dioxin assessment / remediation training program

Part 1: Standard Operating Procedure (SOP) Training

DIOXIN ASSESSMENT / REMEDIATION TRAINING

PROGRAM

Training Materials

September 24 – 26, 2014

DIOXIN ASSESSMENT/REMEDATION TRAINING PROGRAM PART 1 OF 4: STANDARD OPERATING PROCEDURE (SOP) TRAINING

AGENDA

September 24-26, 2014 Melia Hotel – Hanoi, Vietnam

Day 1 – Overview of Training Program and Sampling SOPs for Dioxins September 24 9 AM – 4 PM

Registration – Coffee and Tea 8:30 – 9:00 AM

Opening Remarks 9:00 – 9:10 AM

Overview Presentation of USAID’s Training Program 9:10 – 9:30 AM

Handout and Completion of Pre-Survey Questionnaire 9:30 – 9:45 AM

Break – Coffee and Tea 9:45 – 10:00 AM

Field Sampling Program Preparation 10:00 – 10:15 AM

Surface Soil Sampling SOP PowerPoint on main elements of the SOP (20 minutes) Hands on demonstration (20 minutes) Feedback/comments from attendees on SOP (20 minutes)

10:15 – 11:15 AM

Subsurface Soil Sampling SOP (same format as above) 11:15 – 12:00 PM

Lunch 12:00 – 1:30 PM

Sediment Sampling SOP ( same format as above) 1:30 – 2:30 PM

Break – Coffee and Tea 2:30 – 2:45 PM

QA/QC Sampling and Decontamination Protocols (same format as above) 2:45 – 3:45 PM

Closing Remarks 3:45 – 4:00 PM

Day 2 – MIS Methodology September 25 9 AM – 4 PM



Multi-increment Sampling Metholodogy (MIS) Background and Applications 9:10 – 9:45 AM

MIS Theory/Principles 9:45 – 10:30 AM


MIS Systematic Planning and Statistical Design 11:00 – 12:30 PM

Lunch 12:30 – 2:00 PM

Field Implementation and Lab Processing 2:00 – 3:10 PM

Making Decisions with MIS Results 3:10 – 3:50 PM


Day 3 – Sample Investigation Planning September 26 9 AM – 4 PM



Conceptual Site Model Development 9:10 – 10:00 AM

Elements of a Sampling Plan 10:00 – 10:15 AM


1

SOP Training Agenda September 24-26, 2014

Day 3 – Sample Investigation Planning (continued) September 26 9 AM – 4 PM

Steps for Developing a Sampling Plan & Data Quality Objectives 10:30 – 11:15 AM

Review of Case Studies 11:15 – 12:00 PM

Lunch 12:00 – 1:30 PM

Breakout Group Exercises Conceptual Site Model Data Quality Objectives

1:30 – 2:30 PM

Break – Coffee and Tea 2:30 – 3:00 PM

Report Outs from Group 3:00 – 3:50 PM


2

Overview of USAID’s Environmental Assessment Training Program

24 September 2014

1

Environmental Assessment (EA) at Bien Hoa AirbaseCurrent estimates: > 230,000 cubic meters of dioxin-contaminated soil and sediment (based by historical sampling events)EA will collect additional samples and refine this estimate and evaluate remediation strategiesGVN Partner: Academy of Military Science & TechnologyProject Timeline: 2014-2016

Environmental Remediation at Danang Airport

Environmental Assessment (USAID 2009/10) and Environmental Impact Assessment (GVN 2011)

Estimated ~73,000 cubic meters of dioxin-contaminated soil and sedimentSelected an innovative technology called In-Pile Thermal Desorption (IPTD)

Prime Minister approved the Project in 2011GVN Project Partner: Air Defense Air Force CommandProject Timeline: 2012-2016

“Big Picture” Remediation Strategy Process

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Build containment structure

Excavate and place contaminated material in

structure

Install technology and treat contaminated

material

Capacity Building on Danang Project• 16-hour Construction and Hazardous Waste Operations and

Emergency Response training for over 400 workers at the site• Close technical and managerial collaboration with Air Defense

Air Command, Chemical Command and VRTC, VEA/DXL Laboratory, local drillers and others has led to demonstrative knowledge sharing and transparency in both directions

Objectives of USAID Technical Assistance & Training Program • Overall: To build capacity of GVN officials to conduct

environmental assessment/remediation activities

• Training/Certificate Program: To provide a tangible, sustainable skillset for the GVN

a) to characterize dioxin sites to international standards b) to evaluate and select site-specific remediation strategies

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Method

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• Needs Assessment Workshop (March 2014) provided direct feedback from GVN

• Key findings/requests:1) Standard Operating Procedures (SOPs)2) Field sampling experience and equipment3) Data synthesis/evaluation training4) Remediation technology evaluation training 5) Laboratory training and equipment

• The certificate program addresses the first four requests and combines seminars with practical hands-on training (both in the field and in the classroom)

Training Overview

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Training Part 1Sampling SOPsMIS MethodologyInvestigation Planning(September 2014)

Training Part 2Collecting Environmental Samples(March 2015)

Training Part 3Data Evaluation and Reporting(July 2015)

Training Part 4Remediation Technology Identification and Evaluation(October 2015)

Certificate of Completion

Training Part 1

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• Objectives: – Develop written SOP for dioxin sampling that can be applied to Vietnam– Provide in depth training in MIS methodology– Provide in depth training on developing a comprehensive sampling plan

• Activities:– 1-day SOP workshop with hands on demonstrations– 1-day seminar on MIS theory, principles and application– 1-day workshop with case studies and group exercises to develop

efficient and effective sampling investigations

Training Part 2

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• Objectives: – Provide in depth practical training on dioxin sampling following a

standardized procedure and the MIS methodology

• Activities:– 2-days classroom review– 3-days field training

Training Part 3

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• Objectives: – Provide in depth training and practice in

evaluating results compared to sampling objectives

• Activities:– 1-day theory on data synthesis, evaluation and

presentation– 2-days classroom exercises in actual analysis,

interpretation and presentation

Training Part 4

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• Objectives: – Provide in depth training in evaluating different remediation

technologies using internationally accepted standardized criteria

• Activities:– 1-day review of available technologies– 1-day seminar on alternative comparison– 1-day practical experience

Dioxin/Environmental RemediationStandard Operating Procedure (SOP) Training

Day 1Soil and Sediment Sampling

for Dioxins and Furans

24 September 2014

1

Objectives for Day 1:• Review contents of written SOP• Provide hands on demonstrations of SOP elements• Receive feedback/comments from attendees on SOP

• After training – provide final written SOP to attendees

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Purpose of Standard Operating Procedures• To instruct field technicians on how to prepare for and

implement field sampling program• To ensure scientifically defensible and consistent methods of

sample collection• To maximize data quality

Error introduced through poor technique can undermine theentire sampling program and lead to incorrect results and conclusions.

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Agenda for Day 1:• Preparing for a Field Sampling Program • Surface Soil Sampling SOP • Hands on Demonstration

- Surface soil sample collection- Compositing material for MIS sample collection

• Subsurface Soil Sampling SOP• Hands on Demonstration

- Collecting a subsurface soil MIS composite sampling

• LUNCH

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Agenda for Day 1:• Sediment Sampling SOP• Hands on Demonstration

- Different sediment sampling equipment (grabs, corers)

• QA/QC Sampling• Decontamination Protocols• Hands on Demonstration

- Cleaning/decontaminating sampling equipment- Collecting an equipment rinsate sample

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PREPARING FOR A FIELD SAMPLING PROGRAM

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Field Planning Considerations• Health and safety• Sampling objectives• Sampling locations• Sampling tasks• Field equipment• Sample handling, labeling and shipping procedures• Contact information• Decontamination and disposal of contaminated material• Local approvals and site access• These are typically documented in a Health & Safety Plan, Field

Sampling Plan and QA/QC Plan

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Health & Safety Plan/Planning

• Should be prepared in accordance with recognized and approved health and safety procedures

• Identify specific hazards at the site• Personal protective equipment (Tyvek suits, respirators,

gloves, boots)• Decontamination and disposal of contaminated material• Safe work zones• Procedures in the case of medical emergency• Health and safety monitoring

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Field Sampling Plan/Planning• Sampling objectives, locations, tasks• Specific equipment and supplies should be detailed in site-

specific planning documents for each field program including:– Sampling and documentation equipment/supplies– Health and safety equipment/supplies– Decontamination Equipment/supplies

• Sampling equipment should be regularly inspected and maintained according to manufacturers’ instructions

• Ensure that all equipment has been properly calibrated at the start of sampling*

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Sampling Plan/Planning• Sample documentation content

and procedures:– Date and time of sampling– Station locations– Sample ID’s– Number of increments collected– Descriptions of photos taken

• Supplemental information to document:– Unusual events– Deviations from the SOP– Soil/sediment characteristics

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Dioxin Assessment Location:

Site Name:Start Time:

Date: Finish Time:

Sample collected by (initials): Field Notes Recorded by (initials): Crew Signatures:

Waypoint (UTM) Easting: Northing: Photos:

SAMPLE INFO

Sample Type (circle one): SOIL SEDIMENT

Sampling Device (circle one): Ekman Corer Spade Other (define):

Sampling Method (circle one): Grab Composite (n = )

Sample Depth (m) Sampler Fullness (%)

Texture (e.g., rocky, sandy

Distance from Bank ___________ (m)

Colour: Organic Content:Low Medium High

DESCRIPTION OF SAMPLING LOCATION (AND AREA SURROUNDING, IF APPLICABLE):

SITE MAP (please draw an “X” showing sampling location)

Field Sampling Plan/Planning • Samples should be kept cool (e.g., on ice, 4 degrees Celsius

[ C]) and dark• Samples should be shipped in coolers (with ice-packs) as

soon as possible• Chain of Custody (COC) and Analytical Request forms must

accompany all samples– Keep one copy of the COC and remaining copy(ies) sent with the

samples to the laboratory in a sealed, waterproof bag. – The receiving laboratory will check the COC to ensure all samples are

accounted for and in good condition, and the analyses to be performed

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QA/QC Plan and Planning• Coordinate with analytical laboratory (should be International

Organization for Standardization (ISO) 17025 accredited)• Involve analytical laboratory(ies) in the development of the

planning documents, particularly regarding: – Specific analytical methods– QA/QC protocols to be followed– Number and type of sampling containers– Sample handling requirements– Sample holding times

• Inform them of the schedule for sample delivery• Obtain chain of custody (COC) forms and sample containers*

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SURFACE SOIL SAMPLING SOP

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Before going into the field….

• Have all field planning documents prepared and reviewed by all field team members

• Conduct a field planning meeting to review the field sampling program tasks and procedures

• Assemble all field equipment and supplies• Coordinate with analytical laboratory• Be prepared!

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Wear gloves

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Clean all sample equipment• Clean equipment with a solvent and

metals-free soap (e.g., Liquinox) to remove all large soil particles;

• Rinse equipment two times with deionized or distilled water.

• Rinse equipment three times with environmental grade hexane to remove soil residues;

• Rinse equipment three times with environmental grade acetone to remove any residual materials and assist with hexane evaporation and equipment drying; and

• Wipe sample equipment with paper towels to remove any residual materials and dry equipment.

16

Pre-label jars for each sample, including:• Sample ID;• Site name;• Sample analysis requested;• Date of sampling; and• Name of sampling agency/company.

Sample containers should remain capped at all times, except during sample collection

Containers must be a glass bottle with a Teflon-lined lid (preferably 125 milliliter [mL] heat treated, wide-mouth glass jars) provided by the laboratory

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Plot out grid cells within decision unit area

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Image: ITRC Incremental Sampling Methodology Technical and Regulatory Guidance (February 2012)

Insert soil corer

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• Insert soil corer at least 15 cm

• Twist back and forth• Remove

Retrieve increment samples

20

• Collect the top 10 cm of each increment

• Excluding any surface vegetation

Mix increment samples into composite sample

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• Mix increments together

Sieve (or grind) composite sample

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• Ensure particle size is uniform

Spread and grid composite sample

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• Evenly spread the composite across the tray

• imprint a grid of 6 by 5 into the surface


Collect composite sample

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Transfer equal volumes of the composite sample from each grid section to the heat-treated, wide-mouth glass jars with Teflon® lids, using a stainless steel scoop.

Record sampling information

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• Number of increments collected for each composite sample;

• General appearance of the soil (e.g. grain size, debris, plant material, or biota)

• Other general information described in the “Sample Documentation” slide

Store and ship samples

26

• Store sample containers in a clean, dark shipping container (cooler)

• Store at <4 °C (if sample is destined for extended storage, keep at <-10°C)

• Ship samples as soon as possible to analytical laboratories.

Clean equipment after sampling each decision unit

27

• Clean equipment with a solvent and metals-free soap (e.g., Liquinox) to remove all large soil particles;

• Rinse equipment two times with deionized or distilled water.

• Rinse equipment three times with environmental grade hexane to remove soil residues;

• Rinse equipment three times with environmental grade acetone to remove any residual materials and assist with hexane evaporation and equipment drying; and

• Wipe sample equipment with paper towels to remove any residual materials and dry equipment.

HANDS ON DEMONSTRATION

28

Hands On Demonstration

• Retrieving a surface sample from a soil corer• Grinding, sieving, and compositing a surface soil sample• Spreading and gridding an MIS composite sample• Collecting an MIS composite sample

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QUESTIONS, FEEDBACK, COMMENTS?

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SUBSURFACE SOIL SAMPLING SOP

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Wear Gloves

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Clean all sample equipment

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Pre-label jars for each sample

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35


Soil corer sample collection

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• Relatively shallow subsurface samples (10 cm to 40 cm) can be collected by hand using a soil corer

• Advance the corer into the ground to the desired depth

• Extract the depth of interest from the corer, measuring down from the top of the sample (i.e. from the ground surface)

Drill rig sample collection

37

• Deep samples (e.g., 40 cm or greater) should be collected using a drill rig with a split-spoon sampler with a stainless steel liner

Drill rig sample collection

38

• Attach the properly assembled sampler with appropriate liner to the end of the probe rod

• Insert sampler to the first designated sample depth, adding extension rod(s) as necessary


39

• Upon reaching the designated sample interval, retrieve sampler

• Remove the shoe (or bottom cap) from the barrel, and remove the barrel from the split-spoon head


40

• Once the barrel has been removed it can be separated and the liner containing the sample can be removed from the core sampler


41


Collect increment samples from the core and transfer to the pan for compositing. Use either a wedge from the entire length of the core (core wedge) or slice a full cross-section from the desired depth of the core.

Mix increment samples into composite sample

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43

Spread and grid composite sample

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48


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• Collecting a core wedge or core slice increment for subsurface MIS composite sampling

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51

SEDIMENT SAMPLING SOP

52

Sediment grabs and corer

53

Grab Sampler/ Dimension Sediment Depth Sampled(cm) Volume of Sediment Sample (cm3)

Ekman Grab – Small 0-10

Ekman Grab – Large 0-30

Ponar Grab – Standard 0-10 7,250

Ogeechee Sand Corer 0-50 800

Wear Gloves

54

Clean all sample equipment

55

Pre-label jars for each sample

56


57

• Laying a physical grid on a lake is generally not practical, so create a waypoint list in the GPS and navigate to each of the 30 increment locations.

Prepare for sample collection• Record the location of

each sample site• Take photos of each

sample site and sampling procedure

• In flowing water, face upstream to take sample

• Commence sampling at the furthest downstream site

58

Retrieve increment samples – surface sediment

• Set the grab into the open position• Be careful not to disturb the sediments • Where discernable, collect samples

from depositional areas with small particle sizes

59


• Using a graduated rope attached to the top of the sampler

• Slowly lower the grab until it touches the bottom

• Using an Ekman grab, ensure the messenger (small weight used to trigger the sampler) remains at the surface. Trigger the sampler.

• Using a Ponar grab, the sampler will trigger automatically as soon as it contacts the sediment bed.

60

Retrieve increment samples – surface sediment • After the sampler has been triggered

slowly raise the sampler off the bottom • Fine sediments may be lost if the

sampler is raised too quickly• Ensure the sample meets acceptability

criteria, for example: – desired depth has been achieved, – no loss of sediment sample due to incomplete

closure or tilting of the grab sampler

• If these criteria are not met, the sample should be discarded in a bucket and another sample collected from the site

61


• Open the top of the sampler• Using a stainless steel spoon, collect an

appropriate and equal volume from the top 10 cm of each grab

• Transfer the increment to the stainless steel composite pan

62

Retrieve increment samples – subsurface sediment

• Seat the corer at the desired sample collection location (attach enough additional lengths of core handle extensions to ensure the top is above the water surface).

• Either sink the corer into the sample using body pressure, or utilize the slide hammer to pound the corer into the substrate.

63


• Remove the corer by hand, or use the slide hammer if manual removal is difficult.

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65


Collect increment samples from the core and transfer to the pan for compositing. Use either a wedge from the entire length of the core (core wedge) or slice a full cross-section from the desired depth of the core.

Mix increment samples into composite sample • After all 30 increments have been

placed in the composite pan, mix the sediment sample until it is thoroughly combined into a single homogeneous sample

• Keep the composite container covered between collection of each increment

• Use only sediment samples that do not contain large, foreign objects

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67

Spread and grid composite and collect composite

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• Demonstration of different sediment sampling equipment– Ekman grab– Ponar grab– Ogeechee corer

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75

QUALITY ASSURANCE/QUALITY CONTROL (QA/QC) SAMPLING

76

Why QA/QC?• Quality Assurance (QA) - technical and management

practices to ensure good data

• Quality Control (QC) - aspect of QA that refers to specific measurements used to assess data quality (e.g., lab replicates, blanks)

• Emphasis on QA/QC in planning, field sample collection and laboratory analysis is critical

• Error introduced through poor technique can undermine entire monitoring program and lead to incorrect results and conclusions

77

QC Field Samples• Equipment rinsate samples – ensure decontamination

procedures for sampling equipment are effective• Replicate samples – used to assess precision

– Laboratory split samples– Field duplicate samples– Triplicate MIS samples

78

Equipment Rinsate Samples• Equipment rinsates are used to ensure that decontamination procedures

for sampling equipment are effective

• After decontaminating the sampling equipment, the equipment rinsate sample is collected

• Generally 5% to 10% of the total number of samples collected are equipment rinsate samples

• Equipment rinsate sample collection procedures:

– Place decontaminated sampling equipment (e.g., Ekman grab sampler, soil core, compositing spoon, etc.) in a clean metal tray.

– Rinse the equipment inside the tray with de-ionized or distilled water and ensure the “rinsate” water remains in the tray.

– Pour the rinsate water into a sample jar and label as appropriate

79

Laboratory Split Samples

• Replicates that are collected from a single homogenized composite sample

– Submitted to two or more laboratories for the same analysis, or

– Submitted blind to the same laboratory.

• Used to assess analytical precision of the laboratories

80

Field Duplicate Samples

• Replicates that are collected from the same location and using the same method

• Sent to the same laboratory for the same analysis

• Used to assess sampling precision

81

Evaluating Split and Duplicate Results

82

• Relative percent difference (RPD) between replicates is used to assess precision:

|(A–B) / [(A+B)/2] * 100%|

• “A” represents the concentration in the primary sample

• “B” represents the concentration in the replicate sample

Triplicate MIS Samples

• To calculate standard deviation and 95% UCL of a decision unit, collect a minimum of three MIS samples (i.e., triplicates) from each decision unit

83

DECONTAMINATION PROTOCOLS

84

Decontamination Protocols for Small Reusable Equipment• Clean equipment with a solvent and metals-free soap (e.g.,

Liquinox) to remove all large soil particles; • Rinse equipment two times with deionized or distilled water.• Rinse equipment three times with environmental grade

hexane to remove soil residues;• Rinse equipment three times with environmental grade

acetone to remove any residual materials and assist with hexane evaporation and equipment drying; and

• Wipe sample equipment with paper towels to remove any residual materials and dry equipment

85

Decontamination Protocols for Large Equipment (e.g., drill rig)(continued)

• Requires a bermed decontamination area large enoughto fully contain the equipment

86

Decontamination Protocols for Large Equipment (e.g., drill rig)• Contaminated water is directly collected (steel wash tubs or

within the lined berms)• A wash-down station may be set up to remove contaminated

materials using hot water high-pressure washer• Brushes, soap and water may also be used • Corers should be raised off of the ground for cleaning to

reduce splashback• Decontaminated equipment should be allowed to air dry

before being used again

87

Work Zones• Exclusion Zone• Contamination Reduction Zone• Support Zone

88

Wind direction

Exclusion zone

Contamination reduction zone

Support zone

Exclusion Zone• Hazardous substances are known or suspected to be present. • Used to conduct field investigation. • Personnel enter and exit this zone from designated access points in

the Contamination Reduction Zone

89

Support Zone• A clean area, beyond the outer boundary of the Contamination

Reduction Zone.• There is no contamination in this zone.• Used for administrative, clerical, and other support functions.

90

Contamination Reduction Zone

• Located between the Exclusion Zone and the Support Zone.

• Used for decontamination of workers and equipment by washing boots and equipment

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• Decontaminating sampling equipment• Collecting an equipment rinsate sample

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Day 2MIS Methodology

25 September 2014

1

Multi-increment Sampling (MIS) Methodology

2

Agenda for Day 2:• Introduction: MIS Background• MIS Theory/Principles• MIS Systematic Planning and Statistical Design• Field Implementation and Lab Processing• Making Decision with MIS Results

3

• Where can MIS be used?• When should MIS not be used?• What contaminants are most suitable for MIS?• What effect does sample processing have on contaminant

concentration?• Does MIS miss areas of high concentrations due to

compositing and homogenization?• How does MIS differ from discrete sampling?• How many replicates should be collected?• How are data quality objectives addressed?• How do MIS results relate to action levels?

Objectives of Day 2

4

• Interstate Technology & Regulatory Council (www.itrcweb.org)

• Workshop material based on:– Incremental Sampling Methodology Technology Regulatory and

Guidance Document (ISM-1, February 2012)

• On-line resources:– http://www.itrcweb.org/Guidance/ListDocuments?TopicID=11&SubTo

picID=16– http://www.clu-in.org/live/archive/default.cfm?display=all&group=itrc

Resources

5

MIS BACKGROUND

6

MIS Background Learning Objectives• What is the objective of soil sampling?• How do we collect a representative soil sample?• What are the sources of uncertainty of results?• What is multi-increment sampling (MIS)?• What are the advantages and disadvantages of MIS?• What are some common applications of MIS?

7

Why Collect Soil Samples?Representative Data:• Accurate• Representative• Reproducible• Defensible

….but how do we get it?

Multi-increment sampling (MIS)…..may be your answer…..

8

Are Soil Samples Representative?• Is the sampling event sufficiently planned?

• Is confidence in the sample results high?

• Are samples representative of the entire area sampled?

• Is the contaminantdistribution fully delineated?

• How reproducibleare the data?

9

What Does the Sample Represent?

Representative subsampling

10

Most risk-based environmental criteria based on estimate of mean concentration

• Soil screening levels• Regional screening levels• Site-specific cleanup levels• Exposure point concentrations

11

Uncertainty Sources

• Instrument analysis• Sample preparation• Laboratory sub-

sampling• Field sample collection

12

What is Multi-increment Sampling1 (MIS)?

• Structured composite sampling and processing protocol • Reduces data variability• Provides a reasonably unbiased estimate of mean

contaminant concentrations in a volume of soil targeted for sampling

MIS Objective: To obtain a single sample for analysis that has the mean analyte concentration representative of the decision unit2

1Also called Incremental Sampling Methodology (ISM)2Decision Unit (DU): the smallest volume of soil (or other media) for which a decision will be made based upon MIS sampling

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MIS

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Advantages and Limitations of MISAdvantages of MIS Effect

Improved spatial coverage (increments x replicates)

• Sample includes high and low concentrations in proper proportions

Higher Sample Mass • Reduces errors associated with sampleprocessing and analysis

Optimized processing • Representative subsamples for analysisFewer non-detects • Simplifies statistical analysisMore consistent data • More confident decision

Limitations of MIS Effect

Small number of replicates • Limits Upper Confidence Limit calculation methods

No spatial resolution within Decision Unit

• Limits remediation options within Decision Unit• Limits multivariate comparisons

Assessing Acute Toxicity • Decision Unit has to be very small

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Benefits of MIS• Fewer analyses but a more representative sample

• High quality data leads to a more confident decision

• Potential for cost savings

16

MIS Applications• Regulated sites

• Residential yards

• Former pesticide-applied orchards

• Stockpiled soil

• Post-soil treatment sampling

• Dredged materials

Residential Yard

Soil Stockpile

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MIS Applications (continued)

• Large Areas

– Tailings impoundments

– Agricultural fields

– Floodplain soils

• Transects

• Dredged materials

Mine Tailings Impoundment

Transects

2 miles

Discrete VS. MIS

DU1DU2DU3DU4DU5

Dis

tanc

e

Mine Tailings

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MIS Applications (continued)

• Firing Ranges

• Confirmatory sampling

• Background

• Fill Material Small Arms Firing Range

Post-Excavation Confirmatory Sampling

19

MIS THEORY/PRINCIPLES

20

MIS Theory: Learning Objectives• Soil heterogeneity at two spatial scales makes it difficult to

correctly interpret data results– Two scales are micro-scale and short-scale– Heterogeneity can cause data variability costly decision errors

• Micro-scale heterogeneity is managed by increasing sample mass and improving lab sample processing (required by MIS)

• Short-scale spatial heterogeneity is managed by the field incremental sampling of MIS

ITRC, ISM-1, Sections 2 and 5.3.1

21

Nature of soil and its

contaminant interactions

Contaminant Heterogeneity

Results In:

Sampling Errors

Sampling without addressing it leads to:

Data Variability

Decision Errors

Manifested (observed) as: Which can lead to:

How Soil Heterogeneity Can Cause Decision Errors: Navigation Pane

ITRC, ISM-1, Section 2.1

• Heterogeneity: the condition of being non-uniform

22

Nature of soil and its



Results In:

Sampling Errors


Data Variability

Decision Errors

Manifested (observed) as: Which can lead to:

Soil is a Complex Particulate Material


• Firing Ranges

• Confirmatory sampling

23

A sandy soil, showing variation in particulate size and mineral content (10X magnification)

Pho

to c

redi

t: D

eana

Cru

mbl

ing

Micro-Scale Variation in a Homogeneous-Looking Soil

24

Soil Particle Composition

• Many contaminants adhere to the surfaces of certain minerals

• Organic carbon is composed of complex molecules that act as molecular sponges

Individual soil particles are inorganic mineral or some form of organic

carbon.


25

“Sticky” Minerals• Contaminant

molecules/atoms “stick” well to certain particles

• Smallest particles usually the stickiest – Clays (see photo)– Iron (hydr)oxides

• Stickiness mechanisms– (-) and (+) charges– Surface area

Photo credit: USGS, 2006ITRC, ISM-1, Section 2.2.1.1

Electron microscope photograph of smectite clay – magnification 23,500

26

Contaminants bind preferentially to certain soil minerals Arsenic (whitish color) sorbed to

iron hydroxide particles

Photo courtesy of Roger Brewer, HDOHITRC, ISM-1, Section 2.2 hyperlinks

• Some soil grains have high concentrations, others very low

27

Nature of soil and



Results In:

Sampling Errors


Data Variability

Decision Errors

Which can lead to:

Particulates in Solid Matrices Create “Micro-Heterogeneity”

ITRC, ISM-1, Section 2.5.2

Manifested (observed) as:

• “Micro-heterogeneity” is non-uniformity within the sample jar• Important because contamination is heterogeneous at the same

spatial scale as sample analysis

28

Micro-Heterogeneity Makes Contamination Hard to Interpret

• If contaminant distribution is not uniform in the sample jar, how can analytical data represent the contents of the jar, much less the field? – Huge difference between scale of decision-making and scale of sample

analysis


29

Analysis on 1 Gram of Soil Guides Decisions on Tons

vs.

Photo credits: Roger Brewer, HDOH

30

Short-Scale Field Heterogeneity: Co-located Samples• Shortest spatial scale in the field measured by

“co-located samples” (cm to a few meters apart)

• Samples anticipated to be “equivalent,” but often give very different results

• Chance governs exact location where soil is scooped– Therefore, chance can determine decision

outcome!

• MIS addresses the problems of both micro- and short-scale heterogeneity

Set of co-located samples for uranium (mg/kg)

As 129 221 61 39 14

1 ft apart over 4 ft

Arsenic in residential yard transect (mg/kg)


31

Long-Scale Heterogeneity is Generally at the Scale of Decision-Making

50’

Figure credit: Roger Brewer, HDOH

Results for an actual sampled property. Green circles denote concentrations below the action level; red circles are above the action level.

32

Nature of soil and the

interaction of contaminants


Results In:

Sampling Errors

Sampling without addressing leads to:

Data Variability

Decision Errors

Which can lead to:

Heterogeneity Causes Sampling Errors

ITRC, ISM-1, Section 2.3.2, 2.4.1.1 and 2.2 hyperlinks


• Sampling error occurs when samples fail to represent the original targeted population

33

Concentration Depends on Sample Size and Contaminated Particle MassCommon assumption

The amount of soil analyzed makes no

difference to what results are obtained.

…get different concentration results

Assumption wrong for solids

Extraction Step

Lab Sample

Reported Concentration

Can have the same contaminated particle mass (blue), BUT in different

sample masses (white)…

Concentration (mg/kg) = contaminant mass (mg) / the

soil mass (kg)

34

Smaller Sample Sizes More Prone to Sampling Error than Larger Ones

• Illustration of sampling error: For the blue and green samples, the proportion of nuggets in the samples do not represent the nugget proportion of the population (the large container)

35

Ways to Reduce Sampling Error When Sampling a Jar

• MIS stresses the importance of sample size and techniques to reduce sampling error– Reduce particle size (grinding)

– Increase sample size (i.e., extract a larger analytical sample mass)

– Take many increments to make up the analytical subsample (“incremental subsampling”)

– Use equipment like rotary splitters

ITRC, ISM-1, Table 3-1 and 6.2.2.5 to 6.2.2.7

36

Reducing Short-Scale Sampling Error• Goal is to get THE concentration for a

target soil volume, so…– IDEAL: analyze whole volume as a single

sample

– PRACTICAL: Increase sample size and sampling coverage by taking many small increments across the area and pooling them

• This is what MIS doesSet of co-located

samples for uranium

ITRC, ISM-1, Section 2.6.2.1

37




Results In:

Sampling Errors


Data Variability

Decision Errors

Which can lead to:

Sampling Error Causes Data Variability

ITRC, ISM-1, Sections 2.4.1.3


• Sampling errors contribute to data variability

38

Study Data for Pb: 5 Laboratory Replicate Subsamples from Same Jar

Pb,Unground

Reps

Lab Replicate Number1 2 3 4 5

Pb

conc

entra

tion

(ppm

)

35000

30000

25000

20000

15000

10000

5000

0

DU4 Lab Replicate Analyses on Unground Sample

39

Same Soil Sample After GrindingPre-grind range: Pb 4000-29000 Post-grind range: Pb 4360-5660

~5000 ppm

~5000 ppm

Particle size reduction

DU4 Pb Unground vs. Ground Subsample Replicate

30000

25000

20000

15000

10000

5000

0

Pb

conc

entra

tion

(ppm

)

1 2 3 4 5

Pre-grind repsPost-grind reps

Lab Replicate Number

40

Sample Size Influences Statistical DistributionsSmall sample sizes contribute to skewed

statistical distributions

Adapted from DOE study (Gilbert, 1978)ITRC, ISM-1, Section 2.4.1.3

41




Results In:

Sampling Errors


Data Variability

Decision Errors

Which can lead to:

Concepts Underlying MIS: Avoiding Decision Error

ITRC, ISM-1, Section 2.4.1.3 and 2.4.2


• Decision Error: a decision that would have been made differently if the true condition were known

• Can occur when conclusions are based on data that were significantly influenced by heterogeneity

42

Skewed Data Distributions Promote Decision Errors

Suppose 3 is an action level. The likelihood of single data points exceeding 3 depends on the sample support.

True mean of large batch = 1.92

43

Avoiding Decision Errors

• Pay attention to QC results in the data package!– Suspect sampling error due to micro-scale heterogeneity when

• Lab duplicates do not “match”

• Matrix spikes/matrix spike duplicates do not “match”

– Suspect sampling error due to short-scale heterogeneity when

• Co-located samples do not “match”

– Ensure ISM work plans spell out procedures to detect and control sampling error

44

Summary: MIS Theory• Inadequate management of soil heterogeneity produces highly

variable data sets

• The “maximum concentration” notion is meaningless

• Chance data variability can be misinterpreted to represent the “true” condition for large soil volumes

• Misinterpreting data, especially single data points, can lead to costly decision errors

ITRC, ISM-1, Sections 5, 6, and 7

45

Q&A AND BREAK

46

MIS DESIGN:SYSTEMATIC PLANNING AND STATISTICAL DESIGN

47

MIS Design: Learning ObjectivesLearn how to• Conduct systematic planning steps important to MIS

– Conceptual Site Model (CSM)– Risk pathways and contaminants of concern– Project objectives (Sampling and Data Quality Objectives (DQOs))

• Determine Decision Units (DUs)– Information used to develop DUs– Why DUs are important– Types of DUs– Real world examples

(i.e., case studies)

ITRC, ISM-1, Section 3

MIS

48

MIS Design: Learning Objectives (continued)

Learn how to• Answer common questions about MIS related to

– Sampling design– Data analysis

• Expand understanding of– Statistical theory– Simulation studies conducted by the ITRC MIS Team

49

MIS DESIGN:SYSTEMATIC PLANNING

50

Systematic Planning and Implementation

• Develop Conceptual Site Model (CSM)

• Identify contaminants and project objectives

• Identify data needed and how it will be used

• Define Decision Units (DUs)

• Develop decision statements

• Collect samples to characterize DUs

• Evaluate data

ITRC, ISM-1, Table 3-1

Key Step of MIS

51

Conceptual Site Model (CSM)

DirectExposure

Groundwater

Prevailing WindDirection

Leaching

GrossContamination

Ecotoxicity

Stream

Stream

Discharge toaquatic habitats

Free ProductDissolved plume

Leaching

Drinking Water

VaporIntrusion

Soil

ITRC, ISM-1, Figure 3-2

52

Data/Information Needs• What receptors and pathways are being evaluated? • What are your sampling objectives?• Are there multiple sampling objectives that must be met?• What is the scale of decision making?

The key is the volume over which the mean should be estimated.

53

Example Sampling Objectives

• Estimate the mean concentration of contaminants in a pre-determined volume of soil (i.e., DU)

• Delineate the extent of contamination above screening levels• Estimate the potential risk to receptors posed by the soil

contamination• Evaluate background metals concentrations in soil• Confirmation sampling following remediation

54

Designating Decision Units (DUs)The volume of soil where samples are to be collected and decisions made

based on the resulting data.Exposure AreasSource Areas

Size, shape and type of DU are an outcome of systematic planning and depend on site specific data quality objectives.


55

Why MIS Is Important

Example Soil Plume Map

A B C

Concentrations can vary several orders of magnitude within a DU

at the scale of a discrete sample

Action Level

MeanFreq

.

Area A. Heavy Contamination(DU Mode and Mean Fail Action Level)

Mode Can’t Miss

Area B. Moderate Contamination(DU Mean Fails Action Level)

Action Level

Freq

.

False Negatives

Area C. Low Contamination(DU Mode and Mean Pass Action Level)

Action Level

Freq

.

False Positives

ITRC, ISM-1, Figure 2-15

56

Traditional Site Investigation Approach

• Potential Concerns– Inadequate samples to

define boundaries– High risk of False Negatives

and False Positives– Confusion over single point

“hot spots”– Cost of 30 analyses– Sample points should be

randomly located for estimation of exposure point concentration (EPC)

Proposed Discrete Samples (30)DU-1

57

MIS Approach (Option 1)• Advantages

– More representative– Risk evaluation objective

identified up front– Increments randomly and

evenly spaced to minimize size of hot spot missed

– Quick and cheap if minimal contamination suspected

• Disadvantages– Additional sampling required if

DU fails

Designate an exposure area DU assuming no source area

Increment location

58

MIS Approach (Option 2)

• Advantages– Addresses both source area

and perimeter as well as directional variability if an exceedance is found

– Best approach to minimize additional sampling

– Will minimize remediation volumes if DU exceeds screening level

Four Decision Units

DU-1

DU-3

DU-2

DU-4

59

Suspected Lead Paint and Pesticides Around House and in Yard

Source Area DU: perimeter of

house

Exposure Area DU: remainder

of the yard

Do lead or pesticides exceed action levels around the house or in the yard?

60

Former Pesticide Mixing Area (0.5 acre)

Suspected heavy contamination with arsenic, dioxins (from PCP) and leachable pesticides

50’

61

Former Pesticide Mixing Area

Exposure Area DUs: Maximum 5,000 ft2

Source Area DUs: Heavy contamination + leaching

Perimeter DUs

62

Source Area and Exposure Area DU Designation

Primary objective is to delineate the source area and the extent of contamination.

Exposure Area DUs(arsenic and dioxins;

direct exposure hazards)Source Area DUs

(triazine pesticides;leaching hazards)

63

Former Power PlantProposed Community Center

100’

Transformer repair area

Primary objective is to identify and delineate source area and extent of contamination that exceeds action levels.

64

Former Power PlantDecision Unit Designation

100’*Assuming 3’ depth

*Small Source Area DUs(max 3,000 ft2, 400 yds3)

*Larger Exposure Area DUs(up to 10,000 ft2, 1,000 yds3)

65

Really Big Decision Units (DU)!(400-acre former sugarcane field)

Source Area DU(investigated separately)

Initial Screening DU• Residual pesticide levels?• OK for residential development?

Lot-Scale Resolution• Hypothetical lots• 5,000 ft2 Exposure Area• May also be required

Primary objective is to determine if property can be developed for residential use.

66

Why DUs (and MIS) are Important>Action Level <Action Level

100’PCB sample aliquot = 30 grams (one spoonful of soil)

Discrete data: Estimated 10,000 ft2 soil

?

67

Why DUs (and MIS) are Important> Action Levels < Action Levels

MIS Data: Estimated 25,000+ ft2 soil(perimeter DUs pending)

68

A B C

Why Discrete Samples Miss Contamination in the Field

Area average FAILS(Isolated False Negatives)

Area average PASSES(Isolated False Positives)

Area average FAILS(Majority False Negatives)

AboveActionLevel

BelowActionLevel

69

Excavation Decision UnitsFloor and sides tested as separate DUs

DU-3

DU-1

ITRC, ISM-1, Section 3.3.6 and Figure 3-11

70

Stockpile Decision Units

10 m


71

Subsurface Decision Units

-1.5’

-0.5’

-3.0’

-5.0’

-10’

DU-1

DU-2

DU-3

DU-4

30 Borings (ideal)Core Increments

not to scale


Individual core samples combined to prepare an MIS

sample for each DU

72

Decision Unit (DU) Summary

• Determining DU size and location– Use all available information– Determine Data Quality Objectives

• Establish DUs with risk assessment and remedial goals in mind from the start

• Many random increments required (30 to 50+)– Capture the effects of heterogeneity – Characterize a DU

73

Decision Unit Summary (continued)

• MIS samples– More efficient and cost effective method– Minimizes the chance of missing hot spots– Represent larger volumes accurately– Tight grids of screening data can be useful to locate suspected

source areas for better DU designation, if needed

74

Summary: Systematic Planning• Conduct Systematic Planning

• Develop a CSM before beginning a sampling design• Be sure that sampling design will achieve objectives

• Decision Unit designation• Use all site information to develop DUs• Align scale of decision making with sampling objectives

75

MIS DESIGN:STATISTICAL DESIGN

76

Statistical Design Questions – Data Analysis

What is the statistical foundation for MIS?

1. Does a single MIS sample provide a reasonableestimate of the mean?

2. Can a 95UCL be calculated with MIS data?

95UCL = 95% Upper Confidence Limit of the mean

Section 4.2.1

Section 4.2.2

77

Questions – Sampling Design

3. What sampling design should I use?

4. Can background and site data be compared using MIS?

Section 4.3.4.2

Sections 4.4.3.3 and 7.2.4

78

1. Does a single MIS provide a reasonable estimate of the mean?

• Why would someone collect just 1 MIS?– UCL not required– Save time and expense– Assumption that more sampling wouldn’t change the

decision. For example• Variance among individual increments is low• Mean of DU is far above or below an action level

Answer: • It depends how much error we are willing to accept.


79

1(b). How badly might I underestimate the mean?

CV = 1.0

CV = 3.0

CV = 2.0

Pro

babi

lity

Underestimate of Mean

60%

40%

20%

0%20% 40% 60% 80%

CV=1.0CV=2.0

CV=3.0

*Coefficient of variation (CV) = St Dev / meanITRC, ISM-1, Section 4.2.1, Figure 4-2

CV Frequency Magnitude True Mean Estimate1 33% 10% 400 ppm2 33% 20% 400 ppm3 25% 30 - 60% 400 ppm 160 - 280 ppm

80

2. Can a 95UCL be calculated?

•

• Supported by theory and statistical simulations

• Fewer methods are available than we are used to with discrete sampling:– Chebyshev– Student’s-t

Answer: • Yes, even with as few as 3 MIS samples (replicates).

• Each MIS result provides an estimate of the mean (“x-bar”)

• Parameter estimates are calculated directly from MIS data

81

How much higher is Chebyshev?• Chebyshev will tend to yield 10-45% higher UCLs than Student’s-t depending

on the CV of 3 replicates• Example: Student’s-t = 100 ppm, Chebyshev = 110 -145 ppm

rsXUCL x11

rstXUCL x

r 1,1

Chebyshev

Student’s-t

Che

bysh

ev /

Stu

dent

’s-t

CV of MIS Replicates

1.0

1.5

0 5.0


1.1

1.2

1.3

1.4

4.03.02.01.0

82

3. Is there a preferred MIS sampling design?

83

3. Is there a preferred MIS sampling design?

Systematic Systematic (3 replicates)

Random within GridSimple Random


84

3. Is there a preferred MIS sampling design (continued)?

• Systematic random sampling is most often used because it is the easiest to implement random sampling,

Answer: • Each random sampling design yields unbiased estimates of the

mean and is an acceptable approach in most situations.

Concentration (mg/kg)1000 200

f(x)


85

3(b). How many increments?

• As the number of increments increases:– spatial coverage improves (greater sample density)– lower variability in MIS results (smaller standard deviation)– 95UCL will tend to be closer to the mean

• Size of DU can be a consideration – large DUs may require more increments

Answer: • n = 30: generally, 30 increments per MIS sample provide good

results. Lower numbers are discouraged and higher numbers provide diminishing improvement in statistics.

10 20 30 40 50 60 70 80 90 100

ITRC, ISM-1, Sections 4.3.4.1 and 5.3.1

86

3(c). How many replicates?

• Minimum number to calculate standard deviation (and 95UCL) of MIS results

• More replicates will produce a 95UCL closer to the actual mean, but may not be cost-effective unless the result is near the action level

Answer: • r =3 : for most DUs, three replicates is sufficient.


87

Which Would You Choose and Why?A. n = 30, r = 1B. n = 90, r = 1C. n = 30, r = 3 (so 30 x 3 = 90)

Scenario SpatialCoverage

Analysis Cost

Estimate of Mean

Estimate of Variance

A Low Low Yes NoB 3 x A A Yes NoC 3 x A 3 x A Yes Yes

88

4. Can background and site MIS data be compared?

Answer: • Yes, but statistical tools for comparison are limited.

Background DU-1• Each data sets consists of MIS samples, preferably generated with

similar sampling designs

Concentration (mg/kg)0 100 200

f(x)

Concentration (mg/kg)1000 200

f(x)


89

4. Can background and site MIS data be compared?

Answer: • Yes, but statistical tools for comparison are limited.

Background

DU-1

• Equal central tendency (mean, median) ?• Equal upper tails ?

• Hypothesis testing is limited to parametric tests of the mean:– Assume distribution shape– Use estimates of mean, SD, and number of

replicates• Cannot test upper tails with MIS data


90

5. Example Background ComparisonC

once

ntra

tion

(mg/

kg)

Reference Area(sample mean = 0.17)

Site(sample mean = 0.18)

0.5

0.4

0.3

0.2

0.1

0

ITRC, ISM-1, Section 7.2.4, Figure 7-1

91

Summary: Statistical Design• Mean or 95UCL from MIS data may be used to make decisions

about a site

• 3 replicate samples provide adequate information to calculate a 95UCL

• Systematic random sampling is most commonly used

• About 30 increments per MIS sample is usually sufficient

• Comparisons between MIS data (e.g., site vs. background) are possible, with caution

92

Q&A AND LUNCH

93

FIELD IMPLEMENTATION AND LAB PROCESSING

94

MIS Theory and Design – Summary• Reduce Sampling Errors

– Heterogeneity Rules!

• Plan, Plan, Plan– Involve the entire team– Know your site– Know your objectives– Focus your decisions

• Design for Confidence• The mean is the goal!• Collect replicates to calculate UCL

Plan

Principles

Systematic Planning

Statistical Design

95

Implement, Assess and Apply

Implement

Field Implementation

Lab Processing

Assess Making Decisions

Application

Collect anMIS Sample

Match Lab Process toAnalytes and Objectives

Decision Mechanisms andData Evaluation

Where to Apply MIS

MIS Opportunities

?

96

FIELD IMPLEMENTATION

97

Field Implementation Learning ObjectivesLearn how to:• Collect an MIS sample

– Understand the similarities and differences between surface and subsurface MIS sampling

– Consider issues specific to non-volatile and volatile MIS sampling

– Implement and collect MIS replicate samples

ITRC, ISM-1, Section 5

98

Key Presentation Topics• Sampling design• Sampling tools• MIS surface/subsurface sampling

– Cores and subsampling

• Specific contaminant of concern (COC) considerations– Non-volatile and volatile

• MIS replicates

99

Sample Collection Components• Decision Unit (DU) sampling design

– Simple random sampling– Random sampling within a grid – Systematic random sampling

• Sampling tools– Core shaped– Adequate diameter

• Mass– Increment mass– Sample mass


100

Sampling Designs

Simple Random Random within Grids

Systematic Random

Increments

ITRC, ISM-1, Section 4.3.4.2 & Section 5.3.1, Appendix A1

101

Florida Case Study: Decision Unit (DU) Identification• Identify DU in the field

– Use typical environmental site investigation procedures– Examples

• Survey• GPS • Swing ties

ITRC, ISM-1, Section 9.3 & Appendix C, Section C.3

Decision Unit

102

Increment Locations• Identify increment locations in field

– Utilize similar site investigation tools


103

Sampling Tool Considerations• Criteria - shape

– Cylindrical or core shaped increments – Minimum diameter required – based on particle size of interest

e.g., core diameter >16 mm


104

Additional Considerations• Decontamination

– Not necessary within DU (including replicates)

• Sampling tool– Appropriate for matrix and contaminant of interest


105

Sampling Tool ExamplesSoft Surface Soil

Source: Courtesy http://www.jmcsoil.com/index.htmlhttp://fieldenvironmental.com/evc-incremental-sampler.php

106

Alternate Sampling Tools

Hard Surface Soil

ITRC, ISM-1, Section 5.2; Figure 5-2b

107

Adequate Sample Mass

Ms = • n • Ds • • (q / 2)2

Ms – targeted mass of sample (g)Ds – increment length (cm)n – number of increments

- soil or sediment density (g/cm3)q - diameter of sample core (cm)


• Criteria – mass (non-volatile)

• For Danang

108

Individual core samples combined to prepare an MIS sample for each DU

Subsurface Decision Units (DU)

-1.5’

-0.5’

-3.0’

-5.0’

-10’

DU-1

DU-2

DU-3

DU-4

30 Borings (minimum recommended)Core Increments

not to scale

109

• Preferred increment – entire core interval• Core subsampling alternatives

1. Core wedge2. Core slice

Subsurface Sampling Considerations


110

Core Wedge

Continuous wedge removed from entire length of targeted DU interval for 100% coverage


e.g., wedge width >16 mm

111

Core Slice

Core slice removed from randomly selected interval of targeted DU depth


112

Field Processing for Non-Volatiles• MIS sample processing in a controlled laboratory environment

is recommended to reduce error• Field processing may be applicable if project specific DQOs

can be met


113

Non-Volatile MIS Sample Logistics• Initial MIS samples: typically 600-2,500 grams or more

– Containers, storage– Facilities and equipment for correct processing

and subsampling– Final MIS sample for shipping: 120-mL jar

114

MIS Volatile Sampling Tools• Core type sampler• Typical for VOC soil sampling per SW846 5035A

ITRC, ISM-1, Section 5.4.2 Source: Courtesy www.ennovativetech.com

115

MIS Volatile Samples – Subsurface• Numerous increments collected across core/depth interval

116

Methanol

Soil

MIS Volatile Sample Logistics

ITRC, ISM-1, Section 5.4.2, Figure 5-11

• VOC preservation and analysis– Increments are extruded from sampler directly into appropriate

container with predetermined volume of methanol

– Methanol preserved sample submitted to laboratory

– Note shipping restrictions/requirements

117

Replicates• Provide assurance of accuracy

• Required to determine 95% upper confidence limit

• Increments collected from alternate random locations

– Independent samples, not “splits”

• Minimum 3 replicate set for statistical evaluations

• Additional replicates may be necessary depending on contaminant heterogeneity and project specific DQOs


118

Replicate Spacing and Collection

Decision Unit

Replicate Increment Spacing

Decision Unit

Sample Collection

R1 R2 R3Replicate 1Replicate 2Replicate 3ITRC, ISM-1, Section 5.3.5

119

Field Replicates – Simple Example

Replicate 3

Replicate 2

Replicate 1

Collecting the Samples

120

Replicate/Sampling Reminders

• Replicates – What type– How many– Where/when will they be collected– How will they be evaluated

121

Field Implementation Summary• Determined during Systematic Planning

– Sampling design– Adequate sampling tools– MIS surface/subsurface sampling logistics

• Subsurface cores and subsampling

• Specific contaminant of concern (COC) considerations

• Non-volatile and volatile

• MIS replicates

122

LABORATORY PROCESSING

123

Laboratory/Field ProcessingLearning Objectives

Learn how to:

• Match process options to analytes and data objectives

• Manage sample moisture

• Select/reduce particle size

• Collect subsamples for analysis

• Apply Quality Control

124

Analyte-Matrix Driven Options

• Pick the right option– More representative subsamples– Better precision

• Pick the wrong option– Poor and unknown bias

125

Define the Analytes• Volatile organics• Energetics• Metals, Hg• PCBs• Organochlorine pesticides• Phenoxy acid herbicides• Dioxin• Petroleum hydrocarbons• Semivolatile organics• Other

126

Coordinate VOC Sampling & Analysis• Use methanol preservation

– Methanol transport– Bottle sizes (large, medium, small)

• Analytical sensitivity limitations– Higher reporting limits– Selected Ion Monitoring GC-MS

• Short analyte lists


127

Symbol Key• Good effect

• Bad effect

• Result or statistic gets larger in value

• Result or statistic gets smaller in value

128

Lab* Processing Roadmap

Lab* Processing

Sample Conditioning

Particle Size Reduction

Splitting and Subsampling

*Processing can also be done in the field

129

• Air drying– Room temperature – most common– Ventilation hood– Goal: Crushable agglomerates – Consider volatilization losses

• Boiling point• Binding to soil particles• Potential for Loss Table

– Naphthalene

– Acenaphthene

– Benzo[a]pyrene

• Use other options when drying not appropriate

Condition the Sample


130


Lab* Processing

Sample Conditioning




131

Define Terms: Disaggregating• Breaking all the soil clumps into individual small particles,

but keeping the small pebbles and hard crystalline particles intact


132

Picture from USACE-Alan Hewitt

Define Terms: Milling• Complete particle size reduction of all soil components

including hard crystalline materials to a defined maximum particle size (e.g. < 75 μm)


133

To Mill or Not to Mill? (Particle Size Reduction)• Recommended

– Crystalline particles, fibrous threads, paint chips

– Energetics, metals

• Strengths – Reduces variability

– Reduces subsampling error

– Facilitates mixing

– Improves precision

Picture from USACE-Alan HewittITRC, ISM-1, Section 6.2.2.5

134

• Not recommended

– Volatile, thermally labile, increased “availability”

– Examples• Monochloro PCBs,

reactive SVOCs, decane, elemental mercury

– Limitations• Analyte losses

• Metals contamination

• Potential high bias to metals risk assessment (pebbles)

To Mill or Not to Mill

If uncertain, do milled & unmilled


135


Lab* Processing

Sample Conditioning




136

Subsampling Options• 2-Dimensional Japanese Slabcake

Dry

WetITRC, ISM-1, Section 6.2.2.7

137

Subsampling Tools

• Square straight-sided scoops for dry non-cohesive soil

138

Why Use Large Subsamples?• Larger particles

– Produce larger errors or require larger subsamples

0

50

100

150

200

0 1 2 3 4 5Particle size (mm)

%R

SD

1 g5 g 10 g

30 g


139

Laboratory Quality Control Measures• Laboratory equipment blanks

– Limited clean matrices

• Laboratory control samples (LCS) and matrix spikes– Practicality of large scale spiking in kg samples

• High cost• Limited availability

– Introduced post ISM processing into subsample

• Replicate analyses

140

Lab Processing Summary

• Match processing to the properties of the analyte(s)• Condition the sample – manage soil moisture appropriately

for the analyte• Disaggregate• Mill, if appropriate and necessary• Subsample with appropriate process and tools

141

Question and Answer BreakDecision Unit

142

MAKING DECISIONS WITH MIS RESULTS

143

Making Decisions: Learning ObjectivesLearn how to:

• Use MIS data to make decisions

• Evaluate data

– Identifying sources of error

– Quantify error

– Interpret error

– Isolate sources of error

144

Decision Mechanisms

Making Decisions Using ISM Data

Making Decisions

Data Evaluation

145

Making Decisions

• Decision Mechanism (DM)– Structured approach to making decisions– Identified and agreed upon during Data Quality Objective (DQO)

process– 6 common types of DM

146

DM 1: Compare One MIS Result to Action Level

Decision Unit Action Level

Single Result

ITRC, ISM-1, Section 4.2.1 and Section 7.2.1

147

DM 2: Compare Average MIS Result to Action Level

Decision Unit Action Level

Mean of Replicates


148

Florida Case Study: Decision Mechanism (DM) 2

Discreten = 30

Incr-30n = 3

Incr-100n = 3

DU 2 4.2 5 5.2

DU 3 7.5 10.5 9.5

Mean arsenic concentrations (mg/kg)

149

Decision Unit

Action level or risk assessment

95%UCL

DM 3: Calculate 95%UCL then Compare to Action Level or Use for Risk Assessment


150

Florida Case Study: Decision Mechanism 3: (DU 1)

Discreten = 10

(mg/kg)

Incr-30n = 3

(mg/kg)

Incr-100n = 3

(mg/kg)

Mean 2 1.8 1.7

Std Dev 1.4 0.08 0.03

95UCL 3.0 2.0 1.8

Florida Action Level: 2.1 mg/kg

Arsenic Data (mg/kg)

151

Decision Unit

Comparison

Background

Mean &

S.D. M

ean

& S

.D.

DM 4: Compare to Background

ITRC, ISM-1, Section 4.4.3.3 and Section 7.2.4

152

DM 5: Combining Decision Units

Action Level

DU average andWeighted average


153

DM 6: Extrapolation to Unsampled Areas

Action Level

Sampled Decision Unit

Unsampled Decision Unit

Extrapolate

ITRC, ISM-1, Section 4.4.4.2 and Section 7.2.6

154

Making Decisions Using MIS Data

Making Decisions

Decision Mechanisms

Data Evaluation

155

Data Evaluation Components

Data EvaluationInterpreting error

Identifying sources of error

Quantifying error

Isolating sources of error

156

Identifying Sources of ErrorField• Number of increments • Increment collection• Field processing• Field splitting• DU size and shape

Laboratory• Lab processing• Subsampling• Extraction• Digestion• Analysis

157

Quantifying Error

Decision Unit

Data includes all sources of error

RSD = CV = standard deviation / arithmetic mean

ITRC, ISM-1, Section 4.3.1.3 and Section 7.3

158

Interpreting Error

• “Unacceptable” RSD• Low RSD• High RSD

ITRC, ISM-1, Section 4. 3.4.4 and Section 7.3

Unbiased Biased

Imprecise

Precise

159

Isolating Sources of Error

Adapted from EPA 2011, page 38: http://go.usa.gov/EAE

160

Making Decisions: Summary

• Determine appropriate decision mechanism• Calculate RSD• If RSD unacceptable isolate and quantify sources of error

161

How Does MIS Cost Compare?Elements• Planning• Field Collection• QA/QC Samples• Sample Transport• Sample Processing/Conditioning• Lab Analysis• Overall Sampling/Analysis Portion of Project

162

Bottom Line on Cost Comparisons

Measuring the cost difference between MIS and discrete sampling.

Measuring the cost of making a wrong decision.

163

Overview & Wrap-up

• Unbiased estimate of the mean

• Improved spatial coverage

• Increased sample representativeness

• Control over most common sources of sampling error

• Reduced data variability

• Ability to calculate 95% UCL

MIS Provides:

MIS

164


Day 3Sample Investigation Planning

26 September 2014

1

Topics for Day 3:

• Conceptual Site Model Development• Elements of a Sampling Plan• Steps for Developing a Sampling Plan• Review of Case Studies• Group Exercises and Report Outs

2

CONCEPTUAL SITE MODEL DEVELOPMENT

3

What is a Conceptual Site Model?• Conceptual site model (CSM) or site conceptual model (SCM)• A written or pictorial representation of an environmental

system and the biological, physical, and chemical processesthat determine the transport of contaminants from sources through environmental media to environmental receptors within the system

ASTM International: Standard Guide for Developing Conceptual Site Models for Contaminated Sites (Designation: E1689–95 [Reapproved 2014])

4

What is a Conceptual Site Model?

5

• Identifies potential contaminants• Identifies source(s) of contaminants• Establishes background/baseline levels of contaminants• Characterizes the source(s)• Identifies pathways that contamination could migrate to

receptors (human and ecological)• Identifies potential receptors (human and ecological)• Determines limits of the study area

What is a Conceptual Site Model?

6

ITRC Incremental Sampling Methodology Technical and Regulatory Guidance (February 2012)

• CSM can be presented in narrative, pictorial, and/or table formats


Step 1: Assemble Information

7

• Maps• Aerial

photographs• Cross sections• Environmental

data• Records,

reports, studies • Site visit

Step 2: Identify Contaminants

8

• In ground water• In surface water• In soils• In sediments• In air• In biota

Step 3: Establish Background/Baseline Concentrations of Contaminants

9

• Establish the naturally occurring (or baseline) concentrations of contaminants

• Establish the extent to which contamination exceeds background/baseline levels

Step 4: Characterize Source(s)

10

• Identify source location, boundaries, and volume of contaminated material

• Identify contaminant concentrations at thesource(s)

Step 5: Identify Migration Pathway(s)

11

• Identify how contamination can migrate from source(s) to receptors (human and ecological)

• For example:– Direct contact with contamination source– Migration to groundwater drinking water well– Erosion via surface water flow from contamination source to a lake

used for fishing– Dispersion through air via contaminated dust

• Tracking contaminant migration from sources to receptors (humans or ecological) is one of the most important uses of the conceptual site model!


12

• Groundwater pathway – when hazardous solid/liquid has come in contact with surface/subsurface soil, consider:– Distance from contamination to groundwater – Groundwater flow rates and direction– Geology and hydrology of the site– Presence of drinking water wells


13

• Surface water/sediment pathway should be investigated if: – Contamination is detected in surface water (rivers, lakes, streams,

drainage ditches, etc.) or sediments – Surface water is in contact with a contamination source or a pathway

exists from the source to the surface water

• Air pathway should be evaluated if contaminants in the surface soil, subsurface soil, surface water, or other media are capable of releasing gases or particulate matter to the air


14

• Soil contact pathway should be evaluated if contaminated soils may come into direct contact with receptors – Direct contact with skin– Direct exposure to gamma radiation (radioactively contaminated soil) – Potential for human and ecological receptors (e.g., plants or animals)

to be exposed to contaminants at different soil depths


15

• Biotic pathway should be considered if:– Contaminants in organisms can be transferred along food chains– Contaminants in soil/sediment can be transported by animal

movements

• Some contaminants in soil/sediments can bioaccumulate andbioconcentrate in organisms such as plankton, worms, or herbivores and biomagnify in organisms such as fish, mammals, or birds

• The movement of contaminated biota can transport contaminants (e.g., transplanting fish between ponds)

Step 6: Identify Environmental Receptor(s)

16

• Environmental receptors: human or ecological (plants, animals, etc.)

• Identify environmental receptors currently or potentially exposed to site contaminants– Receptors in direct contact with the contamination source– Receptors along the migration pathways of the contamination source – Potentially exposed human populations– Potentially exposed terrestrial and aquatic habitats

Conceptual Site Model

17

ITRC Incremental Sampling Methodology Technical and Regulatory Guidance (February 2012)

Conceptual Site Model

18


Bien Hoa Airbase Conceptual Site Model• Step 1: Assemble Information

– Population: 100,000 (immediate vicinity) and 1,200 (Airbase)– Historical context: storage and handling of Agent Orange during U.S.-

Vietnam war at the Airbase (Z1 Area, Southwest Area, Pacer Ivy Area) Current land use: military activities, cattle farming, rubber plantations, aquaculture

– Future land use: industrial, commercial, residential, perennial tree land– Drainage: generally west, south and southeast to Dong Nai River– Surface water: 32 lakes (varies, seasonal fluctuation)– Groundwater: 1 to 3 m depth, monitoring program with 6 wells– Environmental Data: sources of collection (O33, MND, Dong Nai DONRE,

etc.)

19

Bien Hoa Airbase Conceptual Site Model• Step 2: Identify Contaminants

– 2,3,7,8-TCDD in soil, sediment, groundwater (?), surface water (?), fish (?), humans (?)

– Arsenic (?), 2,4-D (?), 2,4,5-T (?), other (?)

20

Known Dioxin Contamination

21

Bien Hoa Airbase Conceptual Site Model• Step 3: Establish background/baseline concentrations of

contaminants– Dioxins/furans– Metals, VOCs, SVOCs, PCBs, general chemistry

• Step 4: Characterize sources– Z1 Area, Southwest Area, Pacer Ivy Area, Northeast Area, Northwest

Area– 200,800 m3 contaminated soil (mainly in Z1 Area, Southwest Area,

Pacer Ivy Area)– 29,200 m3 contaminated sediment (mainly in Z1 Lakes, Gate 2 Lake,

Pacer Ivy Lakes)

22

Bien Hoa Airbase Conceptual Site Model• Step 5: Identify migration pathways• Step 6: Identify environmental receptors

23

The Importance of a CSM for Developing a Sampling Plan• Use the CSM to synthesize all available site information and

determine whether information/data is missing• Develop the Sampling Plan to gather the missing information• Avoid collecting unusable data• Design an efficient and effective sampling investigation with

clear objectives

24

Data Gaps at Bien Hoa Airbase to be Addressed with Additional Sampling• What is the full nature and extent (i.e., lateral and vertical

extent) of dioxin contamination on and around the Airbase?• What amount of soil, sediment, and groundwater (if any) must

be addressed to close exposure pathways? • Which lakes require containment/remediation to prevent

human exposure to dioxin contamination?• What is the nature of non-dioxin contamination in the dioxin-

contaminated areas?

25

CONCEPTUAL SITE MODEL DEVELOPMENT

QUESTIONS?

26

ELEMENTS OF A SAMPLING PLAN

27

What is a Sampling Plan?• For purposes of this training, the term “Sampling Plan” refers

to a document that covers/includes all of the following: – Field Sampling Plan (FSP)– Field Work Instructions (FWI)– Sampling and Analysis Plan (SAP)– Quality Assurance Project Plan (QAPP)– Health and Safety Plan (HASP)

• The purpose of this training is to describe WHAT goes into sampling plans and WHY

28

Elements of a Sampling Plan• Site description, location, background• Data quality objectives (DQOs)• Field sampling methods and procedures• Sample handling and custody procedures• Field documentation procedures• Laboratory analytical methods, procedures, quality control• Field quality control• Data validation (data usability assessment)• Data management and reporting• Health and safety procedures

29

Site description, location, background• Summary of Conceptual Site Model (CSM)• The Sampling Plan should be designed to gather the missing

information from the CSM so that decisions can be made for next steps (i.e., remediation)

30

Data Quality Objectives (DQOs)1. State the problem2. Identify the goal of the study3. Identify the information inputs (what data are needed to

achieve the goal)4. Define the boundaries of the study5. Develop the analytic approach/decision rules6. Specify limits on decision errors7. Develop the sampling design

31

Procedures• Field sampling methods and procedures• Sample handling and custody procedures• Field documentation procedures• Laboratory analytical methods and procedures

32

Quality Control• QC sample type, frequency, and performance criteria• Field QC samples: field duplicates, equipment rinsates,

blanks, splits, etc.• Laboratory QC samples: laboratory duplicates, blanks,

laboratory control samples, matrix spikes, surrogate spikes, etc.

33

Data Validation, Management, Reporting• Data review, data verification, data validation

– Field records and forms– Field measurements– Analytical data reports– Analytical data validation– Data usability assessment: precision, accuracy, representativeness,

comparability, completeness, sensitivity

• Data management and reporting

34

Health and Safety• Identify the specific hazards at the site• Identify procedures for field staff to protect themselves from

site hazards, for example:– Personal protective equipment (PPE)– Decontamination– Safe work zones

• Identify procedures to be followed in case of a medical emergency

• Identify any health and safety monitoring to be conducted during sampling activities to protect the field staff

35

BREAK

AFTER BREAK…. STEPS FOR DEVELOPING A SAMPLING PLAN

36

STEPS FOR DEVELOPING A SAMPLING PLAN

37

Elements of a Sampling Plan• Site description, location, background (i.e., CSM)• Data quality objectives (DQOs)• Field sampling methods and procedures• Sample handling and custody procedures• Field documentation procedures• Laboratory analytical methods, procedures, quality control• Field quality control• Data validation (data usability assessment)• Data management and reporting• Health and safety procedures

38

SITE DESCRIPTION, LOCATION, BACKGROUND (I.E., CSM)

39

DATA QUALITY OBJECTIVES (DQOS)

40

Data Quality Objectives (DQOs)

1. State the problem2. Identify the goal of the study3. Identify the information inputs (what data are needed to


41

Step 1: State the problem• Dioxin contamination has been detected in soil at a location

that will be developed into a residential area.

42

Step 2: Identify the goal of the study• Determine whether the dioxin contamination in soil poses an

unacceptable danger to human health or the environment and requires remediation.

43

Step 3: Identify information inputs• Dioxin concentrations in soil• Action level/cleanup level for dioxin that is appropriate for

residential area

44

Step 4: Define the boundaries of the study• Spatial boundaries:

– Property boundary (no dioxin was detected outside the property boundary)

– Surface soil to depth of 15 cm

• Temporal boundaries:– Investigation will begin in 1 month and be completed in 1 year

• Scale of decision to be made (i.e., decision unit):– Decision to be made for each residential lot

• Practical constraints:– Building structures exist on the site

45

Step 5: Develop the analytic approach• If the dioxin concentration in the residential lot is greater than

300 ppt, then the soil in the residential lot will be remediated. [Site is not clean]

• If the dioxin concentration in the residential lot is less than or equal to 300 ppt, then the soil in the residential lot will not be remediated. [Site is clean]

46

Step 6: Specify limits on decision errors• False positive – Type 1 decision error – results say site is not

clean, but it is actually clean• False negative – Type 2 decision error – results say site is

clean, but it is actually not clean• Type 2 decision error is less acceptable than Type 1 decision

error; therefore, Type 2 errors should be minimized• Site clean decisions will be made based on the action level

minus 5% site is clean if dioxin is less than 285 ppt

47

Step 7: Develop the sampling design• Each residential lot will be a decision unit (DU)• 30-point MIS samples will be collected from each DU• Each sample will be analyzed for dioxin using EPA Method

1613B

48

FIELD SAMPLING METHODS AND PROCEDURES

SAMPLE HANDLING AND CUSTODY PROCEDURES

FIELD DOCUMENTATION PROCEDURES

FIELD QUALITY CONTROL

49

Field procedures• SOP describes general method for collecting samples.

Sampling Plan must provide specific detail on:– Location, depth, number of samples to be collected from each area– Number of sample containers, holding times, sample preservation

requirements– Type and frequency of field QC samples– Training/experience required for field team to conduct sampling– Field measurements to be collected, instruments to be used, and

calibration requirements for those instruments– Sample labeling system (i.e., sample IDs)– Field forms (field measurements, sample collection, chain of custody,

corrective action or deviation documentation)– Management of investigation-derived waste

50

LABORATORY ANALYTICAL METHODS/PROCEDURES

51

Laboratory analytical methods/procedures• List the analytical methods to be conducted for each sample

and the laboratory that will conduct analysis• Work directly with the laboratory to determine the following for

each analyte and matrix:– Reporting limits– Method detection limits – Laboratory control limits / measurement performance criteria – percent

recoveries (%R), relative percent difference (RPD)– Laboratory QC checks, frequency, acceptance criteria, and corrective

actions

52

Laboratory analytical methods/procedures

53


54


55

HEALTH AND SAFETY PROCEDURES

56

Health and Safety• Identify the specific hazards at the site• Identify procedures for field staff to protect themselves from

site hazards, for example:– Personal protective equipment (PPE)– Decontamination– Safe work zones

• Identify procedures to be followed in case of a medical emergency

• Identify any health and safety monitoring to be conducted during sampling activities to protect the field staff

57

REVIEW OF CASE STUDIES

58

Case Studies• Develop a conceptual site model (CSM)• Develop data quality objectives (DQOs)

59

GROUP EXERCISES

60

61

REPORT OUTS FROM GROUPS

62

REVIEW OF CASE STUDY AND GROUP EXERCISES

1

Case Study/Group Exercises• Review the Case Study• Breakout Group Exercise #1: Develop a conceptual site

model (CSM)• Report outs from groups• Breakout Group Exercise #2: Develop data quality objectives

(DQOs)• Report outs from groups• Breakout Group Exercise #3: Develop sampling plan• Report outs from groups

2

CASE STUDY

3

123 Tire Company• Former tire manufacturer• 2 hectare site • Industrial buildings – incinerator, shop building, warehouse• Stopped operation in 2005• Identified historical spill of cleaning solvents • Historical stack emissions

4

123 Tire Company Environmental Setting• Residential area 1 kilometer to the east• Groundwater flow direction: south• Wind direction: northeast• Lake northeast of residential area and seasonal stream flows

east to lake – both used for fishing and swimming by locals• Drinking water well south/southeast of residential area

5

Identified contamination• Elevated TCE detected in drinking water well• PAHs and dioxin detected in residential surface soils

6

Develop Data Quality Objectives (DQOs)1. State the problem2. Identify the goal of the study3. Identify the information inputs (what data are needed to


7

Develop Sampling Plan• Number and location of samples• Laboratory analyses• Schedule for investigation• Sampling equipment and supplies• QC samples• Health and safety requirements

8

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Washington, DC 20523 Tel: (202) 712-0000 Fax: (202) 216-3524

www.usaid.gov