Spatially valid data of atmospheric deposition of heavy metals and nitrogen derived by moss surveys for pollution risk assessments of ecosystems

For analysing element input into ecosystems and associated risks due to atmospheric deposition, element concentrations in moss provide complementary and time-integrated data at high spatial resolution every 5 years since 1990. The paper reviews (1) minimum sample sizes needed for reliable, statistical estimation of mean values at four different spatial scales (European and national level as well as landscape-specific level covering Europe and single countries); (2) trends of heavy metal (HM) and nitrogen (N) concentrations in moss in Europe (1990–2010); (3) correlations between concentrations of HM in moss and soil specimens collected across Norway (1990–2010); and (4) canopy drip-induced site-specific variation of N concentration in moss sampled in seven European countries (1990–2013). While the minimum sample sizes on the European and national level were achieved without exception, for some ecological land classes and elements, the coverage with sampling sites should be improved. The decline in emission and subsequent atmospheric deposition of HM across Europe has resulted in decreasing HM concentrations in moss between 1990 and 2010. In contrast, hardly any changes were observed for N in moss between 2005, when N was included into the survey for the first time, and 2010. In Norway, both, the moss and the soil survey data sets, were correlated, indicating a decrease of HM concentrations in moss and soil. At the site level, the average N deposition inside of forests was almost three times higher than the average N deposition outside of forests

Modelling and mapping heavy metal and nitrogen concentrations in moss in 2010 throughout Europe by applying Random Forests models

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 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. 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. 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

Bioindication and modelling of atmospheric deposition in forests enable exposure and effect monitoring at high spatial density across scales

Context: For enhancing the spatial resolution of measuring and mapping atmospheric deposition by technical devices and by modelling, moss is used complementarily as bio-monitor. Aims: This paper investigated whether nitrogen and heavy metal concentrations derived by biomonitoring of atmospheric deposition are statistically meaningful in terms of compliance with minimum sample size across several spatial levels (objective 1), whether this is also true in terms of geostatistical criteria such as spatial auto-correlation and, by this, estimated values for unsampled locations (objective 2) and whether moss indicates atmospheric deposition in a similar way as modelled deposition, tree foliage and natural surface soil at the European and country level, and whether they indicate site-specific variance due to canopy drip (objective 3). Methods: Data from modelling and biomonitoring atmospheric deposition were statistically analysed by means of minimum sample size calculation, by geostatistics as well as by bivariate correlation analyses and by multivariate correlation analyses using the Classification and Regression Tree approach and the Random Forests method. Results: It was found that the compliance of measurements with the minimum sample size varies by spatial scale and element measured. For unsampled locations, estimation could be derived. Statistically significant correlations between concentrations of heavy metals and nitrogen in moss and modelled atmospheric deposition, and concentrations in leaves, needles and soil were found. Significant influence of canopy drip on nitrogen concentration in moss was proven. Conclusion: Moss surveys should complement modelled atmospheric deposition data as well as other biomonitoring approaches and offer a great potential for various terrestrial monitoring programmes dealing with exposure and effects