1. Regulator’s Perspective John Fitzgerald Massachusetts Department of Environmental Protection

2. Top 3 complaints of MADEP regulators l Insufficient site characterization

3. Level of Effort Level of Certainty Diminishing Returns Risk & Uncertainty Continuum Regulatory “Battle Ground” contam tox/mobility site complexity receptor sensitivity

5. Reality… Site Characterization Consulting fees Cleanup(?) Legal fees Finite $ for all expenses!

6. We must get the most Bang for the Buck We must do a better job within budgetary constraints….

7. We must ask ourselves… what the heck are we doing?

8. 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 ?

9. It can be done….affordably Conceptual Site Model Dynamic Work Plans Analytical Hierarchy

10. Screening Analysis Decision Quality Data Optimally using all tools in the tool box

11. Analytical Hierarchy Screening Analysis Decision Quality Data Support representativeness & completeness of “lab” data expand base of data used to make decisions Screening Analysis can perform two functions:

12. PARCCS: –Precision & Accuracy –Representativeness –Comparability –Completeness –Sensitivity Data Usability (“lab” or “screening”) Screening data can play key supporting role

13. Level of Effort Level of Certainty ….Its possible to significantly increase amount and/or representativeness of site data using combination of screening and “lab quality” techniques Cost For the same amount of money…

14. Revolutionary ideas ? Change is hard….

15. 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

16. Details…. Disclaimers…

17. Personal Biases & Perspective… Data ReviewerData Generator Policy Wonk Unabashed cheerleader.. …and wearer of many hats…

18. Step 1: Examine and select the right analytical tools from the tool box 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? Making this work…

19. Step 2: Developing guidelines, SOPs and/or templates for the most common situations most common contaminants most common screening techniques most problematic contaminants

20. Petroleum Releases: We’re #1 Most Common Contaminant 1500 reported spills/year in Massachusetts 75% of contaminated sites in Massachusetts

21. Most Problematic Contaminants Chlorinated Solvents (Groundwater) Heavy Metals (soils)

22. Most Common Screening Techniques PID/FID Meters Gas Chromatographs XRF

23. Other Screening Techniques l Immunoassay Test Kits l UV Fluorescence/Absorbance l Emulsion-based TPH methods

24. Lowly PID/FID meter…. Use 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 How? (i) MADEP #WSC-94-400 (jar hdspace) (ii) MADEP Draft VPH/EPH Policy (6/01)

25. Lowly PID/FID meter…. P/A/R +/- 20% agreement expected for jar headspace duplicates; accuracy function of contam & matrix. Quick & simple testing technique allows for generation of large data set QA/QC Check calibration 1/10 samples (i) Variable responses between PID models; occasional erratic operations C/C

26. Lowly PID/FID meter…. 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) S1 ppmV air; via headspace: 10’s  g/L aqueous; 0.1 mg/kg soil +/-

27. 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….

28. “Field” Gas Chromatograph Use 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 How?No universal SOPs

29. “Field” Gas Chromatograph P/A/R Matrix/sample preparation technique dependent. QA/QC Min 3 point calibration curve; blanks and mid-level calibration check standard every 10 samples or daily

30. “Field” Gas Chromatograph Variability because of lack of standardization (e.g. calibration) C/C S 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)

31. “Field” Gas Chromatograph Biases Dependent upon assumptions; need to design for positive bias Subject to interferences and positive biases like any GC method; soil headspace data order-of- magnitude at best

32. “Field” Gas Chromatograph @ MADEP MADEP workhorse for site invest of most problematic VOCs: chlorinated VOCs and gasoline GC/PID/dry-ELCD (headspace): Systematic, periodic “split” samples taken for conventional analyses; almost always within 30%

33. 3-D plume delineation Infiltration Dissolved plume fresh water lens well groundwater flow “Field” Gas Chromatograph @ MADEP

34. Plume tracked 4400 feet back from well field Up to 77,000  g/L TCE detected at location of former machine shop “Field” Gas Chromatograph @ MADEP PLUME TRACKING

35. XRF (X-Ray Fluorescene) Use 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 How?EPA Method 6200

36. XRF P/A/R 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 QA/QCCalibration verification (+/- 20% of NIST standard) and blanks 1/20 samples

37. XRF Prepared samples have produced excellent correlation with AA/ICP data C/C SSoil: 10’s of mg/kg Biases Can be positive or negative, depending upon a number of factors, including interference from other metals.

38. 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.

39. Northbridge, MA (1997) Manufacturer’s Literature Lead

40. MADEP Data (Amesbury, MA, 1999)

42. Recommended degree of “confirmation” by definitive methods Not a stand-alone data set PID/FID Meters GC hdspce Screening 10% - 20% for aqueous samples if good correlation XRF 5-10% for soil if good correlation

43. Barriers Inertia Concern over qualification of “field screeners” Lack of standard/accepted protocols & guidelines One more thing for a generalist to learn about…

44. 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!