Remote Sensing Techniques for Characterizing Distribution of Impact Area Energetic Compounds

Transcription

1 Remote Sensing Techniques for Characterizing Distribution of Impact Area Energetic Compounds Mr. Mark R. Graves US Army Engineer R&D Center Environmental Laboratory Vicksburg, MS ,

2 Project Description and Objectives Description Application of innovative techniques to develop predictive models of load and spatial distribution of explosive contaminants on impact areas Objectives Focus on the use of geospatial technologies (remote sensing and GIS) to measure surrogate factors which may be related to distribution of explosive contaminants Determine the strength of these relationships using valid statistical techniques Develop a model which can then be used as a non sitespecific methodology for characterizing the spatial distribution of HE residues across impact areas

3 Energetic Compounds TNT RDX Tetryl 1,3,5-TNB 4-Am-DNT 2-Am-DNT NG 2,4-DNT HMX

4 Survey Area and Remote Ft. Ord,, CA Sensing Strategy 2603 ha including boundary roads survey area consisted of 2562 embedded hectares within this area Remote Sensing Helicopter-based magnetometer and EM data transect sampling at 50% density height of 2 to 5.5 meters

5 Remote Sensing Data Collection

6 Data Collection in Progress

7 Remote Sensing Data Collection

8 Magnetometer Data Analytic Signal

9 Magnetometer Data Analytic Signal Classes developed using Jenks Natural Breaks Method -minimizes within class difference and maximizes between class difference

10 Magnetometer Data Analytic Signal

11 Magnetometer Data Analytic Signal

12 Magnetometer Data Analytic Signal

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14 Soil Sampling Site center located using GPS 5m Using pre-cut ropes and compass, corner points were established 25 samples gathered using shovel and/or special shovel device 5m North

15 Soil Sampling

16 Soil Sampling Protocols Soil samples were collected from a 5m by 5m grid associated with the locations identified as potential contaminant source locations. Twenty-five (25) subsamples were systematically sampled and the sediment was extracted to a depth of ~ 2 inches. Soil samples were shipped to CRREL and prepared for evaluation. Contaminant concentrations were measured using a GC-ECD ECD according to Environmental Protection Agency (EPA) SW846 Method 8095 (US EPA 1999).

17 Scatter Plot of Energetics vs Metal Content (nt/m( nt/m)

18 Models Linear regression on untransformed data. Linear regression on transformed data logarithm square root reciprocal

19 Modeling Results Model Intercept Slope R 2 F p-value % % % %

20 Problems Data are not normally distributed. Transformations did not appear to normalize the data. Data are highly skewed. Coefficients of Determination are low. Pearson s Type III distribution appears to best fit the data. Modeled the data using generalized linear modeling with a gamma distribution.

21 Non-Parametric Analyses GridCode Non-Parametric Median Test N Mean GRIDCODE (Analytic Signal Ranges) Score Sum Median Std Deviation Summary: Larger class values of analytic signal are associated with larger values of energetic compounds (subtly at least)

22 Logistic Analysis Observations were classified as Detectable or Not Number of Observations Yes Predicted No Yes (72.7%) 15 (27.3%) No (30.6%) 25 (69.4%) Model Estimate ξ 2 p-value Intercept Slope

23 Logistical Analysis Logistic regression: Results highly significant P < (Likelihood Ratio) P = (Wald) log(odds) = AS (Where AS = mag analytic signal) Prob of detecting energetics: 38% at lowest value of AS (0.1060) 50% at AS of (6.9134) > 80% at AS of ( ) Plot of mean analytic signal versus probability of detection of energetics.

24 Conclusions Remote sensing techniques do help predict where you are most likely to detect energetic compounds However, a relationship of analytic signal to energetic concentrations is weak Due to distributed nature of compounds Photodegradation / vertical transport / etc.

25 Other Comments Ft. Ord was not an ideal place for study Site had been surface-cleared Closed for approximately 10 years Energetic levels detected in soil were very low Support provided by Ft. Ord BRAC office was critical Dealt with media (ABC, CBS news coverage) and public meetings prior to remote sensing flights Cost-shared remote sensing data acquisition (60%) Provided EOD personnel for soil sampling team Team Effort ORNL/Battelle (Jeff Gamey, Les Beard, Bill Doll) (REMOTE SENSING) CRREL (Tom Jenkins, Susan Bigl, Marianne Walsh, Alan Hewitt, Dennis Lambert, Nancy Perron) (SOIL SAMPLING AND ANALYSIS) LSU and ULM (Drs. Jay Geaghan and Dale Magoun) (STATISTICAL ANALYSES)

26 Remote Sensing Techniques for Characterizing Distribution of Impact Area Energetic Compounds Mr. Mark R. Graves US Army Engineer R&D Center Environmental Laboratory Vicksburg, MS ,