diversos sorbentes

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Simultaneous removal of As, Cd, Cr, Cu, Ni and Zn from

stormwater: Experimental comparison of 11 different

sorbents

Hulya Genc--Fuhrman, Peter S. Mikkelsen, Anna Ledin

Institute of Environment & Resources, Technical University of Denmark, Building 115, DK-2800 Kgs. Lyngby, Denmark

a r t i c l e i n f o

Article history:

Received 31 May 2006

Received in revised form

18 October 2006

Accepted 19 October 2006

Available online 14 December 2006

Keywords:

Heavy metals

Stormwater runoff

Secondary treatment

Sorption

Precipitation

Sorbents

A B S T R A C T

The potential of using alumina, activated bauxsol-coated sand (ABCS), bark, bauxsol-

coated sand (BCS), fly ash (FA), granulated activated carbon (GAC), granulated ferric

hydroxide (GFH), iron oxide-coated sand (IOCS), natural zeolite (NZ), sand, and spinel

(MgAl2O4) as sorbents for removing heavy metals from stormwater are investigated in the

present study. The ability of the sorbents to remove a mixture of As, Cd, Cr, Cu, Ni and Zn

from synthetic stormwater samples were evaluated in batch tests at a starting pH of 6.5.

The metal speciation and saturation data is obtained using the PHREEQ-C geochemical

model and used to elucidate the sorption data. It is found that BCS, FA, and spinel have

significantly higher affinity towards heavy metals mainly present as cationic or non-

charged species (i.e. Cd, Cu, Ni and Zn) compared to those present as anionic species (i.e. As

and Cr). However, IOCS, NZ and sand have higher affinity towards As and Cr, while alumina

has equally high affinity to all tested heavy metals. The Freundlich isotherm model isfound to fit the data in many cases, but ill fitted results are also observed, especially for FA,

BCS and GAC, possibly due to leaching of some metals from the sorbents (i.e. for FA) and

oversaturated conditions making precipitation the dominant removal mechanism over

sorption in batches with high heavy metal concentrations and pH. Calculated sorption

constants (i.e. Kd) are used to compare the overall heavy metal removal efficiency of the

sorbents, which in a decreasing order are found to be: alumina, BCS, GFH, FA, GAC, spinel,

ABCS, IOCS, NZ, bark, and sand. These findings are significant for future development of

secondary filters for removal of dissolved heavy metals from stormwater runoff under

realistic competitive conditions in terms of initial heavy metal concentrations, pH and

ionic strength.

&2006 Elsevier Ltd. All rights reserved.

1. Introduction

Urban stormwater runoff, especially road runoff, is rich

in heavy metals that, unlike organic pollutants, are not

degradable in the environment. The major sources of

heavy metals in stormwater runoff are building materials

(e.g. Cu from roofs and Zn from galvanized steel), and

traffic-related sources such as brake linings (Cu, Ni, Cr, Zn,

Pb), tire wear (Zn), and autocatalysts (Pt, Pd, Rh). Due to

short term (e.g. acute toxicity) and long-term (e.g. carcinoge-

nity and reproducing damages) adverse effects of

heavy metals in the aquatic environment, treatment of

stormwater runoff containing heavy metals receives in-

creased attention.

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0043-1354/$ - see front matter&

2006 Elsevier Ltd. All rights reserved.doi:10.1016/j.watres.2006.10.024

Corresponding author. Tel.: +4545 2516 11; fax: +45 4593 2850.E-mail address:[email protected] (H. Genc--Fuhrman).

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Stormwater best management practices (BMPs) are widely

used for handling of stormwater. Although most of the BMPs

are constructed to control the water flow in order to alleviate

peak flows and avoid flooding, more attention has recently

been directed to construct BMPs that also remove pollutants.

The most common BMPs in Denmark are ponds, basins,

wetlands and swales. Of these, some are built to infiltrate

stormwater, whereby pollutants are either accumulated in

the soil or transported to the groundwater. Others are built as

active scavengers for pollutants associated with particles. The

primary inadequacy of such systems is that the colloidal and

truly dissolved fractions cannot be removed via settling;

hence, there is a need for secondary treatment of stormwater,

after passing through a pond, basin or wetland. Filtration of

the stormwater, where both colloidal and truly dissolved

heavy metal fractions are primarily removed via sorption, is

one of the most promising technologies providing that

effective filtration media is used.

Although many studies of heavy metal removal from

stormwater using a variety of sorbents exist, they typically

only report removal percentages and do not document the

experimental conditions thus making the results impossible

to compare directly. Furthermore, the majority of previous

work on heavy metal removal using filtration/adsorption

deals with treatment in single heavy metal systems, and only

a few studies are carried out at realistic experimental

conditions in terms of stormwater (Liu et al., 2005; Pitcher

et al., 2004). However, it is important to understand and

quantify the heavy metal removal properties when they

coexist (Jang et al., 2005;Al-Asheh et al., 2000), and identical

experimental conditions are important when comparing

different sorbents. Consequently, the main objective of the

present study was to evaluate the efficiency of 11 pre-selected

sorbents/filtration materials in batch tests under well-con-

trolled experimental conditions to identify those most

promising for potential use in secondary stormwater treat-

ment systems. Co-existence of the most important heavy

metals in realistic concentrations as well as initial pH and

ionic strength are prioritized properties in this study, whereas

other properties such as the content of organic matter and

colloids are included in a later study focusing only on the

most promising sorbents.

Although a wide range of sorbents are available as possible

filtration media the following 11 are selected based on a

literature survey: activated bauxsol-coated sand (ABCS),

alumina, bauxsol-coated sand (BCS), bark, fly ash (FA),

granulated activated carbon (GAC), granulated ferric hydro-

xide (GFH), iron oxide-coated sand (IOCS), natural zeolite (NZ),

sand, and spinel (MgAl2O4). A mixture of As, Cd, Cr, Cu, Ni

and Zn is chosen, as dissolved fractions of these metals

reportedly pose environmental concerns (Makepeace et al.,

1995). Pb is excluded in this study, as Pb in stormwater is

primarily associated with suspended solids, which are

expected to be satisfactorily removed during pre-filtration

treatment (i.e. by settling). Furthermore, due to increasing

environmental concerns, Pb usage is decreased, which in turn

has significantly reduced Pb concentrations in stormwater

(Furumai et al., 2002). Other metals such as Pt, Pd and Rh were

excluded as they are available in very low concentrations of

less concern.

Among the sorbents tested, alumina and IOCS are com-

monly used metal-oxide-based sorbents for heavy metal

removal from aqueous solutions (Baumgarten and Kirchhau-

sen-Dusing, 1997;Khaoidhiar et al., 2000). Similarly, BCS and

ABCS are previously tested as cost-effective sorbents espe-

cially for As (Genc--Fuhrman et al., 2005). FA is an effective

heavy metal sorbent possibly due to its carbon, silica,

alumina and iron oxides content (Bailey et al., 1999). NZ is

widely available at low cost and has high ion exchange

capacity, thus it may have the potential to be used for

stormwater treatment (Pitcher et al., 2004). Activated carbon

(AC) and GAC are often employed for heavy metal removal

(Chen and Wang, 2000). On the other hand, GFH is effectively

used for arsenate removal (Sperlich et al., 2005) and included

here to observe whether the sorbent is capable of removing

other heavy metals. Bark is a by-product of the timber

industry and its high tannin content, especially polyhydroxy

polyphenol groups, gives bark an ion exchange ability ( Bailey

et al., 1999). Moderate heavy metal removal using bark is

reported (Palma et al., 2003), and bark is suggested as a cost-

effective alternative to AC (Al-Asheh et al., 2000). Sand is also

included in the batch tests for two main reasons: first, sand

filtration is commonly used for stormwater treatment (San-

salone, 1999), and the current study may provide data for

comparison; second, because IOCS, ABCS, and BCS are

prepared by coating a sorbent onto sand, having the removal

data for sand itself is useful for comparative purposes (i.e. to

see the effect of the coating). To the authors knowledge, no

study has yet been published using spinel for heavy metal

removal from water.

2. Materials and methods

2.1. Sorbents

The supplier information and physical characteristics of the

sorbents are provided inTable 1. Specific surface areas of the

samples were measured using the single-point-N2-BET meth-

od. Further details about the various sorbents, their physical

and chemical properties and preparation can be found from

the references cited inTable 1. Similar particle sizes of the

sorbent materials were used, as particle size may have an

effect on the removal (Smith, 1998). All sorbents were sieved

(or crushed if required) to the desired particle size (0.61 mm)and dried at 40 1C for 3 h without additional treatment before

being used in batch experiments, except for sand which was

first acid washed for 24 h using 10% HNO3. Alumina, bark, FA,

GAC, GFH, NZ, sand and spinel were commercially supplied,

while ABCS, BCS and IOCS were prepared in the laboratory.

BCS and ABCS were prepared from the alumina production

industry by-product red mud (Genc--Fuhrman et al., 2005),

while IOCS was prepared in the laboratory by coating goethite

onto sand (Lo et al., 1997). FA is a waste incineration by-

product supplied from a municipal waste incineration plant

(MSWI) in Denmark. There are several NZs but here clinopti-

lolite, the most abundant naturally occurring zeolite, was

used.

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2.2. Heavy metal concentrations

It is documented in the literature that the concentrations of

heavy metals in stormwater vary several orders of magnitude

(Makepeace et al., 1995;Walker et al., 1999). In this study, an

attempt was made to use realistic concentration ranges,

where the lowest values were below the Danish emission

limit values for discharge into fresh surface waters (DELV; see

Table 2) (DME, 1996); while, the highest values were signifi-

cantly higher representing extreme conditions such as

snowmelt or first flush concentrations after long dry weather

periods. The purpose was to evaluate and compare the

sorbents at both highly polluted and slightly polluted condi-

tions, since it is well known that some sorbents are effective

at rather high concentrations, but are less efficient and may

leach specific contaminants at low concentrations.

2.3. Batch experiments

Sorption experiments were carried out using synthetic

stormwater samples in 50 mL conical PE batches at room

temperature (2271 1C). The required concentrations of As, Cd,

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Table 1 Overview and physical characteristics of the sorbents used in the study

Sorbent Supplier pHpzca Par. size, mm Surface areab, m2/g Literature

Aluminac Haldor Topse, Denmark 9.1d 0.61.0 238.9 (Baumgarten and Kirchhausen-Dusing, 1997)

ABCS Prepared in the laboratory 8.3e 00.5 42.2 (Genc--Fuhrman et al., 2005)

Bark Zugol, Sweden 0.61.0 0.32f (Vasconcelos and Bec-a, 1997)

BCS P repared in the l aboratory

8.3

e

00.5 4.6 (Genc-

-Fuhrman et al., 2005)FA MSWI, Denmark 6.2 00.5 2.5 (Wang et al., 2004)

GAC Kemira, Denmark 6.1 0.61.0 784.5 (Marzal et al., 1996)

GFH IPSg, Germany 7.5-8 0.61.0 172.6 (Sperlich et al., 2005)

IOCS Prepared in the laboratory 9 0.61.0 1.5 (Lo et al., 1997)

NZh Euremica, UK 5.6i 01.0 24.2 (Matheickal and Yu, 1997)

Sand Dansand, Denmark 0.7 0.61.0 0.1 (Urbonas, 1999)

Spinelc,j Haldor Topse, Denmark 11k 0.61.0 11.8

a Point of zero charge.b BET-N2single point analysis.c Developed commercially as catalysts.d From (Stumm, 1992).e pHpzc of red mud (Apak et al., 1998).f From the cited literature.g

Ingenieurgesellschaft Prof. Dr. Sieker MBH.h Clinoptilolite ((Ca, Na2, K2)3[Al6Si30O72].24H20).i pHpzc is reported at the presence of cations (Feng et al., 2000).j MgAl2O4.k From (Anderson, 2005).

Table 2 Quantification limits, Danish emission limit values, runoff and initial concentrations in the batch experiments(C0) of the heavy metals.

Heavy metals and concentrations (mg/L)

As Cd Cr Cu Ni Zn

Quantif. limit 1.0 0.05 0.5 1.0 0.5 1.0DCRa 1210 0.0513,730 12300 0.061410 149,000 0.722,000

ACRb 1210 0.311 10230 6.5150 6150 16.6580

DELVc 4 5 7 12 160 110

Batch 1 o1 0.1 o0.5 2.6 0.6 22.5

Batch 2 2.6 3.0 3.5 7.6 12.3 67.6

Batch 3 14 10.4 18 36.1 58.9 340

Batch 4 23 23.1 34.2 20.4 89.9 543

Batch 5 52 221 376 588 178 1350

Batch 6 128 160 212 246 545 3400

Batch 7 396 735 784 1250 2220 13,700

Batch 8 1000 2670 2830 1820 8640 52,300

a Detected.b Average heavy metal concentration ranges reported in the literature for stormwater runoff (Makepeace, 1995).

c Danish emission limit values (DELV) (DME, 1996).

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Cr, Cu, Ni and Zn were obtained by step-by-step diluting their

commercial stock solutions at 1000mg/L in HNO3 to the

desired concentrations. The ionic strength of the water

samples was controlled using 0.01M NaCl, and all samples

had 0.003 M NaHCO3 to minimize the pH changes during

experiments. It was decided not to use a stronger buffer since

this would influence the heavy metal speciation in a way not

realistic for stormwater. After preparing the solutions with

the required heavy metal concentrations, ionic strength, and

buffer; the pH was adjusted to 6.5 using strong acid or base

solutions to resemble the lowest realistic pH observed in

stormwater runoff. Afterwards, 50 mL of the heavy metal

solutions with pH 6.5 were added to the PE flasks with 1g

sorbent to obtain a 20 g/L sorbent dosage. Then, the solutions

and the sorbents were mixed by gently shaking the batches,

and the pH was re-measured and re-adjusted to 6.5 in each

batch within a few minutes. Following the pH adjustment the

batches were capped tightly and shaken for 48h. The pH

values were checked after the completion of the shaking time

to identify possible pH variations from the starting values.

The temporal evolution of the solution pH due to sorbent-

sorbate interactions was monitored in a separate set of

experiments for 48h. The used 48h shaking time and 20g/L

solid/solution ratio may overestimate the actual removal of

metals occurring in real flow-through stormwater filters but

were used here to compare the 11 sorbents under identical

conditions. It is noted that further studies on the effects of

sorption kinetics and filter surface loading are in progress.

Later, the batches were taken from the shaker and

centrifuged for 20min at 2000rpm (894g), and finally the

samples were carefully dispensed to 50 mL PE sample cups,

acidified to pH 1.52 using strong HNO3 and stored at 4 1C

until the heavy metal measurements.

Two sets of control batches were also run. The first set

included 11 batches with different sorbents without any

heavy metal addition, following the same experimental

procedure as described previously to determine whether any

of the sorbents would release heavy metals. The second set of

control batches also followed the same experimental proce-

dure, but without adding any sorbents. This trial included 8

batches with the initial heavy metal concentrations given in

Table 2in order to quantify any losses due to sorption to the

PE flasks or additions due to contamination during the

handling of the samples. No significant metal sorption on to

the flask walls was observed and all concentrations measured

from the second set of control experiments were within75%

of the initial metal concentrations.

All chemicals used were reagent grade and used without

any further purification.

2.4. Heavy metal quantification

All the samples were sent to a certified commercial laboratory

(Analytica, Sweden) for quantification of heavy metal con-

centrations, where inductively coupled plasma atomic emis-

sion spectrometry (ICP-AES) or inductively coupled plasma

sector field mass spectrometry (ICP-SFMS) was used, accord-

ing to USEPA Methods 200.7 (modified) and 200.8 (modified),

respectively. The methods quantification limits are given in

Table 2.

2.5. Heavy metal speciation calculations

Speciation calculations for the heavy metals were carried out

using a computer model, PHREEQ-C version 2 with a

thermodynamic database (i.e. LLNL.DAT) (Parkhurst and

Appelo, 1999), at a wide pH range employing the initial heavy

metal concentrations used in the experiments. The model

was also used for identification of oversaturated conditions of

any relevant metal mineral/salt during the experiments.

2.6. Analyzing sorption data

The solid phase heavy metal concentrations, qe (mg/g), were

determined for each sorbent by analyzing the corresponding

heavy metal concentration before and after the treatment

using the equation below:

qe C0 Ce

X , (1)

whereC0 and Ceare the initial and equilibrium heavy metal

concentrations in the solution (mg/L), and X is the sorbentdosage (g/L).

Freundlich isotherm,

logqe logK 1n

logCe, (2)

whereK is correlated with the quantity of sorbate associated

with the sorbent, and n is the Freundlich isotherm constant

related to the strength of the sorption, was used to investigate

the sorption data further.

The overall efficiency of each sorbent in terms of heavy metal

removal was determined using sorption constants Kd(L/g)

Kd qeCe

, (3)

whereKdvalues were calculated corresponding to all the initial

concentrations used, and average Kd values were calculated

and used to rank the sorbents for each metal.

3. Results

3.1. pH variations during the batch experiments

The pH increased with time for all sorbents except bark,

where the pH decreased (seeFig. 1). It was accentuated that

the pH increases were mainly due to dissolution of the

sorbent minerals in the process of shaking, while for bark the

observed pH decrease was primarily attributed to the release

of natural organic matter containing both acids and phenols

(confirmed by a yellow coloring of the samples during the

batch tests).

In most cases the pH increase was moderate, e.g. from pH

6.5 to 6.8 for ABSC, while for FA a major pH shift was observed

from pH 6.5 to 10.5. It is noted that the reason that the pH is

not changed significantly for ABCS is that ABCS is prepared

from activated bauxsol (Genc--Fuhrman et al., 2004), which

was refluxed by HCl previously, and this process probably

already neutralized the surface. A final pH of 7.7 was observed

in the NZ batches due to hydrolysis of zeolites as well as

cationic exchange. Similar results are reported elsewhere

with a remark that the pH increase is almost unavoidable

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in a NZ heavy metal system (Ouki and Kavannagh, 1997;

Pitcher et al., 2004).

3.2. Heavy metal speciation and saturation conditions

The pH-dependent distributions between the dissolved metal

species (as calculated using PHREEQ-C) are presented inFig. 2

for stormwater composition corresponding to batch 5 (Table2). The results illustrate that both As and Cr are present as

anionic species; and the dominating As specie is HAsO42 in

the pH range 6.510, while Cr is mainly present as CrO42 at pH

values above 6.5. The other metals show somewhat more

complicated patterns, for example the most important Cd

species are CdCl+ and Cd2+ below pH 9.5, whereas Cd(CO3)22

starts dominating above pH 10. The non-charged CuCO3is the

dominating Cu-specie in the pH range 6.59.0, while the

negatively charged species such as Cu(CO3)22 and

CuCO3(OH)22 dominate at higher pH values. The dominating

Ni-specie is Ni2+ until about pH 9.5, succeeded by the

hydroxide species Ni(OH)2and Ni(OH)3 at higher pH. Zinc is

mainly present as Zn2+ below pH 8.5 and as Zn(OH)2 atpH49.0.

It is evident from the results presented in Table 3that the

number of oversaturated minerals/salts is increasing with

increasing pH and increasing initial heavy metal concentra-

tion. Therefore, sorption may no longer be the primary

removal process in those batches, since precipitation of

minerals/salts on both the sorbents and the walls of the PE

flasks may also occur.

3.3. Equilibrium concentrations and sorption isotherms

The results of simultaneous removal of Cd, Cu, Ni, and Zn

using the tested sorbents are presented in Fig. 3on a double

logarithmic scale, while the results for As and Cr are

presented inFig. 4on a double or semi logarithmic scale. In

these figures, the equilibrium heavy metal concentrations (Ce)

are depicted on the horizontal axis vs. the solid phase metal

concentrations (qe) on the vertical axis. As expected the

amount of heavy metal removed increased with increasing

initial heavy metal concentration in nearly all of the batches,

except a few cases where leaching was observed for As and Cr

(hence the semi-logarithmic depiction). Leaching of As was

observed when using bark, BCS, GAC, FA, NZ and sand, while

leaching of Cr was observed when using ABCS, BCS, FA and

spinel. It is highlighted that leaching of As and Cr was also

observed in the control batches involving sorbents but no

heavy metal addition. For example, 7, 114, 10 and 9 mg/L As

leaching was observed from Bauxsol, FA, GAC and NZ,

respectively. In the case of Cr, leaching was 137, 124, 40, 8

and 20mg/L from ABCS, FA, NZ, sand and spinel, respectively.

The calculated Freundlich isotherm parameters (Kandn, cf.

Eq. (2)) are shown inTable 4. Although a high correlation was

observed in some cases, the r2 values were low especially for

bark, BCS and FA. Fitting Langmuir isotherms to the data did

not improve the correlation. It is noted that here although

r2X0:5 signifies a statistically significant correlation, using

the estimated Freundlich isotherms for prediction is consid-

ered highly uncertain unless r2 close to 1.0 is reached.

3.4. Sorbent ranking

The calculated averageKdvalues for each batch are also given

in Table 4, while the overall results are presented in Fig. 5.

Each tested sorbent demonstrates different removal effi-

ciency for each heavy metal, for example, while IOCS

effectively retains As, it fails to repeat its performance for

Cd and Ni. Similarly, FA has very high affinity for Cd and Ni,

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Fig. 1 The variation of pH values as a function of time during the sorption experiments with starting pH of 6.5, initial As, Cd,

Cr, Cu, Ni and Zn concentrations of 1 mg/L, sorbent dosage of 20 g/L, ionic strength of 0.01 M NaCl and 0.003 M HCO3 buffer.

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but it is ineffective for As and Cr (leaching). However, alumina

is consistently effective for all heavy metals tested. Conse-

quently, the overall heavy metal removal efficiency of the

sorbents at the experimental conditions in a decreasing order

is, alumina4BCS4GFH4FA4GAC4spinel4ABCS4IOCS4

NZ4bark4sand.

4. Discussion

4.1. Alumina

At the experimental conditions used alumina is capable of

removing all heavy metals to concentrations below the DELV

except for the batches with the highest concentrations of Cd,

Cu and Zn. This high efficiency is probably due to aluminas

high surface area (Table 1) and favorable heavy metal surface

interactions.

The dominant heavy metal species in the alumina system

(420% at pH 7.4, cf.Fig. 2) are HAsO42, CdCl+ and Cd2+, CrO4

2,

CuCO3, Ni2+ and Zn2+. The alumina surface is expected to be

positively charged at the experimental pH (note that the pHpzcof alumina is about 9, but decreases when forming hydro-

xides), which enhances electrostatic removal of the nega-

tively charged HAsO42 and CrO4

2 species. On the other hand,

alumina performed equally well for the heavy metal cation

species, which can be explained by other removal mechan-

isms like surface complexation (hydroxide groups), pore

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Fig. 2 Speciation of As, Cd, Cr, Cu, Ni and Zn in water obtained using the PHREEQ-C model with ionic strength of 0.01 M NaCl,

0.003M NaHCO3buffer and heavy metal concentrations according to batch 5 (seeTable 2).

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diffusion and specific adsorption. For batch numbers 58

precipitation of Cu, Cd and Zn minerals/salt may also be

counted as a relevant removal process (oversaturation, cf.

Table 3).

4.2. Activated bauxsol-coated sand and bauxsol-coatedsand

ABCS efficiently retains As to values below the DELV

independent of the initial As concentration, and it is also

one of the most efficient sorbents for Cu. However, the

sorbent fails to remove Cr, Cd, Ni, and Zn as efficiently as As

and Cu.BCS performs better than ABCS for removing all metals

except for As. This can be explained by the negatively charged

BSC-surface at the experimental pH (8.6) compared to that of

ABCS (6.8). Consequently, the electrical attraction increases

the removal of positively charged metal ions. Furthermore,

the surface complexation between dissolved species and

oxide- and hydroxide groups on the surface will increase with

increasing pH. Oversaturation occurs only in batches 7 and 8

for ABSC (Table 3), thus sorption may be the dominant

mechanism for ABCS. On the other hand, for BCS it is

expected that e.g. Zn minerals/salts can precipitate in all

batches except for batch 1, and that Cd and Cu minerals/salts

may precipitate for batches 38. It can, therefore, besuspected that precipitation and not sorption is the major

removal process for Zn, Cd, and Cu in the BCS batches.

4.3. Bark

Bark suffered from low heavy metal removal efficiency (see

Fig. 5), and minor As and Cr leaching is observed especially at

low initial heavy metal concentrations (see Fig. 4). Further-

more, water discoloration is observed most likely due to the

release of organic acids and phenols. Among the metals,

especially Cu is known to form strong complexes with natural

organic acids, which can reduce the sorption (Duker et al.,

1995;Dario and Ledin, 1997), and the low averageKdobserved

for Cu is, therefore, probably attributed to this phenomenon.

Considering the low affinity of the bark to the tested heavy

metals and the leaching of organic matter as well as As and

Cr, bark may be considered unsuitable as a secondary

treatment filtration media.

4.4. Fly ash

FA has high affinity for Ni, Zn, Cu and Cd, which at the high

pH during the experiments with FA mainly are present non-

charged or anionic species (i.e. Ni(OH)2, Zn(OH)2, Cd(CO3)22,

Cu(CO3)22). In addition, oversaturated conditions and conse-

quently precipitation of Cd, Cu, Ni and Zn minerals/salts (seeTable 3) will also contribute to the overall removal. The results

indicate that FA has low affinity to Cr and As species.

However, this is due to the considerable leaching from the

sorbent (seeFig. 4b). Despite the high removal efficiency of FA

towards Cd, Cu, Ni and Zn, FA is unsuitable for secondary

treatment of stormwater due to the leaching of As and Cr and

the high effluent pH.

4.5. Granulated activated carbon

GAC is the most effective sorbent for Cr and it has moderate

to high efficiency for Cd, Cu, Ni and Zn. On the other hand, it

has a significantly lower efficiency for As compared to that ofthe other heavy metals (Fig. 5). Oversaturated conditions of

Zn-, Cd- and Cu- minerals/salts may also contribute to the

removal efficiency in the GAC batches in the same manner as

in the BCS batches (Table 3).

4.6. Granulated ferric hydroxide

GFH was the third most effective sorbent when ranked

according to average Kd values (Fig. 5). Previously GFH has

been advocated as a highly efficient sorbent especially for

arsenate removal (Sperlich et al., 2005), but here GFH

demonstrated moderate As removal efficiency and is out-

performed by alumina, IOCS, ABCS, and spinel. This may be

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Table 3 Oversaturated heavy metal minerals/salts, e.g. SI40, where SI is defined as log(IAP/Ks) in the batch experiments,estimated by PHREEQ-C modelling with ionic strength of 0.01M NaCl and 0.03 M NaHCO3buffer

pH Batch 1 Batch 2 Batch 3 Batch 4 Batch 5 Batch 6 Batch 7 Batch 8

5.8 (Bark) Zn, Cd, Cu

6.8 (ABCS) Zn, Cd, Cu Zn,Cd, Cu

7.2 (Sand) Cu, Cd, Zn Zn, Cd, Cu Zn, Cd, Cu7.4 (Alumina) Cu, Cd Zn, Cd Zn, Cd, Cu Zn, Cd, Cu

7.5 (IOCS) Zn, Cd, Cu Zn, Cd, Cu Zn, Cu, Cd Zn, Cd, Cu

7.7 (NZ) Zn, Cd, Cu Zn, Cd, Cu Zn, Cd, Cu Zn,Cd, Cu

7.8 (GFH) Zn, Zn, Cd, Cu Zn, Cd, Cu Zn, Cd, Cu Zn, Cd, Cu

8.6 (BCS, GAC) Zn Zn, Cd, Cu Zn, Cd, Cu Zn, Cd, Cu Zn, Cd, Cu Zn, Cd, Cu Zn, Cd, Cu, As

8.7 (Spinel) Zn Zn, Cd, Cu Zn, Cd, Cu Zn, Cd, Cu Zn, Cd, Cu Zn, Cd, Cu,

Ni

Zn, Cd, Cu, Ni, As

10.6 (FA) Cu Ni, Cd, Cu Zn, Ni, Cd,

Cu

Zn, Ni, Cd,

Cu

Zn, Ni, Cd,

Cu

Zn, Ni, Cd,

Cu

Zn, Ni, Cd,

Cu

Zn, Ni, Cd, Cu, As

W AT E R R E S E A R C H 41 (2007) 591 602 597


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due to the pH differences, as the literature reports higher

arsenate removal efficiencies using GFH at the pH values

below 7.5, e.g. below pHpzc(seeTable 1) while the equilibrium

pH here was 7.8.

Despite its medium As removal efficiency, GFH is the

second most effective sorbent for Cr and it is also effec-

tive for Cd, Ni and Zn. The dominant heavy metal species

at pH 7.8 are HAsO42, CdCl+ and Cd2+, CrO4

2, CuCO3, Ni2+,

and Zn2+ (Fig. 1). The pH is close to pHpzc, e.g. both posi-

tively and negatively charged groups are available at the

sorbent surface. This indicates that the electrostatic

attraction as well as surface complexation can be impor-

tant removal mechanisms. Moreover, the saturation calcula-

tions indicate that Cd, Cu and Zn could also be removed

due to precipitation of their minerals/salt in batches 58 (see

Table 3).

4.7. Iron oxide-coated sand

IOCS has moderate to poor heavy metal removal efficiency

compared to that of the other sorbents, except from bark,

sand and NZ (Fig. 5). Interestingly, IOCS is the second most

ARTICLE IN PRESS

Fig. 3 Cd, Cu, Ni and Zn removal using (a) sorbents with high heavy metal removal efficiency, (b) sorbents with moderate or

low heavy metal removal efficiency; with starting pH of 6.5, sorbent dosage of 20 g/L, ionic strength of 0.01M NaCl, and

0.003M NaHCO3buffer. DELV is Danish emission limit values for fresh surface waters.

W AT E R R E S E A R C H 4 1 ( 2 0 0 7 ) 5 9 1 6 0 2598


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4.11. Adsorption isotherms

Freundlich and Langmuir isotherms are commonly used in

the literature to study heavy metal sorption. Here, it was

observed that the Freundlich isotherm fitted the data better

suggesting heterogeneous surface characteristics. When

evaluating FA, spinel, and BCS (sorbents with high end pH)

sorption isotherms gave lower r2 values especially for Zn, Ni,

Cu and Cd (seeTable 4). In these systems, precipitation may

play a role as removal mechanism besides adsorption, which

in turn may have decreased the capability of the Freundlich

isotherms to predict the removal.

Leaching of e.g. As from FA and bark could also provide

explanation on why the isotherm did not explain the data

very well.

5. Conclusions

In this study, 11 sorbents are examined in batch tests as

potential filter media for secondary treatment of stormwater.

The sorbents are tested in for the combined removal of As,

Cd, Cr, Cu, Ni and Zn removal from synthetic stormwater

samples at a starting pH of 6.5. However, the pH values are

changed to 5.8 (bark), 6.8 (ABCS), 7.2 (sand), 7.4 (alumina), 7.5

(IOCS), 7.7 (NZ), 7.8 (GFH), 8.6 (BCS, GAC), 8.7 (spinel) and 10.6

(FA) during the experiments, suggesting that in a secondary

stormwater treatment system with sufficient reaction time,

the pH of the discharged water is controlled by the sorbent

material rather than the inlet water composition.

It is found that the magnitude of the heavy metal removal

depends on the initial heavy metal concentration and varies

to a great extent among the sorbents. For example, while

sand and bark fail to remove all heavy metals to meet the

DELV; alumina, BCS, GFH and GAC can efficiently remove the

heavy metals down to the DELV. The results from the present

study also show that the heavy metal speciation and the

surface charge are pH dependent, and that several possible

removal mechanisms interact in a complex manner.

The Freundlich isotherm is applied to the sorption data and

the constantsK and n are presented. Although the isotherm

fit the data in many cases, ill fitted results are also observed

especially for FA, BCS and GAC possibly due to leaching of

some metals from the sorbents and oversaturated conditions

making precipitation the dominant removal mechanism over

sorption in batches with high heavy metal concentrations

and pH.

The sorbents are ranked using average Kd values, and

it is found that when overall heavy metal removal is

considered alumina is the most effective followed by BCS,

GFH, FA, GAC, spinel, ABCS, IOCS, NZ, bark, and sand.

It is highlighted that despite the high affinity of FA, NZ

and bark to some heavy metals leaching of especially As and/

or Cr limits their usage. The current study provides important

new information on the efficiency of a range of potential

sorbents in a multiple sorbate system using experimental

conditions, which resemble those of real life stormwater

runoff in terms of initial heavy metal concentration, pH, and

ionic strength.

ARTICLE IN PRESS

Table4Freundlichisotherm

constan

ts(Kandn,cf.Eq.(2))andaverageKd

valuesa

Sorbents

Heavymetals

As

Cd

Cr

Cu

Ni

Zn

K,

L/mg

n

r2

Kd

a,

L/g

K,

L/mg

n

r2

Kd

a,

L/g

K,

L/mg

n

r2

Kd

a,

L/g

K,

L/mg

n

r2

Kd

a,

L/g

K,

L/mg

n

r2

Kd

a,

L/g

K,

L/m

g

n

r2

Kd

a,

L/g

Alumina

1.6

108

0.0

1

0.9

3

5.1

1

6.3

5

0.8

6

0.9

1

6.8

4

0.0

5

0.6

0

0.9

3

0.0

8

0.0

6

0.5

3

0.80

0.8

9

1.4

6

0.6

2

0.7

3

7.4

7

0.05

0.5

4

0.5

7

3.1

5

ABCS

0.3

9

0.2

0

0.6

3

2.2

6

0.3

5

1.0

6

0.6

0

1.1

5

0.0

1

0.9

3

0.8

6

0.0

1

1.0

2

0.7

6

0.58

3.3

6

0.6

1.5

7

0.9

1

0.1

5

0.99

1.3

2

0.6

7

0.6

1

Bark

0.3

8

8.7

4

0.0

9

0.0

0

0.2

6

1.1

5

0.7

8

0.2

2

0.0

6

1.0

4

0.9

6

0.0

2

0.0

9

0.4

2

0.93

0.0

3

0.1

8

1.3

1

0.8

0

0.2

0

0.0

01

0.6

3

0.8

7

0.0

7

BCS

0.0

6

0.7

8

0.5

9

0.0

9

11.1

7

0.8

6

0.5

3

34.7

4

0.1

2

2.8

1

0.3

8

0.0

0

0.3

3

0.4

7

0.32

5.4

4.4

5

1.1

2

0.6

9

7.5

8

3.59

1.2

1

0.2

0

8.3

7

FA

4.3

104

0.4

7

0.5

3

0.1

1

5.0

1

0.6

4

0.5

3

12.3

5

0.0

3

0.1

0.4

6

0.20

2.9

6

0.0

4

0.3

0

0.5

5

8.5

4

0.04

0.4

2

0.8

5

6.3

3

GAC

0.0

9

1.1

8

0.5

5

0.0

2

1.4

5

1.1

0.8

3

2.3

8

0.3

3

0.9

3

0.8

0.7

3

0.3

7

0.8

0.76

1.2

8

0.9

7

0.9

4

0.7

4

2.0

0

0.91

0.9

9

0.5

4

1.3

7

GFH

0.1

3

0.6

5

0.8

7

0.4

9

2.7

4

0.5

3

0.8

9

5.6

4

0.0

3

0.6

0.7

6

0.3

9

0.2

6

0.7

8

0.61

0.8

9

0.1

4

0.5

3

0.5

3

1.8

1

1.21

0.8

5

0.8

1

2.8

7

IO

CS

0.9

3

0.1

9

0.3

8

3.1

6

0.0

7

1.2

5

0.8

9

0.0

6

0.0

4

1.1

6

0.9

6

0.0

5

0.4

1

1.2

9

0.64

0.3

2

0.1

7

2.1

3

0.5

7

0.0

1

0.24

1.3

4

0.9

1

0.0

7

NZ

0.0

4

0.7

9

0.8

0.0

5

0.2

1

1.0

7

0.9

4

0.2

4

0.0

4

1.6

2

0.8

0

0.0

2

0.1

3

0.6

8

0.76

0.7

9

0.5

0

1.4

3

0.9

7

0.1

1

0.36

1.1

5

0.9

8

0.1

9

Sand

0.0

4

1.0

9

0.8

3

0.0

4

0.4

2

3.5

7

0.2

7

0.0

0

0.0

4

1.7

3

0.7

9

0.0

1

0.1

0

1.5

7

0.59

0.0

5

0.1

4

2.9

8

0.4

2

0.0

0

0.03

1.3

7

0.8

5

0.0

0

Spinel

0.2

9

0.5

5

0.5

3

1.0

4

1.7

6

1.1

7

0.6

5

5.1

7

0.0

5

1.2

4

0.6

2

0.0

1

0.0

8

0.6

3

0.71

0.7

9

1.6

7

1.3

3

0.5

2

3.9

3

2.55

1.2

2

0.6

3.2

1

a

AverageofKd

values.

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Acknowledgements

This work is funded by Danish Technical Research Council

(Grant no.: 26-03-0326). Laboratory technician Susanne Kruseand the M.Sc. students Yushan Zhou and Peng Wu are

acknowledged for their contribution during the experiments.

Thanks are also to Haldor Topse A/S, Kemira Denmark and

Ingenieurgesellschaft Prof. Dr. Sieker MBH for supplying the

alumina, GAC, and GFH, respectively.

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diversos sorbentes