Contaminated sites and the extent to which contaminants have spread are usually difficult to comprehend and are therefore often underestimated. As a consequence, these incomplete insights may lead to inappropriate measures, cost overruns and remedial target levels that in practice turn out to be hard to achieve.
While contaminated sites may seem to look like a black box with unknown distribution pathways caused by subsurface infrastructures and soil heterogeneities, it is possible to get a far better understanding by combining some elementary tools that are within reach of any soil investigator. We created a method to help with finding the right way of interpreting data. This consisted of three main parts, starting with the definition of the box: literally define a box of the soil volume that needs to be investigated and interpolate and visualise pollutant data in such a way.
Secondly, show the groundwater flow direction of the aquifers, and thirdly, combine all available borehole information within this model. Combining these facts creates an unseen level of detail shown in three and four dimensions and is easy to understand for everyone involved: the consultant, stakeholder and regulator.
Define the box
Just as in ancient times when people thought the world was as flat as a pancake, today’s interpretation of soil and groundwater contaminations is done one-dimensionally, by plotting the estimated contours of certain threshold values in layers at different depths, as seen in Figure 1. Relations between these different layers, however, are typically difficult to interpret and calculations of the soil volumes above different threshold values of, for instance hydrocarbons or chlorinated solvents, are usually an estimation.
Nowadays, ready to use software tools offer the possibility of creating gorgeous automated and digitised three-dimensional visualisations of different measuring points, displaying pollutants in a 3D view and providing a more detailed overview and better understanding.
For this purpose, soil and groundwater concentration data are visualised with a voxel based three-dimensional visualisation programme designed to display XYZC data where C, the parameter concentration, is a variable at each X, Y, and Z location (borehole depth or monitoring well screen). Interpolations of soil and groundwater monitoring data are ‘Inverse Distance Weighted’ (IDW). IDW-interpolation determines cell values using a linearly weighted combination of a set of monitoring data. This method generates volumetric rendering and three-dimensional data, as presented in Figure 2, displaying the pathway and directions of distribution.
Pathways can be defined in more detail by demarcating cross sections parallel or perpendicular to the groundwater flow direction. These cross sections are then used as a site conceptual model (SCM) and to derive the horizontal fluxes and quantify the annual increase of the soil volume of a specific parameter above a certain threshold value.
Instead of only looking at remediation target levels, this example shows concentration levels that are defined above threshold values on a log scale of 1 – 100,000 ppm, enabling us to calculate the estimated load of contaminant present in the sub soils.
Groundwater flow directions
Pollutant pathways in most cases correspond with the groundwater flow direction, but of course this needs to be verified. For this purpose, maps are produced from irregularly spaced groundwater head data interpolated by kriging, a standard geostatistical gridding method, which generates an estimated surface from a scattered set of points with z-values, corresponding to groundwater heads from several monitoring wells. From the interpolated contour maps the groundwater flow directions are derived for each aquifer, shallow and deep, as seen in Figure 3.
We have to be aware that obtained monitoring head data and derived groundwater flow directions represent the situation of a particular moment and change over time, because of seasonal influences. It’s recommended, therefore, to repeat groundwater head measurements several times a year in order to gain a better insight and a better understanding of changing flow path directions and the long term groundwater behaviour.
The final understanding of the groundwater flow direction and distribution pathway is directly related to the soil composition and soil heterogeneities. Soil composition data obtained from different borehole descriptions are visualised in a layer with varying thickness and permeability, as seen in Figure 4. The different shades of colour, light to dark grey, show the high and less permeable areas within the clay layer. The higher permeable areas explain the groundwater pathway and distribution of a contaminant from a shallow to a deeper aquifer.
Figures 3 and 4 show how the contaminated shallow groundwater infiltrates and spreads through a clay layer to the deeper aquifer, as shown in Figure 5. Within the deeper aquifer, composed of high permeable layers of medium, fine and coarse sand, groundwater contaminations can then spread over a distance of several hundred meters, over a period of decades.
Quantifying annual changes
Knowing the pathway of contaminated groundwater and understanding the spreading behaviour allows us to quantify the annual changes of contaminated soil and groundwater volumes. By defining the plane of distribution perpendicular to the groundwater flow direction (shown in Figure 6) and by knowing the groundwater flow velocity, the annual changes are quantified. Subsequent annual increases of contaminated soil volumes can be judged by regulators and consultants as to whether there is reason for concern or an acceptable risk.
Combined with groundwater modelling software, gridded contamination data is easily exchanged to verify and predict contamination behaviour and remediation progress over time, as seen in Figures 7 and 8. These predictions are verified by comparing the results of frequent updates of the database with new monitoring data and make the management of groundwater contamination data over time extremely beneficial, allowing stakeholders to get a good assessment and understanding.
Case study of a site remediation
In practice this method has proven and shown to be very beneficial, by simplifying the evaluation of a great amount of monitoring data obtained over time, for instance an in situ remediation project MWH carried out in the Netherlands. In this project, the team was able to create a 4D-interpretation from the 3D-interpolations, showing the progress over a longer period of time, seen in Figure 9. The interpretation showed that as a result of the remedial measures, the total load of the groundwater contaminant decreased over time by about 70%.
Total volume reduction showed an increase, due to mobilisation of the contaminant. Within the volume reduction over years a shift is shown, from decreased higher concentrated soil volumes towards an increase of the lowest concentrated soil volume. After this period of active remediation an informed decision can be made to continue with monitoring the site as a cost effective measure to show stability and further volume and concentration decreases over time.
To conclude, cleverly combining all these tools and all available site information has shown to be a very useful and efficient interpretation method, resulting in a better overview and understanding of those ‘mysterious contents’ of a black box with contaminated soils and groundwater. Once a database and a 3D model are set up, it is easy to import new data and to generate output. As a result, fourth dimension data management becomes available as a handsome decision tool for the evaluation of soil remediation performance over time and to assist in coordinating ongoing remediation efforts.
This method then, forms a tool through which the performance of remediation efforts are no longer evaluated and focussed on concentration target levels that in practice seem hard to achieve. Instead, the evaluation – based on contaminant load and volume reduction – provides a better way to assess cost effective remediation measures, compared to the obtained environmental benefits of these measures over time. Implementing such a method will create a more profound basis for a contract agreement between the stakeholder and the contractor and the assessment over time of remedial performance.
Published: 14th Dec 2015 in AWE International