Purpose This study aimed at investigating correlations between heavy metal concentrations in mosses and modelled deposition values as well as other site-specific and regional characteristics to determine which factors primarily affect cadmium, lead and mercury concentrations in mosses. The resulting relationships could potentially be used to enhance the spatial resolution of heavy metal deposition maps across Europe. Materials and methods Modelled heavy metal deposition data and data on the concentration of heavy metals in naturally growing mosses were integrated into a geographic information system and analysed by means of bivariate rank correlation analysis and multivariate decision trees. Modelled deposition data were validated annually with deposition measurements at up to 63 EMEP measurement stations within the European Monitoring and Evaluation Programme (EMEP), and mosses were collected at up to 7,000 sites at 5-year intervals between 1990 and 2005. Results and discussion Moderate to high correlations were found between cadmium and lead concentrations in mosses and modelled atmospheric deposition of these metals: Spearman rank correlation coefficients were between 0.62 and 0.67, and 0.67 and 0.73 for cadmium and lead, respectively (p<0.001). Multivariate decision tree analyses showed that cadmium and lead concentrations in mosses were primarily determined by the atmospheric deposition of these metals, followed by emissions of the metals. Low to very low correlations were observed between mercury concentrations in mosses and modelled atmospheric deposition of mercury. According to the multivariate analyses, spatial variations of the mercury concentration in mosses was primarily associated with the sampled moss species and not with the modelled deposition, but regional differences in the atmospheric chemistry of mercury and corresponding interactions with the moss may also be involved. Conclusions At least for cadmium and lead, concentrations in mosses are a valuable tool in determining and mapping the spatial variation in atmospheric deposition across Europe at a high spatial resolution. For mercury, more studies are needed to elucidate interactions of different chemical species with the moss.

The NERC and CEH trade marks and logos ('the Trademarks') are registered trademarks of NERC in the UK and other countries, and may not be used without the prior written consent of the Trademark owner. 1 European Heavy Metals in Mosses Survey AbstractThe aim of this study was to identify the factors influencing Cd, Hg and Pb concentrations in mosses sampled within the framework of the European Heavy Metals in Mosses Surveys 1990Surveys -2005. The analyses encompassed data from 4661 (1990), 7301 (1995), 6764 (2000) and 5600 (2005) sampling sites. As exemplary case studies revealed that other factors besides atmospheric deposition of metals influence the element concentrations in mosses, the moss datasets of the above mentioned surveys were analysed by means of bi-and multivariate statistics in order to identify factors influencing metal bioaccumulation. In the analyses we used the metadata recorded during the sampling as well as additional geodata e.g. on depositions, emissions and land use. Bivariate Spearman correlation analyses showed the highest correlations between Cd and Pb concentrations in mosses and EMEP modelled total deposition data (0.62 ≤ r s ≤ 0.73). For Hg the correlations with all the tested factors were considerably lower (e.g. total deposition r s ≤ 0.24). Multivariate decision tree analyses by means of Classification and Regression Trees (CART) identified the total deposition as the statistically most significant factor for the Cd and Pb concentrations in the mosses in all four monitoring campaigns. For Hg, the most significant factor 8 in 1990 as identified by CART was the distance to the nearest Hg source recorded in the European Pollutant Emission Register, in 1995 and 2000 it was the analytical method, and in 2005 CART identified the sampled moss species. The strong correlations between the Cd and Pb concentrations in the mosses and the total deposition can be used to calculate deposition maps with a regression kriging approach on the basis of surface maps on the element concentrations in the mosses.

The NERC and CEH trade marks and logos ('the Trademarks') are registered trademarks of NERC in the UK and other countries, and may not be used without the prior written consent of the Trademark owner.

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.

Background In order to map exceedances of critical atmospheric deposition loads for nitrogen (N) surface data on the atmospheric deposition of N compounds to terrestrial ecosystems are needed. Across Europe such information is provided by the international European Monitoring and Evaluation Programme (EMEP) in a resolution of 50 km by 50 km, relying on both emission data and measurement data on atmospheric depositions. The objective of the article at hand is on the improvement of the spatial resolution of the EMEP maps by combining them with data on the N concentration in mosses provided by the International Cooperative Programme on E ects of Air Pollution on Natural Vegetation and Crops (ICP Vegetation) of the United Nations Economic Commission for Europe (UNECE) Long-range Transboundary Air Pollution (LTRAP) Convention. MethodsThe map on atmospheric depositions of total N as modelled by EMEP was intersected with geostatistical surface estimations on the N concentration in mosses at a resolution of 5 km by 5 km. The medians of the N estimations in mosses were then calculated for each 50 km by 50 km grid cell. Both medians of moss estimations and corresponding modelled deposition values were ln-transformed and their relationship investigated and modelled by linear regression analysis. The regression equations were applied on the moss kriging estimates of the N concentration in mosses. The respective residuals were projected onto the centres of the EMEP grid cells and were mapped using variogram analysis and kriging procedures. Finally, the residual and the regression map were summed up to the map of total N deposition in terrestrial ecosystems throughout Europe. Results and discussionThe regression analysis of the estimated N concentrations in mosses and the modelled EMEP depositions resulted in clear linear regression patterns with coe cients of determination of r 2 = 0.62 and Pearson correlations of r p = 0.79 and Spearman correlations of r s = 0.70, respectively. Regarding the German territory a nationwide mean of 18.1 kg/ ha / a (standard deviation: 3.49 kg / ha / a) could be derived from the resulting map on total N deposition in a resolution of 5 km by 5 km. Recent updates of the modelled atmospheric deposition of N provided a similar estimate for Germany. ConclusionsThe linking of modelled EMEP data on the atmospheric depositions of total N and the accumulation of N in mosses allows to map the deposition of total N in a high resolution of 5 km by 5 km using empirical moss data. The mapping relies on the strong statistical relationship between both processes that are physically and chemically related to each other. The mapping approach thereby relies on available data that are both based on European wide harmonized methodologies. From an ecotoxicological point of view the linking of data on N depositions and those on N bioaccumulation can be considered a substantial progress. Keywords