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Targeted and untargeted analysis of organic contaminants from
on-site sewage treatment facilities
Removal, fate and environmental impact
Kristin Blum
Department of Chemistry
Umeå 2018
This work is protected by the Swedish Copyright Legislation (Act 1960:729)
Dissertation for PhD
ISBN: 978-91-7601-836-1
The front cover photo was taken by Patrik Andersson and Kristin Blum during a sampling
trip.
Electronic version available at: http://umu.diva-portal.org/
Printed by: Lars Åberg, KBC Service center
Umeå, Sweden 2018
When everything feels like an uphill struggle, just think of the view
from the top. – Unknown
i
Table of Contents
Abstract .......................................................................................................... ii
Enkel sammanfattning på svenska ............................................................... iv
Abbreviations ................................................................................................ vi
List of Publications ........................................................................................ x
1. Introduction ................................................................................................ 1
2. Background ................................................................................................ 3 2.1. On-site sewage treatment facilities .............................................................................. 3 2.2. Environmental fate ....................................................................................................... 9 2.3. Environmental risk assessment ...................................................................................13 2.4. Contaminant analysis using mass spectrometry .......................................................13
3. Material and Methods ............................................................................... 19 3.1. Sampling ....................................................................................................................... 19 3.2. Sample preparation ..................................................................................................... 23 3.3. Instrumental analysis .................................................................................................. 24 3.4. Data processing ........................................................................................................... 30 3.5. Environmental risk assessment .................................................................................. 32 3.6. Mass fluxes per capita ................................................................................................. 32 3.7. Statistics ....................................................................................................................... 32 3.8. Molecular descriptors ................................................................................................. 34
4. Results and Discussion ............................................................................. 35 4.1. Prioritization of environmentally-relevant contaminants in wastewater .............. 35 4.2. Method development for urban water contaminants ............................................ 40 4.3. Removal patterns in soil beds compared to large-scale STPs ................................. 42 4.4. Wastewater-impacted surface water and sediment ................................................ 43 4.5. Environmental fate of wastewater contaminants .................................................... 45 4.6. Mass fluxes per capita at wastewater-impacted sites ............................................. 48 4.7. Removal in char-fortified soil beds ........................................................................... 50
5. Conclusions and future perspectives ........................................................ 53
6. Acknowledgements ................................................................................... 57
7. References ................................................................................................. 59
ii
Abstract
On-site sewage treatment facilities (OSSFs) are widely used all over the world to
treat wastewater when large-scale sewage treatment plants (STPs) are not
economically feasible. Although there is great awareness that the release of
untreated wastewater into the environment can lead to water-related diseases and
eutrophication, little is known about organic contaminants and their removal by
OSSFs, environmental load and fate. Thus, this PhD thesis aims to improve the
knowledge about treatment efficiencies in current OSSFs, the environmental
impact and fate of contaminants released from OSSFs, as well as how biochar
fortification in sand filter (soil beds) OSSFs might increase removal of these
contaminants. State-of-the-art analytical techniques for untargeted and targeted
analyses were used and the results evaluated with univariate and multivariate
statistics.
Environmentally-relevant contaminants discharged from OSSFs were identified
using untargeted analysis with two-dimensional gas chromatography mass
spectrometry (GC×GC-MS) and a MS (NIST) library search in combination with a
prioritization strategy based on environmental relevance. A method was
successfully developed for the prioritized contaminants using solid phase
extraction and GC×GC-MS, and the method was also applicable to untargeted
analysis. This method was applied to several studies. The first study compared
treatment efficiencies between STP and soil beds and showed that treatment
efficiencies are similar or better in soil beds, but the removal among the same type
of treatment facilities and contaminants varied considerably. Hydrophilic
contaminants were generally inadequately removed in both types of treatment
facilities and resulted in effluent levels in the nanogram per liter range.
Additionally, several prioritized and sometimes badly removed compounds were
found to be persistent, mobile, and bioavailable and two additional, untargeted
contaminants identified by the NIST library search were potentially mobile. These
contaminants were also found far from the main source, a large-scale STP, at Lake
Ekoln, which is part of the drinking water reservoir Lake Mälaren, Sweden. The
study also showed that two persistent, mobile and bioavailable contaminants were
additionally bioaccumulating in perch. Sampling for this study was carried out over
several seasons in the catchment of the River Fyris. Parts of this catchment were
affected by OSSFs, other parts by STPs. Potential ecotoxicological risks at these
iii
sites were similar or higher at those affected by STPs compared to those affected
by OSSFs. Mass fluxes per capita were calculated from these levels, which were
higher at STP-affected than at OSSF-affected sites in summer and autumn, but not
in winter. Possibly, the diffuse OSSF emissions occur at greater average distances
from the sampling sites than the STP point emissions, and OSSF-affected sites may
consequently be more influenced by fate processes.
The studies carried out suggested that there is a need to improve current treatment
technologies for the removal of hydrophilic contaminants. Thus, the final study of
this thesis investigated char-fortified sand filters (soil beds) as potential upgrades
for OSSFs using a combination of advanced chemical analysis and quantitative
structure-property relationship modeling. Removal efficiencies were calculated
from a large variety of contaminants that were identified by untargeted analysis
using GC×GC-MS and liquid chromatography ion mobility mass spectrometry as
well as library searches (NIST and Agilent libraries). On average, char-fortified
sand filters removed contaminants better than sand, partly due to an enhanced
removal of several hydrophilic contaminants with heteroatoms. After a two-year
runtime, sorption and particularly biodegradation must have contributed to the
removal of these compounds.
Generally, the combination of targeted and untargeted analysis has proven
valuable in detecting a large variety of organic contaminants, as well as unexpected
ones. The results imply that OSSFs have similar or better removal efficiencies,
similar or lower environmental risks and similar or lower mass fluxes per capita,
compared to STPs. Biochar fortification can improve the removal of organic
contaminants in soil beds, but further research is needed to find technologies that
reduce the discharge of all types of organic contaminants.
iv
Enkel sammanfattning på svenska
Enskilda avlopp och andra små avloppsreningsanläggningar används över hela
världen för att behandla avloppsvatten när storskaliga reningsverk inte är
ekonomiskt försvarbara. Det är välkänt att utsläpp av obehandlat avloppsvatten i
miljön kan leda till vattenburna sjukdomar och igenväxning. Effekter av utsläpp av
organiska föroreningar från små avlopp är däremot relativt okänt. Denna
avhandling syftar till att förbättra kunskapsläget kring hur organiska föroreningar
avskiljs i små avlopp, om dessa leder till miljöpåverkan samt om befintliga små
avlopp kan förses med biokolfilter för att förbättra reningsgraden. I avhandlingen
har avancerad kemisk analys använts för riktade analyser av välkända miljögifter
och förutsättningslös analys på jakt efter nya, okända föroreningar.
I den förutsättningslösa analysen isolerades föroreningar i avloppsvatten med en
ny fast-fas extraktionsmetod, identifierades med tvådimensionell gaskromatografi
kopplat till masspektrometri följt av ranking och prioritering med avseende på
miljörelevans. Samma metodik användes för riktad analys men då med selektiv
datautvärdering. Resultaten har utvärderats med både traditionell och multivariat
statistik. Avhandlingen är baserad på fyra studier där den första jämför rening i
stora verk med markbäddar, som är en mycket vanlig teknik för små avlopp.
Avskiljningen av föroreningar visade sig vara lika bra eller bättre i markbäddar men
också att den varierar kraftigt mellan anläggningar och mellan kemikalier.
Vattenlösliga föroreningar avlägsnades generellt sämre i båda markbäddar och
konventionella anläggningar. Andra och tredje studien genomfördes i Fyrisåns
avrinningsområde nära Uppsala där vatten och andra matriser provtogs i områden
påverkade av små avlopp eller nedströms stora kommunala reningsverk. Flera av
de föroreningar som den första studien identifierat som svåra att avskilja, visade
sig också vara långlivade, lättrörliga och biotillgängliga (påträffades i fisk). Dessa
kemikalier återfanns också långt från källan och når till Mälaren som är en av
Stockholms och Sveriges viktigaste ytvattentäkter. Utsläppt mängd föroreningar
per person och år beräknades och visade sig vara högre under sommaren och
hösten för vatten påverkade av stora reningsverk jämfört med vatten påverkade av
små avlopp, vilket inte kunde ses under vintern. Utsläppen från små avlopp sker
längre bort från provtagningsplatserna än utsläppen från stora reningsverk vilket
ökar betydelsen av biologisk nedbrytning, UV nedbrytning samt inbindning till
partiklar. En uppskattning av studerade föroreningarnas ekotoxikologiska risker
indikerade lika eller högre risker i områden påverkade av stora reningsverk jämfört
v
med områden påverkade av små avlopp. De tre första studierna visade på ett behov
av förbättrad reningsteknik för vattenlösliga föroreningar vilket var i fokus i den
sista studien. Utsläpp från markbäddar med biokolfilter studerades med avancerad
kemisk analys och multivariat statistik och resultaten pekar på att en biokol-sand
blandning i markbädden ökade avskiljningen av föroreningar jämfört med enbart
sand. Markbäddarna hade använts under två år och skillnaden i avskiljning var
relativt liten men pekade på betydelsen av framförallt biologisk nedbrytning men
också inbindning till kol.
vi
Abbreviations
3-C12-LAB 3-Phenyldodecane
4OP 4-Octyl phenol
6-C12-LAB 6-Phenyldodecane
AHTN Tonalide
ANTC Anthracene
APCI Atmospheric pressure chemical ionization
ATS Aerobic treatment system
BANT Benz(a)anthracene
BCF Bioconcentration factor
BFLA Benzo(b+k)fluoranthene
BOD Biological oxygen demand
BP-1 Benzophenone-1
BPA Bisphenol-A
BPYR Benzo(a)pyrene
CCS Collision cross section
CHR Chrysene
ChV Chronic value
EC50 Half maximal effective concentration
EDC Endocrine disrupting chemical
EHDPP 2-Ethylhexyldiphenyl phosphate
EI Electron ionization
ERA Environmental risk assessment
ESI Electrospray ionization
FLA Fluoranthene
GC×GC Two-dimensional gas chromatography
GC×GC-MS Two-dimensional gas chromatography mass
spectrometry
GC×GC-ToF-MS Two-dimensional gas chromatography time-of-flight
mass spectrometry
GC×GC-HRToF-MS Two-dimensional gas chromatography high resolution time-of-flight mass spectrometer
GC-EI Gas chromatography electron ionization
GC-MS Gas chromatography mass spectrometry
GC-sectorMS Gas chromatography sector field mass spectrometry
GPC Gel permeation chromatography
vii
GW Grey water separation/ Source separation system
H0 Null hypothesis
HCB Hexachlorobenzene
HHCB 1,3,4,6,7,8-Hexahydro-4,6,6,7,8,8,-hexamethyl-
cyclopenta[g]benzopyran (Galaxolide)
HRT Hydraulic retention time
IM Ion mobility
KOC Organic carbon-water partition coefficient
LC50 Median lethal dose
LC-ESI-IM-QToF-MS Liquid chromatography electrospray ionization ion mobility quadrupole time-of-flight mass spectrometer
LC-HRMS Liquid chromatography high resolution mass
spectrometry
LC-IM-MS Liquid chromatography ion mobility mass spectrometry LC-MS Liquid chromatography mass spectrometry
LOD Limit of detection
LOEC Lowest observed effect concentration
Log KOC Logarithm of the organic carbon-water partition
coefficient
Log KOW Logarithm of the octanol-water partition coefficient
LOQ Limit of quantification
MEC Measured environmental concentration
MKK Musk ketone
MKX Musk xylene
MLQ Method limit of quantification
MRM Multiple reaction monitoring
MS Mass spectrometry
MS/MS Tandem mass spectrometry
MSTP Medium-scale sewage treatment plant
MTBT 2-(Methylthio)benzothiazole
nBBSA N-Butylbenzenesulfonamide
NIST National Institute of Standards and Technology
NOEC No observed effect concentration
N-tot Total nitrogen
OC Organic carbon
OCR Octocrylene
OECD Organization for Economic Co-operation and
Development
OSSF On-site sewage treatment facility
viii
PAH Polycyclic aromatic hydrocarbon
PBT Persistent, bioaccumulative, and toxic
PC Principle component
PCA Principle component analysis
PEC Predicted environmental concentration
PLS Partial least squares
PMT Persistent, mobile and toxic
PNEC Predicted no effect concentration
POCIS Polar organic chemical integrative sampler
P-tot Total phosphorous
PYR Pyrene
QqQ Triple quadrupole
QSPR Quantitative structure-property relationship
QToF Quadrupole time-of-flight mass spectrometer
REACH Registration, evaluation, authorization and restriction
of chemicals
SB Soil bed
SPE Solid phase extraction
SPME Solid-phase micro extraction
SRM Selected reaction monitoring
STP Sewage treatment plant
SVHC Substances of very high concern
SW Water solubility
TBEP Tris-(2-butoxyethyl) phosphate
TBP Tributyl phosphate
TCEP Tris-(2-chloroethyl) phosphate
TCIPP Tris-(1-chloro-2-propyl) phosphate
TCP Tricresyl phosphate
TCS Triclosan
TDCPP Tris-(1,3-dichloro-2-propyl) phosphate
TEHP Tris-(2-ethylhexyl) phosphate
TMDD 2,4,7,9-Tetramethyl-5-decyn-4,7-diol
TMF Trophic magnification factor
ToF Time-of-flight mass spectrometer
TPP Triphenyl phosphate
U.S. EPA United States Environmental Protection Agency
USE Ultrasonic extraction
UV Ultraviolet
VIP Variable influence on projection
ix
vPvB Very persistent and very bioaccumulative
αTPA α-Tocopheryl acetate
x
List of Publications
I Blum KM, Anderson PL, Renman G, Ahrens L, Gros M, Wiberg K and
Haglund P, 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,
Science of Total Environment (2017), 575, 265-275
II Blum KM, Anderson PL, Ahrens L, Wiberg K and Haglund P, Persistence,
mobility and bioavailability of emerging organic contaminants
discharged from sewage treatment plants,
Science of the Total Environment (2018), 612, 1532-1542
III Blum KM, Haglund P, Gao Q, Ahrens L, Gros M, Wiberg K and Anderson,
PL, Mass fluxes per capita of organic contaminants from on-site sewage
treatment facilities,
Submitted manuscript (Chemosphere, September 2017)
IV Blum KM, Gallampois C, Anderson PL, Renman G, Renman A, and
Haglund P, Comprehensive assessment of organic contaminant removal
from on-site sewage treatment facility effluent by char-fortified filter
beds,
Manuscript
xi
Author’s contributions
I The author was involved in planning the study, carrying out parts of the
sampling, and was responsible for sample preparation,
GC×GC-(HR)ToF-MS analysis, method development, target and
untargeted data processing, as well as identification, data evaluation,
interpretation and representation. The prioritization strategy was
developed together with the co-authors. The author also wrote,
submitted and revised the paper for publication.
II The author took part in the planning of the study, partly participated in
sampling and was responsible for sample preparation,
GC×GC-HRToF-MS analysis and data processing, partial GC-sectorMS
analysis and data processing, as well as identification, data evaluation,
interpretation and representation. The author also wrote, submitted and
revised the paper for publication.
III The author took part in the planning of the study, partly participated in
sampling and was responsible for sample preparation,
GC×GC-HRToF-MS analysis and data processing, partial GC-sectorMS
analysis and data processing, mass flux per capita estimations, data
interpretation and representation, as well as writing and submitting of
the manuscript.
IV The author carried out sample preparation, untargeted and targeted
GC×GC-HRToF-MS and LC-ESI-IM-QToF-MS analysis and data
treatment of additional data generated using GC-ToF-MS. The author
was also responsible for identification, data evaluation, interpretation
and representation, and wrote the manuscript.
xii
Own publications not included in this thesis
Lindberg RH, Fedorova G, Blum KM, Pulit-Prociak J, Gillman A, Järhult J,
Appelblad P and Söderström H, Online solid phase extraction liquid
chromatography using bonded zwitterionic stationary phases and tandem mass
spectrometry for rapid environmental trace analysis of highly polar hydrophilic
compounds - Application for the antiviral drug Zanamivir,
Talanta (2015), 164-169
Blum KM, Norström SH, Golovko O, Grabic R, Järhult JD, Koba O and Lindström
Söderström H, Removal of 30 active pharmaceutical ingredients in surface water
under long-term artificial UV irradiation,
Chemosphere (2017), 176, 175-182
Gros M, Blum KM, Jernstedt H, Renman G, Rodríguez-Mozaz S, Haglund P,
Anderson PL, Wiberg K, and Ahrens L, Screening and prioritization of
micropollutants in wastewaters from on-site sewage treatment facilities,
Journal of Hazardous Materials (2017), 328, 37-45
1
1. Introduction
Domestic wastewater contains a variety of organic contaminants. However, since
wastewater treatment plants were built to remove pathogens, phosphor and
nitrogen species but not to remove organic contaminants [1], their discharge into
the environment has raised concerns for ecosystem and human health [2].
Whereas centralized sewage treatment plants (STPs) provide the largest economic
benefit if the population is dense and large enough, smaller decentralized on-site
sewage treatment facilities (OSSFs) provide a larger economic benefit for smaller
communities and single households in rural or suburban areas [3]. Thus, they can
be a solution for the 90% of wastewater in developing countries that is not treated
before it is discharged into the environment and the 2.6 billion people worldwide
without proper sanitation [3]. In the industrialized world, 13% (2014) of Sweden’s
population [4], 20% (2011) of the population of the United States [5] and 3.9%
(2007) of Germany’s population [6] do not have access to a central STP. This
corresponds to 26.1 million (2007) housing units in the United States alone [7].
It is already well known that inadequately treated wastewater results in water-
related diseases and eutrophication. However, the removal of emerging organic
contaminants in OSSFs and their environmental impact has rarely been studied.
Therefore, this PhD thesis aims to improve the knowledge about treatment
efficiencies and contaminant discharge of current OSSFs, the environmental load
of these OSSFs, and the fate of released contaminants. Finally, possible
improvements in technology were examined to reduce the discharge.
Throughout this thesis, state-of-the-art analytical techniques including
comprehensive two-dimensional gas chromatography mass spectrometry
(GC×GC-MS) and liquid chromatography ion mobility mass spectrometry
(LC-IM-MS) were used for targeted and untargeted contaminant identification.
Contaminants were identified through library searches and results evaluated using
univariate and multivariate statistics.
2
Objectives
o To identify environmentally-relevant compounds that are discharged from
OSSFs using a GC×GC-MS based screening approach, to develop an
analysis method for these compounds in order to quantify the discharge
and to evaluate treatment efficiencies of OSSFs (Paper I).
o To investigate the fate of wastewater-discharged contaminants in the
aquatic environment in terms of persistence, mobility and bioavailability
(Paper II).
o To determine the environmental load of OSSFs compared to STPs using
mass flux per capita estimations, to evaluate temporal variations and
associated environmental risks (Paper III).
o To screen for contaminants in char-fortified sand filter (soil beds) effluents
using GC×GC-MS and LC-IM-MS, evaluate these filters for the removal of
organic contaminants, and to understand the relationship between
molecular properties and removal (Paper IV).
3
2. Background
2.1. On-site sewage treatment facilities
2.1.1. Technologies
According to data by Statistics Sweden, 28% of housing units (including holiday
houses) [4] and 13% of inhabitants [8] had OSSFs in Sweden in 2014, corresponding
to around 690 000 housing units. Other sources, such as the SMED report in 2015,
report 750 000 housing units with OSSFs in Sweden of which 83% treat sewage and
17% treat greywater only [9]. As source separation systems (greywater systems,
GWs) are a type of OSSF, the statistics for both categories were combined and
proportions for different treatment types recalculated (Figure 1). The most
common treatment technologies are infiltration systems, followed by septic tanks,
GWs and soil beds (or filter beds, SBs). Aerobic treatment systems (ATSs) were the
least common, present in only 2% of the households connected to OSSFs.
Figure 1. Proportions of the different OSSF types in Sweden, calculated and adapted from [9].
A septic tank consists of a container that retains wastewater and allows anaerobic
digestion and sedimentation [10] (Figure 2). Solids and digested organic matter
settle to the bottom, floatable solids rise to the top and are discharged with the
effluent from the tank [11]. In Sweden, septic tanks often have three consecutive
17%
25%
21%
16%
2%
9%
10%
source separation system
infiltration system
septic tanks (without add-on)
soil beds
aerobic treatment system
closed septic tanks
unknown
4
sedimentation compartments (“trekammarbrunn”) from which the sedimented
sludge needs to be removed periodically. The degree of macropollutant removal is
usually assessed with the biochemical oxygen demand (BOD), total nitrogen
(N-tot) that includes organic nitrogen, NH4+, NO3
-, and NO2- species, and the total
phosphorous (P-tot) consisting mainly of polyphosphates, orthophosphate (PO43-),
and organic phosphorous [12]. In septic tanks, the percentages of removed BOD,
N-tot and P-tot are approximately 20 ± 10%, 10 ± 5% and 15 ± 10%, respectively [9].
If the septic tank effluent is drained through leach fields into the ground to remove
macropollutants further, the technology is referred to as an infiltration system
(Figure 2). Other common names are drain field, soil absorption system, leach field
or soil treatment unit [10]. Degradation conditions in the uppermost soil are
usually aerobic and become more anaerobic further downgradient [13]. BOD,
N-tot, and P-tot removal in infiltration systems are around 90 ± 5%, 30 ± 10%, and
50 ± 30%, respectively [9]. Infiltration systems do not have a defined outlet, which
often makes effluent sampling impossible. Therefore, more and more enclosed SBs
with defined outlets are installed. Septic tank effluent is drained through a bed that
consist of layers of soil, gravel and sand and is surrounded by an impermeable
material [14]. The system prevents uncontrolled infiltration and the outlets make
sampling easier. Removal percentages for this type of system are 90 ± 5% for BOD,
25 ± 10% for N-tot and 40 ± 20% for P-tot [9].
ATSs, also called package treatment plants or sequential batch reactors, exist as
continuous or batch flow systems. By actively aerating the wastewater, they
promote biological activity and enhance aerobic degradation processes [15,16].
Some plants additionally use precipitation agents (e.g. ferric chloride) to remove
P-tot by precipitation of Al, Fe and Ca phosphates and sorption to Fe and Al
(hydr)oxide phases [13]. Aerobic treatment systems remove 90 ± 10% of BOD,
40 ± 10% of P-tot and 80 ± 10% of N-tot [9].
Source separation systems (greywater separation, GWs) separate blackwater (from
toilet flushing) and greywater (e.g. water from sinks, showers, bath, washing
machines and dishwashers). Greywater is treated and sometimes recycled on-site,
whereas blackwater is collected and treated separately or transported to a
municipal treatment plant. Other less common treatment technologies exist such
as constructed wetlands, urine separators and membrane bioreactors. Sometimes,
OSSFs are upgraded by filtering effluent through an inorganic sorbent to reduce
the P-tot discharge [17].
5
2.1.2. Removal processes of organic contaminants
For ease of understanding, the word “removal” is used in this thesis to address
losses of the parent compound, thus not only for mineralization but also for e.g.
transformation or sorption. To quantify the removal, removal efficiencies
(treatment efficiencies) were calculated.
Previous studies have found organic wastewater contaminants from various
chemical categories in OSSF effluent (Table 1), with concentrations ranging from
the low nanogram per liter up to several micrograms per liter [10]. Organic
contaminants originate from sources such as food, pharmaceutical intake and
excretion, cleaning and personal care products, furniture, building materials, floor
treatments, and dust. The contaminants reach the treatment facilities from toilets,
washing machines, sinks and bath tubs. Once at OSSFs, the major removal
processes are microbial transformation/biodegradation, as well as sorption to
solids and sedimentation [18,19] (Figure 2). Volatilization can be relevant for some
compounds [20]. The removal processes of on-site sewage treatment plants are
dependent on treatment technology, the wastewater composition and on the
structure and properties of a contaminant [19].
Figure 2. Schematic of an on-site sewage treatment plant and fate processes during treatment and
transport in the vadose zone. Adapted from [21].
A main driving force for retention of lipophilic compounds by soil or sludge
depends on the rejection of the solute by the hydrophilic water (solvophobic effect
[22]), due to the strong hydrogen bonding between the water molecules. Sorption
of these lipophilic compounds is caused by hydrophobic interactions of aromatic
septic tank
leach lineswell
ground water zone
infiltrationvadose zone
aerobic &
anaerobic
biodegradation
sorption
to drinking water to surface water
sources
anaerobic biodegradation
sorption & sedimentation
volatilization
to surface water
6
and aliphatic groups with lipophilic constituents of solids and the cell membranes
of microorganisms [19]. Alternatively, electrostatic interactions between
compounds’ positively charged functional groups and negatively charged surfaces
lead to adsorption [19]. Sorption to soil, as found in soil beds, and infiltration
systems and sorption to sludge, as found in septic tanks, is influenced by the soil
type and organic matter content, pH [23], hydraulic retention time (HRT) [24] and
temperature [25].
Organic carbon-water partition coefficients (KOC) that reflect partitioning into
organic carbon, increase with increasing molecular size and hydrophobicity [23] of
a contaminant. However, biodegradation decreases with a compound’s increasing
hydrophobicity, when sorption becomes more dominant [25,26] due to a reduction
in bioavailability (see section 2.2.3).
Biodegradation largely depends on the susceptibility to microbial attack, and thus
on a compound’s stability and the general microbial activity in the facility due to
nutrient availability and chemical and physical conditions (e.g. temperature).
Thus, it can be separated into ultimate biodegradation (mineralization) and
primary biodegradation (transformation of the parent compound). Furthermore,
biodegradation can be aerobic (e.g. package treatment plants [27], soil beds [28]),
mixed anaerobic-aerobic (e.g. infiltration systems [29],Figure 2) and anaerobic
(e.g. septic tanks [30]). Aerobic bacteria oxidize compounds by using oxygen as an
electron acceptor, whereas anaerobic bacteria use other terminal electron
acceptors such as nitrate, Fe(III), Mn(IV), SO42-, and CO2, thus reducing them
under anoxic conditions [31]. Anaerobic processes are slower than aerobic
processes, since these reactants supply less energy than oxygen. Although
oxidizing conditions have shown improved degradation of organic contaminants
in OSSFs [29,30], reducing conditions can, for instance, favor the degradation of
halogenated substances through reductive dehalogenation [32]. Reductive
dehalogenation belongs to the group of co-metabolism degradation processes [33],
which are defined as being when energy and carbon that are gained from the
compound (secondary substrate) are not used for microbial growth and the
degradation happens at the expense of growth-supporting substrates (primary
substrates) [33].
2.1.3. Removal efficiencies in OSSFs
Schaider et al. (2017) summarized studies of OSSFs and calculated removal
efficiencies where they were not calculated in the original studies (Table 1). In this
7
review and this thesis, removal efficiencies were calculated with the following
equation:
𝑅𝑒𝑚𝑜𝑣𝑎𝑙 𝑒𝑓𝑓𝑖𝑐𝑖𝑒𝑛𝑐𝑦 = 100% − 𝑐𝑜𝑛𝑐𝑒𝑛𝑡𝑟𝑎𝑡𝑖𝑜𝑛 𝑖𝑛 𝑒𝑓𝑓𝑙𝑢𝑒𝑛𝑡
𝑐𝑜𝑛𝑐𝑒𝑛𝑡𝑟𝑎𝑡𝑖𝑜𝑛 𝑖𝑛 𝑖𝑛𝑓𝑙𝑢𝑒𝑛𝑡× 100%
Large variations were found for each compound in different facilities. For instance,
0 to 99.8% of triclosan and 27 to 76% bisphenol A were removed. Other studies
reported that the removal efficiencies were similar in OSSFs (ATSs, SBs) and STPs
(activated sludge treatment and sedimentation tanks) [5,27]. However, anaerobic
septic tanks [5,27] often performed worse than large-scale STPs. This is likely
because aerobic biodegradation and sorption to organic matter are important
removal processes in both STPs and OSSFs, whereas anaerobic septic tanks mainly
remove by sorption and anaerobic biodegradation (Figure 2)
Table 1. Chemical categories and examples of organic wastewater contaminants found in OSSF effluent
and their removal efficiencies in OSSFs summarized by [10].
Category Compound example Removal in OSSFs [10]
Fragrances Citronellol [34], tonalide [35] 29 to 99.5% (galaxolide)
Biocides Triclosan [21,34] 0 to 99.8%
UV stabilizers 2-Phenyl-5-benzimidazolesulfonic acid
[35], oxybenzone [36,37]
41 to 99% (oxybenzone)
Pharmaceuticals Ibuprofen [36,38], carbamazepine
[36,37,39,40]
0 to 85% (carbamazepine)
Hormones Estrogens [35,41] 0 to 94%
(17β-Estradiol)
Sweeteners Acesulfame [42,43] -
Polymer impurities Bisphenol A [36,38] 27 to 76%
Alkyl phenols and
ethoxylates
Nonyl-phenols and ethoxylates [21,41,44] 0 to 70% (octyl phenol)
Removal efficiencies vary considerably for the same contaminants between
different facilities and studies. These variations can be explained by several factors:
o Influent variability: The population discharging to each OSSF is very small
and thus represents only a small fraction of the total population [10]. This
also leads to a high daily and weekly variability in the inflow [45].
o Facility technology variability: Removal processes are dependent on
treatment technology e.g. soil infiltration versus aerobic treatment
systems.
8
o Site-specific variability: Each site varies in HRT, maintenance state and soil
composition. A higher HRT leads to more time for sorption and
biodegradation, limited aeration results in anaerobic conditions, and soil
infiltration depends on the site-specific soil characteristics [46], bedrock
and the groundwater table [12].
o Sampling representability: Grab sampling is often carried out without
adjustment to HRTs. Thus, the influent heterogeneity is problematic due
to a lack of mixing and lack of correspondence between influent and
effluent sample [10]. Some treatment technologies have sampling-related
issues e.g. the very common soil infiltration system rarely has a defined
outlet where effluent sampling could take place.
o Temporal and regional variability: Precipitation can affect removal
efficiencies due to dilution, and temperature differences can affect
removal processes [25]. Water consumption, product use and national
regulations differ regionally [10].
Another phenomenon often observed in studies of OSSFs and STPs are negative
removal efficiencies and negative mass balances due to higher concentrations in
effluent than in influent [47]. Possible explanations are desorption from solids [48],
lack of adjustment for HRTs and flows [49], release from broken-down feces [50],
and deconjugation (retransformation of the metabolite to its parent
compound) [19]. Additionally, an analytical bias could translate to negative
removal efficiencies due to a higher matrix content in the influent than effluent
impacting the extraction efficiency and instrumental sensitivity.
2.1.4. Biochar fortification to improve removal in OSSFs
To reduce the discharge of organic contaminants into the environment, simple and
cost-efficient upgrades for existing OSSFs need to be found. One cost-effective
option is the additional filtration of OSSF effluent though a low-cost sorbent or
adding the sorbent to the soil bed during construction of new SBs. Biochar is such
a cost-effective sorbent and is preferably produced from waste biomass [51].
Biochar is a carbon-rich material that is produced by thermochemical treatment of
biomass using, for instance, pyrolysis, torrefaction or hydrothermal carbonization
[52]. Torrefaction and pyrolysis typically convert biomass to biochar in the absence
of oxygen at 200 to 300˚C [53] and at approximately 350 to 800˚C [54], respectively,
while in hydrothermal carbonization, water-surrounded biomass is converted to
hydrochar under pressures up to 20 bar and at temperatures between 180 and
9
250˚C [54,55]. These approximate temperature-based definitions differ between
studies. Feedstock type, heating rates, and temperature conditions in these
processes determine the biochar characteristics and product fractions [52,55,56].
High carbonization temperatures (400 – 700˚C) have been shown to produce
biochars suitable for the removal of more hydrophobic contaminants due to an
increase in surface area, microporosity and hydrophobicity, while low
temperatures (200 – 300˚C) create biochars that remove polar organic
contaminants better due to more oxygen-containing functional groups and
electrostatic attraction [52,57–59].
Activated carbon is char that has been additionally processed to increase
microporosity and surface area [52]. Granulated activated carbon is widely used in
drinking water treatment to remove organic contaminants [60] and has also been
shown to remove personal care products and pharmaceuticals from wastewater
[61–63]. In addition, biochars have previously been evaluated for the removal of
specific organic water contaminants, such as pesticides [64,65], pharmaceuticals
[66–68], and color and dyes [59,69].
2.2. Environmental fate
2.2.1. Processes and pathways
Environmental fate describes the environmental “behavior” of a compound when
it is released into the environment and is determined by interconnected physical,
chemical and biological processes [70]. OSSF effluent is either discharged directly
to rivers and lakes or it is infiltrated into the soil through the vadose zone, from
where it can reach the subjacent groundwater zone or seep into surface water [12]
(Figure 2). Thus, OSSF-discharged organic contaminants have not only been found
in coastal [71,72] and surface water [73,74], but also in ground and drinking water
wells [29,30,40,75–81]. Amongst other organic contaminants, the insecticide
diethyltoluolamide (DEET), pharmaceuticals such as ibuprofen and
sulfamethoxazole, and the organophosphate tris(2-butoxyethyl) phosphate have
all been found in ground or drinking water wells.
Organic contaminants and their transformation products that enter the aquatic
environment from OSSF effluent can undergo intramedia and intermedia
transportation, dilution, volatilization, and sorption to suspended solids,
sediments and organic matter. Additionally, organic contaminants can be
transformed through biodegradation, direct and indirect photolysis, hydrolysis,
10
oxidation, and reduction [82,83]. To facilitate direct photolysis, the natural
sunlight’s radiation spectrum has to overlap with the contaminant’s absorption
spectrum. In this way, high-energy photons are absorbed, which initiate the
molecule’s transformation [84]. Indirect photolysis is the reaction with
photogenerated species such as photoexcited natural organic matter or reactive
oxygen species [85]. These fate processes are influenced by the contaminant’s
intrinsic properties such as conjugated π-systems, functional groups,
physicochemical properties [84], and by factors such as water flow, pH,
precipitation, temperature, irradiation, and biomass levels [83].
2.2.2. Persistence
Persistence describes the degradability of a compound and is often quantified in a
regulatory context, based on transformation half-lives in different medias that are
measured in laboratory tests such as biodegradability tests in water [86–88].
Current biodegradability tests from the Organization for Economic Co-operation
and Development (OECD) guidelines include three groups: ready biodegradability
tests (e.g. OECD 301), inherent biodegradability tests (e.g. OECD 302), and
simulation tests (e.g. OECD 303) [89]. Annex XIII of the Registration, Evaluation,
Authorization and restriction of Chemicals (REACH) regulation defines a
substance as persistent if its half-life exceeds 60 days, 40 days, 180 days or 120 days
in marine water, fresh water, marine sediment, or fresh water sediment and soil,
respectively. The regulation defines a substance as very persistent if the half-life in
water or sediment and soil is > 60 days, or > 180 days, respectively [90].
The overall persistence of a compound depends on the persistence in the media
into which most of the compound partitions [86,91]. Therefore, it is important to
distinguish between half-lives in an environmental media, environmental
compartment and overall half-lives in the environment [91,92]. As discussed in
earlier chapters, environmental fate, as a result also persistence, is impacted by
environmental conditions such as temperature, pH, microbial activity and
irradiation [86], which control, for instance, photolysis, volatilization and
biodegradation (sections 2.1.2. and 2.2.1). Consequently, large spatial and temporal
variability has been found when investigating the persistence of contaminants in
the field [93,94].
Field studies to determine persistence are rare and generally either utilize mass
fluxes [95] or benchmarking [93,96–98]. In this context, benchmarking is the
normalization of concentrations or rather degradation rates to that of a persistent
11
tracer [88]. If half-lives cannot be calculated, persistence is, for instance, assessed
based on attenuation between study sites [94,99].
2.2.3. Bioavailability
Bioavailability depends on interactions between contaminants, matrix and
organism and is very specific for their individual physical, chemical and biological
properties [100,101]. There have been many definitions of bioavailability in
environmental science; however, one currently often used definition distinguishes
between bioavailability and bioaccessibility. Semple et al. (2004) [102] defined the
bioavailable compound as “that which is freely available to cross an organism’s
cellular membrane from the medium the organism inhabits at a given time” and
the bioaccessible compound as what is additionally potentially available over time
(e.g. through desorption) [102]. In Reichenberg and Mayer (2006)’s [103]
bioavailability definition, they distinguished chemical activity from
bioaccessibility and defined chemical activity as the potential of a contaminant for
physicochemical processes (e.g. diffusion, sorption, partitioning), which does not
include the biological and ecological aspects [103].
Chemical methods to measure bioavailability are, for instance, equilibrium
sampling devices that, by this definition, measure the chemical activity when in
equilibrium with the freely dissolved and reversibly bound fractions [103]. For
instance, the polar organic chemical integrative sampler (POCIS) was
developed [104] to mimic respiratory exposure of aquatic organisms (biomimetric
method [100]). Thus, it can assess the cumulative aqueous exposure to bioavailable
compounds [105]. Another semi-permeable membrane sampling device that
retains the dissolved fraction are solid-phase micro extraction (SPME) fibers that
are coated with a thin polymer used for sampling and subsequent introduction into
a gas chromatograph coupled to a mass spectrometer (GC-MS) [106]. Apart from
these chemical methods that identify and quantify contaminants, there are
biological methods to measure bioavailability that measure the effect of the
bioavailable concentration on an organism such as in earthworm reproduction
tests [107].
2.2.4. Bioaccumulation
Bioaccumulation leads to higher contaminant concentrations in an organism than
derived from its direct environment [31] and happens by all routes of exposure. For
aquatic organisms, these routes are: bioconcentration (direct uptake from water
via respiratory and dermal surfaces), biomagnification (intake of contaminated
food), and ingestion of suspended particles [108,109]. Even without acute or
12
chronic effects, bioaccumulation can impact ecosystems by multi-generation
effects or effects on higher trophic levels [108].
The bioconcentration factor (BCF) can be calculated as the ratio of concentration
in the organism and the freely dissolved concentration in the water at
steady-state [109]. The bioaccumulation factor, which is usually determined from
field data, can be calculated as the ratio of concentration in the organism and the
total concentration in water [109]. Trophic magnification factors (TMFs), that are
calculated from the slope of the function between concentration and trophic level
of the organism, were proposed for assessing bioaccumulation [110]. However, the
European Union’s REACH and the United States Environmental Protection Agency
(U.S. EPA) both define a compound as bioaccumulative if the BCF in aquatic
species is ≥ 2 000 (REACH) and ≥ 1 000 (U.S. EPA), and very bioaccumulative if the
BCF is ≥ 5 000 [87,111].
2.2.5. Mobility
Mobility has been discussed in the past in the context of soil and was defined by
REACH as: “Mobility in soil is the potential of the substance (…), if released to the
environment, to move under natural forces to the groundwater or to a distance
from the site of release” [112]. Although mobility in water is currently the subject
of intense discussion and persistent, mobile and toxic (PMT) criteria have been
proposed to be included for the identification of substances of very high concern
(SVHC), a definition for mobility in water is still lacking. Hence, “the tendency of
a chemical to move in the environment”[113] describes mobility more generally and
is used in this thesis. Additionally, quantitative measures for mobility are lacking
[113], thus several options, usually used for describing water solubility and sorption
tendency, have been proposed [114]. For instance, the water solubility
(SW > 0.15 mg L−1) together with the organic carbon normalized adsorption
coefficient (log KOC < 4.5) for non-ionic compounds [113] or the pH-adjusted
octanol-water partition coefficients (DOW < -1) for ionizable compounds and
log KOW for neutral compounds [114] have all been suggested. Rogers et al. (1996)
[32] suggested a lower sorption potential for log KOW < 4 [32], which is therefore
used in this thesis. These measures, however, do not account for ionic interactions
and, since most natural surfaces are negatively charged, compounds, especially
cationic ones, could adsorb and might not be mobile [114]. Evidently, more research
on the development of better mobility quantifiers is necessary.
13
2.3. Environmental risk assessment
To identify chemicals of environmental concern, assessments need to cover various
aspects of risks to the ecosystem. For instance, chemicals can be very persistent
and very bioaccumulative (vPvB, sections 2.2.2 and 2.2.4), persistent,
bioaccumulative and toxic (PBT), or/ and endocrine disrupting chemicals (EDCs).
Effects on the endocrine system are subtle and can occur at low exposure levels
that may lead to long-term adverse effects, which are not captured with traditional
toxicity tests [31]. vPvB chemicals may as well reach critical levels in biota over time
that could induce adverse effects [31]. However, these highly lipophilic compounds
would also not be covered by standard ecotoxicity tests. Typically, environmental
risk assessments (ERAs) are completed where data on the predicted environmental
concentration (PEC) are compared with a predicted no effect concentration
(PNEC). Here, the measured environmental concentration (MEC) was used instead
of PEC. The effects are assessed by calculating predicted no effect concentrations
(PNECs), defined as “a concentration below which an unacceptable effect will most
likely not occur” [115] and relies on the assumption that the sensitivity of the most
sensitive species can be extrapolated to the entire ecosystem [115]. The European
commission recommends obtaining the PNEC by dividing the lowest short-term
(acute) or long-term (chronic) ecotoxicological endpoint from the most sensitive
species by an assessment factor to accommodate for uncertainties [115].
Uncertainties include laboratory tests to field extrapolation, intra and inter
laboratory variation, intra and inter species variation, short-term to long-term
extrapolation, tests on limited species and mixture effects in the field [115]. For
instance, if only short-term data are available, an assessment factor of 1 000 is
advised and if long-term data for three species representing three trophic levels are
available, the assessment factor can be 10 [116]. Finally, risks are characterized by
calculating the PEC/PNEC or MEC/PNEC ratios (risk quotients) for each
contaminant.
2.4. Contaminant analysis using mass spectrometry
2.4.1. Targeted and untargeted analysis strategies
Instrumental analysis of emerging contaminants can be carried out using targeted
and untargeted data acquisition (Figure 3). Targeted data acquisition aims to
identify and quantify target contaminants using mass spectrometers, such as the
triple quadrupole (QqQ), and carrying out tandem mass spectrometry (MS/MS)
experiments [117]. Specific precursor ions are selected, fragmented and selected
product ions detected, the so-called selected reaction monitoring (SRM)
transitions. If SRM is applied to multiple product ions from one or more
14
precursors, it is called multiple reaction monitoring (MRM). Experiments carried
out using targeted data acquisition techniques cannot be processed in the same
way as untargeted data acquisition [117]. However, experiments carried out using
untargeted data acquisition with MS/MS can be used for target analysis.
Mass spectrometers operating in full-spectrum mode are used in untargeted data
acquisition and allow retrospective identification without selection of analytes in
advance [118]. Data processing workflows differ between data acquired by electron
ionization (EI) in GC-MS and for soft ionization techniques in liquid
chromatography mass spectrometry (LC-MS) and GC-MS (Figure 3). Whereas
workflows for untargeted data acquired by high resolution LC-MS (LC-HRMS) are
commonly separated into target analysis, suspect and non-target screening [119],
workflows in GC-MS are less well defined (Figure 3).
Figure 3. Differences between untargeted and targeted data acquisition and data processing.
2.4.2. Untargeted analysis workflow
A simplified overview of the workflow is shown in Figure 4. Samples are prepared
for untargeted analysis by the extraction of compounds from their original
matrices and by removing potential interferences. It is important to have a generic
method that captures a broad range of analytes [120,121], but still removes matrix
interference sufficiently. The next step is to carry out instrumental analysis.
Instruments that provide high peak capacity, high resolution and accurate mass
information [122] are preferred. To date, most research facilities use Quadrupole
time-of-flight mass spectrometers (QToFs) or ToFs in GC-MS [123–129] and ToFs,
QToFs or Orbitraps in LC-MS [119,120,129–140]. The use of the soft ionization
techniques, for instance atmospheric pressure chemical ionization (APCI) or
Untargeted data acquisition
Suspect
screening
Target
analysis
Targeted
data
acquisition
Target
analysis
GC-EI LC-HRMS
Spectral
library
search
Non-
target
screening
LC-MS and
GC-MS
In-silico
tools
GC-HRMS
Soft-EI/ CI
Unknown
analysis
Target
analysis
15
electrospray ionization (ESI) in LC-MS, enables the detection of molecular ions.
Using a QToF or an Orbitrap, MS/MS spectra are acquired by either data-
independent acquisition (alternating full-spectrum acquisitions at low and high
collision energies) or data-dependent acquisition (switching between
full-spectrum acquisitions and products ion scan in scan cycles based on a product
ion list or ion abundance) [122].
LC-HRMS
Target analysis is carried out using reference standards and produce quantitative
data. Targeted data processing utilizes reference standards or in-house libraries,
built up from reference standards, to match data based on retention time, HRMS
and possibly MS/MS information [119,141]. It can be quantitative or semi-
quantitative [129]. In suspect screening, prior information indicates that certain
molecules could be present in a sample and a suspect list is
created [129]. The molecular formulae of these suspects are used
to calculate exact masses, isotope patterns, and expected
adducts, which are used for identification [122,129]. If MS/MS
spectra are available in vendor libraries, the identification
confidence is increased by matching recorded MS/MS spectra
against them. Suspect screening is initially carried out without
reference standards [119], but may produce semi-quantitative
data if a wide-range of internal and external standards has been
used. Non-target screening tries to identify the remaining
unknown components without prior information available [119].
A component is a group of accurate masses associated with one
compound [129]. The data evaluation includes processing
(feature extraction), filtering (e.g. detection frequency, blank
subtraction), identification including elemental composition
determination from the accurate mass of the molecular ion and
isotope abundance analysis, structural elucidation by search for
the molecular formula in MS libraries and substance databases,
library spectrum matching and ranking of the structure
candidates [122,133,142].
Schymanski et al. (2014) [141] developed identification confidence levels for target,
suspect and non-target screening (Figure 5). The levels run from highest
confidence level 1 to the lowest level 5. Level 5 components only have an accurate
mass, level 4 components have an unequivocal molecular formula assigned using
isotope pattern information, level 3 components have possible structure
candidates utilizing MS/MS fragmentation and/or other experimental data, level 2
Sampling
Sample
preparation
Instrumental
analysis
Data processing
Identification
Prioritization
Filtering
Confirmation
Figure 4. Simplified
screening workflow.
16
components have a probable structure assigned by additional library MS/MS
spectrum match (2a) or in silico fragmentation match (2b), and finally, level 1
components have a confirmed structure by reference standard matching based on
mass, isotope abundance and spacing, fragmentation, and retention time [141].
Target screening starts at level 1, suspect screening at level 3 and non-target
screening at level 5 [129].
Figure 5. Schymanski identification confidence levels for target analysis, suspect screening and non-target
screening in LC-HRMS. Adapted from [129,141].
GC-MS
In GC-MS based screening, the fragmentation reproducibility using EI is taken
advantage of and recorded spectra can be compared to mass spectral libraries such
as the NIST database (> 200 000 substances) [143] (Figure 3). Features with high
spectral similarity and probability count as tentatively identified. In the scope of
this thesis, screening based on GC-EI spectral library searches is referred to as non-
target screening. Since the molecular ion is often lost through hard ionization, the
identification of unknowns remains more complicated and in silico fragmentation
tools such as MetFrag [144] can be used for identification by assuming the largest
fragment is the molecular ion. Alternatively, data acquired using low energy “soft”
EI or chemical ionization (CI) can be used for unknown analysis (Figure 3). First,
the molecular formula is determined from the molecular ion and subsequently
structural candidates are found with the help of databases such as PubChem
(www.ncbi.nlm.nih.gov/pubmed) or ChemSpider (www.chemspider.com/). The
data processing workflow resembles the workflow for LC-HRMS.
The screening for thousands of chemicals makes it necessary to develop
prioritization strategies to focus on the most relevant ones. Especially in
Start Level 1 Confirmed structure
MS, MS/MS, Rt, reference standard
Level 2 Probable structure
MS, MS/MS, (2a) Library MS/MS or (2b) diagnostic evidence
Start Level 3 Tentative candidates
MS, MS/MS
Level 4 Molecular formula
MS, isotopic pattern
Start Level 5 Mass of interest
MS
Suspect
screening
Non-target
screening
Target
analysis
Identification
confidence Data requirements
17
untargeted analysis, the identification of many features without environmental
relevance information makes it sometimes necessary to filter out the most relevant
ones. Different strategies have been used: environmental risk assessment [121],
prioritization scoring based on abundance, detection frequency, exposure and
bioactivity [145] or exposure modeling [146]. The final and optional step would be
to confirm the tentatively identified compounds with reference standards (Figure
4).
2.4.3. Challenges
Untargeted analysis ideally detects all analytes accessible by the method used [133];
hence, unknown and unexpected compounds such as transformation products can
be found [133]. However, untargeted analysis presents challenges. The mass spectra
produced are not necessarily unique as, for example, isomers can result in the same
fragmentation spectrum [147]. In addition, mass abundances can vary when
measured with different instruments which affects the spectral similarity match in
e.g. NIST database searches [147]. Untargeted analysis is also limited by the
exclusion of compounds during sample preparation, chromatography and
ionization [139]. Moreover, special software or programming skills are often
needed to deal with the extensive data mining process [148] and the identification
of unknowns. In general, the handling of the large volume of data generated is
time-consuming [128,148]. Finally, it can be a disadvantage that compounds can
only be semi-quantified and not quantified [122].
2.4.4. Future considerations
Untargeted analysis by LC-HR-MS has already adopted well-accepted
nomenclature for the identification of unknowns [129], whereas nomenclature for
untargeted analysis by GC-MS remains to be defined. For instance, it is common
to describe different types of discovery workflows in GC-MS as untargeted analysis,
non-target analysis, non-target screening or screening. Additionally, LC-HRMS has
well-defined confidence levels [141] but a similar definition of confidence levels for
GC-based techniques is missing. Until consensus has been reached regarding the
proper concepts and terms in GC-MS, similar confidence levels to those in
LC-HRMS could be used (Figure 6). For instance, GC-EI most often yields highly
reproducible fragmentation patterns, which can be associated with compounds
present in the NIST library and give a candidate that is similar to a level 2a
candidate in LC-HRMS (“probable structure” through an unambiguous MS/MS
library match [141]). If the fragmentation does not match well with candidates in
experimental spectral libraries, but a computer-aided “in silico” spectra
interpretation results in a “likely candidate”, it gives an identification confidence
18
similar to level 2b in LC-HRMS. The corresponding workflow could be called
“spectral library search”. If the spectral library search or interpretation produce
several candidates of sufficient quality it gives a level 3 identification confidence,
“tentative candidates” using the LC-HRMS nomenclature [141]). The workflow to
raise a level 3 tentative candidate to a level 2 likely candidate could be called
“unknown analysis”. If the mass spectrum only has low molecular weight
fragments, and larger fragments and molecular ions are missing, the EI spectra
could be complemented with, for instance, low energy “soft” EI or CI spectra. In
addition, consensus structure elucidation could be used to rank the candidates and
find the most likely candidate [149].
Figure 6. Proposed nomenclature and identification confidence levels for untargeted analysis in GC-MS.
Start Level 1 Confirmed structure
Reference standard spectrum + retention time
Start
Level 2 Probable structure a) Library spectrum b) Other evidence
EI-fragmentation spectrum + unambiguous match (2a) library spectrum or (2b) in silico spectrum
Start
Level 3 Tentative candidate(s)
EI-fragmentation spectrum + several library or in silico tool matches
Untargeted analysis
Spectral
library
search
Unknown
analysis
Target
analysisIdentification
confidence Data requirements
19
3. Material and Methods
3.1. Sampling
The work for this thesis involved four sampling campaigns from 2013 to 2016.
Sampling campaigns I and II were carried out for Paper I. The first sampling
campaign aimed to screen a variety of OSSF types in Sweden including SBs, ATSs
and GWs to increase the knowledge of contaminants discharged from OSSFs. The
second sampling campaign, with improved sampling techniques compared to
campaign I, aimed specifically to study SBs and their treatment efficiency for the
removal of contaminants and compare them to STPs. For Papers II and III, a third
sampling campaign was carried out in the catchment of the River Fyris, close to
Uppsala, Sweden. The catchment is affected by a high proportion of the
households (35%) that are connected to OSSFs [150]. Thus, the work for Paper III
aimed to estimate the environmental load per capita from levels detected in the
river water. The work for Paper II concentrated on several environmental matrices
and followed the fate of the contaminants discharged from the large-scale STP
Kungsängsverket downriver in the catchment. To evaluate the removal of
contaminants by biochar in a long-term field setting for Paper IV, samples were
taken from an OSSF field study site that was upgraded with a char-fortified soil
bed.
3.1.1. On-site and municipal sewage treatment facilities
SBs, ATSs and GWs were selected for sampling in Paper I stage I (Table 2) to cover
some of the most widely-used OSSF types in Sweden (section 2.1.1, Figure 1). Since
infiltration systems do not have a defined outlet, these OSSF types were not
covered. Stand-alone septic tanks were also not included as sampling before a
septic tank creates heterogeneity issues.
The campaign was carried out in October and November 2013; at each facility,
influent and effluent was grab sampled. Influent samples were taken after the last
chamber of the septic tank to improve homogeneity. Additionally, samples from
conventional medium-scale STPs (MSTPs) and one large-scale STP were taken for
comparison. Influent/ effluent samples from similar OSSF types were pooled to
avoid poor representativity due to small facility sizes (1 to 40 users connected).
20
Table 2. Pooled samples (SB, ATS, GW), individual samples MSTP1, MSTP2, MSTP3, and STP Paper I
stage I.
Type Sample
name
Total persons connected/
Population equivalents
Number of
facilities
Soil beds SB 60 to 100/- 6
Aerobic treatment systems ATS ~50/- 4
Source separation systems GW ~20/- 3
Medium-scale sewage treatment plant MSTP1 -/125 1
Medium-scale sewage treatment plant MSTP2 -/2 500 1
Medium-scale sewage treatment plant MSTP3 -/1 000 1
Large-scale sewage treatment plant STP -/440 000 1
A second sampling campaign, from September 2015 to November 2015, was carried
out for Paper I stage II. Samples were taken at five SBs as representative of OSSFs
and five STPs (Table 3). SBs represent 16% of facilities in Sweden and function like
soil infiltration systems (25%) (section 2.1.1, Figure 1). To improve OSSF
representativity and removal efficiency reliability compared to stage I, SB influents
and effluents were sampled in a time-integrated manner by collecting an aliquot
every hour for one day. Influent samples were taken from the last stage of the septic
tank, and effluent samples were taken after the SB. Additionally, larger SBs that
were shared by multiple households were selected. The large and medium-scale
STPs were sampled flow-proportionally over one week and one day, respectively.
Table 3. Soil bed and sewage treatment plant samples for removal efficiency evaluation from Paper I
stage II.
Type Name Persons/Population
equivalents
Treatment steps
Soil bed SB1 30 to 40/- S, SB
SB2 60/- S, SB
SB3 14/- S, SB
SB4 80 to 90/- S, SB
SB5 39/- S, SB
Sewage
treatment
plant
STP1 -/ 1200 M, C, S, A, S, (C), (D)
STP2 -/ 110 000 1) M, C, S, A, S, C, (D) 2) M, C, S, BB, S, C, (D)
STP3 -/100 000 M, C, S, A, S, (D)
STP4 -/150 000 M, C, S, A, S, C, (D)
STP5 -/780 000 M, C, S, A, S, C, SF
S = sedimentation, M = mechanical treatment, C = chemical treatment, A = active sludge, BB = bio
bed, D = disinfection, SF = polishing sand filter, optional treatment steps are in brackets, 1) = line 1,
2) = line 2
21
3.1.2. The catchment of the River Fyris
For Paper II and Paper III, the catchment of the River Fyris in Uppsala
municipality (2 200 km2, ~210 000 inhabitants) in Sweden was studied (Figure 7).
Figure 7. Sampling sites from Paper II (A, B, C, S) shown as blue stars and sampling sites from Paper III
(OSSF A, OSSF B, OSSF C, STP A, STP B) shown as red stars. Site A equals STP B and Site S equals OSSF
C. Small brown triangles indicate medium-scale municipal sewage treatment plants (< 6500 persons,
< 4 000 population equivalences) that were in use during 2014/2015. The large triangle indicates
Kungsängsverket sewage treatment plant serving 171 000 people (160 000 population equivalences). The
shaded areas area shows the catchment of River Fyris. Maps were retrieved from
https://vattenwebb.smhi.se/hydronu/, 2017-01-16).
22
Uppsala municipality has a high proportion of households (35%) connected to
OSSFs [150]. The effluent from OSSFs and municipal STPs around Uppsala drains
into the River Fyris, which has a total catchment area of around 2 000 km2 and is
80 km long. The large-scale STP Kungsängsverket also drains into the River Fyris,
which is one of the major contributors of contaminants to Lake Ekoln, a sub-basin
of Lake Mälaren that supplies drinking water to around 2 million inhabitants in
and around Stockholm, Sweden.
Three sites primarily received OSSF effluent, whilst two sites primarily received
STP effluent. One further site was located before the River Fyris flows into Lake
Ekoln and one site was located at Lake Ekoln. The studies for Paper II and
Paper III shared two sampling sites (Site A = STP B and Site S = OSSF C), whereas
OSSF A, OSSF B, and STP A were unique to Paper III and Site B and Site C were
unique to Paper II. However, Paper III only describes grab water samples,
whereas Paper II deals with sediment, fish and effluent samples in addition to grab
samples. Grab and POCIS sampling were carried out over four months covering
four seasons, in December 2014 (winter), March 2015 (spring), June 2015 (summer)
and September 2015 (autumn). POCIS was deployed for two weeks at ~1 m under
the water’s surface. Grab water samples were collected twice in glass bottles, when
deploying and when collecting the POCIS samples, and were pooled before
extraction. Additionally, sediment core samples were taken with the top 4 cm
layers being sliced off (September 2015), a six-day composite effluent sample was
collected from Kungsängsverket STP (November 2015) and perch (Perca fluviatilis)
were caught in the River Fyris after Kungsängsverket STP (June 2014).
3.1.3. Experimental on-site sewage treatment facility site
Paper IV’s study site is described in detail in [151]. Briefly, the wastewater
originated from a total of four households and was distributed on three types of
filter beds (Table 4). The first sorbent bed was sand, the second bed was sand and
hardwood-derived biochar, and the third bed was gas concrete (Sorbulite®) with
biochar.
Table 4. Filter materials and their physical parameters. Adapted from [151].
Parameter Concrete Sand Char
Particle size (mm) 10 – 80 0.3 – 25 0.5 – 20
Porosity (%) (unsaturated) 60 45 50
Char-Concrete Sand Char-sand
Hydraulic retention time (hours) 2 – 2.5 2.1 – 3.0 2.1 – 3.0
23
Outlets enabled effluent sampling and beds were covered to avoid any leakage
caused by precipitation. The wastewater was distributed on the sorbent beds at
timed intervals. After a one-year run period, samples were taken from the influent
and from the three types of effluent (sand, char-sand, char-concrete).
3.2. Sample preparation
Samples were generally prepared with methods suitable for targeted and
untargeted analysis. Thus, preparation was focused on the extraction of a variety
of organic contaminants with different molecular properties and a limited clean-
up to reduce the loss of contaminants during an extensive sample preparation
workflow. The following sections explain concepts and give short summaries of the
methods used, while details including materials are described in their publications.
3.2.1. Water samples
Water samples were filtered before the separate extraction of suspended solids and
water phase in order to assure optimal extraction from both fractions. In Paper I
stage I, filtered water samples were extracted by liquid-liquid extraction with
dichloromethane. Thus, analytes are extracted based on partitioning between the
water and the dichloromethane phase in which the analytes are diverse soluble.
Dichloromethane was selected because it covers extractable compounds in an
extended polarity range. Briefly, water samples were added to a separatory funnel
and extracted three times with dichloromethane. Next, the extracts were combined
and evaporated. The suspended solids on filters were extracted using Soxhlet
extraction with toluene. Soxhlet extraction is a continuous extraction using a
heated solvent in a round bottom flask, a Soxhlet extractor and a reflux condenser.
Both extracts were combined for analysis.
A solid phase extraction (SPE) method for filtered surface water was developed for
Paper II and Paper III and extended to wastewater in Paper I stage II and applied
again to wastewater in Paper IV. The separation in SPE happens due to
physicochemical properties such as polarity or ionic charge. SPE cartridges are
made of different stationary phases for the separation desired. Normal phase
retains polar analytes, reversed phase retains non-polar analytes, anion-exchange
retains anions and cation-exchange retains cations. Reverse-phase Oasis HLB
cartridges were chosen, since the polymer sorbent is made from two monomers,
the hydrophilic n-vinylpyrrolidone and the lipophilic divinylbenzene, and thus has
a wide selectivity and can sorb both hydrophilic and hydrophobic compounds.
Briefly, the cartridges were conditioned with dichloromethane, acetonitrile and
water, and the filtered water samples were loaded. The analytes interacted with the
24
stationary phase and were retrained on the column. Next, the cartridges were
washed, dried under vacuum and the analytes eluted with acetonitrile and
dichloromethane. Analytes retained on Oasis HLB in POCIS samples were eluted
using the same method (Paper II).
In parallel, suspended solids on filters were extracted using ultrasonic extraction
with acetonitrile and dichloromethane (Paper I stage II, Paper II, Paper III, and
Paper IV). The filters were stepwise sonicated with acetonitrile and
dichloromethane and the analytes were dissolved by ultrasound-induced
cavitation. Cavitation is generated when sound waves travel through matter and
cause compression and expansion cycles [152]. The latter produces a negative
pressure so that bubbles build up, expand and collapse. This process facilitates the
leaching of compounds from particles [152]. Water phase extracts and suspended
solid extracts were combined and filtered through sodium sulfate to remove any
water residues before analysis.
3.2.2. Sediment and fish samples
In Paper II, sediment samples were extracted using sequential ultrasonic
extraction (USE) and the fish samples were blended with sodium sulfate and
extracted using column extraction first with acetone/n-hexane (71:29, v:v) and then
with n-hexane/diethyl ether (90:10, v:v) [153]. Since lipids were co-extracted with
this method, and since USE is an exhaustive extraction method and sediment is
rich in matrix, the co-extracted matrix had to be removed prior to analysis. Hence,
to remove large molecules without losing any analytes, gel permeation
chromatography (size exclusion chromatography, GPC) was used after USE or
column extraction. Compounds in GPC are separated according to their
hydrodynamic diameter. Thus, a gel works as a molecular sieve. Small molecules
penetrate into the pores of the gel and are retained on the column, whereas large
molecules pass through and elute first [154].
3.3. Instrumental analysis
Samples for Papers I, II, III and IV were analyzed using GC-MS and samples for
Paper IV were additionally analyzed using LC-MS. The use of GC-MS enabled the
detection of semi-volatile, volatile and lipophilic compounds that are stable at high
temperatures [119], whereas thermolabile and more hydrophilic compounds were
detected using LC-MS [119]. While instrumental and operational details are
explained in the respective papers, the methods used are summarized and
explained in the following sections.
25
3.3.1. Two-dimensional gas chromatography time-of-flight mass
spectrometry
GC×GC-MS was used throughout this work since the coupling of two columns with
different separation mechanisms increases the separation space and thus allows
better separation of analytes of interest from interference in complex samples,
without requiring extensive sample preparation. Hence, GC×GC gives higher
resolving power, peak capacity and provides cleaner MS spectra. In complex
matrices such as wastewater, the probability of a correct assignment and (semi-)
quantification is therefore enhanced.
In Paper I stage I, a low-resolution two-dimensional gas chromatograph coupled
to a time-of-flight mass spectrometer (GC×GC-ToF-MS) was used, with a
(midpolar) 50% phenyl polysilphenylene-siloxane phase in the primary column
and a (nonpolar) 100% dimethylpolysiloxane phase in the secondary column. The
instrument was replaced with a high-resolution GC×GC-ToF-MS for Paper I stage
II, as well as Papers II, III and IV. For this setup, the primary column was coated
with a (nonpolar) 5% phenyl phase and the secondary column was coated with a
(midpolar) 50% phenyl phase. Both instruments were made by Leco (St. Joseph,
MI, USA). Additionally, for improved sensitivity of some target analytes, a
magnetic sector field GC-HRMS (GC-sectorMS) from Waters (Milford, MA, USA)
was used for the targeted analysis described in Papers II and III. Helium was used
as carrier phase in all three instruments. Since GC×GC-ToF-MS was used most
often in this thesis, it is explained in more detail in the following section.
Modulator
1 D column
2D
column
GC oven
Secondary
GC oven
MS
Injector
Figure 8. Schematic of a GC×GC system adapted from [183] to the GC×GC-ToF-MS by Leco used for this
thesis.
26
Primarily, separation in GC happens because of the different volatilities of the
analytes. More volatile compounds partition more into the gas phase and thus
move faster and elute earlier. As a secondary process, analytes are separated by
interaction with the stationary phase. More strongly interacting compounds are
retained longer and elute at higher temperatures. Interactions with the stationary
phase are especially important for the separation of compounds with similar
volatility.
Figure 9. Example of a two-dimensional chromatogram (A) and a one-dimensional chromatogram
showing the zoomed in slices of squalene (B).
2nd dimension
separation
1st dimension
separation
Rt 2
Rt 1
B
Slices
A
Abundance
27
Samples were injected by pulsed splitless injections and introduced onto the
column at a temperature below the boiling point of the solvent (i.e. toluene, boiling
point 111˚C) to ensure condensation of the solvent as a film on the column. The
solvent slowly evaporates, and analytes are focused in a narrow band. For the
comprehensive two-dimensional GC set-up (Figure 8), a modulator was set
between the primary column and the secondary column. The system used had a
cryogenic modulator, which repetitively trapped and released the analytes that
were eluted from the first-dimension column using cold nitrogen and hot air jets.
The modulation ensured that the separation achieved in the first dimension was
not lost in the second dimension. Instead of a classical peak as in one-dimensional
chromatography, each first-dimension peak was cut into several second-dimension
chromatogram slices (Figure 9B) and could be visualized a two-dimensional
chromatogram (Figure 9A).
Analytes are introduced from the capillary column through a transfer line into the
EI source. The EI source consists of a filament that releases electrons that are
accelerated towards an anode, thereby interacting with analytes. An electron is
expelled from the molecule and results in a radical cation (molecular ion) [155].
However, due to the high energy supplied, bonds in molecules can dissociate and
molecules fragment [155]. The most common acceleration potential is 70 eV, due
to the maximum number of ions that can be produced and since small changes in
potential do not significantly affect the fragmentation [155]. The ions are
accelerated towards a mass analyzer that measures the time an ion takes to travel
through a flight tube. The time is then translated into its mass-to-charge ratio
(m/z), so the faster an ion reaches the detector, the lower the m/z. To reach high
resolution, an electrostatic mirror (a reflectron) was used to deflect ions back
through the flight-tube towards the detector. Faster ions penetrate the reflectron
deeper and thus spend a longer time in the device than slower ions. Isobaric ions
(with the same m/z) are thus focused, which increases the resolving power. The
GC×GC-HRToF-MS by Leco used in this thesis had a “folded” flight path design
(multi-reflectron analyzer). The flight path is guided by gridless mirrors and
periodic ion lenses, which compress ion beam dispersion and result in high
resolution [156].
3.3.2. Liquid chromatography ion mobility quadrupole time-of-flight mass
spectrometry
The LC-MS system used for Paper IV consisted of a liquid chromatograph, with
electrospray ionization, coupled to an ion mobility spectrometer that was
connected to a quadrupole time-of-flight mass spectrometer
28
(LC-ESI-IM-QToF-MS). Using ion mobility (IM) had the advantage, that in
addition to the separation achieved by LC-MS, an additional dimension of
separation according to the shape-to-charge ratio was introduced. IM can resolve
isomers and increases the peak capacity [157]. Also, it allows use of the collision
cross section (CCS), which describes rotationally average size and shape [158]
(Figure 10), as a distinct qualifier for analytes in addition to m/z and retention time
[157]. The CCS is specific for each ion and thus increases the certainty of
identification [157].
Figure 10. Movement of a charged molecule through an electric field in an ion mobility drift cell filled with
Nitrogen (N2) gas resulting in a rotationally averaged collision cross section (CCS). Adapted from
Christine Gallampois (unpublished) and from presentation by Frederic Abts (Agilent Technologies,
2017-03-01).
The chromatographic separation was achieved with a reversed phase octadecyl
carbon chain bonded silica column (C18 column) as the stationary phase and
MilliQ water (0.1% formic acid)/methanol (0.1% formic acid) as the mobile phase.
The column was tempered at 40 °C throughout the run. The hydrophobic
molecules retained on the stationary phase were eluted by decreasing the
hydrophilicity of the mobile phase which was achieved by gradually increasing the
methanol content (from 10% to 95% methanol, total run time 60 min). Thus,
interactions decrease between aqueous mobile phase molecules that tend to
promote self-association over interactions with dissimilar solutes [156] (the
solvophobic effect [22] decreases) and hydrophobic compounds partition more
from the hydrophobic stationary phase into the mobile phase. Thus, a
hydrophilicity separation is obtained, and more hydrophilic molecules are retained
less and elute first.
Before introduction of the separated molecules into the ion mobility spectrometer,
the eluted analytes were ionized by ESI, an ionization method where, under
29
atmospheric pressure, an electric field is applied to liquid that passes through a
capillary [155]. Charges accumulate on the surface of the liquid located at the end
of the capillary, which breaks and forms highly charged droplets [155]. The droplets
are dispersed, and heated N2 evaporates solvent molecules from the charged
droplets [155]. With evaporation of the solvent, electrostatic repulsion between
charged species becomes larger than the surface tension, and lead to cycles of
explosions of droplets (Coulomb fission) from which gas-phase charged analyte
molecules are formed [159]. Charged molecules are introduced into the ion funnel
through the transfer line, where they are focused and the remaining gas is removed
[155] (Figure 11). Next, the ions move to the trapping funnel where they are
accumulated and introduced as packages into the drift tube. In the drift tube, the
gaseous ions move through a voltage gradient and energy gained is partially lost
through collisions with N2 molecules [158].
Figure 11. Ion mobility schematic in a LC-ESI-IM-QToF-MS from Agilent Technologies.
The drift time depends on an analyte’s mass, charge and CCS [158]. Since small
molecules have less resistance, they have a shorter drift time. From the drift tube,
the ions reach the rear funnel, where the ions are refocused and introduced into
the QToF mass analyzer. Using a quadrupole consisting of four parallel cylindrical
rods as a mass filter, ions may be filtered by the oscillating voltage applied between
one rod pair and the other pair (a fixed direct current and alternating radio
frequency are applied) [155] and only selected precursors pass through the
oscillating electric field. Between the first quadrupole and the time-of-flight mass
analyzer, a hexapole serves as a collision cell (only radio frequency is applied) [155].
The collision cell is filled with N2, so if voltage is applied, ions undergo collisions
with N2 and fragment. Filtering by m/z and fragmentation in the collision cell are
both optional and their usage depends on the goal of the experiment. Ions passing
through the quadrupole and hexapole are orthogonally accelerated towards a flight
tube, the ToF, which is explained in section 3.3.1.
For Paper IV, different experiments were carried out: i) IM plus full spectrum
acquisition without m/z filtering and fragmentation, ii) IM plus All Ions MS/MS
without m/z filtering but alternating no fragmentation and fragmentation in cycles
using 0 V in the first frame and ramped collision energies from 5 V to 90 V in the
second frame, iii) Targeted MS/MS by filtering for selected precursors that were
Ion
source
Front ion
funnel
Trapping
funnelDrift tube
Rear
funnel
Mass
analyzer
30
then fragmented at 10 V, 20 V and 40 V, and iv) Auto MS/MS by fragmenting the
most abundant precursors at 10 V, 20 V and 40 V.
3.4. Data processing
3.4.1. Two-dimensional gas chromatography time-of-flight mass
spectrometry
Targeted data processing involved searches of mass calibrated GC×GC-HRToF-MS
data files for characteristic ions of target analytes within given retention times and
mass windows. Untargeted data processing of GC×GC-HRToF-MS data included
mass calibration, peak picking, retention index calculation, and NIST library search
(Figure 12). Accurate mass spectra were converted to nominal mass spectra and
were aligned with the Java application GUINEU [160] (Paper II and Paper IV).
Extracted features were filtered based on detection frequency and blank levels and
preliminary identified (NIST) features were manually investigated for the accurate
masses of the expected molecular ion and abundant fragments. For semi-
quantification purposes, the remaining features were reprocessed for characteristic
ions within given mass and retention time windows. Since the analysis for Paper I
utilized a low-resolution GC×GC-ToF-MS, data processing excluded mass
calibration and conversion to nominal mass spectra, but otherwise resembled the
described workflow for Paper II and Paper IV.
3.4.2. Liquid chromatography ion mobility quadrupole time-of-flight mass
spectrometry
Data files were recalibrated using references masses, features were extracted, and
calibrated reference ions were used to calculate CCS values. Features were aligned,
blank subtracted, and compared to both in-house and vendor libraries (Agilent
Technologies). The identification was based on molecular formula, isotope
abundance and spacing for MS data files. In All Ions MS/MS data files, fragments
were additionally used for identification (Figure 12).
Targeted MS/MS runs were carried out for the remaining compounds that were
extracted from the MS data using Workflow I, and the results were compared to
the in-house and Agilent Technologies libraries. If the in-house or Agilent libraries
did not contain any MS/MS spectra, an attempt was made to find the information
in Massbank (https://massbank.eu/MassBank/, Norman Association, retrieved
2017-10-16). For compounds not present in Massbank, the in silico fragmentation
tool MetFrag (https://msbi.ipb-halle.de/MetFrag/) [144] was used for confirmation
or rejection. For the compounds confirmed by Targeted MS/MS (Workflow I) or
31
All Ions MS/MS (Workflow II), linear regression analysis of retention times and the
log KOW values of a set of contaminants were used to check retention time
plausibility. Finally, reference standards were purchased for most of the tentatively
compounds and compounds were considered confirmed if the retention time and
major fragments were in agreement.
Figure 12. Identification workflow for GC×GC-HRToF-MS and LC-ESI-IM-QToF-MS in Paper IV.
GC×GC-HRToF-MS LC-ESI-IM-QToF-MS
Targeted data
processing
Peak picking and
alignment
Blank subtraction +
NIST library search
Detection frequency
filtration
+
manual
investigation
including
soft EI re-run
Tentatively
identified
compounds
Identified
compounds
Feature extraction and
alignment
Targeted MS/MS mode
Library search or Metfrag
fragment confirmation
Feature extraction in influent
sample
MS mode All Ions MS/MS mode
Workflow IIWorkflow I
Blank subtraction +
library search
Identification based on
molecular formula, isotope
abundance and spacing
Library search
Identification based on
molecular formula, isotope
abundance and spacing +
MS/MS fragmentation
Targeted search in MS mode
samples
Tentatively identified compounds
Targeted
reprocessing
Confirmation with reference standards
32
3.5. Environmental risk assessment
The ecotoxicological risks of contaminants found in effluent and surface water for
Papers I and II were assessed by carrying out ERAs. First, the exposure was
evaluated by calculating the PEC for a contaminant found in effluent, using a
dilution factor of 1 000 as advised for local surface water scenarios [161] or using
the MEC for contaminants found in surface water. Next, the effects were assessed
by calculating PNECs. For Papers I and II, the chronic value (ChV) was used,
which is a long-term endpoint and is defined as “the geometric mean of the no
observed effect concentration (NOEC) and the lowest observed effect
concentration (LOEC)” [162] calculated using U.S EPA’s ECOSAR model [163]. The
most sensitive (lowest ChV) was divided by an assessment factor of 10 as advised,
if long-term data for three species representing three trophic levels were available
[116,162]. Finally, the risks were characterized for each contaminant by calculating
the PEC/PNEC or MEC/PNEC ratios (risk quotients).
3.6. Mass fluxes per capita
In Paper III, environmental loads from OSSFs were compared to STPs by
calculating mass fluxes per capita from contaminant levels detected in the river
water. Mass fluxes per capita were obtained by multiplying the measured
environmental contaminant concentrations by the river flow at each sampling site
and dividing the product by the number of people estimated to be connected to
OSSFs or STPs in the corresponding catchment area. Estimated river flows, flow
uncertainties, and catchment sizes, were obtained from the Swedish
Meteorological and Hydrological Institute’s HYPE model [164]. Uncertainties in
mass flux per capita estimations were accounted for by calculating the overall
uncertainty, using error propagation including flow, analytical and capita estimate
uncertainties.
3.7. Statistics
3.7.1. Univariate statistics
The Wilcoxon Rank Sum test is a non-parametric statistical test to assess whether
two independent populations differ and can be used when the data are not
normally distributed [165]. Numerical data are ranked from 1 to n, the ranks are
substituted for the actual data, and the sum of the ranks is calculated for each
group (A,B) [165]. If the null hypothesis is true (H0: A = B) i.e. there is no difference
between the medians of each group, then the sum of ranks of one group should be
around half of the sum of all ranks. The Wilcoxon Rank Sum test was used to test
33
for compound-specific removal efficiency differences between SBs and STPs for
Paper I. The Mann-Whitney test [166] uses similar statistics as the Wilcoxon Rank
Sum test [167] and was used for Paper III to test for differences between mass
fluxes per capita at OSSF- and STP-affected river sites. The work for Paper IV
utilized the Wilcoxon signed-rank test and the Friedman test for dependent
populations. Wilcoxon signed-rank test is also a non-parametric test based on
ranks, but can be used when comparisons are paired, which leads to positive or
negative differences between the pairs in the two sample groups [168]. Ignoring the
sign, ranks are assigned to differences between paired comparisons. Then,
differences that are negative are ascribed a negative sign [168]. Next, the test
statistics are calculated, and it is determined whether the two groups are from
populations with the same distribution (H0: A = B) [169]. The Friedman test [170–
172] is also a rank-based non-parametric test for dependent populations, but is
used to compare three or more sample groups. Ranks are assigned within each row
and the sum of ranks for each group is used to calculate the chi-squared statistics
[167]. For Paper IV, paired contaminant dependent removal efficiencies of two
different sorbents and three different sorbents were compared with the Wilcoxon
signed-rank test and with the Friedman test, respectively. The tests for Paper III
and IV were carried out using OriginLab (Version 9.1.0, Northampton, MA, USA,
www.OriginLab.com).
3.7.2. Multivariate statistics
In principle component analysis (PCA), systematic variation in data consisting of
multiple variables and observations is extracted and projected into a low-
dimensional plane (principle components) [173]. The first principle component
(PC) describes the largest variation in the data, the second principle component is
orthogonal to the first and explains the second largest variation and so on. The
projected observations are represented with score plots and the variables in
loading plots [173]. With PCAs, relationships between observations and variables
can be found and patterns, outliers, and groupings elucidated [173]. The work in
Paper I utilized PCA to study variations in contaminant removal efficiencies for
the different treatment plant types and, for Paper III, PCA was used to study
differences in contaminant mass fluxes per capita between OSSF- and STP-affected
surface water. For both papers, samples were treated as observations and
contaminant concentrations as variables.
Projections to latent structures by means of partial least squares (PLS) relate X and
Y variables to each other and model their association [173]. PLS was used to develop
quantitative structure-property relationship (QSPR) models for Paper IV.
34
Computed molecular descriptors (X variables) were used to characterize the
structural variation of compounds and to predict the measured response, here the
removal efficiencies (Y variables). Variable influence on projection (VIP) values
that are the weighted sum of squares of the PLS weights, summarizing all
components into one VIP vector [173], were used to determine significant
molecular descriptors. Internal cross validation was used to estimate the predictive
power of the principle components by dividing the data into groups and then
developing parallel models from reduced data sets. Models were developed using
the deleted data as a test set and difference between observed and predicted Y
values were calculated into a Q2 value (cross-validated R2) that gives information
about the predictive power of a model [173]. In addition, response permutation
tests were carried out to estimate the significance of the Q2 value. In this test,
randomly ordered Y-response data were modeled and the real Q2 was compared to
Q2 values of models with randomly ordered Y data. Overfitted models give high R2
and Q2 for the permutated response models [173]. Multivariate statistics were
carried out using the software package SIMCA (Version 13.0.3, Umetrics, Sartorius
Stedim Biotech, Umeå, Sweden, https://umetrics.com/).
3.8. Molecular descriptors
Molecular descriptors were calculated for the QSPR modelling in Paper IV. For
charge-dependent descriptors, the contaminant structure’s ionic state was
selected, which was most abundant at the present pH. Two-dimensional molecular
descriptors were calculated for each structure, including partial charge,
connectivity and constitutional descriptors, as well as descriptors based on
Abraham solvation parameters, physicochemical properties, surface area, and
stereochemistry. Descriptors were calculated in ACD/Percepta (Version 2017.1.3,
www.acdlabs.com) and MOE (Version, 2015.1001, Chemical Computing Group Inc.
Montreal Canada, https://www.chemcomp.com/).
35
4. Results and Discussion
4.1. Prioritization of environmentally-relevant contaminants in
wastewater
The work for Paper I aimed to increase the knowledge of contaminants discharged
from OSSFs and to evaluate treatment efficiencies of OSSFs. Thus, it consisted of
a two-staged design (Figure 13) including a screening for environmentally-relevant
compounds (stage I) and an evaluation of removal patterns and environmental
loads of SBs compared to STPs (stage II).
Figure 13. Two-staged design of the work for Paper I. The first stage consisted of sampling, contaminant
screening, prioritization and analyte selection. The second stage consisted of method development,
sampling, removal pattern and load analysis.
In Paper I stage I, the samples were analyzed by GC×GC-ToF-MS using a non-
target screening approach. Peak picking and alignment of peaks resulted in
≥ 200 000 features that were filtered based on blank detection, detection
frequency, and manual inspection, resulting in approximately 300 tentatively
identified compounds. The data set was further reduced by filtering based on
environmental relevance: i) Half-life in water ≥ 60 days (EPI Suite’s BIOWIN 3,
[163]), or ii) BCF ≥ 1 000 (EPI Suite’s BAFBCF, [163]), or iii) PEC/PNEC ≥ 0.01 (see
sections 2.3 and 3.5), or iv) European or Swedish industrial chemical with an acute
(LC50/EC50) or chronic (ChV) ecotoxicity endpoint ≤ 1.0 mg L−1 or ≤ 0.1 mg L−1,
respectively. Industrial chemicals were found from European low and high
production volume chemicals as given in [174], the EINECS database [175], and a
database of chemicals used in large amounts in Sweden compiled by the Swedish
Chemicals Agency [176]. Finally, the list was checked for anthropogenic origin and
obviously biogenic compounds were excluded.
Stage I
Sampling I
GCxGC-MS based non-target
screening
Compound prioritization
Target analyte selection
Stage II
Method development for target
analytes
Sampling II
Removal pattern analysis
Environmental load analysis
36
Environmental relevance filtering, the removal of biogenic compounds from the
data set and targeted reprocessing, resulted in 46 anthropogenic contaminants of
potential environmental concern. These compounds included the pharmaceuticals
acetyl salicylic acid, carbamazepine, ethosuximide, and mirtazapine; the pesticide
2,3-dichlorobenzonitrile, and chlorinated, alkyl and aryl organophosphates; the
UV stabilizers octyl salicylate, oxybenzone and octocrylene; the detergent
impurities 4-phenylundecane, 5-phenylundecane, 4-phenyldodecane and 6-
phenyldodecane, and the surfactants 2,4,7,9-tetramethyl-5-decyn-4,7-diol
(TMDD) and N,N,N′,N′-tetraacetylethylenediamine. These 46 contaminants were
further ranked with a scoring system from 1 to 5 for i) removal efficiency, ii) half-
life, iii) BCF, iv) PEC/PNEC, and v) maximum concentration. The lowest total score
represented the most environmentally-relevant compound. The top ranked
compounds were a fragrance (galaxolide) that scored low in all five categories, a
food additive and cosmetic ingredient (α-tocopheryl acetate) that scored low in
persistence and toxicity, the UV stabilizers (octocrylene and octyl salicylate) that
scored low in bioconcentration and removal efficiency, followed by the surfactant
additives and impurities (TMDD, 4-phenyldodecane, 6-phenyldodecane,
5-phenylundecane, 4-phenylundecane), organophosphorus plasticizers and flame
retardants (Figure 14).
The top ranked chemicals (Figure 14) were studied for Paper I stage II, Paper II,
Paper III and Paper IV. These compounds were complemented with structurally-
related compounds including several alkyl, aryl and chlorinated
organophosphates, nitro and polycyclic fragrances and the UV stabilizer
2
2
2
2
3
2
1
4
1
2
2
2
2
2
1
5
3
5
2
1
5
2
5
5
5
5
2
4
1
3
2
5
4
2
5
2
5
2
2
1
1
2
5
1
3
5
1
3
3
2
1
1
1
4
3
1
3
2
4
3
4
3
1
5
5
0 5 10 15
Galaxolide
a-Tocopheryl acetate
Octocrylene
2,4,7,9-Tetramethyl-5-decyn-4,7-diol
4-Phenyldodecane
Tris(3-chloropropyl) phosphate
Tris(1,3-dichloroisopropyl) phosphate
6-Phenyldodecane
Tri(2-chloroethyl) phosphate
Octyl salicylate
Caffeine
5-Phenylundecane
4-Phenylundecane
Total Score
Removal efficiency Half-life BCF PEC/PNEC Concentration
Figure 14. The top ranked compounds identified by GC×GC-ToF-MS, with the lowest total score and their
scoring in removal efficiency, half-life for aquatic biodegradation, bioconcentration factor, PEC/PNEC and
maximum concentration found in samples.
37
benzophenone-1. Furthermore, the set was complemented with a few reference
compounds to expand the physicochemical domain and facilitate the
interpretation of removal processes such as the classical biocides, triclosan and
hexachlorobenzene, and the polymer impurity bisphenol A. Structures of the
selected compounds and their water solubility (SW) and octanol-water partition
coefficients (log KOW) from the EPI SuiteTM toolbox (www.epa.gov, 2008) are
presented in the following pages. For Papers II, Paper III, and Paper IV, several
polycyclic aromatic hydrocarbons (PAHs) were added to the selection of target
compounds as indicators for road runoff.
Biocides
Hexachlorobenzene (HCB) CAS 118-74-1
SW = 0.0062 mg L-1
log KOW = 5.9
Triclosan (TCS) CAS 3380-34-5 SW = 29 mg L-1
log KOW = 4.7
Thiabendazolea (TBZ) CAS 148-79-8
SW = 340 mg L-1
log KOW = 2.5
Food additive
α-Tocopheryl acetate (αTPA)
CAS 58-95-7 SW = 1.3 10-7 mg L-1
log KOW = 12
Fragrances
Galaxolide (HHCB) CAS 1222-05-5 SW = 1.8 mg L-1
log KOW = 6.3
Musk ketone (MKK) CAS 81-14-1
SW = 1.9 mg L-1
log KOW = 4.3
Musk xylene (MKX) CAS 81-15-2
SW = 0.47 mg L-1
log KOW = 4.5
a Only for Paper I and Paper IV.
38
Tonalide (AHTN) CAS 1506-02-1 SW = 1.3 mg L-1
log KOW = 6.3
Linear alkyl benzenes
3-Phenyldodecanea (3-C12-LAB) CAS 2400-00-2
SW = 0.0030 mg L-1
log KOW = 7.9
6-Phenyldoceanea (6-C12-LAB) CAS 2719-62-2
SW = 0.0032 mg L-1
log KOW = 7.9
Organophosphates
Tributyl phosphate (TBP) CAS 126-73-8
SW = 280 mg L-1
log KOW = 3.8
Triphenyl phosphate (TPP) CAS 115-86-6 SW = 1.9 mg L-1
log KOW = 4.7
Tricresyl phosphateb (TCP) CAS 1330-78-5 SW = 0.36 mg L-1
log KOW = 6.3
Tris(2-chloroethyl) phosphate
(TCEP) CAS 115-96-8
SW = 7 000 mg L-1
log KOW = 1.6
Tris(1,3-dichloro-2-propyl) phosphate (TDCPP)
CAS 13674-87-8 SW = 28 mg L-1
log KOW = 3.6
Tris(1-chloro-2-propyl) phosphate (TCIPP) CAS 13674-84-5 SW = 1 200 mg L-1
log KOW = 2.9
aOnly for Paper I, Paper III, and Paper IV. bApart from tri-p-cresyl phosphate (p,p,p) shown here, there are tri-m-cresyl phosphate (m,m,m) (CAS 563-04-2), tri-o-cresyl phosphate (o,o,o) (CAS 78-30-8), and isomers consisting of combined o, p and m substituents. The CAS 1330-78-5 refers to the isomer mixture and SW and log KOW to the p,p,p isomer.
39
2-Ethylhexyldiphenyl phosphatea (EHDPP)
CAS 1241-94-7 SW = 0.19 mg L-1
log KOW = 5.7
Tris(2-butoxyethyl) phosphate (TBEP)
CAS 78-51-3 SW = 1 100 mg L-1
log KOW = 3.0
Tris(2-ethylhexyl) phosphate (TEHP)
CAS 78-42-2 SW = 0.60 mg L-1
log KOW = 9.5
Plastic/rubber additives
n-Butylbenzenesulfonamide (nBBSA)
CAS 3622-84-2 SW = 400 mg L-1
log KOW = 2.3
Bisphenol A (BPA) CAS 80-05-7
SW = 42 mg L-1
log KOW = 3.6
2-(Methylthio) benzothiazole (MTBT)
CAS 615-22-5 SW = 130 mg L-1
log KOW = 3.2
Surfactants
2,4,7,9-Tetramethyl-5-decyn-4,7-diol (TMDD)
CAS 126-86-3 SW = 26 mg L-1
log KOW = 3.6
4-Octyl phenol (4OP) CAS 1806-26-4 SW = 3.1 mg L-1
log KOW = 5.5
UV stabilizers
Benzophenone-1 (BP-1) CAS 119-61-9
SW = 140 mg L-1
log KOW = 3.1
Octocrylene (OCR) CAS 6197-30-4
SW = 0.0038 mg L-1
log KOW = 6.9
aFor Paper III, 2-Ethylhexyldiphenyl phosphate was wrongly presented as CAS 109925-03-3, SW = 0.12 mg L-1 and log KOW = 6.3.
40
Polycyclic aromatic hydrocarbonsa
Anthracene (ANTC)
CAS 120-12-7 SW = 0.043 mg L-1
log KOW = 4.5
Fluoranthene (FLA) CAS 206-44-0
SW = 0.26 mg L-1
log KOW = 4.9
Pyrene (PYR) CAS 129-00-0
SW = 0.14 mg L-1
log KOW = 4.9
Benz(a)anthracene (BANT) CAS 56-55-3
SW = 0.0094 mg L-1
log KOW = 5.5
Chrysene (CHR) CAS 218-01-9
SW = 0.0020 mg L-1
log KOW = 5.5
Benzo(k)fluoranthene (BFLA) CAS 207-08-9
SW = 0.0008 mg L-1
log KOW = 6.1
Benzo(b)fluoranthene (BFLA) CAS 205-99-2
SW = 0.0015 mg L-1
log KOW = 5.8
Benzo(a)pyrene (BPYR) CAS 50-32-8
SW = 0.0016 mg L-1
log KOW = 6.1
4.2. Method development for urban water contaminants
A SPE method for filtered surface water was developed for Paper II and Paper III
and extended to wastewater for Paper I stage II. Although the method was
developed for the selected target analytes, clean-up was minimized to enable
untargeted analysis of the extracts. The final solid phase extraction method was
tested with triplicate recovery experiments of native and labeled analytes (Figure
15).
Native analytes were recovered with median relative recoveries of 95%, 97%b, and
96% in effluent, influent, and surface water, respectively. Recoveries relative to
structurally identical or carefully matched deuterated/13C-labeled standards were
generally > 50%, except for tris(2-ethylhexyl) phosphate in influent (30 ± 1%) and
surface water (43 ± 11%), and TMDD in effluent (44 ± 3%). Only a few compounds
had recoveries > 150%: 6-Phenyldodecaneb, 3-phenyldodecanec, musk ketone,
a Only for Paper II and Paper III. b The median relative recovery was wrongly described in Paper I as 95% due to an error in the recovery of TCIPP which was, in fact, 159% instead of 15%. c 6-Phenyldodecane and 3-phenyldodecane were not validated in effluent and surface water due to a late delivery of the standards.
41
tris(2-ethylhexyl) phosphate in influent, 4-octyl phenol and tris(1-chloro-2-propyl)
phosphate (TCIPP) in effluent, and hexachlorobenzene in surface water.
Figure 15. Boxplot representing recoveries in influent, effluent, and surface water. The boxes represent the
25th, 50th and 75th percentiles, the whiskers show the 10th and 90th percentiles, the cross represents the mean
and the dots represent data points that lay outside the 10th and 90th percentiles. Analytes included in
wastewater recovery tests (n = 34) do not completely overlap with the analytes included in surface water
tests (n = 28). Analytes are listed in the Supplementary Material of Paper I and Paper III
Recoveries > 100% are caused by matrix enhancement in the liner, column and ion
source [177]. During calibration, analytes bind to active sites and are not
completely released. In turn, when analyzing samples containing matrix, the
matrix competes with analytes to bind to active sites and higher analyte responses
are observed [177]. This effect could have been avoided by extracting a non-spiked
sample and using it for preparation of the calibration standards (“matrix-matched
calibration”) [177] or the standard addition method. By spiking standard to sample
aliquots at different concentrations to obtain a calibration curve and through
extrapolation to the signal measured for the pure sample, the unknown original
sample concentration could have been found. Alternatively, adding analyte
protectants, that exhibit the same effect as matrix, to calibration standards has
been proposed [177]. Recoveries comprised the narrowest interquartile range for
surface water followed by effluent due to the lowest matrix content in this type of
sample.
In conclusion, the developed method had high extraction efficiencies for a large
variety of compounds. However, the method worked best for water with less
influent effluent surface water
25
50
75
100
125
150
175
200R
eco
ve
rie
s (
%)
42
matrix, so best for surface water followed by effluent and finally influent. The
method could be improved for several very hydrophobic compounds and the
missing structurally matched labeled standard could be complemented.
4.3. Removal patterns in soil beds compared to large-scale STPs
For Paper I stage II, the treatment efficiencies of SBs (n = 5) were compared to
STPs (n = 5) for the target contaminants selected using the prioritization strategy
from Paper I stage I; removal efficiencies were correlated to the hydrophobicity of
these compounds to draw conclusions about removal processes.
The removal efficiencies varied considerably between the different facilities, which
can be seen from the whiskers representing the 25th and 75th percentiles (Figure
16). Nevertheless, total and treatment type specific median removal efficiencies
were significantly correlated to the log KOW of each contaminant (Spearman rank
correlation, α = 0.01), due to sorption playing a significant role in the removal of
organic contaminants in both STPs and SBs. However, some of the more
hydrophilic compounds – TMDD, TCIPP, tris(1,3-dichloro-2-propyl) phosphate
(TDCPP), tributyl phosphate (TBP), and tris(2-butoxyethyl) phosphate (TBEP) –
were removed better in SBs, although only significantly for TMDD (33% vs. 0%)
and TBEP (80% vs. 68%) (Wilcoxon Rank Sum test, α = 0.01). This difference could
be due to a higher soil-to-water ratio and an increased sorption of compounds with
polarizable functional groups in SBs.
SBs, which are a type of OSSF, removed organic contaminants as effectively as STPs
with the exceptions of TBEP and TMDD, for which SBs worked significantly better.
However, these compounds were moderately hydrophobic. The removal of more
hydrophilic contaminants was inadequate by both STPs and SBs. For instance, the
median removal efficiencies of the chlorinated organophosphates
tris(2-chloro-ethyl) phosphate (TCEP), TCIPP and TDCPP were < 56% in SBs and
STPs.
For several of the contaminants, removal efficiencies by OSSFs were reported for
the first time. Amongst them, the lowest median removal efficiencies were
2-(methylthio)benzothiazole (MTBT) (64%), TDCPP (56%), TMDD (33%), TCIPP
(34%), and TCEP (19%). The low removal efficiency resulted in median effluent
concentrations of these analytes between 67 (MTBT) and 1 100 ng L-1 (TMDD).
The in-depth investigation of removal patterns was limited to SBs and well-
functioning plants only. Thus, future studies should include a variety of commonly
43
used OSSF types and investigate poor-functioning plants. The results also imply
that upgrades of both STPs and OSSFs are necessary to reduce contamination of
surface water with hydrophilic contaminants. Additionally, OSSFs could
contaminate groundwater, if the water table and OSSF density are high.
Groundwater contamination could especially represent a risk for human exposure,
if drinking water wells are installed too close to OSSFs.
Figure 16. Median removal efficiencies in sewage treatment plants (STPs) and soil beds (SBs) versus the
logarithm of the octanol–water partition coefficient (log KOW) for compounds with detection frequency =
100 % (detected in influent or effluent from the same treatment plant). Whiskers show the 25th and 75th
percentiles and log KOW values were retrieved from EPI Suite’s KOWWIN v.1.68 [163].
4.4. Wastewater-impacted surface water and sediment
For Paper II and Paper III, concentrations of the selected target analytes from
Paper I in OSSF- and STP-impacted surface water were examined, and
contaminants were tentatively identified in surface water affected by the
large-scale STP Kungsängsverket.
Concentrations were strongly impacted by water flow, thus, a low flow resulted in
higher contaminant concentrations. The flow was highest during the spring flood
in March [178,179], followed by December, June and September. Hence, highest
EHDPP
α-TPATCS
AHTNBP TPP
TBP
MTBT
TDCPP
TMDD
TCIPP
TCEP
OC
TEHPHHCBTBP
TBEP
TDCPP
TMDD
0
20
40
60
80
100
1 3 5 7 9 11 13
Media
n r
em
oval
eff
icie
ncy
(%)
log KOW
STP
SB
44
concentrations were detected in September directly after a large-scale STP (STP B
= Site A) for galaxolide (1 200 ng L−1), TMDD (240 ng L−1), TBEP (270 ng L−1),
n-butylbenzenesulfonamide (nBBSA) (130 ng L−1), TCEP (110 ng L−1), TDCPP
(62 ng L−1) and MTBT (44 ng L−1). The lowest concentrations were detected in
March, except TCIPP after the large-scale STP Kungsängsverket (1 100 ng L-1). The
high concentration could be due to increased runoff from roadside snow
contaminated with emissions from polyurethane foam from car interiors [180]. In
addition to the target contaminants, ten contaminants were tentatively identified
in surface water after the STP using GC×GC-MS and a NIST library search.
Amongst them were pharmaceuticals, an insecticide, a fragrance and plastic
impurities: profolol, tolycaine, carbamazepine, diethyltoluamide, rose acetate,
7,9-di-tert-butyl-1-oxaspiro(4,5) deca-6,9-diene-2,8-dion, 4-acetyl-morpholine,
dipropylene glycol, 1-butyl-2-pyrrolidinone, and diphenyl sulfone.
Several contaminants were also found in Lake Ekoln (Site C). They were the target
analytes benzophenone-1, fluoranthene, galaxolide, triclosan, nBBSA, TMDD,
TCEP, TCIPP, TDCPP and TBEP, and the tentatively identified compounds
dipropylene glycol and rose acetate.
Most of the contaminants found were also found in the effluent of the large-scale
STP, except a few were not detected due to higher detection limits (Paper II).
Contaminants found with high detection frequencies (≥ 50%) at OSSF-impacted
sites and in OSSF effluent (Paper I stage II) were galaxolide, TMDD, TCEP, TCIPP
and TDCPP. Contaminants found in OSSF effluent with high detection
frequencies, but rarely in OSSF impacted surface water, were MTBT, TBEP,
6-phenyldodecane, tonalide, octocrylene, triphenyl phosphate and
α-tocopheryl acetate. These compounds were predominantly found in sediment
(Paper II).
Comparing sediment and water composition profiles (Paper II), the more
hydrophilic compounds dominated the water phase, whilst the more hydrophobic
contaminants dominated the sediment phase. For instance, sediment levels
normalized to the organic carbon content (OC) at sites after the STP reached up
to 9 800 ng g−1 OC−1 for individual PAHs, 3 100 ng g−1 OC−1 for galaxolide,
1 400 ng g−1 OC−1 for 6-phenyldodecane, 730 ng g−1 OC−1 for tris(2-ethylhexyl)
phosphate, 640 ng g−1 OC−1 for tonalide, and 210 ng g−1 OC−1 for MTBT.
High environmental levels of galaxolide in surface water after the large-scale STP
presented a high potential ecotoxicological risk (MEC/PNEC = 2.4). Medium
potential risks (MEC/PNEC > 0.1) were also found for the organophosphates TBEP
45
and TCIPP at both STP sites in summer and for the UV stabilizer octocrylene, at
both OSSF- and STP-affected sites.
In conclusion, wastewater contaminants that likely originated from OSSFs and
STPs occurred in water and sediment affected by OSSFs and STPs. Seasonal
changes in flow impacted the water concentration, and several contaminants were
transported far from the source to Lake Ekoln. Lake Ekoln is part of the drinking
water reservoir, Lake Mälaren, that supplies drinking water to around 2 million
inhabitants in and around, Stockholm, Sweden. Thus, both the ecosystem and
human health could be at risk.
4.5. Environmental fate of wastewater contaminants
The aim of the work for Paper II was to assess the fate (persistence, mobility,
bioavailability) of emerging contaminants in the catchment of the River Fyris.
Persistence was assessed by calculating mass flux attenuations, mobility was
assessed by sorption and partition potentials along with water solubility in
addition to detection far from the source in Lake Ekoln, and bioavailability was
assessed by sampling of the bioavailable fraction of contaminants with POCIS
samplers. Additionally, fish were analyzed to determine bioaccumulative
contaminants.
To assess persistence, the mass flux attenuation from Site A to Site B in surface
water during summer and autumn was calculated for the top ten detected
compounds at sites A, B, and C in June and September (detection frequency > 50 %,
n = 6). In addition to an attenuation < 50%, contaminants had to be found far from
the source (Lake Ekoln, Site C) to be considered persistent (Table 5). Compounds
fulfilling these requirements were TCIPP, TDCPP, TCEP, TMDD, nBBSA,
galaxolide and fluoranthene. These compounds have relatively stable molecular
structures e.g. chlorinated aliphatic moieties and stabilizing conjugated π-systems.
Furthermore, attenuations were higher in June and September, possibly due to an
increase in microbial activity at higher temperatures (15 to 17°C in June and
September compared to 3 to 6°C in December) and an increase of indirect
photolysis through excited dissolved organic substances by the higher irradiation
intensity in summer and autumn.
Mobility, which is a compound’s potential to be transported from the source [114]
without removal through for instance sorption to particulate matter, was
estimated for the studied target compounds for Paper II. Regulated quantifiers for
mobility are currently unavailable [113], thus the analytes were evaluated by their
46
log KOW/ log DOW plus SW and experimentally determined log KOC and SW
(section 2.2.5). Log KOC values could only be calculated, if the compound was found
in both water and sediment phase. According to their log KOW (< 4) [32] and
SW (> 0.15 mg L-1) [113], resulting in low sorption potentials, possible mobile target
analytes are TBP, TDCPP, TCIPP, TCEP, TBEP, nBBSA, MTBT, TMDD and
benzophenone-1 (Table 5). The Log DOW evaluation did not yield any additional
mobile contaminants. Additionally, MTBT, TBEP, galaxolide and tonalide are likely
mobile according to SW (> 0.15 mg L-1) and their log KOC (< 4.5) calculated from the
water-sediment distribution [113].
Table 5. Compounds with detection frequency > 50% (n = 6, sites A, B, and C in June and September) plus
mobile compounds), their mobility indicators (log KOW, log KOC, WS) and the mass flux attenuation from
Site A to B, the occurrence in Lake Ekoln (Paper II), SB effluent (Paper I) and OSSF-impacted surface
water (Paper III). Compounds fulfilling mobility criteria (section 2.2.5) [32,113,114] and the persistence
criteria set by the author are marked in bold.
Chemical Log KOW
Log KOC WS in mg L-1
Attenuation A to Ba)
Paper II
Lake Ekoln Paper II
SB effluent Paper I
OSSF surface water Paper III
BP-1 3.1 - 140 b)
galaxolide 6.3 3.4, 4.1 1.8 43 ± 26%
Fluoranthene 4.9 6.5, 4.7 0.26 0 ± 121%
c)
MTBT 3.2 4.0, 3.5 130 23 ± 20%
nBBSA 2.3 - 400 43 ± 12%
TBEP 3.0 3.4 1 100 71 ± 20%
TBP 3.8 - 280 19 ± 35%
TCEP 1.6 - 7 000 47 ± 16%
TCIPP 2.9 - 1 200 24 ± 36%
TDCPP 3.6 - 28 35 ± 23%
TMDD 3.6 - 26 38 ± 14%
a) Mean of June and September with standard error (n = 2) b) Detection frequency (17 %, n = 6) too
low to determine attenuation c) Fluoranthene was not analyzed for Paper I.
Apart from TBP, MTBT, benzophenone-1 and tonalide, these compounds and the
tentatively identified compounds dipropylene glycol (log KOW = -0.64,
SW = 10-6mg L-1) and rose acetate (log KOW = 3.85, SW = 19 mg L-1) were transported
far from the source to Lake Ekoln (Site C), which indicates, along with their
log KOW and SW, high mobility (Table 5). Although the main source for these
contaminants was a large-scale STP, the mobile target contaminants were also
found in SB effluent (Paper I) and in OSSF-impacted surface water (Paper III),
excluding nBBSA and TBEP, respectively. Thus, mobile contaminants are also
discharged from OSSFs and could be transported far from the source.
47
POCIS samplers can show the bioavailability of the target compounds by
mimicking the uptake of the bioavailable fraction in the water. Consequently, only
13 instead of 17 target analytes were found in POCIS samples compared to grab
samples. However, spatiotemporal trends were similar in POCIS and grab samples,
thus, sampled amounts were highest in summer when the flow was lowest. This
resulted in a higher amount available for uptake into biota when biological
productivity was at its highest.
Emerging organic contaminants that were identified for Paper II as being
persistent, mobile and bioavailable were galaxolide, TMDD, TCEP, TDCPP, and
TCIPP. Galaxolide and TMDD were additionally found in fish and thus found to be
bioaccumulative (Figure 17). As mentioned in section 4.4, galaxolide and TCIPP are
a potential risk to the aquatic ecosystem (Paper III). Thus, galaxolide and TCIPP
are not only potentially persistent, mobile and bioavailable, but also an
ecotoxicological threat.
The reoccurrence of these contaminants in the work in Paper I, Paper II and
Paper III, as well as the indication of possible ecosystem and human health risks
demand an improvement in treatment technologies to remove organic
contaminants during wastewater treatment, before they can enter the
environment. Moreover, the field-based studies to investigate persistence could be
complemented with laboratory studies of photolysis and biodegradation to
confirm that these fate processes play a key role in the removal of the studied
Figure 17. Bioavailable, persistent and mobile compounds identified for Paper II.
48
contaminants in surface water. Furthermore, the method to analyze contaminants
in fish did not cover all target analytes due to lipid interference in the GC×GC-MS
analysis (e.g. TCEP and TCIPP). Therefore, the method needs to be improved for
the removal of lipids and extended to the analytes measured in water, POCIS and
sediment. Consequently, further studies of the bioaccumulation of targeted and
untargeted analytes in fish would increase the knowledge of discharge from OSSFs
and STP and the subsequent uptake in biota. Accumulated levels in biota are not
only a threat for the ecosystem, but also humans who could consume these
organisms and could thus be exposed to toxic levels.
4.6. Mass fluxes per capita at wastewater-impacted sites
The environmental loads of on-site sewage treatment plants were compared to
large-scale sewage treatment facilities by calculating mass fluxes per capita at sites
impacted by either of them (Paper III). Mass fluxes per capita were calculated by
multiplication of the measured water concentration at the sampling site with the
river flow at the site and by division with the number of people estimated as having
their housing unit connected to OSSFs and STPs in the catchment area (Figure 18A,
section 3.6).
Mass fluxes per capita are presented in a PCA biplot, a combined loading and score
plot (Figure 18B). Correlation in the positive direction of variables in one PC means
that these variables have a high positive impact on the observations located in the
same PC direction. In Figure 18B, March samples and most STP samples are located
in the positive direction along PC1, because of higher contaminant fluxes than the
samples located in PC2 i.e. June and September samples. Thus, OSSF-affected sites
deviated more from STP sites in June and September than in December and March.
March samples were impacted by an ongoing spring flood [178,179] and levels could
therefore be affected by both dilution and increased runoff (section 4.4).
Of the compounds with the highest detection frequency (> 33%), median mass
fluxes per capita were similar at OSSF- and STP-impacted sites for TDCPP, TCIPP,
TCEP and nBBSA. However, mass fluxes per capita of TBEP, MTBT and galaxolide
were significantly lower (~2 to 3-fold) at OSSF sites than at STP sites
(Mann-Whitney, α = 0.05).
Calculating mass fluxes downstream of OSSFs is challenging, since OSSFs are
diffuse sources and the distance between sampling site and source can influence
the measured levels. Hence, enhanced biodegradation and photodegradation in
June and September as compared to December could affect contaminant levels
49
more at OSSF sites. Therefore, mass fluxes could have been lower at OSSF sites not
due to a lower environmental load, but due to “treatment” along the way to the
recipient and sampling site. Thus, contaminants identified as persistent and
mobile in Paper II – Galaxolide, nBBSA, TCEP, TCIPP, TDCPP and TMDD – should
be less affected by these fate processes than compounds which were less mobile
and persistent – triclosan, fluoranthene, TBEP and MTBT. Thus, the significantly
lower mass fluxes per capita of MTBT and TBEP at OSSF-affected sites could
originate from a higher impact of fate processes on OSSF sites. Alternatively, these
compounds could have been removed by hydrophobicity-driven adsorption
(log KOW = 3.0 to 6.3) in OSSFs due to a higher solid-to-liquid ratio and long
retention time in, for instance SBs, whereas the relatively hydrophilic
contaminants were removed in OSSFs and STPs to a similar degree. In addition,
differences could have originated from a higher use and emission of these
compounds in urbanized areas with a higher percentage of non-residential
buildings, which could have affected the differences: MTBT due to tire abrasion,
TBEP from floor polish and galaxolide from cleaning products.
Figure 18. (A) Schematic overview of sampling sites and (B) Principal Component Analysis biplot of mass
fluxes per capita at OSSF-affected and STP-affected sites (observations) of contaminants (variables) with
detection frequency ≥ 33%. Ellipses represent 50%, 75%, and 100% correlation. Samples were collected at
five sites (OSSF A, B, C and STP A and B) and collected in four seasons (December (D), March (M), June (J)
and September (S), n = 20). Mass flows for < limit of detection (LOD), < limit of quantification (LOQ),
and < method limit of quantification (MQL) were calculated based on LOD/2, LOQ/2, and MQL/2,
respectively.
TMDD
MTBT
TCEP
TCIPP
nBBSA
HHCB
FLA
TCSTDCPP
TBEP
OSSF A.D
OSSF A.M
OSSF A.J
OSSF A.S
STP A.D
STP A.M
STP A.J
STP A.SOSSF B.D
OSSF B.M
OSSF B.J
OSSF B.S
OSSF C.D
OSSF C.M
OSSF C.J
OSSF C.S
STP B.D
STP B.MSTP B.J
STP B.S
PC
2 (
16
%)
-1
-0.5
0
0.5
-1 -0.5 0 0.5
PC 1 (45%)
Contaminant
OSSF
STP
Kungsängsverket
STP 171 000 pers
River Sävja
River Fyris
Björklinge STP
3700 pers
OSSF A660 pers
STP A5500 pers
OSSF B560 pers
STP B198 000 pers OSSF C
10400 pers
Lake Ekoln
River Björklinge Storvreta STP
6400 pers
Lake Mälaren
Vattholma STP
1500 pers
Skyttorp STP600 pers
Gåvsta STP
690 pers
Länna STP1300 pers
A B
50
Although the calculation of mass fluxes per capita is affected by uncertainties from
the analysis, water flow and estimated numbers of people using OSSFs in the
catchment, the estimations suggest that the environmental loads of OSSFs are
similar or weaker compared to STPs. There are several factors that could explain
such differences, including differing patterns of product and material usage
between urban and rural areas, as well as differing treatment technologies and
removal mechanisms between STPs and OSSFs. Further studies of the impact of
OSSFs on the environment are necessary by studying contaminants in recipients
with an exclusive inflow of OSSF effluent, so that results could be solely attributed
to OSSFs. In these studies, different distances between OSSFs and recipient could
also be investigated. Water levels, bioaccumulation in biota and the impact of
seasonal factors such as temperature, irradiation and nutrient availability could be
further investigated.
4.7. Removal in char-fortified soil beds
For Paper IV, the removal of organic contaminants from OSSF effluent using a
stand-alone sand filter, commonly used in SBs, was investigated in a long-term
(two-year) field setting and compared to removal using a char-fortified sand filter
and a char-fortified gas concrete (Sorbulite®) filter. Biochar was investigated as it
may enhance the removal of (semi-)polar contaminants, whilst gas concrete was
included since it is currently used to upgrade OSSFs for the removal of phosphates
[181] and had already been installed at the study site. The removal of compounds
identified by targeted and untargeted analysis with GC×GC-ToF-MS, GC-ToF-MS
and LC-ESI-IM-QToF-MS was thoroughly investigated with QSPR modeling, to
better understand the relationship between molecular properties and compound
removal by biochar.
The comprehensive screening of OSSF samples with three different instruments
resulted in 24 compounds confirmed by reference standards and 47 tentatively
identified compounds. Amongst these were 12 LC compounds on level 1
identification confidence (”confirmed structure”) and five LC compounds on
level 2 identification confidence (“probable structure” confirmed with MS/MS
library) [141] (see section 2.4.2). Twelve target GC-compounds were found that
overlapped with four GC compounds found by library search. A further
47 GC compounds remained tentatively identified with eight GC compounds
confirmed with low energy EI GC-ToF-MS. Two compounds were found using
GC-MS and LC-MS. Amongst the 74 compounds found in total were plasticizers,
UV stabilizers, fragrances, pesticides, surfactant and polymer impurities,
pharmaceuticals and their metabolites and many biogenic compounds.
51
Char-fortified sand filters removed these contaminants the best, followed by sand,
while gas concrete fortified with biochar performed worst (Figure 19). The
difference between the three treatments was significant according to the Friedman
test (α = 0.05) and the treatment efficiencies were significantly higher in
char-fortified sand compared to sand according to the Wilcoxon signed-rank test
(α = 0.05).
Figure 19. Boxplot showing the removal efficiencies of organic contaminants in char-concrete, sand, and
char-sand beds. The boxes represent the 25th and 75th percentiles, the whiskers indicate the 10th and 90th
percentiles, the horizontal lines indicate the median and the crosses represents the mean. Data points
representing the individual removal efficiencies are indicated as dots next to each boxplot (Paper IV).
QSPR modeling showed that sand filtration commonly used in soil bed/ infiltration
OSSFs can remove contaminants that are hydrophobic, have few heteroatoms and
few hydrogen bond donors and acceptors. Char fortification enhances the
treatment efficiencies of such contaminants (Figure 20) and a few hydrophilic
compounds. However, most of the very polar and hydrophilic were also not
completely removed by char-fortified sand, thus additional treatment methods are
needed to remove these compounds.
The difference in removal of individual compounds spanned from -7% to 49%, and
the small (4%), but significant increase in average removal efficiencies in char-
fortified sand, compared to sand, indicates that the difference is attributed to
sorption to biochar. However, microorganisms that colonized the filter beds after
a two-year run in a field setting and formed biofilm, are likely responsible for the
overall removal through biodegradation or sorption in the two filter beds. The
char-concrete sand char-sand
0
10
20
30
40
50
60
70
80
90
100
Rem
ova
l e
ffic
iencie
s (
%)
52
worst removal efficiency was by char-enhanced gas concrete, showing that it was
a poor medium for biofilm formation or that the combination of two coarse
materials reduced the hydraulic retention time to an extent, resulting in reduced
biodegradation and sorption (section 3.1.3,Table 4). Most studies on wastewater
treatment with biochar were carried out in short-term laboratory settings and
therefore discuss sorption as the major removal pathway [182] and ignore
biodegradation. In our study, we show that organic contaminants are still
significantly better removed with char-fortified sand, as compared with sand only,
after a two-year run period. In summary, this study suggests that biodegradation
plays a key role in the overall removal of organic contaminants and biofilm
formation and long hydraulic retention times should be promoted as well as
further investigated. The reduction of the discharge of very polar and hydrophilic
contaminants requires further research on alternative treatment methods, such as
ozonation.
Figure 20. Properties negatively and positively correlated to removal in char-fortified filter beds.
Although a large variety of contaminants with different polarities were covered by
the comprehensive analysis with GC-MS and LC-MS, differences in removal could
be further examined by extending the contaminant range with experiments in
ESI(-) mode to cover, for instance, carboxylic acids and alcohols that preferentially
form negative ions. Additionally, many unidentified features that might be
environmentally-relevant, were not further investigated due to the sole
identification via library searches. Features remained unidentified due to a lack of
library entries (NIST and vendor libraries) and the challenging workflows to
identify unknowns through non-target screening in LC-HRMS and unknown
analysis in GC-MS.
Hydrophobicity
van der Waals surface area (partial positive charge)
Negatively charged
Positively charged
van der Waals surface area (partial negative charge)
Heteroatoms
Polarizability
Polarity
Positive
correlation
Negative
correlation
53
5. Conclusions and future perspectives
Environmentally-relevant contaminants were found (Paper I) by untargeted
contaminant analysis with GC×GC-MS in combination with filtering and
prioritization based on estimated ecotoxicological risks, bioconcentration and
persistence, as well as the determination of removal efficiencies and semi-
quantified concentrations in OSSFs. A method was successfully developed for
these target contaminants and was also applicable to untargeted analysis. The
method was based on solid phase extraction and GC×GC-MS analysis in
wastewater and surface water and was used for subsequent studies (Paper II,
Paper III and Paper IV).
The prioritization strategy for untargeted GC×GC-MS data was successful in
identifying environmentally-relevant compounds discharged from OSSFs, since
several of the prioritized untargeted contaminants were found in high
concentrations in the effluent samples from the second OSSF sampling campaign
(Paper I). Some of these contaminants were also identified as being persistent,
mobile and bioavailable in a field study (Paper II). These contaminants were
galaxolide, TMDD, TCIPP, TCEP and TDCPP. Galaxolide and TMDD were also
found to be bioaccumulative.
Mobility, persistence, bioavailability and especially bioaccumulation could be
studied in other recipients or on a laboratory-scale to confirm the results of this
assessment. For instance, the field-based studies to investigate persistence could
be complemented with laboratory-scale studies of photolysis and biodegradation
to investigate whether these fate processes play a key role in the removal of the
studied contaminants from surface water in brighter and warmer months. Thus,
the impact of seasonal factors such as temperature, irradiation and nutrient
availability could be further investigated. Additionally, it would be simpler to
compare studies, if as a first step, harmonization discussions would lead to a
general agreement on persistence and mobility quantifiers in the field and as a
second step, better quantifiers would be found through further research.
The analysis method for fish did not cover all target analytes due to lipid
interferences and needs to be improved to cover a wider range of analytes. An
improved and extended method to remove lipids from fish extracts could be
valuable to further investigate bioaccumulation of untargeted wastewater
54
contaminants in biota. Accumulated levels in biota are not only a threat to the
ecosystem, but also to the health of fish consumers.
SBs, which are a type of OSSF, removed organic contaminants with a similar
effectiveness to STPs, with the exceptions of TBEP and TMDD for which SBs
worked significantly better. Yet, the removal of in particular more hydrophilic
contaminants was inadequate by both STPs and SBs (Paper I). For instance, the
median removal efficiencies of the chlorinated organophosphates TCEP, TCIPP
and TDCPP were < 56% in both SBs and STPs and their effluent concentrations
were in the nanogram per liter range. The poorer removal of hydrophilic
contaminants by both OSSF and STPs (Paper I), that were later shown to be
mobile, persistent and bioavailable (Paper II), implies that an upgrade of the
treatment facilities is necessary to reduce discharge of organic contaminants into
the environment and to avoid adverse ecological and human health risks.
Despite the similar concentrations and removal efficiencies noted in the study of
paired OSSF and STP influent and effluent samples (Paper I), the surface water
concentrations measured in river water seem to be somewhat lower at OSSF- than
STP-affected sites (Paper II and Paper III). The associated potential
ecotoxicological risks (MEC/PNEC) were higher at sites receiving primarily STP
effluents as compared with sites receiving primarily OSSF effluents (Galaxolide,
TBEP and TCIPP) and similar for octocrylene at OSSF and STP sites. No elevated
risks at the studied sites were found for the other studied contaminants. The
differences between OSSFs and STPs in terms of mass fluxes per capita could be
associated with deviations in user patterns between urban and rural areas as well
as deviations in treatment technologies and removal mechanisms. However, the
pronounced differences in June and September compared to December indicate
that fate processes, such as biodegradation and photolysis, may have influenced
the measured environmental concentrations. OSSFs are diffuse sources and there
were relatively long average distances between the OSSFs and the sampling sites,
whereas STPs are point sources with release points closer to the sampling sites.
Large uncertainties in water flow (up to 36%), estimated people (22%) using OSSFs
in each catchment, and the 95% confidence intervals from the recovery tests used
for the analytical uncertainty that become rather large due to the small sample size
(n = 3), all result in large overall uncertainties of the mass flux per capita
calculations and make interpretation more difficult. Additionally, none of these
sampling sites had direct and sole inflow of OSSF effluents or STPs, thus risks and
loads cannot solely be attributed to either OSSFs or STPs. Hence, further studies
on the impact of OSSFs on the environment are necessary by studying
55
contaminants in recipients mainly receiving OSSF effluent, allowing results to be
more certainly attributed to OSSFs.
OSSF soil beds can be fortified with biochar to enhance the removal of
hydrophobic contaminants and partly to enhance the removal of several
hydrophilic contaminants with heteroatoms (Paper IV). Removal in sand and
char-fortified sand was not solely associated to sorption, but also to
biodegradation. In fact, biodegradation seems to play a significant role after two
years of running in a field-based system (Paper IV). However, further research on
treatment technologies to remove all types of organic contaminants is necessary as
some of these contaminants were neither removed by sand filters nor with a
biochar-fortified sand filter. For this purpose, other sorbent types, such as
granulated activated carbon filtration or technical solutions e.g. ozonation, should
be tested in an OSSF field-scale setting. Such a study, investigating different
sorbents in a field study, is currently being carried out and is part of the same
project as this thesis (“RedMic”).
Untargeted analysis by LC-HRMS has already well accepted nomenclature [129]
and well-defined confidence levels [141] for the identification of unknowns. In
comparison, a nomenclature for untargeted analysis by GC-MS remains to be
defined and a similar definition for confidence levels for GC-based techniques is
missing. Until consensus has been reached regarding the proper concepts and
terms in GC-MS, similar confidence levels as for LC-HRMS could be used. For
instance, GC-EI peaks with good similarity and probability to NIST library entries
or computer-aided “in silico” spectra, which give a “likely candidate”, are similar to
level 2a or 2b candidates in LC-HRMS (“probable structure” [141]), respectively. If
the spectral library search or interpretation produce several candidates of
sufficient quality, it gives a level 3 identification confidence, “tentative candidates”
using the LC-HRMS nomenclature [141]. The additional use of low energy “soft” EI
or CI could increase the identification confidence for some candidates and
consensus structure elucidation could be used for candidate ranking to find the
most likely candidate [149].
Generally, the combination of targeted and untargeted analysis throughout
Paper I, Paper II and Paper IV has been proven valuable to investigate a large
variety of organic contaminants. In addition, the PCA used for Paper I and
Paper III helped to elucidate patterns in removal and mass fluxes per capita,
respectively, and the PLS used for Paper IV visualized the strength of latent
variables instead of single molecular properties to correlate properties and removal
efficiencies. The results from Paper I, Paper II, and Paper III implied that OSSFs
56
have similar or better removal efficiencies and have similar or lower environmental
impact regarding environmental risks and mass fluxes per capita. Biochar
fortification can improve the removal of organic contaminants in SBs, however,
further research on alternative technologies to remove all types of organic
contaminants is needed.
57
6. Acknowledgements
Thanks to my supervisor Peter Haglund for giving me the opportunity to do my
PhD in his lab. Thanks for all the interesting discussions, great scientific advice
and access to a unique selection of instruments. Thanks to my co-supervisor
Patrik Andersson for great discussions, for your support along the way, and
always keeping me on track. Thanks, that I could be part of the project RedMic and
had the opportunity to collaborate with Karin Wiberg, Lutz Ahrens, Gunno
Renman, Meri Gros, Wen Zhang, Qiuju Gao, Pablo Gago Ferrero, Berndt
Björlenius, Jerker Fick, Misse Wester, Erik Kärrman, Henrik Jernstedt,
Rikard Tröger, and Linnéa Tjernström. I also want to thank Stina Jansson and
Erik Björn for advice during my annual evaluations.
Thanks to all my current and former colleagues from Miljökemi for your support,
fruitful discussions, and company during fikas. Thanks to my fellow PhD council
members for interesting discussions and having a lot of fun during our organized
events. I would like to kindly thank Mátyás Ripszám for his technical support,
teaching me a lot about the HRT, and for a lot of fun at the climbing wall. Special
thanks goes to João Figueira for writing data processing scripts for me and for
being the best climbing partner and fellow dwarven companion. I want to
especially thank Christine Gallampois for many scientific discussions,
proofreading my thesis, and having you as a friend. To Mirva, who was one of my
first friends in Umeå and has ever since been a great support along the way. Thanks
also for reading parts of my thesis. Thanks to Sandra for helping me out as a
neighbor, proofreading my thesis, and for being an awesome friend. Thanks to Mar
for all the good times, for proofreading parts of my thesis, and for all the
dissertation tips and tricks. Qiuju, you are awesome, and it was great to work with
you in RedMic. Thanks Darya for your openness and friendship. Ivan, continue to
have a nice life ;). Thanks to Elin Näsström for introducing me to Guineu and
thanks to Ziye Zheng for help with MOE.
Thanks to Anna for our long-lasting friendship, our refreshing weekly lunches, and
being a patient Swedish teacher. Thanks to Dagmar for hanging out with me at
IKSU, but most of all for always smiling and passing on your good mood. Thanks
to Henke for a lot of fun during parties, dinners and pathfinder sessions.
58
Thanks to all the friends who have been a large part of the Umeå experience but
have unfortunately moved away, foremost Palle, Lina, Casper, Jon, Tobi, Moritz,
Hanna, Herja, and Warin. Thanks to Steffi, you have been a great support during
our Bachelor and thanks for all the great hiking adventures we could experience
together. Emmy, Nina, Bela, and Lisa, I am glad to have you as friends. You have
always been there for me, even with thousands of kilometers between us.
To Mama, Papa, Anne, Norman, Gisela, and the rest of my family for your
support and love.
Finally, thanks to Artur for going on this Sweden journey together with me,
enduring me during all this time, being a great support, and giving me your love.
59
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