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Highlights: [•] Accurate estimates of soil hydraulic properties are needed for modelling. [•] Soil hydraulic properties can be measured in different data supports. [•] We used block sequential simulation to integrate data with different supports. [•] We obtained the spatial distribution of SHP and associated uncertainty. [•] Block sequential simulation performed better than direct sequential simulation. [Copyright &y& Elsevier]

• Portable X-ray fluorescence data contributes to accurate soil contamination assessment. • Data uncertainty tackled using local probability distributions to map contamination. • Novel geostatistical modeling strategy supports the use of pXRF data. A significant reduction in the costs associated with contamination assessments can be achieved if traditional soil sampling for contaminated-site characterization is complemented by real-time sampling using proximal soil sensors. Real-time sampling using a portable X-ray fluorescence (pXRF) device is a cheap and fast sampling method to provide more data and reduce the time needed to map soil contamination. The main disadvantage of using pXRF is the degree of uncertainty of these in situ measurements due to the technology's indirect nature, and its sensitivity to soil heterogeneity and soil moisture content. This study evaluates the potential of using both pXRF and traditional soil sampling measurements to accurately map soil contamination due to the presence of heavy metals. The approach proposed uses geostatistical sequential simulation with local probability distributions to characterize and integrate pXRF uncertainty at each sampling location. The resulting maps agree with the contamination map obtained using traditional laboratory data only, in terms of mapping accuracy and extent of contaminated areas. This study shows that with few collocated pXRF and laboratory analytical data it is possible to identify contaminated areas accurately, thus providing a cost-effective solution to work with pXRF data directly. [ABSTRACT FROM AUTHOR]

[Display omitted] • Performance of feasibility studies in mining projects is poor which could be due to omission of quantitative uncertainty assessments in public reporting. • JORC 2012 standards require quantitative uncertainty assessment for geological and spatial domain models. • Current methodologies of quantitative uncertainty assessment and communication exist and can be used in early-stage mining projects. • Professional development and incorporating software and process workflows could be the way to go to improve current reporting practice. Sustainability of mining projects is linked to informed investment decisions based on public reporting of exploration and mineral resource estimation results. In Australia, guidelines for public reporting are established by the Joint Ore Reserves Committee reporting code through the JORC Code (2012). Although assessment of uncertainty in the results reported is a requirement, this is often communicated in a qualitative manner and subjectively evaluated. This can become a liability if not communicated effectively, particularly in early stages of mining projects when spatial domains of geological interpretation and mineralization envelopes influence reliability of resource estimates. This review describes methodologies for quantitative uncertainty assessment and communication, and explores how they could be applied in public reporting practice. The complexity, cost and additional work of doing a quantitative assessment could hinder a straightforward implementation. This could be overcome if mining companies budget for quantitative uncertainty assessment and associated professional development. A compulsory requirement for inclusion of uncertainty assessments in public reporting or use of standardized subjective language would improve industry practice. [ABSTRACT FROM AUTHOR]