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Spatial structures of heavy metals and nitrogen accumulation in moss specimens sampled between 1990 and 2015 throughout Germany

Environmental Sciences Europe201931:33

https://doi.org/10.1186/s12302-019-0216-y

  • Received: 27 March 2019
  • Accepted: 15 May 2019
  • Published:

Abstract

Background

The collection of atmospheric deposition by technical samplers and validated deposition modelling using chemical transport models is spatially complemented by using mosses as bioindicator: since 1990, the European moss survey has been providing data on element concentrations in moss every 5 years at up to 7300 sampling sites. In the moss specimens, heavy metals (since 1990), nitrogen (since 2005) and persistent organic pollutants (since 2010) were determined. Germany participated in all surveys with the exception of that in 2010. In this study, the spatial structures of element concentrations in moss collected in Germany between 1990 and 2015 were comparatively investigated by using Moran’s I statistics and Variogram analysis and mapped by use of Kriging interpolation. This is the precondition to spatially join the moss survey data with data collected at other locations within different environmental networks and to validate spatial patterns of atmospheric deposition as derived by technical sampling and modelling.

Results

The calculated maps reveal a clear and statistically significant decrease of most heavy metals, but not of nitrogen, in moss. Due to decreasing element concentrations and the unchanged application of the element concentration classification for the mapping, the heavy metal maps for the survey 2015 do no longer depict much spatial variation.

Conclusions

Therefore, in an upcoming study, this analysis needs to be complemented for the heavy metals by calculating maps that depict the spatial structure of survey-specific percentile statistics 1990, 1995, 2000, 2005 and 2015.

Keywords

  • Atmospheric deposition
  • European moss survey
  • Geostatistics
  • Kriging interpolation
  • Mapping
  • Variogram analysis

Background

Atmospheric deposition of heavy metals, nitrogen and persistent organic pollutants may impact the integrity of ecosystems so that standards aiming at their protection are failed. For instance, atmospheric deposition is correlated with accumulation of pollutants in soils and sediments as well as in vegetation and, consequently, in food webs [2, 4, 5, 9, 20, 21], Nickel et al. [32, 35, 37, 55, 61, 63]. In Germany, there are eight sites with wet only deposition samplers which are part of the European Monitoring and Evaluation Programme. EMEP is a scientifically based and policy driven programme under the UNECE1 Convention on Long-range Transboundary Air Pollution (CLRTAP) for international co-operation to solve transboundary air pollution problems [72]. In the focus of EMEP, deposition monitoring and modelling are cadmium (Cd), mercury (Hg), lead (Pb) and nitrogen (N). In Europe, the EMEP-deposition network comprises 22 sites where Hg is measured and 66 with Cd and Pb measurements according to a standardised method [1, 6, 67], Travnikov et al. [68]. The respective data are used to validate results derived by EMEP chemical transport models applied to data on emissions and meteorology. Unfortunately, emission data contain some uncertainty [7, 3236], so that ecological risks due to deposition cannot be spatially detailed as needed. Therefore, the concentrations of heavy metals (HM) and nitrogen in atmospheric deposition can be evaluated by complementarily method of moss biomonitoring by using ectohydric mosses which lack any roots, cuticle and epidermis [11, 39]. Therefore, they accumulate dry, wet and occult deposition and enable the quantification of elements far beyond the respective limits of analytical detection [8]. Among others, this is especially true for Pleurozium schreberi (BRID.) MITT. (abbreviated as Plesch), Hypnum cupressiforme HEDW. (abbreviated as Hypcup) and Pseudoscleropodium purum (HEDW.) M.FLEISCH (Synonym Scleropodium purum HEDW. LIMPR.) (abbreviated as Psepur) [16]. These species are appropriate for mapping trends of HM bioaccumulation of atmospheric deposition throughout areas of large spatial extent based on a spatially dense network.

Since 1990, European moss surveys (EMS) were conducted every 5 years. Together with the German moss surveys (GMS), being part of EMS with the exception of 2010, they aim at mapping transboundary air pollution by using moss specimens as bioaccumulation indicators. Sampling, chemical analysis and research data management follow a harmonised experimental protocol [16].

The number of sampling sites in six EMS between 1990 and 2015 ranged between 4499 and 7312 in 20 to 36 participating European countries. The number of sampling sites in five GMS (Fig. 1) was reduced from 1026 (1995) and 1028 (2000), respectively, to 726 (2005) and, further on, to 400 (2015) according to a transparent and statistically sound methodology [34, 45, 46, 59]. In addition to Cd, Hg, Pb and N, aluminium (Al), arsenic (As), chromium (Cr), copper (Cu), nickel (Ni), vanadium (V) and zinc (Zn) were determined in the moss specimens collected in 2015. This article focus on the LRTAP elements Cd, Hg, Pb and N. Additionally, Cr is regarded, since this is one of the elements besides antimony (Sb) and Zn with an intermediate increase in concentration between 2000 and 2005.
Fig. 1
Fig. 1

Sampling sites and elements regarded the German Moss Surveys (GMS) 1990, 1995, 2000, 2005, 2015. SH Schleswig-Holstein, MV Mecklenburg-Western Pomerania, HH Hamburg, NI Lower Saxony, BE Berlin; ST Saxony-Anhalt, BB  Brandenburg, NW North Rhine-Westphalia, SN Saxony, TH Thuringia, HE Hesse, RP Rhineland Palatinate, SL Saarland, BY Bavaria, BW Baden-Wuerttemberg

This study aims at synoptically compare the spatial structures of element concentrations accumulated in moss in terms of surface maps as derived from sample point data by analysis and modelling of the spatial autocorrelation by means of Variogram analysis and, based on the resulting function, mapping by Kriging interpolation [24, 25]. Generalising spatial and temporal sample data is an essential goal of empirical sciences. In particular, spatial generalisation through interpolation is a precondition for connecting and evaluating measurements derived from monitoring networks being incongruent with the moss monitoring network [3236]. The explanation of the course of investigation starts with the sampling procedure followed by the chemical analysis of element concentrations including quality control (QC; “Determination of element concentrations in moss” section). QC is an essential precondition for interpreting measurement differences at several points in space and time as real phenomena but not as artefacts. Then, descriptive statistics (“Descriptive statistics” section) and spatial statistics in terms of Moran’s I, Variogram analysis and Kriging interpolation (“Geostatistics” section) are outlined.

Methods

Determination of element concentrations in moss

The European moss surveys follow a harmonised methodology encompassing the design of the monitoring network, sampling and chemical analyses including QC and data handling. For EMS 2015, this was published by ICP Vegetation [16]. The fundamentals rely on Rühling et al. [48]. They were up-dated continuously [13] and, respectively, specified as, e.g., for Germany [62].

Regarding the reorganisation of the German sampling network, Nickel and Schröder [34] operationalised the given criteria of ICP Vegetation [16]. Examples include: The monitoring network should comprise 1.5 sampling sites within 1000 km2 or at least two sites per EMEP-deposition modelling grid (50 km × 50 km), regions with steep deposition gradients should be sampled at a higher sample point density, moss sampling points should be located close to sites where atmospheric deposition is collected by technical devices, for enabling time trend analyses, the sampling points should remain the same across time, the sampling should be restricted to the three moss species mentioned in “Background” section.

After a thorough common training, the moss specimens were sampled from June 2016 until March 2017 by five experts according to ICP Vegetation [16]. The same is true for the preparation of the moss specimens by another five members of the lab staff which was subjected to continuous QC, too. The mass concentration of total N was performed according to VDLUFA [69] using an Elementar Vario Max. The dry and homogeneous moss material was digested with nitric acid (65%) and hydrogen peroxide (35%) in a microwave Mars 5. The measurements of Al, As, Cd, Cr, Cu, Fe, Ni, Pb, Sb, V and Zn were performed according to ISO [18] using ICP-MS (Agilent 7900 with sample loop). Hg was determined according to ISO [17] by cold vapour atomic absorption spectroscopy (AAS, Mercury) after enrichment with tin(II) chloride.

The limits of quantification for the elements were determined, and the respective results are given in “Quality control of measurement” section. The same applies for the lab-internal QC encompassing for each sampling series the measurement of a blind value and of reference materials to check for recovery and performance. The moss reference materials M2 and M3 ([66] for recommended HM values; [12] for recommended HM and N values) were analysed together with three samples from the EMS 2005 (samples 2071, 3050, 3069) each three times. Lab-external QC was accomplished through certification according to [19] and through national and international ring tests.

Descriptive statistics

For ensuring the comparability of the five GMS, the same descriptive statistical parameters of GMS 2015 were used for all monitoring campaigns [14, 41, 53, 65]. Thus, for each trace element and N, mentioned in “Determination of element concentrations in moss” section, minimum, maximum, arithmetic mean, standards deviation, coefficient of variation in [%], the 20th, 50th, 90th and 98th percentiles were calculated by taking in consideration all sample point data and specifically for moss species and federal states [38]. In addition, the geostatistical surface estimations were also done (“Geostatistics” section) [62].

Geostatistics

For mapping the spatial patterns of deposition-induced bioaccumulation of HM and N and for spatially connecting them with data derived by other environmental monitoring networks, geostatistics [25] was used. Variogram analysis is a geostatistical tool for analysing and modelling spatial autocorrelation of continuous metric variables which are regarded as realisations of random functions (see below). Variogram analysis is a precondition for subsequent spatial surface estimation by Kriging interpolation [24]. Contrary to deterministic interpolation methods, such as inverse distance weighting [3], Kriging uses the geostatistical function derived by Variogram analysis for interpolation. The modelled autocorrelation function informs whether or not and how much the (semi)variance of continuous metric variables is correlated with the distance between measurement points. This function can be evaluated by several characteristics such as the nugget effect indicating measurement variations at spatial ranges below the minimal sample point distance or measurement errors. The range informs about the spatial extent of autocorrelation and, thus, Kriging interpolation. The semivariance reached at the maximum spatial extent of autocorrelation is called sill. The nugget-to-sill ratio [%] is a measure for the strength of spatial autocorrelation: The higher the ratio, the lower the autocorrelation. As a rule of thumb, the nugget-to-sill ratio should not exceed 75%. Ratio values nearby 100% indicate a random distribution of measurements [10, 22, 71].

A complementary statistical means to account for spatial autocorrelation is suggested by Moran [27]. Moran’s I allows for testing whether objects are spatially distributed at random (negative I-values) or clustered (positive I-values) and whether spatial autocorrelation is significant. The range determined by Variogram analysis can be used in Moran’s I statistics to specify the spatial extent of autocorrelation [70].

Following the autocorrelation analysis and modelling, the autocorrelation function was used for Kriging interpolation. Depending from assumptions about the random function, several variants of Kriging interpolation can be applied: two of them are ordinary Kriging, supposing the mean of the random function as constant across the area investigated, and Universal Kriging for data including a deterministic trend [22]. Since most data from environmental surveys do not follow a normal distribution [47], the moss survey data distribution was analysed with regard to their skewness (Sk), and data not normally distributed were subjected to log-transformation [64] and Box-Cox-transformation [49, 71]. The quality of Kriging interpolation was quantified by leave-one-out cross-validation [15, 22]. Thereby, the mean error (ME) and the mean standardized error (MSE) indicate over- and underestimation. Optimal values of both measures would be 0. The root mean square standardised error (RMSSE) accounts for the relation between theoretical and experimental variance. Its optimum value is 1, and RMSSE < 1 indicates underestimation and RMSSE > overestimation. The median of percentage errors (MPE [%]) allows for comparing data covering different orders of magnitude. Cases where cross-validation measures show smaller ranges than empiric measurements indicate a good quality of spatial estimation. To account for this, the corrected mean percentage error (MPEc) can be computed by multiplying MPE with the ratio of the empirical and estimated ranges. Additionally, Olea [40] suggests the correlation coefficient r (Pearson) between estimated and empiric measurements which ideally equal 1.

The geostatistical analyses and modelling were computed by ESRI ArcGIS 10.2‚ Geostatistical Analyst. For ensuring the reproducibility of spatial estimation, the primary data and all statistical measures explained above were documented and archived [62].

Results

Quality control of measurement

The limits of quantitative detection of elements in moss specimens (Table 1) are within the ranges of the reference materials indicated in Tables 2, 3, 4. Similar results are shown in the quality control for Al, As, Cu, Fe, Ni, Sb, V and Zn (Additional file 1: Tables S1–S3).
Table 1

Limits of quantification (LQ) for the elements determined

 

Cd

Cr

Pb

Hg

N

LQa

0.05

0.05

1.00

0.020

0.01

aIn mg/kg (HM) and mass-% (N)

Table 2

Statistical values for determination of nitrogen in reference materials during the measurement period

 

References

Measurements

RR (%)

MV ± SD

MV ± SD

RSD (%)

Carbamide

46.62 ± 2.33

47.05 ± 0.81

1.7

100.9

Glutamic acid

9.52 ± 0.95

9.58 ± 0.18

1.9

100.7

TBK6

0.156 ± 0.01

0.16 ± 0.01

5.8

103.5

MV ± SD mean value ± standard deviation (in mg/kg), RSD relative standard deviation, RR recovery rate, related to the reference, TBK6 Kinesin-like polypeptides 6

Table 3

Statistical values for determinations of heavy metals in reference materials during the measurement period

 

References

Measurements

RR (%)

MV ± SD

MV ± SD

RSD (%)

Cd

0.04 ± 0.004

0.040 ± 0.001

1.7

100.7

Cr

0.04 ± 0.004

0.041 ± 0.001

2.3

101.8

Pb

0.04 ± 0.004

0.041 ± 0.001

1.9

101.3

Hg

2 ± 0.2

2.033 ± 0.054

2.7

101.7

MV ± SD mean value ± standard deviation (in mg/L, µg/L for Hg), RSD relative standard deviation, RR recovery rate, related to the reference

Table 4

Statistical values of the moss reference materials M2 and M3

 

M2

M2a

RR (%)

M3

M3a

RR (%)

Reference

MV ± SD

RSD (%)

Reference

MV ± SD

RSD (%)

Cd

0.454 ± 0.019

0.44 ± 0.01

1.8

97.2

0.106 ± 0.005

0.101 ± 0.01

5.6

95.6

Cr

0.97 ± 0.17

0.89 ± 0.07

7.6

92.2

0.67 ± 0.19

0.58 ± 0.06

11.3

86.0

Pb

6.37 ± 0.43

6.54 ± 0.2

3.6

102.6

3.33 ± 0.25

3.41 ± 0.09

2.7

102.3

Hg

0.058 ± 0.005

0.057 ± 0.001

1.8

99.0

0.035 ± 0.004

0.037 ± 0.006

16.0

105.7

MV ± SD mean value ± standard deviation (in mg/L, µg/L for Hg), RSD relative standard deviation, RR recovery rate, related to the reference

aMeasurements

The element concentrations for M2 and M3 measured in this research did not differ significantly from the respective reference values (RR% < 10%), with the exception of Cr (14%, M3). This means that the recovery rate mainly was above 90% (Table 4). All measurements of elements concentrations in M2 and M3 with the exception of Zn (M2) were within the ranges published by Harmens et al. [12] and Steinnes et al. [66] (Table 4).

Geostatistically analysed, modelled and mapped spatial structures of element concentrations in moss 1990–2015

The presentation of results of geostatistical analysis, modelling and mapping explained in “Descriptive statistics” section focus on those elements relevant for CLRTAP, i.e. Cd, Hg, Pb and N and on Cr representing those three elements which did not show a continuous decrease between 1990 and 2015 but an intermediate increase from 2000 to 2005 (Cr, Sb, Zn). Thereby, the results derived from the moss sampling 2015 are compared to those from previous campaigns. In addition, geostatistical surface estimations of Al, As, Cu, Fe, Ni, Sb, V and Zn concentrations in moss and respective statistical values are provided in the supplement (Additional file 1: Figures S1–S4; Tables S4 and S5).

Cadmium

The GMS 2015 yielded 398 Cd measurement values which were analysed by application of statistical methods explained in “Methods” section. With values between 0.035 and 1.760 mg/kg Plesch shows lower 20th, 50th, 90th and 98th percentiles than Hypcup and Psepur [62]. Regarding the federal states, Cd concentrations in North Rhine-Westphalia show the highest median value (0.189 mg/kg). The Cd median of the federal states Hesse, Mecklenburg-Western Pomerania, Saarland, Saxony and Thuringia exceeds the Germany-wide median (= 0.136 mg/kg Cd). The lowest Cd median below the 20th percentile (= 0.0944 mg/kg) was found in Hamburg (= 0.089 mg/kg) [62].

Due to skewness (Sk = 6.28), Cd measurement values 2015 were transformed (Box Cox) before spatial generalisation by Universal Kriging. The spherical variogram model shows a low, but significant autocorrelation with a range of 223 km and a nugget-to-sill ratio of 0.66. Due to the high nugget effect, there is a considerable smoothing of the estimated values’ map. The results of cross-validation indicate a rather unbiased spatial estimation (MSE = − 0.07; RMSSE = 1.2) with low correlations between measurements and estimations (rp = 0.22). The MPEc accounts for 12.55%. Figure 2 depicts increased Cd concentrations from North Rhine-Westphalia to Saxony. Further the maps show the trends of Cd accumulation in moss specimens collected between 1990 and 2015. During this period, the Germany-wide median of Cd concentrations decreased by 52.5%. Between 1990 and 1995 the median Cd accumulation in moss increased by + 2.1%, and from 1995 to 2005 it decreased by − 28.3%. No changes of the median Cd values throughout Germany could be measured between 2000 and 2005, while from 2005 to 2015 the median Cd concentrations in moss decreased significantly by − 35.2%. Above-average decreased values were found between 2005 and 2015 in Baden-Wuerttemberg (− 35.8%), North Rhine-Westphalia (− 40%), Rhineland Palatinate (− 57.3%), Schleswig-Holstein (− 41.3%) and Saxony-Anhalt (− 37.4%). No increase of median Cd concentrations could be detected during the years 2005 to 2015 [38].
Fig. 2
Fig. 2

Spatial distribution of sample point-specific measured and geostatistically estimated Cd concentrations in moss 1990–2015

Chromium

The moss survey 2015 yielded Cr measurements from 399 sites with values ranging between 0.051 and 4.951 mg/kg. Hypcup exhibited higher 20th, 50th, 90th and 98th percentile values than Plesch and Psepur. Accordingly, the highest value in Germany (4.951 mg/kg) was found in a Hypcup sample in Baden-Wuerttemberg (BW450). Spatial clusters of values in this magnitude can be found in North Rhine-Westphalia (Ruhr Region), along the upper Rhine Valley (Baden-Wuerttemberg), in north and central Hesse, in Saxony-Anhalt and Saxony as well as in Bavaria, Lower Saxony and Saarland. The values in Brandenburg, Hesse, Hamburg, Lower Saxony, North Rhine-Westphalia, Rhineland Palatinate and Saarland are above the Germany-wide Cr median value (= 0.57 mg/kg). The lowest median Cr concentration was found in Mecklenburg-Western Pomerania (= 0.34 mg/kg).

The geostatistical surface estimation of Cr concentrations was performed by application of Ordinary Kriging. Due to skewness (Sk = 3.17), the data were Box-Cox-transformed. An exponential model variogram was fitted to the empirical variogram indicating a low but significant (p < 0.01) spatial autocorrelation with a range of 104 km and a nugget/sill ratio of 0.67. The cross-validation proved a low bias (MSE = − 0.01; RMSSE = 0.96), and the MPEc accounted for 20.38%.

Figure 3 presents the temporal and spatial development of Cr accumulation from 1990 to 2015. The maps corroborate a comprehensive decrease of Cr concentrations throughout Germany. From 2000 to 2005, the values increased remarkably.
Fig. 3
Fig. 3

Spatial distribution of sample point-specific measured and geostatistically estimated Cr concentrations in moss 1990–2015

From 2000 to 2005, the Cr concentrations in moss increased. Hot spots can be found in the Ruhr Region (North Rhine-Westphalia), in the North-West of Lower Saxony, Saxony, Saxony-Anhalt and in Mecklenburg-Western Pomerania. From 2005 to 2015, the Cr accumulation in moss specimens decreased clearly.

The significant decrease of Cr median values between 1990 and 2000 accounted for 58.5% throughout Germany. However, from 2000 to 2005 the German Cr median increased by + 159.3% (p > 0.05). The highest rise was found in Mecklenburg-Western Pomerania (+ 754.5%). From 2005 and 2015, the reduction was 75.8% and between 1990 and 2015 it accounted for 74% [38].

Mercury

In 2015, 397 Hg measurements ranged between 0.0047 mg/kg and 0.1960 mg/kg. Regarding the 20th, 50th, 90th and 98th percentile values, there are no striking differences between the moss species.

Since the Hg measurements were skewed (Sk = 3.33), they were Box-Cox-transformed, then interpolated by means of Ordinary Kriging, and finally back transformed. For the spherical autocorrelation function fitted to the experimental variogram autocorrelation is weak but could be proved to be statistically significant with a sill of 67 km and a nugget-to-sill ratio of 0.63. Cross-validation indicates a rather low bias (MSE = − 0.02; RMSSE = 1.37) with correlations of measurements and estimated values (rp = 0.33). The mean relative corrected deviance between empiric measurements and geostatistically estimated values is low (MPEc = 5.21%).

The Kriging maps in Fig. 4 depict the spatial and temporal trends of Hg bioaccumulation in moss due to atmospheric deposition between 1995 and 2015. The values are low in terms of the European classification of measurement values to be applied. From 1995 to 2000, slight increases can be proved in the Eastern part of Schleswig-Holstein, in the South of Saxony-Anhalt, Northern Thuringia, and in Baden-Wuerttemberg. Large-area Hg bioaccumulation occurred in North Rhine-Westphalia. During 2000 to 2005, further decline of Hg concentrations in moss occurs throughout Germany. From 2005 to 2015, the Hg accumulation decreased in regions located in Bavaria, North Rhine-Westphalia, Saxony, Saxony-Anhalt, Mecklenburg-Western Pomerania. Contrary to that trend, increases of Hg concentrations could be localised in Lower Saxony. During 1990 to 2015, the Rhine valley in Baden-Wuerttemberg, Northern Thuringia, in the Erz Mountains (Saxony) and in Mecklenburg-Western Pomerania are regions with continuous enhanced values.
Fig. 4
Fig. 4

Spatial distribution of sample point-specific measured and geostatistically estimated Hg concentrations in moss 1995–2015

In 2015, the 98th percentile of Hg concentrations in moss amounted to 0.0702 mg/kg, the 90th percentile to 0.054 mg/kg, and the median throughout Germany was 0.0446 mg/kg). The medians measured from 1995 to 2000 increased significantly in Baden-Wuerttemberg, Rhineland Palatinate, Saxony-Anhalt and Thuringia. Across Germany, no such statistically significant trend could be corroborated. From 2000 to 2005, the median of Hg measurements sank in almost all federal states, and the median decreased by 14.6%, and during 2005 to 2015 the median values of Hg concentrations in moss accounted for another 4% (p < 0.05). Since 2005, the trends of Hg bioaccumulation were as follows: Increases could be proved for Brandenburg (+ 27.8%) and Lower Saxony, (+ 14.8%), while reductions were measured in Bavaria, (− 18.2%), North Rhine-Westphalia (− 25.8%), Rhineland Palatinate (− 5.7%) and Saxony (− 13.7%). The trend between 1995 and 2015 is characterised by a significant reduction throughout Germany by 20%, while spatial differences in terms of federal states range between − 51.2% (Bavaria) and + 17.4% (Brandenburg). Hesse, Hamburg, Lower Saxony, Rhineland Palatinate, Schleswig-Holstein, Saarland, Saxony-Anhalt and Thuringia did not show any significant decline since 1995 [38].

Lead

The GMS 2015 yielded Pb concentrations in moss collected at 400 sites across Germany ranging from 0.47 mg/kg to 19.34 mg/kg. In Hypcup, the 20th, 50th and 90th percentile values exceeded the values found in Psepur and Plesch. The 98th percentile accounted for 9.094 mg/kg and the 90th percentile for 4.334 mg/kg. Baden-Wuerttemberg, Hamburg, North Rhine-Westphalia, Schleswig-Holstein, Saarland and Saxony exceed the Germany-wide median (1.830 mg/kg). Lowest medians of measured Pb concentrations were found in Bavaria, Mecklenburg-Western Pomerania and Saxony-Anhalt. The Germany-wide trend of median Pb bioaccumulation exhibits a continuous decrease throughout time (1990–2015). A similar tendency could be found for most of the federal states. It was only Hesse (2000–2005: + 8%) and Hamburg (1990–1995: + 12.3%, and 2005–2015: + 20.4%), whereby these trends were not statistically significant. The decrease of Pb concentrations in moss trends collected in the federal states during 1990–2015 was statistically significant with the exception of Hamburg (n = 3) and ranged between − 81.3% (Schleswig-Holstein) and − 92.1% (Saxony) [38].

The spatial estimation and surface covering mapping of Pb bioaccumulation were calculated by Ordinary Kriging from Box-Cox-transformed data. The original measurement data were clearly skewed (Sk = 4.42). The exponential model variogram indicated a low but significant spatial autocorrelation with a range amounting to 202 km and a nugget-to-sill ratio of 0.70. The MSE (= − 0.04) proved a rather unbiased estimation and the RMSSE (= 1.24) points out an overestimation. The correlation between measurements and estimations was rp = 0.28 and the MPEc was 23.9%.

The Kriging map for the GMS 2015 (Fig. 5) is covered with estimated Pb concentrations < 5 mg/kg. Within this range, regions with estimation > 2.62 mg/kg are located in Hamburg and around (Schleswig-Holstein), in Lower Saxony (Harz mountains), in Saxony, in the Rhine valley (Baden-Wuerttemberg) and in North Rhine-Westphalia.
Fig. 5
Fig. 5

Spatial distribution of sample point-specific measured and geostatistically estimated Pb concentrations in moss 1990–2015

Figure 5 also depicts the spatial and temporal development of Pb concentrations from 1990 to 2015. The maps prove a Germany-wide continuous decrease of Pb bioaccumulation. The most distinct decline was proved for North Rhine-Westphalia, Brandenburg (Southern regions) and Saxony (Lausitz and Erz Mountains).

Nitrogen

From the measured concentrations of N in 400 moss specimens, ranging between 0.80 and 3.49%, a median value of 1.431% was computed. Regarding all descriptive statistical measures computed, Psepur evidenced the highest values. The highest N concentration was found in Mecklenburg-Western Pomerania. N measurements exceeding the 90th percentile (= 2.131%) could only be found in Mecklenburg-Western Pomerania, Schleswig-Holstein, Lower Saxony, North Rhine-Westphalia and Hesse. Ranking the German federal states by N concentrations accumulated in moss, Mecklenburg-Western Pomerania shows the highest median value (= 2.370%), followed by North Rhine-Westphalia, Lower Saxony, Hesse, Thuringia, Schleswig-Holstein and Saxony, all exceeding the Germany-wide median N concentration in moss. The lowest median N values were found for Hamburg and Saarland (1.190% and 1.115%, respectively).

The geostatistical estimation of surface covering N concentration in moss was performed by using second-order Universal Kriging of log-transformed measurement values. The spherical model variogram fitted to the experimental one corroborates a slight but significant spatial autocorrelation with a range of 117 km and a nugget-to-sill ratio of 0.67. However, the estimation is nearly unbiased (MSE = − 0.03; RMSSE = 0.97) with low differences between measured and estimated values (MPEc = 2.96%) which are correlated with rp = 0.57.

Within the spatial patterns depicted in Fig. 6, the highest surface estimations with values > 2.4% cover most of the territory of Mecklenburg-Western Pomerania. Estimations between 2.2 and 2.4% can be observed in Lower Saxony and North Rhine-Westphalia, especially in the western regions close to the border between both German federal states and the Netherlands. N concentrations > 1.6% cover wide areas of Schleswig-Holstein, Hesse, Thuringia and Saxony. N concentrations < 1.0% occur dominantly in the Alps.
Fig. 6
Fig. 6

Spatial distribution of sample point-specific measured and geostatistically estimated N concentrations in moss 2005–2015

The Kriging maps for 2005 and 2015 shown in Fig. 6 indicate that the N bioaccumulation did not change significantly during the last 10 years. However, this Germany-wide statistical statement includes regional decreases in Bavaria and Baden-Wuerttemberg and increases in Lower Saxony, Hamburg and in Mecklenburg-Western Pomerania. Permanent N hot spots existed between 2005 and 2015 in North Rhine-Westphalia and Mecklenburg-Western Pomerania. Inference statistical tests corroborate that the N concentrations did not change significant between 2005 and 2015 neither Germany-wide nor in most of the federal states. Significant increases were proved for Hamburg (− 33.1%), Mecklenburg-Western Pomerania (− 30.4%) and Bavaria (− 13.9%). Significant decreases of N concentrations in moss were determined for Baden-Wuerttemberg (− 13.9%).

Discussion

To evaluate the results presented, the discussion includes not only the elements presented in this article (Cd, Cr, Hg, Pb; N) but also some of those which could not be tackled. The spatial patterns of As, Cd, Ni, Pb, Sb and Zn detected in the GMS 2015 were rather similar to those mapped for the GMS 1995, 2000 and 2005. This similarity was quantified by means of Pearson correlation between the respective Kriging maps which accounted in case of As, Ni and Sb for rP > 0.4 (exceptions: As 2000, Sb 1995) and in case of Cd, Pb and Zn for rp > 0.6. Continuous hot spots existed in the industrialised regions of North Rhine-Westphalia (mainly Cd, Cr, Cu, Fe, Hg, Pb, Sb, V and Zn), in the Rhine-Main Region (primarily Cd, Cr, Cu and Zn), in the industrialised region Halle/Leipzig (especially Cd, Cu, Fe, V and Zn), in Saarland (most notably Al, Cd, Cr, Fe, Pb, V and Zn), in Saxony (particularly As, Cd, Fe, Hg, Pb and Sb), in Mecklenburg-Western Pomerania (first of all u, Fe, Hg and Zn), in the Harz Mountains (above all Cd, Pb and Zn), in the Black forest (in the first instance Pb, Sb), in the upper Rhine valley (mainly Al, As, Cr, Fe and Hg) and in Berlin (primarily Cr and Zn).

The development of bioaccumulation of heavy metals since 2005 is characterised by a Germany-wide significant decrease (p < 0.05). This general trend varies by element and region in terms of federal state. The range of decrease was between − 4% (Hg) to − 75.8% (Cr). Similar was the trend from the first measurement and 2015: The median values of heavy metals decreased significantly since 1990 and of Al, Hg and Sb since 1995. The most distinct decrease was determined for Pb (− 85.9%), the lowest for Hg (− 20%). These trends of bioaccumulation are in agreement with the heavy metal emissions in Germany between 1990 and 2015 [30], especially in case of As, Cd, Ni and Pb. The concentrations of these four heavy metals in moss collected in 2005 on the one hand and modelled atmospheric deposition on the other hand were correlated with rS > 0.3 [38, 50, 51]. According to NaSE [30] emissions from metallurgy (Cd, Ni and Pb), power economy (As, Cd, Ni and Pb), manufacturing and constructing industry (As, Ni and Pb) and traffic (Pb) declined since 1990. However, the decrease of Hg bioaccumulation is less than the reduction of Hg emissions [30]. This is possibly due to long-range transport of gaseous Hg and atmospheric residence times of 6 to 18 months [52]. The concentrations of Cu and Zn in moss contradict the emission trends. At least for Zn, the correlation between the concentration in moss ample in 2005 and the modelled atmospheric deposition is rs < 0.3 [38].

For Cr, good agreement could be identified between the emission trends [30] and the concentrations in moss during the period 1990 to 2015. Strikingly, the Cr concentrations in moss were extraordinarily high in 2005, especially in Mecklenburg-Western Pomerania and conurbations such as Bremen, Hamburg, Dresden, Halle/Leipzig and the Ruhr region. Respective increased values were also reported from Austria and were attributed to a Cr mine on the Kola Peninsula [23, 38].

The spatial pattern of N concentrations in moss specimens collected in 2015 is in a more distinctive agreement than those determined in 2005 with what could be expected from the spatial patterns of potential emission sources: Regions with high spatial livestock density as for instance the Northwestern part of Lower Saxony and North Rhine-Westphalia show high N bioaccumulation and corroborate other investigations [2628, 29, 54]. The N emissions between 2005 and 2015 declined by 23.9% in case of NOx emissions. In 2015, agricultural land use emitted 95% of the NH3 in Germany which increased between 2005 and 2015 by 12% [31]. In conclusion, this should be evidenced by quantitatively correlating the spatial livestock density with the Kriging maps. Further, it should be investigated whether the replacement of Plesch through the more nitrophilous Psepur could be a relevant influence.

Conclusions

The maps given in Figs. 2, 3, 4, 5, 6 are based on an international classification [16] which does not allow detailing much regional variance due to decreasing element concentrations in moss. Therefore, element- and campaign-specific percentile statistics should be computed ensuring to map the still existing spatial variance of element concentrations statistically sound. Based on this, heavy metals integrating Multi Metal Index should be computed and mapped according to Pesch and Schröder [42, 43, 44] and Schröder and Pesch [5660] allowing to comprehend the many data collected from 1990 to 2015, to integrate several elements in one map depicting their spatial patterns according to their percentile statistics and not according to the international classification [16].

Footnotes
1

United Nations Economic Commission for Europe.

 

Abbreviations

AAS: 

atomic absorption spectroscopy

Al: 

aluminium

As: 

arsenic

BB: 

Brandenburg

BE: 

Berlin

BW: 

Baden-Wuerttemberg

BY: 

Bavaria

Cd: 

cadmium

CLRTAP: 

convention on long-range transboundary air pollution

Cr: 

chromium

Cu: 

copper

CV: 

coefficient of variation

EMEP: 

European Monitoring and Evaluation Programme

EMS: 

European moss survey

Fe: 

iron

GMS: 

German moss surveys

HE: 

Hesse

Hg: 

mercury

HH: 

Hamburg

HM: 

heavy metals

Hypcup: 

Hypnum cupressiforme

ICP: 

International Cooperative Programme

ICP-MS: 

inductively coupled plasma–mass spectrometry

ISO: 

International Organization for Standardization

LQ: 

limit of quantification

M2, M3: 

moss reference materials

ME: 

mean error

MPE: 

median of percentage errors

MPEc: 

corrected mean percentage error

MSE: 

mean standardized error

MV: 

Mecklenburg-Western Pomerania

MV: 

mean value

N: 

nitrogen

n: 

sample size

NH3

ammonia

NI: 

lower Saxony

Ni: 

nickel

NOx

nitrogen oxide

NW: 

North Rhine-Westphalia

p: 

level of significance

Pb: 

lead

Plesch: 

Pleurozium schreberi

POP: 

persistent organic pollutants

Psepur: 

Pseudoscleropodium purum

QC: 

quality control

RMSSE: 

root mean square standardised error

RP: 

Rhineland Palatinate

r p

correlation coefficient (Pearson)

RR: 

recovery rate

r s

correlation coefficient (Spearman)

RSD: 

relative standard deviation

Sb: 

antimony

SD: 

standard deviation

SH: 

Schleswig-Holstein

Sk: 

skewness

SL: 

Saarland

SN: 

saxony

ST: 

Saxony-Anhalt

TBK6: 

kinesin-like polypeptides 6

TH: 

Thuringia

UNECE: 

United Nations Economic Commission for Europe

V: 

vanadium

Zn: 

zinc

Declarations

Acknowledgements

This research paper was only possible through the help and support of the Federal Environmental Agency, Dessau-Roßlau, Germany.

Funding

Federal Environmental Agency, Dessau-Roßlau, Germany.

Authors’ contributions

WS headed the computations executed by SN. WS wrote the article. All authors read and approved the final manuscript.

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors’ Affiliations

(1)
Chair of Landscape Ecology, University of Vechta, P.O.B. 1553, 49364 Vechta, Germany

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