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Using a geostatistical approach to characterize and visualize complex contaminated zones: Identifying the potential origin of contaminants among several neighboring industrial sites

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Abstract

Feedback on the application of geostatistics to contaminated sites.
Technical Day: Geostatistics applied to contaminated soils 23/01/2019 1
Using a geostatistical approach to
characterize and visualize complex
contaminated zones: Identifying the
potential origin of contaminants among
several neighboring industrial sites
Michel H. Garcia1, Bertrand Vidart2, Jean-Baptiste Mathieu1
1KIDOVA, 2AECOM
Technical Day: Geostatistics applied to contaminated soils Paris January 23, 2019
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Organic contaminants in non-aqueous phase (NAPL)
Context Methodology Feedback and conclusions
Results
Technical Day: Geostatistics applied to contaminated soils 23/01/2019 3
Organic contaminants in non-aqueous phase (NAPL)
Benzene (LNAPL)
Context Methodology Feedback and conclusions
Results
Soil contaminant
saturation
Groundwater
contaminant
concentration
// groundwater flow groundwater flow
KIDOVA Transpol 2002
Technical Day: Geostatistics applied to contaminated soils 23/01/2019 4
Naphthalene (DNAPL)
Organic contaminants in non-aqueous phase (NAPL)
Context Methodology Feedback and conclusions
Results
Soil contaminant
saturation
Groundwater
contaminant
concentration
// groundwater flow groundwater flow
KIDOVA Transpol 2002
Technical Day: Geostatistics applied to contaminated soils 23/01/2019 5
Organic contaminants in non-aqueous phase (NAPL)
Geostatistics as an investigation science?
Context Methodology Feedback and conclusions
Results
Smoking can seriously harm you and others around you…
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Context Methodology Feedback and conclusions
Results
Relationship between industrial and environmental agency about
managing soil & groundwater contamination in a production site
Long history of studies and works carried out by the industrial
Several surveys to assess the soil and groundwater contamination by
several organic contaminants
Pumping to get rid of and confine the groundwater contamination
Assessment of contaminant masses still in place, carried out by AECOM
without geostatistics
Decisions with the environmental agency for next step
Considering the contaminant masses estimated in the soils
Considering the complexity of the spatial distribution of contaminants
By taking into account the uncertain origin of the contamination
Different industrials at different times
Current presence of other industrials in the neighboring plots
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Expectation from the geostatistics
Better understanding of the contamination
Better representation / visualization of the residual contamination source
shape
In particular
Identification of possible contamination pathways
Identification of the potential contamination source locations
Checking the estimation of soil contaminations
Estimations of contaminant masses in the 3 zones of interest
Two in the plot corresponding to identified source zones
One deep under the neighboring plot
Distinction above and under the groundwater (saturated / unsaturated zone)
Quantification of the uncertainty on estimated contaminant masses
Context Methodology Feedback and conclusions
Results
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Presentation of the case study
Current situation: two production areas in two neighboring
plots owned by two industrials
Multiple contaminants: BTX, VOCs, alcohols, ketones
Available data: contaminant grade data measured in
laboratory, PID measurements on site
Context Methodology Feedback and conclusions
Results
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Data
Context Methodology Feedback and conclusions
Results
Plot 1
88 soil data from samples
Topography
Water table
987 PID measurements
Plot 2
Toluene (ppm) Prob(Toluene > 10 ppm)
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Context Methodology Feedback and conclusions
Results
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Smoothed 2D histogram as correlation models
Relationship between PID and grade data
Context Methodology Feedback and conclusions
Results
Cumulative distribution
of toluene for PID = 0.01
Proportions
Proportions
Cumulative distribution of
toluene for PID = 10 000
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Context Methodology Feedback and conclusions
Results
Geostatistical approach
Estimation by kriging of the probability of exceeding a grade
threshold
Grade data measured in laboratory (hard data)
Prob = 1 if grade > threshold
= 0 else
PID data (soft data)
Prob derived from the correlation model
Grade
Threshold
Prob
Cumulative distribution of
contaminant grade given
the PID measurement
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Context Methodology Feedback and conclusions
Results
Geostatistical approach
Variogram of the probability of exceeding the grade threshold
Toluene for threshold of 10 ppm
Hard probabilities Soft probabilities
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Threshold on toluene
57 grade data > 10 ppm
Proportions
Cumulative distribution
of toluene(> 10 ppm)
Context Methodology Feedback and conclusions
Results
Selection of a grade threshold
Proportions
Threshold 10 ppm
Cumulative
distribution
of toluene
Technical Day: Geostatistics applied to contaminated soils 23/01/2019 15
Threshold on toluene
17 grade data > 100 ppm
Context Methodology Feedback and conclusions
Results
Selection of a grade threshold
Proportions
Threshold 100 ppm
Proportions
Cumulative distribution
of toluene(> 100 ppm)
Cumulative
distribution of
toluene
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Context Methodology Feedback and conclusions
Results
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Context Methodology Feedback and conclusions
Results
Estimating probability maps
Probability that contaminant grade > 10 ppm
Estimation by kriging of the probability of exceeding the
threshold using the grade or PID data
Grade data (hard) PID measurements (soft)
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Context Methodology Feedback and conclusions
Results
Identifying contaminant pathways
Associated with high enough probabilities that toluene grade
> 10 ppm
Sensitivity analysis on probability cutoff
Probability cutoff = 80% Probability cutoff = 70%
Surface at the
probability cutoff
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Context Methodology Feedback and conclusions
Results
Interpretations
Possible contamination pathways
Vertically = gravity, horizontally = water table and groundwater flow
Potential contamination source locations
Left: toluene grade data
and probability cutoff of
80%
Right: PID data and
probability cutoff of 30%.
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Context Methodology Feedback and conclusions
Results
Technical Day: Geostatistics applied to contaminated soils 23/01/2019 21
Context Methodology Feedback and conclusions
Results
Achievement of the study objectives
Better understanding of the contamination
Better representation / visualization of the residual contamination source
shape
+
Identification of possible contamination pathways
Identification of the potential contamination source locations
Checking the estimation of soil contaminations
Estimations of the contaminant masses in the zones of interest
Quantification of the uncertainty on estimated contaminant masses
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Context Methodology Feedback and conclusions
Results
Exploitation of the results by the industrial
Vis-à-vis the environmental agency
Validation of the contaminant masses estimated by zone
Validation of mass balances in the vadose and saturated zones
Better description / 3D visualization of the residual contamination source
Complexity of the shape
Effectiveness of a potential approach by natural attenuation
Confirmation of the interest in a joint approach involving all industrials
Vis-à-vis the neighboring industrial
Better highlighting the relevance of a joint approach
Internally
Communication on the complexity of contaminated soil management
Technical Day: Geostatistics applied to contaminated soils 23/01/2019 23
Thank you for your attention
Context Methodology Feedback and conclusions
Results
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