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Levels and Spatial Distribution of Persistent Organic Pollutants in the
Environment: A Case Study of German Forest Soils
Bernhard Aichner,*
,†
Bernd Bussian,
‡
Petra Lehnik-Habrink,
†
and Sebastian Hein
†
†
Federal Institute for Materials Research and Testing (BAM), Richard-Willstätter-Strße 11, 12489 Berlin, Germany
‡
Federal Environment Agency (UBA), Wörlitzer Platz 1, 06844 Dessau-Roßlau, Germany
*
SSupporting Information
ABSTRACT: The Of/Oh-horizons of 447 forest stands in Germany were evaluated
for concentrations and spatial distribution of selected polycyclic aromatic
hydrocarbons (PAHs), polychlorinated biphenyls (PCBs), and organochlorine
pesticides (OCPs). While concentrations of dichlorodiphenyltrichloroethane
(DDT) and PCBs show similar spatial distribution patterns for all measured
compounds within each compound class, significantly different distributions were
identified for concentrations of low-molecular-weight PAHs [2- and 3-ring PAHs plus
fluoranthene (FLA) and pyrene (PYR)] in contrast to high-molecular-weight PAHs
(4−6-ring PAHs without FLA and PYR). Maxima of persistent organic pollutant
(POP) concentrations could be mostly explained by specific locatable sources.
Because of the slow degradation rates of these target substances, this is especially
relevant for historic contamination sources, such as extensive 1980s DDT usage in
the former German Democratic Republic and industrial facilities that produced
hexachlorobenzene (HCB) or PCBs. A contribution of ubiquitous background pollution derived from long-range atmospheric
transport is likely for some compounds in the studied area, e.g., DDT in the western part of Germany and dieldrin. However,
most target compounds appear to be mainly sourced from local or regional emissions. This is supported by the absence of clear
dependencies between POP concentrations and most evaluated environmental and local parameters. We suggest that these
results generally reflect the distribution of POPs in densely populated and industrialized countries located in temperate regions.
1. INTRODUCTION
Because of their persistence in the environment and potential
toxicological effects, compounds classified as persistent organic
pollutants (POPs) have been the focus of attention since the
middle of the 20th century. Naturally occurring, such as
polycyclic aromatic hydrocarbons (PAHs), or industrial
products, such as polychlorinated biphenyls (PCBs) and
organochlorine pesticides [OCPs, e.g., dichlorodiphenyltri-
chloroethane (DDT), dieldrin, and hexachlorobenzene
(HCB)], they can be detected even in remote regions far
from emission sources.
1,2
Possible harmful effects on organisms
range from neurotoxic to teratogenic, carcinogenic, and
endocrine disruptive effects. This is especially concerning
because the lipophilic properties of these substances favor their
storage in the fat tissue of organisms, which consequently leads
to bioaccumulation through the food chain.
3
The fate of POPs, i.e., the possible pathways in the
environment after release, has been subject to numerous
studies. Most compounds are susceptible to long-range
transport because of their relatively high volatility. Nevertheless,
environmental compartments, such as soils, can act as sinks for
POPs. This is mainly due to the high affinity for organic
compounds to bind to organic carbon and their recalcitrance in
soils.
4,5
In forest stands, the scavenging effect of canopies
enhances POP fluxes from air to soils.
6,7
On the other hand,
soils can also act as a source for some POPs by revolatilization
of compounds with relatively high vapor pressure, such as
HCB, low chlorinated PCBs, or low-molecular-weight PAHs.
8
POPs have been extensively studied in the atmosphere and
atmospheric depositions (see refs 9 and 10) as well as in urban
and agricultural soils (see refs 11−14). In contrast, soil data
from forested regions are mostly available from small-scale
studies only, which either cover a relatively small study area or
are based on a relatively low number of sampling spots.
Exceptions include data from the large-scale programs
MONARPOP (e.g., see refs 15−18), the Swiss soil-monitoring
network (NABO) (e.g., see ref 19), transect studies in the U.K.
and Norway,
20−22
and a comprehensive study conducted in the
German state Bavaria.
23
The MONARPOP program analyzed
POP concentrations at 33 homogeneous forest stands
distributed over the Alps, i.e., at comparable altitude with
similar vegetation, as well as seven altitude profiles. The NABO
project covered the entire area of Switzerland and also included
26 forest stands among the 105 sampling sites. U.K. and
Norway transects cover relatively large gradients of latitude but
report data for PCBs/polybrominated diphenyl ethers
(PBDEs)
21,22
and PAHs
20
only, while the Bavaria study also
Received: May 3, 2013
Revised: September 6, 2013
Accepted: September 19, 2013
Article
pubs.acs.org/est
© XXXX American Chemical Society Adx.doi.org/10.1021/es4019833 |Environ. Sci. Technol. XXXX, XXX, XXX−XXX
includes pesticides HCB and DDT. Other, more small-scale
studies from temperate regions include locations in the Czech
Republic,
24
France (Seine River Basin
25
), Germany (northern
Bavaria
26
and Lower Saxony
27
), Spain (Ebro River Basin
28
),
Sweden,
29
Nepal (Sagarmatha Nationalpark
30
) or U.S.A.
(Maine
31
).
Some of these studies suggest a dependency between POP
concentrations and environmental/local parameters, such as
altitude, latitude, population density, or soil total organic
carbon (TOC). Direct comparison of published data is often
complicated by varying sampling strategies, such as sampled
horizons and soil depths, as well as varying target compounds.
Here, we examine how POPs distribute in soils of temperate
regions across a relatively large scale. This study is based on (a)
447 sampling spots organized in a 16 ×16 km grid covering all
of Germany, including large gradients in terms of altitude,
latitude, and longitude and environmental parameters, such as
soil TOC, pH, temperature, and precipitation, (b) a stand-
ardized sampling, sample preparation, and analysis strategy, and
(c) a broad range of target compounds, which include PAHs,
PCBs, and OCPs. Only sampling spots in forests were selected,
because those are considered sinks for POPs and naturally
located at a certain distance from emission sources in most
cases. In addition to the identification of regional trends in the
mapped data, we evaluated the effect of potential influencing
factors (e.g., climatic, soil, and vegetation parameters) on
pollutant levels.
2. STUDY AREA
Germany is located in central Europe and extends between ca.
5.86°and 15.04°E and 47.27°and 55.06°N, covering a total
area of 357 021 km2. It can be segregated into four major
topographic units: the northern German Lowlands, the central
German Uplands, the Alpine Foreland of southern Germany,
and the Alps in the far south, with the Zugspitze as the highest
point (2962 m) (Figure 1a). About 30% of the total area is
covered by forest (Figure 1b).
Because of its location in the Northern Hemispheric
Westerlies Zone and the influence of the Gulf Stream, the
climate is moderate and temperate. The mean annual
temperature is ca. 9 °C, and the mean annual precipitation
(MAP) is ca. 780 mm.
33
The climate in the northwest is
oceanic with relatively mild winters and cool summers. In
contrast, the eastern climate is relatively continental with
comparably low annual rainfall (<500 mm), higher temper-
atures in summer, and cold winters because of the impact of
cold air masses delivered by the Siberian High. In the
mountainous regions of the Uplands and Alps, climate is
relatively cool and wet, with MAP often exceeding 1000 mm.
33
3. MATERIALS AND METHODS
3.1. Sampling. Principally, a grid of 16 ×16 km covering
the whole of Germany was used for a systematic sampling
scheme (Figure 1a). Sampling points that did not fall on forest
sites were excluded. Along the borders of the German states,
the sampling grid sometimes was shifted by 4 or 8 km.
Consequently, some discontinuities in the mesh of original data
may be observed. Occasionally, additional sampling spots were
selected by local authorities to consider specific regional
topography or emission situations. Thus, in total, 447 spots
were sampled. Basic statistics of environmental parameters of
sampling locations are given in Table 1.
On each sampling spot, a soil pit was dug in the center of the
sampling plot to gain a pedological description of the site and
Figure 1. (a) Topographic map of Germany, German states, and sampling points. (b) Landscape units according to Corine Land Cover 2006.
32
Relevant urbanized areas and brown coal strip-mining districts are annotated.
Environmental Science & Technology Article
dx.doi.org/10.1021/es4019833 |Environ. Sci. Technol. XXXX, XXX, XXX−XXXB
to take material for soil physics tests. Then, material from eight
satellite points was combined to one composite sample.
Distances between the sampling center and satellite points
were 10 m. Samples were taken from the organic layer (Of/Oh-
horizons), excluding the litter horizon (Oi). Sampling was
conducted in principal by two different methods, depending
upon the local situation at the sampling site. If possible, a 30 ×
30 cm metal frame was applied to the soil surface and the
humic layer was gently removed within that frame from the
mineral layer. Alternatively, hand auger equipment was used.
Here, the differentiation of the humic layer and mineral layer
was performed visually. The sampling procedure is described in
detail in BMELV and TI manuals.
34,35
The samples were
transferred to 1 L brown-glass bottles, sealed by alumina foil,
and stored until further pretreatment at −20 °C. The bulk raw
field samples were sieved by 5 mm diameter mesh size sieves
under dry conditions, homogenized with an egg whisk, and
kept in cooled storage. Aliquots of field wet samples for further
analytical steps were obtained with a PT 100 sample divider
(Retsch, Haan, Germany).
3.2. Analysis. Sample preparation and combined multi-
residue analysis of PAHs, PCBs, and OCPs are described in
detail by Lehnik-Habrink et al.
36
In brief, an internal standard
consisting of deuterated PAHs, native PCBs, and 13C-labeled
OCPs was spiked to the sample prior to extraction. Samples
were extracted using pressurized liquid extraction with acetone/
cyclohexane (2:1) as the solvent. For cleanup, gel permeation
chromatography and solid-phase extraction were conducted
(see ref 36 for details).
Target compounds were identified and quantified with a gas
chromatograph (GC) Agilent 7890 connected to a mass
selective detector (MSD) 5975C (Agilent Technologies,
Waldbronn, Germany). The GC was equipped with a
GERSTEL (Mülheim a.d. Ruhr, Germany) cooled injection
system (CIS; start temperature, 50 °C; heating rate, 12 °C/s to
280 °C; isotherm time, 5 min; operated in splitless mode) and
a DB5-MS column (60 m length, 0.25 mm inner diameter, and
0.25 μmfilm thickness). The GC oven was set to a start
temperature of 50 °C (1 min), a heating rate of 50 °C/min to
120 °C, followed by a heating rate of 5 °C/min to 310 °C, and
an isothermal phase of 22 min. Helium was used as the carrier
gas, operated at a constant flow of 1.3 mL/min. The mass
spectrometer was operated in electron impact ionization mode.
The temperatures of the transfer line, ion source, and
quadrupole were set to 300, 230, and 150 °C, respectively.
The mass spectrometer (MS) was operated in selected ion
monitoring mode.
The following compounds were quantified: (a) a total of 16
priority PAHs from the United States Environmental
Protection Agency (U.S. EPA),
37
naphthalene (NAPH),
acenaphthylene (ACY), acenaphthene (ACE), fluorene
(FLUO), phenanthrene (PHE), anthracene (ANT), fluoran-
thene (FLA), pyrene (PYR), benzo[a]anthracene (BaA),
chrysene (CHRY), benzo[b]fluoranthene (BbF), benzo[k]-
flouranthene (BkF), benzo[a]pyrene (BaP), dibenz(a,h)-
anthracene (DahA), benzo[g,h,i]perylene (BghiP), and indeno-
[1,2,3-cd]pyrene (IcdP); (b) a total of 6 principal PCBs,
38
2,4,4′-trichlorobiphenyl (PCB-28), 2,2′,5,5′-tetrachlorobiphen-
yl (PCB-52), 2,2′,4,5,5′-pentachlorobiphenyl (PCB-101),
2,2′,3,4,4′,5′-hexachlorobiphenyl (PCB-138), 2,2′,4,4′,5,5′-hex-
achlorobiphenyl (PCB-153), and 2,2′,3,4,4′,5,5′-heptachlorobi-
phenyl (PCB-180); and (c) a total of 8 OCPs, hexachlor-
obenzene (HCB), 1,2,3,4,10,10-hexachloro-6,7-epoxy-
1,4,4a,5,6,7,8,8a-octahydro-endo-1,4-exo-5,8-dimethanonaph-
thalene (dieldrin), 1,1,1-trichloro-2,2-bis(4-chlorophenyl)-
ethane (4,4′-DDT), 1,1,1-trichloro-2-(2-chlorophenyl)-2-(4-
chlorphenyl)ethane (2,4′-DDT), 1,1-dichloro-2,2-bis(4-
chlorophenyl)ethane (4,4′-DDD), 1,1-dichloro-2-(2-chloro-
phenyl)-2-(4-chlorophenyl)ethane (2,4′-DDD), 1,1-dichloro-
2,2-bis(4-chlorophenyl)ethene (4,4′-DDE), and 1,1-dichloro-
2-(2-chlorophenyl-1)-2-(4-chlorophenyl)ethene (2,4′-DDE).
3.3. Environmental Parameters and Emission Data.
Generally, soil and local environmental parameters (e.g., pH,
TOC, humus type, and forest type) were analyzed by standard
methods as described in the BMELV instruction sheet.
34
Climatic parameters, such as MAP, mean summer temperature
(MST), and mean winter temperature (MWT), were taken
from the German Climate Data Center.
33
Modeled emission
data
39
can be derived from the Centre of Emission Inventories
and Projections (CEIP)
40
in a 50 ×50 km grid. Higher
resolution data were kindly provided by TNO (Utrecht,
Netherlands) and transferred to a 32 ×32 km grid for
comparison to our data set.
3.4. Quality Assurance and Validation of Results. The
used analytical method was validated for the analyses of PAH,
PCB, and OCP at low concentrations in forest soil.
36
During
the measurement campaign, various instruments of quality
assurance were applied. With every daily batch of samples, one
control sample was routinely extracted, purified, and measured.
Relative standard deviations of individual compound concen-
trations in the control sample range from 6.95% (BghiP) to
22.85% (IcdP) and average at 12.15%. Results of ca. 10% of all
samples were double-checked by a replicate analysis (which
included a complete sample preparation). Mainly samples
whose pollutant levels were conspicuous according to their
absolute height (extreme values and outliers) as well as location
(“hotspot”criterion) were chosen. The average relative
standard deviation of compounds in replicate measurements
was 22.5%. Typically, the highest deviations were found for
low-abundant compounds, such as low-chlorinated PCBs and
some DDT metabolites. Limit of quantification (LOQ) and
limit of detection (LOD) were established according to
standard methods
41
and listed in SI-2 of the Supporting
Information.
3.5. Statistics. Data were plotted with the open-source
software SAGA-GIS (University of Hamburg, Germany) using
a16×16 km output grid (see SI-1 of the Supporting
Information). Thus, one grid field represents in general one
sampling point. Because variogram models would have resulted
in large nugget effects and large residuals between original and
Table 1. Basic Statistics of Location Parameters of 447
Sampling Spots
a
altitude
(m) slope
(deg) soil
pH TOC
(%) MAP
(mm) MST
(°C) MWT
(°C)
minimum 3 0.0 2.6 4.8 491 12.5 −3.5
10% 43 0.0 3.1 28.0 580 15.1 −1.0
50% 348 5.0 3.9 37.4 834 16.7 0.7
mean 345 8.3 4.1 36.6 877 16.6 0.5
90% 668 23.7 5.3 44.2 1202 17.9 1.7
maximum 1280 55.0 6.4 49.2 2232 19.1 3.5
a
Soil pH, measured with CaCl2solution; MAP, mean annual
precipitation; MST, mean summer temperature; MWT, mean winter
temperature. Climatic data were derived from the German Climate
Data Center.
33
.
Environmental Science & Technology Article
dx.doi.org/10.1021/es4019833 |Environ. Sci. Technol. XXXX, XXX, XXX−XXXC
interpolated data, missing values were calculated with inverse
distance weight interpolation, using “2”as an inverse distance
power parameter. Principal component analysis (PCA) was
conducted with the software R (R Foundation, Vienna,
Austria). The software SPSS was used to create correlation
matrices, Q−Q plots, and to perform Kruskal−Wallis tests and
multiple linear regression (MLR).
4. RESULTS
To summarize the results, basic statistics of measured
compound concentrations are given in Table 2 (and SI-3 of
the Supporting Information). The data show a strong positive
skew for all parameters (see SI-4 of the Supporting Information
for log-normal Q−Q plots).
Because all measured compounds, with the exception of the
natural PAH contribution, are essentially of anthropogenic
origin, it has been discussed whether local or regional emissions
as well as short- and long-range transport processes may cause
specific spatial distribution patterns. To test this, all results are
displayed by georeferential methods (see panels a−h of Figure
2). The different PCBs displayed similar spatial distributions, as
did DDT and its metabolites (see SI-1 of the Supporting
Information) and are, therefore, treated as ∑PCB and ∑DDx.
In contrast, PAH concentrations show different spatial
distributions for low-molecular-weight PAHs (lmwPAHs), for
FLA and PYR, and for high-molecular-weight PAHs
(hmwPAHs). Therefore they are differentiated into those
groups.
∑PAHs are plotted in Figure 2a, and above-mentioned
groups (∑lmwPAH, ∑FLA + PYR, and ∑hmwPAH) in
panels b−d of Figure 2. ∑lmwPAHs (Figure 2b) clearly show
highest concentrations in the easternmost part of Germany. A
few single spots with enhanced concentrations were also found
Table 2. Basic Statistical Parameters of Measured POP Concentrations (ng/g of dw) in 447 Samples
a
∑16 PAH ∑lmwPAH ∑Fla + Pyr ∑hmwPAH ∑6 PCB ∑DDx Dieldrin HCB
minimum 105 23 22 44 <LOD <LOD <LOD <LOD
10% 283 58 76 142 4 4 <LOD 1
50% 1448 172 361 803 14 31 3 3
mean 2099 353 534 1210 18 146 3 4
90% 4935 845 1216 2759 36 292 5 7
maximum 14889 4424 5054 8613 106 4383 12 24
a
lmwPAH, PAHs with 2 or 3 aromatic rings (NAPH, ACE, ACY, FLOU, PHE, and ANT); hmwPAHs, PAHs with 4−6 aromatic rings without FLA
and PYR (BaA, CHRY, BbF, BkF, BaP, IcdP, DahA, and BghiP). For parameters of single compounds, refer to SI-3 of the Supporting Information.
Figure 2. Spatial distribution of POP concentrations. lmwPAHs, low-molecular-weight PAHs with 2 and 3 aromatic rings (NAPH, ACE, ACY,
FLUO, PHE, and ANT); hmwPAHs, high-molecular-weight PAHs with 4, 5, or 6 aromatic rings but without FLA and PYR (BaA, CHRY, BbF, BkF,
BaP, IcdP, DahA, and BghiP). (∗) Colors of circles are stretched to a linear scale with 90 percentile as the upper limit, except ∑DDx, which is shown
in a logarithmic scale with 95 percentile as the upper limit. Interpolated maps in 16 ×16 km grid with detailed legends for all compounds are shown
in SI-1 of the Supporting Information.
Environmental Science & Technology Article
dx.doi.org/10.1021/es4019833 |Environ. Sci. Technol. XXXX, XXX, XXX−XXXD
at the westernmost edge of Germany as well as in the Harz and
Thuringian Forest. Lowest concentrations were detected in
southern Germany. A comparable distribution pattern was
found for ∑FLA + PYR (Figure 2c). ∑hmwPAHs (Figure 2d)
show highest values in the central German Uplands (Ore
Mountains, Thuringian Forest, and Harz) as well as in the
vicinity of the metropolitan regions of Rhine-Ruhr, Rhine-Main,
and Rhine-Neckar. Relatively high values can also be observed
in the northern part (close to Hamburg), while the southern
part, with a few exceptions of single spots, for instance in the
Black Forest, again has the lowest concentrations.
Considering ∑PCB (Figure 2e) and ∑DDx (Figure 2f), an
east−west trend is observable. Maximum PCB concentrations
can be found around the Rhine-Ruhr metropolitan region and
several other distributed hotspots in western Germany, while
eastern Germany mostly shows very low concentrations. In
contrast, ∑DDx has highest values in the eastern part of
Germany, while values in the west, especially in the southwest,
are very low. One exception is a sampling point north of the
Rhine-Ruhr metropolitan region, which shows maximum DDx
concentrations in our data set. DDx data describe the most
pronounced positive skew, with a 50 percentile of 31.3 ng/g of
dw but maximum values of >4000 ng/g of dw. Results are
therefore plotted in logarithmic scale in Figure 2f. Refer to SI-1
of the Supporting Information for detailed maps with legends
showing concentrations of all single compounds.
Analyzed soils generally contain low amounts of HCB
(Figure 2g) and dieldrin (Figure 2h). For HCB, six samples
showed values below the LOQ (0.6 ng/g of dw). In the case of
dieldrin, 107 samples were below LOD (0.5 ng/g of dw) and
another 28 were below LOQ (1.5 ng/g of dw). Some HCB
hotspots, with values ranging from 8 to 23 ng/g of dw, can be
observed in eastern Germany, southeastern Germany, and
western Germany. Concentrations of dieldrin are spatially
scattered.
5. DISCUSSION
5.1. Levels of POPs. While POPs have been extensively
studied in urban and agricultural areas, data from forested
regions are relatively rare. Additionally, they are difficult to
compare because of varying sampling strategies and target
compounds. Krauss et al.
26
measured concentrations ranging
from 24 to 15 056 (∑20 PAHs) ng/g of dw and from 11.6 to
201.5 (∑12 PCBs) ng/g of dw in the O-horizons of 15 forest
stands in northern Bavaria. A comprehensive study conducted
in Bavaria (397 stands) revealed average concentrations of 1913
(∑16 PAHs), 24.7 (∑6 PCBs), 37.3 (∑6 DDx), and 4.4
(HCB) ng/g of dw (see ref 23; raw data kindly provided by
LfU Bavaria). Except for ∑6 DDx, this is close to the arithmetic
means of our data set (Table 2). The same does apply for a
study conducted in the German state of Lower Saxony (results
for Oh-horizons in ng/g dw: ∑16 PAHs, 61−18266; ∑6
PCBs, <LOQ−195; 4,4′-DDT, <LOQ−708; HCB, <LOQ−42;
see refs 22 and 27).
From adjacent regions, a number of studies were conducted
in the alpine countries,
15−19
in the Czech Republic,
24
France,
25
Figure 3. (a) Average fingerprints of PAHs, PCBs, and DDx in the O-horizons of forest soils. (b) Average fingerprints of DDx in air and aerosol
samples collected in years 2007−2009 at EMEP monitoring sites.
59
(c) Regionalized relative abundances (%) of 4,4′-DDE in forest soils (IDW
interpolation with 2000 m grid size).
Environmental Science & Technology Article
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and U.K. and Norway.
20−22
In comparison to our samples, POP
concentrations seem to be significantly lower in alpine forest
soils (median values of 25 stands in ng/g dw: ∑16 PAHs, 210;
∑6 PCBs, 3.3; ∑6 DDx, 7.8; HCB, 0.5; see ref 15), as well as
in U.K. and Norway soils (AM of 110 spots, also including
grassland: ∑31 PCBs, 6.5; HCB, 1.1; see ref 21). In contrast,
concentrations from nine Czech mountainous forest stands are
slightly higher (∑16 PAHs, 3768; ∑7 PCBs, 22.7; ∑3 DDx,
315.2; HCB, 1.5; see ref 24). In French forest soils (Forest of
Brotonne; n= 6), PCB concentrations were below LOD, while
∑14 PAHs average for 940 ng/g of dw.
25
In Germany, POP reference values do exist in the form of
trigger and intervention values for the soil−human pathway
(e.g., playgrounds and residential areas) and the soil−plant
pathway (agricultural areas). No specific trigger and inter-
vention values for forest soils exist to date. Lowest trigger
values were set for playgrounds (DDT, 40 000 ng/g of dw; BaP,
2000 ng/g of dw; HCB, 4000 ng/g of dw; ∑6 PCB, 400 ng/g
of dw; see ref 42) and are much higher than our measured
concentrations in forest soils.
General precautionary values for soils, not specifically defined
for land-use classes and therefore also including forest soils,
were introduced for ∑16 PAHs, BaP, and ∑6 PCB only.
42
For
our O-horizon data, with an exception of two samples, the
guidance value for soils rich in organic material (>8% TOC)
has to be applied. Here, just three samples have higher
concentrations than the reference value in the case of ∑16
PAHs (10 000 ng/g of dw), while no samples exceed the value
for BaP (1000 ng/g of dw). For ∑6 PCB, also only two
samples are slightly higher than the precautionary value (100
ng/g of dw).
5.2. Spatial Distribution of POPs. 5.2.1. PAHs. PAHs are
produced during incomplete combustion of organic substances.
Thus they have multiple possible sources, such as traffic and
industrial exhausts or wood fire.
43,44
TheaveragePAH
fingerprint of our sample set, which is dominated by 4- and
5-ring PAHs, is shown in Figure 3a. Hotspots of high
concentrations of lmwPAH and hmwPAH can partly be
explained by present and historic contamination sources.
Khalili et al.
45
identified 2 and 3-ring PAHs as major products
from multiple emission sources, including coke ovens, diesel
and gasoline engines, and wood combustion. We interpret
relatively high concentrations of lmwPAH in eastern Germany
to be caused by the vicinity to brown coal strip-mining sites
“Central German District”and “Lausitz District”and the
smaller “Rhinian District”(Figure 1b) as well as to power
plants. lmwPAHs, especially NAPH and PHE, are associated
with coal
46
and might have been released directly to the
atmosphere by the mining process. Furthermore, most brown-
coal fired power plants, are located in the mentioned regions
47
and might have contributed to the region-specificfingerprint by
coal combustion. In comparison to lmwPAHs, concentrations
of hmwPAH are distributed more heterogeneously. Distinct
hotspots were found close to municipal and industrialized
regions, such as Rhine-Ruhr metropolitan region, Hamburg,
and Rhine-Main metropolitan region. Furthermore, we
hypothesize that high levels in central Germany Uplands,
such as the Harz, Thuringia Forest, and Ore Mountains,
indicate that these elevated areas act as a sink for regional PAH
emissions because of enhanced deposition at relatively cool
temperatures and/or high precipitation. The absence of this
effect at other mountainous regions, such as the Black Forest,
Bavarian Forest, or alpine foreland is probably caused by the
larger distance of those regions to major emitters, which are
mainly concentrated in the central western and eastern parts of
Germany.
5.2.2. PCBs. PCBs are industrial chemicals that have been
widely used in multiple applications since 1929 until the early
1970s.
48,49
In addition, they can be produced during
combustion processes. During the early 1980s, in the Federal
Republic of Germany (FRG) as well as in many other
countries, PCBs were first restricted to closed systems and later
completely banned.
50
Nevertheless, its widespread use, ongoing
release from PCB-containing waste, such as transformers,
capacitors, or hydraulic systems, and the characteristic of the
chemicals as extremely stable compounds lead to an ubiquitous
distribution in modern soils.
49
In a global soil survey, Meijer et
al.
4
inferred a correlation between maximum PCB burdens
between 30°and 60°northern longitude and regions of
maximal PCB usage.
In our samples, we see relatively low PCB concentrations
with no significant differences in PCB patterns (Figure 3a).
Correlation coefficients between highly chlorinated congeners
PCB 101, 138, 153, and 180 and ∑PCBs range from 0.91 to
0.99. PCBs 28 and 52 were either below LOQ or LOD in most
samples. The fingerprint is possibly influenced by the average
composition of common technical PCB mixtures that have
been used. Clophen A60 (produced by Bayer) and Aroclors
1254 and 1268 (produced by Monsanto) for instance contained
hexa- and heptachlorobiphenyls, such as congeners 138, 153,
and 180, as most abundant constituents.
48,51
Those are the
dominant compounds in the fingerprint of our analyzed soils.
Other products (e.g., Clophen A30, A40, or A50) contained
higher relative amounts of lower chlorinated congeners,
48
which might have contributed to the low concentrations of
those compounds in the soils. A significant part of tri- and
tetrachlorinated biphenyls might have, however, been removed
by re-evaporation and aerobic microbial degradation.
8
The trend toward relatively high concentrations in the
vicinity of densely populated and industrialized regions is an
indicator of the influence of local and regional sources. These
might mainly be historic. On the other hand, recent studies
have shown that especially cities can be an ongoing source of
PCBs.
52
Indeed, Breivik et al.
49,53
estimated PCB consumptions
and emissions on the basis of population density as a surrogate
(also plotted in ref 54). While the exact amount of produced
PCB in the former German Democratic Republic (GDR) is
unknown,
48,55
emissions have been estimated to be lower than
in the former FRG. This is supported by a 10-fold higher
amount of disposal of PCB-contaminated waste in the former
West Germany
56
and could explain the east−west trend in our
samples.
5.2.3. DDT and Metabolites. The spatial distribution of the
concentrations of DDT and metabolites in German forest soils
can clearly be attributed to historic application. While in the
former FRG, DDT application was phased out in 1972, it was
continuously used in the GDR until 1988.
50,57
Of relevance are
large-scale applications in forestry in the years 1982−1984
combating the bark beetle and the pine moth Lymantria
monacha.
57,58
The “half-time”of DDT in soils has been
estimated to ca. 20−30 years in temperate climates.
31
As a
consequence, 25 years after the mid-1980s, DDx concentrations
up to 4000 ng/g of dw can still be observed in forest soils from
eastern Germany, especially in Brandenburg, the German state
surrounding Berlin. In contrast, soils from western Germany
mainly show values below 100 ng/g of dw. Exceptions are
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single spots, e.g., one sampling point close to the northern edge
of the Rhine-Ruhral metropolitan area, whose high DDx value
is possibly influenced by a large-scale chemical industry in this
region. We interpret the low DDx concentrations as ubiquitous
background contamination, which is lowest in the southwestern
part of Germany. This assumption is supported by the
regionalized DDx fingerprint, which shows higher proportional
abundances of 4,4′-DDE in regions with generally low DDx
concentrations (Figure 3c). Technical DDT mainly consists of
4,4′-DDT (ca. 80−85%) and 2,4′-DDT (ca. 10−15%) and only
low amounts of 4,4′-DDE.
50
However, 4,4′-DDE is the main
compound in air, aerosol, and precipitation samples collected at
different sites in Germany (Figure 3b; see ref 59). This speaks
for wet and dry deposition as the main input path in western
Germany compared to application of technical DDT in the
eastern part. 4,4′-DDE is also a main metabolic product of
especially aerobic 4,4′-DDT degradation.
57,60
Therefore, the
DDx fingerprint in the southwestern part of Germany can
additionally be explained by advanced microbial breakdown of
4,4′-DDT.
5.2.4. HCB. The spatial distribution of HCB is difficult to
interpret because of multiple possible sources of HCB (listed in
refs 11 and 50). First, HCB was applied as a fungicide, but
application in the FRG and GDR was phased out in 1981 and
1984, respectively.
50,61
Second, it is a secondary byproduct in
numerous chemical synthesis processes, which include chlorine
products. As a consequence, a large number of chlorine-based
pesticides and other chemicals contain traces of HCB
contamination. Third, combustion processes can lead to HCB
production if chlorine compounds are involved. Spatial
distributions of generally low concentrations (median of 3
ng/g of dw; Table 2) possibly reflect an ubiquitous background
contamination but also include some hotspots, which could be
attributed to above-mentioned sources. For instance, relatively
high concentrations in eastern Germany (corresponding to
high lmwPAH concentrations) could be explained by industrial
combustion processes. Past HCB production and ongoing
industrial combustion are also a likely source for relatively high
concentrations around the Bavarian Chemical Triangle, in
northeastern Bavaria, in vicinity to the industrialized regions
along the river Rhine, as well as in other regions already
discussed in the context of PCBs.
61
Except for peak values,
spatial HCB distributions should generally not be over-
interpreted because of the low concentrations.
5.2.5. Dieldrin. Dieldrin is a pesticide that was phased out in
Germany in 1971. It is also a metabolic product of aldrin,
another insecticide that was phased out in 1981.
50
Aldrin
concentrations were also determined but were below LOQ in
all of our samples. Low concentrations of dieldrin with high
spatial heterogenity can be interpreted as an ubiquitous
background contamination. Similar to HCB results, the spatial
patterns should not be overinterpreted because of generally low
concentrations (median of 2.7 ng/g of dw).
5.3. Influence of Environmental Parameters. To
understand the spatial distribution of organic pollutants, it is
important to consider local environmental parameters as
potential influencing factors. This is especially the case if the
pollutant levels are interpreted to reflect an ubiquitous
background contamination derived from long-range atmos-
pheric transport rather than from specific local or regional
sources. The relationship between local environmental
parameters and POP concentrations was evaluated with PCA
(Figure 4a) and (MLR; see SI-7 of the Supporting
Information).
5.3.1. Climatic Parameters. On the large scale, temperature
effects can potentially be expected to influence POP
concentrations. First, low air temperatures enhance the
sorption of semi-volatile organic pollutants to aerosols and,
thus, favor their deposition. This effect of higher POP
concentrations at cooler locations, such as higher altitudes,
has been observed in a number of altitude transect studies. In
contrast, no altitude−concentration relationship was found in
others, and the potential of higher altitude locations as sinks for
POPs has been controversially discussed.
18,30,54,62
Second, re-
evaporation of compounds can reduce POP concentrations of
soils. Because of this reason, soils are considered to be an
Figure 4. (a) PCA-loadings plot of pollutant levels and environmental variables (MST), mean summer temperature; MWT, mean winter
temperature; P_ann, mean annual precipitation; data source, German Climate Data Center;
33
SO2, average SO2- emission in 32 ×32 km grid in
2005; data source, CEIP.
40
(b) Cross-plots between soil pH and HCB (above) and soil TOC and ∑PCB (below). For cross-correlation plots of all
compounds and for correlation matrix, refer to SI-5 and SI-6 of the Supporting Information, respectively.
Environmental Science & Technology Article
dx.doi.org/10.1021/es4019833 |Environ. Sci. Technol. XXXX, XXX, XXX−XXXG
important secondary source of semi-volatile compounds, such
as low-chlorinated PCBs and lmwPAH.
29
The major input
paths via wet and dry deposition suggest that POP
concentrations can possibly also be influenced by precipitation
amounts.
Even if condensation at low temperatures at elevated regions
might play a role on a local scale in the central German
Uplands, the results suggest that climatic parameters as well as
altitude cannot explain the POP concentrations of our study
area on the large scale. This is visible from PCA loadings
(Figure 4a), MLR results (see SI-7 of the Supporting
Information), and direct correlation coefficients (see SI-6 of
the Supporting Information). Despite low R2because of the
high amounts of samples, the latter are significant on a 0.01
level between P_ann and ∑lmwPAH (R2=−0.16), ∑PCB (R2
= 0.13), ∑DDx (R2=−0.25), and HCB (R2=−0.21).
Similarly, this trend is also observed for temperatures, especially
summer temperatures. As described in section 5.2, there is an
east−west trend in soil concentrations of many of our target
compounds, which can clearly be attributed to present or
historic sources identified above. At the same time, there is a
climatic east−west trend in Germany with slightly warmer and
drier summers in the eastern part. Thus, we interpret this slight
correlation to be more coincidental than attribute climatic
parameters as the driving forcing of spatial POP distribution.
5.3.2. Influence of Soil and Vegetation. The surface of the
vegetation and canopy renewal rates influence the deposition of
POPs. Higher dry particle and gaseous deposition velocities and
higher total deposition fluxes have been calculated for
deciduous canopies compared to coniferous canopies.
6
However, a few studies have also observed higher POP
concentrations below a coniferous forest than below a
deciduous forest.
19,63
Because of the lipophilic properties of
most POPs, some studies inferred a relationship between POP
concentrations, TOC,
4,8
and pH. The latter was explained by
the influence of pH upon the structure of soil organic matter
and, thus, their binding affinity to hydrophobic substances.
64,65
In our data set, there are indeed detectable relationships
between soil parameters (pH and TOC) and POP concen-
trations. Even though correlations are not significant for all
Figure 5. Box-and-whisker plots of POP concentrations classified by (a) humus type and (b) forest type. Boxes represent 25 and 75 percentiles.
Whisker edges represent the “outer fence”(3 times interquartile ranges) excluding outliers. Dashes indicate maximum values. See SI-9 of the
Supporting Information and the text for significance levels between different groups on the basis of Kruskal−Wallis tests.
Environmental Science & Technology Article
dx.doi.org/10.1021/es4019833 |Environ. Sci. Technol. XXXX, XXX, XXX−XXXH
POP groups, except for dieldrin (see SI-6 of the Supporting
Information), it becomes clear that high POP concentrations
are only found at spots with high TOC contents (Figure 4b; see
SI-8 of the Supporting Information). However, high amounts of
TOC do not necessarily lead to high POP concentrations on a
specific stand. This is plausible because the TOC content is
independent from POP deposition, as opposed to the
molecules of organic pollutants that require organic matter
(in the form of aerosols, tree leaves and needles, and soil
organic matter) as “carriers”. This also reveals that relationships
between POP contents and local parameters do not necessarily
need to be linear.
The PCA-loadings plot and correlation coefficients suggest a
significant negative correlation on a 0.01 level between
concentrations of all POP groups and soil pH. Also, the
MLR indicates that pH has the highest partial contribution to
the overall variance of all analyzed parameters (15% to ∑PAH,
12% to FLA + PYR, and 17% to ∑hmwPAH and ∑PCB; see
SI-7 of the Supporting Information). Indeed, there is a
tendency to lower POP concentrations (as shown for HCB
in Figure 4b; R2=−0.52) at higher pH values. This could be
explained by the higher binding capacity of organic substances
to hydrophobic compounds at lower soil pH, as suggested by
some previous studies.
65−67
However, it is highly likely that
similar industrial emission sources, which enhanced POP
concentrations, have also lead to soil acidification at specific
spots, and we, therefore, see an association between pH and
POP concentrations in our data set. Finally, it is known that
input from coniferous trees decrease soil pH. We found
significantly higher POP concentrations under coniferous trees
compared to deciduous and mixed stands (Figure 5b, p=
0.001; except dieldrin, with p= 0.047 for coniferous versus
mixed stands; see SI-9 of the Supporting Information for all
significant levels of all compounds) similar to the NABO study
conducted in Switzerland.
19
However, this effect is probably
biased by the distribution of forest types in Germany. For
instance, sandy soils in eastern Germany are mainly covered by
coniferous forests, which are located close to the described
major emission sources and elevated regions, such as the central
German Uplands.
Closely related to forest cover are humus types. When
classifying POP concentrations by this parameter, significantly
higher concentrations were found for “mor”and “moder”
stands compared to “mull”stands (Figure 5a; p= 0.001; except
dieldrin, with p= 0.035 for mull versus mor; see SI-9 of the
Supporting Information). Mor and moder humus types are
expected at acidic coniferous stands and favored by low
temperatures and high humidity. Relatively warm and dry
conditions and easily degradable organic matter with a low C/
N ratio, e.g., as derived from deciduous trees, promote the
formation of a “mull”-type humus layer. Therefore, increased
mineralization of target compounds because of enhanced
microbial activity might have contributed to the lower POP
concentrations at mull stands.
5.3.3. Comparison to Emission Data. We included modeled
SO2emission data
39,40
as combustion estimates to our
evaluation. From visual inspections (see emission maps in
cited references), it seems obvious that relatively high emissions
from densely urbanized and industrialized regions can explain
relatively high concentrations of hmwPAHs, PCBs, and also
HCB at proximal sampling sites (panels d, e, and g of Figure 2).
Indeed, correlation coefficients between emission estimates and
POP concentrations are positive but relatively low. However,
because of the large number of data points, they are still
significant on a 0.01 level for all PAH groups as wells as for
PCBs (see SI-6 of the Supporting Information). Because of
multiple influencing factors, especially range and direction of
transport, it is difficult and goes beyond the scope of this study
to assess the potential influence of emitters upon a distinct
sampling spot in a more precise quantitative manner. To also
be taken into consideration, this emission data represent the
status of 2005; therefore, historic emissions are not included.
However, soils are a long-term reservoir of POPs, which is
clearly indicated by high DDx concentrations in our O-horizon
samples, which reflect intense pesticide application in the early
1980s in eastern Germany. Total emission amounts and sources
in Germany have changed over the last 30 years. Especially after
1990, a significant reduction of emissions may have been
achieved because of intensified efforts in improving technical
standards of power plants and industrial facilities. After the
German reunification, this effectively decreased the emissions at
the territory of the former GDR because of economic and
industrial restructuring.
39,47
We therefore hypothesize that a
significant proportion of the observed POP concentrations in
this region (Figure 2) is derived from historic emissions.
Nevertheless, an ongoing input is likely derived from still active
industrial facilities, oil refineries, and power plants, which are
concentrated in the Middle German Chemical Triangle/
Central German District as well as in the Lausitz District
(see Figure 2b).
5.3.4. Concluding Summary. Observed statistic relation-
ships between environmental parameters and POP concen-
trations are weak. This was deduced from a PCA-loadings plot
(Figure 4a), correlation coefficients (see SI-6 of the Supporting
Information), and MLR (see SI-7 of the Supporting
Information). The first two principal components of the PCA
only describe 48% of the variance of POP concentration data.
Further, MLR results suggest that parameters can explain only
up to a maximum of 31% of the variance of analyzed compound
groups. Nevertheless, indications for weak linear correlations as
well as nonlinear dependencies could be observed. The latter is
specifically the case for TOC contents. Here, high POP
concentrations were only found at locations with high TOC
contents while low POP concentrations can be related to the
whole range of TOC contents in our data set. Other
environmental factors, such as soil pH, forest type, and
humus type, might also influence POP concentrations on a
local basis even though coincidental associations might bias
these results. On basis of the spatial distribution of pollutant
levels, we state that present and historic contamination sources
have the strongest influence upon pollutant levels in forest soils
of our study area. Location parameters (e.g., altitude or
temperature) might however be an important influencing factor
on POP concentrations in soils on a more regional or local
scale. We hypothesize that this large-scale distribution is a
plausible scenario for other industrialized countries, at least
those located in temperate regions.
■ASSOCIATED CONTENT
*
SSupporting Information
Interpolated maps and basic statistical parameters of all
measured compounds, information about LOD and LOQ,
Q−Q-plots, cross-plots and correlation coefficients between
compound groups and environmental parameters, MLR-data
and significance levels of Kruskal−Wallis-tests, cross-plots of
Environmental Science & Technology Article
dx.doi.org/10.1021/es4019833 |Environ. Sci. Technol. XXXX, XXX, XXX−XXXI
TOC contents versus POP concentrations. This material is
available free of charge via the Internet at http://pubs.acs.org.
■AUTHOR INFORMATION
Corresponding Author
*E-mail: bernhard.aichner@gmx.de.
Notes
The authors declare no competing financial interest.
■ACKNOWLEDGMENTS
This study was possible as a result of the cooperation between
the federal and state authorities for forest and environment.
Specifically, forest soil samples were taken by the authorities of
the German states and provided for this study. We gratefully
thank Katja Kaminski and Dino Berners for processing the
samples in the BAM laboratory. Environmental and soil
parameters were used with courtesy of the German states as
well as Nicole Wellbrock and Petra Dühnelt (TI Eberswalde).
We thankfully acknowledge Levke Godbersen and Friedrich
Krone (BGR Hannover) for measuring and providing TOC
data and Hugo Denier van der Gon (TNO, Utrecht,
Netherlands) for providing high-resolution emission data.
The authors are thankful for the fruitful discussions with the
colleagues of the state institutions and also with the colleagues
in the Federal Environment Agency (UBA), especially Simone
Schmidt and Jens Utermann. We further acknowledge four
anonymous reviewers whose comments lead to significant
improvements of the manuscript. Financial support by the
research fund of the Federal Environment Agency (UBA) is
gratefully acknowledged (FKZ 3707 71 201).
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