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Assessment of the application of bioanalytical toolsas surrogate measure of chemical contaminants inrecycled water

Frederic D.L. Leusch a,*, Stuart J. Khan b, Somprasong Laingam c,Erik Prochazka a, Suzanne Froscio c, Trang Trinh b, Heather F. Chapman a,Andrew Humpage c

aSmart Water Research Centre, Griffith University Gold Coast Campus, Southport, Qld 4222, AustraliabWater Research Centre, University of New South Wales, Sydney, NSW 2052, AustraliacAustralian Water Quality Centre, SA Water, Adelaide, SA 5001, Australia

a r t i c l e i n f o

Article history:

Received 29 August 2013

Received in revised form

15 November 2013

Accepted 18 November 2013

Available online 28 November 2013

Keywords:

Bioassay

In vitro

Micropollutant

Water quality

Water reclamation plant

Water recycling

* Corresponding author. Tel.: þ61 7 5552 783E-mail address: f.leusch@griffith.edu.au (

0043-1354/$ e see front matter ª 2013 Elsevhttp://dx.doi.org/10.1016/j.watres.2013.11.030

a b s t r a c t

The growing use of recycled water in large urban centres requires comprehensive public

health risk assessment and management, an important aspect of which is the assessment

and management of residual trace chemical substances. Bioanalytical methods such as

in vitro bioassays may be ideal screening tools that can detect a wide range of contaminants

based on their biological effect. In this study, we applied thirteen in vitro assays selected

explicitly for their ability to detect molecular and cellular effects relevant to potential

chemical exposure via drinking water as a means of screening for chemical contaminants

from recycled water at 9 Australian water reclamation plants, in parallel to more targeted

direct chemical analysis of 39 priority compounds. The selected assays provided measures

of primary non-specific (cytotoxicity to various cell types), specific (inhibition of acetyl-

cholinesterase and endocrine receptor-mediated effects) and reactive toxicity (mutage-

nicity and genotoxicity), as well as markers of adaptive stress response (modulation of

cytokine production) and xenobiotic metabolism (liver enzyme induction). Chemical and

bioassay analyses were in agreement and complementary to each other: the results show

that source water (treated wastewater) contained high levels of biologically active

compounds, with positive results in almost all bioassays. The quality of the product water

(reclaimed water) was only marginally better after ultrafiltration or dissolved air floatation/

filtration, but greatly improved after reverse osmosis often reducing biological activity to

below detection limit. The bioassays were able to detect activity at concentrations below

current chemical method detection limits and provided a sum measure of all biologically

active compounds for that bioassay, thus providing an additional degree of confidence in

water quality.

ª 2013 Elsevier Ltd. All rights reserved.

2.F.D.L. Leusch).

ier Ltd. All rights reserved.

wat e r r e s e a r c h 4 9 ( 2 0 1 4 ) 3 0 0e3 1 5 301

1. Introduction

Over the last decade, many populated regions throughout the

world have suffered water supply shortages. While the causes

of these shortages have been variable, extensive droughts and

increasing population demands have been consistent factors.

Large urban centres in Australia, the USA and parts of Asia

and Europe have found traditional groundwater and surface

water sources increasingly limited and difficult to expand.

One consequence has been the growth of long-distance inter-

basin transfers of water from less populous areas. Many large

coastal cities have also begun to develop extensive seawater

desalination capacity. However, these alternative water sup-

plies are commonly energy intensive, costly and not available

in all areas. An increasingly important alternative has been

the use of recycled water for a variety of applications

including potable water reuse.

The use of recycled water in large urban centres requires

comprehensive public health risk assessment and manage-

ment, of which residual trace chemical substances are an

important aspect (Khan and McDonald, 2010; Rodriguez et al.,

2009). A wide variety of substances may be present in

reclaimed water at low concentrations depending on the

water treatment processes applied. Such complex and poorly-

defined mixtures tend to be difficult to characterise and pre-

sent a number of challenges for risk assessment. Direct

chemical analysis is limited by the sheer range of chemicals

potentially present and a lack of suitable analytical methods

for many. In addition, direct chemical analysis cannot

account for potential mixture interaction between individual

chemicals, which may lead to either increased (additivity or

synergism) or decreased (antagonism) biological activity.

Bioanalytical methods such as in vitro bioassays may be ideal

screening tools that can detect a wide range of contaminants

based on their biological activity rather than their chemical

structures. This means that less expectation bias is intro-

duced in the analysis (Escher and Leusch, 2012). When used in

parallel with chemical analysis, “unknown” biologically active

contaminants can be detected and sometimes identified.

Bioanalytical tools have previously been applied to recycled

water quality assessment. Until 2005, most of these examples

focused on assessment of genotoxicity (NRC, 2012), but since

then bioanalytical batteries have started to include additional

endpoints such estrogenicity, bacterial and algal toxicity,

acetylcholinesterase (AChE) inhibition and aryl hydrocarbon

receptor activity (Escher et al., 2009; Leusch et al., 2005; Macova

et al., 2011; Poulsen et al., 2011; Reitsema et al., 2010; Reungoat

et al., 2010). The selection of these additional endpoints is

usually based on chemical consideration (e.g., an algal toxicity

assay is a good indicator of herbicides) or as surrogate mea-

sures (e.g., the bacterial toxicity assay responds to the presence

of a wide variety of compounds; Tang et al., 2013) and not

specifically related to human health considerations.

In this study we have assessed the application of a battery

of in vitro assays selected explicitly for their ability to detect

molecular and cellular effects relevant to potential chemical

exposure via drinking water as a means of screening for

chemical contaminants in recycled water prior to more tar-

geted direct chemical analysis.

2. Materials and methods

2.1. Sites and sample processing

Nine water reclamation plants in 6 Australian states were

sampled. These plants were selected to provide a variety of

treatment technologies (from pond- to membrane-based

systems) in a range of climatic conditions. Samples were

taken between 7am and 1pm. Sample types and a brief

description of each site is provided in Table 1.

Grab samples (2 � 2 L) were taken of the source (treated

wastewater) and product water (reclaimed water) in

methanol-rinsed amber glass bottles. Ultrapure water field

blanks were also taken as negative controls. In addition,

samples of tap water from five Australian capital cities,

bottled water and rainwater were taken for comparison. All

samples were kept on ice until brought back to the labora-

tory. Samples were processed within 12 h by passage

through 6cc Oasis HLB (Waters Corp) and Supelclean coco-

nut charcoal (SigmaeAldrich) cartridges in series, stacked

on top of each other. All cartridges were individually pre-

conditioned with 5 mL methanol followed by 5 mL ultra-

pure water. Once dried, the cartridges were eluted with

2 � 5 mL methanol, the extracts blown down to dryness

under gentle nitrogen stream, and immediately recon-

stituted to 1 mL methanol for a final sample enrichment

factor of 2000�. The same aliquots were used for chemical

and bioassay analysis.

2.2. Bioanalytical tools

Thirteen in vitro bioassays were selected for this project based

on a review of potential human health effects from exposure

to toxic chemicals via drinking water and the current state-of-

the-science of bioanalytical methods (Escher and Leusch,

2012). The selected assays provide measures of primary non-

specific (cytotoxicity to various cell types), specific (inhibi-

tion of AChE and endocrine receptor-mediated effects) and

reactive toxicity (mutagenicity and genotoxicity), as well as

markers of adaptive stress response (modulation of cytokine

production) and xenobiotic metabolism (liver enzyme

induction).

Table 2 provides details on the bioassay battery used in this

study, as well as method references. Additional details on the

bioassay methodology used in this study are available in the

Supplementary information.

2.3. Chemical analysis

A list of 39 priority chemicals for screening analysis were

selected based on criteria such as the availability of chemical

analysis methods, predicted biological activity, actual or

perceived toxicity, presence on industrial inventories and

likelihood of occurrence in recycled water sources. The pri-

ority list (Table 3) includes natural and synthetic hormones,

industrial compounds, a personal care product, pharmaceu-

ticals, a veterinary drug, pesticides, and chlorinated and

brominated disinfection by-products (DBPs). An additional 23

Table 1 e Description of the sites and grab water samples taken for this study. "EP" [ Equivalent Population.

Plant ID Sampling date Sample type n Site and sample description

LAGF May & Jul 2010 Treated sewage 8 Large urban wastewater plant, >500,000 EP. Has gone through

conventional sedimentation, activated sludge digestion (with aerobic

and anaerobic zones) and several days’ retention in stabilisation

lagoons.

Reclaimed water 8 Coagulation, dissolved air floatation/filtration and chlorination.

LAGUV Apr 2010 Treated sewage 4 Large urban wastewater source, >500,000 EP. Has gone through

engineered anaerobic/aerobic lagoons, including activated sludge

treatment, with a total residence time of up to 30 days.

Reclaimed water 4 UV and chlorine disinfection.

UF1 Jul 2010 Treated sewage 3 Large urban wastewater source (mostly domestic), >500,000 EP. Has

gone through conventional activated sludge treatment and settlement

tanks, followed by chloramine disinfection.

Reclaimed water 3 Coagulation, ultrafiltration and further chloramine disinfection.

UF2 Apr & Jul 2010 Treated sewage 8 Mediummunicipal wastewater, mostly residential sewage,<50,000 EP.

Has gone through trickling filters and settlement lagoons.

Reclaimed water 8 Ultrafiltration and chlorination.

RO1 Apr & Jul 2010 Treated sewage 8 Combined from large urban wastewater plants, >500,000 EP. Has gone

through conventional activated sludge treatment and settlement

tanks.

RO effluent 8 Flocculation, chloramination, microfiltration, reverse osmosis,

advanced oxidation.

RO2 Apr 2010 Treated sewage 1 Combined from large urban wastewater plants, approximately 100,000

EP. Has gone through conventional activated sludge treatment and

settlement tanks.

RO effluent 1 Flocculation, chlorination, ultrafiltration, reverse osmosis, advanced

oxidation.

RO3 Apr & May 2010 Treated sewage 4 Large urban wastewater source with a significant industrial

component, approximately 200,000 EP. Has gone through conventional

aerobic/anaerobic activated sludge treatment, clarification and sand

filtration.

RO effluent 4 Microfiltration, reverse osmosis, chlorination and pH adjustment.

RO4 Jul 2010 Treated sewage 4 Combined from large urban wastewater plants, approximately 400,000

EP. Has gone through conventional aerobic/anaerobic activated sludge

treatment, clarification and chlorination.

RO effluent 4 Chloramination, ultrafiltration, reverse osmosis, chlorination. Trials of

different disinfection regimes were underway at the time of sampling.

RO5 Apr & Jul 2010 Treated sewage 6 Large urban wastewater source, >500,000 EP. Has gone through

conventional activated sludge treatment, settlement tanks and

ultrafiltration.

RO effluent 6 Reverse osmosis, UV disinfection.

Bottle Jul 2010 Bottled water 1 Supermarket chain bottled water.

Tap Jul 2010 Tap water 5 Tap water from 5 Australian capital cities.

Rain Jul 2010 Rainwater 1 Water from a private rainwater tank in Queensland.

FB Apr, May & Jul 2010 Field blank 11 Laboratory ultrapure water extracted in same environment and

conditions as the field samples.

wat e r r e s e a r c h 4 9 ( 2 0 1 4 ) 3 0 0e3 1 5302

compoundswere co-analysedwith our chemicalmethods and

their concentrations are reported in Tables SI3 and SI4.

The concentrations of these 39 compounds in the grab

water samples were analysed using a number of instrumental

methods, including HPLC-MS/MS for pharmaceuticals and

personal care products (Vanderford and Snyder, 2006), GC-MS/

MS for N-nitrosamines (McDonald et al., 2012), GC-ECD for tri-

halomethanes (based on method 6232B in APHA/AWWA/WEF,

2012) and GC-MS/MS for steroid hormones (Trinh et al., 2011).

2.4. Data analysis

For non-specific and reactive endpoints (i.e., cytoxocity, geno-

toxicity and mutagenicity), the results are expressed as “toxic

unit” (TU) and “genotoxic unit” (GTU), respectively. The (G)TU

was calculated as 1/REFECQL, where REFECQL is the relative

enrichment of the sample necessary to reach the quantification

limit of the assay (see SI). The REF is calculated by dividing the

sampleenrichment fromtheSPEstep (2000� in thisstudy)by the

dilution required in the assay (between 100e1000�, depending

on the assay). A (G)TU value above 1 indicates that the sample

would have been toxic as sampled; a value below 1 means that

the sample had to be concentrated to produce a toxic response.

All other assays are expressed as “toxic equivalent con-

centrations”, calculated by dividing the ECQL of the reference

compound (concentration of the reference compound at the

quantification limit of the assay, in ng/L or mg/L; see SI) by the

REFECQL (unitless) of the sample. The reference compound for

each bioassay is specified in Table 2.

For data analysis and graphing of water quality data,

samples below detection limit were assigned a value of 1/2 the

detection limit.

Table 2 e Bioassay battery.

Mode of action Endpoint Basis of the bioassay Bioassay name Assay unit or referencecompound

Ref.

Non-specific

toxicity

Cytotoxicity Viability of gastro-intestinal

cells (Caco2) measured by

neutral red assay

Caco2 NRU Toxic Unit (TU) (Modified from Konsoula and

Barile, 2005)

Cytotoxicity Viability of white blood cells

(WIL2NS) measured by

resazurin assay

WIL2NS TOX Toxic Unit (TU) (Modified from Nociari et al.,

1998)

Cytotoxicity Viability of liver cells (C3A)

measured by resazurin

assay

HepaTOX Toxic Unit (TU) (Modified from Nociari et al.,

1998)

Reactive

toxicity

Genotoxicity Formation of micronucleus

in white blood cells

(WIL2NS) measured by flow

cytometry

WIL2NS FCMN Genotoxic Unit (GTU) (Laingam et al., 2008)

Mutagenicity DNA mutation in bacterial

cells (Salmonella)

Ames TA98 � S9

and Ames

TA100 � S9

Genotoxic Unit (GTU) (Ames et al., 1973; Meier,

1988)

Specific

toxicity

Neurotoxicity

(cell-free assay)

Inhibition of

acetylcholinesterase

enzyme activity measured

by conversion of

acetylthiocholine

AChE inhibition Chlorpyrifos equivalent

(ChlorpyEQ)

(Ellman et al., 1961; Hamers

et al., 2000)

Androgenic

endocrine effect

Androgen receptor (AR)

mediated transcriptional

activation measured by

luciferase reporter gene

AR-CALUX

(in þ/� modes)

Dihidrotestosterone

equivalent (DHTEQ) in þmode; Flutamide equivalent

(FluEQ) in � mode

(van der Linden et al., 2008)

Estrogenic

endocrine effect

Estrogen receptor a (ERa)

mediated transcriptional

activation measured by

luciferase reporter gene

ERa-CALUX

(in þ/� modes)

17b-Estradiol equivalent

(EEQ) in þ mode; Tamoxifen

equivalent (TMXEQ) in �mode

(van der Linden et al., 2008)

Glucocorticoid

endocrine effect

Glucocorticoid receptor (GR)

mediated transcriptional

activation measured by

luciferase reporter gene

GR-CALUX Dexamethasone equivalent

(DexaEQ)

(van der Linden et al., 2008)

Progestagenic

endocrine effect

Progesterone receptor (PR)

mediated transcriptional

activation measured by

luciferase reporter gene

PR-CALUX Levonorgestrel equivalent

(LevoEQ)

(van der Linden et al., 2008)

Thyroid

endocrine effect

Thyroid receptor b (TRb)

mediated transcriptional

activation measured by

luciferase reporter gene

TRb-CALUX Triiodothyronine

equivalent (T3EQ)

(van der Linden et al., 2008)

Adaptive stress

response

Modulation of

cytokine

production

Stimulation or inhibition of

cytokine (IL1b) production

in white blood cell (THP1)

measured by enzyme-

linked immunosorbent

assay (ELISA)

THP1 CPA

(in þ/� modes)

Phorbol-12-myristate-13-

acetate equivalent (PMAEQ)

in þ mode; Dexamethasone

equivalent (DexaEQ) in �mode

(Modified from Baqui et al.,

1998)

Xenobiotic

metabolism

Liver enzyme

induction

Induction of CYP1A2

enzyme activity in liver cells

(C3A) measured by

conversion of luciferin-ME

HepCYP1A2 Benzo-a-pyrene equivalent

(BaPEQ)

(Modified from Cali et al.,

2006)

wat e r r e s e a r c h 4 9 ( 2 0 1 4 ) 3 0 0e3 1 5 303

3. Results

3.1. Bioassay results

3.1.1. Effects fingerprintIt was important to determine the biological activity of the 39

compounds screened in each bioassay to understand the

chemical basis of bioassay responses. The biological activity

of the 39 compounds in the full bioassay battery is sum-

marised in Tables 4 and 5. As could be expected, the hor-

mones were very potent in the endocrine assays, and were

genotoxic and cytotoxic at higher concentrations. The hor-

mone estrone and the veterinary growth promoter 17b-tren-

bolone were active in both endocrine and immune assays,

illustrating the well-known relationship between the endo-

crine and the immune systems. All of the pesticides were

slightly estrogenic, albeit with very low potencies (>1 million

Table 3 e Priority chemicals monitored in this study and applicable Australian water recycling guideline.CASRN [ Chemical Abstract Service Registry Number. N/A [ not available.

Chemical name CASRN Class Comment AGWRa

(ng/L)

17b-Estradiol (bE2) 50-28-2 Hormone Natural estrogen, main estrogenic hormone in

humans

175

Estrone (E1) 53-16-7 Hormone Natural estrogen , principal metabolite of estrogen

hormones

30

17a-Estradiol (aE2) 57-91-0 Hormone Natural estrogen, excreted mostly by livestock 175

Estriol (E3) 50-27-1 Hormone Natural estrogen, mostly produced during pregnancy 50

17a-Ethinylestradiol (EE2) 57-63-6 Pharmaceutical Used in contraceptive pills 1.5

Mestranol 72-33-3 Pharmaceutical Used in contraceptive pills, metabolised to

ethinylestradiol

2.5

Testosterone 58-22-0 Hormone Natural androgen, main androgenic hormone in

mammals

7000

Dihydrotestosterone (DHT) 521-18-6 Hormone Natural androgen, very potent androgenic hormone N/A

17b-Trenbolone 10161-33-8 Pharmaceutical (Vet) Growth promoter used in livestock management N/A

Levonorgestrel 797-63-7 Pharmaceutical Progestin, used in contraceptive pills N/A

Bisphenol A (BPA) 80-05-7 Industrial compound Plasticizer 200,000

4-Nonylphenol (4NP) 104-40-5 Industrial compound Degradation product of alkylphenol ethoxylates 500,000

4-t-Octylphenol (4tOP) 140-66-9 Industrial compound Degradation product of alkylphenol ethoxylates 50,000

Atenolol 56715-13-0 Pharmaceutical b-Blocker, used to treat cardiovascular disease 25,000b

Caffeine 58-08-2 Pharmaceutical Psychoactive simulant found in coffee, tea and other

drinks

Provisional

[350]

Carbamazepine (CBZ) 298-46-4 Pharmaceutical Anticonvulsant and mood stabiliser 100,000

Diethyltoluamide (DEET) 134-62-3 Pharmaceutical Active ingredient in insect repellents 2,500,000

Diazepam 439-14-5 Pharmaceutical Benzodiazepine derivative, used as mood stabiliser 2500

Diclofenac 15307-79-6 Pharmaceutical Non-steroidal anti-inflammatory (NSAID), used as

analgesic

1800

Gemfibrozil 25812-30-0 Pharmaceutical Lipid regulator 600,000

Indomethacin 53-86-1 Pharmaceutical Non-steroidal anti-inflammatory (NSAID), used as

analgesic

25,000

Methotrexate 59-05-2 Pharmaceutical Cytotoxic drug used to treat cancer and autoimmune

disease

5b

Paracetamol 103-90-2 Pharmaceutical Over-the-counter analgesic and antipyretic 175,000

Salicylic acid 69-72-7 Pharmaceutical Aspirin metabolite (analgesic, antipyretic and anti-

inflammatory)

105,000

Sulfamethoxazole 723-46-6 Pharmaceutical Antibiotic 35,000

Triclosan 3380-34-5 Personal care product Antibiotic and antifungal Provisional

[350]

Atrazine 1912-24-9 Pesticide Triazine herbicide, photosynthesis inhibitor 40,000

Chlorpyrifos 2921-88-2 Pesticide Organophosphate insecticide 10,000

Diazinon 333-41-5 Pesticide Organophosphate insecticide 3000

Diuron 330-54-1 Pesticide Herbicide, photosystem II inhibitor 30,000

Pentachlorophenol (PCP) 87-86-5 Pesticide Organochlorine pesticide and disinfectant 10,000

Simazine 122-34-9 Pesticide Triazine herbicide, photosynthesis inhibitor 20,000

Trifluralin 1582-09-8 Pesticide Pre-emergence herbicide 50,000

Bromochloroacetic acid 5589-96-8 Disinfection by-product Disinfection by-product Provisional

[14]b

Bromodichloromethane 75-27-4 Disinfection by-product THM disinfection by-product, also used as a flame

retardant

6000

Bromoform 75-25-2 Disinfection by-product Trihalomethane disinfection by-product 100,000

Chloroform 67-66-3 Disinfection by-product Trihalomethane disinfection by-product 200,000

Dibromochloromethane

(DBCM)

124-48-1 Disinfection by-product Trihalomethane disinfection by-product 100,000

N-Nitrosodimethylamine

(NDMA)

62-75-9 Disinfection by-product Disinfection by-product, also present in industrial

wastewater

10

a Australian Guidelines for Water Recycling (NRMMC/EPHC/NHMRC, 2008).b Queensland Public Health Regulation 2005 Schedule 3B (Queensland Government, 2005).

wat e r r e s e a r c h 4 9 ( 2 0 1 4 ) 3 0 0e3 1 5304

times less potent than 17b-estradiol). The pesticides chlor-

pyrifos and diazinon were the only two compounds found to

be neurotoxic in the AChE inhibition assay. None of the

compounds tested induced any activity in the Ames TA98

(with or without S9), TRb-CALUX, or the THP1 cytokine

stimulation (THP1 CPAþ) assays. Only one compound

induced a mutagenic response in the Ames TA100 assay

(bromochloroacetic acid), and only one compound induced a

xenobiotic metabolism response in the HepCYP1A2 assay

(pentachlorophenol).

Table 4 e Relative potency (expressed as log RP) of model compounds in the non-specific and reactive toxicity assays. Bioassays are described in Table 2. “2-AF” [ 2-Aminofluorene; “4-NPD” [ 4-Nitro-o-phenyldiamine; “MMS” [ Methyl methanesulfonate; “S9” [ liver homogenate containing phase I and phase II enzymes (“S9fraction”); “Neg”[Negative: the compound did not induce a detectable biological response above the quantification limit of the assay at the highest concentration tested(maximum possible potency indicated in brackets).

Chemical Cytotoxicity Genotoxicity Mutagenicity

Caco2 NRU WIL2NS TOX HepaTOX WIL2NS FCMN Ames TA98-S9 Ames TA98þS9 Ames TA100-S9 Ames TA100þS9

Methotrexate(IC50 ¼ 3.2E�6M)

Methotrexate(IC50 ¼ 2.6E�6 M)

Methotrexate(IC50 ¼ 3.8E�6M)

MMS(EC05 ¼ 9.4E�5M)

4-NPD(ECIR2 ¼ 1.9E�6M)

2-AF(ECIR2 ¼ 2.2E�6M)

Sodium azide(ECIR2 ¼ 1.5E�7M)

2-AF(ECIR2 ¼ 3.4E�6M)

17b-Estradiol �1.09 �1.39 �1.21 Neg (<1.56) Neg (<�1.29) Neg (<�1.22) Neg (<�2.38) Neg (<�1.04)

Estrone Neg (<�0.53) Neg (<�0.87) Neg (<�0.53) Neg (<1.30) Neg (<�0.60) Neg (<�0.52) Neg (<�1.68) Neg (<�0.34)

17a-Estradiol �1.13 �1.25 �1.05 Neg (<1.56) Neg (<�1.29) Neg (<�1.22) Neg (<�2.38) Neg (<�1.04)

Estriol Neg (<�0.90) �1.19 Neg (<�0.90) Neg (<0.93) Neg (<�0.97) Neg (<�0.89) Neg (<�2.05) Neg (<�0.71)

17a-Ethinylestradiol �1.04 �1.20 �0.92 1.62 Neg (<�1.26) Neg (<�1.18) Neg (<�2.34) Neg (<�1.00)

Mestranol Neg (<�0.86) Neg (<�1.20) Neg (<�0.87) Neg (<0.96) Neg (<�0.94) Neg (<�0.86) Neg (<�2.02) Neg (<�0.68)

Testosterone Neg (<�1.20) Neg (<�1.54) Neg (<�1.20) 0.65 Neg (<�1.27) Neg (<�1.19) Neg (<�2.35) Neg (<�1.01)

Dihydrotestosterone Neg (<�1.15) Neg (<�1.53) �1.19 0.65 Neg (<�1.27) Neg (<�1.19) Neg (<�2.35) Neg (<�1.01)

17b-Trenbolone Neg (<�1.23) Neg (<�1.56) Neg (<�1.23) 0.62 Neg (<�1.30) Neg (<�1.22) Neg (<�2.38) Neg (<�1.04)

Levonorgestrel Neg (<�1.16) Neg (<�1.50) �1.15 0.69 Neg (<�1.23) Neg (<�1.16) Neg (<�2.32) Neg (<�0.98)

Bisphenol A �1.21 �1.61 �1.28 Neg (<0.53) Neg (<�1.37) Neg (<�1.29) Neg (<�2.45) Neg (<�1.11)

4-Nonylphenol �1.10 �1.53 Neg (<�1.32) Neg (<0.99) Neg (<�1.39) Neg (<�1.31) Neg (<�2.47) Neg (<�1.13)

4-t-Octylphenol �1.09 �1.14 Neg (<�1.35) Neg (<1.44) Neg (<�1.41) Neg (<�1.34) Neg (<�2.50) Neg (<�1.16)

Atenolol Neg (<�1.19) Neg (<�1.57) Neg (<�1.24) Neg (<0.60) Neg (<�1.30) Neg (<�1.23) Neg (<�2.39) Neg (<�1.05)

Caffeine Neg (<�1.33) Neg (<�1.71) Neg (<�1.37) Neg (<0.46) Neg (<�1.44) Neg (<�1.37) Neg (<�2.52) Neg (<�1.18)

Carbamazepine Neg (<�1.24) Neg (<�1.62) Neg (<�1.29) Neg (<0.54) Neg (<�1.36) Neg (<�1.28) Neg (<�2.44) Neg (<�1.10)

DEET Neg (<�2.07) Neg (<�2.41) Neg (<�2.08) Neg (<�0.25) Neg (<�2.15) Neg (<�2.07) Neg (<�3.23) Neg (<�1.89)

Diazepam Neg (<�1.50) Neg (<�1.84) Neg (<�1.51) Neg (<0.32) Neg (<�1.58) Neg (<�1.50) Neg (<�2.66) Neg (<�1.32)

Diclofenac Neg (<�1.15) Neg (<�1.49) Neg (<�1.16) Neg (<0.67) Neg (<�1.23) Neg (<�1.15) Neg (<�2.31) Neg (<�0.97)

Gemfibrozil Neg (<�1.22) Neg (<�1.60) Neg (<�1.26) Neg (<0.57) Neg (<�1.33) Neg (<�1.25) Neg (<�2.41) Neg (<�1.07)

Indomethacin Neg (<�1.07) Neg (<�1.44) Neg (<�1.11) Neg (<0.72) Neg (<�1.17) Neg (<�1.10) Neg (<�2.26) Neg (<�0.92)

Methotrexate 0.00 0.00 0.00 3.43 Neg (<�0.07) Neg (<0.00) Neg (<�1.16) Neg (<0.19)

Paracetamol Neg (<�1.44) Neg (<�1.82) Neg (<�1.48) Neg (<0.35) Neg (<�1.55) Neg (<�1.47) Neg (<�2.63) Neg (<�1.29)

Salicylic acid Neg (<�1.48) Neg (<�1.86) Neg (<�1.52) Neg (<0.31) Neg (<�1.59) Neg (<�1.51) Neg (<�2.67) Neg (<�1.33)

Sulfamethoxazole Neg (<�1.25) Neg (<�1.59) Neg (<�1.26) Neg (<0.57) Neg (<�1.33) Neg (<�1.25) Neg (<�2.41) Neg (<�1.07)

Triclosan �0.96 �0.90 �0.83 Neg (<1.59) Neg (<�0.79) Neg (<-0.71) Neg (<�1.87) Neg (<�0.53)

Atrazine Neg (<�1.33) Neg (<�1.66) Neg (<�1.33) Neg (<0.50) Neg (<�1.40) Neg (<�1.32) Neg (<�2.48) Neg (<�1.14)

Chlorpyrifos Neg (<�1.38) Neg (<�1.75) Neg (<�1.42) Neg (<0.41) Neg (<�1.48) Neg (<�1.41) Neg (<�2.57) Neg (<�1.23)

Diazinon �1.79 �2.07 �1.71 Neg (<0.43) Neg (<�1.94) Neg (<�1.87) Neg (<�3.03) Neg (<�1.69)

Diuron Neg (<�1.29) Neg (<�1.63) �1.26 Neg (<0.54) Neg (<�1.36) Neg (<�1.29) Neg (<�2.45) Neg (<�1.10)

Pentachlorophenol �1.10 Neg (<�1.57) Neg (<�1.24) Neg (<0.60) Neg (<�1.30) Neg (<�1.23) Neg (<�2.39) Neg (<�1.05)

Simazine Neg (<�1.05) Neg (<�1.39) Neg (<�1.06) Neg (<0.78) Neg (<�1.12) Neg (<�1.05) Neg (<�2.21) Neg (<�0.87)

Trifluralin Neg (<�0.73) Neg (<�1.07) Neg (<�0.74) Neg (<1.09) Neg (<�0.80) Neg (<�0.73) Neg (<�1.89) Neg (<�0.55)

Bromochloroacetic acid Neg (<�2.08) �2.40 Neg (<�2.12) �0.26 Neg (<�2.19) Neg (<�2.11) �3.05 Neg (<�1.93)

Bromodichloromethane �4.20 �4.05 Neg (<�3.74) Neg (<�1.91) Neg (<�3.81) Neg (<�3.74) Neg (<�4.90) Neg (<�3.55)

Bromoform Neg (<�4.68) �4.00 �3.12 �1.88 Neg (<�3.79) Neg (<�3.71) Neg (<�4.87) Neg (<�3.53)

Chloroform Neg (<�4.75) Neg (<�4.09) �3.86 Neg (<�1.92) Neg (<�3.82) Neg (<�3.75) Neg (<�4.91) Neg (<�3.57)

Dibromochloromethane �4.68 �3.82 �3.12 Neg (<�1.61) Neg (<�3.82) Neg (<�3.74) Neg (<�4.90) Neg (<�3.56)

NDMA Neg (<�2.49) Neg (<�2.83) Neg (<�2.49) Neg (<�0.66) Neg (<�2.56) Neg (<�2.48) Neg (<�3.64) Neg (<�2.30)

Notes: IC50 ¼ Concentration required to cause 50% reduction in cell survival; EC05 ¼ Concentration required to cause 5% effect; ECIR2 ¼ Concentration required to reach an induction ratio of 2.

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Table 5 e Relative potency (expressed as log RP) of model compounds in the specific toxicity, adaptive stress response and xenobiotic metabolism assays. Bioassays aredescribed in Table 2. “BaP” [ Benzo-a-pyrene; “Dexa” [ Dexamethasone; “DHT” [ 5a-Dihydrotestosterone; “Levo” [ levonorgestrel; “PMA” [ Phorbol-12-myristate-13-acetate; “T3” [ Triiodothyronine; “Neg” [ Negative: the compound did not induce a detectable biological response above the quantification limit of the assay at thehighest concentration tested (maximum possible potency indicated in brackets); [ ]* [ extrapolated, low reliability.

Chemical Neurotox Endocrine effect (CALUX) Adaptive stress response Metabolism

AChE AR þ AR � ERa þ ERa � GR PR TRb THP1 þ THP1 � HepCYP1A2

Chlorpyrifos(IC50 ¼

1.8E�9M)

DHT(EC50 ¼

2.4E�10M)

Flutamide(IC50 ¼

1.1E�6M)

Estradiol(EC50 ¼

2.4E�12M)

Tamoxifen(IC50 ¼

4.5E�8M)

Dexa(EC50 ¼

1.1E�9M)

Levo(EC50 ¼

5.4E�10M)

T3(EC50 ¼

6.9E�10M)

PMA(ECIR2 ¼

3.1E�10M)

Dexa(IC50 ¼

2.7E�9M)

BaP(ECIR1.7 ¼1.5E�6M)

17b-Estradiol Neg (<�2.65) �4.40 1.86 0.00 Agonist Neg (<�5.03) Neg (<�5.20) Neg (<�4.32) Neg (<�3.05) Neg (<�3.61) Neg (<�1.39)

Estrone Neg (<�2.65) Neg (<�4.56) 1.17 �1.85 Agonist Neg (<�4.34) Neg (<�4.38) Neg (<�3.63) Neg (<�2.39) �2.83 Neg (<�0.69)

17a-Estradiol Neg (<�2.65) Neg (<�5.26) 1.14 �2.69 Agonist Neg (<�5.03) Neg (<�5.08) Neg (<�4.32) Neg (<�3.09) Neg (<�3.61) Neg (<�1.39)

Estriol Neg (<�2.62) Neg (<�5.21) �0.26 �1.77 Agonist Neg (<�4.71) Neg (<�4.75) Neg (<�4.00) Neg (<�2.76) Neg (<�3.28) Neg (<�1.06)

17a-Ethinylestradiol Neg (<�2.61) Neg (<�5.22) 1.96 0.73 Agonist Neg (<�5.00) �4.39 Neg (<�4.29) Neg (<�3.05) Neg (<�3.57) Neg (<�1.35)

Mestranol Neg (<�2.59) Neg (<�4.81) 0.30 �3.21 Agonist Neg (<�4.67) Neg (<�4.72) Neg (<�3.97) Neg (<�2.73) Neg (<�3.25) Neg (<�1.03)

Testosterone Neg (<�2.62) �0.77 Agonist �5.78 Agonist Neg (<�5.01) Neg (<�4.79) Neg (<�4.30) Neg (<�3.06) Neg (<�3.58) Neg (<�1.36)

Dihydrotestosterone Neg (<�2.62) 0.00 Agonist �4.81 Agonist Neg (<�5.00) �4.92 Neg (<�4.31) Neg (<�3.06) Neg (<�3.58) Neg (<�1.36)

17b-Trenbolone Neg (<�2.65) �0.30 Agonist �4.26 Agonist Neg (<�5.04) �2.23 Neg (<�4.33) Neg (<�3.09) �3.51 Neg (<�1.39)

Levonorgestrel Neg (<�2.59) �0.56 Agonist �5.54 Agonist �4.79 0.00 Neg (<�4.26) Neg (<�3.03) Neg (<�3.55) Neg (<�1.33)

Bisphenol A Neg (<�2.72) Neg (<�5.34) �0.67 �4.84 Agonist Neg (<�5.11) Neg (<�5.15) Neg (<�4.40) Neg (<�3.17) Neg (<�3.69) Neg (<�1.47)

4-Nonylphenol Neg (<�2.74) Neg (<�5.35) �0.53 �4.04 Agonist Neg (<�5.12) Neg (<�5.17) Neg (<�4.42) Neg (<�3.18) Neg (<�3.70) Neg (<�1.48)

4-t-Octylphenol Neg (<�2.77) Neg (<�5.38) �0.39 �4.91 Agonist Neg (<�5.15) Neg (<�4.76) Neg (<�4.45) Neg (<�3.21) Neg (<�3.73) Neg (<�1.51)

Atenolol Neg (<�2.66) Neg (<�5.27) �1.03 Neg (<�7.16) Neg (<�2.06) Neg (<�4.47) Neg (<�5.11) Neg (<�4.35) Neg (<�3.10) Neg (<�3.62) Neg (<�1.40)

Caffeine Neg (<�2.79) Neg (<�5.68) Neg (<�1.04) Neg (<�7.29) Neg (<�2.19) Neg (<�5.18) Neg (<�5.22) Neg (<�4.49) Neg (<�3.24) Neg (<�3.76) Neg (<�1.54)

Carbamazepine Neg (<�2.71) Neg (<�5.32) Neg (<�0.95) Neg (<�7.21) Neg (<�2.17) Neg (<�5.09) Neg (<�5.14) Neg (<�4.39) Neg (<�3.15) Neg (<�3.67) Neg (<�1.45)

DEET Neg (<�2.80) Neg (<�5.06) Neg (<�1.64) Neg (<�8.00) Neg (<�2.90) Neg (<�5.89) Neg (<�5.93) Neg (<�5.18) Neg (<�3.94) Neg (<�4.54) Neg (<�2.24)

Diazepam Neg (<�5.63) Neg (<�5.54) �0.74 �6.95 Agonist Neg (<�5.31) Neg (<�6.34) Neg (<�4.62) Neg (<�3.37) Neg (<�3.89) Neg (<�1.67)

Diclofenac Neg (<�2.58) Neg (<�5.19) Neg (<�0.82) Neg (<�7.08) Neg (<�2.34) Neg (<�4.97) Neg (<�5.01) Neg (<�4.26) Neg (<�3.02) Neg (<�3.54) Neg (<�1.32)

Gemfibrozil Neg (<�2.68) Neg (<�5.81) Neg (<�0.93) Neg (<�7.18) Neg (<�2.08) Neg (<�5.07) Neg (<�5.11) Neg (<�4.36) Neg (<�3.13) Neg (<�3.65) Neg (<�1.42)

Indomethacin Neg (<�2.53) Neg (<�5.42) Neg (<�0.77) Neg (<�7.03) Neg (<�1.93) Neg (<�4.91) Neg (<�4.96) Neg (<�4.21) Neg (<�2.97) Neg (<�3.49) Neg (<�1.27)

Methotrexate Neg (<�2.42) Neg (<�4.31) Neg (<0.33) �5.33 Agonist Neg (<�3.81) Neg (<�3.85) Neg (<�3.10) Neg (<�1.87) Neg (<�2.39) Neg (<�0.17)

Paracetamol Neg (<�2.90) Neg (<�5.79) Neg (<�1.15) �4.51 Agonist Neg (<�5.29) Neg (<�5.33) Neg (<�4.58) Neg (<�3.34) Neg (<�3.87) Neg (<�1.64)

Salicylic acid Neg (<�2.94) Neg (<�5.83) Neg (<�1.18) Neg (<�7.44) Neg (<�2.34) Neg (<�5.33) Neg (<�5.37) Neg (<�4.62) Neg (<�3.38) Neg (<�3.90) Neg (<�1.68)

Sulfamethoxazole Neg (<�2.68) Neg (<�5.57) Neg (<�0.56) �7.00 Agonist Neg (<�5.06) Neg (<�5.11) Neg (<�4.36) Neg (<�3.12) Neg (<�3.64) Neg (<�1.42)

Triclosan Neg (<�2.62) Neg (<�5.23) �0.11 �6.23 Agonist Neg (<�5.01) Neg (<�5.05) Neg (<�4.30) Neg (<�3.06) Neg (<�3.58) Neg (<�1.36)

Atrazine Neg (<�2.75) Neg (<�5.36) Neg (<�0.63) �6.36 Agonist Neg (<�5.13) Neg (<�5.52) Neg (<�4.43) Neg (<�3.19) Neg (<�3.71) Neg (<�1.49)

Chlorpyrifos 0.00 Neg (<�5.32) �0.70 �5.31 Agonist Neg (<�4.65) Neg (<�6.25) Neg (<�4.53) Neg (<�3.28) Neg (<�3.80) Neg (<�1.58)

Diazinon �0.26 Neg (<�6.19) �1.53 �7.29 Agonist Neg (<�5.11) Neg (<�5.73) Neg (<�4.99) Neg (<�3.74) Neg (<�4.26) Neg (<�2.04)

Diuron Neg (<�2.71) Neg (<�5.33) Neg (<�1.43) �6.49 Neg (<�2.11) Neg (<�5.10) Neg (<�5.14) Neg (<�4.39) Neg (<�3.16) Neg (<�3.75) Neg (<�1.46)

Pentachlorophenol Neg (<�2.66) Neg (<�5.27) Neg (<�0.54) �6.88 Agonist Neg (<�5.04) Neg (<�5.09) Neg (<�4.33) Neg (<�3.10) Neg (<�3.62) [ 1.37 ]*

Simazine Neg (<�2.78) Neg (<�5.36) Neg (<�0.36) �6.61 Agonist Neg (<�4.86) Neg (<�4.91) Neg (<�4.15) Neg (<�2.92) Neg (<�3.44) Neg (<�1.22)

Trifluralin Neg (<�2.56) Neg (<�5.05) Neg (<�0.04) �6.12 Agonist Neg (<�4.54) Neg (<�4.59) Neg (<�3.84) Neg (<�2.60) Neg (<�3.12) Neg (<�0.90)

Bromochloroacetic acid Neg (<�2.84) Neg (<�6.15) Neg (<�1.43) Neg (<�8.04) Neg (<�2.94) Neg (<�5.93) Neg (<�5.97) Neg (<�5.23) Neg (<�3.98) Neg (<�4.50) Neg (<�2.28)

Bromodichloromethane Neg (<�6.16) Neg (<�8.78) Neg (<�4.41) Neg (<�10.7) Neg (<�5.76) Neg (<�8.55) Neg (<�9.57) Neg (<�7.86) Neg (<�6.61) �7.01 Neg (<�3.91)

Bromoform Neg (<�6.14) Neg (<�9.03) �3.69 �10.1 Neg (<�5.05) Neg (<�8.52) Neg (<�8.57) Neg (<�7.82) Neg (<�6.58) �6.33 Neg (<�3.88)

Chloroform Neg (<�6.17) Neg (<�8.79) Neg (<�4.42) �9.98 Agonist Neg (<�8.56) Neg (<�8.61) Neg (<�7.85) Neg (<�6.62) Neg (<�7.14) Neg (<�4.92)

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Dibro

moch

loro

meth

ane

Neg(<

�6.17)

Neg(<

�9.06)

Neg(<

�4.41)

Neg(<

�10.7)

�5.47

Neg(<

�8.55)

Neg(<

�8.60)

Neg(<

�7.85)

Neg(<

�6.61)

�6.48

Neg(<

�3.91)

NDMA

Neg(<

�3.21)

Neg(<

�6.52)

Neg(<

�2.15)

Neg(<

�8.41)

Neg(<

�3.31)

Neg(<

�6.30)

Neg(<

�7.32)

Neg(<

�5.60)

Neg(<

�4.35)

Neg(<

�4.87)

Neg(<

�2.65)

Notes:

EC50¼

Conce

ntration

required

toca

use

50%

effect;IC

50¼

Conce

ntration

required

toca

use

50%

reduction

ofth

eeffect;ECIR2¼

Conce

ntration

required

toreach

an

induction

ratio

of2;

ECIR1.7¼

Conce

ntrationrequiredto

reach

aninductionratioof1.7.

wat e r r e s e a r c h 4 9 ( 2 0 1 4 ) 3 0 0e3 1 5 307

3.1.2. Water samplesFig. 1 summarises the biological activity as tested in our

bioassay battery for all water samples. Therewas no biological

activity in the field blanks or the tap water samples and there

was minimal cytotoxicity (0.06 TU in the WIL2NS TOX assay)

in the rain and bottled water samples (Fig. 1, “Blanks”). With

the exception of LAGUV, source water samples (treated

wastewater; Fig. 1, black bars) contained high levels of bio-

logically active compounds, with positive results in almost all

bioassays except the Ames test for mutagenicity, the TRb-

CALUX for thyroid activity and the THP1 CPAþ for immu-

nostimulation. The water quality of the product water

(reclaimed water; Fig. 1, grey bars) was only marginally better

in the non-RO based plants (LAGF, UF1 and UF2), but greatly

improved in the RO-based plants (RO1eRO5) often bringing

biological activity to below detection limit.

3.2. Chemical results

Table 6 presents some of the key chemical results which

illustrate the general trends identified by the full chemical

analysis. Additional chemical data is presented in Tables

SI1eSI4. The trends of the chemical analysis results are

nearly identical to the bioanalytical results with almost no

chemicals detected in the blanks, very low concentrations of a

few compounds in the source water from the LAGUV plant,

and mg/L concentrations of a variety of anthropogenic com-

pounds in source water of the other plants. As with the bio-

analytical results, the concentration of these compoundswere

greatly reduced by the RO-based plants (RO1eRO5), but only

marginally in the non-RO plants (LAGF, UF1 and UF2).

Pharmaceuticals were not detected in RO effluent from

RO1, RO3, RO4 and RO5. Two pharmaceuticals were detected

just above detection limit in product water from RO2. These

were carbamazepine at 8 ng/L (Table 6) and metformin at

12 ng/L (Table SI4), reduced by 99% from the source water

concentrations. These concentrations are several orders of

magnitude lower than their health guideline levels (Table 3).

No personal care products were detected in any of product

water samples from RO1, RO3, RO4 and RO5, but the anti-

bacterial compound triclocarban, used in disinfectants and

soaps, was found at the limit of detection (5 ng/L) in product

water from RO2 (Table SI4).

None of themonitoredpesticideswere detected inany of the

product water samples from RO1, RO3 and RO4, but two of the

monitored herbicides (diuron and simazine) were detected at

ng/L concentrations in product waters from RO2 and RO5. At

RO5, very high simazine concentrations were detected in the

sourcewaters (treatedwastewater) on two of the sampling days

(>20,000 ng/L). On those same two days, simazinewas detected

in the product water, at 112 and 118 ng/L. These concentrations

aremore than 99.5% lower than in the sourcewater, and several

orders of magnitude lower than the recycled water guidelines

(Table 3). Diuron was also detected at 15 ng/L in product water

from RO2, also several orders of magnitude lower than the

guideline value for recycled water (Table 3).

Chlorine DBPs (such as trihalomethanes) were detectable

in product water of a few plants (LAGF, LAGUV, UF2, RO1 and

RO4), as well as in tap water.

Fig. 1 e Average bioassay results for the source (black bars) and product water (grey bars) samples at the nine water

reclamation plants and the four blanks (field blanks, tap, rain and bottled water). Thirteen bioassays were applied covering a

range of modes of action: non-specific (green), reactive (red) and specific toxicity (blue), as well as indicators of adaptive

stress response (orange) and xenobiotic metabolism (brown). Samples are described in Table 1 and bioassays in Table 2.

wat e r r e s e a r c h 4 9 ( 2 0 1 4 ) 3 0 0e3 1 5308

Table 6 e Concentration of selected compounds in the source and product water from nine water reclamation plants and the blank samples (field blanks, tap, rain andbottled water) determined by chemical analysis. Values are mean ± standard error of the mean. Samples are described in Table 1. “4tOP” [ 4-t-Octylphenol;“BPA” [ Bisphenol A; “CBZ” [ Carbamazepine; “DBCM” [ Dibromochloromethane; “Sulfameth” [ Sulfamethoxazole. “ND” [ Not detected. Where a compound wasdetected at least once above themethod detection limit but the average of all themeasures is less than the detection limit, the sample ismarked as “<”. These results arerepresentative of the chemical trends highlighted by chemical analysis, and the summary for all other chemicals is presented in Tables SI1eSI4.

Site Sample n Estrone(ng/L)

BPA(ng/L)

4tOP(ng/L)

CBZ(ng/L)

Sulfameth(ng/L)

Triclosan(ng/L)

Diazinon(ng/L)

Diuron(ng/L)

Simazine(ng/L)

DBCM(ng/L)

LAGF Source 8 1.2 � 0.3 ND (<70) ND (<10) 550 � 12 345 � 28 34 � 9.7 114 � 17 638 � 27 739 � 63 ND (<20)

Product 8 ND (<1) ND (<70) ND (<10) 72 � 47 <5 ND (<10) ND (<5) 446 � 60 735 � 88 3660 � 1610

LAGUV Source 4 ND (<1) ND (<70) ND (<10) ND (<5) 12 � 4.7 ND (<10) ND (<5) ND (<5) ND (<20) ND (<20)

Product 4 ND (<1) ND (<70) ND (<10) ND (<5) ND (<5) ND (<10) ND (<5) ND (<5) ND (<20) 2780 � 767

UF1 Source 3 20 � 7.7 ND (<70) 26 � 8.1 495 � 112 212 � 47 40 � 10 11 � 5.6 53 � 13 568 � 272 ND (<20)

Product 3 19 � 3.6 ND (<70) 17 � 1.1 600 � 20 239 � 11 37 � 4.0 12 � 2.0 64 � 7.4 622 � 145 ND (<20)

UF2 Source 8 8.0 � 1.1 <70 80 � 16 288 � 57 440 � 92 38 � 21 <5 25 � 6.1 175 � 58 <20

Product 8 1.0 � 0.3 ND (<70) 39 � 13 220 � 8.7 166 � 22 18 � 5.6 ND (<5) 16 � 1.1 112 � 20 26 � 4.0

RO1 Source 8 4.0 � 0.4 <70 12 � 2.1 405 � 40 394 � 42 89 � 11 47 � 11 107 � 12 301 � 95 ND (<20)

Product 8 ND (<1) <70 ND (<10) ND (<5) ND (<5) ND (<10) ND (<5) ND (<5) ND (<20) 24 � 7.2

RO2 Source 1 2 ND (<70) ND (<10) 676 498 46 142 68 260 ND (<20)

Product 1 ND (<1) ND (<70) ND (<10) 8 ND (<5) ND (<10) ND (<5) 15 ND (<20) ND (<20)

RO3 Source 4 <1 248 � 82 ND (<10) 484 � 15 228 � 11 230 � 219 73 � 15 54 � 4.0 ND (<20) ND (<20)

Product 4 ND (<1) ND (<70) ND (<10) ND (<5) ND (<5) ND (<10) ND (<5) ND (<5) ND (<20) ND (<20)

RO4 Source 4 <1 <70 ND (<10) 491 � 22 116 � 61 13 � 6.9 12 � 8.6 39 � 4.3 35 � 7.2 208 � 223

Product 4 ND (<1) ND (<70) ND (<10) ND (<5) ND (<5) ND (<10) ND (<5) ND (<5) ND (<20) 39 � 31

RO5 Source 6 4.7 � 2.1 ND (<70) 10 � 2.0 396 � 85 222 � 43 52 � 14 30 � 8.1 96 � 29 6790 � 4580 ND (<20)

Product 6 ND (<1) ND (<70) ND (<10) ND (<5) ND (<5) ND (<10) ND (<5) ND (<5) 45 � 24 ND (<20)

Bottle 1 ND (<1) ND (<70) ND (<10) ND (<5) ND (<5) ND (<10) ND (<5) ND (<5) ND (<20) ND (<20)

Tap 5 ND (<1) ND (<70) ND (<10) ND (<5) ND (<5) ND (<10) ND (<5) ND (<5) ND (<20) 166 � 144

Rain 1 ND (<1) ND (<70) ND (<10) ND (<5) ND (<5) ND (<10) ND (<5) ND (<5) ND (<20) ND (<20)

FB 11 ND (<1) ND (<70) ND (<10) ND (<5) ND (<5) ND (<10) ND (<5) ND (<5) ND (<20) ND (<20)

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wat e r r e s e a r c h 4 9 ( 2 0 1 4 ) 3 0 0e3 1 5310

4. Discussion

Overall, there was a strong agreement between the data

trends of the bioassay battery and conventional chemical

analysis. It is obvious that the 39 compounds monitored here

only represent a fraction of the total number of chemicals in

those water samples, so it was generally not possible to

explain all of the biological activity measured in vitro with the

concentrations of just those 39 compounds. The compounds

were however carefully selected as likely to contribute to the

biological activity or at least indicate the presence of groups of

biologically active compounds (e.g., DBPs). So while it was not

possible to comprehensively link individual compounds to a

bioassay response, it was often possible to link classes of

compounds to specific bioassay responses and use bioassays

as indicators of classes of pollutants in the water sample, as

described below.

4.1. Non-specific toxicity: cytotoxicity

Of the cytotoxicity assays, theWIL2NS TOX appeared to be the

most sensitive, providing a few detections particularly with

treated wastewater samples. No cytotoxicity was detected in

any of the samples with the Caco2-NRU test, although the

method detection limit was 10� higher than the other two

assays (0.5 vs. 0.05 TUs). A simple modification (loading 1%

sample instead of 0.1% as currently performed) would

improve the method detection limit by 10� and make it

comparable to the other tests.

The bottled water and rainwater samples exhibited cyto-

toxicity just above the detection limit (0.06 vs. 0.05 TU). This

“low level toxicity” is not uncommon in water samples that

have been highly concentrated by solid-phase extraction

(SPE), and has been reported previously (Escher et al., 2008;

Macova et al., 2011; Reitsema et al., 2010). The residual toxicity

is most likely due to small amounts of solvent carried over

from the SPE concentration step and is thought to be of no

concern if a) it is not considerably above the detection limit

and b) no other specific and/or reactive toxicity are associated

with the sample e as was the case here.

Slightly higher but consistent cytotoxicity (WIL2NS TOX)

was detectable in most of the source water samples. Notably,

none of the samples exceeded 1 TU, meaning that none of the

samples would have been cytotoxic without sample concen-

tration. These results are comparable to previous studies

(Muller et al., 2007; Reitsema et al., 2010), which reported the

equivalent of 0.03e0.12 TU in treated sewage effluent with the

Microtox assay for bacterial cytotoxicity.

Cytotoxicity (WIL2NS TOX) in the product water of LAGF,

UF1 and UF2 was fundamentally unchanged from that in the

source water (Fig. 1). Ultrafiltration/chloramination (at UF1)

was generally ineffective at removing organic compounds,

with concentrations in the product water on average 8%

higher than in source water at those plants (based on 34

compounds detected in both source and product water; Tables

6 and SI1eSI4). The poor removal efficacy of ultrafiltration for

small organic compounds has been demonstrated before

(Snyder et al., 2007). Ultrafiltration/chlorination (at UF2) was

slightlymore effective, with an average removal of 28% (based

on 23 compounds detected in both source and product water).

Free chlorine has previously been found to oxidise trace

organic compounds to some extent (Snyder et al., 2007), and

chlorination could be responsible for the slightly better

removal compared with chloramination. Dissolved air floa-

tation/filtration (DAFF) and chlorination (at LAGF) was effec-

tive at removing lipophilic compounds (e.g., diclofenac and

gemfibrozil), but less effective with more water-soluble com-

pounds (log Kow < 3; e.g., atenolol and caffeine) (Tables 6 and

SI1eSI4).

Low-level cytotoxicity (WIL2NS TOX) comparable to that

measured in the blank samples was detected at one of the RO

sites (RO4), but was below detection limit for all other RO

effluent samples. Previous studies (Macova et al., 2011; Muller

et al., 2007; Reitsema et al., 2010; Reungoat et al., 2010) have

likewise reported very low cytotoxicity (equivalent of

0.03e0.05 TU) in highly treated water (including RO) using the

Microtox assay for bacterial cytotoxicity, and concluded that it

was likely an artefact of SPE concentration. Very few com-

pounds were detected in RO-treated water, suggesting that RO

is an effective barrier against most trace organic compounds.

4.2. Reactive toxicity: genotoxicity and mutagenicity

Hormones, pharmaceuticals and to a lesser extent some DBPs

caused genotoxicity in theWIL2NS FCMN assay (Table 4). This

was noticeable in the water samples as well, with detectable

genotoxicity in samples that contained domestic wastewater

or DBPs. DBPs have generally been linked to genotoxicity in

water (Richardson et al., 2007), and 2 out of 8 of our priority

compounds that induced genotoxicity were DBPs (bromo-

chloroacetic acid and bromoform; Table 4). DBPs have likewise

previously been associated with mutagenicity (Richardson

et al. 2007). In this project, the trihalomethanes (THMs)

tested did not induce mutagenicity in the Ames test; however

the samples with the highest THM concentrations (Tables 6

and SI2) also were the only ones to cause detectable mutage-

nicity (Fig. 1). This suggests that mutagenicity is likely asso-

ciated with DBPs, but not those monitored here.

A few of the source water samples exhibited low-level

genotoxicity (at LAGF, UF1 and UF2; WIL2NS FCMN),

although none of the samples exceeded 1 GTU. These results

are comparable to previous studies (Reitsema et al., 2010;

Reungoat et al., 2010), which reported the equivalent of <0.1

GTU in treated sewage effluent with the umuC assay for

bacterial genotoxicity.

Non-RO treatment had little impact on genotoxicity, with

genotoxicity (WIL2NS FCMN) in the product water samples

from LAGF, UF1 and UF2 inducing a similar activity as in the

source water (Fig. 1). Chlorine disinfection (at LAGF, LAGUV

and UF2) resulted in a considerable increase in chlorination

DBPs detected at mg/L concentration (Tables 6 and SI2).

Mutagenicitywas only detected in productwater fromLAGF in

the Ames test without S9 fraction (the only sample to test

positive in this assay), which contained the highest concen-

trations of THMs (Tables 6 and SI2). Chloramination (at UF1)

on the other hand did not create chlorinated DBPs, NDMA or a

detectable increase in genotoxicity or mutagenicity.

There was no detectable genotoxicity (WIL2NS FCMN) or

mutagenicity (Ames test) in any of the RO effluent samples.

wat e r r e s e a r c h 4 9 ( 2 0 1 4 ) 3 0 0e3 1 5 311

This is comparable to previous studies (Reitsema et al., 2010;

Reungoat et al., 2010), which reported the equivalent of <0.01

GTU in RO effluents. THMs were detected in product water

samples from RO1 and RO4. THMS are frequently detected in

chlorinated waters, and the concentrations of THMs detected

here (<20e344 ng/L) were within the same range as THMs in

the tap water samples (Tables 6 and SI2). Both were orders of

magnitude below relevant Australian recycled and drinking

water guideline levels (NHMRC/NRMMC, 2011; NRMMC/EPHC/

NHMRC, 2008).

4.3. Specific toxicity: acetylcholinesterase inhibition

The AChE assay was the only cell-free assay used in our

bioassay battery. The lack of a cell membrane, which acts as a

major barrier to toxicant uptake, means that the results of the

AChE assay can be significantly affected by matrix interfer-

ence and may not be suitable for samples rich in organic

matter such as wastewater samples (Neale and Escher, 2013).

Of the tested compounds, chlorpyrifos and diazinon were

potent AChE inhibitors, and the concentrations of those two

compounds explained most of the AChE inhibition in most

samples.

Except for LAGUV, all source water samples caused inhi-

bition of AChE activity (up to 0.10 mg/L ChlorpyEQ in RO2

source water). Bioactivity in secondary treated wastewater

causing inhibition of AChE activity has been reported previ-

ously (Macova et al., 2011; Reungoat et al., 2010). The AChE

inhibitionmeasured in vitrowas well correlated with diazinon

and chlorpyrifos concentrations (Tables 6 and SI2, respec-

tively), which explained 40e97% of the activity at most sites.

This AChE inhibition was not effectively reduced by non-

RO treatment, with concentrations in source and product

water at LAGF, UF1 and UF2 essentially unaffected by treat-

ment. Diazinon was effectively removed by DAFF and chlori-

nation (at LAGF), but surprisingly AChE activity was still

comparable to that in the source water (71 vs. 78 ng/L Chlor-

pyEQ). The reason for this discrepancy is unclear, but could be

due to interference with enzyme activity in this cell-free assay

(Neale and Escher, 2013). Other work has shown that the

inhibitory effects on AChE activity were reduced following

progressive treatment in a sewage treatment plant (Escher

et al., 2008).

AChE inhibition was not detected in any of the RO-treated

samples, and neither were chlorpyrifos or diazinon.

4.4. Specific toxicity: estrogenic activity

Several endocrine endpoints were included in this bioassay

battery. Many compounds were active in the ERa-CALUX

assay, although most non-hormones had much lower po-

tencies than the hormones. Only a few samples induced anti-

estrogenic activity and the causative chemicals are unclear,

although plasticisers and phthalates are likely candidates.

Apart from LAGUV, all source water samples contained

significant estrogenic activity (up to 4.7 ng/L EEQ). Anti-

estrogenic activity could generally not be determined

because of the masking effect of estrogenic activity. There is

an extensive dataset on estrogenicity in treated sewage in

Australia (reviewed in Allinson et al., 2010), and the low ng/L

EEQ in treated wastewater reported here is comparable to

previously reported Australian values. Interestingly, none of

the natural hormones were detected in any of the treated

wastewater samples with the exception of estrone, whichwas

commonly detected (Table 6). Estrone is one of several hor-

mones excreted by humans, and its presence suggests that

other more potent estrogen hormones such as 17b-estradiol

and the synthetic hormone ethinylestradiol may also be pre-

sent, but below the detection limit of the chemical analysis

method (1 ng/L). Those additional compounds could, however,

significantly contribute to the estrogenic activity measured by

the bioassay (with a lower detection limit of 0.05 ng/L EEQ). A

small portion of the measured activity is also due to the

presence of hormone mimics such as the industrial com-

pounds (4-t-octylphenol and bisphenol A) and pesticides

(Table 5).

Ultrafiltration and chlorination/chloramination (at UF1

and UF2) reduced the estrogenic activity of the wastewater

samples by 54e83%, but despite this reduction the estrogenic

activity present in the source water was still clearly detectable

in product water samples (Fig. 1, grey bars). These results are

consistent with expected removal efficacies of endocrine

disrupting compounds by ultrafiltration and chlorination/

chloramination (Snyder et al., 2007). Similar to what was

found in source water, the product water samples contained

no detectable traces of hormones except for low ng/L of

estrone at UF1 and UF2. On the other hand, the very low

concentration of estrone in source water samples at LAGFwas

reduced below detection limit by DAFF. This result was

mirrored in the bioassay data, with no detectable endocrine

activity in LAGF product water. This indicates that DAFF and

chlorination treatment was effective in reducing the concen-

tration of the estrogenic compounds. Steroid hormones are

highly lipophilic and usually well removed by adsorption

mechanisms (Joss et al., 2004; Leusch et al., 2006). The same

trends were also visible for pesticides, with poor removal by

ultrafiltration but efficient removal of lipophilic compounds

by DAFF, with variable (but generally less efficient) removal of

the more water-soluble herbicides.

Two of the five RO effluents produced low and intermittent

estrogenicity (0.08 � 0.07 and 0.17 � 0.15 ng/L EEQ at RO3 and

RO5, respectively). Anti-estrogenic activity was also detected

just above the detection limit at two of the plants, RO1 and

RO2 (2.3 and 4.4 mg/L TMXEQ, respectively). No compounds

were detected in RO effluent samples that could explain the

low estrogenic and anti-estrogenic activities reported by the

ERa-CALUX bioassay. A desktop modelling exercise predicted

concentrations of roughly 0.5 ng/L EEQ in RO reclaimed water

(Leusch et al., 2009), and the concentrations reported here are

slightly lower than that prediction. There are currently no

bioassay-based guidelines for water quality, but the Austra-

lian Guidelines for Water Recycling (NRMMC/EPHC/NHMRC,

2008) set guidelines for estrogens ranging from 1.5 ng/L for

ethinylestradiol to 175 ng/L for 17b-estradiol. The concentra-

tions reported here are much lower than these guideline

levels, and the estrogenic activity unlikely to be of human

health significance considering that dietary intake results in

approximately 1.4 mg/d EEQ (Leusch et al., 2009). Little is

known about anti-estrogens, and most currently identified

anti-estrogens are synthetic drugs (van der Burg et al., 2010).

wat e r r e s e a r c h 4 9 ( 2 0 1 4 ) 3 0 0e3 1 5312

Plasticisers and phthalates are known weak estrogen mimics

and may also be weak anti-estrogens (Ghisari and Bonefeld-

Jorgensen, 2009; Leusch et al., 2009). Considering the large

surface of RO membranes, it is possible that low concentra-

tions of plastics and phthalates leaching from membrane

material could be causing this low level activity.

4.5. Specific toxicity: androgenic activity

Only androgen hormones and pharmaceuticals were biologi-

cally active in the AR-CALUX assay, while many estrogenic

compounds proved to be anti-androgens. No androgen hor-

moneswere detected in any of the samples, and only 1 sample

was active in the AR-CALUX.

The only positive sample in this assay was the source

water to UF2, just above the detection limit at 2 ng/L DHTEQ.

No androgen hormones were detected, however the chemical

detection limit was higher than this at 5 ng/L. It is therefore

unclear what compound is responsible for this activity. Anti-

androgenic activity was not detected in any of the samples.

A previous study in Western Australia (Reitsema et al., 2010)

reported <2.5 ng/L DHTEQ in treated sewage and a Dutch

study (van der Linden et al., 2008) reported 0.73e0.83 ng/L

DHTEQ in treated sewage, both determined by AR-CALUX

assay.

No (anti)androgenic activity was detected in any of the

reclaimed water samples. This is similar to what was reported

in RO effluent in a previous study (Reitsema et al., 2010).

4.6. Specific toxicity: glucocorticoid activity

None of the compounds tested were highly active in the GR-

CALUX assay, however some activity was detected in treated

wastewater samples and product waters from non-RO plants.

Corticosteroids and other pharmaceuticals have been corre-

lated with glucocorticoid activity in wastewater (Schriks et al.,

2010; van der Linden et al., 2008). Corticosteroids are present

both as natural hormones and pharmaceutical agents, used in

severalmedical applications such as asthma inhalers and skin

irritation creams. A positive GR-CALUX result was usually well

correlated with “treated domestic wastewater” and pharma-

ceuticals, and this assay could be used as an indicator of

either.

Low glucocorticoid activity (up to 0.081 mg/L DexaEQ) was

detected in many of the source water samples (at UF1, RO1,

RO3, RO4 and RO5). These concentrations are similar to those

reported in a Dutch study, which reported 0.011e0.038 mg/L

DexaEQ in treated sewage using the GR-CALUX assay (van der

Linden et al., 2008). Corticosteroids are likely to be the most

significant contributors to glucocorticoid activity in these

samples.

Ultrafiltration did not reduce glucocorticoid activity

detected in the UF1 source water. The poor removal of

glucocorticoid activity by UF is not surprising considering that

the molecular weight of most steroids (including corticoste-

roids) is usually in the range of 300e500 Da and that the mo-

lecular weight cut off (MWCO) of ultrafiltration membranes is

usually in the range of 10,000e100,000 Da.

Although glucocorticoid activity was detected in 4 out of 5

RO plant source waters, it was not detected in any of the RO

effluent samples, clearly indicating that RO membranes

(which generally have a MWCO below 300 Da) were an effi-

cient barrier to glucocorticoid compounds.

4.7. Specific toxicity: progestagenic activity

Levonorgestrel, an active ingredient in contraceptive pills,

displayed significant activity in the PR-CALUX (Table 5), but it

was not detected in any of the samples. It is however difficult

to compare the bioassay and the chemical data for levonor-

gestrel due to the significant difference in detection limits: the

chemical detection limit was 5 ng/L, but the PR-CALUX assay

was able to detect 0.01 ng/L LevoEQ. Other researchers have

suggested that natural hormones such as progesterone and

other pharmaceuticals may also be responsible for some of

the PR-like activity detected in environmental water samples

(van der Linden et al., 2008). Similar to the glucocorticoid ac-

tivity results, a positive PR-CALUX result was well correlated

with “treated domestic wastewater” and pharmaceuticals,

and the assay could be used as an indicator of either.

Strong progestagenic activity (up to 5.4 ng/L LevoEQ) was

detected in several source water samples (at UF1, RO1, RO3

and RO5). These concentrations are in the same range as those

reported in a Dutch study, which reported the equivalent of

1.9e2.2 ng/L LevoEQ in treated sewage using the PR-CALUX

assay (van der Linden et al., 2008). This activity is likely due

to natural and synthetic progestagens, such as levonorgestrel,

present below the chemical detection limit (5 ng/L).

Ultrafiltration and chlorination reduced progestagenic ac-

tivity detected in the UF1 source water by 54%. It is likely that

the main removal mechanism was chlorination and not ul-

trafiltration, considering the size of these compounds.

No progestagenic activity was detected in any of the RO

effluent samples, indicating that RO treatment effectively

removed all PR-like activity that was detected in the source

waters to plants RO1, RO3 and RO5. No hormones or industrial

compounds were detected in any of the RO effluent samples.

4.8. Specific toxicity: thyroid activity

None of the 39 compounds tested had any thyroid effect in the

TRb-CALUX (Table 5), and thyroid activity was not detected in

any of the samples (Fig. 1). A recent report suggests that many

thyroid active compounds require metabolic activation (Murk

et al., 2013). While the cell lines used here has been shown to

possess some metabolic activity (Sotoca et al., 2010), it may

not be sufficient and addition of a metabolic step to thyroid

testing in the future may result in increased detection.

4.9. Adaptive toxicity: modulation of cytokineproduction

None of the compounds or samples produced any detectable

activity in the THP1 CPAþ assay for immunostimulation,

however a few compounds tested positive in the antagonist

mode of that assay (THP1 CPA-). In the chemical fingerprint,

estrone was the most active immunosuppressive compound

tested (although it should be noted that its potency was still

nearly 1000� less than the standard used for that assay,

dexamethasone). Some of the THMs were also very lightly

wat e r r e s e a r c h 4 9 ( 2 0 1 4 ) 3 0 0e3 1 5 313

immunosuppressive. A positive THP1 CPA- result always

correlated with the presence of estrone or THMs in the same

sample, but the reverse was not always true (in other words,

the presence of estrone or THMs was not sufficient to pre-

dict THP1 CPA- activity). There was also relatively poor

quantitative agreement between the concentration of THMs

and/or estrone and the measured immunosuppressive ac-

tivity. More research is needed to understand which com-

pounds cause immunosuppressive activity in the THP1 CPA-

assay.

4.10. Xenobiotic metabolism: induction of cytochromeP450 1A2

Cytochrome P450 1A2 (CYP1A2) is one of the main enzymes

used by liver cells to metabolise drugs and other chemicals to

more easily excreted forms. These include the antiulcer drug

omeprazole and polycyclic aromatic hydrocarbon compounds

like benzo-a-pyrene (BaP; Pascussi et al., 2008). In fact, liver

cells are able to detect such compounds through sensors such

as the aryl hydrocarbon receptor (AhR), which in turn increase

the expression of the metabolising enzymes including

CYP1A2. We therefore used the expression level of CYP1A2 as

a marker for the presence of these chemicals in wastewaters.

With the exception of slight activity with pentachloro-

phenol, none of the priority compounds were active in the

CYP1A2 assay (Table 5). Most source water samples (treated

wastewater) exhibited low but consistent induction of liver

enzyme activity (usually <10� the assay detection limit), with

the highest activity in source water samples from RO1, RO2,

RO3 and RO5 (Fig. 1, black bars). The activity was often

correlated with the presence of omeprazole (Table SI3). Sam-

ples from the other sites were close to or below the detection

limit of the assay. To our knowledge there are no other studies

using CYP1A2 activity to assess the bioactivity of wastewaters.

Induction of the AhR is commonly detected in treated sewage

samples (Macova et al., 2011; Reungoat et al., 2010).

Induction of P450 1A2 activity was below detection limit in

product water samples from LAGF and LAGUV (as it was in the

sourcewater for those plants), but increased by 2� and at least

4� at UF1 and UF2, respectively, compared to the levels in the

source water. The highest activity was found in product water

samples from UF2 (up to 186 mg/L BaPEQ), which also had the

highest concentration of omeprazole (Table SI3).

Therewas no detectable induction of P450 1A2 in any of the

RO effluent samples, even though there was detectable ac-

tivity in source waters at RO1, RO2, RO3 and RO5. RO treat-

ment was thus an effective barrier to compounds that can

induce CYP1A2.

5. Conclusions

Without whole animal (in vivo) or epidemiological data, it is

difficult to say how protective of human health this bioassay

battery is. Some of the endpoints used have been shown to be

well correlated with in vivo measurements. For example,

toxicity in the Caco2-NRU test was well correlated with rat

acute toxicity in vivo (Konsoula and Barile 2005) and estro-

genic, androgenic and progestagenic activity in the ERa-, AR-

and PR-CALUX assays, respectively, were well correlated with

in vivo activity (Sonneveld et al., 2011, 2006).

The battery was developed to provide a measure of po-

tential toxicity for human health endpoints that were highly

relevant to exposure to organic contaminants from drinking

water. A couple of relevant endpoints were, however, not

included in the battery: developmental and reproductive

toxicity. Development and reproduction are meta-cellular

events and it is currently not possible to adequately predict

toxicity to these events in humans using in vitro models.

Future developments in biotechnology may make these

possible, and the bioassays to be included in the toolbox

should be constantly re-evaluated.

It should be emphasized that this bioassay battery is not

intended to replace chemical analysis, but rather to supple-

ment conventional methods to provide a measure of non-

target chemical and mixture toxicity assessment. Some

compounds known to be poorly rejected by reverse osmosis

(such as NDMA; Bellona et al., 2004) did not induce any bio-

logical response in our selected assays (note however that

other assays with CYP2E1 and CYP2A6 metabolic activation

can detect genotoxicity from nitrosamines such as NDMA;

Kushida et al., 2000). It is therefore important to continue to

monitor those compounds using conventional chemical

methods (for routine and compliance monitoring) in parallel

with bioassay analysis (for an improved water quality

assessment). Additional high-throughput testing of a variety

of chemicals will also allow us to understand the types of

compounds that can (and more importantly cannot) be

detected by specific in vitro bioassays.

In conclusion:

1. Bioassay and chemical analysis were in agreement and

complementary.

2. Carefully selected indicator chemicals confirmed the

removal trends identified by the bioassay results, but

significantly more chemicals need to be tested in the

bioassay battery to develop an effective chemical finger-

printing capability.

3. The bioassays were able to detect activity at concentra-

tions below current chemical method detection limits and

provided a sum measure of all biologically active com-

pounds for that bioassay, thus providing an additional

degree of confidence in water quality.

4. Source water (treated wastewater) contained high levels of

biologically active compounds, with positive results in

almost all bioassays. The water quality of the product

water (reclaimed water) was only marginally better after

ultrafiltration or dissolved air floatation/filtration, but

greatly improved in the RO-based plants often reducing

biological activity to below detection limit.

5. The lack of biological activity and limited chemical de-

tections in the LAGUV source water suggests that the very

long residence time during prior wastewater treatment

applied at this site (Table 1) is very effective at reducing

chemical contaminants.

6. Reverse osmosis was an effective but not absolute barrier

to trace organic contaminants. All detected organic con-

taminants in RO effluents samples were several orders of

magnitude below available guideline levels.

wat e r r e s e a r c h 4 9 ( 2 0 1 4 ) 3 0 0e3 1 5314

Acknowledgement

Funding and support for this work was provided by the Na-

tional Water Commission (NWC) of Australia under the

Raising National Standard Program (RNS), Water Quality

Research Australia (WQRA), ACTEW/Ecowise, Melbourne

Water, Urban Water Security Research Alliance (UWSRA), SA

Water, Sydney Water, United Water, Water Corporation and

the Western Australian Department of Water.

We thank Pam Quayle, Tarren Reitsema, Dan Inglis, Mel-

ody Lau, Jackson Wong, Nhat Le Minh, James McDonald and

Heather Coleman for their assistance in the laboratory and all

industry partner staff for their assistance in the field work

components. We also thank Paul Smith, Adam Lovell, Mal-

colm Warnecke, Judy Blackbeard, Peter Cox, Simon Toze,

David Halliwell, Brian Priestley, Tarren Reitsema and Paul

Rasmussen for useful discussions throughout this project.

WIL2-NS cells were a gift from Dr Barbara Sanderson (Flinders

University).

Appendix A. Supplementary material

Supplementary material related to this article can be found at

http://dx.doi.org/10.1016/j.watres.2013.11.030.

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