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In many contaminated sites and/or sites affected by a potentially toxic element (PTE) contamination, the spatial variability of soil contamination is a very complex issue.This is because the history of contamination in a specific industrial site is often lost in time and with very different modifications occurring over time. It is even hidden in the case of illegal waste dumping, for which type, quantity and localization of contaminants are unknown. Thus it is not known in advance (i) the spatial distribution of contamination, (ii) the knowledge about how contaminants have been distributed over and beneath the soil, (iii) how far contaminants have been reworked during the life time of the contaminated site.Despite these problems, it is self-evident that a detailed knowledge of the natural and the anthropogenic spatial variability of soil contamination and soil properties is of crucial importance in both site characterization and most importantly in site reclamation.Here we claim that the analysis of soil trenches in combination with pXRF can strongly support the understanding about processes behind soil contaminant distribution and this information can then be used in the following study of contaminated site characterization.This contribution focuses on how to acquire detailed knowledge of the spatial distribution of contamination in an agricultural area of southern Italy, 6 ha of farmland confiscated by the Italian Judiciary due to past illegal burial of industrial tannery wastes causing potential contamination by Cr, Zn and heavy hydrocarbons (C>12). After indirect geophysical and radiometric (i.e. soil gamma ray emissions) prospections, 8 sites for soil profiles and trenches (10 x 1.7 m wide) digging were identified.Over these trenches both morphological (i.e. colour, structure, plant roots, etc.) and elemental total content analysis (Olympus p-XRF) were performed. This analysis enable to identify three type of soil contaminant deposition which affected to various degree the Ap, Bw and C horizons of the investigated Silandic Andosol. The highest Cr and Zn content was found in the B horizon, where levels of 25000 ppm were measured in soil-pockets of muddy-grey materials, very likely hosting the original processing residues.

Today it is evident that there are many contrasting demands on our landscape (e.g. food security, more sustainable agriculture, higher income in rural areas, etc.) as well as many land degradation problems. It has been proved that providing operational answers to these demands and problems is extremely difficult. Here we aim to demonstrate that a spatial decision support system based on geospatial cyberinfrastructure (GCI) can address all of the above, so producing a smart system for supporting decision making for agriculture, forestry, and urban planning with respect to the landscape. In this paper, we discuss methods and results of a special kind of GCI architecture, one that is highly focused on land management and soil conservation. The system allows us to obtain dynamic, multidisciplinary, multiscale, and multifunctional answers to agriculture, forestry, and urban planning issues through the Web. The system has been applied to and tested in an area of about 20 000 ha in the south of Italy, within the framework of a European LIFE+ project (SOILCONSWEB). The paper reports – as a case study – results from two different applications dealing with agriculture (olive growth tool) and environmental protection (soil capability to protect groundwater). Developed with the help of end users, the system is starting to be adopted by local communities. The system indirectly explores a change of paradigm for soil and landscape scientists. Indeed, the potential benefit is shown of overcoming current disciplinary fragmentation over landscape issues by offering – through a smart Web-based system – truly integrated geospatial knowledge that may be directly and freely used by any end user (www.landconsultingweb.eu). This may help bridge the last very important divide between scientists working on the landscape and end users.