Application of time-lapse electrical resistivity tomography and groundwater simulation models to monitor the transport of organic contaminants under unsaturated and saturated conditions

Production of olive oil is one of the most important activities in the Mediterranean area, particularly in Greece, which holds the third place worldwide after Spain and Italy. For every tone of produced olive oil, about 25% of liquid wastes (olive oil mill wastes- OOMWs) enriched with organic load and inorganic constituents are produced. The OOMWs are usually disposed in uncontrolled, unprotected and poorly constructed shallow evaporation ponds, causing several ecological problems such as odour, increased salinity, toxicity to soil and contamination to surface water bodies and groundwater. A pilot study area (an evaporation pond of OOMWs) has been constructed in Western Crete, at Alikianos village, very close (about 15 meters) from Keritis river. During the last decades, geophysical methods have gained popularity as efficient tools for monitoring the changes of subsurface physical properties over time and identifying the spatial distribution of pollutants. The OOMWs are mainly characterized by high electrical conductivity values and high concentration of phenolic compounds. Those characteristics of OOMWs can be used for detecting them and in particular using geoelectrical methods. In the present study, time-lapse electrical resistivity tomography (ERT) and self-potential techniques are used to map and monitor the subsurface contamination caused by OOMW. A three-dimensional finite-element model for groundwater flow and transport is developed to estimate the temporal and spatial distribution of the selected contaminant under unsaturated and saturated conditions. The resulted simulation models are verified by the obtained time -lapse two-dimensional ERT geophysical inversion images. Results of geochemical analysis of soil and liquid samples collected from two soil profiles excavated along the ERT profile have been used for calibration and validation of both simulation and geophysical results

Characterization of groundwater contaminant sources by utilizing MARS based surrogate model linked to optimization model

Unknown groundwater contaminant source characterization is the first necessary step in the contamination remediation process. Although the remediation of a contaminated aquifer needs precise information of contaminant sources, usually only sparse and limited data are available. Therefore, often the process of remediation of contaminated groundwater is difficult and inefficient. This study utilizes Multivariate Adaptive Regression Splines (MARS) algorithm to develop an efficient Surrogate Models based Optimization (SMO) for source characterizing. Genetic Algorithm (GA) is also applied as the optimization algorithm in this methodology. This study addresses groundwater source characterizations with respect to the contaminant locations, magnitudes, and time release in a heterogeneous multilayered contaminated aquifer site. In this study, it is specified that only limited concentration measurement values are available. Also, the contaminant concentration data were collected a long time after the start of first potential contaminant source(s) activities. The hydraulic conductivity values are available at limited locations. The performance evaluation solution results of the developed MARS based SMO for source characterizing in a heterogeneous aquifer site with limited concentration measurement, parameter values, and under hydraulic conductivity uncertainties are shown to be satisfactory in terms of source characterization accuracy

Evaluation of shallow ground water recharge and its potential for dry season irrigation at Brante Watershed, Dangila, Ethiopia

The estimation of crop water demand and understanding groundwater use is an essential component for managing water effectively. Groundwater is the main source of irrigation in Dangila. However, there is a lack of information in the study area on amount of irrigated land, irrigation water use and demand, groundwater recharge. Consequently, the objective of this study is to determine the groundwater recharge and its potential for dry season irrigation. The study was conducted in Brante watershed of 5678 ha located in Dangila woreda, Ethiopia. Water table data from twenty-five wells and discharge data at the outlet of the watershed used to assess recharge amount in 2017. To calculate irrigation water demand, CROPWAT model was used. Questionnaires were undertaken to assess groundwater use. A KOMPSAT-2 image was used to map shallow groundwater irrigated vegetables in February 2017. From the soil water balance method, the annual groundwater recharge was 17,717,690 m3 which is 15.8% of annual rainfall, and recharge amount of 14,853,339 m3 was obtained using water table fluctuation method. From satellite image classification the area coverage of dry season irrigated vegetables (onion, tomato, pepper) below the main road was 4.02 ha. From CROPWAT result, seasonal irrigation water demand for onion, Tomato, and pepper was 333,314, and 261 mm respectively. However, the questioners result indicates that farmers apply in average 20% more water than crop water demand. In the watershed 60,150 m3, 62,750 m3 and 41,603 m3 of water was abstracted for irrigation, domestic and livestock use respectively. The ratio of groundwater use to groundwater recharge at the watershed scale was found to be only 1%. This study indicates that the current use of groundwater was sustainable. For better improvement of household livelihood irrigation can be further expand using ground water. Future work should be performed to determine if the method outlined in this research could be used to accurately estimate available water potential