Regulators Perspective

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Regulators Perspective
John Fitzgerald Massachusetts Department of
Environmental Protection
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Top 3 complaints of MADEP regulators

• Insufficient site characterization

• Insufficient site characterization

• Insufficient site characterization

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Risk Uncertainty Continuum
Level of Certainty
Diminishing Returns
Regulatory Battle Ground
contam tox/mobility site complexity
receptor sensitivity
Level of Effort
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Reality
Cleanup(?)
Site Characterization
Legal fees
Consulting fees
Finite for all expenses!
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We must do a better job within budgetary
constraints.
We must get the most Bang for the Buck
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We must ask ourselves
what the heck are we doing?
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What is the Primary and Over-riding Site
Characterization Objective?
To get the most accurate and precise data
possible ?
To ascertain levels of contaminants at sites
sufficient to make decisions on risk and
remediation ?
To ascertain levels of contaminants at sites
sufficient to make decisions on risk and
remediation ?
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It can be done.affordably
Conceptual Site Model
Dynamic Work Plans
Analytical Hierarchy
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Analytical Hierarchy
Decision Quality Data
Optimally using all tools in the tool box
Screening Analysis
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Analytical Hierarchy
Screening Analysis can perform two functions
expand base of data used to make decisions
Decision Quality Data
Support representativeness completeness of
lab data
Screening Analysis
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PARCCS
Data Usability
(lab or screening)

• Precision Accuracy
• Representativeness
• Comparability
• Completeness
• Sensitivity

Screening data can play key supporting role
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.Its possible to significantly increase amount
and/or representativeness of site data using
combination of screening and lab quality
techniques
For the same amount of money
Level of Certainty
Level of Effort
Cost
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Revolutionary ideas ?
Change is hard.
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Making this work
demystify analytical procedures and data
come to common understanding and level of comfort
on what/when/how to use screening techniques
(e.g., SOPs/Guidelines)
Key in (initially) on most common techniques and
applications for most common problems
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Disclaimers
Details.
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Personal Biases Perspective

Unabashed cheerleader..
and wearer of many hats
Policy Wonk
Data Reviewer
Data Generator
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Step 1 Examine and select the right analytical
tools from the tool box
Making this work
What will the tool be used for? Supporting or
decision quality data?
Is the tool selective and sensitive enough for
the job?
What are the biases and uncertainties?
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Step 2 Developing guidelines, SOPs and/or
templates for the most common situations
most common contaminants
most problematic contaminants
most common screening techniques
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Petroleum Releases Were 1
Most Common Contaminant
1500 reported spills/year in Massachusetts 75 of
contaminated sites in Massachusetts
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Most Problematic Contaminants

• Chlorinated Solvents
• (Groundwater)

Heavy Metals (soils)
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Most Common Screening Techniques
PID/FID Meters
Gas Chromatographs
XRF
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Other Screening Techniques

• Immunoassay Test Kits
• UV Fluorescence/Absorbance
• Emulsion-based TPH methods

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Lowly PID/FID meter.
Establish approx extent/distribution/ levels of
contamination in soil/gw/soil gas at sites contam
by gasoline, light petro VOCs. Support vehicle
only can not be used as decision quality data
Use
(i) MADEP WSC-94-400 (jar hdspace) (ii) MADEP
Draft VPH/EPH Policy (6/01)
How?
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Lowly PID/FID meter.
Check calibration 1/10 samples (i)
QA/QC
/- 20 agreement expected for jar headspace
duplicates accuracy function of contam matrix.
Quick simple testing technique allows for
generation of large data set
P/A/R
Variable responses between PID models occasional
erratic operations
C/C
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Lowly PID/FID meter.
S
1 ppmV air via headspace 10s ?g/L aqueous 0.1
mg/kg soil /-
Volatiles only not qualitative. Low response if
high moisture or total VOC gt 150 ppmv petroleum.
Assume 50 water headspace development 1-2
orders magnitude partitioning soil/headspace.
Less than 100 ppmV usually lt 100 ug/g VOC (ii)
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Lowly PID/FID meter.
Codified as notification trigger (gt 100ppmV) in
Massachusetts Contingency Plan since 1993
Finally achieved respect in 1999, after issuance
of MADEP soil VOC preservation policy, as way to
try to salvage unpreserved lab data.
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Field Gas Chromatograph
Semi qualitative/quantitative, for VOCs in
soil/gw/sg/indoor air, using techniques of
varying accuracy and precision. Used as PARCCS
support for EPA methods and, where supportable,
as part of site decision data
Use
How?
No universal SOPs
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Field Gas Chromatograph
Min 3 point calibration curve blanks and
mid-level calibration check standard every 10
samples or daily
QA/QC
Matrix/sample preparation technique dependent.
P/A/R
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Field Gas Chromatograph
Variability because of lack of standardization
(e.g. calibration)
C/C
Aqueous headspace low ?g/L range Soil gas/indoor
air 10-30 ppbV Soil low mg/kg
range Sensitivity and selectivity dependent upon
detector(s)
S
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Field Gas Chromatograph
Dependent upon assumptions need to design for
positive bias
Biases
Subject to interferences and positive biases like
any GC method soil headspace data
order-of-magnitude at best
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Field Gas Chromatograph _at_ MADEP
GC/PID/dry-ELCD (headspace)
MADEP workhorse for site invest of most
problematic VOCs chlorinated VOCs and gasoline
Systematic, periodic split samples taken for
conventional analyses almost always within 30
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3-D plume delineation
Field Gas Chromatograph _at_ MADEP
well
Infiltration
fresh water lens
groundwater flow
Dissolved plume
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Field Gas Chromatograph _at_ MADEP
Plume tracked 4400 feet back from well field
Up to 77,000 ?g/L TCE detected at location of
former machine shop
PLUME TRACKING
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XRF (X-Ray Fluorescene)
Semi qualitative/quantitative (simultaneous)
screening for multiple elements in soil. Used as
PARCCS support for EPA methods and, for certain
elements and/or with site-specific correlation,
as part of site decision data
Use
How?
EPA Method 6200
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XRF
QA/QC
Calibration verification (/- 20 of NIST
standard) and blanks 1/20 samples
Highly dependent upon sample and preparation
technique in-situ, bag, or cup. Dried, sieved,
grinded homogenized samples may be as good as
laboratory (AA/ICP) data
P/A/R
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XRF
Prepared samples have produced excellent
correlation with AA/ICP data
C/C
S
Soil 10s of mg/kg
Can be positive or negative, depending upon a
number of factors, including interference from
other metals.
Biases
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XRF
Subject to interferences from high moisture,
matrix effects (particle size distribution)
and presence of high conc of other elements
(e.g. lead and arsenic).
Degree of sample preparation dictates level of
achievable accuracy and precision.
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Manufacturers Literature
Lead
Northbridge, MA (1997)
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MADEP Data (Amesbury, MA, 1999)
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MADEP Data (Amesbury, MA, 1999)
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Recommended degree of confirmation by
definitive methods
PID/FID Meters
Not a stand-alone data set
10 - 20 for aqueous samples if good correlation
GC hdspce Screening
5-10 for soil if good correlation
XRF
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Barriers
Concern over qualification of field screeners
Inertia
Lack of standard/accepted protocols guidelines
One more thing for a generalist to learn about
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Conclusions.
Screening data can significantly improve the
effectiveness and cost-effectiveness of site
characterizations.
though we will always need to rely upon the
services of a faithful and trusted lab!