sediment and floodplain remediation: tools for site...

Sediment and Floodplain Remediation: Tools for Site Characterization

Presented by:

Tim Dekker, Joe DePinto, Noemi Barabas

Limno-Tech, Inc.

Ann Arbor, Michigan

Technology Benchmarking Workshop:Technology Benchmarking Workshop:

Sediment and Floodplain RemediationSediment and Floodplain Remediation

March 25March 25--26, 200426, 2004

Presentation Outline

• Goals of Site Characterization – Exposure and risk : Sources – pathways - receptors

– Remediation endpoints

• Site Characterization Framework– Screening investigations, IRAs

– Development of a conceptual model

– Refinement of conceptual model

• Tools for Site Characterization– Hydrology/Hydraulics/Hydrodynamics

– Solids

– Contaminants

– Biota

• Relevance to Selection of Remedial Alternatives


• Goals of Site Characterization– Exposure and risk : Sources – pathways - receptors






– Solids

– Contaminants

– Biota


Goals of Site Characterization

• Focusing on Exposure and Risk

– Site assessment should be risk-based (both human and ecological) rather than mass-based (EPA 11 principles for managing contaminated sediment risks)

– Understanding risk requires understanding of Sources Pathways Receptors

– Goal is to quantify and rank potential pathways for exposure / risk (i.e., build site-specific conceptual models)

Current Challenges to Quantitative Risk Assessment

• Need to develop a better mechanistic understanding of sediment and associated chemical stability– Cohesive sediment erosion– Non-resuspension sediment-water flux

• Need to better understand exchange of contaminants between bedded sediments and floodplain soils– Groundwater – surface water interactions– Floodplain deposition, redistribution

• Need to better quantify food web bioaccumulation– Effects of habitat and food web

structure/function

Sediments/soils

contaminants

biota

Goals of Site Characterization

• Remediation Endpoints

– Risk assessment must recognize bioavailability of contaminants of concern based on:

• Physico/chemical form,

• location relative to exposure pathways

• Quantitatively link loss of beneficial uses to risk pathways to exposure pathways








– Solids

– Contaminants

– Biota


EPA Guidance on CSM Development

ASTM Standard: E1689-95(2003)e1 Standard Guide for Developing Conceptual Site Models for Contaminated Sites

EPA Example CSM

sources pathways receptors

Development of a Conceptual Model

Water

Algae

Buried Sediment

Mixed Layer (~5-10cm)

Upstream Loading

UpstreamFlow

Runoff Loading TributariesAir-Water Exchange

Particle-boundchemical

Settling Resuspension


Burial

Partitioning

Dissolvedchemical

Partitioning

Benthos

Flow

Dispersion

AdvectionDiffusion

Diffusion

PorewaterFlow

PorewaterFlow

Dissolvedchemical

Chemical Decay or Biodegradation


Water

Algae

Buried Sediment


Upstream Loading

UpstreamFlow





Burial

Partitioning

Dissolvedchemical

Partitioning

Benthos

Flow

Dispersion

AdvectionDiffusion

Diffusion

PorewaterFlow

PorewaterFlow

Dissolvedchemical


Primary modes of attenuation:

settling/burial, resuspension/advection, chemical decay/biodegradation


Floodwater

Floodplain Soil

Mixed Layer (~cm)

Upstream Loading

UpstreamFlow

Tributaries

Air-Water Exchange




Burial

Flow

Dispersion

Flow Dispersion

River-floodplain exchange

Refinement of Conceptual Model

• Conceptual model is continually refined by:

– Hypothesis development

– Data gathering

– Hypothesis testing

• Spatial trends over relevant exposure areas

• Time trends over relevant exposure periods

• Time trends over periods of sufficient duration to show important system changes

Conceptual Models are Informed by Spatially and Temporally Appropriate Data

1985 1990 1995 2000 2005 2010 2015 202010

0

101

10 2

103

104

lipid

-no

rmal

ized

PC

B (m

g/kg

)

0 0.5 1 1.5

0

10

20

30

40

50

60

70

Dep

th (c

m)

Cs-137 Activ ity (pCi/g)

Cesium-137/PCB ProfileALG-1segment: 35

0 20 40PCB (mg/kg)

0 0.5 1 1.5

0

10

20

30

40

50

60

70

Dep

th (c

m)



0 20 40 60PCB (mg/kg)

Conceptual Models are Refined and Tested with Data from Numerous Sources

1990 1995 2000 2005 2010 2015 202010-4

10-3

10 -2

10 -1

10 0Water Column PCB Load

half time=4.12 years

1985 1990 1995 2000 2005 2010 2015 202010

-2

10-1

100

101

102 Lipid-normalized PCB

half time=6.40 years

1990 1995 2000 200510

0

101

102

103

TOC-normalized PCBhalf time=7.57 years

Cesium-137/PCB Profile

0 0.5 1 1.5

0

10

20

30

40

50

60

70

Cs-137 Activity (pCi/g)

0 20 40PCB (mg/kg)

half time =6.1 – 7.7 years

Integrating Data Sources and Their Uncertainties

Uncertainty target

Geostatistics

Targeted field sampling/analysis

Prior Information: Industry

Prior Information: NGOs

Prior Information: Zoning

First sampling: screening Presence/Absence

of contaminants

Prediction Uncertainty(Risk Maps)

Decision Making








– Solids

– Contaminants

– Biota


Flow and solids sampling

Cass: 14.2 cms

Saginaw: 114.5 cms

Flint: 21.0 cms

Shiawassee: 11.9 cms

Tittabawassee: 48.2 cms

Chippewa:12.0 cms

Upper Tittabawassee:~30 cms (est)

Pine:8.5 cms

Simple solids balancing

Water

Buried Sediment


Upstream Loading

35,787 MT

Net Settling and Burial24,332 MT

AdvectiveTransport17,321 MT

Biotic Loading5,253 MT

Tributary Loading613 MT

Sediment Characterization: Poling, Bathymetry

• Goals:– Gather basic data

to support hydraulic/hydro-dynamic analyses

– Develop a basic understanding of the character, dynamics, spatial variability of the sediment bed

Titawabassee River: Transect @ RM 23.00

0.0

2.0

4.0

6.0

8.0

10.0

12.0

14.0

0 40 80 120 160 200 240 280

Distance from North Bank (ft)

Dep

th (f

t)

Sand

SandSand

Sand

Sand

Sand


0.0

2.0

4.0

6.0

8.0

10.0

12.0

14.0

0 40 80 120 160 200 240


Dep

th (f

t)

Sand

Sand

Wood

Sand

Sand

Sand

Sand

Sand/Wood

Sand

SandSand/Wood


0.0

2.0

4.0

6.0

8.0

10.0

12.0

14.0

0 20 40 60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 360 380


Dep

th (f

t)

Sand

Clay

Sand

SandSandSand

Sand

Sand

Wood

Deposition measurements

http://mo.water.usgs.gov/indep/heimann/longbranch/images/Figure5b.gif

http://mo.water.usgs.gov/indep/heimann/longbranch/images/figure4.jpg

Feldspar clay pads/ plexiglass plates Sediment traps Dendrogeomorphic

measurements

Geochronological Investigations

0 0.5 1 1.5

0

10

20

30

40

50

60

70

Dep

th (c

m)



0 20 40

PCB (mg/kg)

0 0.5 1 1.5

0

10

20

30

40

50

60

70

Dep

th (c

m)



0 20 40 60

PCB (mg/kg)

kczcv

tc

b −∂∂

−=∂∂

Monitoring/measurement of bank erosion, retreat rate

• Physical observations of bank condition, vegetative cover can be used to infer erodibilityof banks

• Erosion pins or other survey techniques can be used to quantify bank retreat rates

Monitoring/measurement of groundwater/surface water interaction

• Temperature/conductivity probing can be used to detect gradients, indicate extent of GW/SW interaction

• Seepage meters can be used to measure GW/SW seep directly

Contaminant sampling – soils, sediments

• Sampling plan development– Considering exposure

pathways, key receptors

– Iterative, part of CSM refinement

• Methods– Phased analyses

– Geostatisticalconsiderations

– Screening-level analytical methods

Contaminant sampling – water columnFloat Study Longitudinal PCB Profile (July 2000) - C

0

10

20

30

40

50

25303540455055606570758085

River Mile

Wat

er C

olum

n PC

B (n

g/l)

Dates: 7/25/00 - 7/27/00 , Average Flow: 516 cfs Non-detect Detect Kalsim

Float Study Longitudinal PCB Profile (August 2000) - D

0

10

20

30

40

50

25303540455055606570758085

River Mile

Wat

er C

olum

n PC

B (n

g/l)

Dates: 8/30/00 - 8/31/00 , Average Flow: 580 cfs Data Non-detect Kalsim

Time trending analysis

1985 1990 1995 2000 2005 2010 2015 202010

0

101

102

103

104

lipid

-no

rmal

ized

PC

B (m

g/kg

)

half time = 6.40

Lipid-Normalized PCB Trend

1985 1990 1995 2000 2005 2010 2015 202010

-2

10-1

100

101

PCB

(mg/

kg) half time=4.67

1985 1990 1995 2000 2005 2010 2015 20200

1

2

3

4

5

% li

pid

cont

ent

Wet-weight PCB Trend

Lipid Trend

Spatial trending analysis

Polytopic Vector Analysis (PVA)

(borrow from noemi’s presentation)FATE:FATE:Dechlorination

shifts initial ratios

2378T/PCDD = 0.3

Sediment PCDD Patterns

SOURCES:SOURCES:linear mixing

0

2

+0

1

0

+-

ClCl

Cl

Cl

O

O

Mixed source pattern

00.5

1

Hx4Hx6Hx9 Hp Pe T

Some Congeners increase others

decrease

00.5

1

Hx4Hx6Hx9 Hp Pe T

Sample

2378T/PCDD = 0.6 Source patterns are modified by

reactive processes

Infer source distribution and relevant fate

processes...

From final distribution

Traditional PVA: used to model source patterns

Modified PVA, used to model reactive patterns in dioxins/furans

Numerical models (Tiers of modeling complexity)

• Tier 1: Empirical models of trends

• Tier 2: Attenuation rates, key process coefficients (development of conceptual model)

• Tier 3: Phenomenological and mass-balance modeling

• Tier 4: Mechanistic models of hydrodynamics, sediment transport, contaminant fate and transport

Empirical trend analysis and forecasting of future declines in bioavailability

based on water, sediment, and fish data.Ti

er 1

Calculation of key attenuation rates based on determination of process coefficients and forecasting from current sediment concentrations.

Tier

2

Phenomenological and mass balance modeling of sediment bed and overlying water columnTi

er 3

Integrated, mechanistic hydrodynamic, sediment transport, contaminant fate and transport modelsTi

er 4

Incr

easi

ng s

yste

m c

ompl

exit

y


Water

Algae

Buried Sediment


Upstream Loading

UpstreamFlow





Burial

Partitioning

Dissolvedchemical

Partitioning

Benthos

Flow

Dispersion

AdvectionDiffusion

Diffusion

Porewater Flow

Porewater Flow

Dissolvedchemical


Flow/Solids Sampling

Contaminant Sampling: Water Column, Sediments, Floodplain, TSS, Biota

GeochronologicalInvestigations

Geostatistical Tools

Mathematical/Numerical Models:

Statistical Trending Models (Tier 1)Simple Process Models (Tier 2)Mass-Balance Models (Tier 3)Mechanistic Fate and Transport Models (Tier 4)

Sediment/soil physical characterization (probing, PSD, TOC, etc)

Resuspension/Deposition Measurements (In-stream, floodplain)

Bathymetric Studies

Conceptual Site Model

Tools for Site Characterization - Summary

PCA, PVA








– Solids

– Contaminants

– Biota


• Evaluate remediation alts using realistic assumptions for:– Time to complete remediation

– Release during remediation

– Residual contamination

– Potential for recontamination

• Use risk-based approach for prioritization of areas within site:– Utilize spatial statistical methods for comparison

– Base remediation decisions on relative risk reduction over time

• Consideration of alternatives to dredging for sediment remediation– Capping – various materials, including active caps

– In-place remediation – decontamination/isolation/reduction of bioavailability

• Establish, quantify “natural recovery” as a reference for remediation decisions– Understand and quantify processes that contribute to natural

attenuation

How does site characterization impact selection of remedial options?

Biological data (fish)

0 5 100 1000200020 30 400 2 41980199020000

5

100

1000

200020

30

400

2

4

1980

1990

2000

Date Lipid WeightLength PCB

PCB

Weight

Length

Lipid

Date

Tier 1: Simple Statistical Trending Models

Explore for confounding factors in fish data

Tier 4:

Balancing model complexity and utility

Substantial Resources

Limited ResourcesU

tility

/ R

elia

bilit

y

A

B

Complexity / $$$

Optimum point for model development

A

BSubstantial Resources

Limited ResourcesU

tility

/ R

elia

bilit

y

A

B

Complexity / $$$

Optimum point for model development

A

B

A well-constrained model

• Observation of key processes and measurement of relevant rate coefficients. (previous presentation on Element 2: Fate and Transport Processes).

• Calibration to long-term trends, and model validation (See previous papers on Elements 3 and 4)

• Sufficient understanding of the system to indicate that future conditions will be similar to conditions during model calibration, or a means for the model to account for changes. (Sometimes called “permanence” of model predictions)

• Model transparency (no “black box”)

• An understanding of major sources of uncertainty

Occam’s Razor

Occam’s Razor(liberally paraphrased)

Given the choice of multiple explanations, the simplest one is most likely correct.

If you don’t understand all of the model’s theory, or don’t have the data to describe model inputs, you’re probably “over the hump.”

Modeling translation:

sediment and floodplain remediation: tools for site...

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