Science of the Total Environment 575 (2017) 265–275

Contents lists available at ScienceDirect

Science of the Total Environment

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

Non-target screening and prioritization of potentially persistent,

bioaccumulating and toxic domestic wastewater contaminants and their
removal in on-site and large-scale sewage treatment plants
Kristin M. Blum a,⁎, Patrik L. Andersson a, Gunno Renman b, Lutz Ahrens c, Meritxell Gros c,
Karin Wiberg c, Peter Haglund a
a
Dept. of Chemistry, Umeå University, SE-901 87 Umeå, Sweden
b
Dept. of Sustainable Development, Environmental Science and Engineering, KTH Royal Institute of Technology, Stockholm, Sweden
c
Dept. of Aquatic Sciences and Assessment, Swedish University of Agricultural Sciences (SLU), Uppsala, Sweden

H I G H L I G H T S G R A P H I C A L A B S T R A C T

• Contaminants emitted from on-site sew-

age treatment facilities were identified.
• A non-target screening based prioritiza-
tion strategy was established.
• Top-ranked compounds were found at
high levels in a follow-up study.
• TMDD and TBEP were better removed
in small than in large plants.
• Hydrophilic compounds were removed
less efficiently than hydrophobic com-
pounds.

a r t i c l e i n f o a b s t r a c t

Article history: On-site sewage treatment facilities (OSSFs), which are used to reduce nutrient emissions in rural areas,
Received 13 July 2016 were screened for anthropogenic compounds with two-dimensional gas chromatography–mass spectrometry
Received in revised form 16 September 2016 (GC × GC–MS). The detected compounds were prioritized based on their persistence, bioaccumulation,
Accepted 16 September 2016
ecotoxicity, removal efficiency, and concentrations. This comprehensive prioritization strategy, which was
Available online xxxx
used for the first time on OSSF samples, ranked galaxolide, α-tocopheryl acetate, octocrylene, 2,4,7,9-
Editor: D. Barcelo tetramethyl-5-decyn-4,7-diol, several chlorinated organophosphorus flame retardants and linear alkyl benzenes
as the most relevant compounds being emitted from OSSFs. Twenty-six target analytes were then selected for
Keywords: further removal efficiency analysis, including compounds from the priority list along with substances from the
Two-dimensional gas chromatography–mass same chemical classes, and a few reference compounds. We found significantly better removal of two polar con-
spectrometry taminants 2,4,7,9-tetramethyl-5-decyn-4,7-diol (p = 0.0003) and tris(2-butoxyethyl) phosphate (p = 0.005) in
Non-target analysis soil beds, a common type of OSSF in Sweden, compared with conventional sewage treatment plants. We also re-
Ranking port median removal efficiencies in OSSFs for compounds not studied in this context before, viz. α-tocopheryl
Decentralized sewage treatment
acetate (96%), benzophenone (83%), 2-(methylthio)benzothiazole (64%), 2,4,7,9-tetramethyl-5-decyn-4,7-
Removal efficiencies
diol (33%), and a range of organophosphorus flame retardants (19% to 98%). The environmental load
Organic micropollutants
of the top prioritized compounds in soil bed effluents were in the thousands of nanogram per liter range,

⁎ Corresponding author.
E-mail address: kristin.blum@umu.se (K.M. Blum).

http://dx.doi.org/10.1016/j.scitotenv.2016.09.135
0048-9697/© 2016 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
266 K.M. Blum et al. / Science of the Total Environment 575 (2017) 265–275

viz. 2,4,7,9-tetramethyl-5-decyn-4,7-diol (3000 ng L−1), galaxolide (1400 ng L−1), octocrylene (1200 ng L−1),
and α-tocopheryl acetate (660 ng L−1).
© 2016 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license
(http://creativecommons.org/licenses/by-nc-nd/4.0/).

1. Introduction et al., 2010; Wilcox et al., 2009). Removal efficiencies were mainly in-
vestigated in lab-scale (Leal et al., 2010; Teerlink et al., 2012) or field-
Wastewater is commonly treated in sewage treatment plants (STPs) scale experimental facilities (Conn et al., 2010b; Du et al., 2014; Garcia
to reduce the nutrient load into the environment. Whereas centralized et al., 2013) and rarely at real household or community OSSFs (Conn
STPs are only economically sustainable if the population is dense and et al., 2010a, 2006; Wilcox et al., 2009). Furthermore, studies examining
large enough, smaller decentralized on-site sewage treatment facilities the fate of OSSF contaminants in soil are sparse (Carrara et al., 2008;
(OSSFs) provide a larger economic benefit for smaller communities Conn et al., 2010b).
and single households in rural areas (Corcoran et al., 2010). In the Prioritization strategies based on non-targeted data to identify envi-
United States and Sweden, around 20% (Olshammar et al., 2015; U.S. ronmentally relevant contaminants have previously focused on criteria
EPA, 2008) of all households are connected to OSSFs. Sweden has such as ecotoxicity (Bastos and Haglund, 2012), exposure (Rager et al.,
753,000 OSSFs (Olshammar et al., 2015), of which infiltration systems 2016; Singer et al., 2016) or bioactivity (Rager et al., 2016). Other
dominate (25%), followed by septic tanks without further treatment prioritization/ranking strategies have focused on selected groups
(22%), soil beds (SBs) (16%), grey water separation (17%), and aerobic of water contaminants, such as active pharmaceutical ingredients.
treatment systems (ATSs) (2%) (Olshammar et al., 2015). Septic tanks These approaches prioritized based on ecotoxicity data (Sanderson
consist of a container that retains wastewater and allows for sedimenta- et al., 2004), biodegradation, bioaccumulation and ecotoxicity data
tion to occur. Solids and digested organic matter settle to the bottom, (Wennmalm and Gunnarsson, 2005), or prescription dispensation, en-
whereas floatable solids rise to the top and are discharged with the ef- vironmental concentrations, half-lives, octanol-water partition coeffi-
fluent from the tank (U.S. EPA, 2000a). These treatment systems are cients, and ecotoxicity data (Cooper et al., 2008). Attempts have also
nowadays restricted in Sweden unless they are combined with addi- been made to start with large inventories of industrial chemicals or
tional treatment techniques. In soil infiltration systems, the septic tank pharmaceuticals and use prioritization schemes to identify potentially
effluent is infiltrated into the ground at the treatment site to further re- persistent and bioaccumulating substances (Andersson et al., 2011;
move nutrients (macropollutants). SBs are similar to infiltration sys- Howard and Muir, 2011).
tems and consist of layers of soil, gravel, and sand that are surrounded In our study we applied a two-stage strategy (Fig. 1), to increase the
by a less permeable material to prevent uncontrolled infiltration (U.S. knowledge of micropollutants emitted from OSSFs into the environ-
EPA, 2000b). ATSs exist as continuous or batch-flow systems and are ment (Stage I) and to evaluate the treatment efficiency of OSSFs
commonly called package treatment plants. By actively aerating the (Stage II). In Stage I, we aimed to identify and prioritize environmentally
waste water, they promote biological activity and enhance degradation relevant organic contaminants emitted from OSSFs by using a two di-
processes (U.S. EPA, 2000c, 2000d). mensional gas chromatography–mass spectrometry (GC × GC–MS)
Like STPs, OSSFs are primarily designed to remove macropollutants based non-target methodology. The use of GC enabled us to identify
and pathogens rather than micropollutants (Petrovic, 2003), but few persistent and bioaccumulating non-polar compounds, which would
studies have focused on the occurrence of organic micropollutants in be difficult to detect using screening methodologies based on liquid
OSSF effluents. Most of these studies have focused on selected target chromatography (LC). Additionally, the use of GC× GC allowed better
analytes, including fragrances like tonalide (AHTN) (Leal et al., 2010) separation of the analytes from interferences in complex samples with-
and galaxolide (HHCB), the biocide triclosan (TCS) (Conn et al., 2010a, out extensive sample preparation. The resulting compounds were prior-
2010b, 2006), the UV filters 2-phenyl-5-benzimidazolesulfonic acid itized based on removal efficiencies and effluent concentrations along
(Leal et al., 2010) and octocrylene (OC) (Leal et al., 2010), nonylphenols with environmental hazard criteria such as persistence, bioaccumula-
(Conn et al., 2010a, 2010b; Stanford and Weinberg, 2010), bisphenol A tion potential, and toxicity (PBT), and environmentally relevant target
(BPA) (Conn et al., 2010a), and steroid estrogens (Leal et al., 2010; analytes were selected. To widen the physicochemical property domain
Stanford and Weinberg, 2010). Such targeted approaches can oversee these target analytes were supplemented with analogues of the same
a large number of potentially relevant compounds. Non-targeted ap- classes of compounds and a few commonly used reference compounds.
proaches can be used to generate more comprehensive information This facilitated the evaluation of relative removal efficiencies between
about contaminants present in a wastewater sample. We have only different contaminants and different treatment technologies, specifical-
identified one study where non-targeted screening was used to find ly between SBs and STPs, and the quantification of environmental loads
contaminants in grey water extracts by gas chromatography–mass in Stage II (Fig. 1).
spectrometry (GC–MS) (Eriksson et al., 2003). However, this study did To the best of our knowledge, our study is the first to use a compre-
not include any environmental relevance prioritization for the 190 ten- hensive non-targeted approach based on GC×GC–MS, combined with a
tatively identified components. In addition to concerns for emissions to
surface waters, micropollutants that most likely originated from OSSFs
have been detected in nearby ground water or drinking water wells,
e.g. the pesticide diethyltoluamide (DEET) (Del Rosario et al., 2014),
the pharmaceuticals ibuprofen (Carrara et al., 2008; Del Rosario et al.,
2014) and sulfamethoxazole (Godfrey et al., 2007), the plasticizer
tris(2-butoxyethyl)phosphate (TBEP) (Phillips et al., 2015), organo-
phosphorus flame retardants (OPs) (Schaider et al., 2016), per- and
polyfluoroalkyl substances, and steroid hormones (Swartz et al., 2006).
Previous studies have reported similar removal efficiencies for ATSs
and STPs (Du et al., 2014; Garcia et al., 2013; Wilcox et al., 2009) and
worse removal efficiencies in anaerobic septic tanks compared to aero- Fig. 1. Design of the study using comprehensive gas chromatography–mass spectrometry
bic systems (Conn et al., 2006; Du et al., 2014; Garcia et al., 2013; Leal (GC×GC–MS).
 K.M. Blum et al. / Science of the Total Environment 575 (2017) 265–275 267

PBT based prioritization strategy, to identify organic micropollutants in kept at 80 °C for 1 min, raised at a rate of 4 °C min− 1 to 300 °C, and
OSSF effluents. Our study is also the first to compare OSSFs and STPs held isothermal for 3 min. The secondary oven and the modulator
using multivariate analysis and to report removal efficiencies and efflu- were operated with a +15 °C and +55 °C offset, respectively, to the pri-
ent concentrations for OSSFs for a number of emerging micropollutants. mary oven. The modulation period was 3 s with a 0.6 s hot pulse time
and a 0.9 s cooling time. The transfer line temperature was set to
2. Experimental 325 °C, and the ion source temperature was set to 250 °C. Electron ion-
ization at 70 eV was used, and mass spectra were recorded from 45 to
The study was divided into two stages (Fig. 1). The analytical proce- 750 m/z with a 260 s acquisition delay and an acquisition rate of 200 Hz.
dures used throughout Stage I are summarized in Fig. 2, whilst a more The data acquisition and processing was performed as described in
detailed description is given in Sections 2.1 and 2.2. Fig. 2 using the ChromaTOF-GC Software (v4.50.8.0, Leco®). The data
processing included baseline correction, picking of peaks with a
2.1. Non-target screening: identification of environmentally relevant con- signal-to-noise ratio (S/N) ≥ 100, n-alkane retention index calculation,
taminants discharged from on-site sewage treatment facilities (Stage I) area/height calculation based on the total ion chromatogram, and an
NIST-MS library search (covering EI MS spectra for ~240,000 chemicals)
2.1.1. Sampling design and sample collection with a minimum similarity criterion of 65%. The resulting peaks were
Based on the shares of OSSF installations in Sweden (Olshammar aligned using the Java application GUINEU (Castillo et al., 2011) based
et al., 2015), we selected SBs, ATSs, and grey water separation as repre- on retention indices and spectra, and peak areas were normalized to
sentative OSSF treatment systems. Soil infiltration systems were not in- chrysene-d12. The peaks were then filtered based on 13% detection fre-
cluded because they do not have a defined outlet and it is generally not quency (minimum 8 out of 63 samples, including blanks, quality assur-
possible to sample effluents in those facilities. Septic tank influents were ance standards, and technical replicates) and blank levels (normalized
not sampled due to sample heterogeneity problems. peak area of a sample at least 10-fold higher than that of the blank). Fi-
The first sampling campaign was conducted in October and Novem- nally, the spectra of the remaining peaks were searched again against
ber 2013. Influent and effluent wastewater was grab sampled at 13 dif- the NIST-MS library and manually investigated to ensure that peaks
ferent OSSFs in Sweden, including six SBs (1 to 40 population that were misassigned by the library were corrected or excluded. Only
equivalents (PE)), four ATSs (5 to 21 PE), and three grey water separa- tentatively identified compounds of most likely anthropogenic origin
tion systems (2 to 10 PE). Influent samples were taken after the last were considered for the next step, therefore long-chain fatty acids and
chamber of the septic tank to obtain a relatively homogenous sample their esters, which often originate from excreta (Paxéus, 1996), were
of the influent. In addition, samples from conventional activated sludge excluded (Fig. 2).
based treatment plants were taken – three medium-sized STPs (135 PE
to 2500 PE) and one sample from a large STP (440,000 PE) (Supplemen- 2.1.4. Prioritization of chemicals
tal Table S1). The approximately 300 tentatively identified compounds (Supple-
mental Table S2) were further characterized and filtered to isolate the
2.1.2. Liquid-liquid and Soxhlet extraction most environmentally relevant OSSF-specific compounds (Fig. 2). The
Wastewater samples were filtered through 12 μm cellulose nitrate compounds had to be persistent, bioaccumulative, or toxic (PBT) and
membrane filters (GE Healthcare Life Sciences, Buckinghamshire, UK), had to be used or produced in significant quantities. Apart from occur-
and the filters were wrapped in aluminum foil and stored at − 20 °C rence in effluent samples, the prioritized compounds fulfilled at least
until sample preparation. To avoid issues of poor representativity due one of the following PBT cut-off values: i) a half-life in water ≥
to small facility sizes (1 to 40 people connected), we pooled samples 60 days, ii) a bioconcentration factor (BCF) ≥ 1000, iii) a ratio of predict-
from similar treatment types resulting in five samples i.e. influent/ ed environmental concentration to predicted no effect concentration
effluent from SB, ATS and grey water influent. An IS (113 ng chrysene- (PEC/PNEC) ≥ 0.01, or iv) listed as European or Swedish industrial
d12) and 25 mL saturated sodium chloride solution were added to chemicals with an acute (LC50/EC50) or chronic (ChV) ecotoxicity end-
each 500 mL composite water sample. The samples were subsequently point ≤1.0 mg L−1 or ≤0.1 mg L−1, respectively. These thresholds are
extracted with 100 mL, 50 mL, and 50 mL dichloromethane. The com- similar to criteria suggested by REACH and the U.S. EPA for the identifi-
bined extracts were filtered through 10 g sodium sulfate, rinsed with di- cation of PBT chemicals (EPI Suite™ Appendix B, 2004) (Annex XIII,
chloromethane, and evaporated to 1 mL. The filters were extracted 16 h REACH).
by Soxhlet extraction with 250 mL toluene, IS (113 ng chrysene-d12) Aquatic ecotoxicity (LC50, EC50, and ChV), half-lives in water,
was added, and the volume was reduced to 1 mL. The corresponding ex- and BCFs were estimated for each identified compound using the
tracts were combined and analyzed in triplicate with comprehensive ECOSAR, BIOWIN3, and BAFBCF modules in the EPI Suite™ toolbox
two-dimensional gas chromatography and time-of-flight mass spec- (www.epa.gov, 2008). The PEC was calculated by using the maximum
trometry (GC×GC–ToFMS). concentration found in either ATS or SB effluent and multiplying this
value by an uncertainty factor of 10 (due to the semi-quantitative anal-
2.1.3. Comprehensive gas chromatography time-of flight mass spectrometry ysis) and dividing it by a dilution factor of 1000 as recommended for
The samples were analyzed with a Pegasus 4D mass spectrometer local surface water scenarios (Lijzen and Rikken, 2004). The corre-
(Leco Corp., St. Joseph, MI, USA), equipped with an Agilent Technologies sponding PNEC was calculated using the lowest ecotoxicity value from
6890 gas chromatograph (Palo Alto, CA, USA), a secondary oven, and a the ECOSAR model divided by an uncertainty factor of 10 as recom-
dual stage cryogenic (liquid nitrogen) modulator. A BPX50 column mended for long-term data from at least three species representing
(29.5 m, 0.25 mm ID, 0.25 μm film thickness, SGE) was used for first- three tropic levels (Echa, 2008). The used industrial chemicals invento-
dimension separation, and a VF-1ms column (1.2 m, 0.15 mm ID, ries were the European low and high production volume chemicals
0.15 μm film thickness, Agilent Technologies) was used for the (Rännar and Andersson, 2010), the EINECS database (Stenberg et al.,
second-dimension separation. The polar-nonpolar column combination 2009), and a database of chemicals used in large amounts in Sweden
was chosen because it was suspected that the STP samples might con- compiled by the Swedish Chemicals Agency (Fischer, 2011).
tain high levels of petroleum hydrocarbons from storm water runoff. The resulting compounds were manually checked for possible bio-
Helium was used as the carrier gas at 1.0 mL min−1. The extracts were genic or anthropogenic origin and only anthropogenic compounds
injected with a 1 μL pulsed splitless injection. The inlet was purged at were considered for the next step. To improve the semi-quantitative
20 mL min−1 for 1 min, the inlet pulse was 40 psi for 1.5 min, and the data and to detect low-abundance compounds, the GC × GC–MS data
inlet temperature was 280 °C. The primary oven temperature was files were reprocessed with Chroma-ToF using a specific quantification
268 K.M. Blum et al. / Science of the Total Environment 575 (2017) 265–275

mass for each compound and a S/N cut-off of 10 (Fig. 2). The compounds
were also semi-quantified using chrysene-d12. The lower S/N limit and
the integration based on extracted ion chromatograms resulted in
some previously undetected compounds appearing in the blanks. For
those compounds, we calculated MLOQs corresponding to 10 times
the maximum concentration found in one blank (Supplemental
Table S3). If a compound did not appear in any sample at a concentra-
tion higher than the MLOQ, it was excluded.
To rank the compounds that passed the filtering process by environ-
mental relevance, a scoring system was developed (Table 1) and applied
to the dataset. Scores were given from 1 to 5 in five categories (removal
efficiency, half-life, BCF, PEC/PNEC, and maximum concentration in SB
or ATS effluent), and a total score was obtained based on the sum of
the single scores. The lowest score represents the most problematic
chemical.

2.2. Target analysis to evaluate the treatment efficiency (Stage II)

2.2.1. Sampling design and sample collection

Since ATSs represent only a small share of OSSFs (2%) and no major
differences in concentrations or removal efficiency were observed be-
tween ATSs and STPs in the first sampling campaign, we focused on
SBs in the second sampling campaign (Table 2). Notably, these also rep-
resent a large share of facilities (16%) and function similarly to soil infil-
tration systems (25%).
The second sampling campaign was conducted from September
2015 to November 2015. Samples were taken at five SBs as representa-
tives for OSSFs and five STPs (Table 2, Supplemental Tables S4–S5). To
obtain more representative samples and more reliable removal efficien-
cy values than the ones obtained in Stage I, SB influents and effluents
were sampled in a time-integrated manner by collecting an aliquot
every hour for one day. Four samplers were used for time-integrated
sampling: an ISCO 2900 and an ISCO 6712 from Teledyne Isco (Lincoln,
USA) and two Buehler 2000 by Hach Lange (Düsseldorf, Germany). In-
fluent samples were taken from the last stage of the septic tank, and ef-
fluent samples were taken after the SB. The large and medium-scale
STPs were sampled in a flow-proportional manner over one week and
one day, respectively.

2.2.2. Solid phase and ultrasound extraction

Before extraction, 200 mL of effluent and 40 mL of influent water
were filtered through pre-burned GF/B and GF/F glass fiber filters
(Whatman, GE Healthcare Life Sciences, Buckinghamshire, UK). To re-
duce solvent usage, automated solid phase extraction (SPE) and
ultrasonication were used for filtrate and filter samples, respectively. In-
fluent filtrates were diluted with 160 mL Milli-Q water to get a similar
dissolved organic carbon load and extraction efficiency as the effluent,
the pH of the filtrates was adjusted to pH 7 with 1 M hydrochloric
acid, and 30 μL IS mixture was added (Supplemental Table S6). The sam-
ples were extracted using a SmartPrep automated cartridge extractor
(Horizon Technology, Salem, NH, USA) equipped with 200 mg OASIS
HLB 6 cm3 cartridges (Waters, Milford, MA, USA) and using positive
pressure. The sorbents were conditioned with 10 mL dichloromethane,
10 mL acetonitrile, and 10 mL Milli-Q water prior to use. The samples
were loaded at 10 mL min− 1, and the cartridges were washed with
3 mL Milli-Q water and dried under vacuum for 20 min. The bottles
were rinsed with 30 mL Milli-Q water/isopropanol (90:10, v/v) and
loaded on new cartridges with the previously described method to
avoid losses. The cartridges and the filters were stored at −20 °C until

Fig. 2. Workflow of Stage I, GC×GC–ToFMS = comprehensive gas chromatography–time-

of-flight mass spectrometry, S/N = signal-to-noise, TIC = total ion chromatogram, PBT =
persistent/bioaccumulative/toxic, PEC/PNEC = ratio of predicted environmental
concentration/predicted no effect concentration, LC50 = lethal concentration, EC50 =
half-maximal effective concentration, ChV = chronic value, HPVC/LPVC = high
production or low production volume chemicals, EINECS = European Inventory of
Existing Commercial Chemical Substances.
 K.M. Blum et al. / Science of the Total Environment 575 (2017) 265–275 269

Table 1
Scoring system for the 46 identified compounds.

Score 1 2 3 4 5

Removal efficiency Removal b75% Removal b75% Removal N75% Present in effluent, but not 100% removalb
in SB and ATS in SB or ATS in SB and ATSa in influent in SB or ATS
Half-life (days) 180 60 37.5 15 b15
BCF N10,000 N1000 N100 N10 b10
PEC/PNEC N1 N0.1 N0.01 N0.001 b0.001
Missing value
Maximum effluent N1000 N500 N100 N50 b50
concentrationc (ng L−1)

BCF = bioconcentration factor, PEC/PNEC = predicted environmental concentration/predicted no effect concentration, SB = soil bed, ATS = aerobic treatment system.
a
= or present in effluent, but not in influent in ATS and SB.
b
= or below limit of quantification in effluent, but present in influent.
c
= maximum concentration in SB or ATS effluent.

all samples were loaded. The analytes were eluted from the cartridges was 115 s at a flow rate of 25 mL min−1. Helium was used as the carrier
with 8 mL dichloromethane/acetonitrile (80:20, v/v) followed by gas at 1.0 mL min−1. The primary oven temperature was kept at 90 °C
10 mL dichloromethane. for 2 min, raised at 10 °C min− 1 to 335 °C, and held isothermal for
Filters with suspended solids were lyophilized for 45 h, soaked in 2 min. The secondary oven and the modulator were operated at a +
10 mL dichloromethane/acetonitrile (80:20, v/v), and 30 μL IS mixture 10 °C (up to 335 °C) and +15 °C offset, respectively. The modulation pe-
(Supplemental Table S6) was added. Filters were sonicated for 30 min, riod changed over the run, modulating at 1.7 s from start to 780 s, at
the extract was decanted, and 10 mL fresh solvent mixture was added. 2.0 s from 780 s to 1374 s, and at 2.5 s from 1374 s to the end (Supple-
This sonication process was repeated twice and the third sonication mental Table S7) to obtain a sufficient number of modulations across a
step was performed only with dichloromethane. first dimension GC peak. The transfer line temperature was set to
The combined extracts of SPE eluate, flask rinse SPE eluate, and filter 335 °C and the ion source temperature was set to 300 °C. Electron ioni-
extract were filtered through 10 g sodium sulfate. The solvent was ex- zation was performed at 70 eV, and mass spectra were recorded at
changed to toluene, reduced to 500 μL, and 10 μL 13C6-labeled PCB-97 200 Hz from 38 to 480 m/z after a 360 s acquisition delay.
and PCB-188 recovery standard in toluene (Supplemental Table S6) Samples were analyzed in batches (effluent recovery tests, influent
was added for IS recovery calculations. recovery tests, effluent samples, influent samples, and blanks), and
each batch contained a calibration with seven calibration solutions
2.2.3. Comprehensive gas chromatography high-resolution time-of-flight resulting in at least three useful data points for each analyte. The instru-
mass spectrometry ment was tuned in between each set.
Stage II samples were analyzed with a Pegasus 4D HRT mass The ChromaTOF-HRT software (V.1.90, Leco Corp., St. Joseph, MI,
spectrometer (Leco Corp., St. Joseph, MI, USA) equipped with an Agilent USA) was used for data processing. The raw data files were mass cali-
7890 gas chromatograph (Palo Alto, CA, USA). A conventional nonpolar- brated to perfluorotributylamine mass ions, and characteristic target
polar column combination was used because Stage I samples did not analyte ions were searched within a given retention time window and
contain any elevated levels of aliphatic hydrocarbons. The primary col- with a 0.005 Da mass tolerance.
umn was a Rtx-5MS (30.0 m, 0.25 mm ID, 0.125 μm film thickness) from
Restek (Bellefonte, PA, USA). The secondary column, a Restek Rxi-17Sil 2.2.4. Quality assurance and control
MS (2.0 m, 0.25 mm ID, 0.125 μm film thickness) of which 0.6 m were The 26 target analytes were quantified using the ions listed in the
placed inside the secondary oven, was connected to an uncoated apolar Supplemental Table S9. In addition, five 1-substituted linear alkyl
deactivated silica column (1.0 m, 0.25 mm) from Sigma-Aldrich benzenes (LABs) are listed which were used for method development
(Steinheim, Germany) situated in the transfer line. A pulsed splitless in- 1-phenyldecane (1-C10-LAB), 1-phenylundecane (1-C11-LAB), 1-
jection with 50 psi inlet pulse pressure and 3 mL min−1 septum purge phenyldodecane (1-C12-LAB), 1-phenyltridecane (1-C13-LAB), and 1-
flow was used. The inlet pulse lasted 120 s, and the inlet purge time phenyltetradecane (1-C14-LAB). The target analytes were quantified
using the isotope dilution technique with carefully matched labeled IS.
Table 2
Structurally identical deuterated or 13C-labeled standards were used
for 2,4,7,9-tetramethyl-5-decyn-4,7-diol (TMDD), tributylphosphate
Name Type Built, modified Households/Population Treatment steps (TBP), tris(2-chloro-ethyl)phosphate (TCEP), tris(1-chloro-2-
equivalents
propyl)phosphate (TCIPP), tris(1,3-dichloro-2-propyl)phosphate
SB1 SB 2006 9/− S, SB (TDCPP), triphenylphosphate (TPP), benzophenone (BP), OC, hexachlo-
SB2 SB 1993 28/− S, SB
robenzene (HCB), n-butylbenzenesulfonamide (n-BBSA), TCS,
SB3 SB 2010 4/− S, SB
SB4 SB 1992 37/− S, SB thiabendazole (TBZ), BPA, α-tocopheryl acetate (α-TPA), AHTN, and
SB5 SB 2012 13/− S, SB musk xylene, and labeled compounds with similar structural
STP1 STP 1971, 1996 −/1200 M, C, S, A, S, (C), features, physicochemical properties, and extraction efficiencies were
(D) used for LABs, 2-(methylthio)benzothiazole (MTBT), TBEP, tris(2-
STP2 STP 1934, 1939, 1970 −/110,000 1) M, C, S, A, S, C,
ethylhexyl)phosphate (TEHP), 2-ethylhexyldiphenylphosphate
(D)
2) M, C, S, BB, S, (EHDPP), tricresylphosphate (TCP), 4-octyl phenol (4-OP), HHCB, and
C, (D) musk ketone.
STP3 STP 1972, 2015 −/100,000 M, C, S, A, S, (D) Before and after the sampling, Milli-Q water was pumped through
STP4 STP 1940/50, 1960, −/150,000 M, C, S, A, S, C,
each sampler to account for background levels (Blank 1 to Blank 6 in
1990 (D)
STP5 STP 1941, 1971, 2011, −/780,000 M, C, S, A, S, C, SF Supplemental Table S10). In addition, a field blank and a laboratory
2015 blank were processed with Milli-Q water (Blank F1 and Blank L1 in Sup-
S = sedimentation, M = mechanical treatment, C = chemical treatment, A = active
plemental Table S10). Instrumental limit of detection and quantification
sludge, BB = bio bed, D = disinfection, SF = polishing sand filter, optional treatment (LOD and LOQ) were determined by extrapolation to S/N 3 and S/N 10,
steps are in brackets, 1) = line 1, 2) = line 2. respectively. For compounds appearing in the blank, the MLOQ was
270 K.M. Blum et al. / Science of the Total Environment 575 (2017) 265–275

calculated by multiplying the maximum concentration determined in were filtered for occurrence in our effluent samples, and then further fil-
the blanks by 10. Recovery tests were performed for the SPE method tered for PBT properties, production volume or emission potential ac-
using triplicate influent and effluent samples spiked with native cording to Section 2.1.4 and Fig. 2. The compounds that passed these
analytes and the 1-subsituted LABs (Supplemental Table S11). Three filters were manually checked for anthropogenic origin which resulted
non-spiked influent samples, one non-spiked effluent sample, and one in 63 remaining compounds (Supplemental Table S2).
Milli-Q blank were analyzed in each effluent and influent batch. The 63 compounds were re-processed using a compound specific
The target analysis method developed for Stage II was evaluated quantification ion, and were semi-quantified using chrysene-d12,
based on linearity, LOD, LOQ, recoveries, and precision. Good linearities which resulted in a lower percentage of non-detects, but also in
were obtained for both SPE recovery tests and for samples with regres- the elimination of 17 compounds due to elevated blank levels
sion coefficients (R2) ≥ 0.99. Instrumental LOQs ranged from 3.3 pg μL−1 (Section 2.1.4, Supplemental Table S3). Although these background
for TBP to 49 pg μL−1 for TCP, and MLOQs ranged from 23 ng L−1 for TBP compounds potentially have environmental relevance, they are likely
to 1300 ng L−1 for BPA. Native analyte SPE recovery experiments result- not of OSSF origin and thus not in the scope of this study.
ed in excellent median relative recoveries of 95% and 94% in effluent and The final set of anthropogenic contaminants of potential environmen-
influent, respectively. The recovery tests were performed in triplicates, tal concern consisted of 46 compounds (Fig. 2, Supplemental Table S2)
and standard errors ranged from 0.2% for 4-OP and 1-C10-LAB to 13% and included pharmaceuticals, like the pain reliever acetylsalicylic acid,
for HHCB in influent and up to 28% for BP in effluent (Supplemental the stimulant caffeine, the antiepileptic carbamazepine, the anticonvul-
Table S12). sant ethosuximide, and the antidepressant mirtazapine; the OPs TDCPP,
The median absolute IS recoveries in the influent, effluent, and blank TCEP, TCIPP, tris(3-chloropropyl)phosphate (TCPP), TBEP, TBP and TPP;
samples were 189%, 115%, and 92%, respectively. Only TBZ-13C6 and rubber and plastic additives like MTBT and n-BBSA; personal care prod-
nonylbenzene-d24 had recoveries ≤ 50% (Supplemental Tables S13– uct ingredients like α-TPA; the UV stabilizers octyl salicylate (OS),
S15). The high apparent recoveries in the influent might be due to ma- oxybenzone, and OC; LABs like 5-phenylundecane (5-C11-LAB), 4-
trix shielding of active sites in the GC liner resulting in enhanced analyte phenylundecane (4-C11-LAB), 4-phenyldodecane (4-C12-LAB) and 6-
transfer to the column (Rahman and El-Aty, 2013). However, with the phenyldodecane (6-C12-LAB), which are impurities in linear alkyl
extensive use of labeled standards and careful matching of native sulfonates containing detergents; surface-active compounds like TMDD
analytes and IS, matrix enhancement should not significantly affect and N,N,N′,N′-tetraacetylethylenediamine; flavor and fragrances like α-
the final results. cumyl alcohol and HHCB; and pesticides like 2,3-dichlorobenzonitrile
and DEET.
2.2.5. Removal efficiency calculations and statistical analysis By scoring the 46 contaminants based on their removal efficiency,
Sample concentrations below LOQ, LOD, and MLOQ were substituted half-life, BCF, PEC/PNEC, and maximum concentration found in either
with LOQ/2, LOD/2, and MLOQ/2, respectively, for removal efficiency ATSs or SBs (Table 1, Fig. 2), the potential environmental relevance of
calculations. The percentage removal efficiency was calculated the identified compounds could be estimated. Theoretically the scores
as 1 minus the concentration in effluent divided by the concentration can range from 5 to 25, with the most relevant compounds scoring
in influent, times 100. In case of a negative removal efficiency, the lowest (Fig. 3). Individual scores for the 46 compounds are given
the value was set to 0%. Negative removal efficiencies have often in Supplemental Table S18. HHCB scored the lowest with a total score
been reported and have been attributed to the fluid dynamics of the of 8, followed by α-TPA, OC, TMDD, 4-C12-LAB, TCPP, TDCPP, 6-C12-
system (e.g. not taking hydraulic retention times into account), LAB, TCEP, OS, caffeine, 5-C11-LAB, and 4-C11-LAB. HHCB scored low
deconjugation of metabolites, and desorption from return activated due to its high concentration in OSSFs in combination with the risk of
sludge in the secondary treatment process (Blair et al., 2015; Verlicchi causing adverse effects in the environment and its overall low scores
et al., 2012). Analytical bias also cannot be ruled out, because dense in all categories. α-TPA was highly ranked because of a long half-life
samples such as influent are generally more difficult to extract than and high risk for causing adverse effects in the environment, whereas
lean samples such as effluent. OC was ranked high due to its bioconcentration potential, low removal
Principal component analysis (PCA) was performed with SIMCA efficiency, and high PEC/PNEC. TMDD was poorly removed and was
(v.13.0.3, Umetrics, Umeå, Sweden) to study variations in compound- present in high abundance, 4-C12-LAB had a high PEC/PNEC and BCF,
specific removal efficiency for the different treatment plants and tech- and TCPP showed low removal efficiency and high persistence and
niques. Compounds were excluded from data analysis if ≥ 50% of the abundance.
data was missing. Removal efficiencies were mean centered and scaled Eriksson et al. (2003) identified compounds using non-target
to unit variance prior to PCA. screening of grey water that were also highly ranked in our study,
The removal efficiencies and influent and effluent concentrations such as α-TPA, TCEP, TPP, geranyl acetone and caffeine. Octocrylene
were also analyzed for significant differences between SBs and STPs and galaxolide have been detected in grey water (Leal et al., 2010),
with the Wilcoxon's sum rank test (Wilcoxon, 1945), and the correla- and Conn et al. (2010a, 2010b) targeted for TCEP, TCIPP and TDCPP in
tion between the logarithm of the octanol–water partition coefficient OSSF influent and effluent without success. Rager et al. (2016) used LC
(log KOW) and removal efficiencies was tested with Spearman's rank coupled to high-resolution MS to screen for and prioritize contaminants
correlation. based on detection frequency, bioactivity, exposure and abundance in
household dust. Similar to our study, they found TCPP, TCIPP, 4-C12-
3. Results and discussion LAB, and DEET among the top-ranked (n = 25) contaminants. Consider-
ing that various screening and ranking approaches for different kinds of
3.1. Non-target screening: identification of environmentally relevant con- environmental matrices picked up compounds identical to some of our
taminants discharged from on-site sewage treatment facilities (Stage I) priority compounds, our strategy appears to be successful in the identi-
fication of environmentally relevant compounds.
The peak extraction and alignment of all peaks found in all samples Ultimately, the derived scores were used to select chemicals of high
resulted in a total of ≥ 200,000 features as can be seen from the environmental concern to include in Stage II. Low score (top ranked)
workflow schematics (Fig. 2). After detection frequency and blank filtra- chemicals were complemented with structurally related compounds
tion, manual inspection to exclude features with a poor spectral library belonging to same compound classes and some commonly used refer-
match, and exclusion of compounds because they had long alkyl chains ence compounds to reach a total of 26 target analytes (Table 3, Fig. 2).
indicating biogenic origin, approximately 300 compounds remained This extension was done to expand the physicochemical domain
(Supplemental Table S2). These tentatively identified compounds of the studied chemicals and to facilitate the understanding of
 K.M. Blum et al. / Science of the Total Environment 575 (2017) 265–275 271

fundamental removal and degradation processes in OSSFs. The selection removed in SBs compared to STPs (Wilcoxon's sum rank test, α =
criteria for the final list of compounds to include in Stage II are given in 0.01). The median removal efficiencies of TMDD in SBs and STPs were
Table 3. 33% and 0%, respectively, whereas the median removal efficiencies of
TBEP were 80% and 68%, respectively (Table 4).
3.2. Evaluation of soil beds and large-scale sewage treatment facilities PCA was used to analyze and visualize differences in removal pattern
(Stage II) between the two types of sewage treatment. The score plot in Fig. 6A
shows a weak separation of SBs and STPs along PC2 (with SB4 as an out-
3.2.1. Removal pattern lier). As already seen in Fig. 5, STPs seem to be more diverse in their re-
Volatilization, sorption to solids followed by sedimentation, and bio- moval behavior than SBs. However, SB3 also appears to be quite
degradation are reported to be the major removal pathways of contam- different from the rest of the SBs. It was the smallest of the studied
inants in wastewater treatment (Conn et al., 2006; Simonich et al., SBs with 4 households connected and also had the lowest median re-
2002). The sorption potential of an organic compound can often be re- moval efficiency (60%). The plants STP1, STP3, and SB3 differed from
lated to its hydrophobicity using the log KOW (Fernandez et al., 2014). the other plants along PC1 and showed deviating removal efficiencies
Since the hydrophobicity of a chemical influences its affinity to organic for specific chemicals. TCEP, TCIPP and TBZ showed a better removal
matter, and thus its removal efficiency in treatment plants, the calculat- and HHCB, MTBT and BP a worse removal in these plants as compared
ed overall median removal efficiencies of each compound (df = 100%) to the majority of plants (SB1, SB2, SB4, SB5, STP2, STP4, STP5)
were correlated to the log KOW (Fig. 4). SB, STP, and overall median (Fig. 6B, Supplemental Table S19). The main drivers for the separation
removal efficiencies were significantly correlated (Spearman rank of SBs and STPs along PC2 were the better removal of HHCB, AHTN,
correlation, α = 0.01) to the log KOW with a correlation of 85% (p = TBEP, TBP, and TMDD and the worse removal of OC and EHDPP in
0.00003), 82% (p = 0.0001), and 85% (p = 0.00003), respectively. OC, most SBs (Fig. 6B, Supplemental Table S19). The cluster of compounds
EHDPP, α-TPA, TCS, TEHP, and HHCB had the highest overall median re- in region 1 (EHDPP, OC, TCS, TPP, and 6-C12-LAB) are very hydrophobic
moval efficiency (≥90%) and were also the most hydrophobic chemicals (log KOW 4.6 to 8.0), whereas the compounds in region 2 (TBEP, MTBT,
investigated (log KOW 4.8 to 12), whereas TDCPP, TMDD, TCIPP, and TMDD, TCIPP, TDCPP, and TBZ) are in comparison rather hydrophilic
TCEP were removed with reduced efficiency between 22% and 44% (log KOW 2.5 to 3.8) (Fig. 6B). Consequently, many compounds that
and are less hydrophobic (log KOW 1.4 to 3.7). AHTN, BP, TPP, TBP, were better removed in SBs are relatively hydrophilic. SBs contain vari-
TBEP, and MTBT were removed with efficiencies between 64% and ous layers of gravel and sand and have a high solid-to-water ratio, which
87%, and their log KOW is between 3.2 and 5.7. Sorption is crucial during could increase sorption of compounds with moderate hydrophobicity
sedimentation and soil filtration, which explains the higher removal ef- and polar or polarizable functional groups, that might interact with sim-
ficiency of hydrophobic compounds. Volatility did not explain any vari- ilar functional groups in the SB material. Although SBs should be aerated
ation in removal efficiency in our study. Furthermore, it was hard to to promote aerobic biodegradation, anaerobic sections can occur if the
explain the low removal of TMDD and chlorinated OPs (TCEP 22%, SBs do not work properly. In combination with longer residence times
TCIPP 26%, and TDCPP 44%) solely by lipophilicity. TMDD, TCIPP and in SBs, anaerobic sections promote reductive dehalogenation of chlori-
TDCPP have a much lower removal than other target compounds with nated OPs such as TDCPP, TCIPP, and TCEP (Rittmann et al., 1994),
similar log KOW (Fig. 4). Instead, their high water solubility (TMDD 2 g whereas the biodegradation in active sludge treatment in STPs is exclu-
L−1,TCIPP 2 g L−1 and TDCPP 0.1 g L−1) and resistance to biological deg- sively aerobic.
radation (TCIPP 21% and TDCPP 0% degraded; 28 days OECD degrada- Because internal LAB isomers (i.e. phenyl substitution is near the
tion test) may partly explain their low removal (World Health center of the alkyl chain) are more susceptible to biodegradation than
Organization, 1998). external LAB isomers (i.e. phenyl substitution is near the end of the
The compounds with the lowest overall median removal efficiency alkyl chain) (Eganhouse et al., 1983), the ratio between, for example,
(≤80%) and highest occurrence (df = 100%) are presented in a boxplot (6-C12-LAB + 5-C12-LAB) and (4-C12-LAB + 3-C12-LAB + 2-C12-
(Fig. 5). The removal efficiency in STPs (n = 5) varied to a greater LAB) (the internal/external ratio) was previously used to assess biodeg-
extent compared to SBs (n = 5), and the largest variation was radation in the aquatic environment (Takada and Ishiwatari, 1990) and
observed for MTBT with a removal of 0% in STP3 and 94% in STP2. STP treatment efficiencies (Hartmann et al., 2000). Influent has internal/
TMDD (p = 0.0003) and TBEP (p = 0.005) were significantly better external ratios around 1, whereas effluent usually has ratios around 3 or
larger (Isobe et al., 2004). We only had analytical standards available for
6-C12-LAB and 3-C12-LAB, thus we used the ratio between those two
isomers to evaluate the treatment efficiency. In SB3, the ratio between
6-C12-LAB and 3-C12-LAB was 1.5, which indicates overall low microbi-
ological activity and agrees with the results from the PCA removal effi-
ciency analysis.
Few studies have reported the removal efficiencies in OSSFs of sim-
ilar target analytes. The removal of TCS in OSSFs was reported to be 47 ±
18% (Conn et al., 2006), 39% (Conn et al., 2006), 75 ± 23% (Conn et al.,
2006), 98% (Leal et al., 2010), and ≥90% (Conn et al., 2010b) in septic
tanks, wetlands, biofiltration systems, ATSs, and SBs, respectively. ATS
lab-scale experiments showed an average removal efficiency of AHTN,
HHCB, and OC of 32%, 80%, and 91%, respectively (Leal et al., 2010).
Our median removal efficiency was 91% for TCS and 87%, 95%, and 98%
for AHTN, HHCB, and OC, respectively, which is at the upper end of
the results of the cited studies (Table 4). We are aware of only two stud-
ies (Du et al., 2014; Garcia et al., 2013) that have compared OSSF and
STP treatment efficiency by treating STP influent using different OSSF
technologies (ATS and septic systems). The routine water quality
Fig. 3. The top ranked compounds with the lowest total score and their scoring in removal
efficiency, half-life for aquatic biodegradation, bioconcentration factor (BCF), PEC/PNEC
parameters (Garcia et al., 2013) and contaminant concentrations
(predicted environmental concentration/predicted no effect concentration), and (Du et al., 2014; Garcia et al., 2013) did not significantly deviate be-
maximum concentration found in samples. Compound abbreviations are given in Table 3. tween STPs and OSSFs (α = 0.05), but the effluent toxicity was highest
272 K.M. Blum et al. / Science of the Total Environment 575 (2017) 265–275

Table 3
The 26 selected target analytes for Stage II along with compounds classes, abbreviations, corresponding ranks, total score and selection criteria. Reference compounds are marked in italic.

Rank Total score

Class Analyte Abbreviation (1 to 15) (5 to 25) Selection criteria

Biocides Hexachlorobenzene HCB n.d. n.d. Classical persistent organic pollutant, which was previously
detected in STP effluents (Robles-Molina et al., 2013)
Thiabendazole TBZ n.d. n.d. As example for a more polar biocide
Triclosan TCS n.d. n.d. Classical biocide, previously studied in OSSFs (Conn et al.,
2010a, 2010b, 2006; Leal et al., 2010)
Food additive α-Tocopheryl acetate α-TPA 2 12 Top 5 ranking
Fragrances Galaxolide HHCB 1 8 Overall top ranked
Musk ketone n.d. n.d. Common nitro-aromatic musk found in STP effluents
(Heberer, 2002)
Musk xylene n.d. n.d. Common nitro-aromatic musk found in STP effluents
(Heberer, 2002)
Tonalide AHTN n.d. n.d. Commonly found polycyclic musk to complement HHCB
(Heberer, 2002)
Linear alkyl benzenes 3-Phenyldodecane 3-C12-LAB n.d. n.d. External LAB isomer found in detergents as impurity, together
with the internal isomer 6-C12-LAB, it can be used to assess
biodegradation activity (Takada and Ishiwatari, 1990)
6-Phenyldodecane 6-C12-LAB 5 15 Top 5 ranking
Organophosphorus 2-Ethylhexyldiphenylphosphate EHDPP n.d. n.d. Supplement for an aryl organophosphorus flame
flame retardants retardant (Marklund et al., 2005)
Tributylphosphate TBP 6 16 Top 10 ranking
Tricresylphosphate TCP n.d. n.d. Supplement for an aryl organophosphorus flame retardant
Triphenylphosphate TPP 12 22 Moderate score, but typical aryl organophosphorus
flame retardant (Marklund et al., 2005)
Tris(1,3-dichloro-2-propyl)phosphate TDCPP 5 15 Top 5 ranking
Tris(1-chloro-2-propyl)phosphate TCIPP 12 22 Moderate score, but structurally very similar to
tris(3-chloropropyl)phosphate (TCPP) which scored in
the top 5
Tris(2-butoxyethyl)phosphate TBEP 7 17 Top 10 ranking
Tris(2-chloro-ethyl)phosphate TCEP 5 15 Top 5 ranking
Tris(2-ethylhexyl)phosphate TEHP n.d. n.d. Identified during screening, supplement for alkyl
organophosphorus flame retardant (Marklund et al., 2005)
Plasticizer n-Butylbenzenesulfonamide n-BBSA 14 24 Moderate score, but detected previously in STP
effluents (Huppert et al., 1998) and therefore included
as example of a plasticizer.
Polymer impurity Bisphenol A BPA n.d. n.d. Previously studied in OSSFs (Conn et al., 2010a; Leal
et al., 2010)
Rubber additive 2-(Methylthio)benzothiazole MTBT 8 18 Top 10 ranking
Surfactants 2,4,7,9-Tetramethyl-5-decyn-4,7-diol TMDD 4 14 Top 5 ranking
4-Octyl phenol 4-OP n.d. n.d. 4-OP has been found in ground water effected by OSSFs and
studied in OSSFs (Conn et al., 2006; Phillips et al., 2015)
UV stabilizers Benzophenone BP n.d. n.d. To supplement OC with another commonly detected UV
stabilizer (Kasprzyk-Hordern et al., 2009)
Octocrylene OC 3 13 Top 5 ranking

n.d. = rank/score not determined, since reference compound, STP = sewage treatment plant, OSSF = on-site sewage treatment facility, LAB = linear alkyl benzene.

Fig. 5. Removal efficiencies in soil beds (SBs) and sewage treatment plants (STPs) for
compounds with a median removal efficiency ≤80% and a detection frequency = 100%
(detected in influent or in effluent of the same treatment plant). The boxes represent
the 25th and 75th percentiles, the median is indicated as a horizontal line in the box,
Fig. 4. Overall median removal efficiencies in % versus the logarithm of the octanol–water and the mean is presented as a cross. Error bars represent the minimum and maximum
partition coefficient (log KOW) for compounds with detection frequency = 100% (detected removal efficiency measured in SB or STPs. Data points are indicated as dots. *
in influent or effluent of the same treatment plant); log KOW values were retrieved from Significantly better removed by SB than STP (Wilcoxon's sum rank test, α = 0.01).
KOWWIN v.1.68 (www.epa.gov, 2008). Compound abbreviations are given in Table 3. Compound abbreviations are given in Table 3.
 K.M. Blum et al. / Science of the Total Environment 575 (2017) 265–275 273

Table 4 HCB, α-TPA, 6-C12-LAB, 3-C12-LAB, TBP, TPP, TEHP, EHDPP, 4-OP, and
Median removal efficiencies in % for soil beds (SBs) and sewage treatment plants (STPs), BP. 4-OP was identified in STP5 effluent only (10 ng L− 1), and HCB
and the probability (p-value) that the SB removal efficiency is worse than the STP removal
(Wilcoxon's sum rank test) for compounds with a detection frequency = 100% (detected
was found in SB1 and STP2 effluent at around 5 ng L− 1. 3-C12-LAB
in influent or in effluent of the same treatment plant). was only detected in SB3 effluent. BPA, n-BBSA, musk xylene, musk ke-
tone, and TCP were not detected.
Median removal
To the best of our knowledge, only TCS, BPA, AHTN, HHCB, OC, and
efficiency in %
TBEP have been reported before in OSSF effluent (Conn et al., 2010a,
SB STP p-value
2010b, 2006; Leal et al., 2010; Phillips et al., 2015). These studies detect-
n =5 n =5 (α = 0.01) ed TCS at much higher concentrations and reported up to 57,000 ng L−1
Tris(2-chloroethyl)phosphate 19 24 0.7 (Conn et al., 2010a) in septic tank effluent (Conn et al., 2010b, 2006),
2,4,7,9-Tetramethyl-5-decyn-4,7-diol 33 0 0.0003 up to 230 ng L−1 (Conn et al., 2010b) in biofiltration effluent, and up
Tris(1-chloro-2-propyl)phosphate 34 18 0.6 to 350 ng L− 1 (Leal et al., 2010) in aerobically treated grey water.
Tris(1,3-dichloro-2-propyl)phosphate 56 31 0.3
AHTN, HHCB, and OC were detected at 1500 ng L−1, 2100 ng L−1, and
2-(Methylthio)benzothiazole 64 65 0.3
Triphenylphosphate 78 86 0.8 3500 ng L− 1 (Leal et al., 2010), respectively, in aerobically treated
Tris(2-butoxyethyl)phosphate 80 68 0.005 grey water. The HHCB and OC concentrations agree well with the levels
Benzophenone 83 80 0.4 we detected. TBEP was found at concentrations N 20,000 ng L−1 below
Tributylphosphate 83 29 0.2 leach beds of OSSFs in a commercial area (Phillips et al., 2015), which
Tonalide 87 86 0.2
Triclosan 91 96 1
is N20 times higher than what we detected in SB effluent in a residential
Tris(2-ethylhexyl)phosphate 92 94 0.9 setting. BPA was detected up to 13,000 ng L−1 in septic tank effluent
Galaxolide 95 84 0.01 (Conn et al., 2010a), but was not detected above MLOQ (1300 ng L−1)
α-Tocopheryl acetate 96 99 1 in our samples.
2-Ethylhexyldiphenylphosphate 98 99 0.9
Octocrylene 98 99 0.9
4. Conclusions

in one type of OSSF (Garcia et al., 2013). However, they did not examine For the first time, a GC × GC–MS based non-target screening was
SBs, so a direct comparison is not possible. used in combination with PBT prioritization to successfully identify en-
vironmentally relevant compounds discharged from OSSFs. The rele-
3.2.2. Environmental load vance of the selected priority pollutants was confirmed in Stage II,
In 55% of the cases, the maximum concentration detected in SB where the top-ranked compounds were detected with high detection
effluent was higher than the concentration in the STP effluent, but no frequency and at high concentrations in SB effluent samples. The top-
significant differences were found with the Wilcoxon's sum rank test four compounds – HHCB, α-TPA, OC, and TMDD were detected at levels
(α = 0.01). up to 1400 ng L−1, 660 ng L−1, 1200 ng L−1, and 3000 ng L−1 in SB ef-
The most frequently detected compounds were AHTN, TCIPP, TBEP, fluent, respectively. In addition, six contaminants (α-TPA, TMDD, BP,
TDCPP, TCEP, MTBT, and TMDD. Concentrations above 1000 ng L− 1 LABs, TBZ and MTBT) were found for the first time in OSSF effluents. Be-
were detected for TCIPP and TMDD in SB and STP effluent; for cause OSSFs are diffuse sources of contamination, the high levels of con-
HHCB, TBEP, and α-TPA in STP effluent; and for OC in SB effluent taminants in OSSF effluents will taint surface and ground waters,
(Table 5). TDCPP, TCEP, and MTBT were detected at levels higher than potentially impacting not only sensitive ecological systems (Brodin
100 ng L−1 in SB and STP effluent, and AHTN was found at a maximum et al., 2013), but also drinking water supplies (Godfrey et al., 2007;
concentration of 90 ng L− 1. Less frequently detected were TBZ, TCS, Swartz et al., 2006).

Fig. 6. Removal efficiencies of soil beds (SBs) and large sewage treatment plants (STPs) presented in a principle component analysis (PCA) with A) the score scatter plot and B) the loading
plot. The first and second principle component (PC) explain 34% and 21% of the variation, respectively. Compounds in region 1 and 2 have a log KOW of 4.6 to 8.0 and 2.5 to 3.8, respectively.
The more hydrophobic the compound, the darker its dot. Compound abbreviations are given in Table 3.
274 K.M. Blum et al. / Science of the Total Environment 575 (2017) 265–275

Table 5
Detected compounds in soil bed (SB) or sewage treatment plant (STP) effluent and their respective maximum and median concentration and detection frequency (df).

SB effluent STP effluent

Median Maximum df (%) Median Maximum df (%)

Analytea (ng L−1) (ng L−1) n =5 (ng L−1) (ng L−1) n =5

∑Organophosphorus flame retardants (n = 8) 2600 4000 3000 4700

2,4,7,9-Tetramethyl-5-decyn-4,7-diol 1100 3000 100 1500 4200 100
Tris(1-chloro-2-propyl)phosphate 1000 3000 100 1200 1600 100
Galaxolide 600 1400 80 910 990 100
Benzophenone 460 460 20 278 300 40
α-Tocopheryl acetate 290 660 80 110 110 20
Tris(1,3-dichloro-2-propyl)phosphate 260 490 100 460 600 100
Tris(2-butoxyethyl)phosphate 180 950 100 810 2400 100
Tris(2-chloroethyl)phosphate 170 360 100 230 300 100
6-Phenyldodecane 130 170 60 64 67 60
Octocrylene 120 1200 80 32 53 60
3-Phenyldodecane 110 110 20 bLOQ/LOD bLOQ/LOD 0
2-(Methylthio)benzothiazole 67 120 100 92 950 100
Tonalide 59 90 100 69 80 100
Tributylphosphate 54 80 40 47 220 80
Thiabendazole 43 300 60 130 220 40
2-Ethylhexyldiphenylphosphate 38 38 20 14 16 40
Tris(2-ethylhexyl)phosphate 37 41 40 32 32 20
Triphenylphosphate 30 61 60 64 110 40
Triclosan 15 21 40 10 10 60
Hexachlorobenzene 5.3 5.3 20 5 5 20
4-Octyl phenol bLOD bLOD 0 10 10 20
a
Among the studied compounds the following were not detected above methodological limit of quantification (MLOQ), instrumental limit of quantification (LOQ), or detection limit
(LOD) in effluent: BPA, n-BBSA, musk xylene, musk ketone, and TCP.

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