Bioreactor Landfill Moisture Management
Funded by the Urban Waste
Management and Research Center at the
University of New Orleans
Debra Reinhart1, Ph.D., P.E.
University of Central Florida
Timothy Townsend2, Ph.D. Sreeram Jonnalagadda3,
Nitin Gawande4, Pradeep Jain3, Phillip Thomas4
University of Florida
Chris Ziess, Ph.D.
University of Alberta, Canada
1
Professor, University of Central Florida
Associate Professor, University of Florida
3
Graduate Research Assistant, University of Florida
4
Graduate Research Assistant, University of Central Florida
2
The contents of this report reflect the views of the authors, who are responsible for the facts and the
accuracy of the data presented herein. The contents do not necessarily reflect the official views or policies
of the Urban Waste Management & Research Center of the U.S. EPA. This report does not constitute a
standard, specification or regulation. The Urban Waste Management & Research Center does not endorse
products, equipment or manufacturers. Trademarks or manufacturer’s names may appear herein only
because they are considered essential to the object of this report.
ABSTRACT
The presence and movement of moisture in landfilled solid waste play a major
role in the rate of landfill stabilization. Instruments that can monitor the in situ moisture
content of landfilled waste would be of great benefit to landfill operators, especially those
at bioreactors. Two potential technologies were examined in this research: resistance
based and time domain reflectometry (TDR) sensors. One hundred and thirty five
resistance-based sensors and 12 TDR sensors were installed in a leachate recirculation
well field at a bioreactor in Florida. The resistance-based sensors were found to respond
to an increase in moisture resulting from leachate recirculation. The initial spatial average
moisture content determined by the sensor readings (using a laboratory-derived
calibration) was 42% compared to 23% from gravimetric readings. This was attributed to
the greater leachate conductivity values encountered in the landfill compared to that used
in the calibration, inability of the MTG sensor to detect moisture contents below ~35%,
and the potential for the sensors to intercept leachate flow from preferential paths. The
TDR sensors were also found to respond to leachate recirculation. The moisture contents
from the TDR sensors (obtained using laboratory-derived calibration) were compared to
the moisture contents from the resistance-based sensors. The results showed that both
technologies predicted transient moisture changes in the landfill. The heterogeneous
nature of landfilled waste and its variable leachate electrical conductivity were observed
to affect the calibration equations for both moisture measurement technologies. Moisture
measurement devices have advantages over gravimetric moisture measurement
techniques because of their less expensive manufacturing costs, ease in automation and
ability to predict the transient moisture changes with time.
TABLE OF CONTENTS
LIST OF TABLES
LIST OF FIGURES
CHAPTER 1 INTRODUCTION
1.1 Background
1.2 Research Objectives
1.3 Research Approach
CHAPTER 2 LITERATURE REVIEW
2.1 Moisture Content
2.2 Capacitance Probe Technology
2.2.1 Principles of Operation
2.3 Neutron Probe Technology
2.3.1 Principles of Operation
2.4 Electrical Resistance Technology
2.4.1 Principles of Operation
2.4.2 Granular Matrix Sensor
2.5 Time Domain Reflectometry (TDR)
2.5.1 Principles of Operation
2.5.2 Production and Analysis of TDR Wave Forms
2.5.3 Calibration of TDR Sensors for Moisture Measurement
2.6 Summary of the Advantages and Disadvantages of each Technology
CHAPTER 3 SENSOR DEVELOPMENT
3.1 Introduction
3.2 Principles of Operation
3.3 Sensor Description
3.4 Methodology
3.4.1 Particle Size Determination
3.4.2 Effect of liquid electrical conductivity
3.4.3 Sensor Calibration
3.4.4 Field Testing of Sensors
3.4.5 Sensor Behavior under Saturated Condition
3.5 Results and discussion
3.5.1 Particle Size Determination
3.5.2 Effect of Liquid Electrical Conductivity
3.5.3 Sensor Calibration
3.5.4 Field Testing of Sensors
3.5.5 Sensor Behavior under Saturated Condition
3.6 Summary of Results
CHAPTER 4 FIELD EVALUATION OF RESISTIVITY SENSORS FOR IN SITU
MOISTURE MEASUREMENT IN A BIOREACTOR LANDFILL
4.1 Introduction and Background
4.2 Methods and Materials
4.2.1 Site Description
4.2.2 Installation of MTG Sensors
4.2.3 Field Measurements and Field Trials
4.2.4 Conversion of Resistance to Moisture Content
4.2.5 Estimation of Spatial Average of the Moisture Content of the
Landfill
4.3 Results and Discussion
4.3.1 Sensor Output and Performance
4.3.2 Estimation of Moisture Content
4.3.3 Assessment of Sensor for Monitoring Leachate Recirculation
4.3.4 MTG sensor limitations
4.3.5 Conclusions
CHAPTER 5 COMPARISON OF RESISTIVITY AND TIME DOMAIN
REFLECTOMETRY SENSORS FOR ASSESSING MOISTURE
CONTENT IN BIOREACTOR LANDFILLS
5.1 Introduction
5.2 Background
5.2.1 Time Domain Reflectometry (TDR)
5.2.2 Electrical Resistance Technology
5.3 Methods
5.3.1 TDR Sensors
5.3.2 Resistance-Based Sensors
5.3.3 Site Description
5.3.4 Installation of Sensors
5.3.5 Field Measurements and Trials
5.4 Results and Discussion
5.4.1 Discussion of Performance of TDR and MTG Sensors
5.4.2 Estimation of Moisture Content
5.4.3 Assessment of TDR and MTG Sensors for Leachate Recirculation
5.4.4 Advantages and Disadvantages of Each Probe
5.4.5 Summary and Conclusions
CHAPTER 6 MOISTURE BALANCE ON BIOREACTOR LANDFILL
6.1 Background
6.2 Simplified Moisture Balance Equation
6.2.1 Moisture Balance on New River Site
6.2.2 Estimation of Spatial Average of the Moisture Control of the Landfill
6.3 Conclusions
CHAPTER 7 SUMMARY AND CONCLUSIONS
APPENDIX-A SUPPLEMENTAL FIGURES
APPENDIX-B RESISTIVITY SENSOR DATA
APPENDIX-C TDR PROBE DATA
APPENDIX-D EXAMPLE CALCULATION OF CONVERSION TO MOISTURE
CONTENT FROM OBSERVED RESISTANCE AND TEMPERATURE
AND CALIBRATION CURVES
APPENDIX-E CALCULATION FOR THE AVERAGE MOISTURE CONTENT IN
THE LANDFILL AND TABLE ON TIMES OF TRAVEL OF
MOISTURE TO REACH THE SENSOR
APPENDIX-F SCHEMATIC OF THE TDR WAVEFORM AND CALIBRATION
GRAPHS AS PROVIDED BY ZIRCON INC
REFERENCES
PUBLICATIONS
LIST OF FIGURES
2-1.
Phase diagram of solid waste containment unit.
2-2.
Schematic of capacitance probe and frequency reader (Dean et al. 1987).
2-3.
Effect of bound hydrogen on calibration curve (Yuen et al 2000).
2-4.
Effect of neutron capture on the calibration curve (Yuen et al 2000).
2-5.
Watermark 200 Granular Matrix Sensor (McCann et al. 1992).
2-6.
Schematic of AC-half bridge circuit.
2-7.
Schematic illustration of the time domain reflectometry principles showing the
launching of voltage pulse V with the transmitted and reflected components VT
and VR.
Schematic representations of the transmitted and reflected voltage pulses in nonconducting and conducting media (Dalton and Van Genuchten. 1986).
Schematic diagram of the TDR system using parallel wave guide to measure soil
water content. a-Physical arrangement, b-Idealized output display, c-Schematic of
reflections (Cassel et al. 1994).
2-8.
2-9.
3-1
Longitudinal section of the MTG sensor.
3-2
Pictorial view of the MTG sensor.
3-3
Sensor calibration testing apparatus.
3-4
3-5
Fraction saturation vs. resistance curve for all particle diameters of sensor in tap
water (0.3mS/cm)
Combined calibration curves of 2.4-mm sensor in KCl solutions.
3-6
Calibration curve for MTG sensors for varying moisture conductivities.
3-7
Variation in resistance as a function of temperature for MTG sensors.
3-8
Capture of moisture front as a result of infiltration.
3-9
Field experiments with sensors under saturated condition.
4-1
Overview plan of the bioreactor.
4-2
Plan view of the well field.
4-3
Cross section along CC’ of figure 3-2.
4-4
Schematic cross section of injection and monitoring clusters.
4-5
4-7
Response of resistivity sensors at cluster L6W to leachate recirculation (Day 1 =
01/01/2003).
Response of resistivity sensors at cluster MM3E to leachate recirculation (Day 1 =
01/01/2003).
Calculated moisture content from cluster MM3E.
4-8
Monitoring wells surrounding injection cluster CM3.
4-9
Cumulative volume of leachate injected through CM3
4-10
5-2
Response of monitoring clusters surrounding CM3. (A) M3E (B) M3W (C) ML3
(D) ML4 (E) MO5.
Plan of bioreactor showing leachate injection wells and relatively dry monitoring
locations.
Observed TDR waveform from sensor at bioreactor landfill in north central
Florida.
Plan view of the bioreactor.
5-3
Plan view of the MTG and TDR sensor locations.
5-4
5-5
Schematic cross section of cluster E (sensors placed in same hole) and cluster D
(sensors placed in different holes).
Response of sensors to leachate recirculation. A) TDR B) MTG.
5-6
Change in moisture content of TDR and MTG sensor.
5-7
Cumulative volume of leachate injected through CI5.
5-8
Cumulative volume of leachate injected through CF6.
5-9
Response of TDR and MTG sensors located at clusters C and D.
5-10
Response of TDR and MTG sensors located at clusters A, B and E.
5-11
Comparison of moisture contents from TDR and MTG sensors.
6-1
Moisture contours at different levels.
4-6
4-11
5-1
6-2
Cumulative volume of leachate recirculated.
A-1
Plan view of the New River Regional Landfill.
A-2
Plan view of injection and monitoring cluster.
B-1-48 Response of resistivity sensors at cluster location MD3 (Day 1 = 01/01/03).
D-1
F-1
F-2
Calibration curves for MTG sensors for varying moisture conductivities
Schematic sketch of the TDR wave form
Calibration graphs for different probes (Zircon Inc).
LIST OF TABLES
2-10. Reported field capacity of MSW.
2-11. Capture cross section for thermal neutrons of common soil elements (Dickey
1990) units are in barns.
2-12. Advantages and disadvantages of various moisture measurement techniques.
3-1
3-2
3-3
Composition of solid waste used in laboratory experiments (after Tchobanoglous
et al., 1993).
Air drying times for moisture sensors.
4-1
Effect of solid waste density on sensor measurements for 4.0 mS/cm moisture
electrical conductivity.
Volume of leachate injected in different wells.
4-2
Average conductivity measured at NRRL.
4-3
Time of travel in days for the incoming moisture to pass the sensor.
C-1
Calibration coefficients for the twelve probes.
C-2
Dry density of waste material around each probe.
C-3
Characteristics of waveform obtained from TDR probe located near the upper
resistivity sensor at instrumentation cluster MH6.
C-4
Characteristics of waveform obtained from TDR probe located near the upper
resistivity sensor at instrumentation cluster MI7.
C-5
Characteristics of waveform obtained from TDR probe located near the middle
resistivity sensor at instrumentation cluster MI7.
C-6
Characteristics of waveform obtained from TDR probe located near the upper
resistivity sensor at instrumentation cluster MI5.
C-7
Characteristics of waveform obtained from TDR probe located near the middle
resistivity sensor at instrumentation cluster MI5
C-8
Characteristics of waveform obtained from TDR probe located near the lower
resistivity sensor at instrumentation cluster MI6.
C-9
Characteristics of waveform obtained from TDR probe located near the middle
resistivity sensor at instrumentation cluster MI6.
C-10 Characteristics of waveform obtained from TDR probe located near the upper
resistivity sensor at instrumentation cluster MG7.
C-11 Characteristics of waveform obtained from TDR probe located near the middle
resistivity sensor at instrumentation cluster MG7.
CHAPTER 1
INTRODUCTION
1.1 Background
Municipal solid waste (MSW) management has evolved into an important issue in
the past few decades. Approximately 231.9 million tons of MSW were generated in the
United States in 2000 for a per capita generation rate of 4.5 pounds per person per day
(US Environmental Protection Agency 2002). Landfills represent the primary method of
managing this waste. Environmental risks posed by past landfilling practices have
included contamination of ground water and the release of flammable and toxic gases.
Modern sanitary landfills are constructed with engineered leachate collection and landfill
gas extraction systems to address these concerns.
When a landfill is closed, a cap is constructed to prevent external moisture entering
the waste; this is performed to minimize leachate generation. Minimizing waste moisture
content, however, has consequences. Research has demonstrated that biodegradation
rates, and hence methane production rates, of landfilled wastes rise with an increase in
moisture content (Eliasen 1942, DeWalle et al. 1978, Rees 1980, and Pohland 1988). The
movement of the moisture also plays an important role, as it promotes the exchange of
substrates and nutrients, the dilution of inhibitors, and the distribution of micro-organisms
within the landfill environment (Tittlebaum 1982, Pohland 1988). Considering these
advantages, some landfill practitioners and researchers have proposed to increase the
Chapter 1
waste’s moisture content by recirculating leachate or adding water to landfills in an effort
to enhance waste degradation and stabilization.
The Solid Waste Association of North America (SWANA) has defined a bioreactor
landfill as “any permitted Subtitle D landfill or landfill cell where liquid or air is injected
in a controlled fashion into the waste mass in order to accelerate or enhance
biostabilization of the waste” (US EPA, 2002). Recirculation of leachate is the primary
tool for creating bioreactor conditions. Other methods to enhance biostabilization include
waste shredding, addition of nutrients and sludge, pH neutralization, and temperature
control (Pohland 1975, Tittlebaum 1982, Miller et al. 1991). Advantages that enhanced
biostabilization provides include quicker waste stabilization times, faster rates of landfill
gas production, effective management of leachate generated from the landfill, and
accelerated waste settlement to gain more air space.
The critical role of moisture in bioreactor operations suggests that the ability to
measure and track moisture levels in a landfill would be of great value to bioreactor
landfill operators. Moisture content can be measured by collecting waste samples
followed by gravimetric measurement. This technique is disruptive and costly, and
cannot be effectively used for routine monitoring. Various moisture measuring devices
have been developed to measure the in situ moisture content of soils. Examples include
neutron probes, capacitance probes, time domain reflectometry probes and electrical
resistance sensors; these devices were originally developed to measure the moisture
contents of soils for irrigation (McCann et al. 1992, Noborio et al. 1994, Chanasyk and
Naeth 1996, Chanzy et al. 1998). Each moisture measuring technology has specific
Chapter 1
advantages and disadvantages. The application of these technologies to in situ
measurement in landfilled waste is a natural extension. While some work has been
conducted on in situ moisture monitoring of landfills (Holmes 1984, Rosqvist et al. 1997,
Yuen et al. 2000), experience and data are limited and there is no one agreed upon
technology. More work, especially at the operating field scale, is needed.
Gawande et al. (2003) studied the ability of a prototype resistance-based sensor to
measure moisture content of solid waste. This prototype sensor was operated in tap water
and saline conditions and also tested in soil, paper and compost to simulate its operation
under landfill conditions. The study indicated that resistivity sensors show potential for
responding to changes of moisture contents within a landfill. It also suggested that a
single calibration curve obtained at a specified electrical conductivity of the leachate
could be used to calculate the moisture content values from corresponding resistivity
measurements. Such laboratory calibrations were performed and calibration curves were
provided for two conductivities. As a follow up to this work, a large number of the
resistance-based sensors were installed at the New River Regional Landfill (NRRL)
bioreactor in North Central Florida for tracking the in situ moisture content of the
landfilled waste. A major focus of this research work is an evaluation of these sensors,
how they respond to leachate recirculation, and their ability to provide information
necessary to complete a bioreactor water balance. In addition to the resistance sensors,
several time domain reflectometry (TDR) sensors were installed at the NRRL for
comparison purposes. A second focus of this research work is a comparison of the two
sensor technologies in a side-by-side application.
Chapter 1
1.2 Research Objectives
The objectives of this research work were to:

Evaluate the effectiveness of the electrical resistance-based sensors for the in situ
measurement of moisture in the NRRL bioreactor.

Use data from the resistivity sensors to perform a water balance and compare to
actual liquid addition.

Compare the performance of electrical-resistance-based sensors with time domain
reflectometry sensors with respect to measuring the in situ moisture content in the
landfilled solid waste.
1.3 Research Approach
A total of 135 electrical resistance-based moisture sensors in 48 different clustered
locations were installed at the NRRL. Each cluster consisted of either two or three
resistivity sensors placed at different depths (approximately 15 ft, 30 ft and 50 ft). These
sensors were developed and lab-tested before installation (Gawande et al., 2003). They
work on the principle that an estimate of moisture present in the medium between two
concentric electrodes can be obtained from their measured resistance, and that the
moisture content of the medium would be representative of the surrounding waste. Data
from the sensors were collected using a data logger at a frequency of twice per day.
A total of 12 TDR sensors were installed at five different clustered locations at
depths of approximately 6ft, 15ft and 30 ft, also at the NRRL. TDR sensors work on the
Chapter 1
principle that the time taken for an electromagnetic pulse to travel through a wet medium
can be used to measure the relative dielectric constant of the medium. Since the dielectric
constant changes as a function of moisture content, it can be used to estimate the moisture
content of the medium using an appropriate calibration curve. The TDR cluster locations
contained resistivity sensors installed at approximately the same depths as the TDR
sensors. Two of the five cluster locations contained three TDR sensors per location; the
resistivity sensors were placed in adjacent holes at approximately the same depths as the
TDR sensors. In the remaining three locations the TDR and resistivity sensors were
placed next to each other in the same hole. A data logger with a laptop was used to
capture the reflected waveform from the TDR sensors. The captured waveforms were
used to calculate the relative dielectric constant of waste surrounding the TDR sensors (at
least once a week).
A vertical leachate injection well recirculation system was used to recirculate
leachate at the NRRL. In order to efficiently wet the waste, injection wells were installed
in clusters of different depths. Each injection cluster contained three vertical injection
wells with depths averaging 20ft, 40ft and 60ft. Leachate recirculation was initiated on
May 30, 2003. The background data from both resistance as well as TDR sensors were
collected before recirculation of leachate. These data were used as a baseline for
comparison with data acquired after the beginning of recirculation.
1.4 Organization of report
This report is organized into six chapters.
Chapter 2 of this thesis presents
background information and a literature review pertaining to devices used to measure
Chapter 1
moisture in landfills. The results of the field study evaluating the use of electrical
resistance sensors for tracking in situ moisture content in landfills are described in
Chapter 3. A comparison of resistivity and TDR sensors for assessing moisture content in
landfills is made in Chapter 4. A water balance for the NRRL using sensor data and
actual liquid addition data is presented in Chapter 5. The report ends with a summary
and conclusions in Chapter 6. Appendices with tables and information on the calibration
curves and supplemental figures which include data from all the resistance-based sensors
are provided. Much of the information presented in this report has been previously
presented as part of a refereed journal publication (Gawande et al., 2003) and a Master of
Engineering Thesis by Jonnalagadda (2004). Chapters 2, 4 and 5 were adapted from
Jonnalagadda (2004). Chapter 3 is an updated version of Gawande et al. (2003).
Chapter 1
Chapter 1
CHAPTER 2
LITERATURE REVIEW
This chapter provides a brief overview of the various technologies available and
currently used to monitor the in situ moisture content in soils. The potential for
application of these technologies in bioreactor landfills, including their advantages and
disadvantages is discussed.
Bioreactor landfill operators must provide conditions that facilitate higher rates of
degradation of the landfilled waste. This is typically achieved by the addition of moisture
of up to a target of 60% by weight. Previous studies indicated that the availability of
moisture content of about 25 to 60% has shown to exponentially increase methane gas
production. In situ moisture measuring devices could be helpful to regulate the moisture
and control leachate recirculation in the landfill and without them the operators could not
understand the travel patterns of the pumped moisture. Conventional moisture
measurement (gravimetric measurement) is the most reliable technique to find the in situ
moisture contents. In this method waste is excavated from the required depth inside the
landfill and the wet weight of the excavated sample is measured. The sample is oven
dried and its dry weight is calculated. The difference between the wet weight and the dry
weight is the amount of water retained by the sample. However, it is extremely difficult
to measure moisture content using this technique because the waste must be excavated to
measure the in situ moisture content, an expensive and time consuming prospect. These
Chapter 2
difficulties were the impetus for developing more sophisticated in situ moisture
measurement technologies.
The technologies discussed in this chapter are reported to have variable degrees of
success in the agricultural sector to provide in situ moisture measurement for irrigation of
farmlands. Landfills provide some unique challenges in the application of these
technologies, when compared to soils. A few more prominent challenges are the extreme
heterogeneity of waste and varying electrical conductivity of leachate. This chapter
discusses the theory associated with each technology and the advantages and
disadvantages involved in the field application of the devices associated with the
technology. The following sections in this chapter give the definition of moisture content,
types of moisture contents commonly reported in literature, typical values of the moisture
content present in MSW and discussions about various moisture measurement
technologies.
2.1 Moisture Content
The amount of water that is present in the waste can be given by its moisture
content value. The moisture content is expressed in Equation 2.1.1 (Tchobanoglous et al.
1993),
wd 
M 
 * 100
 w 
(2.1.1)
where M is the moisture content (%) and w is the initial weight of sample as delivered, lb
(kg) and d is weight of sample after drying at 105oC, lb (kg). For most MSW in the US,
Chapter 2
the moisture content will vary from 15-40% by weight depending on the composition of
the wastes, the season of the year, and the humidity and weather conditions
(Tchobanoglous et al. 1993). However, in soil science, moisture content is typically
expressed as the volume of water filling the pore space in the soils. This is also known as
volumetric moisture content. A phase diagram is useful to define the various engineering
properties of a porous medium. Figure 2-1 gives the three different phases in the solid
waste containment unit.
Total Mass = Mw+Ms=MT
Total Volume = Vw+Vs+Vs=VT
Mass
Volume
Gas
Mw
Ms
Liquid
Solid
VG
Vv
Vw
Vs
Figure 2-1. Phase diagram of solid waste containment unit
Moisture content is defined as the ratio of mass of water to the total mass and is
given by Equation 2.1.2. Volumetric moisture content is given by Equation 2.1.3.and the
dry density of the waste defined as the ratio of the mass of the waste to the total volume
is given by equation 2.1.4.
Chapter 2
Moisture content (MC) =
Mw
MT
Volumetric Moisture content () =
Dry density  d =
(2.1.2)
Vw
VT
(2.1.3)
Ms
VT
(2.1.4)
The conversion of volumetric moisture content to the gravimetric moisture content
MC could be made if the dry density of the sample is known and is given by equation
2.1.5 as
MC 
 w
 D   w
(2.1.5)
The porosity of the waste is defined as
Porosity (n)
Vv
VT
(2.1.6)
where Vv is the total void space and VT is the total volume. Field capacity is the moisture
content of the porous material at which water no longer drains under gravity and is
defined as the volumetric water content at a soil water suction of 0.33 bars or remaining
after a prolonged period of gravity drainage without additional water supply. The
reported values of field capacity of MSW in literature were given in Table 2-1.
Chapter 2
Table 2-1. Reported field capacity of MSW
Reference
Reported field capacity
% (v/v)
Bengtsson et al. (1994)
44
Zeiss and Major (1993)
14
Korfiatis et al. (1984)
20-30
Remson et al. (1968)
29
2.2 Capacitance Probe Technology
Measurement of soil moisture with a capacitance probe is one of the oldest
engineered in situ soil measurement techniques. Thomas (1966) showed that the
measurement of the dielectric constant of a soil medium could potentially yield results
showing the changes in the moisture content. Since the dielectric constant, , of free
water is 80 and values of typical dry soils are about 4, measurement of the dielectric
constant offers a potentially sensitive determination of soil moisture content (Dean et al.
1987). Hence, changes in the relative dielectric constant of the medium can be attributed
to the changes in the moisture content of the medium.
Chapter 2
2.2.1 Principles of Operation
Dean et al. (1987) described a typical capacitance probe soil moisture measurement
device. This probe consists of two electrodes (upper and lower) separated by a plastic
dielectric. The electrodes and the dielectric are placed in a plastic cylindrical container
known as access tube. For in situ measurements, the access tubes are installed in the field
and care is taken to eliminate the air gaps between the access tube and the soil and
minimize the overall disturbance in the soil. The sensor measures the resonant frequency
of the LC (L = inductance, C = Capacitance) circuit formed by the soil matrix in the
immediate vicinity of the access tube, the access tube itself, plus the air space between
the sensor and the access tube (together referred to as the soil-access tube system). Any
changes in the soil access tube system will result in a change of the resonant frequency.
The capacitance of the soil access tube system is measured and is given by Equation 2.2.1
C  g
(2.2.1)
where C = Capacitance (Farads)
 = the dielectric constant (dimensionless)
g = a geometrical constant depending on the configuration of the system (Farads).
Increase in the soil moisture content results in an increase in capacitance of the soilaccess tube system. Changes in temperature, bulk density and the porosity of the soil
effects the capacitance of the soil-access tube system (Evett 1998). The geometric
Chapter 2
parameter g in equation 2.2.1 can be related to the classical equation for a simple two
electrode plate as given by equation 2.2.2 (Evett 1998):
C  0Ka
a
d
(2.2.2)
where 0= the permittivity of free space (8.9 X 10-12 Fm-1 Farad per meter)
a = the overlapping area (m2) of the plates
d = the thickness (m) of the dielectric separating the plates
Ka = relative dielectric constant
C = capacitance (Farads)
Equation 2.2.2 only applies if the plates are parallel and the dielectric material
separating the plates is uniform. In this case the value of g in equation 2.2.1 is  0
a
. The
d
dielectric between the two electrodes for the soil access tube system is much more
complex and a relationship has not been established for computing g and thus C in
equation 2.2.1 for this geometry. The schematic of a typical sensor is shown in the Figure
2-2. This sensor, unlike the simple plate capacitor, has plates in the form of two surfaces
of a cylinder that are separated by an insulator, and placed in an access tube with soil
outside the plates. Due to this setup, the electric field permeating the soil forms an
elliptical shape originating in one plate and ending in the other. In general, the surface
area of the plates of the capacitance probe electrodes can be easily found but the degree
to which the elliptical electric field lines permeate into the soil is not known. Thus it is
Chapter 2
hard to evaluate a term equivalent to d, in Equation 2.2.2, since the shape of the electric
field in the soil access tube system may be influenced by the soil heterogeneity including
the gaps between the tube wall and soil during the time of installation. The capacitance
probes need to be calibrated before installation in the field. Topp et al. (1980) empirically
established relationship between dielectric constant  and volumetric water content  for
the soils. This relationship depends on soil properties like texture, dry density and the
soil’s temperature. Chanzy et al. (1998) performed field scale evaluation of the
capacitance probes for soil moisture monitoring. The presence of heavy metals and high
salinity of landfills has influence on dielectric constant measurement and hence limits the
application of this technology for in situ moisture content measurements in MSW
landfills.
Frequency reader
Access Tube
Figure 2-2. Schematic of capacitance probe and frequency reader (Dean et al. 1987)
Chapter 2
2.3 Neutron Probe Technology
Neutron probe technology, also known as a neutron scattering method, is an
indirect method of measuring soil moisture content. A radioactive source emits neutrons
through the media and its moisture content is measured by the thermal or slow neutron
density at the collector (Schmugge et al. 1980). Garner and Kirkham (1952) first defined
the principles of the neutron scattering method. The neutron probe has found wide use in
agricultural, hydrological and civil engineering applications (Williams et al 1981).
Smaller and safer radioactive sources have evolved as a result of technological
advancements in neutron probe technology (Evett, 1998). Application of neutron probe
technology is not feasible for certain moisture-measuring situations due to high
regulatory standards for use of radioactive materials. These requirements result in costly
licensing and training for both the companies and the operators, and storage of the
equipment and disposal of the probe with its radioactive source is also expensive and
highly regulated (Evett, 1998).
2.3.1 Principles of Operation
Neutron probes consist of a probe and an electronic counting scale, which are
connected together by an electric cable. In order to measure the moisture content of a
medium at a desired depth, the probe is lowered down an access tube to the required
depth. Access tubes are made of materials that do not slow the neutrons emitted by the
source.
Chapter 2
The principles of neutron probe operation given below were discussed in Gardner
and Kirkham (1952) and are documented by Chanasyk and Naeth (1996), Evett (1998),
Schmugge et al (1980). Neutrons with high energy are emitted by a radioactive source
into the soil and are slowed down by elastic collisions with nuclei of atoms and become
thermalized. The average energy loss is much greater when neutrons collide with atoms
of low atomic weight than collisions with heavier atoms. The hydrogen atom with its low
atomic weight can slow down neutrons more effectively than other elements. The density
of the resultant cloud of slowed neutrons (which are detected by the counter) is taken to
be proportional to the total number of hydrogen atoms per unit volume of the soil (Yuen
et al 2000). The volumetric moisture content can then be determined by an established
calibration curve, assuming that these hydrogen atoms have direct correlation with soil
moisture.
Thomas (2001) documented some of the problems of the neutron probe that
include:

All hydrogen atoms slow the high-energy neutrons. These involve both free water
atoms and bound hydrogen atoms that are part of the molecular structure of
compounds that are not water molecules (Yuen et al 2000).

Some elements other than hydrogen have a propensity to absorb the high-energy
neutrons (Yuen et al. 2000).

Changes in the density of the medium may affect the transmission of the neutron
particles (Yuen et al 2000).
Chapter 2
Count ratio
n
oge
ou
il w
b
ith
So
dh
t
fec
Ef
of
b
yd
nd
dr
hy
no
en
g
o
r
n
ou
the
t in
S
r
ate
n
oge
ou
ob
w
oil
m
for
w
of
nd
h
r
yd
n
ith
Volumetric moisture content obtained by oven drying
Figure 2-3. Effect of bound hydrogen on calibration curve (Yuen et al 2000).
Effect of the presence of bound hydrogen is discussed in Yuen et al 2000. Figure 23 shows the relation between the calibration curves with and without bound hydrogen.
Bound hydrogen will cause the parallel up shift in the calibration curve (assumed a
straight line) with no change in the gradient. The quantity of the bound hydrogen will
determine the amount of up shift in the curve. When this method is applied to MSW
landfills, there would be significant bound hydrogen bias, due to the presence of
materials (like plastic and wood) that have significant amount of bound hydrogen in
MSW (Yuen et al. 2000).
From Figure 2-3, it can be clearly seen that the calibration curve shifted vertically
as a result of the presence of bound hydrogen. Landfills have significant amounts of
organic matter containing bound hydrogen. These bound hydrogen atoms should be taken
into account to get reasonably accurate results for the application of this technology in
Chapter 2
landfills. A standard calibration curve using sand as a datum can be used with reasonable
accuracy when relative (and not absolute) water content is measured (Yuen, 1999). This
calibration curve is limited to organic content of the waste that is not extremely
excessive.
Neutron probe technology assumes that hydrogen atoms from the water molecules
thermalize the neutrons. This technology cannot measure accurate moisture content, if
atoms of any other element are involved in thermalizing the neutrons. When the neutrons
are slowed, some of them are captured by various elements that have the affinity towards
the neutrons. Dickey (1990) suggested that this neutron capture effect could be reflected
graphically as shown in Figure 2-4 (Yuen et al. 2000).
Neutron absorption elements decrease the number of thermalized neutrons and this
reduction is proportional to the moisture content. As shown in the Figure 2-4, neutron
capture effect decreases the gradient of the calibration curve. Table 2-2 lists different
elements and their absorption capacities (Dickey 1990).
Yuen et al. (2000) indicated that iron, potassium and chlorine are common in MSW
landfills and hence a certain degree of neutron absorption bias is expected when this
technology is applied to landfills. Apart from bound hydrogen and neutron capture
effects, the changes in the density of the medium also effect the thermalization of the
neutrons and their transport to the detector, thus affecting the neutron count rate at the
detector.
Chapter 2
Count ratio
e
tur
ap
c
on
utr
e
e
fn
tur
to
ap
fec
c
f
E
n
tro
u
e
e
n
tur
ap
No
c
ron
eut
n
th
Wi
Volumetric moisture content obtained by oven drying
Figure 2-4. Effect of neutron capture on the calibration curve (Yuen et al 2000).
Table 2-2. Capture cross section for thermal neutrons of common soil elements (Dickey
1990) units are in barns
Element
Capture Cross Section
Oxygen
0.0016
Hydrogen
0.2
Silicon
0.16
Carbon
0.0045
Chlorine
33
Boron
795
Aluminum
0.23
Iron
2.5
Chapter 2
Calcium
0.43
Sodium
0.5
Potassium
2.2
Magnesium
0.4
Increase in the density of the medium containing bound hydrogen will increase the
count rate due to the presence of more hydrogen per unit volume of the medium (Yuen et
al. 2000). On the other hand, increasing the density of soil containing neutron absorption
elements would decrease the count rate due to more neutron capture per unit volume of
the medium Yuen et al. (2000).
Yuen et al. (2000) recommended the following field application procedure for
implementation of neutron probe technology in landfills:

Install access tube

Collect MSW samples from drilling during installation to determine gravimetric
moisture content and waste composition

Convert gravimetric to volumetric moisture content

Plot initial volumetric moisture content against depth

Plot subsequent moisture change (use the standard sand curve to calculate the
moisture change from the change in neutron count ratio)
Chapter 2
Yuen et al. (2000) installed the laboratory calibrated neutron probes in a full-scale
operating landfill and their observations for the application of neutron probe technology
in landfills are given below:

Due to the heterogeneous nature of MSW, neutron probe technology cannot be
used to measure the absolute moisture content

Neutron probe technology can be used to measure moisture change with
acceptable errors, provided the presence of neutron capture elements is not
excessive

The use of the standard sand calibration curve tends to underestimate moisture
change slightly

If density is not known and has to be estimated, errors may result in the
conversion of initial gravimetric to volumetric moisture content
2.4 Electrical Resistance Technology
The moisture content of a medium can be determined from the value of the
electrical resistance obtained between the electrodes inserted in the medium. This
technology had been applied for soil moisture measurements using gypsum block sensors
and granular matrix sensors. Electrical resistance of the moisture present in the sensor’s
granular media can be obtained when equilibrium is attained between the moisture
present in the sensor media and the external moisture. By using a calibration curve, this
resistance value can be converted into the corresponding moisture content of the media.
Chapter 2
2.4.1 Principles of Operation
A pair of electrodes inserted into a porous matrix comprises an electrical resistance
soil moisture sensor. Two main types are used, gypsum blocks and granular matrix
sensors. For the soil moisture measurement, the matrix in these sensors consists of a
highly soluble calcium sulfate salt that serves to boost conductivity and insulate the
sensor from the fluctuations in the salinity of the external environment (McCann et al.
1992). However, when used in landfills, it is not essential to add the soluble salt to boost
conductivity as landfills contain highly conductive leachate. When inserted into the
medium the sensors come into hydraulic contact with the soil solution and equilibrium is
attained between the sensor and the medium and the resistance between the electrodes is
measured. To prevent the polarization between the electrodes, measurements are taken
using an alternating current, AC (McCann et al. 1992).
Gypsum block sensors consist of two electrodes encased in a porous block of
gypsum. This block is placed in a medium where the moisture content is to be measured.
The block absorbs moisture from the surrounding media thus making a saturated solution
of calcium sulfate from gypsum. This solution helps in maintaining uniform conductivity
of the medium between the electrodes and makes the electrodes independent of the
variations in conductivity of the surrounding media. Under saturated conditions the
resistance of the sensor is minimal. During drying, the pores of the block are emptied
(larger pores emptied first) making the transmission of electric current progressively
difficult and resulting in an increase in sensor resistance (Thomas, 2001). Since the
relationship between the soil moisture content and the measured electrical resistance is
Chapter 2
constant irrespective of the media, a single calibration curve is required (Skinner et al.
1997). The gypsum block sensors are low maintenance and low cost pieces of equipment
but they are inaccurate in 0-100 kPa range (McCann et al. 1992). In order to incorporate
these difficulties a granular sensor was developed.
Figure 2-5. Watermark 200 Granular Matrix Sensor (McCann et al. 1992).
McCann et al. (1992) described a granular matrix sensor matrix as a uniformly
distributed sand material, unlike the gypsum block sensors in which the entire porous
matrix is gypsum. Figure 2-5 shows the typical example of a granular matrix sensor.
There are two different sections in the granular matrix sensor shown in Figure 2-5, the
transmission section and the measurement section. The transmission section has holes in
Chapter 2
it and a permeable synthetic membrane is used to hold the sand material in place. The
measurement section is sealed externally and has the gypsum block to insulate the sensor
from the changes in the external salinity. For measurement to be detected a finite amount
of moisture must traverse the transmission section and enter the measurement section. In
crossing the gypsum block a saturated solution of calcium sulfate is formed and the
electrodes measure the conductivity of this liquid, which is proportional to the soil water
content (McCann et al. 1992). Rosqvist et al. (1997) conducted a study to measure the
moisture content variation in a pilot-scale landfill using gypsum block sensors. Gypsum
block sensors were placed at 36 points located at three depths in the landfill to measure
moisture content throughout waste mass. However no information about calibration was
reported.
2.4.2 Granular Matrix Sensor
Gawande et al. (2003) developed a unique electrical resistance based granular
matrix sensor that would facilitate the measurement of moisture content in MSW
landfills. The present section gives the background work and operating principles of this
technology. This sensor was designed to measure the electrical resistance occurring
between two electrodes embedded in an insoluble granular media. Resistance is inversely
proportional to the electrical conductivity, which can be correlated to moisture content.
When placed in the landfill, moisture from the surrounding waste enters the sensor matrix
through the capillary action provided by the glass fiber wicks attached to the sensor body.
Change in the moisture content of the sensor granular media changes the measured
resistance. The relationship between sensor resistance and the external moisture content
Chapter 2
were developed assuming that equilibrium exists between the granular media moisture
content and moisture content of surrounding waste. Resistance measurement using this
sensor is shown in the schematic given in figure 2-6. An alternating current AC-half
bridge with 1-k bridge resistor was used to measure the resistance across the sensor
electrodes. The AC-half bridge output
Vs
is used to calculate the sensor resistance by the
Vx
equation below:
V
 X 
Rs  R1 
 ; where X  s
Vx
1 X 
(2.3.1)
where Rs is the sensor resistance and R1 is the bridge resistance with an applied excitation
voltage Vx and Vs is the sensor voltage.
R1
Rs
VS
Vx
Figure 2-6. Schematic of AC-half bridge circuit
Since landfill leachate is highly conductive, the granular media in this sensor did
not contain soluble salt to boost conductivity.
Chapter 2
A total of 138 MTG sensors were installed at an operating bioreactor landfill in
Florida. The field evaluation of these sensors for moisture measurement had been made
in the present study.
2.5 Time Domain Reflectometry (TDR)
Time domain reflectometry (TDR) is one of the accepted techniques in measuring
the in situ moisture content of soils (Topp et al. 1988, Dalton and Van Genuchten 1986,
Topp and Davis, 1985, Topp et al. 1980). The application of this technology involves the
use of a finite transmission lines (coaxial or parallel). An electromagnetic pulse is
transmitted through these lines and its reflection is analyzed to obtain the complete
dielectric frequency spectrum. This technology was first applied by Fellner-Feldegg four
decades ago. Li and Zeiss (2001) reported the moisture content measurement for MSW
materials using TDR technology.
2.5.1 Principles of Operation
TDR technology is similar to the concept that the physical characteristic of the
medium in which an electromagnetic signal is emitted can be found by analysis of the
reflection of this signal. In TDR technology, the physical characteristic of the medium
that is analyzed by the propagated electromagnetic wave is the relative permittivity or the
dielectric constant of the medium. TDR theory states that the time for a transmitted
electromagnetic pulse to be reflected is dependent on the relative permittivity or dielectric
constant of the medium (Thomas, 2001). A basic capacitor theory can be used to explain
the concept of relative permittivity. Dalton and Van Genuchten (1986), Topp et al. 1988,
Chapter 2
Noborio et al. 1994, Nadler et al. 1991, Li and Zeiss (2001) and Thomas (2001) have
documented the concept of relative permittivity and the TDR theory. The theory
described below is referred from the aforementioned articles.
A capacitor is a device that can be used to store electrical charge (Q) when a
voltage (V) is applied across its terminals. For an air filled parallel plate capacitor, the
equation between the stored charge and the applied voltage is:
Q  C0V
(2.5.1)
where CO is the capacitance of the capacitor (the constant relating the charge to the
applied voltage). If an insulating material is placed between the plates of the capacitor the
electric charge generated is seen to increase with the same voltage applied across the
terminals. The parameter used to describe this change of the capacitance of the insulating
medium is the dielectric constant. TDR is able to detect the changes in this constant and
ultimately allows its use in measuring the moisture content. The dielectric constant is
defined in terms of capacitance in the equation given below
C  C0
'
 Co
0
(2.5.2)
where ’ and 0 refer to the dielectric constant of the medium and the air, respectively.
The term  is the relative dielectric constant and is also known as relative permittivity.
The relative permittivity of water is approximately 80 while that of air and soil are
approximately 1 and 2 respectively. It is this large difference in the values of water versus
soil and air that time domain reflectometry ultimately measures to generate a value of
Chapter 2
volumetric water content. A concise description of the application of TDR technology
taken from Dalton and Van Genuchten (1986) is given in the following paragraph. Figure
2-7 gives the schematic of a TDR setup given by Dalton (1992).
The TDR instrument consists of a voltage source and a coaxial cable that is
attached to parallel probes at its end. The probes are inserted into the media in which
moisture content is to be determined. The voltage source produces a fast rise step voltage
pulse (V). This voltage pulse propagates through a coaxial cable and part of this voltage
pulse is transmitted (VT) as an electromagnetic wave along the parallel electrodes that are
inserted into the test medium (Topp, 1988).
Switch
Air soil
interface
VT
Parallel
electrodes
V
VR
Voltage
generator
Figure 2-7. Schematic illustration of the time domain reflectometry principles showing
the launching of voltage pulse V with the transmitted and reflected
components VT and VR.
Chapter 2
The voltage source produces a fast rise step voltage pulse (V). This voltage pulse
propagates through a coaxial cable and part of this voltage pulse is transmitted (VT) as an
electromagnetic wave along the parallel electrodes that are inserted into the test medium
(Topp, 1988). If during propagation the electromagnetic wave passes an interface of
changing impedance (when the voltage leaves the coaxial cable and enters the parallel
probes), a portion of the signal is transmitted through the interface and a portion is
reflected. The first change of impedance is at the beginning of the parallel probes when a
portion (VT) is transmitted. At the end of the parallel probes part of the transmitted pulse
(VT) is reflected by the waste (again due to a change of impedance) and is shown as VR.
If the medium is a perfect insulator, the reflected voltage will be of the same intensity as
the transmitted voltage but if the medium is conductive, the reflected electromagnetic
wave will be attenuated. This is shown in Figure 2-8. The attenuation of the reflected
wave has been researched in some studies conducted to determine the effectiveness of the
TDR technology in measuring the electrical conductivity of soil solutions (Dalton et al.
1984, Dalton and Van Genuchten 1986, Zegelin et al. 1989, Topp et al. 1988, Bonnell et
al. 1991). The examination of the TDR attenuation is, however, very complex as all of
the reflections between the TDR source and the main reflection of interest have to be
measured (Bonnell et al. 1991). The production and analysis of the TDR waveforms is
discussed in section 2.4.2. The effect of this waveform attenuation on the ability to
determine the moisture content of the medium will be discussed next.
Chapter 2
Note that the transmitted VT
and reflected VR are of
equal intensity.
Transmission in nonconductive medium
Reflection
plane
Note the attenuation of
both the transmitted VT
and reflected VR as they
pass through the media.
Transmission in nonconductive medium
Figure 2-8. Schematic representations of the transmitted and reflected voltage pulses in
non-conducting and conducting media (Dalton and Van Genuchten. 1986).
Topp et al. (1980) found that the pulse time, which is nothing but the total time
taken for the voltage pulse to be transmitted and reflected, is proportional to the dielectric
constant of the material. This was obtained by equating the pulse velocity equations
derived from electrodynamics and mechanics theory. From electrodynamics the pulse
velocity can be expressed in terms of relative dielectric constant  and the velocity of
light in free space c as shown in equation 2.5.3 below.
V
C

(2.5.3)
From mechanics the pulse velocity is given by:
Chapter 2
V 
2L
t
(2.5.4)
where L is the length of one of the parallel electrodes and t is the time taken for the pulse
to be transmitted and reflected. The path length of the voltage pulse is twice the length of
the parallel rods as this is the total distance traversed during the transmittal and the
reflection of the signal. Equating 2.5.3 and 2.5.4 and rearranging we may solve for the
dielectric constant as a function of transmittal time:
 Ct 
  
 2L 
2
(2.5.5)
An empirical equation relating the relative dielectric constant and the volumetric
water content of the soil medium was proposed by Topp et al. 1980. This relation is
shown in equation 2.4.6.
2
3
   530  292  5.5  0.043 10000
(2.5.6)
where  is the soil volumetric water content. The above equation is valid for wetted
media whose liquid has conductance less than 8 mS/cm (Dalton 1992). The conductivity
of the medium will factor in the equation 2.5.6 if the conductivity of the medium exceeds
this value. This is due to the attenuation of both the transmitted and reflected voltage
resulting from the leakage of current occurring as a result of the increase in conductance
of the medium. This is one of the limiting factors for the application of this technology in
landfills as the leachate can achieve high values of electrical conductivity. The
production and analysis of TDR waveforms is given in section 2.5.2.
Chapter 2
2.5.2 Production and Analysis of TDR Wave Forms
The basic components for the production of a TDR waveform and an idealized
output display are shown in Figure 2-9. These include a cable tester (labeled TDR
instrument in the figure), a balun (balanced-unbalanced) transformer, a shielded balanced
pair transmission line, and two parallel metal rods that serve as a wave guide (Cassel et
al.1994). These rods are inserted into the soil to be tested.
The voltage source that emits the electromagnetic pulse is the TDR instrument
shown in Figure 2-9. As stated earlier, whenever the propagating electromagnetic pulse
encounters a region of changing impedance a portion of the pulse is reflected and a
portion is transmitted. In Figure 2-9(c) the returning reflections are shown arbitrarily at
30o to the vertical to enable them to be related to the time and voltage display. In the
instrumentation the points of changing impedance are labeled B, C, and D. These
correspond to the pulse entering and leaving both the balun and the soil and the end of the
two parallel metal rods (wave guides).
The TDR instrument emits a voltage pulse Vo at point A. As the voltage pulse
travels downward it comes in contact with the balun and encounters a change in
impedance. Due to this change in impedance part of the voltage pulse is reflected
upwards. The small deflection at point B in Figure 2-9(b) represents this reflection. Most
of the pulse travels downward along the shielded balanced pair transmission line until it
encounters the soil at C. Here a large portion of the pulse is reflected upwards. When the
reflected pulse itself encounters the balun on its travel upwards, part of it will be reflected
Chapter 2
downwards and the remaining portion will arrive at the voltage measuring device and
will be seen as a drop in voltage as shown by point C in Figure 2-9(b).
Figure 2-9 Schematic diagram of the TDR system using parallel wave guide to measure
soil water content. a-Physical arrangement, b-Idealized output display, cSchematic of reflections (Cassel et al. 1994).
The remainder of the original pulse continues its travel downwards through the
parallel metal electrodes (wave guides) until it reaches the end of the wave guides where
practically all of the pulse is reflected upwards. Again the reflected pulse will itself
Chapter 2
undergo reflections as it travels upwards, both at the soil entrance and at the balun. Some
of the original reflected pulse will arrive at the voltage measuring point and will be seen
as a voltage rise at point D. When this examination is done for each of the original pulses
and for each of the reflections the idealized output of Figure 2-9 (b) is obtained.
2.5.3 Calibration of TDR Sensors for Moisture Measurement
Li and Zeiss (2001) developed analytical procedures to measure the in situ moisture
content in MSW materials. Similar to Topp et al. (1980), this study aimed at developing
most appropriate calibration equations for the range of solid waste materials. A fourth
degree polynomial equation was chosen to fit the experimental data and the relationship
between relative dielectric constant of the medium and its moisture content is given by
the Equation 2.4.7.
 a  b  c 2  d 3  e 4
(2.5.7)
where  is the volumetric moisture content and  is the bulk dielectric constant and a, b,
c, d and e are the parameters to be determined.
The apparent dielectric constant  of the air-water-solid medium can be given by the
equation 2.4.8
 V1 air  V2 water  V3 solid
(2.5.8)
where V1, V2, V3 are the volume ratios of air, water and solid and the ranges of air, water
and solid are approximately 1, 3 to 5 and 80 respectively. Hence, small change in the
Chapter 2
moisture content would cause a large change in the bulk dielectric constant of the
medium. The following factors were reported to have effects on TDR moisture
measurement technique:
Porosity: It is measured by the ratio of the volume of the voids to the total volume of the
waste. The higher the porosity of the waste material, the higher the value of bulk
dielectric constant as there would be more volume available to hold water (equation
2.5.8).
Electrical conductivity: Increase in electrical conductivity of medium increases
the measurement of bulk dielectric constant. Dalton (1992), reported that TDR
overestimates the value of moisture content for conductivities greater that 8mS/cm. In a
landfill, the typical conductivity of leachate varies from 2-18 mS/cm (Reinhart and
Townsend, 1997). Another disadvantage is the TDR signal attenuation in the conductive
medium, as reported by Dalton and Van Genuchten (1986). Li and Zeiss (2001) observed
that by coating the TDR probes with plastic, the signal attenuation could be reduced.
Apart from these, the ferrous metals content in the landfill were also reported to influence
the TDR measurements.
Twelve different probes along with resistance based MTG sensors were installed
at 5 clustered locations at New River Regional Landfill. The probes were initially packed
with MSW material with dry densities as given by table C-1 (appendix-C) and the
equation of the form 2.5.7 was derived. Table C-2 gives the coefficients of equation 2.5.7
for the 12 probes. A comparison will be made in this study between these TDR probes
and MTG sensors for calculating the moisture content at these locations.
Chapter 2
2.6 Summary of the Advantages and Disadvantages of each Technology
The summary of the advantages and disadvantages of different moisture
measurement technologies as reported in literature is given in table 2-3. This chapter
gives a brief overview of the existing technologies to determine the in situ moisture
contents. Several innovative techniques are currently being explored that may have
application to bioreactor landfills. Some of them include, gas tracer method, electrical
sounding, electrical 2-D imaging, electromagnetic mapping, and radar profiling. Research
continues to evaluate the use of these techniques at bioreactor landfills.
Table 2-3.Advantages and disadvantages of various moisture measurement techniques
Moisture measurement
Advantages
Disadvantges
Instantaneous measurement
Exhibits sensitivity to
possible with no random error.
variations in media bulk
technique
Capacitance probe
density and texture.
Instrument is relatively
inexpensive.
Changes in electrical
conductivity and temperature
effect the sensor operations.
Neutron probe
Average moisture content over
Absolute moisture content
depths can be determined.
cannot be determined.
Sudden or seasonal changes in
Health risks are associates due
Chapter 2
the soil moisture can be
to radiation hazard.
determined.
Automation is difficult.
Offers large radius of
Density variations in landfill
influence.
have effect on moisture
content measurement.
Electrical resistance probe
Sensors are relatively
Affected by changes in
inexpensive.
electrical conductivity and
temperature.
Technology is non hazardous.
Inconsistent readings at low
Sensor installation is easy.
moisture contents
Automation is possible.
Results affected by electrical
Density does not affect
conductivity and porosity of
readings.
the media.
Presence of metals in landfills
affects the moisture content
measurement.
Time domain reflectometry
Automation possible
Results affected by electrical
conductivity and porosity of
Unaffected by organic content
the media.
changes
Chapter 2
Non hazardous and results are
Presence of metals in landfills
repeatable
affects the moisture content
measurement.
The next two chapters in this thesis examine two different technologies (Electrical
resistance technology and Time domain Reflectometry technology) for field application
to determine the in situ moisture content in a bioreactor landfill located in north central
Florida.
Chapter 2
CHAPTER 3
SENSOR DEVELOPMENT
3.1
Introduction
A composite moisture, temperature, and gas (MTG) sensor was developed that
would enable the in situ measurement of moisture (via the electrical resistance technique)
and temperature, and that would facilitate the collection of a gas sample in landfills.
Electrical resistance was selected for the MTG sensor because of advantages related to
ease of resistance measurement, historically observed good correlation between moisture
content and electrical resistance, and low cost of manufacturing. An objective of the
sensor development work was to develop laboratory data that will permit adjustment of
field measurements to account for these effects. A part of the laboratory work is
explained in Thomas (2001).
The development of this sensor required extensive testing including:
1.
Determination of the optimum granular media particle size with respect to
sensor sensitivity and responsiveness.
2.
Assessment of effect of liquid electrical conductivity on resistance
measurements.
3.
calibration of the sensor for interpretation of in situ moisture content and
assessment of the effect of temperature and dry density.
4.
Field testing of sensors.
5.
Sensor behavior under saturated conditions.
Chapter 3
3.2
Principle of Operation
The MTG sensor was designed to measure the electrical resistance occurring
between two electrodes embedded in an insoluble granular media. Resistance is inversely
proportional to electrical conductivity, which can be correlated to moisture content.
Water moves from the surrounding waste to the granular media through capillary action
enhanced by glass fiber wicks attached to the sensor body. Changing resistance readings
reflect changes in moisture content in the sensor’s granular media. While the moisture
content of the sensor media may be different from that of the waste, it is assumed that a
matric potential equilibrium exists between the sensor media and that of the surrounding
waste, and that a relationship between sensor resistance measurements and surrounding
waste moisture content can be developed. This relationship could be influenced by the
electrical conductivity of the fluid and its temperature at which the sensor resistance is
recorded; these two factors were also investigated.
3.3
Sensor Description
The sensor body was constructed from a 20-cm section of 5-cm diameter PVC
well screen (5 cm ID, 6 cm OD) (Figure 3-1 and 3-2). A slot size of 3 mm was used. Two
1.6-cm thick by 5-cm diameter solid PVC plugs were cut for the top and bottom of the
sensor. Two drainage holes were drilled into the bottom plug to allow moisture to drain
from the sensor.
Chapter 3
Figure 3-1 Longitudinal section of the MTG sensor.
Chapter 3
Figure 3-2 Pictorial view of the MTG sensor.
A 19.6-cm piece of #6 stainless steel rod was inserted through the center of the
top plug and partially into the bottom plug. A hole was drilled in the top plug to allow
the gas sample collection hose to access the main sensor body. The stainless steel rod
served as one electrode and a market-grade stainless steel screen wrapped on the inside of
the sensor body served as the other. The mesh was sized according to the particle size of
the granular media (see below) and was connected to the resistance conductors via a short
piece of 18-gauge copper wire soldered onto the mesh. The bottom plug was first glued
in place, the stainless steel screen and rod were then inserted, granular material was
Chapter 3
added, and the upper plug was glued. The dry packing density of granular material was
1.65 g/cm3 for a diameter of 1.0 mm. A cone tip fitting was placed on the bottom of the
sensor and a coupling was placed on the top of the sensor to facilitate field installation.
Resistance across the sensor electrodes was measured using an AC half-bridge with a 1kOhm, 0.1% completion resistor. In addition a thermocouple (type T) was provided to
measure temperature and a gas tube was inserted through the top plug for gas sampling.
Note that because this sensor is used in landfills that contain highly conductive leachate,
it was not necessary to add a soluble salt to the matrix to boost conductivity.
3.4
Methodology
3.4.1
Particle Size Determination
The optimum particle size of sensor granular media was determined by evaluating
sensor sensitivity and responsiveness. Sensitivity is defined as the ratio between the
change in fraction saturation of the media and the change in the corresponding electrical
resistance. Sensitivity was evaluated by measuring resistance over time as water drained
from a saturated sensor.
Sands with uniform particle sizes having 0.5, 0.7, 1.0, 1.2, and 2.4-mm median
diameters were tested using tap water (specific conductivity of 0.3 mS/cm). The sensor
was inserted into a bucket of water and shaken gently to remove any air bubbles trapped
in the matrix. The sensor was then allowed to remain submerged in the water for
approximately six hours.
The resistance of the sensor while fully submerged was
Chapter 3
recorded. The sensor was then removed from the water and transferred to an electronic
scale (Sartorius BL 6100). The sensor was supported on a metal frame with a water
reservoir below it, shown in figure 3-3. The reservoir served to collect water as it drained
from the sensor. The sensor was suspended so that the weight of the sensor and the
entrained moisture was automatically recorded as the water drained.
Resistance
measurements were recorded and logged using a Campbell Scientific CR 10X datalogger
(recording measurements every five seconds). Resistance and weight measurements
were simultaneously recorded. When the datalogger registered a maximum value of
resistance of 99,999 ohms the experiment was discontinued.
The sensor was then
completely dried to determine its dry weight. The fraction of saturation (S) of the sensor
was determined using Equation 3.1. The volume of voids was determined by calculating
the porosity of the particulates (Bowles, 1992).
S 
 Sensor Wt  Dry Wt of Sensor 


Vol of Voids


 water
(3.1)
Chapter 3
DRYING
SUPPORT
STAND
DATALOGGER
CABLE
WATER
RESERVOIR
SCALE
MOISTURE
SENSOR
Figure 3-3 Sensor calibration testing apparatus.
Responsiveness was evaluated by examining the time required for sensors filled
with granular media having 0.5, 0.7, 1.0, 1.2 and 2.4-mm median diameters to reach
maximum resistance while air drying. These tests would indicate the relative ability of
the sensor to detect change in surrounding waste moisture content for the different
particle sizes.
3.4.2
Effect of Liquid Electrical Conductivity
Experiments were performed to determine the response of the sensor to increases
in specific conductivity of the test liquid.
Three solutions were prepared with specific
Chapter 3
conductivities of 6.6, 13.9, and 22.7 mS/cm. Increments of KCl were added to deionized
water and the conductivities measured until reaching the desired ionic strength. These
values were chosen to approximate the specific conductivity strengths of the New River
Regional Landfill leachate. Leachate obtained from this facility had an average specific
conductivity value of 13.2 mS/cm. Conductivities double and half this value were also
formulated to determine variability patterns.
The conductivities were measured
according to Standard Methods (Franson 1995). The experimental procedure is presented
below.
The sensor was inserted into a volumetric flask containing each respective
solution. The sensor was shaken gently and allowed to saturate for six hours to remove
any trapped air bubbles.
recorded.
The resistance of the sensor while fully submerged was
The sensor was then transferred to the scale and resistance and weight
measurements were simultaneously recorded as above (see Figure 3-3). When a reading
of 99,999 ohms was recorded by the datalogger the experiment was stopped. The sensor
was dried to determine the dry weight of the sensor. The fraction of saturation was
computed as before and the calibration curves (fraction saturation vs resistance)
constructed.
3.4.3
Sensor Calibration
Calibration experiments were conducted with sensors containing 1.0-mm particle
size medium. A 50-kg sample of dry synthetic solid waste was made in the laboratory as
per the composition given in Table 3-1 (Tchobanoglous et.al. 1993). Twenty- liter plastic
buckets with a diameter of 27 cm were used as test cells. Because of the limited size of
Chapter 3
the container, synthetic waste components size was reduced to 5 cm or less. It was felt
that this size reduction would have minimal impact on moisture retention characteristics
of the waste. The bulk density of waste was maintained at values similar to those found
in operating landfills (500 to 950 kg/m3). Calibration experiments were carried out for
various waste moisture contents.
True moisture content of the tested samples was
determined gravimetrically by oven drying 150-g sub-samples (dry minimum) at 75 +
2oC for 48 hours to minimize losses due to combustion.
All of the calibration
experiments were conducted at 22 ± 1 ºC.
Chapter 3
Table 3-1. Composition of solid waste used in laboratory experiments (after
Tchobanoglous et al., 1993)
Component
Food wastes
Paper
Cardboard
Plastics
Textiles
Rubber
Leather
Yard wastes
Wood
Glass
Tin cans
Aluminum
Other metals
Dirt & ash
Total
Percent weight
3.43
40.61
7.23
8.76
2.28
0.63
0.51
9.39
2.03
9.90
7.36
0.63
3.68
3.55
100.00
Since the resistance measured by the sensor is strongly affected by the electrical
conductivity of moisture, the calibration experiment was conducted twice; once with the
added moisture having an electrical conductance of 4.0 mS/cm and the other with the
moisture having a conductance of 8.0 mS/cm.
Potassium chloride was used as an
electrolyte to adjust the conductivity of the moisture. After contact with the waste, final
conductivities increased and were measured for saturated conditions to be 9.7 mS/cm and
12.4 mS/cm, for 4.0 and 8.0 mS/cm experiments, respectively. Results reference initial
electrical conductivities only.
The experimental cell containing a MTG sensor soaked in 8.0 mS/cm KCl
solution was allowed to stabilize for a period of over 30 days to reach a constant
Chapter 3
resistance value at 22oC.
The effect of temperature on sensor readings was then
examined by increasing the temperature as high as 55oC while recording corresponding
resistance readings. In an effort to evaluate the effect of changes in dry density on the
calibration data points, experiments were repeated by changing the dry density while
maintaining similar moisture contents.
3.4.4
Field Testing of Sensors
In an effort to validate laboratory calibration experiments, field experiments were
conducted. Two MTG sensors 15 cm apart were installed in waste at the Orange County
Landfill (Florida) approximately 60 cm below the top cover soil. The field experiments
aimed to provide information on the reproducibility of sensor readings in the highly
heterogeneous landfill environment.
On two occasions, waste samples from the
immediate vicinity of the sensors were removed and brought to the laboratory for
gravimetric moisture content determination.
3.4.5
Sensor Behavior under Saturated Condition
This experiment was conducted to know the behavior of the MTG sensor under
completely saturated condition. The resistance and temperature readings from a sensor
known to be installed at a completely saturated location are expected give indirectly the
electrical conductivity of liquid at the location. Therefore an experiment was performed
in laboratory by placing a MTG sensor in completely saturated condition in potassium
chloride solution with electrical conductivity values ranging from 4.0 to 20.0 mS/cm at
22 ± 1 ºC. Resistance values indicated by sensor and the corresponding temperature were
Chapter 3
noted. Curves were generated for various eC values which read resistance readings as a
function of temperature. Field experiment was conducted with the sensors installed at the
orange county landfill to verify the results from laboratory experiment. A hand suction
pump was used to obtain leachate samples from the gas tube of MTG sensors. The
readings of resistance and temperature were recorded.
3.5
Results and discussion
3.5.1
Particle Size Determination
The curves of fraction saturation of sensor media versus resistance provided information
regarding sensor sensitivity as a function of particle size (see Figure 3-4). Note some
irregularities in response were observed during the first few seconds of data collection
following removal of the sensor from the water tank. Large changes in resistance in
response to small changes in fraction saturation were observed at low values of fraction
saturations and small changes in resistance in response to large changes in fraction
saturation were seen at high values of fraction saturation. Higher fraction saturations are,
unfortunately, of greatest interest in the operation of bioreactor landfills. As the particle
size increased, sensitivity to changes in moisture present increased. However, as particle
size decreased the relationship between fraction saturation and resistance became very
steep resulting in decreased resolution.
The 1.0-mm particle size had the best
combination of sensitivity and resolution.
Chapter 3
1
0.9
Fraction Saturation
0.8
0.7
0.6
0.5-mm
0.7-mm
1.0-mm
Sensor
Sensor
Sensor
0.5
0.4
0.3
1.2-mm
0.2
Sensor
2.4-mm
0.1
Sensor
0
0
1000
2000
3000
4000
5000
Resistance (ohms)
Figure 3-4 Fraction saturation vs. resistance curve for all particle diameters of sensor in
tap water (0.3mS/cm)
Table 3-2 gives the approximate time required for the sensor to reach maximum
resistance while air-dried. The purpose of conducting this test was to determine the
responsiveness (ability of the sensor to detect changes in the external environment) of the
sensor to changes in external conditions. Responsiveness will give an indication of the
time delay the sensor will experience in the landfill in monitoring changes in the moisture
content of the waste, the longer the time the lower the responsiveness of the sensor.
Chapter 3
Table 3-2 Air drying times for moisture sensors
Sensor particle size
Time (Days)
(mm)
0.5
5
0.7
~4–5
1.0
4
1.2
~3–4
2.4
2
The larger sized particles had the greatest responsiveness (the shortest time taken for the
sensor to attain maximum resistance by air drying) with the 2.4-mm particles exhibiting
the greatest responsiveness of all the particle sizes tested (2 days). A progressive decline
in responsiveness was observed with the smaller particle sizes with the 0.5-mm sensor
having the least responsiveness and sensitivity requiring 5 days to reach equilibrium. A
particle size of 1.0 mm was selected for use in all subsequent testing and application as a
compromise among important attributes of the sensor medium.
3.4.2
Effect of Liquid Electrical Conductivity
Leachate characteristics vary due to the leaching and decomposition / stabilization
of waste constituents.
It was therefore necessary to simulate these changes in the
laboratory and determine what effects if any these variations may have on the moisture
Chapter 3
content – electrical resistance relationship of the sensor. The results of these experiments
are shown in Figure 3-5.
Chapter 3
1
0.9
0.8
Fraction Saturation
0.7
0.6
0.5
0.4
0.3
13
.9
m
S/
c
m
0.2
0.1
0
0
500
1000
6.
6
m
S/
c
m
1500
22
.7
m
S/
c
m
2000
2500
Resistance (ohms)
Figure 3-5 Combined calibration curves of 2.4-mm sensor in KCl solutions.
Electrical resistance is inversely proportional to conductance, the higher electrical
conductivity of the solution causes a shift in the curve to the left. There was a shift in the
curve for 13.9 mS/cm conductivity, which could be attributed to experimental error. The
Chapter 3
influence of electrical conductivity was significant therefore this factor was considered as
a variable in sensor calibration.
3.5.3
Sensor Calibration
Figure 3-6 provides the calibration curve for the MTG sensors reading moisture
content of surrounding waste as a function of resistance measured across the granular
matrix. The sensor was sensitive enough to follow the trend of inverse relationship
between moisture contents and resistance values above waste moisture content of nearly
35% (w/w, wet basis). Waste moisture contents below 35% resulted in dry conditions
within the sensor and extremely high resistance values (greater than 30 kOhms) that were
difficult to interpret. In addition the time to reach a stable value took approximately 30
days. This behavior confirms expectations that the sensor cannot accurately measure low
moisture content because of limiting potential for moisture transport. Note that the
resistance vs. moisture content relationship reported for the MTG is a function of pore
size distributions of the surrounding medium and sensor fill material.
Chapter 3
Wet Moisture Content (%)
100
4.0 mS/cm (Data)
8.0 mS/cm (Data)
16.0 mS/cm(Data)
75
50
25
0
0
5
10
15
20
Resistance (k Ohms)
Figure 3-6 Calibration curve for MTG sensors for varying moisture conductivities.
Three very distinct curves were obtained for moisture conductivities of 4.0
mS/cm, 8.0 mS/cm, and 16.0 mS/cm presented in Figure 3-6. Resistance values at
similar moisture contents would vary by as much as 40% suggesting that this parameter
has significant impact on resistance readings in the range of interest for bioreactor
landfills. From these curves, it can be seen that sensitivity is lost at higher conductivities.
Behavior at higher conductivities should be explored in the future to fully characterize
sensor behavior. Based on the experimental data empirical relationships were developed
to describe MC as a function of measured resistance (Equation 3.2, 3.3, and 3.4 for 4.0,
8.0, and 16.0 mS/cm respectively).
Chapter 3
At 4.0 mS/cm:
MC 
21.56
1  0.682 exp  0.0252 R 
MC 
30.068
1  0.568 exp  0.167 R 
(3.2)
At 8.0 mS/cm:
(3.3)
At 16.0 mS/cm:
MC 
34.037
1  0.488 exp  0.4036 R 
(3.4)
Where,
MC = moisture content of solid waste (% wet weight)
R = resistance value measured from the sensor (kOhms)
Figure 3-7 shows the change in the resistance readings with varying temperatures. The
measured resistance values were compared with the predicted values calculated using the
U.S. Geological Survey (1998) method given by Equation 3.5.
Equation 3.6 was
obtained from Equation 3 by substituting the inverse of resistance for conductivity.
Chapter 3
C25 
Cm
1  0.02t m  25
(3.5)
where,
C25  corrected conductivity value adjusted to 25 oC,
C m  actual conductivity measured, and
t m  water temperature at time of C m measurement in oC.
1  0.02t 2  25
R1  R2 

 1  0.02t1  25 
(3.6)
where,
R1  resistance at temperature t1 , and
R2  resistance at temperature t 2
It can be observed from Figure 3-7 that the predicted values of resistance closely
match the measured values. The measured values deviate slightly at higher temperatures,
Chapter 3
which could be due to evaporation of moisture from solid waste as the temperature was
increased. Using Equations 1, 2, and 4, a resistance value for a sensor at a particular
temperature exposed to leachate at a particular conductivity can be converted to the
moisture content of surrounding waste.
Experimental values
Predicted values
Resistance (k Ohm)
1.2
1
0.8
0.6
0.4
20
30
40
50
60
Temperature (o C)
Figure 3-7. Variation in resistance as a function of temperature for MTG sensors.
As seen in Table 3-3, the change in bulk density caused minimal difference in
measured resistance for identical moisture content values for unsaturated waste. Under
saturated conditions, the sensor will measure the resistance of the leachate independent of
the solid matrix, and this value will be same for all values of waste density. However, in
order to determine the corresponding MC when the waste is saturated, an estimate of
density and porosity of solid waste in a landfill will be required. Completely saturated
Chapter 3
conditions can be distinctly identified by resistance values less than 0.05 kOhms
irrespective of conductivity or temperature.
Chapter 3
Table 3-3 Effect of solid waste density on sensor measurements for 4.0 mS/cm
moisture
electrical conductivity
Moisture content of Bulk Density
Resistance
waste w/w (v/v)
Predicted value of MC
using calibration curve
(kg /m3)
(k Ohm)
(w/w)
47.20 (25.43)
539
8.90
48.04
47.00 (29.67)
631
8.10
49.31
62.29 (44.89)
717
2.66
60.70
62.10 (51.59)
831
1.36
64.43
71.00 (60.00)*
845
0.015
68.89
68.28 (65.33)*
957
0.015
68.89
*Completely saturated conditions
3.5.4
Field Testing of Sensors
Sensors installed at the Orange County Landfill were monitored over time. Initial
readings showed resistance readings of 50 to 80 kOhm, which suggested that the moisture
levels were below MTG detection limit (~35 %, w/w, wet basis). Laboratory analysis
indicated a moisture content of 38.8 % (w/w, wet basis), apparently below field capacity
Chapter 3
for this waste. After a few days, there was an increase in the moisture content to
saturated conditions due to infiltration from precipitation. Figure 3-8 shows the capture
of the moisture front detected by the two sensors in similar ways. The sensors recorded
the influx of precipitations over a short period of time, suggesting that they are capable of
sensing rapid changes in waste moisture content with nearly similar resistance readings
for the two sensors.
Resistance (kOhm)
100
Sensor 1
75
Sensor 2
50
25
0
9-Apr
10-Apr
11-Apr
12-Apr
13-Apr
14-Apr
15-Apr
Time
Figure 3-8 Capture of moisture front as a result of infiltration.
Waste samples were removed from an area near the sensors following the rain
event.
These samples clearly had extremely high moisture content, with leachate
draining during waste sample removal.
Sensors reported saturated conditions with
resistance readings of 0.0357 and 0.0378 kOhms for the two different sensors. The
gravimetric analysis of the nearby waste showed a MC of 59 % (w/w, wet basis).
Laboratory analysis may be low due to draining of leachate during sample extraction.
Chapter 3
3.5.5
Sensor Behavior under Saturated Condition
Figure 3-9 shows a set of curves generated for various eC values which gives the
sensor response to changing temperature values. These curves were generated using a
data point at 22 ºC and the values extrapolated using Equation 3.6.
0.04
4.0
mS/cm
8.0
mS/cm
12.0
mS/cm
16.0
mS/cm
20.0
mS/cm
Field Data
Resistance (kOhm)
0.035
0.03
0.025
0.02
eC = 5.92 mS/cm
0.015
0.01
0.005
0
0
10
20
30
40
50
60
Temperature (o C)
Figure 3-9 Field experiments with sensors under saturated condition.
Leachate extracted through gas tubes from the three sensors installed at the
Orange County Landfill showed an eC value of 5.92 mS/cm. More than 1.0 L leachate
was drawn from one sensor; this sensor read a resistance value and the temperature
indicative of leachate eC value close to 5.92 mS/cm as shown in Figure 3-9. Two other
Chapter 3
sensors became dry upon application of suction. These two sensors reported high levels
of resistance, suggesting unsaturated conditions. The resistance and temperature readings
plotted in Figure 3-9 for one saturated sensor were consistent with the laboratory
observations therefore further field experiments were not conducted. This experiment
suggests that it is possible to predict the electrical conductivity of liquid when the sensor
in know to be located at a saturated location.
Chapter 3
3.6
Summary of Results
The resistance-based granular matrix sensors appear to provide a practical method
for measuring the in situ moisture content of solid waste in landfills. The technique is
non-destructive, non-hazardous, economical, and easy to automate. The sensor could be
suitably used to measure in situ moisture content of solid waste with the knowledge of
electrical conductivity of liquid and applying correction for temperature variation. Even
without accurate knowledge of these parameters, the sensors will still provide an
indication of the relative moisture content of the surrounding waste and how the moisture
content is changing as a function of time and landfill operation. In absence of reliable eC
values the MTG sensor is able to identify nearly saturated regions with a response of very
low resistance readings. One of the limitations to the use of this sensor is in regions of
low moisture contents (values of less than 35% (w/w, wet basis)) as will be further
discussed in subsequent chapters.
Chapter 3
CHAPTER 4
FIELD EVALUATION OF RESISTIVITY SENSORS FOR IN SITU MOISTURE
MEASUREMENT IN A BIOREACTOR LANDFILL
4.1 Introduction and Background
Moisture content plays a vital role in the solid waste biodegradation process and thus is a
key parameter of interest for operators of bioreactor landfills (Rees, 1980; Pohland, 1980;
Reinhart and Townsend, 1997). The ability to measure moisture content of the landfilled waste
would be beneficial to operators as they try to evenly and efficiently wet the waste. Gravimetric
measurement is one method for determining moisture content that involves the collection of
waste samples followed by a laboratory measurement. This process can be expensive and time
consuming and is not a practical method for routine moisture content determination. An ideal
moisture measurement technique would be one that allows measurement in situ over time as
leachate recirculation progresses. Many in situ moisture measuring devices have been developed
for use in soil systems. These include neutron probes, capacitance probes, time domain
reflectometry sensors (TDR), and resistance-based sensors. Several researchers have proposed
the use of such probes for tracking in situ moisture content in landfills. Holmes (1984) evaluated
neutron probe technology for determining the in situ moisture content of waste; while it could
not predict the absolute value of moisture content, the change in moisture could be assessed.
Yuen et al. (2000) evaluated the use of laboratory calibrated neutron probes at a full-scale
landfill. This study suggested that neutron probe technology was a practical tool to monitor
moisture change in the landfill. A potentially significant disadvantage associated with this
Chapter 4
technology is the radiation hazard. Li and Zeiss (2001) developed empirical procedures to
measure in situ moisture content of MSW materials with TDR probes. They found that a fourth
degree polynomial equation provided an excellent fit for the determination of moisture content
() from the measured apparent dielectric constant (Ka). The equation coefficients depended on
the material type. However, the porosity of the waste material and electrical conductivity of the
medium was found to affect the Ka- relationship.
Another type of in situ moisture measurement technology is electrical resistance-based,
which was developed more than 40 years ago. Resistivity measurement devices included
traditional gypsum blocks and granular soil matrix resistivity devices. Both types work on the
principal that the moisture content of the medium can be obtained from the resistance between
the pair of electrodes embedded in gypsum or granular soil. Rosqvist et al. (1997) used gypsum
block sensors at thirty-six points and three depths to measure the moisture content variation in a
pilot-scale landfill. Because gypsum block devices had operational difficulties in the field,
granular soil matrix resistivity devices were developed McCann et al. (1992). Gawande et al.
(2003) developed a granular soil matrix resistance sensor for in situ moisture content
measurement in landfills. Tests were conducted to select the optimum granular media particle
size to evaluate sensor sensitivity and responsiveness, and to assess the effect of liquid
temperature and conductivity on resistance measurement. Calibration curves relating the
measured resistance to gravimetric water content were developed. A thermocouple and 0.25-inch
tubing were attached to this device for measurement of temperature and collection of gas
samples respectively. As this sensor could be used for in situ measurement of moisture,
temperature, and gas composition, it was referred to as MTG sensor. A total of 135 sensors have
been installed in waste at a bioreactor landfill in North Central Florida. The paper reports the
`
Chapter 4
results from before and after the start of leachate recirculation and evaluates the effectiveness of
the sensor for in situ moisture measurement.
4.2 Methods and Materials
4.2.1 Site Description
The New River Regional Landfill (NRRL) is located in north central Florida, US, and
consists of several distinct landfill cells (see supplemental figure A-1 for a plan view of the entire
site). The area designated for the bioreactor consisted of parts of two cells covering an area of
approximately 10 acres and containing approximately 0.61 million tons of waste. The bioreactor
area is distinguished from the rest of the landfill by the presence of recirculation devices and an
exposed geomembrane cap. Figure 4-1 presents a plan view of the portion of the landfill site that
includes the bioreactor. The area distinguished as the “bioreactor well field” represents the
section where leachate recirculation was concentrated and this area will be used in upcoming
graphical presentations of the data. Leachate recirculation is performed at the site by means of
vertical injection wells. A series of vertical injection well cluster of different depths are
distributed throughout the landfill. The injection wells connect to a leachate recirculation
manifold. The MTG probes were also installed in clusters. Rows of MTG clusters were installed
in between rows of injection clusters. Figure 4-2 provides a detailed view of the well field
including the relative locations of the sensors, the injection wells, and the data logging station.
Injection wells and MTG clusters that are specifically discussed later in the text have labels
provided (see supplemental figure A-2 for a map showing all labeled clusters). Figure 4-3
presents a cross section of the landfill along section C-C’ (from Figure 4-2.). The height of the
landfill as seen from this figure is approximately 70 ft. A safety zone of approximately 10 ft was
`
Chapter 4
left from the deepest injection well to the bottom of the landfill where the liner was placed.
Leachate generated from the landfill gravity drains to the saw-tooth liner and is collected through
the extraction pipes. The extraction pipes were 100 ft apart and the leachate from each extraction
pipe flows into a header pipe that drains leachate into a wet well where it is pumped to storage
ponds.
Figure 4-1 Overview plan of the bioreactor.
`
Chapter 4
Figure 4-2 Plan view of the well field
`
Chapter 4
Boundary of bioreactor area
Injection well cluster
Landfill Surface
10 ft
Safety zone
2 ft bottom liner
100 ft
2 ft thick bottom liner
Figure 4-3 Cross section along CC’ of Figure 3-2.
4.2.2 Installation of MTG Sensors
A total of 134 vertical injection wells were installed at forty-five different locations, with
most locations consisting of three injection wells at depths averaging 20 ft, 40 ft, and 60 ft. A
total of 135 resistance-based MTG sensors were placed at 48 monitoring clusters with depths
averaging 15 ft, 30 ft, and 50 ft. Moisture monitoring clusters were installed in between rows of
injection clusters. Safety zone of approximately 10 ft was left between the liner system and the
bottom of the deep wells. The holes used for sensor placement were excavated using a 4-inch
truck-mounted power flight auger. The sensor was lowered into the borehole by temporarily
attaching it to a 2-inch diameter PVC pipe and lowering the pipe into the newly excavated hole.
`
Chapter 4
When the sensor reached the required depth, the pipe was detached. Sand was added first,
followed by the addition of bentonite pellets. The purpose of the sand was to bridge the gap
between the sensor and surrounding waste, while the clay was added to hydraulically isolate the
sensor before backfilling with waste. The locations of the clusters were presented in plan view
previously in Figure 4-2. Figure 4-4 presents a representative schematic of the relative locations
of a MTG cluster to an injection well cluster. It is noted that 51 waste samples were collected
during the installation of the injection wells and sensors and analyzed for moisture content
gravimetrically.
Figure 4-4 Schematic cross section of injection and monitoring clusters
`
Chapter 4
4.2.3 Field Measurements and Field Trials
The injection wells and the MTG clusters were installed over a six-week period in the
spring of 2001. The construction of the rest of the bioreactor, which included the installation of
the exposed geomembrane cap and the leachate recirculation piping, was not completed until the
fall of 2002. During this time period, the MTG sensor readings were collected manually
approximately every two weeks. The sensors were connected to the datalogger during the spring
of 2003. Three Campbell Scientific CR10X data loggers installed at various locations on the
landfill, as shown by Figure 4-2, were programmed to collect data at a frequency of twice per
day. Since the readings did not change substantially during the period after construction through
the start of leachate recirculation, only the data-logged values are reported in this report. For
purposes of discussion and presentation, January 1, 2003 was defined as day 1 of the experiment.
Leachate recirculation began on May 30 2003 (day 150), in the injection cluster located at CG3
(Figure 4-2.). Leachate recirculation continued over the following months in a total of 29 wells
in 10 clusters. The cumulative volumes of leachate injected into each recirculation well are
presented in Table 4-1. The reporting period for this chapter goes through November 18, 2003
(day 322). A total of 762,326 gallons of leachate were recirculated through the end of the
reporting period. Flow meters were used at each injection well to measure flow into each well. A
flow meter at the head of the leachate injection header pipe measures the cumulative flow of the
leachate pumped from the storage ponds to the landfill.
4.2.4 Conversion of Resistance to Moisture Content
Gawande et al. (2003) previously presented equations to convert measured resistance values to
moisture content. For the sensors used here, the equations were determined by measuring the
`
Chapter 4
resistance of a sensor that was inserted into laboratory- prepared synthetic waste, as a function of
measured moisture content, and determining a best-fit relationship. (See Appendix-D for
example calculation for the conversion to moisture content from measured resistance a known
temperature)
Table 4-1 Volume of leachate injected in different wells
Cluster Well
Leachate
Injected
(gallons)
CG3-Upper
23,379
CG3-Middle
31,793
CG3-Lower
32,141
CM3-Upper
40,983
CM3-Middle
36,397
CM3-Lower
38,838
CI5-Upper
53,373
CI5-Middle
52,272
CI5-Lower
50,541
CF6-Upper
15,913
CF6-Lower
17,574
CL6-Upper
17,290
CL6-Middle
18,446
CL6-Lower
18,431
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Chapter 4
CC7-Upper
14,862
CC7-Middle
28,874
CC7-Lower
18,453
CO7-Upper
15,307
CO7-Middle
23,065
CO7-Lower
19,686
CF8-Upper
6,296
CF8-Middle
32,146
CF8-Lower
31,015
CL8-Upper
23,373
CL8-Middle
39,539
CL8-Lower
37,505
CN8-Upper
2,467
CN8-Middle
15,643
CN8-Lower
6,724
Total
762,326
As the conductivity of the leachate was known to influence the resistance measured from
the sensor, leachate conductivity measurements routinely made on site were used. Leachate
samples were collected from the manholes on a weekly basis and conductivity was measured. On
one occasion after the start of leachate recirculation, leachate was collected from the injection
wells specifically to measure its conductivity.
`
Chapter 4
4.2.5 Estimation of Spatial Average of the Moisture Content of the Landfill
The resistivity-based moisture sensors were spatially distributed inside the landfill at
depths averaging 15ft, 30ft and 50ft. To provide a graphical representation of changes in
moisture content, distinct moisture contour profiles were computed for each depth. The
bioreactor well field boundary shown in Figure 4-2 was used as the boundary of a control
volume for determining spatial average moisture content of the landfill. A conceptual 3-D GIS
model was used to determine the spatial average. The first step in determining the spatial average
was to distribute the known moisture content values in the landfill into three identical moisture
grids. Arc view GIS 3.2 was used to create the grids with the known moisture content values
obtained from the sensors at each layer by an interpolation process using an inverse distance
algorithm. Then, a series of algebraic operations were performed on the interpolated layers to
create an average grid. Finally, the mean of the average grid was determined which is the
representative spatial average of the gravimetric moisture content in the control volume of the
landfill.
4.3 Results and Discussion
4.3.1 Sensor Output and Performance
A total of 135 resistance-based moisture sensors were installed at three different depths in
the landfill. Leachate recirculation started on day 150 and this chapter reports data collected until
day 322. Both the resistance values in k and the temperature values in degree centigrade were
recorded. During the experimental period, 133 out of 135 installed sensors had resistance values
ranging from 0.005 k to 466.7 k, which indicated that these sensors functioned within the
`
Chapter 4
output range expected for conditions within the landfill. The resistance values measured from the
remaining two sensors indicated that these sensors had failed (they measured 6999 k).
Resistance measurements from the period prior to leachate recirculation ranged from a minimum
resistivity of 0.005k (indicative of wet conditions) to a maximum resistivity of 466.7 k
(indicative of dry conditions). The temperature obtained from the MTG sensors ranged from 26.9
to 57.6 oC with the average temperature of 48.6oC. The optimum temperature for anaerobic
waste decomposition reported in the literature is in the range of 34 to 38oC (Mata Alvarez and
Martina-Verdure, 1986), but temperatures as high as 55oC occur in large landfills. In general, the
temperatures in deeper levels in the landfill were greater than temperatures at upper levels closer
to the surface (see Appendix-B for the temperature data). The waste placed in deeper levels was
older and more compacted than upper levels; it was thus more insulated from ambient
temperatures and was likely more biologically active. Gawande et al. (2003) reported that a
resistance value of 0.05 k or less indicates saturated conditions. The pre-leachate recirculation
baseline data found 18 sensors to have measured resistivity values less than 0.05 k, indicating
that the granular media in these sensors was saturated before recirculation. There were no
changes in the resistivity of these 18 sensors even after the start of recirculation. Half of them are
located at the west corner of the landfill and the temperature range at these sensors was 50-55oC.
Sixty other sensors responded to a passing moisture front after recirculation during the course of
this study.
Figures 4-5 and 4-6 present the typical response of the sensors to leachate recirculation.
The figures show both the changes in resistance and temperature. Leachate recirculation started
on day 150. The three distinct curves in Figures 4-6 and 4-7 correspond to the sensors located at
three different levels present at the monitoring clusters L6W and M3E (see Figure 4-2). A
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Chapter 4
noticeable decline in the resistance is observed, apparently as a result of the leachate
recirculation activity. As stated before, in certain areas of the landfill where the background
resistance was lower than 0.05 k, sensors did not show any appreciable change in resistance
with leachate recirculation (see supplemental figures given in Appendix-B for the response from
all sensors). In general, temperatures also showed a slight response, most noticeably for wells
that were not as wet to begin with. The deeper wells that were already wetted and had higher
temperatures did not show dramatic temperature variations. When leachate recirculation was
temporarily stopped, the resistance and temperature measurements from the sensors gradually
increased, thus indicating the drainage of moisture from the sensor granular media.
4.3.2 Estimation of Moisture Content
As described previously, equations were developed to convert the resistivity
readings to moisture content. The two parameters impacting this conversion were
`
Chapter 4
1000
L6W-Lower
L6W-Upper
L6W-Middle
Resistance (KOHMS)
100
10
1
0.1
Start of Leachate
Recirculation
0.01
0.001
100
150
200
Days
250
300
350
60
L6W-Upper
L6W-Lower
L6W-Middle
Temperature (Deg C)
55
50
45
40
35
Start of Leachate
Recirculation
30
100
150
200
250
300
350
Days
Figure 4-5. Response of resistivity sensors at cluster L6W to leachate recirculation (Day 1 =
01/01/2003)
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Chapter 4
1000
M3E-Lower
M3E-Middle
M3E-Upper
Resistance (KOHMS)
100
10
1
Start of Leachate
Recirculation
0.1
0.01
0.001
100
150
200
250
300
Days
60
Temperature (Deg C)
55
50
45
40
Start of Leachate
Recirculation
M3E-Lower
M3E-Middle
M3E-Upper
35
30
100
150
200
250
300
Days
Figure 4-6. Response of resistivity sensors at cluster MM3E to leachate recirculation (Day 1 =
01/01/2003)
`
Chapter 4
temperature and electrical conductance of leachate. Since the sensors also recorded the
temperatures, their impact was addressed using Equation 3.6. Leachate conductivity, on the other
hand, could not be measured directly for each location. Leachate conductivity was routinely
measured at the site and the values ranged from 2.5 mS/cm to 25.5 mS/cm. The average
conductivity of leachate at NRRL for a time period of 16 months (measured from the weekly
samples that were collected from manholes) was 11.8 mS/cm. The average conductivity of
leachate sampled from 10 injection wells at the northwest corner of the landfill was observed to
be 27.7mS/cm. The nearest leachate collection manholes from these sampled locations were
manholes 3 and 4. The leachate collected from manholes 3 and 4 had higher average
conductivities than leachate from other manholes. Table 4-2 gives the average measured
conductivities from different manholes as well as from the injection wells.
`
Chapter 4
Table 4-2. Average conductivity measured at NRRL
Sampling Location
Conductivity (mS/cm)
Manhole 1
7.58
Manhole 2
8.75
Manhole 3
21.62
Manhole 4
20.24
Manhole 5
15.29
Manhole 6
9.89
CF2-upper
27.9
CF2-middle
27.9
CH2-lower
28.2
CH2-middle
30.4
CE3-middle
20.3
CG3-lower
26.4
CG3-middle
5.35
CI3-upper
29.7
CF4-lower
20.3
CH4-upper
46.9
Figure 4-7 presents the predicted moisture content over time for cluster M3E (the
resistivity values for this sensor were presented in Figure 4-6). The deep sensor was wet to begin
with, with a predicted moisture content of 69 %. The upper and the middle sensors started lower,
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Chapter 4
in the range of 34 % to 36 % and increased to approximately 69% after leachate recirculation
was initiated.
70
% MC
60
50
Start of Leachate
Recirculation
40
M3E-Lower
M3E-Middle
M3E-Upper
30
100
150
200
250
300
Days
Figure 4-7. Calculated moisture content from cluster MM3E
4.3.3 Assessment of Sensor for Monitoring Leachate Recirculation
The data presented in this report represent only the beginning phases of leachate
recirculation at the site; leachate recirculation should continue several more years in the area. A
complete evaluation of the utility of these sensors for tracking moisture flow in bioreactor
landfill therefore is beyond the scope of this work. Some initial observations, however can be
made. To illustrate the response of sensors surrounding an injection well, one specific area is
examined in detail. Figure 4-8 provides a plan view of the area surrounding the injection cluster
CM3 including the surrounding monitoring clusters. A total of 116,218 gallons of leachate was
`
Chapter 4
injected through the three injection wells in this cluster. The cumulative volume of leachate
recirculated through the wells is presented in Figure 4-9. Figure 4-10 shows the changes in the
moisture content of the MTG sensors surrounding this injection cluster. As a result of leachate
injection, the moisture content is observed to increase as shown by Figure 4-10. The cumulative
leachate flow shown in Figure 4-9 is constant from days 217 to 251 indicating that there was no
leachate pumped through the injection cluster in this time. The moisture levels dropped during
this period indicating the drainage of water from the sensor media. This drying cycle is clearly
seen for one of the locations in Figure 4-10. However most of the sensors continued to show the
high moisture levels as seen in Figure 4-10. The typical time of travel for the moisture to reach
the sensors depended upon the relative location of the sensor to the injection well. The time
taken to reach the nearest sensor from injection cluster was 2-3 days (see relative location of
M3E or M3W from CM3 in Figure 4-8). In general, the deeper sensors first encountered the
injected leachate (see Table 4.3).
`
Chapter 4
Table 4-3 Time of travel in days for the incoming moisture to pass the sensor
Sensor
Lower
Middle
Upper
G3E
16
10
25
G3W
Low reading
10
No sensor
MF4
Malfunction
14
37
MF3
14
No sensor
MH3
56
-
-
M3E
Low reading
2
10
M3W
Low reading
8
10
ML4
Low reading
15
-
MO5
63
-
-
L6E
8
8
13
L6W
2
2
14
MK6
46
15
-
ME7
17
-
-
ML8
21
22
46
MJ8
-
59
47
MI9
51
-
-
MM9
29
-
-
MF8
6
21
22
MF9
6
36
-
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Chapter 4
MI5
No sensor
21
21
MH6
12
-
23
MI6
13
12
-
MG5
23
-
-
MC7
15
14
16
MD8
38
36
11
Figure 4-11 shows the leachate injection locations utilized by the end of day 385 (January
2004); these locations are indicated in green. At this time, a majority of sensors reported
moisture content values greater than 60 % (w/w), except for those shown in red in Figure 4-11
and at a few other locations in top layer. The curve for 16.0 mS/cm was used to convert the
resistance values to corresponding moisture contents. Several observations can be made
concerning sensors reporting high MC:
(a) a sudden three log reduction in the resistance
readings occurred within one to three days when moisture arrived at the sensor locations (see
Figure 4-10), (b) most of the sensors reading high MC (above 60 % (w/w)) values continue to
show such values, and (c) most of the low resistance values read by the sensors correspond to
completely saturated condition.
4.3.4 Conclusions
A field evaluation of electrical resistance based sensors for in situ moisture content
determination in a bioreactor landfill was conducted. A total of 135 electrical resistance-based
`
Chapter 4
MTG sensors were installed in the landfill for moisture content determination of the landfilled
waste. Moisture contents were measured before and after
Figure 4-8 Monitoring wells surrounding injection cluster CM3
`
Chapter 4
Cumulative Volume Recirculated (gallons)
50000
40000
30000
20000
10000
200
LOWER
UPPER
MIDDLE
220
240
260
280
300
Days
Figure 4-9. Cumulative volume of leachate injected through CM3
Chapter 6
70
70
60
% MC
% MC
60
50
50
Start of Leachate
Recirculation
Start of Leachate
Recirculation
MO5-Lower
MO5-Middle
MO5-Upper
40
ML3-Lower
ML3-Upper
40
30
30
140
160
180
200
220
240
260
280
100
300
150
200
250
300
Days
Days
70
70
60
% MC
% MC
60
50
50
Start of Leachate
Recirculation
Start of Leachate
Recirculation
40
ML4-Lower
ML4-Middle
ML4-Upper
40
M3W-Lower
M3W-Middle
M3W-Upper
30
30
100
150
Days
200
250
300
100
150
Days
200
250
300
70
% MC
60
Start of Leachate
Recirculation
50
40
M3E-Lower
M3E-Middle
M3E-Upper
30
100
150
Days
200
250
300
Figure 4-10. Response of monitoring clusters surrounding CM3. (A) M3E (B) M3W (C)
ML3 (D) ML4 (E) MO5
`
Chapter 4
`
Chapter 4
Figure 4-11. Plan of bioreactor showing leachate injection wells and relatively dry
monitoring locations.
`
Chapter 4
leachate recirculation. It was observed that 98% of the sensors worked, initial moisture
content measurements indicated that some areas of the landfill were wet as suggested by
resistance values of less than 0.05 k. Upon leachate recirculation the sensors at
monitoring locations read low resistance values as the moisture front arrived at sensor
locations. There was a sudden drop in the resistance values as the moisture front met the
sensors and sensors continue to show high MC values (above 60% (w/w). This
observation may indicate interception of leachate preferential flow paths established
when drilling for sensor placement. Relatively dry areas on the N-E side of landfill
abutting cell-3 showed that leachate injection at other locations had no effect on these
locations. This information suggests preferential flow paths of leachate from the N-E to
N-W direction inside the landfill. As the leachate injection in landfill continues, the MTG
sensors are expected to show high moisture content values from the interception of flow
paths. Sensor placement methods should be adjusted to minimize preferential flow paths.
The resistance-based MTG sensors show the potential moisture distribution in
bioreactor landfills. The sensors were able to track the changes in the moisture contents
and detect the wetting and drying cycles on field. The ability of MTG sensors to measure
various in situ parameters, its low manufacturing costs, and the convenience in
automating the data collection process make it an attractive prospect for use in bioreactor
landfills.
`
Chapter 4
CHAPTER 5
COMPARISON OF RESISTIVITY AND TIME DOMAIN REFLECTOMETRY
SENSORS FOR ASSESSING MOISTURE CONTENT IN BIOREACTOR
LANDFILLS
5.1 Introduction
Landfill bioreactors are designed and operated to enhance the biodegradation
process by increasing moisture levels within the landfill (Pohland, 1975; Rees, 1980;
Reinhart and Townsend, 1997). Moisture levels in a bioreactor landfill can be increased
by adding water or by recirculating the leachate extracted from the landfill. However, it is
a challenge to understand the distribution of recirculated moisture in a landfill because of
the extreme heterogeneity of the landfilled waste. Gravimetric moisture content can be
measured by collecting a waste sample through excavating the waste. This method is not
usually feasible for collection of routine samples. A measure of in situ moisture content
would provide landfill operators very valuable information about the effectiveness of
their leachate recirculation systems. The installation of in situ moisture measuring
devices in landfills may be feasible since they are commonly applied to soil systems.
Several previous studies (Holmes 1984, Rosqvist et al. 1997, Yuen et al. 2000) have
evaluated instrumentation for measuring moisture content in landfilled waste. Two of the
most common devices for measuring in situ moisture content are time domain
reflectometry (TDR) probes and resistance-based moisture sensors.
Chapter 5
Time domain reflectometry (TDR) works on the principal that a change in the in
situ moisture content can be determined by analyzing the changes in the reflected
electromagnetic waveform that is emitted by the TDR source. The resistivity sensors
calculate the electrical resistance of the moisture present in the granular media between
the two concentric electrodes. Increase in the moisture surrounding the waste would
increase the moisture content in the granular media, resulting in the decrease of the
electrical resistance between the electrodes. Resistivity based MTG sensors developed by
Gawande et al. (2003) and modified TDR probes developed with the help of Zircon Inc
were installed on a side-by-side basis at selected locations in a full-scale operating
bioreactor landfill in north central Florida. The objective of this chapter is to compare the
performance of the resistivity and TDR sensors installed at these locations.
5.2 Background
Historically, in situ moisture measurement devices have been used to measure the
moisture content of soils for irrigation. These technologies can also be applied to
landfilled solid waste but this application does pose certain challenges, the most common
being the heterogeneous nature of the waste materials. Previous studies reported time
domain reflectometry, neutron probe technology, electrical resistance technology to have
varying degrees of success when applied to monitor in situ moisture contents in landfills
(Holmes 1984, Rosqvist et al. 1997, Yuen et al. 2000). TDR and resistivity technologies
were used in the study reported in this paper and are thus discussed in greater detail.
`
Chapter 5
5.2.1 Time Domain Reflectometry (TDR)
TDR relies on the concept that the physical characteristics of a medium in which an
electromagnetic signal is emitted can be related to the signals reflection. In TDR
technology, the physical characteristic that is analyzed by a propagated electromagnetic
wave is the relative permittivity or the dielectric constant of the medium. Topp et al.
(1980) first used TDR technology for measurement of moisture content of soils. They
found that the total time taken for the transmitted electromagnetic pulse to get reflected
was dependent on the relative permittivity or dielectric constant of the medium. The
calculated moisture content and the observed dielectric constant followed a fourth degree
polynomial approximation. Li and Zeiss (2001) studied the relationship between the
moisture content and the bulk dielectric constant of various solid waste materials. They
performed series of laboratory experiments with different waste materials and mixtures
with varying conductivities. Similar to Topp et al. (1980), a fourth degree polynomial
provided a satisfactory fit for the determination of moisture content of the solid waste
from the measured apparent dielectric constant. The coefficients of the equation
depended on material type. Some of the advantages of TDR technology are that it is nonhazardous, when used on field are it has the ability to reproduce the results and is inert to
organic carbon content changes.
5.2.2 Electrical Resistance Technology
The moisture content of the medium can be estimated from the electrical resistance
measured between the electrodes inserted in it. Two types of electrical resistance sensors
that have been used to estimate soil moisture content are gypsum block and granular
`
Chapter 5
matrix sensors. A typical gypsum block sensor consists of electrodes embedded in a
gypsum block and the blocks are installed in the soil for which moisture content is to be
determined. When the soils are wet, the external moisture enters the gypsum block and a
soluble calcium sulfate solution is formed as the uniformly conductive media between the
electrodes and the resistance is measured between them. Gypsum block sensors had some
operational difficulties on field such as their inability to show the transient wetting and
drying cycles and to address these operational difficulties, McCann et al. (1992)
developed a granular matrix sensor. A granular matrix sensor consists of a uniformly
distributed material, for e.g. sand that serves as the media between the two electrodes.
Gawande et al. (2003) developed a granular soil matrix resistance sensor for in situ
moisture content measurement in landfill. Tests were conducted to select the optimum
granular media particle size, evaluation of sensor sensitivity, responsiveness and
assessment of the effect of liquid temperature and conductivity on resistance
measurement. Calibration curves relating the measured resistance to gravimetric water
content developed by Gawande et al. (2003) will be used in this study. The fieldevaluation of this sensor for in situ moisture content measurement in a bioreactor landfill
is reported in Chapter 4. The positive aspects of using resistance-based sensors are
cheaper manufacturing and installation costs and the ability to collect and store the data
automatically.
5.3 Methods
Two different types of sensors (TDR probes and resistance-based sensors) were
installed side-by-side in the landfill. The experimental methods focus on measuring the
`
Chapter 5
surrounding waste moisture content using both sensor types, and comparing the two
different technologies.
5.3.1 TDR Sensors
TDR probes used in this study were laboratory calibrated and obtained from the
manufacturer (Zircon Inc). The manufacturer used Campbell Scientific TDR probes for
the calibration experiments. All the information regarding the probe type, its geometry
can be found in the Campbell Scientific instruction manual (TDR 100 instruction manual,
2002). The components of TDR included the pulse generator, a coaxial cable and the
parallel probes. The pulse generator generates voltage pulse (250 mV) that is propagated
through a coaxial cable and is transmitted through parallel probes located at its end. The
propagating voltage pulse will encounter a change in impedance when it leaves the cable
and enters the probes resulting in the reflection of the voltage pulse to a cable tester. The
reflected energy is observed as a waveform that is used to calculate the reflection travel
time of the voltage pulse. The dielectric constant of the medium can be found by using
the reflection travel time and known length of the probes with the equation given below,
 ct 

 2L 
2
 
(5.1)
where  is the unknown bulk dielectric constant to be calculated and c is the velocity of
electromagnetic signal in free space and t is reflection travel time as measured from the
waveform and L is the known length of the probes.
`
Chapter 5
The figure in Appendix E shows the schematic of a typical waveform. Figure 5-1
provides an example of the actual waveform observed from one of the TDR sensors in
NRRL. The first discontinuity in Figure 5-1 is observed when the propagating voltage
pulse leaves the coaxial cable and enters the parallel probes and the second discontinuity
is due to the reflection from the end of the parallel probes. The difference in the travel
times is shown in the form of apparent length displayed on screen as shown by Figure 51. Equation 5.1 can be modified in the form of apparent length of the waveform observed
on screen and given by Equation 5.2,
L 
  a 
 L 
2
(5.2)
where La is the apparent length measured on the screen. The relationship between the
bulk dielectric constant to the moisture content as described by Li and Zeiss (2001) was
used in this study. This is given by Equation 5.3,
  a  b  c 2  d 3  e 4
(5.3)
where  is the volumetric water content and  is the bulk dielectric constant. The
coefficients of the Equation 5.3 depend on the solid waste material type, the porosity of
the material and the electrical conductivity of the liquid.
`
Chapter 5
0.7
0.6
0.5
0.4
L
0.3
0.2
0.1
0
0.1
L1
0.2
55
57
59
61
63
65
Figure 5-1 Observed TDR waveform from sensor at bioreactor landfill in north central
Florida
As shown by Figure 5-1, the distance L1 interprets the difference between the time
taken for the voltage pulse to enter the wave-guide and exit it and L is the total waveform
length. The selection of the total waveform length depends on the length of the probes.
The apparent length as given by TDR 100 manual calculations are shown in Equation
5.4,
 L
  SelectedV p 

Apparent length La   1  * 10m 

 L 
  ActualV p 
`
(5.4)
Chapter 5
where Vp is the cable propagation velocity and depends on the dielectric constant of the
insulating material between the coaxial cable center conductor and the outer shield, L1 is
the length of the measured waveform, and L is the total length of the screen as shown by
Figure 5-1. More details regarding the cable propagation velocity can be found in the
Campbell Scientific TDR instruction manual. The selected Vp and the actual Vp in this
case are 1.0 and 0.99, respectively. A probe offset factor occurred due to the presence of
a probe head that binds the probe rods to the coaxial cable. This factor must be included
while calculating the apparent length. Campbell Scientific suggests a value of 0.08 for the
apparent length correction due to the presence of probe head and the equation for
correction is given below:
La (corrected )  La  0.08 ( probe offset factor)
(5.5)
The bulk dielectric constant of the medium can be found by combining Equations
5.2, 5.4, and 5.5. The calibration equation to convert the obtained bulk dielectric constant
to water content of the waste media surrounding the TDR probes is given by Equation
5.3. The coefficients of this equation depend on the composition of the waste surrounding
the probes and its density. Each probe has been independently packed with the waste that
was excavated from the landfill and calibrated by the vendor to get the coefficients of
Equation 5.3. The densities of the excavated waste surrounding each probe and the
corresponding calibration equation coefficients are given in Appendix C. A total of
twelve TDR probes, along with the excavated waste surrounding them, and twelve
electrical resistance-based MTG sensors were installed at specific locations in an
operating bioreactor landfill in north central Florida. A comparison of moisture content
`
Chapter 5
obtained from both MTG and TDR sensors will be made in this study. Note that Equation
5-3 calculates the volumetric moisture content (see supplemental figure in Appendix E
for the calibration curve). For comparison purposes a conversion is made from
volumetric moisture content to gravimetric moisture content using the dry densities of the
waste surrounding each TDR probe (see Appendix-C for values of dry densities) and is
given by Equation 5.6
  w 
MC  

  d   w 
(5.6)
where  and MC are the volumetric and gravimetric moisture contents of waste and d is
the dry density of the waste surrounding the probe and w is the density of the water.
5.3.2. Resistance-Based Sensors
Gawande et al. (2003) developed a resistance-based moisture sensor that would facilitate
the in situ measurement of moisture content, temperature and gas composition. This
resistivity sensor was designed to measure the electrical resistance of the moisture present
in the granular media between two electrodes. Increase in the moisture content of the
granular media results in decrease in the observed resistance. This sensor was named
MTG as it has the ability to measure the in situ moisture content, temperature and gas
composition. Details pertaining to the field evaluation of MTG sensors are reported in
Chapter 4.
`
Chapter 5
5.3.3 Site Description
The New River Regional Landfill (NRRL) is located in north central Florida, US,
and consists of several distinct landfill cells (see supplemental Figure A-1 for a plan view
of the entire site). The area designated for the bioreactor consisted of parts of two cells
covering an area of approximately 10 acres and containing approximately 0.61 million
tons of waste. The bioreactor area is distinguished from the rest of the landfill by the
presence of recirculation devices and an exposed geomembrane cap. Figure 5-2 presents
a plan view of the portion of the landfill site that includes the bioreactor. The area
distinguished as the “bioreactor well field” represents the section where leachate
recirculation was concentrated. The area where both TDR and MTG sensors have been
installed together is marked as “MTG and TDR sensor locations” and this area will be he
focus of upcoming graphical presentations of the data. Leachate recirculation is
performed at the site by means vertical injection wells. A series of vertical injection well
cluster of different depths are distributed throughout the landfill. The injection wells
connect to a leachate recirculation manifold. Figure 5-3 provides a detailed view of the
MTG and TDR sensor locations and the relative locations of the injection wells. The five
different places on the landfill, where both the types of sensors were installed are
indicated as clusters A through E in this figure. Injection wells and the sensors that are
discussed later in the text have labels provided (see supplemental figure A-2 for a map
showing all labeled clusters). Clusters A, B and E have four (two MTG and two TDR)
sensors located side-by-side in the same hole, and clusters C and D have six (three MTG
and three TDR sensors) located in different holes next to each other, at approximately the
same depths.
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Chapter 5
Figure 5-2 Plan view of the bioreactor
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Figure 5-3 Plan view of the MTG and TDR sensor locations
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Chapter 5
5.3.4 Installation of Sensors
A total of 24 (12 TDR and 12 MTG) moisture content measuring sensors were
installed at five different cluster locations on the landfill. Modified CS610 TDR probes
were used in this study. A hole was drilled into the waste using a 4-inch truck-mounted
power flight auger and a 4-inch PVC pipe was placed in the hole. TDR probes were
dropped down until they reached required depth, adjacent to this probe, a MTG sensor
was placed next. Sand was added first followed by bentonite pellets. The purpose of the
sand was to bridge the gap between the sensors and surrounding waste, while the clay
was added to hydraulically isolate the sensors before backfilling with waste. The
locations of the clusters were presented in plan view previously in Figure 5-3. Figure 5-4
presents a representative schematic of the relative locations of a MTG sensor and TDR
sensors. Only clusters D and E are shown, the remaining clusters A, B have similar
configuration as cluster E and C has similar configuration as cluster D. Note that the
depths of sensors installed in each cluster were not exactly the same. Cluster E was
chosen to represent an example of both MTG and TDR sensors located in the same hole
and cluster D to represent an example of sensors in adjacent holes.
5.3.5 Field Measurements and Trials
The injection wells and the MTG and TDR sensors were installed over a six-week
period in the spring of 2001. The construction of the rest of the bioreactor, which
included the installation of the exposed geomembrane cap and the leachate recirculation
piping, was not completed until the fall of 2002. The TDR data collection started from
spring of 2003. A TDR 100 instrument was used for manual interrogation of each TDR
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Chapter 5
location. A step voltage was applied by this instrument and the reflected voltage is
captured as a waveform on a laptop screen. The waveforms were obtained from each
sensor and were stored at a frequency of at least once per week. The details of data
collection of MTG sensors were given in Chapter 4. Three Campbell Scientific CR10X
data loggers installed at various locations on the landfill, as shown in Figure 4-2, were
programmed to collect data from MTG locations at a frequency of twice per day. For
purposes of discussion and presentation, January 1, 2003 was defined as day 1 of the
experiment. Leachate recirculation first started on day 150. Injection clusters CI5 and
CF6 were used to recirculate leachate in the area where both the TDR and MTG sensors
were installed as shown by Figure 5-3. A total of 33,487 gallons of leachate were injected
in cluster CF6 from day 203 to 217 and 156,186 gallons of leachate were pumped
through CI5 from day 218 to day 322, which is the end of the reporting period for this
thesis.
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Chapter 5
Figure 5-4 Schematic cross section of cluster E (sensors placed in same hole) and cluster
D (sensors placed in different holes)
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Chapter 5
5.4 Results and Discussion
5.4.1 Discussion of Performance of TDR and MTG Sensors
A total of twenty-four sensors, twelve each of the TDR and the resistance-based
MTG sensors were installed at a particular area in the landfill as shown by Figure 5-2.
The background data were collected from January 2003. The data collection continued
after recirculating leachate until November 18, 2003. The typical output of the TDR
probes, the reflected voltage, was recorded as a waveform. The waveforms were used to
calculate the apparent dielectric constant of the surrounding waste media. The output of
the MTG sensors was the resistance of the granular media in k and the temperature in
degree centigrade (0C). The background data suggested that all twelve of the installed
resistance-based sensors were functional, but only none of the twelve TDR sensors were
able to generate a waveform. The reason for the apparent malfunction of the three sensors
was thought to be damage to the probes during installation. Initial conditions showed that
the minimum and maximum measured resistivity and temperature readings from the
MTG sensors were 0.44 k, 18.87 k and 31oC and 51.5oC respectively. The ranges of
temperatures measured in this study were similar to typically observed temperatures in
landfills. The minimum and maximum values of the relative dielectric constants
calculated from the waveforms obtained from the TDR sensors were 5.5 and 25.5
(Appendix C).
Out of the nine locations where the TDR and resistance-based sensors both
functioned, four TDR sensors and five resistance based sensors responded to the leachate
recirculation through day 322. Figure 5-5 gives the typical response of both the types of
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sensors to leachate recirculation. Figure 5-5(a) shows the typical response of the TDR
probe before and after recirculation. An increase in apparent length of the waveform (as
indicated by La in the figure) means an increase in the relative dielectric constant of the
medium, which indicates the increase in the moisture content of the medium (Equation
5.3 and Appendix C). Figure 5-5 (a) clearly shows the increase in the apparent length of
the waveform with recirculation. Figure 5-5(b) shows a sharp decline in resistance
between the electrodes, which indicates the increase in the moisture content of the media
surrounding this sensor.
5.4.2 Estimation of Moisture Content
The interpretation of the TDR waveform is explained in Section 5.3.1. Equations
5.2-5.5 were used to obtain the moisture content from the collected waveform. The
moisture content predicted by the MTG sensors was obtained by converting the resistance
of the sensor at a particular temperature exposed to leachate at a particular conductivity to
the moisture content of the surrounding waste using Equations 3.2, 3.3, and 3.6. Moisture
content measurements using both MTG and TDR sensors are influenced by conductivity
of leachate. The average conductivity of leachate present in this area of the landfill as
collected from manhole for a period of 16 months was found to be 15.29 mS/cm. TDR
probes used in this study were coated with plastic. Li and Zeiss (2001) mentioned that
coating the probes could reduce the attenuation of the electromagnetic signal that is
caused by increase in the electrical conductivity. The calibration equation developed for
the 8.0 mS/cm leachate (Equation 3.3) was used for the conversion of obtained MTG
sensor resistance to moisture content. Based on the measured landfill leachate
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Chapter 5
conductivities, it is possible that the MTG sensors will over-predict the absolute moisture
contents. Figure 5-6 shows the changes in the moisture contents with time as for both the
TDR and MTG sensors located at Cluster-E of Figure 5-3. Note that the sensors at this
cluster are adjacent to each other and placed in a same hole. As shown by this figure,
before recirculation the moisture contents of the sensor were observed to be 35-45 %
(w/w).
A
Reflection Coefficient
0.4
0.2
0.0
-0.2
Pre wetting (Day 146)
Post wetting (Day 276)
-0.4
Apparent length on screen
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Chapter 5
1000
B
Resistance (KOHMS)
100
10
1
0.1
0.01
MI5-Middle
0.001
150
200
250
300
Days
Figure 5-5. Response of sensors to leachate recirculation (A) TDR (B) MTG
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However, after the start of recirculation, the moisture content from the sensors went up to
68 % (w/w). It can be seen that there was a simultaneous increase in the obtained
moisture contents from both the sensors. This is expected as the sensors are next to each
other. As the recirculation continued in CI5 until day 322 (the last day of reported data)
there was no decrease in the moisture around the sensors.
(%) Gravimetric Moisture Content
80
70
60
Start of leachate
recirculation
50
40
30
TDR 11
MI5-Middle
20
150
200
250
300
Days
Figure 5-6. Change in moisture content of TDR and MTG sensor
5.4.3 Assessment of TDR and MTG Sensors for Leachate Recirculation
The injection clusters CI5 and CF6 were primarily used to wet the area surrounding
the TDR and MTG probes as shown in Figure 5-3. A total of 189,673 gallons of leachate
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was injected through these clusters. The cumulative flow distributions through the
different injection wells at these clusters are given in Figures 5-7 and 5-8. The response
of the MTG and TDR sensors to the leachate recirculation is shown in Figure 5-9 and 5-
Cumulative Volume Recirculated (Gallons)
10.
50000
40000
30000
20000
LOWER
MIDDLE
UPPER
10000
240
250
260
270
Days
280
290
Figure 5-7. Cumulative volume of leachate injected through CI5
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300
Cumulative Volume Recirculated (Gallons)
18000
16000
14000
12000
10000
8000
6000
LOWER
UPPER
4000
2000
204
206
208
210
212
Days
214
216
Figure 5-8. Cumulative volume of leachate injected through CF6
Figure 5-9 shows the output of the sensors that were installed in different holes (Clusters
C and D) and Figure 5-10 gives the data obtained from the sensors that were installed in
the same hole (Clusters A, B and E).
80
60
Start of leachate
recirculation
40
20
TDR 1
MH6-Upper
150
200
250
300
Days
`
Gravimetric Moisture Content
Gravimetric Moisture Content
80
60
40
Start of leachate
recirculation
20
TDR 7
MI6-Middle
150
200
Days
250
300
Chapter 5
Gravimetric Moisture Content
80
60
Start of leachate
recirculation
40
TDR 6
MI6-Lower
20
150
200
250
300
Days
Figure 5-9. Response of TDR and MTG sensors located at clusters C and D
Clusters C and D consisted of 12 sensors (six each of MTG and TDR). Out of six
TDR sensors in these clusters, there was no waveform generated from three TDR sensors
indicating their malfunction. The data from the remaining functional sensors are plotted
in the Figure 5-9. As seen from the figures above, all three MTG sensors located in
clusters C and D responded to leachate recirculation and two TDR sensors indicated an
increase in moisture. Clusters A, B and E consisted of twelve sensors (six each of TDR
and MTG). Only the sensors in cluster E have responded to recirculation. The sensors
that showed increase in the moisture content were from clusters C, D and E in Figure 5-3.
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80
60
Start of leachate
recirculation
40
20
TDR 2
MI7-Upper
150
200
250
Gravimetric Moisture Content
Gravimetric Moisture Content
80
60
Start of leachate
recirculation
40
300
150
80
80
70
70
60
50
Start of leachate
recirculation
40
30
TDR 9
MG7-Upper
20
200
250
Gravimetric Moisture Content
Gravimetric Moisture Content
Days
150
TDR 5
MI5-Upper
20
200
300
Start of leachate
recirculation
50
40
30
TDR 10
MI7-Middle
20
150
300
200
250
300
Days
80
80
70
60
Start of leachate
recirculation
40
30
TDR 12
20
MG7-Middle
150
200
250
300
(%) Gravimetric Moisture Content
Gravimetric Moisture Content
250
60
Days
50
Days
70
60
Start of leachate
recirculation
50
40
30
TDR 11
MI5-Middle
20
150
200
250
300
Days
Days
Figure 5-10. Response of TDR and MTG sensors located at clusters A, B and E
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As these sensors were nearer to injection well CI5 than sensors in Clusters A and B, it is
likely that the leachate recirculated through CI5 arrived at Clusters C, D, and E before
reaching sensors in Clusters A and B. Figure 5-10 gives the response of both types of
sensors to leachate recirculation. As shown by this figure, though the initial moisture
contents of the waste predicted by both TDR and MTG sensors were not equal, the
sensors showed simultaneous increase in the moisture contents. The sensors did not show
any drying due to the continuous leachate injection through CI5 (the injection cluster that
primarily wet all the sensors in the area) from day 218 through day 322, which was the
last day of the reported data in this chapter. However, data need to be collected to
investigate the drying cycle. Figure 5-11 gives the plot of moisture contents predicted by
the calibration equations for both the sensor types. Each point shown in this figure
represents the moisture content value as predicted by resistance-based as well as TDR
sensors that were installed adjacent to each other. All the values were chosen from
Figures 5-9 and 5-10. Two values of moisture content (one each from MTG and TDR
sensors before and after recirculation) were selected from the sensors that became wetter
after recirculation and one value was selected from the sensors that did not predict any
change in moisture after recirculation. Note from Figure 5-9 that the MTG sensor (MH6upper) responded to leachate recirculation where as the corresponding TDR sensor (TDR
1) did not show any change and the outlier in Figure 5-11 represents this value. A best-fit
line was plotted using all the points except the outlier.
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Chapter 5
5.4.4 Advantages and Disadvantages of Each Probe
As seen in this section, both MTG and TDR sensors were able to predict the
increase in the moisture content of the medium. However it is difficult to measure exact
moisture contents due to the heterogeneity of the waste. The manufacturing costs of MTG
sensors were much cheaper than the TDR sensors (approximately $25 per MTG and $500
per TDR sensor); hence multiple MTG sensors could be placed in the landfill at the same
manufacturing cost as a TDR sensor. 2D Graph 8
80
r2=0.75
TDR Moisture content (%)
70
60
50
40
30
20
20
30
40
50
60
MTG Moisture content (%)
70
80
Figure 5-11. Comparison of moisture contents from TDR and MTG sensors
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The conductivity of the medium affects the operation of both sensors. While the
effect of conductivity can be reduced by coating the TDR probes, the MTG sensors need
to be calibrated for various conductivity ranges in the landfill. The variation in density of
the landfill affects the moisture measurement using TDR sensors; the density variation
had negligible effect, however, on resistance measurements from MTG sensors. All
twelve MTG sensors worked (100 % success in operation) but only nine out of twelve
TDR sensors worked (75% success in operation). Though the installation procedure was
similar for both the sensors, care needs to be taken in the installation of TDR sensors to
avoid any damages to the probes during installation.
5.4.5 Summary and Conclusions
TDR is one of the widely used technologies for in situ soil moisture measurements.
When applied to landfills, the porosity of the waste and the electrical conductivity of the
leachate had significant effect on the moisture content determination. A total of 12 each
of TDR and resistance-based MTG sensors were installed at a section of the landfill to
measure the in situ moisture content of landfilled waste. This study indicated that TDR
could be used as one of the moisture measurement techniques in the landfills. The
simultaneous increase in the moisture contents of the TDR and MTG sensors in some
clustered locations give an indication that both technologies were capable of estimating
the transient moisture changes in the landfill. However, the values of the obtained
moisture content indicated that there is some degree of error between the two
measurement technologies while predicting the absolute moisture content. As the
leachate electrical conductivity has an effect on both these measurement technologies,
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knowledge of this parameter could be beneficial in calculating the moisture content. Due
to the high cost of manufacturing of TDR sensors, this technology could be limited in its
application to bioreactor landfills where there is a need to measure moisture contents at
multiple locations. Further data need to be collected to investigate the drying cycle.
Future work should aim at simultaneous measurement of moisture content and electrical
conductivity of the landfill leachate with the TDR probes.
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CHAPTER 6
MOISTURE BALANCE ON BIOREACTOR LANDFILL
6.1 Background
Moisture is a critical factor for the degradation of solid waste which consequently
affects the gas production rate in the landfill (Eliasen 1942, DeWalle et al. 1978, Rees
1980, and Pohland 1988). Optimum moisture content to enhance degradation of solid waste
could be achieved by recirculation of leachate and/or addition of water or other sources.
The municipal solid waste landfills NESHAP rules (40 CFR part 63, subpart AAAA)
define a bioreactor as a MSW landfill or a portion of a MSW landfill where any liquid,
other than leachate (leachate includes landfill gas condensate) is added in a controlled
fashion into the waste mass (often in combination with recirculating leachate) to reach a
minimum average moisture content of at least 40 percent by weight to accelerate or
enhance the anaerobic biodegradation of the waste. The NESHAP rules specifically
mention the need for timely control of the bioreactor with respect to landfill leachate and
the generated gas. Moisture budget analysis therefore becomes part of the mandatory
control systems requirement. The moisture budget analysis could be based on simple mass
balance calculations or more complex hydrologic model such as the HELP model
(Schroeder, 1983). The HELP model relies heavily on climatic and landfill material
properties and may not be a very practical method. Reinhart and Townsend (1997) showed
a simplified water balance equation for bioreactor operation (Equation 6.1). US EPA
(2003) summarized the application of the simplified equation and the HELP model with
Chapter 6
example mass balance calculations. The HELP model uses the volumetric moisture content
instead of the weight based approach; therefore US EPA (2003) suggests not using the
HELP model in order to meet the NESHAP requirements.
STORAGE  INFILTRATION  RECIRCULATION  LEACHATE GENERATION
(6.1)
6.2 Simplified Moisture Balance Equation
US EPA (2003) described the use of a simplified equation for the water balance
method (Equation 6.2). Equation 6.2, although simple, may be an adequate tool for
moisture content calculations.
PMC 
LO  M   P  LA  LCH
M
 100
(6.2)
where PMC is the estimated potential moisture content of the waste mass (% moisture
content on a wet weight basis), Lo is the moisture entering with the waste mass (kg
moisture /kg total waste mass as received), M is the total waste mass in the bioreactor cell
on an as received basis (kg total waste mass as received), P is total precipitation (kg total
precipitation), LA is total liquids added to the waste mass, including recirculated leachate
(kg total liquids), and LCH is the total leachate collected (kg total leachate).
6.2.1
Moisture Balance at New River site
The bioreactor site at the New River Regional Landfills (NRRL) in North-Central
Florida is provided with a double liner system at the bottom with a leachate collection
system and a synthetic top cover. Therefore this bioreactor site provides an opportunity to
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apply the simple moisture balance method with greater confidence as infiltration from
precipitation has been eliminated. The moisture content of the landfill prior to leachate
recirculation as calculated from field sampling was observed to be 23% by wet weight.
The final moisture content estimated for the landfilled waste was calculated from the
amount of leachate pumped into the known mass of waste. The first step, shown below,
in this process was to find the initial weight of water stored in the waste. The final weight
of water stored in the waste was found by adding the known weight of water recirculated
to the initial weight of water till the day 322. This calculation is shown in Step 2. The
final moisture content was found by dividing the final weight of water in the waste by the
total weight of the waste (initial weight plus weight of added water; see Step 3). It was
assumed that the pumped leachate was stored in the waste and did not drain from the
landfill.
Step 1
Mass of waste = 0.61 million tons = 0.61  10 6 tons
Initial moisture content (MC) of waste samples = 0.23 (w/w)
Initial volume of water present in the landfill (V1) =
0.23  0.61  10 6 tons  1
metricton
10 6 g
1cm 3
1litre
1gallon




33.74 10 6 gals
3
1.1ton 1metric ton 1g
1000cm 3.78litres
Step 2
Volume of water added until day 322 (V2) = 0.762  10 6 gals
Total water in the landfill until day 322 = V1 + V2 = 34.5  10 6 gals
Mass of water in the landfill =
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Chapter 6
34.5  10 6 gals  3.78
1.1tons
l
cm 3
g
1kg 1metric ton
 1000
1 3 


 143,451 tons
gal
l
1000 kg
1metric ton
cm 1000 g
Step 3
Final Moisture Content =
Final mass of water
Total mass of waste


 

Total mass of waste = 1  0.23 0.61*10 6  0.143 *10 6  0.6127 *10 6 tons
Final Moisture Content =
143,451
 0.2341
0.6127  10 6
The predicted moisture content after day 385 was 24% w/w.
6.2.2
Estimation of Spatial Average of the Moisture Content of the Landfill
The resistivity-based moisture sensors were spatially distributed inside the landfill
at depths averaging 15ft, 30ft and 50ft. To provide a graphical representation of changes
in moisture content, distinct moisture contour profiles were computed for each depth. The
bioreactor well field boundary shown in Figure 4-1 was used as the boundary of a control
volume for determining spatial average moisture content of the landfill. A conceptual 3-D
GIS model was used to determine the spatial average. The first step in determining the
spatial average was to distribute the known moisture content values in the landfill into
three identical moisture grids. Arcview GIS 3.2 was used to create the grids with the
known moisture content values obtained from the sensors at each layer by an
interpolation process using an inverse distance algorithm. Then, a series of algebraic
operations were performed on the interpolated layers to create an average grid. Finally,
the mean of the average grid was determined which was the representative spatial
average of the gravimetric moisture content in the control volume of the landfill.
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The spatial average moisture content was calculated using the method described
earlier. The average moisture content prior to recirculation (Day 150) was calculated to
be 42% w/w and the final average moisture content (Day 319) was calculated to be 48%
by wet weight. The typical range of initial moisture content in the landfills has been
observed to be 15-35 % by weight and reported values of field capacity of MSW are 5570 % by weight (Remsen et al. 1968, Wigh, 1979, Walsh and Kinman, 1982 Bengtsson et
al. 1994, Gawande et al 2003). The initial gravimetric moisture content measured from
the 51 samples analyzed was 23% w/w. The pre-recirculated values obtained using the
resistance probes were thus greater than expected from the literature and the field
analysis. A total of 762, 300 gallons of leachate were recirculated from day 150 to day
322. The predicted value of the moisture content based on this recirculated volume
(initial gravimetric plus that added by recirculation) was calculated to be 24% w/w. This
was observed to be lower that the estimated final moisture content (48% w/w) using the
spatial interpolation.
The background data from the sensors suggested that there were a few locally
saturated areas in the landfill and the number of these locally saturated zones increased
with recirculation. The final spatial average of moisture content was calculated using the
moisture contents obtained from the sensors after leachate recirculation. Since some
sensors in the landfill were already saturated (before leachate recirculation), and a few
more sensors reached saturation in the course of recirculation, the calculation of a spatial
average from these values could lead to an over estimation.
Three distinct moisture contours were plotted with the obtained moisture values
from the sensors corresponding to the three levels. Figure 6-1 shows these moisture
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contours, with the darker zones indicating the higher moisture contents. Again, moisture
content was determined using the 8.0 ms/cm conversion. As shown in the figure 6-1, the
background data before recirculation shows the deeper areas in the landfill with the
higher initial moisture contents. After the start of recirculation, the moisture contours of
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the middle and upper levels became darker indicating the inflow of moisture to these
areas.
Day 150
Day 272
15’
30’
50’
Day 304
Higher moisture
70
69.625
69.25
68.5
67
66
65
60
50
40
30
Lower moisture
Figure 6-1. Moisture contours at different levels
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A possible reason for the high spatial moisture content values obtained was
attributed to the use of 8.0 mS/cm calibration curve. These high MC values and high
leachate conductivities (ranging from 7 to 20 mS/cm) from the leachate collection
manholes prompted the need of extending the calibration process. The calibration of
sensor was conducted for an electrical conductivity value of 16.0 mS/cm. Average MC of
40.0 % w/w was obtained for day 150 using the 16.0 mS/cm curve. It was observed from
the data that a majority of moisture content values for day 150 were in the range of 34 to
36 % w/w and a few sensors also showed moisture contents higher than 60% w/w. This
range between 34 to 36 % w/w represents the minimum values above which sensor can
detect moisture content; whereas the gravimetric MC of samples from the landfill showed
a value of 23 % (w/w). This suggests a limitation of MTG sensosr in predicting MC
below ~35 % w/w and consequently leading to errors in the calculation of average
moisture content for the landfill.
In order to further investigate the effect of leachate recirculation on spatial
average moisture content, the data for day 385 were used. Up to the day 385 about
1,279,800 gallons of leachate were injected in the bioreactor. Figure 6.2 shows the
cumulative volume of the leachate recirculated. A value of 50.4 % average moisture
content for day 385 was obtained as compared to only 24 % using the moisture balance
equation explained in 6.2.1. The MTG data showed 50 sensors with moisture content
values higher than 60 % as interpreted using a calibration curve for eC of 16.0 mS/cm.
The significantly high moisture content values from a majority of sensors were affecting
the spatial averaging. The liquid injection in the NRRL bioreactor is being continued to
date and the sensors showing high moisture contents continue to show high values.
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Cumulative Leachate
Recirculated (gal)
3.50E+06
Total
3.00E+06
Leachate
2.50E+06
Ground water
2.00E+06
1.50E+06
1.00E+06
5.00E+05
0.00E+00
0
100
200
300
400
500
600
Time (days)
Figure 6-2 Cumulative volume of leachate recirculated.
6.3
Conclusions
The simple moisture balance equation provides an accurate assessment of the
moisture content in the landfill. The spatial averaging of moisture content using data from
the MTG sensors is affected by: (a) the inability of the MTG sensor to accurately read
moisture content values below ~35 %, and (b) significantly high moisture content values
read by the sensors through interception of preferential flow paths by the monitoring
wells of the sensors. The error occurring due to the high moisture content values may be
rectified if the liquid injection is stopped for some time and the sensors allowed to attain
an equilibrium with the surrounding waste. Installation techniques sensors in the future
should consider this problem.
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Chapter 6
CHAPTER 7
SUMMARY AND CONCLUSIONS
Knowledge of the in situ moisture content of the waste in a landfill would be
extremely helpful to bioreactor landfill operators. The present study evaluated the field
application of moisture measurement devices for assessing in situ moisture content of
waste in a bioreactor landfill permitting water balance calculations. A total of 135
electrical resistance-based and twelve time domain reflectometry (TDR) sensors were
installed in a bioreactor landfill in north central Florida. In situ moisture content of the
waste was measured using these two different technologies with laboratory-derived
calibration equations. Moisture content for the landfill was calculated using the
instrument measurements and an overall water balance.
It was observed that 98% of the resistance-based sensors performed as expected.
The initial moisture content measurements determined that some areas of the landfill
were wet, as indicated by a resistance value of less than 0.05 k. The resistance values
obtained in this study ranged from a minimum of 0.005 k to a maximum of 466.7 k.
The temperatures obtained from the MTG sensors ranged from 26.9 to 57.7oC with an
average temperature of 48.6oC. The initial moisture content of the landfill estimated from
the sensors was 42%, which was higher than values reported in the literature and values
obtained from collected waste samples. Sixty sensors showed a response to leachate
recirculation. The spatial average of moisture content after 762,300 gallons and
1,279,800 gallons of leachate recirculation was estimated to be 48% and 50.4%
Chapter 7
respectively. These were higher than the expected average moisture content based on the
amount of leachate recirculated. This was attributed to:

Greater leachate conductivity values encountered in the landfill compared to that
used in calibration curves

Leachate flow through preferential paths got intercepted by the sensors monitoring
wells.

Inability of MTG sensors to sense MC values below ~35 % (wet (w/w).
A total of nine out of the twelve installed TDR probes functioned. The data from
the TDR sensors showed that this technology was also able to track changes in moisture
content in the landfill. The simultaneous increase in moisture contents measured by the
TDR and MTG sensors in some clustered locations gave an indication that both the
technologies were compatible for predicting the transient moisture changes in the landfill.
However, the values of the obtained moisture contents indicated that some difference
between the two measurement technologies do exist in predicting absolute moisture
content.
The extreme heterogeneity of the landfilled waste and the varying electrical
conductivity of the leachate were factors that were observed to influence both moisture
measurement technologies. The manufacturing costs of the MTG sensors were less than
the TDR sensors (approximately $25 per MTG and $500 per TDR sensor). The TDR
sensors required a larger borehole, and installation costs were also more expensive
relative to the MTG sensors. The use of multiple TDR locations is thus expensive relative
`
Chapter 7
to the MTG sensors. It was observed that only 75% of TDR sensors functioned as
compared to a high performance rate of 98% for the resistance-based sensors.
Future work should focus on continuing monitoring of the sensors after the
recirculation is stopped to study the drying cycle of the sensors. The TDR sensors also
need to be investigated for the drying cycle in the field. Data from the sensors should be
used to estimate the leachate recirculation travel times. The ability of the sensors to
predict the absolute moisture content should be explored further.
`
Chapter 7
APPENDIX A
SUPPLEMENTAL FIGURES
Bioreactor Area
Figure A-1. Plan view of the New River Regional Landfill
Appendix A
Figure A-2. Plan view of injection and monitoring cluster
Appendix A
APPENDIX B
RESISTIVITY SENSOR DATA
This appendix presents graphs of the data collected from all the resistance-based
sensors present in the landfill. The results cover a period from March 2003 to December
2003.
Appendix B
1000
MD3G
MD3Y
MD3R
100
Resistance (KOHMS)
10
1
Start of Leachate
Recirculation
0.1
0.01
0.001
100
150
200
Days
250
300
350
60
Temperature (Deg C)
55
50
Start of Leachate
Recirculation
45
40
35
D3R
D3Y
D3G
30
100
150
200
Days
250
300
350
Figure B-1 Response of resistivity sensors at cluster location MD3 (Day 1 = 01/01/03)
`
Appendix B
1000
Resistance (KOHMS)
100
10
1
0.1
Start of Leachate
Recirculation
0.01
MF3R
MF3G
0.001
100
150
200
Days
250
300
350
60
Temperature (Deg C)
55
50
Start of Leachate
Recirculation
45
40
35
F3G
F3R
30
100
150
200
Days
250
300
350
Figure B-2 Response of resistivity sensors at cluster location MF3 (Day 1 = 01/01/03)
`
Appendix B
1000
MG3WY
MG3WG
Resistance (KOHMS)
100
10
1
0.1
Start of Leachate
Recirculation
0.01
0.001
100
150
200
Days
250
300
350
60
55
Temperature (Deg C)
50
45
Start of Leachate
Recirculation
40
G3WG
35
G3WY
30
100
150
200
Days
250
300
350
Figure B-3 Response of resistivity sensors at cluster location MG3W (Day 1 = 01/01/03)
`
Appendix B
1000
MG3EG
Resistance (KOHMS)
100
MG3EY
MG3ER
10
1
Start of Leachate
Recirculation
0.1
0.01
0.001
100
150
200
Days
250
300
60
Temperature (Deg C)
55
50
45
40
Start of Leachate
Recirculation
G3EG
G3EY
35
G3ER
30
100
150
200
250
300
Days
Figure B-4 Response of resistivity sensors at cluster location MG3E (Day 1 = 01/01/03)
`
Appendix B
1000
MH3Y
MH3R
MH3G
Resistance (KOHMS)
100
10
1
0.1
Start of Leachate
Recirculation
0.01
0.001
100
150
200
250
300
350
Days
60
Temperature (Deg C)
55
50
45
Start of Leachate
Recirculation
40
35
H3Y
H3R
30
100
150
200
250
300
350
Days
Figure B-5 Response of resistivity sensors at cluster location MH3 (Day 1 = 01/01/03)
`
Appendix B
1000
MJ3G
MJ3Y
MJ3R
Resistance (KOHMS)
100
10
Start of Leachate
Recirculation
1
0.1
0.01
0.001
100
150
200
250
300
350
Days
60
Temperature (Deg C)
55
50
45
40
Start of Leachate
Recirculation
35
J3R
J3Y
30
100
150
200
250
300
350
Days
Figure B-6 Response of resistivity sensors at cluster location MJ3 (Day 1 = 01/01/03)
`
Appendix B
1000
Resistance (KOHMS)
100
10
1
Start of Leachate
Recirculation
0.1
0.01
ML3G
ML3R
0.001
100
150
200
250
300
350
Days
60
Temperature (Deg C)
55
50
45
Start of Leachate
Recirculation
40
35
L3G
L3R
30
100
150
200
250
300
350
Days
Figure B-7 Response of resistivity sensors at cluster location ML3 (Day 1 = 01/01/03)
`
Appendix B
1000
Resistance (KOHMS)
100
10
1
Start of Leachate
Recirculation
0.1
MM3WG
MM3WY
MM3WR
0.01
0.001
100
150
200
250
300
350
Days
60
Temperature (Deg C)
55
50
45
40
Start of Leachate
Recirculation
35
M3WG
M3WY
30
100
150
200
250
300
350
Days
Figure B-8 Response of resistivity sensors at cluster location MM3W (Day 1 = 01/01/03)
`
Appendix B
1000
MM3EG
MM3EY
MM3ER
Resistance (KOHMS)
100
10
1
Start of Leachate
Recirculation
0.1
0.01
0.001
100
150
200
Days
250
300
60
Temperature (Deg C)
55
50
45
40
Start of Leachate
Recirculation
35
M3EG
M3EY
M3ER
30
100
150
200
250
300
Days
Figure B-9 Response of resistivity sensors at cluster location MM3E (Day 1 = 01/01/03)
`
Appendix B
1000
MO3G
MO3Y
MO3R
Resistance (KOHMS)
100
10
1
Start of Leachate
Recirculation
0.1
0.01
0.001
100
150
200
Days
250
300
350
60
Temperature (Deg C)
55
50
45
40
Start of Leachate
Recirculation
35
O3G
O3Y
30
100
150
200
250
300
350
Days
Figure B-10 Response of resistivity sensors at cluster location MO3 (Day 1 = 01/01/03)
`
Appendix B
1000
Resistance (KOHMS)
100
10
1
Start of Leachate
Recirculation
0.1
0.01
MD4G
MD4Y
MD4R
0.001
100
150
200
250
300
350
Days
60
Temperature (Deg C)
55
50
45
Start of Leachate
Recirculation
40
35
D4R
D4Y
D4G
30
100
150
200
250
300
350
Days
Figure B-11 Response of resistivity sensors at cluster location MD4 (Day 1 = 01/01/03)
`
Appendix B
1000
Resistance (KOHMS)
100
10
1
0.1
Start of Leachate
Recirculation
0.01
MF4Y
MF4R
0.001
100
150
200
Days
250
300
350
60
Temperature (Deg C)
55
50
45
Start of Leachate
Recirculation
40
35
F4G
F4R
F4Y
30
100
150
200
250
300
350
Days
Figure B-12 Response of resistivity sensors at cluster location MF4 (Day 1 = 01/01/03)
`
Appendix B
1000
100
Resistance (KOHMS)
10
1
Start of Leachate
Recirculation
0.1
0.01
MH4Y
MH4R
MH4G
0.001
100
150
200
250
300
350
Days
60
Temperature (Deg C)
55
50
45
40
Start of Leachate
Recirculation
35
H4Y
H4R
30
100
150
200
250
300
350
Days
Figure B-13 Response of resistivity sensors at cluster location MH4 (Day 1 = 01/01/03)
`
Appendix B
1000
Resistance (KOHMS)
100
10
1
Start of Leachate
Recirculation
0.1
0.01
MJ4G
MJ4Y
0.001
100
150
200
250
300
350
Days
60
Temperature (Deg C)
55
50
45
40
Start of Leachate
Recirculation
35
J4R
J4G
J4Y
30
100
150
200
250
300
350
Days
Figure B-14 Response of resistivity sensors at cluster location MJ4 (Day 1 = 01/01/03)
`
Appendix B
1000
ML4G
ML4Y
ML4R
Resistance (KOHMS)
100
10
1
Start of Leachate
Recirculation
0.1
0.01
0.001
100
150
200
250
300
350
Days
60
L4Y
L4R
Temperature (Deg C)
55
50
45
40
Start of Leachate
Recirculation
35
30
100
150
200
250
300
350
Days
Figure B-15 Response of resistivity sensors at cluster location ML4 (Day 1 = 01/01/03)
`
Appendix B
1000
MN4G
MN4Y
MN4R
Resistance (KOHMS)
100
10
1
Start of Leachate
Recirculation
0.1
0.01
0.001
100
150
200
250
300
350
Days
60
Temperature (Deg C)
55
50
45
40
Start of Leachate
Recirculation
N4G
N4R
N4Y
35
30
100
150
200
250
300
350
Days
Figure B-16 Response of resistivity sensors at cluster location MN4 (Day 1 = 01/01/03)
`
Appendix B
1000
100
Resistance (KOHMS)
10
1
Start of Leachate
Recirculation
0.1
0.01
MC5G
MC5Y
MC5R
0.001
100
150
200
Days
250
300
350
60
Temperature (Deg C)
55
50
45
40
Start of Leachate
Recirculation
35
C5R
C5Y
C5G
30
100
150
200
250
300
350
Days
Figure B-17 Response of resistivity sensors at cluster location MC5 (Day 1 = 01/01/03)
`
Appendix B
1000
Resistance (KOHMS)
100
10
1
Start of Leachate
Recirculation
0.1
ME5G
ME5Y
ME5R
0.01
0.001
100
150
200
Days
250
300
350
60
Temperature (Deg C)
55
50
45
40
Start of Leachate
Recirculation
35
E5R
E5Y
E5G
30
100
150
200
250
300
350
Figure B-18 Response of resistivity sensors at cluster location ME5 (Day 1 = 01/01/03)
`
Appendix B
1000
Resistance (KOHMS)
100
10
1
Start of Leachate
Recirculation
0.1
MG5G
MG5Y
MG5R
0.01
0.001
100
150
200
250
300
350
Days
60
Temperature (Deg C)
55
50
45
40
Start of Leachate
Recirculation
G5G
G5Y
G5R
35
30
100
150
200
250
300
350
Days
Figure B-19 Response of resistivity sensors at cluster location MG5 (Day 1 = 01/01/03)
`
Appendix B
1000
Resistance (KOHMS)
100
10
1
Start of Leachate
Recirculation
0.1
0.01
MI5Y
MI5R
0.001
100
150
200
250
300
350
Days
60
Temperature (Deg C)
55
50
45
40
Start of Leachate
Recirculation
35
I5Y
I5R
30
100
150
200
250
300
350
Days
Figure B-20 Response of resistivity sensors at cluster location MI5 (Day 1 = 01/01/03)
`
Appendix B
1000
Resistance (KOHMS)
100
10
1
0.1
Start of Leachate
Recirculation
0.01
MK5G
MK5Y
MK5R
0.001
100
150
200
Days
250
300
350
60
Temperature (Deg C)
55
50
45
40
35
Start of Leachate
Recirculation
K5R
K5Y
K5G
30
100
150
200
Days
250
300
350
Figure B-21 Response of resistivity sensors at cluster location MK5 (Day 1 = 01/01/03)
`
Appendix B
1000
Resistance (KOHMS)
100
10
1
0.1
Start of Leachate
Recirculation
0.01
ML5G
ML5Y
ML5R
0.001
100
150
200
Days
250
300
350
60
Temperature (Deg C)
55
50
45
40
35
Start of Leachate
Recirculation
L5R
L5G
L5Y
30
100
150
200
Days
250
300
350
Figure B-22 Response of resistivity sensors at cluster location ML5 (Day 1 = 01/01/03)
`
Appendix B
1000
Resistance (KOHMS)
100
10
1
0.1
Start of Leachate
Recirculation
0.01
MO5G
MO5Y
MO5R
0.001
100
150
200
250
300
350
Days
60
Temperature (Deg C)
55
50
45
40
Start of Leachate
Recirculation
35
O5R
O5Y
O5G
30
100
150
200
250
300
350
Days
Figure B-23 Response of resistivity sensors at cluster location MO5 (Day 1 = 01/01/03)
`
Appendix B
1000
Resistance (KOHMS)
100
10
1
0.1
0.01
MD6G
MD6Y
MD6R
Start of Leachate
Recirculation
0.001
100
150
200
250
300
350
Days
60
Temperature (Deg C)
55
50
45
40
Start of Leachate
Recirculation
35
D6R
D6Y
D6G
30
100
150
200
250
300
350
Days
Figure B-24 Response of resistivity sensors at cluster location MD6 (Day 1 = 01/01/03)
`
Appendix B
1000
Resistance (KOHMS)
100
10
1
0.1
0.01
Start of Leachate
Recirculation
MF6R
MF6Y
0.001
100
150
200
Days
250
300
350
60
Temperature (Deg C)
55
50
45
40
35
Start of Leachate
Recirculation
F6R
F6Y
30
100
150
200
250
300
350
Days
Figure B-25 Response of resistivity sensors at cluster location MF6 (Day 1 = 01/01/03)
`
Appendix B
1000
MH6G
MH6Y
MH6R
Resistance (KOHMS)
100
10
1
0.1
Start of Leachate
Recirculation
0.01
0.001
100
150
200
Days
250
300
350
60
H6G
H6Y
H6R
Temperature (Deg C)
55
50
45
40
Start of Leachate
Recirculation
35
30
100
150
200
Days
250
300
350
Figure B-26 Response of resistivity sensors at cluster location MH6 (Day 1 = 01/01/03)
`
Appendix B
1000
MI6G
MI6Y
MI6R
Resistance (KOHMS)
100
10
1
0.1
Start of Leachate
Recirculation
0.01
0.001
100
150
200
Days
250
300
350
60
I6G
I6Y
I6R
Temperature (Deg C)
55
50
45
40
Start of Leachate
Recirculation
35
30
100
150
200
250
300
350
Days
Figure B-27 Response of resistivity sensors at cluster location MI6 (Day 1 = 01/01/03)
`
Appendix B
1000
100
Resistance (KOHMS)
10
1
0.1
Start of Leachate
Recirculation
0.01
MK6R
MK6G
MK6Y
0.001
100
150
200
Days
250
300
350
60
Temperature (Deg C)
55
50
45
40
Start of Leachate
Recirculation
35
K6R
K6G
K6Y
30
100
150
200
250
300
350
Days
Figure B-28 Response of resistivity sensors at cluster location MK6 (Day 1 = 01/01/03)
`
Appendix B
1000
ML6EY
ML6EG
ML6ER
Resistance (KOHMS)
100
10
1
0.1
Start of Leachate
Recirculation
0.01
0.001
100
150
200
Days
250
300
350
60
L6ER
L6EG
L6EY
Temperature (Deg C)
55
50
45
40
Start of Leachate
Recirculation
35
30
100
150
200
Days
250
300
350
Figure B-29 Response of resistivity sensors at cluster location ML6E (Day 1 = 01/01/03)
`
Appendix B
1000
ML6WG
ML6WR
ML6WY
Resistance (KOHMS)
100
10
1
0.1
Start of Leachate
Recirculation
0.01
0.001
100
150
200
Days
250
300
350
60
L6WR
L6WG
L6WY
Temperature (Deg C)
55
50
45
40
35
Start of Leachate
Recirculation
30
100
150
200
Days
250
300
350
Figure B-30 Response of resistivity sensors at cluster location ML6W (Day 1 = 01/01/03)
`
Appendix B
1000
100
Resistance (KOHMS)
10
1
0.1
0.01
Start of Leachate
Recirculation
MM6G
MM6Y
0.001
100
150
200
250
300
350
Days
60
Temperature (Deg C)
55
50
45
40
Start of Leachate
Recirculation
M6R
M6Y
M6G
35
30
100
150
200
250
300
350
Days
Figure B-31 Response of resistivity sensors at cluster location MM6 (Day 1 = 01/01/03)
`
Appendix B
1000
100
Resistance (KOHMS)
10
1
0.1
Start of Leachate
Recirculation
MO6G
MO6R
0.01
0.001
100
150
200
Days
250
300
350
60
Temperature (Deg C)
55
50
45
Start of Leachate
Recirculation
40
35
O6Y
O6G
30
100
150
200
250
300
350
Days
Figure B-32 Response of resistivity sensors at cluster location MO6 (Day 1 = 01/01/03)
`
Appendix B
1000
Resistance (KOHMS)
100
10
1
0.1
Start of Leachate
Recirculation
MC7G
MC7Y
MC7R
0.01
0.001
100
200
150
Days
250
300
350
60
Temperature (Deg C)
55
50
45
40
35
C7R
C7Y
C7G
30
100
250
200
150
300
350
Days
Figure B-33 Response of resistivity sensors at cluster location MC7 (Day 1 = 01/01/03)
`
Appendix B
1000
Resistance (KOHMS)
100
10
1
0.1
0.01
Start of Leachate
Recirculation
ME7G
ME7Y
ME7R
0.001
100
150
200
Days
250
300
350
60
Temperature (Deg C)
55
50
45
40
35
Start of Leachate
Recirculation
E7R
E7G
E7Y
30
100
150
200
Days
250
300
350
Figure B-34 Response of resistivity sensors at cluster location ME7 (Day 1 = 01/01/03)
`
Appendix B
1000
Resistance (KOHMS)
100
10
1
0.1
Start of Leachate
Recirculation
0.01
MG7Y
MG7R
0.001
100
150
200
Days
250
300
350
60
Temperature (Deg C)
55
50
45
40
35
Start of Leachate
Recirculation
G7R
G7Y
30
100
150
200
250
300
350
Days
Figure B-35 Response of resistivity sensors at cluster location MG7 (Day 1 = 01/01/03)
`
Appendix B
1000
Resistance (KOHMS)
100
10
1
0.1
Start of Leachate
Recirculation
0.01
MI7Y
MI7R
0.001
100
150
200
250
300
350
Days
60
Temperature (Deg C)
55
50
45
Start of Leachate
Recirculation
40
35
I7R
I7Y
30
100
150
200
250
300
350
Days
Figure B-36 Response of resistivity sensors at cluster location MI7 (Day 1 = 01/01/03)
`
Appendix B
1000
Resistance (KOHMS)
100
10
1
Start of Leachate
Recirculation
0.1
MK7G
MK7Y
MK7R
0.01
0.001
100
150
200
250
300
350
Days
60
Temperature (Deg C)
55
50
45
40
Start of Leachate
Recirculation
K7R
K7Y
35
30
100
150
200
250
300
350
Days
Figure B-37 Response of resistivity sensors at cluster location MK7 (Day 1 = 01/01/03)
`
Appendix B
1000
Resistance (KOHMS)
100
10
1
0.1
0.01
Start of Leachate
Recirculation
MM7G
MM7Y
MM7R
0.001
100
150
200
250
300
350
Days
60
Temperature (Deg C)
55
50
45
40
Start of Leachate
Recirculation
M7R
M7Y
M7G
35
30
100
150
200
250
300
350
Days
Figure B-38 Response of resistivity sensors at cluster location MM7 (Day 1 = 01/01/03)
`
Appendix B
1000
Resistance (KOHMS)
100
10
1
0.1
Start of Leachate
Recirculation
0.01
MD8R
MD8Y
0.001
100
150
200
Days
250
300
350
60
Temperature (Deg C)
55
50
45
40
Start of Leachate
Recirculation
35
D8Y
D8R
30
100
150
200
250
300
350
Days
Figure B-39 Response of resistivity sensors at cluster location MD8 (Day 1 = 01/01/03)
`
Appendix B
1000
MF8G
MF8Y
MF8R
Resistance (KOHMS)
100
10
1
0.1
Start of Leachate
Recirculation
0.01
0.001
100
150
200
250
300
350
Days
60
Temperature (Deg C)
55
50
45
40
35
Start of Leachate
Recirculation
F8G
F8Y
F8R
30
100
150
200
250
300
350
Days
Figure B-40 Response of resistivity sensors at cluster location MF8 (Day 1 = 01/01/03)
`
Appendix B
1000
Resistance (KOHMS)
100
10
1
0.1
Start of Leachate
Recirculation
0.01
MH8Y
MH8G
0.001
100
150
200
Days
250
300
350
60
Temperature (Deg C)
55
50
45
40
Start of Leachate
Recirculation
35
H8Y
H8R
H8G
30
100
150
200
Days
250
300
350
Figure B-41 Response of resistivity sensors at cluster location MH8 (Day 1 = 01/01/03)
`
Appendix B
1000
MJ8G
MJ8Y
MJ8R
100
Resistance (KOHMS)
10
1
0.1
Start of Leachate
Recirculation
0.01
0.001
100
150
200
250
300
350
Days
60
J8G
J8Y
Temperature (Deg C)
55
50
45
40
Start of Leachate
Recirculation
35
30
100
150
200
250
300
350
Days
Figure B-42 Response of resistivity sensors at cluster location MJ8 (Day 1 = 01/01/03)
`
Appendix B
1000
100
Resistance (KOHMS)
10
1
Start of Leachate
Recirculation
0.1
0.01
ML8G
ML8Y
ML8R
0.001
100
200
150
Days
250
300
350
60
Temperature (Deg C)
55
50
45
40
Start of Leachate
Recirculation
L8G
L8Y
L8R
35
30
100
250
200
150
300
350
Days
Figure B-43 Response of resistivity sensors at cluster location ML8 (Day 1 = 01/01/03)
`
Appendix B
1000
Resistance (KOHMS)
100
10
1
0.1
Start of Leachate
Recirculation
0.01
MN8G
MN8Y
MN8R
0.001
100
150
200
250
300
350
Days
60
Temperature (Deg C)
55
50
45
40
Start of Leachate
Recirculation
35
N8R
N8Y
N8G
30
100
150
200
250
300
350
Days
Figure B-44 Response of resistivity sensors at cluster location MN8 (Day 1 = 01/01/03)
`
Appendix B
1000
Resistance (KOHMS)
100
10
1
0.1
Start of Leachate
Recirculation
0.01
MC9Y
MC9R
0.001
100
150
200
Days
250
300
350
60
Temperature (Deg C)
55
50
45
40
Start of Leachate
Recirculation
35
C9R
C9Y
30
100
150
200
Days
250
300
350
Figure B-45 Response of resistivity sensors at cluster location MC9 (Day 1 = 01/01/03)
`
Appendix B
1000
100
Resistance (KOHMS)
10
1
0.1
0.01
Start of Leachate
Recirculation
MF9G
MF9Y
MF9R
0.001
0.0001
100
150
200
Days
250
300
350
60
Temperature (Deg C)
55
50
45
40
Start of Leachate
Recirculation
F9G
F9Y
F9R
35
30
100
150
200
250
300
350
Days
Figure B-46 Response of resistivity sensors at cluster location MF9 (Day 1 = 01/01/03)
`
Appendix B
1000
Resistance (KOHMS)
100
10
1
0.1
Start of Leachate
Recirculation
0.01
MI9G
MI9Y
MI9R
0.001
100
150
200
250
300
350
Days
60
Temperature (Deg C)
55
50
45
40
35
Start of Leachate
Recirculation
I9R
I9Y
30
100
150
200
250
300
350
Days
Figure B-47 Response of resistivity sensors at cluster location MI9 (Day 1 = 01/01/03)
`
Appendix B
1000
Resistance (KOHMS)
100
10
1
Start of Leachate
Recirculation
0.1
0.01
MM9G
MM9Y
MM9R
0.001
100
150
200
Days
250
300
350
60
Temperature (Deg C)
55
50
45
40
Start of Leachate
Recirculation
M9G
M9Y
M9R
35
30
100
150
200
Days
250
300
350
Figure B-48 Response of resistivity sensors at cluster location MM9 (Day 1 = 01/01/03)
`
Appendix B
APPENDIX C
TDR PROBE DATA
Table C-1. Calibration coefficients for the twelve probes
Water Content ( )  a  b 2  c 3  d 4  e 5
Probe
a
b
c
1
2.25
5.73
-0.84
2
-0.85
-2.12
1.34
3
9.77
1.08
0.07
4
-6.12
5.12
-0.03
5
4.43
4.00
0.034
6
-0.92
6.28
-0.0015
7
1.08
2.49
-0.16
8
2.26
10.9
-1.95
9
1.52
4.29
-0.28
10
1.88
5.85
-0.51
11
0.73
2.15
0.03
12
-2.36
8.07
-0.93
d
0.07
-0.11
0.0008
-0.0018
-0.005
-0.011
0.037
0.15
0.016
0.024
0.0013
0.052
E
-0.002
0.003
-6.579
2.66
0.0001
0.0003
-0.001
-0.004
-0.0003
-0.0003
-5.99
-0.0009
Table C-2. Dry density of waste material around each probe
Probe
Dry Density
(kg/m3)
1
489.9
2
506.3
3
375.6
4
294.6
5
456.8
6
410.5
7
370
8
620.3
9
431.2
10
422.8
11
320.3
12
405.4
Appendix C
Table C-3. Characteristics of waveform obtained from TDR probe located near the upper
resistivity sensor at instrumentation cluster MH6
Date
Apparent length La (m) Relative dielectric Gravimetric moisture
constant (Ka)
content
01/07/03
2.755
7.128
29.21
03/18/03
2.816
7.459
29.77
04/15/03
2.755
7.128
29.21
05/01/03
2.803
7.388
29.65
05/15/03
2.803
7.388
29.65
05/26/03
2.755
7.128
29.21
06/10/03
2.803
7.388
29.65
07/03/03
2.803
7.388
29.65
07/07/03
2.803
7.388
29.65
07/15/03
2.803
7.388
29.65
07/24/03
2.803
7.388
29.65
07/29/03
2.803
7.388
29.65
08/11/03
2.755
7.128
29.21
08/21/03
2.790
7.316
29.53
08/26/03
2.838
7.578
29.98
08/27/03
2.851
7.653
30.11
08/28/03
2.790
7.316
29.53
09/03/03
2.803
7.388
29.65
09/09/03
2.790
7.316
29.53
09/15/03
2.803
7.388
29.65
09/18/03
2.803
7.388
29.65
09/23/03
2.790
7.316
29.53
09/26/03
2.803
7.388
29.65
09/30/03
2.803
7.388
29.65
10/03/03
2.658
6.621
28.38
10/07/03
2.851
7.653
30.11
10/10/03
2.850
7.649
30.10
10/14/03
2.860
7.726
30.24
10/21/03
2.867
7.744
30.27
10/23/03
2.843
7.611
30.04
10/31/03
2.832
7.548
29.93
11/07/03
2.875
7.787
30.35
11/11/03
2.842
7.603
30.02
11/18/03
2.844
7.616
30.04
`
Appendix C
Table C-4. Characteristics of waveform obtained from TDR probe located near the upper
resistivity sensor at instrumentation cluster MI7
Date
Apparent length La (m) Relative dielectric Gravimetric moisture
constant (Ka)
content
01/07/03
2.785
7.292
28.07
03/18/03
2.785
7.292
28.07
04/15/03
2.755
7.128
27.35
05/01/03
2.755
7.128
27.35
05/15/03
2.755
7.128
27.35
05/26/03
2.755
7.128
27.35
06/10/03
2.803
7.388
28.48
07/03/03
2.803
7.388
28.48
07/07/03
2.803
7.388
28.48
07/15/03
2.803
7.388
28.48
07/24/03
2.755
7.128
27.35
07/29/03
2.755
7.128
27.35
08/11/03
2.755
7.128
27.35
08/21/03
2.790
7.316
28.17
08/26/03
2.803
7.388
28.48
08/27/03
2.803
7.388
28.48
08/28/03
2.779
7.258
27.92
09/03/03
2.779
7.258
27.92
09/09/03
2.755
7.128
27.35
09/15/03
2.803
7.388
28.48
09/18/03
2.755
7.128
27.35
09/23/03
2.790
7.316
28.17
09/26/03
2.755
7.128
27.35
09/30/03
2.755
7.128
27.35
10/03/03
2.851
7.653
29.58
10/07/03
2.851
7.653
29.58
10/10/03
2.843
7.610
29.41
10/14/03
2.811
7.433
28.67
10/21/03
2.866
7.739
29.93
10/23/03
2.824
7.502
28.96
10/31/03
2.832
7.549
29.16
11/07/03
2.830
7.537
29.11
11/11/03
2.838
7.581
29.29
11/18/03
2.844
7.616
29.43
`
Appendix C
Table C-5. Characteristics of waveform obtained from TDR probe located near the
middle resistivity sensor at instrumentation cluster MI7
Date
Apparent length La (m) Relative dielectric Gravimetric moisture
constant (Ka)
content
01/07/03
3.528
11.85
43.56
03/18/03
3.52
11.80
43.49
04/15/03
3.528
11.85
43.56
05/01/03
3.625
12.53
44.34
05/15/03
3.576
12.19
43.94
05/26/03
3.673
12.87
44.74
06/10/03
3.528
11.85
43.56
07/03/03
3.576
12.19
43.94
07/07/03
3.576
12.19
43.94
07/15/03
3.576
12.19
43.94
07/24/03
3.576
12.19
43.94
07/29/03
3.576
12.19
43.94
08/11/03
3.576
12.19
43.94
08/21/03
3.608
12.41
44.19
08/26/03
3.608
12.41
44.19
08/27/03
3.583
12.24
44.00
08/28/03
3.559
12.07
43.81
09/03/03
3.721
13.22
45.15
09/09/03
3.721
13.22
45.15
09/15/03
3.625
12.53
44.34
09/18/03
3.625
12.53
44.34
09/23/03
3.625
12.53
44.34
09/26/03
3.608
12.41
44.19
09/30/03
3.608
12.41
44.19
10/03/03
3.673
12.87
44.74
10/07/03
3.656
12.75
44.59
10/10/03
3.732
13.30
45.25
10/14/03
3.661
12.78
44.64
10/21/03
3.699
13.06
44.96
10/23/03
3.722
13.23
45.16
10/31/03
3.624
12.53
44.34
11/07/03
3.655
12.75
44.33
11/11/03
3.690
13.00
44.89
11/18/03
3.708
13.13
45.04
`
Appendix C
Table C-6. Characteristics of waveform obtained from TDR probe located near the upper
resistivity sensor at instrumentation cluster MI5
Date
Apparent length La (m) Relative dielectric Gravimetric moisture
constant (Ka)
content
01/07/03
2.479
5.733
37.61
03/18/03
2.571
6.181
39.07
04/15/03
2.610
6.375
39.67
05/01/03
2.720
6.941
41.35
05/15/03
2.720
6.941
41.35
05/26/03
2.768
7.199
42.08
06/10/03
3.528
8.561
45.56
07/03/03
2.684
6.754
40.81
07/07/03
2.781
7.271
42.28
07/15/03
2.684
6.754
40.81
07/24/03
2.781
7.271
42.28
07/29/03
2.733
7.010
41.55
08/11/03
2.928
8.082
44.40
08/21/03
2.768
7.199
42.08
08/26/03
2.768
7.199
42.08
08/27/03
2.768
7.199
42.08
08/28/03
2.817
7.462
42.80
09/03/03
2.768
7.199
42.08
09/09/03
2.792
7.331
42.44
09/15/03
3.497
11.638
51.61
09/18/03
3.934
14.811
55.98
09/23/03
3.836
14.073
55.10
09/26/03
4.704
21.334
61.56
09/30/03
4.478
19.301
60.15
10/03/03
4.704
21.334
61.56
10/07/03
4.128
16.334
57.62
10/10/03
4.157
16.583
57.85
10/14/03
4.080
15.966
57.24
10/21/03
4.28
17.602
58.78
10/23/03
4.49
19.407
60.23
10/31/03
4.637
20.724
61.16
11/07/03
4.582
20.229
61.15
11/11/03
4.608
20.464
60.97
11/18/03
4.325
17.978
59.10
`
Appendix C
Table C-7. Characteristics of waveform obtained from TDR probe located near the
middle resistivity sensor at instrumentation cluster MI5
Date
Apparent length La (m) Relative dielectric
Gravimetric
constant (Ka)
moisture content
01/07/03
3.367
10.772
47.10
03/18/03
3.306
10.373
46.04
04/15/03
3.335
10.561
46.55
05/01/03
3.383
10.878
47.38
05/15/03
3.335
10.561
46.55
05/26/03
3.335
10.561
46.55
06/10/03
3.770
13.577
53.74
07/03/03
3.335
10.561
46.55
07/07/03
3.383
10.878
47.38
07/15/03
3.383
10.878
47.38
07/24/03
3.335
10.561
46.55
07/29/03
3.335
10.561
46.55
08/11/03
3.383
10.878
47.38
08/21/03
3.431
11.199
48.21
08/26/03
3.383
10.878
47.38
08/27/03
3.383
10.878
47.38
08/28/03
3.383
10.878
47.38
09/03/03
3.383
10.878
47.38
09/09/03
3.351
10.666
46.83
09/15/03
4.640
20.745
65.24
09/18/03
4.736
21.635
66.23
09/23/03
4.688
21.188
65.74
09/26/03
4.736
21.635
66.23
09/30/03
4.736
21.635
66.23
10/03/03
5.026
24.417
68.81
10/07/03
4.810
22.326
66.94
10/10/03
4.849
22.701
67.31
10/14/03
4.864
22.844
67.44
10/21/03
4.998
24.141
68.59
10/23/03
5.041
24.567
68.93
10/31/03
4.962
23.792
68.30
11/07/03
5.072
24.874
68.29
11/11/03
4.964
23.803
68.30
11/18/03
4.937
23.542
68.07
`
Appendix C
Table C-8. Characteristics of waveform obtained from TDR probe located near the lower
resistivity sensor at instrumentation cluster MI6
Date
Apparent length La (m) Relative dielectric
Gravimetric
constant (Ka)
moisture content
01/07/03
2.449
5.587
44.10
03/18/03
2.571
6.181
46.40
04/15/03
2.562
6.133
46.22
05/01/03
2.562
6.133
46.22
05/15/03
2.562
6.133
46.22
05/26/03
2.610
6.375
47.09
06/10/03
2.562
6.133
46.22
07/03/03
2.610
6.375
47.09
07/07/03
2.610
6.375
47.09
07/15/03
2.610
6.375
47.09
07/24/03
2.610
6.375
47.09
07/29/03
2.610
6.375
47.09
08/11/03
2.610
6.375
47.09
08/21/03
2.658
6.621
47.94
08/26/03
2.658
6.621
47.94
08/27/03
2.658
6.621
47.94
08/28/03
2.658
6.621
47.94
09/03/03
3.646
12.682
60.58
09/09/03
3.334
10.558
57.48
09/15/03
3.236
9.930
56.35
09/18/03
3.334
10.558
57.48
09/23/03
3.383
10.879
58.01
09/26/03
4.040
15.645
63.66
09/30/03
3.383
10.879
58.01
10/03/03
3.923
14.728
62.82
10/07/03
3.481
11.536
59.03
10/10/03
3.675
12.894
60.84
10/14/03
3.734
13.322
61.35
10/21/03
3.919
14.701
62.79
10/23/03
4.029
15.562
63.59
10/31/03
3.931
14.795
62.88
11/07/03
4.922
23.398
69.87
11/11/03
3.817
13.929
62.01
11/18/03
3.786
13.702
61.77
`
Appendix C
Table C-9. Characteristics of waveform obtained from TDR probe located near the
middle resistivity sensor at instrumentation cluster MI6
Date
Apparent length La (m) Relative dielectric
Gravimetric
constant (Ka)
moisture content
01/07/03
2.602
6.334
32.35
03/18/03
2.694
6.805
34.44
04/15/03
2.706
6.872
34.74
05/01/03
2.706
6.872
34.74
05/15/03
2.706
6.872
34.74
05/26/03
2.658
6.621
33.62
06/10/03
2.61
6.375
32.53
07/03/03
2.658
6.621
33.62
07/07/03
2.658
6.621
33.62
07/15/03
2.658
6.621
33.62
07/24/03
2.61
6.375
32.53
07/29/03
2.61
6.375
32.53
08/11/03
2.706
6.872
34.74
08/21/03
2.706
6.872
34.74
08/26/03
2.706
6.872
34.74
08/27/03
2.706
6.872
34.74
08/28/03
2.658
6.621
33.62
09/03/03
2.996
8.477
41.75
09/09/03
2.996
8.477
41.75
09/15/03
4.205
16.972
66.46
09/18/03
4.398
18.602
68.15
09/23/03
4.301
17.778
67.42
09/26/03
4.785
22.087
67.62
09/30/03
4.301
17.778
67.42
10/03/03
4.714
21.426
68.28
10/07/03
4.398
18.602
68.15
10/10/03
4.396
18.593
68.14
10/14/03
4.373
18.39
67.99
10/21/03
4.612
20.497
68.70
10/23/03
4.892
23.114
65.79
10/31/03
4.861
22.815
66.44
11/07/03
5.14
25.55
53.19
11/11/03
4.934
23.52
64.69
11/18/03
4.817
22.40
67.17
`
Appendix C
Table C-10. Characteristics of waveform obtained from TDR probe located near the
upper resistivity sensor at instrumentation cluster MG7
Date
Apparent length La (m) Relative dielectric Gravimetric moisture
constant (Ka)
content
01/07/03
2.921
8.045
36.43
03/18/03
2.694
7.797
35.91
04/15/03
2.803
7.388
35.04
05/01/03
2.851
7.653
35.61
05/15/03
2.851
7.653
35.61
05/26/03
2.803
7.388
35.04
06/10/03
2.851
7.653
35.61
07/03/03
2.851
7.653
35.61
07/07/03
2.851
7.653
35.61
07/15/03
2.9
7.923
36.18
07/24/03
2.851
7.653
35.61
07/29/03
2.851
7.653
35.61
08/11/03
2.851
7.653
35.61
08/21/03
2.9
7.923
36.18
08/26/03
2.934
8.117
36.58
08/27/03
2.9
7.923
36.18
08/28/03
2.9
7.923
36.18
09/03/03
2.924
8.06
36.47
09/09/03
2.9
7.923
36.18
09/15/03
2.9
7.923
36.18
09/18/03
2.9
7.923
36.18
09/23/03
2.851
7.653
35.61
09/26/03
2.886
7.846
36.02
09/30/03
2.851
7.653
35.61
10/03/03
2.996
8.477
37.32
10/07/03
2.851
7.653
35.61
10/10/03
2.959
8.263
36.88
10/14/03
2.985
8.416
37.19
10/21/03
2.985
8.412
37.19
10/23/03
2.937
8.135
36.62
10/31/03
2.962
8.277
36.91
11/07/03
2.938
8.142
36.63
11/11/03
2.947
8.192
36.74
11/18/03
2.941
8.161
36.67
`
Appendix C
Table C-11. Characteristics of waveform obtained from TDR probe located near the
middle resistivity sensor at instrumentation cluster MG7
Date
Apparent length La (m) Relative dielectric Gravimetric moisture
constant (Ka)
content
01/07/03
2.851
7.653
38.44
03/18/03
2.938
8.142
39.07
04/15/03
2.9
7.923
38.79
05/01/03
2.9
7.923
38.79
05/15/03
2.948
8.198
39.13
05/26/03
2.9
7.923
38.79
06/10/03
2.928
8.082
38.99
07/03/03
2.928
8.082
38.99
07/07/03
2.928
8.082
38.99
07/15/03
2.977
8.362
39.34
07/24/03
2.977
8.362
39.34
07/29/03
2.928
8.082
38.99
08/11/03
2.977
8.362
39.34
08/21/03
2.977
8.362
39.34
08/26/03
3.025
8.646
39.68
08/27/03
3.011
8.561
39.58
08/28/03
2.962
8.279
39.23
09/03/03
3.011
8.561
39.58
09/09/03
2.962
8.279
39.23
09/15/03
3.025
8.646
39.68
09/18/03
2.977
8.362
39.34
09/23/03
3.001
8.503
39.51
09/26/03
2.914
8.002
38.89
09/30/03
3.011
8.561
39.58
10/03/03
3.025
8.646
39.68
10/07/03
3.059
8.847
39.93
10/10/03
3.04
8.736
39.79
10/14/03
3.019
8.609
39.64
10/21/03
3.023
8.636
39.67
10/23/03
2.997
8.483
39.49
10/31/03
3.009
8.555
39.57
11/07/03
3.006
8.535
39.55
11/11/03
3.020
8.614
39.64
11/18/03
3.020
8.615
39.64
`
Appendix C
APPENDIX D
EXAMPLE CALCULATION OF CONVERSION TO MOISTURE CONTENT FROM
OBSERVED RESISTANCE AND TEMPERATURE AND CALIBRATION CURVES
The equations used for calculating the moisture content (wet weight basis) have
been described Gawande et al. (2003). They are as follows:
30.068
(1)
1  0.568 * exp  0.167 R 
where MC is the moisture content of solid waste (% wet weight) R is the resistance value
At 8.0 mS/cm:
MC 
measured from the sensor (k). The 8mS/cm refer to the electrical conductivity of the
solution used to wet the waste samples during calibration.
To incorporate the effect of temperature, a temperature correction was suggested is
shown as follows:
1  0.02t 2  25
(2)
R1  R2 

 1  0.02t1  25 
where R1is the resistance at temperature t1 and R2 is the resistance at temperature t2.
Equations 2 and 3 are used for the conversion of the measured resistance value at a
certain temperature to the corresponding gravimetric moisture content. For example, if an
MTG sensor in the landfill outputs a resistance value of 1k at in situ temperature of
44oC, then the moisture content at a conductivity 8 mS/cm was calculated by the
following steps.
Given that R2 = 1 k and t2 = 44oC,
Apply the temperature correction using equation 3 and the measured resistance
value at a calibration temperature of 22oC.
Appendix D
1  0.0244  25
R1 = 1 k 

1  0.0222  25
R1 = 1.468 k
Then convert to moisture content at a leachate electric conductivity 8 mS/cm by
equation (2)
30.068
1  0.568 exp  0.167 *1.468
MC = 54.16 % by weight
MC 
Laboratory experiments were conducted to obtain the relationship between the
measured resistance and moisture content for conductivities of 4 mS/cm and 8 mS/cm.
This relationship is shown by the calibration curves give below.
Wet Moisture Content (%)
100
4.0 mS/cm (Data)
8.0 mS/cm (Data)
16.0 mS/cm(Data)
75
50
25
0
0
5
10
15
20
Resistance (k Ohms)
Figure D-1 Calibration curves for MTG sensors for varying moisture conductivities
`
Appendix D
APPENDIX E
CALCULATION FOR THE AVERAGE MOISTURE CONTENT IN THE LANDFILL
AND TABLE ON TIMES OF TRAVEL OF MOISTURE TO REACH THE SENSOR
The moisture content of the landfill prior to leachate recirculation as calculated
from field sampling was observed to be 23% by wet weight. The final moisture content
estimated for the landfilled waste was calculated from the amount of leachate pumped
into the known mass of waste. The first step in this process was to find the initial weight
of water stored in the waste. The final weight of water stored in the waste was found by
adding the known weight of water recirculated to the initial weight of water. This
calculation is shown in step 2. The final moisture content was found by dividing the final
weight of water in the waste by the total weight of the waste (initial weight plus weight of
added water; see step 3). It was assumed that the pumped leachate was stored in the waste
and did not drain from the landfill.
Step 1
Mass of waste = 0.61 million tons = 0.61  10 6 tons
Initial moisture content (MC) of waste samples = 0.23 (w/w)
Initial volume of water present in the landfill (V1) =
0.23  0.61  10 6 tons  1
metricton
10 6 g
1cm 3
1litre
1gallon




33.74 10 6 gals
3
1.1ton 1metric ton 1g
1000cm 3.78litres
Step 2
Volume of water added until day 322 (V2) = 0.762  10 6 gals
Total water in the landfill until day 322 = V1 + V2 = 34.5  10 6 gals
Appendix E
Mass of water in the landfill =
1.1tons
l
cm 3
g
1kg 1metric ton
34.5  10 gals  3.78
 1000
1 3 


 143,451 tons
gal
l
1000 kg
1metric ton
cm 1000 g
Step 3
6
Final Moisture Content =
Final mass of water
Total mass of waste
Total mass of waste =  0.61*106    0.143 *106   0.653 *106 tons

 

Final Moisture Content =
143,451
= 0.235
0.653  10 6
So, the predicted moisture content is 24%
The travel times of pumped moisture to reach the sensor is given in the table E-1.
Travel times could be found by measuring the number of days taken for the pumped
water through the injection well to reach the nearest moisture sensors surrounding this
well.
Table E-1: Time of travel in days for the incoming moisture to pass the sensor
Sensor
Lower
Middle
Upper
G3E
16
10
25
G3W
Low reading
10
No sensor
MF4
Malfunction
14
37
MF3
14
No sensor
MH3
56
M3E
Low reading
2
10
M3W Low reading
8
10
ML4
Low reading
15
MO5
63
L6E
8
8
13
L6W
2
2
14
MK6
46
15
ME7
17
ML8
21
22
46
MJ8
59
47
MI9
51
MM9
29
MF8
6
21
22
`
Appendix E
MF9
MI5
MH6
MI6
MG5
MC7
MD8
6
No sensor
12
13
23
15
38
`
36
21
12
14
36
21
23
16
11
Appendix E
APPENDIX F
SCHEMATIC OF THE TDR WAVEFORM AND CALIBRATION GRAPHS AS
PROVIDED BY ZIRCON INC
TDR waveform provided by the manufacturer (Zircon inc) could be shown by the
schematic given below.
Figure F-1 Schematic sketch of the TDR wave form
Appendix F
The relationship between the volumetric moisture content and the measured
dielectric constant obtained from the waveform from the TDR sensors is shown in the
figure below. As mentioned in the text, 12 TDR probes (numbered from 1 to 12) were
installed in the landfill. These probes were calibrated on site with the excavated waste by
the vendor (Zircon Inc). The calibration equations were developed by increasing the
moisture content (by adding water or leachate with conductivities 0.7 S/m or 1.4 S/m to
the waste samples) and measure the relative dielectric constant from the observed
waveform. The details of the conductivities of liquid used for calibrating twelve different
TDR probes are given by the legend shown in the figure below.
Dielectric constant versus actual water content for different
MSW
Volumetric water content
100
90
80
70
60
50
40
30
20
10
0
0
5
10
15
20
25
30
Dielectric constant
No.1-1.4S/m
No. 2-water
No.3-water
No.4-water
No.5-1.4S/m
No.6-water
No.7-water
No.8-water
No.9-0.7s/m
No.10-0.7S/m
No.11-0.7S/m
No.12-1.4S/m
Figure F-2 Calibration graphs for different probes (Zircon Inc)
`
Appendix F
LITERATURE
Bengtsson, L., D. Bendz, W. Hogland, and H. Rosqvist (1994). “Water balance for
landfills of different age.” Journal of Hydrology, 158, 203-217.
Bonnell, R. B., R. S. Broughton, and P. Enright (1991). “The measurement of soilmoisture and bulk-salinity using time domain reflectometry.” Canadian Agricultural
Engineering, 33(2), 225-229.
Bowles, J.E., 1992. Engineering Properties of Soils and Their Measurement. McGrawHill, New York, N.Y.
Cassel, D. K., R. G. Kachanoski, and G. C. Topp (1994). “Practical considerations for
using a TDR cable tester.” Soil Tech, 7(2), 113-126.
Chanasyk, D. S., and M. A. Naeth (1996). “Field measurement of soil moisture using
neutron probes.” Canandian Journal of Soil Science, 76, 317-323
Chanzy, A., J. Chadoeuf, J. C. Gaudu, D. Mohrath, G. Richard, and L. Bruckler (1998).
“Soil moisture monitoring at the field scale using automatic capacitance probes.”
European Journal of Soil Science, 49, 637-648.
Dalton, F. N., W. N. Herkelrath, D. S. Rawlins, and J. D. Rhoades (1984). “Time domain
reflectometry: Simultaneous measurement of soil water content and electrical
conductivity with a single probe.” Science, 224, 989-990.
Dalton, F. N., and M. Th. Van Genuchten (1986). “The time domain reflectometry
method for measuring soil water content and salinity.” Geoderma, 1986, 38, 237-250.
Dalton, F. N. (1992). “Development of time domain reflectometry for measuring soil
water content and bulk soil electrical conductivity.” Soil Science Society of America,
SSSA Spec. Publ, 30, 143-167
Dean, T. J., J. P. Bell, and A. J. B. Baty (1987). “Soil moisture measurement by an
improved capacitance technique, Part I. Sensor design and performance.” Journal of
Hydrology, 93, 67-78.
DeWalle, F. B., E. S. K. Chian, and E. Hammerberg (1978). “Gas production from solid
waste in landfill.” Journal of the Environmental Engineering Division, ASCE, 104, 415.
Dickey, G. L. (1990). “Factors affecting neutron gauge calibration”, In Irrigation and
Drainage Proceeding of the National Conference of ASCE, Irrigation and Drainage
Division, Durango, CO, July 11-13, ASCE, New York, 192-201.
Eliassen, R. (1942). “Decomposition of Landfills.” American Journal of Public Health.
32, 1029.
References
Evett, S. R. (1998). “Some aspects of the time domain reflectometry (TDR), neutron
scattering and capacitance methods of soil water content measurement.” IAEA- Tecdoc1137, 5-49.
Franson, M.A. 1995. ed. Standard Methods for the Examination of Water and
Wastewater. 19th edition. American Public Health Association. Washington, DC.
Gardner, W., and D. Kirkham (1952). “Determination of soil moisture by neutron
scattering.” Soil Science, 73, 391-401.
Gawande, N. A., D. R. Reinhart, P. A. Thomas, P. T. Mccreanor, and T. G. Townsend
(2003). “Municipal solid waste in situ moisture measurement using an electrical
resistance sensor.” Waste Management, 23, 667-674.
Holmes, R. (1984). “Comparison of different methods of estimating infiltration at a
landfill site in south Essex with implications for leachate management control.” Quarterly
Journal of Engineering Geology, London, 17, 9-18.
Jonnalagadda, S. (2004). “Resistivity and time domain reflectometry sensors for
assessing in situ moisture content in a bioreactor landfill.” ME thesis, Department of
Environmental Engineering Sciences, University of Florida, Gainesville, Florida.
Korfiatis, G. P., A. C. Demetracopoulos, E. L. Bourodimos, and E. G. Nawy (1984).
“Moisture transport in a solid waste column.” Journal of Environmental Engineering,
ASCE, 110(4), 780-796.
Li, R. S., and C. Zeiss (2001). “In situ moisture content measurement in MSW landfills
with TDR.” Environmental Engineering Science, 18(1), 53-66.
Mata-Alvarez, J., and A. Martina-Verdure (1986). “Laboratory simulation of muncipal
solid waste fermentation with leachate recycle.” Journal of Chemical Technology, 36,
547-556.
McCann, I. R., D. C. Kincaid, and D. Wang (1992). “Operational characteristics of the
watermark model-200 soil water potential sensor for irrigation management.” Applied
Engineering in Agriculture, 1992, 8(5), 603-609.
Miller, L. W., J. F. K. Earle, T. G. Townsend, C. W. Bartlett, and H. Lee (1991).
“Leachate recycle and the augmentation of biological decomposition at MSW landfills.”
Report No. 91-3, University of Florida, College of Engineering, Gainesville, FL.
Nadler, A., S. Dasberg, and I. Lapid (1991). “Time domain reflectometry measurements
of water content and electrical conductivity of layered soil columns.” Soil Society of
America Journal, 55, 938-943.
Noborio, K., K. J. McInnes, and J. L. Heilman (1994). “Field measurements of soil
electrical conductivity and water content by TDR.” Computers and Electronics in
Agriculture, 11, 131-142.
References
Pohland, F. G. (1975). “Sanitary landfill stabilisation with leachate recycle and residual
treatment.” EPA-600/2-75-043. US Environmental Protection Agency, Cincinnati, OH.
Pohland, F. G. (1988). “Leachate recycle as landfill management option.” Journal of the
Environmental Engineering Division, ASCE, 106, 1057
Pohland, F. G., and S. R. Harper (1980). “Critical Review and Summary of Leachate and
Gas Production from Landfills.” EPA/600/2-86/073. US Environmental Protection
Agency, Cincinnati, OH.
Rees, J. F. (1980). “Optimisation of methane production and refuse decomposition in
landfills by temperature control.” Journal of Chemical Technology and Biotechnology,
30, 458.
Reinhart, D. R., and T. G. Townnsend (1997). Landfill bioreactor design and operation.
Lewis publishers, Boca Raton, FL.
Remson, I., and A. Fungaroli (1968). “Water movement in an unsaturated sanitary
landfill.” Journal of the Sanitary Engineering Division, 94, SA2, 307-317.
Rosqvist, H., D. Bendz, C. Oman, and J. E. Meije (1997). “Water flow in a pilot-scale
landfill.” Proceedings of Sardinia. 6th Internatonal Landfill Symposium. CISA, Caligari,
Italy.
Schmugge, T. J., T. J. Jackson, and H. L. McKim (1980). “Survey methods for soil
moisture determination.” Water Resources Research,16(6), 961-979.
Schroeder, P.R., Gibson, A.C., and Smolan, M.D. (1983). Hydrologic evaluation of
landfill performance, Report:EPA/530-SW-84-009 & 10, HELP model documentation
and user’s guide, Vol I & II. US Environmental Protection Agency, Cincinnati, Ohio.
Skinner, A., C. Hignett, and J. Dearden (1997). “Resurrecting the gypsum block for soil
moisture measurement.” Australian Viticulture, 13-19.
Tchobanoglous, G., H. Theisen, and S. Vigil (1993). Integrated Solid Waste
Management. McGraw-Hill, New York.
TDR 100 Instruction Manual (2002). Campbell Scientific Inc, Alberta.
Thomas, A. M. (1966). “In situ measurement of moisture in soil and similar substances
by 'fringe’ capacitance.” Journal of Science Instruments, 43, 21-27.
Thomas, P. A. (2001). “The testing and evaluation of a prototype sensor for the
measurement of moisture content in bioreactor landfills.” MS thesis, College of
Engineering and Computer Sciences, University of Central Florida, Orlando, Florida.
Tittlebaum, M. E. (1982). “Organic carbon content stabilization through landfill leachate
recirculation.” Journal of Water Pollution Control Federation, 54(5), 428.
References
Topp, G. C., J. L. Davis, and A. P. Annan (1980). “Electromagnetic determination of soil
water content: measurements in coaxial transmission lines.” Water Resources Research,
16, 574-582.
Topp, G. C., and J. L. Davis. (1985). Comment on “Monitoring the unfrozen water
content of soil and snow using the Time Domain Reflectometry” by Jean Stein and
Douglas L. Kane, Water Resources Research. 21:1059-1060.
Topp, G. C., M. Yanuka, W. D. Zebchuk, and S. Zegelin (1988). “Determination of
electrical conductivity using time domain reflectometry: soil and water experiments in
coaxial lines.” Water Resources Research, 24(7), 945-952.
US Environmental Protection Agency (2002). “Municipal solid waste in the United
States: 2000 Facts and Figures.” Office of solid waste and emergency response (5305W).
EPA530-R-02-001. US Environmental Protection Agency, Cincinnati, OH.
US Environmental Protection Agency (2003). “Example moisture mass balance
calculations for bioreactor landfills” Office of air quality planning and standards.
EPA456-R-03-007. US Environmental Protection Agency, Cincinnati, OH.
Walsh, J. J., and R. N. Kinman (1982). “Leachate and gas production under controlled
moisture conditions”. EPA-600-9-82-002. US Environmental Protection Agency,
Cincinnati, OH.
Wigh, R. J. (1979). “Boone County Field study Interim Report.” EPA-600-2-79-058. U.
S. Environmental Protection Agency, Cincinnati, OH.
Williams, J., B. G. Williams, J. W. Holmes, and R. E. Winkworth. (1981). “Application
in agriculture, forestry, and environmental science.” Soil water assessment by the neutron
method, E. L. Greacen, ed., Commonwealth Scientific and Industrial Research
Organization, Australia.
Yuen, S. T. S. (1999). “Bioreactor landfills promoted by leachate recirculation-A fullscale study.” Doctor of Philosophy thesis, Dept of Civil and Environmental Engineering,
University of Melbourne, Melbourne.
Yuen, S. T. S., McMohan, T. A, and J. R. Styles (2000). “Monitoring in situ moisture
content of MSW landfills.” Journal of Environmental Engineering, 1088-1095.
Zegelin, S. J., I. White, and D. R. Jenkins (1989). “Improved field probes for soil water
content and electrical conductivity measurement using time domain reflectometry.”
Water Resources Research, 25, 2367-2376.
Zeiss, C., and W. Major (1993). “Moisture Flow Through Municipal Solid Waste: Pattern
and Characteristics.” Journal of Environmental Systems, 22(3), 211-232
References
PUBLICATIONS
Gawande, N. A., D. R. Reinhart, P. A. Thomas, P. T. Mccreanor, and T. G. Townsend (2003).
“Municipal solid waste in situ moisture measurement using an electrical resistance sensor.”
Waste Management, 23, 667-674.
Jonnalagadda, S., P. Jain, N. Gawande, T. Townsend, and D. Reinhart (2004). “Field evaluation
of resistivity sensors for in situ moisture measurement in a bioreactor landfill.” In preparation.
Jonnalagadda, S., P. Jain, N. Gawande, T. Townsend, and D. Reinhart (2004). “Comparison of
resistivity and time domain reflectometry sensors for assessing moisture content in bioreactor
landfills.” In preparation.