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ARTICLE OPEN
In vitro bioassays for monitoring drinking water quality of tap
water, domestic filtration and bottled water
Beate I. Escher
1,2
✉, Jordi Blanco
3
, Josep Caixach
4
, Dora Cserbik
5,6,7
, Maria J. Farré
8,9
, Cintia Flores
4
, Maria König
1
, Jungeun Lee
1
,
Jo Nyffeler
1
, Carles Planas
4
, Paula E. Redondo-Hasselerharm
5,6,7,12
, Joaquim Rovira
3,10
, Josep Sanchís
8,9,13
, Marta Schuhmacher
10
and
Cristina M. Villanueva
5,6,7,11
© The Author(s) 2023
BACKGROUND: Location-specific patterns of regulated and non-regulated disinfection byproducts (DBPs) were detected in tap
water samples of the Barcelona Metropolitan Area. However, it remains unclear if the detected DBPs together with undetected
DPBs and organic micropollutants can lead to mixture effects in drinking water.
OBJECTIVE: To evaluate the neurotoxicity, oxidative stress response and cytotoxicity of 42 tap water samples, 6 treated with
activated carbon filters, 5 with reverse osmosis and 9 bottled waters. To compare the measured effects of the extracts with the
mixture effects predicted from the detected concentrations and the relative effect potencies of the detected DBPs using the
mixture model of concentration addition.
METHODS: Mixtures of organic chemicals in water samples were enriched by solid phase extraction and tested for cytotoxicity and
neurite outgrowth inhibition in the neuronal cell line SH-SY5Y and for cytotoxicity and oxidative stress response in the
AREc32 assay.
RESULTS: Unenriched water did not trigger neurotoxicity or cytotoxicity. After up to 500-fold enrichment, few extracts showed
cytotoxicity. Disinfected water showed low neurotoxicity at 20- to 300-fold enrichment and oxidative stress response at 8- to 140-
fold enrichment. Non-regulated non-volatile DBPs, particularly (brominated) haloacetonitriles dominated the predicted mixture
effects of the detected chemicals and predicted effects agreed with the measured effects. By hierarchical clustering we identified
strong geographical patterns in the types of DPBs and their association with effects. Activated carbon filters did not show a
consistent reduction of effects but domestic reverse osmosis filters decreased the effect to that of bottled water.
IMPACT STATEMENT: Bioassays are an important complement to chemical analysis of disinfection by-products (DBPs) in drinking
water. Comparison of the measured oxidative stress response and mixture effects predicted from the detected chemicals and their
relative effect potencies allowed the identification of the forcing agents for the mixture effects, which differed by location but were
mainly non-regulated DBPs. This study demonstrates the relevance of non-regulated DBPs from a toxicological perspective. In vitro
bioassays, in particular reporter gene assays for oxidative stress response that integrate different reactive toxicity pathways
including genotoxicity, may therefore serve as sum parameters for drinking water quality assessment.
Keywords: Water quality; Bioassay; Oxidative stress; Neurotoxicity; Disinfection by-products
Journal of Exposure Science & Environmental Epidemiology (2024) 34:126–135; https://doi.org/10.1038/s41370-023-00566-6
Received: 1 March 2023 Revised: 26 May 2023 Accepted: 1 June 2023
Published online: 16 June 2023
1
Helmholtz Centre for Environmental Research –UFZ, Department of Cell Toxicology, Leipzig, Germany.
2
Eberhard Karls University Tübingen, Environmental Toxicology,
Department of Geosciences, Tübingen, Germany.
3
Laboratory of Toxicology and Environmental Health, School of Medicine, Universitat Rovira i Virgili, Reus, Spain.
4
Mass
Spectrometry Laboratory/Organic Pollutants, Institute of Environmental Assessment and Water Research, IDAEA-CSIC, Barcelona, Spain.
5
ISGlobal, Barcelona, Spain.
6
Universitat
Pompeu Fabra, UPF, Barcelona, Spain.
7
CIBER Epidemiología y Salud Pública, CIBERESP, Madrid, Spain.
8
Catalan Institute for Water Research, ICRA, Girona, Spain.
9
University of
Girona, Girona, Spain.
10
Environmental Engineering Laboratory, Universitat Rovira i Virgili, Tarragona, Spain.
11
Hospital del Mar Medical Research Institute, IMIM, Barcelona, Spain.
12
Present address: IMDEA Water, Madrid, Spain.
13
Present address: Catalan Water Agency, Barcelona, Spain. ✉email: beate.escher@ufz.de
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BACKGROUND
Water is a limited natural resource that is under pressure from
human activity. The quality of our drinking water is threatened by
the growing use of a wide range of chemicals that end up in the
water cycle [1]. The scale of the challenge posed by drinking water
contamination will increase in the future due to the increasing
need for clean water, globally growing water scarcity due to
climate change and a steep increase in the use of chemicals. We
are all exposed to a cocktail of contaminants in drinking water [2]
that may not be removed by standard treatments including
pesticides, pharmaceuticals, personal care products (e.g., UV
filters), ingredients from consumer products (e.g., poly- and
perfluorinated compounds) and microplastics, or indeed are
generated by drinking water production itself, e.g., distribution
network materials, disinfection by-products (DBPs), and other
transformation products [3].
While drinking water contaminants are present in mixtures [4],
previous epidemiological research has focused on single chemicals
or limited chemicals groups [5]. All chemicals contribute to mixture
effects, even at low concentrations that, alone, might be below the
threshold of effect [6,7]. Animal studies suggest that the single-
chemical paradigm underestimates cumulative effects of chemical
mixtures [8]. The assessment of biological responses through
in vitro assays has emerged as a useful tool to evaluate drinking
water quality to provide insights into risks from (unknown) complex,
low-level mixtures of micropollutants and DBPs [9].
In vitro bioassays have been widely applied in the past to assess
drinking water quality in a comprehensive manner [9]. Source
water can be contaminated with a wide range of organic
micropollutants, which are typically reduced by drinking water
treatment but depending on the treatment technology may still
be present in drinking water, and with DBPs, which are formed
during disinfection processes. Organic micropollutants and DBPs
contribute to the mixture effects detected with in vitro bioassays.
It is possible to differentiate between the contribution of
micropollutants and DBPs to the measured mixture effect by
measuring the in vitro effect of the SPE extract of water sampled
directly before and after chlorination/disinfection [10]. Micropol-
lutants cause diverse adverse outcome pathways among them
endocrine disruption, reproduction toxicity, but also adaptive
stress responses and carcinogenicity/mutagenicity. Accordingly
diverse test batteries have been developed to capture the
diversity of micropollutants’toxicity (reviewed in [11]).
Most DBPs cause reactive toxicity, mainly oxidative stress
response and genotoxicity/mutagenicity [12,13]. The largest
database of genotoxicity and cytotoxicity data on Chinese
hamster ovary (CHO) cells dates back to 2000 [14] and has been
further expanded over the years [15] and applied in numerous
studies that tested drinking water [16,17] and other water types
[18]. Batteries of in vitro bioassays have been used to quantify
different aspects of reactive toxicity pathways [9,19]. The
oxidative stress response quantified with a reporter gene assays
indicative of the keap-nrf2-ARE pathway was shown to be a good
measure of the specific effects of reactive DBPs, also because the
soft electrophilic character of many DBPs lead only indirectly to
genotoxicity [19]. As genotoxicity occurs often at similar or only
slightly lower concentrations than cytotoxicity and cytotoxicity is a
more integrative parameter where all chemicals and DBPs
contribute to, albeit with different potency, water quality has also
been assessed directly by cytotoxicity in CHO cells [20,21].
DBPs have been reported to be neurotoxic [22] and activate the
Nrf2-mediated oxidative stress response pathway [23] by reducing
intracellular glutathione and increasing ROS [24]. Further there is
some but not strong evidence that DBP exposure may adversely
affect neuropsychological development [25]. Therefore, we also
included for the first time a novel neurotoxicity assay based on the
neurite outgrowth inhibition and cytotoxicity in differentiated
neuronal SH-SY5Y cells [26,27] in the evaluation of drinking water
and extracts of drinking water.
Testing water samples directly in in vitro assays has often not
resulted in detectable response. DPB mixtures in drinking water
have to be enriched up to several hundred times to show
measurable activity. For non-volatile DBPs, solid-phase extraction
(SPE) has shown best recoveries [28,29]. Volatile DBPs can be
enriched with a purge-trap method but as the known volatile
DBPs contribute only a minor fraction to the overall mixture effect
with non-volatile DBPs dominating mixture toxicity [28], most
studies to date applied SPE for sample preparation.
The Barcelona metropolitan area (BMA) exhibits unique and
valuable characteristics to make it a suitable setting to conduct
water studies. Drinking water is supplied from different sources
(mainly Llobregat and Ter rivers), providing diverse water zones in
a well-defined geographical area. This provides variability in the
chemical concentration and composition, which allows to identify
links with biological responses. In addition, the Llobregat river is
intensively impacted by human activity, leading to the eventual
Graphical Abstract
B.I. Escher et al.
127
Journal of Exposure Science & Environmental Epidemiology (2024) 34:126 – 135
Content courtesy of Springer Nature, terms of use apply. Rights reserved
occurrence of chemicals of industrial origin [30]. Previous studies
in the area showed that levels of other drinking water
contaminants e.g. arsenic were very low [31].
Regulated and non-regulated DBPs were measured in tap water
samples collected at the 42 postal codes of the BMA [32]. DBPs
were also quantified in tap water samples filtered with activated
carbon (AC) and reverse osmosis (RO), as well as in bottled water
[32]. Drinking water samples were analyzed for 11 haloacetic acids
(HAAs), 4 trihalomethanes (THMs), 4 haloacetonitriles (HANs), 2
haloketones (HKs), chlorate, chlorite, and trichloronitromethane
(TCNM). The median concentration of total THMs, HAAs and HANs,
TCP, chlorite and chlorate in tap water were 42, 18, 3.2, 1.2, 53.9
and 214 μg/L, respectively. Chlorate, THMs, HAAs, and HANs were
quantified in 98–100% tap water samples. Although both
brominated and chlorinated DBPs were present; brominated
species were found in a larger number of samples. AC filters
reduced DBP levels in the range of 27–80%, and RO reduced DBP
concentrations ≥98% [32]. In bottled water, only chlorate was
detected in 3 out of 10 brands, with a median concentration of
13.0 µg/L [32].
OBJECTIVES
The objective of this study was to complement the previous work
on exposure to regulated and non-regulated DPB in the BMA [32],
micropollutants [33] and micro(nano) plastic [34] by effect-based
method that capture the entirety of mixture exposure of extracted
micropollutants and DBPs with a focus on cytotoxicity, activation
of oxidative stress response and neurotoxicity. Iceberg modelling,
a specific form of mixture toxicity modelling, was used to identify
mixture effect drivers for the endpoint of oxidative stress
response.
AC filters and home RO systems have become quite popular for
polishing tap water at the home. Previous studies have demon-
strated the variable removal efficacy of effects measured with
in vitro bioassays by point-of-use filters, ranging from 25 to 100%
depending on the filter type, age and condition. We also
evaluated point-of-use filter that were in use at 11 out of the 42
households, where tap water was sampled. Bottled water was also
extracted and tested for comparison with the tap and
filtered water.
METHODS
Water samples
The sites were selected and sampling was performed as described
previously [32]. 42 water samples were collected from taps in homes across
different postcodes in the BMA. The sample codes are the postcodes. In
the homes of postcodes 08001, 08006, 08008, 08013, and 08017 pitcher-
type AC filters and in 08028 faucet AC filters were also sampled to obtain a
snapshot of the realistic exposure. Reverse osmosis (RO) was used in the
homes sampled in postcodes 08002, 08018, 08019, 08024, and 08029 and
both tap water and RO water were sampled. Out of the 10 bottled water
samples from the previous work [32], 9 were tested in the bioassays.
Physical parameters
The pH, total hardness was measured as CaCO
3
using EDTA titration, free
chlorine and total chlorine, conductivity and total organic carbon (TOC)
had been reported by Redondo-Hasselerharm et al. [32]. and are reprinted
in Table S1.
Chemical analysis
Concentrations of THMs, HAAs, HANs, HKs and TCNM as well as chlorite
and chlorate had been reported by Redondo-Hasselerharm et al. [32].
Inorganic chlorite and chlorate cannot be enriched by SPE. Since THMs and
TCNM are too volatile to be captured by SPE and would require specific
bioassay formats for volatile chemicals [35], the comparison with bioassay
data focused on HAAs, HKs and HANs, and the detected concentrations
[32] are reprinted in Table S2. This is also justified because volatile DBPs are
often less cytotoxicity than non-volatile DBPs [36] and under real-life
scenarios the volatile DBPs contribute less to mixture toxicity [28,37]. The
detected DBPs were MBAA, bromoacetic acid; DCAA, dichloroacetic Acid;
BCAA, bromochloroacetic acid; DBAA, dibromoacetic acid; TCAA, trichlor-
oacetic acid; BDCAA, bromodichloroacetic acid; DBCAA, dibromochloroa-
cetic acid; TBAA,tribromoacetic acid; 1,1,1-TCP, 1,1,1-
trichloropropanone(acetone); DCAN, dichloroacetonitrile; BCAN, bromo-
chloroacetonitrile; DBAN, dibromoacetonitrile [32].
Sample preparation for bioassays
Samples were extracted with SPE at the ICRA laboratories. Samples were
acidified with HCl to reach pH 2.5–3. Two liters of water were enriched for
each sample using 12 cc 500 mg Oasis HLB SPE cartridges. SPE blanks were
2 L of ultrapure water (HPLC grade) run in parallel to the samples in the
same setups. The cartridges were dried under vacuum and sent at room
temperature to UFZ Leipzig. They were stored at −20 °C prior to elution.
The cartridges were eluted without vacuum with 20 mL of ethyl acetate
followed by 10 mL methanol, then all extracts were blown down and
resolubilized with 1 mL methanol.
Relative enrichment factor of the samples
All samples were enriched from 2 L to 1 mL, yielding an enrichment factor
of the SPE of 2000. An aliquot of the enriched sample extract was then
added to a dosing vial, the solvent was blown down to dryness and the
sample was resolubilized with cell assay media. Therefore, the bioassay
contained no residual solvent. A solvent blank using the same volume of
ethyl acetate and methanol (10 mL each), blown down and reconstituted
with medium, was also run to ensure that there were no interferences from
residuals in the solvents.
The sample was transferred from the dosing vial into a 96 well plate and
serially diluted in test media and 30 µL of this dosing solution was
transferred to 384-well plates that contain cells in 10 µL medium.
The final relative enrichment factor (REF) is the combination of the
enrichment of the extract and the dilution in the bioassay [38]and
represents the enrichment of the original water sample in each bioassay.
The REF is equivalent to concentration and is expressed in the units
[L
water sample
/L
bioassay
].
AREc32 assay for activation of oxidative stress response
The SPE extracts were tested in the AREc32 assay for activation of oxidative
stress response. The AREc32 assay was performed according to [39] with
some modifications. Briefly, the extracts were serially diluted in DMEM with
10% fetal bovine serum (FBS) and added to a 384 well plate containing
cells at a final density of 8.33 × 10
4
cells/mL. The plates were incubated at
37 °C for 24 h, then luciferase production was measured using luciferin and
ATP as substrate and luminescence relative light units RLU were recorded.
The measure of effect was the induction ratio IR, which is ratio of the RLU
of the sample at a given concentration divided by the mean of the RLU of
the unexposed cells.
Live-cell analysis using IncuCyte S3 live cell imaging system (Essen
BioScience, Ann Arbor, Michigan, USA) was used to assess confluency,
which served as a proxy for cytotoxicity. Confluency was measured 48 h
after seeding (24 h after dosing) using phase contrast images [40]. The
extracts were tested up to a REF of 500. tert-Butylhydroquinone (tBHQ) was
the positive reference compound for AREc32.
An inhibitory concentration IC
10
for 10% cytotoxicity was derived from
the confluency measurements using a linear concentration-response
regression with intercept of 0 according to [40]. The cytotoxicity can also
be expressed as toxic units TU
bio
, which is the inverse of the IC
10
.
TUbio ¼1
IC10
Only concentrations below IC
10
and up to a maximum IR of 5 were used
for concentration-response assessment of the activation of ARE. Here we
applied a linear concentration-response regression with intercept of IR 1
[39]. An IR of 1.5 corresponds to a 50% increase over the IR of the
unexposed cells, and the associated concentration was used as effect
concentration EC
IR1.5
[39].
Neurite outgrowth inhibition assay
The SPE extracts were tested in a neurotoxicity assay that was based on the
cytotoxicity and neurite outgrowth inhibition in differentiated SH-SY5Y
B.I. Escher et al.
128
Journal of Exposure Science & Environmental Epidemiology (2024) 34:126 – 135
Content courtesy of Springer Nature, terms of use apply. Rights reserved
cells obtained from Sigma-Aldrich, 94030304 [27]. Briefly, the cells were
plated in a collagen-coated black/clear flat bottom 384-well plate (Corning,
354667) and exposed with the serially diluted extracts. Prior to testing the
methanol extracts were solvent-exchanged into medium to assure that the
effects were not impacted by the presence of solvents. The extracts were
tested in 11 different concentrations up to a REF of 300. After incubation of
the plate at 37 °C for 24 h, image analysis was performed with IncuCyte S3.
Cell viability was quantified from fluorescence images after staining with
Nuclear Green LCS1 (Abcam, ab138904) and propidium iodide (Sigma-
Aldrich, 81845). The number of total and dead cells were derived to
determine cytotoxicity in neuronal cells.
Inhibition in neurite outgrowth was measured using phase-contrast
image and the length of neurite was quantified with IncuCyte NeuroTrack
software module. 10% effect concentration for cytotoxicity and neurite
outgrowth inhibition were expressed as IC
10
and EC
10
, respectively.
Narciclasine was used as positive reference compound for
neurotoxicity assay.
Direct testing of unenriched water samples in the neurite
outgrowth inhibition assay
The water was also tested in its entirety after filtration in another setup of
the neurite outgrowth inhibition in differentiated SH-SY5Y cells [27]. The
experimental procedure is detailed in Text S1.
Iceberg modelling for oxidative stress response
Bioanalytical equivalent concentrations (BEQ) can be used to compare the
predicted biological effect from the concentrations of the detected DBPs
with the biological effect. The concept of BEQ implies that chemicals act
concentration-additive as was demonstrated multiple times for designed
complex mixtures in equipotent concentration ratios and concentration
ratios of occurrence for the AREc32 assay [37,41,42]. The BEQ
bio
can be
derived directly from the effect concentration measured in the sample by
relating it to the effect concentration of a reference chemical [38]. In
previous work we have used MCAA as reference compound for DBPs [43]
but given that MCAA was below the limit of detection in all samples and it
is of low potency we used DBAN, the most potent DPB in the AREc32 assay
among the tested DBPs [19] as reference chemical, as was also done in a
previous mixture study [37]. The DBAN-EQ
bio
can then be calculated by
Eq. 2with EC
IR1.5
of 0.15 µM or 29 µg/L of DBAN.
DBAN EQbio ¼ECIR1:5ðDBANÞ
ECIR1:5ðwater extractÞ(1)
If the concentration of the detected DBPs i,C
i
, is multiplied by their
relative effect potency REP
i
, we obtain the DBAN equivalent concentration
of this DBP i, DBAN-EQ
chem
(i). If all DBPs act concentration-additive in
mixtures, which has been established for drinking water DBPs in the
AREc32 assay [37], the DBAN-EQ
chem
(i) can be summed up to yield the
measure of the mixture effect of the detected DBPs, DBAN-EQ
chem.
(Eq. 2).
DBAN EQchem ¼X
n
i¼1
DBAN EQchem iðÞ
¼X
n
i¼1
ECIR1:5ðDBANÞ
ECIR1:5ðiÞCi¼X
n
i¼1
REPiCi
(2)
The mixture effect from the detected chemicals DBAN-EQ
chem
can now
be compared with the measured mixture effect in the bioassay expressed
as DBAN-EQ
bio
to assess the contribution of the detected chemicals (Eq. 3).
%effect explained by detected chemicals ¼DBAN EQchem
DBAN EQbio
(3)
RESULTS
Oxidative stress response
Some of the extracts were slightly acidic, as evidenced by a
discoloration of the phenol red medium and the pH of the
bioassay medium had to be neutralized with 0.3 to 0.5 µL of 5 M
NaOH solution. The pH shift could only be measured with pH
paper because the sample volume of 120 µL of the dosing vial was
too low to use a pH electrode.
The acidity in the highly enriched samples might have well
been caused by the dissociation of the HAAs when dissolving the
neutral form of the HAA eluted from the SPE cartridge with
solvent in medium that is only weakly buffered at pH 7.4, which
would lead to a dissociation of the HAA that leads to a proton
release and hence acidification of the medium. In the dosing vial,
the sample had an REF of 2000 and the decrease in pH was higher
for samples with high sum concentrations of HAAs (Fig. S1). The
samples with the highest acidification had sum concentrations of
HAAs of 0.05 to 0.25 µM at REF 1, which corresponds to 100 to
500 µM HAA at REF 2000, which could bring about such a pH shift,
when the HAAs deprotonate in the medium that is weakly
buffered at pH 7.4.
Although all highly enriched SPE extracts of the water samples
were slightly acidic, the acidity of the extracts was not caused by
the experimental procedure because blanks, bottled water and
water filtered by RO did not exhibit pH shifts, while the original
tap water and the AC-filtered water had pH values lowered by one
to two pH units (Fig. S1).
Even if the pH were not optimal, the AREc32 assay results would
not be impacted as we have demonstrated that AREc32 can be
adapted to growth on medium from pH 6.5 to pH 8 without
change in sensitivity towards chemicals and cells that were not
adapted but challenged with pH during testing also delivered
robust results (unpublished data). This is different from the
neurotoxicity assay, which is sensitive to pH shifts as
described below.
The positive control tert-butylhydroquinone (tBHQ) in the
AREc32 assay had an EC
IR1.5
of 8.34 ± 0.15 µM (Fig. S2a, Table S3),
which is in the range of previous literature data [39]. SPE blanks
had EC
IR1.5
values ranging from REF 81 to > 500 (Fig. S2b, Table S3),
i.e., were much less potent than the tap water, but in the same
range of effects of RO water and bottled water. The solvent blanks
showed no effects (Fig. S2c, Table S3).
None of the samples showed cytotoxicity (Fig. 1a) or activation
of oxidative stress response (Fig. 1b) without substantial enrich-
ment. The tap water had EC
IR1.5
ranging from REF 9 to 141
(example of one concentration-response curve in Fig. S2d, EC
IR1.5
in Table S3). Cytotoxicity occurred at 3 to 34 times higher REF (IC
10
for cytotoxicity in Table S3) than activation of oxidative stress
response. 12 of 42 tap water samples were not cytotoxic up to REF
500, which means that the tap water showed a rather specific
oxidative stress response. Water treated with AC filters had similar
oxidative stress response before and after filtration and cytotoxi-
city was small and not impacted by filtration (Fig. 1a, b, Table S3).
As discussed by Redondo-Hasselerharm et al. [32], these were
pitcher-type AC filters that were in households, so it cannot be
assured that they were well maintained and not overloaded.
After reverse osmosis even the low cytotoxicity had disap-
peared (IC
10
> 500) and oxidative stress response was active only
at REF > 150 (Fig. 1, Table S3). Bottled water showed no
cytotoxicity up to REF 500 with exception of one out of 9 bottles,
with an IC
10
of 246 and activation of oxidative stress response had
EC
IR1.5
> REF 100 (example of one concentration-response curve in
Fig. S2e, Fig. 1, Table S3).
Upon direct comparison of sample types (Fig. 1b), it is evident,
that tap water and tap water after AC filtration had the same
ranges of effect concentrations and the water had to enriched 10
to 100-fold to case the 10% activation of the oxidative stress
response pathway (exception 08021, which had an EC
IR1.5
> 100).
Reverse osmosis-treated tap water and bottled water had
consistently lower effects in the range of the SPE blanks. One of
five SPE blanks had a slightly increased effect, the source of which
could not be identified.
Fig. 2shows the spatial distribution of cytotoxicity and oxidative
stress response on a map of the BMA. On first sight, the
distribution of effects appears quite uniform across the entire
BMA with slightly higher activation of oxidative stress response in
B.I. Escher et al.
129
Journal of Exposure Science & Environmental Epidemiology (2024) 34:126 – 135
Content courtesy of Springer Nature, terms of use apply. Rights reserved
the Northwestern region of BMA, where also some cytotoxicity
was detectable. The site 08039 was higher than other sites at the
coastal region but that was a public water fountain where the
highest concentration of DBAA (13.9 µg/L) and TBAA (6.6 µg/L)
and DBAN (4.2 µg/L) were measured. Bromine containing DBPs are
known to be more toxic than their chlorinated analogues [44] and,
in particular, DBAN is the most cytotoxic DBP with the highest
activation of oxidative stress response included in the study
(Table S5 [19]).
Neurotoxicity
Tap water samples. None of the directly tested tap water samples
showed any neurotoxic effects (Text S2). Unenriched water did not
induce diminution of cell viability (MTT) or increase of LDH
Fig. 1 Cytotoxicity, oxidative stress, and neurotoxicity. Comparison of the effect concentrations of the different water types: aIC
10
for
cytotoxicity in AREc32. bEC
IR1.5
for oxidative stress response in AREc32. cEC
10
for neurite outgrowth inhibition in SH-SY5Y cells (left y-axis)
and IC
10
for cytotoxicity in SH-SY5Y cells (right y-axis, grey symbols). Data are in Tables S1, S2. The line is at REF 500/300, the highest tested
concentration in AREc32/SH-SY5Y and the symbols stand for experiments without detected effect ( > 500/ > 300).
Fig. 2 Mixture effects. Distribution of mixture effects across the drinking water supply of Barcelona Metropolitan Area (BMA) expressed as
inhibitory concentration IC
10
for 10% reduction of cell viability in the AREc32 cell line and effect concentration EC
IR1.5
for activation of
oxidative stress response. Data are from Table S3. The units are relative enrichment factors REF, and samples that did not show cytotoxicity up
to a 500-fold enrichment had no bar. The y-axis is inverse (1/IC
10
or 1/EC
IR1.5
) because a low IC
10
/EC
IR1.5
refers to a high cytotoxicity or effect
but the legend refers to the original IC
10
/EC
IR1.5
.
B.I. Escher et al.
130
Journal of Exposure Science & Environmental Epidemiology (2024) 34:126 – 135
Content courtesy of Springer Nature, terms of use apply. Rights reserved
leakage (Fig. S3), nor did they increase the propidium iodide
permeabilization (Fig. S4). The neuronal phenotype differentiation
and neurite length of the differentiated SH-SY5Y cells were not
affected.
SPE extracts. Only selected SPE extracts were evaluated with the
screening neurotoxicity assay that was tuned for water quality
assessment [45] because of the challenge of the acidity of the SPE
extracts described below. The concentration-response curves for
cytotoxicity and inhibition of neurite outgrowth are depicted in
Fig. S5.
The EC
10
for neurite outgrowth inhibition of the positive control
narciclasine was 9 nM (Table S4), which is consistent with earlier
work. The SPE blanks showed no cytotoxicity nor effects up to REF
300. The high acidity of some of the extracts posed a problem for
this assay despite the attempt to neutralize the sample after
dissolving in medium and before dosing with cell debris as is
visible in the images of the neutralized extract of tap water 08008,
while tap water 08022 only showed decreased neurite numbers
and lengths and decreased cell number as compared to the
unexposed cells (Fig. S6).
Apart from 08025, which had an IC
10
of 98 (Table S4), none of
the tested samples caused cytotoxicity up to a REF of 100, three
tap water samples and all filtered and bottled water even up to a
REF of 300. The neurite outgrowth was inhibited by all tap water
samples (with exception of 08008, which had shown the pH
problems) and by none of the other samples. The EC
10
for neurite
growth inhibition ranged from 20 to 51, which means the water
had to be enriched 20 to 51 times to show 10% neurotoxic effects.
Although fewer samples were tested in the neurotoxicity assays
due to the pH shift issue, the same picture emerged (Fig. 1c) as for
AREc32 (Fig. 1a, b), although the AC filters removed the
neuroactive chemicals to below the limit of detection. We have
not fingerprinted single DPBs in this neurotoxicity assay yet, but it
is often highly responsive to pesticides and other water pollutants
that are more hydrophobic [45], so that the sorption to AC might
have been more efficient for the chemicals causing neurotoxicity.
DISCUSSION
Comparison of oxidative stress response with literature data
and other water types
The oxidative stress response was in the same range or slightly
higher than in previously tested drinking water (Hebert et al. [46]
and Neale et al. [10]) but clearly less active than in surface water
during rain events [45] and wastewater treatment plant effluent
(WWTP) (Fig. S7a). None of the tap water samples exceeded the
proposed effect-based trigger value EBT-EC
IR1.5
of 6 (REF) [41].
AREc32 can be impacted by both, DBPs and micropollutants,
but most of the oxidative stress response in unchlorinated
samples remained unexplained to date [41]. If one measures
activation of the oxidative stress response directly before and after
chlorination, it is possible to derive the contribution of DBPs to the
overall effect, but this is not feasible here because we only
sampled the treated or treated and filtered tap water, which is
much more realistic of human exposure than previous work on
drinking water treatment plants and assessment of DBP formation
potential [47].
There was no direct correlation between cytotoxicity and
activation of oxidative stress response (Fig. S8a). Cytotoxicity can
be considered an effect-scaled sum parameter for all chemicals
present in a sample and acting together, while only a fraction of
micropollutants [48] and many but not all DBPs [19] activate the
oxidative stress response.
Organic matter plays an important role for the formation of
DBPs [47] but neither the cytotoxicity (Fig. S8b) nor the activation
of the oxidative stress response (Fig. S8c) was directly correlated
with the TOC.
Neurotoxicity
To our knowledge no neurotoxicity assay has been applied to
drinking water samples yet, so we can only compare to other
water types. The tap water showed equal to lower effects
compared to wastewater treatment plant effluent and surface
water collected during rain events (Fig. S7b). As effect concentra-
tions for individual DBPs in the neurotoxicity assay are not
available, iceberg modelling could not be performed. Only one
extract of water from postcode 08008 was tested in the
neurotoxicity assay before and after AC filtration (Table S4) but
due to the acidity only cell debris were observed. In contrast, after
RO no effects could be observed (Table S4) and there were no
issues with acidification, but no matching tap water sample could
be tested due to limited sample volume availability.
Comparison of detected DBPs and oxidative stress response
Iceberg modelling helps to understand the contribution of
individual detected DBPs to the measured effect and how much
of the mixture effect is contributed to by DBPs and micropollu-
tants not quantified or not toxicologically characterized. Here we
performed the iceberg modelling with the detected concentra-
tions of HAAs, HKs and HANs (Table S2 [32]), and their EC
IR1.5
were
taken from the literature (Table S5 [19]).
The DBAN-EQ
chem
reported in Table S6 comprise the contribu-
tion of HAAs, HKs and HANs to the mixture effect. The sum of the
DBAN-EQ
chem
(i) of the HAAs, DBAN-EQ
chem
(ΣHAA), contributed
very little to the DBAN-EQ
chem
(Eq. 3, Fig. 3). DBAN-EQ
chem
was
dominated by HANs (DBAN-EQ
chem
(ΣHAN)), and particularly the
most potent DBAN (Fig. 3).
DBAN-EQ
chem
(ΣHAN) explained 5% to 460% of the DBAN-EQ
bio
,
while the HAA explained a mere 0.005% to 5.6% of the DBAN-
EQ
bio
. If present at all, the HK 1,1,1TCP contributed 8.4% to the
DBAN-EQ
bio
at postcode 08020 but was typically < 1% (Fig. 3).
That DBAN-EQ
chem
exceeded DBAN-EQ
bio
by up to a factor of 4
(Table S6) is most likely caused by differences in sample
preparation methods. The DBPs were extracted with group-
specific sample preparation and targeted analytical methods [32],
while the water was extracted with SPE. HANs are a technically
Fig. 3 Iceberg modeling. Comparison of the bioanalytical equiva-
lent concentrations DBAN-EQ
bio
with the DBAN-EQ
chem
and
percentage of bioassay response explained by the detected
chemicals. All data in Table S6.
B.I. Escher et al.
131
Journal of Exposure Science & Environmental Epidemiology (2024) 34:126 – 135
Content courtesy of Springer Nature, terms of use apply. Rights reserved
challenging group of DBPs: HANs are not volatile enough to be
captured by a purge and trap method but are also not well
recovered by SPE [28]. For chemical analysis HANs were extracted
by liquid-liquid salted microextraction and gas chromatography
but this extraction method would not keep the mixture intact and
not extract HAAs. Therefore, for water sample preparation for
bioanalysis, SPE remains the preferred method. Even if it does not
capture all individual DBPs with high yield, it captures a bigger
diversity than any specialized extraction methods [28]. The SPE
recovery of DCAN was negligible and that of DBAN around 20%
[28], which would explain the factor 4 overestimation of DBAN-
EQ
chem
. HAA were recovered by SPE between 10 and 66% [28]. In
addition, HANs are instable in bioassay medium and form HAAs
[49]. Mono-HANs are fairly stable, but Di-HANs have been reported
to be reduced by 40% (DBAN), 68% (BCAN) and 85% (DCAN) in
plate-based bioassays and cell culture medium with 10% fetal
bovine serum [49]. As the HAAs are less potent than the
corresponding HANs, this would also explain part of the
overestimation of DBAN-EQ
bio
by DBAN-EQ
chem
.
The THMs chloroform (TCM), bromodichloromethane (BDCM),
dibromochloromethane (DBCM) and bromoform (TBM) had also
been included in the analysis in the previous study [32]. Due to
their high volatility, they were not extracted by SPE [28], but one
can estimate their additional effect load. Despite the relatively
high concentrations of 0.31 to 36.3 µg/L for TCM, 0.32 to 11.5 µg/L
for BDCM, 1.7 to 26.5 µg/L for DBCM and 0.17 to 57.7 µg/L for TBM
[32], due to the low effect potencies [19], the sum of THMs would
add less than 1% (0.04 to 0.87%) additional DBAN-EQ
chem
to the
DBAN-EQ
bio
of the extracted mixtures, which is negligible (data
and calculations not shown). This is consistent with what has been
found in previous work where THMs were negligible contributors
to mixture effects [28,37,46].
Cytotoxicity was 3 to 23 times less sensitive than the activation
of oxidative stress response and therefore many samples were not
active up to REF 500. A comprehensive study of drinking water
from 6 US drinking water treatment plants found that the
mammalian cell cytotoxicity index (CTI), which is equivalent to the
TU used in the present study, was also dominated by the HANs
[50].
Spatial distribution of DBPs and effects
BMA receives water from Ter and Llobregat river, the latter being
richer in bromide concentration [51]. Therefore, the relative
portion of Br-BPs vs Cl-DBPs varies greatly within the region
studied as reported by Redondo-Hasselerharm et al. [32]. In the
Northwestern area of BMA, the water comes from the Ter river
with a lower concentration of bromide. There was a clear spatial
distribution of concentrations with chlorinated DBPs occurring at
higher concentrations in the North, brominated DBPs rather in the
South and East and 1,1,1TPP only in the Northwest (Fig. S9). While
HAAs and the HK were not detected in several sampling sites,
HANs were ubiquitous (Fig. S9). The concentrations in Fig. S9 were
already converted to DBAN-EQ
chem
(i) to allow a direct comparison
between effect contribution of individual DBPs.
In the Northwestern area of BMA (postcodes 08006, 08013,
08016, 08021 to 08027, 08031, 08032, 08035 and 08041, 08042)
concentrations of DCAA, BCAA, DBAA, TCAA were substantially
higher than that of all HANs (ratio ΣHAA/ΣHAN > 9) but due to the
higher REP of HANs, the DBAN-EQ
chem
(ΣHAN) were 32 to 5388
times higher than DBAN-EQ
chem
(ΣHAA). In these samples the
concentration ratios DBAN/DCAN were smaller than 1, often even
smaller than 0.1 or no DBAN detected.
Despite these regional difference in contribution of Cl-DBPs and
Br-DBPs, the DBAN-EQ
chem
was always dominated by HANs due to
their high potency, and HAAs had only a minor contribution to
DBAN-EQ
chem
(Fig. 4a and Table S6). 1,1,1TCP was only detected in
a few samples that were at the same time very low in HAA in the
postcodes 08006, 08013, 08016, 08021 to 08028, 08031, 08032,
080315, 08041. Sample 08018 had only HANs and no detected
HAAs or 1,1,1-TCP but was as potent as other samples. In the
coastal areas (postcodes 08001 to 08005, 08007 to 08011, 08014,
08018 to 08020, 08039) DBAN was 10 times higher than DCAN or
no DCAN was detected and HAAs were in a similar concentration
range as HANs.
Hierarchical clustering depicted in Fig. 4a only clustered the
sample locations and depicts DBAN-EQ
bio
, DBAN-EQ
chem
and
DBAN-EQ
chem
(i). In the Northwestern area of BMA (Fig. 2,
postcodes 08006, 08016, 08022 to 08026, 08031, 08032, 08035
and 08041, 08042) less than 50% of the effect could be explained
by the HANs presumably due to the absence or low concentration
of the brominated DBAN, but the HAAs also had less than 0.1%
contribution to the mixture effects (Fig. 4a). However, 1,1,1-TCP
was detected at these sites and contributed 0.4 to 8.4% of the
biological effect. Presumably other DBPs that were not in the
target analysis or not characterized in the AREc32 could have
contributed to the mixture effect.
In Fig. 4b, the DBAN-EQ
chem
were compared with DBAN-EQ
bio
as
well as TOC and cytotoxicity DBAN-EQ
chem
(ΣHAN). The original
clustering was performed after scaling (Fig. S10) but in Fig. 4b, the
original data are plotted on a logarithmic scale for easier
comparison. DBAN-EQ
chem
(1,1,1-TCP) and DBAN-EQ
chem
(ΣHAA)
clustered together and were an independent group from all
others, indicating that they did not influence the mixture effect
very much due to their low potency. DBAN-EQ
bio
then clustered
with the TOC concentration, which can be explained by TOC being
an important precursor of DBPs, although we had not observed a
correlation between TOC and DBAN-EQ
bio
. Those two were
associated with cytotoxicity on the next level of clustering, while
the cluster with DBAN-EQ
chem
(ΣHAN) connected all of them on the
highest level.
Efficacy of point-of-use filters
Consistent with the observations in the cell assays, where the AC
filters did not reduce the cytotoxicity and effects, the filtration also
had a low and variable effect on the removal of TOC and DBPs.
Three of six AC filter did not change the TOC concentrations, only
two lowered it and one even increased the TOC by 38% (Table S7).
While the concentrations of HAAs were reduced by 18 to 91% and
those of HANs by 47 to 100%, this did not directly translate to the
reduction of the predicted mixture effect DBAN-EQ
chem
because
different DBPs were detected before and after the filter and there
was no consistent picture on the removal of DBAN-EQ
chem
.
Sometimes the more potent DBPs were removed, sometimes
those of lower potency.
What is striking is that the measured mixture effect for oxidative
stress response (DBAN-EQ
bio
) was only decreased by 49 to 72% in
three filters, but one filter showed no removal (4%) and for one
filter the toxicity increased by 44% and for another one it tripled.
As these filters were not run under optimal conditions but reflect
real-life scenarios, we can conclude as in the previous study [32]
that domestic filters need to be used according to the
manufacturer’s instruction and sufficiently often replaced when
the AC become saturated and DBPs and other chemicals can
break through. The filter that showed a three-times increased
oxidative stress response had a recent cartridge change reported
by the participant (personal communication).
In contrast, the five RO filters all reduced the TOC (Table S1)
substantially and no more DBPs were detected (Table S2). This
aligns well with the substantial but not full reduction of mixture
effects described by DBAN-EQ
bio
, which were reduced by 88 to
92% (Table S7).
SIGNIFICANCE
This study builds up on three earlier communications of the
presence of DBPs [32], micropollutants [33] and micro(nano)
B.I. Escher et al.
132
Journal of Exposure Science & Environmental Epidemiology (2024) 34:126 – 135
Content courtesy of Springer Nature, terms of use apply. Rights reserved
plastic [34] in the same water samples but provides important
additional insights into the potential harm DBPs may cause.
Bioassays are useful screening tools for drinking water quality and
removal of DBPs. Even if DBPs fall below the limit of detection,
their concentrations are likely not zero but the non-detectable
DBPs still contribute to the mixture effect. Moreover, there are
many more DBPs than typically covered by chemical analysis and
we do not even know how many there really are. Therefore,
bioassays are complementary to chemical analysis and can be
used to assess the overall burden of DBPs already scaled for effect-
potency.
Although the pattern, type and concentration of DBPs showed a
substantial regional variability, the effects were much less variable
across the entire BMA. The mixture effects were mainly dominated
by non-regulated DBPs.
We propose to complement the chemical analysis of regulated
DBPs by bioassays because they provide a sum parameter for all
DBPs and other micropollutants present in a drinking water
sample. This requires the definition of effect-based trigger (EBT)
values, which differentiate acceptable from poor water quality.
Tentative EBTs values for oxidative stress response already exist
and none of the investigated water samples exceeded these EBTs.
Before bioassays can be used for regulatory drinking water
monitoring EBTs must be defined for all bioassays of relevance
for DBPs.
DATA AVAILABILITY
The data generated and analyzed during this study can be found in within the
published article, reference [32] and in the Supplementary Information file.
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ACKNOWLEDGEMENTS
We thank Patricia González, Anna Gómez, Sònia Navarro, and Lai a Font-Ribera (Public
Health Agency of Barcelona) for providing valuable information on Barcelona’s
drinking water supplies. We thank Rita Schlichting for help with the data evaluation.
B.I. Escher et al.
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Content courtesy of Springer Nature, terms of use apply. Rights reserved
AUTHOR CONTRIBUTIONS
BE, CMV, CF, MJF, CP, and JC conceptualized the study and designed the
methodology. PERH collected the drinking water samples. JS, CF and CP conducted
contributed to the laboratory analysis of chemicals. PERH and DCS prepared
databases of chemical concentrations. JL and MK performed the bioassay
experiments with SPE extracts, JB, JR and MS performed the direct neurotoxicity
testing of water samples. BE carried out formal data analysis, JN performed advanced
data analysis. BE drafted the manuscript, which was reviewed and edited by all
coauthors. BE, CMV and CF administered the project and acquired the financial
support.
FUNDING
This project has been partially funded by Ajuntament de Barcelona (Institut de
Cultura, Pla Barcelona Ciencia 2019. #19S01446-006), and partly funded by Instituto
de Salud Carlos III and co-funded by European Union (ERDF) “A way to make Europe”
(PI20/ 00829). We acknowledge support from the Spanish Ministry of Science and
Innovation and State Research Agency through the “Centro de Excelencia Severo
Ochoa 2019-2023”Program (CEX2018-000806-S and CEX2018-000794-S, for ISGlobal
and IDAEA-CSIC, respectively), and support from the Generalitat de Catalunya
through the CERCA Program. MJF acknowledges her Ramón y Cajal fellowship (RyC-
2015-17108), from the AEI-MICIU. JR acknowledge Grant IJC 2018-035126-I funded by
MCIN/AEI and by “ESF Investing in your future”. We gratefully acknowledge access to
the platform CITEPro (Chemicals in the Environment Profiler) funded by the
Helmholtz Association for bioassay measurements and financial support of the work
at UFZ from the Helmholtz POF IV Topic 9 “Healthy Planet- towards a non-toxic
environment”.Wefinally would like to acknowledge all the volunteers that
participated in the project by providing access to water samples. Open Access
funding enabled and organized by Projekt DEAL.
COMPETING INTERESTS
The authors declare no competing interests.
ETHICAL APPROVAL
The study was approved by the Parc de Salut Mar Ethics committee.
ADDITIONAL INFORMATION
Supplementary information The online version contains supplementary material
available at https://doi.org/10.1038/s41370-023-00566-6.
Correspondence and requests for materials should be addressed to Beate I. Escher.
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© The Author(s) 2023
B.I. Escher et al.
135
Journal of Exposure Science & Environmental Epidemiology (2024) 34:126 – 135
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