Environ. Sci. Technol. 2008, 42, 8661–8667
Total Nitrogen Content and δ15N Signatures in Moss Tissue: Indicative Value for Nitrogen Deposition Patterns and Source Allocation on a Nationwide Scale H A R A L D G . Z E C H M E I S T E R , * ,†,‡ ANDREAS RICHTER,§ STEFAN SMIDT,| D A N I E L A H O H E N W A L L N E R , ‡,⊥ INGRID RODER,⊥ SABINE MARINGER,§ AND WOLFGANG WANEK§ Faculty of Life Sciences, University of Vienna, Althanstrasse 14, 1090 Vienna, Austria, Ecotox-Austria, Company for Monitoring Environmental Pollution, Fleschgasse 22, 1130 Vienna, Austria, Department of Chemical Ecology and Ecosystem Research, Faculty of Life Sciences, University of Vienna, Althanstrasse 14, 1090 Vienna, Austria, Federal Research and Training Centre for Forests, Natural Hazards and Landscape (BFW), Senkendorff-Gudent-Weg 8, 1130 Vienna, Austria, and Umweltbundesamt, Spittelauer La¨nde 5, 1090 Vienna, Austria
Received July 5, 2008. Revised manuscript received September 17, 2008. Accepted September 25, 2008.
To evaluate a new N-monitoring program in the framework of the UN-ECE ICP-Vegetation program using mosses as bioindicators, 490 moss samples were collected at 220 sites in Austria and analyzed for total N (N content) and δ15N signatures. Within-site variability of N content and δ15N signatures was tested for the first time on a large scale and was extremely low compared to between-site variability. N content in moss tissue ranged between 0.76% and 1.99% and δ15N signatures between -10.04 and -2.45. Altitude was significantly correlated with N content (P ) 0.021) and δ15N signatures (P < 0.001). When comparing moss data to deposition data from 35 measurement sites, significant correlations between N content and N deposition (P ) 0.014) were found. Increasing δ15N signatures provided evidence for a change in N source and its respective isotopic composition with altitude, e.g., due to longdistance transport of reactive N or as a result of changes in the wet:dry deposition ratio. Our study underlines that N deposition can generally be estimated by N content in mosses on a large scale, but that this approach has certain limitations, especially in areas with large differences in altitude and precipitation.
* Corresponding author e-mail:
[email protected]; tel: +43-1-8792994; fax: +43-1-8792994. † Faculty of Life Sciences, University of Vienna. ‡ Ecotox-Austria, Company for Monitoring Environmental Pollution. § Department of Chemical Ecology and Ecosystem Research, Faculty of Life Sciences, University of Vienna. | Federal Research and Training Centre for Forests, Natural Hazards and Landscape (BFW). ⊥ Umweltbundesamt. 10.1021/es801865d CCC: $40.75
Published on Web 11/04/2008
2008 American Chemical Society
Introduction Nitrogen input into ecosystems is increasing on a worldwide level (1, 2), as are the detrimental effects of enhanced N input on mostly oligotrophic habitats (e.g., bogs, dry grasslands, lakes) and selected inhabitants of a wide range of systems (3). Critical loads (N ha-1yr-1) are exceeded in Europe for numerous areas and habitats (4). Human activities have greatly increased the abundance of reactive forms of N that are responsible for other forms of environmental damage as well (climate change, ozone production, etc.). This urgently calls for deposition measurements and N-monitoring networks. Structured N-monitoring networks should be favored that combine highly sophisticated Nmonitoring sites with an inexpensive, wide-ranging biomonitoring network (5). The latter could be provided by biomonitors, and moss has been targeted by many groups and programmes (e.g., UN-ECE ICP-Vegetation) for this purpose. Deposition methods based on technical equipment are well developed and have been extensively implemented. This study considers two deposition monitoring networkssboth part of national and international networkssand compares them to a moss monitoring network. A wide range of monitors and in particular biomonitors for N deposition has been described in the past decade (6, 7). Some biological monitors are based on predictable alterations in species composition and diversity due to enhanced nitrogen deposition. Species changes are thought to result from shifts in competition reflecting the different abilities of the species to use enhanced nitrogen (8, 9). Nitrogen tissue concentration is another approach to monitor nitrogen deposition. A series of organisms has been used, but these showed different outcomes under comparable conditions according to their different strategies regarding N uptake (10). Mosses have been reliable indicators for numerous atmospheric pollutants like (heavy) metals, PAHs, PCDD/ F, and dioxins for more than three decades (11-13). In the past few years, monitoring of nitrogen deposition by mosses has proliferated. The results demonstrate that mosses provide a fairly accurate picture of the airborne nitrogen deposition because the N content of moss increases with N deposition (7, 14, 15). The N content of mosses reflects N deposition better than N concentration of most of the other plant species. This is because mosses obtain nitrogen mainly from atmospheric deposition; for the species used for monitoring, there is hardly any interference by soil nitrogen. To date, the analysis of the N content of moss tissue has been used to map nitrogen deposition on smaller scales only (7, 16). To further investigate the quantitative importance of different sources of N deposition, the metal contents and stable N isotopes (15N:14N ratio, expressed relative to an international standard as δ15N value) of bryophytes have successfully been applied (17-19). The two main sources of atmospheric N released by humans are oxidized and reduced compounds. They can have different δ15N signatures: NOx tends to have high δ15N signatures compared to NHy, which usually has lower δ15N signatures (17). According to their direct N uptake from the atmosphere, it is generally accepted that δ15N signatures of mosses reflect those of nitrogen deposited from the atmosphere (16, 17). Given that major sources of deposited nitrogen differ in their δ15N values, a source attribution is possible based on moss δ15N measurements. VOL. 42, NO. 23, 2008 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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The aim of this study was therefore (a) to test whether data obtained from the biomonitoring method (N content, δ15N signatures and 15 other elements) correlate with data from N-monitoring networks based on physical and chemical measurements (wet deposition of Ntot, NO3-, NH4+, SO4, H+, and S). As the accuracy of every monitoring method inherently entails variation between small and large scales (20), we (b) tested for the first time whether within-site variation is smaller than between-site variation of N content and δ15N in moss samples on a regional/national scale. Furthermore, we (c) investigated whether δ15N values and metal contents of moss tissues allow the main N sources of deposition to be differentiated across a national scale. These three research questions were tested with 490 samples from 220 sampling sites according to the nationwide moss monitoring program within the UN-ECE Vegetation Programme on Heavy Metal Monitoring (21).
Material and Methods Moss Samples were taken in September 2005 at 220 sites distributed on a regular grid across Austria. This corresponds to a density of approximately 2.5 sites per 1000 km2. The sampling sites have been part of a national monitoring program within the framework of the UN-ECE ICP Vegetation program on monitoring heavy metals by mosses (21). The 220 sampling sites covered a strong climatic gradient from suboceanic climatic conditions in the west of Austria to a continental climate in the eastern parts. Sampling sites ranged from 120 to 1800 m asl. Site vegetation was characterized and classified into four types: woods, meadows, seminatural sampling sites (fens, bogs, heathlands), and urban surroundings. This site classification was taken as the basis for analyzing potential N-sources. The sampling protocol was according to the international protocol of the UN-ECE program for sampling mosses to estimate atmospheric heavy metal deposition (22). Sampling sites were located at open sites (e.g., clearances, open meadows, bogs); any shielding by canopy was avoided (neither trees nor bushes or tall herbs). Samples, consisting of 5 subsamples, were taken within a 50 × 50 m area to achieve homogeneity of subsamples. At 68 sites, the five subsamples were analyzed separately to investigate the local variability with regard to N content and δ15N signatures. Moss species for monitoring were selected for comparability in morphological structure and pattern of N uptake: Hylocomium splendens (Hedw.) B.S. & G. (50% of the total samples), Pleurozium schreberi (Brid.) Mitt. (30%), Abietinella abietina (Hedw.) Fleisch (10%), Hypnum cupressiforme Hedw. s.str. (6%) and Scleropodium purum (Hedw.) Limpr. (4%). Before analysis, mosses were cleaned from obvious contamination (soil particles, litter etc.), and only green and yellow-green parts of the mosses were taken. Therefore, the concentrations of N and δ15N reported in this study represent a deposition period of approximately two years before sampling. Concentrations of Metals (Al, As, Cd, Co, Cr, Cu, Fe, Hg, Ni, Mo, Pb, Sb, S, V, and Zn) and S in moss were compared to N content and δ15N signatures. Metal data were taken from (23). All elements were analyzed from the same moss samples and sites used for analysis of N content and δ15N signatures. Metal data were taken as indicators for possible sources and pathways of N-deposition. Chemical Analyses of Moss Samples (N content, δ15N). Bryophyte materials were dried for 24 h at 80 °C. Samples were homogenized in a ball mill (Retsch MM2, Haan, Germany) and aliquots of 1 to 2 mg of each sample were weighed into tin capsules and analyzed for N concentration and nitrogen isotope abundances by isotope ratio mass spectrometry (IRMS) at the Department of Chemical Ecology 8662
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and Ecosystem Research, University of Vienna. The continuous-flow IRMS system consisted of an elemental analyzer (EA 1110, CE Instruments, Milan, Italy) which was connected to an IRMS (DeltaPLUS, Finnigan MAT, Bremen, Germany) by a ConFlo III interface (Finnigan MAT). Reference gas (high purity N2 gas, Air Liquide, Vienna, Austria) was calibrated to the atmospheric N2 standard (at-air) using IAEA-NO-3, IAEAN-1 and -2 reference materials (International Atomic Energy Agency, Vienna, Austria). The natural abundance of 15N was calculated as follows: δ15N[% o vs at - air] ) (Rsample ⁄ Rstandard - 1) × 1000 (1) where R is the ratio of 15N/14N. The standard deviation of repeated measurements of a laboratory standard was 0.15‰ for δ15N and 0.01% for N. Deposition Data were obtained from two monitoring networks: (1) A network within the framework of European Forest Monitoring (UN-ECE ICP Forest; 20 sites; established by the BFW (Federal Centre for Forest Research) and (2) a network within a national program (15 sites), which also comprised three stations within the EMEP network. Deposition data were taken only from those stations that had data for the time frame within which the collected moss material grew (2004 and 2005). Overall, data on the deposition of N, NO3-, NH4+, SO4, H+, and S and mean annual precipitation were available from 35 stations. Data were taken in compliance with national law (24) and international regulations (25). Data of network (1) have been published by (26) and data of network (2) by (27-31). Precipitation Data for all 220 sites were derived from (32) if not covered by the above-mentioned stations. The Austrian average annual precipitation during the study period was slightly above 1000 mm (33). Therefore, this value was taken as a threshold for the division into sites with annual precipitation below average (low precipitation sites) and above average (high precipitation sites). Statistical Analysis. Spearman rank correlations were calculated to study relations between element concentrations and parameters derived from physical measurements as well as data obtained by other sources (e.g., type of site, altitude). An ANOVA was performed to calculate the relationship between various parameters. The Kruskal-Wallis Test was calculated to test differences according to moss species. The Durbin-Watson Statistic was used to test for autocorrelation or serial correlation in the residuals of a least-squares regression analysis. The significance level for all statistical analyses is P < 0.01, if not stated otherwise. PCA was initially used for data reduction of metals from all 220 sites to identify an appropriate number of metals that explain most of the variance. Subsequently, selected metals and all other parameters from 36 deposition sites were used to generate hypotheses regarding causal mechanisms between these variables. Geostatistical Analysis was performed by “kriging”, which is a geostatistical interpolation technique that uses a linear combination of surrounding sampled values to generate predicted values for unmeasured locations. Attempting to minimize the error variance and systematically setting the mean of the prediction errors to zero, kriging generates the weights applied to each surrounding sample, yielding optimal and unbiased estimates. The best estimation model for generating the output surface is developed in a variogramanalysis. On the basis of the estimation model, the precision of the prediction results can be characterized in crossvalidation. The method applied here is ordinary kriging, the most commonly used type of kriging which assumes a constant but unknown mean.
FIGURE 1. N content of moss tissue at 220 sampling sites in Austria and interpolation by kriging; nugget:sill ) 0.61; cross validation: mean standardized ) -0.002; root-mean-square standardized ) 0.985.
FIGURE 2. δ 15N signatures of moss tissue at 220 sites in Austria and interpolation by kriging; nugget:sill ) 0.52; cross validation: mean standardized ) -0.005; root-mean-square standardized ) 1.024.
Results The N content in moss tissue ranged between 0.76% and 1.99%, with an average of 1.21% ( 0.25 (Figure 1). The mean δ15N signature value was -6.04 ( 1.27, with site averages ranging between -10.04 and -2.45 (Figure 2). Data for N content and δ15N signature were normally distributed. There was no significant correlation between N content and δ15N signatures. Sampling sites with more than 1000 mm precipitation and those with less rain differed significantly (P < 0.001) in their δ15N signatures but not in N content. Altitude and N content (P ) 0.021) and altitude and δ15N signatures (P < 0.001, Figure 3) were significantly correlated, while classification of site vegetation was unrelated to N content and δ15N.
The δ15N signatures differed significantly between Abietinella abietina (Hedw.) Fleisch. and the other species (P < 0.001). Thus, all tests using δ15N signatures were performed by including and excluding this species. However, the results in terms of significances did not change between these tests. Here, we therefore show only results that include data from all sites and all species. Across all sampling sites, we found significant correlations between N content and the following elements (elements given in descending order of correlation coefficient; Table 1): S, Cu, Pb, Zn, Cd, Sb, Hg, and Ni. δ15N signatures correlated strongly with Pb and weaker with Fe and Al (Table 1). Testing Within- and Between-Site Variance by ANOVA of five subsamples each at 68 sites revealed significant variance (P < 0.001) for both variables (N content, δ15N VOL. 42, NO. 23, 2008 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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FIGURE 3. Linear regression analysis for variables altitude (m) and δ15N (‰) from all 220 sampling sites (R2 ) 26%; P ) 0.000).
TABLE 1. Correlation Coefficients of Parameters Derived from All 220 Sites
15N
δ signature %N site altitude Pb V S Zn Fe Cu Al Cr Ni Cd Mo Co Hg Sb As a
P < 0.05,
b
δ 15N signature
%N
1.000 -0.032 -0.043 0.514c -0.235c -0.035 -0.076 -0.099 -0.164a -0.009 -0.136a -0.036 -0.019 -0.063 0.007 0.058 -0.127 0.010 -0.114
-0.032 1.000 0.056 -0.155a 0.459c -0.170a 0.731c 0.427c 0.140a 0.488c 0.123 0.026 -0.178b 0.322c 0.000 -0.110 0.229c 0.233c 0.088
P < 0.01; c P < 0.001.
TABLE 2. Correlation Coefficients of All Parameters from Measurement Sites all stations (n ) 35)
stations >1000 mm precipitation (n ) 14)
N content δ 15N signature N content δ 15N signature -0.05 0.25 0.22 -0.11 0.41b 0.09 0.23 -0.14 0.02
H+ NH4+-N NO3--N S N NH4+/NO3site altitude precipitation a
P < 0.05,
b
0.08 -0.12 0.04 0.14 0.14 -0.09 -0.22 0.64c 0.16
0.040 0.566a 0.645a 0.081 0.519a -0.020 0.363 -0.349 0.319
0.408 -0.703b -0.467 -0.144 -0.044 -0.281 0.47a 0.65b -0.200
P < 0.01; c P < 0.001.
signature). Within-site variation was very low compared to between-site variation, especially for δ15N signatures. Average within-site variance was 0.4‰ for δ15N signature compared to 5.9‰ between-site variance of 68 sites. For N content, within- and between-site variance was 0.02% and 1.28%, respectively. Comparison of Moss and Deposition Data. N Content. All 35 sites for which N deposition data were available together revealed a significant correlation between N content and N deposition (P ) 0.013), though not with NH4+ or NO3deposition (Table 2). Dividing areas according to their precipitation regime, we found a correlation between N content and N deposition (P ) 0.05) for high precipitation 8664
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FIGURE 4. Principle component analysis of all data obtained by moss analysis (N content, δ15N, Al, Cd, Mo, Pb, Zn) and deposition data (N total, S total, NO-3, NH4+, NO3,-/ NH4+ relation, H+, precipitation, altitude, type of site) for 35 stations; component 1 ) 29.9%, component 2 ) 21.3%.
FIGURE 5. Principle component analysis of all data obtained by moss analysis (N content, δ15N) and deposition data (N total, S total, NO3,- NH4+, NO3,-/ NH4+ relation, H+, precipitation, altitude, type of site) for 14 stations with a precipitation > 1000 mm; component 1 ) 42.3%, component 2 ) 18.4%. sites (>1000 mm yr-1). At these sites, moss N content and NO3- deposition (P ) 0.013) as well as NH4+ deposition (P ) 0.035) were correlated (Table 2). In contrast, at low precipitation sites (
