1 / 26

DOE Site Preliminary Sampling Strategy Using VSP

DOE Site Preliminary Sampling Strategy Using VSP. Brent Pulsipher and Brett Matzke Amoret Bunn and Brad Fritz DOE ASP Workshop September 2012. Outline. Site Background and Problem Statement Pertinent Decision Rules Sampling Strategies Explored Examination of Historical Data

gautam
Download Presentation

DOE Site Preliminary Sampling Strategy Using VSP

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. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. DOE Site Preliminary Sampling Strategy Using VSP Brent Pulsipher and Brett Matzke Amoret Bunn and Brad Fritz DOE ASP Workshop September 2012

  2. Outline • Site Background and Problem Statement • Pertinent Decision Rules • Sampling Strategies Explored • Examination of Historical Data • Recommended Sampling Design and Approach • Potential Cost Savings • Depth Issue – MI sampling? • VSP Virtues • VSP Improvement Recommendations

  3. Pertinent Background • Lead Arsenate was a common pesticide for orchards across the U.S. from 1890s through 1948 • Today – still detect Pb and As in soil • Future land uses include vulnerable receptors (e.g., residences or elementary schools where children come into contact with the soil) • Many possible sites;  ~50 decision units (0.1 to 1 sq. km) • Decision units categorized into similar groupings based on photos, site use, etc. • Some historical data available • Field sampling tools (e.g., XRF) not sufficiently sensitive to confidently quantify contamination at concentration levels of concern

  4. Decision Units 1954 2004

  5. Changes Over 70 years

  6. Changes Over 70 years

  7. Changes Over 70 Years

  8. Sampling Strategy Considerations –Multiple Decision Rules • Primary decision rule: Mean concentration > threshold. • If the true mean concentration for Pb or As at any one decision unit is greater than the prescribed action level, then the decision unit is classified as “contaminated”. Otherwise, it is classified as “acceptable”. • Hotspot decision rule: Single measurement > 2*threshold • If any single measurement > twice the action level used for the mean test then further investigation needed. • How large of a hotspot must be detected? • Determined by typical size of area that would have been affected. • Hot Area decision rule: 10% of measurements > threshold • What if there is a spatial cluster of elevated results? • If 10% or more of the measured values exceed the action level used for the mean test, then further investigation is needed.

  9. Candidate Sample Approaches • Collaborative Sampling (Cheap w/ few lab samples) • XRF is cheaper than lab samples • XRF isn’t sensitive enough at levels of concern • Multiple Increment Sampling • Concerned about dilution effect and losing spatial information • Ordinary Sampling • Standard deviation estimates varied significantly with time/space • Sequential Sampling

  10. Sample Requirements • Number of samples per decision unit depends on • Decision thresholds • Tolerance for incorrect decision risk • Sampling approach (collaborative, sequential, ordinary, stratified…) • Statistical distribution assumptions • Standard deviation between samples • Size of decision unit (for hotspot concern)

  11. Standard Deviation Estimation (As) • Examined historical data • DU #1 • As • Std. Dev. appears spatially dependent

  12. Standard Deviation Estimation (As) • Examined historical data • DU #1 • As • Std. Dev. appears temporally dependent

  13. Standard Deviation Estimation (Pb) • Examined historical data • DU #1 • Pb • Std. Dev. appears spatially dependent

  14. Data Evaluation on Other Sites • No generally consistent standard deviation estimate • %RSD may be more consistent • Sequential sampling approach recommended Data Set 1 Data Set 2 Data Set 3 Data Set n

  15. Sequential Sampling Approach • Iterative sampling/analysis technique (VSP supported) • Three possible outcomes • Confidently conclude site is acceptably clean • Confidently conclude site is contaminated • Not sufficient confidence to reach decision…gather more data Implementation: • Classify each decision unit into M groups/categories • Take 10-15 samples from one or more representative decision units from each of the M groups • Alternatively gather n samples from each of the selected decision units but only analyze 10-15 of them. • Perform statistical test on data results from each selected decision unit

  16. VSP Sequential Sampling DQOs Specified 15 initial systematic random samples

  17. VSP Sequential Analysis • Performs statistical test • Determines whether sufficient evidence to make a decision or need to take more samples • Achieves acceptable decision error tolerances • Specifies where to obtain samples • UTM sample coordinates output • Easily transferred to sampling team GPS unit

  18. Benefits of Sequential Approach • Standard deviation estimates obtained/used directly • Decisions may be reached for some decision units without over-sampling – Cost/time savings • If additional data required, good basis for determining how many more samples are needed (use std. dev. estimate from that group) • If decisions are reached for initially selected decision unit(s) in group, then proceed with no more than 10-15 samples in each of other units in that group • Caveat: Mean must be normally distributed; Usually ok if based on 10-15 samples and data are not highly skewed. • Can evaluate and use non-parametric test or increase conf. requirement to 99% as a double-check • Test is fairly robust to non-normality departures

  19. Ordinary Sampling Approach • Number of samples per decision unit required varying • Action Level (AL) • Lower Boundary of Grey Region (LBGR) (established by looking at what might be considered background) • % RSD • Parametric or Non-Parametric method • Equivalent to maximum allowed for sequential approach

  20. Meeting the Hotspot Objective • For each decision unit, can determine largest possible undetected hotspot

  21. Hotspot Objective • If mean < threshold, then can augment samples to achieve hotspot detection objective

  22. Potential Cost Savings • Assuming cost of analysis = $100/sample • Assuming 50% of the 50 DUs fail initial mean test (n=10) • Assuming other 25 DUs decision is made with n=20 • Assuming no additional sampling/analysis required after mean test failure • Comparing to Ordinary Sampling approach (n=36 /DU) • Anticipated Savings of $105K

  23. Depth Issue • Depth of sampling? • Consider initially sampling at various depths to confirm depth profile? • Consider compositing (multiple increment) across depth? Figure 2. Vertical distribution of arsenic and lead in six lead arsenate-contaminated orchard soils .

  24. Hot Area Concern • Perform Geospatial Analysis to Identify and Bound Hot Areas

  25. VSP Virtues • Visualization helps communicate analyses and planned approach • Easily explored sampling requirements under different assumptions and varying approaches • VSP supports several possible sampling approaches that can potentially save $$$

  26. Recommended VSP Improvements • Need better guidance on when normality assumptions may/may not be appropriate • Sequential sampling that supports multiple analytes • Placement of increments in MI sampling at depth and spatially • Better layer views: Multiple maps, images selected or deselected. • Comparisons with ProUCL

More Related