Chemical and Biological Analyses of Selected
Endocrine Disruptors in Wastewater Treatment
Plants in South East Queensland, Australia
Benjamin L. L. Tan B.Sc. (Hons), M.Med.Sc.
Submitted in fulfillment of the requirements for the degree of
Doctor of Philosophy
Australian School of Environmental Studies
Faculty of Environmental Sciences
Griffith University
Queensland
Australia
September 2006
SYNOPSIS
Studies in North America, Europe, Japan and Australia have reported the presence of
endocrine disrupting compounds (EDCs) in wastewater treatment plants (WWTPs) effluent
could affect physiological and reproductive function in exposed fish consistent with
exposure to hormonally active chemicals. The occurrence of EDCs in rivers and receiving
environments situated near WWTPs raises concern over the removal efficacy of these
compounds by conventional treatment processes.
The main aim of this study was to utilize chemical analyses to assess concentrations of
selected endocrine disruptors as well as a biological assay to measure the potential
estrogenic effects of EDCs present in water discharged from wastewater treatment plants in
South East Queensland, Australia. Currently, there are few reported studies on the
estrogenic effects of EDCs released from WWTPs into receiving environments in Australia.
Two field sampling methods were used. Grab sampling with subsequent extraction using a
solid-phase extraction (SPE) technique and passive sampling utilizing EmporeTM (styrene-
divinylbenzene copolymer) disk were used in this study. A gas chromatography-mass
spectrometric (GC-MS) method was successfully developed to simultaneously analyze 15
environmentally ubiquitous EDCs including phthalates, alkylphenols, tamoxifen, androgens
and estrogens. Application of these methods for the determination of target EDCs in
wastewater samples in this study showed 80 – 99% removal of most EDCs from influent to
effluent, despite the wastewater treatment plants having different treatment processes.
It was observed that the passive samplers accumulated less EDCs than predicted when
compared to the grab samples. This is probably caused by, but may not be limited to,
biofouling, low flow rate, biodegradation and temperature which can progressively reduce
the uptake of compounds into the sampler. A future challenge would be to improve the
reliability of passive samplers by reducing or controlling the environmental conditions that
may impact on the passive sampler performance.
ii
Stir bar sorptive extraction (SBSE) in combination with thermal desorption coupled to GC-
MS was successfully applied to analyze a range of EDCs in wastewater, biosolids and
sludge. The technique was shown to be very versatile, shortening extraction time, reducing
sample volume needed as well as being sensitive for the analysis of a wide range of EDCs.
The results showed that there were high amounts of phthalates, alkylphenols and female
hormones present in the raw influent wastewater and biosolids of the WWTP samples.
For the complimentary bioassay, a proliferation assay using human breast cancer cell line
MCF-7 (E-Screen assay) was used to determine estrogen equivalents (EEqs) in grab and
passive samples from five municipal WWTPs. EEq concentrations derived by E-Screen
assays for the grab samples were between 108 – 356 ng/L for the influents and <1 – 14.8
ng/L for the effluents with the exception of one effluent sample which was at 67.8 ng/L
EEq. In most wastewater samples, the natural estrogens contributed to 60% or more of the
EEq value. Based on the chemical and in vitro biological analyses results and coupled with
reported no observed effect concentration (NOEC) in vivo studies (mainly based on fish
vitellogenin studies), the risk of EDCs found in effluents of the monitored WWTPs having
a significant impact on the receiving environment is reasonably low.
Furthermore, a fugacity-based analysis was employed to model the fate of selected
industrial chemicals with endocrine disrupting properties in a conventional activated sludge
WWTP. Using mass balance principles, a fugacity model was developed for correlating and
predicting the steady state-phase concentrations, the process stream fluxes, and the fate of
four phthalates and four alkylphenols in a WWTP. The relative amounts of chemicals that
are likely to be volatilized, sorbed to sludge, biotransformed, and discharged in the effluent
water was assessed. Results obtained by applying the model for the eight compounds
compared satisfactorily with data from the WWTP. All eight EDCs modelled in this study
had high removal efficacy from the WWTP. Apart from benzyl butyl phthalate and
bisphenol A, the majority is removed via biotransformation followed by a lesser proportion
removed through primary sludge. Fugacity analysis provides useful insight into compound
fate in a WWTP and with further calibration and validation the model should be useful for
correlative and predictive purposes.
iii
In conclusion, the complementary chemical and biological analyses used in this study
provided a comprehensive assessment which showed that the EDCs discharged from the
monitored WWTPs would be expected to have a low impact on the receiving environments.
Keywords: Wastewater treatment plant; Grab sampling; Passive sampling; Stir bar sorptive
extraction; Gas chromatography-mass spectrometry; E-screen assay; Estrogen equivalent;
Fugacity modelling
iv
ACKNOWLEDGEMENTS It is a great pleasure to thank the many people who made this thesis possible.
It is difficult to overstate my gratitude to my PhD supervisors, Drs. Heather Chapman,
Darryl Hawker, Jochen Müller and Louis Tremblay. With their enthusiasm, inspiration,
patience and great efforts to explain things clearly and simply, they helped to make this
PhD venture a great journey. Throughout my thesis-writing period, they provided
encouragement, sound advice, good teaching, good companies, and lots of great ideas. I
would have been lost without them.
I would like to thank Dr. Frédéric Leusch for being a good friend and helping out with most
of the field sampling and laboratory work while at the same time testing the limits of his
olfactory senses. Many thanks go out to Rene Diocares for technical advice and support on
the GC-MS, Katherine Trought, Tamara Ivastinovic, and Ngari Teakle for their help in the
E-Screen assay. I am grateful to the faculty and staff at Griffith University, National
Research Centre for Environmental Toxicology (EnTox), Landcare Research, NZ and
Queensland Health Pathology and Scientific Services (QHPSS) who have made this project
enjoyable, especially Eri Takahashi, Dr. A’edah Abu Bakar, Henrique Anselmo, Brad
Polkinghorne, Eva Holt, Heather Brown, Jason Dunlop, Mary Hodge, Anita Kapernick,
Scott Stephens, Andrew Watkinson, Dr. Simon Costanzo and Colm Cahill. Special thanks
also go to my lively aikido mates for helping me keep calm and collected during the testing
times of my PhD, especially Dr. Daniel James, Steve Dows, Dr. Bruce Tranter, Dan Brown,
Gary Weigh, Gabrielle Paynter, Chris Cobban and Tim Piatkowski.
I would like to acknowledge with gratitude Griffith University, Corporative Research
Centre for Water Quality and Treatment (CRC WQT), EnTox, QHPSS and the Australian
Research Council (ARC) for their financial support and for giving me a chance to
contribute towards the field of Environmental Toxicology.
Lastly, and most importantly, I wish to thank my parents, Susan and David, and sisters,
Yvonne and Yvette, for their love, guidance, support, encouragement and patience
throughout my life. To them I dedicate this thesis.
v
DECLARATION OF ORIGINALITY
The experimentation, analyses, presentation and interpretation of results presented in this
thesis represent my original work that has not previously been submitted for a degree or
diploma in any university. To my best knowledge and belief, this thesis contains no
material previously published or written by another person except where due reference is
made within the thesis itself.
______________________________________________
(Benjamin L.L. Tan)
vi
TABLE OF CONTENTS
Synopsis ii
Acknowledgement v
Declaration of originality vi
Table of contents vii
List of tables xii
List of figures xiv
List of abbreviations xviii
Publications resulting from this research xxi
Other publications related to this research xxii
Chapter 1: Thesis objectives 1
1.1 General introduction 1
1.2 Aims and objectives 3
1.3 Research questions 4
1.4 Thesis format 5
1.5 References 6
Chapter 2: Literature review 8
2.1 Introduction 8
2.2 The endocrine system 12
2.3 Endocrine disrupting compounds (EDCs) 13
2.4 Chemical properties of selected endocrine disruptors 14
2.4.1 Estrogens 19
2.4.2 Tamoxifen 21
2.4.3 Androgens 22
2.4.4 Alkylphenols 23
2.4.5 Phthalates 25
2.5 Endocrine disruption 26
2.5.1 Mechanisms of endocrine disruption 26
2.5.2 Other factors affecting the activity of endocrine disruption 28
vii
2.5.3 Endocrine disruptors in wildlife (vertebrates/invertebrates) 29
2.5.4 Endocrine disruptors in discharge and surface water 31
2.6 Methodologies for detection and monitoring of endocrine disruptors 33
2.6.1 Chemical analytical techniques 33
2.6.1.1 Extraction methods for water 33
2.6.1.1.1 Direct sampling: solid phase extraction (SPE) 34
2.6.1.1.2 Passive sampling 35
2.6.1.1.3 Stir bar sorptive extraction (SBSE) 38
2.6.1.2 Extraction methods for sludge 38
2.6.1.3 Gas chromatography-mass spectrometry (GC-MS) 39
2.6.2 Biological testing 40
2.6.2.1 In vitro bioassays 40
2.6.2.1.1 Receptor binding assay 40
2.6.2.1.2 Estrogen receptor (ER) activation assays 41
2.6.2.2 Whole animal assays (in vivo) 42
2.7 EDCs fate modelling 43
2.8 Risk assessment of EDCs 44
2.9 Conclusions 46
2.10 References 46
Chapter 3: Evaluation of grab and passive sampling methods to determinate selected
endocrine disrupting compounds in municipal wastewaters 66
3.1 Abstract 66
3.2 Introduction 66
3.2.1 Sampling kinetics of EDCs with the EmporeTM disk sampler 69
3.3 Materials and methods 71
3.3.1 Chemicals and reagents 71
3.3.2 Sample collection 72
3.3.3 Processing of grab samples 73
3.3.3.1 SPE extraction procedure 73
3.3.3.2 Grab sampling SPE recovery experiment 74
3.3.4 Processing of passive samples 75
3.3.4.1 Passive sampler pre-deployment conditioning 75
viii
3.3.4.2 Passive sampler calibration experiment 75
3.3.4.3 Passive sampler extraction 76
3.3.5 Derivatization procedure 76
3.3.6 GC-MS analysis 77
3.4 Results and discussion 79
3.4.1 Calibration of passive sampler 79
3.4.2 Environmental monitoring 87
3.5 Conclusions 96
3.6 References 97
Chapter 4: Stir bar sorptive extraction and trace analysis of selected endocrine
disrupting compounds in water, solids and sludge samples by thermal desorption with
gas chromatography-mass spectrometry 103
4.1 Abstract 103
4.2 Introduction 103
4.3 Materials and methods 105
4.3.1 Chemicals and reagents 105
4.3.2 Instrumentation 105
4.3.3 SBSE procedure 109
4.3.4 Sludge/water partitioning experiment 110
4.3.5 Environmental monitoring 111
4.4 Results and discussion 111
4.4.1 SBSE recovery and partitioning experiments 111
4.4.2 Environmental monitoring 113
4.5 Conclusions 117
4.6 References 117
Chapter 5: Comprehensive study of selected endocrine disrupting compounds using
grab and passive sampling at selected wastewater treatment plants in South East
Queensland, Australia. 1. Chemical analysis 121
5.1 Abstract 121
5.2 Introduction 121
5.3 Materials and methods 124
ix
5.3.1 Chemicals and reagents 124
5.3.2 Sampling sites 124
5.3.3 Grab sample collection and extraction 125
5.3.4 Passive sampler conditioning and extraction 128
5.3.5 GC-MS derivatization procedure 129
5.3.6 GC-MS analysis 129
5.3.7 Centrifuged solids and sludge analysis 130
5.4 Results and discussion 132
5.4.1 Grab sampling 136
5.4.2 Passive sampling 141
5.4.3 Solids and sludge analysis 145
5.5 Conclusions 147
5.6 References 147
Chapter 6: Comprehensive study of selected endocrine disrupting compounds using
grab and passive sampling at selected wastewater treatment plants in South East
Queensland, Australia. 2. In vitro biological screening 153
6.1 Abstract 153
6.2 Introduction 153
6.3 Materials and methods 155
6.3.1 Sampling sites 155
6.3.2 Grab sample collection and extraction 156
6.3.3 Passive sampler conditioning and extraction 157
6.3.4 Cell proliferation assay 158
6.4 Results and discussion 161
6.4.1 Estrogenic activity of WWTPs samples 161
6.4.2 Comparison between the estrogenic activity of passive sampler and grab
sampler 165
6.4.3 Comparison of E-Screen assay and analytical chemistry 166
6.5 Conclusions 170
6.6 References 171
x
Chapter 7: Modelling of the fate of selected alkylphenols and phthalates in a
municipal wastewater treatment plant in South East Queensland, Australia 176
7.1 Abstract 176
7.2 Introduction 176
7.3 Process description 180
7.4 The fugacity approach 184
7.5 Results and discussion 189
7.6 Conclusions 197
7.7 References 198
Chapter 8: General discussion and conclusion 201
8.1 General discussion 201
8.2 General conclusion 205
8.3 Future research 206
8.4 References 207
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LIST OF TABLES
Table 2.1. Biochemical properties of selected endocrine disruptors 16
Table 2.2. Daily excretion (μg) of estrogenic steroids by humans 20
Table 3.1. Retention time and ions used for quantification in GC-MS detection of the
selected EDCs and their respective recoveries with SPE and EmporeTM disk extractions 78
Table 3.2. Selected physiochemical properties and sampling rates of test analytes for the
passive sampler (EmporeTM disk) based on the laboratory calibration at 24°C 84
Table 3.3. Concentration of EDCs detected in grab samples from WWTP J 92
Table 3.4. Concentration of EDCs detected grab samples from WWTP M which practices
water recycling 93
Table 3.5. Concentration of EDCs detected using grab and passive sampling methods in the
wetlands of WWTP N 94
Table 4.1. Log Kow, theoretical recoveries, spiked sludge recoveries, retention time, ions
used for quantification in SBSE GC-MS detection 107
Table 4.2. EDCs concentration present in raw influent, anaerobic, aerobic and anoxic zones
of the bioreactor at WWTP J determined by SBSE 116
Table 5.1. Description of the 5 activated sludge wastewater treatment plants in this
study 125
xii
Table 5.2. Log Kow, retention time and ions used for quantification in GC-MS analysis for
the detection of the selected EDCs and their respective extracted recoveries with SPE and
EmporeTM disk, and sampling rates for the target compounds using the EmporeTM
disk 127
Table 5.3. Log Kow, theoretical recoveries, retention time, ions used for quantification in
SBSE GC-MS analysis and detection 132
Table 5.4. Selected analytes present in WWTP A 133
Table 5.5. Selected analytes present in WWTP B 134
Table 5.6. Selected analytes present in WWTPs C, D and E 135
Table 6.1. Description of the 5 conventional activated sludge wastewater treatment plants in
this study 156
Table 6.2. Estrogenicity of individual compounds when tested with E-Screen assay 160
Table 6.3. Aqueous estrogen equivalent comparison between the chemical and biological
analyses and the different sampling methods 164
Table 7.1. Measured phthalates and alkylphenols present in the WWTP A 182
Table 7.2. Selected physical properties at 25 °C of the phthalates and alkylphenols used in
this study 183
Table 7.3. Estimated and measured removal efficiencies of selected compounds with
endocrine disrupting properties in WWTP A 192
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LIST OF FIGURES
Figure 2.1. Water supply catchments for nearly half of South East Queensland, Australia
region (SEQ Water, 2002) 11
Figure 2.2. Water storage status of Wivenhoe, Sometset and North Pine Dams suplying
potable water to South East Queensland, Australia (SEQ Water, 2006) 12
Figure 2.3. Chemical structure of natural estrogens 17
Figure 2.4. Chemical structure of androgens 17
Figure 2.5. Chemical structure of pharmaceutical drugs 18
Figure 2.6. Chemical structure of alkylphenols 18
Figure 2.7. Chemical structure of phthalates 18
Figure 3.1. Chromatogram of the selected EDCs 79
Figure 3.2. Examples of time series uptake data in SDB-RPS EmporeTM disk for selected
EDCs in calibration experiment (a) nonylphenol; (b) bisphenol A; (c) estrone and (d)
dibutyl phthalate 85
Figure 3.3. Relationship between log Kow and log KSW for SDB-RPS EmporeTM disk,
including available literature data (Verhaar et al., 1995; Green and Abraham, 2000; Mayer,
2000; Stephens et al., 2005) 86
Figure 3.4. Correlation between measured EDC concentration obtained from grab sampling
and passive sampling at different sites along WWTPs A, B, C, D and E in South East
Queensland, Australia 87
xiv
Figure 3.5. Examples of variation of concentrations for (a) 4-tert-octylphenol and (b)
nonylphenol using grab sampling at WWTP M at selected time intervals over a period of 7
days in 2005 95
Figure 4.1. A. SBSE desorption chromatogram of phthalates, tamoxifen, acyl derivative of
alkylphenols, estrogens and androgen. B. Total ion chromatogram (A) enlarged to show
smaller compound peaks of the chromatogram (the chromatogram for tamoxifen was
removed to give a clearer view of androsterone and etiocholanolone) 108
Figure 4.2. Recovery of EDCs in water and sludge phases when 1 L of bioreactor sample
from WWTP J was spiked at a concentration of 500 ng/L (mean ± standard deviation) 112
Figure 4.3. Changes in the log Kp (sludge/water partition coefficient) of EDCs onto
bioreactor sludge with log Kow (octanol/water partition coefficient) 113
Figure 5.1. Elimination of estrogens during passage through the 5 WWTPs located in
Southeast Queensland, Australia. α-E2 = 17α-estradiol, β-E2 = 17β-estradiol, BDL = below
detection limit. Grab samples collected were from the influent (Inf), bioreactor-anaerobic
(bio-ana), bioreactor-aerobic (Bio-ae), bioreactor (Bio), return activated sludge (RAS),
clarifier (Clar), effluent (Eff), point of discharge in the river or outflow (Dis) and 1 km
downstream from outlet (Riv) 141
Figure 5.2. Correlation between measured EDCs obtained from grab sampling and passive
sampling at different sites at WWTPs A, B, C, D and E 145
Figure 6.1. Comparison of the estrogen equivalent concentration (EEq) determined in the
E-Screen assay with those calculated from the results of chemical analysis of the grab
samples from the influent and effluent of selected five WWTPs in Southeast Queensland,
Australia (Chapter 5). Columns represent the mean ± standard deviation. Inf = influent, Eff
= effluent, BDL = below detection limit 165
xv
Figure 6.2. Correlation between the measured E-Screen assay estrogen equivalent
concentrations (EEq) and the predicted EEq from the results of the grab and passive
samples from all five WWTPs 169
Figure 6.3. Contribution of steroidal estrogens to total estradiol equivalent concentration
(EEq) calculated from results of GC-MS in selected WWTP samples of five WWTPs in
Southeast Queensland, Australia. α-E2 = 17α-estradiol, β-E2 = 17β-estradiol. Grab samples
collected were from the influent (Inf), bioreactor-anaerobic (bio-ana), bioreactor-aerobic
(Bio-ae), bioreactor (Bio), return activated sludge (RAS), clarifier (Clar), effluent (Eff),
point of discharge in the river or outlet (Dis) and 1km downstream from outlet (Riv) 170
Figure 7.1. Diagram of (A) water (m3 h-1) and (B) solids (g h-1) balances for WWTP A.
65% biosolids removal in the primary settling tank is assumed 183
Figure 7.2. Diagram of fugacity transport/process parameters (D) in WWTP A. P =
primary settling tank, Bio = bioreactor, F = final settling tank, B = biodegradation, V =
volatilization 188
Figure 7.3. Process details of fate, D (mol Pa-1 h-1), f (Pa), k (h-1) and Z (mol m-3 Pa-1), for
(A) diethyl phthalate and (B) dibutyl phthalate in WWTP A. Data in bold are the fluxes for
the various processes (g h-1). ZW of diethyl phthalate and dibutyl pthalate are 37.2 mol m-3
Pa-1 and 11.16 mol m-3 Pa-1, respectively 193
Figure 7.4. Process details of fate, D (mol Pa-1 h-1), f (Pa), k (h-1) and Z (mol m-3 Pa-1), for
(A) benzyl butyl phthalate and (B) di-(2-ethylhexyl) phthalate in WWTP A. Data in bold
are the fluxes for the various processes (g h-1). ZW of benzyl butyl phthalate and di-(2-
ethylhexyl) phthalate are 13.0 mol m-3 Pa-1 and 0.576 mol m-3 Pa-1, respectively 194
Figure 7.5. Process details of fate, D (mol Pa-1 h-1), f (Pa), k (h-1) and Z (mol m-3 Pa-1), for
(A) nonylphenol and (B) 4-tert-octylphenol in WWTP A. Data in bold are the fluxes for the
various processes (g h-1). ZW of nonylphenol and 4-tert-octylphenol are 9.09 ×10-2 mol m-3
Pa-1 and 1.80 mol m-3 Pa-1, respectively 195
xvi
Figure 7.6. Process details of fate, D (mol Pa-1 h-1), f (Pa), k (h-1) and Z (mol m-3 Pa-1), for
(A) 4-cumylphenol and (B) bisphenol A in WWTP A. Data in bold are the fluxes for the
various processes (g h-1). ZW of 4-cumylphenol and bisphenol A are 5.03×102 mol m-3 Pa-1
and 1.74×105 mol m-3 Pa-1 196
Figure 7.7. Correlation between the estimated and measured effluent compound
concentrations from WWTP A 197
xvii
LIST OF ABBREVIATIONS
Andr. = Androsterone
APE = Alkylphenol ethoxylate
BAC = Biologically activated carbon
BBP = Benzyl butyl phthalate
BDL = Below detection limit
BNR = Biological nutrient removal
BPA = Bisphenol A
BSTFA = N,O-bis-(trimethylsilyl)trifluoroacetamide
CD-FBS = Charcoal-dextran treated fetal bovine serum
CP = 4-Cumylphenol
CV = Coefficients of variation
DBP = Dibutyl phthalate
DDT = Dichlorodiphenyltrichloroethane
DEHP = Di-(2-ethylhexyl) phthalate
DEP = Diethyl phthalate
DNA = Deoxyribonucleic acid
DOP = Dioctyl phthalate
E1 = Estrone
E2 = 17β-Estradiol
E3 = Estriol
EC50 = Effective concentration which produces 50% of the maximum possible response
EDC = Endocrine disrupting compound
EC95 = Effective concentration which produces 95% of the maximum possible response
EE2 = 17α-ethynylestradiol
EEq = Estrogen equivalent
ER = Estrogen receptor
ERBA = Estrogen-receptor binding assay
Etio. = Etiocholan-3α-ol-17-one
FST = Final settling tank
GAC = Granular activated carbon
xviii
GC-MS = Gas chromatography-mass spectrometry
GPC = Gel permeation chromatography
HEPES = 4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid
HPLC = High performance liquid-chromatography
HS-SBSE = Headspace stir-bar sorptive extraction
i.d. = Internal diameter
IC50 = The concentration required to inhibit 17β-estradiol binding by 50%
LLE = Liquid-liquid extraction
LOEC = Lowest observed effect concentration
Log Kow = Log octanol/water partition coefficient
NA = Not analyzed
NOAEL = No observed adverse effect level
NOEC = No observed effect concentration
NP = Nonylphenol
OP = 4-tert-Octylphenol
PAH = Polyaromatic hydrocarbon
PCB = Polychlorinated biphenyls
PE = People equivalent
PEC = Predicted environmental concentration
PNEC = Predicted no effect concentration
POCIS = Polar organic chemical integrative sampler
PRC = Performance reference compound
PST = Primary settling tank
RAS = Return activated sludge
RPE = Relative proliferative effect
rpm = Revolutions per minute
RPP = Relative proliferative potency
SBSE = Stir-bar sorptive extraction
SD = Standard deviation
SIM = Selected ion monitoring
SPE = Solid-phase extraction
SPMD = Semi-permeable membrane device
SPME = Solid-phase microextraction
xix
TIE = Toxicity identification evaluation
TMS = Trimethylsilyl
UK = United Kingdom
USA = United States of America
UV = Ultraviolet
VOC = Volatile organic chemicals
VTG = Vitellogenin
WWTP = Wastewater treatment plant
xx
PUBLICATIONS RESULTING FROM THIS RESEARCH Tan, B.L.L., Hawker, D.W., Müller, J.F., Leusch, F.D.L., Stephens, B.S., Tremblay, L.A.,
Chapman, H.F., (submitted for publication). Evaluation of grab and passive sampling
methods to determinate endocrine disrupting compounds in municipal wastewaters.
Tan, B.L.L., Hawker, D.W., Müller, J.F., L.A., Chapman, H.F., (submitted for publication).
Stir bar sorptive extraction and trace analysis of selected endocrine disruptors in water,
biosolids and sludge samples by thermal desorption with gas chromatography-mass
spectrometry.
Tan, B.L.L., Hawker, D.W., Müller, J.F., Leusch, F.D.L., Tremblay, L.A., Chapman, H.F.,
2007. Comprehensive study of endocrine disrupting compounds using grab and passive
sampling at selected wastewater treatment plants in South East Queensland, Australia.
Environ. Int. doi:10.1016/j.envint.2007.01008.
Tan, B.L.L., Hawker, D.W., Müller, J.F., Leusch, F.D.L., Tremblay, L.A., Chapman, H.F.,
2007. Modelling of the fate of selected endocrine disruptors in a municipal wastewater
treatment plant in South East Queensland, Australia. Chemosphere
doi:10.1016/j.chemosphere.2007.02.057.
xxi
OTHER PUBLICATIONS RELATED TO THIS RESEARCH
Leusch, F.D.L., Tan, B.L.L., Tremblay, L.A., Chapman, H.F., 2005. Endocrine disruptors
in sewage: Perception vs. reality. Proceedings of AWA OzWater 2005, 8-12 May 2005,
Brisbane, QLD, Australia.
Tan, B.L.L., Hawker, D.W., Tremblay, L.A., Chapman, H.F., 2005. Endocrine disruptors in
sewage effluent: the effects of formaldehyde preservation and the handling of sewage
samples. Proceedings of the Australian Water Association ‘Contaminants of Concern in
Water’ Conference, June, Canberra, CD-ROM.
Leusch, F.D.L., Chapman, H.F., van den Heuvel, M.R., Tan, B.L.L., Gooneratne, S.R.,
Tremblay, L.A., 2006. Bioassay-derived androgenic and estrogenic activity in
municipal sewage in Australia and New Zealand. Ecotoxicol. Environ. Saf. 65, 403 –
411.
xxii
Chapter 1: Thesis objectives
1.1 General introduction
The endocrine system is diverse and complex, with varied and sophisticated
mechanisms that control hormone synthesis, release and activation, transport as well as
metabolism and delivery to the surface or interior of cells upon which they act
(Greenspan and Strewler, 1997). Endocrine disrupting compounds (EDCs) are defined
as exogenous substances or mixtures that can alter the function(s) of the endocrine
system and may cause health effects in an intact organism, or its progeny (WHO, 2002).
Industrial, agricultural and municipal wastes usually contain EDCs resulting in exposure
of organisms in the environment to unusually high concentrations of natural and
anthropogenic compounds that can elicit biological effects (Purdom et al., 1994;
Routledge and Sumpter, 1996). In wastewater, these compounds are sometimes able to
pass through the wastewater treatment system and reach receiving environments. It has
been demonstrated in Europe (Lavado et al., 2004; Diniz et al., 2005) and the USA
(Folmar et al., 1996; McArdle et al., 2000) that male fish held in treated wastewater
effluents or in rivers below wastewater treatment plants (WWTPs) showed a
pronounced increase of estrogen-dependent plasma vitellogenin concentrations. In egg-
laying vertebrates such as fish, estrogens activate the hepatic synthesis of vitellogenin.
This response has been suggested as a biomarker of exposure to estrogen active
substances (Sumpter and Jobling, 1995; Folmar et al., 1996).
As they are part of complex effluents, EDCs exist as mixtures. Individual compounds
within mixtures may vary greatly in estrogenic potency and may interact with each
other in an unpredictable manner. Measuring the concentrations of EDCs present in
water or solid phases typically involves extraction and analysis steps. It is essential to
develop effective methods that can extract multiple EDCs simultaneously from water
samples. Solid-phase extraction (SPE) is commonly used for spot or grab sample
extraction because of the large choice of sorbents for trapping targeted analytes. Aquatic
EDC monitoring programs are generally based on collection of discrete samples of
water phases. In environments where the contaminant concentrations may vary over
time, it is often desirable to expand the time window and increase the resolution by
taking more samples. Such pseudo time-integrated sampling of water, be it automatic or
manual, is both costly and cumbersome, and rarely used in large scale monitoring
studies. Passive sampling methods may represent a versatile tool in aquatic monitoring
1
programs, allowing a time-integrated monitoring of organic pollutants directly in the
aqueous phase as an alternative to conventional sampling techniques (Stuer-Lauridsen,
2005). During the past few years, miniaturization has become a dominant trend in
analytical chemistry with the development of stir-bar sorptive extraction (SBSE),
commercialized under the name Twister (Gerstel, Mülheim an der Ruhr, Germany). The
main advantages of this method are high sensitivity and a wide application range that
include extraction of volatile aromatics, halogenated solvents, polycyclic aromatic
hydrocarbons, polychlorinated byphenyls, pesticides, preservatives, odour compounds,
organotin compounds and EDCs from a variety of matrices (Tienpont et al, 2003;
Kawaguchi et al., 2005; Nakamura et al., 2005; Zuin et al., 2005; Duran Guerrero et al.,
2006).
Established analytical protocols are available for many of the compounds implicated as
being EDCs. Biological methods can also be used as screens to determine if EDC-active
compounds are present in a given environmental sample. In vitro and in vivo bioassays
offer a rapid, sensitive and relatively inexpensive solution to some of the limitations of
instrumental analysis. These bioassays can be used as tools to measure relevant
endpoints used for risk assessment of EDCs on the receiving environments. The
bioassays can be carried out concurrently with chemical methods to establish cause and
effect relathionships and to quantify the EDC activity present (Cech et al., 1998). New
and revised toxicological testing methods are being developed around the world
incorporating molecular and cellular biology and they hold promise for reducing whole
animal testing.
Several researchers have proposed and reported mathematical models which can be
used to quantify the distribution and fate of polycyclic aromatic hydrocarbons,
pharmaceuticals, pesticides, natural hormones and xenoestrogens in WWTPs (Clark et
al., 1995; Byrns, 2001; Khan and Ongerth, 2002 and 2004; Johnson and Williams,
2004). Clark et al. (1995) have modelled and analyzed the fate of organic chemicals in a
WWTP using fugacity modelling equations that describe the partitioning,
biodegradation, and volatilization or stripping behavior of chemical, which can be
solved to give an overall mass balance.
In South East Queensland, Australia, water levels in the major dams that supply potable
water to Brisbane are currently at an all time low of less than 30% of their maximum
2
capacities because of the sparse rainfall in the catchments areas over the past few years
(Figures 2.1 and 2.2). The state government has also proposed to add recycled water
from WWTPs into the region’s dam as a measure to ensure the water level in the dam
does not fall below 10%. With this new proposal, there are concerns from the local
community over the presence of toxic substances, including EDCs, which might not be
fully removed by the WWTPs.
Currently, there are very few studies that address the removal efficacies of EDCs in
different treatment technologies of WWTPs in Australia. Furthermore, the comparison
between a variety of sampling techniques and analytical (chemical and biological)
results are rarely carried out in the various treatment trains of a WWTP to give an
overall EDC assessment of the plant removal efficacy and the effects of effluent
discharge have on the receiving environment. Within this context, this PhD research
presents several different analytical approaches to address the assessment of EDCs in
Australia.
1.2 Aims and objectives
The main aim of this research was to determine EDC concentrations and total estrogenic
activity in WWTPs in South East Queensland, Australia and to evaluate the practicality
of various collection and extraction methods. This was addressed using chemical
techniques to quantify the EDCs. In addition, a biological assay was utilized to predict
likely impacts in receiving environments. The five objectives of this research are listed
below:
(a) Development of suitable extraction technique for chemicals with endocrine
disrupting activity
The first objective was to develop a robust extraction technique that could extract
estrogenic compounds in wastewater and sludge with high recovery. Three methods
were used for this purpose; solid phase extraction (SPE) for grab sampling,
EmporeTM disk as the matrix for passive sampling and stir bar sorptive extraction as
a new extraction method for water and sludge (Chapter 3).
3
(b) Assessment of estrogenic compounds present in wastewater samples using
chemical analysis
Two new gas chromatography-mass spectrometry methods were developed to measure a
range of selected EDCs present in wastewater and sludge samples. Fifteen EDCs
were selected based on their potency and ubiquity in WWTPs and the receiving
environments. These compounds include the natural female and male hormones,
phthalates, alkylphenols and tamoxifen (Chapter 3, 4 and 5).
(c) Assessment of biological response using in vitro assay
The MCF-7 cell proliferation assay or E-Screen was used to determine the level of
estrogenic activity of the various wastewater samples (Chapter 6).
(d) Integration of chemical and biological techniques
Both chemical and biological assays were used to determine the estrogenicity of the
wastewater (influent and effluent) collected from 5 WWTPs in South East
Queensland, Australia. The comparison of efficacy of these 5 WWTPs at removing
estrogenic compounds and activity was assessed. Furthermore based on the results,
an estimation of hazard towards aquatic organisms was made based on the effluent
released into receiving environments (Chapter 5 and 6).
(e) Fugacity fate modeling for EDCs in a WWTP
Based on the selected EDCs concentrations in the WWTP, their fate was modelled
using a fugacity format with equations describing the partitioning, biodegradation,
and volatilization or stripping behavior of chemical, which can be solved to give an
overall mass balance. With this particular model, the various EDC removal
pathways from the WWTP can be identified (Chapter 7).
1.3 Research questions
i) Passive samplers allow time integrated evaluation of EDCs in WWTPs
Since passive samplers are time integrated and cost effective, it was predicted that
this technique would more easily provide ambient field EDC concentrations in
WWTP samples compared to the grab samples (Chapter 3, 5 and 6).
ii) Combining chemical and biological analyses will give a thorough interpretation
of estrogenicity
4
Chemical and biological analyses have been shown to have their own advantages
and disadvantages. It was predicted that combining the results from the chemical
and biological assays will provide a more complete understanding of estrogenic
activity and the compounds most likely responsible in order to trace the source of
the release or problem (Chapter 5 and 6).
iii) Estrogenic activity in WWTPs and receiving environments are caused by natural
hormones
Since natural hormones are in general more potent than industrial estrogen mimics,
it was predicted that even if there are trace amounts of estrogens found in
wastewater as compared to the high concentrations of industrial estrogen mimics,
the majority of estrogenic activity would be attributed to the natural estrogens
(Chapter 5 and 6).
iv) WWTPs significantly remove EDCs by the end of the treatment process
The activated sludge treatment process of a WWTP was predicted to be the most
effective step in biodegrading or removing a large portion of EDCs from wastewater
(Chapter 4, 5, 6 and 7).
v) EDCs fugacity fate modeling will provide a good understanding of EDC
removal
Using specific fugacity based equations, chemical physical properties and field
monitoring data, it was predicted that fate modeling of EDCs removal pathways in a
WWTP can be undertaken to reflect the ambient removal mechanisms (Chapter 7).
1.4 Thesis format
Except for Chapter 2, the chapters in this thesis are structured as stand-alone scientific
papers. This has led to some overlap in the material and methods section (particularly
between Chapter 3, 4, 5 and 6). Some material has been deliberately excluded from the
general introduction and literature review to avoid repetition in the introductions to data
chapters. The specific discussions in each chapter include most of the discussion
material, while a more concise general discussion at the end is aimed to highlight
synergies between the different chapters and to show the coherence of the overall
purpose of the research.
5
1.5 References
Byrns, G., 2001. The fate of xenobiotic organic compounds in wastewater treatment
plants, Water Res. 35, 2523 – 2533.
Clark, B., Henry, J.G., Mackay, D., 1995. Fugacity analysis and model of organic
chemical fate in a sewage treatment plant. Environ. Sci. Technol. 29, 1488 – 1494.
Diniz, M.S., Peres, I., Pihan, J.C., 2005. Comparative study of the estrogenic responses
of mirror carp (Cyprinus carpio) exposed to treated municipal sewage effluent
(Lisbon) during two periods in different seasons. Sci. Total Environ. 349, 129 –
139.
Duran Guerrero, E., Natera Marin, R., Castro Mejias, R., Garcia Barroso, C., 2006.
Optimisation of stir bar sorptive extraction applied to the determination of volatile
compounds in vinegars. J. Chromatogr. A 1104, 47 – 53.
Folmar, L.C., Denslow, N.D., Rao, V., Chow, M., Crain, A., Enblom, J., Marcino, J.,
Guillette, L.J., 1996. Vitellogenin inductions and reduced serum testosterone
concentrations in feral male carp (Cyprinus carpio) captured near a major
metropolitan sewage treatment plant. Environ. Health Perspect. 104, 1096 – 1101.
Greenspan, F.S., Strewler, G.J. (Eds.), 1997. Basic and Clinical Endocrinology. 5th
edition. Appleton and Lange, Stamford, CT, pp. 1 – 36.
Johnson, A.C., Williams R.J., 2004. A model to estimate influent and effluent
concentrations of estradiol, estrone and ethinylestradiol at sewage treatment works.
Environ. Sci. Technol. 38, 3649 – 3658.
Kawaguchi, M., Sakui, N., Okanouchi, N., Ito, R., Saito, K., Nakazawa, H., 2005. Stir
bar sorptive extraction and trace analysis of alkylphenols in water samples by
thermal desorption with in tube silylation and gas chromatography-mass
spectrometry. J. Chromatogr. A 1062, 23 – 29.
Khan, S.J., Ongreth, J.E., 2002. Estimation of pharmaceutical residues in primary and
secondary sewage sludge based on quantities of use and fugacity modelling. Water
Sci Technol. 46, 105 – 113.
Khan, S.J., Ongreth, J.E., 2004. Modelling of pharmaceutical residues in Australian
sewage by quantities of use and fugacity calculation. Chemosphere 54, 355 – 367.
Lavado, R., Thibaut, R., Raldúa, D., Martín, R., Porte, C., 2004. First evidence of
endocrine disruption in feral carp from the Ebro River. Toxicol. Appl. Pharmacol.
196, 247 – 257.
6
McArdle, M., Elskus, A., McElroy, A., Larsen, B., Benson, W., Schlenk, D., 2000.
Estrogenic and CYP1A response of mummichogs and sunshine bass to sewage
effluent. Mar. Environ. Res. 50, 175 – 179.
Nakamura, S., Daishima, S., 2005. Simultaneous determination of 64 pesticides in river
water by stir bar sorptive extraction and thermal desorption-gas chromatography-
mass spectrometry. Anal. Bioanal. Chem. 382, 99 – 107.
Purdom, C.E., Hardiman, P.A., Bye, V.J., Eno, N.C., Tyler, C.R., Sumpter, J.P., 1994.
Estrogenic effects of effluents from sewage treatment works. Chem. Ecol. 8, 275 –
285.
Routledge, E.J., Sumpter, J.P., 1996. Estrogenic activity of surfactants and some of their
degradation products assessed using a recombinant yeast screen. Environ. Toxicol.
Chem. 15, 241 – 248.
Stuer-Lauridsen, F., 2005. Review of passive accumulation devices for monitoring
organic micropollutants in the aquatic environment. Environ. Pollut. 136, 503 – 524.
Sumpter, J.P., Jobling, S., 1995. Vitellogenin as a biomarker for estrogenic
contamination of the environment. Environ. Health Perspect. 103 (Suppl. 7), 173 –
178.
Tienpont, B., David, F., Benijts, T., Sandra, P., 2003. Stir bar sorptive extraction-
thermal desorption-capillary GC-MS for profiling and target component analysis of
pharmaceutical drugs in urine. J. Pharm. Biomed. Anal. 32, 569 – 579.
WHO, 2002. Global assessment of the state-of-science of endocrine disruptors.
Damstra, T., Barlow, S., Bergman, A., Kavlock, R., Van der Kraak, G. (Eds.),
International Program on Chemical Safety, World Health Organization.
Zuin, V.G., Montero, L., Bauer, C., Popp, P., 2005. Stir bar sorptive extraction and
high-performance liquid chromatography-fluorescence detection for the
determination of polycyclic aromatic hydrocarbons in Mate teas. J. Chromatogr. A
1091, 2 – 10.
7
Chapter 2: Literature review
2.1 Introduction The environment and organisms that live in it can be exposed to chemicals, including
those which may have endocrine disrupting activity, from such sources as agricultural
chemical use, industrial and commercial discharges to waterways and sewers as well as
excretion of natural and synthetic hormones by animals and humans to sewers. These
waters discharge, either directly or after treatment, to rivers or oceans. Human exposure
to chemical contaminants can be via food (naturally occurring contaminants, pesticide
residues, contaminants from transport or storage containers), through use of domestic
and consumer products (food packaging materials, pharmaceuticals products) and
potentially from drinking water.
Australia is a highly urbanised country, with its main population centres located on the
coastal fringe; and a limited number of smaller cities located inland. Thus, the bulk of
sewage effluent from the human population in Australia is treated and discharged to the
ocean. Agricultural runoff (including pesticides and fertilizers) from farming land in
the relatively small crescent of arable country running down the east coast into South
Australia, has the potential to find its way into creeks, streams and rivers which feed
into the Murray-Darling River system (Australia’s largest river catchment), the
Murrumbidgee River, or into a number of other rivers running east to the coast from the
Great Dividing Range. The Murrumbidgee and the Darling Rivers ultimately join the
Murray before it flows west, where it is used for irrigation and for drinking water.
Thus, in Australia, with respect to human health and exposure to endocrine disrupting
chemical contaminants in water, agricultural chemical runoff to rivers is likely to be of
greater concern than hormone discharge to city sewers (Falconer et al., 2003).
Because Australia is a dry continent, it has had to rely on very large reservoirs for the
supply of drinking water. In most States and Territories of Australia, these reservoirs
have highly protected catchments (e.g. Melbourne, Canberra, Sydney) and the water
supplies to their capital cities are of high quality. However, Adelaide, the capital of
South Australia, has to rely heavily on water taken from the Murray River, with the rest
of its supply obtained from reservoirs which have some agricultural land in their
catchments. Perth, the capital of Western Australia, relies on both reservoirs (with
protected catchments) and groundwater, for which there is the potential for
8
contamination from chemicals leaching into the sandy soil on which Perth is built.
Outside of the capital cities most country towns, apart from those located on large
rivers, rely on reservoirs which often collect from rivers and streams draining
agricultural catchments. Private dams in farming areas are quite likely to be
contaminated by agricultural runoff. Both dams and rainwater tanks in rural areas may
be contaminated if, for example, there is aerial spraying of crops.
In South East Queensland, Australia, water levels in the major dams that supply potable
water to Brisbane are <30% capacity at the time of writing. This is due to sparse rainfall
in the catchments over the past few years (Figures 2.1 and 2.2) and rapid population
growth in the region. This situation has resulted in water restrictions and conservation
techniques being implemented by the state government in an attempt to safeguard the
continuous supply of water for the future of South East Queensland. The addition of
reclaimed wastewater to water storages is being increasingly viewed as an important
element of water resources planning in Australia in order to stretch a scarce resource.
For this to be able to occur, the population health and ecological effects of exposure to
chemical contaminants in recycled water need to be well understood. When these
schemes are proposed, health concerns are voiced by local communities about the
possibility of toxic substances including endocrine disruptors entering their drinking
water supply that might not be completely removed by the wastewater treatment plants
(WWTPs) potentially leading to negative health outcomes.
Thus, any action with respect to drinking water contamination needs to be appropriately
targeted. For the bulk of the Australian population, contaminated water is not a
problem. However, the water supply industry is becoming increasingly aware of
endocrine disruptors as an issue for consideration.
Australia is moving to re-use its water more efficiently. As greater attention is paid to
reprocessed water and uses to which it is put, there is no doubt that endocrine disrupting
compound issues will assume greater importance. For example, it is likely that water
control authorities will adapt some of the newly developed biochemical assays for
testing inflows and outflows from treatment works. While strongly supporting improved
screening and testing for potential endocrine disruptors, Australian regulatory agencies
consider that endocrine disruption is but one part of a spectrum of effects that chemicals
can cause if animals and humans are exposed to concentrations which overwhelm
9
normal inactivation processes such as metabolism and excretion. That is, endocrine
disruption is not considered to be an adverse end-point per se, but rather is a mode or
mechanism of action potentially leading to other toxicological or ecotoxicological
outcomes e.g. reproductive, developmental, carcinogenic or ecological effects; these
effects are routinely considered in reaching regulatory decisions (at least for pesticides,
food additive chemicals and high production volume industrial chemicals for which the
required toxicology database is extensive).
In addition to endocrine disruption, there are other physiological mechanisms which can
be affected by excessive chemical exposure and chemical assessment should not unduly
focus entirely on carcinogens or endocrine disrupters but take into account all toxic end-
points of concern. Nevertheless, the focus on endocrine systems has led to an
acceleration of research and testing on a range of suspected problem chemicals and, in
many countries, has helped attract greater government and private funding for research.
10
Figure 2.1. Water supply catchments for nearly half of South East Queensland,
Australia region (SEQ Water, 2002).
11
Wivenhoe Dam
Figure 2.2. Water storage status of Wivenhoe, Sometset and North Pine Dams suplying
potable water to South East Queensland, Australia (SEQ Water, 2006).
2.2 The endocrine system The endocrine system and the nervous system are the major means by which the body
transmits information between different cells and tissues. This information results in the
regulation of most bodily functions. The endocrine system uses hormones to convey its
information. The endocrine system is diverse and complex, with varied and
sophisticated mechanisms that control hormone synthesis, release and activation,
transport as well as metabolism and delivery to the surface or interior of cells upon
which they act. Other mechanisms regulate the sensitivity of cells in target tissues to
hormones and the specific responses elicited by hormones. A hormone is defined as a
substance released by an endocrine gland and transported through the bloodstream to
another tissue where it acts to regulate functions of the target tissue (Greenspan and
Strewler, 1997). These actions are typically mediated by binding of the hormone to
receptor molecules. The receptor must be able to distinguish the hormone from a large
number of other molecules to which they are exposed to and transmit the binding
information to post-receptor events. Hormones are allosteric effectors that alter the
conformations of the receptor proteins to which they bind (Greenspan and Strewler,
1997).
Hormones produce their biological effects through interaction with high-affinity
receptors which are, in turn, linked to one or more effector systems within the cell. The
% F
ull
Somerset Dam North Pine Dam Average total system
12
effectors involve many different components of the cell’s metabolic machinery, ranging
from ion transport at the cell surface to stimulation of the nuclear transcriptional
apparatus. Steroids and thyroid hormones exert their effects in the cell nucleus, although
regulatory activity in the extranuclear compartment has also been documented. Peptide
hormones and neurotransmitters, on the other hand, trigger a plethora of signalling
activities in the cytoplasmic and membrane compartments while at the same time
exerting parallel effects on the transcriptional apparatus (Greenspan and Strewler,
1997).
2.3 Endocrine disrupting compounds (EDCs) It is now well established that there is a vast array of chemicals discharged into the
environment that can mimic (agonise) or block (antagonise) the action of hormones. A
hormone agonist is a compound that binds to a receptor and transmits binding into a
hormone response, while an antagonist is a compound that binds to a given receptor and
does not transmit the binding into a receptor response. The binding of an antagonist also
blocks binding of agonists and thereby prevents their actions, thus defining the term
antagonist. An endocrine disrupting compound is defined as an exogenous agent that
interferes with the synthesis, storage or release, transport, metabolism, binding, action
or elimination of natural blood-borne hormones responsible for the regulation of
homeostasis and the regulation of development process (Kavlock et al., 1996). Amongst
the important endocrine disruptors are those compounds suspected of interfering with
the normal action of the steroidal hormone estrogen through its receptor (i.e. estrogen
agonists and antagonists). The fact that these hormones play a critical role in the normal
development of the reproductive tract and sexual differentiation of the brain is well
documented (Cooper and Kavlock, 1997).
Disruption of sexual differentiation following exposure to estrogen has also been
demonstrated in various aquatic species, such as the turtle, which show temperature-
dependent sexual differentiation. Placement of either estrogen or some hydroxylated
polychlorinated biphenyls (PCBs) that are estrogen agonists directly on the egg have
been shown to alter sexual differentiation (Crews et al., 1995). Similar findings have
been reported in birds (Fry and Toone, 1981). Environmentally released chemicals may
also have anti-androgenic properties. Anti-androgenic compounds bind to the androgen
receptor, but block its transcriptional activity. Compounds such as the vinclozolin
metabolite M2 and the dichlorodiphenyltrichloroethane (DDT) metabolite, p,p’-DDE,
13
inhibit androgen binding to the androgen receptor (Kelce et al., 1994 and 1995) and
androgen-induced transcriptional activity (Wong et al., 1995). In vivo studies of
vinclozolin and p,p’-DDE have shown that these compounds inhibit androgen action in
developing, pubertal and adult male rats (Gray et al., 1994; Kelce et al., 1995).
Recently, concern has been expressed over the possibility that some synthetic chemicals
present in surface waters and aquatic sediments may adversely affect reproduction in
fish due to the possibility of pseudohermaphroditism and smaller testes weight (Purdom
et al., 1994; Sumpter, 1995).
2.4 Chemical properties of selected endocrine disruptors The exogenous chemicals in Table 2.1 with their molecular structures shown in Figures
2.3 – 2.7, have attracted much attention because even at low concentration levels they
are suspected of interfering with reproductive and behavioral health in humans and
wildlife, through disturbance of their endocrine system. Furthermore, these chemicals
are ubiquitous in the environment and have some of the highest potential as EDCs
compared to other known endocrine disruptors. From the physicochemical properties of
these compounds, it can be seen that most of them are in the range of low to moderately
hydrophobic organic compounds of mainly low volatility. It is expected that the
sorption on soil or sediment could be a significant factor in reducing their aqueous
phase concentration.
While reproductive toxicology studies in animals are typically required for regulation of
pesticides, many other chemicals in use have not been routinely screened for endocrine
disruption activity before being introduced for commercial use. Consequently the
significance of current concentrations of exposure to environmental estrogens or other
hormonally active compounds is unclear. To effectively assess the exposure to such
chemicals for endocrine disruption activity, the need for a rapid and sensitive screening
technique becomes apparent.
Because of the importance of the estrogen receptor (ER) in determining the
estrogenicity potential of a chemical which mimics or blocks the activity of natural
estrogens by specifically binding to the ER, there have been a number of attempts to
model the relationship between the structures of chemicals and estrogen receptor
binding affinity. Extensive binding studies of 17β-estradiol analogues have indicated a
comprehensive binding property (Anstead et al., 1997; Brzozowski et al., 1997). That is,
14
15
the ER can bind with a wide variety of non-steroidal compounds, which are structural
analogues of the alkyl substituted phenol moiety of the 17β-estradiol. For this steroid it
has been proposed that hydrogen bonding between the phenolic hydroxyl group and the
binding site in the ER, and also the hydrophobic and steric properties are important for
the binding affinity (Anstead et al., 1997; Brzozowski et al., 1997). For other
compounds, binding affinity depends on the extent of structural similarity with the
natural substrate.
One thing that has become very clear is the enormous difference in potency of
chemicals possessing estrogenic activity and probably other types of estrogenic activity
(Table 2.1). The most potent are the natural estrogens, such as 17β-estradiol and the
synthetic estrogen 17α-ethynylestradiol. Most, and perhaps all xenoestrogens (synthetic
chemicals that mimic the effect of estrogens) are much less potent, usually by 3 or 4
orders of magnitude, but sometimes even more. Thus, to obtain the same degree of
estrogenic response, it is usually necessary for the organism to be exposed to a much
higher concentration of xenoestrogen than that of 17β-estradiol and 17α-
ethynylestradiol. Obviously potency needs to be considered along with environmental
concentrations. Essentially all of the evidence to date suggests that it is the potent
steroidal estrogens that are the primary causative agents leading to feminization of fish
(Desbrow et al., 1998). Despite the general agreement that steroidal estrogens cause
much of the feminization of fish that has been reported, there appear to be at least a few
specific locations where concentrations of alkylphenolic chemicals, in particular
nonylphenol, are high enough that they contribute to the feminization, or may even be
the major causative chemicals (Solé et al., 2000; Sheahan et al., 2002; Todorov et al.,
2002).
16
Tabl
e 2.
1. B
ioch
emic
al p
rope
rties
of s
elec
ted
endo
crin
e di
srup
tors
.
Com
poun
d Ty
pe o
f com
poun
d M
olec
ular
w
eigh
t M
eltin
g po
int (
°C)
Boi
ling
poin
t (°C
) So
lubi
lity
in w
ater
(g
/100
mL)
Lo
g K
ow a
EEq
(est
roge
n eq
uiva
lent
) b
17β-
estra
diol
N
atur
al st
eroi
d es
troge
n 27
2 17
3 -
1.0
×10-3
4.
01
1.0
(Dre
wes
et a
l., 2
005)
c 17α-
estra
diol
N
atur
al st
eroi
d es
troge
n 27
2 17
3 -
1.0
×10-3
4.
01
0.10
(Kui
per e
t al.,
199
7) d
Estro
ne
Nat
ural
ster
oid
estro
gen
270
255
- 3.
0 ×1
0-3
3.13
0.
01 (L
eusc
h et
al.
2006
a) c
Estri
ol
Nat
ural
ster
oid
estro
gen
288
282
- B
arel
y so
lubl
e 2.
45
0.30
(Gut
endo
rf a
nd W
este
ndor
f, 20
01) c
17α-
ethy
nyle
stra
diol
Fe
mal
e co
ntra
cept
ive
296
142
– 14
6 -
4.8
×10-4
3.
67
1.25
(Gut
endo
rf a
nd W
este
ndor
f, 20
01) c
Test
oste
rone
N
atur
al st
eroi
d an
drog
en
288
152
– 15
6 -
3.9
×10-5
3.
32
1 ×1
0-5 (L
eusc
h et
al.
2006
a) c
Etio
chol
anol
one
Nat
ural
ster
oid
andr
ogen
29
0 18
1 –
184
-
Bar
ely
solu
ble
3.69
5×
10-7
(Leu
sch
et a
l. 20
06a)
d A
ndro
ster
one
Nat
ural
ster
oid
andr
ogen
29
0 18
1 –
184
-
Bar
ely
solu
ble
3.69
5×
10-7
(Leu
sch
et a
l. 20
06a)
d Ta
mox
ifen
Bre
ast c
ance
r tre
atm
ent
drug
37
2 96
– 9
8 -
<0.0
1 6.
30
4 ×1
0-5 (G
uten
dorf
and
Wes
tend
orf,
2001
) c
Non
ylph
enol
Su
rfac
tant
, pla
stic
izer
22
0 -
293
– 29
7 3.
9 ×1
0-4
5.76
8
×10-5
(Leu
sch
et a
l. 20
06a)
c
4-te
rt-oc
tylp
heno
l Su
rfac
tant
, pla
stic
izer
20
6 79
17
5 3.
0 ×1
0-4
5.50
6
×10-5
(Leu
sch
et a
l. 20
06a)
c
4-cu
myl
phen
ol
Surf
acta
nt, p
last
iciz
er
212
74 –
76
335
1 –
10
4.12
3
×10-4
(Ter
asak
i et a
l., 2
005)
e
Bis
phen
ol A
Pl
astic
izer
22
8 15
0 –
159
220
0.01
– 0
.03
3.32
3
×10-5
(Leu
sch
et a
l. 20
06a)
c
Die
thyl
pht
hala
te
Surf
acta
nt, p
last
iciz
er
222
-3
298
0.09
2.
42
5 ×1
0-7 (H
arris
et a
l., 1
997)
c D
ibut
yl p
htha
late
Pl
astic
izer
27
8 -3
5 34
0 1.
3 ×1
0-3
4.50
3
×10-7
(Kör
ner e
t al.
2001
) c B
enzy
l but
yl
phth
alat
e Pl
astic
izer
31
2 -4
0.5
370
2.7
×10-4
4.
73
2 ×1
0-7 (K
örne
r et a
l. 20
01) c
Dio
ctyl
pht
hala
te
[di-(
2-et
hylh
exyl
) ph
thal
ate]
Plas
ticiz
er
391
-47
386
3.4
×10-5
7.
60
<<1
×10-7
(Har
ris e
t al.
1997
) c
a Log
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as p
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cted
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2.1
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pute
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gram
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Virt
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ompu
tatio
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hem
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Lab
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(200
5).
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, EEq
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1).
OH
H
H
HOH
OHCH3 CH3
H Figure 2.3. Chemical structure of natural estrogens. Figure 2.4. Chemical structure of androgens.
17β-Estradiol
HHOH
17α-Estradiol
OH OCH3
H
H
HOH
CH3
H OH
Estrone
HHOH
Estriol
Testosterone
O
H
CH3
H
H
CH3
HOH
Androsterone
O
H
CH3
H
H
CH3
HOH
Etiocholanolone
O
CH3 H
H
CH3OH
H
17
ON
Tamoxifen 17α-Ethynylestradiol OH
H
H
H
CH3OH
CH
Figure 2.5. Chemical structure of pharmaceutical drugs.
OHCH3
CH2C(CH3)3
CH3
OH C9H19 Nonylphenol 4-tert-octylphenol
CH3
Figure 2.6. Chemical structure of alkylphenols. Figure 2.7. Chemical structure of phthalates.
CH3 OH OH
CH3
OH
4-cumylphenol
CH3
Bisphenol A
O
O
O
OO
O
O
Diethyl phthalate
O
Dibutyl phthalate
CH3O
O
O
O
O
Benzyl butyl phthalate
O
O
O
CH2
CH2
CH3
Di-(2-ethylhexyl) phthalate
18
2.4.1 Estrogens
The estrogens (17β-estradiol, estriol and estrone) are predominantly female hormones,
which are important for maintaining the health of the reproductive tissues, breasts, skin and
brain. 17α-Ethynylestradiol on the other hand is a synthetic steroid used as a contraceptive.
All vertebrate animals, including humans, can excrete steroidal hormone from their bodies,
which end up in the environment through sewage discharge and animal waste disposal. The
hormones 17β-estradiol and estrone are naturally excreted by women (2 –12 and 3 – 20
μg/person/day, respectively) and female animals, as well as by men (estrone 5
μg/person/day) (Gower, 1975). Pregnant women have been measured to excrete 260
μg/person/day of 17β-estradiol, 600 μg/person/day of estrone and 6000 μg/person/day of
estriol (Fotsis et al., 1980). However, Berg and Kuss (1992) demonstrated from a survey of
220 pregnant women that women could vary quite markedly in their excretions between
one another, and depending on the stage of their pregnancy. Based on the survey and
previous measurements of human estrogen excretion, Johnson et al. (2000) estimated the
daily excretion of estrogen by males and various categories of females (Table 2.2). From
such data on daily human excretion of estrogens, dilution factors and previous field
measurements, ng/L concentrations of estrogens are expected to be present in aqueous
environmental samples from English rivers (Johnson et al., 2000). These steroids have been
detected in effluents of sewage treatment plants and surface water (Ternes et al., 1999a).
They may interfere subsequently with the normal functioning and development in wildlife
(Jobling et al., 1998). Vitellogenesis (plasma vitellogenin induction) and feminization in
male fish have been observed in British rivers and are attributed to the presence of
estrogenic compounds (Desbrow et al., 1998; Jobling et al., 1998). Concentrations as low as
1 ng/L of estradiol led to the induction of vitellogenin (egg protein normally found in
female fish) in male trout (Purdom et al., 1994; Hansen et al., 1998).
In humans and animals, estrogens undergo various transformations, mainly in the liver.
They are frequently oxidized, hydroxylated, deoxylated or methylated prior to the final
conjugation with glucuronic acid or sulphate. 17β-estradiol is rapidly oxidized to estrone,
which can be further converted into estriol, the major excretion product. Many other polar
metabolites such as 16-hydroxy-estrone, 16-ketoestrone or 16-epiestriol are formed and can
also be present in urine and faeces. The contraceptive ingredient mestranol is converted
after administration into 17α-ethynylestradiol by demethylation (Ternes et al., 1999a). 17α-
19
ethynylestradiol is mainly eliminated as conjugates, whereas other metabolic
transformations occur, but are of minor relevance. Therefore, estrogens are excreted mainly
as inactive conjugates with sulphate and glucuronic acid. Although steroid conjugates do
not possess a direct biological activity, they can act as precursor hormone reservoirs able to
be reconverted to free steroids by bacteria in the environment (Baronti et al., 2000; Ternes
et al., 1999a). Due to the presence of microorganisms in raw sewage and sewage treatment
plants, these inactive conjugates of estrogenic steroids are cleaved, and active estrogenic
steroids may be released to the environment (Baronti et al., 2000; Ternes et al., 1999a).
In an aerobic batch experiments with activated sludge, 17β-estradiol was oxidized to
estrone, which was eliminated from the activated sludge tank without any further
transformation observed (Ternes et al., 1999b). The contraceptive 17α-ethynylestradiol was
largely persistent under selected aerobic conditions, whereas mestranol was rapidly
eliminated and small portions of 17α-ethynylestradiol were formed by demethylation. In
another experiment (Layton et al., 2000), 70 – 80% of added 17β-estradiol was mineralised
to CO2 within 24 hours by biosolids from WWTPs, whereas the mineralization of 17α-
ethynylestradiol was 25 – 75 fold less. 17α-ethynylestradiol was also reported to be
degraded completely within 6 days by nitrifying activated sludge resulting in the formation
of hydrophilic compounds (Vader et al., 2000).
Table 2.2. Daily excretion (μg) of estrogenic steroids by humans a.
Category 17β-estradiol Estrone Estriol 17α-ethynylestradiol Males 1.6 3.9 1.5 - Menstruating females
3.5 8 4.8 -
Menopausal females
2.3 4 1 -
Pregnant women 259 600 6000 - Women on contraceptives
- - - 35
a Estrogen concentrations taken from Johnson et al. (2000).
20
2.4.2 Tamoxifen
Considerable attention has been paid to the mechanism of action of triphenylethylenic
antiestrogens after they were demonstrated to antagonize the development of breast
cancers, especially those expressing the estrogen receptor α. Among these drugs, the partial
anti-estrogenic tamoxifen has become a reference compound in view of its high clinical
efficacy and lack of major side effects (Favoni and de Cupis, 1998; Green and Furr, 1999;
Prichard, 2000; Plouffe, 2000). Tamoxifen has a non-steroidal triphenylethylene structure
which competes with estrogen for binding sites in the breast (Figure 2.5). At present anti-
estrogenic properties make tamoxifen the endocrine treatment of choice for all stages of
breast cancer. In addition, tamoxifen has a variety of other mechanisms which may mediate
its effect – such as the induction of transforming growth factor β from stromal fibroblasts,
the reduction in circulating levels of insulin-like growth factor I, inhibition of angiogenesis
and induction of apoptosis (Neven and Vergote, 2001).
Experimental studies conducted with the MCF-7 breast cancer cell line have clearly shown
that short term exposure to tamoxifen, as well as to its active metabolite 4-
hydroxytamoxifen, leads to a significant increase of ER content or up regulation (Kiang et
al., 1989; Gyling and Leclercq, 1990; Leclercq et al., 1992). Actually, additional
investigations with other partial anti-estrogens reveal that ER up regulation could be a
characteristic feature of this particular class of pharmacological compounds (Jin et al.,
1995; Legros et al., 1997). This behavior contrasts with that observed with other ligands
(i.e. estrogens, pure antiestrogens), which down regulate the receptor (Dauvois et al., 1993;
Devin-Leclerc et al., 1998).
ER up regulation upon tamoxifen treatment is associated with its strong anchorage to the
nuclear matrix (Oesterreich et al., 2000), which results in a progressive loss of 17β-estradiol
binding ability (El Khissiin et al., 2000). The “partial anti-estrogenicity” of tamoxifen
suggests that this tamoxifen-receptor complex which is unable to bind with 17β-estradiol
would not mediate transcription under an estrogenic stimulus while it may still respond to
signals generated by peptide growth factors (cross-talk mechanisms) (Lee et al., 2000;
Sakamoto et al., 2002). On the other hand, such ER accumulation does not seem to be
directly responsible for the cytostatic or cytotoxic effects of tamoxifen, since it is observed
in MCF-7 sublines resistant to high doses of this drug (Leclercq et al., 1992; Jin et al.,
21
1995). Up till now, there are still no studies reporting the impact tamoxifen has on the
environment and wildlife.
2.4.3 Androgens
Androgens (testosterone, etiocholanolone and androsterone) are predominantly male
hormones that stimulate or control the development and maintenance of masculine
characteristics in vertebrates by binding to androgen receptors. Androgen concentrations in
humans are generally much higher than estrogen concentrations. For example, plasma
testosterone concentrations are 3000 – 10,000 ng/L in adult males and 200 – 750 ng/L in
adult females, while 17β-estradiol plasma concentrations are usually 10 – 60 ng/L in adult
males and 30 – 400 ng/L in adult females although they can be as high as 350 – 2000 ng/L
during pregnancy (Tietz, 1987). Kirk et al. (2002) reported that most of the androgenic
activity in municipal sewage with a predominantly domestic input is most likely caused by
androgens excreted by humans. Leusch et al. (2006b) found raw and treated wastewater
from WWTPs located in South East Queensland, Australia and New Zealand to have on
average 50 – 100 fold higher androgenic activity than estrogenic activity. Androgenic
activity in raw wastewater in the United Kingdom which ranged from 113 – 4300 ng/L
androgenic equivalents was also found by Kirk et al. (2002). As was the case with
estrogenic activity, WWTPs with activated sludge treatment were more effective than
trickling filters at removing the androgenic activity, with 82 – 99% net removal in activated
sludge plants compared to 57% in the tricking filter plant (Leusch et al., 2006b). Similar to
estrogens, sorption to activated sludge appears to be the major mechanism involved in
removing androgens from the aqueous phase (Esperanza et al., 2004; Layton et al., 2000).
Little is known about the effects of exposure of fish to androgenic chemicals. The lowest
observable effect concentration for induction of the male-specific protein, spiggin, in
female stickle backs (Gasterosteus aculeatus) after 3 – 5 weeks of exposure to
dihydrotestosterone was 2000 – 3000 ng/L (Katsiadaki et al., 2002), suggesting that fish
may not be susceptible to androgenic chemicals below the µg/L concentration. However,
some studies have shown masculinization of mosquitofish exposed to paper mill effluents
containing ng/L concentrations of the steroid androstenedione (Ellis et al., 2003; Jenkins et
al., 2001).
22
2.4.4 Alkylphenols
Alkylphenol ethoxylates (APE) are a class of surfactants which are manufactured by
reacting an alkylphenol (e.g. nonylphenol and octylphenol) with ethylene oxide. An APE
molecule consists of two parts: the alkylphenol and the ethoxylate moiety. This structure
makes APEs soluble in water and helps disperse dirt and grease from soiled surfaces into
water. Alkylphenols have been found in various aquatic environments as products of
biological degradation of alkylphenol ethoxylates, which are used for a variety of industrial
applications due to their potential efficiency and low cost. Alkylphenols themselves are
also used as antioxidants and a stabilizer of plastics by some industries. Since alkylphenols
are more toxic, persistent, and estrogenic to aquatic living organisms than the ethoxylate
surfactants, the presence of alkylphenols in the environment has recently become of some
concern.
Alkylphenols such as nonylphenol, octylphenol, cumylphenol and bisphenol A, have been
shown to elicit estrogenic hormonal activity by binding specifically to estrogen receptors
(Soto et al., 1992; White et al., 1994; Hu and Aizawa, 2003). While there are significant
differences in the receptor-binding affinity of the various phenolic compounds, their
biological activity and the significance of exposure to them, even to those chemicals with
weak estrogenicity, they are nonetheless important because of their environmental
prevalence (Thiele et al., 1997). The structural feature responsible for the estrogenic
activity of alkylphenolic chemicals was found from the results of recombinant yeast
screening (Routledge and Sumpter, 1996). The estrogenicity is very dependant on the size
and degree of branching of the alkyl group, and its position on the phenol ring (Routledge
and Sumpter, 1997). The maximum response is found with eight carbons and a tertiary
branched structure. Other authors have also reported similar results (Taira et al., 1999, Blair
et al., 2000, Nishihara et al., 2000). The estrogenicity is dependent on the carbon number of
the straight chain alkyl group when the carbon number is less than seven.
Alkylphenols and APEs enter the environment primarily via industrial and municipal
WWTP effluent (liquid and sludge), but also direct discharge such as pesticide application.
The distribution of alkylphenols and their ethoxylates have been documented in many
studies in North America and Europe. Nonylphenol and octylphenol have been detected in
ambient air, water, soil, sediment and biota (Ying et al., 2002a).
23
Nonylphenol is widely used as plastic additive and antioxidant. A derivative of
nonylphenol, nonylphenol ethoxylate, is commonly used as a non-ionic surfactant in
detergents, paints, emulsifying agents, pesticides, herbicides as well as a dispersing agent
for industrial applications such as production of paper, fibre, metal and agriculture
chemicals (White et al., 1994, Nimrod and Benson, 1996; Khim et al., 1999). The in vitro
estrogenicity activity of nonylphenol was reported to be 10-6 times less than 17β-estradiol at
a minimum (Jobling and Sumpter, 1993) to 2 ×10-3 times less at a maximum (Flouriot et al.,
1995). The no observed adverse effect level (NOAEL) for nonylphenol is 50 mg/kg body
weight (de Jager et al., 1999a and b).
Octylphenol is used for the production of octylphenol ethoxylates, a class of non-ionic
surfactants with a wide range of application. Octylphenol has been shown to weakly bind to
the estrogen receptor and to have weak estrogen-like activity in some in vitro screening
assays, with potency of octylphenol relative to estradiol of approximately 10-3 to 10-7(White
et al., 1994). In vivo screening assays have been variable with uterothropic responses and
other short-term changes occurring only at high doses, if at all (Gray and Ostby, 1998;
Williams et al., 1996). Madsen et al. (2002) found that a concentration of 4-tert-octylphenol
at 50 mg/kg body weight caused significant induction of vitellogenin in flounder
(Platichthys flesus).
Bisphenol A is a compound widely used as the monomer for the production of
polycarbonate plastic such as in baby bottles, and is a major component of epoxy resin used
for lining of food cans and dental sealants (Staples et al., 1998). To date, there have been
many reports detecting bisphenol A in the environment (Gonzalez-Casado et al., 1998,
Staples et al., 1998), baby food bottles (Mountfort et al., 1997), plastic waste (Yamamoto
and Yasuhara, 1998), and living organisms including humans (Miyakoda et al., 1999; Tan
and Mustafa, 2003). The safety of bisphenol A has become a controversial issue because it
not only possesses estrogenic endocrine disrupting effects (Krishnan et al., 1993; Brotons et
al., 1995), but also may be carcinogenic (Ashby and Tennant, 1988; Suarez et al., 2000).
There have been many reports concerning the disorders of reproductive organs when rats
and mice were exposed to bisphenol A in the prepubertal period (Vom Saal et al., 1998;
Stoker et al., 1999; Takao et al., 1999; Long et al., 2000; Tan et al., 2003). Bisphenol A was
able to activate estrogen receptors at concentrations lower than 1 μM (Paris et al., 2002),
24
however the NOAEL for bisphenol A was set at 50 mg/kg body weight (Tyl et al., 2002). 4-
cumylphenol, just like bisphenol A, is commonly used in the manufacture of plastic
polymers and has been found to be a weak estrogen mimic (Hashimoto et al., 2001).
2.4.5 Phthalates
Phthalate esters are plasticizers used largely in the production of polyvinyl chloride
products to make them flexible and workable and, to a lesser degree, in paints, lacquers,
and cosmetics (Skinner, 1992; Harris et al., 1997). The physical rather than chemical
incorporation of phthalates in the polymeric matrix ensures that they are widespread
contaminants. Release of phthalates into the ecosystem or in wastewater effluents occurs
during the production phase and via leaching and volatilization from plastic products during
their usage and/or after disposal (Staples et al., 1997). Phthalates have been detected in
water, and air (Fatoki and Vernon, 1990). They have also been found in foods, especially in
fatty foods, as they can migrate out of food packaging materials (Sharman et al., 1994;
Petersen, 1991). Some phthalates are suspected of disrupting the endocrine system,
especially by mimicking estrogens (Harris et al., 1997). This assertion was primarily based
upon work conducted in vitro, using receptor binding assays or reporter cell systems, but
estrogenic activity was not a consistent finding.
The competitive binding of a phthalate to the estrogen receptor was first reported for
hepatic receptors derived from rainbow trout. Di-(2-ethylhexyl) phthalate (DEHP) did not
affect 17β-estradiol at a concentration of 2 μM, but there was a decrease in 17β-estradiol
binding at higher concentrations, with a maximum of 25% reduction at 1 mM that was
suggestive of DEHP binding to the receptor. Dibutyl phthalate (DBP) was without effect at
a concentration of 80 nM, but induced a contraceptive-related decrease in 17β-estradiol
binding at higher concentrations, with an apparent IC50 (the concentration required to
inhibit 17β-estradiol binding by 50%) of 1 mM (Moore, 2000). Benzyl butyl phthalate
(BBP) inhibited 17β-estradiol binding at all concentrations (estimated between 80 nM and
50 μM), with an IC50 of approximately 10 μM and maximum inhibition of 60% (Moore,
2000). Diethyl phthalate (DEP) displayed weak binding to Xenopus laevis liver cytosol,
with an IC50 of 12 μM, representing a relative binding affinity of approximately 0.003
compared to 17β-estradiol (Lutz and Kloas, 1999).
25
In an in vivo study, BBP, DBP, DEHP were assessed for estrogenic activity following
administration as four daily doses (20, 200, or 2000 mg/kg/day) to ovariectomized rats.
None of the phthalates stimulated either absolute or relative uterine weight increases in
immature animals, or vaginal epithelium cornification in mature animals (Zacharewski et
al., 1998a). In contrast, known estrogenic chemicals (including 17β-estradiol) stimulated
uterine weight increase, vaginal cornification, and lordosis (Zacharewski et al., 1998a).
Benzyl butyl phthalate (BBP) is a phthalate ester that is present in paper and paperboards
used as packaging materials for aqueous, fatty, and dry food (IARC, 1982). BBP has been
tested for its estrogenic properties in vivo and in vitro. Uterothrophy and vaginal cell
cornification tests carried out on ovariectomized female Sprague-Dawley rats have shown
no estrogenic effects of BBP (Zacharewki et al., 1998b; Gray et al., 1999). In contrast, BBP
exerted estrogenic activities in several in vitro tests: MCF-7 cell proliferation, estrogen
receptor binding in rat uterus, and yeast transfected with human ER (Jobling et al., 1995;
Harris et al., 1997; Zacharewski et al., 1998b; Andersen et al., 1999).
2.5 Endocrine disruption
2.5.1 Mechanisms of endocrine disruption
A biologically active chemical can disrupt the endocrine system of an organism in a wide
variety of ways. The following are some examples, focusing particularly on the sex
hormone disruptors:
i) Binding to and activating the estrogen receptors (therefore acting as an estrogen) by
mimicking the female hormone 17β-estradiol.
One complexity of this mode of action is the fact that there are a variety of estrogen
receptors, present in a wide range of tissues. It has been found that if several chemicals that
can bind and activate the estrogen receptor are added together, their effects will usually be
additive, so that effects of small quantities of a range of estrogenic chemicals can add
together into a much larger effect (Soto et al., 1995). Chemicals such as benzyl butyl
phthalate and di-n-butyl phthalate have been shown to add their effects to any natural
estrogen present (Jobling et al., 1995).
26
ii) Binding with but not activating the estrogen receptor (therefore acting as an anti-
estrogen).
For example, dioxin and furans work as anti-estrogenic agents through binding with the
aryl hydrocarbon receptor and estrogen receptor; however the aryl hydrocarbon ligand-
receptor complex may block estrogen receptor action in estrogen-responsive cells by DNA
binding competition (Krishnan and Safe, 1993; Klinge et al., 1999).
iii) Binding with other receptors.
There are many other receptors involved in the hormonal system, for example androgen
receptors for male hormones. This binding can either activate the receptor, or inactivate it,
as seen in anti-androgenic like effect of the DDT metabolite p,p’-DDE (Kelce, 1995).
iv) Modifying the metabolism of natural hormones.
Some chemicals such as the pesticides lindane and atrazine, can affect the metabolic
pathway of estradiol, producing more estrogenic metabolites such as 16α-hydroxyestrone,
potentially leading to an increased risk of breast cancer (Bradlow et al., 1995). Other
chemicals can activate enzymes which speed up the metabolism of hormones. The testes
contain specific enzymes to metabolise estrogens, breaking them down rapidly to a form
which can no longer bind to their receptor (Toppari et al., 1996). However, if these
enzymes are affected by a xenoestrogen, this metabolism will be reduced, increasing the
exposure of the testes to estrogen. This could be particularly relevant during fetal
development, when there are high concentrations of estrogen (Toppari et al., 1996).
v) Modifying the number of hormone receptors in a cell.
Complex mechanisms control the number of hormone receptors present in cells. A
chemical may reduce or increase the number of receptors, and so affect the existing
response to natural or synthetic hormones. For example, DDT and related compounds act
in a number of ways to disrupt endocrine function by binding with the estrogen receptor
(via mimicry and antagonism), altering the pattern of synthesis or metabolism of hormones
and modifying hormone receptor levels (Welch et al., 1969; Soto et al., 1995; Lascombe et
al., 2000; Rajapakse et al., 2001).
27
vi) Modifying the production of natural hormones.
Chemicals can affect natural hormone production by interfering with other signalling
systems, such as other hormone systems like the thyroid system, or the immune and
nervous systems. Chemicals such as pentachlorophenol affect the thyroid system by
reducing levels of thyroid hormone possibly through a direct effect on the thyroid gland
(Beard and Rawling, 1999). Polychlorinated biphenyls also affect the thyroid hormone by
binding to the serum transport protein, transthyretin and thyroid-binding globulin, but not
to the thyroid hormone receptor (Cheek et al., 1999). The authors suggest that disruption of
thyroid hormone transport is one of the mechanisms by which organochlorine compounds
alter thyroid homeostasis.
2.5.2 Other factors affecting the activity of endocrine distuption
There are many variables which determine whether an endocrine disrupting chemical
actually exerts a biological effect, including uptake, distribution, mode of action and nature
of exposure.
i) Uptake and distribution
To elicit an effect, first the chemicals must enter the body, either through ingestion of food
or drinks, by cutaneous adsorption, for example from cosmetics or inhalation. The
chemical will then be distributed through the body, usually by the circulatory system.
Several systems exist in the body to detoxify chemicals, notably the liver enzymes. These
systems remove chemicals by a combination of breaking them down and attaching them to
other chemicals, which promote their excretion, usually through the kidneys and into the
urine. Some chemicals are not removed effectively by these processes and are retained in
body. Those chemicals that are lipophilic and metabolised poorly or slowly can accumulate
and be stored in the body fat. The fat stored in the body can be mobilized during stressful
periods, malnutrition, or in pregnancy, releasing the stored chemicals into the blood stream.
ii) Bioavailability
Natural hormones such as estrogens have their concentration in the blood modified by sex
hormone binding globulin and albumin, which bind the majority of the hormones in the
blood, so regulating their availability to bind to receptors and initiate responses (Arnold et
al., 1996). Sex hormone binding globulin binds very strongly and specifically to estradiol,
28
whilst albumin binding is weaker and less specific. Arnold et al. (1996) have investigated
how these two compounds affect the availability of 17β-estradiol, diethylstilbestrol,
octylphenol and o,p’-DDT by observing how their presence affects binding to a human
estrogen receptor (expressed by a yeast). They found that the xenoestrogens bound far less
to the albumin and the sex hormone binding globulin, leaving more free compounds
available to bind to the receptor and initiate an estrogenic response.
iii) Exposure
The time of exposure to an endocrine disruptor can be crucial, as some stages of
development, for instance during early childhood or puberty, are far more sensitive to the
detrimental effects of these chemicals. The duration of exposure to an endocrine disruptor
also contributes to the biological effects of the chemical; a chronic exposure would
certainly cause a more serious effect as compared to an acute exposure to the EDCs.
2.5.3 Endocrine disruptors in wildlife (vertebrates/invertebrates)
Most of the evidence for endocrine disruption in wildlife has come from studies on species
living in, or closely associated with, the aquatic environment, which is perhaps not
surprising given the fact that our rivers and oceans act as a basin for the discharge of many
chemicals in large volume. The effects found include altered/abnormal blood hormone
concentrations, reduced fertility and fecundity, masculinization of females and
feminization of males. Information on the impact of endocrine disruptors on wildlife
populations is however, limited to a few species in several vertebrate and invertebrate taxa.
Furthermore, the responses and relative sensitivities of different animal species to
endocrine disruptors have not been comprehensively compared and may vary, both within
and between taxa (Jobling et al., 2003). However, by comparing the results of different
studies, done by different groups of researchers for different reasons it is possible to reach
some tentative conclusion on the effects of EDCs. What is clear is that most, if not all,
vertebrates (fish, amphibians, reptiles, birds and mammals) do respond in a similar way,
with a similar degree of sensitivity, to both steroidal hormones and xenoestrogens and also
probably antiandrogens (Sumpter and Johnson, 2005). This is not surprising when it is
realized that there are essentially no differences in the specificities of the estrogen receptors
across the wide range of vertebrates (Sumida et al., 2003).
29
The majority of laboratory-based studies on endocrine disruptors in vertebrates have
focused on the effects of estrogenic chemicals, because many of the effects seen in
vertebrate wildlife are believed to have resulted from disruption from this axis. In
vertebrates, estrogens play a fundamental role in both reproduction and somatic cell
function, sexual differentiation, development of secondary sex characteristics, ovulation,
the regulation of mating and breeding behaviors, and the regulation of calcium and water
homeostasis (Fairbrother, 2000). Thus, for example 17β-estradiol, whether of endogenous
or exogenous origin, is a very powerful estrogen, and induces vitellogenin production in all
oviparous (egg-laying) vertebrates (Sumpter and Johnson, 2005). Likewise, nonylphenol is
a weak estrogen, acting through estrogen receptors, in all vertebrates in which it has been
studied (White et al., 1994). Thus, in the case of vertebrates, it appears that one animal’s
endocrine disruptors is another animal’s endocrine disruptor (though there will be subtle
differences between species, may probably caused by differing pharmacokinetics), due
largely to the fact that their endocrine systems show many similarities (Sumpter and
Johnson, 2005).
Few studies have, however, examined the effects of endocrine disruption on invertebrates,
mainly due to the general lack of knowledge of their basic endocrine physiology (Jobling et
al., 2003). It is very unlikely, however, that all invertebrates respond in the same way as do
vertebrates to EDCs, and equally unlikely that all invertebrates respond in the same way
(Sumpter and Johnson, 2005). For example, steroidal estrogens such as 17β-estradiol and
17α-ethynylestradiol are extremely potent estrogens in fish (Thorpe et al., 2003), but have
very little, if any, endocrine effect on at least some groups of invertebrates (Breitholz and
Bengtsson, 2001; Segner et al., 2003), though even with this example some researchers
have reported reproductive effects on one group of invertebrates, molluscs, at
concentrations similar to those that cause feminization in fish (Jobling et al., 2003). From
the work that has been conducted with invertebrate taxa (the crustacea and the insecta), it
seems that many of the physiological functions that are under hormonal control have no
vertebrate comparison (DeFur et al., 1999). Although vertebrate-like sex steroid hormones
have been discovered in many invertebrate groups, their function, in most cases remains
equivocal. Only in molluscs (slugs and snails) have a role for vertebrate type sex steroids
been suggested (Bettin et al., 1996; Geraerts and Joosse, 1984). Mechanistic studies on the
30
induction of imposex and intrasex in prosobranch molluscs indicate that steroids
(particularly testosterone) may play an important role in manifestation of these
abnormalities (Bettin et al., 1996; Gooding and LeBlanc, 2001). Furthermore, imposex and
intersex can be induced by exposure to androgens or androgen mimics. As Oehlmann and
Schulte-Oehlmann (2003) have pointed out, there are more than 30 different invertebrate
phyla, whereas in contrast all vertebrates comprise only part of a single phylum. It would
therefore be surprising if an EDC caused the same effect in all invertebrates.
2.5.4 Endocrine disruptors in discharge and surface water
The centralization of sewerage and the widespread introduction of secondary biological
sewage treatment have brought enormous benefits to society and the environment. Given
the relatively short hydraulic residence time (a few hours), the large reduction in the
amount of natural and xenobiotic organic molecules that occur in a WWTP is remarkable
(Johnson and Sumpter, 2001). However, an increasing number of studies, including studies
within Australia, have shown that effluent from sewage treatment plants contains EDCs
(Braga et al., 2005; Leusch et al., 2006a and b). Feminization in male fish, including
skewed sex ratios in exposed fish populations and oocytes in the gonads of males
downstream from sewage discharges have been linked to the occurrence of estrogenic
compounds in the effluents. Natural estrogens such as those regulating the female
reproductive cycle are excreted both by women and men in the population (Table 2) and
occur in sewage (Desbrow et al., 1998; Ternes et al., 1999a and b; Belfroid et al., 1999;
Larsson et al., 1999; Spengler et al., 2001). Estrogens used as contraceptives and
pharmaceuticals are also excreted and have been found in municipal wastewater. In
addition, synthetic compounds such as nonylphenol and its derivatives, and bisphenol A
mimic estrogens, and have also been detected in wastewater (Krishnan et al., 1993; Giger
et al., 1994; Desbrow et al., 1998; Ternes et al., 1999a and b; Belfroid et al., 1999; Larsson
et al., 1999; Baronti et al., 2000; Körner et al., 2000; Spengler et al., 2001).
In a study of seven sewage treatment works in the UK, sewage treatment effluents
contained estrone (E1) at concentrations of 1 – 80 ng/L, 17β-estradiol (E2) at
concentrations of 1 – 50 ng/L, and 17α-ethynylestradiol (EE2) at concentrations of 0 – 7
31
ng/L (Desbrow et al., 1998). Effluents from three Dutch WWTPs contained E1, E2 and
EE2 at concentrations of <0.4 – 47, <0.6 – 12 and <0.2 – 7.5 ng/L, respectively (Belfroid et
al., 1999). In sixteen German municipal sewage treatment effluents, estrogens were
measured at concentrations of up to 70 ng/L for E1, 3 ng/L for E2, and 15 ng/L for EE2,
while in 10 Canadian plants corresponding concentrations were up to 48 ng/L E1, 64 ng/L
E2, and 42 ng/L EE2 (Ternes et al., 1999a). Similar concentrations of estrogens were also
found in WWTPs in Switzerland and Austria (Joss et al., 2004). In Sweden, a minor
sewage treatment plant discharged up to 5.8 ng/L of E1, 1.1 ng/L of E2, and 4.5 ng/L of
EE2 (Larsson et al., 1999). This effluent also contained 840 ng/L of 4-nonylphenol and 490
ng/L of bisphenol A. Leusch et al. (2005; 2006a and b) have detected significant amounts
of both estrogenic (<4 – 185 ng/L estrogen equivalent) and androgenic (1920 – 9330 ng/L
testosterone equivalent) activity in raw influent from municipal WWTPs in Australia and
New Zealand. Subsequent treatment of raw sewage successfully removed most of the
activity so that the estrogenicity and androgenicity associated with the final effluent were
very low (<1 – 4.2 ng/L estrogen equivalent and <6 – 736 ng/L testosterone equivalent,
respectively) (Leusch et al., 2005; 2006b). Braga et al. (2005) reported average
concentrations of estrone, 17β-estradiol and 17α-ethynylestradiol in raw wastewater in
Australia were 55, 22 and <5.0 ng/L respectively and noted >85% removal for the
estrogens after treatment at the WWTPs surveyed.
Microorganisms may hydrolyse sulphate and glucuronide conjugates of excreted estrogenic
steroids in treatment effluent, and most probably also in sewage prior to or during
treatment (Baronti et al., 2000). Although these conjugates are not active estrogens,
hydrolysis transforms them into active products that may exert their effects on the hormone
regulation of organisms in receiving waters. Estrogenic steroids may also be transformed or
absorbed in the sewage treatment process. Depending on the treatment method, high rates
of removal of natural and synthetic estrogens in biological sewage treatment plants were
found in Brazil and Germany (Ternes et al., 1999a). Activated sludge was found to be more
efficient than biological filters, and the extent of removal differed among the estrogenic
steroids. Sewage treatment processes in Italy removed 61 – 95% of these estrogenic
compounds with median concentrations of E1, E2 and EE2 of 9.3, 1.0, and 0.45 ng/L,
respectively, in effluents after treatment (Baronti et al., 2000). Membrane filtration and
32
advanced chemical oxidation effectively removed estrogens in Japanese sewage treatment
effluent (Shishida et al., 2000).
Rivers and other water bodies in Netherlands that received treated wastewater contained
from <0.3 – 5.5 ng/L E2 and <0.1 – 4.3 ng/L EE2 (Belfroid et al., 1999). In Italy,
concentrations of 0.11 and 0.04 ng/L were reported for these two estrogens in the river
Tiber at a point downstream from a sewage treatment plant, and 15 rivers and streams in
Germany were found to contain less than the quantitation limit (0.5 ng/L) of estrogenic
compounds (Ternes et al., 1999a; Baronti et al., 2000). Varying concentrations of E1 (<5 –
112 ng/L), E2 (<5 – 200 ng/L), EE2 (<5 – 831 ng/L), testosterone (<5 – 214 ng/L),
bishenol A (<90 – 1200 ng/L), 4-nonylphenol (50 – 4000 ng/L) and other compounds such
as pesticides, phthalates, surfactants and pharmaceuticals with endocrine disrupting
properties were found in 139 streams (downstream of intense urbanization and livestock) in
USA (Kolpin et al., 2002).
2.6 Methodologies for detection and monitoring of endocrine disruptors
2.6.1 Chemical analytical techniques
Established analytical methods are available for many of the compounds implicated as
being EDCs. Most developed countries have established regulatory authorities,
requirements for chemical analysis and methods for testing for pesticides, metals, industrial
chemicals and PCBs in food or the environmental materials (WHO/IPCS, 2002). However
for some of the EDCs such as hormones, drugs, and personal care product ingredients the
analytical methods are less well developed. There are potentially significant classes of
compounds that are not studied in detail due to a lack of suitable instrumental techniques or
analytical standards. Chemical analyses can also be costly and time consuming.
2.6.1.1 Extraction methods for water
Many potential endocrine disruptors exist as mixtures. Individual compounds within
mixtures may vary greatly in estrogenic potency and may interact with each other in an
unpredictable manner. While this may not be such a problem for a simple matrix containing
only a few well, defined contaminants, in the majority of cases there will be too many
33
chemical components to easily identify those that are hormonally active. Some EDCs are
transported to the aquatic environment through atmospheric transport as well as via soil
runoff, erosion and leaching. The sediment of the hydrosphere plays an important role in
storage of such chemicals. EDCs can also be released unknowingly from untreated
wastewater into the rivers and estuarine, and eventually the aquatic environment may
contain a mixture of various chemical compounds of different concentrations and
toxicological potencies. To measure the concentration of EDCs present in water or
sediment would typically involve extraction and analytical steps which will be discussed in
the following sections.
2.6.1.1.1 Direct sampling: solid-phase extraction (SPE)
Solid-phase extraction (SPE) is a commonly used sample extraction method. It is a very
active field of separation science and more than 50 companies currently make products for
SPE (Barcelo and Hennion, 1997; Thurman and Mills, 1998; Simpson, 1998). Disposable
cartridges for SPE have been available for more than 20 years, yet SPE development has
been slow for many reasons. Liquid-liquid extraction (LLE) has remained the preferred
technique for the work up of liquid samples for several years especially in the
environmental field. Increased development of SPE has occurred during the past five or six
years, with many improvements in format, automation and introduction of new phases. One
reason was the desire to decrease organic solvent usage in laboratories which has
encouraged the requirements of solvent-free procedures and has greatly contributed to the
growth of SPE at the expense of liquid-liquid extraction procedures (Hennion, 1999).
Interest in polar analytes such as some degradation products of organic micropollutants has
also highlighted the need for alternative methods to LLE because many polar analytes are
often partly soluble in water and cannot be extracted with good recoveries whatever the
organic solvent selected (Chiron et al., 1993; Barcelo and Hennion, 1997). At the same
time, the availability of cleaner and more reproducible sorbents than in the past has also
helped its increasing acceptance by regulatory agencies. Other reasons for the growing
interest in SPE techniques are the large choice of sorbents for trapping polar analytes with
the capability for new ones. As EDCs of varying concentrations and properties tend to
occur simultaneously in natural waters, it is essential to develop an effective method that
can extract multiple EDCs simultaneously from water samples.
34
The selection of an appropriate SPE cartridge with particular sorbent materials plays a key
role in the achievement of high and reproducible recovery for contaminants. The most
commonly used sorbents are porous silica particles surface-bonded with C18 or other
hydrophobic alkyl groups and polymeric sorbents such as styrene-divinylbenzene and
activated charcoal. Furthermore, some hydrophilic groups, such as sulfonic acid and N-
vinylpyrrolidone groups are often added into the polymeric sorbents to enhance water
movement which make the sorbent more efficient. The recovery of organic compounds by
SPE is highly dependent on the polarity of the eluents. Solvents used as eluents are usually
acetone, dichloromethane, ethyl acetate, hexane and methanol. Natural waters can have
different salinities (e.g. freshwater, seawater). It is well known that the aqueous solubility
of many organic compounds decreases with increasing salt concentration, thus their
extraction efficiency in SPE is likely to increase. Generally, speciation of weakly acidic
compounds in aqueous solutions depends on the solution properties, such as its pH value.
Acidification of water solution is likely to decrease the dissociation of weakly acidic
analytes leading to increasing extraction efficiency of the target compounds if the non-
dissociated form binds strongly to the SPE cartridges (Liu et al., 2004).
The EDCs shown in Table 2.1, have a wide range of aqueous solubility which will give an
indication of the best selection of SPE sorbent needed to extract them. Most of these
chemicals have been successfully extracted with high recovery when different SPE
cartridges are used to suit the compound; however, no published studies have shown that
there is a single SPE sorbent that can extract all of these EDCs with a high recovery rate.
2.6.1.1.2 Passive sampling
Passive sampling is based on the free flow of analyte molecules from the sampled medium
as a result of a difference in chemical potential (Górecki and Namieśnik, 2002). It can be
used for the determination of both inorganic and organic compounds in a variety of
matrices, including air, water and soil. Systematic attempts have been made in the last
decade to develop passive sampling systems that accumulate chemicals while deployed for
a fixed period in a water body and from which time-weighted average exposure
concentrations can be calculated. The equipment used for passive sampling and/ or
extraction is simple and reliable; this is of great importance, since sampling sites are often
situated far from laboratory where further stages of analysis must be performed. The next
35
principal advantage of passive approach over grab sampling is that one device is necessary
at a given sampling location for the duration of the sampling (Namieśnik et al., 2005). In
grab sampling, where the sample represents the conditions at the sampling site at a given
moment in time, the number of samples collected over the duration of sampling can be
large if the same time-averaged information is to be obtained. Since only a few analyses are
necessary over the monitoring period for the passive sampler, analytical costs can be
reduced substantially.
Several novel passive sampling devices suitable for monitoring a range of non-polar and
polar organic chemicals, including pesticides, pharmaceutical/ veterinary drug and other
emerging pollutants of concern have recently been developed (Allen et al., 2006). Semi-
permeable membrane devices (SPMDs) have increasingly attracted attention as a passive
sampling tool in both marine and freshwater environments. SPMDs consist of a thin layer
of an appropriate neutral lipid (usually triolein, which acts as the major sequestration
medium) sealed within layflat, low density polyethylene plastic tubing (USGS, 2002).
Dissolved aqueous lipophilic contaminants are sampled by the SPMDs, and the processes
involved are believed to mimic bioconcentration in living organisms (Axelman et al.,
1999). SPMDs are seen as having several advantages over bioaccumulator organisms such
as bivalve shellfish. Amongst the strongest reasons for their use are the lack of differences
in contaminant accumulation due to sex, reproductive state or age; the fact that their use
poses no difficulties with high mortality in polluted environments; and their inability to
metabolise chemicals of interest (Peven et al., 1996). The United States Geological Survey
(USGS), in their extensive on-line information concerning SPMDs, state that the
accumulation of contaminants by the lipid (e.g. triolein) is affected by physicochemical
properties of the contaminants, such as the octanol/water partition coefficient (log Kow),
molecular weight, volatility and polarity, and also by features of the exposure environment
such as temperature, flow rate, and degree of biofouling on the membrane surface (USGS,
2002). Peven et al. (1996) noted that relatively water-insoluble substances with high
molecular weight are generally accumulated at a reduced rate. This relationship may be due
to restrictions of pore size (approximately 10Å) in the SPMD membrane (Huckins et al.,
1990).
36
Particle loaded membranes have been recently introduced as another aquatic passive
sampling device. The particle loaded membranes or Empore disks (manufactured by 3MTM)
allow for increased flow rates and efficiency in extracting semi-polar compounds such as
many of the EDCs mentioned previously. It was originally produced as a solid phase
extraction disk (C18 or styrenedivinylbenzene), but with a few minor adjustments to the
device, it can be deployed into the aqueous environment as an effective passive sampler.
The Empore disk passive sampler has been used monitor polycyclic aromatic hydrocarbons
(PAHs) and pesticides in water bodies (Vrana et al., 2005, Stephens et al., 2005). To date,
there are very few studies reported on EDCs where Empore disks are used as passive
samplers. Vermeirssen et al. (2005) used a passive sampler system (polar organic chemical
integrative sampler, POCIS) to measure estrogenicity in rivers and lakes in Switzerland and
also correlating the results with that from grab and bioaccumulation of estrogens in brown
trout. Alvarez et al. (2005) on the other hand compared a novel passive sampler to standard
water-column sampling for 96 organic contaminants associated with wastewater effluents
entering a New Jersey stream; they reported the passive sampling technique offers an
efficient and effective alternative for detecting organic wastewater-related contaminants in
our waterways for wastewater contaminants.
Results obtained with passive samplers can be interpreted at different levels of complexity.
The most basic modelling concerns the comparison of peak patterns in biota and passive
sampler, or between passive samplers exposed at different locations (Allan et al., 2006).
Samplers can be used to investigate temporal trends in concentrations of waterborne
contaminants and to evaluate the location of point and diffusive contaminant sources (Allan
et al., 2006). In more complex applications, exposure concentrations in the field can be
determined after the passive sampling exchange kinetics have been measured in the
laboratory using known exposure concentrations (Stephens et al., 2005, Vrana et al., 2006).
In order to predict time-weighted average water concentrations of contaminants from levels
accumulated in passive sampler, extensive calibration studies are necessary to characterise
the uptake of chemicals into a passive sampler. Uptake of chemicals depends upon their
physico-chemical properties, but also upon the sampler design and is influenced by
environmental variables such as temperature, flow rate, turbulence and biofouling of the
sampler surface (Booij et al., 1998, Vrana et al., 2006, Allan et al., 2006).
37
2.6.1.1.3 Stir bar sorptive extraction (SBSE)
During the past few years, miniaturisation has become a dominant trend in analytical
chemistry. Examples of miniaturisation in sample preparation techniques are micro liquid-
liquid extraction (in-vial extraction), ambient static headspace and disk cartridge SPE.
Recently, Baltussen et al. (1999) developed a new extraction technique based on the same
extraction principles as solid-phase microextraction (SPME) but the sorbent,
(polydimethylsiloxane, PDMS), is placed on a stir bar. This technique is known as stir-bar
sorptive extraction (SBSE) and the coated stir bars are commercialised under the name
TwisterTM (Gerstel, Mülheim an der Ruhr, Germany). The amount of PDMS on the stir bar
is higher than the amount on a SPME fibre so higher recoveries and therefore sensitivities
are expected when using SBSE. While high recoveries (>50%) are only obtained for solutes
with log Kow >4 using SPME, the recovery obtained by SBSE is higher than 50% for
solutes with log Kow >2 (Baltussen et al., 1999). Its main advantage is high sensitivity and
wide application range that includes volatile aromatics, halogenated solvents, polycyclic
aromatic hydrocarbons, polychlorinated biphenyls, pesticides, preservatives, odour
compounds and organotin compounds (Tienpont et al, 2003; Nakamura and Daishima,
2005; Zuin et al., 2005; Duran Guerrero et al., 2006).
The stir bar is immersed in the sample or placed in the headspace (HS-SBSE) for a period
of time at a fixed temperature and the analytes are extracted. SBSE can be used with both
gas-chromatography (GC) and high performance liquid-chromatography (HPLC) but it is
more frequently combined with GC because the desorption step is straightforward. In this
case the coated stir bar is usually introduced into a glass thermal desorption tube after
extraction and then this is placed in a thermal desorption unit mounted on the GC
(Baltussen et al., 1999). The use of SBSE to detect EDCs in aqueous samples is at an early
stage. Kawaguchi et al. (2004a and b; 2005) have managed to develop several SBSE
methods to detect alkylphenols as well as estrone, 17β-estradiol and 17α-ethynylestradiol in
water samples. A method for detecting phthalates in drinking water was also developed by
Serodio and Nogueira (2006).
2.6.1.2 Extraction methods for sludge
To date, studies devoted to the determination of EDCs in solid environmental samples have
been scarce in comparison with those carried out in aqueous media. Samples comprising
38
environmental matrices such as sludge are complex and may be subject to interferences that
significantly affect the extraction and separation steps of the analysis. In general, liquid
samples are easier to handle than solid ones. In the determination of EDCs in solid
environmental matrices, where the target analytes are present at very low concentrations
along with a large number of potentially interfering compounds, it is essential to carry out
effective sample pre-treatment, which normally include both an extraction and a
purification step prior to analysis by GC or HPLC. Extraction of the target analytes from
the solid matrix has normally been performed by Soxhlet, sonication or by simple blending
or stirring of the sample with polar organic solvents or mixtures of them (Ternes et al.,
2002). Extraction and clean-up of sludge extracts, when performed, has been carried out by
SPE, liquid-liquid extraction, gel permeation chromatography (GPC) and semi-preparative
HPLC (Ternes et al., 2002; Díaz-Cruz et al., 2003). The latest technology in solids
extraction, the accelerated solvent extraction (ASE) which has been proven to reduce
extraction time and solvent consumption as compared to Soxhlet extraction (Noppe et al.,
2007); however the extracted sample still has to undergo cleanup before analysis. The
analysis of organic compounds at low concentrations in sediment and sludge is a challenge,
since a lot of matrix impurities have to be removed by cleanup steps (Ternes et al., 2002).
Problems might occur with sludge from different origins, because their composition can
differ enormously.
2.6.1.3 Gas chromatography-mass spectrometry (GC-MS)
Generally, the analysis of EDCs has been accomplished by electrochemical methods and
chromatographic techniques, such as high-performance liquid chromatography (HPLC)
with ultraviolet, florescence, electrochemical, or mass spectrometric (MS) detection, as
well as gas chromatography (GC) coupled with sensitive and specific detection systems,
such as MS or MS-MS (Liu et al., 2004). GC-MS is usually the preferred analytical tool
used in the determination and analysis of EDCs such as estrogens, phthalates and
alkylphenols (Liu et al., 2004, Psillakis et al., 2004). Some EDCs such as the estrogens and
the alkylphenols which have a hydroxyl group within the molecule have to derivatized with
N,O-bis-(trimethylsilyl)trifluoroacetamide (BSTFA), which leads to the formation of
trimethylsilyl (TMS) derivatives. Derivatization is undertaken to reduce the polarity of the
more polar compounds (Helaleh et al., 2001). The phenol-silylate or TMS derivative is
more volatile and affords a better detection limit and better chromatographic resolution.
39
Free phenols can form hydrogen bonds with GC column stationary phases or with material
of the injector chamber, which results in peak tailing and interferes with integration
(Helaleh et al., 2001).
2.6.2 Biological testing
New and revised toxicological testing methods are being developed around the world
incorporating molecular and cellular biology and they hold promise for reducing whole
animal testing. Biological methods can be used as screens to determine if endocrine
disrupting-active compounds are present in a given environmental sample. These can be
carried out concurrently with chemical methods to establish cause and effect and to
quantify the EDCs present (Cech et al., 1998). The majority of tests developed so far
include in vitro bioassays for assessing estrogenic and anti-estrogenic substances and in
vivo methods using fish or wildlife. Ideally a battery of screening tests should be used to
address the range of different mechanisms of EDCs.
In vitro and in vivo bioassays are useful techniques for the determination of receptor-
mediated activities in environmental samples containing complex mixtures of
contaminants. The bioassays determine contamination by pollutants with specific modes of
action and also accommodate possible interaction between compounds (Geisy et al., 2002).
Extracts from various matrices can be tested to evaluate their biological activities and
identify those samples that require further investigation using resource intensive analytical
techniques. In vitro and in vivo bioassays offer a rapid, sensitive and relatively inexpensive
solution to some of the limitations of instrumental analysis. Methods that rely on biological
activity are finding increasing utility as screening tools, because the detailed chemical
nature of the endocrine disrupting sample may be unknown and the biological method may
be the best or only indicator of EDC activity.
2.6.2.1 In vitro bioassays
2.6.2.1.1 Receptor binding assay
Steroid hormones such as estradiol act on their target cells by binding to specific, high-
affinity receptors within the cell nucleus. Receptor binding assays have therefore been
developed to assess the ability of substances to bind directly to the hormone receptor.
40
Receptor binding assays measure binding of agonists or antagonists to a specific cellular
receptor. This provides an assessment of endocrine disrupting activity potential at the
molecular level of biological organisation. Thus, this represents the first level of signal
transduction of these hormones to modulate the expression of specific genes. The potency
of compounds is possibly dictated by their relative affinity to these receptors. Receptor
binding affinity measurements can then be used to indicate the potential of specific
compounds or mixtures of compounds to act as EDCs. The concentrations able to be
detected with some of these tests can be in the pg/L range (Soto et al., 1995). An estrogen-
receptor binding assay (ERBA) using sheep uterus estrogen receptors has recently been
used to derive estrogen equivalents (EEq) from sewage effluent in Australia (Leusch et al.,
2006a and b). The results from the studies done by Leusch et al. (2006a and b) show that
there is a mixture of compounds within the sewage effluent that have endocrine disruption
activity.
Receptor binding assays have been widely used because they are easy to perform, rapid and
relatively cheap, making them a good choice for large-scale screening. Nevertheless, some
limitations of receptor binding assays include their inability to distinguish between
agonistic and antagonistic effects, as binding to the receptor may not necessarily result in
transcriptional activation (as in the case of antagonist). They are also only suitable for the
detection of hormone-receptor mediated effects and cannot detect pro-estrogens (due to
absence of metabolism in the cell-free system) (Baker, 2001).
2.6.2.1.2 Estrogen receptor (ER) activation assays
ER activation depends on the ability of estrogens to induce cellular responses in target
organs such as fish liver cells (hepatocyte bioassay) (Stephensen et al., 1998) or human
breast cancer cells such as MCF-7 cells (Körner et al., 1999). When primary cell cultures
are used, a particular protein such as vitellogenin which is a protein unique to egg
development, can be measured as the endpoint. One of the advantages of using whole cell
bioassays is the ability to differentiate a hormone agonist from a hormone antagonist (both
of which may show receptor affinity). Cell proliferation can also be induced at very low
concentrations of estrogenic substances. The MCF-7 cell line, which is derived from a
human breast adenocarcinoma, is well established as a model of estrogen-responsive cells
(Soule et al., 1973). MCF-7 cells have been recommended because of their reproducible
and stable estrogen sensitivity (Soto et al., 1992). The proliferation test with MCF-7 cells
41
(E-Screen assay) has already been proven to be specific for a number of tested chemicals
which are known to be estrogenic in vivo (Soto et al., 1995). This assay also reveals
whether the compound is a partial or a full agonist by comparing the maximal cell number
obtained with the test compound with that obtained with 17β-estradiol (Körner et al., 1999).
Examples of E-Screen tests on individual EDC compounds have been previously shown in
Table 2.1. Results have shown such tests or assays to be a good tool compared to the
receptor binding assay with which to measure the estrogen equivalent of a potential EDC.
The relevance of in vitro assays for the evaluation of biological effects in intact organisms
through xenoestrogenic exposure might be limited however since important processes such
as uptake, bioaccumulation and metabolic activation or degradation of these compounds are
not taken into account (Zacharewski, 1997; EMWAT, 1997; Tyler et al., 1998, Soto et al.,
1992).
2.6.2.2 Whole animal assays (in vivo)
Together with the in vitro work, a number of in vivo assays have also been developed and
applied to the assessment of endocrine disrupting activity of pure compounds or
environmental samples. Within the group of in vivo test systems one might distinguish in
vivo exposure and in vivo effect markers. The most frequently used in vivo exposure marker
with regard to the aquatic environment, is the vitellogenin (VTG)-assay. VTG is the female
yolk precursor protein which is synthesized in the liver and translocated to the ovaries (Bun
and Idler, 1983). It is normally absent or present at very low concentrations in male fish but
due to the constitutive presence of the VTG gene, VTG can be induced in male fish upon
estrogenic exposure (Sumpter and Jobling, 1995; Folmar et al., 1996; Jobling et al., 1996;
Tyler et al., 1996). As a result it has demonstrated that VTG can be a good biomarker for
xenoestrogenic exposure. However, the relationship between changes of VTG
concentrations in both males and females and potential adverse impact on gonads,
reproductive success or development of progeny are still uncertain. In vivo markers of
effects on gonadal structure, reproductive function and long term reproductive success are
most relevant and should be identified (Arcand-Hoy and Benson, 1998). These studies
however have a high cost and are time consuming, but are necessary in order to assess
risks.
42
Therefore, hazard identification strategies for determining potential endocrine disrupting
effects of new and existing compounds recommend the use of both rapid in vitro and in
vivo assays in the screening-prioritisation step (EMWAT, 1997; EDSTAC, 2000). In vivo
screening is considered essential in the screening-prioritisation stage as such a system
integrates metabolism and all potential modes of action of a compound. In vitro screening
assays and structure-activity relationships are considered useful at this screening level of
assessment, especially if a particular mode of action is suspected. They however cannot
stand on their own in their present state of development; they are not sufficient to exclude
or confirm concern for endocrine modulating effects (EMWAT, 1997).
2.7 EDCs fate modelling Traditional risk assessment involves a comparison of the predicted environmental
concentration (PEC) of a chemical with the predicted no effect concentration (PNEC) on
target species. The PEC is derived from the amount of substance used per year, the size of
the human population, the volume of wastewater produced per capita per day, and the
amount of removal in treatment, with the environmental dilution factor set at 10 (Sumpter
and Johnson, 2005). However, the available dilution in river catchments can be
considerably less and is enormously variable in both the temporal and spatial senses
(Kolpin et al., 2004). Discovering the significance of the key environmental processes
which determine the fate of chemicals is one of the most important benefits of a modelling
exercise. Software such as EXAMS, which incorporates hydrology and all potential
chemical fate processes to predict concentrations for river reaches, will represent an
increasingly important resource for environmental chemists, biologist, and regulators alike,
their principal advantage being in telling scientists where and potentially when, to look for
human-derived chemicals and their effects (Williams et al., 1999; Williams et al., 2003).
Several researchers have proposed and reported mathematical models which can be used to
quantify the distribution and fate of polycyclic aromatic hydrocarbons, pharmaceuticals,
pesticides, natural hormones and xenoestrogens in WWTPs (Clark et al., 1995, Byrns,
2001; Khan and Ongerth, 2002, 2004; Johnson and Williams, 2004). These models consider
the major abiotic and biotic processes which influence the intermedia distribution and
eventual fate of the organic compounds. Under optimal operating conditions, a WWTP may
43
remove a large percentage, for example 70 – 100%, of many organic pollutants from the
wastewater, but treatment efficiency varies (Clark et al., 1995). Clark et al. (1995) have
modelled and analysed the fate of organic chemicals in a WWTP in fugacity format using
equations describing the partitioning, biodegradation, and volatilization or stripping
behavior of chemical, which can be solved to give an overall mass balance. According to
Clark et al. (1995), the fugacity analysis facilitates insight into the variations in the nature
of the fundamental removal mechanisms that apply to chemicals of different properties.
Byrns (2001) reported the fate and distribution of hydrophobic chemicals is controlled
largely by the physicochemical properties and the high rates of advective transport within a
typical treatment plant. Biodegradation has most influence on those compounds with
moderate log Kow values in the range of 1.5 – 4 (Byrns, 2001). In general terms, very
soluble compounds appear to be removed as much by advective transport into the final
effluent as by biodegradation. Compounds with strong hydrophobic characters are not
significantly removed by biochemical reactions but are generally removed through sorption
to sludge particulates and transfer to the sludge processing systems (Byrns, 2001).
2.8 Risk assessment of EDCs Both the National Research Council (NRC) and American Council of Safety and Health
(ACSH) consensus reports concluded that environmental chemicals can display endocrine-
disruptive activity, particularly after extreme exposures and in laboratory settings (NRC,
1999; ACSH, 1999). In the case of xenoestrogens this activity is explained by the
promiscuity of ER. This promiscuity is based on the fact that these chemicals share
characteristics in common with estradiol, usually an unencumbered phenol configuration
which enables the chemical to interact with first point of contact in the binding pocket of
the receptor. The fact that the remaining portions of these compounds are hindrances for a
good fit into the binding pocket accounts for the low affinity of these compounds for the
receptor and, hence, low biological activity (Witorsch, 2002).
The position of the NRC and ACSH that there is little evidence of endocrine-disruptive
effects under non-extreme conditions in humans is based not only on lack of consistent
epidemiologic evidence (e.g., breast cancer induced by organochlorines and trends of
declining sperm count) but also on the relatively low hormonal (or anti-hormonal) activity
44
of environmental chemicals (NRC, 1999; ACSH, 1999). In other words, evidence to date
that a “sea of estrogens” evoking adverse health effects as suggested in an earlier report
(Sharpe and Skakkebaek, 1993) is not compelling. However it would still seem prudent to
investigate new chemicals that seem to emerge yearly and that are becoming ubiquitous in
our environment for their endocrine-disruptive properties as there is a wide void of
knowledge surrounding chemical toxicity that needs to be filled.
A biological-based assessment of a specific risk to organisms or their progeny arising from
exposure to EDC should consider, among others, the following points:
i) The identification of critical effects; dose-response curves of different effects will
identify dose concentrations inducing minimal increases in response rate (Piersma et
al., 2000).
ii) The assessment of the actual exposure and different susceptibility of target tissues, as
observed, e.g. in reproductive tissues, hypothalamus and pituitary with regard to
estrogen receptor α expression following neonatal exposure of rodents to DES,
bisphenol A and octylphenol (Khurana et al., 2000).
iii) The study of mechanism(s) to identify possible characteristics of susceptibility and also
to study gene-environment interactions in endocrine-related reproductive and
developmental disorder.
iv) The elaboration of a model to estimate the possible additional risk deriving from
combined exposures.
v) The proper interpretation of experimental results on the basis of sound and updated
endocrinological knowledge. At present, attention is focused on chemicals interacting
with sex steroids and, to a lesser extent, thyroid hormones. However, any hormone-like
or anti-hormone effect should be viewed in the context of the complex network of
endocrine homeostasis.
In assessing the effects of EDCs, there is a need to account for possible interactions since,
generally, humans and wildlife are often exposed to mixtures of chemicals, and there is a
potential for simultaneous exposure to several EDCs. As previously discussed, the
estrogenicity and the effects of individual estrogens, industrial and pharmaceutical
compounds, randomised mixture effects as normally occurring in polluted field sites have
still not been studied. All kinds of interactions are obviously possible, including additivity,
45
synergism, potentiation, or antagonism. As in the case of exposure to mixture of chemicals
causing endocrine-independent effects, there is still no general valid methodology to
evaluate EDC mixtures.
2.9 Conclusions In conclusion, when proper endpoints are investigated, studies on reproductive and
developmental effects are likely to provide critical information for risk assessment of most
EDCs. The investigation of the biological effects of EDCs is likely to make an important
contribution in elucidating some basic events in pathophysiology. In the meantime,
toxicological risk assessment of EDCs needs to focus on dose-response assessment of
endocrine-mediated effects, as well as on possible susceptibility factors and
additive/synergistic effects of combined exposures. This will allow an efficient exploitation
of resources and, most important, an effective protection of the public with special
attention to the most vulnerable population subgroups.
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65
Chapter 3: Evaluation of grab and passive sampling methods to
determinate selected endocrine disrupting compounds in municipal
wastewaters
3.1 Abstract Two methods, grab sampling with subsequent extraction using solid-phase extraction (SPE)
and passive sampling utilizing EmporeTM styrene-divinylbenzene copolymer as the matrix
were used to monitor selected endocrine disrupting compounds (EDCs) in wastewater from
several municipal wastewater treatment plants in South East Queensland, Australia. The
selected wastewater treatment plants comprised six conventional activated sludge plants, a
modern biological nutrient removal plant and a tricking filter plant with its effluent
discharged into a constructed wetland. A gas chromatography-mass spectrometric method
was successfully developed to simultaneously analyze 15 environmentally ubiquitous
EDCs including phthalates, alkylphenols, tamoxifen, androgens and estrogens. EDC
extraction recoveries ranged from 52 – 122% for the SPE and 12 – 133% for the EmporeTM
disk. This study showed that the calibrated passive samplers (with the given assumptions)
gave reduced EDC concentrations as compared to the grab samples. On the other hand, it
could not be concluded since the grab sampling results were not fully representative of the
average concentration per se. A future challenge for this type of passive sampler is to
improve the robustness by reducing or controlling the impact of environmental conditions
and biofouling on sampler performance. The proposed monitoring methods showed
acceptable recovery and reproducibility for target compounds at ng/L concentration.
Application of the methods for the determination of these target EDCs in wastewater
samples in this study showed >90% removal of EDCs, despite the wastewater treatment
plants having different treatment processes.
3.2 Introduction In recent years, there has been growing concern over the harmful effects environmental
contaminants have on aquatic organisms. The aquatic environment and resident organisms
have the potential to be exposed to multiple chemical compounds, including those with
endocrine disrupting activities. Sources of contaminants include chemicals from
66
agricultural activities, industrial discharges to waterways, and excretion of natural and
synthetic hormones by animals and humans to sewers (Birkett and Lester, 2003).
At present, the endocrine disrupting compounds (EDCs) receiving the most attention are
those that mimic estrogens. Due to the large number of potential EDCs, it is most likely that
aquatic organisms are exposed to not a single agent but a mixture of numerous EDCs
(Desbrow et al., 1998; Ternes et al., 1999a; Baronti et al., 2000; Petrovic 2002; Tan and
Mustafa, 2003). The EDCs that are ubiquitous in the wastewater and the receiving
environment and have been reported to have the highest estrogenic potentials are the
natural estrogens, pharmaceuticals such as synthetic steroids in female contraceptive pills,
the breast cancer treatment drug, tamoxifen, pesticides, surfactants and plasticizers (Birkett
and Lester, 2003). It is generally accepted that EDCs are at least partially responsible for
disruption of reproduction and development in some wildlife populations (Tyler et al.,
1998; Vos et al., 2000). The effects found include altered or abnormal circulating plasma
steroid concentrations, reduced fertility and fecundity, masculinization of females and
feminization of males.
Estrogens and androgens, naturally excreted by humans and livestock, can frequently be
found in effluent as products of incomplete breakdown of free steroids, conjugates and
steroid metabolites (Laganà et al., 2004). In recent years, phthalates which are used in a
wide array of plastic products and surfactants have also attracted much attention because
even at relatively low or trace concentration levels they are suspected of interfering with
reproductive and behavioral health in humans and wildlife, through disturbance of the
endocrine system (Petrovic et al., 2002; Psillakis and Kalogerakis, 2003). Also of interest in
the context of this work are alkylphenols and tamoxifen. Alkylphenols such as octylphenol
and nonylphenols are used to synthesize alkylphenol ethoxylates which are commonly used
as surfactants and emulsifiers in household and industrial products (Ferguson et al., 2001).
There is a general trend in Europe of phasing out alkylphenol polyethoxylates in household
cleaning products in favor of readily available substitutes such as alcohol ethoxylates
(Renner, 1997). The phase-out which began in 1995 was led by findings that show
alkylphenol polyethoxylate breakdown products (alkylphenols) are highly toxic to aquatic
organisms, pose environmental risks because of their occurrence, persistence, and
67
concentration. Monitoring and management of these chemicals has become a key challenge
for water authorities.
As different EDCs of varying concentration tend to occur simultaneously in natural waters,
it is essential to develop effective method that can extract multiple EDCs simultaneously
from water samples (Liu et al., 2004). Detection and measurement of EDCs in wastewaters
are challenging procedures since wastewater treatment plants (WWTPs) receive complex
and variable mixtures of organic and inorganic substances. These may be co-extracted with
the analytes of interest. This makes qualitative assessments doubtful and quantification
difficult owing to matrix-induced signal suppression effects or isobaric spectral
interferences from complex sample extracts (Zöllner et al., 1999). The use of the solid-
phase extraction (SPE) procedure that selectively extracts a range of semi-polar compounds
from the wastewater sample greatly reduces the problem of interferences present in the
chromatograms and signal suppression.
Aquatic EDC monitoring programs are generally based on collection of discrete samples of
water phases. In environments where the contaminant concentrations may vary over time, it
is often desirable to expand the time window and increase the resolution by taking more
samples. Such pseudo time-integrated sampling of water, be it automatic or manual, is both
costly and cumbersome, and rarely used in large scale monitoring studies. There remains a
need for a robust sampling technique that allows time integrated sampling as well as an
analytical method that can be used to target a broad range of EDCs. Furthermore,
wastewater grab samples have to be extracted reasonably quickly after collection since
biodegradation within the sample over time will give a lessened compound concentration as
opposed to the true WWTP concentration. Passive sampling methods may offer a versatile
tool in aquatic monitoring programs, allowing a direct time-integrated monitoring of
organic pollutants in the aqueous phase as an alternative to conventional sampling
techniques (Stuer-Lauridsen, 2005). A passive sampling device for chemicals is an object
that collects or accumulates analytes without provision of energy from an external source.
The aim of this study was to evaluate both sampling and analytical methods that allow the
design of sensitive, cost-effective and ideally continuous (time integrated) monitoring of
68
EDC concentrations in WWTP samples. WWTPs in general were chosen for the passive
sampling deployment sites since they may have high daily fluctuations of influent inputs
and chemical compound concentrations and their effluent represents a major source of
EDCs into the receiving environment. This research is important because currently there
are no reported studies that use a passive sampling technique to monitor a wide range of
EDCs in wastewater. Furthermore the development of a new gas chromatography-mass
spectrometric method that allows simultaneous separation and analyses a wide range of
EDCs including phthalates, tamoxifen, alkylphenols and natural steroids would be useful to
determine the fate of EDCs in various WWTPs. The major challenge is trying to choose the
right grab sample extraction and passive sampler matrices that afford the best extraction
recoveries since these EDCs ranged from fairly hydrophilic to hydrophobic. This could also
be one of the main reasons why there are very few reported studies in the literature on the
wide range of EDCs present in wastewater.
3.2.1 Sampling kinetics of EDCs with the EmporeTM disk sampler
The mass transfer of an analyte from water to the sampler includes diffusive and interfacial
transport steps across several barriers. Assuming a rapid establishment of steady-state
conditions, the flux of an analyte is constant and equal in each of the individual barriers.
This also assumes that sorption equilibrium exists at all compartment interfaces. For
modeling purposes, the resistances of all barriers to the mass transfer of analytes are then
additive and independent (Scheuplein, 1968; Flynn and Yalkowsky, 1972).
Applying the assumptions given above and making the assumption of constant water
concentration, it can be shown that the amount of a given chemical accumulated from
water in the receiving phase of the passive sampler can be described by the following
equation:
⎥⎦
⎤⎢⎣
⎡⎟⎟⎠
⎞⎜⎜⎝
⎛−−×−+= t
VKAk
mVKCmtmSSW
oSSSWWSS exp1)]0([)0()( (1)
where mS is the mass of analyte in the receiving phase, mS (0) is the analyte mass in the
receiving phase at the start of exposure, CW represents the concentration of the analyte in
69
water during the deployment period, KSW is the receiving phase-water distribution
coefficient, VS is the volume of the receiving phase, ko is the overall mass transfer
coefficient, A is the exposed passive sampler surface area, and t equals exposure time.
The time taken for the sampler to reach half of its equilibrium concentration t1/2, is
classically solved as:
⎟⎟⎠
⎞⎜⎜⎝
⎛−=⎟⎟
⎠
⎞⎜⎜⎝
⎛−=
uptake
SW
loss kK
kt 2
12
1
2/1lnln (2)
where kloss is the first order rate constant for loss of compound from the receiving phase,
and kuptake is the first order rate constant for uptake of compound into the receiving phase.
Since, S
Suptake V
Rk = (3)
t1/2 could also be written as:
⎟⎟⎠
⎞⎜⎜⎝
⎛−=
SS
SW
VRK
t 21
2/1ln
(4)
In the initial uptake phase, where the loss rate is very small, chemical uptake is linear or
integrative over time. In this situation, the amount accumulated in the sampler is a simple
linear function of time:
mS (t) = mS (0) + CW RS t = mS(0) + CW kuptakeVS t (5)
where RS is the sampling rate of the system, representing the equivalent extracted water
volume per unit of time.
70
The concentration in the sampler increases over time as it approaches equilibrium and loss
of compound from the sampler becomes more important. Thus concentration of analyte in
the water can be written as:
([ ])tkVKtm
ClossSSW
SW −−
=exp1
)( (6)
Where, SSW
Sloss VK
Rk = (7)
The time taken to attain equilibrium is influenced by sampler and analyte characteristics, as
well as the environmental conditions: flow, biofouling and temperature (Bartkow et al.,
2005).
3.3 Materials and methods
3.3.1 Chemicals and reagents
Hydrochloric acid, benzyl butyl phthalate, dibutyl phthalate, diethyl phthalate, dioctyl
phthalate or di-(2-ethylhexyl) phthalate, 4-tert-octylphenol, nonylphenol (technical grade),
4-cumylphenol, bisphenol A, estrone, 17α-estradiol, 17β-estradiol, estriol, tamoxifen and
N,O-bis(trimethylsilyl)trifluoroacetamide (BSTFA) (also containing trimethylchlorosilane)
were all purchased from Sigma (St Louis, MO, USA). It should be noted that technical
nonylphenol is a mixture of isomers with regard to branching of the alkyl chain. The
composition of technical nonylphenol is not known in detail; however, it does not contain
4-n-nonylphenol (Wheeler et al., 1997). 4-n-Nonylphenol was purchased from Lancaster
Synthesis (Morecambe, England). Androsterone and etiocholan-3α-ol-17-one were
purchased from Riedel-de Haën AG (Seelze, Germany). 17β-estradiol-2,4,16,16-d4 was
purchased from CDN Isotopes (Quebec, Canada). The solvents (methanol, n-hexane, and
acetone) were of HPLC grade and obtained from Merck (Darmstardt, Germany). Separate
stock solutions of individual EDCs were prepared in methanol (100 mg/L). A working
mixture containing each compound at 10 mg/L was also prepared. Internal standard
solutions, (5 mg/L) of 4-n-nonylphenol (Günther et al., 2001; Naassner et al., 2002) and
71
17β-estradiol-2,4,16,16-d4 were prepared in n-hexane:acetone (50:50, v/v). All standard
solutions were stored at -18 °C prior to use.
3.3.2 Sample collection
This study investigated eight different WWTPs from South East Queensland, Australia and
their efficacies at EDC removal in the final treatment stages were compared. Out of the
eight, only three WWTPs (WWTPs J, M and N) were intensively studied, whereas the other
five (WWTPs A, B, C, D and E) were used in a preliminary field passive sampler study.
WWTP J is a modern biological nutrient removal plant equipped with the latest technology
for water recycling. WWTP M is a conventional activated sludge WWTP which also
tertiary treats its effluent for water recycling purposes. WWTP N is a trickling filter plant
which discharges its effluent into a constructed wetland for further chemical contaminant
degradation. WWTPs J and N receive their influent from domestic discharge of their
respective townships. WWTP M on the other hand receives its influent from domestic,
medical and industrial discharges and is the biggest of the three WWTPs.
The key aim in using WWTPs J and M was to assess the SPE extraction method, thus only
grab samples were collected. WWTP J was monitored through its complete treatment
process train in order to see which section of the modern treatment process was responsible
for the highest removal of the EDCs. Accordingly, water samples from WWTP influent,
aerobic and anoxic zones of the bioreactor as well as clarifier, sand filter, ozone,
biologically activated carbon (BAC) and ultraviolet (UV) disinfection stages were collected
using methanol (HPLC grade) rinsed 1 L amber bottles.
Since WWTP M recycles its effluent, it was of interest to know if the tertiary treatment
would consistently remove EDCs. Forty-two 1 L water samples of effluent and ultra-
filtration water were collected daily at 0900 h, 1200 h and 1500 h for 7 days from WWTP
M which treats water for an experimental water recycling scheme just recently introduced
in a small residential area in South East Queensland, Australia. Furthermore, the results
from WWTP M would show whether variation in concentrations would be observed in
hourly or daily grab samples.
72
Both grab and passive samplers were used to collect samples from the wetland of WWTP N
in order to assess whether the passive sampler was able to accumulate any EDCs that the
grab sample did not detect. Triplicates of 1 L water samples were collected from each of
the seven compartments of the constructed wetland of WWTP N; effluent is discharged into
the wetland to remove residual contaminants. The compartments or cell of the constructed
wetland are as follows: N-1 is the first wetland cell that the effluent is discharged into,
followed by flow into cells N-2, N-3, N-4, N-5, N-6 and finally N-7 before it is released
into a creek. Typical advection time from the first cell to the last cell is 14 days. A total of
14 polar passive samplers (2 passive samplers per cell) were deployed at WWTP N for 4
days at the sites from where the grab samples were collected. The samples were transported
to the laboratory on ice and extraction was carried out within 4 – 6 hours in order to
minimize microbial degradation of the target compounds. Reproducibility of the grab
samples was evaluated at both WWTPs M and N.
A preliminary deployment of passive samplers was also carried out at several sections
along the treatment process train of five conventional activated sludge WWTPs (WWTPs
A, B, C, D and E) located within South East Queensland, Australia to compare the
correlation between EDCs concentration from the grab and passive samplers. Six grab
samples were taken at each treatment process train (influent, bioreactor, clarifier and
effluent) together with 2 co-deployments of passive samplers. Furthermore, since each
sampling site and treatment compartment has very different flow rates, results from this
work should afford an indication as to whether flow rate plays an important role in EDCs
uptake by passive sampler.
3.3.3 Processing of grab samples
3.3.3.1 SPE extraction procedure
A number of commercial SPE cartridges with different sorbent materials (C2, C8, C18,
ENV+ and Oasis HLB matrices) were investigated. Of all the cartridges, Waters Oasis HLB
cartridges showed the best recoveries overall and were therefore used for further grab
sampling testing (Leusch et al., 2006a).
73
Oasis HLB 6 cm3 extraction cartridges, supplied by Waters Corporation (Milford, MA,
USA), were used to extract the EDCs from aqueous samples. Before processing the sample,
the cartridges were fitted onto a vacuum manifold (Supelco) which was connected to a
pump and the cartridges were conditioned with 5 mL of n-hexane:acetone (50:50, v/v),
followed sequentially by 5 mL of methanol and 10 mL of Milli-Q purified water (purified
by Milli-Q Synthesis A10 System, Millipore, Bedford, MA, USA).
Prior to extraction, each 1 L sample was centrifuged at 3000×g for 30 minutes at 4 °C using
an IEC PR-7000M refrigerated centrifuge to remove suspended particulate matter that
might block the SPE cartridges. The aqueous sample was poured into a 1 L amber bottle
without disturbing the compacted particulate material at the bottom of the centrifuge
container. Hydrochloric acid was used to adjust the pH of the water samples to 2 – 3 before
passing it through the conditioned Oasis HLB cartridge. Acidification decreases the
dissociation of weakly acidic analytes such as phenols, leading to increased extraction
efficiency of the non-dissociated target compounds (Liu et al., 2004). After the sample was
passed through the cartridge, 5 mL of Milli-Q water was passed and later left on the
vacuum manifold to dry for 2 hours (-70 kPa).
The retained compounds were eluted with 5 mL of methanol followed by 5 mL of n-
hexane:acetone (50:50, v/v). The combined solution was placed on a heating plate set at 70
°C and evaporated to dryness under a gentle nitrogen stream.
3.3.3.2 Grab sampling SPE recovery experiment
Recovery measurement was done in triplicates by spiking 1 L Milli-Q water (pH 2) at
concentrations of 100, 50 and 20 ng/L of a mixture of the target compounds, and passing
the water through the Oasis HLB cartridges. Recoveries for the test analytes from the SPE
were between 52 – 122%; reproducibility of the spiked grab samples was acceptable, with
most coefficients of variations (CVs) of the target compounds <15% (Table 3.1).
74
3.3.4 Processing of passive samples
3.3.4.1 Passive sampler pre-deployment conditioning
Passive sampling techniques are usually used for monitoring non-polar organic compounds.
With the availability of polar samplers such as the POCIS (polar organic chemical
integrative sampler) and particle loaded membranes or EmporeTM disk type samplers, they
also show potential for monitoring compounds with a range of polarity (Kingston et al.,
2000; Alvarez et al., 2004; Vermeirssen et al., 2005, Stephens et al., 2005; Vrana et al.,
2006). In this study, the EmporeTM disk was observed to allow increased uptake rates and
efficiency in extracting many of the target EDCs.
Prior to deployment of the passive sampler into any water bodies, it had to be conditioned.
A 47 mm diameter EmporeTM SDB-RPS high performance extraction disk supplied by 3M
(St Paul, MN, USA) was used as the matrix for the passive sampler. The EmporeTM disk
was placed on a filtration apparatus and 10 mL of methanol was passed through the disk at
low vacuum. This was followed by passing through 100 mL of Milli-Q water, also at low
vacuum. The filtration process was stopped immediately before the last mL of the water
passed through the disk to ensure the disk remained saturated with water. The wet
EmporeTM disk was then removed and fitted onto a patented Teflon deployment device
(Kingston et al., 2001) and kept wet with Milli-Q water until deployment.
3.3.4.2 Passive sampler calibration experiment
In order to measure concentration of a test analyte in an actual field sample, firstly the
passive sampler has to be calibrated for each test analyte so that concentrations or
compounds accumulated can be related to ambient concentrations. This involves
determining the sampling kinetics of the EDCs. This passive sampler calibration method
was based on that described by Stephens et al. (2005). In this study, up to 10 passive
samplers were deployed in a high volume exposure tank (1400 L). The tank water (24 °C)
was spiked at a nominal concentration of 600 ng/L for each analyte. To ensure uniform
hydrodynamic conditions in the vicinity of all samplers, 10 passive samplers were placed
on a horizontal turntable with a diameter of 60 cm. The turntable was vertically connected
to a shaft driven by an overhead stirrer. All parts of the turntable in contact with water and
tank were made of stainless steel. This carousel device was rotated at 8 rpm and an average
75
bulk flow rate over the sampler of approximately 0.14 m/s was estimated after the
experiment. The exposure lasted approximately 4 days, during which duplicate samplers
were removed at set time intervals and analyzed to determine the concentrations of
accumulated test chemicals. At the same time as samplers were removed, 1 L of water was
also collected from the exposure tank and the concentration of analyte in the water
determined via SPE extraction. Reproducibility was determined using normalized
differences for passive samplers (replicates = 2). The normalized differences were
calculated for replicates x and y according to:
1002/)(
(%) ×⎥⎦
⎤⎢⎣
⎡+
−=
yvaluexvalueyvaluexvaluesdifferenceNormalized (8)
3.3.4.3 Passive sampler extraction
After exposure, the Teflon sampler was carefully dismantled and the EmporeTM disk was
carefully rinsed with Milli-Q water. The disk was then fitted onto the filtration apparatus
and vacuum applied for about 15 minutes to dry the disk completely. Then, 6 mL of
methanol followed by 6 mL n-hexane:acetone (50:50, v/v) were passed through the disk at
low vacuum. The solution was collected in a 15 mL collection vial and was placed on a
heating plate set at 40 °C and evaporated to dryness under a gentle nitrogen stream.
Recoveries for the analytes from the EmporeTM disk were between 12 – 133% (coefficients
of variation <15%) (Table 3.1).
3.3.5 Derivatization procedure
Some EDCs such as the estrogens and the alkylphenols which have a hydroxyl group
within the molecule have to be derivatized with BSTFA, which leads to the formation of
trimethylsilyl (TMS) derivatives. The high polarity and low vapor pressure of these
compounds give rise to poor chromatographic performance and as a consequence
derivatization was carried out. The phenol-silylate or TMS derivative is more volatile and
affords a better detection limit. All dried SPE and EmporeTM disk samples were subjected
to the same GC-MS sample preparation. The individual sample was reconstituted with 110
μL n-hexane:acetone (50:50, v/v). Then 20 μL of internal standards (5 mg/L of 4-n-
nonylphenol and 17β-estradiol-2,4,16,16-d4) was added to the solution followed by 20 μL
76
of the derivatization agent, BSTFA. The solution was then transferred into a 2 mL GC-MS
vial (Agilent technologies, CA, USA) which was capped securely and then placed in a
water bath heated to 70 °C for 30 minutes. The derivative was then cooled to room
temperature and subjected to GC-MS analysis.
3.3.6 GC-MS analysis
GC-MS analyses were performed using an Agilent 6890 gas chromatograph with a model
5973 mass-selective detector (Agilent Technologies, Palo Alto, CA, USA). Separation was
accomplished on a DB-5MS fused silica column (30 m × 0.25 mm i.d.; 0.5 μm film
thickness, Agilent Technologies). The oven temperature program was 4 minutes at 50 °C, 8
°C/minute to 150 °C, 7 °C/minute to 250 °C, 8 °C/minute to 300 °C and then hold at 300
°C for 4 minutes. Column pressure was set at 70 kPa. Helium was used as the gas carrier at
a constant flow of 1.2 mL/minute. The transfer line was set at 300 °C and the source at 250
°C. Sample injection (2 µL) was in splitless mode.
In the quantitative procedure, standard solutions of compounds were prepared and spiked
into Milli-Q water to cover the calibration range. The spiked water was then extracted using
either SPE or EmporeTM disk methods and analysed via GC-MS. Quantitative analysis was
performed in the selected ion monitoring (SIM) mode in order to maximize sensitivity. The
concentrations were calculated relative to the internal standard added to the sample prior to
analysis. Recoveries of the compounds using the SPE and passive sampling (EmporeTM
disk) extraction methods were in the range of 12 – 133%; limit of detections for the
compounds are in the range of 1 – 5 ng/L (Table 3.1). A typical total ion chromatogram
from a prepared solution of selected EDCs is shown in Figure 3.1; the ions monitored for
each compound are listed in Table 3.1. Analysis of phthalates in environmental samples is
problematic because blanks with variable amounts of phthalates (mainly dibutyl phthalate
and dioctyl phthalate) are often encountered. This is due to the presence of phthalates in
many products used in laboratory including chemicals and glassware.
77
Table 3.1. Retention time and ions used for quantification in GC-MS detection of the
selected EDCs and their respective recoveries with SPE and EmporeTM disk extractions. Compound Log Kow
a Retention time (min)
Target ion (m/z)
Reference ion (m/z)
Average recovery, SPE, (%)
Average recovery, EmporeTM disk (%)
Diethyl phthalate (DEP)* 2.42 20.53 149 177 108 44.8 4-tert-octylphenol 5.50 21.01 207 278 63.5 35.1 Nonylphenol (technical grade)
5.76 22.60 193 207 77.9 39.8
4-Cumylphenol 4.12 25.04 269 284 83.0 72.2 4-n-nonylphenol (internal standard)
5.76 25.36 179 292 - -
Dibutyl phthalate (DBP)* 4.50 26.05 149 233 93.6 80.4 Bisphenol A 3.32 29.56 357 372 97.5 77.1 Benzyl butyl phthalate (BBP)*
4.73 31.36 149 206 101 97.8
Androsterone 3.69 33.17 272 347 112 133 Etiocholanolone 3.69 33.36 272 244 122 127 Dioctyl phthalate (DOP)* 7.60 33.44 149 279 117 109 Estrone 3.13 34.69 342 327 96.7 71.5 17α-Estradiol 4.01 34.69 416 285 102 120 17β-Estradiol 4.01 35.15 416 285 120 113 17β-Estradiol-2,4,16,16-d4 (internal standard)
4.01 35.15 420 287 - -
Tamoxifen* 6.30 35.51 371 372 52.3 12.4 Estriol 2.45 37.28 311 386 79.5 72.4
*Compound is not affected by BSTFA derivatization. a Log Kow values for all compounds as predicted from ALOGPS 2.1 computer program provided by Virtual
Computational Chemistry Laboratory (VCCLAB, 2005; Tetko et al., 2005).
78
DEP
4-tert-Octylphenol
Nonylphenol
4-Cumylphenol
4-n-Nonylphenol
DBP
Bisphenol A
BBP Androsterone
Etiocholanolone
Estrone DOP
17β-Estradiol
17α-Estradiol
17β-Estradiol-2,4,16,16-d4
Tamoxifen
Estriol
Figure 3.1. Chromatogram of the selected EDCs.
3.4 Results and discussion 3.4.1 Calibration of passive sampler
The EmporeTM disk was originally produced as a solid phase extraction disk [SDB-RPS or
a poly(styrene-divinylbenzene) copolymer that has been modified with sulfonic acid groups
to make it hydrophilic)], but with a few minor adjustments to the device, it was deployed
into the aqueous environment as an effective passive sampler. The results of the laboratory
EDC calibration of the passive sampler showed that the fifteen compounds monitored in
this study were taken up differentially from the mixed solution into the EmporeTM disk
sampler even though they are exposed to the same experimental conditions. The normalized
differences for the passive samplers of the calibration study were mostly <20%. Most of the
compounds appear to approach equilibrium between the water and the passive sampler after
4 days of exposure. Nonylphenol had a curvilinear uptake into the passive sampler with
very little compound degradation in the water even after 4 days (Figure 3.2a). The other
EDCs with a similar uptake profile to nonylphenol were 4-tert-octylphenol and 4-
cumylphenol. Bisphenol A had a similar profile to nonylphenol although the uptake profile
slowly approached equilibrium towards the end of the 4-day experiment. Concomitantly,
79
the concentration of bisphenol A in the water showed a slight decrease after 4 days (Figure
3.2b). 17α-estradiol and estriol had similar uptake profiles to bisphenol A. In contrast,
estrone exhibited a very linear uptake profile without any sign of reaching equilibrium after
4 days; however the water concentration of estrone appear to be gradually increasing from
the first to the third day, with a small decrease on the forth day (Figure 3.2c). The gradual
increment of the estrone concentration could be due to the conversion of 17β-estradiol in
the mixture to estrone (Ternes et al., 1999b). Dibutyl phthalate on the other hand had a
linear uptake phase that lasted only 1.5 days, approaching equilibrium from the second day
onwards with a slow decrease of compound from the sampler (Figure 3.2d). This
compound seems to degrade relatively quickly in the water and could be the cause of the
slow loss from the sampler. Diethyl phthalate, benzyl butyl phthalate, dioctyl phthalate,
tamoxifen, androsterone, etiocholanolone and 17β-estradiol had similar uptake and loss
profile as shown in Figure 3.2d. The fact that 17β-estradiol concentration was decreasing
and at the same time estrone concentration was increasing suggests the transformation of
17β-estradiol to estrone.
From the calibration experiment in the exposure tank (Table 3.2), sampling rates (RS) for
the selected EDCs were calculated using an algorithm presented by Stephens et al. (2005),
and their partition coefficient (KSW) and time required to accumulate 50% of the
equilibrium concentration (t1/2) were calculated based on equations and theories shown in
Vrana et al. (2006) and Stephens et al. (2005). Table 3.2 and Figure 3.2 clearly show that
each EDC has its own distinct kinetic uptake rate and profile. Compounds within a
particular molecular structural group such as the alkylphenols and phthalates may have
similar degradation rates and sampling profiles. Alvarez et al. (2004) and Matthiessen et al.
(2006) reported sampling rates for a range of hydrophilic organic compounds to be
between 0.03 – 0.12 L/d using POCIS disks which are several times lower than the
sampling rates calculated in this study using the EmporeTM disk (1.12 – 3.23 L/d). The
increased sampling rates using the EmporeTM disk would be ideal for field sampling
experiments at heavily polluted sites such as WWTP treatment processes with high
incidences of biodegradation and biofouling occurring on the passive sampler matrix since
deployment period can be reduced to just a couple of days while ensuring target
compounds will be taken up to detectable amounts by the passive sampler during that short
time frame.
80
Mayer et al. (2000) and Stephens et al. (2005) illustrated a linear log-log relationship
between Kow and Ksw for a range of hydrophobic compounds (Figure 3.3). Verhaar et al.
(1995) also presented a similar relationship for moderately polar compounds. As seen in
Figure 3.3 the measured log Ksw values of the EDCs deviate from Verhaar’s relationship
(1995), but are similar to those of Stephens et al. (2005) and Green and Abraham (2000),
partitioning much more favorably into the EmporeTM disk matrix. Such a high disk affinity
is an excellent property for a passive sampler matrix as the ambient phase volume extracted
by the disk at equilibrium is high and therefore lower method quantitation limits are
feasible (Stephens et al., 2005). Adsorption phenomena and steric considerations of the
EmporeTM disk matrix may explain the deviation from a strictly hydrophobic-based model
of equilibrium partitioning. Furthermore, the difference between analyte characteristics,
molecular size and even the passive sampling matrices (C18 was used in Verhaar’s study
while SDB-RPS was used in this study) may contribute to the deviation when compared to
Verhaar’s results (Verhaar et al., 1995).
Experimentally calculated sampling rates from the calibration tank experiment (Table 3.2)
might not reflect the sampling rates of the environmental deployment site since the water
temperature, water movement and flow rate can be irregular and biofouling can occur on
the surface of the passive sampler. As shown in Figure 3.4, when several passive samplers
were deployed at several sites along WWTP A, B, C, D and E’s process train, the aqueous
concentrations calculated from the passive sampler is on average 1.2 to >10 times lower
than the concentrations measured from the grab samples; however the correlation between
the two sample collection methods was relatively good (F-test, p<0.001, Figure 3.4). There
was substantial biofouling on some of the EmporeTM disk upon retrieval after four days.
Passive samplers collected from the influent had the lowest compound uptake as compared
to the grab samples (Figure 3.4). This could be due to the fact that the passive sampler in
the influent had a build-up of insoluble particulate matter that could hinder the absorption
of chemicals into the EmporeTM disk even though the flow rate of the influent was quite
high. Furthermore, there might be biodegradation of absorbed compounds occurring on the
matrix itself. Even though the clarifier and the bioreactor passive samplers had almost
similar uptake profiles, they however experienced different conditions that might
contribute to the reduced uptake of compounds (Figure 3.4). The bioreactor had a relatively
high concentration of microorganisms that may cause biofouling on the surface and in the
81
matrix of the EmporeTM disk. On the other hand, although the concentration of
microorganisms in the clarifier is significantly lower than in the bioreactor, the low water
flow rate needed for the settling of sludge in the clarifier could be a contributing factor
towards the reduced uptake rate. The site with roughly similar EmporeTM disk uptake
profile as those exhibited by the laboratory calibration study was the effluent (Figure 3.4).
This could be explained by the fact that the effluent has a relatively high flow rate and low
concentrations of microorganisms and suspended solids which are similar to conditions of
the laboratory calibration study.
Huckins et al. (2006) reported several cases with 20 – 70% impedance of uptake of
polyaromatic hydrocarbons (PAHs) by semi-permeable membrane devices (SPMDs)
caused by biofouling. Richardson et al. (2002) showed that biofouling limited uptake of
contaminants in SPMDs, and that before use of passive samplers in a particular locality,
steps should be made towards identification of the extent of fouling via a calibration study;
most preferably aided by the use of performance reference compounds (PRCs). An
approach using PRCs could compensate for the reduction in uptake due to biofouling, and
thus better relate passive sampler contaminant concentrations to ambient water values
(Richardson et al., 2002; Vrana et al., 2006); however, PRCs may affect bioassay results.
The importance of the flow for the mass transport over the surface of the passive sampler
has been debated intensely and an important assumption for calculation of aqueous
concentration from accumulated amount is that the rate limiting step is the crossing of the
matrix or polymeric membrane (SPMD) rather than the aqueous boundary layer (Stuer-
Lauridsen et al., 2005). A study done by Booij et al. (1998) reported that the change in
mass transfer coefficient for compounds into SPMDs with log Kow >4 was 3 – 4 times
higher for an increase in velocity from 0.03 to 30 cm/s. In flow experiments by Huckins et
al., (2000), only a 50% increase in SPMD sampling rates was found for flow regimes from
0.004 to 0.2 cm/s suggesting little influence from aqueous biofouling layer diffusion. Green
and Abraham (2000) also noticed that the uptake rate for EmporeTM disk is affected by
differences in flow rates. In many environments, flow is considerably slower than the 30
cm/s and the exposure regime may influence the observed uptake. Vrana and Schüürmann
(2002) also concluded that flow under environmental conditions affected the sequestration
in the SPMD. Information on flow, temperature and the measurement of a PRC may
provide valuable information to correct for flow conditions. However, in this study PRCs
82
could not be used because the pre-concentrated passive sampler extracts were also used in a
bioassay to determine their estrogenicity and PRCs would interfere with the measurement.
Generally, passive samplers offer great potential for application in air and aquatic
environments using different designs and calibration and may complement the use of a
biomimetic sampling to estimate organism exposure. Biomimetic equilibrium sampling
using EmporeTM disks can mimic partitioning of contaminants between the ambient water
and an aquatic organism. This approach assumes that the freely dissolved contaminant
concentrations will represent bioavailability. Passive samplers do not exhibit differential
contaminant accumulation due to sex, reproductive state or age. Furthermore their use
reduces difficulties such as high mortality in polluted environments observed with live
organisms. Passive samplers also do not metabolize target chemicals (Peven et al., 1996).
However, for substances that may be biotransformed in the organism, the method will
overestimate the concentration (Vrana et al., 2005).
This study has shown that SDS-RPS EmporeTM disks are suitable passive samplers for
accumulating semi-polar compounds such as EDCs and also have the ability to sample
large volumes of water. Passive sampling also reduces the effort required for deployment
and sample processing compared to other commonly used methods. It is important to
consider the mechanisms of the exchange process between aqueous phase and the sampler
components. The rate-limiting step in the uptake to the receiving phase (in the absence of
fouling) may be controlled by diffusion in the receiving phase matrix or across the aqueous
diffusive boundary layer at the membrane-water interface (Flynn and Yalkowsky, 1972).
Water turbulence affects the thickness of the unstirred layer of water that forms part of the
diffusion-limiting barrier near the sampler surface, and consequently also affects the mass
transfer of the analytes. The rate-limiting step depends on the type and properties of the
receiving phase, the environmental conditions prevailing during sampling and the
properties of the compound being sampled (Vrana et al., 2005). In this study, the aim was
to observe feasibility of using passive samplers in WWTPs thus specific issues of
environmental factors governing the uptake rates were not addressed in detail. Future
studies will employ the use of a polysulfonic membrane to protect the surface of the
exposed EmporeTM disk against biofouling, as well as potentially obviate the effects of
83
variable flow and thus improve the consistency of the compound uptake rate and the
prediction of ambient water concentration.
Table 3.2. Selected physiochemical properties and sampling rates of test analytes for the
passive sampler (EmporeTM disk) based on the laboratory calibration at 24°C. Compound Log Kow
a Partition coefficient,
KSW
Sampling rate, RS (L/d)
t1/2 (d) kloss (d-1) % of equilibrium
concentration attained b
Phthalate Diethyl phthalate 2.4 4.69 1.27 9.24 8.72×10-7 26.8 Dibutyl phthalate 4.5 4.62 2.48 3.98 2.00×10-6 51.1 Benzyl butyl phthalate 4.7 5.00 2.39 9.89
8.04×10-7 25.0
Dioctyl phthalate 7.6 3.68 1.57 0.73 1.10×10-5 98.1 Alkylphenols 4-tert-octylphenol 5.5 4.54 2.78 2.97 2.70×10-6 62.0 Nonylphenol 5.8 4.54 3.23 2.54 3.13×10-6 67.5 4-Cumylphenol 4.1 4.47 2.47 2.84 2.82 ×10-6 63.5 Bisphenol A 3.3 4.20 1.71 2.22 3.63×10-6 72.8 Estrogens Estrone 3.1 4.36 2.21 2.47 3.25×10-6 68.7 17α-Estradiol 4.0 4.28 1.97 2.32 3.24×10-6 68.7 17β-Estradiol 4.0 4.41 1.84 3.36 2.41×10-6 57.8 Estriol 2.5 3.77 1.12 1.26 6.40×10-6 89.9 Androgens Etiocholanolone 3.7 4.49 2.00 3.68 2.18×10-6 54.2 Androsterone 3.7 4.49 1.84 3.98 2.00×10-6 51.2
a Log Kow values for all compounds as predicted from ALOGPS 2.1 computer program provided by Virtual
Computational Chemistry Laboratory (VCCLAB, 2005; Tetko et al., 2005). b Assuming concentration in field study does not exceed 500 ng/L and the EmporeTM disk was deployed for
approximately 4 days. t1/2 – time required to accumulate 50% of the equilibrium concentration.
kloss – rate constant for loss of compound from the receiving phase.
84
(d) Dibutyl phthalate
0
200
400
600
800
1000
1200
0 1 2 3 4 5
Time (days)
Acc
umul
ated
mas
s Em
pore
(ng)
0
100
200
300
400
500
600
Con
cent
ratio
n SP
E (ng
/L)
(a) Nonylphenol
0
1000
2000
3000
4000
5000
6000
0 1 2 3 4 5
Time (days)
Acc
umul
ated
mas
s Em
pore
(ng)
0
100
200
300
400
500
600
700
Con
cent
ratio
n SP
E (ng
/L)
(b) Bisphenol A
0
500
1000
1500
2000
2500
3000
0 1 2 3 4 5
Time (days)
Acc
umul
ated
mas
s Em
pore
(ng)
0
100
200
300
400
500
600
700
Con
cent
ratio
n SP
E (ng
/L)
(c) Estrone
0
1000
2000
3000
4000
5000
0 1 2 3 4 5Time (days)
Acc
umul
ated
mas
s Em
pore
(ng)
0
200
400
600
800
1000
1200
1400
Con
cent
ratio
n SP
E (ng
/L)
SPEEmpore
SPEEmpore
SPEEmpore
SPEEmpore
Figure 3.2. Examples of time series uptake data in SDB-RPS EmporeTM disk for selected
EDCs in calibration experiment (a) nonylphenol; (b) bisphenol A; (c) estrone and (d)
dibutyl phthalate.
85
0
1
2
3
4
5
6
0 2 4 6 8log Kow
log
Ksw
Stephens (C18)Stephens (SDB)Green (C18-diuron)
Veerhaar (C18)
Figure 3.3. Relationship between log Kow and log KSW for SDB-RPS EmporeTM disk,
including available literature data (Verhaar et al., 1995; Green and Abraham, 2000; Mayer,
2000; Stephens et al., 2005).
Mayer (C18)Verhaar (C18)
This study (SDB)
86
Influent, y = 0.18x, R2 = 0.46, p<0.001
Bioreactor, y = 0.33x, R2 = 0.72, p<0.001
Clarifier, y = 0.30x, R2 = 0.44, p<0.001
Effluent, y = 0.68x, R2 = 0.57, p<0.001
0.1
1
10
100
1000
0.1 1 10 100 1000 10000
Concentration grab sampling (ng/L)
Con
cent
ratio
n pa
ssiv
e sa
mpl
ing
(ng/
L)
InfluentBioreactorClarifierEffluent
Isometric line
Figure 3.4. Correlation between measured EDC concentration obtained from grab sampling
and passive sampling at different sites along WWTPs A, B, C, D and E in South East
Queensland, Australia.
3.4.2 Environmental monitoring
The occurrence of the selected EDCs has been studied in typical WWTP water and
receiving environments. Values obtained from the grab samples at WWTPs J, M and N
show that variable concentrations, from a few ng/L for estrogens to approximately 22,000
ng/L for diethyl phthalate were found (Tables 3.3, 3.4 and 3.5). Many were frequently
below their limit of detection. The reproducibility of the grab sample collection and
analysis was evaluated at WWTP N and the results showed that for most chemicals the
coefficients of variations (CVs) were <30%. Considering the complexity of the extraction
and analysis, this was found to be an acceptable result. As expected, the estrogens estrone
and 17β-estradiol were identified in influent of WWTP J at low concentrations (Table 3.3).
This is because influent samples at this site contain mainly urban discharge in which
steroidal estrogens certainly exist as a consequence of human excretion. Studies on the
estrogen profile of urine samples indicate that women can excrete about 7 µg of estrone, 2.4
µg of 17β-estradiol and 4.6 µg of estriol per day which can subsequently reach WWTPs
87
(Desbrow et al. 1998). As shown in Tables 3.3 and 3.4, the estrogens concentrations of the
effluent grab samples were below the detection limit for WWTP J and M; however, trace
amounts of estrogens were found in selected compartments of the constructed wetland of
WWTP N (Table 3.5). In fact, there was a rise in estrone concentration from the grab
samples of wetland compartments N1 – N7 of WWTP N (Table 3.5). This finding is
substantially in accordance with data reported in the literature (Ternes et al., 1999a;
Johnson et al., 2000; Baronti et al., 2000; Liu et al., 2004; Laganà et al., 2004). According
to Laganà et al. (2004), the occurrence of estrogens in effluent may be the result of either
incomplete removal of these compounds during biological treatment and/or release of the
active estrogenic forms from conjugates during the same treatment processes. Estrogens are
mainly excreted as conjugates of sulfuric and glucoronic acids. In this form, they do not
possess a direct biological activity, but they can act as precursor hormone reservoirs able to
be deconjugated into the parent compounds during the wastewater treatment (Johnson et al.,
2000). This process is well known for estrone, the most abundant estrogen excreted by
women. In addition, it is also suggested to be the by-product of biodegradation of 17β-
estradiol in wastewater (Ferguson, 2000). Nevertheless, the wetland data show that low
concentrations of estrogens that are released by the WWTP are slowly removed mostly
through microbial degradation or sorption to sediment (Birkett and Lester, 2003).
As for the alkylphenols, they were present in the grab samples at the WWTPs at
concentrations ranging from 2 – 330 ng/L for 4-tert-octylphenol, 55 – 6650 ng/L for
nonylphenol, 7 – 13 ng/L for 4-cumylphenol and <1 – 19 ng/L for bisphenol A (Tables 3.3,
3.4 and 3.5). High concentrations of phthalates were also observed in the first few removal
steps at WWTP J (Table 3.3). The presence of alkylphenols and phthalates in WWTP water
is readily explained by their extensive domestic and industrial uses. However the values
reported in this study are still considered low when compared to results from studies
conducted in Europe (Blackburn and Waldock, 1995; Solé et al., 2000; Laganà et al., 2004).
As seen in Table 3.4, ultrafiltration, which was the tertiary treatment process used by
WWTP M, was not an effective treatment process to remove alkylphenols and phthalates.
According to Birkett and Lester (2003), the best tertiary removal methods for EDCs are UV
with TiO2 catalyst and granular activated carbon (GAC) filtration which has been shown to
remove volatile organic chemicals (VOC), pesticides and herbicides, trihalomethane
88
compounds, phthalates, alkylphenol, steroids, radon, solvents and hundreds of other
anthropogenic chemicals found in water.
From the data shown in WWTPs J, M and N, the efficiency in removing the selected EDCs
is >90% for the more potent EDCs such as the natural hormones, but much less for the
alkylphenols and phthalates, mainly because of major resistance of alkylphenols and
phthalates to microbial degradation occurring during treatment (Birkett and Lester, 2003).
In addition, alkylphenols are generated during the breakdown of alkylphenol
polyethoxylates, one of the world’s largest groups of surfactants (Laganà et al., 2004).
Concentrations of bisphenol A are expected to be relatively low as it is readily
biodegradable (Staples et al., 2000). On the whole, the advance treatment processes
employed at these three different WWTPs are quite reliable and effective at removing
EDCs. Dioctyl phthalate and diethyl phthalate dominated the phthalate concentrations at all
WWTPs grab water samples that ranged from 189 – 6470 ng/L and <1 – 22,000 ng/L,
respectively.
The concentrations of compounds in aqueous phase of the wetlands of WWTP N derived
using passive sampling methods are shown in Table 3.5. The majority of the chemical
concentrations measured using the passive samplers were lower than those found in the
grab sampler. As previously discussed, the difference in the wetland passive and grab
sample values could be due to the very low flow rate in the wetland that is resulting in a
reduced uptake of chemicals into the passive sampler since no biofouling was observed on
the EmporeTM disk upon retrieval. An exception was the androgenic compounds,
etiocholanolone and androsterone, found in the passive sampler deployed in N-1 of
WWTP N but not in the corresponding grab sample. These androgenic concentrations were
still low when compared to studies done by Leusch et al. (2006b). Passive samplers may
provide representative information on EDCs concentration over a period of time with lower
sampling frequency than that in grab sampling. However, it is important to recognize that,
in most cases, the aqueous concentration estimated using passive samplers reflects only the
truly dissolved contaminant fraction and is not necessarily equal to the concentration
measured in grab sampling, particularly with very hydrophobic compounds in the presence
of elevated concentrations of dissolved organic matter (Vrana et al., 2005). In many aquatic
systems, contaminant concentrations are not constant, but fluctuate or occur in the form of
89
unpredictable pulses. As shown in Figure 3.5, 4-tert-octylphenol and nonylphenol
concentrations from grab samples of WWTP M varies between <1 – 8 ng/L and 15 – 185
ng/L, respectively for effluent samples and <1 – 14 ng/L and 33 – 172 ng/L, respectively
for ultrafiltration samples. Most of the other compounds from WWTP M have similar
concentration fluctuations which vary between 1 – 2 orders of magnitude. The CVs of the
compound concentrations calculated from the entire grab sample collection period were
between 25 – 184% (Table 3.4) as apposed to <30% for most compounds when triplicates
of grab samples were taken at a particular time frame at WWTP N. Such a huge daily or
even hourly concentration variations would be assumed to happen for most compounds
found in various WWTP treatment processes, thus highlighting the problem associated with
grab sampling when only a few samples are collected and assuming the mean ‘snapshot’
compound concentrations will reflect the overall ambient concentrations. Concentrations
from integrative passive samplers reflect a certain exposure period, but more research is
needed to quantitate the uptake in passive samplers in scenarios involving pulsed and
discontinuous exposure in order to provide sufficient evidence of realistic concentration
estimates using passive samplers. Such work is essential if regulators are to use passive
samplers in monitoring programs (Vrana et al., 2005).
It has been demonstrated in Europe (Lavado et al., 2004; Diniz et al., 2005) and the USA
(Folmar et al., 1996; McArdle et al., 2000) that male fish held in treated wastewater
effluents or in rivers below WWTPs showed a pronounced increase of estrogen-dependent
plasma vitellogenin concentrations. Vitellogenin is a glycolipophosphoprotein precursor to
egg yolk produced in the liver of sexually mature female fish under estrogenic stimulation.
Although it is not normally synthesized in males, the gene is present in both sexes and
vitellogenin expression can be induced in males exposed to exogenous estrogens (Denslow
et al., 1999). The lowest effect concentrations for the induction of vitellogenin production
reported in the literature were 0.3 ng /L estrogen equivalent in immature male rainbow trout
after 28 weeks of dosing (Sheahan et al., 1994) and 10 ng/L estrogen equivalent in male
rainbow trout and roach exposed for three weeks in flow through aquaria (Routledge et al.,
1998). Jobling et al. (1995) reported effects on vitellogenin production and testicular
growth in sexually maturing male rainbow trout exposed during three weeks to a
concentration of 2 ng/L estrogen equivalent. The concentration of compounds in the final
effluent from WWTPs J, M and N measured in this work appear to have a cumulative
90
91
estrogen equivalent concentration well below the values reported to induce vitellogenin
induction in fish. From this study it could be concluded that the 3 different treatment
technologies, biological nutrient removal (WWTP J), conventional activated sludge
(WWTP M) and trickling filter with discharge into the wetland (WWTP N) were
effectively removing most EDCs monitored to concentrations where endocrine disrupting
effects would be negligible. However, since only a selected number of EDCs were
monitored in this study, there might be other unknown chemicals exuding an estrogenic
effect. Further studies will include the use of both chemical and biological assays to
determine the effects of EDCs have on the Australian environment.
Con
cent
ratio
n (n
g/L)
C
ompo
und
Influ
ent
Bio
reac
tor
Nitr
ifica
tion/
de
nitri
ficat
ion
Cla
rifie
r Sa
nd fi
lter
Ozo
ne
Bio
logi
cally
ac
tivat
ed
carb
on
Ultr
avio
let
Phth
alat
es
Die
thyl
pht
hala
te
2200
0 11
9 12
6 16
0 52
9 75
.1
43.3
71
.2
Dib
utyl
pht
hala
te
1560
70
.4
70.2
20
3 15
3 23
3 91
9 98
.9
Ben
zyl b
utyl
pht
hala
te
1010
21
.1
29.6
B
DL
453
17.9
38
.2
23.1
D
ioct
yl p
htha
late
64
70
1360
97
6 12
40
1540
12
00
666
711
A
lkyl
phen
ols
4-te
rt-oc
tylp
heno
l 33
0 4.
70
5.6
9.7
21.0
6.
3 8.
8 3.
8 N
onyl
phen
ol
6650
12
7 11
9 15
8 21
5 74
.9
76.9
78
.4
4-C
umyl
phen
ol
BD
L B
DL
BD
L B
DL
BD
L B
DL
BD
L B
DL
Bis
phen
ol A
B
DL
19.4
7.
1 10
.7
17.5
8.
2 11
.0
7.4
Ph
arm
aceu
tical
Ta
mox
ifen
BD
L B
DL
BD
L B
DL
BD
L B
DL
BD
L B
DL
E
stro
gens
Es
trone
32
.8
7.2
6.8
9.8
17.2
6.
3 B
DL
BD
L 17α-
Estra
diol
B
DL
BD
L B
DL
BD
L B
DL
BD
L B
DL
BD
L 17β-
Estra
diol
15
.6
3.4
BD
L 7.
3 25
.9
3.4
BD
L B
DL
A
ndro
gens
Et
ioch
olan
olon
e N
A
NA
N
A
NA
N
A
NA
N
A
NA
A
ndro
ster
one
NA
N
A
NA
N
A
NA
N
A
NA
N
A
Tabl
e 3.
3. C
once
ntra
tion
of E
DC
s det
ecte
d in
gra
b sa
mpl
es fr
om W
WTP
J.
92
BD
L: b
elow
det
ectio
n lim
it (<
1 ng
/L).
NA
: Not
ana
lyze
d.
93
Table 3.4. Concentration of EDCs detected grab samples from WWTP M which practices
recycling.
Effluent Ultrafiltration Compound
Concentration (ng/L) CV (%) Concentration (ng/L) CV (%)
Dioctyl phthalate 437 ± 166 38.0 416 ± 106 25.4
Dibutyl phthalate 101 ± 44.5 44.1 86.8 ± 27.2 31.6
Nonylphenol 82.8 ± 40.9 49.4 73.1 ± 35.1 48.1
4-tert-octylphenol 4.4 ± 3.2 72.9 2.7 ± 4.5 168
Bisphenol A 11.5 ± 11.3 98.3 4.8 ± 8.9 184
CV: Coefficients of variation. Out of the 15 compounds, only the above 5 compounds were
detected in the samples. Values represent mean ± standard deviation (n = 21).
94
Tabl
e 3.
5. C
once
ntra
tion
of E
DC
s det
ecte
d us
ing
grab
and
pas
sive
sam
plin
g m
etho
ds in
the
wet
land
s of W
WTP
N.
Con
cent
ratio
n (n
g/L)
N
-1
N-2
N
-3
N-4
N
-5
N-6
N
-7
Com
poun
d
Gra
b Pa
ssiv
e G
rab
Pass
ive
Gra
b Pa
ssiv
e G
rab
Pass
ive
Gra
b Pa
ssiv
e G
rab
Pass
ive
Gra
b Pa
ssiv
e Ph
thal
ates
D
ieth
yl p
htha
late
20
1 ±1
2.3
7.8
42.6
±6
.7
1.9
29.4
±2
.2
4.2
21.5
±1
.3
6.8
3.8
±6.5
1.
6 B
DL
2.6
BD
L 1.
3
Dib
utyl
pht
hala
te
110
±16.
6 25
.1
54.7
±3
.5
9.6
49.4
±1
3.1
13.2
28
.2
±2.0
11
.7
13.3
±2
.1
8.5
12.5
±0
.9
8.7
16.6
±0
.2
9.5
Ben
zyl b
utyl
ph
thal
ate
20.0
±1
.5
9.4
BD
L 1.
0 B
DL
1.6
BD
L 2.
0 B
DL
BD
L B
DL
BD
L B
DL
BD
L
Dio
ctyl
pht
hala
te
1594
±2
180
211
342
±23.
5 16
5 18
9 ±6
.8
177
318
±19.
5 18
5 23
1 ±4
1.6
191
349
±241
19
5 27
4 ±1
08
179
Alk
ylph
enol
s
4-
tert-
octy
lphe
nol
161
±26.
3 1.
4 10
9 ±4
8.3
BD
L 10
7 ±1
8.4
BD
L 13
5 ±1
5.1
1.1
204
±51.
9 4.
6 14
8 ±8
3.2
BD
L 15
7 ±6
6.0
BD
L
Non
ylph
enol
17
3 ±5
.4
60.5
12
6 ±3
.9
36.1
92
.3
±17.
5 9.
9 10
7 ±1
3.6
17.3
57
.0
±15.
1 3.
6 54
.9
±13.
9 2.
6 66
.9
±12.
2 2.
2
4-C
umyl
phen
ol
9.7
±1.0
B
DL
8.8
±1.2
B
DL
7.0
±0.8
B
DL
8.9
±0.6
B
DL
10.1
±1
.3
BD
L 11
.6
±2.4
B
DL
12.7
±2
.8
BD
L
Bis
phen
ol A
5.
4 ±1
.0
1.4
10.4
±2
.4
BD
L 4.
5 ±0
.3
BD
L 3.
5 ±0
.4
1.5
2.7
±0.5
B
DL
2.1
±0.3
B
DL
2.5
±2.0
B
DL
Phar
mac
eutic
al
Tam
oxife
n B
DL
BD
L B
DL
BD
L B
DL
BD
L B
DL
BD
L B
DL
BD
L B
DL
BD
L B
DL
BD
L E
stro
gens
Es
trone
B
DL
BD
L 1.
9 ±0
.2
BD
L 2.
0 ±0
.4
BD
L 2.
2 ±0
.1
BD
L 2.
2 ±0
.1
BD
L 4.
3 ±0
.2
1.5
6.3
±0.7
1.
1 17α-
Estra
diol
B
DL
2.4
BD
L B
DL
BD
L B
DL
BD
L B
DL
BD
L B
DL
BD
L B
DL
BD
L B
DL
17β-
Estra
diol
B
DL
4.42
B
DL
BD
L B
DL
BD
L B
DL
BD
L B
DL
BD
L B
DL
BD
L B
DL
BD
L Es
triol
B
DL
BD
L B
DL
BD
L B
DL
BD
L B
DL
BD
L B
DL
BD
L B
DL
BD
L B
DL
BD
L A
ndro
gens
Et
ioch
olan
olon
e B
DL
5.3
3.9
±4.5
B
DL
BD
L B
DL
BD
L B
DL
BD
L B
DL
BD
L B
DL
BD
L B
DL
And
rost
eron
e B
DL
2.3
BD
L B
DL
BD
L B
DL
BD
L B
DL
BD
L B
DL
BD
L B
DL
BD
L B
DL
BD
L: b
elow
det
ectio
n lim
it (<
1 ng
/L).
Val
ues r
epre
sent
mea
n ±
stan
dard
dev
iatio
n, n
= 3
for g
rab
sam
plin
g; n
= 2
for p
assi
ve sa
mpl
ing.
Cal
cula
tion
of c
ompo
und
wat
er c
once
ntra
tions
usi
ng th
e pa
ssiv
e sa
mpl
er w
as b
ased
on
equa
tion
6.
(a) 4-tert-octylphenol
0
2
4
6
8
10
12
14
1609
00 h
1200
h
1500
h
0900
h
1200
h
1500
h
0900
h
1200
h
1500
h
0900
h
1200
h
1500
h
0900
h
1200
h
1500
h
0900
h
1200
h
1500
h
0900
h
1200
h
1500
h
Conc
entra
tion
(ng/
L)
Effluent
Ultrafiltration
2 June 3 June 4 June 5 June 6 June 7 June 8 June
(b) Nonylphenol
020406080
100120140160180200
0900
h
1200
h
1500
h
0900
h
1200
h
1500
h
0900
h
1200
h
1500
h
0900
h
1200
h
1500
h
0900
h
1200
h
1500
h
0900
h
1200
h
1500
h
0900
h
1200
h
1500
h
Con
cent
ratio
n (n
g/L)
EffluentUltrafiltration
2 June 3 June 4 June 5 June 6 June 7 June 8 June
Figure 3.5. Examples of variation of concentrations for (a) 4-tert-octylphenol and (b)
nonylphenol using grab sampling at WWTP M at selected time intervals over a period of 7
days in 2005.
95
3.5 Conclusions
Two different sampling methods have been developed and compared for the extraction of
analysis of important EDCs including, estrogens, androgens, alkylphenols, phthalates and
tamoxifen. The usefulness of the passive sampler method in monitoring obviously depends
on its ability to sequester EDCs of priority, but it must also be versatile and practical and
have predicable accumulation characteristics. The robust SPE extraction method that was
also used in this study shows great potential at extracting a wide range of EDCs with high
and consistent recovery for most compounds.
Both grab and passive sampling have advantages and disadvantages; the conventional grab
sampling method is currently the preferred method of choice in monitoring work, although
it only gives a concentration value at a particular moment in time. Multiple grab samples at
different time periods have to be taken to give a temporal picture of the compound
concentration at a particular site. The passive sampling method on the other hand holds
promise since it employs a small sampling device (which simplifies transportation to and
from the sampling site and storage) and also lowers the consumption of solvents and
reagents during subsequent processing. A further challenge is to improve the robustness by
reducing or controlling the impact of environmental conditions and biofouling on the
sampler performance.
The proposed monitoring methods showed acceptable recovery and reproducibility for
target compounds at ng/L concentration. The methods were successfully applied for the
determination of these target EDCs in wastewater samples and the WWTPs monitored in
this study showed >90% removal of EDCs. The three different treatment technologies,
biological nutrient removal, conventional activated sludge and trickling filter with
discharge into the wetland were equally as efficient at removing the majority of the EDCs
monitored to concentrations where endocrine disrupting effects would be negligible.
96
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102
Chapter 4: Stir bar sorptive extraction and trace analysis of selected
endocrine disrupting compounds in water, solids and sludge samples by
thermal desorption with gas chromatography-mass spectrometry
4.1 Abstract
There are currently limited methods to extract organic compounds from solids or sludge
due to the presence of high lipid content and tedious post extraction sample clean-up which
would eventually lead to compound loss. Stir bar sorptive extraction (SBSE) in
combination with thermal desorption coupled to gas chromatography-mass spectrometry
(GC-MS) was successfully applied to analyze a range of endocrine disrupting compounds
(EDCs) in wastewater, solids and sludge. The SBSE method relies on the sorption of
selected compounds and sample clean-up is not required. The targeted EDCs include sex
steroid hormones, phthalates, alkylphenols and tamoxifen. The technique was shown itself
to be very versatile and sensitive for the analysis of a wide range of EDCs. Recovery for the
EDCs using this analytical technique ranged from 44 – 128%. When this analytical
technique was applied to measure EDCs concentration in a biological nutrient removal
(BNR) wastewater treatment plant located in South East Queensland, Australia, the results
showed that there were high amounts of phthalates, alkylphenols and female hormones
present in the raw influent wastewater and solids. These concentrations were dramatically
reduced after passing through the various treatment zones of the bioreactor (anaerobic,
aerobic and anoxic).
4.2 Introduction Endocrine disrupting effects in the aquatic environment, such as the feminization of male
fish has been strongly associated with the presence of hormones in river water (Ternes et al.
2002). Other substances commonly found in surface waters such as alkylphenols,
phthalates, polychlorinated byphenyls (PCBs), phytoestrogen and androgens are also
suspected of influencing the endocrine system (Desbrow et al., 1998; Ternes et al. 2002;
Tan and Mustafa, 2003). Predominant among the estrogens detected worldwide in
wastewater treatment plants (WWTPs) and their receiving environments are estrone, 17β-
estradiol and 17α-ethynylestradiol. Laboratory studies have shown that selected EDCs can
103
be potent and exert estrogenic effects at concentrations as low as 1 ng/L in water
(Routledge et al., 1998). Concentration levels of only a few nanograms per liter have been
reported in WWTP effluent and river water (Desbrow et al., 1998; Belfroid et al., 1999;
Larsson et al., 1999; Leusch et al., 2006). Their log Kow range of 3.1 – 7 indicates that
EDCs tend to be lipophilic and should mostly sorb onto organic matter such as sludge
(Table 4.1). This assumption is supported by the detection of high concentrations of
estrogens in water released by dewatering sewage sludge (Matsui et al., 2000). Therefore, a
potential contamination of soil with EDCs may be caused by the application of digested
sludge from municipal WWTPs onto agricultural fields (Ternes et al., 2002).
Analytical methods for the determination of EDCs have frequently been described in the
literature for aqueous matrixes, such as wastewater and river water; and are frequently
based on solid phase extraction (SPE). However, there are limited published methods that
describe the simultaneous determination of ubiquitous EDCs in sewage sludge. Recently, a
new sorptive extraction technique using a stir bar coated with polydimethylsiloxane
(PDMS) was developed and is known as the stir bar sorptive extraction (SBSE). Its main
advantages are its high sensitivity and extensive application over a range of compounds
including volatile aromatics, halogenated solvents, polycyclic aromatic hydrocarbons
(PAHs), polychlorinated biphenyls (PCBs), pesticides, preservatives, odor compounds and
organotin compounds (Baltussen et al., 1999; Kawaguchi et al., 2004). Moreover, SBSE
with in situ acylation has been effectively used in the determination of alkylphenols and
natural estrogens in river water samples (Kawaguchi et al., 2004).
The aim of this work was to investigate a rapid and reliable method to detect selected EDCs
both WWTP wastewater and solids/sludge using SBSE. The novel technique of extracting
EDCs from solids/sludge using the SBSE method has not been reported in any previously
published works. This method has the potential to considerably reduce extraction and
analysis time when compared to the usual SPE or liquid-liquid extraction method. In
addition, this method requires no solvent and very small sample size, i.e. a few grams of
sludge or milliliters of aqueous solution.
104
4.3 Materials and methods
4.3.1 Chemicals and reagents
Benzyl butyl phthalate, dibutyl phthalate, diethyl phthalate, dioctyl phthalate [di-(2-
ethylhexyl) phthalate], 4-tert-octylphenol, nonylphenol (technical grade), 4-
cumylphenol, bisphenol A, estrone, 17β-estradiol, tamoxifen, sodium carbonate, acetic
anhydride and hydrochloric acid were all purchased from Sigma (St Louis, MO, USA). 4-n-
Nonylphenol was purchased from Lancaster Synthesis (Morecambe, England).
Androsterone and etiocholan-3α-ol-17-one were purchased from Riedel-de (Haën Seelze,
Germany). 17β-estradiol-2,4,16,16-d4 was purchased from CDN Isotopes (Quebec,
Canada). The solvents (methanol, n-hexane, and acetone) were of HPLC grade and
obtained from Merck (Darmstardt, Germany). Separate stock solutions of individual EDCs
(100 mg/L) were made up in methanol. From these solutions, a working mixture containing
each compound at 10 mg/L was prepared. Internal standard solutions (5 mg/L) of 4-n-
nonylphenol (Günther et al., 2001; Naassner et al., 2002) and 17β-estradiol-2,4,16,16-d4
were prepared in n-hexane:acetone (50:50, v/v). All standard solutions were stored at -18
°C prior to use. The water used was from a Milli-Q Synthesis A10 System (Millipore,
Bedford, MA, USA). Stir bars coated with a 0.5 mm thick PDMS layer (24 µL) were
obtained from Gerstel (Mülheim an der Ruhr, Germany). The stir bars could be used more
than 50 times with appropriate re-conditioning (the stir bars were conditioned for 2 hours at
300 °C in a flow of helium). For the extraction, 20 mL headspace vials from Agilent
Technologies (Palo Alto, CA, USA) were used.
4.3.2 Instrumentation
An Agilent 6890 gas chromatograph with model 5973 mass-selective detector (Agilent
technologies, Palo Alto, CA, USA) was used for analysis. Stir bar sample introduction via
thermal desorption was performed using a Gerstel TDS 2 thermodesorption system with a
Gerstel CIS 4 programmable temperature vaporization (PTV) inlet (Gerstel). The
temperature of TDS 2 was programmed to increase from 20 °C (held for 1 minute) to 280
°C (held for 5 minutes) at a rate of 60 °C min-1. The desorbed compounds were cryofocused
in the CIS 4 at -150 °C. After desorption, the temperature of CIS 4 was programmed to
increase from -150 °C to 300 °C (held for 10 minutes) at a rate of 12 °C s-1 to inject the
105
106
trapped compounds into the analytical column. The injection was performed in the splitless
mode (Kawaguchi et al., 2004). Separation was accomplished on a DB-5MS fused silica
column (30 m × 0.25 mm i.d.; 0.5 μm film thickness, Agilent Technologies). The oven
temperature program was 4 minutes at 50 °C, 8 °C/minute to 150°C, 7 °C/minute to 250 °C
and then 8 °C/minute to 300 °C and then hold at 300 °C for 4 minutes. Column pressure
was set at 70 kPa. Helium was used as the gas carrier at a constant flow of 1.2 mL/minute.
The transfer line was set at 300 °C and the source at 250 °C. The mass spectrometer was
operated in the selected ion-monitoring (SIM) mode with electron impact ionization of 70
eV. The ions monitored in the SIM mode are shown in Table 4.1 and the chromatogram of
the compounds is shown in Figure 4.1. Quantitative analysis was performed in the SIM
mode in order to maximize sensitivity. The concentrations were calculated relative to the
internal standards added to the sample prior to analysis.
10
7
Tabl
e 4.
1. L
og K
ow, t
heor
etic
al re
cove
ries,
spik
ed sl
udge
reco
verie
s, re
tent
ion
time,
ions
use
d fo
r qua
ntifi
catio
n in
SB
SE G
C-M
S de
tect
ion.
C
ompo
und
Log
Kow
a Th
eore
tical
reco
very
(%
) SP
ME
reco
very
(%)
Spik
ed sl
udge
re
cove
ry (%
) e
Ret
entio
n tim
e (m
inut
es)
Targ
et
ion
(m/z
) R
efer
ence
io
n (m
/z)
Die
thyl
pht
hala
te (D
EP)
2.4
51.7
12
.5 b
44.2
21
.7
149
177
4-te
rt-O
ctyl
phen
ol
5.5
99.9
82
.6 c
NT
NT
NT
NT
Acy
l der
ivat
ive
of 4
-tert-
octy
lphe
nol
5.6
99.9
-
90.0
23
.3
135
177
Non
ylph
enol
6.
0 10
0 83
.5 c
NT
NT
NT
NT
Acy
l der
ivat
ive
of n
onyl
phen
ol
6.1
100
- 61
.7
24.9
13
5 17
7 4-
Cum
ylph
enol
4.
1 96
.9
- N
T N
T N
T N
T A
cyl d
eriv
ativ
e of
4-c
umyl
phen
ol
4.2
97.4
-
58.6
27
.5
197
198
Dib
utyl
pht
hala
te (D
BP)
4.
6 99
.0
- 60
.5
27.5
14
9 22
3 4-
n-N
onyl
phen
ol (i
nter
nal s
tand
ard)
6.
0 10
0 -
NT
NT
NT
NT
Acy
l der
ivat
ive
of 4
-n-n
onyl
phen
ol
(inte
rnal
stan
dard
) 6.
1 10
0 -
NT
27.7
22
0 22
1
Ben
zyl b
utyl
pht
hala
te (B
BP)
4.
8 99
.4
59.3
b
74.5
32
.9
149
206
Bis
phen
ol A
3.
6 91
.3
98.0
c N
T N
T N
T N
T A
cyl d
eriv
ativ
e of
bis
phen
ol A
6.
6 10
0 -
70.1
33
.0
213
312
Dio
ctyl
pht
hala
te (D
OP)
7.
6 10
0 3.
7 b
128
35.0
14
9 27
9 Et
ioch
olan
olon
e 3.
1 73
.8
- N
T N
T N
T N
T A
cyl d
eriv
ativ
e of
etio
chol
anol
one
4.1
96.6
-
NT
35.6
29
0 29
1 A
ndro
ster
one
3.1
73.8
-
NT
NT
NT
NT
Acy
l der
ivat
ive
of a
ndro
ster
one
4.1
96.6
-
NT
35.7
29
0 29
1 Ta
mox
ifen
6.3
100
- 88
.8
37.1
37
1 37
2 Es
trone
3.
4 86
.6
107
d N
T N
T N
T N
T A
cyl d
eriv
ativ
e of
est
rone
3.
5 88
.6
- 49
.4
37.3
27
0 27
1 17β-
Estra
diol
3.
9 95
.4
100
d N
T N
T N
T N
T A
cyl d
eriv
ativ
e of
17β
-est
radi
ol
4.0
96.2
-
56.6
37
.9
272
273
17β-
Estra
diol
-2,4
,16,
16-d
4 3.
9 95
.4
- N
T N
T N
T N
T A
cyl d
eriv
ativ
e of
17β
-est
radi
ol-
2,4,
16,1
6-d 4
(int
erna
l sta
ndar
d)
4.0
96.2
-
NT
37.9
27
6 N
T
a Log
Kow
val
ues
for
all c
ompo
unds
as
pred
icte
d fr
om A
LOG
PS 2
.1 c
ompu
ter
prog
ram
pro
vide
d by
Virt
ual C
ompu
tatio
nal C
hem
istry
Lab
orat
ory
(VC
CLA
B, 2
005;
Tetk
o et
al.,
200
5).
SPM
E (s
olid
-pha
se m
icro
extra
ctio
n) re
cove
ries w
ere
repo
rted
by b
Luks
-Bet
lej e
t al.
(200
1), c
Bas
heer
et a
l. (2
005)
and
d Mita
ni e
t al.
(200
5).
e Coe
ffic
ient
s of v
aria
tion
<15%
; NT
= no
t tes
ted.
Etiocholanolone
Androsterone
Estrone
17β-Estradiol
17β-Estradiol-2,4,16,16-d4
B
Diethyl phthalate
4-tert-Octylphenol
Nonylphenol
4-Cumylphenol
Dibutyl phthalate
4-n-Nonylphenol
Benzyl butyl phthalate
Bisphenol A
Dioctyl phthalate
Tamoxifen
17β-Estradiol
Estrone
B
A
Figure 4.1. A. SBSE desorption chromatogram of phthalates, tamoxifen, acyl derivative of
alkylphenols, estrogens and androgen. B. Total ion chromatogram (A) enlarged to show
smaller compound peaks of the chromatogram (the chromatogram for tamoxifen was
removed to give a clearer view of androsterone and etiocholanolone).
108
4.3.3 SBSE procedure
Solids and/or sludge were obtained from centrifugation of grab wastewater sample to
remove excess water. 1 g of wet solids/sludge was placed in a 20 mL headspace vial and 20
μL of internal standards (5 mg/L of 4-n-nonylphenol and 17β-estradiol-2,4,16,16-d4) was
added to the solids/sludge. Then 10 mL of Milli-Q water was added to the sample, followed
by 0.5 g of sodium carbonate as a pH adjustment agent (pH >11), and 0.5 mL of acetic
anhydride as derivatization agent. Derivatized compounds enhance detection peak on the
GC-MS because the acylated derivative is more volatile and less polar as compared to its
parent compound. One stir bar was added to each vial and sealed with the screw top cap.
SBSE was performed at room temperature for 5 hours while stirring at 500 rpm
(Kawaguchi et al. 2004). After the extraction, the stir bar was removed, rinsed with Milli-Q
water and dried with lint-free tissue. The stir-bar was then placed in a glass thermal
desorption (TD) tube and desorbed in the TD system, for delivery to the GC-MS analysis.
The solids/sludge in the glass vial was filtered through a filter paper, oven dried and
weighed in order to express concentration in solids/sludge on a mass basis. Wastewater
sample was also analyzed using a similar SBSE method as mentioned above; the only
difference is the solids/sludge was replaced by 10 mL of wastewater and no Milli-Q water
was added.
Table 4.1 shows the log Kow and the theoretical recoveries of the compounds investigated
in this work, assuming that the majority of the compounds attached to the sludge will
desorb into the 10 mL water and be taken up by the stir bar after 5 hours of extraction. The
log Kow values were calculated using an ALOGPS 2.1 computer program provided by
Virtual Computational Chemistry Laboratory (VCCLAB, 2005; Tetko et al., 2005).
Theoretical recoveries were calculated with the following equations:
Theoretical recovery = β
β
ow
ow
KK+1
= 1
1+owKβ
where PDMSw VV=β , VPDMS is the volume of PDMS on the stir bar and Vw, the volume of
water (Kawaguchi et al., 2004). According to Kawaguchi et al. (2004), when sample
109
volume was increased, the recovery of estrogens was decreased. When the compounds
theoretical aqueous recoveries for the SBSE were compared with solid-phase
microextration (SPME) recoveries from various reported studies, both methods of
extraction showed similar recoveries for the estrogens and alkylphenols; however the
phthalates’ recoveries were much lower for the SPME compared to SBSE (Table 4.1). The
SBSE recoveries were also comparable to SPE (solid-phase extraction) technique reported
in an earlier study (Chapter 3). Although the extraction phase (polydimethylsiloxane,
PDMS) in SBSE is similar to that in SPME, its amount is 50 – 250 times larger, thus
extraction sensitivity is increased by several folds (Kawaguchi et al., 2006).
The actual sludge recovery was calculated by comparing the area ratio of stir bar extracted
standard compounds from water with that of the spiked sludge. Recoveries corrected by
internal standards were done in triplicates at 3 different spike concentrations (10, 25 and
500 ng/g sludge). Since a blank sludge was not easily obtained, freeze-dried sludge from a
municipal WWTP (WWTP J) was ultrasonicated with methanol for 20 minutes and the
solvent discarded, this step was repeated three more times to ensure the sludge had a
minimal amount of the EDCs present. The recoveries were calculated by subtracting the
results from the non-spiked samples from those of the spiked samples.
4.3.4 Sludge/water partitioning experiment
A partitioning experiment was carried out to observe the partitioning characteristics of the
targeted EDCs in both water and sludge phase. Triplicates of 1 L bioreactor samples from
WWTP J were spiked with 500 ng/L of the selected EDCs. The samples were centrifuged at
3000×g for 30 minutes at 4 °C (IEC PR-7000M refrigerated centrifuge) to separate the
aqueous phase from the sludge; and both portions were subjected to SBSE. From the SBSE
result, the sludge/water partition coefficient (Kp, L/kg) for each compound was calculated
as follows:
w
sp C
CK =
where Cs is the concentration of the EDC adsorbed by sludge (ng/kg), while Cw is the
concentration of the compound in water (ng/L).
110
4.3.5 Environmental monitoring
The SBSE method was also tested on an in field monitoring study. Concentrations of the
selected EDCs were measured in water and solids of raw influent, and also sludge of
anaerobic, aerobic and anoxic zones of the bioreactor of WWTP J located in South East
Queensland, Australia. This WWTP is a municipal biological nutrient removal (BNR) plant
which receives its influent from domestic discharge. Samples were taken at two different
times (November 2004 and March 2005).
4.4 Results and discussion
4.4.1 SBSE recovery and partitioning experiments
The SBSE method showed acceptable recovery and reproducibility with most coefficients
of variations of the target compounds <15% (Table 4.1). The results showed that theoretical
recoveries of EDCs with hydroxyl groups were increased by derivatization.
The recovery percentage of targeted compounds in both water and sludge phase of the
partitioning experiment are shown in Figure 4.2. Most of the EDCs were present in higher
concentrations in the water phase compared to the sludge phase. From the partitioning
results, nonylphenol was observed to have recoveries higher than 100% in the water phase
of the spiked bioreactor sample. This could be explained by inconsistent concentrations
present from sample to sample collected from WWTP J (Figure 4.2). Tamoxifen on the
other hand showed a low recovery in the sludge phase and below detectable limit in the
water phase, which could be due to a strong adsorption hysteresis to the sludge, or
biodegradation prior to extraction (Figure 4.2).
The EDCs have an overall increase in sludge/water partition coefficients as log Kow
increases (Figure 4.3). Hydrophilic compounds such as estrone and 17β-estradiol tend to
partition more in favor of the aqueous phase whereas lipophilic compounds such as
tamoxifen and dioctyl phthalate tend to partition more in favor of the sludge phase. A good
positive linear correlation was found when the sorption coefficients were compared with
the log Kow of the EDCs (F-test, R2 = 0.55, p<0.01, Figure 4.3).
111
Patititioning of EDCs in spiked bioreactor sample
0
20
40
60
80
100
120
140
160
Die
thyl
pht
hala
te
4-te
rt-O
ctyl
phen
ol
Non
ylph
enol
(tec
h.)
4-C
umyl
phen
ol
Dib
utyl
pht
hala
te
Bis
phen
ol A
Ben
zyl b
utyl
phth
alat
e
Dio
ctyl
pht
hala
te
Estro
ne
17β-
Estra
diol
Tam
oxife
n
Compound
Rec
over
y (%
)
WaterSludge
Figure 4.2. Recovery of EDCs in water and sludge phases when 1 L of bioreactor sample
from WWTP J was spiked at a concentration of 500 ng/L (mean ± standard deviation).
112
y = 0.45x - 0.65, R2 = 0.55, p<0.01
0
1
2
3
4
0 2 4 6 8
Log Kow
Log
KP
10
Figure 4.3. Relationship of log Kp (sludge/water partition coefficient) of EDCs onto
bioreactor sludge with log Kow (octanol/water partition coefficient). Error bars represent
standard deviations.
4.4.2 Environmental monitoring
The majority of the compounds with high concentrations detected in solids/sludge of
WWTP J were phthalates and alkylphenols (Table 4.2). As expected, there were high
concentrations of phthalates, alkylphenols hormones in the influent water and solids
samples which decreased through the different treatment removal process in the WWTP. In
a study done by Ternes et al. (2002), 37 ng/g of estrone were detected and up to 49 ng/g of
17β-estradiol in activated and digested sludge of two municipal WWTPs in Germany. Most
of the results of this study show much lower concentrations; however 37.5 ng/g of estrone
and 12.2 ng/g 17β-estradiol were detected in the solids of the raw influent. Nonylphenol,
bisphenol A and phthalates in water and sludge fell within the range detected by Fromme et
113
al. (2002) and Gibson et al. (2005). Concentrations of nonylphenol in sludge of up to 3.6
μg/g and octylphenol at 200 ng/g have been reported in raw sludges (Bolz et al., 2001).
The phthalate burden in both water and sludge is mainly from dioctyl phthalate, whilst
benzyl butyl phthalate is only present at concentrations near the detection limit. Reported
studies from Germany have shown phthalate concentrations ranging from 0.21 – 110 μg/g
in sludge (Schnaak et al., 1997; Fromme et al., 2002). It was shown that bisphenol A, being
easily degraded, is only present in influent water at low concentrations (Fromme et al.,
2002). These results were confirmed in this study as bisphenol A was only detected in
influent particulates and not in the bioreactor sludge (Table 4.2).
According to a study done by Van Emmerik et al. (2003), sorption of 17β-estradiol from
aqueous solution to selected soil minerals ranged from 10% - 65%. Their results show that
17β-estradiol is adsorbed at the surfaces of goethite, koalinite and illite, but taken up in the
interlayer spaces of montmorillonite. According to Lai et al. (2000), although the presence
of organic carbon was not a prerequisite for sorption, the sorption of estrogens to sediments
seems to be correlated to the total organic carbon content. Some studies conducted with
activated sludge point to aerobic biodegradation as the principal mechanism contributing to
the removal of estrogens from aqueous phase, while losses by sorption effects are
considered highly unlikely (Ternes et al., 1999b; Baronti et al., 2000). However, other
studies indicate that the main mechanism for eliminating steroidal hormones in WWTPs is
sorption onto particles and not biotransformation (Huang and Sedlak, 2001). Both sorption
and aerobic biodegradation are hypothesized as the main mechanisms for their elimination
from aquatic environments; however, there is no clear agreement about their relative
importance (Kuster et al., 2004). Tan et al. (2005) reported both sorption and
biodegradation are involved in the removal of EDCs from wastewater samples. According
to this study’s results, sorption played a more important role for wastewater removal of
hydrophobic compounds whereas aerobic biodegradation was the main mechanism for
hydrophilic compounds. Adsorbed compounds will eventually biodegrade over time since
the contact of compounds onto the microorganism-rich solids will facilitate faster
biodegradation. Both 17β-estradiol and 17α-ethynylestradiol have been shown to be
susceptible to photodegradation, with estimated half-lives of 10 d under ideal conditions in
English Rivers (Jurgens et al., 2002). Therefore, this suggests that the removal of the
114
115
estrogenic activity is enhanced by the combined actions of biological treatment
technologies and photodegradation.
Some groups of compounds are degraded during the aerobic treatment processes to more
hydrophobic compounds, in part the alkylphenol ethoxylates, which include the
nonylphenol ethoxylates. These compounds degrade to short (1 to 2 carbon) chain
ethoxylates or to the parent alkylphenol, which both increases lipophilicity and enhances
their estrogenicity (Brunner et al., 1988). However, during aerobic digestion, further
removal of residual ethoxylate groups occurs, and nonylphenol persists in digested sludges
(Johnson and Sumpter, 2001) with similar behavior reported for octylphenol (Ball et al.,
1989). Natural and synthetic estrogens are also removed during activated sludge processes
with losses of 20 – 90% occurring; the compounds removal mechanisms could be
biotransformation and/or binding to the solids (Ternes et al., 1999a; Ternes et al., 1999b;
Belfroid et al., 1999; Johnson and Sumpter, 2001).
116
Tabl
e 4.
2. E
DC
s co
ncen
tratio
n pr
esen
t in
raw
influ
ent,
anae
robi
c, a
erob
ic a
nd a
noxi
c zo
nes
of th
e bi
orea
ctor
at W
WTP
J d
eter
min
ed b
y SB
SE.
A
mou
nt in
wat
er (n
g/L)
A
mou
nt in
solid
s or s
ludg
e (n
g/g)
R
aw in
fluen
t R
aw in
fluen
t A
naer
obic
A
erob
ic
Ano
xic
Com
poun
d
Nov
. 200
4 M
arch
200
5 M
arch
200
5 N
ov. 2
004
Mar
ch 2
005
Nov
. 200
4 M
arch
200
5 N
ov. 2
004
Mar
ch 2
005
Pht
ha
late
D
ieth
yl p
htha
late
10
2 59
.3
389
6.5
18.2
9.
4 9.
2 13
.0
3.5
Dib
utyl
pht
hala
te
37.4
24
.7
138
16.3
51
.5
6.1
16.4
12
.7
12.1
B
enzy
l but
yl
phth
alat
e B
DL
6.3
62.0
B
DL
58.7
B
DL
BD
L B
DL
BD
L
Dio
ctyl
pht
hala
te
BD
L 23
80
2030
0 27
30
461
3430
12
64
1680
73
4
A
lkyl
phen
ols
4-
tert-
octy
lphe
nol
4.4
2.6
248
BD
L 10
.9
BD
L 0.
26
BD
L 0.
23
Non
ylph
enol
12
0 70
.3
9610
50
.8
406
65.3
21
.4
86.7
36
.7
4-C
umyl
phen
ol
BD
L 2.
3 25
.5
BD
L 0.
78
BD
L 6.
0 B
DL
5.1
Bis
phen
ol A
B
DL
3.8
704
BD
L B
DL
BD
L B
DL
BD
L B
DL
Phar
mac
eutic
al
Ta
mox
ifen
BD
L B
DL
BD
L B
DL
BD
L B
DL
BD
L B
DL
BD
L
E
stro
gens
Estro
ne
BD
L 14
.5
37.5
B
DL
4.0
BD
L B
DL
BD
L 4.
5 17β-
Estra
diol
B
DL
BD
L 12
.2
3.6
0.39
1.
0 1.
1 1.
5 0.
83
And
ro
gens
Et
ioch
olan
olon
e B
DL
BD
L B
DL
BD
L B
DL
BD
L B
DL
BD
L B
DL
And
rost
eron
e B
DL
BD
L B
DL
BD
L B
DL
BD
L B
DL
BD
L B
DL
*BD
L –
belo
w d
etec
tion
limit
(<1
ng/L
for w
ater
and
<0.
02 n
g/g
for s
ludg
e).
4.5 Conclusions The WWTP monitored in this study showed that the majority of EDCs was removed during
the activated sludge stage while on the whole, BNR treatment processes showed good
removal efficacy of EDCs. The determination of trace amounts of estrogenic substances in
water and solids/sludge has been successfully carried out using SBSE with in situ
derivatization followed by thermal desorption GC-MS. The method has many practical
advantages such as small sample volume (10 mL aqueous or <1 g sludge sample) and
simplicity of extraction since SBSE does not involve solvent wastage or tedious sample
clean-up steps that will eventually lead to lowered compound recoveries. Furthermore, the
SBSE can detect EDCs with a very low detection limit even though relatively small sample
volumes are involved.
4.6 References Ball, H.A., Reinhard, M., McCarty, P.L., 1989. Biotransformation of halogenated and
nonhalogenated octylphenol polyethoxylate residues under aerobic and anaerobic
conditions. Environ Sci Technol. 23, 951 – 961.
Baltussen, E., Sandra, P., David, F., Cramers, C. 1999. Stir bar sorptive extraction (SBSE),
a novel extraction technique for aqueous samples: Theory and principles. J.
Microcolumn Sep. 11, 737 – 747.
Baronti, C., Curini, R., D'Ascenzo, G., Di Corcia, A., Gentili, A., Samperi, R., 2000.
Monitoring natural and synthetic estrogens at activated sludge sewage treatment plants
and in a receiving river water. Environ. Sci. Technol. 34, 5059 – 5066.
Basheer, C., Parthiban, A., Jayaraman, A., Lee, H.K., Valiyaveettil, S., 2005.
Determination of alkylphenols and bisphenol-A: A comparative investigation of
functional polymer-coated membrane microextraction and solid-phase microextraction
techniques. J. Chromatogr. A 1087, 274 – 282.
Belfroid, A.C., Van der Horst, A., Vethaak, A.D., Schäfer, A.J., Rijs, G.B., Wegener, J.,
Cofino, W.P., 1999. Analysis and occurrence of estrogenic hormones and their
glucuronides in surface water and wastewater in the Netherlands. Sci. Total. Environ.
225,101 – 108.
117
Bolz, U., Hagenmaier, H., Korner, W., 2001. Phenolic xenoestrogens in surface water,
sediments, and sewage sludge from Baden-Wurttemberg, South-West Germany.
Environ. Pollut. 115, 291 – 301.
Brunner, P.H., Capri, S., Marcomini, A., Giger, W., 1988. Occurrence and behavior of
linear alkylbenzenesulfonates, nonylphenol, nonylphenol mono- and nonylphenol
diethoxylate in sewage and sewage-sludge treatment. Water. Res. 22, 1465 – 1472.
Desbrow, C., Routledge, E.J., Brighty, G.C., Sumpter, J.P., Waldock, M., 1998.
Identification of estrogenic chemicals in STW effluent. 1. Chemical fractionation and in
vitro biological screening. Environ. Sci. Technol. 32, 1549 – 1558.
Fromme, H., Küchler, T., Otto, T., Pilz, K., Müller, J., Wenzel, A., 2002. Occurrence of
phthalates and bisphenol A and F in the environment. Water Res. 36, 1429 – 1438.
Gibson, R., Wang, M., Padgett, E., Beck, A.J., 2005. Analysis of 4-nonylphenols,
phthalates, and polychlorinated biphenyls in soils and biosolids. Chemosphere 61, 1336
– 1344.
Günther, K., Dürbeck, H.-W., Kleist, E., Thiele, B., Prast, H., Schwuger, M., 2001.
Endocrine-disrupting nonylphenols – ultra-trace analysis and time-dependent trend in
mussels from the German bight. Fresenius J. Anal Chem. 371, 782 – 786.
Huang, C., Sedlak, D.L., 2001. Analysis of estrogenic hormones in municipal wastewater
effluent and surface water using enzyme-linked immunosorbent assay and gas
chromatography/tandem mass spectrometry. Environ. Toxicol. Chem. 20, 133 – 139.
Johnson, A.C., Sumpter, J.P., 2001. Removal of endocrine-disrupting chemicals in
activated sludge treatment works. Environ. Sci. Technol. 35, 4697 – 4703.
Jurgens, M.D., Holthaus, K.I.E., Johnson, A.C., Smith, J.J.L., Hetheridge, M., Williams,
R.J., 2002. The potential for estradiol and ethinylestradiol degradation in English rivers.
Environ. Toxicol. Chem. 21, 480 – 488.
Kawaguchi, M., Ishii, Y., Sakui, N., Okanouchi, N., Ito, R., Inoue, K., Saito, K., Nakazawa,
H., 2004. Stir bar sortive extraction with in situ derivatization and thermal desorption-
gas chromatography-mass spectrometry in the multi-shot mode for determination of
estrogens in river water samples. J. Chromatogr. A 1049, 1 – 8.
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methods for environmental and biomedical analysis. J. Pharm. Biomed. Anal. 40, 500 –
508.
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Lai, K.M., Johnson, K.L., Scrimshaw, M.D., Lester, J.N., 2000. Binding of Waterborne
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Larsson, D.G.J., Adolfsson-Erici, M., Parkkonen, J., Petterson, M., Berg, A.H., Olsson, P.-
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A.M.E., Tremblay, L.A., 2006. Development of methods for extraction and in vitro
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2000. Estrogen and estrogen mimics contamination in water and the role of sewage
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xenoestrogens 4-nonylphenol and bisphenol A by high-performance liquid
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Chromatogr. A 945, 133 – 138.
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1998. Identification of Estrogenic Chemicals in STW Effluent. 2. In vivo responses in
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119
samples. Proceedings of the Australian Water Association ‘Contaminants of Concern in
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Ternes, T.A., Andersen, H., Gilberg, D., Bonerz, M., 2002. Determination of estrogens in
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VCCLAB (Virtual Computational Chemistry Laboratory), 2005. ALOGPS 2.1. Website
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120
Chapter 5: Comprehensive study of selected endocrine disrupting
compounds using grab and passive sampling at selected wastewater
treatment plants in South East Queensland, Australia. 1. Chemical
analysis
5.1 Abstract Fifteen endocrine disrupting compounds (EDCs) were monitored at 5 wastewater treatment
plants (WWTPs) in South East Queensland, Australia. Water samples were collected using
grab and time-integrated passive sampling methods. The grab samples were extracted using
solid phase extraction (SPE); centrifuged solids were extracted using stir bar sorptive
extraction (SBSE) while EmporeTM disks were used as the accumulation matrix for passive
samplers. All extracts were analyzed by gas chromatography-mass spectrometry (GC-MS).
The results show that the WWTPs sampled efficiently remove the EDCs investigated. The
passive sampler results showed lower EDCs concentrations as compared to the grab
sample, a decrease probably caused by, but not limited to biofouling, low flow rate,
biodegradation and temperature which can progressively reduce the uptake of compounds
into the sampler. At this stage, grab sampling is the most reliable method for field
monitoring; nevertheless, passive sampler is a useful sampling tool but the method requires
more research to ensure that the obtained information can be interpreted appropriately.
Although alkylphenols and phthalates were detected at higher concentrations in the
wastewater samples as compared to natural hormones, the environmental risk may be
negligible as their estrogenic potencies is several orders of magnitude lower than that of the
natural estrogens. Removal efficacy of most estrogenic and xenoestrogenic compounds
from the conventional activated sludge or biological nutrient removal (BNR) WWTPs
monitored in this study was in the range of 80 – >99%.
5.2 Introduction Environmental pollution by endocrine disrupting compounds (EDCs) remains an important
environmental issue however the ecotoxicological and human health risks remain unclear.
EDCs are defined as exogenous substances or mixtures that alter the function(s) of the
endocrine system and consequently causes health effects in an intact organism, or its
progeny (WHO, 2002). Given the complexity of the endocrine system, it is not unexpected
121
that a range of natural and anthropogenic substances thought to cause endocrine disruption
is wide. Industrial, agricultural and municipal waste usually contains a variety of EDCs
resulting in exposure of organisms in the environment to unusually high concentrations of
natural and synthetic compounds that can elicit biological effects (Purdom et al., 1994;
Routledge et al., 1998). The wide variety of sources and substances that can disrupt the
endocrine system represents an enormous challenge to environmental managers, industry
and government. Many of the known industrial estrogen mimics as well as natural
estrogens eventually end up in the aquatic environment via wastewater discharge.
Municipal and industrial wastewater treatment plants (WWTPs) encounter a multitude of
relatively persistent organic compounds derived from domestic and industrial applications.
Those compounds are sometimes able to pass through the wastewater treatment system and
reach receiving environments. It has been demonstrated in Europe (Lavado et al., 2004;
Diniz et al., 2005) and the USA (Folmar et al., 1996; McArdle et al., 2000) that male fish
held in treated wastewater effluents or in rivers below WWTPs showed a pronounced
increase of estrogen-dependent plasma vitellogenin concentrations. Vitellogenin is an egg
yolk precursor protein expressed only in female fish and not normally synthesized in males.
However, in the presence of estrogenic EDCs, males can express the vitellogenin gene in a
dose dependent manner (Sumpter and Jobling, 1995; Folmar et al., 1996). The use of
vitellogenin gene expression in male fish can be used as a molecular marker of exposure to
estrogenic EDCs (Jobling et al., 1996; Tyler et al., 1996; Leusch et al., 2005).
Many communities worldwide use water resources for potable water production that
contain a significant portion of treated wastewater (Filali-Meknassi et al., 2004). Existing
WWTPs have often been designed for optimal CNP (carbon, nitrogen and phosphorus)
removal. Partial removal of EDCs occurs as a positive side effect (Filali-Meknassi et al.,
2004). It should be recognized that, whereas transformation and degradation processes may
remove a large fraction of EDCs, physical processes (coagulation, sedimentation, filtration,
and membrane separation) might accumulate EDCs in phases such as sludge which may
then need further treatment (Filali-Meknassi et al., 2004). While secondary and tertiary
wastewater treatment can show high EDC removal efficiency from influent to effluent,
traditional wastewater treatment is not always an effective barrier to trace contaminants
(Filali-Meknassi et al., 2004). Removal rates published in the literature vary greatly due to
122
local conditions and the nature of the contaminant (Lazier and MacKay, 1993; Nimrod and
Benson, 1997; Ankley et al., 1998; Ota et al., 2000; Hernando et al., 2004; Sarmah et al.,
2006; Leusch et al., 2006a and b). The main characteristic that determines the fate of a
given contaminant in the water cycle is its ability to interact with particulates. These
particulates can be naturally occurring (clay, sediments, colloids coated with natural
organics, microorganisms) or added during treatment (activated sludge, powdered activated
carbon, ion exchange resin, coagulants). The fate of trace contaminants such as EDCs or
pharmaceuticals in the environment and in WWTPs is largely dependent on particle-
contaminant interactions, which are not well understood (Filali-Meknassi et al., 2004).
At the moment, little is known about the efficacies of different process characteristics
within a WWTP or even comparison of various WWTP treatment technologies that affect
removal of EDCs in WWTPs. Furthermore, there are not enough published results that
address temporal variability of EDC concentrations and in specific the retention of EDCs in
WWTP. This knowledge is important to decide appropriate steps that should be taken to
minimize the risk of EDCs emission into the environment.
The purpose of this study was to determine and compare the concentrations of selected
EDCs (different natural hormones and industrial estrogen mimics) at various treatment
trains from five WWTPs with different treatment technologies in South East Queensland,
Australia using grab and passive sampling techniques. Two different sampling techniques
were employed (grab and passive sampling) to provide a better understanding of the most
suitable technique that is both cost effective and able to reliably monitor a wide range
EDCs found in the WWTPs. This study also sought to evaluate whether the concentration
of EDCs in the WWTPs effluent might cause estrogenic effects to the biota of the receiving
environment in South East Queensland. The results of the instrumental analysis were
compared with estrogenic potency of wastewater samples as determined by a biological
assay (Chapter 6).
123
5.3 Materials and methods
5.3.1 Chemicals and reagents
Benzyl butyl phthalate, dibutyl phthalate, diethyl phthalate, dioctyl phthalate [di-(2-
ethylhexyl) phthalate], 4-tert-octylphenol, 4-nonylphenol (technical grade), 4-
cumylphenol, bisphenol A, estrone, α-estradiol, 17β-estradiol, estriol, tamoxifen,
hydrochloric acid, sodium carbonate, acetic anhydride and N,O-
bis(trimethylsilyl)trifluoroacetamide (BSTFA) (with trimethylchlorosilane) were all
purchased from Sigma (St Louis, MO, USA). 4-n-Nonylphenol was purchased from
Lancaster Synthesis (Morecambe, England). It should be noted that the technical 4-
nonylphenol employed is a mixture of isomers with regard to branching of the alkyl chain.
The composition of technical 4-nonylphenol is not known in detail; however, the product
does not contain 4-n-nonylphenol. Androsterone and etiocholan-3α-ol-17-one were
purchased from Riedel-de Haën AG (Seelze, Germany). 17β-estradiol-2,4,16,16-d4 was
purchased from CDN Isotopes (Quebec, Canada). The solvents (methanol, n-hexane, and
acetone) were of HPLC grade and obtained from Merck (Darmstardt, Germany). Separate
stock solutions (100 mg/L) of individual EDCs were made up by dissolving an appropriate
amount of each substance in methanol. From these stock solutions, a working mixture
containing each compound at 10 mg/L was prepared. Internal standard solutions (5 mg/L)
of 4-n-nonylphenol (Günther et al., 2001; Naassner et al., 2002) and 17β-estradiol-
2,4,16,16-d4 were prepared in n-hexane:acetone (50:50, v/v). All standard solutions were
stored at -18 °C prior to use.
5.3.2 Sampling sites
The study was focused on five WWTPs in South East Queensland, Australia. All five
WWTPs utilize activated sludge as the secondary biological treatment step, but otherwise
vary greatly in their influent source, treatment capacity and specific treatment processes
(Table 5.1). The study was designed to cover the efficiencies of different treatment trains
by sampling at various stages in the treatment plant (WWTPs A and B). For the remainder
of the WWTPs (WWTPs C, D and E), the key focus was on comparing the overall removal
and to relate this to the specifics of the individual treatment. WWTP B is the smallest of the
five plants which uses revolving plates or spirals partially submerged to aerate the
124
bioreactor as compared to a conventional activated sludge WWTP such as WWTPs A, C
and E which had specific sections for anoxic, anaerobic and diffused aeration bioreactors.
WWTP D is a ‘biological nutrient removal’ plant. The processed effluents of these five
WWTPs are either released into rivers, creeks, dams and the open sea. Two of these
WWTPs (WWTPs B and E) have tertiary treatment facilities that treat a portion of the
effluent for industrial and domestic non-potable water reuse purposes. The five WWTPs
mentioned in this chapter are the same WWTPs A, B, C, D and E mentioned previously in
Chapter 3.
Table 5.1. Description of the 5 activated sludge wastewater treatment plants in this study. WWTP Average dry
weather flow (m3/day)
People equivalent
(PE)
Source a Grab sampling date
Location of grab sample collected b
Location of passive sampler
deployment b
A 60,000 240,000 Dom, Ind
1, 3, 5 August 2005
Inf, Bio-ana,Bio-ae, RAS, Clar, Eff, Dis, Riv
Inf, Bio-ana, Bio-ae, Clar, Eff, Dis, Riv
B 6,000 26,000 Dom, Ind, Bm
8, 10, 12 August 2005
Inf, Bio, RAS, Clar, Eff
Inf, Bio, Clar, Eff
C 26,000 100,000 Dom 22, 24, 25 August 2005
Inf, Eff Eff
D 12,000 45,000 Dom, Ind
22, 24, 25 August 2005
Inf, Eff Eff
E 140,000 700,000 Dom, Ind
22, 24, 25 August 2005
Inf, Eff Eff
a Sources of influent: domestic (Dom), industrial (Ind) and biomedical (Bm). b Collection location were from the influent (Inf), bioreactor-anaerobic (Bio-ana), bioreactor-aerobic (Bio-ae),
bioreactor (Bio), return activated sludge (RAS), clarifier (Clar), effluent (Eff), point of discharge in the river
or outlet (Dis) and 1km downstream from outlet (Riv). Passive samplers were deployed on the first day and
retrieved on the last day of the grab sample collection.
5.3.3 Grab sample collection and extraction
Duplicates of 1 L grab samples were collected at different treatment stages within the
WWTPs for 3 selected days (Table 5.1). All sampling was done between 0900 – 1200h and
the water temperature at the time of sampling was approximately 19 – 21 °C. Grab samples
were collected in methanol-rinsed 1 L amber glass bottles and were kept on ice until
extraction within 12 h of collection. SPE extraction cartridges (Oasis HLB 6 cm3 extraction
125
126
cartridges, supplied by Waters Corporation, Milford, MA, USA) were used to extract the
EDCs from the aqueous sample. Before processing the sample, the cartridges were fitted
onto a vacuum manifold (Supelco) which was connected to a vacuum pump and the
cartridges were sequentially conditioned with 5 mL of n-hexane:acetone (50:50, v/v), 5 mL
of methanol and 10 mL of Milli-Q purified water (purified by Milli-Q Synthesis A10
System, Millipore, Bedford, MA, USA).
Prior to extraction, each 1 L sample was centrifuged at 3000×g for 30 minutes at 4 °C using
an IEC PR-7000M refrigerated centrifuge; this step is important when dealing with
wastewater samples containing particulate matter that might block the SPE cartridges. The
supernatant of the sample was poured into a 1 L amber bottle without disturbing the
compacted particulate material at the bottom of the centrifuge container. Hydrochloric acid
was used to adjust the pH of the water samples to 2 – 3 before passing it through the
conditioned Oasis HLB cartridge. After the sample was passed through the cartridge, 5 mL
of Milli-Q water was passed and later left on the vacuum manifold to dry for 2 hours (-70
kPa).
The retained compounds were eluted with 5 mL of methanol followed by 5 mL of n-
hexane:acetone (50:50, v/v). The combined solution was placed on a heating plate set at
40 °C and evaporated to dryness under a gentle nitrogen stream. Recoveries for the test
analytes from the SPE were between 52 – 122% (Table 5.2).
Com
poun
d Lo
g K
ow a
Ret
entio
n tim
e (m
in)
Targ
et
ion
(m/z
) R
efer
ence
ion
(m/z
) SP
E re
cove
ry
(%)
Empo
reTM
dis
k re
cove
ry (%
) Em
pore
TM d
isk
sam
plin
g ra
te, R
S (L/
d) b
Die
thyl
pht
hala
te (D
EP)*
2.
4 20
.53
149
177
108
44.8
1.
27
4-te
rt-oc
tylp
heno
l 5.
5 21
.01
207
278
63.5
35
.1
2.78
N
onyl
phen
ol
(tech
nica
l gr
ade)
5.
8 22
.60
193
207
77.9
39
.8
3.23
4-C
umyl
phen
ol
4.1
25.0
4 26
9 28
4 83
.0
72.2
2.
47
4-n-
nony
lphe
nol
(inte
rnal
st
anda
rd)
5.8
25.3
6 17
9 29
2 -
- -
Dib
utyl
pht
hala
te (D
BP)
* 4.
5 26
.05
149
233
93.6
80
.4
2.48
B
isph
enol
A
3.3
29.5
6 35
7 37
2 97
.5
77.1
1.
71
Ben
zyl
buty
l ph
thal
ate
(BB
P)*
4.7
31.3
6 14
9 20
6 10
1 97
.8
2.39
And
rost
eron
e 3.
7 33
.17
272
347
112
133
1.84
Et
ioch
olan
olon
e 3.
7 33
.36
272
244
122
127
2.00
D
ioct
yl p
htha
late
(DO
P)*
7.6
33.4
4 14
9 27
9 11
7 10
9 1.
57
Estro
ne
3.1
34.6
9 34
2 32
7 96
.7
71.5
2.
21
17α-
Estra
diol
4.
0 34
.69
416
285
102
120
1.97
17β-
Estra
diol
4.
0 35
.15
416
285
120
113
1.84
17β-
estra
diol
-2,4
,16,
16-d
4 (in
tern
al st
anda
rd)
4.0
35.1
5 42
0 28
7 -
- -
Tam
oxife
n*
6.3
35.5
1 37
1 37
2 52
.3
12.4
-
Estri
ol
2.5
37.2
8 31
1 38
6 79
.5
72.4
1.
12
Tabl
e 5.
2. L
og K
ow,
rete
ntio
n tim
e, i
ons
used
for
qua
ntifi
catio
n in
GC
-MS
anal
ysis
for
the
det
ectio
n of
the
sel
ecte
d ED
Cs
and
thei
r
resp
ectiv
e ex
tract
ed re
cove
ries w
ith S
PE a
nd E
mpo
reTM
dis
k, a
nd sa
mpl
ing
rate
s for
the
targ
et c
ompo
unds
usi
ng th
e Em
pore
TM d
isk.
a Log
Kow
or l
og ‘o
ctan
ol/w
ater
par
titio
n co
effic
ient
’ val
ues
for a
ll co
mpo
unds
as
pred
icte
d fr
om A
LOG
PS 2
.1 c
ompu
ter p
rogr
am p
rovi
ded
by V
irtua
l Com
puta
tiona
l
Che
mis
try L
abor
ator
y (T
etko
et a
l., 2
005)
.
127
b Em
pore
TM d
isk
sam
plin
g ra
te, R
S was
take
n fr
om C
hapt
er 3
.
*Com
poun
d is
not
aff
ecte
d by
BST
FA d
eriv
atiz
atio
n.
5.3.4 Passive sampler conditioning and extraction
The passive sampling component of this study used the naked 47 mm diameter styrene-
divinylbenzene EmporeTM SDB-RPS high performance extraction disk (3M, St Paul, MN,
USA) which have shown to be suitable for the sequestration of polar to semi-polar organic
pollutants (Stephens et al., 2005). Parallel to this study, a laboratory calibration of the
EmporeTM disk was carried out in a 1400 L rotation tank that contained known
concentrations of the selected EDCs (Chapter 3). Preparation of the passive sampler
included conditioning of the sampler using a manifold with 10 mL of methanol followed by
approximately 100 mL of Milli-Q water using low vacuum. The conditioning was stopped
before all the Milli-Q water was passed through the disk to ensure that the EmporeTM disk
was fully saturated. The wet EmporeTM disk was then transferred into a Teflon housing
(Kingston et al., 2001) that secures the disk and which was then topped with Milli-Q water
until deployment. Two passive samplers were deployed at selected sites along the various
treatment trains of the five WWTPs (Table 5.1) and retrieved 4-days later. The passive
sampler was only deployed for 4 days to minimize the possibility of biofouling and in
consideration of the half life of the chemicals of interest. Upon retrieval, the EmporeTM disk
contained in the Teflon housing was rinsed with Milli-Q water and the housing was topped
up with Milli-Q water, sealed again and transported to the laboratory on ice. The passive
sampler was stored at 4 °C and extraction of the target compounds was carried out within
48 h of collection.
Prior to extraction, the Teflon sampler was carefully dismantled and the EmporeTM disk
was fitted onto the filtration apparatus and vacuum applied for about 15 minutes to dry the
disk completely. Then 6 mL of methanol followed by 6 mL n-hexane:acetone (50:50, v/v)
were passed through the disk under low vacuum. The combined filtrate was collected in a
15 mL collection vial, placed on a heating plate set at 40 °C and evaporated to dryness
under a gentle nitrogen stream. Recoveries for the test analytes from the EmporeTM disk
were between 12 – 133% (Table 5.2).
128
Calculations of the mean water concentrations from passive sampling data were based on a
model assuming a first order uptake of selected EDCs into the sampler.
⎥⎦
⎤⎢⎣
⎡⎟⎟⎠
⎞⎜⎜⎝
⎛−−
=
SSW
sSSW
SW
VKtR
VK
tmC
exp1
)(
where CW represents the mean concentration of the analyte in water during the deployment
period, mS is the mass of analyte in the receiving phase after a period of time (t), KSW is the
receiving phase-water distribution coefficient, VS is the volume of the receiving phase and
RS is the sampling rate of the system, representing the equivalent extracted water volume
per unit of time. KSW and RS were obtained in a separate calibration experiment (Chapter 3)
(Table 5.2).
5.3.5 GC-MS derivatization procedure
The relatively high polarity of some compounds of interest gave rise to poor
chromatographic peaks and derivatization was carried out to reduce their polarity. All dried
SPE and EmporeTM disk samples were subjected to the same GC-MS sample preparation.
Each sample was reconstituted with 110 μL n-hexane:acetone (50:50, v/v). Following this,
20 μL of internal standard (5 mg/L of 4-n-nonylphenol and 17β-estradiol-2,4,16,16-d4) was
added to the solution. Next, 20 μL of the derivatization agent, BSTFA was added to the
solution which was then transferred into a 2 mL GC-MS vial (Agilent technologies, CA,
USA) and capped securely. The vial was then placed in a water bath heated to 70 °C for 30
minutes. The derivatized solution was then cooled to room temperature and analyzed using
GC-MS.
5.3.6 GC-MS analysis
GC-MS analyses were performed using an Agilent 6890 gas chromatography coupled to an
Agilent 5973 mass-selective detector (Agilent Technologies, Palo Alto, CA, USA).
Separation was accomplished on a DB-5MS fused silica column (30 m × 0.25 mm i.d.; 0.5
μm film thickness, Agilent Technologies). The oven temperature program was 4 minutes at
50 °C, 8 °C/minute to 150 °C, 7 °C/minute to 250 °C, 8 °C/minute to 300 °C and then held
129
at 300 °C for 4 minutes. Column pressure was set at 70 kPa. Helium was used as the gas
carrier at a constant flow of 1.2 mL/minute. The transfer line was heated to 300 °C and the
source at 250 °C. Sample injection (2 µL) was in splitless mode.
In the quantitative procedure, standard solutions of compounds were prepared and spiked
into Milli-Q water to cover the calibration range (1 – 500 ng/L). The spiked water was then
extracted using either SPE or EmporeTM disk methods and analysed via GC-MS.
Quantitative analysis was performed in selected ion monitoring (SIM) mode in order to
maximize sensitivity. The concentrations were calculated relative to the internal standard
added to the sample prior to analysis. Limits of detection for the compounds in the
wastewater samples were in the range of 1 – 5 ng/L (Table 5.2).
5.3.7 Centrifuged solids and sludge analysis
Up to the present time, concentrations of EDCs in solids, particulate, sediment and sludge
samples and their effects on the environment have been rarely reported in the literature
since the extraction process for solids is tedious and time consuming. Extraction may
involve various pretreatment processes, long periods of soxlet extraction and post
extraction clean-up steps to remove lipids and other contaminants that could block the GC-
MS column or produce undesired or interfering peaks on the chromatogram. Inevitably, all
of these pre and post clean-up step will considerably reduce the recovery of the analytes of
interest.
A new sorptive extraction technique that uses a stir bar coated with polydimethylsiloxane
(PDMS) known as stir bar sorptive extraction (SBSE) was developed by Gerstel (Mülheim
an der Ruhr, Germany). With the SBSE technique employed in this work, time and
compound recovery are not compromised (Baltussen et al., 1999). Furthermore, since there
is no solvent use, experimental waste is eliminated. Its main advantage is high sensitivity
and a wide application range that includes volatile aromatics, halogenated solvents,
polycyclic aromatic hydrocarbons (PAHs), polychlorinated biphenyls (PCBs), pesticides,
preservatives, odor compounds, organotin compounds and EDCs (Baltussen et al., 2002;
Kawaguchi et al. 2006). SBSE with in situ derivatization has been successfully used for the
130
determination of phenolic and carbonyl compounds in various samples (Kawaguchi et al.,
2004, 2006).
The SBSE method used in this study is described in detail in Chapter 4. Briefly,
approximately 1 g of wet centrifuged solids/sludge was placed in a 20 mL headspace vial
and 20 μL of internal standard solution (5 mg/L of 4-n-nonylphenol and 17β-estradiol-
2,4,16,16-d4) was added to the centrifuged solids followed by 10 mL of Milli-Q water, 0.5
g of sodium carbonate as a pH adjustment agent (pH >11), and 0.5 mL of acetic acid
anhydride as the derivatization agent. One stir bar, coated with a 0.5 mm thick PDMS layer
(24 µL) (Gerstel, Mülheim an der Ruhr, Germany), was added to each vial and sealed with
the screw top cap. SBSE was performed at room temperature for 5 hours while stirring at
500 rpm (Kawaguchi et al. 2004). After the extraction, the stir bar was then placed in a
glass thermal desorption (TD) tube and the target compounds were desorbed from the TD
system into the GC-MS. The sludge remaining in the glass vial was filtered through a filter
paper, oven dried at 40 °C for approximately 5 days and weighed. The GC-MS retention
time, monitored ions and recoveries for the selected EDCs extracted are shown in Table
5.3.
131
132
Table 5.3. Log Kow, theoretical recoveries, retention time, ions used for quantification in
SBSE GC-MS analysis and detection a. Compound Log Kow
b Retention time (min)
Target ion (m/z)
Reference ion (m/z)
Spiked sludge
recovery (%) c
Diethyl phthalate (DEP)* 2.4 21.65 149 177 44.2 4-tert-octylphenol 5.5 23.34 135 177 90.0 Nonylphenol (technical grade) 5.8 24.86 135 177 61.7 4-Cumylphenol 4.1 27.48 197 198 58.6 Dibutyl phthalate (DBP)* 4.5 27.53 149 223 60.5 4-n-Nonylphenol (internal standard)
5.8 27.71 220 221 -
Benzyl butyl phthalate (BBP)* 4.7 32.89 149 206 74.5 Bisphenol A 3.3 32.99 213 312 70.1 Dioctyl phthalate (DOP)* 7.6 34.98 149 279 128 Etiocholanolone 3.7 35.60 290 291 - Androsterone 3.7 35.71 290 291 - Tamoxifen* 6.3 37.07 371 372 88.8 Estrone 3.1 37.34 270 271 49.4 17β-Estradiol 4.0 37.91 272 273 56.6 17β-Estradiol-2,4,16,16-d4 (internal standard)
4.0 37.91 276 - -
* Compound not affected by acetic anhydride derivatization reagent. a SBSE calibration data was taken from Chapter 4. b Log Kow values for all compounds as predicted from ALOGPS 2.1 computer program provided by Virtual
Computational Chemistry Laboratory (Tetko et al., 2005). c Coefficients of variation <15%.
5.4 Results and discussion The various sampling techniques (grab and passive sampling) and extraction methods (SPE,
EmporeTM disk and SBSE) showed good recoveries and reproducibility (Tables 5.2 and 5.3)
with most coefficients of variations of the target compounds <15%. Using the methods
described above, grab and passive wastewater samples from the 5 different WWTPs in
South East Queensland, Australia are summarized in Tables 5.4, 5.5 and 5.6. Most of the
samples had analyte concentrations greater than the limit of detection and alkylphenols and
phthalates showed higher concentrations compared to natural hormones at most sites. The 5
WWTPs had high removal efficiencies for most of the EDCs monitored.
Tabl
e 5.
4. S
elec
ted
anal
ytes
pre
sent
in W
WTP
A.
M
ean
conc
entra
tion
(gra
b sa
mpl
es, n
g/L
± SD
, n =
6; p
assi
ve, n
g/L,
n =
2; s
olid
s, ng
/g ±
SD
, n =
3)
Site
Sa
mpl
ing
tech
niqu
e D
EP
DB
P B
BP
DO
P O
P N
P C
P B
PA
And
r. Et
io.
E1
α-E2
β-
E2
E3
Gra
b
1080
±7
4.5
201
±17.
9 13
4 ±3
1.0
716
±206
22
9 ±6
4.0
3070
±1
190
10.2
±1
3.6
140
±30.
9 20
40
±728
26
8 ±5
7.2
13.1
±3
.2
BD
L 16
.6
±3.7
11
0 ±2
4.5
Pass
ive
140
58.9
24
.5
202
17.8
29
7 B
DL
36.6
44
1 62
.3
2.9
13.6
36
.9
43.4
Influ
ent
Solid
s 10
.6
±18.
4 94
8 ±6
50
261
±346
38
000
±531
0 39
1 ±2
16
6820
±2
810
5.3
±6.4
27
1 ±4
66
BD
L B
DL
BD
L B
DL
BD
L B
DL
Gra
b
50.9
±0
.7
24.5
±8
.1
62.5
±3
7.5
262
±64.
2 10
1 ±3
0.6
536
±41.
7 1.
0 ±1
.7
111
±50.
0 B
DL
2.3
±4.0
58
.5
±36.
4 11
.2
±5.7
12
3 ±7
0.8
69.9
±2
7.6
Pass
ive
18.8
10
.0
15.7
15
3 11
.6
114
BD
L 26
.3
25.0
7.
0 35
.8
BD
L B
DL
BD
L
Bio
reac
tor
(ana
ero-
bic)
Sl
udge
8.
3 ±8
.0
36.6
±1
3.0
14.3
±3
.1
6630
±3
580
27.6
±3
.9
206
±58.
7 1.
8 ±1
.7
9.8
±8.5
B
DL
BD
L B
DL
BD
L B
DL
BD
L
Gra
b
5.7
±10.
0 16
.4
±1.4
65
.7
±37.
6 44
7 ±3
71
135
±48.
9 47
9 ±7
2.9
5.0
±2.0
10
6 ±5
6.8
BD
L B
DL
95.3
±6
0.5
6.1
±1.4
50
.5
±9.4
14
.8
±25.
7 Pa
ssiv
e 20
.0
6.6
35.1
17
7 14
.3
145
BD
L 19
.2
BD
L B
DL
3.1
BD
L 3.
0 B
DL
Bio
reac
tor
(aer
obic
)
Slud
ge
35.5
±2
0.3
55.4
±4
.3
12.3
±1
2.1
6200
±1
410
37.3
±1
3.4
245
±1
13
11.5
±1
0.0
23.1
±2
1.4
BD
L B
DL
BD
L B
DL
BD
L B
DL
Gra
b
1.6
±2.7
15
.1
±2.3
35
.1
±6.7
35
6 ±1
40
34.9
±0
.8
398
±71.
3 3.
1 ±5
.4
74.0
±2
6.3
BD
L 5.
6 ±9
.8
171
±42.
3 21
.7
±10.
1 19
0 ±1
38
DB
L R
etur
n ac
tivat
ed
slud
ge
Slud
ge
39.9
±2
4.3
149
±80.
4 25
.7
±9.4
99
10
±277
0 35
.0
±2.9
42
9
±238
1.
5 ±1
.3
3.8
±4.3
B
DL
BD
L 17
.5
±30.
3 B
DL
17.3
±3
0.0
BD
L
Gra
b
12.4
±5
.9
31.8
±3
.0
121
±63.
8 39
3 ±1
23
31.0
±4
.6
386
±65.
9 B
DL
118
±72.
4 B
DL
BD
L 10
7 ±1
2.6
BD
L 19
.7
±3.9
B
DL
Cla
rifie
r
Pass
ive
1.1
5.5
3.1
193
2.2
14.7
B
DL
4.0
BD
L B
DL
7.1
1.1
6.2
BD
L G
rab
4.
9 ±3
.7
34.3
±7
.0
75.7
±4
0.4
589
±331
23
.5
±3.8
33
5 ±9
6.1
1.9
±4.6
86
.7
±51.
3 B
DL
BD
L 41
.9
±7.0
1.
7 ±3
.7
1.6
±4.0
B
DL
Efflu
ent
Pass
ive
13.5
B
DL
7.2
75.5
12
.5
129
BD
L 23
.1
BD
L B
DL
27.6
B
DL
7.4
BD
L G
rab
36
.9
±35.
6 10
2 ±9
1.3
60.8
±4
0.4
595
±303
8.
5 ±2
.2
118
±28.
2 B
DL
40.0
±1
6.8
BD
L 1.
4 ±3
.4
21.2
±5
.4
BD
L 10
.1
±17.
0 11
.2
±27.
5 Po
int o
f di
scha
rge
or o
utflo
w
Pass
ive
BD
L 27
.6
BD
L 82
9 3.
8 28
.3
BD
L 8.
8 B
DL
BD
L 4.
1 B
DL
20.1
B
DL
Gra
b
12.5
±8
.6
46.4
±1
9.4
13.2
±2
.7
644
±253
1.
3 ±0
.5
47.9
±7
.0
BD
L 5.
5 ±2
.4
BD
L B
DL
1.5
±0.9
B
DL
7.3
±6.4
B
DL
1km
dow
n-st
ream
Pa
ssiv
e B
DL
7.2
BD
L 15
1 B
DL
BD
L B
DL
BD
L B
DL
BD
L B
DL
BD
L B
DL
BD
L B
DL
= B
elow
det
ectio
n lim
it (<
1 ng
L-1
); D
EP =
Die
thyl
pht
hala
te,
DB
P =
Dib
utyl
pht
hala
te;
BB
P =
Ben
zyl
buty
l ph
thal
ate;
DO
P =
Dio
ctyl
pht
hala
te;
OP
= 4-
tert-
octy
lphe
nol;
NP
=
Non
ylph
enol
(te
chni
cal g
rade
); C
P =
4-cu
myl
phen
ol; B
PA =
Bis
phen
ol A
; And
r. =
And
rost
eron
e; E
tio. =
Etio
chol
anol
one;
E1
= Es
trone
; α-E
2 =
17α-
Estra
diol
; β-E
2 =
17β-
Estra
diol
; E3
=
Estri
ol. T
amox
ifen
was
not
det
ecte
d in
any
of t
he si
tes.
Val
ues r
epre
sent
mea
n ±
stan
dard
dev
iatio
n (S
D).
13
3
Tabl
e 5.
5. S
elec
ted
anal
ytes
pre
sent
in W
WTP
B.
M
ean
conc
entra
tion
(gra
b sa
mpl
es, n
g/L
± SD
, n =
6; p
assi
ve, n
g/L,
n =
2; s
olid
s, ng
/g ±
SD
, n =
3)
Site
Sa
mpl
ing
tech
niqu
e D
EP
DB
P B
BP
DO
P O
P N
P C
P B
PA
And
r. Et
io.
E1
α-E2
β-
E2
E3
Gra
b
2200
±5
57
294
±73.
9 80
.8
±18.
4 31
2
±103
45
.9
±7.4
38
1 ±7
3.4
BD
L 28
47
±261
0 41
2 ±6
13
27.6
±6
7.6
1.7
±2.6
B
DL
3.2
±5.3
B
DL
Pass
ive
115
34.3
15
8 34
0 47
.8
33.3
B
DL
914
333
41.5
B
DL
10.5
B
DL
155
Influ
ent
Solid
s 22
.3
±21.
8 56
9 ±4
94
73.9
±8
4.2
2460
0 ±1
8000
97
.9
±86.
0 83
9 ±7
16
BD
L 22
.7
±23.
8 B
DL
BD
L B
DL
BD
L B
DL
BD
L
Gra
b
20.3
±1
8.7
49.0
±5
8.3
14.0
±1
0.4
211
±4
5.3
16.2
±6
.4
115
±43.
0 B
DL
186
±62.
6 61
.3
±31.
2 7.
9 ±4
.9
8.6
±4.2
B
DL
31.3
±1
3.7
127
±257
Pa
ssiv
e 2.
3 6.
2 4.
8 16
9 3.
7 25
.6
BD
L 58
.0
BD
L B
DL
1.9
2.0
7.5
32.7
Bio
reac
tor
Slud
ge
18.0
±7
.9
46.6
±3
4.3
22.5
±2
1.8
6250
±5
870
16.1
±2
0.6
78.2
±1
17
1.7
±1.1
8.
0 ±4
.8
BD
L 52
9 ±1
300
33.8
±5
1.3
BD
L 2.
2 ±3
.7
BD
L
Gra
b
13.1
±1
3.6
106
±144
61
.6
±1.7
24
7 ±2
9.7
15.8
±6
.2
115
±42.
3 3.
2 ±0
.5
158
±67.
4 B
DL
BD
L 17
.1
±11.
4 4.
5 ±7
.9
36.7
±2
0.5
102
±26.
8 R
etur
n ac
tivat
ed
slud
ge
Slud
ge
17.2
±1
2.0
12.6
±3
.4
11.0
±9
.5
2200
±1
480
5.6
±2.4
20
.5
±5.1
B
DL
3.1
±1.2
B
DL
BD
L B
DL
BD
L B
DL
BD
L
Gra
b
23.9
±2
0.5
72.4
±6
4.1
26.5
±1
5.7
276
±95.
7 17
.6
±5.7
11
3 ±4
5.1
BD
L 19
2 ±3
0.6
BD
L B
DL
6.2
±6.1
B
DL
27.0
±2
6.1
47.5
±8
2.3
Cla
rifie
r
Pass
ive
BD
L 11
.3
0.7
200
BD
L 5.
6 B
DL
7.6
BD
L B
DL
BD
L B
DL
1.8
BD
L G
rab
14
.5
±15.
6 27
.3
±21.
6 15
.1
±8.4
20
3 ±1
05
10.4
±5
.5
57.3
±3
7.4
2.6
±2.2
37
.2
±31.
8 B
DL
BD
L B
DL
BD
L B
DL
BD
L Ef
fluen
t
Pass
ive
BD
L 4.
4 2.
3 25
7 1.
0 4.
6 B
DL
2.0
BD
L B
DL
BD
L B
DL
BD
L B
DL
BD
L =
Bel
ow d
etec
tion
limit
(<1
ng L
-1);
DEP
= D
ieth
yl p
htha
late
, DB
P =
Dib
utyl
pht
hala
te; B
BP
= B
enzy
l but
yl p
htha
late
; DO
P =
Dio
ctyl
pht
hala
te; O
P =
4-te
rt-oc
tylp
heno
l;
NP
= N
onyl
phen
ol (t
echn
ical
gra
de);
CP
= 4-
cum
ylph
enol
; BPA
= B
isph
enol
A; A
ndr.
= A
ndro
ster
one;
Etio
. = E
tioch
olan
olon
e; E
1 =
Estro
ne; α
-E2
= 17α-
Estra
diol
; β-E
2 =
17β-
Estra
diol
; E3
= Es
triol
. Tam
oxife
n w
as n
ot d
etec
ted
in a
ny o
f the
site
s. V
alue
s rep
rese
nt m
ean
± st
anda
rd d
evia
tion
(SD
).
13
4
135
Tabl
e 5.
6. S
elec
ted
anal
ytes
pre
sent
in W
WTP
s C, D
and
E.
M
ean
conc
entra
tion
(gra
b sa
mpl
es, n
g/L
± SD
, n =
6; p
assi
ve, n
g/L,
n =
2; s
olid
s, ng
/g ±
SD
, n =
3)
Site
Sa
mpl
ing
tech
niqu
e D
EP
DB
P B
BP
DO
P O
P N
P C
P B
PA
And
r. Et
io.
E1
α-E2
β-
E2
E3
WW
TP
C
G
rab
22
10
±283
17
3 ±2
0.2
62.1
±1
9.5
461
±15.
5 20
9 ±2
22
1410
±1
140
BD
L 10
4 ±1
1.6
2400
±4
93
395
±152
8.
3 ±1
.8
BD
L 18
.1
±5.1
11
1 ±9
6.2
Influ
ent
Solid
s 53
6 ±1
11
615
±143
33
0 ±4
10
1520
0 ±1
2800
57
5 ±3
63
4260
±1
070
7.6
±2.9
14
.2
±24.
7 B
DL
BD
L 32
.2
±27.
4 B
DL
BD
L B
DL
Gra
b
23.9
±7
.9
49.6
±2
8.0
20.6
±8
.0
354
±301
14
.2
±7.9
17
4 ±6
9.5
3.5
±2.2
11
.6
±7.3
B
DL
BD
L B
DL
BD
LB
DL
BD
L Ef
fluen
t
Pass
ive
1.2
7.4
BD
L 21
1 2.
1 19
.6
BD
L 1.
7 B
DL
BD
L B
DL
BD
L2.
9 B
DL
WW
TP
D
G
rab
80
80
±434
0 80
4 ±4
74
4100
±5
590
2240
±1
380
307
±133
49
90
±196
0 B
DL
153
±89.
4 B
DL
BD
L B
DL
BD
L22
6 ±1
61
185
±227
In
fluen
t
Solid
s 34
9 ±5
1.5
767
±88.
5 35
70
±578
0 60
300
±116
00
561
±71.
3 98
30
±110
0 3.
6 ±3
.5
12.4
±1
3.2
BD
L B
DL
14.6
±2
5.2
B
DL
BD
L
Gra
b
15.3
±8
.2
68.2
±5
2.3
82.6
±5
7.4
359
±376
5.
4 ±1
.6
56.7
±1
0.6
2.3
±1.2
12
.4
±8.7
26
.4
±41.
4 5.
4 ±8
.4
1.3
±1.1
B
DL
BD
L B
DL
Efflu
ent
Pass
ive
BD
L 7.
5 2.
9 24
6 3.
7 21
.8
BD
L 4.
9 B
DL
BD
L 1.
4 3.
0 6.
1 B
DL
WW
TP
E
G
rab
60
20
±166
0 65
7 ±8
7.6
547
±152
11
40 ±
115
350
±85.
4 53
20
±200
0 B
DL
BD
L B
DL
BD
L 18
.3
±4.4
B
DL
221
±63.
0 15
5 ±2
7.5
Influ
ent
Solid
s 25
4 ±9
5.0
993
±192
60
4 ±7
06
4510
0 ±4
3300
54
3 ±1
74
8100
±3
900
8.9
±5.0
B
DL
BD
L B
DL
BD
L B
DL
BD
L B
DL
Gra
b 7.
7 ±5
.6
27.0
±9
.5
10.7
±1
.9
208
±96.
6 9.
5 ±2
.2
74.8
±2
7.5
BD
L 11
.9
±8.1
B
DL
11.2
±2
.0
6.7
±1.8
B
DL
BD
L B
DL
Efflu
ent
Pass
ive
BD
L 6.
1 1.
2 15
7 3.
6 25
.0
BD
L 2.
6 B
DL
BD
L 4.
2 B
DL
3.1
BD
L
BD
L =
Bel
ow d
etec
tion
limit
(<1
ng L
-1);
DEP
= D
ieth
yl p
htha
late
, D
BP
= D
ibut
yl p
htha
late
; B
BP
= B
enzy
l bu
tyl
phth
alat
e; D
OP
= D
ioct
yl p
htha
late
; O
P =
4-te
rt-
octy
lphe
nol;
NP
= N
onyl
phen
ol (
tech
nica
l gra
de);
CP
= 4-
cum
ylph
enol
; BPA
= B
isph
enol
A; A
ndr.
= A
ndro
ster
one;
Etio
. = E
tioch
olan
olon
e; E
1 =
Estro
ne; α
-E2
= 17α-
Estra
diol
; β-E
2 =
17β-
Estra
diol
; E3
= Es
triol
. Tam
oxife
n w
as n
ot d
etec
ted
in a
ny o
f the
site
s. V
alue
s rep
rese
nt m
ean
± st
anda
rd d
evia
tion
(SD
).
5.4.1 Grab sampling
EDCs were detected in grab sampling collected from all five WWTPs in all stages of the
treatment and post-treatment effluent. All five had varying concentrations of EDCs
throughout the different stages of the treatment train; tamoxifen was the only monitored
EDC that was not detected in any of the wastewater samples.
Typically in terms of detectable concentrations, nonylphenol (381 – 5320 ng/L), bisphenol
A (104 – 2847 ng/L), diethyl phthalate (1080 – 8080 ng/L), dioctyl phthalate (312 – 2240
ng/L) and androsterone (412 – 2400 ng/L) were found at relatively high concentrations in
the influents of WWTPs A, B, C, D and E (Tables 5.4, 5.5 and 5.6). As shown in Table 5.1,
since the majority of receiving influents from the five WWTPs are from domestic and
industrial sources, high concentrations of alkylphenols and phthalates are not unusual.
Nonylphenol polyethoxylates and their parent compound, nonylphenol are often used as
surfactants in many household products and industrial applications. Diethyl phthalate,
dioctyl phthalate and bisphenol A are frequently used as plasticizers in the manufacturing
of a variety of consumer products. The presence of androsterone and etiocholanolone
(which are major metabolites of testosterone and androstenedione) at these relatively high
concentrations in the raw influent of the WWTPs (Table 5.4, 5.5 and 5.6) are not unusual
either. Leusch et al. (2006a) for example, reported concentrations of 1920 – 9330 ng/L
testosterone equivalent in raw influent from WWTPs in Australia and New Zealand. Kirk et
al. (2002) found that most of the androgenic activity in municipal wastewater with a
predominantly domestic input is from androgens excreted by humans. Androgen
concentrations in humans are generally much higher than estrogen concentrations. For
example, plasma testosterone concentrations are 3000 – 10,000 ng/L in adult males and 200
– 750 ng/L in adult females, while 17β-estradiol plasma concentrations are usually 10 – 60
ng/L in adult males and 30 – 400 ng/L in adult females, although they can rise to as high as
350 – 2000 ng/L during pregnancy (Tietz, 1987). Concentrations of androgens in
wastewater would therefore be expected to be much higher than those of estrogens.
Other key compounds in the influents of these WWTPs that were not detected at high
concentrations but would certainly contribute to a major portion of the sample’s
estrogenicity are the estrogens, estriol (110 – 185 ng/L) and 17β-estradiol (3.2 – 226 ng/L)
(Tables 5.4, 5.5 and 5.6). The concentrations of 17β-estradiol in the influents of WWTPs A,
136
B and C were relatively lower when compared to WWTPs D and E; this may be due to the
fact that a large portion of the estrogens found in WWTPs A, B and C was in the
conjugated forms (glucoronides and sulfides). Since estrogens are released from humans in
conjugated forms (D’Ascenzo et al., 2003), the higher occurrence of 17β-estradiol in
WWTPs D and E could be caused by the deconjugation of estrogens by microorganisms
present in the sewerage system during wastewater transport from the various domestic
sources to these WWTPs.
As seen in the monitored treatment trains of WWTPs A and B (Table 5.4 and 5.5), there is
a relatively huge decrease in concentration for most of the alkylphenols, phthalates, estriol
and androgens (androsterone and etiocholanolone) from influent to bioreactor. The high
concentrations of androgens in the influent were reduced to below detectable
concentrations after the bioreactor treatment train of the WWTPs (Table 5.4 and 5.5). This
could be attributed to the fact that a large portion of the compounds was either adsorbed to
solids in the primary settling tank or biodegraded by the microorganisms present in the
bioreactor. In aerobic conditions, the oxidative shortening of the polyethoxylate chain of
the alkylphenol polyethoxylates occurs rapidly. However, complete mineralization is
unlikely due to the presence of the highly branched alkyl group on the phenolic ring, the
aromatic ring itself and their limited water solubility (Birkett and Lester, 2003). In activated
sludge, diethyl and dibutyl phthalate have been observed to degrade rapidly with
approximately 90% removal within 3 – 8 days (Birkett and Lester, 2003). Dioctyl phthalate
removal was slower (20% less after 8 days) with the degradation rate constant apparently
inversely proportional to alkyl side chain length (Birkett and Lester, 2003). Because of this,
larger alkylphenols and phthalates may not be completely removed from the effluent as
shown in this study. Aqueous concentrations of these compounds do not vary greatly from
bioreactor to clarifier which shows that although the clarifier treatment has an important
role in settling out the sludge, this process train does not contribute much to the removal of
endocrine disruptors. On the other hand, the natural estrogens, 17β-estradiol and estrone, do
not follow a similar fate as the other compounds monitored in the WWTPs. Both WWTPs
A and B had relatively low 17β-estradiol concentrations in the influents but increased by
several folds in the bioreactor treatment train of the WWTPs; the 17β-estradiol
concentrations then gradually decreases through the different downstream treatment
sections of the plants. This is likely due to the fact that the majority of 17β-estradiol was in
137
a conjugated form in the influent, which was then cleaved by the microbial activity found in
the anaerobic and aerobic bioreactor chambers (Ternes et al., 1999b; D’Ascenzo et al.,
2003; Braga et al., 2005). There was a rise in estrone concentrations from influent to
bioreactor of WWTPs A and B (likely caused by the transformation of 17β-estradiol to
estrone in the bioreactor) followed by decreasing concentrations as it is slowly degraded
(Figure 5.1).
All five WWTPs showed good removal efficacies since most of the endocrine disruptors
were greatly reduced in concentration from influent to effluent (Tables 5.4, 5.5 and 5.6).
Nonylphenol and bisphenol A which were found at high concentrations in the influent were
reduced by 85 – >99% and 38 – >99% (median = 90%) respectively, by the WWTPs.
Removal efficacies of >99% were observed for diethyl phthalate, 17β-estradiol, estriol,
androsterone and etiocholanolone. A study by Leusch et al. (2006a) in Australian WWTPs
had similar effluent estrogen and alkylphenol concentrations. They recorded 17β-estradiol,
estrone, nonylphenol, octylphenol and bisphenol A at ranges of 8 – 28 ng/L, 5 – 585 ng/L,
60 – 20,100 ng/L, 55 – 798 ng/L and 103 – 667 ng/L, respectively. Another Australian
study has also shown comparable results with average 17β-estradiol and estrone
concentrations of 22 ng/L and 55 ng/L, respectively (Braga et al., 2005). The low ng/L to
below the detection limit concentrations for natural estrogen in effluents found in this study
were also similar to those obtained by Ternes et al. (1999a) in effluents of German and
Canadian WWTPs which were in the low ng/L values. The concentrations of alkylphenols
and phthalates found in the effluent of the WWTPs in this study were lower by factors of at
least 3 – 10 than those found by monitoring studies carried out in Canada, Japan and
Europe (Di Corcia et al., 1994; Blackburn and Waldock, 1995; Bennie, 1999; Matsui et al.,
2000; Spengler et al., 2001).
On the other hand, dioctyl phthalate was reduced by 18 – 35% by WWTPs A, B and C
treatment technologies while WWTPs D and E manage to reduce this compound by 82 –
84%. It was observed despite the lower removal efficacies of dioctyl phthalate by WWTPs
A, B and C as compared to WWTPs D and E, the concentration of this compound found in
the effluents of all five WWTPs did not differ greatly (203 – 589 ng/L, median = 354 ng/L).
The non-polar property of dioctyl phthalate (log Kow = 7.6) could be one of the reasons for
the low removal by the WWTPs as compared to the polar diethyl phthalate (log Kow =
138
2.42). Dioctyl phthalate has been known to be a persistent organic pollutant and is not
readily biodegraded by microorganisms found in the bioreactor (Birkett and Lester, 2003).
Estrone was found to be higher in the effluent than in the influent (WWTPs A and D) or
even having lowered removal efficacy (63 – 99% for WWTPs B, C and E) when compared
to the other natural estrogens. Ternes et al. (1999b) suggested that the presence of estrone
in WWTP effluents and rivers is presumably a result of the relative high stability of estrone
within the WWTP, the cleavage of glucoronide conjugates from both estrone and 17β-
estradiol and the oxidation of 17β-estradiol to estrone. Besides oxidation and demethylation
reactions, glucoronide conjugates can be hydrolyzed on contact with activated sludge
(Ternes et al., 1999b). Komori et al. (2004) have also observed concentrations of
conjugated estrogens in influent higher than those of unconjugated (free) estrogens. In
Germany, predominantly estrone found in effluent had a maximum concentration of 70
ng/L and a median value of 9 ng/L while 17β-estradiol was near the detection limit of 1
ng/L. In Canada the median value for estrone was 3 ng/L (Ternes et al., 1999a). The results
from this study were in the same range of concentration as that found in WWTP effluents in
Germany and Switzerland by Spengler et al. (2001) and Joss et al. (2004).
According to Thorpe et al. (2003) median EC50 (effective concentration which produces
50% of the maximum possible response) for a significant induction of the egg yolk
precursor vitellogenin in juvenile female rainbow trout were between 19 – 26 ng/L for 17β-
estradiol, 60 ng/L for estrone, and between 0.95 – 1.8 ng/L for 17α-ethynylestradiol.
Yokota et al. (2001) reported that the lowest observed effect concentration (LOEC) and no
observed effect concentration (NOEC) of 4-nonylphenol on reproductive status of the
medaka (Oryzias latipes) over two generations of continuous exposure were 17.7 and 8.2
μg/L, respectively. Since the measured concentrations of selected endocrine disruptors from
the effluent and river samples in this study were lower than the EC50 of the compounds, this
suggests the risk of endocrine disrupting effects towards wildlife of the receiving
environments is relatively low.
When the in vitro estrogenicity data (Chapter 6) was compared with the target analyte
concentrations in this study, it was found that the natural estrogens were the most potent
estrogenic compounds among the EDC analytes of interest. Most of the other industrial
estrogen mimics such as the phthalates and alkylphenols, although detected at most sites at
139
concentrations three orders of magnitude higher than that of the estrogens, still had a lower
cumulative estrogenic effects than the estrogens. Figure 5.1 shows the natural hormones
were clearly being efficiently removed at all five WWTPs to below concentrations that
would result in significant estrogenic effect in the receiving biota (Chapter 6).
This work shows that conventional activated sludge treatments from all five WWTPs were
particularly effective and removed 80 – >99% of most EDCs in the raw wastewater. The
conventional activated sludge process has previously been shown to be very effective at
removing estrogenic hormones and other lipophilic contaminants from the aqueous phase
of wastewater (Baronti et al., 2000; Byrns, 2001; Joss et al., 2004; Leusch et al., 2006a).
140
0
50
100
150
200
250
300
350
400
450
Inf
Bio
-ana
Bio
-ae
RA
S
Cla
r
Eff
Dis
Riv
.
Inf
Bio
RA
S
Cla
r
Eff Inf
Eff Inf
Eff Inf
Eff
Con
cent
ratio
n (n
g/L)
Estriolb-E2a-E2Estrone
β-E2
α-E2
WWTP A WWTP B WWTP C
WWTP D
WWTP E
BDL BDL
Figure 5.1. Elimination of aqueous estrogens during passage through the 5 WWTPs located
in South East Queensland, Australia. α-E2 = 17α-estradiol, β-E2 = 17β-estradiol, BDL =
below detection limit. Grab samples collected were from the influent (Inf), bioreactor-
anaerobic (bio-ana), bioreactor-aerobic (Bio-ae), bioreactor (Bio), return activated sludge
(RAS), clarifier (Clar), effluent (Eff), point of discharge in the river or outflow (Dis) and 1
km downstream from outlet (Riv).
5.4.2 Passive sampling
Currently, there are only a few reported studies that measure EDCs in the environment
using passive samplers. This is probably because these compounds have only relatively
recently been classified as compounds of environmental concern and the recent emergence
of semi-polar samplers means calibration and validation issues still need to be addressed.
EDC concentration from passive sampling as shown in Tables 5.4, 5.5 and 5.6 are lower as
compared to the grab samples by factors of between 2 to >10, depending on the location.
141
Figure 5.2 shows the good correlation between the passive and the grab sampling
compound concentration (F-test, p<0.001) with the majority of the plots falling below the
isometric line, suggesting that the uptake rate in the passive samplers deployed in the field
was generally lower compared to the uptake rate calibrated in the laboratory.
Approximately 12% of the passive sampling concentrations were higher than the grab
sampling concentration, 12% of the passive sampler compound concentrations were within
a factor of 2 less than the grab, while 28% of the passive sampler compound concentrations
were more than a factor of 10 lower than the comparable grab sample concentrations
(Figure 5.2). Passive samplers collected from the influent had the lowest compound
uptake as compared to the grab samples (Figure 5.2). This could be due to the fact that the
passive sampler in the influent had a build-up of insoluble particulate matter that could
hinder the absorption of chemicals into the EmporeTM disk even though the flow rate of the
influent was quite high. Furthermore, there might be biodegradation of absorbed
compounds occurring on the matrix itself. Even though the clarifier and the bioreactor
passive samplers had similar compound uptake profiles with concentrations higher than the
influent, they experienced particular conditions that might contribute to the reduced uptake
of compounds (Figure 5.2). The bioreactor had a relatively high concentration of
microorganisms that might cause biofouling and biodegradation on the surface of the
exposed EmporeTM disk. In the clarifier although the concentration of microorganisms is
significantly lower than in the bioreactor, the low water flow rate needed for the settling of
sludge could be the contributing factor for the reduced uptake rate. The site with roughly
similar EmporeTM disk uptake profile as those exhibited by the laboratory calibration study
was the effluent (Figure 5.2). This could be explained by the fact that the effluent has high
flow rate and low concentrations of microorganisms which are similar to conditions of the
laboratory calibration study. Other factors that could affect the comparison are that the grab
water samples did not reflect the average concentration but rather represented a period
when concentrations were generally elevated in the system. Furthermore some of the
chemicals that were determined using SPE extraction may not have been available long
enough for absorption to the passive sampler in the field. Lower flow rates have been
shown to reduce rates of uptake into the passive sampler matrix (Booij et al., 1998) and
could be a significant cause of this result. Calibration of the EmporeTM disks for this study
was done in a tank filled with distilled water and circulated at a set flow rate. These
laboratory parameters and conditions were certainly different to those found in the WWTPs
142
which could explain the differences in concentration and uptake of passive samplers in the
field study (Booij et al., 1998). Biofouling can affect the overall resistance to mass transfer
by increasing the thickness of the diffusion barrier (Vrana et al., 2005a). Performance
reference compound (PRC) allow quantification of variation in sampling rates due to
differences in environmental exposure conditions, and thus better relate passive sampler
contaminant concentrations to ambient water values. However, PRCs could not be used in
this study because the passive sampler extracts were also used in a complementary bioassay
study to determine whole sample estrogenicity; thus PRCs spiked into the passive sampler
matrix would have interfered with the results (Chapter 6).
According to Vrana et al. (2005b), varying exposure conditions with a passive sampler can
sometimes increase sampling rates of compounds by more than one order of magnitude by
increasing the water turbulence around the interface of the sampler. This confirms that the
uptake of some compounds is indeed governed by diffusion across the aqueous boundary
layer. The mass transfer conditions at the boundary layer for a non-streamlined body with a
complicated geometry are difficult to model and for practical purposes are non-predictable
(Vrana et al., 2005b). To predict water concentrations of contaminants from concentrations
accumulated in passive samplers, extensive calibration studies are necessary to characterize
the uptake of chemicals under various exposure conditions. Uptake kinetics of chemicals
depends upon not only the physicochemical properties of the diffusand, but also the
sampler design and environmental variables, such as temperature, water turbulence and
biofouling of the sampler (Vrana et al. 2005b).
In this study, the passive sampling results showed that although some EDCs were detected
at low concentrations at the time when the grab samples were taken, they were present at
higher concentrations at other time periods and were subsequently taken up by the passive
samplers. For example 17α-estradiol and higher concentrations of 17β-estradiol were
detected in the influent of WWTP A when the passive sampler was used (Table 5.4). On the
other hand, the increase in estrogens concentrations found from the passive samplers could
be most likely due to the biotransformation of the conjugated estrogens absorbed by the
sampler to their unconjugated form by microbial activity. Joss et al. (2004) reported a clear
daily fluctuation of WWTPs influent load and the concentration of estrone, 17β-estradiol
and 17α-ethynylestradiol could fluctuate between the range of 5 – 43 ng/L, 2 – 12 ng/L and
143
0.2 – 3.2 ng/L, respectively (average of six 8 h composite samples taken within 2 days).
Given the same influent load variability at the WWTPs of this study, then it is possible that
grab samples were taken when the concentrations of most EDCs were highest; whereas
since the passive sampler is time integrated, the passive sampler is giving an average EDCs
concentration for the period it was deployed. In this case, the results of the passive samplers
would reflect the average compound concentration for approximately 4 days whereas the
grab samples are giving results at that particular sampling moment. Future studies will
include 24 h grab sample wastewater collection with passive sampler deployment to give a
more definitive understanding of passive sampler uptake theory and to determine the effects
biofouling and flow rate have on uptake rate of compounds. Polysulfonic membranes could
also be used to protect the surface of the EmporeTM disk against degradation and
biofouling.
Even though passive samplers have limitations that can sometimes be difficult to overcome,
principally the possible effects of environmental conditions (e.g. temperature and water
movement) on analyte uptake, there are many significant advantages, including simplicity,
low cost, no power requirement, no need for expensive or complicated equipment,
unattended operation, and the ability to produce precise results (Namieśnik et al., 2005).
Overall, it is clear that the full potential of passive sampling techniques is not yet fully
realized, and more research is needed that involves calibration of the passive sampler with
various environmental conditions before it can replace the grab sampling technique.
144
Influent, y = 0.18x, R2 = 0.46, p<0.001
Bioreactor, y = 0.33x, R2 = 0.72, p<0.001
Clarifier, y = 0.30x, R2 = 0.44, p<0.001
Effluent, y = 0.68x, R2 = 0.57, p<0.001
0.1
1
10
100
1000
0.1 1 10 100 1000 10000
Concentration grab sampling (ng/L)
Con
cent
ratio
n pa
ssiv
e sa
mpl
ing
(ng/
L)
InfluentBioreactorClarifierEffluent
Isometric line
Figure 5.2. Correlation between measured EDCs obtained from grab sampling and passive
sampling at different sites at WWTPs A, B, C, D and E.
5.4.3 Solids and sludge analysis
Most of the target compounds monitored in this study were present in the solids/sludge
samples of the five WWTPs (Tables 5.4, 5.5 and 5.6). The influent particulates of WWTPs
A, B, C, D and E have the highest concentration of EDCs when compared to the sludge of
bioreactor and return activated sludge (RAS). Of the analytes, the highest concentration
detected in the influent solids was dioctyl phthalate (15,200 – 60,300 ng/g) followed by
nonylphenol (839 – 9830 ng/g). Low concentrations of natural estrogens were found in the
influent particulates of WWTPs C and D.
The majority of compounds found in the sludge of the bioreactors and RAS of WWTPs A
and B was phthalates and alkylphenols and the concentration of these compounds tended to
stay fairly constant. This could be due to the fact that approximately 98% of the RAS was
recycled back to the bioreactors of the plant. The phthalates and alkylphenols (log Kow >5)
tend to be present in higher concentration in sludge compared to the aqueous phase mainly
because these chemicals are more hydrophobic as compared to the natural hormones (Table
145
5.2). Byrns (2001) showed that compounds above log Kow of around 4 were not greatly
influenced by the effects of biodegradation. A study by Cheng et al. (2000) reported sludge
concentrations for di-(2-ethylhexyl) phthalate or dioctyl phthalate from municipal WWTPs
in Taiwan, Europe and the USA, ranging from 4 – 661 µg/g. Concentrations of nonylphenol
and 4-t-octylphenol in sewage sludge of Canadian WWTPs ranged from 137 – 479 µg/g
and 9.2 – 12.1 µg/g, respectively (Lee and Peart, 1995). These values are higher than those
found in this study. Compounds with a strong hydrophobic character (e.g. phthalates,
alkylphenols) are, in general, not significantly removed by biochemical processes. The
principal removal mechanism for these compounds is through sorption to sludge particles
and transfer to the sludge processing systems and, to a limited extent, removal in the final
effluent associated with suspended solids (Byrns, 2001). Therefore, a major sink for the
recalcitrant and hydrophobic compounds is the waste biomass and sludges produced during
treatment as shown in this study. Thus, the long-term ecotoxicological effects including
EDCs on terrestrial organisms need to be assessed when sludges are disposed to
agricultural land (Byrns, 2001).
Etiocholanolone, estrone and 17β-estradiol were also detected in the bioreactor of WWTP
B (529 ng/g, 33.8 ng/g and 2.2 ng/g, respectively); none of these natural hormones were
detected in the RAS of WWTP B which suggests rapid and complete degradation of the
compounds. This again emphasizes the importance of the aerobic bioreactor in the
degradation of natural hormones. The estrogen concentrations in our sludge study were
much lower when compared to those in a Japanese study by Takigami et al. (2000), who
found a 17β-estradiol sludge concentration of 100 ng/g. According to Ternes et al. (1999b),
the glucoronic acid moiety of 17β-estradiol glucoronides and other estrogen glucoronides is
cleaved and the estrogen is further oxidized to estrone or other estrogen metabolites when
in contact with activated sludge in a municipal WWTP. It took approximately 1 – 3 h to
metabolize 95% of 17β-estradiol to estrone, and after 5 hours both 17β-estradiol and
estrone vanished in a control activated sludge batch experiment (Ternes et al., 1999b). It
took 20 – 30 h to convert 70% of conjugated 17β-estradiol to the oxidized form (estrone) in
the same activated sludge batch experiment (Ternes et al., 1999b).
146
5.5 Conclusions Overall, the WWTPs monitored in this study were very effective at removing estrogenic
compounds. However, low concentrations of industrial estrogen mimics and natural
estrogens were frequently detected in WWTP discharges, due to their incomplete removal
during passage through the WWTPs. These concentrations were similar to those reported
by other researchers in Australia and New Zealand in similar studies. Co-deployed passive
samplers showed reduced EDCs uptake rates which could be due to biofouling and/or other
environmental factors such as flow rates. The passive sampling method for EDCs has a few
challenges that need to be resolved before it can replace conventional grab sampling
techniques. Until these uncertainties are addressed, use of passive samplers for EDCs in
field sampling should only be employed as a qualitative rather than a quantitative measure.
Analysis of the solids and sludge samples showed that hydrophobic industrial estrogen
mimics are more likely to adsorb onto these particulates and will take a longer time to
biodegrade as compared to the natural estrogens. Future studies need to assess the chronic
effect of exposure to low concentration of EDCs found in the environment on the unique
fauna and flora of Australia.
5.6 References Ankley, G., Mihaich, E., Stahl, R., Tillitt, D., Colborn, T., McMaster, S., Miller, R.,
Bantle, J., Campbell, P., Denslow, N., Dickerson, R., Folmar, L., Fry, M., Giesy,
J., Gray, L.E., Guiney, P., Hutchinson, T., Kennedy, S., Kramer, V., LeBlanc,
G., Mayes, M., Nimrod, A., Patino, R., Peterson, R., Purdy, R., Ringer, R., Thomas,
P., Touart, L., Van Der Kraak, G., Zacharewski, T., 1998. Overview of a workshop on
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Baltussen, E., Sandra, S., David, F., Cramer, C.J., 1999. Stir bar sorptive extraction
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Baronti, C., Curini, R., D’Ascenzo, G., Di Corcia, A., Gentili, A., Samperi, R., 2000.
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treatment processes. CRC Press LLC, Boca Raton, Florida, pp. 119 – 125.
Blackburn, M.A., Waldock, M.J., 1995. Concentrations of alkylphenols in rivers and
estuarines in England and Wales. Water Res. 29, 1623 – 1629.
Booij, K., Sleiderink, H.M., Smedes, F., 1998. Calibrating the uptake kinetics of
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Braga, O., Smythe, G.A., Schafer, A.I., Feitz, A.J., 2005. Fate of steroid estrogens in
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Byrns, G., 2001. The fate of xenobiotic organic compounds in wastewater treatment plants.
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Cheng, H.F., Chen, S.Y., Lin, J.G., 2000. Biodegradation of di-(2-ethylhexyl) phthalate in
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D'Ascenzo, G., Di Corcia, A., Gentili, A., Mancini, R., Mastropasqua, R., Nazzari, M.,
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Diniz, M.S., Peres, I., Pihan, J.C., 2005. Comparative study of the estrogenic responses of
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Filali-Meknassi, Y., Tyagi, R.D., Surampalli, R.Y., Barata, C., Riva, M.C., 2004.
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148
Folmar, L.C., Denslow, N.D., Rao, V., Chow, M., Crain, D.A., Enblom, J., Marcino, J.,
Guillette, L.J., 1996. Vitellogenin induction and reduced serum testosterone
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Chapter 6: Comprehensive study of selected endocrine disrupting
compounds using grab and passive sampling at selected wastewater
treatment plants in South East Queensland, Australia. 2. In vitro
biological screening
6.1 Abstract The proliferation assay using the human breast cancer cell line MCF-7 (E-Screen assay)
was used to determine estrogen equivalents (EEq) in grab and passive samples from five
municipal wastewater treatment plants (WWTPs) in South East Queensland, Australia. EEq
concentrations derived by E-Screen assays for the grab samples were between 108 – 356
ng/L for the influents and <1 – 14.8 ng/L for the effluents with the exception of one
effluent sample which was at 67.8 ng/L EEq. The EEq values for the passive samples were
several times lower than those of the grab samples; several environmental factors have been
suspected as causing the reduced uptake of EDCs into the passive sampler. The passive
sampler method employed in parallel with the grab sampling method in the WWTPs for
EDCs has a few challenges that need to be addressed before being considered as
replacement for the conventional grab sampling technique. The efficiency of the WWTPs
to remove estrogenic activity was >95%. The EEqs of the E-Screen and those calculated
from the results of extensive chemical analyses using the estradiol equivalency factors were
comparable for most of the WWTPs samples. In most wastewater samples, the natural
estrogens contributed to >60% of the EEq value.
6.2 Introduction Endocrine disrupting compounds (EDCs) have emerged as a major environmental issue in
the last decade, generating a vast amount of attention among the worldwide scientific
communities (Birkett and Lester, 2003). Many EDCs or potential endocrine disruptors were
formerly classified as organic micropollutants. These compounds include the degradation
products of alkylphenols polyethoxylates (APEs), polyaromatic hydrocarbons (PAHs),
polychlorinated biphenyls (PCBs), phthalates, polybrominated flame retardants, dioxins,
furans, herbicides, pesticides, pharmaceutical drugs and steroid hormones. There is
increasing concern that the release of EDCs from wastewater treatment plant (WWTP)
discharges is affecting reproductive processes in exposed freshwater and marine organisms
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(Purdom et al., 1994; Rodgers-Gray et al., 2000; Vermeirssen et al., 2005). In the UK,
WWTP effluent containing estrogenic activity have been associated with feminization of
male fish and skewed sex ratios in exposed fish populations which may potentially affect
population densities (Purdom et al., 1994; Rodgers-Gray et al., 2000; Sheahan et al., 2002).
Several studies have concluded that in vitro test methods are most appropriate for large
scale monitoring programs due to their low cost and high throughput capability (Beresford
et al., 2000; Leusch et al., 2006a and b). In vitro assays are based on well-understood
pathways and modes of action and usually have clearly defined endpoints. An additional
very important advantage is that in vitro assays can also provide direct information of the
estrogenic activity of complex mixtures of EDCs that are likely to occur in water
irrespective of the causative compounds. The human breast cancer cell line (MCF-7)
proliferation assay or E-Screen assay have been proven to be a useful first rapid screening
assay to evaluate estrogenic activity (Andersen et al., 1999). MCF-7 cell line, which is
derived from a human breast adenocarcinoma, is well established as a model of estrogen-
responsive cells (Soule et al., 1973, Soto et al., 1992). MCF-7 cells have been
recommended because of their reproducible and stable estrogen sensitivity (Soto et al.,
1992). The E-Screen has already been proven to be specific for a number of tested
chemicals which are known to be estrogenic in vivo (Soto et al., 1995). This assay also
reveals whether the compound is a partial or a full agonist by comparing the maximal cell
number obtained with the test compound with that obtained with 17β-estradiol (Körner et
al., 1999).
To date, even though there are a lot of studies that determine the estrogenicity of WWTP
effluent and river samples using a wide array of in vitro assays, there are however, very
little published studies that compare results of WWTPs with different treatment
technologies and treatment process trains. Furthermore, the correlation between chemical
analysis and in vitro biological assays results of WWTPs samples are very rarely reported.
This study measured the total estrogen activity in raw and treated wastewater of five major
municipal WWTPs in South East Queensland, Australia using the E-Screen assay. The five
WWTPs chosen include typical activated sludge and biological nutrient removal (BNR)
treatment processes; these WWTPs service Brisbane and the major suburbs in South East
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Queensland, Australia, with varying sources of influent. Both grab and passive sampling
were used to get a better understanding of which method will be the most suitable method
for sampling the fluctuating estrogenic activity of the WWTPs. In addition, a recently
developed GC-MS method was used to analyze the concentrations of fifteen ubiquitous
EDCs that are commonly found in WWTPs (Chapter 5). Finally, complementary chemical
(Chapter 5) and biological assay data were compared to estimate the magnitude of
contribution of these selected EDCs to total estrogenic activity in wastewater and the
efficacy of these WWTPs at removing EDCs.
6.3 Materials and methods
6.3.1 Sampling sites
The study was focused on five WWTPs in South East Queensland, Australia. All five
WWTPs utilize activated sludge as the secondary biological treatment step, but otherwise
vary greatly in their influent source, treatment capacity and specific treatment processes
(Table 6.1). The study was designed to cover the efficiencies of different treatment trains
by sampling at various stages in the treatment plant (WWTPs A and B). For the remainder
of the WWTPs (WWTPs C, D and E), the key focus was on comparing the overall removal
and to relate this to the specifics of the individual treatment. Furthermore, WWTP B is a
smaller plant which uses revolving plates or spirals partially submerged to aerate the
bioreactor as compared to a conventional activated sludge WWTP such as WWTPs A, C
and E which had specific anoxic, anaerobic and diffused aeration compartments in the
bioreactor. WWTP D is a ‘biological nutrient removal’ plant. The processed effluents of
these WWTPs are either released into rivers, creeks, dams or the open sea. WWTPs B and
E have additional tertiary treatment processes that treat a portion of the effluent for
industrial and non-potable household purposes in an attempt to provide some relief for the
water shortage crisis in South East Queensland, Australia. The five WWTPs stated in this
chapter are the same WWTPs A, B, C, D and E stated previously in Chapters 3 and 5.
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Table 6.1. Description of the 5 conventional activated sludge wastewater treatment plants in
this study. WWTP Average dry
weather flow (m3/day)
People equivalent
(PE)
Source a Grab sampling date
Location of grab sample collected b
Location of passive sampler
deployment b
A 60,000 240,000 Dom, Ind
1, 3, 5 August 2005
Inf, Bio-ana,Bio-ae, RAS, Clar, Eff, Dis, Riv
Inf, Bio-ana, Bio-ae, Clar, Eff, Dis, Riv
B 6,000 26,000 Dom, Ind, Bm
8, 10, 12 August 2005
Inf, Bio, RAS, Clar, Eff
Inf, Bio, Clar, Eff
C 26,000 100,000 Dom 22, 24, 25 August 2005
Inf, Eff Eff
D 12,000 45,000 Dom, Ind
22, 24, 25 August 2005
Inf, Eff Eff
E 140,000 700,000 Dom, Ind
22, 24, 25 August 2005
Inf, Eff Eff
a Sources of influent: domestic (Dom), industrial (Ind) and biomedical (Bm). b Collection location were from the influent (Inf), bioreactor-anaerobic (Bio-ana), bioreactor-aerobic (Bio-ae),
bioreactor (Bio), return activated sludge (RAS), clarifier (Clar), effluent (Eff), point of discharge in the river
or outlet (Dis) and 1km downstream from outlet (Riv). Passive samplers were deployed on the first day and
retrieved on the last day of the grab sample collection.
6.3.2 Grab sample collection and extraction
Duplicates of 1 L grab samples were collected from the different treatment stages at the
WWTPs. Two polar passive samplers were also deployed at selected treatment stages along
the WWTP process chain and retrieved four days later. All sampling was done between
0900 – 1200 h during the month of August 2005 and the water temperature at the time of
sampling was approximately 19 – 21 °C. Samples were collected in methanol-rinsed 1 L
amber glass bottles and kept on ice until extraction within 12 h of collection. Oasis HLB 6
cm3 extraction cartridges (Waters Corporation, Milford, MA, USA), were used to extract
the EDCs from the aqueous sample. Before processing the sample, the cartridges were
fitted onto a Vacuum Manifold (Supelco) which was connected to a vacuum pump. The
cartridges were sequentially conditioned with 5 mL of n-hexane:acetone (50:50, v/v), 5 mL
of methanol and 10 mL of Milli-Q purified water (Milli-Q Synthesis A10 System,
Millipore, Bedford, MA, USA).
Prior to extraction, each 1 L sample was centrifuged at 3000×g for 30 minutes at 4 °C (IEC
PR-7000M refrigerated centrifuge); this step was necessary because wastewater samples
have high amounts of undissolved particulate matter that might block the SPE cartridges.
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The supernatant was poured into a 1 L amber bottle without disturbing the compacted
particulate material in the pellet. Hydrochloric acid was used to adjust the water samples to
between pH 2 – 3 before passing it through the conditioned Oasis HLB cartridge to extract
chemicals of interest. After the sample was passed through the cartridge, 5 mL of Milli-Q
water was passed and later left on the vacuum manifold to dry for 2 hours at a vacuum
pressure of approximately -70 kPa.
The retained compounds were eluted with 5 mL of methanol followed by 5 mL of n-
hexane:acetone (50:50, v/v). The combined eluted sample was placed on a heating plate set
at 40 °C and evaporated to dryness under gentle nitrogen stream.
6.3.3 Passive sampler conditioning and extraction
For the passive sampling, 47 mm diameter EmporeTM SDB-RPS high performance
extraction disk (3M, St Paul, MN, USA) was conditioned using 10 mL of methanol
followed by 100 mL of Milli-Q water using a low vacuum on a manifold. The ensure that
the disk remained saturated with water, the conditioning was stopped with a few
millimeters of water in the reservoir. The wet disk was then transferred into a Teflon
deployment device as described and patented by Kingston et al., (2001). The device was
filled with Milli-Q water, sealed and stored refrigerated until use. It should be noted that the
sampler was used in a naked configuration with no membrane. Two passive samplers were
deployed at selected sites along the various treatment trains of the five WWTPs (Table 6.1)
and retrieved 4-days later. Upon retrieval, the EmporeTM disk contained in the Teflon
housing was rinsed with Milli-Q water and the housing was topped up with Milli-Q water,
sealed again and transported to the laboratory on ice. The passive sampler was stored at 4
°C and extraction of the target compounds was carried out within 48 h of collection.
After approximately 4 days of exposure in the field, the Teflon sampler was dismantled and
the EmporeTM disk was rinsed with Milli-Q water. The disk was then fitted onto a filtration
apparatus and dried completely for about 15 minutes under vacuum. Extraction of the disk
was carried out with 6 mL of methanol and 6 mL of hexane:acetone on the manifold at a
low vacuum. The eluted sample was collected in a 15 mL collection vial and was placed on
a heating plate set at 40 °C and evaporated to dryness under gentle nitrogen stream.
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Based on the calibration study done in Chapter 3, the EmporeTM disk sampling rate for 17β-
estradiol was set at 1.84 L/d. This sampling rate was used to provide a semi-quantitative
estimate of the average estrogen concentrations using the cell proliferation assay at the
sampling sites during the period of passive sampler deployment.
6.3.4 Cell proliferation assay
The E-Screen assay is based on an increased growth of MCF-7 cells in the presence of
estrogenic substances. When a range of concentrations is tested, the method can
differentiate between agonists, partial agonist, and inactive compounds (Korach and
McLachlan, 1995). For the E-Screen, each extracted sample from either the grab or the
passive sampler was reconstituted in 100 µL of ethanol.
The MCF-7 breast cancer cell line was obtained from American Type Culture Collection
(ATCC, Virginia, USA) and routinely grown in Dulbecco’s Modified Eagle Medium
(GIBCO, USA) containing 10% fetal bovine serum (Sigma-Aldrich, USA), 10 mM HEPES
buffer (JRH, USA), 0.1 mM non-essential amino acids (GIBCO, USA), 0.4 mM Glutamax
(GIBCO, USA), 1% of 5000 units penicillin/5000 µg streptomycin (BioWhittaker, USA)
and 0.01 mM sodium pyruvate (JRH, USA), in a T75 flask (Iwaki, Japan) at 37 °C with 5%
CO2 in a humidified atmosphere (95% relative humidity). Cells were passaged at 70 – 80%
confluence, about twice a week by trypsinization. MCF-7 was used from the 6th passage of
the frozen stock until the 20th passage.
The E-Screen assay in this study was similar to that reported by other authors (Jones et al.,
1998; Andersen et al., 2002; Minervini et al. 2005) but with some modifications to cell
seeding number and cell proliferation measurement technique. At the start of each
experiment, MCF-7 cells were seeded in 96 multiwell plates (Iwaki, Japan) at 2000
cells/well, in 200 µL of phenol red-free Dulbecco’s Modified Eagle Medium containing
10% charcoal-dextran treated fetal bovine serum (CD-FBS, Thermo Electron Corporation,
Australia), 10 mM HEPES buffer, 0.1 mM non-essential amino acids, 0.4 mM Glutamax,
1% of 5000 units penicillin/ 5000 µg streptomycin and 0.01 mM sodium pyruvate. After 24
h, the medium from each well was aspirated and replaced with 200 µL of phenol red-free
medium containing a series of dilution volumes of the stock solutions of wastewater
extracts. Nine dilution volumes were tested in two replicates per sample; six wells per assay
158
without hormones or sample acted as negative control. 17β-estradiol (Sigma, Montana,
USA) in twelve final concentrations between 10-13 M – 10-9 M was the internal positive
control in each assay. On the sixth day (5 days after exposure), cell proliferation was
measured using the CellTiter 96® AQueous One Solution Cell Proliferation Assay (Promega,
Wisconsin, USA). The assay was performed by adding 20 µL of the CellTiter 96® AQueous
One Solution reagent directly to culture wells, incubating for 4 h and then recording
absorbance at 490nm with a BMG Labtech FLUOstar OPTIMA plate reader (BMG
LABTECH GmbH, Germany). The quantity of formazan product as measured by the
amount of 490 nm absorbance is directly proportional to the number of living cells in
culture (Promega Corporation, 2005).
Absorbance from each set wells was plotted against chemical concentration, and a Verhulst
curve fitted by least-squares regression using a Microsoft® Office Excel 2003 module
written by F. Leusch (unpublished). The estrogenicity of the test compounds was quantified
with two parameters: (i) the relative proliferative potency (RPP), which is the ratio of the
lowest concentration of 17β-estradiol required for maximal cell yield divided by the lowest
concentration of the tested compound required for maximal yield; and (ii) the relative
proliferative effect (RPE), which is the ratio of the maximum cell yield obtained from the
test sample compared to 17β-estradiol (Soto et al., 1995). For statistical consistency,
“maximal cell yield” was set at EC95 (effective concentration which produces 95% of the
maximum possible response) and the concentration of the chemical at EC95 was calculated
with the following equation (based on the Verhulst curve):
slopeECEC 1
595logloglog 5095 ×⎟
⎠⎞
⎜⎝⎛+=
Full agonistic activity was considered when RPE was greater than 70%, partial agonistic
activity with RPE ranged from 25 – 70%, and no effect when RPE was less than 25% (Soto
et al., 1995). The estrogen equivalency factor for the selected compounds as shown in
Table 6.2, is the total concentration of estrogenic active compounds normalized to the
natural estrogen 17β-estradiol. The estrogen equivalency factor can also be used to predict
the estrogen equivalent concentrations (EEqs) of measured individual compound
concentrations from the chemical analysis. The estrogen equivalency factor value is
159
calculated as the quotient of the EC50 (effective concentration which produces 50% of the
maximum possible response) values of 17β-estradiol and the sample.
Estrogen equivalency factor = EC50 [17β-Estradiol] / EC50 [target compound]
The additive behavior of the estrogenic activity of single substances in mixtures has been
demonstrated for the E-screen assay (Körner et al., 1999; Soto et al., 1995), and therefore,
the total content of estrogenic active substances in environmental samples can be
determined quantitatively by calculating EEqs. In this study the RPP of the E-Screen assay
was used as a measure of EEq.
Table 6.2. Estrogenicity of individual compounds when tested with E-Screen assay. Compound Estrogen equivalency factor
(E-Screen assay) RPE (%)
Phthalate Diethyl phthalate 5 × 10-7 (Harris et al., 1997) 30 (Harris et al., 1997) Dibutyl phthalate 3 × 10-7 (Körner et al., 2001) 63 (Körner et al., 2001) Benzyl butyl phthalate 2 × 10-7 (Körner et al., 2001) 80 (Körner et al., 2001) Dioctyl phthalate DND (<<1 × 10-7) (Harris et al., 1997) 20 (Harris et al., 1997) Alkylphenols 4-tert-octylphenol 6 × 10-5 (Leusch et al., 2006b) 62 (Leusch et al., 2006b) Nonylphenol 8 × 10-5 (Leusch et al., 2006b) 46 (Leusch et al., 2006b) 4-Cumylphenol NA NA Bisphenol A 3 × 10-5 (Leusch et al., 2006b) 126 (Leusch et al., 2006b) Pharmaceutical Tamoxifen 5 × 10-4 (Fang et al., 2000) 11 (Fang et al., 2000) Estrogens Estrone 0.01 (Leusch et al., 2006b) 77 (Leusch et al., 2006b) 17α-Estradiol 0.1 (Soto et al., 1995) 90 (Fang et al., 2000) 17β-Estradiol 1.0 (Soto et al., 1995) 100 (Soto et al., 1995) Estriol 0.4 (Fang et al., 2000) 82 (Fang et al., 2000)
DND – did not displace at the highest concentration tested.
NA – not available.
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6.4 Results and discussion The E-Screen assay was proven to have good reproducibility with coefficients of variations
(CVs) <15% for 17β-estradiol spiked samples. The CVs of field samples were higher due to
the day to day EDCs concentration variations. The proliferative response of the wastewater
samples were dose dependent and decreased with subsequent dilution by steroid-free cell
culture medium. It was observed that the influent and bioreactor samples were cytotoxic at
higher dilutions than that of the effluent samples. However, this incidence did not impair
the calculation of EEq and RPE values. Estrogenic activity removal efficiencies by the
WWTPs were good (>90%).
6.4.1 Estrogenic activity of WWTPs samples
Estrogenic activity was detected at all sites using both grab and passive sampling
techniques with concentration expressed as EEq ranging from <1 – 356 ng/L (Table 6.3).
The proliferative effect of the WWTP sample on the MCF-7 cells relative to that of the
positive control, 17β-estradiol, is represented as RPE in Table 6.3.
The measured EEqs for the influent grab samples of WWTPs A, B, C, D and E ranged from
108 – 356 ng/L; the highest EEq was detected in the influent of WWTP E (Table 6.3). The
observation of the high EEq of WWTP E could be attributed to the relatively high people
equivalent, differences in plant efficacy compared to other plants and/or chemical
constituents of plant influent. The RPE of the influent grab samples from WWTP A and C
showed full agonistic activity (RPE >70%) whereas the influents from the other WWTPs
had partial agonistic activity (RPE 25 – 70%) suggesting that the activity is promoted by a
range of EDCs (Table 6.3). The E-Screen influent results from all five WWTPs showed
EEq ranging from 108 – 356 ng/L. Leusch et al. (2005, 2006a and b) have reported
estrogenic activity from <4 – 185 ng/L EEq (using both the sheep estrogen-receptor
competitive binding assay and E-Screen assay) in the influent of municipal WWTPs in
Australia and New Zealand, almost half the results from this study. Since sampling was
done in the morning, it was predicted that high input of wastewater from domestic sources
during the early morning could cause the increase of estrogenic activity as compared to
estrogenic activity shown by Leusch et al. (2006a and b). Furthermore sampling was taken
during the coldest season of the year for South East Queensland, Australia which could
161
influence the activity of microbial community and metabolic pathways. A reduction in
temperature will cause reduced WWTPs treatment efficiency as the metabolic rate of
microorganisms present in the various treatment trains to slow down (Birkett and Lester,
2003).
Based on the measured EEq of the treatment train largely from grab samples from WWTPs
A and B, it was observed that the primary settling tank and/or bioreactor were responsible
in removing or degrading a large portion of compounds with estrogenic effects; 62 – 65%
of estrogenic activity was removed by the time the wastewater reached the bioreactor
(Table 6.3). The desorption of EDCs from the higher solids/sludge content (v/v) of the
return activated sludge (RAS) as compared with that in the bioreactor would have occurred
during sample processing which resulted in the elevated EEq concentrations when
compared to the bioreactor. Another 16 – 32% of estrogenic activity was removed by the
time the secondary treated water reaches the clarifier and <10% of influent EEqs of
WWTPs A and B were detected in the effluent. The high estrogenic activity in the raw
influent from this study should not be cause for concern since most of the treated effluents
from the WWTPs have relatively low estrogenic activity.
WWTPs A, B, C and D showed good estrogenic activity removal efficacies from influent to
effluent ranging from 93 – >99% whereas WWTP E should slightly lowered removal
efficacy of 81% (Table 6.3 and Figure 6.1). RPEs exhibited by the effluents of the five
WWTPs were generally from full to partial agonistic activity. The grab sample at 1 km
downstream from WWTP A did not show any detectable levels of estrogenic activity.
Estrogenicity in the final effluent from this work was similar to results reported in the
literature, <1 – 4.1 ng/L in Australia and New Zealand (estrogen receptor binding assay,
Leusch et al., 2006a and b), <1 –16 ng/L in the Netherlands (ER-CALUX assay, Murk et
al., 2002), 4 – 35 ng/L in Japan (yeast assay, Onda et al., 2002), <1 – 7.8 ng/L in Germany
(E-Screen, Körner et al., 2001) and <3 – 13 ng/L in the United Kingdom (yeast assay, Kirk
et al., 2002). Among the five WWTPs, only WWTP E had effluent grab sample of 67.8
ng/L EEq which was several folds higher than the other four WWTPs. Even though the
WWTP E effluent had a relatively high EEq, since the discharge of WWTP E effluent is
into the sea, it is likely that the high dilution factor would cause the estrogenic activity to
fall to undetectable concentrations. Desbrow et al. (1998) has also discovered similar high
162
concentrations of 17β-estradiol and estrone that were present in the effluent of several UK
WWTPs at measured concentrations ranging from 1 ng/L to almost 50 and 80 ng/L,
respectively. The high effluent estrogen concentrations reported by Desbrow et al., (1998)
were mainly from WWTPs with just a primary settling tank as the major treatment process
and these effluents are discharged into rivers. This study shows that even though the five
monitored WWTPs had different tertiary treatments (or in some cases none at all), they do
have similar secondary treatment processes that give the highest contribution in terms of
estrogenic removal efficacy.
In this study, only the direct EDCs exposure through water phase was looked at since
effluent from WWTPs that are released into the receiving environments are practically free
of solids. However, in order to fully characterize the adverse estrogenic risk exerted on the
receiving environments, solids or sediment EDC loads must be addressed. Even though
selected EDCs were measured from the solids using the stir bar sorptive extraction (SBSE)
chemical analysis for the specific sites along the treatment train of the WWTPs (Chapter 5),
the solids amount from the 1 L wastewater sample collected was just too small to get
enough extract for the E-Screen assay.
163
Table 6.3. Aqueous estrogen equivalent comparison between the chemical and biological
analyses and the different sampling methods. Site Sampling
technique Measured
EEq* (ng/L) Measured RPE
(%) EEq predicted from chemical analysis (ng/L)
Predicted/ measured EEq
(%) WWTP A
Grab 205 ±192 112 ±50.6 50.1 ±11.2 24.4 Influent Passive 10.0 153 44.1 441
Grab 135 ±182 187 ±49.3 146 ±80.0 108 Bioreactor (anaerobic) Passive 12.0 136 47.7 398
Grab 77.3 ±23.7 173 ±117 56.6 ±17.8 73.2 Bioreactor (aerobic) Passive 0.8 101 3.1 388
Return activated sludge
Grab 121 ±70.1 204 ±73.7 194 ±139 160
Grab 45.1 ±55.6 239 ±132 20.8 ±4.0 46.1 Clarifier Passive 0.9 103 6.6 733
Grab 14.8 ±18.3 95.7 ±42.8 2.2 ±4.4 14.9 Effluent Passive 0.8 106 7.9 988
Grab 3.5 ±5.5 91.7 ±78.0 13.7 ±25.3 391 Point of discharge Passive 2.5 108 2.7 108
Grab BDL BDL 7.3 ±6.4 NA 1km downstream Passive 1.5 0.52 BDL 0.1
WWTP B Grab 108 ±69.4 50.0 ±19.5 3.3 ±5.4 3.06 Influent
Passive 40.8 160 18.5 45.3 Grab 37.6 ±13.1 49.7 ±29.8 69.6 ±90.7 185 Bioreactor
Passive 14.1 76.6 11.7 83.0 Return activated sludge
Grab 44.3 ±9.3 47.5 ±10.8 68.1 ±29.4 154
Grab 3.3 ±2.4 55.4 ±13.6 41.3 ±50.9 1250 Clarifier Passive 1.5 50.4 1.9 127
Grab 1.0 ±1.0 42.0 ±35.0 BDL 10.0 Effluent Passive 0.2 22.1 BDL 0.5
WWTP C Influent Grab 221 ±230 72.9 ±35.0 51.7 ±34.1 23.4
Grab 4.9 ±3.3 104 ±68.7 BDL 0.4 Effluent Passive 6.8 50.9 3.1 45.6
WWTP D Influent Grab 310 ±227 55.8 ±4.1 282 ±230 91.0
Grab 5.9 ±5.9 65.9 ±30.4 BDL 0.3 Effluent Passive 0.5 43.0 6.7 1340
WWTP E Influent Grab 356 ±139 61.4 ±52.7 268 ±71.5 75.3
Grab 67.8 ±59.7 95.1 ±74.7 BDL 0.1 Effluent Passive 2.5 37.7 3.3 132
* Estrogen equivalent (EEq) based on Relative proliferative potency (RPP) obtained from the E-Screen assay.
Values represent mean ± standard deviation, n=6 for grab sampling; n=2 for passive sampling).
RPE = Relative proliferative effect, BDL = Below detection limit (<0.1 ng/L); NA = not available.
164
0
100
200
300
400
500
600
Oxley Carole Park Elanora Beenleigh Luggage Point
STP
Estro
gen
equi
vale
ntInf (E-Screen)Inf (GC-MS)Eff (E-Screen)Eff (GC-MS)
BDL BDL BDL BDL
WWTPA B C D E
Figure 6.1. Comparison of the estrogen equivalent concentration (EEq) determined in the
E-Screen assay with those calculated from the results of chemical analysis of the grab
samples from the influent and effluent of selected five WWTPs in South East Queensland,
Australia (Chapter 5). Columns represent the mean ± standard deviation. Inf = influent, Eff
= effluent, BDL = below detection limit.
6.4.2 Comparison between the estrogenic activity of passive sampler and grab sampler
In this study, the passive samplers presented reduced EEq concentrations when compared
to the grab samples (Table 6.3). Environmental factors and WWTP conditions such as
biofoulding, biodegradation, temperature, differences in flow rates were likely to contribute
towards the reduced uptake of the EDCs into the EmporeTM disk (Chapter 5). On the other
hand since passive sampler is a time-integrated sampling device, it could be giving an
average estrogenic activity. Joss et al. (2004) noted a relatively high variation of estrogen
concentration in the influent (up to a factor of 10) between different plants over the
sampling period (average of six 8 h composite samples taken within 2 days). However in
this study, it was observed that biofouling might have decreased the uptake ability of the
device because there was the presence of a visible biofilm covering the exposed surface of
the passive sampler matrix particularly in the influent and bioreactor compartments. More
165
calibration work is needed to prove the validity of this assumption since biodegradation of
EDCs on the EmporeTM disk matrix could be a possible reason for the lowered EEq as
compared to the grab sample. Future work is required to introduce a performance reference
compound (PRC) that is similar in structure to a specific EDC into the passive sampler
matrix which could compensate for the reduction in uptake due to biofouling,
biodegradation and differential flow rates; thus better relate passive sampler contaminant
concentrations to ambient water value. Finding a suitable PRC that would not interfere with
the E-Screen assay or any other in vitro assay results however could be difficult since the
PRC compound and concentration infused into the passive sampler should be both non-
toxic and non-estrogenic to the MCF-7 cells. According to Vermeirssen et al. (2005),
passive samplers are particularly useful in ecosystems that experience very dynamic
hydrological conditions and where it is otherwise difficult to assess the exposure of wildlife
to estrogenic compounds. Moreover passive samplers have been reported to accumulate
polychlorinated biphenyls and estrogens in a similar rate to that of brown trout (Salmo
trutta) (Meadows et al., 1998; Vermeirssen et al., 2005). Hence, passive samplers can be
used as an alternative bioaccumulative entity for in vivo studies at heavily polluted sites
which would certainly cause fish mortality.
Nevertheless, passive samplers still offer the ability to integratively sample a range of
environmental contaminants over an exposure period, to mimic biological uptake while
potentially avoid heterogeneity and cleanup problems implicit with biological matrices
(Verhaar et al., 1995; Namieśnik et al., 2005; Stephens et al., 2005). It was proposed that
the time integrated passive samplers would be a better sampling method for WWTPs since
grab wastewater samples have high amounts of undissolved particulate matter that needs
pretreatment such as filtration and centrifugation which could clog up the SPE cartridge.
6.4.3 Comparison of E-Screen assay and analytical chemistry
For each estrogenic compound detected in a grab wastewater sample by GC-MS, the
measured molar concentration was multiplied by its estrogenic potency relative to 17β-
estradiol, determined previously in the E-Screen assay obtained from literature (Table 6.2).
The estrogenic equivalent factor determined in the E-Screen assay for various estrogenic
compounds analyzed in the wastewater samples (Chapter 5) were the basis for comparison
of the results of chemical and biological analyses. The sum of the single compound EEq
166
value was compared with the EEq concentration as determined for the wastewater extract in
the E-Screen assay. The results of the predicted EEq based on the measured concentrations
of 15 estrogenic compounds from the GC-MS analysis are shown in Table 6.3 and Figure
6.1. For most of WWTP samples, the EEq of the chemical and biological analyses were in
the same order of magnitude (Table 6.3). For the majority of the samples, the EEq
calculated from the grab sample via GC-MS were lower than that determined in the E-
Screen assay. The large differences in the results of the chemical and biological analysis for
influent samples from WWTPs A, B and C could be that the presence of other EDCs such
as conjugated estrogens not measured by the GC-MS may be contributing to the estrogenic
activity in the E-Screen assay. The same could be said about effluent samples in WWTP E.
Overall, however, there is a good correlation between the EEq of the E-Screen assay and
the predicted GC-MS analyses (F-test, R2 = 0.68, p<0.001, Figure 6.2). The levels of
estrogenic activity in undiluted final effluents from WWTP A, B, C and D ranged from <1
– 14.8 ng/L. With the dilution effect associated with discharge into the environment, the
potential for exposure adverse estrogenic effects in exposed wildlife is unlikely. This was
clearly demonstrated with the effluent from WWTP A which showed an EEq of 14.8 ng/L
which was already reduced to 3.5 ng/L at the point of discharge and undetectable 1 km
downstream. However, the effluent EEq for WWTP E was a few folds higher than its
predicted EEq (Table 6.3). Even though this effluent is discharged into the sea where it is
rapidly diluted, it remains a cause for concern that the plant is not effectively removing
xenoestrogens as the other WWTPs. Future studies will be carried out if concentrations at
this plant remain relatively high or the observed concentrations were atypical.
Figure 6.3 shows the contribution of all analyzed steroidal estrogens (17α-estradiol, 17β-
estradiol, estrone and estriol) to the total EEq calculated from the results of GC-MS
analysis (see companion paper) for all WWTPs samples. The steroidal estrogens accounted
for 60% to almost 100% of the total EEq of which estrone and 17β-estradiol, if detectable,
made the highest contribution (Figure 6.3). Although the detected concentrations of
xenoestrogens were generally as much as 1000 fold higher than those of the steroids, they
accounted for only a small fraction of the EEq because of their low estrogenic potencies
(estrogen equivalency factor <1 × 10-4) (Table 6.2). It was also observed that estriol and
17β-estradiol were the main contributors toward estrogenicity of the influent for all of the
WWTPs, whereas estrone was the main contributor towards estrogenicity of the effluent
167
except for WWTP A (Figure 6.3). This means that a large portion of the estriol and 17β-
estradiol present in the influent have been biotransformed into estrone by the treatment
train processes of the WWTPs. Even though 17β-estradiol was the main contributor
towards estrogenicity of the effluent for WWTP A, the concentration present was relatively
low.
Several studies on the estrogenicity of wastewater have reported that samples contained
more estrogenic activity based on bioassays than predicted from chemical analysis (Soto, et
al., 2005; Sarmah et al., 2006). This suggests that the chemicals measured in the analytical
assay did not explain all the estrogenicity of the sample, and that other unknown chemicals
were contributing to the activity. There is also a possibility that the interaction of different
EDCs present in the sample could have an estrogenic synergistic or additive estrogenic
effect on the MCF-7 cells (Soto et al. 2006). Kramer et al. (1998) reported an EC50 for
inhibition of egg production in female fathead minnows was 120 ng/L of 17β-estradiol
while the EC50 (effective concentration which produces 50% of the maximum possible
response) for induction of vitellogenin production in males was 251 ng/L of 17β-estradiol.
Biomarkers of exposure to 17β-estradiol were already induced at lower concentrations.
According to Thorpe et al. (2003) median EC50 for a significant induction of the egg yolk
precursor vitellogenin in juvenile female rainbow trout were between 19 – 26 ng/L for 17β-
estradiol, 60 ng/L for estrone, and between 0.95 – 1.8 ng/L for 17α-ethynylestradiol.
Exposure to 10 – 100 ng/L of 17β-estradiol for 110 days induced intersex in adult Japanese
medaka (Oryzias latipes) (Metcalfe et al., 2001). A 3-week exposure to a concentration
between 1 and 10 μg/L of octylphenol caused significant production of vitellogenin in male
rainbow trout, whereas a dose of 100 μg/L of octylphenol caused a significant increase of
vitellogenin in male roach (Routledge et al., 1998). This suggests that certain species of fish
are more sensitive to estrogen mimics compared to others. In summary, the EEq
concentrations measured in the river and effluent from this study are highly unlikely to
cause a biological effect or risk in organisms of the receiving environments since the EEqs
measured were below the 10 ng/L threshold as reported in literature.
168
y = 1.1x, R2 = 0.68, p < 0.001
0.1
1
10
100
1000
0.1 1 10 100 1000
EEq predicted from chemical analysis (ng/L)
EEq
mea
sure
d fr
om E
-Scr
een
assa
y (n
g/L)
Figure 6.2. Correlation between the measured E-Screen assay estrogen equivalent
concentrations (EEq) and the predicted EEq from the results of the grab and passive
samples from all five WWTPs.
169
0
10
20
30
40
50
60
70
80
90
100
Inf
Bio-
ana
Bio-
ae
RAS Cla
r
Eff
Dis
Riv
.
Inf
Bio
RAS Cla
r
Eff
Inf
Eff
Inf
Eff
Inf
Eff
Estro
gen
in %
tota
l EEq
Estriolb-E2a-E2Estroneα-E2 β-E2
WWTP A WWTP B WWTP
C WWTP
D WWTP
E
Figure 6.3. Contribution of steroidal estrogens to total estrogen equivalent concentration
(EEq) calculated from results of GC-MS in selected WWTP samples of five WWTPs in
South East Queensland, Australia. α-E2 = 17α-estradiol, β-E2 = 17β-estradiol. Grab
samples collected were from the influent (Inf), bioreactor-anaerobic (Bio-ana), bioreactor-
aerobic (Bio-ae), bioreactor (Bio), return activated sludge (RAS), clarifier (Clar), effluent
(Eff), point of discharge in the river or outlet (Dis) and 1km downstream from outlet (Riv).
6.5 Conclusions The activated sludge WWTPs monitored in this study have shown to be efficient in
removing estrogenic substances and activity from influent to effluent (81 – >99%) which
were similar to recently reported studies carried out by other researchers in Australia. Most
of the monitored WWTPs effluent showed low estrogenic activities except WWTP E.
Further monitoring work will be carried out at WWTP E to observe if this situation is
continuing and the specific treatment technology that is responsible for removing
estrogenic activity will be examined. The use of a combination of biological and chemical
techniques has been utilized in studies on the samples from WWTPs to both confirm that
they exhibit estrogenic activity and to identify the compounds responsible for the estrogenic
170
effect. For any chemical or mixture, the initial question must be whether or not it exhibits
any estrogenic activity. Several studies have concluded that for cost effectiveness and to
facilitate the relatively rapid screening of a large number of compounds, in vitro test
methods are most appropriate because they can integrate effects of chemicals that may not
be measured in a limited analytical screen. The E-Screen assay was used in this study
because it can discriminate among agonists and antagonists and in addition it lends itself to
the study of interactions such as additivity, synergism, and antagonism. In vitro bioassays
should always be coupled with chemical analysis to give a comprehensive view of the total
estrogenic activity and also possibly single out specific EDCs that might be responsible for
the estrogenicity of a particular sample. For example, in this study, 17β-estradiol was
responsible for most of the estrogenicity in the influent, while estrone was responsible for
most of the estrogenicity in the effluent. This information may help WWTP engineers and
operators to design more efficient WWTPs in the future. The passive sampling method used
in parallel with the grab sampling method in the WWTPs for EDCs suffers from problems
that need to be dealt with before it can fully replace the conventional grab sampling
technique. Passive samplers are still viewed as potential sampling methods since they can
act as integrative samplers over a long period of time for fluctuating concentrations and
seasonal variations.
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Kramer, V.J., Miles-Richardson, S., Pierens, S.L., Giesy, J.P., 1998. Reproductive
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wildlife/human connection. Princeton Scientific Publishing, Princeton, NJ, pp. 295 –
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Soto, A.M., Maffini, M.V., Schaeberle, C.M., Sonnenschein, C., 2006. Strength and
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1995. The E-Screen assay as a tool to identify estrogens: an update on estrogenic
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175
Chapter 7: Modelling of the fate of selected alkylphenols and phthalates
in a municipal wastewater treatment plant in South East Queensland,
Australia
7.1 Abstract The aim of this study was to develop a fugacity-based analysis of the fate of selected
industrial compounds (alkylphenols and phthalates) with estrogenic properties in a
conventional activated sludge wastewater treatment plant (WWTP A) in South East
Queensland, Australia. Using mass balance principles, a fugacity model was developed for
correlating and predicting the steady state-phase concentrations, the process stream fluxes,
and the fate of four phthalates and four alkylphenols in WWTP A. Input data are the
compound’s physicochemical properties, measured concentrations and the plant’s operating
design and parameters. The relative amounts of chemicals that are likely to be volatilized,
sorbed to sludge, biotransformed, and discharge in the effluent water was determined. Since
it was difficult to predict biotransformation, measured concentrations were used to calibrate
the model in terms of biotransformation rate constant. Results obtained by applying the
model for the eight compounds showed <40% differences between most of the estimated
and measured data from WWTP A. All eight compounds that were modelled in this study
had high removal efficacy from WWTP A. Apart from benzyl butyl phthalate and
bisphenol A, the majority is removed via biotransformation followed by a lesser proportion
removed with the primary sludge. The most critical and uncertain variable is the
biodegradation rate constant. Fugacity analysis provides useful insight into compound fate
in a WWTP and with further calibration and validation the model should be useful for
correlative and predictive purposes.
7.2 Introduction There is increasing evidence that endocrine disrupting compounds (EDCs) can have
harmful effects on aquatic organisms. Some of the compounds with high estrogenic activity
include natural and synthetic estrogens as well as chemicals from household or industrial
processes such as alkylphenols and phthalates. These endocrine disruptors from domestic,
agricultural or industrial sources may be released directly or indirectly to the aquatic
environment (Birkett and Lester, 2003). Wastewater treatment plants (WWTPs) appear to
176
be one of the major secondary sources of pollution because these compounds may not be
totally removed or degraded by chemical, physical and biological treatment processes
within the plants.
The removal of endocrine disruptors in wastewater treatment processes is dependent on the
inherent physicochemical properties of the compounds and on the nature of the treatment
processes involved (Birkett and Lester, 2003). There are four major removal pathways for
organic compounds during conventional wastewater treatment; namely (i) adsorption onto
suspended solids or association with fats and oils, (ii) biodegradation, (iii) chemical
(abiotic) degradation by processes such as photolysis and hydrolysis and (iv) volatilization.
The hydrophobic nature of many EDCs causes them to sorb onto particulates. This suggests
that the general effect of wastewater treatment processes would be to concentrate organic
pollutants, including EDCs in the wastewater sludge. Mechanical techniques, such as
sedimentation would result in significant removal from the aqueous phase to primary and
secondary sludges (Birkett and Lester, 2003).
A compound’s physicochemical characteristics can be used to predict physical processes,
such as sorption, volatilization and dissolution. Knowledge of chemical partitioning
between the aqueous and solid phases is needed to assess pathways of EDC transport and
transformation. A conventional WWTP is typically a three-stage process consisting of
preliminary treatment, primary sedimentation and secondary treatment (Hamer, 1997;
Birkett and Lester, 2003). More recent technologies include advanced tertiary treatments
i.e. ozonation, ultrafiltration, sand/carbon filtration, ultraviolet (UV) disinfection and
reverse osmosis. Wastewater sludge is a complex mixture of fats, proteins, amino acids,
sugars, carbohydrates, lignin, celluloses, humic material and fatty acids (Birkett and Lester,
2003). In secondary sludge, the large amounts of live and dead microorganisms provide a
large surface area (0.8 – 1.7 m2 g-1) for interaction with the compound (Rogers, 1996).
Those EDCs that preferentially adsorb onto the suspended particulates do so because of
their hydrophobic properties. The Kow values often correlate with the degree of association
between an organic compound and the solid phase (Dobbs et al., 1989; Byrns, 2001). Log
Kow values increase with increasing lipophilicity and correlate inversely with aqueous
solubility.
177
Chemical factors, such as structure, together with environmental factors influence
biodegradation which is not as predictable as processes such as volatilization and sorption
(Meakins et al., 1994; Alcock et al., 1999). Molecular mass or size can limit active
transport, solubility can result in competitive partitioning, and toxicity can result in cell
damage or enzyme inhibition. Unlike naturally occurring compounds, anthropogenic
compounds tend to be relatively resistant to biodegradation. This is partly due to the fact
that microorganisms lack the necessary enzymes required for transformation, so a longer
acclimation period may be required. The importance of biotransformation increases with
sludge retention time and increasing log Kow of the selected compound. Biotransformation
rates have been found to increase to a maximum at log Kow 3 – 3.5 and then decline rapidly
as sorption to sludge dominates the removal mechanisms for more hydrophobic compounds
(Danielsson and Zhang, 1996).
Apart from biodegradation, abiotic chemical reactions can also be responsible for the
transformation of compounds. Photolysis, exposure to (UV) sunlight, may degrade certain
compounds to simpler compounds, rendering them more susceptible to biodegradation
(Birkett and Lester, 2003). Suspended particulates are responsible for a large amount of
turbidity in the watercourse. They decrease the proportion of irradiating light to the target
compounds, as a high clarity of the water is usually required in order for UV to reach
compounds in the water column (Birkett and Lester, 2003). Hydrolysis is usually the most
important chemical transformation. Hydrolysis is a nucleophilic displacement reaction that
can occur when molecules have linkages separating highly polar groups (Rogers, 1996).
Factors such as pH, temperature, moisture and inorganic matter can also have an effect on
chemical degradation rates (Meakins et al., 1994; Alcock et al., 1999). Volatilization is the
transfer of a compound from the aqueous phase to the atmosphere from the surface of open
tanks such as clarifiers. However in practice, the majority of volatilization losses occur
through air stripping in the aeration tank. A proportion may be lost during sludge treatment
at the dewatering or thickening stage, particularly if the sludge is aerated or agitated. Low
molecular mass, non-polar compounds with low aqueous solubilities and high vapor
pressures are known to be transferred to the atmosphere during aeration in wastewater
treatment (Byrns, 2001).
178
Several studies have proposed and reported mathematical fugacity models which can be
used to quantify the distribution and fate of xenobiotic compounds in WWTPs (Clark et al.,
1995; Byrns, 2001; Khan and Ongerth, 2002, 2004; Johnson and Williams, 2004). These
models consider the major abiotic and biotic processes which influence the intermedia
distribution and eventual fate of the organic compounds. Under optimal operating
conditions, a WWTP may remove a large percentage for example, 70 – 100%, of many
organic pollutants from the wastewater, but treatment efficiency varies (Clark et al., 1995).
Apart from these few cited works there are even fewer reported studies on the fugacity
modelling of compounds that are estrogenic mimics within a WWTP. The complexity of
wastewater sampling and analysis coupled with the intricate design of the WWTP fugacity
model and its various unknowns could be one of the reasons for the lack of published
studies.
The purpose of this current research was to test a simple fugacity-based model’s ability to
predict the fate of selected phthalates and alkylphenols in a conventional activated sludge
WWTP (WWTP A) located in South East Queensland, Australia. This major WWTP
(people equivalent capacity of 240,000) receives its influent from both domestic and
industrial discharges; and its configuration is typical of those currently found in South East
Queensland, Australia. To date, none of the reported fugacity modelling studies has
previously investigated the fate and behavior of phthalates and alkylphenols commonly
found in WWTPs. Phthalates have a wide variety of industrial, agricultural and domestic
applications, but by far the most important is their use as plasticizers that improve the
flexibility and workability of polymeric materials. The physical rather than chemical
incorporation of phthalates in the polymeric matrix ensures that they are widespread
contaminants (Psillakis et al., 2004). Penetration of phthalates into the ecosystem or in
wastewater effluent occurs during the production phase and via leaching and volatilization
of plastic products during their usage and/or after disposal (Staples et al., 1997).
Alkylphenols are used to manufacture household surfactants, pesticide formulations,
industrial products, flame retardants and polycarbonate and epoxy resins. In recent years,
phthalates and alkylphenols have attracted much attention because they are suspected of
interfering with reproductive and behavioral health in humans and wildlife, through
disturbance of the endocrine system (Petrović et al., 2001; Ying et al., 2002; Fromme et al.,
2002). As previously reported in Chapters 3, 4, 5 and 6, EDC studies sampling influent and
179
effluent from WWTPs have shown significant removal, however with this fugacity
modelling framework, the relative importance of particular fate processes can be identified.
WWTP fate modelling for volatile organic compounds has been performed by Clark et al.
(1995), so the aim of this study was to observe if a similar predictive fate model was
possible for 2 families of EDCs. This fugacity framework can then subsequently be used to
direct a more informed and targeted search for EDCs residue in wastewater.
7.3 Process description Conceptually, WWTP A is considered as a series of interlinked “boxes” or compartments in
which the influent sewage is treated starting with primary sedimentation followed by
biological treatment which consists of anaerobic and aerobic zones. The final treatment
phase is the clarification sludge settling followed by effluent discharged into the Brisbane
River. In this study, the main focus was to model the fate of four phthalates [diethyl
phthalate, dibutyl phthalate, benzyl butyl phthalate and di-(2-ethylhexyl) phthalate] and
four alkylphenols [4-tert-octylphenol, nonylphenol (technical grade), 4-cumylphenol and
bisphenol A]. The initial task was to establish the water and solids mass balances for the
plant. The data from these initial calculated balances was then applied to deduce a mass
balance and thus fate of the selected compounds in these compartments, solely from a
knowledge of wastewater concentrations and the physicochemical properties specifically
molecular mass, aqueous solubility and vapor pressure. Chemical concentration
measurements were based on WWTP A grab sampling results reported in Chapter 5 (Table
7.1). The water and solids/sludge samples collected at selected sites along the treatment
process train in WWTP A were extracted using solid phase extraction (SPE) and analyzed
for the selected EDCs using gas chromatography-mass spectrometry (Chapter 5). Daily
water and solids flow measurements were obtained from the WWTP A operators (Figure
7.1). The physical properties of these compounds are shown in Table 7.2. The compounds
modelled in this study ranged from mid polar to non-polar compounds and do not volatilize
readily.
To facilitate calculation, equilibrium partitioning (equal fugacity) is assumed to exist for
the compound between the water and solids phase in each part of the treatment train. This
assumption appears to be justified, since for most compounds, equilibrium is substantially
180
181
approached during the treatment residence time (Clark et al., 1995). In the aerobic section
of the bioreactor, the off-gas airstream is assumed to reach equilibrium with the water
phase. Also, in each compartment, a steady-state situation (i.e. no accumulation) was
assumed to apply.
Con
cent
ratio
ns in
wat
er (n
g L-1
) and
solid
s (ng
g-1
) D
EP
DB
P B
BP
DEH
P O
P N
P C
P B
PA
Site
Wat
er
Solid
s /s
ludg
e W
ater
So
lids
/slu
dge
Wat
er
Solid
s /s
ludg
e W
ater
So
lids
/slu
dge
Wat
er
Solid
s /s
ludg
e W
ater
So
lids
/slu
dge
Wat
er
Solid
s /s
ludg
e W
ater
So
lids
/slu
dge
Influ
ent
1080
10
.6
201
948
133.
6 26
1.3
716
3800
0 22
9 39
1 30
70
6820
10
.2
5.3
140
271
Bio
reac
tor
(ana
erob
ic)
50.9
8.
3 24
.5
36.6
62
.5
14.3
26
2 66
30
101
27.6
53
6 20
6 1.
0 1.
8 11
0 10
.0
B
iore
acto
r (a
erob
ic)
5.7
35.5
16
.4
55.4
65
.7
12.3
44
7 62
00
135
37.3
47
9 24
4 5.
0 11
.5
106
23.1
Fina
l se
ttlin
g ta
nk
12.4
N
A
31.8
N
A
121
NA
39
3 N
A
31.0
N
A
386
NA
1.
0 N
A
118
NA
Ret
urn
activ
ated
sl
udge
1.6
39.9
15
.1
149
35.1
25
.7
356
9910
34
.9
35.0
39
8 42
9 3.
1 1.
5 74
.0
3.8
Efflu
ent
4.9
NA
34
.4
NA
75
.7
NA
58
9 N
A
23.5
N
A
335
NA
1.
9 N
A
86.7
N
A
Poin
t of
disc
harg
e 36
.9
NA
10
2 N
A
60.8
N
A
595
NA
8.
5 N
A
118
NA
B
DL
NA
40
.0
NA
1km
dow
n st
ream
(r
iver
bank
)
12.5
N
A
46.4
N
A
13.2
N
A
644
NA
1.
3 N
A
47.9
N
A
BD
L N
A
5.5
NA
DEP
= d
ieth
yl p
htha
late
, DB
P =
dibu
tyl p
htha
late
; BB
P =
benz
yl b
utyl
pht
hala
te; D
EHP
= di
-(2-
ethy
lhex
yl) p
htha
late
(als
o kn
own
as ‘d
ioct
yl p
htha
late
’); O
P =
4-te
rt-
octy
lphe
nol;
NP
= no
nylp
heno
l (te
chni
cal g
rade
); C
P =
4-cu
myl
phen
ol; B
PA =
bis
phen
ol A
; NA
= n
ot a
vaila
ble;
BD
L =
belo
w d
etec
tion
limit
(<1
ng L
-1).
Tabl
e 7.
1. M
easu
red
phth
alat
es a
nd a
lkyl
phen
ols p
rese
nt in
the
WW
TP A
a .
182
a Val
ues t
aken
from
Cha
pter
5.
Table 7.2. Selected physical properties at 25 °C of the phthalates and alkylphenols used in
this study. Compound Molar mass
(g mol-1) Vapor pressure
(Pa) Aqueous solubility (mg L-1)
Henry’s law constant, H
(Pa m3 mol-1)
Log Kow
Diethyl phthalate a 222 1.33 ×10-1 1.10 ×103 2.69×10-2 2.38 Dibutyl phthalate a 278 3.60 ×10-3 1.12 ×101 8.96×10-2 4.45 Benzyl butyl phthalate a 312 6.67 ×10-4 2.70 7.72 ×10-2 4.59 Di-(2-ethylhexyl) phthalate a 391 1.33 ×10-5 3.00 ×10-3 1.74 7.50 4-tert-octylphenol b 206 3.41 ×10-2 1.26 ×101 5.57 ×10-1 4.12 Nonylphenol c 220 3.00 ×10-1 6.00 11.0 4.48 4-Cumylphenol d 212 1.69 ×10-4 1.80 ×101 1.99×10-3 4.12 Bisphenol A e 228 5.33 ×10-3 2.10 ×102 5.76×10-6 3.32
Values for compound physical properties taken from a Staples et al. (1997); b Ahel and Giger (1993) and
Verevkin (1999); c Müller and Schlatter (1998); d General Electric Company (2005); e Staples et al. (1998).
AirA
EffluentW6
(1163) W1
(583)
Primary settling
tank
Bioreactor (anaerobic)
Final settling
tank
Bioreactor (aerobic) Inflow
Primary sludge W2 (2.6)
Return activated sludge Waste activated
sludge
W3 (580) W8
(571)
WWAS (12.5) WRAS (583)
EffluentS6
(2330400) S1
(162000)
Primary settling
tank
Bioreactor (anaerobic)
Final settling
tank
Bioreactor (aerobic) Inflow
Primary sludge S2 (105300)
Return activated sludge Waste activated
sludge
S3 (56700) S8
(5710)
SWAS (48750) SRAS (2273700)
B Air
Figure 7.1. Diagram of (A) water (m3 h-1) and (B) solids (g h-1) balances for WWTP A.
65% solids removal in the primary settling tank is assumed.
183
7.4 The fugacity approach The use of fugacity in mass balance models of this type has been reviewed by Mackay
(1991) and Clark et al. (1995). Fugacity (f, Pa) is a criterion of equilibrium related to
chemical potential and is used as a surrogate for chemical concentration C (mol m-3) such
that C = Zf, where Z is a phase fugacity capacity constant (mol m-3 Pa-1) and is specific to
the compound, the phase in which the compound resides, and temperature. Values of Z for
each compound in each phase can be calculated with high or low Z values reflecting high or
low chemical concentrations expected in that phase at equilibrium.
The three phases under consideration are air, water and solids/sludge. Calculation of Z
values starts in the air phase. Here Z is RT1 for all compounds where R is the gas constant
(8.314 Pa m3 mol-1 K-1) and T is absolute temperature (298 K). For compounds in water, ZW
is 1/H where H is the Henry’s law constant (Pa m3 mol-1) (Table 7.2). For solids:
WW
BB Z
CCZ ×= (1)
where CB and CW are measured biomass and aqueous concentrations that are assumed to
represent equilibrium (Mackay, 1991).
Necessary in the calculations are D (mol Pa-1 h-1) values which may be effectively regarded
as fugacity rate constants where magnitude is a measure of the relative importance of that
fate to input or output. When multiplied by the prevailing fugacity, the flux of a particular
chemical with respect to a particular transport process is determined. Three types of D
values were used. For advective transfer, the fugacity rate constant is:
D = Q × Z (2)
where Q is the volumetric flow rate of the phase (m3 h-1). For biotransformation, the
fugacity rate constant is:
D = k × V × Z (3)
184
where k is a first-order rate constant (h-1), and V is the phase volume (m3). The D value for
volatilization is expressed as:
D = KV × A × Z (4)
where A is air/water interfacial area, KV is the overall mass transfer coefficient (m h-1),
which can be expressed as the combination of the water side and air side mass transfer
coefficients, KW and KA, as well as the dimensionless Henry’s law constant, H’:
'111HKKK AWV
+= (5)
For this study, KW is assigned a default value of 0.05 m h-1 and KA a value of 5 m h-1 for all
chemicals. Using measured concentrations and a mass balance approach assuming steady-
state exists, the fugacity in the compartment is calculated, then the mass balance equation
solved for the degradation D value (if applicable). From this the proportion of molecules
undergoing each considered fate can be derived.
Input flux (mol h-1) = f × (Output D values) = f (ΣD) (6)
where ΣD represents the sum of the D values for all the possible fates and f is the fugacity
of the selected compound in the tank or compartment. A diagram showing the D values to
assess the chemical fate for each pathway in relation to WWTP A process is shown in
Figure 7.2.
For the primary settling tank (PST), the mass balance equation, assuming steady-state, may
be written as (Figure 7.2):
Input flux to PST = fP (D2 + D3 + DPV + DPB) (7)
Specifically, the input flux of the selected chemical is the sum of dissolved and sorbed
contributions. This is expressed by:
185
Input flux to PST = fI (QW1ZW + QS1ZS) , but since C = Zf, (8)
Input flux to PST = QW1CW1 + QS1CS1 (9)
where, fP is the fugacity for a chemical in the PST, fI is the fugacity in the influent; ZW and
ZS are the fugacity capacity constants for water and solids, while CW1 is the measured
concentration of the selected compound in the influent and CS1 is the measured
concentration of the compound that is sorbed to the solids in the influent. For the PST
biotransformation fugacity rate constant, DPB:
DPB = kPB × VS(PST) × ZB (10)
where VS(PST) is the volume of solids in the PST. This approach assumes biotransformation
occurs in or on solids, hence the employment of ZB and not ZW. The magnitude of DPB was
obtained from the mass balance equation (7). In order to estimate the rate constant kPB then,
it is necessary to know solids volume in the PST. For the latter, Clark et al. (1995) assumes
a volume fraction of 1/200 in a typical PST. Given there is 1.43 ×103 m3 of water in the
PST, it is expected that a volume of 7.30 m3 of solids is present (assuming the density of
solids is 1 g cm-3). In this manner, kPB was able to be predicted. kPB is site specific and only
applies to the modelled compounds in WWTP A. The area of the PST is 487.5 m2 and was
used to calculate volatilization fugacity rate constant of the PST, DPV.
If we assume some biotransformation in the primary settling tank, but very little in the
anaerobic zone of the bioreactor, fugacity of the selected compound in anaerobic zone is
also the fugacity in primary settling tank because equilibrium volatilization or sorption will
not decrease fugacity. Biotransformation however, will decrease fugacity. Input into
anaerobic zone of bioreactor is the result of advection from the primary settling tank, plus
the contribution from return activated sludge (RAS). The input into the anaerobic zone is
then assumed to equal output, which will also be the input into aerobic zone. The mass
balance expression for the aerobic zone of the bioreactor is then:
Input flux to bioreactor (aerobic) = fPD5 = fB (D6 + DBioB + DBioV) (11)
186
where fB is the fugacity in the aerobic zone of the bioreactor. The aerobic volatilization
fugacity rate constant (DBioV) was treated as an advective air flow.
DBioV = QAir × ZAir (12)
where QAir is the volumetric flow rate of air through the diffusers (4.96 ×103 m3 h-1) and
ZAir is 4.04 × 10-4 mol m-3 Pa-1. The aerobic biotransformation fugacity rate constant (DBioB)
was calculated using the following equation:
DBioB = kBioB × VS(Bio) × ZB (13)
The measured solids concentrations in the aerobic zone of the bioreactor is 2.33 kg m-3, and
the volume of aerobic zone is 1.07×103 m3. This means the volume of solids present in the
aerobic zone, VS(Bio), is 2.49 m3. With this information kBioB was able to be predicted for the
selected compounds.
For the final settling tank (FST), the concentrations of compounds of interest were not
determined. We therefore assume the ZB value of the FST to be the similar to that of the
aerobic zone of the bioreactor. This means, the physical and chemical characteristics of the
solids were assumed to be the same in both compartments. Furthermore, it was assumed
that the solids concentration in the FST was the same as in the effluent (10 g m-3) (Figure
7.1). The mass balance expression for the FST assuming steady-state then is:
Input flux to the FST = fBD6 = fF (D7 + D8 + DFB + DFV) (14)
where fF is the fugacity of the FST (Figure 7.2). The fugacity rate constant for RAS (D7)
was derived from volumetric water and solids flow rate data (Figure 7.1) as well as ZB and
ZW where the former is derived from the measured concentration of solids associated EDCs.
For the calculation of the effluent fugacity rate constant, D8, we assume ZB of effluent is the
same as ZB in FST (which is the same as in the aerobic zone of the bioreactor). The FST
area of 314 m2 was used to calculate the FST volatilization fugacity rate constant, DFV. In
order to calculate the FST biotransformation fugacity rate constant, DFB:
DFB = kFB × VS(FST) × ZB (15)
187
the volume of solids in the FST, was calculated first. To derive this, note that the residence
time for water was 4.8 h. Therefore if input flux of water is 1163 m3 h-1 (Figure 7.1), the
volume of water will be 2.42×102 m3. Based on the data collected in the study, 0.01 g of
solids was present in a liter of FST water sample, so the concentration of solids in the FST
is assumed to be 10 g m-3. This means the amount of solids in the FST is 2.42×103 g or 2.43
×10-3 m3. Multiplying the effluent fate or transport parameter (D8) by fF afford the flux of
EDCs dissolved and associated with solids. Dividing by volumetric flow rates of the
advecting phases (water and solids) in effluent, results in concentrations that may be
compared with measured data.
Measured effluent concentrations were from samples taken where the effluent from all
process trains of WWTP A was combined. The effluent from the WWTP is then discharged
into the Brisbane River via a 2 – 3 km long pipeline. Davie et al. (1990) have determined
the volumetric flow rate near the mouth of the Brisbane River to be 5.75×103 m3 h-1, and
this data was used together with effluent concentrations to calculate the “point of
discharge” concentrations for the selected compounds after mixing with river water.
Primary settling
tank
Bioreactor (anaerobic)
Bioreactor (aerobic)
Final settling
tank D1
DPV
D2 DPB
D3
D4
D5
DBioV
DBioB
D6
DFV
D8 DFB
D7 D4 = D7
Inflow
Volatilization
Biodegradation Primary sludge
Biodegradation
Volatilization Volatilization
Biodegradation
Effluent
Return activated sludge
Waste activated sludge
Figure 7.2. Diagram of fugacity transport/process parameters (D) in WWTP A. P = primary
settling tank, Bio = bioreactor, F = final settling tank, B = biodegradation, V =
volatilization.
188
7.5 Results and discussion The fugacity fate modelling for all the eight compounds showed that each compound has
very distinct pathways in which these chemicals are predicted to be removed or channelled
through along the various process trains of WWTP A. The combined effects of sorption,
advection, volatilization and biotransformation will, in any particular system govern the
overall removal of the selected compound from the wastewater. Figures 7.3 – 7.6 and Table
7.3 give the complete process details of the computed fate of the eight selected compounds
at 25 ºC. Table 7.3 also gives the predicted and the measured compound concentration in
the effluent and point of discharge. The majority of the phthalates and alkylphenols were
removed from the wastewater mainly through biotransformation which largely occurs in the
PST. However, 4-tert-octylphenol and 4-cumylphenol had higher incidences of
biotransformation in the FST as compared to the PST. Diethyl phthalate had the highest
proportion of removal via biotransformation (99%) whereas benzyl butyl phthalate had the
lowest (44%).
Overall, for all compounds, a smaller portion was adsorbed to the primary sludge (<1 –
22%), whereas the remainder is released to the effluent (2 – 47%). The compounds with
approximately 47% of the compound mass remaining in effluent were benzyl butyl
phthalate and bisphenol A; this result appeared to be due to reduced biotransformation.
The compound that was more hydrophobic, in this case di-(2-ethylhexyl) phthalate (log Kow
= 7.5), had a higher sorption to the solids as compared to the other semi-hydrophobic
compounds (log Kow = 2.38 – 4.59). As expected, removal of the EDCs through
volatilization was negligible since the vapour pressures of the selected compounds were
low.
Most of the selected compounds had negative biotransformation rate constants, k, for the
aerobic zone and FST because the measured compound concentration in solids actually rose
slightly on progressing from the anaerobic to aerobic zone of the bioreactor or from the
aerobic zone to the FST (Table 7.2). It could be a result of analytical variability or other
compounds, metabolites or polymers that are being transformed into the selected
compounds faster than it could be degraded within the aerobic zone which could explain
the rise. Studies have shown that under aerobic conditions, the oxidative shortening of the
189
polyethoxylates chain of the alkylphenol polyethoxylates occurs easily and rapidly to form
alkylphenols and alkylphenol carboxylates; ultimately the biodegradation of these
metabolites occurs more slowly, because of the benzene ring and their limited aqueous
solubility (Birkett and Lester, 2003). However, reported studies have shown that the
alkylphenol, bisphenol A, is easily removed during activated sludge treatment processes by
a biodegradation mechanism and thus the acclimation period required is short (Staples et
al., 1998). In activated sludge, diethyl and dibutyl phthalate have been shown to rapidly
degrade by approximately 90% within 3 and 8 days, respectively; di-(2-ethylhexyl)
phthalate removal was slower (20% after 8 days) (O’Grady et al., 1985).
The phase fugacity capacity constant for solids of the anaerobic zone (ZB) is different to
that calculated for the PST for all compounds; this is not unreasonable given significant
sedimentation in the PST and that the bioreactor receives RAS. There are also slight
differences between the ZB of the aerobic zone and the RAS for most compounds. The rise
or fall of ZB between compartments is usually within 1 – 2 orders of magnitude. Fugacities,
f, for all selected compounds change slightly from the PST to FST because of the different
characteristics of the solids found in the various zones. The computed input fluxes
compared reasonably favourably with the summed output fluxes for all selected compounds
which showed that the fugacity approach and assumptions of steady state in PST, bioreactor
and FST as well as solids/aqueous equilibrium were valid.
The measured effluent values were mostly lower than the predicted concentrations however
dibutyl phthalate, di-(2-ethylhexyl) phthalate and 4-cumylphenol concentrations were
underestimated by the fugacity model. These differences in effluent concentrations
presumably reflect contributions from the other process trains such as the anoxic bioreactor
and effluent chlorination treatment which were not taken into account by the fugacity
model. Results obtained by applying the model for the eight compounds showed <40%
differences between most of the estimated and measured data from WWTP A. All eight
compounds that were modelled in this study had high removal efficacy from WWTP A and
good correlation was reported between the measured and estimated effluent compound
concentrations (Figure 7.7, linear regression, R2 = 0.89, p<0.001). Measured concentrations
at the point of discharge were much greater than the predicted, implying that the effluent
was not well mixed at the river sampling sites. Even 1 km downstream from the discharge,
190
191
concentrations were still greater than the predicted river concentrations, which showed that
mixing was still not complete. Hydrodynamic modelling studies done by Fryar et al. (2002)
confirms that lateral and vertical mixing was rarely complete in the Brisbane River even 2
km from the discharge. This is of course based on the assumption that there are no EDCs
upstream from the point of discharge. In reality, motorboats and catamarans can usually be
found throughout the Brisbane River; these forms of transport could leak fuel that may
contain trace amounts of phthalates and alkylphenols. Furthermore, there are several
WWTPs situated upstream of the WWTP A discharge which could also contribute towards
the discrepancies between the estimated and measured data. The selected compounds could
also be present throughout the Brisbane River at varying low concentrations caused by
surface water runoff, desorption and resuspension from sediments and bacterial degradation
(Birkett and Lester, 2003). The modelled WWTP A operating conditions are based on the
assumption of average dry weather flow. Rainfall events may be expected to have a
significant impact on the EDC concentrations.
Tabl
e 7.
3. E
stim
ated
and
mea
sure
d re
mov
al e
ffic
ienc
ies o
f sel
ecte
d co
mpo
unds
with
end
ocrin
e di
srup
ting
prop
ertie
s in
WW
TP A
.
Com
poun
d Pr
oces
s det
ails
D
EP
DB
P B
BP
DEH
P O
P N
P C
P B
PA
Mod
el in
put f
lux
(mol
h-1
) 2.
85×1
0-3
9.73
×10-4
3.
85×1
0-4
1.68
×10-2
9.
55×1
0-4
1.32
×10-2
3.
21×1
0-5
5.48
×10-4
M
odel
out
put f
lux
(mol
h-1
) 2.
74×1
0-3
1.13
×10-3
4.
71×1
0-4
1.70
×10-2
1.
37×1
0-3
1.25
×10-2
1.
76×1
0-4
6.29
×10-4
C
ontri
butio
ns to
out
put f
lux:
B
iotra
nsfo
rmat
ion
(mol
h-1
),
[% to
war
ds o
utpu
t flu
x]
2.71
×10-3
[98.
91]
1.05
×10-3
[92.
92]
2.09
×10-4
[44.
37]
1.26
×10-2
[74.
12]
1.20
×10-3
[87.
59]
1.09
×10-2
[87.
20]
1.73
×10-4
[98.
30]
2.34
×10-4
[3
7.20
] Pr
imar
y sl
udge
(mol
h-1
),
[% to
war
ds o
utpu
t flu
x]
8.32
×10-7
[0.0
3]
1.27
×10-5
[1.1
2]
4.16
×10-5
[8.8
2]
3.75
×10-3
[22.
06]
8.95
×10-5
[6.5
3]
5.79
×10-4
[4.6
3]
2.70
×10-7
[0.1
5]
9.86
×10-5
[15.
76]
Efflu
ent (
mol
h-1
), [%
tow
ards
out
put f
lux]
3.
39×1
0-5
[1.2
4]
6.75
×10-5
[5.9
7]
2.21
×10-4
[46.
92]
6.60
×10-4
[3.8
8]
8.61
×10-5
[6.2
8]
1.01
×10-3
[8.0
8]
2.75
×10-6
[1.5
6]
2.96
×10-4
[47.
06]
Estim
ated
eff
luen
t con
cent
ratio
n (n
g L-1
) 12
.4
31.7
12
1 39
4 31
.0
387
1.0
118
Mea
sure
d ef
fluen
t con
cent
ratio
n (n
g L-1
) 4.
9 34
.3
75.7
58
9 23
.5
335
1.9
86.7
Estim
ated
poi
nt o
f dis
char
ge
conc
entra
tion
(ng
L-1)
3.41
×10-6
2.
39×1
0-5
5.26
×10-5
4.
10×1
0-4
1.63
×10-5
1.
33×1
0-4
1.32
×10-6
6.
05×1
0-5
Mea
sure
d co
ncen
tratio
n at
poi
nt
of d
isch
arge
(ng
L-1)
36.9
10
1 60
.8
8.5
8.5
118
BD
L 40
.0
Mea
sure
d co
ncen
tratio
n 1
km
dow
nstre
am (n
g L-1
) 12
.5
46.4
13
.2
1.3
1.3
47.9
B
DL
5.5
DEP
= d
ieth
yl p
htha
late
, DB
P =
dibu
tyl p
htha
late
; BB
P =
benz
yl b
utyl
pht
hala
te; D
EHP
= di
-(2-
ethy
lhex
yl)
phth
alat
e; O
P =
4-te
rt-oc
tylp
heno
l; N
P =
nony
lphe
nol
(tech
nica
l gra
de);
CP
= 4-
cum
ylph
enol
; BPA
= b
isph
enol
A; B
DL
= be
low
det
ectio
n lim
it (<
1 ng
L-1
).
Bio
trans
form
atio
n re
mov
al fl
ux w
as b
ased
on
the
cum
ulat
ive
posi
tive
fluxe
s onl
y. R
emov
al fl
ux v
ia v
olat
iliza
tion
is n
eglig
ible
for a
ll co
mpo
unds
.
192
DBi
oB=-
7.31
×104
k Bio
B =-0
.13
DFB
=-1.
88×1
06 k F
B =-3
.35×
103
D8=
2.
26×1
04
D2=
1.35
×102
DBi
oV=2
.00
DPB
=4.4
0×10
5 k P
B=1.
66×1
02
D5=
5.
72×1
04 D
6=
5.84
×105
D1=
2.1
7×10
4
f I=1.
31×1
0-7
D3=
2.
16×1
04 PS
T f P
=6.1
7×10
-9
Z B=3
.63×
102
Bio
reac
tor
(ana
erob
ic)
f P=6
.17×
10-9
Z B=6
.07×
103
Bio
reac
tor
(aer
obic
) f B
=6.9
1×10
-10
Z B=2
.32×
105
FST
f F=1
.50×
10-9
Z B=2
.32×
105
DPV
=9.8
5×10
-1
DFV
=6.3
5×10
-1
D7=
2.13
×106 ; Z
B(RA
S)=9
.29×
105
A
DBi
oB=-
2.37
×104
k Bio
B =-0
.25
DFB
=1.4
1 ×1
04 k F
B =1.
54×1
02
D8=
6.
60×1
03
D2=
1.61
×103
DBi
oV=2
.00
DPB
=1.1
4×10
5 k P
B=1.
04
D5=
5.
17×1
04 D
6=
1.01
×105
D1=
1.50
×104
f I =6.
47×1
0-8
D3=
7.
32×1
03 PS
T f P
=7.8
9×10
-9
Z B=1
.50×
104
Bio
reac
tor
(ana
erob
ic)
f P=7
.89×
10-9
Z B=1
.67×
104
Bio
reac
tor
(aer
obic
) f B
=5.2
8×10
-9
Z B=3
.77×
104
FST
f F=1
.02×
10-8
Z B=3
.77×
104
DPV
=9.7
9×10
-1
DFV
=6.3
1×10
-1
D7=
3.15
×104 ; Z
B(RA
S)=1
.10×
104
B
0.63
1 0.60
3
1.35
×10-6
1.85
×10-4
2.96
×10-2
7.83
×10-2
3.06
×10-7
8.96
×10-2
-1.1
2×10
-2
0.71
9
2.11
×10-7
7.53
×10-3
-0.6
26
0.27
0 0.25
0
2.15
×10-6
3.53
×10-3
1.16
×10-2
0.11
3
2.94
×10-6
0.14
8
-3.4
8×10
-2
8.93
×10-2
1.79
×10-6
1.87
×10-2
4.00
×10-2
Figu
re 7
.3. P
roce
ss d
etai
ls o
f fat
e, D
(mol
Pa-1
h-1
), f (
Pa),
k (h
-1) a
nd Z
(mol
m-3
Pa-1
), for (
A) d
ieth
yl p
htha
late
and
(B) d
ibut
yl p
htha
late
in
WW
TP A
. Dat
a in
bol
d ar
e th
e flu
xes f
or th
e va
rious
pro
cess
es (g
h-1
). Z W
of d
ieth
yl p
htha
late
and
dib
utyl
pth
alat
e ar
e 37
.2 m
ol m
-3 P
a-1 a
nd
11.1
6 mol
m-3
Pa-1
, res
pect
ivel
y.
19
3
DBi
oB=2
.24×
102
k Bio
B =3.
70×1
0-2
DFB
=-2.
53 ×
104
k FB =
-4.3
0×10
3
D8=
7.
41×1
03
D2=
2.70
×103
DBi
oV=2
.00
DPB
=1.3
3×10
4 k P
B=7.
23×1
0-2
D5=
2.
20×1
04 D
6=
2.07
×104
D1=
1.16
×104
f I=3.
31×1
0-8
D3=
8.
95×1
03 PS
T f P
=1.5
4×10
-8
Z B=2
.53×
104
Bio
reac
tor
(ana
erob
ic)
f P=1
.54×
10-8
Z B=2
.97×
103
Bio
reac
tor
(aer
obic
) f B
=1.6
2×10
-8
Z B=2
.43×
103
FST
f F=2
.98×
10-8
Z B=2
.43×
103
DPV
=9.7
9×10
-1
DFV
=6.3
0×10
-1
D7=
2.91
×104 ; Z
B(RA
S)=9
.51×
103
DBi
oB=1
.00×
103
k Bio
B =5.
01×1
0-2
DFB
=-1.
51 ×
104
k FB =
-7.7
8×10
-2
D8=
3.
77×1
02
D2=
3.23
×103
DBi
oV=2
.00
DPB
=9.1
4×10
3 k P
B=4.
11×1
0-2
D5=
3.
47×1
04 D
6=
1.94
×104
D1=
5.2
9×10
3
f I=3.
18×1
0-6
D3=
2.
07×1
03 PS
T f P
=1.1
6×10
-6
Z B=3
.06×
104
Bio
reac
tor
(ana
erob
ic)
f P=1
.16×
10-6
Z B=1
.46×
104
Bio
reac
tor
(aer
obic
) f B
=1.9
8×10
-6
Z B=8
.03×
103
FST
f F=1
.75×
10-6
Z B=8
.03×
103
DPV
=9.1
8×10
-1
DFV
=5.9
1×10
-1
D7=
3.67
×104 ; Z
B(RA
S)=1
.60×
104
B
A
0.12
0
6.40
×10-2
4.70
×10-6
1.30
×10-2
4.30
×10-2
0.10
6
1.01
×10-5
0.10
5
1.13
×10-3
0.27
1
5.86
×10-6
6.89
×10-2
-0.2
35
6.58
4.15
4.16
×10-4
1.46
0.93
915
.7
1.55
×10-3
15.0
0.77
4
25.1
4.04
×10-4 0.
258
-10.
3
Figu
re 7
.4. P
roce
ss d
etai
ls o
f fat
e, D
(mol
Pa-1
h-1
), f (
Pa),
k (h
-1) a
nd Z
(mol
m-3
Pa-1
), for (
A) b
enzy
l but
yl p
htha
late
and
(B) d
i-(2-
ethy
lhex
yl)
phth
alat
e in
WW
TP A
. D
ata
in b
old
are
the
fluxe
s fo
r th
e va
rious
pro
cess
es (
g h-1
). Z W
of
benz
yl b
utyl
pht
hala
te a
nd d
i-(2-
ethy
lhex
yl)
phth
alat
e ar
e 13
.0 m
ol m
-3 P
a-1 a
nd 0
.576
mol
m-3
Pa-1
, res
pect
ivel
y.
19
4
Figu
re 7
.5. P
roce
ss d
etai
ls o
f fa
te, D
(m
ol P
a-1 h
-1),
f (Pa
), k
(h-1
) an
d Z
(mol
m-3
Pa-1
), for
(A)
nony
lphe
nol a
nd (
B)
4-te
rt-oc
tylp
heno
l in
WW
TP A
. Dat
a in
bol
d ar
e th
e flu
xes f
or th
e va
rious
pro
cess
es (g
h-1
). Z W
of n
onyl
phen
ol a
nd 4
-tert-
octy
lphe
nol a
re 9
.09
×10-2
mol
m-3
Pa-1
and
1.80
mol
m-3
Pa-1
, res
pect
ivel
y.
DBi
oB=-
6.33
k B
ioB =
-5.5
0×10
-2
DFB
=-6.
22 ×
101
k FB =
-2.8
3×10
5
D8=
5.
22×1
01
D2=
2.16
×101
DBi
oV=2
.00
DPB
=4.0
5×10
2 k P
B=2.
73×1
0-1
D5=
1.
87×1
02 D
6=
2.14
×102
D1=
8.10
1
f I=1.
53×1
0-4
D3=
6.
42×1
01 63
×PS
T f P
=2.6
8×10
-5
Z B=2
.03×
102
Bio
reac
tor
(ana
erob
ic)
f P=2
.68×
10-5
Z B=3
.49×
101
Bio
reac
tor
(aer
obic
) f B
=2.4
0×10
-5
Z B=4
.62×
101
FST
f F=1
.93×
10-5
Z B=4
.62×
101
DPV
-1
DFV
-6
D
=6.8
2×10
=7.3
4×10
7=2.
75×1
02 ; ZB(
RAS)
= 9.
79×1
01
DBi
oB=-
8.29
×102
k Bio
B =-6
.70×
10-1
D
FB=7
.96×
103
k FB =
6.62
×103
D8=
1.
03×1
03
D2=
3.29
×102
DBi
oV
DPB
=1.9
6×10
3 k P
B=8.
72×1
0-2
D5=
3.
24×1
03 D
6=
3.25
×103
D1
103
f I=6.
17×1
0-7
D3=
1.
22×1
03
=2.0
0
=1.5
5×PS
T f P
=2.7
2×10
-7
Z B=3
.08×
103
Bio
reac
tor
(ana
erob
ic)
f P=2
.72×
10-7
Z B=4
.92×
102
Bio
reac
tor
(aer
obic
) f B
=3.6
4×10
-7
Z B=4
.97×
102
FST
f F=8
.36×
10-8
Z B=4
.97×
102
DPV
-1
=9.6
5×10
DFV
-1=6
.22×
10
D7=
5.16
×103 ; Z
B(RA
S)=
1.81
×103
A
2.92
×10-8
1.06
×10-2
4.02
×10-3
2.90
0.22
11.
130.
378
1.10
0.12
7-3
.34×
10-2
-0.2
642.
38
1.17
B
1.50
×10-4
0.24
4
-6.2
2×10
-2
1.07
×10-5
1.77
×10-2
0.13
7
5.41
×10-5
0.19
7
6.84
×10-2
0.18
2
1.84
×10-2
0.11
0
8.89
×10-2
19
5
Figu
re 7
.6. P
roce
ss d
etai
ls o
f fat
e, D
(mol
Pa-1
h-1
), f (
Pa),
k (h
-1) a
nd Z
(mol
m-3
Pa-1
), for (
A) 4
-cum
ylph
enol
and
(B) b
isph
enol
A in
WW
TP
A. D
ata
in b
old
are
the
fluxe
s for
the
vario
us p
roce
sses
(g h
-1).
Z W o
f 4-c
umyl
phen
ol a
nd b
isph
enol
A a
re 5
.03×
102 m
ol m
-3 P
a-1 an
d 1.
74×1
05
mol
m-3
Pa-1
.
DBi
oB=-
2.74
×106
k Bio
B =-9
.50×
10-1
D
FB=1
.53×
107
k FB =
5.45
×103
D8=
2.
94×1
05
D2=
2.88
×104
DBi
oV=2
.00
DPB
=3.0
9×10
6 k P
B=1.
62
D5=
2.
70×1
06 D
6=
3.28
×106
D1=
3.36
×105
f I=9.
56×1
0-11
D3=
3.
07×1
05 PS
T f P
=9.3
8×10
-12
Z B=2
.61×
105
Bio
reac
tor
(ana
erob
ic)
f P=9
.38×
10-1
2
Z B=9
.05×
105
Bio
reac
tor
(aer
obic
) f B
=4.6
9×10
-11
Z B=1
.16×
106
FST
f F=9
.38×
10-1
2
Z B=1
.16×
106
DPV
=9.8
4×10
-1
DFV
=6.3
5×10
-1
D7=
8.47
×105 ; Z
B(RA
S)=
2.44
×105
DBi
oB=-
4.14
×107
k Bio
B =-0
.44
DFB
=3.9
1×10
7 k F
B =4.
27×1
02
D8=
9.
96×1
07
D2=
3.53
×107
DBi
oV=2
.00
DPB
=4.2
7×10
7 k P
B=1.
75×1
0-2
D5=
2.
39×1
08
19
6
D6=
2.
90×1
08 FS
T f F
=2.9
7×10
-12
Z B=3
.78×
107
D1
108
=1.5
6×f I=
3.52
×10-1
2 D
3=
1.20
×108
PST
f P=2
.77×
10-1
2
Z B=3
.35×
108
Bio
reac
tor
(ana
erob
ic)
f P=2
.77×
10-1
2
Z B=1
.58×
107
Bio
reac
tor
(aer
obic
) f B
=2.6
7×10
-12
Z B=3
.78×
107
DPV
=9.8
4×10
-1
DFV
=6.3
4×10
-1
D7=
1.22
×108 ; Z
B(RA
S)=9
.05×
106
A
B
6.81
×10-3
6.14
×10-3
1.96
×10-9
5.72
×10-5
6.10
×10-4
5.37
×10-3
1.99
×10-8
3.26
×10-2
-2.7
2×10
-2
1.68
×10-3
1.26
×10-9
5.84
×10-4
3.04
×10-2
0.12
5
2.70
×10-2
6.21
×10-1
0
2.23
×10-2
7.58
×10-2
0.15
1
1.22
×10-9
0.7
-2.2
5×10
-2
8.26
×10-2
4.29
×10-1
0
6.74
×10-2
2.65
×10-2
17
y = 1.2x - 22.5, R2 = 0.89
0
100
200
300
400
500
600
700
0 100 200 300 400 500
Measured concentration (ng L-1)
Estim
ated
con
cent
ratio
n (n
g L
-1)
Figure 7.7. Correlation between the estimated and measured effluent compound
concentrations from WWTP A.
7.6 Conclusions The fate of eight anthropogenic estrogens in conventional activated sludge WWTP in South
East Queensland was modelled and analyzed using fugacity equations describing
partitioning, biodegradation, and volatilization. Using a mass balance approach and
measured concentrations, biotransformation rate constants were derived and relative
importance of fate investigated. All eight compounds that were modelled in this study had
high removal efficacy from WWTP A and the fugacity model was able to predict values
closely related to the measured concentrations. Apart from benzyl butyl phthalate and
bisphenol A, most of the compounds showed lesser than 8% remaining in the effluent.
Biotransformation was the most effective step followed by removal through primary
sludge. However, the most critical and uncertain variable is the biodegradation rate
constant. The conceptual model presented in this paper and the subsequently derived
predicted concentrations highlight priorities for further research work into EDCs residue in
197
wastewater. Additional sample collections, compartments, equations or measurements of
other EDCs at various sites not modelled in this study could be used in future fugacity
modelling to improve the model design which predicts compound concentrations closer to
the ambient values. Fugacity modelling gives an insight into the variations in basic removal
mechanisms that apply to compounds of different properties.
7.7 References Ahel, M., Giger, W., 1993. Aqueous solubility of alkylphenols and alkylphenol
polyethoxylates. Chemosphere 26, 1461 – 1470.
Alcock, R.E., Sweetman, A., Jones, K.C., 1999. Assessment of organic contaminant fate in
wastewater treatment plant. I. Selected compounds and physicochemical properties.
Chemosphere 38, 2247 – 2262.
Birkett, J.A., Lester, J.N. (Eds.), 2003. Endocrine disruptors in wastewater and sludge
treatment processes. CRC Press LLC, Boca Raton, Florida, pp. 1 – 135.
Byrns, G., 2001. The fate of xenobiotic organic compounds in wastewater treatment plants.
Water Res. 35, 2523 – 2533.
Clark, B., Henry, J.G., Mackay, D., 1995. Fugacity analysis and model of organic chemical
fate in a sewage treatment plant. Environ. Sci. Technol. 29, 1488 – 1494.
Danielsson, L.G., Zhang, Y.H., 1996. Methods for determining n-octanol-water partition
coefficient, TrAC-Trends Anal. Chem. 15, 188 – 196.
Davie, P., Stock, E., Choy, D.L. (Eds.), 1990. The Brisbane River: A source book for the
future. Australian Littoral Society / Queensland Museum Brisbane, Australia, pp.1 –
427.
Dobbs, R.A., Wang, L., Govind, R., 1989. Sorption of toxic organic compounds on waste
water solids: correlation with fundamental properties, Environ. Sci. Technol. 23, 1092 –
1097.
Fromme, H., Kuchler, T., Otto, T., Pilz, K., Muller, J., Wensel, A., 2002. Occurrence of
phthalates and bisphenol A and F in the environment. Water Res. 36, 1429 – 1438.
Fryar, R., Botev I., Regan, B., 2002. Using three dimensional models to manage outfalls
and minimize environmental impact – modeling Moreton bay and the Brisbane River.
198
Proceedings from the AWA Regional Conference, Mooloolaba, Australia, November
2002.
General Electric Company, 2005. US high production volume (HPV) chemical challenge
program. Robust summary: p-cumylphenol (CAS No 599-64-4). Pittsfield, MA, USA,
pp.1 – 58.
Hamer, G., 1997). Microbial consortia for multiple pollutant biodegradation. Pure Appl.
Chem. 69, 2343 – 2356.
Johnson, A.C., Williams R.J., 2004. A model to estimate influent and effluent
concentrations of estradiol, estrone and ethinylestradiol at sewage treatment works.
Environ. Sci. Technol. 38, 3649 – 3658.
Khan, S.J., Ongreth, J.E., 2002. Estimation of pharmaceutical residues in primary and
secondary sewage sludge based on quantities of use and fugacity modelling. Water Sci
Technol. 46, 105 – 113.
Khan, S.J., Ongreth, J.E., 2004. Modelling of pharmaceutical residues in Australian sewage
by quantities of use and fugacity calculation. Chemosphere 54, 355 – 367.
Mackay, D., 1991. Multimedia environmental models: the fugacity approach. Lewis Pub/
CRC Press, Boca Raton, FL, pp. 67 – 257.
Meakins, N.C., Bubb, J.M., Lester, J.N., 1994. The fate and behaviour of organic micro
pollutants during wastewater treatment. Intern. J. Environ. Poll. 4, 27 – 58.
Müller, S., Schlatter, C., 1998. Natural and anthropogenic environmental oestrogens: the
scientific basis for risk assessment. Oestrogenic potency of nonylphenol in vivo – a case
study to evaluate the relevance of human non-occupational exposure. Pure Appl. Chem.
70, 1847 – 1853.
O'Grady, D.P., Howard, P.H., Werner, A.F., 1985. Activated sludge biodegradation of 12
commercial phthalate esters. Appl. Environ. Microbiol.49, 443 – 445.
Petrović, M., Eljarrat, E., López, M.J., Barceló, D., 2001. Analysis and environmental
levels of endocrine-disrupting compounds in freshwater sediments. Trends Anal. Chem.
20, 637 – 648.
Psillakis, E., Mantzavinos, D., Kalogerakis, N., 2004. Monitoring the sonochemical
degradation of phthalate esters in water using solid-phase microextraction.
Chemosphere 54, 849 – 857.
Rogers, H.R., 1996. Sources, behaviour and fate of organic contaminants during sewage
treatment and in sewage sludges. Sci. Total Environ. 185, 3 – 26.
199
Staples, C.A., Dorn, P.B., Klecka, G.M., O’Block S.T., Harris, L.R., 1998. A review of the
environmental fate, effects, and exposures of bisphenol A. Chemosphere 36, 2149 –
2435.
Staples, C.A., Peterson, D.R., Parkerton, T.F., Adams, W.J., 1997. The environmental fate
of phthalate esters: a literature review. Chemosphere 35, 667 – 749.
Verevkin, S.P., 1999. Thermochemistry of phenols: quantification of the ortho-, para-, and
meta-interactions in tert-alkyl substituted phenols. J. Chem. Thermodyn. 31, 559 – 585.
Ying G., Williams, B., Kookana, R., 2002. Environmental fate of alkylphenols and
alkylphenol ethoxylates – a review. Environ. Int. 28, 215 – 226.
200
Chapter 8: General discussion and conclusion
8.1 General discussion The occurrence of the estrogenic compounds and their activity that were in wastewater
treatment plants (WWTPs) and in the receiving environment in South East Queensland,
Australia showed the WWTPs were efficient at removing EDCs. The results reported in this
study showed estrogen and estrogen mimic concentrations in influent and effluent samples
were quite similar to those found by researchers in Australia, New Zealand, Europe and
Japan (Spengler et al., 2001; Joss et al., 2004; Kawaguchi et al., 2005; Leusch et al., 2006).
The selected estrogens monitored in the selected WWTPs typically had much lower
concentrations, <1 – 190 ng/L in wastewater and <1 – 33.8 ng/g in solids. Although
concentration as high as 2400 ng/L of androgens were found in influent, the androgens
were among the few compounds monitored that showed a sharp drop to below detectable
levels from influent to clarifier. Most of the compounds monitored in this study were
reduced significantly by the treatment processes of the WWTP. The industrial estrogen
mimics such as the alkylphenols and phthalates often exhibited the highest concentrations
among the measured compounds and were present at all stages of the treatment process
train within the WWTP (Chapters 3, 4 and 5). Phthalate and alkylphenol concentrations
ranged from <1 – 8080 ng/L and <1 – 5320 ng/L, respectively for wastewater concentration
whereas solids concentration had much higher concentrations of phthalates (8.3 – 45,100
ng/g) and alkylphenols (1.7 – 9830 ng/g). According to Birkett and Lester (2003), it is
presumed that most hydrophobic estrogen mimics such as the phthalates and alkylphenols
were more likely to sorb onto solids and removed through the primary sludge whereas the
semi-hydrophilic EDCs such as the natural hormones were removed through the process of
biodegradation. The samples with the highest estrogenic potency were mostly found in the
influent with estrogen equivalent as high as 356 ng/L measured by the E-Screen assay
(Chapter 6). The measured estrogenicity of the extracted samples using the E-Screen assay
were positively correlated (p<0.001) with the predicted chemical analysis derived
concentration although the majority of the time the bioassay showed higher concentrations
of estrogen equivalent when compared to the chemical analysis. This is not unusual since
only a limited number of EDCs could be measured using a chemical analysis while
bioassays measured the cumulative EDCs effect and interaction of the extracted analytes.
201
However, the use of both chemical and biological assays should be used simultaneously to
measure the estrogenic potency and also to identify the main compound present in a
particular sample. Furthermore, with the advanced technology of the various biological and
analytical tools in recent years, environmental sampling and extraction have become less
laborious. A good example is the stir bar sorptive extraction (SBSE) used in the sludge and
water analysis which minimizes sample volume by a factor of a hundred and cuts down
sample extraction time substantially compared to the conventional soxlet extraction
technique (Chapter 4). Even though the SBSE technology is fairly recent, it has become an
increasingly popular analytical tool in the environmental, health and medical research fields
(Tienpont et al., 2003; Nakamura et al., 2005; Zuin et al., 2005; Duran Guerrero et al.,
2006).
Sampling methods used in this study include grab and passive sampling. Both techniques
have advantages and disadvantages (Chapter 3, 4 and 5). The conventional grab sampling
method gives a more accurate and reliable ambient concentration. However because a grab
sample will give only a ‘snapshot’ concentration of a particular moment in time, several
collections have to be taken over time to ensure fluctuation of compound concentrations is
accounted for. In this case, the time integrated passive sampling method could be an
advantage since an average of compound concentration over a certain period of time can be
collected with just one sampler. The drawback of this sampling method is that extensive
calibrations have to be performed before field sampling (Chapter 3). Environmental factors
such as differences in flow rate, biofouling, biodegradation, and temperature have been
reported to affect the uptake of compounds into the passive sampler (Vrana et al., 2005).
Furthermore a good understanding of compound physico-kinetics and environmental flow
dynamics are needed to model the uptake and loss of compounds via diffusion from passive
samplers. In this study, limiting environmental mentioned above have been assumed to
cause the reduced uptake by the passive sampler compared to the grab sampler. However,
because wastewater has been reported to have fluctuating compound concentrations
depending on several factors such as temperature, frequency of source discharge, weather,
etc., the grab samples could be reporting compound concentrations at their highest peak
rather than the average daily or monthly concentrations (Joss et al., 2004). In spite of that,
fluctuation of compound concentrations were only found in the early parts of treatment
trains of the WWTPs and usually decreasing to a constant average concentration by the
202
time it reaches the end of the treatment process of the WWTPs; even at this stage the
passive sampler showed under representation of compound concentration as compared to
the grab samples. Because the passive sampling method was a pilot study to test the
feasibility of using this sampling technique for WWTP treatment, the polar passive sampler
(EmporeTM disk) was not extensively calibrated for the various environmental factors
limiting the uptake of compounds into the passive sampler when deployed. Until these
conditions or challenges are addressed to create a more reliable sampling method, the
results from the passive samplers of this study should be considered qualitative rather than
quantitative. Overall, the passive sampling technique is a useful promising method for
future WWTPs monitoring.
Predicting compound fate in WWTPs using a fugacity model with equations describing the
partitioning, biodegradation, and volatilization or stripping behavior of chemical has proved
to be useful in understanding the physico-dynamics of a particular compound (Byrns,
2001). Fugacity modelling also facilitates insight into the variations in the fundamental
removal mechanisms that apply to chemicals of different properties (Chapter 7). The
alkylphenols and phthalates that were modelled in this study had high removal efficacy
from WWTP A. Apart from benzyl butyl phthalate and bisphenol A, all other EDC showed
<8% remaining in the effluent. The majority of the semi-polar compounds were removed
via biotransformation (37 – 99%) followed by removal through primary sludge (<1 – 22%).
However, the most critical and uncertain variable is the biodegradation rate constant. The
natural estrogens fate could not be modeled mainly because of some missing concentrations
data at crucial compartments that are needed to complete the fate modelling. Furthermore,
the relative complexity of biotransformation of conjugated estrogens to free estrogens and
biodegradation of the free estrogens is shown by the fluctuation from low 17β-estradiol
concentrations in the influent to high concentrations in the bioreactor (Chapter 5). This
means adding a few more unknown biotransformation predictions or assumptions could
leave the whole estrogen fate model fairly weak. The conceptual model presented in this
study and the subsequently derived predicted concentrations highlight priorities for further
research work into EDCs residue in wastewater.
Environmental risk assessment aims to evaluate the potential impact of individual
substances on the environment by examining both exposure and effects on the ecosystem of
203
their emission (European Commission, 2003). According to Thorpe et al. (2003) median
EC50 for a significant induction of the egg yolk precursor vitellogenin in juvenile female
rainbow trout were between 19 – 26 ng/L for 17β-estradiol, 60 ng/L for estrone, and
between 0.95 – 1.8 ng/L for 17α-ethynylestradiol. Exposure to 10 – 100 ng/L of 17β-
estradiol for 110 days induced intersex in adult Japanese medaka (Oryzias latipes)
(Metcalfe et al., 2001). A 3-week exposure to a concentration between 1 and 10 μg/L of
octylphenol caused significant production of vitellogenin in male rainbow trout, whereas a
dose of 100 μg/L of octylphenol caused a significant increase of vitellogenin in male roach
(Routledge et al., 1998). Yokota et al. (2001) reported that the lowest observed effect
concentration (LOEC) and no observed effect concentration (NOEC) of 4-nonylphenol on
reproductive status of the medaka (Oryzias latipes) over two generations of continuous
exposure were 17.7 and 8.2 μg/L, respectively. Based on the chemical and in vitro
biological analyses results presented in Chapters 3, 4, 5, 6 and 7, and coupled with reported
NOEC in vivo studies (mainly based on fish vitellogenin studies), the risk of EDCs found in
effluents of the monitored WWTPs having a significant impact on the receiving
environment is relatively low. Most of the compound concentrations in the effluents were
well below the NOEC of the selected compounds. Even if high compound concentrations or
estrogenic activity were detected in the samples, dilution factors taken into account upon
release into rivers and sea will significantly reduce estrogenic effects to yet lower
concentrations (Chapters 5 and 6). A thorough EDC risk assessment of all the major
WWTPs in this study could not be done because of limited dose-response data. The
majority of NOEC in vivo studies for the endocrine disruptors have limited period of
exposure and only focuses on one or two endpoints such as vitellogenin production in
males and target tissue morphology. A two-generation study is an apical test with a high
power to identify whether or not effects occur, especially as a result of multiple, minor hits
on growth, development, survival and reproductive competence (Mantovani, 2002). The
two-generation study is the only current regulatory protocol where male and female animals
(the F1 generation) are exposed from gamete stage through to sexual maturation and
production of offspring. Accordingly, the test is indicated as the tool of choice for risk
assessment of compounds, such as EDCs, which may affect prenatal and/or postnatal
development, besides the organogenetic phase. However, in order to achieve this aim,
relevant endpoints have to be investigated within the basic frame of the protocol
(Mantovani, 2002).
204
8.2 General conclusion In conclusion, the estrogenic compound concentration and activity of treated wastewater in
South East Queensland, Australia is very low, as shown by the chemical and biological
analyses as well as the compound fate fugacity modelling. This suggests that the potential
for significant impact the EDCs released from the WWTPs into the receiving environments
is minimal. However, surveillance monitoring should be carried out periodically to ensure
WWTPs are functioning optimally in removing EDCs and other toxic waste especially if
these wastewaters are channelled into dams for the use of recycled water as proposed by the
state government to solve the water shortage crisis. Such programs will also lead to trust
and reassurance among the local community towards recycled water.
With regards to the initial research questions:
i) Passive sampling technique shows potential as a replacement for grab sampling if
intense calibration is carried out taking into account the various environmental factors
limiting the uptake of EDCs compounds (Chapter 3, 5 and 6). But for the time being,
the grab sampling technique is the most reliable technique for quantitative
environmental sampling.
ii) The use of both chemical and biological assays in combination is useful in predicting
individual EDC concentrations and the combined estrogenicity exerted by the mixture
(Chapter 5 and 6).
iii) The concentrations of natural hormones found in WWTPs contributed 60 – >99% of the
estrogenic activity when compared to those of industrial estrogen mimics even though
these natural hormones were detected at concentrations several times lesser than the
other estrogen mimics (Chapter 5 and 6).
iv) ‘Biological nutrient removal’ and the conventional activated sludge WWTPs have been
shown to be efficient at reducing EDC concentrations and estrogenicity often to below
detectable limits (Chapter 3, 4, 5 and 6).
v) EDCs fugacity fate modelling did provide a good understanding EDCs removal as long
as the model is kept relatively simple (Chapter 7).
205
8.3 Future research The uncertainties associated with wastewater discharge and recycled water should be
addressed through fundamental and applied research. Because either unintentional or
planned water recycling will be practiced in most communities, the risk and benefits
associated with chemical contaminants must be quantified. Research is needed to identify
contaminants of concern and to evaluate and optimize treatment systems. Given the wide
range of physical and chemical properties of these contaminants, a feasible treatment
strategy might consist of multiple chemical barriers to contaminants such as membrane
treatment, granular activated charcoal (GAC), ozone or UV disinfection followed by soil
aquifer treatment, coupled with a comprehensive monitoring program. Such monitoring
programs should be accompanied by periodic reviews of contaminants potentially present
in wastewater effluent.
Concentrations of EDCs in wastewater streams have been shown to cause feminization of
male fish. The likely compounds responsible are the natural mammalian and synthetic
estrogens, as well as estrogens and anti-estrogens of plant origin. These appear to be
resistant to primary sewage treatment, and to some secondary and tertiary treatments. A
broad scale survey of individual EDCs using chemical analysis in the Australian
environment is probably not required at the present time as there are simply too many
compounds. However, a monitoring capability for EDCs using bioassays in Australian
wastewaters and receiving environments is necessary, and can be extended using the
methodologies currently being validated in the USA and elsewhere. It is possible to use
biomarkers to test for hormonal activity (i.e. those in effluents, present in surface waters or
reported from other monitoring programs). These biological markers for exposure can
usefully be validated for use in Australia, prior to any detailed investigation of EDC
impacts. When there is clear biomarker evidence of EDC contamination, there may be a
need to evaluate actual effects of the constituent compounds through a TIE (toxicity
identification evaluation) approach. The development of an assessment process should be
based on integrated effects, which would include endocrine disruption as well as other
toxicological effects such as genotoxicity, mutagenicity, carcinogenicity and cytotoxicity.
206
It is apparent from the information available that adverse effects on the health of human
populations have occurred from exposure to toxic and EDCs through accidental exposure,
occupational exposure and pharmaceutical drug treatment. Consumption of chemically
contaminated food has also been shown repeatedly to result in short and long-term harm to
health. However, the evidence for environmental concentrations of EDCs having
demonstrable detrimental effects on human populations is far from clear. As many
compounds being investigated for activity are also toxic through other mechanisms, the
proportional effects of endocrine disruption may be minimal. The question of relevance of
environmental exposure to EDCs to human health will not be resolved until prospective
epidemiological studies, in which exposure is monitored closely, have been continued for a
decade or longer. Furthermore, a knowledge of baseline concentrations in clinically normal
individuals is essential for determining the limits between good health and disease and for
understanding the changes produced by estrogen mimics.
8.4 References Birkett, J.A., Lester, J.N. (Eds.), 2003. Endocrine disruptors in wastewater and sludge
treatment processes. CRC Press LLC, Boca Raton, Florida, pp. 1 – 232.
Byrns, G., 2001. The fate of xenobiotic organic compounds in wastewater treatment plants,
Water Res. 35, 2523 – 2533.
Duran Guerrero, E., Natera Marin, R., Castro Mejias, R., Garcia Barroso, C., 2006.
Optimisation of stir bar sorptive extraction applied to the determination of volatile
compounds in vinegars. J. Chromatogr. A 1104, 47 – 53.
European Commission, 2003. Technical guidance document on risk assessment (TGD).
Part II, Technical Report, Institute of Health and Consumer Protection, European
Chemicals Bureau, European Commission (EC).
Joss, A., Andersen, H., Ternes, T., Richle, P.R., Siegrist, H., 2004. Removal of estrogens in
municipal wastewater treatment under aerobic and anaerobic conditions: consequences
for plant optimization. Environ. Sci. Technol. 38, 3047 – 3055.
Kawaguchi, M., Sakui, N., Okanouchi, N., Ito, R., Saito, K., Nakazawa, H., 2005. Stir bar
sorptive extraction and trace analysis of alkylphenols in water samples by thermal
desorption with in tube silylation and gas chromatography-mass spectrometry. J.
Chromatogr. A 1062, 23 – 29.
207
Leusch, F.D.L., Chapman, H.F., van den Heuvel, M.R., Tan, B.L.L. Gooneratne, S.R.,
Tremblay, L.A., 2006. Bioassay-derived androgenic and estrogenic activity in
municipal sewage in Australia and New Zealand. Ecotoxicol. Environ. Saf. 65, 403 –
411.
Mantovani, A., 2002. Hazard indentification and risk assessment of endocrine disrupting
chemicals with regard to developmental effects. Toxicol. 181 – 182, 367 – 370.
Metcalfe, C.D., Metcalfe, T.L., Kiparissis, Y., Koenig, B.G., Khan, C., Hughes, R.J.,
Croley, T.R., March, R.E., Potter, T., 2001. Estrogenic potency of chemicals detected
in sewage treatment plant effluents as determined by in vivo assays with Japanese
medaka (Oryzias latipes). Environ. Toxicol. Chem. 20, 297 – 308.
Nakamura, S., Daishima, S., 2005. Simultaneous determination of 64 pesticides in river
water by stir bar sorptive extraction and thermal desorption-gas chromatography-mass
spectrometry. Anal. Bioanal. Chem. 382, 99 – 107.
Routledge, E.J., Sheahan, D., Desbrow, C., Brighty, G.C., Waldock, M., Sumpter, J.P.,
1998. Identification of estrogenic chemicals in STW effluent. 2. In vivo responses in
trout and roach. Environ. Sci. Technol. 32, 1559 – 1565.
Spengler, P., Korner, W., Metzger J.W., 2001. Substances with estrogenic activity in
effluents of sewage treatment plants in Southwestern Germany. 1. Chemical analysis.
Environ. Chem. 20, 2133 – 2141.
Tienpont, B., David, F., Benijts, T., Sandra, P., 2003 Stir bar sorptive extraction-thermal
desorption-capillary GC-MS for profiling and target component analysis of
pharmaceutical drugs in urine. J. Pharm. Biomed. Anal. 32, 569 – 579.
Thorpe, K.L., Cummings, R.I., Hutchinson, T.H., Scholze, M., Brighty, G., Sumpter, J.P.,
Tyler, C.R., 2003. Relative potencies and combination effects of steroidal estrogens in
fish. Environ. Sci. Technol. 37, 1142 – 1149.
Vrana, B., Mills, G.A., Allan, I.J., Dominiak, E., Svensson, K., Knutsson, J., Morrison, G.,
Greenwood, R., 2005. Passive sampling techniques for monitoring pollutants in water.
Trends Anal. Chem. 24, 845 – 868.
Yokota, H., Seki, M., Maeda, M., Oshima, Y., Tadokoro, H., Honjo, T., Kobayashi, K.,
2001. Life-cycle toxicity of 4-nonylphenol to medaka (Oryzias latipes). Environ.
Toxicol. Chem. 20, 2552 – 2560.
208
Zuin, V.G., Montero, L., Bauer, C., Popp, P., 2005. Stir bar sorptive extraction and high-
performance liquid chromatography-fluorescence detection for the determination of
polycyclic aromatic hydrocarbons in Mate teas. J. Chromatogr. A 1091, 2 – 10.
209
