Timing and setting of skarn and iron oxide formation at the Smältarmossen calcic iron skarn deposit, Bergslagen, Sweden

Abundant iron oxide deposits including banded iron formations, apatite iron oxide ores, and enigmatic marble/skarn-hosted magnetite deposits occur in the Palaeoproterozoic Bergslagen region, southern Sweden. During the last 100 years, the latter deposit class has been interpreted as contact metasomatic skarn deposits, metamorphosed iron formations, or metamorphosed carbonate replacement deposits. Their origin is still incompletely understood. At the Smältarmossen mine, magnetite was mined from a ca. 50-m-thick calcic skarn zone at the contact between rhyolite and stratigraphically overlying limestone. A syn-volcanic dacite porphyry which intruded the footwall has numerous apophyses that extend into the mineralized zone. Whole-rock lithogeochemical and mineral chemical analyses combined with textural analysis suggests that the skarns formed by veining and replacement of the dacite porphyry and rhyolite. These rocks were added substantial Ca and Fe, minor Mg, Mn, and LREE, as well as trace Co, Sn, U, As, and Sr. In contrast, massive magnetite formed by pervasive replacement of limestone. Tectonic fabrics in magnetite and skarn are consistent with ore formation before or early during Svecokarelian ductile deformation. Whereas a syngenetic–exhalative model has previously been suggested, our results are more compatible with magnetite formation at ca. 1.89 Ga in a contact metasomatic skarn setting associated with the dacite porphyry.     

Chloride in precipitation and streamwater for the upland catchment of river severn,mid-wales; some consequences for hydrochemical models

Variations in the concentration of Cl in rainfall and stream runoff are presented for two catchments in the Hafren forest of mid-Wales, Great Britain. Despite the large fluctuations in rainfall concentrations, Cl in the streamwater remains relatively constant. Using the two-reservoir Birkenes model, an attempt was made to simulate observed Cl in streamwater. The original model was unable to reproduce the observations and several modifications are suggested to provide better simulations. The resulting model is not the only one capable of reproducing the observations; other hydrochemical models will most probably also achieve this although emphasis will in each case be placed on different aspects. In this paper, it is suggested that the stochastic properties of water movement and chemical processes can account for the streamwater chemistry responses observed. On the catchment scale these processes will lead to an apparently deterministic behaviour that may well be described by simple relationships.     

A note on the gravitational field of Phobos

Phobos' proximity to the Roche limit, and some of its consequences deserving attention by the Phobos' mission, are the subject of this note.     

Equilibrium and structural studies of silicon(iv) and aluminium(iii) in aqueous solution. V. Acidity constants of silicic acid and the ionic product of water in the medium range 0.05–2.0 M Na(CI) at 25°C

The apparent ionization constants for silicic acid, k₁ and k₂, and the ionic product of water, k_w, have been determined in 0.05, 0.1, 0.2, 0.4 and 2.0 M Na(CI) media at 25°C. The medium dependence of these constants was found to fit equations of the form

$\text{log}\text{k}{}_{\mathrm{i}}\text{=}\text{log}\text{K}{}_{\mathrm{i}}\text{+a}{}_{\mathrm{i}}\text{I}{}^{\mathrm{<mtext>1</mtext><mtext>2</mtext>}}\text{(1+I}{}^{\mathrm{<mtext>1</mtext><mtext>2</mtext>}}\text{)+b}{}_{\mathrm{i}}\text{I}$

where K₁ is the ionization constant in pure water, α_i and b_i are parameters of which b_i has been adjusted to present data. The following results were obtained (α_i, b_i): pK₁ = 9.84, (1.022, ?0.11); pK₂ = 13.43, (2.044, ?0.20); and pK_w = 14.01 (1.022, ?0.22). k_i values are collected in Tables I and II. Attempts have been made to explain the medium dependence of k₁ and k₂ with weak sodium silicate complexing according to the equilibria

$\text{Na}{}^{\mathrm{+}}\text{+}\text{SiO(OH)}{}^{\mathrm{?}}{}_3\text{?}\text{NaSiO(OH)}{}_3\text{;k}{}_{11}$

$\text{Na}{}^{\mathrm{+}}\text{+}\text{SiO}{}_2\text{(OH)}{}^2{}_2\text{?}\text{NaSiO}{}_2\text{(HO)}{}^{\mathrm{?}}{}_2\text{; k}{}_{21}$

giving k₁₁ = 0.37M^?1 and k₂₁= 3.0M^?1. However, these weak interactions cannot be interpreted unambiguously from potentiometric data at different 1-levels. Probably the medium dependence could equally well be expressed by variations in the activity coefficients.The measurements were performed as potentiometric titrations using a hydrogen electrode. The average number of OH^- reacted per Si(OH)₄, Z, has been varied within the limits 0 ? Z ? 1.1 and B₁, the total concentration of Si(OH)₄, between 0.001 M and 0.008 M. k₁ was evaluated from experimental data with B ? 0.003 M, and k₂ with B ? 0.008 M and Z ? 0.95.     

Predicting riverine dissolved silica fluxes to coastal zones from a hyperactive region and analysis of their first-order controls

Silicate weathering and resulting transport of dissolved matter influence the global carbon cycle in two ways. First by the uptake of atmospheric/soil CO₂ and second by providing the oceanic ecosystems via the fluvial systems with the nutrient dissolved silica (DSi). Previous work suggests that regions dominated by volcanics are hyperactive or even “hot spots” concerning DSi-mobilization. Here, we present a new approach for predicting DSi-fluxes to coastal zones, emphasizing “first-order” controlling factors (lithology, runoff, relief, land cover and temperature). This approach is applied to the Japanese Archipelago, a region characterized by a high percentage of volcanics (29.1% of surface area). The presented DSi-flux model is based on data of 516 catchments, covering approximately 56.7% of the area of the Japanese Archipelago. The spatial distribution of lithology—one of the most important first order controls—is taken from a new high resolution map of Japan. Results show that the Japanese Archipelago is a hyperactive region with a DSi-yield 6.6 times higher than the world average of 3.3 t SiO₂ km⁻² a⁻¹, but with large regional variations. Approximately 10% of its area exceeds 10 times the world average DSi-yield. Slope constitutes another important controlling factor on DSi-fluxes besides lithology and runoff, and can exceed the influence of runoff on DSi-yields. Even though the monitored area on the Japanese Archipelago stretches from about 31° to 46°N, temperature is not identified as a significant first-order model variable. This may be due to the fact that slope, runoff and lithology are correlated with temperature due to regional settings of the Archipelago, and temperature information is substituted to a certain extent by these factors. Land cover data also do not improve the prediction model. This may partly be attributed to misinterpreted land cover information from satellite images. Implications of results for Earth System and global carbon cycle modeling are discussed.