Malcolm Coull

Concentrations and geographic distribution of selected organic pollutants in Scottish surface soils.

Concentrations of selected persistent organic pollutants (POPs) representing three chemical classes (polycyclic aromatic hydrocarbons (PAH), polybrominated diphenyl ethers (PBDE) and polychlorinated biphenyls (PCB) and the organic pollutant diethylhexyl phthalate (DEHP), were determined in surface soil samples (0-5 cm) collected at 20 km grid intersects throughout Scotland over a three-year period. Detectable amounts of all chemical classes and most individual congeners were present in all samples. There were no consistent effects of soil or vegetation type, soil carbon content, pH, altitude or distance from centres of population on concentrations which exhibited extreme variation, even in adjacent samples. It is concluded that soil POPs and DEHP concentrations and associated rates of animal and human exposure were highly variable, influenced by multiple, interacting factors, and not clearly related to local sources but possibly related to wet atmospheric deposition and the organic carbon content of the soil.

Long term temporal and spatial changes in the distribution of polychlorinated biphenyls and polybrominated diphenyl ethers in Scottish soils

Long term changes in polychlorinated biphenyl (PCB) and polybrominated diphenyl ether (PBDE) concentrations in soil from four transects across Scotland were measured in three surveys conducted between 1990 and 2007-9. Overall PCB level declined during this period (22.5 to 4.55 ng/g, p<0.001) but PBDEs increased (0.68 to 2.55 ng/g, p<0.001), reflecting the ban on PCB use in the 1980s while PBDE use increased until about 2004 when the use of penta-mix congener ceased in Europe. The proportion of lighter PCB congeners (28+52) present declined (p<0.001) primarily between 1990 and 1999. However, the proportion of lighter PBDE congeners (47+99) in the soil samples increased (p<0.01) from 1990 to 1999 and declined (p<0.001) thereafter, probably reflecting the introduction of legislation banning penta-BDE products and the degradation of lighter congeners and their translocation. PCBs were slightly higher in two southernmost transects but PBDE concentrations were significantly higher (p<0.001) in the two southern transects than in the two northern transects. This may reflect proximity to areas of high population and industrial activity. It is concluded that temporal and spatial changes in the distribution of PCBs and PBDEs reflect geography, physical processes and legislation.

Controls on soil solution nitrogen along an altitudinal gradient in the Scottish uplands

Nitrogen (N) deposition continues to threaten upland ecosystems, contributing to acidification, eutrophication and biodiversity loss. We present results from a monitoring study aimed at investigating the fate of this deposited N within a pristine catchment in the Cairngorm Mountains (Scotland). Six sites were established along an elevation gradient (486-908 m) spanning the key habitats of temperate maritime uplands. Bulk deposition chemistry, soil carbon content, soil solution chemistry, soil temperature and soil moisture content were monitored over a 5 year period. Results were used to assess spatial variability in soil solution N and to investigate the factors and processes driving this variability. Highest soil solution inorganic N concentrations were found in the alpine soils at the top of the hillslope. Soil carbon stock, soil solution dissolved organic carbon (DOC) and factors representing site hydrology were the best predictors of NO(3)(-) concentration, with highest concentrations at low productivity sites with low DOC and freely-draining soils. These factors act as proxies for changing net biological uptake and soil/water contact time, and therefore support the hypothesis that spatial variations in soil solution NO(3)(-) are controlled by habitat N retention capacity. Soil percent carbon was a better predictor of soil solution inorganic N concentration than mass of soil carbon. NH(4)(+) was less affected by soil hydrology than NO(3)(-) and showed the effects of net mineralization inputs, particularly at Racomitrium heath and peaty sites. Soil solution dissolved organic N concentration was strongly related to both DOC and temperature, with a stronger temperature effect at more productive sites. Due to the spatial heterogeneity in N leaching potential, a fine-scale approach to assessing surface water vulnerability to N leaching is recommended over the broad scale, critical loads approach currently in use, particularly for sensitive areas.

E-SMART: Environmental Sensing for Monitoring and Advising in Real-Time

Smart monitoring, using real-time environmental sensing with links to server-side data processing/modeling, allows progression from data acquisition to useful information generation. The use of modern technology such as mobile phones to provide imagery and other types of data along with GPS-derived coordinates enables researchers and stakeholders to integrate ground-based observations with existing datasets. We have developed an infrastructure linking mobile communications, server-side processing and storage of data and imagery, and field-based access to existing spatial datasets. This infrastructure has been used for the development of a number of mobile phone apps (applications) and web-based applications, and has proved useful for stakeholders in agriculture, science and policy. In addition to giving information on the capacity development, we demonstrate useful applications relating to the upload, interpretation and integration of data (e.g. automated interpretation of soil profile imagery, carbon content estimation from soil colour) while focusing on the technical aspects of the underpinning system.

Estimating Soil Properties with a Mobile Phone

Several soil properties can be used to estimate soil health and suitability for specific land use. These properties include, but are not restricted to, organic matter content, pH, cation exchange capacity, C/N ratio, texture and structure. These properties provide broad information about the capacity of the soil to provide nutrients, water and physical support to crops. They also provide information about soil erosion and compaction risk. The measurement of these properties is traditionally carried out through laboratory analysis which delays decision-making. Some of these properties can be estimated from an understanding of the soil-forming characteristics and visual analysis of the soil profile. Here, a method is presented that automates estimating soil fertility properties using image analysis of field-based topsoil images, including image morphometrics. A database of Scottish soil samples has been used to generate a model, which links spatial data sets and image analysis to produce estimates of soil fertility properties. A mobile phone app has been produced that provides an estimate of soil organic matter rapidly and for free.

Neural network integration of field observations for soil endocrine disruptor characterisation.

A neural network approach was used to predict the presence and concentration of a range of endocrine disrupting compounds (EDCs), based on field observations. Soil sample concentrations of endocrine disrupting compounds (EDCs) and site environmental characteristics, drawn from the National Soil Inventory of Scotland (NSIS) database, were used. Neural network models were trained to predict soil EDC concentrations using field observations for 184 sites. The results showed that presence/absence and concentration of several of the EDCs, mostly no longer in production, could be predicted with some accuracy. We were able to predict concentrations of seven of 31 compounds with r(2) values greater than 0.25 for log-normalised values and of eight with log-normalised predictions converted to a linear scale. Additional statistical analyses were carried out, including Root Mean Square Error (RMSE), Mean Error (ME), Willmotts index of agreement, Percent Bias (PBIAS) and ratio of root mean square to standard deviation (RSR). These analyses allowed us to demonstrate that the neural network models were making meaningful predictions of EDC concentration. We identified the main predictive input parameters in each case, based on a sensitivity analysis of the trained neural network model. We also demonstrated the capacity of the method for predicting the presence and level of EDC concentration in the field, identified further developments required to make this process as rapid and operator-friendly as possible and discussed the potential value of a system for field surveys of soil composition.

Predicting Sample Source Location from Soil Analysis Using Neural Networks

A system combining a national soils database with a neural network was developed for prediction of source location for soil samples. The neural network was trained to predict environmental characteristics, which can be of crucial importance to investigating officers in a police operation or to environmental agencies attempting to locate the source of a pollutant. When coupled with maps of environmental conditions and a generalized opinion pool approach, the system was used to produce weighted maps of source location. The system was capable of reducing search areas of a sample source to less than 0.1% of the total area.

Automated Soil Physical Parameter Assessment Using Smartphone and Digital Camera Imagery

Here we present work on using different types of soil profile imagery (topsoil profiles captured with a smartphone camera and full-profile images captured with a conventional digital camera) to estimate the structure, texture and drainage of the soil. The method is adapted from earlier work on developing smartphone apps for estimating topsoil organic matter content in Scotland and uses an existing visual soil structure assessment approach. Colour and image texture information was extracted from the imagery. This information was linked, using geolocation information derived from the smartphone GPS system or from field notes, with existing collections of topography, land cover, soil and climate data for Scotland. A neural network model was developed that was capable of estimating soil structure (on a five-point scale), soil texture (sand, silt, clay), bulk density, pH and drainage category using this information. The model is sufficiently accurate to provide estimates of these parameters from soils in the field. We discuss potential improvements to the approach and plans to integrate the model into a set of smartphone apps for estimating health and fertility indicators for Scottish soils.