A quick methodology for the evaluation of preliminary toxicity levels in soil samples associated to a potentially heavy-metal pollution in an abandoned ore mining site.

The risk assessment of pollution associated with metal extraction process is a social responsibility. The initial goal of this type of assessment should be the use of a methodology able to combine the use of easy application, low cost and high efficiency-techniques to provide the necessary information with the least amount of investment in time and money as possible. A suitable option maybe a combination of a simple, quick and reliable analytical technique such as PXRF with bioassays and chemometric tools like HCA, PCA or LDA as a viable approach to carrying out a preliminary estimation of phytotoxicity levels associated to the soil sampled in a metal-contaminated area. The method we propose has been shown to be a quick, economical and reliable tool for use in the first stages of environmental risk characterization in mining areas. Particularly, the use of root elongation (RE) as the classification criteria provides a viable approach for selecting the final samples (or zones) in which an "in-depth" investigation plan will be designed as part of a future remediation strategy.

The use of a combined portable X ray fluorescence and multivariate statistical methods to assess a validated macroscopic rock samples classification in an ore exploration survey.

The combination of "ex situ" portable X ray fluorescence with unsupervised and supervised pattern recognition techniques such as hierarchical cluster analysis, principal components analysis, factor analysis and linear discriminant analysis have been applied to rock samples, in order to validate a "in situ" macroscopic rock samples classification of samples collected in the Boris Angelo mining area (Central Chile), during a drill-hole survey carried out to evaluate the economic potential of this Cu deposit. The analysed elements were Ca, Cu, Fe, K, Mn, Pb, Rb, Sr, Ti and Zn. The statistical treatment of the geological data has been arisen from the application of the Box-Cox transformation used to transform the data set in normal form to minimize the non-normal distribution of the data. From the statistical results obtained it can be concluded that the macroscopic classification applied to the transformed data permits at least, to distinguish quite well in relation to two of the rock classes defined (70.5% correctly classified (p<0.05)) as well as for four of the five alteration types defined "in situ" (75% of the total samples).