Document 1958442

The measurement of chemical persistence in the field
by benchmarking-Theory and Experiment
Hongyan Zou
The measurement of chemical
persistence in the field by
benchmarking
Theory and Experiment
Hongyan Zou
©Hongyan Zou, Stockholm University 2015
ISBN 978-91-7649-146-1
Printer: Holmbergs, Malmö 2015
Distributor: Department of Environmental
Analytical Chemistry
Science
and
To my family
永远爱你们
Abstract
Persistence is one of the core criteria in chemical exposure and hazard
assessment. It is often defined as the half-life for the removal of a chemical
from a specified environment by transformation. Chemicals with long
transformation half-lives may pose high risks for wildlife or humans and be
subject to long-range transport to remote areas. It is challenging to measure
persistence directly in the field in view of the complexity of the natural
environment and spatial and temporal variability in environmental
conditions that may affect degradation.
The mass balance approach is the most commonly used method for field
measurement of persistence. In this thesis an alternative to the traditional
mass balance approach that uses benchmarking is proposed and evaluated
using models and field application. The benchmarking approach compares
the relative behavior of chemicals, rather than measuring the absolute value
of a property. The unknown property (persistence in this thesis) of test
chemicals can be estimated by comparison against another chemical for
which this property is known.
In Paper I, the potential of benchmarking to measure persistence in the field
was evaluated by modeling. A framework for applying benchmarking to
measure persistence in the field was developed. Lake systems with hydraulic
residence times of the order of months were identified as appropriate field
sites to measure the persistence of chemicals that are close to the regulatory
thresholds, which are also on the scale of months. Field studies in two
Swedish lakes were conducted. Both are shallow lakes, whereas Norra
Bergundasjön (Paper II) has a longer residence time (four months) than
Boren (one to two months; Paper III). In Paper II the benchmarking
approach was tested to measure the persistence of a group of chemicals that
were expected to stay in the water phase. Acesulfame K (artificial
sweetener) without observable degradation in the lake was used as the
benchmark chemical. The persistence of 9 pharmaceuticals and one X-ray
contrast agent was measured to range from <1-2 days (ketoprofen) to 5805700 days (carbamazepine). The results obtained using the benchmarking
approach agreed well with the mass balance approach, indicating that the
benchmarking approach can be a valid and useful method to measure
persistence in the field. In Paper III the seasonality in chemical persistence
was investigated by benchmarking. The seasonal difference in chemical
persistence was found to be largest between spring and autumn. The
persistence of 5 chemicals in spring were lower than in autumn, mainly
attributed to lower temperature and less sunlight in autumn. The spatial
variation of the persistence of chemicals was observed by comparing the
persistence of chemicals in spring in the two lakes. Thus benchmarking is a
i
useful tool to study the temporal and spatial variation of persistence in the
real environment.
Paper IV explores the potential of benchmarking thoroughly and the
application of benchmarking in a regulatory context. Benchmarking could
facilitate more field measurements of persistence, leading to a better
understanding of the temporal and spatial variability of persistence in
various environments and a basis for lab-to-field extrapolation. Besides
quantitative estimation of persistence in the field, benchmarking can be
applied to determine the relative magnitude of persistence, called threshold
benchmarking which could be a valuable tool in regulatory processes.
ii
Sammanfattning
Kemisk persistens är ett mått på hur långlivade kemikalier är i miljön. Det är
också ett av huvudkriterierna i riskbedömning av kemikalier och styr kemisk
exponering. Persistens definieras som halveringstiden för en kemikalie att
avlägsnas från en angiven miljö via kemisk omvandling. Kemikalier med
långa halveringstider kan innebära stora risker för djur och människor och
kan även transporteras långa avstånd till avlägsna områden. Det finns endast
ett begränsat antal mätningar av kemisk persistens i fält, främst på grund av
bristen på lämpliga metoder. Det är en utmaning att mäta persistensen direkt
i fält på grund av komplexiteten i form av skiftande förhållanden på olika
platser och vid olika tidpunkter, omständigheter som kan påverka
nedbrytningen.
Den vanligaste metoden för fältmätning av kemisk persistens är den så
kallade massbalansmetoden. I denna avhandling föreslås ett alternativ till
denna metod, nämligen användning av benchmarking. Benchmarkingmetoden utvärderas här i modeller och i fält. I den föreslagna metoden
jämförs det relativa beteendet hos kemikalier istället för att mäta egenskapen
i absoluta mått. Den okända egenskapen som ska bestämmas hos
testkemikalierna (kemisk persistens i denna avhandling) kan uppskattas i
jämförelser med en referenskemikalie vars egenskaper är kända. I papper I,
används modellering för att utvärdera den teoretiska potentialen för
benchmarking-metoden. Ett ramverk utvecklas för hur man kan tillämpa
metoden för att mäta persistens i fält.
För att bestämma persistensen hos kemikalier som ligger nära de
regulatoriska tröskelvärdena dvs med månadslånga halveringstider, visade
sig sjösystem med uppehållstider på vattnet i samma tidsrymd som
halveringstiden vara bäst lämpade. Fältstudier i två svenska sjöar (Norra
Bergundasjön i papper II och Boren i papper III) genomfördes. Båda är
grunda sjöar, Norra Bergundasjön med en längre uppehållstid (fyra månader)
än Boren (en till två månader). Provtagningskampanjerna för de två
fältstudierna startade vid samma tid på året, på senvåren. I Boren (papper
III) fortsatte provtagningen även under höst och vinter. I papper II testades
benchmarking-metoden för att mäta en grupp svårnedbrytbara kemikalier
som främst förekommer i vattenmassan. Sötningsmedlet Acesulfam K som
inte visade någon märkbar nedbrytning i sjön användes som
referenskemikalie.
Persistensen
hos
9
läkemedel
och
ett
röntgenkontrastmedel bestämdes vara mellan <1-2 dagar (ketoprofen) upp
till 580-5700 dagar (karbamazepin). Resultaten från benchmarking-metoden
överensstämde väl med massbalansmetoden, vilket indikerar att
benchmarking kan vara en användbar metod för att mäta persistens i fält. I
papper III undersöktes årstidsvariationer i kemisk persistens med hjälp av
iii
benchmarking. Skillnaden i kemisk persistens var störst mellan vår och höst.
Persistensen för fem kemikalier under våren var lägre än under hösten, vilket
främst kan hänföras till lägre temperatur och mindre solljus på hösten. Den
rumsliga variationen av substansernas persistens undersöktes genom att
jämföra persistensen uppmätt under våren i både Boren och Norra
Bergundasjön. Benchmarking visade sig vara ett användbart verktyg för att
studera den tidsmässiga och rumsliga variationen av persistens uppmätt i
fält.
I Papper IV undersöks potentialen för benchmarking mer ingående och dess
tillämpning i regulatoriska sammanhang. Metoden kan underlätta för fler
fältmätningar av persistens, vilket leder till en bättre förståelse av den
tidsmässiga och rumsliga variationen i olika miljöer. Vidare kan
fältmätningar utgöra grunden för extrapolering från laborativa försök till
naturliga miljöer. Förutom en kvantitativ uppskattning av persistensen i fält,
kan benchmarking användas för att fastställa den relativa storleken på
persistens, så kallad tröskel-benchmarking som föreslås kunna vara ett
värdefullt redskap i det regulatoriska arbetet.
iv
List of papers
Paper I: Evaluation of the potential of benchmarking to facilitate the
measurement of chemical persistence in lakes.
Zou, H.; MacLeod, M.; McLachlan, M. S. Chemosphere 2014, 95, 301–309.
Paper II: Using chemical benchmarking to determine the persistence of
chemicals in a Swedish lake.
Zou, H.; Radke, M.; Kierkegaard, A.; MacLeod, M.; McLachlan, M. S.
Environ. Sci. Technol. 2015, 49 (3), 1646-1653
Paper III: Seasonality in chemical persistence in a Swedish lake assessed
by benchmarking.
Zou, H.; Radke, M.; Kierkegaard, A.; McLachlan, M. S. Submitted to
Environmental Science and Technology.
Paper IV: Using benchmarking to strengthen the assessment of
persistence.
McLachlan, M. S.; Zou, H.; MacLeod, M. Manuscript.
Contributions to the papers
Paper I
I built the model, created different scenarios, ran the model and interpreted
the results. I took the lead in writing the paper.
Paper II
I was responsible to find such lakes that met our requirements and involved
in communicating with STP personnel. I was involved in planning the field
campaign. I was responsible for collecting lake water samples and
transporting wastewater samples taken by STP personnel and also for all lab
analysis. I was also responsible for all the calculations. I took the lead in
writing the paper.
Paper III
I was involved in planning the sampling campaign and communicating with
STP personnel. I was responsible for taking lake water samples and
transporting wastewater samples taken by STP personnel and all lab
analysis. I did all the calculations and took the lead in writing.
Paper IV
I was involved in the discussions and some parts of the writing and
responsible for most of the literature work.
v
Contents
Abstract ................................................................................................... i
Sammanfattning ..................................................................................... iii
List of papers .......................................................................................... v
Contributions to the papers ...................................................................... v
Contents ................................................................................................ vi
Abbreviations ....................................................................................... vii
1. Introduction........................................................................................ 1
1.1 Persistence of chemicals .............................................................................. 1
1.2 The factors that influence persistence ....................................................... 1
1.3 Persistence measurements ........................................................................... 4
2. Objectives .......................................................................................... 6
3. Methods .............................................................................................. 7
3.1 Multimedia fate models ............................................................................... 7
3.2 The benchmarking approach....................................................................... 9
3.3 PPCPs in the aquatic environment ........................................................... 10
3.4 Sampling and analysis ............................................................................... 12
3.4.1 Sampling campaign ........................................................................... 12
3.4.2 Analysis ............................................................................................... 13
4. A summary of the results................................................................... 15
4.1 Theoretical evaluation of benchmarking to measure persistence in the
field ..................................................................................................................... 15
4.2 Test the method: using benchmarking to measure persistence in the
field and comparing benchmarking with mass balance ............................... 19
4.3 Application of the method: using benchmarking to study the temporal
and spatial variability of persistence in the field .......................................... 21
4.4 Potential of the benchmarking approach, especially in a regulatory
context ................................................................................................................ 22
5. Conclusions...................................................................................... 24
6. Future outlook .................................................................................. 25
Acknowledgement................................................................................. 27
References ............................................................................................ 29
vi
Abbreviations
CEPA
Canadian Environmental Protection Act
CSA
Chemical safety assessment
HDPE
High-density polyethylene
KOW
Octanol-water partition coefficient
KAW
Air-water partition coefficient
MDL
Method detection limit
P
Persistent
PBTs
Persistent, bioaccumulative, and toxic substances
POPs
Persistent organic pollutants
PPCPs
Pharmaceutical and personal care products
QSARs
Quantitative Structure-Activity relationships
QSPRs
Quantitative Structure-Property relationships
REACH
UNECE
Registration, Evaluation, Authorization and Restriction
of CHemicals
United Nations Economic Commission for Europe
UNEP
United Nations Environment Programme
vPvB
very persistent, very bioaccumulative
WWTPs
Wastewater water treatment plants
vii
1. Introduction
1.1 Persistence of chemicals
To protect humans and the environment from chemicals that may exert
adverse effects, there are a variety of regulatory frameworks for the
identification and evaluation of problematic chemicals, for instance, REACH
(Registration, Evaluation, Authorization and Restriction of Chemicals),
UNEP (the United Nations Environment Programme) Stockholm
Convention on Persistent Organic Pollutants (POPs), and the UNECE (the
United Nations Economic Commission for Europe) POP Protocol.
(Moermond et al., 2012; van Wijk et al., 2009). Under REACH, chemical
safety assessment (CSA) is required to be conducted for chemicals
manufactured or imported in annual amounts of over 10 tonnes unless
exemption is granted (ECHA, 2014). For the CSA, a PBT (Persistent,
Bioaccumulative, Toxicity) or vPvB (very Persistent, very Bioaccumulative)
assessment is required. Persistence is one of the criteria included in PBT
assessment and is often defined as degradation half-life. Chemicals with high
persistence have a high potential to accumulate in the environment and
living organisms. Persistent chemicals can be transported to remote pristine
areas far away from where they are produced or used. Furthermore, after
emissions cease it will take a long time for the concentrations of persistent
chemicals to drop down to a low level.
Different persistence criteria are used in different frameworks for persistence
assessment. For REACH, the thresholds of a chemical to be considered as
persistent are 60 days in marine water, 40 days in fresh water, 180 days in
marine sediment and 120 days in fresh water sediment while CEPA
(Canadian Environmental Protection Act) specifies 182 days in water and
365 days in sediment (Boethling et al., 2009). Unlike many chemical
properties, persistence is not an intrinsic property of the substance but
environment-dependent. The chemical-specific properties together with
environmental conditions determine the degradation rate of chemicals
(Boethling et al., 2009). Thus challenges arise in the light of the influences
of environmental conditions when evaluating persistence and classifying
chemicals as P (persistent).
1.2 The factors that influence persistence
Chemical degradation can occur via a range of biotic and abiotic processes,
including biodegradation, hydrolysis, and photolysis etc. (Battersby, 1990;
1
Boethling et al., 2009). The molecular structure of the chemical determines
which transformation pathway is important and how fast the chemical can be
degraded. Environmental factors can also influence transformation rates.
Therefore, persistence can be expected to vary in space and time
where/when these influencing factors differ (Bartholomew and Pfaender,
1983; Mackay et al., 2001; Ahtiainen et al., 2003; Boxall et al., 2004;
Labadie and Budzinski, 2005; Vieno et al., 2005; Bending et al., 2006;
Storck et al., 2012).
Biologically mediated degradation (enzymatic or metabolic reactions) plays
a very important role in determining the fate of organic chemicals in the
natural environment (Schwarzenbach et al., 2003). Biodegradation here does
not mean mineralization but a transformation that alters the original
chemical structure to a different one. Mineralization of chemicals refers to
the conversion of chemicals to CO2 or other stable inorganic forms such as
CH4. The rates of biodegradation in surface media (water, soil or sediment)
depend on temperature, ionic strength, pH, redox potential and the
distribution and concentrations of nutrients and other chemical substrates
(Boethling et al., 2009). The redox potential in particular has considerable
effects on transformation products and rates. Under different redox
conditions, the terminal electron acceptor can be sulfate, nitrate or acids like
acetate and formate (anaerobic biodegradation) rather than oxygen (aerobic
biodegradation). The impacts of pH and temperature are diverse and not easy
to predict, and they can be both direct and indirect (Rosso et al., 1997;
Siddique et al., 2002). Normally biodegradation rates are slower at lower
temperature, but different microorganisms have different optimal
temperature conditions and they are adapted to prevailing environmental
conditions. Therefore, the transformation rate cannot be reliably related to
temperature. For the influence of pH, some chemicals dissociate to different
extents at different pHs and the biodegradability might be affected when one
form is more easily transported into the cell and metabolized than another. In
lab studies, an initial lag time before degradation starts is often a sign of
biodegradation (Swinnen et al., 2004). Biodegradation can be distinguished
from abiotic losses like hydrolysis and photolysis using sterile and dark
controls that are often included in biodegradation tests (e.g. OECD 301,
OECD 308, OECD 309). The differences in the concentrations (often higher
in the lab than in the environment) of test chemicals applied in the laboratory
standardized tests and occurring in the environment (Alexander, 1985), the
unrealistic ratio between sediment and water in the lab (in higher-tier
simulation tests; Klecka and Muir, 2008), and the temporal and spatial
variation of other environmental factors contribute to the uncertainty of
biodegradation rate estimation. Accordingly, estimation of biodegradation
rates is an important source of uncertainty of the assessment of persistence in
the chemical safety assessment (Ahtiainen et al., 2003).
2
Hydrolysis provides the baseline loss rate for any chemical in the aqueous
environment (OECD 111; Boethling et al., 2009). Hydrolysis can be an
important loss process of a chemical in the environment, especially for
chemicals that are dissolved in water such as pesticides and plasticizers
(Mabey and Mill, 1978). It mainly depends on pH and temperature in the
environment (Mabey and Mill, 1978). In practice, the hydrolysis degradation
half-life is required to be determined at several temperatures and pHs in the
lab. This measurement in the lab is readily performed. The pHs and
temperatures in the environment are also easily measured, so the validity of
extrapolating hydrolysis rates from laboratory measurements to
environmental conditions is relatively solid compared with biodegradation
and photolysis. However, hydrolysis can also be affected by other factors.
Perdue and Wolfe (1982) and Georgi et al. (2008) showed that the content of
humic substances has an influence on the hydrolysis kinetics of pollutants.
This example indicates that some uncertainties may be induced when
measuring the hydrolysis rate in labs and doing lab-to-field extrapolation.
In the environment, photolysis, another abiotic transformation process, also
takes place for some organic compounds (Zepp and Cline, 1977; Boreen et
al., 2003; Boethling et al., 2009). In laboratory studies, photon fluxes,
generated for example by xenon lamps or mercury lamps, are used to
bombard compounds in pure water or in the gaseous phase, and the
measured disappearance of chemical is used to estimate the photolysis rate.
However, in the natural environment, compounds might absorb to particulate
or dissolved organic matter and become unavailable for photolysis reactions.
Chemicals may also be transported and accumulated in the sediment and
became inaccessible for photodegradation. Photolysis in the environment
can be direct and indirect. Particulate or dissolved organic carbon in the
environment may attenuate the sunlight and thus lead to lower direct
photolysis rate (Zepp and Cline, 1977). Nevertheless, particulate or
dissolved organic matter may also promote indirect photodegradation and in
some cases they may have chemical or physical interactions with the
chemical (Miller and Zepp, 1979; Zepp et al., 1984; Boethling et al., 2009).
Temperature has little effects on photolysis while pH may have some effects
since the UV absorption specturms are different for the dissociated and
neutral forms (Boethling et al., 2009). Thus the photolysis rate may be
reduced or enhanced in the environment compared with the measured values
in the lab tests in pure water or the gaseous phase. Considering the
influences of different environmental factors on different transformation
processes, the degradation rate of a chemical will be very variable in the
environment and this variability is difficult to predict from laboratory data
alone.
3
1.3 Persistence measurements
Current regulations prescribe biodegradation tests in the laboratory for
persistence assessment. For instance, in the European REACH regulation
(ECHA, 2014), ready biodegradability (OECD 301), inherent
biodegradability (OECD 302 A-C), and, if necessary, higher-tier tests, i.e.
simulation tests (OECD 303) or transformation tests (e.g. OECD 307, 308
and 309) are used to screen or evaluate persistence. In the absence of
available lab data, Quantitative Structure-Activity Relationships (QSARs) or
Quantitative Structure-Property Relationships (QSPRs) can be used as a
screening tool to estimate biodegradability or half-life, whereby they
generally have the same empirical basis, being based on the laboratory
degradation tests (Raymond et al., 2001; Philipp et al., 2007; Papa and
Gramatica, 2008; Blotevogel et al., 2011; Guillén et al., 2012; Rauerst et al.,
2014). In biodegradability tests (e.g. OECD 301 A-F, 302 A-D, 310 and
311), a solution, or suspension, of the test substance in a mineral medium is
inoculated and incubated. A relatively higher and often unrealistic
concentration is applied and non-specific parameters are measured such as
dissolved organic carbon, oxygen uptake and gas production etc. Simulation
tests are performed under more environmentally relevant conditions and
designed to represent a specific environmental compartment (e.g. water,
water/sediment etc.) (Bowmer et al., 2004). A lower (more environmentrelevant) concentration and indigenous biomass, media, relevant solids (i.e.
soil, sediment, activated sludge or other surfaces) are used in simulation
tests. In contrast with biodegradability tests that can only provide an
indication of whether chemicals undergo primary or ultimate degradation,
simulation test can give quantitative information on the rate of
biotransformation of chemicals. However, the simulation tests can be
influenced by the experimental temperature, pH or other factors and these
factors are essential environmental properties that can influence the
persistence of chemicals in the real environment. Therefore, if laboratory
tests are to be used to estimate the environmental persistence of a chemical,
it is necessary to demonstrate the correspondence between the laboratory test
results and the persistence of chemicals in the environment. Furthermore,
one needs to establish under what range of environmental conditions a
reasonable agreement between laboratory test result and environmental
persistence can be expected.
To our knowledge, there is limited evidence demonstrating the alignment
between persistence measured in laboratory tests and in the real
environment. This is because there are limited field measurements of
persistence. Even though in some regulations weight-of-evidence evaluation
that integrates different sources of degradation data is advocated for
persistence assessment, field measurements have been seldom used in
4
persistence assessment because there are not many field measurements of
persistence.
To directly measure persistence in the field is challenging because of the
complexity and variability of the real environment. One method is to do
semi-field measurements in mesocosms, which are a scaled-up experiment in
which one tries to reproduce a small portion of the natural environment
under controlled conditions (Wakeham et al., 1983; Kangas and Adey, 1996;
Zhou et al., 2013). Its scale lies in between laboratory and real
environmental. Mesocosms have been used to study the behavior and
depletion of pesticides and pharmaceuticals in aquatic systems (Knuth et al.,
2000; Thompson et al.s, 2004; Zhang et al., 2013). However, the full
complexity of the environment still cannot be included and the methodology
has problems with reproducibility and repeatability. Among the potential
artifacts, wall effects and the water-sediment interactions in the real
environment need to be considered in mesocosm experiments (Nally, 1997;
Berg et al., 1999; Ahn and Mitsch, 2002).
Another method to measure persistence in the field is to conduct a full
chemical mass balance of the total inputs into the system and the outputs out
of the system. Some excellent studies have been done in Lake Greifensee
and Lake Zurich in Switzerland for pharmaceuticals and UV-filter chemicals
(Buser et al., 1998; Poiger et al., 2001; Tixier et al., 2003; Poiger et al.,
2004). A mass balance necessitates quantifying all the important mass fluxes
of chemicals in natural systems. The real environment is characterized with
large spatial and temporal variability. Thus, doing a mass balance for a
specific environment can be difficult, time-consuming and resource-costly.
For instance, it would be impossible to quantify the inputs or outputs in the
case of inability to access the sampling sites; it would be difficult to quantify
the mass flow rate in the case of inhomogeneous distribution of the chemical
in the environment (e.g. a lake) or an intermittent emission source and so on.
Furthermore, for chemicals for which dissipation processes other than
degradation and advection (e.g. volatilization to air, burial in sediment etc.)
play an important role in the loss of chemicals from the environment of
interest (e.g., a lake), the characterization of these processes would be
difficult and laborious if using the mass balance approach. If the dissipation
processes and not degradation dominate the loss of chemicals in the
environment, it would be more difficult to quantify the loss by degradation
or the half-life due to degradation. Therefore, alternatives to the mass
balance approach to do field measurements of persistence could be useful.
This is especially important for chemicals with half-lives close to or
exceeding regulatory thresholds, because in regulations (e.g. REACH)
degradation half-life is used as the threshold to classify a chemical as
persistent.
5
2. Objectives
The overall objective of this thesis is to evaluate the potential of the
benchmarking approach to measure persistence in the field (Figure 1). The
benchmarking approach compares the behavior of the two chemicals (the
test chemical of interest and the benchmark chemical), in contrast to the
mass balance approach, which measures the absolute value of chemical
fluxes. The benchmarking approach may overcome some of the limitations
of the mass balance approach mentioned above.
To achieve the overall objective, three hypotheses were tested:
1. It is possible in principle to apply the benchmarking approach to measure
chemical persistence in the field (theoretical evaluation; Paper I).
−This was done by using a simple one-box multimedia fate and transport
model.
2. The benchmarking approach can be applied in practice to measure the
persistence of long-lived chemicals in lakes and the results agree with the
traditional mass balance approach (test the method; Paper II).
−This was tested using a group of non-volatile PPCPs (pharmaceuticals
and personal care products).
3. The benchmarking approach can be used to investigate the temporal and
spatial variability of persistence in lakes (application of the method; Paper
III).
−This was also tested using non-volatile PPCPs.
Last, Paper IV thoroughly discusses the potential of the benchmarking
approach to measure persistence in the field and proposes the application of
benchmarking in a regulatory context.
Figure 1: Illustration of this project.
6
3. Methods
3.1 Multimedia fate models
There are numerous anthropogenic chemicals with a variety of properties
(water solubility, vapor pressure, biodegradability, etc.) present in the
environment, and many more are being introduced each year. In order to
evaluate the concentrations and behavior of these chemicals in different
environmental media (air, water, soil, sediment and living organisms etc.),
models can be a valuable and useful alternative to field or lab work,
considering insufficient resources such as time, money and equipment etc.
(Webster et al., 1998; MacLeod et al., 2010; Buser et al., 2012). What
models do is to simplify the complexity of the environment by making
several assumptions. In models, several boxes representing different
environmental media are constructed with defined boundary conditions.
Parameterized with physicochemical properties of chemicals, emissions,
environmental properties and boundary conditions, models can estimate the
levels of chemicals in every medium and fluxes between media. Models can
provide a guide for field or lab studies. They can also prioritize chemicals
that pose the greatest concern. Thus, models can be used as a preliminary
tool for chemical hazard and risk assessment of chemicals and are included
in the weight-of-evidence evaluation in national and international
frameworks and treaties (Boethling et al., 2009).
Multi-media mass balance models are the most commonly used models to
estimate the fate, transport and distribution of organic chemicals in the
environment or in organisms (Buser et al., 2012). Baughman and Lassiter
first developed such models for aquatic systems (Baughman and Lassiter,
1978), which were later on adapted and extended by Mackay (Mackay,
1979). In these models, the world is divided into different compartments
such as air, water, soil and sediment, or different organs in organisms. Each
compartment is considered to be homogenous and a mass balance equation is
formulated for each compartment based on the law of conservation of mass.
Each compartment can also be further subdivided into different boxes with
specified volume, composition and density to achieve higher spatial
resolution. Multimedia fate models with different levels of complexity (from
Level I to IV models) are available (Mackay and Paterson, 1991; Cahill and
Mackay, 2003). The simplest case is Level I, which is a model of an isolated
system and assumes thermodynamic partitioning equilibrium between
phases. Compared with Level I, Level II models are more realistic
representations that allow chemical reaction and advection in an open
system, but assume steady state and retain the equilibrium assumption. For
Level III models, equilibrium is no longer assumed and internal
7
disequilibrium can develop because of interphase resistances. However, the
steady state constraint still applies. Level IV models use differential
equations to express the time-dependent concentrations of chemicals at nonsteady state. Emissions or system properties can change over time. Level IV
models in principle reproduce the environment better and provide more
accurate chemical concentrations (Cahill and Mackay, 2003). Some
examples of multimedia fate models are SimpleBox (Klasmeier et al., 2006),
QWASI (Level III/IV) (Mackay and Diamond, 1989), ChemCAN (Level III)
(Mackay et al., 2009) and CoZMo-POP (Level IV) (Wania et al., 2006).
Based on the purpose of each study, one can choose models with different
complexity and find the right balance between the necessary detail to
represent the problem and the parameters needed for model inputs. It is not
always good to choose a high-resolution model even though it provides a
better representation of the environment. More complex models usually have
greater data input requirements and need more computing time. Simple
models may be able to achieve the same goal with fewer inputs, for instance
when ranking chemicals for their relative persistence. They are particularly
attractive for international regulation purposes (Mackay et al., 2001). Cahill
and Mackay (2003) compared the results from Level II models, lowresolution Level IV models and high-resolution Level IV models for 10
chemicals and concluded that each model can be applied in its respective
application scope. They concluded that a low-resolution Level IV model
often provides adequate regional information when chemicals are emitted to
air or water compared with high-resolution Level IV models. Simple Level
II models can be useful for establishing the partitioning tendencies and for
ranking chemicals based on their relative persistence at steady state.
In this project, we focused on aquatic systems, in particular on lake systems
with long hydraulic residence times in order to measure the persistence of
chemicals on the scale of months. To better characterize the hydraulics of the
lake system, we started working on well-mixed lake systems. Well-mixed is
a good approximation of the hydraulics in many lakes especially in spring
and autumn. The model that we used was a simple one-box Level II steadystate model. It is not possible to replicate all features of the environment in a
model. As mentioned above, the features of the environment that one
chooses to address depend on the purposes of the model application and the
availability of input information. A simple model could be a better option in
aspects of pedagogical elegance and ease of parameterization if it can
achieve the same goal compared with more complex models.
8
The one-box model used in
this thesis is illustrated in
Figure 2. This model was
used to evaluate the
potential of benchmarking
to measure persistence in the
field (Paper I) and to
quantify the persistence in
the field studies (Papers II
and III). In the model, a Figure 1: One-box model used in this project
whole lake was considered (Paper I).
as one box consisting of
water, suspended particles and surficial non-buried sediments. Figure 2
shows the mass transfer across the boundaries of the system. There are
chemical inputs, which are not distinguished further but could include the
contribution from inflowing water, inflowing groundwater, direct emissions,
etc. There are four removal processes out of the box including volatilization,
water advection, sediment burial and degradation (e.g. hydrolysis,
photolysis, biodegradation, etc.). Equations for each process were compiled
in a mass balance (see eq. 1 in Paper I) and coded in Excel. Note the
assumptions used in the model, i.e. steady-state, well-mixed, partitioning
equilibrium between the water and the sediment, and first-order chemical
transformation kinetics in the lake. The lakes that we chose to work on in
this thesis are shallow lakes. Thus no strong stratification and well-mixed
conditions were expected. The well-mixed assumption was tested through
comparing the chemical concentrations at different locations in the lake
(Papers II and III). Considering the chemicals that were studied and the
long hydraulic residence time of the lakes, a near steady state situation was
expected. This was tested by investigating the temporal variation of the
emissions, i.e., from the WWTP in this thesis (Papers II and III).
Importantly, a dynamic (non-steady state) model was run to further check
the validity of the steady state assumption. Since the lakes are shallow,
wind-induced resuspension events are relatively frequent, encouraging
exchange between the water column and sediment. Thus the assumption of
equilibrium between water and sediment is reasonable. The chemicals that
were selected in Paper II and III have low concentrations in the lake (most
of them are in the ng/L range). Therefore first-order transformation kinetics
is also a reasonable assumption.
3.2 The benchmarking approach
Given the limitations discussed above of other methods to measure
persistence (laboratory tests, QSARs or QSPRs, mesocosms and mass
9
balance studies) and the needs to measure persistence directly in the field,
alternative approaches to overcome the limitations are motivated and
necessitated. The benchmarking approach provided such opportunities.
In the benchmarking approach, the behavior of two chemicals (the test
chemical and the benchmark chemical) are compared to estimate the
unknown property (here persistence) of the test chemical (Figure 3). The
benchmark
chemical
behaves in the same way
as the test chemical, with
the (possible) exception
of the unknown property
of
interest.
The
persistence
of
the
benchmark chemical is
known. In a lake system,
the persistence of the test Figure 2: Illustration of benchmarking to measure
chemical
can
be the persistence of test chemical using benchmark
determined by comparing with a single emission source (from Paper I).
the
ratio
of
the
concentrations (or mass flows) of the test and benchmark chemicals in the
inlet with the ratio in the outlet, if the hydraulic residence time between the
inlet and outlet can be characterized.
The benchmarking concept has been employed in many contexts. In the
assessment of ecological risk, the reported concentrations are compared with
a set of toxicological benchmarks to screen chemicals (Suter II and Tsao,
1996; Bu et al., 2013). Cowan-Elsberry et al. suggested that the ratio of
exposure and emissions of candidate chemicals can be benchmarked to the
ratio of known POPs in order to obtain information about relative risk of
candidate chemicals when combined with information on their relative
toxicity (van Wijk et al., 2009). PCB180 was used as a benchmark to assess
the bioaccumulation of three semi-volatile compounds (Kierkegaard et al.,
2011). All of the usages of ‘benchmark’ above are evaluative in that
benchmarks served to assess the relative magnitude of a given property
between different chemicals. The application of the benchmarking concept
in connection with the assessment of persistence is described in details in
Paper IV. Two examples are the use of known chemicals for measuring
atmospheric photodegradation rate in lab studies and the use of fluorescent
dyes as tracers in river fate studies.
3.3 PPCPs in the aquatic environment
In the field studies in this thesis (Paper II and III), a group of PPCPs was
selected to study. The occurrence of PPCPs in the environment including
10
surface water, groundwater, drinking water and soils etc. is frequently found
and has become a contamination issue of concern in recent years (HallingSørensen et al., 1998; Miège et al., 2009; Walters et al., 2010). The exposure
of aquatic life to PPCPs is particularly troublesome where there is
continuous chemical emission and multigenerational exposure, even to low
concentrations (Daughton and Ternes, 1999). Some PPCPs are classified as
persistent and bioaccumulative in the aquatic environment (Khetan and
Collins, 2007; Howard and Muir, 2011). After administration or usage, the
original molecules or metabolites of PPCPs can be excreted or washed into
sewage systems and consequently emitted into receiving waters if there is
incomplete removal through wastewater water treatment plants (WWTPs)
(Kasprzyk-Hordern et al., 2009). WWTPs are considered to be significant
sources and in many cases the major routes of PPCPs into surface water
(Ellis, 2006). The chemical concentrations in the wastewater are potentially
variable over time. There have been some studies of seasonal variations of
PPCP concentrations in the influent and effluent of WWTPs (Loraine and
Pettigrove, 2006; Yu et al., 2013).
The concentrations of PPCPs in surface water also vary over time (Poiger et
al., 2004; Vieno et al., 2005; Stamm et al., 2008; Daneshvar et al., 2010; Liu
and Wong, 2013; Yu et al., 2013). This is determined by the temporal
variability in emissions into the lake and the following environmental
behavior of chemicals in the lake. The variation in the concentrations of
chemicals in the water can further influence the chemical exposure in the
lake ecosystem. If the season of high concentrations coincides with
development phases during which the organisms are particularly sensitive to
the PPCPs then the ecotoxicological consequences could be more severe.
Thus, to understand the variation of chemical exposure over time or the
seasonality in chemical exposure in aquatic ecosystems, one needs to
investigate the seasonality in both emissions and the processes that occur in
the water after emission, especially the seasonality in chemical persistence in
the water.
For the field studies in this thesis (Paper II and III), we studied ten to
twelve pharmaceuticals, one X-ray contrast agent and one artificial
sweetener (Table 1). Most of them are hydrophilic and non-volatile, which
means that once emitted to a lake they mainly stay in the water phase.
Advection out of the lake can be the dominant physical loss process for these
chemicals, and there are no requirements to quantify other physical removal
processes like burial and volatilization.
11
Table 1 Chemicals studied in this thesis (for the chemical properties, see Table 1
in Paper II and Table S1 in Paper III).
Chemicals
Use
Paper in the
thesis
Acesulfame K
Acetaminophen
Bezafibrate
Carbamazepine
Climbazole
Diclofenac
Diatrizoic acid
Fluconazole
Furosemide
Gemfibrozil
Hydrochlorothiazide
Ibuprofen
Ketoprofen
Sulfamethoxazole
Artificial sweetener
Analgesic and antipyretic
Blood lipid regulator
Anti-epileptical drug
Antifungal agent
Anti-inflammatory drug
X-ray contrast agent
Antifungal drug
Diuretic drug
Blood lipid regulator
Diuretic drug
Anti-inflammatory drug
Anti-inflammatory drug
Antibiotic
Paper II & III
Paper III
Paper II & III
Paper II & III
Paper II & III
Paper II & III
Paper II & III
Paper II & III
Paper II & III
Paper II & III
Paper II & III
Paper III
Paper II & III
Paper II & III
3.4 Sampling and analysis
3.4.1 Sampling campaign
In Paper II, the sampling campaign was designed to fulfil the two aims:
testing the benchmarking approach to measure persistence in the field and
comparing the benchmarking approach with the mass balance approach. It
was conducted in a Swedish lake, Norra Bergundasjön, with one major water
inlet and one large WWTP discharge. Wastewater samples from the WWTP
(Input) and the lake water samples at the lake outlet (Output) were collected
in late spring 2013. To quantify the chemical input with the inflowing water
from Södra Bergundasjön, samples of the inflowing water were collected.
This input was expected to be negligible due to there being no significant
wastewater discharges to the
watershed upstream of Norra
Bergundasjön.
Since
the
chemicals
studied
were
expected to mainly stay in the
water phase, there was no need Figure 3: An illustration of the sampling
to quantify the other two campaign.
12
physical loss processes, i.e. volatilization and sediment burial. Figure 4
shows a guide for the sampling campaign. In order to apply the
benchmarking and mass balance approaches, the two assumptions need to be
tested. The well-mixed assumption was tested by looking at whether the
concentrations of the chemicals were homogenously distributed in the lake.
To this end, several samples were taken at three locations in the lake in
addition to the outlet. To test the steady state assumption, 7 day flowproportional wastewater effluent samples were collected for two months
from the WWTP in late spring 2013.
In Paper III, the sampling campaign was designed to investigate the
seasonality of chemical persistence using the benchmarking approach. It was
conducted in Boren, another Swedish lake with one major water inlet and
one large WWTP discharge. The wastewater samples (Input), the samples of
the inflowing water from Vättern (Input) and the outlet water (Output) were
collected in late spring, late autumn and winter 2013. The well-mixed
assumption was tested in the same way as in Paper II. For the steady state
assumption, samples from the WWTP and the inflowing water from Vättern
were taken over extended periods of time. In both papers, the absolute mass
flow rates into the lake (from the wastewater samples (Paper II) or from
both the wastewater samples and the inflowing water samples (Paper III))
and out of the lake (from the outlet water samples) were used for the mass
balance approach, while the ratios of the benchmark and the test chemicals
were used for the benchmarking approach. Detailed information about the
lake properties and the sampling campaigns are described in Papers II and
III.
In the two WWTPs in Papers II and III, a permanently installed flowproportional sampling system was used to sample the effluent samples. The
samples were kept at 2 °C in the sampler, and they were transferred every 24
hour to 2 L HDPE bottles and frozen. After 7 days, aliquots of each daily
sample were combined to give a weekly flow-proportional sample. For lake
water samples, grab samples were collected in HDPE bottles.
3.4.2 Analysis
In the benchmarking approach, the ratio of the concentrations (or mass flow
of chemicals) was used for measuring persistence in the lakes. Thus a major
goal of the analytical method was to maximize the precision of the
measurement of the test chemical / benchmark chemical concentration ratio,
so as to better distinguish the difference in the ratio between the inlet and the
outlet. The analytical method was developed first based on the lake water
taken from Boren (Paper III) where the chemicals had low concentrations
(<1 ng/L) because of high dilution effects from Vättern. Therefore, methods
with better recoveries were needed. Most of chemicals that were studied are
13
pharmaceuticals, most of which are acidic or neutral (see Table 1 in Paper
II). An artificial sweetener (acesulfame K) and an X-ray contrast agent
(diatrizoic acid) were also studied. According to Lavén et al. (2009), Yu and
Wu (2011) and Al-Qaim et al. (2014), basic, neutral and acidic
pharmaceuticals can be extracted by Oasis MAX, HLB and MCX cartridges.
After testing the three extraction sorbents using Milli-Q water, HLB was
shown to have the best recoveries for most of the chemicals except
acesulfame K and diatrizoic acid. To improve the recoveries of these two
chemicals, two further sorbents were added based on the literature (Ordóñez
et al., 2012). A self-packed cartridge was prepared, containing three sorbents
(from top to bottom: Oasis MAX/ Oasis HLB/ Isolute ENV+). Compared
with using the single sorbent Oasis HLB, the recoveries were better for all of
the chemicals studied when Milli-Q water was tested. For the extraction of
lake water (Boren), different extraction volumes were tested and 1.5 L was
finally used considering the sensitivity of the instrument and the matrix
effects. Unfortunately, the recoveries of acesulfame K and diatrizoic acid
from lake water were lower compared with Milli-Q water. Nevertheless, we
could still quantify these two chemicals with enough sensitivity, because: 1.
they had sufficiently high concentrations in the lake water; 2. isotopesubstituted internal standards were used for every test chemical, which
corrected for the recoveries. The recoveries were determined using the
method reported by Lavén et al. (2009). For Boren lake water, the recoveries
were higher than 53% for most chemicals (except for acesulfame K and
diatrizoic acid; see Figure S3 in Paper III). For lake water from Norra
Bergundasjön, the recoveries were lower (except for acesulfame K and
diatrizoic acid; see Figure S3 in Paper II), which might be a result of dirtier
matrix in Norra Bergundasjön. For wastewater extraction, due to much
higher concentrations of chemicals compared with lake water, only Oasis
HLB was used.
The general procedure for the lake water analysis is illustrated in Figure 5.
1.5 L (Paper III) or 500 mL (Paper II) of lake water was spiked with 3 mL
of an isotope-substituted internal standard solution. Then the water was
vacuum filtered, pH adjusted to 3, and extracted on SPE cartridges. The 6
mL cartridges were packed with three sorbents (from top to bottom: Oasis
MAX /Oasis HLB/ Isolute ENV+, 120 mg/220 mg/140 mg, respectively).
After extraction, the cartridges were eluted with 7 mL methanol, 7 mL 2 %
NH4OH in methanol, and 7 mL 2% formic acid in methanol. The combined
eluates were evaporated to dryness under a nitrogen stream. The final extract
was reconstituted in 150 µL H2O:ACN (80:20, v:v, 10 mM HAc). The
extracts were analyzed on UHPLC/MS/MS (ACQUITY UPLC System with
a Xevo TQ-S mass spectrometer from Waters). An ACQUITY UPLC HSS
T3 column (1.8 µM, 2.1×100 mm, Waters) was employed. The mobile phase
was a binary gradient of water and acetonitrile. Detailed descriptions of the
analytical method and quality control are given in Papers II and III.
14
Figure 4: Experimental steps for lake water analysis used in Paper II and III.
4. A summary of the results
4.1 Theoretical evaluation of benchmarking to measure
persistence in the field
To test the first hypothesis whether it is possible in principle to apply the
benchmarking approach to measure persistence in the field (the first
hypothesis), a model was used (Paper I). It was a simple one-box
multimedia fate and transport model as introduced in section 3.1. It was run
using four different scenarios for a group of hypothetical persistent
chemicals. The base-case scenario was conducted for Boren (the study site in
Paper III). The chemicals were defined with the logarithms of KOW
(octanol-water partition coefficient; from -3 to 10) and KAW (air-water
partition coefficient; from −10 to 4). The model was used to: 1. identify the
dominant physical removal process(es), which includes all the elimination
processes except degradation, i.e. volatilization (V), advection (A) and
sediment burial (B); 2. calculate the maximum value of the persistence that
can be measured for each hypothetical chemical. The results were shown in
chemical partitioning space plots with log KOW and log KAW as the x-axis and
y- axis, respectively. The dominant physical removal process(es) and the
maximum measurable persistence for a chemical depend on both chemical
and lake properties (see Figure 2 and 3 in Paper I). The model results
demonstrate that it is feasible in principle to apply the benchmarking
approach to measure persistence in a lake. The persistence of test chemicals
15
theoretically could be assessed by quantifying the concentration (or mass
flow) ratio of the test and the benchmark chemicals at the inlet and the outlet
of the lake. Furthermore, benchmarking can allow us to measure the two
pseudo-first-order rate constants for volatilization and burial. This is
advantageous for chemicals for which volatilization or burial or both are
important physical removal processes.
Equation 10 in Paper I gives the basic and generic mathematical description
of persistence (t0.5R) determined using the benchmarking concept. It can be
reduced to:
ln(2)𝜏
𝑡0.5R = 𝐼Test 𝐶W,Test
(1)
(
𝐼BM
)/(
𝐶W,BM
)−1
where  is defined as a characteristic residence time, day-1. For instance,
when only advection is the dominant physical removal process,  is equal to
the hydraulic residence time of the lake. I is the mass flow rate into the lake,
g/day; CW is the concentration of chemical in the lake water, g/m3; ‘Test’ and
‘BM’ refer to the test and the benchmark chemicals, respectively. This
equation was based on the assumption that the benchmark chemical has the
same dominant physical removal process as the test chemical, as this would
reduce the uncertainty from the system and chemical properties. A good
benchmark chemical is one that comes from the same emission sources as
the test chemicals, has an emission rate that is correlated with that of the test
chemicals at the time scale of the hydraulic residence time of the lake, and
has the same dominant physical removal process(es) as the test chemicals.
One should note that the persistence defined in equation (1) is not a singlemedium half-life but environmental persistence that is for the whole lake
system (water plus non-burial sediments). It does not specify how much of
the chemicals is transformed in water and how much in sediment.
Nevertheless, the half-lives in whole lake system that consider the mass
distribution in the whole system (i.e. the single-medium half-lives are
weighted based on the mass fraction in each medium) are more
environmental-relevant. Single-medium half-lives cannot reflect the whole
environment.
A framework to conduct benchmarking experiments was provided in Paper
I. It is illustrated in Figure 6 below. For a group of test chemicals of interest,
an approximation of the degradation half-lives is made. In parallel, several
candidate lakes that are expected to satisfy the assumptions for the
benchmarking approach are identified. The model is parameterized using the
chemical and lake properties and then used to identify the dominant physical
process(es) for the test chemicals. A partitioning space plot showing the
dominant physical process can be created using the model. Figure 7 gives an
example that is from the result of the base-case scenario for Boren.
Depending on where the test chemicals lie in the partitioning space, a
16
benchmark chemical can be selected. For instance, for chemicals that locate
in the yellow region, i.e. only water advection is a major physical removal
process, a chemical that locates in the same region (i.e. has the same
physical removal processes as the test chemicals) can be used as the
benchmark chemical. This means the benchmark chemical can be chosen
from anywhere in the yellow region. At the same time, the requirements to
be a good benchmark chemical as mentioned above should be considered
(e.g., from the same emission sources as the test chemicals). The boundaries
between regions shift with the lake properties (see Figure 2 in Paper I).
Chemical partitioning space plots give a very useful basis for selecting
suitable benchmark chemicals. Further considerations include analytical
limitations and the nature of the major source of the chemicals and the
design of the sampling campaign. After choosing the benchmark chemical,
the maximum measurable persistence can be calculated and presented in
chemical partitioning space plots. For a given candidate lake, the maximum
measurable persistence can be compared with the estimated half-lives in the
first step in the framework. For example, the difference in the ratios of
concentrations at the inlet and the outlet may not be distinguishable for
chemicals with long half-lives in a lake that has a short hydraulic residence
time. Thus, combining both the chemical and lake properties, the most
appropriate lake system can be selected. Alternatively, if only one candidate
lake is available, one may need to develop analytical methods with higher
precision together with a better sampling campaign design.
Based on the partitioning space plot showing the dominant physical removal
process(es) for the selected lake, one can know whether there is a need to
quantify the other two physical removal processes besides advection. The
advection rate can generally be readily measured in the field. For test
chemicals for which volatilization or burial play a role (all regions except the
yellow region in Figure 7), the volatilization or burial rate needs to be
quantified. This can be done by using appropriate diagnostic chemicals (see
Eq. 8 and 9 in Paper I). The ability to quantity the two pseudo-first-order
rate constants for volatilization and burial is another advantage of the
benchmarking approach. Finally, after the field sampling and laboratory
analysis, the value of (ITest/IBM) / (CEff,Test/CEff,BM) can be measured and the
persistence of the test chemicals of interest can be estimated using the
equation above.
17
Figure 5: The framework of conducing benchmarking experiments, modified
from Figure S3 in Paper I.
Figure 6: The chemical partitioning
space plot showing the dominant
physical removal process(es) for a
group of hypothetical persistent
chemicals in Boren (Figure 2A in
Paper I).
*A physical removal process (advection A,
volatilization V or sediment burial S) was
defined as dominant if it contributed ≥80%
to the total loss of the chemical from the
system. If no single process contributed
≥80%, then the two strongest processes
were considered dominant provided they
contributed ≥80%; otherwise all three
processes were considered important.
18
4.2 Test the method: using benchmarking to measure
persistence in the field and comparing benchmarking
with mass balance
In Paper II, the practical applicability of the framework was explored and
the validity of the benchmarking approach against the mass balance
approach was investigated (the second hypothesis). The first task, as
specified in the framework introduced in section 4.1, was selecting a lake
that can meet the requirements for the benchmarking approach (e.g. wellcharacterized emissions of chemicals such as WWPTs, well-mixed, near
steady state). To measure persistence that is close to the regulatory
thresholds (e.g. 60 days in freshwater according to REACH), a lake with a
hydraulic residence time in the order of months is preferred. There are tens
of thousands of lakes in Sweden and in the end two were selected (Norra
Bergundasjön in Paper II and Boren in Paper III). Both lakes are shallow,
receive wastewater effluent from a WWTP and have a hydraulic residence
time of months. Norra Bergundasjön has a longer residence time (four
months) than Boren (one to two months). Both WWTPs discharge the
wastewater effluent directly into the lake close to the inlet. As outlined in
section 3.3, several pharmaceuticals, one artificial sweetener and one X-ray
contrast agent were studied. They were expected to be detectable in the lake.
For these chemicals, as in the framework in the previous section, advection
was identified to be the major physical loss process (see the partitioning
space plot in Figure S2 in Paper II). That is to say, there were no needs to
quantify the other two physical removal processes, volatilization and burial,
which simplified both the benchmarking and the mass balance experiments.
Especially for the mass balance approach, it would be difficult to quantify
volatilization and burial losses. Among the chemicals studied, the artificial
sweetener acesulfame K was reported to be persistent in aquatic
environments, i.e. the degradation rate was close to 0 (Buerge et al., 2009).
Acesulfame K is non-volatile and hydrophilic and mainly stays in the water
after emission, which means advection is the only dominant physical
removal process. Furthermore, acesulfame K has the same source as the
other chemicals in this study, i.e. WWTP effluent. All of these factors made
acesulfame K a good potential benchmark chemical according to the
requirements discussed in section 4.1.
The sampling campaign is summarized in section 3.4 and was conducted in
spring 2013. The two assumptions, the lake being well-mixed and near
steady state, were found to be reasonable. The mass balance result showed
no observed degradation for acesulfame K (Figure 2 and Table S11 in Paper
19
II). Therefore, for the benchmarking approach, acesulfame K was used as
the benchmark chemical to quantify the persistence of the other test
chemicals. The half-lives of the chemicals (ten pharmaceuticals and one Xray contrast agent) in the lake were measured to range from several days to
one thousand days (Table 2 in Paper II). The difference in the estimated
half-lives between the benchmarking and mass balance methods was small
(1−21%) and the uncertainties in these estimates were similar (with a
difference for most chemicals <15%) between the two methods. Therefore,
the benchmarking approach is a valid and useful method to measure
persistence in the field and the practical applicability of benchmarking was
demonstrated.
The similar performance of the mass balance and the benchmarking
approaches was attributed to the selection of a lake system that was suitable
for both approaches. A high quality estimate of the mass flow of chemicals
into the lake from the WWTP can be achieved by means of flowproportional sampling over a sufficient period of time. This resulted in
comparable uncertainties in the ratio between the concentrations of the
benchmark and the test chemicals (used in the benchmarking approach) and
in the chemical mass flow (used in the mass balance approach). In situation
where flow-proportional sampling is not available, e.g. when using grab
sampling, a better performance of the benchmarking approach may be
obtained compared with mass balance. This is because the uncertainty in the
ratio between the concentrations of the benchmark and the test chemicals
may be lower than the uncertainty in the chemical mass flow. When there is
only one dominant source of chemicals to the system like in Paper II, the
benchmarking approach does not need to measure chemical mass flow but
only the ratio between the concentrations of the benchmark and the test
chemicals and the residence time of the water. The ratio between the
concentrations of the benchmark and the test chemicals and its uncertainty
can be measured as in Paper II. The residence time of water can be
estimated by using hydraulic residence time of the lake (i.e. volume divided
by the outflow rate) or using a persistent tracer. In effect, the problem of
measuring the persistence in the field becomes a problem of measuring the
residence time of the water. The measurement of the residence time of the
water is much easier than the measurement of persistence. As introduced in
section 1.3, the benchmarking approach is particularly advantageous
compared with the mass balance approach when the mass flow of chemicals
is variable (e.g. an intermittent emission source), difficult to quantify (e.g. no
access to the sampling sites), resource-costly, or when there are needs to
quantify physical loss processes other than advection (i.e. volatilization and
sediment burial).
20
4.3 Application of the method: using benchmarking to
study the temporal and spatial variability of persistence
in the field
The temporal and spatial variability of persistence in the field (the third
hypothesis) was explored in another Swedish lake, i.e. Boren. As mentioned
above, the hydraulic residence time of Boren is shorter than Norra
Bergundasjön, i.e. one to two months. This allowed exploration of
persistence on a seasonal time scale. Therefore, Boren was chosen for this
study. The sampling campaign in Boren started at the same time as in Norra
Bergundasjön, i.e. late spring 2013, and continued during late autumn and
winter. Another two pharmaceuticals (acetaminophen and ibuprofen; see
Table 1 in the thesis) were added into the study in addition to the 12
chemicals included in Paper II.
The two assumptions (i.e. well-mixed and steady state) were also checked
and shown to be reasonable. Acesulfame K was again used as the benchmark
chemical. The chemical concentrations were much lower in Boren than in
Norra Bergundasjön due to a greater dilution of the WWTP effluent, but it
was still possible to quantify the persistence of 7 chemicals (see Figure 3 in
Paper III). There was no difference in persistence observed between autumn
and winter. This is likely because the environmental conditions were similar
during these two seasons, amongst other reasons because no ice was formed
during that winter. A difference in persistence was found between spring and
autumn, ranging from 24% to a factor of 20. Five chemicals had longer halflives in autumn than in spring, which was attributed to lower temperature
and/or less irradiation in autumn. Among these five chemicals,
hydrochlorothiazide had the largest difference in persistence between spring
and autumn, i.e. a factor of 20. This directly contributed to the big difference
(an order of magnitude) in the concentrations of hydrochlorothiazide in the
lake between spring and autumn, while the contributions of the seasonality
in emissions and the seasonality in water flow was negligible. The exposure
of aquatic life to chemicals is directly linked to the concentrations of
chemicals in the water. Therefore, for hydrochlorothiazide, the seasonality in
the persistence determined the seasonality in the exposure of aquatic life. For
other chemicals, seasonable variability in chemical emissions and water flow
also influenced the concentrations of chemicals in the lake. For chemicals
such as bezafibrate, climbazole and diclofenac, even though the persistence
in autumn was much longer than in spring, the concentrations were still
lower in autumn. This is because the persistence of these chemicals was
close to the lake’s hydraulic residence time. Compared to the seasonality in
the chemical inputs, the seasonality in the persistence had limited influence
on the seasonality in the concentrations in the lake. Overall, to understand
the seasonality in the exposure of the aquatic life, the seasonality in chemical
21
inputs, water flow and chemical persistence, as well as the relative
magnitude of the persistence and the hydraulic residence time need to be
considered.
The spatial variability of the persistence was studied by comparing the
persistence of eight chemicals measured in late spring 2013 in the two lakes
(Norra Bergundasjön and Boren). Bezafibrate, climbazole, gemfibrozil and
sulfamethoxazole had longer half-lives in Boren while hydrochlorothiazide
had a shorter half-life in Boren. For bezafibrate and climbazole, the halflives were 30-60% longer in Boren. Gemfibrozil and sulfamethoxazole had
no observable degradation in Boren while in Norra Bergundasjön they were
39-59 days and 21-34 days respectively. The difference in half-lives might
be explained by the nutrient status of the lakes. Norra Bergundasjön is a very
hypertrophic lake with strong algae blooms during spring and summer, while
Boren is a non-eutrophied lake. On the one hand this could lead to higher
biological activity and more biotransformation; on the other it could
attenuate light penetration and thereby reduce photodegradation. For
bezafibrate, climbazole, gemfibrozil and sulfamethoxazole, longer half-lives
in Boren were consistent with slower biotransformation in Boren. For
hydrochlorothiazide, abiotic transformation such as hydrolysis and
photolysis might be more relevant. The higher pH and less light attenuation
in Boren might explain some of the difference in half-life of
hydrochlorothiazide in the two lakes. A detailed discussion of how
environmental factors may contribute to the differences in the degradation
half-lives of chemicals in the two lakes can be found in Paper III.
4.4 Potential of the benchmarking approach, especially
in a regulatory context
Paper IV further discusses the potential of the benchmarking approach and
also the application of the benchmarking approach in a regulatory context.
As introduced in section 1.3 in this thesis, persistence as one of the criteria
for chemical hazard assessment is often measured under controlled
conditions in the laboratory. Based on Papers I, II and III, it was
demonstrated theoretically and experimentally that the benchmarking
approach can be a useful tool to measure persistence directly in the field
without system manipulation for existing chemicals being released from
urban environments. One can employ the benchmarking approach to
measure persistence in different aquatic systems and compare the persistence
in these systems (such as tropical vs boreal, oligotrophic vs eutrophic, saline
vs fresh or in dynamic conditions). This will help to better understand the
influences of different environmental factors and then the temporal and
spatial variability of persistence in the real environment. Eventually a
22
persistence map may be produced. Another opportunity with benchmarking
is to generate a rich database of field persistence measurements, which could
be used to compare the laboratory test results with the measured values in
the real environment. This can lead to the creation of an empirical basis for
lab-to-field extrapolation. In the best case lab tests could be used to predict
the persistence in different environments.
Besides quantitative estimation of persistence, benchmarking can also be
tailored to measure the relative persistence of the test and the benchmark
chemical. This concept, called threshold benchmarking, was introduced in
Paper II and IV. Threshold benchmarking can overcome problems of labto-lab, site-to-site or person-to-person variations in persistence
measurements. For threshold benchmarking to be most effective, the
benchmark chemical used should have a persistence that is equal to or close
to the persistence threshold defined by regulations. By observing the change
of the concentration ratio between the test and benchmark chemical over
time, one could know the relative magnitude of the half-lives of the two
chemicals; if the ratio increases, the test chemical is classified as persistent
and vice versa. Threshold benchmarking can be used both in the lab and
field. The rank order measured by threshold benchmarking in the field can
be compared with the rank order in lab tests, which can facilitate lab-to-field
extrapolation. It would be good to include threshold benchmarking in
regulatory processes. This would enrich the scientific quality of regulatory
decision making. Instead of investing time and money on calibration of the
laboratory tests, direct field measurements by threshold benchmarking could
be a wiser option. Paper IV also proposes using reference lake systems with
well-characterized properties (e.g. the lakes in Papers II and III) to measure
the relative persistence of chemicals and study the temporal and spatial
variability of persistence. This can also be useful in regulatory processes.
The limitations of the application of the benchmarking approach were also
discussed in Paper IV. For instance, the concentrations of chemicals need to
be measurable and no significant sources may be present between the inlet
and outlet of the lake.
23
5. Conclusions
In this thesis, the benchmarking approach was proposed to measure
persistence in the field. To assess this approach, as specified in Objectives
section, three hypotheses were tested, which correspond to the three papers,
Papers I, II and III. The first hypothesis was that in principle it is possible
to apply the benchmarking approach to measure chemical persistence in the
field. In Paper I, this hypothesis was demonstrated to be true by modeling.
Careful attention needs to be paid to the selection of benchmark chemicals. It
is based on both the chemical properties of the test chemicals of interest and
the lake properties. Chemical partitioning space plots provide a useful guide
to select benchmark chemicals and lake systems. A framework for how to
employ benchmarking to measure persistence in the field was developed.
The benchmarking technique also allows one to quantify volatilization and
sediment burial processes by using the appropriate diagnostic chemicals.
The second hypothesis is that the benchmarking approach can be applied in
practice to measure the persistence of long-lived chemicals in lakes and the
results agree with the traditional mass balance approach. This was
demonstrated to be true in Paper II. The persistence of 10 non-volatile
chemicals was measured in a Swedish lake by both the benchmarking and
mass balance approaches. The results of the two approaches agreed well.
The benchmarking approach was illustrated to be a useful method to
measure persistence in the field.
The third hypothesis is that the benchmarking approach can be used to
investigate the temporal and spatial variability of persistence in lakes. This
was answered in Paper III. The temporal and spatial variability were
successfully explored in another Swedish lake. For the chemicals studied,
there were differences in chemical persistence between spring and autumn,
but no difference between autumn and winter. The temperature and
irradiation during different seasons mainly contributed to the seasonal
variation of persistence. Spatial variation of the persistence of chemicals was
found between the two Swedish lakes, which may be explained by different
nutrient status and other factors such as pH, light penetration depth etc.
The overall aim of this thesis was to evaluate the potential of the
benchmarking approach to measure persistence in the field. This was
achieved theoretically and experimentally in Papers I, II and III. The
potential of benchmarking was further thoroughly discussed in Paper IV.
The ability of the benchmarking approach to measure persistence in the field
can enable more measurements of persistence in various environments. A
database of field measurements of persistence can be built. The field
measurements of persistence can deepen the understanding of the temporal
and spatial variability of persistence in the real environment and be used to
24
facilitate lab-to-field extrapolation. Threshold benchmarking that measures
the relative persistence of chemicals is proposed for regulatory processes.
Well-characterized systems like the two lakes in this thesis can be used as
reference lakes (standardized experiments) to collect reliable information
about the absolute and relative magnitude of chemical persistence in the
environment.
In summary, the benchmarking approach opens the door to measuring
persistence in a much wider range of environments than present.
6. Future outlook
As suggested in Paper IV, more field measurements of persistence should
be conducted. In this thesis, two Swedish lakes were selected to measure the
persistence of some PPCPs by the benchmarking approach. Lakes, reservoirs
or estuaries in other geographical locations where different climate
conditions are anticipated could be the next step to go. Based on the
framework provided in Paper I, a system that satisfies the requirements such
as well-mixed and characterized sources etc. can be selected. One can
always use a hydraulic model to calculate the residence time of the water
between the inlet and the outlet. For this reason the system needs not be at
steady state. In case of non-steady state, sampling in shorter time intervals
over a long enough time period is needed in the chemical sources and in the
lake to characterize the ratio of the concentrations (or the mass flow) of the
benchmark and the test chemicals. If flow-proportional sampling is not
possible, grab sampling can be used and it can give a better estimation of
persistence by benchmarking compared with the mass balance method. If the
ratio of the concentrations (or the mass flow) of the benchmark and the test
chemicals is relatively constant over the study period, the requirements can
be loosened and the sampling period can be short. The integration of models,
good sampling design and modern techniques in trace analysis makes it
possible to measure persistence in the field by benchmarking.
The range of the test chemicals should be extended, i.e. to chemicals with
other physicochemical properties, for instance, more volatile or more
hydrophobic chemicals. The chemicals that locate in other regions (the
regions except the yellow region) in Figure 7 have not only advection but
volatilization or burial or both as dominant physical removal processes. Then
one needs to quantify the volatilization and burial processes. The
quantification of these two processes can be done by using a diagnostic
chemical for which volatilization or burial is the only important removal
process, and a benchmark chemical for which advection is the only loss
process. It might be more difficult to measure the concentrations of these
chemicals in the water since they tend to go to the air or the sediment. In this
case, a sensitive analytical method would be required.
25
In lab tests of persistence, the rank order of chemical persistence can be
determined by threshold benchmarking. To use lab tests to predict the
relative magnitude of persistence in the field, the rank order of persistence of
chemicals in the field should be measured and compared with the lab
measurements. Also, the variation of the rank order in space and time needs
to be investigated. More studies can be conducted in the two Swedish lakes
in this thesis (Papers I and II). Lake water and sediment samples from the
lake can be taken into the lab and incubated. The rank order of the
persistence of the chemicals studied in the two lakes in Papers I and II can
be compared with the results of the lab studies. Also the chemicals studied in
Papers I and II can be expanded as mentioned in the last paragraph.
It is notable that the persistence measured in this thesis is not a singlemedium half-life but applies to the whole lake system. The existing
regulations or guidelines only include single-medium half-lives as the
criterion for persistence assessment. The disappearance of chemicals from
the whole environment is not only determined by the half-life in each
medium but the partitioning between media. Chemicals are transformed fast
in one medium, but the elimination from the whole system can be limited by
the much slower transformation in another medium. The criterion for
persistence in the whole system should be proposed in regulations, since it is
more environment-relevant than single-medium half-lives.
In existing regulations, laboratory standard tests are used to measure
persistence. The results can be variable between labs and be different from
the persistence in the real environment. As mentioned above, threshold
benchmarking can be used in the lab and field. The results in the lab and
field can be compared to calibrate the lab tests. Therefore, threshold
benchmarking should be included in regulatory processes because it would
improve the scientific quality of persistence assessment.
Given all that, models are needed to measure persistence. Also, decisionmakers should be involved in the context of proposing the persistence
criterion for the whole system in addition to single-medium half-lives and
facilitating more field measurements of persistence for lab-to-field
extrapolation. Multidisciplinary cooperation is thus motivated, i.e.
contributions from chemists, hydrologists, modelers, and decision-makers.
26
Acknowledgement
I am profoundly grateful to my main supervisor, Michael McLachlan. A big
thank you for giving me such a great opportunity to start my PhD in ACESo
(or ITMo) and be involved in this Unilever project. I did really learn a lot
from the scientific discussions with you. How to think more structurally,
how to jump out from detailed information to get a bigger picture, how to
write and think in a more logic way ….and now I am confident of what I am
doing. Thank you very much for your trust that I can solve the problem and
your encouragement when I was down.
Thank you, Matthew MacLeod, as my co-supervisor. I feel happy to be in
your group in the first year of the PhD studies. I got a lot of inspiration of the
project and enthusiasms for modeling! Although the first draft of the 1st
paper was not included in the thesis, I really appreciate and learned a lot
from you. Also thank you for the books that you recommended! You let me
know that to be a scientist I also need to be an ‘artist’!
Many thanks to my co-supervisor Amelie Kierkegaard. It was you that
guided me into the laboratory world in the beginning! Thank you for the
inspiration of ‘recovery’ problem. Thank you for giving me so many good
suggestions for the papers and for the life ! A big thank you for sampling.
We had nice trips to Motala and Växjö! Thank you so much, Michael and
Amelie !
I am lucky to have Michael Radke as my co-supervisor. Your experience in
pharmaceutical studies and instrumental analysis helped me throughout one
after another experimental problem. Thank you for spending time giving me
suggestions and comments for the papers after you moved to Germany.
I also wanna express my gratitude to Elena Gorokhova being my external
supervisor. Thank you for your support!
Thank you Cecilia, Ann-Karin and Anneli for helping the sampling. Thank
you Todd for great scientific discussion of the project.
Thank you Lotta for your administrative support. Many thanks to Tomas
Alsberg for the help of the instrument.
I want to give my thanks to Anna’s group. Thank you for the activities,
support and presents. Thank you Anna for the comments on the
presentations.
Fiona, my big sister! No words can express my appreciation and luckiness to
have you along with me in this journey! You came to me during my darkest
days in my life. I became more and more positive. We not only share our life
experience and also discuss scientific questions. I learned a lot from you. I
even learned baking (for me this is a difficult task).
Dimi, my ‘Chinese’ brother! We had a very good time in the same office for
more than one year. Big thanks for your caring and delicious food! You are
27
so talented in learning new things especially Chinese and Asian food! Thank
you for saving me not falling into the water on the boat in the heavy rain!!
Wouter, big thank you for buying me beer when I was sad! You gave me
lots of help for the instrumental analysis. Thank you for your good
suggestions for my project and papers. Thank you for driving that long
together with Dimi and Fiona to help me with field sampling.
Thank you Marko as my officemate for two years! Very good memories.
We talked about our phd life and asked each other how the project was
going. It is always very nice to talk with you. Thank you for cheering me up
when I was not feeling confident! Thank you for the help in the lab and
sharing your lab experience.
Thank you, Damien, for your fast volunteering to help my field sampling
together with Michael and Dimi! We had a great trip to Motala. Hope that
you enjoyed canoe paddling.
Ioannis and Evi, thank you for the invitation to your wedding in Greece.
Ioannis, thank you for the jokes, happy conversations and good food! Nice
trip in Prage and Brno!
Seth, thank you for the juice maker! Along this phd journey, you are always
positive and gave me a lot of positive energy for life and also for work!
Thank you for the party in your place, Kerstin. Stathis, thanks for the
laughs and jokes!
Bo, thank you for the delicious and spicy food .
Thank you everyone in ACESo. I really enjoyed working in this fantastic
environment.
Ping, I am so lucky to have you as a great friend. We had so much fun
together. Even though our subject is not in the same area, you always tried to
help me find my confidence back!
I want to thank Xu. Thanks a lot for the laughs and life sharing.
Thank you Jerry for being a good accompany in my first conference in US.
Also a big thank you for great conversations about science and life.
Thank you for your caring and the cakes, Xiaojing.
Qiong, thank you so much for encouraging me and for introducing me to the
badminton group. Badminton really changed my life here in Sweden.
Tongmei, you are a wonderful accompany to be together with. Thank you
for comforting me when I was depressed .
Lidi and Peng, thank you so much for organizing parties and being there for
my 30th birthday . Thank you Qiang for being the ‘coordinator’.
Thank you Genping to support me and to finally let me manage the project.
Your advice really helped me make the right decision.
Mom and dad, without your support, I would never be able to come to
Sweden and start my master studies here. 我爱你们。I promise that I will
live happily and won’t let you down. I will be positive and take it easy to
pursue my success in my life and career. 爸，我知道你一定为我骄傲，我
做到了。Love you, my brother.
28
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