Deana
M.
Crumbling,
M.
S.

Deana
M.
Crumbling,
M.
S.

Technology
Innovation
Office
Technology
Innovation
Office
U.
S.
Environmental
Protection
Agency
U.
S.
Environmental
Protection
Agency
Washington,
D.
C.

Washington,
D.
C.

(
703)
603
(
703)
603­
0643
0643
crumbling.
deana@
epa.

crumbling.
deana@
epa.
gov
gov
224
224th
th
ACS
National
Meeting
ACS
National
Meeting
Principles
of
Environmental
Analysis:
Two
Decades
Later
Principles
of
Environmental
Analysis:
Two
Decades
Later
Boston,
MA
Boston,
MA
August
21,
2002
(
Handouts)

August
21,
2002
(
Handouts)

Environmental
Quality:

Environmental
Quality:

Closing
the
Decision
Closing
the
Decision­
Data
Loop
Data
Loop
2
USEPA
USEPA
Technology
Innovation
Office
Technology
Innovation
Office

Advocates
for
better
technologies
and
strategies
to
Advocates
for
better
technologies
and
strategies
to
clean
up
contaminated
sites:

clean
up
contaminated
sites:

 
Site
investigation/
characterization
Site
investigation/
characterization
»
How
to
exploit
benefits
of
field
analytical
methods??

How
to
exploit
benefits
of
field
analytical
methods??

 
Site
remediation
Site
remediation
 
Monitoring
during
or
after
remedial
action
Monitoring
during
or
after
remedial
action

Acts
as
an
Acts
as
an
agent
for
change
agent
for
change
and
and
bridge
bridge
 
Disseminates
others'
good
ideas
Disseminates
others'
good
ideas

Cleanup
Information
Website:
http://

Cleanup
Information
Website:
http://
cluin
cluin.
org
.
org
3
Data
Quality
Remedial
systems
optimization
Decision
quality/
uncertainty
mgt
Sound
Science
PBMS
DL
QL
Dynamic
Work
Plans
Field
Analytical
Methods
DQOs
Budgets
NELAC
QA
Risk
Assessment
IDQTF
education
States
Enforcement
contracting
Decision
Theory
Precautionary
principle
political
&

economic
constraints
Innovation
SAPs
QAPPs
Lab
Certification
4
Putting
all
the
Pieces
Together:

Manage
Decision
Uncertainty
5
OSWER
Mandate
for
"
Sound
Science"

OSWER
Mandate
for
"
Sound
Science"

Using
Using
SOUND
SCIENCE
SOUND
SCIENCE
in
the
cleanup
of
contaminated
sites
means
that
the
in
the
cleanup
of
contaminated
sites
means
that
the
the
scale
of
data
generation
and
interpretation
the
scale
of
data
generation
and
interpretation
must
closely
"
match"

must
closely
"
match"

the
scale
of
project
decisions
the
scale
of
project
decisions
being
based
on
that
data.

being
based
on
that
data.

Sound
science
also
means
managing
Sound
science
also
means
managing
uncertainty
uncertainty
since
an
since
an
exact
exact
match
usually
is
not
feasible.

match
usually
is
not
feasible.

The
current
environmental
data
quality
model
is
The
current
environmental
data
quality
model
is
inadequate
to
ensure
that
this
matching
occurs.

inadequate
to
ensure
that
this
matching
occurs.
6
Using
Experience
&
Knowledge
Using
Experience
&
Knowledge
Key
Features
of
Successful
Innovative
Projects:

Key
Features
of
Successful
Innovative
Projects:


Project
Project­
specific
specific
planning
planning
(
vs.
rote
process)

(
vs.
rote
process)


Multidisciplinary
team
Multidisciplinary
team

Stakeholders
involved
Stakeholders
involved

Created
opportunities
Created
opportunities
for
real
for
real­
time
decision
time
decision­
making
making
that
saved
significant
time
and
$$

that
saved
significant
time
and
$$


Real
Real­
time
decisions
need
real
time
decisions
need
real­
time
data:

time
data:
flexibility
flexibility
to
to
select
and
modify
methods
according
to
select
and
modify
methods
according
to
actual
actual
decision
making
needs
decision
making
needs

Project
Project­
specific
Conceptual
Site
Model
specific
Conceptual
Site
Model

to
identify
data
gaps
to
identify
data
gaps

evolve
in
real
evolve
in
real­
time
w/
dynamic
work
plans
time
w/
dynamic
work
plans
7

The
"
Triad
Approach"
articulates
a
paradigm
to
The
"
Triad
Approach"
articulates
a
paradigm
to
institutionalize
institutionalize
decision
decision
uncertainty
management
as
uncertainty
management
as
applied
to
contaminated
sites
applied
to
contaminated
sites

Triad
Approach
Triad
Approach
=
Integrates
=
Integrates
systematic
planning
systematic
planning,

,

dynamic
work
plans
dynamic
work
plans,
and
,
and
real
real­
time
analysis
time
analysis
to
to

time
&
costs
time
&
costs
and
and

decision
certainty
decision
certainty

Theme
for
the
Triad
Approach
=

Theme
for
the
Triad
Approach
=
Explicitly
identify
and
Explicitly
identify
and
manage
the
largest
sources
of
decision
error,
especially
the
manage
the
largest
sources
of
decision
error,
especially
the
sampling
sampling
representativeness
of
data
representativeness
of
data

Holistic
integration
of
innovative
data
generation
and
Holistic
integration
of
innovative
data
generation
and
interpretation
tools
interpretation
tools
The
Triad
as
a
Systems
Framework
The
Triad
as
a
Systems
Framework
8
The
Current
Environmental
The
Current
Environmental
Data
Quality
Model
Data
Quality
Model
Hinders
Decision
Quality
Hinders
Decision
Quality
9
The
SYSTEM
functions
as
if
it
The
SYSTEM
functions
as
if
it
believes
that 

believes
that 

Screening
Methods
Screening
Data
Uncertain
Decisions
"
Definitive"

Methods
"
Definitive"

Data
Certain
Decisions
Methods
=
Data
=
Decisions
Distinguish:

Distinguish:

Analytical
Methods
Analytical
Methods
from
from
Data
Data
from
from
Decisions
Decisions
10
Distinguishing
Concepts
Distinguishing
Concepts
Analytical
Methods
Overall
Data
Quality
Decision
Quality
Manage
Uncertainty
in
Decision
Making
Clarify
Assumptions
Draw
Conclusions
Representative
Sampling
Manage
Uncertainty
in
Data
Generation
Data
Assessment/

Analytical
Integrity
Non­
scientific
considerations
Method
Modifications
Method
Selection
Analytical
Quality
for
Samples
11
First
Generation
Data
Quality
Model
First
Generation
Data
Quality
Model
Assumptions
Assumptions

"
Data
quality"
depends
on
analytical
methods
Data
quality"
depends
on
analytical
methods

Using
regulator
Using
regulator­
approved
methods
ensures
"
definitive
approved
methods
ensures
"
definitive
data"

data"


QC
checks
that
use
ideal
matrices
are
representative
of
QC
checks
that
use
ideal
matrices
are
representative
of
method
performance
for
real
method
performance
for
real­
world
samples
world
samples

Laboratory
QA
is
substitutable
for
project
QA
Laboratory
QA
is
substitutable
for
project
QA

One
One­
size
size­
fits
fits­
all
methods
eliminate
the
need
for
all
methods
eliminate
the
need
for
analytical
chemistry
expertise
analytical
chemistry
expertise
12
Non
Non­
Representative
Representative
Sample
Sample
Perfect
Perfect
Analytical
Analytical
Chemistry
Chemistry
+

"
BAD"
DATA
BAD"
DATA
Distinguish:

Distinguish:

Analytical
Quality
Analytical
Quality
from
from
Data
Quality
Data
Quality
Reality:
Data
used
for
Project
Decision
Reality:
Data
used
for
Project
Decision
Making
is
Generated
on
Making
is
Generated
on
Samples
Samples
13
The
Data
Quality
"
Chain"

The
Data
Quality
"
Chain"

Sampling
Sampling
Analysis
Analysis
Sample
Support
Sampling
Design
Sample
Preservation
Sub­
Sampling
Sample
Preparation
Method(
s)
Determinative
Method(
s)

D
E
C
I
S
I
O
N
Goal
Result
Reporting
Making
D
E
C
I
S
I
O
N
Extract
Cleanup
Method(
s)

All
links
in
the
Data
Quality
chain
must
be
intact
for
Decision
Quality
to
be
supported
!

e.
g.,
Method
8270
14
#
1
#
1
#
2
#
2
#
3
#
3
The
decision
driving
sample
collection:

Assess
contamination
resulting
from
atmospheric
deposition
Sample
Support:
Critical
to
Representativeness
Sample
Volume
&
Orientation
Given
that
the
dark
surface
layer
is
the
soil
layer
impacted
by
atmospheric
deposition
relevant
to
this
project:

Which
sample
support
(
the
white
areas:
#
1,
#
2,
or
#
3)

provides
a
sample
that
is
representative
of
atmospheric
deposition
for
this
site?
15
Sample
Support:
Critical
to
Representativeness
Sample
Volume
&
the
"
Nugget
Effect"

Project
managers
seldom
(
if
ever)
ask
whether
the
volume
of
the
sample
being
collected
can
alter
the
concentration
of
contaminants
in
that
sample.

In
other
words,
it
is
a
working
assumption
that
contaminants
are
homogeneously
distributed
at
the
scale
of
the
sample.

Is
this
a
reasonable
assumption
for
contaminants
released
and
transported
by
heterogeneous
mechanisms,
and
that
preferentially
partition
to
minerals
and
organic
carbon
that
are
themselves
heterogeneously
distributed?
16
Same
contaminant
mass
in
nugget,
but
different
sample
volumes
produce
different
concentrations.

Sample
Prep
Sample
Support:
Critical
to
Representativeness
Sample
Volume
&
the
"
Nugget
Effect"

Will
a
certain
sample
volume
detect
nugget­
like
contamination?
If
yes,
what
will
the
concentration
be?
17
Sample
Location
~
95%
Analytical
(
between
methods)

Analytical
(
between
methods)
~
5%

5%

Example
of
Variability:

Example
of
Variability:

Sample
Location
vs.
Analytical
Method
Sample
Location
vs.
Analytical
Method
39,800
On­
site
41,400
Lab
500
On­
site
416
Lab
164
On­
site
136
Lab
27,800
On­
site
42,800
Lab
24,400
On­
site
27,700
Lab
1,280
On­
site
1,220
Lab
1
2
7
6
3
4
5
331
On­
site
286
Lab
18
Partitioning
Data
Uncertainty
Partitioning
Data
Uncertainty
Std
Dev
Std
Dev
Sampling
Sampling
:
Std
Dev
Std
Dev
Analytical
Analytical
=
Samp
Samp:
Anal
Anal
Ratio
Ratio
Example
using
a
Example
using
a
Brownfields
Brownfields
project
data
set
project
data
set
(
scrap
yard
site
with
contaminated
soil)

(
scrap
yard
site
with
contaminated
soil)

Effect
of
general
Effect
of
general
env
env.
matrix
on
analytical
variability
for
B(
a)
P
.
matrix
on
analytical
variability
for
B(
a)
P
Using
LCS
data
Using
LCS
data
(
no
matrix
effect)

(
no
matrix
effect)
:
6,520
6,520
:
4.4
4.4
=
1464
1464
:
1
Using
MS/
MSD
data
Using
MS/
MSD
data
(
matrix
(
matrix
incl'd
incl'd)
:
6,520
6,520
:
12.7
12.7
=
513
513
:
1
Different
metals
Different
metals
(
LCS
data
used
to
estimate
analytical
variability)

(
LCS
data
used
to
estimate
analytical
variability)

High
spatial
variability,

High
spatial
variability,
Pb
Pb
3255
3255
:
3
=
1085
1085
:
1
Natural
background
present,

Natural
background
present,
As
As
22.4
22.4
:
7
=
3
:
1
19
What
is
"
Data
Quality"?

What
is
"
Data
Quality"?

Data
Quality
=
The
ability
of
data
to
Data
Quality
=
The
ability
of
data
to
provide
information
that
meets
user
needs
provide
information
that
meets
user
needs

Users
need
to
make
correct
decisions
Users
need
to
make
correct
decisions

Data
quality
is
a
function
of
data's 

Data
quality
is
a
function
of
data's 

 
ability
to
ability
to
represent
represent
the
"
true
state"

the
"
true
state"
in
the
context
in
the
context
of
the
decision
to
be
made
of
the
decision
to
be
made
»
The
decision
defines
the
scale
for
the
"
true
state"

The
decision
defines
the
scale
for
the
"
true
state"

 
information
content
information
content
(
including
its
uncertainty)

(
including
its
uncertainty)
20
Data
Quality
Data
Quality
+

Representative
Representative
Sampling
Sampling
Representative
Representative
Analysis
Analysis
(
sample
selection
and
sample
integrity)
(
method
selection
and
(
method
selection
and
method
integrity)

method
integrity)

Collection
technique
Sample
Support
Design
Preservation
Holding
time
Expertise
Lab
integrity/
Lab
QC
Sample
behavior
21
Second
Generation
Data
Quality
Model
Second
Generation
Data
Quality
Model
Scientific
Foundation
Scientific
Foundation

"
Data
quality"
=
data's
ability
to
support
Data
quality"
=
data's
ability
to
support
decisions
decisions

Anything
that
compromises
data
representativeness
Anything
that
compromises
data
representativeness
compromises
data
quality
compromises
data
quality

Data
representativeness
=
(#
1)
sampling
Data
representativeness
=
(#
1)
sampling
representativeness
+
(#
2)
analytical
representativeness
representativeness
+
(#
2)
analytical
representativeness

Project
Project­
specific
planning
critical
to
match
scale(
s)
of
data
specific
planning
critical
to
match
scale(
s)
of
data
generation
with
scale(
s)
of
decision
generation
with
scale(
s)
of
decision­
making.

making.


Technical
expertise
is
required
to
manage
data
Technical
expertise
is
required
to
manage
data
representativeness
representativeness
(
i.
e.,
assess,
control,
or
interpret
sampling
(
i.
e.,
assess,
control,
or
interpret
sampling
and
analytical
uncertainties
in
relation
to
the
decision)

and
analytical
uncertainties
in
relation
to
the
decision)
22
Institutionalize
Uncertainty
Institutionalize
Uncertainty
Management
Management
Using
Modern
Tools
Using
Modern
Tools
23
A
Systems
Approach
Framework
A
Systems
Approach
Framework
The
Triad
Approach
The
Triad
Approach
Systematic
Project
Planning
Dynamic
Work
Plan
Strategy
Real­
time
Measurement
Technologies
24
Unifying
Concept
for
Triad:

Unifying
Concept
for
Triad:

Managing
Uncertainty
Managing
Uncertainty

Manage
uncertainty
about
project
goals
Manage
uncertainty
about
project
goals
 
Identify
decision
goals
with
tolerable
overall
uncertainty
Identify
decision
goals
with
tolerable
overall
uncertainty
 
Identify
major
uncertainties
(
cause
intolerable
decision
error)

Identify
major
uncertainties
(
cause
intolerable
decision
error)

 
Identify
the
strategies
to
manage
each
major
uncertainty
Identify
the
strategies
to
manage
each
major
uncertainty

Manage
uncertainty
in
data
Manage
uncertainty
in
data
 
Sampling
uncertainty:

Sampling
uncertainty:
manage
sample
representativeness
manage
sample
representativeness
 
Analytical
uncertainty:

Analytical
uncertainty:
especially
if
field
methods
are
used
especially
if
field
methods
are
used

Multidisciplinary
expertise
critical
Multidisciplinary
expertise
critical
 
A
TEAM
A
TEAM
is
the
best
way
to
bring
needed
knowledge
to
bear
is
the
best
way
to
bring
needed
knowledge
to
bear
Systematic
planning
is
used
to
proactively 

Systematic
planning
is
used
to
proactively 
Will
the
Real
"
Screening
Data"
Please
Stand
Up?

Will
the
Real
"
Screening
Data"
Please
Stand
Up?

Costly
definitive
analytical
methods
Cheaper/
screening
analytical
methods
High
spatial
density
Low
DL
+
analyte
specificity
Manages
analytical
uncertainty
=
analytical
representativeness
=
analytical
quality
(
usability)

Definitive
analytical
quality
Screening
sampling
quality
Manages
sampling
uncertainty
=
sampling
representativeness
=
sampling
quality
(
usability)

Definitive
sampling
quality
Screening
analytical
quality
Marrying
Analytical
Methods
to
Make
Sound
Decisions
Marrying
Analytical
Methods
to
Make
Sound
Decisions
Involving
Heterogeneous
Matrices
Involving
Heterogeneous
Matrices
Costly
definitive
analytical
methods
Cheaper/
screening
analytical
methods
High
spatial
density
Low
DL
+
analyte
specificity
Manages
analytical
uncertainty
Manages
sampling
uncertainty
Collaborative
Data
Sets
Costly
definitive
analytical
methods
Cheaper/
screening
analytical
methods
High
spatial
density
Low
DL
+
analyte
specificity
Manages
analytical
uncertainty
Manages
sampling
uncertainty
Reliable
(
yet
Cost­
Effective)
Scientifically
Defensible
Decisions
Together

Decision
Quality
Data
"
Good"
Data
Quality
is
Achieved
when
ALL
Relevant
Good"
Data
Quality
is
Achieved
when
ALL
Relevant
Uncertainties
are
Managed
Uncertainties
are
Managed
28
Estimating
Respective
Uncertainties
Estimating
Respective
Uncertainties
¢
¢
¢
¢
¢
¢
¢
¢
¢
¢
¢
¢
¢
¢
¢
¢
¢
¢
¢
¢
¢
¢
¢
¢
¢
¢
¢
¢
¢
¢
¢
¢
¢
¢
¢
¢
$
$
$

$
$
$
$
$
$

$
$
$

Ex
1
Ex
2
Ex
3
Fixed
Lab
Analytical
Uncertainty
Sampling
Uncertainty
Ex
1
Sampling
Uncertainty
Controlled
through
Increased
Density
Field
Analytical
Data
Ex
2
Fixed
Lab
Data
Decreased
Sampling
Variability
after
Removal
of
Hotspots
Ex
3
Remove
hot
spots
To
This
From
This
29
Sample
Representativeness
is
Key!

Sample
Representativeness
is
Key!


Cheaper
analytical
technologies
permit
Cheaper
analytical
technologies
permit
increased
sample
increased
sample
density
density..


Real
Real­
time
measurements
support
time
measurements
support
real
real­
time
decision
time
decision­

making
making..

 
Rapid
feedback
for
course
correction
Rapid
feedback
for
course
correction

smarter
sampling
smarter
sampling
 
New
software
New
software
pkg's
pkg's
available
for
statistical/

available
for
statistical/
geostatistical
geostatistical
decision
support
decision
support

Focus
on
Focus
on
overall
data
uncertainty:

overall
data
uncertainty:
analytical
uncertainty
analytical
uncertainty
is
often
a
relatively
small
fraction
is
often
a
relatively
small
fraction..

Finally
Finally
able
to
address
this
issue
able
to
address
this
issue
defensibly
and
affordably!

defensibly
and
affordably!
30
Case
Study:
Wenatchee
Tree
Fruit
Site
Case
Study:
Wenatchee
Tree
Fruit
Site

Pesticide
IA
kits
guide
dynamic
work
plan:
remove
Pesticide
IA
kits
guide
dynamic
work
plan:
remove
and
segregate
contaminated
soil
for
disposal
and
segregate
contaminated
soil
for
disposal
230
230
IA
analyses
IA
analyses
(
w/
thorough
QC)

(
w/
thorough
QC)
+
29
29
fixed
fixed­
lab
lab
samples
for
33
analytes
samples
for
33
analytes
Managed
Managed
sampling
uncertainty
sampling
uncertainty:

:

achieved
very
high
confidence
that
achieved
very
high
confidence
that
all
contamination
above
action
all
contamination
above
action
levels
was
located
and
removed
levels
was
located
and
removed
Managed
Managed
field
analytical
field
analytical
uncertainty
uncertainty
as
additional
QC
on
as
additional
QC
on
critical
samples:
confirmed
&

critical
samples:
confirmed
&

perfected
field
kit
action
levels)

perfected
field
kit
action
levels)


Clean
closure
data
set
Clean
closure
data
set
 
33
fixed
lab
samples
for
analyte
33
fixed
lab
samples
for
analyte­
specific
pesticide
analysis
specific
pesticide
analysis
 
Demonstrate
Demonstrate
full
full
compliance
with
compliance
with
all
all
regulatory
requirements
for
regulatory
requirements
for
all
all
33
pesticide
analytes
to
>
95%
statistical
confidence
33
pesticide
analytes
to
>
95%
statistical
confidence
the
first
time
the
first
time!


Projected
cost:
~$
1.2M;
Actual:
$
589K
(
Save
~
50%)

Projected
cost:
~$
1.2M;
Actual:
$
589K
(
Save
~
50%)


Field
work
completed:
<
4
months;
single
mobilization
Field
work
completed:
<
4
months;
single
mobilization
http://

http://
cluin
cluin.
org/
char1_

.
org/
char1_
edu
edu..
cfm
cfm#
site_
char
#
site_
char
31
The
Challenge
The
Challenge
Can
Regulatory
Practice
Keep
Can
Regulatory
Practice
Keep
Pace
with
Evolving
Science?

Pace
with
Evolving
Science?
32
Disconnect
Lagging
Practices
Reality
Reality
Perceived
Perceived
reality
reality
Institutionalized
Institutionalized
Procedures,

Procedures,

Program
Guidance
Program
Guidance
Time
Time
Experience
&
investment
Experience
&
investment
in
R&
D
produce
in
R&
D
produce
°
Better
technology
tools
Better
technology
tools
°
More
experience
More
experience
°
More
complete
knowledge
More
complete
knowledge
°
Better
models
Better
models
~
1980
~
1980
Present
Practice
Based
on
Sound
Science
Evolving
from
First
Approximations
Evolving
from
First
Approximations
33
Perceived
Perceived
reality
reality
Institutionalized
Procedures,

Institutionalized
Procedures,

Program
Guidance
Program
Guidance
Realign
by
controlled,

gradual
transition
Reality
Reality
Practice
Based
on
Sound
Science
Second
Generation
Practices:
Option
1
Second
Generation
Practices:
Option
1
34
Perceived
Perceived
reality
reality
Institutionalized
Procedures,

Institutionalized
Procedures,

Program
Guidance
Program
Guidance
Reality
Reality
Practice
Based
on
Sound
Science
Second
Generation
Practices:
Option
2
Second
Generation
Practices:
Option
2
vs.

incremental
improvements
that
continually
lag
behind
35
First
Transition
Steps
First
Transition
Steps

Articulate
an
overall
vision
and
strategy
to
modernize
site
Articulate
an
overall
vision
and
strategy
to
modernize
site
cleanup
activities
and
programs
cleanup
activities
and
programs
 
View
Triad
pilot
projects
as
View
Triad
pilot
projects
as
both
both
teaching
and
learning
tools:

teaching
and
learning
tools:

perfect
scientific
best
practice
1st,

perfect
scientific
best
practice
1st,
then
then
write
technical
guidance
write
technical
guidance

Revise
and
clarify
the
data
quality
model
to
match
current
Revise
and
clarify
the
data
quality
model
to
match
current
scientific
understanding
scientific
understanding
 
Use
intuitive
terminology
that
avoids
misconceptions,
and
Use
intuitive
terminology
that
avoids
misconceptions,
and
clarifies
and
enlightens
(
rather
than
obscures)
concepts
clarifies
and
enlightens
(
rather
than
obscures)
concepts
 
Conceptually
link
data
quality
to
managing
decision
uncertainty
Conceptually
link
data
quality
to
managing
decision
uncertainty
 
Retool
common
phrasing.
Example:
"
Define
the
nature
and
Retool
common
phrasing.
Example:
"
Define
the
nature
and
extent
of
contamination
extent
of
contamination
at
the
scale
of
decision
at
the
scale
of
decision­
making
making"


Educate
about
uncertainty
management
(
decisions
&
data)

Educate
about
uncertainty
management
(
decisions
&
data)


Explicitly
support
multi
Explicitly
support
multi­
disciplinary
project
teams
disciplinary
project
teams
36
TIO
Efforts
to
Provide
Support
TIO
Efforts
to
Provide
Support

Public
outreach:
published
articles
Public
outreach:
published
articles
 
ES&
T
feature
article
(
Oct
2001)

ES&
T
feature
article
(
Oct
2001)


"
PM's
Handbook
of
Technical
Best
Practices
to
Implement
"
PM's
Handbook
of
Technical
Best
Practices
to
Implement
the
Triad
Approach"
(
in
development)

the
Triad
Approach"
(
in
development)

 
Hyper
Hyper­
linked
Internet
linked
Internet­
based
"
how
based
"
how­
to"
map
to
existing
guidance
and
to"
map
to
existing
guidance
and
technical
technical
information
supporting
Triad
implementation
information
supporting
Triad
implementation
 
Designed
to
evolve
and
incorporate
new
ideas
as
practitioner
and
Designed
to
evolve
and
incorporate
new
ideas
as
practitioner
and
programmatic
experience
grows
programmatic
experience
grows
»
Serve
as
hub
to
capture
and
disseminate
institutional
knowledge,

Serve
as
hub
to
capture
and
disseminate
institutional
knowledge,
lessons
lessons
learned,
and
successful
innovation
learned,
and
successful
innovation

"
Triad
Campaign"
Partners:

"
Triad
Campaign"
Partners:

 
US
Army
Corps
of
Engineers
(
Handbook
partner,
practitioner
outre
US
Army
Corps
of
Engineers
(
Handbook
partner,
practitioner
outreach)

ach)

 
Argonne
Argonne
National
Lab
(
technical
&
practitioner
expert,
university
outre
National
Lab
(
technical
&
practitioner
expert,
university
outreach)

ach)

 
ITRC
Sampling
Characterization
and
Monitoring
Team
(
State
outrea
ITRC
Sampling
Characterization
and
Monitoring
Team
(
State
outreach)

ch)

 
and
more 

and
more 
37
The
Diffusion
of
Innovation
The
Diffusion
of
Innovation
"
At
first
people
At
first
people
refuse
to
believe
refuse
to
believe
that
a
strange
that
a
strange
new
thing
can
be
done;

new
thing
can
be
done;

­­

­­
then
they
begin
to
then
they
begin
to
hope
hope
it
can
be
done;

it
can
be
done;

­­

­­
then
it
is
done
and
all
the
world
wonders
why
it
then
it
is
done
and
all
the
world
wonders
why
it
was
not
done
centuries
ago."

was
not
done
centuries
ago."

 
Francis
Hodges
Burnett
Francis
Hodges
Burnett
