL, xylenes.
Enhancements in VOC concentrations, OH reactivity and radical production at high temperatures
a, AVOC concentrations at temperatures greater than 35 °C (over 9 days) normalized to the average concentrations in the temperature range of 20–30 °C at the SAES site during the summer of 2018. The horizontal lines and squares represent medians and mean values, respectively, with the edges of boxes representing the 25th and 75th percentiles. The whiskers indicate the minimum and maximum values. b, Simulated enhancements of OH reactivity during the heatwave (25–30 July) relative to the monthly averages. The enhancement of total OH reactivity based on the VOC measurements (not including OVOCs) is shown in grey compared with simulations with or without OVOCs in the adj case. The impacts induced by meteorological conditions, BVOCs and AVOCs are indicated with transparent, semi-transparent and solid bars, respectively. c, Simulated P(ROx) (bars) and relative contributions of major reactions (pies) in different cases (Methods). The cases labelled ‘HW’ indicate average values during the heatwave. In the pie chart, the black numbers indicate the relative contributions of photolysis of carbonyl compounds. Changes in total and carbonyl-related P(ROx) between different cases are indicated by the grey numbers. All carbonyl-related reactions are outlined with dashed lines in the legend. GLY, glyoxal; MGLY, methylglyoxal.
Impacts of AVOC emissions enhancements on O3 chemistry during the heatwave
a,b, Simulated changes in MDA8 O3 (a) and the daytime (6:00–18:00 lt) P(Ox) (b) in the YRD. Large cities are marked with circles. c,d, Diurnal variations in O3 (c) and P(Ox) (d) in Shanghai. The shaded areas in c represent ±0.5σ from the averages. The shaded areas in d indicate the differences between the cases with (dotted lines) and without (solid lines) the temperature effect of AVOC emissions. Changes in P(Ox) were examined under four emissions scenarios: (1) present emissions (black), (2) a 50% reduction in NOx (orange), (3) a 50% reduction in evaporative emissions of AVOCs (eAVOCs; green) and (4) concurrent reductions in both NOx and eAVOCs (blue).
How temperature-induced AVOC emissions are likely to exaggerate O3 pollution in AVOC-sensitive megacities during heatwaves
Increased AVOC emissions from anthropogenic non-combustion sources enhance OVOC concentrations, promoting radical production and O3 formation during heatwaves.
Increased urban ozone in heatwaves due to temperature-induced emissions of anthropogenic volatile organic compounds

January 2025

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175 Reads

Urban ozone (O3) pollution correlates with temperature, and higher O3 often occurs during heatwaves, threatening public health. However, limited data on how anthropogenic volatile organic compound (AVOC) precursor emissions vary with temperature hinders understanding their impact on O3. Here we show that the increase in non-combustion AVOC emissions (for example, from volatile chemical products) during a heatwave in Shanghai contributes significantly to increased O3, on the basis of ambient measurements, emissions testing and air quality modelling. AVOC concentrations increase ~twofold when the temperature increases from 25 °C to 35 °C due to air stagnation and increased emissions. During the heatwave, higher concentrations result in an 82% increase in VOC OH reactivity. Air quality simulations reveal that temperature-driven AVOC emissions increases account for 8% (1.6 s–1) of this reactivity increase and enhance O3 by 4.6 ppb. Moreover, we predict a more profound (twofold) increase in OH reactivity of oxygenated VOCs, facilitating radical production and O3 formation. Enhanced AVOC emissions trigger O3 enhancements in large cities in East China during a heatwave, and similar effects may also happen in other AVOC-sensitive megacities globally. Reducing AVOC emissions, particularly non-combustion sources, which are currently less understood and regulated, could mitigate potential O3 pollution in urban environments during heatwaves.






Pollution resistance mechanism for Saturn’s ring particles
a, Micrometeoroid impacts on Saturn’s rings occur at impact velocities of ~30 km s–1. b, The impactor materials are highly shocked (>100 GPa) and form hot expanding vapour (>10,000 K). Only a small fraction of the ring particles (mass comparable to the impactor) is vaporized. c, The impact-generated vapour expands with a high velocity (on average >14 km s–1), producing atoms/molecules and forming nanoparticles as condensates. The silicate vapour is more prone to condensation than water vapour. d, Atoms or molecules are ionized and nanoparticles are charged in Saturn’s magnetosphere, and impactor materials are removed from the ring plane by direct collision with Saturn, by escape from Saturn’s gravitational field or by being dragged into Saturn by interaction with the electromagnetic field. Credit: d, NASA Goddard Space Flight Center.
Time evolution of our SPH impact simulation (vimp = 30 km s–1)
Impacts occur in the x–z plane in the −z direction (vertical impact). Cross sections (particles within y = ±0.1Rimp) are plotted. The top and bottom panels show cases of the H2O ice EOS and the SiO2 rock EOS, respectively. The left and right halves of each panel show the temperature and pressure using the colour contour. Plotted time is the case where the impactor radius is rimp = 10 μm, and it is linearly scaled by rimp.
Cumulative distribution of the peak temperature and peak pressure that our modelled impactor experiences
a, Peak temperature. b, Peak pressure (see also Supplementary Fig. 1). Solid and dashed lines represent cases of the H2O ice EOS and the SiO2 rock EOS, respectively, for different impact velocities. Here target material is not included because the impactor’s materials, potential ring darkening material, are the focus of this study.
Numerical results of vapour expansion and condensation processes
a, Silicate vapour expansion (SiO2 ANEOS EOS): (i) T−P evolution; (ii) 1/ρvap−T evolution. Black lines correspond to cases of vimp = 30, 40 and 50 km s–1 cases, as marked. The initial conditions are obtained from the impact simulations. b, The same as a but for the case of water vapour (five-phase H2O EOS). In all cases of water vapour, because the vapour density is too low when the system reaches the two-phase boundary, viable nucleation hardly occurs, remaining as neutral/ionized atoms and/or molecules. The grey lines in a (i) and b (i) indicate the two-phase boundary. The blue and red lines in a (ii) and b (ii) indicate the two-phase boundaries that separate the liquid and vapour regions from the liquid–vapour region, respectively (the vapour dome). The Hugoniot curve for vimp = 5− 52 km s–1 is displayed with orange lines, and open circles are plotted at intervals of 1 km s–1.
Numerical results of the dynamical evolution of charged particles for different q/m ratios
a, Fifteen randomly selected particle trajectories out of a total of 10⁵ trajectories. b, The fraction of particles with different fates: collision with Saturn (green lines), ejection from the system (cyan lines), re-collision with rings (red lines; corresponds to the accretion efficiency of impactor’s material, η value) and total removal from the rings (collision with Saturn or ejection from the system; black lines). c, Close-up of b (only the η value is shown; red lines).
Pollution resistance of Saturn’s ring particles during micrometeoroid impact

December 2024

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19 Reads

Saturn’s rings have been estimated to be as young as about 100 to 400 million years old according to the hypothesis that non-icy micrometeoroid bombardment acts to darken the rings over time and the Cassini observation indicated that the ring particles appear to be relatively clean. These young age estimates assume that the rings formed out of pure water ice particles with a high accretion efficiency of impacting non-icy micrometeoroid material (η ≳ 10%). Here we show, using numerical simulations of hypervelocity micrometeoroid impacts on a ring particle, that non-icy material may not be as readily accreted as previously thought. We found that the complete vaporization and expansion of non-icy impactor material on energetic collision with a ring particle leads to the formation of charged nanoparticles and ions that are subsequently removed from the rings through collision with Saturn, gravitational escape or electromagnetic drag into Saturn’s atmosphere. Despite uncertainties in our models that assume no porosity, strength or ring particle granularity, we suggest minimal accretion of non-icy materials would occur following micrometeoroid impact. This pollution resistance mechanism implies a low accretion efficiency (η ≲ 1%). Thus we suggest that the apparent youth of Saturn’s rings could be due to pollution resistance, rather than indicative of young formation age.


Contrasting sulfur speciation at ambient and entrapment temperatures under reducing and oxidizing conditions
a–d, Raman spectra of fluids trapped as ~20-µm-diameter SFIs under reducing (a,b) and oxidizing (c,d) conditions. The raw Raman spectra presented here are vertically offset for better readability, except for those in the insets. Insets show detailed spectral features in areas marked with dashed rectangles.
Source data
Sulfur speciation in an aqueous supercritical fluid phase at 2 kbar and 875 °C
a–c, Sulfur speciation as determined experimentally in this study (a), calculated using the experimentally derived equilibrium constants of Binder and Keppler⁷ (b) and calculated using the thermochemical data reported in Supplementary Table 5 with the S3⁻ radical included in the calculation (c). Note that only those sulfur species that reached a concentration of at least 1% of total sulfur at any of the considered fO2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$f_{{\mathrm{O}}_2}$$\end{document} values are visualized. In a, the horizontal error bars correspond to the estimated 2σ error in fO2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$f_{{\mathrm{O}}_2}$$\end{document} (that is, 0.3logfO2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$0.3 \log f_{{\mathrm{O}}_2}$$\end{document} units²⁸) and the vertical error bars correspond to the estimated 1σ error on sulfur speciation that arises from the peak fitting of one Raman spectrum per experiment.
Source data
Raman spectra showing the contrasting detection of S2⁻ and S3⁻ with different excitation wavelengths
a,b, Spectra of S2⁻ (a) and S3⁻ (b) were collected under temperature and fO2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$f_{{\mathrm{O}}_2}$$\end{document} conditions at which the respective species showed the strongest signals in the resonant Raman spectra of fluids containing 1 mol NaCl per kg H2O. S2⁻ remained below the detection limit in its non-resonant Raman spectrum collected at 532 nm excitation (a), and S3⁻ remained below the detection limit in its non-resonant Raman spectrum collected at 405 nm excitation (b). The raw Raman spectra presented here were vertically offset for better readability. Insets show detailed spectral features in areas marked with dashed rectangles.
Source data
Sulfur speciation and equilibria in a calc-alkaline magmatic system related to arcs
aThis work. bJugo et al.³⁸. cBénard et al.⁴¹. dDing et al.⁵¹. eBinder and Keppler⁷. fDrummond⁴³. gNi and Keppler¹⁸. hWallace and Edmonds²⁰. ⁱRoberts et al.⁴⁷. (aq), aqueous species; (g), gaseous species; (m), species in silicate melt; (s), solid species; Me, divalent metal (for example, Ca²⁺).
Gold solubility at 2 kbar and 875 °C
a,b, Total gold solubility (a) and the corresponding fraction of reduced sulfur species (b) in our experimental fluids. In a, the drop in gold solubility with increasing fO2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$f_{{\mathrm{O}}_2}$$\end{document} corresponds to the drop in HS⁻ + H2S in the fluid. No maximum in gold solubility is observed at the centre of the sulfur redox transition (~NNO + 0.2) where the concentration of sulfur radical ions should be the highest (Fig. 2c). In a and b, the horizontal error bars correspond to the estimated 2σ error in fO2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$f_{{\mathrm{O}}_2}$$\end{document} (that is, 0.3logfO2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$0.3 \log f_{{\mathrm{O}}_2}$$\end{document} units²⁸); in a, the vertical error bars correspond to the estimated 1σ error around the arithmetic mean of the data shown as filled symbols, and in b they correspond to the estimated 1σ error on sulfur speciation that arises from the peak fitting of one Raman spectrum per experiment. c, Gold solubility and speciation predicted by thermodynamic model calculations. Note that the model severely underestimates the gold solubility and the concentration of HS⁻ and Au–HS– species in the fluid. Moreover, the Au–HS– species under reducing conditions are dominated by Au(HS)2⁻, and Au–OH complexes are not shown as their predicted concentration remained below 1 µg g⁻¹ under all fO2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$f_{{\mathrm{O}}_2}$$\end{document} conditions. Note that the sulfur redox transition occurs at about 0.5logfO2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$0.5 \log f_{{\mathrm{O}}_2}$$\end{document} units higher in the model calculation (Fig. 2), and the predicted gold solubility pattern is offset accordingly compared with the experimental data.
Source data
Sulfur species and gold transport in arc magmatic fluids

December 2024

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582 Reads

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1 Citation

The sulfur species present in magmatic fluids affect the global redox cycle, the Earth’s climate and the formation of some of the largest and most economic ore deposits of critical metals. However, the speciation of sulfur under conditions that are relevant for upper crustal magma reservoirs is unclear. Here we combine a prototype pressure vessel apparatus and Raman spectroscopy to determine sulfur speciation in arc magmatic fluid analogues in situ over a range of geologically relevant pressure–temperature–redox conditions. We find that HS⁻, H2S and SO2 are the main sulfur species in the experimental fluids, while the concentrations of S⁶⁺ species and S2⁻ and S3⁻ sulfur radical ions are negligible, in contrast to previous experimental work. The measured gold solubilities in the experimental fluids are highest when sulfur is dominantly present as HS⁻ and H2S species and greatly exceed thermodynamic predictions. Our results imply that HS⁻, rather than sulfur radicals, accounts for the high solubilities of gold in magmatic–hydrothermal fluids. We also find that magmatic sulfur degassing is a redox process under oxidizing conditions and may lead to additional magma oxidation beyond that imparted by slab-derived fluxes and crystallization.






Acoustic transect across Gotland Basin showing sulfate and methane profiles
a, Northwest to southeast transect showing acoustic profile and core locations. The stratigraphy of the deposits is tracked by coloured lines that show the transitions from late glacial silt, Baltic Ice Lake clay, Yoldia Sea clay and Ancylus Lake clay to modern Littorina Sea mud (that is, Holocene mud). m.b.s.l., metres below sea level. Inset: methane profile from station 51 in expanded CH4 scale. Methane data that may be compromised by degassing are indicated by the red plus symbols. b, Sulfate and methane profiles from stations along the transect. Stations with a relatively high CH4 flux to the sediment surface co-occur with the thickest Holocene mud deposits. In the station 51 panel, the faint red line connects to the deeper methane concentration, which is off scale but can be seen in a(inset). Values of RSWI/SMT show the inefficiency of AOM.
Methane flux to the SWI calculated from this study and from other published Baltic Sea data
Brown areas represent the presence of Holocene mud (Geological Survey of Denmark and Greenland). Credit: basemap, World Ocean Base: Esri, GEBCO, Garmin, NaturalVue; World Ocean Reference: Esri, GEBCO, Garmin, NGS.
AOM efficiency in the Baltic Sea and in other marine sediments around the world
a, Log–log plot of JSWI versus JSMT for this study, for the additional Baltic sites and for the global inventory from ref. ²⁷. The dark-grey line is the linear fit to the log–log data: log(JSWI) = −0.5 + 0.9 log(JSMT) (R² = 0.4). Solid and dot–dash light-grey lines above and below the linear fit denote that 100% and 1% of the methane leaks to the sediment surface, respectively. b, The relative frequency distribution (%) of RSWI/SMT (each bin is 10%) across the Baltic Sea (combining this study and published Baltic sites, n = 95) and the global database (n = 41). c, Log–log plot of methane flux to SWI versus SMT depth. The black line is the linear fit to the log–log data: log(JSWI) = 3.8 − 1.3 log(SMT) (R² = 0.3). d, The same as c, but with linear axes and indication of the 40 cm SMT depth (dashed line).
Comparison of geochemical and reaction rate profiles
a–l, Comparison of geochemical and reaction rate profiles at station 24 from Bornholm Basin (a–f) and station 73 from Gotland Basin (g–l). Data are represented with symbols, and results of a multicomponent diagenetic model with solid curves. The dashed lines indicate the SMT depth. a,g, Sulfate (SO4²⁻) and methane (CH4); b,h, δ¹³C-CH4; c,i, reactive POC (% of dry weight); d,j, ammonium (NH4⁺) and DIC. Note that ammonium concentrations are multiplied by 20 to see the monotonic, concave-down profiles, consistent with diffusion-dominated transport. Solid curves show that model-predicted profiles are consistent with data. e,f,k,l, CH4 reaction rates (e,k) and organic matter (OM) remineralization rates (f,l) predicted by the model. The model predicts a peak in AOM rate at station 24 (e), consistent with the shift in δ¹³C-CH4 in the SMT (b), and no AOM at station 73 (k), consistent with no shift in δ¹³C-CH4 in the SMT zone (h). The higher efficiency of AOM at station 24 than station 73 correlates with a lower OM remineralization rate in the SMT (f,l).
Relationship between methane flux to the bottom water and methane flux to the SWI
The linear fit suggests that ~76% of the methane leaks across the SWI and may be released to the bottom waters. The solid line (1:1 fit) indicates 100% methane leakage.
Methane leakage through the sulfate–methane transition zone of the Baltic seabed

December 2024

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346 Reads

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1 Citation

Anaerobic oxidation of methane at the sulfate–methane transition in marine sediments is generally considered to be a near-perfect barrier against methane release from the seabed, but the mechanisms involved are not well understood. On the basis of a survey of Baltic Sea sediments we show that a highly variable amount (0–100%) of subseafloor methane leaks through the sulfate–methane transition. The diffusive methane flux to the sediment–water interface is often high, reaching over 2 mmol m⁻² d⁻¹. Even though anaerobic methane oxidation is thermodynamically and kinetically favoured where methane fluxes are high, there is no evidence of methane oxidation in concentration, isotope and modelling results. Cores that lacked anaerobic methane oxidation had high modelled organic matter mineralization rates, suggesting that a possible mechanism could be high electron donor availability due to elevated H2 concentrations, as has been predicted by laboratory studies. We show that methane leakage across the sulfate–methane transition is widespread in organic-rich marine sediments.


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