Environmental Pollution 190 (2014) 115e122
Contents lists available at ScienceDirect
Environmental Pollution
journal homepage: www.elsevier.com/locate/envpol
Occurrence and transport of 17 perfluoroalkyl acids in 12 coastal rivers
in south Bohai coastal region of China with concentrated
fluoropolymer facilities
Pei Wang a, b, Yonglong Lu a, *, Tieyu Wang a, Yaning Fu a, b, Zhaoyun Zhu a, b, Shijie Liu a, b,
Shuangwei Xie a, b, Yang Xiao a, b, John P. Giesy c
a
b
c
State Key Lab of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
Graduate University of Chinese Academy of Sciences, Beijing 100049, China
Department of Veterinary Biomedical Sciences and Toxicology Centre, University of Saskatchewan, Saskatoon, Saskatchewan, Canada
a r t i c l e i n f o
a b s t r a c t
Article history:
Received 4 January 2014
Received in revised form
19 March 2014
Accepted 21 March 2014
Available online 17 April 2014
Perfluoroalkyl acids (PFAAs) are emerging contaminants that have raised great concern in recent years.
While PFAAs manufacturing becomes regulated in developed countries, production has been partly
shifted to China. Eight fluoropolymer manufacturing facilities located in the South Bohai coastal region,
one of the most populated areas of China, have been used to manufacture PFAA-related substances since
2001. The environmental consequence of the intensive production of PFAAs in this region remains largely
unknown. We analyzed 17 PFAAs in twelve coastal rivers of this region, and found staggeringly high
concentrations of perfluorooctanoic acid (PFOA) ranging from 0.96 to 4534.41 ng/L. The highest concentration was observed in the Xiaoqing River which received effluents from certain fluoropolymer facilities. Principal component analysis indicated similar sources of several perfluoroalkyl carboxylic acids
(PFCAs) in all rivers, which indicated that atmospheric transport, wastewater treatment and surface
runoff also acted as important supplements to direct discharge to surface water.
Ó 2014 Elsevier Ltd. All rights reserved.
Keywords:
Perfluoroalkyl acids
PFOA
Spatial analysis
Rapid urbanization
Fluoropolymer facility
1. Introduction
Perfluoroalkyl acids (PFAAs) have unique properties including
surface activity, repellency of water and oil, and resistance of acid
and heat (Giesy and Kannan, 2002). In industry, they are widely
used in manufacturing processes and products (Giesy et al., 2006),
but when dispersed into the environment, they can transport over
long distances and accumulate to toxic concentrations, due to their
persistence (Giesy et al., 2010). Ionic PFAAs, mostly known as perfluoroalkyl carboxylic acids (PFCAs) and perfluoroalkyl sulfonic
acids (PFSAs), are relatively soluble compared to many of the
organochlorine compounds with similar molecular size such as
DDT and Hexachlorocyclohexane. Thus, water is the primary
reservoir of PFAAs and the major medium for their transportation
(Prevedouros et al., 2005).
Concerns over sources, transport and fate of PFAAs in the
aquatic environment have grown rapidly in recent years.
* Corresponding author.
E-mail address: yllu@rcees.ac.cn (Y. Lu).
http://dx.doi.org/10.1016/j.envpol.2014.03.030
0269-7491/Ó 2014 Elsevier Ltd. All rights reserved.
Perfluorooctane sulfonic acid (PFOS) is the most frequently
detected PFAA that is released from a variety of diffuse sources,
and is usually the predominant PFAAs detected in aquatic biota
(Giesy et al., 2010). Perfluorooctanoic acid (PFOA) is discharged
primarily from point-sources in industrial regions, especially from
manufacturing facilities (Pistocchi and Loos, 2009). Wastewater
treatment plants (WWTPs) are also important point sources of
releasing PFAAs, along with more diffuse inputs including rain,
dry deposition and release during the use of products (Muller
et al., 2011). The majority of PFAAs reach the coastal marine
environment dissolved in water, while for long chain PFCAs (C12e
C15), about a half of the load was absorbed to particles during
transportation (Zushi et al., 2012). Higher affinity for organic
carbon leads to enhanced sorption of longer chain PFCAs to particles and solids in sediment and sludge (Armitage et al., 2009).
The transportation of PFAAs in water indicated that rivers would
be the main source of PFAAs for coastal water. Due to the restriction on the production and use of C8 PFOS and PFOA, the C4
and C6 chemicals have been developed well to adequately replace
most current C8 and higher homologues. However, this has also
led to the emergence of short chain PFAAs in the environment
116
P. Wang et al. / Environmental Pollution 190 (2014) 115e122
(Moller et al., 2010; Oliaei et al., 2013). This suggested the
importance of the measurement of PFAAs with different carbon
chain lengths with different properties, in order to trace the trend
of PFAAs contamination in the environment.
The Bohai-Rim Economic Circle is a highly urbanized and
industrialized region in Northern China (Fig. S1). There are more
than 40 rivers flowing into the Bohai Sea, a semi-enclosed sea.
Based on estimation of mass fluxes of several chemical pollutants
(i.e. petroleum hydrocarbons, heavy metals) to the Bohai Sea, the
rivers contributed 50%e70% of the total inventory among the five
sources: rivers, drains, atmospheric deposition, cultivation and
non-point sources (Wang and Li, 2006). The authors have conducted systematic studies on PFAAs in the northern part of the
Bohai coastal region since 2008, and found that PFAAs were widely
distributed in the environmental matrices with PFOA dominant in
the Northern Bohai coastal region (Wang et al., 2011b), PFOS was
dominant in the aquatic products in Tianjin (Chen et al., 2011),
PFAAs concentrations in surface water were correlated with the
level of industrialization in Northern China (Wang et al., 2011a),
Fluoropolymer production in the northern Bohai coastal region had
posed potential impacts to local soils (Wang et al., 2013b), and the
PFOS emissions from industrial and domestic sources in the eastern
coastal region of China were identified and estimated (Chen et al.,
2009; Xie et al., 2013a, 2013b). Results of the studies conducted
by other researchers in the northern part also indicated the presence of great concentrations of PFAAs in river water, sediment, soil,
precipitation, organisms and human blood from various sources
including fluorine industry parks (Jin et al., 2007; Bao et al., 2009,
2010; Liu et al., 2009; Li et al., 2011; Pan et al., 2011). In recent
years, urbanization has also sped up in the southern part of the
Bohai coastal region, while information on the concentrations of
PFAAs in the rapidly urbanized southern portion of Bohai-Rim is
still limited.
This study is an extension of our research on tracing the
source and fate of PFAAs from adjacent riverine and estuarine
areas of the Bohai Sea. The major aim was to investigate the key
species of PFAAs in southern Bohai coastal region, and to identify
their potential sources and fate. With less usage and strict control
over emissions of PFOS in the world, it is still necessary to
measure the whole series of known PFCAs and PFSAs in surface
water for understanding the status and trends of their production and presence in the environment. Spatial analysis of integrated geographic information will help to discover potential
effluents and understand fate and transport from sources to
surface water and from rivers to the sea. The hydrological cycle
between land and ocean brings PFAAs from the land to the sea
through river flow. Thus, the quantification of PFAAs loading in
each river to the southern Bohai Sea will contribute information
not only to understanding the behavior of PFAAs, but also to
more accurate modeling of trace contaminants in these systems
for estimating potential risks.
2. Materials and methods
2.1. Collection of water samples
Twelve coastal rivers within latitude 38 220 north and longitude 120 440 east,
which flow into the Bohai Sea from the south were selected. For each river, at least 2
sites were chosen considering different volumes of discharge at distances of 20e
30 km from the estuary (Fig. S1). In September 2011, 35 samples of surface water
were collected using 1 L polypropylene bottles, stored in icebox during transportation and left in room temperature as all samples were extracted within one
week after arrival in the lab, the left was stored at 20 C for long-term storage.
Parameters including turbidity, pH, dissolved oxygen, conductance, water temperature, concentrations of chloride ion and fluorine ion, and salinity were measured in
situ using a HQd Portable and Benchtop Meter Configurator (HACH Company, USA)
(Table S2). Before analysis, all the samples were left to stand for 24 h to settle nonsuspended substances, and 400 mL supernatant for each sample was taken for the
following analysis.
2.2. Extraction, identification and quantification of target analytes
17 PFAAs, including 13 PFCAs with carbon lengths from C4 to C18, and 4 PFSAs
were identified and quantified (Table S3). A 400 mL aliquot of unfiltered water was
extracted using OASIS WAX-SPE following published methods (Taniyasu et al.,
2005) with some modifications. Briefly, prior to loading samples, the Oasis WAX
cartridges (6 cc, 150 mg, 30 mm, Waters, Milford, MA) were preconditioned with
4 mL of 0.1% NH4OH in methanol, 4 mL of methanol, and 4 mL of Milli-Q water.
After loading the samples, cartridges were washed with 4 mL 25 mM ammonium
acetate (pH 4) and air-dried overnight. Target analytes were then eluted with 4 mL
of methanol and 4 mL of 0.1% NH4OH in methanol, respectively. The latter fraction
was reduced to 0.5 mL under high purity nitrogen (99.999%, Haidian District, BJ)
and passed through a nylon filter (13 mm, 0.2 mm, Chromspec, Ontario, Canada),
then transferred into a 1.5 mL PP snap top brown glass vial with polyethylene (PE)
septa. Individual PFAA was separated and quantified via Agilent 1290 Infinity HPLC
System coupled to an Agilent 6460 Triple Quadrupole LC/MS System (Agilent
Technologies, Palo Alto, CA) that was operated in the negative electrospray ionization (ESI) mode. Conditions under which the instrument was operated were
listed in Table S4.
2.3. QA/QC
Field blanks, transport blanks, procedure blanks and solvent blanks were conducted with every sample set. External standard curves of 9-point ranging from
0.01 ng/mL to 100 ng/mL were prepared for quantification of individual PFAA with
coefficients (r2) for all target analytes exceeding 0.99. The limit of detection (LOD)
and limit of quantification (LOQ) were defined as the peak of analyte that needed to
yield a signal-to-noise (S/N) ratio of 3:1 and 10:1, respectively. Matrix spike recoveries ranged from 75% to 126%, while procedure recoveries ranged from 77% to
122%. Concentrations of PFAAs were not corrected for recoveries. Detailed QA/QC
measurements of PFAAs in water were given in the Supplementary data.
2.4. Statistical and spatial analysis
Statistical analysis was performed by use of SPSS Statistics V20.0 (SPSS Inc.
Quarry Bay, HK). During the analysis, values of concentrations less than the LOQ
werep
set
ffiffiffi to one-half of the LOQ, and those less than the LOD were assigned values of
LOD/ 2 (Bao et al., 2010). Prior to the principal component analysis (PCA), tests of
normality were carried out to ensure that data met the assumptions used for further
analysis. More details on PCA were given in the Supplementary data. Spatial distributions of PFAAs were analyzed using the Arcmap module in ArcGIS V10.0 software (ESRI, Redland, CA). Layers including Digital Elevation Model (DEM) of land and
sea, land use and vegetation were obtained from the National Geomatics Center of
China (Haidian District, BJ).
3. Results and discussion
3.1. PFAAs in South Bohai coastal rivers
Among the 17 PFAAs quantified, the concentrations of perfluorododecanoic acid (PFDoA), perfluorotridecanoic acid (PFTrDA),
perfluorotetradecanoic acid (PFTeDA), perfluorohexadecanoic acid
(PFHxDA), Perfluorooctadecanoic acid (PFODA) and perfluorodecanesulfonate (PFDS) were less than the LOQ in all samples, therefore they were not discussed further in this or the
following sections. Concentrations of the remaining PFAAs were
listed in Fig. 1 and Table S5.
PFAAs were detected in all the rivers with concentrations of sum
P
PFAAs ( PFAAs) ranging from 2.21 to 5068.97 ng/L. In the Xiaoqing
River, PFOA was the dominant PFAA with a mean concentration of
P
3112.28 ng/L, which contributed 90.1% of the
PFAAs, and was
followed by short chain PFCAs, including PFBA (mean concentration of 49.80 ng/L, 1.4%), PFPeA (mean concentration of 70.97 ng/L,
2.0%), PFHxA (mean concentration of 123.34 ng/L, 3.5%) and PFHpA
(mean concentration of 91.72 ng/L, 2.6%). For concentrations of the
other long chain PFCAs and all PFSAs, the total contribution was less
than 1%. In the remaining 11 rivers, the mean concentration of
P
PFAAs was 25.78 ng/L with the average contribution of individual
PFAA in decreasing order of percentage: PFOA (38.2%) > PFBA
(19.5%) > PFOS (14.3%) > PFHxA (6.7%) > PFPeA (6%) > PFBS
(3.9%) > PFNA (4.6%) > PFHpA (4.3%) > PFDA.
(1.1%) > PFHxS (1%) > PFUdA (0.2%) (Fig. 2a). PCA analysis on the
11 PFAAs and 9 water parameters showed that PFBA, PFPeA, PFHxA,
and PFOA were associated when the Xiaoqing River was excluded
P. Wang et al. / Environmental Pollution 190 (2014) 115e122
117
Fig. 1. Concentrations of the 11 PFAAs (ng/L) in South Bohai coastal rivers.
due to the extremely high levels of PFOA (Fig. 2b). When the
Xiaoqing River was included, the association among the four PFCAs
became much stronger (Fig. 2c). This indicated that these compounds might come from similar sources. However, there were still
differences in the two scenarios, which might explained by the
different weights of various sources to the rivers. Concentrations of
PFOS ranged from 0.40 to 12.78 ng/L with a mean concentration of
3.09 ng/L for these rivers. The highest concentration of PFOS was in
SR-1, where concentrations of PFBS (24.19 ng/L) and PFHxS
(0.59 ng/L) were also highest. SR-1 was located in an estuary where
there might be local releases of PFSAs (Table S1). Furthermore,
concentrations of PFOS and PFBS were always correlated in the two
P
Fig. 2. (a) Mean contribution of individual PFC to PFCs in the Xiaoqing River and the remaining 11 rivers. Result of PCA using concentrations of 11 PFCs and 9 water parameters in
all rivers (b) and 11 rivers excluding the Xiaoqing River (c).
118
P. Wang et al. / Environmental Pollution 190 (2014) 115e122
scenarios of PCA (Fig. 2b and c), which indicated a probable coemission of these two PFSAs in the study area.
The profile of PFAAs showed that the south Bohai coastal rivers
were mainly contaminated by PFOA followed by shorter chain
PFCAs and PFOS. In recent studies, PFOA has been found to be the
predominant PFAA in North Bohai coastal rivers (Wang et al.,
2011b), rivers in Tianjin (Pan et al., 2011), Dianchi Lake (Zhang
et al., 2012), Hanjiang River (Wang et al., 2013a), the Huaihe River
Basin and Taihu Lake in China (Yu et al., 2012); Water samples from
Hanoi city and its surrounding areas in Vietnam (Kim et al., 2013);
Yodo River basin in Japan (Lien et al., 2008); the watershed of River
Po in Northern Italy (Loos et al., 2008); and Mediterranean coastal
rivers in Spain (Sánchez-Avila et al., 2010). However, only water in
the Yodo and Po Rivers contained concentrations of PFOA that
exceeded 1000 ng/L, which was comparable to this study (Table S7).
As far as we know, this is the highest concentration of PFOA in river
water of China that has ever been reported.
3.2. Spatial analysis of the fluoropolymer industry as the source
High levels of PFOA implied local point sources in this area. As
a result, the fluoropolymer industry in Shandong Province was
investigated and the major manufacturing facilities were found
located along the Xiaoqing River (Fig. 3), with production begun
in 2001. The manufacturing history of the profile of PFAAs and
related products in these facilities is unknown, but until now,
fluorinated refrigerants, intermediates for production of pesticides and medicine, polytetrafluoroethylene (PTFE) and tetrafluoroethylene (TFE) have been the main products of these
facilities. Facility 1 is the largest with an annual capacity of
37,000 tons of PTFE, 50,000 tons of TFE, 10,000 tons of hexafluoropropylene (HFP), and more than 200,000 tons of different
types of fluorinated refrigerants by the end of 2012 (Fig. 4)
(Dongyue Group Limited, 2012). The fluoropolymers production
capacities of the other facilities ranged from hundreds to thousands of tons. PFOA is mainly produced and used as ammonium
perfluorooctanoate (APFO) and further used as important processing additives for production of fluoropolymers and fluoroelastomers. For example, high purity APFO is used primarily in
the dispersion polymerization process to produce PTFE. PTFE has
unique properties like repellence to acid and alkali, thermal
resistance, almost insoluble in solvents etc. Thus it has been used
in many industrial and consumer products, including soil, stain,
grease, and water resistant coatings on textiles and carpet; uses
in the automotive, mechanical, aerospace, chemical, electrical,
medical, and building/construction industries; personal care
products; and non-stick coatings on cookware (European
Commission, 2010). The emission of PFOA came from both the
PTFE production discharge and applications of PTFE products
(Fig. 4). Further study is needed to estimate the weights of the
two ways for PFOA emissions. The fluorinated refrigerants (like
R22), TFE and HFP are all important intermediates to produce
PTFE and other fluoropolymers in different processes, with
limited information on their emission of related PFAAs. While
fluorinated ethylene propylene (FEP) is a modified material to
PTFE and the production of FEP is still at the primary stage.
Studies on these intermediates and new materials are also
important not only to quantify known perfluoroalkyl and polyfluoroalkyl substances (PFASs), but also to qualify unknown
species.
Spatial analysis of PFAAs levels, rivers, and production facilities
together with the results obtained by other researchers indicated
that facilities along the Xiaoqing River and its tributaries exhibited
the greatest emissions of wastes, including sewage discharged
Fig. 3. Spatial distribution of PFOA in South Bohai coastal rivers, combined with available studies by other researchers and locations of manufacturing facilities.
P. Wang et al. / Environmental Pollution 190 (2014) 115e122
119
Fig. 4. Annual production capacity (kiloton, kt) of facilities surveyed in this study and the main industrial process for the production of TFE and PTFE.
directly to the river, and consequently resulted in the highest
concentrations of PFOA measured in this study (Fig. 3). In addition,
atmospheric transport and subsequent degradation of PFOA precursors including fluorotelomer alcohols (FTOHs) and perfluorooctane sulfonyl fluoride (POSF)-based chemicals (e.g.,
perfluorooctyl sulfonamidoethanols) could account for as much as
10% of PFOA emissions (Pistocchi and Loos, 2009). In a study conducted at one of 3M’s largest fluoropolymer facilities in Minnesota
(USA), WWTPs from both industrial and domestic discharges
played a key role in the PFAAs releases to surface waters, and
stormwater runoff from PFAAs-related commercial and industrial
releases might also be a significant source of PFAAs to the surface
water (Oliaei et al., 2013). So the significant correlations among
PFCAs with carbon chain lengths from 4 to 8 in all rivers could be
explained by the direct emission of waste from manufacturing facilities, atmospheric transport and degradation of precursors, input
from WWTPs, or stormwater runoff, where the weights of these
pathways might vary for different rivers. For the Yellow River, there
are two main reasons for less concentrations of PFAAs, especially
that of PFOA. One is that the riverbed of the section in Shandong
Province has an average height of 4e6 m above the land surface,
there is almost no possibility for it to receive waste from local facilities, and contribution from the upstream was also limited. The
other is that the relatively greater water discharge compared to
other rivers in this study would lead to more dilution (Table S1).
Although in 2006, the eight major fluoropolymer and fluotelomer
manufactures joined the US EPA 2010/2015 PFOA Stewardship
Program working toward elimination of PFOA, its precursors and
related higher homologue chemicals from emissions and their
products by 2015, facilities in this region as well as other facilities in
China are still scaling up production to meet domestic and international demand without sufficient regulations on PFAAs emission
(Wang et al., 2013b). The trend that PFOA levels were the highest
among all PFAAs detected in this study was consistent with the
results of other studies conducted in this area. Concentrations of
PFOA in Mollusks were predominant with the highest concentration of 126 ng/g dry weight observed in Laizhou Bay, the estuary of
Xiaoqing River, which was almost 40 times higher than that of PFOS
(Pan et al., 2010). In the region of Zouping County where facilities 1,
2 and 3-1 were located, the median concentration of PFOA in the
whole human blood was 3.26 ng/mL, while the median concentration of PFOS was 2.19 ng/mL, which were the highest and the
lowest among all cities investigated in Bohai Rim, respectively (Guo
et al., 2011). PFOA was the dominant PFAA in precipitation across
eastern and central China with a maximum concentration of 88 ng/
L observed in the city of Weifang (Zhao et al., 2013), where there is
only small scale fluorinated chemical manufacturing compared to
those in the Xiaoqing River basin. However, Tai’an city was also on
the list of investigation in the precipitation study and had a much
lower concentration of PFOA, but it is closer to the fluoropolymer
manufacturing facilities than Weifang City. In this study, geomorphic analysis indicated that Mount Tai (the peak in Shandong
Province) might be a natural block for the transport of volatile
PFAAs in the atmosphere (Fig. S1). This result demonstrated that
the mass loading of PFAAs for atmospheric deposition might be
determined more by regional conditions than by local conditions.
The major emission of PFOS in Shandong Province came from
textile treatment and metal plating (Xie et al., 2013b). Unlike PFOA,
release of PFOS from these kinds of manufactures is distributed
more like non-point sources (Pistocchi and Loos, 2009), and no
direct-emissions from these industries were observed in this study.
Furthermore, PFOS and PFOA chemicals are still used in some
pesticides with exceptions to the phase-out action. For example,
sulfluramid will be phased-out by the year 2016 (Fluoride Action
Network Pesticide project). In Shouguang County, which is the
most famous production base of vegetables in China, although
pesticide-free vegetables are dominating nowadays, the residues of
PFAAs and their precursors that will degrade to PFAAs could be an
issue for public health (Houde et al., 2011).
3.3. Influence of waves and currents on dissipation of PFOA
PFAAs undergo a mixing process and are dissipated by waves
and currents when they move from the rivers to the sea. The process of dissipation in saline waters of estuaries when calculating the
mass flux of PFAAs has been discussed previously (McLachlan et al.,
2007). In this study, on a smaller scale, the effect of the estuarine
drainage areas (EDA) showed more interesting characteristics of
pollution dispersion within proximate rivers. Salinity was used to
explain the extent to which water mixes (Fig. S3). However, it must
120
P. Wang et al. / Environmental Pollution 190 (2014) 115e122
be illustrated that the water was sampled in different times of the
day while the tidal time varied daily, so the salinity only represented the status at the moment of sampling. The results showed
that in all the rivers salinity in the EDA were greater than those at
upstream locations despite the difference at the time of sampling.
As an almost enclosed sea, the Bohai Sea has an estimated mixing
time of approximately 30 years. For some of the small bays within
the Bohai Sea, the turnover time could be even longer. It has been
reported that poor exchange of seawater has led to accumulation of
containments (i.e. chemical oxygen demand, heavy metals, nutrient
salts) in these bays (Wang and Li, 2006), which indicated that in
addition to the dilution process, part of the concentration of PFAAs
in the EDA water might be contributed by intrusions of salty water,
especially from adjacent rivers. The Yellow River represents a
special case, in which the large discharge would make the estuary
less influenced by seawater. When a numerical model was used to
simulate wave-induced, near-shore currents and transport of pollutants in Bohai Bay, it was concluded that due to the action of
waves in the near-shore zone with shallow water, pollutants were
transported parallel to the shoreline (Sun and Tao, 2006). For the
three rivers discharging into Bohai Bay, little influence on concentrations of PFOA was observed between the upstream and the EDA
sites, so the influence of waves on these three rivers was not
obvious. However in Laizhou Bay, in addition to greater salinity,
concentrations of PFOA in EDA of the Mi, Sha, Wang and Huangshui
Rivers were significantly higher than those at the upstream locations. In the Yellow, Xiaoqing, Wei, Jiaolai and Jia Rivers there was
no significant difference in concentrations of PFOA as a function of
salinity. This indicated that the majority of the PFOA at MR-1, SR-1,
WR-1 and HS-1 might come from XQ-1 with the waves and currents along the shoreline. Although concentrations of PFOA were
less at other EDA sites, they might increase notably with the rising
tide. The ebb-tide (recession of sea level) in Laizhou Bay would lead
to a current from the top of the bay eastward to HS-1 (Zhang, 2007),
which is in good agreement with the results of the present study
(Fig. 3). More attention should be paid to the influence of salinity
changes arisen by waves and currents in the EDA as it could influence the property of PFAAs and also the physiology of organisms,
which would consequently contribute to sorption and bioaccumulation of PFAAs in these organisms (Houde et al., 2011).
3.4. Riverine input of PFAAs to the Bohai Sea
Mass flux will provide information on the environmental inventory of PFAAs. In order to further eliminate the influence of
residual seawater, sites used to calculate the mass flux of PFAAs
were chosen based on salinity. Although the salinity of some EDA
water was less than the freshwater threshold during ebb-tide,
considering the frequent mixing, upstream sites of EDA were
used to estimate loadings of PFAAs from rivers to the Bohai Sea. The
mass flux was calculated based on instantaneous concentrations of
PFAAs multiplied by the average annual water discharge data to
give a rough yet valuable approximation (Table 1) (McLachlan et al.,
P
2007; Filipovic et al., 2013). The mass flux of PFOA and PFAAs in
the Xiaoqing River were 3.6 tons and 4 tons per year, which
accounted for 90% and 80% of those among all the rivers, respectively. Even though the concentrations were less, there was the
largest discharge in this study, and thus the Yellow River accounted
P
for the second largest mass of PFAAs and PFOS, which were about
0.4 ton and 0.07 ton per year, respectively.
Excluding the extensive production capacity along the Xiaoqing
River and the huge discharge of the Yellow River, mass flux of PFOS
and PFOA into the Bohai Sea from the southern coastal rivers in this
study was larger but still comparable to that of northern coastal
rivers, which were calculated to be 0.02 and 0.2 ton per year for
PFOS and PFOA, respectively. (Wang et al., 2011a) In comparison,
the mass flux of PFOA from main rivers in the European Continent
was estimated to be 14.3 tons per year (McLachlan et al., 2007).
However, when averaged by area and population, the emission of
PFOA in the South Bohai area was about 20 times greater than that
in the European Continent, respectively, which posed a much
heavier burden to the local environment.
3.5. Assessment of risks
Concentrations of PFOA in the Xiaoqing River exceeded several
drinking water criteria including the New Jersey guidance for PFOA
in drinking water (40 ng/L), the US EPA provisional health advisories for PFOA (400 ng/L), and the Health Canada drinking water
guidance value for PFOA (700 ng/L) (New Jersey Department of
Environmental Protection (NJDEP), 2007; USEPA, 2009; Paterson
et al., 2012). The residents in the study area have not used the
river water as drinking water for a long time. However, according to
a study conducted on transport of PFOA near a fluoropolymer
manufacturing facility, the atmospheric deposition would not only
influence concentrations in surface waters, but also the underlying
aquifer by migration downward with precipitation and river
recharge (Davis et al., 2007). So there is still a large potential risk to
the local drinking water system. Meanwhile, river water is used for
irrigation, which might pose risks due to PFAAs in soils and subsequent accumulation into crops and vegetables and eventually
accumulation in humans. None of the concentrations of PFOS or
PFOA measured in this study exceeded any water quality criteria for
the protection of freshwater aquatic organisms including criteria
maximum concentration (CMC) and criteria continuous concentration (CCC). For example, the CMC was calculated to be 3.78 mg/L
Table 1
Estimated mass flux of PFAAs in rivers to the Bohai Sea (kg/year).
Rivers
PFBA
PFPeA
PFHxA
PFHpA
PFOA
PFNA
PFDA
PFUdA
PFBS
PFHxS
PFOS
P
PFAAs
Zhangweixin River
Majia River
Tuhai River
Yellow River
Xiaoqing River
Mi River
Wei River
Jiaolai River
Sha River
Wang River
Jie River
Huangshui River
Total
1.2
6.2
26.4
120.2
63.8
1.2
24.2
2.0
0.6
<0.1
0.1
0.1
246.0
0.5
3.2
15.0
17.8
84.7
0.4
4.5
1.4
0.1
<0.1
<0.1
<0.1
127.5
0.4
3.7
26.1
21.7
147.6
0.6
6.2
1.1
0.2
<0.1
<0.1
<0.1
207.6
0.3
3.6
13.6
14.7
115.6
0.5
5.8
0.4
0.2
<0.1
<0.1
<0.1
154.7
3.4
18.8
128.0
131.9
3648.6
3.9
37.0
2.1
1.7
<0.1
0.1
0.3
3975.9
0.4
1.4
14.9
20.3
4.1
0.3
6.8
0.3
0.2
<0.1
<0.1
<0.1
48.7
0.1
0.5
5.4
1.3
2.2
0.1
2.3
0.1
<0.1
<0.1
<0.1
<0.1
12.1
<0.1
0.04
1.0
0.6
0.2
<0.1
0.3
<0.1
<0.1
<0.1
<0.1
<0.1
2.2
<0.1
1.0
9.4
25.6
1.3
0.1
0.7
<0.1
<0.1
<0.1
<0.1
<0.1
38.2
<0.1
0.2
<0.1
10.4
0.1
<0.1
2.2
0.1
<0.1
<0.1
<0.1
<0.1
13.1
0.9
4.7
18.5
69.3
1.4
1.4
38.0
1.5
0.2
0.01
0.1
<0.1
136.0
7.2
43.2
258.3
433.9
4069.6
8.6
127.8
9.1
3.3
0.1
0.3
0.6
4961.9
P. Wang et al. / Environmental Pollution 190 (2014) 115e122
for PFOS and 45.54 mg/L for PFOA, while CCC was 0.25 mg/L for
PFOS and 3.52 mg/L for PFOA that were derived for the protection of
freshwater aquatic life in China (Yang et al., 2014). The CMC for
PFOS (21 mg/L) and PFOA (25 mg/L), CCC for PFOS (5.1 mg/L) and
PFOA (2.9 mg/L) and avian wildlife value (AWV) for PFOS (47 ng/L)
were also derived based on toxicology data of organisms resident in
North America (Giesy et al., 2010). When biodiversity in the main
EDAs in China was investigated by use of the ShannoneWiener
index, Laizhou Bay exhibited relatively greater indices, indicative of
good biodiversity and a constant environment (Huang et al., 2012),
and was also consistent with the in-situ findings of this study.
However, considering the almost lacking degradation property of
PFOA, the scaling-up of its production, less water-mobility in
Laizhou Bay and intensive fishery in this area, especially the
aquaculture and salt fields in and around the coastal mudflat
created by the rising tide (Fig. 3), further research is needed to
evaluate the health risk of fish consumption for local residents to
make sure that the pollution is controllable. Furthermore, the
wave-induced near-shore current is usually considered to be the
reason for sediment suspension in the near-shore zone (Sun and
Tao, 2006), and many aquatic organisms ingest particles in water
(Jeon et al., 2010). This would increase the bioconcentration factors
of the aquatic ecosystem and also the overall risk.
4. Conclusions
The present study gave a general characterization of the PFAAs
pollution in the main rivers of the rapidly urbanized South Bohai
coastal region with PFOA dominant in pretty high concentration.
Previous studies on source identification would use a combination
of geographic information, such as population density and land use
as indicators for the influence of urbanization on PFAAs emissions,
especially for PFOS (Murakami et al., 2008; Pistocchi and Loos,
2009; Zushi and Masunaga, 2009). Because the majority of the
PFOA loads are emitted from industries, distributions of fluoropolymer industry in Shandong Province were investigated and
the main facilities were identified so that monitoring could be
conducted to establish the distribution, status and trend in concentrations of PFAAs in this region. The data on the concentrations
of PFAAs was also combined with GIS data including land and sea
DEM, and vegetation data to provide a visual description of
contamination in the region. For China, the scaling-up of fluoropolymer production would predictably bring actions on risk
assessment and regulation in the future, and results in this study
would provide valuable information. It may also provide hint for
other countries with rapid urbanization to take precautionary
approach to tackling the emerging pollution.
Acknowledgments
This study was supported by the National Natural Science
Foundation of China under Grant No. 41371488 and 41171394, the
International Scientific Cooperation Program with Grant No.
2012DFA91150, and the Key Project of the Chinese Academy of
Sciences under Grant No. KZZD-EW-TZ-12. We would like to thank
the editors and reviewers for their valuable comments and suggestions. Prof. Giesy was supported by the Einstein Professor Program of the Chinese Academy of Sciences and the Canada Research
Chair program.
Appendix A. Supplementary data
Supplementary data related to this article can be found at http://
dx.doi.org/10.1016/j.envpol.2014.03.030.
121
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1
Supplementary data
2
Occurrence and transport of 17 perfluoroalkl acids in
3
12 coastal rivers in south Bohai coastal region of
4
China with concentrated fluoropolymer facilities
5
a,b
a，*
a
a,b
a,b
a,b
Pei Wang ,Yonglong Lu ,Tieyu Wang , Yaning Fu , Zhaoyun Zhu , Shijie Liu ,
6
Shuangwei Xiea,b, Yang Xiaoa,b, John P. Giesyc
7
8
9
10
a
State Key Lab of Urban and Regional Ecology, Research Center for Eco-environmental
Sciences, Chinese Academy of Sciences, Beijing 100085, China
b
c
Graduate University of Chinese Academy of Sciences, Beijing 100049, China
Department of Veterinary Biomedical Sciences and Toxicology Centre, University of
11
Saskatchewan, Saskatoon, Saskatchewan, Canada
12
Corresponding author:
13
Tel: 86-10-62917903; Fax: 86-10-62918177; E-mail: yllu@rcees.ac.cn
14
Pages: 19
15
Tables: 7
16
Figures: 3
S1
17
Content
18
Water samples collection
19
Standards and Reagents
20
Quality Assurance and Quality Control (QA/QC)
21
Principal Component Analysis (PCA)
22
Tables
23
Table S1. Description of sampling locations, and average annual river discharge at locations used
24
25
to calculate mass flux of PFAAs.
Table S2. Parameters measured with in situ water samples. (DO: Dissolved oxygen; WT: water
26
temperature; CD: Conductivity; CCl-: concentration of chloride ion; CF-: concentration
27
of fluoride ion; ORP: oxidation reduction potential.)
28
Table S3. 17 PFAAs measured in this study with QA/QC information including monitoring
29
transitions, matrix spike recovery (MSR), procedure recovery (PR), limit of detection
30
(LOD), and limit of quantification (LOQ) (Mean: Arithmetic mean; SD: Standard
31
deviation; n indicates the number of samples analyzed.)
32
Table S4. Conditions for HPLC and ESI- MS.
33
Table S5. Concentrations of PFAAs (ng/L) in the south Bohai coastal rivers.
34
Table S6. PCA results with 5 components extracted to explain over 80% of total variance for the
35
36
12 rivers (a) and 11 rivers not including the Xiaoqing River (b).
Table S7. PFOA levels in surface water collected from the south Bohai coastal rivers compared
37
with previous studies by other researchers.
38
Figures
39
Figure S1. Sampling locations (red plots) in the south Bohai coastal rivers of China.
S2
40
Figure S2. The chromatogram of 17 PFAAs by MRM with the concentration of 0.05 ng/mL.
41
Figure S3. Salinity of water samples. Sites in estuaries are denoted as red bars with green bars
42
representing the location where the discharge was measured and the blue bars representing the
43
most upstream locations where samples were collected.
44
References
S3
45
Water samples collection
46
For each river at least two sites were chosen. The sites in the estuarine drainage areas (EDA)
47
were used to evaluate the influence of seawater on the dispersion of riverine PFAAs, so they
48
were located as closely to the mouth of the river as possible, but not in the sea. Locations
49
immediately at the upstream of the EDA that were not influenced as much as possible by tides,
50
industrial or municipal wastewater discharges, or tributaries, were used to calculate the mass flux
51
of riverine PFAAs to the sea. Additional upstream locations were used to assess changes in the
52
concentrations of PFAAs along the riverine sections in the study area. No floods or heavy rain
53
occurred during the sampling period. Water samples were collected as close as possible to a
54
point equidistant from each bank. A 1L PP bottle was opened and closed beneath the surface of
55
the water, and filled and emptied three times before the actual samples were taken. Water was
56
collected excluding the surface film(McLachlan et al., 2007).
57
58
Standards and Reagents
59
Seventeen native PFAAs including Perfluorobutanoic acid (PFBA), Perfluoropentanoic acid
60
(PFPeA), Perfluorohexanoic acid (PFHxA), Perfluoroheptanoic acid (PFHpA), Perfluorooctanoic
61
acid (PFOA), Perfluorononanoic acid (PFNA), Perfluorodecanoic acid (PFDA),
62
Perfluoroundecanoic acid (PFUdA), Perfluorododecanoic acid (PFDoA), Perfluorotridecanoic
63
acid (PFTrDA), Perfluorotetradecanoic acid (PFTeDA), Perfluorohexadecanoic acid (PFHxDA),
64
Perfluorooctadecanoic acid (PFODA), Potassium Perfluorobutanesulfonate (PFBS), Sodium
65
Perfluorohexanesulfonate (PFHxS), Potassium Perfluorooctanesulfonate (PFOS),
66
Sodium Perfluorodecanesulfonate (PFDS) and 5 mass-labeled PFAAs including PFBA [1,2,3,4
67
13
C], PFOA [1,2,3,4 13C], PFDoA [1,2 13C], PFHxS [1,2 18O] and PFOS [1,2,3,4 13C] were
S4
68
purchased from Wellington Laboratories with purities of >98% (Guelph, Ontario, Canada).
69
Mixed standards were prepared in 100% methanol and stored at 4 ℃. HPLC grade methanol, and
70
acetonitrile were purchased from J.T. Baker (Phillipsburg, NJ, USA). Ammonium acetate (~98%)
71
and ammonium hydroxide solutions (28%~30% NH3 basis) were purchased from Sigma-Aldrich
72
Co. (St. Louis, MO, USA). Milli-Q water was obtained from a Milli-Q synthesis A10 (Millipore,
73
Bedford, MA, USA) and used throughout the experiment.
74
75
76
77
78
Quality Assurance and Quality Control (QA/QC)
The usage of polytetrafluoroethylene (PTFE) or other fluoropolymer materials was avoided
during sample collection and extraction, to minimize the background contamination.
Blank experiments including field blank, transport blank, procedure blank were prepared using
79
Milli-Q water and were analyzed routinely to check for contamination during sampling and
80
extraction. However, it must be illustrated that certain parts in the 6460 mass spectrum were
81
made of PTFE that were not replaced in this experiment. The solvent blank was prepared using
82
100% methanol and ran after 10 samples during instrumental analysis to monitor the background
83
contamination of the instrument and to minimize cross contamination. No detectable PFAAs
84
were observed over LOQ in all field, transport and solvent blanks. Concentrations greater than
85
the LOD in blanks were not used to correct sample concentrations in this study.
86
Replicate experiments were performed including sample replicate and injection replicate.
87
Sample replicates were conducted using another 400 mL in the same 1L water samples; and
88
injection replicates were conducted by measuring the extract twice during instrumental analysis.
89
Four replicates for each replicate experiment were carried out during the analysis, with RSD%
90
less than 10%.
S5
91
In order to elevate the sensitivity of the instrument, 6 time segments were applied in MS QQQ
92
analysis according to different retention times for the 17 native PFAAs (Fig. S1). Optimized △
93
EMV of 200-400V was then applied to each time-segment to increase the ratio of signal-to-noise.
94
Concentrations of PFAAs were quantified using external calibration curves containing a
95
concentration series of 0.01, 0.05, 0.1, 0.5, 1, 5, 10, 50, and 100 ng/mL. The curves for all
96
PFAAs showed strong linearity with r2 > 0.99 and deviation of calibration points less than ±20%
97
from its theoretical value. A concentration of 10 ng/mL was also used as a check of calibration
98
standard (CCS) and ran after every 10 samples. When the deviation of a CCS was more than ±
99
20% from its theoretical value, a new calibration curve was prepared. For concentrations of
100
PFAAs in any extracts measuring over 100 ng/mL the first time, the extract would be diluted to
101
fit the range of the calibration series and measured again.
102
In order to assess overall extraction efficiency, three kinds of recovery experiments were
103
conducted.(Loi et al., 2011) For procedure recovery and matrix spike recovery tests, 20 ng
104
mixtures of 17 native PFAAs standards were spiked into 400mL Milli-Q water and 400mL water
105
samples (in small concentrations of PFAAs) via 4 duplicates, respectively. A surrogate recovery
106
test was prepared along with the matrix spike recovery to check for any ionization suppression or
107
enhancement during instrumental analysis. 13C and 18O labeled standards containing [1, 2, 3, 4
108
13
109
PFOS were used as surrogates, which cover different carbon chain lengths of PFCAs and PFSAs.
110
Results are listed in Table S3.
C] PFBA, [1, 2, 3, 4 13C] PFOA, [1, 2 13C] PFDoA, [1, 2 18O] PFHxS, and [1, 2, 3, 4 13C]
S6
111
The limit of detection (LOD) was defined as the minimum concentration that provides a
112
signal/noise (S/N) > 3 (peak height), while the limit of quantification (LOQ) was defined as the
113
minimum concentration providing S/N >10. Both values were determined in three successive
114
injections with a standard deviation less than 20% (Table S3).
115
116
117
Principal Component Analysis (PCA)
PCA was introduced in this study to characterize the potential relationship of the distribution
118
among 11PFAAs and 9 water parameters by extracting two principle components through
119
dimension reduction. The missing values were set as ‘excludes cases listwise’. Kaisex-Meyer-
120
Olkin test was used to test if the partial correlation among variances was small and Bartlett’s test
121
of Sphericity was used to test if the variances were independent among each other. The variances
122
in this study had passed all the test. The principle components were extracted using correlation
123
matrix, while the rotation of the factors was set as varimax with Kaiser Normalization. Results of
124
the scree test indicated that the eigenvalue of the first three components located in the steep
125
polyline before the elbow appeared in the scree plot. The first five components explained over 80%
126
of the cumulative variance with each component contributing at least 7%. The results of the PCA
127
can meet the demand for further analysis.
128
S7
129
Table S1. Description of sampling locations, and average annual river discharge at locations used
130
to calculate mass flux of PFAAs.
Discharge1
m3/s
Site
Ambient description
ZW-1
ZW-2
ZW-3
MJ-1
MJ-2
MJ-3
TH-1
TH-2
TH-3
YR-1
YR-2
YR-3
YR-4
YR-5
YR-6
XQ-1
XQ-2
XQ-3
MR-1
MR-2
MR-3
WH-1
WH-2
WH-3
JL-1
JL-2
JL-3
SR-1
SR-2
WR-1
WR-2
JR-1
JR-2
HS-1
HS-2
Estuary, Huanghua harbor, factories around
50% Wasteland and 50% farmland, planting cotton and corn
Farmland and villages, planting cotton and corn
Estuary, fishing village, salt field, crabs and seabirds
Cotton land, villages around, good ecological environment
Corn land, fishing boats, the source of drinking water
Estuary, wind power, shipyard
Farmland and villages, good ecological environment
Downstream of a park, good ecological environment
Huanghe delta, in a park, a lot of reed
Green land, few farmland
Green land, few farmland
Farmland in good condition, planting cotton and corn
Riverbed, near a park
Green land, soil corrosion, unsuitable for farming
Estuary, fishing village, salt field
Farmland, planting cotton and corn, power plant around
Wasteland, factories around
Estuary, salt field, factories around
Wasteland, lots of fishermen
Wasteland with greenbelt
Estuary, fishing boats, wind mill, harbor under construction
Corn land, villages around, domestic garbage
Downstream of a dam, farmland
Estuary, wind mill, seabirds
Farmland, downstream of an iron mine
Farmland, lots of fishermen, factories around
Estuary, wind mill, salt field
Wasteland, lots of stone material factory
Estuary, lots of fishing boats, factories around
Farmland, good ecological environment
Estuary, wasteland
Wasteland, factories around, bad smell in air
Estuary, sand beach
Orchard, the source of drinking water
131
132
133
1
8.27
55.83
107.64
1008.23
60.28
13.95
116.20
14.48
8.00
1.00
1.17
3.07
Longitude
Latitude
117.84509
117.71553
117.514805
117.908725
117.59283
117.420544
118.03777
118.07113
117.85723
119.1545
118.8248
118.53272
118.29218
118.06085
117.67832
118.97909
118.68804
118.42622
119.13739
118.87563
118.7894
119.46481
119.45213
119.37873
119.55236
119.59309
119.5318
119.73192
119.86956
119.94564
120.09361
120.26695
120.38986
120.51944
120.64941
38.26311
38.09021
37.971917
38.176113
37.97155
37.777257
38.125231
37.66212
37.50457
37.75972
37.75225
37.60389
37.49843
37.33766
37.261017
37.2745
37.23415
37.13554
37.13926
37.04269
36.84446
37.05461
36.8755
36.57984
37.0947
36.9292
36.74178
37.10948
37.01559
37.39772
37.25268
37.52762
37.40725
37.74471
37.55727
Average annual river discharge data was taken from the Ministry of Water Resources of the
People’s Republic of China databases closest to the sampling sites (http://www.mwr.gov.cn/) and
corrected by in-situ measurements.
S8
134
135
136
Table S2. Parameters measured with in situ water samples. (DO: Dissolved oxygen; WT: water
temperature; CD: Conductivity; CCl-: concentration of chloride ion; CF-: concentration of fluoride
ion; ORP: oxidation reduction potential)
Site
Turbidity
pH
ZW-1
ZW-2
ZW-3
MJ-1
MJ-2
MJ-3
TH-1
TH-2
TH-3
YR-1
YR-2
YR-3
YR-4
YR-5
YR-6
XQ-1
XQ-2
XQ-3
MR-1
MR-2
MR-3
WH-1
WH-2
WH-3
JL-1
JL-2
JL-3
SR-1
SR-2
WR-1
WR-2
JR-1
JR-2
HS-1
HS-2
103.0
97.2
189.3
146.7
8.9
11.9
158.7
14.4
37.5
673.0
869.0
827.3
930.3
379.0
189.7
100.2
58.7
34.4
27.6
21.4
26.0
13.5
24.6
11.3
26.2
37.2
5.7
12.2
17.0
14.6
2.6
44.5
69.0
17.3
1.0
8.04
7.94
8.48
8.19
7.88
7.88
7.77
8.28
8.06
8.34
8.20
8.24
8.14
8.34
8.40
7.71
7.74
7.59
7.12
8.07
8.15
7.98
8.12
8.09
7.99
8.16
7.99
8.65
7.63
8.51
8.10
7.72
6.63
7.79
8.16
DO
mg/L
4.94
5.34
9.59
7.34
4.40
5.36
6.57
10.21
9.62
9.20
8.88
8.74
8.63
8.76
8.98
8.39
5.23
2.45
8.20
8.75
9.42
7.67
9.52
9.73
9.48
10.61
8.43
12.35
5.58
13.12
9.49
3.42
5.87
7.02
11.32
WT
℃
22.5
22.5
22.8
23.6
23.8
23.5
23.2
23.1
23.9
19.1
19.6
19.8
19.9
22.4
23.4
21.6
20.0
20.5
19.9
20.0
19.0
22.1
22.2
21.9
23.7
22.0
21.3
23.2
20.2
23.4
21.4
25.5
24.6
24.3
20.0
CD
µs/cm
28700
3880
9610
6990
2600
2640
22000
1803
2500
767
759
760
764
754
750
3630
1842
1923
20740
707
622
9030
712
654
6090
1533
1672
1062
515
13160
773
1987
3180
21290
769
137
S9
Salinity
PSU
18.70
2.17
5.65
3.96
1.38
1.38
13.76
0.95
1.32
0.45
0.44
0.44
0.44
0.41
0.41
2.04
1.02
1.06
13.89
0.38
0.33
5.36
0.36
0.36
3.41
0.80
0.93
0.57
0.27
7.86
0.42
1.01
1.68
12.96
0.44
CClmg/L
22500
2938
9215
2049
1600
915
9565
375
396
215
181
181
180
136
111
1556
734
504
10800
69
83
5227
182
130
2615
359
324
392
113
6705
136
616
358
11100
114
CFmg/L
0.465
0.496
0.537
1.010
1.020
0.644
0.948
0.454
0.418
0.375
0.389
0.399
0.376
0.345
0.332
0.755
0.633
0.476
0.343
0.243
0.221
0.212
0.224
0.192
1.080
1.380
1.920
0.225
0.261
0.317
0.185
0.440
0.404
0.276
0.172
ORP
mV
208.5
233.7
212.7
210.1
214.4
220.3
228.9
194.9
195.2
210.0
226.6
219.4
231.9
204.1
258.3
215.8
213.7
216.8
22.4
214.5
213.2
209.5
225.9
173.1
229.1
224.0
227.4
222.5
214.9
154.0
207.3
183.0
114.8
221.6
204.1
138
Table S3. 17 PFAAs measured in this study with QA/QC information including monitoring
139
transitions, matrix spike recovery (MSR), procedure recovery (PR), limit of detection (LOD),
140
and limit of quantification (LOQ) (Mean: Arithmetic mean; SD: Standard deviation; n indicates
141
the number of samples analyzed.)
Analyte
PFCAs
PFBA
PFPeA
PFHxA
PFHpA
PFOA
PFNA
PFDA
PFUdA
PFDoA
PFTrDA
PFTeDA
PFHxDA
PFODA
PFSAs
PFBS
PFHxS
PFOS
PFDS
Surrogates
MPFBA
MPFOA
MPFDoA
MPFHxS
MPFOS
MS/MS transition
MSR (n=4)
% Mean
±SD
PR (n=4)
% Mean
±SD
LOD
(ng/L)
LOQ
(ng/L)
213.0 → 169.1
263.0 → 218.9
313.0 → 269.0
363.0 → 318.9
413.0 → 368.9
463.0 → 419.0
513.0 → 468.9
563.0 → 519.0
613.0 → 569.0
662.9 → 619.0
713.1 → 669.0
813.0 → 769.0
913.0 → 869.0
93±12
100±3
111±3
106±4
105±4
111±5
115±4
104±10
87±7
75±7
82±8
121±6
126±12
105±7
99±9
112±7
106±10
112±9
115±8
121±12
108±15
97±8
77±4
77±5
119±12
116±6
0.13
0.05
0.04
0.06
0.05
0.06
0.05
0.03
0.05
0.05
0.04
0.05
0.06
0.63
0.15
0.15
0.15
0.19
0.13
0.15
0.08
0.13
0.15
0.13
0.11
0.18
299.0 → 80.0
399.0 → 80.0
498.9 → 80.0
599.0 → 79.9
101±5
110±4
93±8
76±4
102±8
113±10
122±10
89±11
0.03
0.01
0.03
0.03
0.09
0.06
0.10
0.06
217 → 172
417 → 372
615 → 570
403 → 103
503 → 99
105±13
107±12
88±11
107±12
102±7
107±11
113±9
101±9
115±8
113±10
‐
‐
‐
‐
‐
‐
‐
‐
‐
‐
142
S10
143
Table S4. Conditions for HPLC and ESI- MS.
HPLC conditions
Analytical column
Aglient ZORBAX Eclipse Plus C18, 2.1×100 mm, 3.5μm
Guard column
Agilent 1290 Infinity In-line filter with 0.3μm SS frit
Column temperature
40 ℃
Injection volume
5 μL
Mobile phase
A= 2 mM ammonium acetate
B= 100% Acetonitrile
Run time
16 min + 4 min post time
Flow rate
0.3 mL/min
Gradient
Time (min)
Mobile phase
0
20% B
14
90% B
16
90% B
MS conditions
Acquisition parameters
ESI mode, negative ionization; MRM
Source gas temperature
350 ℃
Source gas flow rate
9 L/min
Nebulizer pressure
40 psi
Capiliary
3500 V negative
Delta EMV(-)
200-400 V
144
S11
145
site
ZW-1
ZW-2
ZW-3
MJ-1
MJ-2
MJ-3
TH-1
TH-2
TH-3
YR-1
YR-2
YR-3
YR-4
YR-5
YR-6
XQ-1
XQ-2
XQ-3
MR-1
MR-2
MR-3
WH-1
WH-2
WH-3
JL-1
JL-2
JL-3
SR-1
SR-2
WR-1
WR-2
JR-1
JR-2
HS-1
HS-2
Table S5. Concentrations of PFAAs (ng/L) in the south Bohai coastal rivers.
PFCAs
PFBA
6.09
4.55
3.29
4.69
3.53
2.90
5.48
7.78
3.81
4.55
3.78
3.41
2.89
6.41
2.51
36.23
33.56
79.61
4.99
2.63
2.58
2.95
6.59
3.48
6.48
4.39
9.39
7.49
2.48
1.40
<0.63
1.52
2.58
3.92
1.43
PFPeA
2.01
1.73
1.47
1.39
1.79
1.72
2.42
4.42
1.82
0.75
0.56
0.52
0.84
0.92
0.50
59.65
44.57
108.68
2.10
0.95
1.27
0.74
1.22
1.00
3.11
3.01
6.06
3.05
0.57
0.65
<0.15
0.31
0.28
0.45
0.18
PFHxA
2.22
1.69
1.91
1.42
2.11
1.54
3.01
7.68
2.10
0.83
0.68
0.62
0.49
1.08
0.60
97.26
77.61
195.15
8.43
1.41
1.28
1.03
1.68
1.30
2.38
2.48
4.73
4.40
0.76
0.67
<0.15
0.44
0.44
0.37
0.26
PFHpA
1.40
1.02
1.22
1.00
2.02
1.53
1.72
4.02
0.86
0.61
0.46
0.56
0.31
0.88
0.46
72.18
60.82
142.16
2.11
1.06
0.63
0.74
1.58
1.30
0.63
0.84
1.16
4.28
0.76
0.40
<0.15
0.39
0.43
0.31
0.30
PFOA
23.79
13.04
18.02
12.04
10.68
10.06
20.51
37.70
8.26
2.73
4.15
2.14
0.96
3.74
2.86
2883.20
1919.23
4534.41
72.30
8.96
7.37
7.60
10.11
7.18
4.53
4.63
7.78
26.89
6.60
9.89
0.97
2.17
2.33
6.05
2.79
PFSAs
PFNA
3.32
1.55
2.74
1.36
0.79
0.97
1.35
4.39
0.82
0.85
0.64
0.64
0.17
1.04
0.90
3.34
2.13
2.70
0.65
0.65
0.51
1.03
1.85
0.77
0.68
0.75
0.95
3.05
0.79
0.40
0.32
0.37
0.26
0.24
0.24
146
147
148
S12
PFDA
0.67
0.48
0.64
0.25
0.28
0.45
0.23
1.60
<0.15
<0.15
<0.15
<0.15
0.20
0.17
0.33
2.21
1.17
2.04
<0.15
0.31
0.28
0.17
0.61
0.15
0.20
0.17
0.19
1.72
0.16
<0.15
<0.15
<0.15
<0.15
<0.15
<0.15
PFUdA
0.09
<0.08
<0.08
<0.08
<0.08
<0.08
<0.08
0.29
<0.08
<0.08
<0.08
<0.08
<0.08
<0.08
<0.08
0.15
0.09
0.19
<0.08
<0.08
<0.08
<0.08
0.09
<0.08
<0.08
<0.08
<0.08
0.50
<0.08
<0.08
<0.08
<0.08
<0.08
<0.08
<0.08
PFBS
0.13
<0.09
0.20
0.62
0.57
<0.09
0.56
2.75
<0.09
0.98
0.81
0.75
0.72
1.86
0.83
1.16
0.70
0.63
0.57
0.19
0.09
<0.09
0.19
<0.09
<0.09
<0.09
<0.09
24.19
<0.09
<0.09
0.33
0.50
0.89
0.37
0.45
PFHxS
0.08
<0.06
0.14
<0.06
0.10
0.10
0.19
<0.06
<0.06
0.16
0.33
0.37
0.22
0.15
0.47
0.07
0.07
0.14
0.12
<0.06
<0.06
0.27
0.59
0.51
0.19
0.18
0.28
0.59
0.10
0.11
0.13
0.09
<0.06
<0.06
0.15
PFOS
2.60
3.30
4.80
2.25
2.64
1.85
1.35
5.45
0.97
2.28
2.18
1.47
0.95
5.37
2.65
2.22
0.73
3.28
0.55
3.28
6.66
3.58
10.38
3.83
2.25
3.31
5.55
12.78
0.95
0.92
0.46
1.85
1.46
3.71
0.40
∑PFAAs
42.40
27.34
34.43
25.01
24.51
21.12
36.82
76.08
18.63
13.74
13.59
10.47
7.75
21.63
12.10
3157.68
2140.68
5068.97
91.81
19.44
20.66
18.11
34.88
19.51
20.44
19.75
36.09
88.94
13.17
14.45
2.21
7.64
8.68
15.42
6.18
149
Table S6. PCA results with 5 components extracted to explain over 80% of total variance for the
150
12 rivers (a) and 11 rivers not including the Xiaoqing River (b).
151
(a)
Rotated Component Matrix
a
Component
1
2
3
4
5
PFPeA
.989
-.020
-.059
.005
.013
PFHpA
.988
-.018
-.062
.017
-.013
PFHxA
.988
-.023
-.052
.014
-.020
PFOA
.986
-.030
-.055
.021
-.011
PFBA
.983
.018
-.039
.005
.029
PFDA
.780
.539
.022
-.130
.083
PFUdA
.362
.879
-.005
-.141
-.064
PFBS
.000
.861
-.073
-.084
-.128
PFOS
-.042
.820
-.110
-.126
.134
PFNA
.522
.584
.302
-.144
.196
PFHxS
-.152
.544
-.191
.310
.082
pH
-.283
.517
-.060
.427
.504
DO
-.448
.481
-.124
.328
-.058
Cl
-.049
-.046
.981
-.059
-.026
Salinity
-.057
-.104
.969
-.105
-.109
Conductivity
-.059
-.108
.967
-.124
-.089
Turbidity
-.102
-.105
-.072
.775
.138
WT
-.223
.099
.164
-.759
.172
ORP
.071
.167
-.210
.184
.834
F
.070
-.217
.003
-.360
.619
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
a
a. Rotation converged in 5 iterations.
152
153
154
S13
155
(b)
Rotated Component Matrix
a
Component
1
2
3
4
5
PFUdA
.963
-.046
.041
-.070
.010
PFDA
.932
.040
.175
.003
-.075
PFHpA
.837
.057
.316
-.319
-.080
PFBS
.836
-.123
-.082
-.029
.061
PFOS
.793
-.152
.160
.163
.050
PFNA
.767
.323
.276
.031
-.059
Cl
.004
.976
.014
-.067
-.110
Salinity
-.055
.956
.043
-.186
-.126
Conductivity
-.058
.955
.043
-.161
-.150
.347
.019
.903
-.120
-.052
-.210
-.038
.887
.149
-.180
PFBA
.475
.147
.756
-.023
.083
ORP
.127
-.150
.159
.900
.060
PFOA
.371
.437
.214
-.749
.050
PFHxA
.434
.173
.579
-.640
.043
pH
.424
-.041
.022
.580
.482
WT
.199
.126
.034
.175
-.808
-.215
-.023
-.077
.240
.664
DO
.351
-.190
-.039
-.020
.602
PFHxS
.417
-.187
-.021
.220
.421
PFPeA
F
Turbidity
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
a
a. Rotation converged in 6 iterations.
156
157
S14
158
Table S7. PFOA levels in surface water collected from the south Bohai coastal rivers compared
159
with previous studies by other researchers.
Regions
South Bohai, China
North Bohai, China
Tianjin, China
Dianchi Lake, China
Huai River, China
Hanjiang River, China
Tai Lake, China
Hanoi City, Vietnam
Yodo River, Japan
River Po, Italy
Mediterranean coastal
rivers, Spain
Year
n
2011
2008
2008
2010
2011
2010
2011
2011
2004,2005
2007
35
36
23
26
9
69
8
41
81
19
min
0.96
nd
4.66
3.41
6.2
nd
23
nd
4.2
1
2009
6
0.79
160
S15
PFOA (ng/L)
median
mean
7.78
277.02
3.47
14.1
12
12
15
18
48.5
81
61.5
56
30
43.1
10
101.04
5.5
5.1
reference
max
4534.41
81.7
22.9
35.44
47
256
71
100
2568
1270
This study
Wang et al. 2011
Pan et al. 2011
Zhang et al. 2012
Yu et al. 2012
Wang et al. 2013
Yu et al. 2012
Kim et al. 2013
Lien et al. 2008
Loos et al. 2008
9.63
Avila et al.2010
161
Figure S1. Sampling locations (red plots) in the south Bohai coastal rivers of China.
162
163
(The acronyms: ZW for sites in Zhangweixin River, MJ for sites in Majia River, TH for sites in
164
Tuhai River, YR for sites in Yellow River, XQ for sites in Xiaoqing River, MR for sites in Mi
165
River, WH for sites in Wei River, JL for sites in Jiaolai River, SR for sites in Sha River, WR for
166
sites in Jie River, and HS for sites in Huangshui River.)
167
S16
168
Figure S2. Chromatogram of 17 PFAAs by MRM with the concentration of 0.05 ng/mL.
169
S17
170
Figure S3. Salinity of water samples (PSU). Sites in estuaries are denoted as red bars with green
171
bars representing the locations where the discharges were measured and the blue bars
172
representing the most upstream locations where samples were collected.
173
174
S18
175
References
176
177
178
179
180
181
182
Loi, E. I. H., Yeung, L. W. Y., Taniyasu, S., Lam, P. K. S., Kannan, K., Yamashita, N. 2011.
Trophic Magnification of Poly- and Perfluorinated Compounds in a Subtropical Food
Web. Environmental Science & Technology. 45(13): 5506-5513.
McLachlan, M. S., Holmström, K. E., Reth, M., Berger, U. 2007. Riverine Discharge of
Perfluorinated Carboxylates from the European Continent. Environmental Science &
Technology. 41(21): 7260-7265.
183
S19