RECENT ADVANCES IN OUR UNDERSTANDING OF SEDIMENTTOWATER CONTAMINANT FLUXES: THE SOLUBLE RELEASE FRAC

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RECENT ADVANCES IN OUR UNDERSTANDING OF
SEDIMENT-TO-WATER CONTAMINANT FLUXESTHE SOLUBLE
RELEASE FRACTION

• Louis J. Thibodeaux, Jesse Coates Professor
• Gordon A. and Mary Cain Department of Chemical
  Engineering, Louisiana State University, Baton
  Rouge, LA
• Acknowledgements Michael Erickson of Blasland,
  Bouck Lee, Inc. and Carrie Turner of
  Limno-Tech, Inc.
• A keynote presentation at 5th Int. Symp. On
  Sediment Quality Assessment of Aquatic Ecosystems
  and Public Health. Oct. 16-18, 2002. Chicago, IL.
  

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INTRODUCTION and OUTLINE

• The sediment bed chemical release process is a
  key factor effecting water quality.
• Getting the process correct is needed for
  confident forecasting.
• Water quality models are undergoing
  reformulation and restructuring of both particle
  and soluble processes plus procedural changes in
  the calibration hierarchy.
• Focus of this presentation is on the soluble
  release fraction.

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INTRODUCTION and OUTLINE continued

• After covering the definitions and origins of the
  soluble release concepts some laboratory and
  field data will be presented followed by a
  ranking of the likely theoretical suspects to
  explain it mechanism.
• A coupled bioturbation driven, water-side
  boundary regulated process is offered as the
  likely mechanism.
• Field data from the Hudson River Thompson Island
  Pool(TIP) will be presented in some detail.
• Closure will cover model successes, outstanding
  uncertainties and needs for further
  investigations.

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REVIEW OF THE PARTICLE RESUSPENSION PROCESSIt
occurs during storm events, primarily. The
easily erodable material is quickly suspended and
the bed surface becomes armored. Little more if
any further erosion of the surface occurs even if
the storm persist for a long time-period.
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FAILURE OF THE PARTICLE RESUSPENSION MODEL AT LOW
FLOWS

• An early model hypothesis since the hydrophobic
  organic chemicals(HOCs) are strongly bound to
  solids only the particles need to be tracked in
  the system.
• Soluble release was included as a bed-side
  molecular diffusion process.
• No muddy water present and the total suspended
  solids (TSS) concentration were low. The chemical
  release remained significant.
• High flow events are few in number and endure
  for brief time-periods whereas the low flows
  endure for very long time-periods.

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THREE YEAR HUDSON RIVER DATA
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COMING TO GRIPS WITH THE NEW PROCESS

• During low-flow time-periods adjust the particle
  re-suspension model parameters using the total
  chemical concentration in the water column.
  (USGS,WDNR).
• Adopt a strict calibration hierarchy that
  decouples the particles and the chemical
  processes (Connolly. et al.).
• Change from one particle size to
  multiple(gt3)size classes including very fine
  ones(Ziegler, et al., USAE).
• Introduce the chemical dissolution theory rate
  equation to quantify the soluble fraction and
  obtain on-site data to quantify the mass-transfer
  coefficients(MTCs).

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DEFINITION AND MEASUREMENT OF THE SOLUBLE RELEASE
MTC
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FIELD MEASURED Kf VALUES(CM/DAY)

• Graphical data of measured Kf values vs. Julian
  day follow for the Grasse River, the Hudson River
  and the Kalamazoo.
• Notice that the Kf vs. J-day function for each
  river is unique as are the ranges of the
  numerical values.
• Water quality modelers input the Kf vs. J-day
  function to drive the MTC variability in the
  soluble release rate equation.

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GRASSE RIVER Kf
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HUDSON RIVER Kf
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KALAMAZOO RIVER Kf
RIVERINE
RIVERINE
IMPOUNDMENT
LAKES
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ANOTHER SURPRISE WAS THE SIZE OF THE SOLUBLE
FRACTION !
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THE USUAL SUSPECTS of THEORETICAL PROCESSES
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FEEDING TYPES OF BENTHIC ORGANISMS
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BIOTURBATION DEPTHS
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LABORATORY EXPERIMENTS WITH OLIGOCHAETES
Chemical Kd(L/Kg)
Kf(cm.day) Dibenzofuran 105
1.4 - 2.2 Phenanthrene
330 1.6 - 2.9 Trifluarlin
840 0.34 - 5.9 Pentachlorobenzen
e 1120 4.3 - 7.0 Pyrene
1230 3.3 -
6.2 Hexachlorobenxene 2240 6.8 -
8.9
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DETAILED PCB FIELD STUDY

• The Thompson Island pool, a six mile section on
  the Upper Hudson River.
• Chemistry on 12 congeners over a Koc range of
  log(4.40) to log (6.18).
• 512 observations on Kf.
• Data collected over a four year time
  period(1996-1999).
• Observations on Kf for clear water flows. This
  means those flow rates lt 10,000cfs and TSS in
  range of 1 to 10 mg/L.

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RAW Kf DATA
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SUMMARY OF LAB. AND FIELD EVIDENCE ON Kf

• Field values range from 1 to 100 cm/day.
• They increase in magnitude with increasing
  chemical hydrophobicity.
• Generally the numerical values higher for rivers
  than lakes(?).
• Each aquatic system has a a yet-to-be-fully
  explained unique annual cycle behavior pattern.

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THEORETICAL EQUATION FOR THE BIOTURBATION DRIVEN
PROCESS
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PROCESS CARTOON
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THEORETICAL BEHAVIOR WITH SOME OLD DATA
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THE BUTCHER/GARVEY PROCESS MODEL(20th SETAC
Conf.,1999)

• They observed the field measured Kf increased
  with increasing Koc of the congener.
• Proposed a simultaneous release model with a
  pore-water term and a particle release term.
• The contributions(Kpw) and(Kp) in the rate
  equation were linear and additive.
• It provided a reasonable correlation of the data
  but algorithm curvature was problematic and the
  rate equation was without theoretical support.

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TRANSPORT PARAMETERS-TIP DATA
REGRESSION DERIVED________________ Season
B Db RR
Linear RR
(cm/day) (cm2/day) ___________________
_________________________________ Early Spring
18.7 .0302 0.77
0.54 Spring 32.4
.0191 0.96 0.82 Summer
51.8 .00956 0.99
0.96 Fall 10.5
.00336 0.74
0.27 Winter 35.5
.00898 0.78 0.80
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SUMMARY OF THEORY AND PROPOSED MODEL

• Rate equation is one of chemical solubilization
  based of Ficks first law of diffusion.
• Model fabricated from existing, well known
  individual processes particle bioturbation, LEA
  at S/W interface and transport through waterside
  boundary layer.
• Transparent algorithm with algebraic coupling of
  transport coefficients(Db B) and thermodynamic
  parameter (KdKocfoc).

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SUMMARY OF CONSISTANCY BETWEEN MODEL AND DATA

• The thermodynamic functions are consistent Kf
  increases with increasing Koc and then flatten
  out.
• The extracted transport parameters, Db for
  particle biodiffusion and B for the water-side
  boundary layer, are in good agreement with
  literature reported values.

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UNCERTAINTIES

• Other benthic processes may explain the same
  data. For example gas generation and some macro
  fauna inject fine particles directly into the
  boundary layer.
• The cause of the annual cycle Kf behavior is
  unknown. Enhancing and attenuating factors
  include SAV emergence, bloom and die-off,
  bottom feeding on algae, sunlight and temperature
  on formation of algal mats, seasonal flow
  variations, ice cover, etc.

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CONCLUDING REMARKS

• Modelers must use the correct process mechanism
  and algorithm in order to make creditable
  long-term concentration predictions.
• The bioturbation driven process model explains
  many key observations.
• We are generally ignorant about many aspects of
  the chemical release processes in aquatic
  ecosystems.
• More lab. and field data is need alternative
  models are needed as well.
• We need to de-mystify the Kf annual cycle
  behavior patterns.