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ORIGINAL RESEARCH
published: 11 September 2018
doi: 10.3389/fchem.2018.00407
Frontiers in Chemistry | www.frontiersin.org 1September 2018 | Volume 6 | Article 407
Edited by:
Teresa A.P. Rocha-Santos,
University of Aveiro, Portugal
Reviewed by:
Gerrit Renner,
Hochschule Niederrhein, Germany
Monica F. Costa,
Universidade Federal de Pernambuco,
Brazil
*Correspondence:
Sherri A. Mason
mason@fredonia.edu
Specialty section:
This article was submitted to
Analytical Chemistry,
a section of the journal
Frontiers in Chemistry
Received: 25 April 2018
Accepted: 20 August 2018
Published: 11 September 2018
Citation:
Mason SA, Welch VG and Neratko J
(2018) Synthetic Polymer
Contamination in Bottled Water.
Front. Chem. 6:407.
doi: 10.3389/fchem.2018.00407
Synthetic Polymer Contamination in
Bottled Water
Sherri A. Mason*, Victoria G. Welch and Joseph Neratko
Department of Chemistry, State University of New York at Fredonia, Fredonia, NY, United States
Eleven globally sourced brands of bottled water, purchased in 19 locations in nine
different countries, were tested for microplastic contamination using Nile Red tagging. Of
the 259 total bottles processed, 93% showed some sign of microplastic contamination.
After accounting for possible background (lab) contamination, an average of 10.4
microplastic particles >100 um in size per liter of bottled water processed were found.
Fragments were the most common morphology (66%) followed by fibers. Half of these
particles were confirmed to be polymeric in nature using FTIR spectroscopy with
polypropylene being the most common polymer type (54%), which matches a common
plastic used for the manufacture of bottle caps. A small fraction of particles (4%) showed
the presence of industrial lubricants. While spectroscopic analysis of particles smaller
than 100 um was not possible, the adsorption of the Nile Red dye indicates that these
particles are most probably plastic. Including these smaller particles (6.5–100 um), an
average of 325 microplastic particles per liter of bottled water was found. Microplastic
contamination range of 0 to over 10,000 microplastic particles per liter with 95% of
particles being between 6.5 and 100 um in size. Data suggests the contamination is
at least partially coming from the packaging and/or the bottling process itself. Given
the prevalence of the consumption of bottled water across the globe, the results of this
study support the need for further studies on the impacts of micro- and nano- plastics
on human health.
Keywords: plastic pollution, microplastic, consumables, human health, FTIR, Nile Red, drinking water
INTRODUCTION
Plastic is defined as any synthetic or semi-synthetic polymer with thermo-plastic or thermo-
set properties, which may be synthesized from hydrocarbon or biomass raw materials (UNEP,
2016). Plastics production has seen an exponential growth since its entrance on the consumer
stage, rising from a million tons in 1945 to over 300 million tons in 2014 (PlasticsEurope, 2015).
Some of the features of plastic that make it so attractive from a manufacturing standpoint are of
concern when it comes to its environmental impact. It is very light-weight allowing it to be easily
transported over long distances, and it is durable being resistant to breakage and biodegradation.
Its durability is inherently connected to its chemical structure. Being composed largely, if not
entirely, of hydrocarbon chains, the lack of double bonds or other functional groups provides
an inherent stability to its molecules, and its synthetic nature means that the vast majority of
microorganisms haven’t evolved to utilize plastic as a food source. Thus, while plastic will break
into smaller and smaller particles via photo-oxidative mechanisms, the fundamental molecular
structures of the material change very little throughout that process. Plastics become microplastics
become nanoplastics, but they are all plastics, just of increasingly smaller size, allowing them to be
more easily ingested and perhaps even cross the gastrointestinal tract to be transported throughout
a living organism (Brennecke et al., 2015; Sharma and Chatterjee, 2017).
Mason et al. Bottling More Than Just Water?
With the rise in plastics manufacture, there has been an
associated rise in plastic pollution of the external environment.
The first reports date back to the early 1970’s (Carpenter and
Smith, 1972) and most famously within the world’s oceans (e.g.,
Moore et al., 2001; Eriksen et al., 2014), but more recently plastic
pollution has been found within freshwater lakes, inland seas,
rivers, wetlands and organisms from plankton to whales (and
nearly every species in between; Eriksen et al., 2013; Baldwin
et al., 2016; Horton et al., 2017; Lusher et al., 2017).
As its ubiquity in the external environment has been
increasing, this has lead more researchers to investigate various
consumables for the presence of plastic. The first such study
focused on bivalves intended for human consumption (Van
Cauwenberghe and Janssen, 2014). More recent studies have
focused on fish (such as anchovies), as well as mussels (Rochman
et al., 2015; Tanaka and Takada, 2016; Lusher et al., 2017). Two
studies have noted the presence of microplastics within beer
(Liebezeit and Liebezeit, 2014; Kosuth et al., 2018). Starting
with a 2015 study of Chinese Sea Salt brands, several additional
studies have established the presence of microplastics within
these human consumables as well (Yang et al., 2015; Iñiguez
et al., 2017; Karami et al., 2017; Kosuth et al., 2018). The first-
ever investigation of plastic pollution within globally sourced
tap water (a total 159 samples from seven geographical regions
spanning five continents) was published just earlier this year
(Kosuth et al., 2018).
As research into the occurrence of plastic pollution has
increased, sampling and analysis methods are continually
evolving as well. Within the aqueous environment, volume-
reduced (using neuston nets) or bulk sampling followed by
density separation, filtration/sieving and visual identification
have been the most commonly employed methods (Hidalgo-Ruz
et al., 2012). Given the time-consuming nature of these methods
of sample processing, as well as the potential for misidentification
using visual cues alone, one focus area for plastics pollution
research (especially at the micro- and nano- scale) is development
of methods for high-throughput with increased polymeric
confirmation. Several recent studies have supported the use of
Nile Red (NR) as an accurate stain for the rapid detection and
quantification of microplastics given its selectivity adsorption
and fluorescent properties. Maes et al. (2017) specifically tested
the preferential adsorption of NR for polymeric materials relative
to common organic (algae, seaweeds, wood and feathers) and
inorganic (shells) environmental contaminants. Like Maes et al.
(2017) and Erni-Cassola et al. (2017) validated the use of this
stain with analysis using FTIR to verify the polymeric content
of fluorescing particles. Both of these studies concluded from
their efforts that NR can be used for the rapid detection of
microplastics without the need for additional spectroscopic
analysis (thereby reducing the time needed to analyze an
environmental sample). These studies suggest that the adsorption
of NR alone is sufficient to identify a particle as polymeric in
nature. A conclusion further supported by the inclusion of this
method within the recent review of analytical methodologies for
microplastic monitoring by Renner et al. (2018).
Here we present a study utilizing Nile Red for the detection
of microplastic within 11 globally- sourced brands of bottled
water. In total 259 bottles of water from 11 brands were processed
across 27 different lots (an identification number assigned by a
manufacturer to a particular production unit) purchased from
19 locations in nine countries. For 10 brands we tested 2–3 lots
each, while for one brand only one lot was tested. Within each
lot, we generally tested 10 bottles (bottle volume 500–600 mL
each) from the case. However, for one lot, several bottles from
the case were seized by customs allowing only nine bottles to be
tested, while for two other lots the volume of water per bottle was
significantly greater (0.750–2 L) and thus only four (2 L bottles)
or six bottles (750 mL bottles) were processed. One of the bottled
water lots was packaged in glass (Gerolsteiner, 750 mL, six glass
bottles processed); all other samples were packaged in plastic. All
bottles had plastic bottle caps.
MATERIALS AND METHODS
Sample Collection
Sample lots were procured with an eye to geographic diversity
(five continents are represented), size of the national packaged
drinking water market (China, USA, Brazil, India, Indonesia,
Mexico), and high per captia consumption of packaged
drinking water (Lebanon, Mexico, Thailand, USA; Table 1).
Leading international brands in this study included Aquafina,
Dasani, Evian, Nestle Pure Life, and San Pellegrino. Leading
national brands included Aqua (Indonesia), Bisleri (India),
Epura (Mexico), Gerolsteiner (Germany), Minalba (Brazil), and
Wahaha (China).
As many bottled water brands are simply filtered municipal
tap water, sample lots were purchased from a number of
locations to increase the likelihood of diverse bottling sources.
For example, cases of the Mexican brand Epura were purchased
from Tijuana in Baja California state, Reynosa on the Texas
border (1,200 miles east of Tijuana), and Mexico City (1,400 miles
south of Tijuana). This pattern is repeated with the other brands.
Sample Processing
The bottles within most (9 out of 11 brands) lots came
in containers of 500–600 mL per bottle, while two of the
brands contained 0.75–2 L per bottle. For those samples with
500–600 mL per bottle, 10 bottles were randomly chosen from
the lot, while for the 750 mL samples, six bottles were chosen,
and for the 2 L sample, four bottles were randomly chosen, and
placed under a laminar flow fume hood. While under the fume
hood, each bottle was opened and injected with a specific volume
of Nile Red solution (prepared in acetone to 1 mg mL−1) to
yield a working concentration of 10 ug mL−1 (Maes et al., 2017)
and re-capped. Nile Red adsorbs to the surface of plastics, but
not most naturally occurring materials, and fluoresces under
specific wavelengths of light (Erni-Cassola et al., 2017). Bottles
were allowed to incubate with the injected dye for at least 30 min.
The bottled water was then vacuum filtered through a glass fiber
filter (Whatman grade 934-AH, 55 mm diameter, 1.5 um pore).
Filters were examined under an optical microscope (Leica
EZ4HD, 8–40×zoom, integrated 3 Mpixel camera) using a blue
crime light (Crime-Lite 2, 445–510 nm, Foster & Freeman) to
elicit fluorescence, which was visualized through orange filter
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Mason et al. Bottling More Than Just Water?
TABLE 1 | Selected market assessment data utilized to determine the countries of origin and brands tested within this study.
Brand sales ranking Country sales ranking in world
Brand Parent company Country In country In world
Aqua Danone (France) Indonesia 1 3 4 (by volume)
Aquafina Pepsico USA 2 7 2 (by volume)
Bisleri Bisleri (Indian) India 1 10 6 (in sales)
Dasani Coca-Cola USA 1 4 2 (by volume)
Epura Proprietary brand of GEPP Mexico 1 — 1 (per capita)
Evian Danone USA
United Kingdom
France
1 (UK)
2 (France)
3 1 (in sales)
Gerolsteiner GmbH & Co. KG GERMANY 1 — 4 (per capita)
8 (in sales)
Minalba Edson Queiroz Group Brazil — — 5 (in sales)
Nestle Pure Life Nestle Lebanon 1 1 (parent company) —
San Pellegrino Nestle Italy — 1 (parent company) 3 (per capita)
9 (in sales)
Wahaha Hangzhou Wahaha Group China 1 1 1 (by volume)
Dashes (—) indicate missing information.
viewing googles (Foster & Freeman, 529 nm). All particles larger
than ∼100 um (which are large enough to be visible to the
naked eye and manipulated with tweezers) were photographed,
enumerated and typed with respect to morphology (Fragment,
Fiber, Pellet, Film, or Foam). Additionally the first 3–5 particles
were analyzed via FTIR (PerkinElmer Spectrum Two ATR;
450 cm−1 to 4,000 cm−1, 64 scans, 4 cm−1 resolution; ATR
correction) to confirm polymeric identity (Spectrum 10 software
suite).
After removal of all particles >100 um, the filter with
fluorescing particles was photographed (8×zoom) through
an orange camera filter (Foster & Freeman, 62 mm diameter,
529 nm) in four separate quadrants. To ensure no overlap of the
quadrant photographs identification marks were made on the
filters prior to turning the filter 90 degrees to take the subsequent
photo. In fact, given the zoom factor of the microscope, quadrant
photos did not obtain full (100%) coverage of the filter. Each
photographed quadrant was analyzed using a software program
entitled “Galaxy Count” developed by a former astrophysicist
for this specific purpose and briefly described here. Given the
fluorescing particles relative to the non-fluorescing background,
“Galaxy Count” is able to enumerate the number of particles
(as bright spots) in order to quantifying the number of smaller
microplastics. To do this, the operator of the software sets a
threshold value which is used to convert the quadrant images
to black (background filter) and white (fluorescing particles).
The software then digitally counts the number of white spots
(“stars”) against the dark background (“the night sky”). At the
8×magnification in which the quadrant photos were taken, 1
pixel was equal to 6.5 um. Thus, while the filter pore size was
1.5 um, the smallest size particle visualized through the use of
the combination of photography and software was 6.5 um. There
could certainly be particles smaller than 6.5 um, but the method
employed here would not be able to assess their presence. Due
to the programmatic setting of the threshold value, all digital
counts were conducted by two different researchers working
independently of one another to account for possible variability.
Microplastic counts for particles >100 um (referred to as
“NR +FTIR confirmed particles”) are reported for each bottle.
These particles are the ones that were further analyzed by
FTIR and thus the types of polymers are also reported. Smaller
microplastic particles (6.5–100 um; referred to as “NR tagged
particles”), counted using the “Galaxy Count” software, are
similarly reported for each bottle by summing over the four
quadrants (each quadrant being reported as the average of the
two researchers).
Quality Assurance and Quality Control
As the “Galaxy Count” software was created specifically for
this project in order to verify its accuracy four solutions were
created using DI water containing 0, 20, 50 or 100 polyethylene
microspheres (Cospheric, PE micropheres, D=1.25 g mL−1,
75–90 um diameter). These solutions were created by one
researcher, but processed “blind” by another researcher in a
manner identical to the samples themselves (NR injection,
incubation, filtering, quadrant photographing and analysis by
the “Galaxy Count” software). Additionally the analysis of all
filter quadrants by the “Galaxy Count” software for all samples
were conducted “blindly” by two separate researchers. These two
counts were compared to one another for accuracy, in addition
to being averaged for reported numbers.
In order to prevent/reduce potential contamination
throughout the sample processing from external sources,
such as airborne fibers, work occurred in a laminar airflow
cabinet (Mott manufacturing, Phoenix Controls, serviced
annually in September) and the workspace was wiped down
every week. All glassware was covered with a watch glass when
not in use and washed thoroughly between trials. Filters were
inspected under a microscope prior to use, and a cotton lab
coat and sterling nitrile powder free exam gloves were worn
throughout the experimental procedure.
To account for possible lab contamination that could be
coming from atmospheric deposition, the chemicals used, the
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Mason et al. Bottling More Than Just Water?
glassware or other aspects of the testing environment, lab blanks
containing deionized water (used to wash all glassware) or
acetone (used to prepare the Nile Red solution) were processed in
a manner identical to the samples themselves. Particle densities
within samples were reduced based upon the average densities
across all lab blanks.
RESULTS
Overview
A total of 259 individual bottles from across 11 different
brands and 27 different lots were analyzed for microplastic
particulate, subdivided into two size fractions: so-called “NR
+FTIR confirmed particles,” which are >100 um, and “NR
tagged particles,” which are 6.5–100 um. As quadrant photos
did not provide full (100%) coverage of the filter, it is likely
that “NR tagged particles” are underestimated. Since individual
bottles contained varied water volumes, from 500 mL to 2 L,
absolute counts for each bottle and size fraction were divided
by sample volume to calculate (raw) densities of microplastic per
liter (microplastic particles/L or MPP/L).
Thirteen lab blanks using laboratory deionized water or
acetone were processed using methods identical to those for the
bottled water samples. For “NR +FTIR particles” (>100 um)
the average density was found to be 4.15 MPP/L, with a range
of 0–14 MPP/L, while within the smaller “NR tagged particles”
(6.5–100 um) the average density was 23.5 MPP/L, with a range
of 7–47 MPP/L. Reported microplastic densities for the bottled
water samples are calculated (by size fraction) from raw densities
less the average from laboratory blanks (Table 1). If raw densities
had less than or equal quantities relative to the laboratory blanks,
their values were set to zero. Given that quadrant photos did not
obtain full (100%) coverage of the filter and that raw densities
were reduced by lab blanks, reported densities are expected
to be reasonable but conservative accounting of microplastic
contamination. Total densities were calculated by summing
across the size fractions (Table 2).
Seventeen bottles out of the 259 bottles analyzed (∼7%)
showed no microplastic contamination in excess of possible
laboratory influence indicating that 93% of the bottled water
tested showed some sign of microplastic contamination. The
densities of microplastic contamination are quite variable
ranging from the 17 bottles with no contamination to one bottle
that showed an excess of 10,000 microplastic particles per liter
(Table 2). The variabilities seen in the individual bottles, even
among the same lot and brand, is similar to what is seen in
sampling open bodies of water (Yonkos et al., 2014). Patterns in
such sampling can be rather stochastic due to the large number
of factors that can affect the occurrence of plastic particles
(especially at the microscale), like particle-fluid dynamics, as well
as variabilities within the manufacturing process itself, leading
to the large variabilities seen within the samples. This erraticism
highlights the need for large sample sizes, such as that employed
here, in order to average across the variabilities to produce a
realistic depiction.
Table 3 provides the mean (by size fraction and total), as
well as the minimum and maximum, microplastic densities
(in MPP/L) for each lot averaged across all the bottles tested.
When averaging across the individual bottles, all 27 lots tested
showed some quantity of microplastic contamination (Table 2).
Within brands there is significant variability between different
lots, which could be owing to a number of factors, such as
water source, different bottling facilities, or the conditions and/or
length of time involved in shipping from bottling facilities
to purchase location. The 17 individual bottles that showed
no microplastic contamination in excess of possible laboratory
background (Table 1) originated from seven lots (∼25%) of the
27 tested. Thus, microplastic contamination was found within all
bottles in 75% of the lots analyzed.
When averaged across all lots and all brands, 325 MPP/L
were found within the bottled water tested [broken down as
an average of 10.4 MPP/L occurring within the larger size
range (>100 um) and an average 315 MPP/L within the smaller
size range (6.5–100 um)]. While all bottled water lots tested
showed some sign of microplastic contamination (Table 2),
there was significant variation among the brands (Figure 1).
Averaging across lots by brand, Nestle Pure Life and Gerolsteiner
showed the highest average densities at 930 and 807 MPP/L,
respectively, while San Pellegrino and Minalba showed the
lowest microplastic contamination with 30.0 and 63.1 MPP/L,
respectively (Figure 1). Error bars in Figure 1 represent one
standard deviation and are quite large given the large variability
among the individual bottles for each lot (Table 2), as well as the
variation among lots of the same brand (Table 3).
Of all the lots tested, only one was packaged in glass rather
than plastic: Gerolsteiner (NV No. AC-51-07269). While these
samples revealed microplastic contamination, they did so at
lower level as compared to the other lots (Tables 2,3). Further,
the same brand of water but packaged in plastic instead of
glass was also tested (Gerolsteiner, 07.142018 2). While both of
these packaged waters have the same water source, there was
considerably less microplastic contamination within the water
bottled in glass as compared to that packaged in plastic (204
vs. 1,410 MPP/L, respectively). This indicates that some of the
microplastic contamination is likely coming from the water
source, but a larger contribution might be originating from the
packaging itself.
NR +FTIR Confirmed Particles (>100 um)
In total nearly 2,000 microplastic particles >100 um were
extracted from all of the filters, with nearly 1,000 (∼50%) being
further analyzed by FTIR. Obtained FTIR spectra (after applied
ATR correction) were compared to libraries of known spectra
using the included PerkinElmer Spectrum 10 software suite in
order to confirm and identify the polymeric content of the
particles. All particles analyzed were either best matched to
a polymer, plastic additive or known plastic binder providing
additional supporting evidence that Nile Red selectively adsorbed
to microplastic particles within the bottled water. With this
spectroscopic confirmation, it can be concluded that on average
each bottle of water contains at least 10.4 MPP/L (Table 3). While
this analysis confirmed the polymeric nature of these particles, a
match of 70% or greater was required in order to assign polymer
identity. In total over 400 particles (20% of all extracted plastic
particles >100 um and 40% of those analyzed by FTIR) met this
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Mason et al. Bottling More Than Just Water?
TABLE 2 | Microplastic particle densities by bottle and size fraction for each brand and lot number.
Microplastics densities (MPP/L) by bottle
NR +FTIR confirmed particles (>100 um) NR tagged particles (6.5–100 um) Reported total densities (MPP/L)
Brand Lot number Purchase location 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10
Aqua IB 101119 Jakarta, Indonesia 6 8 8 4 11 9 4 9 3 6 127 52 55 57 12 0 0 0 0 0 133 60 62 62 23 9 4 9 3 6
Aqua BB 311019 08:11 PSRL6 Bali, Indonesia 3 9 1 9 8 8 3 19 26 21 2 37 0 142 0 0 7 602 1,466 4,692 4 47 1 152 8 8 10 621 1,492 4,713
Aqua BB 311019 09:50 STB1 Medan,Indonesia 0 1 3 9 8 0 6 36 8 0 36 94 30 43 41 0 25 3,687 12 5 36 95 32 52 48 0 31 3,722 20 5
Aquafina Oct0719 0121PF100375 Amazon.com 10 8 14 8 24 14 20 28 10 14 87 37 74 35 132 313 139 1,268 137 153 96 44 87 42 155 326 158 1,295 146 166
Aquafina BN7141A04117 Chennai, India 22 22 10 16 4 2 10 10 6 16 127 171 71 94 180 1 253 131 212 389 148 192 80 109 183 2 262 140 217 404
Bisleri HE.B.No.229 (BM/AS) Chennai, India 38 28 18 8 8 8 14 10 26 24 76 75 144 37 98 32 50 206 2,163 5,207 113 102 161 44 105 39 63 215 2 ,188 5,230
Bisleri MU.B.No.298 (MS/AD) Mumbai, India 14 8 12 6 8 12 2 12 6 10 66 8 17 125 6 20 0 1,799 0 0 79 15 28 130 13 31 2 1,810 6 10
Bisleri SO.B.No.087 (AS/LB) New Delhi, India 0 0 0 0 0 2 4 0 0 0 0 0 0 32 0 0 0 0 0 0 0 0 0 32 0 2 4 0 0 0
Dasani Oct 0118NHBRB Amazon.com 22 18 12 4 12 20 8 16 22 14 169 99 292 116 74 130 186 99 168 173 190 116 303 119 85 149 193 114 189 186
Dasani P18NOV17CG3 Nairobi, Kenya 26 0 8 14 2 0 6 2 2 4 0 7 226 56 8 16 28 1 13 332 26 7 233 69 9 16 33 2 14 335
E-Pura 17.11.18 Mexico City, Mexico 9 11 38 26 31 38 36 4 4 28 2 0 946 1,292 1,167 667 2,232 7 56 268 11 11 983 1,318 1,198 704 2,267 11 60 296
E-Pura 14.10.18 Tijuana, Mexico 18 14 21 9 3 3 0 6 4 1 0 78 12 2 0 12 6 6 6 2 18 92 32 11 3 14 6 12 10 3
E-Pura 09.08.18 Reynosa, Mexico 0 0 0 1 – – – – – – 0 0 0 148 – – – – – – 0 0 0 149 – – – – – –
Evian PRD 03 21 2017 14:02 Amazon.com 18 18 26 10 38 24 22 20 40 46 239 207 156 176 98 222 105 212 153 148 256 224 181 185 135 245 126 231 192 193
Evian PRD 05 24 17 11:29 Fredonia, NY, USA 4 0 2 0 0 2 2 2 4 0 253 0 77 29 3 50 1 47 96 15 256 0 78 29 3 51 2 48 99 15
Gerolsteiner 07.142018 2 07.07.2017 Fredonia, NY, USA 10 8 2 10 10 10 20 24 20 36 180 35 45 2 13 56 154 3,431 4,974 5,071 189 42 46 11 22 65 173 3,454 4,993 5,106
Gerolsteiner NV No. AC-51-07269 Amazon.com 8 11 5 8 12 11 – – – – 3 11 4 173 504 479 – – – – 10 21 9 180 516 490 – – – –
Minalba FAB: 211017 09:06SP Sao Paulo, Brazil 4 4 4 0 4 4 2 4 2 0 5 0 14 6 7 17 48 0 79 199 9 4 17 6 11 20 50 4 81 199
Minalba FAB: 160817 15:05SP Aparecida de Goiania,
Brazil
4 0 8 4 0 6 10 2 15 6 0 0 3 43 0 10 11 5 0 0 4 0 11 47 0 16 20 7 15 6
Minalba FAB: 091217 16:53SP Rio de Janeiro, Brazil 2 0 4 0 6 0 0 0 0 39 37 0 2 0 54 0 32 25 479 824 39 0 6 0 60 0 32 25 479 863
Nestle Pure Life 100517 278WF246 Amazon.com 24 38 22 22 28 28 32 28 38 40 101 1,074 30 106 110 1,249 622 1,511 7,322 10,351 124 1,111 51 127 137 1,276 653 1,538 7,359 10,390
Nestle Pure Life P: 4/11/17 01:34 AZ Beirut, Lebanon 12 18 18 6 12 12 12 8 6 8 64 136 27 0 27 21 40 57 14 0 75 153 44 6 38 32 51 64 19 8
Nestle Pure Life 730805210A 23:28 Bangkok, Thailand 2 28 8 4 28 8 66 12 18 8 140 147 83 23 398 4 3,461 87 105 60 141 174 90 26 425 11 3,526 98 122 67
San Pellegrino BBE 11.2018 10 Amazon.com 1 4 4 0 2 2 0 2 2 1 74 29 38 0 27 15 30 6 34 35 74 33 41 0 28 17 30 7 35 36
Wahaha 20171102 1214JN Jinan, China 9 11 4 26 18 4 1 11 4 3 225 198 65 705 54 26 61 62 34 37 234 209 69 731 71 30 62 73 39 39
Wahaha 20171021 3214GH Beijing, China 0 0 9 4 4 4 3 21 4 – 178 101 39 9 106 55 42 0 21 – 178 101 48 13 110 60 45 21 25 –
Wahaha 20171103 2106WF Qingdao, China 4 8 4 1 1 8 4 3 11 1 86 108 44 0 0 158 39 0 87 104 91 116 48 1 1 165 44 3 98 105
Dashes (—) indicate lots in which <10 bottles were processed. NR, Nile Red.
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Mason et al. Bottling More Than Just Water?
TABLE 3 | Microplastic densities (MPP/L), by size fractions and total, averaged across all bottles within the same lot.
Average microplastic densities (MPP/L)
NR +FTIR confirmed
particles
NR tagged particles Total
Brand Lot Purchase location (>100 um) (6.5–100 um) Average Minimum Maximum
Aqua IB 101119 Jakarta, Indonesia 6.68 30.4 37.1 3 133
Aqua BB 311019 08:11 PSRL6 Bali, Indonesia 10.5 695 705 1 4,713
Aqua BB 311019 09:50 STB1 Medan, Indonesia 6.93 397 404 0 3,722
Aquafina Oct0719 0121PF100375 Amazon.com 14.8 237 252 42 1,295
Aquafina BN7141A04117 Chennai, India 11.6 162 174 2 404
Bisleri HE.B.No.229 (BM/AS) Chennai, India 18.0 808 826 39 5,230
Bisleri MU.B.No.298 (MS/AD) Mumbai, India 8.85 204 213 2 1,810
Bisleri SO.B.No.087 (AS/LB) New Delhi, India 0.57 3.15 3.72 0 32
Dasani Oct 0118NHBRB Amazon.com 14.6 150 165 85 303
Dasani P18NOV17CG3 Nairobi, Kenya 6.28 68.3 74.6 2 335
E-Pura 17.11.18 Mexico City, Mexico 22.3 664 686 11 2,267
E-Pura 14.10.18 Tijuana, Mexico 7.76 12.2 20.0 3 92
E-Pura 09.08.18 Reynosa, Mexico 0.21 37.1 37.3 0 149
Evian PRD 03 21 2017 14:02 Amazon.com 26.0 171 197 126 256
Evian PRD 05 24 17 11:29 Fredonia, NY, USA 1.51 56.7 58.2 0 256
Gerolsteiner 07.142018 2 07.07.2017 Fredonia, NY, USA 14.8 1,396 1,410 11 5,106
Gerolsteiner NV No. AC-51-07269 Amazon.com 8.96 195 204 9 516
Minalba FAB: 211017 09:06SP Sao Paulo, Brazil 2.56 37.5 40.1 4 199
Minalba FAB: 160817 15:05SP Aparecida de Goiania, Brazil 5.30 7.19 12.5 0 47
Minalba FAB: 091217 16:53SP Rio de Janeiro, Brazil 5.01 145 150 0 863
Nestle Pure Life 100517 278WF246 Amazon.com 29.8 2,247 2,277 51 10,390
Nestle Pure Life P: 4/11/17 01:34 AZ Beirut, Lebanon 11.0 38.2 49.3 6 153
Nestle Pure Life 730805210A 23:28 Bangkok, Thailand 18.0 450 468 11 3,526
San Pellegrino BBE 11.2018 10 Amazon.com 1.68 28.6 30.3 0 74
Wahaha 20171102 1214JN Jinan, China 9.10 147 156 30 731
Wahaha 20171021 3214GH Beijing, China 5.53 61.2 66.7 13 178
Wahaha 20171103 2106WF Qingdao, China 4.40 62.7 67.1 1 165
Minimum and maximum densities within the lot are also provided. NR, Nile Red.
threshold for identity confirmation and only those results are
presented here
Polypropylene was found to be the most common polymeric
material (54%) with Nylon being the second most abundant
(16%; Figure 2). Polypropylene is a polymer often used
to make plastic bottle caps, along with polyethylene,
which corresponded to 10% of the particles analyzed.
Interestingly, 4% of retrieved particles were found to have
signatures of industrial lubricants coating the polymer (not
shown).
As is common practice in plastic pollution research, all
microplastics >100 um were visually characterized according
to their morphology: Fragment, Fiber, Pellet, Film, or Foam.
Fragments were found to be the most common type of particle
(66%), followed by fibers (13%) and films (12%; Figure 3).
The 13% of particles described as fibers (Figure 3) compares
well with the 17% of particles that were confirmed by FTIR
to be composed of fiberous polymers, most notably Nylon
(Figure 2).
NR Tagged Particles (6.5–100 um)
In order to verify the effectiveness of the “Galaxy Count” software
to count microplastics smaller than ∼100 um, the software was
tested using solutions with known quantities (0,20, 50 or 100)
of microspheres (diameters 75–90 um) processed in a manner
identical to all samples and lab blanks. The “Galaxy Count”
of fluorescing particles on the filter quadrant photos agreed
very well with the actual count of particles included within
the solutions (Figure 4). The excellent agreement with these
test solutions supports the use of this tool for quantifying the
numbers of smaller particles within the bottled waters analyzed,
while the y-intercept of the least-squares fit further supports that
the study is likely undercounting particles, especially within this
smallest size range.
All counts using the “Galaxy Count” software were conducted
independently by two different researchers owing to possible
variabilities in software settings. As shown in Figure 5, the
agreement in counts between the two researchers is excellent
providing additional support to the effectiveness and validity
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Mason et al. Bottling More Than Just Water?
FIGURE 1 | Microplastic density averaged across individual bottles and lots by brand. Blue bars are densities for “NR +FTIR confirmed particles” (>100 um); Orange
bars are for “NR tagged particles” (6.5–100 um). Error bars are one standard deviation. Percentages are for the contribution to the total for “NR tagged particles”
(6.5–100 um); Contribution of larger particles can be inferred.
FIGURE 2 | Polymeric content of microplastic particles >100 um found within
bottled water. PP, polypropylene; PS, polystyrene; PE, polyethylene; PEST,
polyester +polyethylene terephthalate; Others includes Azlon, polyacrylates
and copolymers.
in using the software to count the smaller particles within the
bottled water.
Given the limitations of the lab, particles <100 um (the so-
called “NR tagged particles”) were not able to be confirmed as
polymeric through spectroscopic analyses (FTIR and/or Raman
FIGURE 3 | Morphologies of microplastics >100 um found within bottled
water.
spectroscopy). However, in testing of various stains and dyes
that could be employed for microplastic detection and analysis
within environmental samples with a greater potential for
misidentification and false positives (i.e., sediments and open-
water environmental samples) both Maes et al. (2017) and
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Mason et al. Bottling More Than Just Water?
FIGURE 4 | Comparison of counts using the “Galaxy Count” software relative to the known number of microplastic particles within four test solutions.
FIGURE 5 | Comparison of microplastic counts by the “Galaxy Count” software for particles <100 um within all 259 bottles tested by two researchers working
independently of one another.
Erni-Cassola et al. (2017) concluded that Nile Red (NR) was
very selective, especially within the time scales of incubation
employed, and could be used for the rapid detection of
microplastics without the need for additional spectroscopic
analysis. To be sure that is why this stain was employed for
this study. Additionally FTIR analysis was done on fluorescing
particles >100 um and every particle analyzed was confirmed to
be polymeric. Even further, NR is well-established to selectively
Frontiers in Chemistry | www.frontiersin.org 8September 2018 | Volume 6 | Article 407
Mason et al. Bottling More Than Just Water?
adsorb to hydrophobic (“water-fearing”) materials and, as such,
will not adsorb to the only contents reasonably expected to be
within bottled water, water and/or its mineral components. In
addition, Schymanski et al. (2018) reported Raman confirmed
densities of particles within a similar size range and even smaller
(5–500 um) in bottles of German bottled mineral water. Thus, at
a minimum while particles <100 um were not spectroscopically
confirmed to be microplastics, particles are rationally expected to
be plastic or of some other anthropogenic origin.
DISCUSSION
Part of the impetus for this study was as a follow-up to a tap water
study released (in part) in September 2017 (Kosuth et al., 2018).
The methods used in this study differed slightly in comparison
to this earlier study, most notably in the use of a different stain.
Rose Bengal was used in the earlier study, while Nile Red was used
here. The two dyes have opposite affinities. While plastics adsorb
Nile Red (allowing their easy detection via fluorescence), they do
not adsorb Rose Bengal. The affinity of plastics to adsorb Nile Red
allows smaller particles to be detected as compared to the Rose
Bengal method, as noted by a recent study by Erni-Cassola et al.
(2017). Thus, only our data on particles >100 um is comparable
to the data in this previous tap water study.
We found roughly twice as many plastic particles (>100 um)
within bottled water as compared to tap water on average
(10.4 vs. 5.45 particles/L). While fibers made of 97% of the
microplastics within the tap water study, they only composed
13% of the particles within bottled water. Instead fragments
were the most common particle morphology (65%) within
bottled water. These results indicate that the main source of
the microplastic particulate is different. Given the fragment
morphology combined with the fact that 4% of the particles were
found to have signatures of industrial lubricants, the data seems
to suggest that at least some of the plastic contamination may be
coming from the industrial process of bottling the water itself.
As polypropylene was the most common polymer found, the
fragments could also be breaking off the cap, even entering the
water through the simple act of opening the bottle.
More recently Schymanski et al. (2018) published their study
on microplastic contamination of packaged mineral water. They
tested a wider variety of packaging media from returnable and
single-use plastic bottles to cartons to glass, while this study
almost exclusively focused on single-use plastic bottles (having
only one lot packaged in glass as an alternative). They did test
fewer bottles overall as compared to this study. In order to
compare these two studies, then, only their data for single-use,
plastic beverage bottles is utilized. Within those confines, they
tested a total of 11 bottles in comparison to our 259. While they
do not specify how many different brands, for one brand they
tested two different lots (purchased 6 weeks apart), but only tested
one lot for the others.
The average microplastic density across all brands, lot
numbers and bottles analyzed (325 MPP/L) is significantly higher
in this study as compared to that reported by Schymanski
et al. (2018) (14 MPP/L). This difference could be owing to
a number of factors. First, as they report they only counted
particles for which they could fully confirm the polymeric nature
using Raman spectroscopy. We used the adsorption of Nile
Red as our frontline confirmation of microplastic identity, using
FTIR on particles simply to provide more information as to
the specific polymer. As the authors note, while Raman can
analyze smaller particles than FTIR, the laser intensity can cause
the particle to decompose before an adequate spectra can be
obtained. Schymanski et al. (2018) did not include these particles
in their counts leading to a reduction in their calculated densities.
Further, as our data shows there can be substantial variability
between brands and between lots. Our significantly larger sample
set provides a greater accounting of that variability.
Another difference between our studies is distribution of
polymer types. Schymanski et al. (2018) found PEST (the
combination of polyester and polyethylene terephthalate) to
be the dominant polymeric material of their particulate
contaminants, while that same categorization only accounted for
6% of our analyzed particles. Here polypropylene was found to
be the dominant plastic (54%), which only accounted for 1% of
their particles. However, our two studies are not fully comparable
with regard to this analysis. Schymanski et al. (2018) analyzed
and determined polymeric identity for all particles counted, while
we only did so for particles >100 um. It is quite possible that
the smaller particles we were unable to analyze were mainly
composed of the polymers within the PEST category, which
would very much alter our percentages. Nevertheless, we both
do reason from our data that the packaging of the water itself
is a likely source of contamination, though for us it appears to be
the caps, while for Schymanski et al. (2018) it appeared to be the
bottle.
Despite the differences between our studies some similarities
do exist. We both found polyethylene accounting for ∼10% of
the polymeric contaminants. Additionally, we both found smaller
particles provided a larger contribution to the total number of
particles as compared to the larger particles (>100 um). Across all
samples, 95% of our particles were <100 um, while Schymanski
et al. (2018) found they accounted for 98% of their counts. Even
further, taken together, these two studies do support the very
basic point that there are microplastics within bottled water and
at least some of this contamination may arise from the industrial
process of bottling the water, as well as from the packing material
itself.
CONCLUSIONS
Twenty-seven different lots of bottled water from 11 different
brands purchased in 19 locations across nine different countries
were analyzed for microplastic contamination using a Nile
Red stain, which adsorbs to polymeric material and fluoresces
under specific wavelengths of incident light. The use of the
fluorescent dye allowed for smaller particles to be detected
as compared to a similar study of tap water using a
Rose Bengal stain, though the analytical methods employed
for their enumeration restricted the lower size limit to
6.5 mm.
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Mason et al. Bottling More Than Just Water?
Of the 259 total bottles analyzed, 93% showed signs of
microplastics. There was significant variation even among
bottles of the same brand and lot, which is consistent
with environmental sampling and likely resulting from the
complexities of microplastic sources, the manufacturing process
and particle-fluid dynamics, among others. As bottle volume
varied across brands, absolute particle counts were divided by
bottle volume in order to produce microplastic particle densities
that were comparable across all brands, lots and bottles. These
densities were reduced by lab blanks in order to account for any
possible contamination. Given our use of lab blanks, the inability
to photograph the full filter, the lower limit of one pixel being
equivalent to 6.5 mm, and control runs of the software employed
to digitally count particles <100 mm, the numbers reported here
are very conservative and likely undercounting, especially with
regard to smaller microplastics (<100 mm), which were found to
be more prominent (on average 95%) as compared to particles
>100 mm (on average 5%).
Infrared analysis of particles >100 mm in size confirmed
microplastic identity and found polypropylene to be the most
common (54%) polymeric material (at least with regard to these
larger microplastics), consistent with a common plastic employed
to manufacture bottle caps. Smaller particles (6.5–100 mm)
could not be analyzed for polymer identification given the
analytical limits of the lab. While these smaller particles could
not be spectroscopically confirmed as plastic, Nile Red adsorbs
to hydrophobic (“water-fearing”) materials, which are not
reasonably expected to be naturally found within bottled water.
Our FTIR analysis of larger (>100 um particles) fluorescing
particles, all of which were confirmed to be polymeric, provides
additional support of the selective binding of NR to microplastic
particles within the samples. Even further, Schymanski et al.
(2018) did spectroscopically confirm (via Raman) particles
within this smaller size range in German bottled water as
being polymeric in nature provide additional support for their
presence. Given this and following the conclusions of prior
studies (Erni-Cassola et al., 2017; e.g., Maes et al., 2017) the
adsorption of Nile Red alone was used to confer microplastic
identity to these smaller particles. As the specific polymer content
could not be determined, they could very well show a different
compositional pattern as compared to the larger particles
analyzed. This could explain the difference in our polymeric
compositional analysis relative to a very recent and similar
analysis of bottled mineral waters by Schymanski et al. (2018),
which found PEST (polyester+polyethylene terephthalate) to
be the most common polymeric material, consistent with a
common plastic employed to manufacture the bottle itself. Either
way both studies indicate that at least part of the microplastic
contamination is arising from the packaging material and/or the
bottling process itself.
Beyond the polymeric identity of the microplastics, the
morphology of the particles also provides an indication as to a
different source of contamination relative to an earlier study on
globally sourced tap water. In this prior study 83% of the 159
samples were show to contain anthropogenic debris and 98%
of those particles were microfibers. In comparison, this study
found microplastic contamination within 93% of the individual
bottles (and in all of the brands and lots tested) with only 13% of
the particles being categorized as microfibers. The vast majority
(65%) of the microplastics were identified as fragments indicating
a different source of the contamination relative to the tap water.
Even further, the bottled water contained on average nearly
twice as much microplastic contamination (within the same size
range, i.e., >100 um) as compared to tap water (10.4 vs. 5.45
particles/L). While the impacts of microplastic contamination on
human health are still unknown, these results strongly support
a reduction in the bottling of water and in the consumption of
bottled water, especially within locations in which clean, safe tap
water exists.
DATA AVAILABILITY
The raw data supporting the conclusions of this manuscript will
be made available by the authors, without undue reservation, to
any qualified researcher.
AUTHOR CONTRIBUTIONS
SM designed the study, supervised the work, ensured
quality control and wrote the manuscript. VW was the lead
laboratory research assistant and conducted all aspects of the
laboratory analysis. JN assisted in and conducted laboratory
analyses.
ACKNOWLEDGMENTS
The authors wish to thank Orb Media who conducted the market
analysis to determine the top selling bottled water brands within
each region and facilitated the purchase and delivery of all
samples to our lab.
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Conflict of Interest Statement: The authors declare that the research was
conducted in the absence of any commercial or financial relationships that could
be construed as a potential conflict of interest.
Copyright © 2018 Mason, Welch and Neratko. This is an open-access article
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original author(s) and the copyright owner(s) are credited and that the original
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