Evaluation of dispersivity coefficients by means of a laboratory image analysis

Reconstructing the release history of a contaminant source with different precision via the ensemble smoother with multiple data assimilation

Identifying a contaminant time-varying release history is an ill-posed problem but crucial for groundwater contamination issues. A precise inversed release history offers a promising estimation of contaminant movement and is of great importance for environmental monitoring and further management. In this paper, a recent emerging data assimilation method, the ensemble smoother with multiple data assimilation (ES-MDA) is employed to handle this conundrum. The study starts with some synthetic cases in which several factors are analyzed, such as the observation data frequency, covariance inflation schemes, iteration numbers used in the ES-MDA for the purpose of identifying a time-varying contaminant injection event with different precision. The results show that the ES-MDA performs well in recovering the release history when the injection is discretized into 50 or 100-time steps but encounters fluctuation problems in the cases with 300-time steps. Further comparison reveals that the observation data frequency is a very influential factor, while the number of iterations or the kind of covariance inflation used has a lesser effect. Nevertheless, this is a first test in a non-synthetic environment, in which the ES-MDA has proven its ability to recover the release history in two close-to-reality sandbox experiments. The outcome shows that the ES-MDA with Rafiee's inflation scheme has the ability to capture the main pattern of the release history. But in order to move one more step to field cases, a more detailed description of uncertainties or elaborated parameterization of the time functions is paramount.

Experimental evaluation of self-remediation mechanism by groundwater flow in unconfined aquifers

The main goal of this study is to investigate the effect of soil properties such as permeability on the dispersion and movement of a water dissolved contaminant in three types of soil in saturated and 2D conditions. The experimental modeling was conducted using a constructed sand box. In order to evaluate the effect of soils particle size on the distribution and self-remediation of the contaminant, three types of soil, as coarse, medium, and fine-grained sand were used. Results of experiments showed that, at the first 25% of the test time, the contaminated area reduction rate in all three specimens varies significantly, so that for the medium and coarse sand, it is 2.2 and 3 times that of fine sand, respectively. The contaminant width reduction at the first 25% of the test time was 5%, 6%, and 35% for the fine, medium, and coarse sand, respectively, while the contaminant length reduction was 13%, 18%, and 37% for the fine, medium, and coarse sand, respectively. In addition, by comparing the contaminant movement in the saturated and semi-saturated areas, it was observed that the longitudinal and transverse movement of the contaminant under the water level are almost 2.5 times of the semi-saturated area. PRACTITIONER POINTS: Reduction rate of solution area in fine, medium and coarse-grained sample are nearly convex, linear and concave-shaped, respectively. The remediation process in saturated zones is implemented in both directions with higher intensity in a shorter time than unsaturated zones. In the strip formed plumes, the volume of the self-remediation is proportional to the time intervals during the test. In the elliptic masses the self-purification amount is lower at the beginning, due to the small cross-section ending of the contamination mass.

Towards predicting DNAPL source zone formation to improve plume assessment: Using robust laboratory and numerical experiments to evaluate the relevance of retention curve characteristics

We conducted multiple laboratory trials in a robust and repeatable experimental layout to study dense non-aqueous phase liquid (DNAPL) source zone formation. We extended an image processing and analysis framework to derive DNAPL saturation distributions from reflective optical imaging data, with volume balance deviations < 5.07%. We used a multiphase flow model to simulate source zone formation in a Monte Carlo approach, where the parameter space was defined by the variation of retention curve parameters. Integral and geometric measures were used to characterize the source zones and implemented into a multi-criteria objective function. The latter showed good agreement between observation data and simulation results for effective DNAPL saturation values > 0.04, especially for early stages of DNAPL migration. The common hypothesis that parameters defining the DNAPL-water retention curves are constant over time was not confirmed. Once DNAPL pooling started, the optimal fit in the parameter space was significantly different compared to the earlier DNAPL migration stages. We suspect more complex processes (e.g., capillary hysteresis, adsorption) to become relevant during pool formation. Our results reveal deficits in the grayscale-DNAPL saturation relationship definition and laboratory estimation of DNAPL-water retention curve parameters to overcome current limitations to describe DNAPL source zone formation.

Visualization study on aniline-degrading bacteria AN-1 transport in the aquifer with the low-permeability lens

The geological conditions of the contaminated sites will affect the migration of microorganisms in the underground environment. In order to study the effect of low-permeability lens on bacterial transport, green fluorescent protein labeling combined with light transmission method was used to reveal the bacterial transport in the heterogeneous aquifer. The experiment has the advantages of real-time monitoring and no disturbance. The results showed that the bacteria gave priority to bypass the lens to flow away. The lens had a significant effect on hindering the bacterial transport due to adsorption and straining. The larger permeability coefficient ratio between the bulk media and the low-permeability lens was, the more obvious the obstruction was. AN-1 cannot enter the lens until the ratio decreased to the order of 10². With the increase of the flow velocity, the bacterial plume changed a lot. The higher flow velocity reduced the adsorption and retention of AN-1 to the media, resulting in some microorganisms remaining in the pores washed down. When the flow came to 2.0 m·d^-1, AN-1 cannot adhere to the media due to the excessive fluid shear stress.

Quantification of Uncertainties from Image Processing and Analysis in Laboratory-Scale DNAPL Release Studies Evaluated by Reflective Optical Imaging

Subsurface DNAPL (dense non-aqueous phase liquid) contamination from (un-) intentional spilling typically leads to severe environmental hazards. A large number of studies have demonstrated the relevance of DNAPL source zone geometry for the determination of contaminant plume propagation in groundwater. Optical imaging represents a promising non-invasive method for identifying DNAPL saturation without disturbing multiphase flow dynamics. However, workflow and image analysis methodologies have not been sufficiently developed or described for general application to related experimental efforts. For example, the choice of dye(s) used for phase colorization affects image processing and can bias final estimations of DNAPL saturations. In this study, we perform a series of DNAPL migration and entrapment studies in transparent tanks that are filled with three different types of porous media. Different dyes are used and raw images are acquired. Subsequently, these are used to evaluate a suite of image processing and analysis approaches, which are organized into a workflow. Our approach allows for us to identify key image processing and analysis steps that introduce the most error. Applicable dye configurations led to uncertainties of up to 41% depending on the selection of processing steps. Based on these findings, it was possible to delineate a flexible framework for image processing and analysis that has the potential for transfer and application in other tank experiment setups.

Contaminant back-diffusion from low-permeability layers as affected by groundwater velocity: A laboratory investigation by box model and image analysis

Low-permeability lenses represent potential sources of long-term release when filled from contaminant solute through direct contact with dissolved plumes. The redistribution of contaminant from low to high permeability aquifer zones (Back-Diffusion) was studied. Redistribution causes a long plume tail, commonly regarded as one of the main obstacles to effective groundwater remediation. Laboratory tests were performed to reproduce the redistribution process and to investigate the effect of pumping water on the remediation time of these contaminated low-permeability lenses. The test section used is representative of clay/silt lenses (k≈1∗10^-10m/s/k≈1∗10^-7m/s) in a sand aquifer (k≈1∗10^-3m/s). Hence, an image analysis procedure was used to estimate the diffusive flux of contaminant released by these low-permeability zones. The proposed technique was validated performing a mass balance of a lens saturated by a known quantity of tracer. For each test, performed using a different groundwater velocity, the diffusive fluxes of contaminant released by lenses were compared and the remediation times of the low-permeability zones calculated. For each lens, the obtained remediation timeframes were used to define an analytical relation vs groundwater velocity and the coefficients of these relations were matched to grain size of the low-permeability lenses. Results show that an increase of the velocity field is not useful to diminish the total depletion times as the process mainly diffusive. This is significant when the remediation approach relies on pumping technology.

Null-space Monte Carlo particle tracking to assess groundwater PCE (Tetrachloroethene) diffuse pollution in north-eastern Milan functional urban area

The Lombardy Region in Italy is one of the most urbanized and industrialized areas in Europe. The presence of countless sources of groundwater pollution is therefore a matter of environmental concern. The sources of groundwater contamination can be classified into two different categories: 1) Point Sources (PS), which correspond to areas releasing plumes of high concentrations (i.e. hot-spots) and 2) Multiple-Point Sources (MPS) consisting in a series of unidentifiable small sources clustered within large areas, generating an anthropogenic diffuse contamination. The latter category frequently predominates in European Functional Urban Areas (FUA) and cannot be managed through standard remediation techniques, mainly because detecting the many different source areas releasing small contaminant mass in groundwater is unfeasible. A specific legislative action has been recently enacted at Regional level (DGR IX/3510-2012), in order to identify areas prone to anthropogenic diffuse pollution and their level of contamination. With a view to defining a management plan, it is necessary to find where MPS are most likely positioned. This paper describes a methodology devised to identify the areas with the highest likelihood to host potential MPS. A groundwater flow model was implemented for a pilot area located in the Milan FUA and through the PEST code, a Null-Space Monte Carlo method was applied in order to generate a suite of several hundred hydraulic conductivity field realizations, each maintaining the model in a calibrated state and each consistent with the modelers' expert-knowledge. Thereafter, the MODPATH code was applied to generate back-traced advective flowpaths for each of the models built using the conductivity field realizations. Maps were then created displaying the number of backtracked particles that crossed each model cell in each stochastic calibrated model. The result is considered to be representative of the FUAs areas with the highest likelihood to host MPS responsible for diffuse contamination.

Contaminant source reconstruction by empirical Bayes and Akaike's Bayesian Information Criterion

The objective of the paper is to present an empirical Bayesian method combined with Akaike's Bayesian Information Criterion (ABIC) to estimate the contaminant release history of a source in groundwater starting from few concentration measurements in space and/or in time. From the Bayesian point of view, the ABIC considers prior information on the unknown function, such as the prior distribution (assumed Gaussian) and the covariance function. The unknown statistical quantities, such as the noise variance and the covariance function parameters, are computed through the process; moreover the method quantifies also the estimation error through the confidence intervals. The methodology was successfully tested on three test cases: the classic Skaggs and Kabala release function, three sharp releases (both cases regard the transport in a one-dimensional homogenous medium) and data collected from laboratory equipment that consists of a two-dimensional homogeneous unconfined aquifer. The performances of the method were tested with two different covariance functions (Gaussian and exponential) and also with large measurement error. The obtained results were discussed and compared to the geostatistical approach of Kitanidis (1995).