**Atmospheric Deposition and Element Accumulation in Moss Sampled across Germany 1990–2015: Trends and Relevance for Ecological Integrity and Human Health**

**Angela Schlutow <sup>1</sup> , Winfried Schröder 2,\* and Stefan Nickel <sup>3</sup>**


**Abstract:** Deposition of N and heavy metals can impact ecological and human health. This stateof-the-art review addresses spatial and temporal trends of atmospheric deposition as monitored by element accumulation in moss and compares heavy metals Critical Loads for protecting human health and ecosystem's integrity with modelled deposition. The element accumulation due to deposition was measured at up to 1026 sites collected across Germany 1990–2015. The deposition data were derived from chemical transport modelling and evaluated with regard to Critical Loads published in relevant legal regulations. The moss data indicate declining nitrogen and HM deposition. Ecosystem and human health Critical Loads for As, Ni, Zn, and Cr were not exceeded in Germany 2009–2011. Respective Critical Loads were exceeded by Hg and Pb inputs, especially in the low rainfall regions with forest coverage. The Critical Load for Cu was exceeded by atmospheric deposition in 2010 in two regions. Human health Critical Loads for Cd were not exceeded by atmospheric deposition in 2010. However, the maximum deposition in 2010 exceeded the lowest human health Critical Load. This impact assessment was based only on deposition but not on inputs from other sources such as fertilizers. Therefore, the assessment should be expanded with regard to other HM sources and specified for different ecosystem types.

**Keywords:** bioaccumulation; biomonitoring; critical loads; deposition forests; chemical transport modelling; geographic information system; heavy metals; mapping

#### **1. Introduction**

Emissions of elements from natural and anthropogenic sources come down to earth as wet, occult [1,2], or dry deposition at locations distant from their origin where they accumulate in biomass and soils [3]. The geographical pattern of element deposition and accumulation is influenced by chemical and physical element characteristics, meteorological and topographical conditions, land use, and vegetation structure. Potential impacts on human health and ecosystems integrity through heavy metal (HM) accumulation in food chains, and acidification and eutrophication of soils and limnic ecosystems [4–6] are intended to be avoided through the Convention on Long-Range Transboundary Air Pollution [7,8] addressing Cd, Pb, and Hg, as well as N and S. The European Monitoring and Evaluation Programme (EMEP) is to collate emission data, to collect atmospheric deposition Europe-wide by technical devices, and to calculate and map atmospheric deposition by chemical transport models such as the Long Term Ozone Simulation—EURopean Operational Smog model (LOTOS-EUROS) and EMEP [9–21]). This monitoring and modelling data can be validated and complemented by monitoring the bioaccumulation of elements in moss [22–24]. In Europe, HM (since 1990), N (since 2005), and persistent organic pollutants (POP; since 2010) were determined in moss specimens sampled in a rather dense

**Citation:** Schlutow, A.; Schröder, W.; Nickel, S. Atmospheric Deposition and Element Accumulation in Moss Sampled across Germany 1990–2015: Trends and Relevance for Ecological Integrity and Human Health. *Atmosphere* **2021**, *12*, 193. https:// doi.org/10.3390/atmos12020193

Academic Editor: Daniele Contini Received: 14 January 2021 Accepted: 26 January 2021 Published: 31 January 2021

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**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

spatial pattern. Since 2000, this European Moss Survey (EMS) is part of the International Cooperative Programme on Effects of Air Pollution on Natural Vegetation and Crops (ICP Vegetation). Since 1990, the EMS was conducted every five years and covered up to 7300 sampling sites in up to 36 European countries enabling to map spatial patterns of bioaccumulation and to derive deposition estimates by regression modelling [25–29].

High concentrations of atmospheric pollutants can result in exceeding Critical Levels of atmospheric concentrations and Critical Loads (CL) of atmospheric deposition. CL are defined as quantitative estimates of exposure to one or more pollutants deposited from air to the ground below which significant harmful effects on specified sensitive elements of the environment do not occur according to present knowledge. Critical levels are defined as concentrations of pollutants in the atmosphere above which direct adverse effects on receptors, such as human beings, plants, ecosystems, or materials, may occur according to present knowledge [7,8].

The chemical elements regarded in this investigation are given in Figure 1. The data on element concentrations in moss collected in Germany were analysed by a broad range of statistical methods focusing among others on following five key issues:


**gure 1.** Sampling sites and elements regarding the German Moss Surveys 1990, 1995, 2000, 2005, and 2015. SH = **Figure 1.** Sampling sites and elements regarding the German Moss Surveys 1990, 1995, 2000, 2005, and 2015. SH = Schleswig-Holstein; MV = Mecklenburg-West 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 article aims at presenting the current state of knowledge on atmospheric HM deposition and bioaccumulation and the assessment of its relevance for ecosystem integrity and human health in Germany. Both these aspects of the relevance of HM input are interlinked. For example, excessive pollution of arable and grassland ecosystems contributes to the exposure on humans via the food chain. Damage to forest ecosystems reduces their recreational effect on humans. The article concentrates on two of the five key issues investigated in the framework of the German moss surveys: 1. Summarising EMS data collected from 1990 to 2015 across Germany by percentile statistics and calculation of elements and surveys integrating index scores 2. Reporting on the latest assessment of atmospheric heavy metal deposition with regard to ecological integrity and human health in Germany.

#### **2. Materials and Methods**

#### *2.1. Bioaccumulation of Atmospheric Deposition of HM in Moss*

Sampling and chemical analysis of moss specimens as well as classification and mapping of element concentrations determined follow a harmonised methodology (for EMS 2015 refer to ICP Vegetation [27]). Between 1990 and 2015, the number of moss sampling sites in Europe ranged between 4499 and 7312 in 20–36 countries. In the German Moss Survey, moss specimens were collected at 592 (1990 [30]), 1026 (1995 [31,32], 1028 (2000 [28]), 726 (2005 [33]), and 400 (2015 [34]) sites in forested areas. The reduction of sampling sites was performed according a statistically sound methodology [33,35]. Germany did not take part in the EMS 2010.

The international classification of element concentrations in moss [27] is too coarse to display the spatial variance of decreasing element concentrations. Additionally, the extensive data on up to 40 metal elements collected between 1990 and 2015 every five years at up to 1028 sites across Germany were summarised as far as possible in terms of a multi-metal index (MMI). Thereby, mapping of spatial and temporal trends was preserved. To this end, the element-specific data on HM accumulation was divided into 10 percentiles, which were then transformed into MMI score values ranging from 1 to 10.

The statistical analyses presented in this review regard HM concentrations that were measured in moss specimens collected in Germany 1990–2015. The following percentile statistics were calculated and mapped for spatial point data as well as for geostatistical surface estimations [23]:


The results for Element-specific quantiles integrating all surveys 1990–2015 (b) and MMI90-2015 and MMI95-2015 scores are presented in Section 3.1.

#### *2.2. Assessing Impacts of Atmospheric Deposition*

#### 2.2.1. Assessment Values

The second aim of this contribution was to assess potential HM deposition effects on ecosystems and human health on the basis of legal requirements and environmental quality objectives. As HM can be transported through the atmosphere over long distances and across national borders, both national and international regulations and assessment methods were considered thereby. The regulations and recommendations compiled in Tables 1 and 2 contain different categories of assessment values, which differ with regard to their protective purpose, the respective level of protection, and protective objective. For this reason, this study uses the overarching term "assessment value" but takes over the nomenclature of quotations from the rules and regulations. In addition, a distinction is made between precautionary assessment values and those which serve to avert danger. Precautionary assessment values indicate limits of resilience (concentrations in environmental compartments or substance flows) below which there is no concern of significant impairment of ecosystems and their functions and services to humans. They apply generally, i.e., beyond the sphere of influence of concrete facilities, projects, or management measures, and they are independent of usage claims. In law, the concept of danger is always linked to a certain probability of the occurrence of significant, harmful changes. In principle, assessment values that serve to avert hazards permit higher pollutant concentrations or inputs than precautionary ones. As a rule, they serve to assess concrete (including planned) facilities, projects, or management measures and are derived from specific uses (e.g., test values and measure values in soil protection). Table 1 compiles the assessment values used in this study to compare them with CL. Due to the methodological differences in their derivation, they are only comparable to each other to a limited extent and with CL. The differences, some of which are clear, are due to different levels of protection, protection objectives, and the relationship between effects (Table 2).


**Table 1.** Assessment values for heavy metal fluxes (g ha−<sup>1</sup> a −1 ) for the protection of ecosystems and human health.

<sup>1</sup> TA Luft [36] = Technical Instructions for Air pollution control (deposition values to protect human health). <sup>2</sup> TA Luft [35] = Technical Instructions for Air pollution control (deposition values as reference points for the special case examination to protect environment). <sup>3</sup> BBodSchV [37] = Federal Soil Protection Ordinance (permissible additional load according to §11 para. 2). <sup>4</sup> 39th BImSchV [38] = 39th Federal Immission Control Ordinance. <sup>5</sup> Converted from assessment values for concentrations (Tables 33 and Table 34 in [17]) published in Directive 2004/107/EC [39] and Directive 2008/50/EC [40]. <sup>F</sup> For field. <sup>G</sup> For grassland. <sup>C</sup> For coniferous forest. <sup>D</sup> For deciduous forest. <sup>H</sup> For housing settlement.

**Table 2.** Compilation of categories of assessment values from legal, sublegal regulations, and recommendations for air pollution control for heavy metal fluxes for the protection of ecosystems, protected goods, levels and objectives, and impact indicators.


\* It is legally binding that values for the permissible additional load are derived. The values themselves are rather indicative, as there are no concrete prescribed applications. Installations or input values due to managemen<sup>t</sup> are subject to other technical laws. BBodSchV [38] = Federal Soil Protection Ordinance; CLRTAP [7,8] = Convention on Long-range Transboundary Air Pollution; EC10 = ECx is the effect concentration at which x% effect (mortality, inhibition of growth, reproduction, . . . ) is observed compared to the control group; HC5 = Hazardous concentration for 5% of the species; LOEC = Lowest Observed Effect Level; NOEC = No Observed Effect Concentration; PNEC = Predicted No Effect Concentration; TA Luft [36] = Technical Instructions Air.

The protection of human health and ecosystems and their functions against adverse effects from air pollutant deposition is generally ensured if HM inputs are completely avoided. However, this is currently not a realistic assumption. On the basis of empirical evidence, it is assumed that the protection of these objects may be reached if specific critical concentrations or loads of HM in environmental media are not exceeded. Thereby, only with the calculation of CL the balance between inputs and outputs can be proved.

#### 2.2.2. Basics for the Determination of Critical Loads for Heavy Metal Deposition

CL for Cd, Pb, and Hg have already been calculated for the entire EMEP region. They serve as policy advice, in particular to examine and justify whether further emission reductions are necessary. To date, they have not been designed as binding air concentration or deposition values. CL indicate the total input rate below which adverse effects on ecosystems and human health (paths atmosphere-soil-groundwater for drinking water use and atmosphere-soil-food wheat (only for Cd)) can be excluded in the long term according to current knowledge. Consequently, if CLs are complied with, risk minimisation is achieved below the classic danger threshold, which means that the assessment values are very precautionary.

The CL concept focuses on the budgets of substances in ecosystems. Ecosystem specific features (soil, climate, use, etc.) are taken into account when calculating the critical load values. As a result, there is not only one CL, but rather a range of values that allows a comprehensive, regionalised representation of the sensitivity of ecosystems, food crops, and drinking water to HM.

In addition to natural and semi-natural ecosystems, agricultural land is also considered both as ecosystems and as areas where human and eco-toxicological values must be respected. CL aimed at protecting ecosystems are hereinafter referred to as CL(M)eco. CL aiming at protecting human health, e.g., drinking water, are abbreviated CL(M)drink and those aimed at protecting food for humans CL(M)food, where (M) stands for heavy metal and can be replaced by the respective element symbol (Cd, Pb, Hg, . . . ). The determination of CL(M)eco was based exclusively on eco-toxicological threshold values. This means that the CL(M)eco were determined on the basis of effects. Experimentally determined zero effect threshold values (NOEC or PNEC) were used as "critical limits" in the calculation of the CL(M)eco. For the CL(M)drink, internationally agreed critical concentrations were used in drinking water and for CL(Cd)food in food wheat.

For Germany, an assessment of the input rates into ecosystems in the equilibrium of inputs and outputs will be carried out according to the CL concept [34]. Their mapping for Germany is carried out on a scale of 1:1 million and provides an overview of the sensitivity of terrestrial ecosystems to nine HM. Ecosystem integrity and human health are regarded as protection goals.

By definition, CL for HM are the highest total input rate of HM under consideration (from atmospheric deposition, fertilisers, and other anthropogenic sources) below which no long-term adverse effects on human health and on the structure and function of ecosystems are to be expected according to the current state of knowledge [7,8]. CL are calculated according to the mass balance approach assuming a chemical equilibrium in the system under consideration and a steady state at a concentration level defined by the critical limit. This is an impact-based derived limit concentration in certain ecosystem compartments below which significant adverse effects on human health as well as on defined sensitive components of ecosystems can be excluded according to the current state of knowledge.

Cd has been identified as an important pollutant in relation to the maintenance of food quality for the protection of human health. With this metal, uptake from the soil into the vegetation is comparatively high, so that accumulations in the soil entail the potential danger of health effects via plant food. Wheat was selected as the indicator plant. Wheat grain accounts for a significant proportion of food in Germany (as in Europe) and its cultivation accounts for a large proportion of agricultural land in Germany (and other European countries) [41]. CLs for the protection of drinking water are mapped for all ecosystem types. CL were therefore determined for three objects of protection:


#### 2.2.3. Calculation of Critical Loads for Heavy Metals in Germany

The methodological approach for the calculation of CL for HM in this study follows [7,8] (Chapter V.5). All relevant fluxes into or from a certain soil layer, in which the essential substance conversions occur or in which the receptors have their distribution focus and which is therefore relevant for the effects in the system, were compared. The consideration of HM fluxes, reserves, and concentrations refers to mobile or potentially mobilizable metals, only they are relevant for the consideration of substance fluxes.

The mass balance equation includes as output paths from the terrestrial ecosystem the uptake into the biomass with subsequent harvest and the output with the leachate flow as follows:

CL(M) = M\_<sup>u</sup> + M\_le(crit) where:

CL(M) = Critical Load of the metal M (g ha−<sup>1</sup> a −1 )

M<sup>u</sup> = Net uptake of the metal M into harvestable plant parts (g ha−<sup>1</sup> a −1 ).

Mle(crit) = Tolerable (critical) leaching of the metal M from the considered soil layer with exclusive consideration of vertical rivers (leachate) (g ha−<sup>1</sup> a −1 ).

The inclusion of further terms was in accordance with the recommendations of the Expert Panel for HM to the ICP Modelling & Mapping [41,42]. For the CL calculation, the necessary data were spatially linked with GIS software ArcView, 10.2.1 and transferred to an Access 2000 database. Both original data such as precipitation and derived data such as values for the organic matter content (OM) and pH values derived from the land use-differentiated soil overview map of Germany, 1:1,000,000 BÜK1000N were used. The storage, evaluation, and presentation of the data were performed in polygons, which result from the intersection of the input data.

Identifying the geographical distribution and magnitude of total (the sum of wet and dry) deposition of atmospheric pollutants is crucial to determine the areas, populations, ecosystems, and farmlands that are most vulnerable to its negative effects and that would most benefit from measures to control excessive pollutant loads. To this end, the relevance of HM deposition for ecological integrity and human health in terms of CL were compared to the respective atmospheric deposition modelled with the chemical transport model LOTOS-EUROS. CL and CL exceedances by modelled atmospheric deposition were mapped for Germany on a scale of 1:1 Mio.

The deposition dataset [18] contains information on the concentrations and deposition fluxes of various HM (As, Cd, Cr, Cu, Ni, Pb, V, Zn) for the years 2009, 2010, and 2011. The deposition data sets (dry and wet) were combined to the total deposition, converted into the unit of measure of the critical load [g ha−<sup>1</sup> a −1 ] and blended with the critical load receptor areas. The CL exceedance rates were calculated as follows:

MinExcCL(M)eco = (MinMdep + MinMfertilizer)-CL(M)eco and MaxExcCL(M)eco = (MaxMdep + MaxMfertilizer)-CL(M)eco

where:

MinExcCL(M)eco = Minimum ecosystem critical loads exceedance in the German receptor areas due to total deposition from the air and fertiliser inputs

MaxExcCL(M)eco = Maximum ecosystem critical loads exceedance in the German receptor areas due to total deposition from the air and fertiliser inputs

MinMdep = Minimum of the total deposition from the air in the German receptor surfaces, corresponds to the highest minimum of the three years 2009–2011

MaxMdep = Maximum of total deposition from the air in the German receptor surfaces corresponds to the highest maximum of the three years 2009–2011

MinMfertilizer = Minimum of the metal inputs with the fertilization in the German receptor surfaces

MaxMfertilizer = Maximum of the metal inputs with the fertilization in the German receptor areas

CL(M)eco = Median of the Ecosystem Critical Loads for the metal in the German receptor surfaces

#### 2.2.4. Modelling Heavy Metal Deposition

The deposition dataset was produced by use of the chemical transport model LOTOS EUROS [18] and contains information on the atmospheric concentrations and deposition of various HM (Cd, Pb, Ni, As, Zn, Cu, V, and Cr) for the years 2009, 2010, and 2011. The data include information on the centroid coordinates of the degree cells with the deposition data. The modelled data for Cd and Pb were provided in the 0.125◦ × 0.0625◦ network of the Long/Lat system and the data for the other HM in the 0.5◦ × 0.25◦ resolution. The methodology of the deposition calculation is explained by Schaap et al. [18], where the different scales of the deposition data were also justified. The available point data (centroids of the grid cells and intersection points of the grid cells) were assigned to the planar degree grid, which has the corresponding mesh size of the respective resolution. This step was necessary to compare the deposition data with the critical load data set.

The datasets on dry and wet atmospheric deposition were combined to the total deposition, converted into the unit of measure of the CL (g ha−<sup>1</sup> a −1 ) and blended with the critical load receptor areas covering Germany. After a detailed examination of all years (2009, 2010, and 2011) for all heavy metals, the data for 2010 turned out to be the highest. For this reason, the full-year presentation is limited to 2010.

#### **3. Results and Discussion**

#### *3.1. Trends of HM Bioaccumulation Integrating Metal Elements and Surveys 1990–2015*

The surveys integrating metal concentrations classified by use of percentile statistics (Section 2.1.) were transformed to scores ranging from 1 to 10 and aggregated for each of the 3 km by 3 km grid cells covering Germany. Based on this procedure a Multi Metal Index (MMI) encompassing all data collected in the framework of EMS 1990 to 2015 and integrating Cr, Cu, Fe, Ni, Pb, V, and Zn (MMI90-2015) and MMI95-2015 (Al, As, Cd, Cr, Cu, Fe, Hg, Ni, Pb, Sb, V, Zn) was calculated and mapped (Figure 2).

In the following, the spatial patterns depicted in Figure 2 are summarized and the median MMI values for Germany are referred to. Thereby, the geostatistically estimated median MMI are added in squared brackets by those calculated from the sample point measurements. In 1990, almost the whole territory of Germany is covered by MMI90- 2015 values exceeding MMI = 6.0 (median 8.3 [7.7]). The survey 1995 (median = 7.3 [6.7]) indicated a slight area-wide decline of the MMI compared with that conducted in 1990. This trend was continued in the survey 2000 (median = 4.6 [4.4]). However, from 2000 to 2005, this trend changed and the MMI increased (median = 5.3 [5.1]) due to increasing bioaccumulation of Cr, Hg, Sb, and Zn. Until the survey in 2015, again a Germany-wide decrease of MMI90-2015 could be corroborated (median = 1.7 [2.0]). In 2015, areas with MMI90-2015 exceeding 4.0 could only be determined for North Rhine-Westphalia and the upper Rhine valley (Baden-Wuerttemberg) (Figure 2 Above).

**Figure 2.** Spatial patterns of Multi Metal Index (MMI) integrating Cr, Cu, Fe, Ni, Pb, V, and Zn based on data collected from 1990 to 2015 (**Above**). Spatial patterns of MMI integrating Al, As, Cd, Cr, Cu, Fe, Hg, Ni, Pb, Sb, V, and Zn based on data collected from 1995 to 2015 (**Below**).

The maps depicting the spatial patterns MMI95-2015 integrating Al, As, Cd, Cr, Cu, Fe, Hg, Ni, Pb, Sb, V, and Zn measured in moss sampled 1995–2015 (Figure 2 Below) also indicate a clear decrease between 1995 and 2000 from 7.5 [6.6] to 5, 1 [4.8]. From 2000 to 2005 the MMI90-2015 turned to 5.3 [5.0]. Finally, during the years 2005–2015 the MMI95-2015 decreased to 2.0 [2.3]. The spatial patterns and hot spots of MMI95-2015 are similar to MMI90-2015 with correlation coefficients (Spearman) between r<sup>s</sup> = 0.83 (year 2015, *p* < 0.01) and r<sup>s</sup> = 0.98 (year 2000, *p* < 0.01) and highest values in the Ruhr region (North Rhine-Westphalia) and in the upper Rhine valley (Baden-Wuerttemberg).

− −

The element and surveys integrating trends of bioaccumulation of HM atmospheric deposition depicted in Figure 2 and described the two preceding paragraphs can be explained by looking at the element- and survey-specific quantiles (Section 2.1.) and at the element-specific quantiles integrating surveys (Section 2.1.). From that we can derive information identifying the elements with a decreasing but discontinuous trend (Table 3).

N, which is not in the focus in this review, did not show any statistically significant time trend from 2005 to 2015 but a change of the geographical location of its hot spots. Regarding the development of Cd, Cr, Hg, and Pb accumulation in moss since 2005, a Germany-wide decrease could be detected and proved as statistically significant (*p* < 0.05). In terms of median values, the decline of HM bioaccumulation ranged between −4% (Hg) and −50.4% (Pb). The same tendency could be corroborated for comparisons between EMS 2015 values with those in the first year of sampling. Since 1990 (in case of Hg since 1995) the median values of all HM were reduced significantly. The most distinct decline was found for Pb (−85.9%) and the lowest for Hg (−20%). The trends for Cd and Pb are in line with respective emission data covering the period 1990 to 2015 [43]. For Cd, the Spearman correlation coefficient rs > 0.3 (*p* < 0.01) was also found between concentrations in moss sampled in 2005 and according modelled atmospheric deposition, which was calculated by use of LOTOS-EUROS chemical transport modelling. The same holds true for Pb (Section 3.2). According to [43] 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, due to the compulsory introduction of unleaded petrol in 1988 in Germany and 2000 in the EU) declined since 1990. However, the decrease of Hg bioaccumulation is less than the reduction of Hg emissions [43]. This is possibly due to long-range transport of gaseous Hg and atmospheric residence times of 6 to 18 months [18]. The concentrations of Cu and Zn in moss contradict the emission trends. At least for Cu, the Spearman correlation between the concentration in moss collected in 2005 and the modelled atmospheric deposition is rs 0.22 (*p* < 0.01), for Zn, respectively, rs < 0.2 (*p* < 0.01). For Cr, good agreement could be identified between the emission trends [43] 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-West Pomerania and conurbations such as Bremen, Hamburg, Dresden, Halle/Leipzig, and the Ruhr region. Respective increased values were also reported from Austria.

**Table 3.** Element-specific trends of heavy metal (HM) bioaccumulation in moss (Germany, 1990–2015).


*Al, As, Cd, Hg, Sb* were monitored since *1995*, Cr, Cu, Fe, Ni, Pb, V, and Zn since 1990. \* The international classification of element concentrations in moss [27] does not allow displaying spatial variance with decreasing element concentrations. Therefore, the data on HM accumulation was divided into ten percentile classes (Section 2.1).

#### *3.2. Atmospheric Heavy Metal Deposition*

The ranges of modelled HM deposition in Germany for the year 2010 [18] are shown in Table 4. For Hg, deposition data in Germany are only available as modelled EMEP data 2013 on a 50 <sup>×</sup> 50 km<sup>2</sup> grid (EMEP 2015) 5–95 [10,11]. The ranges of the 5th percentile to 95th percentile vary from 0 to 0.76 g ha−<sup>1</sup> a −1 for deciduous forest, from 0.16 to 0.87 g ha−<sup>1</sup> a −1 for coniferous forest, from 0.8 to 0.35 g ha−<sup>1</sup> a −1 for acre, from 0 to 0.31 g ha−<sup>1</sup> a −1 for marshes, from 0.08 to 0.34 g ha−<sup>1</sup> a −1 for grassland, and from 0.08 to 0.34 g ha−<sup>1</sup> a −1 for grassland. For Thallium deposition data are not available for the whole of Germany, but only from a few measuring stations.

#### *3.3. Heavy Metal Inputs from Other Sources*

In addition to the atmospheric inputs of HM presented in Section 3.2, the following other input pathways play an important role in soil pollution. The respective values were collected by Knappe et al. [44] from nationwide surveys and they are given in Table 5:



**Table 4.** Background atmospheric total deposition of heavy metals (g ha−<sup>1</sup> a −1 ) [18].

**Table 5.** HM inputs from fertilisation in agriculture and forestry (in g ha−<sup>1</sup> a −1 ) in Germany [44].


#### *3.4. Critical Load Exceedances Due to Atmospheric Deposition*

After comparing the deposition data available from this study for 2009–2011 [18] with the respective CL, three exceedances occurred: Pb (drinking water and ecosystem protection) and Cu (ecosystem protection) (Figures 3–5). The critical load exceedance for Pb deposition from air with the protection target drinking water quality can only be expected on a relatively small receptor surface. Between 1.32% (2011) and 2.44% (2010) of the receptor surface have an increased risk for drinking water quality due to airborne pollutant deposition of Pb. Critical load exceedance with airborne Cu deposition with ecosystem integrity as a protection target is also relatively low. The values vary between 0.35% (2011) and 1.16% (2010) of the receptor areas. A significantly higher proportion of

land is affected by a critical load exceedance in airborne Pb deposition with the protection objective of ecosystem integrity. Here, space shares between 5.18% (2011) and 14.36% (2010) are achieved. In absolute terms, this corresponds to 14,494 km<sup>2</sup> (2011) and 40,181 km<sup>2</sup> (2010). The focus is on the Leipzig and Thuringian basins, the loess areas (including the northern Harz foothills and the Lower Saxony area), the Erzgebirge foothills, the Lower Saxony highlands, parts of the Mecklenburg Lake District backcountry and parts of the Ruhr area (Lower Rhine lowlands and Cologne Bay). These areas are already identified as particularly sensitive to Pb deposition. Maps of the exceedance rates of the CL for Pb (drinking water and ecosystem protection) and Cu (ecosystem protection) are shown in Figures 3–5. There is no need to map exceedances of other CL as there are no exceedances of these CLs in 2010.

**Figure 3.** Exceedance of Critical Loads with the protection objective of drinking water quality by atmospheric Pb deposition.

**Figure 4.** Exceedance of Critical Loads with the aim of protecting ecosystem integrity by atmospheric Pb deposition.

**Figure 5.** Exceedance of Critical Loads with the protection objective ecosystem integrity by atmospheric Cu deposition.

These results based on the deposition calculations with the LOTOS-EUROS model differ significantly from the deposition calculations with the EMEP model [18]. The LOTOS-EUROS model consistently results in lower medians and maxima than EMEP. The EMEP/LOTOS-EUROS ratios correlate very strongly with the deposition height, i.e., where EMEP calculates particularly high deposition (e.g., Pb in the Ruhr area or Cd in North Rhine-Westphalia), the differences between the two models are also highest. Conversely, the low deposition calculated with LOTOS-EUROS, e.g., at altitudes in southern Bavaria, is higher than with EMEP (Cd: 45% higher, Pb: 12% higher).

The integrative analysis of the LOTOS-EUROS models (2005, 2007–2011, Germany) with the geostatistical area estimates of the heavy metal contents in moss (EMS, 2005, Germany) showed stronger statistical correlations than the correlation with measured concentrations in moss [24]. For Cu, the correlation is weak. At Pb, the mean correlations to LOTOS-EUROS were stronger than to EMEP, but strongest to the arithmetic mean of LOTOS-EUROS and EMEP. It follows from this that if EMEP background deposition were applied, the proportion of areas with critical load exceedances would be higher. However, the comparison of the geostatistical area estimates of the heavy metal contents in moss with the EMEP results also leads to the conclusion that the LOTOS-EUROS results for the Pb deposition are closer to reality than the EMEP data and thus at least for Pb the exceedance rates shown above have lower uncertainties than when using EMEP deposition. The uncertainties of the LOTOS-EUROS results cannot be measured quantitatively, because there are not enough measurement data containing wet and dry deposition at the same time.

#### *3.5. Statistical Evaluation of Critical Load Exceedances*

In the following, the values for the total deposition for 2010 by Schaap et al. [18] and for Hg from the EMEP dataset for Germany 2013 are compared with the CL for 2016 (minimum, 5-percentile, 95-percentile and median). This comparison shows possible risks for ecosystems and human health for the areas that cannot be represented in the German data set due to its small scale. Since the 2010 deposition dataset shows higher average deposition than the 2009 and 2011 data sets for Germany, this comparison also tends to show more unfavourable conditions, so that the risk assessment is conservative.

The German datasets for CL and deposition were calculated on the basis of input data collected on a scale of 1:1 million. The mapping units of BÜK1000N, CORINE 2006 [45] and the site types classified by climate zones are not homogeneous in reality. They may contain

sprays that are more sensitive to heavy metal ingress. The German dataset of CL 2016 [34] and the exceedances of CL in 2010 is therefore not applicable for large-scale area-based evaluations or even for site-specific statements. A rough orientation for individual sites can only be derived from the German dataset if it can be demonstrated that the same site conditions prevail for specific areas or sites as were used as a basis in the German CL(M) and deposition datasets. In order to nevertheless be able to derive a statement on the risks also for areas that cannot be represented at a scale of 1:1 million, the respective worst-case values are compared with each other, i.e., the lower range limit of the CL with the upper range limits of the deposition. It is assumed that the parameter values of the site types that are not representative in terms of area on a scale of 1:1 million and are not represented in the German data sets would not lie outside the ranges of the CL and atmospheric deposition in Germany.

The comparison of the ranges of CL and deposition shows the risk of whether and to what extent, in the worst case, further accumulation of HM in ecosystems or in groundwater or in the soil of wheat fields above the critical concentrations could take place if the annual deposition rates were to remain constant at the 2010 level in the future. However, it should be pointed out here that the deposition data used for comparison, which were determined on the basis of models throughout Germany [18], could be significantly higher locally, e.g., on areas with industrial and/or traffic concentrations or in the vicinity of a particularly high-emission plant.

#### 3.5.1. Hg

Since no Germany-wide deposition calculations including dry deposition for Hg are available [17], the assessment of the total atmospheric load situation can only be made on the basis of the EMEP deposition mapping [10,11] (Table 6).

**Table 6.** Comparison of the Hg deposition (in g ha−<sup>1</sup> a −1 ) in the year 2013 [10,11] with the critical loads (in g ha−<sup>1</sup> a −1 ) of the receptor surfaces in Germany.


Based on the evaluation of the EMEP total deposition values in a 50 km × 50 km grid [10,11], it can be assumed that a proportion of Germany's ecosystem receptor areas, i.e., those with high to medium sensitivity, are contaminated by Hg inputs above the critical load. Sensitive ecosystem types include in particular the unused deciduous forests of dry, nutrient-poor sites such as dry oak forests in the arid regions of Brandenburg and Saxony-Anhalt or beech forests along the coasts. The CL for drinking water protection in Germany are also clearly exceeded in the worst case. This means that in the worst case, Hg can accumulate in the soil or groundwater as soon as the critical limits in soil and soil water have been reached. However, the buffer capacity of the humus-rich forest soils in particular is very high for Hg, so that actual damage to ecosystem compartments, even if CL are exceeded, may only be expected after decades or centuries.

#### 3.5.2. Cd

The comparison of the Germany-wide raster maps of the total deposition of Cd in 2010 determined by Schaap et al. [18] with the CL determined in this study is given in Table 7.


**Table 7.** Comparison of the Cd deposition (in g ha−<sup>1</sup> a −1 ) with the assessment values (in g ha−<sup>1</sup> a −1 ) and statistical evaluation of the area share of protected receptor areas in Germany, 2010, against limit values exceeded.

The maximum atmospheric deposition in 2010 may have exceeded the CL for the protection of ecosystems, drinking water, and food in a few areas if maximum deposition rates hit areas with very low CL (<2.3 g ha−<sup>1</sup> a −1 ). This means that in the worst case an unacceptable accumulation of Cd in soil and drinking water could occur as long as the deposition is above the respective critical load and the critical limits have already been reached. However, when the inputs from atmospheric deposition and fertilisers are determined summarily, there is a risk that arable land will be exceeded if maximum total inputs are applied to high to average sensitive soils.

The comparison of the maximum inputs of Cd by fertilisation with CL(Cd)food shows that risks to human health from the consumption of wheat products of German origin cannot be excluded, since an intolerable increase in the Cd content in the soil can at the same time lead to an intolerable increase in the Cd content in the wheat grain [44].

#### 3.5.3. Pb

The comparison of the Germany-wide raster maps of the total deposition of Pb in 2010 calculated by Schaap et al. [18] with the CL determined in this paper is shown in Table 8.

**Table 8.** Comparison of Pb deposition 2010 (in g ha−<sup>1</sup> a −1 ) with the Critical Loads (in g ha−<sup>1</sup> a −1 ) of receptor surfaces in Germany.


The maximum atmospheric deposition in 2010 has exceeded the CL for the protection of ecosystems and drinking water in a large part of the areas. This means that an unacceptable accumulation of Pb in soil and especially in drinking water occurs as soon as the critical limits in soil and soil water are reached. When the inputs from atmospheric deposition and fertilization are determined summarily, there is an excess risk for all arable land and grassland if maximum total inputs are applied to high to medium sensitive soils.

#### 3.5.4. As

Table 9 compares the grid maps of the total deposition of As in 2010 calculated throughout Germany by Schaap et al. [18] with the CL determined in this study.


**Table 9.** Comparison of As deposition in 2010 (in g ha−<sup>1</sup> a −1 ) with the CL (in g ha−<sup>1</sup> a −1 ) of receptor surfaces in Germany.

Even in the worst case (maximum deposition meets minimum CL), the CL for the protection of ecosystems and drinking water are clearly undercut. Even if the fertiliser inputs in the maximum are added to the total atmospheric inputs in the maximum, the CL for ecosystem or drinking water protection are not exceeded. If, in the critical load calculation for ecosystem protection, the minimum threshold [46] used instead of the critical concentration in the leachate according to Doyle et al. [47] (cited in: [48]), a minimum critical load of 2.9 g ha−<sup>1</sup> a <sup>−</sup><sup>1</sup> would be obtained. In the worst-case scenario, this minimum would also not be exceeded by the maximum deposition. The same applies to the alternatively calculated minimum critical load for drinking water protection.

#### 3.5.5. Cu

The comparison of the grid maps of the total deposition of Cu in 2010 calculated by Schaap et al. [18] throughout Germany with the CL determined in this paper is shown in Table 10.

**Table 10.** Comparison of the 2010 Cu deposition (in g ha−<sup>1</sup> a −1 ) with the critical loads (in g ha−<sup>1</sup> a −1 ) of the receptor surfaces in Germany.


The maximum atmospheric deposition in 2010 [18] has exceeded the CL for ecosystem protection on part of the areas. This means that there will be an unacceptable accumulation of Cu in soil and/or groundwater as soon as the critical limits in soil are exceeded. When the inputs from atmospheric deposition and fertilisation are determined summarily, there is an exceedance risk for all arable land and grassland when maximum total inputs meet high to average sensitive soils.

#### 3.5.6. Zn

The comparison of the grid maps of the total deposition of Zn in 2010 calculated by Schaap et al. [18] throughout Germany with the CL determined in this paper is shown in Table 11**.**


**Table 11.** Comparison of the Zn deposition 2010 (in g ha−<sup>1</sup> a −1 ) with the critical loads (in g ha−<sup>1</sup> a −1 ) of the receptor surfaces in Germany.

Even in the worst case (maximum deposition meets minimum CL), the CL for the protection of ecosystems and drinking water are barely undershot. The minimum inputs of Zn with fertilisers are already so high that there is a high risk of the CL for ecosystem protection being exceeded for all arable land and grassland. Since limit concentrations for Zn in drinking water are not specified, a critical load for drinking water protection cannot be determined.

#### 3.5.7. Cr

The comparison of the grid maps of the total deposition of Cr in 2010 calculated by Schaap et al. [18] throughout Germany with the CL determined in this paper is shown in Table 12.

**Table 12.** Comparison of Cr deposition 2010 (in g ha−<sup>1</sup> a −1 ) and critical loads (in g ha−<sup>1</sup> a −1 ) of receptor areas in Germany.


The maximum atmospheric deposition in 2010 may not have exceeded the CL for the protection of ecosystems and drinking water in any area. This means that in the worst case there is no unacceptable accumulation of Cr in soil and drinking water. If, in the critical load calculation for ecosystem protection, instead of the critical concentration in the leachate according to Crommentuijn et al. [49] (cited in: [48]), the minimum threshold [46] was used as an alternative, a minimum critical load of 2.1 g ha−<sup>1</sup> a <sup>−</sup><sup>1</sup> would be obtained. In the worst case, however, this minimum would be exceeded by the maximum deposition. The same applies to the alternatively calculated minimum critical load for drinking water protection. There is also an exceedance risk for a part of the arable land in Germany when the entries from atmospheric deposition and fertilisation are determined summarily, if maximum total entries hit soils with high to average sensitivity.

#### 3.5.8. Ni

Table 13 compares the grid maps of the total deposition of Ni in 2010 calculated by [18] throughout Germany with the CL determined in this study.


**Table 13.** Comparison of Ni deposition in 2010 (in g ha−<sup>1</sup> a −1 ) with the critical loads (in g ha−<sup>1</sup> a −1 ) of receptor areas in Germany.

The maximum atmospheric deposition in 2010 may not have exceeded the CL for ecosystem protection in any area. This means that in the worst case there is no unacceptable accumulation of Ni in the soil. Since limit concentrations for Ni in drinking water are not specified, a critical load for drinking water protection cannot be determined. There is no risk that the CL for the protection of forest, arable and grassland ecosystems will be exceeded when the inputs from atmospheric deposition and fertilisation are summarily determined, even if maximum total inputs would occur on high to average sensitive soils. If instead of the WHAM modelling results [50] for the critical concentration in leachate, the critical load calculation for ecosystem protection were to alternatively use the minimum threshold [46], a minimum critical load of 7.9 g ha−<sup>1</sup> a <sup>−</sup><sup>1</sup> would be obtained. In the worst case, this minimum would also not be exceeded by the maximum deposition. The same applies to the alternatively calculated minimum critical load for drinking water protection.

#### 3.5.9. Tl

CL for Tl for the protection of ecosystems cannot yet be determined, because there is no valid database for the derivation of impact-based ecosystem critical limits. A provisional rough estimate of the risk of Tl inputs into Germany's receptor ecosystems can be based on a calculated balance of inputs and outputs in the ranges typical for Germany (Table 14).


**Table 14.** Calculation of the acceptable Tl discharge in the typical German range (minimum and maximum).

The average Tl content in vegetable biomass on unpolluted soils is 0.05 g t−<sup>1</sup> dry matter [51]. Thus, the range of Tl outputs with the biomass harvest is obtained by multiplying this typical concentration in plant stands of unpolluted soils by the minimum and maximum yields in Germany. In leachate, the minimum threshold of 0.0002 g m−<sup>3</sup> [46] may be used as a critical limit. This threshold value is an ecotoxicologically determined threshold value for a Tl limit concentration.

Since no nationwide deposition surveys are available, no assessment of the pollution situation can be made. However, a measuring station in Dortmund, for example, recorded an annual average concentration in 2013, which converted into a deposition rate of 0.15 g ha−<sup>1</sup> a −1 [52]. This value is within the range for the acceptable discharge rate. Thus, a risk cannot be excluded that sensitive ecosystems could be overburdened in the long term.

#### 3.5.10. V

CL for V for the protection of ecosystems cannot yet be determined because there is no valid database for the derivation of impact-based ecosystem-critical limits. A provisional rough estimate of the risk of V inputs into Germany's receptor ecosystems can be based on a calculated balance of inputs and outputs in the ranges typical for Germany (Table 15).

**Table 15.** Calculation of the acceptable V discharge in the typical German range (minimum and maximum).


Biomass harvesting is one of acceptable cutting methods. The average V content in vegetable biomass can be assumed to be 0.7 g t−<sup>1</sup> dry matter [53]. The ranges of the V outputs with the biomass harvest then result from multiplying this typical concentration in plant stands of unpolluted soils with the minimum and maximum yields in Germany. Furthermore, leaching with the seepage can be taken into account as an acceptable discharge, whereby the critical concentration of the metal in the leachate can be assumed to be the negligibility threshold of 0.004 g m−<sup>3</sup> [46]. This human-toxic threshold value is lower than the ecotoxicological threshold value for a V limit concentration.

The maximum deposition 2010 [18] for forest and grassland is slightly above the minimum acceptable deposition. In the worst case (maximum deposition meets areas with minimal cut-offs), a risk of impairment of ecosystem functions cannot be ruled out.

#### *3.6. Comparison and Discussion of Assessment Values, Risk Assessment of Heavy Metal Inputs*

When comparing the deposition calculated by Schaap et al. [18] as an area-wide dataset for Germany with the assessment values, the differences between the calculation results of the deposition (with EMEP model, LOTO-EUROS model, derived from mosses) and between assessment values are of great importance. The dataset for MH deposition calculated for Germany corresponds methodically roughly to the German dataset for the total deposition of N, which is used for the assessment of environmental impacts of projects and plants as the data basis for determining the background deposition. However, such an application is not recommended for the datasets of the total heavy metal deposition due to the high uncertainties [18]. However, it is in line with expectations that this background deposition will not exceed assessment values for the assessment of the environmental impacts of plants or projects.

#### 3.6.1. Protection of Human Health

Comparing the assessment values based on human toxicological thresholds and relating to the total general exposure with the corresponding airborne input rates (for Hg:

EMEP deposition grid data for 2013; for all other metals deposition grid data for 2010 from Schaap et al. [18], then the following undercuttings and exceedances become obvious (Table 16). A comparison of the plant-related assessment values for deposition according to TA Luft [36] with the background loads does not provide any information on the currently existing risks and is therefore not made in the following section. Alternatively, it is indicated to what extent the background deposition already exhausts the values according to Table 6 or Table 8 of the TA Luft [36] or comparable assessment values.

**Table 16.** Assessment values for heavy metal (in g ha−<sup>1</sup> a −1 ) fluxes for the protection of human health and their exceedance/undercutting by atmospheric deposition (for Hg: EMEP in 2013 [10,11]; for all others German dataset in 2010 from Schaap et al. [18]).


**<sup>1</sup>** Converted into input rates using the mean deposition velocities in the various vegetation complexes. <sup>+</sup> Critical values for human health are not exceeded. A risk can be excluded. − Critical values for human health are exceeded in worst cases. A risk cannot be excluded regionally. − − Critical values for human health are exceeded. There is a regional risk.

> Although the immission limit values and target values of the 39th Federal Immission Control Ordinance [38] are in principle suitable as assessment values for endangering human health, the concentrations stated are not directly comparable with the deposition of the German dataset. If the concentrations are converted into input rates using the mean deposition velocities in the various vegetation complexes, taking into account the proportions of coarse and fine fractions in the dust, different permissible input rates are obtained for coniferous and deciduous forest and for arable land.

> The EU Position Paper [54] specifies immission target values (concentrations in the particulate matter fraction PM10) for As, Cd, and Ni. For Cd, a deposition threshold derived from concentration values is also proposed. These are proposals derived from human toxicological data. These assessment values are therefore suitable for the risk assessment of total entries for human health. The assessment concentrations for As and Ni were converted into allowable input rates as indicated above. For the protection of drinking water, the CL (CL(M)drink) for the atmospheric total heavy metal inputs for the German receptor surfaces on a scale of 1:1 million were determined as assessment values for the assessment of the CL(M)drink, in which the limit concentrations from the German Drinking Water Ordinance [55] were included as critical threshold values. These are identical to the corresponding limit concentrations of the WHO guideline [56]. The CL for drinking water protection were determined taking into account the different leachate rates and vegetation types. In this respect, they show a higher degree of differentiation than the absolute assessment values of the 39th BImSchV [38] and the TA Luft [36]. Entries at the CL level lead to an equilibrium between total input and unpolluted output and thus guarantee the precautionary avoidance of an accumulation of HM in drinking water above the limit

values. Thus, the following differentiated picture emerges with regard to the risk to human health from inputs of the individual HM under consideration.

#### Hg

The 39th BImSchV [38] and the EU Position Paper [54] do not contain assessment values for Hg. Based on the EMEP deposition grid map of Germany for the year 2013, the CL for drinking water protection CL(Hg) drink were exceeded in the German dataset 2016 [34] with a focus on southeast Brandenburg, northeast Saxony, and southwest Saxony-Anhalt. The maximum deposition exhausts the assessment value of TA Luft [36] (Table 6) to 22% and the assessment value of TA Luft [36] (Table 8) to 0.8%.

#### Cd

For the protection of plant food (wheat grain), a critical load for Cd inputs on wheat fields (CL(Cd)food) was determined as the assessment value, in which the Cd limit concentration for wheat was included in accordance with the recommendation of the manual [8,57]. This is half of the limit value set in the EU regulation (EC No. 1881/2006). The CL(Cd)food was not exceeded in 2010 by the atmospheric Cd deposition in the receptor surfaces of the German CL dataset 2016 [34]. However, it should be noted that only a fraction of the heavy metal load on agricultural soils results from the atmosphere. In particular, the comparison of Cd inputs by fertilisation with CL(M)food shows the risk of harmful accumulation in wheat fields. Since the current content of Cd in wheat correlates with the content in soil [44], there is currently a risk potential for human health from the consumption of wheat products of German origin. The CL for drinking water protection in the receptor areas of the German dataset 2016 [34] are not exceeded by the atmospheric deposition in 2010 (Table 4). In the German dataset 2016 [34], it may be possible that areas where maximum deposition rates meet a very low critical load (<2.3 g ha−<sup>1</sup> a −1 ) (worst case) may not be mapped due to scale conditions. In these cases, the maximum atmospheric deposition in 2010 (Table 4) may have exceeded the CL for the protection of drinking water and food. This means that in the worst case, Cd may accumulate in drinking water and wheat products as long as the deposition is above the respective critical load and the critical limits are exceeded.

The CL(Cd)drink and CL(Cd)food are predominantly in the range of the range from the EU position paper [54], but also go deeper than this. The target value for the Cd entry from the EU position paper is far below the atmospheric deposition 2010 [18]. The maximum deposition exhausts the assessment value of TA Luft [36] (Table 6) at 27% and the assessment value of TA Luft [36] (Table 8) at 21%. If the assessment value for the concentration from the 39th BImSchV [38] were alternatively converted into a deposition, this would result in assessment values of 2.5–7 g ha−<sup>1</sup> a −1 , which would not be exceeded by the deposition in the German dataset or in the worst case. However, this is only an auxiliary calculation for a rough comparison of the exceedance rates of CL(M)drink and CL(M)food.

#### Pb

The CL for the protection of drinking water will be exceeded by atmospheric Pb deposition in 2010 [18] on 2.41% of the receptor areas in Germany, predominantly in the state of Brandenburg, in Leipzig and in the Ruhr area. This area proportion may be higher, since the German dataset 2016 [34] may not include areas where maximum deposition rates meet a very low critical load (worst case) (Table 8). The maximum deposition [18] exhausts 19% of the assessment value of the TA Luft [36] (Table 6) and 11% of the assessment value of the TA Luft [36] (Table 8). If the assessment value for the concentration from the 39th BImSchV [38] were alternatively converted into a deposition rate, this would result in assessment values of 250−716 g ha−<sup>1</sup> <sup>a</sup> −1 , which are not exceeded by the deposition of the German dataset [18] and also in the worst case.

#### As

The CLs for drinking water protection are not exceeded in the receptor areas of the German dataset 2016 [34] by the atmospheric deposition in 2010 [18]. Even in the worst case (maximum deposition rates meet the lowest critical load), which does not occur in the German dataset 2016 [34] but could occur on a larger scale, exceeding the CL for drinking water protection is ruled out. Even if a critical load calculation for drinking water protection is carried out alternatively on the basis of the minimum threshold value [46], the resulting minimum critical load in the worst case would not be exceeded by the maximum deposition. The Minority threshold [46] corresponds to the base value.

The maximum deposition exhausts 6% of the assessment value of the TA Luft [36] (Table 6) and 0.02% of the assessment value of the TA Luft [36] (Table 8). If the assessment value for the concentration from the 39th BImSchV [38] were alternatively converted into a deposition, this would result in assessment values of 2.5–6 g ha−<sup>1</sup> a −1 , which are not exceeded by the deposition of the German dataset and also in the worst case.

#### Cu

The CL for drinking water protection in the receptor areas of the German dataset 2016 [34] are not exceeded by the atmospheric deposition in 2010 [18]. Even in the worst case (maximum deposition rates meet the lowest critical load), which does not occur in the German dataset 2016 [34], but could occur on a larger scale, exceeding the CL for drinking water protection is ruled out.

#### Zn

The CL for drinking water protection in the receptor areas of the German dataset 2016 [34] are not exceeded by the atmospheric deposition in 2010 [18]. Even in the worst case (maximum deposition rates meet the lowest critical load), which does not occur in the German dataset 2016 [34] but could occur on a larger scale, exceeding the CL for drinking water protection is ruled out.

#### Cr

The CL for drinking water protection in the receptor areas of the German dataset 2016 [34] are not exceeded by the atmospheric deposition in 2010 [18]. Even in the worst case (maximum deposition rates meet the lowest critical load), which does not occur in the German dataset 2016 [34] but could occur on a larger scale, exceeding the CL for drinking water protection is ruled out. However, if a critical load calculation for drinking water protection was carried out alternatively on the basis of the minimum threshold value [46], the resulting minimum critical load would be exceeded by the maximum deposition [18] in the worst case.

#### Ni

The BTrinkwV [55] does not specify a limit concentration for Ni, therefore no critical load for drinking water protection is calculated in this study. If a critical load calculation for drinking water protection is carried out alternatively on the basis of the minimum threshold value [46], the resulting minimum critical load in the worst case would not be exceeded by the maximum deposition. The maximum deposition exhausts 12% of the TA Luft [36] (Table 6) assessment value.

If the assessment value for the concentration from the 39th BImSchV [38] were alternatively converted into a deposition, this would result in assessment values of 10–28 g ha−<sup>1</sup> a −1 , which would not be exceeded by the deposition of the German dataset and also in the worst case. If the target values of the EU position paper [54) are converted into deposition rates (5–72 g ha−<sup>1</sup> a −1 ), in the worst case (maximum deposition meets minimum permissible input) there is an exceedance risk on arable land and grassland.

#### Tl

No limit concentration for Tl is specified in the BTrinkwV [55]. Therefore, no critical load for the drinking water protection CL(Tl)drink was calculated in this study. A human toxicological minimum threshold is also not available [46]. An exceedance of the immission values for heavy metal deposition according to TA Luft [36] (Tables 6 and 8 ) by the diffuse total pollution from the long-distance transport of the metal in the atmosphere cannot be determined nationwide. However, the assessment values are far above, for example, the annual average concentration measurement values 2013 of the LANUV NRW [52] in Dortmund converted into deposition rates.

#### V

The BTrinkwV [55] does not specify a limit concentration for V. Therefore, no critical load for the drinking water protection CL(Ni)drink is calculated in this study. A risk assessment using an input/output balance based on a human toxicological minimum threshold [46] shows that a health risk cannot be safely excluded by the inputs in 2010.

3.6.2. Protection of Terrestrial Ecosystems (in Particular Soils) from Harmful Changes

If one compares the assessment values with the corresponding aerial input rates (for Hg: EMEP deposition grid data for 2013 [10,11]; for all other metals deposition grid data for 2010 according to [18], the following under- and overruns result (Table 17). However, the assessment of risks from airborne inputs on the basis of the assessment values from the various statutory regulations and recommendations must be considered taking into account different levels of protection, protection objectives, and impact thresholds. The assessment values of the TA Luft [36], the 39th BImSchV [38], and the EU Position Paper [54] are based on human toxicological threshold values but are also intended for the protection of plants and the environment in general, whereby it is assumed that the ecosystem compartments are not more sensitive than humans. After conversion of the permissible concentrations from the 39th BImSchV [38] into permissible annual input rates, there are no exceedances in 2010 due to the atmospheric background deposition [18]. The assessment values from the EU position paper [54] for Cd will not be exceeded in 2010, as will the assessment values for As after conversion. The converted lowest assessment values for Ni are exceeded in the worst case on fields and grassland.


**Table 17.** Assessment values for heavy metal fluxes to protect ecosystems and their exceedance/undercutting by atmospheric deposition (for Hg: EMEP in 2013 [10,11]; for all others German dataset in 2010 [18]).

<sup>1</sup> Converted into input rates using the mean deposition velocities in the various vegetation complexes. <sup>+</sup> Critical values for ecosystems are not exceeded. A risk can be excluded. − Critical values for ecosystems are exceeded in worst cases. A risk cannot be excluded regionally. −− Critical values for ecosystems are exceeded. There is a regional risk.

At the same time, a comparison of the plant-related assessment values for metal inputs (assessment value according to LUA Brandenburg [58], TA Luft [36], permissible additional load according to BBodSchV [37]) with the background loads does not provide any information on the currently existing spatially widespread risks for ecosystems. The comparison can only serve to roughly estimate to what extent the permissible total load has already been exhausted by the background load. The uncertainties of the deposition mapping and its small scale do not permit concrete spatial statements for individual sites. It was therefore to be expected that the atmospheric entries for 2010 would not exceed the plant-related assessment values.

In the following, only the risks of terrestrial ecosystems, including soil, are discussed on the basis of assessment values based on ecotoxicological thresholds, i.e., from the BBod-SchV [37], the Brandenburg enforcement aid for the FFH-habitats impact assessment [58] and the CL for ecosystem protection identified in Schröder et al. [34]. As the pathways of action of HM to all compartments of a terrestrial ecosystem usually run across the soil, the assessment values for the protection of soil functions can at the same time be regarded as relevant assessment values for the protection of ecosystems.

The CL for ecosystem protection [34] for the heavy metal inputs of Hg, Cd, Pb, As, Cu, Zn, Cr, and Ni (CL(M)eco) are differentiated by ecosystem type. The input values are determined soil- and vegetation-specifically. The critical threshold values (critical concentration in the soil for the maintenance of microbial functions, for the protection of plants, invertebrates, and soil microorganisms) are comparable with the criteria for determining the precautionary values in the Federal Soil Protection Ordinance [37]. However, the precautionary values of the BBodSchV [37] are stated as concentrations and are therefore not directly comparable with the flow rates of the CL.

According to current knowledge, compliance with CL(M)eco with the inclusion of all input paths permanently (for all time) excludes the possibility of risks arising, provided that the critical limits (critical concentrations in the indicators considered) have not yet been exceeded. If they have already been exceeded, compliance with the critical load leads to a long-term, gradual reduction up to the critical limits.

The assessment values of the ´Brandenburgische FFH-Vollzugshilfe´ [58] are given as maximum permissible concentrations in the soil. In addition, plant-related irrelevance thresholds can be calculated. A comparison of the permissible enrichment rate in 100 years with background deposition therefore also does not lead to a real risk assessment. In the following, therefore, only the extent to which the total permissible positions calculated in this way are already exhausted by the entries at background level is given.

In the following, an assessment of the risk of harmful changes in terrestrial ecosystems based on the current 2010 and 2013 HM deposition under consideration is nevertheless to be carried out, taking into account the limited comparability of the assessment values.

#### Hg

On the basis of the EMEP deposition grid map of Germany for 2013 [10], the CLs for ecosystem protection in the German dataset 2016 [34] are exceeded, with a focus on North Rhine-Westphalia, southeast Brandenburg, north-east Saxony, and southwest Saxony-Anhalt.

In addition, further diffuse Hg entries may have to be taken into account, which aggravates the situation. However, due to the high buffering capacity of the soils for Hg, it cannot be concluded from a CL(Hg)eco exceedance that there is an immediate risk.

The permissible annual additional load of Hg according to BBodSchV [37] in the amount of 1.5 g ha−<sup>1</sup> a −1 , if the precautionary value of the Hg concentration in the soil according to BBodSchV [37] has already been reached or exceeded, is far above the maximum of the EMEP deposition in 2013 [10,11]. Fifty-eight percent of the maximum atmospheric background deposition uses this value, but the permissible additional load also applies to all other input paths in total. The irrelevant plant-related additional load extrapolated from the irrelevance threshold according to LUA Bbg [58] using the example of a cambisol to 100 years is already 44% exhausted by the maximum atmospheric background deposition in 2013 [18].

#### Cd

The CL for ecosystem protection in the receptor areas of the German dataset 2016 [34] are not exceeded by the atmospheric deposition 2010 [18]. In the German dataset 2016 [34], it may be possible that areas where maximum deposition rates meet a very low critical load (<2.3 g ha−<sup>1</sup> a −1 ) (worst case) may not be depicted due to scale. In these cases, the maximum atmospheric deposition in 2010 may have exceeded the CL for ecosystem protection. The permissible annual Cd input rate according to BBodSchV [37] of 6 g ha−<sup>1</sup> a −1 ), if the precautionary value (Cd concentration in soil) according to BBodSchV [37] has already been reached or exceeded, is far above the maximum of the EMEP deposition in 2013 [10,11]. Thirty-nine percent of the maximum atmospheric background deposition uses this value, but the permissible additional load applies to all input paths.

The irrelevant plant-related additional load extrapolated from the irrelevance threshold according to LUA Bbg [58] using the example of a cambisol to 100 years is already 47% exhausted by the maximum atmospheric background deposition in 2010 [18].

#### Pb

The CL for Pb inputs for the protection of ecosystems are exceeded by the atmospheric deposition in 2010 [18] on 14.11% of the receptor areas in Germany, predominantly in the Leipzig and Thuringian bight, in the Harz foreland and in the Ruhr area. This area share may be higher, since the German dataset 2016 [34] may not include areas where maximum deposition rates meet a very low critical load (worst case). The permissible annual Pb input rate under the BBodSchV [37] of 400 g ha−<sup>1</sup> a −1 , if the precautionary value has already been reached or exceeded, is well above the maximum of the 2010 deposition [18]. The maximum atmospheric background deposition exhausts 22% of this value, but the permissible additional load applies to all entry paths. The irrelevant plantrelated additional load extrapolated from the irrelevance threshold according to LUA Bbg [58] using the example of a cambisol to 100 years is used up by 11% by the maximum atmospheric background deposition in 2010 [18].

#### As

The CL for ecosystem protection in the receptor areas of the German dataset 2016 [34] are not exceeded by the atmospheric deposition 2010 [18]. Even in the worst case (maximum deposition rates meet the lowest critical load), which does not occur in the German dataset 2016 [34] but could occur on a larger scale, exceeding the CL for ecosystem protection is ruled out. Even if a critical load calculation for ecosystem protection was carried out alternatively on the basis of the minimum threshold value [46], the resulting minimum critical load in the worst case would not be exceeded by the maximum deposition [18]. A permissible additional annual input rate of As according to BBodSchV [37] is not specified.

#### Cu

In 2010 [18], the Cu deposits exceeded the CL(Cu)eco on 1.16% of the receptor areas in Germany, predominantly in the Berlin environs and the Ruhr area. This area proportion may be higher, since the German CL dataset 2016 [34] may not reflect areas where maximum deposition rates meet a very low critical load (worst case). The permissible annual input rate of Cu according to BBodSchV [37] of 360 g ha−<sup>1</sup> a −1 , if the permissible Cu concentration in the soil according to BBodSchV [37] has already been reached or exceeded, is far above the maximum of the deposition 2010 [18]. The maximum background deposition exhausts this value to 8%, but the permissible additional load applies to all input paths.

#### Zn

The CL for ecosystem protection in the receptor areas of the German data set 2016 [34] are not exceeded by the atmospheric deposition 2010 [18]. Even in the worst case (maximum deposition rates meet the lowest critical load), which does not occur in the German data set 2016 [34] but could occur on a larger scale, exceeding the CL for ecosystem protection is ruled out. The permissible annual input rate of Zn according to BBodSchV [37] of 1200 g ha−<sup>1</sup> a −1 , if the permissible Zn concentration in the soil according to BBodSchV [37] has already been reached or exceeded, is far above the maximum of the deposition in 2010 [18]. The maximum background deposition exhausts this value by 6%, but the permissible additional load applies to all input paths.

#### Cr

The CL for ecosystem protection in the receptor areas of the German dataset 2016 [34] are not exceeded by the atmospheric deposition 2010 [18]. Even in the worst case (maximum deposition rates meet the lowest critical load), which does not occur in the German CL dataset 2016 [34] but could occur on a larger scale, exceeding the CL for ecosystem protection is ruled out. However, if a critical load calculation for ecosystem protection was carried out alternatively on the basis of the minimum threshold value [46], the resulting minimum critical load would be exceeded by the maximum deposition in the worst case. The permissible annual input rate of Cr according to BBodSchV [37] of 300 g ha−<sup>1</sup> a −1 , if the permissible Cr concentration in the soil according to BBodSchV [37] has already been reached or exceeded, is far above the maximum of the deposition in 2010 [18]. The maximum background deposition exhausts this value by 1.3%, but the permissible additional load applies to all input paths.

#### Ni

The CL for ecosystem protection in the receptor areas of the German CL dataset 2016 [34] are not exceeded by the atmospheric deposition 2010 [18]. Even in the worst case (maximum deposition rates meet the lowest critical load), which does not occur in the German CL dataset 2016 [34], but could occur on a larger scale, exceeding the CL for ecosystem protection is ruled out. Even if a critical load calculation for ecosystem protection was carried out alternatively on the basis of the minimum threshold value [46], the resulting minimum critical load would not be exceeded by the maximum deposition [18] in the worst case. The permissible annual Ni input rate according to BBodSchV [37] of 100 g ha−<sup>1</sup> a −1 , if the permissible Ni concentration in the soil according to BBodSchV [37] has already been reached or exceeded, is far above the maximum of the deposition in 2010 [18]. The maximum background deposition exhausts this value by 7%, but the permissible additional load applies to all input paths.

The maximum atmospheric background deposition in 2010 [18] exploits 4.6% of the irrelevance threshold according to LUA Bbg [58] extrapolated from the example of a cambisol to 100 years of irrelevant plant-related additional pollution.

#### Tl

Since no nationwide deposition surveys are available, no assessment of the pollution situation can be made. However, a measuring station in Dortmund, for example, recorded an annual average concentration in 2013, which converted to a deposition rate of 0.15 g ha−<sup>1</sup> a −1 [52]. A risk assessment by means of an input/output balance based on an ecotoxicological minimum threshold [46] shows that a risk cannot be safely excluded, at least in the Ruhr region. Tl is a highly toxic element for living organisms, comparable to the effect of Hg [59]. Tl is hardly transported in the soil and is thus strongly enriched in the rooted topsoil during prolonged input [59,60] states that 80% of anthropogenic Tl is stored in humus-rich topsoil. This results in an obviously neglected need for research. The impact-based derivation of assessment values and at the same time the inventory of the current deposition in Germany are urgently required.

#### V

A risk assessment by means of an input/output balance based on a human toxicological minimum threshold [46], which is lower than the ecotoxicological threshold determined, results in a health risk from the inputs in 2010 in the worst case. For example, the maximum deposition in 2010 [18] (Table 4) for forest and grassland is slightly above the minimum acceptable deposition. In the worst case (maximum deposition meets areas with minimal outcropping), a long-term risk of impairment of ecosystem functions cannot be ruled out. Assessment values for V are not given in the BbodSchV [37].

#### **4. Future Research**

The German Moss Monitoring 2020 does not continue the monitoring network 2015 and the range of measured elements except for POPs. This is a cause for concern, because in that way it is not possible to detect a trend reversal like for Cr, Sb, Zn, and standstill of concentrations of Al, As, Cd, Cu, Hg, Ni, V between 2000 and 2005. The Moss Survey 2025 should therefore again focus on the internationally agreed measurement spectrum (Al, As, Cd, Cr, Cu, Fe, Hg, Ni, Pb, Sb, V, Zn; N; POP). From a political and ecological point of view, not only increases in concentrations are worth reporting, but also standstills and (further) declines.

**Author Contributions:** Conceptualization, S.N., A.S., and W.S.; methodology, A.S.; data curation, S.N. and A.S; writing—original draft preparation, W.S.; writing—review and editing, W.S.; supervision, W.S.; project administration, W.S.; funding acquisition, W.S. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the Federal Environment Agency, Germany.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** The provision of data is in preparation.

**Acknowledgments:** We would like to thank Gudrun Schütze (Federal Environment Agency) for her constructive professional support of the project.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


### *Article* **First Results on Moss Biomonitoring of Trace Elements in the Central Part of Georgia, Caucasus**

**Omari Chaligava <sup>1</sup> , Igor Nikolaev <sup>2</sup> , Khetag Khetagurov <sup>2</sup> , Yulia Lavrinenko <sup>2</sup> , Anvar Bazaev <sup>3</sup> , Marina Frontasyeva 1,\* , Konstantin Vergel <sup>1</sup> and Dmitry Grozdov <sup>1</sup>**


**Abstract:** The moss biomonitoring technique was used for assessment of air pollution in the central part of Georgia, Caucasus, in the framework of the UNECE ICP Vegetation. A total of 35 major and trace elements were determined by two complementary analytical techniques, epithermal neutron activation analysis (Na, Mg, Al, Cl, K, Ca, Sc, Ti, V, Cr, Mn, Fe, Co, Ni, Zn, Se, B, Rb, Sr, Zr, Mo, Sb, I, Cs, Ba, La, Ce, Nd, Sm, Eu, Tb, Yb, Hf, Ta, W, Th, and U) and atomic absorption spectrometry (Cu, Cd, and Pb) in the moss samples collected in 2019. Principal Component Analyses was applied to show the association between the elements in the study area. Four factors were determined, of which two are of geogenic origin (Factor 1 including Na, Al, Sc, Ti, V, Cr, Fe, Co, Ni, Th, and U and Factor 3 with As, Sb, and W), mixed geogenic–anthropogenic (Factor 2 with Cl, K, Zn, Se, Br, I, and Cu) and anthropogenic (Factor 4 comprising Ca, Cd, Pb, and Br). Geographic information system (GIS) technologies were used to construct distributions maps of factor scores over the investigated territory. Comparison of the median values with the analogous data of moss biomonitoring in countries with similar climatic conditions was carried out.

**Keywords:** moss biomonitoring; trace elements; atmospheric deposition; neutron activation analysis; atomic absorption spectrometry; multivariate statistics

#### **1. Introduction**

At present air pollution is recognized as the fifth largest threat to human health [1]. Air pollution and the associated problems are not confined by any geopolitical boundaries. The European Directives on air quality related to particulate matter (PM), heavy metals, and polycyclic aromatic hydrocarbons in ambient air [2,3], define target and limit values in the monitoring and further control of the pollutants. During the last several decades, biomonitoring surveys considering the use of an organism as a monitor of environmental pollution [4] have become a valuable complement to instrumental measurements. Widespread species that reliably reflect air pollution represent a simple and cost-effective alternative for instrumental measurements, thus enabling measurements with much higher spatial resolution. Mosses are recognized as good biomonitors of air pollution due to their specific morpho-physiological features: the lack of a root system, large surface area, and a high cation-exchange capacity of cell membranes, which represent their adaptations to nutrition from the air. Mosses are ubiquitous species and they have been extensively used in large-scale studies for biomonitoring of trans-boundary air pollution [5] known as passive moss biomonitoring [4]. The moss biomonitoring method, in combination with

**Citation:** Chaligava, O.; Nikolaev, I.; Khetagurov, K.; Lavrinenko, Y.; Bazaev, A.; Frontasyeva, M.; Vergel, K.; Grozdov, D. First Results on Moss Biomonitoring of Trace Elements in the Central Part of Georgia, Caucasus. *Atmosphere* **2021**, *12*, 317. https:// doi.org/10.3390/atmos12030317

Academic Editor: Antoaneta Ene

Received: 30 December 2020 Accepted: 24 February 2021 Published: 28 February 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

nuclear and related analytical techniques, has been regularly used for the last 25 years in Western European countries to study atmospheric deposition of heavy metals (HM). Over the past 15 years it has spread to Eastern Europe [5]. The first moss survey in Georgia was undertaken in 2014 [6] and the results included in the Report on the European Moss survey 2015–2016 [5] along with data obtained in the next surveys [7,8]. Rocks of different composition, age and stability are spread on the territory of Georgia. The high degree of the relief-dissection is due to strong tectonic movement and intense erosion processes in the Caucasus region. At certain locations of Georgia the depths of erosion-cuts exceeds 2000 m [9].

There is a need to investigate whether mosses sampled in this region can be used as biomonitors of atmospheric heavy metal deposition given the rather high contribution of mineral particles to the metal concentration in mosses. The present research was carried out by the Georgian and Russian teams aimed to cover white spots in the map of this territory of Caucasus.

#### **2. Experimental**

#### *2.1. Study Area*

The study area is located in Georgia between coordinates: 42◦40′ N latitude and 43◦17′ E longitude for the North, 41◦22′ N latitude and 43◦46′ E longitude for the South, 42◦35′ N latitude and 43◦13′ E longitude for the West, and 41◦40′ N latitude and 44◦41′ E longitude for the East. Elevation ranges from 651 to 2132 m a.s.l.

The South Caucasus region is highly prone to natural disasters, and its mountainous regions are particularly high risk areas. Natural phenomena common in the region include landslides and mudflows, floods, flash floods, droughts, avalanches, rainstorms, and earthquakes. The countries are located in a region of moderate to very high seismic activity and are therefore particularly prone to earthquakes, which can have devastating consequences for lives, buildings and infrastructure. This seismic activity can also trigger secondary events such as landslides, avalanches and flash floods in mountainous areas [10].

The mountainous regions of the South Caucasus have a wide range of climatic zones, from cold temperate alpine peaks to temperate, humid and arid landscapes.

The relief of Georgia is characterized by complex hypsometric and morphographic features: heavily dissected mountain slopes, deep erosive gorges, intermountain depressions, flat lowlands, plains, plateaus, and uplands. The most important landforms found in the territory of Georgia are erosive, volcanic, karst, gravitational, and old glacial landforms [11,12].

The climate in the high-mountains contributes to the formation of eternal snows and glaciers. Mountain meadow soils prevail in the highlands, and brown forest soils on the plains. Landscape and ecosystems of each sampling site differ considerably and depend on wind direction. Study area is located outside industrial zones; however, it may experience a long-range transport of pollutants due to resuspension of soil particles.

During the summer season, the main source of air pollution is traffic. It should be noted that as of 2018, 45.5% of vehicles were over 20 years old. Diesel fuel quality and requirements remain a particularly problematic issue in the country [13].

The main industrial activities taking place in the mountainous regions of Central Caucasus are related to the extraction and processing of natural resources. Mining activities alter the structure of the landscape, which can have severe consequences. Mining and processing activities often create toxic waste, which can have adverse impacts on the surrounding environment. In the Ambrolauri region, near village Uravi arsenic mining sites are situated. When the mining sites were abandoned in 1992, approximately 100,000 tons of wastes containing arsenic were left in surface areas. These sites are situated in the basins of the Rioni river, and there was an existing high risk of arsenic leakage [14,15].

#### *2.2. Moss Sampling*

Passive moss biomonitoring was performed in compliance with the guidelines of the Convention on Long-Range Transboundary Air Pollution (CLRTAP) and International Cooperative Program on Effects of Air Pollution on Natural Vegetation and Crops monitoring manual Moss Manual 2020 [16]. The following regions are represented: Racha-Lechkhumi and Kvemo Svaneti, Shida Kartli, Mtskheta-Mtianeti, Kvemo Kartli, and Samtskhe-Javakheti. Overall, thirty-five moss samples (*Hylocomium splendens* (Hedw.) Schimp. (4), *Hypnum cupressiforme* Hedw. (12)), *Pleurozium schreberi* (Brid.) Mitt (5), and *Abietinella abietina* (Hedw.) M. Fleisch) (14) were collected during summer 2019. (The number of samples of each type is given in brackets). Three first moss species are recommended for biomonitoring purposes in the Moss Manual-2020 [15], However in some sampling sited the only available species was *Abietinella abietina* (Hedw.) M. Fleisch) which was considered suitable for sampling due to the closeness of its morphological properties with the mosses listed in the Moss Manual. The sampling map is given in Figure 1. From a map Ecosystems of South Caucasus (Figure 2) one can obviously see the variety of ecosystems and climatic zones of the sampled areas.

**Figure 1.** Sampling map.

Samples were collected at least 300 m from the main roads and settlements and at least 100 m away from the side roads, mainly from open areas to avoid the impact of higher vegetation. Longitude, latitude, and elevation were noted for every sampling location using the global positioning system.

**Figure 2.** Ecosystems of South Caucasus [15].

Samples were collected at least 300 m from the main roads and settlements and at least 100 m away from the side roads, mainly from open areas to avoid the impact of higher vegetation. Longitude, latitude, and elevation were noted for every sampling location using the global positioning system.

For each sampling site, details (date of the sampling, weather condition, nearby vegetation, topography, and land use) were noted. Five to ten sub-samples were collected within an area of 50 m × 50 m and mixed in one composite sample. Samples were stored and transported in tightly closed paper bags. To prevent any contamination of the samples, sampling, and sample handling in the field and in the laboratory were performed using disposable polyethylene gloves (without talc) for each sample.

#### *2.3. Sample Preparation and Elemental Analysis*

Each sample was cleaned from extraneous materials in a chemical laboratory. Only green and green-brown shoots were taken and dried to a constant weight at 30–40 ◦C for 48 h. The elemental analysis of each sample was performed using instrumental neutron activation analysis (INAA) and atomic absorption spectrometry (AAS). The procedure of moss preparation for INAA and AAS is described in our previous study [8].

Moss samples were subjected to INAA at the neutron activation analysis facility REGATA of the IBR-2 reactor of the FLNP, JINR (Dubna, Russia). To determine elements with short lived isotopes (Mg, Al, Cl, Ca, Ti, V, Mn, I) samples were irradiated for 3 min and measured for 20 min. To determine elements with long lived isotopes (Na, K, Sc, Cr, Fe, Co, Ni, Zn, As, Se, Br, Rb, Sr, Zr, Mo, Sn, Sb, Cs, Ba, La, Ce, Nd, Sm, Eu, Tb, Yb, Hf, Ta, W, Au, Th and U) samples were irradiated for 3 days, re-packed, and measured twice using HPGe detectors after 4 and 20 days of decay, respectively. The calculation of element concentrations was performed using software developed at FLNP JINR [17].

The AAS was used to determine amounts of Cu, Cd, and Pb in the moss samples using the iCE 3300 AAS atomic absorption spectrometer with electrothermal (graphite furnace) atomization (Thermo Fisher Scientific, Waltham, MA, USA).

The calibration solutions were prepared from a 1 g/L stock solution (AAS standard solution; Merck, DE).

#### *2.4. Quality Control of ENAA and AAS*

In order to evaluate the precision and accuracy of the results, the certified reference materials and standards were used, namely NIST SRM 1575a—Trace Elements in Pine Needles, NIST SRM 1547—Peach Leaves, NIST SRM 1633b—Constituent Elements in Coal Fly Ash, NIST SRM 1632c—Trace Elements in Coal (Bituminous), IRMM SRM 667— Estuarine Sediment, NIST SRM 2711—Montana Soil, NIST SRM 2710—Montana Soil.

Table 1 shows the differences between certified and calculated values of concentrations, where "SRM" were used as standards for calculations of concentrations for SRMs in the column "Sample". Most differences between certified and obtained values are lower than 2 σ. There are no such data for elements Mo, Sn, W, and Au because their certified values are in the irradiated SRM only.

**Table 1.** Epithermal Neutron Activation Analysis (ENAA): obtained and certified values of reference materials, mg/kg.


A comparison of heavy metal concentrations obtained using the AAS with the standard values are presented in Table 2. The difference between the certified and measured elements contents of the certified material varied between 1% and 5%.

**Table 2.** Comparison of the atomic absorption spectrometry (AAS)-obtained heavy metal concentrations with the standard values, mg/kg.


#### *2.5. Data Analysis Using PCA*

Principle Component Analysis (PCA) is a special case of factor analysis, which transforms the original set of intercorrelated variables into a set of uncorrelated variables that are linear combinations of the original variables. The first principal component is the linear combination of the variables that accounts for a maximum of the total variability of the data set. The second principal component explains a maximum of the variability not accounted for by the first component, and so on. The objective is to find a minimum number of principal components that explain most of the variance in the data set. The principal components are statistically independent and, typically, the first few components explain almost all

the variability of the whole data set. The minor principal components, which explain only a minor part of the data, can be eliminated, thus simplifying the analysis. Further, these minor components contain most of the random error, so eliminating them tends to remove extraneous variability from the analysis. A wide range in concentrations makes normalization of the data necessary if all the elements are to be given equal weight in the analysis. The values used in the PCA are made dimensionless by this transformation [18]).

#### *2.6. Construction of GIS Maps*

The ArcGis 10.6 software (Esri, Redlands, California, USA) was used to build distribution maps of factor scores over the study area. We are using the OpenLayers library and a few backgrounds like "Oceans", "Gray", "World", "OSM", etc., to generate maps.

#### **3. Results and Discussion**

A summary of the results from the 2019 moss sampling over the study area is presented in Table 3 along with similar data obtained in previous surveys in Georgia in 2014–2017 [8], North Macedonia [19], Bulgaria [20], and pristine country Norway [21]. Data from North Macedonia and Bulgaria were obtained by INAA in Dubna, at the IBR-2 reactor of FLNP JINR using the same hard- and software, whereas Norwegian data is a result of ICP-MS. The Table 3 contains the medians and the lower and upper concentration quartiles of all components. Variability of elemental concentrations is reflected by the total range, which often spans approximately two to three orders of magnitude. Direct comparison of the medians does not show great difference in the elemental concentrations for Georgia and the Balkan countries, whereas maximal values of such element as As and Mo, both in 2014–2017 and 2019, exceed those for North Macedonia and Bulgaria, and it is five times higher than the maximum in Norway. This phenomenon is easily explained by mining and processing of arsenic and the presence of polymetallic ores abundant in the Caucasian Mountains. To demonstrate special behavior of As, the Summary Results for arsenic, iron, zinc, and nickel are presented in Figure 3 from which a strong local As contamination is evident, whereas Fe shows normal distribution, and the others are close to normal. In comparison with Norway, a country with fewer anthropogenic influences, Georgia has higher median values for the elemental content in mosses for almost all air pollution elements (As, Cd, Co, Cr, Cu, Hg, Ni, and Pb) [21].

**Figure 3.** Summary Report for As and some selected elements created byMinitab® 19.


**Table 3.** Comparison of the results obtained in present study with the countries allocated in relatively the same geographical belt. Norway chosen as a pristine area. Concentration is in mg/kg.

\* Elements determined by AAS marked with asterisks.

It is also clearly confirmed by principal component analysis (PCA) used to classify the elements with respect to contribution sources.

PCA was carried out by using the Statistical Package STATISTICA 13.0 Results of factor analysis are presented in Table 4. Communality values close to 1 suggest that the extracted factors explain much of the variance of the individual variable.

**Table 4.** Rotated factor loadings for the Central Georgia data set (36 samples). Varimax normalized. Extraction: Principal components (Marked loadings are > 0.6).


The data set analyzed includes results for 24 trace elements and major components. The PCA indicates four factors, which explain 82% of the total variance.

To visualize the results obtained, the graph on Factor Loadings was built (see Figure 4).

**Figure 4.** Factor Loadings, Factor 1 vs. Factor 2 vs. Factor 3. Rotation: Varimax normalized. Extraction: Principal components.

The results of factor scores are presented in the form of distribution geographic information system (GIS) maps. See Figure 4.

Factor 1 is loaded with Na, Al, Sc, Ti, V, Cr, Fe, Co, Ni, Th, and U, and represents mainly a combination of light and heavy crust component elements in the form of soil dust. It has almost 35% of the total variability and is the strongest factor (Figure 5). The contents of these elements in the moss samples are significantly influenced by the mineral particles that are carried into the atmosphere by winds, and their spatial distribution mainly depends on urban activities that are not related to industrial activities. High contents of elements of this geochemical association have been found in samples taken from the sampling points 28–29 (Racha-Lechkhumi and Kvemo Svaneti region, Ambrolauri municipality); 4 (Racha-Lechkhumi and Kvemo Svaneti region, Oni municipality); 10 (Mtskheta-Mtianeti region, Akhalgori municipality); 13 (Mtskheta-Mtianeti region, Akhalgori municipality); 22–23 (Kvemo Kartli region, Tetritskaro municipality); and 26 (Samtskhe-Javakheti region, Ninotsminda municipality).

Factor 2 contains Cl, K, Zn, Se, Br, I, and Cu and represents a combination of two sub-factors, a marine one: halogens Cl, Br, I and Se [22], and the second one possibly is due of some local agricultural activity. Zinc, potassium and copper are essential elements for several biochemical processes in plants [23]. The concentrations of heavy metals such as zinc and copper in the environment are currently increasing, due mainly to human activities. Copper is still used for protecting purposes in agriculture: it prevents and cure diseases, which can have adverse effects on crop yields and quality. Factor 3 includes As (0.89), Sb (0.85), and W (0.72) which are characteristic for ores used for arsenic extraction. In particular, in the village of Uravi (Ambrolaur region, Western Georgia) a mining and chemical factory functioned during the Soviet era. Arsenic has been mined and processed there for almost 60 years.

**Figure 5.** Factor Scores.

Factor 4 is represented by Ca (0,80) Cd (0.64), Pb (0.64), and Br (0.65) of local anthropogenic origin due to closeness to urban areas. Lead and cadmium enter the environment in the form of impurities in fertilizers, halides and oxides of these metals, as well as bromides which are contained in the exhaust gases of cars, as part of the waste generated during the extraction and processing of used batteries [24]. The highest contents of these elements are found in the moss samples collected from the sampling points 28–33 (Racha-Lechkhumi and Kvemo Svaneti region, Ambrolauri municipality); 17–18 (Samtskhe-Javakheti region, Borjomi municipality); 27 (Samtskhe-Javakheti region, Akhalkalaki municipality); 22–23 (Kvemo Kartli region, Tetritskaro municipality); 24–25 (Kvemo Kartli region, Tsalka municipality); 9 (Mtskheta-Mtianeti region, Akhalgori municipality); and 35 (Mtskheta-Mtianeti region, Dusheti municipality).

#### **4. Conclusions**

For the first time atmospheric deposition of trace elements using moss biomonitoring technique was studied in Central Georgia in 2019. By the comparison of the obtained values for a broad set of elements (Al, As, Ba, Ca, Cd, Co, Cr, Cu, Fe, Hg, K, Li, Mg, Mn, Mo, Ni, Na, Pb, Rb, Sr, V, Zn, Th, and U) with the data from previous surveys in other parts of Georgia and in the countries of the similar climatic conditions (North Macedonia and Bulgaria) it was shown that air pollution in Central Georgia does not exceed mean values for these European countries, whereas data for potentially toxic elements such as As, Cd, Co, Cr, Cu, Ni, Pb, and Zn exceed the ones in Norway used as an example of a pristine country of Europe. Of the four factors, determined by PCA one factor (F4) is purely anthropogenic (Ca, Cd, Pb, and Br) and it is explained by the relevant high factor scores in the urban areas where they may come from fertilizers, halides, and oxides as well as bromides of these metals, which are contained in the exhaust gases of cars, as part of the waste generated during the extraction and processing of used batteries, etc. High As and W loadings if factor 3 are explained by intense mining activity for more than 60 years of As extraction from ores rich in this element and accompanying elements such as antimony and tungsten. A strong marine component (Cl, Br, I, and Se) in factor 2 is provided by the location of Georgia between two seas—the Black and the Caspian ones. In this factor

2 elements of marine component are mixed with Zn, K, and Cu due to most probably agricultural activity. Factor 1 represents mainly a combination of light and heavy crust component elements in the form of soil dust.

**Author Contributions:** Conceptualization, O.C., I.N., K.K., Y.L. and M.F.; Formal analysis, O.C.; Investigation, I.N., K.K., Y.L., A.B., K.V. and D.G.; Methodology, M.F.; Software, O.C.; Writing original draft, M.F.; Writing—review & editing, I.N. and K.K. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was supported by the grant of the Plenipotentiary of Georgia at JINR in 2020 (Order #37 of 22.01.2020).

**Data Availability Statement:** Not applicable.

**Acknowledgments:** This research was supported by the grant of the Plenipotentiary of Georgia at JINR in 2020 (Order #37 of 22.01.2020). The authors express their gratitude to Richard Hoover for editing the English.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


### *Article* **Estimating Background Values of Potentially Toxic Elements Accumulated in Moss: A Case Study from Switzerland**

**Stefano Loppi 1,\* , Zaida Kosonen <sup>2</sup> and Mario Meier <sup>2</sup>**


**Abstract:** Although the use of moss as biomonitor of air pollution is relatively simple, the interpretation of the data needs reference values. Background values for Cd, Cu, Pb, and Zn accumulated in moss samples from Switzerland, collected every five years from 1995 to 2015 in the framework of the European Moss Survey, were statistically estimated. These background values can be used as reference for the assessment of spatial and temporal trends, to be expressed in terms of bioaccumulation ratios with actual values. The use of annual background values is of great importance to identify spatial trends, while period-wide background values identify temporal trends. The latter are consistent with those reported in other comprehensive similar biomonitoring studies in Europe and are required to be updated in time, possibly every five years. The use of cutoff values to be used as benchmark for bioaccumulation ratios is invaluable in having a scale for assessing ecological quality.

**Keywords:** biomonitors; PTE; temporal change; atmospheric pollution; deposition; heavy metal

**Citation:** Loppi, S.; Kosonen, Z.; Meier, M. Estimating Background Values of Potentially Toxic Elements Accumulated in Moss: A Case Study from Switzerland. *Atmosphere* **2021**, *12*, 177. https://doi.org/10.3390/ atmos12020177

Academic Editor: Antoaneta Ene Received: 29 December 2020 Accepted: 26 January 2021 Published: 29 January 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

#### **1. Introduction**

The term "heavy metal", although widely used in the literature, is now deprecated and it has been suggested to replace it with the term of potentially toxic element (PTE), especially in the case of environmental studies [1]. Having a wide variety of emission sources such as motor vehicles, heating systems, industrial plants, etc., PTEs are an important component of air pollution and have a great epidemiological concern because of their persistence in the environment and the negative effects on human health [2].

Since about 80% of the EU population lives in urban areas, urban air pollution affects the quality of life of most citizens. Consequently, urban air pollution has largely been investigated (see, e.g., [3]) and many urban areas have an air quality monitoring network to monitor whether the set environmental standards are met or not. However, studies on atmospheric deposition at remote areas to evaluate the impact of PTEs on ecosystems are less common [4,5] and an evaluation on background pollution in pristine areas is only very rarely accessed. This is often due to economic constraints related to the establishment and maintenance of sophisticated and costly equipment. In such cases, the use of living organisms may be very useful to complement the data obtained by physico-chemical measurements. Since any change taking place in the environment has a significant effect on the biota, biological monitoring (biomonitoring) is a very effective early warning system to detect environmental changes [6].

Carpet-forming moss species are among the most valuable biomonitors of atmospheric pollution as they are highly dependent on wet and dry atmospheric deposition for nutrients and lack a waxy cuticle and stomata, allowing the absorbance of contaminants over the whole moss surface [7]. Additionally, mosses can accumulate persistent pollutants, such as PTEs and are used to measure the amounts of pollutants in the ecosystem that are biologically available [8]. The abundance of these moss species in Scandinavia gave rise to the beginning of the moss monitoring survey making it possible to monitor PTE

pollution on a multicountry-wide scale [9]. Thanks to the standardized method and the relatively low costs as well as the ease of collection of samples, this well-established technique of moss monitoring has been adopted also in other European countries coordinated by an International Cooperative Program on Effects of Air Pollution on Natural Vegetation and Crops (ICP Vegetation) under the United Nations Economic Commission for Europe (UNECE) Geneva Air Convention. Through this program, mosses have been used to evaluate atmospheric deposition of PTEs at several European countries every five years since 1990 [10]. This enabled not only the comparison of spatial patterns of PTE deposition, but also the detection of temporal trends. Presently, also other pollutants have been added to the survey such as nitrogen, persistent organic pollutants (POPs), and, more recently, also microplastics. Although outside of Europe mosses have been sometimes used as biomonitors of air pollution, the wide use of the moss monitoring technique is mainly restricted to Europe, where ca. 80% of the studies have been performed [11].

Switzerland has been participating in the moss monitoring project since 1990 and the Swiss outcomes showed a general decline in element concentrations in time [12], likely determined by the closure of several small industries, mostly metallurgic, as well as improved abatement technology of waste incinerators, the ban of leaded fuel, and the wide use of catalytic converters in cars. This hypothesis is corroborated by the consistency of the temporal trends in moss with those of emission data for some elements [12]. The main aim of the European Moss Survey is to determine spatial differences and temporal changes in the atmospheric deposition of PTEs, estimated by their concentrations in moss. Therefore, it is of paramount importance that these concentrations are properly evaluated in terms of deviation from reference conditions, i.e., that the magnitude of pollution phenomena can be clearly depicted. As environmental quality standards (EQSs) set by legislation for the concentration of PTEs in biomonitors are missing, the interpretation of PTEs' contents in moss requires the estimation of deviation from an unaltered reference (background) condition. Therefore, the first step in any biomonitoring survey should be the definition of appropriate background values to be used as reference. In a second step, this reference can be used to calculate the extent of the deviation from this background condition.

In this paper we aimed to estimate background values for some PTEs, namely Cd, Cu, Pb, and Zn accumulated in moss collected at remote sites of Switzerland in the framework of the European Moss Survey. These background values could be used as reference for the assessment of spatial and temporal pollution phenomena. Bioaccumulation differences between moss species, although sometimes reported as important [13], being also still unclear if this variation is species- or habit-specific, were outside the scope of this paper and were not considered.

#### **2. Materials and Methods**

#### *2.1. Selection of Background Sites and Moss Sampling*

Sampling sites for the European Moss Survey were evenly distributed across the five biogeographic regions of Switzerland: Jura, Plateau, and Northern, Central, and Southern Alps, which differ in elevation, geology, and meteorological conditions as well as in flora, fauna, and population density (Figure 1). Ten remote sites were selected as pristine areas in order to estimate the background deposition. All these sites were situated in alpine regions and were under the influence of only a very modest human activity. Therefore, these sites can be regarded as representative of the "natural" situation of Switzerland.

**Figure 1.** The five biogeographic regions of Switzerland: Jura (J), Plateau (P), Northern Alps (NA), Central Alps (CA), Southern Alps (SA). Adapted from Gutersohn [14]. **Figure 1.** The five biogeographic regions of Switzerland: Jura (J), Plateau (P), Northern Alps (NA), Central Alps (CA), Southern Alps (SA). Adapted from Gutersohn [14].

The moss species *Hypnum cupressiforme* Hedw. and *Pleurozium schreberi* (Willd. Ex Brid) Mitt. were sampled, the former mostly at lowland sites and the latter mostly at Alpine sites. The moss samples were collected from tree trunks, in open areas such as forest clearings, at least 3 m away from the edge of the tree canopy, following the Moss Monitoring protocol [15]. At each site five subsamples were collected. Sampling took place every five years, namely 1990, 1995, 2000, 2005, 2010, and 2015, at the same sites and in the same period of the year (from April to October). According to the five-year cycle, the 2020 moss monitoring survey is currently ongoing, with samples being prepared for analysis and preliminary results expected by the end of 2021. The moss species *Hypnum cupressiforme* Hedw. and *Pleurozium schreberi* (Willd. Ex Brid) Mitt. were sampled, the former mostly at lowland sites and the latter mostly at Alpine sites. The moss samples were collected from tree trunks, in open areas such as forest clearings, at least 3 m away from the edge of the tree canopy, following the Moss Monitoring protocol [15]. At each site five subsamples were collected. Sampling took place every five years, namely 1990, 1995, 2000, 2005, 2010, and 2015, at the same sites and in the same period of the year (from April to October). According to the five-year cycle, the 2020 moss monitoring survey is currently ongoing, with samples being prepared for analysis and preliminary results expected by the end of 2021.

#### *2.2. Chemical Analysis 2.2. Chemical Analysis*

In the laboratory, the samples were cleaned from dead or extraneous material such as litter, needles, soil, insects, etc., and only the green shoots roughly corresponding to the last three years of growth were cut for the chemical analyses. An equal amount of moss biomass was taken from each subsample and combined to form a single composite sample, taken as representative of the site following the Moss Monitoring protocol [15,16]. Samples were then dried at 40 °C. The chemical analyses were performed immediately after complete collection. Due to the time laps, the analyses were performed in different laboratories using the following analytical methods. Prior to the mineralization, each sample was pulverized in liquid nitrogen. Approximately 200 mg of moss powder was then mineralized in a microwave digestion system (Milestone Ethos 1) with 7 mL of HNO<sup>3</sup> and 3 mL of H2O2, using hermetic Teflon vessels at 130 °C and high pressure (100 bar). The mineralized and diluted solutions (up to 50 mL) were analyzed by inductively coupled plasma mass spectrometry (ICP-MS Perkin Elmer—Sciex, Elan 6100). The results are expressed on a dry weight basis (µg g−1 dw). Analytical quality was checked by analyzing several Standard Reference Materials: the moss standards M2 and M3 (*Pleurozium schreberi* [17,18]), BCR 61 (*Platihypnidium rapariodides*) and BCR 62 (*Olea europea*) [19], and LMS 2 (a laboratory internal moss reference material, [20]). In the laboratory, the samples were cleaned from dead or extraneous material such as litter, needles, soil, insects, etc., and only the green shoots roughly corresponding to the last three years of growth were cut for the chemical analyses. An equal amount of moss biomass was taken from each subsample and combined to form a single composite sample, taken as representative of the site following the Moss Monitoring protocol [15,16]. Samples were then dried at 40 ◦C. The chemical analyses were performed immediately after complete collection. Due to the time laps, the analyses were performed in different laboratories using the following analytical methods. Prior to the mineralization, each sample was pulverized in liquid nitrogen. Approximately 200 mg of moss powder was then mineralized in a microwave digestion system (Milestone Ethos 1) with 7 mL of HNO<sup>3</sup> and 3 mL of H2O2, using hermetic Teflon vessels at 130 ◦C and high pressure (100 bar). The mineralized and diluted solutions (up to 50 mL) were analyzed by inductively coupled plasma mass spectrometry (ICP-MS Perkin Elmer—Sciex, Elan 6100). The results are expressed on a dry weight basis (µg g−<sup>1</sup> dw). Analytical quality was checked by analyzing several Standard Reference Materials: the moss standards M2 and M3 (*Pleurozium schreberi* [17,18]), BCR 61 (*Platihypnidium rapariodides*) and BCR 62 (*Olea europea*) [19], and LMS 2 (a laboratory internal moss reference material, [20]).

#### *2.3. Statistical Analysis 2.3. Statistical Analysis*

For each element, the background data set was first checked for outliers using the Tukey test; in case an outlier emerged, its value was replaced by the median value of the remaining data set [21]. Based on this data set, for each element and for each year, median values and confidence limits were estimated (note that the confidence limits are not necessarily symmetric around the sample estimate, as is the case when standard errors are used to construct the confidence intervals) by bootstrapping [22]. For each element, For each element, the background data set was first checked for outliers using the Tukey test; in case an outlier emerged, its value was replaced by the median value of the remaining data set [21]. Based on this data set, for each element and for each year, median values and confidence limits were estimated (note that the confidence limits are not necessarily symmetric around the sample estimate, as is the case when standard errors are used to construct the confidence intervals) by bootstrapping [22]. For each element, the sig-

the significance of differences between years was evaluated by means of a pairwise

nificance of differences between years was evaluated by means of a pairwise permutation test [23], correcting for multiple testing according to Benjamini and Hochberg [24].

#### **3. Results and Discussion**

Based on our estimates of background concentrations (Table 1), very different trends emerged for the four elements investigated. The background value of Cd showed a continuous and constant decrease, with differences requiring 10 years (two moss monitoring project sampling campaigns) to become significant. Lead showed a sharp decrease in the first two periods and from the year 2000 differences became insignificant. Copper, although with some higher values, did not show any temporal trend. Values for Zn, although decreased from 1990 to 2010, remained, overall, quite constant, without any significant difference through years.

**Table 1.** Median and 95% confidence limits (c.l.) of Cd, Cu, Pb, and Zn concentrations (µg g−<sup>1</sup> dw) in moss at background sites of Switzerland. Different letters in a column indicate statistically significant (*p* < 0.05) differences.


An important consequence of this methodological approach for estimating background element concentrations in moss is that, in addition to a proper selection of background sites, background values may change in time due to efforts to reduce emissions. Also, legal reference values for environmental pollutants measured instrumentally are updated in time, with the progress of measurement techniques, increase of knowledge, and decreasing environmental concentrations. In this light, the assessment of background values of PTEs should be intended as a dynamic process, with periodically updating with the most recent data.

To detect a significant accumulation for a given PTE, values must be significantly different from those measured at background areas. A commonly adopted approach in this sense (see, e.g., [25]) is to use the upper limit of the confidence interval as threshold. After having assessed this background threshold, a very simple and reliable method to estimate the degree of deviation from background conditions is to calculate the ratio between the concentration of a given element to its background value. This approach has the great advantage of allowing element- and species-specific differences to be overcome, thus permitting the use of a single interpretative scale in any circumstance. This method has been implemented, e.g., in the European Water Framework Directive (WFD), using so-called ecological quality ratios (EQRs), and is now successfully used is many studies [26]. Following this approach, Cecconi et al. [27] suggested an interpretative scale for Italian foliose lichens based on bioaccumulation ratios (B ratios), i.e., ratios of actual values to background values, established according to the 25th, 75th, 90th, and 95th percentiles of the frequency distributions as follows: <1 no bioaccumulation, 1–2.1 low bioaccumulation, 2.1–3.4 moderate bioaccumulation, 3.4–4.9 high bioaccumulation, >4.9 severe bioaccumulation.

On the basis of this scale, we compared the background values estimated for each period with those measured at the remaining 65 sites [12] distributed among the five Swiss biogeographic regions sampled in the corresponding year (Table 2). This comparison shows an overall absence of bioaccumulation or low bioaccumulation for all elements at all regions, with the notable exception of Pb at the Southern Alps, which showed values up to severe bioaccumulation. On an average basis, the Southern Alps were always the region with the highest bioaccumulation. However, maximum values of B ratios (data not shown) indicated a very wide array of variation, with some values reaching severe bioaccumulation for all the four investigated elements. The Southern Alps are exposed to winds from the South and, therefore, also to pollution coming from that direction (e.g., from the Po Plain, a nearby and highly polluted area of Italy), and at the same time they act as a barrier to air pollutants for the other Swiss regions.

**Table 2.** Bioaccumulation ratios (median values) at the five Swiss biogeographic regions during the six European moss surveys, using annual background values and the remaining 65 sampling sites. See Figure 1 for explanation of regions.


Estimated element background values in moss for the whole period 1990–2015 (Table 1) are consistent with those estimated for the lichen *Flavoparmelia capera* (a species with ecological requirements similar to those of *H. cupressiforme* and *P. schreberi*) from Italy (Cecconi et al. 2019): Cd = 0.18 µg g−<sup>1</sup> dw, Cu = 6.2 µg g−<sup>1</sup> dw, Pb = 2.4 µg g−<sup>1</sup> dw, and Zn = 35.3 µg g−<sup>1</sup> dw. Additionally, these data also match background values estimated with a Bayesian approach in the lichen *Evernia prunastri* from Tuscany, Italy [28]: Cd = 0.13 µg g−<sup>1</sup> dw, Cu = 5.1 µg g <sup>−</sup><sup>1</sup> dw, Pb = 2.4 µg g−<sup>1</sup> dw, and Zn = 22.5 µg g−<sup>1</sup> dw. Overall background values are also in line with the lowest values of the scales adopted to map element bioaccumulation in the European moss surveys [10]: Cd = 0.1 µg g−<sup>1</sup> dw, Cu = 4 µg g−<sup>1</sup> dw, Pb = 2 µg g−<sup>1</sup> dw, and Zn = 20 µg g−<sup>1</sup> dw.

Using these whole-period background values (upper limit of 95% confidence limits), ratios with values measured at the five Swiss biogeographic regions during the six surveys (Table 3) are indicative of temporal changes. At all regions, a gradual decrease can be seen for Cd and a marked drop for Pb; values of Zn also indicated a modest decreasing trend from 1995 to 2015, while Cu values remained quite constant. Also, in the case of temporal changes, the Southern Alps were the Swiss region with the highest bioaccumulation ratios.


**Table 3.** Bioaccumulation ratios (median values) at the five Swiss biogeographic regions during the six European moss surveys, using whole-period background values. See Figure 1 for explanation of regions.

The concentrations of trace elements (Cel) accumulated by biomonitors can be converted into estimates of PTE deposition rates (D) [3–5,29] according to the formula:

$$\mathbf{D} = \mathbf{C}\_{\text{el}} \cdot \mathbf{R} \cdot \mathbf{t}^{-1} \tag{1}$$

where R is the weight/area ratio and is the time period the sample is covering. Assuming that the final concentrations in moss represent an average equilibrium of three years with the environmental conditions at the site and knowing that pleurocarpous moss species have a weight/area ratio of 175 g m−<sup>2</sup> [30], we were able to estimate annual element depositions rates at background Swiss sites as follows: Cd = 8 g km−<sup>2</sup> y −1 , Cu = 0.4 kg km−<sup>2</sup> y −1 , Pb = 0.2 kg km−<sup>2</sup> y −1 , and Zn = 1.9 kg km−<sup>2</sup> y −1 . Estimated values of Cd and Pb were consistent with the lowest annual total deposition of these elements modeled for Switzerland (Cd < 10 g km−<sup>2</sup> y −1 , Pb < 0.28 kg km−<sup>2</sup> y −1 ) in the framework of the EMEP (European Monitoring and Evaluation Program) project, a co-operative program for monitoring and evaluation of the long-range transmission of air pollutants in Europe [31].

According to Aboal et al. [32], estimation of bulk atmospheric deposition from moss data is possible only for some PTEs such as Cd and Pb, because these elements are almost exclusively of atmospheric origin. However, for other elements this relationship is more labile since element concentrations in moss represent a steady state of non-equilibrium with the surrounding environment, rather than a time-integrated measure of element deposition [33]. Nevertheless, despite these limitations, which are intrinsic of a biological surrogate method, moss monitoring remains a very useful and efficient way of determining spatial and temporal patterns of PTEs, allowing the detection of relevant element sources and temporal trends. From this perspective, small- as well as large-scale biomonitoring surveys have, thus, their own value in regional, national, and international projects aiming at evaluating biological effects of airborne PTEs.

#### **4. Conclusions**

We statistically estimated background values for Cd, Cu, Pb, and Zn accumulated in moss from Switzerland using 10 remote sites. Our data showed that background values change over time and can be used as reference, when assessing spatial and temporal trends expressed in terms of bioaccumulation ratios with values at other sites. The use of annual background values is of great importance to identify spatial trends, while the background value over all sampling periods helps to identify temporal trends. The latter are consistent with those reported in other comprehensive, similar biomonitoring studies and require to be updated in time, possibly every five years. The use of cutoff values to be used as benchmark for bioaccumulation ratios is invaluable in having a scale for assessing ecological quality.

**Author Contributions:** S.L. conceived and designed the study; M.M. provided the data; Z.K. managed and reprocessed the raw data; S.L. and Z.K. analyzed the data; S.L. wrote the paper; Z.K. and M.M. supervised the text. All authors have read and agreed to the published version of the manuscript.

**Funding:** The moss collection and chemical analyses were funded by the Swiss Federal Office for the Environment. (FOEN).

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Raw data on moss data can be found in the publication FOEN: Bern, Switzerland, 2018.

**Acknowledgments:** We would like to thank the Swiss Federal Office for the Environment (FOEN) for funding the moss study in the context of the ICP Vegetation European Moss. We are grateful to the various Institutes for analyzing the moss samples: WSL, Birmensdorf, Switzerland (1990); BGR, Hannover, Germany (1995); LUFA, Hamlen, Germany (2000); VAR Tervuren, Belgium (2005, 2010); and SJI Ljublijana, Slovenia (2015). Our gratitude also goes to all the field and laboratory assistants of the various institutions for analyzing the samples and to the people who collected the mosses.

**Conflicts of Interest:** The authors declare no conflict of interest.

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