A modified normalized model for predicting effective soil thermal conductivity

Effective soil thermal conductivity (λ _eff) describes the ability of a multiphase soil to transmit heat by conduction under unit temperature gradient. It is a critical parameter for environmental science, earth and planetary science, and engineering applications. Numerous models are available in the literature, but their applicability is generally restricted to certain soil types or water contents (θ). The objective of this study was to develop a new model in the similar form of the Johansen 1975 model to simulate the λ _eff(θ) relationship of soils of various soil textures and water contents. An exponential type model with two parameters is developed and a new function for calculating dry soil thermal conductivity is presented. Performance of the new model and six other normalized models were evaluated with published datasets. The results show that the new model is able to well mimic λ _eff(θ) relationship of soils from sand to silt loam and from oven dry to full saturation. In addition, it has the best performance among the seven models under test (with root-mean-square error of 0.059 W m^?1 °C^?1, average deviations of 0.0009 W m^?1 °C^?1, and Nash–Sutcliffe efficiency of 0.994). The new model has potential to improve the reliability of soil thermal conductivity estimation and be incorporated into numerical modeling for environmental, earth and engineering studies.     

Estimation of commercial diamond grades based on microdiamonds: a case study of the Koidu diamond mine,Sierra Leone

This paper documents the application of a microdiamond-based approach to the estimation of diamond grade in the Pipe 1 kimberlite at the Koidu mine in Sierra Leone. A geological model of Pipe 1 was constructed to represent the distribution and volume of the dominant kimberlite units within the pipe. Bulk samples, along with representative microdiamond samples, were collected from these units at surface and were used to define the ratio between microdiamond stone frequency (+212 μm stones per kilogram) and recoverable macrodiamond grade (+1.2 mm carats per tonne; 1 carat = 0.2 g). These ratios were applied to a comprehensive, spatially representative microdiamond sample dataset and were combined with a spatial model of country-rock xenolith dilution within the pipe to estimate +1.2 mm recoverable grades. The resource estimate was reconciled with subsequent production results in the elevation range 160 to 100 m above sea level. Production results for each of the six 10 m benches covering this elevation range were compared to the estimated average grades for these zones in the pipe. For the five cases where most of the kimberlite mass on a given bench is represented in the production data, the results show a maximum discrepancy of 6% between predicted and reported production grade with no indication of any consistent bias. This indicates that, when supported by a sound geological model and suitable microdiamond and macrodiamond data, the microdiamond-based estimation approach can provide reliable constraints on macrodiamond grade, even in the case of geologically complex bodies such as Koidu Pipe 1.

     

Automated Kerogen Classification in Microscope Images of Dispersed Kerogen Preparation

We develop the classification part of a system that analyses transmitted light microscope images of dispersed kerogen preparation. The system automatically extracts kerogen pieces from the image and labels each piece as either inertinite or vitrinite. The image pre-processing analysis consists of background removal, identification of kerogen material, object segmentation, object extraction (individual images of pieces of kerogen) and feature calculation for each object. An expert palynologist was asked to label the objects into categories inertinite and vitrinite, which provided the ground truth for the classification experiment. Ten state-of-the-art classifiers and classifier ensembles were compared: Naïve Bayes, decision tree, nearest neighbour, the logistic classifier, multilayered perceptron (MLP), support vector machines (SVM), AdaBoost, Bagging, LogitBoost and Random Forest. The logistic classifier was singled out as the most accurate classifier, with an accuracy greater than 90. Using a 10 times 10-fold cross-validation provided within the Weka software, we found that the logistic classifier was significantly better than five classifiers (p<0.05) and indistinguishable from the other four classifiers. The initial set of 32 features was subsequently reduced to 6 features without compromising the classification accuracy. A further evaluation of the system alerted us to the possible sensitivity of the classification to the ground truth that might vary from one human expert to another. The analysis also revealed that the logistic classifier made most of the correct classifications with a high certainty.     

Recent crustal movements in the Sierra Nevada—Walker lane region of California—Nevada: Part i, rate and style of deformation

This review of geological, seismological, geochronological and paleobotanical data is made to compare historic and geologic rates and styles of deformation of the Sierra Nevada and western Basin and Range Provinces. The main uplift of this region began about 17 m.y. ago, with slow uplift of the central Sierra Nevada summit region at rates estimated at about 0.012 mm/yr and of western Basin and Range Province at about 0.01 mm/yr. Many Mesozoic faults of the Foothills fault system were reactivated with normal slip in mid-Tertiary time and have continued to be active with slow slip rates. Sparse data indicate acceleration of rates of uplift and faulting during the Late Cenozoic. The Basin and Range faulting appears to have extended westward during this period with a reduction in width of the Sierra Nevada.The eastern boundary zone of the Sierra Nevada has an irregular en-echelon pattern of normal and right-oblique faults. The area between the Sierra Nevada and the Walker Lane is a complex zone of irregular patterns of hörst and graben blocks and conjugate normal-to right- and left-slip faults of NW and NE trend, respectively. The Walker Lane has at least five main strands near Walker Lake, with total right-slip separation estimated at 48 km. The NE-trending left-slip faults are much shorter than the Walker Lane fault zone and have maximum separations of no more than a few kilometers. Examples include the 1948 and 1966 fault zone northeast of Truckee, California, the Olinghouse fault (Part III) and possibly the almost 200-km-long Carson Lineament.Historic geologic evidence of faulting, seismologic evidence for focal mechanisms, geodetic measurements and strain measurements confirm continued regional uplift and tilting of the Sierra Nevada, with minor internal local faulting and deformation, smaller uplift of the western Basin and Range Province, conjugate focal mechanisms for faults of diverse orientations and types, and a NS to NE—SW compression axis (σ₁) and an EW to NW—SE extension axis (σ₃).     

Turbulent Pressure Statistics in the Atmospheric Boundary Layer from Large-Eddy Simulation

We use large-eddy simulation (LES) to study the turbulent pressure field in atmospheric boundary layers with free convection, forced convection, and stable stratification. We use the Poisson equation for pressure to represent the pressure field as the sum of mean-shear, turbulence–turbulence, subfilter-scale, Coriolis, and buoyancy contributions. We isolate these contributions and study them separately. We find that in the energy-containing range in the free-convection case the turbulence–turbulence pressure dominates over the entire boundary layer. That part dominates also up to midlayer in the forced-convection case; above that the mean-shear pressure dominates. In the stable case the mean-shear pressure dominates over the entire boundary layer.We find evidence of an inertial subrange in the pressure spectrum in the free and forced-convection cases; it is dominated by the turbulence–turbulence pressure and has a three-dimensional spectral constant of about 4.0. This agrees well with quasi-Gaussian predictions but is a factor of 2 less than recent results from direct numerical simulations at moderate Reynolds numbers. Measurements of the inertial subrange pressure spectral constant at high Reynolds numbers, which might now be possible, would be most useful.     

In the other 90%: phytoplankton responses to enhanced nutrient availability in the Great Barrier Reef Lagoon

Our view of how water quality effects ecosystems of the Great Barrier Reef (GBR) is largely framed by observed or expected responses of large benthic organisms (corals, algae, seagrasses) to enhanced levels of dissolved nutrients, sediments and other pollutants in reef waters. In the case of nutrients, however, benthic organisms and communities are largely responding to materials which have cycled through and been transformed by pelagic communities dominated by micro-algae (phytoplankton), protozoa, flagellates and bacteria. Because GBR waters are characterised by high ambient light intensities and water temperatures, inputs of nutrients from both internal and external sources are rapidly taken up and converted to organic matter in inter-reefal waters. Phytoplankton growth, pelagic grazing and remineralisation rates are very rapid. Dominant phytoplankton species in GBR waters have in situ growth rates which range from approximately 1 to several doublings per day. To a first approximation, phytoplankton communities and their constituent nutrient content turn over on a daily basis. Relative abundances of dissolved nutrient species strongly indicate N limitation of new biomass formation. Direct ((15)N) and indirect ((14)C) estimates of N demand by phytoplankton indicate dissolved inorganic N pools have turnover times on the order of hours to days. Turnover times for inorganic phosphorus in the water column range from hours to weeks. Because of the rapid assimilation of nutrients by plankton communities, biological responses in benthic communities to changed water quality are more likely driven (at several ecological levels) by organic matter derived from pelagic primary production than by dissolved nutrient stocks alone.     

An analytic similarity theory for the planetary boundary layer stabilized by surface buoyancy

An analytic solution for a steady, horizontally homogeneous boundary layer with rotation, % MathType!MTEF!2!1!+- % feaafeart1ev1aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn % hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr % 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqaqpepeea0xe9qqVa0l % b9peea0lb9Lq-JfrVkFHe9peea0dXdarVe0Fb9pgea0xa9W8qr0-vr % 0-viWZqaceaabiGaciaacaqabeaadaqaaqaaaOqaaiaadAgaaaa!38AA! \[ f \] , and surface friction velocity, û_*, subjected to surface buoyancy characterized by Obukhov length L, is proposed as follows. Nondimensional variables are % MathType!MTEF!2!1!+- % feaafeart1ev1aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn % hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr % 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqaqpepeea0xe9qqVa0l % b9peea0lb9Lq-JfrVkFHe9peea0dXdarVe0Fb9pgea0xa9W8qr0-vr % 0-viWZqaceaabiGaciaacaqabeaadaqaaqaaaOqaaiabeA7a6jabg2 % da9iaadAgacaWG6bGaai4laiabeE7aOnaaBaaaleaacqGHxiIkaeqa % aOGaamyDamaaBaaaleaacqGHxiIkaeqaaOGaaiilaiqadwhagaqcai % abg2da9iabeE7aOnaaBaaaleaacqGHxiIkaeqaaOGabmyvayaajaGa % ai4laiqadwhagaqcamaaBaaaleaacqGHxiIkaeqaaOGaaiilaiqads % fagaqcaiabg2da9iqbes8a0zaajaGaai4laiaadwhadaWgaaWcbaGa % ey4fIOcabeaakiqadwhagaqcamaaBaaaleaacqGHxiIkcaGGSaaabe % aaaaa!5587! \[ \zeta = fz/\eta _ * u_ * ,\hat u = \eta _ * \hat U/\hat u_ * ,\hat T = \hat \tau /u_ * \hat u_{ * ,} \] , where carets denote complex (vector) quantities; Û is the mean velocity; % MathType!MTEF!2!1!+-% feaafeart1ev1aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn% hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr% 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqaqpepeea0xe9qqVa0l% b9peea0lb9Lq-JfrVkFHe9peea0dXdarVe0Fb9pgea0xa9W8qr0-vr% 0-viWZqaceaabiGaciaacaqabeaadaqaaqaaaOqaaiqbes8a0zaaja% aaaa!3994!\[\hat \tau \]is the kinematic turbulent stress; and % MathType!MTEF!2!1!+- % feaafeart1ev1aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn % hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr % 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqaqpepeea0xe9qqVa0l % b9peea0lb9Lq-JfrVkFHe9peea0dXdarVe0Fb9pgea0xa9W8qr0-vr % 0-viWZqaceaabiGaciaacaqabeaadaqaaqaaaOqaaiabeE7aOnaaBa % aaleaacqGHxiIkaeqaaOGaeyypa0JaaiikaiaaigdacqGHRaWkcqaH % +oaEdaWgaaWcbaGaamOtaaqabaGccaWG1bWaaSbaaSqaaiabgEHiQa % qabaGccaGGVaGaamOuamaaBaaaleaacaWGJbaabeaakiaadAgacaWG % mbGaaiykamaaCaaaleqabaGaeyOeI0IaaGymaiaac+cacaaIYaaaaa % aa!4B1F! \[ \eta _ * = (1 + \xi _N u_ * /R_c fL)^{ - 1/2} \]is a stability parameter. The constant % MathType!MTEF!2!1!+- % feaafeart1ev1aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn % hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr % 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqaqpepeea0xe9qqVa0l % b9peea0lb9Lq-JfrVkFHe9peea0dXdarVe0Fb9pgea0xa9W8qr0-vr % 0-viWZqaceaabiGaciaacaqabeaadaqaaqaaaOqaaiabe67a4naaBa % aaleaacaWGobaabeaaaaa!3A81! \[\xi _N \] is the ratio of the maximum mixing length(% MathType!MTEF!2!1!+-% feaafeart1ev1aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn% hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr% 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqaqpepeea0xe9qqVa0l% b9peea0lb9Lq-JfrVkFHe9peea0dXdarVe0Fb9pgea0xa9W8qr0-vr% 0-viWZqaceaabiGaciaacaqabeaadaqaaqaaaOqaamaaBaaaleaaca% WGTbaabeaaaaa!38DD!\[_m \]) to the PBL depth, % MathType!MTEF!2!1!+- % feaafeart1ev1aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn % hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr % 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqaqpepeea0xe9qqVa0l % b9peea0lb9Lq-JfrVkFHe9peea0dXdarVe0Fb9pgea0xa9W8qr0-vr % 0-viWZqaceaabiGaciaacaqabeaadaqaaqaaaOqaaiaadwhadaWgaa % WcbaGaey4fIOcabeaakiaac+cacaWGMbaaaa!3B7C! \[ u_ * /f \] , for neutrally stable conditions; and % MathType!MTEF!2!1!+-% feaafeart1ev1aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn% hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr% 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqaqpepeea0xe9qqVa0l% b9peea0lb9Lq-JfrVkFHe9peea0dXdarVe0Fb9pgea0xa9W8qr0-vr% 0-viWZqaceaabiGaciaacaqabeaadaqaaqaaaOqaaiaadkfadaWgaa% WcbaGaam4yaaqabaaaaa!39AA!\[R_c\](the critical flux Richardson number) is the ratio % MathType!MTEF!2!1!+- % feaafeart1ev1aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn % hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr % 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqaqpepeea0xe9qqVa0l % b9peea0lb9Lq-JfrVkFHe9peea0dXdarVe0Fb9pgea0xa9W8qr0-vr % 0-viWZqaceaabiGaciaacaqabeaadaqaaqaaaOqaaiaadYgadaWgaa % WcbaGaamyBaaqabaGccaGGVaGaamitaaaa!3B5C! \[ l_m /L \] under highly stable conditions. Profiles of stress and velocity in the ocean (<0) are given by % MathType!MTEF!2!1!+- % feaafeart1ev1aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn % hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr % 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqaqpepeea0xe9qqVa0l % b9peea0lb9Lq-JfrVkFHe9peea0dXdarVe0Fb9pgea0xa9W8qr0-vr % 0-viWZqaceaabiGaciaacaqabeaadaqaaqaaaOqaamaaxacabaGabm % yDayaajaGaeyypa0ZaaiqaaqaabeqaaiabgkHiTiaadMgacqaH0oaz % caWGLbWaaWbaaSqabeaacqaH0oazcqaH2oGEaaGccaqGGaGaaeiiai % aabccacaqGGaGaaeiiaiaabccacaqGGaGaaeiiaiaabccacaqGGaGa % aeiiaiaabccacaqGGaGaaeiiaiaabccacaqGGaGaaeiiaiaabccaca % qGGaGaaeiiaiaabccacaqGGaGaaeiiaiaabccacaqGGaGaaeiiaiaa % bccacaqGGaGaaeiiaiaabccacaqGGaGaaeiiaiaabccacaqGGaGaae % iiaiaabccacaqGGaGaaeiiaiaabccacaqGGaGaaeiiaiaabccacaqG % GaGaaeiiaiaabccacaqGGaGaaeiiaiaabccacaqGGaGaaeiiaiaabc % cacaqGGaGaaeiiaiaabccacaqGGaGaaeiiaiaabccacaqGGaGaaeii % aiaabccacaqGGaGaaeiiaiaabccacaqGGaGaaeiiaiaabccacaqGGa % GaaeiiaiaabccacaqGGaGaaeiiaiaabccacaqGGaGaaeiiaiaabcca % caqGGaGaaeiiaiaabccacaqGGaGaeqOTdONaeyizImQaeyOeI0Iaeq % OVdG3aaSbaaSqaaiaad6eaaeqaaaGcbaGaeyOeI0IaamyAaiabes7a % KjaadwgadaahaaWcbeqaaiabes7aKjabe67a4naaBaaameaacaWGob % aabeaaaaGccqGHsisldaWcaaqaaiabeE7aOnaaBaaaleaacaGGQaaa % beaaaOqaaiaadUgaaaWaamWaaeaaciGGSbGaaiOBamaalaaabaWaaq % WaaeaacqaH2oGEaiaawEa7caGLiWoaaeaacqaH+oaEdaWgaaWcbaGa % amOtaaqabaaaaOGaey4kaSIaaiikaiabes7aKjabgkHiTiaadggaca % GGPaGaaiikaiabeA7a6jabgUcaRiabe67a4naaBaaaleaacaWGobaa % beaakiaacMcacqGHsisldaWcaaqaaiaadggaaeaacaaIYaaaaiabes % 7aKjaacIcacqaH2oGEdaahaaWcbeqaaiaaikdaaaGccqGHsislcqaH % +oaEdaqhaaWcbaGaamOtaaqaaiaaikdaaaGccaGGPaaacaGLBbGaay % zxaaGaaeiiaiaabccacaqGGaGaaeiiaiabeA7a6naaBaaaleaacaaI % WaaabeaakiabgwMiZkabeA7a6jabg6da+iabgkHiTiabe67a4naaBa % aaleaacaWGobaabeaaaaGccaGL7baaaSqabKazbaiabaGabmivayaa % jaGaeyypa0JaamyzamaaCaaajqMaacqabeaacaWGPbGaeqiTdqMaeq % OTdOhaaaaaaaa!C5AA! \[ \mathop {\hat u = \left\{ \begin{array}{l} - i\delta e^{\delta \zeta } {\rm{ }}\zeta \le - \xi _N \\ - i\delta e^{\delta \xi _N } - \frac{{\eta _* }}{k}\left[ {\ln \frac{{\left| \zeta \right|}}{{\xi _N }} + (\delta - a)(\zeta + \xi _N ) - \frac{a}{2}\delta \end{array} \right.}\limits^{\hat T = e^{i\delta \zeta } } \] where % MathType!MTEF!2!1!+- % feaafeart1ev1aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn % hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr % 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqaqpepeea0xe9qqVa0l % b9peea0lb9Lq-JfrVkFHe9peea0dXdarVe0Fb9pgea0xa9W8qr0-vr % 0-viWZqaceaabiGaciaacaqabeaadaqaaqaaaOqaaiabes7aKjabg2 % da9maabmaabaGaamyAaiaac+cacaWGRbGaeqOVdG3aaSbaaSqaaiaa % d6eaaeqaaaGccaGLOaGaayzkaaWaaWbaaSqabeaacaaIXaGaai4lai % aaikdaaaGccaGG7aGaamyyaiabg2da9iabeE7aOnaaBaaaleaacqGH % xiIkaeqaaOGaaiikaiaaigdacaGGVaGaeqOVdG3aaSbaaSqaaiaad6 % eaaeqaaOGaey4kaSIaamyDamaaBaaaleaacqGHxiIkaeqaaOGaai4l % aiaadAgacaWGmbGaamOuamaaBaaaleaacaWGJbaabeaakiaacMcaca % GGOaGaaGymaiabgkHiTiabeE7aOnaaBaaaleaacqGHxiIkaeqaaOGa % aiykaiaacUdaaaa!5CB6! \[ \delta = \left( {i/k\xi _N } \right)^{1/2} ;a = \eta _ * (1/\xi _N + u_ * /fLR_c )(1 - \eta _ * ); \] and ₀ is the nondimensional surface roughness. The constants are% MathType!MTEF!2!1!+-% feaafeart1ev1aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn% hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr% 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqaqpepeea0xe9qqVa0l% b9peea0lb9Lq-JfrVkFHe9peea0dXdarVe0Fb9pgea0xa9W8qr0-vr% 0-viWZqaceaabiGaciaacaqabeaadaqaaqaaaOqaaiaadkfadaWgaa% WcbaGaam4yaaqabaaaaa!39AA!\[R_c \]= 0.2 and% MathType!MTEF!2!1!+-% feaafeart1ev1aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn% hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr% 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqaqpepeea0xe9qqVa0l% b9peea0lb9Lq-JfrVkFHe9peea0dXdarVe0Fb9pgea0xa9W8qr0-vr% 0-viWZqaceaabiGaciaacaqabeaadaqaaqaaaOqaaiabe67a4naaBa% aaleaacaWGobaabeaaaaa!3A81!\[\xi _N \]= 0.052. The solutions for the atmosphere are similar except û is the nondimensional velocity The model produces satisfactory predictions of geostrophic drag and near-surface current (wind) profiles under stable stratification.     

Effects of anthropogenic seawater acidification on acid-base balance in the sea urchin Psammechinus miliaris

The purple-tipped sea urchin, Psammechinus miliaris, was exposed to artificially acidified seawater treatments (pH(w) 6.16, 6.63 or 7.44) over a period of 8 days. Urchin mortality of 100% was observed at pH(w) 6.16 after 7 days and coincided with a pronounced hypercapnia in the coelomic fluid producing an irrecoverable acidosis. Coelomic fluid acid-base measures showed that an accumulation of CO(2) and a significant reduction in pH occurred in all treatments compared with controls. Bicarbonate buffering was employed in each case, reducing the resultant acidosis, but compensation was incomplete even under moderate environmental hypercapnia. Significant test dissolution was inferred from observable increases in the Mg(2+) concentration of the coelomic fluid under all pH treatments. We show that a chronic reduction of surface water pH to below 7.5 would be severely detrimental to the acid-base balance of this predominantly intertidal species; despite its ability to tolerate fluctuations in pCO(2) and pH in the rock pool environment. The absence of respiratory pigment (or any substantial protein in the coelomic fluid), a poor capacity for ionic regulation and dependency on a magnesium calcite test, make echinoids particularly vulnerable to anthropogenic acidification. Geological sequestration leaks may result in dramatic localised pH reductions, e.g. pH 5.8. P. miliaris is intolerant of pH 6.16 seawater and significant mortality is seen at pH 6.63.     

The magnetisation of Rosemary Bank Seamount,Rockall Trough,northeast Atlantic

Rosemary Bank is a non-uniformly magnetised seamount in the northern Rockall Trough. The reversely magnetised major component of the anomaly field was simulated by a numerical method and modelled using the Talwani three-dimensional magnetics program. The results suggest a higher Koenigsberger ratio than earlier reported for Rosemary Bank and a remanent magnetisation vector compatible with post-Jurassic formation and probably of a Late Cretaceous to Tertiary age. The limited depth to the base of the model implies that Rosemary Bank post-dates the underlying basement in agreement with a volcanic origin. The residual of the observed anomaly field is interpreted as being caused by normally magnetised bodies within and on top of the bank. This suggests subsequent volcanic activity during an interval of normal polarity.