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MODELED PCB WEATHERING SERIES IN PRINCIPAL COMPONENTS SPACE:

CONSIDERATIONS FOR MULTIVARIATE CHEMICAL FINGERPRINTING

Glenn W. Johnson Energy and Geoscience Institute

Department of Civil & Environmental Engineering University of Utah, Salt Lake City, UT

Michael J. Bock

ENVIRON International Portland, ME

Introduction

• Well-recognized that PCBs can weather after release to the ambient environment. • Degree of weathering differs for different congeners. Lighter congeners are more susceptible to

weathering Degree of weathering is dependent on the material

to which PCBs are adsorbed. •We have seen this empirically, both in bench-scale lab experiments, and field studies.

Johnson PCDD/F - INEF 2013 – Penn State University

Example: Volatilization Experiments

Residual from Arolcor 1242 experiment looked more like A1248. How do such alteration series manifest in multivariate space?

Chiarenzelli, et al., 1997. (ES&T, 31: 587). Reported results of a series of laboratory volatilization experiments for four Aroclors.

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Lake Hartwell, South Carolina

Weathering of PCBs • Just two examples

•A number of studies have demonstrated this.

•Such alteration is most dramatic when “lighter” Aroclors are weathered (e.g. Aroclor 1242).

• Tremendous implications for PCB fingerprinting/forensics.

•In this paper we demonstrate the influence this has on a common PCB fingerprinting tool: Principal Components Analysis (PCA).

• We use fugacity to simulate the weathering of Aroclor 1242 in different environments.

• We show the data from these weathering models, and how the changes in congener profiles manifest in PCA score-plots.

Johnson PCDD/F - INEF 2013 – Penn State University

Principal Components Analysis (PCA)

• Widely used in Environmental Forensics and beyond. • Can be a stand-alone Exploratory Data Analysis Method. • Or as an intermediate step in receptor modeling • Objective: In its most common form - A data visualization tool.

PCA scores plot •I’ll give you two simple PCB examples Both are three source systems (i.e. 3 Aroclors) First Example is strongly clustered Second Example has the same three sources, but Mixtures

Johnson PCDD/F - INEF 2013 – Penn State University

PCBs: Aroclor Congener Compositions Data from Frame, et al., 1996

Figure from Johnson, et al., 2006 (p. 182)

Johnson PCB - INEF 2013 – Penn State University

Data Set 1: A Stongly Clustered Data Set From Johnson, et al. (2007)

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From: Johnson, et al., 2007: Chapter 7 (p. 231)

Same 3 Sources, but Mixtures

Fugacity Modeling •Fugacity is a quantitative measure of the “escaping” tendency of a chemical from one phase to another.

• Objective: Predict concentrations of a chemical in multiple phases (e.g., air, soil, and water) based on equilibrium partitioning equations.

•It has been applied to PCBs but rarely to full PCB congener profiles.

•Bock and Johnson recently developed a fugacity model based on Land Applied Biosolids model (LABS) of Hughes and Mackay.

• Model used to estimate the mass loss over time and alteration of congener profiles over time.

Fugacity Modeling: Formulation •Starting Composition: Full 209 congener-composition of Aroclor 1242 (Frame, et al.1996 – Spl A3).

•Fugacity equations used to model congener specific weathering due to volatilization and solubilization/leaching,

•Modified LABS (Hughes and Mackay 2011). – Model simulated single release of PCBs to soil/sed – Koc values based on Poly parameter linear-free energy

relationships (PP-LFER) of Hawthorne et al.(2011) – Two types of organic matter substrates were modeled:

1. Natural Organic Mater (NOM) (surrogate for fresh) 2. Coal Tar (CT) (surrogate for decayed organic matter)

Model Formulation •In the full paper (Bock and Johnson, in prep), we will report model results for five separate environments.

•For purposes of PCA demonstration, the type of environment modeled was submerged sediment, with parameters shown below.

Model Run

Aroclor Source Matrix

Depth (cm)

Diffusivity in Water (m2/h)

Porewater Exchange/ Leaching

Rate (mm/day)

Boundary Layer (mm)

Diffusion Path

Length (mm)

Proportion of Volume

as Air

Proportion of Volume as Water

Organic Matter

Model Duration

1 Aroclor 1242

Submerged Sediment

5 0.000002 500 4 4 0.01 0.49 CTb 50 years

2 Aroclor 1242

Submerged Sediment

5 0.000002 500 4 4 0.01 0.49 NOMa 50 years

a NOM - Natural Organic Matter as surrogate for fresh organic materialb CT - Coal Tar as a surrogate for decayed organic matter

Table 1. Model Parameters

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PCA Applied to A1242 Volatilization Series • Starting composition: Frame (1996) Aroclor 1242 • 31 samples

– 27 weathering intervals and – 4 Aroclors as references

• Congeners filtered to exclude variables that never exceed 0.2% of total PCBs

– Reduced data set from 209 congeners to 130 congeners.

• We used pretty standard data pre-treatment for PCA – Normalize samples to percent – Normalize congeners by auto-scaling (Z-transform)

• 3 Principal Components account for 95% of variance…BUT

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Look More Closely at Goodness of Fit

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Let’s Look at Goodness of Fit

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What do the degraded samples look like as compared to Aroclors?

Zoom in on 3 Congeners

1242 Congener Profiles: 12 Hours w/ Coal Tar as a surrogate for decayed organic matter

1242 Congener Profiles: 24 Hours w/ Coal Tar as a surrogate for decayed organic matter

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1242 Congener Profiles: 48 Hrs (2 Days) w/ Coal Tar as a surrogate for decayed organic matter

1242 Congener Profiles: 4 Days w/ Coal Tar as a surrogate for decayed organic matter

1242 Congener Profiles: 8 Days w/ Coal Tar as a surrogate for decayed organic matter

1242 Congener Profiles: 30 Days w/ Coal Tar as a surrogate for decayed organic matter

1242 Congener Profiles: 60 Days w/ Coal Tar as a surrogate for decayed organic matter

1242 Congener Profiles: 90 Days w/ Coal Tar as a surrogate for decayed organic matter

1242 Congener Profiles: 0.5 Years w/ Coal Tar as a surrogate for decayed organic matter

1242 Congener Profiles: 1 Year w/ Coal Tar as a surrogate for decayed organic matter

1242 Congener Profiles: 2 Years w/ Coal Tar as a surrogate for decayed organic matter

1242 Congener Profiles: 3 Years w/ Coal Tar as a surrogate for decayed organic matter

1242 Congener Profiles: 4 Years w/ Coal Tar as a surrogate for decayed organic matter

1242 Congener Profiles: 5 Years w/ Coal Tar as a surrogate for decayed organic matter

1242 Congener Profiles: 10 Years w/ Coal Tar as a surrogate for decayed organic matter

1242 Congener Profiles: 15 Years w/ Coal Tar as a surrogate for decayed organic matter

1242 Congener Profiles: 20 Years w/ Coal Tar as a surrogate for decayed organic matter

1242 Congener Profiles: 25 Years w/ Coal Tar as a surrogate for decayed organic matter

1242 Congener Profiles: 30 Years w/ Coal Tar as a surrogate for decayed organic matter

1242 Congener Profiles: 35 Years w/ Coal Tar as a surrogate for decayed organic matter

1242 Congener Profiles: 40 Years w/ Coal Tar as a surrogate for decayed organic matter

1242 Congener Profiles: 45 Years w/ Coal Tar as a surrogate for decayed organic matter

1242 Congener Profiles: Years w/ Coal Tar as a surrogate for decayed organic matter

Unaltered 1242

Aroclor 1242 Fugacity Modeled Weathering Series (CT Model) Plotted in 3 PC Space….

Not a straight line between A1242 and A1248. A curvilinear trajectory through multivariate space

NOM Model: 0.5 Years w/ NOM as a surrogate for decayed organic matter

The model with coal tar as our organic matter took 25 years to get this point

w/ NOM as a surrogate for decayed organic matter NOM Model: 4 Years

Unaltered 1242

Aroclor 1242 Fugacity Modeled Weathering Series (NOM Model) Plotted in 3 PC Space….

Not a straight line between A1242 and A1248. A curvilinear trajectory through multivariate space

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Lake Hartwell, South Carolina

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Lake Hartwell PCBs – Multiple Source / Multiple Alteration Conceptual Model

Volatilization

Dechlorination

4 Component Model

Conclusions •Rate at which PCBs weather (and profiles change) depends greatly on the type of organic matter present in soil/sediment.

•In multivariate space, weathered patterns (dechlorination and volatilization) do not manifest as simple linear mixing of end members. Rather: curvilinear lines through multi-dimensional space.

•Linear Mixing Models (PCA-based or otherwise): Resolved fingerprint patterns are interpretable in context of weathering patterns published in the literature.

It may not be linear and it may not be mixing. •But PCA & related methods still a good way to gain insight. Just be aware of limitations of the conceptual model:

Conclusions (Cont.) •A non-linear trend in PCA - may result in apparent need to retain more principal components / sources to accurately back-calculate data, and still there will be non-random lack of fit

•If you suspect alteration, perhaps relax your goodness-of-fit criterion.

•If alteration suspected - can’t blindly interpret end-members as “sources” mixing proportions.

Receptor Modeling: 4 Patterns • End-member 1 appeared to match a

relatively unaltered, homogenized 1248/1254 source

• Aroclor 1248 not a suspected source • The true source was suspected to be

devolatilized 1242

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Lake Hartwell PCBs – CMB Approach

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Lake Hartwell PCBs – Receptor Modeling

EM-2: Devol 1242/1016

EM-1: 1248/1254

EM-4: Dechlorination 2 (H’)

EM-3: Dechlorination 1 (C)

Conceptual Model

Surface Soil

Air Transport

Air Pore Water Mineral Grains

Volatilization Aroclor in

Surface Soil

Leaching

Fugacity Modeling

Model Formulation •Starting Composition: Full 209 congener-composition of Aroclor 1242 (Frame, et al., 1996).

•ackay Level III (non-equilibrium, non-steady state) – Based on BASL model, single input of Arcolor

http://www.trentu.ca/academic/aminss/envmodel/models/BASL4110.html

– Congener specific • Physical, chemical properties based on Oberg 2001 (QSAR

based) http://www.tomasoberg.com/qspr/papersource.html – Currently models volatilization and dissolution/leaching,

modeling of dechlorination theoretically possible if rates and pathways are completely described

– Can be adapted to model bioaccumulation

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In Presence of Alteration • Some of the corners are not source compositions. • The are products of alteration. • We carry on, treating degradation end-products as if

they behave (geometrically) as linearly mixing sources.

• But if we look at experimental data with single source and alteration - probably not that simple:

Aroclor 1248 Dechlorination – Next few slides from Johnson & Quensen (2000). Organohalogen

Compounds. 45: 280-283 – Aroclor 1248 dechlorinated in lab by anaerobic micro-organisms eluted

from Hudson River sediments. – 20 week experiment, with 3 replicates collected at four week intervals. – Congener specific PCB data were reported based on quantitation of 87

peaks.

In PCA: 3 Corners / 3 “Sources”

Unaltered Aroclor 1248

IUPAC Congener Number

Intermediate Dechlorination

IUPAC Congener Number

End-Member 3: End-Point Dechlorination

IUPAC Congener Number

Fugacity Modeling •A framework for predictive modeling for movement of chemicals from one phase to another

•Fugacity is a quantitative measure of “escapability” •Objective: predict concentration in multiple phases (air soil, water) based on equilibrium partitioning equations.

•Has been applied to PCBs (Harner, 1995; Sweetman, 2009)

• Bock & Johnson recently developed fugacity model based on LABS model of Hughes and Mackay. – Used to estimate mass loss of PCBs over time, and – Evaluate alteration of PCB congener profiles due to

differential weathering. – Such data used here, plotted in PCA space to evaluate

degradation series.