slide1 l.
Download
Skip this Video
Loading SlideShow in 5 Seconds..
Case Example: Using a Stratified Sampling Design & Field XRF to Reduce the 95% UCL for Residential Soil Lead PowerPoint Presentation
Download Presentation
Case Example: Using a Stratified Sampling Design & Field XRF to Reduce the 95% UCL for Residential Soil Lead

Loading in 2 Seconds...

play fullscreen
1 / 18

Case Example: Using a Stratified Sampling Design & Field XRF to Reduce the 95% UCL for Residential Soil Lead - PowerPoint PPT Presentation


  • 115 Views
  • Uploaded on

Case Example: Using a Stratified Sampling Design & Field XRF to Reduce the 95% UCL for Residential Soil Lead. Deana Crumbling, EPA/OSRTI/TIFSD crumbling.deana@epa.gov 703-603-0643 2009 EPA Annual Quality Conference. What things increase the interval between the sample mean & UCL?.

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about 'Case Example: Using a Stratified Sampling Design & Field XRF to Reduce the 95% UCL for Residential Soil Lead' - may


Download Now An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
slide1
Case Example: Using a Stratified Sampling Design & Field XRF to Reduce the 95% UCL for Residential Soil Lead

Deana Crumbling, EPA/OSRTI/TIFSD

crumbling.deana@epa.gov 703-603-0643

2009 EPA Annual Quality Conference

what things increase the interval between the sample mean ucl
What things increase the interval between the sample mean & UCL?
  • High variability in data set
  • Data set is from a non-normal or non-parametric distribution
  • Small number of physical samples in the statistical sample

What creates high data variability?

  • True changes in matrix concentrations across space
  • Inadequate soil sample homogenization
  • Artifact caused small analytical subsample mass
variability as an artifact of small analytical sample mass
Variability as an artifact of small analytical sample mass

As analytical sample volumes increase, data variability decreases & distribution goes from lognormal to normal

(assumes whole sample is measured)

reduce the ucl by addressing

}

Physical manipulation of sample, increase volume (MIS) and/or sufficient replicate analyses

Reduce the UCL by addressing:
  • Variability artifacts
  • Non-normal statistical distributions
  • Small number of physical samples in the statistical sample
  • High variability due to true variation

By procedures that support:

  • Sample homogenization
  • Increased sample mass
  • True changes in matrix concentrations across space
can anything be done about true spatial variations in concentration
Can anything be done about true spatial variations in concentration?

(Statistical) Stratified Sampling Design

  • Methods for Evaluating the Attainment of Cleanup Standards Volume 1: Soils and Solid Media”, 1989, section 6.4 http://www.cluin.org/download/stats/vol1soils.pdf
  • Guidance on Choosing a Sampling Design for Environmental Data Collection (EPA QA/G-5S), 2002, Chap 6. http://www.epa.gov/quality/qs-docs/g5s-final.pdf
  • Data Quality Assessment: Statistical Methods for Practitioners (EPA QA/G-9S), 2006, section 3.2.1.3 http://www.epa.gov/quality/qs-docs/g9s-final.pdf
  • Purpose: determine the overall mean & UCL for a decision unit (DU) when different sections of the DU have different means & standard deviations (SDs).
what makes a stratified design different

**

1100

1040

18 *

20 *

25 *

22 *

16 *

15 *

21 *

“Dividing by 12” assumes equal weight is given to each sample (1/12th of total area)

What Makes a Stratified Design Different?

120 *

184 *

155 *

To calculate average over the entire area, routine practice is that data go straight into a database, and then…

Sum(all) = 2736; then 2736 ÷ 12 = 228 ppm

slide7

1100

1040

* 5% of area; ave = 1070 *

120 *

184 *

155 *

18 *

20% of area

ave = 153

20 *

25 *

75% of area

ave = 20

22 *

16 *

15 *

21 *

Area

High

Mid

Low

Routine

Stratified

Mean

1070

153

20

228

99

SD

42

32

4

398

80

95% UCL

434 (Δ=196)

143 (Δ=44)

But the CSM supports partitioning the site into 3 distinct portions based on similar populations

20(0.75) + 153(0.20) + 1070(0.05) = 99 ppm

A spatially weighted mean makes a difference!

basic principles of a stratified sampling design
Basic Principles of a Stratified Sampling Design

The CSM is the basis for defining both the DU & its strata

  • Decision Unit (DU) = a unit for which a decision is made: a single drum, a batch of drums, risk exposure unit, remediation unit, etc.
  • The DU is the volume & dimensions over which an average conc is desired
  • Strata are created by different release or transport mechanisms – cause different contaminant patterns in within the DU
    • Target properties like conc level & variability differ from strata to strata w/in the DU
basic principles cont d
Basic Principles (cont’d)
  • DU is delineated (stratified) into non-overlapping subsections according to the CSM
  • Each stratum’s area/volume is recorded as a fraction of the DU’s area/volume
  • Each stratum’s conc mean & SD determined
  • The means & SDs are weighted and mathematically combined  overall mean & UCL for the DU
  • Can apply stratification to data analysis even if not planned into sampling, but must have spatial info & final CSM available
benefits of a stratified sampling design
Benefits of a Stratified Sampling Design
  • Small areas of very high or low conc do not bias the overall mean of the DU.
  • Reduces variability (SD) in the DU data set
    • Reduces statistical uncertainty (as distance between mean & UCL)
  • Preserves spatial information to identify source/transport mechanisms & support remedial design.
slide11

Case Example: XRF with stratified sampling design

Properties in old town near Pb battery recycling plant

XRF Pb data from bagged soil samples (~300 gram)

Plastic bag of soil

decision goals

Data Collection Design

  • Property divided into 3 sections (strata)
    • Front yard (likely “same” conc within & own SD)
    • Side yard (ditto)
    • Back yard (ditto)
  • Each stratum 5 ~equal subsections (sample units)
    • 1 grab (or MIS) sample (300-400 g) into plastic bag
    • 5 sample units/stratum or 15 sample units/DU (the EU)
Decision Goals
  • Resolve confusion over past conflicting data.
  • Determine mean (95% UCL) for exposure unit (entire yard): 500 ppm risk-based A/L; if over, cleanup high contamination areas
  • Pb source? Suggested by spatial contaminant pattern (does facility have liability?)
slide13

{

Side Yard: 5 Bagged Samples

{

{

Front Yard: 5 Samples

Back Yard: 5 Samples

House Footprint

Preliminary CSM of Simplified Property

Action Level (entire yard) = 500 ppm

Area fraction = 0.25

Area fx = 0.15

Area fx = 0.60

Potential release: Traffic (facility truck, Pb gasoline); Pb house paint; facility’s atmospheric deposition; combination. Expected Pb conc: Higher.

Potential release: Pb paint; atmos dep. Pb conc: Uncertain (near road, house?)

Potential release: Pb paint (near structures); atmos dep. Expected Pb conc: Lower.

xrf bag analysis
XRF Bag Analysis
  • 4 30-sec XRF readings on bag
    • (2 on front & 2 on back)
  • Results entered real-time into pre-programmed spreadsheet
  • Spreadsheet immediately calculates:
    • ave & SD for each bag
    • ave & SD within each strata (yard section),
    • ave & UCL for the decision unit (entire property).
    • the greater of within-bag vs. between-bag variability
  • IFstatistical uncertainty interferes w/ desired decision confidence for DU:
    • Use #4 & a series of decision trees to reduce statistical uncertain until confident decision possible
minimizing variability improves statistical confidence in epcs
Minimizing Variability Improves Statistical Confidence in EPCs

NOTE: “Routine” calculation applies same weighting to data points & database loses their spatial representativeness

Note: ½ CI width = mean-to-UCL width

data used to mature the csm

House Footprint

Data Used to Mature the CSM

Preliminary CSM: an informed hypothesis about strata boundaries

Mature CSM: Data confirms or modifies hypothesis about strata boundaries

progressive data uncertainty management
Progressive Data Uncertainty Management

* Normal z-distribution used for the XRF instrument’s

counting statistics, rest of rows use the t-distribution

slide18

Questions ?

Deana M. Crumbling, M.S.

U.S. EPA, Office of Superfund Remediation & Technology Innovation

1200 Pennsylvania Ave., NW (5203P)

Washington, DC 20460

PH: (703) 603-0643

crumbling.deana@epa.gov

www.triadcentral.org