Comparison between measurements with passive sampling devices (DGT, POCIS, SBSE) and biota
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Louise FOAN
Jean‐Louis GONZALEZ
Ifremer Méditerranée (La Seyne‐sur‐Mer)
"Biogeochemistry and Ecotoxicology" Unit
Comparison between measurements
with passive sampling devices
(DGT, POCIS, SBSE) and biota
State of the art and review of available data
International Passive Sampling Workshop (IPSW)
Thursday 27th June, 2013
Context
o Water Framework Directive (2000/60/CE)
• 41 priority substances (Annex IX & X):
‐ metals: Cd, Hg, Ni, Pb.
‐ organic pollutants: PAHs, pesticides…
o Directive on Environmental Quality Standards (2008/105/CE)
• EQS defined in surface waters (coastal, transitional and continental)
for the 41 priority substances of the WFD (Annex I):
Measures on non filtered water except metals: dissolved fraction
(filtration at 0,45 µm or equivalent preliminary treatment)
• Possibility of using integrative matrices for studying long term evolution:
biota, sediments or passive samplers.
2
Context
o Active water sampling
• Analytical difficulties:
‐ sample representativity (spot sampling)
‐ sample stability (analyte loss or contamination)
‐ sensitivity (insufficient LOQs to attain 1/3 EQS)
• Speciation is not studied
no information on fate, bioavailability and toxicity
o Biota
• Easy sampling procedure
• Less analytical difficulties
• Information on pollutant bioavailablity
o Passive sampling
• Less analytical difficulties
• Green chemistry
• Information on pollutant speciation
3
Biota
o Bioconcentration
Mercury PCB
(Csea water = 0,03 µg L-1) (Csea water = 0,002 µg L-1)
Phytoplancton - 4.106
Plants 1.103
Zooplancton - 5.106
Invertebrates 1.105 4.106
Fish 1.104 1.107
Birds - 5.107
Mammals - 8.107
Source : Bliefert and Perraud (2008)
o Passive/active biomonitoring
• Passive: extensive studies (long‐term, high spatial resolution)
• Active: intensive studies with homogeneous populations
o Marine/continental studies
• Marine: national programs (RNO & RINBIO in France)
• Continental: few studies as more complex systems & various species
4
Passive sampling devices
o Metals
DGT (Diffusive Gradient in Thin film)
o Organic micropollutants
SPMD (Semi‐Permeable Membrane Device)
LDPE (Low Density PolyEthylene)
MESCO (Membrane‐Enclosed Sorptive COating)
Silicone rod
SBSE (Stir Bar Sorptive Extraction)
POCIS (Polar Organic Chemical Integrative Sampler)
Chemcatcher®
Mazzella et al.
log cut-off point (nm)
(2011) Suspended matter
Colloids
Dissolved
5
DGT POCIS
Metals Pesticides
Alkylphenols
Pharmaceuticals
Magnetic PDMS Glass
SBSE bar phase envelop
PAHs
PCB
Pesticides
Can micropollutant bioavailibility be
predicted with passive sampling devices?
6
Comparison PS vs biota
64 studies between 1992 and 2012
Marine River
Studies in the natural sediments Laboratory studies water
environment 14% 4%
Sea water
Open sea Continental 18%
waters Continental
11% sediments
23%
18%
Artificial
Coastal
fresh water
waters Transitional 32%
53% waters
13%
Artificial
sea water
14%
Primary Biota Others Passive samplers
producers 13%
10% POCIS
Benthic 6%
organisms DGT
SBSE
19% 39%
Bivalves 6%
51%
Fish SPMD
20% 37%
7
Studies with DGTs
o Metals measured
Most studies: Cu, Cd, Ni, Pb, Zn
Isolated studies: Al, Cr, Co, Fe, Hg, Mn, Sb, Sn
Specific DGTs for monomethylmercury
o “DGT‐labile” fraction
Free ions + mineral complexes + “weak” organic complexes
Significant differences between metals:
Cd : DGT‐labile fraction ~ dissolved fraction (mineral complexes)
Cu : DGT‐labile fraction
DGTs vs biota
Laboratory studies
Metal Biota Correlation between Source
DGT & biota data
Cu Trout gills r = 0,691 Luider et al. (2004)
(Oncorhynchus mykiss) p < 0,0001
Al Trout gills r = [0,75‐0,85] Røyset et al. (2005)
(Salmo truta L.) p < 0,05
Cd Amphipods r = 0,968 Pellet et al. (2009)
(Gammarus pulex) p < 0,05
62Ni Bivalves relation log‐log linear Bourgeault et al. (2012)
(Dreissena polymorpha) r = 0,9996
p < 0,001
MM199Hg Bivalves r = 0,94 Clarisse et al. (2012)
(Macoma balthica) p < 0,001
9
DGTs vs biota
In situ studies
Metal Biota Correlation between Source
DGT & biota data
Cd, Cr, Pb, Zn Mosses r = [0,61‐0,76] Diviš et al. (2007)
(Fontinalis antipyretica) p < 0,05
Cu Bivalves r = 0,787 Jordan et al. (2008)
(Saccostrea glomerata) p < 0,001
Cd Bivalves r = 0,790; p < 0,005 Schintu et al. (2008)
Pb (Mytilus galloprovincialis) r = 0,728; p < 0,05
Pb Algae r = 0,993 Schintu et al. (2010)
(Padina pavonica) p < 0,05
MMHg (Macoma arenaria) r = 0,99 Best et al. (2009)
p < 0,001
10Environmental parameters
o Influence on metal speciation
pH
Natural Organic Matter (NOM)
Impact on accumulation by DGTs & biota
o Competition with metals
Others cations: Ca…
Impact on bioaccumulation
o Biotic ligand model
Biological membrane = ligand
Integrates speciation models
& competition models
Luider et al. (2004)
11Environmental parameters
Influence of natural organic matter
Cd influx in Gammarus pulex (µg.g‐1.L‐1) in function of dissolved Cd [Cd]w, inorganic Cd [Cd]inorg and DGT‐labile
[Cd]DGT in mineral water (●) and water doped with organic ligands : EDTA at 10 µg.L‐1 ( ); humic acids (∆) at 5
and 10 mg.L‐1 (Pellet et al., 2009).
Better estimation of the bioavailable fraction with DGTs
12Environmental parameters
Influence of natural organic matter
Study with bivalves Dreissena polymorpha (Bourgeault et al., 2012)
Study with mosses Fontinalis antipyretica (Ferreira et al., 2008)
13Physiological parameters
o Main physiological parameters
growth (dry mass, condition index, …)
nutrition (ingestion rates, assimilation efficiency…)
excretion (elimination rate)
o Environmental parameters which affect the biota
temperature
food quality and quantity
hydrodynamism
Ni bioaccumualtion with bivalves Dreissena polymorpha (Bourgeault et al., 2012)
14Biodynamic model
Concentration in the Biovailable concentration
organism (µg/g) in the water (µg/L) Concentration in
food (µg/g)
dCorg
ku.Cw AE.IR.Cf ( ke g ).Corg
dt Growth rate (d‐1)
Sampling rate Assimilation Ingestion rate Elimination rate
(L/g/d) efficiency (%) (g/g/d) (d‐1)
(Casas, 2005; Pan & Wang, 2008; Bourgeault et al., 2011)
Modelisation with DGT data for Cw has given reliable results,
even during studies performed in situ
15Studies in sediments
o Passive sampling in sediments with DGTs
DGTs measure the mobile fraction of metals
Accumulation from interstitial waters
Mobilization of “labile” metals adsorbed to particulate phase
DIFS model gives the dynamic response of sediments
o DGT vs biota
Often correlations between data from the 2 matrices
Function of the metals studied
Important differences between species due to their diet
Mobile fractions vary with sediment types: sand >> clay
DGTs give a better estimation of the bioavailable fraction
except for detritus feeders
16Studies with POCIS
o Micropollutants measured
Alkylphenols
Estrogens
Perfluoroalkylated compounds
o POCIS
Reliable time‐intergrated measures of hydrophilic pollutants
Results correlated with YES (Yeast Estrogen Screen) bioessays
POCIS are a usefull tool for evaluating estrogenic activity
o Biomonitoring
Fish (plasma, bile) & bivalves
No significant correlation with concentrations in water
Metabolization of the compounds
Biota is not reliable for monitoring the compounds studied
17Studies with SBSE
Magnetic PDMS Glass
bar phase envelop
o Micropollutants measured
Polycyclic aromatic hydrocarbons (PAHs)
Polychlorinated biphenyls (PCBs)
Organochlorine pesticides (OCPs)
o SBSE
Main drawbacks:
‐ not time‐integrated
‐ concentrations often < LOQ
Ideal for use in controlled conditions
Few studies in situ
o Biomonitoring
Fish plasma & bivalves
Metabolization of PAHs observed in fish plasma
Bioconcentration factors determined in situ with SBSE data
1819
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