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Regulator’s Perspective. John Fitzgerald Massachusetts Department of Environmental Protection. Top 3 complaints of MADEP regulators. Insufficient site characterization. Insufficient site characterization. Insufficient site characterization. Risk & Uncertainty Continuum. Level of Certainty.

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regulator s perspective
Regulator’s Perspective

John Fitzgerald

Massachusetts Department of Environmental Protection

top 3 complaints of madep regulators
Top 3 complaints of MADEP regulators
  • Insufficient site characterization
  • Insufficient site characterization
  • Insufficient site characterization
slide3

Risk & Uncertainty Continuum

Level of Certainty

Diminishing Returns

Regulatory “Battle Ground”

contam tox/mobility site complexity receptor sensitivity

Level of Effort

reality
Reality…

Cleanup(?)

Site Characterization

Legal fees

Consulting fees

Finite $ for all expenses!

slide6

We must do a better job within budgetary constraints….

We must get the most Bang for the Buck

we must ask ourselves
We must ask ourselves…

what the heck are we doing?

what is the primary and over riding site characterization objective
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 ?

it can be done affordably
It can be done….affordably

Conceptual Site Model

Dynamic Work Plans

Analytical Hierarchy

slide10

Analytical Hierarchy

Decision Quality Data

Optimally using all tools in the tool box

Screening Analysis

slide11

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

parccs
PARCCS:

Data Usability

(“lab” or “screening”)

  • Precision & Accuracy
  • Representativeness
  • Comparability
  • Completeness
  • Sensitivity

Screening data can play key supporting role

slide13

….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

revolutionary ideas
Revolutionary ideas ?

Change is hard….

making this work
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

slide16

Disclaimers…

Details….

slide17

Personal Biases & Perspective…

Unabashed cheerleader..

…and wearer of many hats…

Policy Wonk

Data Reviewer

Data Generator

step 1 examine and select the right analytical tools from the tool box
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?

step 2 developing guidelines sops and or templates for the most common situations
Step 2: Developing guidelines, SOPs and/or templates for the most common situations

most common contaminants

most problematic contaminants

most common screening techniques

petroleum releases we re 1
Petroleum Releases: We’re #1

Most Common Contaminant

1500 reported spills/year in Massachusetts

75% of contaminated sites in Massachusetts

most problematic contaminants
Most Problematic Contaminants

Chlorinated Solvents

(Groundwater)

Heavy Metals (soils)

most common screening techniques
Most Common Screening Techniques

PID/FID Meters

Gas Chromatographs

XRF

other screening techniques
Other Screening Techniques
  • Immunoassay Test Kits
  • UV Fluorescence/Absorbance
  • Emulsion-based TPH methods
lowly pid fid meter
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?

lowly pid fid meter25
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

lowly pid fid meter26
Lowly PID/FID meter….

S

1 ppmV air; via headspace: 10’s g/L aqueous; 0.1 mg/kg soil +/-

Volatiles only; not qualitative. Low response if high moisture or total VOC > 150 ppmv petroleum. Assume 50% water headspace development; 1-2 orders magnitude partitioning soil/headspace. Less than 100 ppmV usually < 100 ug/g VOC (ii)

lowly pid fid meter27
Lowly PID/FID meter….

Codified as notification trigger (> 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….

field gas chromatograph
“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

field gas chromatograph29
“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

field gas chromatograph30
“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

field gas chromatograph31
“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

field gas chromatograph @ madep
“Field” Gas Chromatograph @ 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%

3 d plume delineation
3-D plume delineation

“Field” Gas Chromatograph @ MADEP

well

Infiltration

fresh water lens

groundwater flow

Dissolved plume

field gas chromatograph @ madep34
“Field” Gas Chromatograph @ 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

xrf x ray fluorescene
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

slide36
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

slide37
XRF

Prepared samples have produced excellent correlation with AA/ICP data

C/C

S

Soil: 10’s of mg/kg

Can be positive or negative, depending upon a number of factors, including interference from other metals.

Biases

slide38
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.

slide39

Manufacturer’s Literature

Lead

Northbridge, MA (1997)

recommended degree of confirmation by definitive methods
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

barriers
Barriers

Concern over qualification of “field screeners”

Inertia

Lack of standard/accepted protocols & guidelines

One more thing for a generalist to learn about…

conclusions
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!

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