Boyd, Robin - ISM Presentationmseca.org/.../2016-02-17_-_Boyd_Robin_-_ISM_Presentation.pdf · 2016. 2. 17. · 2/17/2016 3 Multi Increment ® Sampling Page 7 Why Should I use Multi

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Why Most Soil Data is WorthlessIs Yours?

Robin Boyd – AECOM

Charles Ramsey - ENVIROSTAT

2014

Multi Increment ® Sampling

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Sampling Categories

•Probabilistic (statistical designs)

– Multi Increment®

•Non-Probabilistic (judgmental)

– Discrete (a.k.a. grab)

– Composite (spatial or temporal weighted average)


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Characteristics Unique to Probabilistic Sampling?

• Allows one to make inferences about areas not sampled

• Allows you to express results with a scientific degree of confidence (i.e., error bars)

• Results are independent of the sampler

• Scientifically defensible

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Requirement to be a probabilistic Sample

• Every member of the population of interest (i.e., grain of soil) must have an equal, non-zero, probability of being included in the sample


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Why Perform Probabilistic Sampling?

• Make conclusions with fewer resources

• Provide results that are independent of the sampler

• Make a statistical inference

• Assign a statistical level of confidence

• Reproducible data

• Defensible data


•Only probabilistic samples can be used to estimate sampling error

•A minimum of 3 field replicate samples are required to estimate the total sampling error

•One of the 3 field replicates should be flagged for the collection of 3 laboratory replicates to estimate the total laboratory error– This allows you to know the source of the majority of your

sampling error

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Why Should I use Multi Increment Sampling?

Discrete Samples Often Fail

Example Soil Plume

Map

A B C

Concentrations can vary several orders of magnitude within a DU at the scale of a discrete

sample aliquot.

Action Level

MeanFre

q.

Area A. Heavy Contamination

(DU Mode & Mean Fail Action Level)

ModeCan’t Miss

Area B. Moderate Contamination

(DU Mean Fails Action Level)

Action Level

Fre

q.

False Negatives

Area C. Low Contamination

(DU Mode & Mean Pass Action Level)

Action Level

Fre

q.

False PositivesITRC, ISM-1: Figure 2-15

A B C

Discrete Sample Heterogeneity in the Field

Area average FAILs

(Isolated False “Clean Spots”)Area average PASSES

(Isolated False “Hot Spots”)

Area average FAILS

(Majority False “Clean Spots”)

> Action Level

< Action Level

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Discrete Sampling Study

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Photo Courtesy of Roger Brewer, Hawaii Dept. Health

Discrete Sampling Study

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Paradigm Shift

Probabilistic SamplingDiscrete Sampling

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Concepts

•Scale of Sampling vs. Scale of Decision Making

•All Sample Results are an Average

•Concentration Changes with Mass

•Scale of Sampling Must Match Scale of Decision Making

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Representative Sample?

SiteSample

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Planning

•Failing to Plan is Planning to Fail

•Determine the Population of Interest (part of the DQO process)

– Population of Interest is the same as Scale of Decision Making

•Scale of Decision Making Dictates the Scale of Sampling

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What is Multi Increment ® Sampling?

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Actual Site Showing Decision Unit Design

A Decision Unit is the smallest volume of soil you wish to make a decision about.

• 4 Source Area DUs

• 8 Perimeter DUs for delineation

Heterogeneity

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What Information does your laboratory result provide?

• An estimate of the mean of your parameter of concern, which changes with the mass of the sample.

What changes from sample type to sample type?

• The volume or mass over which the estimated mean is representative; and

• The amount of error associated with the estimate of the mean.

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Lab Result is an Average

Laboratory Process

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Acidic or Extracting Solution

Field Sample

Laboratory Subsample

1 - 30 grams

Laboratory Extraction

WEIGHTED AVERAGE

OF ALL GRAINS IN

THE SUBSAMPLE

Laboratory Analysis

Sampling Results are Always Averages

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1 gramPb = 195 ppm

Heterogeneity at the Laboratory

1 gram subsamples from same field sample jar

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195 mg/kg

45 mg/kg

567 mg/kg

73 mg/kg

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Concepts

•Scale of Sampling vs. Scale of Decision Making

•All Samples are Averages

•Concentration Changes with Mass

•Scale of Sampling Must Match Scale of Decision Making

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Concentration Changes with Mass

1 gram(Pb = 195 ppm)

10 grams(Pb = 1,300 ppm)

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Representative Sample?

Site

Sample

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Types of Sampling

•Probabilistic

– Results are independent of the sampler– Allows you to make inferences about locations not

sampled– Allows you to express your results with a scientific

degree of confidence

•Non-probabilistic

– Result depends on the sampler– Results cannot be extrapolated beyond the mass

analyzed by the laboratory

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Site Scale

100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100

100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100

100 100 100 100 100 100 100 100 900 3000 3100 800 100 100 100 100 100 100 100 100

100 100 100 100 100 100 100 1200 1500 6300 5000 2200 600 100 100 100 100 100 100 100

100 100 100 100 100 100 100 4100 5900 7700 6800 3400 1800 100 100 100 100 100 100 100

100 100 100 100 100 100 100 1000 5000 6500 6100 3000 900 100 100 100 100 100 100 100

100 100 100 100 100 100 100 200 1500 2000 1900 1000 100 100 100 100 100 100 100 100

100 100 100 100 100 100 100 100 700 800 700 600 210 100 100 100 100 100 100 100

100 100 100 100 100 100 100 100 650 240 100 210 590 100 100 100 100 100 100 100

100 100 100 100 100 100 250 620 250 100 100 110 550 110 100 100 100 100 100 100

100 100 100 100 210 600 600 200 100 100 100 100 500 150 100 100 100 100 100 100

100 100 100 170 580 200 100 100 100 100 100 100 250 350 100 100 100 100 100 100

100 100 100 550 180 100 100 100 100 100 100 100 120 450 100 100 100 100 100 100

100 100 300 300 100 100 100 100 100 100 100 100 100 400 130 100 100 100 100 100

100 100 450 100 100 100 100 100 100 100 100 100 100 200 350 100 100 100 100 100

100 120 400 100 100 100 100 100 100 100 100 100 100 100 350 120 100 100 100 100

100 350 150 100 100 100 100 100 100 100 100 100 100 100 130 300 100 100 100 100

100 300 100 100 100 100 100 100 100 100 100 100 100 100 100 120 250 100 100 100

270 150 100 100 100 100 100 100 100 100 100 100 100 100 100 100 170 200 100 100

120 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 200 120 100

100 1292 1016 100

100 836 687 100

178 148 176 100

142 100 111 127

332

200'

10'

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Concepts

• Scale of Sampling vs. Scale of Decision Making

• All Samples are Averages

• Concentration Changes with Mass

• Scale of Sampling Must Match Scale of Decision Making

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Field Sampling Scale

20 30 50 20 250 60 20 170 30 180 10 80 40 10 80 70 100 150 60 80

10 140 80 70 100 50 10 10 130 50 170 80 70 10 90 50 30 20 10 40

180 10 190 40 20 130 100 10 20 30 10 90 50 130 460 60 130 50 60 150

50 130 40 60 60 20 10 80 10 10 60 40 60 80 60 20 30 10 90 40

150 10 80 50 130 10 770 90 100 10 80 60 80 190 10 90 40 80 70 100

30 10 80 50 80 50 130 40 10 80 10 80 50 60 80 100 10 90 50 30

10 210 130 80 90 20 10 60 10 90 20 10 40 10 40 30 410 40 60 130

100 70 60 30 80 30 10 80 130 40 70 60 150 60 150 60 70 60 80 120

140 150 70 10 640 30 20 10 10 60 80 120 10 30 680 20 980 80 50 80

130 90 40 110 70 130 50 60 250 80 50 80 60 20 30 50 20 10 40 30

160 170 80 80 30 20 50 30 20 10 40 30 230 10 10 80 70 60 150 60

210 170 80 60 120 80 60 130 810 60 150 60 80 100 10 90 210 20 70 40

530 150 30 20 80 20 80 120 10 30 80 20 10 20 130 40 60 60 110 150

390 280 200 510 80 50 80 60 20 30 50 20 40 10 80 70 100 150 60 80

360 290 40 260 110 40 30 10 10 10 80 70 60 380 90 50 30 20 10 40

390 320 350 310 480 150 60 80 100 10 90 40 80 130 40 60 130 50 60 150

410 380 340 270 300 110 40 10 80 50 190 20 50 30 20 10 40 30 10 10

1800 4690 2550 320 250 180 30 100 80 80 50 80 60 130 50 60 150 60 80 100

2800 3800 520 350 300 690 190 120 50 10 60 20 80 120 10 30 280 20 10 20

4400 1010 470 290 280 210 140 70 160 50 40 110 50 30 20 10 40 30 10 10

81% are below 150, the average for the 100 ft2 area.

10'

1506"

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Laboratory Subsampling Scale

10 20 30 10 90 50 130 250 10 10 90 50 10 10 40 70 60 150 60 150

80 610 10 60 40 60 580 90 50 12000 40 60 10 10 60 80 120 10 30 680

10 10 810 10 10 190 40 40 60 25000 60 80 10 10 80 50 80 60 20 30

210 10 10 600 76400 40 60 60 80 16500 4400 50 80 60 20 30 360 20 40 10

10 10 380 51000 40000 80 70 80 50 20 10 40 30 10 10 540 13900 770 60 380

10 10 690 88370 25000 90 40 10 40 70 60 150 60 80 100 12800 90 40 80 130

10 10 57000 60 80 10 30 710 10 80 120 10 520 250 380 840 10 10 10 10

10 20 30 10 90 90 50 130 60 540 80 60 10 20 30 10 90 50 630 10

80 10 10 60 40 740 60 620 10 440 30 230 80 10 10 60 40 60 80 10

80 70 100 10 80 60 120 80 60 130 10 840 60 60 10 220 10 660 90 50

190 40 20 1370 30 260 80 20 80 120 10 10 10 290 10 10 10 60 40 60

40 60 10 90 50 280 30 10 90 750 10 90 50 10 10 5050 10 80 60 80

10 10 560 40 60 10 10 60 10 60 60 40 60 480 28900 370 50 10 250 50

10 10 2280 60 80 20 10 80 70 10 80 60 80 100 41700 510 20 20 10 40

230 10 10 80 650 370 110 190 40 350 10 80 50 66300 57800 39300 10 70 60 150

10 10 20 10 40 220 10 40 60 60 20 10 40 55300 880 410 640 80 120 10

50 80 60 20 30 50 20 40 10 2160 70 40 770 60 150 60 110 60 80 60

40 30 470 58300 10 80 670 60 45000 10 80 60 80 120 10 30 680 40 30 230

150 71200 79500 1500 10 90 40 80 130 50 50 80 50 80 560 20 30 40 20 10

10 10 40 30 230 10 10 860 40 60 40 30 230 10 10 10 40 60 60 10

94% are below 2550, the average

concentration of entire discrete sample.

2550

6"

0.3"

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Skewed Environmental Data

Fre

qu

en

cy

MODE

Concentration

MEDIAN

MEAN

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Pierre Gy’s Sampling Theory

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Correct Sampling Requires that every member of the population of interest has an EQUAL probability of being included in the sample!

– This applies to all sampling, whether it is in the field or in the laboratory prior to analysis.


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Sampling Theory incorporates science into sampling and permits us to:

1. Estimate total error from sampling

2. Determine how mass impacts sampling error

3. Determine how particle size impacts sampling error

4. Determine the best sampling tools to use

5. Determine where to sample, and

6. Determine whether sampling can meet our DQOs

Reference: ENVIROSTAT


All Sampling Errors are the result of one basic phenomenon;

Heterogeneity.

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Primary Sampling Errors

•Fundamental Error

•Grouping & Segregation Error

• Increment Delimitation Error

• Increment Extraction Error

•Periodic Error

•Preparation Error

•Analytical Error

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2 Primary Types of Heterogeneity

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Compositional Heterogeneity•Differences between particles•Unaffected by homogenization•Leads to Fundamental Error

Distributional Heterogeneity•Differences between increments•Causes Grouping & Segregation Error

Fundamental Error

The variance of the fundamental error (FE) can be estimated as follows:

SFE2 = C d3/Ms where:

Assumption: Ms << ML /10

SFE2 = variance of the fundamental error

C = constant comprised of 4 variablesd = sieve that will pass 95% of the Lot

Ms = Mass of sampleML = Mass of Lot

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Fundamental Error Cont.

•FE cannot be eliminated

•FE can be estimated prior to sampling

•FE can be minimized by collecting sufficient mass for the grain size “d” of the population of interest

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Grouping and Segregation Error

•Related to FE in that the more compositional heterogeneity is present, the more GSE

•Driven by distributional heterogeneity

•Controlled/minimized by collecting many random increments (typically 30 or more)

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Increment Delimitation Error

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Scoop Trowel

Post-Hole

Digger

Partially Penetrating

Core

Fully Penetrating

Core

Map View

Side View

X X X X

X X X

X = Biased= Correct

XX X X

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Increment Extraction Error

•This could be called the Increment Recovery Error

•Once correct delimitation is achieved, the entire increment, no more and no less, must become part of the sample

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Pierre Gy’s Sampling Theory

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• To understand how much sample to collect and how and where to collect it requires an understanding of sampling errors.

• Only by understanding these sampling errors can we hope to collect representative samples.

How is Multi Increment ® Sampling Done?

Hypothetical Decision Unit Showing Triplicate Samples.

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ITRC, ISM-1, Section 3

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What is Multi Increment ® Sampling ?

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Flags showing increment locations.


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•Designated target volume of soil (“Decision Unit”);

•Collect 30-50+ “increments” of soil in a systematic, random manner;

•Combine to form an “incremental” sample;•Typical mass 1-2kg.

Estimating Sampling Error Cont.

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Primary Increment Locations

Duplicate Increment Locations

Triplicate Increment Locations

The percentage of DUs that should have replicate samples collected depends on your DQOs as well as things such as:

•Land Use•Soil composition/grain size•Topography•Release mechanism•Constituent of Concern•Anticipated concentration

DU with Replicates

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Estimating Sampling Error Cont.

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ANALYTE UNITS Result Result Result Mean SD CV t-95 UCL Cheb. 95 UCLAluminum mg/Kg 442 893 669 668 226 0.34 1,048 N/A

Manganese mg/Kg 91.4 71.2 97.7 87 14 0.16 110 N/A

Aroclor 1260 ug/Kg 300 110 170 193 97 0.50 N/A 438

Benzo(a)pyrene mg/Kg 0.035 0.059 0.033 0.042 0.01 0.34 0.067 N/A

Data and Error Calculations

Ravine Disposal Area

Surface Disposal Site

•Heavily vegetated site

•53 Discrete Sampling Locations

•Judgmental Samples

•6 Samples > Screen

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Result < Screening CriteriaResult ≥ Screening Criteria


Planned Remedial Action

•Hot Spot Removal

•Estimated 18 CY

• Is this reasonable?

•Have you done something similar?

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End Result

•Excavate Hot Spots

•Multi Increment Confirmation Samples

•Repeated Over-Excavation

•Over 1,400 CY Removed

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Multi Increment ® Sampling for VOCs

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Slide Courtesy of Marvin Heskett, Element Environmental, LLC


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53

-2m

0m

-4m

-6m

-8m

DU-1

DU-2

DU-3

DU-4

Ideally 30+ Borings

Core Increments

Subsurface Decision Unit Layers

ITRC, ISM-1, Section 3.3.4 and Figure 3-8

Multi Increment ® Laboratory Processing

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• REQUIRED!!!• Air dry the entire sample, • Sieve to < 2mm or grind

your dry sample• Subsample for each

analysis (30-50 increments)• Rotary splitter or MI

• Analyze at least 10 - 30 grams to minimize fundamental error

• Only use a lab that has experience w/ MI sampling

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Summary

•All concentrations are weighted means, regardless of how the sample is collected;

•Concentration is mass dependent;

•The scale of sampling must be equal to the scale of decision making to make sound decisions;

– Concentrations from masses different than the mass of interest are not representative; and

– Discrete samples cannot represent a heterogeneous material unless your scale of decision making is 1 to 30 grams.

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Training

A. Guidance Documents Available

a) Hawaii Department of Health (www.hawaiidoh.org)

b) Alaska Department of Environmental Conservation (www.dec.state.ak.us/spar/guidance.htm#cleanup)

c) USACE (http://www.hnd.usace.army.mil/oew/policy/IntGuidRegs/IGD%209-02v2.pdf)

d) Laboratory Subsampling (EPA/600/R-03/027)

e) ITRC (Spring 2012)

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Training cont.

B. Courses/Resources

a) Envirostat, Inc.: Chuck Ramsey (www.envirostat.org)

b) Francis Pitard Sampling Consultants, LLC: Francis Pitard(www.fpscsampling.com)

c) ITRC Internet based training (2012)

d) Robin Boyd, AECOM (808) 356-5376, [email protected]

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Where Do You Want to Be?Multi Increment® Sampling

ProbabilisticDiscrete SamplingNon-Probabilistic

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Robin BoydTechnical Development [email protected]

Questions?