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A QSAR-like analysis of the adsorption of endocrinedisrupting compounds, pharmaceuticals, and personalcare products on modified activated carbons

Adam M. Reddinga,*, Fred S. Cannona, Shane A. Snyderb, Brett J. Vanderfordb

aDepartment of Civil and Environmental Engineering, The Pennsylvania State University, 212 Sackett Bldg, University Park, PA 16802, USAbSouthern Nevada Water Authority, Las Vegas, NV 89153, USA

a r t i c l e i n f o

Article history:

Received 10 October 2008

Received in revised form

6 May 2009

Accepted 16 May 2009

Published online 27 May 2009

Keywords:

Activated carbon

Endocrine disrupting compounds

Pharmaceuticals

QSAR

Chi index

* Corresponding author. Tel.: þ1 814 880 337E-mail address: adam.redding@gmail.com

0043-1354/$ – see front matter ª 2009 Elsevidoi:10.1016/j.watres.2009.05.026

a b s t r a c t

Rapid small-scale column tests (RSSCTs) examined the removal of 29 endocrine disrupting

compounds (EDCs) and pharmaceutical/personal care products (PPCPs). The RSSCTs

employed three lignite variants: HYDRODARCO 4000 (HD4000), steam-modified HD4000,

and methane/steam-modified HD4000. RSSCTs used native Lake Mead, NV water spiked

with 100–200 ppt each of 29 EDCs/PPCPs. For the steam and methane/steam variants,

breakthrough occurred at 14,000–92,000 bed volumes (BV); and this was 3–4 times more bed

volumes than for HD4000. Most EDC/PPCP bed life data were describable by a normalized

quantitative structure–activity relationship (i.e. QSAR-like model) of the form:

BVp ¼�ðTPV� rmcÞðe0:2812�pHs Þ

CV� Co

��0:2758� 8cp þ 0:0011� FOSA

�

where TPV is the pore volume, rmc is the apparent density, CV is the molecular volume, Co

is the concentration, 8cp depicts the molecule’s compactness, and FOSA is the molecule’s

hydrophobic surface area.

ª 2009 Elsevier Ltd. All rights reserved.

1. Introduction, overview, and objectives preventing, controlling, and curing diseases; then through the

Environmental engineers, scientists, and consumers have

considerable interest in predicting the extent to which envi-

ronmentally important compounds can be removed from

potable water sources by means of treatment unit operations.

This pursuit is particularly keen for endocrine disrupting

compounds (EDCs) and pharmaceutical and personal care

products (PPCPs). Analysts are finding these EDCs/PPCPs in

a wide array of fresh water sources at the parts per trillion

(ppt) level (Snyder, 2003; Snyder et al., 2003). These are often

the same chemicals that consumers ingest as a means of

1; fax: þ1 814 863 7304.(A.M. Redding).

er Ltd. All rights reserved

natural processes of human digestion and wastewater pro-

cessing, these EDCs/PPCPs find their way to down-stream

water sources.

Research continues to clarify the toxicological significance

of these trace EDCs/PPCPs in drinking water. The concerns of

consumers have caused increased regulatory focus on this

issue even though the EDCs/PPCPs appear at reportedly low

levels (Snyder, 2003; Snyder et al., 2003). Additionally, at

elevated concentrations, some EDCs/PPCPs appear to have

alarming effects on fish (van Aerle et al., 2001). Improved

analytical methods make possible the accurate and precise

.

w a t e r r e s e a r c h 4 3 ( 2 0 0 9 ) 3 8 4 9 – 3 8 6 13850

detection of these compounds at these ppt levels (Snyder

et al., 1999, 2000, 2001a,b, 2004; Trenholm et al., 2006; Van-

derford et al., 2003; Yoon et al., 2003). Given that EDCs and

PPCPs occur at ppt levels, removal of these compounds can be

challenging (Snyder, 2003). However, activated carbon has

proven well-suited to the task of removing low-level organic

contaminants from water.

The authors herein had earlier developed modified acti-

vated carbons that removed organic compounds for prolonged

time (Nowack et al., 2004; Rangel-Mendez and Cannon, 2005).

Specifically, this work demonstrated that by thermally treat-

ing lignite carbon with steam, or with methane plus steam,

the physicochemical properties of the commercial lignite-

based granular activated carbon (GAC) were altered to where

they removed 2-methylisoborneol (MIB) for 6–8 times longer

than their commercial lignite or bituminous counterparts,

when MIB was spiked at ppt levels. For these carbons, higher

pore volume and higher slurry pH corresponded to higher

removal of MIB, natural organic matter, and other

compounds. Because the odorant MIB possesses properties

similar to many EDCs/PPCPs (e.g. similar molecular weight,

solubility, and partitioning), the authors hypothesized that

these thermally modified lignite carbons would likewise

provide increased removal of EDCs and PPCPs.

To appraise this, the Penn State activated carbon team

collaborated with the Southern Nevada Water Authority

analytical team to process EDC/PPCP-spiked Lake Mead water

through conventional and modified activated carbons. For

these trials, the Lake Mead water was concurrently spiked at

ppt levels with 29 EDC/PPCP compounds that were monitored

by HPLC–MS, plus another 31 EDC/PPCP compounds that could

be monitored by GC–MS. The analysis of the GC–MS

compounds was not pursued due to technical issues. The

HPLC–MS compounds exhibited typically shaped GAC break-

through curves, with no breakthrough initially, then gradually

increasing breakthrough. These RSSCT results pertaining to

the lignite-based HYDRODARCO 4000 (HD4000), as compared

to the steam-modified lignite (STEAM), have appeared in

Snyder et al. (2007), with bed lives to 5%, 10%, and 20%

breakthrough reported for 28 EDCs/PPCPs. For the work

herein, the authors have added the results of the methane–

steam tailored lignite variant to that of earlier data set.

The regularity of these RSSCT breakthrough curves indi-

cated that this data set offered a valuable resource for devising

an initial QSAR-like predictive model for EDC/PPCP removal

through activated carbon beds. Clearly, any such compre-

hensive model that is applied to all molecules and all acti-

vated carbons in all native waters would be well beyond the

scope of any single journal paper. The authors, however,

sought to utilize this novel data set as a starting point that

other subsequent research could build upon.

Specifically, the objectives of the work herein were to (a) test

the hypothesis that thermally modified lignite variants would

offer prolonged bed life for removing EDCs/PPCPs at ppt levels

and (b) test the hypothesis that this breakthrough behavior

could be well-described by a QSAR-like model that included

relevant properties of the activated carbon, relevant proper-

ties of the molecules, and a few operational parameters.

To the authors’ knowledge, the RSSCT breakthrough of as

many ppt-level compounds concurrently in a native water like

this had not previously been studied by others; however, the

concurrent adsorption of EDCs/PPCPs at ppt levels in batch

tests has been demonstrated (Westerhoff et al., 2005). Inter-

estingly, as recently as 10–20 years ago, the prospect of

monitoring ppt levels of these compounds would have been

daunting. Because of this, virtually all of the prior molecular-

structure modeling in the refereed literature that has

appraised multiple compound sorption behavior has done so

at the ppb or even ppm levels (e.g. Blum et al., 1994; Brasquet

et al., 1997). Often, these models have been used for isotherm

conditions in deionized water, which can pose distinctions

from flow-through column conditions where background

NOM is present. The concentration disparities meant that the

parameters that affected ppb or ppm sorption performance

could be distinct from those that influenced the ppt sorption.

For example, at the ppt level, the compounds’ interactions

with the activated carbon surface could be considerably more

important than the compounds’ interactions with other

adsorbate molecules; this further meant that there could be

less relevance to the idea of pore-filling, where molecules

collect to form their own liquid-like sorbed phase (e.g. Dubinin

and Stoeckli, 1980; Manes and Hofer, 1969).

The experimental protocol employed herein aimed to offer

both practical engineering water treatment value and scien-

tific significance. Specifically, as a practical matter, the

authors chose organic species that represented common

EDCs/PPCPs. Moreover, the authors intentionally used a native

water for the spiked source, since in water treatment appli-

cations, there would always be an NOM background. With

regard to scientific significance, the authors have endeavored

to discern whether an equation like a quantitative structure–

activity relationship (QSAR) could be applied to this EDC/PPCP

breakthrough data. QSARs have related the activity of

a compound to chemical or physical characteristics that have

been obtained from structure or experimental data. In well-

characterized systems, QSARs have predicted activity (e.g.

partitioning, transport, adsorption) when experimental data

for the compound of interest are not available, including

Henry’s Law constants (Brennan et al., 1998), octanol–water

partitioning (Kier and Hall, 1986), pharmacological efficacy

(Pan et al., 2006), and activated carbon adsorption isotherms at

ppm levels (Blum et al., 1994; Brasquet et al., 1997; Qi et al.,

2000).

To this end, the authors found that such a QSAR-like

equation (Eq. (1)) could indeed predict the breakthrough of

most compounds, when the equation included just a few

QSAR descriptors of the organic species, plus a few parame-

ters that pertained to the activated carbon character.

Compound descriptors were selected through multi-variable

linear regression and then validated using an approach for

internal model validation, known as leave-one-out cross-

validation (LOOCV), with predictive error minimized accord-

ing to the prediction sum-of-squares (PRESS) statistic (Gra-

matica, 2007; Picard and Cook, 1984). LOOCV is particularly

well-suited to small data sets where additional data are either

difficult or impossible to obtain.

With regard to the activated carbon, the pertinent char-

acteristics in Eq. (1) are as follows: (1) the carbon’s internal

pore volume in the 4–1000 A width range (herein identified as

the total pore volume – TPV), (2) the activated carbon’s slurry

Table 1 – BET surface area, slurry pH, and pore volume ofGAC samples.

GAC sample HD4000 STEAM M/S

Slurry pH (pHs) 4.99 9.67 10.2

Mini-column apparent density

(rmc, g/mL)

0.33 0.33 0.33

Micropores (mL/g, 4.06–21.7 A) 0.15 0.25 0.31

Mesopores (mL/g, 21.7–504 A) 0.37 0.45 0.47

Total pore volume (TPV)

(mL/g, 4.06–1000 A)

0.54 0.73 0.1

BET surface area (m2/g) 607 848 1031

w a t e r r e s e a r c h 4 3 ( 2 0 0 9 ) 3 8 4 9 – 3 8 6 1 3851

pH, as an indicator of the carbon’s surface chemistry (pHs),

and (3) its apparent density in the column (rmc) (kept constant

herein). Prior work at Penn State (Nowack et al., 2004; Rangel-

Mendez and Cannon, 2005) and by many others (Leng and

Pinto, 1997; Moreno-Castilla, 2004) has shown that the pore

volume and slurry pH influence the extent of adsorption.

With regard to the EDC/PPCP compounds, Eq. (1) includes

three parameters, namely (1) the molecule’s ‘‘compactness’’,

as depicted by the compounds’ 8th-order simple-path Chi

index (8cp – unitless) (Kier and Hall, 1986). This parameter

depicts the number of unique 8-bond paths (non-hydrogen) in

a molecule; with more 8-bond paths, the value of the 8cp

descriptor increases, and the more compact the molecule

becomes relative to its molecular weight (see Support online

material for representative calculation). Also, the equation

includes (2) the molecule’s hydrophobic surface area (FOSA,

A2/molecule), as an indicator of how much of the molecular

surface can hydrophobically interact with the activated

carbon graphene layers, which are also generally hydrophobic

by nature. And finally, (3) the volume of Avogadro’s number of

molecules (CV, mL/mol). This CV is distinguished from the

molar volume (i.e. volume of one mole of pure compound),

which would effectively depict the inverse of the density of

a discrete organic liquid phase. The normalization to molec-

ular volume herein was included to account for the effect of

size exclusion during adsorption. For example, the size of the

adsorbate has been linked to both the access to the internal

pore structure and the available surface area for sorption

(Summers and Roberts, 1988; Kilduff et al., 1996a).

Eq. (1) also includes two parameters that pertain to the

RSSCT operations, namely (1) the molar concentration of the

EDC/PPCP compound (Co, nmol/L), and (2) the bed volumes

(BV) to characteristic initial breakthrough (L/L) for each of the

EDCs/PPCPs that were included in the spiked matrix.

BVp ¼�ðTPV� rmcÞðe0:2812�pHs Þ

CV� Co

��0:2758� 8cp þ 0:0011

� FOSA�

(1)

With an R2 of 0.861, the overall model (Eq. (1)) fits to the

performance of 23 out of the 29 EDCs/PPCPs that could be

analyzed by HPLC–MS. Of the remaining six, two exhibited no

breakthrough for one or several of the RSSCT runs, and four

had characteristics that caused them to be statistical out-

liersdsuch as an exceptionally large molecular volume (see

below).

2. Experimental

2.1. Pore volume distribution and BET surfacearea analysis

Pore volume distributions and BET surface areas (Table 1) of

the GAC samples were determined by interpretation of argon

adsorption isotherms following the protocol of Moore et al.

(2003). The adsorption of gaseous argon onto the GAC surface

was recorded through a pressure range of 10�6 to 0.993 atm

using ASAP 2000 Physisorption Instruments (Micromeritics

Instrument Co., Norcross, GA), with duplicate analyses. The

resulting argon adsorption isotherm data were processed

according to the density functional theory via the Micro-

meritics DFT Plus (V2.02) software to produce a pore volume

distribution for the sample.

2.2. Slurry pH

To monitor the net surface pH, a 0.010 M solution of potas-

sium chloride was prepared from nitrogen-sparged deionized

water. A suspension of carbon sample was prepared by

weighing 0.100 g of thoroughly washed 170 US Mesh� 200 US

Mesh GAC into a 20 mL glass scintillation vial and then adding

15 mL of 0.010 M KCl solution. After 2 h on a shaker table, the

pH of the supernatant was measured using a Mettler–Toledo

DG115-SC pH probe (Mettler–Toledo, Inc., Columbus, OH) that

was calibrated with reference standard buffers at pHs 4, 7, and

10 (VWR, Inc., West Chester, PA). The pH was measured again

after 24 h to verify that equilibrium had been reached. Values

listed below in Table 1 are the average of the three analyses,

with a standard deviation of 0.21. The authors noted that the

slurry pHs of these carbons did not change while the dry

carbon samples were stored in a vacuum desiccator for up to 1

year.

2.3. Activated carbon materials and preparation

The commercial lignite HD4000 used in this research origi-

nated from Norit Americas, Inc., Marshall, TX, USA. The

lignite variants were prepared utilizing a high-temperature

steam (STEAM) or methane/steam (MS) modification protocol

as described by Rangel-Mendez and Cannon (2005).

GAC samples for use in RSSCTs were prepared by grinding

the as-received commercial GAC and then wet-sieving it using

a 170-US mesh (0.090 mm) sieve combined with a 200-US

mesh (0.075 mm) sieve to obtain the 170� 200 fraction. In wet-

sieving, the surface of the sieve was rinsed with distilled water

to remove fine particles and prevent particles from sticking to

each other or to the surface of the mesh. Dry-sieving would

not have been adequate as electrostatics would have pre-

vented the correct particle size from passing the mesh. After

rinsing with about 0.5 L of additional distilled water per gram

of ground GAC, the samples were dried at 105 �C for 24 h

before storage. Samples were stored in a vacuum desiccator

for short periods (days) or under a slightly pressurized (2 psig)

nitrogen atmosphere for long-term storage (months). The

storage techniques aimed to minimize the exposure to

atmospheric oxygen and moisture.

Fig. 2 – Volumetrically normalized adsorption (nL

compound per mL TPV) versus slurry pH for five

compounds: testosterone (-), ethynylestradiol (B),

diazepam (C), naproxen (6), and ibuprofen (:). These

compounds displayed the typical response to changing

slurry pH of the GAC. The value used for slope, as per Eq.

(3), was the average of all compounds’ slopes.

w a t e r r e s e a r c h 4 3 ( 2 0 0 9 ) 3 8 4 9 – 3 8 6 13852

2.4. Rapid small-scale column tests

The bench-scale evaluation consisted of RSSCTs as a measure

of GAC adsorption performance with EDCs/PPCPs. The RSSCTs

simulated a full-scale GAC bed of #12� 40 US mesh size grains

operating at a 7.6 min empty bed contact time, with protocol

as presented previously (Snyder et al., 2007). A 7.6-min empty

bed contact time translates to 70,000 bed volumes of water

processed per year. The EDC/PPCP tests herein employed

constant diffusivity similitude because of the compound’s low

molecular weight (average 257 Da) and relatively weak

polarity (refer to Crittenden et al., 1986, 1991).

These RSSCT experiments used the influent from a water

treatment plant at Lake Mead, NV, USA. This water had

a natural organic matter content of 3.0 mg/L TOC (Shimadzu

TOC-VCSN, Columbia, MD, USA). Water was stored at 4 �C in

stainless steel containers until testing. A total of 60 EDCs/

PPCPs were collectively dissolved in either methanol or

methylene chloride and dosed to reach concurrent concen-

trations of 100–200 ppt (0.24–1.49 nmol/L) for each compound

in the influent water (Snyder et al., 2007). Effluent concentra-

tions were measured for those 29 compounds that could be

monitored via high pressure liquid chromatography–mass

spectrometry, as per the analytical method presented in

Snyder et al. (2007). The STEAM and MS GACs slightly raised

the effluent pH for the first w900 bed volumes; and thereafter,

the effluent pH returned to the influent pH (8.05).

3. Results and discussion

3.1. Outline of model development steps

In light of the considerable complexity of the model development,

this text first presents a step-by-step outline of the approach

used herein. Details are included in the sections that follow.

Step 1: The authors conducted RSSCTs with natural Lake Mead

water that was concurrently spiked with many EDCs/PPCPs

Fig. 1 – Bed volumes treated at characteristic initial breakthroug

showed no breakthrough during the HD4000 test length. Proges

test length of the M/S carbon. Oxybenzone exhibited no breakth

arranged in order of M/S breakthrough.

and analyzed the effluents for these species. The authors also

characterized the three lignite GACs relative to pore volume

distribution, slurry pH, surface area, etc.

Step 2: The authors calculated a characteristic initial break-

through (BVobs, L/L) for each of these compounds from the

RSSCT data of each lignite variant (Fig. 1).

Step 3: The authors compiled and computed numerous (>40)

molecular descriptors for the EDCs/PPCPs (see Support online

material, Tables S1–S5).

Step 4: The authors plotted the GAC slurry pH (x-axis) versus

the volumetrically normalized breakthrough ( y-axis) (e.g.

Fig. 2) and characterized this plot as per Eq. (2) (where

parameters are as listed above and below):

h for the 28 EDCs/PPCPs exhibiting breakthrough. Triclosan

terone breakthrough was observed only during the longer

rough during any of the three test lengths. Compounds are

w a t e r r e s e a r c h 4 3 ( 2 0 0 9 ) 3 8 4 9 – 3 8 6 1 3853

BVobs ��

CV� Co

TPV� rmc

�¼ ez�pHs (2)

For each compound’s curve, the z-coefficient was calculated

and the z-values fell within a narrow range with an average of

z¼ 0.2812. This average z-value was used in the subsequent

model development.

Step 5: The authors calculated the ‘‘Relative Adsorbability’’

observed for each EDC/PPCP by plotting the observed charac-

teristic breakthrough (BVobs, y-axis) versus the volumetric

normalization (x-axis), now including the influence of slurry

pH (pHs) as per Step 4 above (e.g. Fig. 3). For each molecule’s

plot with the three GACs, the Relative Adsorbability was the

slope of this linear relationship (Eq. (3)).

BVobs ¼�ðTPV� rmcÞðe0:2812�pHs Þ

CV� Co

�� Relative Adsorbabilityobs

(3)

Step 6: The authors determined which molecular properties

could account for the variation in the compound’s Relative

Adsorbability by conducting a multi-variable regression

(Minitab V15, State College, PA) with an equation (Eq. (4)) of the

form (where ‘‘a’’ and ‘‘b’’ are best-fit coefficients):

Relative Adsorbability ¼ a�molecular descriptor1 þ b

�molecular descriptor2/ (4)

The authors exhaustively evaluated the molecular parameters

that produced the highest correlation between the predicted

and observed Relative Adsorbabilities. The best-fit coefficients

and the selected parameters ( p< a¼ 0.001) were then inserted

into the final equation (Eq. (1) shown again below for clarity),

Fig. 3 – Relative Adsorbability (RA) for several compounds

(as defined per Eq. (3)): androstenedione (C), caffeine (B),

diazepam (:), gemfibrozil (6), ibuprofen (-), and

sulfamethoxazole (,). Each dashed line shows the linear

regression and associated slope (RA) for each compound’s

data with the origin assigned as the intercept.

which thus offered a prediction of bed volumes (BVp) to

characteristic breakthrough (Fig. 4).

BVp ¼�ðTPV� rmcÞðe0:2812�pHs Þ

CV� Co

��0:2758� 8cp þ 0:0011

� FOSA�

(1)

Step 7: After determining the significant predictors (which

were 8cp and FOSA), the authors statistically evaluated if any

other parameters could be added that were significant in the

correlation between BVobs and BVp. Again, if p< a¼ 0.001, then

it was construed as significant. No other parameters were

found to be significant among more than 40 that were

examined.

Step 8: The authors tested this QSAR-like model by internal

validation per leave-one-out cross-validation (LOOCV), with

the final model form corroborated by a minimized predictive

sum-of-squares (PRESS-statistic) (Gramatica, 2007; Picard and

Cook, 1984).

Step 9: The authors examined the EDC/PPCP compounds that

were statistical outliers in this BVobs versus BVp analysis and

found that some of these also had physical–chemical outlier

characteristics. These characterizations could aid scientists in

extending the breadth of subsequent QSAR-like model

applicability.

3.2. GAC characteristics and RSSCT removalof EDCs/PPCPs

For the three lignite GAC variants, the pore volume, slurry pH,

surface area, and mini-column apparent density are pre-

sented in Table 1. The pore volume distributions appear in

Support online material, Fig. S1. Notably, for the three

carbons, the ratio of micropores-to-total pores was relatively

uniform. This meant that it was not possible to statistically

Fig. 4 – Predicted (Eq. (1), BVp) versus observed initial

breakthrough (Fig. 1, BVo) for 23 EDCs/PPCPs as described

by the QSAR-like model. Adsorption generally increased in

this order: M/S (,) > STEAM (6) > HD4000 (B). The

breakthrough of fluoxetine with steam (:) was a statistical

outlier (>3 standard deviations).

Fig. 6 – Estradiol breakthrough. Dashed lines indicate

w a t e r r e s e a r c h 4 3 ( 2 0 0 9 ) 3 8 4 9 – 3 8 6 13854

distinguish whether, for example, micropores or total pores

were more correlated to sorption performance (refer to

Nowack et al., 2004). Thus, the model herein used total pore

volume (width< 1000 A).

The RSSCT results have been plotted as the relative

concentration (C/Co) versus bed volumes of water processed

(Figs. 5 and 6; and Support online material, Figs. S2–S27).

STEAM and M/S removed EDCs/PPCPs 3–4 times longer (i.e.

14,000–92,000 BV for initial breakthrough) than did HD4000

(i.e. 2000–26,000 BV for initial breakthrough) (Fig. 1).

The most readily sorbed group of compounds were the

steroids: androstenedione, estradiol, estriol, estrone, ethy-

nylestradiol, progesterone, and testosterone (Fig. 1). On

observation, these steroids were found to be quite similar to

one another in their molecular volume, which averaged to

84 mL/mol.

regression through available data (C/Co < 0.50, circled

points). The method of data interpretation is demonstrated

here (dotted line) where the M/S slope is used to define

breakthrough with STEAM. Influent concentration (Co)

equaled 0.66–0.85 nmol/L (179–232 ppt).

3.3. Computing the characteristic initial breakthrough

The authors sought to calculate the point of characteristic

initial breakthrough in a standardized manner while

including as much data as possible for each EDC/PPCP. To do

so, the authors fit a linear regression to all the data points with

0< C/Co� 0.50, plus the last C/Co¼ 0.00 data point before

breakthrough when possible. The authors defined the inter-

section of this regression line with the x-axis as the charac-

teristic initial breakthrough, as shown by an example in Fig. 5.

If only one data point or no points were available below

C/Co¼ 0.50, the first two available data points were used.

Support online material includes the breakthrough curves and

regressions for the additional test compounds not shown here

(Figs. S2–S27).

Occasionally, effluent sample selection offered insufficient

data to adequately define the initial breakthrough of an EDC/

PPCP with the STEAM carbon. This occurred with five

compounds where there was only one or two data points

above C/Co¼ 0. In those instances, the slope of the break-

through curve was assumed to be parallel to that observed for

the same EDC/PPCP compound with the M/S carbon (i.e. the

Fig. 5 – Meprobamate breakthrough. Circled points indicate

those used for interpretation of characteristic initial

breakthrough. Influent concentration (Co) equaled 0.86–

1.20 nmol/L (188–262 ppt).

carbon with the most similar pore volume distribution and

slurry pH) (see Fig. 6 for an example). It is noted that in more

than half of the cases where considerable data were available

for both STEAM and MS, the two breakthrough curves were

indeed parallel to one another. With this approach, the

authors employed more than w80% of the data points that

exceeded detection, so as to mathematically and narrowly

define the characteristic initial breakthroughs.

The authors note that other equation forms for charac-

teristic breakthrough could have been used (and indeed were

appraised), but the approach herein used the most data. With

the standard deviation inherent in analyzing these

compounds at the ppt level, the more comprehensive use of

the data set meant a more accurate and precise definition of

breakthrough. Also, as a practical matter, this ‘‘zero-break-

through’’ characterization reflects the consumer’s desire to

drink water without detectable EDCs/PPCPs.

3.4. Molecular descriptor calculations

The authors employed QikProp (Schrodinger LLC, New York,

NY, USA), a molecular analysis program to compute various

properties of the 29 EDCs/PPCPs herein. These properties

included molecular volume, various types of solvent-acces-

sible surface areas (1.4 A probe radius), dipole, polarizability,

and ionization potential. Two-dimensional ‘‘mol’’ files of the

compounds were acquired from the National Institute of

Standards and Technology WebBook (http://webbook.nist.

gov). The LigPrep computer program (Schrodinger LLC, New

York, NY, USA) produced optimized three-dimensional mol

files from the two-dimensional files.

The authors used the computer program Molconn-Z V4.10

(Edusoft, La Jolla, CA, USA) to calculate Chi connectivity

indices and kappa shape (topological) indices for the pertinent

EDCs/PPCPs. These descriptors have frequently appeared in

QSARs (Kier, 1986; Kier and Hall, 1986). The calculations

included the indices up to the 8th order, including simple-

Fig. 7 – Relative Adsorbability as calculated for all 22

compounds that exhibited breakthrough with all three

carbons.

w a t e r r e s e a r c h 4 3 ( 2 0 0 9 ) 3 8 4 9 – 3 8 6 1 3855

path, valence-path, valence-path-cluster, and valence-chain

types (Kier and Hall, 1986). For Chi indices above the 8th order,

many of the compounds produce ‘‘zero’’ values and the

molecule is thus too small to be described by indices that were

higher than the 8th order. A full list of these properties and

descriptors can be found in Support online material (Tables

S1–S5).

3.5. Accounting for the influence of the carbon’s slurrypH

To address the influence of GAC slurry pH (pHs), the authors

first plotted pHs versus the normalized adsorption for each

compound, as it appears in Fig. 2 and Support online material

(Figs. S28–S30). Because pH is a logarithmic parameter, the

authors characterized each curve by an exponential expres-

sion (see Section 3.1, Eq. (2)).

In Eq. (2), ‘‘z’’ reflected the influence of pHs on adsorption. It

is noted that since the slurry pHs were somewhat similar

between the STEAM lignite (9.67) and the MS lignite (10.2), the

two points corresponding to these did not vary greatly for any

given compound. Most importantly, the z-values showed

minimal variation (average¼ 0.2812, standard deviation¼0.033) and ranged from 0.230 to 0.357 (see Fig. 2 and Support

online material (Figs. S28–S30)). Thus, the selection of the

average z¼ 0.2812 was effectively based on 72 data points.

This average pre-exponential z was then used in Step 5 of the

QSAR-like model development, as below.

3.6. Relative Adsorbability

As in Step 5 of the QSAR-like model development, the authors

calculated a value herein termed the ‘‘Relative Adsorbability.’’

To find this value for each EDC/PPCP, the authors fit a line to

the plot of volumes of water processed at characteristic initial

breakthrough (L water/L carbon bed) versus the pore-to-

molecule ratio (x-axis) as per Eq. (3) (see Section 3.1). For the 22

compounds where this could be calculated, the pore-to-

molecule ratios ranged from 10,000 to 120,000 (mL of total

pores/L of GAC bed)/(nL of molecules/L of water). These plots

are shown for the 22 compounds in Fig. 3 and Support online

material (Figs. S25–S27).

For a given compound, the slope of the plot indicated the

Relative Adsorbability observed. These plots served as

a means of discerning a volume-to-volume based comparison

of adsorption behavior; and they posed some similarities to

the gravimetric-based isotherms. The difference is that for

these plots, the selection of GACs offered a range of total pore

volume; whereas in isotherms, the selection of GAC offers

a range of mass. Moreover, the Relative Adsorbability plots

employ column flow-through data at very low concentrations;

whereas isotherms employ batch data that must be collected

at inherently higher concentrations if true differences are to

exceed standard errors.

The Relative Adsorbabilities ranged from 0.13 to 1.24 (nL

molecules sorbed/mL pores available) (Fig. 7); this was an

order of magnitude span for these 22 EDCs/PPCPs (see Relative

Adsorbability (RA) in Table 2). The data were not sufficient to

calculate the Relative Adsorbability for progesterone or tri-

closan, as these compounds did not show breakthrough with

all three GACs. Using Fig. 7 and Table 2 data, one could

calculate the volume (nL) of all the compounds sorbed at

breakthrough, relative to the total pore volume. This

comparison showed that only w10�3 to 10�4 percent (%) of the

pores were occupied by these EDCs/PPCPs. Moreover, one

could compute that less than 0.2% of the pore surface would

have been covered with these EDCs/PPCPs.

3.7. Multi-variable linear regressions for identifyingsignificant molecular descriptors

As the sixth step, the authors compared the observed Relative

Adsorbabilities (Section 3.6) to the set of molecular properties

and descriptors in a stepwise linear regression, as per the

standard expression:

Relative Adsorbability ¼ a�molecular descriptor1 þ b

�molecular descriptor2/ (4)

The regression analysis identified 8cp and FOSA as the only

two statistically significant parameters. The best-fit coeffi-

cients for this regression against Relative Adsorbability were

as follows: a¼ 0.2758 and b¼ 0.001098. For this, the R2 corre-

lation was 0.851 when including 22 of the EDCs/PPCPs (see

Fig. 8).

These parameters were then included in the overall

equation to yield the final QSAR-like model (Eq. (1) and Fig. 4).

This equation described the characteristic breakthrough for 23

of the test compounds, with a coefficient of determination (R2)

of 0.861.

Six compounds were not included in Fig. 4 regression; these

are the italicized compounds in Table 2. Two of the excluded

compounds exhibited no breakthrough for one or more

RSSCTs. Four of the excluded compounds were statistical

outliers; of these, some had chemical properties that could

have contributed to their outlier status, as discussed below.

As mentioned above, two descriptors, the 8th-order

simple-path Chi index (8cp) and the hydrophobic component

of the solvent-accessible surface area (FOSA, A2/molecule)

offered good statistical correlation. The 8cp and FOSA values

Table 2 – Influent concentrations and molecular properties of the 29 EDCs/PPCPs.

Compound RA(nL/mL)

Mol. weight(Da)

log Kow pKa log D(pH¼ 8.05)

Co (nmol/L) CV(mL/mol)

FOSA(A2)

8cp

HD4000 STEAM M/S

Acetaminophen 0.240 151.2 0.46 9.38 �0.89 0.82 1.13 0.71 40.25 93.6 0.136

Androstenedione 1.244 286.2 2.75 NA 2.75 0.71 0.75 0.89 83.35 389.8 2.717

Atrazine 0.628 215.7 2.61 1.7 �3.74 0.89 1.21 1.25 60.43 314.7 0.511

Caffeine 0.813 194.1 �0.07 10.4 �0.07 1.09 1.32 1.49 49.17 239.0 0.506

Carbamazepine 0.761 236.3 2.45 7.00 1.36 0.63 0.90 1.03 69.07 43.2 1.748

DEET 0.501 191.3 2.18 4.5 2.18 0.94 1.25 1.45 59.99 292.9 0.245

Diazepam 0.618 284.7 2.82 3.4 2.82 0.47 0.60 0.76 80.88 120.3 1.696

Diclofenac 0.244 294.0 0.7 4.51 �2.84 0.41 0.51 0.60 73.53 24.4 1.066

Dilantin 0.258 252.3 2.47 8.33 2.01 0.61 0.74 0.84 69.47 0.0 1.299

Erythromycin NA 734.5 3.06 8.88 3.00 0.02 0.02 0.01 189.2 802.5 3.239

Estradiol 1.192 272.4 4.01 10.4 4.01 0.66 0.81 0.85 80.02 304.1 2.693

Estriol 0.977 288.4 2.45 10.4 2.45 0.76 0.82 0.98 81.47 266.9 2.826

Estrone 1.098 270.4 3.13 10.4 3.13 0.78 0.91 0.98 79.08 300.2 2.693

Ethynylestradiol 0.947 296.4 3.67 10.4 3.67 0.64 0.77 0.84 87.34 306.2 2.978

Fluoxetine 0.683 309.1 4.05 8.7 3.31 0.27 0.48 0.46 81.04 156.9 1.218

Gemfibrozil 0.493 250.3 4.77 4.42 1.14 0.62 0.86 0.93 73.01 366.2 0.607

Hydrocodone 0.617 299.4 2.16 7.32 2.09 0.47 0.89 0.92 84.85 383.5 4.659

Ibuprofen 0.217 206.3 3.97 4.91 0.83 0.70 1.03 1.07 61.20 278.5 0.586

Lopromide NA 791.1 �2.05 10.2 �2.05 0.24 0.30 0.25 142.2 363.4 1.551

Meprobamate 0.272 218.3 0.7 10.9 0.07 0.86 0.95 1.20 53.73 234.4 0.167

Naproxen 0.430 230.3 3.18 4.15 �0.72 0.53 0.97 0.99 65.74 179.9 1.206

Oxybenzone NA 228.1 3.79 7.77 3.33 0.01 0.04 0.04 64.60 92.2 0.671

Pentoxifylline 0.624 278.3 0.29 0.97 0.29 0.61 0.66 0.83 72.46 366.2 1.207

Progesterone NA 314.5 3.87 NA 3.87 0.57 0.57 0.72 92.44 430.0 3.085

Sulfamethoxazole 0.130 253.3 0.89 5.5 0.89 0.58 0.71 0.54 64.83 96.4 0.492

TCEP 0.329 285.5 1.44 NA 1.44 0.52 0.72 1.02 56.13 241.0 0.188

Testosterone 1.184 288.4 3.32 NA 3.32 0.68 0.71 0.84 84.29 393.9 2.717

Triclosan NA 287.5 4.76 7.9 4.38 0.29 0.37 0.58 67.24 0.0 0.890

Trimethoprim 0.893 290.3 0.91 7.12 0.86 0.55 0.82 0.88 84.22 295.5 1.244

RA¼ Relative Adsorbability, Co¼ influent concentration, CV¼molecular volume, FOSA¼hydrophobic portion of solvent-accessible surface

area, 8cp¼ 8th order simple-path Chi index, Kow¼ log octanol–water partition coefficient. Outliers (>3 standard deviations), relative to the

averages for these values, are italicized. Italicized compounds were outliers from the final model as listed in Table 4. DEET: N,N-diethyl-meta-

toluamide, TCEP: tris(2-chloroethyl) phosphate.

Fig. 8 – Predicted versus observed Relatively Adsorbability

for 22 EDCs/PPCPs. Predictions were made using the 8cp

index and FOSA. Two compounds were outliers (Tables 3

and 4) on the basis of: (1) influent concentration (caffeine

(B)) and (2) 8cp value (hydrocodone (,)).

w a t e r r e s e a r c h 4 3 ( 2 0 0 9 ) 3 8 4 9 – 3 8 6 13856

for these EDCs/PPCPs are listed in Table 2. The 8cp Chi index

encodes for the number of unique 8-bond paths present in

a molecule, excluding bonds to hydrogen (Kier and Hall, 1986).

As the number of paths increases, the value of the 8cp index

will increase, and the compactness, relative to the molecular

weight, increases. This in turn corresponds to faster adsorp-

tion kinetics and greater access into smaller pores. Thus, for

the 22 compounds (Fig. 8), the positive correlation of the 8cp

index with the initial breakthrough implied that an increased

degree of compaction benefited adsorption. Kilduff et al.

(1996b) observed a similar phenomenon with the adsorption

of humic acids onto activated carbon: increasing ionic

strength caused macromolecules to coil and reduce their

effective size such that adsorption improved via increased

access to smaller pores and enhanced diffusivity.

The regression analysis selected the simple index (8cp) as

statistically significant, as opposed to the valence index (8cpv);

this result posed important implications. The simple index is

purely a structural descriptor and considers only the

compactness of the molecule, regardless of its heteroatom

substituents. Thus it encodes for only the s electrons. This

contrasts to the valence index which also accounts for

p-electrons and lone pair electrons additionally (Kier and Hall,

1986).

w a t e r r e s e a r c h 4 3 ( 2 0 0 9 ) 3 8 4 9 – 3 8 6 1 3857

The hydrophobic surface area (FOSA) represents the

aliphatic portion of the solvent-accessible surface area that is

composed of saturated carbons and their attached hydrogen.

It is noted that the FOSA value does not include any molecular

surface area due to p-bonds (PISA) (see discussion below). The

significance of the FOSA descriptor is logical, given that

hydrophobic interactions are known to strongly enhance

adsorption extent and kinetics. As a corollary to Traube’s rule

(Ward, 1946), the adsorbability of aliphatic acids increased

with increasing hydrocarbon length and their corresponding

increase in hydrophobicity (Moreno-Castilla, 2004). Likewise,

this relationship was manifested in our QSAR model herein:

adsorption increased with increasing hydrophobic surface

area, as would be expected.

Implied support for the selection of the 8cp and FOSA

variables can be found elsewhere in the literature. In their

application of the solvophobic theory to the adsorption of

homologous series of alcohols and ketones, the work of Belfort

et al. (1984) in isotherm tests demonstrated that for a set of six

hexanol isomers (2,3-dimethyl 2-butanol through 1-hexanol)

adsorption increased with increased branching, not with

decreased molecular size (i.e. molecular volume). Though not

explicitly calculated by Belfort et al. for this group of isomers,

the adsorbability bears some correlation to cavity surface area

(A2, R2¼ 0.82). Interestingly, when the cavity surface area (i.e.

molecular surface area) is normalized by the molecular

volume (A2/A3), then the correlation with adsorbability

increases noticeably (R2¼ 0.86).

3.8. Regression statistics and leave-one-outcross-validation

The authors used a stepwise linear regression to identify the

significant predictors. With each successive step of the

regression, predictors and outlier observations were identified

(Table 3: starting with Model A and ending with Model H). The

statistics generated for each step are also listed (e.g. best-fit

coefficient and predictor p-value). Because of the moderate

size of the dataset, the authors used an internal validation

method, namely leave-one-out cross-validation (LOOCV) with

the predicted sum-of-squares (PRESS-statistic). This LOOCV

approach was used to verify the choice of model. In LOOCV,

one observation at a time is removed from the data set, and

the remaining observations are used successively to predict

the deleted observation (Gramatica, 2007; Picard and Cook,

1984). The PRESS-statistic is then the sum of the error for

predicting each deleted value using LOOCV; the model with

the lowest relative PRESS is thus the preferred model. Values

of the PRESS-statistic are also listed in Table 3. Additionally,

the statistical significance (i.e. p-value) is listed for each of

these other molecular parameters when added as a third

predictor in the model (Model H) where 8cp and FOSA were

already identified as predictors (Support online material,

Tables S1–S5).

3.9. Significance of other molecular descriptors

The authors compared many molecular descriptors to the

observed activity (i.e. adsorption) because each descriptor

encoded unique structural information. The large number of

descriptors required a rigorous critical significance level ( p< a

¼ 0.001) when accepting or rejecting predictors. Specifically,

Tables S1–S5 in Support online material list the statistical

significance of each molecular descriptor when added as an

additional predictor (i.e. in addition to FOSA and 8cp) for

Relative Adsorbability. As indicated, none of these QSAR

descriptors were significant ( p< a¼ 0.001); thus these others

were not included in the final equation. Indeed, the next

smallest p-value amongst these other 40 descriptors was 0.202

(Support online material, Table S4), which was far above 0.001.

Several of the particular parameters that posed no statis-

tical significance at these low ppt levels included Kow, log Kow,

log D, PISA, the valence index (8cpv), log S, or log S (at pH 8.05). It

is notable that the log of the octanol–water partition coeffi-

cient (log Kow) was not predictive of the observed break-

through. This compares to previous work by others that

showed that log Kow was important at the ppm concentration

level (Abe et al., 1988; Hu et al., 1998; Walters and Luthy, 1984).

Indeed, substituting log Kow for the ‘‘affinity’’ term (FOSA) in

the regression greatly reduced the quality of the correlation

(R2¼ 0.705). Further, if log Kow were to be added to the model

as a predictor alongside 8cp and FOSA, the resulting p-value of

0.878 (Support online material, Table S3 and Table 3: Model I)

identifies it as offering no more significant correlation. During

the research herein, the very low aqueous concentrations of

the adsorbates (ppt) appeared to greatly reduce the relevancy

of log Kow and log D (Table 3: Model J), and log S (Support

online material, Table S3). A similar irrelevancy of log Kow at

the ppt level has been observed by others (McDonough et al.,

2008; Yu et al., 2008) who conducted ppt single solute batch

experiments at ppt levels.

The authors are not aware of other studies that have

appraised concurrent sorption in flow-through tests of

numerous EDCs/PPCPs at the ppt level; the distinctions pose

important phenomenological differences. For example,

because Kow values are derived from the interaction of

a molecule immersed in liquid phases, the polar chemistry of

the entire molecule can influence partitioning. In contrast, for

these ppt-level RSSCTs, the 60 sorbed EDC/PPCP molecules

covered less than 0.2% of the GAC surface; thus interactions

with the GAC surface may have been more important than

interactions with other EDC/PPCP molecules.

Additionally, the authors observed that p surface area

(PISA) did not correlate with breakthrough (Table 3: Model K)

although 15 of the 22 compounds contained aromatic rings.

Others have noted that p–p dispersion interactions with these

rings were dominant in aromatic adsorption at much higher,

part-per-million concentrations (Radovic et al., 1997). But, for

this set of compounds, the PISA was not a significant

descriptor of the observed adsorption at the ppt level.

3.10. Breadth of QSAR-like model applicability

The QSAR-like model herein (Eq. (1)) utilized relatively few

parameters for characterizing the adsorption of numerous

EDCs/PPCPs. With regard to the activated carbon parameters,

this QSAR-like model appraised three lignite variants. With

regard to molecular parameters, the authors observed that the

model offered the greatest accuracy for those compounds

where influent concentration (Co) averaged 0.24–1.25 nmol/L,

Table 3 – Statistics produced from the linear stepwise regression for selecting predictors, using leave-one-out cross-validation (LOOCV) with the predicted sum-of-squares(PRESS-statistic) to verify model selection.

Model A B C D E F G H I J KSample size (n) 24 23 22 24 23 22 22 22 22 22 22

Predictor 1 8cp8cp

8cp8cp

8cp8cp

8cp8cp

8cp8cp

8cp

Coefficient 0.914 0.2938 0.3112 0.4119 0.1546 0.2553 0.2730 0.27579 0.2787 0.2727 0.271

p-value (a¼ 0.001) <0.001 <0.001 <0.001 <0.001 0.003 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001

Predictor 2 – – – – FOSA FOSA FOSA FOSA FOSA FOSA FOSA

Coefficient 0.00102 0.00111 0.00106 0.00110 0.00116 0.00110 0.00102

p-statistic (a¼ 0.001) 0.034 0.001 0.001 <0.001 <0.001 <0.001 <0.001

Predictor 3 – – – – – – – – Log Kow Log D PISA

Coefficient �0.00387 0.00291 0.00006

p-statistic (a¼ 0.001) 0.878 0.880 0.794

Constant 0.355 0.250 0.207 – 0.171 0.04533 0.01627 – – – –

p-value (a¼ 0.001) <0.001 0.002 0.007 0.147 0.577 0.818

PRESS-statistic 2.28 1.02 0.81 1.06 2.06 0.67 0.49 0.45 0.53 0.52 0.50

R-squared (%) 44.2 67.9 74.4 – 55.2 80.8 86.3 – – – –

R-squared (%, Pred) 16.0 62.5 69.8 – 24.0 75.5 81.8 – – – –

F-statistic 17.4 44.4 58.2 221.9 12.9 42.2 59.7 302.4 191.8 191.8 192.3

Outlier statistics Standardized residual

Hydrocodone �3.06 R R R �3.47 R R R R R R

Caffeine 1.42 2.12 R R 1.38 2.41 R R R R R

R¼ removed from sample set as a statistical outlier.

Steps proceeded from Model A to Model H, where Model H was selected, based on a minimum PRESS value. Two outliers were identified in model selection, hydrocodone and caffeine; these are shown

along with their standardized residual values. Additional possible models (I–K) are included to show the statistics generated when including additional probable predictors (e.g. Log Kow).

wa

te

rr

es

ea

rc

h4

3(2

00

9)

38

49

–3

86

13

85

8

Table 4 – Compounds that were excluded as statistical outliers, the properties that identified them as outliers, and result ifbreakthrough is nonetheless predicted.

Compound Reason for outlier status Complete data set Effect

Caffeine High average Co (1.30 nM/L) Avg Co¼ 0.72, StDev¼ 0.29 Under predicted

Erythromycin Low average Co (0.018 nM/L) Avg Co¼ 0.72, StDev¼ 0.29 Over predicted

High molar

refractivity (189 mL/mol)

Avg CV¼ 77.7, StDev¼ 28.0

Hydrocodone High 8cp value (due to cross-linking, 8cp¼ 4.66) Avg 8cp¼ 1.55, StDev¼ 1.17 Over predicted

Lopromide High molar refractivity (142 mL/mol) Avg CV¼ 77.7, StDev¼ 28.0 Over predicted

Oxybenzone No breakthrough during tests Avg Co¼ 0.72, StDev¼ 0.29 N/A

Low average Co (0.029 nM/L)

Triclosan Simple index (8cp) cannot account for influence

of two chlorinated aromatic rings

Maximum of one chlorinated

aromatic ring per molecule

Under predicted

w a t e r r e s e a r c h 4 3 ( 2 0 0 9 ) 3 8 4 9 – 3 8 6 1 3859

molecular volume (CV) was 40–87 mL/mol, 8cp was 0.13–3.3,

and halogenation was limited to one aromatic ring. For four of

the compounds, at least one of these parameters was outside

this range (italicized in Table 2). The properties of these outliers

are listed in Table 4. For example, the molecular volumes of

erythromycin (189.2) and iopromide (142.2) are nearly twice as

high as for the other compounds. Hydrocodone has an 8cp that

is nearly twice as high as for other species. Also, triclosan has

three chlorine substituents attached to its ring structure; the

influence of this would not be seen in the 8cp index.

For three compounds, oxybenzone, progesterone, and tri-

closan, breakthrough was not observed in every case. No

breakthrough of oxybenzone was observed in any test,

apparently due to the low influent concentrations (Co¼ 0.01–

0.04 mmol/L); the model thus correctly predicts that break-

through would have occurred well beyond the sampling range

of the tests herein (e.g. HD4000 w 320,000 BV, STEAM w 405,000

BV, MS w 520,000 BV). Progesterone and triclosan did not

breakthrough during the HD4000 and STEAM tests, but did

breakthrough for the MS run. For progesterone, the model

predicts that breakthrough should have been observed with

HD4000 at w18,000 BV, but breakthrough was not observed.

Then, for STEAM the predicted progesterone breakthrough is

w92,000 BV, which was past the range of sampling herein. For

triclosan the model predicts breakthrough at w9000 BV for

HD4000; and at w36,000 BV for STEAM; but no breakthrough

was observed. As indicated below, triclosan has three chlorine

substituents on rings, which apparently render it an outlier.

In follow-up studies, which could be designed to include

a broader range of compounds, scientists could include

a spectrum of parameters that extend beyond the applicable

ranges above. For example, a set of compounds could be

intentionally selected that offered a range of chlorine heter-

oatomsdfrom none to four, so as to specifically address the

impact of such heteroatoms. As a practical first step, the

authors selected the compounds herein on the basis of their

environmental significance, rather than on the basis of

a particular span of some targeted parameter.

Further understanding the adsorption of EDCs/PPCPs from

a natural water requires consideration of the natural organic

matter (NOM) present. It stands to reason that the NOM in this

surface water (3.0 mg/L TOC) competed with the target

compounds by both occupying adsorption sites and blocking

access to pores. Because competition with NOM is strongly

size-dependent (Newcombe et al., 2002), the low variation in

molecular volume for the fitted compounds (average¼ 72 mL/

mol, standard deviation¼ 13 mL/mol) likely caused similar

levels of NOM competition for all EDCs/PPCPs. Because only

the level of NOM (as TOC) was measured herein, it was well

beyond the scope of this study to appraise the influence of

NOM character.

4. Conclusions

For 23 compounds, BVs to initial breakthough showed good

correlation with a relationship including GAC total pore

volume (TPV), GAC mini-column apparent density (rmc), GAC

slurry pH (pHs), volume of compound adsorbed (CV� Co), the

compound’s 8th-order simple Chi index (8cp), and the com-

pound’s hydrophobic surface area (FOSA).

Clearly, as a first attempt at devising a QSAR-like model for

characterizing ppt-level sorption in activated carbon columns

under real-world NOM conditions, the model will be limited in

its breadth of application. For example, the authors herein

employed three lignite-based activated carbons and these all

had similar proportions of micropores-to-total pores (where

total pores were micropores plus mesopores). Also, the molar

concentrations of the compounds were all similar; and the

model may not well-characterize behavior outside this

concentration range. Moreover, the conforming EDCs/PPCPs

hosted somewhat similar substituent groups. Nonetheless,

this QSAR-like model offers an important first step in pre-

dicting EDC/PPCP breakthrough behavior that other scientists,

engineers, and water quality specialists can build on, as yet

more analytical data are collected.

Through the process of developing this QSAR-like model,

the authors identified more than 40 molecular descriptors that

lacked statistical significance. For example, the octanol–water

partition coefficient (Kow) was not significant, even though

log Kow has been deemed important when modeling ppm-

level sorption.

As a practical conclusion regarding the overall bed life for

removing these EDCs/PPCPs, the modified lignite carbons

(STEAM and M/S) offered bed lives of a half a year to more than

a year for many of these compounds, when they were present

at intentionally high ppt levels (i.e. higher than they often

appear in native water supplies). The conventional activated

carbon would offer a shorter bed life in comparison. This is the

case when the activated carbon performed with a 7.6 min

w a t e r r e s e a r c h 4 3 ( 2 0 0 9 ) 3 8 4 9 – 3 8 6 13860

empty bed contact time, where compound removal was

simulated by constant diffusivity.

Acknowledgements

The authors thank Norit Americas for providing the

commercial carbon sample. This work was funded through

the National Science Foundation (grant #0202177), the

Consortium for Premium Carbon Products from Coal (CPCPC),

the City of Cincinnati, OH, USA and the American Water

Works Research Foundation (AwwaRF).

Appendix

Supplementary data associated with this article can be found

in the online version, at 10.1016/j.watres.2009.05.026.

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