Triad-Friendly Approaches to Data Collection Design

Transcription

1 Triad-Friendly Approaches to Data Collection Design

2 Alternative Triad-Friendly Approaches Collaborative Data Sets Weight-of-evidence approaches Using lower analytical quality data for search, higher analytical quality data for population characterization Using lower analytical quality data for search, and higher analytical quality data sets for clarification and as QA/QC Blending two different data sets statistically Dynamic Data Collection Programs Multi-increment sampling and adaptive compositing strategies Sequential probability ratio test/barnard s t test Adaptive cluster sampling GeoBayesian approaches

3 A Second-Generation Data Quality Model (for Heterogeneous Matrices) Cheaper/rapid (lab? field? std? non-std?) analytical methods Costlier/rigorous (lab? field? std? non-std?) analytical methods Targeted high density sampling Low DL + analyte specificity Manages CSM & sampling uncertainty Manages analytical uncertainty Collaborative Data Sets Collaborative data sets complement each other so all sources of data uncertainty are managed; when using either kind of data alone will not produce reliable information. 3

4 1980 s Paradigm $ $ $ $ $ $ Triad Traditional Paradigm vs. Triad Fixed Lab Analytical Uncertainty Ex 1 Sampling Uncertainty Remedy: remove hot spots CSM & cleanup incomplete; repeat as needed $ $ $ $ $ $ DONE Rapid Analytical Data Ex 2 Sampling Uncertainty Controlled through Increased Sampling Density to Segregate Populations Ex 1 Ex 2 Ex 3 Fixed Lab Data Ex 3 Decreased Sampling Variability after Removal of Hotspots

5 Collaborative Data Sets Collaborative data sets: refer to using data from multiple sources to support decisionmaking. can be used to support static or dynamic work strategies. Usually include a relatively small number of high quality (but expensive) analytics, and a larger number of lower quality (but much cheaper) analytics. Recall what lower quality means: Higher detection limits (perhaps even higher than cleanup requirement), and/or Greater variability in analytical results, and/or Greater potential for interferences and bias, and/or Measuring something that s different from, but linked to, the true parameter of concern.

6 Collaborative Data Sets and Weight of Evidence Locate and remove buried waste pits. Collaborative data sets: Historical air photos Non-intrusive geophysics Passive soil gas analysis Limited intrusive GeoProbe sampling (~40 locations) Alternative: using hot spot approach, 137 intrusive sampling locations.

7 Search and Stratification Using Collaborative Data Cheaper, lower quality analytical data identifies areas of concern. More expensive, higher analytical quality data provides more definitive information about population characteristic (e.g., average contaminant concentration). Only requirement for cheaper technique is that it has sufficient detection capabilities to identify areas that would be of concern (not necessarily below cleanup requirements).

8 Quantitatively Blending Data for Population Mean Estimation Assumptions: data are unbiased, detection limits below requirements, linear correlation exists, Goal is to estimate average concentration level. Question: What s the best combination of two methods?

9 Collaborative Data in a Clarifying Role Linear regressions often don t work. Outlier problems. Non-linear relationships. Non-detects. Result: two data sets cannot be merged quantitatively. Th230 (pci/g) Th230 vs Gross Activity R 2 = Gross Activity (cpm)

10 Non-Parametric Techniques Work Well for Establishing Relationships Decision focus is yes/no. Heavy lifting done by cheaper analytical technique. More definitive methods clarify inconclusive results. Value depends on: strength of the relationship between two techniques, and the spatial distribution of contamination. Re latio ns hip Be twe e n Gamma Walkve r Data and Tho rium /40 12/ / K-16K 16K-20K 20K + Co unts per Minute (x 1000)

11 Example: Hot Spot Search and the Looking for hot spots (1,500 over 900 square feet) Same hot spot search strategy as described before. Two methods: Real-time method that is ¼ the cost used at each point. Standard method used to clarify uncertainty. Non-parametric relationship between two methods. Scattered Site

12 Collaborative Data Set 172 real-time measurements and 9 follow-up standard analyses. Performance Same conclusions as traditional method. Cost savings of 70%.

13 Multi-Increment Sampling Can Control Spatial Heterogeneity Multi-increment composites can be a very effective tool both in search and population characterization for stretching budget. Multi-increment sampling reduces the effects of spatial variability on sampling uncertainty. Multi-increment sampling can be used to address both short scale spatial variability (e.g., for searching) as well as longer scale spatial variability (e.g., determining decision unit means).

14 Multi-Increment Sampling Applied to the Scattered Site Looking for hot spots (1,500 over 100 square yards) Same hot spot search strategy as described before. 7-point increments applied to each grid node.

15 7-Increment Sampling Improves 100% of problem areas identified. Incorrectly called 2% of clean locations contaminated. Missed 45% of contaminated locations. Compositing resulted in more hits! Performance

16 Adaptive Compositing Strategies Can Reduce Sampling Costs Applicable when action level is significantly greater than background levels. Aggregate samples (single or multiincrement) into larger composites. Develop investigation levels for larger composites that indicate when analyses of contributing multi-increment samples are necessary.

17 Adaptive Compositing Strategies and the Scattered Site Goal: Identify hot spots Background is 700 ppm, action level 1,500 ppm Four multi-increment sample composites Investigation level is 900 ppm Results: same performance as multi-increment sampling Analytical costs reduced by ~50%.

18 Adaptive Composite Strategies Using Multi- Increment Sampling and Collaborative Data Sets Brings it all together: Multi-increment sampling Adaptive compositing strategies Collaborative data sets. Same performance from an error perspective as straight multi-increment sampling for scattered site hot spot example. Upshot is that this brings further cost-reductions to the table (~80% over single sample, standard analytics hot spot search for the scattered site).

19 Sequential Probability Ratio Test Adaptively addresses population characterization (e.g., estimating the mean). Two flavors, depending on whether sample result variability is known ahead of time or not. If not, a minimum of 10 samples required. Samples are distributed across an area in a manner that leads to even coverage. Tests can be run at any point in time to determine if requirements have been met.

20 Adaptive Cluster Sampling Used to delineate boundaries of contamination within a decision unit, while also providing information about the average contamination level for the unit. Grid laid over decision unit. An initial # of samples determined and systematically distributed. Sampling takes place. If a result is above the requirement, adjacent grid nodes are then sampled and analyzed. This continues until the final round of samples yields no exceedances.

21 Adaptive GeoBayesian Approaches Used for searching and boundary delineation (yes/no sample results). Spatial autocorrelation explicitly addressed. Can roll in soft information using Bayesian techniques. Explicitly addresses decision errors.

22 Example: Surface Contamination Event Surface soil contamination problem. Resulted from spillage from the lagoon. 7,940 sq m actually contaminated, an area unknown to the responsible party. Soft information available for the site includes: Terrain Contour Lines Utility Bldg. Road Slope of land; Waste Lagoon Location of barriers to flow; Location of source. Owner will remediate anything with greater than 20% chance of being contaminated. Road

23 Initial Conceptual Site Model Based on soft information, assign probability of contamination being present. Map shows this CSM pictorially, along with the boundary that captures the everything with a probability > 20% based on the CSM. This CSM drives subsequent sampling decisions and becomes an important point of concurrence for stakeholders.

24 Sampling Progression Samples are collected sequentially and analyzed with an appropriate real-time method. CSM updated with current sampling results. CSM drives subsequent sample location selection. In this example, locations are selected to maximize the area with less than 0.2 probability of contamination.

25 Sampling Can Continue Until Goals are Achieved Classification of Soils at 80% Certainty Level % of Volume 100 Contaminated Uncertain Clean Number of Samples 50

26 Adaptive Sampling Realities Strength is ability to modify sampling program to fit reality as it unfolds. This makes answering the question of How many samples? harder. Requires flexible contracting mechanisms and careful budget forecasting to be effective.