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Members of the public in England were invited in 2010 to take part in a national metals survey, by collecting samples of littoral sediment from a standing water body for geochemical analysis. To our knowledge, this is the first national sediment metals survey using public participation and reveals a snapshot of the extent of metals contamination in ponds and lakes across England. Hg, Ni, Cu, Zn and Pb concentrations exceeding sediment quality guidelines for the health of aquatic biota are ubiquitous in ponds and lakes, not just in areas with a legacy of industrial activity. To validate the public sampling approach, a calibration exercise was conducted at ten water bodies selected to represent a range of lakes found across England. Sediment concentrations of Hg, Ni, Cu, Zn and Pb were measured in samples of soil, stream and littoral and deep water sediment to assess inputs. Significant differences between littoral sediment metal concentrations occur due to local variability, but also organic content, especially in upland, peat soil catchments. Variability of metal concentrations between littoral samples is shown to be low in small (<20 ha) lowland lakes. Larger and upland lakes with more complex inputs and variation in organic content of littoral samples have a greater variability. Collection of littoral sediments in small lakes and ponds, with or without voluntary participation, can provide a reliable sampling technique for the preliminary assessment of metal contamination in standing waters. However, the heterogeneity of geology, soils and history/extent of metal contamination in the English landscape, combined with the random nature of sample collection, shows that systematic sampling for evaluating the full extent of metal contamination in lakes is still required.

The London Region Atlas of Topsoil Geochemistry (LRA) is a further step towards understanding the chemical quality of soils in London, following a previous project called London Earth carried out by the British Geological Survey (BGS) (Johnson et al., 2010[1]). The main advantage of the LRA is that it includes soil geochemical data from the counties surrounding London; placing the city within the context of its rural hinterland, allowing assessments of the impact of urbanisation on soil quality. The London Region Atlas of Topsoil Geochemistry is a product derived from the BGS Geochemical Baseline Survey of the Environment (G-BASE[2]) project. The London Region Geochemical Dataset (LRD, n=8400), on which the atlas is based, includes TOPSOIL data from two complementary surveys: i) the urban London Earth (LOND) and ii) the rural South East England (SEEN). The LRA covers the Greater London Authority (GLA) and its outskirts in a rectangular area of 80x62 km. This extends from British National Grid coordinates Easting 490000–570000, and Northing 153000–215000. The urban LOND and the rural SEEN surveys contribute with 6801 and 1599 samples respectively to the LRD. The concentrations of 44 inorganic chemical elements (Al2O3, CaO, Fe2O3, K2O, MgO, MnO, Na2O, P2O5, SiO2, TiO2, Ag, As, Ba, Bi, Br, Cd, Ce, Co, Cr, Cs, Cu, Ga, Ge, Hf, I, La, Mo, Nb, Nd, Ni, Pb, Rb, Sb, Sc, Se, Sn, Sr, Th, U, V, W, Y, Zn and Zr), loss on ignition (LOI) and pH in topsoil are included in the LRA. For each element, a map showing the distribution in topsoil across the atlas area and a one-page sketch of descriptive statistics and graphs are presented. Statistics and graphs for whole dataset (LRD), London urban subset (LOND) and London surroundings rural subset (SEEN), as well as graphs of topsoil element concentrations over each simplified geology unit are shown. The LRD has been used already in a study aiming to detect geogenic (geological) signatures and controls on soil chemistry in the London reg

Purpose – The purpose of this paper is to introduce and define Cognitive Big Data as a concept. Furthermore, it investigates what is really “new” in Big Data, as it seems to be a hyped-up concept that has emerged during recent years. The purpose is also to broaden the discussion around Big Data far beyond the common 4V (velocity, volume, veracity and variety) model. Design/methodology/approach – The authors established an expert think tank to discuss the notion of Big Data, identify new characteristics and re-think what really is new in the idea of Big Data, by analyzing over 60 literature resources. They identified typical baseline scenarios (traffic, business processes, retail, health and social media) as a starting point from which they explored the notion of Big Data from different perspectives. Findings – They concluded that the idea of Big Data is simply not new and recognized the need to re-think a new approach toward Big Data. The authors also introduced a five-Trait Framework for “Cognitive Big Data”, socio-technical system, data space, data richness, knowledge management (KM)/decision-making and visualization/sensory presentation. Research limitations/implications – The viewpoint is centered on cognitive processes as KM process. Practical implications – Data need to be made available in an understandable form for the right application context and in the right portion size that it can be turned into knowledge and eventually wisdom. The authors need to know about data that can be ignored, data that they are not aware of (dark data) and data that can be fully utilized for analysis (light data). In the foreground is the extension of human mental capabilities and data understandability. Social implications – Cognitive Big Data implies a socio-technological knowledge system. Originality/value – Introduction of cognitive Big Data as concept and framework.

What is really 'new' in Big Data? - Big Data seems to be a hype that has been emerging during the past years. But it requires a more thorough discussion beyond the very common 3V (velocity, volume, and variety) approach. We established an expert group to re-discuss the notion of Big Data, identify new characteristics, and re-think what actually really is new in the idea of Big Data by analysing over 100 literature resources. We identified typical baseline scenarios (traffic, business processes, retail, health, and social media) as starting point, from which we explored the notion of Big Data from a different viewpoint. We concluded, that the idea of Big Data is simply not new, as well as we need to re-think our approach towards Big Data. We introduce a fully new way of thinking about Big Data, and coin it as the trend of 'Cognitive Big Data'. The publications introduces a basic framework for our research results. However, this work remains work-in-progress, and we will continue with a refinement of the Cognitive Big Data Framework in one future publication.

Media industry is undergoing a very intensive transformation from traditional Media towards fully digital media environments. Within that market space, the environment as well as consumer behavior is changing very rapidly. In particular inside media houses various changes are taking place. One of these changes is the introduction or improvement of IT infrastructure to support business activities - in short, introducing or improving information systems and management in eMedia and creativity industry. In view a focus on advanced information management has a vital role of having a major impact in creating competitive advantage in media companies. In this respect, one of the biggest challenges is how to efficiently manage the ever changing massive amount of mostly unstructured and diverse data. This data consists of traditional business information (e.g. Customer Relationship Management (CRM), Supplier Relationship Management (SRM), Financial Supply Chain Management (FSCM)), information directly related to media content (e.g. media information, metadata, content information), as well as consumer data (e.g. social media, customer databases). Traditional media companies will need to invest in latest IT technology which is able to manage flexibly, quickly and efficiently the large amounts of structured and unstructured data. On the other hand the challenge is to deliver structured data in form of business intelligence throughout the organizational levels, between business partners, and the complete media eco-system. Within the scope of this paper we present a first problem definition of information systems and management in eMedia industries.

Regulatory authorities need ways to estimate natural terrestrial gamma radiation dose rates (nGy h−1) across the landscape accurately, to assess its potential deleterious health effects. The primary method for estimating outdoor dose rate is to use an in situ detector supported 1 m above the ground, but such measurements are costly and cannot capture the landscape-scale variation in dose rates which are associated with changes in soil and parent material mineralogy. We investigate the potential for improving estimates of terrestrial gamma dose rates across Northern Ireland (13542 km2) using measurements from 168 sites and two sources of ancillary data: (i) a map based on a simplified classification of soil parent material, and (ii) dose estimates from a national-scale, airborne radiometric survey. We used the linear mixed modelling framework in which the two ancillary variables were included in separate models as fixed effects, plus a correlation structure which captures the spatially correlated variance component. We used a cross-validation procedure to determine the magnitude of the prediction errors for the different models. We removed a random subset of 10 terrestrial measurements and formed the model from the remainder (n = 158), and then used the model to predict values at the other 10 sites. We repeated this procedure 50 times. The measurements of terrestrial dose vary between 1 and 103 (nGy h−1). The median absolute model prediction errors (nGy h−1) for the three models declined in the following order: no ancillary data (10.8) > simple geological classification (8.3) > airborne radiometric dose (5.4) as a single fixed effect. Estimates of airborne radiometric gamma dose rate can significantly improve the spatial prediction of terrestrial dose rate.

The Southern Uplands-Down-Longford Terrane in south-east Northern Ireland is prospective for Caledonian-age, turbidite-hosted orogenic gold mineralisation with important deposits at Clontibret in the Republic of Ireland and in Scotland. Geochemical and geophysical data from the DETI-funded Tellus project have been used, in conjunction with other spatial geoscience datasets, to map the distribution of prospectivity for this style of mineralisation over this terrane. A knowledge-based fuzzy logic modelling methodology using Arc Spatial Data modeller was utilised. The prospectivity analysis has identified several areas prospective for turbidite-hosted gold mineralisation, comparable to that at Clontibret and gold occurrences in the Southern Uplands of Scotland. A number of these either coincide with known bedrock gold occurrences or with areas considered prospective and targeted by previous exploration work, validating the predictive capability of the exploration model devised and its translation into a GIS-based prospectivity model. The results of the modelling suggest that as in other parts of the Southern Uplands the coincidence of regional strike-parallel structures and intersecting transverse faults are highly prospective, as these are likely to create zones of anomalous stress for fluid flow and deposit accumulation. Those areas in which there are no known gold occurrences are considered to be favourable targets for further exploration and should be followed up.

Jurassic ironstones consist of several formations, which, in the East Midlands, crop out in a thin band across the region. They stretch from north east of Gainsborough in Lincolnshire, through east Leicestershire, Northamptonshire and then curve gently towards the south-east, through Oxfordshire. Ironstones have been worked over a long period for their iron content, mainly opencast but with a limited number of underground workings (e.g. Hollingworth and Taylor, 1951; Tonks, 1989, 1991, 1992; Whitehead et al, 1952). Although no longer used as an iron ore, they are still quarried in a few places for buildings stone and as a source of aggregates. The Jurassic ironstones are known to be associated with a higher than average radon risk, as determined by house and soil gas data (e.g. Appleton and Ball, 1995, Sutherland and Sharman, 1996), and this is not uniform across the ironstones. High resolution airborne survey data (HiRES-1, Peart et al, 2003) showed elevated levels of eU (equivalent uranium, derived from the 214Bi gamma peak) and eTh (equivalent thorium, derived from the 208Tl gamma peak) for the ironstones, but also showed variability in these elements. It has been suggested that the primary source of radon in the ironstones is in phosphates (Sutherland, 1992) but the regional variations have not been investigated in detail. The study presented here was carried out in order to examine possible geological factors that might give rise to geographical variations in radon both within each ironstone formation and between formations. The objectives were as follows: (i) To characterise the large to medium (i.e. regional to outcrop) scale variation in the radioactivity of the ironstones, using airborne data acquired by the HiRES-1 project, and fieldbased gamma spectrometry. (ii) To sample ironstones from a number of locations and undertake laboratory-based petrographic, geochemical and radiographic studies in order to determine the mineralogy/petrology of radiogenic miner

The spatial distribution of 137Cs across the landscape and the processes controlling its redistribution are of interest because (i) 137Cs has been widely used to quantify the movement of soil and sediments and (ii) substantial fallout of 137Cs after the Chernobyl accident has led to contamination of foodstuffs in some places. A high-resolution airborne geophysical radiometric survey of Northern Ireland has provided an opportunity to study the distribution and possible redistribution of 137Cs. The 137Cs activity (recorded at 1·2 million points) is distributed in a series of bands oriented approximately 160° and 115° clockwise from north. Geostatistical analysis of the data shows a strong, short-range structure (correlation ranges between 0·6 and 8 km) in 137Cs activity across the vast majority of the region; the spatial distribution shows association with a published, coarse-scale depositional pattern of 137Cs from Chernobyl. Two indices of land form derived from a digital elevation model, namely compound topographic index and the length–slope factor of the Revised Universal Soil Loss Equation, account for only 3% of the variance in 137Cs activity. In contrast, soil type and land cover in combination (including their interaction) account for 20% of the variance. In areas that received moderate fallout from Chernobyl, soil type alone accounts for a substantial proportion of the spatially correlated 137Cs activity. We attribute this to each soil type having a fairly uniform radiocaesium interception potential that differs from those of other soil types and that this potential controls the vertical migration of 137Cs. Over the granitic Mourne Mountains there is a strong spatial cross-correlation between 137Cs activity and airborne estimates of soil potassium, suggesting that the latter provides a measure of the soil's radiocaesium interception potential; this is probably dominated by the quantity of the mineral illite

Mercury is present in trace amounts only in most natural circumstances but anthropogenic sources such as coal combustion[1] enhance environmental loadings. High Hg concentrations are of concern as it has immunotoxic, genotoxic and teratogenic effects on humans and animals

The spatial distribution of caesium-137 (137Cs) across the land is of much interest because it can tell us about the redistribution of the radionuclide as a result of soil erosion, differential migration through the soil—or its complement, differential retention in the soil. Any such inferences from survey measurements depend on the assumption of a broadly even distribution from weapons testing fallout, and the substantial deposition of 137Cs in rain following the Chernobyl accident on 26 April 1986. Deposition from the latter was not uniform over large areas, of course, and measurements across northern England showed that the magnitude of 137Cs deposition depended largely on the distribution of convective rainfall in the days immediately after the accident. There were too few measurements of 137Cs deposition at close spacings to estimate local variation. Twenty years after the deposition from Chernobyl a detailed airborne radiometric survey of the whole of Northern Ireland was flown. Flights were made along transects 200 m apart with recordings at 80 m intervals along the flight lines to give more than one million data in total. We have used the data to investigate the spatial distribution of 137Cs. Our initial geostatistical analyses suggested substantial short-range variation in the distribution of 137Cs. We wished to determine whether soil erosion or soil type could account for this.We made further detailed analyses using the terrain parameter compound topographic index as an index of soil erosion and deposition and soil maps to account for the migration or retention of 137Cs. The concentration of 137Cs in the soil is greatest where most rain fell in the few days after the accident. However, the local variances are of similar magnitudes across the the majority of the province. The global variogram of the radionuclide shows a large proportion of the spatially correlated variance occurring within 700 m and a longerrange structure extending to 15 km. Local variogra

222Rn, a natural radioactive gas produced by the radioactive decay of 238U, accounts for about 50 % of the total radiation dose to the average person in the UK. Geology is the most important factor controlling the source and distribution of radon; which has been linked to an increased risk of lung cancer. In order to prevent the public receiving high exposures to radon, it is necessary to identify those areas most at risk. We present results of new mapping of radon potential for Scotland using a method that allows the spatial variation in radon potential to be delineated both within and between geological groupings. The main geological and geochemical associations with moderate to high radon potential areas are described. The highest radon potential values in Scotland are associated with U-rich, highly evolved Siluro-Devonian biotite granite intrusions, notably those clustered within a zone to the west of Aberdeen and at Helmsdale, in Caithness. U mineralisation plays a role in areas including the Helmsdale granite and the Middle Old Red Sandstone of the Orcadian Basin. Elevated radon potential is also associated with limestones - where fracture permeability is influential - and with Ordovician-Silurian greywackes. The radon potential of unconsolidated deposits, and how this affects the radon potential of the underlying bedrock, reflects both their permeabilities and their compositions.

An assessment of the natural radiation dose to wildlife in England and Wales was made to determine the contribution it may make to the total radiation dose estimated during environmental impact assessments. Significant use was made of systematic datasets for environmental media (stream sediments, stream waters and soils), in particular those produced by the Geochemical Baseline Survey of the Environment (G-BASE) project. This provided extensive, although variable, coverage for different elements and sample types after normalisation of data to account for changes in sampling and analysis over time. Almost complete coverage for K in stream sediments was achieved by merging G-BASE andWolfson Atlas data. This required normalisation of theWolfson data to the G-BASE results. Coverage was improved greatly for U and Th in sediments, and K, U and Th in soils, by using the strong relationship between soils and sediments and geology (both solid and superficial) to extrapolate the data. The total U, Th and K data were used to derive activity concentrations of 238U and 232Th series radionuclides and 40K. External dose rates to wildlife were then estimated from derived media concentrations; internal dose rates were estimated from measured activities in biota or activities predicted using recommended concentration ratios.

Soil scientists require cost-effective methods to make accurate regional predictions of soil organic carbon (SOC) content. We assess the suitability of airborne radiometric data and digital elevation data as covariates to improve the precision of predictions of SOC from an intensive survey in Northern Ireland. Radiometric data (K band) and, to a lesser extent, altitude are shown to increase the precision of SOC predictions when they are included in linear mixed models of SOC variation. However the statistical distribution of SOC in Northern Ireland is bimodal and therefore unsuitable for geostatistical analysis unless the two peaks can be accounted for by the fixed effects in the linear mixed models. The upper peak in the distribution is due to areas of peat soils. This problem may be partly countered if soil maps are used to classify areas of Northern Ireland according to their expected SOC content and then different models are fitted to each of these classes. Here we divide the soil in Northern Ireland into three classes, namely mineral, organo-mineral and peat. This leads to a further increase in the precision of SOC predictions and the median square error is 2.2 %2. However a substantial number of our observations appear to be mis-classified and therefore the mean squared error in the predictions is larger (30.6 %2) since it is dominated by large errors due to mis-classification. Further improvement in SOC prediction may therefore be possible if better delineation between areas of large SOC (peat) and small SOC (non-peat) could be achieved.

An assessment of the natural radiation dose to wildlife in England and Wales was made to determine the contribution it may make to the total radiation dose estimated during environmental impact assessments. Significant use was made of systematic datasets for environmental media (stream sediments, stream waters and soils), in particular those produced by the Geochemical Baseline Survey of the Environment (G-BASE) project. This provided extensive, although variable, coverage for different elements and sample types after normalisation of data to account for changes in sampling and analysis over time. Almost complete coverage for K in stream sediments was achieved by merging G-BASE and Wolfson Atlas data. This required normalisation of the Wolfson data to the G-BASE results. Coverage was improved greatly for U and Th in sediments, and K, U and Th in soils, by using the strong relationship between soils and sediments and geology (both solid and superficial) to extrapolate the data. The total U, Th and K data were used to derive activity concentrations of 238U and 232Th series radionuclides and 40K. External dose rates to wildlife were then estimated from derived media concentrations; internal dose rates were estimated from measured activities in biota or activities predicted using recommended concentration ratios.

An airborne geophysical survey of the whole of Northern Ireland was flown by the Geological Survey of Northern Ireland in 2005 and 2006 as part of the Tellus project funded by the Department of Enterprise, Trade and Investment of Northern Ireland and by the Rural Development Programme through the Northern Ireland Programme for Building Sustainable Prosperity. Magnetic, electromagnetic and radiometric data were acquired at 200 m line spacing and a flying height of 56 m (240 m over built-up areas) using the Joint Airborne Capability of the British Geological Survey and the Geological Survey of Finland. In total, 82,000 line-km of data were acquired. The radiometric data were collected as full 256 channel gamma-ray spectra every 1 s or approximately 70 m of ground coverage. The Tellus project also included a baseline geochemical survey of Northern Ireland for which 22,000 samples, of soils, stream sediments and stream waters, were collected. A preliminary assessment of the radiometric data has been carried out to illustrate the type of features present in the dataset, at both regional and local scales, and provide pointers to possible areas of future research. At the scale of the whole of Northern Ireland the data reflect the major solid geological subdivisions; the Lower Palaeozoic greywackes and Palaeogene intrusives of the SE have relatively high K, U and Th contents. In contrast the Palaeogene basalts in the NE have much lower radioelement concentrations, although Palaeogene rhyolites, and inliers of Palaeozoic and Precambrian rocks in this area, have higher values. Surrounding the basalts to the south and west, the Triassic sedimentary rocks have moderate K, eU and eTh levels. The more heterogeneous geology of the western part of Northern Ireland is reflected in the radiometric data with juxtaposed areas of relatively high and low radioelement content. Formation boundaries within the basement and Upper Palaeozoic rocks, sometimes faulted, are clear in places as EN