www.irstea.fr

Mazzella, N., Byers, H., Bernard, M., Fauvelle, V.,

Lissalde, S., Guibaud, G., Booij, K.

Experimental evaluation of temperature and flow velocity on

pesticide uptake by POCIS and suggestion of a dimensionless

number correlation for sampling rate prediction

9th International Passive Sampling Workshop, Toronto June 1-2, 2017

2

time

ku

ke

Ksw

Ns

tk

swwstseeKCMN

1)(e

usw

k

kK With

tR

NC

s

Sw

Time Weighted

Average (TWA)

Concentrations sws

se

KM

Rk

Kinetics and calibration Water

Boudary

Layer

3

atrazine

diuron

0

0.05

0.1

0.15

0.2

0.25

High data dispersion

- various flowing conditions (0 to dozens of cm.s-1)

- various temperatures, salinity/conductivity

- various surface exposures (18 or 45 cm2)

- various sorbents (but Oasis HLB is mainly used)

…

L.d-1

Alvarez, 1999

Alvarez et al., 2004

Hernando et al., 2005

Mazzella et al., 2007

Martinez Bueno et al., 2009

Lissalde et al., 2011

Ibrahim et al., 2013

Kinetics and calibration

4

Kinetics and calibration

0,0

0,5

1,0

1,5

2,0

2,5

ku (

L g

-1 d

-1)

log Kow

0,0

0,5

1,0

1,5

2,0

2,5

3,0

0 1 2 3 4 5

ku

(l j-1

g-1)

Log Kow

ku (

L g

-1 d

-1)

Mazzella et al. J Chromatogr A (2007)

Lissalde et al. . J Chromatogr A (2011)

Poulier et al. ESPR (2014)

ku (or Rs) not

correlated/predictable with log Kow

s

su

M

Rk

5

Prediction of sampling rates

Miller et al., ES&T (2016)

Artificial neural networks

A recent attempt to predict (in silico) Rs data with some physico-chemical and

structural properties, but remains an empirical approach… And does not take

into account flow velocity and temperature effects

Input

Log D

Log P

nC

TPSA

nHacc

Output

BCF

kD

Rs

6

Sswmmwws kKkKkkR

A 1111

0

Prediction of sampling rates and consideration of

environmental effects

Sswmmwws kKkKkR

A 1111

0

Approaches Relationships Pros and cons References

Increasing membrane

resistance (i.e. addition

of diffusive gel)

• Can reduce bias up to ±20 %

• But potential increase of lag-phase and

Rs decrease

Chen et al., 2013

Suggestion of

confidence interval

• Can be sufficient for some regulatory

monitoring?

• But accepting larger uncertainties at

lower flow velocity (e.g. -80% to +130%)

Poulier et al.,

2014

Empirical relationships

between Rs and flow

velocity ν

• Accurate description of analyte

accumulation with flow velocity

• Non zero Rs and Rs(max) are not modelled

Li et al. 2010

Taking kw explicitly into

account

• Most accurate method when flow velocity

is determined during lab calibration

• But which method? Alabaster

dissolution? PRC from POCIS or non-

polar PSD?

Booij and

Smedes, 2010

Booij et al. 2017

mmww kKk

11

n

s cR

)2( )()( réplabssituins RkR

wmwms AkKAkR

111

25.0 ork

WBL

Membrane

Sorbent

7

Prediction of sampling rates and consideration of

environmental effects

Design of Experiment for temperature and flow effect study

Compounds log Kow Dw

(10-6 cm2.s-1)

Simazine 2.18 5.31

Azoxystrobine 2.5 3.79

Dimethomorph 2.68 4.43

Atrazine 2.71 4.57

Diuron 2.87 4.52

Isoproturon 2.87 3.73

Metolachlor 3.13 4.44

Alachlor 3.09 4.4

Terbuthylazine 3.4 5.87

Acetochlor 4.14 5.31

8

Temp.

Flow

Interactions

Prediction of sampling rates and consideration of

environmental effects

Design of Experiment for simultaneous study of temperature and flow effects

Rs (

mL.

d-1

)

Predominant effect of the flow velocity

on Rs for pesticides with log Kow=2-4

and Dw=3.7-5.8 cm2.s-1

9

Sswmmwwos kKkKkkR

A 1111

Prediction of sampling rates and consideration of

environmental effects

Use of dimensionless number correlations for flow velocity

Flow specific Compound specific

5.0

)5.0(5.0)5.0()1(

5.0

5.0Re

1

UbLaD

U

ScaD

L

k mmm

w

m

ww

For n=0.5 and considering a constant temperature (18°C), then:

cUbko

5.01

ShD

L

k ww

1 mn ScaSh Re

10

Mas

s tr

ansf

er

resi

stan

ce 1

/ko (

d.c

m-1

) Prediction of sampling rates and consideration of

environmental effects

10 and 25°C

Flow velocity U (cm.s-1)

Literature data: only Oasis HLB

and 45 cm2

Mazzella et al. 2007

Lissalde et al. 2011

Fauvelle et al. 2012

Morin et al. 2013

Di Carro et al. 2014

Arhens et al. 2015 059.0061.0

1 5.0 Uko

b c

c parameter

variations:

0.043 - 0.065 d.cm-1

11 M

ass

tran

sfe

r re

sist

ance

1/k

o (

d.c

m-1

)

Flow velocity U (cm.s-1)

Prediction of sampling rates and consideration of

environmental effects

12

mm

w La

U

k

15.0)5.0(

5.064.11

Viscosity η

Temperature (K)

)/(

)( 10 CTB

T A

10°C

25°C

18°C

)10()10(1 )/(5.064.1)/( CTBCTB

o

cUbk

Use of dimensionless number correlations

for both flow velocity and temperature

Prediction of sampling rates and consideration of

environmental effects

m

mwmKk

21

13

Prediction of sampling rates and consideration of

environmental effects

Modelling of “average” Rs with flow velocity and temperature

(mL.d-1)

os kAR .

14

Prediction of sampling rates and consideration of

environmental effects

Linear form

cU

bk

11

0

Mas

s tr

ansf

er

resi

stan

ce 1

/ko (

d.c

m-1

)

Flow velocity U (cm.s-1)

95 %

Confidence

interval

15

Conclusions and further works

Predominant effect of the flow velocity on the Rs, whatever the temperature

For the range of polarity and structures considered, the coefficients of the model

seem to converge possibility of predicting the Rs for a wide range of

moderately polar pesticides?

Possibility of neglecting the temperature effect with a Maximum Permissible

Error of ±65% (i.e. confidence interval for flow velocity modelling only)

However, we need a reliable flow velocity estimate by using:

o Alabaster dissolution?

o PRC from non-polar PSD?

o PRC from POCIS, since the desorption rate also appears to be correlated with

the flow velocity?