CEP-A 02 Fate & Transport in Vadose plus.ppt · CEP—A 01 Introduction to Environmental Chemistry Chemistry for Environmental Professionals – APPLIED 2 ERTP Training Courses Course

Offi ce of Solid Waste andEmergency Response(5201G) February 2015

Chemistry for Environmental Professionals – Applied

Superfund

United StatesEnvironmental ProtectionAgency

Student Manual

Agenda

CHEMISTRY FOR ENVIRONMENTAL PROFESSIONALS — APPLIED

DAY and TIME SUBJECT SPEAKER

Day 1

8:15 – 8:30 a.m. Orientation/Introduction

8:30 – 9:30 a.m. Fate & Transport in the Vadose Zone

9:40 – 10:40 a.m. Fate & Transport in Ground Water

10:50 – 12:00 a.m. Process Chemistry – Module Option I

12:00 – 1:00 p.m. LUNCH

1:00 – 2:10 p.m. Process Chemistry – Module Option II

2:20 – 3:30 p.m. Process Chemistry –Module Option III

3:40 – 4:50 p.m. Process Chemistry – Module Option IV

Day 2

8:15 – 10:00 a.m. Data Usability

10:10 – 11:40 a.m. Data Quality Objectives

11:40 – 12:00 p.m. Closing Remarks/Evaluation

CEP—A 01 Introduction to Environmental Chemistry

Chemistry for Environmental Professionals – APPLIED 1

presented byTetra Tech, Inc.

for theU.S. Environmental Protection Agency's

Environmental Response Team

Chemistry for Environmental Professionals

- Applied -

Environmental ResponseTraining Program (ERTP)

Office of Solid Waste and Emergency Response (Superfund)

United States Environmental Protection Agency

Environmental Response Team

Office of Superfund Remediationand Technology Innovation

OSWER

U.S. EPA

ERT

OSRTI

• Are offered tuition-free for environmental and response personnel from federal, state, and local agencies

• Vary in length from one to five days

• Are conducted at EPA Training Centers and at other locations throughout the United States

ERTP Training Courses



ERTP Training Courses

Course Descriptions, Class Schedules, and Registration Information are available at:

• www.trainex.org

• www.ertpvu.org

Course Objectives

• Basic Chemistry

– Inorganic Chemistry

– Organic Chemistry

• Analytical Chemistry

• Chemical Fate and Transport

• Chemical Processes

• Data Evaluation

Course Materials

• Student Registration Card

• Student Evaluation Form

• Course Agenda

• Student Manual

• Facility Information

• Student Handouts



Facility Information

• Parking

• Classroom

• Restrooms

• Water fountains, snacks, refreshments

• Lunch

• Telephones

• Emergency telephone numbers

• Alarms and emergency exits

VIBRATE MODE

Please . . .In consideration of your fellow students and the instructors, please silence all cell phones and pagers

CEP—A 02 Chemical Fate and Transport in the Vadose Zone


Fate and Transport Processes

in the Environment

Percolation

SURFACEWATER

PhotochemicalReactions

MicrobialTransformations

RunoffVolatilization

Retardation

Degradation

Degradation

VADOSE ZONE

CAPILLARY FORCES

GROUNDWATER

Adsorption

DiffusionSolubility

Dispersion

Dispersion

Retardation

Diffusion

Adsorption

Chemical Fate and Transport in the Vadose Zone



Fate and Transport in the Vadose Zone Objectives

• Define terms: Vadose zone, capillary fringe, and matric potential

• Processes which distribute contaminants within the vadose zone: volatilization, solubility, and adsorption

• Movement and/or dilution of a contaminant plume: advection, diffusion, and dispersion

Vadose Zone

Unsaturated(vadose)

Capillary fringe

GW Flow

Saturated(unconfined aquifer)

Vadose Zone: Capillary Fringe

Coarse grain

Medium grain

Fine grain

Capillary fringe



Distribution of Water and Other Fluids in Vadose Zone

• Matric potential

• Adhesive and cohesive forces

• Wilting point

• Field capacity

• Horizontal and vertical distribution

Matric Potential

Vadose

GW flow

Adhesiveforces

Cohesiveforces

Free water

10,000

0

Saturated

Soil Peds or Particles Wetting

• Wetting of non-wetted soil

• Below soil's field capacity

• "Rolling off a duck's back"



Soil Peds or Particles Wetting (cont.)

• Water wet peds

• Contaminant wet peds

Contaminants Considered

• Metal ions

• LNAPLs

• DNAPLs

• Landfill / sewage leachate

Partitioning of Contaminant in Vadose Zone

• Solubility

• Volatilization

• Henry's Law

• Adsorption



Free-phasecontaminant

Soil H2O

Vapor

Solubility

• Free-phase – 100% liquid contaminant

• Dissolved phase – contaminant soluble in H2O

Free-phasecontaminant

Vapor

Soil mineraladsorption



Dissolvedphase

Soil H2O



Volatilization

• Vapor pressure

• Transport of contaminant vapor

Vapor

Vapor Pressure

all values reported at 20°C

Chemical VP (mm Hg) MW (g)

Acetone 180 58.08

Benzene 75 78.12

TCE 65 131.39

Phenanthrene <<1.0 178.24

PCB <<1.0 296.00



Vapor Pressure

Molecular Weight

Vapo

r Pre

ssur

e

Acetone

Benzene

TCE

Phenanthrene

PCB

Vapor Pressure Curves

10 20 30 40 50 60 70 80 90 100Temperature (C°)

800

700

600

500

400

300

200

100

Vap

or P

ress

ure

(mm

Hg)

Vapor Pressure Triple Point for Water

Temperature (°C)

Pres

sure

(atm

)

3740.01

0.006

218

waterice

gasTtp

Tcr



Vapor Pressure (cont.)

Conditions that affect volatilization:

• Concentration of contaminant vapor in soil-air

• Soil moisture content

• Soil matric potential

• Soil temperature

• Atmospheric pressure

Henry's Law Constant

• Describes the partitioning of a contaminant between air (vapor phase) and water (liquid phase)

• Vapor is non-reactive in water

Henry's Law

Compound VP (mmHg) Sol.(mg/L) HL ( )

VC 2,300 1,100 6.9 × 10–1

Benzene 76 1,780 5.4 × 10–3

TCE 58 1,100 8.9 × 10–3

MEK 71.2 268,000 2.7 × 10–5

PCP 0.00011 1 2.8 × 10–6

Atm-m3

mol

HL depends on VP and sol1



Henry's Law ConstantContaminant partitioning between soil-air and soil-liquid

Cw

Ca

Ca

Ca

Henry's Law Constant

How to quantify this process?

H = =

H =

H =

Ca Vapor pressureCw Solubility

atm-Lmole

VPCO2(g) (in atm)

Molar conc. CO2(aq) /liter

Example CO2

Adsorption

• Soil's adsorbent materials

• Retards liquid contaminant transport in vadose



Adsorbent Materials

• NOM – natural organic matter

– Nonpolar organic compounds

– Low solubility in soil-water with high surface areas

• Clays and metal ion complexes

• Adsorptive forces

Natural Organic Matter

• Fulvic acid

• Humin and humic acid

• Biopolymers

– Lignin

– Cellulose

– Proteins

Clays and Other Minerals

• Clays

– Montmorillonite

– Illite

– Kaolinite

• Metallic complexes

• Colloids



Crystalline Structure of Clay Minerals

OxygenAl

Other Adsorbent Environments

• Van der Waal's forces

• Chemisorption

– Sorption to NOM

– Sorption to metal complexes

Estimating a Soil's Adsorptive Capacity

• Cation exchange capacity (CEC)

• Adsorption coefficients

• Water solubility

• Cation exchange capacity (CEC)

• Adsorption coefficients

• Water solubility



[soil] 2H+ + Cu+2 [soil] Cu+2 + 2H+

CEC units:

– meq /100 g of soil

– centimoles of positive charge/kg soil

Cation Exchange Capacity (CEC)

Quantifying CEC

One equivalent (eq) of an ion is the weight which will

completely react with an equivalent of another species

1 eq =

Example:1 eq Cu+2 = = 31.773 g63.546g

2

Weight of 1 mole of ionValence charge of the ion

Defining milliequivalent (meq):

1 meq =

Example:

1 meq Cu+2 = × = .031773 g = 31.773 mg

Quantifying CEC

1eq1000

63.546g2

11000



CEC

Examples:

• Clays: 2–150 meq /100 g of soil

• Soil organic matter: >200 meq /100g of soil

• Sand: 2–7 meq /100 g of soil

Transport Processes of a Contaminant's Vapor

• Advection

• Gravity-driven

• Diffusion

• Dispersion – tortuosity of pathways

Transport Processes

• Primary porosity pathways

• Secondary porosity pathways



Primary Porosity Pathways

Secondary Porosity Pathways

• Faults

• Fractures and joints

• Bedding plane partings

• Exfoliation

Secondary Porosity Pathways



Advection

• Vapor movement by pressure gradient

• Causes of gradient

• Gas velocity

Gravity-driven

• Vapor density of pure air – 1200 g/m3 @ 20ºC

• Density of contaminant vapors

• Source of information – NIOSH Pocket Guide

Gravity-driven

Pure air 1200 g/m3

DCE 1949 g/m3

o-Dichlorobenzene 1211 g/m3

Benzene 1409 g/m3

DCM 2274 g/m3

Vapor Density



Diffusion of Vapors

• Solubility of contaminant in air spaces

• How large are the spaces?

• How continuous are the spaces?

Diffusion of VaporsConcentration vs. Distance from Source

Concentration gradient = = cx

x1

x2

Dispersion

Process which results in dilution of contaminant plume's concentration



Dispersion

Contaminant movement

Soil particles

Dispersion – Tendency for asolute to spread from thepath that it would beexpected to follow underadvective transport

The Real World

Case Study

CEP—A 03 Chemical Fate and Transport in Groundwater


Chemical Fate and Transport

in Groundwater

Fate and Transport in Groundwater Objectives

• Define hydrogeologic terms: Aquifer (unconfined or confined), hydraulic gradient, hydraulic conductivity, porosity, and permeability

• Define oxidation/reduction components: Electron donor, electron acceptor, metabolic by-products, and energy

Fate and Transport in Groundwater Objectives

• Describe how groundwater chemical parameters such as Eh, pH, chemical concentration, and specific conductance/total dissolved solids change in response to hydrocarbon contamination

• Define geochemical parameters: Alkalinity, hardness, and complexation

• Define contaminant retardation



Percolation

SURFACEWATER

PhotochemicalReactions

MicrobialTransformations

RunoffVolatilization

Retardation

Degradation

Degradation

VADOSE ZONE

CAPILLARY FORCES

GROUNDWATER

Adsorption

DiffusionSolubility

Dispersion

Dispersion

Retardation

Diffusion

Adsorption

What is an Aquifer?

• Matric potential: adhesive and cohesive forces are satisfied

• Free water now available to move through aquifer

AquitardLow K

Unconfined aquifer

VadoseWater level line

What is an Aquifer?

Unconfined Aquifer



Confined aquifer

AquitardLow K

potentiometric line

What is an Aquifer?

Confined Aquifer

AquitardLow K

• Geologic material

– Heterogeneity

– Porosity and permeability

– Mineralogy

Physical and Chemical Properties of Aquifers

Physical and Chemical Properties of Aquifers

• Hydrogeologic parameters

– Hydraulic gradient

– Hydraulic conductivity (K)

– Groundwater flow direction and velocity

– Matric potential

• Background chemistry of groundwater



• Metal ions

• LNAPLs

• DNAPLs

• Landfill/sewage leachate

Contaminants Considered

Electron donor + electron acceptor

(Metabolic) by-products + energy

Oxidation-Reduction Reactions


Electron donors:

• Petroleum hydrocarbons (LNAPLs)

• Landfill leachate and natural organic carbon

Electron acceptors:

• O2, NO3-, Mn+4, Fe+3, SO4

-2, CO2



Metabolic by-products:

• CO2, N2, Mn+2, Fe+2, H2S, CH4

• Energy for microbes, e.g., e-, ATP


Parameters to Evaluate Chemical Changes

• Eh / pH-related reactions

• Dissolved gases

• Alkalinity / pH buffer

• Specific conductance / total dissolved solids

• Hardness

• Complexation

Distance from source (or time)

Chemical Parameter Changes from LNAPL Contamination

Pa

ram

eter

co

nce

ntr

atio

n



Eh - pH

I0

I2

I4

I6

I8

I10

I12

I14

neutral

oxidizing

reducing

Eh (volts)

pH (s.u.)

+ 1.0

+ 0.8

+ 0.6

+ 0.4

+ 0.2

0.0

- 0.2

- 0.4

- 0.6

- 0.8

- 1.0

0 5 10

0.9

0.6

0.3

0.0

-0.3

Eh(volts)

pH (s.u.)

Pb

Pb2+

PbO2

Eh - pH Diagram for Lead (Pb)

BTEX concentrationHigh LowMedium

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vel )

Contaminated Groundwater Chemistry

Dissolved Hydrocarbon

"Dump"

Aquitard



Oxidation-Reduction Reactions in Contaminated Groundwater

Benzene oxidation - iron reduction:

C6H6 + 5O2 + Fe(OH)3

6CO2 + 7H+ + 9e- + Fe+2 + H2OBenzene


Oxidation of Hydrocarbons, e.g., Benzene

GW

Chemical oxygen demandMedium LowHigh

Aquitard

Landfill

GW

Oxygen concentrationMedium LowHigh

Aquitard

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"Dump"

Benzene oxidation - manganese reduction:

C6H6 + 5O2 + Mn(OH)4

6CO2 + 6H+ + 8e- + Mn+2 + 2H2O

Benzene




G W

Medium LowHigh

Aquitard

Landfill Manganese reduction

Mn+2 concentration

G W

Medium LowHigh

Aquitard

Landfill Iron reduction

Fe+2 concentration

Contaminated Groundwater ChemistryOxidation of Hydrocarbons, e.g., Benzene Iron and Manganese Reduction

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Benzene oxidation - sulfate reduction:

C6H6 + 5O2 + SO4-2

6CO2 + H+ + 2e- + HS- + 2H2O

G W

Medium LowHigh

Aquitard

Landfill

Sulfate concentration

G W

Medium LowHigh

Aquitard

Landfill

Sulfide concentration


Oxidation of Hydrocarbons, e.g., Benzene Sulfate Reduction/Sulfide Oxidation

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Benzene oxidation - denitrification:

2NO3- + 2H+ + C6H6 + 5O2

6CO2 + N2 + 4H2O

Medium LowHigh

Aquitard

G W

Medium LowHigh

Aquitard

Nitrate concentrationAmmonium concentration

G W


Oxidation of Hydrocarbons, e.g., Benzene Nitrification of Ammonia

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"Dump""Dump"

pH Changes

Example:

Benzene oxidation - iron reduction:

C6H6 + 5O2 + Fe(OH)3

6CO2 + 7H+ + 9e- + Fe+2 + H2O


Benzene



G W

Medium LowHigh

Aquitard

Landfill

G W

MediumLow High

Aquitard

Landfill

pH CO2(g) concentration


pH Changes

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Oxidating Hydrocarbons, e.g., Benzene Dissolved Gases

GW

"Dump"

Methane concentrationMedium LowHigh

Aquitard

GW

Medium LowHigh

Aquitard

Landfill

CO (g) concentration2

GW

Chemical oxygen demandMedium LowHigh

Aquitard

Landfill

GW

"Dump"

Oxygen concentrationMedium LowHigh

Aquitard


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HCO3-1 + CO3

-2 + OH- + CaCO3

• Provides buffer for pH changes

• H2O + CO2 = H2CO3

• H2CO3 = HCO3- + H+

• HCO3- = H+ + CO3

-2

Alkalinity



Elev

atio

n(fe

et a

bove

mea

n se

a le

vel)

Total Alkalinity as HCO3-

Landfill

Aquitard

MediumHigh Low

Alkalinity

Cations

Ca+2

Mg+2

Na+

K+

Fe+2

H+

Anions

HCO

CO

OH-1

Cl -1

SO

NO

Dissolved Ions

-1

3

-2

4

-2

3

-13

Specific Conductance vs. Total Dissolved Solids

Specific Conductance (SC) vs. Total Dissolved Solids (TDS)

• High SC measurements

• High TDS



Total dissolved solids

Leaking UST

Free-phase LNAPL liquid

MediumHigh Low

Total Dissolved Solids

Elev

atio

n(fe

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bove

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a le

vel)

Calcium and magnesium salts of

• Sulfate

• Chloride

• Nitrate

• Carbonate

• Bicarbonate

noncarbonate or permanent hardness

carbonate or temporary hardness

Hardness

Type

Soft

Moderately hard

Hard

Very hard

mg/L expressed as CaCO3

0 - 60

61 - 120

120 - 180

>180

Hardness



7

Me

tal

so

lub

ility

(m

ob

ility

)

pH (s.u.)

Pb(OH)+1Pb(OH)2

Fe(OH)3

Fe(OH)+2

Al(OH)3

Al(OH)+2

Al(OH)4-1

Pb(OH)3-1

Fe(OH)4-1

"Classic" Inorganic Solubility vs. pH

Complexation / Colloids

• Can change actual inorganic solubility

• Eh-pH dependent

• Humic and fulvic acid complexes

• Oxyhydroxide colloids, e.g., FeO – OH

• Other ionic colloids

Fe FeHO OHO

O O

O O

O O

Fe Colloid [FeO2(OH)-1]

Fe FeHO OHO



OO

O

O

HOC

O

O O

OH

OO

OH

HH

H

H

HH

H

H

H

H

H HC

C

CC

C

C

CC

C

C

C

C

C

C O HH

OH

C

O

OH

O

O

OHH

M+

M

M

Fulvic Acid Metal (M+2) Complex

Bedrock

Groundwater flow

Source

What about DNAPL Compounds?

LNAPL SourceDNAPL Source

Anoxic Oxygen begins to increaseOxic

Backgroundchemistry

Transformation of Chlorinated Solvents i.e., PCE




• Reductive dechlorination of DNAPLs by microbial degradation:

– PCE, TCE, TCA

• LNAPL electron donors

• DNAPL by-products:

– TCE, DCE and VC

• Benzene oxidation - PCE reduction:

– C2Cl4 + C6H6 + 5O2 + 2H2O

– 6CO2 + C2HCl3 + Cl- + 9H+ + 8e-

• Iron reduction - vinyl chloride oxidation:

– Fe(OH)3 + C2H3Cl + 2O2

– 2CO2 + e- + Cl- + 3H2O + Fe+2

PCE

VC

Contaminated Groundwater

Retardation Process



– Rd = 1+

– Adsorption Kd

Retardation (Rd)

Kd ρBΘ

• Kom, Koc

• Kow

• fom, foc

• Kd

Adsorption Coefficients

Cs

Cw (mmol/l)

Kd = = Koc x foc

Cs

Cw

Benzene Distribution



Case Study

The Real World

CEP—A 04 Data Usability


DATA USABILITY

for Environmental Data Generation

WARNINGAlthough these data have beengenerated and reviewed inaccordance with standard QA/QCprocedures, we make nojudgements or guaranteesregarding their ultimate usability

It is the responsibility of the userto determine whether this data isuseable for their intendedpurpose.

Signed: Al T. Chemist

Data Usability Objectives

• Objectives:

• Define detection limits, Sample Quantitation Limit (SQL), Method Detection Limit (MDL), and Instrument Detection Limit (IDL)

• Define data qualifiers, “U", “J", and “R"

• Define data quality indicators, precision, accuracy, etc.

Data Producers Chemists and Sampling Specialists

• Chemists

– Develop analytical methods– Perform analyses

– "Lords" of analytical chemistry

• Sampling Specialists– Develop sampling methods

– Write and implement sampling plans

– "Lords" of sampling world



Decision-Makers Project Managers, RPMs, etc.

• Decision-Makers

– Primary data users

– Not necessarily chemists or sampling experts

– Must be able to interpret information generated by data producers

– Must be assured that data is adequate for its intended use, i.e., decision to be made

Data Gathering System

What we often have is a failure to communicate

• Decision-Makers

– Must specify objectives for data collection activities

– Make decision based on what’s produced

• Data Producers

– Interpret data users' stated objectives

– Prepare and execute sampling plan

– Present data

Data Gathering System

Communication is essential to ensure data isappropriate for its intended use.



Environmental Data Quality

What are the Issues to be Discussed?

• General requirements

• Measurement limits

• Data qualifiers

• Data quality indicators

• Technical and legal defensibility


What are the General Requirements?



• Measurement sensitivity requirements– Parts per million (ppm) or parts per billion (ppb)

– Air, water, soil, biota, waste, etc.







• Data type

– Analyte-specific– Laboratory vs. field

– "Broad" spectrum preferred (at least one per medium)





• Data type

– Analyte-specific– Laboratory vs. field

– "Broad" spectrum preferred (at least one per medium)

• Media

Measurement Limits

Can be:

• Instrument detection limit (IDL)

• Method detection limit (MDL)

• Sample quantitation limit (SQL)

• Others (e.g., CRQL, PQL, LOD)



Instrument Detection Limit

• Describes instrument sensitivity under ideal conditions

• Operational definition: 3 × SD of seven replicate analyses at lowest concentration that is statistically different from blank

• Neither method-specific nor sample-specific

• Least useful measurement for assessing data quality

Instrument Detection Limit

Analyte

IDL

Instrument Noise

3 x SD

Method Detection Limit

• Minimum analyte concentration that can be reliably identified by a specific analytical method

• Operational definition: 3 × SD of seven replicate spike samples run according to the complete method

• Accounts for sample size, reagents, preparation, etc.

• Not sample-specific; determined under ideal conditions



Method Detection Limit

Analyte

Old IDL

3 x SD

MDL

Sample Quantitation Limit

• Operational definition: MDL adjusted to reflect sample-specific action, e.g., extra dilution, sample size adjustment

• Useful measurement limit

• SQL may vary from sample to sample


= 10)Diluted sample X (Df

Conc. read Actual SQL by analyst sample conc.

25 7500 ?250 400 4000*

*(IR) × (Df) = Actual concentration inoriginal sample

400 × 10 = 4000

Sample X

Linear Range

50 100 150 200 250 300 350 400 450 500

Concentration (mg/m 3 )




Diluted sample X (Df = 10)

Conc. read Actual SQL by analyst sample conc.

25 7500 ?250 400 4000*

*(IR) × (Df) = Actual concentration inoriginal sample

400 × 10 = 4000

Sample X

Linear Range

50 100 150 200 250 300 350 400 450 500

Concentration (mg/m 3 )

Measurement Limits

Others• Contract-Required Quantitation Limit (CRQL)

– Performance standard for EPA's Contract Lab Program (CLP); Applies to organic analyses (CRDL applies to inorganic analyses)

• Practical Quantitation Limit (PQL)– Lowest limit reliably achievable under routine laboratory

conditions• Limit of Detection (LOD)

– Lowest concentration level that can be determined to be statistically different from a blank (99% confidence)

Data Validation and Data Usability

• Data validation is defined as those procedures used to determine whether the sample analyses meet the predetermined performance criteria for the analytical method used.

• The impact of the specific performance acceptance criteria is noted by appending qualifiers on each data point, as required.

• Data usability refers to the process of evaluating the data validation results and determining the confidence with which any data point may be used.



Data Validation and Data Usability

• Usability is determined by evaluating the data validation qualifier applied and the laboratory QC results.

• Data quality indicators (DQIs) are qualitative and quantitative measures of data quality “attributes,” which are descriptors used to express various properties of analytical data.

• Thus, DQIs are the various measures of the individual data characteristics that collectively comprise the general, all encompassing term “data quality”.

Qualified Data

• Chemists' "codes" to identify data inadequacies

• Indicative of QC problems with a sample or set of samples

• Data may be qualified by:

– Laboratory: good

– Independent third party: better

Qualified Data

Common Data Qualifiers

U The chemical was analyzed, but was not detected. The associated numerical value is the sample quantitation limit (or other measurement limit).

J The chemical is present. The associated numerical value is of less certain quantitation (an estimated quantity).

R The data are unusable (chemical may or may not be present). Resampling and reanalysis are necessary for verification.

Qualifier Analyses Explanations



Applying Data Qualifiers to Quantitation Limits

Sample 1 = "U" – not detectedSample 2 = "J" – uncertain quantitation, estimated valueSample 3 = no qualifier

- Certain Detection- Certain Quantitation

- Certain Detection (analyst's judgment)- Uncertain Quantitation

- Uncertain Detection- Uncertain QuantitationSample 1

Sample 2

Sample 3

Con

cent

ratio

n be

nzen

e (µ

g/l)

SQL

Applying Data Qualifiers - Student Exercise

- Certain Detection- Certain Quantitation

- Certain Detection (analyst's judgment)- Uncertain Quantitation

- Uncertain Detection- Uncertain QuantitationSample 1

Sample 2

Sample 3

Con

cent

ratio

n be

nzen

e (µ

g/l)

SQL

7

6

5

4

3

2

1

0

Sample 1 = Sample 2 = Sample 3 =

What are the reported results?

Data Quality Indicators - PARCC Parameters

• Tools for describing, specifying, defining data quality

– Precision

– Accuracy

– Representativeness

– Comparability

– Completeness

• Specify in Quality Assurance Project Plan (QAPP) and /or Sampling and Analysis Plan (SAP)



Precision

• Measurement of the degree of agreement among individual measurements

• Usually evaluated using duplicate samples

• Evaluates relative percent difference (RPD)

RPD = x 100| S1 – S2 |

(S1 + S2) / 2

Precision

RPD = x 100 = x 100 = 26%| 100 – 130 |

(100 + 130) / 230115

TCE Analysis100 µg/l

TCE Analysis130 µg/l

Sample 1DuplicateSample 1

Is this acceptable?

Accuracy

• Closeness of a measurement to the true value

• Usually evaluated using spiked samples

• Results reported as "bias" (high or low), a component of accuracy

• Use of field spikes is not recommended for soils



Precision and Accuracy

Low Precision + Low Bias =Low Accuracy

High Precision + High Bias =Low Accuracy

Low Precision + High Bias =Low Accuracy

High Precision + Low Bias =High Accuracy

Measurement Error

What factors affect Precision and Accuracy?

• Random Errors

– Uncertainties inherent in every physical measurement

– As likely to be positive as negative (unbiased)

– Limit precision of measurement

– Detectable via multiple measurement, e.g., duplicates

Measurement Error

What factors affect Precision and Accuracy?

• Systematic Errors

– Defect in method, sampling technique or design, instrument malfunction, or analyst error

– Results in "biased" data, i.e., the error tends to be mostly high or mostly low



Measurement Error

Random Error100

Systematic Error100

True Value

True Value

= Sample Value

Evaluating Bias – Example

Analysis Analysis

20 ppb Pb

Is this the true value?

25 ppb Pb

Expected value =30 ppb Pb

Sample 1Sample 1 +10 ppb Pb(spiked)

Evaluating Bias – Example

% recovery = x 100

% recovery = x 100 = 50%

(spiked result) – (unspiked result)amount spiked

25 ppb – 20 ppb10 ppb

Is this acceptable?



Representativeness

• Data must be representative of exposure or assessment area

• Representative samples depend on study objective

– Average vs. maximum exposure, upper percentile, etc.

– Surface soil interval

– Temporal and / or spatial variability

• Non-representative samples introduce bias into data

Comparability

• Qualitative evaluation – can different data sets be combined? Confidence with which one data set can be compared with another.

• Consider differences in:

– Sampling design– Analytical methods

– Sample preparation

– Laboratories

– Media variability

Completeness

• Measure of the amount of usable data

• Acceptable data must be available for "decision-critical" samples

• Data may be incomplete for a number of reasons:

– Sample loss – broken bottle or couldn't collect sample

– Analytical loss – mercury analysis qualified "r," rejected



Technical and Legal Defensibility

• Standard Operating Procedures (SOPs)

• Standard Analytical Methods

• Quality Assurance / Quality Control (QA/QC) Samples

Standard Operating Procedures

• SOPs – detailed standard and accepted procedures, protocols, and methods for specific tasks

• Developed by recognized authority (EPA, ASTM, state water department)

Standard Analytical Methods

• Analytical methods

– EPA

– USGS

– OSHA

– ASTM

• Recognized by courts

• Increase comparability



QA/QC Samples

Field samples

– Field blanks

– Trip blanks

– Rinsate blanks

– Duplicates

QA/QC Samples

• Laboratory samples

– Reagent blank / method blank

– Matrix spike / matrix spike duplicate

– Duplicates

– Performance evaluation (PE) samples

QA/QC Limits of Acceptability

• Permissible blank contamination

• Duplicate sample precision

• Matrix spike sample accuracy



The Real World

Case Study

The Real World - Case Study

Given Ohio EPA site assessment data:

• What was the sample matrix for DRM001?

• Convert DRM003 result for xylenes (total) to %

• Why might some results be reported, although below the SQL? (e.g., methylene chloride for DRM001)

The Real World - Case Study

Given Ohio EPA site assessment data:

• Suggest reasons why the SQL associated with sample DRM003 is so high

• What decisions, if any, might the data support?



Summary

Considerations for Data Usability

• What is the intended use of the data?

• What are the required quantitation limits?

– Were these limits achieved?

• Have the data been qualified?

Summary

Considerations for Data Usability

• Is the precision (random variability) known?

– Will it affect the usability of the data?

• Is the magnitude and direction of bias known?

– Will it affect the usability of the data?

CEP—A 05 The Data Quality Objectives Process


TheData Quality Objectives

ProcessFor Environmental Data Generation

Data Quality Objectives Process

Objectives

• Describe the components of a Quality Management System, including objectivity of approach, use of the “scientific method”, and acceptability of results

• Identify and define the 7 DQO process steps

• Define false positive and false negative decision errors

Quality

What is Quality?

• Peculiar and essential character

• Fitness for intended use

• Quality can be "good" or "bad"

• Customers prefer "good" quality and good value



Quality

What is Quality?

• A wise customer

– Knows data needs – Needs are clearly

communicated

– Checks data before using

Quality

What is "good" Quality?

• Doing things right the first time

• Adherence to standards

• Meeting expectations precisely or within tolerable limits

• Appropriate for the intended use

Quality

What is "good" Quality?

• Good quality is not an accident

• It is the result of:– Planning– Assessment– Investment– Communication

• Organizations must employ management systems to ensure goals are attained



Quality

Quality Management System (QMS)

• A management system for ensuring that quality goals are attained (QA/QC)

• EPA’s plan for ensuring data quality

– EPA Quality Program Policy, CIO 2106.0– EPA Procedure CIO 2105.0 (formerly EPA Directive

5360.1 A2)

– Policies and program requirements

Quality

Quality Management System (QMS)

Quality Management System(QMS)

Quality Assurance(QA)

Quality Control(QC)

Quality Management System

Quality Assurance

• QA: System of management activities designed to ensure that a process, service, or product is of the required quality

– Quality assurance manager (QAM)

– Quality management plan (QMP)- Policies and procedures

- Authority and responsibilities


(QMS)

Quality Assurance

(QA)

Quality Control

(QC)




Quality Control

• QC: Technical activities that measure performance relative to standards for verification that those standards are met

– Calibrating instruments

– Duplicate samples, etc.

• Is a quality assurance project plan (QAPP) QA or QC?


(QMS)

Quality Assurance

(QA)

Quality Control

(QC)

Quality Assurance Project Plan (QAPP)

• QAPP must address:

– Project management and responsibilities– Measurement methods and procedures

– Assessment/oversight activities

– Data validation and usability criteria

• Problem: How does the decision-maker formulate and then communicate data needs?

Data Quality Objectives (DQO) Process

• Structured tool for project planning that consists of seven iterative steps

• Provides communication between the data collector and the data user

• Ensures data will be appropriate to support decision-making

• Provides basis for developing QAPP and/or sampling and analysis plan (SAP)/field sampling and analysis plan



Data Quality Objectives (DQO) Process

• Conceptual Site Model (CSM)

– Presents overall understanding of the site– Updated throughout the life of site activities

– Important communication tool for regulators, remediating parties, and stakeholders

– Development of this model is critical for determining potential exposure routes (for example, ingestion and inhalation) and for suggesting possible effects of the contaminants on human health and the environment

Scope of the DQO Process

Who uses the DQO process?

• Applicable to all types of environmental sites• Applicable to various levels of investigation

– Initial investigation through cleanup confirmation

• U.S. EPA-developed guidance

– Guidance on Systematic Planning Using the Data Quality Objectives Process (EPA/240/B-06/001)

– Systematic Planning: A Case Study for Hazardous Waste Site Investigations (EPA/240/B-06/004)

– Data Quality Objectives Process for Hazardous Waste Site Investigations (EPA/600/R-00/007)

Why The DQO Process

To enhance the probability of arriving at a decision that is morally, ethically, and legally defensible



Seven Steps of the DQO Process

1. State the Problem to be Solved

2. Identify the Decision to be Made

3. Identify the Inputs to the Decision

4. Define the Study Boundaries

5. Develop a Decision Rule

6. Specify Tolerable Limits on Decision Errors

7. Optimize the Design for Obtaining Data

DQO Process Example Site

X XX

X

X

X

X

X

X

X X X XLagoon

Facility

Houses with Drinking Water (DW) Wells

Monitoring Wells (MW)

X

X

X

X

X

N

Step 1: State the Problem

Clearly define the problem so that the focus of the project will be unambiguous.

• What is the environmental and regulatory context?

• What are the known or suspected contaminants, sources, pathways, and receptors?

• What is the appropriate regulatory authority?

• What are the limits on budget, equipment, and time?



Step 1: State the Problem

• Concisely describe the problem to be solved.

• A concise problem statement describes:– The problem as it is currently understood

– The conditions that are causing the problem

In order to…….

Step 2: Identify the Decision

When you know the problem, what do you need to decide?

Identify the questions that must be addressed in order to resolve the problem statement, in this case including

• whether the lagoon is releasing contaminants to the aquiferand

• whether nearby DW wells are contaminated above health-based standards or action levels

Step 2: Identify the Decision

• For decisions to be valid, there must be two or more alternative actions

• Data are needed to choose between the alternative actions

• For example: Further action required? No further action required? Constituents in DW wells are above health-based standards or action levels or are not above health-based standards or action levels.



Step 3: Identify Inputs to the Decision

What information is needed to support the decision? Identify the information that needs to be obtained and the measurements that need to be taken to resolve the decision statement.

• In this case, is there a release from the lagoon?– GW samples from monitoring wells– Monitoring well depths and construction– Background GW samples– Source samples from lagoon– GW flow direction– Historical information

Step 3: Identify Inputs to the Decision

What information is needed to support the decision?

• In this case, have residential wells been affected, and at what level?

– DW well samples

– Background samples– Residential well depths and construction

– Identify appropriate health-based standards / action levels

Step 4: Define Study Boundaries

What are the study’s spatial and temporal boundaries? Determine generally when and where data should be collected.

• Spatial – Limited to aquifer(s) in actual or potential contact with the lagoon

• Temporal – Potential health effects dictate that residential well study be done quickly. Confirmation of release from the lagoon should take into account seasonal GW flow variability.



Step 5: Develop a Decision Rule

Integrate information into a single statement that describes the logical basis for choosing among alternative actions.

• Develop a decision rule as an “If…, then” statement that incorporates the parameter of interest, the unit of decision making, the action level, and the action(s) that would result from the resolution of the decision.

Step 5: Develop a Decision Rule

"If..., then" statement

• If mean contaminant concentration in monitoring wells is greater than background, then GW contamination exists and further investigation is needed to determine what / if remedial response is necessary

• If maximum contaminant concentration in DW wells is above action levels, then an immediate response is required (carbon adsorption, bottled water, etc.) to protect residential population

Step 6: Specify Limits on Decision Errors

What amount of error is tolerable? Define the decision maker’s tolerable decision error rates based on a consideration of the consequences of making an incorrect decision

• Consider consequences of making an incorrect decision

• Must communicate with decision-maker

• Select analytical methods and QA/QC procedures that will provide appropriate data for the decision



Step 6: Specify Limits on Decision Errors

Two types of error to consider, which affect decisions:

• Sampling Error - This error (variability) is influenced by the sample collection design, the number of samples, and the actual variability of the population over space and time

• Measurement Error - This error (variability) is influenced by imperfections in the measurement and analysis system

The goal of planning is to develop a data collection design that reduces the chance of making a decision error to a tolerable level

Decision Errors

• False Positive– A contaminant is not actually present above decision

point but is mistakenly identified as such– Example: laboratory contamination of sample with

phthalates

• False Negative– A contaminant exceeds the decision level but is not

correctly quantified– Example: error in extraction procedure produces extract

with too little contaminant

False Positive Decision Errors

• Example: A release to the aquifer is identified when in reality there has been no release

• Consequences:

– Expend resources on unnecessary remediation or further study

– No adverse consequences to human health



False Negative Decision Errors

• Example: A release to the aquifer which exceeds health-based action levels is not identified

• Consequences:– No further resources are expended for investigation or

cleanup– Potential exists for adverse human health effects– In this example, the false negative decision error carries

the more severe consequences

Specify Limits on Decision Errors

Pro

bab

ility

of

Dec

idin

g t

he

Par

amet

er

Ex

ceed

s th

e A

ctio

n L

evel

True Value (ppm)Action Level

40 50 60 70 80 90 100 110 120 130 140 150 160 170 180

Gray region: Large decision error ratesdefault to more conservative decision

Step 7: Optimize the Design

Evaluate information from previous steps and generate alternative data collection designs. Choose the most resource-effective design that meets the planning process objectives.

• Develop resource-effective sampling and analysis design that satisfies all DQOs

• Document operational details (QA/QC, sampling locations, chain-of-custody, etc.)



The Real World

Case Study

Case Study

• Situation June 1996: Source removal completed

– 14,000 drums excavated and disposed

– 10,000 yd3 impacted soil treated and disposed

– What additional threats might remain? [Problem]

– Use DQO process (steps 2–4 only)

Case Study – DQO Steps

• Step 1: State the problem:

– Authority: Superfund– Resources: $30-300K (10–100 samples); 6 mo.

Timeframe (non-time-critical)

– Team members: Group of 3–5 students

• Step 2: Identify decision(s): include rationale (any and all pathways you might wish to consider)



Case Study – DQO Steps

• Step 3: Identify inputs: (sediment pathway only)

• Step 4: Define study boundaries: (sediment pathway only); locations, depth interval, sampling strategy, etc.

Summary

• Quality Management System (QMS) – Planning, management, and auditing (QA)– Measurement and assessment (QC)

• DQO process– Seven-step process– Requires knowledge about chemistry, fate, and

analytical methods, among other items

• Decision errors– False positive/false negative– Must specify acceptable limits

Documents

CEP-A 02 Fate & Transport in Vadose plus.ppt · CEP—A 01 Introduction to Environmental Chemistry Chemistry for Environmental Professionals – APPLIED 2 ERTP Training Courses Course