Modelling of PFOS fate and transport
Thomas Franz, Adam Dawe, Lauren McDonald, Franz Environmental Inc.
Chris>ne Levicki, Health Canada John Miller, Environment Canada
Conceptual model
PFOS source area (e.g. FFTA)
Dissolved phase
Leachate
Groundwater flow
Drinking Water
Surface Water
• Par>>oning from soil to pore water (to soil gas)
• Transport of soil leachate through unsaturated zone
• Mixing of soil leachate into groundwater
• Transport in aquifer
Model capabilities • The model was designed to back-‐calculate soil concentra>on at PFOS source loca>on for a given concentra>on in groundwater or surface water
• Now also does “forward” calcula>ons by simula>ng PFOS transport • from soil to groundwater to receptor • within groundwater to receptor
CCME, 2006
• SQG = DF1 x DF2 x DF3 x DF4 x DF5 x CMAC
10 m
CMAC-‐DW
CMAC-‐AW
New: dilu%on at receptor (e.g. in wellbore or at groundwater -‐ surface water interface)
CCME, 2006
• SQGPW = DF1 x DF2 x DF3 x DF4 x DF5 x CMAC
10 m
CMAC-‐DW
CMAC-‐AW
CCME, 2006
• SQGPW = DF1 x DF2 x DF3 x DF4 x DF5 x CMAC
10 m
CMAC-‐DW
CMAC-‐AW
1 1 1
~7.3 ~2.4 to ~3.4
Cs = Kd Cw
Sorp%on
Higgins & Luthy, 2006) Tang et al., 2010
• PFOS-‐surface electrosta>c • PFOS-‐PFOS electrosta>c repulsion • Hydrophobic sorp>on
foc increases à Kd increases Higgins & Luthy, 2006)
What affects sorption? • Hydrophobic sorp%on
• Hydrophobic interac>on is the dominant mechanism of PFOS sorp>on to organic carbon,
• Strong hydrophobic nature of its perfluoroalkyl chain (Higgins and Luthy, 2006).
• Hydrophobic interac>on can also arise between the hydrophobic chains of different PFOS molecules.
• Koc and foc (frac>on of organic carbon) are used to calculate adsorp>on coefficient (Kd)
What affects sorption? • Hydrophobic sorp%on
• PFOS sorp>on increases in oil contaminated soil
Oil contaminated soil à Kd increases Chen et al., 2009
Higgins & Luthy, 2006)
What affects sorption? • PFOS-‐surface electrosta%c interac%on due to pH.
• PFOS is nega>vely charged under all environmentally relevant pH values.
• A mineral surface becomes more posi>vely charged (or less nega>vely charged) at lower pH.
• Enhanced electrosta>c afrac>on force (or reduced electrosta>c repulsion force) results between the nega>vely charged PFOS molecules and the more posi>vely charged mineral surface at lower pH.
pH decreases à Kd increases
What affects sorption? • PFOS-‐PFOS electrosta%c interac%on.
• Two adjacent PFOS molecules on a surface will repel each other due to their nega>vely charged sulfonate head groups.
• A strong PFOS-‐PFOS repulsion tends to prevent these molecules gehng close to each other.
• Thus, a solu>on with high ionic strength has a tendency to promote PFOS adsorp>on as a result of the suppressed electrosta>c repulsive force.
Higgins & Luthy, 2006)
What affects sorption? • PFOS-‐surface electrosta%c interac%on due to pH.
• PFOS is nega>vely charged under all environmentally relevant pH values.
• A mineral surface becomes more posi>vely charged (or less nega>vely charged) at lower pH.
• Enhanced electrosta>c afrac>on force (or reduced electrosta>c repulsion force) results between the nega>vely charged PFOS molecules and the more posi>vely charged mineral surface at lower pH.
pH decreases à Kd increases
(Higgins & Luthy, 2006; Chen et al., 2009; Pan and You, 2010; You et al., 2010)
What affects sorption? • PFOS-‐surface electrosta%c interac%on due to ionic strength.
• Electrosta>c interac>on can be significantly weakened at higher ionic strength due to the “double layer compression effect”.
• For a posi>vely charged mineral surface, the amount of adsorbed PFOS tends to be reduced due to the weaker electrosta>c afrac>on. However, for a nega>vely charged surface, adsorp>on tends to increase as a result of weaker electrosta>c repulsion.
e.g. Ca2+ decreases à Kd increases
Reviewed partitioning studies • Method
• Selected studies with low dissolved PFOS concentra>ons • Went to original papers • Eliminated duplica>ons and copies • Problem: many studies are for marine sediments (not soil)
• Results • PFOS Koc ranges from 229 to 6310 (Kd from 0.08 to 250 L/kg) • Median Koc 1441 L/kg (log Koc = 3.16 L/kg)
• Programmed spreadsheet to determine median Koc • Can add data to this spreadsheet and/or manually override Koc • Programmed pseudo-‐func>on to simulate pH dependency
Typical Koc’s (L/kg) • Benzene = 165.5 • Toluene = 268 • B(a)P = 787000 • Naphthalene = 1837 • TCE = 67.7 • Acenaphthene = 6123
• PFOS = 229 -‐> 6310
Authors Year Kd units Koc units Soil type / soil source Enevoldsen & Juhler 2010 15 L/kg 1500 L/kg Jyndevad (Denmark) soil, agricultural topsoil, A horizon, sandy soil Enevoldsen & Juhler 2010 17 L/kg 4048 L/kg Sj. Odde (Denmark) soil, agricultural topsoil, A horizon, clayey soil Ferry et al 2012 1.23 L/kg 3514 L/kg Minnesota aquifer material from landfill Ferry et al 2012 0.08 L/kg 229 L/kg same microcosm, but at end of 740 d study 3M 2001 18.3 L/kg 704 L/kg clay 3M 2001 9.72 L/kg 374 L/kg clay loam 3M 2001 35.3 L/kg 1260 L/kg sandy loam 3M 2001 7.42 L/kg 571 L/kg river sediment Chen et al., 2012 2012 38.0 L/kg 2659 L/kg marine sediment, S1, from Dalian coastal area, China Chen et al., 2012 2012 25.7 L/kg 2596 L/kg marine sediment, S2, from Dalian coastal area, China Chen et al., 2012 2012 25.1 L/kg 3101 L/kg marine sediment, S3, from Dalian coastal area, China Chen et al., 2012 2012 20.0 L/kg 2660 L/kg marine sediment, S4, from Dalian coastal area, China Chen et al., 2012 2012 15.8 L/kg 3774 L/kg marine sediment, S5, from Dalian coastal area, China Chen et al. 2009 12.3 L/kg 1349 L/kg soil from paddyfield in Panjin, China Higgins and Luthy 2006 16 L/kg 372 L/kg freshwater sediments (rivers and lakes) from USA Ahrens et al. 2011 1.5 L/kg 5012 L/kg sandy river sediment from Kogaigawa, Japan Ahrens et al. 2011 50.6 L/kg 3162 L/kg muddy river sediment from Sakuragawa, Japan Ahrens et al. 2011 27.6 L/kg 2512 L/kg muddy marine sediment from Tokyo Bay Ahrens et al. 2010 126 L/kg 6310 L/kg marine sediment cores from Tokyo Bay, Japan Kwadijk et al. 2010 224 L/kg 1445 L/kg 19 sediment samples from rivers, lakes, canals in Netherlands Labadie & Chevreuil 2011 251 L/kg 5012 L/kg sediment from Orge River, France (near Paris) Johnson et al. 2007 2.81 L/kg Ofawa sand Johnson et al. 2007 5.31 L/kg 265.5 L/kg kaolinite Johnson et al. 2007 7.52 L/kg 376 L/kg Lake Michigan sediment Johnson et al. 2007 7.88 L/kg goethite Johnson et al. 2007 8.9 L/kg high iron sand Johnson et al. 2007 18.3 L/kg 610 L/kg clay Johnson et al. 2007 9.72 L/kg 324 L/kg clay loam Johnson et al. 2007 35.3 L/kg 1177 L/kg sandy loam Johnson et al. 2007 7.42 L/kg river sediment
Model limitations • Equilibrium par>>oning between soil, water, vapour phases
• Assumes rela>vely low concentra>ons in soil / groundwater • PFOS concentra>ons should be less than 10 mg/L • Solubility = 500 to 600 mg/L (in freshwater), 12 mg/L (in seawater)
• Homogeneous geology • However, can be different in unsaturated / saturated zones
• Uniform, unidirec>onal groundwater flow • Constant source concentra>on
Primary data requirements • Coordinates: x, y, z • Geometry of PFOS source • Frac>on of organic carbon (foc) • Unsat zone thickness • Average linear groundwater velocity • Aquifer thickness • Dispersivity
Model comparison of predicted vs measured ground water concentrations Four sites evaluated for transport modeling and empirical Kd calcula>ons: • All at civilian or military airports • 3 FFTAs • 1 disposal site
Dissolved phase
Koc, foc-‐unsat, m
Koc, foc-‐sat, b, v, n, alfa
C=?
Disposal Site Soil and Ground Water Concentrations
33.0
11.8
21
34.5
26.1
3
0.077
0.160
33.0 = Ground Water Concentra>on, units of ug/L
0.077 = Soil Concentra>on, units of mg/Kg
Oil Disposal Pit
28
FFTA 1 – Shallow Ground Water Contours
Fire Training Area
FFTA 1 – Soil and Ground Water Concentrations
4400
2100
600
2.8
<0.02 = Ground Water Concentra>on, units of ug/L
2800 = Soil Concentra>on, units of mg/Kg
Fire Training Area
Model result with literature Koc (Koc = 1445 L/kg; Kd,sat = 2.17 L/kg)
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
0 5 10 15 20 25 30 35 40 45 50
Concen
tra>
on (m
g/L)
Distance from source (m)
Predicted vs Measured PFOS Concentra>ons in Ground Water
Predicted
Measured
Model “calibration” • Match between observed and modelled PFOS concentra>ons was not very good using literature Koc (Kd) values
• Approach to improve model results:
• 1) Derived site-‐specific Kd values based on co-‐located soil and groundwater samples from the site(s) -‐> re-‐run model
• 2) Adjust Kd to obtain best fit -‐> “brute force”
Empirical Koc’s • Calculated “empirical” Kd by comparing co-‐located soil and groundwater sample concentra>ons
• Calculated Koc from Kd = Koc foc • foc based on site-‐specific data
• Empirical Koc values range from 85 to 7619 (L/kg) • Literature Koc values range from 229 to 6310 (L/Kg)
Model result with median empirical Kd (Kd = Koc foc = 1.6 L/kg ; Koc = 424 L/kg)
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
0 5 10 15 20 25 30 35 40 45 50
Concen
tra>
on (m
g/L)
Distance from source (m)
Predicted vs Measured PFOS Concentra>ons in Ground Water
Predicted
Measured
Brute Force – best fit • Excel SOLVER used to adjust Kd
-‐> obtain best fit between observed and modelled PFOS concentra>ons
Model result with best fit Kd (Kd sat = 0.1 Kd unsat) (Kd,sat = 0.514 L/kg; Koc = 342 L/kg)
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
0 5 10 15 20 25 30 35 40 45 50
Concen
tra>
on (m
g/L)
Distance from source (m)
Predicted vs Measured PFOS Concentra>ons in Ground Water
Predicted
Measured
Adjust other model parameters
• Koc alone is not the problem… • Modified transverse (horizontal) dispersivity to achieve befer match
• Likely unrealis>c dispersivity value
Adjust transverse dispersivity (transverse = longitudinal dispersivity) (Kd,sat = 1.2 L/kg)
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
0 5 10 15 20 25 30 35 40 45 50
Concen
tra>
on (m
g/L)
Distance from source (m)
Predicted vs Measured PFOS Concentra>ons in Ground Water
Predicted
Measured
Conclusions • Simple mathema>cal model for PFOS fate & transport has been developed.
• Runs in Excel, easy to use, has a series of limita>ons. • Limita>ons are similar to other models used for guideline development and risk assessment
• Use with cau>on for site specific applica>ons • Primary afenua>on process for PFOS is par>>oning (sorp>on).
• Greatest uncertainty for modelling of PFOS stems from par>>oning coefficient • Review of Koc’s
• Empirical Koc’s range from 85 to 7619 L/kg • Literature Koc’s range from 229 to 6310 L/kg
• Model results match field data befer for Koc’s at the low end of the range (i.e. less sorp>on / more mobile)
Recommenda>ons
• For assessment of PFOS fate & transport, we need to collect • foc data in unsaturated soil zone • foc data in aquifer (groundwater transport) zone • Collect data pairs of “co-‐located” soil and groundwater directly below source to determine site-‐specific Kd
• pH
Acknowledgments • Sanya Petrovic, Health Canada • Brian Asher, Health Canada • Luigi Lorusso, Health Canada • Jo-‐Ann Aldridge, Environment Canada • Philippa Coureton, Environment Canada
Disposal Site Soil and Ground Water Concentrations
33.0
11.8
21
34.5
26.1
3
0.077
0.160
33.0 = Ground Water Concentra>on, units of ug/L
0.077 = Soil Concentra>on, units of mg/Kg
Oil Disposal Pit
28
Cold lake – best solution, solve for unsat and sat Kd (0 <= unsat kd <= sat Kd
0
0.005
0.01
0.015
0.02
0.025
0.03
0.035
0.04
0 50 100 150 200 250 300 350 400
Concen
tra>
on (m
g/L)
Distance from source (m)
Predicted vs Measured PFOS Concentra>ons in Ground Water
Predicted
Measured
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