Supplemental data for
“Modeling and predicting competitive sorption of organic compounds in soil”
in Environmental Toxicology and Chemistry
Isabel R. Faria, Thomas M. Young*
Agricultural and Environmental Chemistry and Department of Civil and Environmental
Engineering, University of California – Davis, One Shields Av, Davis, California 95616, USA
* Corresponding author phone: (530) 754 9399
email: tyoung@ucdavis.edu
TABLE OF CONTENTS
S1 – ANALYTICAL METHOD
S2 – CONCENTRATION OF COMPETITOR IN MULTI-SOLUTE EXPERIMENTS
S3 – MULTI-SOLUTE EXPERIMENTAL DATA, IAST MODEL AND POTENTIAL MODEL RESULTS
S4 – COMPARING MULTI-SOLUTE MODELS
S5 – COMPETITIVE EFFECT
S6– DEGREE OF COMPETITION
S-1
S1 – ANALYTICAL METHOD
The instrument used for the systems of 1,2DCB+BZ, CB and NP consisted of an Agilent 6890
Gas chromatograph – Mass spectrometer (GC-MS) with a DB-624 (30 m x 0.25 mm, 1.4 m)
capillary column by Agilent J&W. The system was run on positive electron ionization (EI) (70
eV) in selected ion monitoring (SIM) mode. The temperature program started at 35 oC (below
the boiling point of the solvent) ramped to 245 oC (30 oC/min) with a solvent delay of 4.5 to 7
min and a total run time of 11 min. 1 or 2 L samples were injected in split/splitless mode with
an inlet temperature of 220 oC. The interface between the GC oven and the mass detector was
maintained at 260oC to minimize peak tailing.
For systems of 1,2DCB+PHN, PY the instrument used was an Agilent 6890 GC-MS with a
Agilent J&W DB-5MS (30 m x 0.25 mm, 0.25 m) column in Positive EI (70 eV), SIM mode. The
temperature program was 35 oC ramp to 320 oC (15 oC/min) with a solvent delay of 4.5 min and
a total run time of 20 min. 1 L of sample was injected in split/splitless mode with an inlet
temperature of 280 oC and an interface temperature of 325 oC. For all the GC-MS runs helium
was used as the carrier gas at a flow rate of 1 or 2 mL/min. When possible, one quantifying and
two qualifying ions were used to identify each compound. Since BZ and the PAHs do not
produce significant fragments, one quantifying and one qualifying ion were used for
identification purposes.
The multi-solute systems of 1,2DCB + chlorobenzenes with more than one chlorine were
analyzed using an Agilent 6890 Gas chromatograph – Electron capture detector (GC-ECD)
system with an Agilent J&W DB-VRX (30 m x 0.45 mm, 2.55 m) capillary column. The
temperature program started at 40 oC with an inlet temperature of 220 oC and a detector
temperature of 300 oC. The carrier gas was helium at 30 mL/min and nitrogen was used as
makeup gas at a flow rate of 60 mL/min.
All samples were run in duplicate and the average response was used to quantify the samples
by means of external calibration. After injection of every eight unknown samples a known
concentration sample was analyzed to check for system drift.
S-2
S2 – CONCENTRATION OF COMPETITOR IN MULTI-SOLUTE EXPERIMENTS
Table S1 – Concentration of competitor in multi-solute experiments
Primary
solute
12DCB
(0.1 mg/L)
Competitor Concentration (mg/L)
BZ
0
0.01
0.1
1
5
15
30
CB
0
0.05
0.18
0.49
1.68
6.73
16.5
14DCB
0
0.022
0.071
0.3
0.91
3.23
11.3
TCB
0
0.027
0.076
0.24
0.68
2.03
5.8
Table S2 – Average % recovery for each solute
BZ
CB
NP
12DCB
14DCB
TCB
TeCB
PHN
Average
recovery (%)
86
89
97
99
99
96
94
96
S-3
TeCB
0
0.0032
0.0093
0.033
0.13
0.52
1.69
NP
0
0.01
0.05
0.1
0.5
1.5
3
PHN
0
0.0035
0.0081
0.023
0.075
0.232
0.81
PY
0
0.005
0.01
0.05
0.1
---
S3 – MULTI-SOLUTE EXPERIMENTAL DATA, IAST MODEL AND POTENTIAL MODEL RESULTS
Data
Potential
IAST
Yolo
1400
12 DCB K OC
1200
1000
800
600
400
200
0
0.001
0.01
0.1
1
10
Qe CB (cm3/kg OC)
Data
Potential
IAST
Yolo
1000
1000
800
800
600
400
600
400
200
200
0
0.001
0.01
0.1
0
0.001
1
Qe NP (cm3/kg OC)
1
10
Data
Potential
IAST
Yolo
1200
1000
800
12DCB K OC
12DCB K OC
0.1
1400
1000
600
400
800
600
400
200
200
0
0.01
0.1
1
0
0.001
10
Data
Potential
IAST
Yolo
1200
1000
12DCB K OC
800
600
400
200
0.01
0.1
Qe PHN (cm3/kg OC)
0.01
0.1
1
10
Qe TeCB (cm3/kg OC)
Qe TCB (cm3/kg OC)
12DCB K OC
0.01
Qe 14DCB (cm3/kg OC)
Data
Potential
IAST
Yolo
1200
0
0.001
Data
Potential
IAST
Yolo
1200
12DCB K OC
12DCB K OC
1200
1
Yolo
1000
900
800
700
600
500
400
300
200
100
0
0.001
S-4
Data
Potential
IAST
0.01
0.1
Qe PY (cm3/kg OC)
1
Data
Potential
IAST
5000
4500
4000
3500
3000
2500
2000
1500
1000
500
0
0.0001
Data
Potential
IAST
Forbes
3500
3000
12 DCB K OC
12DCB K OC
Forbes
2500
2000
1500
1000
500
0.001
0.01
0.1
0
0.01
1
Qe CB (cm3/kg OC)
Data
Potential
IAST
Forbes
3000
3000
2500
2000
12DCB K OC
12DCB KOC
Data
Potential
IAST
Forbes
2500
1500
1000
500
2000
1500
1000
500
0
0.001
0.01
0.1
1
0
0.01
10
0.1
Qe NP (cm3/kg OC)
10
Data
Potential
IAST
Forbes
2500
2500
2000
2000
12DCB K OC
12DCB K OC
1
Qe 14DCB (cm3/kg OC)
Data
Potential
IAST
Forbes
3000
1500
1000
1500
1000
500
500
0
0.01
0.1
Qe TCB
1
(cm3/kg
0
0.001
10
0.1
1
10
Qe TeCB (cm3/kg OC)
Data
Potential
IAST
3000
3000
2000
2000
12DCB K OC
2500
1500
1000
Data
Potential
IAST
Forbes
2500
1500
1000
500
500
0
0.001
0.01
OC)
Forbes
12DCB K OC
10
1
0.1
Qe BZ (cm3/kg OC)
0.01
0.1
1
Qe PHN (cm3/kg OC)
0
0.01
10
0.1
Qe PY (cm3/kg OC)
S-5
1
Qe BZ
Data
Potential
IAST
Peat
2500
2000
1500
1000
500
0.01
(cm3/kg
0.1
0
0.001
1
(cm3/kg
1
10
OC)
Data
Potential
Peat
2500
IAST
2000
12DCB K OC
2000
1500
1000
1500
1000
500
500
0
0.001
0.01
0.1
1
0
0.001
10
0.01
Data
Potential
IAST
Peat
2500
(cm3/kg
1
10
OC)
Data
Potential
IAST
Peat
2500
2000
12DCB K OC
2000
1500
1000
1500
1000
500
500
0
0.01
0.1
1
0
0.001
10
0.01
0.1
Qe TeCB
Qe TCB (cm3/kg OC)
Peat
Data
Potential
IAST
(cm3/kg
1
10
OC)
Data
Potential
IAST
Peat
3000
2500
12DCB K OC
2000
1800
1600
1400
1200
1000
800
600
400
200
0
0.001
0.1
Qe 14DCB
Qe NP (cm3/kg OC)
12DCB K OC
0.1
Qe CB
Data
Potential
IAST
2500
12DCB K OC
0.01
OC)
Peat
12DCB K OC
Data
Potential
IAST
3000
12DCB K OC
12DCB K OC
Peat
5000
4500
4000
3500
3000
2500
2000
1500
1000
500
0
0.0001
0.001
2000
1500
1000
500
0.01
0.1
1
10
0
0.01
Qe PHN (cm3/kg OC)
0.1
Qe PY (cm3/kg OC)
1
Figure S1 – Multi-solute experimental data (12DCB + competitor), IAST model and Potential model
results for sorbates BZ, CB, NP, 14DCB, TCB, TeCB, PHN and PY as competitors in sorbents Yolo,
Forbes and Peat.
S-6
S4 – COMPARING MULTI-SOLUTE MODELS
To quantify the relative predictive ability of both models the average percent error of the
model fit was determined according to the equation:

n

i 1
Error (%)  
2
 KOC  K OC,m  

 


KOC

 
  100
df
(S1)
where KOC is the obtained experimental data and KOC,m is the modeled data. df is the number of
degrees of freedom given by the number of data points minus the number of fitting parameters
of the model. Applying Equation S1 to the comparison of the two model results for Yolo, Forbes
and peat are summarized in Table 2 in the manuscript.
S-7
S5 – COMPETITIVE EFFECT
S-8
Figure S2 – Competitive effect shown for all sorbates in a) Yolo, b) Forbes and c) Peat
respectively.
Table S3 - aqueous phase concentrations corresponding to the sorbed volume in Figure S3
BZ
CB
NP
14DCB
TCB
TeCB
PHN
Min-Max
Ce (mg/L)
0.002 - 3.5
0.03 - 13.5
0.001 - 3.4
0.001 - 8.5
0.006 - 3.0
0.0003 - 0.4
0.00005 - 0.04
S-9