Post on 20-Mar-2020
EMERGING CONTAMINANTS IN ECOSYSTEMS:
NEW CHALLENGES FOR WATER REUSE IMPLEMENTATION AND
MECHANISMS OF PERFLUOROCHEMICAL BIOACCUMULATION
A DISSERTATION
SUBMITTED TO THE DEPARTMENT OF
CIVIL AND ENVIRONMENTAL ENGINEERING
AND THE COMMITTEE ON GRADUATE STUDIES
OF STANFORD UNIVERSITY
IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY
Heather Nicole Bischel August 2011
http://creativecommons.org/licenses/by-nc/3.0/us/
This dissertation is online at: http://purl.stanford.edu/zs920pn1994
© 2011 by Heather Nicole Bischel. All Rights Reserved.
Re-distributed by Stanford University under license with the author.
This work is licensed under a Creative Commons Attribution-Noncommercial 3.0 United States License.
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I certify that I have read this dissertation and that, in my opinion, it is fully adequatein scope and quality as a dissertation for the degree of Doctor of Philosophy.
Richard Luthy, Primary Adviser
I certify that I have read this dissertation and that, in my opinion, it is fully adequatein scope and quality as a dissertation for the degree of Doctor of Philosophy.
Martin Reinhard
I certify that I have read this dissertation and that, in my opinion, it is fully adequatein scope and quality as a dissertation for the degree of Doctor of Philosophy.
Laura MacManus-Spencer
Approved for the Stanford University Committee on Graduate Studies.
Patricia J. Gumport, Vice Provost Graduate Education
This signature page was generated electronically upon submission of this dissertation in electronic format. An original signed hard copy of the signature page is on file inUniversity Archives.
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Abstract Technological innovations developed in response to pressing water supply needs in
populated arid regions have led to the recovery of municipal wastewater for beneficial
reuse worldwide. At a time when rapid urbanization, severe droughts, and public
concern introduce complex management challenges for water reuse, the persistence of
residual and byproduct pharmaceutical and industrial chemicals in treated municipal
effluent gives rise to new technological hurdles. Bioaccumulation of synthetic organic
chemicals in environments downstream of wastewater effluent discharge and recycled
water use poses an ecological risk and introduces a potential pathway of human
exposure to these contaminants. This dissertation assesses management challenges for
water reuse implementation in Northern California; identifies opportunities of water
reuse for ecosystem enhancement; explores the bioaccumulation of one class of
persistent and toxic unregulated chemical contaminants, perfluoroalkyl acids (PFAAs);
and evaluates mechanisms of bioaccumulation via an in-depth study of PFAA
interactions with a model serum protein.
Chapter 1 provides background and outlines research objectives governing this
dissertation. In Chapter 2, major factors that influenced the implementation of
nonpotable water reuse in Northern California are presented based on a survey of
program managers. Capturing experiences of managers in urban and peri-urban regions
of California provides context for the historical developments of water reuse and the
sources of barriers to implementation. Results demonstrate that in recent times, water
reuse is driven more often by water supply needs rather than by wastewater discharge
limitations. From a management perspective, economic issues stand as the largest
hindrance to successful project implementation, while negative perceptions of water
reuse less frequently inhibit nonpotable water reuse projects. Analysis conducted in
Chapter 3 indicates that while ecosystem protection goals are frequently drivers of
water reuse programs, few water reuse projects have been implemented in California
explicitly for ecosystem enhancement or wildlife habitat creation. Augmentation of
degraded wetlands with recycled water represents an opportunity for expansion of
inland water reuse programs. However, detection of persistent, unregulated
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contaminants in recycled water presents management challenges for these projects.
The ability to accurately predict the bioaccumulative potential of chemicals in
aquatic organisms is an essential component to assessing the human health and
ecological risk of trace micropollutants. The bioaccumulation of PFAAs presents a
particularly intriguing case. Unlike other persistent organic pollutants, PFAAs do not
preferentially accumulate in lipids and fatty tissue but rather in body compartments with
high protein content, including the liver, kidneys, and serum. In Chapter 4, PFAA
concentrations detected in the livers of white sturgeon from the San Francisco Bay are
presented as a brief study on the environmental prevalence and bioaccumulation of
these chemicals. A fugacity-based approach that utilizes protein-water distribution
coefficients (KPW) based on interactions with model proteins is introduced as a useful
parameter to characterize the bioaccumulation and in vivo bioavailability of PFAAs.
Based on this modeling paradigm, noncovalent interactions of long-chain perfluoroalkyl
acids with bovine and human serum albumins (BSA and HSA, respectively) are
characterized in Chapter 5. Results suggest binding through specific high affinity
interactions at low PFAA:albumin mole ratios. In an effort to reduce the
bioaccumulation of PFAAs in humans and wildlife, fluorochemical manufacturers have
recently shifted production to shorter chain-length compounds. In Chapter 6,
associations of perfluoroalkyl carboxylates (PFCAs) with 2 to 12 carbons (C2 – C12) and
perfluoroalkyl sulfonates with 4 to 8 carbons (C4, C6, and C8) with BSA and
physiochemical binding mechanisms are evaluated at physiologically relevant
PFAA:albumin mole ratios and various solution conditions using equilibrium dialysis,
nanoelectrospray ionization mass spectrometry, and fluorescence spectroscopy.
Measured log KPW values for C4 to C12 PFAAs confirm that protein associations as
characterized in this model scenario prove to be greater in magnitude for PFAAs than
lipid-based partitioning coefficients. Association constants determined for
perfluorobutanesulfonate and perfluoropentanoate with BSA are on the order of those
for long-chain PFAAs, suggesting that physiological implications of strong binding to
albumin may be important for short-chain PFAAs.
In the final chapter, conclusions are drawn for research objectives outlined initially,
and future research directions are identified. The presented evaluation of management
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challenges for water reuse implementation provides context for issues surrounding
chemicals of emerging concern (CECs) in recycled water. However, uncertainty
regarding bioaccumulation of CECs from recycled water used for direct habitat
enhancement or creation remains a concern. Investigation of mechanisms influencing
PFAA bioaccumulation provides insight into one class of CECs now detected in
sensitive aquatic ecosystems. As the elimination of one unsafe chemical does not
guarantee the safety of its commercial replacements, presented findings further
contribute to ongoing efforts to characterize the physiochemical properties and
anticipated environmental fate of compounds used to replace long-chain perfluorinated
chemicals.
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Acknowledgement First, I thank my advisor Richard Luthy for thoughtful mentoring, consistent
guidance, and for always challenging each of his students to keep the big picture in
mind. Dick is living reassurance that kindness and compassion go hand-in-hand with
effectiveness and impact – a wonderful role model to have. A special thanks to Laura
MacManus-Spencer for invaluable cross-coast collaboration (including many hours on
the phone and conference-roommate chats) and for a delightful friendship. I also thank
Martin Reinhard, Jim Leckie, and Buzz Thompson, for joining my dissertation
committee.
One of the most enjoyable components of my work has been the opportunity to
interact with and learn from so many extraordinary people. Many thanks to the entire
Luthy Research Group for plenty of support and encouragement; to: Chris, Laura, and
Pam for helping me get started in the lab; to YeoMyoung and Eunah – organic
chemistry extraordinaires – from whom I learned so much as teachers and teaching
partners; Sarah for office-time advice; Jay for mini-debriefs; Aude for her work and
grounded perspectives on water reuse for ecosystems; Jeanne and Diana for a bit of
tennis; Chinghong, Lilli, Sungwoo, Yuan, and Yongju for research advice with a smile;
and Niveen for summertime chats as I finished up.
I am also tremendously grateful to: Gregory Simon and Tammy Frisby for diving
right in with recycled water field trips and helping me view our work from different
perspectives; Sophie Egan for hours of meticulous work, and contagious enthusiasm;
the many water reuse professionals who volunteered their time to participate in our
project and answer our phone calls; the professors at Cal who helped set me on this path
(Go Bears!) and the ones at Stanford who opened up even more; my best bud (at
Stanford), Liv Walter, for giving me the keypad code and keeping her door unlocked;
my dear friend Blythe Layton for countless hugs and laughs; the LCMS csars of the past
for keeping it running; the EES moles– too numerous to name – for advice and beer;
Royal, of course, who always lent a helpful hand; EFML nerds, especially Liv, Joel,
Sarah, Erin, Jaime, and Yacoub for crossing the great divide for many meals and a few
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crafts together; ESW friends, especially Eric, Grace, Kathy, Kristof, Milena, Sophie,
and Julie Chow, for trying to save the world; the San Francisco Bay and the Sierras for
inspiration and fun; my awesome housemates – from Cal Ave. to Cowper; my long-time
friends Gracie, Celia, Ycxia, Edna, Rachel, Christi, Heena, and Kofi and my family –
Dad, Mom, Tyler, Mandy and Drew – for lots of love and support, even from afar.
Finalement, à mon mec, Nico – une joie de vivre depuis mon premier jour à
Stanford. Merci à tous!
Support for my work was provided by the Stanford Woods Institute for the
Environment (Environmental Ventures Project), the National Science Foundation
Graduate Research Fellowship, and the National Defense Science & Engineering
Graduate Fellowships.
"When the well's dry, we know the worth of water."
- Benjamin Franklin (1706-1790), Poor Richard's Almanac.
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Contents Abstract .......................................................................................................................... v
Acknowledgement ..................................................................................................... viii
Contents ......................................................................................................................... x
List of Tables .............................................................................................................. xii
List of Figures .............................................................................................................. xv
Introduction ................................................................................................................... 1
1.1 Motivation and Background ................................................................................... 1
1.2 Research Objectives and Précis .............................................................................. 6
Management experiences and trends for water reuse implementation in Northern
California ........................................................................................................... 9
2.1 Introduction ............................................................................................................. 9
2.2 Methodology ......................................................................................................... 11
2.3 Analysis of Water Reuse in California ................................................................. 13
2.4 Drivers of Water Reuse Implementation in Northern CA .................................... 16
2.5 Challenges for Water Reuse Implementation in Northern CA ............................. 22
2.6 Significance ........................................................................................................... 28
2.7 Supporting Information ......................................................................................... 30
Water reuse for ecosystem enhancement: Matching opportunity with need ........ 51
3.1 Introduction ........................................................................................................... 51
3.2 Few Existing Examples of Water Reuse for Direct Ecosystem Enhancement in
California .................................................................................................................... 52
3.2 Identifying Opportunities for Ecosystem Enhancement ....................................... 57
3.3 Technological and Management Challenges ........................................................ 63
3.4 Significance ........................................................................................................... 68
xi
Exposure of perfluorinated chemicals to San Francisco Bay white sturgeon and
mechanisms of bioaccumulation ................................................................... 69
4.1 Introduction .......................................................................................................... 69
4.2 Predictive models for PFAA bioaccumulation ..................................................... 71
4.3 Materials and Methods ......................................................................................... 74
4.4 Results and Discussion ......................................................................................... 77
4.5 Significance .......................................................................................................... 83
Investigating binding to a model protein: Noncovalent interactions of long-chain
perfluoroalkyl acids with serum albumin .................................................... 85
5.1 Introduction .......................................................................................................... 85
5.2 Materials and Methods ......................................................................................... 88
5.3 Results and Discussion ......................................................................................... 94
5.4 Significance ........................................................................................................ 105
5.5 Supporting Information ...................................................................................... 107
Strong associations of short-chain perfluoroalkyl acids with serum albumin and
investigation of binding mechanisms .......................................................... 123
6.1 Introduction ........................................................................................................ 123
6.2 Methods .............................................................................................................. 126
6.3 Results and Discussion ....................................................................................... 128
6.4 Significance ........................................................................................................ 139
6.5 Supporting Information ...................................................................................... 142
Conclusions ............................................................................................................... 161
7.1 Summary Conclusions ........................................................................................ 161
7.2 Future Work ........................................................................................................ 166
7.3 Final Thoughts .................................................................................................... 168
References ................................................................................................................. 171
xii
List of Tables Table 2.1. Percent of respondents indicating a specific factor as a Driver or one of the
three Most Important Drivers. .......................................................................... 17
Table 2.2. Percent of respondents indicating a specific factor as a Hindrance or one of
the three Most Important Hindrances. .............................................................. 23
Table 2.3S. Chi square analysis of drivers of program implementation by self-reported
date of implementation. .................................................................................... 40
Table 2.4S. Chi square analysis of hindrances to program implementation by self-
reported date of implementation. ...................................................................... 41
Table 2.5S. Chi square analysis of categorized drivers of program implementation by
self-reported date of implementation. ............................................................... 42
Table 2.6S. Chi square analysis of categorized hindrances to program implementation
by self-reported date of implementation. .......................................................... 43
Table 2.7S. Chi square analysis of drivers of program implementation by self-reported
total annual reclaimed water use. ...................................................................... 44
Table 2.8S. Chi square analysis of hindrances to program implementation sorted by
total annual reclaimed water use. ...................................................................... 45
Table 2.9S. Chi square analysis of categorized drivers of program implementation by
self-reported total annual reclaimed water use. ................................................ 46
Table 2.10S. Chi square analysis of hindrances to program implementation by self-
reported total annual reclaimed water use. ....................................................... 47
Table 2.11S. Representation of recycled water beneficial uses from the 2010 Survey of
Northern California (n = 69) agencies and the State Water Resources Control
Board 2009 Municipal Wastewater Recycling Survey (n = 143). .................... 48
Table 2.12S. Milestones for California water reuse and statewide recycling goals. .... 49
Table 2.13S. San Francisco Bay Area Recycled Water Coalition 2011 Project Summary
(64) .................................................................................................................... 50
Table 3.1. Projects in Northern California utilizing recycled water for ecosystem
enhancement or treatment wetlands for wastewater effluent polishing. ........... 55
xiii
Table 4.1. Analyte primary and secondary transitions monitored, internal standards
(IS), average concentration of MDL samples (with standard deviation of 12
replicates), and calculated MDLs. .................................................................... 76
Table 5.1. Percent bound and log KPW for PFNA binding to 500 µM albumin
determined by equilibrium dialysis and LC-MS/MS. ...................................... 96
Table 5.2. Association constants (Ka) and binding stoichiometries (n) for PFOA and
PFNA binding to BSA determined by equilibrium dialysis. ............................ 98
Table 5.3. Summary of PFCA-albumin association constants (Ka) and binding
stoichiometries over a range of ligand:protein mole ratios ([L]:[P]). ............ 104
Table 5.4S. Association constants (Ka,1) and binding stoichiometries (n1 and n2) for
PFOA binding to 1 µM BSA as determined by equilibrium dialysis for a range
of applied weighting factors. .......................................................................... 114
Table 5.5S. Association constants (Ka,1) and binding stoichiometries (n1 and n2) for
PFNA binding to 1 µM BSA as determined by equilibrium dialysis for a range
of applied weighting factors. .......................................................................... 114
Table 5.6S. Association constants (Ka,1 and Ka,2) and binding stoichiometries (n1 and
n2) for PFOA and PFNA and binding to 1 µM BSA as determined by
equilibrium dialysis using Equation 5.16S with no weighting factor (WF) and a
1/[Free PFAA] weighting factor. .................................................................... 115
Table 5.7S. Association constants (Ka,1) and binding stoichiometries (n1) for PFOA and
PFNA binding to 1 µM BSA as determined by equilibrium dialysis for a subset
of the total data and a one-class binding model. ............................................ 117
Table 5.8S. Estimated association constants calculated from nanoESI-MS results for 50
µM BSA exposed to PFOA (25, 50, and 100 µM). ........................................ 120
Table 5.9S. Estimated association constants calculated from nanoESI-MS results for 50
µM BSA exposed to PFNA (25, 50, and 100 µM). ........................................ 120
Table 5.10S. Estimated association constants calculated from nanoESI-MS results for
50 µM BSA exposed to PFDA (25, 50, and 100 µM). ................................... 121
Table 5.11S. Estimated association constants calculated from nanoESI-MS results for
50 µM BSA exposed to PFOS (25 µM). ........................................................ 121
xiv
Table 6.1. Fraction of perfluoroalkyl acids (PFAAs) bound to 200 µM bovine serum
albumin (BSA) and log protein-water distribution coefficients for PFAAs with
BSA measured over a range of equilibrium free PFAA reservoir concentrations.
......................................................................................................................... 129
Table 6.2S. Dialysis mass balance, reservoir matrix and bovine serum albumin (BSA)
spike recovery results, and liquid chromatography tandem mass spectrometry
(LC-MS/MS) transitions monitored. ............................................................... 146
Table 6.3S. Protein-water distribution coefficients for PFAAs with BSA. ............... 147
Table 6.4S. Measured incremental mass shifts (ΔM) from measured BSA peak (P) to
BSA-PFAA peaks (P + jL) for representative spectra in manuscript Figure 6.2
and Supporting Information Figure 6.7S. ....................................................... 149
Table 6.5S. Average fraction of PFAAs bound to BSA (197 ± 2 µM) for a range of pH
conditions. ....................................................................................................... 152
Table 6.6S. Slope of linear regressions for average number of PFAAs bound to BSA,
!
" , versus pH (6 to 9) in equilibrium dialysis tests. ........................................ 153
xv
List of Figures Figure 1.1. Structures and names of perfluoroalkyl acids (PFAAs) included in this
study. .................................................................................................................. 4
Figure 2.1. Timeline of statewide water recycling goals and production volumes, major
drought periods, and select water recycling laws and policies in California
during the implementation period for survey respondents. .............................. 10
Figure 2.2. A snapshot of water reuse facilities in California from the National
Database of Water Reuse Facilities (Annual Production, reported as Facility
Production Average Annual Actual in million gallons) and the California 2009
Municipal Water Recycling Survey (Annual Reuse, reported as Total Reuse for
2009 in AFY). ................................................................................................... 15
Figure 2.3. Beneficial uses of recycled water in Northern California in 2001 and 2009.
.......................................................................................................................... 18
Figure 2.4. Results of χ2 analyses by implementation date for specific factors indicated
as one of the Three Most Important Drivers (top) or more generally a Driver of
implementation (bottom). ................................................................................. 20
Figure 2.5S. Distribution of survey invitations and responses collected from Northern
California counties. ........................................................................................... 37
Figure 2.6S. Agencies binned by annual recycled water flow (AFY) in 2001 and 2009.
.......................................................................................................................... 38
Figure 2.7S. Representation of total annual reclaimed water deliveries reported from
the 2010 Survey of Northern California agencies (n = 64) and the State Water
Resources Control Board 2009 Municipal Wastewater Recycling Survey (n =
143). .................................................................................................................. 39
Figure 3.1. Distribution of California Rapid Assessment Method (CRAM) overall
wetland scores, included for estuarine (saline and non-saline) and riverine
(confined and non-confined), and wastewater facilities with tertiary treatment
capacity. ............................................................................................................ 59
xvi
Figure 3.2. Response frequencies for 2010 Survey respondents who rated five broad
categories of hindrances to implementation of future water reuse programs for
ecosystem enhancement. ................................................................................... 66
Figure 4.1. Study area. ................................................................................................. 74
Figure 4.2. Measured PFAA concentrations (ng/g ww) in white sturgeon fish livers (n =
15). .................................................................................................................... 78
Figure 4.3. Stable isotopes, fish length, and muscle Hg concentration plotted with white
sturgeon, striped bass, or leopard shark liver PFOS concentrations on the
ordinate. ............................................................................................................ 81
Figure 5.1. Equilibrium dialysis results for PFOA (a) and PFNA (b) where
!
" is the
average number of PFAA molecules bound per albumin. ................................ 97
Figure 5.2. Representative spectra from 2500-4800 m/z for BSA, BSA exposed to
PFOA (50 µM), and BSA exposed to PFNA (50 µM) in 9 mM ammonium
acetate (pH 7). ................................................................................................. 100
Figure 5.3. Mass spectra for the +16 charge state of 50 µM BSA in 9 mM ammonium
acetate (pH 7) with PFOA (left) and PFNA (right). ....................................... 101
Figure 5.4S. Structures and names of perfluoroalkyl acids (PFAAs) used in this study.
......................................................................................................................... 107
Figure 5.5S. Samples taken prior to equilibration in the reservoir from control bags
containing only buffer and the PFAA spike are compared to samples taken from
test bags containing 1 µM BSA with the same PFAA spike. ......................... 110
Figure 5.6S. Reservoir samples taken over time in a PFNA equilibrium dialysis test
indicate equilibrium of the system after 24 hours. .......................................... 111
Figure 5.7S. Reservoir samples taken over time in a PFOA equilibrium dialysis test
suggest equilibrium of the system after approximately 48 hours. .................. 111
Figure 5.8S. Measured total and free PFOA and PFNA concentrations taken at
equilibrium from dialysis bag and reservoir samples, respectively. ............... 112
Figure 5.9S. Standard deviations of triplicate measurements of bound PFAAs (Sbound)
were linearly correlated with free PFAA concentrations, as shown above for
PFNA in 1µM BSA equilibrium dialysis tests. ............................................... 113
xvii
Figure 5.10S. Equilibrium dialysis results for PFOA and PFNA up to a 5:1 ligand to
protein mole ratio and 1 µM BSA where ν is the average number of PFAA
molecules bound per albumin. ........................................................................ 116
Figure 5.11S. Representative mass spectra for the +16 charge state of 50 µM BSA in 9
mM ammonium acetate (pH 7) with PFDA (left, cone voltage = 100V) and
PFOS (right, cone voltage = 130 V). .............................................................. 118
Figure 5.12S. Representative deconvoluted spectrum for 50 µM BSA in 9 mM
ammonium acetate (pH 7) with 100 µM PFOA (cone voltage = 100V) used for
determination of Ka. ....................................................................................... 119
Figure 6.1. Measured BSA-water distribution coefficients (KPW) for perfluoroalkyl
sulfonates (PFSAs, ) and perfluoroalkyl carboxylates (PFCAs, ) with
fluorocarbon tail lengths of 4 to 11. ............................................................... 131
Figure 6.2. Deconvoluted spectra of 50 µM BSA alone or with 50 µM PFPeA, PFHxA,
PFHpA, PFOA, or PFNA. .............................................................................. 132
Figure 6.3. Effect of ionic head group on binding of equivalent chain length PFAAs to
BSA. ............................................................................................................... 135
Figure 6.4. Effect of pH on
!
" , the concentration of PFAA bound to BSA normalized to
the total protein concentration. ....................................................................... 137
Figure 6.5S. Structures and names of perfluoroalkyl acids (PFAAs) included in the
present study. .................................................................................................. 145
Figure 6.6S. Total PFAA analyte concentration in the bound phase (CP, [g bound
PFAA / mL BSA]) versus total aqueous PFAA concentration (CW, [g free PFAA
/ mL water]). ................................................................................................... 147
Figure 6.7S. Representative deconvoluted spectra of 50 µM BSA alone or with 50 µM
TFA, PFPrA, PFBA, PFBS, PFHxS, or PFOS. .............................................. 148
Figure 6.8S. Representative deconvoluted spectra of PFPeA and PFNA (100 µM) with
BSA (50 µM) collected at a 10 V collision energy (left) and representative
spectra of PFNA (100 µM) with BSA (50 µM) at 10, 30, 50 or 70 eV collision
energy (right). ................................................................................................. 150
Figure 6.9S. Fluorescence spectra of BSA at pH 6 (solid line), 7 (long dashed line), 8
(short dashed line), or 9 (dotted line). ............................................................ 151
xviii
Figure 6.10S. Average number of bound perfluoroalkyl carboxylates (PFCAs, ) or
perfluoroalkyl sulfonates (PFSAs, ) per BSA,
!
" (µM PFAAbound / µM BSA),
measured in dialysis bags containing 200 µM BSA in a PFAA-spiked reservoir
at pH 7. ............................................................................................................ 152
Figure 6.11S. Effect of pH on the average number of PFHpA (), PFOA (), PFNA
(), or PFDA () molecules bound to BSA. .................................................. 153
Figure 6.12S. Changes in the fluorescence of BSA with added PFNA (top) or PFOS
(bottom) at pH 6 (), 7 (), 8 (), or 9 (). ................................................... 154
Figure 6.13S. The binding of PFNA (a) and PFOS (b) to BSA, plotted as the degree of
saturation (Y) versus total PFAA concentration, at pH 6 (), 7 (), 8 (), or 9
(). .................................................................................................................. 155
Figure 6.14S. Dependence of estimated binding constant (KHill) on pH for the binding
of PFNA () and PFOS () to BSA. .............................................................. 156
Figure 6.15S. Changes in the fluorescence of BSA with added PFNA (top) or PFOS
(bottom) at 0.21 M (), 0.30 M (), or 0.41 M () ionic strength and pH 7. 157
Figure 6.16S. The binding of PFNA (a) and PFOS (b) to BSA, plotted as the degree of
saturation (Y) versus total PFAA concentration, at 0.21 M (), 0.30 M (), or
0.41 M () ionic strength and pH 7. ............................................................... 158
Figure 6.17S. Dependence of estimated binding constant (KHill) on ionic strength for
the binding of PFNA () and PFOS () to BSA at pH 7. .............................. 159
1
Chapter 1
Introduction
1.1 Motivation and Background
Wastewater recycling, or water reuse, is becoming critically important for improved
water management and ecosystem protection and rehabilitation in semi-arid regions of
the American West as increasing water demands deplete freshwater supplies. Although
the amount of municipal wastewater recovered for the augmentation of water supplies
and pollution abatement is increasing globally (1), untapped wastewater resources
remain abundant, even in water-starved regions. In California alone, where reuse of
municipal wastewater more than doubled from 1970 to 2002, only 10% of available
treated effluent is recycled (2, 3). Water resource opportunities arising from advanced
wastewater treatment technologies are tempered by numerous challenges experienced
by managers during the implementation of recycled water programs in California and
elsewhere. For example, recent decisions to implement new water reuse projects are
complicated by considerable uncertainty regarding the risk of unregulated chemical
contaminants in recycled water (4). Reclaimed wastewater intended for a range of
2
beneficial uses, including agricultural irrigation and habitat enhancement, may contain
detectable quantities of chemicals with largely unknown ecological effects (5, 6).
Water reuse challenges and opportunities. Despite efforts to encourage and
support water reuse programs at the state and federal levels (2), not all projects are
successful. California failed to reach statewide goals for water reuse in 1982, 2000, and
most recently in 2010. Public opposition has led to the suspension or abandonment of
several large water reclamation projects for indirect potable use in California (2, 7),
which remains a small fraction of California’s recycled water portfolio. Whereas
negative public perception stands as a key barrier to overcome for potable reuse, other
challenges associated with nonpotable reuse programs include a mélange of funding,
regulatory, and technical hurdles (8). Pressing water supply concerns related to dramatic
population growth and periodic, yet often severe, regional droughts necessitate a
thorough understanding of past experiences in water reuse implementation to identify
the sources of such failures and to focus efforts on effectively implementing new water
reuse programs. Further, one largely underutilized opportunity for recycled water use is
that for natural system enhancement, in which recycled water may serve as a hydrologic
resource for wetlands, lakes, and streams (9, 10). Natural processes may be harnessed to
remove contaminants of concern in treatment or polishing wetlands and augmented
river or stream systems (11, 12). Wetland treatment projects are attractive as a cost-
saving measure over expensive treatment and distribution facilities (12), though many
unresolved issues remain with respect to water reuse for ecosystem enhancement. New
opportunities to couple reuse of wastewater with the needs of streams and wetlands and
strategies to facilitate implementation of such projects, including quantification of
auxiliary benefits realized via ecosystem enhancement projects, require identification.
Chemicals of emerging concern. Chemicals of emerging concern (CECs) have
come to the attention of water reuse policymakers and regulatory bodies (13).
Addressing issues of CECs is especially acute for sensitive aquatic ecosystems that
serve as receiving waters for wastewater discharge or recycled water used in ecosystem
enhancement projects. Contamination of aquatic systems by chemical micropollutants is
a key environmental challenge facing humanity (14). The remarkable diversity of
structural properties of CECs and attendant uncertainty arising from unknown toxicity
3
mechanisms and thresholds necessitates research that assesses impacts on aquatic life
and human health, explores cost-effective and appropriate treatment technologies, and
identifies benign products and processes (14). As one example of the regulatory and
technological challenges facing the management of CECs, the United States
Environmental Protection Agency (U.S. EPA) identified nearly 26,000 substances as
potential candidates for the 2009 Candidate Contaminant List 3 (CCL3), a list used to
prioritize research and data collection efforts for unregulated contaminants. In the final
CCL3, this universe of contaminants was whittled down to 104 chemicals or chemical
groups and 12 microbial contaminants for their potential to present health risks through
drinking water exposure (15). Two compounds included in the U.S. EPA CCL3,
perfluorooctanoate (PFOA) and perfluorooctanesulfonate (PFOS), are representative of
a family of compounds commonly referred to as perfluorochemicals (PFCs) or
perfluoroalkyl acids (PFAAs, Figure 1.1).
Perfluorinated chemicals. Perfluoroalkyl acids are remarkably stable due to
complete fluorination of the saturated carbon chain. The high-energy carbon-fluorine
bond imparts resistance to photolysis, hydrolysis, microbial degradation, and
metabolism by vertebrates (16, 17). The unique chemical properties of PFAAs have
been capitalized over the past half century in the production of a variety of industrial
and consumer products including textile coatings, food-contact paper, fire-fighting
foams, repellants, paints, and cosmetics (16, 18-20). Amongst this diverse class of
synthetic surfactants, PFOA and PFOS are final degradation products of a host of parent
compounds that include fluorotelomer alcohols (21), n-ethyl sulfonamido ethanols (22,
23), and polyfluoroalkyl phosphate surfactants (19). Today, PFAAs are recognized to be
extremely persistent in the environment as well as globally distributed,
bioaccumulative, and toxic (24-26).
4
Figure 1.1. Structures and names of perfluoroalkyl acids (PFAAs) included in this
study. Compound abbreviations and notations (C2 – C14) are listed. PFSAs and PFCAs
have a fluorocarbon tail length of n + 1 and m + 1, respectively.
Wastewater treatment plants are likely dominant sources of PFAAs to the
environment (27-29). The observed environmental concentrations of PFOS, which
exhibits a range of ecotoxicological endpoints, are generally greater than those of other
PFAAs (30). Subchronic exposure of PFOS to animals causes significant weight loss
coupled with hepatotoxicity and reduction in thyroid hormones and serum cholesterol
(31). PFOA is likely to be carcinogenic, according to a U.S. EPA Science Advisory
Board, by inducing liver adenomas via activation of the peroxisome proliferator-
activated receptor (32). Although the toxicity of PFOA and PFOS is well studied (26,
31), more information concerning the modes of toxic action is needed to quantify risks
of exposures to PFAA mixtures for a range of species (33). Vast differences in the
elimination half-life for a variety of PFAAs are observed between species, with several
years expected for humans (26).
Responding to comprehensive research documenting widespread occurrence of
5
long-chain PFAAs in humans and wildlife (25, 34), the 3M Company eliminated
production of perfluorooctane sulfonyl fluoride (POSF)-based materials, including
PFOA and PFOS. Several companies followed suit, committing to eliminate emissions
of PFOA and related compounds by 2015 (35). However, legacy products remain in
use, and production continues globally for perfluoroalkyl compounds of varying chain
lengths (C4 – C15) (20). Reformulation of product contents to include substitute
compounds and modified PFAAs, such as those based on C4-sulfonyl chemistries,
necessitates research regarding these compounds (35). A shorter half-life in organisms
of perfluorobutanesulfonate (PFBS) likely reduces its bioaccumulation (36, 37) but is
unlikely to affect its persistence. The environmental and human health implications of
increased production and use of short-chain PFAAs requires further study.
Biological accumulation of perfluoroalkyl acids. Although perfluorinated
compounds are detected ubiquitously in organisms, little is known about the
mechanisms of PFAA bioaccumulation and the processes by which PFAAs are
introduced into the aquatic food web. PFAAs exhibit biouptake patterns divergent from
well-characterized hydrophobic organic contaminants. Despite relatively low
hydrophobicity, perfluorinated compounds with greater than seven fluorinated carbons
bioaccumulate and biomagnify in aquatic food webs (18, 25, 38). Rather than
partitioning to adipose tissue, PFAAs are detected predominantly in protein-rich
compartments such as the liver, kidney and blood (39-42). The bioconcentration factor
(BCF) of PFOS, relating ambient water concentrations to measured tissue
concentrations, ranges from approximately 1,000 to more than 5,000 for bluegill and
rainbow trout fish species, depending on the method of determination and organ
considered (30). The structures of perfluoroalkyl carboxylates (PFCAs) and
perfluoroalkyl sulfonates (PFSAs), two homologue groups of PFAAs, resemble those of
fatty acids and hydrocarbon-based detergents, but the perfluorinated tail renders the
compounds both hydrophobic and oleophobic (16, 33). The nature of PFAA structure
and bioaccumulation suggests an importance of protein interactions (43). However, the
sorptive capacity of animal protein is rarely incorporated in biouptake models to
improve estimations of chemical distribution and bioaccumulation of persistent organic
pollutants (44).
6
1.2 Research Objectives and Précis
Amongst a broad array of challenges, negative public perception of health risks,
actions of influential stakeholders in a changing regulatory environment, and limited
availability of financial assistance may affect the implementation of water reuse
programs. High costs, including those for treatment facilities and distribution systems,
may be exacerbated by limited financial and technological capacity to eliminate trace
contaminants. In the face of new knowledge surrounding chemicals of emerging
concern, coupled with advanced analytical techniques to evaluate the presence of
chemicals, future challenges associated with water reuse programs in California may be
different than historical practices and experiences. Work presented in Chapter 2,
Management experiences and trends for water reuse implementation in Northern
California,1 assesses the greater context of water reuse in California and contains
results from a survey of water reuse managers and professionals in Northern California.
Chapter 3, Water reuse for ecosystem enhancement: Matching opportunity with need,
delves into the role of water reuse for ecosystems, describing existing programs and
broadly identifying opportunities for new enhancements. The analysis draws on
responses from the previously discussed survey and complementary databases to outline
specific challenges for habitat enhancement using tertiary treated wastewater. Together,
these chapters seek to provide insight on the following questions:
• What are the major drivers and barriers to water reuse in Northern
California, and how have these factors evolved through time?
• To what extent has water reuse been applied for the direct benefit of
ecosystems, and what major challenges are associated with the
implementation of water reuse for ecosystem enhancement?
Despite an indication that positive environmental impact is a benefit of water reuse
projects, relatively few projects have been implemented for ecosystem enhancement in
California.
1 The results presented in this chapter are submitted as a Research Article by Heather N. Bischel, Gregory Simon, Tammy M. Frisby, and Richard G. Luthy for the journal Environmental Science & Technology.
7
One potential challenge associated with the implementation of water reuse for
ecosystem enhancement is uncertainty regarding the bioaccumulation of unregulated
chemical contaminants. Chapter 4, Exposure of perfluorinated chemicals to San
Francisco Bay white sturgeon and mechanisms of bioaccumulation, sets the stage for
understanding the role of bioaccumulation of chemicals of emerging concern in
ecosystems as well as mechanisms associated with PFAA bioaccumulation. PFAA
concentrations detected in white sturgeon fish livers from organisms in the San
Francisco Bay are presented as a case study. As perfluorinated chemicals receive
increasingly more attention and are more carefully examined for their potential
ecosystem effects, bioaccumulation processes based on biologically relevant
mechanisms are considered. This chapter asks:
• What dominant processes govern the bioaccumulation of PFAAs, and how
can these processes be captured in bioaccumulation models?
As mentioned, PFAAs do not preferentially accumulate in lipids and fatty tissue but
rather in body compartments with high protein content, including the liver, kidneys, and
serum. Such observations bring question to the appropriateness of using octanol-water
partition coefficients (Kow), which are commonly applied for modeling the
bioaccumulation of persistent organic pollutants, to describe the environmental behavior
of PFAAs.
For PFAAs, molecular interactions with proteins likely contribute to PFAA
bioaccumulation mechanisms. As such, quantitatively determined associations between
perfluorinated chemicals and proteins may be useful parameters to more accurately
describe observed bioaccumulation. In Chapters 5 and 6, processes influencing the
bioaccumulation of these unique compounds are evaluated through associations of
PFAAs with proteins, utilizing bovine serum albumin as a model protein. These
chapters seek to address the following questions:
• How do long-chain PFAAs associate with the model protein, serum albumin,
at physiologically relevant PFAA:albumin mole ratios?
• What analytical tools are appropriate for quantitatively determining PFAA-
albumin associations?
• Given a shift in production of fluorinated compounds to shorter-chain length
8
compounds, how will a reduction in perfluoroalkyl chain length affect
protein-water distribution coefficients?
• What physiochemical mechanisms govern interactions of PFAAs with serum
albumin?
In Chapter 5, Investigating associations with a model protein: Noncovalent
interactions of long-chain perfluoroalkyl acids with serum albumin, 1 association
constants (Ka) and binding stoichiometries for PFAA-albumin complexes are quantified
over a range of physiologically relevant PFAA:albumin mole ratios. Binding
interactions between PFAAs with eight to ten perfluoroalkyl carbons and the model
protein bovine serum albumin (BSA) are studied using equilibrium dialysis with liquid
chromatography tandem mass spectrometry and nanoelectrospray ionization mass
spectrometry. Chapter 6, Strong associations of short-chain perfluoroalkyl acids
(PFAAs) with serum albumin and investigation of binding mechanisms2, expands on
the previous chapter to evaluate associations of PFCAs with 2 to 12 carbons (C2 – C12)
and PFSAs with 4 to 8 carbons (C4, C6, and C8) with BSA at physiologically-relevant
PFAA:albumin mole ratios. Protein-water distribution coefficients (KPW) are quantified,
providing interpretation of hydrophobicity, steric hindrances, and electrostatic effects
on interactions with albumin. This work comprises a thorough evaluation of molecular
interactions of PFAAs with albumin using several analytical tools, a wide range of
ligand and substrate concentrations, a series of fluorochemical chain lengths and two
anionic head group moieties, and varied solution conditions. Chapter 7, Conclusions,
contains final remarks on the research objectives as well as a discussion of research
needs.
1 The results presented in this chapter originally appeared as a Research Article in the journal Environmental Science & Technology: (45) Bischel, H. N.; MacManus-Spencer, L. A.; Luthy, R. G. Noncovalent interactions of long-chain perfluoroalkyl acids with serum albumin. Environ. Sci. Technol. 2010, 44 (13), 5263-5269. 2 The results presented in this chapter are in press as a Research Article for the journal Environmental Toxicology & Chemistry by Heather N. Bischel, Laura A. MacManus-Spencer, Chaojie Zhang, and Richard G. Luthy.
9
Chapter 2
Management experiences and trends for
water reuse implementation in Northern
California
2.1 Introduction
California is at the forefront of recycled water use, treating municipal wastewater to
a high enough degree that it can be returned to the water supply for a variety of
beneficial uses including landscape irrigation (46-48), agriculture (49, 50), ecosystem
enhancement (9), industrial cooling and processing (47, 51), groundwater recharge and
indirect potable reuse (51-53). From 1970 to 2002, reuse of municipal wastewater more
than doubled in California from 175,000 acre-ft per year (AFY) to approximately
525,000 AFY. Yet this growth fell short of the state’s goal to reuse 700,000 AFY by
2000 (2, 3). California’s goal to increase reuse by 2 million acre-feet by 2030 over 2002
levels (54) will require a portfolio of projects for a range of beneficial uses. Given
multiple failures to attain statewide recycling goals (Figure 2.1), questions remain as to
10
the sources of such difficulties as well as the feasibility of reaching near-term goals
described in California’s State Water Board Strategic Plan Update of 2008-2012 (55).
Figure 2.1. Timeline of statewide water recycling goals and production volumes, major
drought periods, and select water recycling laws and policies in California during the
implementation period for survey respondents. Refer to the Supporting Information for
a description of major laws and policies.
Despite efforts to encourage and support water reuse programs at the state and
federal levels (e.g., (2) and (54)), not all projects are successful, and nonpotable reuse
projects frequently fall short of planned delivery goals (56, 57). Public opposition has
led to the suspension or abandonment of several large water reclamation projects for
indirect potable reuse in California (2, 7). Considering the promise of recycled water for
augmenting water supplies in the West and pressing water supply concerns related to
dramatic population changes and climate change, assessment of past and current
experiences in water reuse implementation will aid in more effectively promoting,
evaluating, and implementing water reuse. This paper contributes to this task by
evaluating the experiences and perspectives of current water reuse project managers in
Northern California to understand recent developments and major issues confronting
11
recycled water projects in the region.
Specifically, our study reveals the following: (1) In Northern California, water reuse
programs are widely distributed across 48 counties and, though more numerous than
programs in the 10 Southern California counties, are often smaller in the volumes of
reclaimed water delivered annually. This finding highlights the importance of capturing
experiences of managers in rural regions of California, which likely differ from
experiences in highly urbanized centers. (2) Regulatory requirements that limit
discharge played an important role in motivating many water reuse programs in
Northern California. However, a trend away from reuse as a wastewater disposal issue
is documented in Northern California, as water supply and reliability become more
prevalent drivers of water reuse. (3) Although ecosystem enhancement or protection
goals are frequently cited as drivers of water reuse, such goals are rarely the most
important drivers for reuse programs. Few water reuse programs in California have
been implemented for the purpose of ecosystem enhancement. (4) Negative perceptions
of water reuse were not frequently major hindrances to implementation of water reuse
programs in Northern California. Public perception of water reuse may be positively
influenced by a shift in view of recycled water towards that of a valuable resource and
as public knowledge of water supply challenges increases. (5) Economic issues stand as
the largest hindrance to successful project implementation from a management
perspective. In particular, smaller water reuse programs are less frequently incentivized
by federal or state grants and loans, while larger programs have somewhat greater
challenges associated with distribution system (pipeline) costs.
2.2 Methodology
Data sources. Primary data on water reuse agencies, practices, and management
experiences were collected via an online questionnaire of Northern California water
reuse managers conducted for the present study in 2010 (2010 Survey). Additional data
on water reuse agency characteristics were obtained from the California State Water
Resources Control Board (SWRCB) 2001 Water Recycling Survey released in 2002
(2001 Survey, (3)), the National Database of Water Reuse Facilities (National Database,
(58)), and the 2009 California Municipal Wastewater Recycling Survey, a follow-up
12
survey from the SWRCB released in April 2011 (2009 Survey, (59)). Municipal water
recycling agencies in Northern California (defined as the 48 counties northward of the
southern boundaries of Monterey, Kings, Tulare, and Inyo counties) listed on the
National Database and the 2001 Survey were invited to participate in the 2010 Survey.
Fieldwork administration and questionnaire. Data were collected online from
February to April 2010 using electronic surveys sent to general managers or
water/wastewater directors from 134 agencies in 41 Northern California counties using
a distribution list compiled from the SWRCB 2001 Survey and the publicly available
National Database (58). The questionnaire, which is described further in the Supporting
Information, was developed based on case study research, literature review and site
visits at water and wastewater facilities and agencies with programs implemented for
agriculture, landscape irrigation, industrial power plant cooling, and ecosystem
enhancement. Respondents were asked a number of questions related to the drivers and
challenges experienced in implementing their agency’s water reuse program with
additional survey components addressing responses to recent recycled water policy in
California and future expectations for programs in development. Prior to distribution,
survey testing by several consultants and project staff was conducted for usability and
content feedback.
Categorization and statistical tests. The analyses conducted for 2010 Survey
results provide quantitative confirmation of trends that have been previously discussed
and valuable insights into the characteristics of water reuse in Northern California.
Results represent quantitative response data and are supported by qualitative
descriptions of drivers and barriers experienced in program implementation. Chi square
analysis was conducted on two by two contingency tables constructed from frequency
results of specific drivers (Table 2.1) and hindrances (Table 2.2) to program
implementation to assess relationships between categorical variables. For simplicity in
additional analysis and discussion, the list of specific drivers and hindrances was
consolidated into eight and nine categorical variables, respectively. Chi square analysis
was also performed on these data, and categories were used to contextualize qualitative
responses to survey questions (See Tables 2.3S – 2.10S for full results). The
presentation of representative respondent quotations, extracted primarily from responses
13
to two questions – the single most important driver or hindrance to implementation –
provide context for the diversity of experiences evident throughout the results.
Respondent information and survey limitations. A total of 71 distinct agencies, a
53% response rate, are represented by 2010 Survey responses. Because some parent
utilities represent multiple recycled water facilities, a total of 81 unique production
facilities are represented by responses; however, most agencies (83%) represent only
one recycled water production facility, and another 7% represent a unique distribution
facility coupled to a production facility. Respondents consist of internal public agency
managers or utility staff. The survey completion rate was 40% of invited participants.
Therefore, the response fractions reported for each question indicate values for that
particular question. Respondent agencies for the 2010 Survey were distributed widely
across Northern California though survey representation appears somewhat weak for the
number of agricultural programs relative to the 2009 Survey data (Figure 2.5S and
Table 2.11S). The median year of recycled water program implementation, based on
self-reported implementation dates for 56 respondents, was 1991, with the earliest
reported implementation occurring in the early 1960’s.
2.3 Analysis of Water Reuse in California
Recycled water distribution falls short of statewide goals. Figure 2.1 displays a
timeline of statewide water recycling goals and production volumes (2, 3, 13, 57, 59,
60). According to the 2009 Survey results, recycled water used in California in 2001
included 491,992 AFY from municipal facilities, with the additional volume attributed
to private facilities (59). The newest data from the California SWRCB indicates
California municipal wastewater facilities recycled a total of 723,845 AFY in 2009 (59).
This represents an increase of more than 230,000 AFY from levels in 2001, yet once
again falls short of goals for recycling set by the State of California by nearly 300,000
AFY (Figure 2.1 and Table 2.12S). Although the SWRCB 2009 Survey may
underrepresent current reuse volumes due to the low survey response rate, the results
underline a need to identify continuing challenges associated with implementation of
water reuse programs and to evaluate strategies to develop new recycled water
programs and expand existing distribution networks.
14
Northern California context. Our analysis shows that only 20% of the observed
state-wide increase in reuse between 2002 and 2009 occurred in the Northern 48
counties of California, where 120 municipal agencies recycled 127,000 AF in 2002, and
173,000 AF was produced from 143 agencies in 2009. Recycled water programs in
Northern California are generally smaller in volume (median = 347 AFY in 2009) than
programs in the ten Southern California counties (median = 1064 AFY in 2009), where
82 municipal agencies recycled 365,000 AFY of water in 2002, increasing to a total of
551,000 AFY of water in 2009 by 104 agencies (Figure 2.2 and Figure 2.6S). Water
reuse programs are frequent across rural Northern California and agricultural areas in
the Central Valley (Figure 2.2), typically at much lower volumes than urban areas
generating larger volumes of wastewater. Though reuse in Northern California
represents a lesser fraction of overall reuse in the state, challenges associated with the
implementation of smaller, rural programs are important to consider in developing the
total portfolio of state projects. Several larger programs have been implemented over
the last decade in Northern California, and more are likely to be developed in large
urban centers. However, recycled water program size has remained relatively stable on
average in Northern California.
15
Figure 2.2. A snapshot of water reuse facilities in California from the National
Database of Water Reuse Facilities (Annual Production, reported as Facility Production
Average Annual Actual in million gallons) and the California 2009 Municipal Water
Recycling Survey (Annual Reuse, reported as Total Reuse for 2009 in AFY). In the
inset box plot, the boundary of the box indicates the upper and lower quartiles; a line
within the box indicates the median; whiskers above and below the box demarcate 1.5
times the interquartile distance with outlying points also shown.
16
2.4 Drivers of Water Reuse Implementation in Northern CA
Various social, economic, and environmental factors have been identified as drivers
of water reuse by governments and stakeholders globally (2, 8, 56, 61). These driving
forces include: drought, demand due to population and economic growth, wastewater
management, ecological protection, availability near urban areas, and availability of
proven treatment technologies (8, 56). To establish a forum for free-form responses
regarding principal driving forces behind recycled water implementation in Northern
California, respondents first considered the relative importance of several broad
categories of drivers. The fraction of respondents indicating each broad category as a
very important driver or a driver, respectively, was: regulatory requirements (0.59,
0.27), water shortages (0.49, 0.34), economic concerns (0.28, 0.37), recycled water
policy (0.23, 0.49), and influential stakeholders (0.21, 0.33).
To further gauge the extent to which a range of specific factors motivated water
reuse in Northern California, respondents were asked to select factors that drove
program implementation. Amongst a list of 19 specific factors (Table 2.1), 63% of
respondents indicated “wastewater discharge volume requirements” as a driver of
implementation, with 49% of respondents selecting this factor as one of the three most
important drivers. “Water shortages due to reduced supply” was cited as a driver by
65% of respondents and by 42% of respondents as one of the three most important
drivers of implementation. Together, these two factors were cited by 80% of all
respondents. Expressing a common experience for the most important driver of program
implementation, one respondent described that their “initial recycled water program was
established as a wastewater disposal option out of concern for discharge capacity…
Expansions to the recycled water system since 2005 were based on prudent use of water
resources and extending the limited potable supply.”
17
Table 2.1. Percent of respondents indicating a specific factor as a Driver or one of the
three Most Important Drivers. Responses (n = 65) were further categorized as shown
and are sorted from top to bottom by the highest frequency categorized Most Important
Driver.
Categorized Factor
Most Impt. Driver
Driver Specific Factor Most Impt. Driver
Driver
Wastewater discharge requirements
51% 65% wastewater discharge volume requirements
51% 65%
Water supply needs
49%
69% water shortages due to reduced supply
42% 65%
water shortages due to increased demand
17% 42%
seawater intrusion 5% 6%
Local, regional, or state policy and mandates
45%
68% basin plan water quality objectives
25% 43%
regional or local recycled water policy goals or mandates
20% 42%
state recycled water policy goals or mandates
14% 31%
climate change adaptation plans
0% 5%
Institutional control
29%
58% need for reliable water supply 26% 52%
need for increased institutional control of water
3% 20%
Economic/financial incentives
26%
51% availability of federal/state grants or loans
18% 32%
cost of alternative freshwater sources
9% 32%
Ecological goals or requirements
18%
51% ecological protection or enhancement goals
12% 49%
ecological protection or enhancement requirements
6% 20%
Influential stakeholders
11%
34% large volume user(s) 6% 28% citizen initiative 5% 12%
Technological advancements
3% 22% technological advancements 3% 18%
Other 18% 18% other 18% 18%
18
Figure 2.3. Beneficial uses of recycled water in Northern California in 2001 and 2009.
See Supporting Information for a description of categories.
In addition to specific regulatory requirements, state recycled water policy goals or
mandates were selected as a driver by nearly a third (29%) of 2010 Survey respondents
and as one of the three most important drivers by 13% of respondents. Additionally,
24% of respondents selected basin plan water quality objectives as one of the three most
important drivers of implementation. Such objectives may relate to discharge volume
requirements: one respondent who described Basin Plan Water Quality Objectives as
the single most important driver of their program’s implementation stated, “Reducing
our volume discharged to surface water helps us to meet increasingly more stringent
effluent discharge loading requirements.” Drivers of implementation, other than those
shown in Table 2.1, identified by respondents (n = 12 total) often reflected site-specific
conditions including a need for a specific effluent disposal method or location, the cost
of disposal, a water conservation Executive Order, and the need for replacement water.
Notably, “Ecological protection or enhancement goals” were drivers for the
implementation of many programs (49%) but were rarely the most important drivers for
these programs (12%). In 2001 and 2009, only 6-7% of reuse was for natural
system/wildlife enhancement (Figure 2.3).
Controlling wastewater discharge and the role of regulation.
“We needed a method [to] dispose of treated effluent. The only viable
alternative was recycling.”
Results demonstrate that regulatory requirements, such as those limiting discharge
19
of wastewater, have historically played an important role in driving the implementation
of water reuse in Northern California. The California Department of Public Health
establishes state public health criteria for wastewater reclamation via Title 22 for
bacterial quality, treatment types and levels, and facility reliability. Individual Regional
Water Quality Control Boards (RWQCBs) and local water and health agencies may also
develop more stringent policies and programs related to recycled water use (2). In free-
form responses, respondents who cited regulatory requirements as a very important
category of drivers (n = 28) noted a range of specific regulatory pressures that drove the
implementation of their program (see Supporting Information for details). Various
agencies were mandated or recommended to reduce percolation and increase reuse, cap
discharge flows despite population growth, and eliminate point source discharges or
meet dilution requirements in receiving waters during a particular time period (e.g.,
summer months).
Transitioning from wastewater discharge control to recycled water as a
resource.
“The original driver is not the current driver. Currently water supply and
reliability is the most important driver.”
Water shortages are commonly experienced throughout California, with several
severe droughts throughout the period of implementation represented by survey
responses (Figure 2.1). California’s elaborate system of dams, canals, aqueducts,
groundwater basins, and levees mediates the dichotomy between the state’s water
sources and demand centers, where 75% of the state’s precipitation falls north of
Sacramento, and 75% of demand occurs in the population and farming centers to the
south (62). Because of the interconnectedness of water infrastructure in the state and the
dependence of the largest urban centers on imported water, Northern California is not
immune to challenges associated with limited water supplies. The growing awareness
and response to water supply challenges are reflected in agency experiences. Programs
implemented after 1990 were more likely to cite water shortages due to increased
demand as a driver than older programs (p < 0.01) and were somewhat more likely to
indicate water shortages due to reduced supply as a driver (0.1 < p < 0.2, Figure 2.4).
Conversely, wastewater discharge volume requirements were more frequently indicated
20
as one of the three most important drivers of implementation by agencies with reported
implementation dates before 1991 (p < 0.05), suggesting that early implementation of
water reuse in the region was driven more frequently by such regulatory requirements.
Newer recycled water programs were also more likely to cite the need for reliable water
supply as an important driver of implementation (p < 0.01).
Figure 2.4. Results of χ2 analyses by implementation date for specific factors indicated
as one of the Three Most Important Drivers (top) or more generally a Driver of
implementation (bottom).
When expanding on the role of water shortages in driving program implementation
21
(n = 25), respondents frequently cited recycled water as a replacement source for
potable water supplies, where there may be a site-specific water need or shortage (e.g.,
for golf courses or parks) or a cap on freshwater source allocations (e.g., through
externally controlled piped sources). Increased demand without additional supplies was
also evident in several cases of overdraft of groundwater systems leading to degraded
water quality. Water shortages and reliability planning resulting from droughts were
noted separately as driving forces. For example, the 1976-1977 drought was followed
by the adoption of the Policy and Action Plan for Water Reclamation in California by
the SWRCB and subsequent increased funding recycled water planning studies (60).
Such opportunistic funding support strategies may continue to be important to capitalize
on increased incentive for water reuse implementation during periodic drought periods
in the region. Interestingly, only 5% of respondents in the present survey indicated
climate change adaptation plans as a driver of recycled water program implementation.
However, guidance by the California Natural Resources Agency (63) and Department
of Water Resources (5) incorporate recycled water as a drought-proof and sometimes
energy efficient water management strategy to complement climate change adaptation
measures. As these goals filter from state planning to local practices, state policies for
climate change adaptation will likely become more influential in recycled water
implementation.
Although water shortages were not directly an issue during project implementation
for some older projects, anticipated water shortages and need for long-term reliable
sources are now critical issues, especially following the 2007 – 2009 drought in
California. Projects that were implemented initially due to wastewater requirements
may expand or find new benefits of reuse due to water supply challenges. One
respondent illustrated this changing paradigm, stating:
“Fifteen years ago when we started our program, public acceptance was
an issue. People did not understand recycled water, and we spent a lot of
time educating potential customers and marketing recycled water. There
was some 'fear factor' slowing the expansion. However, things have
changed completely with the worsening drought, delta water problems,
climate change awareness, and the public's desire to be 'green' and
22
recycle everything now. We currently cannot get the water out to
customers fast enough.”
Implementation to increase reliability of potable water supplies (e.g., by sustaining
groundwater supplies for drinking), supplement water supply needs, or free up
freshwater entitlements for use elsewhere were described as other drivers of
implementation.
2.5 Challenges for Water Reuse Implementation in Northern
CA
Challenges for water reuse projects include a need for public education, lack of
available funding, recovery of capital costs for dual distribution systems, a need for
improved documentation of economic benefits of water reuse, political support, a need
for additional research for innovative technologies, public perception, flawed or
unevenly applied regulations and standards, and concerns and liability over the
unknown long-term health effects of chemical contaminants (2, 8). When asked to
select factors that hindered program implementation at the respondent’s site from a list
of 20 specific options, 87% of respondents cited financial or economic challenges as
one of the three most important hindrances to water reuse implementation (Table 2.2).
One respondent commenting on the single most important hindrance to implementation
simply stated, “These projects are big ticket items outside the range of a rate base.”
Specific hindrances from the financial or economic challenges list included: availability
of federal/state grants or loans, capital costs for construction of recycling plant facilities,
cost of alternative freshwater sources, costs for pipeline construction, and ongoing
operations & maintenance cost recovery. Together, these factors dominated the
selection of the most important challenges relative to other categories shown in Table
2.2 consistently through time. Despite various sources of policy and financial support
for water reuse in California, lack of sufficient funding may be the main factor
preventing recycling goals from being achieved (59).
23
Table 2.2. Percent of respondents indicating a specific factor as a Hindrance or one of
the three Most Important Hindrances. Responses (n = 54) were further categorized as
shown and are sorted from top to bottom by the highest frequency categorized Most
Important Hindrance.
Categorized Hindrance
Most Impt. Hind.
Hind. Specific Hindrance Most Impt. Hind.
Hind.
Economic/ financial disincentives
87% 94% capital costs for construction of recycling plant facilities
56% 85%
costs for pipeline construction 48% 80% ongoing operations & maintenance cost recovery
26% 61%
availability of federal/state grants or loans
24% 54%
cost of alternative freshwater sources 7% 26% Perceptions and social attitudes
26% 61% perceived human or environmental health risks due to constituents of emerging concern
13% 48%
social attitudes/public perception 13% 33% perception that recycled water will lead to more development
4% 22%
perception that recycled water will reduce property value
4% 6%
Who pays system costs
20% 59% issue of who pays for program capital or operating costs
20% 59%
Regulatory constraints
15% 52% complexities/conflicts of water law and/or regulation
9% 37%
slow regulatory process in permitting 7% 30% Water quality impacts
13% 48% downstream water quality impacts/NPDES constraints
7% 31%
detection of constituents of emerging concern
4% 33%
effluent residuals (e.g., brine) disposal 2% 11% User acceptance
9% 37% user acceptance 9% 37%
Institutional issues
11% 30% institutional coordination 9% 28% loss of projected users 2% 6%
Technical issues/ treatment
7% 31% technical issues/treatment processes 7% 31%
Uncertainty over future recycled water uses
4% 13% uncertainty over future recycled water uses
4% 13%
Other 9% 11% other 9% 11%
24
Challenges in the next most-cited category, public perception and social attitudes,
were indicated as an important hindrance by only 26% of respondents. Specific
hindrances categorized under public perception and social attitudes challenges were:
constituents of emerging concern, perception that recycled water will lead to more
development, perception that recycled water will reduce property value, and the more
general factor of social attitudes/public perception. In addition to those in Table 2.2,
other factors hindering program implementation identified by individual respondents
included soil salinity, lack of seasonal storage, and overcoming opposition from
influential stakeholders.
Economic constraints and financial implications of challenges.
“Generally in the industry and specifically for us, the cost of pipelines is
really the only reason we haven't been recycling more.”
Several examples of recycled water programs in Northern California provide
context for the expected costs of recent treatment facilities and distribution systems. For
16 projects seeking regional federal funding as part of the San Francisco Bay Area
Recycled Water Coalition, the total costs ranged from $220/AF to $3400/AF, with a
$1200/AF median value, assuming a 20-year period for recycled water generated at the
initial project yield (Table 2.13S, (64)). Recycled water deliveries expected for these
projects range from 115 AFY initially to up to 28,000 AFY in the future. A City of Palo
Alto analysis indicates an annualized cost of $2700/AF (over 30 years, in March 2008
dollars) expected for expansion of distribution facilities. This compares with a projected
cost of $1,600/AF by 2015 for wholesale purchase of potable water from the San
Francisco Public Utilities Commission (SFPUC) (65). An earlier phase of the Palo Alto
project completed in 2009 came to approximately $3.4 million/mile of pipeline for
construction base contract of approximately 5 miles of pipeline along US Highway 101
to the neighboring City of Mountain View (66). A project under analysis by the SFPUC
estimates $9.4 million (including a 30% contingency) for approximately 6.5 miles of
pipeline construction costs as part of a $153 million recycled water treatment and
distribution system (67, 68).
2010 Survey respondents were asked to characterize, as quantitatively as possible,
the impact of cited hindrances to implementation in terms of program cost, scope, and
25
timing. Respondents indicated that hindrances led to a change in program cost (n = 9),
reduced program scope (n = 5), delay of implementation (n = 14), project cancellation
(n = 7), or other (n = 1). For a subset of these responses, estimated costs associated with
impacts (n = 21) ranged from $50,000 to almost $100 million per agency. Estimates by
respondents for changes in program cost represented construction cost increases over
time, costs for additional studies, increased staff time, additional testing “beyond
reasonable needs,” “huge” impacts from years of delay, costs to upgrade to tertiary
treatment, costs for new processes, and a combination of changes in program scope,
changes in design, addition of professional consultants or a combination of conveyance
pipes, distribution piping, tanks and pressure stations.
Issues related to institutional coordination were also noted for increasing project
costs. Nearly a third of respondents indicated institutional coordination as a hindrance
to implementation. One example described:
“While water agencies need recycled water to help them with long term
supply issues, they cannot justify the increased costs and thus tend to be
unsupportive. Water agencies are also concerned about loss of revenue
with recycled water projects. If the water agency is not the same as the
recycled water agency (as in our area), implementation of recycled water
projects means a loss of revenue for the water district as customers are
shifted to the recycled water agency. This means that the potable water
agency must raise rates for the remaining customer base, which is very
difficult in today's economic climate.”
Limited role of negative perceptions.
“In 1984, the biggest hindrance was the negative perception by
landowners next to the farms scheduled to receive recycled water today.
Today the biggest hindrance is cost.”
Since the 1970s, a significant amount of research has investigated reasons for public
resistance to recycled water (69, 70). Although public perceptions of risks are identified
as key impediments in the adoption of indirect potable water reuse (71-73), nonpotable
water reuse programs generally receive public support (56). Thus, opposition
surrounding high-profile indirect potable reuse is likely unrepresentative of the
26
landscape of challenges faced by managers of nonpotable reuse programs distributed
throughout California cities and rural areas. A notable contrary case developed when
homeowners actively opposed the use of recycled water for landscape irrigation in
Redwood City, CA (46). While utilities and consultants have developed more
appropriate modes of communicating with the public, some members of the public
remain skeptical about the safety of the practice, especially as projects are proposed in
their community and the likelihood of human contact increases (74-76). Organizational
trust correlates with intended behavior towards using recycled water and may be an area
of further focus for institutional practices to increase public acceptance (77), and
principles of fairness and equity are significant to people’s decision-making (69).
Analyses emphasize the importance of public engagement early during project
conception and continuously throughout planning, design, and construction (2, 56).
The primary drivers of water reuse programs may also influence public opposition
or acceptance. An early public opinion study in California indicated that those who
believed water supply augmentation was necessary in California were somewhat less
likely to be opposed to reclaimed water for drinking than those who did not believe that
water was scarce (70). Consequently, public education efforts to effectively
communicate the need for water reuse are important. In the present study, respondents
who cited wastewater discharge volume requirements as a driver of implementation
were somewhat more likely to also cite a specific factor within the category of public
perceptions and social attitudes as a hindrance (0.2 > p > 0.1). As freshwater supply and
distribution agencies experience increased demands and pressures on existing resources,
greater public awareness of augmentation needs may reduce challenges associated with
public perceptions. Conversely, in communities where the drivers of recycled water are
discharge-based, rather than supply driven, public perception problems may arise more
readily.
“Perceived human or environmental health risks due to constituents of emerging
concern” was cited as a hindrance to implementation by almost half of respondents.
Yet, this factor was not correlated to program implementation date, reminding us that
unknown or unregulated contaminants change in specific definition with time, but have
challenged managers for decades. Concern for residuals in recycled water has been
27
expressed in various forms. In the 1970’s and 80’s, issues of public perception were
difficult to overcome, as recycled water was relatively unfamiliar and long-term safety
of reuse for high-contact uses was unproven. Today, CECs are a topic for technological
research and a source of concern for recycled water managers (13). Noting this issue as
an additional challenge to cost hindrances, one respondent commented that “opponents
are also trying to use the issue of emerging constituents as a way to portray the project
in a negative light.” Public perception of recycled water continues to be an important
non-technical challenge for water reuse implementation, especially with regards to
CECs. However, the present study finds that economic issues, rather than public
perception, stand as the largest hindrance to nonpotable reuse implementation for
Northern California programs.
Responses to recycled water policy. In 2009, the California State Water Resources
Control Board adopted a California Recycled Water Policy “to increase the use of
recycled water from municipal wastewater sources.” Providing statements towards the
beneficial uses of recycled water, the State Water Board “strongly supports recycled
water as a safe alternative to potable water for such approved uses.” Despite the
policy’s stated objectives, whether the water reuse policy will actually accelerate efforts
to develop and maintain new recycled water projects remains unclear. The legislation
itself takes on a hopeful tone by striving for, among other items, increased use of
recycled water “over 2002 levels by at least one million acre-feet per year (AFY) by
2020 and by at least two million AFY by 2030” (54).
Recycled water managers were questioned about their expectations concerning how
the California Recycled Water Policy of 2009 will facilitate or hinder the
implementation of new recycled water programs. Survey responses reveal both support
and trepidation towards the policy, with a greater number of respondents voicing
concern that the policy will hinder project implementation. According to respondents, a
perceived beneficial impact of the policy stems from standardized and consistent
guidance for recycled water projects. For example, the water reuse policy established a
Blue Ribbon Panel for evaluating contaminants of emerging concern that will apply to
all projects across California and also contains language endorsing water reuse under
the California Environmental Quality Act (CEQA) (13). Second, many respondents
28
viewed the policy favorably due to its singular management structure. The
establishment of an overarching permitting process, and of salt and nutrient
management requirements, in particular, drew positive reviews. As one manager put it,
“The standardization of salinity and nutrient management provisions among the various
regional boards should facilitate reuse and make it easier for some projects to get
permitted.” Thus, for water reuse project managers, the provision of administrative,
legal and scientific continuity across state, regional and local agencies was perceived as
the most beneficial aspect of the policy.
Much of the skepticism expressed for the 2009 policy may be traced to funding
issues. A majority of respondents (19 of 30 question responses) felt the policy would
obstruct new projects through onerous regulatory and cost requirements. According to a
number of managers, while statewide project streamlining and standardization is
important, ultimately the fate of projects will depend on adequate funding support. A
common refrain amongst respondents was a concern over added administrative layers
that will arise with new oversight and reporting requirements. In sum, the perceived
presence of additional financial costs and administrative requirements have led nearly 2
of every 3 survey respondents to suggest the 2009 water reuse policy will in some way
hinder new project implementation. From a management perspective, results suggest
that the 2009 policy has done little to alter the perceived drivers and hindrances of water
reuse project implementation for managers in Northern California.
2.6 Significance
A diverse body of responses from the 2010 Survey illuminates a number of
influential drivers of water reuse implementation, including the protection of
ecosystems, meeting wastewater discharge requirements, and needs for water supply
and reliability. We continue to detect manifestations of the intrinsic links between water
supply and quality: threats of long-term diminished water quality (e.g., seawater
intrusion) necessitates new water conservation and reuse measures, while new water
supplies of altered quality may galvanize community opposition. Although water supply
agencies increasingly face challenges associated with population growth and drought,
wastewater agencies have traditionally approached recycled water as an issue of
29
disposal. This push/pull duality that either push implementation forward (via regulatory
requirements for wastewater discharge) or pull agencies into recycled water programs
(by increased demand for water) is apparent. Results provide evidence of changing
perspectives towards recycled water management, from a waste disposal issue towards a
water supply resource opportunity.
Failure to meet statewide reuse goals results largely from lack of sufficient funding
for water recycling, as the cheapest recycled water opportunities have already been
exploited (2). Following three years of drought and recent passage of the Safe, Clean,
and Reliable Drinking Water Supply Act of 2010 by the State of California that
included $1.25 billion general obligation bond proposal for Water Recycling and Water
Conservation, the physical and political climates may be ripe for aggressive
implementation of new water reuse programs, where financially viable, socially
accepted, and technically sound. Yet the legislature’s 2010 decision to postpone the
water bond initiative for at least two years (62) is testament to the realities of financial
limitations for new water infrastructure in California.
Supporting Information Available. Contains (1) methodological details, (2) a
brief description of recycled water policy and regulation in California, and (3)
additional analysis and summary tables.
Acknowledgement. We thank the numerous participants in the project and survey
respondents for their generous donation of time and thoughtful contributions. This work
was funded by the Stanford University Woods Institute for the Environment
Environmental Venture Projects, the Bill Lane Center for the American West, the NSF
Graduate Research Fellowship Program, and the NSF Engineering Research Center for
Re-inventing Urban Water Infrastructure (UrbanWaterERC.org). We especially thank
Sophie Egan for detailed technical assistance.
Publication Information. Reproduced with permission from Environmental
Science & Technology, submitted for publication. Unpublished work copyright 2011
American Chemical Society.
30
2.7 Supporting Information
Description of data sources. Invited and respondent agency locations for the
present study (2010 Survey) are shown by county in Figure 2.5S. The California State
Water Resources Control Board (SWRCB) 2001 Water Recycling Survey (2001
Survey, (3)) compiled data on planned direct reuse of treated municipal wastewater in
California, excluding industrial reuse. The 2009 California Municipal Wastewater
Recycling Survey (2009 Survey) was conducted by the SWRCB Water Recycling
Funding Program (WRFP), compiling volumetric data for municipal wastewater
recycling facilities to determine progress towards goals set in 2008 and 2009 by the
California State Water Board Strategic Plan Update and the Recycled Water Policy.
Although the 2009 Survey represents the best available information on current water
recycling volumes in California, the survey itself was completed initially by only 18%
of agencies invited to participate (118 agencies responding). SWRCB staff collected
additional data through recycled water annual reports, agency websites, telephone
communication, and carry-over of data from the 2001 Survey, assuming volumes
reported in 2001 remained the same in 2009. Updates in the beneficial use categories
noted in the 2009 Survey as compared to the 2001 Survey include: Golf Course
Irrigation was separately quantified from Landscape Irrigation in the 2009 Survey;
Wildlife Habitat & Miscellaneous Enhancement in the 2001 Survey was labeled Natural
Sys. Restoration, Wetlands, Wildlife Habitat in the 2009 Survey; and Wastewater
Treatment Plant uses were not specified in the 2009 survey (included as Other for 2001
Survey results in manuscript Figure 2.3). Recreational Impoundments was 0% in 2009
and <1% in 2001. The 2001 Survey included private agencies, which were excluded in
the 2009 Survey. Annual flows reported in Northern and Southern CA for the 2001 and
2009 Surveys are compared in Figure 2.6S.
The National Database of Water Reuse Facilities (National Database, (58)) is
maintained by the WateReuse Association. The database was initially populated in 2006
and may be updated manually by agency representatives (78). The National Database
was accessed for the California Query in January 2010 for the 2010 Survey. Private
facilities and individuals from the 2001 Survey were excluded from the initial
31
distribution list for the 2010 Survey, and surveys were not sent to several agencies that
requested exclusion when compiling contact information. Additional information on
survey respondent characteristics was collected from the member-accessible portion of
the National Database in July-August 2010. Data for average annual production
volumes (million gallons) reported by water reuse facilities in California were accessed
from the National Database on August 16, 2010. Facility locations for mapping
purposes were obtained from zip codes available in the National Database or from
online searches for the facility location. Data from the 2010 Survey are not intended to
update or compare directly with the 2009 Survey results. 2010 Survey respondent
characteristics are described to assess representation of surveyed agencies compared to
agencies in the sampling frame.
Questionnaire. For the 2010 Survey, the invited participant was asked to complete
the survey or designate an individual familiar with the implementation of the water
reuse program to complete the survey. Coding entries for respondent “Position” shows
that 72% of respondents were directors/managers of the agency/utility (e.g., Public
Works Director, General Manager, Deputy Director, Division Manager), 14% of
respondents were engineers (e.g., Chief Engineer, Senior Engineer), 10% were
operators or other technical positions (e.g., Chief Plant Operator, Operations Director),
and 4% of respondents did not list a position. In the final dataset, one response per
agency was retained. Three agencies submitted two responses, so the more complete
survey was retained. One respondent did not input agency identification information;
responses from this individual were not excluded. In statistical analysis, quantitative
results were omitted for two respondents who indicated that their facilities do not
produce recycled water; these responses were retained for qualitative analysis purposes.
In the questionnaire, reclaimed or recycled water were noted to be synonymous and
defined as: “water that is used more than one time before it passes back into the natural
water cycle, or wastewater that has been treated to a level that allows for its reuse for a
beneficial purpose.” Driving forces and barriers to water reuse program implementation
in the region were examined through several qualitative and quantitative components.
Respondents rated the importance of five broad categories of potential drivers
(economic concerns, regulatory requirements, recycled water policy, water shortages,
32
and influential stakeholder groups) and five broad categories of hindrances (cost
recovery, human health or water quality concerns, institution/management issues,
influential stakeholder groups, and salt and nutrient management) for program
implementation on a three-point scale (Not a Driver/Hindrance, Driver/Hindrance, or
Very Important Driver/Hindrance). Additionally, from lists of specific factors
(manuscript Tables 2.1 and 2.2) respondents selected all factors that were considered
drivers/hindrances of program implementation and three factors considered to be the
most important drivers/hindrances for the implementation of their agency’s recycled
water program. In several cases, respondents marked a specific factor as one of the three
most important drivers/hindrances without selecting this same factor as a
driver/hindrance. In these cases, responses were updated such that all most important
drivers/hindrances were necessarily considered drivers/hindrances. Respondents were
given opportunity to insert other categorical and specific factors. To encourage free-
form descriptive responses and for clarification, respondents were further invited to
describe components of the broad categories previously marked as very important.
Subsequent questions and focus sections on the role of influential stakeholders,
constituents of emerging concern, and ecosystem enhancements will be the subject of
future analysis.
Categorization and statistical tests. Response data were categorized by self-
reported values of total reclaimed water use and date of program implementation such
that an equal number of respondents were in each category. Thus, recycled water
program size was operationally categorized as programs with self-reported total annual
reclaimed water deliveries greater or less than the median value (990 AFY), and the
date of implementation was categorized as either before or after the median date of
implementation, 1991. Analysis was omitted when the frequency in any contingency
table bin was less than five and p > 0.1 or when the frequency in any bin was equal to
zero. When the frequency of a bin was between 1 and five and p < 0.1 for the
association test, the results were listed in parentheses. Chi square analyses are
summarized in Table 2.3S to 2.10S.
33
Characteristics of 2010 Survey respondent agencies. Based on cross checking of
categorizations available in the member-accessible National Database, which lists all
respondent agencies in the 2010 Survey, respondent agencies represent Public Utilities
(45%), Private Utilities (6%), or Special Districts (23%). The remaining 23% of
respondent agencies were not categorized in the National Database. Most agencies
(76%) conduct “Both Production and Distribution” of recycled water, with 7%
categorized as “Only Production” and 7% as “Only Distribution;” 6% of respondents
represent other agencies (that may have been involved by funding, management of
construction, etc.), and 4% (3 agencies) are not categorized. Non-zero total annual
reclaimed water deliveries reported for 63 respondent agencies ranged from 6 AFY to
28,000 AFY, with a median value of 920 AFY. With the inclusion of delivery rates
from the 2009 Survey for three agencies where a value was not entered and data were
available in the SWRCB survey, the median reclaimed water delivery value was 990
AFY. Responses captured a larger flow range than reported in the 2009 Survey and
yielded a larger median annual recycled water volume (Figure 2.7S).
Historically, agricultural water reuse predominated in California, occurring in the
Central Valley in places where farmland was located adjacent to wastewater treatment
facilities (2). In recent years, agricultural reuse volumes have remained relatively stable,
becoming a smaller fraction of total reuse as new industrial and commercial uses are
developed. However, significant population growth, particularly in the Central Valley,
creates challenges for new or increased wastewater discharge in largely agricultural
areas, especially for environments with limited assimilative capacity (55). Beneficial
uses of recycled water reported in the 2009 and 2010 Surveys are displayed in Table
2.11S. Additionally, between 2002 and 2009, fourteen agencies in Northern California
reported reduced volumes, averaging 61% of 2002 levels recycled in 2009, including
two null values reported in 2009 for previously recycling agencies. From 1970 to 1977
and 1977 to 1987 various agencies were dropped from the latter surveys of water reuse
in California due to: (1) discontinued reuse, (2) treatment plants shut down or converted
to a wet weather plant with the construction of regional facilities, (3) changes in
reporting criteria or interpretation, (4) name changes, or (5) inadvertent omission (60).
34
California recycled water policy and regulation. Several milestones in recycled
water policy and regulation have been implemented in California since the Porter
Cologne Act of 1969 that formulated state and regional water quality control regulatory
bodies (e.g., Table 2.12S). As required by Assembly Bill No. 331 passed by the
California Legislature and signed into California law on October 7, 2001, a Recycled
Water Task Force was convened by the California Department of Water Resources
(DWR) in 2002 to address issues related to the impediments or constraints related to
increasing water recycling. Six issues areas were identified: funding/CALFED
coordination; public information, education, and outreach; plumbing code/cross-
connection control; regulations and permitting; economics of water reclamation; and
science and health issues of indirect potable reuse (2). In February 2009, the California
SWRCB adopted a Recycled Water Policy to encourage expanded reuse in California
(54). In a progressive move to address issues of trace contaminants in recycled water,
the policy led to a series of recommendations on chemicals of emerging concern,
published in 2010 (13).
Funds have been made available through state and federal financial assistance and a
series of bond initiatives from 1978 – 2002, totaling close to $132 million in planning
and construction grants and $509 million in low interest loans for water recycling
projects distributed by the State Water Board from 1978 to 2006 (79). An additional
$275 million in construction grants and $11.5 million in planning grants was distributed
by the U.S. Bureau of Reclamation Title XVI program through 2007 in California (79).
The 2003 Recycled Water Task Force estimated an $11 billion dollar investment was
required at the time to reach 2030 water recycling goals (2), and the 2008-2012 State
Water Board Strategic Plan Update estimates $300 million in annual grants and loans
are required (55).
2010 Survey respondents noted specific sources of regulatory requirements,
mandates, or strong recommendations that influenced program implementation,
including: National Pollutant Discharge Elimination System (NPDES) permits,
Regional Water Quality Control Boards (RWQCBs) and the SWRCB, Basin Plans, the
Porter Cologne Act, Waste Discharge Requirements, waste production located outside
of city limits for a treatment system (e.g., for a college campus), a water right
35
requirement to move golf courses off river water to recycled water, construction
approvals for new stand-alone subdivisions requiring production of recycled water, and
California Energy Commission recommendations to use recycled water for power plant
cooling water. Several other responses inserted by respondents as very important drivers
(n = 9), including flow limits on discharges, need for wastewater disposal, and RWQCB
requirements, supported the theme of regulatory requirements as important drivers of
implementation.
Small- versus large-volume recycled water programs. A chi square analysis
indicated that respondents representing the lower 50% of reuse programs by volume
(small-volume reuse programs) were somewhat more likely to cite wastewater
discharge volume requirements as a driver for implementation (0.1 < p < 0.2). At the
same time, small-volume programs were somewhat less likely to indicate the
availability of federal and state grants and loans as a driver of program implementation
(0.1 < p < 0.2) than programs with total annual reclaimed wastewater deliveries greater
than the median reported value. One respondent noted, “In rural foothill communities,
regulations tend to push agencies towards recycling because there are no viable
alternatives.” An effort to coordinate funding proposals was successfully implemented
by the San Francisco Bay Area Recycled Water Coalition (BARWC), a partnership of
17 agencies formed to secure federal funding under Title XVI of the 1992 Reclamation
Wastewater and Groundwater Study & Facilities Act. Typical project costs and the
federal shares of funding for the BARWC are shown in Table 2.13S (64). This
exemplifies the potential of collaborative funding efforts. Such efforts to pool resources
regionally, rather than compete individually for funding, may prove beneficial for rural
communities seeking facility upgrades for implementation of smaller-volume programs.
Respondents representing small-volume programs also provided somewhat greater
recognition of the role of influential stakeholders in driving program implementation,
citing either citizen initiatives or large volume users as drivers of implementation more
frequently than respondents from large volume programs (0.1 < p < 0.2). Larger volume
programs were more likely than smaller volume programs to be implemented due to a
need for reliable water supplies (0.1 < p < 0.2).
36
Impacts of hindrances. Cost impacts estimated by respondents for project delays
caused by cited hindrances specifically ranged from $50,000 to $25,000,000 due to time
delays, increased material costs, California Environmental Quality Act (CEQA)
documentation, staff time to contact interested parties, and for additional storage ponds
required for groundwater recharge. “Delays in customer connections caused by slow
regulatory approval and issues related to customer acceptance” led to lost revenues
estimated at $1,000,000 for one project. Another respondent noted that lack of local
project funding led to the loss of state grant funding and the cancellation of a project.
One respondent described a revenue challenge experienced in transitioning to recycled
water, “Recycled water is also typically discounted by 20 to 25% in Northern California
to encourage customers to connect, so even if the water and recycled water agency is
the same, the agency loses revenue by connecting recycled water customers while also
having to pay for the expensive recycled water infrastructure required.” A contingency table chi square analysis indicated that respondents who identified at
least one negative impact of stated hindrances to implementation were somewhat more
likely to cite costs for pipeline construction as one of the three most important
challenges as compared to those agencies who did not experience significant negative
impacts (p < 0.1). Programs recycling more than the median flow were also somewhat
more likely to cite costs for pipeline construction as a hindrance than smaller projects
(0.1 < p < 0.2), underlining the need for support of distribution system costs as recycled
water receiving sites are located further from recycled water production facilities.
37
Figure 2.5S. Distribution of survey invitations and responses collected from Northern
California counties. The number of respondents from a given county is displayed, with
the number of agencies invited to participate in a county indicated in parentheses.
38
Figure 2.6S. Agencies binned by annual recycled water flow (AFY) in 2001 and 2009.
39
1
10
100
1000
104
105
2010 Survey 2009 Survey
Tota
l R
euse (
AF
Y)
Figure 2.7S. Representation of total annual reclaimed water deliveries reported from
the 2010 Survey of Northern California agencies1 (n = 64) and the State Water
Resources Control Board 2009 Municipal Wastewater Recycling Survey (n = 143).
Both plots exclude zero-volume survey responses.
1 Includes reclaimed water deliveries values from the 2009 Survey for three agencies in which a value was not entered in the 2010 Survey and data was available in the SWRCB 2009 Survey.
40
Table 2.3S. C
hi square analysis of drivers of program im
plementation by self-reported date of im
plementation. O
lder and recent
implem
entation dates were operationally defined as before and after the reported m
edian value (1991), respectively. 1
D
river M
ost Important D
river
n = 54
Statistical tests (df = 1)
n = 54 Statistical tests
(df = 1)
Older
impl.
date, %
Recent
impl.
date, %
χ2
p O
lder im
pl. date, %
Recent
impl.
date, %
χ2
p
need for reliable water supply
17%
39%
8.45 0.004
9%
19%
1.98 0.16
water shortages due to increased dem
and 9%
30%
8.13
0.004 2%
13%
(4.94)
(0.03) w
astewater discharge volum
e requirements
37%
28%
1.98 0.16
33%
19%
4.59 0.03
water shortages due to reduced supply
22%
44%
(8.47) (0.004)
15%
26%
2.23 0.14
basin plan water quality objectives
26%
19%
1.29 0.26
19%
7%
(3.56) (0.06)
ecological protection or enhancement goals
26%
24%
0.13 0.72
7%
4%
regional or local recycled water policy goals or
mandates
19%
19%
0.01 0.93
4%
11%
state recycled water policy goals or m
andates 15%
15%
0.01
0.94 6%
7%
availability of federal/state grants or loans
6%
22%
(6.72) (0.01)
2%
11%
(3.84) (0.05)
cost of alternative freshwater sources
7%
24%
(6.14) (0.01)
2%
7%
1 A
nalysis is omitted w
hen the frequency in any contingency table bin was less than five and p > 0.1 or w
hen the frequency in any bin was equal to
zero. When the frequency of a bin w
as between 1 and five and p < 0.1 for the association test, the results are listed in parentheses.
41
Tab
le 2
.4S.
Chi
squ
are
anal
ysis
of h
indr
ance
s to
pro
gram
impl
emen
tatio
n by
sel
f-re
porte
d da
te o
f im
plem
enta
tion.
Old
er a
nd re
cent
impl
emen
tatio
n da
tes w
ere
oper
atio
nally
def
ined
as b
efor
e an
d af
ter t
he re
porte
d m
edia
n va
lue
(199
1), r
espe
ctiv
ely.
1
H
indr
ance
M
ost I
mpo
rtan
t Hin
dran
ce
n
= 44
St
atis
tical
test
s (d
f = 1
) n
= 44
St
atis
tical
test
s (d
f = 1
)
Old
er
impl
. da
te, %
Rec
ent
impl
. da
te, %
χ
2 p
Old
er
impl
. da
te, %
Rec
ent
impl
. da
te, %
χ
2 p
avai
labi
lity
of fe
dera
l/sta
te g
rant
s or l
oans
20
%
34%
2.
21
0.14
7%
14
%
user
acc
epta
nce
16%
27
%
1.59
0.
21
7%
2%
dow
nstre
am w
ater
qua
lity
impa
cts/
NPD
ES c
onst
rain
ts
11%
20
%
1.19
0.
28
0%
5%
dete
ctio
n of
con
stitu
ents
of e
mer
ging
con
cern
11
%
20%
1.
19
0.28
2%
0%
co
sts f
or p
ipel
ine
cons
truct
ion
39%
48
%
30%
25
%
0.88
0.
35
perc
eive
d hu
man
or e
nviro
nmen
tal h
ealth
risk
s due
to
cons
titue
nts o
f em
ergi
ng c
once
rn
23%
32
%
0.78
0.
38
5%
9%
issu
e of
who
pay
s for
pro
gram
cap
ital o
r ope
ratin
g co
sts
27%
36
%
0.73
0.
39
7%
11%
co
mpl
exiti
es/c
onfli
cts o
f wat
er la
w a
nd/o
r reg
ulat
ion
16%
23
%
0.48
0.
49
5%
2%
capi
tal c
osts
for c
onst
ruct
ion
of re
cycl
ing
plan
t fac
ilitie
s 41
%
45%
30
%
27%
0.
42
0.52
so
cial
atti
tude
s/pu
blic
per
cept
ion
18%
18
%
0.05
0.
82
5%
9%
slow
regu
lato
ry p
roce
ss in
per
mitt
ing
16%
16
%
0.04
0.
84
2%
5%
ongo
ing
oper
atio
ns &
mai
nten
ance
cos
t rec
over
y 30
%
32%
0.
00
0.94
14
%
14%
0.
03
0.85
pe
rcep
tion
that
recy
cled
wat
er w
ill le
ad to
mor
e de
velo
pmen
t 11
%
14%
0.
03
0.86
2%
2%
te
chni
cal i
ssue
s/tre
atm
ent p
roce
sses
5%
23
%
(6.3
8)
(0.0
1)
2%
0%
inst
itutio
nal c
oord
inat
ion
5%
20%
(5
.13)
(0
.02)
2%
7%
ef
fluen
t res
idua
ls (e
.g. b
rine)
dis
posa
l 2%
11
%
(2.6
9)
(0.1
0)
0%
2%
1 Ana
lysi
s is o
mitt
ed w
hen
the
freq
uenc
y in
any
con
tinge
ncy
tabl
e bi
n w
as le
ss th
an fi
ve a
nd p
> 0
.1 o
r whe
n th
e fr
eque
ncy
in a
ny b
in w
as e
qual
to
zero
. Whe
n th
e fr
eque
ncy
of a
bin
was
bet
wee
n 1
and
five
and
p <
0.1
for t
he a
ssoc
iatio
n te
st, t
he re
sults
are
list
ed in
par
enth
eses
.
42
Table 2.5S. C
hi square analysis of categorized drivers of program im
plementation by self-reported date of im
plementation. O
lder and
recent implem
entation dates were operationally defined as before and after the reported m
edian value (1991), respectively. 1
D
river M
ost Important D
river
n = 54
Statistical tests (df = 1)
n = 54 Statistical tests
(df = 1)
O
lder im
pl. date, %
Recent
impl.
date, %
χ2
p O
lder im
pl. date, %
Recent
impl.
date, %
χ2
p
Institutional control 19%
41%
8.39
0.004 11%
20%
1.80
0.18 W
ater supply needs 24%
44%
(7.10)
(0.01) 15%
33%
6.02
0.01 Influential stakeholders
9%
20%
2.77 0.10
6%
2%
Wastew
ater discharge requirements
37%
28%
1.98 0.16
33%
19%
4.59 0.03
Economic/financial incentives
20%
30%
1.35 0.24
7%
17%
Other policy and regulation
33%
33%
0.02 0.88
22%
20%
0.13 0.72
Ecological goals or requirements
26%
26%
0.01 0.91
11%
6%
1 A
nalysis is omitted w
hen the frequency in any contingency table bin was less than five and p > 0.1 or w
hen the frequency in any bin was equal to
zero. When the frequency of a bin w
as between 1 and five and p < 0.1 for the association test, the results are listed in parentheses.
43
Tab
le 2
.6S.
Chi
squ
are
anal
ysis
of c
ateg
oriz
ed h
indr
ance
s to
pro
gram
impl
emen
tatio
n by
sel
f-re
porte
d da
te o
f im
plem
enta
tion.
Old
er
and
rece
nt im
plem
enta
tion
date
s wer
e op
erat
iona
lly d
efin
ed a
s bef
ore
and
afte
r the
repo
rted
med
ian
valu
e (1
991)
, res
pect
ivel
y.1
H
indr
ance
M
ost I
mpo
rtan
t Hin
dran
ce
n
= 44
St
atis
tical
test
s (d
f = 1
) n
= 44
St
atis
tical
test
s (d
f = 1
)
O
lder
im
pl.
date
, %
Rec
ent
impl
. da
te, %
χ
2 p
Old
er
impl
. da
te, %
Rec
ent
impl
. da
te, %
χ
2 p
Wat
er q
ualit
y im
pact
s 14
%
27%
2.
53
0.11
2%
5%
U
ser a
ccep
tanc
e 16
%
27%
1.
59
0.21
7%
2%
Pu
blic
per
cept
ion
and
soci
al a
ttitu
des
36%
32
%
1.19
0.
28
11%
16
%
0.24
0.
62
Econ
omic
/fina
ncia
l dis
ince
ntiv
es
48%
50
%
0.93
0.
33
43%
45
%
Who
pay
s for
pro
gram
cos
ts
27%
36
%
0.73
0.
39
7%
11%
R
egul
ator
y co
nstra
ints
25%
30
%
0.08
0.
78
7%
7%
Tech
nica
l iss
ues/
treat
men
t pro
cess
es
5%
23%
(6
.38)
(0
.01)
2%
0%
In
stitu
tiona
l iss
ues
7%
20%
(3
.42)
(0
.06)
5%
7%
1 A
naly
sis i
s om
itted
whe
n th
e fr
eque
ncy
in a
ny c
ontin
genc
y ta
ble
bin
was
less
than
five
and
p >
0.1
or w
hen
the
freq
uenc
y in
any
bin
was
equ
al to
ze
ro. W
hen
the
freq
uenc
y of
a b
in w
as b
etw
een
1 an
d fiv
e an
d p
< 0.
1 fo
r the
ass
ocia
tion
test
, the
resu
lts a
re li
sted
in p
aren
thes
es.
44
Table 2.7S. C
hi square analysis of drivers of program im
plementation by self-reported total annual reclaim
ed water use. Sm
all and
large volume program
s were operationally defined as agencies w
ith total reclaimed w
ater use less than and greater than the reported
median value, respectively. 1
D
rivers T
hree Most Im
portant Drivers
n = 65
Statistical tests (df=1)
n = 65 Statistical tests
(df=1)
Sm
all V
olume
%
Large
Volum
e %
χ
2 p
Small
Volum
e %
Large
Volum
e %
χ
2 p
availability of federal/state grants or loans 8%
23%
6.79
0.01 3%
14%
5.11
(0.02) need for increased institutional control of w
ater 14%
5%
3.91
(0.05) 2%
2%
large volume user(s)
18%
9%
3.03 0.08
6%
0%
need for reliable water supply
20%
31%
2.60 0.11
11%
15%
0.60 0.44
wastew
ater discharge volume requirem
ents 35%
28%
2.09
0.15 29%
22%
1.87
0.17 regional or local recycled w
ater policy goals or m
andates 23%
15%
1.89
0.17 11%
6%
state recycled water policy goals or m
andates 11%
18%
1.65
0.20 6%
6%
w
ater shortages due to reduced supply 29%
34%
0.37
0.54 20%
20%
0.01
0.92 cost of alternative freshw
ater sources 17%
15%
0.12
0.73 5%
5%
technological advancem
ents 8%
9%
0.08
0.78 2%
2%
ecological protection or enhancem
ent goals 23%
25%
0.02
0.90 5%
8%
basin plan w
ater quality objectives 20%
20%
0.01
0.92 14%
11%
0.42
0.52 w
ater shortages due to increased demand
20%
20%
0.01 0.92
9%
6%
1 A
nalysis is omitted w
hen the frequency in any contingency table bin was less than five and p > 0.1 or w
hen the frequency in any bin was equal to
zero. When the frequency of a bin w
as between 1 and five and p < 0.1 for the association test, the results are listed in parentheses.
45
Tab
le 2
.8S.
Chi
squ
are
anal
ysis
of h
indr
ance
s to
pro
gram
impl
emen
tatio
n so
rted
by to
tal a
nnua
l rec
laim
ed w
ater
use
. Sm
all a
nd la
rge
volu
me
prog
ram
s wer
e op
erat
iona
lly d
efin
ed a
s age
ncie
s with
tota
l rec
laim
ed w
ater
use
less
than
and
gre
ater
than
the
repo
rted
med
ian
valu
e, re
spec
tivel
y.1
H
indr
ance
s T
hree
Mos
t Im
port
ant H
indr
ance
s
n
= 52
St
atis
tical
test
s (d
f = 1
) n
= 52
St
atis
tical
test
s (d
f = 1
)
Sm
all
Vol
ume
%
Lar
ge
Vol
ume
%
χ2
p Sm
all
Vol
ume
%
Lar
ge
Vol
ume
%
χ2
p
cost
of a
ltern
ativ
e fr
eshw
ater
sour
ces
8%
19%
2.
38
(0.1
2)
2%
6%
dete
ctio
n of
con
stitu
ents
of e
mer
ging
con
cern
10
%
21%
2.
07
0.15
0%
4%
sl
ow re
gula
tory
pro
cess
in p
erm
ittin
g 10
%
21%
2.
07
0.15
2%
6%
pe
rcep
tion
that
recy
cled
wat
er w
ill le
ad to
mor
e de
velo
pmen
t 6%
15
%
2.00
(0
.16)
2%
2%
cost
s for
pip
elin
e co
nstru
ctio
n 33
%
46%
1.
72
(0.1
9)
19%
29
%
0.73
0.
39
user
acc
epta
nce
13%
25
%
1.63
0.
20
4%
6%
inst
itutio
nal c
oord
inat
ion
10%
17
%
0.84
0.
36
8%
2%
2.55
(0
.11)
pe
rcei
ved
hum
an o
r env
ironm
enta
l hea
lth ri
sks d
ue to
co
nstit
uent
s of e
mer
ging
con
cern
19
%
27%
0.
36
0.55
6%
6%
capi
tal c
osts
for c
onst
ruct
ion
of re
cycl
ing
plan
t fa
cilit
ies
40%
44
%
29%
27
%
0.82
0.
37
dow
nstre
am w
ater
qua
lity
impa
cts/
NPD
ES c
onst
rain
ts
13%
17
%
0.05
0.
82
4%
2%
ongo
ing
oper
atio
ns &
mai
nten
ance
cos
t rec
over
y 27
%
33%
0.
03
0.86
10
%
17%
0.
84
0.36
is
sue
of w
ho p
ays f
or p
rogr
am c
apita
l or o
pera
ting
cost
s 27
%
33%
0.
03
0.86
10
%
10%
0.
07
0.79
com
plex
ities
/con
flict
s of w
ater
law
and
/or r
egul
atio
n 17
%
19%
0.
02
0.89
6%
4%
so
cial
atti
tude
s/pu
blic
per
cept
ion
15%
17
%
0.01
0.
93
6%
8%
1 Ana
lysi
s is o
mitt
ed w
hen
the
freq
uenc
y in
any
con
tinge
ncy
tabl
e bi
n w
as le
ss th
an fi
ve a
nd p
> 0
.2 o
r whe
n th
e fr
eque
ncy
in a
ny b
in w
as e
qual
to
zero
. Whe
n th
e fr
eque
ncy
of a
bin
was
bet
wee
n 1
and
five
and
p <
0.1
for t
he a
ssoc
iatio
n te
st, t
he re
sults
are
list
ed in
par
enth
eses
.
46
Table 2.9S. C
hi square analysis of categorized drivers of program im
plementation by self-reported total annual reclaim
ed water use.
Small and large volum
e programs w
ere operationally defined as agencies with total reclaim
ed water use less than and greater than the
reported median value, respectively. 1
D
rivers T
hree Most Im
portant Drivers
n = 65
Statistical tests (df = 1)
n = 65 Statistical tests
(df = 1)
Sm
all V
olume
%
Large
Volum
e %
χ
2 p
Small
Volum
e %
Large
Volum
e %
χ
2 p
Wastew
ater discharge requirements
35%
28%
2.09 0.15
29%
22%
1.87 0.17
Influential stakeholders 20%
12%
1.99
0.16 11%
0%
8.09
(0.004) Econom
ic/financial incentives 20%
29%
1.87
0.17 11%
14%
0.26
0.61 O
ther policy and regulation 29%
34%
0.37
0.54 23%
18%
0.74
0.39 Institutional control
26%
31%
0.37 0.54
12%
17%
0.55 0.46
Ecological goals or requirements
23%
26%
0.14 0.71
8%
11%
0.34 0.56
Technological advancements
11%
9%
0.14 0.71
2%
2%
1 A
nalysis is omitted w
hen the frequency in any contingency table bin was less than five and p > 0.1 or w
hen the frequency in any bin was equal to
zero. When the frequency of a bin w
as between 1 and five and p < 0.1 for the association test, the results are listed in parentheses.
47
Tab
le 2
.10S
. Chi
squ
are
anal
ysis
of h
indr
ance
s to
pro
gram
impl
emen
tatio
n by
sel
f-re
porte
d to
tal a
nnua
l rec
laim
ed w
ater
use
. Sm
all
and
larg
e vo
lum
e pr
ogra
ms
wer
e op
erat
iona
lly d
efin
ed a
s ag
enci
es w
ith t
otal
rec
laim
ed w
ater
use
les
s th
an a
nd g
reat
er t
han
the
repo
rted
med
ian
valu
e, re
spec
tivel
y.1
H
indr
ance
s T
hree
Mos
t Im
port
ant H
indr
ance
s
n =
52
Stat
istic
al te
sts (
df =
1)
n =
52
Stat
istic
al te
sts (
df =
1)
Sm
all
Vol
ume
%
Lar
ge
Vol
ume
%
χ2
p Sm
all
Vol
ume
%
Lar
ge
Vol
ume
%
χ2
p
Reg
ulat
ory
cons
train
ts
19%
33
%
1.88
0.
17
6%
10%
U
ser a
ccep
tanc
e 13
%
25%
1.
63
0.20
4%
6%
0.
08
0.77
In
stitu
tiona
l iss
ues
10%
19
%
1.39
0.
24
8%
4%
Publ
ic p
erce
ptio
n an
d so
cial
atti
tude
s 25
%
35%
0.
55
0.46
10
%
15%
0.
41
0.52
W
ater
qua
lity
impa
cts
19%
27
%
0.36
0.
55
8%
4%
1.15
0.
28
Who
pay
s for
pro
gram
cos
ts
27%
33
%
0.03
0.
86
10%
10
%
0.07
0.
79
Tech
nica
l iss
ues/
treat
men
t pro
cess
es
13%
15
%
0.00
0.
96
4%
4%
1 Ana
lysi
s is o
mitt
ed w
hen
the
freq
uenc
y in
any
con
tinge
ncy
tabl
e bi
n w
as le
ss th
an fi
ve a
nd p
> 0
.1 o
r whe
n th
e fr
eque
ncy
in a
ny b
in w
as e
qual
to
zero
. Whe
n th
e fr
eque
ncy
of a
bin
was
bet
wee
n 1
and
five
and
p <
0.1
for t
he a
ssoc
iatio
n te
st, t
he re
sults
are
list
ed in
par
enth
eses
.
48
Table 2.11S. R
epresentation of recycled water beneficial uses from
the 2010 Survey of Northern C
alifornia (n = 69) agencies and the
State Water R
esources Control B
oard 2009 Municipal W
astewater R
ecycling Survey (n = 143). Volum
es for each beneficial use were
not collected in the 2010 Survey.
A
gricultural Irrigation
Landscape
Irrigation1
Industrial
Wildlife
Habitat
Enhance-m
ent. 2
Com
mercial/
Residential
Buildings 3
Groundw
ater R
echarge R
ecreational Im
poundment
Geotherm
al/ E
nergy Production
Other
2009 Total R
euse (AFY
) 4 91,360
22,556 13,975
12,071 7,371
2,500 0
12,665 10,049
2009 Frequency Percent
60.8%
32.9%
7.7%
4.9%
2.8%
0.7%
0.0%
0.7%
9.1%
2010 Frequency Percent
42.0%
52.2%
24.6%
21.7%
7.2%
11.6%
7.2%
N/A
24.6%
1 Landscape Irrigation and G
olf Course Irrigation w
ere combined from
the 2009 Survey results in this table. 2 Labeled as N
atural Sys. Restoration, Wetlands, W
ildlife Habitat in 2009 Survey results.
3 Labeled as Com
mercial in 2009 Survey results.
4 No agencies in N
orthern California indicated Seaw
ater Intrusion Barrier, Surface Water Augm
entation, or Indirect Potable Reuse as beneficial uses in the 2009 Survey results.
49
Table 2.12S. Milestones for California water reuse and statewide recycling goals.1
Year Name (Ref.) Description 1967 California Legislature supports
policies and laws to promote water recycling
Declared that “the state undertake all possible steps to encourage development of water reclamation facilities…to help meet the growing water requirements of the state.” (California Water Code, Section 13512)
1969 Porter-Cologne Water Quality Control Act (62)
Established State Water Resources Control Board and nine Regional Water Quality Control Boards (California Water Code, Division 7).
1974 Water Reuse Law of 1974 Enacted with the mission that “the primary interest of the people of the State in the conservation of all available water resources requires the maximum reuse of reclaimed water in the satisfaction of requirements for beneficial uses of water.” (California Water Code, Section 461)
1977 Policy and Action Plan for Water Reclamation in California and Executive Order B-36-77
Encouraged water reclamation and funding to support legislative directives. Established Office of Water Recycling and goal “to make available an additional 400,000 acre-feet by 1982.”
1991 California Water Recycling Act of 1991
Established statewide goal to recycle 700,000 AFY by 2000 and 1,000,000 AFY by 2010. (California Water Code, Section 13577)
1994 Statement of Support for Water Reclamation
Joint statement signed by State Water Resources Control Board, the USEPA, California Conference of Environmental Health Directors, Department of Water Resources, U.S. Bureau of Reclamation, and Water Reuse Association of California to pursue and develop policies and regulations that reduce constraints and promote water reclamation.
2000 Water Recycling in Landscaping Act
Required local public or private entities that produce or will provide recycled water to notify the local agency and required local agencies in turn to adopt and enforce a recycled water ordinance requiring recycled water use. (Senate Bill 2095)
2001 Recycled Water Task Force (2) Assembly Bill 331 required the Department of Water Resources to convene a Recycled Water Task Force "to identify opportunities/constraints to increase the industrial and commercial use of recycled water."
2006 Assembly Bill 371 (2) Included statement that agencies should take appropriate steps to implement the Recycled Water Task Force recommendations to meet the goal of recycling one million acre-feet per year of water by 2010. Required installation of piping for landscape irrigation if recycled water will be provided.
2008 State Water Board Strategic Plan Update (55)
Stated goal to recycle 1,250,000 AFY by 2015.
2009 Recycled Water Policy (54) Established goal to recycle 1,525,000 AFY by 2020 and 2,525,000 AFY by 2030.
2010 Science Advisory Panel report on Monitoring Strategies for Chemicals of Emerging Concern (CECs) (13)
A Science Advisory Panel was established by the 2009 Recycled Water Policy “to provide guidance for developing monitoring programs that assess potential CEC threats from various water recycling practices.”
1 Includes Water Recycling Laws and Policies summarized in (79) Water Recycling Funding Program Division of Financial Assistance Strategic Plan; California State Water Resources Control Board: Sacramento, 2007. Excludes summary of funding sources (e.g., Bond Laws).
50
Table 2.13S. San Francisco Bay Area Recycled Water Coalition 2011 Project Summary
(64)
Project Yield
(AFY), Project
Yield (AFY), Future
Total Cost
Federal Share of
Cost
Cost per ac-ft1
South Bay Water Recycling Phase 1.d.
2000 3000 $39.2 M $9.8 M $980
South Santa Clara County Recycled Water Project
1790 2440 $28 M $4.2 M $780
Antioch Recycled Water Project 490 850 $12.5 M $0.875 M $1300 South Bay Advanced Recycled Water Treatment Facility
6720 28000 $53 M $5 M $390
Central Contra Costa Sanitary District (CCCSD)-Concord Recycled Water Project
255 255 $7.2 M $1.8 M $1400
Contra Costa County Refinery Recycled Water Project
5600 22500 $25 M $6.25 M $220
Central Redwood City Recycled Water Project
1075 3170 $32 M $8 M $1500
Central Dublin Recycled Water Distribution and Retrofit Project & other projects
215 215 $4.6 M $1.15 M $1100
Delta Diablo Sanitation District (DDSD) Recycled Water Advanced Treatment and Expansion Project
3900 12500 $25 M $6.25 M $320
Dublin San Ramon Services District (DSRSD) Recycled Water Expansion Project
350 3250 23.85 M $5.96 M $3400
Hayward Recycled Water Project 3760 3760 27 M $6.75 M $360 Ironhouse Sanitary District Recycled Water Project
910 1320 26 M $6.5 M $1400
Palo Alto Recycled Water Pipeline Project
1000 1500 33 M $8.25 M $1700
Petaluma Recycled Water Project, Phases 2A, 2B and 3
1610 3280 24 M $6 M $750
Pleasanton Recycled Water Project 440 1840 20 M $5 M $2300 Yountville Recycled Water Project 115 400 3 M $0.75 M $1300
1 Assumes 20-years of Project yield.
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Chapter 3
Water reuse for ecosystem enhancement:
Matching opportunity with need
3.1 Introduction
Water and wastewater treatment systems were developed during the twentieth
century as two separate systems that served mutually excusive goals of water supply
and protection of the integrity of receiving waters. Upgrades of wastewater treatment
facilities to meet more expansive regulatory requirements improved ambient water
quality. Yet increase in urban water demand has come at the expense of aquatic
ecosystems. Approximately 91% of historical California wetlands, including 85% of
saline wetlands and 92% of freshwater tidal wetlands, have been lost due to
urbanization (80). Urban and peri-urban development, and a traditional emphasis of
engineers on the prevention of floods and disposal of wastewater, has adversely
impacted urban hydrology and damaged aquatic ecosystems. In California, more than
half (62%) of estuarine wetlands exhibit medium to poor health due to modification of
physical structure, including levees and transportation infrastructure that have changed
52
the shape and reduced the size of wetlands (80). In these regions, non-natural tidal and
freshwater hydrology couple with excessive sediment supplies to reduce physical
complexity and wetland health.
Effective management of urban water can benefit aquatic habitat. Given the
availability of tertiary treated recycled water within the San Francisco Bay Area (81)
and potentially throughout California, the question arises whether some portion of
available highly treated recycled water can be used for beneficial wetland enhancement
and creation or stream augmentation. Redesign of urban hydrology in a manner that
enhances existing aquatic habitat has the potential to provide new sources of water
storage, while restoring the integrity and improving the aesthetics of watersheds and the
urban environment.
In Chapter 2, the growth of water reuse in Northern and Southern California was
documented using statewide survey data and ground-truthing to evaluate major trends in
the size, location, and form of projects implemented over the past half-century. The
objectives of the present chapter are to characterize existing and potential cases of water
reuse for natural system enhancement in California and to outline perceived challenges
associated with the implementation of water reuse projects for ecosystem enhancement.
Projects in California in which environmental enhancement drove the project design,
distinct from discharge of highly treated wastewater with incidental environmental
benefit, are identified. Opportunities for new projects are evaluated based on responses
to the previously described survey of water reuse managers (2010 Survey), an
assessment conducted in the San Francisco Bay Area by a regional coalition of
municipalities, and a statewide projection of wetland condition. Lastly, general issues
and challenges associated with wetland creation and enhancement as well as stream
augmentation with recycled water are discussed.
3.2 Few Existing Examples of Water Reuse for Direct
Ecosystem Enhancement in California
Several databases were queried and assessed to compile data on the use of
wastewater for the benefit of ecosystems: the California State Water Resources Control
53
Board (SWRCB) recycled water surveys (3, 59) conducted in 2001 (2001 Survey) and
2009 (2009 Survey), the National Database of Water Reuse Facilities (National
Database, (58)), the Treatment Wetland Database (TWDB), and the 2010 Survey
conducted as part of this research. Brief descriptions of identified projects that are
located in Northern California are given in Table 3.1.
According to the 2009 Survey, water reuse for ecosystem enhancement totaled
27,849 AFY, representing 4% of total wastewater reuse in the state. A total of 17
programs listed either “Wildlife habitat or misc. enhancement” on the 2001 Survey or
“Natural System Restoration, Wetlands, Wildlife Habitat” on the 2009 Survey. Eight of
these projects are located in the northern 48 counties of California, and the remaining
projects are located in the ten southernmost counties. The average size of Northern
California projects listed on the 2009 Survey was 1,700 AFY. The National Database
notes a total of six projects, three of which are in Northern California, with “Natural
System Restoration – Wetlands” as a beneficial use category. Five of the National
Database listings are represented on the 2001 or 2009 Surveys. Other agencies with
wildlife enhancement beneficial uses noted on the 2001 and 2009 Surveys are listed in
the National Database without beneficial use categorizations.
Wetlands ecosystems that serve as polishing for secondary treated wastewater may
also be considered water reclamation systems. Due to their ability to accept large
quantities of effluent, their partly oxic and partly anoxic soils, and resilient aquatic plant
species, wetlands are particularly suitable for wastewater purification (82). The TWDB
contains system descriptions and performance data on pilot and full-scale constructed
wetlands (83). At the time of access (May 2011), the database lists 11 unique systems in
California, with several additional pilot systems: Arcata Treatment Marsh, Gustine,
Hayward, Hemet/San Jacinto, Kelly Farm, La Franchi, Las Gallinas Sanitary District,
Manila Community Treatment Plant, Mt. View Marsh, Richmond, and Sacramento
Demonstration Wetland. Except for the Hemet/San Jacinto, all of these systems are in
Northern California and several overlap with systems identified via the 2001 and 2009
Surveys. Wetlands as treatment systems are an attractive options for small communities
that may be disproportionately affected by the construction, operation, energy, and labor
costs associated with centralized “concrete and steel” water pollution control facilities
54
(84).
Additional data regarding wastewater reuse for ecosystem enhancement were
collected via an online questionnaire of Northern California water reuse managers
(2010 Survey), which was discussed in detail in Chapter 2. If “Wildlife habitat
enhancement” was selected as a direct beneficial use of the treated wastewater in the
initial background section of the survey, respondents were asked to list the type (e.g.,
wetland enhancement or restoration, stream augmentation, freshwater marsh, etc.),
location, and volume of recycled water for existing uses of recycled water from their
agency’s program for ecosystem enhancement purposes. Fourteen respondents indicated
“Wildlife habitat enhancement” as a direct beneficial reuse utilized by their agency, and
seven of these respondents provided further information on the systems. Descriptions
listed in Table 3.1 from the 2010 Survey were included only if the system was also
listed on either the 2001 Survey, the 2009 Survey, the National Database, or the TWDB.
As a result, three project descriptions, the Dow Wetlands Preserve in Antioch supported
with approximately 0.1 MGD by the Delta Diablo Sanitation District, a Moss Landing
research project using 0 to 8 MGY, and habitat enhancement projects coupled to
development near the Sutter Creek Wastewater Treatment Plant in Amador County,
were described by respondents but not included in Table 3.1.
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Table 3.1. Projects in Northern California utilizing recycled water for ecosystem
enhancement or treatment wetlands for wastewater effluent polishing.
Name Agency Noted in 2001 or
2009 Survey
Noted in 2010 Survey
Noted in TWDB
Arcata Enhance-ment Wetlands
City of Arcata Y2 Y Y A free-surface constructed wetland operating year round with continuous loading of secondary treated chlorinated effluent, the Arcata Enhancement Wetlands became fully operational in 1986 at a cost of just over $500,000 (1986 base year of capital cost). With an approximately 15 ha footprint, three wetland cells (Allen, Gearheart, and Hauser Marshes) operate in series for tertiary treatment of solids, organics, and nutrients as well as for habitat creation/enhancement, recreation, research, and acting as nature preserve (83). Additional references: (9, 85).
Calera Creek Wetlands
City of Pacifica Y N N The Calera Creek wetland restoration was conducted to improve riverine waters and wetland ecosystem function and to create habitat for the threatened California Red-Legged Frog and endangered San Francisco Garter Snake. Pacifica used a Hydro Geomorphic model for planning their wetland restoration projects. The treatment facility utilizes ultraviolet disinfection (83). In 1999, Pacifica delivered an average of 2,020 AFY (1.8 MGD) to existing wetlands (81). The City of Pacifica reported 3,280 AFY of recycled water use for Natural System Restoration, Wetlands, Wildlife Habitat in the SWRCB 2009 Survey (59).
Emily Renzel Wetlands
City of Palo Alto N2 Y N The Emily Renzel Wetlands restoration project in the Palo Alto Baylands comprises a 15-acre freshwater pond that receives 1 to 2 million gallons per day of pumped reclaimed water from the nearby Palo Alto Regional Water Quality Control Plant. When completed in 1992, it was one of only three projects in the State using reclaimed wastewater to develop freshwater marshes for birds. In 1999, Palo Alto delivered an average of 280 AFY (0.25 MGD) to existing wetlands (81). The City of Palo Alto does not report any use of recycled water for ecosystem enhancement on the 2002 or 2009 SWRCB Surveys, but the wetland enhancement project is listed on the National Database of Water Reuse Facilities.
Kelly Farm Santa Rosa N Y Y A small free surface constructed wetland (4 ha; 5 cells) that receives advanced secondary treated effluent, Kelly Farm became fully operational in 1990 (83). The marsh uses about 20 million gallons of water per year from the City of Santa Rosa Laguna Treatment Plant. This treatment facility also supports riparian revegetation projects that may use 20,000 gallons per day in the dry season.1
Hayward Marsh
Union Sanitary District (USD) Y2 Y Y The original two-phase implementation completed in 1980 and 1988 restored nearly 400 acres of the 1800 acres of Hayward shoreline (12). The 5-cell free surface wetland treatment system (~60 ha) receives conventional secondary effluent for year-round operation (83). In 1999, USD delivered an average of 11,000 AFY (10 MGD) to existing wetlands (81). In 2009, USD reported its total recycled water use, 3,493 AFY, as that for natural system restoration, wetlands, or wildlife habitat (59).
Gustine constructed wetlands
City of Gustine N N Y Fully operational in 1988 at a total cost of $882,000, the 9.6 ha free-surface constructed wetland utilizes 24 marsh cells to manipulate hydraulic detention time after receiving effluent from up to 11 oxidation ponds operated in series (83, 84). The City of Gustine only reports water reuse for irrigation in the 2002 or 2009 SWRCB Survey.
1 Additional information from 2010 Survey response. 2 “National System Restoration – Wetlands” beneficial use also listed on the National Database of Water Reuse Facilities.
56
Name Agency Noted in 2001 or
2009 Survey
Noted in 2010 Survey
Noted in TWDB
Las Gallinas, San Rafael
Las Gallinas Sanitary District (LGSD) N N Y The 20-acre free surface constructed freshwater marsh/pond was designed with varying depths and vegetation in a single unit to incorporate different wildlife habitat types (86). An additional 40 acres of storage ponds are used to irrigate pasture. Including land acquisition, the total cost of the reclamation system was $8.6 million, with state and federal Clean Water Grant funds covering 87.5% of the costs. LGSD reports 378 AFY for agricultural irrigation on the 2009 SWRCB Survey but does not indicate reuse for an environmental purpose (59).
La Franchi Santa Rosa N N Y La Franchi became fully operational as a free-surface constructed wetland (0.1 ha) treating low rate pond secondary-treated agricultural and animal waste in 1991 (83, 87). Because this recycling does not occur from a municipal wastewater treatment facility, the listing is not reported on the SWRCB Surveys.
Manila wetlands
Manila Comm. Treatment N N Y The free surface constructed wetland in Manila, CA operates year round with continuous loading after passing through a low-rate pond for secondary treatment in Manila, CA (83).
Moorhen Marsh and McNabney Marshes
Mt. View Sanitary District (MVSD) Y Y Y Moorhen Marsh is a 21-acre constructed wetland that is 100% fresh water and effluent dominated. MVSD cites the marsh as the first to use conventional secondary treated effluent as its primary water source. The adjacent McNabney Marsh (formerly known as Shell Marsh) is estuarine and seasonally saline, in total consisting of 130-acre restored, seasonally tidal wetland (88). The free-surface constructed wetland, reported as 3 cells and 37 ha in the TWDB, became operational in 1974 at a system cost of $90,000 and annual operation and maintenance cost of $20,000 (1978 base year of costs) (83). In 1999, MVSD delivered an average of 1,000 AFY (0.9 MGD) to existing wetlands (81).
Sacramento Demon-stration Wetlands
Sacramento Regional WWTP N N Y Treatment of municipal disinfected secondary effluent via a full scale free surface constructed wetland (8.9 ha) that utilizes 10 cells began in 1994, operating at about 1 MGD (83, 89), The Sacramento Regional County Sanitation District did not report reuse on the SWRCB 2009 Survey (59).
Wetland Enhance-ment / Restoration
Sonoma County Water Agency Y Y N The Sonoma County Water Agency includes four wastewater treatment facilities. Of these, two report use of recycled water for natural system enhancement or restoration on the SWRCB 2009 Survey: the Sonoma Valley Treatment Plant reported 100 AFY for wildlife of 1,600 AFY total reuse in 2009, and the Russian River Treatment Plant reported 90 AFY for wildlife of 150 AFY total reuse in 2009 (59).
Many 2010 Survey respondents considered the major beneficiaries from the
implementation of recycled water programs to include environmental groups in addition
to natural habitats. Examples cited of environmental and public benefits were: less
reliance on Delta Water, stakeholders concerned with protection of water quality in
Clear Lake, restoring water levels at Lake Merced in San Francisco (e.g., Cal Trout and
Natural Heritage Institute), and reduced discharge flows to Monterey Bay. Beneficiaries
cited also included general environmental advocacy groups (e.g., in San Jose/Santa
Clara) as well as natural habitat and recreational users (e.g., birders).
57
3.2 Identifying Opportunities for Ecosystem Enhancement
Although opportunities to reuse reclaimed water may be gleaned by quantification
of water needs for various applications (1), the needs of ecosystems are less practically
quantified. However, the system hydrology and measurable ecosystem characteristics
are intricately linked. Urbanized estuaries tend to have lower wetland health due to
hydrologic and biotic community structures (80). Water source, velocity, flow rate,
renewal rate, and inundation frequency influence the chemical and physical properties,
and thus biological structure, of wetland substrate (84). A major recommendation of the
Surface Water Ambient Monitoring Program (SWAMP) includes the need to increase
the size of estuarine wetlands to reduce the effects of stressors such as terrestrial
predators (80). Water movement through wetlands tends to have positive impacts on the
ecosystem, promoting increased regional production (84). For streams that have
experienced significant flow reductions due to anthropogenic influences, augmentation
using highly treated recycled water may beneficially impact the stream via increased
summer flows, improved water quality, support of healthier riparian areas, lowered
stream temperatures, enhanced fish and wildlife habitat, and improved aesthetics (90).
Based initially on this premise, artificial augmentation of wetlands, including riparian
corridors that have experienced significant flow reductions and altered hydrologic
regimes, represents potential opportunities for habitat restoration.
California assessment to match opportunity with need. Rapid assessment
methods represent a potential cost effective and consistent mechanism to monitor
relative wetland and riparian health, evaluating complex ecological condition using
observable field indicators. The California Rapid Assessment Method (CRAM) was
developed to assess the health of California wetlands along a continuum of conditions
based on attributes and metrics identified from a literature review and selected for
appropriate accuracy, precision, robustness, ease of use, and cost (91). The analysis
assumes that ecosystem condition, and the ability to support wildlife, may be measured
by structural characteristics and increases with complexity and size. Seven wetland
classifications were selected for the CRAM: riverine and riparian, estuarine, lacustrine,
depressional, wet meadows, vernal pools, and playas. The goal of the CRAM
58
assessment is to “provide rapid, scientifically defensible, standardized, cost-effective
assessments of the status and trends in the condition of wetlands and related policies,
programs and projects throughout California.” Subsequent validation of the CRAM
methodology indicates that the score corresponds with multiple independent
assessments of condition for avian diversity, plant community composition, and benthic
macroinvertebrate indices (92).
Four attributes, landscape context, hydrology, physical structure, and biotic
structure, are each characterized semi-quantitatively based on narrative analyses on a
series of metrics that are further assigned an ordinal or interval score relative to a
pristine condition (91). The hydrology attribute incorporates three metrics: water
source, hydroperiod, and hydrologic connectivity (92). External stressors (e.g.,
anthropogenic influences or natural disturbances to the wetland) are documented
separately from the wetland condition. The CRAM score, expressed as percent possible
ranging from 25 to 100 (80), summarizes the condition, or health, of a wetland or
riparian habitat relative to its maximum achievable condition based on a field visit by
two trained individuals. CRAM scores falling between 25 and 44 are indicative of poor
estuarine wetland health while 44 to 63 indicates medium to poor health. Other widely
used wetland assessment methods include the Hydrogeomorphic Method (HGM), the
Index of Biotic Integrity (IBI), and the Habitat Evaluation Procedure (HEP). These
methods are generally much more time and cost intensive and thus are generally
unavailable at a statewide level (91). For the present assessment, CRAM data were
obtained in July and August 2010. The CRAM scores categorize water bodies as
Estuarine Saline, Estuarine Non-saline, Riverine Confined, or Riverine Non-confined.
59
Figure 3.1. Distribution of California Rapid Assessment Method (CRAM) overall
wetland scores, included for estuarine (saline and non-saline) and riverine (confined and
non-confined), and wastewater facilities with tertiary treatment capacity.
Monitoring wetlands on a broad scale provides general information about
opportunities for wetland enhancement using recycled water. For this purpose, the
locations of tertiary treatment facilities were identified for proximity to wetland
ecosystems under stress. In Figure 3.1 the location of California tertiary treatment
facilities is overlaid with CRAM scores (93). As a conservative approach, only
wastewater facilities that currently utilize tertiary treatment, as indicated by the National
Database, are included. Typically tertiary treated recycled water for general purpose
irrigation comprises additional steps of coagulation, filtration and disinfection beyond
60
secondary treated wastewater. According to the National Database, advanced/tertiary
treatment technologies used by water reuse facilities in California include carbon
adsorption, ion exchange, disk filters, media filtration, ultrafiltration, nanofiltration,
reverse osmosis, and chemical precipitation. The treatment facility locations shown in
Figure 3.1 were generated from the zipcode of the facilities. Further refinement with
GIS mapping and system features are underway. Analysis of the location of tertiary
treatment facilities as generated from the zipcode was compared to the location of the
lower 50% of CRAM scores as a proxy for distance to wetlands. There were 27 low-
quality wetland sites within 10 miles of a wastewater treatment plant. Additional
evaluations at an ecoregion level can inform prioritization of restoration projects.
San Francisco Bay regional assessment. Of California’s 44,456 acres of perennial
tidal estuarine wetlands, 77% are located in the San Francisco Bay Estuary (80). The
1999 Bay Area Water Recycling Master Plan (BAWRMP) contains the most
comprehensive assessment of opportunities for wetland and stream augmentation using
tertiary treated recycled water for the San Francisco Bay region (81). The goals of the
Bay Area Regional Water Recycling Program environmental enhancement committee
were to identify potential environmental enhancement projects, use a watershed context
to evaluate environmental issues, and identify and develop action plans to address
regional environmental issues in recycled water implementation. The March 1999
Baylands Ecosystems Habitat Goals Report was utilized to frame the analysis of
potential for ecosystem enhancement using recycled water. The team evaluated 16
potential wetland restoration locations and 13 possible stream augmentation sites for
ecosystem enhancement, totaling of 13,000 AFY and 19,000 AFY, respectively (81).
The evaluation was not comprehensive, and further identification of potential sites
should be conducted, especially as new wetland assessment techniques are developed
and implemented. The application of recycled water for environmental enhancement
requires further investigation to determine the value of this option as well as appropriate
water quality criteria.
The BAWRMP evaluation of wetland sites involved potential site identification
from an initial market assessment, evaluating wetland water demand based on acreage
and a wetland water application rate (10 AF/acre based on wetland biological
61
requirements), and site evaluation of potential benefits and impacts, as well as
implementation strategies, for 16 potential sites (81). Possible site benefits included
habitat, species, and habitat diversity and management benefits, as well as the potential
to intercept non-point source pollution runoff and improve aesthetics. Potential adverse
impacts considered included the conversion of an existing valuable habitat, impact to
existing habitat or special status species, inconsistency with habitat management plans,
possible bioaccumulation of pollutants based on potential design, and impact on
biological resources from pipeline infrastructure.
The BAWRMP goals for 2010 were to develop an additional 11,000 AFY for
streamflow augmentation and 9,500 AFY for wetland enhancement or creation (81).
This was expected at a total cost of approximate $15 million for wetlands and $0.9
million for streams (discounted using 6.875% nominal discount rate; reported in 1997
dollars). Sites included in these goals included stream augmentation projects at San
Francisquito Creek by the City of Palo Alto facility, San Mateo Creek by South Bay
Systems Authority, Pillarcitos Creek by the San Francisco International Airport facility,
and the Guadalupe River by San Jose/Santa Clara (SJSC) among several other sites.
Based on the database analysis, many of the projects identified by the BAWRMP as
potential enhancement sites have likely not been implemented. The relatively large
number of projects expected for environmental uses by 2010 may have been a result of
low costs of additional treatment expected in the modeling scenarios. At the time of the
BAWRMP, the San Jose Coyote Creek study was underway, with note that additional
treatment beyond tertiary filtration may be a conclusion of that study. As will be
described in more detail later, this project was canceled in 2008 following extensive
water quality analysis that showed the presence of perfluorinated chemicals.
Additional opportunities in Northern California. In addition to identifying
existing sites, respondents from the 2010 Survey of Northern California water reuse
facilities were questioned regarding “opportunities to expand the use of recycled water
for restoration or protection of natural environments.” Respondents were asked to list
future opportunities identified to use or expand use of recycled water from their
recycled water program for ecosystem enhancement purposes. Several agencies noted
future opportunities for wetland or salt pond restoration, creation, or expansion:
62
• South Bay wetland creation (1-5 MGD) and bittern ponds habitat restoration
(5-15 MGD), City of San Jose;
• Napa Salt Marsh wetland reclamation (up to approximately 5 MGD), Napa
Sanitation District;
• Duer and Irwin Creek (estimated to use about 20,000 gallons per day each),
City of Santa Rosa;
• Wildlife habitat, Sacramento Regional County Sanitation District;
• Salt ponds restoration/wetland enhancement, Sonoma County Water
Agency;
• Carmel River Lagoon, Carmel Area Wastewater District;
• Possible wetland enhancement, Laurel Pond, Mammoth Community Water
District;
• Expansion of Hayward Marsh (north and south of existing marsh), East Bay
Dischargers Authority;
• Closed-loop constructed wetlands, Northwest Regional Treatment Plant,
Lake County Sanitation District.
Several respondents noted particular ecosystem enhancements expected due to
increased treatment or reduced discharge:
• Churn Creek, City of Shasta Lake (looking to increase treatment levels to
allow more discharge; could increase fish spawning);
• Pajaro Valley sloughs and Monterey Bay National Marine Sanctuary (~7
MGD reduced discharge), Pajaro Valley Water Management agency;
• Reduce nutrients to Monterey Bay, Marina Coast Water District.
Providing additional commentary on this form of ecosystem benefit, one respondent
noted, “I believe that since the run-off from recycled water is in the creeks, it helps
provide a habitat for certain species. So it does enhance the ecosystem. We have never
seen any adverse affects from recycle water run-off and/or use, only good things like
water and lower rates to use it.”
In response to whether future opportunities to use or expand use of recycled water
for ecosystem enhancement purposes, 32 respondents indicated that no future
opportunities have been identified. In space for comment, several of these respondents
63
provided additional input:
• “Restrictive Regional Water Quality Control Board requirements for
discharging recycled water to local creeks due to downstream potable water
recharge diversions would seem to seriously limit or eliminate ecosystem
enhancement opportunities.”
• “In fact we would like to divert a percentage [of] current flow from wildlife
enhancement to industrial use.”
• “Wildlife/habitat enhancement is not a consistent user of water. We have
consistent users who could use the water instead, if we can make it
available.”
• “Unlikely District will be able to expand system due to new state general
order. May not be able to meet new regulatory standards.”
While regulatory requirements may be a consistent driver of program
implementation, such requirements may also limit the use of recycled water for
ecosystem enhancement. This assessment represents a limited patchwork of projects,
and further systematic identification of sites is necessary as part of future work.
3.3 Technological and Management Challenges
The BAWRMP provides a key resource for framing the issues associated with
wetland creation and enhancement using recycled water as the main source and for
conducting a preliminary assessment of potential benefits and adverse impacts of such
projects (81). Key issues for wetland creation and enhancement with recycled water
include flow velocity and erosion, habitat conversion, ecosystem buffers, control of
non-native species, vector control, water quality, flood control, public access, land
ownership/land use impacts, and water supply dedication/environmental management
sustainability. For stream augmentation, general issues include elevated water
temperature, decreased water quality (e.g., for temperature, nutrient load, total dissolved
solids, and trace organics), increased warm-water predator populations, inadvertent salt
marsh conversions, needs in addition to flow augmentation (especially when this is not
the priority obstacle to restoration), and flow timing. San Francisco Bay Area streams
are typically seasonal streams, and the creation of perennial streams could have adverse
64
effects on indigenous fauna. Some streams (e.g., San Mateo Creek) may have been
perennial historically and therefore warrants inclusion as possible augmentation sites.
Water quality. Undisinfected secondary treated effluent is the miminum water
quality requirement for wetlands (81). Wetland systems can further filter particular
matter, reduce suspended solids (SS), remove biochemical oxygen demand (BOD),
remove and store nutrients, and attenuate some trace chemical contaminants (11, 94).
Due to these features and other environmental benefits, as well as escalating costs of
construction, operation, and distribution associated with conventional treatment, interest
in wetlands as wastewater treatment systems has increased (12, 82, 84). However, the
presence of waterborne pathogens, nutrients and other aesthetic issues (e.g., odors)
could preclude the use of effluent in the urban environment such that more advanced
treatment technologies may be necessary for protection of ecosystem health. Further,
despite potential benefits associated with natural attenuation of pharmaceuticals,
personal care products, and other commercial and industrial chemicals of emerging
concern in effluent-dominated wetlands and rivers (6, 11, 95, 96), recalcitrant chemicals
may persist in treatment wetlands and receiving waters. The bioaccumulation of trace
organic contaminants in biota residing in treatment wetlands (97) and effluent-
dominated natural systems (98) is a concern. Research is needed on the most effective
means of treating wastewater prior to reuse for habitat enhancement.
2010 Survey analysis of drivers and challenges for water reuse for ecosystems.
“In most cases, using recycled water to offset potable use is more
desirable than using limited recycled supplies for habitat enhancement.
Therefore the most important barrier is that environmental enhancement
is not the highest and best use.”
When respondents from the 2010 Survey were asked to consider potential drivers
and challenges for future water reuse projects for ecosystem enhancement, several
important management issues were raised and overall concern for funding and
skepticism for practicality arose. Respondents rated the importance of five broad
categories of potential drivers and hindrances for future use of recycled water for
wildlife or habitat enhancement on a three-point scale. The fraction of respondents
indicating each broad category as a very important driver or a driver (n = 36),
65
respectively, was: regulatory requirements (0.61, 0.28), public value of natural
environments (0.44, 0.41), recycled water policy (0.37, 0.40), water shortages (0.33,
0.50), and influential stakeholder groups (0.34, 0.43). Other drivers listed by
respondents were funding and seasonal viability. The response frequencies for rated
hindrances, including cost recovery, water quality concerns, lack of influential
proponents, lack of a policy mandate, and quantification of ecosystem benefits, are
displayed in Figure 3.2. Other challenges inserted by respondents included lack of
available recycled water (or incoming wastewater) and operational reliability.
When asked to describe the most important driver for the implementation of
recycled water programs for wildlife or habitat enhancement (n = 38), many
respondents noted the role of regulatory requirements in reducing discharge, requiring
increased treatment prior to discharge, or increasing the cost of discharge. However,
regulatory allowance for wildlife or habitat enhancement was also viewed as a
limitation. Others expressed concern over the practicality of such projects, lack of
funding, or higher priority uses such as industrial cooling towers and turf irrigation.
Stakeholder support was considered important, though one responded noted, “The issue
is do stakeholders really want environmental improvement or is it just ‘code’ for
stopping growth.”
Augmentation of natural systems with recycled water that is coupled to revenue
generating reuse options may provide sources of treated wastewater for environmental
uses. For example, flow regimes that mimic naturally occurring ephemeral streams may
receive water in winter months while tertiary treated effluent is otherwise used for
irrigation in summer months. For programs using tertiary treatment for irrigation during
the summer months, one respondent noted, “There might be some possibility of using
the water during winter months to wetland storage.” However, concern over reliability
during wet season operations was expressed. Another respondent described, “Our
existing recycled water storage reservoir (175 acre-feet) is home for over 100 species of
birds. Future expansion might use wetlands treatment. The problem with this is that the
wetlands treatment system would work well in the dry season but would likely fail in
the wet season.”
66
Figure 3.2. Response frequencies for 2010 Survey respondents who rated five broad
categories of hindrances to implementation of future water reuse programs for
ecosystem enhancement.
Many respondents listed cost recovery and inability to pay for habitat enhancement
projects as the most important hindrances to the implementation of recycled water
programs for wildlife or habitat enhancement. Reiterating previous specific cost
concerns, one respondent stated that the greatest challenge is “as always, the cost of
building pipelines to take water from the production site to the use site.” Next most
frequently cited hindrances were regulatory restrictions that may be difficult to meet, as
well as the “level of water quality effluent the wetlands oversight will allow.” Concern
was expressed that runoff into receiving waters may be perceived as a discharge. Others
cited a lack of available recycled water (due to full dedication to other uses) or available
land.
Given the cautionary responses and cost recovery concerns for the use of recycled
water for ecosystem enhancement, the technological advancements that are made with
respect to water reuse for ecosystems must be coupled with relevant management
solutions for practical challenges associated with regulatory hurdles and funding.
Management scenarios may require economic justification and incentives for positively
utilizing tertiary treated water in such new ways. One particular area of research need is
67
with regards to ecosystem services of water reuse wetland systems. Ecosystem services
and values of managed wetlands include water storage, flood protection, water quality
improvement, aquifer maintenance, carbon sequestration, recreation, education and
provision of traditional wildlife habitat (e.g., migratory birds) (99-102). A limited
number of life cycle assessments have been conducted to compare the environmental
impacts of wetland treatment systems to conventional wastewater treatment (103), but
quantifying the magnitude, timing, and spatial variation of ecosystem services provided
by managed wetlands remains a challenge (104).
Case study. The Santa Clara Valley Water District (SCVWD) recently explored the
use of recycled water for stream augmentation, a project that was eventually cancelled
in 2008. The San Jose/Santa Clara Water Pollution Control Plant was required to reduce
freshwater discharges to Artesian Slough to protect the endangered California clapper
rail and salt marsh harvest mouse (81). At the same time, regulatory agencies
recognized that removal of all effluent flow could damage existing wetlands. The
discharge restriction led to the creation of the South Bay Water Recycling Program
(81). Recognizing the potential for tertiary treated municipal wastewater to provide
additional water to a river ecosystem, the SCVWD planned to use tertiary-treated
wastewater from the City of San Jose, CA to augment Coyote Creek as a research
demonstration (105). The project was expected to serve as an analysis for stream
enhancement via recycled water augmentation throughout the County (106); however,
the project was immersed in debates over the use of recycled water in degraded urban
streams and lacked clear metrics for evaluating success. Furthermore, this project was
complicated by uncertainty regarding the risk of unregulated emerging contaminants in
reclaimed wastewater. Plumlee et al. (5) showed that in the case of the San Jose/Santa
Clara Water Pollution Control Plant, measurable levels of perfluorooctane sulfonate
(PFOS) in recycled water effluent would increase surface water concentrations of the
receiving waters above concentrations projected to be protective of avian life. Although
emerging contaminants such as PFAAs may be detected in wastewater and recycled
water effluent, these chemicals are not explicitly regulated by Title 22 requirements for
recycled water. Following the reporting of these results to the SCVWD, the project was
cancelled in February 2008.
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3.4 Significance
An evaluation of the 2001 and 2009 California Municipal Wastewater Recycling
Surveys conducted by the California State Water Resources Control Board, the National
Database of Water Reuse Facilities, the Treatment Wetland Database, and a 2010
survey of recycled water managers reveals that few projects have been implemented in
Northern California for direct natural system enhancement or habitat creation. Despite
identification of potential restoration sites in the San Francisco Bay Area, relatively few
have been developed. Further work towards identifying specific locations and assessing
potential environmental benefits of such projects is needed.
As a relatively new and innovative use of recycled water, wastewater reclamation
for ecosystem enhancement demands further assessment. At the site level, experiments
to measure ecosystem response to restoration projects and to quantify biotic response to
altered or modified flows are needed. Uncertainties concerning water quality and
ecosystem impact are barriers to the use of water reuse for ecosystems. The interplay of
trace organic contaminants and ecosystems presents an important area of research. One
particular challenge associated with widespread use of synthetic chemicals and
discharge of wastewater to natural systems is the potential for bioaccumulation of
persistent organic pollutants downstream of discharges. The SCVWD case provides one
example in which uncertainty about emerging contaminants, and specifically the
bioaccumulative potential of perfluorinated compounds, played an influential role in
project implementation. Issues of trace contaminants in recycled water were recently
addressed by a California Science Advisory Panel (13). However, the objectives of the
panel were to evaluate human and environmental exposures resulting from landscape
irrigation, indirect potable reuse via surface spreading, and indirect potable reuse via
groundwater injection. The analysis excluded other forms of reuse, including
environmental applications of recycled water.
Acknowledgment. We thank Aude Martin and Sophie Egan for contributions to this
project.
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Chapter 4
Exposure of perfluorinated chemicals to
San Francisco Bay white sturgeon and
mechanisms of bioaccumulation
4.1 Introduction
The ability to accurately predict the bioaccumulation of chemicals in aquatic
organisms is an essential component to assessing the human health and ecological risk
of chemical pollutants. Bioaccumulation is the increase in the concentration of a
substance in an organism from the intake of contaminated water, food, and air and
results from a greater rate of contaminant uptake compared to that of elimination via
metabolism or excretion (107). There is strong evidence from field biomonitoring
studies and food web analyses in marine and freshwater ecosystems that C8-C12
perfluorocarboxylates (PFCAs), as well as C6 and C8 perfluoroalkyl sulfonates (PFSAs),
bioaccumulate and biomagnify in aquatic organisms (25). Although the global
70
distribution of perfluoroalkyl acids (PFAAs) in biota is well documented (24, 25), less
is known about the biological and chemical processes governing the introduction and
propagation of these compounds in food webs. Traditional evaluations of contaminant
impacts on biological systems rarely consider basic physiological and ecological
processes that drive differences in exposures among species and that may explain
within-species contaminant concentration distributions (108). For PFAAs in particular,
further challenges arise in modeling bioaccumulation because the behavior of PFAAs in
the environment is not obviously deduced from their physiochemical properties.
PFAAs are ubiquitous in coastal and marine systems (27, 109-111), where
characteristic salinity gradients may influence PFAA chemistry as well as organism
physiology (112). The relatively high water solubilities, low Henry's constants, and
amphiphilic nature of PFAAs imply the importance of water partitioning in the
environmental fate of these compounds (16, 113). In rainbow trout, direct uptake of
aqueous PFAAs in freshwater conditions exceeds that of dietary accumulation (114,
115). However, relatively low environmental concentrations of PFAAs suggest the
importance of considering bioaccumulation in aggregate, as accumulation from both
water and food characteristically contribute to chronic exposures. The importance of
PFAA uptake from sediments has been demonstrated in field biomonitoring work and
an assessment of uptake in a freshwater benthic organism (110, 116). In sediments
collected from throughout the San Francisco Bay region, PFAAs were present at low
ng/g concentrations (27).
Study objectives. This chapter commences with a description of traditional
bioaccumulation models and shortcomings of such approaches for capturing the
environmental behavior of PFAAs, providing justification for the elucidation of
mechanistic PFAA bioaccumulation parameters in subsequent chapters. Secondly, an
inter-species comparison of PFAA concentrations in white sturgeon from San Francisco
Bay is presented. Quantitative ecological parameters including trophic position and
foraging location are utilized to evaluate influences of ecological processes on PFAA
liver tissue concentrations. Stable isotope ratios for nitrogen (δ15N) and carbon (δ13C)
act as naturally occurring intrinsic tracers by providing integrated measures of trophic
relationships and feeding locations along a salinity gradient (108).
71
4.2 Predictive models for PFAA bioaccumulation
Predictive approaches for quantifying the extent of bioaccumulation in organisms in
aquatic food webs are important as organisms may be exploited as monitors of
environmental contamination, and consumption of contaminated organisms can result in
high dosage exposures of toxic chemicals. The quantity of a chemical in a target organ
or tissue is controlled by contaminant uptake and retention from environmental
exposures and determines the organism’s toxic response (107). Existing estimation
methods for the bioaccumulation of organic substances in aquatic organisms are
categorized in a tiered predictive approach:
Tier 1: A simplistic correlation whereby the bioconcentration factor is a
function of the compound’s octanol-water partition coefficient;
Tier 2: A mass balance model for bioaccumulation in which uptake and loss
processes are empirically quantified in an organism at steady state;
Tier 3: A detailed prediction of biomagnification in a food chain involving fish
and air-breathing animals.
The simplest Tier 1 approach, used for initial screening purposes, relies on empirical
correlations of bioconcentration with octanol-water partition coefficients (KOW). In this
case, the bioconcentration factor (BCF) is defined as the ratio of the total chemical
concentration in an organism to the total chemical concentration in water. This method
is particularly useful because BCFs are expensive to measure and estimations from
octanol-water partition coefficients are rapidly applied. However, for ionic species,
traditional linear or bilinear relations between log BCF and log KOW do not hold (117).
Meylan et al. (1999) developed a more detailed prediction of bioconcentration factors
from a database of 84 ionic compounds, including 41 carboxylic acids and 37 sulfonic
acids and salts (117):
log KOW < 5, log BCF = 0.50
log KOW 5 to 6, log BCF = 0.75
log KOW 6 to 7, log BCF = 1.75
log KOW 7 to 9, log BCF = 1.00
log KOW > 9, log BCF = 0.50
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The above relationships exclude compounds with long alkyl chains (≥11 carbons)
because five chemical species with long alkyl chains in the database exhibited log KOW
values ranging from 1.2 to 1.78 with higher corresponding log BCF values, averaging
1.85.
Log BCF values for PFOA, PFDA, perfluoroundecanoate (PFUnA),
perfluorododecanoate (PFDoA), perfluorotetradecanoate (PFTA) (C8 to C14 PFCAs)
and perfluorohexanoate (PFHxS) and PFOS (C6 and C8 PFSAs) ranged from 0.6 to 4.36
for rainbow trout carcass (114). Experimentally determined values for the n-octanol
distribution coefficient (log D), defined as the partitioning between water and n-octanol
for the total concentration of protonated and deprotonated forms of the PFAA, are 1.92
for PFOA, 2.57 ± 0.07 for PFNA, 2.90 ± 0.10 for PFDA, and 2.45 ± 0.08 for PFOS (17,
118). Calculations for log KOW, estimating partitioning of the protonated species, vary
widely for each PFAA, ranging from < 1 to 6.3 for PFOS alone. SPARC 2009 software
estimates log KOW values of 2.91 to 6.38 for C4 to C10 PFCAs and 4.67 for PFOS (17).
Based on the correlations above, log KOW likely underestimates bioconcentration factors
for perfluoroalkyl acids. Further, for equivalent perfluoroalkyl chain length, PFSAs
generally have higher bioconcentration factors than PFCAs (37, 114, 119), a feature lost
in the generalized relationship of log BCF with log KOW.
The mechanism of PFAA accumulation is likely different than lipid partitioning,
which is common to neutral hydrophobic organic contaminants (HOCs). Rather, PFAAs
may be proteinophilic (113), exhibiting bioaccumulative properties similar to fatty acids
(109). However, proteins have not been incorporated into PFAA bioaccumulation
models, and little is known about the sorptive capacity of proteins as a biological
compartment (44). Whereas KOW can be used to predict the accumulation and
environmental fate of HOCs, the amphiphilic nature of PFAA anionic surfactants
renders such descriptors unsuitable for evaluating the biological fate of PFAAs (25). In
fact, the limited utility of a lipid-normalization paradigm was noted due to artificially
high biota-sediment accumulation factors (BSAF) determined for PFAAs taken up by
the aquatic oligochaete, Lumbriculus variegatus (116). In general, PFAA
bioaccumulative potentials may be underestimated by calculations involving log KOW
(18).
73
Perfluoroalkyl carboxylates exhibit similar lipophilicity relative to equivalent chain
length sulfonates (17, 120). Differences in the electron withdrawing nature of the
sulfonate and carboxylate groups may alter the hydrophobicity of the perfluoroalkyl
chain, such that electrostatic and hydrophobic components of free energy may yield
overall equivalent values for PFCA and PFSA compounds with the same perfluoroalkyl
chain length (17). However, an increased electrostatic contribution to the Gibbs free
energy may yield more favorable values for proteinophilic partitioning for PFSAs
relative to equivalent perfluoroalkyl chain length PFCAs (17).
Equilibrium partitioning approach. Protein may represent a significant fraction of
tissue content; typical models do not account for the body burden of chemicals
attributed to this fraction. Whereas L. variegatus has 12.2 ± 1.6% lipid content by dry
weight, the organisms have a higher protein content by dry weight at 47.4 ± 8.3% (121).
Considering lipid, protein, and water fractions of organism tissues, the concentration of
a compound in an organism based on fugacity capacity theory may be assumed to be a
linear function of the contributing tissue constituents (44):
Corg = flip Clip + fprot Cprot + fw Cw (4.1)
where flip, fprot, and fw are the fractions of lipid, protein, and water relative to the whole
body tissue, and Clip, Cprot, and Cw are the respective concentrations of PFAAs in lipid,
protein, and water in the organism. In the case of PFAAs, protein-rich tissue may be the
dominant compartment for PFAA partitioning. The total sorptive capacity for the
organism tissue for PFAAs may be largely influenced by the sorptive capacity of
protein tissue. Thus, a protein-water partition coefficient (KPW), analogous to partition
coefficients between two bulk solvents (i.e., KOW), should be incorporated into Equation
4.1 to more accurately estimate a bioconcentration factor using aqueous concentrations
(Caq):
Corg = flip KOW Caq + fprot KPW Caq + fw Cw (4.2)
Bovine serum albumin (BSA) was previously utilized as a model protein for
establishing KPW for a series of HOCs in the above relationships (44).
74
Figure 4.1. Study area. Fish samples were collected from north San Francisco Bay,
including San Pablo Bay and Suisun Bay. Figure from Stewart et al. (108).
4.3 Materials and Methods
Sample collection. In the fall and early winter of 1999-2000, researchers from the
US Geological Survey (USGS) collected fish samples from Suisun Bay and San Pablo
Bay located in the San Francisco Bay estuary (Figure 4.1). This region, seaward of the
Sacramento-San Joaquin River system, is a part of the migration corridor for the large
fish species assessed in this study. Field sampling and fish tissue sample preparation
and storage by corroborating laboratories are further described by Stewart et al. (108).
Analyses in this chapter focus on white sturgeon liver samples (n = 15). Tissue
concentrations are also measured in striped bass (n = 1) and leopard shark (n = 1) liver
samples for comparison of physiological parameters. Sample collection occurring
during a time when anadromous fish species, such as striped bass (December 1999
collection) and white sturgeon (January 2000 collection), were likely to have spent
several weeks or months feeding in the region. Additional data provided by Robin
Stewart (USGS) for these samples included tissue type (muscle or liver), general sample
collection location, fish length, muscle tissue carbon isotope tissue (δ13C), muscle and
liver tissue nitrogen isotope data (δ15N), and selenium and mercury muscle tissue
75
concentrations (108). White sturgeon liver samples were 52 ± 11% protein and 24 ±
12% fat by dry weight (Anresco, Inc. analysis, Methods AOAC 992.15 and 960.39, 18th
ed.). Liver and muscle tissue samples were received by Christopher Higgins of Stanford
University from the USGS and frozen in sample containers at -4°C until analysis.
Extraction. Samples were analyzed for a series of perfluorinated chemicals using a
method modified from Stevenson et al. (122). Samples were transferred to -80˚C at least
one night before processing. Tissues were freeze dried at -80°C and homogenized with
a mortar and pestle. Ground, freeze-dried liver tissue (~150 mg) was extracted with
acetonitrile (3 × 5 mL) in a 50-mL polypropylene tube. For each addition of acetonitrile,
the tube was vortexed, sonicated in a heated bath (60°C, 10 min), centrifuged at 3100
rpm (10 min), and transferred via glass pipette to 15-mL glass tubes. Extract was
concentrated (N2 concentrator at 40 °C), supplemented with an 80-µL aliquot of glacial
acetic acid (1% by volume), and brought to 8 mL with acetonitrile. For purification, 1.8
mL of extract was added to ENVI-Carb (25-50 mg) in a microcentrifuge tube, which
was vortexed and then centrifuged for 30 minutes at 14,000 rpm. Extract (1.2 mL) was
transferred to a second microcentrifuge tube and centrifuged for 30 minutes at 14,000
rpm before final analysis.
LC-MS/MS analysis. High-performance liquid chromatography tandem mass
spectrometry (LC-MS/MS) as reported by Higgins et al. (27) was used to determine
PFAA concentrations. Perfluorooctanoic acid (PFOA, 96%) and perfluorodecanoic acid
(PFDA, 98%), were from Aldrich Chemical Co. (Milwaukee, WI). Potassium
perfluorooctane sulfonate (PFOS, 98%) was from Fluka through Sigma-Aldrich (St.
Louis, MO). Perfluorononanoic acid (PFNA, 97%) was from Sigma-Aldrich (St. Louis,
MO). Mass labeled internal standards [13C5] PFNA, [13C2] PFDA, [13C2] PFOS, N-
deuterioethylperfluoro-1-octanesulfonamidoacetic acid ([D5]–N-EtFOSAA) were from
Wellington Laboratories (Guelph, ON, Canada), and [13C2] PFOA was from Perkin-
Elmer Life Sciences (Boston, MA). Two mass transitions were monitored for each
analyte (Table 4.1). Analyte-dependent mass-labeled internal standards (IS) spiked
immediately prior to final LC-MS/MS analysis were used for peak verification and
normalization. Quantification was achieved using a 1/x weighted standard calibration
curve (8-12 points) for the primary mass transition and confirmed by the secondary
76
transition. Calibration standards were prepared from a mixed stock solution in 70:30
methanol/aqueous ammonium acetate (0.01%) and were run at the beginning and end of
each LC-MS/MS sample batch. The stock solution contained all analytes, and analyte
concentrations were corrected for impurities. The instrument LOQ was determined as
the lowest calibration curve point with a signal to noise ratio greater than 30:1, an
accuracy between 70 and 130%, and a peak area at least twice that of the largest blank
peak for that sample batch.
Table 4.1. Analyte primary and secondary transitions monitored, internal standards
(IS), average concentration of MDL samples (with standard deviation of 12 replicates),
and calculated MDLs.
Analyte Primary
trans. (m/z)
Secondary trans. (m/z)
IS IS trans. (m/z)
Conc. (ng/g dw)
MDL (ng/g ww)
PFOA 413 > 369 413 > 169 [13C2] PFOA 415 > 370 7.8 ± 0.5 1.3 PFNA 463 > 419 463 > 219 [13C5] PFNA 468 > 423 10. ± 3 6.9 PFDA 513 > 469 513 > 219 [13C2] PFDA 515 > 470 2.5 ± 0.2 0.4 PFOS 499 > 99 499 > 80 [13C2] PFOS 503 > 99 47 ± 2 6.4 PFDS 599 > 99 599 > 80 [D5] N-
EtFOSAA 589 > 419 5.7 ± 0.3 0.81
Quality assurance and data analysis. Solvent blanks were run every six samples
to monitor instrument background. To monitor ion suppression and enhancement,
matrix spike (MS %) recoveries were determined for each extract by performing a
second analysis of each extract, spiked with a known concentration of PFAA analyte.
Tissue concentrations are reported for MS % recoveries that were between 70 and
130%. Tissue samples were analyzed in triplicate; relative standard deviations of PFOS
replicates averaged 5% for white sturgeon samples. In several instances, tissue PFAA
concentrations were at or close to the LOQ, yielding at least one replicate above and
one below the LOQ. In these cases, the tissue concentration was reported as the average
of the measured concentration for replicates above the LOQ and the LOQ for
concentrations below the LOQ. The method detection limits (MDL) determined from
the extraction of replicate fish liver tissue samples (n = 12) are displayed in Table 4.1.
The MDL was calculated as the product of the relative standard deviation of sample
replicates and the student’s t-statistic (99% confidence level) for 11 degrees of freedom.
77
PFOA was spiked into dried tissue whereas other values were determined from analytes
already present in the extracted tissue. A least-squares regression linear fit with
corresponding coefficient of determination (R2) was calculated and displayed for each
correlation plot. Data were analyzed in Microsoft Office Excel (Microsoft Corporation;
Redmond, WA) and Kaleidagraph (Synergy Software Systems; Dubai, United Arab
Emirates).
4.4 Results and Discussion
White sturgeon PFAA tissues concentrations in the San Francisco Bay. White
sturgeon liver PFAA concentrations (ng/g wet weight) are displayed in Figure 4.2.
PFOS was detected in 14 of 15 samples, ranging in concentration from 14 ng/g ww to
180 ng/g ww. PFOS was also detected in the striped bass (83 ng/g ww) and leopard
shark (37 ng/g ww) samples. PFOS was below the LOQ in four white sturgeon muscle
tissue samples also analyzed as well as in side-by-side extracted blanks. For
comparison, concentrations of PFOS were 180-680 ng/g ww in livers of polar bears
from Alaska, up to 2570 ng/mL in blood plasma of bald eagles less than 200 days old,
and as high as 300 ng/g ww in fish (24). PFDS was detected in 13 of 15 white sturgeon
fish liver samples, ranging from 4.1 ng/g ww to 16.8 ng/g ww. PFDA was detected in 6
samples (0.9 – 8.2 ng/g ww), though five additional samples exhibited low PFDA MS%
recoveries and are thus not included. PFNA was greater than the LOQ in 12 of 15 white
sturgeon fish liver samples, ranging from 2.2 ng/g ww to 20.1 ng/g ww. However, 10 of
these samples were less than the MDL. PFNA concentrations above the LOQ, but not
necessarily above the MDL, are displayed in Figure 4.2. PFOA concentrations were
near or below the LOQ for all samples analyzed. Additionally, because PFOA was
detected in one of five blank samples (at a level near the LOQ), tissue concentrations
are not reported for this analyte. Few samples exhibited PFOA concentrations higher
than the LOQ in a global study of birds, fish, and marine mammals (24). PFOS
exhibited relatively high concentrations with wide variability; thus further analysis
regarding correlations with physiological and ecological parameters is limited to
comparisons with PFOS.
78
Figure 4.2. Measured PFAA concentrations (ng/g ww) in white sturgeon fish livers (n =
15). The tissue samples were archival specimens from animals taken from North San
Francisco Bay in December 1999 and January 2000. The boundary of the box indicates
the 25th and 75th percentile; a line within the box marks the median; whiskers above
and below the box indicate the maximum and minimum concentrations; outlying points
(>1.5 times the upper quartile) are shown as open circles. Number of detects for each
PFAA are also shown; samples with concentrations below the LOQ were excluded from
the plot.
An ecological perspective on contaminant variability: Influence of trophic level
and feeding location. Stable isotope ratios for nitrogen (δ15N) and carbon (δ13C) act as
naturally occurring intrinsic tracers by providing integrated measures of trophic
relationships and feeding locations along a salinity gradient. Isotope results are
presented as deviations from standard reference materials, where: δX = [Rsample/Rstandard
– 1] × 103. Here, X is 13C or 15N and R is 13C/12C or 15N/14N. Because 15N becomes
enriched with increasing trophic level (by 2.5 – 5% between prey and predator) without
varying along a salinity gradient in lower trophic level organisms (bivalves and
zooplankton), this ratio can serve as a quantitative measure of trophic position (108). A
higher δ15N for an individual within a given species may indicate consumption of higher
trophic level biota, due to the introduction of more prey-predator trophic steps from
79
baseline organism nitrogen signatures to the higher trophic position of the individual
under consideration.
Figure 4.3a displays PFOS white sturgeon concentrations with trophic position,
measured by muscle and liver δ15N. White sturgeon have enriched δ15N over lower
trophic organisms, such as the clams that are found as a dominant food items in
sturgeon digestive tracts (108). A wide range of PFOS concentration occurs with little
variability in δ15N. Notably, the highest liver PFOS concentration corresponds to the
highest trophic position white sturgeon. PFOS concentrations increase with trophic
position, as concentrations in predatory animals exceeded concentrations in their diets
(24). The high trophic position of this individual may result from increased
consumption of higher trophic level biota through a piscivorous dietary pattern rather
than more typical clam-based consumption. Additionally, this sturgeon was second to
the smallest, by length, of all white sturgeon considered, and PFOS concentrations
decreased with increasing fish length (Figure 4.3c). Although Martin et al. suggest that
the half-life for PFAAs in trout may be much longer for a larger fish of the same species
(37), growth dilution can be an important determinant of concentration for a substance
with slow uptake or clearance rates (107). The striped bass and leopard shark samples
are included in Figure 4.3 and, as expected, do not reflect the length trend for white
sturgeon. The high trophic level of the striped bass individual corresponds with a
relatively high liver PFOS concentration, whereas the leopard shark PFOS
concentration falls below the average PFOS concentration despite its slightly higher
trophic position.
Stable carbon isotope ratios can identify contributions of different foods in a diet by
tracking distinct isotopic signatures of food types. In estuaries, δ13C shows little to no
enrichment with trophic level but is enriched in algae with increasing salinities due to
the influence of δ13C in dissolved inorganic carbon that is incorporated into algae. As
these distinct isotopic signatures are incorporated into the base of the food web, the δ13C
of consumers will reflect their predominant foraging location, as determined by the
salinity gradient (108). Figure 4.3b displays the relationship between PFOS
concentration and foraging location, increasing on the ordinate from the freshwater
eastern reaches of the estuary to the more saline Suisun Bay. When excluding the high
80
trophic position outlier, PFOS liver concentrations decrease with increasing salinity.
Thus, fish spending a greater time feeding in more saline environments appear to
exhibit lower PFOS liver concentrations. Little is known regarding the affect of changes
in salinity on PFAA accumulation and toxicity, however an increase in distribution
coefficient with increasing water salinity suggests that long-chain PFAAs may “salt-
out” onto particles. This led to an increase in bioaccumulation in Pacific oysters
(Crassostrea gigas), filter-feeding bivalves that accumulate contaminants through
ingestion of contaminated particles (112). For organisms in which aqueous uptake is
more important than dietary accumulation, such as rainbow trout in which the blood-
water interface of gills is a major route of uptake and clearance (37), a decrease in
accumulation with increased salinity may be postulated. Additionally, freshwater
sources of PFAAs, such as wastewater treatment plant effluent (27), likely influence site
specific accumulation of PFAAs.
The relationship of total mercury concentration (organic and inorganic Hg) with
PFOS liver concentrations is displayed in Figure 4.3d. With the exception of the
sturgeon outlier, there appears to be a slight inverse relationship between these two
contaminant concentrations. Although both mercury (in its methylated form) and PFOS
are organic contaminants, PFOS sorption behavior does not typically follow the
paradigm of hydrophobic, lipophilic organic contaminants because of its surfactant
properties. The weak relation between Hg and PFOS is confounded by analysis of
different fish organs, with potentially different sorption properties. The physiochemical
behavior of PFAAs cannot be expected to predictably mimic other organic
contaminants (43). The influence of the unique PFAA chemical properties on
mechanisms controlling bioaccumulation requires further study. No significant
relationship was evident between selenium and PFOS concentrations.
81
Figure 4.3. Stable isotopes, fish length, and muscle Hg concentration plotted with white
sturgeon, striped bass, or leopard shark liver PFOS concentrations on the ordinate. (a)
Muscle and liver δ15N serve as measures of organism trophic position. A high trophic
position outlier was excluded from the linear regression. (b) δ13C represents integrated
measure of organism feeding location, indicating foraging location along a salinity
gradient. Liver PFOS concentration decreases with increasing salinity gradient. (c) An
increase in fish length corresponded to a decrease in liver PFOS concentration. (d) With
the exception of the high trophic position sturgeon outlier, PFOS liver concentration
(ng/g ww) yields and inverse relationship with total muscle mercury concentration (µg/g
dw).
Additional factors contributing to PFAA bioaccumulation. In addition to fish
size, trophic position, and foraging location, other factors not considered in this analysis
influence the exposure and retention of PFOS in white sturgeon tissue. For example,
82
fish age and sex may influence elimination rates of contaminants (37). PFAA source
locations in relation to foraging location are important; PFOS concentrations in
relatively industrialized regions may be several times greater than those in isolated areas
(24). Further, although PFOS is metabolically inert (37), precursors to this compound
are not – yielding additional sources of PFOS and other PFAAs in organisms. The role
of precursor compounds as sources of PFAAs has been studied extensively. N-ethyl
perfluorooctane sulfonamidoethanol (N- EtFOSE), produced directly and attached to
phosphate esters in paper coatings, degrades to N-ethyl perfluorooctane sulfonamido
acetic acid (N-EtFOSAA) in wastewater treatment processes (22). PFOS is the terminal
metabolite of this microbial degradation pathway. N-EtFOSE may also be stripped to
the atmosphere from treatment facilities (22) and oxidized to PFCAs and PFSAs (123).
N-EtFOSAA has been detected in natural San Francisco Bay sediments at levels often
exceeding PFOS (27) and oxidizes to perfluorooctane sulfonamide (FOSA) and PFOA
in hydroxyl-mediated photolysis (23). Recent studies elucidate the biotransformation
from such biologically labile precursor compounds to PFAAs as terminal metabolites.
N-EtFOSAA appears to undergo biotransformation to PFOS in an aquatic oligochaete
(116), likely contributing to organism PFOS body burden. PFCAs may form from
fluorotelomer alcohols (FTOHs) via biotransformation in rats and other organisms (124,
125), oxidation in the atmosphere (21), and indirect photolysis (126). 8:2 FTOH (127)
and fluorotelomer acrylates (128) biotransform to PFOA in rainbow trout.
Depuration and elimination rates of PFAAs from fish vary with PFAA chain length
(a proxy for hydrophobicity) as well as head group type (sulfonates or carboxylates)
(37). Such chemical properties may influence the extent of accumulation within a
certain organ, with blood concentrations in rainbow trout exhibiting higher
concentrations than the kidney and liver (114). Chemical properties influence the
interaction of PFAAs with gill membranes, an important site for elimination of
contaminants in fish (37). Controls of contaminant elimination on a cellular level may
contribute to PFOS bioaccumulation. For example, organic anion transporters play a
role in pharmacokinetics of PFAAs (129). Additionally, greatest inhibition of cellular
efflux transporter activity, which serves as a first line of defense against toxic
compounds (130), occurred with exposure to the longer chain acids, PFNA and PFDA,
83
for the marine mussel Mytilus californianus (122).
4.5 Significance
Perfluorinated compounds are bioaccumulative and ubiquitous among
environmental samples. In order to better understand the accumulation and variability of
concentrations measured within a species collected over a limited spatial and temporal
range, ecological and physiological processes must be considered. Wide variability of
concentrations of perfluorooctane sulfonate can occur within a single species type.
PFOS concentrations decreased for sturgeon feeding primarily in more saline
environments. Although correlations such as those presented are useful in developing an
assessment of the fate of PFOS in an ecosystem, a complete understanding of the
ecological and physiological diversity that influences contaminant concentrations
requires analysis of mechanistic processes such as elimination and uptake rates,
compound specific properties, and ecosystem dynamics such as contaminant sources
and transport processes. Even simplistic Tier 1 screening measures for evaluating the
bioaccumulative potential of new chemicals, a necessity for effective decision-making,
generally do not incorporate expected bioaccumulation mechanisms relevant to PFAAs.
In the following two chapters, the binding of perfluoroalkyl acids to a model protein,
BSA, is explored. In Chapter 5, commonly utilized dissociation constants relating free
chemical concentrations to protein-bound concentrations are determined for PFOA and
PFNA. Analytical methods are applied over a wide range of concentrations to assess the
contribution of different binding regimes at varied concentrations and for comparison to
literature values. In Chapter 6, a protein-water partition coefficient is quantified for C5 –
C10 PFCAs as well as C4, C6, and C8 PFSAs, and physiochemical mechanisms of
interactions are explored.
Acknowledgment. This work was conducted while funded by the National Defense
Science and Engineering Graduate Fellowship. Thanks to Christopher P. Higgins and
Laura A. MacManus-Spencer for laboratory guidance and feedback. We thank Robin
Stewart for provision of tissue samples and insights on analysis and interpretation.
84
85
Chapter 5
Investigating binding to a model protein:
Noncovalent interactions of long-chain
perfluoroalkyl acids with serum
albumin1
5.1 Introduction
Used throughout the past half-century in a variety of industrial and commercial
applications, perfluoroalkyl acids (PFAAs) are a class of environmentally persistent
anionic surfactants detected globally in water, air, sediment, and biota (20, 25). Field
1 Reproduced (with modifications) with permission from Bischel, H. N.; MacManus-Spencer, L. A.; Luthy, R. G. Noncovalent interactions of long-chain perfluoroalkyl acids with serum albumin. Environ. Sci. Technol. 2010, 44 (13), 5263-5269. Copyright 2010 American Chemical Society.
86
and laboratory studies indicate that perfluorooctanesulfonate (PFOS) and
perfluorocarboxylates (PFCAs) with greater than seven fluorinated carbons
bioaccumulate and biomagnify in aquatic food webs (25, 37, 113). Tissue distribution
studies show PFAA concentrations are greatest in body compartments high in protein
content, such as the liver, kidney and blood of organisms (24, 40). Typical
concentrations of perfluorooctanoate (PFOA) and PFOS in the serum of non-
occupationally exposed humans are 4 – 7 and 25 – 46 ng/mL, respectively (131). The
half-life of PFOA in serum varies widely by species and sex and is considered long (3.1
– 4.4 years) in human blood (132). Species-specific differences in PFAA distribution
patterns and retention may be influenced by active uptake by organic anion transporters
(41). Studies assessing PFAA-protein interactions (42, 133-138) may shed light on the
tissue distribution patterns, bioaccumulation, and in vivo bioavailability of these
chemicals.
Protein binding. The binding of PFAAs to proteins was first reported in the 1950s,
when PFAAs were investigated for their ability to aid in protein precipitation (139). In
the 1960s, organofluorine compounds were first detected in human blood serum (140).
Serum albumin, the most abundant protein in blood plasma (35 – 50 g/L) (141), binds a
variety of endogenous and exogenous ligands including fatty acids, amino acids, metals,
and pharmaceuticals (142) and was reported as the major binding protein for PFOA in
blood (42). PFAA-protein interactions result from the unique surfactant nature of
PFAAs. The highly hydrophobic perfluorocarbon tail paired with a strongly polar
carboxylate or sulfonate head group resembles the structure of fatty acids and facilitates
both hydrophobic and ionic interactions with proteins. In fact, PFOA binds to liver- and
kidney-fatty acid binding proteins (135), and PFAAs may interfere with the normal
binding of fatty acids or other endogenous ligands to liver-fatty acid binding protein
(136). However, the rigidity of the perfluorocarbon tail differs from the relatively more
fluid hydrocarbon tail (143), limiting extrapolation of fatty acid – albumin binding
results to their fluorinated counterparts.
Studies of PFAA-albumin interactions using spectroscopic methods (137, 144-146),
electrophoresis (144), 19F NMR (42, 137), dialysis (139, 147), and surface tension (144,
145) report a wide range of association constants (102 – 106 M-1) with most values
87
suggesting relatively weak binding (<104 M-1). However, PFAAs are highly bound to
proteins in rat, monkey, and human plasma (138), suggesting stronger, specific binding.
Many studies have used relatively high PFAA:albumin mole ratios; results from such
studies are difficult to extrapolate to physiological protein and substrate concentrations
and may explain weaker observed binding.
In this study, bovine serum albumin (BSA) serves as a model protein for
characterizing PFAA-protein interactions. BSA is widely used as a model protein in
evaluating protein-ligand interactions, as the sequences of human and bovine serum
albumins are highly conserved (44, 142, 148). Here association constants (Ka) and
stoichiometries for PFAA-albumin complexes are quantified over a range of
PFAA:albumin mole ratios via equilibrium dialysis and automated nanoelectrospray
ionization mass spectrometry (nanoESI-MS), with the specific goal of providing
quantitative binding data at low ligand:protein mole ratios. Additional tests are
performed with human serum albumin (HSA) for comparison to results for PFAA-BSA
interactions. Quantification of PFCA-BSA association constants at physiologically
relevant PFAA:protein mole ratios using equilibrium dialysis, the most
thermodynamically sound and straightforward protein-ligand analysis technique (149),
has not been reported. Prior equilibrium dialysis studies (139, 147) reporting PFOA-
albumin affinities at relatively high PFAA:albumin mole ratios (>3) used less sensitive
and selective analytical methods (e.g. surface tension and spectrophotometry) to
measure PFOA concentrations.
Analysis via automated nanoESI-MS provides complementary information about
PFAA-BSA interactions at low PFAA:albumin mole ratios and is explored as a
technique to more rapidly evaluate the binding affinities and stoichiometries.
Automated nanoESI-MS overcomes some disadvantages of conventional ESI-MS such
as irreproducibility due to non-automated sample introduction. This method, with
nL/min flow rates, is gentler than conventional ESI-MS as complexes are transferred
from solution to the gas phase, reducing disruption of intact protein-ligand complexes
(150). Analysis of PFOA with rat serum albumin (RSA) (42) and rat-specific proteins
(135) via electrospray ionization mass spectrometry (ESI-MS) demonstrated intact
complexes. However, challenges of noisy mass spectra due to additional protein adducts
88
(135) and limits in instrument sensitivity and reproducibility (133) were reported in
conventional ESI-MS PFAA-protein binding studies.
Although PFAAs are consistently detected in protein-rich tissues, this concept has
yet to be incorporated into PFAA bioaccumulation models. Models utilizing a lipid
partitioning paradigm may underestimate the bioaccumulative potential of PFAAs
(113), although this remains to be further tested. PFAA-protein association constants
may thus contribute to improved predictive models for the bioaccumulation of these
chemicals.
5.2 Materials and Methods
Materials. Essentially fatty acid free human serum albumin (HSA, 96%), Cohn
Fraction V protease free, essentially γ-globulin free bovine serum albumin (BSA, 99%)
and ammonium acetate were from Sigma-Aldrich, Inc. (St. Louis, MO). Fraction V
fatty acid-free BSA (99.9%) was from EMD Biosciences, Inc. Standards of
perfluorooctanoic acid (PFOA, 96%) and perfluorodecanoic acid (PFDA, 98%) were
from Aldrich Chemical Co. (Milwaukee, WI). Perfluorononanoic acid (PFNA, 97%)
and potassium perfluorooctane sulfonate (PFOS, 98%) were from Fluka through Sigma-
Aldrich (St. Louis, MO). The internal standard [13C5] PFNA was from Wellington
Laboratories (Guelph, ON, Canada) and [13C2] PFOA was from Perkin-Elmer Life
Sciences (Boston, MA). Internal standards had purities greater than 98%, as reported by
the suppliers.
Equilibrium dialysis. Purity-corrected stock solutions of PFOA and PFNA were
prepared in 50 mM sodium phosphate buffer (pH 7.4) in polypropylene containers. A
sonicating bath (~38 °C) was used to assist in the dissolution of PFAAs without the use
of an organic co-solvent. Stock solutions of BSA were prepared fresh daily in the
sodium phosphate buffer for dialysis experiments. Spectra/Pore dialysis membrane
tubing with 6000-8000 Da molecular weight cutoff (Spectrum Laboratories, Inc.,
Rancho Domingo, CA) was cut into 7.5-cm pieces, soaked in deionized water for 30
minutes, and rinsed with deionized water followed by Milli-Q water. Dialysis tests were
prepared with either 500 µM albumin (human or bovine) exposed to a single
concentration of PFNA or 1µM BSA exposed to a range of PFOA or PFNA
89
concentrations. For all tests, a known volume of BSA or HSA solution was added to the
dialysis bag, spiked with a PFAA solution prepared in the same buffer, and brought to 3
mL or 10 mL. Molecular weights of 66430 Da (151) and 66248 Da (152) were used to
calculate the final concentrations of BSA and HSA, respectively. The dialysis bag,
which is impermeable to the protein and its complexes but freely permeable to PFAAs,
was equilibrated in 300 or 500 mL of the sodium phosphate buffer in polypropylene
dialysis reservoirs at laboratory temperatures (approximately 21 °C). Controls were
prepared using a buffer-only solution in dialysis bags with a PFAA spike, and blanks
were prepared using a BSA solution with no PFAA spike. Free and bound PFAA
concentrations were determined via liquid chromatography tandem mass spectrometry
(LC-MS/MS). Details on sample preparation and LC-MS/MS analysis are available in
the Supporting Information. Instrumentation and operating parameters were previously
reported (27).
Dialysis bag and reservoir sample preparation. For 500 µM albumin tests,
dialysis reservoirs (300µL of 50 mM sodium phosphate, pH 7.4) were prepared in
triplicate or quadruplicate with albumin in dialysis bags exposed to a single
concentration of PFNA. Dialysis bags were equilibrated in separate reservoirs for 96
hours prior to sampling. PFNA concentrations were measured at equilibrium both inside
the dialysis bag and in the external reservoir. Extraction of PFNA from albumin
samples taken from dialysis bags was performed using a method modified from
Stevenson et al. (122). For triplicate 500 µL samples from dialysis bags, albumin was
precipitated from solution and PFNA extracted with acidified acetonitrile (1% v/v
glacial acetic acid) in a 15-mL polypropylene tube. After addition of the acidified
acetonitrile (9.5 mL), the tube was vortexed (30 sec), sonicated (60 °C, 10 min), and
centrifuged (3000 rpm, 10 min). Extracts were purified using a dispersed sorbent
(ENVICarb, 25-50 mg) by vortexing 1.8 mL of extract in polypropylene
microcentrifuge tubes containing the sorbent followed by centrifugation (14000 rcf, 10
min). Samples were further diluted in acetonitrile as needed. Glass HPLC vials
contained the acetonitrile extract (200 µL), HPLC grade water (200 µL), 1:1 v/v
methanol:buffer (100 µL), and an internal standard prepared in HPLC grade water that
was spiked prior to analysis (100 µL). Spike/recovery experiments (n = 7) were used to
90
determine the efficiency of the extraction procedure. The averages of PFNA recoveries
were 90.2% from 1µM BSA and 80.5% from 500µM, and average PFOA recovery was
81.8% from 1µM BSA. Reported concentrations were not corrected for the extraction
efficiencies. Matrix-matched calibration curves were prepared with PFAA standards in
a 1:1 methanol:buffer solution.
For 1 µM dialysis tests and control reservoirs, PFOA or PFNA concentrations were
measured in triplicate at the initiation of the test (0 hours) and at equilibrium both inside
the dialysis bag and in the external reservoir (48 hours). In order to ensure equilibrium
was reached in test reservoirs, the final bag and reservoir concentrations were required
to be equal in control experiments conducted on the same day. Dialysis bag samples
(500 µL each) were prepared using the above method for a subset of the PFNA tests
(when free PFNA was greater than 330 µM) or added to an equal volume of methanol
in polypropylene microcentrifuge tubes, vortexed, and diluted into the analytical range
of the LC-MS/MS when necessary using a 1:1 methanol:buffer solution. Data were
restricted to those with a PFAA mass balance of 70-130%, as calculated using initial
and final samples from the dialysis bag and external reservoir. A comparison of BSA
tests and buffer controls spiked with PFAAs in the dialysis bag showed agreement in
determined concentrations (See Supporting Information, Figure 5.5S), suggesting
limited matrix effects on the signal from this method. All reservoir samples were
prepared in 1:1 methanol:buffer, and samples were stored at 4°C until analysis.
Standard 12-point calibration curves were also prepared in 1:1 (v/v) methanol:buffer
and all samples and standards were spiked prior to analysis with a mass-labeled internal
standard prepared in HPLC grade water.
LC-MS/MS analysis. Equilibrium dialysis samples were analyzed for PFOA or
PFNA via liquid chromatography tandem mass spectrometry (LC-MS/MS). Samples
and standards were injected (40 µL) onto a 40 mm x 2.1 mm Targa Sprite C18 column
(5-µm particle size, Higgins Analytical, Mountain View, CA) equipped with a C18
guard column (Higgins Analytical) and analyzed via LC-MS/MS using instrumentation,
chromatography conditions, and negative electrospray ionization multiple reaction
monitoring (MRM) mode operating parameters previously described (27). Analyte
transitions and internal standards used to monitor and quantify each analyte are reported
91
by Higgins et al. (116). Blanks were injected every 3-6 samples to monitor carry-over,
and standards were run before and after samples to monitor instrument drift. Initial
eluent conditions were 35% methanol:65% 2 mM aqueous ammonium acetate.
Methanol was ramped to 100% over 7.5 min, held at 100% for 2.5 min, reverted to 35%
over 0.5 min, and held until 13 min at a flow rate of 0.25 mL/min. Optima-grade
methanol was from Fisher Scientific (Fair Lawn, New Jersey). A VICI Cheminert
system (Valco Instruments Co., Inc.) was employed for the first 4 minutes to divert
buffer salts away from the source needle. Data were processed using Analyst software
version 1.4.2. The detection limit was defined as the lowest calibration standard for the
transition used for quantitation with 70 to 130% accuracy, at least twice the peak area of
blanks, and a signal-to-noise ratio greater than 3:1. Detection was confirmed by the
second monitored transition.
Nanoelectrospray ionization mass spectrometry. Analysis of noncovalent PFAA-
BSA interactions was conducted by automated nanoelectrospray ionization mass
spectrometry (nanoESI-MS) for the PFAAs shown in Figure 5.4S. A 9 mM ammonium
acetate buffer (pH 7) was selected to minimize interfering adducts during electrospray
ionization. Individual stock solutions of PFAAs (1 mM) were prepared with the
ammonium acetate buffer in polypropylene bottles. A sonicating bath (~38°C) was used
to assist in the dissolution of PFAAs. Stock solutions of BSA (100 µM) were prepared
fresh daily in 9 mM ammonium acetate (pH 7) at room temperature and dialyzed
overnight, with exchange of the same buffer in an external reservoir. BSA solutions
were transferred to polypropylene microcentrifuge tubes, spiked with a PFAA in buffer,
and diluted with buffer to a final BSA concentration of 50 µM. PFAA-BSA solutions
were prepared over a range of ligand:protein mole ratios (0:1, 0.1:1, 0.5:1, 1:1, 2:1, 4:1,
and 8:1) and were allowed to equilibrate for one or more hours before same-day
analysis. Samples tested after approximately one month indicated no change in
equilibrium.
A Waters Micromass Q-Tof API-US quadrupole time-of-flight mass spectrometer
(Micromass, Milford, MA) equipped with an Advion Triversa Nanomate nano-
electrospray robot (Advion BioSystems, Inc. Ithaca, NY) was used for nanoESI-MS
analysis. Samples in the ammonium acetate buffer were infused through the ESI Chip (5
92
µm) allowing nL/min flow rates. Optimal conditions of instrument pressures and
voltages were determined by maximizing intensities of peaks due to PFAA-BSA
complexes while maintaining peak resolution and ion current. Typical operating
conditions for the Q-Tof and nanomate in positive-ion mode were: spray voltage, 1.88
kV; sample pressure, 0.5 psi; extraction cone voltage, 6.0 V; source temperature,
100°C; desolvation temperature, 50°C; desolvation gas flow rate, 80 L/hr. Argon was
the collision gas. Backing (2-3 mbar), Penning (~10-5 mbar), and Tof pressures (~10-7
mbar) were adjusted each test day for detection of protein-ligand complexes. In titration
experiments, mass spectra were acquired for 4 min at cone voltages of 100 V or 130 V
and the collision energy was set at 10 V. Mass signals were collected over the scan
range m/z 1000–5000. Data were processed using MassLynx software version 4.1 from
Waters. Mathematical deconvolution of multiply-charged ion spectra of native BSA
using MaxEnt produced an accurate mass of BSA, which was consistent with the
theoretical molecular weight of BSA (66.4 kDa). For quantifying association constants,
deconvoluted spectra acquired with 20 iterations using MaxEnt were smoothed, and a
10-channel center was applied. Spectra were integrated from the expected mass of the
protein or ligand-bound protein to the next expected mass with a signal to noise ratio
greater than 2:1. Unless otherwise stated, statistical comparisons were conducted using
a student’s two-tailed t-test assuming equal variance.
Binding model. The binding of ligand, L, to binding site, j, on protein, P, can be
described by a series of stepwise equilibria for a total of n binding sites:
P + L! PL1
PL1+ L! PL
2
!
PLj!1 + L" PL
j (5.1)
!
PLn!1 + L" PL
n
The general form for stoichiometric binding constants describing binding sites that
act independently from each other is given by:
93
Ka, j=
[PLj]
[PLj!1][L]
(5.2)
The average number of bound ligands per protein molecule,
!
" , can be expressed in
terms of the association constant, Ka, the free ligand concentration, [L], and the total
number of binding sites on the protein, n, yielding (153):
! =
j( Ka, j)[L]
j
1
j
!j=0
n
"
1+ j( Ka, j)[L]
j
1
j
!j=0
n
" (5.3)
The full form of this equation can be simplified by considering classes of binding
sites with similar affinities, as described by Scatchard (154). For ionic surfactants such
as fatty acids binding to water-soluble proteins, high and low affinity binding sites may
be considered, with high-affinity interactions dominant at low [L]:[P] mole ratios (141,
155, 156). Thus, for two classes of binding sites, the equation becomes:
! =n1K
a,1[L]
1+Ka,1[L]
+n2K
a,2[L]
1+Ka,2[L]
(5.4)
where
!
" is equivalent to the ratio of the bound PFAA concentration to the total protein
concentration. Over a narrow range of low [L]:[P] mole ratios, where high affinity
binding dominates, data may be best described with a one-class model. For equilibrium
dialysis results, a nonlinear curve fit based on a Levenberg-Marquardt algorithm that
iteratively minimizes the sum of the squared error (Chisq) between the original data and
the calculated fit was applied to results for bound PFAAs using Kaleidagraph software
(See Supporting Information, Tables 5.4S – 5.7S). Fits were allowed an error of 0.1%,
and initial guess values in the iterations were unity for n1 and Ka,1.
Binding theory applied to nanoESI-MS results. For a series of stepwise
equilibria, with a total of n binding sites, the total protein ([P]o) and total ligand ([L]
o)
concentrations can be expressed as:
[P]o= [P]+[PL]+[PL
2]+...+[PL
n] (5.5)
[L]o= [L]+[PL]+ 2[PL
2]+...+ n[PL
n] (5.6)
The total protein can be expressed in terms of R, the ratio of complex to free protein:
94
Rj=[PL
j]
[P] (5.7)
yielding:
[P]o= [P]+ R
1[P]+ R
2[P]+...+ R
n[P] (5.8)
The free protein concentration can then be written as:
[P]=[P]
o
1+ R1+ R
2+...+ R
n
(5.9)
The free ligand concentration [L] can be expressed as:
[L]= [L]o! R
1[P]! 2R
2[P]!...! nR
n[P] (5.10)
= [L]o![P]
o(R
1+ 2R
2+...+ nR
n)
1+ R1+ R
2+...+ R
n
(5.11)
The stepwise association constants can then be expressed as:
Ka, j=
[PLj]
[PLj!1][L]
=Rj
Rj!1[L]
=Rj
Rj!1([L]o !
[P]o(R
1+ 2R
2+...+ nR
n)
1+ R1+ R
2+...+ R
n
)
(5.12)
The association constant for the case of one bound ligand (j = 1) is:
Ka,1=
[PL]
[P]([L]o![PL])
=R1
[L]o![P]
oR1
1+ R1
(5.13)
5.3 Results and Discussion
Equilibrium dialysis: PFNA-albumin binding at low [L]:[P] mole ratios.
Equilibrium dialysis provides direct measurement of free and bound PFAA
concentrations, minimizing uncertainty from indirect methods such as fluorescence
spectroscopy (137). To quantitatively assess the binding of PFNA to albumin at
physiologically relevant [L]:[P] mole ratios, equilibrium dialysis was conducted with
500 µM HSA or BSA and 0.24 ± 0.08 µM or 0.32 ± 0.07 µM PFNA, respectively, in 50
mM sodium phosphate buffer. Based on measured equilibrium concentrations, at low
[L]:[P] mole ratios (10-3 – 10-4) greater than 99.9% of PFNA was bound to both HSA
and BSA (Table 5.1). The percent bound was also high for BSA dialysis tests in 50 mM
sodium phosphate buffer with an additional 9 g/L NaCl (representative of physiological
95
salinity) although the high salt concentration slightly reduced the fraction of bound
PFNA. Further investigation into the effects of ionic strength and the presence of
competing ligands, such as fatty acids, is needed.
Debruyn and Gobas (44) utilize BSA and HSA binding data to assess the sorptive
capacity of animal protein for a range of neutral hydrophobic organic contaminants
(HOCs) by calculating distribution coefficients between BSA or HSA and water.
Though representation of PFAA-protein binding by a distribution coefficient may be
acceptable only at very low solute concentrations when the binding isotherm tends
towards linearity, the calculation of such a distribution coefficient may be useful in
PFAA bioaccumulation models. The ratio of analyte concentration in the bound phase
to that in the aqueous phase (KPW) is determined by the protein concentration, [P], the
fraction bound to protein (fbound), and the partial specific volume of protein in aqueous
solution (ρalbumin):
KPW =CP
CW
=fbound
!albumin ![P](1" fbound ) (5.14)
Log KPW values for the low PFNA:albumin mole ratio tests are presented in Table 5.1.
A partial specific volume of 0.733 ml/g was used in calculations for BSA and HSA
(141). The measured value of KPW for PFNA is greater than all protein distribution
coefficients presented by Debruyn and Gobas, where log KPW ranged from less than 0.1
to 3.5 (44). Log KPW for PFNA is greater than an experimentally determined octanol-
water distribution coefficient (log KOW = 2.57 ± 0.07) (118), highlighting the potential
influence of interactions with nonlipid materials on PFNA distribution in organism
tissues. Log KPW for neutral HOCs was also generally greater than Log KOW for less
lipophillic compounds (log KOW < 2) (44). Confirming results obtained for PFNA with
essentially fatty acid free BSA (99.93 ± 0.01% bound for a 6 ± 1 × 10-4 [L]:[P] mole
ratio, corresponding to a log KPW of 4.80 ± 0.08) suggest that trace fatty acids in the
essentially γ-globulin free BSA used in this and subsequent tests had little influence on
the binding affinities.
96
Table 5.1. Percent bound and log KPW for PFNA binding to 500 µM albumin
determined by equilibrium dialysis and LC-MS/MS.1
Albumin Conditions Percent Bound Log KPW [Ligand]: [Protein]
mole ratio HSA 50 mM sodium
phosphate, pH 7.4 99.95 ± 0.01% 4.93 ± 0.05 5 ± 1 × 10-4
BSA 50 mM sodium phosphate, pH 7.4
99.92 ± 0.01% 4.74 ± 0.05 6 ± 1 × 10-4
BSA 50 mM sodium phosphate, pH 7.4; 9 g/L NaCl
99.89 ± 0.01% 4.56 ± 0.05 10 ± 1 × 10-4
Equilibrium dialysis: PFAA-BSA binding over a wide range of [L]:[P] mole
ratios. Equilibrium dialysis was used to quantify free and albumin-bound PFAAs in an
equilibrated system over a wide range of PFNA and PFOA concentrations and 1 µM
BSA; [L]:[P] mole ratios ranged from 0.02 to 120 in these experiments. Concentrations
in all initial reservoir samples (0 hours) were below the detection limit, and a null value
was used for mass balance calculations. Initial dialysis bag PFAA concentrations ranged
from 1.6 µM to 2700 µM prior to equilibration in reservoirs. Dialysis bags reached
equilibrium with external reservoirs within 48 hours (see Supporting Information,
Figures 5.6S and 5.7S), when final samples were taken. The average relative standard
deviations for final bag and reservoir concentrations ranged from 4 – 6% for PFOA and
PFNA. Initial bag concentrations, which generally required additional dilution of
samples prior to analysis, had higher relative standard deviations (9% for PFOA and
11% for PFNA) than those for final bag concentrations. Average mass balance results
for control experiments (94%, n = 5 for PFOA and 107%, n = 7 for PFNA) indicate that
PFAAs do not significantly bind to the dialysis membrane or reservoir vessels.
Equilibrium dialysis results for PFOA and PFNA with 1 µM BSA are displayed in
Figure 5.1 along with 500 µM BSA and HSA results for PFNA. Although anionic
1 Errors represent 95% confidence intervals and were calculated from the root mean squared error of all results conducted at a 500 µM albumin concentration (n = 13). Means and error calculations for Log KPW were performed on the log-transformed data.
97
surfactants may cause protein denaturation, this is not expected to occur over the
concentration range tested (157), as reservoir concentrations were well below the
critical micelle concentrations (CMCs) of PFOA and PFNA (8.7 – 10.5 mM and 2.8 –
5.6 mM, respectively) (16). Total PFOA and PFNA concentrations, representing the
sum of free and bound PFAA concentrations, were measured inside dialysis bags at
equilibrium. Final bag concentrations were greater than reservoir concentrations for all
tests, indicating that PFAAs were bound to BSA and that PFAA-BSA complexes were
retained in the dialysis bags. Osmotic dilution of the retentate was assumed to be
negligible. Bound PFAA concentrations were calculated from concentrations measured
inside the dialysis bag in excess of free concentrations.
Figure 5.1. Equilibrium dialysis results for PFOA (a) and PFNA (b) where
!
" is the
average number of PFAA molecules bound per albumin. Data represent averages of
triplicate measurements from each test reservoir or dialysis bag. PFOA and PFNA data
from experiments conducted with 1 µM BSA were fit using Equation 5.4.
Association constants and binding stoichiometries for PFOA- and PFNA-BSA
complexes determined via equilibrium dialysis with 1 µM BSA are reported in Table
5.2. Data were fit both over the full range of test concentrations using Equation 5.4 and
up to a 5:1 PFAA:BSA mole ratio using a one-class binding model. Further details of
the fitting approaches and results are available in the Supporting Information. To reduce
the number of parameters being simultaneously solved in Equation 5.4 and thus reduce
the error in the solved parameters, association constants of 630 M-1 and 8000 M-1 were
98
employed for Ka,2 for PFOA and PFNA, respectively. These values were determined
under the same buffer conditions but at higher PFAA:albumin mole ratios (15 – 200)
using 19F NMR (137). An increased error was observed at higher reservoir
concentrations when samples were diluted into the analytical range of the LC-MS/MS
(see Supporting Information, Figure 5.9S). Due to a strong influence of the two highest-
concentration PFOA data points, which also indicated weak binding at a high mole
ratio, these data points were excluded from the fit presented in Table 5.2 for the PFOA
two-class model. The effects of applying various weighting factors for the full PFOA
and PFNA data sets were tested and yielded similar results to those in Table 5.2. The
primary association constants determined are similar for PFOA and PFNA, on the order
of 106 M-1 with binding stoichiometries of one to four or five. BSA had 150 ± 20
secondary binding sites for PFOA and 31 ± 2 secondary binding sites for PFNA. A
modest effect of albumin concentration on calculated PFAA association constants, in
which increased protein concentration decreased affinities, has been previously
observed (137). This effect was not evaluated in detail in the present study. However,
applying a one-class binding model to the results at a single PFNA:albumin mole ratio
with 500 µM BSA or HSA, and assuming n = 3, yields similar PFNA-albumin
association constants (on the order of 106 M-1) to those from 1 µM albumin tests (Table
5.3).
Table 5.2. Association constants (Ka) and binding stoichiometries (n) for PFOA and
PFNA binding to BSA determined by equilibrium dialysis.
Compound Ka,1 (M-1) n1 R2 [Ligand]: [Protein]
range Model
PFOA 0.20 (± 0.14) × 106 4.3 (± 2.0) 0.772 0.04 – 5 One-class PFOA 1.4 (± 1.9) × 106 1.4 (± 0.5) 0.913 0.04 – 70 Two-class PFNA 1.1 (± 0.2) × 106 4.6 (± 0.3) 0.943 0.02 – 5 One-class PFNA 3.3 (± 3.2) × 106 2.9 (± 0.7) 0.953 0.02 – 120 Two-class
NanoESI-MS results. In nanoESI-MS experiments, soft ionization maintained
protein-ligand complexes in the gas phase such that PFAA-BSA complexes were
distinctly observed relative to free BSA at a 0.5 ligand:protein mole ratio and greater.
99
ESI-MS is widely used to study noncovalent interactions of small molecules with
proteins (150, 158, 159). During the transfer of ions from the liquid to gas phase, even
weak interactions are largely preserved (159). Exposure of BSA to PFAAs causes a
shift in the observed m/z depending on the number of bound PFAA molecules. The
charge state of peaks and number of bound ligands is determined by:
m
z=MWBSA + j !MWPFAA + i !H[ ]
i+
i (5.15)
where MWBSA and MWPFAA are the molecular weights of BSA and the PFAA,
respectively, j is the number of PFAAs bound per BSA molecule, and i is the charge
state of the protein in the gas phase, produced in positive ion mode. Results show that
multiple PFAAs are bound per BSA molecule at a 1:1 PFAA:BSA mole ratio, with
peaks present at j = 1 and j = 2. This pattern is repeated over a range of charge states
and is most intense at i = 16 and 17 (Figure 5.2).
Representative nanoESI-MS spectra for the most intense charge state (+16) of 50
µM BSA exposed to a range of PFOA and PFNA concentrations are shown in Figure
5.3. Representative spectra for PFDA-BSA and PFOS-BSA complexes are included in
the Supporting Information (Figure 5.11S). Deconvolution of results yields spectra with
peaks at m = MWBSA + j × MWPFAA (e.g., see the Supporting Information, Figure
5.12S). Results for PFOA and PFNA suggest a maximum of eight PFAA molecules
bound per BSA molecule at a 4:1 PFAA:BSA mole ratio. At higher ligand
concentrations (8:1 PFAA:BSA, data not shown), the number of bound PFAAs detected
continues to increase. Han et al. detected up to six binding sites at an 8:1 mole ratio of
PFOA to rat serum albumin using conventional ESI-MS (42). The use of nanoESI-MS
here may have reduced disruption of PFAA-albumin complexes during transfer to the
gas phase, resulting in enhanced detection of intact complexes. At 1:1 and 2:1
PFAA:BSA mole ratios, up to two to four bound PFOA and PFNA molecules were
detected. Binding observed by nanoESI-MS was not categorized into primary or
secondary binding classes, although equilibrium dialysis results suggest one to four or
five primary binding sites may be occupied at similar concentration ratios. Overlap of
ligand-bound protein charge states limits the utility of nanoESI-MS results at elevated
ligand concentrations.
100
Figure 5.2. Representative spectra from 2500 – 4800 m/z for BSA, BSA exposed to
PFOA (50 µM), and BSA exposed to PFNA (50 µM) in 9 mM ammonium acetate (pH
7). BSA concentrations are constant at 50 µM. Individual charge states are indicated for
the multiply charged BSA or PFAA-BSA complex.
ESI-MS may also be used to quantify protein-ligand association constants.
Association constants determined via ESI-MS are in good agreement with solution-
based methods for a variety of protein-ligand complexes with affinities ranging from
102 to 106 M-1 (150, 158, 160). However, limitations exist in extrapolating results from
gas-phase measurements to solution-phase behavior. In particular, obtaining high
resolution for proteins in the high-mass range is difficult, and nonspecific binding may
be observed at high ligand:protein mole ratios (158) as solution-phase equilibrium
changes during the evaporation and droplet fission processes (159). The detection of
solution phase specific and non-specific binding is desired, while binding that may
occur during transfer of molecules to the gas phase is not.
101
Figure 5.3. Mass spectra for the +16 charge state of 50 µM BSA in 9 mM ammonium
acetate (pH 7) with PFOA (left) and PFNA (right). The number of PFAA molecules
associated with BSA and the mole ratio of PFAA to BSA concentrations, [L]:[P], are
indicated.
Mathematically deconvoluted spectra of native and ligand-bound BSA were used to
quantitatively analyze PFAA-protein complexes. Because the ligand is small compared
to the protein (tested PFAAs are less than 1% of the molecular weight of BSA) the
surface properties of the ligand-bound and free protein are expected to be similar. As
such, the ionization and detection efficiencies of the free and ligand-bound proteins are
assumed to be similar, so the concentration ratio of complex to free protein, R, is
102
equivalent to the abundance ratio of bound and free protein in the gas phase (161).
Deconvoluted spectra were used to determine the abundance ratio and calculate Rj for
each bound complex (Equation 5.7). Rj was in turn used to calculate stepwise
association constants (Equation 5.12).
Applying Equation 5.12 to deconvoluted spectra for 0.5, 1, and 2 PFOA:BSA,
PFNA:BSA, and PFDA:BSA mole ratios yields association constants for three binding
sites reported in the Supporting Information (Tables 5.8S – 5.11S). For PFOS,
decreased resolution limited calculation of association constants at higher concentration
ratios; only the 0.5 PFOS:BSA results are displayed. Calculated PFOA-, PFNA-, and
PFDA-BSA association constants (Ka,1 – Ka,3) range from 0.9 × 104 M-1 to 5.1 × 105
M-1, 1.4 × 104 M-1 to 3.9 × 105 M-1, and 1.3 × 104 M-1 to 4.5 × 104 M-1, respectively. For
PFOA and PFNA, Ka,1, Ka,2, and Ka,3 are not statistically different from one another.
The relative similarity among the three Ka values suggests a single binding class.
Additionally, Ka,1 results for PFOA, PFNA, and PFDA collected at the 0.5 PFAA:BSA
mole ratio were not statistically different. Two tests were conducted with cone voltage
at 100 V and one test at 130 V. Data collected at a cone voltage of 130 V yielded
statistically lower Ka,1 and Ka,2 values for each PFCA-BSA as compared to data
collected at 100 V (one-tailed t-test, p < 0.1). A higher cone voltage was used to
improve peak resolution, but resulting in-source dissociation of bound ligands could
lead to calculation of artificially low association constants. In future studies, solution
additives may be used to stabilize complexes for prevention of in-source dissociation
(161).
Results collected at 100 V to limit in-source dissociation and low mole ratios to
limit nonspecific binding, which may occur as ligand concentrations increase, are most
appropriate in this study for comparison to solution-based results. The average Ka,1
values for data collected at 100 V and a 0.5 mole ratio are 1.3 × 105 M-1 for PFOA and
2.6 × 105 M-1 for PFNA. Although these results must be interpreted with caution given
the wide range of values, results are within an order of magnitude of association
constants calculated via equilibrium dialysis.
Binding regimes. Binding of PFOA and PFNA at concentrations spanning several
orders of magnitude and 1 µM BSA was fit well by a two binding class equation,
103
whereas a one-class equation was applicable for a narrower range of results at lower
PFAA concentrations. A companion study investigating the binding strength of PFCAs
with BSA and HSA using fluorescence spectroscopy and 19F NMR over a range of
PFCA concentrations (100 nM – 2 mM) suggests PFCA-BSA association constants of
~105 M-1 and 102 M-1 for primary and secondary binding sites, respectively (137).
Taken together with available literature data and results presented here (Table 5.3), the
data sets suggest two major binding regimes: strong specific associations at low
PFCA:albumin mole ratios and weaker nonspecific associations at higher mole ratios.
Specific interactions may be similar to those proposed for fatty acids. Albumin is
known to have a highly flexible conformation in which hydrophobic “pockets” hold the
hydrocarbon tails of fatty acids, and charged residues contribute an electrostatic
component to the binding (155). Although albumin has a net negative charge in
solution, it generally has a greater affinity for small, negatively charged hydrophobic
molecules (142).
PFOA-HSA interactions studied via zeta-potential measurements and ion selective
electrodes also demonstrate specific interactions at low ligand concentration, where
almost all PFOA molecules are bound to HSA (156). Initial binding to high affinity
sites, where Gibbs energies of interaction are at a minimum, stabilizes the HSA
structure (144). The interaction was more favorable at lower concentrations (~1 mM),
and increased at saturation (>10 mM PFOA with 20 µM HSA), where the hydrophobic
effect predominated (157). Using electrophoretic mobility, Blanco et al. showed binding
to high-energy sites for low PFOA concentrations with three proteins of varying size
and alpha-helix contents (162). This interaction was more favorable than that for
sodium caprylate, such that PFOA binding may be more energetically favorable than
the hydrogenated counterpart, which is less hydrophobic and less surface active than the
fluorinated surfactant.
104
Table 5.3. Summary of PFCA-albumin association constants (Ka) and binding
stoichiometries over a range of ligand:protein mole ratios ([L]:[P]).
Ligand Protein Method Ka (M-1) n [L]:[P] range Ref. PFOA BSA Dialysis 1.4 (± 1.9) × 106 1.4 ± 0.5 0.04 – 70 This
paper.1 BSA NanoESI-
MS 1.3 × 105 Up to 8 0.1 – 4 This
paper. HSA ζ-
potential2 2.4 × 104 - ~1.5 (157)
RSA Micro-SEC3
2.8 (± 0.6) × 103 7.8 ± 1.5 1 – 4 (42)
HSA Micro-SEC
2.6 (± 0.3) × 103 7.2 ± 1.3 1 – 4 (42)
HSA Dialysis4 3.12 × 104 13 ~4 – 30 (147) BSA 19F NMR 6.3 (± 0.8) × 102 - 72 – 200 (137) RSA 19F NMR 3.4 (± 1.2) × 103 - 8 – 400 (42) BSA Dialysis 3.2 × 102 85 ~3 - 110 (139) HSA ISE5 1.44 × 105 1414 ± 28 ~180-520 (156)6 HSA ISE 3.17 × 104 2565 ± 52 ~520-1100 (156)6
PFNA HSA Dialysis 2.1 ± 0.3 × 106 - 5 ± 1 × 10-4 This paper.7
BSA Dialysis 1.4 ± 0.4 × 106 - 6 ± 1 × 10-4 This paper.7
BSA Dialysis 3.3 (± 3.2) × 106 2.9 ± 0.7 0.02 – 120 This paper.1
BSA NanoESI-MS
2.6 × 105 Up to 8 0.1 – 4 This paper.
BSA 19F NMR 8 (± 5) × 103 - 15 – 100 (137) HSA 19F NMR 2 (± 3) × 104 - 8 – 32 (137)
1 Primary association constant listed 2 Electrophoretic mobility measured at pH 10; Ka calculated from ΔG = -25 kJ/mol 3 Micro size exclusion chromatography 4 Conducted at 37 °C 5 Ion-selective electrode 6 Hill binding constants reported for two binding regimes with positive cooperativity 7 Ka calculated for one PFAA:albumin mole ratio assuming n = 3
105
Results presented here demonstrate that PFNA is highly bound to BSA (>99%) at
low [L]:[P] mole ratios (< 10-3). Vanden Heuvel et al. (134) determined that 80% of 100
µM PFDA remained bound to 80 µM BSA after 60 minutes of extensive extraction with
organic solvents. This was attributed to covalent binding of the carboxylate head group
to protein sulfhydryl groups. Isolated albumins normally contain 0.5 – 0.7 moles of free
SH per mole of protein molecule (141). For comparison to prior results, at a 1.25 mole
ratio of free PFOA and PFNA to BSA (80 µM) and using equilibrium dialysis values
for a one-class binding model reported in Table 5.3, we calculate that greater than 98%
of PFAAs are bound to albumin.
5.4 Significance
A standard solution-based method (equilibrium dialysis) was compared with a
modern mass spectrometric approach (nanoESI-MS), providing complementary
information about the strength of PFAA-protein binding interactions and number of
binding sites at low ligand:protein mole ratios. Results presented, together with
previously published data, suggest stronger specific associations at low PFAA:albumin
mole ratios and weaker nonspecific associations at higher mole ratios. Equilibrium
dialysis yields primary association constants of ~106 M-1 for PFOA and PFNA, for a
class of one to five high affinity binding sites. A high protein-water partition coefficient
for PFNA (log KPW > 4) relative to neutral HOCs supports the characterization of
specific binding at low ligand concentrations.
NanoESI-MS is a useful technique for more rapidly characterizing PFAA-protein
interactions. However, a wide range of calculated association constants and sensitivity
of complexes to instrument conditions limit the utility of nanoESI-MS as a fully
quantitative method. Stoichiometry values obtained from mass spectrometry
demonstrate up to eight bound PFAAs per BSA molecule at a 4:1 mole ratio. Binding
constants from nanoESI-MS experiments are on the order of 105 M-1 for both PFOA and
PFNA, lower but in qualitative agreement with solution-based values determined via
equilibrium dialysis.
Because Kow may underestimate the bioaccumulative potential of PFAAs, a serum
protein association constant or protein-water distribution coefficient may be useful in
106
characterizing the bioaccumulative potential and in vivo bioavailability of long-chain
PFAAs. However, as proportions of various proteins vary among species and in time,
and likely also have different affinities for PFAAs, further analysis is required to test
the ability of protein partitioning to enhance perfluoroalkyl acid bioaccumulation
models.
Supporting Information. Contains (1) analytical and experimental details, (2)
results of fitting approaches, and (3) additional and summary results from nanoESI-MS.
Acknowledgment. This work was supported by the National Defense Science and
Engineering Graduate Fellowship, the Stanford University UPS Foundation and Woods
Institute for the Environment, and the National Science Foundation Graduate Research
Fellowship Program. We thank Pavel Aronov and Allis Chien from the Stanford
University Mass Spectrometry Laboratory.
107
5.5 Supporting Information
Figure 5.4S. Structures and names of perfluoroalkyl acids (PFAAs) used in this study.
Equilibrium dialysis results fitting approach. As previously described, a
nonlinear curve fit was applied to equilibrium dialysis results using the two-class
binding equation:
! =n1K
a,1[L]
1+Ka,1[L]
+n2K
a,2[L]
1+Ka,2[L]
(5.16S)
where
!
" is average number of bound ligands per protein molecule, L is the free ligand
concentration, Ka,1 and Ka,2 and are the association constants and n1 and n2 are the total
number of binding sites for each class of binding sites. For results presented in Tables
5.4S and 5.5S, association constants of 8000 M-1 for PFNA and 630 M-1 for PFOA were
inserted for Ka,2 in Equation 5.16S. Initial guess values for n2 were 30 and 100 for
PFNA and PFOA data, respectively. A range of initial guess values for n2 was tested for
the non-weighted PFNA and PFOA data fits in Tables 5.4S and 5.5S and did not
influence the fit in these cases. Parameters determined for Equation 5.16S without
insertion of values for Ka,2 showed greater error on determined binding stoichiometries
and association constants for n2 and Ka,2 and are presented in Table 5.6S. For these fits,
initial guess values were Ka,2 = 630 M-1 and n2 = 100 for PFOA and Ka,2 = 8000 M-1 and
n2 = 30 for PFNA. The results for PFAA bound to albumin are obtained by subtracting
the measured free (reservoir) PFAA concentrations from the corresponding total (final
bag) concentrations, displayed in Figure 5.8S. The error of the bound concentrations is
thus a propagation of error of both the free PFAA concentration and the total PFAA
concentration. The standard deviations of bound concentrations (Sbound) may be
calculated from the standard deviation of the reservoir triplicate samples for a single
108
point (Sfree) and the standard deviation of the final bag samples (Stotal) as:
Sbound = (Sfree )2+ (Stotal )
2 (5.17S)
These deviations were linearly correlated with the free and total PFAA
concentrations, such that at higher concentrations, the error of measured values
increases. This correlation is displayed in Figure 5.9S for measured free PFNA
concentrations in tests with 1 µM BSA. Consequently, a weighting factor may be
applied using the Kaleidagraph software when fitting the data to Equation 5.16S.
However, because standard deviations were determined only from triplicate samples at
each point, weighting bound concentrations in the fitted equation by the standard
deviation for that point was not performed. Alternatively, fits were tested with
weighting factors inversely proportional to the free, total, or bound measured PFAA
concentrations or the square of these values. Although fit parameters for PFOA (Table
5.4S) and PFNA (Table 5.5S) changed with different weighting factors applied, results
were consistently of the same order of magnitude for primary association constants and
number of primary binding sites. For these fits, initial guess values for n2 were 100 for
PFOA and 30 for PFNA, unless otherwise noted. In some cases for PFOA the fit did not
converge or yield physiologically relevant results for a fitted parameter, so the initial
guess was modified and data refit. Due to the large standard deviation and strong
influence on the fitted parameters of measurements at the two highest free PFOA
concentrations, these two data points were excluded from the non-weighted PFOA fits
presented in Tables 5.4S and 5.6S. Non-weighted fits for the full PFOA dataset did not
yield physiologically relevant results.
PFOA and PFNA data sets span several orders of magnitude, and as expected, are
not fit well by a model that represents only one binding class (including only Ka,1 and
n1). However, a subset of the experimental results (data for PFAA:albumin mole ratios
less than 1 or 5) was generally fit well by a one class model (Figure 5.10S), although
several outliers reduced the R2 for the fit of the non-weighted PFOA data. Fits utilizing
a one-class model yielded primary association constants similar to those obtained with a
two-class model applied to the full PFAA:albumin mole ratio range (Table 5.7S). Errors
in Tables 5.4S through 5.7S represent the standard error calculated by the Kaleidagraph
software for each parameter.
109
Statistical comparisons for nanoESI-MS results. Statistical comparisons of
results presented in Tables 5.8S – 5.11S were conducted using a student’s two-tailed t-
test assuming equal variance. PFOA and PFNA, Ka,1, Ka,2, and Ka,3 values are not
statistically different from one another (p > 0.1). For PFDA, pooled results for Ka,1 from
all exposure concentrations collected at 100 V are significantly greater than Ka,2 (p <
0.1) and Ka,3 (p < 0.05), indicating a somewhat stronger first binding site. However, all
PFDA-BSA measured affinities are on the order of 104 M-1, and Ka,2 and Ka,3 are not
statistically different. Ka,1 for PFDA was significantly less than Ka,1 for PFOS (p < 0.05)
although both values are on the order of 104 M-1. Pooled results for Ka,1 from data
collected at three concentration ratios and 100 V were compared between each PFAA.
Results for PFDA Ka,1 were significantly less than that for PFOA (p < 0.1) , PFNA (p <
0.05), and PFOS (p < 0.05). No other comparisons for Ka,1 determined at 100 V were
statistically significant. Further, Ka,1 results for PFOA, PFNA, and PFDA collected at
the 0.5 PFAA:BSA mole ratio and 100 V or 130 V were not statistically different. For
PFDA and PFOS, the averages of Ka,1 calculated at a PFAA:BSA mole ratio of 0.5 and
100 V are 3.5 × 104 M-1 and 7.4 × 104 M-1, respectively.
110
Figure 5.5S. Samples taken prior to equilibration in the reservoir from control bags
containing only buffer and the PFAA spike are compared to samples taken from test
bags containing 1 µM BSA with the same PFAA spike. Points fall along the 1:1 line
(plotted), indicating minimal effects from the BSA matrix in LC-MS/MS sample
analysis. The average relative standard deviation of initial bag samples from tests was
9% for PFOA and 11% for PFNA. Error bars represent 95% confidence intervals for
triplicate samples from the same dialysis bag.
111
Figure 5.6S. Reservoir samples taken over time in a PFNA equilibrium dialysis test
indicate equilibrium of the system after 24 hours. At equilibrium, control bag and
reservoir sample concentrations were also equivalent (data not shown). Error bars
represent 95% confidence intervals for triplicate samples from the same reservoir.
Figure 5.7S. Reservoir samples taken over time in a PFOA equilibrium dialysis test
suggest equilibrium of the system after approximately 48 hours. Samples were taken
after 48 hours for all PFOA tests. Error bars represent 95% confidence intervals for
triplicate samples from the same reservoir.
112
0.01
0.1
1
10
100
1000
0.01 0.1 1 10 100
Tota
l [P
FO
A] (!
M)
Free [PFOA] (!M)
0.1
1
10
100
1000
0.01 0.1 1 10 100
Tota
l [P
FN
A] (!
M)
Free [PFNA] (!M)
Figure 5.8S. Measured total and free PFOA and PFNA concentrations taken at
equilibrium from dialysis bag and reservoir samples, respectively. Results for bound
PFAA are obtained by subtracting the measured free PFAA concentrations from the
corresponding total concentrations.
113
Figure 5.9S. Standard deviations of triplicate measurements of bound PFAAs (Sbound)
were linearly correlated with free PFAA concentrations, as shown above for PFNA in
1µM BSA equilibrium dialysis tests. Consequently, a weighting factor may be
employed to account for larger error at higher measured concentrations.
114
Table 5.4S. Association constants (Ka,1) and binding stoichiometries (n1 and n2) for
PFOA binding to 1 µM BSA as determined by equilibrium dialysis for a range of
applied weighting factors. In these fits, 0.00063 µM-1 was inserted for Ka,2 in Equation
5.16S. Fits did not converge or yield physiologically relevant results for the non-
weighted or 1/
!
" -weighted full PFOA dataset.
Weighting Factor Ka,1 (µM-1) n1 n2 R2 Chisq
None 1.4 ± 1.9 1.4 ± 0.5 153 ± 18 0.913 14.25 1/[Free PFAA] 1.9 ± 2.2 1.3 ± 0.8 110 ± 81 0.869 1.74 1/[Total PFAA] 1.8 ± 3.3 1.3 ± 1.1 106 ± 87 0.834 1.41 1/[Free PFAA]2 2.4 ± 2.4 1.1 ± 1.0 160 ± 514 0.946 0.76 1/[Total PFAA]2 2.5 ± 4.8 1.1 ± 1.6 135 ± 622 0.919 0.22 1/
!
" 2 2.6 ± 2.0 1.1 ± 0.4 43 ± 32 0.765 4.69
Table 5.5S. Association constants (Ka,1) and binding stoichiometries (n1 and n2) for
PFNA binding to 1 µM BSA as determined by equilibrium dialysis for a range of
applied weighting factors. In these fits, 0.008 µM-1 was inserted for Ka,2 in Equation
5.16S.
Weighting Factor Ka,1 (µM-1) n1 n2 R2 Chisq
None 3.3 ± 3.2 2.9 ± 0.7 31.0 ± 2.2 0.953 41.82 1/[Free PFAA] 1.9 ± 1.0 3.5 ± 1.0 27.9 ± 10.0 0.938 4.77 1/[Total PFAA] 1.7 ± 1.8 3.6 ± 1.7 27.2 ± 11.9 0.955 1.47 1/
!
" 2.0 ± 1.7 3.3 ± 1.1 27.9 ± 5.3 0.959 4.63 1/[Free PFAA]2 2.4 ± 0.9 3.0 ± 1.1 39.8 ± 54.2 0.766 34.09 1/[Total PFAA]2 1.6 ± 2.4 3.6 ± 4.0 27.5 ± 85.5 0.918 0.55 1/
!
" 2 1.6 ± 1.5 3.5 ± 2.1 25.0 ± 16.3 0.946 1.11
115
Table 5.6S. Association constants (Ka,1 and Ka,2) and binding stoichiometries (n1 and
n2) for PFOA and PFNA and binding to 1 µM BSA as determined by equilibrium
dialysis using Equation 5.16S with no weighting factor (WF) and a 1/[Free PFAA]
weighting factor.
Com-pound WF Ka,1
(µM-1) n1 Ka,2 (µM-1) n2 R2 Chi-sq
PFOA None 3.0 ± 5.1 1.0 ± 0.5 6.1 ± 7.8 × 10-03 22 ± 20 0.917 13.7 PFOA 1/[Free
PFAA] 3.1 ± 6.8 0.9 ± 1.4 3 ± 11 × 10-02 7 ± 11 0.891 1.4
PFNA None 1.7 ± 1.1 3.9 ± 0.8 3 ± 23 × 10-04 400 ± 3100 0.968 28.6 PFNA 1/[Free
PFAA] 1.7 ± 0.9 3.8 ± 1.2 1 ± 14 × 10-03 100 ± 1200 0.940 4.6
116
Figure 5.10S. Equilibrium dialysis results for PFOA and PFNA up to a 5:1 ligand to
protein mole ratio and 1 µM BSA where ν is the average number of PFAA molecules
bound per albumin. Data represent average of triplicate measurements from each test
reservoir or dialysis bag. PFOA and PFNA data were fit using a one-class binding
model with no weighting factor (top) and with a 1/[Free PFAA] weighting factor
(bottom).
117
Table 5.7S. Association constants (Ka,1) and binding stoichiometries (n1) for PFOA and
PFNA binding to 1 µM BSA as determined by equilibrium dialysis for a subset of the
total data and a one-class binding model.
Compound Weighting Factor Ka,1 (µM-1) n1 R2 Chisq
[Ligand]: [Protein]
range PFOA None 1.1 ± 0.2 2.1 ± 0.2 0.988 0.01 0.04 – 1 PFOA None 0.20 ± 0.14 4.2 ± 1.9 0.767 1.8 0.04 – 5 PFOA 1/[Free
PFAA] 1.7 ± 1.7 1.5 ± 0.8 0.894 0.9 0.04 – 5
PFNA None 1.3 ± 0.8 4.6 ± 1.5 0.900 1.1 0.02 – 1 PFNA None 1.2 ± 0.2 4.6 ± 0.3 0.948 1.7 0.02 – 5 PFNA 1/[Free
PFAA] 1.6 ± 0.7 4.2 ± 1.0 0.930 3.8 0.04 – 5
118
Figure 5.11S. Representative mass spectra for the +16 charge state of 50 µM BSA in 9
mM ammonium acetate (pH 7) with PFDA (left, cone voltage = 100V) and PFOS (right,
cone voltage = 130 V). The m/z for free BSA and PFAA-BSA peaks and the mole ratio
of PFAA to BSA concentrations, [L]:[P], are indicated.
119
Figure 5.12S. Representative deconvoluted spectrum for 50 µM BSA in 9 mM
ammonium acetate (pH 7) with 100 µM PFOA (cone voltage = 100V) used for
determination of Ka. Peaks correspond to the number of bound PFOA molecules (j), as
indicated.
120
Table 5.8S. Estimated association constants calculated from nanoESI-MS results for 50
µM BSA exposed to PFOA (25, 50, and 100 µM).
[PFOA]: [BSA] 0.5 1 2
Cone Voltage Ka,1 (M-1) Ka,1 (M-1) Ka,2 (M-1) Ka,1 (M-1) Ka,2 (M-1) Ka,3 (M-1)
100 1.9 × 105 5.1 × 105 3.1 × 105 1.4 × 105 1.2 × 105 6.5 × 104 100 6.3 × 104 1.5 × 105 1.0 × 105 4.8 × 104 4.3 × 104 3.5 × 104 130 3.0 × 104 1.3 × 104 1.4 × 104 8.9 × 103 1.0 × 104 1.2 × 104
Table 5.9S. Estimated association constants calculated from nanoESI-MS results for 50
µM BSA exposed to PFNA (25, 50, and 100 µM).
[PFNA]: [BSA] 0.5 1 2
Cone Voltage Ka,1 (M-1) Ka,1 (M-1) Ka,2 (M-1) Ka,1 (M-1) Ka,2 (M-1) Ka,3 (M-1)
100 3.8 × 105 1.3 × 105 9.6 × 104 1.8 × 105 1.4 × 105 7.6 × 104 100 1.4 × 105 2.6 × 105 1.7 × 105 4.0 × 104 4.2 × 104 3.5 × 104 130 4.9 × 104 1.7 × 104 1.8 × 104 1.4 × 104 1.8 × 104 1.6 × 104
121
Table 5.10S. Estimated association constants calculated from nanoESI-MS results for
50 µM BSA exposed to PFDA (25, 50, and 100 µM).
[PFDA]: [BSA] 0.5 1 2
Cone Voltage Ka,1 (M-1) Ka,1 (M-1) Ka,2 (M-1) Ka,1 (M-1) Ka,2 (M-1) Ka,3 (M-1)
100 4.0 × 104 2.9 × 104 1.8 × 104 2.4 × 104 1.7 × 104 1.6 × 104 100 3.1 × 104 4.5 × 104 2.9 × 104 4.3 × 104 3.3 × 104 2.0 × 104 130 1.3 × 104 1.7 × 104 1.6 × 104 1.4 × 104 1.3 × 104 1.3 × 104
Table 5.11S. Estimated association constants calculated from nanoESI-MS results for
50 µM BSA exposed to PFOS (25 µM). Poor resolution limited quantitative analysis of
PFOS at 50 and 100 µM.
[PFOS]: [BSA] 0.5
Cone Voltage Ka,1 (M-1)
100 9.6 × 104 100 5.2 × 104 130 8.9 × 104
122
123
Chapter 6
Strong associations of short-chain
perfluoroalkyl acids with serum albumin
and investigation of binding mechanisms
6.1 Introduction
The unique chemical properties of perfluoroalkyl acids (PFAAs), a class of stable
anionic surfactants, have been capitalized upon since the 1940s in the production of a
variety of industrial and consumer products (20). In response to comprehensive research
documenting the environmental persistence and widespread occurrence of PFAAs in
humans and wildlife (25, 34), 3M Company voluntarily eliminated perfluorooctane
sulfonyl fluoride (POSF)-based materials including perfluorooctanoate (PFOA) and
perfluorooctane sulfonate (PFOS) from production. Eight companies subsequently
committed to eliminating emissions of PFOA and related compounds by 2015 as part of
a U.S. Environmental Production Agency PFOA Stewardship Program (35).
124
Internationally, however, perfluoroalkyl compounds of varying chain lengths (C4 to C15)
are still manufactured, and legacy products remain in use globally (20). The phase-out
of PFOS has been accompanied by a shift in production to shorter-chain length
compounds, including those based on C4-sulfonyl chemistries (35). The apparently
efficient clearance of perfluorobutane sulfonate (PFBS), observed in several organisms,
reduces this compound’s bioaccumulative potential (36, 37).
The structures of two homologue groups, perfluoroalkyl carboxylates (PFCAs) and
perfluoroalkyl sulfonates (PFSAs), resemble those of fatty acids and hydrocarbon-based
detergents (Figure 6.5S), but the high-energy carbon-fluorine bond imparts resistance to
hydrolysis, photolysis, microbial degradation, and metabolism by vertebrates and
renders the perfluoroalkyl tail both hydrophobic and oleophobic (16, 33). Rather than
partitioning to adipose tissue, PFAAs are detected predominantly in protein-rich
compartments such as the liver, kidney and blood (39-42), and PFOS concentrations
have been shown to positively correlate with tissue and fluid protein content (39).
Recent field monitoring data for C7 to C14 PFCAs demonstrate a curvilinear
relationship between protein-normalized trophic magnification factors in a marine
mammalian food web and protein-water distribution coefficients (KPW) estimated from
a relationship between octanol-water partition coefficients and affinities for bovine
serum albumin (BSA) (43). KPW may be incorporated into predictive models to improve
estimations of chemical distribution and bioaccumulation of organic contaminants (44).
A globular protein consisting of 583 amino acid residues, BSA (66430 Da) is widely
utilized as a model protein in biophysical, biochemical, and physicochemical studies, as
BSA binding sites accommodate a wide variety of endogenous and exogenous ligands.
Albumin is located to some degree in every fluid of the body, accounting for 60% of
total serum proteins at concentrations of 35 to 50 g/L and exhibiting extravascular
concentrations of 10 to 30 g/L in the skin, muscle, liver, gut and subcutaneous
compartment (151). Despite a proposal that PFAA biomagnification patterns may
correlate with binding to serum proteins, empirical data on the protein binding behavior
of PFAAs are limited, especially for short-chain PFAAs. Chen and Guo (146) measured
displacement of fluorescent probes on human serum albumin (HSA) to calculate
association constants for PFBS and the C4 PFCA on the order of 106 M-1. However,
125
other commonly utilized fluorescence methods may not be applicable to the study of
short-chain PFAAs with albumin (163, 164).
A wide-range of association constants (102 to 106 M-1) for C8 and C9 PFAAs with
rat (42), human (42, 137, 146, 147, 156, 157, 163), and bovine (45, 137, 164) albumins
are reported. Primary association constants for PFOA and perfluorononanoate (PFNA)
with BSA suggest binding through specific high affinity interactions. High log KPW
values (>4) were determined for PFOA and PFNA at physiological PFAA:albumin
mole ratios (45). Site-specific binding of PFOA and perfluorohexanoate (PFHxA) to
albumin has the potential to disrupt endogenous functions via displacement of the fatty
acid, oleate (165), which exhibits 107 to 108 M-1 association constants with albumin for
its first five binding sites (166). However, C4 to C10 PFAAs did not displace steroid
hormones from avian serum proteins at physiological concentrations (133). In addition
to active uptake of PFAAs by transporter systems (129), PFAA-albumin binding may
play a role in elimination kinetics (165). Binding of small molecules to albumin controls
their free concentrations in blood and influences the duration of action, as generally
only the unbound fraction can exit the vascular component. Interactions of PFAAs with
other biomolecular targets including liver fatty acid binding proteins may also play a
role in PFAA bioaccumulation (167) and affect the pharmacokinetics of fatty acids or
other endogenous ligands (136).
The objective of the present study was to examine the interactions of a series PFCAs
(C5 to C12) and even chain length PFSAs (C4 to C8) with BSA. The fraction of PFAAs
bound to BSA and protein-water distribution coefficients are determined at
physiologically relevant ligand:protein mole ratios using equilibrium dialysis and liquid
chromatography tandem mass spectrometry (LC-MS/MS), a direct approach that allows
evaluation of binding parameters at low ligand concentrations. Further analyses via
dialysis, nanoelectrospray ionization mass spectrometry (nanoESI-MS), and
fluorescence spectroscopy offer insights into the mechanisms of PFAA-albumin
interactions by evaluating various solution- and chemical structure-specific parameters
potentially affecting the binding of PFAAs to albumin. In particular, the influence of
fluorocarbon carbon chain length, ionic head group, and solution pH and ionic strength
are assessed.
126
6.2 Methods
Equilibrium dialysis. A purity-corrected equimolar stock solution of PFCAs (C5 to
C12, and C14) and PFSAs (C4, C6, and C8) and a separate concentrated solution of PFNA
were prepared in HPLC water and diluted in 50 mM sodium phosphate buffer in
polypropylene containers without the use of an organic co-solvent. BSA solutions were
prepared fresh daily in glass volumetric flasks using 50 mM sodium phosphate buffer at
the desired pH and concentrations measured using absorbance readings from a
NanoDrop-1000 spectrophotometer (NanoDrop Technologies, Rockland, DE, USA).
Polypropylene dialysis reservoirs containing the same buffers (250 to 500 mL) and
spiked with PFAAs were sampled in triplicate to determine initial (0 h) and final (120
h) PFAA concentrations. The buffer system was selected for consistency with previous
analyses (45, 137). Dialysis reservoir pH and temperature measurements were taken
with an Orion 5 Star Multimeter (Thermo Fisher Scientific, Waltham, MA, USA).
Dialysis bags containing 2 mL of BSA solution were secured with dialysis clips, added
to reservoirs, and equilibrated for 120 h at room temperature (21.2 ± 0.4 °C) prior to
sampling. Six reservoirs were prepared at pH 7.0 with a range of mixed PFAA
concentrations and one dialysis bag each ([BSA] = 199 ± 4 µM) and were sampled in
triplicate at equilibrium to determine KPW values. In these tests, individual free PFAA
concentrations at equilibrium ranged from 0.009 to 0.7 µM (Table 6.1), such that total
free PFAA concentrations ranged from 0.8 to 5 µM. Additional dialysis reservoirs
prepared at pH 6.1, 7.0, 8.0, and 8.9 with 2.8 ± 0.6 µM total PFAAs each contained
three dialysis bags ([BSA] = 197 ± 2 µM) that were sampled in duplicate for PFAAs.
Over the pH range studied, the PFAAs tested were in ionized form in solution (168);
any changes in protonation state upon binding to BSA are not detectable by this
method. Unless otherwise stated, error bars represent one standard deviation. Control
dialysis bags containing a buffer-only solution were equilibrated in a PFAA-spiked
reservoir to confirm equilibration and diffusion of PFAAs through the dialysis
membrane. Control results indicated that perfluorotetradecanoic acid (PFTA) does not
diffuse freely into the dialysis bag, so results for PFTA were excluded.
Details of post-dialysis sample preparation, PFAA analysis via LC-MS/MS, and
127
mass balance results are available in the Supporting Information. Samples and standards
were analyzed using a Shimadzu LC system (LC10ADvp pumps controlled by an
SCL10Avp controller, Columbia, MD, USA) coupled to a Sciex API 3000 triple
quadrupole mass spectrometer (MDS Sciex, Ontario, Canada) operating in negative
electrospray ionization multiple reaction monitoring (MRM) mode. Analyte separation
was achieved using a 40 mm x 2.1 mm Targa Sprite C18 column (5-µm particle size,
Higgins Analytical, Mountain View, CA, USA) with a C18 guard column (Higgins
Analytical).
Nanoelectrospray ionization mass spectrometry. Sample preparation, as well as
instrument parameters and conditions, were previously described (45). Briefly,
individual PFAAs prepared in 9 mM ammonium acetate (pH 7) at room temperature
were added to BSA (50 µM) in polypropylene microcentrifuge tubes, brought to a 1:1
or 2:1 PFAA:BSA mole ratio, and equilibrated for one or more hours before same-day
analysis. Samples were analyzed using an Advion Triversa Nanomate nano-electrospray
robot (Advion BioSystems, Ithaca, NY, USA) coupled with a Waters Micromass Q-Tof
API-US quadrupole time-of-flight mass spectrometer (Micromass, Milford, MA, USA).
Instrument gas pressure and voltage settings were selected to maximize peak intensities
while maintaining resolution and ion current. For static collision energy tests, mass
spectra were acquired for 4 min with cone voltage set at 100 V and laboratory scale
collision energies set to 10 eV for singly charged ions. In duplicate collision induced
dissociation (CID) tests for each tested PFAA-BSA complex, the collision energy was
ramped from 10 to 90 eV, with spectra acquired for 2 min at each setting. Additional
details are available in the Supporting Information.
Fluorescence spectroscopy. PFAA stock solutions were prepared in 50-mL
polypropylene centrifuge tubes (Corning, NY, USA) in HPLC grade water and were
sonicated at 35 °C to dissolve. Solutions of BSA were prepared in glass volumetric
flasks in 50 mM sodium phosphate buffer (pH 6.1, 7.0, 8.1, or 9.1) immediately prior to
each experiment. A stock solution of sodium chloride (3.31 M) was prepared in HPLC
grade water as needed. Fluorescence titrations were conducted for PFNA or PFOS with
BSA in solutions buffered at pH 6.1, 7.0, 8.1, or 9.1 without added sodium chloride and
at pH 7.0 with added sodium chloride to achieve an ionic strength of 0.21, 0.30, or 0.41
128
M. Test solutions (14) were prepared in 15-mL polypropylene centrifuge tubes and
allowed to equilibrate overnight at 4 °C. The concentration of BSA was constant at 4
µM, a concentration that was selected, along with instrumental parameters, to maximize
both sensitivity and resolution. One sample was prepared with no added PFAA, while
the other 13 contained a range of PFAA:BSA mole ratios (0.1:1 through 60:1).
Emission scans were collected in a 1-cm quartz cuvette (Starna Cells; Atascadero, CA,
USA) using a PTI QuantaMaster spectrofluorometer (Photon Technology International;
Birmingham, NJ, USA). The excitation wavelength was 295 nm, and the emission was
recorded from 305 to 450 nm with a step size of 1 nm and an integration time of 0.1 s.
All monochromator slit widths were 4 nm.
6.3 Results and Discussion
Effect of chain length on binding of PFAAs to BSA. Dialysis experiments allow
direct measurement of PFAA-BSA binding through partitioning of PFAAs between an
external reservoir and BSA-containing dialysis bags. Measured concentrations of C2 to
C12 PFCAs and C4 to C8 PFSAs in the dialysis bag relative to the external reservoir
indicate free movement of PFAAs through the dialysis membrane and retention of
PFAA-BSA bound complexes in the dialysis bag. At a BSA concentration of 200 µM
(13 g/L) and a range of PFAA mixture concentrations, PFCAs and PFSAs with four to
ten fluorinated carbons were highly bound (>95%) to BSA across the range of free
PFAA concentrations tested (Table 6.1). This was consistent even for the short-chain
compounds PFBS, PFPeA, and PFHxA, for which reduced affinity for BSA was
expected. The lower fraction bound for PFDoA relative to other compounds in the
mixed reservoir could reflect a decrease in affinity at this longer chain length; however
the relatively large error associated with these results and the mass balance calculations
limits full interpretation. In a separate reservoir spiked only with PFNA, 98.2% of
PFNA was bound to BSA at equilibrium, a result in close agreement with that obtained
in the mixed PFAA experiments.
129
Table 6.1. Fraction of perfluoroalkyl acids (PFAAs) bound to 200 µM bovine serum
albumin (BSA) and log protein-water distribution coefficients for PFAAs with BSA
measured over a range of equilibrium free PFAA reservoir concentrations. For the
fraction of PFAA bound to BSA in each tested reservoir, errors represent 95%
confidence values; standard errors for log KPW are from regressions performed using
Kaleidagraph software.
Analyte Reservoir
[PFAA] Range (µM)1
Log KPW Fraction Bound
perfluorobutanesulfonate (PFBS)
0.010 to 0.57 3.86 ± 0.07 99.0 ± 0.5%
perfluorohexanesulfonate (PFHxS)
0.012 to 0.27 4.3 ± 0.1 99.2 ± 0.5%
perfluorooctanesulfonate (PFOS)
0.013 to 0.26 4.1 ± 0.1 99.1 ± 0.4%
perfluoropentanoate (PFPeA)
0.024 to 0.56 3.40 ±0.02 96.6 ± 0.8%
perfluorohexanoate (PFHxA)
0.009 to 0.43 4.05 ± 0.02 99.2 ± 0.3%
perfluoroheptanoate (PFHpA)
0.054 to 0.30 4.23 ± 0.08 99.3 ± 0.1%
perfluorooctanoate (PFOA)
0.023 to 0.32 4.14 ± 0.04 99.1 ± 0.2%
perfluorononanoate (PFNA)
0.064 to 0.46 4.05 ± 0.08 98.9 ± 0.3%
perfluorodecanoate (PFDA)
0.016 to 0.47 3.86 ± 0.08 98 ± 1%
perfluoroundecanoate (PFUnA)
0.026 to 0.38 3.7 ± 0.2 95 ± 3%
perfluorododecanoate (PFDoA)
0.031 to 0.68 3.3 ± 0.1 80 ± 10%
Protein-water distribution coefficients were determined for each PFAA by a linear
regression of the ratio of analyte concentration in the bound phase (Cp) to that in the
aqueous phase (Cw) (Figure 6.1). This ratio may be represented by:
KPW=CP
CW
=fbound
ralbumin
![P](1" fbound
) (6.1)
1 n = 6; n = 5 for PFHpA
130
where [P] is the protein concentration (g/mL), fbound is the fraction bound to protein, and
ρalbumin is the partial specific volume of protein in aqueous solution (0.733 mL/g, (141)).
KPW thus has units of [g bound PFAA / mL BSA]/[g free PFAA / mL water] and
represents the distribution of the combined anionic and neutral forms of PFAAs
between BSA and the aqueous buffer. At the low PFAA concentrations tested, the
binding isotherm for total PFAAs, represented by the sum of the concentrations of
tested PFAAs, is linear (Figure 6.6S). Values of log KPW for individual PFAAs range
from 3.3 to 4.3 (Table 6.1). The highest Cw value for PFBS, PFPeA, and PFHxA (Cw >
0.4 µM) was excluded in the KPW regressions because these binding isotherms were
nonlinear at higher concentrations. All data were included in nonlinear regressions
performed to determine association constants for the short-chain PFAAs. Data obtained
at relatively higher PFAA aqueous concentrations here and in previous work (45)
demonstrate an expected nonlinear binding relationship, and illustrate that application
of KPW should be limited to a narrow concentration range where binding may be
approximated as linear. At the PFAA:BSA ratios tested, log KPW results were
consistently greater than or at the high end of the range of BSA-water distribution
coefficients compiled for traditional hydrophobic organic contaminants (log KPW = 0.09
to 3.5) (44).
An increase in KPW with increasing chain length was observed for PFCAs with 4-6
fluorinated carbons. For PFCAs with greater than 6 fluorinated carbons, KPW values
generally decreased, though the differences between 6 and 7, 7 and 8, and 9 and 10
fluorinated carbons were not significant. Kelly et al. (43) estimated KPW values for
PFCAs with BSA based on a generalized BSA-ligand relationship with octanol-water
partition coefficients (KOW). Reported log KPW values increased linearly from 2.0 for
PFHpA to 5.0 for PFDoA; however, details of the KPW calculation were not provided.
The trend predicts an increase in affinity for BSA with increased hydrophobicity of the
perfluoroalkyl tail but does not account for increased steric hindrance associated with
longer fluorocarbon tails. Increased rigidity of the fluorocarbon tail and changes in
molecular geometry that begin at C8 and become more pronounced for PFCAs with ten
carbons and longer may influence PFAA partitioning and binding behavior (165, 169).
Hydrophobic binding cavities in BSA may have limited ability to accommodate these
131
larger, less flexible ligands.
0
1x104
2x104
3x104
2 4 6 8 10 12
PFSAs
PFCAs
KP
W
Number of fluorinated carbons
Figure 6.1. Measured BSA-water distribution coefficients (KPW) for perfluoroalkyl
sulfonates (PFSAs, ) and perfluoroalkyl carboxylates (PFCAs, ) with fluorocarbon
tail lengths of 4 to 11. Error bars are standard errors for regressions performed in
Kaleidagraph software.
Direct binding observed by nanoESI-MS. NanoESI-MS, employing a soft
ionization technique, is used here to confirm direct binding of PFCAs (C2 – C9) and
PFSAs (C4 – C8) to BSA and determine stoichiometries of binding at 1:1
PFAA:albumin mole ratios. ESI-MS has been widely used to study proteins in their
native conformation and non-covalent interactions of protein-ligand complexes
preserved in the gas phase (170), although to our knowledge this is the first reported
detection of short-chain PFCA and PFSA complexes with BSA by nanoESI-MS.
Several studies have analyzed intact PFOA- and PFNA-protein complexes via ESI-MS
to elucidate the stoichiometry of binding (42, 45, 133, 135, 167). Although caution must
be taken when quantifying gas-phase affinities using this method (45) for comparison to
solution-based results, the technique has been increasingly used to determine
stoichiometries of specific binding for protein-ligand complexes.
132
Figure 6.2. Deconvoluted spectra of 50 µM BSA alone or with 50 µM PFPeA, PFHxA,
PFHpA, PFOA, or PFNA. The location of free BSA (P) and successive bound PFNA
ligands (P+L), (P+2L), and (P+3L) are denoted on the uppermost spectrum. The peak of
each first bound PFAA used to confirm the expected incremental mass shift (ΔM) from
the free BSA peak is indicated by a star (*).
Representative deconvoluted mass spectra for 1:1 mole ratio mixtures of BSA and
several PFAAs are displayed in Figure 6.2 and Figure 6.7S. Deconvoluted spectra
exhibit distinct peaks for at least one bound PFAA per BSA. Despite somewhat broad
peaks, the mass increment of noncovalent PFAA-BSA complex peaks (P + jL) from the
initial protein peak (P) generally corresponds well with the theoretical molecular mass
of the ligands (ΔM, Table 6.4S). Protein-bound complexes were also maintained for C2-
C4 PFCAs in the gas phase, with an observable change in the native BSA spectrum even
with the addition of the C2 PFCA. Peaks corresponding to two bound PFPrA and PFBA
(P + 2L) were visible as shoulders at the expected BSA-2(PFAA) complex mass on the
133
broader peaks. Adduct ions in the protein spectra, which may be partially attributed to
contamination of the protein with low molecular weight cations (e.g., sodium) via
contact with laboratory glassware, may mask the binding of small or weakly bound
ligands and also limit evaluation of binding at low ligand:albumin mole ratios. For
higher molecular weight PFAAs, the pattern of native BSA was preserved with the
addition of successive PFAA ligands leading to a repetition of the native BSA peak
shape at intervals corresponding to the mass of each bound PFAA. Evidence of multiple
PFAAs bound per BSA molecule was distinctly visible for PFNA and the C4-C8 PFSAs
(2-3 PFAAs bound at a 1:1 mole ratio). A rough calculation previously indicated that
one PFOS binds per albumin molecule (133). However, we present clear evidence of
multiple associations of PFOS with a given albumin molecule at a 1:1 PFOS+BSA mole
ratio. Binding sites detected in this manner likely represent both strong and weak
associations at the relatively high ligand:albumin mole ratio tested. Lower numbers of
C4 and shorter PFCAs bound at equivalent mole ratios may indicate that secondary
binding sites or nonspecific interactions for these compounds, experienced at higher
ligand concentrations, are weaker than comparable interactions of longer-chain PFAAs
with BSA.
Because the precise binding location for various chain-length PFAAs on albumin
has not been established, the full albumin molecule was used in this study to establish
specific binding of the full range of PFCAs and PFSAs to BSA, especially for short-
chain PFAAs, without selecting for particular domains on the protein. In three
homologous domains, albumin contains seven fatty acid (FA) binding sites including
four low affinity sites (Sites 1, 3, 6, and 7) and three high affinity sites (Sites 2, 4, and
5) that are likely candidates for PFAA binding (146). Existing evidence suggests that
PFHxA binds to Sudlow’s drug binding site II, which overlaps with FA Sites 3 and 4,
whereas PFOA preferentially binds to Sudlow’s drug binding site I at FA Site 7 (165).
Use of truncated albumin (e.g., recombinant HSA domain II (171)) in future work could
improve nanoESI-MS mass resolution and, if coupled with newly developed
competitive binding nanoESI-MS techniques (171), may provide further information on
site-specific associations.
Effect of ionic head group on PFAA-BSA binding. The protein-water distribution
134
coefficient determined for PFBS was significantly greater than that for the equivalent
fluoroalkyl chain length carboxylate, PFPeA. This corresponded to an association
constant for PFBS (Ka = 7.0 ± 3.6 ×106 M-1) that was more than three times greater than
that for PFPeA (Ka = 2.0 ± 0.8 ×106 M-1). Association constants (ratio of bound to free
reactants) were calculated using a one-class binding model that relates the ratio of the
bound PFAA concentration to the total protein concentration,
!
" , to the association
constant, Ka, the free ligand concentration, and the total number of binding sites on the
protein (45). At the concentrations tested, PFBS and PFPeA exhibited non-linear
isotherms, where a greater fraction of PFBS was bound to BSA relative to PFPeA over
the full range tested in dialysis experiments (Figure 6.3).
NanoESI-MS was exploited to further evaluate the effect of the ionic head group on
PFAA-BSA binding. The stability of two pairs of compounds of equal fluorinated alkyl
chain length (4 and 8) and a sulfonate or carboxylate moiety was observed by collision-
induced dissociation (CID) at 2:1 PFAA:BSA mole ratios. Collision-induced
dissociation provides a relatively rapid way to probe the stability of binding in the gas
phase. Studies have suggested a link between the energy required for dissociation of
gas-phase complexes and solution-phase binding constants, although the relative
stability of protein-ligand complexes in the gas phase and in solution influences the
interpretation of such measurements (172). For example, although binding in a
hydrophobic pocket may minimize the effects of water on protein-ligand binding in
solution (173), hydrophobic interactions may not persist in the gas phase, and
desolvation of the protein may have unknown influences on ligand interactions (172).
135
Figure 6.3. Effect of ionic head group on binding of equivalent chain length PFAAs to
BSA. In equilibrium dialysis tests (a), PFBS () exhibits higher affinity for BSA than
PFPeA () across the full concentration range tested. Data are displayed as the ratio of
the bound PFAA concentration to the total protein concentration (
!
" ) versus the free
PFAA concentration and were fit with a one-class binding model to determine Ka (R2 =
0.906 and 0.973 for PFBS and PFPeA, respectively). The measured fraction of PFAAs
bound to BSA in nanoESI spectra (b) decreased with increasing collision energy for
PFCAs ( and ) at 40 to 60 eV, whereas PFSA-BSA ( and ) complexes did not
dissociate over the range of collision energies.
A markedly different result was observed for PFBS and PFOS compared to PFPeA
and PFNA: the PFSAs did not dissociate from BSA over the range of tested collision
energies (CE), whereas PFCAs of identical chain lengths dissociated from BSA
between 40 to 60 eV (Figure 6.3). Representative deconvoluted mass spectra for PFPeA
and PFNA at the 2:1 PFAA:BSA mole ratio and spectra demonstrating dissociation of
PFNA with increasing CE are displayed in Figure 6.8S. The larger size of the sulfonate
moiety relative to the carboxylate may play a role in explaining the different behavior of
the PFSAs and PFCAs. PFAAs may interact with positive residues on BSA, including
lysine (pKa 10.53) and arginine (pKa 12.48) (174), and electrostatic interaction between
two oppositely charged molecules may be enhanced in the gas phase and become
relatively difficult to disrupt when complexes are desolvated (172). Additionally, BSA
and BSA-PFAA complexes were monitored in positive ionization mode, so the
136
dissociation of PFAAs in an anionic form is not expected. Although both PFCAs and
PFSAs are expected to be charged at physiological pH in solution, the carboxylate
anions may be more amenable to the capture of a proton as compared to the sulfonate
prior to dissociation from BSA in the ESI source and collision cell. Solution-phase
methods may be more appropriate to provide insight into the potential contribution of
electrostatic interactions to PFAA-BSA binding.
Effect of pH on PFAA-BSA binding. The contribution of electrostatic interactions
to protein-ligand binding is often assessed by changing the pH of the solution (175).
However, the analysis and interpretation of the influence of pH on ligand interactions
with BSA is complicated by the fact that BSA undergoes conformational changes with
changes in pH, potentially altering binding site characteristics. Conformational changes
can occur through the folding and unfolding of the tertiary and secondary structure of
the protein. In the case of BSA, successive multi-step, reversible transformations occur
with increasing pH, from fast (F) to normal (N) near pH 4, and to basic (B) and aged
(A) forms near pH 8 and 10, respectively. At near neutral pH (pH 7.4), albumin has a
net charge of -17 and is heart-shaped in the N form (151, 176). Over the pH range tested
(pH 6 to 9), BSA begins in the N form at lower pH and transitions to the B form. An
analysis of the native fluorescence of BSA reveals a decrease in fluorescence intensity
with increasing pH and a slight blue shift at pH 9 (Figure 6.9S). These spectroscopic
changes confirm the conformational changes in BSA as a function of pH.
137
Figure 6.4. Effect of pH on
!
" , the concentration of PFAA bound to BSA normalized to
the total protein concentration. Binding of PFBS (), PFPeA (), and PFHxA () to
BSA (a) decreased with increasing pH while binding of PFHxS (), PFOS (), PFUnA
(), and PFDoA () (b) increased with pH.
For all tested PFAAs, equilibrium binding to BSA was high across all tested pH
levels, ranging from 85% bound for PFPeA at pH 9 to greater than 99% for several
compounds and pH conditions (Table 6.5S). Similar to KPW results described earlier, at
pH 7 the average number of PFCA molecules bound per BSA molecule (
!
" ) increases
for the C4 to C6 compounds and decreases above C8 (Figure 6.10S). There was no
increase observed in bound PFSAs with increasing chain length at pH 7. A plot of the
138
dialysis bag concentration vs. reservoir pH reveals an overall increase in binding of
PFBS, PFPeA, and PFHxA to BSA with pH, whereas a negative relationship was
obtained for several longer chained PFAAs: PFHxS, PFOS, PFUnA, and PFDoA
(Figure 6.4 and Table 6.6S). Little to no overall trend in binding with pH was observed
for PFHpA, PFOA, PFNA, and PFDA (Figure 6.11S). The opposite trend in
!
" observed
for short- and long-chain PFAAs with pH may result from different conformational
changes experienced from binding at different locations on the protein.
Fluorescence analysis was used to further probe the binding of PFNA and PFOS –
two PFAAs with equivalent chain length but different ionic head groups – to BSA. The
binding of PFAAs to serum albumin evokes changes in the protein’s native fluorescence
(Figure 6.12S); these spectroscopic changes may be used to estimate binding constants
and stoichiometries, as recently described (163). For both PFAAs, the estimated binding
constant, KHill, determined from a plot of the degree of saturation (Y) vs. total PFAA
concentration (Figure 6.13S), increases from pH 6 to pH 7, but no further change is
observed at higher pH (Figure 6.14S). This trend is similar to that obtained for these
PFAAs by equilibrium dialysis (Figures 6.4 and 6.11S); however, as the fluorescence
data are obtained at higher PFAA:BSA mole ratios, these data are not directly
comparable. Given the changes in native fluorescence of BSA as a function of pH, the
differences in binding affinity observed here are likely due to conformational changes in
the protein. However, electrostatic changes could also play a role.
One would expect enhanced electrostatic interactions with decreasing pH as
repulsion from negatively charged amino acid residues is reduced; however, BSA
supports a more compact conformation as it approaches its isoelectric point (~pH 4.8),
which may reduce accessibility of surfactants to the hydrophobic cavities of the binding
sites (148). Gelamo et al. (148) observed a lower binding constant for sodium dodecyl
sulfate (SDS) with BSA at pH 5 as compared to pH 7 and pH 9. The compact structure
may have a more pronounced effect on binding of the longer, more hydrophobic
PFAAs, while electrostatic interactions may play a larger role in binding for short-chain
PFAAs. The difference in responses of PFAA-BSA affinity with pH suggests that short-
and long-chain PFAAs may bind at different locations on albumin.
To specifically investigate the role of electrostatics, the binding of PFNA and PFOS
139
to BSA as a function of ionic strength was explored through fluorescence titrations by
adding varying concentrations of sodium chloride to the titration solutions. The results
are shown in Figures 6.15S and 6.16S. For both PFAAs, there was no difference in
estimated binding affinity over a physiologically relevant ionic strength range (Figure
6.17S). This result supports the hypothesis that the observed changes in PFAA-BSA
binding with changing pH result primarily from conformational changes in the protein
and not changes in electrostatics.
6.4 Significance
In recent attempts to include protein associations in bioaccumulation models, which
traditionally approximate organism or tissue sorptive capacity using only lipid content,
researchers utilized BSA-ligand relationships to report log KPW values for a range of
hydrophobic organic contaminants (44). Empirical data for log KPW of a series of PFAA
cogeners are reported in the present study, yielding relatively large values from 3.3 to
4.3. Considered analogous to octanol-water partition coefficients, such parameters may
be useful to more accurately predict chemical accumulation and distribution. However,
use of KPW to describe binding between macromolecules and small molecule ligands
should be conducted with caution and limited to a narrow concentration range over
which binding may be approximated as linear.
Results from the present study indicate increased binding with chain length for C4 to
C6 PFCAs. Above C8, KPW decreased with increasing chain length. Additionally,
differences in binding of PFBS and PFOS relative to PFPeA and PFNA, respectively,
observed via dialysis and collision induced dissociation indicate an electrostatic
component to interactions with BSA. Fluorescence results for PFOS and PFNA suggest
that these affects are minimal in solution. A number of studies have illustrated an
important role of the hydrophobic driving force on PFAA environmental partitioning.
However, increased rigidity associated with long-chain PFCAs may contribute to the
observed nonlinear relationship of KPW with the fluorocarbon tail length.
Prior studies suggest that the binding of short-chain PFAAs may be different in
nature than that of long-chain PFAAs (133, 165). PFOA and PFHxA may bind
primarily to different sites on HSA, demonstrating that an increase in the fluorinated
140
carbon tail length by only two units may have a substantial effect on the nature of
PFCA-albumin interactions (165). Fluorescence studies indicate that long-chain PFAAs
have a greater influence on albumin conformation than short-chain PFAAs (e.g., PFBS,
PFPeA, and PFHxA) (163, 164), and the Trp binding site on HSA, which overlaps with
or exists in the hydrophobic cavity of Fatty Acid Site 7 in Subdomain IIA, may have a
preference for long-chain PFAAs (146). We find strong binding of short-chain PFAAs
to BSA, suggesting that reduced hydrophobicity and steric hindrances of short-chain
PFCAs and PFBS, which may limit observable conformational changes in albumin by
fluorescence methods, do not correspond to low affinity for albumin. In the present
study, pH-induced changes in binding affinity observed via equilibrium dialysis support
evidence that short- and long-chain PFAAs bind at different locations on BSA.
Fluorescence titrations suggest that the observed pH dependence of binding is due to
conformational changes in the protein.
An effort to reduce the bioaccumulation of PFAAs in humans and wildlife has led to
shifts in fluorochemical production to shorter chain-length compounds. Association
constants obtained for PFBS and PFPeA are useful to compare amongst various PFAAs
and with other exogenous and endogenous ligands in blood. Results in the present study
indicate that short-chain PFAAs bind strongly to BSA at low PFAA:albumin mole
ratios, suggesting that physiological implications of strong binding to albumin may be
important for short-chain PFAAs. However, these results contrast with limited evidence
that short-chain PFAAs are less bioaccumulative than long-chain PFAAs (36, 37),
highlighting a need for additional protein association measures for bioaccumulation
modeling beyond a single-protein KPW. Further research is needed to investigate the
binding of short-chain perfluorinated compounds to a multitude of potential protein and
transporter targets and to further understand the influence of such interactions on
biological uptake, retention, and functioning.
Supporting Information. Contains (1) additional analytical and experimental
details, (2) Figures 6.5S – 6.17S and (3) Tables 6.2S – 6.6S.
Acknowledgment. The present study was supported by the National Science
141
Foundation Graduate Research Fellowship Program, the Stanford University Woods
Institute for the Environment, and Union College. We thank Pavel Aronov from the
Stanford University Mass Spectrometry Laboratory for nanoESI-MS instrument
operation and technical input.
Publication Information. Reproduced with minor modifications from Bischel, H.
N.; MacManus-Spencer, L. A.; Zhang, C.; Luthy, R. G. Strong associations of short-
chain perfluoroalkyl acids with serum albumin and investigation of binding
mechanisms. Environmental Toxicology & Chemistry, Copyright © 2011 Society of
Toxicology and Chemistry, Wiley-Blackwell Publisher.
142
6.5 Supporting Information
Materials. Fraction V fatty acid-free bovine serum albumin (BSA, 99.9%) was
from EMD Biosciences, Inc. Perfluoropropanoic acid (PFPrA, 97%), perfluorobutanoic
acid (PFBA, 97%), perfluoropentanoic acid (PFPeA, 97%), perfluorononanoic acid
(PFNA, 97%), potassium perfluorobutane sulfonate (PFBS, 98%), and potassium
perfluorohexane sulfonate (PFHxS, >98%) were from Sigma-Aldrich (St. Louis, MO).
Perfluoroheptanoic acid (PFHpA, 99%), perfluorooctanoic acid (PFOA, 96%),
perfluorodecanoic acid (PFDA, 98%), perfluoroundecanoic acid (PFUnA, 95%),
perfluorododecanoic acid (PFDoA, 95%), and perfluorotetradecanoic acid (PFTA, 97%)
were from Aldrich Chemical Co. (Milwaukee, WI, USA). Triflouroacetic acid (TFA,
>99.5%), perfluorohexanoic acid (PFHxA, >97%), and potassium perfluorooctane
sulfonate (PFOS, 98%) were from Fluka through Sigma-Aldrich (St. Louis, MO, USA).
Mass labeled internal standards [13C5] PFNA, [13C2] PFDA, [13C2] PFOS, N-
deuterioethylperfluoro-1-octanesulfonamidoacetic acid ([D5]–N-EtFOSAA) were from
Wellington Laboratories (Guelph, ON, Canada), and [13C2] PFOA was from Perkin-
Elmer Life Sciences (Boston, MA, USA). Labeled internal standards had purities
greater than 98%, as reported by the suppliers. Structures of perfluoroalkyl acids
(PFAAs) included in this study are displayed in Figure 6.5S. Spectra/Pore dialysis
membrane tubing (6000 to 8000 Da molecular weight cutoff), polypropylene dialysis
reservoirs and clips were from Spectrum Laboratories (Rancho Domingo, CA, USA).
Data were analyzed in Microsoft Office Excel (Microsoft Corporation; Redmond, WA,
USA) and Kaleidagraph (Synergy Software Systems; Dubai, United Arab Emirates).
Post-dialysis sample preparation and liquid chromatography tandem mass
spectrometry (LC-MS/MS) analysis. Triplicate reservoir samples (0.5 mL) were
added to an equal volume of methanol in HPLC vials or polypropylene microcentrifuge
tubes for further dilution with 1:1 methanol:buffer. Reservoir samples were analyzed by
LC-MS/MS following the addition of 7:3 v/v methanol:1% aqueous NH4OH (100 µL)
and a mixed internal standard solution prepared in HPLC grade water (100 µL).
Dialysis bag samples (0.5 mL or 0.2 mL) taken at equilibrium into 15-mL
polypropylene centrifuge tubes were extracted using acetonitrile (1% v/v glacial acetic
143
acid) as previously described (45), using 9.5 mL acidified acetonitrile for 0.5 mL
samples and 3.8 mL for 0.2 mL samples. Samples were vortexed (30 s), sonicated (10
min, 60 °C), and centrifuged (15 min, 3000 rpm). An aliquot from each extraction (1.8
mL) was transferred to a polypropylene microcentrifuge tube containing ENVICarb
(Supelco, Bellefonte, PA, USA), vortexed, and centrifuged (30 min, 14000 rcf).
Additional dilutions were performed as needed to bring the expected sample
concentration into the range of the LC-MS/MS calibration standards. High pressure
liquid chromatography (HPLC) vials contained the acetonitrile extract (200 µL), HPLC
grade water (200 µL), 7:3 v/v methanol:1% aqueous NH4OH (50 µL), and internal
standard in HPLC grade water (45 µL). Reservoir samples and dialysis bag extracts
were stored at 4°C until analysis. Reservoir and BSA matrix spike recovery results are
shown in Table 6.2S. Matrix-matched calibration standards were prepared with PFAA
stock solutions in 7:3 v/v methanol:1% aqueous NH4OH. Further details of LC-MS/MS
analysis are available elsewhere (45). Mass transitions monitored for quantitation and
confirmation are shown in Table 6.2S.
The average mass balance of PFAAs in pH 7.0 dialysis reservoirs (n = 6) was
between 80% and 130% for all compounds (Table 6.2S). The mass balance on PFTA
includes only initial and final reservoir samples (n = 7), as PFTA did not diffuse into
dialysis bags. A decrease in measured BSA concentrations was observed between initial
and final time points, but no BSA was detected in external reservoirs; the decrease may
have been due to osmotic dilution or sorption to the apparatus. Therefore, BSA
concentrations measured at equilibrium were used in calculations. BSA was
equilibrated separately in blank reservoirs. Occasional low levels of several PFAAs
were detected in either blank reservoir or blank dialysis bag measurements. The lowest
reported test dialysis PFAA concentrations were always more than twice that detected
in a blank. For reservoir matrix spike recoveries, a known standard was spiked into 50
mM sodium phosphate buffer at each pH (n = 6) and compared to the standard, which
was prepared in pH 7 buffer. For BSA spike recoveries, freshly prepared BSA (200 µL,
pH 7) was spiked with a mixed PFAA standard (n = 12) and extracted using the
previously described procedure. The average reservoir and BSA matrix spike recovery
results were between 87 to 123% and 95 to 101%, respectively (Table 6.2S). All PFAAs
144
were below the detection limit for side-by-side extractions of blank BSA and sodium
phosphate buffer solutions.
Nanoelectrospray ionization mass spectrometry analysis. BSA prepared in 9 mM
ammonium acetate (pH 7) at room temperature was dialyzed overnight to aid in
removal of salts prior to PFAA exposure. Mass signals were collected over the scan
range m/z 1000 to 5000. The three most intense charge states (+15 to +17) occurred
from m/z 3800 to 4700 for free BSA and BSA-PFAA complexes. Multiply-charged
mass spectra were mathematically deconvoluted using m/z 3800 to 4700 and MaxEnt in
MassLynx software version 4.1 from Waters. Spectra were deconvoluted with 10
iterations using MaxEnt and smoothed. For CID integrations, a 10-channel center was
applied to deconvoluted results, and free BSA was operationally defined as the
integrated spectral area up to a mass of 66690 Da, which is less than the expected mass
of the first-bound PFPeA. The fraction of bound PFAA was then calculated using the
integrated area above the expected mass of the first-bound tested PFAA relative to the
free BSA area.
145
Figure 6.5S. Structures and names of perfluoroalkyl acids (PFAAs) included in the
present study. Compound abbreviations and notations adopted for reference in the
manuscript (C2 to C14) are listed. PFSAs and PFCAs have a fluorocarbon tail length of n
+ 1 and m + 1, respectively.
146
Table 6.2S. Dialysis mass balance, reservoir matrix and bovine serum albumin (BSA)
spike recovery results, and liquid chromatography tandem mass spectrometry (LC-
MS/MS) transitions monitored. Errors represent 95% confidence intervals.
Analyte
Test Reservoir
Mass Balance
Reservoir matrix spike
recovery
BSA spike
recovery
Primary transition monitored
(m/z)
Secondary transition monitored
(m/z)
Internal standard
Internal standard transition monitored
(m/z) PFBS 90 ± 16% 103 ± 21% 95 ± 3% 299 > 80 299 > 99 [13C2] PFOS 503 > 99 PFHxS 87 ± 13% 122 ± 24% 97 ± 5% 399 > 80 399 > 99 [13C2] PFOS 503 > 99 PFOS 95 ± 27% 120 ± 24% 99 ± 6% 499 > 80 499 > 99 [13C2] PFOS 503 > 99 PFPeA 103 ± 19% 87 ± 24% 101 ± 5% 263 > 219 263 > 69 [13C2] PFOA 415 > 370 PFHxA 119 ± 19% 98 ± 23% 95 ± 4% 313 > 269 313 > 119 [13C2] PFOA 415 > 370 PFHpA 107 ± 19% 103 ± 21% 97 ± 4% 363 > 319 363 > 169 [13C2] PFOA 415 > 370 PFOA 103 ± 23% 108 ± 10% 94 ± 3% 413 > 369 413 > 169 [13C2] PFOA 415 > 370 PFNA 107 ± 43% 112 ± 21% 97 ± 4% 463 > 419 463 > 169 [13C5] PFNA 468 > 423 PFDA 106 ± 18% 93 ± 11% 99 ± 3% 513 > 469 513 > 219 [13C2] PFDA 515 > 470 PFUnA 99 ± 14% 105 ± 13% 101 ± 5% 563 > 519 563 > 269 [D5] N-
EtFOSAA1 589 > 419
PFDoA 129 ± 58% 123 ± 21% 93 ± 5% 613 > 569 613 > 319 [D5] N-EtFOSAA
589 > 419
PFTA 92 ± 31% 119 ± 14% 98 ± 7% 713 > 669 713 > 169 [D5]–N-EtFOSAA
589 > 419
1 N-deuterioethylperfluoro-1-octanesulfonamidoacetic acid
147
Figure 6.6S. Total PFAA analyte concentration in the bound phase (CP, [g bound
PFAA / mL BSA]) versus total aqueous PFAA concentration (CW, [g free PFAA / mL
water]). The slope of the linear regression (R2 = 0.923) yields an apparent PFAA-BSA
distribution coefficient (log KPW = 3.92 ± 0.06).
Table 6.3S. Protein-water distribution coefficients for PFAAs with BSA. Standard
errors are from regressions performed using Kaleidagraph software.
Analyte Number of Fluorinated
Carbons log KPW R2
PFBS 4 3.86 ± 0.07 0.921 PFHxS 6 4.3 ± 0.1 0.707 PFOS 8 4.1 ± 0.1 0.782 PFPeA 4 3.40 ±0.02 0.994 PFHxA 5 4.05 ± 0.02 0.993 PFHpA 6 4.23 ± 0.08 0.918 PFOA 7 4.14 ± 0.04 0.973 PFNA 8 4.05 ± 0.08 0.904 PFDA 9 3.86 ± 0.08 0.893 PFUnA 10 3.7 ± 0.2 0.651 PFDoA 11 3.3 ± 0.1 0.738
148
Figure 6.7S. Representative deconvoluted spectra of 50 µM BSA alone or with 50 µM
TFA, PFPrA, PFBA, PFBS, PFHxS, or PFOS.
149
Table 6.4S. Measured incremental mass shifts (ΔM) from measured BSA peak (P) to
BSA-PFAA peaks (P + jL) for representative spectra in manuscript Figure 6.2 and
Supporting Information Figure 6.7S.
Ligand Theoretical
ligand molecular
weight (g/mole)
Measured incremental mass (ΔM)
[(P+L)-P] [(P+2L)-P]/2 [(P+3L)-P]/3
PFBS 299 299 301 3001 PFHxS 399 400 400 399 PFOS 499 500 500 5102 TFA 113 117 ND ND PFPrA 163 164 1631 ND PFBA 213 213 2131 ND PFPeA 263 265 2651 ND PFHxA 313 314 313 ND PFHpA 363 364 363 ND PFOA 413 414 413 ND PFNA 463 463 463 4641
1 Peak visible as a shoulder to broad spectrum. 2 A fourth shoulder peak identified for PFOS at ΔM = 489 mass units.
150
Figure 6.8S. Representative deconvoluted spectra of PFPeA and PFNA (100 µM) with
BSA (50 µM) collected at a 10 V collision energy (left) and representative spectra of
PFNA (100 µM) with BSA (50 µM) at 10, 30, 50 or 70 eV collision energy (right).
Charge states are displayed for raw spectral results.
151
Figure 6.9S. Fluorescence spectra of BSA at pH 6 (solid line), 7 (long dashed line), 8
(short dashed line), or 9 (dotted line). (a) Raw data; (b) Data normalized to the
maximum intensity in each spectrum.
152
Table 6.5S. Average fraction of PFAAs bound to BSA (197 ± 2 µM) for a range of pH
conditions. Errors represent standard deviations of results from triplicate dialysis bags
after 120 hours equilibration.
Analyte pH 6 pH 7 pH 8 pH 9 PFBS 98.2 (± 0.2)% 97.2 (± 0.9)% 96.6 (± 0.5)% 95.6 (± 0.7)% PFHxS 99.3 (± 0.1)% 99.3 (± 0.2)% 99.0 (± 0.1)% 99.2 (± 0.3)% PFOS 99.10 (± 0.04)% 99.3 (± 0.1)% 98.6 (± 0.1)% 99.1 (± 0.2)% PFPeA 92 (± 1)% 91 (± 2)% 86 (± 2)% 84 (± 1)% PFHxA 97.8 (± 0.3)% 97.5 (± 0.5)% 97.00 (± 0.03)% 95.6 (± 0.3)% PFHpA 98.9 (± 0.1)% 99.1 (± 0.2)% 98.9 (± 0.1)% 98.4 (± 0.2)% PFOA 99.0 (± 0.1)% 98.9 (± 0.2)% 98.6 (± 0.1)% 99.0 (± 0.1)% PFNA 98.87 (± 0.05)% 98.9 (± 0.2)% 97.8 (± 0.4)% 99.2 (± 0.1)% PFDA 97.7 (± 0.2)% 98.9 (± 0.1)% 98.2 (± 0.3)% 98.2 (± 0.1)% PFUnA 96.4 (± 0.3)% 98.6 (± 0.2)% 96.9 (± 0.3)% 97.9 (± 0.3)% PFDoA 94.2 (± 0.4)% 96.7 (± 0.9)% 90 (± 2)% 96.2 (± 0.1)%
Figure 6.10S. Average number of bound perfluoroalkyl carboxylates (PFCAs, ) or
perfluoroalkyl sulfonates (PFSAs, ) per BSA,
!
" (µM PFAAbound / µM BSA),
measured in dialysis bags containing 200 µM BSA in a PFAA-spiked reservoir at pH 7.
Results illustrate a chain-length dependence of PFCA binding to BSA.
153
Figure 6.11S. Effect of pH on the average number of PFHpA (), PFOA (), PFNA
(), or PFDA () molecules bound to BSA.
Table 6.6S. Slope of linear regressions for average number of PFAAs bound to BSA,
!
" , versus pH (6 to 9) in equilibrium dialysis tests.
Analyte Slope R2 PFBS -0.005 0.639 PFHxS 0.0097 0.855 PFOS 0.0146 0.924 PFPeA -0.0061 0.965 PFHxA -0.0052 0.887 PFHpA 0.0293 0.001 PFOA 0.1563 0.009 PFNA 0.7645 0.055 PFDA 1.2569 0.272 PFUnA 0.0089 0.570 PFDoA 0.0041 0.592
154
Figure 6.12S. Changes in the fluorescence of BSA with added PFNA (top) or PFOS
(bottom) at pH 6 (), 7 (), 8 (), or 9 (). (a) and (c): Both PFNA and PFOS cause a
dose-dependent blue shift in the wavelength of maximum emission of BSA; there is no
significant difference over the range of tested pH. (b) and (d): Both PFNA and PFOS
cause a dose-dependent decrease in the fluorescence emission of BSA; the extent of this
decrease is diminished as the pH increases.
155
Figure 6.13S. The binding of PFNA (a) and PFOS (b) to BSA, plotted as the degree of
saturation (Y) versus total PFAA concentration, at pH 6 (), 7 (), 8 (), or 9 (). The
degree of saturation was calculated as by Hebert and MacManus-Spencer (163).
156
Figure 6.14S. Dependence of estimated binding constant (KHill) on pH for the binding
of PFNA () and PFOS () to BSA.
157
Figure 6.15S. Changes in the fluorescence of BSA with added PFNA (top) or PFOS
(bottom) at 0.21 M (), 0.30 M (), or 0.41 M () ionic strength and pH 7. (a) and (c):
Both PFNA and PFOS cause a dose-dependent blue shift in the wavelength of
maximum emission of BSA; there is no significant difference over the range of tested
ionic strength. (b) and (d): Both PFNA and PFOS cause a dose-dependent decrease in
the fluorescence emission of BSA; there is no significant difference over the range of
tested ionic strength.
158
Figure 6.16S. The binding of PFNA (a) and PFOS (b) to BSA, plotted as the degree of
saturation (Y) versus total PFAA concentration, at 0.21 M (), 0.30 M (), or 0.41 M
() ionic strength and pH 7. The degree of saturation was calculated as by Hebert and
MacManus-Spencer (163).
159
Figure 6.17S. Dependence of estimated binding constant (KHill) on ionic strength for
the binding of PFNA () and PFOS () to BSA at pH 7.
160
161
Chapter 7
Conclusions
7.1 Summary Conclusions
In this thesis, drivers and hindrances for water reuse implementation in Northern
California were assessed, and opportunities of water reuse for natural system
enhancement were identified (Chapters 2-3). Subsequently, the bioaccumulation of
perfluoroalkyl acids (PFAAs), focusing on behavior of binding to proteins as a
proposed biological partitioning parameter, was evaluated (Chapters 4-6). In response to
the research questions outlined in Chapter 1, the following conclusions regarding these
topics can be drawn:
What are the major drivers and barriers to water reuse in Northern
California, and how have these factors evolved through time? Despite growth of
water reuse throughout California, the state has failed to meet recycling goals
established over the past several decades. In Chapter 2, major factors that influenced the
implementation of water-recycling projects in the region were presented based on a
survey of water reuse program managers and facility representatives in Northern
162
California. The study revealed that regulatory requirements limiting discharge played an
important role in motivating many water reuse programs in the region. However, a trend
away from reuse as a disposal issue was documented, as water supply and reliability
become more prevalent drivers of water reuse. Respondents cited economic challenges
as the greatest barrier to successful project implementation. In particular, managers of
smaller water reuse programs more frequently experienced challenges in acquiring
grants and loans, while larger programs had somewhat greater challenges associated
with distribution system costs. Issues of cost recovery were also expressed as barriers to
implementation of water reuse for ecosystem enhancement. Negative perceptions of
water reuse were not frequently major hindrances to implementation of water reuse
programs in the region. Public perception of water reuse may be positively influenced
by a shift in view of recycled water as a valuable resource and as public knowledge of
water supply challenges increase. However, almost half of survey respondents cited
perceived human or environmental health risks due to constituents of emerging concern
as a hindrance to recycled water program implementation. Today, trace chemicals
detected in effluent and receiving waters represent a technological challenge and a
source of concern for recycled water managers.
To what extent has water reuse been applied for the direct benefit of
ecosystems, and what major challenges are associated with the implementation of
water reuse for ecosystem enhancement? Although ecosystem enhancement or
protection goals were frequently cited as drivers of water reuse, such goals were rarely
the most important drivers for program implementation. A survey of databases and
input from managers in Northern California indicates that few water reuse programs in
California have been implemented for the explicit purpose of ecosystem enhancement.
A past effort by the Bay Area Water Recycling Program to identify potential new
programs in the San Francisco Bay region framed important issues to evaluate potential
wetland or stream augmentation sites, but this analysis was far from comprehensive.
Additionally, relatively little progress has been made towards implementation of water
reuse for ecosystem enhancement over the past decade in California. A newly
developed and validated rapid assessment method for California wetlands represents a
potential tool for identifying opportunities for water reuse for natural system
163
enhancement. This method does not evaluate the capacity of wetlands to improve water
quality; rather, it indicates physical and biological attributes that link to the ecosystem’s
ability to support flora and fauna. However, if the links between hydrologic regimes and
wetland condition indicators are well established, the assessments may be utilized to
characterize opportunities for ecosystem enhancement using tertiary treated wastewater.
Amongst a range of challenges for implementing these types of projects, understanding
the bioaccumulation of chemicals of emerging concern represents a particular research
need. Because of their unique chemical properties, extreme environmental persistence,
and elusive bioaccumulation mechanisms, perfluoroalkyl acids were selected for further
analysis.
What dominant processes govern the bioaccumulation of perfluoroalkyl acids
(PFAAs), and how can these processes be captured in bioaccumulation models?
PFAA concentrations detected in white sturgeon fish livers from organisms in the San
Francisco Bay, presented in Chapter 4, contribute to literature on the widespread
detection of PFAAs in the environment and relative dominance of
perfluorooctanesulfonate (PFOS) in biological samples. PFOS was detected in 14 of 15
white sturgeon fish livers, ranging in concentration from 14 ng/g ww to 180 ng/g ww.
Correlations of fish liver concentrations with organism stable isotope ratios demonstrate
the importance of ecological approaches to understanding biomagnification from varied
food sources. However, even simplistic Tier 1 screening measures for evaluating the
bioaccumulative potential of new chemicals, a necessity for effective decision-making,
generally do not incorporate expected bioaccumulation mechanisms relevant to PFAAs.
Traditional models based on legacy hydrophobic organic contaminants correlate
bioconcentration factors with octanol-water partition coefficients (KOW). These models
are insufficient for capturing the nature of biological uptake of ionic species such as
PFAAs. Because interactions of PFAAs with tissue and serum proteins likely contribute
to their tissue distribution and bioaccumulation patterns, an empirical fugacity approach
that incorporates protein binding, considered analogous to KOW, may improve predictive
measures. Protein-water distribution coefficients (KPW) based on ligand associations
with bovine serum albumin (BSA) are proposed as biologically relevant parameters to
describe the environmental behavior of PFAAs.
164
How do long-chain perfluoroalkyl acids interact with the model protein, serum
albumin, at physiologically relevant PFAA:albumin mole ratios? Previous studies
report a wide range of association constants between perfluorooctanoate (PFOA) and
albumin, with most values suggesting relatively weak binding (<104 M-1), likely
resulting from the use of high PFAA:albumin mole ratios. In Chapter 5, association
constants (Ka) and binding stoichiometries for PFAA-albumin complexes were
quantified over a wide range of PFAA:albumin mole ratios. Primary association
constants for PFOA or perfluorononanoate (PFNA) with BSA determined via
equilibrium dialysis are on the order of 106 M-1 with one to three primary binding sites,
comparable to the affinity of fatty acids with albumin. PFNA was greater than 99.9%
bound to BSA or human serum albumin (HSA) at a physiological PFAA:albumin mole
ratio. Nanoelectrospray ionization mass spectrometry (nanoESI-MS) reveals PFAA-
BSA complexes with up to eight occupied binding sites at a 4:1 PFAA:albumin mole
ratio. Association constants estimated by nanoESI-MS are on the order of 105 M-1 for
PFOA and PFNA and 104 M-1 for perfluorodecanoate and PFOS.
What analytical tools are appropriate for quantitatively determining PFAA-
BSA associations? Work presented in Chapters 5 allows comparison of a standard
solution-based method (equilibrium dialysis) with a modern mass spectrometric
approach (automated nanoESI-MS). Along with fluorescence spectroscopy techniques
applied in Chapter 6, and further investigation conducted by MacManus-Spencer et al.
(137), these methods provide complementary information about the strength of PFAA-
albumin binding interactions, the number of binding sites at low ligand:protein mole
ratios, and physiochemical mechanisms of interactions. As evident in previous studies
of PFAA-albumin interactions that utilized spectroscopic methods, electrophoresis, 19F
NMR, and surface tension, limitations associated with analytical methods often require
high PFAA concentrations in experimental analysis. Equilibrium dialysis, a
thermodynamically sound and straightforward protein-ligand analysis technique,
produces reliable data for PFAAs with less than 12 perfluoroalkyl carbons. When
coupled with sensitive detection methods, achievable via liquid chromatography tandem
mass spectrometry (LC-MS/MS), dialysis can yield binding parameters at
physiologically relevant PFAA:BSA mole ratios. NanoESI-MS is a useful technique for
165
rapid characterization PFAA-protein interactions and may provide utility for screening
small molecule interactions with proteins. However, challenges with spectral resolution,
sensitivity of complexes to instrument conditions, and questions regarding physiological
relevance of a gas-phase approach limit the utility of nanoESI-MS as a fully
quantitative method for characterizing PFAA-BSA interactions. Fluorescence
spectroscopy allows for analysis of a wide range of PFAA:albumin mole ratios, and,
though an indirect method, offers insights into conformational changes in the protein
that occur upon ligand binding and with changes in solution conditions.
How will a reduction in perfluoroalkyl chain length affect protein-water
distribution coefficients? A general shift in commercial production of PFAAs from
long- to short-chain chemicals is underway, yet empirical data on the environmental
behavior of short-chain PFAAs are limited. In Chapter 6, associations of perfluoroalkyl
carboxylates (PFCAs) with 2 to 12 carbons (C2 – C12) and perfluoroalkyl sulfonates
(PFSAs) with 4 to 8 carbons (C4, C6, and C8) with BSA are evaluated at low
PFAA:albumin mole ratios and various solution conditions using equilibrium dialysis,
nanoelectrospray ionization mass spectrometry, and fluorescence spectroscopy. Log
KPW values for C4 to C12 PFAAs range from 3.3 to 4.3, greater in magnitude than
octanol-water distribution coefficients for PFAAs. Corresponding association constants
determined for perfluorobutanesulfonate and perfluoropentanoate with BSA are high
(Ka ~106 M-1), and the C4-sulfonate exhibits increased affinity relative to the equivalent
chain-length PFCA. Association constants determined for perfluorobutanesulfonate and
perfluoropentanoate with BSA (Ka ~ 106 M-1) are on the order of those for long-chain
PFAAs, suggesting that physiological implications of strong binding to albumin may be
important for short-chain PFAAs.
What physiochemical mechanisms govern interactions of perfluoroalkyl acids
with serum albumin? Results in Chapters 5 and 6 suggest binding through specific
high affinity interactions at low PFAA:albumin mole ratios. Affinity for BSA increases
with PFAA hydrophobicity but decreases from the C8 to C12 PFCAs, likely due to steric
hindrances associated with longer and more rigid perfluoroalkyl chains. Differences in
binding of the C4-sulfonate relative to the C5-carboxylate, observed via dialysis and
collision induced dissociation, indicate an electrostatic component to interactions with
166
BSA. PFAA-BSA fluorescence titrations conducted at varying pH and ionic strength
support evidence that an observed dependence of binding on pH is due to
conformational changes in the protein. For strongly associating ligands such as PFAAs,
use of KPW to describe binding to albumin should be conducted with caution and limited
to a narrow concentration range over which binding may be approximated as linear.
Additionally, although a serum protein association constant may be a useful parameter
to contribute to the characterization of PFAA bioaccumulative potential, the parameter
has limitations. Serum albumin represents one amongst a multitude of protein targets
for in vivo perfluoroalkyl binding. Additional protein targets or mixtures require
identification to more fully capture the observed bioaccumulation trends of PFAAs.
7.2 Future Work
The work conducted in this dissertation suggests several lines of future research,
including but not limited to, those discussed below.
Identifying needs of water and wastewater agencies, evaluating and quantifying
success, and recommendations for institutional arrangements.
Research presented in Chapters 2 and 3 comprise a regional assessment of water
reuse opportunities and challenges for existing water recycling facilities. As a practical
approach, we inferred that the challenges overcome by implemented programs
represents a minimum of potential challenges facing those who have not yet
implemented programs. However, as a follow-up step, wastewater and water agencies
that do not produce and utilize recycled water should be approached to better
understand the needs of these agencies. In particular, wastewater facilities with potential
couplings to wetland creation or enhancement opportunities may be a first cohort to
further assess interest, potential, and agency needs for ecosystem enhancement using
recycled water. Secondly, additional metrics to evaluate the degree of success for a
recycled water program require development. In the present study, all operational
programs were equally weighted in the analysis. An assessment of the degree of success
would facilitate identification of strategies for particularly successful programs as well
as lessons from those that faced more significant hurdles. Finally, surprisingly little
167
information is available regarding effective pricing structures and institutional
mechanisms to collaboratively develop and operate recycled water programs. Yet, as
shown in this work, economic hindrances frequently limit reuse development and
expansion. Case study analysis of the particular scenarios in which a recycled water
system is limited due to potential revenue reductions for a water purveyor would be
valuable. Future economic analyses should be coupled to a tight feedback loop to
potential recycled water producers and distributors. The existing National Database of
Water Reuse Facilities, which does not include pricing structures or funding
mechanisms, is a practical choice for compiling and sharing such data.
Ecosystem services of wetlands that reclaim tertiary treated wastewater.
Though the needs of our ecosystems, and their responses to human perturbations,
are inherently difficult to quantify, estimating the ecological capital generated from the
implementation of water reuse for ecosystem enhancement in terms that relate to human
activities and investment may lead to management decisions that yield overall greater
environmental and human benefit. In addition to creating recreational opportunities and
improving urban aesthetic, wetlands have the potential to provide water storage
capacity, treat for bulk water quality (e.g., biochemical oxygen demand), remove
nutrients, sequester (or emit) greenhouse gases including carbon dioxide and nitrous
oxide, and attenuate organic contaminants through photolytic and phytotransformation
as well as sorption and redox processes. In order to aid managers in evaluating the
economic potential for various natural system water reuse options, these processes
require quantification and comparison with more traditional recycled water uses through
life cycle assessment approaches.
In situ bioaccumulation and passive sampling of micropollutants in water reuse
systems.
In order to conduct broad assessments of ecosystem services generated from
recycled water wetlands, in situ characterization of micropollutant attenuation and
bioaccumulation should be conducted. Application of passive samplers in wetland water
reuse systems could yield design insights for physical structures and vegetative
168
communities that promote natural attenuation of trace contaminants. Today, many
apolar, persistent organic pollutants are monitored using passive samplers that
accumulate hydrophobic organic contaminants preferentially compared to water.
Passive samplers may also be biomimetic, emulating the body burden of biota. The
Polar Organic Chemical Integrative Sampler (POCIS) has also been applied for a range
of pharmaceuticals and personal care products, though not commonly for perfluorinated
compounds. The properties of PFAA binding to albumins or other proteins may be
exploited to develop new passive sampling devices for these anionic contaminants.
Bioaccumulation measurement and modeling for persistent, ionic organic pollutants
A pollutant-specific study of biological accumulation processes, such as that for
PFAAs, provides insights into additional needs for bioaccumulation approaches for
emerging chemical contaminants. However, with approximately seven new chemicals
on the horizon each day in the United States alone, decisions to accept or reject
compounds for widespread use require rapid analytical approaches. New screening
methods are especially necessary for compounds that fall outside the range of
predictability of log KOW-bioconcentration relationships (e.g., large, ionizable chemicals
that may metabolize or concentrate in specific organism body compartments). In the
present study, dialysis measurements were augmented by nanoESI as a rapid technique
to analyze small-molecule binding to proteins. However the nanoESI technique is
neither universally applicable nor fully physiologically relevant. Development of
additional techniques that rapidly capture relevant mechanisms of bioaccumulation is
needed.
7.3 Final Thoughts
For the design of our chemicals and engineered systems, the best models are those
that mimic natural systems. This applies at all scales and requires continuous attention
and adaptation. Full-cycle processes that return used water to the environment in
planned ways have potential to meet both human and ecosystem needs. Chemicals that
provide products and services to improve quality of life can be designed to readily
transform to benign products. As one of thousands of chemicals developed and utilized
169
for the betterment of society, perfluorooctanoate exemplifies the all too frequent story
of an unregulated chemical discovered to have negative environmental and human
health effects. Only due to cumulative results of incremental research, leading to public
concern and eventual collaborative industrial and governmental action, is the chemical
phased out of production. It is unfortunate that eliminating the use of one unsafe
chemical does not prevent substitution with another that is equally bad or worse. As
technologies evolve and new policies are developed, moving away from reactionary
measures towards preemptive approaches that limit widespread production and use of
potentially harmful chemicals, prior to their environmental detection, is imperative.
170
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