w a t e r r e s e a r c h 7 3 ( 2 0 1 5 ) 2 3 1 e2 4 1
Available online at www.sciencedirect.com
ScienceDirect
journal homepage: www.elsevier.com/locate/watres
Bioassay-directed identification of organic
toxicants in water and sediment of Tai Lake, China
Xinxin Hu a,b, Wei Shi a,*, Nanyang Yu a, Xia Jiang c, Shuhang Wang a,
John P. Giesy a,d,e,f,g, Xiaowei Zhang a, Si Wei a, Hongxia Yu a,*
a
State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University,
Nanjing 210023, People's Republic of China
b
Shandong Academy of Environmental Science, Jinan, People's Republic of China
c
Key Laboratory of Environmental Protection of Lake Pollution Control, Chinese Research Academy of
Environmental Science, Beijing, People's Republic of China
d
Department of Veterinary Biomedical Sciences and Toxicology Centre, University of Saskatchewan, Saskatoon,
Saskatchewan, Canada
e
Department of Zoology, Center for Integrative Toxicology, Michigan State University, East Lansing, MI, USA
f
School of Biological Sciences, University of Hong Kong, Hong Kong, China
g
Department of Biology and Chemistry and State Key Laboratory for Marine Pollution, City University of Hong Kong,
Hong Kong, China
article info
abstract
Article history:
The government of China has invested large amounts of money and manpower into
Received 10 June 2014
revision of water quality standards (WQS). Priority organic pollutants have been screened
Received in revised form
for WQS establishment using the potential hazard index method, however, some unsus-
6 January 2015
pected chemicals that could cause adverse effects might have been ignored. A large
Accepted 22 January 2015
number of chemicals exist in environment and there might be interactions between or
Available online 31 January 2015
among chemicals especially those with the same mode of action. Therefore, a toxicitydirected analysis, based on acute toxicity to Daphnia magna, was conducted for organic
Keywords:
extracts of water and sediment from Tai Lake (Ch: Taihu) to determine toxicants respon-
Tai Lake
sible for adverse effects. Extracts of five of twelve samples of water and all extracts of
Organic extract
sediment were acutely toxic. Based on toxic units, water from location L1 in July and
Toxicity-directed analysis
sediments from locations L1 and L4 during several months would be expected to result in
Chlorpyrifos
some toxicity. Twenty one (21) organophosphorus pesticides, 25 organophosphorus pes-
Cyfluthrin
ticides and 10 pyrethroids were detected in samples, extracts of which caused toxicity to D.
Predominant pollutants
magna. Chlorpyrifos and cyfluthrin were identified as predominant pollutants in organic
extracts of sediments, accounting for up to 71% and 57% of bioassay-derived toxicity
equivalents (BEQs), respectively. Chlorpyrifos was identified as the major contributor to
toxicity of organic extracts of surface water, accounting for 71% to 83 % of BEQs. The
putative causative agents were confirmed by use of three lines of evidence, including
statistical correlation, addition of key pollutants or synergists. Greater attention should
* Corresponding authors. Tel./fax: þ86 25 8968 0356.
E-mail addresses: njushiwei@nju.edu.cn (W. Shi), yuhx@nju.edu.cn (H. Yu).
http://dx.doi.org/10.1016/j.watres.2015.01.033
0043-1354/© 2015 Elsevier Ltd. All rights reserved.
232
w a t e r r e s e a r c h 7 3 ( 2 0 1 5 ) 2 3 1 e2 4 1
be paid to chlorpyrifos and cyfluthrin, neither of which is currently on the list of priority
pollutants in China. Bioassay-directed analysis should be added for screening for the
presence of priority organic pollutants in environmental media.
© 2015 Elsevier Ltd. All rights reserved.
1.
Introduction
Chinese surface water quality standards (WQS) include
criteria mainly for inorganic contaminants, while few criteria
have been established for organic pollutants (SEPA, 2002).
However, contamination of some bodies of water in China
with organic pollutants is serious and, because of the
complexity, persistence, bioaccumulation and toxic effects of
organic pollutants, has been attracting more and more
attention (Spearow et al., 2011; Kidd et al., 2007). Hydrophobic,
organic pollutants can be adsorbed by sediments (Liu et al.,
2009; Kuivila et al., 2012), where they can persist and cause
toxic effects to benthic organisms. The government of China
is adding criteria for organic pollutants for various basins, and
several hundred million RMB has been invested to select
model organisms, screen priority pollutants, and revise the
present water quality standards (WQS).
In this long-term program to revise WQS, Tai Lake (Ch:
Taihu) has been chosen as the first demonstration area
because it is important for production of rice and also because
it has a commercial fishery. It is also used as a major source of
drinking water in one of the most populous and economically
developed regions of China. Organochlorine pesticides
(OCPs), organophosphorus pesticides (OPs), polychlorinated
biphenyls (PCBs) and polycyclic aromatic hydrocarbons
(PAHs), all of which can be toxic to aquatic organisms (USEPA,
2007), have been detected in water and sediments of Tai Lake
(Shi et al., 2011; Ta et al., 2006; Wang et al., 2003).
Decreases in populations of fishes and richness of the
zooplankton community in Tai Lake have attracted increasing
attention (Zhu, 2004; Fan, 1996). The organic pollutants can
cause decreases in populations of aquatic organisms
(Spearow et al., 2011; Kidd et al., 2007). However, the existing
surface water quality standards include only three classes of
organic contaminants, including volatile phenols, petroleum
compounds and anionic surfactants (SEPA, 2002). Addition of
classes of organic contaminants to the WQS for protection of
aquatic organisms in Tai Lake is necessary. Screening and
evaluation of listed priority organic toxicants in Tai Lake has
been undertaken to develop a baseline information on the
current status of concentrations of these toxicants against
which future trends can be compared. Using potential hazard
index method, five chemicals, including pyrene, dimethyl
phthalate (DMP), di-n-butyl phthalate (DNBP), di(2-ethylhexyl)
phthalate (DEHP) and 1,4-dichlorobenzene, have been identified as priority organic pollutants in Tai Lake. However, potential effects of un-listed pollutants that might be present
were unknown and if only those on the priority list are
considered in the monitoring program, some toxicants which
could cause toxicity to organisms might be ignored. Because
there are criteria for only a limited set of organic contaminants that might occur in surface waters and sediments, thus
it was deemed necessary to screen and evaluate toxicity of
contaminants in water or sediment. This was undertaken by
use of a screening-level bioassay in the context of a bioassaydirected identification scheme.
Because organic compounds occur in mixtures, it is difficult to evaluate the potential hazards of organic pollutants in
surface water and sediment based on measurements of their
individual concentrations. Also, there might be contaminants
present that are not identified because they are not on the list
of priority pollutants and for which there might not be
methods or authentic standards available. Effect-directed
analysis (EDA) is one useful method for evaluating potential
toxicity and identifying the responsible organic toxicants in
extracts of environmental samples, which has been used in
identifying major toxicants in samples of surface water,
sediment and effluents (Bandow et al., 2009; Grung et al., 2007)
and have been described and reviewed in detail (Hecker and
Giesy, 2011). However, the volume of environmental samples
that is available for fractionation and identification of the
major pollutants by use of EDA is often limited. Mass balance
(or potency balance) analysis can be used to assess potential
risks of chemicals detected in samples and calculate their
contributions to the observed toxicity of extracts of environmental samples. Once suspect key toxicants have been identified, confirmation of putative causative agents is essential.
Spiking of suspect toxicants has been used as a approach for
confirming the suspect toxicants (Bandow et al., 2009; Schwab
et al., 2009). Moreover, the presence of synergists or antagonists can modulate toxic potencies of some classes of pollutants and also needs to be considered as a confirmation
approach. Since some compounds with similar properties
could react similarly, confirmation of specific causative agents
using only one approach may not be definitive. Therefore, use
of multiple techniques and lines of evidence for confirmation
is suggested.
Daphnia magna is a resident aquatic crustacean and used as
one of the six families of aquatic species for which data are
required in development of WQS in China (Yang et al., 2012;
Yan et al., 2012). D. magna has been used to evaluate toxicity
of a range of types of samples (Ribe et al., 2012; Diamantino
et al., 1998). In the present study, acute toxicity was determined by use of D. magna exposed to organic extracts of water
or sediment of Tai Lake collected from several locations,
during several months.
The objectives of this study were to: 1) examine toxicity of
organic extracts of water and sediment from Tai Lake in
different months and different locations; 2) determine concentrations of organic pollutants in water and sediment from
w a t e r r e s e a r c h 7 3 ( 2 0 1 5 ) 2 3 1 e2 4 1
Tai Lake; 3) identify organic pollutants that contributed to
toxicity of organic extracts of water and sediment and 4)
determine potential additional contaminants to be added to
the priority pollutants list for which WQS need to be derived.
2.
Materials and methods
2.1.
Chemicals and materials
Chemicals used for bioassays included chlorpyrifos (CPY,
>98.5% purity), cyfluthrin (>99.4% purity) and triphenyl
phosphate (TPP, >99% purity). Chlorpyrifos was purchased
€ fers (Augsburg, Germany).
from Labor Dr. Ehrenstorfer-Scha
Cyfluthrin and TPP were purchased from AccuStandard (New
Haven, CT, USA). Descriptions of OPs, pyrethroids, OCPs and
nitro-anilines used for instrumental analysis are given in
Table S1 in the supporting information (SI).
2.2.
Sampling locations
Pollution of Tai Lake is greatest in the northern portion in
areas such as Meiliang and Zhushan Bays (Guo et al., 2012;
Wang et al., 2012; Lu et al., 2013). The surrounding terrestrial
landscape of these regions is farmland and agricultural
effluent is a primary source of contaminants. Both water and
sediments were collected in May, July and October, 2009 from
four locations including L1, L2, L3 and L4 in the northern part
of Tai Lake (Fig. S1 in SI). At each location, 10 L of water and
2000 g wet mass (wm) of sediment were collected and placed
into brown, glass bottles pre-cleaned with Milli-Q water,
methanol (Tedia Co. Ltd, Fairfield, OH, USA), acetone (Tedia
Co. Ltd, Fairfield, OH, USA), dichloromethane (Tedia Co. Ltd,
Fairfield, OH, USA) and n-hexane (Merck, Darmstadt, Germany). Detailed information on sampling locations, sampling
time, sediments collection and samples storage is shown in
supporting information.
2.3.
Sample preparation
Samples of water and sediment were extracted by use of solid
phase extraction (SPE, Oasis HLB cartridges, Waters, Milford,
MA, USA) and accelerated solvent extraction (ASE, Dionex ASE
300, Dionex, Idstein, Germany), respectively. Extracts of water
and sediment samples were separated into two aliquots for
use in instrumental analysis or bioassays. The aliquot for use
in instrumental analysis was concentrated, blown to dryness
and reconstituted in 0.5 mL and 1 mL dichloromethane for
water and sediments, respectively. The other aliquot for use in
bioassays was concentrated, blown to dryness and reconstituted in 0.5 mL and 1 mL DMSO for water and sediments,
respectively. Greater details of these methods are provided in
the supporting information.
2.4.
233
randomly selected to expose to the treatment or control
conditions for 48 h. Seven concentrations were used for each
organic extract. There were four replicates for each concentration and five animals in each breaker. Extracts of water
samples in DMSO were diluted to provide concentrations
equivalent to 40-, 20-, 10-, 5-, 2.5-, 1.25-, 0.625-fold relative to
the original concentrations in water. Extracts of sediments in
DMSO were diluted into concentrations equivalent to 25, 12.5,
6.25, 3.13, 1.56, 0.78, 0.39 mg/mL (mg-dry mass (dm) per mL of
test solution). Tests were conducted in 10 mL of solution in
25 mL glass breakers. Blanks (no solvent) and solvent controls
(0.5% DMSO) were also performed. Tests were conducted
under a 14:10 lightedark cycle at 22 ± 1 C. Immobilization of
D. magna after 48 h was used as the measurement to determine mortality. If D. magna did not move after gentle agitation
of the solution, they were considered to be immobilized. Potassium dichromate was used as a positive control, reference
toxicant. D. magna were treated with vehicle or various concentrations of potassium dichromate with three replications
and repeated once a week. The EC50 of potassium dichromate
was calculated in control charts by use of normal statistical
procedures. If EC50s for potassium dichromate were between
0.9 and 1.7 mg/L, the test conditions and sensitivities of D.
magna were similar among tests and the tests deemed valid
(ISO, 1996).
2.5.
Instrumental analyses
Qualitative analysis of organic pollutants in water and sediment extracts was conducted by use of Thermo TSQ Quantum
Discovery triple-quadrupole mass spectrometer (San Jose, CA,
USA) in selected reaction monitoring (SRM) mode. Organic
pollutants in the water or sediment extracts detected in the
qualitative analysis were mainly pesticides, including OCPs,
OPs, pyrethroids and nitro-anilines. Concentrations of OPs
and OCPs in water and sediment were quantified by use of a
Thermo Single Quadrupole GCeMS (San Jose, CA, USA) in
selected ion monitoring (SIM) mode. Concentrations of pyrethroids and nitro-anilines were quantified using Thermo TSQ
Quantum Discovery triple-quadrupole mass spectrometer
(San Jose, CA, USA) in selected reaction monitoring (SRM)
mode. Recoveries of pesticides were determined by use of
external standards. Limits of detection (LODs) of pesticides
were defined based on a signal three times the background
noise (S/N ¼ 3). Internal standards parathion-d10, 13C-PCB 141
and PCB-189 from Sigma (St. Louis, MO, USA) were spiked into
extracts before chemical analysis to quantify OPs, OCPs and
pyrethroids according to previously published methods (Li
et al., 2010; Wang et al., 2010; Tao et al., 2010). Average recovery, limit of quantification (LOQ) and LOD of each chemical
are given in Table S2 in the supporting information. None of
the procedural blanks contained detectable concentrations of
target compounds. Detailed information about instrumental
analysis is showed in supporting information.
Toxicity testing
2.6.
The water flea (D. magna) was used as the test organism in 48h tests that followed OECD protocols (OECD, 1984) with some
modifications. To evaluate acute toxic potency of organic extracts of water or sediment, young D. magna (<24 h old) were
Relative toxic potencies of tested samples
Since concentrations of measured toxicants indicated that the
OP insecticide CPY would likely be a primary toxicant in the
tested samples, CPY was chosen as the standard compound to
234
w a t e r r e s e a r c h 7 3 ( 2 0 1 5 ) 2 3 1 e2 4 1
Table 1 e EC50s for the detected chemicals for D. magna
from the present study and previous literature sources.
Chemicals
Chlorpyrifos
Chlorpyrifos
Bolstar
Diazinon
Disulfoton
Ethoprop
Fenthion
Methyl parathion
Phorate
Coumaphos
Dichlorvos
Tefluthrin
L-Cyhalothrin
Permethrin
Cyfluthrin
Cyfluthrin
Cypermethrin
Fenvalerate
Deltamethrin
Dichloran
Pendimethalin
HCH
Hexachlorobenzene
Heptachlor
Aldrin
Heptachlor epoxide
Oxychlordane
Chlordane
Endosulfan I
Endosulfan II
Dieldrin
Endrin
p,p0 -DDD
p,p0 -DDT
Methoxychlor
Mirex
EC50 (mg/L)
7.4
6.8
5.1
6.1
1.3
9.3
5.7
2.6
3.7
1.9
8.5
7.0
2.3
7.2
1.4
8.1
7.2
9.0
5.9
2.1
2.8
5.2
4.7
7.8
3.0
2.4
1.3
9.8
1.2
1.5
1.5
5.9
8.9
1.2
1.8
2.0
1
10
101
101
101
101
101
102
102
101
101
102
101
101
102
103
102
102
101
101
102
103
101
103
103
102
101
103
103
Reference
(Palma et al., 2008)
This study (Fig. S3 in SI)
(USEPA, 2007)
(Jemec et al., 2007)
(USEPA, 2007)
(USEPA, 2007)
(USEPA, 2007)
(Baun et al., 2008)
(USEPA, 2007; Johnson, 1986)
(USEPA, 2007)
(USEPA, 2007)
(USEPA, 2007)
(USEPA, 2007)
(USEPA, 2007)
(USEPA, 2007)
This study (Fig. S4 in SI)
(USEPA, 2007; EPA/OTS., 1985)
(USEPA, 2007)
(Day and Maguire, 1990)
(USEPA, 2007)
(USEPA, 2007)
(USEPA, 2007)
(USEPA, 2007)
(USEPA, 2007)
(USEPA, 2007)
(USEPA, 2007)
(USEPA, 2007)
(Moore et al., 1998)
(Wan et al., 2005)
(Wan et al., 2005)
(USEPA, 2007)
(USEPA, 2007)
(USEPA, 2007)
(USEPA, 2007)
(USEPA, 2007)
(USEPA, 2007)
quantify toxic potency of the tested samples. Bioassayderived toxicity equivalents of water and sediment were
calculated relative to CPY (BEQ) by dividing the concentration
of CPY that caused 50% immobilization of D. magna by the
volume of extracts of samples that produced equivalent
immobilization (50%) of D. magna (Eq. (1)).
BEQ ¼
2.7.
EC50 of CPY
enrichment factors of sample extracts
(1)
Potency balance analysis
Potencies relative to CPY (RePs) of individual chemicals were
calculated by dividing concentration of CPY which produced
50% immobilization of D. magna by concentrations of individual compounds that caused 50% immobilization (Eq. (2)).
REPi ¼
EC50 of CPY
EC50 of compoundi
(2)
EC50s for CPY and cyfluthrin to D. magna were determined
in the present study. EC50s from literature sources were used
for other individual compounds (Table 1). EC50 values of
chemicals which had more than one reported EC50s were reported as the geometric mean of all the reported EC50s.
Calculated toxicity equivalents of individual compounds
(CEQ) were derived by multiplying their concentrations in
P
samples by their respective ReP values. Total CEQs ( CEQs) of
tested samples were the sum of CEQs of all individual compounds (Eq. (3)).
X
CEQ ¼
N
X
RePi concentrations of individual compoundi
i¼1
(3)
A potency balance analysis was conducted to compare the
BEQs determined in the bioassay with CEQs calculated from
measured concentrations in samples to determine if the BEQs
of the tested samples had been accounted for. Relative contributions of individual measured toxicants to the total
toxicity equivalents determined in the bioassay (BEQ) in each
sample were determined as the ratio of CEQs for individual
contaminants and BEQ of the tested sample.
2.8.
Confirmation
In order to confirm that putative causative agents predicted
from instrumental analysis and potency balance analysis
were the probable cause of observed toxicity of organic extracts of water and sediments, confirmation tests were conducted. One confirmation approach was statistical
correlation, where BEQs of a series of samples were regressed
against total CEQs based on concentrations of individual
contaminants in samples and their RePs relative to CPY.
Constituents of sediment organic extracts were complex, and
putative causative agents could be several suspect toxicants.
Therefore, another two lines of evidence were conducted to
further determine toxicants that might have caused toxicity of
sediment extracts. One confirmatory test was addition of
suspect key toxicants, in which extracts from a control sediment were spiked with known amounts of suspect key toxicants based on their concentrations measured in sediments
from Tai Lake using instrumental analysis. Acute toxicities of
these artificial extracts to D. magna were then determined.
Acute toxicity of the extract of the un-spiked, control sediment was also determined. Since the other lines of evidence
suggested that OPs might be the cause of observed toxicity, a
third approach in which TPP was added as a synergist was
used to confirm toxicity of OPs. TPP is a known inhibitor of the
enzyme carboxylesterase (CbE). CbE could protect against
toxic effects of OPs (Maxwell, 1992). It was found that TPP
could significantly increase the toxicity of OPs to D. magna
(Barata et al., 2004). Tests were conducted by use of previously
published methods (Barata et al., 2004; Amweg and Weston,
2007). Each series of concentrations of extracts of sediments
was tested in parallel with and without addition of 30 nM TPP
and differences between the observed toxicity with and
without TPP amendment were compared. If a significant difference was observed, it was concluded that toxicity was
increased by TPP and it was likely that OPs in the samples
were responsible for at least some of the observed toxicity.
The maximum concentration of TPP (30 nM) that could be
applied without causing toxicity to D. magna was determined
w a t e r r e s e a r c h 7 3 ( 2 0 1 5 ) 2 3 1 e2 4 1
by use of the methods of Barata et al. (2004) and a range of
doses and the maximum concentration that did not cause
toxicity to D. magna relative to control treatments.
2.9.
Data analysis
Immobilization of D. magna and EC50 values of organic extracts
of samples were derived by use of SPSS 11 (SPSS Inc., Chicago,
IL, USA). Values were reported as mean ± SD (n ¼ 4). Correlations between BEQs and CEQs of samples were analyzed by
regression analysis based on Origin 8.5 (Origin software Inc.,
San Clemente, CA, USA). The regression lines were fitted with
intercepts forced through zero. Homogeneity of variances and
normality of data were tested by use of Levene's Test and
ShapiroeWilks normality test, respectively. When necessary,
data were log-transformed to obtain homogeneity of variances and normality. When these assumptions were met,
significant differences between sample extracts and control
extract and between toxicity of sediment extracts with or
without TPP were analyzed by one-way analysis of variance
(ANOVA), followed by Duncan's multiple comparisons test
(SPSS 11). The level of significance was set as (p < 0.01) or
(p < 0.05), which were designated by ** or *, respectively.
3.
Results and discussion
3.1.
Observed toxicity of sample extracts
The EC50 of the reference toxicant, potassium dichromate
(K2CrO7), to D. magna was 9.0 101 ± 8.0 102 mg/L (Fig. S2
in SI), which met requirements of ISO 6431 (ISO, 1996) for a
valid test. This result indicated that the test results were
reliable and there was little variation in sensitivities of D.
magna among periods, during which tests were conducted.
Toxic potencies of organic extracts of water and sediment
represented by BEQs are given (Table 2). No extracts of water
collected in May were acutely toxic to D. magna. Extracts of
water from two locations (locations L1 and L4) in July and
three locations (locations L1, L2 and L4) in October showed
significant acute toxicity with the BEQs greater than 0.0069 mg
CPY/L. Greater frequencies of toxicities were observed for
extracts of water collected in July or October. Also, BEQs of
samples collected in July or October were greater than those
observed in May, and BEQs for samples collected in July were
the greatest (Fig. S5 in SI). These results might have been due
to runoff from agricultural fields and aquaculture base in July.
Summer (July to October) is the season of harvesting aquatic
products. Also during summer, multiple pesticides such as
chlorpyrifos and pyrethroids were applied in agriculture. July
is in the rainy season and the heaviest rain period is from June
to September in the Tai Lake region. In this region, residues of
multiple pesticides could be washed into waters from farmland and aquaculture. This could be seen in the level of
contamination of locations L1 and L4. Geographically, extracts
of water collected in July and October from location L1 (Fig. S5
in SI), which is located in Zhushan Bay at the northern part of
Tai Lake, caused significant immobilization of D. magna. There
is no water quality criterion (WQC) for CPY in China, and the
USEPA acute freshwater quality criterion for CPY is 0.083 mg/L.
235
The hazard quotient (HQ) derived by dividing the concentration of BEQs of water samples from location L1 in July to the
USEPA acute freshwater quality criterion for CPY, was 3.01,
which was greater than 1.0. This result indicted that water
from location L1 in July would result in some toxicity. HQs of
other water samples were all less than 1.0. BEQs at location L1
were greater than others, which might be due to releases of
residues from surrounding farmland, where rice and wheat
are the main crops.
All extracts of sediments from the four locations collected
during different months caused significant, acute toxicity to D.
magna with BEQs ranging from 0.041 to 0.53 mg CPY/g, dm.
Toxicities of extracts of sediments were obvious and detected
frequently, which might be because relatively hydrophobic
toxicants were easily absorbed to sediments (Liu et al., 2009;
Kuivila et al., 2012). Toxicities of organic extracts of sediments collected from the same location in May and July were
not significantly different (Fig. S6 in SI). Greatest BEQs in
sediments were observed in October with values of 0.53, 0.060,
0.058 and 0.11 mg CPY/g, dm at locations L1, L2, L3 and L4,
respectively. BEQs of sediments from location L1 were greater
than those of sediments from other locations during all the
three months (Fig. S6 in SI). BEQs of sediments from location
L1 in May, July and October were 0.46, 0.44 and 0.53 mg CPY/g,
dm, respectively. No sediment quality criteria for CPY have
been promulgated in China or other countries. A sediment
quality criteria (SQC) equation based on equilibrium partitioning theory was used to create SQC for CPY (SQCeqp-CPY)
(Ditoro et al., 1991; Giesy et al., 1999). Detailed information
about the methods used to calculate the SQCeqp-CPY and values
of parameters chosen are shown in supporting information.
The calculated SQCeqp-CPY was 0.087 mg/g, dm sediment. HQs,
dividing BEQs of sediments from location L1 collected in
different months to the SQCeqp-CPY, ranged from 5.06 to 6.09,
which were greater than 1.0. HQs of sediments from location
L4 collected in different months ranged from 1.11 to 1.26,
which were also greater than 1.0. The results illustrated that
sediment from locations L1 and L4, especially from location
L1, would pose potential harm to benthic and planktonic
organisms.
3.2.
Potency balance analysis
In water, four OPs and eight OCPs but no pyrethroids and
nitro-anilines, were detected. Total concentrations of OPs and
OCPs ranged from 0.089 to 2.5 102 and 2.2 to 24 ng/L,
respectively (Table S3 in SI). Concentrations of CPY in water
collected in July were greater than those observed during
other months, which is consistent with previously reported
results (Palma et al., 2010). This result illustrates that agricultural effluent was likely the primary source of CPY at these
locations. It has been reported that the half-life of CPY in
water ranged from 29 to 74 d (Racke, 1993), which could
explain the fact that concentrations of CPY in water of Tai
Lake were lesser in October than in July. Calculated toxicity
equivalents (CEQs) for individual OPs and OCPs in waters are
shown in Tables S5 and S6 of the SI. CEQs for CPY ranged from
9.0 105 to 0.18 mg/L among samples of water. These values
exceeded those for other compounds in all waters. The
greatest CEQ for CPY was 0.18 mg/L in water collected from
236
w a t e r r e s e a r c h 7 3 ( 2 0 1 5 ) 2 3 1 e2 4 1
Table 2 e BEQs of water and sediment samples, the calculated CEQs for OPs, OCPs and pyrethroids in samples and the
contributions of total CEQs to their corresponding BEQs.
Sample
Water
Sediment
a
b
c
d
Location-month
BEQa
L1-5c
L2-5
L3-5
L4-5
L1-7
L2-7
L3-7
L4-7
L1-10
L2-10
L3-10
L4-10
L1-5
L2-5
L3-5
L4-5
L1-7
L2-7
L3-7
L4-7
L1-10
L2-10
L3-10
L4-10
N.D.d
N.D.
N.D.
N.D.
0.25
N.D.
N.D.
0.011
0.037
0.035
N.D.
0.0069
0.46
0.041
0.046
0.10
0.44
0.042
0.046
0.097
0.53
0.060
0.058
0.11
CEQ
CEQ-OPs
CEQ-OCPs
CEQ-pyre
P
CEQ
2.6 104
9.0 105
0.0038
0.0061
0.28
0.014
0.0094
0.013
0.031
0.037
0.0078
0.0061
0.19
0.018
0.030
0.056
0.28
0.025
0.031
0.033
0.40
0.031
0.044
0.083
5.2 106
1.9 105
3.5 106
5.2 106
6.7 105
1.3 105
1.9 106
2.9 105
6.6 106
4.4 106
2.3 105
5.7 106
0.0048
8.6 104
0.0010
0.0055
0.026
0.0012
0.0015
9.8 104
0.0047
2.0 105
0.0012
3.9 105
N.D.
N.D.
N.D.
N.D.
N.D.
N.D.
N.D.
N.D.
N.D.
N.D.
N.D.
N.D.
0.058
0.032
0.018
0.023
0.058
0.010
0.010
0.018
0.022
0.014
0.016
0.025
2.6 104
1.1 104
0.0038
0.0061
0.28
0.014
0.0094
0.013
0.031
0.037
0.0078
0.0061
0.26
0.052
0.049
0.085
0.36
0.036
0.042
0.052
0.42
0.045
0.062
0.11
Contribution (%)b
e
e
e
e
110
e
e
114
83
107
e
88
56
125
107
82
83
85
91
54
79
76
108
99
The unit of the BEQs and CEQs of water samples is mg CPY/L, and the unit of the BEQs and CEQs of sediment samples is mg CPY/g dm (dry mass).
P
The contributions of CEQs to their corresponding BEQs.
The number 5 means May, 7 means July and 10 means October.
Not detected.
location L1 in July. CEQs for OCPs were all less than
8.7 105 mg CPY/L. Total CEQs for OPs and OCPs were all
greater than 0.0038 mg CPY/L except for locations L1 and L2 in
May (Table 2).
For the five water samples whose extracts caused acute
toxicity to D. magna, total CEQs for OPs and OCPs accounted
for 83%e114% of the corresponding BEQs (Table 2). Contributions of OCPs to BEQs of samples were less than 0.3%. Therefore, OPs were the primary constituents contributing to
toxicity of water extracts. CPY alone accounted for 71% to 83 %
of the BEQs and 60% to 99 % of the total CEQs. Therefore, CPY
was the primary putative cause of toxicity of organic extracts
of water from Tai Lake to D. magna. Water quality criterion for
CPY for protection of aquatic organisms has been established
in some countries and regions, such as the USA, Canada,
Ontario, Australia and Europe (USEPA, 2002; CCME, 1991;
OMEE, 1994; ANZECC, 2000; Lepper, 2002). While there is no
water quality criterion (WQC) for CPY in China, concentrations of CPY at some locations in the present study, such as
L1-7, L1-10, and L2-10, were greater than criteria established
by other countries or regions.
In sediments, seven OPs, nine pyrethroids, eighteen OCPs
and one nitro-aniline were detected. In order to determine
their concentrations and their contributions to the acute
toxicity to D. magna in the tested samples, they were further
quantified (Tables S4 in SI). Total concentrations of OPs ranged
from 38.5 to 953 ng/g, dm and the detection frequency of CPY
was greatest (83.3%) with concentrations ranging from 23 to
266 ng/g, dm. Concentrations of CPY observed in this study
were similar to those observed in sediments in California, USA
with values ranging from not detected (N.D.) to 220 ng/g, dm
(Phillips et al., 2012). CEQs for CPY ranged from N.D. to 0.27 ng/
g, dm with the greatest CEQ in sediment from L1 in October
(Tables S5 in SI). CEQs for fenthion comprised less than 10% of
total CEQs in most sediments. Contributions of the sum of
CEQs for disulfoton, ethoprop, bolstar, coumaphos and
methyl parathion to total CEQs from most locations were also
negligible (<10%). Total concentrations of the nine pyrethroids
ranged from 2.1 to 17 ng/g. Cyfluthrin was frequently detected
in sediments with CEQs greater than those for other pyrethroids in sediments from most locations (Tables S7 in SI).
Similarly, cyfluthrin was also frequently detected in California, USA (Amweg et al., 2006; Weston et al., 2005; Holmes et al.,
2008; Phillips et al., 2010). Other pyrethroids, such as cypermethrin and permethrin, were also frequently detected in
sediments, however, their CEQs were negligible at those locations. Although dichloran was detected in L1-10, its CEQ
was also negligible (6.0 107). Concentrations of OCPs ranged
from 7.8 to 1016 ng/g, dm. Although frequencies of detection
of OCPs were relatively great, CEQs for OCPs were relatively
small (Tables S8 in SI). Their contributions to the total CEQs in
different sediments were minimal with contributions ranging
from as little as 0.04% to 7.1% of the corresponding total CEQs.
Total CEQs for OPs, pyrethroids and OCPs in sediments ranged
from 0.036 to 0.42 mg CPY/ng, dm, and greater CEQs were
observed in sediments from location L1. The greatest CEQ of
0.42 mg CPY/ng, dm was observed at location L1 during
October. Total CEQs for sediments from location L4 were also
w a t e r r e s e a r c h 7 3 ( 2 0 1 5 ) 2 3 1 e2 4 1
greater than those for locations L2 and L3 (Table 2). This result
illustrated that pollution at location L1 was greater than those
at other locations, and CEQs of pesticides in October were
greater than those in other months.
In sediments, total CEQs for OPs, pyrethroids and OCPs
accounted for 54%e125% of BEQs (Table 2). The sum of CEQs
for all OPs could account for 34%e76% of BEQs, and up to 71%
of BEQs could be attributed to CPY. CEQs for pyrethroids
could account for from 4.0% to 78% of BEQs. Among the pyrethroids, as much as 57% of BEQs could be accounted for by
cyfluthrin. The sum of CEQs for OPs and pyrethroids
accounted for 93%e100% of total CEQs and 53%e123% of
BEQs, while contributions of OCPs to BEQs of sediments were
lesser and accounted for almost none of the BEQs with a
maximum of 5.9% of BEQs. These results indicated that OPs
and pyrethroids were the primary organic contaminants in
these sediments. The sum of CEQs for CPY and cyfluthrin
accounted for more than 53% of BEQs in most sediments.
Therefore, CPY and cyfluthrin were determined to be major
putative causative agents of toxicity observed in extracts of
sediments. Since there had been no previous studies of
toxicity of sediments in Tai Lake, it was not possible to
compare the results obtained in this study to those of previous studies. Also, since this was the first comprehensive
study of organic pollutants, including pesticides, to be conducted on water and sediments of Tai Lake, it was not
possible to compare the CEQs calculated from the concentrations given here to those from other studies. Results of
previous studies have indicated that OPs and pyrethroids do
not have the same mode of toxic action and OPs can synergize or retard potencies of pyrethroids to a variety of organisms (Ahmad et al., 2009; Denton et al., 2003; Khan et al.,
2013). Possible synergism or antagonism between OPs and
pyrethroids would make greater or lesser discrepancies between BEQs and CEQs and a greater proportion of toxic potency would have been unaccounted for. Mechanisms of
action of mixtures of compounds are complicated and vary
among ratios and organisms, which might be additive,
antagonistic or synergistic interaction (Ahmad et al., 2009;
Denton et al., 2003; Khan et al., 2013). Moreover, there is no
fully applicable and reliable model to calculate the toxicity of
mixtures of pesticides. Therefore, the simple additive model
was used in the present study according to previous studies
(Khim et al., 1999; Brack and Schirmer, 2003; Qiao et al., 2006).
For this reason, it was deemed absolutely necessary to verify
the results by use of multiple confirmation steps.
3.3.
3.4.
237
Addition of suspect key toxicants
To further confirm the causative toxicants identified by the
potency balance and the regression analyses, known concentrations of CPY and cyfluthrin were added to organic extracts from control sediment to approximate concentrations
measured in sediments of Tai Lake (Table S4). Survivals of D.
magna in all tests with extracts of un-spiked, control sediments were equal to or greater than 90%. The BEQs for the
spiked sediments accounted for 49%e131% of BEQs in sediments from Tai Lake. Addition of putative toxicants to some
sediment extracts caused more than 100% of BEQs of raw
sediments. This effect indicated that there might be synergistic effects between these toxicants. BEQs for corresponding
sediments spiked with known concentrations in mixtures of
CPY and cyfluthrin were in good agreement with BEQs of
sediments in which the same concentrations of these two
pesticides had been measured (Fig. 2). This result was further
Correlation between BEQs and CEQs
When BEQs and CEQs of the five water samples whose organic
extracts exhibited acute toxicity to D. magna were compared
by regression analysis, the coefficient of determination (r2)
between BEQs and CEQs for CPY was 0.998 (Fig. 1A). This
correlation, along with the results of the potency balance
analysis, supported the hypothesis that CPY was a likely
causative agent for the observed toxicity. In sediments, the r2
between BEQs and total CEQs for CPY and cyfluthrin was 0.943
(Fig. 1B). This correlation together with the results in potency
balance analysis, suggested that CPY and cyfluthrin were the
contributors to acute toxicity of sediment extracts.
Fig. 1 e (A) Correlation between calculated toxicity
equivalents (CEQs) for chlorpyrifos (CPY) based on
concentrations in water and toxicity equivalents based on
acute toxicity to Daphnia magna (BEQs) for water samples
and (B) correlation between the total CEQs of CPY and
cyfluthrin based on their concentrations in sediments and
the BEQs for sediment samples.
238
w a t e r r e s e a r c h 7 3 ( 2 0 1 5 ) 2 3 1 e2 4 1
confirmation that these two insecticides were likely causative
agents of observed toxicity of organic extracts of sediment
from Tai Lake.
3.5.
Effect of synergist
Addition of 30 nM TPP to extracts of reference sediments
caused no acute toxicity to D. magna. Toxic potencies of all
extracts of sediments were greater in the presence of added
TPP (Fig. 3), which suggested that toxicities of sediment extracts were caused by OPs. In the present study, toxicities of
extracts of sediments from eight locations, were approximately 1.8- fold greater in the presence of TPP (from 1.2 to 2.3)
than the same extracts without TPP. This result is consistent
with the synergistic effect of TPP on chlorpyrifos obtained by
Barata et al. (2004). It demonstrated that the putative
contaminant, chlorpyrifos, which is an OP, could have been
the cause of the toxicity to D. magna observed in extracts from
these locations. Chlopyrifos was not detected in sediments
from locations L2-5, L4-7 or L2-10. The toxicity of extracts of
sediment from location L3-5 was 3.8- fold greater in the
presence of TPP than the same extract without TPP. This result
indicated that there might be other toxicants contributing to
toxicity caused by extracts of these sediments. The synergistic
effect of TPP on toxicity of these toxicants was stronger than
that for chlorpyrifos, so it was likely that observed toxicity was
caused by another OP. Carbofuran, which can also be synergized by TPP (Barata et al., 2004) could possibly be responsible
for this observed effect. The effect of adding the synergist for
OPs was consistent with OPs being primary constituents
causing the observed toxicity of extracts of sediments, but it is
still possible that other pollutants might have been present in
sediments and have contributed to toxicities of sediments
from some locations. The use of the synergist was an effective
tool to confirm the toxicity of OP insecticides to D. magna.
Although some previous studies have used synergists in the
confirmation of suspect pollutants in toxicity identification
Fig. 2 e Correlation between toxicity equivalents based on
bioassays (BEQs) for organic extracts of control sediment to
which known concentrations of suspect key pollutants
measured in sediments had been added and BEQs
observed for sediments from Tai Lake.
Fig. 3 e Toxicity equivalents based on bioassays (BEQs) for
sediments from Tai Lake before and after addition of the
OPs synergist TPP.
evaluations (TIEs) and piperonylbutoxide (PBO) was the common synergist, addition of TPP had not been used as a
confirmatory test in previous studies (Amweg and Weston,
2007; USEPA, 1993). In the present study, addition of TPP as a
synergist was relatively simple and feasible and the results
were consistent with the observed toxicities being caused by
OPs.
4.
Conclusions
In conclusion, toxicities of organic extracts of simultaneously
collected water and sediment extracts from Tai Lake were
determined using D. magna acute toxicity test. Organic extracts of five samples of water caused significant toxicity to D.
magna with BEQs ranging from 0.0069 to 0.25 mg CPY/L, and
extracts of all 12 sediments caused significant toxicity to D.
magna with BEQs ranging from 0.041 to 0.53 mg CPY/g, dm.
Hazard quotients of water from location L1 in July and sediments from locations L1 and L4 collected in May, July, October
were greater than 1.0, which indicated that water from location L1 in July and sediments from locations L1 and L4 from
different months would result in some toxicity. Potential risk
would be posed to aquatic and benthic organisms by water
and sediment in Tai Lake, especially samples from location L1.
The greatest toxic potency was observed in July for water and
in October for sediment. The toxicities of both water and
sediment samples from location L1 were greater than those
observed for other locations. Agricultural chemicals were the
primary pollutants of concern. In the present study, CPY and
cyfluthrin were identified as the major organic toxicants
contributing to the potential toxicity in extracts of water and
sediment from Tai Lake by combining bioassays, potency
balance analysis, instrumental analysis and confirmation.
However, these compounds are not listed in the priority
organic pollutants screened by use of potential hazard index
w a t e r r e s e a r c h 7 3 ( 2 0 1 5 ) 2 3 1 e2 4 1
method. It indicated that the previous method for screening
priority organic pollutants was still needed to improve and
evaluate according to the identification results obtained in the
present study. Moreover, more toxicity data of other model
organisms are also needed for reliable identification results of
priority organic pollutants. The present study suggested that
the series of procedures including bioassays, instrumental
analysis, potency balance analysis and confirmation tests
were useful and reliable for identifying and confirming the
priority organic pollutants of environmental samples and
should be added into the revision of water quality standards.
Acknowledgments
This work was supported by Natural Science Foundation of
China (21307054), Major State Basic Research Development
Program (2013AA06A309), Natural Science Foundation of
Jiangsu Province (BK20130551), Specialized Research Fund for
the Doctoral Program of Higher Education (20130091120013),
Jiangsu Provincial Environmental Monitoring Research Fund
(1212), Major Science and Technology Program for Water
Pollution Control and Treatment (2012ZX07506-004-004). Prof.
Giesy was supported by the program of 2012 “High Level
Foreign Experts” (#GDW20123200120) funded by the State
Administration of Foreign Experts Affairs, the P.R. China to
Nanjing University and the Einstein Professor Program of the
Chinese Academy of Sciences. He was also supported by the
Canada Research Chair Program, a Visiting Distinguished
Professorship in the Department of Biology and Chemistry
and State Key Laboratory in Marine Pollution, City University
of Hong Kong.
Appendix A. Supplementary data
Supplementary data related to this article can be found at
http://dx.doi.org/10.1016/j.watres.2015.01.033.
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Murowchick, J.B., 2010. Sediment contamination of residential
streams in the metropolitan Kansas City area, USA: part I.
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pesticide-related compounds. Arch. Environ. Contam. Toxicol.
59 (3), 352e369.
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Evaluations. Phase III Toxicity Confirmation Procedures for
Samples Exhibiting Acute and Chronic Toxicity. EPA/600/R92/081. U.S. Environmental Protection Agency, Washington,
DC.
USEPA, 2007. ECOTOX User Guide: ECOTOXicology Database
System. Version 4.0. Avaiable. http://www.epa.gov/ecotox/.
USEPA, 2002. National Recommended Water Quality Criteria.
EPA-822-R-02e047. Office of Water, Office of Science and
Technology, U.S. Environmental Protection Agency, Ed.,
Washington, DC.
Wan, M.T., Kuo, J.N., Buday, C., Schroeder, G., Van Aggelen, G.,
Pasternak, J., 2005. Toxicity of alpha-, beta-, (alpha plus beta)endosulfan and their formulated and degradation products to
Daphnia magna, Hyalella azteca, Oncorhynchus mykiss,
Oncorhynchus kisutch, and biological implications in streams.
Environ. Toxicol. Chem. 24 (5), 1146e1154.
w a t e r r e s e a r c h 7 3 ( 2 0 1 5 ) 2 3 1 e2 4 1
Wang, D.L., You, J., Lydy, M.J., 2010. Sediment matrix effects in
analysis of pyrethroid insecticides using gas chromatographymass spectrometry. Arch. Environ. Contam. Toxicol. 59 (3),
382e392.
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Persistent organic pollutants in water and surface sediments
of Taihu Lake, China and risk assessment. Chemosphere 50
(4), 557e562.
Wang, X., Xu, J., Guo, C., Zhang, Y., 2012. Distribution and sources
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Liang, L.J., Liu, Z.T., 2012. Development of aquatic life criteria
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1
Supporting Information
2
Bioassay-Directed Identification of Organic Toxicants in Water and
3
Sediment of Tai Lake, China
4
Xinxin Hua,b, Wei Shia,*, Nanyang Yua, Xia Jiangc, Shuhang Wanga, John P. Giesya,d,e,f,g, Xiaowei
5
Zhanga, Si Weia, Hongxia Yua,*
6
7
a
8
Nanjing University, Nanjing, People’s Republic of China,
9
b
10
c
11
Academy of Environmental Science, Beijing, People’s Republic of China,
12
d
13
Saskatchewan, Saskatoon, Saskatchewan, Canada,
14
e
15
Lansing, MI, USA,
16
f
17
China,
18
g
19
university of Hong Kong, Hong Kong, SAR, People’s Republic of China
State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment,
Shandong Academy of Environmental Science, Jinan, People’s Republic of China,
Key Laboratory of Environmental Protection of Lake Pollution Control, Chinese Research
Department of Veterinary Biomedical Sciences and Toxicology Centre, University of
Department of Zoology, and Center for Integrative Toxicology, Michigan State University, East
School of Biological Sciences, University of Hong Kong, Hong Kong, SAR, People’s Republic of
Department of Biology and Chemistry and State Key Laboratory for Marine Pollution, City
20
21
Corresponding author.
22
Prof. Hongxia Yu, PhD: School of the Environment, Nanjing University, Nanjing,
23
210023, China. Tel.: +86 25 8968 0356, Fax: +86 25 8968 0356, E-mail:
24
xinxin20111006@126.com
25
Wei
26
Nanjing,210023,China.Tel.: +86 25 8968 0356, Fax: +86 25 8968 0356,
27
E-mail:njushiwei@nju.edu.cn
Shi,
PhD:School
of
the
S1
Environment,
NanjingUniversity,
28
Supporting Information, Materials and methods.
29
Sampling locations. Water and sediments were collected at four locations in the
30
northern part of Tai Lake, including locations L1, L2, L3 and L4. Specific data on
31
timing and amount of pesticides used near the study sites during the study period is
32
unavailable. However, application of the target pesticides near study sites could be
33
qualitatively assessed. Sites L2 and L3 were located in the central, open water area of
34
Tai Lake. Sites L1 and L4 were located along the edge of Tai Lake, the surrounding
35
terrestrial landscape of which is farmland and aquaculture. Pesticides, such as the OP,
36
chlorpyrifos and pyrethroids, were used to control pests and diseases of crops and
37
aquaculture. During the rainy season, which lasts from May to September, multiple
38
pesticides might be washed into this region of Tai Lake. It has been reported that
39
concentrations of some pollutants are greater during the rainy season than during the
40
dry season (Zhang et al., 2012). In the present study, in May, July and October 2009,
41
10-L samples of water and 2,000 g wet mass (wm) of sediment were collected from
42
each location and placed into brown glass bottles pre-cleaned with Milli-Q water,
43
methanol (Tedia Co. Ltd, Fairfield, OH, USA), acetone (Tedia), dichloromethane
44
(Tedia) and n-hexane (Merck Darmstadt, Germany). Samples were collected from the
45
surface (0-10 cm) of sediments. Before sampling, bottles were pre-washed three times
46
with the samples to be placed into them. Samples were transported to the laboratory on
47
ice immediately and stored at 4 °C. Samples of water were extracted and sediments
48
were freeze-dried within 24h. Some benthic organisms samples, such as Bellamya
S2
49
aeruginosa, Corbicula fluminea and Limnodrilus hoffmeisteri were observed in the
50
sediment.
51
Sample preparation. Samples of water were extracted by use of modifications of
52
previously described methods (Shi et al., 2011a; Shi et al., 2011b). Briefly, an aliquant
53
of 8 L of water from each location was passed through 500 mg Oasis HLB cartridges
54
(Waters, Milford, MA, USA) that had been pre-activated with n-hexane,
55
dichloromethane, methanol and Milli-Q water. Approximately 2L of water was passed
56
through each cartridge. Extracts were eluted successively with 10 mL n-hexane, 10 mL
57
n-hexane and dichloromethane (4:1, v/v), and then 10 mL dichloromethane and
58
methanol (1:1, v/v). Eluates from one sample of water were separated into two aliquots
59
for use in instrumental analysis and bioassays, respectively.
60
Samples of sediment were freeze-dried and homogenized, and then extracted by
61
use of accelerated solvent extraction (ASE, Dionex ASE 300, Dionex, Idstein,
62
Germany) method. Ten grams of each sample (dry weight) were extracted in triplicate
63
with n-hexane: dichloromethane: acetone (1:1:1, v/v/v). Each extract was separated
64
into two aliquots for use in instrumental analysis and bioassays, respectively.
65
For instrumental analysis, one aliquot of each sample was concentrated to 2mL,
66
and activated copper granules were added to remove sulfur. For clean-up and
67
fractionation, extracts of both water and sediment were passed through 10 g florisil
68
(60-100 mesh size; Sigma Chemical Co., St. Louis, MO, USA) columns which had
69
been pre-rinsed with n-hexane. Each column was eluted with 100 mL n-hexane, 100
70
mL n-hexane and dichloromethane (4:1, v/v), 100 mL dichloromethane and methanol
S3
71
(1:1, v/v), followed by 100 mL acetone. Eluates were concentrated and blown to
72
dryness and extracts of water were reconstituted in 0.5 mL dichloromethane and
73
extracts of sediments were reconstituted in 1 mL dichloromethane. For bioassays, the
74
other aliquot was concentrated and blown to dryness and extracts of water samples
75
were reconstituted in 0.5 mL dimethyl sulfoxide (DMSO, Sigma)and extracts were
76
reconstituted in1 mL DMSO for sediment samples. Extracts were stored in -20 °C. The
77
limit of detection (LOD) was defined as 3 times the standard deviation (SD) of
78
procedural blanks (n = 3).The limit of quantification (LOQ) was defined as 10 times the
79
SD of procedural blanks (n = 3).The procedural blanks consisted of tap water which
80
was treated identically to samples. Recoveries were determined by spiking standards
81
into tap water.
82
For bioassays, extracts of water samples dissolved in DMSO were diluted to 40,
83
20, 10.5, 2.5, 1.25 and 0.625 times the original concentrations in water samples. And,
84
the final concentrations of tested extracts of sediments were 25, 12.5, 6.25, 3.13, 1.56,
85
0.78, 0.39 mg dry sediments /mL. Tap water and tap water containing 0.5% DMSO
86
were used as blank and vehicle controls, respectively.
87
Instrumental analysis. Pesticides including OCPs and OPs were detected using a
88
Thermo Single Quadrupole GC-MS in selected ion monitoring (SIM) mode (San Jose,
89
CA, USA). Helium was used as carrier gas and flow rate was set at 1.0 mL/min. For
90
analyzing OCPs, 1.5 μL of extract was injected into the column using a pulsed splitless
91
injection. The oven temperature was set at 150 °C, heated to 290 °C at 4 °C/min, then
92
heated to 310 °C at 15 °C/min, and held at 310 °C for 5 min. For analyzing OPs, 1.5 μL
S4
93
of extract was injected into the column using a pulsed splitless injection. The oven
94
temperature was set at 50 °C, held at 50 °C for 1 min, heated to 100 °C at 10 °C/min,
95
then heated to 280 °C at 7 °C/min, and held at 280 °C for 2 min.
96
Quantitative analysis of pyrethroid insecticides was performed by use of a
97
Thermo TSQ Quantum Discovery triple-quadrupole mass spectrometer (San Jose, CA,
98
USA) in selected reaction monitoring (SRM) mode. Helium was used as carrier gas and
99
flow rate was set at 1.0 mL/min. A pulsed splitless injector was used for injecting 1.5
100
μL of extract for analyzing pyrethroids. The temperature of inlet was set as 220 °C. The
101
initial oven temperature was set as 70 °C, held at 70 °C for 1 min, heated to 130 °C at
102
25 °C/min, heated to 160 °C at 15 °C/min, heated to 210 °C at 5 °C/min, and then
103
heated to 290 °C at 25 °C/min, held at 290 °C for 5 min.
104
Supporting Information, Results and discussion.
105
Toxicities of chlorpyrifos and cyfluthrin. Toxicities of chlorpyrifos and cyfluthrin to
106
D. magna were examined in the present study because concentrations of these
107
compounds in the tested samples were high (Tables S3 and S4 in SI).The EC50 values of
108
chlorpyrifos and cyfluthrin obtained in the present study were 0.68 and 0.081 μg/L,
109
respectively.
110
Calculation of sediment quality criteria (SQC) for CPY. A sediment quality criteria
111
(SQC) equation (SQC=focKocFCV) based on equilibrium partitioning theory was used
112
to create SQC for different chemicals, where foc is the mass of organic carbon in
113
sediment, Koc is organic carbon partition coefficient for individual chemicals (Koc was
114
usually replaced by Kow), and FCV means final chronic value (Ditoro et al., 1991;
S5
115
Green et al., 1996). In the present study, SQC for CPY were calculated based on the
116
above equation with foc of 0.01134 (Yao et al., 2012), log Kow of 5.25 (Green et al.,
117
1996), and FCV of 0.041μg/L (USEPA, 1996), and the calculated SQC for CPY was
118
0.087 μg/g sediment (dry mass).
119
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120
Ditoro, D. M., Zarba, C. S., Hansen, D. J., Berry, W. J., Swartz, R. C., Cowan, C. E.,
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Pavlou, S. P., Allen, H. E., Thomas, N. A., Paquin, P. R., 1991. Technical basis for
122
establishing sediment quality criteria for nonionic organic-chemicals using equilibrium
123
partitioning. Environ. Toxicol. Chem. 10 (12), 1541-1583.
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Green, A. S., Chandler, G. T., Piegorsch, W. W., 1996. Life-stage-specific toxicity of
125
sediment-associated chlorpyrifos to a marine, infaunal copepod. Environ. Toxicol.
126
Chem. 15 (7), 1182-1188.
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Shi, W., Wang, X. Y., Hu, G. J., Hao, Y. Q., Zhang, X. W., Liu, H. L., Wei, S., Wang,
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X. R., Yu, H. X., 2011a. Bioanalytical and instrumental analysis of thyroid hormone
129
disrupting compounds in water sources along the Yangtze River. Environ. Pollut. 159,
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441-448.
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Shi, W., Hu, X. X., Zhang, F. X., Hu, G. J., Hao, Y. Q., Zhang, X. W., Liu, H. L., Wei, S.,
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Wang, X. R., Geisy, J. P., Yu, H. X., 2011b. Occurrence of thyroid hormone activities in
133
drinking water from eastern China: contributions of phthalate esters. Environ. Sci.
134
Technol. 46, 1811-1818.
135
USEPA, 1996. Ambient water quality criteria for chlorpyrifos. EPA 440/5-86-005.
136
Washington, DC.
S6
137
Yao, X., Zhu, G. W., Cai, L. L., Zhu, M. Y., Zhao, L. L., Gao, G., Qin, B. Q., 2012.
138
Geochemical characteristics of amino acids in sediments of Lake Taihu, a large,
139
shallow, eutrophic freshwater lake of China. Aquat. Geochem. 18 (3), 263-280.
140
Zhang, Y., Shi, G. L., Guo, C. S., Xu, J., Tian, Y. Z., Feng, Y. C., Wang, Y. Q., 2012.
141
Seasonal variations of concentrations, profiles and possible sources of polycyclic
142
aromatic hydrocarbons in sediments from Taihu Lake, China. J. Soils Sediments
143
(6), 933-941.
144
145
146
147
148
S7
12
149
Supporting Information, Fig. S1. Map of sampling locations in Tai Lake.
150
151
152
S8
153
Supporting Information, Fig. S2. Dose-response relationship for immobilization of
154
Daphnia magna exposed to a series of potassium dichromate (K2Cr2O7) in acute
155
toxicity tests. Values are expressed as mean ± SD (n=4).
156
157
S9
158
Supporting Information, Fig. S3. Dose-response relationship for immobilization of
159
Daphnia magna exposed to a series of chlorpyrifos in acute toxicity tests. Values are
160
expressed as mean ± SD (n=4).
161
162
163
S10
164
Supporting Information, Fig. S4. Dose-response relationship for immobilization of
165
Daphnia magna exposed to a series of cyfluthrin in acute toxicity tests. Values are
166
expressed as mean ± SD (n=4).
167
168
S11
169
Supporting Information, Fig. S5. Bioassay-derived toxicity equivalents (BEQs) of
170
organic extracts of waters from different locations in May, July and October. The
171
BEQ are reported to their equivalent concentrations in un-extracted water.
172
173
174
S12
175
Supporting Information, Fig. S6. Bioassay-derived toxicity equivalents (BEQs) in
176
organic extracts of sediment from different locations in May, July and October.
177
Concentrations are expressed as equivalent concentrations of un-extracted sediments.
178
179
180
S13
181
Supporting Information, Table S1. The information for pesticides used for instrumental analysis.
Pesticides
CAS no.
Purity
Suppliers
Dichlorvos
62-73-7
99.8%
AccuStandard (New Haven, CT, USA)
Mevinphos
7786-34-7
99.8%
AccuStandard (New Haven, CT, USA)
Demeton
8065-48-3
99.8%
AccuStandard (New Haven, CT, USA)
Ethoprop
13194-48-4
99.8%
AccuStandard (New Haven, CT, USA)
Phorate
298-02-2
99.8%
AccuStandard (New Haven, CT, USA)
Diazinon
333-41-5
99.8%
AccuStandard (New Haven, CT, USA)
Disulfoton
298-04-4
99.8%
AccuStandard (New Haven, CT, USA)
Methyl parathion
298-00-0
99.8%
AccuStandard (New Haven, CT, USA)
Ronnel
299-84-3
99.8%
AccuStandard (New Haven, CT, USA)
Chlorpyrifos
2921-88-2
98.5%
AccuStandard (New Haven, CT, USA)
Fenthion
55-38-9
99.8%
AccuStandard (New Haven, CT, USA)
Trichloronate
327-98-0
99.8%
AccuStandard (New Haven, CT, USA)
Merphos
150-50-5
99.8%
AccuStandard (New Haven, CT, USA)
Tokuthion
34643-46-4
99.8%
AccuStandard (New Haven, CT, USA)
Fensulfothion
115-90-2
99.8%
AccuStandard (New Haven, CT, USA)
Bolstar
35400-43-2
99.8%
AccuStandard (New Haven, CT, USA)
Coumaphos
56-72-4
99.8%
AccuStandard (New Haven, CT, USA)
Dichloran
99-30-9
99.8%
AccuStandard (New Haven, CT, USA)
Tefluthrin
79538-32-2
99.8%
AccuStandard (New Haven, CT, USA)
Pendimethalin
40487-42-1
99.8%
AccuStandard (New Haven, CT, USA)
Tetrachlorvinphos
961-11-5
99.8%
AccuStandard (New Haven, CT, USA)
L-Cyhalothrin
91465-08-6
99.8%
AccuStandard (New Haven, CT, USA)
Permethrin
52645-53-1
99.8%
AccuStandard (New Haven, CT, USA)
Cyfluthrin isomers
68359-37-5
99.4%
AccuStandard (New Haven, CT, USA)
Cypermethrin isomers
52315-07-8
99.8%
AccuStandard (New Haven, CT, USA)
Fenvalerate
51630-58-1
99.8%
AccuStandard (New Haven, CT, USA)
Deltamethrin
52918-63-5
99.8%
AccuStandard (New Haven, CT, USA)
α-BHC
319-84-6
99.8%
Sigma(St. Louis, MO, USA)
Hexachlorobenzene
118-74-1
99.8%
Sigma(St. Louis, MO, USA)
β-BHC
319-85-7
99.8%
Sigma(St. Louis, MO, USA)
γ-BHC
58-89-9
99.8%
Sigma(St. Louis, MO, USA)
δ-BHC
319-86-8
99.8%
Sigma(St. Louis, MO, USA)
Heptachlor
76-44-8
99.8%
Sigma(St. Louis, MO, USA)
Aldrin
309-00-2
99.8%
Sigma(St. Louis, MO, USA)
Isodrin
465-73-6
99.8%
Sigma(St. Louis, MO, USA)
Heptachlor epoxide B
1024-57-3
99.8%
Sigma(St. Louis, MO, USA)
Oxychlordane
26880-48-8
99.8%
Sigma(St. Louis, MO, USA)
Heptachlor epoxide A
28044-83-9
99.8%
Sigma(St. Louis, MO, USA)
γ-Chlordane
57-74-9
99.8%
Sigma(St. Louis, MO, USA)
o,p'-DDE
3424-82-6
99.8%
Sigma(St. Louis, MO, USA)
Endosulfan
959-98-8
99.8%
Sigma(St. Louis, MO, USA)
α-Chlordane
5103-71-9
99.8%
Sigma(St. Louis, MO, USA)
p,p'-DDE
72-55-9
99.8%
Sigma(St. Louis, MO, USA)
Dieldrin
60-57-1
99.8%
Sigma(St. Louis, MO, USA)
o,p'-DDD
53-19-0
99.8%
Sigma(St. Louis, MO, USA)
Endrin
72-20-8
99.8%
Sigma(St. Louis, MO, USA)
Endosulfan I
33213-65-9
99.8%
Sigma(St. Louis, MO, USA)
o,p'-DDD
72-54-8
99.8%
Sigma(St. Louis, MO, USA)
o,p'-DDT
789-02-6
99.8%
Sigma(St. Louis, MO, USA)
p,p'-DDT
50-29-3
99.8%
Sigma(St. Louis, MO, USA)
Methoxychlor
72-43-5
99.8%
Sigma(St. Louis, MO, USA)
Mirex
2385-85-5
99.8%
Sigma(St. Louis, MO, USA)
S14
182
Supporting Information, Table S2. Recoveries, limits of quantification (LOQ) and limits of
183
detection (LOD) for analytes.
Pesticides
Organophosphorus
pesticides
Pyrethroid pesticides
Organochlorine
pesticides
Nitro-anilines
Dichlorvos
Mevinphos
Demeton (a)
Ethoprop
Phorate
Demeton (b)
Diazinon
Disulfoton
Methyl parathion
Ronnel
Chlorpyrifos
Fenthion
Trichloronate
Merphos
Tokuthion
Fensulfothion
Bolstar
Coumaphos
Tefluthrin
Tetrachlorvinphos
L-Cyhalothrin
Permethrin1
Permethrin2
Cyfluthrin isomers
Cypermethrin isomers
Fenvalerate1
Fenvalerate2
Deltamethrin
α-BHC
Hexachlorobenzene
β-BHC
γ-BHC
δ-BHC
Heptachlor
Aldrin
Isodrin
Heptachlor epoxide B
Oxychlordane
Heptachlor epoxide A
γ-Chlordane
o,p’-DDE
Endosulfan
α-Chlordane
p,p'-DDE
Dieldrin
o,p'-DDD
Endrin
Endosulfan I
p,p'-DDD
o,p'-DDT
p,p'-DDT
Methoxychlor
Mirex
Dichloran
Pendimethalin
S15
Recovery
(%)
110
84
91
90
89
90
93
91
86
86
100
91
93
92
94
82
92
90
90
95
90
82
93
89
88
93
84
93
90
90
91
86
89
90
93
84
88
87
91
90
93
89
92
88
91
84
87
97
91
98
86
97
91
88
91
RSD (%)
1.7
130
6.3
8.1
8.9
4.8
2.1
8.2
5.2
5.7
7.2
3.2
110
6.2
5.6
0.97
9.7
3.1
3.6
2.0
3.0
0.36
1.3
6.3
6.9
2.7
3.6
6.5
4.5
2.3
4.2
5.8
4.5
4.5
6.4
4.4
5.1
7.1
7.4
8.5
5.3
3.0
2.5
5.9
5.8
6.4
7.6
1.2
9.0
3.4
2.5
1.5
5.1
8.8
1.3
LOD
(ng/L)
5.3×10-1
6.7×10-1
4.0×10-1
3.6×10-1
3.6×10-1
4.1×10-1
5.5×10-1
5.2×10-1
3.7×10-1
4.1×10-1
4.6×10-1
5.8×10-1
5.4×10-1
5.1×10-1
4.0×10-1
5.6×10-1
4.1×10-1
1.3×100
6.0×10-2
2.5×10-1
2.0×10-2
2.0×10-2
1.1×10-1
7.0×10-2
7.0×10-2
4.0×10-2
4.0×10-2
4.0×10-2
2.1×10-1
2.0×10-1
2.1×10-1
2.0×10-1
2.0×10-1
1.9×10-1
2.1×10-1
2.3×10-1
2.2×10-1
2.2×10-1
2.4×10-1
4.6×10-1
2.4×10-1
3.1×10-1
2.5×10-1
2.4×10-1
1.7×10-1
2.6×10-1
2.8×10-1
2.0×10-1
2.1×10-1
1.9×10-1
1.8×10-1
5.9×10-1
2.8×10-1
5.0×10-2
6.0×10-2
LOQ
(ng/L)
1.8×100
2.2×100
1.3×100
1.2×100
1.2×100
1.4×100
1.8×100
1.8×100
1.3×100
1.4×100
1.5×100
1.9×100
1.8×100
1.7×100
1.3×100
1.9×100
1.4×100
4.4×100
1.9×10-1
8.2×10-1
6.0×10-2
6.0×10-2
3.7×10-1
2.3×10-1
2.2×10-1
1.3×10-1
1.4×10-1
1.3×10-1
7.1×10-1
6.6×10-1
6.9×10-1
6.8×10-1
6.7×10-1
6.2×10-1
6.9×10-1
7.7×10-1
7.5×10-1
7.2×10-1
7.9×10-1
1.5×100
8.1×10-1
1.0×100
8.3×10-1
8.0×10-1
5.7×10-1
8.6×10-1
9.2×10-1
6.8×10-1
7.0×10-1
6.2×10-1
6.0×10-1
2.0×100
9.3×10-1
1.8×10-1
1.9×10-1
Supporting Information, Table S3. Concentrations of pesticides (ng/L) in samples of water from Tai Lake.
Pesticides
In May
OCPs
a
Ethoprop
Disulfoton
Chlorpyrifos
Fenthion
∑OPs
α-BHC
β-BHC
γ-BHC
δ-BHC
Heptachlor
Aldrin
Heptachlor
epoxide B
Oxychlordane
∑OCPs
L2-5
L3-5
L4-5
L1-7
L2-7
L3-7
L4-7
L1-10
L2-10
L3-10
L4-10
N.D.
N.D.
0.26
N.D.
0.26
0.50
1.8
0.56
0.49
N.D.
N.D.
N.D.
N.D.
N.D.
0.089
N.D.
0.089
0.38
1.4
0.58
N.D
0.36
0.57
N.D.
N.D.
0.33
3.8
N.D.
4.1
0.83
0.49
0.70
0.60
N.D.
N.D.
N.D.
N.D.
0.44
6.1
N.D.
6.5
0.60
2.8
0.57
N.D.
N.D.
N.D.
N.D.
N.D.
N.D.
179
35
245
2.3
4.2
2.7
0.24
N.D.
0.99
11
N.D.
0.54
4.0
1.8
10
0.22
0.60
0.36
0.30
0.29
0.38
N.D.
1.1
N.D.
4.7
0.64
8.2
0.22
0.76
0.29
N.D.
N.D.
N.D.
N.D.
N.D.
N.D.
8.1
1.8
12
1.7
4.1
1.7
N.D.
N.D.
0.87
N.D.
N.D.
4.6
31
N.D.
36
1.1
1.7
1.3
1.0
N.D.
N.D.
N.D.
N.D.
N.D.
22
17
39
0.59
0.69
0.39
N.D.
0.25
N.D.
N.D.
N.D.
N.D.
6.3
N.D.
6.3
1.3
3.0
1.3
1.3
0.53
0.40
N.D.
N.D.
N.D.
5.7
3.9
9.5
1.2
2.0
1.2
N.D.
N.D.
N.D.
N.D.
1.5
5.2
N.D.
3.9
N.D.
3.2
N.D.
4.4
N.D.
24
N.D.
2.4
0.49
2.2
N.D.
9.6
N.D.
5.8
N.D.
2.5
1.5
10
N.D.
5.6
The number 5 means May, 7 means July and 10 means October;
b
In October
b
L1-5
OPs
In July
a
Not detected.
S16
Supporting Information, Table S4. Concentrations of pesticides (ng/g) in sediments from Tai Lake.
Pesticides
In May
L2-5
L3-5
L4-5
L1-7
L2-7
L3-7
L4-7
b
N.D.
59
70
167
53
N.D.
N.D.
348
4.1
6.4
N.D.
N.D.
N.D.
N.D.
N.D.
N.D.
N.D.
N.D.
111
38
N.D.
149
0.073
1.8
0.29
0.15
2.8
2.1
N.D.
N.D.
14
25
12
N.D.
N.D.
51
0.074
1.8
0.070
N.D.
1.3
1.6
8.5
N.D.
N.D.
53
27
N.D.
N.D.
88.5
0.057
1.4
0.15
0.11
2.0
1.8
N.D.
N.D.
197
127
42
N.D.
27
393
N.D.
0.30
2.3
1.1
5.7
7.6
N.D.
N.D.
N.D.
23
16
N.D.
N.D.
38.5
N.D.
0.040
0.17
N.D.
0.98
1.1
N.D.
N.D.
N.D.
28
23
N.D.
N.D.
51
N.D.
0.063
0.11
N.D.
1.0
0.85
N.D.
N.D.
N.D.
10
61
134
85
70
N.D.
N.D.
N.D.
N.D.
7.3
21
63
47
N.D.
N.D.
N.D.
N.D.
N.D.
4.8
51
90
42
N.D.
N.D.
N.D.
N.D.
N.D.
5.5
45
121
67
34
N.D.
N.D.
N.D.
N.D.
17
147
157
103
66
N.D.
N.D.
N.D.
N.D.
2.4
71
91
44
27
N.D.
N.D.
N.D.
N.D.
2.1
64
135
71
34
N.D.
a
L1-5
OPs
Pyrethroids
OCPs
Ethoprop
Disulfoton
Methyl parathion
Chlorpyrifos
Fenthion
Bolstar
Coumaphos
∑OPs
Tefluthrin
L-Cyhalothrin
Permethrin1
Permethrin2
Cyfluthrin isomers
Cypermethrin
isomers
Fenvalerate1
Fenvalerate2
Deltamethrin
∑pyrethroids
α-BHC
β-BHC
γ-BHC
δ-BHC
Heptachlor
In July
S17
L1-10
In October
L2-10
L3-10
L4-10
N.D.
N.D.
N.D.
N.D.
281
N.D.
N.D.
281
N.D.
0.070
0.45
0.23
1.8
2.1
62
N.D.
380
266
246
N.D.
N.D.
953
N.D.
0.39
1.0
0.52
1.5
2.0
N.D.
N.D.
N.D.
N.D.
258
N.D.
N.D.
258
N.D.
0.083
0.18
0.14
1.5
1.2
N.D.
N.D.
31
33
22
N.D.
N.D.
87
N.D.
0.19
0.33
0.24
1.7
1.7
17
9.8
N.D.
78
41
N.D.
N.D.
146
N.D.
1.0
0.57
0.29
2.3
2.1
N.D.
N.D.
N.D.
4.7
25
72
25
N.D.
N.D.
2.4
3.7
0.19
12.2
73
122
47
54
N.D.
N.D.
N.D.
N.D.
3.1
2.3
2.1
2.0
N.D.
1.4
N.D.
N.D.
N.D.
4.1
28
50
26
N.D.
19
N.D.
N.D.
0.062
6.4
1.5
2.4
N.D.
N.D.
1.2
Nitro-anilines
a
Aldrin
Heptachlor epoxide
B
Oxychlordane
Heptachlor epoxide
A
γ-Chlordane
Endosulfan
α-Chlordane
Dieldrin
Endrin
p,p'-DDD
p,p'-DDT
Methoxychlor
Mirex
∑OCPs
Dichloran
∑nitro-anilines
N.D.
100
N.D.
38
N.D.
32
N.D.
72
N.D.
213
N.D.
53
N.D.
64
26
N.D.
40
187
N.D.
N.D.
21
48
N.D.
N.D.
108
135
45
54
32
61
79
95
174
N.D.
76
65
62
67
48
70
218
249
N.D.
N.D.
54
59
N.D.
N.D.
11
7.4
31
21
164
18
N.D.
N.D.
N.D.
946
N.D.
N.D.
10
6.1
N.D.
N.D.
N.D.
4.2
N.D.
N.D.
12
300
N.D.
N.D.
29
13
N.D.
N.D.
N.D.
3.4
N.D.
N.D.
21
373
N.D.
N.D.
48
22
N.D.
N.D.
71
14
4.5
N.D.
50
721
N.D.
N.D.
N.D.
N.D.
N.D.
N.D.
N.D.
39
39
N.D.
N.D.
937
N.D.
N.D.
N.D.
N.D.
N.D.
N.D.
N.D.
7.1
N.D.
N.D.
N.D.
434
N.D.
N.D.
N.D.
N.D.
N.D.
N.D.
N.D.
7.7
N.D.
235
5.3
744
N.D.
N.D.
N.D.
N.D.
N.D.
N.D.
N.D.
N.D.
N.D.
N.D.
8.0
274
N.D.
N.D.
N.D.
N.D.
N.D.
N.D.
N.D.
26
N.D.
N.D.
N.D.
1016
1.8
1.8
N.D.
N.D.
N.D.
N.D.
N.D.
N.D.
N.D.
N.D.
N.D.
7.8
N.D.
N.D.
6.7
N.D.
N.D.
N.D.
N.D.
N.D.
N.D.
N.D.
13
325
N.D.
N.D.
0.94
N.D.
N.D.
N.D.
N.D.
N.D.
N.D.
44
1.8
51
N.D.
N.D.
The number 5 means May, 7 means July and 10 means October;
b
Not detected.
S18
Supporting Information, Table S5. CEQs for individual OPs in water and sediment samples.
Sample
Location
Chlora
Fen
Disul
Ethop
Para
Bol
Coum
Water
L1-5b
2.6×10-4
N.D.c
N.D.
N.D.
N.D.
N.D.
N.D.
L2-5
9.0×10-5
N.D.
N.D.
N.D.
N.D.
N.D.
N.D.
L3-5
0.0038
N.D.
1.7×10-5
N.D.
N.D.
N.D.
N.D.
L4-5
0.0061
N.D.
2.3×10-5
N.D.
N.D.
N.D.
N.D.
L1-7
0.18
0.0042
N.D.
N.D.
N.D.
N.D.
N.D.
L2-7
0.0040
2.1×10-4
2.8×10-5
N.D.
N.D.
N.D.
N.D.
L3-7
0.0047
7.6×10-5
N.D.
7.9×10-6
N.D.
N.D.
N.D.
L4-7
0.0081
2.2×10-4
N.D.
N.D.
N.D.
N.D.
N.D.
L1-10
0.031
N.D.
2.4×10-4
N.D.
N.D.
N.D.
N.D.
L2-10
0.022
0.0021
N.D.
N.D.
N.D.
N.D.
N.D.
L3-10
0.0062
N.D.
N.D.
N.D.
N.D.
N.D.
N.D.
L4-10
0.0057
4.6×10-4
N.D.
N.D.
N.D.
N.D.
N.D.
L1-5
0.17
0.0063
0.0031
N.D.
0.018
N.D.
N.D.
L2-5
N.D.
0.013
N.D.
N.D.
N.D.
0.0050
N.D.
L3-5
0.025
0.0014
N.D.
N.D.
0.0035
N.D.
N.D.
L4-5
0.053
0.0032
N.D.
6.2×10-5
N.D.
N.D.
N.D.
L1-7
0.127
0.0050
N.D.
N.D.
0.051
N.D.
0.094
L2-7
0.023
0.0019
N.D.
N.D.
N.D.
N.D.
N.D.
L3-7
0.028
0.0027
N.D.
N.D.
N.D.
N.D.
N.D.
L4-7
N.D.
0.033
N.D.
N.D.
N.D.
N.D.
N.D.
L1-10
0.27
0.029
N.D.
4.5×10-4
0.100
N.D.
N.D.
L2-10
N.D.
0.031
N.D.
N.D.
N.D.
N.D.
N.D.
L3-10
0.033
0.0027
N.D.
N.D.
0.082
N.D.
N.D.
L4-10
0.078
0.0049
5.1×10-4
1.3×10-4
N.D.
N.D.
N.D.
Sediment
a
Chlor=chlorpyrifos; Fen=fenthion; Disul=disulfoton; Ethop= ethoprop; Nal=naled; Para=methyl
parathion; Bol=bolstar; Coum=coumaphos;
b
The number 5 means May, 7 means July and 10 means October;
c
Not detected.
S19
Supporting Information, Table S6. CEQs for individual OCPs in water samples.
Chemical
L1-5a
L2-5
L3-5
L4-5
L1-7
L2-7
L3-7
L4-7
L1-10
L2-10
L3-10
L4-10
α-BHC
6.6×10-7
5.0×10-7
1.1×10-6
8.0×10-7
3.1×10-6
2.9×10-7
2.9×10-7
2.2×10-6
1.4×10-6
7.8×10-7
1.7×10-6
1.6×10-6
β-BHC
2.3×10-6
1.9×10-6
6.4×10-7
3.6×10-6
5.6×10-6
8.0×10-7
1.0×10-6
5.3×10-6
2.2×10-6
9.1×10-7
3.9×10-6
2.6×10-6
γ-BHC
7.4×10-7
7.7×10-7
9.3×10-7
7.5×10-7
3.6×10-6
4.8×10-7
3.8×10-7
2.2×10-6
1.7×10-6
5.1×10-7
1.6×10-6
1.5×10-6
δ-BHC
6.5×10-7
N.D.b
7.9×10-7
N.D.
3.3×10-7
3.9×10-7
N.D.
N.D.
1.3×10-6
N.D.
1.7×10-6
N.D.
Heptachlor
N.D.
3.1×10-6
N.D.
N.D.
N.D.
2.5×10-6
N.D.
N.D.
N.D.
2.1×10-6
4.6×10-6
N.D.
Aldrin
N.D.
1.3×10-5
N.D.
N.D.
2.2×10-5
8.8×10-6
N.D.
2.0×10-5
N.D.
N.D.
9.1×10-6
N.D.
Heptachlor
N.D.
N.D.
N.D.
N.D.
3.2×10-5
N.D.
N.D.
N.D.
N.D.
N.D.
N.D.
N.D.
7.8×10-7
N.D.
N.D.
N.D.
N.D.
N.D.
2.5×10-7
N.D.
N.D.
N.D.
7.8×10-7
N.D.
epoxide B
Oxychlordane
a
The number 5 means May, 7 means July and 10 means October;
b
Not detected.
S20
Supporting Information, Table S7. CEQs for individual pyrethroids in sediment samples.
Location
Cyflua
Cyper
Del
Fen-1
Fen-2
L-Cy
Per-1
Per-2
Tef
L1-5b
N.D.c
N.D.
N.D.
N.D.
N.D.
0.019
N.D.
N.D.
0.039
L2-5
0.024
0.0018
N.D.
N.D.
N.D.
0.0055
3.3×10-4
1.7×10-4
7.1×10-4
L3-5
0.010
0.0014
N.D.
N.D.
N.D.
0.0052
7.9×10-5
N.D.
7.2×10-4
L4-5
0.017
0.0015
N.D.
N.D.
N.D.
0.0041
1.7×10-4
1.2×10-4
5.5×10-4
L1-7
0.047
0.0065
N.D.
N.D.
N.D.
8.7×10-4
0.0026
0.0012
N.D.
L2-7
0.0082
9.6×10-4
N.D.
N.D.
N.D.
1.2×10-4
2.0×10-4
N.D.
N.D.
L3-7
0.0083
7.2×10-4
N.D.
N.D.
N.D.
1.9×10-4
1.3×10-4
N.D.
N.D.
L4-7
0.015
0.0018
N.D.
N.D.
N.D.
2.1×10-4
5.1×10-4
2.6×10-4
N.D.
L1-10
0.012
0.0018
1.9×10-6
0.0018
0.0028
0.0012
0.0011
5.9×10-4
N.D.
L2-10
0.012
0.0010
BRL
N.D.
N.D.
2.5×10-4
2.0×10-4
1.6×10-4
N.D.
L3-10
0.014
0.0014
BRL
N.D.
N.D.
5.5×10-4
3.9×10-4
2.8×10-4
N.D.
L4-10
0.019
0.0018
6.0×10-7
N.D.
N.D.
0.0030
6.5×10-4
3.3×10-4
N.D.
a
Cyflu=cyfluthrin isomers; Cyper=cypermethrin isomers; Del=deltamethrin; Fen-1=fenvalerate 1;
Fen-2= fenvalerate 2; L-Cy=L-cyhalothrin; Per-1=permethrin 1; Per-2=permethrin 2;
Tef=tefluthrin;
b
The number 5 means May, 7 means July and 10 means October;
c
Not detected.
S21
Supporting Information, Table S8. CEQs for individual OCPs in sediment samples.
Chemical
L1-5a
L2-5
L3-5
L4-5
L1-7
L2-7
L3-7
L4-7
L1-10
L2-10
L3-10
L4-10
α-BHC
8.0×10-5
2.8×10-5
6.7×10-5
5.9×10-5
1.9×10-4
9.3×10-5
8.4×10-5
3.2×10-5
9.6×10-5
3.0×10-6
3.7×10-5
1.9×10-6
β-BHC
1.8×10-4
8.4×10-5
1.2×10-4
1.6×10-4
2.1×10-4
1.2×10-4
1.8×10-4
9.5×10-5
1.6×10-4
2.8×10-6
6.6×10-5
3.1×10-6
γ-BHC
1.1×10-4
6.2×10-5
5.5×10-5
8.8×10-5
1.4×10-4
5.8×10-5
9.4×10-5
3.3×10-5
6.2×10-5
2.6×10-6
3.4×10-5
N.D.b
δ-BHC
Heptachlor
9.2×10-5
N.D.
N.D.
4.5×10-5
8.7×10-5
3.6×10-5
4.4×10-5
N.D.
7.1×10-5
N.D.
N.D.
N.D.
N.D.
N.D.
N.D.
N.D.
N.D.
N.D.
N.D.
N.D.
N.D.
1.2×10-5
1.7×10-4
1.0×10-5
N.D.
N.D.
N.D.
N.D.
N.D.
N.D.
N.D.
6.0×10-4
9.0×10-4
N.D.
4.8×10-4
N.D.
2.8×10-4
1.1×10-4
9.2×10-5
2.0×10-4
6.0×10-4
1.5×10-4
1.8×10-4
N.D.
5.3×10-4
N.D.
1.4×10-4
N.D.
5.6×10-5
2.4×10-5
1.7×10-5
4.1×10-5
9.1×10-5
4.0×10-5
3.2×10-5
2.5×10-5
1.1×10-4
N.D.
2.8×10-5
N.D.
3.8×10-4
1.5×10-4
1.7×10-4
2.7×10-4
N.D.
1.9×10-4
1.9×10-4
2.0×10-4
7.1×10-4
N.D.
1.7×10-4
N.D.
7.8×10-5
7.1×10-5
2.0×10-4
3.3×10-4
N.D.
N.D.
N.D.
N.D.
N.D.
N.D.
4.6×10-5
6.5×10-6
3.3×10-6
2.7×10-6
5.7×10-6
9.8×10-6
N.D.
N.D.
N.D.
N.D.
N.D.
N.D.
N.D.
N.D.
2.1×10-4
N.D.
N.D.
N.D.
N.D.
N.D.
N.D.
N.D.
N.D.
N.D.
N.D.
N.D.
1.0×10-4
N.D.
N.D.
N.D.
N.D.
N.D.
N.D.
N.D.
N.D.
N.D.
N.D.
N.D.
0.0019
N.D.
N.D.
8.2×10-4
N.D.
N.D.
N.D.
N.D.
N.D.
N.D.
N.D.
N.D.
Aldrin
Heptachlor
epoxide B
Oxychlordane
Heptachlor
epoxide A
γ-Chlordane
Endosulfan
α-Chlordane
Dieldrin
Endrin
S22
p,p'-DDD
p,p'-DDT
Methoxychlor
Mirex
a
0.0014
3.2×10-4
2.6×10-4
0.0010
0.0030
5.4×10-4
5.9×10-4
N.D.
0.0020
N.D.
N.D.
N.D.
N.D.
N.D.
N.D.
0.0024
0.021
N.D.
N.D.
N.D.
N.D.
N.D.
N.D.
N.D.
N.D.
N.D.
N.D.
N.D.
N.D.
N.D.
8.9×10-5
N.D.
N.D.
N.D.
N.D.
1.6×10-5
N.D.
4.1×10-6
7.2×10-6
1.7×10-5
N.D.
N.D.
1.8×10-6
2.8×10-6
N.D.
N.D.
4.3×10-6
6.1×10-7
The number 5 means May, 7 means July and 10 means October;
b
Not detected.
S23