A pre-study of an intelligent self-organizing wireless sensor network for monitoring contaminant plume movement in a heterogeneous porous media

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A pre-study of an intelligent self-organizing wireless sensor

network for monitoring contaminant plume movement in a

heterogeneous porous media

A.A. Duijster

Faculty of Environmental Hydrogeology, Utrecht University, Budapestlaan 4 3584 CD Utrecht, The Netherlands

Center for Experimental Study of Subsurface Environmental Processes (CESEP), Colorado School of Mines, 1500 Illinois St. Golden, Co 80401, United States of America

Abstract

This study is performed to gain better insights in experimental laboratory tank design to simulate a heterogeneous subsurface. Laboratory experiments were performed combined with a modelling study using Modflow with MT3D in the GMS interface. Different sand types were used for the packing of a two-dimensional laboratory tank. Fluorescien was used as tracer and was injected through a well. The goal of this study is to create an interesting natural plume, which changes shape and direction over time and space. Such a plume is desirable for the validation and calibration of an intelligent self-organizing wireless sensor network for monitoring contaminant plume movement in heterogeneous porous media, which is the overall goal of this research.

Fiber optical sensors (fluorimeter probes) are used for automated concentration data at different locations in the experimental setup. The fluorimeter probe signals, however, were not consistent over time which made model calibration difficult. A homogeneous and a heterogeneous experiment were performed. The hydraulic conductivities in the heterogeneous experiment corresponded better with the model, but there was still a difference of more than 20 percent over some areas. Hydraulic conductivities of the different sand types were strongly related to the tightness of the packing in the laboratory tank. Fine sands (110 and 140) were especially difficult to pack tightly, their hydraulic conductivity therefore tend to be larger in the laboratory experiments. Coarser sands (50 and 70), however, were packed to tight, their hydraulic conductivity therefore turned out to be significantly lower in the laboratory experiments.

A heterogeneous packing together with an extended low hydraulic conductive layer into the direction of the flow is capable of creating an interesting natural plume, which changes shape and direction over time and space. The model was not capable of simulating the laboratory experiment well enough. Therefore extraction wells at many locations in the experimental setup are recommended for better validation and especially for better calibration.

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1 Introduction

Recent development accounts for a more extensive use of wireless network technology. Sensors are becoming smaller in size with more power and with lower production costs. The fields in which wireless network technology is used are therefore increasing. Traditionally the main driving force behind research in sensor networks is military application. Recently, however, there has been a diversification towards the development of civilian applications such as environmental monitoring, habitat monitoring, monitoring in living areas, structural monitoring and health monitoring (Khemapech, et al. 2005). Remediation of contaminated groundwater systems has become an issue of crucial importance concerning the protection of public health and preservation of environmental quality. Natural Attenuation (NA) and Enhanced Natural Attenuation (ENA) are considered to be promising remediation techniques in the field of groundwater remediation (Gutierrez-Neri, et al. in preparation). Analytical and numerical models designed to simulate transport of chemicals in the subsurface are useful tools for decision making and risk analysis of hazardous waste sites. In order to achieve accurate model predictions it is important to select the appropriate parameter values. Because the migration of solute transport is mainly controlled by geological complexity of formation, these model parameters need to reflect the influence of the underlying heterogeneous structure (Fernandez-Garcia, 2005). This is the reason why field tracer tests and monitoring of the subsurface is required. A significant part of the overall costs for remediation using NA and ENA are monitoring costs.

Traditional monitoring methods for contaminant concentrations are usually done manually by taking samples at monitoring wells. The disadvantage of manual sampling is that there is always a time period in between the samplings. Samples then have to be taken to a lab for further analysis. Manual sampling is therefore a money and time consuming process.

Wireless sensing can produce real-time data and eliminate these disadvantages. However, using a wireless sensing system has its own disadvantages, and problems should be overcome in order to make it a useful tool for remediation. Sensors have to be calibrated which could implement a new source of error to the data. In some application scenarios, replenishment of power resources might be too expensive or even impossible. Sensor node lifetime, therefore, shows a strong dependence on battery lifetime (Akyildiz, et al. 2002). The systems must be designed to withstand specific conditions, such as temperature, pressure, vibration or chemicals. A well designed wireless sensing network is of the essence. The design of sensor networks for the natural environment requires technologies from different research areas: Sensing, communication, computer technology and earth science (Martinez, et al. 2004).

Research goals and objectives:

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advance the sensor networking based monitoring technology for decision making and design. In order to test the wireless sensor network a three dimensional laboratory experiment will be performed. The 3D tank should be packed to create an interesting tracer plume for comparison with realistic contaminant plumes in naturally heterogeneous porous media. To create a design for the packing of the 3D tank a modelling study using Modflow with MT3D is performed. The model, however, has to be validated to make sure that the simulated tracer plume corresponds to the real tracer plume in the 3D laboratory experiment. In order to validate the model, smaller 2D experiments are performed for different packings.

Approach:

In the overall approach, sensors are placed at fixed known positions at different locations in the subsurface. Sensor data are used in an inverse model to estimate the flow and transport parameters. A forward model is fed with the new estimated parameters to make a new prediction of plume migration. For example, predictions of the direction of plume migration are used in the form of configuration instructions to the sensor network to activate sensors downstream.

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2 Literature

2.1 Wireless sensor networks

2.1.1 Sensor network applications

Nowadays sensor networks are applied in different fields which can be categorised as follow:

1. Military applications 2. Environmental applications 3. Health applications

4. Home/School/Office applications 5. Commercial business applications

The sensor network applications can also be objective-orientated categorised in five categories as follow by Khemapech, et al. 2005:

1. Military

2. Public Security/Warning

 Environmental Observation and Forecasting  Health monitoring

 Structural Monitoring 3. Education

 Environmental Observation and Forecasting  Health Monitoring

 Structural Monitoring  Habitat Monitoring  Smart Classroom

4. Business Competitiveness Improvement  Tracking (inventory system)  Smart Office

5. Quality-of-Live Improvement

 Environmental Observation and Forecasting  Health Monitoring

 Tracking (Traffic monitoring)  Smart Home/Office

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2.1.2 Sensor node components

A sensor node contains four basic components: a sensing unit, a processing unit, a transceiver unit and a power unit. They may also have application dependent additional components like a location finding system, a power generator or a mobilizer (Akyildiz, et al. 2002). The sensing unit measures physical data from the target area. The continual incoming sensor waves are digitised by an analog-to-digital converter (ADC) and then delivered to the processing unit for further analysis. The processing unit plays an important role in managing collaboration with other sensors to achieve the predefined tasks (Khemapech, et al. 2005). The processing unit needs storage for tasking and it needs to minimise the size of transmitted messages by local processing and data aggregation (Khemapech, et al. 2005). For the transceiver three different communication techniques can be used: Optical (laser), infrared, and radio-frequency (RF). Laser communication requires a line of sight and is sensitive to atmospheric conditions. However, laser consumes less energy than RF and provides high security. The most often used is the RF, but it needs an antenna. Infrared, like laser, needs no antenna but is limited in its broadcasting capacity (Khemapech, et al. 2005). As mentioned before the power unit plays an important role in wireless sensing networks. In many applications the power unit consists of a battery. Replacing or recharging of these batteries may often be impossible because of the harsh environments. Current power units are developed to be able to renew their energy from solar or vibration energy (Khemapech, et al. 2005). For energy saving, sensor nodes or components on sensor nodes can be shut down and reactivated when required. This is called Dynamic Power Management (DPM). Another method for saving power is Dynamic Voltage Scheduling (DVS), which allows the power to vary (Khemapech, et al. 2005).

2.1.3 Sensor network design

For environmental sensor network design it is important to know the factors in the environment that affect the wireless sensing network. Such factors include, size and

conditions of operating environment, sensor network topology and transmission media.

Other factors important for designing a wireless sensor network are: production costs,

scalability, hardware constraints, data sending rates with respect to possible congestion, power consumption, and fault tolerance.

Size and conditions of operating environment: The size of the environment is important for the number of sensor nodes needed in the network and the distance over which the data has to be transmitted. Other environmental conditions are of great importance for the network design. The sensor nodes for instance can be under high pressure in the bottom of the ocean, under extreme heat and cold such as in an engine and in an arctic region respectively (Akyildiz, et al. 2002).

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Transmission media: Most current hardware for sensor nodes are based upon RF. What type of radio frequency is the best for the sensor network system depends on the type of transmission medium. For instance, marine applications may require the use of the aqueous transmission medium. For the aqueous transmission medium one would like to use long-wavelength radiation that can penetrate the water surface. Also power consumption and distance between the transmitter and receiver should be taken into account for choosing the radio frequency. However, the types of frequency bands that are available to use for the wireless sensor network are limited. What often can be used are the industrial, scientific and medical (ISM) bands, which offer license-free communication in most countries (Akyildiz, et al. 2002). Other types of communication links can be formed by using infrared or optical media.

Production costs: The price of one sensor node is dependant on the functionalities of the node. The cost of a single node is very important to justify the overall costs of the network. If the costs of the network are more expensive than deploying traditional sensors, then the sensor network is not considered to be cost-justified (Akyildiz, et al. 2002).

Scalability: The sensor network systems must be able to work with the amount of sensor nodes used in the system.

Hardware constraints: The sensing units need the four basic components as previously discussed. Most of the sensing tasks require the knowledge of position and therefore a location finding system has to be added to the sensing unit. Also an additional power replenishing unit has to be added to assure a long life time of the sensor unit. These sensor components are effecting the power consumption; operation in high volumetric densities; production costs; (un)attended operation; and the environment (Akyildiz, et al. 2002).

Congestion: In situations of high data sending rates, sensor networks are likely to face congestion problems. During for instance a natural disaster or a military attack, data delivery in sensor networks can be heavy during these events. Each sensor has limited resources including memory (Khemapech, et al. 2005). During large data delivery events it is therefore possible that data is lost. When using RF, the nature of the radio signal itself varies over time. This makes the congestion problem worse. Most research on developing congestion control is based on monitoring communication channels and buffers to adjust data sending rate from the neighboring nodes (Khemapech, et al. 2005). Power consumption: If data is to be collected for longer periods of time, power consumption plays one of the most important roles in wireless sensor network design. To avoid constant battery maintenance, low power use is essential. Low maintenance is a design goal as wireless sensor networks should work independently without much intervention (Hart, et al. 2006). Methods for saving power and renewable energy options were mentioned in the previous paragraph.

Fault tolerance: The failure of sensor nodes should not affect the overall task of the sensor network (Akyildiz, et al. 2002).

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possible (Akyildiz, et al. 2006). Wireless communication within the subsurface is not yet proved to be effective. Therefore surface stations, which are connected to the subsurface sensor nodes, are used for wireless communication. An optional design for a wireless sensor system to track contaminant plume migration through the subsurface is shown in figure 1. The subsurface-nodes communicate to the surface-nodes and the surface-nodes are capable of transmitting the data further to other surface-nodes by wireless communication.

Figure 1. Schematic representation of a wireless sensor network design for monitoring contaminant plume migration through the subsurface.

2.2 Information from breakthrough curves

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t t T L n q v  (1)

Where: v = The interstitial velocity [m∙s-1] q = The darcy velocity [m3∙s-1∙m-2] n = porosity [-]

Lt = The travel distance [m]

Tt = The travel time [s]

In 1856 Henry Darcy performed an experimental study of one-dimensional water flow moving through a pipe filled with sand. He found that the flow was proportional to the cross-sectional area of the pipe and the head loss along the pipe. He also found that the flow was inversely proportional to the flow length (Fetter, 1999). From these experiments, Darcy came up with the following relationship known as Darcy’s law (equation 2). Equation 2 is only valid under conditions where fluid properties like viscosity and density are constant.

q  K h (2)

Where:

q = Darcy velocity, which is the discharge per unit area perpendicular to

the flow [m3∙s-1∙m-2 = m∙s-1]

K = Proportionality constant known as the hydraulic conductivity tensor

[m∙s-1

] h

 = Gradient of hydraulic head [-]. Described by h=z+P/ρg

By calculating the Darcy velocity from the breakthrough curves and knowing the porosity together with the gradient of hydraulic head, the average hydraulic conductivity can be calculated between the two wells.

2.3 Modelling experiments

2.3.1 Groundwater flow model

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The two-dimensional movement of ground water of constant density through a porous media may be described by the partial-differential equation 3.

t h S W z h K z x h K x L T s                          (3)

Where: K = Hydraulic conductivity in the longitudinal direction x [m∙s_L -1]

T

K = Hydraulic conductivity in the transverse direction z [m∙s-1] h= Potentiometric head [m]

W= Volumetric flux per unit volume representing sources and/or sinks of water, with W<0.0 for flow out of the ground-water system, and W>0.0 for flow into the system [s-1]

s

S = Specific storage of the porous material [m-1]

t = Time [s]

In general Ss, KL and KT may be functions of space and W may be functions of space and

time (W(x,z,t)). Equation 3, together with specification of flow and/or head conditions at the boundaries of an aquifer system and specifications of the initial-head conditions constitutes a mathematical representation of a ground-water flow system. Except for very simple systems, analytical solutions of equation 3 are rarely possible, so various numerical methods must be employed to obtain approximate solutions (Harbaugh, 2005). Modflow uses the finite-difference method for solving three-dimensional ground-water flow systems.

2.3.2 Solute transport model

To model the tracer transport, the MT3DMS model is used. MT3DMS is a new version of the Modular 3-D Transport model (MT3D), where MS denotes the Multi-Species structure for accommodating add-on reaction packages. In this study only one tracer material is used so multi-species modelling will not be needed. Similar to the original MT3D code, MT3DMS is developed for use with any block-centered finite-difference flow model such as MODFLOW and is based on the assumption that changes in the concentration field will not affect the flow field significantly. For this study Modflow-2000 and MT3DMS are combined in the GMS interface. MT3DMS can use three major classes of transport solution techniques, i.e., the standard finite difference method, the particle-tracking-based Eulerian-Lagrangian methods and the higher-order finite-volume method. Each solution technique has its own strengths and limitations.

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z C v x C v z C D x C D t C z x T L _              2 2 2 2 (4)

Where: C = Concentration of the tracer material [kg∙m-3]

DL = Longitudinal Hydrodynamic Dispersion Coefficient [m2∙s-1]

DT = Transverse Hydrodynamic Dispersion Coefficient [m2∙s-1]

vx = Average interstitial flow velocity in the direction of the flow [m∙s-1]

x = Distance in the direction of the flow [m]

z = Distance in the vertical direction perpendicular to the flow [m] t = Time [s]

The longitudinal and transverse hydrodynamic dispersion coefficients are related by equation 5 and 6. eff z T x L L D v v a v v a D    2 2 (5) eff x T z l T D v v a v v a D    2 2 (6)

Where: a = The longitudinal dispersivity in the direction x [m] L T

a = The transverse dispersivity in the direction z [m]

eff

D = The effective diffusion coefficient [m2∙s-1]

eff aq

D D (xxx)

Where Daq is the aqueous diffusion coefficient, which is the diffusion in water without a

porous media. And where ω is a coefficient related to the tortuosity (Bear, 1972). The tortuosity is an indication of the shape of the flow-path that water molecules follow in a porous medium. If L is a straight distance between the ends of a tortuous flow-path of length Le, the tortuosity T, can be defined as T = Le/L (Fetter, 1999). The tortuosity has

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3 Material and Methods

3.1 Small 2D tank experiment using a fine sand inclusion

The first experiment was performed to see whether an inclusion is able to split the fluorescent tracer plume and change its direction and shape. The inclusion consists of fine sand with a low hydraulic conductivity (K) of 2.9 meter per day. This is a low hydraulic conductivity in comparison with the surrounding background sand which has a hydraulic conductivity of 28.7 meter per day. To create a steady uniform flow two head devices are used with a head difference of 3 cm. The two constant heads, shown in figure 2A, are connected with the upstream side and the downstream side of the two-dimensional tank. At both sides a layer of coarse sand was used to create a uniform flow over the whole length of the tank.

The first experiment is performed using a uniform packing with a fine sand inclusion in the middle. To form the inclusion fine sand is saturated with water and frozen into a round 2D shape (figure 2B). The 2D tank is tightly packed with the surrounding sand under saturated conditions. Halfway the packing the water level is lowered, keeping all the sand saturated with water due to capillary forces, to place the frozen fine sand inclusion. Thereafter the rest of the packing could be finished (figure 2C). After having packed the tank an injection well (figure 2D) is placed inside the coarse sand layer at the same elevation as the inclusion. The injection well has three openings at different elevations. A Harvard injection pump was used for injecting a tracer in the upstream part of the tank. To create a visual plume, a blue dye is added to the injection water. The injection rate is one tenth of the overall discharge of the 2D tank. This injection rate had to be small enough not to disturb the flow field. For this experiment an injection rate of 60 mL/h is used which is injected for 3 hours.

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C D

Fig 2. A) Tank setup with the constant heads and the large water reservoir. B) Frozen sand inclusion. C) Packing of the tank. D) Injection well with three ports.

3.2 Fiber optical fluorimeter

The tracer type used in this study is fluorescein disodium salt (C20H10O5Na2). This is a

non-hazardous tracer also called Uranine. A fiber optical fluorimeter with different probes was used for automated tracer concentration measurements. The fluorimeter consists of a light source and a photomultiplier with a current-voltage converter. An optical fiber transmits the light from the light source to the measurement medium. This light stimulates the fluorescent tracer present in the measurement volume and part of the emitted light from the tracer is then transmitted back to the photomultiplier of the fluorimeter by the other receiving optical fiber. An electrical signal, corresponding to the measured light intensity, is created by the photomultiplier with a current-voltage converter. This system is connected to a data collecting and processing system. The optical fiber probe and the tip of the optical fiber is shown in figure 3.

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Figure 4. 19-channel fiber optical fluorimeter

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Figure 5. Optical fiber probe with his protective brass cap.

3.3 Large two-dimensional experiments

3.3.1 General setup

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A B

Fig 6. A) Large tank setup with the two constant heads. B) Large tank setup with injection pump and optical sensors

3.3.2 Homogeneous experiment

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Fig 7. Schematic representation of the homogeneous experiment.

A difficulty encountered with installing the probes into the tank is that no air should be trapped inside the caps and no sand should be able to migrate into the caps. Therefore, the caps and the probes were glued in a bucket which was constantly filled with CO2 gas.

After the glue dried the bucket was filled with water and the CO2 gas was able to dissolve

into the water. Before starting the experiment three pore volumes of water were flushed through the tank in order to have steady state conditions. Finally, the injection pump was checked for air inside the pump before the start of the experiment. For the homogeneous experiment an injection rate of 200 mL/h was used over a time period of three hours.

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3.3.3 Heterogeneous experiment

For the heterogeneous experiment the coarse sand between the screens and the sides of the tank is replaced with gravel. This is to avoid clogging and to create a better uniform flow. Five different sand types were used to create the heterogeneous packing, shown in table 1.

Sand no. K[m/day] Porosity [-] Bulk density [kg/m3]

#8 13.1 ·102 0.399 1.59·103

#50 28.7 0.426 1.52·103

#70 12.2 0.418 1.54·103

#110 4.38 0.334 1.76·103

#140 2.86 0.358 1.70·103

Table 1. Sand type characteristics obtained by Sakaki and Komatsu in 2007.

The goal of this experiment is to create an interesting tracer plume that is forced to change shape and direction due to the heterogeneous formations in the underground. The 140 sand together with 110 are used to form a barrier in the middle of the tank, which represents a less permeable layer, simulating a clay layer often found in the natural underground. The water tends to flow through the sand with the largest hydraulic conductivity and therefore adjusts its flow path when encountering heterogeneities. The tracer plume will try to avoid the less conductive sands and flow around it, splitting the plume into two main directions. Fiber optical fluorimeter probes are located at several locations and heights to monitor the flow through the tank. Seven probes are used for monitoring, their locations are shown in figure 9.

Fig 9. Schematic representation of the heterogeneous experiment.

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placed to form the tracer plume (figure 10). During the packing the seven probes and the injection well are placed with the same technique as described in the homogeneous case. Hand made screens are used to divide the different types of sand blocks during the packing, shown in figure 10A. At the end of the packing piezometers are installed in the gravel at both sides of the tank (figure 10B). The piezometers are used to control the constant heads stay constant over time. The Harvard injection pump is used with an injection rate of 60 mL/h over a time period of 3 hours.

A B

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4 Results

4.1 Small 2D tank experiments

The main objective of the small 2D tank test experiment is to see whether a fine sand lens is able to displace the tracer flow and create a barrier to split the tracer plume.

In figure 11 the dyed tracer plume can be seen in the beginning of the experiment (A) and at the end of the experiment (B). Figure 11 A shows that the tracer is injected through three holes in the injection well. Due to the barrier formed by the fine sand lens the upper tracer flow is forced to curve to a higher vertical level. The same effect can be observed for the lower tracer plume which is forced, however, to a lower vertical level. The middle tracer plume starts at a similar elevation as the fine sand lens and is therefore the most interesting. The water flow is hindered the most by the fine sand lens and from figure 11A can be seen that most of the tracer plume is forced to change its course downwards and follows the edge of the fine sand lens. While a smaller part of the dyed water from the centre tracer plume is forced up and follows the upper edge of the fine sand lens. This can be observed more clearly from figure 11B. Some of the tracer plume is flowing through the fine sand lens with very low concentration, this, however, can not be visually observed.

Noticeable immediately downstream of the fine sand lens is that the tracer plumes stream back into the direction and elevation with which they started upstream of the fine sand lens. So the tracer plumes are mainly just flowing around the fine sand lens without significantly changing their flow paths downstream of the fine sand lens. In order to split a tracer plume into different directions more heterogeneity should be implemented into the experiment and the lenses should extend more over the length of the tank into the direction of the flow to create a more structural change in plume shape and its migration direction.

A B

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4.2 Validation and calibration of the fiber optical fluorimeter probes

The sensors are validated for reproducibility of the same concentration of fluorescence. The probes are installed in a plastic beaker filled with a solution of fluorescence with a specific concentration.

The first observation made is that probe signals depend on the location of the probes, this is due to the reflection from the wall of the beaker. The probes are also tested on their reproducibility of the signals when installed in saturated sand with the same fluorescence concentration. The conclusion is that the probes are sensitive to reflection of the sand. Therefore, the caps, shown in figure 5, are applied to the probes to protect them from reflection.

From figure 12 can be observed that for each probe the intensity of the signal increases within approximately the first 90 minutes. After the first 90 minutes, however, the signal intensity seems to be constant over time for most probes. The reason for this increase in signal can be due to the warming up of the sensor system, however, after replacing the probes into another concentration solution the same effect happened without switching the system off. Somehow it has to do with the probes having to adapt to new concentrations.

Sensor signals over time for constant concentration (2500 PPB)

500 1000 1500 2000 2500 0 100 200 300 400 500 Time (minutes) S ign a l Probe 7 Probe 8 Probe 9 Probe 10 Probe 11 Probe 12 Probe 14

Fig 12. This graph shows the non-consistency of the individual sensor probes over time.

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In the results of the first homogeneous experiment one probe gave a peek value of a little more than 3500 PPB, which is remarkably higher than the initial tracer concentration. This indicates that the calibration curves are not accurate enough. Therefore a second calibration with the same standards was performed, the results are shown in figure 14. By comparing figure 13 and 14 one can see that, indeed for probe 7 the two calibration curves are different. For the second calibration curve, obtained after the first homogeneous experiment, a larger signal intensity is needed for the same concentration in comparison with the first calibration curve. This can only be observed for probe 7. For most other probes, however, the opposite is observed. In the second calibration curve, lower sensor intensities are related to the same concentrations for most probes except for probe 7. It is remarkable that the second calibration, performed in a similar way using the same standards and under similar conditions after the first homogeneous experiment, shows significantly different calibration curves. For further experiments the last calibration curves (figure 14) are used to relate signal intensity to fluorescence concentration.

Calibration of the sensor probes 1

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Calibration of the sensor probes 2 0 500 1000 1500 2000 2500 0 500 1000 1500 2000 2500 3000 Fluorescence concentration (PPB) S ign a l Probe 7 Probe 8 Probe 9 Probe 10 Probe 11 Probe 12 Probe 14

Fig 14. Second calibration results for each probe.

4.3 Large 2D tank experiments

4.3.1 Homogeneous

The aim of the homogeneous experiment is to perform an in-situ test of the fiber optical fluorimeter and to validate and calibrate the Modflow and MT3D models.

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Homogeneous experiment 0 500 1000 1500 2000 2500 0 0.2 0.4 0.6 0.8 1 Time (days) C o n c e n tr a ti o n ( P P B ) probe 7 Probe 8 Probe 9 Probe 11 Probe 14

Figure 15. Fluorescence breakthrough curves for the 5 different probe locations of the homogeneous experiment.

Homogeneous model simulation

0 500 1000 1500 2000 2500 0 0.2 0.4 0.6 0.8 1 Time (days) C o n c e n tr a ti o n ( P P B ) Probe 7 Probe 8 Probe 9 Probe 11 Probe 14

Figure 16. Fluorescence breakthrough curves for the 5 different probe locations of the homogeneous model simulation.

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The experiment is homogeneously packed with the same sand type and therefore a uniform flow and a uniform hydraulic conductivity should be expected. Unfortunately table 2 shows that this is not the case. In the last column of table 2 the average velocity and average hydraulic conductivity is calculated.

Sakaki and Komatsu (2007) performed constant-head column tests to obtain the hydraulic conductivities for different sand types, the results are shown in table 1. When comparing the hydraulic conductivity with those found by Sakaki and Komatsu (2007) for type 50-sand (50-sand used in this experiment), the values do not correspond. The value found by Sakaki and Komatsu (2007), 28.7 m/day, is about twice as high as the average value of 14,6 m/day obtained in this experiment.

Although the sand is tightly packed the difference in hydraulic conductivity is too large for it to be a plausible explanation. While the head gradient is held constant over time for the homogeneous experiment, the extra injected tracer water is not taken into account for the calculation of the hydraulic conductivity. If taken into account it may lead to larger values of hydraulic conductivity. From this comparison, however, the accuracy of the head gradient can be questioned together with the information that the sensors provide about the time to peak.

Probe numbers 7-8 8-9 9-11 11-14 7-14 (average) Distance between probes [m] 0.10 0.10 0.10 0.10 0.40

Time [d] 0.066 0.04 0.083 0.082 0.271

Interstitial velocity [m/d] 1.52 2.50 1.20 1.22 1.47 Darcy velocity [m/d] 0.65 1.07 0.51 0.52 0.63 Hydraulic conductivity [m/d] 15.1 24.9 11.9 12.1 14.6 Table 2. Calculation of velocity and hydraulic conductivity with data obtained from the sensors. Probe numbers 7-8 8-9 9-11 11-14 7-14 (average) Distance between probes [m] 0.10 0.10 0.10 0.10 0.40

Time [d] 0.029 0.029 0.037 0.035 0.131

Interstitial velocity (v) [m/d] 3.45 3.45 2.70 2.86 3.05 Darcy velocity (q) [m/d] 1.47 1.47 1.15 1.22 1.30 Hydraulic conductivity [m/d] 34.8 34.8 27.2 28.8 30.8 Table 3. Calculation of velocity and hydraulic conductivity with data obtained from the model

The homogeneous experiment is simulated by modelling using Modflow and MT3D. Concentration values are obtained at the same positions of the sensor probes in the experiment. The tracer injection is simulated using ten injection wells located at similar positions as the ten holes in the experimental injection well. Inputs for hydraulic conductivity and porosity are used from table 1 and a constant head gradient of 3 cm over a length of 71 cm is also included in the model. Due to the large flow velocity it is assumed that there is no diffusion. In the model a dispersivity value was used related to the sand type and grid size used in the model. Possible flow disturbance due to the sensor probes are assumed to be negligible and are therefore not included in the model.

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conductivity is larger than the last two distances. This is probably due to the extra pressure induced while injecting the tracer over the first 3 hours (0.125 day). The average hydraulic conductivity is 30.8 m/day which is about twice as high as the average hydraulic conductivity obtained from the experimental data with the sensors. The average hydraulic conductivity value of 30.8 m/day from the model, however, is in the same order of magnitude as those obtained from the constant head tests performed by Sakaki and Komatsu in 2007, 28.8 m/day. The slightly larger hydraulic conductivities in table 3 are probably due to not taking into account the extra injected tracer water for calculating the hydraulic conductivity. Therefore the values in table 3 are in good comparison with the values obtained by Sakaki and Komatsu in 2007.

Figure 16 shows the results of the model simulation. The scales of the axes are the same as in figure 15. This enables a better comparison between the experimental results and model simulation.

The simulated breakthrough curves show a different shape and a maximum concentration which is constant over a certain time period compared to the experimental breakthrough curves. The breakthrough curves for the sensors located closer to the point of tracer injection have a steeper gradient than the breakthrough curves of the sensors located further away. This is due to dispersion and was to be expected. A similar behaviour was expected for the experimental breakthrough curves. They do show that the sensors downstream have more dispersion than the sensors upstream. However, the dispersion is more than in the model simulations.

All probes from the experiment show a gradual increase of concentration over time and a more rapid decline. The signal of the sensors tend to increase during the initial period of time over which the sensors have contact with larger concentrations. This behaviour is shown in figure 12. Probe 14 showed a very gradual decline, which was not the case for the other probes. In theory a gradual decline of concentration over time can be subscribed to a large retardation due to adsorption. However, a non-reacting tracer is used for this experiment and adsorption should not occur. Another possibility is that some of the tracer solution was trapped inside the cap of the probe which resulted in the gradual decline of concentration over time.

The experimental breakthrough curves peak too sharply knowing a constant tracer concentration of 3000 PPB was injected over a period of three hours with a constant injection rate.

4.3.2 Heterogeneous

4.3.2.1 The Model

Before starting a heterogeneous experiment different designs were made using the models Modflow and MT3D in the GMS interface. The overall goal of the heterogeneous experiment is to create a packing which is suitable for a proof of concept of an intelligent self-organizing wireless sensor network to monitor contaminant plume movement in naturally heterogeneous porous media. Therefore, a requirement for the heterogeneous packing is that it should be able to create an interesting plume by splitting it into half or by forcing the plume to migrate into different directions.

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A B

C D

Fig xxxan. Animation of plume migration through a heterogeneous packing with concentration contour lines at four different time steps. Modflow and MT3D in the GMS interface.

One design for the heterogeneous packing of the two-dimensional tank is shown in figure 17. The sand number 8, 50, 70, 110, and 140 (from coarse to fine) are indicated by the following colours respectively: dark green, orange, white, brown, and light green. Characteristics of those sand types are shown in table 1.

The first small tank experiment shows that by using a single inclusion the tracer flow is temporary disturbed but no structural changes are made in plume shape and flow direction. To change the shape and the direction of the tracer plume a more heterogeneous pattern should be introduced. To split the plume into different flow direction a more extended low hydraulic conductive layer should be introduced into the heterogeneous pattern. This layer is created by using the less hydraulic conductive 110 and 140 sands shown in figure 17.

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The model shows that using a packing design as in figure 17, an interesting tracer plume is developed for a proof of concept of an intelligent self-organizing wireless sensor network to monitor contaminant plume movement in naturally heterogeneous porous media.

4.3.2.2 The laboratory experiment

An laboratory experiment is performed, using a similar heterogeneous packing used in the model of figure 17, to validate whether the model is capable of predicting concentration values over time in a laboratory experiment. A schematic representation of the heterogeneous laboratory experiment is shown in figure 9. Probes are installed at seven locations for real-time concentration data during the experiment. Three probes are installed at a similar elevation in the less hydraulic conductive layer. Two probes are installed at a similar elevation in the more hydraulic conductive layers, above and below the less hydraulic conductive layer. This enables to obtain concentration data over time through the high hydraulic conductive layers and in the low hydraulic conductive layer, to track the tracer plume migration.

During the laboratory experiment discharge measurements are taken manually over time. The results are shown in figure 18. The discharge during the beginning of the experiment is about 10 percent larger than the discharge a day later. During the first 3 hours (0.125 day) the tracer is injected with a rate of 0.06 l/hour. This explains a large part of the decrease in discharge one day after start of the experiment. Taking into account the change in discharge due to the tracer injection it can be concluded that the discharge is constant over time within a range of 5 percent. There is thus no major clogging during the experiment and the head gradient (dh/dx) can be considered to be constant over time.

Discharge measurments during the heterogeneous experiment

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0 0.5 1 1.5 2 time [days] D is c ha rge [ l/ ho ur]

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4.3.2.3 Comparison between the model and laboratory data

In the model the concentration data over time are obtained at the same locations as in the laboratory experiment. In figure 19 the experimental breakthrough curves are compared with the model breakthrough curves. The results are divided into three graphs. One graph for the sensor locations in the low hydraulic conductive layer (figure 19B) and two other graphs for the sensor locations in the higher hydraulic conductive layers (figure 19A and 19C). The locations of the sensors are shown in figure 9.

Plume migration through the upper layer

0 500 1000 1500 2000 0 0.2 0.4 0.6 0.8 1

Time after start of injection (days)

C o n c e n tr a ti o n ( P P B ) Probe 14 / location 1 Probe 9 / Location 5 Model Location 1 Model Location 5 A

Plume migration through the middle layer

0 500 1000 1500 2000 2500 3000 0 0.2 0.4 0.6 0.8 1

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Plume migration through the lower layer 0 100 200 300 400 500 0 0.2 0.4 0.6 0.8 1

C o n c e n tr a ti o n ( P P B ) Probe 11 / Location 3 Probe 7 / Location 7 Model location 3 Model Location 7 C

Fig 19. Comparison between experimental breakthrough curves and breakthrough curves obtained by the model. A) Two locations in the upper high hydraulic conductive layer. B) Three locations in the middle low hydraulic conductive layer. C) Two locations in the bottom high hydraulic conductive layer.

Model location 1 in figure 19A has a steeper and higher breakthrough curve compared to model location 5 which is at a similar elevation but is located further downstream. This is expected due to the increase of dispersion further downstream. The same process is shown in the low hydraulic conductive layer in figure 19B for model locations 2, 4 and 6. However, when looking at the bottom layer this is not the case. In model location 7, which is further downstream, the tracer is more dispersed but the amplitude is higher than in model location 3.

For the breakthrough curves obtained by the sensor probes in the laboratory experiment it is more difficult to observe the process of dispersion. The base of the experimental breakthrough curves, which contains information about the amount of dispersion, is often not significantly larger at locations further downstream.

On top of this many experimental breakthrough curves located further downstream show a larger amplitude than breakthrough curves located further upstream. This is different from what is seen in the model, and is not expected.

This increases the uncertainty of the capability of the sensors to obtain accurate concentration data over time. Not knowing whether the sensor probes were measuring the same tracer concentration inside the cap than outside the cap together with the large uncertainty in the calibration makes comparing the sensor data with other sensor data and sensor data with the model data challenging.

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conductive layer (figure 19B) all the sensor probes measured larger concentration values than the model. However, when looking at the high hydraulic conductive layer in the bottom of the tank (figure 19C) both probes measured lower concentrations than the model.

By looking at the timing of the peaks in the breakthrough curves gives information about the hydraulic conductivity, which is related to the type of sand and tightness of the packing of the tank.

In figure 19A at location 1 the concentration starts increasing at approximately the same time for both the model and sensor data. In the same layer at location 5 both sensor and model data start to increase in concentration in the same time period. However, the timing of the peaks is significantly different. Looking closer at the sensor data on location 5 the breakthrough curve shows two peaks. The smaller peak corresponds well with the model peak.

In figure 19B can be seen that all breakthrough curves obtained from the model have a delay in comparison with the breakthrough curves obtained from the sensor data. This is the case for both the start of concentration increase and the time to peak.

Figure 19C shows that both sensor and model breakthrough curves at location 3 start increasing in concentration at approximately the same time and the time to peak corresponds well. At location 7 both the time to peak and start of concentration increase are slightly earlier for the breakthrough curve obtained by sensor probe 7 than for the model breakthrough curve. The model breakthrough curve also has a longer retardation zone.

In table 4A, 4B and 4C the average hydraulic conductivities are calculated for the sensor data and for the model data in the same layers. Table 4A shows that the hydraulic conductivities calculated with the model and the sensor data are not too different from each other in the upper, high hydraulic conductive, layer.. The same accounts for Table 4B over a distance of 0.30 meter in the middle, low hydraulic conductive, layer. However the average hydraulic conductivities between the first two sensor probes (two and four) over a distance of 0.15 meter differ from 10.6 m/d for the sensor data to 14.0 m/d for the model data. This is a change of more than 20 percent.

Looking at the hydraulic conductivities in the bottom, high hydraulic conductive, layer in table 4C, the hydraulic conductivity for the sensor data is 26.3 m/d and for the model data 19.5 m/d. This is also a change of more than 20 percent.

Time [d] Distance [m] v [m/d] q [m/d] K [m/d]

Sensor data (1 – 5) 0.41 0.30 0.73 0.31 7.3

Model data (1 – 5) 0.45 0.30 0.67 0.28 6.7

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Time [d] Distance [m] v [m/d] q [m/d] K [m/d] Sensor data (2 – 4) 0.12 0.15 1.25 0.45 10.6 Model data (2 – 4) 0.09 0.15 1.65 0.59 14.0 Sensor data (4 – 6) 0.31 0.15 0.49 0.16 3.8 Model data (4 – 6) 0.31 0.15 0.48 0.16 3.8 Sensor data (2 – 6) 0.43 0.30 0.70 0.24 5.7 Model data (2 – 6) 0.40 0.30 0.75 0.26 6.1

Table 4B. Hydraulic conductivities calculated with the breakthrough curves between the three probe locations two and four, four and six and two and six. These probes are located in the the middle, low hydraulic conductive, layer

Time [d] Distance [m] v [m/d] q [m/d] K [m/d]

Sensor data (3 – 7) 0.11 0.30 2.63 1.11 26.3

Model data (3 – 7) 0.15 0.30 1.95 0.82 19.5

Table 4C. Hydraulic conductivities calculated with the breakthrough curves between the bottom, high hydraulic conductive, layer.

It is surprising that there is a large difference in hydraulic conductivity between the first two sensors in the middle, low hydraulic conductive, layer and the last to sensors in this same layer. For the model data this difference is even larger than for the sensor data. The surprisingly large hydraulic conductivity in the first part of the middle layer can only be explained by the extra pressure during the first three hours (0.125 day) of tracer injection. Also surprising is that the average hydraulic conductivity between location two and four is larger than the hydraulic conductivity calculated in the top layer consisting of sands (50 and 70) that have a relative large hydraulic conductivity compared with the sands in the middle layer (110 and 140).

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5 Conclusions

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6 Future recommendations

 Install sufficient extraction wells to obtain accurate concentration data over time at many locations which are needed for validation and calibration.

 Install thin injection wells little upstream of the sensor probes to enable in-situ calibration of each individual sensor probe.

 Take into account the difference in hydraulic conductivity due to the tightness of the packing in the laboratory experiments while designing packings using a groundwater flow model.

 When using a solute transport model for plume prediction, use concentration data from extraction wells to calibrate the hydraulic conductivities and dispersivities.

7 References

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 Bear, D.A., 1972. Dynamics of fluid in Porous media New York: America Elsevier Publishing Company, 764 pp.

 Carman, P.C. 1937. Fluid flow through a granular bed. Institute of Chemical Engineers London. 15, 150-156.

 Fernandez-Garcia, D., Illangasekare, T.H. and H. Rajaram. 2005. Differences in the scale-dependence of dispersivity estimated from temporal and spatial moments in chemically and physically heterogeneous porous media. Advances in water resources. Vol 28. 7. pp 745-759.

 Fetter, C.W., 1999. Contaminant Hydrology second edition, Prentice-Hall International.

 Freeze, R.A. and J.A. Cherry. 1979. Groundwater. Englewood Cliffs, N.J.: Prentice Hall, 604 pp.

 Harbaugh, A.W., 2005. Modflow-2005, The U.S. Geological Survey Modular Ground-Water Model--The Ground-Water Flow Process. Book 6 Chap 16.

 Hart, J.K. and K. Martinez. 2006. Environmental Sensor Networks: A revolution in the earth system science? Earth-Science Reviews 78, 177-191.

 Khemapech, I., Duncan, I. and A. Miller. 2005. A Survey of Wireless Sensor Networks Technology. School of computer Science, University of St. Andrews.

A pre-study of an intelligent self-organizing wireless sensor network for monitoring contaminant plume movement in a heterogeneous porous media