Better Site Characterization through Incremental Sampling Methodology Mark Bruce Ph. D.

Transcription

1 Better Site Characterization through Incremental Sampling Methodology Mark Bruce Ph. D. 2014, TestAmerica Laboratories, Inc. All rights reserved. TestAmerica & Design are trademarks of TestAmerica Laboratories, Inc. October 29, 2014

2 Chasing Uncertainty Sources Instrumental analysis Sample preparation Laboratory sub-sampling Field sample collection 2

3 Does the decision unit fit in the sample jar? Representative subsampling 3

4 Why is this important? _ X with known and less uncertainty Representative subsampling 4

5 Cost Implications of ISM Lower remediation cost Lower envir. liability cost Lower measurement cost 5

6 Incremental Sampling Systematic Random Design Random starting locations in first grid Increment collection point 6

7 ITRC Guidance Incremental Sampling Methodology Team ~ ~ Formed Jan TestAmerica recruited by the US Army Corps of Engineers 7

8 Paint the Big Picture Plan Implement Assess 8

9 Focus on Planning Plan Implement Assess Develop Conceptual Site Model Identify Data Quality Objectives Consider end use of data Base decision on mean or UCL? Develop Decision Units 9 Determine increments & replicates

10 Implement the Plan Plan Implement Assess Collect field samples Process samples at laboratory 10

11 Assess the results Plan Implement Assess Are the data sufficient for decision making? Manage risk / remediate as needed 11

12 ISM Guidance Introduction Laboratory Process Case Studies ISM Principles Making Decisions Delineation Decisions 12 Systematic Planning Statistical ISM Design Field Implementation Regulatory Concerns Case Studies Stakeholder Input

13 Introduction ISM for Environmental Sampling Limitations of Traditional Approaches How ISM addresses these Limitations Comparing ISM to Compositing Frequently Asked Questions Document Organization Key Terms 13

14 ISM Principles Introduction Soil Heterogeneity Foundation Concepts of Sampling Sampling Considerations Gy Theory and the Source of Sampling Error Sampling Approaches ~ (Discrete, Composite, Incremental) 14

15 Systematic Planning Introduction Intent of Decision Units Decision Units (DU) Establishing new DUs Hot spots 15

16 Statistical ISM Design Estimating the Mean Concentration Uncertainty in Estimates of the Mean Evaluate Sampling Performance Areas for Further Study 16

17 Field Implementation Introduction Sampling Tools Field Collection Field Processing Options 17

18 Laboratory Processing & Analysis Introduction Laboratory Processing Laboratory Analysis Quality Assurance 18

19 Making Decisions Introduction Decision Mechanisms Error Assessment 19

20 Regulatory Concerns Introduction Perception Issues Regulatory Challenges State of Knowledge, Experience and Training Implementation Issues 20

21 Case Studies PCB Contaminated Landfill Petroleum Contaminated Soil Stockpile Former Golf Course Field Demonstration Hawaiian Homelands Development 21

22 Stakeholder Input Citizen, tribal, community, environmental advocate Find all contamination, facilitate cleanup Desire to understand characterization & cleanup plans Avoid averaging away hot spots Explain ISM approach early in the process 22

23 ISM Limitations Limitation Smaller number of replicates Effect Limits calculation methods for Upper Confidence Limit No spatial resolution within Decision Unit Limits remediation options within Decision Unit Limits multivariate comparisons 23

24 ISM Advantages Advantage Better spatial coverage Higher Sample Mass Optimized processing Fewer non-detects Effect Includes high & low concentrations in proper proportions Reduces errors associated with sample processing and analysis Representative subsamples for analysis Simplifies statistical analysis More consistent data More confident decision 24

25 ISM Guidance Introduction Laboratory Process Case Studies ISM Principles Making Decisions Delineation Decisions 25 Systematic Planning Statistical ISM Design Field Implementation Regulatory Concerns Case Studies Stakeholder Input

26 ISM Principles Nature of Soil & Interaction of Contaminants With Soil Results In 26

27 ISM Principles Heterogeneity Sampling w/o addressing leads to 27

28 ISM Principles Sampling Errors Manifested (observed) in 28

29 ISM Principles Unknown Data Variability which can lead to 29

30 ISM Principles Decision Errors 30

31 Representativeness Which is more likely to represent the true mean? Average from 3 discrete samples One 30-increment sample 50 ft 50 ft 50 ft 50 ft 31

32 Representative Sample A representative sample is one that contains a subset of all the constituents of a population in exactly the same proportion that they are present in the target population. Represents 32

33 ISM Guidance Introduction Laboratory Process Case Studies ISM Principles Making Decisions Delineation Decisions 33 Systematic Planning Statistical ISM Design Field Implementation Regulatory Concerns Case Studies Stakeholder Input

34 The Decision Unit: A Key Concept for ISM Decision Unit (DU) smallest volume of soil (or other media) for which a decision will be made based upon ISM sampling. A DU may consist of one or more Sampling Units (SUs). 34

35 Source Area DUs Above Ground Storage Tank Transformer Pad 35

36 Side Exposure Areas DUs Residential Yard Backyard DU-1 House Side 36 DU-2 Front yard DU-3

37 Combined DUs Future residential lots, DUs sized as exposure areas (EUs) Pesticide mixing area, DUs sized to assist remediation 37

38 Subsurface DUs Borings into DUs for increment collection Stacked spill area DUs 0 m -2 m -4 m -6 m DU-1 DU-2 DU-3 DU-4-8 m 38 Separate DU increments

39 Confirmation Sampling DUs Walls and Floor of an Excavation Site DU-2 DU-2 DU-3 DU-1 39

40 ISM Guidance Introduction Laboratory Process Case Studies ISM Principles Making Decisions Delineation Decisions 40 Systematic Planning Statistical ISM Design Field Implementation Regulatory Concerns Case Studies Stakeholder Input

41 How Many Increments? Depends on heterogeneity ~ Multiple distributions ~ Large nugget effect increments usually enough 3 replicates recommended 41

42 Single ISM result ~ Under estimate > 50% of the time Calculate the Upper Confidence Limit ~ Based on Student s t distribution UCL X Uncertainty in Estimates of the Mean t ( 1 )( r 1) S X r ~ Based on Chebyshev inequality UCL X 1/ 1 S X r 42

43 ISM Guidance Introduction Laboratory Process Case Studies ISM Principles Making Decisions Delineation Decisions 43 Systematic Planning Statistical ISM Design Field Implementation Regulatory Concerns Case Studies Stakeholder Input

44 44 Sampling Tools

45 45 Surface Samples

46 Collection Pattern Example: 60 increment triplicate 46

47 47 Subsurface Samples

48 48 Subsurface Sample: Whole Core

49 Sample Mass M s = n D s ( / 2) 2 M s mass of sample (g) - soil or sediment density (g/cm 3 ) n number of increments see D s increment length (cm) - diameter of sample core (cm) 49

50 50 Volatile Organic Compound Sampling

51 ISM Guidance Introduction Laboratory Process Case Studies ISM Principles Making Decisions Delineation Decisions 51 Systematic Planning Statistical ISM Design Field Implementation Regulatory Concerns Case Studies Stakeholder Input

52 52 No Universal Lab Sample Processing

53 Laboratory Analyses Choose the Best ISM Processes ISM Field Sampling Yes VOCs Analysis Needed? Laboratory Processing Separate Sample Container for % Moisture Determination Determine the Number & Mass of Soil Increments Collected in Methanol No Yes VOCs Analysis Needed? No % Moisture Determination Determine the Number & Mass of Soil Increments Collected % Moisture Determination % Moisture Reporting Weighing - Methanol vol/weight Determination Volatiles Analysis Bulk Sample Mass Reduction (Optional ; Not Recommended) Yes Processing in Field? Other on-site Processing Air Drying - Sieving - Subsampling On-Site No % Dry Weight Reporting Specify Air Drying on-site to facillitate sieving Sieving On-Site: Specify Mesh Size of Screen Subsampling On- Site: Determine the Number & Mass of Soil Increments Sample Containerized, Packaged and Process "As Received" Sample Conditioning ( Matrix and Analyte Specific ) Disaggregation Drying "Chopping" (May require drying) Air Oven Freeze Drying Drying Drying Water Addition GO TO BOX "A" A ON NEXT PAGE Sample Mixing (Optional) Certification Preparatory Methods Analytical Method Modifications Particle Size Selection and/or Reduction Methods/ Contamiants Method Considerations Limitations Sieving Dish and Puck Mill Mortar and Pestle Ball Mill Pulverizers Other Fundamental Error and Sample Mass Consideration/Requirements Analytical Subsampling Techniques Inorganics Organics Sectorial Splitters Riffle Splitters Rotary Splitters 1 or 2-Dimensional Japanese Slabcake Other Quality Assurance/Quality Control 53 OAF Sample Ready for Analytical Preparation/Extraction Blanks Laboratory Replicates Spikes (Blank or Matrix?) Reference

54 Include Lab Processing in Project Planning Lab 54

55 Choosing ISM processing options without understanding the project objectives is like writing a love letter and addressing it to whom it may concern 55

56 Two Fundamental Decisions What are you looking for? ~ Complete list of individual analytes Where are you looking for it? ~ What particles in the sample container should be included in the sample? ~ Exposed surfaces and pores of the particles? ~ Interior of the particles? 56

57 Two Trade-Off Decisions To air dry or not? Mechanical processing Analyte retention To mill or not? Improved precision Analyte representativeness 57

58 ISM Guidance Introduction Laboratory Process Case Studies ISM Principles Making Decisions Delineation Decisions 58 Systematic Planning Statistical ISM Design Field Implementation Regulatory Concerns Case Studies Stakeholder Input

59 Making Decisions Introduction Decision Mechanisms Error Assessment 59

60 Decision Mechanisms Structured approach to making decisions avoid confusion Six Common Types of Comparisons 60

61 Compare One ISM Result to Action Level Decision Unit Single Result Action Level 61

62 Compare Average ISM Result to Action Level Decision Unit Mean of Replicates Action Level 62

63 Compare 95% Upper Confidence Limit to Action Level Decision Unit 95% UCL Action Level 63

64 Technical Summary Study allowed comparison of a number of factors that affected reproducibility of results. For analytes presented: Factor A Factor B Comments 64 Field collection techniques Laboratory subsampling and milling 8330B milling technique 8330B analytical equipment Discrete, box, and wheel Avg RSD = 115% 8330A Avg RSD = 22% ISM Avg RSD = 22% 8330B Avg RSD = 6% ~5x better precision with ISM ~3x better precision with ISM Puck mill Ball mill Difference inconclusive HPLC/UV HPLC/MS/MS Slightly better precision with HPLC/UV

65 ISM Guidance Introduction Laboratory Process Case Studies ISM Principles Making Decisions Delineation Decisions 65 Systematic Planning Statistical ISM Design Field Implementation Regulatory Concerns Case Studies Stakeholder Input

66 66 Previous Anti-compositing regulations

67 67 States with Some Regulator Acceptance of ISM

68 EPA Regional Support 68

69 Federal Guidance Method 8330B Appendix A October 2006 Method 3050 Sw-846 Update VI? Uniform Federal Policy Quality Assurance Project Plan Template For Soils Assessment of Dioxin Sites September Superfund Lead- Contaminated Residential Sites Handbook Late 2012

70 ISM Guidance Introduction Laboratory Process Case Studies ISM Principles Making Decisions Delineation Decisions 70 Systematic Planning Statistical ISM Design Field Implementation Regulatory Concerns Case Studies Stakeholder Input

71 Michigan ISM contact : ~ John Bradley ~ Michigan Department of Environmental Quality ~ BRADLEYJ1@michigan.gov ~ (517) Analytes ~ VOCs, SVOCs, PAHs, Metals, Hg Sites ~ Parks, Superfund, manufacturing facility 71

72 Ohio ISM contact : ~ Martin Smith ~ Ohio Environmental Protection Agency ~ Martin.Smith@epa.state.oh.us ~ (614) Analytes ~ SVOCs, PAHs, Metals, Hg Sites ~ Ordinance manufacturing, urban garden, chromium process stockpiles 72

73 The Cost Savings of ISM Reduced overall analytical costs significantly Maintain analytical spend and structure sampling design to reduce uncertainty Large potential ripple effects for: ~ Liability insurance coverage ~ Remediation costs when site concentration near decision threshold 73

74 Better Precision Changes Decisions Florida Golf Course - Arsenic Data (mg/kg) Discrete n = 10 (mg/kg) Incr-30 n = 3 (mg/kg) Incr-100 n = 3 (mg/kg) Mean Std Dev %UCL FDEP SCTL: 2.1 mg/kg ITRC ISM team Florida golf course case study

75 ITRC Guidance & Training ITRC ISM guidance publically available ~ Feb 15, 2012 ~ Internet based training ~ Live Nov. 4 & ~ Archived versions from 2012, 2013, ~

76 Purpose of ISM Representative samples Better data Better decisions 76

77 Contact Information Patricia McIsaac Industry Marketing Manager Mark Bruce Ph. D. Corporate Technical Director