Spatial Assessment of Soil Contamination through GIS Data Management

Abstract: Spatial data management within the environmental field has a large range of application possibilities and comes with great advantages. In this study methods and technologies for spatial data management of soil contamination has been assessed in Geographical Information Systems (GIS), in order to identify in which way spatial data applications and tools can contribute with valuable information for these type of projects. The spatial assessment has been applied on a case study site in Kagghamra, Stockholm, exposed to high levels of contaminants, arsenic in particular. Subjects that have been evaluated are arsenic contamination distribution pattern, estimation of volume contaminated soil and amount of samples needed for spatial analyses. Furthermore, two versions of an exploratory soil sensitivity estimation model based on site specific ground and landscape parameters as well as literature references have been developed. The data management included large quantities of primary and secondary data of the commination levels as well as geological and ground properties. First hand collected geophysical field data obtained from Electromagnetic (EM) and Induced Polarisation (IP) measurements was also interpreted. The benefits of using geophysical measurements in soil contamination projects has been investigated. In this case the benefits were few due to difficult measuring conditions with disturbance noise. Spatial interpolations with the Natural Neighbour  (NN) technique are proven to be useful in transforming point contamination data into continuous layers. From the interpolation surfaces (arsenic distribution map) a variety of information can be extracted, such as a first hand volume estimation of contaminated soil, possibilities of reduction in amount of field sampling or to investigate statistical information and relations to different site specific ground conditions. The soil sensitivity estimation models are combined maps consisting of data layers that are relevant for the arsenic behaviour and interaction in the subsurface. Site specific Model (1) is based the data layers Soil type, Iron level, Soil depth, Slope  and illustrates mainly areas exposed to high concentrations of arsenic as high sensitivity areas. The more general, literature supported Model (2) also includes Vegetation cover and Topographic Wetness Index (TWI) and is not related highly to the arsenic distribution in the site area, but could contribute with general implications of sensitive areas if applied on a another, larger site area. Efficient management of large data quantities, economic and time saving benefits from less physical sampling and good representation and visualisation possibilities of the site conditions, as a tool for stakeholder communication and decision-making are the main contributions from the spatial data management.