Procedures for Determining Site-Specific Background Conditions and Their Impact on SiteRemediation

CPANS – 2012 Spring Conference

April 24, 2012

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Authors and Presenter

Authors

Anne G. Way

Tai. T. Wong

Yong Li

James G. Carss

Presenter

Anne Way, P. Chem. anne.way@parsons.com

O’Connor Associates - A Parsons CompanyCalgary, Alberta

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Outline

What is background? Provincial and

Federal Guidance Methodology

Picking locations Calculation What about outliers?

Case studies

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What is Background?

Concentration of a substance in the environment that can be attributed to natural sources Can also include anthropogenic sources, as long as they

are not specifically related to site activities

Can vary regionally and locally based on the soil and bedrock present So use of federal/regional background data for site-

specific remediation is generally not a good idea

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Provincial and Federal Guidance

SourceMethod Description

ReferenceSoil Groundwater

Ontario MOE 97.5 percentile 97.5 percentile

ON MOE 2011. Rationale for the Development of Soil and Groundwater Standards for Use at Contaminated Sites in Ontario

BC MOE

95 percentileOutlier removal:data ≥ Q3 + (1.5 x IQR)data ≤ Q1 – (1.5 x IQR)

95 percentile

BC MOE 2004. Protocol 9 for Contaminated Sites – Determining Background Groundwater QualityBC MOE 2005. Technical Guidance on Contaminated Sites #16, “Soil Sampling Guide for Local Background Reference Sites

AEW95 percentileOutlier removal:data ≥ 2 x stdev + mean

Not specified AENV 2009. Subsoil Salinity Tool

USEPA

95 % UPL (normal dist)Outlier removal:Rosner or Dixon’s test95 percentile (non-parametric)Outlier removal:Rosner or Dixon’s test

95 % UPL (normal dist)Outlier removal:Rosner or Dixon’s test95th percentile (non-parametric)Outlier removal:Rosner or Dixon’s test

Singh and Singh 2010. ProUCL Version 4.1.00 Technical Guide (Draft), EPA/600/R-07/041

Other mean + (3 x stdev) mean + (3 x stdev)

“Recommended” Procedures for Calculating Background

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Methodology - Locations

Background locations need to match onsite conditions, but are unaffected by anthropogenic activities As close in distance to site as possible

Can use non-impacted onsite areas in SOME cases (EPA 2002) Not influenced by site activities (upgradient, up-wind, up-hill) Match geological strata represented by site characterization data

Representative of range of soil samples to which they will be compared (more than one area may be required)

In most cases, this idealized background location does not exist

EPA. 2002. Guidance for Comparing Background and Chemical Concentrations in Soil for CERCLA Sites . EPA 540-R-01-003, U.S. Environmental Protection Agency, Office of Emergency and Remedial Response, Washington, DC.

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Methodology - Locations

Complications Complex site history Incomplete site characterization/conceptual site model Minimal resources Limited availability of background information

e.g., Sites located within cities

How many locations? The more the better!

Larger number of samples more accurate estimate lower error rates

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Methodology - Calculations

Histograms Assess shape of data

Symmetric (normal distribution) Skewed (logarithmic, other)

Assess spread of data Tightly clustered around a certain value? Stay within certain limits?

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Methodology - Calculations

Box and whisker plots Shows the shape, central tendency and variability of the data Useful for comparing several data sets

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Methodology - Calculations

Percentiles The nth percentile has n % of the data below it and

(100-n) % of the data above it Based on your current data set

Will change with additional data

Prediction Limits (PLs) The upper bound of the associated prediction limit (UPL)

“about 95% of the time, or I am 95% confident that, the next future observation taken will be less than X”

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Methodology – What About Outliers?

“An observation that does not conform to the pattern established by other observations” (Hunt et al. 1981) An unavoidable problem

Sources of outliers Recording, transcription, data-coding errors Calibration problems, unusual sampling conditions Manifestations of larger spatial or temporal variability than

expected e.g., small-scale variability within individual soil samples

Indication of unsuspected contamination

Hunt, W.F., Jr., Akland, G., Cox, W., Curran, Frank,N., Goranson, S., Ross, P. Sauls, H., and Suggs, J. 1981. U.S. Environmental Protection Agency Intra-Agency Task Force Report on Air Quality Indicators, EPA-450/4-81-015. Environmental Protection Agency, National Technical Information Service, Springfield, Va.

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Methodology – What About Outliers?

May bea) True measurements of conditions on-site

b) An actual error

Must identify which class the outlier falls into! Both outlier tests and a qualitative review of field and

laboratory data should be used to determine if the data point should be eliminated from the data set Many different types of outlier analysis

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Methodology – What About Outliers?

EPA (2002) recommends 5 steps to treat outliers1. Identify extreme values that may be potential outliers

Box plots, histograms

2. Apply statistical tests Dixon’s, Rosner’s, others….

3. Review statistical outliers with qualitative field and laboratory data Decide on their class (true measurement or error)

4. Conduct data analysis with and without outliers

5. Document everything!

EPA. 2002. Guidance for Comparing Background and Chemical Concentrations in Soil for CERCLA Sites . EPA 540-R-01-003, U.S. Environmental Protection Agency, Office of Emergency and Remedial Response, Washington, DC.

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Case Study 1

Site in Alberta Former oilfield facilities: well, pump jack, scrubber shack, storage

tanks, pipelines Currently agricultural land use Stratigraphy: silt and/or clayey silt with inter-bedded

discontinuous sand lenses Fine-grained, fine-textured soils

Salinity related contaminants of concern in soil Tier 1 Salt Contamination Remediation Guidelines

(SCARG) evaluation Need to calculate background for EC and SAR

Limited site characterization and budget

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Case Study 1

Well Head

Area of Potential Impact

Background locations

Background locations Representative of un-

impacted soil Collected near area of

potential salinity impact

Used to identify a soil rating category Upper limit of the soil

rating category becomes the guideline

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Case Study 1

Histograms not very useful in this case

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Case Study 1

Boxplots of background data vs. Area of Potential Impact (AOPI) Data

EC and SAR data distributions are very similar in background vs. AOPI IQR is smaller in the AOPI since there

is more data Medians very similar No identified “potential outliers”

Indicates that any elevated EC/SAR located in the AOPI may be natural and not-site related

All depths included

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Case Study 1

Calculation of Background

Sub-divided into different depths intervals

USEPA UPL method could not be performed: Data not quite

normal Not enough data

(8-10 min)

Electrical Conductivity (dS/m) 0 – 0.3 m 0.3 – 1 m 1 – 1.5 m > 1.5 mSummary Stats (w/o outlier removal)

Number of Observations 6 7 7 22

Average 0.4 0.5 4.2 7.8

Standard Deviation 0.14 0.14 5.9 2.4

Maximum 0.6 0.7 14.0 13.0

Minimum 0.3 0.4 0.4 3.7

Background Calculation Methods

ON MOE (97.5th Percentile) 0.6 0.7 13.4 12.5

BC MOE (95th Percentile) 0.6 0.7 12.8 12.0

AEW (95th percentile with outlier removal) 0.6 0.7 12.8 12.0

USEPA 95% UPL with outlier removal (normal dist)

97.5th Percentile with outlier removalID0.6

ID0.7

ID13.4

ID12.5

Other (average + 3 Stdev) 0.9 0.9 24.7 15.0

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Case Study 1

Calculation of Background

Sub-divided into different depths intervals

USEPA UPL method could not be performed: Data not quite

normal Not enough data

(8-10 min)

Electrical Conductivity (dS/m) 0 – 0.3 m 0.3 – 1 m 1 – 1.5 m > 1.5 mSummary Stats (w/o outlier removal)

Number of Observations 7 6 6 22

Average 1.07 2.7 6.5 11.3

Standard Deviation 2.1 3.26 5.3 4.5

Maximum 5.4 7.0 14.0 15.0

Minimum 0.16 0.23 0.7 1.0

Background Calculation Methods

ON MOE (97.5th Percentile) 4.8 7.0 13.5 15.0

BC MOE (95th Percentile) 4.1 7.0 12.9 15.0

AEW (95th percentile with outlier removal) 0.3 7.0 12.9 15.0

USEPA 95% UPL with outlier removal (normal dist)

97.5th Percentile with outlier removalID0.3

ID7.0

ID13.5

ID15.0

Other (average + 3 Stdev) 7.4 12.5 22.3 24.8

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Case Study 1

Within each depth interval, background is calculated and used to identify a soil rating category

Upper limit of the soil rating category becomes the guideline for that depth interval

Identified Soil Rating Category for SAR 0 – 0.3 m 0.3 – 1 m 1 – 1.5 m > 1.5 mBackground Calculation Methods

ON MOE (97.5th Percentile) Fair (8) Fair (8) Unsuitable (14) Unsuitable (15)

BC MOE (95th Percentile) Fair (8) Fair (8) Unsuitable (13) Unsuitable (15)

AEW (95th percentile with outlier removal) Good (4) Fair (8) Unsuitable (13) Unsuitable (15)USEPA

95% UPL with outlier removal (normal dist)97.5th Percentile with outlier removal

IDGood (4)

IDFair (8)

IDUnsuitable (14)

IDUnsuitable (15)

Other (average + 3 Stdev) Fair (8) Unsuitable (13) Unsuitable (22) Unsuitable (25)

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Case Study 1

Excavation required based on BC, MOE,

AEW, USEPA and “other” method

Additional excavation required based on AEW and USEPA method

Different excavation area depending on background calculation method

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Case Study 2

Former fertilizer facility in Manitoba Fertilizer contaminants in soil and groundwater Native soil profile: inter-layered silt and clay to

4.4 mbg, fractured bedrock below Groundwater in overburden: 1 mbg Depth to groundwater in bedrock: 11 to 14 mbg Groundwater ingestion pathway a concern

Water wells within 500 m of the site No aquitard between the impacted zone within

overburden and the underlying bedrock aquifer

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Case Study 2

Calculation of background nitrate in groundwater required Possible non-site related anthropogenic sources from residential

septic tanks located upgradient of site Suspect that background nitrate is greater than the CCME drinking

water standard of 10 mg/L Required a soil clean-up criteria to delineate site-related nitrate

impacts Calculated based on background groundwater criteria

Complications Choosing appropriate background locations Potential seasonality of groundwater concentrations Difficulty in determining groundwater flow direction

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Case Study 2

Site

500 m radius line

Downgradient locations in green

Background locations in blue Representative of un-impacted soils Takes into account potential, non-site

related sources of nitrate Collected upgradient from site

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Case Study 2

No significant seasonal fluctuationsSite data has different distribution than background data

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Case Study 2

All data, fall and spring distributions are very similar to each other in both background and site data Negligible seasonal variability

Background data distributions different than Site data distributions IQR is larger in the site data More identified “potential outliers”

on site Indicates that elevated nitrate

concentrations onsite are site-related (above background)

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Case Study 2

Background calculation method comparison

Since no significant seasonal fluctuations, only calculated for all seasons

All site-specific backgrounds are above CCME drinking water standard of 10 mg/L

Nitrate (mg/L) All SeasonsSummary Stats (w/o outlier removal)

Number of Observations 53Average 7.2

Standard Deviation 3.6Maximum 20.0Minimum 1.1

Background CalculationsON MOE (97.5th Percentile) 15.7

BC MOE (95th Percentile) 13.2AEW (95th percentile with outlier removal) 11.0

USEPA 95% UPL with outlier removal (normal dist)

97.5th Percentile with outlier removalNA

15.7Other (average + 3 Stdev) 18.0

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Case Study 2

b

aWOCOCwatersoil

HfKCC

'

Partitioning calculation for site-specific soil criteriaUsing standard CCME parameters

Nitrate Groundwater (mg/L) Soil (mg/kg)

Background Calculations

ON MOE (97.5 Percentile) 15.7 3.4

BC MOE (95 Percentile) 13.2 2.8

AEW (95 percentile with outlier removal) 11.0 2.4

USEPA 95% UPL with outlier removal (normal dist)

97.5 Percentile with outlier removalNA15.7 3.4

Other (average + 3 Stdev) 18.0 3.9

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Case Study 2Different excavation area depending on background calculation method

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Conclusions

BC MOE and ON MOE methods are very similar

Methods including outlier analysis yield different results than those that don’t Take care in the identification and treatment of outliers

Other methods (e.g, average + 3 x stdev) often yield background values that are above the data maximum Use at your own discretion