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Received: 24 April 2020 /Accepted: 22 September 2020 /Published online: 30 September 2020
Water Air Soil Pollut (2020) 231: 512
https://doi.org/10.1007/s11270-020-04881-8
Comparison of Spider Web and Moss Bag Biomonitoring
to Detect Sources of Airborne Trace Elements
Neele van Laaten &Dirk Merten &Wolf von Tümpling &
Thorsten Schäfer &Michael Pirrung
Abstract Atmospheric particulate matter has become a
major issue in urban areas from both a health and an
environmental perspective. In this context, biomonitor-
ing methods are a potential complement to classical
monitoring methods like impactor samplers, being spa-
tially limited due to higher costs. Monitoring using
spider webs is compared with the more common moss
bag technique in this study, focusing on mass fractions
and ratios of elements and the applicability for source
identification. Spider webs and moss bags with Hypnum
cupressiforme were sampled at the same 15 locations
with different types of traffic in the city of Jena, Germa-
ny. In the samples, mass fractions of 35 elements, main-
ly trace metals, were determined using inductively
coupled plasma-optical emission spectroscopy (ICP-
OES) and inductively coupled plasma-mass spectrome-
try (ICP-MS) after aqua regia digestion. Significantly
higher mass fractions in spider webs than in moss bags
were found, even after a much shorter exposure period,
and could not be ascribed completely to a diluting effect
by the biological material in the samples. Different
mechanisms of particle retention by the two materials
are therefore assumed. More significant correlations
between elements have been found for the spider web
dataset. Those patterns allow for an identification of
different sources of particulate matter (e.g. geogenic
dust, brake wear), while correlations between elements
in the moss bags show a rather general anthropogenic
influence. Therefore, it is recommended to use spider
webs for the short-term detection of local sources while
moss bag biomonitoring is a good tool to show a
broader, long-term anthropogenic influence.
Keywords Biomonitoring .Spider webs .Moss bags .
Urban particulate matter .Heavy metals
1Introduction
Particulate matter (PM) in the atmosphere is regarded as
one of the major environmental and health issues world-
wide. This is of special importance in urban areas where
people are exposed to enhanced levels of PM (Furusjö
et al. 2007; Landrigan et al. 2018). The exposure to dust
particles leads to health issues as premature mortality
with up to 3.15 million estimated deaths per year world-
wide, (lung) cancer, and a variety of respiratory and
cardiovascular diseases (Lelieveld et al. 2015;WHO
2013). Furthermore, the atmospheric transport of
Electronic supplementary material The online version of this
article (https://doi.org/10.1007/s11270-020-04881-8)contains
supplementary material, which is available to authorized users.
N. van Laaten :D. Merten :T. Schäfer :M. Pirrung
Institute of Geosciences, Applied Geology, Friedrich Schiller
University Jena, Burgweg 11, 07749 Jena, Germany
N. van Laaten (*)
International Max Planck Research School for Global
Biogeochemical Cycles, Max Planck Institute for
Biogeochemistry, Hans-Knöll-Straße 10, 07745 Jena, Germany
e-mail: neele.van-laaten@uni-jena.de
W. von Tümpling
Helmholtz Centre for Environmental Research –UFZ,
Brückstraße 3a, 39114 Magdeburg, Germany
#The Author(s) 2020
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512 Page 2 of 17 Water Air Soil Pollut (2020) 231: 512
particles influences the atmosphere itself and element
cycling in the environment: PM scatters and absorbs
radiation, and particles can act as cloud condensation
nuclei (Gieré and Querol 2010). Enhanced levels of PM
thus influence both weather and, on a longer time
scale, climate. With deposition, particles can intro-
duce metals into ecosystems. Known examples are
the deposition of metals onto soils or into forest
ecosystems (Fang et al. 2005).
Threshold values for atmospheric PM and metals
attached to it have thus been set (e.g. European Union
2004, 2004/107/EC; European Union 2008,2008/50/
EC) and monitoring stations have been installed in many
urban areas. Due to the high costs and need for space,
most of the cities only hold one or a few stations (Kardel
et al. 2011). As levels and composition of PM can
change rapidly within hundreds or even tens of meters,
those stations have only a poor spatial coverage
(Salmond and McKendry 2009). Simple and cost-
effective complementary tools are the biomonitoring
techniques (Ștefănuțet al. 2019): Airborne pollutants
are adsorbed by a variety of biological materials that are
sampled in the area of interest and analyzed. Typical
matrices include not only mosses, lichen, and plant
leaves but also tree bark and needles (Norouzi et al.
2015; Tretiach et al. 2011; Urbat et al. 2004). The
advantage of biomonitoring methods is their lack of
need for both power supply and maintenance, leading
to low sampling costs. Furthermore, plants are widely
distributed; hence, a large number of sampling sites are
accessible (Berisha et al. 2017;Vukovićet al. 2016).
An advanced technique is the moss bag biomonitor-
ing. There has been a growing scientific interest in this
technique within the last decades, as it is more control-
lable than classical moss monitoring and can be applied
at every desired location (Ares et al. 2012). Moss of
selected species from a remote place is put into bags of
plastic mesh and exposed to ambient air at the locations
of interest (AničićUroševićand Milićević2020). After-
wards, the mass fractions of selected elements or chem-
ical compounds in the individual samples are deter-
mined (Capozzi et al. 2020; Di Palma et al. 2017;
Shvetsova et al. 2019).
Another relevant biomonitoring technique is the col-
lection of spider webs, to whose adhesive silk dust
particles can attach. Spiders are widespread, well-
known arthropods that can survive adverse environmen-
tal conditions like heavy metal pollution due to strong
physiological responses including the production of
detoxifying enzymes (Ayedun et al. 2013;Babczyńska
et al. 2006). Their webs can be found at many locations
in urban areas and can be collected easily, for example,
from fences, handrails, and walls (Rybak et al. 2012;
Xiao-li et al. 2006). Despite the fact that this is a non-
invasive method (if webs are not collected too often, e.g.
every 2 weeks), this method has only been studied a few
times so far (e.g. Górka et al. 2018; Rybak 2015;Xiao-li
et al. 2006). However, the results point out that it is a
promising technique for monitoring of PM. More recent
studies have also focused on indoor air pollution, using
webs of naturally occurring as well as laboratory-bred
spiders (Rutkowski et al. 2019; Rybak et al. 2019).
To the best of our knowledge, no systematic
comparison between the sampling of spider webs
and moss bag biomonitoring has been done so far.
Both methods have been applied individually, show-
ing their suitability to assess levels of atmospheric
pollution. However, the question arises if both
methods lead to the same conclusions. This would
mean that one method can replace the other or
sampling campaigns can be combined, exploiting
the advantages of both methods. Mass fractions of
numerous trace elements (mainly heavy metals) in
the spider web and moss bag samples from the same
locations shall be determined and compared in this
work. The questions we address in this paper are as
follows: (a) Do the two biological materials show
the same retention of dust particles? (b) Do the
individual datasets contain patterns that can be used
to identify sources of PM? And (c) Are the patterns
and the resulting grouping of samples similar for
both biomonitoring methods?
2MaterialsandMethods
2.1 Monitoring Sites
For sampling, 15 locations in the city of Jena were
chosen (Fig. 1). Jena is a medium-sized city in Central
Germany without big industries; therefore the local air
quality is expected to be mainly influenced by traffic on
two railroad lines, two federal highways and a motor-
way. As the cityis located in a valley formed by the river
Saale, the sampling locations cover its north-south ori-
entation and include locations with car traffic (prefix
CA), tram and train transport (TR) as well as areas
reserved only for pedestrians (PD).
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Water Air Soil Pollut (2020) 231: 512 Page 3 of 17 512
2.2 Moss Bag and Spider Web Biomonitoring
The preparation of moss bags was performed combining
the approach of Adamo et al. (2008) with recommenda-
tions for more uniform exposure given by Ares et al.
(2012). Hypnum cupressiforme was collected in a pine
forest about 15 km south of Jena (UTM 32 N E
687024 N 5631097). Foreign objects like pine needles
and soil fragments as well as dead moss material were
removed manually before the moss was rinsed three
times with deionized water and dried at 40 °C. The bags
were made of polyester mesh (16 × 16 cm, 2-mm mesh
Fig. 1 Map of the city of Jena including traffic routes and the
sampling locations coded according to the nearby type of traffic.
CA, car traffic; CA/TR, car and tram/train traffic; PD, pedestrian
areas; TR, tram/train traffic. The map has been created using
SRTM3 topography data (USGS 2004) and GMT (Wessel et al.
2013)
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512 Page 4 of 17 Water Air Soil Pollut (2020) 231: 512
size) sewed with nylon thread and rinsed with diluted
HNO
3
(Merck, subboiled) and ultrapure water (genPure
UV-TOC, Thermo Scientific) before use. A flat design
hasbeenchosenasitisexpectedtobemostcomparable
with two-dimensional spider webs. Three grams of the
dry moss was filled into each bag and the bags were
stored in a polyethylene (PE) pouch until exposure. The
bags were hung up at lamp posts 2.5 m above ground
level, one of the heights that yielded the most replicable
results in Ares et al. (2014). Plastic mountings that kept
the moss away from the metal lamp poles were used for
this purpose. After 10 weeks (from June to August
2017), the bags were removed. In the middle of this
exposure period, webs of orb-weaving spiders
(Araneidae) were sampled at the same locations. Addi-
tional samples were taken every 2 weeks at locations
TR-ARE and CA-BUR to check for temporal variabil-
ity. Spider webs were collected from the upper half of
handrails (0.5–1.2 m above ground level) by coiling the
webs up on the upper half of a plastic straw (polypro-
pylene, PP). Since the handrails were rather narrow
(maximum 10 cm), they do not effectively shield the
webs from vertical dust deposition. Webs from the
lower half of handrails were not sampled as they are
expected to be influenced by wear of the underlying
road surface. All intact webs available at one site made
up one sample containing tens up to two hundred of
intertwined individual webs. Since orb-weaving spiders
renew their web almost every day (Nentwig 1980), the
sampled webs reflect mainly PM from the day of the
sampling, not exceeding a period of 6 days since the
latest rainfall (that would have destroyed all older webs).
Lamp posts for moss bag exposure were either directly
above the handrails or maximum 10 m away but with
the same distance to the next road or tram tracks. All
samples were stored in individual PE bags during trans-
portation to the lab.
2.3 Sample Preparation and Analysis
Mosses were removed from the bags and dried at 40 °C.
Each sample was cryo-milled using liquid nitrogen and
a ceramic mortar and pestle. The spider webs were first
removed from the plastic straws. Coarse objects like
insects or hairs were sorted out manually using plastic
tweezers before drying at 40 °C. All samples were
digested with aqua regia (related to DIN EN
16174:2012) using the microwave system MARS 5
Xpress and vessels of perfluoroalkoxy alkane (both:
CEM GmbH). For this, 6 ml 35% HCl (supra quality,
Carl Roth) and 2 ml 65% HNO
3
(Merck, subboiled)
were added to up to 200 mg of the individual samples. A
pre-reaction of 20 min took place in open vessels. Af-
terwards, the vessels were closed; the mixtures were
heated to 175 °C within 15 min and kept at 175 °C for
10 min. After cooling down, the digestion mixtures were
filled up to 25 ml with ultrapure water in volumetric
flasks (polymethylpentene, PMP, Vitlab) and trans-
ferred to 50-ml centrifuge tubes (PP, Greiner Bio-
One). After centrifugation (3000 rpm, 15 min), the clear
supernatants were transferred to 30-ml sample bottles
(high-density PE, Thermo Scientific) and stored until
further processing.
In addition, total digestions of selected samples were
performed for the purpose of quality control, using a
pressure digestion system (DAS, Pico Trace) with ves-
sels of polytetrafluoroethylene. For this, 2.5 ml 65%
HNO
3
(Merck, subboiled) was added to 50 mg of the
sample, heated up to 45 °C within 1 h, and kept at 45 °C
for 1 h. After cooling down, 2.5 ml 40% HF and 3 ml
70% HClO
4
(both: Suprapur, Merck) were added;
the mixture was heated up to 180 °C within 8 h
and kept at that temperature for 12 h. The cooled
mixture was heated up to 180 °C again within 4–
5 h in the evaporation mode and kept there for
14 h to evaporate the acids. The remaining solids
were dissolved in 2 ml HNO
3
(Merck, subboiled),
0.6 ml HCl (Suprapur, Carl Roth), and 7 ml ultra-
purewaterwithin8hat150°Candfilledupto
25 ml with ultrapure water in volumetric flasks
(PMP, Vitlab).
Mass fractions of Al, Ca, Fe, K, Mg, Mn, Na, P, S, Si,
SrandTiwereanalyzedbyICP-OES(inductively
coupled plasma-optical emission spectroscopy, 725ES,
Agilent Technologies) and mass fractions of Ag, As, B,
Ba, Cd, Co, Cr, Cs, Cu, La, Li, Mo, Nb, Ni, Pb, Rb, Sb,
Sn, V, W, Y, Zn and Zr were analyzed by ICP-MS
(inductively coupled plasma-mass spectrometry,
XSeries II, Thermo Scientific).
Mass fractions of organic and total carbon as well as
nitrogen were determined in selected samples using a
Vario EL cube element analyzer (Elementar
Analysensystem GmbH).
2.4 Quality Control
For quality control, standard reference materials SRM
1648a Urban Particulate Matter, SRM 1575 Trace
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Water Air Soil Pollut (2020) 231: 512 Page 5 of 17 512
Elements in Pine Needles (both: National Institute of
Standards and Technology) and IPE 952 Grass mixture
(Wageningen Evaluating Programs for Analytical Lab-
oratories) were digested with aqua regia and analyzed
according to the description above.
Almost all recovery rates for elements in biological
reference materials (SRM 1575, IPE 952) are greater
than 90% (lowest rate: 76± 9%). For SRM 1648a (ur-
ban particulate matter), recovery rates are greater than
90% for about one-third of the elements but substantial-
ly lower (below 70%) only for Al, Cr, K, Na, Rb, and Ti.
The latter might be due to the fact that urban particulate
matter often contains some geogenic particles of which
silicates like feldspar (containing Al, K, Na, and Rb) as
well as chromite (FeCr
2
O
4
) are not dissolved in aqua
regia (Salminen et al. 2005).
Recovery rates were also calculated relatingthe mass
fractions after aqua regia digestion to those after total
digestion for both sample materials. They are greater
than 90% for Ca, Co, Cu, Fe, K, Mg, Mn, Mo, P, Sb, Sn
and Sr and substantially lower (below 70%) for Cr, Cs,
Na, Nb, Ni, Rb, Ti and Zr. However, as the tendencies
are similar for both sample materials and for SRM
1648a, element mass fractions cannot be regarded as
total mass fractions, but element patterns can be com-
pared with each other. (Detailed numbers can be found
in Online Resource 1.)
2.5 Microscopy
Microscopic pictures of selected sample materials were
taken with the digital microscope KEYENCE VHX-
6000 (KEYENCE GmbH) with a magnification of ×
100–× 1000. Material from three exemplary sampling
locations (one per type of nearby traffic) as well as non-
exposed moss and freshly built webs were regarded to
get an optical, qualitative impression on particle reten-
tion. Non-milled moss from moss bags was placed
directly on the object plate while spider webs were
collected on glass slides at the monitoring sites, embed-
ded in a thermoplastic mounting medium (Cargille
Meltmount*1.582, Cargille-Sacher Laboratories Inc.)
and covered with a cover glass.
2.6 Data Handling and Statistical Evaluation
Data pre-treatment, calculations, and univariate statistics
were done using MS Excel 2010 (Microsoft Corpora-
tion). Mass fractions below the limit of detection (LOD)
were replaced by a random value between zero and the
LOD and element measurement series with more than
10% of values below the LOD were excluded from
further examination (namely Ag and As). The data
was checked for outliers according to Dixon (1951,P
= 99%) and statistical parameters as well as the test for
normal distribution according to David et al. (1954,P=
99%) were calculated for the data without outliers.
Significant differences between spider webs and moss
bags were tested for each element using the Wilcoxon
signed-rank test (Bortz et al. 2008, pp. 259–261, P=
99%). Additionally, the Spearman rank correlation be-
tween elements in spider webs and moss bags was
calculated for each element. Cluster analyses of the
autoscaled data including outliers were done with the
software R using Ward’s algorithm with squared Eu-
clidean distances. Graphical visualizations were edited
with CorelDRAW Graphics Suite 2017 (Corel
Corporation).
3 Results and Discussion
3.1 Microscopy
Microscopic images of the two different sample mate-
rials were kept to get an optical overview of particle
retention of the materials (Fig. 2). Particles can be seen
on the surface of moss material after exposure to ambi-
ent air (Fig. 2c–g) but not before (panel a). Black parti-
cles are attached to the surface of moss exposed at a car
traffic location (panel c) and to a smaller extent to moss
exposed at a pedestrian area (panel g). In contrast, on
moss exposed at a tram and train transport location
(panel e) many big, white, sub-rounded particles can
be found. Those are likely quartz and/or feldspar sand
and silt particles added on the tracks to increase the
adhesion of the wheels (Arias-Cuevas and Li 2011).
Fresh spider webs (panel b) have characteristic sticky
droplets to which particles adhere after exposure (panels
d, f, h; for comparison, see Vollrath and Tillinghast
1991). The highest amount of PM can be found on webs
from a car traffic location (panel d). This is mainly
black, flocculent material similar to brake wear (see
Online Resource 2), while some oval, translucent parti-
cles were identified as windblown plant material. At the
tram and train transport location (panel f), fewer and
smaller particles can be found. Compared with the moss
sample there are less translucent mineral particles but
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Fig. 2 Microscopic images of sample material (left: moss, right:
spider webs) from locations with different types of traffic. amoss
prior to exposure, bfresh spider web early in the morning, c,d
CA-PAR (car traffic), e,fTR-ARE (tram and train transport), g,h
PD-IGW (pedestrian area)
512 Page 6 of 17 Water Air Soil Pollut (2020) 231: 512
some bigger, brownish particles that are considered as
plant material. Possibly the big and heavy minerogenic
particles found on mosses are too heavy to attach to
spider webs while particles of biologic origin might not
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Water Air Soil Pollut (2020) 231: 512 Page 7 of 17 512
be distinguished optically from the moss material.
Those differences are expected to influence the
element patterns discussed in the following. Only
very few particles adhere to the spider web taken
at a pedestrian area (panel h).
Overall, the microscopic images show that the ratio
of deposited PM to biological material is much larger for
spider webs than for moss bag samples. Besides, parti-
cles can be seen and zoomed in better on spider webs as
the structure is less complex and can be fixed on a glass
slide. This easy optical inspection of spider web samples
strengthens the superior suitability of spider webs to
sample PM as stated by Rybak and Olejniczak (2014).
3.2 Moss Bags Vs. Spider Webs: Chemical
Composition
In accordance with the microscopic images, mass frac-
tions of elements in spider webs are generally higher
than in moss bag samples with factors ranging from 2 to
15 for most of the elements (e.g. B: 21.3 μg/g in spider
webs and 8.96 μg/g in moss bags or Sn: 13.1 μg/g in
spider webs and 0.895 μg/g in moss bags). For Na and
P, the factors are 29 and 36, respectively. Detailed
numbers can be found in Table 1(and
Online Resource 3). While a majority of mass fractions
is in the range of trace components (1000 μg/g ≥medi-
an ≥1μg/g), a higher number of minor components
(10% ≥median ≥1000 μg/g) can be found for spider
webs and the number of ultra-trace components (medi-
an ≤1μg/g) is higher for moss bag samples. The differ-
ence between mass fractions of elements in the two
sample materials is significant (Wilcoxon signed-rank
test, P= 99%) for all elements except Cd and Pb. This
does also become visible in Fig. 3. It displays mass
fractions of elements in the samples normalized to mass
fractions in the upper continental crust, the latter of
which are expected to reflect the abundance of elements
in geogenic (natural) dust particles. Only for Cd and Mn
the mass fractions are higher in moss bag samples.
Element patterns for moss bag and spider web sam-
ples in Fig. 3are expected to reflect the general urban air
pollution in the study area. Their shapes look similar for
most of the elements with comparably high normalized
mass fractions, hinting to an enrichment of these ele-
ments, for B, Cd, Cu, Mo, Ni, P, Pb, S, Sb, Sn and Zn.
While P and S occur in the biological materials, the
listed metals and metaloids are known to be mainly of
anthropogenic origin. They might be derived from
sources of PM such as vehicular emissions (e.g. Cd,
Cu, Pb), lubricant or fossil fuel combustion in general
(Cd, Ni, Pb, Zn), coal combustion (Pb), brake and tire
wear for Cd, Cu, Pb, Sb, Sn and Zn and abraded steel
particles for B, Mo and Ni (Enamorado-Báez et al.
2015; Johansson et al. 2009; Rampazzo et al. 2008;
Salminen et al. 2005; Suvarapu and Baek 2017; Viana
et al. 2008; Zhu et al. 2015). It has to be kept in mind
that low valuesfor Al, Nb, Si, Ti and Zr in Fig.3are due
to their incomplete digestion with aqua regia.
Across all orders of magnitude, mass fractions for
moss bag samples are in the range of mass fractions for
moss bag samples using Hypnum cupressiforme in ur-
ban areas in the literature. Adamo et al. (2011) for
example found 0.26 μg/g of Cd, 5.4 μg/g of Cr, and
1140 μg/g of Mg in a study in Naples, Italy, while
Tretiach et al. (2011) found 0.42 μg/g, 5.05 μg/g, and
1020 μg/g respectively in a study from Trieste, Italy.
Median mass fractions in moss bags in this study were
0.291 μg/g Cd, 2.28 μg/g Cr, and 1160 μg/g Mg. Re-
sults of moss bag biomonitoring (using Hypnum
cupressiforme) might therefore be compared across dif-
ferent cities. However, they do likely not stress local
pollution features but rather reflect a general urban air
pollution. More pronounced differences between cities
with different sizes, urban architectures, and climate
zones would be expected if mostly local pollution fea-
tures were reflected. Besides, the mass fractions are
higher than those in naturally occurring Hypnum
cupressiforme from Ljubljana municipality, stressing
the enrichment of metals and metalloids during expo-
sure to ambient air (Berisha et al. 2017). For spider
webs, more differences between mass fractions in the
literature and mass fractions determined in this study
have been found. Often, only well-known heavy metals
like Cd, Cu, Pb and Zn, which are expected to be
derived mainly from anthropogenic sources, were
regarded in spider webs (e.g. Rybak et al. 2012;Rybak
et al. 2015). Xiao-Li et al. (2006) for example found
mass fractions one to two orders of magnitude higher for
Cd (0.85–3.37 μg/g) and Pb (35.4–290 μg/g) in webs
from Wuhan (China) than we found in this work with
median mass fractions of 0.260 μg/g for Cd and
11.1 μg/g for Pb. Higher levels of PM and its metal
mass fractions meet the expectations for the megacity of
Wuhan in comparison with the medium-sized city of
Jena. Besides, older webs (age up to 60 days) were used
in the cited study which might also be a reason for
higher mass fractions compared with those in 1-day-
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Tab l e 1 Median (μg/g) and interquartile range (IQR) of the measured mass fractions in moss bags and spider webs (n= 15). Minor components: 10% ≥median ≥1000 μg/g, trace
components: 1000 μg/g ≥median ≥1μg/g, ultra-trace components: median ≤1μg/g, underlined: mass fraction in moss bags higher than in spider webs, bold: data without the (upwards)
outlier (n= 14), italics: no normal distribution, m,numberofelementspercolumn
Minor components Trace components Ultra-trace components
Moss bags Spider webs Moss bags Spider webs Moss bags Spider webs
Median IQR Median IQR Median IQR Median IQR Median IQR Median IQR
Al 3650 2570 714 171
B 8.96 5.30 21.3 6.0
Ba 45.2 14.1 161 285
Ca 5160 250 12,900 10,900
Cd 0.291 0.032 0.260 0.100
Co 2.44 0.92 0.379 0.121
Cr 2.28 0.92 29.9 25.1
Cs 0.104 0.016 0.500 0.384
Cu 7.85 2.97 79.8 74.2
Fe 9630 9380 766 142
K1320 240 12,300 2200
La 3.32 2.59 0.600 0.202
Li 5.60 4.54 0.560 0.232
Mg 1160 70 3000 1510
Mn 522 90 168 145
Mo 3.60 4.36 0.329 0.086
Na 3700 1630 127 48
Nb 0.133 0.044 0.940 0.600
Ni 2.60 0.65 18.6 10.8
P 13,900 5460 387 79
Pb 9.31 2.61 11.1 4.7
Rb 2.44 0.47 10.9 4.5
S 1000 120 9150 2350
Sb 6.30 9.62 0.459 0.176
Si 1140 320 5490 3000
Sn 13.1 12.5 0.895 0.431
Sr 15.3 2.9 56.4 37.0
512 Page 8 of 17 Water Air Soil Pollut (2020) 231: 512
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Tab l e 1 (continued)
Minor components Trace components Ultra-trace components
Moss bags Spider webs Moss bags Spider webs Moss bags Spider webs
Median IQR Median IQR Median IQR Median IQR Median IQR Median IQR
Ti 28.2 8.1 252 184
V 1.52 0.28 10.0 5.2
W0.092 0.023 0.415 0.238
Y1.66 1.17 0.347 0.092
Zn 42.3 14.0 375 293
Zr 7.33 2.73 0.661 0.232
m5 9 162012 4
Water Air Soil Pollut (2020) 231: 512 Page 9 of 17 512
old webs in the present study. Some differences might
also be due to the various digestion methods used as
they can show different recovery rates. Silicate particles
for example can only be digested totally by HF that was
used by Adamo et al. (2011) while most of the other
studies cited used digestion with HNO
3
and H
2
O
2
(Berisha et al. 2017;Rybak2015; Xiao-li et al. 2006).
To better compare mass fractions for the two mate-
rials and correct them for the diluting effect of the
biological carrier material, the mean amount of the
biomass in the two materials has been calculated. Mass
fractions of the biomass were projected using mean
mass fractions of organic carbon in the samples and a
sum formula describing the biomass. Formoss bags, the
sum formula C
12
H
20
O
10
(cellulose) has been used and
for spider webs, we applied C
3.38
H
5.01
N
1.06
O
1.32
, a sum
formula approximated from the fractions of amino acid
residues named by Work and Young (1987). Mass
fractions measured in the samples were subsequently
corrected for the biomass as given in Eq. (1)wherew
i,cor
is the corrected mass fraction of the element i,w
i
is the
measured mass fraction of the element i(both in μg/g),
and w
bio
is the mass fraction of the biomass (in g/g).
wi;cor ¼wi∙1
1−wbio
ð1Þ
Figure 4shows boxplots for the sum of all mass
fractions measured and for sums of mass fractions
corrected for the biomass. For the latter, the difference
between the sample types decreases noticeably from a
factor of 6.2 to 1.2. Still, the difference is significant for
about half of the elements (B, Ca, Cd, Cr, Cu, Fe, K,
Mg, Mn, Na, P, Pb, S, Sb, Sn, Sr, Zn, Zr). This leads to
two different conclusions: Either the diluting influence
of the biomass cannot be corrected for completely by
this approach or the influence can be corrected for
completely and the remaining differences are due to
systematic contrasts of the retention of PM by the two
biological materials. The latter might also be deduced
from element patterns in Fig. 3. Differences between the
element patterns of moss bags and spider webs can be
found for Cd, Mn and Pb with comparably high values
for moss bag samples. While Mn occurs in the moss
material itself as a micronutrient, the differences for Cd
and Pb cannot be explained by the carrier material itself
and likely hint to different particle retention mechanisms
of spider webs and moss bags.
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Fig. 3 Mass fractions of elements determined in moss bag and
spider web samples normalized to mass fractions in the upper
continental crust (UCC) given by Wedepohl (1995). The
normalization has been performed to allow for an easy optical
comparison of elements with different natural abundances
512 Page 10 of 17 Water Air Soil Pollut (2020) 231: 512
Independent from the significant difference, the
corrected mass fractions are in the same range. For some
comparison that might be helpful, e.g. to qualitatively
differentiate between polluted and unpolluted areas or
when using data gained with both methods
complementary.
3.3 Correlation Coefficients as a Tool to Identify
SourcesofPM
For an explicit source identification, single samples and
the variation of the data can be exploited. The spider
web data shows a higher variation (higher interquartile
ranges compared with the median) than the moss bag
data (Table 1and Fig. 3). Spider webs thus might reflect
small variations in trace element compositions of PM
better than moss bags. The higher variation of the spider
web data is most likely due to the smaller leveling and
diluting influence of the biological material (as it can be
inferred from the microscopic images). However, the
biological carrier material itself might be more hetero-
geneous for spider webs, introducing some of the vari-
ations, as they are woven by individual orb-weaving
spiders (mainly Araneus diadematus).
Spearman rank correlation coefficients (r
s
, calculated
using the mass fractions measured) are a statistical tool
that examinesthe variation of a dataset and is often used
to describe the relationship between two variables. In
this work, groups of elements with significant correla-
tion are used to describe different sources of PM. Only
for some elements, significant correlations (|r
s
|≥0.65,
P= 99%) are found for both spider webs and moss bags
and a higher number is found for the spider web than for
the moss bag dataset (Table 2, numbers in
Online Resource 3). Thus, a stronger relationship be-
tween the elements in spider webs is deduced. For a
better understanding, joint coefficients can be regarded
as forming sub-matrices with predominantly significant
coefficients that are ascribed to the different sources of
PM or influences of the sample material. In this sense,
the sub-matrix of K, P and S likely describes the dilution
of PM by the biological material while correlations of K,
P and S with other elements are negative and the ele-
ments are included in both spider webs and mosses
(Rachold et al. 1992; Strasburger et al. 2014; Work
and Young 1987). Al, Co, La, Nb, Ti, V, Y and Zr form
another sub-matrix that is ascribed to PM of geogenic
origin, suchas natural and anthropogenically influenced
soil erosion. La, Nb, Y and Zr are common ele-
ments found in high mass fractions in the Quater-
nary loess deposits of Central Germany which are
also present in the surroundings of Jena (Salminen
et al. 2005;Seidel1993). Cu, Sb, Sn and Zn
correlating with each other have been ascribed to
brake wear in the literature (Berisha et al. 2017;
Furusjö et al. 2007; Johansson et al. 2009). Their
sub-matrix likely describes brake wear as a part of
the influence of automobile traffic to PM. A dif-
ferentiation of geogenic/natural and anthropogenic
sources of PM is thus possible with both methods.
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Water Air Soil Pollut (2020) 231: 512 Page 11 of 17 512
An additional sub-matrixof Cr, Fe, Mo and Ni can be
found for the spider web data but not for the moss bags.
Those elements are ascribed to the abrasion of rail and/
or tram tracks consisting of steel alloys containing all
four elements (Johansson et al. 2009;LahdGeageaetal.
2007). Furthermore, the sub-matrix describing
(resuspended) geogenic PM contains more elements
for the spider web dataset than for the moss bags.
Additional elements are typical for either calcareous
rocks (Ca, Mg, Sr) or marine evaporates (B, Cs, Li) that
can also be found in the surroundings of Jena (Salminen
et al. 2005;Seidel2003).
In contrast, solely for the moss bag dataset elements
correlating not only with a few other ones but also with a
high number of elements can be found. Those elements
are likely derived from different sources. Especially Co,
Cr, Fe, Ni and Zn have been described as being mainly
of anthropogenic origin and derived from various
sources (Dongarrà et al. 2007; Rybak 2015; Suvarapu
and Baek 2017).
The fact that there are only very few significant
correlation coefficients between the sample materials
(Table 3) further stresses the differences between the
two monitoring methods. If the sample materials would
accumulate PM in identical ways, significant correlation
coefficients for the same element in moss bag and spider
web samples should be expected. A correlation like that
can only be found for Cu, Mo, Sb, Sn and Zn that have
been already ascribed to brake wear or car traffic in a
broader sense. Most striking is also the correlation of Ba
in moss bags with Al, Ca, Cd, Cs, La, Li, Mg, Pb, Sr, Y
and Zr in spider webs. Most of the latter elements have
already been discussed as being of geogenic/natural
origin. Cd and Pb are almost completely of anthropo-
genic origin but can often only be ascribed to a more
diffuse pollution rather than a single source
(Enamorado-Báez et al. 2015; Suvarapu and Baek
2017). Ba, correlating with all of these elements, can
be derived from both natural and anthropogenic sources
like sedimentary rocks, unleaded fuel, lubricant oils, and
brake fillings (Sternbeck et al. 2002). This correlation
between the sample materials might therefore be due to
diffusely distributed PM from different sources, which
is entrapped in the moss bags.
Overall, differences in correlation coefficients match
the hypothesis that the influence of local sources (in this
case types of traffic) is more pronounced in the spider
web dataset while a more diffuse anthropogenic influ-
ence can be seen in the moss bag dataset.
3.4 Cluster Analyses
To further identify sources of PM and effectively dis-
tinguish or group sampling locations, multivariate
methods can be applied. In some studies, this has al-
ready been done but only for single datasets (e.g.
Barandovski et al. 2015; Rybak 2015;Ștefănuțet al.
2019). Here, cluster analyses have been performed with
the mass fractions measured in moss bags and spider
webs, and the resulting dendrograms are contrastedwith
each other (Fig. 5). At a relative distance of 15%, four
stable clusters can be found for the moss bag samples.
While the location with the highest traffic volume (CA-
PAR) is clearly cut off, the other clusters cannot be
ascribed to a specific source or circumstance. They
contain locations with different types of nearby traffic,
Fig. 4 Boxplot of the sum of all element mass fractions measured
in moss bag and spider web samples and of mass fractions
corrected for the amount of biomass (cor)
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Tabl e 2 Joint correlation matrices (Spearman rank correlation r
s
)
for moss bags and spider webs. Only significant correlations (|r
s
|≥
0.65, P= 99%) are regarded. The color code shows if the correla-
tion is significant only for the moss bag dataset (61 significant
correlations), the spider webs dataset (114significant correlations)
or both (85 significant correlations); - indicates a negative corre-
lation. Different sub-matrices are formedfor moss bags and spider
web samples
NaZnSnSb
CuMoNi
Cr
-
FeMn
BaCdCo
-
V
-
-
Pb
-
-
Zr
-
-
La
-
-
Significant rsfor both
sample matrices
Significant rsfor moss
bags
Significant rsfor
spider webs
Negative correlation
Nb
-
-
Ti
-
-
-
Mg
-
-
Cs
-
-
Y
-
-
BSr
-
-
Li
-
-
Ca
-
-
Al
-
-
WSi
-
-
PSKRb
Rb
K
S
P
Si
W
Al
Ca
Li
Sr
B
Y
Cs
Mg
Ti
Nb
La
Zr
Pb
V
Co
Cd
Ba
Mn
Fe
Cr
Ni
Mo
Cu
Sb
Sn
Zn
Na
512 Page 12 of 17 Water Air Soil Pollut (2020) 231: 512
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Fig. 5 Dendrograms (Ward’s algorithm, squared Euclidean dis-
tances) depicting the cluster analyses of moss bags (a) and spider
webs (b). At 15% relative distance the sampling locations are
separated according to the nearby sort of traffic (CA, car; PD,
pedestrian; TR, tram/train; CA/TR, car and tram/train) within the
spider web dataset. A similar pattern cannot be seen within the
moss bag dataset
Water Air Soil Pollut (2020) 231: 512 Page 13 of 17 512
different elevations, and different locations in the town.
In contrast, at 15% relative distance, the spider web
samples are clearly distinguished according to the near-
by type of traffic, coinciding with the results of Rybak
and Olejniczak (2014), who suggested spider webs as a
useful indicator of traffic emissions but with respect to
polycyclic aromatic hydrocarbons. This direct compar-
ison witnesses that spider webs reflect small-scale dif-
ferences in anthropogenic PM better than moss bags.
3.5 Possible Differences Due to the Methodology
Overall, the differences between moss bag and spider
web biomonitoring might not only be due to differences
in texture and relation of sample material to PM as
described above but also to different requirements/
characteristics of the sampling approaches. These char-
acteristics must also be taken into consideration when
selecting one method overthe other. While orb webs are
renewed nearly every day, mosses need several weeks to
accumulate a significant amount of PM (Aničić
Uroševićand Milićević2020;Aresetal.2012;Nentwig
1980). Thus, the latter give integrated values reflecting a
longer period while spider webs allow for a better tem-
poral resolution. Still, moss bags and spider webs in this
study are expected to reflect the same PM that did not
change substantially over the exposure period of the
moss bags. The repeated sampling and analysis of webs
Table 3 Spearman rank correlation coefficients between moss bag and spider web data. Only significant correlations (|r
s
|≥0.65, P=99%)
are shown. A distinct correlation between the same elements in the two matrices can only be calculated for Cu, Mo, Sb, Sn and Zn
Spider webs Al Ca Li Sr Y Cs Mg Nb La Zr Pb Cd Cr Ni Mo Cu Sb Sn Zn
Moss bags
Nb 0.65 0.75
Zr 0.67
Ba 0.68 0.77 0.75 0.79 0.69 0.78 0.66 0.68 0.69 0.65 0.71
Ni 0.67
Mo 0.67 0.80 0.77 0.82 0.75
Cu 0.86 0.84 0.85 0.85
Sb 0.65 0.73 0.66 0.76
Sn 0.83 0.89 0.84 0.85
Zn 0.75 0.71 0.78 0.71
Na 0.65
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
512 Page 14 of 17 Water Air Soil Pollut (2020) 231: 512
from two locations (TR-ARE and CA-BUR) every
2 weeks during the moss bag’s exposure period showed
comparably low standard deviations for element mass
fractions. The selection of one or the other biomonitor-
ing method therefore also depends on the desired tem-
poral resolution.
Furthermore, the sampling height is different for the
two methods: Mosses were sampled 2.5 m above
ground level, and the spider webs at 0.5–1.2 m. The
height of the spider web sampling has been predefined
by the height of the structures (handrails) from which
they have been sampled. Moss bags had to be exposed
in greater height to prevent losses due to vandalism.
Near ground level emissions like those from traffic are
probably more pronounced next to the surface while the
influence of mixing, leading to less local variation,
increases with height. Even though Capozzi et al.
(2016) described a main influence by traffic for moss
bags at a height of 4 m, an influence of mixing already in
a height of 2.5 m cannot be ruled out completely by
means of the results presented. Air circulation will also
transport mainly smaller particles into a height of 2.5 m
compared with a height between 0.5 and 1.2 m. This
could also be seen in the microscopic images (Fig. 2).
Bigger particles have a stronger influence on the mass
fractions of elements which likely leads to a better
discrimination of sources or sampling locations. From
a health perspective, a lower height might be of greater
interest, as this is the height of inhalation for children,
which suffer disproportionally much from PM exposure
(Fang et al. 2005; Landrigan et al. 2018). Still, it might
not be possible to expose moss bags next to ground level
as they will likely be damaged or stolen by people. As
spider web samples consist of a high number of individ-
ual webs that do not attract as much attention as moss
bags, a considerable effect of vandalism is not expected
for the spider webs.
4 Conclusion
In this work, two different biomonitoring methods for
(trace) elements in particulate matter (PM) have been com-
pared, focusing on their potential use for monitoring and
source identification. Element mass fractions are significant-
ly higher in spider webs than in moss bag samples. A
calculation to account for the diluting effect of the biological
material leads to fewer but still existing significant differ-
ences, hinting to different adsorptions of dust particles. This
can also be seen partially in microscopic images of the
samples. Element patterns, correlation coefficients, and clus-
ter analyses show some differences for the two sample
materials. For spider webs, they can clearly be ascribed to
different sources of PM, leading to a clustering of the
sampling locations in accordance with the type of nearby
traffic. This source identification is less pronounced for the
moss bag dataset with an undefined clustering of the sam-
pling locations. However, a single moss bag sampling
campaign reflects PM from a longer period of time (several
weeks) than one sampling campaign of orb webs (one to a
few days). As a result, it is recommended to use moss bags
for long-term screening on a rather regional scale. For a
local, short-term source identification spider web (orb web)
data should be used to exploit the higher variance in the
data, the smaller influence of the biological material, and the
stronger relationships between the elements as found in this
study. Further studies might focus on possibly different
capture mechanisms for PM of the biological materials,
which has not been a major part of this study.
Acknowledgments We would like to thank the central labora-
tory for water analytics & chemometrics at Helmholtz Centre for
Environmental Research (Magdeburg) for performing element
analyses of carbon and nitrogen and the group of Microbial
Communication at Friedrich Schiller University Jena for providing
the equipment for cryo-milling. Access to the digital microscope
was kindly provided by the group of General and Historical
Geology at Friedrich Schiller University Jena. Additionally, we
would like to thank the Kommunalservice Jena for permitting the
use of street lamps for the installation of moss bags and Dietrich
Berger, Friedrich Schiller University Jena, for the identification of
the moss species. The anonymous reviewers are kindly acknowl-
edged for their constructive comments and suggestions.
Authors’Contributions All authors contributed to the study
conception and design. Material preparation, data collection, and
analysis were performed by Neele van Laaten, Michael Pirrung,
Dirk Merten, and Wolf von Tümpling. The first draft of the
manuscript was written by Neele van Laaten and all authors
commented on previous versions of the manuscript. All authors
read and approved the final manuscript.
Funding Open Access funding enabled and organized by
Projekt DEAL. The first author received a scholarship from the
International Max Planck Research School for Global Biogeo-
chemical Cycles.Data AvailabilityNot applicable.
Compliance with Ethical Standards
Conflict of Interest The authors declare that they have no
conflict of interest.
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Water Air Soil Pollut (2020) 231: 512 Page 15 of 17 512
Code Availability Not applicable.
Open Access This article is licensed under a Creative Commons
Attribution 4.0 International License, which permits use, sharing,
adaptation, distribution and reproduction in any medium or format,
as long as you give appropriate credit to the original author(s) and
the source, provide a link to the Creative Commons licence, and
indicate if changes were made. The images or other third party
material in this article are included in the article's Creative Com-
mons licence, unless indicated otherwise in a credit line to the
material. If material is not included in the article's Creative Com-
mons licence and your intended use is not permitted by statutory
regulation or exceeds the permitted use, you will need to obtain
permission directly from the copyright holder. To view a copy of
this licence, visit http://creativecommons.org/licenses/by/4.0/.
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