CHARACTERIZATION OF ACTIVATED CARBON FOR TASTE AND ODOUR CONTROL
by
Kyla Miriam Smith
A thesis submitted in conformity with the requirements for the degree of Master of Applied Science Graduate Department of Civil Engineering
University of Toronto
© Copyright by Kyla Miriam Smith (2011)
ii
CHARACTERIZATION OF ACTIVATED CARBON FOR TASTE AND
ODOUR CONTROL
Kyla Smith
Master of Applied Science
Graduate Department of Civil Engineering University of Toronto
2011
ABSTRACT
Iodine number, BET surface area, taste and odour compound isotherms, and trace capacity
number tests were used to rank five different granular activated carbons according to
thermodynamic adsorption performance. These tests were compared to expected activated carbon
service life and loading results of rapid small-scale column tests (RSSCTs) run with water from
two lake sources spiked with geosmin and 2-methylisoborneol (MIB). Trace capacity number,
used to specifically identify high adsorption energy sites on activated carbon, was hypothesized
to be correlated to geosmin/MIB breakthrough and loading performance of different activated
carbons. This study found no such clear correlation. However, when only bituminous coal
activated carbons were considered, correlations to MIB breakthrough were strengthened. Natural
organic matter (NOM) adversely affected adsorption, resulting in decreased RSSCT throughput
to breakthrough in surface water with higher total organic carbon (TOC). Methods for improving
characterization tests and RSSCTs when NOM is present are discussed.
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ACKNOWLEDGMENTS I would like to thank my supervisor, Ron Hofmann, for his guidance, support and for providing
the opportunity for me to be a part of the Drinking Water Research Group. Thank you also to
Susan Andrews for assisting with data analysis, troubleshooting and for providing helpful
feedback.
I am grateful to Jinwook Kim for countless hours of help and advice in the lab, Fariba Amiri and
Russell D’Souza for assistance in the lab and Sean OToole for returning many (many!) times to
help fix the GCMS. Giovanni Buzzeo, Alan McClenaghan and Joel Babbin were extremely
helpful in construction and in hauling barrels of water. I would also like to thank Tom Hartig,
Dave McNamara and their colleagues at Calgon for their advice and direction. Thank you Laura
Meteer, Aaron Wood and Richard Jones for helping to coordinate visits for water collection at
the Georgina Water Treatment Plant and the Ajax Water Supply Plant.
This work was partially funded by the Natural Sciences and Engineering Research Council of
Canada, Calgon Carbon Corporation, the Region of York and the Region of Durham.
To the DWRG team, I really could not have asked for a better group of people to work alongside.
Special thanks to those who helped me move thousands of kilos of water from the 1st floor to the
4th and to Scott, Juan and Bryony who enthusiastically agreed to go on field trips to various water
treatment plants.
To Heather, Jon, Sarah, Emma and Juan: thank you for helping these final months in the lab, for
your support and feedback in writing and data analysis and for your day-to-day encouragement.
Finally, to my incredible, supportive family and friends, I am so grateful for your love and
encouragement.
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TABLE OF CONTENTS ABSTRACT ................................................................................................................................................................II
ACKNOWLEDGMENTS........................................................................................................................................ III
TABLE OF CONTENTS ......................................................................................................................................... IV
LIST OF TABLES.................................................................................................................................................... VI
LIST OF FIGURES.................................................................................................................................................VII
GLOSSARY........................................................................................................................................................... VIII
1 INTRODUCTION AND RESEARCH OBJECTIVES...................................................................................1
1.1 BACKGROUND ............................................................................................................................................1 1.2 RESEARCH OBJECTIVES ..............................................................................................................................2 1.3 DESCRIPTION OF CHAPTERS........................................................................................................................3 1.4 REFERENCES...............................................................................................................................................3
2 LITERATURE REVIEW..................................................................................................................................4
2.1 GRANULAR ACTIVATED CARBON IN WATER TREATMENT .........................................................................4 2.1.1 BACKGROUND ............................................................................................................................................4 2.1.2 PRODUCTION OF ACTIVATED CARBON .......................................................................................................4 2.1.3 ADSORPTION MECHANISMS.........................................................................................................................5 2.1.4 USES FOR ACTIVATED CARBON..................................................................................................................8 2.2 TASTE AND ODOUR ISSUES.........................................................................................................................9 2.2.1 TASTE AND ODOUR COMPOUNDS ...............................................................................................................9 2.2.2 ODOUR THRESHOLD CONCENTRATION.....................................................................................................11 2.2.3 COMPETITION WITH NOM........................................................................................................................11 2.3 ACTIVATED CARBON CHARACTERIZATION FOR MICROPOLLUTANT CONTROL.........................................12 2.3.1 DETERMINATION OF PHYSICAL PROPERTIES.............................................................................................12 2.3.1.1 DISTRIBUTION OF ENERGY SITES..............................................................................................................15 2.3.2 DETERMINATION OF ADSORPTION CAPACITY...........................................................................................16 2.3.3 RAPID SMALL-SCALE COLUMN TESTS......................................................................................................18 2.3.3.1 ASSUMPTIONS MADE WITH THE RSSCT...................................................................................................23 2.3.3.2 CONSTANT OR PROPORTIONAL DIFFUSIVITY? ..........................................................................................25 2.3.4 THE ACCELERATED COLUMN TEST ..........................................................................................................26 2.4 CURRENT SELECTION METHOD FOR PURCHASING CARBONS ...................................................................29 2.5 REFERENCES.............................................................................................................................................31
3 ASSESSMENT OF ACTIVATED CARBON CHARACTERIZATION TESTS FOR TASTE AND ODOUR CONTROL.................................................................................................................................................36
ABSTRACT...............................................................................................................................................................36 3.1 INTRODUCTION.........................................................................................................................................37 3.2 EXPERIMENTAL ........................................................................................................................................40 3.3 RESULTS AND DISCUSSION .......................................................................................................................39 3.4 SUMMARY AND CONCLUSIONS .................................................................................................................54 3.5 REFERENCES.............................................................................................................................................56
4 THE EFFECTS OF COMPETITIVE ADSORPTION BETWEEN T&O COMPOUNDS AND NOM ON CHARACTERIZATION TESTS AND RSSCTS ...................................................................................................59
ABSTRACT...............................................................................................................................................................59 4.1 INTRODUCTION.........................................................................................................................................60 4.2 SELECTION OF THERMODYNAMIC CHARACTERIZATION TESTS FOR PREDICTING TASTE AND ODOUR
CONTROL..................................................................................................................................................61 4.3 OPTIMIZING RSSCTS FOR PREDICTING ADSORPTION OF TASTE AND ODOUR COMPOUNDS .....................66 4.4 SUMMARY AND CONCLUSION...................................................................................................................71
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4.5 REFERENCES.............................................................................................................................................73
5 SUMMARY AND RECOMMENDATIONS .................................................................................................75
5.1 SUMMARY ................................................................................................................................................75 5.2 CONCLUSIONS ..........................................................................................................................................75 5.3 RECOMMENDATIONS ................................................................................................................................76 5.4 REFERENCES.............................................................................................................................................76
A: DEFINITIONS .................................................................................................................................................77
B: MATERIALS AND METHODS (CHAPTER 3) ..........................................................................................79
B.1 DESCRIPTION OF ACTIVATED CARBONS ...................................................................................................79 B.2 ACTIVATED CARBON PREPARATION.........................................................................................................81 B.3 SAMPLE WATER PREPARATION ................................................................................................................83 B.4 TASTE AND ODOUR COMPOUND PREPARATION........................................................................................84 B.5 ACTIVATED CARBON PHYSICAL CHARACTERISTICS.................................................................................86 B.5.1 APPARENT DENSITY .................................................................................................................................86 B.6 ACTIVATED CARBON ACTIVITY INDICES ..................................................................................................88 B.6.1 IODINE NUMBER TEST ..............................................................................................................................88 B.6.2 TRACE CAPACITY NUMBER ......................................................................................................................92 B.6.3 TRACE CAPACITY NUMBER GAS PHASE ...................................................................................................93 B.7 TOTAL ORGANIC CARBON........................................................................................................................96 B.8 RAPID SMALL-SCALE COLUMN TESTS......................................................................................................98 B.9 TASTE AND ODOUR COMPOUND ANALYSIS ............................................................................................107 B.10 QUALITY CONTROL TESTS......................................................................................................................111 B.11 DATA ANALYSIS.....................................................................................................................................112
C: ADDITIONAL RESULTS (CHAPTER 3) ..................................................................................................115
C.1 TRACE CAPACITY NUMBER TEST ...........................................................................................................115 C.2 TRACE CAPACITY NUMBER GAS PHASE TEST ........................................................................................116 C.3 PORE SIZE DISTRIBUTION .......................................................................................................................117 C.4 MIB AND GEOSMIN BREAKTHROUGH AND LOADING RESULTS ..............................................................118 C.5 CORRELATION TESTS ..............................................................................................................................126 C.6 RAW DATA .............................................................................................................................................128
D: EXPERIMENTAL DESIGN.........................................................................................................................130
E: QAQC .............................................................................................................................................................132
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LIST OF TABLES TABLE 2.1 CHEMICAL AND PHYSICAL CHARACTERISTICS OF GEOSMIN AND MIB (PIRBAZARI ET AL., 1992)................10 TABLE 2.2 COMPARISON OF CALCULATIONS USED FOR RSSCT AND ACT ..................................................................27 TABLE 3.1 PROPERTIES OF FIVE ACTIVATED CARBONS USED IN THIS STUDY................................................................34 TABLE 3.2 SMALL-COLUMN RSSCT PARAMETERS FOR ALL FIVE CARBONS ................................................................37 TABLE 3.3 EXAMINATION OF CORRELATION BETWEEN CHARACTERIZATION TESTS (LEAST-SQUARES LINEAR
REGRESSION) ......................................................................................................................................................40 TABLE 3.4 BED VOLUMES TO MIB BREAKTHROUGH (20 % OF C0), RANKING IN PARENTHESES. ..................................45 TABLE 3.5 COMPARISON OF MIB BREAKTHROUGH (20 % OF C0) TO CHARACTERIZATION RESULTS (R2
VALUES, +/-
INDICATES POSITIVE OR NEGATIVE SLOPE IN LINEAR REGRESSION ANALYSIS) ....................................................47 TABLE 3.6 ADSORPTION
A AND TRANSPORT
B PORE VOLUMES FOR FIVE CARBONS (CALGON, 2009)..............................47
TABLE 3.7 COMPARISON OF LOADING (AT 50,000 BED VOLUMES) AND CHARACTERIZATION RESULTS (R2 VALUES, +/-
INDICATES POSITIVE OR NEGATIVE SLOPE IN LINEAR REGRESSION ANALYSIS) ....................................................51 TABLE 3.8 COMPARISON OF TOC LOADING (AT 50,000 BED VOLUMES) AND CHARACTERIZATION RESULTS (R2
VALUES, +/- INDICATES POSITIVE OR NEGATIVE SLOPE IN LINEAR REGRESSION ANALYSIS).................................54
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LIST OF FIGURES FIGURE 2.1 ACTIVATED CARBON’S INTERNAL STRUCTURE: MULTIPLE LAYERS OF GRAPHITE PLATES IN RANDOM
ARRANGEMENT .....................................................................................................................................................5 FIGURE 2.2 MASS TRANSFER ZONE THROUGH AN ACTIVATED CARBON COLUMN...........................................................7 FIGURE 2.3 GEORGINA WATER TREATMENT PLANT GAC CONTACTOR ..........................................................................8 FIGURE 3.1 ELEMENTS OF RSSCT SET-UP: SAMPLING PORTS [A], FLOATING LID [B], FULL RSSCT SET-UP [C] .........37 FIGURE 3.2 RSSCT SYSTEM SCHEMATIC, TOTAL OF 6 GAC COLUMNS IN RSSCT SET-UP...........................................38 FIGURE 3.3 PARALLEL COLUMNS (CARBON A) SHOWING REPRODUCIBLE MIB BREAKTHROUGH CURVES...................42 FIGURE 3.4 COMPARISON OF DUPLICATED COLUMN RESULTS FOR GEOSMIN BREAKTHROUGH....................................42 FIGURE 3.5 MIB BREAKTHROUGH CURVE FOR CARBON B WITH GOMPERTZ CURVE FIT..............................................43 FIGURE 3.6 EXAMPLE OF MIB BREAKTHROUGH CURVES, LAKE SIMCOE.....................................................................44 FIGURE 3.7 EXAMPLE OF GEOSMIN BREAKTHROUGH CURVES, LAKE SIMCOE ..............................................................44 FIGURE 3.8 COMPARISON OF MIB BREAKTHROUGH USING CARBON B FROM TWO SOURCE WATERS ..........................45 FIGURE 3.9 COMPARISON OF IODINE NUMBERS TO MIB BREAKTHROUGH RESULTS FOR CARBONS IN FOUR WATERS...48 FIGURE 3.10 COMPARISON OF TCN VALUES TO MIB BREAKTHROUGH RESULTS FOR CARBONS IN FOUR WATERS ......48 FIGURE 3.11 MIB LOADING CAPACITY, LAKE SIMCOE ................................................................................................50 FIGURE 3.12 LOADING CAPACITY OF FIVE CARBONS AT 50,000 BED VOLUMES IN TWO SOURCE WATERS ....................50 FIGURE 3.13 TOC BREAKTHROUGH CURVES, LAKE SIMCOE.......................................................................................52 FIGURE 3.14 TOC LOADING FOR ALL FIVE CARBONS, LAKE SIMCOE...........................................................................53
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GLOSSARY
Roman Letters
Å angstrom (equivalent to 10-10 metre or 0.1 nanometer)
C0,i initial bulk phase concentration
Dg,i combined solute distribution parameter (dimensionless)
Dgp,i pore solute distribution parameter (dimensionless)
Dgs,i surface solute distribution parameter (dimensionless)
Ds,i surface diffusivity
Dp,i pore diffusivity
Edp,i pore diffusion modulus (dimensionless)
Eds,i surface diffusion modulus (dimensionless)
Ki Freundlich isotherm capacity constant
kf,i film transfer coefficient
l/ni Freundlich isotherm intensity constant (dimensionless)
L length of fixed bed
Q flow rate
R carbon particle size (mm)
Re Reynolds number (dimensionless)
Sc Schmidt number (dimensionless)
Sti Stanton number (dimensionless)
t real or elapsed time
vi interstitial velocity: v /ε
v approach velocity
X defines the dependence of the intraparticle diffusion coefficient on particle
size
Greek Letters
ε void fraction (dimensionless)
ρ density
τ fluid residence time in packed bed, empty bed contact time (EBCT)
μ viscosity of water
ix
Terms
Adsorbate substance that is being adsorbed
Adsorbent solid material to which compound (or adsorbate) is being adsorbed
(i.e., activated carbon)
Acronyms
DFPSDM dispersed-flow pore-surface-diffusion model
EBCT empty bed contact time (min)
GAC granular activated carbon
LC large column
RSSCT rapid small-scale column test
SC small column
x
GOVERNING EQUATIONS OF THE RSSCT
SC
SCSC Velocity
LengthEBCT
RSSCTs
LC
SC
SC,S
LC,S
X
LC
SC
LC
SC
t
t
D
D
R
R
EBCT
EBCT
0
2
X
LC
SC
LC,p
SC,p
R
R
D
D
X
LC
SC
LC,S
SC,S
R
R
D
D
RSSCT – Assuming Constant Diffusivity (X=0)
LC
SC
LC
SC
LC
SC
t
t
R
R
EBCT
EBCT
2
SC
LC
LC
SC
R
R
v
v
RSSCT – Assuming Proportional Diffusivity (X=1)
LC
SC
LC
SC
LC
SC
t
t
R
R
EBCT
EBCT
LC
min,SC
SC
LC
LC
SC
Re
Re
R
R
v
v
1
1 INTRODUCTION AND RESEARCH OBJECTIVES
1.1 BACKGROUND Granular activated carbon (GAC) is often used to remove natural organic matter, colour, and
micropollutants during drinking water treatment (AWWA, 2006). In the Great Lakes region, in
Canada and the United States, seasonal taste and odour episodes caused by geosmin and 2-
methylisoborneol (MIB) drive utilities to invest millions of dollars on activated carbon
contactors or filter caps to minimize consumer complaints of adverse tastes and/or smells in their
water.
Appropriate selection of GAC for taste and odour control remains a challenge as specific
information about the GAC’s adsorption performance specific to a utility’s water source is often
limited. GAC adsorption performance is affected by several factors including the organic and
inorganic chemical composition of the specific natural water being treated and the target
compounds to be removed, as well as the physical and chemical properties of the activated
carbon (Karanfil, 2006).
Often utilities will select a carbon on the basis of traditional carbon characterization tests, such as
iodine number, that may not be representative of taste and odour compound adsorption (Chen et
al., 1997). More appropriate information and characterization tests would be useful to reliably
and accurately predict adsorption performance for geosmin and MIB removal for a particular
water treatment utility. Conducting pilot or even rapid small-scale column tests can be time-
consuming and costly. This study aimed to gain an understanding of whether simpler laboratory-
scale characterization tests, or combinations of tests, can provide accurate predictions of GAC
adsorption performance.
In this study, five characterization tests were used to rank five different activated carbons
according to adsorption performance. The results of these tests were then compared to the results
of kinetic carbon column tests (rapid small-scale column tests – RSSCTs) run with two different
lake waters. The intent was to determine if any of the carbon characterization tests provided
2
useful predictions of GAC effectiveness and service life for taste and odour control. By running
RSSCTs, potentially confounding but ‘real life’ parameters of performance such as competitive
adsorption and characteristics of the source water were taken into account. Equipped with
information on which characterization tests provide the most accurate information on adsorption
performance, utilities could better ensure that the most appropriate GAC is chosen for their
source water.
1.2 RESEARCH OBJECTIVES
The overall objective of this research was to compare various carbon characterization tests used
to determine GAC adsorption capacity for geosmin and MIB with test results from kinetic, rapid
small-scale column tests (RSSCTs). Adsorption characterization tests and RSSCTs were
conducted to:
(1) Characterize the adsorption capacity of five commonly used types of GAC according to
BET surface area, iodine number, geosmin and MIB isotherms, trace capacity number
(TCN) and the trace capacity number – gas phase (TCNG).
(2) Characterize the five GACs according to MIB, geosmin and TOC breakthrough and
loading capacity according to results from RSSCTs.
(3) Investigate the relationship between the various measured carbon characteristics (iodine
number, surface area, etc.) and geosmin/MIB removal in RSSCTs.
3
1.3 DESCRIPTION OF CHAPTERS
Chapter 2 Literature review/background information on activated carbon characterization
methods and taste and odour issues.
Chapter 3 Evaluation of characterization tests for selecting carbon and a comparison of these
test results to adsorption performance with RSSCTs.
Chapter 4 Literature review and recommendations regarding the effect of competitive
adsorption between NOM and taste and odour compounds for adsorption sites of GAC.
Chapter 5 Summary of significant findings and recommendations for future research.
1.4 REFERENCES American Water Works Association (2006) AWWA Standard: Granular Activated Carbon,
ANSI/AWWA B604-05. American Water Works Association Research Foundation, Denver, CO.
Chen G., Dussert B.W. and Suffet I.H. (1997) Evaluation of granular activated
carbons for removal of methylisoborneol to below odor threshold concentration in drinking water. Water Research 31(5), 1155-1163.
Karanfil T. (2006) Activated carbon adsorption in drinking water treatment. Activated Carbon
Surfaces in Environmental Remediation. Elsevier Ltd., 345-373.
4
2 LITERATURE REVIEW
2.1 GRANULAR ACTIVATED CARBON IN WATER TREATMENT Granular activated carbon has a very large internal surface area (>500 m2/g) making it suitable
for the adsorption of a variety of contaminants during water treatment. The adsorption process
onto carbon is by no means a new water treatment technology with records showing carbon
being used for water treatment as early as 2,000 B.C. (Baker, 1949). Today, activated carbon
with its unique high adsorptive capacity is used worldwide for various applications. Within the
United States (US), 80 % of the total demand for activated carbon is for liquid-phase
applications, 55 % of which is used for the removal of water contaminants (Marsh and
Rodriguez-Reinoso, 2006).
2.1.1 BACKGROUND
Activated carbon in water treatment is used in both powder and granular form. Powdered
activated carbon (PAC) (particles < 0.05 mm) is added to the water in batches and left for a
specified contact time before being removed by flocculation, sedimentation and/or filtration
(Sontheimer et al., 1988). Granular activated carbon (GAC) (particles 0.3 - 3 mm) is placed in
the water treatment train as a fixed bed adsorber or as a cap on a granular media filter. GAC’s
higher initial cost over PAC is usually justified as GAC contactors are simpler processes to
operate, more efficient in the use of the carbon, and are capable of being reused (Herzing et al.,
1977). This research focused solely on the use of GAC.
2.1.2 PRODUCTION OF ACTIVATED CARBON
Activated carbon can be produced from almost any carbonaceous material. Common materials
used include bituminous coal, lignite coal, coconut shells and wood. These raw materials are
fired in the absence of oxygen in a step referred to as carbonization. The activation process that
follows carbonization is a carefully controlled process in which the carbon is heated at extremely
high temperatures (315 - 925ºC) in the presence of carbon dioxide or steam. This activation
process disrupts the orderly arrangement of the graphitic plates of the carbon creating a vast
5
network of pores of different shapes and sizes throughout the cross-linked graphitic crystallite
planes (Figure 2.1). As a result, activated carbon exhibits an extremely large surface area (greater
than 500 m2/g) and the capacity to adsorb dissolved organic material.
Figure 2.1 Activated carbon’s internal structure: multiple layers of graphite plates in random arrangement
(Image: Calgon Carbon Corporation)
2.1.3 ADSORPTION MECHANISMS
Adsorption of trace contaminants may be due to various combinations of chemical, electrostatic
and physical interactions (Karanfil, 2006). A carbon’s adsorption capacity and surface chemistry
are two main factors affecting its capacity to remove a given micropollutant. Adsorption capacity
is usually attributed to a carbon’s internal pore volume (Considine et al., 2001). The activated
carbon’s surface chemistry is also important as the adsorption of the pollutant is preceded by the
adsorbate displacing the water to reach the surface of the carbon (Considine et al., 2001,
Pendleton et al., 1997). There are two broad adsorption mechanisms: physiosorption and
chemisorption.
In water treatment, physiosorption or physical adsorption is the principle mechanism for the
removal of organics (MWH, 2005). There are three main interactions that should be considered
when examining physical adsorption onto an activated carbon: adsorbate-water interactions,
adsorbate-carbon surface interactions and water-carbon surface interactions. Adsorption capacity
is determined by the strength of the adsorbate-carbon surface interactions compared to the other
interactions. Adsorbate-carbon surface interactions depend on both physical and chemical factors
6
(Karanfil, 2006). Physical factors such as size distribution of pores across the activated carbon
and adsorbate molecular dimensions, will determine the accessible surface area available for
adsorption (Karanfil, 2006).
The principle attractive force between the adsorbate and the activated carbon is the dispersion
force or London-van der Waals force. Adsorbate molecules are attracted by van der Waals forces
and attach themselves to the surface of the activated carbon. As van der Waals forces are directly
related to the polarizability of the adsorbate and the adsorbent, a stronger attraction between
adsorbate and the activated carbon surface will exist with increasing polarizability and size.
Therefore, larger and more non-polar compounds will adsorb more easily and strongly to
activated carbon. Physical adsorption is a reversible process and thus desorption of adsorbate
compounds must always be considered.
When examining chemisorption or chemical adsorption, the type of reaction occurring on the
surface of the carbon, the molecular structure of the adsorbate and the chemistry of the solution
all affect the adsorbate-carbon surface interactions. Chemical adsorption and physical adsorption
are sometimes difficult to differentiate. Chemical adsorption occurs as the reaction between the
adsorbate and the surface of the activated carbon forming a covalent or an ionic bond. The
charged surface attracts opposite charges and repels like charges as stated by Coulomb’s law
(MWH, 2005).
The specific adsorption mechanisms for the two major taste and odour compounds, 2-
methylisoborneol (MIB) and geosmin, are still being explored in the research, however, several
hypotheses have been made. The main mechanism generally agreed upon in the literature for the
adsorption of micropollutants is that of physical adsorption in micropores. Within the
micropores, opposing pore walls are close enough together to create a site of overlapping
adsorption forces and the pore size is similar to the molecular size of the adsorbates being
targeted for removal. Several studies also show that contaminants with low solubility and a
molecular size and shape similar to those of the pore sites available in the activated carbon are
the most readily adsorbed (AWWARF, 2007). Newcombe et al. (1997) speculated that the most
likely adsorption mechanism for MIB is hydrophobic attraction to the carbon surface or through
a specific mechanism involving the alcohol functional group. Other studies on the removal of
7
organic compounds agree with the removal mechanism being hydrophobic interactions
(AWWARF, 2007).
Mass Transfer Zone
In a downflow activated carbon filter, as the adsorbate enters the top of the bed, it continues to
saturate the bed beginning at the inlet, but follows a distinctive concentration profile as it
continues down the bed. The area in the activated carbon bed where adsorption is occurring is
known as the mass transfer zone (MTZ). The MTZ is the length of bed required for the adsorbate
to be transferred from solution into the carbon. Once the front of the MTZ reaches the effluent,
breakthrough of the adsorbate has occurred (Figure 2.2). The concentration found in the effluent
will continue to increase until it approaches the influent concentration at which point the
activated carbon bed is considered to be exhausted or spent. The term breakthrough is usually
defined according to the treatment objective. In drinking water applications, the odour threshold
concentration (see Section 2.2.2), or concentration at which consumers detect a particular
compound, is the most apt way with which to define breakthrough. When consumer complaints
begin to be reported due to taste or odour in their drinking water, the treatment objectives of the
activated carbon are no longer being met.
Figure 2.2 Mass transfer zone through an activated carbon column
(adapted from Vermeulen, 1958) (C0: influent concentration, C: Effluent concentration, tb: time to breakthrough, te: time to bed exhaustion)
1 0
C/C
0
Used/spent carbon Unused carbon
tb te Operating time, t
8
2.1.4 USES FOR ACTIVATED CARBON
In the modern era, the earliest applications of activated carbon were for industrial use in
decolourization of sugar in the late 18th century. Activated carbon went on to be used for the
treatment of polluted air, recycling of solvents and in the purification of by-products from
chemical, pharmaceutical and food manufacturing processes (Sontheimer et al., 1988). In 1920,
activated carbon increasingly became popular as part of water treatment processes, mainly for
the removal of taste and odour compounds. The highly controlled and precise activation process
used to produce activated carbon is capable of producing specific carbons with different
properties appropriate for a wide variety of applications.
A very common application for activated carbon in drinking water treatment today is for the
control of taste and odour episodes. Some utilities apply seasonal control measures such as the
use of PAC; however, many have installed granular activated carbon contactors that remain
operational all year round. A properly designed and operated GAC contactor can be operated for
several years to reduce T&O compound concentrations (Figure 2.3). Length of use depends on
source water characteristics including the presence of NOM which compete for adsorption sites
with taste and odour target compounds (Newcombe et al., 1997; Pelekani and Snoeyink, 1999),
the activated carbon chosen (Chudyk et al., 1979; Lalezary et al., 1986; Newcombe et al., 2002)
and the flow rate.
Figure 2.3 Georgina water treatment plant GAC contactor
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2.2 TASTE AND ODOUR ISSUES
The aesthetics of drinking water has a large influence on consumer perception of their drinking
water. A utility ultimately seeks to make the water potable and palatable. It is speculated that
taste and odour episodes will continue to increase with the presence of zebra mussels clarifying
water in the Great Lakes, leading to increased temperatures and resulting improved conditions
for the growth of algae (Anderson and Quartermaine, 1998). More frequent complaints in certain
municipalities may therefore be expected from consumers due to taste and odour in their
drinking water. Consumers may also perceive a risk to their health due to taste and/or odour in
their water, resulting in a loss of consumer confidence.
A survey completed of 377 water utilities in Canada and the US by the American Water Works
Association (AWWA) stated that, “fiscal resources spent by water utilities to control taste and
odor problems averages $67,800, representing an average of 4.5 percent of their total budget”
(Suffet et al., 1996). Tools to predict adsorption capacity, not only for organic compounds in
general, but specifically for taste and odour, are therefore needed to allow utilities to make the
most cost-effective choice.
2.2.1 TASTE AND ODOUR COMPOUNDS The known causes of taste and odour (T&O) are summarized in a drinking water taste and odour
wheel by Suffet et al. (1999). Of particular interest for the Great Lakes region in Canada and the
US are the two naturally occurring compounds which produce earthy-musty odours in water,
geosmin and 2-methylisoborneol (MIB). While other compounds also cause earthy-musty odours
(2-isopropyl-3-methoxy pyrazine (IPMP), 2-isobutyl-3-methoxy pyrazine (IBMP), and 2,3,6-
trichloroanisole (TCA)), geosmin and MIB are considered the major compounds in the earthy-
musty category (Lalezary et al., 1986) and are the most commonly identified (Rao et al., 2003).
Physical and chemical characteristics of geosmin and MIB are shown in Table 2.1.
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Table 2.1 Chemical and physical characteristics of geosmin and MIB (Pirbazari et al., 1992) Name Geosmin 2-Methylisoborneol (MIB) Molecular structure*
Molecular formula
C12H22O C11H20O
Molecular weight 182 g/mol 168 g/mol Kow 3.70 3.13
*(Image source for structures: National Library of Medicine, ChemIDplus Advanced, http://chem.sis.nlm.nih.gov/chemidplus/)
Geosmin and MIB are produced naturally by planktonic and benthic algae, most commonly
cyanobacteria, fungi, bacteria and actinomycetes (Lloyd et al., 1998). Specifically, geosmin is
produced by blue-green algae such as Oscillatoria simplicissima and Anabaena scheremetievi
and MIB is a product of certain blue-green algae (Oscillatoria curviceps and Oscillatoria tenius)
and Actinomycetes (Herzing, 1977). Both compounds are considered semi-volatile and produce
an earthy, musty odour in drinking water. Taste and odour episodes are a seasonal issue in the
Great Lakes region, with studies from Lake Ontario, Canada showing that episodes occur in the
summer months due to high water temperatures creating algae blooms and bacterial growth in
the lake (Rao et al., 2003; Ridal et al., 2001). Rao et al. (2003) reported that geosmin production
peaks annually but is not always found to be at offensive levels.
Both geosmin and MIB are low molecular weight, tertiary alcohols. MIB has a hydrocarbon
skeleton containing one hydroxyl group, making it relatively hydrophobic (Considine et al.,
2001; Pendleton et al., 1997). MIB has a molecular weight of 168 g/mol (Newcombe et al.,
2002a) and is roughly spherical in shape with a diameter of 0.6 nm (Pendleton et al., 1997).
1,2,7,7-tetramethyl-2-norborneol, or geosmin, which directly translates to earthy smell, has a
molecular weight of 182 g/mol.
11
MIB has generally been found to be the least adsorbable compound to activated carbon of the
five earthy-musty compounds listed above and is therefore often chosen as the model compound
in activated carbon taste and odour studies (Chen et al., 1997). Other studies have shown that
MIB is readily adsorbed by microporous carbon unless competing with compounds for
adsorption (Newcombe et al., 2002b). Adsorption of MIB has also been found to be more
affected than geosmin by the presence of humic acid and glycolic acid. Sugiura et al. (1997)
hypothesized this was due to the difference of pore size required by geosmin and acids for
adsorption as well as the difference in molecular structure of the two musty odour compounds.
2.2.2 ODOUR THRESHOLD CONCENTRATION
The odour threshold concentration (OTC) of a compound is the concentration at which
consumers can detect that compound in their water. Both geosmin and MIB may be detected by
humans at extremely low concentrations. A recommended reduction of MIB to below 10 ng/L
was given by Chen et al. (1997). Although the OTC for both these compounds varies slightly
across the literature, thresholds as low as 9 ng/L and 4 ng/L have been reported for MIB and
geosmin, respectively (Kim et al., 1997; Pirbazari et al., 1993).
2.2.3 COMPETITION WITH NOM
Commercial activated carbons are generally designed for drinking water treatment applications
to deal with the removal of small molecular weight hydrophobic organic contaminants and not
specifically the removal of dissolved organic material (DOM) (Dastgheib et al., 2004). Although
its primary use may not be to remove DOM, competition of DOM with targeted compounds
necessitates the understanding of the competition effects that exist between these compounds.
Adsorption capacity for trace contaminants has been reported to be reduced due to competition
with natural organic matter (NOM) in numerous studies. The extent to which competitive
adsorption has an effect is dependent on the initial concentration of the trace contaminant (Najm
et al., 1991), the molecular structures of the NOM and the trace contaminants (Herzing et al.,
1977; Newcombe et al., 1997, 2002b) and the type of activated carbon (Chen et al., 1997;
Pelekani and Snoeyink, 1999).
12
Competition of trace contaminants with NOM for adsorption sites will be described in more
detail in Chapter 4.
2.3 ACTIVATED CARBON CHARACTERIZATION FOR MICROPOLLUTANT
CONTROL In order to choose the most appropriate activated carbon for micropollutant removal, a clear
understanding of its adsorption capacity is required. Carbon characterization for adsorption
capacity is conducted using three main parameters: physical properties, activity indices (isotherm
tests) and kinetic tests. Ideally, a combination of all of these parameters should be used to enable
a utility to make a more accurate and educated choice when purchasing an activated carbon.
When it comes time for a utility to purchase an activated carbon, the carbon manufacturer will
generally provide information on the adsorption capacity of the carbon. An activated carbon’s
adsorption capacity is commonly described first by the carbon’s physical characteristics and
secondly by using simple lab-scale tests such as the iodine number or the tannin number. Studies
have shown, however, that these tests are not always reliable in predicting activated carbon
performance. Although they serve as a good starting point to narrow down the choice of
activated carbon, more precise tests catered to taste and odour adsorption capacity are needed.
Chen et al. (1997) suggests the use of isotherm tests using the compound of interest (i.e.,
geosmin or MIB) in organic pure water1 noting that they are the true representation of the
inherent adsorption potential of a particular activated carbon. Beyond isotherm tests, additional
tests need to be conducted in order to consider kinetics within the activated carbon bed and the
influence of characteristics from the natural source water that will vary from season to season.
Rapid small-scale column tests are an example of a bench-scale test used to design and evaluate
full-scale granular activated carbon contactors.
2.3.1 DETERMINATION OF PHYSICAL PROPERTIES
The physical properties of an activated carbon contribute information needed to understand a
carbon’s adsorption capacity. Activated carbon’s porous nature is the key component
contributing to its ability to adsorb large quantities of organics (Sontheimer et al., 1988). When
examining the porosity of an activated carbon, pore size, pore shape, pore volume, pore size
1 Organic pure water was obtained by glass-distilling Milli-Q® water.
13
distribution, and surface area of the carbon are all important parameters to consider (Sontheimer
et al., 1988).
The adsorption process takes place in four steps: bulk solution transport, film diffusion transport,
pore and surface transport and adsorption (Hand et al., 1983). These steps occur on the outer
surface of the adsorbent as well as within the carbon’s pore structure. An understanding of the
pore size distribution provides key information on the adsorption process and, subsequently, how
a particular activated carbon will perform in adsorbing an adsorbate of interest.
Pores within an activated carbon are split into four size categories by the Union of Pure and
Applied Chemistry (IUPAC): macropores (≥500 Å), mesopores (20 - 500 Å), secondary
micropores (7 - 20 Å) and primary micropores (≤7 Å) (Lastoskie et al., 1993). Although there is
very little adsorption that occurs in the macropores, this region is very important in the diffusion
process (Sontheimer et al., 1988). The surface area in macro- and mesopores is very small and
thus the amount of material adsorbed on these sites is considered negligible. There is an inverse
relationship between pore size and surface area. Thus, a larger number of small pores for a given
pore volume will yield a larger surface area for the activated carbon (MWH, 2005). The majority
of the internal surface area of an activated carbon is within the micropores. As a result, most of
the adsorption of organic compounds occurs in the micropores (Karanfil, 2006).
Several tests exist to measure the pore volume and surface area of an activated carbon. These
tests involve exposing the activated carbon to a certain amount of adsorbate (in liquid or gas
form) and measuring the quantity of adsorbate that is taken up by the carbon. Various dosages of
carbon are exposed to the quantity of adsorbate until equilibrium is reached (at a constant
temperature) indicating what is referred to as the adsorption isotherm for that adsorbate. Mercury
porosimetry is often used to evaluate the pore volume and surface area distribution in the
macropore and mesopore range. Nitrogen isotherms (at liquid temperature, 77K) are commonly
used to determine mesopore and micropore volumes. Brunauer, Emmett and Teller (1938)
developed an isotherm method that is still commonly used to indicate the specific surface area of
an activated carbon, the BET surface area.
14
Adsorption Isotherms
At equilibrium, an adsorption phase concentration, or amount of adsorbate (mg) per gram of
adsorbent, can be calculated using Equation 2-1; where qe is the adsorbent phase concentration
after equilibrium (mg adsorbate/g adsorbent), C0 is the initial concentration of the adsorbate
(mg/L), Ce is the equilibrium concentration of the adsorbate after adsorption has occurred
(mg/L), V is the volume of liquid in the reactor and m is the mass of the adsorbent (g). Three
main theories exist to determine adsorption capacity of adsorbents using this adsorption phase
concentration and the data from the isotherm test: Freundlich, Brunauer Emmett and Teller
(BET) and Langmuir theories.
m
)VC(Cq e0
e
(2-1)
The Freundlich isotherm is most commonly used to describe adsorption capacity of activated
carbon in the water treatment context. The Freundlich isotherm is an appropriate empirical
equation as it describes heterogeneous adsorbents, or adsorbents with varying site energies, such
as activated carbon (MWH, 2005). The Freundlich isotherm is the following (Equation 2-2):
nefe CKq
m
x 1 (2-2)
where x/m is the mass of the adsorbate (mg) adsorbed per unit mass of the adsorbent (g) after
equilibrium, Kf is the Freundlich capacity factor ((mg adsorbate/g adsorbent) x (L water/mg
adsorbate)1/n), 1/n is the Freundlich adsorption intensity parameter (unitless) and the other terms
are as defined above. The Freundlich intensity parameter varies widely for each adsorbate being
considered and must be determined for each compound being studied.
The BET theory describes the adsorption of gases onto a solid surface with the assumption that
adsorption occurs in multiple layers. The BET theory accounts for multiple layers in which
adsorption occurs, however, it maintains that site energy is the same for the first layer and equal
to free energy of precipitation for subsequent layers. This differs from the Langmuir model
which assumes that adsorption site energy is the same for all sites and that the largest capacity
occurs on one monolayer. This assumption makes the Langmuir equation invalid for activated
carbon adsorption measurement as activated carbon, as previously mentioned, has a wide range
of pore sizes that will continue to adsorb organics even as the adsorbate concentration increases.
15
Pore Size Distribution
Pore size distribution in carbon is an important property influencing the adsorption process
(Pelekani and Snoeyink, 1999). The pore size distribution provides information on the fraction of
total pore volume that will be available for adsorption by an adsorbate of a certain size. Pore size
distribution has a large impact on competitive adsorption (Pelekani and Snoeyink, 1999).
Newcombe et al. (1997) studied the pore size distribution of an activated carbon using adsorption
of nitrogen (77K) and BET plot and noted that the adsorption of different size fractions of natural
organic matter (NOM) had significant effects on the surface area and pore volume distributions
available to MIB. When NOM consisted of small compounds similar to MIB, competitive
adsorption competition was greatest due to direct competition between NOM and MIB for the
pore sites.
Additional Important Physical Characteristics
Other physical characteristics such as apparent density, moisture content, hardness and abrasion
number are also important. The apparent density is defined as the mass of carbon per unit volume
of carbon bed, including the pore volume (MWH, 2005). Apparent density enables the packed
density of a carbon bed to be determined. Activated carbons with higher density are generally
preferred. Hardness and abrasion number are both important parameters to consider to minimize
costs due to loss of carbon from carbon contactors. Both parameters are also indicators of a
carbon’s ability to withstand frequent backwashing and repeated handling during regeneration.
2.3.1.1 DISTRIBUTION OF ENERGY SITES Adsorption energy will vary within the porous structure of an activated carbon. Adsorption
energy is determined by the amount of surface area with which an adsorbate comes in contact.
Adsorption energy for micropollutants is greatest in micropores because multiple contact points
exist between the adsorbate and the activated carbon surface allowing for multiple surface forces
to overlap resulting in increased adsorption forces (Pelekani and Snoeyink, 1999; Karanfil,
2006). Within the carbon the larger pore sizes are considered lower energy pores. If the graphitic
plates within the carbon are further apart, the adsorption energy decreases. If the plates are too
far apart, then no adsorption energy exists, however, the space, known also as transport pores,
provides avenues for the adsorbates to enter the wide variety of adsorption pore structures (CCC,
2002).
16
Different activated carbons will display different distributions of adsorption energy, each
providing varying proportions of high versus low energy pores. The raw material and the
activation process play a large role in producing the activated carbon best suited for specific
applications. If a particular pore does not have the required amount of pore energy to remove a
specific compound, the compound will not be adsorbed in that location. Compounds requiring
less energy may adsorb to lower energy adsorption sites. Therefore, if the purpose of an activated
carbon application is to remove larger and more easily adsorbed compounds (i.e., low solubility,
high molecular weight) an activated carbon with more low energy pores would be selected.
Easily adsorbed compounds can adsorb in all of the adsorption pore structure, in both high and
low energy sites. Generally, carbons with high overall surface area will be best suited for
removal of these compounds.
A specific consideration would include if the intended use for the activated carbon involves
removal of compounds more difficult to adsorb (i.e., high solubility, low molecular weight) or
compounds at trace concentration levels, a carbon with more high energy pores would be
required. Studies have shown that compounds are likely to adsorb in a pore approximately the
same size as the adsorbate due to more contact points with the carbon and a resulting more
favourable adsorption energy (Newcombe et al., 1997). Lower energy pores would not be
utilized by these compounds.
2.3.2 DETERMINATION OF ADSORPTION CAPACITY As described in Section 2.3.1, adsorption isotherms are helpful in determining the adsorption
capacity of an activated carbon. However, adsorption isotherms, as thermodynamic tests, will
only provide a guideline to what type of carbon is needed for a particular purpose, i.e., taste and
odour control.
Iodine number
Iodine number is the most common indicator of activated carbon adsorption capacity provided to
utilities by carbon manufacturers. The iodine number is the measure of iodine (I2) adsorbed from
a 0.1 N solution by a gram of activated carbon when the residual concentration of the solution is
17
0.02 N. The iodine number measures total energy sites in an activated carbon rather than
specifying between low and high energy sites.
Additional Conventional Equilibrium Adsorption Capacity Tests
Other adsorption isotherms are conducted to provide information on the amount of a certain
surrogate that will be adsorbed to the activated carbon. Surrogates include phenol, butane,
molasses and tannic acid. The phenol test is the amount of carbon required to reduce phenol
concentration from 200 g/m3 to 20 g/m3. Phenol has a high aqueous solubility and is a potential
pollutant in certain source waters. A butane activity test can be conducted to determine the
micropore volume of an activated carbon. It is a measure of the ability of a carbon to remove
butane from dry air. The molasses test reveals the amount of carbon necessary to decolourize a
standard molasses. Due to the large molecular weight of the colour producing substances in the
molasses, this test is not as applicable to the drinking water industry but rather is relevant for the
sugar processing industry and decolourization purposes. The tannin value test measures the
concentration of GAC (in mg/L) that is required to reduce the standard tannic acid concentration
from 20 mg/L to 2 mg/L (AWWA, 2006). These surrogates, each with differing molecular sizes,
provide information on the pore sites with corresponding sizes that exist within the carbon.
Trace Capacity Number (TCN) test
In order to determine the adsorption capacity for trace contaminants, a more appropriately sized
surrogate is necessary. The Trace Capacity Number (TCN) test is performed to measure the trace
adsorption capacity of carbon. The TCN method has been verified for coconut carbons and
bituminous coal based virgin, reactivated, and calcined carbons (CCC, 1999). The trace capacity
number test is conducted in both liquid (TCN) and gas phase (TCNG) using acetoxime solution
and tetrafluoromethane gas, respectively, as indicator compounds to determine adsorption
capacity.
The liquid phase TCN test is included in the appendix of the AWWA Granular Activated Carbon
Standard (ANSI/AWWA B604-05) as a surrogate adsorption capacity test to the iodine number
test for GAC (AWWA, 2006). The method involves three different known weights of carbons
being treated with a standard acetoxime solution for a specific contact time. The trace capacity
number is the mass (mg) of acetoxime adsorbed onto 1 mL of activated carbon at a 30 mg/L
18
residual concentration. This value indicates the trace adsorption capacity of the carbon being
tested.
The gas phase TCN test or TCNG is defined as the ratio (in g/100mL) of the mass of
tetrafluoromethane (CF4) adsorbed by a volume of activated carbon sample when the carbon is
saturated with CF4 vapour. The TCNG method is modified based on the butane number method
as described in ASTM D5742-95.
2.3.3 RAPID SMALL-SCALE COLUMN TESTS Several studies have noted that conventional equilibrium tests such as iodine and tannin numbers
are inconsistent at predicting adsorption capacity of a carbon (Chen et al., 1997; Sontheimer et
al., 1988). Adsorption isotherms such as the Freundlich isotherm test using the contaminant of
interest were found to be a better indicator of performance. These adsorption isotherms,
however, are thermodynamic tests and do not provide any information on the kinetics of
adsorption. Information on adsorption kinetics is required to compile a more complete
understanding of GAC performance. Kinetic bench-scale or pilot tests are needed to ensure that
the carbon chosen best suits the utilities’ source water.
Rapid small-scale column tests (RSSCTs) are kinetic tests used extensively to help in the design
and evaluation of full-scale GAC adsorption processes. There are three main advantages in using
a RSSCT: (1) a RSSCT can be completed in a fraction of the time it would be required to do a
pilot study, (2) extensive isotherm or kinetic studies are not needed to predict full-scale
performance from a RSSCT and (3) only a small volume of water is needed for a RSSCT,
allowing the test to be completed easily in a laboratory setting (Crittenden, 1987).
Several articles over the past 20 years have described the design and the successful
implementation of the RSSCT. This section serves to show the development and discussion of
the RSSCT in the literature.
A mathematical model of the adsorption process in a packed media bed was developed prior to
the RSSCT. It is called the dispersed-flow, pore-surface-diffusion model (DFPSDM) and was
referenced by Crittenden et al. (1987). The DFPSDM was used in scaling down the full-scale
19
adsorber as it contains many of the mechanisms that occur in fixed-bed adsorption. The
DFPSDM maintains that the adsorption process is a function of (1) advective flow, (2) axial
dispersion and diffusion, (3) liquid-phase mass transfer resistance, (4) local adsorption
equilibrium at the exterior surface of the carbon, (5) surface diffusion, (6) pore diffusion, and (7)
competitive equilibrium of solutes upon the carbon surface.
Work by Berrigan (1985) reviewed the DFPSDM and noted the presence of six dimensionless
groups in the governing equations of the model. It was proposed that if these 6 groups were kept
constant between the small and large carbon columns, there would be exact similitude between
the two columns. Caveats to the RSSCT are that backwashing effects are not considered in the
model, the scaling procedure is based on the DFPSDM and therefore will only work in situations
where the DFPSDM applies and finally, the effect of biological activity within the carbon bed is
ignored (Crittenden, 1987). RSSCTs also are generally run using a single batch of water and
therefore will not take into account variations of water quality (i.e., seasonal or climate event
related) at full-scale.
The six dimensionless groups are:
1. Surface solute distribution parameter, Dgs,i
i
ieais C
qDg
,0
,,
1
(2-3)
) )( (
) 1)( )( (
ionconcentratphasebulkinitialfractionvoid
fractionvoidqFreundlichdensitycarbon
2. Pore solute distribution parameter, Dgp,i
1,
pipDg (2-4)
) (
) 1)( (
fractionvoid
fractionvoidfractionpore
Note: Void fraction is equal to the pore fraction plus the empty space between the carbon
particles in the carbon bed.
20
3. Modified Stanton number, Sti
R
kSt i,f
i
1 (2-5)
)fraction void)(radius particle carbon(
)fraction void)(bed in time residence fluid)(tcoefficien transfer film(
1
4. Pore diffusion modulus, Ed p,i
2R
DgDEd i,pi,p
i,p
(2-6)
2)radius particle carbon(
)bed in time residence fluid)(parameter ondistributi solute pore)(ydiffusivit pore(
5. Surface diffusion modulus, Ed s,i
2R
DgDEd i,si,s
i,s
(2-7)
2)radius particle carbon(
)bed in time residence fluid)(parameter ondistributi solute surface)(ydiffusivit surface(
6. Peclet number, Pe i,D
i
S
i De
vL
Pe (2-8)
tydispersivi axial
velocity) erstitial)(intlength bed(
The Stanton number represents film transfer effects, the Peclet number represents dispersive
effects, and intraparticle diffusion is represented by the Surface and Pore Diffusion moduli.
Berrigan (1985) notes that in most fixed-bed adsorption processes, the rate of film transfer
(shown by the Stanton number) is rarely the limiting mass transfer mechanism. The surface
diffusion modulus, however, is often an important consideration with mass transport often being
controlled by surface diffusion. Berrigan states that there is only one set of circumstances where
all six dimensionless parameters can be maintained equal between the large and small columns.
For this to occur, surface diffusivity, DS, must be independent of carbon particle size (i.e.,
constant diffusivity). The dimensionless parameters are used to create the following two
21
governing equations that must be satisfied to ensure similitude between the large and small
columns:
LC
SC
SC,S
LC,S
LC
SC
LC
SC
t
t
D
D
R
R
EBCT
EBCT
2
(2-9)
and with DS,LC = DS,SC this equation reduces to:
LC
SC
LC
SC
LC
SC
t
t
R
R
EBCT
EBCT
2
(2-10)
For the case of constant diffusivity, to ensure similitude in terms of dispersion and film transfer
effects (St and Pe), the following equation is used:
SC
LC
LC
SC
R
R
v
v (2-11)
Combining the above 2 equations, it is determined that the small column length is dictated by
carbon particle sizes:
LCSC
LCSC v
R
Rv
LCLC
SCSC EBCT
R
REBCT
2
Since, Velocity Approach
LengthEBCT
LC
LC
LC
SC
SC
SC
Velocity Approach
Length
R
R
Velocity Approach
Length2
LC
LCSC
LCLC
LC
SCSC Velocity Approach
Velocity ApproachR
RLength
R
RLength
2
The above equation reduces to: LCLC
SCSC Length
R
RLength
(2-12)
Crittenden et al. (1987) argue that intraparticle diffusivity normally controls the adsorption
process. Therefore, Equation 2-11 may be ignored to an extent without compromising the results
22
in an effort to have more control over the calculated small column length. The limitation is that
the Reynolds number (2-14) for the small column should remain in the mechanical dispersion
region, which evidence suggests corresponds to a minimum value of approximately 1 (Crittenden
et al., 1991). As such, Equation 2-11 can be modified to yield a recommended minimum small
column superficial velocity:
LC
min
SC
LCLCSC
SC
LCLC Re
Re
R
Rvv
R
Rv
1 (2-13)
LCiLC
dvRe (2-14)
Where, d = diameter of particles (m) i = interstitial velocity (m/s) [approach velocity/porosity of bed] = density of the fluid (kg/ m³) μ = dynamic viscosity of fluid (Pa·s or kg /m·s) LC = large column
In the more general circumstance where surface diffusivity may vary with particle size
(proportional diffusivity), one cannot simultaneously satisfy all six dimensionless parameters. In
this case, Crittenden et al. (1987) suggest ensuring solely that similitude in terms of intraparticle
diffusivity is maintained (i.e., Equation 2-9). Efforts to maintain similar Stanton and Peclet
numbers are abandoned.
Another important parameter in the RSSCT is one which allows the comparison of breakthrough
curves, regardless of bed size and is termed bed volumes (BV) (Equation 2-15):
EBCT
t
V
VBV
F
W (2-15)
Where VW is the volume of water treated, VF is the volume of the carbon filter bed, t is the time
(min) over which the water passed through the bed and EBCT is the empty bed contact time
(min).
23
2.3.3.1 ASSUMPTIONS MADE WITH THE RSSCT Constant Diffusivity
To obtain the RSSCT equation: LC
SC
LC
SC
LC
SC
t
t
R
R
EBCT
EBCT
2
the following assumptions are made:
1. Constant surface diffusivity (surface diffusivity of GAC is identical in both full-scale
and RSSCT systems)
2. The solute distribution parameters are the same for carbons in both small and large
columns, implying that the following properties are identical:
a. Equilibrium capacity
b. Bed void fractions
c. Carbon particle densities
d. Influent concentrations
3. Pore diffusivities and surface diffusivities are identical in large and small columns.
4. The LC
SC
t
tratio indicates the time saved using a RSSCT and is determined by the amount
of reduction of the large column GAC particles.
Equation SC
LC
LC
SC
R
R
v
v ensures that Reynolds number of the small-scale process is the same as the
large scale process (with SCv and LCv being the superficial velocities, or loading rates of the
small and large columns, respectively).
Proportional Diffusivity
The EPA Bench- and Pilot-Scale manual highlights that several researchers have had positive
results scaling their column tests, especially for removal of NOM as measured by TOC and
UV254, assuming proportional diffusivity (McGuire et al., 1989; Summers and Crittenden; 1989
Summers et al., 1992; Wallace et al., 1988).
To obtain the RSSCT equation for proportional diffusivity: LC
SC
LC
SC
LC
SC
t
t
R
R
EBCT
EBCT
the
following assumptions are made:
24
1. Surface diffusivity is linearly dependent on/proportional to carbon particle size
2. Surface diffusion is the controlling process
3. Similitude for St and Pe numbers is abandoned
Example Calculation Using Proportional Diffusivity For Carbon A, 100 x 325 mesh screens used for RSSCT – Geometric Mean Particle Size is 0.0890mm. EBCTSC = 0.39 min EBCTLC = 7.5 min RSC = 0.089 mm RLC = 1.6 mm (8 x 30 mesh)
LC
SC
LC
SC
LC
SC
t
t
R
R
EBCT
EBCT
And, VLC = 4.1 gpm/ft2
RSC = 0.089 mm RLC = 1.6 mm Re SC,min = 1 (*as suggested in Crittenden et al., 1991)
Re LC = 11648
LC
SC
SC
LC
LC
SC
R
R
V
V
Re
Re min,
11648
1
0890
61
14 2
mm.
mm.
ft/gpm.
VSC = 0.0015gpm/ft2 = 2.45 L/s/m2
Hydraulic loading rate of small-scale column (VSC) is 2.45 L/s/m2. As stated in Crittenden et al. (1991) the minimum column-diameter to particle size ratio should
be 50 to avoid channelling within the column. Therefore,
Column diameter = 0.46cm = 0.046mm Particle size (small-scale) = 0.089mm
Ratio 0.046:0.089 is greater than 50, thus minimizing the ‘wall effect’.
25
2.3.3.2 CONSTANT OR PROPORTIONAL DIFFUSIVITY?
In the event where it is unknown as to whether surface diffusivity is independent (constant
diffusivity) or dependent (proportional diffusivity) on carbon particle size, isotherm tests can be
conducted as described by Hand et al. (1983) to determine the relationship.
In order to determine the appropriate scaling factor for the mini-column tests, batch tests using
carbon of different sizes along with the adsorbent of interest, in the water matrix of interest, are
performed, with a plot of C/Co versus t/(RSC)2 and t/(RLC)2 examined. In the plot, for a given
C/Co, the average ratio of t/(RSC)2 to t/(RLC)2 is equal to the ratio of Ds,SC to Ds,LC. The ratio could
then be used in Equation 2-14 to obtain the governing equation for the mini-column tests.
LC
SC
SC,S
LC,S
LC
SC
LC
SC
t
t
D
D
R
R
EBCT
EBCT
0
(2-16)
Where, R = carbon particle size t = real or elapsed time C/C0 = effluent concentration divided by influent concentration DS = surface diffusion coefficient SC = small-scale LC = large scale In the case of proportional diffusivity, with similitude for St and Pe numbers abandoned, it is
only Equation 2-14 which governs the design. This means that different combinations of
superficial velocity, vSC, and column length, LSC, can be selected, so long as the ratio of LSC/vSC
is equal to EBCTSC. Crittenden et al. (1987) advise, however, that to ensure that dispersion
effects remain negligible, velocities and lengths need to be selected according to Equation 2-13.
Studies conducted to date have found that when NOM is present PD-designed columns best
predicted NOM breakthrough curves (Summers et al., 1989). The CD-designed columns
predicted earlier breakthrough as particle size decreased (EPA, 1996).
26
2.3.4 THE ACCELERATED COLUMN TEST The ACT and RSSCT are very similar. The ACT test is the in-house small-scale column test
used by Calgon Carbon Corporation to predict activated carbon performance. The principal
difference lies in the exponent that is used in the scaling factor equations. This exponent is
derived experimentally rather than by making assumptions about the system having constant or
proportional diffusivity.
The governing equation for the ACT relates the length of the mass transfer zones to the mean
carbon diameter:
SC
LC
SC
LC
R
R
MTZ
MTZ (2-17)
Calgon Carbon Corporation (CCC) determines the alpha factor experimentally by adsorbing a
solution of acetoxime in three identical micro-column tests where the columns contain the same
mass of carbon but with different mean particle diameters. The alpha factor CCC consistently
found in their tests was approximately 1.1 under most drinking water conditions.
The MTZ is defined here as the amount of time between 1 % and 50 % breakthrough of
acetoxime. A plot of ln(MTZ) versus ln(R) yields the alpha factor as the slope. Note that any
percent breakthrough can be used for the determination of the alpha factor, as long as it is
consistent among the three micro-columns. Equation 2-15 therefore is similar to the RSSCT
Equation 2-9, where the ratio of MTZs is equivalent to the ratio of treatment times, tSC and tLC:
RSSCT equation: LC
SC
SC,S
LC,S
LC
SC
LC
SC
t
t
D
D
R
R
EBCT
EBCT
2
Re-written ACT equation:
LC
SC
LC
SC
LC
SC
R
R
t
t
MTZ
MTZ (2-18)
As mentioned above, Calgon reports that the alpha factor is normally 1.1. The derivations of the
RSSCT suggest that the scaling factor, if controlled by intraparticle diffusivity, can be between 2
(constant diffusivity) and 1 (diffusivity is exactly linearly proportional to particle size). It is
27
perhaps logical that the “true” situation should fall between 1 and 2: i.e., 1.1. No information is
available, however, to support the claim that 1.1 is almost always the appropriate scale factor.
With the ACT, to select the length of the columns, the following equation is used:
LC
SC
lC
LC
SC
SC
LC
SC
R
R
v
Lv
L
EBCT
EBCT (2-19)
Calgon recommends that similar approach velocities be used for the small- and large-columns,
reducing Equation 2-19 to:
LC
SCLCSC R
RLL (2-20)
By keeping the large- and small-column approach velocities equal, the ACT violates the only
circumstance reported by Berrigan (1985) where all 6 dimensionless parameters can be similar
between large and small columns: i.e., constant diffusivity in which the ratio of approach
velocities must equal the ratio of carbon particle sizes (Equation 2-11). However, by selecting an
alpha factor not equal to 2, the ACT automatically rejects constant diffusivity. The RSSCT also
violates this condition in proportional diffusivity designs where the approach velocities are
arbitrarily ignored provided that the corresponding Reynolds Numbers remain in the mechanical
dispersion regime (Equation 2-13).
A Comparison of the RSSCT and the ACT A summary comparison of the RSSCT and ACT is given in Table 2.2. Table 2.2 Comparison of calculations used for RSSCT and ACT
Equations RSSCT ACT
Column Length
LC
scLCSC R
RLL
where, 1 2
LC
scLCSC R
RLL
where, = 1.1
Flow Rate (Q) Q = vSC x areaSC
where, vSC =calculated superficial velocity in the small column
Q = vLC x areaSC
where, vLC = the given loading rate/approach velocity of the large
column
VelocitySC ( v SC)
LC
minSC,LC
SC
LCSC Re
Rev
R
Rv v SC =
SC
SC
A
Q
28
To determine the volume of water needed for a RSSCT, two derivations are possible:
Derivation 1:
TSCSCSC tQVol where, VolSC is the minimum volume of influent water needed and Q is the
flow rate and tT is the total run time and where the subscript SC indicates small column.
Substitute, Q = VA into the above equation
TSCSCSCSC tAvVol (2-21)
Since, LC
SC
LC
SC
LC
SC
R
R
EBCT
EBCT
t
t
LC
SCLCSC R
Rtt
Therefore, Equation (2-21) becomes:
LC
SCLCSCSCSC R
RtAvVol
And LC
min,SC
SC
LC
LC
SC
Re
Re
R
R
v
v substituted into the above equation gives:
LCSCLC
min,SCLC
LC
SCLCSC
LC
min,SC
SC
LCLCSC tA
Re
Rev
R
RtA
Re
Re
R
RvVol
Similarly, Derivation 2: Volume of Water Needed = BV x Volume of Carbon (m3) = BV x LSC x AreaSC
Recall, SC
LC
LC
SC
R
R
v
v and LC
SC
LCSC v
R
Rv
SCLC
min,SCLC
SC
LCSCSCSCSC EBCT
Re
Rev
R
RAreaEBCTvArea
BV
Needed Volume
29
LC
SCLC
LC
min,SCLC
SC
LCSC R
REBCT
Re
Rev
R
RArea
BV
Needed Volume
As seen above, volume is only a function of the small column area since the other components
(EBCTLC and vLC) are fixed.
In contrast, for ACT:
11.
LC
SCLCLCSCSCLCSC R
REBCTvAreaEBCTvArea
BV
Needed Volume
As seen above, with the ACT protocol, the volume will change with a change in carbon sizes.
EBCT reduces to Length/Approach Velocity (V) as shown below:
SC
SC
SCSC
SCSC
SC
SCSC V
L
AV
AL
Q
VolumeEBCT
Surface Loading Rate is the same as approach velocity (m/hr).
A
QdingRateSurfaceLoa
LC
SC
SC,S
LC,S
LC
SC
LC
SC
t
t
D
D
R
R
EBCT
EBCT
In an ACT, velocities are the same for small and large scale columns. Therefore, the above
equation becomes:
LC
SC
LC
LC
SC
SC
LC
SC
R
R
V
LV
L
EBCT
EBCT LC
LC
SCSC L
R
RL
2.4 CURRENT SELECTION METHOD FOR PURCHASING CARBONS The following section serves to explain how utilities are assisted in choosing an activated carbon
for drinking water treatment. Most of the information was obtained through communication with
Calgon Carbon Corporation, a major manufacturer of activated carbon.
30
Filtrasorb is Calgon Carbon Corporation’s main product line available to drinking water
treatment utilities. When assisting a utility in the selection of an activated carbon, mesh size is
one influencing factor. For example, F300 (8 x 30 mesh) may be chosen over F400 (12 x 40
mesh) as a coarser mesh size allows for deeper carbon beds without as much headloss resulting
in a longer service life. Therefore, utilities installing carbon for total organic carbon (TOC) and
disinfection by-product (DBP) removal tend to choose a coarser carbon product.
For TOC removal, carbons with high iodine numbers are generally preferred due to larger overall
adsorption capacity for organics. A carbon that has been activated for longer will have less
micropores and a higher iodine number but a lower trace capacity number. Another Calgon
product, F600, has a low iodine number but a high TCN value. Reactivation of a carbon will also
contribute to a decrease in micropores, thus lowering the TCN value. Reactivation therefore is
considered a better choice for utilities using carbon for TOC removal as the carbon structure is
opened up, resulting in larger pores in the reactivated product.
Other parameters considered are hardness and abrasion values. During backwash, carbons with
lower hardness and abrasion values will generate fine particles. These fine particles will increase
headloss in the carbon bed and result in a loss of carbon mass. Ash content is also an important
parameter to consider as this is additional carbon mass that is not needed and reduces the overall
efficiency of the carbon on a per mass basis. It also increases the cost effectiveness of the
activated carbon on a per mass basis.
31
2.5 REFERENCES American Water Works Association (2006) AWWA Standard: Granular Activated Carbon,
ANSI/AWWA B604-05. American Water Works Association Research Foundation, Denver, CO.
American Water Works Association Research Foundation (AWWARF) (2007) Removal of
EDCs and pharmaceuticals in drinking and reuse treatment processes. American Water Work Association Research Foundation, Denver, CO.
Anderson B.C. and Quartermaine L-K. (1998) Tastes and odors in Kingston’s municipal
drinking water: A case study of the problem and appropriate solutions. Journal of Great Lakes Research 24(4), 859-867.
Baker M.N. (1948) The Quest for Pure Water. American Water Works Association Inc., New
York. Berrigan J.K. (1985) Scale-up of rapid small-scale adsorption tests to fixed-scale adsorbers:
Theoretical and experimental basis. Masters Thesis from Michigan Technological University. Brunauer S., Emmett P.H. and Teller E. (1938) Adsorption of gases in multimolecular layers.
Journal of the American Chemical Society 60, 309-19. Calgon Carbon Corporation (CCC) (1999) Determination of trace capacity number (TM-79).
Standard operating procedure for trace capacity number test method provided by Calgon. Calgon Carbon Corporation, Pittsburgh, PA.
Chen G., Dussert B.W. and Suffet I.H. (1997) Evaluation of granular activated
carbons for removal of methylisoborneol to below odor threshold concentration in drinking water. Water Research 31(5), 1155-1163.
Chudyk W.A., Snoeyink V.L., Beckmann D., Temperly T.J. (1979) Activated carbon versus
resin adsorption of 2-methylisoborneol and chloroform. Journal of American Water Works Association 71(9), 529-538.
Considine R., Denoyel R., Pendleton P., Schumann R. and Wong S.H. (2001) The influence of
surface chemistry on activated carbon adsorption of 2-methylisoborneol from aqueous solution. Colloids and Surfaces A: Physicochemical and Engineering Aspects 179(13), 271-280.
Crittenden J.C., Berrigan J.K. and Hand D.W. (1986) Design of rapid small-scale adsorption
tests for a constant diffusivity. Journal of Water Pollution Control Federation 58(4), 312-9. Crittenden J. C., Berrigan J. K. and Hand D.W. (1987) Design of rapid fixed-bed adsorption tests
for non-constant diffusivities. Journal of Environmental Engineering 113(2), 243-259.
32
Crittenden J.C., Reddy P.S., Arora H., Trynoski J., Hand D.W., Perram D.L. and Summers R.S. (1991) Prediction of GAC performance with RSSCTs. Journal of American Water Works Association 83(1), 77-87.
Dastgheib S.A., Karanfil T. and Cheng W. (2004) Tailoring activated carbons for enhanced
removal of natural organic matter from natural waters. Carbon 42, 547-557. Gillogly T. E. T., Snoeyink V. L., Vogel J. C., Wilson C. M. and Royal E. P. (1999) Determining
GAC bed life. Journal of American Water Works Association 91(8), 98-110. Hand D.W., Crittenden J.C., ASCE M. and Thacker W.E. (1983) User-oriented batch reactor
solutions to the homogeneous surface diffusion model. Journal of Environmental Engineering 109(1), 82-101.
Herzing D., Snoeyink V. and Wood N. (1977) Activated carbon adsorption of the odorous
compounds 2-methylisoborneol and geosmin. Journal of American Water Works Association 69(4), 223-228.
Huang C., Benschoten J.E.V. and Jensen J.N. (1996) Adsorption kinetics of MIB and geosmin.
Journal of American Water Works Association 22, 116-128. Karanfil T. (2006) Activated carbon adsorption in drinking water treatment. Activated Carbon
Surfaces in Environmental Remediation. Elsevier Ltd, 345-373. Karanfil T. and Kilduff J. (1999) Role of granular activated carbon surface chemistry on the
adsorption of organic compounds 1. Priority Pollutants. Environmental Science and Technology 33(18), 3217-3224.
Karanfil T., Kitis M., Kilduff J. E. and Wigton A. (1999) Role of granular activated carbon
surface chemistry on the adsorption of organic compounds 2. Natural organic matter. Environmental Science and technology 33(18), 3225-3233.
Khiari D. and Watson S. (2007) Tastes and odours in drinking water: Where are we today?
Water Science and Technology 55(5), 365-366. Kim Y., Lee Y., Gee C. and Choi E. (1997) Treatment of taste and odor causing substances in
drinking water. Water Science and Technology 35(8), 29-36. Lalezary S., Pirbazari M., Dale M., Tanaka T. and McGuire M. (1988) Optimising the removal
of geosmin and 2-methylisoborneol by powdered activated carbon. Journal of American Water Works Association 80(3), 73-80.
Lalezary S., Pirbazari M. and McGuire M. (1986) Evaluating activated carbons for removing low
concentrations of taste-and-odor producing organics. Journal of American Water Works Association 78(11), 76-82.
Lastoskie C., Gubbins K. E. and Quirke N. (1993) Pore size distribution analysis of microporous
carbons: A density functional theory approach. Journal of Physical Chemistry 97, 4786-4796.
33
Lloyd S.W., Lea J.M., Zimba P.V. and Grimm C.C. (1998) Rapid analysis of geosmin and 2-
methylisoborneol in water using solid phase micro extraction procedures. Water Research 32(7), 2140-2146.
Marsh H. and Rodríguez-Reinoso F. (2006) Activated carbon. Elsevier, Amsterdam, Boston.
Permanent link: http://simplelink.library.utoronto.ca/url.cfm/59084. McGuire M.J., Davis M.K., Liang S., Tate C.H., Aieta E.M., Wallace I.E., Wilkes D.R.,
Crittenden J.C. and Vaith K. (1989) Optimization and economic evaluation of granular activated carbon for organic removal. AWWARF, Denver CO.
MWH. (2005) Water treatment: principles and design, 2nd edition. John Wiley & Sons, Hoboken,
New Jersey. Najm I.N., Snoeyink V.L and Richard Y. (1991) Effect of initial concentration of a SOC in
natural water on its adsorption by activated carbon. Journal of American Water Works Association 83(8), 57-63.
Newcombe G., Drikas M. and Hayes R. (1997) Influence of characterised natural organic
material on activated carbon adsorption: II. Effect on pore volume distribution and adsorption of 2-methylisoborneol. Water Research 31(5), 1065-1073.
Newcombe G., Morrison, J. and Hepplewhite C. (2002a) Simultaneous adsorption of MIB and
NOM onto activated carbon. I. Characterization of the system and NOM adsorption. Carbon 40(12), 2135-2146.
Newcombe G., Morrison J., Hepplewhite C. and Knappe D. (2002b) Simultaneous adsorption of
MIB and NOM onto activated carbon. II. Competitive effects. Carbon 40(12), 2147-2156. Oswald E. and Warmate S. (1999) Determination of trace capacity number. Calgon carbon
corporation test method. Calgon Carbon Corporation, Pittsburgh, PA. Pelekani C. and Snoeyink V.L. (1999) Competitive adsorption in natural water: Role of activated
carbon pore size. Water Resources 33(5), 1209 – 1219. Pendleton P., Wong S.H., Schumann R., Levay G., Denoyel R. and Rouquerol J. (1997)
Properties of activated carbon controlling 2-methylisoborneol adsorption. Carbon 35(8), 1141-1149.
Pirbazari M., Borow H., Craig S., Ravindran V. and McGuire M.J. (1992) Physical chemical
characterization of five earthy-musty-smelling compounds. Water Science and Technology 25(2), 81-88.
Pirbazari M., Ravindran V., Badriyha B.N., Craig S. and McGuire M.J. (1993) GAC adsorber
design protocol for the removal of off-flavors. Water Research 27(7), 1153-1166.
34
Randtke S.J. and Snoeyink V.L. (1983) Evaluating GAC adsorptive capacity. Journal of American Water Works Association 75, 406–413.
Rao Y.R., Skafel M.G., Howell T. and Murthy R.C. (2003) Physical processes controlling taste
and odour episodes in Lake Ontario drinking water. Journal of Great Lakes Research 29(1), 70-78.
Ridal J., Brownlee B., McKenna G. and Levac N. (2001) Removal of taste and odour compounds
by conventional granular activated carbon filtration. Water Quality Research Journal of Canada 36(1), 43-54.
Sontheimer H., Crittenden J.C. and Summers R.S. (1988) Activated Carbon for Water Treatment,
2nd Edition, DVGW-Forschungsstelle, University of Karlsruhe, Karlsruhe, Germany. Distributed in the US by the American Water Works Association, Inc.
Suffet I.H., Corado A., Chou D., McGuire M.J. and Butterworth S. (1996) AWWA taste and
odor survey. Journal of American Water Works Association 88(4), 168-180. Suffet I.H., Khiari D. and Bruchet A. (1999) The drinking water taste and odor wheel for the
millennium: beyond geosmin and 2-methylisoborneol. Water Science & Technology 40(6), 1-13.
Sugiura N., Nishimura O., Kani Y., Inamori Y. and Sudo R.(1997) Evaluation of activated
carbons for removal of musty odor compounds in the presence of competitive organics. Environmental Technology 18(4), 455-459.
Summers R.S. and Crittenden J.C. (1989) The use of mini-columns for predicting full-scale GAC
performance. In proceedings: AWWARF/USEPA Conference for the design and use of granular activated carbon: Practical aspects. American Water Works Association, Cincinnati, OH.
Summers R.S., Cummings L., DeMarco J., Hartman D.J., Metz D.H., Howe E.W., MacLeod B.
and Simpson M. (1992) Standardized Protocol for the Evaluation of GAC. American Water Work Association Research Foundation and American Water Works Association, Denver, CO.
Summers R.S., Haist B., Koehler J., Ritz J., Zimmer G. and Sontheimer H. (1989) The influence
of background organic matter on GAC adsorption. Journal of American Water Works Association 81(5), 66-74.
Summers R. S., Hooper S. M., Solarik G., Owen D. M. and Hong S. (1995) Bench-scale
evaluation of GAC for NOM control. Journal of American Water Works Association 87(8), 69-80.
Summers R.S. and Roberts P. (1988) Activated carbon adsorption of humic substances. II. Size
exclusion and electrostatic interactions. Journal of Colloid and Interface Science 122(2), 382-396.
35
Sontheimer H., Summers R.S. and Crittenden J.C. (1988) Activated Carbon for Water Treatment, 2nd English Edition. Denver, CO: American Water Work Association Research Foundation.
Wallace I.E., Aieta E.M., Tate C.H., Crittenden J.C., McGuire M.J. and Davis M.K. (1988) The
application of the rapid small-scale column test to model organic removal by granular activated carbon. Proceedings of the American Water Works Association Annual Conference, Orlando, Fl. American Water Works Association.
U.S. Environmental Protection Agency (USEPA) (1996) ICR Manual for Bench- and Pilot-Scale
Treatment Studies. Report No. EPA 814/B-96-003, Cincinnati, OH.
36
3 ASSESSMENT OF ACTIVATED CARBON CHARACTERIZATION
TESTS FOR TASTE AND ODOUR CONTROL
ABSTRACT Iodine number, BET surface area, geosmin and MIB isotherms, and TCN(G) tests are shown to
be inconsistent in predicting site-specific performance for taste and odour control (e.g. geosmin
and MIB) under the conditions tested. Tests expected to predict trace contaminant removal
(TCN, TCNG) did not correlate well with RSSCT breakthrough results (R2 < 0.50 for MIB) and
only negatively correlated to geosmin loading results with Lake Simcoe waters. Correlations
were thought to be adversely affected by the inclusion of Carbon D results which is a lignite coal
carbon as opposed to a bituminous coal and has a larger transport-to-adsorption pore ratio.
Correlations with MIB breakthrough results were improved when Carbon D was removed from
the analysis. However, the conclusion remains that no characterization test was applicable for
predicting RSSCT results across the different carbons and natural waters used in this study.
Although certain carbons (B and E) stood out in adsorption performance for both loading and
breakthrough, no correlation existed that pointed towards a test that would aid in the selection of
these carbons. Kinetic considerations, differences in water matrices and competition with NOM
were speculated to be the reasons that no correlation was seen with thermodynamic tests. Further
research is recommended to consider the confounding effects of natural organic matter on both
the characterization test results and the accuracy of RSSCTs for predicting large scale adsorption
performance.
37
3 ASSESSMENT OF ACTIVATED CARBON CHARACTERIZATION
TESTS FOR TASTE AND ODOUR CONTROL
3.1 INTRODUCTION Granular activated carbon (GAC) is a popular choice for taste and odour removal in drinking
water treatment plants. Selection of an appropriate GAC remains a challenge utilities face as
carbon characterization tests are often not specific to a utility’s source water parameters or to
taste and odour compounds. One of the most important carbon characteristics is adsorption
capacity, commonly determined by the carbon’s iodine number. However, because iodine is
easily adsorbed to all adsorption sites within a carbon it may not accurately indicate the removal
of trace contaminants (Chen et al., 1997), which require high energy sites for adsorption
(Newcombe et al., 1997). Other simple, lab-based tests that provide information about adsorption
capacity for trace contaminants would be valuable to a utility at the time of purchase of an
activated carbon for taste and odour control. The aim of this study was to assess the relationship
between typical characterization tests and GAC performance for taste and odour removal in
natural water matrices.
Thermodynamic characterization tests describe the adsorption capacity of a carbon at
equilibrium. These tests, including iodine, phenol and tannin tests, are often chosen because they
are simple tests to run at bench scale. The surrogates used in these tests, i.e., iodine, phenol, and
tannin, quantify adsorption capacity for compounds of similar molecular size and weight. These
surrogates, each with differing molecular sizes, provide information on the pore sites with
corresponding sizes that exist within the carbon but may not be appropriate for predicting the
removal of lower molecular weight taste and odour compounds. Another thermodynamic test,
BET surface area, is an isotherm test developed by Brunauer, Emmett and Teller (1938) that
indicates the total surface area of an activated carbon which corresponds to the overall adsorption
capacity of a carbon.
Taste and odour compounds, geosmin and 2-methylisoborneol (MIB), have relatively low
molecular weights (<200 g/mol) and weak affinities for activated carbon. Studies have also
shown that these compounds require primarily micropores (<20 Å) for adsorption (Newcombe et
38
al., 1997, 2002). The trace capacity number (TCN) and trace capacity number gas phase (TCNG)
have been introduced to predict trace contaminant removal using surrogates (acetoxime
[(CH3)2C=NOH] and tetrafluoromethane [CF4], respectively). These surrogates may be more
appropriate for the description of sites available to trace contaminants such as geosmin and MIB.
Additionally, isotherm tests, measuring the total adsorption capacity of a carbon at equilibrium,
may be conducted using the taste and odour compounds of interest (i.e., geosmin and MIB).
These results may provide more accurate measurements of adsorption capacity of the compounds
of interest (Chen et al., 1997).
Thermodynamic tests, however, will not accurately describe adsorption capacity of a carbon in a
large-scale treatment plant as they do not take into account the kinetics of adsorption, the
presence of natural organic matter (NOM) or changes in source water matrices; all of which will
influence competitive adsorption on taste and odour compounds. Backwashing, causing the
restratification of the large-scale carbon bed and potential disturbance to the mass transfer zone
is also not accounted for with thermodynamic tests. Biological activity may be an additional
cause for the reduction of taste and odour compounds at large-scale along with adsorption.
Kinetic tests, such as rapid small-scale column tests (RSSCTs) or pilot-scale tests, may be
performed to simulate the dynamic process of adsorption within the activated carbon bed.
RSSCTs were developed by Crittenden and his team of researchers in 1987 as a tool to mimic
large scale drinking water carbon columns in a laboratory environment (Crittenden et al., 1987).
RSSCTs are continuous-flow column tests that are conducted at bench-scale. The relationship
between the empty bed contact time (EBCT), column length, operation time and hydraulic
loading of the small- and large-scale columns is determined with the use of multiple equations
and is a function of the ratio between the granular activated carbon (GAC) particles sizes used in
both the full-scale treatment plant and the RSSCT. The main advantages to using the RSSCT are:
(A) the RSSCT takes a fraction of the time to complete compared to a pilot or full-scale run, (B)
extensive isotherm and kinetic studies are not needed to predict the performance of a full-scale
system and (C) only a small volume of water is needed for the test (Crittenden et al., 1991).
RSSCTs do not usually take into account backwashing or biological activity but have been
shown in several studies to be a helpful tool in predicting breakthrough curves of pilot- and full-
scale adsorbers (Crittenden et al., 1991, Hand et al., 1989, Summers et al., 1992). Summers et al.
(1992) compared the relative rankings of GAC performance at field- and lab-scale comparing
39
different carbons in different GAC-use scenarios. The authors noted the RSSCTs predicted the
appropriate ranking of GAC at field-scale in the majority of cases, with the RSSCTs not
indicating the appropriate GAC in only 1 of 36 cases. RSSCTs were used in this study in place of
pilot or full-scale tests due to limitations in time and resources.
The development of the RSSCT is discussed in detail by Berrigan (1985) and Crittenden et al.
(1986, 1987, 1991) and is summarized in chapter two of this document. The governing equation
scaling a full-scale water treatment to a bench-scale system is as follows:
LC
SC
X
LC
SC
LC
SC
t
t
R
R
EBCT
EBCT
2
(3-1)
Where, SC refers to the small columns and LC refers to large columns or the full-scale water
treatment columns that are being simulated; RSC and RLC are the activated carbon particle sizes
for both the large and small columns; and tSC and tLC are the corresponding run times for both
systems. X dictates the dependence of intraparticle diffusivity on particle size, where X = 1
would indicate linearly proportional relationship to particle size (proportional diffusivity, PD)
and X = 0 would indicate constant diffusivity (CD).
Studies conducted to date have found that PD-designed columns (X = 1) most closely predicted
NOM breakthrough curves (Summers et al., 1989). The CD-designed columns predicted earlier
breakthrough as particle size decreased (EPA, 1996). Studies completed by Calgon report that an
X value of 1.1 provides best results when predicting breakthrough of micropollutants in real
water matrices (Communication with CCC, 2009) (Equation 2).
LC
SC
.
LC
SC
LC
SC
t
t
R
R
EBCT
EBCT
11
(3-2)
Traditional carbon characterization methods, such as the iodine number test, are limited in their
ability to predict performance for taste and odour control (e.g. geosmin and MIB) in natural
waters. This study aimed to evaluate whether alternative characterization tests, specifically the
40
TCN and TCNG, were more accurate in predicting GAC performance for geosmin and MIB
removal. RSSCTs were used to model large-scale adsorption performance and provided a
measure of effectiveness of the thermodynamic tests. A comparison of the characterization test
results for five carbons was completed to determine which best predicts the adsorption
performance of an activated carbon for taste and odour compounds compared to the results from
the RSSCTs.
3.2 EXPERIMENTAL Materials
Adsorbents. Five different types of granular activated carbon were used in this research. General
properties and results from the common characterization tests are shown for each carbon in Table
3.1. The carbons will be referred to in this chapter as Carbons A through E and are described in
more detail in Appendix B.
34
Table 3.1 Properties of five activated carbons used in this study
Carbon Sample
Raw Material Activation
Process
Apparent Density (g/mL)
Iodine Number (mg/g)
TCN (mg/mL)
TCNG (g/100cm3) 1
BET Surface
Area (m2/g) 1
Adsorption Isotherm
MIB q10 (ng/mg)2
Adsorption Isotherm Geosmin
q10 (ng/mg)2
A Bituminous coal Steam 0.58 869 10.96 5.4 625 230 982
B Bituminous coal Steam 0.575 961 11.9 6.4 683 302 732
C Bituminous coal Steam 0.65 813 15.7 7.7 584 282 442
D Lignite coal Steam 0.37 633 4.6 3.5 539 83 112
E Bituminous/
Subbituminous coal Steam 0.454 1001 8.3 5.1 797 399 798
1BET surface area and TCNG results from Zhang (2008) 2Single-solute bottle point adsorption (Freundlich) isotherm parameters, q10 = adsorption capacity of carbon at effluent concentration of 10 ng/L (Zhang, 2008)
35
Sample Water. RSSCTs were run using four different batches of water (referred to as Water 1,
2, 3 and 4) from two different sources. Water 1, 2 and 3 were obtained from Lake Simcoe and
Water 4 from Lake Ontario. Water obtained from Lake Simcoe from the Georgina drinking water
treatment plant had undergone pre-chlorination for zebra mussel control at the intake pipe and
membrane filtration. Water was collected immediately after membrane filtration, prior to
entering the activated carbon adsorbers, to obtain a representative sample of the water that would
be entering the full-scale adsorbers. Water obtained from Lake Ontario from the Ajax Water
Supply Plant had also undergone pre-chlorination for zebra mussel control at the intake pipe. The
direct filtration plant uses alum as coagulant and the water was collected from a flocculated
water sample line prior to passing through the activated carbon beds.
The water samples were filtered through a 1 µm fibrous polypropylene string-wound cartridge
(EW-01508-77, Cole-Parmer, Anjou, QC) followed by a 0.5 µm pleated cartridge filter (RK-
01512-86, Cole-Parmer, Anjou, QC) to remove any particulate matter and undissolved NOM.
Water was stored at 2 - 4°C for up to two months.
Adsorbates. Geosmin and 2-methylisoborneol (MIB) were selected as the two taste and odour
compounds for this study as they are prevalent in the Great Lakes region in Canada and the
United States. Both compounds were obtained neat, in solid form, from Wako Chemicals USA
Inc. A mixed stock solution of 1 mg/L geosmin and MIB was prepared in Milli-Q® water ready
for spiking the influent water. All solutions were kept in amber vials, head-space free, with caps
sealed with Parafilm®, at 2 - 4ºC. Solutions were analyzed in the GCMS prior to a new RSSCT
run to ensure that stock solution concentrations had not changed. The stock solution was found to
be stable by comparing the concentration of a stock solution made five months earlier to a new
stock solution (MIB: P value = 84 %, geosmin: P value = 37 %, 95 % confidence level, average
change in stock solution concentration = 1.5 ng/L for MIB and 1.7 ng/L for geosmin).
Methods
Analytical Methods. TOC samples were analyzed using an Aurora 1030 TOC Analyzer (O. I.
Analytical) and the method based on Standard Method 5310 D: Wet Oxidation Method (APHA,
AWWA, and WEF, 2005). Geosmin and MIB were extracted by headspace solid phase micro-
extraction (HSSPME) and quantified using gas chromatography-mass spectrometry (GCMS).
36
Geosmin and MIB were measured using the SPME method and quantified by using two internal
standards, d5-Geosmin and s-BMP (2-sec-butyl-3-methoxyprazine). The analysis was carried out
using a Varian® 3800 Gas Chromatograph with a Varian® Ion-trap Mass Spectrometer Detector,
using electron impact (EI) ionization and autosampler.
Thermodynamic Tests. Iodine numbers for all five carbons were measured following Standard
Method D4607 (AWWA, 2005). Additional details on this and other methods used are given in
Appendix B. The trace capacity number (TCN) of the carbon sample was determined using the
test method for the determination of acetoxime number in ANSI/AWWA B604-05 (AWWA,
2006). The carbon capacity (mg/mL) at 30 mg/L residual acetoxime concentration specifies the
trace adsorption capacity of the carbon. The trace capacity number gas-phase (TCNG) of the
carbon sample was determined using the gas adsorption method TM-85 developed by Calgon
Carbon Corporation (CCC, 2003). Tetrafluoromethane activity (TCNG) is defined as the ratio
(g/100mL) of the mass of tetrafluoromethane (CF4) adsorbed by a volume of activated carbon
sample when the carbon is saturated with tetrafluoromethane vapour under specific test method
conditions. These conditions include ensuring that the activated carbon sample has less than 0.1
wt% moisture and the pores are empty of any adsorbate, including ambient air, when the test
tube containing the carbon is weighed. This is achieved by weighing the carbon samples when
the carbon is over 70°C. CF4 vapour has low affinity for activated carbon and hence the
conditioning of the sample is paramount. The tube is filled with carbon using a vibrating feeder
to ensure maximum packing. The TCNG method is modified from the butane number method
described in ASTM D5742-95 (AWWA, 2005). Results for the five carbons TCNG values were
obtained by Zhang (2008). Additional carbon characterization parameters (BET surface area and
isotherm values for geosmin and MIB) were obtained by a previous study completed by Zhang
(2008).
RSSCT. RSSCTs were designed using scaling equations developed by Crittenden et al. (1991).
RSSCTs for this study were designed using a diffusivity factor (X) of 1.1 as shown in Equation
2. A large-scale system with an EBCT of 7.5 minutes, a carbon contactor length of 1.5 metres
and a surface loading rate of 10 m/hr was modeled using RSSCTs for each carbon. RSSCT
parameters for all five carbons are provided in Table 3.2.
37
Table 3.2 Small-column RSSCT parameters for all five carbons
Carbon EBCT (min)
Loading Rate (m/h)
Carbon Depth (cm)
Mass (g)
A 0.31 11.3 5.9 0.5673 B 0.55 11.2 10.4 0.9898 C 0.52 10.8 9.3 1.0046 D 0.38 11.5 10.2 0.4531 E 0.55 11.1 7.3 0.7664
Full-scale 7.5 10 1.25 m The activated carbon was packed into 0.46 cm inside diameter stainless steel columns with a 2
cm support base of glass wool. A glass wool pre-filter was installed prior to the carbon filter to
remove any remaining particulate matter in the influent water in order to reduce the chance of
clogging in the column. Stainless steel reservoirs held the sample water and contained Teflon®-
lined (3 mm) polystyrene foam floating lids inserted to minimize the loss of geosmin and MIB to
the atmosphere. See Figure 3.1 for a photo of the RSSCT apparatus elements and Figure 3.2 for a
schematic including hardware specifics. Please refer to Appendix D for additional details on the
RSSCT system.
Figure 3.1 Elements of RSSCT set-up: sampling ports [A], floating lid [B], full RSSCT set-up [C]
B A
C
38
Figure 3.2 RSSCT system schematic, total of 6 GAC columns in RSSCT set-up
Breakthrough. RSSCTs provide results on the amount of bed volumes (i.e., volume of water)
that are treated before a carbon will need to be replaced or regenerated. The breakthrough value
is an arbitrary value that depends on the ultimate purpose of an activated carbon bed. Most
activated carbon applications in the Great Lakes region are to prevent taste and odour
compounds from being detected by consumers. Thus, the use of the odour threshold
concentration is reasonable as a breakthrough value as this is the value at which consumers will
39
begin to complain about an unpleasant taste or odour to their water. The odour threshold for
geosmin and MIB varies across the literature. A recommended reduction of MIB to below 10
ng/L was recommended (Chen et al., 1997). Although the OTC for both these compounds varies
slightly across the literature, thresholds as low as 9 ng/L and 4 ng/L have been reported for MIB
and geosmin, respectively (Kim et al., 1997; Pirbazari et al., 1993). As the main purpose of the
study was to compare characterization tests for activated carbon, a clear breakthrough value was
needed as breakthrough results were difficult to distinguish between carbons at low
concentrations. In order to examine the correlations between different characterization tests and
the RSSCT results, the breakthrough value chosen for this study was 20 % of the influent
concentration. RSSCTs were compared by examining the throughput (bed volumes) at 20 %
breakthrough.
3.3 RESULTS AND DISCUSSION An overview of the results were chosen for inclusion in this chapter, additional results may be
found in Appendix C.
Summary of Characterization. Several common adsorption capacity tests were performed on
the five carbons and the results are summarized in Table 3.1.
Table 3.3 shows the results of testing the correlation between the characterization tests. All
characterization tests were conducted using lab-grade water. TCN and TCNG are strongly
correlated (R2 = 0.97, P<0.05) as expected since both are thought to reflect the amount of high
energy adsorption sites within the carbon. Iodine number correlated well with BET surface area
as iodine easily adsorbs to activated carbon and is therefore a good measure of the total pore
adsorption capacity. BET and iodine also correlated well to the MIB isotherm results (R2 = 0.81,
P<0.05). This is less expected since it is hypothesized that MIB requires high energy sites and
therefore TCN or TCNG would be expected to predict MIB or geosmin adsorption. No
characterizations correlated well with the geosmin isotherm results. Correlations to breakthrough
will be examined later in the chapter.
Iodine is said to be easily adsorbed onto carbon and less preferential to high energy sites.
Therefore, it is considered a more general indicator, providing the overall pore volume of a
40
carbon and total adsorption sites available rather than being a specific indicator of high energy
sites. Chen et al. (1997) noted that conventional equilibrium tests, such as iodine number, are not
sufficient for predicting carbon performance. The trace capacity number test using acetoxime is
thought to be a better indicator of high energy sites available on a carbon and thus a more
appropriate test for trace contaminant removal. However, TCN(G) tests did not correlate to MIB
and geosmin isotherms indicating that perhaps these two compounds do not require the same
pore sites as acetoxime for adsorption.
Adsorption isotherm tests indicate that the carbon of choice varies depending on which taste and
odour compound is being targeted as certain carbons exhibit higher affinity for the adsorption of
one compound over the other. For example, Carbon A would be chosen based on its ranking for
highest removal of geosmin according to the isotherm test (982 ng/mg) but Carbon E would be a
more suitable choice for highest ranking in MIB adsorption (399 ng/mg). In Lake Ontario,
geosmin is the main taste and odour compound found. However, often both MIB and geosmin
occur simultaneously and need to be removed. An isotherm combining both compounds mixed in
solution would be recommended for future research.
Table 3.3 Examination of correlation between characterization tests (least-squares linear regression)
TCN TCNG BET Surface
Area MIB q10 Geosmin q10
Method R2
P Value
R2 P
Value R2
P Value
R2 P
Value R2
P Value
Iodine 0.1489 0.5212 0.1833 0.472 0.8097 0.0375 0.8721 0.0202 0.6838 0.0842
TCN 0.9686 0.0024 0.0001 0.9858 0.2179 0.428 0.1249 0.5596
TCNG 0.0072 0.8923 0.2872 0.3519 0.854 0.6334
BET 0.7734 0.0493 0.4252 0.2331
MIB q10 0.4372 0.2243
Geosmin q10 As can be seen from Table 3.1 there is no clear choice for best overall activated carbon when
taking all the thermodynamic characterization test results into account. Carbon B would appear
to be the most consistent high ranking carbon for adsorption capacity. Carbon E consistently
ranks highest amongst all the characterization tests with the exception of the TCN and TCNG
tests.
One main adjustment that could be made to some of the characterization tests mentioned above is
to take into account natural organic matter (NOM) and the confounding effects it will have on
41
the adsorption of taste and odour compounds. This concept will be further explored in Chapter 4.
Isotherm tests run with carbons that have been preloaded with NOM would potentially provide
better information on the adsorption capacity of those carbons specific to the source water of
interest (Chen et al., 1997; Corwin, 2010).
RSSCTs. In order to verify the reliability of the characterization tests for specific source waters,
kinetic tests (RSSCTs) were conducted. A total of four batches of water were tested using up to
five carbons for each batch of water. The RSSCT system was expanded during the research
period allowing for all five carbons to be tested simultaneously for Water 4 only.
QAQC for RSSCTs. QAQC tests were performed in order to determine the limitations
associated with running a RSSCT including changes in water matrices and reproducibility of the
data. Water for a RSSCT needs to be stored for the duration of the test and changes in the water
matrix could occur. Two RSSCTs were run under identical conditions (carbons and water) to
determine if the source water changed over a period of one month. Results were found to be
reproducible, implying that no significant change in the water matrix was seen during storage
and that results between RSSCTs could be compared (P = 0.0025). In order to verify for
reproducibility of the data within a single run, parallel columns packed with the same carbon
were used as a control. Results were found to be reproducible (Figure 3.3). Analytical variability
was seen after the first RSSCTs were run and therefore GCMS effluent samples were duplicated
in the subsequent run. Duplicate results varied on average by 4.0 ng/L for MIB and 1.9 ng/L for
geosmin (standard deviation ranged between 0.03 - 21.0 for MIB and 0.01 - 12.2 for geosmin).
Breakthrough curves derived from the RSSCT results showed some variability while still
presenting a clear overall curve. Variability within the curves was seen primarily as positive
error, possibly from loose carbon reaching the effluent sample or through adsorption of MIB on
septa in caps used from previous research. Variability, however, was similar across the parallel
columns (Figure 3.4) and overall trends for breakthrough were still observed.
42
0.00
0.20
0.40
0.60
0.80
1.00
0 20,000 40,000 60,000 80,000 100,000
Bed Volumes of Water Treated
MIB
Co
nce
ntr
atio
n C
/C0
Carbon A - 1 Carbon A - 2
Figure 3.3 Parallel columns (Carbon A) showing reproducible MIB breakthrough curves
0.00
0.20
0.40
0.60
0.80
1.00
0 20,000 40,000 60,000 80,000 100,000
Bed Volumes of Water Treated
Ge
osm
in C
onc
ent
ratio
n C
/C0 Carbon A - 1 Carbon A - 2
Figure 3.4 Comparison of duplicated column results for Geosmin breakthrough
In order to determine bed volume values (throughput) to breakthrough of MIB and geosmin
while dealing with variability in the data, a logistic curve (Gompertz) was fitted to data (Figure
3.5). An asymmetrical logistic curve was suggested by Clark (1987) and the Gompertz curve was
used for simplicity as it fit breakthrough curves well (R2>0.80 for MIB). Geosmin, as will be
discussed broke through much later than MIB or did not reach 20 % breakthrough, and therefore
it was not always possible to include it in the analysis. The Gompertz regression fit was used to
minimize error caused by scatter in individual data points during extrapolation of breakthrough
values. Once curves were fitted to results, an estimate of breakthrough as a function of bed
volumes of water treated was made.
43
Bed Volumes of Water Treated
0 20000 40000 60000 80000 100000
MIB
Con
cen
tra
tion
C/C
0
0.0
0.2
0.4
0.6
0.8
1.0
Carbon BGompertz curve
Figure 3.5 MIB breakthrough curve for Carbon B with Gompertz curve fit
RSSCT RESULTS MIB and Geosmin Breakthrough. MIB consistently crossed the 20 % breakthrough value
before geosmin for all carbons and all batches of water. This was expected as several studies
have shown that MIB is less readily adsorbed than geosmin to GAC (Chen et al., 1997),
presumably reflecting MIB’s lower hydrophobicity (log Kow of 3.13) (Pirbazari et al., 1992) and
higher aqueous solubility (194.5 mg/L) than geosmin (log Kow of 3.7 and solubility at 150.3
mg/L). Another factor influencing the adsorption of MIB in this case could be the competing
organic compounds in the natural water. As seen in a study by Sugiura et al. (1997), MIB may be
more affected by the presence of humic acid than geosmin during adsorption, as MIB
consistently was found to breakthrough earlier in all batches of water studied. It may be
recommended that MIB alone be studied in the future to save resources and time. This would
ensure that a conservative estimate be made for geosmin as well. Examples of breakthrough
curves for MIB and geosmin are presented in Figure 3.6 and Figure 3.7, respectively.
44
Bed Volumes of Water Treated
0 10000 20000 30000 40000 50000
MIB
Con
cent
ratio
n C
/C0
0.0
0.2
0.4
0.6
0.8
1.0
Carbon ACarbon BCarbon CCarbon DCarbon E
Figure 3.6 Example of MIB breakthrough curves, Lake Simcoe
Bed Volumes of Water Treated
0 10000 20000 30000 40000 50000
Geo
smin
Con
cent
ratio
n C
/C0
0.0
0.2
0.4
0.6
0.8
1.0
Carbon ACarbon BCarbon CCarbon DCarbon E
Figure 3.7 Example of geosmin breakthrough curves, Lake Simcoe
Note: Curves for Carbon B, D and E did not converge due to scatter or minimal breakthrough (C/C0<0.08) When considering solely RSSCT results for carbon performance in the removal of MIB, a carbon
with the highest throughput at the breakthrough value would be the carbon of choice. As seen in
Table 3.4, Carbons B and Carbon E treated the most bed volumes of water prior to breakthrough
of MIB. MIB results alone are shown as MIB breakthrough consistently occurred before geosmin
breakthrough. In order to make conclusions on an activated carbon best suited for geosmin
45
removal alone, longer RSSCTs would need to be conducted as geosmin did not reach 20 %
breakthrough for all carbons.
Table 3.4 Bed volumes to MIB breakthrough (20 % of C0), ranking in parentheses.
Note: Rank 1 = highest bed volumes or throughput at breakthrough value
Water 11 Water 21 Water 31 Water 4
Carbon A - - 12,564 (3) 7,336 (4) Carbon B 42,145 (1) 20,336 (1) 24,467 (2) 60,562 (2) Carbon C 7,612 (3) 3,088 (3) - 6,686 (5) Carbon D 13,339 (2) 14,977 (2) - 19,824 (3) Carbon E - - 34,752 (1) 79,620 (1)
1For Waters 1, 2 and 3, only three carbons were tested simultaneously When comparing the MIB and geosmin breakthrough results from the RSSCTs from two
separate source waters, the implications of competing organics is clear. Lake Simcoe has almost
double the TOC content of Lake Ontario (4.02 mg/L and 2.30 mg/L, respectively) and, therefore,
the lag in MIB breakthrough seen in the Lake Ontario water relative to Lake Simcoe water
(Figure 3.8) likely reflects the lower adsorption competition or pore blockage by NOM. Similar
patterns in lag between the two water MIB breakthrough curves were seen for all carbons, with
curves matching closely initially and a lag developing between adsorption curves in Lake
Ontario and Lake Simcoe waters past 20,000 bed volumes.
0.00
0.20
0.40
0.60
0.80
1.00
0 10,000 20,000 30,000 40,000 50,000 60,000 70,000
Bed Volumes of Water Treated
MIB
C/C
o
Carbon B Lake OntarioCarbon B Lake Simcoe
Figure 3.8 Comparison of MIB Breakthrough using Carbon B from two source waters
[Influent average TOC: Lake Simcoe = 4.02 mg/L and Lake Ontario = 2.30 mg/L]
46
Comparison of Characterization Tests to RSSCT Breakthrough Results
Correlations were made to compare the results from the characterization tests (iodine, TCN(G),
BET surface area, MIB and geosmin isotherms) to the MIB and geosmin breakthrough obtained
from the RSSCTs. For most comparisons, no strong correlation was seen (R2<0.50) (Table 3.5).
Evidently, no single thermodynamic test, applicable to all types of carbons, would accurately
predict adsorption performance of an activated carbon for taste and odour removal. As
mentioned, kinetic considerations, differences in water matrices and competition with NOM may
be reasons why no correlation was seen. Additional breakthrough values could be compared to
see if correlations may be seen between tests.
Figure 3.9 and Figure 3.10 are two examples of correlation tests between RSSCT breakthrough
results and the characterization tests. It is interesting to note that in both figures, Carbon D
appears to be an anomaly in the analysis. A positive correlation between the characterization
tests (excluding the TCN(G)) and the MIB breakthrough results was seen on the test completed
with Water 3, the one test that did not include Carbon D (R2 = 0.94 - 0.97) (Table 3.5). When
Carbon D was removed from the correlation tests between thermodynamic tests and MIB
breakthrough, R2 values increased by 0.11 - 0.45 (Table 3.5). Insufficient data for the other two
waters meant that further confirmation of the anomalous Carbon D results was not possible.
Carbon D ranks lowest of all carbons according to all the isotherm tests and is also shown to
have the lowest volume of primary micropores (< 8 Å) (Zhang, 2008, see Appendix C). Carbon
D is also a lignite coal activated carbon in contrast to the other carbons which are bituminous
coal. However, Carbon D outperforms Carbons A and C in the RSSCTs for removal of geosmin
and MIB, showing the importance of examining kinetics when considering adsorption. The
kinetics of adsorption for Carbon D appears to be faster than Carbon A and C allowing more
geosmin and MIB to be removed. One explanation for this could be that Carbon D has a higher
transport to adsorption pore ratio which would allow geosmin and MIB to move more easily (and
quickly) into the carbon structure to be adsorbed (Table 3.6). This hypothesis is supported by
findings from Newcombe et al. (2002a). Transport pores are larger than the largest adsorption
pores and serve as diffusion paths within the carbon structure to transport adsorbates. Transport
pores do not have adsorption capabilities but simply transport adsorbates to adsorption sites. An
overall correlation between the adsorption to transport ratio and breakthrough results was not
seen, however, and therefore it seems as though this phenomenon does not hold true for the other
47
four carbons. Carbon D does, however, seem to have the advantage of faster kinetics allowing it
to outperform Carbons A and C for loading and breakthrough. These rankings may change,
however, if a different breakthrough value were chosen. Timed batch adsorption tests to show
the kinetics of each carbon would help to further explore these findings.
Table 3.5 Comparison of MIB breakthrough (20 % of C0) to characterization results
(R2 values, +/- indicates positive or negative slope in linear regression analysis)
Water 1 Water 2 Water 3 Water 4 Water 4 w/o Carbon D
Iodine Number 0.55 (+) 0.06 (+) 0.97 (+) 0.48 (+) 0.93 (+)
TCN 0.001 (+) 0.28 (-) 0.47 (-) 0.06 (-) 0.49 (-)
TCNG 0.004 (+) 0.25 (-) 0.03 (-) 0.01 (-) 0.24 (-)
MIB Isotherm 0.19 (+) 0.02 (-) 0.94 (+) 0.43 (+) 0.76 (+)
BET Surface Area 0.80 (+) 0.25 (+) 0.95 (+) 0.76 (+) 0.87 (+)
Table 3.6 Adsorptiona and transportb pore volumes for five carbons (Calgon, 2009)
Adsorption Pore Volume (mL/g)
Transport Pore Volume (mL/g)
Transport : Adsorption Pore
Ratio Carbon A 0.331 0.293 0.88 Carbon B 0.384 0.218 0.57 Carbon C 0.308 0.147 0.48 Carbon D 0.348 0.795 2.28 Carbon E 0.458 0.399 0.87
a Pore sites in which adsorption occurs; b Transport pores are larger than the largest adsorption pores and do not have adsorption capabilities but instead act as pathways to adsorption pore sites.
48
Water 1y = 83.567x - 46016
R2 = 0.5501
Water 2y = 13.407x + 2043.9
R2 = 0.0622
Water 3y = 161.36x - 128343
R2 = 0.9674
Water 4y = 159.86x - 101936
R2 = 0.4821
0
10,000
20,000
30,000
40,000
50,000
60,000
70,000
80,000
90,000
600 650 700 750 800 850 900 950 1000 1050
Iodine Number (mg/g)
Be
d V
olu
mes
to
MIB
Bre
akt
hro
ugh
Water 1
Water 2
Water 3
Water 4
Linear (Water 1)
Linear (Water 2)
Linear (Water 3)
Linear (Water 4)
Carbon D
Carbon A
Carbon E
Carbon B
Carbon C
Figure 3.9 Comparison of iodine numbers to MIB breakthrough results for carbons in four waters
Water 1y = 81.114x + 20162
R2 = 0.0006
Water 2y = -829.56x + 21704
R2 = 0.281
Water 3y = -4056.5x + 66061
R2 = 0.4653
Water 4y = -1937.6x + 54747
R2 = 0.058
0
10,000
20,000
30,000
40,000
50,000
60,000
70,000
80,000
90,000
0 2 4 6 8 10 12 14 16
TCN (mg/mL)
Bed
Vo
lum
es t
o M
IB B
reak
thro
ug
h
Water 1
Water 2
Water 3
Water 4
Linear (Water 1)
Linear (Water 2)
Linear (Water 3)
Linear (Water 4)
Carbon D
Carbon A
Carbon E
Carbon B
Carbon C
Figure 3.10 Comparison of TCN values to MIB breakthrough results for carbons in four waters
49
As shown in Figure 3.10 (for MIB), TCN was not seen to be an accurate indicator of MIB and
geosmin removal. As TCN(G) does not correlate with either loading or breakthrough results
(except inversely with geosmin loading), the surrogates used in these tests do not appear to be
appropriate for these two taste and odour compounds.
Overall, the highest ranked carbons for breakthrough, Carbon B and Carbon E, were also the
carbons with the highest overall surface area (BET area), highest iodine values, and MIB
isotherm values and were ranked 2nd and 3rd in terms of the geosmin isotherm value. These,
however, are individual observations that should be considered with caution as they were not
supported by an overall correlation between the tests including all carbons. All comparisons were
made to 20 % breakthrough and therefore it would be interesting to examine whether a lack of
correlation is seen at different breakthrough values. RSSCTs would need to be run for a longer
period of time to allow for this analysis to be possible.
Total Loading Capacity for MIB and Geosmin. Another important parameter that can be
examined using RSSCTs is the total loading capacity of an activated carbon. Cumulative loading
capacity was plotted and the point at which the loading data reaches an equilibrium indicates the
carbon’s total loading capacity. Since equilibrium was not reached for each carbon, an arbitrary
bed volume of 50,000 was chosen to compare loading results. This value was chosen as each
RSSCT was run to at least 50,000 bed volumes and is close to the total loading capacity for most
carbons (Figure 3.11). This is equivalent to approximately nine months run time in the full-scale
treatment plant. The rationale behind loading capacity from RSSCT results is that as a
thermodynamic consideration it, in theory, would better correlate to isotherm tests (such as the
iodine number and TCN). Corwin (2010) showed, however, that equilibrium reached in RSSCTs
will be an apparent capacity due to the presence of dissolved organic matter causing fouling of
the activated carbon. This is also clearly shown in this study’s results where the loading capacity
of geosmin and MIB is 1.5 - 3 times higher in carbons tested with Lake Ontario water (TOC =
2.30 mg/L) versus carbons tested with Lake Simcoe water (TOC = 4.02 mg/L) (Figure 3.12).
Loading capacity of the carbons measured here is the apparent adsorption capacity with NOM
fouling and therefore a correlation to thermodynamic tests would not be expected.
50
0
2,000
4,000
6,000
8,000
10,000
12,000
0 10,000 20,000 30,000 40,000 50,000 60,000 70,000 80,000 90,000 100,000
Bed Volumes
MIB
Loa
din
g (n
g/g)
Carbon A
Carbon B
Carbon C
Carbon D
Carbon E
Figure 3.11 MIB loading capacity, Lake Simcoe
0
5,000
10,000
15,000
20,000
25,000
Carbon A Carbon B Carbon C Carbon D Carbon E
Carbon
Load
ing
Cap
acity
at
50,
000
Bed
Vol
umes
MIB Lake Simcoe Geosmin Lake Simcoe
MIB Lake Ontario Geosmin Lake Ontario
Figure 3.12 Loading capacity of five carbons at 50,000 bed volumes in two source waters [Influent average TOC: Lake Simcoe = 4.02 mg/L and Lake Ontario = 2.30 mg/L]
Comparison of Characterization Tests to RSSCT Loading Results
Correlations were prepared to compare the results from the characterization tests (iodine,
TCN(G), BET surface area, MIB and geosmin isotherms) to the MIB and geosmin loading
results at 50,000 bed volumes obtained from the RSSCTs. For most comparisons, no strong
correlation was seen (R2<0.60) (Table 3.7).
51
Table 3.7 Comparison of loading (at 50,000 bed volumes) and characterization results (R2 values, +/- indicates positive or negative slope in linear regression analysis)
Characterization Test
MIB Loading
Lake Simcoe
Geosmin Loading
Lake Simcoe
MIB Loading
Lake Ontario
Geosmin Loading
Lake Ontario
Iodine Number 0.21 (+) 0.03 (-) 0.04 (+) 0.001 (+) TCN 0.39 (-) 0.94 (-) 0.33 (-) 0.37 (-)
TCNG 0.30 (-) 0.91 (-) 0.18 (-) 0.21 (-) MIB Isotherm 0.13 (+) - 0.058 (+) -
Geosmin Isotherm - 0.01 (-) - 0.17 (-) BET Surface Area 0.57 (+) 0.04 (+) 0.28 (+) 0.15 (+)
The MIB and geosmin isotherm data also proved to be a poor indicator of total loading capacity
in natural waters. Since isotherm tests indicate the total adsorption capacity of a carbon (at
equilibrium) for a particular compound, it was expected that the total loading capacity values
obtained from the RSSCTs would correlate well with the isotherm results. The MIB and geosmin
isotherm tests, however, were run on lab-grade water. This suggests that not taking NOM into
account affects the reliability of the characterization tests.
TOC Breakthrough. Total organic carbon (TOC) is the most commonly used surrogate measure
for natural organic matter (NOM) in drinking water. TOC is an important parameter to examine
both for separate removal (to reduce DBP formation) and for consideration of competition for
adsorption sites with taste and odour compounds. TOC breakthrough and loading was tested
using RSSCTs and is presented in this section. Comparisons to characterization test were
conducted with loading results alone as TOC breakthrough occurred very quickly. TOC data for
Water 4 was lost due to analytical equipment malfunction, hence, only comparisons made with
Waters 1, 2 and 3 will be presented here.
The immediate and sharp breakthrough curves for all carbons indicate that NOM compounds
pass through the carbon column much faster than the taste and odour compounds. TOC’s rapid
movement through the carbon column is attributed to the slower adsorption kinetics of NOM (Li
et al., 2003). This also results in what is referred to in the literature as the preloading effect. As
NOM passes quickly through the carbon it occupies or blocks adsorption sites, preloading the
carbon with NOM (Li et al., 2003; Pelekani and Snoeyink, 1999; Summers et al., 1989). The
NOM travels quickly through the bed, preloading the carbon bed with NOM before the geosmin
52
and MIB have passed through the column. There did appear to be steady removal of some
remaining TOC in both Lake Ontario and Lake Simcoe waters (Figure 3.13) where the
concentration in the effluent remains just below the influent concentration, a phenomenon noted
previously by others and referred to as pseudo steady-state (EPA, 1996). The TOC breakthrough
behaviour followed similar trends to other curves in the literature including an immediate
breakthrough of TOC of nonadsorbable NOM (5 - 20 %), 50 % breakthrough values ranging
between 1,000 to 6,000 bed volumes, and TOC curves stabilizing as slower adsorption or
biodegradation occurs (EPA, 1996). The time (or volume of water) required for the TOC to reach
a certain threshold (i.e., 50 % breakthrough) can be influenced by the EBCT, characterization of
NOM, influent water quality (TOC concentration, pH) and GAC type (Zachman et al., 2007).
TOC Loading. Steady removal of TOC may also be seen when examining the TOC loading
capacity results (Figure 3.14) where certain carbons continued to adsorb TOC (i.e., Carbon E)
while others reached total loading capacity very early on in the run (i.e., Carbon C). This may be
attributed to the activation process of these carbons. Carbons with lower TCN values have
typically longer activation times, opening up more pore spaces available for TOC adsorption
(Communication with CCC, 2010). Carbons D and E have the lowest TCN values but exhibited a
high loading capacity for TOC compared to the other carbons. Although the carbon column was
saturated early on for TOC adsorption, geosmin and MIB were still being removed.
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
0 10,000 20,000 30,000 40,000 50,000 60,000 70,000 80,000
Bed Volumes Treated
Con
cen
trta
ion
(mg
/L)
Carbon A
Carbon B
Carbon C
Carbon D
Carbon E
Influent
Figure 3.13 TOC breakthrough curves, Lake Simcoe
53
0
20
40
60
80
100
120
0 10,000 20,000 30,000 40,000 50,000 60,000 70,000 80,000
Bed Volumes Treated
TO
C L
oad
ing
(m
g/g
)
Carbon A Carbon B Carbon C Carbon D Carbon E
Figure 3.14 TOC loading for all five carbons, Lake Simcoe
Activated carbon beds are commonly used for disinfection by-product (DBP) precursor removal
in drinking water treatment plants. Total loading capacity analyses such as the one shown in
Figure 3.14 would be helpful information for a utility choosing a carbon for this purpose. A
carbon with a high loading capacity and with higher throughput to equilibrium would be best
suited for such a utility (e.g. Carbon E). Also, a carbon with the largest throughput in number of
bed volumes treated to 50 percent TOC breakthrough would be an important consideration for
DBP control. Finally, a fraction of the TOC was seen to be nonadsorbable, which is typical for
GAC adsorbers (EPA, 2003). Results for TOC analysis from a RSSCT must be used with caution
as they do not represent seasonal variability of the source water. A representative batch of water
is important and other factors such as biodegradation within the bed will not be included (EPA,
2003).
Comparison of Characterization Tests to TOC Loading Results
When TOC loading results (at 50,000 bed volumes) were compared to characterization tests, a
negative correlation was seen with TCN results for all waters (R2>0.76) (Table 3.8). A high TCN
value implies a low degree of activation during GAC fabrication and this tends to limit the
creation of mesopores within the carbon structure and limit the capacity for TOC adsorption
(communication with CCC, 2010). A clear positive correlation between overall surface area
(BET surface area) and TOC loading was seen only in Water 3 (R2 = 0.99) (Table 3.8). Carbon
54
D’s high ranking of TOC loading capacity compared to the other carbons was unexpected as it
has the smallest overall surface area and iodine number. This anomalous result would have
skewed the correlation of the BET surface area and the TOC loading results. Carbon D’s
different raw material (lignite coal) and high transport to adsorption pore ratio may have
influenced this result as discussed in the previous section.
Table 3.8 Comparison of TOC loading (at 50,000 bed volumes) and characterization results
(R2 values, +/- indicates positive or negative slope in linear regression analysis)
Characterization Test
TOC Loading Water 1
TOC Loading Water 2
TOC Loading Water 3
Iodine Number 0.23 (-) 0.32 (-) 0.78 (+) TCN 0.93 (-) 0.98 (-) 0.76 (-)
TCNG 0.91 (-) 0.96 (-) 0.22 (-) BET Surface Area 0.05 (-) 0.11 (-) 0.99 (+)
3.4 SUMMARY AND CONCLUSIONS Iodine number, BET surface area, and TCN(G) tests are shown to be inconsistent in predicting
site-specific service life for taste and odour control (e.g. geosmin and MIB) under the conditions
tested. Tests expected to predict trace contaminant removal (TCN, TCNG) did not correlate well
with RSSCT breakthrough results (R2<0.50 for MIB) and only negatively correlated to geosmin
loading results with Lake Simcoe waters (TCN: R2 = 0.94, TCNG: R2 = 0.91). A positive
correlation existed between BET surface area (R2=0.95), MIB isotherm (R2 = 0.94), iodine
number (R2 = 0.97) and the MIB breakthrough results on the RSSCT completed with Water 3,
the one test that did not include Carbon D. Correlations were thought to be adversely affected by
the inclusion of Carbon D results which is a lignite coal activated carbon as opposed to a
bituminous coal and has a larger transport-to-adsorption pore ratio. Additional investigation
could be done comparing multiple bituminous carbons with multiple lignite carbons to examine
the trends within raw materials. However, the conclusion remains that no characterization test
was applicable for predicting RSSCT results across the different carbons and natural waters used
in this study. Carbons B and E ranked highest relative to the other carbons for loading capacity
and breakthrough. According to the manufacturers, both carbons are designed to have a high
capacity for the removal of both high and low molecular weight compounds suggesting a wider
pore distribution range. Other studies have also shown that carbons with a wide range of pore
55
sizes are less affected by competitive adsorption of NOM on trace contaminants (Ebie et al.,
2001; Newcombe et al., 2002b; Pelekani and Snoeyink, 1999). Although certain carbons (B and
E) stood out in adsorption performance for both loading and breakthrough, no correlation existed
which pointed towards a test that would aid in the selection of these carbons. Kinetic
considerations, differences in water matrices and competition with NOM were also speculated to
be reasons no correlation was seen with thermodynamic tests. Further research is recommended
to consider the confounding effects of natural organic matter on both the characterization test
results and the accuracy of RSSCTs for predicting large scale adsorption performance.
With the two waters tested in this study, a general water treatment plant was modeled and the
carbon columns in the RSSCTs were continuously dosed with 100 ng/L geosmin and MIB.
Concentrations in both source waters would not reach these concentrations normally. RSSCTs
modeled after longer EBCTs would also be interesting to study to determine if kinetics was a
factor in the lack of correlation between RSSCTs and thermodynamic tests. With additional tests,
different breakthrough values could also be examined to see if correlations between tests are
affected.
Without a consistent isotherm test capable of predicting the service life of an activated carbon
contactor for taste and odour control, water treatment utilities are faced with the challenging task
of selecting an appropriate activated carbon for their specific source water. Thermodynamic tests
do not show a clear correlation with taste and odour adsorption in RSSCTs. Tests designed
specifically to measure the high energy sites on a carbon and that were hypothesized to correlate
well with geosmin and MIB, did not correlate with RSSCTs. Utilities seeking to determine which
carbon will have a longer service life for taste and odour control would, therefore, be required to
run RSSCT or pilot-scale studies.
56
3.5 REFERENCES American Public Health Association (APHA), American Water Works Association (AWWA),
and Water Environment Federation (WEF) (2005). Standard Methods for the Examination of Water and Wastewater, 21st Edition. AWWA, Washington, DC.
American Water Work Association (2006) AWWA Standard: Granular Activated Carbon,
ANSI/AWWA B604-05. American Water Work Association Research Foundation, Denver, CO.
Berrigan J.K. (1985) Scale-up of rapid small-scale adsorption tests to fixed-scale adsorbers:
Theoretical and experimental basis. Masters Thesis from Michigan Technological University, Department of Chemical Engineering.
Brunauer S., Emmett P.H. and Teller E. (1938) Adsorption of gases in multimolecular layers.
Journal of the American Chemical Society 60, 309-19. Calgon Carbon Corporation (2005) Accelerated Column Test: Methodology to Rapidly
Determine Activated Carbon Adsorption. Calgon Carbon Corporation, Pittsburgh, PA. Calgon Carbon Corporation (2003) Determination of the carbon tetrafluoride
(tetratfluoromethane) activity of activated carbon (trace capacity number gas phase or TCNG) (TM-85). Standard operating procedure provided by Calgon Carbon Corporation, Pittsburgh, PA.
Chen G., Dussert B.W. and Suffet I.H. (1997) Evaluation of granular activated
carbons for removal of methylisoborneol to below odor threshold concentration in drinking water. Water Research 31(5), 1155-1163.
Clark R.M. (1987) Evaluating the cost and performance of field-scale granular activated carbon
systems. Environmental Science & Technology 21(6), 573-580. Corwin C.J. (2010) Trace Organic Contaminant Removal from Drinking Waters by Granular
Activated Carbon: Adsorption, Desorption, and the Effect of Background Organic Matter. Ph.D. Thesis submitted to the University of Colorado, Boulder, Colorado, Department of Civil, Environmental, and Architectural Engineering.
Crittenden J.C., Berrigan J.K. and Hand D.W. (1986) Design of rapid small-scale adsorption
tests for a constant diffusivity. Journal of Water Pollution Control Federation 58(4), 312-9. Crittenden J. C., Berrigan J. K. and Hand D.W. (1987) Design of rapid fixed-bed adsorption tests
for non-constant diffusivities. Journal of Environmental Engineering 113(2), 243-259. Crittenden J.C., Reddy P.S., Arora H., Trynoski J., Hand D.W., Perram D.L. and Summers R.S.
(1991) Prediction of GAC performance with RSSCTs. Journal of American Water Works Association 83(1), 77-87.
57
Environmental Protection Agency (EPA) (1996) ICR Manual for Bench- and Pilot-Scale Treatment Studies (EPA 814/B-96-003) US Environmental Protection Agency, Cincinnati, OH.
Environmental Protection Agency (EPA) and NSF (2003) Environmental Technology
Verification Protocol: Protocol for Equipment Verification Testing for Removal of Precursors to Disinfection By-Products. NSF International, Ann Arbor, MI.
Hand D.W., Crittenden J.C., Arora H., Miller J.M. and Lykins Jr. B.W.(1989) Designing fixed-
bed adsorbers to remove mixtures of organics. Journal of American Water Works Association 81(1), 67-77.
Kim Y., Lee Y., Gee C. and Choi E. (1997) Treatment of taste and odor causing substances in
drinking water. Water Science and Technology 35(8), 29-36. Li Q., Snoeyink V.L. Mariñas B.J. and Campos C. (2003) Pore blockage effect of NOM on
atrazine adsorption kinetics of PAC: the roles of PAC pore size distribution and NOM molecular weight. Water Research 37, 4863-4872.
Newcombe G., Drikas M. and Hayes R. (1997) Influence of characterised natural organic
material on activated carbon adsorption: II. Effect on pore volume distribution and adsorption of 2-methylisoborneol. Water Research 31(5), 1065-1073.
Newcombe G., Morrison, J. and Hepplewhite C. (2002a) Simultaneous adsorption of MIB and
NOM onto activated carbon. I. Characterization of the system and NOM adsorption. Carbon 40(12), 2135-2146.
Newcombe G., Morrison J., Hepplewhite C. and Knappe D. (2002b) Simultaneous adsorption of
MIB and NOM onto activated carbon. II. Competitive effects. Carbon 40(12), 2147-2156. Pelekani C. and Snoeyink V.L. (1999) Competitive adsorption in natural water: Role of activated
carbon pore size. Water Resources 33(5), 1209 – 1219. Pirbazari M., Borow H., Craig S., Ravindran V. and McGuire M.J. (1992) Physical chemical
characterization of five earthy-musty-smelling compounds. Water Science and Technology 25(2), 81-88.
Pirbazari M., Ravindran V., Badriyha B.N., Craig S. and McGuire M.J. (1993) GAC adsorber
design protocol for the removal of off-flavors. Water Research 27(7), 1153-1166. Sugiura N., Nishimura O., Kani Y., Inamori Y. and Sudo R. (1997) Evaluation of activated
carbons for removal of musty odor compounds in the presence of competitive organics. Environmental Technology 18(4), 455-459.
Summers R.S., Cummings L., DeMarco J., Hartman D.J., Metz D.H., Howe E.W., MacLeod B.
and Simpson M. (1992) Standardized Protocol for the Evaluation of GAC. American Water Work Association Research Foundation and American Water Works Association, Denver, CO.
58
Summers R.S., Haist B., Koehler J., Ritz J., Zimmer G. and Sontheimer H. (1989) The influence
of background organic matter on GAC adsorption. Journal of American Water Works Association 81(5), 66-74.
Zachman B.A., Rajagopalan B., Summers R.S. (2007) Modeling NOM breakthrough in GAC
adsorbers using nonparametric regression techniques. Environmental Engineering Science 24(9), 1280-1296.
Zhang X. (2008) Selecting activated carbon for micropollutant removal in drinking water
treatment: Trace capacity number test. Masters Thesis, University of Toronto, Department of Civil Engineering, Toronto, ON.
59
4 THE EFFECTS OF COMPETITIVE ADSORPTION BETWEEN T&O
COMPOUNDS AND NOM ON CHARACTERIZATION TESTS AND RSSCTS
LITERATURE REVIEW AND RESEARCH RECOMMENDATIONS
ABSTRACT To assist utilities in making the most cost effective choice of GAC for taste and odour control, an
understanding of competitive adsorption with natural organic matter (NOM) in the water is
necessary. NOM influences the adsorption capacity of an activated carbon for trace organic
contaminants by competing for adsorption sites and blocking adsorption pores. This section will
examine the existing literature on taking competitive adsorption into account with respect to
isotherm tests and RSSCTs. Recommendations for future research involving both isotherm tests
and RSSCTs to predict GAC capacity for taste and odour control in the presence of background
organic matter will be given.
60
4 THE EFFECTS OF COMPETITIVE ADSORPTION BETWEEN T&O
COMPOUNDS AND NOM ON CHARACTERIZATION TESTS AND RSSCTS
LITERATURE REVIEW AND RESEARCH RECOMMENDATIONS
4.1 INTRODUCTION Identifying bench-scale tests that accurately predict the removal of trace contaminants in the
presence of natural organic matter (NOM) remains a challenge that is currently studied by the
research community (Corwin, 2010). Rapid small-scale column tests (RSSCTs) are a helpful tool
used to assess granular activated carbon (GAC) for trace contaminant removal at bench scale. In
addition, isotherms offer information on adsorption capacity of a carbon. RSSCTs offer the
additional benefit of modeling not only the thermodynamics of a GAC system but also the
kinetics of adsorption, a parameter important to consider when examining the performance of
GAC for trace contaminant removal (Corwin, 2010). As was shown in Chapter 3, no clear
correlation existed between the isotherm tests and breakthrough values for the five carbons
tested. This was hypothesized to be caused by the presence of NOM in the water affecting the
adsorption of taste and odour compounds. The presence of NOM will affect both the results of
the isotherm tests and the RSSCTs for trace contaminant removal. Further research is needed to
improve the accuracy of RSSCTs and isotherm tests when NOM is present.
This chapter will be divided into two main sections. The first section will continue the discussion
regarding the search for a simple bench-scale test that accurately predicts carbon adsorption
performance in the presence of competing organics. This will include thoughts on how to
improve the thermodynamic tests conducted in Chapter 3 to achieve better adsorption
performance predictions. The second section will present a summary of the latest research
describing improvements to the RSSCT design and RSSCT results to produce more accurate
predictions of adsorption performance at large-scale. This will allow future RSSCT results to be
analyzed more accurately and provide utilities with the best information possible.
The focus of this chapter remains on taste and odour compounds, namely MIB and geosmin. As
previously mentioned, one of the main uses of granular activated carbon contactors in Ontario is
61
for taste and odour control. GAC contactors designed and tested for taste and odour control (i.e.,
MIB) and removal of disinfection by-product precursors will also serve to safeguard treated
water against many other trace organic contaminants (e.g. atrazine, DEET, caffeine, some
pharmaceuticals) (Corwin, 2010).
4.2 SELECTION OF THERMODYNAMIC CHARACTERIZATION TESTS FOR
PREDICTING TASTE AND ODOUR CONTROL Several studies have demonstrated that natural organic matter (NOM) has a negative impact on
trace contaminant removal by GAC through either direct competition for adsorption sites or by
pore blockage (Corwin and Summers, 2010; Knappe et al., 1997; Summers et al., 1989).
Newcombe et al. (1997, 2002) demonstrated how low molecular weight NOM (<600 g/mol)
compounds were found to have a greater competition effect on the adsorption of MIB (molecular
weight of 168 g/mol) than the larger molecular weight (>1000 g/mol) NOM compounds due to
direct competition for adsorption sites. Pore blockage was also shown to play an important role
in both the equilibrium capacity and the kinetics of adsorption by completely blocking an
adsorption site or by slowing the diffusion of a compound into a site partially blocked by NOM.
Pelekani and Snoeyink (1999) showed that the competitive effects of NOM depend on the pore
size distribution of an activated carbon. Microporous carbons (<20 Å) were most affected by low
molecular weight NOM and mesoporous (20–500 Å) carbons by higher molecular weight NOM.
This suggests that MIB and geosmin, both with relatively low molecular weights, would be most
adversely affected by the presence of NOM with low molecular weights.
Performing isotherm tests in the presence of background organic matter has been presented in
several studies. For comparability, isotherms using lab-grade water have been used to provide
baseline information on the equilibrium capacity of different carbons (Chen et al., 1997).
However, in order to assess adsorption capacity performance of a carbon in natural waters,
isotherm tests using these natural waters are required. Summers et al. (1989) suggested using
isotherm tests conducted with carbons that have been preloaded with NOM to take into account
competitive effects. In the study, the researchers found that adsorption capacity results from
trichloroethene (TCE) isotherm studies conducted with preloaded carbon were very similar to
results from a TCE column test. A main challenge to preloaded isotherm tests is the
determination of preloading time for the activated carbon prior to performing the isotherm test.
62
Corwin (2010) and Speth (1991) determined preloading time by running RSSCTs and directly
removing carbon from the columns at specific sampling times to obtain an accurately preloaded
carbon sample. Qi et al. (1992) found good agreement in a comparison of the adsorption capacity
results of preloaded isotherm tests to pilot plant capacity results. Reasons limiting the strength of
this relationship were speculated to be caused by displacement effects that occur in GAC
columns not being taken into account. Speth (1991) also found that the column adsorption
capacity was well represented by the preloaded isotherms.
Najm et al. (1991) developed a novel method to predict isotherms for trace organic contaminants
in the presence of competing background organic matter termed the equivalent background
compound (EBC) method. This method requires Freundlich isotherm values for the target
compound both in distilled water and the water being treated. The ideal adsorbed solution theory
(IAST) is then used to calculate the initial concentration and Freundlich parameters (K and 1/n)
of the background organic matter. These results can be used to simulate the effects of
background organic matter on the adsorption of the target compounds. Najm et al. (1991)
reported successful prediction of the equilibrium capacity of powdered activated carbon (PAC)
for different initial concentrations of 2,4,6-trichlorophenol (TCP) in several natural waters. Other
authors have also reported success in quantifying competition using the EBC method but have
listed some limitations (Heijman and Hopman, 1999; Qi et al., 1992; Yu et al., 2008).
Limitations include: the EBC constants for NOM are dependent on the target compound being
studied and specific water type, constants specific to the compound and natural water need to be
determined through additional testing, only thermodynamic (equilibrium) information is
obtained, and, finally, the method is conducted using PAC and the effect of pore blocking is
increased with larger particle sizes (Corwin, 2010; Graham et al., 2000; Heijman and Hopman,
1999). The EBC requires significant experimental testing and mathematical modeling before it
may be applied which may also be a deterrent for researchers. However, the EBC is a helpful
tool in pinpointing the specific NOM that will affect the adsorption of the target compound.
Overall effects of NOM on the carbon column bed will not be considered as the EBC represents
only the NOM that affects a particular target compound (Qi et al., 1992).
63
Source Water-Specific Isotherm Tests
In order to account for preloading effects by NOM, isotherm tests conducted with natural water
would be better suited for determining GAC adsorption capacity of taste and odour compounds.
The thermodynamic characterization tests reported in Chapter 3 (TCN(G), iodine number, MIB
and geosmin isotherms) were all conducted with lab-grade water. Based on the literature listed
above, it is recommended that these tests be run with preloaded carbon or using the EBC method.
These tests could assist a utility in selecting the activated carbon with the highest adsorption
capacity specific to their source water. In the study by Summers et al. (1989), RSSCTs were run
with natural water and carbon samples removed at different depths and times which provided
GAC with varying levels of preloaded NOM. Corwin (2010) ran RSSCTs with the natural water
without the target of interest and took carbon samples from the column at regular intervals. At
the time of sampling, the GAC was mixed and a representative sample taken before repacking
the remaining carbon back into the column. The flow rate was adjusted to maintain the
appropriate EBCT for the preloading run. Results from the preloading RSSCTs showed a
continuous breakthrough curve indicating that mixing and repacking of the carbon did not disrupt
the mass transfer zone (Corwin and Summers, 2010). Speth (1991) also obtained preloaded
carbon samples for isotherm tests from column tests, however, samples were taken from the top
of the carbon column. Qi et al. (1992) noted that isotherm tests run with natural water can help in
predicting a carbon’s capacity but that kinetic tests should also be conducted to take into account
displacement effects. Partial or complete pore blockage which slows or blocks the diffusion of
adsorbates into the pores in a kinetic process would not be consistently predicted in isotherm
tests. Pore blockage by large molecular weight NOM has been shown to affect the kinetics of
adsorption of the target compound but displays little effect on the adsorption capacity (Li et al.,
2003). Kinetic tests must also be conducted to improve accuracy of the predictions of full-scale
GAC adsorption capacity (Chen et al., 1997). In addition to running isotherm tests in natural
waters, batch rate experiments would be another tool to introducing the kinetic element of
adsorption. These tests run simultaneously with RSSCTs would provide an interesting
comparison of the performance of the five carbons.
Selection of Appropriate Isotherm Test
As was discussed in Chapter 3, acetoxime (TCN test) is a surrogate used to determine the amount
of high energy sites of an activated carbon. When tested on five carbons, certain carbons showed
64
a greater affinity for the adsorption of acetoxime (higher TCN numbers), indicating these
carbons have a higher number of high energy sites. However, when RSSCTs were run with taste
and odour compounds, which also require high energy sites, the high ranking carbons according
to TCN did not perform as well as expected. No correlation between TCN and MIB
breakthrough was seen. The first consideration would be that acetoxime is not an appropriate
surrogate for MIB. There was not a strong correlation seen between TCN and MIB and geosmin
isotherm results (R2<0.22). Acetoxime or acetone oxime ((CH3)2C=NOH) has a low molecular
weight of 73 g/mol. Therefore, the competitive effects on this compound could be different than
those on taste and odour compounds (molecular weight of MIB = 168 g/mol, geosmin = 182
g/mol). Competitive effects between NOM and acetoxime may in fact be more exaggerated,
resulting in sites needed for acetoxime adsorption to be more easily blocked by NOM. The other
explanation would be that there is a high fraction of similarly sized NOM competing directly for
acetoxime sites. If the relative competition varied in magnitude between acetoxime and MIB
with NOM, rankings or relative performance of the carbons would be affected when considering
breakthrough curves.
Newcombe et al. (2002b) observed that the trends or ranking of different carbons from isotherm
tests in the presence and absence of NOM are consistent. Performing isotherm tests specific to
the target compound would, therefore, presumably be more effective and accurate. Zhang (2008),
however, found that the rankings between carbons varied with isotherms conducted in lab-grade
and NOM water. Results from Chapter 3 showed that MIB isotherm results correlated well with
20 % breakthrough results between bituminous coal-based carbons in both waters (R2 = 0.94 in
Lake Simcoe and R2 = 0.76 in Lake Ontario). The MIB isotherm test in this case was conducted
with lab-grade water. Although this might be a simple test in the case of taste and odour control
in Ontario where there are two main compounds of interest, it would not predict which carbon
would be best suited for a host of target compounds (i.e., pesticides and pharmaceuticals). It
does, however, allow utilities to narrow down their choices of activated carbons and only run
RSSCTs on one or two of the most promising carbons for taste and odour control. Additional
testing of MIB isotherms in both natural and lab-grade water would help to test this hypothesis.
As mentioned in Chapter 3, Carbon D was an anomaly in the results. It ranked the lowest for all
characterization tests relative to the four other carbons and yet performed better than two of the
65
other carbons in kinetic tests (RSSCTs). It is unclear as to what improved its MIB adsorption
performance in the RSSCTs both for breakthrough and loading (relative to other carbons). One
hypothesis is that the large number of transport pores would have allowed the MIB to reach the
sites available quickly, supporting other research that a carbon with a wide range of pore sizes is
best for minimizing competition effects (Ebie et al., 2001; Newcombe et al., 2002b; Pelekani and
Snoeyink, 1999). Although Carbon D ranked the lowest carbon for MIB adsorption capacity
according to the MIB isotherm, it outperformed Carbons A and C on MIB loading results (at
50,000 bed volumes in RSSCT results) (Figure 3.12). This suggests that Carbon D’s faster
kinetics place it at an advantage over carbons with apparently slower kinetics (Carbon A and C).
It was also seen that Carbons A and C have lower total MIB loading capacity values than Carbon
D from the RSSCT tests (Figure 3.11). This may be caused by these carbons having slower
kinetics, fouling on the carbons causing slower kinetics or an EBCT in the RSSCT that did not
allow sufficient time for these carbons to adsorb higher volumes of MIB. RSSCTs run with
varying EBCTs would help to assess if a change in carbon performance would be seen. It is also
recommended that a comparison be done between the results of MIB isotherm tests and batch
rate MIB isotherm tests for the five carbons. If, for example, Carbon A and Carbon C have lower
loading capacity in the RSSCTs relative to Carbon D due to slower adsorption kinetics, batch
rate experiments would show this trend. Carbon D may also rank higher in MIB batch rate
relative to the other carbons regardless of its low MIB isotherm value.
Characterization of NOM to Help Select an Appropriate Activated Carbon
The continued research into the competitive effects of NOM on trace contaminant adsorption
highlights the complexity of predicting trace contaminant removal in the presence of NOM.
NOM in itself is highly complex with a wide range of molecular weights with differing chemical
properties. Emerging equipment for the characterization of NOM, such as the liquid
chromatography organic carbon detector (LC-OCD), could be useful in pinpointing which NOM
fractions are present in the water, leading to a better understanding of the competition for
adsorption pores with taste and odour compounds. By characterizing Lake Ontario and Lake
Simcoe waters with the LC-OCD or other NOM characterization tests, a better understanding
would emerge of the competition effects acting in the research presented in Chapter 3.
Knowledge of an activated carbon’s pore size distribution and the NOM molecular weight
66
fractions could lead to more optimal selection of an activated carbon for a utility’s specific
source water needs.
As previously mentioned the two main mechanisms in adsorption competition with NOM are
pore blockage and direct competition for adsorption sites. By identifying the molecular weight
fractions of the specific NOM in the natural waters, a clearer sense of whether direct competition
between the low molecular weight NOM and the taste and odour compounds is the main cause of
competitive adsorption as Newcombe et al. (1997, 2002) reported. Preloaded isotherms and
batch rate experiments using preloaded NOM could determine if this hypothesis is correct.
Several studies have reported that carbons with a wider range of pore sizes display a lower
competitive effect of NOM on trace contaminants (agricultural chemicals, MIB and atrazine)
(Ebie et al., 2001; Newcombe et al., 2002b; Pelekani and Snoeyink, 1999).
4.3 OPTIMIZING RSSCTS FOR PREDICTING ADSORPTION OF TASTE AND
ODOUR COMPOUNDS A thorough understanding of the confounding impact of NOM on trace contaminant adsorption
in RSSCTs would be valuable in providing utilities with the most accurate guidance in activated
carbon selection. RSSCTs are not immune to adverse effects on results by the presence of NOM.
Crittenden et al. (1991) acknowledged that rapid small-scale column tests (RSSCTs) have
limitations in predicting large-scale adsorption performance of trace contaminants when NOM is
present. As described in Chapter 2, studies conducted to date have shown that the constant
diffusivity (CD) model is more accurate when NOM is not present but that the proportional
diffusivity (PD) model should be used to accurately predict NOM removal (Corwin, 2010).
Recent research has been presented allowing for adjustments to be made to RSSCT results to
increase accuracy for scale-up. The currently used scaling equations for the RSSCT do not
account for the confounding effects of NOM on the adsorption of different contaminants. NOM
is shown to affect both the adsorption kinetics and the adsorption capacity of a carbon (Corwin,
2010). To account for these effects, two main RSSCT adjustments are presented by Corwin
(2010): (1) assigning a target contaminant-specific diffusivity factor (kinetics) for the RSSCT
scaling equations and (2) applying a fouling factor to RSSCT results to account for adsorption
capacity differences between small and large particles.
67
Adjustment #1: Determination of specific diffusivity factors for MIB and geosmin
As has been shown in previous studies, neither the constant diffusivity (CD) or proportional
diffusivity (PD) approach yields scalable results for RSSCTs in which trace contaminants and
NOM compete for adsorption sites (Corwin and Summers, 2010; Crittenden et al., 1991). The
difference between these two approaches is the diffusivity factor or X value seen in Equation 4-
1.
LC
SC
X
LC
SC
LC
SC
t
t
R
R
EBCT
EBCT
2
(4-1)
X defines the dependence of intraparticle diffusion on GAC particle size. In the RSSCT
conducted for Chapter 3, an X value of 0.9 was selected as previous research had found this
diffusivity factor yielded the best results (Communication with CCC, 2009). This decision to
select a diffusivity factor between 0 and 1 is supported by work completed by Corwin (2010).
Many studies have shown that the diffusivity of DOM is linearly proportional to GAC particle
size but little research has been conducted to determine the diffusivity factor of trace organics in
the presence of DOM (Corwin, 2010). The author’s study presents diffusivity factors for various
compounds ranging between 0.4 and 1.1 (Corwin, 2010). Corwin (2010) concludes that the
diffusivity factor is compound dependent and cites using the differential column batch reactor
(DCBR) kinetic test to determine the diffusivity factor. The DCBR method is described in more
detail below. Corwin recommends first designing a RSSCT using the PD approach in order to
correctly predict dissolved organic carbon breakthrough curves for full-scale adsorbers. Corwin
then offers a method to convert the PD-RSSCT breakthrough curves to those for a trace
contaminant using its specific diffusivity factor and a pore surface diffusion model (PSDM)
(detailed method in Corwin, 2010). The reasoning behind this approach is to save the time of re-
running RSSCTs for multiple compounds using the same source water. If Corwin’s study is
reproducible, future taste and odour studies could use the geosmin and MIB specific diffusivity
factors on PD RSSCTs measuring TOC. This recommendation by Corwin would need to be
verified.
Corwin noted that a compound’s intraparticle diffusivity factor did not vary significantly with
preloading time or between different types of water. However, diffusivity factors were lower
between tests run with lab-grade water and NOM water suggesting that additional verification
68
should be completed to see if a compound’s diffusivity factor can be applied across different
waters. Since pore blockage is found to have more of an effect on adsorption in larger particles,
Corwin expected this to be the main mechanism affecting the dependency of diffusion on particle
size. The presence of NOM would result in a diffusivity factor that is nonconstant (X>0). In the
absence of NOM (lab-grade waters), and therefore a scenario without pore blockage, it was
expected that the diffusivity factor be close to zero. This was not the case. Although the
diffusivity factors were lower than those in NOM water, diffusivity factors were also
nonconstant in the lab-grade water tests. Corwin hypothesized that a second mechanism, other
than pore blockage, is causing the dependence of intraparticle mass transfer on GAC particle size
(increasing X above 0 or constant diffusivity). This second mechanism is suggested to be the
heterogeneity in the internal pore structure of the carbon between different GAC particle sizes.
Although total pore volumes between large and small carbon particles would be similar, internal
pore structures may vary and diffusion paths are shorter within smaller particles. The
accumulation of NOM on the carbon surface (and pore blockage) would contribute to this
mechanism in natural waters. Further research into these considerations should be made, since, if
this hypothesis is validated, the different internal pore structure between different types of
carbons would affect selection of diffusivity factors for a compound.
The kinetics of adsorption, through the mass transfer zone, is controlled by two main
mechanisms: the film mass transfer (liquid-phase) and intraparticle mass transfer (diffusion
within a particle). The use of RSSCT scaling equations where X is not equal to zero violates the
assumption of the RSSCT equation that film mass transfer and intraparticle mass transfer are
perfectly matched (Crittenden et al., 1987). Therefore, the separate contributions made by the
film and intraparticle mass transfer must be examined to determine which is rate-limiting.
Studies have shown that the presence of NOM on the surface of GAC can reduce the film mass
transfer rate of trace organic contaminants (Carter and Weber, 1994; Yu et al., 2008). The Biot
number is the film mass transfer rate divided by the intraparticle mass transfer rate. The
importance of each mechanism can be determined based on the Biot number, i.e., for Biot
numbers less than 1, film diffusion is the rate-limiting mechanism, a Biot number of 5 indicates
equal contributions and Biot numbers between 50 - 100 indicate that adsorption kinetics are
controlled by intraparticle diffusion (Sontheimer et al., 1988). The spread of the mass transfer
zone will be partially influenced by film transfer for Biot numbers between 0.5 and 100. Corwin
69
(2010) reported that Biot numbers for trace contaminants range between 30 and 90 for typical
loading rates of 5 to 25 m/hr. Therefore, intraparticle mass transfer dominates but film transfer
kinetics should not be completely ignored, especially if loading rates are low. Crittenden et al.
(1987) also stated that when Biot numbers for adsorbates are high, RSSCTs designed according
to CD poorly predicted performance at large-scale.
Determining the Diffusivity Factor for a Trace Contaminant
This section explains a method for determining a compound’s diffusivity factor.
To determine the diffusivity factor for MIB and geosmin (or other trace contaminants) the
differential column batch reactor (DCBR) method developed by Hand et al. (1983) would be
used. The DCBR is a series of batch tests using carbons with different diameters along with the
adsorbent of interest, in the water matrix of interest. The ultimate goal of a batch rate test is to
eliminate the film mass transfer resistance and measure the intraparticle kinetics of a compound
in GAC. The DCBR is an apparatus capable of varying the mixing intensity in order to compare
the rate data at different mixing intensities. When the rate data for increasing mixing intensities
is identical, film mass transfer resistance has been eliminated (Hand et al., 1983). Samples are
taken at varying times and a plot of C/Co versus t/(dSC)2 and t/(dLC)2 is prepared. In the plot, for a
given C/Co, the average ratio of t/(dSC)2 to t/(dLC)2 is equal to the ratio of Ds,SC to Ds,LC (Equation
4-2).
LC
SC
SC,S
LC,S
X
LC
SC
LC
SC
t
t
D
D
d
d
EBCT
EBCT
2
(4-2)
Corwin (2010) used the DCBR results from three different GAC particle sizes to determine the X
value for the specific compound. The best fit X value is determined by normalizing time (x-axis)
by X
LC
SC
d
d
2
for all particle sizes. A manual search for X is then used to determine the best fit
value which results in all data points collapsing to form a single curve. The best fit value was
verified by ensuring the lowest mean square error of the data compared to a log-linear fit through
all data points. DCBR tests must be conducted for each compound of interest in the natural water
of interest. Corwin (2010) reports that the diffusivity factor for MIB was 0.40 and did not vary
70
significantly with different loading times or between different waters. This result could be
verified.
Adjustment #2: Determination of Fouling Index (SFY) for Source Water
This section provides a summary of Corwin’s method for applying a fouling index to account for
fouling by NOM in a specific water. To aid in improving the accuracy of RSSCTs, fouling
indexes specific to Ontario waters would be helpful. This range of fouling indexes would be,
according to Corwin, necessary parameters for the accurate scaling-up of results from RSSCTs to
full-scale.
The presence of NOM has been shown to influence not only adsorption kinetics but also to
cause adsorption capacity differences between different particle sizes. This results in inaccurate
scaling of results from RSSCT to large-scale systems. Corwin concludes that a RSSCT,
regardless of whether PD or CD is used, will not accurately predict large-scale performance
unless a fouling index is used to correct for differences in adsorption capacity. Corwin
hypothesizes that pore blockage by NOM is the main mechanism responsible for the dependence
of adsorption capacity on GAC particle size, noting that as GAC particle size increases so does
the microporous surface area behind a constricted pore. Therefore, adsorption capacity decreases
per mass of adsorbent of larger GAC particles. Corwin presents a methodology which accounts
for NOM preloading effects on GAC particle size. Fouling of a GAC by NOM must be scaled
differently to large scale than scaling applied to take into account adsorption kinetic differences
between different compounds. This dependence on particle size is shown to increase when the
ratio between concentrations of trace organic contaminants to DOM increases. The fouling
index is not compound specific but will be C0/DOC0 ratio specific.
In order to determine the fouling index for a source water, a RSSCT would need to be performed
with different GAC particle sizes for a given trace contaminant. The scaling factor of the
governing equation (Equation 4-3), which calculates the ratio of the diameters of the large to
small column carbon particles, is raised to an exponent, Y (Equation 4-4).
SF dp,LC
dp,SC
(4-3)
71
SFY (4-4) Once the fouling index has been determined, both the compound specific diffusivity factor and
the fouling index would be applied to the RSSCT results to improve the accuracy of scaling up
the results. In order to normalize RSSCT results according to the fouling index, the breakthrough
curve bed volumes (x-axis) are divided by the fouling index. Pilot plant results would also be
helpful in verifying the results from applying the two RSSCT adjustments suggested by Corwin
(2010).
In summary, Corwin (2010) recommends that a PD-RSSCT be conducted with the natural water
followed by two adjustments. The breakthrough results for individual trace organic contaminants
can be determined by first adjusting the results according to the compound-specific diffusivity
factor, followed by an additional adjustment with the fouling index, if required.
4.4 SUMMARY AND CONCLUSION Further research is needed to determine the most appropriate lab-based characterization test for
predicting GAC performance for taste and odour control, if indeed such a test can be appropriate
at all. The confounding factor of NOM presents a challenge to selecting a carbon based on
thermodynamic characterization tests alone. Selecting the appropriate thermodynamic test and
conducting it on the natural water of interest are two important steps in improving the selection
of GAC for a utility’s needs. RSSCTs designed and scaled to take into account the confounding
effects of NOM would be an additional tool to improving this selection. Although isotherm tests
could aid in the preliminary selection of a carbon for taste and odour control, RSSCTs or pilot-
scale tests would still be needed for breakthrough information (i.e., OTC for taste and odour
compounds) and GAC contactor design (i.e., EBCT, carbon usage rates, rankings according to
breakthrough values).
One of the principal goals of this research is to assist utilities in purchasing the most appropriate
GAC for taste and odour control. The two principle areas for future research highlighted in this
chapter include: continued research into appropriate lab-scale testing for the selection of a carbon
for taste and odour control (in the presence of competing organics) and the improvement of
RSSCT results for scale-up to full-scale systems. This research could include:
72
1. Continued investigation into the appropriateness of MIB and geosmin isotherms and batch
rate experiments as bench-scale tests for taste and odour performance.
2. NOM characterization for different source waters (i.e., Lake Ontario and Lake Simcoe). This
information could be paired with information on pore size distribution of available activated
carbons.
3. Diffusivity factors for geosmin, MIB and other contaminants of interest providing RSSCT
scaling equations appropriate for each compound.
4. Determination of fouling index (SFY) parameters that best correct a utility’s RSSCT
breakthrough curves to mimic large scale performance of GAC (source water-specific).
5. Comparison of pilot plant results verifying RSSCT results.
The information obtained from the points listed above could help utilities in their selection of
activated carbons for taste and odour control in the presence of competing NOM. The
documentation of this information, specifically the diffusivity factors for geosmin, MIB and
other trace contaminants would be pertinent to any utilities running RSSCTs.
73
4.5 REFERENCES
Carter M.C. and Weber Jr. W.J. (1994) Modeling adsorption of TCE by activated carbon preloaded by background organic matter. Journal of Environment Science & Technology 28, 614-623.
Chen G., Dussert B.W. and Suffet I.H. (1997) Evaluation of granular activated
carbons for removal of methylisoborneol to below odor threshold concentration in drinking water. Water Research 31(5), 1155-1163.
Crittenden J. C., Berrigan J. K. and Hand D.W. (1987) Design of rapid fixed-bed adsorption tests
for non-constant diffusivities. Journal of Environmental Engineering 113(2), 243-259. Crittenden J.C., Reddy P.S., Arora H., Trynoski J., Hand D.W., Perram D.L. and Summers R.S.
(1991) Prediction of GAC performance with RSSCTs. Journal of American Water Works Association 83(1), 77-87.
Corwin C.J. (2010) Trace Organic Contaminant Removal from Drinking Waters by Granular
Activated Carbon: Adsorption, Desorption, and the Effect of Background Organic Matter. Ph.D. Thesis submitted to the University of Colorado, Boulder, Colorado, Department of Civil, Environmental, and Architectural Engineering.
Corwin C.J. and Summers R.S. (2010) Scaling trace contaminant adsorption capacity by granular
activated carbon. Environmental Science & Technology 44, 5403-5408. Ebie K., Li F., Azuma Y., Yuasa A. and Hagishita T. (2001) Pore distribution effect of activated
carbon in adsorbing organic micropollutants from natural water. Water Research 35(1), 167-179.
Graham M.R., Summers R.S., Simpson M.R. and Macleod B.W. (2000) Modeling equilibrium
adsorption of 2-methylisoborneol and geosmin in natural waters. Water Research 34(8), 2291-2300.
Hand D.W., Crittenden J.C., ASCE M. and Thacker W.E. (1983) User-oriented batch reactor
solutions to the homogeneous surface diffusion model. Journal of Environmental Engineering 109(1), 82-101.
Heijman S.G.J. and Hopman R. (1999) Activated carbon filtration in drinking water production:
model prediction and new concepts. Colloids and Surfaces A: Physiochemical and Engineering Aspects 151, 303-310.
Knappe D.R.U., Snoeyink V.L., Roche P., Prados M.J. and Bourbigot M-M. (1997) The effect of
preloading on rapid small-scale column test predictions of atrazine removal by GAC adsorbers. Water Research 31(11), 2899-2909.
Li Q.L., Snoeyink V.L., Marinas B.J., Campos C. (2003) Elucidating competitive adsorption
mechanisms of atrazine and NOM using model compounds. Water Research 37(4), 773-784.
74
Najm I.N., Snoeyink V.L., Richard Y. (1991) Effect of initial concentration of a SOC in natural
water on its adsorption by activated carbon. Journal of American Water Works Association 83(8), 57–63.
Newcombe G., Drikas M. and Hayes R. (1997) Influence of characterised natural organic
material on activated carbon adsorption: II. Effect on pore volume distribution and adsorption of 2-methylisoborneol. Water Research 31(5), 1065-1073.
Newcombe G., Morrison, J. and Hepplewhite C. (2002a) Simultaneous adsorption of MIB and
NOM onto activated carbon. I. Characterization of the system and NOM adsorption. Carbon 40(12), 2135-2146.
Newcombe G., Morrison J., Hepplewhite C. and Knappe D. (2002b) Simultaneous adsorption of
MIB and NOM onto activated carbon. II. Competitive effects. Carbon 40(12), 2147-2156. Pelekani C. and Snoeyink V.L. (1999) Competitive adsorption in natural water: Role of activated
carbon pore size. Water Resources 33(5), 1209 – 1219. Qi S., Snoeyink V.L., Beck E.A., Koffskey W.E. and Lykins Jr. B.W. (1992) Using isotherms to
predict GAC’s capacity for synthetic organics. Journal of American Water Works Association 84(9), 113-120.
Sontheimer H., Crittenden J.C. and Summers R.S. (1988) Activated Carbon for Water Treatment,
2nd Edition, DVGW-Forschungsstelle, University of Karlsruhe, Karlsruhe, Germany. Distributed in the US by the American Water Works Association, Inc.
Speth T.F. (1991) Evaluating capacities of GAC preloaded with natural water. Journal of
Environmental Engineering 117(1), 66- 79. Summers R.S., Haist B., Koehler J., Ritz J., Zimmer G. and Sontheimer H. (1989) The influence
of background organic matter on GAC adsorption. Journal of American Water Works Association 81(5), 66-74.
Yu Z., Peldszus S., and Huck P.M. (2008) Adsorption characteristics of selected pharmaceuticals
and an endocrine disrupting compound – Naproxen, carbamazepine and nonylphenol – on activated. Water Research 42, 2873-2882.
75
5 SUMMARY AND RECOMMENDATIONS
5.1 SUMMARY Adsorption characterization and rapid small-scale column tests (RSSCTs) were performed on
five different granular activated carbons. The adsorption characterization tests, conducted with
lab-grade water, ranked the carbons in terms of overall adsorption capacity (iodine number),
trace contaminant removal (TCN(G)), specific compound removal (MIB and geosmin isotherms)
and overall surface area (BET). These results were then compared to the RSSCT breakthrough
and loading results run in natural waters.
It was hypothesized that the breakthrough curves for MIB and geosmin would correlate well with
the TCN(G) tests as they indicate the availability of high energy sites within the carbon structure.
However, no clear correlation was seen. Several explanations for this lack of correlation were
discussed, including: competitive adsorption by natural organic matter, the inappropriateness of
acetoxime as a surrogate for MIB/geosmin, and that the TCN(G) test does not take into account
kinetics. A positive correlation existed between iodine number, BET surface area, MIB isotherm
results and MIB breakthrough for bituminous carbons but no correlation existed when all carbons
were included in the analysis. The appropriateness of these tests should be further explored with
additional waters and different activated carbons.
The competition of natural organic matter with trace contaminants (e.g., geosmin and MIB) for
adsorption sites is recognized in the literature as a complex issue. The results from this study
confirm this. The importance of considering the adverse effects of competing organics in both
thermodynamic and kinetic tests is emphasized. Characterization tests conducted with natural
water, MIB and geosmin batch rate isotherms and adjustments to RSSCTs are recommended.
5.2 CONCLUSIONS TCN(G) results did not correlate well with RSSCT breakthrough results (R2 < 0.50 for MIB)
and only negatively correlated to geosmin loading results with Lake Simcoe waters (TCN R2
= 0.94, TCNG R2 = 0.91).
76
A positive correlation existed between BET surface area (R2 = 0.95), MIB isotherm (R2 =
0.94), iodine number (R2 = 0.97) and the MIB breakthrough results when Carbon D was not
included in the analysis.
No clear correlation was seen between characterization results and RSSCT MIB and geosmin
loading results (except for a negative correlation with geosmin loading results in Lake
Simcoe water).
Lake Simcoe water experienced earlier MIB breakthrough than Lake Ontario water, likely
because of higher TOC and therefore a higher amount of pore blockage or direct competition
with NOM for adsorption sites.
Carbon D was considered to be an anomaly in the results, perhaps due to its unique raw
material (lignite coal) or high transport to adsorption pore ratio relative to the other carbons.
5.3 RECOMMENDATIONS Conduct adsorption characterization tests in natural waters to observe the effect of competing
organics. Both isotherms and batch rate isotherm tests in natural waters are recommended.
Further examine the possibility that, in the presence of NOM, iodine number and MIB
isotherm tests are promising lab-based tests for choosing an appropriate activated carbon for
taste and odour control.
Characterize the NOM in both waters to determine if NOM molecular weight fractions exist
in the waters that are competing directly for acetoxime, MIB or geosmin sites.
Apply the fouling index and compound-specific diffusivity factors to future RSSCT results
according to Corwin (2010).
Run pilot-scale experiments to further validate RSSCT results and correlations with full-scale
performance.
5.4 REFERENCES Corwin C.J. (2010) Trace Organic Contaminant Removal from Drinking Waters by Granular
Activated Carbon: Adsorption, Desorption, and the Effect of Background Organic Matter. Ph.D. Thesis submitted to the University of Colorado, Boulder, Colorado, Department of Civil, Environmental, and Architectural Engineering.
77
A: DEFINITIONS Interstitial Velocity
In order to pass through narrow points in a column or
through a bed of carbon particles, the water has to travel
faster. Otherwise the water would become backed up or
would have to leak out of the column. Therefore a
Reynolds number specific to porous media flow is
required (Darcy’s Law). The velocity value used in this
calculation is termed interstitial velocity and is the
approach velocity divided by the porosity of the bed.
Interstitial velocity is utilized in the Reynolds Number
calculation for large columns:
LCLC,i
LC
dvRe
fluid of itycosvis dynamic
particles of diametervelocity erstitialintfluid of densityReLC
Approach Velocity = the approach velocity is equal to the filter/column loading rate. It is the
velocity of the water arriving at the top of the filter actual velocity of the water running through
the columns. Approach velocity (m/s) is equal to the volumetric flow rate (m3/s) divided by the
cross-sectional area of the filter column (m2). The water inside the filter will accelerate to the
interstitial velocity as the flow is restricted to a smaller cross-sectional area.
Reynolds Number: Reynolds number (Re) is a dimensionless number that provides a ratio of
inertial forces to viscous forces. This provides the relative importance of these two types of
forces for given flow conditions.
A
QDVDVDRe
Approach Velocity (e.g. 5m/hr)
78
Where, V = mean fluid velocity (m/s) d = diameter of particles (m) i = interstitial velocity (m/s) [approach velocity/porosity of bed] = density of the fluid (kg/ m³) μ = dynamic viscosity of fluid (Pa·s or kg /m·s) LC = large column = kinematic viscosity ( = μ/) (m²/s) Q = volumetric flow rate (m³/s) A = column cross-sectional area (m²)
Dynamic viscosity is the kinematic viscosity multiplied by the density of the fluid (νρ = μ).
Viscosity is the measure of resistance of a fluid.
79
B: MATERIALS AND METHODS (CHAPTER 3)
B.1 DESCRIPTION OF ACTIVATED CARBONS
Five different types of granular activated carbon were used in this research and are labelled Carbon A – E. The following are short
descriptions of the different carbons:
Carbon A is developed for drinking water purposes for the removal of taste and odour compounds, disinfection by-products and other
dissolved organic compounds. The activation process was carefully controlled to create an equal combination of low and high energy
pores to allow for the effective adsorption of a broad range of high and low molecular weight organic compounds.
Carbon B is similar to Carbon A, however, has a higher volume of low energy pores (as measured by the iodine number) while
maintaining a high trace capacity number for the adsorption of both high and low molecular weight compounds.
Carbon C is developed to optimize the removal of trace contaminants from water by ensuring a maximum distribution of high energy
adsorption sites in the carbon structure.
The carbons above are all made from select grades of bituminous coal capable of withstanding the various abrasive steps of water
treatment (backwashing, air scouring and hydraulic transport).
Carbon D is developed for water treatment. It has a wide pore size distribution and large pore volume and is specified to have a rapid
adsorption rate and high adsorption capacity for dissolved organics.
Carbon E is developed specifically for drinking water treatment and to have a high capacity for the removal of natural organics,
colour bodies, pesticides, detergents, chlorinated solvents and taste and odour compounds.
80
Table B.1 Properties of activated carbon used in research
Carbon Raw Material Apparent Density (g/mL)
Iodine Number (mg/g)
TCN (mg/mL)
TCNG (g/100cm3) 1
BET Surface Area (m2/g) 1
Adsorption Isotherm MIB
q10 (ng/mg)2
Adsorption Isotherm Geosmin
q10 (ng/mg)2 A Bituminous coal 0.58 869 10.96 5.4 625 230 982 B Bituminous coal 0.575 961 11.9 6.4 683 302 732 C Bituminous coal 0.65 813 15.7 7.7 584 282 442 D Lignite coal 0.37 633 4.6 3.5 539 83 112
E Bituminous/
Subbituminous coal
0.454 1001 8.3 5.1 797 399 798
1BET surface area and TCNG results from Zhang (2008) 2Single solute bottle point adsorption (Freundlich) isotherm parameters, q10 = adsorption capacity of carbon at effluent concentration of 10 ng/L (Zhang, 2008) Table B.2 General activated carbon characteristics
Carbon Abrasion Number (%/mm)
Molasses decolourizing
efficiency Mesh Size
Ash by weight (%)
Tannin Value (mg/L)
A 78 8 x 30 8 B 75 12 x 40 8 C 80 12 x 40 6 D 70 85 8 x 30 150 E 75 230 12 x 40
81
The information in this section was obtained from communication with activated carbon
manufacturers (December, 2010).
Coarser carbons will allow for deeper beds (and longer service life) without as much headloss.
Utilities selecting GAC for TOC removal and DBP precursor removal would therefore tend to
choose the carbons with a larger mesh size.
Carbons that have undergone longer activation times should display a lower TCN value but
higher iodine number. This is seen here if Carbons A and C are compared. Carbon C has a
larger TCN (more micropores preserved due to a shorter activation time) but a smaller iodine
number. For TOC removal, a highly activated carbon is desirable.
Hardness and abrasion number are important parameters to consider as backwash will
generate fines which increase headloss and lose carbon mass.
Ash content is important because ash is wasted mass that a utility will still pay for.
Coal-based carbon tends to be better for TOC and DBP precursor removal for surface waters.
Acid-rinse can be used on coal-based GAC to remove any arsenic and antimony that may be
present.
Custom reactivation is starting to become more common. Note that it is often not really
“custom”— a common reactivation process may be used. Custom simply refers to the fact that
carbon from Plant A is kept segregated from all other carbon and returned to Plant A after
reactivation. The economics of custom reactivation are driven by the amount of make-up
carbon needed to account for losses in the process, which in turn is a function of hardness.
Reactivated carbon is often better for TOC removal because more of the carbon structure is
opened up (but TCN will be lowered).
In-house models at the carbon manufacturer can be used to predict how long a GAC will last
for T&O control.
Physical parameters (hardness, ash and custom reactivation economics) is a key selling point
for activated carbon
B.2 ACTIVATED CARBON PREPARATION
Activated carbon was prepared in identical fashion for all tests: iodine number, TCN and the
RSSCTs. The particle diameter for each of these tests differs, however, and this parameter is
82
outlined in the specific test sections found below. The general preparation of carbon requires the
following steps: selecting a representative sample and drying the carbon samples.
The activated carbon used in this research, although originally from different manufacturers, was
prepared to specific mesh sizes and supplied by Calgon Carbon Corporation. Once received, in
order to obtain a representative sample for a given test, the coning and quartering technique was
used (Figure B.1). This is done to ensure that a range of particle sizes is obtained as carbon
particles may have settled in the container during shipment. The carbon is poured onto a flat
surface and formed into a conical pile. The pile is then evenly flattened into a circular, flat cake.
The circle is divided into quarters and two of the opposite quarters are removed. The remaining
carbon is scooped into another conical pile and the process is repeated until the desired quantity
of sample is obtained.
Figure B.1 Representative Activated Carbon Sample: Coning and Quartering Technique
Once a representative sample of carbon was obtained the carbon was dried in the oven at 150ºC
for a minimum of three hours. Samples were then placed in a desiccator to reach ambient
temperature before use.
Step 1: Form carbon into a conical pile (side view) Step 2: Flatten into a circular cake (side view) Step 3: Quarter pile
Step 4: Remove opposite quarters. Repeat process until desired quantity of sample is obtained.
83
B.3 SAMPLE WATER PREPARATION
Once back at the laboratory, all water samples were filtered through a 1 µm fibrous
polypropylene string-wound cartridge (EW-01508-77, Cole-Parmer, Anjou, QC) followed by a
0.5 µm pleated cartridge filter (RK-01512-86, Cole-Parmer, Anjou, QC) to remove any
particulate matter and undissolved NOM. Table B.3 outlines the influent water preparation steps.
Table B.3 Influent Water Preparation
1. Filter water through a 1 µm fibrous polypropylene string-wound cartridge (EW-01508-77, Cole- Parmer, Anjou, QC). 2. Re-filter water through a 0.5 µm pleated cartridge filter (RK-01512-86, Cole-Parmer, Anjou, QC). 3. Spike water with combined geosmin and MIB stock solution to obtain concentration of 100 ng/L. *Be sure to invert stock solution several times before opening vial to ensure solution is well mixed. 4. Stir for 10 minutes.
84
B.4 TASTE AND ODOUR COMPOUND PREPARATION Geosmin and MIB Solutions
Geosmin (CAS #19700-21-1) and MIB (CAS #2371-42-8) were purchased neat from Wako
Chemicals (Richmond, VA). MIB and geosmin stock solution to be used for influent spiking was
prepared in the lab using Milli-Q® water to avoid TOC production from compounds diluted in
methanol solution. Pure MIB (20 mg/vial, solid phase) and geosmin (20 mg/vial, liquid phase)
were dissolved in Milli-Q® water in aluminum foil wrapped, capped 100 mL volumetric flasks
on a magnetic stir plate. The original vials containing 20 mg geosmin and 20 mg MIB were
rinsed ~100 times each with Milli-Q® water. The aluminum foil-wrapped flasks were allowed to
mix overnight. Both solutions, expected to be 200 ppm, were analyzed in the GCMS to obtain
their exact concentrations. From this analysis, intermediate solutions of 100 ppm for both
geosmin and MIB were made.
Finally, a mixed stock solution of 1 ppm geosmin and MIB solution was prepared ready for
spiking the influent water. All solutions were kept in amber vials, head-space free, with caps
sealed with Parafilm®, at 2 - 4ºC. Solutions were analyzed in duplicate in the GCMS prior to a
new RSSCT run in order to ensure that stock solution concentrations had not changed. If
variation in GCMS results exists, additional solutions should be analyzed to verify the
concentration.
Both solutions were analyzed on the GCMS to test exact concentrations. From these results, an
intermediate solution of 100 ppm was made for both geosmin and MIB. From these solutions, an
additional dilution and combined solution of geosmin (1 ppm) and MIB (1 ppm) was made to
facilitate ease in spiking the influent water. All stock solutions were kept in amber vials,
headspace free at 2 - 4ºC.
Geosmin and MIB standards are synthetically produced. Geosmin is a mixture of the D- and L-
forms of trans-1, 10-dimethyl-tran-(9)decanol (Figure B.2):
85
Figure B.2 Chemical Structure of GSM
Naturally occurring geosmin is one isomer, however, the synthetic geosmin contains more than
one isomer, but not all are odorous. GCMS characteristics, however, match that of the natural
isomer.
MIB standard is a single isomer, 1,2,7,7-tetramethyl-exo-bicyclo[2,2,1]heptan-2-ol (Figure B.3).
Figure B.3 Chemical structure of 2-methylisoborneol
As MIB is a hydrocarbon skeleton containing one hydroxyl group it is relatively hydrophobic
(Considine, 2001).
For the synthetic compounds used, the molecular weight, boiling point, and appearance are all in
line with the natural products. There is potentially a slight difference in purity and isomer form.
The minimum specification by GC for purity is >98 % for both geosmin and MIB, with results
normally >99 % (in communication with Wako Chemicals Inc. 2010, email received March 1,
2010).
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Table B.4 Molecular structure of geosmin and MIB
Compound Molecular
Weight C H O Boiling Point (ºC)
Geosmin 182.31 79.06 % 12.16 % 8.78 % 260 MIB 168.28 78.51 % 11.98 % 9.51 % 207
MIB and geosmin stock solutions in methanol were purchased from Sigma-Aldrich Corporation
(Oakville, ON) for preparation of running standards and calibration curves. Stock solutions were
kept in amber 2 mL vials at 2 - 4ºC.
B.5 ACTIVATED CARBON PHYSICAL CHARACTERISTICS
B.5.1 APPARENT DENSITY
Apparent density was determined in the lab according to test method ASTM D2854-96 (Table
B.5, Table B.6). To determine the apparent density, a small batch of the granular activated
carbon sample was placed on a vibrating feeder and allowed to free fall into a calibrated 100 mL
graduated cylinder. The mass from the graduated cylinder was weighed and the weight used to
calculate the apparent density.
Table B.5 Apparatus required for apparent density test
Device Description
Graduated cylinder Glass, 100 mL, calibrated Funnels Stainless steel, to fit into graduated cylinder and to serve as reservoir funnel Vibrating feeder, controller
FMC Syntron magnetic feeder, model F-TOC Electric controller, model F-TO
Balance Capable of weighing to 0.0001 g Desiccator Oven Drying carbon at 105ºC (overnight) or 150ºC (3 hours)
Table B.6 Test method for apparent density
Assemble the Apparatus 1. Assemble and align the apparatus as shown in Figure B.4. 2. Calibrate the feed rate to a desired speed by adjusting the controller setting and/or raising or lowering the reservoir funnel. Note: The speed should not be less than 0.75 or exceed 1.0 mL/s. Preparation of Carbon Samples 1. Obtain a representative sample of activated carbon (Figure B.1). 2. Dry the carbon at 150°C for 3 hours in the oven or 105°C overnight. 3. Allow it to cool in a desiccator. Test Procedures 1. Carefully place the sample into the reservoir funnel (Figure B.4).
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2. Add the sample to the cylinder using the vibrating feeder, allowing the carbon to fall through the feed funnel into the cylinder. 3. Transfer the contents of the cylinder to a balance pan and weigh to the nearest 0.1 g. Calculations
“as received” apparent density: ADas (g/mL) = smillilitreinvolumecarbon
gramsincarbonactivatedofmass
“dry basis” apparent density: ADdry basis (g/mL) =
100
moisture%1ADas
Figure B.4 Schematic of apparent density test apparatus setup
Apparent density (AD) values were also provided by Calgon for the carbon samples prepared for
the test runs in this study (Table B.7). These values were used in data analysis calculations.
Table B.7 Apparent density values for five carbons
Carbon Apparent Density
(g/mL)
A 0.58
B 0.58
C 0.65
D 0.37
E 0.45
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B.6 ACTIVATED CARBON ACTIVITY INDICES
B.6.1 IODINE NUMBER TEST The iodine number is the traditional test used to determine adsorption capacity of an activated
carbon. Iodine number is the measure of iodine (I2) adsorbed from a 0.1 N solution by a gram of
activated carbon when the residual concentration is 0.02 N after adsorption. Iodine number of
carbon samples is determined following the Standard Method D4607. The appropriate activated
carbon is prepared by grinding the carbon until 95 % or more passes through a 325 mesh screen.
The reagents and devices required for the iodine test are provided in Table B.8 and Table B.9,
respectively. The iodine test method outline is presented in Table B.12.
Table B.8 Iodine number test - Reagents Reagent Supplier and Purity Iodine, I2 [7553-56-2] Sigma-Aldrich, resublimed crystals, USP Grade Potassium Iodide, KI [7681-11-0] Sigma-Aldrich Sodium Thiosulfate solution, Na2S2O3.5H2O [7772-98-7]
Aldrich, 0.10 N, volumetric standard [319546]
Hydrochloric Acid, HCl [7647-01-0] E.M.Science, ACS Grade Potato Starch [9005-25-8] Sigma-Aldrich Milli-Q® water Generated in-house (Millipore Milli-Q® UVplus)
Table B.9 Apparatus required for iodine number test
Device Description
Flasks Erlenmeyer, Pyrex, 250 mL with glass stoppers Beakers Griffin-type, low form, 250 mL or similar Funnels Stemless, Pyrex, 100 mm top inside diameter Bottles Amber, 2 L, for storage of iodine solutions Pipettes Volumetric, to deliver 10 mL and 100 mL Volumetric flasks Pyrex, with stopper, 1 L Graduated cylinders Glass, to deliver 100 mL Burette 50.0 mL, divided into at least 0.1 mL increments Filter paper 18.5 cm prefolded paper, Whatman No. 2V Balance Capable of weighing to 0.0001 g Weighing paper Hot plate
Table B.10 Test solution preparation Iodine Stock Solution (1.0N) 1. Weigh 127.0 g of iodine crystals and 191.0 g of KI into a large beaker. 2. Mix the dry iodine and potassium iodine, add 100 mL of Milli-Q® water and stir well. 3. Continue adding small increments of water until the total volume is 250 to 300 mL. Allow the solution to stand a minimum of 6 hours. 4. Transfer the solution to a 1 L volumetric flask and fill to the mark with Milli-Q® water. Note: Stock solution is stable for a month if properly stored.
89
Iodine Working Solution (0.1 N) 1. Pipette 100.0 mL of the stock solution into a 1 L volumetric flask 2. Top volumetric flask to 1 L with Milli-Q® water. 3. Cap flask with stopper and mix by inverting 15 times. 4. Standardize the iodine solution using sodium thiosulfate solution. Note: The 0.10 N iodine solution is stable for only 8 hours.
Hydrochloric Acid Solution (5 % wt) 1. Add 70 mL of concentrated HCl to 550 mL of Milli-Q® water and mix well. 2. Use graduated cylinder for measurement of volume. Starch Indicator Solution 1. Mix 1.0 ± 0.5 g of soluble starch with 5 to 10 mL of cold water to make a paste. 2. Add 25 ± 5 mL of water while stirring the starch paste. 3. Pour the mixture, while stirring, into 1 L of boiling water and boil for 4 to 5 min.
Table B.11 Standardization of iodine solution Standardization of Iodine Solution 1. Pipette 25.0 mL of the 0.1 N iodine solution into a 250 mL Erlenmeyer flask. 2. Titrate with standardized 0.1000 N sodium thiosulfate until a pale yellow colour. 3. Add 5 drops of starch indicator. 4. Continue the titration until one drop produces a colourless solution. 5. Record the volume of sodium thiosulfate used. Calculation N2 = (S × N1)/I Where: N2 = standard iodine solution normality, N S = sodium thiosulfate, mL N1= standard thiosulfate solution normality, N I = iodine solution used, mL Analysis 1. The titration step should be done in triplicate and the normality results averaged. 2. Report the normality to the nearest 0.0001 N. 3. Additional replication should be done if the range of values exceeds 0.003 N. 4. The iodine solution concentration must be 0.100 ± 0.001 N. Note: If this requirement is not met, remake the iodine solution.
Table B.12 Iodine number test method outline
Carbon Preparation and Weighing 1. Use ‘Coning and Quartering Technique’ to obtain a representative sample of pulverized carbon. 2. Remove excess moisture by drying in the oven at 150ºC for 3 hours. 3. Place sample to cool to ambient temperature in a desiccator. 4. Tare a clean, dry 250 mL Erlenmeyer flask on a balance. 5. Weigh the appropriate amount of dried carbon into the Erlenmeyer flask. 6. Record this weight. 7. Cap Erlenmeyer flask with a stopper to prevent moisture from reaching the carbon. Adsorption Isotherm Test Procedures 1. Pipette 10.0 mL of 5 wt% HCl into each flask containing carbon.
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2. Stopper and gently swirl each flask until the carbon is completely wet. 3. Loosen the stoppers to vent the flasks. 4. Place the flask on a hot plate in a fume hood and bring the contents to a boil. 5. Allow to boil for 30 ± 2 seconds. 6. Remove the flask from the hot plate and allow the contents to cool to room temperature. 7. Pipette 100.0 mL of standardized iodine solution into each flask. 8. Immediately stopper the flask and shake the contents vigorously for 30 ± 1 seconds. Sample Collection 1. Quickly filter each mixture by gravity through a folded filter paper into a beaker. 2. Discard the first 20 to 30 mL filtrate and collect the remaining filtrate. 3. Mix the filtrate by swirling the beaker. 4. Pipette 50.0 mL of each filtrate into a clean 250 mL Erlenmeyer flask. 5. Stopper the flask until analysis of the filtrate. Titration 1. Titrate with standardized 0.1000 N sodium thiosulfate until filtrate becomes a pale yellow colour. 2. Add 5 drops of starch indicator solution. 3. Continue the titration drop by drop until one drop produces a colourless solution. 4. Record the volume of sodium thiosulfate used. 5. Calculate the residual filtrate normality (CR).
In order to determine the iodine number, a solution of 0.1 N standardized iodine solution is
added to three different weights of carbon samples (Table B.13) in three 250 mL Erlenmeyer
flasks (Figure B.5). The contents of the flask are shaken vigorously for 30(±1) seconds and then
filtered through filtered paper (18.5 cm prefolded paper, Whatman No.2V). The filtrate is
titrated using sodium thiosulfate solution to measure the remaining iodine. The residual filtrate
normality CR and iodine number can be calculated using the equations listed in Table B.14.
Table B.13 Example of carbon weights required for iodine number test
Carbon Weights of Carbon (g) A 0.9, 1.1, 1.3 B 0.8, 1.0, 1.2 C 0.9, 1.15, 1.4 D 1.25, 1.5, 1.75 E 0.8, 1.0, 1.2
Table B.14 Analysis of iodine test data
Calculation of X/M (Iodine Adsorbed per gram of Carbon) A = (N2) (12693.0) Where: N2 = normality of the iodine solution, N B = (N1) (126.93) Where: N1 = normality of the sodium thiosulfate solution, N
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DF = (I + H)/F Where: DF = dilution factor I = iodine solution used, mL (100 mL) H = volume of 5 % hydrochloric acid used, mL F = volume of filtrate used in the titration step, mL X/M = [A – (DF)(B)(S)]/M Where: X/M = iodine adsorbed per gram of carbon, mg/g S = volume of sodium thiosulfate used in the titration step, mL M = amount of carbon used, g Calculation of Filtrate Normality (CR) CR = (N1)(S)/F Where: CR = normality of the filtrate, N N1 = normality of the sodium thiosulfate solution, N F = volume of filtrate used in the titration step, mL Iodine Number Iodine Number = (X/M)D [reported to nearest whole number] log D = 1/n [log0.02 – log CR] Where: D = correction factor CR = final residual iodine concentration 1/n = slope from iodine isotherm
Figure B.5 Iodine number test set-up
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B.6.2 TRACE CAPACITY NUMBER
The trace capacity number (TCN) of the carbon sample was determined using the colorimetric
method TM-79 developed by Calgon Carbon Corporation (CCC, 1999). Acetoxime test solution
is mixed with three different weights of carbon samples for a contact time of ten minutes. The
mixture solution is filtered and the residual concentration is analyzed for absorbance at 220 nm
using a Diode Array Spectrophotometer (HP 8425A).
From the determined residual concentrations, the ratio of the adsorbed acetoxime to the
corresponding carbon dosage is calculated. This ratio is plotted against the residual acetoxime
concentration on a logarithmic scale, where the acetoxime carbon loading corresponding to 30
mg/L acetoxime concentration is determined. This carbon loading specifies the trace organic
adsorption capacity of the carbon. The reagents and devices needed to determine the TCN are
listed in Table B.15 and Table B.16, respectively. The TCN method is outlined in
Table B.17 .
Table B.15 Trace capacity number – Reagents
Reagent Supplier and Purity
Acetone Oxime (acetoxime) (CH3)2C=NOH [127-06-0]
Sigma-Aldrich, purity 98 %
Potassium dihydrogen phosphate, KH2PO4 [7778-77-0]
EMD, ACS Grade
Sodium Hydroxide, NaOH, [1310-73-2] EMD, ACS Grade
Table B.16 Apparatus required for trace capacity number test Device Description Analytical balance Capable of weighing to 0.0001 g Drying oven Capable of maintaining 150ºC Desiccator With calcium chloride desiccant Spectrophotometer Capable of absorbance readings at 220nm Quartz cuvettes UV range, 1 cm size Timer/stopwatch Heating plate Spatula Magnetic stirrer Minimum 3 magnetic stirrers needed to maintain
equivalent stirring velocity with minimum temperature rise during operation, Fisher 14-493-1205 or equivalent
Magnetic stir bars Flasks Erlenmeyer, Pyrex, 250 mL capacity Bottles Amber, 2 L, for storage of acetoxime solution
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Pipettes Volumetric, to deliver 10 mL and 25 mL capacity Volumetric flasks Pyrex, with stopper, 50 mL Graduated cylinders Glass, to deliver 100 mL Burette 50.0 mL, divided in at least 0.1 mL increments Syringe Disposable, B-D, 20 mL, slip tip without needle Syringe filter Acrodisk, 25 mm diameter x 0.8 μm pore size Thermometer Glass, -1 to 101ºC in 10/10ºC graduations Table B.17 Trace capacity number – Method Outline Preparation of Buffer Solution (pH 7.0) 1. Dissolve 13.608 g of KH2PO4 and 2.328 g of NaOH into 2.0 L Milli-Q® water. 2. Adjust pH to 7.00 ± 0.05 by additions of NaOH or KH2PO4. Preparation of Acetoxime Stock Solution (200 mg/L) 1. Transfer 0.2000 ± 0.002 g acetoxime into 1.0 L volumetric flask. 2. Dilute with pH 7 buffer. Preparation of Five Calibration Standards 1. Prepare six calibration standards: reagent blank, 25, 50, 100, 150, 200 mg/L by adding the appropriate amount of acetoxime stock solution to 50 mL volumetric flasks. 2. Once flask is topped up with buffer solution, invert 15 times to mix. Preparation of Acetoxime Running Standards 1. Pipette 12.5 mL acetoxime stock solution into a 50 mL volumetric flask. 2. Fill flask with pH 7 buffer solution. 3. Use stopper to cap flask and invert 15 times to mix. Carbon Weighing 1. Place representative sample of activated carbon into oven for 3 hours at 150ºC. 2. Keep in desiccator until carbon has reached ambient temperature. 3. Weigh the appropriate amount of dry carbon into 250 mL Erlenmeyer flask. 4. Record weight. Adsorption Isotherm Test 1. Place volumetric flask with acetoxime stock solution into a 25 ± 0.2C water bath for 30 minutes. 2. Transfer 100 mL of acetoxime solution Erlenmeyer flasks containing carbon. 3. Gently swirl flask to wet the carbon. 4. Place each flask on stir plate with a magnetic stirring bar. 5. Stir for 10 minutes at a moderate speed. 6. Remove from stir plate and allow 5 minutes for carbon to settle. 7. Collect 30 mL of filtrate using a syringe with a nylon, 24mm diameter, 0.8 µm pore size filter disc. 8. Measure the residual acetoxime solution concentration with a spectrophotometer at a wavelength of 220 nm.
B.6.3 TRACE CAPACITY NUMBER GAS PHASE
The trace capacity number gas-phase (TCNG) of the carbon sample was determined using the
gas adsorption method TM-85 developed by Calgon Carbon Corporation (CCC, 1999).
Tetrafluoromethane activity (TCNG) is defined as the ratio (g/100mL) of the mass of
tetrafluoromethane (CF4) adsorbed by a volume of activated carbon sample when the carbon is
94
saturated with tetrafluoromethane vapour under specific test method conditions. The CF4 vapour
has low affinity for activated carbon and hence the conditioning of the sample is paramount. The
activated carbon sample must have less than 0.1 wt% moisture and the pores must be empty of
any adsorbate, including ambient air, when the test tube containing the carbon is weighed. The
tube is filled with carbon using a vibrating feeder to ensure maximum packing. The TCNG
method is modified from the butane number method described in ASTM D5742-95. The reagents
and devices used to determine the TCNG are listed in Table B.18 and Table B.19, respectively.
The TCNG method is outlined in Table B.20. A butane adsorption apparatus was utilized for the
TCNG method and is displayed in Figure B.6.
Table B.18 Trace capacity number gas phase test – Reagent Reagent Supplier and Purity
Tetrafluoromethane (CF4) [CAS No: 75-73-0] BOC gases, 99.996 %, C.P. Grade
Table B.19 Apparatus required for trace capacity number gas phase test Device Description
Butane adsorption apparatus Received from Calgon Carbon Corporation Heating coil Gas heat exchange cooper coil about 1.9 m long in the bath Temperature controller Heater/cooler circulating device to maintain a temperature of
25±0.2°C Plastic container Sufficient depth to submerge entire carbon bed and large enough to
contain the temperature controller Flow meter Capable of delivering the gas at 0 to 500 mL/min Sample tube Fabricated tubes Vibrating feeder/controller FMC Syntron magnetic feeder, model F-TOC
Electric controller, model F-TO Reservoir/ feed funnels Fabricated of stainless-steel Balance Capable of weighing to 0.001 g Bubble flow meter
Table B.20 Trace capacity number gas phase test – Method outline Sample Tube Calibration 1. Clean and dry the sample tube. 2. Fill the sample tube with Milli-Q® water through the narrow side stem. 3. Clamp the sample tube in an upright position and stopper the narrow side stem. 4. Remove the water from the sample tube using a pipette to the top of the retainer plate. 5. Fill the sample tube with 16.7 ± 0.05 mL of water using the burette. 6. Mark the tube at the level of the meniscus. Butane Adsorption Apparatus Calibration 1. Fill the bubble flow meter with soapy water into the rubber bulb. 2. Connect the bubble flow meter to the outlet port of the butane adsorption apparatus. 3. Turn on the gas cylinder and open the inlet port of the apparatus. 4. Squeeze the rubber bulb. 5. Measure the time required for a soap bubble to travel a 10.0 mL volume.
95
ondssectraveltobubblebytakenTime
minsec/sintpofixedtwobetweenVolumerateFlow
60
6. Regulate the flow of the Tetrafluoromethane to be 250 ± 5 mL/min. Fill sample tube 1. Weigh the sample tube and the cork stopper to the nearest 0.001 g. 2. Record the tare weight. 3. Dry the sample in the oven at 150˚C for 3 hours. 4. Fill the sample tube to the mark with the carbon at a uniform rate using a vibrating feeder. 5. Re-weigh the warm filled sample tube to the nearest 0.001 g. 6. Record the weight of the carbon, the sample tube and the cork stopper (W). 7. Cool the filled sample tube in a desiccator prior to adsorption. Note: Do not allow the sample to cool below 70˚C prior to weighing W, since the carbon can adsorbs up to 1 wt % nitrogen and oxygen from ambient air. If the sample cools below 70˚C, reheat and try again. Adsorption Test Procedures 1. Set the water bath to maintain a temperature of 25 ± 0.2°C. 2. Place the filled sample tube in the water bath. 3. Connect the CF4 delivery line to the tube. 4. Pass the CF4 down flow through the carbon bed for a minimum of 20 minutes. 5. Weigh the sample tube. 6. Reconnect the sample tube to the apparatus for extra 5 minutes and weigh again. 7. Repeat until the mass of the sample is constant to within ± 0.005 g. 8. Turn off the purge flow. 9. Remove the sample tube from the apparatus. 10. Wipe dry and weigh to the nearest 0.001 g (S) including the cork stopper. Calculations Tetrafluoromethane Activity (g Tetrafluoromethane/100 mL carbon)
TCNG = 100AD
TW
WS
Where: AD = Apparent density (g/mL) Note: Report tetrafluoromethane activity to nearest 0.01 % Conversion from TCNG to TCN TCN acetoxime = 57.6valueTCNG432.3
96
Figure B.6 TCNG adsorption apparatus (Image source: Zhang, 2008)
B.7 TOTAL ORGANIC CARBON
Total organic carbon (TOC) concentration of the samples was determined by oxidizing the
organically bound carbon into carbon dioxide which is then quantified. The method used was
based on Standard Method 5310 D: Wet Oxidation Method (APHA, AWWA, and WEF, 2006)
and TOC analysis was completed using an Aurora 1030 TOC Analyzer (O. I. Analytical). The
Aurora TOC Analyzer operates with the following general procedure: the sample is acidified to
pH 2 or less converting inorganic carbon species to CO2. It is then purged to remove the
inorganic carbon and oxidized with persulphate in an autoclave at 116 to 130°C. The CO2
produced from the sample is quantified using nondispersive infrared spectrometry.
97
Table B.21 Total organic carbon – Reagents Reagent Supplier and Purity
Milli-Q® Water Prepared in the laboratory Sulphuric Acid, H2SO4 [7664-93-9] VWR International, 98+ % Potassium hydrogen phthalate, C8H5KO4 [877-24-7]
Aldrich, 98+ %
Sodium persulphate, Na2(SO4)2 [7775-27-1] Aldrich, 98+ %, anhydrous Phosphoric acid, H3PO4 [7664-38-2] Nitrogen gas, N2 [7727-37-9] Praxair, Ultrapure
Table B.22 Apparatus required total organic carbon analysis Device Description
Balance 0.1 g and 0.0001 g accuracy Cylinder Glass, 25 mL Beaker Glass, 500 mL Volumetric flasks Glass, 50, 500 and 1000 mL Amber bottles 50, 1000 mL
Table B.23 Total organic carbon analysis – Method outline Preparation of 5% Phosphoric Acid Solution 1. Add 25 mL phosphoric acid to 500 mL water in a beaker and mix well. Preparation of 100 g/L Sodium Persulphate Solution 1. Dissolve 50 g reagent in water and bring volume to 500 mL using volumetric flask. Preparation of Calibration and Calibration Verification Stock Solutions 1.0 mg/mL (1000 mg/L) 1. Dissolve 2.1254 g of anhydrous C8H5KO4 in about 500 mL Milli-Q® water and bring volume to 1L with Milli-Q® water using a volumetric flask. 2. Fill a 1 L amber bottle with the stock solution. 3. Preserve the solution by acidifying to pH < 2 with H2SO4. 4. Cap with a Teflon®-lined septum screw cap. 5. Store the stock solution in an amber bottle in the dark at 2 - 4°C. Note: Two separate stock solutions (same concentration) should be made for making calibration and calibration verification standards. Preparation of Calibration Standard Solution (for mid range concentration (1 - 10mg/L)) 1. Prepare a 10 mg/L calibration standard solution by diluting 0.5 mL of calibration stock solution into 50 mL of Milli-Q® water using a 50 mL volumetric flask. Discard after use. 2. Preserve the calibration standards by acidifying to pH < 2 with 3 drops of H2SO4. 3. Cap with a Teflon®-lined septum screw cap. 4. Analyze immediately. Preparation of Check Standard (for mid range concentration (1 - 10 mg/L)) 1. Prepare a 3.0 mg/L check standard by diluting 1.5 mL of calibration verification stock solution into 500 mL of Milli-Q® water using a volumetric flask. 2. Fill a 40 mL amber vial with the running standard solution. 3. Acidify the running standard to pH < 2 with 3 drops of H2SO4. 4. Cap with a Teflon®-lined septum screw cap. 5. Analyze immediately. Note: One large volume of running standard solution should be prepared and divided into aliquots for
98
each of the 40 mL vials that are to be filled. A check standard should be analyzed every ten samples, after a reagent blank. Preparation of Reagent Blanks 1. Fill a 40 mL amber vial with Milli-Q® water. 2. Acidify the reagent blank to pH < 2 with H2SO4. 3. Cap with a Teflon®-lined septum screw cap. 4. Analyze immediately. Note: A reagent blank should be analyzed every ten samples. Sampling and Storage 1. Collect samples in 40 mL amber vials with Teflon®-lined septa screw caps. Note: For collecting tap water let the water run for a few minutes before collecting the sample. 2. Acidify the samples to pH < 2 with H2SO4. 3. Store samples at 2 - 4°C for up to 14 days. Analysis Operate the TOC analyzer according to the operation instructions.
Total Organic Carbon Control Chart (Check standard = 3 mg/L)
0
0.5
1
1.5
2
2.5
3
3.5
4
1 2 3 4 5 6 7 8 9 10 11
Check Standard #
To
tal O
rgan
ic C
arb
on C
once
ntr
atio
n (m
g/L
)
Check Standard results
UCL (+3 S.D.)
UWL (+2 S.D.)
Mean
LWL (-2 S.D.)
LCL (-3 S.D.)
Figure B.7 Control chart for TOC check standard (3 mg/L)
B.8 RAPID SMALL-SCALE COLUMN TESTS
Rapid small-scale column tests or RSSCTs were developed by Crittenden and his team of
researchers in 1987 as a method for mimicking large scale drinking water carbon columns in a
laboratory environment (Crittenden et al., 1987). RSSCTs are continuous-flow column tests that
are conducted at bench-scale. The relationship between the empty bed contact time (EBCT),
99
column length, operation time and hydraulic loading of the small- and large-scale columns is
determined with the use of multiple equations and is a function of the ratio between the granular
activated carbon (GAC) particles sizes used in both the full-scale treatment plant and the RSSCT.
The main advantages to using the RSSCT are: (A) the RSSCT takes a fraction of the time to
complete compared to a pilot or full-scale run, (B) extensive isotherm and kinetic studies are not
needed to predict the performance of a full-scale system and (C) only a small volume of water is
needed for the test (Crittenden et al., 1991). The design of the RSSCT is based on fixed-bed
mass transfer models and kinetic phenomenon associated with adsorption. The set of
mathematical equations used to determine the column test parameters are explained in Chapter 2.
An example of the derived large- and small-scale parameters for one carbon, Carbon B, is listed
in Table B.24. RSSCT parameters for all five carbons are provided in Appendix D. Large-scale
refers to parameters used from a full-scale water treatment plant and small-scale refers to the
micro-column set-up in the laboratory.
Table B.24 Parameters for large-scale water treatment and small-scale column test (Carbon B) Parameter General Large-Scale Small-Scale EBCT (min) 7.50 0.55 Length (m) 1.25 0.10 VelocityLC (m/hr) 10.00 11.2
Flow rateLC (m3/min) 2.32 3.10E-06
Particle diameter (R) (mm) 0.96 0.09 RSSCT exponent 1.1 Reynolds number 7.64 Remin 0.70 Porosity of carbon bed 0.4 Dynamic viscosity of water at 20ºC (N•s/m²)
0.001
Density of water (kg/m3) 1000 Apparent density of carbon (g/cm3) 0.575 Table B.25 describes the carbons and source water tested in each run.
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Table B.25 RSSCT conditions Run Time (days)
Run Dates Carbons Tested Source Water SC1 LC1
Bed Volumes Treated
Run 1
(June 2009) B C D - - - 21 458 88,000
Run 2
(July 2009) B C D - - -
April sample from Georgina
Plant, Lake Simcoe 13 276 53,000
Run 3
(August 2009) B A E - - -
August sample from Georgina
Plant, Lake Simcoe
20 376 72,000
Run 4
(June 2010) A A B C D E
May sample from Ajax Plant,
Lake Ontario 39 512-938
100,000-180,000
1SC (small column) is the run time for the RSSCT, LC (large column) is the calculated equivalent full-scale water treatment run time.
Column Test Set-Up
To begin running the column test, the prepared geosmin and MIB water was pumped into three
50 L stainless steel reservoirs. The reservoirs contained Teflon®-lined (3 mm) polystyrene foam
floating lids inserted to minimize the loss of geosmin and MIB to the atmosphere. These
reservoirs were placed on a shelf above the column system and contained the feed water for two
carbon columns each. The water flowed through Masterflex® (L/S® 16) tubing into positive
displacement, reciprocating piston pumps (Eldex Optos Model 2SM high pressure metering
pump, reproducibility ± 0.3 %) which then ran the spiked geosmin and MIB solution through
stainless steel tubing (0.2 cm (1/16”)OD) to the columns. The pumps are capable of withstanding
up to 6000 psi and delivering flow rates between 0.01 to 10 mL/min. The water first passed
through a column containing a glass wool pre-filter to remove any remaining particulate matter
in the influent water in order to reduce the chance of clogging in the column and the tubing. The
water then flowed into a column containing activated carbon with a 2 cm support base of glass
wool. Upon exiting the carbon column, the water passed either to waste or to a sampling port for
regular sampling. Stainless steel columns (0.456 cm (0.2”)ID) and stainless steel tubing were
used in the micro-column test set-up to minimize adsorption of geosmin and MIB to the
apparatus. See Figure B.8 for a photo of the RSSCT apparatus elements and Figure B.9 for a
schematic with hardware specifics.
101
B A
102
C
Figure B.8 Elements of RSSCT set-up: sampling ports [A], floating lid [B], full RSSCT set-up [C]
103
Figure B.9 RSSCT system schematic, total of 6 GAC columns in RSSCT set-up
104
Carbon Parameters for Column Design
The full- and small-scale mean particle diameters (MPDs) for the five carbons were provided by
Calgon as the carbons were prepared and pulverized by Calgon and delivered to the University of
Toronto. These MPDs (Table B.26) were used in the design of the column test.
Table B.26 Mean particle diameters (MPDs) of activated carbons, full-scale and small-scale
Carbon Full-Scale (12x40)
(mm) Column (100x325)
(mm) A 1.60 0.089 B 0.96 0.090 C 1.06 0.093 D 1.32 0.088 E 0.98 0.091
RSSCT sampling and maintenance
The system flow rates were checked daily for each column. Effluent and influent samples were
taken once a day from each of the columns. Additional total organic carbon (TOC) samples were
taken for the first two days as breakthrough was seen to occur very quickly with TOC. A total of
4 - 6 TOC samples should be taken for the first two days of the run. Influent samples were taken
directly from a sampling port below the reservoir. For effluent samples, a 500 mL Tedlar® bag
was attached to the effluent port to minimize the loss of geosmin/MIB to the atmosphere. TOC
samples were transferred to amber 40 mL vials and geosmin/MIB samples to amber 20 mL vials.
Duplicate samples of geosmin/MIB were taken for testing variability and for back-up (in case of
GCMS problems). Geosmin/MIB sample vials were sealed with Parafilm®. All samples were
stored at 2 - 4°C. See Figure B.8 for a photo of the RSSCT apparatus elements and Figure B.9
for a schematic including hardware specifics. TOC samples were analyzed every couple days and
geosmin/MIB samples when GCMS time allowed, but no longer than 2 weeks after sampling
date.
Additional maintenance
The pumps need to be primed if the pump has been turned off or is offline for a period of time.
This should be done immediately when the pump is turned on. A syringe should be kept by the
pumps at all times. Care must be taken to insert the syringe straight into the pump. The tubing
exiting the pump should be checked regularly for air bubbles. If air bubbles are seen, the pump
needs to be re-primed to ensure a constant flow rate.
105
Water leakage at some location in the column system during the run may occur. To check for the
cause of this leak, begin by disconnecting the connectors starting from the pump. Only reattach
the next section of the column system when it is clear that the flow rate is constant for the
segment being checked. Continue to reattach the segments of the column system to find the
location of the leak. A common location is at the base of the carbon columns. The connector at
the base has a built-in fine mesh on 4 of the 5 columns. This mesh may become blocked and may
need to be cleaned or replaced. The connector can be soaked in methanol and cleaned using the
ultrasonic-cleaner (Fritsch).
Routine maintenance is required with the Optos pumps. Full details may be found in the
equipment manual. It is recommended that the piston is cleaned with methanol regularly (after
each RSSCT run or minimum every 6 months). The piston seal should also be checked to see if it
needs replacement. Piston seals may need to be replaced every 6 months to 1 year depending on
use of the system.
Post-Filter Run Steps:
Empty carbon and glass wool columns.
Run Milli-Q® water through the empty columns for 1 - 2 hours to rinse out the system.
Turn pumps off and perform routine maintenance (above).
Improvements and Recommendations with RSSCT System
The following steps could be followed to help further improve the accuracy of the RSSCT system
and results:
Improve accuracy of measurement of total volume of water treated from one column
Check for loss of MIB/geosmin in the system
Determine a method for measuring the volumes in reservoirs. This would be helpful if spiking
geosmin and MIB directly into the reservoirs instead of a common mixing tank.
Additional Notes
The currently used GCMS is highly prone to needle bending and/or the stripping of the SPME
fibre. Selection and proper use of vial caps for analysis was seen to reduce this problem. Bi-
metal 20 mm crimp caps with Teflon/silicone liners (Varian) were used as they caused the least
106
amount of needle bending of the vial caps tested. Care should be taken to not overly tighten the
caps and to ensure that the cap is secured evenly underneath the glass vial rim. Even using these
precautions, needle bending/stripping still occurred and, therefore, it is highly recommended that
the GCMS is checked regularly during sample analysis. The recommended SPME fibre is the 23-
gauge, 50/30um, DVB/CAR/PDMS for automated holder (gray, notched) (Supelco, product
number: 57299-U). The autosampler should also be checked regularly to ensure that the
alignment is correct for piercing the septa and at the injection port.
107
B.9 TASTE AND ODOUR COMPOUND ANALYSIS
Geosmin and MIB analyses were conducted using a Varian® 3800 Gas Chromatograph with a
Varian® Ion-trap Mass Spectrometer Detector (GCMS), using electron impact (EI) ionization and
an autosampler. These analyses were conducted in the Drinking Water Research Group
laboratory, Department of Civil Engineering, University of Toronto. Analyses followed the
solid-phase micro-extraction (SPME) gas chromatography method described in Standard Method
6040D (APHA, 2005). The GCMS operating conditions are displayed in Table B.27. The
molecular structures of the compounds used in the taste and odour analysis are provided in Table
B.4.
Table B.27 GCMS instrument operating conditions *All analyses are conducted on a Varian® 3800 gas chromatograph coupled with a Varian® Ion-trap mass spectrometer* Parameter Description Column VF-5MS capillary column (30 m × 0.25 mm, I.D., 0.25 µm film
thickness) Carrier gas Helium at 1 mL/min @ 25C Injection method Temperature: 250C
Desorbing time: 5 min Mode: Splitless Split Valve: Open after 2 min, Flow @ 50 mL/min Injection Volume: 1 µL @ normal speed
Auto sampler method Syringe: SPME Fibre Supelco Divinylbenzene/Carboxen/Polydimethylsiloxane (DVB/CAR/PDMS), df 50/30 μm, needle size 23 gauge Agitator Temperature: 65.0C Pre-incubation time: 5 min Extraction agitation speed: 400 rpm Extraction time: 30 min
GC method Initial: starts from 40C, holds for 2 min; Ramp: 1 increases to 250C at 15C/min; Equilibration: hold at 250C for 7 min
MS conditions Scan Mode: SIS (Single Ion Selection) Ionization Type: EI Emission current: 30 uAmps Scan average: 3 microscans (0.89 s/scan) Multiplier Offset: 150 volts
Samples were collected in 250 mL air-tight Tedlar® bags and transferred into two vials, 20 mL
amber vials for taste and odour analysis and 40 mL amber vials for TOC analysis (VWR
International, Mississauga, ON). The 20 mL amber vials for collection and analysis of taste and
odour compounds were first prepared by adding sodium azide (0.18 g/L) to prevent the
108
biodegradation of geosmin and MIB prior to analysis (Pei, 2003). QAQC tests showed that
geosmin and MIB samples could be preserved at 2 - 4ºC in the dark for over two weeks. For
TOC sample vials, three drops of sulphuric acid (H2SO4, VWR International, 98+ %) were added
to preserve the samples until analysis. TOC analysis is described in Appendix B.7.
Sample preparation for analysis involved adding 10 mL of sample into a 20 mL clear vial
(Supelco, Bellefonte, PA) with 3.5 mg of reagent grade sodium chloride (NaCl). Internal
standards (d5-Geosmin, s-BMP) were added at a concentration of 100 ng/L for Runs 1 to 3 and
25 ng/L for Run 4. The change was made to conserve internal standard stock solution and
provide a concentration within the expected range of concentrations for the breakthrough curves.
The vial was capped with a Teflon®-lined septum magnetic crimp cap (Supelco, Bellefonte, PA)
and placed in the GCMS sample tray.
The method for analysis of taste and odour using a SPME fibre is as follows. The autosampler
begins by taking the sample vial and delivering it to the spinning box. The temperature of the
spinning box is preset to 65°C ± 1°C at a rotation speed of 400 rev/min. The vial is placed in the
spinning box for 5 minutes to dissolve the NaCl. The needle containing a 1 cm long SPME fibre
(23 gauge, Supelco) is then inserted into the vial through the septum, and the fibre is extended
into the vial’s headspace for exactly 30 minutes. At the end of the contact time, the fibre is
retracted back into the needle. The needle inserts directly into the GCMS injection port, and the
GCMS run begins. After five minutes of desorption, the fibre is retracted back into the SPME
holder, a new sample is put into the spinning box, and a new sample extraction process begins.
Glassware and needle preparation is extremely important when dealing with trace levels of any
compound. In order to properly clean the glassware and needles used in the preparation of stock
and standard solutions, they were triple rinsed in each of dichloromethane, acetone and methanol
solutions. Specific preparation procedures are listed in Table B.29. The reagents required for
geosmin and MIB analysis are shown in Table B.28.
109
Table B.28 Geosmin and MIB analysis - Reagents
Reagent Supplier and Purity
2-Methylisoborneol, MIB [2371-42-8] Sigma Aldrich (47523-U), 100 μg/mL in methanol
(±)-Geosmin [16423-19-1] Sigma Aldrich (47522-U), 100 μg/mL in methanol
d5-Geosmin [CAS]
2-sec-butyl-3-methoxyprazine) s-BMP [CAS]
Methanol [67-56-1] Sigma Aldrich (646377), ≥99.9 %, Chromasolv® Plus
Acetone [67-64-1] Sigma Aldrich (323772-2L), ≥99.5 %, Reagent Plus
Dichloromethane [75-09-2] EMD (DX0838), HPLC grade
Table B.29 Method outline for geosmin and MIB analysis Glassware Preparation 1. Rinse the glassware three times with DCM. 2. Rinse the glassware three times with acetone. 3. Rinse the glassware three times with methanol. 4. Allow the glassware to dry in the fume hood. Once dry, glassware is ready for use. Geosmin and MIB Stock Solution Preparation (100 mg/L) 1. Carefully break open the 1 mL ampoule along the break line. Use several paper towels while breaking open the ampoule to avoid contact with sharp glass. 2. Transfer the stock solution with a Pasteur pipette into a GC vial. 3. Place the GC vial in a 50 mL Falcon conical polypropylene tube, and label the tube with appropriate WHMIS labels. Place the tube in a sealable bag. 4. Store the stock solution at 2 - 4°C. Geosmin and MIB Stock Solution (10 mg/L) (methanol based) *The calculations to determine the required volumes of stock solution are based on the equation C1V1=C2V2; this equation can be used if preparing solutions with different concentrations or different volume flasks* 1. Partially fill a 2 mL volumetric flask with methanol. 2. Use a 250 μL micro-syringe to transfer 200 µL of 100 mg/L stock into the volumetric flask. 3. Top volumetric flask to 2 mL with methanol using a Pasteur pipette. 4. Cap flask with stopper and mix by inverting 15 times. Geosmin and MIB Working Stock Solution (10 μg/L) (methanol based) 1. Partially fill a 10 mL volumetric flask with methanol. 2. Transfer 10 µL of 10 mg/L stock into the volumetric flask. 3. Top volumetric flask to 10 mL with methanol using a Pasteur pipette. 4. Cap flask with stopper and mix by inverting 15 times. 5. Store stock solutions in 2 mL amber vials at 2 - 4°C. Internal Standard Spiking Solution (10 μg/L) “2-Internal Mix” 1. Partially fill a 5 mL volumetric flask with methanol. 2. Use a 10 μL micro-syringe to transfer 5 µL of d5-Geosmin and s-BMP working solution into the same volumetric flask. 3. Top volumetric flask to 5 mL with methanol. 4. Cap flask with stopper and mix by inverting 15 times.
110
SPME Fibre Conditioning 1. The SPME fibre should be changed after the analysis of 100 samples. 2. Condition the SPME fibre by heating it in a GC injection port at 270°C for one hour. Calibration Standards Prepare six (6) calibration standards: 1. Pipette 10 mL of Milli-Q® water into a sample extraction vial that contains 3.5 g of NaCl. 2. Inject the appropriate volume of 10 µg/L combined geosmin and MIB working stock solution. 3. Spike 25 µL of 10µg/L “2-Internal Mix” into the vial to achieve 25 ng/L. Note: Expel the solution from the syringe into the water to avoid evaporation. 4. Repeat the above steps and prepare 5, 10, 30, 50, 80, 100 ng/L standard solutions. 5. Do not store; analyze immediately. Blank Sample Preparation 1. Pour 10 mL of Milli-Q® into a sample extraction vial (with 3.5 g NaCl). 2. Spike 25 µL of 10 µg/L “2-Internal Mix” into the vial to achieve 25 ng/L. 3. Analyze a blank sample after every 10 samples. Running Standards Preparation (30 ng/L of Geosmin and MIB in Milli-Q® Water) 1. Pipette 10 mL of Milli-Q® water into a sample extraction vial that contains 3.5 g NaCl. 2. Inject 30 µL of geosmin, MIB combined stock solution (10 µg/L) into the vial. 3. Spike 25 µL of 10 µg/L “2-Internal Mix” into the vial to achieve 25 ng/L. 4. Analyze one running standard after every 10 samples.
The taste and odour compound concentrations were calculated by determining the correlation of
the sample’s response ratio (ratio of sample’s response to the internal standard) with a calibration
curve. The calibration curve was determined using standards prepared with geosmin and MIB
and d5-Geosmin and s-BMP as internal standards.
A calibration curve was prepared using standards of MIB, geosmin (compounds of interest), and
d5-Geosmin, s-BMP (internal standards). A new calibration curve was prepared each day of
analysis to act as additional quality control. Possible variation in results because of SPME fibre
changes, frequent equipment malfunction, and the fact that the equipment was being used
continuously for various experiments made it prudent to run calibration curves with each sample
set. Each sample batch included a blank sample at the beginning of the run, six (6) standard
solutions for developing a calibration curve and a running standard and blank standard after
every 10 samples. All samples were tested in duplicate and an average result taken in order to
account for any method variation in compound detection. Quality control charts were used to
track the concentrations of the running standards throughout the different runs as an indication of
method performance (Figure E.5, Figure E.6).
111
B.10 QUALITY CONTROL TESTS
Preservation of Samples
A bactericidal agent, sodium azide, was added to each sample vial to stabilize the solutions with
respect to biodegradation. Pei (2003) found that the addition of sodium azide to geosmin and
MIB samples helped keep samples stable for at least two weeks in the cold room (2 - 4ºC).
Variability of GCMS Results
Analytical variability was seen from the GCMS results. Variability was seen to be greatest with
MIB samples (14 ng/L between MIB samples and 7 ng/L between geosmin samples based on a
100 ng/L sample). In order to account for this variability, samples were run in duplicate on the
GCMS and averaged.
MIB and Geosmin Degradation in Influent Reservoirs
The influent reservoirs were designed to hold enough water to allow the system to run for up to
five days (dependent on column flow rate) without requiring a refill. Prior to running the column
test, however, a QAQC test was completed to examine degradation of geosmin and MIB over
this time. Prior to this test, another QAQC test showed that of three reservoir types, the glass and
stainless steel showed the least amount of loss of the taste and odour compounds. This test
compared a stainless steel reservoir with a floating lid to a sealed glass reservoir. Overall, the
stainless steel reservoirs performed slightly better, with both geosmin and MIB compound
concentrations remaining constant over the six day period (Figure B.10).
020406080
100120140160
Day 1 Day 2 Day 3 Day 6
Co
nce
ntr
atio
n (
ng
/L)
Geosmin - SS Geosmin - Glass
MIB - SS MIB - Glass
Figure B.10 QAQC to compare stainless steel and glass reservoirs for influent water
112
Stability of Influent Stock Solutions
A QAQC test done in the DWRG lab also confirmed that the stock samples remained stable for
at least five months. Influent stock solutions (1 mg/L geosmin and MIB each) kept as long as
five months did not degrade significantly while kept at 2 - 4ºC in amber vials
B.11 DATA ANALYSIS
Correction Factor Applied to Results from Runs 1, 2 and 3
The analysis of the data from the first three runs involved incorporating a correction factor for
the laboratory model design. The original model used one model for all five carbons, thereby
discounting the need to consider individual mean particle diameters (MPDs) of the different
carbons. The use of incorrect MPDs meant that instead of a large column EBCT of 7.5 minutes
being modeled, different EBCTs were measured for each carbon (Table B.30).
In order to compare each of the carbons breakthrough curves and rank them accordingly, it was
important to normalize the results so that each RSSCT mimicked a large column with the same
EBCT (7.5min). To do this, the treated water bed volumes from the small column runs were
multiplied by [7.5 minutes]/[actual simulated large column EBCT (last column in Table B.30)].
The actual simulated large column EBCT was calculated using the following:
Knowing from the governing equations: 11.
LC
SC
LC
SC
MPD
MPD
EBCT
EBCT
, therefore:
Actual simulated EBCTLC = Actual EBCTSC applied (min) 11.
SC
LC
MPD
MPD
For example, for car bon Carbon A:
LCMPD = 1.6
SCMPD =0.0890
EBCTSC applied = 0.339
Actual simulated EBCTLC = 0.339min 11
08900
61.
.
.
= 8.1 minutes
Applying this new EBCTLC to the results (Carbon A):
113
If geosmin = 20 ng/L at 20,000 bed volumes then 20 ng/L geosmin would be expected at 20,000
x [7.5/8.1] = 18,518 bed volumes in an equivalent large column with an EBCT of 7.5 minutes.
Table B.30 Comparing EBCT of small and large columns before and after the correction factor has been applied.
Carbon
Large column MPD (mm)
Small column MPD (mm)
Desired large
column EBCT for modeling
(min):
Desired small column EBCT for modeling
(min):
Actual small column EBCT applied (min):
Actual simulated
large column EBCT (min):
A 1.6 0.089 A 0.313 A 8.1 B 0.96 0.090 B 0.555 B 4.6 C 1.06 0.093 C 0.516 C 4.9 D 1.32 0.088 D 0.381 D 6.7
E 0.98 0.091
7.5
E 0.549 E
0.339
4.6
Data shown in the results section have been adjusted with the appropriate correction factors (Table B.31).
Table B.31 Correction factors used for Runs 1, 2, and 3 for five carbons
Carbon Correction factor* A 0.9 B 1.6 C 1.5 D 1.1 E 1.6
* Note: Correction factor = [7.5 minutes]/[actual simulated large column EBCT] Data Analysis of Run 4 Results All breakthrough curves were fitted with the Gompertz curve (a sigmoid function) to extract bed
volume values to breakthrough. The data was analyzed in Sigmaplot® (Systat Software Inc.).
Sample curve fittings are shown in Figure B.11 .
114
Bed Volumes of Water Treated
0 20000 40000 60000 80000
MIB
Con
cent
ratio
n C
/C0
0.0
0.2
0.4
0.6
0.8
1.0
Carbon BCarbon ACarbon E
Figure B.11 Examples of Gompertz curve fittings
115
C: ADDITIONAL RESULTS (CHAPTER 3)
C.1 TRACE CAPACITY NUMBER TEST
The trace capacity number (TCN) test determines the mass (mg) of acetoxime adsorbed onto 1
mL of activated carbon at a 30 mg/L residual concentration and is said to be indicative of the
trace organic adsorption capacity of the activated carbon (CCC, 1999; AWWA, 2006). The
adsorption isotherms for all five carbons are presented in Figure C.1.
Carbon A: y = 0.4459x + 0.6177
R2 = 0.9923
Carbon B: y = 0.5048x + 0.5717
R2 = 0.9974
Carbon D: y = 0.4478x + 0.6047
R2 = 0.9977
Carbon C: y = 0.4293x + 0.7485
R2 = 0.9983
Carbon E: y = 0.4364x + 0.4448
R2 = 0.9978
0.0000
0.2000
0.4000
0.6000
0.8000
1.0000
1.2000
1.4000
1.6000
1.8000
0.0000 0.5000 1.0000 1.5000 2.0000 2.5000
Log [Residual Filtrate Concentration (mg/L)]
Lo
g [
Ace
toxi
me
Ab
sorb
ed p
er W
eig
ht
of
Car
bo
n (
mg
/g)]
Carbon A Carbon B Carbon C Carbon D Carbon E
Figure C.1 Adsorption isotherms for trace capacity number Table C.1 presents the TCN values of all five carbons. TCN values are presented on a volume
basis (mg/mL) by multiplying the adsorption capacity at 30 mg/L residual acetoxime
concentration (mg/g) by the apparent density of the carbon.
116
Table C.1 Trace capacity number test results
Carbon Sample TCN Values
(mg/mL) Rank
A 10.96 3 B 11.94 2 C 15.71 1 D 4.58 5 E 8.31 4
According to the TCN results, the carbon with the highest capacity for trace contaminant
adsorption is Carbon C.
C.2 TRACE CAPACITY NUMBER GAS PHASE TEST
Trace capacity numbers (gas phase) for all five carbons were obtained from previous tests done
in the DWRG lab by Zhang (2008) (Figure C.2). Carbon ranking from the trace capacity number
gas phase test was identical to the trace capacity number test:
Carbon C > Carbon B > Carbon A > Carbon E > Carbon D
Therefore, Carbon C would be said to display the highest adsorption capacity in both the TCN
and TCNG tests. Carbon D’s adsorption performance was found to the lowest of the five
carbons.
5.43
6.43
7.72
3.51
5.05
0.00
1.00
2.00
3.00
4.00
5.00
6.00
7.00
8.00
9.00
A B C D E
Carbon Sample
TC
NG
Nu
mb
er
(g/1
00
cm
3)
Figure C.2 Trace capacity gas-phase results (adapted from Zhang, 2008)
117
C.3 PORE SIZE DISTRIBUTION
Figure C.3 Pore size distribution (Adapted from Zhang, 2008) Gravimetric rapid pore size distribution tests (GPRD) were conducted by Calgon Carbon
Corporation (Pittsburgh, PA) and presented in a previous study by Zhang 2008, shown here in
Figure C.3. The micropore distribution across the five carbons studied ranked as follows:
Carbon E > Carbon B > Carbon A > Carbon C > Carbon D.
118
C.4 MIB AND GEOSMIN BREAKTHROUGH AND LOADING RESULTS
Breakthrough Curves for Lake Simcoe Water
Run 1 MIB Geosmin
Bed Volumes of Water Treated
0 20000 40000 60000 80000 100000
MIB
Con
cen
tra
tion
C/C
0
0.0
0.2
0.4
0.6
0.8
1.0
F400F600HD3000F400F600HD3000
Bed Volumes of Water Treated
0 20000 40000 60000 80000 100000
Ges
omin
Con
cent
ratio
n C
/C0
0.0
0.2
0.4
0.6
0.8
1.0
F400 F600HD3000F400HD3000F600
Carbon B Carbon C Carbon D Carbon B Carbon C Carbon D
Carbon B Carbon C Carbon D Carbon B Carbon D Carbon C
119
Run 2 MIB Geosmin
Bed Volumes of Water Treated
10000 20000 30000 40000 50000 60000
MIB
Con
cen
trat
ion
C/C
0
0.0
0.2
0.4
0.6
0.8
1.0F400F600HD3000F600HD3000F400
Bed Volumes of Water Treated
0 10000 20000 30000 40000 50000 60000
Geo
smin
Con
cent
ratio
n C
/C0
0.0
0.2
0.4
0.6
0.8
1.0
F400F600HD3000F600HD3000
Carbon B Carbon C Carbon D Carbon C Carbon D Carbon B
Carbon B Carbon C Carbon D Carbon C Carbon D
120
Bed Volumes of Water Treated
0 10000 20000 30000 40000 50000 60000 70000
Geo
smin
Con
cent
ratio
n C
/C0
0.0
0.2
0.4
0.6
0.8
1.0
F400F300GAC1240F300
Run 3 MIB Geosmin
Bed Volumes of Water Treated
0 10000 20000 30000 40000 50000 60000 70000
MIB
Con
cent
ratio
n C
/C0
0.0
0.2
0.4
0.6
0.8
1.0
F400F300GAC1240F400F300GAC1240
Carbon B Carbon A Carbon E Carbon B Carbon A Carbon E
Carbon B Carbon A Carbon E Carbon A
121
Breakthrough Curves for Lake Ontario Water
Run 4
MIB Geosmin
Bed Volumes of Water Treated
0 20000 40000 60000 80000 100000 120000 140000
MIB
Con
cen
trat
ion
C/C
0
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
F400F600HD3000HD3000F600F400
Bed Volumes of Water Treated
0 20000 40000 60000 80000 100000 120000 140000
Geo
smin
Con
cent
ratio
n C
/C0
0.0
0.2
0.4
0.6
0.8
1.0
F400F600HD3000F600HD3000
Carbon B Carbon C Carbon D Carbon D Carbon C Carbon B
Carbon B Carbon C Carbon D Carbon C Carbon D
122
Run 4 Continued MIB Geosmin
Bed Volumes of Water Treated
0 20000 40000 60000 80000 100000 120000 140000
MIB
Con
cen
trat
ion
C/C
0
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
F300AF300BGAC1240F300BGAC1240F300A
Bed Volumes of Water Treated
0 20000 40000 60000 80000 100000 120000 140000
Geo
smin
Con
cent
ratio
n C
/C0
0.0
0.2
0.4
0.6
0.8
1.0
F300AF300BGAC1240F300BF300A
Carbon A-1 Carbon A-2 Carbon E Carbon A-2 Carbon E Carbon A-1
Carbon A-1 Carbon A-2 Carbon E Carbon A-2 Carbon A-1
123
Loading Curves for Lake Simcoe Water
MIB Geosmin
0
2,000
4,000
6,000
8,000
10,000
12,000
0 10,000 20,000 30,000 40,000 50,000 60,000 70,000 80,000 90,000 100,000
Bed Volumes
MIB
Loa
din
g (n
g/g
)
Carbon A
Carbon B
Carbon C
Carbon D
Carbon E
0
2,000
4,000
6,000
8,000
10,000
12,000
14,000
16,000
18,000
0 10,000 20,000 30,000 40,000 50,000 60,000 70,000 80,000 90,000 100,000
Bed Volumes
Geo
smin
Lo
adin
g (
ng/
g)
Carbon A
Carbon B
Carbon C
Carbon D
Carbon E
124
Loading Curves for Lake Ontario Water
MIB Geosmin
0
5,000
10,000
15,000
20,000
25,000
30,000
35,000
40,000
45,000
50,000
0 20,000 40,000 60,000 80,000 100,000 120,000 140,000 160,000 180,000
Bed Volumes
MIB
Lo
ad
ing
(n
g/g
)
Carbon A - 1
Carbon A - 2
Carbon B
Carbon C
Carbon D
Carbon E
0
5,000
10,000
15,000
20,000
25,000
30,000
35,000
40,000
45,000
50,000
0 20,000 40,000 60,000 80,000 100,000 120,000 140,000 160,000 180,000
Bed Volumes
Geo
smin
Loa
ding
(n
g/g
)
Carbon A - 1
Carbon A - 2
Carbon B
Carbon C
Carbon D
Carbon E
125
Table C.2 Bed volumes to MIB and geosmin breakthrough (20 % of C0)
Note: Shaded areas indicate carbons were not run with that batch of water Table C.3 MIB and geosmin loading capacity at 50,000 bed volumes
Carbon A Carbon B Carbon C Carbon D Carbon E
Water MIB Geosmin MIB Geosmin MIB Geosmin MIB Geosmin MIB Geosmin
1 6,894 6,012 2,895 3,947 5,486 9,463 2 6,032 5,621 2,819 4,114 6,191 9,001 3 4,888 7,119 6,047 7,194 8,456 8,708 4 9,985 10,841 19,738 18,679 12,957 15,170 20,307 21,810 24,159 23,763
Note: Shaded areas indicate carbons were not run with that batch of water
Carbon A Carbon B Carbon C Carbon D Carbon E
Water MIB Geosmin MIB Geosmin MIB Geosmin MIB Geosmin MIB Geosmin
1 42,145 65,545 7,612 15,740 13,339 60,055 2 20,336 below 20 % 3,088 17,758 14,977 61,069 3 12,564 76,843 24,467 below 20 % 34,752 below 20 % 4 7,336 64,426 60,562 below 20 % 6,686 34,330 19,824 88,496 79,620 below 20 %
126
C.5 CORRELATION TESTS
Correlations for Lake Simcoe Water
MIB Loading Geosmin Loading
R2 = 0.298
R2 = 0.3907
0
4
8
12
16
20
0 2,000 4,000 6,000 8,000 10,000
MIB Loading Capacity (ng/g)
TC
N V
alu
e (
mg
/mL
)
0
4
8
12
16
20
TC
NG
Va
lue
(g/1
00
cm3 )
TCN TCNG Linear (TCNG) Linear (TCN)
R2 = 0.9137
R2 = 0.9348
0
4
8
12
16
20
0 2,000 4,000 6,000 8,000 10,000
Geosmin Loading Capacity (ng/g)
TC
N V
alue
(m
g/m
L)
0
4
8
12
16
20
TC
NG
Val
ue
(g/1
00cm
3 )
TCN TCNG Linear (TCNG) Linear (TCN)
R2 = 0.2124
0
200
400
600
800
1000
1200
0 2,000 4,000 6,000 8,000 10,000
MIB Loading Capacity (ng/g)
Iod
ine
Nu
mb
er
(mg
/g)
R2 = 0.0268
0
200
400
600
800
1000
1200
0 2,000 4,000 6,000 8,000 10,000
Geosmin Loading Capacity (ng/g)
Iodi
ne N
umbe
r (m
g/g)
Figure C. 4 Comparing MIB and geosmin loading results to iodine and TCN(G) results, Lake Simcoe Note: Each data point represents one set of data points for an activated carbon. Loading results indicate loading capacity at 50,000 bed volumes.
127
Correlations for Lake Ontario Water
MIB Loading Geosmin Loading
R2 = 0.1826
R2 = 0.3309
0
4
8
12
16
20
0 5,000 10,000 15,000 20,000 25,000 30,000
MIB Loading Capacity (ng/g)
TC
N V
alue
(m
g/m
L)
0
4
8
12
16
20
TC
NG
Val
ue
(g/1
00cm
3 )
TCN TCNG Linear (TCNG) Linear (TCN)
R2 = 0.2107
R2 = 0.3689
0
4
8
12
16
20
0 5,000 10,000 15,000 20,000 25,000
Geosmin Loading Capacity (ng/g)
TC
N V
alue
(m
g/m
L)
0
4
8
12
16
20
TC
NG
Val
ue
(g/1
00cm
3 )
TCN TCNG Linear (TCNG) Linear (TCN)
R2 = 0.0401
0
200
400
600
800
1000
1200
0 5,000 10,000 15,000 20,000 25,000 30,000
MIB Loading Capacity (ng/g)
Iodi
ne N
umbe
r (m
g/g)
R2 = 0.0008
0
200
400
600
800
1000
1200
0 5,000 10,000 15,000 20,000 25,000
Geosmin Loading Capacity (ng/g)Io
dine
Num
ber
(mg/
g)
Figure C.5 Comparing MIB and geosmin loading results to iodine and TCN(G) results, Lake Ontario Note: Each data point represents one set of data points for an activated carbon. Loading results indicate loading capacity at 50,000 bed volumes.
128
C.6 RAW DATA
Breakthrough and loading curves may be found in Appendix C.5. This section serves to describe
the electronic files where the remainder of the raw data may be found.
Breakthrough and Loading Data
Lake Simcoe, Runs 1, 2 and 3
Excel file: Runs123
All sample data and calibration curves for runs using Lake Simcoe water are combined in this
folder. Raw data (from GCMS) is included in worksheet tabs entitled “Samples Run 1”,
“Samples Run 2”, etc. Calibration curves are found in tabs leading up to this Sample worksheet
(e.g. for Run 1: Calibration R1.1, Calibration R1.2, etc.). Data analysis including C/C0, loading
capacity, area count ratio, bed volumes treated, etc. is completed in worksheet tabs entitled “Run
1”, “Run 2” and “Run 3”. A summary of results for all three runs is included in the worksheet
“Run1-3 Summary”.
Lake Ontario, Run 4
Excel file: Run4
All sample data and calibration curves for Lake Ontario water (Run 4) are in this folder. The
RSSCT column design is also included in the first worksheet of this file (“Calculations”). Raw
data (from GCMS) is included in worksheet tabs entitled “Sample”. Data analysis including
C/C0, loading capacity, area count ratio, bed volumes treated, etc. is completed in worksheet
“Run 4”, scroll down to Row 103 to see the summary tables showing information for all five
carbons. Correlation calculations are shown in worksheets entitled “Breakthrough correlations”
and “Loading correlations”. The following tabs “Summary table” and “Characterization Tests”
show the results for the characterization tests for all five carbons along with the breakthrough (20
%) and loading capacity (at 50,000 bed volumes) for all four waters tested (Run 1, 2, 3 and 4).
Figures and tables summarizing correlation results are also included in these two worksheets.
129
TOC Data
Excel files: TOC_Runs123 and TOC_Run4
TOC data is organized in worksheets according to runs.
Sigmaplot Analysis – Gompertz Curve Fits
Sigmaplot notebook: Gompertz_Curves_Runs1234
All Gompertz curve fittings for breakthrough data may be found in this file. Figures are divided
into sections according to Runs and compound (e.g. MIB Run 1, Geosmin Run 1, etc.).
Gompertz parameters and statistics are included in each section for each curve and are labelled
according to carbon.
Characterization Tests
Iodine Number: Excel file: Iodine Number Test (feb09)
TCN: Excel file: TCN_MASTER
QAQC
All QAQC test results are kept in the folder entitled “QAQC”. These include experimental and
analytical QAQC tests that were completed.
130
D: EXPERIMENTAL DESIGN
Table D.1 RSSCT design parameters (right) according to large-scale column values (left)
Small-Scale Columns Large-Scale Column
(values based on treatment plant being modeled)
Column No. 1 and 2
(duplicate) 3 4 5 6
Values needed to be input:
Values needed to be input: Carbon A
Carbon B
Carbon C
Carbon E
Carbon D
EBCTLC (min) 7.5
Particle Diameter (RLC)
(mm) 1.6 0.96 1.06 0.98 1.32
LengthLC (m) 1.25
Particle Diameter (Rsc) (mm)
0.0890 0.0900 0.0930 0.0910 0.0880
VelocityLC (m/hr) 10 AD (g/mL) 0.58 0.58 0.65 0.45 0.37
Scaling Factor Exponent 1.1 ReynoldsSC,min 0.70 0.70 0.70 0.70 0.70
Porosity of Carbon Bed 0.4
Dynamic Viscosity of Water at 20ºC (N•s/m²)
0.001
Small Column Diameter (m)
0.0046 0.0046 0.0046 0.0046 0.0046
Density of Water (kg/m3) 1000 Scaling Factor (RLC/RSC) 17.98 10.67 11.40 10.77 15.00
EBCTSC (min) 0.31 0.55 0.52 0.55 0.38
ReynoldsLC 11.11 6.67 7.36 6.81 9.17
Minimum VelocitySC
(m/hr) 11.3 11.2 10.8 11.1 11.5
Maximum VelocitySC
(m/hr) 179.8 106.7 114.0 107.7 150.0
Input the desired small column velocity to be between the range of minimum and
maximum velocity values listed above: Velocity Selected (m/hr) 11.3 11.2 10.8 11.1 11.5
LengthSC (m) 0.059 0.104 0.093 0.102 0.073
Q (mL/min) 3.13 3.10 2.99 3.07 3.19
Weight Carbon Needed
(g) 0.5673 0.9898 1.0046 0.7664 0.4531
Volume of CarbonSC
(mL) 0.98 1.72 1.54 1.69
Run time (days) 26.0 46.2 43.0 45.8 31.8
NOTES: The scaling factor exponent is the exponent on the term (RSC/RLC) in the governing RSSCT equations. It is 1.1 for Calgon's ACT, 1.0 for proportional diffusivity, and 2.0 for constant diffusivity.
131Table D.2 RSSCT design parameters for Run 4
Carbon EBCT (min)
Loading Rate (m/h)
Carbon Depth (cm)
Mass (g)
A 0.31 11.3 5.9 0.5673 B 0.55 11.2 10.4 0.9898 C 0.52 10.8 9.3 1.0046 D 0.38 11.5 10.2 0.4531 E 0.55 11.1 7.3 0.7664
Full-scale 7.5 10 1.25 m
132
E: QAQC
QAQC for RSSCTs
Source Water I – Lake Simcoe
Runs 1 and 2 were replicates. The intent of repeating RSSCTs for the same carbons using the
same water (collected in April 2009) was to determine experimental variability and determine
whether water stored for more than 2 months would show varying results (Figure E.1). Results
from Runs 1 and 2 were reproducible for Carbons C and D. Comparison of Carbon B in Runs 1,
2, and 3 showed no significant difference when comparing Runs 1 and 3 or 2 and 3 (P>0.05), but
a significant difference (P = 0.003) when comparing Runs 1 and 2 (Figure E.2). Figure E.3
shows the geosmin results for Carbon C from both Run 1 and Run 2.
0
1
0 10,000 20,000 30,000 40,000 50,000Bed Volumes of Water Treated
MIB
Con
cen
tra
tion
(C/C
0)
B JuneB JulyC JuneC JulyD JuneD July
Figure E.1 Comparison of MIB results from Runs 1 and 2
133
0
10
20
30
40
50
60
70
0 10,000 20,000 30,000 40,000 50,000
Bed Volumes of Water Treated
MIB
Con
cen
trat
ion
(ng/
L)
Carbon B Run 1 = June
Carbon B Run 2 = July
Carbon B Run 3 = August
Figure E.2 Comparison of Carbon B for Runs 1, 2 and 3
0
10
20
30
40
50
60
70
80
90
100
0 20,000 40,000 60,000 80,000
Bed Volumes of Water Treated
Geo
smin
Con
cent
ratio
n (n
g/L
)
Carbon C Run 1 = June
Carbon C Run 2 = July
Average
Figure E.3 Comparison of geosmin results for Carbon C between Run 1 and Run 2
A two-factor without replication test was run on both Carbon C curves using a 5 per cent
significance level. The P value result was greater than 5 % (P>0.05) indicating no significant
difference between the curve and that the data was therefore reproducible.
Run 3 was conducted in August 2009 using the same source water as Runs 1 and 2 but testing
two new carbons, Carbon A and Carbon E. Carbon B was packed into the third column to act as
a control since Carbon B had been run using the same water in June/July. Comparison of Run 3
with Run 1 and Run 2 Carbon B results showed no significant difference (P>0.05).
134
Following Runs 1 to 3, an expanded RSSCT set-up was built allowing six columns to run
simultaneously instead of three. The new experimental protocol therefore was to duplicate one
carbon and test a total of five carbons at one time. Data analysis from the first three runs also
prompted the analytical duplication of samples as analytical variability was seen between MIB
samples.
Source Water II – Lake Ontario
In order to ensure reproducibility of the data in Run 4, Carbon A was packed in two columns.
The results from Carbon A-1 and Carbon A-2 are shown in Figure E.4. The two-factor without
replication test results again indicated that there was no significant difference between the
geosmin and MIB duplicated columns (P>0.05). This indicates that the data from Run 4 is
reproducible.
Figure E.4 Comparison of duplicated Carbon A column results for MIB (top) and geosmin (bottom), Lake Ontario
0.00
0.20
0.40
0.60
0.80
1.00
0 20,000 40,000 60,000 80,000 100,000
Bed Volumes of Water Treated
Ge
osm
in C
on
cen
tra
tion
C/C
0 Carbon A - 1 Carbon A - 2
0.00
0.20
0.40
0.60
0.80
1.00
0 20,000 40,000 60,000 80,000 100,000
Bed Volumes of Water Treated
MIB
Co
nce
ntr
atio
n C
/C0
Carbon A - 1 Carbon A - 2
135
Analytical QAQC for RSSCT results
As described in section B.9, each sample batch run on the GCMS included a blank sample at the
beginning of the run, six (6) standard solutions for developing calibration curve and a running
standard and blank standard after every 10 samples. Samples from the RSSCTs were tested in
duplicate and an average result taken in order to account for any method variation in compound
detection. Quality control charts (Figure E.5, Figure E.6) were used to track the concentrations of
the running standards.
MIB Quality Control Chart for Running Standard (30 ng/L)
0
10
20
30
40
50
60
70
80
90
100
0 5 10 15 20 25 30 35 40 45 50
Sample
MIB
Co
nc
entr
ati
on
(n
g/L
)
June 2009July 2009
September 2009
June, July 2010
Upper CL (+3 S.D.)
Upper WL (-2 S.D.)
Mean
Lower WL (-2 S.D.)
Lower CL (-3 S.D.)
Figure E.5 MIB quality control chart (30ng/L)
136
Geosmin Quality Control Chart for Running Standard (30 ng/L)
0
10
20
30
40
50
60
70
80
90
100
0 5 10 15 20 25 30 35 40 45 50
Sample
Ge
os
min
Co
nc
en
tra
tio
n (
ng
/L)
June 2009
July 2009
September 2009June, July 2010
Upper CL (+3 S.D.)
Upper WL (-2 S.D.)
Mean
Lower WL (-2 S.D.)
Lower CL (-3 S.D.)
Figure E.6 Geosmin quality control chart (30ng/L)
