Methodology for non-target screening of sewage sludge using comprehensive two-dimensional gas chromatography coupled to high-resolution mass spectrometry
Methodology for non-target screening of sewage sludge using comprehensive two-dimensional gas chromatography coupled to high-resolution mass spectrometry
Cathrin Veenaas 0 1
Peter Haglund 0 1
0 Department of Chemistry, Faculty of Science and Technology, Umeå University , 90187 Umeå , Sweden
1 Cathrin Veenaas
To investigate the wide range of pollutants occurring in sewage sludge, an analytical method for comprehensive nontarget screening is needed. To the best of our knowledge, no procedures currently exist for the full screening of organic contaminants in sewage sludge, which is the ultimate goal of this project. We developed non-discriminating sample preparation methods for gas chromatography-mass spectrometry (GC-MS) analysis. Pressurized liquid extraction (PLE) was used for extraction, with in-line (silica gel selective PLE, SPLE) or off-line clean-up (gel permeation chromatography, GPC). This combination allowed the analysis of non-polar compounds of all sizes and small semi-polar and non-polar compounds. The results show that the combination of SPLE and PLE with GPC is suitable for analysis of established as well as new contaminants. Both methods were validated for 99 compounds with different properties. For all GC suitable analytes, either one of the methods produced acceptable recoveries (64 to 136%). As a test, the two methods were used for non-target screening of Swedish sewage sludge. A tiered approach was used to tentatively identify the sludge contaminants. In total, 1865 and 1593 compounds were found of which 321 and 192 compounds were tentatively identified for the PLE and SPLE method, respectively. For a comprehensive coverage of contaminants, the two methods should be used together, with the PLE method covering a wider polarity range and the SPLE method a wider size range. In addition, polar substances will require liquid chromatography-mass spectrometry analysis, the method for which will be developed soon.
Non-target screening; Sewage sludge; Method development; GC-HRMS; GC × GC
Parts of this work have been awarded with the ABC Poster Prize at the
23rd Analytical Days of the Analytical Division of the Swedish Chemical
Society, Umeå, Sweden, June 14th-17th, 2016.
Globally, there are more than 100,000 chemicals currently
used every day [
]. Many of these chemicals, among them
potential pollutants, are disposed of in wastewater and hence
enter sewage treatment plants (STPs). STPs are used to
remove nutrients, but also some metals and organic chemicals,
from urban water to create a less contaminated effluent.
Consequently, STPs form a link between the technosphere
and the environment. A by-product of the sewage treatment
process is sewage sludge—a solid product that contains
nutrients as well as pollutants. These nutrients make the sewage
sludge attractive for applications such as fertilizer for
agriculture, provided that the contaminant levels are not too high.
Statistics have shown that 54% of the sewage sludge in
Europe and 61% of the North American sewage sludge are
used in land applications, whereas 10 and 17%, respectively,
are placed in landfills [
]. The rest of the sludge is either
combusted, disposed of, or reused in other ways.
In order to safely dispose of or reuse the sewage sludge in
agriculture the European Union directive 86/278/EEC forces
member states to monitor heavy metal concentrations in
sludge and soil on a regular basis when sewage sludge is used
as fertilizer [
]. Similarly, the US government has defined
maximum pollutant loadings for sewage sludge when used as
fertilizer on agricultural crops. Again, heavy metals were the only
regulated pollutants [
]. For EU member states, national
requirements apply as well. Some of these are much stricter than the EU
laws (e.g., in Denmark, Finland, Sweden, and the Netherlands)
and some of them also include organic contaminants in sewage
]. In Sweden, for example, maximum loadings for
polychlorinated biphenyls (PCBs), nonylphenol ethoxylates
(NPEs), polycyclic aromatic hydrocarbons (PAHs), and toluene
are defined and those are measured before sludge is spread on
]. Nevertheless, sewage sludge might still pose a risk
when used on arable land as in all cases only target compounds
are monitored and some crops are known to take up pollutants
from soil [
]. This study aims to develop a method that enables
a comprehensive screening of sewage sludge and thereby
allowing detection and monitoring of currently unknown organic
contaminants present in sewage sludge. Although the literature
contains many examples that deal with the analysis of sewage
sludge, no study so far has involved non-target screening of
sewage sludge, which is the scope of this study. Traditionally,
sludge has been extracted using Soxhlet and ultrasound
extraction, but nowadays, these methods are often replaced with
pressurized liquid extraction (PLE), as highlighted by Zuloaga et al.
]. By reducing solvent consumption and process time,
providing improved extraction rates, and enabling extraction of polar as
well as non-polar compounds [
], PLE would appear to be a
suitable method for the comprehensive extraction of sewage
sludge. In addition, PLE provides the opportunity of an in-cell
clean-up using, for example, Florisil, silica gel, alumina , or
combinations of them [
]. Such procedures, known as selective
PLE (SPLE), decrease the amount of co-extracted interfering
matrix compounds from solid samples, such as lipids or humic
and fulvic acids [
] and may, therefore, enable direct analysis
after extraction [
]. SPLE has been applied to various matrices
such as soil and sediment [
], food and feed samples [
and sewage sludge [
] and dates back to 1996 when the use
of alumina was suggested in a Dionex application note to retain
In SPLE, there is a balance between the polarity range of
chemicals extracted and the purity of the extracts. Polar
solvents or solvent mixtures will extract a wide range of
chemicals but will also extract more of the matrix. Although
in some cases, only filtering and/or derivatization is required
before analysis by gas chromatography-mass spectrometry
], some analysts apply further clean-up prior
to analysis [
] in order to reduce interference and
improve the limit of detection (LOD) [
]. Conventional PLE
will, in most cases, require further clean-up. For a non-target
screening, a non-destructive clean-up is generally used, such
as gel permeation chromatography (GPC) [
partitioning, or adsorption chromatography.
The combination of PLE and GPC has previously been
used for target and non-target analysis of solid matrices [
]. While PLE can be employed with various different types
of solvent, a binary mixture of a non-polar solvent (such as
n-hexane) and a more polar solvent (such as dichloromethane
(DCM)) is often used . For example, Kim et al. [
extracted polychlorinated dibenzo-p-dioxins, dibenzofurans, and
biphenyls from animal feed using n-hexane/DCM (1:3) and
Muscalu et al. [
] extracted halogenated organic compounds
from soil, sediment, and sludge using n-hexane/DCM (3:1).
However, no studies have focused on a comprehensive
nontarget screening of organic contaminants in sewage sludge.
The current study aimed to develop procedures for
nontarget screening of sewage sludge. For this purpose, PLE with
off-line GPC clean-up was compared to SPLE with in-cell
silica clean-up. The extraction efficiency and the amount of
co-extracted matrix were assessed for several solvents and
solvent mixtures. As a test of utility, the two methods were
used for non-target screening of Swedish sewage sludge
samples. The number of sample constituents captured and the
spectra quality (percentage of peaks that could be tentatively
identified) obtained with the two non-target screening
techniques were compared. There was also an assessment of
whether a combination of the two approaches would enlarge
the chemical domain covered. Finally, the potential need for
complementary LC-MS analyses was discussed.
Materials and methods
The method development and evaluation included two
methods for extraction, PLE and SPLE, two methods for
solvent evaporation, Turbovap and Rotavap, and two methods
for sulfur removal, using acid-activated copper and tetra butyl
ammonium sulfite (TBA) reagent, respectively. In addition, a
method validation study was carried out using spiked and
unspiked sewage sludge.
The 8270 MegaMix® standard (see Electronic Supplementary
Material (ESM) Table S1 for compound information) was
bought from Restek (Bellefonte, PA, USA). Deuterated
PAHs (see ESM Table S2 for more information) were
obtained from Cambridge Isotope Laboratories (Tewksbury, MA,
USA). Sand (Fontainebleau PROLABO®) and 2-propanol
(HiPerSolv Chromanorm 100%, PROLABO) were purchased
from VWR (Leuven, Belgium), whereas concentrated
hydrochloric acid was obtained from VWR, Fontenay-sous-Bois
(France). Sodium sulfate, silica gel 60, acetone (≥99.8%),
nhexane (≥98.0%), and cyclohexane were obtained from
Merck KGaA (Darmstadt, Germany). DCM (99.99% purity),
isooctane (99.94% purity), ethyl acetate (99.96% purity), and
methanol (99.99% purity) were purchased from Fisher
Scientific (Loughborough, UK). Copper of mesh size 10–40
(≥99.9% purity) and sodium sulfite (≥98%) were acquired
from Sigma-Aldrich (St. Louis, MO, USA). TBA hydrogen
sulfate was purchased from Molekula (Shaftesbury, UK).
Glass fiber filter papers (GFFs) with a diameter of 27 mm
were acquired from Dionex (Sunnyvale, CA, USA). An
Omnifit glass column (L 50 cm, i.d. 25 mm) from Diba
Industries Ltd. (Cambridge, United Kingdom) and SX-3
Bio-Beads from Bio-Rad Laboratories AB (Hercules, CA,
USA) were used for GPC.
Sludge sampling and sample pre-treatment
Digested, dewatered sludge (15 days in digester) was obtained
from the STP in Umeå, Sweden. Samples were taken in the
morning and frozen immediately until further use. Prior to
extraction, the samples were freeze-dried using a Lyovac GT 2
(SRK System Technik GmbH, Riedstadt, Germany) equipped
with an Edwards High Vacuum Pump E2M2, and the dry
weight was determined (~33.6%). Afterwards, the sludge was
homogenized using a mortar and pestle. As a third step directly
before extraction, a filling material, either pre-cleaned (PLE
with acetone) sand or pre-baked (550 °C) sodium sulfate, was
mixed with the dried sludge (approximately 3:2, w/w) to create
a homogeneous mixture that filled the extraction cells evenly.
The detailed procedures are described below.
Extraction equipment and conditions
Sample extraction was carried out using a Dionex™ ASE™
350 system equipped with 22-mL stainless steel extraction
cells under the following conditions: 120 °C, 5 min static
extraction, 3 extraction cycles, 100% flush volume, and 60 s
nitrogen purge. To reduce the risk of contamination, high
purity solvents were used. During the method development,
different solvents and solvent combinations were tested. The
solvent volume used for the extraction resulted in
approximately 50 mL under the specified conditions. More
information on the solvents can be found in the respective sections
below. In addition, the extraction cells, sand, and GFFs were
pre-cleaned using the PLE system with acetone under the
following conditions: 100 °C, 1 min static extraction, 3
extraction cycles, 100% flush volume, and 60 s nitrogen purge.
PLE method development experiments
Non-polar solvents such as n-hexane are expected to release
less co-extracted matrix but may not exhaustively extract
contaminants. Binary solvent mixtures generally offer better
extraction efficiencies. The method development therefore
included the following solvents and solvent mixtures: n-hexane,
n-hexane/DCM (80:20, v/v), and n-hexane/DCM (50:50, v/v).
The polar modifier selected, DCM, is aprotic and known to
efficiently desorb difficult to extract compounds such as PAHs
from solid matrices [
]. Moreover, conventional PLE and
SPLE with silica were compared. To assess the suitability of
the methods, the co-extracted matrix amount and extraction
efficiency were determined and compared.
For the evaluation of the co-extracted matrix, 1-g sludge
aliquots mixed with sand for homogenization were extracted as
described above. After extraction, the solvent was fully
evaporated and the residue was determined gravimetrically
(d = 0.001 g). For the extraction efficiency evaluation, analytical
standards were spiked to sand as follows: the PLE cells were
filled with a GFF and pre-cleaned sand and spiked with
approximately 1 μg of the 8270 MegaMix standard and the SPLE
extraction cells were filled with a GFF, 5 g silica gel 60 (dried
at 130 °C for 12 h or overnight), a second GFF on top, and
precleaned sand and spiked with approximately 1 µg of the 8270
MegaMix standard. A blank containing one GFF and sand and
one GFF, silica gel 60, another GFF, and sand were prepared for
the PLE and SPLE method, respectively. After solvent exchange
to isooctane and volume reduction to about 1 mL,
d10phenanthrene (approximately 544 ng per sample) was added as
the volumetric standard. Analysis was carried out using an
Agilent 7890A GC (Agilent Technologies, St. Clara, CA,
USA) coupled to a high-resolution (HR) time-of-flight (TOF)
MS (HRT; Leco Corp. St. Joseph, MI, USA) with electron
impact (EI) ionization. The instrument was equipped with a Gerstel
CIS4 inlet, which was operated in pulsed splitless mode. The
splitless time was 105 s with an inlet purge flow of 25 mL/min
and septum purge flow of 3 mL/min. A 30-m DB-5MS Ultra
Inert column (0.25 mm i.d., 0.25 μm film thickness) from
Agilent was used. The oven program was as follows: 80 °C
(3.8 min), 15 °C/min, and 300 °C (6.5 min). Helium was used
as carrier gas with a flow of 1 mL/min. The transfer line was held
at 300 °C. The ion source temperature was 250 °C and 12 spectra
per second were recorded in the range from m/z 38 to 400.
A calibration curve with ten points ranging from 1 to
1000 ng/mL was prepared. A linear regression curve with a
fixed intercept at zero was used for the determination of the
Solvent evaporation experiments
The MegaMix standard was used for testing two methods of
solvent evaporation. The first method used a Turbovap
concentration workstation (Biotage AB, Uppsala, Sweden),
operated at 35 °C and 500 mbar, and the second method used a
rotary evaporator (Rotavap) from Heidolph (Schwabach,
Germany), operated at 50 °C with a pressure below 150 mbar.
The Turbovap was used with 60 mL PLE vials and the
Rotavap with 100 mL pear-shaped flasks, which were tilted
to create a horizontal solvent surface and minimize the
deposition of chemical residues on dry walls. A MegaMix aliquot
equivalent of 1 μg of each analyte was added to 50 mL
isooctane, which was then evaporated to 1 mL using the two
techniques. The experiments were carried out in triplicate
and a blank containing only solvent was included. A mix of
deuterated PAHs was added as a volumetric standard and the
samples were analyzed with GC-MS, as described in the
BMethod development^ section.
PLE method validation experiments
A total of 99 analytes, including the MegaMix 8270, PCBs,
organophosphates, fragrances, pesticides, and others, were
used for validation of the final method. For information about
native standards/analytes and labeled standards including their
spiking levels, please refer to the ESM Tables S1, S3, and S4.
The added amounts of native analytes were higher than of
labeled standards in order to sufficiently exceed the intrinsic
sludge levels. Three sets of samples were prepared in triplicate
for each method: (i) 1 g sewage sludge spiked with native and
labeled compounds, (ii) sewage sludge spiked with labeled
compounds, and (iii) inert material (pre-baked sodium
sulfate) spiked with labeled compounds (blanks).
Each set was extracted using PLE and SPLE with n-hexane/
DCM (80:20, v/v), leading to a total of 24 samples. Samples
extracted with PLE (not SPLE) were further cleaned by using
GPC (mobile phase, cyclohexane/ethyl acetate (3:1); flow, 5 mL/
min; fraction, 23–59 min). The combination of cyclohexane and
ethyl acetate is commonly used in GPC [
]. The flow was
adjusted not to exceed the maximum column pressure while
the collection window was determined by injecting the
MegaMix and collecting fractions for subsequent GC analysis
to determine when the compounds elute. The column was
packed in-house with approximately 45 g SX-3 Bio-Beads and
was compressed to a bed height of 40 cm. For all samples and
blanks, sulfur was removed using TBA sulfite reagent as
explained below. The analysis was carried out using the GC-HRT
system described above, equipped with a secondary oven and a
quad jet two stage thermal (liquid nitrogen) modulator for GC ×
GC analysis. The first column was a 30-m Rtx-5MS (0.25 mm
i.d., 0.25 μm film thickness), and the second column was a 1.1 m
Rxi-17Sil MS column (0.25 mm i.d., 0.25 μm film thickness),
both from Restek. The oven programs were as follows: 90 °C
(2 min), 5 °C/min, and 300 °C (5 min) for the first oven and
105 °C (2 min), 5 °C/min, and 300 °C (8 min) for the second.
The modulator had a temperature offset of 15 °C relative to the
secondary oven, and the modulation period was 4 s with a hot jet
and cold jet duration of 1.2 and 0.8 s, respectively. The transfer
line was held at 325 °C. The ion source temperature was 250 °C,
and 150 spectra per second were recorded in the range from m/z
38 to 1000.
A six-point calibration curve was prepared of which a
linear regression curve (intercept at zero) was created for the
quantification of the analytes. Information about the linear
range and R2 can be found in Table S5 (see ESM). Before
calculating the recovery, the amount of analyte detected in
the unspiked sewage sludge, if present, was subtracted from
the amount detected in the spiked sewage sludge.
Sulfur removal experiments
Sulfur removal using activated copper and a TBA sulfite
reagent were compared using triplicate treatments for recovery
of the following contaminants (all at 1 ng/μL in isooctane): the
8270 MegaMix, an organochlorine pesticide mix (GC
multiresidue pesticide standard no. 2), and an
organophosphorus pesticide mix (GC multiresidue pesticide standard no. 8)
from Restek and diazinon, 2-(methylthio) benzothiazole, and
thiabendazole from Dr. Ehrenstorfer GmbH (Augsburg,
Germany). The recovery of each analyte was determined
using GC × GC-MS, as described in the BMethod validation^
For sulfur removal with copper, the copper was activated
using concentrated hydrochloric acid and then rinsed each
three times with Milli-Q water (Merck Millipore), methanol,
and DCM. The activated copper was added in small portions
(~½ teaspoon) to the samples until freshly added copper no
longer discolored. Samples were kept overnight in the fridge
and more copper was added if additional discoloring was
visible the next day.
For sulfur removal using the TBA sulfite, a reagent mixture
was prepared and used as described by Jensen et al. [
brief, TBA sulfite reagent was prepared by mixing 1.695 g
TBA hydrogen sulfate with 50 mL Milli-Q water followed by
threefold extraction, each with 15 mL n-hexane for removal of
impurities. Afterwards, the solution was saturated with 12.5 g
sodium sulfite. Samples in 2 mL isooctane were mixed with
1 mL 2-propanol and 1 mL TBA sulfite reagent. The mixture
was shaken and sodium sulfite was added in 100-mg portions
until a solid residue remained after shaking. Then, 5 mL
MilliQ water was added and the mixture was shaken for another
minute. Afterwards, the mixture was centrifuged (10 min,
2000 rpm) and the supernatant was transferred.
The limit of quantification (LOQ) and LOD were derived
from method validation blank values (see section BPLE
method validation experiments^) where possible. In all other cases,
they were determined using the standard deviation of the
triplicate injections of the lowest point of the standard curve. The
formulae for the LOD and LOQ are as follows:
LOD ¼ 3:3
LOQ ¼ 10
where σ is the standard deviation of the response (blank or
standard dilution close to the LOQ, respectively) and S being
the slope of the standard curve.
The peak finding and library search for the non-target
application were carried out using the ChromaTOF
software (version 1.90.60) from Leco Corporation in
connection with the NIST MS library (2011). For a peak to be
accepted, the following criteria had to be fulfilled: (i) the
area of the peak in the sample had to be at least three
times higher than the area of the same peak in the blank
and (ii) the peak had to be found in at least two out of
three sample replicates. The stepwise procedure of
identifying and classifying peaks in sludge chromatograms was
1. Peaks occurring in the blank (in high enough
concentrations) as well as the sample were removed (as defined
2. Features that occurred only in one of the triplicates were
3. Peaks were classified into groups according to the rules in
Table 1 in combination with the regions defined in Table 1
and Fig. 2 (see BResults^ section). All classification
regions followed the upwards trend (increasing second
dimension retention time) caused through the isothermal
(starting at 41 min) in the end of the oven temperature
4. The remaining peaks were identified using the NIST
library (similarity and probability), fragmentation
patterns, and, where possible, retention indexes. Only
hits with a similarity match greater than 500 were
displayed. To reduce the amount of peaks to look at,
only peaks that had a first hit with either a high
similarity (>750) or a high probability (>7000) were
considered. For compounds where no retention index was
found, a simple linear regression model using
retention times of standard analytes and their boiling points
was used for giving an approximate retention time.
Retention times were used for exclusion purposes
rather than confirmation.
5. Chlorine and bromine filters were applied. Firstly,
ChromaTOF’s built-in chlorine and bromine filters were
used. In addition, our own filter criteria were applied
6. The mass defect was used to identify chlorinated and
brominated compounds using 81Br–79Br and 37Cl–35Cl
(nominal isotope spacing divided by exact isotope
spacing), respectively, as reference for normalization. The
mass spectrum was summed over a range of 10 min each.
Since the raw chromatograms/spectra were used and
peaks were identified manually, peaks that were missed
in the peak picking process during the data processing
could also be identified.
The in silico fragmentation tool MetFrag [
] was used to
identify unknown chlorinated compounds (steps 5 and 6
above). MetFrag uses compound structures stored in
databases (e.g., PubChem or Chemspider) to predict the
fragmentation of small molecules. Those fragmentation patterns are
then compared to a spectrum that is inserted by the user. The
similarity of the spectrum inserted by the user to the predicted
fragmentation is then given. Originally, MetFrag was
developed for tandem MS data but, it can also be applied for EI MS
Here, the internet database Chemspider was used as a
source for candidate structures matching the neutral mass of
the highest m/z present in the spectrum, with a 5 ppm mass
tolerance. The electron ionization spectra for the unknown
compounds were exported from ChromaTOF and compared
to the fragments generated by MetFrag from [M+] using a
5 ppm or 0.001 mDa tolerance. Only compounds including
(at least) carbon, hydrogen, and chlorine were considered. The
Chemspider data source count and reference count were taken
into account in scoring the results. Hereby, the spectral match
was weighted with 100%, while the data source count and
reference count were weighted with 50% each.
During the method development, PLE and SPLE extraction
efficiencies were compared for different solvents or solvent
mixtures. For both methods, there was an improvement in
extraction efficiency when changing from n-hexane as the
pure extraction solvent to the 20% DCM in n-hexane mixture
but almost no improvement when increasing the DCM
percentage to 50%. For PLE and SPLE, the median recovery
percentages for the 20% DCM in n-hexane mixture were
almost identical at 71 and 76%, respectively. The 10-percentile
values did, however, differ greatly, with greater than 10-fold
higher recovery values for PLE (48%) than for SPLE (3%),
which is no surprise as SPLE is a more selective extraction
technique. For both methods, early eluting compounds
showed lower extraction efficiencies or recoveries than later
eluting compounds. An evaluation of evaporation methods
was, therefore, carried out prior to the validation study.
The amount of co-extracted matrix increased with the
amount of DCM used in the extraction solvent mixture and
was higher for PLE than SPLE in all cases. The percentages of
co-extracted material using n-hexane, 20% DCM in n-hexane,
and 50% DCM in n-hexane were 5, 6, and 7% for PLE and
0.7, 1.6, and 2.5% for SPLE, respectively. As the extraction
efficiency was considerably higher for the 20% DCM mixture
than for pure n-hexane, without showing a significant increase
for the 50% DCM mixture, the less polar solvent mixture was
Abundance of m/z 73.047 ± 0.001 is ≥80% abundance of base mass AND
Abundance of m/z 147.065 ± 0.001 is ≥80% abundance of base mass
m/z 149.023 ± 0.001 is the base mass
m/z 59.037 ± 0.001 is the base mass
m/z 58.041 ± 0.001 is the base mass
Abundance of m/z 57.070 ± 0.001 is ≥75% abundance of base mass AND
Mass m/z 41.039 ± 0.001 is present
Abundance of m/z 57.070 ± 0.001 is ≥90% abundance of base mass AND
Abundance of m/z 71.086 ± 0.001 is ≥10% abundance of base mass AND
Abundance of m/z 43.055 ± 0.001 is ≥10% abundance of base mass
Abundance of m/z 55.054 ± 0.001 is ≥75% abundance of base mass
Abundance of m/z 60.021 ± 0.001 is ≥60% abundance of base mass AND
Abundance of m/z 73.029 ± 0.001 is ≥75% abundance of base mass
Loss of Cl2 from the molecular ion OR
Loss of Cl from the molecular ion OR
Loss of HCl from the molecular ion OR
Loss of Cl and gain of H from the molecular ion OR
Abundance of CCl (m/z 46.968 ± 0.001)
Loss of Br2 from the molecular ion OR
Loss of Br from the molecular ion OR
Loss of HBr from the molecular ion OR
Loss of Br and gain of H from the molecular ion
The corresponding regions for the range between 10 and 41 min are given in the table, where possible, or shown in Fig. 2 in the BResults^ section
a The classification regions are becoming broader towards the end of the run
chosen for both PLE and SPLE. This mixture also releases
slightly less matrix compared to the more polar mixture.
The graph comparing Rotavap versus Turbovap
evaporation for solvent volume reduction (Fig. 1) clearly
shows that the ratio is above one predominantly and,
thus, that Rotavap gives a better result, i.e., higher
recovery of analytes. Only bis(2-ethylhexyl) adipate
showed a slightly better recovery using Turbovap.
However, the difference is not significant. As expected,
the difference between the methods was relatively small
for high molecular weight analytes such as large PAHs
and larger for low molecular weight analytes such as
mono- and di-substituted benzenes, phenols, and
anilines. On average, the recoveries can be improved by
approximately 20% by using Rotavap instead of
Turbovap. Rotary evaporation was therefore used during
the method validation.
The method validation included two different ways of
extraction and clean-up, PLE followed by GPC, and SPLE with
silica as the in-cell clean-up sorbent. Both methods used
20% DCM in n-hexane as the extraction solvent. The two
methods were able to extract most of the analytes. However,
some analytes had to be excluded from the dataset due to poor
GC performance (see Table 2, footnote). A few other
compounds (2,4-dimethylphenol, 2,4-dichlorophenol, bisphenol
A) also suffered from relatively poor chromatography and
their results are, therefore, slightly more uncertain, as
illustrated by a relatively high variation among replicates. Those data
were still kept in the dataset for comparison purposes.
In general, the PLE method worked better for most
compounds (Table 2), although there were problems with
analyzing a few large PAHs (likely due to the relatively narrow (too
short) collection windows used in the GPC). The SPLE also
worked well for many compounds. It does, however, show the
expected losses of polar analytes, such as the
organophosphates (OPs), several phenolic compounds, some phthalates,
diazinon, and carbamazepine due to the sorption to the silica
Taking both methods into account, the non-polar and
moderately polar compounds (left half of Table 2 and pesticides
and fragrances) showed recoveries ranging from 64 to 136%,
while the LOD values ranged from as low as 0.02 ng/g for
bis(2-chloroethoxy) methane to 76 ng/g for
benzo(g,h,i)perylene (data from ESM Table S5). Several of the more polar
compounds (right half of Table 2) did, however, show
relat i ve l y l o w r e c ov e r i es , i nc l u d i n g m an y O P s , s om e
chlorophenols, and 1,2-dinitrobenzene. These, and other more
polar compounds, would be better analyzed using a
complementary LC-MS method.
The data for the validation study (Table 2) were produced
using sulfur removal with a TBA sulfite reagent, much
because it has been claimed to be a soft method [
method was, however, found to be difficult to work with and
the process time-consuming. An alternative technique using
copper was therefore tested to improve the method further.
The recoveries of roughly 100 compounds were
determined for the two sulfur removal techniques: copper and
TBA sulfite reagent. Diethyl phthalate, bis(2-ethylhexyl)
phthalate, and bis(2-ethylhexyl) adipate showed high blank
values for both procedures and were therefore excluded from
the dataset. In addition, endrine ketone was excluded from the
dataset due to poor reproducibility, i.e., it had a high standard
deviation for both procedures. The recovery values for the
remaining compounds using the copper treatment ranged from
42 to 114%, while TBA sulfite reagent treatment resulted in
recoveries from 38 to 127%. Median values were 90 and 85%
for the copper and TBA sulfite reagent treatments,
respectively. The 10-percentile and 90-percentile were calculated as 79
and 98% for copper and 53 and 98% for TBA sulfite reagent,
respectively. Thus, the copper treatment was found to be
slightly better with both a higher median recovery and a
narrower recovery range. The copper treatment was also
found to be much more user-friendly and therefore more
In total, after removing the blanks and peaks that
occurred in only one replicate, 1865 and 1593 peaks were
obtained from the ChromaTOF peak finding algorithm,
using sewage sludge processed with the PLE and SPLE
methods, respectively. Some 633 (34%) and 378 (19%)
peaks had a spectral match with a similarity equal to, or
higher than, 75% for the PLE and SPLE methods,
respectively, using the NIST library. Figure 2 shows the
complexity of the PLE and SPLE sample extracts, and
Table 3 shows how many compounds could be
identified or characterized using the different techniques.
Since a tiered approach was used, compounds that were
identified or characterized at an early stage do not
TCEP tris(2-chloroethyl) phosphate, TDCPP tris(1,3-dichloropropyl) phosphate, TBEP tris(2-butoxy-ethyl) phosphate, EHDPP 2-ethylhexyldiphenyl
a 3- and 4-Nitroaniline, 4-chloraniline, 2- and 4-nitrophenol, 2,4-dinitrophenol, dinitro-o-cresol and hexachlorocyclo-pentadiene were removed from the
dataset due to poor GC performance, and PCB 169 and dioctyl phthalate were removed due to discrepancies among the replicates
of the run caused through the constant temperature set at the end of the
oven program (isothermal). The second dimension scale has been offset
by 2 s to enhance the presentation
appear in later stages. Therefore, the number of
identified compounds generally decreases in the later stages.
In total, 174 and 45 compounds were uniquely
identified (tentatively) using the PLE and SPLE method,
respectively. In addition, 147 compounds were
tentatively identified through both methods.
The automatic peak classification detected a large number
of alkanes, alkenes, and related compounds such as long-chain
ketones, aldehydes, amides, as well as free fatty acids and
methyl derivatives thereof. It also detected a large number of
phthalates. Figure 3 shows the distribution of the classified
compounds, as well as four groups of compounds,
alkyl-benzenes, flavor and fragrances, PAHs and derivatives, and
steroids, which contain constituents with high structural
similarity that are difficult to identify correctly without using
reference materials. More information on the alkyl-benzenes,
flavor and fragrances (mainly terpenoid and musk compounds),
PAHs and derivatives, and steroids can be found in the ESM
Roughly 15% of the compounds detected (17% for PLE
and 12% for SPLE) could be assigned CAS numbers. A list,
Fig. 3 Pie charts showing the
number of automatically
classified compounds (through
classification regions and rules;
tier 1) and other grouped
polycyclic aromatic compounds,
steroids, and flavor and fragrance
compounds (tiers 2 and 3) for the
PLE and SPLE method,
respectively. nd not detected, n
total number of classified and
grouped compounds depicted in
including tentatively identified compounds that were found in
the final extracts from the PLE and SPLE methods (tiers 2 and
3), is shown in Table 4. This table also contains the first
dimension retention times, the second dimension retention
times, the nominal molecular weights, the mass deviation
(ppm) from the theoretical molecular ion or major fragment
ion mass, and the score and probability produced from reverse
searches of the NIST library. The compounds identified were
loosely divided into the following groups: alkyl-phenols,
extractives (of plant origin), organophosphate esters,
pharmaceuticals and personal care products (PPCP), stabilizers
(antioxidants and UV absorbers), as well as other halogenated
compounds and process chemicals.
Further searches for halogenated chemicals using
halogen-specific filters (tier 4) or mass defect plots (tier
5) revealed 17 additional chlorinated compounds, 11
using Cl/Br filters and six using mass defect plots
(Table 5). All chemicals that could be tentatively
identified were aromatic compounds. Five compounds could
be tentatively identified using MetFrag. The final
searches, using mass defect filters, captured only
In SPLE with silica, the polar sorbent retains polar compounds
such as phenols, leading to very low recoveries. Non-polar
compounds such as PAHs show no difference in extraction
efficiency between PLE and SPLE since they are not affected
by the silica. As mentioned before, the amount of co-extracted
matrix increases with increasing percentage of DCM. DCM is
more polar than n-hexane and, for this reason, it has the ability
to elute more polar compounds but also more matrix
compounds, e.g., humic and fulvic acids from the sewage sludge.
The challenge is to find the proper balance between analyte
extraction and matrix retention. For sewage sludge, 20%
DCM in n-hexane seems to be the best compromise.
Methyl dehydroabietate X
Dehydroabietic acid X
Tris(1,3-dichloroisopropyl)phosphate (TCPP) X
Triphenyl phosphate (TPP) X
2-Ethylhexyl diphenyl phosphate (EHDPP) X
Cresyl diphenyl phosphate (CDPP, 2 isomers) X
Isopropyl-phenyl diphenyl phosphate (iPrDPP) X
Dicresyl phenyl phosphate (DCPP, 2 isomers) X
Tricresyl phosphate (TCP, 3 isomers) X
Diphenylmethoxy acetic acid X
Phenyl tetradecyl carbonate X
Butylated hydroxytoluene (BHT) X
Vitamin E γ
Vitamin E α
Vitamin E α acetate
2-Ethylhexyl salicylate X
Phenyl cinnamonitrile X
CAS numbers and IUPAC names are listed in Table S7 in the supplementary material
a Mass deviation was calculated from a fragment ion.
b 1-Decyl-2-pyrrolidinone is in the NIST library. The spectral match was good; however, the retention time did not match. 1-Dodecyl-2-pyrrolidinone
showed a good retention time match but has no corresponding spectrum in the NIST library. Hence, no similarity and probability values are given
In the PLE method, the clean-up was carried out using
size exclusion since common matrix compounds such as
lipids and humic and fulvic acids are big molecules.
However, there is also a risk of losing other large
GCamenable compounds. The combination of both methods
will allow a comprehensive screening of GC-amenable
analytes for non-target screening of sewage sludge. The
PLE method allows detection of compounds that are
nonpolar or moderately polar, but rather small, while the
SPLE method allows the detection of relatively
nonpolar compounds of all sizes. The more polar analytes
would have to be analyzed by LC-MS, which is currently
being evaluated. Thus, using three complementary
methods, a highly comprehensive non-target screening of
environmentally relevant organic contaminants in sewage
sludge and similar matrices (e.g., soil and sediment) could
be achieved, see Fig. 4. Admittedly, very large (molecular
weight of 2000 and above) non-polar contaminants would
still not be possible to analyze by the three proposed
methods (Fig. 4), but those are generally not bioavailable
(i.e., too large to pass biological membranes).
During the method development, it was realized that the
extraction efficiency for the studied analytes correlated with the
retention time. Early eluting compounds show, in general,
lower extraction efficiencies compared to later eluting
compounds. Since a non-polar column was used here, the elution
order is determined by the boiling point of the analytes. Early
eluting compounds have a lower boiling point, while late
eluting compounds have a higher one. Thus, the boiling point of
Halogenated compounds detected using halogen filters (tier 4) or mass defect plots (tier 5) on the final extracts of the PLE or SPLE methods
Only tentative structures/formulae are given. CAS numbers and IUPAC names (if applicable) are listed in Table S7 in the supplementary material
a Mass deviation was calculated from a fragment ion
b These spectra for these compounds were processed using MetFrag and the compounds were identified as a result thereof
the analytes seems to correlate with the recovery of the PLE
and SPLE method. There is a possibility that compounds with
a lower boiling point were lost in the subsequent evaporation
step, which was therefore further evaluated.
The results of the comparison of Rotavap and Turbovap
clearly show that Rotavap is the technique of choice for
solvent evaporation for the tested compounds. The advantage of
Rotavap is that the walls of the small pear-shaped flasks used
in this study are constantly covered with a film of solvent. This
prevents analytes from transferring to the gas phase by
evaporation or adsorbing strongly to the glass walls. In the
Turbovap on the other hand, a constant flow of air creates a
vortex that speeds up the evaporation but also leads to the
formation of a film of dry sample on the vial walls, which
may cause a loss of analyte by evaporation or adsorption.
Hence, greater losses and lower recoveries are to be expected
for relatively volatile analytes using the Turbovap.
The method validation showed that most of the analyzed
compounds had a reasonable recovery in at least one of the
methods (Table 2). Some compounds, such as anilines,
yielded bad recovery percentages using both PLE and SPLE.
These compounds are hard to analyze using GC in general. A
method for LC-MS analysis is currently being developed that
will be suitable for these compounds, as discussed above.
High recoveries (>100%) were observed for several
compounds, e.g., low molecular weight PAHs and PCBs. For these
compounds, the amount used to spike the samples before the
extraction was rather low compared to typical concentrations
found in sewage sludge. The natural occurrence of the
analytes in the sludge, thus, could contribute to the amount
detected. The fact that PCB 118 and PCB 105 yield high
recovery percentages supports this hypothesis since they are
major congeners in technical mixtures. Additionally, the
recoveries of the 13C–labeled PCBs that were added as internal
standards were calculated. The tetra- to hepta-CBs had
recoveries between 71 and 102% for PLE and 84 and 110% for
SPLE, which is fit for purpose for non-target screening
However, for some polar compounds, such as
bisphenol A and some other phenolics, recoveries and
standard deviations were higher than acceptable for at
least one of the methods. These high recoveries and
variabilities could result from a matrix enhancement
effect. When matrix is present, as in the sample, these
matrix compounds can bind to active sites in the GC
inlet system, column, or ion source making them
unavailable for analytes. During instrument calibration,
no such matrix compounds are introduced and therefore
analytes can bind to active sites. This effect is called
matrix induced chromatographic response enhancement
(MICRE) and has been described previously [
MICRE can be avoided by adding so-called analyte
protectants to the sample and/or standard directly before
]. These analyte protectants bind to the
active surfaces in the GC system, mainly the inlet,
thereby facilitating analyte transfer. Since the use of
analyte protectants could improve the analysis of polar
compounds, the authors recommend the use of those for
future analyses. In addition, more internal standards
could be added allowing a better matching of analytes
and internal standards.
It should be emphasized, however, that in non-target
screening, it will never be possible to match analytes and
internal standards perfectly and that reference compounds
are lacking generally. Thus, the analyses will be
semiquantitative at best, although the relative concentrations
among similar samples can be determined much more
accurately. A tiered approach is therefore recommended: first,
prioritize between the tentatively identified compounds, then
confirm the identity of top-priority compounds, and finally
develop quantitative analytical methods for the selected
Sulfur removal with copper and with TBA sulfite
reagent produced similar results for the tested analytes.
However, the variability among recoveries for copper
treatment is lower and both the median as well as
10percentile are higher. The US EPA stated in their
method 3660B that treatment with copper powder (fine
granular) may affect certain pesticides. Some of the
mentioned pesticides (heptachlor, malathion, ethion, and
diazinon) were included in the current study but no
major losses could be seen. The recoveries for these
analytes ranged from 82 to 92%. The reason for this
may be that we used a more coarse copper granulate
than what is recommended in method 3660B. The main
advantage of the copper treatment, however, is its ease
of use and speed. It will therefore be used in future
suspect and non-target screening studies.
Applying the non-target screening methods to a sewage
sludge sample revealed that both methods are very good
at detecting non-target compounds. Although more
compounds were identified using the PLE method (see
Table 3), the SPLE method gave additional information.
This is to be expected as the two methods cover
different parts of the chemical property (polarity and size)
space, as shown in Fig. 4.
Figure 3 clearly shows that there is a difference in
polarity among compounds detected by the PLE and
SPLE method, respectively. The majority of classified
compounds that were found using the SPLE method
were relatively non-polar compounds, such as alkanes
or alkenes, alkyl-benzenes, alkyl-PAHs, and PAHs. In
contrast, more polar compounds, such as alkanals,
phthalates, fatty acids, and methyl derivatives, and
long-chain amides were found using the PLE method.
This can easily be explained by the loss of polar
compounds due to sorption to the silica gel used in the
Size-dependent differences in compounds detected by
the two methods are clearly shown in Fig. 2. The GC ×
GC region between 25 and 40 min first dimension
retention time and 2.5 and 3.0 s second dimension
retention time, which corresponds to the unresolved complex
mixture (UCM) of crude oil or weathered petroleum,
differs greatly for the two methods. The SPLE sample
is much richer in UCM than the PLE sample, most
likely due to losses of big molecules in the GPC step
of the PLE method. A similar observation was made for
the last part of the analysis (40 min or later): 26% of
the compounds detected using the SPLE method (mainly
steroids) eluted in this chromatographic region whereas
only 16% of the compounds using the PLE method
elute in this region. Vitamins E α and E γ, vitamin E
α acetate, and 2,4-bis(2-phenylpropan-2-yl)phenol were
also among the big compounds that were lost in the
PLE method but found using the SPLE method
The non-target screening of sludge using the SPLE and
PLE methods revealed many compounds besides the
classified or grouped chemicals. Tables 4 and 5 show the
compounds that could be tentatively identified. These
compounds were primarily of anthropogenic origin
(organophosphates, PPCPs, synthetic antioxidants, UV screens and
stabilizers, pesticides and other chlorinated compounds, and
process chemicals), which may be of environmental relevance,
while some were of biogenic origin (e.g., extractives and
vitamins). The tables appear to indicate that the PLE method
revealed many more unique compounds than the SPLE
method. However, many of the compounds that were grouped
(PAHs, alkyl-PAHs, N/S/O-PAHs, steroids, and flavor and
fragrances) could be tentatively identified using both methods
(ESM, Table S6), and some were only tentatively identified
using the SPLE method (e.g., large alkyl-benzenes). Hence,
combining both methods increases the amount of information
made available. The range of compounds that can be captured
using the two non-target screening methods (Tables 4 and 5) is
quite wide, including small and large compounds, e.g.,
mamino-phenylacetylene (117 g/mol) and vitamin E α acetate
(473 g/mol), as well as non-polar and relatively polar
compounds (e.g., PCBs and organophosphate esters and phenols).
The results also illustrate how the NIST library can be used
to identify compounds effectively by using both the similarity
and probability scores. As shown in Table 3, the majority of
tentatively identified compounds (83% for PLE and 91% for
SPLE) were identified using the NIST library similarity
scores, with a cutoff at 75% (tier 2). By using such a cutoff,
only a limited number of spectra have to be manually
reviewed but at the risk of losing information for low level
contaminants affected by instrument noise. Some of these
contaminants, those with distinct spectra, may be found by
probability sorting the data that have similarity scores <75%
(tier 3). In this way, 5 and 11% of the tentatively identified
compounds using PLE and SPLE, respectively, could be
However, the majority of existing organic chemicals do not
have an MS spectrum in the NIST library. Therefore,
additional techniques are needed to identify compounds of particular
concern, such as halogenated compounds. An attempt was
made to screen for chlorinated and brominated compounds
(tiers 4 and 5), as these compounds are generally
environmentally relevant (c.f. PCBs, PBDEs, dioxins). Eleven compounds
were found using halogen-specific filters (tier 4) and six
compounds were found using mass defect plots (tier 5); all of these
were chlorinated biphenyls. Molecular formula information
for the tier 4 compounds could be generated using information
about accurate mass and isotopic patterns. In some cases,
searches in databases such as Chemspider and SciFinder
resulted in a plausible candidate structure, e.g.,
9,10di(chloromethyl)-9,10-dihydroanthracene. In other cases, it
was possible to extract a spectrum manually that was missed
in the original peak picking and find a plausible hit in NIST.
This resulted in the discovery of triphenylchloromethane,
p-(6-chloro-4-phenyl2-quinolyl)aniline. Finally, an attempt was made to interpret
the remaining spectra manually, resulting in two additional
tentative identifications: one DDT metabolite and one
chlorinated PAH. The spectra of all unknown chlorinated
compounds that could not be assigned a tentative molecular
structure were analyzed using MetFrag. This resulted in
the identification of five additional compounds: (i) one
compound which is likely to be dichloroxylenol, a commercial
], (ii) 4-(3,4-dichlorophenyl)tetralone and one
of its isomers that are potential impurities in the
pharmaceutical Sertraline [
], and (iii) two compounds belonging to
the classes of dichlorophenyl coumarins or dichloroflavones,
which are highlighted in patents suggesting their use in tire
rubber production [
]. To our knowledge, none of these
compounds were detected in environmental samples up to
now. Spectra for these compounds and other compounds that
are not listed in NIST are given in the ESM (Figs. S1–S7).
Some of the other compounds that were tentatively
identified have, to our knowledge, not previously been reported
in sludge or in environmental samples either, e.g., the
chlorinated PAH derivatives and triphenylchloromethane.
However, similar compounds have been reported.
Chlorinated PAHs have been found in incinerator flue gas,
car exhaust, and urban air [
is quite reactive and is frequently used in organic synthesis.
It may be degraded to triphenylmethane that has been found
in sediment .
Overall, the two proposed methods (PLE with GPC
and SPLE, followed by sulfur removal with copper) in
combination with a soon-to-be developed LC-MS
method will provide a comprehensive methodology for the
screening of a large variety of compounds with different
properties in sewage sludge. This should also be
feasible for other similar environmental matrices such as soil
and sediment. It should be realized that there is an
overlap between the applicability domains of the three
methods. Once the methodology is fully developed, this
should be highly advantageous. It should, for instance,
be possible to use EI spectra from GC-MS to verify the
identity of compounds originally detected by LC-MS
and vice versa: it should be possible to use LC-MS data
to generate molecular ion information that is often
missing in GC-MS spectra. This could lead to the discovery
of many more new and emerging chemicals in samples
of environmental relevance, which could be subject to
targeted measurement campaigns, environmental risk
assessments, and STP improvement initiatives.
Acknowledgements The authors would like to thank the Swedish EPA
for funding through their Environmental Screening Program (Contract
2219-13-002). They are grateful to Matyas Ripszam for help with
instrumentation and Darya Kupryianchyk for help with lab work. The authors
thank Per Liljelind and Christine Gallampois for fruitful discussions and
Compliance with ethical standards
Conflict of interest The authors have no conflict of interests to declare.
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