Regulator’s Perspective

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

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

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

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

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

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 ? 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. Analytical Hierarchy Decision Quality Data Optimally using all tools in the tool box Screening Analysis

11. 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

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

13. ….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

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. Disclaimers… Details….

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

18. 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?

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

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

24. 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?

25. 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

26. 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)

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 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

29. “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

30. “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

31. “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

32. “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%

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

34. “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

35. 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

36. 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

37. 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

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

40. MADEP Data (Amesbury, MA, 1999)

41. MADEP Data (Amesbury, MA, 1999)

42. 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

43. Barriers Concern over qualification of “field screeners” Inertia 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!