Pesticide and trace metal occurrence and aquatic benchmark exceedances in surface waters
and sediments of urban wetlands and retention ponds in Melbourne, Australia, 2010
Supplementary Information
Graeme Allinson 1,2, Pei Zhang 3,1, AnhDuyen Bui 3,1, Mayumi Allinson 1,2, Gavin Rose 1,3,
Stephe Marshall 1, Vincent Pettigrove 1
1
Centre for Aquatic Pollution Identification and Management (CAPIM), The University of
Melbourne, Parkville, Victoria 3010 Australia
2
Future Farming Systems Research, Department of Environment and Primary Industries, DPI
Queenscliff Centre, Queenscliff, Victoria 3225, Australia
3
Future Farming Systems Research Division, Department of Environment and Primary
Industries, Ernest Jones Drive, Macleod, Victoria 3085, Australia
SI1: Determination of pesticides in water and sediment samples
No one analytical method is appropriate for the measurement of all of the herbicides investigated
in this study. Several different methods based on solid-phase extraction (SPE) were used to
prepare the different sample matrices and chemicals. These SPE methodologies were validated
and accredited by the Australian National Association of Testing Authorities to ISO 17025
standard. The method limit of reporting (LOR) was determined as the lowest concentration of a
chemical that can be reliably quantified (95 % confidence interval) in the matrix in question.
Sediment samples were dried and ground, and then shaken (5g) for 30 min with 30 mL of 35%
water/65%acetone (v/v, adjusted to pH <3) on a mechanical shaker. After shaking the mixture was
sonicated for 15 min, and then centrifuged at 2800 rpm for 5 min. The supernatant liquid was
passed through a glass fibre filter and collected in a 250mL flask. The extraction was repeated
with 30 mL of solvent mixture and the combined filtered extract was concentrated to around 20mL
on a rotary evaporator at 30°C under 95 kPa vacuum. The concentrated extract was transferred
into a 250 mL measuring cylinder. The rotary evaporator flask was rinsed with 1mL MeOH,
which was added to the concentrated extract along with sufficient deionised water to make the
final volume 240mL. This aqueous sediment extract was split into two parts, which were
subsequently subjected to the different SPE clean up procedures described below.
For analytes most appropriately measured by gas chromatography (GC), such as a range of
volatile organophosphates (OP), fungicides, organochlorine (OC) and synthetic pyrethroid (SP)
chemicals, a sub-sample (water, 500 mL pH adjusted to <2; sediment aqueous extract, 160 mL)
was extracted with a [C18, 1500mg/83 mL] Enviro Clean® Universal 525 Extraction Cartridge;
(UCT, Bristol, PA, USA) to remove interferences before GC determination. The cartridge was
pre-conditioned with 10 mL of 50:50 ethyl acetate/dichloromethane (v/v), and then 10 mL
methanol before loading the aqueous extract. Compounds of interest were eluted from the
cartridge with first 10 mL ethyl acetate, then 2 x 10 mL 50:50 ethyl acetate/dichloromethane (v/v).
The combined eluates were concentrated using a rotary evaporator at 30 ̊C under 95 kPa vacuum
to about 5mL, and dried using anhydrous sodium sulphate. The extract was then transferred into a
test tube and evaporated to near dryness under nitrogen. Residues were reconstituted in 0.2 mL
acetone and 1.8 mL hexane. Sulphur was removed using copper granules, and the extract was split
into two.
The first aliquot (1mL) of the extract was used to determine a range of volatile fungicides. The
concentrations of the fungicides bupirimate, chlorothalonil, fenarimol, iprodione, procymidone,
buprofezin (insecticide) and the benzamide herbicide propyzamide, were determined using a
Varian 3400 CX or 3800 capillary gas chromatograph fitted with nitrogen-phosphorus detector
(Varian, Mulgrave, Australia). An aliquot of hexane extract was simultaneously injected onto two
parallel columns (15 m, 0.32 ID 0.25µm film, 95 % dimethyl - 5 % diphenylpolysiloxane
stationary phase (J&W™ DB-5) and a 15 m, 0.32 ID 0.25µm film, 50 % dimethyl – 50 %
diphenylpolysiloxane stationary phase (J&W™ DB-17)) via a split/splitless injector with a split
ratio of 1:20. The GC oven was temperature-programmed (120oC 0–2 min, 120–300oC at
20oC/min, held 300oC for 1 min) for optimum separation efficiency. The injector and detector
temperatures were set at 280oC and 320oC, respectively. Helium was used as carrier gas. Varian
Star software (V6.0) was used to manage the chromatographic data. The organic residues were
quantified by comparison with external standards. The LORs in water samples were: bupirimate
and buprofezin, 0.1 g/L; chlorothalonil, fenarimol, and iprodione, 0.2 g/L; procymidone, 0.5
g/L; propyzamide 1 g/L, respectively. The LORs for these pesticides in sediment samples were:
bupirimate and buprofezin, 50 g/kg; fenarimol, iprodione, procymidone, 150 g/kg;
propyzamide 250 g/kg; and chlorothalonil, 500 g/kg (dry weight), respectively.
The first aliquot (1mL) of the extract was also used to determine a range of OPs. The
concentrations of the OPs azinphos ethyl, chlorpyrifos, chlorpyrifos methyl, diazinon, ethion,
fenchlorphos, fenitrothion, fenthion, malathion, mevinphos, methidathion, parathion ethyl,
parathion methyl and prothiofos were determined by external calibration using a Varian 3400 CX
or 3800 capillary gas /-programmed (120oC 0–2 min, 120–300oC at 20oC/min, held 300oC for 1
min) for optimum separation efficiency. The injector and detector temperatures were set at 280oC
and 320oC, respectively. Helium was used as carrier gas. Varian Star software (V6.0) was used to
manage the chromatographic data. The organic residues were quantified by comparison with
external standards. The LORs in water samples were: chlorpyrifos, 0.04 g/L; azinphos ethyl,
chlorpyrifos methyl, diazinon, ethion fenchlorphos, fenitrothion, fenthion, malathion, parathion
ethyl and parathion methyl, 0.05 g/L; methidathion, mevinphos, and prothiofos, 0.1 g/L;
respectively. The LOR for these pesticides in sediment samples were: ethion, fenitrothion,
fenthion, parathion methyl, 3 g/kg; chlorpyrifos, chlorpyrifos methyl, diazinon, malathion,
methidathion, mevinphos, parathion ethyl, prothiofos 4 g/kg; azinphos ethyl 10 g/kg; and
fenchlorphos 12 g/kg (dry weight), respectively. Note that the OP pesticides azinphos methyl,
fenamiphos, dimethoate, omethoate, dichlorvos and trichlorfon were determined by LC-MS/MS
and conditions and LORs are reported below
The second aliquot of the extract (1 mL) was subjected to further clean up by first conditioning a
Bond Elute® 500 mg florisil cartridge with 1 mL of hexane. Using a Rapid Trace® automated
SPE instrument. Then loading 1 mL of the sample to separate organochlorine (OC) and synthetic
pyrethroid (SP) compounds. The OC and SP analytes were eluted from the florisil cartridge with 3
mL of 50: 48.5: 1.5 dichloromethane, hexane and acetonitrile (v/v/v). The eluate was evaporated
to dryness under nitrogen and reconstituted in 1 mL hexane. This hexane solution was directly
injected into a capillary gas chromatograph with dual electron capture detectors(GC-ECDs) to
determine the SPs, whereas a further one to ten dilution of the aliquot was performed before
injection into GC-ECD for the determination of the OCs.
The concentrations of the SPs bifenthrin, cyhalothrin, cyfluthrin, cypermethrin, deltamethrin,
esfenvalerate, fenvalerate and permethrin were determined using a Varian 3400 CX or 3800
capillary GC-ECD (Varian, Mulgrave, Australia). An aliquot of hexane extract was
simultaneously injected onto two parallel columns (15 m, 0.32 ID 0.25µm film, 95 % dimethyl - 5
% diphenylpolysiloxane stationary phase (J&W™ DB-5) and a 15 m, 0.32 ID 0.25µm film, 50 %
dimethyl – 50 % diphenylpolysiloxane stationary phase (J&W™ DB-17)) via a split/splitless
injector with a split ratio of 1:20. The GC oven was temperature-programmed (260oC 0–5 min, to
300oC at 10oC/min, held 300oC for 6 min) for optimum separation efficiency. The injector and
detector temperatures were set at 280oC and 350oC, respectively. Helium was used as carrier gas.
Varian Star software (V6.0) was used to manage the chromatographic data. The organic residues
were quantified by comparison with external standards. The LORs in water samples were:
cyhalothrin 0.01 g/L; bifenthrin, cyfluthrin, deltamethrin and fenvalerate 0.02 g/L;
cypermethrin and esfenvalerate 0.05 g/L; and permethrin 0.1 g/L; respectively. The LORs for
these pesticides in sediment samples were: cyhalothrin, 2 g/kg; cyfluthrin, deltamethrin,
esfenvalerate and fenvalerate, 4 g/kg; bifenthrin and cypermethrin 5 g/kg; and permethrin 20
g/kg (dry weight), respectively.
The concentrations of the OCs aldrin, BHC-alpha, BHC-beta, BHC-delta, cis-chlordane, dieldrin
endosulfan sulphate, endosulfan-alpha, endosulfan-beta, endrin, HCB, heptachlor, heptachlor
epoxide, lindane, oxychlordane, p,p'-DDD, p,p'-DDE, p,p'-DDT, and trans-chlordane were
determined using a Varian 3400 CX or 3800 capillary gas chromatograph fitted with electron
capture detectors (Varian, Mulgrave, Australia). An aliquot of hexane extract was simultaneously
injected onto two parallel columns (15 m, 0.32 ID 0.25µm film, 95 % dimethyl - 5 % diphenyl
polysiloxane stationary phase (J&W™ DB-5) and a 15 m, 0.32 ID 0.25µm film, 50 % dimethyl –
50 % diphenyl polysiloxane stationary phase (J&W™ DB-17) or alternatively a 15 m, 0.32 ID
0.25µm film, 50 % phenyl polysiloxane/ 50% cyanopropylmethyl polysiloxane stationary phase
(Rtx™ 225)) via a split/splitless injector with a split ratio of 1:20. The GC oven was set at 200oC,
isothermal. The injector and detector temperatures were set at 280oC and 350oC, respectively.
Helium was used as carrier gas. Varian Star software (V6.0) was used to manage the
chromatographic data. The organic residues were quantified by comparison with external
standards. The LORs in water samples were: aldrin, BHC-delta, cis-chlordane, endrin, HCB,
heptachlor, heptachlor epoxide, lindane, oxychlordane, p,p'-DDD, p,p'-DDE, and trans-chlordane
0.002 g/L; BHC-alpha and dieldrin 0.003g/L; BHC-beta, endosulfan sulphate, endosulfanalpha, endosulfan-beta and p,p'-DDT 0.005 g/L; respectively. The LORs for these pesticides in
sediment samples were: BHC-delta and heptachlor epoxide 2 g/kg; BHC-alpha, HCB, Lindane
and p,p'-DDE 3 g/kg; aldrin, cis-chlordane, dieldrin, endrin, heptachlor, oxychlordane and transchlordane 4 g/kg; BHC-beta, endosulfan-alpha, endosulfan-beta, p,p'-DDD and p,p'-DDT 5
g/kg; and endosulfan sulfate 7 g/kg (dry weight), respectively.
For analytes most appropriately measured by liquid chromatography (LC), such as a range of
triazine/triazine herbicides, and many fungicides, a sample (water, 100 mL; sediment, 80 mL of
sediment aqueous extract solution) was loaded onto to a Bond Elute® PPL 500 mg/ 3mL SPE
cartridge for LC-tandem mass spectrometry (LC-MS/MS). The cartridge was pre-conditioned with
5mL MeOH followed by 5 mL deionised water, followed by loading of the sample and by elution
with 5mL of acetonitrile. The eluate was evaporated to dryness under a stream of nitrogen. The
residues were dissolved in 1mL 50% MeOH: H2O (v/v). The final extract was filtered through a
0.45 m PTFE syringe filter before analysis by LC-MS/MS
The concentrations of the herbicides atrazine, cyanazine, hexazinone, metribuzin, prometryn,
simazine, terbutryn and the atrazine metabolites desethyl atrazine (DEA), deisopropyl atrazine
(DIA) and hydroxyl atrazine (HA) were determined using a ‘triazines screen’ using a Varian
1200L Quadrupole LC-MS/MS (Varian, Mulgrave, Australia) operating in the positive ion
electrospray mode. The triazine/triazole herbicides were separated from other extract components
with a Varian C18, 5µm, Luna column (150 mm x 2.0 mm). The HPLC column was maintained at
25oC. The mobile phase consisted of (A) 20 % methanol in 5 mM ammonium acetate and (B) 90
% methanol in 5 mM ammonium acetate with the following gradient: 80 % A (7 min), 70% A (12
min), 100 % B (13 min) and 100% A (7 min) at a flow rate of 0.2 ml/min. Varian Workstation
(V6.0) was used for data processing. Residues were quantified using external standards and each
standard set was assayed a minimum of three times during each sample batch run. Sample and
recovery concentrations were calculated from a linear regression of the standards. The tandem
mass spectrometer was operated in the multiple reaction monitoring (MRM) mode. Method LORs
in water were 0.001 g/L for atrazine, cyanazine, hexazinone, prometryn simazine and terbutryn;
and were 0.002 g/L for metribuzin, DEA, DIA and HA, respectively. The LORs for these
pesticides in sediment were 5 g/kg (dry weight) for all analytes except prometryn (2 g/kg).
The concentrations of 40 polar pesticides (azinphos methyl, azoxystrobin, boscalid, carbaryl,
cyproconazole, cyprodinil, dichlorvos, difenoconazole, dimethoate, dimethomorph, fenamiphos
fenoxycarb, fipronil, imidacloprid, indoxacarb, linuron, metalaxyl, methiocarb, methomyl,
myclobutanil, omethoate, oxadixyl, penconazole, pendimethalin, pirimicarb, prochloraz,
propargite, propiconazole, pymetrozine, pyraclostrobin, pyrimethanil, spinosad, tebuconazole,
tebufenozide, tetraconazole, thiodicarb, triadimefon, triadimenol, trichlorfon, and trifloxystrobin)
were determined using a multi-residue screen using a Varian 1200L Quadrupole LC-MS/MS
(Varian, Mulgrave, Australia) operating in the positive ion electrospray mode. The pesticides were
separated from other extract components with a Varian Pursuit C18 column (150 mm x 2.0 mm)
fitted with a Pursuit C18 guard column. The HPLC column was maintained at 25oC. The mobile
phase consisted of (A) 20 % methanol in 5 mM ammonium acetate and (B) 90 % methanol in 5
mM ammonium acetate with the following gradient: 100 % A–100% B (0–15 min), 100 % B (15–
28 min), 100 % B–100 % A (28–30 min) with a flow rate of 0.2 ml/min. Varian Workstation
(V6.0) was used for data processing. Residues were quantified using external standards and each
standard set was assayed a minimum of three times during each sample batch run. Sample and
recovery concentrations were calculated from a linear regression of the standards. The tandem
mass spectrometer was operated in the multiple reaction monitoring (MRM) mode. The method
LORs in water were: metalaxyl, myclobutanil, pyraclostrobin, fenamiphos and trifloxystrobin,
0.001 g/L; azoxystrobin, boscalid, carbaryl, cyprodinil, dimethoate, dimethomorph, fenoxycarb,
imidacloprid, methiocarb, methomyl, omethoate, penconazole, pirimicarb, propiconazole,
tebufenozide and triadimefon, 0.002 g/L; difenoconazole, indoxacarb, linuron, oxadixyl,
prochloraz, pymetrozine, pyrimethanil tetraconazole and trichlorfon, 0.004 g/L; fipronil and
dichlorvos and triadimenol 0.005 g/L; azinphos methyl, cyproconazole and tebuconazole 0.01
g/L; pendimethalin and propargite 0.05 g/L; spinosad and thiodicarb 0.1 g/L, respectively;
LORs for these pesticides in sediment were: carbaryl, metalaxyl, and oxadixyl tebufenozide, 1
µg/kg; boscalid, linuron, methiocarb, myclobutanil and triadimenol, 2 g/kg; azoxystrobin,
fenoxycarb pirimicarb and trifloxystrobin, 3 g/kg; pyraclostrobin tebuconazole, tetraconazole
and triadimefon, 4 g/kg; dimethoate, fenamiphos, fipronil, imidacloprid, indoxacarb,
penconazole, spinosad and trichlorfon, 5 g/kg; cyproconazole, omethoate and pyrimethanil, 10
g/kg; dimethomorph and propiconazole, 12 g/kg; azinphos methyl, cyprodinil and propargite,
15 g/kg; methomyl, difenoconazole and prochloraz, 20 g/kg; dichlorvos and pendimethalin, 25
g/kg; pymetrozine, 250 g/kg; thiodicarb, 500 g/kg (dry weight), respectively.
For every analytical batch of water and sediment samples extracted, a sample was randomly
selected for spike recovery determinations. Water samples were spiked with the reported OCs at
0.02g/L, reported OPs at 0.1g/L NPD screen at1g/L, SPs at 0.2g/L triazines at 0.1g/L and
LC-MS/MS screen compounds at 0.1g/L. Water OC recoveries ranged from 112% for αendosulfan to 26% for HCB with DDT and isomers in the range of 107-64% and dieldrin = 99% .
Recoveries in the OP screen ranged from 143% for mevinphos to 81% for fenchlorphos.
Recoveries for SPs ranged from cyhalothrin at 96% to bifenthrin at 54%. Recoveries for NPD
screen analytes ranged from 145% for chlorothalonil to 25% for buprimate. Triazine recoveries
ranged from hexazinone 98%, atrazine 81%, simazine 82%, to 2-hydoxy-atrazine, 37%.
Recoveries of LC-MS/MS analytes were above 70% except for methiocarb, 54%, prochloraz,
63%, propargite, 2%, dimethoate, 68%, dichlorvos 41%, cyprodinil, 67%, indoxacarb,665,
omethoate, 52%, pendimethalin, 11%, spinosad, 6%, thiodicarb, 1% and trichlorfon 7%. The
measured recoveries are reflected in the water LORs reported above.

Sediment samples were spiked with the reported OCs at 8 g/kg, reported OPs at 0.05 g/g NPD
screen at 0.2 g/g, SPs at 0.02 and 0.04 g/g triazines at 0.02 g/g and LC-MS/MS screen
compounds at 0.02 g/g. Water OC recoveries ranged from 124% for β-BHC to 14% for HCB
with DDT and isomers in the range of 44-48% and dieldrin = 49%. Recoveries in the OP screen
ranged from 92% for mevinphos to 19% for fenthion. Recoveries for SPs ranged from αcypermethrin at 57% to permethrin at 44%. Recoveries for NPD screen analytes ranged from 99%
for iprodione to 36% for propyzamide. Triazine recoveries ranged from simazine 64%, atrazine
62%, to 2-desisopropyl-atrazine, 35%. Recoveries of LC-MS/MS analytes ranged from
trichlorfon, 90% to methomyl, 7%, thiodicarb, 1% and difenoconazole at 9%. The measured
recoveries are reflected in the sediment LORs reported above.
Sample results reported were not corrected for recoveries from spiked samples.
Table SI 1: Pesticide residues observed in water samples
CAPIM
site #
Dimethoate
Fenamiphos
Dieldrin
Fenvalerate
Atrazine
Hexazinone
Metribuzin
Prometryn
Simazine
Terbutryn
DEA
DIA
(g/L)
300
0.004
ND
ND
ND
0.006
ND
ND
ND
0.037
ND
ND
ND
301
ND
ND
ND
ND
0.006
ND
ND
ND
0.039
0.001
ND
ND
302
ND
ND
ND
ND
0.001
ND
ND
ND
0.058
ND
ND
ND
303
ND
0.005
ND
ND
0.004
ND
ND
0.160
0.070
ND
ND
0.010
304
ND
ND
ND
ND
ND
ND
ND
ND
0.869
0.002
ND
0.14
305
0.002
ND
ND
ND
0.013
ND
ND
ND
0.122
0.002
ND
ND
306
ND
ND
ND
ND
0.003
ND
ND
ND
0.049
0.022
ND
ND
307
ND
ND
ND
ND
0.038
ND
ND
ND
0.022
ND
ND
0.004
308
ND
ND
ND
ND
0.022
ND
ND
ND
0.097
0.002
ND
0.005
309
ND
ND
ND
ND
ND
ND
ND
ND
0.100
ND
ND
0.006
310
ND
ND
ND
ND
ND
ND
ND
ND
0.027
0.002
ND
ND
311
ND
ND
ND
ND
ND
ND
ND
ND
0.310
ND
ND
0.036
312
ND
ND
0.002
ND
0.029
ND
ND
ND
0.499
0.022
ND
0.034
313
ND
ND
ND
ND
0.007
ND
ND
ND
0.012
ND
ND
ND
314
ND
ND
ND
ND
0.015
ND
ND
ND
4.78
0.001
ND
0.22
315
ND
ND
ND
ND
0.002
ND
ND
ND
0.023
ND
ND
ND
316
0.002
ND
ND
0.033
1.65
0.014
0.987
ND
1.72
0.002
0.165
0.245
317
ND
ND
ND
ND
0.003
ND
ND
ND
0.005
ND
ND
ND
318
ND
ND
ND
ND
0.007
ND
ND
ND
0.099
ND
ND
0.010
319
0.002
ND
0.002
ND
0.029
ND
ND
ND
0.202
0.002
ND
0.028
0.015
ND
ND
0.557
0.001
ND
0.047
320
ND
ND
ND
ND
0.026
321
0.001
ND
0.004
ND
0.033
ND
ND
ND
0.459
ND
ND
0.065
322
ND
ND
0.003
ND
ND
ND
ND
ND
0.093
ND
ND
0.01
324
ND
ND
ND
ND
0.002
ND
ND
ND
0.036
ND
ND
ND
LOR
0.002
0.001
0.003
0.02
0.001
0.001
0.002
0.001
0.001
0.001
0.002
0.002
DEA, desethylatrazine; DIA, desisoprylatrazine; ND, not detected
Table SI 1: (continued)
CAPIM
site #
Methomyl
Imidacloprid
Oxadixyl
Carbaryl
Pirimicarb
Metalaxyl
300
ND
ND
ND
0.004
ND
ND
301
ND
ND
ND
ND
ND
302
ND
ND
ND
ND
ND
303
0.011
0.493
0.012
ND
0.018
304
ND
ND
ND
ND
ND
305
ND
0.004
ND
ND
ND
306
ND
ND
ND
ND
307
ND
ND
ND
ND
308
ND
0.006
ND
309
ND
ND
ND
310
ND
0.013
311
ND
312
ND
313
314
315
Azoxy
strobin
Dimetho
morph
Myclobutanil
Triadi
menol
Fipronil
Tebucon
azole
Propicon
azole
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
0.006
0.003
ND
ND
ND
ND
ND
ND
ND
0.191
0.178
0.002
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
0.004
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
0.001
ND
ND
ND
ND
ND
ND
ND
ND
0.001
0.001
ND
ND
ND
ND
0.010
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
0.001
0.001
ND
ND
0.002
0.004
ND
ND
ND
0.005
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
0.002
ND
ND
ND
ND
0.002
ND
ND
ND
ND
ND
ND
0.002
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
0.065
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
0.008
ND
ND
ND
ND
ND
ND
ND
ND
ND
0.021
0.022
0.011
0.001
ND
ND
0.016
ND
ND
0.005
ND
ND
ND
ND
ND
ND
ND
ND
(g/L)
316
ND
ND
ND
ND
0.001
317
ND
ND
ND
ND
ND
318
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
319
ND
ND
ND
ND
ND
0.001
ND
ND
ND
0.002
ND
ND
ND
320
ND
ND
ND
ND
ND
0.003
ND
ND
ND
ND
ND
ND
ND
321
ND
ND
ND
ND
ND
0.001
ND
ND
ND
0.004
ND
ND
ND
322
ND
0.007
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
324
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
LOR
0.002
0.002
0.004
0.002
0.002
0.001
0.002
0.002
0.001
0.005
0.005
0.01
0.002
Table SI 2: Pesticide residues observed in sediment samples
CAPIM Site #
Bifenthrin
Permethrin
Fenamiphos
p,p'-DDE
p,p'-DDD
Dieldrin
BHC-beta
trans-Chlordane
cis-Chlordane
(g/kg (dry weight))
300
20
ND
ND
ND
ND
ND
ND
ND
ND
301
59
34
ND
4.7
ND
ND
ND
ND
ND
302
ND
ND
ND
ND
ND
ND
ND
ND
ND
303
ND
ND
2
ND
ND
ND
ND
ND
ND
304
ND
ND
ND
ND
ND
ND
ND
ND
ND
305

ND
ND
ND
ND
ND
ND
ND
ND
306
35
ND
ND
ND
ND
ND
ND
ND
ND
307
ND
ND
ND
ND
ND
ND
ND
ND
ND
308
ND
ND
ND
ND
ND
ND
ND
ND
ND
309
ND
ND
ND
ND
ND
ND
ND
ND
ND
310
29
ND
ND
ND
ND
ND
ND
ND
ND
311
35
ND
ND
ND
ND
ND
ND
ND
ND
312
12
28
ND
ND
ND
1.7
ND
1
ND
313
ND
ND
ND
ND
ND
ND
ND
ND
ND
314
ND
ND
ND
5.4
ND
ND
ND
ND
ND
315
ND
ND
ND
2.0
ND
ND
ND
ND
ND
316
ND
ND
ND
ND
ND
ND
ND
ND
ND
317
ND
ND
ND
6.8
ND
ND
ND
ND
ND
318
ND
ND
ND
ND
ND
ND
ND
ND
ND
319
27
ND
ND
ND
ND
5.4
ND
ND
1.5
320
ND
ND
ND
ND
ND
ND
ND
ND
ND
321
ND
ND
ND
ND
3.4
11
15
ND
ND
322
ND
ND
ND
8.6
3.0
ND
ND
ND
ND
324
ND
ND
ND
ND
ND
ND
ND
ND
40
LOR
5
20
2
3
5
4
5
4
4
Table SI 2: (continued)
CAPIM
Site #
Buprofezin
Chlorothalonil
Atrazine
Simazine
HA
Imidacloprid
Metalaxyl
Azoxystrobin
Linuron
Trifloxystrobin
(g/kg (dry weight))
300
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
301
ND
ND
ND
ND
ND
ND
ND
ND
ND
1
302
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
303
ND
ND
ND
ND
ND
8
1
7
1
ND
304
ND
ND
ND
2.4
ND
ND
ND
ND
ND
ND
305
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
306
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
307
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
308
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
309
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
310
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
311
ND
ND
ND
ND
3.2
ND
ND
ND
ND
ND
312
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
313
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
7.0
314
ND
ND
ND
7.1
ND
ND
ND
ND
ND
315
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
316
ND
ND
1.9
2.2
1.5
ND
ND
ND
ND
ND
317
14
ND
ND
ND
ND
ND
ND
ND
ND
1
318
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
319
ND
53
ND
1.7
ND
ND
ND
ND
ND
ND
320
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
321
ND
ND
ND
4.1
2.4
ND
ND
ND
ND
ND
322
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
324
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
LOR
50
500
5
5
5
5
1
1
1
1
HA, 2-hydroxy atrazine
SI2: Bioanalytical testing using recombinant receptor-reporter gene assays
For each site, an aliquot of water sample (1L) was extracted for the measurement of receptor
(hormonal) activity using a yeast-based bioassay. The sample preparation methods for these tests
are described elsewhere (Shiraishi et al. 2000; Allinson et al. 2007, 2008, 2010), but, in short,
involved filtration and adding buffer solution to the sample to ensure an acid pH (according to
JEA 1998), filtration through GF/C filters to remove particulate matter, and then solid phase
extraction (C18 FF disk; Empore; 47 mm; 3M, MN, USA) with methanol and evaporation.
Measurement of estrogenic activity was undertaken after further sample treatment. In short, the
sample was re-suspended in a mixture of 3:1 hexane: dichloromethane (1 mL), and loaded onto a
florisil column (Strata FL-PR Florisil; 500 mg; 3 mL; Phenonomex). For all samples, elution
protocols separated the extract into three fractions, first a 3:1 hexane:dichloromethane fraction
(H/D), second a 1:9 acetone:dicholoromethane fraction (A/D), and finally a methanol fraction
(MeOH). The A/D fraction contains the steroid hormones, and the separation was undertaken to
minimise the effects of matrix components on the bioassay systems and eliminate anti-estrogenic
compounds.
Measurement of estrogenic activity was undertaken with a yeast two-hybrid recombinant receptorreporter gene bioassay system in accordance with the method of Shiraishi et al. 2000 (described in
English in Allinson et al. 2010) using yeast cells (Saccharomyces cerevisiae Y190) into which the
human estrogen receptor ERα or the estrogen receptor from Japanese medaka (Oryzias latipes)
had been inserted (hERα and medERα, respectively; Nishikawa et al. 1999). Positive controls
were used with all assays: hERα and medERα assays, 17β-estradiol (Sigma, MO, US). A solvent
(vehicle) control (DMSO, Sigma-Aldrich, MO, US) was used in yeast bioassays, and methanol
(Sigma-Aldrich, MO, US) was used in P.B. test. The agonist activities of the A/D fraction of the
sample extracts were measured. The bioassay method’s limits of reporting (LOR) for the hERα
and medERα systems were 0.4 and 1.0 ng/L 17β-estradiol equivalents (EEQ), respectively.
SI3: Risk Assessment methods
Risk Quotient (RQ) Method
This method to evaluate the potential risk to aquatic organisms and ecosystems is a deterministic
method in which the risk ratio is expressed as measured environmental concentrations (MECs)
divided by predicted no-effect concentration (PNEC) or reported no effect concentrations
(NOEC), and is calculated using:
RQ = MEC/PNEC
(1)
An RQ of more than 1 is considered problematic. In this study we followed the process outlined
by Thomateau et al. (2013) in which the median and maximum measured environmental
concentrations (MECs) were used to generate general case (RQmedian) and the worst case (RQmax)
scenarios. NOEC data on the chronic (long-term) ecotoxicological effects of observed residues
extracted from the IUPAC Pesticides Properties Database (University of Hertfordshire 2013) was
used for the calculation of the PNECs. Where NOEC data was not found in the IUPAC Pesticides
Properties Database, short term EC50 (lethal/effect) data was obtained from the PAN Pesticides
Database (Kegley et al. 2011). The PNEC values were calculated by dividing the lowest long term
NOEC (or, where lacking, the short term EC50 (lethal/effect)) of the most sensitive species by an
assessment factor, which was 10 where data was available from at least three trophic levels (fish,
macroinvertebrates, aquatic plants and algae phytoplankton), 50 for where data was available from
only two trophic levels, and 100 for all other cases.
Table SI 3: Summary of information used to calculate RQ for each pesticide
Chemical
MEC
Median
Ecotoxicological effect value
Max
A&A
(2000) a
Fish
b
(g/L)
simazine
atrazine
DIA
metalaxyl
terbutryn
imidacloprid
dimethoate
propiconazole
dieldrin
pirimicarb
triadimenol
azoxystrobin
hexazinone
carbaryl
DEA
dimethomorph
fenamiphos
fenvalerate
fipronil
methomyl
metribuzin
myclobutanil
oxadixyl
prometryn
tebuconazole
0.095
0.007
0.031
0.002
0.002
0.008
0.002
0.005
0.003
0.001
0.004
--
4.78
1.65
0.245
0.191
0.022
0.493
0.004
0.022
0.004
0.018
0.016
0.178
0.015
0.004
0.165
0.002
0.005
0.033
0.010
0.011
0.987
0.002
0.012
0.160
0.021
3.2
13
0.15
0.001
3.5
-
700
2000
9100
9020
400
68
< 18000
3130
147
210
56
3.8
15
76
5600
200
12
b
Aquatic
invertebrates b
Aquatic
plants b
Algae
(g/L)
2500
250
36000
1800
40
310
0.9
100
44
50000
250
5
10
0.08
68
1.6
320
1000
2000
10
300
19
69500
4900
640
72
13700
160
8
> 105000
10.5
144
600
100
36000
10000
32000
320
50000
1000
800
9800
10000
19
2660
100
EC50
c
100 d
1
0.3
100
3062
Selected
PNEC
Risk
factor
RQmed *
RQmax
300
19
100
9100
1
1800
40
68
0.3
0.9
100
44
72
210
100
5
3.8
0.08
15
1.6
8
200
3062
10.5
10
10
10
100
10
10
10
10
10
50
10
10
10
10
10
100
10
10
10
10
50
10
10
10
50
10
0.003
0.004
0.031
0.000
0.020
0.000
0.001
0.001
0.083
0.010
0.000
-
0.159
0.868
0.061
0.000
0.220
0.003
0.001
0.003
0.458
0.200
0.002
0.040
0.002
0.000
0.165
0.004
0.013
4.125
0.007
0.344
1.234
0.000
0.000
0.762
0.021
*, Calculated only for chemical with more than 4 reported residues; A&A (2000), ANZECC and ARMCANZ (2000) water quality guidelines; a, 95% species protection
levels; b, data from IUPAC Pesticide Properties Database (University of Hertfordshire 2013), or c, Kegley et al. (2011); d, EC50 for DEA used as a surrogate for DIA.
Table SI 4: Summary of information used to calculate TUmax for each trophic level for pesticide observed in water samples
Chemical
simazine
atrazine
DIA
metalaxyl
terbutryn
imidacloprid
dimethoate
propiconazole
dieldrin
pirimicarb
triadimenol
azoxystrobin
hexazinone
carbaryl
DEA
dimethomorph
fenamiphos
fenvalerate
fipronil
methomyl
metribuzin
myclobutanil
oxadixyl
prometryn
tebuconazole
.
MEC
Max
4.78
1.65
0.245
0.191
0.022
0.493
0.004
0.022
0.004
0.018
0.016
0.178
0.015
0.004
0.165
0.002
0.005
0.033
0.010
0.011
0.987
0.002
0.012
0.160
0.021
Ecotoxicological effect value
Fish
Zooplankton
110964
14983
g/L)
422,800
299,488
93467
2742
156050
9583
5726
5.4
153000
18750
75235
694317
4675
51500
7100
103168
1219
6750
182
40
2500
149
152000
1479
6200
234
2.15
128.3
2540
91912
4200
320,000
5050
4400
33000
3.2
4.6
100
53
35360
240
530000
40000
490
Aquatic plants
TUf
TUzp
TUap
TUalg
-2.7
-2.0
-1.3
-1.5
-2.7
-2.1
-5.3
-3.2
-3.4
-1.3
-2.4
-1.9
Algae
(Log10TU)
2670
170
100
50
10
2.7
2867
24.5
2667
21
41
12
-4.4
-4.0
-4.9
-5.3
-5.7
-5.1
-5.5
-6.4
-5.4
-3.1
-6.9
-6.1
-5.6
-7.7
-6.1
-5.4
-5.5
-5.3
-5.5
-5.5
-4.7
-3.3
-5.2
-2.9
-7.0
-5.6
-6.5
-4.7
-1.8
-4.1
-5.4
-5.0
-6.3
-7.4
-4.5
-5.3
-7.2
-2.8
-2.1
-4.0
-3.7
-4.6
-5.1
-7.6
-5.4
-4.4
Assessment of potential impact of pesticides in the water column on macroinvertebrates,
aquatic plants and algae using the Toxicity Unit concept
The toxic unit (TU) concept compares the detected concentration of chemical with the respective
toxicity of the substance. We calculated the toxic unit (TU) for each chemical in each water
samples according to Liess and Von Der Ohe (2005):
Log TU = log (Cp / Toxp)
where TU is the toxic unit of the pesticide (presented as the logarithm); Cp is the concentration of
pesticide observed in the sample; and Toxp is a measure of the toxicity value of the pesticide
active ingredient. The potential effect on fish of individual chemicals was assessed for all
chemicals detected in water samples by calculating TUf using the maximum observed
concentration and, where available, the average species LC50 for the rainbow trout
(Onchorynchus mykis); where this summary was not reported by Kegley et al. (2011), any
alternative summary for a trout was used. Similarly, the potential effect of observed chemicals on
zooplankton was assessed for all chemicals detected in water samples by calculating TUzp using
the maximum observed concentration and, where available, the average species LC50 for Daphnia
magna; where this summary was not reported by Kegley et al. (2011), any alternative zooplankton
summary was accepted; where no summary data was reported, an intermediate value for D. magna
48 h LC50 (mortality) was used, or, in extremis, 48h EC50 for immobilisation (terbutryn) or
intoxication (triadimenol, oxadixyl). For the 6 detected photosystem II inhibiting herbicides,
maximum toxicity units for aquatic plants (TUap) and phytoplankton (TUalg) were also calculated
using 7d Lemna population abundance EC50 data, and 4-7 d Selenastrum capricornutum
population abundance data, respectively (Table SI 4). Liess and Von Der Ohe (2005) reported a
significant change in community structure between TU(D. magna) < -4 and >-3, and so a TU of -3 and
higher is considered to pose some risk to assessed organisms.
The presence of multiple pesticides in surface water is common in monitoring programs (Gillom
et al., 2006; Gregoire et al., 2010). These mixtures of different chemicals have the potential for
additive, synergistic or antagonistic effects on toxicity (ANZECC and ARMCANZ 2000). There is
an increasing acknowledgement that toxic effects can occur at much lower concentrations where
chemicals are present as mixtures (Baas et al., 2009). So, for all the samples with pesticide
detections above the LOR, the sum of toxic units (ΣTU) was calculated for each site following the
method reported by Bundshuh et al. (2014) for fish, zooplankton, aquatic plants and algae. For
data presentation purposes, negative log ΣTU was then calculated for each site, and the data
presented as box-and-whisker plots (Figure SI 1). The ΣTU values were then compared to the
European Commission’s unified principles (UP) guideline values established for fish, aquatic
invertebrates and algae, by the European Commission (2011); the number of exceedances of the
UP was expressed relative to the total number of ΣTU available. The UPs suggest that no pesticide
should be registered if the TU for algae (chronic toxicity) or invertebrates and fish (acute toxicity)
in surface waters exceeds 0.1 and 0.01, respectively.
Figure SI 1: Summary of ∑TU for fish, zooplankton, aquatic plants and algae calculated on the
basis of all measured pesticides in the grab water samples from the 24 wetlands and ponds.
Note: the lower the log ΣTU, the higher the toxicity; , arithmetic mean; dividing line within data boxes, data
median; upper and lower boundaries of boxes, 75th and 25th percentile of data; error bars represent the range. The
dotted line represents the Uniform Principles (UP_of the European Union for daphnids and fish (-log 0.01), while the
dashed line represents that for algae (-log 0.1).
Assessment of potential impact of multiple pesticides in sediments using the Toxicity Unit
(TU) method
The toxic unit (TU) concept compares the detected concentration of chemical with the respective
toxicity of the substance. We calculated the toxicity unit for a pesticide (TUp) by dividing the
measured sediment concentration (MSCp) by threshold effect concentration for the pesticide
(TECp):
TUp = MSCp/ TECp
For these calculations, the MSCp is typically based on whole sediment concentrations normalised
to 1% OC. However, because this study used only the < 64 m fraction, and normalising data
from this sediment fraction to 1%OC is considered inappropriate by ANZECC and ARMCANZ
(2000), our calculations took the very conservative approach of considering measured
concentrations are concentrations normalised to 1%OC.
There are few published threshold values for most of the pesticides observed in the sediments, and
so the TECp used in this study were based on the 10 day sediment toxicity thresholds for Hyalella
Azteca for bifenthrin (0.52 g/g OC), permethrin (10.8 g/g OC), p,p'-DDD (1300 g/g OC), p,p'DDE (8300 g/g OC) and dieldrin (2000 g/g OC ) reported in Ding et al. (2010). Kemble et al.
(2013), suggest that even though the TECp are derived using the results of 10 day spiked-sediment
toxicity tests conducted with H. azteca, such pesticide toxicity thresholds have a similar intent as
probable environmental effect (PECs, i.e., as estimates of concentrations above which toxicity is
expected to sediment-dwelling organisms), so the pesticide toxicity quotient is analogous to a PEC
quotient (PECQ) value.
Kemble et al. (2013) suggest that for individual contaminants there is higher likelihood of toxicity
for samples with PECQ values >1, and these values were used when assessing the potential impact
of sediment pesticides using this method (Table SI 5).
Table SI 5 Summary of individual pesticide TU
Site
Pesticide
Bifenthrin
Permethrin
p,p'-DDE
Toxicity Unit
p,p'-DDD
g/kg (dry weight) normalised to 1%OC)



Dieldrin













































































































































Bifenthrin
Permethrin


p,p'-DDD
p,p'-DDE
Dieldrin







































































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2.4 Assessment of potential impact of trace metals in sediments using the Threshold Effects
Concentration (TEC) and Probable Effects Concentrations (PEC) method
Threshold and probable effect concentrations (TECs and PECs, effectively) can be used to
evaluate relationships between sediment contamination and sediment toxicity (MacDonald et al.
2000). In this case, the TEC of an individual metal/metalloid (TECMi) is defined as a contaminant
concentration below which adverse effects on sediment-dwelling organisms are expected not to
occur. The PEC of an individual metal/metalloid (PECMi) is defined as a contaminant
concentration above which adverse effects on sediment-dwelling organisms are expected to occur
more often than not. The TEC and PEC quotients for individual PAH (TECQMi and PECQMi) were
calculated as the measured environmental (sediment) concentration (MECMi) divided by
MacDonald et al.’s (2000) consensus-based threshold effect and probable effect concentrations:
TECQMi = MECPMi/ TECMi
(4)
PECQMi = MECMi/ PECMi
(5)
Sediment concentrations below the limit of quantification were excluded from the prediction of
TECQMi and PECQMi to avoid overestimation of risks by including compounds that are probably
absent.
The TECQMi,site and PECQMi,site were calculated by averaging the TECQMi and PECQMi values for
all contaminants in a sample (MacDonald et al. 2000). Kemble et al. (2013) suggest that there is
higher likelihood of toxicity for samples with TECQMi and PECQMi values >1, and a higher
likelihood of combined toxicity effects where the mean TECQMi,site and PECQMi,site is > 0.5 and >
0.2, respectively, and these values were used when assessing the potential impact of sediment
trace metal concentrations using this method (Table SI 2.4(a,b)).
MacDonald et al. (2000) used a measure of total PAH concentration when combining that data
with other parameters to assess the likelihood of toxicity with a range of contaminants. With only
a limited number of PAHS being observed, we used the mean TECQsite values and mean PECQsite
values to derive a measure of PECQsite , with threshold levels of <0.5 (little likelihood of
toxicity), and >0.1 (likelihood of toxicity) used when assessing the potential impact of sediment
trace metal concentrations using this method (Table SI 2.5)
Table SI 2.4(a): TECQs derived using sediment quality guidelines for trace metals in freshwater ecosystems that reflect TECs (i.e., concentrations below
which harmful effects are unlikely to be observed)
TECQmi
Site #
As
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
324
% TECQMi > 1
% PECQMi > 2
Cd
0.6
1.3
0.6
1.0
2.0
2.0
5.0
0.5
0.9
3.0
0.8
2.0
0.9
0.5
8
4
17
17
Cr
Cu
0.4
0.9
1.2
0.6
0.9
0.8
1.9
0.9
0.8
0.9
0.6
0.9
1.0
1.0
1.0
0.6
0.7
0.6
1.4
1.8
0.9
1.5
0.6
0.2
21
0
0.7
1.7
0.8
0.4
0.7
0.5
1.1
0.7
0.6
2.3
1.3
2.2
2.4
0.5
4.7
1.2
0.8
1.3
1.3
3.7
1.4
5.9
1.5
0.3
58
25
No TEC reported by MacDonald et al. (2000) for Co, Mo, or Se.
Hg
0.6
1.7
3.3
2.8
1.1
0.6
1.1
0.6
21
8
Pb
Ni
Zn
0.3
1.3
0.5
0.3
0.4
0.4
0.8
0.5
0.4
2.6
1.0
2.5
2.7
0.4
16.1
1.2
0.9
1.6
0.8
4.1
1.4
5.9
3.2
0.3
42
29
0.4
1.2
1.7
0.7
0.7
1.1
1.9
1.4
1.0
1.4
0.8
1.5
1.5
1.3
1.8
0.9
1.0
0.8
2.3
2.9
1.6
2.6
0.6
0.3
58
13
0.5
3.4
0.7
0.1
0.2
0.5
1.2
0.4
0.5
1.7
2.7
4.1
12.2
0.3
19.2
2.2
2.3
1.6
1.7
11.7
2.9
18.7
4.8
0.5
63
46
n
6
5
5
5
5
5
5
5
5
6
6
8
7
5
7
6
6
6
6
7
6
7
7
6
% TECQMi,site > 0.5
TECQMi,site
(average)
0.5
1.7
1.0
0.4
0.6
0.7
1.4
0.8
0.7
1.6
1.3
1.9
3.5
0.7
6.8
1.8
1.0
1.2
1.4
4.0
1.5
5.3
1.7
0.3
88
Table SI 2.4(b): PECQs derived using sediment quality guidelines for trace metals in freshwater ecosystems that reflect PECs (i.e., concentrations above
which harmful effects are likely to be observed)
PECQMi
Site #
As
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
324
% PECQMi > 1
% PECQMi > 2
Cd
0.2
0.4
0.2
0.3
1.5
0.2
0.4
0.4
0.3
0.6
0.2
0.3
0.2
0.4
4
0
0
0
Cr
Cu
0.2
0.4
0.5
0.2
0.3
0.3
0.7
0.4
0.3
0.3
0.3
0.4
0.4
0.4
0.4
0.2
0.3
0.2
0.5
0.7
0.3
0.6
0.2
0.1
0
0
0.1
0.4
0.2
0.1
0.1
0.1
0.2
0.2
0.1
0.5
0.3
0.5
0.5
0.1
1.0
0.2
0.2
0.3
0.3
0.8
0.3
1.2
0.3
0.1
4
0
No PEC reported by MacDonald et al. (2000) for Co, Mo, or Se.
Hg
0.1
0.3
0.6
0.5
0.2
0.1
0.2
0.2
0.1
0
0
Pb
Ni
Zn
0.1
0.4
0.1
0.1
0.1
0.1
0.2
0.1
0.1
0.7
0.3
0.7
0.8
0.1
4.5
0.3
0.3
0.5
0.2
1.1
0.4
1.7
0.9
0.1
13
4
0.2
0.6
0.8
0.3
0.3
0.5
0.9
0.7
0.5
0.7
0.4
0.7
0.7
0.6
0.8
0.4
0.5
0.4
1.1
1.3
0.7
1.2
0.3
0.1
13
0
0.1
0.9
0.2
0.0
0.0
0.1
0.3
0.1
0.1
0.4
0.7
1.1
3.2
0.1
5.1
0.6
0.6
0.4
0.5
3.1
0.8
4.9
1.3
0.1
25
17
n
6
5
5
5
5
5
5
5
5
7
6
8
6
5
8
6
5
7
5
7
7
8
7
5
% PECQMi,site > 0.2
PECQMi,site
(average)
0.2
0.5
0.3
0.2
0.2
0.2
0.5
0.3
0.2
0.4
0.3
0.5
1.0
0.3
1.8
0.3
0.4
0.3
0.5
1.2
0.4
1.3
0.5
0.1
75
References
Allinson G, Allinson M, Salzman S, Shiraishi F, Myers J, Theodoropoulos T, Hermon K,
Wightwick A (2007) Hormones in treated sewage effluent. Final Report. A report prepared
for the Victorian Water Trust. Department of Primary Industries, Queenscliff, Australia. 65pp.
Available on-line: http://www.ourwater.vic.gov.au/programs/victorian-watertrust/publications Last accessed: 4 January 2010
Allinson G, Allinson M, Shiraishi F, Salzman SA, Myers JH, Hermon KM, Theodoropoulos T
(2008) Androgenic activity of effluent from forty-five municipal waste water treatment plants
in Victoria, Australia. WIT Trans Ecol Environ 110:293-304.
Allinson M, Shiraishi F, Salzman SA, Allinson G (2010). In vitro and immunological assessment
of the estrogenic activity and concentrations of 17β-estradiol, estrone and ethinyl estradiol in
treated effluent from forty-five waste water treatment plants in Victoria, Australia. Arch
Environ Contam Toxicol 58: 576–586.
ANZECC and ARMCANZ (2000) Australian and New Zealand guidelines for fresh and marine
water quality. Australian and New Zealand Environment and Conservation Council and
Agriculture and Resource Management Council of Australia and New Zealand, Canberra,
Australia.
Baas J, Jager T, Kooijman SALM (2009). A model to analyze effects of complex mixtures on
survival. Ecotox Environ Safe 72: 669-676.
Bundschuh M, Goedkoop W, Kreuger J (2014). Evaluation of pesticide monitoring strategies in
agricultural streams based on the toxic-unit concept — Experiences from long-term
measurements. Sci Total Environ 484: 84–91.
Ding Y, Harwood AD, Foslund HM, Lydy MJ (2010). Distribution and toxicity of sedimentassociated pesticides in urban and agricultural waterways from Illinois, USA. Environ Toxicol
Chem 29(1):, 149–157.
European Commission (2011). Implementing Regulation (EC) No 1107/2009 of the European
Parliament and of the Council as regards uniform principles for evaluation and authorisation
of plant protection products. Off J Eur L155: 127–75.
Gillom RJ, Barbash JE, Crawford CG, Hamilton PA, Martin JD, Nakagaki N, Nowell LH, Scot
JC, Stackleberg PE, Thelin GP, Wolock DM (2006). The quality of our nation's waters pesticides in the nation's streams and groundwater, 1992 - 2001. U.S Geological Survey
Circular 1291. United States Geological Survey, Reston, Virginia, USA.
Gregoire C, Payraudeau S, Domange N (2010). Use and fate of 17 pesticides applied on a
vineyard catchment. Int J Environ Anal Chem 90: 406 - 420.
JEA (2008) Draft manual for surveying exogenous endocrine disrupting chemicals (water,
sediment, aquatic organisms). Japan Environment Agency, Water Quality Bureau, Water
Quality Management Division, Tokyo, Japan. Available online (in Japanese):
www.env.go.jp/chemi/end/manual/water.html Last Accessed 3 August 2009.
Kegley SE, Hill, BR, Orme S, Choi AH (2011). PAN Pesticide Database. Pesticide Action
Network, North America; San Francisco, CA. Available online http://www.pesticideinfo.org
Last accessed 22 May 2014.
Kemble NE, Hardesty DK, Ingersoll CG, Kunz JL, Sibley PK, Calhoun DL, Gilliom RJ, Kuivila
KM, Nowell LH, Moran PW (2013). Contaminants in stream sediments from seven United
States metropolitan areas: Part II—Sediment toxicity to the amphipod Hyalella azteca and the
midge Chironomus dilutes. Arch Environ Contam Toxicol 64: 52–64.
Liess M, Carsten Von Der Ohe PC (2005). Analyzing effects of pesticides on invertebrate
communities in streams. Environmental Toxicology and Chemistry 24(4): 954–965.
MacDonald DD, Ingersoll CG, Berger TA (2000). Development and evaluation of consensusbased sediment quality guidelines for freshwater ecosystems. Arch Environ Contam Toxicol
39: 20–31.
Nishikawa J-I, Saito K, Goto J, Dakeyama F, Matsuo M, Nishihara T (1999) New screening
methods for chemicals with hormonal activities using interaction of nuclear hormone receptor
with coactivator. Toxicol App Pharmacol 154:76-83.
Shiraishi F, Shiraishi H, Nishikawa J, Nishihara T, Morita M (2000) Development of a simple
operational estrogenicity assay system using the yeast two-hybrid system. Kankyo Kagaku
10: 57-64 (in Japanese)
Thomatou A-A, Zacharias I, Hela D, Konstantinou I (2013) Determination and risk assessment of
pesticide residues in lake Amvrakia (W. Greece) after agricultural land use changes in the
lake's drainage basin, Int J Environ Anal Chem 93(7): 780-799.
University of Hertfordshire (2013) The Pesticide Properties DataBase (PPDB) developed by the
Agriculture & Environment Research Unit (AERU), University of Hertfordshire, 2006-2013.
Available online: http://sitem.herts.ac.uk/aeru/iupac/index.htm
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Pesticide and trace metal occurrence and aquatic benchmark