CPANS – 2012 Spring Conference

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

April 24, 2012

Authors and Presenter

Authors Anne G. Way Tai. T. Wong Yong Li James G. Carss Presenter Anne Way, P. Chem. [email protected]

O’Connor Associates - A Parsons Company Calgary, Alberta 2

Outline

  What is background?

Provincial and Federal Guidance  Methodology    Picking locations Calculation What about outliers?

 Case studies 3

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 4

Provincial and Federal Guidance

Source Ontario MOE BC MOE AEW USEPA Other

“Recommended” Procedures for Calculating Background Soil

Method Description

Groundwater

Reference 97.5 percentile 95 percentile

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

97.5 percentile 95 percentile

ON MOE 2011. Rationale for the Development of

Soil and Groundwater Standards for Use at Contaminated Sites in Ontario

BC MOE 2004. Protocol 9 for Contaminated Sites

– Determining Background Groundwater Quality

BC MOE 2005. Technical Guidance on

Contaminated Sites #16, “Soil Sampling Guide for Local Background Reference Sites

95 percentile

Outlier removal: data ≥ 2 x stdev + mean

95 % UPL (normal dist)

Outlier removal: Rosner or Dixon’s test

95 percentile (non-parametric)

Outlier removal: Rosner or Dixon’s test

mean + (3 x stdev) Not specified 95 % UPL (normal dist)

Outlier removal: Rosner or Dixon’s test

95th percentile (non-parametric)

Outlier removal: Rosner or Dixon’s test

mean + (3 x stdev)

AENV 2009. Subsoil Salinity Tool Singh and Singh 2010. ProUCL Version 4.1.00

Technical Guide (Draft), EPA/600/R-07/041

5

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.

6

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 7

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?

8

Methodology - Calculations

 Box and whisker plots   Shows the shape, central tendency and variability of the data Useful for comparing several data sets 9

Methodology - Calculations

 Percentiles  The n th 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” 10

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.

11

Methodology – What About Outliers?

   May be a) b) True measurements of conditions on-site 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 12

Methodology – What About Outliers?

 EPA (2002) recommends 5 steps to treat outliers 1.

2.

3.

 Identify extreme values that may be potential outliers Box plots, histograms  Apply statistical tests Dixon’s, Rosner’s, others…. Review statistical outliers with qualitative field and  laboratory data Decide on their class (true measurement or error) 4.

5.

Conduct data analysis with and without outliers 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.

13

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 14

Area of Potential Impact Background locations Well Head

Case Study 1     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 15

Case Study 1

Histograms not very useful in this case 16

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 17

Case Study 1

Electrical Conductivity (dS/m) 0 – 0.3 m 0.3 – 1 m 1 – 1.5 m > 1.5 m

Summary Stats (w/o outlier removal) Number of Observations Average Standard Deviation Maximum 6 0.4

0.14

0.6

7 0.5

0.14

0.7

7 4.2

5.9

14.0

22 7.8

2.4

13.0

Minimum 0.3

ON MOE (97.5

th Percentile) BC MOE (95 th Percentile) AEW (95 th percentile with outlier removal) 97.5

th USEPA 95% UPL with outlier removal (normal dist) Percentile with outlier removal Other (average + 3 Stdev) 0.6

0.6

0.6

ID 0.6

0.9

0.4

0.4

3.7

Background Calculation Methods 0.7

13.4

12.5

0.7

12.8

12.0

0.7

12.8

12.0

ID 0.7

0.9

ID 13.4

24.7

ID 12.5

15.0

   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) 18

Case Study 1

Electrical Conductivity (dS/m) 0 – 0.3 m 0.3 – 1 m 1 – 1.5 m > 1.5 m

Summary Stats (w/o outlier removal) Number of Observations Average Standard Deviation Maximum 7 1.07

2.1

5.4

6 2.7

3.26

7.0

6 6.5

5.3

14.0

22 11.3

4.5

15.0

Minimum 0.16

0.23

0.7

1.0

Background Calculation Methods ON MOE (97.5

th Percentile) BC MOE (95 th Percentile) AEW (95 th percentile with outlier removal) 97.5

th USEPA 95% UPL with outlier removal (normal dist) Percentile with outlier removal Other (average + 3 Stdev) 4.8

4.1

0.3

ID 0.3

7.4

7.0

7.0

7.0

ID 7.0

12.5

13.5

12.9

12.9

ID 13.5

22.3

15.0

15.0

15.0

ID 15.0

24.8

   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) 19

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

ON MOE (97.5

BC MOE (95 th th Percentile) Percentile) Fair (8) Fair (8) AEW (95 th percentile with outlier removal) Good (4) 97.5

th USEPA 95% UPL with outlier removal (normal dist) Percentile with outlier removal Other (average + 3 Stdev) ID Good (4) Fair (8)

0.3 – 1 m

Fair (8) Fair (8) Fair (8)

1 – 1.5 m > 1.5 m

Background Calculation Methods Unsuitable (14) Unsuitable (13) Unsuitable (13) Unsuitable (15) Unsuitable (15) Unsuitable (15) ID Fair (8) Unsuitable (13) ID Unsuitable (14) Unsuitable (22) ID Unsuitable (15) Unsuitable (25) 20

Case Study 1

Different excavation area depending on background calculation method

Excavation required based on BC, MOE, AEW, USEPA and “other” method Additional excavation required based on AEW and USEPA method

21

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 22

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 23

Site 500 m radius line

Case Study 2

Background locations in blue

 Representative of un-impacted soils  Takes into account potential, non-site  related sources of nitrate Collected upgradient from site

Downgradient locations in green

24

Case Study 2

No significant seasonal fluctuations Site data has different distribution than background data 25

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) 26

Case Study 2

Nitrate (mg/L) All Seasons

Summary Stats (w/o outlier removal) Number of Observations 53 AEW (95 th Average Standard Deviation Maximum Minimum 1.1

Background Calculations ON MOE (97.5

th Percentile) 15.7

BC MOE (95 th Percentile) percentile with outlier removal) 97.5

th USEPA 95% UPL with outlier removal (normal dist) Percentile with outlier removal Other (average + 3 Stdev) 7.2

3.6

20.0

13.2

11.0

NA 15.7

18.0

   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 27

Case Study 2

Nitrate Groundwater (mg/L)

ON MOE (97.5 Percentile) BC MOE (95 Percentile) AEW (95 percentile with outlier removal) USEPA 95% UPL with outlier removal (normal dist) 97.5 Percentile with outlier removal Other (average + 3 Stdev) 15.7

13.2

11.0

NA 15.7

18.0

Soil (mg/kg)

Background Calculations 3.4

2.8

2.4

3.4

3.9

C soil

Partitioning calculation for site-specific soil criteria Using standard CCME parameters 

C water K OC f OC

 

W



H



b

' 

a

28

Case Study 2 Different excavation area depending on background calculation method 29

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 30