SEPARATING ANTHROPOGENIC FROM NATURAL ANOMALIES IN ENVIRONMENTAL GEOCHEMISTRY

Environmental geochemistry has attracted increasing interest during th e last decade. In Sweden, geochemical mapping is carried out with meth ods that allow the data to be used in environmental research, includin g sampling plant roots and mosses from streams, soils and bedrock. The se three sample types form an integrated strategy in environmental res earch, as well as in geochemical exploration. However, one problem tha t becomes prominent in geochemical mapping is to distinguish the signa ls derived from natural sources from those derived from anthropogenic sources. So far, this has mostly been done by using different types of samples, for example, different soil horizons. This is both expensive and time-consuming. We are currently developing alternative statistic al solutions to this problem. The method used here is PLSR (partial le ast squares regression analysis). In this paper, we present an initial discussion on the applicability of PLSR in differentiating anthropoge nic anomalies from natural contents. PLSR performs a simultaneous, int erdependent principal component analysis decomposition in both X- and Y-matrices, in such a way that the information in the Y-matrix is used directly as a guide for optimal decomposition of the X-matrix. PLSR t hus performs a generalized multivariate regression of Y on X overcomin g the multicollinearity problem of correlated X-variables. The advanta ge of PLSR is that it gives optimal prediction ability in a strict sta tistical sense.Bedrock geochemistry from different lithologies in the mapping area in southern Sweden (Y-matrix) is analyzed together with s tream or soil data (X-matrix). By modelling the PLS-regression between these two data sets, separate multivariate geochemical models based o n the different bedrock types were developed. This step is called the training or modelling stage of the multivariate calibration. These cal ibrated models are subsequently used for predicting new (X) geochemica l samples and estimating the corresponding Y-variable values. Informat ion is obtained on how much of the metal contents in each new geochemi cal sample correlate with the different modelled bedrock types. By com puting the appropriate X-residuals, we obtain information on the anthr opogenic impact that is also carried by these new samples. In this way , it is possible from one single geochemical survey to derive both con ventional geochemical background data and anthropogenic data, both of which can be readily displayed as maps. The present study concerns dev elopment of data analysis methods. Examples of the applications of the methodology are presented using Pb and U. The results show the share of these contents in different sampling media that is derived from bed rock on the one hand, and from anthropogenic sources, on the other.