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Atmospheric Environment 49 (2012) 186e196

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Atmospheric Environment

journal homepage: www.elsevier .com/locate/atmosenv

Trends in atmospheric nitrogen and sulphur deposition in northern Belgium

Jeroen Staelens a,b,*,1, Karen Wuyts a,c, Sandy Adriaenssens a, Philip Van Avermaet d, Hilde Buysse d,Bo Van den Bril d, Edward Roekens d, Jean-Pierre Ottoy e, Kris Verheyen a, Olivier Thas e,Ellen Deschepper e,2

a Laboratory of Forestry, Ghent University, Geraardsbergsesteenweg 267, B-9090 Gontrode, Belgiumb Laboratory of Applied Physical Chemistry (ISOFYS), Ghent University, Coupure 653, B-9000 Gent, BelgiumcDepartment of Bioscience Engineering, University of Antwerp, Groenenborgerlaan 171, CGB-V5.08, B-2020 Antwerpen, Belgiumd Flemish Environment Agency, Air Quality Networks, Kronenburgstraat 45, B-2000 Antwerpen, BelgiumeDepartment of Applied Mathematics, Biometrics and Process Control (BIOSTAT), Ghent University, Coupure 653, B-9000 Gent, Belgium

a r t i c l e i n f o

Article history:Received 30 August 2011Received in revised form25 November 2011Accepted 28 November 2011

Keywords:Acidifying depositionAir pollutionEutrophicationSemiparametric modelsTime trend

* Corresponding author. Laboratory of Forestraardsbergsesteenweg 267, B-9090 Gontrode, Belgiufax: þ32 9 264 9092.

E-mail address: jeroen_staelens@yahoo.com (J. Sta1 Present address: Flemish Environment Agency,

nenburgstraat 45, B-2000 Antwerpen, Belgium.2 Present address: Biostatistics Unit, Faculty of M

5K3 Ghent University Hospital, De Pintelaan 185, 900

1352-2310/$ e see front matter � 2011 Elsevier Ltd.doi:10.1016/j.atmosenv.2011.11.065

a b s t r a c t

Temporal trends (2002e2009) in air concentrations and wet and dry atmospheric deposition of inorganicnitrogen (N) and sulphur (S) were determined for nine stations in northern Belgium (Flanders). Wetdeposition of NH4

þ, NO3� and SO4

2� was measured with wet-only precipitation collectors and airconcentrations of NH3, NO2 and SO2 with passive samplers. Dry deposition was calculated from the airconcentrations and literature-based deposition velocities. Generalized additive models were used toassess seasonal and long-term trends of biweekly measurements. Kendall tests on annual data were alsoapplied but found to be less powerful. There was no trend in wet N deposition, while wet deposition ofSO4

2� and air concentrations of NH3 and SO2 decreased significantly (P < 0.05) at seven of the ninestations. For NO2, no significant long-term trend was detected, but opposite to the other compounds, theNO2 concentration tended to increase at all stations. Overall, inorganic N deposition and potentiallyacidifying deposition (N þ S) to grassland decreased significantly at seven stations. The N and N þ Sdeposition to grassland in 2009 was generally below deposition targets for 2010, but the difference wasnot significant when accounting for data uncertainty using a bootstrap resampling procedure. For moststations, atmospheric deposition to heathland and deciduous forest insignificantly exceeded the targets,while deposition to coniferous forest was significantly too high. Consequently, additional policymeasures are needed to reach deposition targets in order to prevent further eutrophication and acidi-fication of (semi)natural ecosystems and to protect groundwater layers in Flanders.

� 2011 Elsevier Ltd. All rights reserved.

1. Introduction

Anthropogenic emissions of sulphur dioxide (SO2), nitrogenoxides (NOx) and ammonia (NH3) contribute to environmentalproblems affecting human and ecosystemhealth such as productionof tropospheric ozone and aerosols, acidification and eutrophicationof ecosystems, soil nutrient depletion, reduction in diversity of plantand animal species, climate change and stratospheric ozonedepletion (Sutton et al., 2011). Trends in air and precipitation

y, Ghent University, Ger-m. Tel.: þ32 9 264 9031;

elens).Air Quality Networks, Kro-

edicine and Health Sciences,0 Gent, Belgium.

All rights reserved.

concentration andwet deposition havemost often been analysed bysimple linear least-squares regressions of annual data (Brani�s, 2008;Fowler et al., 2005; H�unová et al., 2004), although multiple linearregressions (Thoni et al., 2008), non-linear regressions (Seto et al.,2002) and nonparametric Kendall and seasonal Kendall tests(Fagerli and Aas, 2008; van der Swaluw et al., 2011) on annual ormonthly data have been reported as well. However, trends inenvironmental data can also be analysed statistically by the moreinnovative method of generalized additive models (Hastie andTibshirani, 1990), as has been reported for trends in water qualityvariables (Ferguson et al., 2008; Clement and Thas, 2009) and airpollutant concentrations (Pearce et al., 2011).

Flanders, the northern region of Belgium, is exposed to nitrogen(N) and sulphur (S) deposition levels that are amongst the highest inEurope (de Vries et al., 2007), with a high deposition of reduced N(NHx) in particular. Monitoring of wet-only precipitation composi-tion started in 1984, but at three stations only, with a less suitable

J. Staelens et al. / Atmospheric Environment 49 (2012) 186e196 187

device setup and incomplete data series. Therefore, a new deposi-tion monitoring network in line with international standards wasset up in 2001. The focus of this network, operated by the FlemishEnvironment Agency (www.vmm.be), was onwet deposition but airconcentration measurements by diffusive samplers were includedas well. However, time trends of measured air concentrations andatmospheric N and S deposition in Flanders have not yet beenreported. Therefore, the aim of this study was to examine trends inmeasured wet deposition (NH4

þ, NO3� and SO4

2�) and air concentra-tions (NH3, NO2 and SO2) and derived dry and total deposition (NHx,NOy, SOx, N and N þ S) at nine stations in Flanders during2002e2009. Time trends were analysed per station with Kendalltests using annual data and with generalized additive models usingbiweekly data. Furthermore, air concentrations and depositions in2009 were compared with policy targets using bootstrap resam-pling to account for data variability and uncertainty.

2. Material and methods

2.1. Study sites

The study sites were located in northern Belgium (Flanders),which borders to the North Sea in the west and has a temperatemaritime climate. Long-term (1981e2010) mean annual precipita-tion is 852 mm in the centre of this region and mean annual

Fig. 1. Location of deposition monitoring stations in northern Belgium (Flanders). BON: BoRetie; TIE: Tielt-Winge; WIN: Wingene; ZWE: Zwevegem.

temperature is 10.5 �C (Royal Meteorological Institute of Belgium;www.meteo.be). Annual precipitationmeasured in thepresent study,averagedovernine stations (Fig.1) and eight years (2002e2009),was743mm,which is 12% lower than themeanamount at a reference sitein the south of the region (www.meteo.be). The mainwind directionis southwest, followed by northeast, and rainfall is mostly associatedwith southwest wind (www.meteo.be).

The N and S deposition monitoring network consists of ninestations (Fig. 1) selected to meet, as far as possible, the require-ments of ISO 5667-8 for wet deposition sampling and Directive1999/30/EC for ambient air concentration sampling. However,required distances to highways, industrial installations and urbanareas could not be completely respected because of the highlyfragmented, urbanized and industrialized landscape with a densetraffic network. All stations are fenced and located on or nearbygrassland within nature reserves or extensively managed agricul-tural areas. Site altitude ranges from 5 to 92m a.s.l. (SupplementaryTable S.1). The monitoring station at Mol was relocated by 10 km toRetie in 2005 and both locations were considered as one station(RET) for the analysis.

2.2. Data collection and chemical analysis

Each station was equipped with a wet-only precipitationsampler, a tipping bucket gage and passive samplers. All stations

nheiden; GEN: Gent; KAP: Kapellen; KOK: Koksijde; MAA: Maasmechelen; RET: Mol/

Table 1Dry deposition velocity (cm s�1; minimum, maximum and mean value) of NH3, NO2

and SO2 indicative of four vegetation types as derived from a literature survey.

Vegetation type NH3 NO2 SO2

Min. Max. Mean Min. Max. Mean Min. Max. Mean

Grassland 0.7 1.5 1.1 0.1 0.4 0.25 0.3 1.5 0.9Heathland 0.8 2.2 1.5 0.1 0.4 0.25 0.8 1.6 1.2Deciduous forest 0.8 3.0 1.9 0.1 0.4 0.25 0.3 1.5 0.9Evergreen forest 2.0 3.8 2.9 0.1 0.4 0.25 0.3 1.7 1.0

J. Staelens et al. / Atmospheric Environment 49 (2012) 186e196188

have been in use from 1 January 2002 until 31 December 2009,except for Bonheiden that was in use from 22 January 2002 on. Forone station (RET), data were missing from 10 February 2004 to 5June 2005. Air concentrations were not measured in 2004.Precipitation was collected by automatic wet-only samplers (NSA181/KD, Eigenbrodt) with an orifice diameter of 25.4 cm and acti-vated by resistance-driven or optical sensors (RS 85 and IRSS 88,Eigenbrodt). Precipitation was collected weekly until the end of2007 and biweekly afterwards and stored in darkness in 5-L PEbottles at 4 �C. The amount of precipitation was measured bytipping bucket collectors (C101 A, Lastem) with a 16 cm orificediameter until 2008 and with weighing gauges (Pluvio2 200, Ott) in2009. Passive samplers at a height of 3 m and protected fromprecipitation were used to adsorb NH3, NO2 and SO2. Air concen-trations were measured over four-week periods until the end of2003 and over two weeks from 2005 on. Ammonia was measuredusing diffusive samplers (Radiello), first in duplicate and from 2007on with three replicates. Until February 2006, SO2 and NO2 weremeasured in duplicate by combined samplers (Radiello) andthereafter with three replicates of another sampler type (IVL) pergas (see Ferm and Rodhe, 1997, for more detailed information).

Precipitation samples remained cool and in darkness duringsampling, transport and storage. Within one month NO3

�, SO42� and

NH4þ were analysed according to ISO-10304 by ion chromatography

(Dionex DX300 and DX120 until 2005, Dionex ICS1000 afterwards)after passing 20 and 5 mm in-line filters. Samples with a volume<50 ml (equivalent to 1.0 mm of precipitation) were not analyseduntil 2003 andmissing values (n¼ 115 in 2 yr) were replaced by theannual mean volume-weighted solute concentration. From 2004till 2008, samples <100 ml (2.0 mm) were added to and analysedvolume-weighted with the sample of the following collectionperiod (n ¼ 105 in 5 yr). From 2008 on, biweekly samples <100 mlwere not analysed, nor added to the next sample (n ¼ 3 in 2 yr).

Within one week after exposure, NH3 samplers and SO2eNO2samplers were desorbed with ultrapure water, which was analysedusing spectrophotometry (NH3) or ion chromatography (SO2 andNO2; Swaans et al., 2007). The air concentrations for the Radiellosamplers were calculated from the desorbed ion amounts usinga temperature-dependent diffusivity, based on a laboratory vali-dation for NH3 (Swaans et al., 2005) and controlled factoryinstructions for NO2 and SO2 (Swaans et al., 2007). The IVL samplersfor NO2 and SO2 were sent to Sweden for analysis by spectropho-tometry (NO2) and ion chromatography (SO2), from which airconcentrations were determined accounting for temperature-dependent diffusivity (Ferm and Rodhe, 1997). The precision andaccuracy of the measurements is described in detail in theSupplementary Information (Methods A).

2.3. Data processing

Wet deposition of NH4þ, NO3

� and SO42� was calculated per

sampling period by multiplying the precipitation amount by thewet-only ion concentrations. Dry deposition of NH3, SO2 and NO2was estimated by multiplying the air concentrations derived fromdiffusive sampler measurements by a literature-based averagedvalue for the dry deposition velocity (vd). The value of vd amongstothers depends on the considered gas, meteorological conditions,surface roughness and stomatal characteristics, and thus variesover time and with vegetation type. Based on a literature survey ofpeer-reviewed gradient or inferential model studies, a range andmean of gas-specific vd values were determined for four vegetationtypes (Table 1). The survey is described in detail in theSupplementary Information (Methods B). The aim of calculatingdry (gas) deposition and total (wetþ gas) deposition per vegetationtype was to allow an assessment of the potential range of N and S

depositions and the exceedance of policy targets for varying typesof vegetation cover. Since wet deposition was measured directlyand dry deposition was estimated based on dry deposition veloci-ties, the reported wet deposition is more accurate and precise thanthe dry deposition.

The variables studiedwere precipitation amount (mmperiod�1),air concentration (mg m�3) of NH3, NO2 and SO2, and wet, dry andtotal deposition (molc ha�1 period�1) of NHx, NOy, SOx, inorganic N(NHx þ NOy) and the sum of potentially acidifying N and S (N þ S).The deposition of different compounds was estimated for the fourvegetation types, but in the trend results we focus on the depositionto grassland. Deposition fluxes are given in molc of protons (Hþ)ha�1 period�1 by considering that one mole of NHx and NOy formsone Hþ in the soil and that one mole of SOx results in two Hþ. Thiscalculation accounts for soil microbial transformation of NH4

þ toNO3

� with the release of Hþ. Nitrite contributed less than 1% to thepotentially acidifying wet deposition (results not shown) and wasnot further taken into account. Weekly precipitation and wet-onlydeposition values were summed to biweekly data. To cope withthe four-weekly air concentration data in 2002e2003, theseconcentrations values were attributed to the first two weeks ofa four-week period while the remaining two weeks were consid-ered as having missing values.

2.4. Statistical analysis

Time trends were investigated per station by two methods:Kendall tests using annual data and generalized additive models(GAMs) using biweekly data. First, Kendall tests (Kendall, 1975)were used to detect monotonically increasing or decreasing trendsin annual precipitation, air concentrations and deposition fluxes ata station. This non-parametric regression method is based on theranks of the observations and does not assume a fixed relationshipin the time trend. When a linear trend can be assumed, the medianslope and trend line is calculated non-parametrically (Fig. 2). Toexamine a common time trend over the nine stations, a regionalKendall test (Helsel and Frans, 2006) was used. This test corrects forthe spatial dependence between stations by estimating a spatialcovariance structure based on the data.

Second, time trends were investigated by GAMs. Based onbiweekly data, the models considered both a long-term trend overthe study period as well as a seasonal trend over the year (Fig. 3):

yt ¼ C þ f1ðx1tÞ þ f2ðx2tÞ þ 3t (1)

in which yt is the dependent variable, C is a constant term indi-cating the mean over time, f1(.) and f2(.) are functions of the inde-pendent variables x1t and x2t for a seasonal and long-term trend,respectively, and 3t is a stochastic error term with a mean of zero.The error terms 3t were considered to be correlated according toa first order autoregressive covariance structure, which assumesa constant correlation rn between residuals that are shifted with ntime steps. Each model was first estimated by assuming indepen-dent error terms, after which the correlation was estimated from

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NH3 (K = -0.42*)NO2 (K = 0.20)SO2 (K = -0.43*)

NH3 (K = -0.14*)NO2 (K = 0.20)SO2 (K = -0.05)

NH3 (K = -0.31*)NO2 (K = 0.43)SO2 (K = -0.26)

NH3 (K = -0.03)NO2 (K = 0.35)SO2 (K = -0.19)

NH3 (K = -0.33**)NO2 (K = 0.23)SO2 (K = -0.11)

NH3 (K = -0.31*)NO2 (K = 0.23)SO2 (K = -0.58)

NH3 (K = -0.30*)NO2 (K = 0.13)SO2 (K = -0.24*)

NH3 (K = -0.27**)NO2 (K = 0.16)SO2 (K = -0.29*)

Fig. 2. Time trends in air concentration (mg m�3) of NH3, NO2 and SO2 based on annual data and Kendall tests at nine measuring stations. Lines visualize the non-parametricallyestimated median slope K (*P < 0.05; **P < 0.01).

J. Staelens et al. / Atmospheric Environment 49 (2012) 186e196 189

model residuals with a one-step time shift to derive the variance-covariance matrix.

For the seasonal trend f1(.) a circular smoother based ongoniometric functions (Giannitrapani et al., 2005) was used toassure data continuity over the study years, with a span of threemonths. The long-term trend f2(.) was modelled by twoapproaches: a nonparametric smoother based on local weightedlinear regressions with a span of one year, and a parametric linearfirst order term. Nonparametric GAMs were used to examine thesignificance of a long-term variation over the study period. As thisapproach has the disadvantage that a trend is difficult to quantify,the long-term trend was also modelled as a linear term, so that Eq.(1) became a semiparametric GAM. This allowed quantifying theassumed linear slope and standard deviation on the parametric

trend term. Approximate F tests for linearity were used to test ifa long-term trend could be modelled linearly. The long-term trendin nonparametric GAMs was visualized in combination with refer-ence bands (Bowman and Azzalini, 1997), which indicate the ex-pected mean in case of a non-significant term, along with a 95%confidence interval. This allowed a visual check of both the shape ofthe nonparametric trend as well as the deviation from the referenceband (Fig. 4). In addition, approximate F tests were calculated withas null hypothesis that there was no long-term trend (Hastie andTibshirani, 1990; Ferguson et al., 2008). Next to the analysis ofbiweekly data per station, GAMs were used to analyse data from allstations together.

Finally, the variability on the air concentration and deposition ofN and S was examined by a parametric bootstrap. In this way,

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Fig. 3. (aec) Biweekly measured air concentrations of NH3, NO2 and SO2 (mg m�3, dots) at one station (BON) with estimated mean trend (central line) and 95% confidence interval(outer lines) according to generalized additive models, (def) estimated additive term for long-term trend per gas and (gei) estimated additive term for seasonality per gas.

J. Staelens et al. / Atmospheric Environment 49 (2012) 186e196190

annual air concentrations of NOx and SO2 could be compared withlimit values for the protection of ecosystems according to Directive2008/50/EC (20 mg SO2 m�3 and 30 mg NOx m�3). Regarding NOx,the NO2 concentrations measured with passive samplers weremultiplied by 1.17 to account for the unmeasured NO, based on datafrom active monitoring devices in the same region (www.vmm.be).For NH3, air concentrations were compared with the current criticallevel in Europe (8 mg NH3m�3; Cape et al., 2009). Annual depositionwas compared with Flemish deposition targets for 2010, 2015 and2030, which are based on Directive 2001/81/EC. The distribution ofeach variable was estimated by resampling (n ¼ 50,000) theobserved data with replacement using the residual differencebetween observed and modelled values. The only assumption ofthis procedure is that the fitted GAM is a correct model estimate.Therefore, nonparametric GAMs were used with a smoother-basedapproach for the long-term trend. To maintain the correlationstructure of the data, residuals of each N and S depositioncompound were resampled simultaneously. To account for theuncertainty in the dry deposition velocities, uniform distributionsranging from the minimum to the maximum derived vd value(Table 1) were used in the bootstrap. All statistical analyses weredone in R 2.13.0 (R Development Core Team, 2011). The GAM scriptwas based on Ferguson et al. (2008) to include a circular smootherfor the seasonal trend in the GAMs.

3. Results

3.1. Kendall tests on annual data vs. generalized additive modelsusing biweekly data

According to the Kendall tests based on annual data, significant(P < 0.05) long-term time trends were detected for 28% of the

tested variables (48 of the 171 cases; indicated in bold inTables 2e4) and only five variables had a trend that was significantat the 1% level (P< 0.01). According to the GAMs based on biweeklydata, in contrast, a significant (P < 0.05) time trend was found for48% of the tested variables (82 of the 171 cases), the majority ofwhich was significant at the 1% (n ¼ 70) or 0.1% level (n ¼ 51). Alltrends that were significant according to the Kendall tests were alsosignificant according to the GAMs. The largest difference in resultsbetween the two methods was found for the total NHx deposition,for which the GAMs on biweekly data resulted in significant trendsfor seven of the nine stations, while the Kendall tests on annualdata found no significant trends.

For the nine stations together, the regional Kendall test resultedin a significant (P < 0.05) time trend for nine of the 19 variables, i.e.the air concentration of NH3 and SO2, the dry deposition of NHx,SOx, N and N þ S, and the total deposition of SOx, N and N þ S. Alsofor the GAMs, tests were carried out for all stations together, but thelong-term variation could not be assumed to be equal at eachstation. As a result, a common GAM including ‘station’ as a variablewas not useful in this study.

3.2. Semiparametric vs. nonparametric generalized additive models

Nonparametric GAMs with a smoother function for the long-term trend generally resulted in almost straight trend lines, asillustrated for NH3 (Fig. 4). Approximate F tests for linearityconfirmed that long-term trends during the study period could bemodelled linearly for 83% of the tested variables (Tables 2e4;P> 0.05 in 142 of the 171 cases). However, for the remaining 17% ofthe variables with a significant non-linearity, a visual check of thereference band figures indicated a limited deviation of linearity(e.g. Fig. 4b, d, f, g). Therefore, in case the F tests indicated

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Fig. 4. Additive smoother-based term for long-term trends (line) of air concentration of NH3 (mg m�3) and reference bands (shading) indicating ‘no long-term trend’ at nine stations.Dots are the residuals of semiparametric GAMs based on biweekly data (cf. Eq. (1)).

J. Staelens et al. / Atmospheric Environment 49 (2012) 186e196 191

significant non-linear trends, estimated linear slope values werestill presented in brackets (Tables 2e4). Approximate F tests for nolong-term trend (results not shown) and visual comparison ofreference bands with smooth time trends (e.g. Fig. 4) resulted insimilar significant long-term trends as the linear trend approach,confirming the validity of the applied semiparametric GAMapproach.

3.3. Wet-only deposition and air concentrations

From 2002 to 2009, no significant long-term trend was found inprecipitation amount (P > 0.40) and wet deposition of NH4

þ

(P > 0.16) and NO3� (P > 0.25) (Table 2). Wet deposition of SO4

2�, in

contrast, declined significantly at seven of the nine stations andcould be approximated by a linear relationship with a slopeof �13 � 6 (WIN) to �21 � 6 (BON) molc ha�1 yr�1. The combinedwet deposition of potentially acidifying NH4

þ, NO3� and SO4

2�

decreased significantly at one station only (BON). The GAMs indi-cated generally no significant seasonal trend in the wet NH4

þ, NO3�

or SO42� deposition.

The NH3 concentration decreased significantly at all stationsexcept for two (BON and MAA) (Table 3). The largest decrease inNH3 (�0.69 � 0.13 mg m�3 yr�1) was found at a station with a highinitial NH3 concentration (ZWE; 9.3 mg NH3 m�3 in 2002; Fig. 2h).For NO2, no significant trend was found during the study period,but in contrast to NH3, increasing NO2 concentrations were

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Fig. 5. Biweekly nitrogen and sulphur (NHx þ NOy þ SOx) deposition (molc ha�1 2 weeks�1) to grassland vegetation (cf. Table 1) (dots) with estimated mean trend (central line) and95% confidence interval (outer lines) according to generalized additive models at nine stations.

J. Staelens et al. / Atmospheric Environment 49 (2012) 186e196192

observed at all stations (Fig. 2). The SO2 concentration declinedsignificantly at seven of the nine stations, two of which (KAP andTIE) could not be approximated by a linear relationship. For the fivestations with a linear decline in SO2, the slope of the trend was of

Table 2Slope and significance of linear time trends in measured precipitation amount(mm yr�1) and wet deposition (molc ha�1 yr�1) of NH4

þ, NO3�, SO4

2�, the sum of NH4þ

and NO3� (N) and the sum of the three compounds (Nþ S) for grassland vegetation at

nine stations (cf. Table 1) according to generalized additive models based onbiweekly data.

Station Water NHþ4 NO�

3 SO2�4 N N þ S

BON �6.8 �11.7 �6.9 �21.1*** �18.6 �39.7*

GEN 14.3 6.5 6 �5.1 12.4 7.4KAP 8.2 �5.3 �5.3 �16.3* �10.6 �27.0KOK 8.1 �1.6 0.3 �4.6 �1.3 �5.9MAA 6.7 �0.3 �4.6 �15.8* �5 �20.7RET 1.7 �12.4 �6.2 �19.6** �18.6 �38.2TIE �3.2 �5.8 �4.8 L14.2*** �10.6 �24.8WIN 6.5 �10.8 �2.4 �13.0* �13.2 �26.2ZWE �3.3 �7.3 �5.2 �17.4*** �12.5 �29.9

*P < 0.05; **P < 0.01; ***P < 0.001. Bold values indicate that the long-term trend inthe annual data was significant (P < 0.05) according to Kendall tests.

the same order (�0.26 to �0.35 mg SO2 m�3). The GAMs indicateda significant seasonality in gas concentrations, with higher valuesduring summer for NH3 and during winter for SO2 and NO2(Fig. 3gei).

3.4. Dry and total deposition

The trends in the derived dry deposition reflected the trends inmeasured air concentrations (Table 3). For the stations with a signifi-cant time trend, the decline in calculated dry deposition to grasslandwasmorepronounced forNH3 (�59�21to�129�29molcha�1yr�1)than for SO2 (�13 � 5 to �43 � 8 molc ha�1 yr�1). The temporalevolution of the dry deposition ofNwas similar to that ofNH3becauseof the absence of significant trends in dry NO2 deposition. The drydeposition of N þ S to grassland declined significantly (�41 � 13to �171 � 33 molc ha�1 yr�1) at all stations, except for one (TIE).

For total deposition (Table 4), the trends in N deposition reflectedthose of dry deposition because of the lack of wet N depositiontrends. The total NHx deposition to grassland declined significantlyat seven of the nine stations, and could, except for one station (RET),

Table 3Slope and significance of linear time trends in measured air concentration (mg m�3 yr�1) and derived dry deposition (molc ha�1 yr�1) of NH3, NO2, SO2, the sum of NH3 and NO2

(N) and the sum of the three compounds (N þ S) for grassland vegetation at nine stations (cf. Table 1) according to generalized additive models based on biweekly data.

Station Air concentration (mg m�3 yr�1) Dry deposition (molc ha�1 yr�1)

NH3 NO2 SO2 NH3 NO2 SO2 N N þ S

BON �0.11 0.05 L0.50*** �21.5 0.8 L43.9*** �21.3 L63.9***

GEN (L0.36***) 0.09 �0.31* (L74.3***) 1.5 �27.6* (L82.0***) (L111.5***)KAP L0.28** �0.11 (�0.45***) L58.4** �1.9 (�40.2***) �46.0* L73.3**

KOK (L0.40***) 0.08 L0.42*** (L80.6***) 1.4 L36.9*** L78.9*** L102.2***

MAA �0.10 0.04 (�0.20***) �19.9 0.6 (�17.7***) 19.7 1.9RET (L0.29**) �0.21 L0.28*** (L60.9**) �3.6 L25.2*** L72.6** L98.5***

TIE (L0.28***) �0.01 (�0.07) (L56.4***) �0.2 (�6.4) (L59.7***) �41.3**

WIN L0.47*** 0.20 �0.15* L95.5*** 3.5 �12.9* L102.5*** L111.6***

ZWE L0.63*** 0.03 L0.33*** L128.3*** 0.5 L29.7*** L135.8*** L170.5***

*P < 0.05; **P < 0.01; ***P < 0.001. Values in brackets indicate that the long-term trend significantly (P < 0.05) deviated from a linear relationship according to approximate Ftests. Bold values indicate that the long-term trend in the annual data was significant (P < 0.05) according to Kendall tests.

J. Staelens et al. / Atmospheric Environment 49 (2012) 186e196 193

be approximated by a linear trend with slopes of �58 � 20to�125� 30molc ha�1 yr�1 (�0.8 to�1.8 kg N ha�1 yr�1). Total SOx

deposition to grassland decreased significantly at all stations, butthe trends generally could not be approximated by a linear rela-tionship according to the approximate F test. Finally, the total N þ Sdeposition to grassland (Fig. 5) decreased significantly at sevenstations, six of which had a linear decline over time (�89 � 31to �186 � 32 molc ha�1 yr�1). At one station (MAA) no significantreduction in both N and N þ S deposition was detected.

3.5. Exceedance of air concentration and deposition policy targets

For NOx and SO2, the annual air concentrations (Fig. 2) did notexceed limit values for the protection of ecosystems, except for twostations in 2003 (BON and GEN). The annual NH3 concentrationswere below the critical level as well, except for one station in 2002(ZWE) and for a second station where the concentration was (notsignificantly) lower than 8 mg m�3 in 2007 and 2009 only (WIN)(Fig. 2).

Calculated total deposition in 2009, averaged over the ninestations, was 21, 25, 28 and 37 kg N ha�1 yr�1 for grassland,heathland, deciduous and coniferous forest, respectively. The Ndeposition in 2009 significantly exceeded the Flemish target for2010 (21.7 kg N ha�1 yr�1) for ten out of 36 combinations of stationand vegetation type (Fig. 6a). At one station (WIN), this target wassignificantly exceeded for all vegetation types, while at six otherstations this held true for coniferous forest only. For approximatelyone third of the cases (14 out of 36), N deposition was higher thanthe 2010 target, but the difference was not significant at the 5%

Table 4Slope and significance of linear time trends in total deposition (molc ha�1 yr�1) ofNHx, NOy and SOx, the sum of NHx and NOy (N) and the sum of the three compounds(Nþ S) for grassland vegetation at nine stations (cf. Table 1) according to generalizedadditive models based on biweekly data.

Station NHx NOy SOx N N þ S

BON �28.6 �5.6 (L54.5***) �34.7 (L65.6*)GEN �62.2** 8.2 �34.1** �58.5* �89.5**

KAP �58.5** �1.6 (L59.8***) �62.9** L125.1***

KOK �73.9*** 3.6 (�42.7***) �67.0** L88.9**

MAA 14.6 �3.4 (�24.9**) 5.3 �5.8RET (�81.3***) �8.4 �44.2*** �111.7*** �185.9***

TIE �58.1** �1.5 (L14.3*) �64.2** �40.9WIN �93.7*** 2.6 (L16.5*) L102.2*** �90.9**

ZWE �125.5*** 0.9 L36.6*** L129.3*** L155.4***

*P < 0.05; **P < 0.01; ***P < 0.001. Values in brackets indicate that the long-termtrend significantly (P < 0.05) deviated from a linear relationship according toapproximate F tests. Bold values indicate that the long-term trend in the annual datawas significant (P < 0.05) according to Kendall tests.

level. For only four combinations of vegetation type and station(grassland at BON and grassland, heathland and deciduous forest atTIE) the N deposition was significantly below the 2010 target.

Regarding total potentially acidifying deposition (N þ S),station-averaged values in 2009 were 1960, 2274, 2441 and3067 molc ha�1 yr�1 for grassland, heathland, deciduous andconiferous forest, respectively. The 2010 target for N þ S(2050 molc ha�1 yr�1) was significantly exceeded for the same tencombinations of station and vegetation type as for N alone (Fig. 6b),while for 15 cases the N þ S deposition was higher than the target,but the exceeding was not significant. For only three cases (grass-land at BON and grassland and heathland at TIE), the depositionwas significantly below the 2010 target.

Compared with the medium-term target for 2015, the N depo-sition in 2009 was significantly exceeded for 13 of the 36 combi-nations, while the Nþ S depositionwas significantly too high for 16of the 36 combinations (Fig. 6). Compared with the policy target for2030, the N deposition in 2009 was significantly higher for 27combinations, i.e. for all vegetation types at six stations and forconiferous forest at the other stations. A similar result was obtainedfor the N þ S deposition.

4. Discussion

4.1. Methodological limitations

Trends in N and S gases were analysed for measured airconcentrations as well as for estimated dry deposition, and thelatter was calculated using literature-based values for the drydeposition velocity (vd). By assuming constant vd values over time,the uncertainty in the vd value did not affect the deposition trendand consequently the significance of the trends was identical for airconcentrations and dry deposition. The SO2 to NH3 concentrationratio affects the deposition of both SO2 (Fowler et al., 2001) and NH3(Neirynck et al., 2005) because of their effect on the pH of waterfilms on vegetation. We still assumed a constant vd because theobserved SO2:NH3 ratio remained similar at most stations (Fig. 2),except for a slight decrease at two sites (Fig. 2a, i). Another inter-action is that lower SO2 concentrations decrease the rate ofconversion of NH3 to NH4 SO4 particles (Sutton et al., 2009), so thatmore NH3 is available for the formation of particulate NH4 NO3

(Fagerli and Aas, 2008).The definition of potentially acidifying deposition in this study

assumes complete nitrification of NHx within soils and does notaccount for neutralizing effects of Naþ, Kþ, Ca2þ and Mg2þ.However, the actual N and S deposition can be meaningfully largerthan estimated based on precipitation and gases. First, occultdeposition by fog and clouds was not included, which is generally

BON GEN KAP KOK MAA RET TIE WIN ZWE

Site code

0

2000

4000

6000

N +

S d

epos

ition

(mol

c ha-1

yr-1

)

Target 2010Target 2015Target 2030

0

20

40

60

80a

b

N d

epos

ition

(kg

N h

a-1 y

r-1)

GrasslandHeathlandDeciduous forestConiferous forest

Fig. 6. Total atmospheric deposition in 2009 of (a) inorganic nitrogen (N) and (b) the sum of N and sulphur (N þ S) for four vegetation type at nine stations. Vertical bars indicatestandard deviations determined by bootstrap resampling. Horizontal lines indicate policy targets for atmospheric deposition in 2010, 2015 and 2030.

J. Staelens et al. / Atmospheric Environment 49 (2012) 186e196194

important in mountainous areas but has been estimated tocontribute less than 5% to the N and S input to a forest in a neigh-bouring region (Vermeulen et al., 1997). Second, other reactive Ngases than NO2 and NH3 can contribute to atmospheric N deposi-tion. For example, for a Scots pine forest in the study region, drydeposition of HNO3 and HNO2 was 4.1 kg N ha�1 yr�1 (1999e2001)or 13% of the dry N deposition (Neirynck et al., 2007). Third, drydeposition of aerosols containing NH4

þ, SO42� and NO3

� is notmeasured by diffusive gas samplers, but can be important, partic-ularly for tall vegetation types such as forests. In 2006e2007, theseions contributed on average 12.6 mg m�3 or 41% to particulatematter (PM10) in Flanders (Vercauteren et al., 2011). For a Scots pineforest, dry deposition of particulate NH4

þ and NO3� was

9.8 kg N ha�1 yr�1 or almost half of the gaseous dry NH3 and NO2deposition (Neirynck et al., 2007). In the Netherlands, N and Scontaining aerosol was estimated to contribute up to one third ofthe dry gas deposition to forest (Erisman et al., 1997). Trends in gasand particulate N and S compoundsmay also differ (Fagerli and Aas,2008). Both NH4

þ and SO42� aerosols have a longer atmospheric

lifetime than their precursor gases and can be transported overlonger distances (Sutton et al., 2009), so that trends in aerosolconcentrations can depend more upon trends in gas emissionsa long distance upwind (Jones and Harrison, 2011).

4.2. Annual vs. biweekly data and linearity of long-term trends

The present study considered a time period of eight year, withonemissing year for gas measurements and estimated dry and totaldeposition. Because of this relatively short period, Kendal tests onthe annual concentrations and deposition fluxes were based on

only seven or eight data values. In the future, longer time series willlikely allow detecting more significant time trends. We also usedGAMs based on biweekly data, which were more powerful inrevealing significant time trends than Kendall tests on annual data.Consequently, this comparison indicates that summarizing datainto annual values leads to information losses. The semiparametricGAM approach, with linearly assumed long-term time trends, wasgenerally acceptable for the considered period. Nevertheless,longer time series will also allow more precise analyses of trends.The semiparametric GAM approach can then be modified toquantitatively describe non-linear trends including logarithmic,exponential or other multi-order relationships.

4.3. Air concentration and deposition trends and exceedance ofpolicy targets

According to the semiparametric GAMs, the sum of wet andgaseous inorganic N deposition to grassland decreased significantlyat seven of the nine measuring stations. Based on the linear GAMslopes, the annual N deposition has been reduced by7e14 kg N ha�1 or by 18e38% in eight years compared with thedeposition in 2002. This decline was due to a reduction in the NH3concentration (Fig. 2), as no significant trends in wet N depositionor NO2 concentration were detected. In the Netherlands(1992e2008), wet deposition measured at 11 stations declinedsignificantly (P < 0.05) with 37% for NH4

þ and 28% for NO3� (van der

Swaluw et al., 2011).For S, at six of the nine stations a significant decrease occurred in

both wet deposition (27e44% in eight years compared with 2002based on GAM slopes) and SO2 concentration and dry deposition

J. Staelens et al. / Atmospheric Environment 49 (2012) 186e196 195

(41e79%). Total SOx deposition declined significantly (32e46%) atall stations. In the Netherlands, wet deposited SO4

2� decreased by59% over 16 years (van der Swaluw et al., 2011). In Hungary, wetSO4

2� deposition and SO2 concentrations decreased by ca. 30 and80%, respectively (1993e2001; H�unová et al., 2004), and also in theUK, larger reductions in S dry deposition (�74%) than in wetdeposition (�45%) have been reported (1986e2001; Fowler et al.,2005). A stronger decline in SO2 than in SO4

2� precipitationconcentrations has been attributed tomore efficient SO2 depositiondue to a strong decrease in the SO2:NH3 ratio (Fowler et al., 2001).

The observed trends can be explained by emission reductions inFlanders and abroad, although an effect of possible changes inmeteorological conditions cannot be excluded. During 2002e2009,the relative decrease in precursor gas emissions in Flanders washigher for SO2 (�47%) than for NH3 (�24%) and NOx (�25%)(Table S.4). Due to long-range transport of SOx and NOy, a largefraction of the N and S deposition in Flanders originates fromemissions abroad. Total emissions in Belgium and its southwestneighbouring countries (France and the UK) in 2002e2009 showedsimilar trends as in Flanders (�54% SO2 and �31% NOx), except forNH3 (�8%), and the same held when including Germany, Luxem-burg and The Netherlands as well (�50% SO2, �29% NOx and �7%NH3) (Table S.4). Comparing emission and deposition trends indi-cates that halving SO2 emissions has been reflected in a 32e46%decrease in SOx deposition. For NHx, which is deposited closer toemission sources than SO2 and NO2, the observed depositionreduction (�18e38%) is in line with the Flemish NH3 emissionreduction (�24%). For NO2, emission reductions are not reflected ina decrease in NO2 concentration or NOx deposition. Likewise, non-linearities in emissionedeposition relationships have been re-ported for the UK, where a 40% reduction in NOx emissiondecreasedwet NOy deposition by<10% (Fowler et al., 2005). Next toprecursor emissions, air quality is affected by meteorologicalconditions (Pearce et al., 2011). Although meaningful long-termchanges in meteorological variables and processes cannot beexcluded, potential meteorological effects of e.g. ongoing increasesin mean annual temperature or precipitation (www.meteo.be)during the measuring period may be small compared with emis-sion changes.

The total N þ S deposition to grassland also decreased signifi-cantly at seven stations, six of which were in common with thestations with a significantly decreasing trend in N deposition. Thedecrease in NH3 concentrations and NHx deposition was mostpronounced at the two stations (WIN and ZWE) with the highestvalues in 2002, which are located in a region with intensive live-stock breeding, while trends were smaller or insignificant at loca-tions with lower initial levels. Similarly, in the Czech Republic thelargest decrease in SO2 air concentrations (1993e2001) occurred inmore polluted areas (H�unová et al., 2004).

Seasonality was observed in the ambient gas concentrations.Similarly, higher SO2 concentrations in winter than summer havebeen reported for Hungary, which was attributed to increasedconsumption of fossil fuels during the cold season (H�unová et al.,2004). In Prague, both SO2 and NO2 concentrations varied season-ally, also with higher winter values (Brani�s, 2008).

The higher reduction in NH3 concentration and N deposition atmore polluted stations implicates that emission reductionmeasures have been successful in lowering the highest ambientNH3 concentrations and deposition inputs. However, from anecosystem point of view, even deposition fluxes below policytargets exceed critical N loads aimed at protecting plant speciesdiversity (5e20 kg N ha�1 yr�1; Bobbink and Hettelingh, 2011).Besides, even N deposition levels below currently adopted critical Nloads can in the long-term be harmful for plant species compositionand number (Clark and Tilman, 2007). Similarly, adverse effects of

NH3 are observed at concentrations well below the critical level of8 mg m�3 (Cape et al., 2009). Meaningful time lags can exist in therecovery of ecosystems after atmospheric deposition has reachedcritical loads of acidification or eutrophication (e.g. Staelens et al.,2009). Important time lags also exist between deposition reduc-tions and improvements of the chemical quality of groundwater.For a chalk aquifer in southern Belgium, for instance, 17 of the 24sampling points were characterized by a significant upward trendin NO3

� concentration and further major degradation is expectedwithin 10e70 years when no measures are taken today (Aguilaret al., 2007). Therefore, additional policy measures are needed toreach N deposition targets and prevent further eutrophication of(semi)natural ecosystems and to protect groundwater layers inFlanders.

Acknowledgements

JS and KW were funded as postdoctoral fellow of the ResearchFoundation e Flanders (FWO) and the Special Research Grant ofGhent University (BOF), respectively. SA was granted by FWOresearch project G.0205.08N. This work was financially supportedby the Flemish Environment Agency. We thank two anonymousreviewers for their constructive comments.

Appendix. Supplementary material

Supplementary material associated with this article can befound, in the online version, at doi:10.1016/j.atmosenv.2011.11.065.

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