Objective: This study explores the statistical relations between the concentration of nine heavy metals(HM) (arsenic (As), cadmium (Cd), chromium (Cr), copper (Cu), mercury (Hg), nickel (Ni), lead (Pb),vanadium (V), zinc (Zn)), and nitrogen (N) in moss and potential explanatory variables (predictors)which were then used for mapping spatial patterns across Europe. Based on moss specimens collected\ud in 2010 throughout Europe, the statistical relation between a set of potential predictors (such as the atmospheric deposition calculated by use of two chemical transport models (CTM), distance from emission sources, density of different land uses, population density, elevation, precipitation, clay content of soils) and concentrations of HMs and nitrogen (N) in moss (response variables) were evaluated by the use of Random Forests (RF) and Classification and Regression Trees (CART). Four spatial scales were regarded: Europe as a whole, ecological land classes covering Europe, single countries participating in the European Moss Survey (EMS), and moss species at sampling sites. Spatial patterns were estimated by applying a series of RF models on data on potential predictors covering Europe. Statistical values and resulting maps were used to investigate to what extent the models are specific for countries, units of the Ecological Land Classification of Europe (ELCE), and moss species.\ud Results: Land use, atmospheric deposition and distance to technical emission sources mainly influence the element concentration in moss. The explanatory power of calculated RF models varies according to elements measured in moss specimens, country, ecological land class, and moss species. Measured and predicted medians of element concentrations agree fairly well while minima and maxima show considerable differences. The European maps derived from the RF models provide smoothed surfaces of element concentrations (As, Cd, Cr, Cu, N, Ni, Pb, Hg, V, Zn), each explained by a multivariate RF model and verified by CART, and thereby more information than the dot maps depicting the spatial patterns of measured values.\ud Conclusions: RF is an eligible method identifying and ranking boundary conditions of element concentrations in moss and related mapping including the influence of the environmental factors

Since 1990, every 5 years, moss sampling is conducted within the European moss monitoring programme to assess the atmospheric deposition of airborne pollutants. Besides many other countries, Germany takes regularly part at these evaluations. Within the European moss monitoring 2015, more than 400 moss samples across Germany were taken according to a harmonized methodology for the assessment heavy metal and nitrogen input. In a pilot programme, eight of these sites were chosen for additional investigations on a broad range of organic contaminants to evaluate their accumulation in moss and thereby their presence in atmospheric deposition in Germany. Target compound classes comprised polycyclic aromatic hydrocarbons (PAH), polychlorinated dibenzodioxins and –furans (PCDD/F), dioxin-like and non-dioxin-like polychlorinated biphenyls (dl-PCB, ndl-PCB), polyfluorinated alkyl substances, classical flame retardants as well as emerging chlorinated and brominated flame retardants. In total, 120 target compounds were analysed. For some analytes, comparisons of accumulation in moss and tree leave samples were possible. Except for certain flame retardants, PFAS, and ndl-PCB, substances of all other compound classes could be quantified in moss samples of all sites. Concentrations were highest for PAH (40–268 ng g−1) followed by emerging flame retardants (0.5–7.7 ng g−1), polybrominated diphenyl ethers (PBDE; 0.3–3.7 ng g−1), hexabromocyclododecane (HBCD; 0.3–1.2 ng g−1), dl-PCB (0.04–0.4 ng g−1) and PCDD/F (0.008–0.06 ng g−1). Results show the widespread atmospheric distribution and deposition of organic contaminants across Germany as well as the suitability of moss as bioaccumulation monitor for most of these compound classes. Compared to nearby tree leaf samples, accumulation potential of moss appeared to be higher for pollutants of high octanol–air partition coefficient (KOA) and octanol–water partition coefficient (KOW).

Background: This article presents statistical analyses of elements concentrations in mosses collected in two monitoring programmes: 1. The German moss monitoring as part of the European Moss Survey conducted in 1990, 1995, 2000, 2005 and 2015 at 592, 1026, 1028, 726 and 400 sites across Germany, respectively, and chemically analysed according to harmonised methods throughout Europe; and 2. The moss surveys additionally performed throughout Bavaria, federal state of Germany. The evaluations of the present study are intended to examine indications whether the element accumulations are specific to moss species and whether, therefore, conversion factors should be used. Such observations and recommendations have so far been limited to spatially confined areas and relatively few moss samples and were derived from different studies without a common methodology. Therefore, the objective of this investigation was to analyse large and long-term datasets from moss surveys for differences in element concentrations in different moss species. This analysis was based on data derived from several moss species collected across Germany and specimens of only one moss species collected in the monitoring programme performed by the environmental authority of Bavaria. So far, the presented investigation is the most comprehensive one in terms of running time, spatial density of the measurement network and method harmonisation. Results: The present study does neither verify nor falsify the hypothesis of moss species-specific element accumulations in mosses. This, like the other studies, is not possible because of its empirical design, since that would require strictly controlled laboratory experiments. However, this investigation yielded hints, that the differences in element accumulation in several moss species across German surveys between 1990 and 2015 are in the range of the intra- and inter-specific variance reported. Conclusion: It is, therefore, advisable to continue dispensing with conversion factors so far.

This paper aims to investigate the correlations between the concentrations of nine heavy metals in moss and atmospheric deposition within ecological land classes covering Europe. Additionally, it is examined to what extent the statistical relations are affected by the land use around the moss sampling sites. Based on moss data collected in 2010/2011 throughout Europe and data on total atmospheric deposition modelled by two chemical transport models (EMEP MSC-E, LOTOS-EUROS), correlation coefficients between concentrations of heavy metals in moss and in modelled atmospheric deposition were specified for spatial subsamples defined by ecological land classes of Europe (ELCE) as a spatial reference system. Linear discriminant analysis (LDA) and logistic regression (LR) were then used to separate moss sampling sites regarding their contribution to the strength of correlation considering the areal percentage of urban, agricultural and forestry land use around the sampling location. After verification LDA models by LR, LDA models were used to transform spatial information on the land use to maps of potential correlation levels, applicable for future network planning in the European Moss Survey. Correlations between concentrations of heavy metals in moss and in modelled atmospheric deposition were found to be specific for elements and ELCE units. Land use around the sampling sites mainly influences the correlation level. Small radiuses around the sampling sites examined (5 km) are more relevant for Cd, Cu, Ni, and Zn, while the areal percentage of urban and agricultural land use within large radiuses (75–100 km) is more relevant for As, Cr, Hg, Pb, and V. Most valid LDA models pattern with error rates of < 40% were found for As, Cr, Cu, Hg, Pb, and V. Land use-dependent predictions of spatial patterns split up Europe into investigation areas revealing potentially high (= above-average) or low (= below-average) correlation coefficients. LDA is an eligible method identifying and ranking boundary conditions of correlations between atmospheric deposition and respective concentrations of heavy metals in moss and related mapping considering the influence of the land use around moss sampling sites.