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.     

Compartmentalisation and groundwater–surface water interactions in a prospective shale gas basin: Assessment using variance analysis and multivariate statistics on water quality data

An environmental concern with hydraulic fracturing for shale gas is the risk of groundwater and surface water contamination. Assessing this risk partly involves the identification and understanding of groundwater–surface water interactions because potentially contaminating fluids could move from one water body to the other along hydraulic pathways. In this study, we use water quality data from a prospective shale gas basin to determine: if surface water sampling could identify groundwater compartmentalisation by low-permeability faults; and if surface waters interact with groundwater in underlying bedrock formations, thereby indicating hydraulic pathways. Variance analysis showed that bedrock geology was a significant factor influencing surface water quality, indicating regional-scale groundwater–surface water interactions despite the presence of an overlying region-wide layer of superficial deposits averaging 30–40 m thickness. We propose that surface waters interact with a weathered bedrock layer through the complex distribution of glaciofluvial sands and gravels. Principal component analysis showed that surface water compositions were constrained within groundwater end-member compositions. Surface water quality data showed no relationship with groundwater compartmentalisation known to be caused by a major basin fault. Therefore, there was no chemical evidence to suggest that deeper groundwater in this particular area of the prospective basin was reaching the surface in response to compartmentalisation. Consequently, in this case compartmentalisation does not appear to increase the risk of fracking-related contaminants reaching surface waters, although this may differ under different hydrogeological scenarios.     

Cytochrome P4501A biomarker indication of the timeline of chronic exposure of Barrow's goldeneyes to residual Exxon Valdez oil

We examined hepatic EROD activity, as an indicator of CYP1A induction, in Barrow’s goldeneyes captured in areas oiled during the 1989 Exxon Valdez spill and those from nearby unoiled areas. We found that average EROD activity differed between areas during 2005, although the magnitude of the difference was reduced relative to a previous study from 1996/1997, and we found that areas did not differ by 2009. Similarly, we found that the proportion of individuals captured from oiled areas with elevated EROD activity (?2 times unoiled average) declined from 41% in winter 1996/1997 to 10% in 2005 and 15% in 2009. This work adds to a body of literature describing the timelines over which vertebrates were exposed to residual Exxon Valdez oil and indicates that, for Barrow’s goldeneyes in Prince William Sound, exposure persisted for many years with evidence of substantially reduced exposure by 2 decades after the spill.     

Identification of rural land management signals in runoff response

Rural land management signals in catchment‐scale runoff have proven difficult to identify in general. The Pontbren experimental catchment in upland Wales, UK, provides a new data set with which to address this challenge. This data set includes more than 4 years of data from six tipping bucket rainfall gauges and eight stream flow gauges representing different land management regimes at different scales. Data‐based mechanistic rainfall–runoff models were fitted to this data set using the CAPTAIN toolbox. The spatial and temporal variabilities of model parameters were identified and interpreted where possible. The analysis highlighted a dependency between the modelled residence time and the presence of agriculturally improved grassland, which produced a flashier response than grassland in a more natural condition. Another factor found to strongly affect the spatial variability of runoff response was the presence of lakes, while catchment area had a less pronounced effect, and the influence of trees, steepness and soil type could not be identified. Some time variability of response was observed but this was not consistent across the catchment and could not easily be interpreted. Copyright © 2010 John Wiley & Sons, Ltd.