E.L. Ander

G-BASE : baseline geochemical mapping of Great Britain and Northern Ireland

The Geochemical Baseline Survey of the Environment is a long-established, high-resolution regional geochemical mapping project run by the British Geological Survey. A project initially stimulated by mineral exploration and to assist geological mapping has successfully evolved into a survey that has many environmental applications. Much of the current demand for surface geochemical data is driven by legislation. The geochemical mapping, originally based on the collection of drainage samples, has expanded to include soils and a further suite of determinands in stream waters. The methodology of the project and application of the baseline data are described and issues common to worldwide geochemical mapping programmes are discussed.

GSUE: urban geochemical mapping in Great Britain

The British Geological Survey is responsible for the national strategic geochemical survey of Great Britain. As part of this programme, the Geochemical Surveys of Urban Environments (GSUE) project was initiated in 1992 and to date, 21 cities have been mapped. Urban sampling is based upon the collection of top (0.05 to 0.20 m) and deeper (0.35 to 0.50 m) soil samples on a 500 m grid across the built environment (one sample per 0.25 km2). Samples are analysed for c. 46 total element concentrations by X-ray fluorescence spectrometry, pH and loss on ignition as an indicator of organic matter content. The data provide an overview of the urban geochemical signature and because they are collected as part of a national baseline programme, can be readily compared with soils in the rural hinterland to assess the extent of urban contamination. The data are of direct relevance to current UK land use planning, urban regeneration and contaminated land legislative regimes. An overview of the project and applications of the data to human health risk assessment, water quality protection and contaminant source identification are presented.

Arsenic retention and release in ombrotrophic peatlands.

Organic matter can play an important role in the mobility and fate of As in the environment, but there is a lack of data on As biogeochemistry in ombrotrophic peatlands. The aim of this study was to investigate As retention and release in atmospherically contaminated ombrotrophic peat soils in the Peak District National Park (UK). Solid phase As concentrations in the peat soils exceed 25 mg kg(-1). Solid phase As and Fe concentrations are closely correlated at sites where the peat is subjected to drying and oxic conditions. In a wetter zone of the bog, solid phase As and Fe distributions are decoupled, suggesting that As retention in these systems is not solely controlled by the presence of Fe oxides. Comparison of solid phase As and Pb distributions reveals that As has been subjected to post-depositional mobility in areas of water table fluctuation. Conversely, at permanently waterlogged locations As is immobile. Detailed stream water sampling reveals that As is released from the organic-rich uplands soils into the fluvial system. Dissolved As concentrations are highly variable, with values ranging from 0.20 to 7.28 microg l(-1). Stream water As concentrations are elevated during late summer stormflow periods when there has been re-wetting of the peat after significant water table draw-down. Dissolved As is strongly correlated to dissolved organic carbon under stormflow and baseflow. The results of this study suggest that organic matter plays an important role in As dynamics in ombrotrophic peatlands, but further work is needed to identify the exact As binding and release mechanisms. Drying and re-wetting of ombrotrophic peat soils and associated changes in redox status has the potential to lead to increased As mobility. Further work is needed to provide information on how predicted climate change will influence As cycling at sites containing a legacy of atmospheric contamination.

Urinary arsenic profiles reveal exposures to inorganic arsenic from private drinking water supplies in Cornwall, UK

Private water supplies (PWS) in Cornwall, South West England exceeded the current WHO guidance value and UK prescribed concentration or value (PCV) for arsenic of 10 μg/L in 5% of properties surveyed (n = 497). In this follow-up study, the first of its kind in the UK, volunteers (n = 207) from 127 households who used their PWS for drinking, provided urine and drinking water samples for total As determination by inductively coupled plasma mass spectrometry (ICP-MS) and urinary As speciation by high performance liquid chromatography ICP-MS (HPLC-ICP-MS). Arsenic concentrations exceeding 10 μg/L were found in the PWS of 10% of the volunteers. Unadjusted total urinary As concentrations were poorly correlated (Spearman’s ρ = 0.36 (P < 0.001)) with PWS As largely due to the use of spot urine samples and the dominance of arsenobetaine (AB) from seafood sources. However, the osmolality adjusted sum, U-AsIMM, of urinary inorganic As species, arsenite (AsIII) and arsenate (AsV), and their metabolites, methylarsonate (MA) and dimethylarsinate (DMA), was found to strongly correlate (Spearman’s ρ: 0.62 (P < 0.001)) with PWS As, indicating private water supplies as the dominant source of inorganic As exposure in the study population of PWS users.

Liver copper concentrations in cull cattle in the UK: are cattle being copper loaded?

With the release of the Department for the Environment, Food and Rural Affairs/Advisory Committee on Animal Feed Guidance Note for Supplementing Copper to Bovines it was noted that the current copper status of the national herd was not known. Liver samples were recovered from 510 cull cattle at a single abattoir across a period of three days. The samples were wet-ashed and liver copper concentrations determined by inductively coupled plasma mass spectrometry analysis. Breed, age and previous location information were obtained from the British Cattle Movement Service. Dairy breeds had higher liver copper concentrations than beef breeds. Holstein-Friesian and ‘other’ dairy breeds had 38.3 per cent and 40 per cent of cattle above the Animal Health and Veterinary Laboratories Agency (AHVLA) reference range (8000 µmol/kg dry matter), respectively, whereas only 16.9 per cent of animals in the combined beef breeds exceeded this value. It was found that underlying topsoil copper concentration was not related to liver copper content and that age of the animal also had little effect on liver concentration. In conclusion, over 50 per cent of the liver samples tested had greater-than-normal concentrations of copper with almost 40 per cent of the female dairy cattle having liver copper concentrations above the AHVLA reference range, indicating that a significant proportion of the UK herd is at risk of chronic copper toxicity.

Iodine source apportionment in the Malawian diet

The aim of this study was to characterise nutritional-I status in Malawi. Dietary-I intakes were assessed using new datasets of crop, fish, salt and water-I concentrations, while I status was assessed for 60 women living on each of calcareous and non-calcareous soils as defined by urinary iodine concentration (UIC). Iodine concentration in staple foods was low, with median concentrations of 0.01 mg kg−1 in maize grain, 0.008 mg kg−1 in roots and tubers, but 0.155 mg kg−1 in leafy vegetables. Freshwater fish is a good source of dietary-I with a median concentration of 0.51 mg kg−1. Mean Malawian dietary-Iodine intake from food, excluding salt, was just 7.8 μg d−1 compared to an adult requirement of 150 μg d−1. Despite low dietary-I intake from food, median UICs were 203 μg L−1 with only 12% defined as I deficient whilst 21% exhibited excessive I intake. Iodised salt is likely to be the main source of dietary I intake in Malawi; thus, I nutrition mainly depends on the usage and concentration of I in iodised salt. Drinking water could be a significant source of I in some areas, providing up to 108 μg d−1 based on consumption of 2 L d−1.

Combining two national-scale data sets to map soil properties, the case of available magnesium in England and Wales: Combining two soil surveys

Summary Given the costs of soil survey it is necessary to make the best use of available datasets, but data that differ with respect to some aspect of the sampling or analytical protocol cannot be combined simply. In this paper we consider a case where two datasets were available on the concentration of plant‐available magnesium in the topsoil. The datasets were the Representative Soil Sampling Scheme (RSSS) and the National Soil Inventory (NSI) of England and Wales. The variable was measured over the same depth interval and with the same laboratory method, but the sample supports were different and so the datasets differ in their variance. We used a multivariate geostatistical model, the linear model of coregionalization (LMCR), to model the joint spatial distribution of the two datasets. The model allowed us to elucidate the effects of the sample support on the two datasets, and to show that there was a strong correlation between the underlying variables. The LMCR allowed us to make spatial predictions of the variable on the RSSS support by cokriging the RSSS data with the NSI data. We used cross‐validation to test the validity of the LMCR and showed how incorporating the NSI data restricted the range of prediction error variances relative to univariate ordinary kriging predictions from the RSSS data alone. The standardized squared prediction errors were computed and the coverage of prediction intervals (i.e. the proportion of sites at which the prediction interval included the observed value of the variable). Both these statistics suggested that the prediction error variances were consistent for the cokriging predictions but not for the ordinary kriging predictions from the simple combination of the RSSS and NSI data, which might be proposed on the basis of their very similar mean values. The LMCR is therefore proposed as a general tool for the combined analysis of different datasets on soil properties. Highlights Differences in sample support mean that two datasets on a soil property cannot be combined simply. We showed how a multivariate geostatistical model can be used to elucidate the relationships between two such datasets. The same model allows soil properties to be mapped jointly from such data. This offers a general basis for combining soil datasets from diverse sources