Integrative Monitoring Analysis Aiming at the Detection of Spatial and Temporal Trends of Metal Accumulation in Mosses

摘要

Goal and Scope. The UNECE heavy metals in Mosses Surveys provide data on the accumulation of metals in naturally growing mosses throughout Europe. Using Germany as an example, this article concentrates on the elaboration and application of methods of data analysis that are necessary for a comprehensive interpretation of spatial and temporal trends in metal accumulation. Methods and Results. The sampling of mosses, and the chemical analysis of Cr, Cu, Fe, Ni, Pb, Ti, V and Zn in 1990,1995 and 2000 are summarized briefly. The variogram analyses detect distinct autocorrelation structures in the sample data which may consequently be estimated for all sites in Germany without monitoring metal accumulation by means of ordinary kriging. A procedure for the geostatistical detection of spatial outliers was developed and applied and, after the elimination of the spatial outliers, several measurements were seen to indicate an adequate quality of the geostatistical estimations. The cluster analyses of the z-transformed estimation data result in a nominal multielement index that indicates regional metal accumulation types over time. Percentile statistics serve for computation of an ordinal, scaled, multi-element accumulation index which is spatially differentiated over time in terms of multivariate, statistically defined ecoregions. The integrative statistical analysis reveals, from 1990 to 2000, that the metal accumulation declines up to 80% in some of the ecoregions. Hot spots of metal accumulation are mapped and interpreted by means of metadata analysis. Conclusions. Dot maps depict the spatial structure of the metal accumulation without spatial bias. This information, detailed with respect to metal species and space, should be generalized for better supporting the interpretation. The combination of geostatistical analysis and estimation, percentile and multivariate statistics is suitable for the calculation of indices that serve for a comprehensive mapping of metal accumulation in the ecoregions over time, for quantifying the bias of the surface estimation, and for mapping spatial outliers and hot spots of metal accumulation.