Separating anthropogenic from natural anomalies in environmental geochemistry

Environmental geochemistry has attracted increasing interest during the last decade. In Sweden, geochemical mapping is carried out with methods that allow the data to be used in environmental research, including sampling plant roots and mosses from streams, soils and bedrock. These three sample types form an integrated strategy in environmental research, as well as in geochemical exploration. However, one problem that becomes prominent in geochemical mapping is to distinguish the signals 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 statistical solutions to this problem. The method used here is PLSR (partial least squares regression analysis). In this paper, we present an initial discussion on the applicability of PLSR in differentiating anthropogenic anomalies from natural contents. PLSR performs a simultaneous, interdependent 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 thus performs a generalized multivariate regression of Y on X overcoming the multicollinearity problem of correlated X-variables. The advantage of PLSR is that it gives optimal prediction ability in a strict statistical sense. Bedrock geochemistry from different lithologies in the mapping area in southern Sweden (Y-matrix) is analyzed together with stream or soil data (X-matrix). By modelling the PLS-regression between these two data sets, separate multivariate geochemical models based on the different bedrock types were developed. This step is called the training or modelling stage of the multivariate calibration. These calibrated models are subsequently used for predicting new (X) geochemical samples and estimating the corresponding Y-variable values. Information is obtained on how much of the metal contents in each new geochemical sample correlate with the different modelled bedrock types. By computing the appropriate X-residuals, we obtain information on the anthropogenic impact that is also carried by these new samples. In this way, it is possible from one single geochemical survey to derive both conventional geochemical background data and anthropogenic data, both of which can be readily displayed as maps. The present study concerns development 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 bedrock on the one hand, and from anthropogenic sources, on the other.