The EPA 7-Step DQO Process

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The EPA 7-Step DQO Process

Step 7 - Optimize Sample Design

(70 minutes)

Presenter:

Sebastian Tindall

Day 2 DQO Training CourseModule 7

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Terminal Course Objective

To be able to use the output from the previous DQO Process steps to select sampling and analysis designs and understand design alternatives presented to you for a specific project

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Step Objective:Identify the most resourceeffective data collectionand analysis design thatsatisfies the DQOsspecified in the preceding 6steps

Step 7: Optimize Sample Design

Step 4: Specify Boundaries

Step 2: Identify Decisions

Step 3: Identify Inputs

Step 1: State the Problem

Step 5: Define Decision Rules

Step 6: Specify Error Tolerances

Step 7: Optimize Sample Design

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Information IN Actions Information OUT

From Previous Step To Next Step

Select the optimal sample size that satisfies the DQOs for each data collection design option

For each design option, select needed mathematical expressions

Check if number of samples exceeds project resource constraints

Decision Error Tolerances

Gray Region

Optimal Sample Design

Go back to Steps 1- 6 and revisit decisions. Yes

No

Review DQO outputs from Steps 1-6 to be sure they are internally consistent

Step 7- Optimize Sample Design

Develop alternative sample designs

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Gray Region



Develop alternative sample designsThe outputs should provideinformation on the contextof, requirements for, and constraints on data collectiondesign.



No

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No







Gray Region




Based on the DQO outputs from Steps 1-6, for each decision rule develop one or more sample designs to be considered and evaluated inStep 7.

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No







Gray Region




For each option, pay close attention to the Step 4 outputs defining the population to be representedwith the data:• Sample collection method• Sample mass size• Sample particle size• Etc.

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Gray Region




Remember:Sampling Uncertainty is decreasedwhen sampling density is increased.



No

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Types of Designs

Simple Random

Statistical Methods for Environmental Pollution Monitoring, Richard O. Gilbert, 1987

Systematic Grid with random start Geometric Probability or “Hot Spot” Sampling Stratified Random

– Stratified Simple Random– Stratified Systematic Grid with random start

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Simple Random

Definition- choice of sampling location or time is random

Assumptions– Every portion of the population has equal

chance of being sampled Limitation-may not cover area

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Simple Random

To generate a simple random design:– Either grid the site - set up equal lateral

triangles or equal side rectangles and number each grid, use a random number generator to pick the grids from which to collect samples

– Randomly select x, y, z coordinates, go to the random coordinates and collect samples

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Example - Simple Random Using Coordinates

- A Random ly Selected Sam pling Location

- Denotes random length & width Coordinates walked-off bysam pling team

N168'

14

7'

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Systematic Grid, Random Start

Definition-taking measurements at locations or times according to spatial or temporal pattern (e.g., equidistant intervals along a line or grid pattern)

Assumptions– Good for estimating means, totals and patterns of

contamination– Improved coverage of area

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Systematic Grid, Random Start (cont.)

Limitations– Biased results can occur if assumed pattern of

contamination does not match the actual pattern of contamination

– Inaccurate if have serial correlation NPDES outfall

– Periodic recurring release; time dependent Groundwater:

– seasonal recurrence; water-level dependence

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- Equally Spaced SamplingLocations

Nc

- Randomly Selected StartingLocation

Hot spot

Remember:Start at random locationMove in a pre-selected pattern across the site, making measurements at each point

Systematic Grid, Random Start (cont.)

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Geometric Probability or Hot-Spot Sampling

Uses squares, triangles, or rectangles to determine whether hot spots exist

Finds hot spot, but may not estimate the mean with adequate confidence

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Number of samples is calculated based on probability of finding hot area or geometric probability

Geometric Probability or Hot-Spot Sampling (cont.)

Assumptions– Target hot spot has circular or elliptical shape

– Samples are taken on square, rectangular or triangular grid

– Definition of what concentration/activity defines hot spot is unambiguous

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Limitations– Not appropriate for hot spots that are not elliptical


– Not appropriate if cannot define what is hot or the likely size of hot spot

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Example Grid for Hot-Spot Sampling

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In order to use this approach the decision makers MUST – Define the size of the hot spot they wish to find– Provide rationale for specifying that size.– Define what constitutes HOT (e.g., what

concentration is HOT)– Define the effect of that HOT spot on achieving

the release criteria


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Stratified Random

Definition-divide population into strata and collect samples in each strata randomly

Attributes– Provides excellent coverage of area– Need process knowledge to create strata– Yields more precise estimate of mean– Typically more efficient then simple random

Limitations– Need process knowledge

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Example - Stratified SimpleRandom

Strata 1

Strata 2

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Sampling Approaches

Sampling Approach 1– Simple Random

– Traditional fixed laboratory analyses

Sampling Approach 2– Systematic Grid

– Field analytical measurements

– Computer simulations

– Dynamic work plan

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Approach 1 Sample DesignCS

Plan View

Former PadLocation

RunoffZone

0 50 100 150 ft0 15 30 46 m

BufferZone

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Design ApproachesApproach 1

Collect samples using Simple Random design.

Use predominantly fixed traditional laboratory analyses and specify the method specific details at the beginning of DQO and do not change measurement objectives as more information is obtained

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Gray Region




1. Statistical Method/Sample Size Formula2. Cost Function



No

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No







Gray Region




1. Statistical Method/Sample Size FormulaDefine suggested method(s) for testing the statistical hypothesis and define sample size formula(e) that corresponds to the method(s).

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No







Gray Region




Perform a preliminary DQA:• Generate frequency distribution histogram(s) for each population• Select one or more statistical methods that will address the PSQs• List the assumptions for choosing these statistical methods• List the appropriate formula for calculating the number of

samples, n

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HistogramCS

0

1

2

3

0 7 14 21 28 35

Pb Concentration (mg/kg)

Fre

qu

en

cy

0

1

2

3

4

0 85 95 105 115

U Concentration

Fre

qu

en

cy0

1

2

3

4

0 1.7 3.4 5.1 6.8

TPH Concentration

Fre

qu

en

cy

0

1

2

3

0 0.8 1.6 2.4 3.2

Arochlor 1260

Fre

qu

en

cy

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3 Approaches for Calculating n

Normal approach Skewed approach FAM/DWP approach

– Badly skewed or for all distributions use computer simulation approach

• e.g., Monte Carlo

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How Many Samples do I Need?

Begin With the Decision in Mind

Optimal Sampling Design

Alternative Sample Designs

, , , Correct Equation for n (Statistical Method)

Population Frequency Distribution

Contaminant Concentrations in the Spatial Distribution of the Population

The end

Data• field• onsite

methods• traditional

laboratory

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Logic to Assess Distribution and Calculate Number of Samples

SkewedCalculate the number of

samples based on skeweddistributions (e.g.,

nonparametric tests suchas WSR or WRS)

Is frequencydistribution fromeach populationsymmetrical orapproximatelysymmetrical?

SymmetricalUse equations based onsymmetrical distribution.

Option 1 Option 2

Badly SkewedBadly skewed or for any

distribution, use computersimulations

(e.g.,Monte Carlo) to performcalculations to estimate the

number of samples

Yes

No

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Normal Approach

Due to using only five samples for initial distribution assessment, one cannot infer a ‘normal’ frequency distribution

Reject the ‘Normal’ Approach and Examine ‘Non-Normal’or ‘Skewed’ Approach

CS

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Logic to Assess Distribution and Calculate Number of Samples

SkewedCalculate the number of

samples based on skeweddistributions (e.g.,

nonparametric tests suchas WSR or WRS)

Is frequencydistribution fromeach populationsymmetrical orapproximatelysymmetrical?

SymmetricalUse equations based onsymmetrical distribution.

Option 1 Option 2

Badly SkewedBadly skewed or for any

distribution, use computersimulations

(e.g.,Monte Carlo) to performcalculations to estimate the

number of samples

Yes

No

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Gray Region




Using the formulae appropriate to these methods, calculate the number of samples required, varying , for a given .Repeat the same process using new s.

Review all of calculated sample sizes and along withtheir corresponding levels of , , and .

Select those sample sizes that have acceptable levels of , , and associated with them.



No

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Pb, U, TPH (DRO/GRO) Because there were multiple COPCs with

varied standard deviations, action limits and LBGRs, separate tables for varying alpha, beta, and (LBGR) delta were calculated

For the U, Pb, and TPH, the largest number of samples for a given alpha, beta and delta are presented in the following table

CS

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Pb, U, TPH Based on Non-Parametric Test CSS a m p l e S i z e s B a s e d o n V a r y i n g E r r o r T o l e r a n c e s a n d L B G R

L e a d , U r a n i u m , a n d T P H

M i s t a k e n l y C o n c l u d i n g < A c t i o n L e v e l

s = 1 0 . 5 ( U ) = 0 . 0 1 = 0 . 0 5 = 0 . 1 0

S a m p l e s i z e f o r m u l a : 2)(

)(16.1

12

2

2211

Sn

W i d t h o f t h e G r a y R e g i o n , ( ) = 2 4 0 – 2 2 9 . 5 = 1 0 . 5 ( t o t a l e r r o r e s t i m a t e )

= 0 . 1 0 1 9 1 2 9

= 0 . 2 0 1 5 9 7

Mis

tak

enly

Con

clu

din

g>

Act

ion

Lev

el

= 0 . 3 0 1 3 8 5

W i d t h o f t h e G r a y R e g i o n , ( ) = 2 4 0 – 1 9 2 = 4 8 ( 2 0 % o f a c t i o n l e v e l )

= 0 . 1 0 4 3 2

= 0 . 2 0 4 2 2

Mis

tak

enly

Con

clu

din

g>

Act

ion

Lev

el

= 0 . 3 0 4 2 2

W i d t h o f t h e G r a y R e g i o n , ( ) = 2 4 0 – 1 2 0 = 1 2 0 ( 5 0 % o f a c t i o n l e v e l )

= 0 . 1 0 4 2 2

= 0 . 2 0 4 2 1

Mis

tak

enly

Con

clu

din

g>

Act

ion

Lev

el

= 0 . 3 0 4 2 1

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Aroclor 1260- Non-Parametric Test

For PCBs, the Aroclor 1260 has the greatest variance and using the standard deviation results in a wide gray region

The following table presents the variation of alpha, beta and deltas for Aroclor 1260

CS

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Aroclor 1260- Non-Parametric Test CSS a m p l e S i z e s B a s e d o n V a r y i n g E r r o r T o l e r a n c e s a n d L B G R

P C B s ( b a s e d o n A r o c l o r 1 2 6 0 )

M i s t a k e n l y C o n c l u d i n g < A c t i o n L e v e l

s = 0 . 8 8 ( A - 1 2 6 0 ) = 0 . 0 1 = 0 . 0 5 = 0 . 1 0

S a m p l e s i z e f o r m u l a : 2)(

)(16.1

12

2

2211

Sn

W i d t h o f t h e G r a y R e g i o n , ( ) = 1 – 0 . 1 2 = 0 . 8 8 ( t o t a l e r r o r e s t i m a t e ) a

= 0 . 1 0 1 9 1 2 9

= 0 . 2 0 1 5 9 7

Mis

tak

enly

Con

clu

din

g>

Act

ion

Lev

el

= 0 . 3 0 1 3 8 5

W i d t h o f t h e G r a y R e g i o n , ( ) = 1 – 0 . 8 0 = 0 . 2 0 ( 2 0 % o f a c t i o n l i m i t )

= 0 . 1 0 2 9 6 1 9 4 1 4 9

= 0 . 2 0 2 2 9 1 4 1 1 0 3

Mis

tak

enly

Con

clu

din

g>

Act

ion

Lev

el

= 0 . 3 0 1 8 6 1 0 8 7 5

W i d t h o f t h e G r a y R e g i o n , ( ) = 1 - 0 . 5 0 = 0 . 5 0 ( 5 0 % o f a c t i o n l i m i t )

= 0 . 1 0 5 0 3 3 2 5

= 0 . 2 0 4 0 2 4 1 8

Mis

tak

enly

Con

clu

din

g>

Act

ion

Lev

el

= 0 . 3 0 3 3 1 9 1 3

a T h e t o t a l e r r o r e s t i m a t e f o r s u b s u r f a c e c o n c e n t r a t i o n s e x c e e d s t h e a c t i o n l i m i t , t h u s i n a p p r o p r i a t e l y m o v i n g t h e L B G R b e l o w z e r o . O n l y t h e s u r f a c e c o n c e n t r a t i o n e r r o r e s t i m a t e i s c o n s i d e r e d h e r e f o r t h a t r e a s o n .

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No







Gray Region




2. Cost FunctionFor each selected sample size, develop a costfunction that relates the number of samples to the total cost of sampling and analysis.

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No







Gray Region




In order to develop the cost function, the aggregate unit cost persample must be determined. This is the cost of collecting one sample and conducting all the required analyses for a given decision rule.

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AUSCA$ = USC$ + USA$i Where (here):USC$ = Unit Sample Collection CostUSA$ = Unit Sample Analysis CostAUSCA$ = Aggregate Unit Sample Collection and Analysis Costj = Number of analytical methods planned

Aggregate Unit Sampling and Analysis Cost

i=1

j

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CSApproach 1 Sampling Design (cont.) Surface Soils S&A Costs

Lab Analytical Cost Without PCBs

Unit Sample Analysis Cost

Pb by ICP/AES $35U by ICP/AES $65TPH (GRO) by GC $65TPH (DRO) by GC $85

Total USA$ $250

Unit Sample Collection Cost $50AUSCA$ = USC$ + total USA$ $300

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CSApproach 1 Sampling Design (cont) Sub-surface Soils S&A Costs

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CSApproach 1 Sampling Design (cont.)

Surface Soils

Lab Analytical Costs for PCBs by GCPolychlorinated biphenyls 150.00$

Total USA$ 150.00$ Unit Sample Collection Cost 50.00$ AUSCA$ = USC$ + total USA$ 200.00$

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Sub-surface Soils

Lab Analytical Costs for PCBs by GCPolychlorinated biphenyls 150.00$

Total USA$ 150.00$ Unit Sample Collection Cost 100.00$ AUSCA$ = USC$ + total USA$ 250.00$

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No







Gray Region




Merge the selected sample size outputs with the Aggregate Unit Sample Collection and Analysis cost output.

This results in a table that shows the product of each selected sample size and the AUSCA$.

This table is used to present the project managers and decision makers with a range of analytical costs and the resulting uncertainties.

From the table, select the optimal sample size that meets the project budgetand uncertainty requirements.

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SHOW EXCEL File

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Approach 1 Based Sampling Design Design for Pb, U, TPH

– Alpha = 0.05; Beta = 0.2; Delta = total error

– The decision makers agreed on collection of 9 surface samples for Pb, U and TPH (GRO & DRO) from each of the two surface strata, for a total of 18 samples using a stratified random design

– For the sub-surface, 9 borings/probes will be made in each of the two subsurface stratum at random locations; one sample will be collected at a random depth down to 10 feet from each boring, to assess migration through the vadose zone, for a total of 18 samples

Design for PCBs– Alpha = 0.05; Beta = 0.20; Delta = 0.50 (50% of the AL)

– The decision makers agreed on collection of 24 surface samples from each of the two surface strata; total of 48 samples using a stratified random design

– For the sub-surface, 24 borings/probes will be collected from each of the two subsurface stratum at random locations, collected at a random depth down to 10 feet for a total of 48 samples

CS

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Approach 1 Sample Locations(Surface Strata)

CS

Plan View

Former PadLocation

RunoffZone

0 50 100 150 ft0 15 30 46 m

BufferZone

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Remediation Costs*CS

*Does not include layback area

DR#

Description/Depth Area(ft2)

Volume(yd3)

Cost*

1a Pad & Run-off Zone, 0-6” 12,272 227 $45,4001b Buffer Zone (excluding

Pad and Run-off area), 0-6”42,884 794 $158,800

2a Pad & Run-off Zone,6”-10”

12,272 4,318 $863,600

2b Buffer Zone (excludingPad and Run-off area),6”-10”

42,884 15,089 $3,017,800

* Assume $200 per yd3 for all COPCs

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Approach 1 Based Sampling Design Compare Approach 1 costs versus remediation

costs – Approach 1 S&A costs

• $11,700 (Pb, U, TPH) + $21,600 (PCBs) = $33,300

– Remediation costs• Cost to remediate surface soil under footprint of pad and

buffer area: $204,200• Cost to remediate subsurface soil under footprint of pad and

buffer area: $3,881,400

CS

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No







Gray Region



Develop alternative sample designsIf no sample design meets the error tolerances within the budget: relax one or more of the constraints or request more funding, etc.

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Design ApproachesApproach 2: Dynamic Work Plan (DWP) & Field Analytical Methods (FAMs)

Use DWP to allow more field decisions to meet the measurement objectives and allow the objectives to be refined in the field using DWP

Manage uncertainty by increasing sample density by using field analytical measurements

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Approach 2 Sampling Design Phase 1: Pb, U, TPH, PCBs

– Perform field analysis of the four strata on-site using XRF (Pb & U), on-site GC (TPH), and Immunoassay (PCBs) methods. Take into account the chance of false positives at the low detection levels

– This will produce a worse-case distributions that will be used to calculate the number of confirmatory samples for laboratory analysis for the surface and below grade strata

CS

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Phase 1: Pb, U, TPH, PCBs– Provide detailed SOPs for performance of FAMs: XRF,

GC, & Immunoassay analysis– Divide both surface strata into triangular grids– Use systematic sampling, w/random start (RS), to locate

sample points; sample in center of each grid Pad & Run-off zone

CSM expects contamination more likely here 10 ft equilateral triangle: 43.35 ft2

Pad + Run-off zone = 12,272 ft2

283 sample points

Buffer area: Also 283 sample points CSM expects contamination less likely here Thus, grid triangle has larger area

Approach 2 Sampling Design (cont.)CS

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Phase 1: Pb, U, TPH, PCBs– Sub-surface strata: Pad & Run-off zone

Use Direct Push Technology (DPT) to collect Push at all surface sample points > ALs Minimum sample locations: 40 (+ 10 >ALs) = ~50 Collect sub-surface samples every 3 feet 50 X 3 = 150 sub-surface samples in this strata Use systematic sampling, w/random start (RS), to locate sample

points

– Buffer area CSM expects contamination less likely here Thus, fewer sample points Same >ALs rationale as above 50 X 3 = 150 sub-surface samples in Buffer area Use systematic sampling, w/RS, to locate sample points

Approach 2 Sampling Design (cont.)CS

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Stratified Systematic Grid with Random Start(Surface Strata)

CS

Not to scaleSquares will be adjusted according to Step 7 design

NFootprint ofConcrete Pad(Stratum 1)

Runoff Zone(Stratum 1)

Buffer Zone(Stratum 2)

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Phase 2: Pb, U, TPH, PCBs– Evaluate the FAM results and construct FDs for each COPC

– Using Monte Carlo method, evaluate the alpha, beta and delta and resulting n based on the XRF, on-site GC, and Immunoassay data and select a value (worst case) for n to confirm the FAM data, using traditional laboratory analysis for each of the four strata

– For this Case Study, we will assume that number came out to be 9 per strata or 36 confirmatory lab samples


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Surface Soils SC&SA Costs

U by Field XRF $1.5Pb by Field XRF $1.5TPH (GRO) on-site GC $25TPH (DRO) on-site GC $25

PCBs by IMA kits $50

Total USA$ $103


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CSApproach 2 Sampling Design (cont)

Sub-surface Soils SC&SA Costs

U by Field XRF $1.5Pb by Field XRF $1.5TPH (GRO) on-site GC $25TPH (DRO) on-site GC $25

PCBs by IMA kits $50

Total USA$ $103


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COPC (Method) Number of Samples, n AUSCA$Total SC&SA Cost

U and Pb (XRF); TPH (On-site GC); PCBs (IMA kits); Surface Soils (strata 1 & 2) 566 $128 $72,448U and Pb (XRF); TPH (On-site GC); PCBs (IMA kits); Sub-Surface Soils (strata 1&2) 300 $153 $45,900Total $118,348

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CS

Number of Samples, n AUSCA$

Total Sampling and Analytical Cost

Subtotal - Onsite $118,348Pb, U, TPH, PCBs -Surface 18 $250 $4,500Pb, U, TPH, PCBs - Suburface 18 $275 $4,950Subtotal - Lab $9,450

Total Costs On-site and Lab Methods $127,798

Confirmatory Traditional Laboratory Analyses Costs

Approach 2 Sampling Design (cont.)

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Evaluate costs of Approach 2 vs. remediation costs– Sampling and analysis (S&A) costs $127,798– Original budget for S&A $45,000

– Remediation cost• Cost to remediate surface soil under footprint of pad and

buffer area: $204,200• Cost to remediate subsurface soil under footprint of pad

and buffer area: $3,881,400


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CS

Approach

# Samples, n On-Site

(Surface / Sub-surface)

# Samples, n Off-Site



1 none 66/66 $33,3002 566/300 18/18 $127,798

Comparison Costs

Remediation Costs:•Surface - $204,200•Sub-surface - $3,881,400

Approach 2 Sampling Design (cont.)

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A Visual Decision Strategy

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V ES A

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V is u a l D Q A V is u a l F it V is u a l Te s t

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Approach 2b Sampling & Lab Analyses

Remember:Sampling Uncertainty is decreasedwhen sampling density is increased

n = m * k Select k of specified Mass/diameter3

– FE² 22.5 * d³ / M (to control sampling error)

Prepare m multi-increment samples for lab analysis Perform lab analyses on m samples

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n = m * k

k = 3k = 3

m = 2

Laboratory

Collect “n” samples

Group into “k”

Combine “k”into “m”composites

Remember;we want the

AVERAGEover theDecision Unit

Approach 2b Sampling & Lab Analyses

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Approach 2b Sampling Design Phase 2b: Pb, U, TPH, PCBs

– Let n = 283 (for each Surface strata); n = 150 (for each Sub-surface strata)

– Select appropriate values for m and k, based on cost and managing uncertainty

k = 3 (Surface); k = 3 (Sub-surface); add $5 to SC cost m = 94 (each Surface strata); Total = 188 Surface samples m = 50 (each Sub-surface strata); Total = 100 Sub-surface samples

– Perform field analysis of the four strata on-site using XRF (Pb & U), on-site GC (TPH), and Immunoassay (PCBs) methods.

– Again, this will produce worse-case distributions that will be used to evaluate and errors and to calculate the number of confirmatory samples for laboratory analysis for the surface and below grade strata; Still assume 36 total

CS

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Stratified Systematic Grid with Random Start(Surface Strata)

CS

Not to scaleSquares will be adjusted according to Step 7 design

NFootprint ofConcrete Pad(Stratum 1)

Runoff Zone(Stratum 1)

Buffer Zone(Stratum 2)

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CSApproach 2b Sampling Design (cont.)

SC Costs: Surface 564 30 $16,920U and Pb (XRF); TPH (On-site GC); PCBs (IMA kits); Surface Soils (strata 1 & 2) 188 $103 $19,364

SC Costs: Sub-Surface 300 $55 $16,500U and Pb (XRF); TPH (On-site GC); PCBs (IMA kits); Sub-Surface Soils (strata 1&2) 100 $128 $12,800Total $65,584

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CS

Number of Samples, n AUSCA$


Subtotal - Onsite $65,584Pb, U, TPH, PCBs -Surface 18 $250 $4,500Pb, U, TPH, PCBs - Suburface 18 $275 $4,950Subtotal - Lab $9,450

Total Costs On-site and Lab Methods $75,034

Confirmatory Traditional Laboratory Analyses Costs

Approach 2b Sampling Design (cont.)

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Evaluate costs of Approach 2b vs. remediation costs– Sampling and analysis (S&A) costs $75,034– Original budget for S&A $45,000

– Remediation cost• Cost to remediate surface soil under footprint of pad and

buffer area: $204,200• Cost to remediate subsurface soil under footprint of pad

and buffer area: $3,881,400

CSApproach 2b Sampling Design (cont.)

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CS

Approach

# Samples, n On-Site


# Samples, n Off-Site



1 none 66/66 $33,3002 566/300 18/18 $127,7983 188/100 18/18 $75,034

Remediation Costs:•Surface - $204,200•Sub-surface - $3,881,400

Approach 2b Sampling Design (cont.)

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CS

Approach 2b Was SelectedMost Cost-Effective

andBest Management of Uncertainty

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Measure both gasoline & diesel range fractions (GRO/DRO)

Ship & process all samples in one batch to decrease cost.

QC defined per SW 846 [1 MS/MSD, 1 method blank, 1 equipment blank (if equipment is reused), 1 trip blank for GRO only].

Cool GRO/DRO to 4°C, +/- 2°C. QAP written and approved before implementation.

CSQC and Analysis DetailsUsed in All Approaches

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Steps 1- 6

Step 7

Optimal Design

Iterative Process

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Gray Region




Justification for a judgmental sampling design• Timeframe• Qualitative consequences of an inadequate sampling

design (low, moderate, severe)• Re-sampling access after decision has been made

(accessible or inaccessible)



No

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WARNING!!If a judgmental design is selected in lieu of a statistical design the

following disclaimer must be stated in the DQO Summary Report:

“Results from a judgmental sampling design can only be used to make decisions about the locations from which the samples were taken and cannot be generalized or extrapolated to any other facility or population, and error analysis cannot be performed on the resulting data. Thus, using judgmental designs prohibits any assessment of uncertainty in the decisions.”

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Gray Region



Develop alternative sample designsThe output is the most resource-effective design forthe study that is expected toachieve the DQOs.



No

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Data Quality Assessment

Guidance for Data Quality Assessment,EPA QA/G9, 2000

Step 1: Review DQOs and Sampling Design Step 2: Conduct Preliminary Data Review Step 3: Select the Statistical Test Step 4: Verify the Assumptions of the Test Step 5: Draw Conclusions From the Data

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To succeed in a systematic planning process for environmental decision making, you need need Statistical Support:Statistical Support:

One or more qualified statisticiansqualified statisticians, experienced in environmental data collection designsenvironmental data collection designs and statistical statistical data quality assessmentsdata quality assessments of such designs.

Summary

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Summary (cont.) Going through the 7-Step DQO Process will

ensure a defensible and cost effective sampling program

In order for the 7-Step DQO Process to be effective: – Senior management MUST provide support

– Inputs must be based on comprehensive scoping and maximum participation/contributions by decision makers

– Sample design must be based on the severity of the consequences of decision error

– Uncertainty must be identified and quantified

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Gray Region






No

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End of Module 7

Thank you

The EPA 7-Step DQO Process

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