Roland PESCH, Marcel HOLY and Winfried SCHRÖDER
The GI_Forum Program Committee accepted this paper as reviewed full paper.
The primary goal of the UN ECE Moss Survey is to assess and map the bioaccumulation of metals and nitrogen in terrestrial ecosystems across Europe. Several studies claim that local and regional factors may overlay the deposition effects on the bioaccumulation. In this paper this hypothesis is investigated using available data as well as bivariate correlation analysis and decision tree models. The study focuses on two elements: Antimony was chosen from the list of analysed metal elements due to its relation to traffic and industrial emissions. Secondly, the nitrogen bioaccumulation was evaluated statistically since it was expected to be strongly correlated with agricultural land use. In fact, the results of the analyses differ with regard to the two elements according to the emission situation.
Nevertheless, it can also be concluded that next to the emission situation other factors influence the bioaccumulation significantly.
The UN ECE moss surveys of 1990, 1995, 2000 and 2005 enabled to map spatiotemporal trends of the metal concentrations in mosses throughout Europe (Harmens et al. 2006). The goal of these national surveys was to assess the atmospheric bioaccumulation of metals and, from 2005 on also nitrogen (N) in background areas that are not directly influenced by emission sources like motorways, urbanised areas or industries. In Germany the spatial patterns of the metal bioaccumulation mirror the location of large industrial and urbanised areas in each of the four surveys (Schröder & Pesch 2004; Pesch et al. 2007). Hot spots were detected predominantly in the urbanised and industrialised Ruhr Area, the densely populated Rhein-Main region and in the historically grown industrial regions of former East
Germany (e.g. Halle-Leipzig region). Since 1990 continuously decreasing bioaccumulation trends can be observed for almost all metal elements although there are exceptions: In 2005 chromium (Cr), copper (Cu), antimony (Sb) and zinc (Zn) by average show significantly higher concentrations in the mosses than in 2000 (Pesch et al. 2007). Based on these and many further investigations Markert (2007) concluded that the moss method has proven to be an efficient and sensitive model-programme for environmental monitoring. However, in addition to the broad-scale investigations in Europe several studies revealed that the element concentrations in mosses are associated with confounding factors like different moss species, canopy drip, precipitation, elevation and the sea-spray-effect (Zechmeister et

Roland Pesch, Winfried Schröder and Marcel Holy al. 2003). Nevertheless, for some of these factors controversial results have been reported:
Regarding two studies performed in different parts of the Alps Gerdol et al. (2002) and
Zechmeister (1995) came to different conclusions concerning the correlation of the bioaccumulation of lead (Pb) with increasing altitude. Regarding the bioaccumulation tendencies of different moss species the international literature provides plenty of examples where scientists report different results (e.g. Thöni et al. 1996, Köhler & Peichl 1993). One reason for these controversies may be that most of the performed studies rely on low amounts of sampling data. Therefore the results lack of statistical robustness. Hence, in
Germany the available measurement data and sampling sites describing metadata from the three surveys 1990, 1995 and 2000 were investigated statistically. Different methods were applied complementarily to test the hypothesis if factors other than deposition loads affect the spatial patterns of metal bioaccumulation. The studies were performed with bivariate correlation and contingency analyses (Pesch & Schröder 2006a) and with decision tree models (Pesch & Schröder 2006b, Schröder et al. 2007). As a result from all these studies it can be concluded that mainly the moss species and variables associated with canopy drip, elevation, precipitation and the sea-salt-effect seem to influence the spatial variation of the metal concentrations in mosses. These factors proved to be correlated with the metal bioaccumulation as strong as the distances of the monitoring sites to potential emission sources and as strong as aerial percentages of different types of land use categories around the sampling sites.
The goal of this study is to analyse the element loads in mosses measured in the campaign
2005 with help of non parametric correlation analysis and two decision tree models. In comparison to the already published studies additional surface data on precipitation and different land use density measures were calculated and included in the hypothesis testing.
The study concentrates on two elements: Antimony (Sb) was chosen from the list of analysed metal elements due to its proven relation to traffic, coal combustion and industrial processes like the manufacturing of glass and ceramics (Peichl et al. 1994; Siewers et al.
1998). Furthermore, Sb was one of the metal elements showing a significant increase from
2000 to 2005. The second element to be investigated was N due to its relevance for the eutrophication and acidification of ecosystems and the hypothesized strong correlation with agricultural land use. Furthermore, N was measured in 2005 for the first time.
Monitoring data. In 2005 chemical measurements on Sb and N were performed for mosses collected at 726 sites from all over Germany. The underlying empirical design regarding moss sampling and preparation followed the recommendations and mandatory obligations put down in a UN ECE experimental protocol (Harmens et al. 2006). Only three moss species
Pleurozium schreberi (P.s.), Scleropodium purum (S.p.)
and
Hypnum cupressiforme
(H.c.)
were to be collected, preferably in September and October. Defined critical distances were to be kept from trees and bushes to avoid the direct influence of canopy drip and from potential emission sources in the neighbourhood of the sampling sites to avoid the overrepresentation of local emission phenomena. The according distances and other sampling sites describing information on, e.g., growth type and occurrence of the sampled moss specie, surrounding land use and vegetation as well as topographical aspects were

Statistical Investigation of the Atmospheric Accumulation of Antimony and Nitrogen in Mosses documented in a sampling protocol and later on digitised and linked to the measurement data with the aid of a web based moos geodatabase (Kleppin et al. 2007). The measurements were performed with instrumental techniques like ICP MS or OES (metals) or C/N Analysers (N). Preceding the chemical analysis the mosses were cleaned and prepared: Only the three-year growth part was taken, so each measurement value corresponds to a three-year bioaccumulation sum.
Fig. 1:
Different Land Use Percentages within 5 km of each 100 x 100 m² Grid Cell calculated from Corine Landcover 2000 + Major Traffic Ways
Surface data.
To supplement the site-specific data with regard to information on the atmospheric emissions of metals and N, density measures were calculated from a map on land use derived from the Corine land cover map 2000 (Keil et al. 2005) and a map on
German main roads and motorways (ESRI 2003): In a first step all linear vector objects from the road network layer were buffered with regard to the critical distance declared in the sampling protocol (= 300 m). The buffer was then transformed into a 100 x 100 m² raster and intersected with the Corine Landcover map 2000 rasterised with regard to the same resolution. Next, the percental proportion of selected land use categories in a radius of
300 m, 1 km and 5 km around each raster cell was calculated and then projected onto the respective grid cells. This procedure was performed for land uses related to urban and agricultural activities. Additional percentages were calculated regarding areas affected by road traffic (according to the 300 m road buffers) with radiuses of 1 km and 5 km and for forest areas for 5 km. The latter percentage measure was included in the analyses due to experiences made in the Czech moss surveys, where the metal bioaccumulation and the percentage of forests in a radius of 5 km around the monitoring site correlated significantly
(Sucharaová and Suchara 2004). It was assumed that this measure enables to indicate the filtering of dust particles out of the atmosphere. Additionally, monitoring sites representing background levels of bioaccumulation could be identified. At last, the canopy drip effect of trees was parameterised by recoding the Corine Landcover grid in terms of the percentage of forests within each 100 x 100 m² grid cell. To this end, grid cells with forests land use ratios of above 50 percent were coded 1, otherwise 0.
To account for the influence of precipitation on the bioaccumulation the German National
Meteorological Service (DWD) provided monthly averaged precipitation measurement data

Roland Pesch, Winfried Schröder and Marcel Holy regarding all accumulation and sampling periods of all four monitoring campaigns 1990,
1995, 2000 and 2005. To intersect the respective information with the monitoring sites the precipitation sums for both accumulation and sampling periods were analysed geostatistically (Pesch et al. 2007): With regard to the accumulation periods 1000 x 1000 m² precipitation sum maps were calculated for both 2 and 3 years periods preceding the respective first sampling of each campaign. Regarding the sampling periods according geostatistical maps were calculated for September and October, since in these months the main part of the sampling was conducted.
The sea spray-effect was parameterised in terms of a 1000 x 1000 m² grid map quantifying the distances of all grid cells to the coastlines of the North or the Baltic Sea. The grid cells were afterwards subdivided into distance classes: grid cells with their centre in a distance between 0 and 10 km from the nearest coastline were assigned to class 1, such with a distance between 10 and 20 km were assigned to class 2 and so on. In order to minimize interactions between the sea spray-effect and co-variables such as altitude, all grid cells with midpoints farther than 100 km away from the shore were assigned to a single class with the value 11.
Fig. 2:
Methodology to Statistically Investigate Sb and N Accumulation in Mosses
The methodology to statistically investigate the association between the Sb- and Nconcentrations in the mosses and potential confounding factors for the bioaccumulation relies on bivariate correlation analysis and two decision tree models (Figure 2). The
Spearman correlation algorithm was applied to calculate coefficients of correlation

Statistical Investigation of the Atmospheric Accumulation of Antimony and Nitrogen in Mosses regarding the metrically and ordinally scaled predictor variables. Regarding the decision tree models two algorithms were applied: Classification and Regression Trees (CART –
Breimann et al. 1984) and Chi-square Automatic Interaction Detector (CHAID – Kass
1980). All GIS-calculations were performed with the help of ESRI-software (Arcview 3.3 including Spatial Analyst 1.1). The statistical calculations were done with SPSS Answer
Tree 3.1 (decision tree analyses) and SPSS 15.0 (all other statistical analyses).
Decision tree models like CART and CHAID are used to identify latent structures in datasets consisting of large numbers of objects that are described by a multitude of variables with different scale dignities. Decision tree models aim at computing an explanation or prediction model for a variable of interest (the target or dependent variable) from a given set of variables assumed to have an influence on this variable (predictor or independent variables). The algorithms thereby rely on the consecutive segmentation of the entire data set (root node) into subclasses or sub-nodes. The subdivisions are performed on basis of the features of the predictor variables with the goal of optimising a certain statistical target criteria. The latter differs regarding CART and CHAID.
CART subdivides the sample data set by choosing that predictor resulting in the highest increase of homogeneity (improvement) with regard to the features of the target variable.
The determination of the group-internal homogeneity depends on the scale of the target variable. For metrically scaled target variables the calculation of homogeneity is carried out by determining the class-internal variance. The improvement per split then is determined on the basis of the reduction of the variance of the whole sample in relation to the variance of the newly created tree level. CART performs only binary splits and can analyse metrically, ordinally and nominally scaled data for both target and predicting variables.
In contrast to CART, CHAID presupposes a conversion of metrically scaled predictors into an ordinal scale. The correlations between the target variable and the respective predictor variables are determined by inferential statistical methods which are appropriate with regard to the scale of the respective variable. Provided the target variable is metrically scaled Fstatistics are used to investigate the null hypothesis that there is no correlation between the target variable and a chosen predictor variable. The computation of the CHAID dendrogram complies with a sequence of mergings and segmentations: In the first step the characteristics of each predictor are merged into a set of as few as possible significantly different classes which are in turn ranked on the basis of their statistical association with the target variable:
That predictor with the lowest p-level is chosen to split the root- and sub-nodes. Compared to CART, CHAID enables to perform multiple splits.
The complexity of decision trees are usually controlled by user-defined settings in terms of stopping criteria. Regarding the CART- and the CHAID analyses performed in this study, the trees were grown in a way that the minimum number of cases per sub-node did not fall below seven, corresponding to about 1 % of the original data set. A minimum improvement of 1 % of the original variance was defined as a threshold to create a new split in the CART analyses. In the CHAID analyses a level of significance of 0,01was determined to merge the original number of predictor categories and to choose one of the newly aggregated predictors in order to split root- and sub-nodes.

Roland Pesch, Winfried Schröder and Marcel Holy
Tab. 1:
Bivariate Non Parametric Correlations (Spearman) between Sb and N and
Confounding Factors (bold: p < 0,05; grey and bold: p < 0,01)
Independent Variable
Forest areas within 5 km radius
Urban areas within 300 m radius
Urban areas within 1 km radius
Sb N n K n K
684 0,22 685 -0,18
725 0,05 725 0,02
718 0,09 719 0,01
Urban areas within 5 km radius
Traffic density within 1 km Radius
Traffic density within 5 km Radius
Agricultural areas within 300 m radius
683 0,19 684 0,09
725 0,08 725 0,07
725 0,14 725 0,09
718 -0,15 719 0,24
Agricultural areas within 1 km radius
Agricultural areas within 5 km radius
725 -0,09 725 0,19
682 -0,22 683 0,21 precipitation within sample period - September 337 0,14 337 0,05 precipitation within sample period - October 262 0,19 263 -0,08 precipitation within growth period - 2 years precipitation within growth period - 3 years
Distance to North / Baltic Sea
Elevation
Inclination
Height of surrounding trees
Distance to tree crown
Distance to bushes
Distance to human settlement
725
725
725
724
340
725
725
725
495
0,29
0,27
0,27
0,19
-0,08
0,18
-0,25
-0,05
0,02
725
725
725
725
340
725
725
725
496
-0,03
-0,03
0,09
-0,03
-0,06
0,23
-0,34
-0,04
-0,05
Distance to road
Distance to motorway
Distance to agricultural land unit
Distance to factory farming
Distance to industry
604 0,01 605 -0,07
193 -0,38 194 -0,04
369 0,25 370 -0,13
28 0,05 28 -0,01
78 0,00 79 0,19
Bivariate Correlations.
The results of the non parametric correlation analyses between the
Sb- and N-concentrations on the one hand and the either metrically or ordinally scaled sampling sites describing metadata and land use density measures on the other hand is depicted in Table 1. It can be seen that Sb significantly (p < 0,01) correlates with variables related to urban land use, traffic, agriculture, elevation, precipitation as well as the sea-salteffect and canopy drip. The highest correlation was found for the distance of the monitoring site to the nearest motorway (-0,38), indicating a strong influence of the traffic emissions on the Sb-loads in the mosses. The N bioaccumulation shows highly significant correlations to the percentage of agriculture within 300 m, 1 km and 5 km of the monitoring site and variables related to the filter effect of the trees. In comparison to Sb the agriculture indices are positively correlated with the N loads, meaning that the higher the percentage of agricultural land use within the surroundings of the monitoring site, the higher the N loads in the mosses. The filter effect of the trees at the sampling sites as parameterised by the distance to the tree crowns and the height of surrounding trees were similarly correlated with the concentrations of N and Sb in the mosses.
Decision tree models.
Due to the high skewness (7,9) of the Sb value distribution the measurements were transformed lognormally before the decision tree analyses. Figures 3 and 4 depict the first two levels of the CHAID-tree for Sb (Figure 3) and of the CART-tree for N (Figure 4):

Statistical Investigation of the Atmospheric Accumulation of Antimony and Nitrogen in Mosses
Fig. 3: CHAID-Tree for Sb (Ln) in the Moss Survey 2005 (Levels 1 and 2)
Regarding the
Sb-bioaccumulations
the moss species showed the highest degree of significance in the CHAID-analyses and were therefore used to divide the root node into two sub-nodes (Figure 3). Sub-node 10 with sites where
P.s., H.c., Hylocomium splendens
(H.s.) and
Brachytecium rutubularum (B.r.)
were collected by average had higher Sb loads than sub-node 11 with
S.p.
and
Rhytidiadelphus squarrosus
(
R.s
.). On the second level those sites with
P.s., H.c., H.s., B.r.
were split into two sub-nodes by the distance of the sampling sites to the nearest tree crown. Here, sites sampled nearer to trees by average revealed higher Sb-loads. Monitoring Sites where
S.p.
and
R.s.
were collected were grouped into three sub-nodes by the distance of the monitoring site to the nearest motorway. The complete CHAID-tree reaches down to the sixth level leading to 17 terminal nodes and explaining 39,2 % of the total variance (not depicted in Figure 5). In the lower parts of the tree mainly predictors related to urban land use, the canopy drip effect and the precipitation were chosen as splitting variables.
The CART-tree for the Sb-loads in the mosses encompasses 16 terminal nodes on seven levels explaining 42,1 % of the total variance. Like CHAID, CART chose the moss species to subdivide the root node into two sub-nodes, characterised by the same combination of moss species. These then were further split by the precipitation sum for the two years preceding the moss collection and by the aerial percentage of urban land units within 5 km of the monitoring site. In lower parts of the CART-tree mainly predictors related to urban land use, traffic, canopy drip and precipitation were used as splitting variables.

Roland Pesch, Winfried Schröder and Marcel Holy
Fig. 4:
CART-Tree for N in the Moss Survey 2005 (Levels 1 and 2)
Regarding the
N-bioaccumulations
CART selected the distance of the monitoring sites to the nearest tree crowns to divide the root node into two sub-nodes (Figure 4). Sites sampled less than 10 m from tree crowns thereby show by average higher N-loads. On the second tree level the percentage of forest areas within 5 km of the monitoring site was used to separate sites more likely to be influenced by the filter effect. Sites sampled more than 10 m afar from the nearest tree crown were separated by the percentage of agricultural land use within 1 km of the monitoring site. Those sites located in areas with a higher agricultural density show higher N-bioaccumulations. The complete CART tree reaches down to the sixth level, results in 15 terminal nodes and explains 37,8 % of the total sample variance
(not depicted in Figure 4). In levels 3 to 6 mostly predictors related to canopy drip, agricultural and urban land use and moss specific criteria were taken as predictor variables.
Like the case for Sb the CHAID-tree for the N-loads in the mosses shows a similar structure than the corresponding CART-tree. Again the distance of the monitoring sites to the nearest tree was chosen to subdivide the root node into sub-nodes. Further splitting is done by the height of the surrounding trees, the percentage of agricultural areas in the vicinity of the sampling sites, the altitude and the growth type of the moss. In summary, the CHAID-tree lead to eleven terminal nodes on four levels and accounts for 27,8 % of the entire root variance.

Statistical Investigation of the Atmospheric Accumulation of Antimony and Nitrogen in Mosses
The results of the presented study confirm the assumed bioaccumulation influences regarding Sb and N. It could be shown that mainly variables related to traffic and urban influences significantly correlate with the Sb-accumulation in the mosses. N on the other hand was proven to be mainly associated to measures used for parameterising the probability of emissions from agricultural land use. Nevertheless, for both elements the emission situation does not fully determine what is measured in the mosses. Other significant influences detected are canopy drip, moss specific criteria, precipitation sums for the accumulation and sampling periods, elevation and the sea spray-effect. This should lead to more empirical research on the bioaccumulation of metals and N in mosses since these factors may bias the detection of spatial patterns and temporal trends. Regarding the N loads in the mosses in 2005 the respective spatial patterns did not fully confirm assumed deposition patterns as especially in regions characterised by high cattle densities, e.g., in
Lower Saxony, relatively low nitrogen loads were measured.
The Sb- and N-concentrations in the mosses were investigated geostatistically before performing the analyses presented in this paper. In either case, existing, but low spatial autocorrelation could be detected and modelled by means of variogram analysis. The respective semivariogram models exhibit high nugget variances with nugget / sill ratios of
0.81 (Sb) and 0.68 (N), respectively. Since the spatial density of the moss monitoring sites is furthermore rather low (Mean nearest neighbour distance = 15.7 km) it was assumed that the investigated association patterns are not dependent regarding their location. Provided this assumption holds true, the results of the applied statistical methods can be seen as good.
Nevertheless, future works should concentrate on applying the same statistical design on different strata of the data.
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