Detection of temporal trends in atmospheric deposition of inorganic nitrogen and sulphate to forests in Europe

Atmospheric deposition to forests has been monitored within the International Cooperative Programme on Assessment and Monitoring of Air Pollution Effects on Forests (ICP Forests) with sampling and analyses of bulk precipitation and throughfall at several hundred forested plots for more than 15 years. The current deposition of inorganic nitrogen (nitrate and ammonium) and sulphate is highest in central Europe as well as in some southern regions. We compared linear regression and Mann e Kendall trend analysis techniques often used to detect temporal trends in atmospheric deposition. The choice of method in ﬂ uenced the number of signi ﬁ cant trends. Detection of trends was more powerful using monthly data compared to annual data. The slope of a trend needed to exceed a certain minimum in order to be detected despite the short-term variability of deposition. This variability could to a large extent be explained by meteorological processes, and the minimum slope of detectable trends was thus similar across sites and many ions. The overall decreasing trends for inorganic nitrogen and sulphate in the decade to 2010 were about 2% and 6%, respectively. Time series of about 10 and 6 years were required to detect signi ﬁ cant trends in inorganic nitrogen and sulphate on a single plot. The strongest decreasing trends were observed in western central Europe in regions with relatively high deposition ﬂ uxes, whereas stable or slightly increasing deposition during the last 5 years was found east of the Alpine region as well as in northern Europe. Past reductions in anthropogenic emissions of both acidifying and eutrophying compounds can be con ﬁ rmed due to the availability of long-term data series but further reductions are required to reduce deposition to European forests to levels below which signi ﬁ cant harmful effects do not occur according to present knowledge. © 2014 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license


Introduction
Forest ecosystems have been exposed to increased atmospheric deposition of sulphur (S) in the form of sulphate (SO 4 2À ) and inorganic nitrogen (N) since the 1950s, resulting from anthropogenic emissions of sulphur dioxide (SO 2 ), nitrogen oxides (NO x ) and ammonia (NH 3 ).Deposition of these compounds is a major driver for various changes in forest ecosystems.It may alter nutrient limitations and lead to increased forest growth and carbon (C) sequestration (e.g. de Vries et al., 2008;Solberg et al., 2009), accelerate soil acidification (e.g.Ulrich et al., 1980) and eutrophication effects (e.g.Aber et al., 1998) as well as mobilising aluminium in soil solution to levels that are toxic for roots (Cronan et al., 1989).Eutrophication effects include loss of nutrients by leaching, elevated nitrate (NO 3 À ) levels in percolation and runoff water (Dise et al., 2009), nutrient imbalances in trees, and altered susceptibility to pests and diseases (Flückiger and Braun, 1999).Determination of temporal trends of atmospheric deposition of S and N compounds to forests is therefore of considerable interest.Measures were implemented to reduce the emissions of S and N compounds during the last three decades (Reis et al., 2012).Deposition assessments in long time series are required (i) to monitor the success of these measures in reducing deposition and (ii) to investigate the impact of deposition on the long-term stability of forest and its ecosystem services at selected intensively monitored sites (Paoletti et al., 2010).
For this purpose, temporal trend analyses based on bulk precipitation and throughfall measurements performed under the framework of the International Cooperative Programme on Assessment and Monitoring of Air Pollution Effects on Forests (ICP Forests) are regularly carried out at the intensively monitored sites of the ICP Forests Level II network and published on pan-European level (e.g.Lorenz and Granke, 2009;Granke and Mues, 2010;Waldner et al., 2012).Further trend analyses of parts of the ICP Forests deposition data and other data have been carried out at the national, regional and European levels using various methods (Meesenburg et al., 1995;Kvaalen et al., 2002;H unov a et al., 2004;Rogora et al., 2006;Fagerli and Aas, 2008;Vanguelova et al., 2010;Graf Pannatier et al., 2011;Oulehle et al., 2011;Pihl Karlsson et al., 2011;Staelens et al., 2012;Verstraeten et al., 2012;Johnson et al., 2013;Marchetto et al., 2013).
However, the commonly reported absolute trend slopes and percentage of statistically significant trends vary and seem to be partly contradicting.This may be due to the variation of methods used in these studies, e.g.different trend analysis techniques, variations in length and temporal resolution of time series, spatial variation of emission time trends or other factors influencing deposition.
The main aims of this study were to: ▪ determine and explain the minimum detectable trend on a single plot with deposition measurements carried out according to the ICP Forests manual ▪ investigate the influence of trend analysis technique, time series length and temporal resolution on the detection of statistically significant trends ▪ assess bulk deposition (BD) and throughfall deposition (TF) of SO 4

2À
, nitrate (NO 3 À ) and ammonium (NH 4 þ ) and their trends across Europe at ICP Forests sites
In general, collectors (3e20 replicates) are placed in the forest based on a random or fixed systematic design in order to cover the spatial variation (Switzerland: Thimonier, 1998;United Kingdom: Houston et al., 2002;Belgium: Staelens et al., 2006).Samples are collected at least monthly (typically fortnightly or weekly), filtered, and stored below 4 C before chemical analyses are performed to determine the concentrations of SO 4 2À , NO 3 À , and NH 4 þ .The laboratory results are checked for internal consistency based on the conductivity, the ion balance, the concentration of total N and the sodium to chloride (Na/Cl) ratio, and analyses are repeated if suspicious values occur (Mosello et al., 2005(Mosello et al., , 2008;;ICP Forests, 2010).The quality assurance and control (QA/QC) procedures further include the use of control charts for internal reference material to check long-term comparability within national laboratories, as well as participation in periodic laboratory ring tests (e.g.Marchetto et al., 2009) and field inter-comparisons (Draaijers et al., 2001;Zlindra et al., 2011a) to check the international comparability.
Data were reported annually to the pan-European data centre, checked for consistency and stored in the programme database.

Data processing
Data for the period from 1999 to 2010 were used in this analysis.Precipitation and throughfall data sampled during more than 330 days per year, and with concentration values for more than 300 days per year were included.Sampling periods with mean precipitation below 0.1 mm day À1 were counted even if no chemical analyses could be performed.
Data from each sampling period were interpolated to regular monthly and annual data by: (i) splitting each sampling period overlapping two consecutive months by distributing precipitation quantity in proportion to the duration of the new sampling periods; (ii) setting deposition ¼ 0 for periods with missing concentrations and mean precipitation <0.1 mm day À1 ; (iii) calculating TF and BD (Q$c$10 À2 , in kg ha À1 ) by multiplication of the precipitation quantity (Q, in L m À2 ), the concentrations (c, in mg L À1 ) and the unity conversion factor 10 À2 ; (iv) summing up to fluxes by month and year, respectively.
Mean annual fluxes of SO 4 2À eS and the inorganic N species NO 3 À eN and NH 4 þ eN for 2010 were calculated for 286, 282, and 287 TF plots and 266, 265, and 268 BD plots, respectively.

Trend analyses
We analysed the temporal trends of individual time series for sets of plots with continuous measurements from 2007 to 2010 (4 years), from 2005 to 2010 (6 years), from 2003 to 2010 (8 years), from 2001 to 2010 (10 years) as well as for 1999 to 2010 (12 years).We checked that time series were normally distributed and showed a seasonal pattern (see Annex).
For each time series, we calculated a relative slope rslope (yr À1 ), as an estimated mean relative change per year, with rslope ¼ b=meanðyÞ; (1) where b (kg ha À1 yr À2 ) is the estimator for the absolute trend resulting from the trend analyses and mean (y) (kg ha À1 yr À1 ) the mean value of the time series.

Temporal variability (background signal)
The temporal variability of the original data (CV0), data after removing estimated temporal trend (CV1), and data after removing temporal trend and seasonality (CV2) were determined for each time series and averaged for each parameter (see equation (3) in Annex).

Minimum detectable trends
Minimum detectable trends rslope emp min were derived empirically from the p-values and the rslope results of the individual trend analyses for each combination of parameter, time series length and trend analysis technique.The rslope emp min value above which the majority of tests identify a trend as significant, with p < 0.05 (at significance level 95%), was determined by fitting a Gauss shaped function through the band of p-to rslope values of the test results (see Annex).
Secondly, minimum detectable trends were modelled based on the temporal variability of the overall dataset with where n years is the duration of time series in years, n is the number of observations (n ¼ n years for annual and n ¼ 12$n years for monthly data), CV the coefficient of variation of the temporal variability (see deposition are attributable to anthropogenic emission of NO x , SO 2 and NH 3 (Reis et al., 2012).Other contributions are of natural origin.CV1: coefficient of variation after correction for linear trend, CV2: coefficient of variation after correction for linear trend and seasonality.
For example, parts of the high SO 4 2À deposition along the coast occur together with high Cl À deposition (e.g. at some Norwegian coastal sites), which is typical for SO 4 2À originating from sea salt (Granke and Mues, 2010).
The measurements support the findings of modelling and mapping approaches (e.g.Posch et al., 2012) according to which atmospheric deposition of SO 4 2À and N compounds still exceeds critical loads in parts of Europe.Critical loads apply to total deposition (TD), i.e. the sum of wet and dry deposition.In forests, TD of N is typically a factor of 1e2 higher than TF, due to uptake by plant tissue and through stomata in the canopy (Draaijers and Erisman, 1995).For SO 4

2À
, TD is generally assumed to be equal to TF (Draaijers and Erisman, 1995).For N, the ranges from 5 to 15 and from 10 to 20 kg ha À1 yr À1 have been proposed as empirical critical loads for coniferous and broadleaved deciduous woodland, respectively (Bobbink and Hettelingh, 2011).

Trend analyses and derivation of minimum detectable trends
The slope estimates resulting from the trend analysis techniques LR, MK, SMK and PMK agreed well.The agreement between trend techniques increased with length of the time series, and with increasing rslope (Fig. 2, left-hand side).There was less agreement between trend analysis techniques in terms of identifying a trend as being significant or not (Fig. 2, right hand side).
The minimum detectable trend rslope emp min decreased with increasing length of the time series and was typically smaller for methods applied to monthly data (SMK, PMK) compared to tests applied to annual data (MK, LR), as shown in Fig. 3 for SO 4 2À , NO 3 À and NH 4 þ in TF.

Temporal variability
The temporal variability of deposition varied little from plot to plot or from ion to ion (Table 1).The temporal variability was on average about 20e60% higher for monthly data than for the annual sums.The corrections for linear trends, and for seasonality, reduced the temporal variation on average by about 5e10%.
The temporal variability was quite similar for all ions and not much higher than that of precipitation quantity Q (L m À2 yr À1 ), which might be surprising at first glance (Table 1).Andersson et al. ( 2006) used a chemistry transport model (CTM) and estimated that the average European land-area inter-annual variability of SO 4 2À and inorganic N deposition, due to meteorological variability, ranged from 11 to 14% for TD and to about 20% for wet deposition.Kryza et al. (2012) confirmed that meteorology can lead to an interannual variation of 20% and stated that precipitation quantity is generally the more important factor, except for regions such as the UK, where the circulation pattern might become more important.Therefore, it is likely that most of the temporal variability is explained by the variability of air circulation, i.e. the source region and pollution level of the air masses, and the precipitation, i.e. the scavenging of the gaseous and particulate compounds transported in the atmosphere.For most compounds, the temporal signals of the emissions in a region, e.g. from fossil fuel burning, are probably much smoother than those of deposition.However, NH 4 þ shows a slightly higher overall variability than NO 3 À and SO 4 2À which may be caused by spatially and temporally more variable emission sources.The emissions from agricultural land in the form of NH 3 are a major source of NH 4 þ in precipitation and throughfall, and the emissions are themselves strongly influenced by local weather conditions (Wichink Kruit et al., 2012).The temporal variability found here is likely to be valid for other substances transported over similar pathways.It seems that the method to estimate rslope min presented here is generally applicable for most of the major compounds in BD and TF, even when using just the temporal variability values shown in Table 1.

Estimated minimum detectable trends
The minimum detectable trend rslope emp min determined empirically from the trend test results can to a large extent be explained by the mean short-term temporal variability.The rslope emp min (equation (5), Annex) correlated well to the rslope mod min (equation ( 2)) estimated from CV1 and CV2 values in Table 3.The PMK test showed the highest scatter.The co-variable not considered in equation ( 2) but used in this PMK test may be a reason for this higher scatter.For the parameter c 7 in equation ( 2) ðrslope emp min =rslope mod min Þ we found values between 1 and 2.5 that have little dependence on the trend technique applied, the parameter or the time series length (Table 3).
Monthly data involve more data points than annual data, which seems to be favourable for detecting trends despite (i) the uncertainty of monthly data interpolation and (ii) their generally higher temporal variability.

Comparison of minimum detectable trend to sources of uncertainty of the measurements
This study suggests that the data quality objective to 'detect a change of 30% in 10 years' which is defined in the ICP Forests manual (ICP Forests, 2010) seems realistic.It has to be mentioned that this study only investigated the uncertainty related to the statistical methods.However, uncertainties related to the steps prior to the trend analyses (Thimonier, 1998;Houston et al., 2002;Bleeker et al., 2003;Erisman et al., 2003;Staelens et al., 2006;Marchetto et al., 2011;Zlindra et al., 2011b) were on average lower in magnitude than the uncertainty resulting from the temporal variability of the deposition.

Deposition trends
The results on minimum detectable trends are reflected in the stronger agreement of slopes between trend techniques and the smaller scattering of slope among plots for longer time series and for PMK and SMK compared to LR and MK (Table 2).
The trends (Table 2) agree well with the findings of earlier studies (Table 3), which is more obvious when comparing rslope values.The low percentages of significant trends found in several studies are to a large extent due to the expected trends being low compared to minimum detectable trend.No mean rslope is given in Table 3 because the slope values of non-significant changes are often omitted in literature.
Between the peak emission in the 1980s and the turn of the millennium as well as for the decade around the millennium, rslope values for SO 4 2À were typically between À5% and À10% in central Europe and between À12 and þ3% in northern and western Europe (Table 3).The percentage of plots with significant trends was especially high in central Europe.In comparison, the rslope values of significant and nonsignificant changes of N deposition were lower, typically between þ1 and À5%, and the percentage of plots with significant trends was also lower, especially when the time series were short.
For the 10 year period, typical rslope values for N compounds were around À2% per year (Table 2).Hence, typically about 10 years of data were required to detect such a trend on a plot with statistical significance with PMK (Fig. 4).For SO 4 2À with typical rslope values of 4e6%, the corresponding requirement was about 6 years of data.
The strongest decreasing trends during the 10 year period were found in northern central Europe from Belgium and the Netherlands to Germany and for N compounds the region of strongest trends (0.2 to À0.15 kg ha À1 yr À2 ) extended further to Switzerland, France, Italy, Czech Republic, Slovakia and Denmark.
Sites with non-significant changes in deposition were distributed all over Europe (Fig. 5).In the 6 year period, stable or slightly increasing SO  , has become marginal in recent years, while increasing traffic counteracts the effect of stricter emission norms for vehicles.
Other reasons for changes in deposition to forest areas are changes in the tree stand structure, such as the reduction of the number of trees due to bark beetle attacks as reported for a Czech forest (Boh a cov a et al., 2010), forest age, or high levels of nitrate in insect frass falling from the canopy as reported for sites in the UK (Pitman et al., 2010).
For inorganic N especially, the decreasing trends seem too slight to avoid exceedance of the critical loads for acidification and eutrophication in different parts of European forests in the near future (Reis et al., 2012).Further reduction of N emissions is needed to prevent air pollution effects on forests.

Conclusions
The selection of the trend analysis technique had an effect on trend detection.There was a strong agreement between estimated  trend slopes from the different techniques, but SMK and PMK tests applied to monthly data tended to detect smaller trends with statistical significance than LR or simple MK techniques applied to annual data and these tests are therefore recommended for trend analysis.
A consistent relationship between the rslope and p-value of the trend tests was obvious for a given length of time series.The choice of the trend analysis technique, the investigated fluxes and the specific element or ion had less influence on the minimum detectable trend slope rslope min .It seems likely that the minimum detectable trend rslope min can be derived from the mean temporal variability caused mainly by meteorological phenomena.
For time series with a length of 10 years, the rslope min for inorganic N compounds and SO 4 2À seemed to be a change of around 3e4% per year for tests applied to the monthly data in this study.
In more than half of the sites a decrease in SO 4 2À deposition was strong enough to be identified as statistically significant at the plot level in the periods 2001e2010 and 2005e2010.For deposition of inorganic N compounds, relative changes were smaller and significant decreasing trends were only found for about a quarter of the plots.Overall, decreasing trends for SO 4 2À and inorganic N compounds of about À6% and À2% per year respectively were typical for the 10 year period up to 2010.Trend estimates of individual sites however ranged from À15% to 7% per year.The strongest decreasing trends were found for sites in western central Europe in regions with relatively high deposition fluxes whereas stable or slightly increasing deposition during the last 5 years were found in and east of the Alpine region as well as in northern Europe.
For inorganic N compounds, the trends in atmospheric deposition (BD and TF) as a result of emission reductions in Europe are unlikely to be detected with statistical significance in time series shorter than 10 years.For SO 4 2À , typical trends were stronger, especially in the 1990s, and could be detected even in shorter time series.The deposition trends can to a large extent be attributed to the reductions of the emissions of air pollutants achieved between 1990 and 2010.Despite decreasing trends at numerous plots, total deposition of inorganic N compounds and SO 4 2À to forests still exceeds critical loads in parts of Europe.Continued long-term deposition monitoring will be necessary to demonstrate the effectiveness of emission reduction measures and to investigate observed effects on the ecosystems caused by deposition.
The applied methods for determination of atmospheric deposition fluxes have been further developed and harmonized by numerous scientists in the Expert Panel on Deposition of ICP Forests subsequently chaired by G. L€ ovblad, E. Ulrich, N. Clarke and K. Hansen.Applying these methods involved numerous technicians for the installation and maintenance of about 4000 samplers, collection of roughly a million samples, and chemical analyses of approximately 200,000 pooled samples, as well as on-going supervision by some 40þ scientists.Data transmission involved national focal centres and the data centre of ICP Forests that was subsequently at Alterra, Wageningen (FIMCI), at the Joint Research Centre in Ispra and at the Programme Coordination Centre of ICP Forests in Hamburg.Data transmission included sophisticated conformity and plausibility checks developed by the involved database specialists.The comparability of the laboratory analyses has been improved by the activities of the Working Group on QA/QC in laboratories initiated and supported by R. Mosello, N. K€ onig, K. Derome, the late John Derome, A. Kowalska, A. Marchetto and others.For field installations, similar activities were coordinated by G. Draaijers, A. Bleeker, J. Erisman, E. Ulrich, and D.
Zlindra.Data quality objectives and other methodological improvements were the results of activities initiated by the QA/QC committee of ICP Forests chaired by M. Ferretti.The atmospheric deposition measurements typically involved access being granted by the land owners, financial support from the participating countries and the EU, as well as support from subordinated governmental organisations such as communal or forest services.The EU partially funded the deposition network under the Council Regulation (EEC) 3528/86 on the 'Protection of Forests against Atmospheric Pollution' and the Regulation (EC) No 2152/2003 concerning monitoring of forests and environmental interactions in the community (Forest Focus) and by the project LIFE 07 ENV/D/000218 "Further Development and Implementation of an EU-level Forest Monitoring System (FutMon)".
The presented evaluation involved national representatives responsible for the deposition measurements.Thanks to all who contributed.

Checking normal distribution and seasonality
The ShapiroeWilk test (R function 'shapiro.test', c.f. Royston, 1982) was applied to each data series to check whether deposition values were normally distributed.To test for seasonality, we further carried out a linear regression (R function 'lm') for the monthly data (y) with a model of two superposed harmonic waves with wavelengths of one and half a year, respectively, i.e.
(3) where y (kg ha À1 yr À1 ) is the deposition, t years (years, as a continuous number) the time, t ¼ 2 p t years , ε the remainder and the intercept a (kg ha À1 yr À1 ), the slope b (kg ha À1 yr À2 ), and c 1 to c 4 (kg ha À1 yr À1 ) are parameters.Seasonality was assumed if at least one of the seasonality terms (c 1 to c 4 ) was identified as being significant (p-value < 0.05).
The seasonality test confirmed seasonality for 85% of the time series.The remaining time series often had one of the seasonality terms (c 1 ec 4 ) almost reaching the p < 0.05 threshold for significance (97% of p-values <0.2).Therefore, seasonality was assumed and SMK and PMK were applied to all time series.

Determining minimum detectable trend from individual trend results
The relative slope values (rslope) were plotted against the pvalues (p) for each combination of trend analysis techniques, flux, ion and period, to investigate patterns that may be used to define a minimum detectable trend for deposition data.As shown in Fig. 6 for the example of NO 3 À TF series analysed with LR, we found most pvalues to be within a narrow band with the shape of a Gaussian curve when plotted against the rslope.This band was narrower for longer time series and wider for shorter time series.In the 10 years time series of NO 3 À in TF tested with LR, most plots with absolute values of rslope above about 5% per year have significant trends (p < 0.05), whilst plots with rslope below 5% have trends that are not significant (p > 0.05) for many plots.Hence, we can assign a minimum detectable trend rslope min of about 5% for the 10 years time series.With a non-linear regression (R function 'nls'), we fitted a Gaussian shaped curve to the points on the rslope vs. p-value diagram for trend test results of bulk and throughfall deposition series of the same variable, the same length, and trend analysis technique.The curve was described by where c 5 ¼ 0.8 is the amplitude that in contrast to the normal distribution was fixed, m the rslope value of the peak and s a measure of the horizontal aperture of the Gaussian curve, which was used to derive the minimum detectable trend.
We defined the minimum detectable trend rslope emp min as the value above which the majority of tests identify a trend as significant, with p < 0.05 (at significance level 95%).In a visual assessment of the Gauss shaped curves fitted through the points on the perslope-diagram, we found, that rslope min corresponds well to rslope emp min ¼ c 6 s up fit ; (5) where s up fit is the upper value of the confidence interval for s that resulted from fitting the equation ( 4) to the data points, and c 6 ¼ 2 a parameter.
Temporal variability (background signal) The temporal variability was explored for the (i) original data (y), (ii) the data after removing estimated temporal trend (y1) and (iii) the data after removing temporal trend and seasonality (y2).Trends and seasonality were removed with, y1 ¼ y À b t years þ meanðyÞ; (6) y2 ¼ y À mean month ðyÞ þ meanðyÞ; (7) where mean(y) is the overall mean, and mean month (y) the mean over one month in all years.Hereafter we estimated the coefficients of variation CV0, CV1 and CV2 for y, y1 and y2, respectively.The resulting temporal variability values are summarised in Table 1 and illustrated in Figs.7 and 8.

Relation between background signal and minimum detectable trend
In order to estimate the effect of the temporal variability on the value of the minimum detectable trend, we applied a Student's ttest to a two step stair approximation of a linear trend.As a working hypothesis, we assumed that (i) the time series of n years (years) length is split into two halves, (ii) the mean of the values of the second half differs by Dm from the mean of values of the first half, with Dm ¼ 1=2 n years rslope m (8 where m is the mean of all values, and (iii) the temporal variability results in a normal distribution around the mean with s given with where CV can be approximated with CV1 for annual and CV2 for monthly data.
In this case, the minimum detectable trend rslope min can be modelled based on the temporal variability when inserting equations ( 8) and ( 9) into the test equation of the Student's t-test, which is and for rslope we get the equation ( 2).Note that in eq (2), c 7 in theory is assumed to be c 7 ¼ 1.We used CV ¼ CV1 of annual data for LK and MK, and CV ¼ CV2 of monthly data for SMK and PMK.We then compared these modelled rslope mod min estimates (equation ( 2)) with the rslope emp min values derived from trend test results (equation ( 5)) and derived empirical c 7 values.

Fig. 3 .
Fig. 3. Minimum detectable trends derived from the p-value to rslope plots of trend analyses with LR, and MK of annual, SMK and PMK of monthly SO 4 2À , NO 3 À and NH 4 þ TF deposition time series with continuous data from 2007 to 2010 (4 years), from 2005 to 2010 (6 years), from 2003 to 2010 (8 years), from 2001 to 2010 (10 years), and from 1999 to 2010 (12 years).

Fig. 6 .
Fig. 6.Relative slope (rslope) and p-value of linear regression (LR) trend test for annual NO 3 À throughfall deposition time series groups from 2007 to 2010 (4 years), from 2005 to 2010 (6 years), and from 2001 to 2010 (10 years) with a Gaussian shaped curve fitted to each group using non-linear regression techniques.Trend tests with p-value <0.05 (black horizontal line) are significant (at 95% significance level).The intersections of the curves with the horizontal line (circles) were used as empirical values for the minimum detectable trend (rslope min ), i.e. the rslope range outside which the majority of the trends are significant.
southern Baltic and the central Hungarian area, and in some Mediterranean regions in Spain, France, southern Italy and Greece (Fig.1).Highest inorganic N BD (not shown) and TF deposition was recorded in northern central Europe, as for SO 42À , but also in southern Germany and the Swiss Plateau and further to the west, in northern France, the central UK and Ireland.The regions bordering the Alps in the south and some sites in Spain and in southern France also showed relatively high N deposition.Considerable parts of the regionally higher inorganic N and SO 4 2À

Table 1
Temporal variability of annual and monthly deposition of NH 4 þ eN, NO 3 À1 yr À1 ) and precipitation quantity Q (L m À2 yr À1 ) time series from plots with continuous data from 2001 to 2010 (10 years).

Table 3
Ranges of relative trends of S and N deposition (maxjmin rslope in % yr À1 ) in Europe and percentage of plots with significant trends found by other studies.