Integrative exposure assessment through classification and regression trees on bioaccumulation of metals, related sampling site characteristics and ecoregions

Integrative evaluation of data derived from biomonitoring and models indicating atmospheric deposition of heavy metals

Atmospheric deposition of heavy metals (HM) can be determined by use of numeric models, technical devices and biomonitors. Mainly focussing on Germany, this paper aims at evaluating data from deposition modelling and biomonitoring programmes. The model LOTOS-EUROS (LE) yielded data on HM deposition at a spatial resolution of 25 km by 25 km throughout Europe. The European Monitoring and Evaluation Programme (EMEP) provided model calculations on 50 km by 50 km grids. Corresponding data on HM concentration in moss, leaves and needles and soil were derived from the European Moss Survey (EMS), the German Environmental Specimen Bank (ESB) and the International Co-operative Programme on Assessment and Monitoring of Air Pollution Effects on Forests (iCP Forests). The modelled HM deposition and respective concentrations in moss (EMS), leaves and needles (ESB, iCP Forests) and soil (iCP Forests) were investigated for their statistical relationships. Regression equations were applied on geostatistical surface estimations of HM concentration in moss and then the residuals were interpolated by use of kriging interpolation. Both maps were summed up to a map of cadmium (Cd) and lead (Pb) deposition across Germany. Biomonitoring data were strongly correlated to LE than to EMEP. For HM concentrations in moss, the highest correlations were found for the association between geostatistical surface estimations of HM concentration in moss and deposition (LE).

Estimation of plant sampling uncertainty: an example based on chemical analysis of moss samples

In order to estimate the level of uncertainty arising from sampling, 54 samples (primary and duplicate) of the moss species Pleurozium schreberi (Brid.) Mitt. were collected within three forested areas (Wierna Rzeka, Piaski, Posłowice Range) in the Holy Cross Mountains (south-central Poland). During the fieldwork, each primary sample composed of 8 to 10 increments (subsamples) was taken over an area of 10 m² whereas duplicate samples were collected in the same way at a distance of 1-2 m. Subsequently, all samples were triple rinsed with deionized water, dried, milled, and digested (8 mL HNO₃ (1:1) + 1 mL 30 % H₂O₂) in a closed microwave system Multiwave 3000. The prepared solutions were analyzed twice for Cu, Fe, Mn, and Zn using FAAS and GFAAS techniques. All datasets were checked for normality and for normally distributed elements (Cu from Piaski, Zn from Posłowice, Fe, Zn from Wierna Rzeka). The sampling uncertainty was computed with (i) classical ANOVA, (ii) classical RANOVA, (iii) modified RANOVA, and (iv) range statistics. For the remaining elements, the sampling uncertainty was calculated with traditional and/or modified RANOVA (if the amount of outliers did not exceed 10 %) or classical ANOVA after Box-Cox transformation (if the amount of outliers exceeded 10 %). The highest concentrations of all elements were found in moss samples from Piaski, whereas the sampling uncertainty calculated with different statistical methods ranged from 4.1 to 22 %.

Spatially resolved distribution models of POP concentrations in soil: a stochastic approach using regression trees

Background concentrations of selected persistent organic pollutants (polychlorinated biphenyls, hexachlorobenzene, p,p'-DDT including metabolites) and polyaromatic hydrocarbons in soils of the Czech Republic were predicted in this study, and the main factors affecting their geographical distribution were identified. A database containing POP concentrations in 534 soil samples and the set of specific environmental predictors were used for development of a model based on regression trees. Selected predictors addressed specific conditions affecting a behavior of the individual groups of pollutants: a presence of primary and secondary sources, density of human settlement, geographical characteristics and climatic conditions, land use, land cover, and soil properties. The model explained a high portion of variability in relationship between the soil concentrations of selected organic pollutants and available predictors. A tree for hexachlorobenzene was the most successful with 76.2% of explained variability, followed by trees for polyaromatic hydrocarbons (71%), polychlorinated biphenyls (68.6%), and p,p'-DDT and metabolites (65.4%). The validation results confirmed that the model is stable, general and useful for prediction. The stochastic model applied in this study seems to be a promising tool capable of predicting the environmental distribution of organic pollutants.

Assessing spatial patterns of metal bioaccumulation in French mosses by means of an exposure index

BACKGROUND, AIM AND SCOPE

The European Heavy Metals in Mosses Surveys (UNECE-ICP Vegetation) is a programme performed every 5 years since 1990 in at least 21 European countries. The moss surveys aim at uncovering the spatiotemporal patterns of metal and nitrogen bioaccumulation in mosses. In France, the moss survey was conducted for the third time in 2006. Five hundred thirty-six monitoring sites were sampled across the whole French territory. The aim of the presented study is to give an integrative picture of the metal bioaccumulation for the entire French territory without geographical gaps. Furthermore, confounding factors of the metal bioaccumulation in mosses should be investigated.

MATERIALS AND METHODS

Element loads of arsenic (As), cadmium (Cd), chromium (Cr), copper (Cu), iron (Fe), mercury (Hg), nickel (Ni), lead (Pb), antimony (Sb), vanadium (V) and zinc (Zn) measured in the French campaign 2006 were aggregated to a multi-metal index (MMI). This index was first introduced in the German moss surveys and represents the mean rank of each monitoring site or estimated raster cell regarding all elements referred to. Hence, the spatial variability of the metal bioaccumulation in France could be assessed as a whole. A comparison of the MMI map with the spatial patterns of the Cu loads in mosses was then drawn, as Cu originates to a large extent from urban sources. Applying CHAID, the MMI and the Cu loads in the mosses were further investigated with regard to confounding factors. The said results were discussed on the basis of recent scientific publications.

RESULTS

The MMI surface map shows high values in strongly industrialized and urbanized regions as well as at sites of high altitude, lying, for example in the Massif Central and the French Alps. Accordingly, the CHAID decision tree consequently shows the altitude to be the statistically most significant influencing factor of the MMI followed by the sampled moss species. As for the MMI map, the surface map for Cu mirrors urban agglomerations, as high values can be found in the areas of Greater Paris, Lyon and Marseille. The CHAID tree for Cu revealed the sampled moss species and the ratio of urban land uses within 5 km of the sampling sites to be the main influencing factors.

DISCUSSION

The aggregation of metal bioaccumulation data was adopted for the French monitoring campaign. The influence of altitude, moss species-specific accumulation rates and urban emissions on the bioaccumulation is confirmed by international scientific publications. Nevertheless, the confounding factors in France differ from those derived from the German data, where the MMI was mainly associated to canopy drip effects and the growth patterns of the sampled mosses.

CONCLUSIONS

The Cu and the MMI maps give a comprehensive overview of the metal bioaccumulation in France without geographical gaps. Hence, this approach allows summarising the spatial patterns of eleven element loads in mosses by use of geostatistics and percentile statistics.

RECOMMENDATIONS AND PERSPECTIVES

The presented metal integrating approach should be applied on data from past French moss surveys and on those to come. Additionally, the decision tree analyses should be carried out to examine possibly changing boundary conditions of the metal accumulation in mosses over time.

Monitoring nitrogen accumulation in mosses in central European forests

In order to assess whether nitrogen (N) loads in mosses reflect different land uses, 143 sites in North Rhine-Westphalia, the Weser-Ems Region and the Euro Region Nissa were sampled between 2000 and 2005. The data were analysed statistically with available surface information on land use and forest conditions. N bioaccumulation in mosses in the Weser-Ems Region with high densities of agricultural land use and livestock exceeded the concentrations in the more industrialised Euro Region Nissa. In all three study areas agricultural and livestock spatial densities were found to be positively correlated with N bioaccumulation in mosses. In North Rhine-Westphalia, the N concentrations in mosses was also moderately correlated with N concentrations in leaves and needles of forest trees. The moss method proved useful to assess the spatial patterns of N bioaccumulation due to land use.

Nitrogen and metals in two regions in Central Europe: significant differences in accumulation in mosses due to land use?

The study was conducted to test the hypothesis that the regional variability of nitrogen (N) and metal accumulations in terrestrial ecosystems are due to historical and recent ways of landuse. To this end, two regions of Central Europe were selected for investigation: the Weser-Ems Region (WER) and the Euro Region Nissa (ERN). They were assumed to have land use-specific accumulation profiles. Thus, the metal and N accumulations in both regions were examined by means of geostatistically based comparative moss analysis. The sampling and chemical analysis of mosses were conducted in accordance with the convenient guidelines and methods, respectively. The spatial representativity of the sampling sites was computed by means of a land classification which was calculated for Europe by means of classification trees and GIS-techniques. The differences of deposition loads were tested for statistical significance with regard to time and space. The measurement values corroborated the decline of metal accumulation observed since the beginning of the European Metals in Mosses Surveys in 1990. The metal loads of the mosses in the ERN exceeded those in the WER significantly. The opposite holds true for the N concentrations: those in the WER were significantly higher than those in the ERN. The reduction of emissions from power plants, factories and houses was strongly correlated with the decline of deposition and bioaccumulation of metals. As proved by the European Metals in Mosses Surveys, this tendency is due to successful environmental policies. But no such success could be verified by monitoring the accumulation of N in mosses.