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WATER RESEARCH A Journal of the International Water Association

Editor-in-Chief Mark van Loosdrecht Delft University of Technology Department of Biochemical Engineering Julianalaan 67 2628 BC Delft The Netherlands Tel: +31 15 27 81618 E-mail:[email protected]

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Influence of salt, pH and polyelectrolyte on the pressure electro-dewatering of sewage sludge M. Citeau, O. Larue, E. Vorobiev* Laboratoire de Transformations Inte´gre´es de la Matie`re Renouvelable, Universite´ de Technologie de Compie`gne, BP 20529, 60205 Compie`gne Cedex, France

article info

abstract

Article history:

This paper deals with the influence of pH, salt and polyelectrolytes on the electro-dew-

Received 9 June 2010

atering (EOD) of agro-industrial sludge at 3% w/w of dry matter. Initially, a selection of

Received in revised form

polyelectrolyte types and doses was carried out for mechanical dewatering tests. Subse-

3 January 2011

quent EOD tests were carried out in a laboratory two sided filter press at constant electric

Accepted 4 January 2011

current density of 80 A/m2 and at pressure of 5 bar. It was found that whatever was the

Available online 13 January 2011

initial value of pH, salt content or polyelectrolyte type, the EOD progressed always towards the same equilibrium point at around 50% w/w of dry matter. EOD rate and energy input

Keywords:

was not affected by the presence of polyelectrolyte whatever was its charge density and

Electroosmotic dewatering

molecular weight. However, EOD rate and specific energy consumption and repartition of

Pressure consolidation

liquid at anode and cathode sides were strongly influenced by the salt content (adjusted by

Sludge

Na2SO4) or by the initial pH (adjusted with H2SO4 or NaOH). EOD performed better at lower

Conditioning

salt content and at slightly acid pH. In optimum conditions, the process (EOD) required 2 h

Polyelectrolyte

to reach dry matter of 40% w/w with specific energy consumption of 0.25 kWh/kg of water

Salt

removed for the treatment of conditioned sludge. For comparison, compression without electric field at 5 bar required 11 h to reach 22% w/w of dry matter. This work emphasizes and demonstrates that the electrolytic hydroxide and hydronium ions formed at the electrodes have considerable influence in the course of EOD. ª 2011 Elsevier Ltd. All rights reserved.

1.

Introduction

Disposal of sewage sludge is troublesome for municipalities and industries. It contains a lot of water which accounts for high cost of transport and disposal. High water retention in sludge comes from extracellular polymer substances (EPSs) produced by bacteria during sewage process. EPSs are constituted of polysaccharides, proteins and lipids. EPSs form negatively charged polymer network which is highly hydrated (Mikkelsen and Keiding, 2002). Sludge is initially dewatered by filtration-compression or centrifugation and the obtained cake is eventually dried. In order to improve the mechanical

dewatering, the sludge is conditioned with positively charged organic or inorganic polymers (polyelectrolyte). It reduces the stabilizing effect of EPS and promotes the co-aggregation of particles and EPSs in the sludge. Despite of the conditioning, the sludge keeps a gel like structure and extremely high compressibility which makes the dewatering quite difficult. For instance, sludge after thickening contains 1e5% w/w of dry matter. After a filtration/consolidation, the typical dry matter is only 15e25% w/w with a belt filter and 25e35% w/w with a filter press. Thus, new technologies are required to remove more water and reduce the downstream costs. Some alternatives were

* Corresponding author. Tel.: þ 33 3 44 23 52 73; fax: þ 33 3 44 23 19 80. E-mail address: [email protected] (E. Vorobiev). 0043-1354/$ e see front matter ª 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2011.01.001

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explored consisting in intensifying the mechanical dewatering processes: by local heating of filter cake (Couturier et al., 2007), by ultrasounds or electric field (Tarleton and Wakeman, 1990), etc. The enhancement of conventional pressure consolidation by an electric field, known as electroosmotic dewatering (EOD), gave some encouraging results for sewage sludge (Friehmelt and Gidarakos, 1996; Barton et al., 1999; Mujumdar and Yoshida, 2008). After building mechanically a filter cake, an electric field can be applied between two electrodes surrounding the filter cake. An electroosmotic flow is created by the motion of charged liquid relative to the fixed layer of charged particles. Then the electroosmosis effect is able to remove more water from the cake. Researchers from CSIRO (Commonwealth Scientific and Industrial Research Organization) reported results obtained with a bench-scale filter press for different qualities of sewage sludge. The sludge could be dewatered up to 40% w/w of cake solids against 15e31% without any current (depending on the type of sludge). However, differences of energy consumption between the sludges were significant: from 0.06 kWh/kg to 0.1 kWh/kg of water removed. In addition, the sludge shows markedly distinct responses to the various selected dewatering parameters: the flocculants dose, the applied pressure, the timing of application of the electrical power and the addition of fibrous materials or electrolytes (Miller et al., 1998). Electrical devices are easily adaptable to the technology of industrial filters such as plate or diaphragm filter-presses (Kondoh and Hiraoka, 1990; Saveyn et al., 2006) and belt filter press (Smollen and Kafaar, 1994; Miller et al., 1999; Raats et al., 2002). Mahmoud et al. (2010) in recent review of this technology point out that some difficulties still subsist for a widespread commercialization. The continuous application of electric field is rather criticized for its high-energy consumption sometimes as high as thermal one when parameters are not optimized. Besides, EOD requires corrosion resistant electrodes which may be costly. Then, to be viable at large commercial scale, it is needed to better understand key roles of each EOD parameter on the dewatering rate, dewatering extent and the energy consumption. It will allow proposing realistic methods for sludge chemical conditioning likely to improve EOD, adaptations of the dewatering machines like filter press and accurate sizing of the generators. Some solutions for decreasing the energy consumption can be explored through the electrical treatment itself: timing of electric field application and energy input distribution in time. Friehmelt et al. (1995) showed that mechanical dewatering should precede the electro-pressure dewatering. Different electrical modes are possible: constant current, constant voltage and variable current and voltage. The electrical current interruptions and electrode polarity reversals were also reported to be efficient for decreasing the total energy input (Rabie et al., 1994; Gopalakrishnan et al., 1996; Yoshida, 2000). Energy may be saved also acting on the electrical charge of particles surface and on the ions in the interstitial liquid of material. It can be varied through addition of salts and polyelectrolyte. Researchers already reported strong effects of electrolytes and pH on the EOD (Lockhart, 1983; Miller et al., 1998). The influence of polyelectrolyte on the EOD has not yet been well clarified. Lockhart (1983) showed for kaolinite and

clays that cationic flocculants slow down the EOD flowrate. Besides, Dussour et al. (2000) reported that surfactants and anionic polymers increase the speed of EOD for kaoline filter cake. But, concerning sewage sludge, results are more contrasted. Smollen and Kafaar (1994) claimed that addition of polyelectrolyte provides an excess charge in the liquid phase playing in favor of EOD. Kondoh and Hiraoka (1993) observed that by using polyaluminum chloride PAC injection method, the clogging of filter media can be avoided and the treatability of EOD is improved about 1.5e2 times and power consumption decreased by around 50% compared to EOD without PAC. On the contrary, Saveyn et al. (2005) did not notice any marked difference of EOD for a large range of polyelectrolytes. This work aims at showing the effect of polyelectrolytes with different charge density and molecular weight and also the effect of salt and pH on EOD. Procedure of selection of polyelectrolyte types and doses is first detailed in this paper. It was carried out through full mechanical dewatering tests including drainage and compression.

2.

Material

The activated sludge comes from an agro-industrial factory (France). Its dry matter content was 1e3% w/w, it had an average pH 5.7 and an electrical conductivity ranging between 2.4 mS cm1 and 4.6 mS cm1. The size of particles, measured by Mastersizer X (Malvern Instrument) was on average 234 mm (10% inferior to 55 mm and 90% inferior to 470 mm). The zeta potential of particles, measured by Zetasizer (Malvern Instrument), depends on the pH as shown in Fig. 1. Isoelectric point corresponding to a zero zeta potential is obtained at pH 2.5. In alkaline conditions, hydroxide ions adsorb on particles and increase their negative surface charge. The sludge was stored at 4  C. For each test, a sample of 200 mL was maintained for 30 min in a worm water to reach a temperature around of 20  C before conditioning. Table 1 gives an overview of the polyelectrolytes supplied by SNF

Fig. 1 e Evolution of the zeta potential of sludge particles with the pH. The error bars represent the standard deviation.

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Table 1 e Characteristics of the polyelectrolytes used for the sludge conditioning (SNF products). Product

Mw(Da)

Charge density

Flosperse 9000 Flosperse 1000 Floquat FL4820 Flopam FO4990SH Flopam EM840TBD

19 600 83 500 Low Mw High Mw High Mw

Highly Highly Highly Highly Highly

anionic anionic cationic cationic cationic

State Dispersion Dispersion Dispersion Powder Emulsion

FLOERGER (Andre´zieux, France). For the modifications of pH, sulfuric acid and sodium hydroxide were used. Sodium sulfate was used for modifying the electrolyte concentration.

sludge is homogenous. Then, the quantity of sludge deposited on the filter corresponds to the same quantity of filtrate recovered. In that case, the ratio S of volume of cake VC over the volume of filtrate V remains identical at any time: S ¼ ðVCN =VN Þ ¼ ðVC =VÞ

dV rg ½V0  ð1 þ SÞV
¼ SaV dt m þ Rm A

    VN þ VCN V V t¼ KA  ð1 þ gÞln 1  VN VCN VN VN

3.1.

Conditioning

with

Drainage under gravity

A first work consisted in finding right doses of polyelectrolyte yielding close mechanical dewatering performances (Olivier et al., 2004). The conditioned sludge was filtered under gravity in a vertical cylindrical cell of 200 cm3 (cross sectional area of 24.6 cm2). The filter cloth was made from nylon with an aperture of 1 mm (Sefar Fyltis, Nitex 03-1/1). A sludge quantity of 190 mL was introduced slowly in the cell. Afterwards, to start the drainage, the valve of filtrate pipe was opened and the weight of filtrate collected on a balance was recorded by a computer. Model of filtration under gravity was used to calculate the specific cake resistance a of conditioned sludge. Two hydraulic resistances govern the filtrate flowrate: the filter cake and filter cloth. So the Darcy law can be written as: 1 dV 1 P ¼ A dt m ðahc þ Rm Þ

(1)

where A is the filter surface, V is the filtrate volume, Rm is the filter cloth resistance (Rm ¼ 3.1$109 m1), hC is filter cake height and a  hC represents the filter cake resistance. The pressure of filtration P depends on the sludge height which corresponds to the height of cake and height of the suspension above the cake hS:   V0  V  VC A

P ¼ rghS ¼ rgðh  hC Þ ¼ rg

(2)

V0 in Eq. (2) is the initial volume of sludge and VC is the volume of filter cake (VC ¼ A$hC). Severin and Grethlein (1996) assumes that liquid and solid phase are incompressible and that the

(4)

Severin and Grethlein (1996) obtained a model of drainage under gravity by integrating the Eq. (4).

Methods

3.2.

(3)

where VCN and VN are the volume of filter cake and filtrate at infinite time, respectively. Substituting P in Eq. (1) by Eqs. (2) and (3) yields:

3.

Polyelectrolyte solution was prepared from the products of Table 1 diluted in water at 10 g/L; 3 g/L and 5 g/L for the dispersions, the powders and the emulsions, respectively. Polyelectrolyte solution was added to the 200 mL of sludge. Quantity of polyelectrolyte solution was fixed according to the wanted dose of polyelectrolyte in g of active polyelectrolyte/kg of dry sludge (g/kg DS). The blending was stirred at 200 rpm for 20 s and at 120 rpm for 40 s to promote the floc growth using the Jar Test 11197 (Bioblock Scientific).

2169

K¼

rg ; ma

g¼

(5)

ARm VCN a

This relation gives the evolution of the volume of filtrate in the time. Drainage was performed until the filtrate flowrate becomes negligible, that is, after 6 h. The volume of the cake at infinite time VCN was considered as the total volume of sludge remaining in the filter cell after 6 h.

3.3.

Compression and electro-compression tests

The experimental setup for compression or pressure electrodewatering is presented in Fig. 2. It comprises a cylindrical laboratory filter-press cell (cake cross section: 24.6 cm2), a DC power supply Consort E861 (0-1A; 0e600 V), a multimetre Fluke 45, two precision balances and a peristaltic pump. The filter-press cell is made from insulating material to avoid passage of current from the walls. For non conditioned sludge, the filter cell loading was made by peristaltic pump under pressure of 100 kPa. The flocs of conditioned sludge were too large for such a feeding. That is why the conditioned sludge was drained for 1 h in a Buchner, preliminary to EOD. The filter press cell is then manually filled with 67 mL of drained sludge. The initial sludge thickness in the cell was 2.7 cm (dry matter of 5.5  0.5 g). The sludge was covered at each cell side by filter clothes of 1 mm aperture (Sefar Nitex 031/1). The DSA electrodes are disc shaped made from ruthenium coated titanum. They are manufactured by Industrie De Nora (Italy) and supplied by ECS (Electro Chemical Services, France). One is placed behind the filter cloth at cathode side. The other is mounted flush with the piston behind the filter cloth. For tests of compression only, constant pressure 500 kPa was applied to the piston. For EOD tests, the constant pressure was combined to a simultaneous current intensity of 0.2 A (that is, a current density of 80 A/m2). Experiments were monitored by a PC through an IEEE interface. Parameters sampled by the computer are: the filtrate weights at the cathode and anode sides, the voltage and the current intensity. The temperature of the filter cake was also measured by a thermocouple which was placed at the cathode side in

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Fig. 2 e Schematic presentation of the experimental setup. Lab filter press was used for compression without electric field or for electro-compression.

the cake. Sludge and filter cake dry matters were determined after drying for 24 h at 105  C. The EOD results were analyzed through the evolution of dry matter and the specific energy consumption in kWh/kg of water removed. The electrical conductivity of sludge and pH of filtrates were measured with a multi-parameter analyzer Consort C532 (Fischer Bioblock Scientific) at a temperature of 25  C. Experiments were repeated twice in order to confirming the tendencies. The curves represent average results and the error bars represent the standard deviation.

4.

Results and discussion

4.1.

Selection of the polyelectrolytes

Fig. 3 presents pictures of sludge conditioned with increasing doses of the polyelectrolytes mentioned in Table 1. First three columns show the sludge conditioned with cationic polyelectrolytes: Flopam FO4990SH (high Mw), FL4820 (low Mw) and Flopam EM840TBD (high Mw). FO4990SH and FL4820 are linear chain polymers. EM840TBD has a cross-linked backbone. Pictures show that at same dose, larger flocs were produced with FO4990SH compared to the other tested polyelectrolytes. It is known from previous studies that a polyelectrolyte having a high Mw and linear chain like FO4990SH tends to fix on specific particle sites and aggregate particles by chemical bridges. Most of the polymer remains free in the liquid. Low doses around 3e8 g/kg DS are usually enough to trigger off flocculation by bridging of sewage sludge (Saveyn et al., 2008). This is confirmed with FO4990SH. Distinct flocculation appeared for 3e4 g/kg DS. The low Mw cationic polymers like FL4820 proceed rather by charge neutralization and electrostatic patching for flocculating the particles. This flocculation mechanism requires higher dose of flocculent (Hogg, 2000). The flocs formed are smaller as seen in Fig. 3 (column 2). A maximal flocculation was

reached at 12 g of FL4820/kg DS. Above it, a re-stabilization of the sludge was observed presumably because of the reversal of the charge of particles active sites to positive. This confirms the mechanism of charge neutralization with low Mw cationic polymers. EM840TBD polymer with cross-linked backbone probably gives rise to both mechanisms: charge neutralization and particles bridging. As a result, the required dose for flocculation was much higher than FO4990SH (10 g/kg DS for EM840TBD vs. 4 g/kg DS for FO4990SH). But, the flocculation was more resistant than FO4990SH to shear stress and flocs looked more compact. Combining FL4820 to FO4990SH yielded similar flocculation as EM840TBD. Flocs with 12 g of FL4820/kg DS þ 2e4 g/kg DS of FO4990SH were actually comparable to those with 10e12 g of EM840TBD/kg DS. Finally, highly cationic and high Mw polymers looked much more efficient for flocculating this sludge. But, it may be assumed that their high cationic charge risks neutralizing the particles and therefore alter the EOD step. It was demonstrated in previous works that the decrease of particles surface charge (in absolute value) is actually assorted to a decline of EOD (Iwata et al., 1991). To avoid this supposed drawback, anionic low Mw polymers Flosperse 1000 and 9000 were used here to add more negative charges to the particles. On the contrary of cationic polymers, Flosperse at 5 g/kg DS had for effect to stabilize the sludge. Even if the presence of Flosperse may be beneficial for the EOD stage, the particles stabilization was detrimental to the mechanical dewatering. To obtain a high speed of mechanical dewatering, Flosperse was combined to FO4990SH. It is wished here that FO4990SH improves the performance of mechanical dewatering but without changing much the surface charge of particles previously modified by Flosperse. Due to the flocculation mechanism by bridging, it could be expected that the FO4990SH contributes much less to the electrical charge of particle surface than the Flosperse. As seen in Fig. 3 (column 5), the sludge stabilized with

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Fig. 3 e Pictures of the sludges conditioned with polyelectrolytes shown in Table 1.

Flosperse 1000 required more FO4990SH than the sludge treated by FL4820 to yield the flocculation. The combination Flosperse þ FO4990SH generated small flocs with granular aspect. Similar results were obtained with the combination Flosperse 9000 and FO4990SH (not represented in Fig. 3).

4.2.

Choice of polyelectrolyte dosing

4.2.1.

Drainage under gravity

Conditioned sludges presented in Fig. 3 were drained under gravity. Fig. 4a presents the volume of filtrate vs. time for the

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Fig. 4 e (a) Evolution of the filtrate volume during the drainage under gravity for the sludge conditioned with EM840TBD, (b) fitting of an experimental curve with the model of Eq. (5). The error bars represent the standard deviation.

sludge treated by EM840TBD. The slope evolution of Vet curves shows that filtrate flowrate increased with the polyelectrolyte dose until 12 g/kg DS. Above that dose, the gain in filtration rate was insignificant. Same behavior was observed for the other sludge conditionings mentioned in Fig. 3 (that is, an increase of filtration rate until a certain polyelectrolyte dose). The drainage curves in Fig. 4a display a fast dewatering followed by a slow extraction of filtrate. Chu and Lee (2004) have described the dewatering behavior of sewage sludge. They have studied the flocs structure in sludge by confocal laser scanning microscopy. Flocculated sludge exhibits a loosely packed global structure with large pores and a compactly packed local structure. Fluid flow simulation showed that most of the fluid flow occurs through the large pores because of the high resistance of smaller pores to the flow. Then, the first part of the curves in Fig. 4a reflects the hydraulic resistance of the large pores structure. It can be assumed that the specific cake resistance (a in Eq. (5)) remains constant in that first part. For the second part of curve, most of water is released from larger pores. Sludge then enters in consolidation under its own weight. Capillary forces tend also to retain the water. Some water can be anyway released from inner flocs at slow rate even after extended drainage time. The second part of curve is marked by a change in the specific cake resistance. The empirical relation of Eq. (5) supposes that the specific cake resistance is constant. Then, Eq. (5) is only valid for the first part of drainage. Fig. 4b presents the fitting of Eq. (5) with experimental Vet data made with the software “Table Curve 2D”. The coefficients K and g appearing in Eq. (5) were identified and used to calculate a. As presented by solid line in Fig. 4, just the first part of the curve is well correlated by the model (Eq. (5)). A deviation starts occurring after around V ¼ 80 mL when the sludge entered in consolidation (increasing of a). This deviation volume was each time around Vw80 mL whatever was the conditioning treatment. Note that the pressure drop during filtration under gravity is very low (at beginning of filtration: P ¼ rgh0 ¼ 450 Pa for h0 ¼ 7.5 cm). In Eq. (5), the viscosity of filtrate was considered as the viscosity of pure water (103 Pa s  104). Fig. 5 shows the evolution of

specific cake resistance with the dose of EM840TBD. The addition of 4, 9; 12; 15 g EM840TBD/kg DS reduced the specific cake resistance by 50, 80 98, and 99% compared to the raw sludge, respectively. The reduction of specific cake resistance compared to the raw sludge is not significant for doses higher than 12 g/kg DS. Therefore, this dose can be considered as an optimum. The same procedure was repeated with the other polyelectrolytes in Fig. 3. Table 2 gives the specific cake resistances obtained at their optimum dosing. Lower values of a for EM840TBD and FL4820 þ FO4990SH indicate a better sludge structure for drainage under gravity. According to Fig. 3, the flocs formed with Flosperse þ FO4990SH look smaller than that of EM840TBD or FL4820 þ FO4990. Then, the drainage under gravity is slowed down since there is less interspaces between flocs (that is, a higher hydraulic resistance).

Fig. 5 e Specific resistance of the sludge during the drainage vs. the dose of polyelectrolyte EM840TBD. The error bars represent the standard deviation.

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Table 2 e Specific resistance to filtration under gravity and evolution of dry matter during compression at 5 bar of the raw sludge and conditioned at optimum polyelectrolytes dose. Conditioning

Drainage under gravity

Compression at 5 bar

Specific cake resistance a in m/kg Raw sludge 12 g EM840TBD/kg DS 5 g Flosperse 1000/kg DS þ 6 g FO4990/kg DS 5 g Flosperse 9000/kg DS þ 8 g FO4990/kg DS 12 g Floquat FL4820/kg DS þ 1 g FO4990/kg DS

4.2.2.

1.1.1011 2.3.109 7.3.1010 2.9.1010 2.5.109

Compression

Electro-osmotic dewatering

DjzjE m

(6)

Eq. (6) describes the electroosmotic flow qE for a straight cylindrical capillary with walls having a zeta potential z (Smoluchowski, 1922). E is the electric field intensity (V/m), D is the dielectric constant of liquid (F/m). More detailed models describing the consolidation rate for a porous material under dc electric current are available (Kobayashi et al., 1979; Iwata et al., 2007; Curvers et al., 2007). While introducing i the current density in A/m2 of draining surface and l the electrical conductivity of the porous medium (S/m), Eq. (6) becomes: qE ¼

Djzji ml

Cake solids at 11 h,% (1%)

10.0 11.8 10.8 10.3 9.3

11.7 15.7 14.2 13.6 13.2

16.5 23.2 22.0 22.0 21.1

4.3.1.

It is convenient for further discussions to present the results in the light of the classical HelmholtzeSmoluchowski equation: qE ¼

Cake solids at 1 h,% (1%)

(6.109) (9.108) (3.109) (3.109) (2.109)

After 6 h drainage the dry matter was 8.3  1% w/w. Drainages were completed by compression at 5 bar. Table 2 gives the dry matter evolution for the selected polyelectrolytes at optimum doses previously defined. Whatever the conditioning treatment indicated in Table 2, after 11 h of compression, the conditioned sludge achieved 22  1% w/w of dry matter against 16.5  1% w/w for the raw sludge. The kinetics of compression was close for each conditioning treatment. However, a significant gain of 2 points dry matter was observed with the use of EM840TB compared to the combination FL4820 þ FO4990SH. Cross-linked structure of EM840TBD presumably confers higher resistance of flocs to creep deformation. The best preservation of their structure leads to an easier release of water. Despite of lower efficiency during drainage, Flosperse þ FO4990SH sludge made up of smaller and compact flocs performed better during compression and catch on the combination FL4820 þ FO4990SH.

4.3.

Cake solids at 5 min, % (1%)

(7)

In agreement with Eq. (7), previous works on EOD evidenced that the two most important parameters are the electrical charge on particles surfaces (reflected by z) and the applied current density. The electro-kinetics is more effective for particles which have high IzI and at higher current density (Mujumdar and Yoshida, 2008). However, the presence of electrolytes and thus the material conductance does not play a negligible role (Lockhart, 1983).

Effect of electrolyte

Tests were carried out with sludge treated by 12 g/kg DS of EM840TBD (electrical conductivity measured at 3.6 mS/cm). Electrolytes content was adjusted down by washing the sludge with water or up by adding some sodium sulfate. Fig. 6a presents the influence of initial conductivity of sludge (0.3e9.5 mS/cm: electrical conductivity of liquid phase of sludge) on the evolution of its dry matter during the EOD at 5 bar and at 80 A/m2. The overall volume of water extracted (anode þ cathode) was increased slightly from 52 mL to 58 mL with electrical conductivity rise from 0.3 mS/cm to 9.5 mS/cm. Consequently, the final dry matter of sludge was improved of 45%e53% w/w, as shown in Fig. 6a. In addition, EOD enables to decrease significantly the compression time: 2 h to reach 40% w/w at 0.3 mS/cm. Meanwhile, the compression without electric field required 11 h to reach 22% w/w at 9.5 mS/cm. Fig. 6b presents the specific energy consumption for the same tests. Current density remains constant. Voltage (not represented here) increased during the EOD due to an increasing electrical resistance of the dewatered material. For instance, at 5.9 mS/cm, the voltage was 8 V at beginning of the test and 36 V at the end. The average voltage for the whole test depended on the electrolyte content (average of 15 V at 9.5 mS/ cm against 35 V at 0.3 mS/cm). The voltage is proportional to i=l (see Eq. (7)) but it is worthwhile to note that the conductance l is not only the electrical conductivity of the liquid phase. If it was the case, the average voltage should be increased by a factor 31 (¼ 9.5/ 0.3) for the test at 0.3 mS/cm compared to the one at 9.5 mS/ cm. As above mentioned, this is not verified. The conductance l gathers the electrical conductivity of particles and liquid in the material. For moderate and high electrolyte concentration, the conductivity of material is governed by the conductivity of liquid phase. But at low electrolyte concentrations, the specific conductivity of the material is enhanced by the presence of solid particles (Helfferich, 1962). At low electrolyte concentration, even if the voltage was higher, the water removal rate was also higher as shown in Fig. 6a. As a result, the specific energy consumption in kWh/kg of water removed was almost identical from 0.3 mS/cm to 5.9 mS/cm. The end of EOD is marked by a rise in voltage. Then, for the process efficiency, it is better to stop the EOD earlier at around 40% w/w of dry matter to limit the energy consumption at 0.20e0.25 kWh/kg of water removed. It represents 2.3e2.9 MJ/kg of water of primary energies consumed

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Fig. 6 e (a) Dry matter, (b) specific energy consumption, (c) volumes of filtrate at cathode side and at (d) anode side during the EOD at 5 bar and 80 A/m2 of the sludge conditioned with EM840TBD (12 g/kg DS) in presence of different electrolytes content (thus different electrical conductivity of liquid phase). Curve in dashed line represents the compression without electric field of sludge with EM840TBD (12 g/kg DS) at 9.5 mS/cm pH inserts represent the pH of filtrate at end of test. The error bars represent the standard deviation and the average standard deviation of specific energy consumption is around 2.5.102 kWh/kg of removed water.

(assuming electricity is produced in France, Ecoinvent database). For comparison, thermal drying processes require 2.8e4.7 MJ/kg of water evaporated (Ecoinvent database, heating by natural gas, fuel). Then, the energy efficiency for this electrical process looks on average a bit higher than a thermal drying process (but only until 40% w/w sludge dry matter). These results emphasize the interest of EOD for such sewage sludge. However, the electrolyte content should not be too high. At 9.5 mS/cm, the electroosmosis efficiency dropped as seen in Fig. 6b. Prejudicial effect of high electrolyte content on the EOD efficiency was evidenced in Lockhart (1983) for kaolinite, in Larue et al. (2006) for bentonite and in Larue et al. (2001) for silica filter cakes. The decline was noticed usually above 10e15 mS/cm (around 0.1 M of salt). The transfer of electrolytic ions through the electrical double layer of electrode starts to be altered at high electrolyte concentration. This phenomenon is known as the polarization of electrodes (Lockhart, 1983). In addition, the electrical double layer of particles

itself is compressed at high ionic strength. The zeta potential of particles (in absolute value) and hence the electroosmotic flux decline as indicated by the Eq. (7). More energy is then required to extract the same water quantity than at lower electrolyte contents. On the contrary, previous works carried out with mineral particles reported that moderate salt content, around 102 M is advantageous (Lockhart, 1983; Ju et al., 1991). Electroosmosis then removed more water at lower energy demand compared to minerals prepared with pure water. This is not verified here for the sewage sludge. At lower electrolyte content (0.3 mS/cm corresponding to around 103 M of salt), the water removal was faster and specific energy consumption was slightly below than the one at moderate salt content (that is for 3.6 and 5.9 mS/cm corresponding to around 1.5  102e3  102 M of Na2SO4). The presence of cationic polymer (EM840TBD) in the washed sludge may probably enhance the EOD by bringing excess charges in the liquid as suggested by Smollen and Kafaar (1994).

w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 1 6 7 e2 1 8 0

Filtrates from anode and cathode were separated. Fig. 6c and d show the filtrate amounts collected at cathode and anode sides, respectively. During a compression without electric field, amounts extracted at both sides were similar. But during EOD, the repartition of filtrate at each side depends widely on the electrical conductivity. At 0.3 mS/cm, the volume of water extracted at cathode side is 4 times higher than at anode. The electric charge of sludge in absolute value (see Fig. 1, IzI ¼ 15 mV) accounts for the creation of an electroosmosis unidirectional flow toward the cathode. But, at higher salt content (9.5 mS/cm), the volume extracted at cathode is close to the anode’s one. At beginning of test at 9.5 mS/cm and up to 10000 s, the curves of EOD and compression without electric field in Fig. 6c and d are almost confounded. These results indicate that sludge particles in those conditions (9.5 mS/cm and 12 g EM840TBD/kg DS) have reached their isoelectric point. Therefore, only the compression is playing a role and the electric field reveals inefficient. After 10 000 s for the test at 9.5 mS/cm, Fig. 6c and d show that more water than the compression without electric field was removed at cathode and anode sides. Yoshida et al. (1999) have already described the consequences of electrolysis of water at electrodes during current application. For electrodes made from insoluble material such as the electrodes used in this work (ruthenium coated titanium), the following electrolysis reactions happen: Anode : Cathode :

2 H2 OðlÞ /4 Hþ þ O2ðgÞ þ 4 e 2 H2 OðlÞ þ 2 e / 2 OH ðaqÞ þ H2ðgÞ

(8) (9)

The electric current is circulating between electrodes thanks to the motion of electrolytic and native ions through the material (Tuan and Sillanpa, 2010). Therefore, according to Eqs. (8) and (9), the pH becomes basic at the cathode and acid at anode sides (Yoshida, 1993). The OH and Hþ electrolytic ions have for effect to increase the ionic strength (and thus decrease IzI). But these ions are not indifferent to the particles and may adsorb on their surface (and thus increase IzI). The modification of particle charge by adsorption of OH or Hþ is evidenced in Fig. 1. Finally, another effect may influence the EOD: the ohmic heating. The material is acting as an electrical resistance and thus heat during the current application. Contribution of ohmic heating to the dewatering was already demonstrated in previous works (Weber and Stahl, 2002; Larue and Vorobiev, 2004; Curvers et al., 2007). It was quantified here. Fig. 7 shows the temperature of sludge during the tests of Fig. 6. Ohmic heating (proportional to i2/l) involved an increase from 20  C (ambient temperature) to a final temperature around 56  C for any studied conductivity of sludge. Therefore, the average tests temperature was 38  C. EOD is facilitated at higher temperature in reason of the decrease of viscosity of water (from 103 Pa s at 20  C to 7.104 Pa s at 38  C). Owing to the ohmic effect, it can be calculated from the Eq. (7) that the electroosmotic flux was increased by a factor 1.4 on average. In some experiments a sudden rise of filtration rate was noticed. This phenomenon was more pronounced at higher salt content (electrical conductivity of sludge 9.5 mS/cm, Fig. 6c and d). It can be speculated that the fast temperature rise in the core of cake is responsible for the re-start of liquid

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flowing. It was shown previously that a temperature gradient can be formed through the filter cake due to Joule heating effect. The magnitude of temperature would be increased with higher electrolyte concentration (Tang et al., 2004). In our study the temperature inside of the filter cake was not measured. It should be done in the future studies. It can also be supposed that the changes of the particles electrical charge due to the adsorption of electrolysis ions OH and Hþ modify the structural properties of filter cake and lead to the sudden increase of liquid flow.

4.3.2.

Effect of polyelectrolyte

The presence of polyelectrolyte without any salt addition did not change much the electrical conductivity and pH which remain at 3.6 mS/cm and pH 5.3. Fig. 8a shows the dry matter evolution during the EOD for the sludge conditionings shown in Table 2. Fig. 8b presents the specific energy consumption for the same tests. It is observed that the studied polyelectrolytes had not any significant effect on the electroosmotic dewatering. These results confirm some previous studies made on sewage sludge (Gingerich et al., 1999; Saveyn et al., 2005; Laursen and Jensen, 1993). For instance, Saveyn et al. (2005) investigated the effect of various kinds of polyelectrolytes with various degrees of cationicity and molecular weight. They noted that the polyelectrolyte characteristics and dose have major effect only on pressure driven dewatering of sewage sludge but have not any significant effect on the electroosmotic transport of water. They and Mikkelsen (2003) suggested that the majority of the surface charges inside the sludge remain unaffected by the conditioning, even at pronounced overdosing. The total amount of charges present in activated sludge could be several orders of magnitude larger than the amount that is neutralized by polyelectrolyte. The sludge cake can be seen as an exocellular polymeric matrix entrapping the sludge particles. A large amount of surface charges of particles are not affected by the polyelectrolyte.

Fig. 7 e Temperature evolution during the EOD at 5 bar and 80 A/m2 for the sludge conditioned with different salt concentrations. The average standard deviation of temperature is around 3  C.

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Fig. 8 e (a) Dry matter and (b) specific energy consumption during the EOD at 5 bar and 80 A/m2 of the sludge conditioned with different polyelectrolytes at doses defined in Table 2. The error bars represent the standard deviation and the average standard 37 deviation of specific energy consumption is around 2.5.102 kWh/kg of removed water.

Saveyn et al. (2005) made a schematic representation of conditioned activated sludge filter cake as shown in Fig. 9. This explains why the sludge can be flocculated even if the overall charge of the filter cake remains negative. Cationic polyelectrolytes surround particles bearing negative charge and entrap them in floc. These flocs are linked together by bridging effect. Large pore channels are formed between the flocs. Based on this picture of sludge, during a filtration, the liquid flows preferentially through the interfloc space offering less resistance to the flow. It explains an easy drainage of sludge under gravity. But, the intrafloc water is only accessible during mechanical a compression and an electro-dewatering.

Fig. 9 e Schematic representation of the conditioned sludge indicating interfloc and intrafloc water fractions (source: Saveyn et al., 2005). The chains marked with the plus signs indicate the cationic polyelectrolytes; the spheres with the negative signs indicate the sludge particles with a negative surface charge.

4.3.3.

Effect of pH

The electrolysis ions OH and Hþ formed at the electrodes have considerable influence on the course of EOD. Measures of pH were made on the final filtrates at anode and cathode sides for the tests dealing with the effect of electrolyte content (Fig. 6c and d). For the test at 9.5 mS/cm (initial pH at 5.3), the filtrate pH reached 1.4 at anode and 12.5 at cathode (after 8.5 h of EOD). It corresponds to a quantity of Hþ at anode or OH at cathode of about 1  103 mol. As mentioned in Faraday’s law, the quantity of electrolysis OH and Hþ ions is proportional to the current intensity I multiplied by time t (nOH ¼ nHþ ¼ I:t=F with F ¼ 96500 C/mol). Based on calculation from Faraday’s law, for this test, the quantity of OH and Hþ ions produced after 8.5 h at 0.2 A (80 A/m2) was around 0.066 mol. It means that 98.5% of electrolysis ions which were produced during the test were incorporated inside the sludge. Only 1.5% was released in filtrates. Principle of electroneutrality accounts for the relatively low presence of electrolysis ions in filtrate. Electroneutrality imposes that as much as positive charges counterbalances the negative charges in filtrate. Electroosmotic flow at cathode contains mainly cations (since particles are negatively charged). Meanwhile, most part of electrolysis OH ions migrates inside the filter cake by electromigration and diffusion. During EOD, a strong gradient of pH settles inside the filter cake since OH migrates toward the anode and Hþ toward the cathode. Strong pH gradient was already evidenced by Yoshida et al. (1999) for bentonite filter cake which was submitted to a direct electrical current. The authors reported that it was also related to a strong gradient of zeta potential from the cathode to the anode. Throughout the cake, OH and Hþ annihilate to form water. Their fluxes inside the filter cake are important but do not account for the dewatering observed in EOD. Indeed, when the ions re-combine in water inside the filter cake, their progression by electric field effect is stopped. The main displacement of water is in fact ensured by the electroosmotic flow of ions propagating from layer to layer in the cake thanks to the stationary phase of charged particles.

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The approximate velocity of OH and Hþ can be determined knowing their conductance ðlHþ ¼ 35 mS m2 mol1 and lOH ¼ 19:8 mS m2 mol1 at 25  CÞ such as: vi ¼ mi E with mi ¼

li jzi jF

(10)

where zi is the valence of ion. The electric field intensity was around E ¼ 400 V m1 at beginning for the test at 9.5 mS/cm. Then, the application of Eq. (10) gives vHþ ¼ 1:5  104 m=s and vOH ¼ 0:8  104 m=s. The initial thickness of filter cake was 0.027 m. If there was no particle on their way, the time of OH or Hþ to cross the cake would be around 190 and 320 s, respectively. Obviously, this calculation is not realistic since it does not take into account tortuous flow path in filter cake, porosity and interaction with particles (adsorptionedesorption on particles) which may slow down considerably this electromigration. However, the calculation emphasizes the presence of strong fluxes of these ions inside the sludge creating a high

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gradient of pH immediately after the current application. It is remarkable in Fig. 6c that the pH of filtrate at cathode side at the end of test was the same whatever was the duration of current application (pH 12.4e12.5). This was not verified at anode side. The pH of filtrate obtained at anode was not identical for all the tests of Fig. 6d. It comes from the relative absence of electroosmotic flow going to the anode side for the test at lower electrolyte concentration (0.3 and 3.6 mS/cm). Therefore the few negative ions (or anions) extracted by electroosmosis are counterbalanced by a small amount of electrolysis Hþ (pH ¼ 3.1, that is, 1  108 mol of acid). For the tests at higher electrolyte concentrations (9.5 mS/cm), an electroosmotic flow appears in the course of EOD at anode side as above mentioned. It is interesting to change the initial pH of sludge to check the influence of electrolytic ions in new conditions. Fig. 10aec presents the volume of filtrate removed at anode and cathode sides during the EOD of sludge conditioned with EM840TBD

Fig. 10 e Comparison of the filtrate volumes at cathode and anode sides during EOD (5 bar; 80 A/m2) and during compression without electric field (5 bar; 0 A/m2) for the sludge conditioned with EM840TBD (12 g/kg DS) at different initial pH: (a) pH 2.6; (b) pH 5.3; (c) pH 9.5. And (d) evolution of specific energy consumption during the EOD tests at different pH. The curves for the compression without electric field cases represent average results of both sides. The error bars represent the standard deviation and the average standard deviation of specific energy consumption is around 2.5. 102 kWh/kg of removed water.

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(12 g/kg DS) at pH of 2.6, 5.3 and pH 9.5. The natural pH of sludge with EM840TBD was 5.3. Its pH was changed to 2.6 or 9.5 by washing with H2SO4 or NaOH solution. Fig. 10aec shows also in dashed lines curves of compression without electric field for the conditioned sludge. Classical compressions kinetics was quite close at pH of 2.6, 5.3 and pH 9.5. Same amounts were extracted at anode and cathode sides. The EOD tests, shown in Fig. 10aec, indicate that the overall volume of water removed (anode þ cathode) was almost the same V ¼ 51e54 mL (final dry matter around 50% w/w) for any studied pH of the sludge. But during the EOD, the repartition of filtrates at both sides depended widely on the initial pH. At pH 2.6, the sludge is at the isoelectric point as shown in Fig. 1. The electric field had not any effect at beginning. Start of EOD matches with the curve of compression without electric field at both sides as shown in Fig. 10a. After 1 h of current application, as the pH increased at cathode side, the particles acquired negative charge. An electroosmotic flow of cations was then created which accounts for the acceleration of filtrate removal at cathode side as observed in Fig. 10a. At pH 5.3, as sludge particles are negatively charged throughout the cake, the electroosmotic flow started at once at cathode side with the current application as observed in Fig. 10b. The filtrate volume removed at anode was lower than the one obtained by compression without electric field. Rapid migration of water towards the cathode side accounts for the deficit observed at anode. At pH 9.5, Fig. 10c shows that the extraction at cathode is much more difficult than at pH 5.3. Some explanations may be advanced. Higher pH involves a higher content of hydroxides at cathode compared to pH 5.3. Due to their electromigration, the hydroxides ions may oppose a higher “resistance” to the progression of the electroosmotic flow toward the cathode. It is reasonable explanation while considering the speed of the main electroosmotic flux at around 2  106 m/s calculated from Fig. 10c (cathode side) at pH 9.5. Test at pH 9.5 exhibited a jump in flowrate at 10 000 s at anode side. Isoelectric point is probably reached at this point. Larue and Vorobiev (2004) already showed that due to the electrolysis ions, a pH drop occurred during the electrofiltration of kaolin, causing the coagulation of kaolin particles and a rise in hydraulic cake permeability. Same effect is observed on the sewage sludge. Finally, Fig. 10d shows the specific energy consumption for the tests with conditioned sludge at different initial pH. At low pH, the electroosmosis effect is delayed as above mentioned and at high pH, the migration of OH hinders the electroosmotic flow. As a result, the EOD performed better at slightly acid pH conditions (pH 5.3).

5.

Similarly to some previous studies on EOD of sewage sludge, it was found that whatever their charge density (anionic or cationic) and molecular weight, the polyelectrolytes have no significant influence on the EOD rate and energy consumption. It means that it is possible to use highly cationic and high Mw polymers, more efficient for a first stage of mechanical dewatering, without damaging the performances of a subsequent EOD. The absence of notable impact comes probably from the nature of sewage sludge. The total amount of charges present in activated sludge could be several orders of magnitude larger than the amount that is neutralized by polyelectrolytes (Saveyn et al., 2005). Therefore, the particles charge and thus the electroosmotic flux cannot be much altered by the presence of polyelectrolyte at doses currently employed in wastewater treatment plants (3e8 g/kg DS). However, the increase of salt content was detrimental to EOD rate and started to be prejudicial for the specific energy consumption (in kWh/kg of water removed) above 10e15 mS/ cm (0.1 M of Na2SO4). This work emphasizes that the electrolytic ions hydroxide and hydronium formed at the electrodes have considerable influence in the course of EOD. These ions can change the charge of particles at anode and cathode sides and thus the intensity of the electroosmotic flux. Best EOD performances were obtained for the sludge conditioned with 12 g/kg DS of highly cationic polyelectrolyte EM840TBD (SNF FLOERGER), at an electrolyte concentration in liquid phase of about 103 M of salt and at slightly acid pH. Fig. 11 makes a summary of the results by drawing the evolution of cake thickness during drainage under gravity followed by EOD for the raw sludge and sludge conditioned with 12 g/kg DS of EM840TBD. The first part of the curve corresponds to the cake formation by drainage under gravity. The polyelectrolyte addition results in a time savings of 1 h 40

Conclusions

The influence of salts and polyelectrolytes on the electroosmotic dewatering of sludge was studied. Experiments were carried out on agro-industrial sewage sludge at 80 A/m2 and at 5 bar in a laboratory two sided filter press equipped with electrodes of ruthenium coated titanium.

Fig. 11 e Evolution of the cake thickness of the raw sludge and conditioned with EM840TBD during the filtration stage followed by the EOD or compression without electric field. The error bars represent the standard deviation.

w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 1 6 7 e2 1 8 0

compared to the raw sludge during that drainage stage. Further EOD allows a cake thickness reduction of 77%, compared to 37% for the compression without electric field on the raw sludge. For the process efficiency, it is anyway better to stop the electroosmotic process after 2 h while the cake thickness was 0.67 cm, corresponding to 40% w/w of dry matter. Specific energy consumption is then around 0.25 kWh/ kg of water removed.

Acknowledgements The authors gratefully acknowledge the financial and support received for this research from the Agence Nationale de la Recherche (ANR-08-ECOT-018-004).

references

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BSM-MBR: A benchmark simulation model to compare control and operational strategies for membrane bioreactors Thomas Maere a,*, Bart Verrecht b, Stefanie Moerenhout a, Simon Judd b, Ingmar Nopens a a

BIOMATH, Department of Applied Mathematics, Biometrics and Process Control, Ghent University, Coupure Links 653, B-9000 Gent, Belgium b School of Water Sciences, Cranfield University, SIMS, Building 52, Cranfield, Bedfordshire MK43 0AL, UK

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Article history:

A benchmark simulation model for membrane bioreactors (BSM-MBR) was developed to

Received 29 September 2010

evaluate operational and control strategies in terms of effluent quality and operational

Received in revised form

costs. The configuration of the existing BSM1 for conventional wastewater treatment

7 January 2011

plants was adapted using reactor volumes, pumped sludge flows and membrane filtration

Accepted 10 January 2011

for the water-sludge separation. The BSM1 performance criteria were extended for an MBR

Available online 18 January 2011

taking into account additional pumping requirements for permeate production and aeration requirements for membrane fouling prevention. To incorporate the effects of elevated

Keywords:

sludge concentrations on aeration efficiency and costs a dedicated aeration model was

BSM

adopted. Steady-state and dynamic simulations revealed BSM-MBR, as expected, to out-

Control

perform BSM1 for effluent quality, mainly due to complete retention of solids and

MBR

improved ammonium removal from extensive aeration combined with higher biomass

Modelling

levels. However, this was at the expense of significantly higher operational costs. A

Operational cost

comparison with three large-scale MBRs showed BSM-MBR energy costs to be realistic. The

Optimization

membrane aeration costs for the open loop simulations were rather high, attributed to non-optimization of BSM-MBR. As proof of concept two closed loop simulations were run to demonstrate the usefulness of BSM-MBR for identifying control strategies to lower operational costs without compromising effluent quality. ª 2011 Elsevier Ltd. All rights reserved.

1.

Introduction

The use of membrane bioreactors (MBRs) for wastewater treatment has increased significantly over the last 15 years due to technological advances and generally decreasing membrane costs. The high effluent quality offered compared to conventional activated sludge (CAS) systems makes MBRs especially suited for reuse (Judd, 2008). Their widespread application, however, is still limited by comparatively high life cycle costs over more conventional available options (Kinnear et al., 2010). Marginal decreases in both capital and

operational costs can be hugely influential in determining selection of MBRs, particularly at large-scale. For conventional wastewater treatment plants (WWTPs) and MBRs, mathematical models like the ASM family (Henze et al., 2000) are widely used for studying process behavior, system design and process optimization (Fenu et al., 2010a; Gernaey et al., 2004; Verrecht et al., 2010a). The latter has also led to the development of dedicated tools such as the COST/IWA Benchmark Simulation Model No. 1 (BSM1) (Copp, 2002; Jeppsson and Pons, 2004), which is a standardised simulation procedure for the design and evaluation of control

* Corresponding author. Tel.: þ32 92645937; fax: þ32 92646220. E-mail addresses: [email protected] (T. Maere), [email protected] (B. Verrecht), [email protected] (S. Moerenhout), [email protected] (S. Judd), [email protected] (I. Nopens). 0043-1354/$ e see front matter ª 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2011.01.006

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List of symbols and abbreviations 1

AE aeration energy (kWh d ) AEbioreactor contribution to aeration energy by fine bubble aeration (kWh d1) AEmembrane contribution to aeration energy by coarse bubble aeration (kWh d1) total aeration energy (kWh d1) AEtotal AOTE actual oxygen transfer efficiency (%) ASM activated sludge model ASM1 activated sludge model no. 1 5-day biological oxygen demand (g m3) BOD5 BSM1 benchmark simulation model no. 1 BSM1_LT long-term benchmark simulation model no. 1 BSM2 benchmark simulation model no. 2 BSM-MBR benchmark simulation model for membrane bioreactors C actual oxygen concentration in the aeration tank (g m3) CAS conventional activated sludge COD chemical oxygen demand (g m3) power factor (kWs kg1) cp C*(20) dissolved oxygen saturation concentration in clean water at 20  C and 1 atm (g m3) constant for unit conversion () cSI C*(T) dissolved oxygen saturation concentration for clean water at temperature T at sea level (g m3) ,av C* (T) average dissolved oxygen saturation concentration for clean water in an aeration tank for a given temperature T at sea level (g m3) DO dissolved oxygen concentration (g m3) e blower efficiency () EQI effluent quality index (kgPU d1) F correction factor for fouling of the air diffusers (1 for clean diffusers) g gravitational acceleration (m s2) h depth of the aeration tank (m) HRT hydraulic retention time (h1) IQI influent quality index (kgPU d1) LMH unit for flux, i.e. l m2 h1 MBR membrane bioreactor ME mixing energy (kWh d1) n air constant () N nitrogen (g m3) NO nitrite plus nitrate nitrogen concentration (gN m3) mass percentage of oxygen in air (%) OA,m volume percentage of oxygen in air (%) OA,v OCI operational cost index (d1) volume percentage of oxygen in air leaving the Oout surface of the aeration tank (%) OTR oxygen transfer rate (g d1) atmospheric pressure (Pa) Patm pressure at the bottom of the aeration tank (Pa) Pd PE pumping energy (kWh d1) PEeffluent contribution to pumping energy by effluent flow (kWh d1)

PF_Qx

pumping energy factor for sludge flow x (kWh m3) PEsludge contribution to pumping energy by all sludge flows (kWh d1) absolute inlet pressure (Pa) pin absolute outlet pressure (Pa) pout PU pollution unit () airflow rate (Nm3 d1) QA effluent flow rate (m3 d1) Qe influent flow rate (m3 d1) Qi average influent flow rate (m3 d1) Qi,av peak instantaneous influent flow rate (m3 d1) Qi,max internal nitrate recirculation flow rate (m3 d1) Qint return activated sludge flow rate (m3 d1) Qr waste flow rate (m3 d1) Qw R universal gas constant (J mol1 K1) specific membrane aeration demand per unit of SADm membrane area (Nm3 h1 m2) 3 alkalinity concentration (molHCO SALK 3 m ) soluble inert organic material concentration SI (gCOD m3) soluble biodegradable organic nitrogen SND concentration (gN m3) ammonia plus ammonium nitrogen SNH concentration (gN m3) SNH,limit_violations number of exceedances of effluent SNH over 4 gN m3 () 95th percentile for effluent SNH (gN m3) SNH,95 nitrite plus nitrate nitrogen concentration (gN m3) SNO dissolved oxygen concentration (g m3) SO SOTE standard oxygen transfer efficiency (% m1) SP sludge production for disposal (kgTSS d1) total sludge production (kgTSS d1) SPtotal SRT sludge retention time (d1) soluble, readily biodegradable organic material SS concentration (gCOD m3) t time (d) T temperature of the mixed liquor ( C) evaluation period (d) Tev absolute inlet temperature (K) Tin TKN total Kjeldahl nitrogen concentration (g m3) TN total nitrogen concentration (gN m3) TNlimit_violations number of exceedances of effluent TN over 18 gN m3 () 95th percentile for effluent TN (gN m3) TN95 TSS total suspended solids concentration (g l1) 95th percentile for effluent TSS (g l1) TSS95 w mass air flow rate (kg s1) WWTP wastewater treatment plant autotrophic biomass concentration (gCOD m3) XBA heterotrophic biomass concentration (gCOD m3) XBH particulate inert organic material concentration XI (gCOD m3) particulate biodegradable organic nitrogen XND concentration (gN m3) particulate organic material concentration from XP biomass decay (gCOD m3)

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XS y a b

particulate, slowly biodegradable organic material concentration (gCOD m3) aerator depth (m) clean to process water correction factor () salinity-surface tension correction factor ()

strategies for conventional WWTPs in terms of effluent quality and operational costs, comprising a detailed description of plant layout, models, input and evaluation criteria. More recently, the importance of integrated control, plantwide optimization and long-term evaluation was recognized within the wastewater treatment community and led to the development of BSM1_LT (Rosen et al., 2004) and BSM2 (Jeppsson et al., 2006; Nopens et al., 2010). The widespread use of BSM, with more than 300 publications based on BSM1/2, clearly indicates the usefulness of such a tool for the wastewater research community. In this study, a dynamic benchmark simulation model for MBRs (BSM-MBR) is proposed as a platform to evaluate their operational and control strategies. Control systems have already been proven for optimizing operational costs and effluent quality for CAS plants (Olsson et al., 2005). The application of conventional control strategies for aeration, recirculation pumping, carbon addition, etc. to MBRs is, however, yet to be thoroughly investigated. In terms of quantifying operational costs for MBRs, thus far simple static spreadsheet models have been mainly adopted based on rules of thumb and steady-state operation (Verrecht et al., 2008; Yoon et al., 2004). Although useful, these models may lead to erroneous conclusions by not taking dynamic behavior and system configuration into account, and precluding the evaluation of process control. These aspects can all be explored using BSM-MBR.

2.

Materials and methods

BSM-MBR is based on BSM1 (Alex et al., 2008; Copp, 2002). The modification of BSM1 to provide BSM-MBR was conducted using the modelling and simulation software WEST (MOSTforWATER NV, Kortrijk, Belgium). Basic information on the BSM1/ BSM-MBR influent files is given in Table 1. For BSM-MBR, the influent was assumed to already have passed pretreatment, i.e. coarse screens, grit chamber, grease trap and fine sieves.

2.1.

Model configuration

2.1.1.

Biokinetics

As for BSM1, ASM1 (Henze et al., 2000) was used as biological process model for BSM-MBR. The BSM1 biokinetic parameter values were judged adequate for BSM-MBR; no consensus currently exists on updating the biokinetic values for MBRs due to contradictory literature findings (Fenu et al., 2010a), and the default parameter values have been shown to be sufficient (Verrecht et al., 2010b).

2.1.2.

Membrane separation

Separate filtration tanks were used, as is common in almost all submerged hollow fibre (HF) systems and many flat sheet

rA rsludge 4 u

density of air at standard conditions (g m3) the density of sludge (kg m3) temperature correction factor for oxygen transfer () a factor exponent coefficient ()

(FS) ones, to provide flexibility in membrane operation and cleaning (Itokawa et al., 2008), notwithstanding increased pumping requirements. The characteristics and operation of the membrane modules were based on commercially available HF systems; minor modifications would be required for a flat sheet configuration to be represented. All solids were assumed to be retained by the membrane. Fouling of the membranes was not modelled as such, since no consensus on its mechanisms has been reached. Coarse bubble aeration was incorporated in the model for fouling control so that its impact on biology and operational costs, assuming constant permeability, could be assessed. The design net flux was set to 20 l m2 h1 (LMH). Peak flows were assumed to incur a 100% increase in net flux to 40 LMH (Garce´s et al., 2007). Backwashing and relaxation were not physically modelled. 71500 m2 of membranes, divided over 8 separate 3.5m-high membrane tanks, were provided, enabling BSM-MBR to treat the peak instantaneous storm flow with one membrane tank out of service (worst-case scenario). 1500 m3 of membrane tank volume was assumed to be required based on a packing density of 47.5 m2 membrane area per m3 tank volume, which is at the lower end of values reported in literature (Judd and Judd, 2010). A conservative specific membrane aeration demand (SADm) of 0.3 Nm3 h1 per m2 of membrane area was chosen based on literature values for hollow fibre systems (Judd and Judd, 2010), resulting in a maximum of 21450 Nm3 h1 for coarse bubble aeration of the membranes. The target membrane tank total suspended solids (TSS ) concentration was 10 g l1.

2.1.3.

Tank sizing

BSM-MBR was given a total bioreactor volume of 7500 m3, including the membrane tanks, resulting in an HRT of 3 h at peak instantaneous storm flow and 9.8 h at average dry weather flow, which is within but at the lower end of values reported for large MBRs in Europe (Itokawa et al., 2008).

Table 1 e Flow-weighted average influent composition for BSM1 and BSM-MBR. Compound SI SS XI XS XBH SNH SND XND SALK Qi,av Qi,max

Unit

Dry weather

Rain weather

Storm weather

gCOD m3 gCOD m3 gCOD m3 gCOD m3 gCOD m3 gN m3 gN m3 gN m3 3 molHCO 3 m m3 d1 m3 d1

30.00 69.50 51.20 202.32 28.17 31.56 6.95 10.59 7.00 18446.33 32180.00

25.96 60.13 44.30 175.05 24.37 27.30 6.01 9.16 7.00 21319.75 52126.00

28.03 64.93 51.92 193.32 27.25 29.48 6.49 10.24 7.00 19744.72 60000.00

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Compared to BSM1 the total BSM-MBR volume was lower by 37.5%, while the bioreactor volume was actually 25% higher. As with BSM1, the total bioreactor volume was split into 5 zones: 2 anoxic zones followed by 3 aerobic zones, including the membrane tanks. The anoxic volume fraction was set to 40%. Thus, all zones were sized at 1500 m3. To accommodate a worst-case scenario of 25% of the bioreactor volume being out of service, BSM-MBR was split up in 4 equal parallel lanes, as is actually the case for numerous full-scale WWTPs. As such, the actual volume of all 5m-high biological tanks was 375 m3.

2.1.4.

Sludge flows

To keep the sludge concentration in the membrane tanks within reasonable limits and distribute it more evenly over the whole plant, sludge was recirculated from the membrane tanks to the first aerobic zone at 55338 m3 d1, i.e. 3 times the average DWF. Sludge was also recirculated from the second aerobic zone to the first anoxic zone at the same rate to recycle nitrate. Waste sludge was taken from the membrane tank recirculation loop (200 m3 d1) to maintain an SRT between 25 and 30 days as is common for MBRs (Itokawa et al., 2008). The general layout and flow scheme of BSM-MBR is shown in Fig. 1.

2.1.5.

1

In BSM1 the oxygen transfer rate (OTR - g d ) in the aerobic tanks is controlled by adapting the oxygen mass transfer coefficient. The aeration energy (AE e kWh d1) consumed is calculated from this coefficient according to an empirical formula. Using the equations of BSM1 for BSM-MBR would overlook the pivotal negative influence of elevated sludge concentrations, which is paramount in MBR systems, on oxygen transfer efficiency (Henkel et al., 2009). For this reason, and to allow differentiation between coarse and fine bubble aeration, a more fundamental and extensive aeration model was adopted, combining several literature findings (Germain et al., 2007; Judd and Judd, 2010; Stenstrom and Rosso, 2008; Tchobanoglous et al., 2003; Verrecht et al., 2008): OTR ¼ QA ,rA ,OA;m ,AOTE ðb,C;av ðTÞ  CÞ ðT20Þ AOTE ¼ SOTE,y, ,4 ,a,F C ð20Þ 
1  Pd Oout þ ,C ðTÞ, Patm OA;v 2

(1)

(2)

(3)

a ¼ eu,TSS

(4)

Pd ¼ Patm þ rsludge ,g,h

(5)

Oout ¼

2.2.

Evaluation criteria

The evaluation criteria of BSM1, these being the effluent quality index (EQI - kgPU d1) and the operational cost index (OCI - d1), were used for BSM-MBR, with the latter adapted with reference to energy demand in kWh d1 from aeration (AE ), pumping (PE ) and mixing (ME ).

2.2.1.

Aeration

C;av ðTÞ ¼

The dissolved oxygen saturation concentration for clean water at temperature T at sea level (C*(T ) e g m3) was calculated with the equation suggested by Benson and Krause (1984). The parameter values for Eq. (1) to (6) are given in Table 2. The chosen values may be regarded as mean values, or at least within the range of cited literature values. Parameter values for a specific MBR system could differ from the values reported here. For open loop operation (without control strategies implemented) a fine bubble aeration flow of 6500 Nm3 h1 was selected, of which 4250 Nm3 h1 for the first aerobic zone and the remainder for the second aerobic zone. The maximum possible fine bubble aeration was set at 7000 Nm3 h1 per zone, based on manufacturer data. The membrane tanks had no additional fine bubble aeration.

100,OA;v ,ð1  AOTEÞ ð1  OA;v ,AOTEÞ

Fig. 1 e BSM-MBR layout and flow scheme.

(6)

Aeration energy

The aeration energy for BSM-MBR was split into the contributions from fine bubble aeration in the bioreactors (AEbioreactor) and coarse bubble aeration in the membrane unit (AEmembrane). Both were calculated by integration of the expression for power requirement for adiabatic compression (Tchobanoglous et al., 2003) over evaluation period Tev: AE ¼

24 , Tev

ZTev 0

n  
ZTev wðtÞ,R,Tin pout 24 , 1 ,dt ¼ ,cp , wðtÞ,dt cSI ,n,e pin Tev

(7)

0

Table 2 e Oxygen transfer (top) and aeration energy (bottom) model parameter values. Parameter

Unit 

b F g OA,m OA,v Patm rA rsludge SOTE T y 4 u cSI e n pin pout R Tin

e m s2 % % Pa g m3 kg m3 % m1  C m e e e e e Pa Pa J mol1 K1 K

a Coarse bubble aeration. b Fine bubble aeration.

Value 0.95 0.9a0.7b 9.81 23.2 21 101325 1200 1000 2a6b 15 3.5a5b 1.024 0.05a0.083b 29.7 0.5 0.283 101325 140660a155375b 8.314 293.15

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Eq. (7) combined with the parameter values in Table 2 provided a power consumption of 0.019 kWh Nm3 of air for coarse bubble aeration and 0.025 kWh Nm3 for fine bubble aeration, comparable with literature values (Verrecht et al., 2008).

2.2.2.

Pumping energy

As with BSM1, the PE for the sludge (PEsludge) was derived from three pumped sludge flows: the internal nitrate recirculation flow (Qint - m3 d1), the waste flow (Qw - m3 d1) and the return activated sludge flow (Qr - m3 d1). A value of 0.0075 kWh m3 was chosen for the pumping energy factors PF_Qint and PF_Qr, based on values for the MBR plants in Nordkanal and Varsseveld (De Wever et al., 2009). The value of 0.05 kWh m3 for PF_Qw was taken from BSM1. Pumping relating to effluent (or permeate) production (PEeffluent) was calculated in the same way as for the sludge flows, with PF_Qe set to 0.075 kWh m3 based on values for Nordkanal, Varsseveld (De Wever et al., 2009) and Schilde (Fenu et al., 2010b). Changes in PF_Qe due to a varying filtration cycle, filtration flux and fouling behavior were ignored.

2.2.3.

Mixing energy

The total mixing energy (ME - kWh d1) comprised the energy used for mixing the anoxic, aerobic tanks and membrane tanks. The anoxic tanks were mixed constantly and required 0.008 kW mixing power per m3 tank volume (Fenu et al., 2010b; Tchobanoglous et al., 2003), yielding a constant ME of 576 kWh d1. The threshold value for sufficient aeration for mixing was set at 165 Nm3 h1 for an aerobic tank and 120 Nm3 h1 for a membrane tank, based on a value of 2.2 m3 h1 per m2 ground surface area (Water Environment Federation, 2009). Below the threshold additional mechanical mixing at 0.008 kW m3 was assumed necessary.

2.3.

Simulation procedure

Steady-state and dynamic simulations with BSM-MBR were performed in the same way as described for BSM1, i.e. steadystate simulation up to 10 times the sludge age followed by three weeks of dynamic dry weather and a last week, the evaluation

Table 3 e Comparison of BSM-MBR and BSM1 steadystate open loop effluent results. Compound SI SS XI XS XBH XBA XP SO SNO SNH SND XND SALK TSS

Unit

BSM1

BSM-MBR

gCOD m3 gCOD m3 gCOD m3 gCOD m3 gCOD m3 gCOD m3 gCOD m3 g m3 gN m3 gN m3 gN m3 gN m3 3 molHCO 3 m g m3

30.00 0.89 4.39 0.19 9.78 0.57 1.73 0.49 10.42 1.73 0.69 0.01 4.13 12.50

30.00 0.67 0.00 0.00 0.00 0.00 0.00 8.11 12.57 0.07 0.58 0.00 3.85 0.00

Table 4 e Steady-state open loop BSM-MBR results for reactor zones 1 to 5. Compound SI SS XI XS XBH XBA XP SO SNO SNH SND XND SALK TSS

Unit

1 3

gCOD m gCOD m3 gCOD m3 gCOD m3 gCOD m3 gCOD m3 gCOD m3 g m3 gN m3 gN m3 gN m3 gN m3 molHCO 3 m3 g m3

2

3

4

5

30.00 30.00 30.00 30.00 30.00 2.25 1.31 0.85 0.77 0.67 2678.62 2678.62 3554.43 3554.43 4722.18 82.52 76.19 65.13 59.35 67.25 2699.15 2697.86 3573.19 3572.44 4739.59 233.30 233.07 311.13 311.33 413.41 1781.17 1782.50 2372.10 2373.11 3155.87 0.01 0.00 2.46 2.19 8.11 4.09 1.48 10.08 11.54 12.57 8.57 9.22 1.58 0.33 0.07 1.08 0.68 0.65 0.63 0.58 5.38 5.16 4.73 4.40 5.14 5.07 5.30 4.14 3.95 3.85 5606.08 5601.19 7406.98 7403.00 9823.72

period, of dynamic dry, rain or storm weather. Closed and open loop results refer to BSM-MBR simulations respectively with and without control strategies implemented. The full membrane and biological capacity was used in all simulations.

3.

Results and discussion

3.1.

Steady-state open loop evaluation

The steady-state results for the open loop case of BSM-MBR are shown in Tables 3 and 4. The results in Table 3 show that BSM-MBR performs better in terms of effluent TSS and COD compared to BSM1, mostly because of the full retention of particulates by the membranes. In terms of N removal, it can be observed that superior nitrification is obtained in BSMMBR. Effluent nitrate concentrations are however higher for BSM-MBR than BSM1 due to excessive aeration providing complete nitrification, while influent carbon for denitrification is limited with a COD to TN ratio of only 6.93 gCOD gN1. Moreover, less nitrate and more oxygen is recycled back to the anoxic zone of BSM-MBR compared to BSM1, causing a reduced denitrification performance. The high DO levels in zones 3, 4 and 5 in Table 4 also indicate inhibited simultaneous nitrification-denitrification. The total SRT for BSM-MBR is 27.4 days, which is within the intended limits. The anoxic mass fraction amounts to only 31%, despite an anoxic volume fraction of 40%, due to the steep TSS gradient along the different reactor zones: 5.6 g l1, 7.4 g l1 and 9.8 g l1 for the anoxic, aerobic and membrane tanks respectively.

3.2.

Dynamic open loop evaluation

The dynamic dry, rain and storm weather results for the open loop case of BSM-MBR are shown in Tables 5 and 6. From Table 5 it is clear that the use of membrane filtration instead of secondary clarification ameliorates adverse effects of rain and storm weather conditions on effluent quality, since sludge wash-out is not possible. The dynamic dry weather results in Table 5 are comparable to the steady-state results in Table 3 for BSM-MBR, but not for BSM1 in terms of SNH and SNO.

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Table 5 e Comparison of BSM-MBR and BSM1 dynamic open loop flow proportionally averaged effluent results for dry, rain and storm weather. Compound

SI SS XI XS XBH XBA XP SO SNO SNH SND XND SALK TSS TKN TN COD BOD5 Qe

Unit

3

gCOD m gCOD m3 gCOD m3 gCOD m3 gCOD m3 gCOD m3 gCOD m3 g m3 gN m3 gN m3 gN m3 gN m3 3 molHCO 3 m g m3 gN m3 gN m3 g m3 g m3 m3 d1

BSM1

BSM-MBR

Dry

Rain

Storm

Dry

Rain

Storm

30.00 0.97 4.58 0.22 10.22 0.54 1.76 0.75 8.82 4.76 0.73 0.02 4.46 12.99 6.75 15.57 48.30 2.77 18061.33

22.84 1.13 5.64 0.34 12.86 0.64 2.07 0.85 6.96 4.98 0.82 0.02 5.14 16.16 7.37 14.32 45.52 3.47 23808.19

26.30 1.11 5.64 0.32 11.88 0.59 1.91 0.76 7.48 5.35 0.80 0.02 4.87 15.26 7.63 15.11 47.76 3.23 20658.08

30.00 0.70 0.00 0.00 0.00 0.00 0.00 7.58 12.74 0.12 0.60 0.00 3.85 0.00 0.72 13.46 30.70 0.18 18246.31

22.85 0.72 0.00 0.00 0.00 0.00 0.00 7.00 11.20 0.12 0.61 0.00 4.49 0.00 0.74 11.93 23.58 0.18 23993.17

26.29 0.74 0.00 0.00 0.00 0.00 0.00 6.99 11.78 0.13 0.62 0.00 4.19 0.00 0.76 12.54 27.03 0.18 20843.08

Apparently the nitrification capacity of BSM1 is at times insufficient during dynamic simulations. BSM-MBR has 12.5% more aerobic volume compared to BSM1 and also carries more than two times the biological mass per unit volume. The excessive membrane aeration in BSM-MBR further ensures DO levels sufficiently high to maintain nitrification capacity during dynamic conditions. The impact of influent dynamics on TSS and DO concentrations throughout BSM-MBR is clearly visible in Fig. 2. With every peak flow sludge is washed out the anoxic tanks towards the membrane tanks. The TSS concentrations in the

first and second aerobic zone are stable (not shown). Having constant internal nitrate recirculation and return activated sludge flows is clearly insufficient for maintaining a stable sludge distribution over the plant at all times. The combination of a higher demand for oxygen during peak flows and less efficient aeration at high TSS induces high variability in the DO levels of the membrane tanks during dynamic simulations. The DO in the other aerobic zones is also highly variable. Even under normal dry weather conditions the DO in the second aerobic zone fluctuates from 0.25 mg l1 to 6 mg l1. The former has, as mentioned before, little effect on effluent

Table 6 e Comparison of BSM-MBR and BSM1 dynamic open loop effluent quality and operational cost performance criteria for dry, rain and storm weather. Criterion

IQI EQI TN95 SNH,95 TSS95 TNlimit_violations (18 gN m3) SNH,limit_violations (4 gN m3) SPtotal e SP AE e AEbioreactor e AEmembrane PE e PEsludge e PEeffluent ME OCI

Unit

1

kgPU d kgPU d1 gN m3 gN m3 g m3 e % of time e % of time kgTSS d1 kgTSS d1 kWh d1 kWh d1 kWh d1 kWh d1 kWh d1 kWh d1 kWh d1 d1

BSM1

BSM-MBR

Dry

Rain

Storm

Dry

Rain

Storm

52081.40 6690.73 18.54 8.88 15.75 5 8.18 7 62.50 2670.32 2435.67 3341.39 3341.39 e 388.17 388.17 e 240.00 16147.92

52081.40 8936.23 17.79 9.47 21.69 3 4.32 7 63.24 2737.14 2352.32 3341.39 3341.39 e 388.17 388.17 e 240.00 15731.18

54061.50 8022.77 18.72 9.78 20.79 4 8.48 7 64.43 2914.53 2599.36 3341.39 3341.39 e 388.17 388.17 e 240.00 16966.34

52081.40 3286.54 16.83 0.37 0.00 0 0.00 0 0.00 1961.12 1961.12 13558.87 3878.45 9680.42 2208.54 840.07 1368.47 576.00 26148.99

52081.40 3790.07 15.75 0.37 0.00 0 0.00 0 0.00 1974.90 1974.90 13558.87 3878.45 9680.42 2639.56 840.07 1799.49 576.00 26648.91

54061.50 3499.88 16.74 0.38 0.00 0 0.00 0 0.00 2166.26 2166.26 13558.87 3878.45 9680.42 2403.30 840.07 1563.23 576.00 27369.45

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Fig. 2 e Impact of dry, rain and storm weather influent dynamics on TSS and DO in the membrane tanks, DO in the second aerobic zone and TSS in the first anoxic zone. The 2nd and 3rd day of the 7 day evaluation period are shown.

ammonium concentrations because of the excessive membrane aeration, the latter causes severe oxygen poisoning of the first anoxic zone. BSM-MBR performs 51% (dry weather), 58% (rain weather) and 56% (storm weather) better than BSM1 in terms of EQI (Table 6), and no effluent limits are violated. Nonetheless, BSM-MBR effluent TN can be high at times (as indicated by TN95) due to poor denitrification (as indicated by SNH,95). Compared to the dry weather situation, the BSM-MBR EQI increases 15% and 6% for the rain and storm weather case respectively, whereas the corresponding BSM1 EQI figures are 34% and 20%. BSM-MBR is thus more stable than BSM1 when subjected to varying influent conditions. However, the superior effluent quality of BSM-MBR incurs a cost 61e69% higher than that of BSM1 depending on influent dynamics, according to the OCI. Other than for sludge disposal, all costs are increased significantly (140% for mixing, 306% for aeration and up to 580% for pumping). The higher mixing costs can be attributed to the larger anoxic volume to be mixed and the higher energy factor for mixing selected to incorporate the influence of elevated TSS on mixing. Care should be taken when comparing aeration costs between BSM1 and BSM-MBR, since their respective aeration models

differ significantly. However, aeration costs can be expected to be higher for MBRs than CAS plants. 71% of the aeration costs for BSM-MBR are linked with the coarse bubble aeration for membrane fouling control, while it was calculated that the latter accounts for only 30e31% of oxygen transferred into the system with 2e3% of the total oxygen lost through the effluent. The elevated pumping energy costs for BSM-MBR can mostly be attributed to permeate production through membrane filtration. Also, more sludge is being pumped

Table 7 e Overview of total and specific energy costs for the MBRs of Schilde (Fenu et al., 2010b), Varsseveld (De Wever et al., 2009), Nordkanal (Brepols et al., 2010) and BSM-MBR under dry weather conditions. Energy cost (kWh m3) ME PEsludge PEeffluent AEbioreactor AEmembrane Total

Schilde

Varsseveld

Nordkanal

BSM-MBR

0.05 0.10 0.07 0.07 0.23 0.52

0.04 0.11 0.12 0.24 0.34 0.85

0.11 0.01 0.02 0.11 0.45 0.71

0.03 0.05 0.07 0.21 0.53 0.90

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Table 8 e BSM-MBR dynamic closed loop effluent quality and operational cost performance criteria for dry, rain and storm weather. Criterion

IQI EQI TN95 SNH,95 TNlimit_violations (18 gN m3) SP AE e AEbioreactor e AEmembrane PE e PEsludge e PEeffluent ME OCI

Unit

1

kgPU d kgPU d1 gN m3 gN m3 e % of time kgTSS d1 kWh d1 kWh d1 kWh d1 kWh d1 kWh d1 kWh d1 kWh d1 d1

Dry

Rain

Storm

Dry

Rain

Storm

52081.40 3224.22 17.46 0.17 4 0.03 1961.17 13142.86 3462.44 9680.42 2208.55 840.07 1368.48 576.00 25733.28

52081.40 3717.95 16.21 0.18 1 0.01 1975.06 13106.08 3425.66 9680.42 2639.56 840.07 1799.49 576.00 26196.96

54061.50 3461.05 17.25 0.17 4 0.03 2166.11 13234.11 3553.69 9680.42 2403.30 840.07 1563.23 576.00 27043.97

52081.40 3203.27 17.37 0.17 4 0.02 1961.20 9122.00 3525.63 5596.38 2208.55 840.07 1368.48 576.00 21712.54

52081.40 3702.66 16.13 0.18 1 0.01 1975.07 10120.26 3471.58 6648.68 2639.56 840.07 1799.49 576.00 23211.16

54061.50 3440.81 17.17 0.18 4 0.02 2166.14 9584.75 3612.54 5972.21 2403.30 840.07 1563.23 576.00 23394.74

around in BSM-MBR than BSM1. The significant decrease in sludge production for disposal, 16e19%, can be explained by the more than three times longer SRT of BSM-MBR compared to BSM1 (Lubello et al., 2009).

3.3.

Comparison with full-scale MBRs

The total specific energy requirement of modern, optimized large-scale MBR plants is reported as being in the range 0.6e1 kWh m3 (Lesjean et al., 2009). Table 7 provides a breakdown of energy costs per m3 of permeate for three large-scale MBR plants (Schilde, Varsseveld and Nordkanal) compared with the dry weather open loop results of BSM-MBR. Notwithstanding some energy costs being very plant specific, it seems that the BSM-MBR energy costs are comparable with those from full-scale plants. Only membrane aeration costs are consistently higher for BSM-MBR, since the membrane aeration was constantly applied to all membranes in the open loop simulations for BSM-MBR, whereas in reality membrane tanks are taken in and out of service depending on influent flow and membrane flux. The MBRs of Schilde, Varsseveld and Nordkanal are to some extent optimized, which BSM-MBR in its open loop form by definition is not.

3.4.

Closed loop performance

The impact of imposing a basic control and novel operational strategy for regulating aeration was studied for illustrative purposes.

3.4.1.

DO þ SADm control

DO control

DO control

An aeration control scheme was implemented maintaining the DO concentration in the second aerobic zone at 1.5 mg l1 using a PI controller to adjust the fine bubble aeration in both the first and second aerobic zone. Moreover, 50% more air was sent to the first than the second aerobic zone, since it receives a higher load, unless the maximum aeration capacity has been reached. The DO sensor and actuator performance was assumed to be ideal, i.e. without noise or delay. The proportional gain of the controller was tuned to 500 Nm3 h1 and the integral time to

0.002 d. The results in Table 8 show the proposed DO control strategy impact on effluent quality being marginally beneficial, if not the contrary, compared to the open loop case, with EQI decreasing 1e2% depending on the weather conditions, but the TN effluent limit also being violated in each case. The cost of fine bubble aeration decreased significantly, 8e12% compared with the open loop case, albeit with only a minor impact on overall OCI since the latter is dominated by sludge disposal and membrane aeration costs.

3.4.2.

DO and SADm control

Extending the former control scheme to link membrane aeration to flux, assuming this to have no major adverse effects on membrane fouling and sustainable flux (Garce´s et al., 2007; Stone and Livingston, 2008), was tested. SADm was assumed to decrease linearly from 0.3 to 0.15 Nm3 h1 m2 with fluxes decreasing from 20 to 10 LMH. Beyond these limits SADm remained constant. Again, sensor and actuator performance was assumed ideal. The results in Table 8 show the SADm control scheme to have a minor effect on effluent quality, with EQI decreasing by 0e1% compared to the closed loop case with only DO control. The membrane aeration costs decrease by 42, 31 and 38% for the dry, rain and storm case respectively, while the fine bubble aeration costs increase marginally, i.e. 1e2%, to satisfy biological oxygen demand. Interestingly, diminishing membrane aeration has only minor effect on oxygen transfer since the latter still accounts for 27e29% of the oxygen transferred to the system (results not shown). The explanation lies in the lower DO levels obtained in the membrane tanks when membrane aeration is lowered. This increases the driving force for oxygen transfer, while, depending on the weather conditions, also 15e24% less oxygen is lost through the effluent. Compared to the open loop case, the overall OCI decreases by 13e17%. The results thus show large potential for saving energy by having proportional membrane aeration without compromising effluent quality. The latter may, however, be compromised when proportional membrane aeration is used combined with other operational and control strategies. Also, a thorough investigation of the technical feasibility and fouling control effectiveness of proportional membrane aeration is needed.

w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 1 8 1 e2 1 9 0

4.

Conclusions

 A benchmark simulation model for MBRs (BSM-MBR) has been developed. The existing BSM1 for a conventional WWTP was used as starting point and updated in terms of reactor volumes, membrane filtration, aeration capacity and sludge flows. The BSM1 performance criteria were extended for an MBR taking into account additional pumping requirements for permeate production and aeration requirements for fouling suppression. A dedicated aeration model was used to incorporate the effects of elevated sludge concentrations on aeration efficiency and costs.  Steady-state and dynamic open loop simulations revealed the effluent quality of BSM-MBR to be up to 58% better than that of BSM1, mainly thanks to the complete retention of solids and improved ammonium removal due to extensive aeration in combination with more biological mass. However, this was at the expense of significantly higher operational costs. Only the sludge disposal costs decreased for the BSM-MBR, due to the higher SRT.  Impaired denitrification performance was evident due to oxygen poisoning of the first anoxic zone and a reduced anoxic mass fraction related to the steep TSS gradient along the bioreactor zones. Furthermore, the TSS gradient was found to be highly susceptible to influent flow dynamics, also having repercussions on aeration efficiency.  A comparison with three large-scale MBRs showed BSMMBR energy costs to be realistic. The membrane aeration costs for the open loop simulations were high due to the lack of optimization.  Two closed loop simulations were run to show the potential of control strategies applied to BSM-MBR for diminishing operational costs by 13e17% depending on influent dynamics, without compromising effluent quality.

Acknowledgements The authors want to thank Christoph Brepols (Erftverband) for his input on MBR costs and control strategies and Lorenzo Benedetti and Phuong Thu Pham (BIOMATH) for their assistance in modelling BSM-MBR. Thomas Maere is supported by the Institute for Encouragement of Innovation by means of Science and Technology in Flanders (IWT).

references

Alex, J., Benedetti, L., Copp, J., Gernaey, K.V., Jeppsson, U., Nopens, I., Pons, M.-N., Rieger, L., Rosen, C., Steyer, J.P., Vanrolleghem, P., Winkler, S., 2008. Benchmark Simulation Model No. 1 (BSM1). Division of Industrial Electrical Engineering and Automation, Lund University. http://www.benchmarkwwtp.org/. Benson, B.B., Krause, D., 1984. The concentration and Isotopic Fractionation of oxygen dissolved in Fresh-water and Seawater in Equilibrium with the Atmosphere. Limnology and Oceanography 29 (3), 620e632. Brepols, C., Schafer, H., Engelhardt, N., 2010. Considerations on the design and financial feasibility of full-scale membrane

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bioreactors for municipal applications. Water Science and Technology 61 (10), 2461e2468. Copp, J.B., 2002. The COST Simulation Benchmark - Description and Simulator Manual. Office for Official Publications of the European Communities, Luxembourg. De Wever, H., Brepols, C., Lesjean, B., 2009. Decision Tree for Fullscale Submerged MBR Configurations. Final MBR-Network Workshop, 31 March - 1 April 2009 (Berlin, Germany). Fenu, A., Guglielmi, G., Jimenez, J., Sperandio, M., Saroj, D., Lesjean, B., Brepols, C., Thoeye, C., Nopens, I., 2010a. Activated sludge model (ASM) based modelling of membrane bioreactor (MBR) processes: a critical review with special regard to MBR specificities. Water Research 44 (15), 4272e4294. Fenu, A., Roels, J., Wambecq, T., De Gussem, K., Thoeye, C., De Gueldre, G., Van De Steene, B., 2010b. Energy audit of a full scale MBR system. Desalination 262 (1e3), 121e128. Garce´s, A., De Wilde, W., Thoeye, C., De Gueldre, G., 2007. Operational Cost Optimisation of MBR Schilde. 4th IWA International Membranes Conference: Membranes for Water and Wastewater Treatment, 15e17 May 2007 (Harrogate, UK). Germain, E., Nelles, F., Drews, A., Pearce, R., Kraume, M., Reid, E., Judd, S.J., Stephenson, T., 2007. Biomass effects on oxygen transfer in membrane bioreactors. Water Research 41 (5), 1038e1044. Gernaey, K.V., van Loosdrecht, M.C.M., Henze, M., Lind, M., Jorgensen, S.B., 2004. Activated sludge wastewater treatment plant modelling and simulation: state of the art. Environmental Modelling & Software 19 (9), 763e783. Henkel, J., Lemac, M., Wagner, M., Cornel, P., 2009. Oxygen transfer in membrane bioreactors treating synthetic greywater. Water Research 43 (6), 1711e1719. Henze, M., Gujer, W., Mino, T., van Loosdrecht, M., 2000. Activated Sludge Models: ASM1, ASM2, ASM2d and ASM3. IWA Publishing, London. Itokawa, H., Thiemig, C., Pinnekamp, J., 2008. Design and operating experiences of municipal MBRs in Europe. Water Science and Technology 58 (12), 2319e2327. Jeppsson, U., Pons, M.N., 2004. The COST benchmark simulation model - current state and future perspective. Control Engineering Practice 12 (3), 299e304. Jeppsson, U., Rosen, C., Alex, J., Copp, J., Gernaey, K., Pons, M.N., Vanrolleghem, P.A., 2006. Towards a benchmark simulation model for plant-wide control strategy performance evaluation of WWTPs. Water Science and Technology 53 (1), 287e295. Judd, S., 2008. The status of membrane bioreactor technology. Trends in Biotechnology 26 (2), 109e116. Judd, S.J., Judd, C., 2010. The MBR Book - Second Edition: Principles and Applications of Membrane Bioreactors in Water and Wastewater Treatment. Elsevier, London, UK. Kinnear, D.J., Pellegrin, M.-L., Cross, T.B., Condran, M.J., Kochaba, T., Haney, C.M., 2010. Comparing Membrane Bioreactors and Conventional Activated Sludge Processes for Low Nutrient Limits Membrane Applications 2010, 6e9 June 2010, Anaheim, California. Lesjean, B., Ferre, V., Vonghia, E., Moeslang, H., 2009. Market and design considerations of the 37 larger MBR plants in Europe. Desalination and Water Treatment 6 (1e3), 227e233. Lubello, C., Caffaz, S., Gori, R., Munz, G., 2009. A modified Activated Sludge Model to estimate solids production at low and high solids retention time. Water Research 43 (18), 4539e4548. Nopens, I., Benedetti, L., Jeppsson, U., Pons, M.N., Alex, J., Copp, J.B., Gernaey, K.V., Rosen, C., Steyer, J.P., Vanrolleghem, P.A., 2010. Benchmark Simulation Model No 2: finalisation of plant layout and default control strategy. Water Science and Technology 62 (9), 1967e1974. Olsson, G., Nielsen, M.K., Yuan, Z., Lynggaard-Jensen, A., Steyer, J.P., 2005. Instrumentation, Control and Automation in Wastewater Systems. IWA Publishing, London. Rosen, C., Jeppsson, U., Vanrolleghem, P.A., 2004. Towards a common benchmark for long-term process control and

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monitoring performance evaluation. Water Science and Technology 50 (11), 41e49. Stenstrom, M.K., Rosso, D., 2008. Aeration and mixing. In: Henze, M., van Loosdrecht, M., Ekama, G.A., Brepols, C. (Eds.), Biological Wastewater Treatment: Principles, Modelling and Design. IWA Publishing, London. Stone, M., Livingston, D., 2008. Flat Plate MBR Energy Consumption - Village of Dundee, MI. WEF Membrane Technology Conference 27e30 January 2008, Alexandria, Virginia. Tchobanoglous, G., Burton, F.L., Stensel, H.D., 2003. Wastewater Engineering: Treatment and Reuse. McGraw-Hill, Boston. Verrecht, B., Judd, S., Guglielmi, G., Brepols, C., Mulder, J.W., 2008. An aeration energy model for an immersed membrane bioreactor. Water Research 42 (19), 4761e4770.

Verrecht, B., Maere, T., Benedetti, L., Nopens, I., Judd, S., 2010a. Model-based energy optimisation of a small-scale decentralised membrane bioreactor for urban reuse. Water Research 44 (14), 4047e4056. Verrecht, B., Maere, T., Nopens, I., Brepols, C., Judd, S., 2010b. The cost of a large-scale hollow fibre MBR. Water Research 44 (18), 5274e5283. Water Environment Federation, 2009. Energy Conservation in Water and Wastewater Facilities. McGraw-Hill Professional. Yoon, S.H., Kim, H.S., Yeom, I.T., 2004. The optimum operational condition of membrane bioreactor (MBR): cost estimation of aeration and sludge treatment. Water Research 38 (1), 37e46.

w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 1 9 1 e2 1 9 8

Available at www.sciencedirect.com

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Nitrate reduction using nanosized zero-valent iron supported by polystyrene resins: Role of surface functional groups Zhenmao Jiang a,b, Lu Lv a,*, Weiming Zhang a, Qiong Du a, Bingcai Pan a,*, Lei Yang a, Quanxing Zhang a a b

State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210093, PR China College of Resource and Environment, Southwest University, Chongqing 400716, PR China

article info

abstract

Article history:

To probe the role of host chemistry in formation and properties of the inside nano-zero

Received 12 October 2010

valent iron (nZVI), we encapsulated nZVI within porous polystyrene resins functionalized

Received in revised form

with eCH2Cl and eCH2Nþ(CH3)3 respectively and obtained two hybrid nZVIs denoted

14 December 2010

CleSeZVI and NeSeZVI. 14.5% (in Fe mass) of nZVI particles were distributed in NeS

Accepted 11 January 2011

within a ring-like region (about 0.10 mm in thickness) of size around w5 nm, whereas only

Available online 18 January 2011

4.0% of nZVI particles were entrapped near the outer surface of CleS of size > 20 nm.

Keywords:

under acidic pH (3.0e5.5). 97.2% of nitrate was converted into ammonium when intro-

Nano-ZVI

ducing 0.12 g NeSeZVI into 50 mL 50 mg N/L nitrate solution, while that for CleSeZVI was

Polymeric resins

79.8% under identical Fe/N molar ratio. Under pH ¼ 2 of the effectiveness of nZVI was 88.8%

eCH2Nþ(CH3)3 is more favorable than eCH2Cl to inhibit nZVI dissolution into Fe2þ ions

Surface chemistry

for nitrate reduction, whereas that for CleSeZVI was only 14.6% under similar conditions.

Nanocomposite

Nitrate reduction by NeSeZVI exhibits relatively slower kinetics than CleSeZVI, which may be related to different nZVI distribution of both composites. The coexisting chloride and sulfate co-ions are favorable for the reactivity enhancement of NeSeZVI whereas slightly unfavorable for CleSeZVI. The results demonstrated that support chemistry plays a significant role in formation and reactivity of the encapsulated nZVI, and may shed new light on design and fabrication of hybrid nZVIs for environmental remediation. ª 2011 Elsevier Ltd. All rights reserved.

1.

Introduction

Nanoscale zero-valent iron (nZVI) has been extensively studied for environmental remediation of a variety of contaminants including halogenated organic substance (Song and Carraway, 2005), heavy metallic ions (Hou et al., 2008), arsenate (Kanel et al., 2006) perchlorate (Cao et al., 2005) and nitrate (Choe et al., 2000; Sohn et al., 2006; Yang and Lee, 2005). As compared to the bulk or microscale iron particles, nZVI possesses high specific surface area and reactivity as a strong reducing agent.

nZVI is now employed for in situ and ex situ remediation. For in situ application, nZVI can be directly injected into the contaminated sites as slurry or contained in permeable reactive barriers (PRBs). In a PRB structure, groundwater flows passively through an engineered nZVI wall while contaminants are precipitated, adsorbed, or transformed in contact with the nZVI surface. Dozens of pilot and large-scale applications have also been conducted and demonstrated that rapid in situ remediation with nZVI can be achieved (Elliott and Zhang, 2001; Glazier et al., 2003; Zhang, 2003).

* Corresponding authors. Tel.: þ86 25 8698 0390. E-mail addresses: [email protected] (L. Lv), [email protected] (B. Pan). 0043-1354/$ e see front matter ª 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2011.01.005

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Alternatively, nZVI can also be employed in ex situ application after its incorporation with solid hosts as hybrid ones (Ponder et al., 2000; Zheng et al., 2008; Hoch et al., 2008). Note that nZVI incorporation is requisite for its ex situ remediation because nZVI is prone to aggregate during synthesis and application and thereafter result in diminished reactivity (He et al., 2007). In addition, separation of nZVI particles from contaminated zones is still a difficult and economic unfavorable task. nZVI incorporation into large solid particles could result in its facile separation from aqueous system. The widely used supports for nZVI incorporation include granular activated carbon (Zhu et al., 2009; Choi and Al-Abed, 2010), palygorskite (Frost et al., 2010), and zeolite (Lee et al., 2007; Wang et al., 2010). One of the major concerns for nZVI remediation is how to inhibit its aggregation or improve its stability during synthesis and application of nZVI particles. According to conventional DLVO theory (Behrens et al., 2000), aggregation of nZVI is greatly associated with van der Waals attraction and electrostatic double layer repulsion interaction between nZVI particles, and the water chemistry environment surrounding nZVI, including ionic strength, the presence of natural organic matter and the coexisting colloids greatly affects the stability of nZVI particles, and thus, addition of surfactants (i.e., cetylpyridinium chloride) (Chen et al., 2004), polymers (i.e., poly(acrylic acid) and PolyFlo resin) (Schrick et al., 2004) usually would improve the stability of ZVI nanoparticles in aqueous systems. As for hybrid nZVIs, if we treat the support materials as the solid solvent, it would also be expected that the microenvironment surrounding nZVI, i.e., the support chemistry, would affect the formation and properties of the entrapped nZVI in a similar manner to water chemistry except for the steric effect caused by their rigid nano-porous matrix. Unfortunately, almost all the related references concerning hybrid nZVIs were mainly focused on their fabrication and characterization as well as their preliminary properties for removal of various pollutants (Li et al., 2007; Xiong et al., 2007, 2009). To the best of our knowledge, there is no report currently available to address the role of support chemistry in formation and properties of nZVIs. Particularly, there usually exist a variety of functional groups (hydroxyl, carboxyl, amino, sulfonic group, etc.) binding the inner surface of porous supports including carboneous materials (Zhu et al., 2009; Choi and Al-Abed, 2010), silica (Zheng et al., 2008), and polymeric resins (Zhang et al., 2008a, 2008b). Nevertheless, we are still not aware of the effect of these grafted functional groups on the particle size, distribution and reductive activity of the incorporated nZVI. The main objective of the current study is to preliminary probe the role of support functional groups in the performance of hybrid nZVIs. First we synthesized two polystyrene resins covalently binding neutral eCH2Cl and eCH2Nþ(CH3)3 respectively. Afterward, we employed them to stabilize nZVI because the surface chemistry of the host polymers could be well regulated, and their application as host materials to ova´ et al., 2007; fabricate hybrid catalysts (Gasparovic Dodouche et al., 2009) and adsorbents (Zhang et al., 2008a, 2008b) was extensively explored. Effect of both functional groups on the distribution and stability of nZVI as well as its efficiency for nitrate reduction were examined under different

solution chemistry. To the best of our knowledge, it would be the first report on the role of surface functional groups of host materials in formation and effectiveness of nZVI.

2.

Materials and methods

2.1.

Materials

All chemicals were reagent grade and used without further purification. The polystyrene-divinylbenzene copolymer (SteDVB) of crosslinking density w8% was kindly supplied by Zhengguang Industrial Co., Ltd. China. Its structure information is listed in Table 1. It was employed as the starting material to prepare the host polymers of different surface functionalities. Prior to use, it was extracted with ethanol for 4 h in a Soxlet apparatus, and then vacuum-dried at 40  C for 24 h. All the test solutions were prepared by ultrapure water from an Aquapro AFZ-0501-u system (18.25 MU cm).

2.2.

Preparation of hybrid nZVIs

2.2.1.

Synthesis of the host polymers

SteDVB host polymers of different surface functionalities (i.e., CleS and NeS) were prepared according to Eqs. (1) and (2). In detail, 100 g of SteDVB beads was added into 400 mL chloromethyl ether, and 50 g of anhydrous zinc chloride was introduced into the suspension as catalyst, where chloromethylation occurs in terms of FriedeleGrafts reaction as Eq. (1). The reaction lasted 16 h at 308 K and finally we obtained CleS beads. To synthesize NeS, 50 g of the CleS particles were soaked in 50 mL dichloroethane at 298 K for 12 h. The residue dichloroethane was decanted and the swollen CleS particles were filtered into a blank flask. 200 mL trimethylamine solution (30% in mass) was then introduced dropwise into the flask at 10 mL/min. The flask was set in a water bath at 318 K and after 10 h of amination as Eq. (2), the residue trimethylamine was decanted and the aminated CleS beads, i.e., NeS, were rinsed with 100 mL alcohol and 500 mL distilled water in sequence. Prior to use, both CleS and NeS were rinsed with NaOH (5 wt. %), deionized water, HCl (5 wt. %) and alcohol in a glass column in order, and then dried under vacuum at 40  C for 24 h.

CH

CH 2

CH2

CH ZnCl2 CH3OCH2Cl 308 K

St-DVB

CH

CH2Cl

(1)

Cl-S

CH2

CH

CH2

C2H4Cl2 N(CH3)3 CH2Cl Cl-S

298 K

CH2N(CH3)3 N-S

(2)

w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 1 9 1 e2 1 9 8

Table 1 e Physicochemical properties of hybrid nZVIs and their host polymers. Properties

NeS NeSeZVI CleS CleSeZVI

Matrix polystyrene-divinylbenzene 7.91 11.6 15.4 54.3 BET surface area (m2/g) Average pore diameter (nm) 28.4 14.5 11.5 62.1 Surface functional group eCH2eNþ(CH3)3 eCH2Cl ZVI content (in Fe mass%) 0 14.5 0 4.0

2.2.2.

Impregnation of nZVI within host polymers

Fabrication of both hybrid nZVIs, namely CleSeZVI and NeSeZVI, was schematically illustrated in Fig. 1. In detail, 10.0 g of dry NeS beads was added into 500 mL binary solution containing 2.0 M FeCl3 and 2.0 M HCl. The FeCl 4 anions formed were captured by NeS through preferable ion exchange with the counter Cl ion of NeS (Zhang et al., 2008a), and served as the precursor of nZVI. For CleS, 10.0 g of the polymeric beads were added into 500 mL FeCl3$6H2O ethanolewater (V:V ¼ 2:3) solution with about 58% FeCl3 in mass, and Fe(III) ions were immersed within the pores of CleS. Both NeS and CleS contained suspensions were rotated by using an end-over-end shake for around 10 h. Afterward, the supernatant was decanted, and the solid beads were rinsed five times with alcohol, and then introduced into NaBH4 solution (1% in mass) for 15 min at 20  C under the ultrasonic shaking. Preliminary study indicated that 15 min is enough for Fe(III) reduction as ZVI. As 3þ ions would be reduced into expected, the loaded FeCl 4 or Fe ZVI, and we obtained two polymeric hybrids NeSeZVI and CleSeZVI. Both resulting hybrids were present as black spherical beads and vacuum-dried before characterization. For nitrate reduction both hybrids were freshly prepared everyday and rinsed by deoxygenated water prior to use.

2.3.

Nitrate reduction

Nitrate reduction was initiated by introducing 0.12 g NeSeZVI or 0.42 g CleSeZVI beads into a glass flask containing 50 mL nitrate solution (50 mg N/L) deoxygenated by a N2 stream. Due to the different ZVI loadings for both hybrids, their doses were

2193

deliberately determined to keep equal amount of ZVI for comparability during the test. Solution pH was adjusted by HCl (1.0 M) or NaOH (1.0 M). Nitrate, nitrite as well as ammonia in solution were all measured to determine the reduction products of nitrate. When nitrogen balance for the reaction was performed, the hybrid polymers were desorbed by 2 M NaCl solution, and nitrate and nitrite potentially sequestrated by both hybrid reductants were rinsed into the eluate for further measurement. To determine the reduction kinetics, 2.34 g NeSeZVI or 8.34 g CleSeZVI beads were added into 1000 mL solution containing 50 mg N/L nitrate, and desirable amount of sodium chloride or sulfate was introduced to test the effect of co-ions. The reaction temperature was set as 298 K and the rotation rate was 120 rpm. Different hybrid nZVI dosages were also selected to keep equal amount of nZVI for nitrate reduction. A 2.0 mL solution at various time intervals was sampled, and nitrate, nitrite, and ammonium were measured to get a timedependent profile.

2.4.

Characterization

Surface area and pore size distribution of both hybrid polymers were determined by a Micromertics ASAP-2010C automatic analyzer (Micromeritics Col Inc., Australia). The Fe distribution within the polymer beads was observed by SEMEDS (S-3400N HITACHI Japan). The microscopic features of the resulting hybrids were observed by high-solution TEM (JEOL JEM-100S electron microscope) operating at 200 kV with a resolution of 0.23 nm. The XRD analysis was performed with a system equipped with a graphite monochromator and Cu Ka radiation with a scanning rate of 10 /min. The hybrid polymers were vacuum-dried at 30  C before characterization and ground for TEM and XRD analysis.

2.5.

Analysis

The concentration of nitrate and nitrite in solution were determined by ion chromatography (Dionex 1000) with

Fig. 1 e Schematic illustration for fabrication of (a) NeSeZVI and (b) CleSeZVI.

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a column of IonPac AS11-HC (4 mm  250 mm). 20 mM KOH solution was used as the mobile phase at a flow rate of 1.0 mL/ min. Ammonia was analyzed by a UVeVis spectrophotometer (JH 752 Shanghai Jinghua Co., Ltd.) at a wavelength of 420 nm after Nessiler reactions (Eaton et al., 1995). The amount of Fe loaded onto the polymeric hosts was determined by an atomic absorption spectrophotometer (TAS-990 PGENERAL) after being extracted by H2SO4 (10 wt.%) solution.

3.

Results and discussion

3.1. Role of surface groups in particle size and stability of nZVI 3.1.1.

Size and dispersion of nZVIs

Some important physicochemical properties of the solid samples employed in the study are identified and shown in Table 1. Much higher ZVI loadings of NeSeZVI than CleSeZVI were mainly attributed to the preferable binding of FeCl 4 by NeS than a simple immersion into CleS, i.e., more ZVI 3þ precursors were preloaded within NeS (FeCl 4 ) than CleS (Fe ). For both hybrid polymers, ZVI loadings resulted in a significant increase in BET surface area and a decrease in average pore diameter. As suggested, the preloaded nZVI particles would block some inner pores or make the pores narrower. On the other side, they could provide more accessible surface and thereby increased the BET surface area. Similar results were also reported by Li concerning the effect of nZVI loadings on the pore volume and surface area of the resulting hybrid (Li et al., 2007). TEM images of both hybrids depicted in Fig. 2 showed that nanosized ZVI particles were dispersed into the inner surface of porous polymeric hosts. However, the nZVI particle size of NeSeZVI is obviously much smaller (around 5 nm) than that of CleSeZVI (>20 nm), that is, the ammonium group plays more favorable role than the chloromethyl group in nZVI dispersion within the host polymers, and nZVI of smaller particle size would exhibit larger accessible surface areas and higher reactivity (Huang et al., 1998). The role of the positively charged ammonium group is similar to polymeric surfactants in aqueous solution as for nZVI dispersion if we take the host polymer as the solid solvent for nZVI (Wilcoxon and Provencio, 1999), and it might also be interpreted by DLVO theory (Behrens et al., 2000). However, more systematic research is still required to reveal the underlying mechanism. nZVI distribution within both polymeric beads was detected by SEM-EDS images and is presented in Fig. 3 a and b. It can be seen that nZVI was uniformly distributed in a ring-like region of NeS, whereas most of nZVIs were dispersed near outer surface of CleS. Comparing Fig. 3 a and c, we can see that FeCl 4 prior to NaBH4 reduction was uniformly distributed throughout the whole NeS spherical beads. Thus, nZVI distribution in NeSeZVI does not result from the distribution of ammonium groups in NeS, but is possibly related to KBH4 diffusion into the polymeric phases during nZVI formation. ova´ Similar phenomenon was also observed by Gasparovic ova´ et al., 2007) when they prepared resinet al. (Ga sparovic supported metal nanoparticles through H2 reduction, and it might serve as an effective approach to controlling nZVI distribution within a solid support.

XRD spectra of CleSeZVI and NeSeZVI are depicted in Fig. 4. For NeSeZVI, the signals of iron (peak at 44.9 ) were detected obviously, whereas the signals of the iron oxide shell were not found. It is possibly because little iron oxide was formed or the formed iron oxide was poorly crystalline (Sohn et al., 2006), and the wide peak width of NeSeZVI may be associated with the fine or ultrafine ZVI particles (Idakiev et al., 2007). For CleSeZVI several weak signals of the iron oxide shell (peaks at 26.7 , 35 and 61 for Fe3O4, and peak at 55.7 for Fe2O3) were observed (Huang and Zhang, 2004). In addition, no significant variation was observed in XRD spectra of NeSeZVI samples before and after air exposure for 15 days, but stronger signals of iron oxide and weaker signal of zero-valent iron were recorded for CleSeZVI after air exposure for 15 days. We know that bare nZVI is very reactive and even ignites when exposure to air or water. Obviously, immobilization within NeS is an effective approach to stabilizing nZVI and would create potential to long-term and continuous use of nZVI for pollutant detoxification and other applications. However, why NeS instead of CleS is favorable to protect nZVI from oxidation is unknown, and further study is required on the subject.

3.1.2.

pH-dependent stability of nZVI

One important feature concerning the hybrid nZVIs is iron leaching when applied in aqueous system. The pH-dependent iron release from both hybrid nZVIs in the background of 0.1 M KNO3 is illustrated in Fig. 5. When pH is less than 2.5, there

Fig. 2 e TEM of (a) NeSeZVI and (b) CleSeZVI beads (the dark regions represent ZVI particles while the white and gray regions represent the support polymer).

w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 1 9 1 e2 1 9 8

2195

Fig. 3 e Fe element distribution of polymeric beads as observed by SEM plus EDS (a. NeSeZVI; b. CleSeZVI; c. NeSeFeClL 4 ).

exists no significant difference between both hybrids for iron leaching, and almost all the immobilized nZVI were dissolved as ferric ions in equilibrium. Further increase in pH to 3e5.5 results in more nZVI of CleSeZVI dissolved than NeSeZVI, suggesting that nZVI stability of NeSeZVI is more favorable than CleSeZVI under identical pH values. As for the potential mechanism, we suppose that the positively charged ammonium groups of NeS results in unfavorable Hþ dispersion within polymeric phase due to the Donnan repulsion effect (Cumbal and SenGupta, 2005), and lower Hþ concentration surrounding nZVI is reasonably favorable for its stability.

3.2.

Role of surface groups in nitrate reduction by nZVI

Nitrate reaction by nZVI is a redox process in nature. According to previous studies (Cheng et al., 1997; Su and Puls, 2004), nZVI could generally reduce nitrate into ammonia, and nitrite usually occurs as intermediate. We conducted a nitrogen balance for nitrate reduction by NeSeZVI and the results (Fig. S1 of Supporting Information) further demonstrated that ammonia and nitrite are the only two products during the reaction. Thus, we employed the transformation ratio from nitrate to ammonia and nitrite as a parameter to evaluate the efficiency of both nZVI hybrids for nitrate reduction, and effect of solution pH, reaction time, and the coexisting chloride and sulfate was examined.

3.2.1.

pH-dependent efficiency

Effect of solution pH on nitrate reduction by NeSeZVI and CleSeZVI is presented in Fig. 6. In general, acidic pH leads to

faster reduction of nitrate and stronger activity of both hybrid nZVIs (Huang and Zhang, 2004; Xiong et al., 2007), and neutral or basic water is unfavorable for nitrate reduction. The reaction is even ceased when pH is larger than 4 (Huang et al., 1998). Such results for both hybrids are understandable and in good agreement with bare nZVI particles as reported elsewhere (Xiong et al., 2009). In general, the overall reaction of nitrate reduction by nZVI can be represented as (Su and Puls, 2004; Sohn et al., 2006) 0 þ þ 2þ þ 3H2 O NO 3 þ 4Fe þ 10H 4NH4 þ 4Fe

(3)

and lower solution pH is favorable for nitrate reduction by nZVI according to Eq. (3). On the other side, lower pH helps to remove the iron oxide formed in surface during ZVI oxidation and continuously makes the fresh surface of ZVI exposed to solution (Huang and Zhang, 2004). In addition, higher ammonia and lower nitrite production were observed for NeSeZVI than CleSeZVI during nitrate reduction. From the results in Fig. 6, it can be determined that about 88.8% nZVI of NeSeZVI were oxidized into Fe(II) ions by nitrate at pH ¼ 2, whereas only 14.6% nZVI of CleSeZVI participated in nitrate reduction. As for NeSeZVI reaction at pH ¼ 5, 97.2% of the products were ammonium (the others were nitrite), while that for CleSeZVI was only 79.8%. Such results further suggest that NeSeZVI is a more efficient reductant than CleSeZVI. Considering that Fe/N molar ratio of both reaction systems was kept equal, different nitrate reduction efficiency may be attributed to different sizes of nZVI particles of both hybrids. As mentioned above, nZVI particles for NeSeZVI were sized about 5 nm whereas those

2196

w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 1 9 1 e2 1 9 8

a

40 35

N-S-ZVI after air exposure for 15 days Transformation ratio (%)

30

N-S-ZVI

N-S

25 20 15 10 5 0

10

20

30

40

50

60

2

70

b Cl-S-ZVI after air exposure for 15 days

Cl-S

40

50

60

70

2 (deg)

Fig. 4 e XRD pattern of (a) NeSeZVI and (b) CleSeZVI beads (fresh ones or after air exposure for 15 days) as compared to their host polymers.

Fe release content (%)

80

N-S-ZVI Cl-S-ZVI

60

40

0 2.5

3.0

3.5

12

Kinetics

Kinetics of nZVI reaction is another important aspect concerning its application for environmental remediation. Here we examined nitrate reduction kinetics by both hybrid nZVIs to preliminarily probe the effect of polymeric surface groups on nitrate reduction kinetics, and effect of the coexisting chloride and sulfate was also considered because various coions like chloride and sulfate are ubiquitously present in nitrate contaminated waters (Su and Puls, 2004). Ammonium production was employed to evaluate the reduction kinetics because it was the main product of nitrate reduction, as we observed in the preliminary study (Fig. S1). Note that nitrate removal is not a suitable parameter to evaluate the reactivity of nZVI because nitrate can also be preferably sequestrated by the host anion exchanger NeS through electrostatic attraction. Fig. 7 depicts time-dependent ammonium production for nitrate reduction in the absence and presence of chloride and sulfate by both hybrids. To quantify the kinetics, the available data were fit by traditional pseudo-first order kinetic model according to the previous studies (Alowitz and Scherer, 2002; Park et al., 2009).
d½Ct ¼ Kobs ½Ce ½Ct dt

20

2.0

10

Fig. 6 e Effect of initial solution pH on nitrate reduction by NeSeZVI (solid) and CleSeZVI (empty) (-,ammonia; :6nitrite) (ZVI for all the reaction systems was set as 400 mg Fe/L, and initial nitrate concentration was 3.57 mM).

3.2.2. 30

8

for CleSeZVI were larger than 20 nm, and nZVI of smaller particle size exhibits higher reactivity because more reaction sites are available than those of larger particle size (Huang et al., 1998).

Cl-S-ZVI

20

6

Initial pH

2 (deg)

10

4

4.0

4.5

5.0

5.5

equilibrium pH

Fig. 5 e Fe release from both nZVI hybrids under different solution pHs (ZVI content in both S/L mixtures was set as 400 mg Fe/L).

(4)

where [C]t is the ammonium concentration at time t and [C]e is the ammonium concentration in equilibrium; Kobs (min1) is the observed first-order rate coefficient. By integration we can obtain, ½Ct ¼ ½Ce 1  eKobs t




(5)

Kobs values for nitrate reduction onto both hybrids were

w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 1 9 1 e2 1 9 8

a

effect emerges within sulfate ion. Su and Puls (Su and Puls, 2004) found that many anionic ligands including sulfate play inhibitive role in nitrate reduction by ZVI because of their complexation with ferric oxides. As for the enhancement effect on NeSeZVI, we suggested that added chloride or sulfate co-ions are favorable for desorption of nitrate ions binding NeS through electrostatic attraction and thereafter increase nitrate concentration within the porous phase of the polymeric beads. Consequently, nitrate reduction would be enhanced since its kinetics greatly depends on the substrate concentration.

14

+

NH4 -N production (mg/L)

12 10 8 6 4 -1

2

N-S-ZVI kobs=0.0057±0.0008 min ; R =0.946 -

2

-1

N-S-ZVI+Cl

-1

N-S-ZVI+SO4

0 0

100

200

2

kobs=0.0093±0.0009 min ; R =0.958 2-

b

4.

2

kobs=0.0064±0.0008 min ; R =0.992

300

400

500

600

700

800

t (min) 2.5

2.0

1.5

1.0

+

NH4 -N production (mg/L)

2197

-1

Cl-S-ZVI 0.5

2

kobs=0.0260± 0.0045 min ; R =0.957 -

-1

2

Cl-S-ZVI+Cl kobs=0.0211± 0.0045 min ; R =0.921 2-

Cl-S-ZVI+SO4

-1

2

kobs=0.0206± 0.0037 min ; R =0.888

0.0 0

100

200

300

400

500

Conclusions

The present study demonstrated that surface functional groups of polystyrene hosts play significant role in the particle size, distribution and reactivity of the incorporated nZVI. In general, the positively charged ammonium group is more favorable than the neutral chloromethyl group to form smaller nZVI particles and thereby enhance their reactivity for nitrate reduction. Also, the ammonium groups would inhibit nZVI dissolution into Fe2þ ions under acidic pH (3.0e5.5). Due to the different nZVI distribution, nitrate reduction by NeSeZVI exhibits relatively slower kinetics than CleSeZVI. The coexisting chloride seems favorable for the reactivity enhancement of NeSeZVI whereas slightly unfavorable for CleSeZVI. These results reported herein indicated that surface functional groups of the support materials must be considered when fabrication of highly efficient hybrid nZVIs for environmental remediation.

t (min)

Fig. 7 e Nitrate reduction kinetics by NeSeZVI (a) and CleSeZVI (b) in the absence and presence of chloride/ sulfate anions (initial solution pH was 6; initial nitrate was 3.57 mM, chloride, 28.57 mM; and sulfate 28.57 mM; ZVI for all the reaction mixtures was the same and equal to 400 mg Fe/L. Lines represent the pseudo-first order model.).

calculated through iterative algorithm and are presented in Fig. 7. The results available indicated that Kobs of nitrate reduction by NeSeZVI is obviously smaller than CleSeZVI regardless of the presence of co-ions, though nZVI particle size of the former is significantly smaller than the latter. Different reduction kinetics may rely on different nitrate diffusion behavior from solution to both solids, i.e., nitrate diffusion from solution to the active sites of nZVI of CleSeZVI is faster than NeSeZVI because nZVI distribution of CleSeZVI is much closer to the solideliquid interface of the polymer/ solution reaction systems. Another possible reason is that, due to the exclusion effect of the immobilized eCH2Nþ(CH3)3 groups of NeS, Hþ concentration within NeSeZVI would be much lower than CleSeZVI. As indicated by Eq. (3), lack of sufficient Hþ is unfavorable for nitrate reduction by nZVI. In addition, from Fig. 7 we can see that both chloride and sulfate ions inhibit nitrate reduction by CleSeZVI whereas enhance the reaction by NeSeZVI, and a stronger inhibition

Acknowledgments This research is supported by the Scientific Research Foundation of Graduate School of Nanjing University, Program for New Century Excellent Talents in University of China (NCET07-0421), and Natural Scientific Foundation of China (Grant No. 51078179 and 21007023).

Appendix. Supporting information The Supporting information associated with this article can be found in the on-line version at doi:10.1016/j.watres.2011. 01.005.

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Available at www.sciencedirect.com

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Bioaugmented membrane bioreactor (MBR) with a GAC-packed zone for high rate textile wastewater treatment Faisal Ibney Hai a,*, Kazuo Yamamoto a, Fumiyuki Nakajima a, Kensuke Fukushi b a b

Environmental Science Center, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan Integrated Research System for Sustainability Science, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8654, Japan

article info

abstract

Article history:

The long-term performance of a bioaugmented membrane bioreactor (MBR) containing

Received 18 November 2010

a GAC-packed anaerobic zone for treatment of textile wastewater containing structurally

Received in revised form

different azo dyes was observed. A unique feeding strategy, consistent with the mode of

13 January 2011

evolution of separate waste streams in textile plants, was adopted to make the best use of the

Accepted 13 January 2011

GAC-zone for dye removal. Dye was introduced through the GAC-zone while the rest of the

Available online 22 January 2011

colorless media was simultaneously fed through the aerobic zone. Preliminary experiments confirmed the importance of coupling the GAC-amended anaerobic zone to the aerobic MBR

Keywords:

and also evidenced the efficacy of the adopted feeding strategy. Following this, the robust-

Anaerobic decoloration

ness of the process under gradually increasing dye-loading was tested. The respective

Granular activated carbon (GAC)

average dye concentrations (mg/L) in the sample from GAC-zone and the membrane-

Membrane bioreactor (MBR)

permeate under dye-loadings of 0.1 and 1 g/L.d were as follows: GAC-zone (3, 105), permeate

Textile wastewater

(0, 5). TOC concentration in membrane-permeate for the aforementioned loadings were

White-rot fungi

3 and 54 mg/L, respectively. Stable decoloration along with significant TOC removal during a period of over 7 months under extremely high dye-loadings demonstrated the superiority of the proposed hybrid process. ª 2011 Elsevier Ltd. All rights reserved.

1.

Introduction

Textile wastewater is a complex and highly variable mixture of many polluting substances including dye (Robinson et al., 2001). Azo dyes make up the majority (60e70%) of the dyes applied in textile processing industries (Hunger et al., 2004). Several physicochemical decoloration techniques have been reported in the literature (e.g. adsorption, membrane separation, advanced oxidation process); none, however, has appeared as a panacea due to high cost, low efficiency and limited versatility (Hai et al., 2007). Biodegradation is an environmentally friendly and cost competitive alternative. However, azo dyes are xenobiotic compounds and due to their electron withdrawing nature, they tend to persist under aerobic environment

(Knackmuss, 1996). On the other hand, decoloration through reductive cleavage of azo bond (eN]Ne) under anaerobic condition has been reported (van der Zee and Villaverde, 2005). The reduction of many azo dyes is, however, a rather slow process (Kapdan et al., 2003; Manu and Chaudhari, 2003; Me´ndez-Paz et al., 2005). In different experimental systems, redox mediators such as quinones and flavine-based compounds have been demonstrated to accelerate azo dye reduction by shuttling reducing equivalents from an electron-donating cosubstrate to the azo linkage (Cervantes et al., 2001; Field and Brady, 2003; Rau et al., 2002). Although the redox mediator dosage levels are low, continuous dosing implies continuous expense and continuous discharge of these biologically recalcitrant compounds.

* Corresponding author. School of Civil, Mining and Environmental Engineering, The University of Wollongong, New South Wales 2522, Australia. Tel.: þ61 2 4221 3177. E-mail address: [email protected] (F.I. Hai). 0043-1354/$ e see front matter ª 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2011.01.013

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w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 1 9 9 e2 2 0 6

Therefore, it is desirable to immobilize the redox mediator in the bioreactor. Activated carbon (AC) adsorption has long been used as the polishing decoloration step in the industry (Rozzi et al., 1999). Interestingly it contains surface quinone structures (van der Zee et al., 2002). In view of the simultaneous adsorption and catalytic capacity of activated carbon, biologically activated carbon (BAC) processes have been explored for anaerobic decoloration in a few studies with encouraging results (Mezohegyi et al., 2007; Ong et al., 2008; van der Zee et al., 2002). It is, however, noteworthy that the formation of highly toxic aromatic amines during anaerobic azo dye decoloration renders a polishing aerobic step a must (van der Zee and Villaverde, 2005; You et al., 2010). The above mentioned studies exploring BAC process address only a part (i.e., decoloration) of the complex issue of textile wastewater management. In this context it is interesting to note that unlike bacterial activated sludge process, aerobic white-rot fungi can degrade wide varieties of recalcitrant compounds including textile dyes (Fu and Viraraghavan, 2001). In fact we previously developed a membrane bioreactor (MBR) implementing a mixed microbial community dominated by white-rot fungi, and demonstrated improved color as well as total organic carbon (TOC) removal as compared to conventional MBR (Hai et al., 2006, 2008b). An MBR was utilized as its several characteristics, such as membrane interception and long sludge retention time, help to prevent washout of the bioaugmented inoculants. We also demonstrated that direct addition of powdered activated carbon (PAC) into the bioaugmented aerobic MBR brings about added advantages including co-adsorption of dye and enzyme onto activated carbon and subsequent enzymatic dye degradation (Hai et al., 2008b). The dye loading rate in that study, however, was rather limited. We envisaged that the integration of an activated carbon-catalyzed anaerobic reactor and aerobic bioaugmented MBR may enable high rate decoloration and TOC removal. In order to develop a high rate decoloration and TOC removal process, this study explored an innovative MBR with a granular activated carbon (GAC)-packed anaerobic zone beneath the main aerobic zone which contained the membrane module and a mixed microbial community of fungi and bacteria. It was expected that following the primary anaerobic decoloration in the GAC-packed zone, the completion of color and organics removal would be accomplished in the membrane-coupled aerobic zone. Furthermore, a unique wastewater feeding strategy, consistent with the mode of evolution of separate waste streams in textile plants, was adopted in this study. This article reports the effect of such feeding mode and the long-term overall treatment performance of the explored scheme. This is the first report on excellent dye removal performance under very high dye loading with such a membrane-based hybrid process.

2.

Materials and methods

2.1.

Microorganism and synthetic wastewater

The white-rot fungi Coriolus versicolor NBRC 9791 obtained from the NITE Biological Resource Center (NBRC), Japan was

used for this study. Although white-rot fungi have been widely reported to excrete a variety of extracellular enzymes under carbon or nitrogen limitation, reports on enzyme secretion under nutrient sufficient condition (Laugero et al., 1996) are also available. It was confirmed that the collected strain was capable of secreting laccase enzyme in nutrient sufficient media (Hai et al., 2008b). Therefore, a nutrient sufficient synthetic wastewater containing dye and starch (2 g/L)dtwo common components in real textile wastewaterdalong with urea (0.1 g/L) and other nutrients, was utilized in this study. Details regarding the media have been documented elsewhere (Hai et al., 2008a). For the first 210 days of continuous operation only the azo dye acid orange II was utilized; while for the rest of the operation period all four dyes as listed in Table 1 were fed into the reactor.

2.2.

Design and feeding mode of the bioreactor

A laboratory-scale, cylindrical PVC reactor (Diameter ¼ 6.7 cm, Height ¼ 24 cm)dwith a working volume of 0.85 L and containing a GAC-packed anaerobic zone beneath the main aerobic zonedwas operated under a total hydraulic retention time (HRT) of 1 day (Fig. 1). The performance of the MBR was first observed without any GAC-zone. Then the amount of GAC was gradually increased from 0 to 30 g to 60 g and finally to 120 g. The corresponding heights of the aerobic zone were 21, 18 and 12 cm, respectively. GAC F400-OS (average particle size 1.1 mm) received from Calgon Mitsubishi Chemical Corporation, Japan was utilized in this study. The GAC was always washed with Milli-Q water to remove very fine particles and wetted for 24 h before use. In order to avoid the influence of initial dye adsorption, GAC was saturated with dye before addition to the reactor. This was done by pumping through the wetted GAC an amount of dye equivalent to five times the maximum dye adsorption capacity of GAC as estimated by an adsorption isotherm (not shown). Initially, saturated GAC was mixed with partially digested sludge (collected from a long time operated bioaugmented MBR), added into the reactor as slurry and allowed to settle. The aerobic zone, on the other hand, was initially inoculated with pure fungus culture; however, bacterial contamination occurred in absence of any specific means to avoid that, and eventually a stable combined culture of bacteria (40%) and fungi (60%) was obtained in line with a previous study (Hai et al., 2008b). No sludge was withdrawn from the MBRs and no further addition of fresh fungal culture into the MBR was required to maintain fungal dominance. At the time of increasing the amount of GAC in the aerobic zone, the air-diffuser of the reactor was lifted up to allow settling of certain amount of sludge and then a certain amount of GAC was added. The GAC-zone was firm enough not to float and a fairly clear demarcation of the two zones was possible. Unlike the nutrient-deficient hardly-biodegradable dye bath effluent, different other streams of wastewater in a textile mill, namely, scouring and desizing-effluent, usually contain high concentrations of relatively easily degradable organics. Instead of mixing the different streams originating from a textile plant, efficient use of GAC exclusively for dye adsorption may be made by feeding only the dye effluent through GAC. In our study, all the dye along with a small amount of starch

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Table 1 e Outline of the GAC-MBR operation. Aim

No.

Days

Amount of GAC (g)

Loading through X,Ya (g/L.d) Starchb X

A. Effect of GAC-zone

B. Effect of feeding mode

C. Performance under stepwise increased dye loading

I II III IV I II III I II III IV I

14 7 7 14 21 21 14 90 30 30 60 21

e 30 60 120 120

120

e

Dyeb Y

2

X

Y

e

0.1

0.25

1.75

0.1

0

2 1 0.25 0.25

0 1 1.75 1.75

0.1 0.05 0.1 0.1 0.25 0.5 1.0 1

0 0.05 0

D. Effect of simultaneous feeding 120 0.25 of different dyes In runs A-C only dye #1 was fed, while all four dyes were fed in run D.

1.75

Overall loadingc, (g/L.d) Starch

Dye

TOC

2

0.1

0.944

2

0.1

0.944

2

0.1 0.25 0.5 1.0 0.1

0.944 1.03 1.16 1.44 1.39

0 0

2

a X : GAC-zone, Y : aerobic zone (see Fig. 1). b Only starch and dye have been shown as other components were always fed from ‘Y’, except only when mixed wastewater was introduced through ‘X’ or simultaneously from ‘X’, ‘Y’(Trial# B-I, B-II). c Since HRT ¼ 1 day, numerical values of loading and concentration are the same.

(to sustain biological activity in this zone) was fed through the GAC-zone. Same volume of the mediadcontaining rest of the components and representing the effluent from units other than dye bath within a textile plantdwas simultaneously fed from the top of the reactor. The overall dye-loading was

stepwise increased from 0.1 to 1 g/L.d. Before that, however, the performance of the MBR was observed while feeding the mixed wastewater (containing all the components including dye) through the GAC-zone, or simultaneously through aerobic and GAC-zone, in order to assess the benefit of the feeding strategy explored in this study. The pH of the wastewater was 4.5, while continuous monitoring revealed the maintenance of a pH of 6  0.5 in the bioreactor without any specific control. The experimental plan has been detailed in Table 1. A control MBR with the same design and feeding mode, as in ‘C’ in Table 1, except that it contained an anaerobic sludge bed devoid of GAC, was operated in order to assess the performance of the anaerobic zone with/without GAC.

2.3.

Fig. 1 e Schematic of the bioaugmented MBR with GACpacked zone (Hydrophilic Polyethylene Hollow fiber module at inset; X, Y: feeding through GAC and aerobic zone, see Table 1 for details).

Membrane module

A 4.5 cm compact bundle (packing density ¼ 56%) of microporous (0.4 mm), hydrophilicaly treated, polyethylene hollowfibers, obtained from Mitsubishi Rayon, Japan was utilized in this study. As the height of the aerobic zone was gradually reduced, the available 22 cm full-length bundle was cut (and resealed) to fit to that zone. During the final trial, the module had an effective fiber-length and surface area of 5.5 cm and 0.256 cm2, respectively. Due to the small volume of the reactor and the large surface area of the module, application of only a very low average flux of 0.0033 m3/m2.d with a 6 min/50 min (on/off) mode was required. Ex-situ chemical cleaning of the module (backwashing with a NaOCl solution containing

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250 mg Cl/L) was applied only once (on day 180) during the whole operation.

2.4.

Analytical methods

Samples from a port located just below the top of the GACzone were collected to assess the extent of decoloration in the GAC-packed zone, while measurements on membranepermeate revealed the extent of overall removal. Total organic carbon was measured with a TOC/TN analyzer (TOC-V, Shimadzu, Japan). High performance liquid chromatography (HPLC) using a diode array detector (DAD) was utilized to measure the concentration of dye(s). For the HPLC-DAD analysis, two eluents, acetonitrile and water, in gradient proportions were utilized in conjunction with a Spherisorb ODS2 column (200 by 4.6 mm; 5 mm particles). The corresponding injection volume, flow rate, detection wavelength and column-temperature were 100 mL, 0.8 mL/min, 210 nm and 35  C, respectively. Membrane-permeate samples were analyzed as collected, while samples collected from within MBR were centrifuged under 2150g to obtain the supernatant and then analyzed for color. The concentration of only the parent compound(s) was monitored. Oxidation-reduction potential (ORP) and dissolved oxygen (DO) were measured to confirm the establishment of anaerobic environment in the GAC-zone. Mixed liquor suspended solids (MLSS) concentration was measured according to the standard methods (Clescerl et al., 2005). The relative abundance of fungi/bacteria in MLSS was monitored by a microscopic and a size-based fractionation method (Hai et al., 2009). Transmembrane pressure (TMP), as an indicator of membrane fouling, was continuously monitored using a vacuum pressure gauge (GC 61, Nagano Keiki Co. Ltd., Japan).

3.

Results and discussion

In order to confirm that the color and TOC removal data discussed in this article were obtained under stable operating conditions, periodic monitoring of the stability of the bioaugmented culture in the aerobic MBR and the hydraulic performance of the membrane were performed (data not shown) with the methods outlined in section 2.4. This section will focus on the color and TOC removal performance of the integrated as well as the unit processes.

3.1. MBR

Importance of coupling GAC-zone to bioaugmented

When the wastewater with a dye loading of 0.1 g/L.d was fed to the aerobic bioaugmented MBR having no GAC-zone, the dye concentration in the membrane-permeate was 12 mg/L (Table 2). After adding dye-saturated GAC (initial weight 30 g), and passing dye through this zone (while introducing the rest of the media through the aerobic zone) dye in the treated effluent dropped to 1 mg/L only, the corresponding concentration in sample from GAC-zone being 26 mg/L. The dye concentration in the sample from GAC-zone gradually dropped as the amount of GAC was increased (30e120 g). ORP and DO measurements confirmed that due to vigorous aeration in

Table 2 e Effect of GAC-zone on overall dye removal (Dye loading [ 0.1 g/L.d). Amount of GAC, g

0 30 60 120

Dye concentration after removal (mg/L) After GAC-zone

In permeate

e 26 8 3

12 1 1 0

the aerobic zone at the top, anaerobic environment was not completely established in the GAC-zone when the GAC weight was less than 120 g. Our observation confirms that, with the GAC-packed anaerobic zone, an excellent overall decoloration can be achieved in the bioaugmented MBR; however, in order to establish anaerobic environment, a certain height of that zone needs to be maintained.

3.2.

Effect of adopted feeding mode

Separate wastewater-streams originate from different plants in a textile mill (Hai et al., 2007). If those streams are mixed and fed through GAC, many other compounds, in addition to dye, in that mixed textile effluent can adsorb on GAC (Hai et al., 2007; Hao et al., 1999). To make the best use of the GAC-zone exclusively for dye decoloration, we adopted a unique feeding strategy of passing dye (along with a certain amount of easily degradable organics) through GAC, while simultaneously introducing the rest of the uncolored effluent through the aerobic zone. In order to confirm the efficacy of the proposed feeding strategy, the performance of the MBR was observed under three distinct feeding modes as shown in Fig. 2. The best decoloration both in anaerobic and aerobic zone were obtained with the proposed feeding mode. Reduced anaerobic decoloration was observed when a significant portion or whole of the uncolored fraction of the wastewater along with dye was fed through the GAC-zone. This may be attributed to the competitive adsorption of dye and other compounds on GAC, affecting GAC-catalyzed anaerobic dye degradation. However, a reduced decoloration was also observed in the aerobic zone in this case. This was accompanied by a drop in MLSS concentration in the aerobic zone (Fig. 2). Apparently the substantial adsorption of easily degradable substrate on GAC created an artificial nutrient-deficiency in the aerobic zone, and this led to reduced MLSS concentration and deteriorated aerobic decoloration. The observed effect of MLSS concentration on removal is in line with that of Ren et al. (Ren et al., 2005) who reported low microbial metabolism and consequently low COD removal when the MLSS concentration was below a threshold value of 6 g/L. Furthermore similar effect of substrate deficiency on

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1000

Dye loading Concentration after GAC zone Concentration in permeate

800

4 dyes

Dye loading (mg/L.d) Dye concentration (mg/L)

600 400 200

40 30 20 10

a

0 0

30

60

90

120

150

180

210

240

Time (Day)

Fig. 3 e Long-term decoloration under gradually increased dye loading (a. temporary deterioration due to air-diffuser malfunctioning).

Fig. 2 e Effect of feeding mode on MLSS (aerobic) and dye removal (Dye loading [ 0.1 g/L.d).

MLSS concentration and decoloration was reported by Hai et al. (Hai et al., 2008a). Our observations substantiate the efficacy of the adopted feeding strategy as opposed to the conventional practice of completely mixing the dye effluent with the other uncolored streams emanating from a textile mill.

3.3. Long-term performance under stepwise increased dye-loading Although the total HRT utilized in this study was longer than that used in conventional MBRs, a logical way of assessing the reactor performance can be to look at the loading rate. As the dye loading was stepwise increased from 0.1 to 0.25, 0.5 and finally to 1.0 g/L.d, respectively over an operation period of 7 months, the reactor maintained excellent decoloration (Fig. 3). The membrane-permeate was virtually colorless up to the loading of 0.5 g/L.d. Even under the highest loading, the average dye concentration in the membrane-permeate was only 5 mg/L. Van Der Zee et al. (van der Zee et al., 2002) previously demonstrated excellent decoloration of hydrolyzed reactive red 2 under a loading of 0.18 g/L.d in a GAC-packed UASB reactor. Ong et al. (Ong et al., 2008) demonstrated high decoloration of acid orange II under a dye loading of 0.6 g/L.d in a GAC-biofilm sequencing batch reactor. On the other hand, Mezohegy et al. (Mezohegyi et al., 2007) reported excellent decoloration of acid orange II in an up flow AC-packed reactor under a rather high dye loading of 18 g/L.d. However, judging from the very small reactor size (9 mL) and the requirement of continuous bubbling of helium to maintain strict anaerobic

condition, the study of Mezohegy et al. (Mezohegyi et al., 2007) appears to be more of a proof-of-concept and the scale-up potential of that system is questionable. The relative contribution of the anaerobic and aerobic zone to decoloration under various loadings manifested the importance of an integrated process (Table 3). As noted earlier, GAC can perform both adsorption as well as electron shuttling role (essential for anaerobic reduction). The dynamics of adsorption and biodegradation and consequently the dye removal obviously depends on dye loading. The contribution of the bioaugmented aerobic zone to completion of color removal was more convincing under the higher dye-loadings. For instance, when the loading was only 0.1 g/L.d, even the sample from the GAC-zone was almost colorless. Nevertheless, in case of loading of 1 g/L.d, the dye concentration in the sample from the GAC-zone was 105 mg/L. The corresponding dye concentrations in the supernatant of the aerobic zone mixed liquor and membrane-permeate were 16 and 5 mg/L, respectively (Table 3). This observation confirmed that following the initial anaerobic decoloration the bioaugmented culture in the aerobic zone played an important role to improve color removal, and, finally, the membrane contributed to moderate additional removal. Since a microfiltration membrane, which is unable to retain soluble dye by its own, was utilized in this study, the additional dye removal by the membrane can be attributed to the dye retention onto the cake-layer accumulated over the membrane. Such additional removal of dye by membrane in MBR is in line with previous reports (Hai et al., 2009). It is worth-noting here that, in conventional sequential anaerobiceaerobic processes the aerobic stage contributes mainly to organics removal and rarely to decoloration (van der Zee and Villaverde, 2005). However, in our study, which involved a mixed microbial community dominated by fungi, the aerobic stage contributed significantly to decoloration as well. The importance of combining bioaugmented MBR (in contrast to conventional MBR) with GAC-catalyzed anaerobic process lies herein.

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Table 3 e Average dye and organics removal in anaerobic and aerobic zone under different loadings of acid orange II dye. Reactor type

Overall loading (g/L.d)

Concentration after removal (mg/L) After anaerobic zone

MBR with GAC-packed anaerobic zone

MBR with sludge-only anaerobic zone

In permeate

Dye

TOC

TN

Dye

Dye

TOC

TN

0.1 0.25 0.5 1.0 0.25

0.94 1.03 1.16 1.44 1.03

0.055 0.067 0.087 0.127 0.067

3 8 30 105 240

0 1 1 5a 96

3 10 30 54 e

2 2 10 21 e

a Corresponding dye concentration in aerobic zone supernatant was 16 mg/L.

It is also notable from the data in Table 3 that that the reactor with a GAC-packed anaerobic zone outperformed the one with sludge-only anaerobic zone, confirming the importance of presence of GAC in the anaerobic zone. GAC-packed anaerobic zone and the bioaugmented aerobic zone are therefore essential to form an efficient color removal process. In addition to dye, textile wastewater contains many other colorless organics. Moreover, it has been reported that aromatic amines arising from anaerobic reduction of azo dyes are very toxic (O’Neill et al., 2000). Although aerobic removal of aromatic amines has been reported in a few studies (IsIk and Sponza, 2004), controversies exist (Lourenco et al., 2000). Hence, not only decoloration but also confirmation of removal of TOC is essential. It is noteworthy that in all the above mentioned studies (Mezohegyi et al., 2007; Ong et al., 2008) showing high rate decoloration in AC-packed systems, completion of organics removal under high loading was a special concern. In our study, almost complete removal of TOC and TN was achieved under the lowest (0.1 g/L.d) dyeloading. However, in contrary to the stable decoloration over all the dye-loadings, the total organics removal (especially that of TOC) deteriorated to some extent under the higher dyeloadings (Fig. 4, Table 3). Although the contribution of dye to TOC and TN was rather low for a dye-loading of 0.1, over a 50% increase in overall TOC-loading occurred when the dye-loading was raised to 1 g/L.d (Table 1). Increased loading 1500

TOC loading (Dye+starch) TOC loading (starch) Concentration in permeate

1400 1300 TOC loading (mg/L.d) TOC concentration (mg/L)

1200 1100

4 dyes

1000 900 800 90 80 70 60 50 40 30 20 10 0

a 0

on the aerobic zone may have caused the observed moderate decline in TOC removal rate. Nevertheless, it should be mentioned that, even under the highest loading, the average TOC and TN in the membrane-permeate was only 54 and 21 mg/L, respectively corresponding to over 96% TOC removal.

3.4.

The robustness of the developed process was tested by feeding four structurally different dyes (Table 1) in equal loading rates (0.25 g/L.d) resulting in a total dye loading of 1 g/L.d. No deterioration of the decoloration rate was observed during this investigation. Mezohegyi et al. (Mezohegyi et al., 2009) previously tested decoloration of 6 azo dyes in an up flow AC-packed bioreactor; however in that study a single dye was fed during any specific run. Stable decoloration under concomitant high loading of structurally different dyes is, therefore, another unique aspect of the current study. It is notable that owing to the different carbon contents of the dyes tested, a slight drop in TOC-loading occurred during this period, and this corresponded to a slight improvement in TOC (Fig. 4, from day 210). In fact during an extended observation under a slightly longer HRT a near-complete TOC removal was achieved (data not shown). Stable decoloration along with significant TOC removal over a prolonged period under extremely high dye-loadings evidences the superiority of the proposed hybrid process. While the current study confirmed the viability of fungibacteria bioaugmentation in a lab scale MBR, and the overall hybrid process demonstrated excellent removal performance, issues such as fungal enzyme loss and bacterial disruption of fungal activity may require further special attention in case of real scale application. Related strategies to tackle such shortcomings have been already pointed out in our previous publications (Hai et al., 2008b, 2009) and currently application of such strategies in conjunction with the proposed hybrid process is under investigation.

3.5. 30

60

90

120

150

180

210

240

Time (Day)

Fig. 4 e Long-term TOC removal under gradually increased dye loading (a. temporary deterioration due to air-diffuser malfunctioning).

Performance with structurally different dyes

Hydraulic performance of the membrane

The focus of this study was the removal performance of the proposed hybrid process. Nevertheless it is noteworthy that in addition to the accomplishment of significant color, TOC and TN removal, the membrane fouling in this study was rather minimal. This was manifested by the slight fluctuation of TMP (around 3 kPa) for most part of the operation period (data

w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 1 9 9 e2 2 0 6

not shown). Ex-situ chemical cleaning of the module was applied only once (on day 180) during the whole operation. The fouling avoidance capacity of a spacer-filled, compact module developed on the principle of minimizing intrusion of sludge was demonstrated by Hai et al. (Hai et al., 2008a). Apparently the restricted sway of fibers in the short and rigid module in this study also prevented intrusion of sludge and thereby mitigated fouling. Investigations under realistic higher fluxes are currently ongoing to substantiate the efficacy of short and compact modules.

4.

Conclusion

Stable decoloration along with significant organics (TOC, TN) removal over a prolonged period under extremely high dyeloadings was observed in a bioaugmented aerobic MBR with a GAC-packed anaerobic zone. The GAC-packed anaerobic zone played the key role in decoloration, while the aerobic zone was vital for TOC removal. However, in contrast to the limited role of aerobic stage in decoloration in conventional sequential anaerobiceaerobic processes, the aerobic stage in the developed MBR contributed significantly to decoloration under the higher dye-loadings. Our data also evidenced the suitability of a unique wastewater feeding strategy whereby separate streams emanating from a textile plant are selectively split between the GAC-packed and the aerobic zone.

Acknowledgment The authors would like to extend thanks to Japan Society for the Promotion of Science for the financial support. Thanks are also due to Mitsubishi Rayon Co. Ltd., Japan and Calgon Mitsubishi Chemical Corporation, Japan for their supply of membrane modules and activated carbon, respectively. Dr. Long D. Nghiem of University of Wollongong, Australia is thanked for his useful suggestions during preparation of this manuscript.

references

Cervantes, F.J., van der Zee, F.P., Lettinga, G., Field, J.A., 2001. Enhanced decolourisation of acid orange 7 in a continuous UASB reactor with quinones as redox mediators. Water Science and Technology 44, 123e128. Clescerl, L.S., Greenberg, A.E., Eaton, A.D., 2005. Standard methods for examination of water & wastewater 21st ed. American Public Health Association. Field, J.A., Brady, J., 2003. Riboflavin as a redox mediator accelerating the reduction of the azo dye mordant yellow 10 by anaerobic granular sludge. Water Science and Technology 48, 187e193. Fu, Y., Viraraghavan, T., 2001. Fungal decolorization of dye wastewaters: a review. Bioresource Technology 79, 251e262. Hai, F.I., Yamamoto, K., Fukushi, K., 2006. Development of a submerged membrane fungi reactor for textile wastewater treatment. Desalination 192, 315e322. Hai, F.I., Yamamoto, K., Fukushi, K., 2007. Hybrid treatment systems for dye wastewater. Critical Reviews in Environmental Science and Technology 37, 315e377. Hai, F.I., Yamamoto, K., Fukushi, K., Nakajima, F., 2008a. Fouling resistant compact hollow-fiber module with spacer for

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submerged membrane bioreactor treating high strength industrial wastewater. Journal of Membrane Science 317, 34e42. Hai, F.I., Yamamoto, K., Nakajima, F., Fukushi, K., 2008b. Removal of structurally different dyes in submerged membrane fungi reactorebiosorption/PAC-adsorption, membrane retention and biodegradation. Journal of Membrane Science 325, 395e403. Hai, F.I., Yamamoto, K., Nakajima, F., Fukushi, K., 2009. Factors governing performance of continuous fungal reactor during non-sterile operation - the case of a membrane bioreactor treating textile wastewater. Chemosphere 74, 810e817. Hao, O.J., Kim, H., Chiang, P.-C., 1999. Decolorization of wastewater. Critical Reviews in Environmental Science and Technology 30, 449e505. Hunger, K., Gregory, P., Miederer, P., Berneth, H., Heid, C., Mennicke, W., 2004. Important Chemical Chromophores of Dye Classes. Wiley-VCH Verlag GmbH & Co. KGaA. IsIk, M., Sponza, D.T., 2004. Monitoring of toxicity and intermediates of C.I. direct black 38 azo dye through decolorization in an anaerobic/aerobic sequential reactor system. Journal of Hazardous Materials 114, 29e39. Kapdan, I.K., Tekol, M., Sengul, F., 2003. Decolorization of simulated textile wastewater in an anaerobiceaerobic sequential treatment system. Process Biochemistry 38, 1031e1037. Knackmuss, H.-J., 1996. Basic knowledge and perspectives of bioelimination of xenobiotic compounds. Journal of Biotechnology 51, 287e295. Laugero, C., Sigoillot, J.C., Moukha, S., Frasse, P., Bellon-Fontaine, M.N., Bonnarme, P., Mougin, C., Asther, M., 1996. Selective hyperproduction of manganese peroxidases by Phanerochaete chrysosporium l-1512 immobilized on nylon net in a bubble-column reactor. Applied Microbiology and Biotechnology 44, 717e723. Lourenco, N.D., Novais, J.M., Pinheiro, H.M., 2000. Reactive textile dye colour removal in a sequencing batch reactor. Water Science and Technology, 321e328. Me´ndez-Paz, D., Omil, F., Lema, J.M., 2005. Anaerobic treatment of azo dye acid orange 7 under fed-batch and continuous conditions. Water Research 39, 771e778. Manu, B., Chaudhari, S., 2003. Decolorization of indigo and azo dyes in semicontinuous reactors with long hydraulic retention time. Process Biochemistry 38, 1213e1221. Mezohegyi, G., Kolodkin, A., Castro, U.I., Bengoa, C., Stuber, F., Font, J., Fabregat, A., Fortuny, A., 2007. effective anaerobic decolorization of azo dye acid orange 7 in continuous upflow packed-bed reactor using biological activated carbon system. Industrial & Engineering Chemistry Research 46, 6788e6792. Mezohegyi, G., Fabregat, A., Font, J., Bengoa, C., Stuber, F., Fortuny, A., 2009. Advanced bioreduction of commercially important azo dyes: modeling and correlation with electrochemical characteristics. Industrial & Engineering Chemistry Research 48, 7054e7059. O’Neill, C., Lopez, A., Esteves, S., Hawkes, F.R., Hawkes, D.L., Wilcox, S., 2000. Azo dye degradation in an anaerobic-aerobic treatment system operating on simulated textile effluent. Applied Microbiology and Biotechnology 53, 249e254. Ong, S.-A., Toorisaka, E., Hirata, M., Hano, T., 2008. Granular activated carbon-biofilm configured sequencing batch reactor treatment of C.I. acid orange 7. Dyes and Pigments 76, 142e146. Rau, J.R., Knackmuss, H.-J., Stolz, A., 2002. Effects of different quinoid redox mediators on the anaerobic reduction of azo dyes by bacteria. Environmental Science & Technology 36, 1497e1504. Ren, N., Chen, Z., Wang, A., Hu, D., 2005. Removal of organic pollutants and analysis of MLSS-COD removal relationship at different HRTs in a submerged membrane bioreactor. International Biodeterioration & Biodegradation 55, 279e284. Robinson, T., McMullan, G., Marchant, R., Nigam, P., 2001. Remediation of dyes in textile effluent: a critical review on

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current treatment technologies with a proposed alternative. Bioresource Technology 77, 247e255. Rozzi, A., Malpei, F., Bonomo, L., Bianchi, R., 1999. Textile wastewater reuse in northern Italy (Como). Water Science and Technology 39, 121e128. van der Zee, F.P., Villaverde, S., 2005. Combined anaerobiceaerobic treatment of azo dyesea short review of bioreactor studies. Water Research 39, 1425e1440.

van der Zee, F.P., Bisschops, I.A.E., Lettinga, G., Field, J.A., 2002. Activated carbon as an electron acceptor and redox mediator during the anaerobic biotransformation of azo dyes. Environmental Science & Technology 37, 402e408. You, S.-J., Damodar, R.A., Hou, S.-C., 2010. Degradation of reactive black 5 dye using anaerobic/aerobic membrane bioreactor (MBR) and photochemical membrane reactor. Journal of Hazardous Materials 177, 1112e1118.

w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 2 0 7 e2 2 1 2

Available at www.sciencedirect.com

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Chromium removal by combining the magnetic properties of iron oxide with adsorption properties of carbon nanotubes V.K. Gupta a,b,*, Shilpi Agarwal b, Tawfik A. Saleh a a b

Chemistry Department, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia Department of Chemistry, Indian Institute of Technology Roorkee, Roorkee 247667, India

article info

abstract

Article history:

The adsorption features of multiwall carbon nanotubes (MWCNTs) with the magnetic

Received 1 July 2010

properties of iron oxides have been combined in a composite to produce a magnetic

Received in revised form

adsorbent. Composites of MWCNT/nano-iron oxide were prepared, and were characterized

12 January 2011

by X-ray diffraction (XRD), field emission scanning electron microscope (FESEM) and

Accepted 15 January 2011

Fourier transform infrared spectroscopy (FTIR). XRD suggests that the magnetic phase

Available online 22 January 2011

formed is maghemite and/or magnetite. FESEM image shows nano-iron oxides attached to a network of MWCNTs. The adsorption capability of the composites was tested in batch

Keywords:

and fixed bed modes. The composites have demonstrated a superior adsorption capability

Multi-wall carbon nanotube/nano-

to that of activated carbon. The results also show that the adsorptions of Cr(III) on the

iron oxide composite

composites is strongly dependent on contact time, agitation speed and pH, in the batch

Chromium

mode; and on flow rate and the bed thickness in the fixed bed mode. Along with the high

Fixed bed

surface area of the MWCNTs, the advantage of the magnetic composite is that it can be

Batch mode

used as adsorbent for contaminants in water and can be subsequently controlled and

XRD

removed from the medium by a simple magnetic process. ª 2011 Elsevier Ltd. All rights reserved.

SEM FTIR

1.

Introduction

Heavy metals like Cr, Pb, Hg and Cd are common pollutants in environment. Chromium, one of these metals exists in two stable oxidation states, Cr (III) and Cr (VI). Chromium remains the sole leather-tanning chemical since its first successful trial by an American dye chemist, Augustus Schultz, in his two-bath method (Stellmach, 1990). Tanneries use basic chromium sulfate (CrOHSO4), which contains chrome in the trivalent oxidation state. Other main sources of chromium pollution are mining; cement ceramics and glass industries, uses in dyes, electroplating, and production of steel and other metal alloys, photographic material and corrosive paints

(Rana et al., 2004; Rutland, 1991). According to EPA drinking water standards, the maximum limit of chromium in drinking water is 0.1 mg/L which is based on total chromium (US EPA, 2011). The presence of strong oxidants can change Cr (III) to Cr (VI) (Tadesse et al., 2006). Various naturally available adsorbents like wool, olive cake, sawdust, pine needles, almond shells, cactus leaves, charcoal used tyres, soot, hazelnut shell, coconut shell charcoal, banana peel, seaweed, dead fungal biomass, cyanobacterium, and green alga were used for the removal of chromium (Gupta et al., 1999, 2001; Gupta and Ali, 2004; Ali and Gupta 2007; Gupta and Rastogi, 2008, 2009; Gupta et al., 2009; Singh et al., 2007). However, many of these naturally available adsorbents have

* Corresponding author. Chemistry Department, Indian Institute of Technology Roorkee, Roorkee 247667, India. Tel.: þ91 1332 285801; fax: þ91 1332 273560. E-mail address: [email protected] (V.K. Gupta). 0043-1354/$ e see front matter ª 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2011.01.012

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low chromium adsorption capacity and slow process kinetics. Thus, there is a need to develop innovative adsorbents useful both for industry and for the environment. Due to a large surface area, small, hollow, and layered structures, carbon nanotubes (CNTs) have already been investigated as promising adsorbents for various organic pollutants and metal ions and can be easily modified by chemical treatment to increase their adsorption capacity (Chen et al., 2009b). CNTs have been used for the treatment of heavy metals contaminated aqueous solutions. Unlike many adsorbents, CNTs possess different features that contribute to the superior removal capacities; such as fibrous shape with high aspect ratio, large accessible external surface area, and well developed mesopores. The pores have been reported to be mostly mesopores due to a high van der Waals interaction forces along the length axis (Wang et al., 2006; Inoue et al., 1998; Girifalco et al., 2000). The uses of CNTs as support of metallic oxides have still been reported (Xie and Gao, 2007; Takenaka et al., 2008). The materials have the advantages of high sorption capacity, large surface area and supported metallic oxides. Chemical oxidation polymerization, followed by the carbonization process has been employed to produce iron oxide-impregnated magnetic CNT composites. A solvo-thermal method has been also used to synthesize CNTeiron oxide composites. Other effective approaches, including the arc-discharge technique and electrolysis deposition, to prepare CNT-based functional materials have been developed (Zhenyu et al., 2005). The adsorption behavior of carbon nanotubeeiron oxides magnetic composite has been investigated for the removal of Pb(II) and Cu(II) from water (Peng et al., 2005), Ni(II) and Sr(II) (Chen et al., 2009a) and cationic dyes (Gong et al., 2009). The application of magnetic particle technology to solve environmental problems has received considerable attention in recent years. For this, the objective of this study was to prepare and characterize carbon nanotube/nano-iron oxide composites and to demonstrate how it could be utilized for the removal of chromium (III) in such simple batch mode and subsequent fixed bed treatment methods.

2.

Experimental

2.1.

Materials

Nitric acid, chromium nitrate and ferric and ferrous chlorides were all obtained from SigmaeAldrich. Chromium solutions of different initial concentrations were prepared by diluting the stock solution in appropriate proportions. In order to prevent metal contamination from laboratory glassware, glassware was kept overnight in a 10% (v/v) HNO3 solution. Activated carbon (Darco G-60) was from Fisher Scientific Company. All other chemicals were of analytical grade. Stock solutions were prepared daily by dissolving in distilled water.

2.2.

Synthesis of MWCNTs/nano-iron oxide

The multiwall carbon nanotubes (MWCNTs) used in this study as a building block, were of more than 95% purity and procured commercially. MWCNTs was of the following specifications; purity, >95%; outer diameter, 30e50 nm; inside diameter,

5e10 nm; 30e50 nm; length, 10e20 mm. The purification process using HNO3 was performed to remove impurities if any and to modify the surface of the tube with carbonyl and hydroxyl groups. The purification process was accomplished by stirring MWCNTs in concentrated nitric acid at 70  C for 12 h, followed by filtering and washing with distilled water, and then drying at 110  C for 6 h. Then, MWCNTs were oxidized by refluxing with 50% nitric acid at 120  C for 12 h under stirring conditions. The product was then filtered and rinsed with doubly distilled water and dried overnight in the oven. The preparation of MWCNTs/nano-iron oxide composites was accomplished as follows. All glassware was cleaned by aqua regia freshly prepared prior to use. A mixed solution of 0.1 M ferric chloride hexahydrate and 0.05 M ferrous chloride tetrahydrate with a molar ratio of one to two was prepared. Subsequently, a specific amount of oxidized MWCNTs was suspended in the mixed solution for 2 h. Then, at constant temperature of 70  C, 5 M- NH4OH solution was added drop wise to precipitate iron oxides. The mixture was adjusted to pH 10 and then aged for 1 h under stirring. After the completion of the reaction, the suspension was allowed to cool. The product was separated by magnet then washed by distilled water and ethanol respectively. The obtained composite was dried in an oven at 100  C for 2 h.

2.3.

Characterization methods

The size and morphology of the magnetic composites were characterized by scan electron microscopy (SEM) using a field emission scanning electron microscope (FESEM, FEI NovaNano SEM-600, Netherlands). The structure phases and average size of the synthesized adsorbents was analyzed by X-ray diffraction (XRD) (Shimadzu XRD Model 6000). Infrared absorption spectroscopy (IR) spectra were measured at room temperature on a Fourier transform infrared (FTIR) spectroscopy using the KBr Pellet technique.

2.4.

Adsorption experiments

2.4.1.

Batch adsorption experiments

Batch adsorption experiments were conducted by adding the composite mass to 50 mL of different Cr(III) test solutions at different pH (3e7) in an Erlenmeyer flask (100 mL capacity). The initial solution pH was adjusted using 0.1 M HCl or 0.1 M NaOH. The flasks were agitated at different speed (0-to-150 rpm) in a rotary shaker for different contact time (10e60 min). The composite mass was separated from the test solution by magnet followed by filtration using a vacuum filter. For the fixed bed system, the column surrounded with a magnet was packed with the synthesized composites. Then the prepared Cr(III) solutions were passed through to study the adsorption capacity. The column diameter and length used in every experiment were kept constant with a bed depth of 1 cm and diameter of 0.3 mm. The different layer thickness of the adsorbent and the flow rate of the solutions were used as specified for each experiment. All experiments were carried out at ambient temperature (25  2  C). The amount of chromium adsorbed was calculated from the difference between its concentration in test solution and in the supernatant liquid. The initial and final concentrations of chromium were analyzed by using inductively

w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 2 0 7 e2 2 1 2

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Fig. 1 e SEM images of (A) oxidized MWCNTs (B) MWCNTs/nano-iron oxide.

coupled plasma mass spectrometry (ICP-MS). The samples were filtered prior to spray chamber applications to remove the possible agglomerates.

3.

Results and discussion

3.1.

Characterization of MWCNT/nano-iron oxide

After the synthesis of MWCNT/nano-iron oxide composite, a test with the magnet showed that the whole material is magnetic and completely attracted to the magnet. The morphologies of uncoated MWCNTs and synthesized MWCNT/ nano-iron oxide were obtained by SEM. Fig. 1A and B shows the SEM images of oxidized MWCNTs and the composites, respectively. SEM image (Fig. 1B) of the composites depicts an entangled network of oxidized MWCNTs with clusters of iron oxides

attached to them. Surface area of the prepared composite was measured using BET method. The specific surface area of MWCNT/nano-iron oxide composite was 92 m2/g. Under the reaction conditions employed, four iron oxides are commonly formed. These are Fe3O4 (magnetite), g- Fe2O3 (maghemite), a-Fe2O3 (hematite) and a-FeO(OH) (goethite). Among them two magnetite and maghemite are magnetic (Perez et al., 1998). Fig. 2A and B shows the X-ray diffraction patterns of MWCNTs and MWCNTs/nano-iron oxide composites. The two peaks corresponding to the structure of MWCNTs also exist in the XRD pattern of the magnetite composites. The XRD pattern of the magnetic composites reveals a cubic iron oxide phase. The presence of maghemite and magnetite as the magnetic phase in the composite is supported by their indexes diffraction peaks as shown in Fig. 2B. The average grain size ‘d’ of the iron oxide was estimated by using the standard equation known as Debye Scherrer formula (Yu et al., 2009). d¼

0:9l bcosq

where l ¼ wavelength of the X-ray, b ¼ FWHM (Full Width at Half Maximum) width of the diffraction peak, q ¼ diffraction angle. The average size of iron oxide was 18 nm.

Fig. 2 e X-ray diffraction of (A) oxidized MWCNTs (B) MWCNTs/nano-iron oxide where Mn: Magnetite (Fe3O4); Mh: Maghemite (g-Fe2O3).

Fig. 3 e FTIR spectra of (A) oxidized MWCNTs (B) MWCNTs/ nano-iron oxide.

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FTIR measurements are used in order to confirm the formation of iron oxide. FTIR spectra, Fig. 3A and B of oxidized MWCNTs and synthesize MWCNTs/nano-iron oxide were performed for a better comprehension of the structure and composition of these materials. An absorption band revealing the vibrational properties of FeeO bond is observed for in around 477 cm1. This band is mainly assigned to the stretching vibrations of FeO (yFeO). The broad absorption peaks in the range of 3300e3500 cm1 correspond to eOH group, indicating existence of the hydroxyl groups on the surface of the composites or it can be attributed to the adsorption of some atmospheric water during FTIR measurements. Those at 1570e1655 cm1 are the C]O stretching mode of the functional groups on the surface of the MWCNTs. The two peaks at 2920 and 2854 cm1 correspond to the CeH stretch vibration, originated from the surface of tubes, Fig. 3A, are obviously weak in Fig. 3B.

3.2.

Results of batch experiments

3.2.1.

Effect of contact time

The influence of contact time on the adsorption capacity of activated carbon, MWCNTs and the produced composite is depicted in Fig. 4. Less than 5% adsorption by activated carbon was observed. According to the literature reports activated carbon is expected to have a poor adsorption capacity for removal of contaminants comparing with MWCNTs. It is clearly stated that one of the disadvantages of using activated carbon is that it presents problems with the adsorption of hydrophilic substances (Kandah and Meunier, 2007; Pillay et al., 2009). Both MWCNTs and the MWCNTs/nano-iron oxide by contrast, show a good ability to remove Cr(III) from aqueous solution. The MWCNTs/nano-iron oxide, however, show a greater ability, approximately 90% adsorption after 60 min contact time. This can be explained by the additional adsorbing sites that provided by the oxygen atoms of iron oxide nanoparticles, average size is 18 nm, Fig. 2B, on the

Fig. 4 e The effect of contact time on the amount of Cr(III) adsorbed on different adsorbents (Conditions: initial Chromium concentration 20 ppm; Dosage of adsorbent [ 50 mg; pH 6; Agitation speed [ 150 rpm).

Fig. 5 e The effect of pH on the amount of Cr(III) adsorbed on the MWCNT/nano-iron oxide (Conditions: as in Fig. 4).

surface of MWCNTs which are also available for electrostatic interaction with the chromium. It should be noticed that in the composite there are two sorts of adsorbing sites; MWCNTs and iron oxide nanoparticles.

3.2.2.

Effect of pH

The pH effect on the adsorption of Cr(III) onto the prepared composite was studied by evaluating the adsorption at pH values of 3, 4, 5, 6, and 7 as shown in Fig. 5. It was found that the composite effective for the adsorption of Cr(III) above pH 3 and below pH 7, since Cr(III) at strongly acidic media did not adsorb to the composite. Fig. 5 illustrates that maximum adsorption capacity at pH 5e6. The result could be explained based on the reported speciation diagrams (Zhang et al., 2008; Richard and Bourg, 1991; Chuan and Liu, 1996). It has been reported that at pH less than 3.6, Cr(III) is present as Cr3þ while at pH higher than 4 and lower than 6.5, it is present as Cr(OH)2þ Cr(OH)þ 2 . However, it is present as neutral Cr(OH)3 species at pH between 7 and 12. On the other hand, the point of zero charge (PZC) of oxidized MWCNTs was reported to be approximately 4 (Lu and Chiu, 2006; Gao et al., 2009). Thus, when it is placed in aqueous

Fig. 6 e The effect of agitation speed on the amount of Cr(III) adsorbed on the MWCNT/nano-iron oxide.

w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 2 0 7 e2 2 1 2

Fig. 7 e The effect of dosage on the amount of Cr(III) adsorbed on the MWCNT/nano-iron oxide.

solutions of pH below its PZC ¼ 4 it becomes protonated and exhibits a positive net charge on its surface, thus it could not well adsorb the Cr3þ. In contrast, when placed in solutions above its PZC ¼ 4, the net surface charge turns negative by deprotonation and it could adsorbs the positively charged chromium species. At pH 7, the chromium presents as neutral Cr(OH)3 species which has low affinity for electrostatic interaction with the positively charged composite of MWCNTs. Therefore, at pH 7, columbic or electrostatic interactions do not favor the adsorption of chromium. The maximum Cr(III) removal was 82 and 88% at pH 5 and 6, respectively. The association between Cr(III) and the composite is therefore governed by Cr(OH)2þ species, since Cr(OH)2þ dominates the system within this pH range (Richard and Bourg, 1991; Chuan and Liu, 1996).

Fig. 9 e The effect of layer thickness on the amount of Cr(III) adsorbed on the MWCNT/nano-iron oxide.

3.2.4.

Effect of agitation speed

The effect of agitation speeds was investigated by a range from 0 rpm to 150 rpm, Fig. 6. The adsorption of Cr(III) was low without or at low agitation speed and rose as the agitation speed was increased to 150 rpm. This effect can be attributed to the decrease in boundary layer thickness around the adsorbent particles being a result of increasing the degree of mixing.

3.3.

Results of fixed bed experiments

3.3.1.

Effect of flow rate

Experiments were performed with flow rates of 1e5 mL/min whereas the thickness of the adsorbent was 3 mm. As depicted in Fig. 8, it was observed that the lower the flow rate the higher the chromium removal. This is due to the more contact time when the flow rate is low.

Effect of bed thickness

The removal of Cr(III) by MWCNTs/nano-iron oxide fixed bed composite of different thickness at a constant flow rate of 1 mL/min. As shown in Fig. 9, by increasing the thickness of the fixed bed layer, the uptake of chromium ions increases. Increasing the fixed bed layer leads to increase of the available interaction sites of the composite that provided more sites for adsorption of chromium ions with a thicker layer and the efficiency is increased by allowing sufficient time for the adsorbate to diffuse into the adsorbent. When the thickness of the layer of the composite was increased from 0.5 mm to 3 mm, the percentage removal was increased from 40% to 90%. Comparing with its efficiency in batch mode, the prepared adsorbent displayed the main advantage of separation convenience when a fixed bed column was used. This is because the chromium anions are forced to interact with the active adsorbing sites on the large surface-area composite during the penetration.

4.

Fig. 8 e The effect of flow rate (mL/min) on the amount of Cr(III) adsorbed on the MWCNT/nano-iron oxide.

Effect of dosage of adsorbent

Various amounts of adsorbent ranging from 5 mg to 100 mg were used. The percentage removal of Chromium ions varied linearly with the amount of the adsorbent as shown in Fig. 7.

3.3.2. 3.2.3.

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Conclusion

The MWCNT/nano-iron oxide magnetic composites were prepared. Characterization using XRD suggests that the magnetic phase formed is maghemite and magnetite. SEM image shows clusters of nano-iron oxides attached to a network

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of MWCNTs. The adsorbents show good adsorption capacity for chromium ions. The adsorption capability of the composite is higher than that of MWCNTs and activated carbon. In the batch mode, it was found that the adsorption capability is increased by increasing the agitation speed and it is pH dependent. The experiments performed in the fixed bed mode revealed that the removal capability of the composites for chromium increases with decreasing the flow rate. Our results demonstrate that the MWCNTs/nano-iron oxide magnetic composites, whose surface area is large, are very promising materials as adsorbent for contaminants in water with good performances. The material reported here could be used as a base for encapsulation in reverse osmoses systems. We believe it is required to have a small material with large surface area and good ability to remove pollutants, especially for compact columns used in water filtration units.

Acknowledgement Tawfik A. Saleh acknowledges the support of King Fahd University of Petroleum and Minerals, (KFUPM) Dhahran, Saudi Arabia for this work.

references

Ali, I., Gupta, V.K., 2007. Advances in water treatment by adsorption technology. Nature Protocols 1 (6), 2661e2667. Chen, C., Hu, J., Shao, D., Li, J., Wang, X., 2009a. Adsorption behavior of multiwall carbon nanotube/iron oxide magnetic composites for Ni(II) and Sr(II). Journal of Hazard Materials 164 (2e3), 923e928. Chen, C.L., Wang, X.K., Nagatsu, M., 2009b. Europium adsorption on multiwall carbon nanotube/iron oxide magnetic composite in the presence of polyacrylic acid. Environmental Science & Technology 43, 2362e2367. Chuan, M.C., Liu, J.C., 1996. Release behavior of chromium from tannery sludge. Water Research 30, 932e938. Gao, Z., Bandosz, T.J., Zhao, Z., Han, M., Qiu, J., 2009. Investigation of factors affecting adsorption of transition metals on oxidized carbon nanotubes. Journal of Hazardous Materials 167 (2009), 357e365. Girifalco, L.A., Hodak, M., Lee, R.S., 2000. Carbon nanotubes, buckyballs, ropes, and a universal graphitic potential. Physical Review B 62 (19), 13104e13109. Gong, J.L., Wang, B., Zeng, G.M., Yang, C.P., Niu, C.G., Niu, Q.Y., Zhou, W.J., Liang, Y., 2009. Removal of cationic dyes from aqueous solution using magnetic multi-wall carbon nanotube nanocomposite as adsorbent. Journal of Hazard Materials 164 (2e3), 1517e1522. Gupta, V.K., Ali, I., 2004. Removal of lead and chromium from wastewater using bagasse fly ashda sugar industry waste. Journal of Colloid Interface Science 271, 321e328. Gupta, V.K., Rastogi, A., 2008. Sorption and desorption studies of chromium(VI) from nonviable cyanobacterium Nostoc muscorum biomass. Journal of Hazard Materials 154, 347e354. Gupta, V.K., Rastogi, A., 2009. Biosorption of hexavalent chromium by raw and acid treated green alga Oedogonium hatei from aqueous solutions. Journal of Hazard Materials 163, 396e402. Gupta, V.K., Carrott, P.J.M., Ribeiro Carrott, M.M.L., Suhas, 2009. Low cost adsorbents: Growing approach to wastewater

treatment - A review. Critical Reviews in Environmental Science and Technology 39, 783e842. Gupta, V.K., Mohan, D., Sharma, S., Park, K.T., 1999. Removal of chromium(VI) from electroplating industry wastewater using bagasse fly ash e a sugar industry waste material. The Environmentalist 19, 129e136. Gupta, V.K., Gupta, M., Sharma, S., 2001. Process development for the removal of lead and chromium from aqueous solutions using red muddan aluminium industry waste. Water Research 35, 1125e1134. Inoue, S., Ichikuni, N., Suzuki, T., Uematsu, T., Kaneko, K., 1998. Capillary condensation of N2 on multiwall carbon nanotubes. Journal of Physical Chemistry B 102 (24), 4689e4692. Kandah, M.I., Meunier, J.L., 2007. Removal of nickel ions from water by multiwalled carbon nanotubes. Journal of Hazardous Materials 146, 283e288. Lu, C., Chiu, H., 2006. Adsorption of zinc(II) from water with purified carbon nanotubes. Chemical Engineering Science 61, 1138e1145. Peng, X., Luan, Z., Di, Z., Zhang, Z., Zhu, C., 2005. Carbon nanotubes-iron oxides magnetic composites as adsorbent for removal of Pb(II) and Cu(II) from water. Carbon 43 (4), 880e883. Perez, O.P., Umetsu, Y., Sasaki, H., 1998. Precipitation and densification of magnetic iron compounds from aqueous solution at room temperature. Hydrometallurgy 50, 223e229. Pillay, K., Cukrowska, E.M., Coville, N.J., 2009. Multi-walled carbon nanotubes as adsorbents for the removal of parts per billion levels of hexavalent chromium from aqueous solution. Journal of Hazardous Materials 166, 1067e1075. Rana, P., Mohan, N., Rajagopal, C., 2004. Electrochemical removal of chromium from wastewater by using carbon aerogel electrodes. Water Research 38, 2811e2820. Richard, F.C., Bourg, A.C.M., 1991. Aqueous geochemistry of chromium: a review. Water Research 25, 807e816. Rutland, F.H., 1991. Environmental compatibility of chromiumcontaining tannery and other leather product wastes at land disposal sites. Journal of the American Leather Chemists Association 86, 364e375. Singh, A.K., Gupta, V.K., Gupta, B., 2007. Chromium(III) selective membrane sensors based on Schiff bases as chelating ionophores. Analytica Chimica Acta 585 (1), 171e178. Stellmach, J.J., 1990. The commercial success of chrome tanning: a study and commemorative. Journal of the American Leather Chemists Association 85 (11), 407e454. Tadesse, I., Isoaho, S.A., Green, F.B., Puhakka, J.A., 2006. Lime enhanced chromium removal in advanced integrated wastewater pond system. Bioresource Technology 97, 529e534. Takenaka, S., Arike, T., Matsune, H., Tanabe, E., Kishida, M., 2008. Preparation of carbon nanotube-supported metal nanoparticles coated with silica layers. Journal of Catalysis 257, 345e355. US EPA, 2011. Ground Water and Drinking Water, Current Drinking Water Standards, EPA 816-F-02. Wang, X.Z., Li, M.G., YChen, W., Cheng, R.M., Huang, S.M., Pan, L. K., Sun, Z., 2006. Electrosorption of ions from aqueous solutions with carbon nanotubes and nanofibers composite film electrodes. Applied Physics Letters 89, 053127e053134. Xie, X., Gao, L., 2007. Characterization of a manganese dioxide/ carbon nanotube composite fabricated using an in situ coating method. Carbon 45 (12), 2365e2373. Yu, C.H., Al-Saadi, A., Shih, S., Qiu, L., Tam, K.Y., Tsang, S.C., 2009. Immobilization of BSA on silica-coated magnetic iron oxide nanoparticle. Journal of Physical Chemistry C 113, 537e543. Zhang, N., Suleiman, J.S., He, M., Hu, B., 2008. Chromium(III)imprinted silica gel for speciation analysis of chromium in environmental water samples with ICP-MS detection. Talanta 75, 536e543. Zhenyu, S., Zhimin, L., Wang, Y., Han, B., Du, J., Zhang, J., 2005. Fabrication and characterization of magnetic carbon nanotube composites. Journal of Material Chemistry 15, 4497e4501.

w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 2 1 3 e2 2 2 6

Available at www.sciencedirect.com

journal homepage: www.elsevier.com/locate/watres

The effect of anoxia and anaerobia on ciliate community in biological nutrient removal systems using laboratory-scale sequencing batch reactors (SBRs) Donata Dubber*, N.F. Gray Centre for the Environment, Trinity College Dublin, Dublin 2, Ireland

article info

abstract

Article history:

Little is known about the effect of anaerobic and anoxic stages on the protozoan

Received 20 October 2010

community in the activated sludge process and how this subsequently affects perfor-

Received in revised form

mance. Using a laboratory-scale BNR system the effect of different periods of anoxia on

17 January 2011

both the protozoan community and performance efficiency have been examined. Four

Accepted 19 January 2011

SBRs were operated at two cycles per day using a range of combined anoxic/anaerobic

Available online 31 January 2011

periods (0, 60, 120 and 200 min). Effluent quality (TOC, BOD, TP, TN, NH4eN, NO3eN and NO2eN), sludge settleability and ciliate community (species diversity and abundance) were

Keywords:

analysed over a periods of up to 24 days of operation. The species richness and total

Protozoa

abundance of ciliates were found to decrease with longer anoxic/anaerobic periods. Both,

Protists

positive and negative significant correlations between the abundance of certain species

Wastewater treatment

and the period of anoxia was observed (e.g. Opercularia microdiscum, Epicarchesium gran-

Activated sludge

ulatum), although other species (i.e. Acineria uncinata, Epistylis sp.) were unaffected by

Phosphorus removal

exposure to anoxia. In the laboratory-scale units, the 60 min anoxic/anaerobic period

Process performance

resulted in good process performance (TOC and BOD removal of 97e98% respectively), nitrification (80e90%), denitrification (52%) but poor levels of biological P-removal (12%); with the protozoan community moderately affected but still diverse with high abundances. Increasing the length of anoxia to up to 200 min did not enhance denitrification although P-removal rates increased to between 22 and 33%; however, ciliate species richness and total abundance both decreased and sludge settleability became poorer. The study shows that activated sludge ciliate protozoa display a range of tolerances to anoxia that result in altered ciliate communities depending on the length of combined anoxic/anaerobic periods within the treatment process. It is recommended that anoxic/anaerobic periods should be optimised to sustain the protozoan community while achieving maximum performance and nutrient removal. ª 2011 Elsevier Ltd. All rights reserved.

1.

Introduction

Nutrients in wastewater such as phosphates and nitrogen compounds lead to accelerated eutrophication in natural water bodies such as rivers, lakes, estuarines and coastal

waters. Biological nutrient removal (BNR) from domestic and industrial wastewaters is a key factor in preventing eutrophication in receiving waters being one of the most economical and efficient methods of nutrient control (Akpor et al., 2008). This is reflected in the rapid increase in the use of

* Corresponding author. E-mail address: [email protected] (D. Dubber). 0043-1354/$ e see front matter ª 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2011.01.015

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BNR systems since the introduction of the EU Urban Wastewater Treatment Directive (91/271/EEC) which specifies nitrogen and phosphorus limits for effluents discharged to sensitive areas. To integrate biological nutrient removal (BNR) into the activated sludge process anaerobic, anoxic and aerobic cycles are needed. This has lead to a rising importance of sequencing batch reactors (SBRs) which provide a better operation management of the mixed liquor with excellent control over oxygen and redox conditions, employing separate aerobic, anoxic and anaerobic cycles (Carucci et al., 1994; Gray, 2004; Hu et al., 2005; Obaja et al., 2005; Spagni et al., 2007). Therefore, unlike conventional activated sludge systems, the biota in BNR systems experience unique kinetic and metabolic stresses arising from redox shifts. Apart from the operational steps required for the nutrient removal, anoxia can also occur during sludge separation, storage, return, under-aeration and overloading (Gray, 2004). Comparative studies of the protozoan community in wastewater treatment plants operating under a wide variety of conditions have concluded that certain species display higher tolerances to low dissolved oxygen (Esteban et al., 1991a; Madoni et al., 1993; Lee et al., 2004). However, few studies have specifically examined the effect of anoxia/anaerobia on protozoan communities or which species can endure the complete absence of dissolved oxygen. MaurinesCarboneill et al. (1998) found that protozoa and metazoa in activated sludge disappeared completely after three days of anaerobiosis. Toman and Rejic (1988), using a laboratory-scale reactor, found that exposure to either zero or very low oxygen concentrations induced by intermittent 24 h interruptions in the aeration neither adversely affected performance nor the activated sludge biocenosis. Little is known about the long term effect of the stress caused by the repeated exposure of shorter periods of anoxia/anaerobia, as it occurs in BNR systems, on the development or maintenance of protozoan species. Due to the important role of protozoan in the purification process (Curds et al., 1968; Curds and Fey, 1969), it would be detrimental to SBR and BNR operational performance if the alternating oxidation-reduction potential (ORP) adversely affect the protozoan community. Enabling protozoan community structure to be predicted in relation to anoxia will permit more effective process management resulting in optimum treatment capability. Thus the aim of this study was to determine the effect of anoxia/anaerobia on both the protozoan community and performance efficiency in BNR activated sludge systems. Information concerning the ability of ciliates to tolerate anoxia was also obtained and tolerant and sensitive species identified.

2.

Material and methods

2.1.

Laboratory-scale sequencing batch reactors (SBRs)

Four identical 3.4 L volume laboratory-scale SBRs were constructed as outlined in Fig. 1. A magnetic stirrer (SB161, Stuart Scientific, UK) ensured homogeneous mixing during the reaction periods. Aeration was supplied by an aquarium air pump through a diffuser, obtaining dissolved oxygen concentrations

between 1 and 2 mg L
1 in the aeration phase. The reactors were operated at two cycles per day using different combined anoxic/anaerobic periods increasing from 0, 60, 120 and 200 min in reactor 1, 2, 3 and 4 respectively. Detailed cycle time configurations for the laboratory-scale SBRs can be found in Table 1. During each cycle, 1.7 L effluent was decanted and replaced with synthetic sewage (i.e., 50% volumetric exchange ratio) giving a HRT of 1 day (Ndon, 2007). OECD synthetic sewage (Christofi et al., 2003; Gendig et al., 2003) was used as the feed for the lab scale plant. The 100 fold concentrated stock solution was stored at
18  C, thawed when required and diluted to the necessary concentration to refill the sewage reservoir and to provide the desired sludge loading of 0.1 g BOD5 g
1 MLSS d
1. To avoid a decrease in the reactor pH during nitrification, as slightly acidic conditions are known to adversely affect the ciliate community (Cybis and Horan, 1997), NaHCO3 was added to the synthetic sewage at a final concentration of 0.6 g L
1 (Christofi et al., 2003). The storage containers were kept cooled at an approximate temperature of 4e10  C to reduce bacterial growth and prevent degradation of the sewage. Each reactor was fitted with two peristaltic pumps (iProcess, USA), for feeding and for drawing off effluent and excess sludge, respectively. The reactor MLSS was maintained at between 3000 and 3400 mg L
1 by wasting excess sludge on a batch basis before the start of the settling period. The SBR operation cycles (Table 1) were automatically controlled via a computer and programmable external timer power control units (IP Power 9258, Audon Electronics, UK). To monitor the operating conditions, each reactor was equipped with an ORP (platinum-rod electrode ORP-31C, single junction Ag/AgCl Gel reference, Nico2000 Ltd., UK) and a pH electrode (ELIT P11, AgCl reference, Nico2000 Ltd., UK). The electrodes were connected to the computer through an analyser (8 Channel Analyser ELIT 9808, Nico2000 Ltd., UK) and readings were recorded every 5 min. The reactors were operated under identical conditions each time using mixed liquor from different full scale WWTP as a seed. During the first experiment the reactors were operated for 16 days which was extended to 24 days in the second experiment to determine whether there were significant changes in the developments of protozoan communities were observable over a longer period.

2.2.

SBR inoculums

The sludge used to seed the reactors for the first experiment was obtained from Leixlip Wastewater Treatment Plant (WWTP), a medium sized (45 000 p.e.) conventional plant with completely mixed aeration tanks treating mainly domestic (80%) wastewater. For the second run mixed liquor was sourced from Swords WWTP (60 000 p.e.). This plant is an extended aeration BNR system, incorporating both anoxic and anaerobic periods, which treats mainly domestic (95%) wastewater.

2.3.

Microscopic analysis of protozoan community

Microscopic analyses of the mixed liquor were carried out at the start, after 8, 16 days and also in experiment 2 after 24 days. Ciliate enumeration was performed using phase

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pH

2

aeration

effluent ORP

Electrode connector

pump

pump

4

influent

3

Sewage reservoir

magnetic stirrer

1

Timer unit 1

2

3

4

Fig. 1 e Configuration of the laboratory-scale SBRs.

contrast microscopy at 100magnification with species differentiation and identification at higher magnifications up to 400 depending on the size of species using the keys of Foissner et al. (1991, 1992; 1994; 1995) Curds (1969) and Curds et al. (2008). The estimation of ciliate population densities were based on enumerations using four 25 mL sub-sample replicates. With an average process time of 20e30 min per replicate, depending on species abundances, the total number of replicates that could be counted at one day was limited to four. According to Dubber and Gray (2009) this ensures a species recovery of approximately 75% of all except the rarest species (i.e. those comprising <1% of the total protozoan abundance).

2.4.

Process performance and physico-chemical analysis

To monitor process performance and nutrient removal associated with the prevalent protozoan community effluent samples have been collected after 1, 8, 16 days and also after 24 days in experiment 2. Biological oxygen demand (BOD5), total organic carbon (TOC), total phosphorus (TP), total nitrogen (TN), ammonia, nitrate and nitrite concentrations have been measured according to Standard Methods (APHA,

2005). Because the COD analysis produces hazardous wastes, including mercury and hexavalent chromium, estimation of process performance by COD removal was avoided. Instead, TOC together with the BOD5 was used as a performance parameter (Dubber and Gray, 2010). The BOD5 was measured respirometrically using the Oxitop system (Reuschenbach et al., 2003) suppressing nitrification by the addition of 0.5 mg L
1 allythiourea (ISO, 2003). TOC was measured by thermocatalytic oxidation with a high temperature TOC analyser (vario TOC cube, Elementar, Germany). Using the same analyser, the total bound nitrogen was determined. The phosphorus compounds were oxidised to ortho-phosphates applying the acid persulfate method, then the ascorbic acid method was used to measure total phosphorus. The ammonia was determined using an ion-selective electrode (ELIT 8051, Nico, 2000) and nitrate concentrations were measured using the automated cadmium reduction method (Lachat flow injection analyser Quick Chem 8500, Lachat Instruments, US). Nitrite was measured colourmetrically using the same Lachat flow injector that was used for nitrate. The same methods were used to characterise and quantify the influent composition. To include the variation of influent composition that occurred due to storage in the refill tanks influent samples

Table 1 e Cycle time configurations for the laboratory-scale SBRs. Reactor 1 2 3 4

Cycle time [min]

Number of cycles/day

Aeration off [min]

Aeration on [min]

Settle [min]

Draw and fill [min]

Ratio aerobic/ anaerobic time

720 720 720 720

2 2 2 2

60 120 200

610 550 490 410

60 60 60 60

50 50 50 50

9.17 4.08 2.05

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were taken from the freshly prepared dilution and after 12 h of storage just before the start of the 2nd cycle. The mixed liquor suspended solids (MLSS) and the sludge volume index (SVI) of the mixed liquor were determined using Standard Methods (APHA, 2005). While the MLSS was monitored throughout the whole experiment the SVI was only measured at the end. However, the settleability in all reactors was observed relative to each other throughout the experiment by comparison of the sludge height after settling. Using the wasted volume of mixed liquor and its MLSS concentration the amount of solids wasted on average per day was estimated.

2.5.

Statistical analysis

The effluent quality (in terms of TOC, BOD5, TP, TN, NH4eN, NO3eN and NO2eN) as well as the counted protozoan abundances have been analysed for statistical differences between the four SBRs. In order to quantify the treatment performance of the reactors, removal rates [%] were calculated for organic matter (BOD5, TOC) and nutrients (NH4, TP, TN) using equation (1) with cin and cout being the concentrations in influent and effluent respectively.
 cout R ¼ 100
,100 (1) cin Propagation of uncertainty was used to obtain the standard deviation for the calculated removal rates. Equation (2) was applied to equation (1) with sin and sout being the standard deviations obtained from the measurements of influent and effluent concentrations respectively. s
ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2
2ffi vR vR (2) ,sin þ ,sout sR ¼ vcin vcout To detect significant differences in removal rates between the reactors, 95% confidence intervals were calculated. In order to further assess the similarity of protozoan community structure between the different reactors cluster analysis was employed using total ciliate abundances. The cluster analysis was performed with standardised variables using the single linkage (nearest neighbour) method and the

squared Euclidean distance. For all statistical analyses the programmes SPSS 14 and Minitab 15 were used.

3.

Results and discussion

3.1.

Operational conditions

3.1.1.

Sludge loading and influent characteristics

The sludge loading of the reactors was successfully maintained between 0.08 and 0.1 g BOD g
1 MLSS d
1 throughout the first experiment with an average of 0.092  0.007 g g
1d
1. During the second experiment the average sludge loading was 0.098  0.004 g g
1d
1. The variation in the sludge loading between the 4 reactors was 0.003 and 0.0017 g g
1d
1 in the first and second experiment, respectively. The characteristics of the synthetic sewage feed are summarised in Table 2 which shows that small changes in quality occurred during storage in the refill tanks.

3.1.2.

Sludge production

The amount of solids wasted from the reactors per day to maintain the operating MLSS varied from 386 to 445.6 mg during experiment 1. The highest sludge production was observed in reactor 3 and 4. During the second experiment between 274.2 and 367.5 mg solids were wasted per day. Again more solids were produced in reactor 3 and 4 than in the other reactors. Lee and Welander (1996) have shown that the feeding activity of rotifers have a strong influence on sludge production. During the first experiment rotifers were not quantified but in the second experiment rotifer abundance was observed to decline with increasing period of aeration disruption within the cycle which could explain the higher sludge production observed in reactor 3 and 4.

3.1.3.

ORP and pH monitoring

In previous studies (Yu et al., 1997; Akin and Ugurlu, 2005; Spagni et al., 2007) it has been shown that with online ORP and pH measurements it is possible to obtain important information about the processes in the reactors and to identify the end of denitrification and nitrification. The ORP gives a measure of the general condition of the liquid and whether the oxidation or the reduction reactions dominate as a process

Table 2 e OECD sewage composition and influent characteristics in the used concentration. Composition of 100 fold concentrated stock solution Constituents Peptone Meat extract Urea NaCl CaCl2 2H2O MgSO4 7H2O K2HPO4

Measured influent characteristics
1

Concentration [g L ]

Parameter

Influent concentrations Mean values [mg L
1]

16.0 11.0 3.0 0.7 0.4 0.2 2.8

BOD5 TOC Total N NH3eN NO3eN NO2eN Total organic Na Total P

298.7  15.4 175.9  7.3 86.8  2.3 15.4  2.7 0.042  0.007 0.022  0.007 71.3  3.6 8.5  0.3

a calculated as TON ¼ TN
NH3eN
NO3eN - NO2eN.

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(Gray, 2004). This is mainly dependent on DO concentrations but the ORP as an indicator of the DO provides much better information about the processes at low DO concentrations and in the anoxic phase (Fuerhacker et al., 2000; Akin and Ugurlu, 2005; Hu et al., 2005). Fig. 2 shows typical ORP and pH profiles in the four reactors that were recorded during both experiments. With the substrate consumption being a reduction reaction the ORP starts decreasing at the beginning of the cycle in each reactor. However, only in the reactors 2, 3 and 4 the DO decreases to zero, reaching anoxic conditions with the ORP decreasing further to negative values. This is due to the reactors not being supplied with oxygen during their first operational step. At an ORP level of around
170 to
200 mV a sharp decrease in the ORP can be noticed that usually appears when nitrate concentrations are close to zero (Yu et al., 1997). After this “nitrate knee” the ORP decreases further and with no nitrogen bound oxygen present the

a

ORP elbow

nitrate apex

200

conditions in the reactor change from anoxic to true anaerobic (Akin and Ugurlu, 2005). Simultaneously due to OHproduction during denitrification the pH increases and reaches a maximum with a subsequent decrease due to the formation of fatty acids under anaerobic conditions. This bending point in the pH profile, which occurs at the same time as the nitrate knee, is known as the “nitrate apex” (Yu et al., 1997; Spagni et al., 2007). With the onset of aeration the ORP increases, nitrification occurs and the pH decreases due to Hþ release. It eventually reaches a “valley” that indicates the depletion of ammonia and is clearly visible in the recorded profiles (“ammonia valley”, Fig. 2). After an increase the pH forms a plateau with the values staying constant, indicating the end of the phosphate uptake (Akin and Ugurlu, 2005). With the end of nitrification and a decrease in bacterial respiratory the DO increases so that a break point in the ORP profile (ORP elbow) can be observed as well (Fig. 2a) but it is not always as visible

9.5

150

9

8.5

50 0 0

50

100

150

200

250

300

350

400

450

500

550

600

650

700

8

pH

ORP level [mV]

100

-50 7.5

-100 -150

7

-200

ammonia

-250

6.5

valley

nitrate knee

-300

6

Time [min]

b

ORP elbow

nitrate apex

200

9.5

150 9

50

8.5

0 -50

0

50

100

150

200

250

300

350

400

450

500

550

600

650

700

8

pH

ORP level [mV]

100

-100 7.5

-150

ammonia

-200 -250

nitrate knee

-300

7

valley

6.5

Time [min] ORP Reactor 1

ORP Reactor 2

ORP Reactor 3

ORP Reactor 4

pH Reactor 1

pH Reactor 2

pH Reactor 3

pH Reactor 4

Fig. 2 e Typical ORP and pH profile in the four reactors during a treatment cycle in a) experiment 1 (seed from Leixlip WWTP) and b) experiment 2 (seed from Swords WWTP); aeration disruptions of 0, 60, 120 and 200 min in reactor 1, 2, 3 and 4 respectively.

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as the ammonia valley in the pH profile (Akin and Ugurlu, 2005; Spagni et al., 2007). Consulting the ORP and pH profiles throughout the experiment it shows the prevalent conditions in the reactors that result from the different cycle settings and indicates the reactions that are taking place. Although being continuously aerated, in the first experiment reactor 1 occasionally reached anoxic conditions with ORP values below 0 mV for up to 50 min (Table 3). This was probably due to a high respiration in response to feeding that could not be fully satisfied by the oxygen supply. However, this was not observed during experiment 2 with reactor 1 remaining aerobic throughout each cycle (Table 3). Without anoxia no denitrification could take place and therefore no nitrate knee or apex was observed (Fig. 2). In reactor 2 anoxic periods were obtained with an average length of 144e176 min during experiment 1 and 90e113 min during experiment 2 (Table 3). True anaerobic conditions were usually not reached in this reactor. The protozoan community in reactor 3 was exposed to long anoxic/anaerobic periods up to 193 and 200 min (Table 3) towards the end of which conditions frequently turned anaerobic. On average these anaerobic intervals were 60e79 min and 42e58 min long during experiment 1 and 2 respectively (Fig. 3). In the ORP profile of reactor 4 the nitrate knee was usually clearly visible (Fig. 2) so that full denitrification had been reached resulting in anaerobic periods of between 125e158 min and 126e138 min during experiment 1 and 2 respectively (Fig. 3).

3.2.

Process performance and effluent quality

3.2.1.

Effluent BOD5 and TOC

3.2.2.

Nutrient removal

The ammonia valley was usually clearly visible in all the reactors (Fig. 2) confirming that nitrification was taking place. During experiment 1, ammonia removal varied between 78 and 91% with similar removal rates from 73 up to 90% in experiment 2 (Fig. 4a). Throughout both experiments the final effluent ammonical-N concentrations in all reactors was <3 mg NH4eN L
1. While no significant differences in ammonia removal between the different reactors were detected (Fig. 4a), Lee and Oleszkiewicz (2003) consistently observed greater nitrification rates in alternating anoxic/ aerobic SBRs than in solely aerobic reactors. After further investigations they excluded the possibility that under anoxic/ anaerobic conditions the decreased activity of rotifers was responsible for the increased nitrification due to reduced grazing pressure on nitrifying bacteria. Instead they proposed that the difference was due to the feast/famine phenomena. If biomass is subjected to starvation, followed by a substraterich environment then a higher substrate uptake rate will be observed. When exposed to a feasting period the amount of energy required for metabolic functions increases dramatically over that of a non-stressed population due to the additional energy that is necessary for the repair process (Lee and Oleszkiewicz, 2003). Although ammonia is available during the anoxic phase it can’t be utilised by obligate aerobic autotrophs due to the lack of oxygen nor can they store it under anoxic conditions the way facultative heterotrophic aerobes store energy from substrate, so that a similar feast/famine phenomenon can develop (Lee and Oleszkiewicz, 2003). However, in the present study such a phenomenon was not observed. Similar final effluent total nitrogen (TN) concentrations of 40e70 mg L
1 and 30e60 mg L
1 were observed in the reactors during experiment 1 and 2, respectively. During experiment 1 maximum nitrogen removal rates were achieved in reactor 4 (56%  1.4) while during the second experiment maximum

Throughout both experimental runs organic removal was high in all reactors with the effluent BOD5 ranging from 2 to 12.5 mg L
1 (95e99% removal) and TOC 2.89e5.57 mg L
1 (96e98% removal) with no significant differences detected between the treated reactors.

Table 3 e Average length of aerobic and anoxic/anaerobic times (ORP < 0 mV) observed in the reactors at a) experiment 1 (seed from Leixlip WWTP) and b) experiment 2 (seed from Swords WWTP). a)

Reactor Reactor Reactor Reactor

1e16 cycles time ORP<0 mV [min]

aerobic time [min]

ratioa

time ORP<0 mV [min]

aerobic time [min]

17 184 173 244

592 425 436 365

34.8 2.3 2.5 1.5

27 131 194 263

582 478 415 346

1 2 3 4

b)

Reactor Reactor Reactor Reactor

17e32 cycles

1e16 cycles

1 2 3 4

time ORP<0 mV [min]

aerobic time [min]

0 91 172 274

609 518 437 335

a ratio ¼ aerobic time/anaerobic time.

17e32 cycles ratioa

5.7 2.5 1.2

time ORP<0 mV [min]

aerobic time [min]

0 90 183 271

609 519 427 338

ratioa 21.4 3.7 2.1 1.3

33e48 cycles ratioa

5.7 2.3 1.2

time ORP<0 mV [min]

aerobic time [min]

0 113 200 253

609 496 409 356

ratioa

4.4 2.0 1.4

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a

anoxic

Reactor 4

anaerob aerobic

Reactor 3

Reactor 2

Reactor 1 0

100

200

300

400

500

600

Time [min]

b

anoxic

Reactor 4

anaerob aerobic

Reactor 3

Reactor 2

Reactor 1

0

100

200

300

400

500

600

Time [min]

Fig. 3 e Observed length of aerobic (ORP > 0 mV) anoxic (0 mV > ORP > L190 mV) and anaerobic (ORP < L190 mV) periods in the 4 reactors depending on their cycle time settings in a) experiment 1 (seed from Leixlip WWTP) and b) experiment 2 (seed from Swords WWTP).

removal occurred in reactor 3 (66%  4). In the first experiment the TN-removal in reactors 2, 3 and 4, comprising anoxic periods, was always significantly higher than in reactor 1 (Fig. 4b) being on average 32e36% higher. Likewise in experiment 2 the aerobic reactor (reactor 1) had the lowest nitrogen removal efficiency (30e39%) with those with anoxic periods (reactors 2 to 4) achieving 46e66% TN-removal with the latter two occasionally performing significantly better (Fig. 4b). This suggests that the anoxic periods in reactor 2 were not always long enough to ensure complete denitrification. On average the difference between reactor 2 and reactors 3 and 4 was 8 and 10% respectively. The highest final effluent nitrate concentrations were recorded in reactor 1 at 61e64 mg NO3-N L
1 and 58e66 mg NO3-N L
1 during experiment 1 and 2 respectively. During experiment 1 no significant difference was detected between the final nitrate concentrations of reactors 2 and 3 (30e39 mg NO3eN L
1) with reactor 4 achieving significantly lower concentrations at 28e33 mg NO3eN L
1 corresponding to the longest aeration stop in its cycle. A similar trend was visible in experiment 2 where effluent nitrate concentrations decreased when the length of the aeration disruption in the treatment cycles of the reactors increased. Throughout both experiments the nitrite concentrations in the effluent showed no particular pattern or trend between the reactors. Nitrite levels ranged from 19 to 66 mg NO2eN L
1 and 44e85 mg NO2eN L
1 during experiments 1 and 2 respectively over the first 16 days of operation. This rose to 110 and

1813 mg NO2eN L
1 in reactor 1 and 4 respectively after 24 days during the extended experiment 2. These high concentrations are due to incomplete nitrification or denitrification processes. Since the factors that would affect nitrification, such as water temperature, toxic compounds in the influent, pH, BOD removal, sludge retention time (SRT) and MLSS were maintained at the same level between all reactors, the high nitrite concentrations in reactor 4 must be due to low DO concentrations causing incomplete nitrification during the aeration period. Since reactor 4 has the longest anoxic/ anaerobic period it consequently has a very short aeration period that could cause difficulties in supplying the oxygen that is needed to ensure complete nitrification. The observed ammonia valley in the pH profile only indicates the depletion of ammonia and its conversion to nitrite so that with its occurrence complete nitrification can’t be assumed. However, the ORP elbow occurs with the decrease in bacterial respiratory due to complete nitrification (Akin and Ugurlu, 2005; Spagni et al., 2007). The oxygen consumption is reduced and the DO increases. This break point was always visible in the recorded profiles. It usually appeared up to 200 min before the end of the aeration period reaching a constant redox potential similar to the one observed in the other reactors. This suggests that there could be another process responsible for the high nitrite concentration in reactor 4. A nitrite build-up can also occur as a result of the predominant presence of incomplete denitrifiers within denitrifying bacterial communities (Drysdale et al., 2001). Drysdale et al. (2001) isolated and

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characterised the ordinary heterotrophic organisms in an NDBEPR (Nitrification Denitrification Biological Enhanced Phosphorus Removal) system according to their ability to reduce nitrates and/or nitrites under anoxic conditions. They found that 95.6% of the total denitrifying heterotrophic bacteria they isolated were capable of nitrate reduction

compared to only 35.8% that were able to reduce nitrites. The other 64.2% of the isolates were missing the required nitrite reductase enzymes (Drysdale et al., 2001). Ekama and Wentzel (1999) who determined denitrification kinetics for NDBEPR processes noticed an initial nitrite build-up as the nitrite reduction rate was found to be only approximately 1/10th of

Fig. 4 e Ammonia removal (a), total nitrogen (b) and total phosphorus (c) removal in the SBRs with different length of aeration disruption throughout experiment 1 and 2 (Error bars represent 95% confidence interval).

w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 2 1 3 e2 2 2 6

the nitrate reduction rate. The fact that in the present study a significant nitrite build-up was only observed after 24 days in reactor 4 could mean that the frequent exposure to prolonged anaerobic conditions in the long run may have affected the denitrifying bacterial community. Further investigation would be needed to clarify this. However, since other operational factors that affect denitrification, such as water temperature, mixing conditions, SRT and HRT were constant throughout the duration of the run and identical between all reactors they can’t be responsible for a possible incomplete denitrification. Final effluent total phosphorous (TP) concentrations ranged from 6 to 8 mg L
1 and 6e10 mg L
1 during experiment 1 and 2 respectively. In experiment 1 a significant increase in TPremoval with longer aeration interruptions was clearly discernible (Fig. 4c). In reactors 3 and 4, where anaerobic conditions occurred, a maximum TP- removal rate of between 20 and 33% was observed respectively. During experiment 2 an undefined pattern over the first 12 days was observed with negative TP- removal rates at times. These values suggest that P-release into the final effluent occurred due to the sludge becoming anaerobic during the settling phase. However, another possibility is that due to a lack of volatile fatty acids (VFAs) secondary phosphorus release occurred during the anaerobic stage. Unlike the phosphorus released in the presence of VFAs (primary release) the phosphorus being released in the absence of VFAs will not be removed by the phosphorus accumulating organisms (PAOs) in the subsequent aerobic stage (Danesh and Oleszkiewicz, 1997). After 16 days however TP- removal rates had become stable and reached maximum values of up to 24%. Contrary to the results from the first experiment no significant differences were detected between reactors with different periods of anoxia and anaerobia (Fig. 4c). Maximum removal rates could probably be improved by using a synthetic feed of a different composition. While the OECD synthetic sewage is based solely on peptone as an organic substrate it is known that PAOs prefer acetate and glucose as a carbon source (Kargi and Uygur, 2003). Acetate is essential for the poly-hydroxy butyrate (PHB) synthesis under anaerobic and anoxic conditions while glucose or similar carbohydrates are required for the energy generation to be used in PHB or poly-phosphate synthesis. The presence of those carbon sources might have resulted in improved P-removal (Kargi and Uygur, 2003). However, real sewage doesn’t directly provide these compounds either so that the used OECD sewage formula reflects the real scenario better than synthetic sewages that artificially enhance nutrient removal. Also Carucci et al. (1994) have shown that there is a competition for organic substrate between PAOs and denitrifying bacteria so that nitrates inhibit EBPR (Enhanced Biological Phosphorus Removal). In lab scale SBRs they observed an increase in Premoval efficiency when nitrate concentrations in the feed were reduced or nitrification failed (Carucci et al., 1994). In this study high denitrification rates were obtained so that a large amount of COD might have been used up by denitrifying bacteria thereby limiting the activity of PAOs.

3.3.

Settleability

The settleability in all reactors was very poor with values for the SVI ranging from 107.3 to 193.3 mL g
1 in experiment 1 and

2221

119.4e271.6 ml g
1 in experiment 2, with poorest settleability in the two reactors exposed to the longest periods of anoxia and anaerobia. A few days after the start and throughout each experiment the trend was noticeable that the settleability was best in the aerobic control reactor and worsened in the reactors with anaerobic periods. In contrast, granular sludges with good settling characteristics have been reported in SBRs with alternating anaerobic/anoxic/aerobic periods (Danesh and Oleszkiewicz, 1997; Hu et al., 2005). For similar anoxic periods (60 and 120 min) and ratios of aerobic/anoxic cycle times (1 and 3) as used in the present study (Table 1), Hu et al. (2005) found significantly improved settling properties (SVI ¼ 98 ml g
1) compared to their aerobic control reactor (SVI ¼ 239 ml g
1). They consider the aggregation to be a protecting mechanism for the microorganisms from toxic compounds or stressful environments such as anoxic/anaerobic conditions, resulting in better settleability. Danesh and Oleszkiewicz (1997) observed better settling characteristics for sludges with higher P-removal capabilities and explained this with the higher relative abundance of PAOs in the microbial community. The P-accumulating bacteria are thought to be good floc formers and due to their accumulated phosphorus they are heavier and thus settle more rapidly (Danesh and Oleszkiewicz, 1997). A possible reason why the formation of a well settling and granular sludge during these experiments failed could be that the long settling period of 60 min did not select for rapid settling flocs (Hu et al., 2005). Furthermore the use of synthetic sewage could also be the reason for the general poor settleability. With real influent colloids and particles would normally be introduced into the reactors. The adsorption of suspended and colloidal matter onto the flocs promotes the creation of denser flocs ensuring a better settleability (Gray, 1990).

3.4.

Protozoan community

The protozoan species recorded throughout both experiments are listed in Table 4. Compared to the seeding sludge originally used in the experiment all the reactors demonstrated a discernible change in community structure during the experimental period. This was expected since the bacterial community acclimatises to the synthetic sewage which consequently affects the protozoan community. While the system had not reached steady-state conditions during the experimental period, the effects of anoxia and anaerobiosis on the evolution of the community structure can be seen by direct comparison with the aerobic control reactor.

3.4.1.

Community complexity

The most complex protozoan community was observed in reactor 2 with an aeration disruption of 60 min and anoxic periods of between 100 and 160 min. Compared to the number of species recorded in the inoculated sludge the reduction in community complexity ranged from 0 to 20% in reactor 2, while in reactor 1, 3 and 4 a reduction of 9e40%, 9e60% and 50e73% was observed, respectively (Table 5). This suggests that short periods of anoxia do not reduce but enhance protozoan community complexity in activated sludge by providing an additional niche. This is confirmed by Perez-Uz et al. (2010) who studied three N-removal full scale plants and found a higher

2222

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Table 4 e Protozoan species and their occurrence throughout a) experiment 1 (seeding sludge from Leixlip WWTP) and b) experiment 2 (seeding sludge from Swords WWTP); aeration disruptions 0, 60, 120 and 200 min in reactor 1, 2, 3 and 4 respectively. a)

8 days

Vorticella sp. Epistylis entzii Epistylis coronata Epistylis cambari Epicarchesium granulatum Opercularia microdiscum Thuricola kellicottiana Trochilia minuta Chilodonella uncinata Acinera uncinata Cinetochilum margaritaceum

16 days

start

R1

R2

R3

R4

x x x x x

x

x x x x x

x

x x x

x x x x x

R1

R2

x

x

x x

x x

x

x

x x x x

x x x x x x

x x x

b)

x x

x

x x x

x

R1

R2

R3

R4

R1

R2

R3

x x

x x

x

x

x

x x

x x

x x

x x x x x

x x x x x

x x x x x

x x x x x

x x x x x

x x x x

x

x

x x x

x x x

x x x

x

x

x

x x

16 days

start

x x x x x x x

R4

x x

x x

x

x

x

x

x

8 days

Vorticella convallaria Vorticella sp. Vorticella microstoma Epistylis entzii Epistylis coronata Epicarchesium granulatum Opercularia microdiscum Thuricola kellicottiana Aspidisca lynceus Euplotes affinis Trochilia minuta Chilodonella uncinata Acinera uncinata Litonotus sp. Drepanomonas revoluta

R3

x

x

x

x

x

x

diverse protozoan communities in these systems incorporating anoxic stages compared to conventional systems. However, with the introduction of anaerobic periods in the treatment cycle a significant number of species couldn’t be detected anymore, increasing as the anaerobic period increases (Table 5). The more protozoan species that are present in a system the higher the degree of stability in biological functions resulting in a higher resistance to disturbances (Liu et al., 2008). Thus, the length of anaerobia should be kept as short as possible to minimise the reduction in community complexity. However, a compromise between effective P-removal and sustaining protozoan diversity has to be made.

3.4.2.

24 days R4 x x

x

R1

R2

R3

R4

x x x x x

x x

x x x

x

x x x x

x

x x

x

x

x x

x

x

x

x

x

x

x

Species tolerances

Looking at species abundances of the different treatments clear trends are visible that support the hypothesis that protozoa have different tolerance levels to anoxia and anaerobia (Fig. 5). Chilodonella uncinata was only observed in the continuously aerated control (reactor 1), which suggest that this species is intolerant to anoxic conditions (Fig. 5). Further sensitive species include Epicarchesium granulatum and Cinetochilium margaritaceum, both becoming undetectable as soon as conditions within the treatment cycle turn anaerobic as in reactor 3 and 4 with an aeration disruption of 120 and 200 min, respectively (Fig. 5a, b). In both experimental runs the

Table 5 e Reduction in community complexity [%] compared to recorded species richness in sludge from Leixlip WWTP in experiment 1 and Swords WWTP in experiment 2 used for seeding the reactors. Reduction in community complexity [%] Reactor Reactor Reactor Reactor

1 (continuously aerated) 2 (60 min aeration stop) 3 (120 min aeration stop) 4 (200 min aeration stop)

Experiment 1

Experiment 2

8 days

16 days

8 days

16 days

24 days

40 20 50 50

20 10 60 60

27 18 36 73

9 0 9 55

27 18 45 64

Fig. 5 e Total abundance of selected species after 16 days in the reactors of a) experiment 1 (seed from Leixlip WWTP), b) after 16 days in experiment 2 (seed from Swords WWTP) and c) after 24 days of experiment 2 (Error bars represent 95% confidence interval).

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abundance of E. granulatum gradually decreased with increasing length of aeration interruptions from reactor 1 till 4. Similar trends were detected in experiment 2 for Epistylis entzii and D. revoluta (Fig. 5b, c). At all times Trochilia minuta was almost exclusively observed in reactor 2 suggesting that the short times of anoxic conditions favour this species and provide a niche for it to compete with other species. Throughout the second experiment Vorticella convallaria endured short times of anoxia as created by an aeration disruption of 60 min in reactor 2 but decreased in numbers till the species became undetectable under prolonged anaerobic conditions (Fig. 5b, c). Species, which were able to survive in sufficient numbers over 16 or up to 24 days under frequent exposure to prolonged anoxic/anaerobic periods, were: Acineria uncinata, Opercularia microdiscum, Epistylis cambari and Epistylis coronata (Table 4). Vorticella microdiscum was observed throughout the second experiment and tolerated a repeated exposure to anaerobic periods. No significant differences in abundances for A. uncinata were observed in reactor 1, 2 and 3 but although still detected in reactor 4 and therefore being able to survive prolonged anaerobic conditions numbers of this species were significantly reduced (Fig. 5a). A species that increased in numbers with increasing length of anoxic/anaerobic conditions was O. microdiscum (Fig. 5a). In previous studies O. microdiscum was found to show a high resistance to extreme environmental conditions such as presence of toxicity, extreme temperature, high organic matter and low DO (Esteban et al., 1991b; Madoni et al., 1993; Lee et al., 2004). With its tolerance and adaptation to changes O. microdiscum in this experiment survives the anoxic and anaerobic conditions better than other protozoans (Esteban et al., 1991b; Madoni et al., 1993) resulting in densities increasing with longer anoxic/anaerobic periods due to the decreasing competition by other species. Further explanation for the better resistance shown by this species is given by Esteban et al. (1991a) who suggest that being a colonial organism might help the species to endure certain conditions. However, this study shows that such a general explanation

might not be suitable as with increasing length of aeration interruptions other colonial species like E. granulatum and Epistylis entzii gradually decreased in numbers. Vorticella microstoma was detected during the second experiment but only in the reactors exposed to prolonged anoxic and anaerobic conditions with highest abundances in reactor 4 (Fig. 5b, c). This is in close agreement with findings in previous studies where this species was found to be related with low DO levels showing a high resistance to the influence of anoxia (Toman and Rejic, 1988; Madoni et al., 1993; Madoni, 1994; Lee et al., 2004). The fact that the abundances of O. microdiscum and V. microstoma also increased over time within the anaerobic reactors suggests that they must either reproduce within an anoxic/anaerobic environment or at least only need a short time to recover sufficiently from the exposure to those conditions. To clarify this, further investigations would be required. Although V. microstoma was identified as a polysaprobic species with a saprobic value of 3.5 (Foissner et al., 1992) and both species, O. microdiscum and V. microstoma, were previously found to be related with high effluent BOD5 (Poole, 1984; Madoni et al., 1993; Salvado et al., 1995), in this study no evidence has been found that effluent quality declined in the presence of those species. Reactors with ciliate community clearly dominated or even consisting only of O. microdiscum and V. microstoma still delivered effluents of very good quality with BOD5 values between 3.5 and 8.5 mg L
1. However, since previous observations (Poole, 1984; Madoni et al., 1993; Salvado et al., 1995) have been based on full scale plant studies and thus on the use of real sewage the reason for not detecting such a relationship could be connected to the characteristics of the synthetic sewage. V. microstoma is frequently present in the plant during the first phase of colonisation but is substituted by V. convallaria which become dominant during stable conditions. When there is a extreme reduction in the dissolved oxygen concentration in the mixed liquor, an alternation of the two species can be observed, due to their different degree of tolerance to the lack of oxygen (Madoni, 1994). This behaviour was also observed during experiment 2 (Fig. 5b, c).

Total ciliate abundance [ind mg-1]

5000 4500 4000 3500 3000 2500 2000 1500 1000 500 0 start

8 days

16 days

start

Experim ent 1 continuously aerated

60 min no aeration

8 days

16 days

24 days

Experim ent 2 120 min no aeration

200 min no aeration

Fig. 6 e Total ciliate abundance in the SBRs with different length of aeration disruption throughout experiment 1 and 2 (Error bars represent 95% confidence interval).

w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 2 1 3 e2 2 2 6

3.4.3.

Total protozoan abundance

Total ciliate abundance was reduced by length of anoxic/ anaerobic exposure (Fig. 6). In experiment 1 the number of ciliates counted in reactor 4 ranged from 400 to 800 ind mg
1 and was always significantly lower than in the other reactors where abundances varied between 1000 and 2700 ind mg
1. In the second experiment ciliate numbers dropped dramatically from nearly 4000 down to only 230 ind mg
1 within the first 8 days in the same reactor. The reduced ciliate abundance resulted in the effluent becoming very cloudy with an elevated BOD5. While the BOD5 of the other three reactors effluents ranged from 5 to 9 mg L
1, the effluent in reactor 4 had a BOD5 of 12.5 mg L
1. These observations agree with earlier findings (Curds et al., 1968; Esteban et al., 1991a; Salvado et al., 1995). Curds et al. (1968) found that in a protozoan free lab scale treatment plant very turbid effluent of inferior quality (high BOD5, organic carbon and non-settleable suspended solids) was produced. With the inoculation of ciliate seed the clarity as well as BOD5 and suspended solid concentrations of the effluent greatly improved. They were able to demonstrate that the effluent turbidity is related to the number of free swimming bacteria and that the presence of ciliates with their ability to feed upon those bacteria and suspended solids is responsible for a clear, high quality effluent. Esteban et al. (1991a) also observed a significant decrease of effluent COD and colour following an increase in ciliate concentrations. However, in the present study the low ciliate abundances observed in reactor 4 recovered again after 16 days and reached average numbers comparable to the reactors exposed to shorter anoxic/anaerobic periods. This is due to the establishment of fewer tolerant species that reach high densities of >1000 ind mg
1 and start dominating the community (Fig. 5b, c). Simultaneously the clarity and the BOD5 of the reactors effluent greatly improved.

3.4.4.

Community similarities

Cluster analysis revealed that the protozoan communities that had evolved in the reactors under exposure to different anoxic and anaerobic periods were quite dissimilar. After 16 days the communities showed similarities of less than 51% and 60% in experiment 1 and 2 respectively and less than 40% after 24 days in experiment 2. Considering that all other parameters including sludge loading, SRT and HRT were maintained at identical rates in all treatments, these findings suggest that the aeration conditions play a major role in how protozoan communities develop.

4.

Conclusions

 Aeration conditions play a major role in how protozoan communities develop.  Activated sludge ciliate protozoa display a range of tolerances to anoxia and anaerobia. Most sensitive were Chilodonella uncinata, Epicarchesium granulatum and Cinetochilium margaritaceum. Species that survive longer times of anoxic and anaerobic conditions are Opercularium microdiscum, Vorticella microstoma, Epistylis coronata and Acineria uncinata.  By creating a new niche short times of anoxia (up to 60 min) enhance protozoan community complexity with

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abundances only being moderately affected. Under these conditions denitrification is improved but P-removal is still poor.  Increasing the time of anoxia and introducing anaerobic conditions (time of aeration interruption >60 min) protozoan community complexity decreases. Species abundances can increase over time with the establishment of fewer tolerant species. A radical decrease in protozoan abundance can lead to a cloudy final effluent with increased BOD5.  As P-removal only occurs when anaerobic conditions are present within the cycle a compromise has to be made between effective P-removal and sustaining protozoan diversity by keeping the length of anaerobia as short as possible.

Acknowledgements This research has been funded by Science Foundation Ireland under the Research Frontiers Programme (Grant number: 07/ RFP/EEEOBF117).

references

Akin, B.S., Ugurlu, A., 2005. Monitoring and control of biological nutrient removal in a Sequencing Batch Reactor. Process Biochem. 40, 2873e2878. Akpor, O.B., Momba, M.N.B., Okonkwo, J.O., Coetzee, M.A., 2008. Nutrient removal from activated sludge mixed liquor by wastewater protozoa in a laboratory scale batch reactor. Int. J. Environ. Sci. Technol. 5, 463e470. APHA, 2005. Standard Methods for the Examination of Water and Wastewater, 21th ed. American Public Health Association, AWWA, WEF, Baltimore. Carucci, A., Majone, M., Ramadori, R., Rossetti, S., 1994. Dynamics of phosphorus and organic substrates in anaerobic and aerobic phases of a sequencing batch reactor. Water Sci. Technol. 30, 237e246. Christofi, N., Aspichueta, E., Dalzell, D., De la Sota, A., Etxebarria, J., Fernandes, T., Gutierrez, M., Morton, J., Obst, U., Schmellenkamp, P., 2003. Congruence in the performance of model nitrifying activated sludge plants located in Germany, Scotland and Spain. Water Res. 37, 177e187. Curds, C.R., 1969. An Illustrated Key to the British Freshwater Ciliated Protozoa Commonly Found in Activated Sludge, Technical Paper No.12. Water Pollution Research Laboratory, London. Curds, C.R., Cockburn, A., Vandyke, J.M., 1968. An experimental study of the role of the ciliated protozoa in the activatedsludge process. Wat. Pollut. Control 67, 312e329. Curds, C.R., Fey, G.J., 1969. Effect of ciliated protozoa on fate of Escherichia coli in activated-sludge process. Water Res. 3, 853e867. Curds, C.R., Warren, A., Salvado´ Cabre´, H., Roberts, D., 2008. An Atlas of Ciliated Protozoa Commonly Found in Aerobic Sewage-treatment Processes. An Aid to Monitor Treatment -plant Performance. Natural History Museum, London. Cybis, L.F.D., Horan, N.J., 1997. Protozoan and metazoan populations in sequencing batch reactors operated for nitrification and/or denitrification. Water Sci. Technol. 35, 81e86. Danesh, S., Oleszkiewicz, J.A., 1997. Use of a new anaerobicaerobic sequencing batch reactor system to enhance

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biological phosphorus removal. Water Sci. Technol. 35, 137e144. Drysdale, G.D., Kasan, H.C., Bux, F., 2001. Assessment of denitrification by the ordinary heterotrophic organisms in an NDBEPR activated sludge sytem. Water Sci. Technol. 43, 147e154. Dubber, D., Gray, N.F., 2009. Enumeration of protozoan ciliates in activated sludge: Determination of replicate number using probability. Water Res. 43, 3443e3452. Dubber, D., Gray, N.F., 2010. Replacement of chemical oxygen demand (COD) with total organic carbon (TOC) for monitoring wastewater treatment performance to minimize disposal of toxic analytical waste. J. Environ. Sci. Health Part A ToxicHazard. Subst. Environ. Eng. 45, 1595e1600. Ekama, G.A., Wentzel, M.C., 1999. Denitrification kinetics in biological N and P removal activated sludge systems treating municipal wastewaters. Water Sci. Technol. 39, 69e77. Esteban, G., Tellez, C., Bautista, L.M., 1991a. Dynamics of ciliated protozoa communities in activated-sludge process. Water Res. 25, 967e972. Esteban, G., Tellez, C., Bautista, L.M., 1991b. Effects of habitat quality on ciliated protozoa communities in sewagetreatment plants. Environ. Technol. 12, 381e386. Foissner, W., Berger, H., Kohmann, F., 1992. Taxonomische und o¨kologische Revision der Ciliaten des Saprobiensystems, Band II: Peritrichia, Heterotrichia, Odontostomatida. Landesamt fu¨r Wasserwirtschaft, Mu¨nchen. Foissner, W., Berger, H., Kohmann, F., 1994. Taxonomische und o¨kologische Revision der Ciliaten des Saprobiensystems, Band III: Hymenostomata, Prostomatida, Nasulida. Landesamt fu¨r Wasserwirtschaft, Mu¨nchen. Foissner, W., Blatterer, H., Berger, H., Kohmann, F., 1991. Taxonomische und o¨kologische Revision der Ciliaten des Saprobiensystems, Band I: Cyrtophorida, Oligotrichida, Hypotrichida, Colpode. Landesamt fu¨r Wasserwirtschaft, Mu¨nchen. Foissner, W., Blatterer, H., Berger, H., Kohmann, F., 1995. Taxonomische und o¨kologische Revision der Ciliaten des Saprobiensystems, Band IV: Gymnostomatea, Loxodes, Suctoria. Landesamt fu¨r Wasserwirtschaft, Mu¨nchen. Fuerhacker, M., Bauer, H., Ellinger, R., Sree, U., Schmid, H., Zibuschka, F., Puxbaum, H., 2000. Approach for a novel control strategy for simultaneous nitrification/denitrification in activated sludge reactors. Water Res. 34, 2499e2506. Gendig, C., Domogala, G., Agnoli, F., Pagga, U., Strotmann, U.J., 2003. Evaluation and further development of the activated sludge respiration inhibition test. Chemosphere 52, 143e149. Gray, N.F., 1990. Activated Sludge - Theory and Practice. Oxford University Press, Oxford, New York, Tokyo. Gray, N.F., 2004. Biology of Wastewater Treatment, second ed. Imperial College Press, London. Hu, Z.Q., Ferraina, R.A., Ericson, J.F., MacKay, A.A., Smets, B.F., 2005. Biomass characteristics in three sequencing batch reactors treating a wastewater containing synthetic organic chemicals. Water Res. 39, 710e720. ISO, 2003. Water Quality: Determination of Biochemical Oxygen Demand after N Days (BODn), Part 1: Dilution and Seeding Method with Allylthiourea Addition. ISO 5815-1:2003. International Organization for Standardization, Geneva.

Kargi, F., Uygur, A., 2003. Effect of carbon source on biological nutrient removal in a sequencing batch reactor. Bioresour. Technol. 89, 89e93. Lee, N.M., Welander, T., 1996. Use of protozoa and metazoa for decreasing sludge production in aerobic wastewater treatment. Biotechnol. Lett. 18, 429e434. Lee, S., Basu, S., Tyler, C.W., Wei, I.W., 2004. Ciliate populations as bio-indicators at Deer Island treatment plant. Adv. Environ. Res. 8, 371e378. Lee, Y., Oleszkiewicz, J.A., 2003. Effects of predation and ORP conditions on the performance of nitrifiers in activated sludge systems. Water Res. 37, 4202e4210. Liu, J., Yang, M., Qi, R., An, W., Zhou, J., 2008. Comparative study of protozoan communities in full-scale MWTPs in Beijing related to treatment processes. Water Res. 42, 1907e1918. Madoni, P., 1994. A sludge biotic index (SBI) for the evaluation of the biological performance of activated-sludge plants based on the microfauna analysis. Water Res. 28, 67e75. Madoni, P., Davoli, D., Chierici, E., 1993. Comparative-analysis of the activated-sludge microfauna in several sewage-treatment works. Water Res. 27, 1485e1491. Maurines-Carboneill, C., Morin, L., Pernelle, J.J., Derlet, N., Sachon, G., Leblon, G., 1998. Reviviscence of aerobic chemoheterotrophic bacteria in an activated sludge pilot plant after a prolonged absence of oxygen. Water Res. 32, 2211e2219. Ndon, U.J., 2007. Analysis and kinetics of the sequencing batch reactors. Environ. Eng. Sci. 24, 505e514. Obaja, D., Mace, S., Mata-Alvarez, J., 2005. Biological nutrient removal by a sequencing batch reactor (SBR) using an internal organic carbon source in digested piggery wastewater. Bioresour. Technol. 96, 7e14. Pe´rez-Uz, B., Arregui, L., Calvo, P., Salvado´, H., Ferna´ndez, N., Rodrı´guez, E., Zornoza, A., Serrano, S., 2010. Assessment of plausible bioindicators for plant performance in advanced wastewater treatment systems. Water Res. 44, 5059e5069. Poole, J.E.P., 1984. A study of the relationship between the mixed liquor fauna and plant performance for a variety of activated sludge sewage treatment works. Water Res. 18, 281e287. Reuschenbach, P., Pagga, U., Strotmann, U., 2003. A critical comparison of respirometric biodegradation tests based on OECD 301 and related test methods. Water Res. 37, 1571e1582. Salvado, H., Gracia, M.P., Amigo, J.M., 1995. Capability of ciliated protozoa as indicators of effluent quality in activated-sludge plants. Water Res. 29, 1041e1050. Spagni, A., Lavagnolo, M.C., Scarpa, C., Vendrame, P., Rizzo, A., Luccarini, L., 2007. Nitrogen removal optimization in a sequencing batch reactor treating sanitary landfill leachate. J. Environ. Sci. Health Part A Toxic-Hazard. Subst. Environ. Eng. 42, 757e765. Toman, M., Rejic, M., 1988. The effects of low concentrations and short-period lack of dissolved-oxygen upon the organisms of the concomitant biocenosis in activated-sludge. Z. Wasser Abwass. Forsch 21, 189e193. Yu, R.F., Liaw, S.L., Chang, C.N., Lu, H.J., Cheng, W.Y., 1997. Monitoring and control using on-line ORP on the continuousflow activated sludge batch reactor system. Water Sci. Technol. 35, 57e66.

w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 2 2 7 e2 2 3 4

Available at www.sciencedirect.com

journal homepage: www.elsevier.com/locate/watres

Feasibility study of moving-fiber biofilm membrane bioreactor for wastewater treatment: Process control Jirachote Phattaranawik a,*, TorOve Leiknes b a

Research and Technology Office, SCG Chemicals Co., Ltd., 271 Sukhumvit Rd., Map ta phut, Rayong 21150, Thailand Department of Hydraulic and Environmental Engineering, Norwegian University of Science and Technology, S.P. Andersens veg 5, N-7491 Trondheim, Norway b

article info

abstract

Article history:

Non-biodegradable solid wastes of non-intact membrane fibres/flatsheets and modules

Received 23 March 2010

disposed from membrane bioreactor (MBR) plants are in a great concern for environmental

Received in revised form

impact. Estimated cumulative amount of the module solid wastes from European countries

27 December 2010

in the next five years should be larger than 1000 tons in which a proper management

Accepted 21 January 2011

strategy and reuse for the disposed solid waste are urgently required. This article was

Available online 31 January 2011

aimed to propose an alternative to make uses of the non-intact membrane fibres for the aerobic biofilm supports and to study the feasibility on process operation of novel moving-

Keywords:

fiber biofilm MBR. A system of moving-fiber biofilm membrane bioreactor was designed

Membrane bioreactor

and evaluated experimentally, including an upflow anaerobic sludge reactor, an aerobic

Membrane solid waste

moving-fiber biofilm reactor, and a submerged membrane filtration unit. Start-up method

Moving-fiber biofilm

and operating conditions to control

Membrane fouling

investigated. Organic removal rates, optimum operating conditions for the system, and

the biofilms growing on the moving fibers were

membrane fouling rates at various membrane aeration rates and permeate fluxes were monitored to evaluate the performance of the proposed BF-MBR process. ª 2011 Elsevier Ltd. All rights reserved.

1.

Introduction

Membrane bioreactor (MBR) processes have been rapidly developed for wastewater treatment and drinking water applications due to high demands for clean water. Process efficiency of MBR in terms of low energy consumption and high productivity has been significantly improved over the last five years due to large research efforts and marketing competitions. However, a negative impact from such rapid technology development is an increase in the amount of hazardous wastes generated as the technology is commercialized. Wastes from wastewater treatment MBR plants are generated in many ways such as i) sludge disposal from solid

retention time (SRT) control, ii) chemical wastes from membrane cleaning processes, iii) solid wastes of non-intact membrane modules disposed from the plant. Both disposed sludge wastes and chemical cleaning wastes can be managed properly by appropriate sludge handling system and chemical/biological treatment of the cleaning waste. Replacements of the non-intact modules by new membrane module sets are commonly due to serious fiber breakage, loss of integrity, nonrecoverable membrane fouling, and an end of membrane life span (approximately 10 years), which generate large amounts of the solid wastes. However, appropriate management strategies for handling solid wastes of the non-intact membrane modules have not been fully developed by the

* Corresponding author. E-mail addresses: [email protected], [email protected] (J. Phattaranawik). 0043-1354/$ e see front matter ª 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2011.01.016

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industry. To get attention from MBR research and industrial sectors, the amount of the disposed membrane modules from MBR plants should be revealed. The amount of the modules wastes potentially disposed from MBR plants in European countries can be estimated from the amount of membrane surfaces and modules installed in the last ten years (Lesjean and Huisjes, 2008). In an estimation, Zenon and Kubota membrane modules are selected to represent the amounts of hollow fiber modules and flat-sheet modules since they have dominated the European markets during this period (Lesjean and Huisjes, 2008; Pearce, 2008a; Pearce, 2008b). Characteristics and designs of hollow fiber modules and flat-sheet modules are considerably different, and consequently the amount of the solid wastes from both types of modules should be determined separately. Table 1 shows the shipping module weights per membrane surface area and the dry weights of membrane plus reinforcement per membrane surface area for both hollow fiber and flat-sheet modules. The weights of the hollow fibers and the flat-sheet membranes are fairly comparable and contribute approximately 9e28% of the total weights of the commercial membrane modules. The module weight per membrane area of the flat-sheet module is significantly heavier than that of the hollow fiber module because of much lower membrane packing density. This may imply that MBR plants using flatsheet membrane modules would have a tendency to generate a larger amount of solid wastes of the non-intact modules. However, the amount of the flat-sheet module solid wastes can be substantially reduced if module structures, plates and frames are designed to be reusable, such that most of the components can be reused during membrane replacement. Fig. 1 shows the estimated annual amounts of solid wastes of the disposed modules and membrane fibers/flat-sheets between the years 2009e2015. In 2009, European MBR plants are estimated to generate the membrane module solid wastes of approximately 50 tons in which the membrane wastes account for only 5 tons or 10% of the total membrane module wastes. Due to high demands in MBR plants, the amount of the membrane module solid wastes is predicted to increase, then peaking in year 2014 with approximately 500 tons of the solid module wastes generated due to peaking number of the installed membrane surfaces in year 2004 (Lesjean and Huisjes, 2008). The global amount of the module solid wastes may be 2e4 times higher than those generated in European countries due to the increasing number of MBR

Table 1 e Shipping weights of membrane modules and membranes & reinforcements per surface membrane area for Zenon and Kubota (Pearce, 2008a; Pearce, 2008b). Membranes and module weights Shipping weight of membrane module per membrane surface area (kg/m2) Dry weight of membrane & reinforcement per membrane surface area (kg/m2)

Zenon

Kubota

0.9e1.2 (avg:1.1)

3.6e4.6 (avg:4)

0.3e0.4 (avg:0.35)

0.1e0.2 (avg:0.15)

Amont of solid memnbrane module wastes (ton)

w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 2 2 7 e2 2 3 4

600 500

Module wastes Membrane wastes

400 300 200 100 0 2009

2010

2011

2012 Year

2013

2014

2015

Fig. 1 e Prediction in amounts of solid wastes of membrane modules and membrane fibers/sheets in Europe from 2009 to 2015.

plants commissioned in China (Wang et al., 2008) and North America (Yang et al., 2006) during the period of 1999e2006. A large amount of the module solid wastes needs to be properly managed, and reuse of components in the solid wastes is a necessary strategy for a sustainable development and implementation of MBR technology. However, recycling of the various components used in membrane modules is a challenge. An alternative approach to reuse the disposed membrane modules for another application instead of filtration may be useful. If the disposed modules are reused in similar works/applications/locations but in different purposes, costs for transportation and modification for these disposed modules would not be high, which makes the module recycling method more promising. Therefore, a method is proposed in this study to reuse the disposed modules from the MBR plants in wastewater treatment application. The non-intact module was modified to form the supports for aerobic biofilms and integrated to a system of MBR for wastewater treatment. The purpose of the present study was to investigate the feasibility of using the non-intact membrane modules as the biofilm supports for wastewater treatment and to determine the operating conditions to control the BF-MBR process. Biofilm membrane bioreactor (BF-MBR) was chosen for this investigation because it offered many benefits over an activated sludge membrane bioreactor (ASMBR) such as i) lower viscosity of mixed liquor suspended solid (MLSS), and ii) potentially less problems of cake deposition, clogging in membrane filtration modules. More information of process characteristics and potential benefits of BF-MBR has been found in previous studies (Leikness and Ødegaard, 2007; Phattaranawik and Leiknes, 2010). In addition, multifunctional biological reactions are able to take place in the biofilm layer under aerobic environment such as simultaneous nitrification and denitrification or Anammox process (Szatkowska et al., 2007) and simultaneous nitrification and phosphorus removal (Helness and Ødegaard, 1999). The aerobic movingfiber biofilm reactor in this article is designed and built on a modified free-end hollow fiber membrane module. Sustainable operating conditions of a BF-MBR system were studied for process control. An aerobic moving-fiber biofilm reactor adapted from used/disposed membrane filtration module was evaluated. Characteristics of this new integrated biological process with membrane filtration were preliminarily studied.

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w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 2 2 7 e2 2 3 4

The effects of hydraulic retention time (HRT), membrane aeration rates, and permeate fluxes on the effluent quality and on membrane fouling rates were determined.

Material and methods

The BF-MBR pilot plant configuration used in this study is illustrated in Fig. 2, consisting of an upflow anaerobic activated sludge bioreactor, an aerobic moving-fiber biofilm reactor, and a submerged membrane filtration unit. The anaerobic bioreactor has dual functions: anaerobic digestion of organic substances in the wastewater influent and digestion of excess aerobic sludge to reduce amount of disposed sludge waste. Characteristics and operating conditions of the BF-MBR system are listed in Table 2. Semi-synthetic food-processing wastewater was used for all experiments, consisting of a mixture of municipal wastewater and concentrated synthetic food-processing wastewater. The municipal wastewater collected from the public sewer in Trondheim, Norway flowed gravitationally from a 10 m3 storage tank to the coarse particle settler and then

Air-sealed tank

Supernatant

P

Digital pressure sensor Air bubbling

Free-end fibers Up-flow anaerobic activated sludge bioreact

Solid settling direction

Aerobic movingfiber biofilm reactor

Overflow

Up flow

Wastewater influent

Solid waste of excess sludge

Submerged membrane filtration

2.

mixed with the concentrated synthetic food-processing wastewater under temperature control at 20
C. The concentrated synthetic food-processing wastewaters is a mixture of molasses and Salmon extract (peptone) representing food industrial wastewater in Trondheim, Norway (from, i.e. chocolate factory and fishery processing factory (mainly Salmon)), which were prepared from 37 g/L molasses, 3.4 g/L Salmon peptone, 6.7 g/L NH4Cl, 2.1 g/L NaHCO3, 0.2 g/L MgSO4, and 0.2 g/ L K2HPO4. Typically, an industrial wastewater is mixed with municipal wastewater before reaching the wastewater treatment plant, thus reducing the strength of the wastewater (i.e. high COD) and consequently enhancing the biological degradability. The characteristics of the wastewater influent (after mixed), the solution in the membrane tank, and the permeate are listed in Table 3. The wastewater influent was pumped to the bottom of the upflow anaerobic activated sludge bioreactor. Using air-lift effect in the aerobic moving-fiber biofilm reactor module and water level difference between the anaerobic bioreactor and the aerobic biofilm reactor, the supernatant (with little amount of suspended solid) from the anaerobic bioreactor flowed to the bottom of the aerobic biofilm reactor.

Membrane aeration P

Biofilm aeration

DAQ and computer Zone for low dissolved oxygen level and particle settling

Recycle flow Heater and circulator

Municipal wastewater

Permeate flux

Storage tank 10 m3

Mixer Coarse particle settler

Raw municipal wastewater from Trondheim community

Solid waste

Concentrated synthetic food-processing wastewater

Settled coarse particles Fig. 2 e Systematic diagram of moving-fiber BF-MBR system.

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w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 2 2 7 e2 2 3 4

Table 2 e Process characteristics and operations for the moving-fiber biofilm MBR system. Characteristics of bioreactors and membrane module used in experiments I. Anaerobic activated sludge reactor

II. Aerobic moving-fiber biofilm reactor

III. Submerged membrane module and membrane fibers

e Effective volume: 10.5 L e Total suspended solid concentration of anaerobic sludge: 20e25 g/L e Solid retention time (SRT) ¼ 110 days and effective HRT: 7 h e Upflow velocity ¼ 0.6 mm/s e Effective volume: 2.5 L e Effective surface area for biofilm attachment: 3 m2 e Fiber diameter: 1.45 mm with nominal pore size: 0.08 mm e Specific surface area: 689.6 m2/m3 e Configuration: Free top-end fiber, movable freely e Aeration rate: 5 L/min e Membrane material: Polysulfone (PS) e Apparent HRT: 1.7 h and effective HRT: 34 min (with recycle flow) e Membrane area: 0.068 m2 e Fiber outside diameter: 2.6 mm and effective length: 34.8 cm e Nominal pore size: 0.05 mm e Membrane material: Polyethersulfone (PES) e Configuration: Free top-end fiber, movable freely e Module material: high-grade acrylic e Membrane area per membrane tank volume (membrane concentration): 9.71 m2/m3 (9.71  103 m2/L) e Aeration rate (Superficial air velocity): 1e5 L/min (1.6e8.2 cm/s)

The effluent from the aerobic biofilm reactor also flowed gravitationally to the membrane tank. The membrane filtration unit was constructed with a KMS Puron fiber module with free topended configuration placed in a 3.6 cm internal column in the membrane tank. The recycle flow from the bottom of the membrane tank to the anaerobic tank was used to i) enhance the nitrogen removal, ii) recycle an excess aerobic sludge to the anaerobic tank for sludge digestion/stabilization, and iii) minimize the TSS concentrations in the membrane tank to prevent potential sludging problems in the filtration unit and minimize membrane fouling potential. In all experiments, the recycle flow from the bottom of the membrane tank to the anaerobic bioreactor was set at two times the influent flow rates. The moving-fiber biofilm reactor was constructed from a used membrane module supplied by POLYMEM (France), which is suitable for the biofilm application because i) the small diameter fiber provides high surface area density for biofilm attachment in a compact-sized bioreactor, and ii) freely-moving top end of fibers combined with aeration provides a high shear rate in a side-stream module configuration that prevents fiber clogging problem from a severe solid accumulation as well as efficient mass transfer of oxygen and nutrients to the biofilm. Characteristic of biofilm in the aerobic reactor in this study was different from a typical membrane aerated biofilm reactor (MABR) (Brindle and Stephenson, 1996; Casey et al., 1999) since the membranes used for MBR process

are hydrophilic and become wetted when they are in contact with water or liquid. In MABR, a biofilm grows on a gaspermeable membrane surface in which oxygen gas diffuses through membrane pores, and air/oxygen stream flows in fiber/tube lumens without direct contacts between airebiofilm and airewater. The wetted membranes in this study are not practical for gas permeation. In this investigation, aeration in the biofilm reactor provides both mixing in the reactor and oxygen supply to the biofilms for biological activities. The biofilms in the moving-fiber biofilm reactor growing on the external surfaces on the fibers are aerated by a strong bubbling flow which can control a detachment rate of the biofilms from the fibers (similar to membrane fouling control in MBR operation). Minimum aeration rate to control the biofilm stability in the biofilm module needs to be determined experimentally which is very important for process operation. The moving fiber for biofilm attachment has a specific surface area of 689.6 m2/m3, which is relatively higher than the surface area density of the moving bed (K1) typically reported at approximately 500 m2/m3. The anaerobic activated sludge bioreactor and aerobic moving-fiber biofilm reactor contributed 80% and 20% of total HRT, respectively. The anaerobic bioreactor was relatively larger than the aerobic bioreactor because the anaerobic bioreactor needs to handle both sludge stabilization and anaerobic digestion of organics in the wastewater influent. Additional volume in the anaerobic bioreactor is also required for anaerobic sludge digestion and settlement. SRT in the anaerobic bioreactor (as sludge digester) was a significant parameter for operation (Appels et al., 2008), set at 110 days in the experiments. MasterFlex computerized peristaltic pumps with speed control of 0.25% were used to control the flowrate of the wastewater influent, the recycle flow, and to create the vacuum for permeate production. SMC digital pressure sensor: PSE563-NO1 (compound pressure) and temperature sensor: IPAQ-H with Pt-100 were used to monitor transmembrane pressures and temperatures in the membrane tank, respectively. National Instrument DAQ card: USB 6210 and LabVIEW 8.2 were used for data acquisition. Spectral absorbance measurements were carried out at the wavelength of 254 nm by UVeVisible Hitachi U-3000 spectrophotometer. Color was measured by Norwegian standard method at wavelength of 410 nm. Dissolved organic carbons (DOCs)

Table 3 e Average characteristics of semi-synthetic food industrial wastewater influent, solution in membrane tank, and permeates from the biofilm MBR process. Characteristics Total suspended solid (TSS), mg/L Total COD (mg O2/L) Filter COD (mg O2/L) Color (mg Pt/L) NH4eN (mg/L) BOD5 (mg O2/L) Turbidity (NTU) Dissolved organic carbonDOC (mg/L)

Wastewater Membrane Permeate influent tank 94

55

e

480 320 N/A 36 239 92 85

180 96 230 0.5 e 44 38

45 e 110 0.3 0.04 0.1 21

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(after filtered) were measured by Tekmar Apollo 9000 TOC Combustion Analyzer. Turbidities were monitored by Hach 2100N Turbidimeter. Conductivities, pH, and dissolved oxygen concentrations were measured by Mettler Toledo SG3 with InLab 737, Mettler Toledo SG2 with InLab 413, and WTW Oxi 330i with DO probe CellOx 325, respectively. Total suspended solid (TSS) concentrations were measured by standard weighting method with glass filter. Chemical oxygen demand (COD), filtered COD, and ammonium concentrations (NeNH3) in the biofilm MBR system were measured by Hach-Lange cuvette test kits: LCK 114, 314, 614, 114, 302, 303, 238, and 338. The Hach-Lange photometric cuvette tests were calibrated and evaluated to meet the standards with ISO 8466-1, DIN 38402 A51 and DIN 32645. Concentrations of soluble microbial products (SMP) were not measured in this study since the membrane fouling in BF-MBR is very likely to be controlled by submicron particles (Leiknes et al., 2006; Ivanovic et al., 2006). The submerged membrane filtration module was cleaned after TMPs reached 0.32 bar by 3 g/L citric acid for 6 h followed by 2% w/w NaOCl for 6 h. Clean water permeate fluxes were tested after cleaning to confirm 100% permeability recovery.

3.

Results and discussion

3.1. Aeration demands on biofilm control, and nitrification in the aerobic moving-fiber biofilm reactor Optimum operating conditions and controls for the aerobic moving-fiber biofilm reactor were experimentally determined before the biofilm reactor was integrated in the BF-MBR system. Trials of the aerobic biofilm reactor performances and start-up times were conducted with both clean fibers and fouled fibers in the POLYMEM module (without cleaning after filtration stopped with final TMP of approximately 0.1 bar). Total COD, filter COD and ammonia concentrations were monitored daily to observe organic removal in the aerobic reactor. It was found that approximately 35% COD removal and 70% ammonia removal (at HRT of 4 h and aeration of 5 L/min) were obtained from the moving-fiber biofilm reactor after only 3 days when starting with the uncleaned membrane fibers. However, similar COD and ammonia removal rates were obtained after 8 weeks operation starting with the cleaned membrane fibers. The start-up time of the moving-fiber biofilm reactor using the uncleaned fibers was significantly shorter. Aeration in the aerobic biofilm reactor/module is required for two main reasons: i) mass transfer of oxygen for the biodegradation process (i.e. COD removal and nitrification), and ii) preventing accumulation of excess sludge produced in the fiber bundle. Trials to determine the minimum aeration demand were carried out at various air flow rates with HRT at 4.5 h monitoring each experiment for the duration of 1 week. The effect of aerations on nitrification and COD removal efficiencies in the moving-fiber aerobic reactor are showed in Fig. 3. Both total COD removal and nitrification efficiencies increased with increasing aeration rate because of higher dissolved oxygen (DO) level in the aerobic reactor. Minimum air flow rate for nitrification (>88%) was found at 1.5 L/min (superficial air velocity at 0.71 cm/s) and corresponding dissolved oxygen (DO) level in the biofilm module was

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approximately 3.7 mg/L. The total COD removal efficiency was higher than 40% when aeration rate was higher than 2.5 L/min and slightly increased to 44% when the aeration rate reached to 5 L/min. The biofilm aerations at 1.5 L/min and 2.5 L/min were likely to be sufficient for nitrification and the COD removal in short-term operations. Aeration demand for COD removal was likely to be higher than that for nitrification. However, long-term stability of the moving-biofilm reactor is very important and needs to be investigated for practical operation. It was found that at this air flow rate of 1.5 L/min, large amount of sludge was trapped inside the biofilm module with the fiber clogging by solid biomass observed after 3 weeks operation and the nitrification efficiency dropping by approximately 50%. Higher aeration than 1.5 L/min was more practical for the moving-fiber biofilm operation. Thick biofilms and severe accumulation of biomass solid may decrease or block mass transfer of oxygen across the biofilms. Higher aeration was required to purge the accumulated biomass in the fiber bundle and to control the biofilm thickness on the moving fibers by inducing higher shear forces to remove excess biomass. The problems of low nitrification and fiber clogging by biomass accumulation in the biofilm module were overcome when the air flow rate was greater than 4 L/min (superficial air velocity at 1.9 cm/s in the biofilm module). An air flow rate of 5 L/min (superficial air velocity at 2.4 cm/s) was therefore set in the biofilm reactor for all subsequent experiments. Apparently, tests of aeration demands for both nitrification and biofilm control revealed that minimum aeration demand per surface area for biofilms on the moving fibers in this study was approximately 0.08 m3/m2 h. Direct observation of the biofilm growth on the moving-fiber supports revealed that the biofilm thickness was relatively thin, compared to the biofilms observed in moving bed reactors. Biomass retained on the moving-fiber surface as biofilm was experimentally found at approximately 3.8 g/m2 (dry weight basis) which was significantly lower than that retained in the moving bed (K1) surface at approximately 15 g/m2 (Phattaranawik and Leiknes, 2010). These thin biofilms on the moving-fiber support would give benefits on lower mass transfer resistance of oxygen flux across the biofilm and provide an effective biofilm surface area more or less equivalent to the membrane surface area.

Fig. 3 e COD removal rates and nitrification rates at various aeration rates in the moving-fiber biofilm reactor.

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Fig. 4 e Effect of HRT on COD removals and membrane fouling rates.

Other biological characteristics of the moving-fiber biofilm reactor such as biomass production rate and oxygen uptake rate per surface area will be measured and discussed in our future publications. Nitrification efficiencies in the aerobic moving bed biofilm reactor and conventional aerobic activated sludge bioreactor were comparable (Yang et al., 2009) and both higher than 80%. The moving-fiber biofilm reactor in this study also provided relatively comparable nitrification efficiencies reported from conventional membrane aerated biofilm reactor (MABR) with internal aeration and aerated submerged biofilm reactor (ASBF) with external aeration (Choi et al., 2010). Biofilm reactor characteristic of ASBF (Choi et al., 2010) is similar to the moving-fiber biofilm reactor characteristic, and reported COD removal together with nitrification efficiencies were also comparable to the measured COD removal and nitrification efficiencies from this study.

3.2. ratio

Selections of total HRT of the system and recycle

Optimum total HRT of the BF-MBR system was experimentally selected from COD removal tests and membrane fouling experiments. The BF-MBR process was operated without a recycle flow for HRT section experiments, and the total HRT varied from 5e12 h. The membrane filtration module was operated at constant flux of 22 L/m2 h and membrane aeration of 3 L/min to determine membrane fouling rates at various HRT. The experimental results showed that lower membrane fouling rates (dTMP/dt) and higher degree of organic removals (i.e. lower COD in the effluent from the biofilm reactor) were obtained with increasing HRT. Fig. 4 shows the effect of total HRT in the BF-MBR system on COD removals and membrane fouling rates. While the HRT increased, COD removal efficiencies increased but the average membrane fouling rates decreased. The COD removal efficiency was higher than 50% when the HRT was greater than 8.7 h and slightly increased to 53% when reaching HRT of 12.7 h. Longer HRT typically allowed higher degree of biodegradation for wastewater organic substances and consequently provided better COD

removal and lower membrane fouling. When the total HRT was greater than 8.7 h, insignificant reductions in the membrane fouling rates were found, and consequently the optimum total HRT was determined to be 8.7 h. This equates to a HRT of 6.9 h in the anaerobic bioreactor and HRT of 1.8 h was in the aerobic biofilm reactor. Therefore, a total HRT of 8.7 h was maintained for all experiments. Fig. 5 shows the effect of recycle ratio on COD removals and membrane fouling rates in the BF-MBR system at HRT of 8.7 h. Ratios of recycle flow rate to influent flow rates varied from 1 to 4 while maintaining total HRT at 8.7 h. Significant increase in the COD removal efficiency was obtained when the recycle flow changed from 0 to 1. When the recycle flow ratios were higher than 2, there was slight change in the COD removal efficiency. However, when the recycle flow ratio was higher than 3, the COD removal efficiencies were likely to be lower because too high recycle flow can promote the wash-out effect in both anaerobic bioreactor and aerobic biofilm reactor. The average membrane fouling rates were significantly reduced when the recycle flow ratios changed from 1 to 2. There were no obvious reductions in the membrane fouling rates at the recycle flow ratios from 2 to 3 and 4. Recycling the effluent from the aerobic biofilm reactor to the anaerobic bioreactor typically allowed re-biodegradation of the organics in the wastewater, resulting in higher COD removal efficiencies and lower membrane fouling rate. A recycle flow ratio of 2 was subsequently used for all experiments. Based on the experimental results described above, the BFMBR pilot plant was operated under these operating conditions: HRT ¼ 8.7 h, recycle ratio ¼ 2, biofilm aeration ¼ 5 L/min, and the permeate quality from the submerged membrane module was monitored. Average removal efficiencies of filter COD and total COD were 70% and 63%, respectively. Average FCOD removal rate per fiber surface area in the moving-fiber biofilm reactor operated at such conditions was found at 5.8 g FCOD/m2 day with the FCOD loading rate of 9 g FCOD/ m2 day and effective HRT of 34 min, and the average nitrification rate per fiber surface area was found at 1.1 g NH4e N/m2 h. BOD5 in the permeate was negligible, implying that most of the biodegradable organic substances in the wastewater were removed (see Table 3). Average TSS in the membrane tank was relatively low at 55 mg/L. CODs and DOCs in the permeates

Fig. 5 e Effect of recycle ratio on COD removals and membrane fouling rates.

w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 2 2 7 e2 2 3 4

Fig. 6 e Developments of trans-membrane pressures at various aeration rates and flux of 17.6 L/m2 h.

were found to be lower than 50 mg O2/L and 25 mg/L, respectively. Color of the permeate was relatively high which was attributed to the composition of the synthetic food-processing wastewater containing molasses. The permeate, however, may be further chemically treated by conventional oxidation process for color reduction.

3.3. Performance of membrane filtration and membrane fouling rates Membrane clogging or sludging in the membrane filtration module was not found in any of the experiments due to the low TSS environment created in the BF-MBR system. Most of the large biological flocs/particles were observed to settle at the bottom of the membrane tank and subsequently recycled to the anaerobic bioreactor for the sludge digestion. However, methane gas production rate was ignored in this study. The membrane fouling rates from the BF-MBR system described in terms of TMP development over time at various permeate fluxes and membrane aeration rates were experimentally assessed to determine sustainable operating conditions. The effect of membrane aeration intensities on the membrane fouling rates at permeate flux of 17.6 L/m2 h is shown in Fig. 6. Higher air scouring rates on the fiber surfaces decreased membrane fouling rates (TMP increase rates). Aeration rates greater than 3 L/min (4.9 cm/s superficial air velocity) were found to give a better membrane performance at the operating conditions tested. No significant difference in the fouling rates was observed for the experiments with aeration rates of 4 and 5 L/min. Fig. 7 shows the average membrane fouling rates at

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fluxes of 17.6 and 20.2 L/m2 h and various superficial air velocities. Membrane filtration at permeate flux of 20.2 L/m2 h provided significantly higher membrane fouling rates compared to those at flux of 17.6 L/m2 h for all aeration intensities tested. The TMP profiles at flux of 20 L/m2 h were very steep which implies that a sustainable operation did not take place at this flux with membrane aeration lower than 5 L/ min (8.2 cm/s for superficial air velocity). Therefore, sustainable operating conditions for the BF-MBR system in this study could be obtained with a maximum permeate flux of approximately 18 L/m2 h and a minimum superficial air velocity at approximately 5 cm/s. The fouling rates found in this BF-MBR using the moving-fiber biofilms were in a similar range of the fouling rates reported from AS-MBR processes (Judd, 2006)

4.

Conclusions

Cumulative amount of the solid wastes of the non-intact membrane modules potentially disposed from the MBR plants around the world was predicted to be larger than 2000 tons after 2015. The proposed method to reuse the disposed modules was preliminarily studied for a feasibility to use the method for practical wastewater treatment application. A feasibility of the BF-MBR system using the disposed module as the aerobic biofilm reactor was investigated and experimentally demonstrated. The design of the biofilm MBR system has involved an attempt to control and minimize the suspended biological solids in the system and to overcome any possible problems from fiber clogging in the biofilm module and the membrane filtration module. The excess sludge aerobically produced from the biofilms was recycled to the anaerobic bioreactor for the sludge digestion. The moving fibers had slightly higher surface area density than the typical moving bed (K1). The biofilm on the moving fibers needed to be operated under strong shear stress to control the biofilm growth, and minimum biofilm aeration was experimentally found at 0.08 m3/m2 h in this study. The COD removal rate in the biofilm reactor was found at 5.8 g FCOD/m2 day with the FCOD loading rate of 9 g FCOD/m2 day and effective HRT of 34 min, and the nitrification rate per fiber surface area was found at 1.1 g NH4eN/m2 h. Direct observations revealed that visible biofilm on the moving fibers were relatively thin, and biomass retained on the moving fibers was significantly lower than that on the moving bed (K1). The effluent from the system had COD lower than 50 mg O2/L with extremely low ammonia and BOD5. Sustainable operating conditions for the membrane filtration in this study were obtained when permeate flux was lower than 18 L/m2 h and superficial air velocity was higher than 5 cm/s. Reuse of the non-intact membrane module for the biofilm supports was proved to be feasible for wastewater treatment application.

Acknowledgments Fig. 7 e Average membrane fouling rates at various superficial air velocities and fluxes of 17.6 and 20.2 L/m2 h.

The authors would like to thank European Commission for financial support for project 018480: EUROMBRA, a specific

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targeted research project financially supported by the European Commission under the Sixth Framework Programme (Priority “Global Change and Ecosystems”). Special thanks goes to POLYMEM (Toulouse, France) for the hollow fiber module, and Koch Membrane Systems for the PURON membrane fibers and technical supports. Jirachote Phattaranawik would like to thank TRF Senior Research Scholar Grant, Thailand Research Fund (TRF) for support.

references

Appels, L., Baeyens, J., Degreve, J., Dewil, R., 2008. Principles and potential of the anaerobic digestion of waste-activated sludge. Progress in Energy and Combustion Science 34 (6), 755e781. Brindle, K., Stephenson, T., 1996. The application of membrane biological reactors for the treatment of wastewaters. Biotechnology and Bioengineering 49 (6), 601e610. Casey, E., Glennon, B., Hamer, G., 1999. Review of membrane aerated biofilm reactors. Resources, Conservation and Recycling 27 (1), 203e215. Choi, Y., Johnson, K., Hayes, D.F., Sung, N.C., Xu, H., 2010. Dissolved organic matter and nitrogen removal by advanced aerated submerged bio-film reactor. Desalination 250, 368e372. Helness, H., Ødegaard, H., 1999. Biological phosphorus and nitrogen removal in a sequencing batch moving bed biofilm reactor. Water Science and Technology 40 (4e5), 161e168. Ivanovic, I., Leiknes, T., Ødegaard, H., 2006. Influence of loading rates on production and characteristics of retentate from a biofilm membrane bioreactor (BF-MBR). Desalination 199 (1e3), 490e492.

Judd, S., 2006. The MBR book: Principles and Applications of Membrane Bioreactors in Water and Wastewater Treatment. Elsevier, London, UK. Leiknes, T., Bolt, H., Engmann, M., Ødegaard, H., 2006. Assessment of membrane reactor design in the performance of a hybrid biofilm membrane bioreactor (BF-MBR). Desalination 199 (1e3), 328e330. Leikness, T., Ødegaard, H., 2007. The development of a biofilm membrane bioreactor. Desalination 202 (1e3), 135e143. Lesjean, B., Huisjes, E.H., 2008. Survey of the European MBR market: trends and perspectives. Desalination 231 (1e3), 71e81. Pearce, G., 2008a. Introduction to membranes e MBRs: manufacturers’ comparison: part 1. Filtration & Separation 45 (2), 28e31. Pearce, G., 2008b. Introduction to membranes e MBRs: manufacturers’ comparison: part 2 e supplier review. Filtration & Separation 45 (3), 30e32. Phattaranawik, J., Leiknes, T., 2010. Study of hybrid vertical anaerobic sludge-aerobic biofilm membrane bioreactor for wastewater treatment. Water Environment Research 82 (3), 273e280. Szatkowska, B., Cema, G., Plaza, E., Trela, J., Hultman, B., 2007. A one-stage system with partial nitritation and anammox process in the moving-bed biofilm reactor. Water Science and Technology 55 (8e9), 19e26. Wang, Z., Wu, Z., Mai, S., Yang, C., Wang, X., An, Y., Zhou, Z., 2008. Research and applications of membrane bioreactors in China: progress and prospect. Separation and Purification Technology 62 (2), 249e263. Yang, W., Cicek, N., Ilg, J., 2006. State-of-the-art of membrane bioreactors: worldwide research and commercial applications in North America. Journal of Membrane Science 270 (1e2), 201e211. Yang, S., Yang, F., Fu, Z., Lei, R., 2009. Comparison between a moving bed membrane bioreactor and a conventional membrane bioreactor on organic carbon and nitrogen removal. Bioresource Technology 100, 2369e2374.

w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 2 3 5 e2 2 4 6

Available at www.sciencedirect.com

journal homepage: www.elsevier.com/locate/watres

Integrated analysis of water quality parameters for cost-effective faecal pollution management in river catchments Daniel Ekane Nnane*, James Edward Ebdon, Huw David Taylor Environment & Public Health Research Unit, School of Environment & Technology, University of Brighton, Cockcroft Building, Lewes Road, Brighton BN2 4GJ, UK

article info

abstract

Article history:

In many parts of the world, microbial contamination of surface waters used for drinking,

Received 28 August 2010

recreation, and shellfishery remains a pervasive risk to human health, especially in Less

Received in revised form

Economically Developed Countries (LEDC). However, the capacity to provide effective

24 January 2011

management strategies to break the waterborne route to human infection is often thwarted by

Accepted 24 January 2011

our inability to identify the source of microbial contamination. Microbial Source Tracking (MST)

Available online 1 February 2011

has potential to improve water quality management in complex river catchments that are either routinely, or intermittently contaminated by faecal material from one or more sources,

Keywords:

by attributing faecal loads to their human or non-human sources, and thereby supporting more

Faecal

rational approaches to microbial risk assessment. The River Ouse catchment in southeast

Hazard management

England (U.K.) was used as a model with which to investigate the integration and application of

Low-cost approach

a novel and simple MST approach to monitor microbial water quality over one calendar year,

Microbial source tracking

thereby encompassing a range of meteorological conditions. A key objective of the work was to

Sentinel

develop simple low-cost protocols that could be easily replicated. Bacteriophages (viruses)

Watershed

capable of infecting a human specific strain of Bacteroides GB-124, and their correlation with presumptive Escherichia coli, were used to distinguish sources of faecal pollution. The results reported here suggest that in this river catchment the principal source of faecal pollution in most instances was non-human in origin. During storm events, presumptive E. coli and presumptive intestinal enterococci levels were 1.1e1.2 logs higher than during dry weather conditions, and levels of the faecal indicator organisms (FIOs) were closely associated with increased turbidity levels (presumptive E. coli and turbidity, r ¼ 0.43). Spatio-temporal variation in microbial water quality parameters was accounted for by three principal components (67.6%). Cluster Analysis, reduced the fourteen monitoring sites to six representative ‘sentinel’ sites. The correlation coefficient between presumptive E. coli and phages of Bacteroides GB-124 was very small (r ¼ 0.05) whilst that between turbidity and suspended solids was high (r ¼ 0.62). Variations in climate, animal and anthropogenic interferences were all, either directly or indirectly, related to faecal contamination. The findings show the importance of meteorological conditions, such as storm events, on microbial water quality, and suggest that any future increases in the frequency of storm events (associated with climate change) are likely to result in a greater incidence of FIO/pathogen loads. This low-cost approach could help to predict spatio-temporal ‘hotspots’ of elevated waterborne disease risk. The work also represents an important step towards integrating novel MST tools into river catchment modelling. ª 2011 Elsevier Ltd. All rights reserved.

* Corresponding author. Tel.: þ44 1273 643455; fax: þ44 1273 642285. E-mail addresses: [email protected] (D.E. Nnane), [email protected] (J.E. Ebdon), [email protected] (H.D. Taylor). 0043-1354/$ e see front matter ª 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2011.01.018

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1.

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Introduction

More than a billion people have no access to safe drinking water, and over 2 million die each year from water-related diarrhoea. Diarrhoeal disease is one of the leading causes of morbidity and mortality in Less Economically Developed Countries (LEDC) (Unicef and Who, 2009). In LEDC, surface waters are highly vulnerable to anthropogenic faecal pollution resulting from rapid population growth (Godfrey et al., 2006). Such faecal contamination potentially has profound and severe implications for public health, particularly in small communities in LEDC, where surface waters are often a source of drinking and bathing water (Pedley and Howard, 1997). However, it is not just human populations in LEDC that are at risk from poor microbial water quality. Rising demands on water resources raise concerns about longer-term sustainable provision of safe drinking water in many More Economically Developed Countries (MEDC). Intensive agriculture and large-scale construction of roads and settlements have led to a sharp rise in the contribution of non-point sources (NPS) by increasing the volume of run-off during rain events (Servais et al., 2007). In addition, the impact of both point sources (PS) and NPS of pollution may be exacerbated by the possible effects of climate change through more frequent flooding (Moore et al., 1997). According to Domingo et al. (2007), waterborne human infectious diseases associated with faeces from humans and animals are becoming a greater concern globally, and they place an enormous burden on the human population of many countries. Efforts to reduce waterborne disease rates are taking place against a backdrop of the Millennium Development Goals (MDG) (UN, 2009), which aim to reduce by half the proportion of people without sustainable access to safe drinking water by 2015. To protect and manage surface water quality in EU Member States, the revised Bathing Water Directive (2006/7/EC) and the Water Framework Directive (WFD) (2000/60/EC) have been established. The WFD requires all EU Member States to manage both PS and NPS of pollution, so as to achieve ‘good ecological status and quality’ in their watercourses by 2015 (EU, 2000). Whilst faecal indicator organisms (FIOs) have long been used to identify faecal contamination, they do not distinguish sources of contamination. Their use may therefore underestimate potential risks to human health, as human faecal material may constitute a greater risk of disease because of the presence of human specific pathogens (Sinclair, 2009). However, the implementation of effective, simple, low-cost Microbial Source Tracking (MST) techniques within river catchments may significantly improve water quality by enhancing public health protection in a proactive and preventative manner. MST may provide a useful framework for differentiating sources of faecal contamination, and could support rational source protection strategies (Cotruvo, 2004). For example, in the U.S., MST may assist environmental managers to comply with Total Maximum Daily Load regulations required by the U.S. Environmental Protection Agency. In Europe, its most obvious current application is in developing ‘bathing water profiles’ required under the revised EU bathing water Directive (EU, 2006). At a global level, MST is likely to prove an important tool for the development and

implementation of Water Safety Plans (WSP), as prescribed by the WHO (2008). The aim of WSP is to ensure the consistent safety and acceptability of a drinking water supply, through the use of comprehensive risk assessment and risk management approaches. Identifying potential spatio-temporal faecal ‘hotspots’ within a river catchment using appropriate MST methods within a well-designed monitoring programme, would support a ‘multiple-barrier approach’ to source water evaluation, selection and protection. Managing faecal pollution can be especially challenging for many LEDC because public resources can be inadequate, and reliable information about the extent, sources, risks and severity of faecal pollution may be limited at best (Jenkins et al., 2009). Therefore, simple procedures and tools that might potentially be applied in LEDC are urgently needed to assess the microbiological quality of water as a health protection measure. Therefore, the overall aim of this study was to combine the use of traditional water quality parameters with novel MST tools in an attempt to develop low-cost protocols for microbial water quality monitoring and risk assessment under various meteorological conditions. The specific objectives of the study were to: 1) evaluate the potential usefulness of a previously isolated Bacteroides strain GB-124 as a marker for identifying and quantifying human sources of faecal contamination; 2) suggest a protocol for gathering and analysing relevant data in order to develop rational and effective low-cost techniques and mitigation approaches; 3) identify appropriate chemophysical parameters that might provide simple and low-cost alternatives to FIO enumeration; and 4) suggest a protocol for identifying spatio-temporal ‘hotspots’ of faecal contamination in river catchments.

2.

Materials and methods

2.1.

The River Ouse catchment

The River Ouse catchment in southeast England was selected for this study because of its proximity to our laboratory facilities, and because of the ready availability of information on land-use and local pollution issues. The River Ouse contains 273 km of main river-channel, and drains 396 km2 of land to its tidal limit. The catchment is predominantly rural, with only 7% of the land classed as ‘urban’ (Environment Agency, 2006). Agriculture is the principal land-use by area, but this potential source of faecal pollution is supplemented by over twenty municipal wastewater treatment works (WWTW) discharging partially treated wastewaters into the river system (Ebdon et al., 2007).

2.2.

River water sampling

A total of 350 1-L grab samples of river water were collected at least biweekly from fourteen monitoring sites (Table 1, Fig. 1) over the period October 2007 to September 2008. These sites included locations impacted by recognised PS and/or NPS of faecal pollution (both human and non-human). Samples were taken manually by clamping 1000 ml sterile polyethylene bottles (Sterilin, UK) to an extendible sampling pole. All

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Table 1 e River water monitoring sites investigated. Site ID

Name of site a

1

Barcombe Mills

2 3

Clapper’s Bridgea Ditchling

4 5 6 7 8 9 10 11 12 13 14

Goldbridgea Isfielda Lindfield Longford Plumpton Scaynes Hill Sheffield Park Spatham Lane Streat Lane Swansyard Farm Wales Farm

Site description and reason for choosing site Just above the tidal limit, most downstream and therefore impacted by all the other sites. Downstream of Ditchling WWTW; human and non-human inputs. 1 km from chalk spring emerging from aquifer; possibly wild animals inputs; upstream of Ditchling WWTW. Rural; human and non-human inputs. Rural; non-human inputs. Upstream of Scaynes Hill WWTW; non-human inputs. Human and non-human inputs. Non-human inputs, near Plumpton windmill. 0.5 km downstream of Scaynes WWTW; human and non-human inputs. Near steam train station; human and non-human inputs. 0.5 km downstream of Ditchling WWTW; human and non-human inputs. 1.6 km downstream of Ditchling WWTW; human and non-human inputs. Mainly non-human inputs. Dairy Farm; mainly non-human inputs.

a Gauging stations monitored by UK Environment Agency.

samples were taken from the centre of the river or stream, in a non-intrusive manner so as not to disturb sediment (Kleinheinz et al., 2006). The samples were placed in a cool box, maintained at circa 4  C, and transported to the laboratory within 2 h. All samples were analysed within 6 h of sampling.

2.3.

Enumeration of FIO

Presumptive Escherichia coli (E. coli) and presumptive intestinal enterococci levels were detected and enumerated, at least in

duplicate, using ISO methods 9308e1 and 7899e2, respectively. M-FC agar (Difco, BDMS, UK) and m-Enterococcus agar (Difco, BDMS, UK) was used for presumptive E. coli and presumptive intestinal enterococci analyses respectively. Presumptive E. coli and presumptive intestinal enterococci were incubated at 44 (0.5) C for 21(3) h and 36 (2) C for 44 (4) h, respectively. On m-FC agar, all blue colonies were counted and recorded as presumptive E. coli whilst on m-Enterococcus agar; all light and dark red colonies were counted and recorded as presumptive intestinal enterococci.

Fig. 1 e The study area showing catchment boundaries, major WWTW, and monitoring sites.

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Sulfite Polymyxin Sulfadiazine (SPS) agar (Difco, UK), prepared in accordance with the manufacturer’s instructions, was used to detect and enumerate spores of Clostridium perfringens. Ten ml. of each water sample were heat-shocked in a water bath (Grant OLS 200) to 78e80  C, for 10 min (Araujo et al., 2004). One ml. of each heat-shocked water sample was aseptically inoculated into SPS agar (cooled to 50e55  C) in sterile Petri dishes. The Petri dishes and agar were incubated anaerobically at 37  C for 24e48 h. Anaerobic conditions were generated in incubation jars using AnaeroGen sachets (Anaerogen, Oxoid, UK).

2.4.

Enumeration of bacteriophages

A novel MST method involving the detection of bacteriophages capable of infecting a strain GB-124 of anaerobic gut bacterium (Bacteroides) previously isolated from municipal wastewater (Payan et al., 2005) and shown to be restricted to human sewage (Ebdon et al., 2007), was also applied to the river samples. Bacteriophages infecting Bacteroides strain GB124 and somatic coliphages (infecting E. coli strain WG-5) were enumerated by the double-agar-layer method (Tartera et al., 1992) in accordance with ISO 10705e4 (Anon, 2001) and ISO 10705-2 (Anon, 2000), respectively. Enumeration was undertaken in duplicate and average results were expressed as plaque-forming units (PFU) per 100 ml of sample. All samples were filtered (to remove background bacterial contamination) using 0.22 mm polyvinylidene difluoride membrane syringe filters (Millipore, US) and 1 ml of the filtered water sample was added to 1 ml of exponentially growing host strain in 2.5 ml of semi-solid modified Scholtens’ agar for somatic coliphages and semi-solid Bacteroides phage recovery medium agar for phages of Bacteroides. This mixture was stirred and gently poured onto previously prepared

modified Scholtens’ agar or Bacteroides phage recovery medium agar bases for the corresponding bacteriophages in 90 mm diameter Petri dishes. The overlays were inverted and incubated for 18e24 h at 37  C (Muniani-Mujika et al., 2003). For phages of Bacteroides GB-124, anaerobic conditions were generated in incubation jars using AnaeroGen sachets (Anaerogen, Oxoid, UK).

2.5.

Determining chemophysical concentrations

Conductivity, pH level, redox potential, and water temperature were measured in the field at each monitoring site, using a Hanna field meter (Hanna Instruments, Italy). Dissolved oxygen (DO) was also measured on-site using a YSI 550A meter (YSI, Yellow Springs, OH). Turbidity was measured using a Model 2100P turbidimeter (Hach Company, Loveland, USA). Suspended solids (SS) were determined by the ISO 872 filtration method (Anon, 2005). Ammoniacal-nitrogen (NH3eN), nitrate-nitrogen (NO3eN), and orthophosphate (PO4eP) were determined spectrophotometrically using a DR/2400 spectrophotometer (Hach Company, Loveland, USA) in accordance with standard methods (APHA, AWWA, and WEF, 2005). All instruments were calibrated prior to use. Rainfall and river discharge (Q) data were obtained from the UK Environment Agency.

2.6.

Statistical analyses

The Spearman’s rank correlation coefficient (r) was used to determine the degree of association between all parameters. KruskaleWallis test and Wilcoxon signed test (Mill et al., 2006) were employed to determine the spatial and longitudinal variations, respectively in determined parameters from all monitoring sites. Principal Component Analysis with Varimax

Table 2 e Seasonal levels of pooled data of chemophysical, nutrients and microbial parameters at the fourteen monitoring sites. Parameter

Autumn (n ¼ 98) Min

Md

Max

Spring (n ¼ 98) Min

Md

Max

Summer (n ¼ 84) Min

Md

Max

Winter (n ¼ 70) Min

Md

P-value

Max

Phages of Bacteroides GB-124 0.00 2.30 3.40 0.00 2.00 3.15 0.00 0.00 0.00 0.00 2.30 3.48 (logpfu/100 ml) Clostridium perfringens (logcfu/100 ml) 0.00 0.00 3.40 0.00 2.18 3.70 0.00 2.18 3.59 0.00 0.90 3.76 E. coli (logcfu/100 ml) 0.00 2.90 4.53 1.60 2.78 5.48 2.00 3.10 5.61 2.00 3.06 5.02 HPC (logcfu/100 ml) 5.00 6.00 7.61 4.88 5.98 6.78 0.00 5.48 6.72 4.70 5.98 6.78 Intestinal enterococci (logcfu/100 ml) 0.00 2.92 4.80 0.00 2.54 5.37 2.00 2.90 5.33 1.20 2.70 4.90 Somatic coliphages (logpfu/100 ml) 0.00 3.74 4.78 0.00 3.71 4.95 2.00 3.79 4.90 0.00 3.79 4.64 Dissolved oxygen (mg/l) 3.39 12.30 29.51 2.00 19.05 24.55 2.00 6.61 9.77 10.72 19.95 26.30 Conductivity (mS/cm) 23.99 67.61 407.38 19.95 60.26 562.34 36.31 141.25 501.19 33.88 91.20 204.17 pH 6.76 4.90 8.51 7.41 7.76 8.51 7.41 7.76 8.32 0.88 7.76 8.32 Redox potential (mV) 89.13 151.36 263.03 123.03 177.83 234.42 91.20 147.91 173.78 93.32 131.83 177.83 Suspended solids (mg/l) 1.00 10.96 0.19 1.00 15.85 52.48 1.00 14.13 131.83 3.02 16.98 199.53 4.79 13.18 17.38 6.92 10.23 16.60 12.59 15.85 18.62 3.09 6.92 14.79 Temperature ( C) Turbidity (NTU) 1.58 6.03 36.31 1.45 9.77 107.15 1.86 7.24 147.91 2.24 19.05 165.96 1.00 1.05 2.69 1.00 1.12 8.91 1.00 1.07 14.13 1.00 1.15 12.88 NH3eN (mg/l) 0.10 2.00 13.18 0.20 1.70 10.23 8.91 1.00 9.33 0.10 1.42 10.72 NO3eN (mg/l) 0.13 2.04 24.55 0.21 1.10 7.59 0.14 1.95 21.88 0.10 1.07 26.30 PO4eP (mg/l)

<.01 <.01 <.01 <.01 <.01 <.01 .634 <.01 <.01 .669 .106 .976 <.01 <.01 <.01 <.01

Min ¼ Minimum; Md ¼ Median; Max ¼ Maximum; HPC ¼ heterotrophic plate count; cfu ¼ colony forming units; pfu ¼ plaque forming units; NTU ¼ Nephelometric Turbidity Units. Italic numbers denote significant P-values, P < .05. NB: If no microorganisms were detected, both log cfu/100 ml and log pfu/100 ml were reported as 0.

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rotation (so that the loadings tend to be either high (near plus or minus one) or low (near zero) (Rogerson, 2010), and therefore aid in the interpretation) and Cluster Analysis were applied to the dataset following z-transformation. ‘Statistical Package for the Social Sciences’ (SPSS) 15.0 for Windows was used for the various statistical analyses.

3.

Results and discussion

3.1. Seasonality and spatial variation in water quality parameters In order to determine seasonal variations in water quality parameters, the dataset was divided into four temporal subgroups: namely, spring (March to May), summer (June to August), autumn (September to November), and winter (December to February) (Table 2). This approach was considered valid because rainfall and agricultural practices tend to

follow fairly distinct seasonal patterns in both temperate and tropical regions. The KruskaleWallis test revealed statistically significant (P ¼ 0.001) spatial and temporal variations in recorded levels of presumptive E. coli, presumptive intestinal enterococci, spores of C. perfringens, somatic coliphages, phages of Bacteroides GB-124, conductivity, turbidity, pH, NH3eN, NO3eN, and PO4eP. The descriptive statistics of chemophysical parameters and microbial levels are shown in Table 2. Standard deviations (not shown in Table 2) enabled us to appraise how the four seasons compared in terms of the magnitude of change in chemophysical and microbial parameters. The highest standard deviation in levels of presumptive E. coli, spores of C. perfringens, and somatic coliphages were observed in summer whilst the highest standard deviations in levels of presumptive intestinal enterococci and phages of Bacteroides GB-124 were observed in autumn. The greatest fluctuation in levels of NH3eN was observed in winter. For NO3eN, the highest and lowest variations were observed in summer and winter, respectively. Davie (2008)

Table 3 e Longitudinal variation in chemophysical and microbial water quality from upstream to downstream water monitoring sites. Parameter NH3-N (mg/l) PO4-P (mg/l) Dissolved oxygen (mg/l) Conductivity (mS/cm) Redox potential (mV) Temperature ( C) NO3-N (mg/l) Turbidity (NTU) Suspended solids (mg/l) pH Escherichia coli (cfu/100 ml) Clostridium perfringens (cfu/100 ml) Intestinal enterococci (cfu/100 ml) Somatic coliphages (pfu/100 ml) Bacteroides GB-124 (pfu/100 ml)

Site 6 (n ¼ 25) Mean 0.1 0.8 14.9 60.5 153.6 12.2 2.5 13.1 14.9 7.7 572 40 1845 1800 172 Site 12 (n ¼ 25) Mean

NH3-N (mg/l) PO4-P (mg/l) Dissolved oxygen (mg/l) Conductivity (mS/cm) Redox potential (mV) Temperature ( C) NO3eN (mg/l) Turbidity (NTU) Suspended solids (mg/l) pH Escherichia coli (cfu/100 ml) Clostridium perfringens (cfu/100 ml) Intestinal enterococci (cfu/100 ml) Somatic coliphages (pfu/100 ml) Bacteroides GB-124 (pfu/100 ml)

0.23 4.9 14.1 136.3 156.8 11.2 1.7 17.5 16.4 8.0 7100 206 4038 15228 212

Site 9 (n ¼ 25) Mean 0.2 2.1 14.3 79.9 155.2 11.9 2.2 13.1 14.2 7.7 2998 288 2363 8720 328 Site 2 (n ¼ 25) Mean 0.2 4.5 13.2 144.9 151.5 11.8 2.5 26.7 20.5 8.0 5180 194 2699 14620 388

P-value .001b .001b .003a .010b .030b .001b .190 .093 .750 .902 .001b .001b .001b .001b .003b P-value

.010a .059 .027a 0.327 .044a .002b .006b .001b .176 .150 .583 .975 .819 .746 .089

Site 10 (n ¼ 25) Mean

Site 1 (n ¼ 25) Mean

0.2 3.3 14.0 74.5 157.4 11.8 2.2 15.2 14.2 7.7 2640 294 2455 9864 380

0.1 1.7 14.1 152.2 148.6 12.5 2.2 14.1 15.1 8.0 646 78 1556 5240 236

Site 4 (n ¼ 25) Mean 0.1 2.1 13.8 70.5 155.6 12.0 2.8 11.7 12.3 7.8 2710 186 1337 8276 252

Site 1 (n ¼ 25) Mean 0.2 1.7 14.1 152.2 148.6 12.5 2.2 14.1 15.1 8.0 646 78 1556 5240 236

P-value refers to Wilcoxon signed test for each parameter comparing upstream and downstream levels (longitudinal variation). Italic numbers denote statistically significant differences, P < 0.05. a Levels of parameter significantly lower downstream. b Levels of parameter significantly higher downstream.

P-value .001b .001a .049b .031b .052 .007b .224 .312 .686 .001b .007a .006a .003a .001a .663 P-value

.001b .009a .031b .043b .036a .024b .173 .052 .024b .001b .001a .011a .031b .001a .856

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studied seasonal nitrate levels in southeast England and reported similar results. The greatest variation in PO4eP concentration was observed in autumn and large increases in PO4eP concentrations were observed during dry periods at monitoring sites downstream of WWTW. High levels of somatic coliphages, phages of Bacteroides GB-124 and PO4eP were observed at sites 11 and 2, both of which are downstream of the Ditchling WWTW (population equivalent 1621). The temporal and spatial variations observed across all monitoring sites most likely suggest influences from a range of urban and agricultural activities, rainfall, and ‘hydrobiogeochemical’ processes acting within the catchment. Since rainfall levels are generally lower during summer, it may be reasonably assumed that PS are relatively more important sources of faecal pollution during this period (except during summer rainstorms), whilst NPS are relatively more important in winter. At times during the summer months, certain tributaries of the River Ouse consist mainly of final municipal wastewater effluent returns (SOCS, 2010). High FIO levels (greater than 4 Log cfu/100 ml) were observed in summer during periods of intense rainfall and high discharge (greater than 0.35m3/s). The high FIO levels observed during the summer months could also be a consequence of longer bacterial survival times (Plummer and Long, 2007). The reductions in FIO observed in winter compared with summer may be attributable to a diminution of surface sources of FIO, as a consequence of previous flushing of FIO stores under wetter conditions, reduced faecal inputs to grazing land as a result of winter housing of livestock, and a reduction in the amount of slurry spread during the winter period (Kay et al., 2008). The presence of high NO3eN concentrations at sites 6 (1.12 mg/l), 11 (1.04 mg/l), and 12 (1.01 mg/l) may be a consequence of run-off from agricultural fields on which inorganic fertilisers had been applied. Partially treated wastewater

effluents released into the River Ouse and some of its tributaries are probably responsible for much of the background level of NO3eN, but it is perhaps surprising that the summer levels of nitrate are not higher compared with the winter period. This observation could be attributed partly to nitrate assimilation from the water by growing aquatic plants, autotrophic activity (Jarvie et al., 2008), and a decrease in DO concentration, which consequently enhances the denitrification process (Carlyle and Hill, 2001) during summer months. According to Davie (2008), NO3eN levels may peak during the autumn to spring period because of prevalent agricultural practices in southeast England: during autumn and winter periods there is flushing of NO3eN, which has previously accumulated during dry periods in the soils of intensively farmed agricultural lands. Other sources of NO3eN include, inter alia, wastewater discharges, silage pits, slurry-holding tanks and septic tanks (Gray, 2008). In river systems, the main source of dissolved phosphate is normally from partially treated wastewater effluents, since in the absence of specific phosphate-stripping units, wastewater treatment processes remove very little of the phosphate from detergents present in wastewater (Davie, 2008).

3.1.1.

Longitudinal variation in water quality parameters

Concentrations of some chemophysical parameters (conductivity, dissolved oxygen, temperature, turbidity, suspended solids and pH) significantly increased from upstream to downstream monitoring sites (Table 3). By contrast, levels of microbial parameters significantly decreased at downstream sites except for Site 9 that received wastewater effluents from WWTW. The Wilcoxon signed test results (Table 3) showed that Site 2, downstream of Site 12, recorded significantly higher levels of temperature, NO3eN, and turbidity compared with Site 12. Such downstream increase in NO3eN is also reported by Edwards and Withers (2008) following a study of some freshwaters in the UK.

Table 4 e Significant Spearman rank correlations between chemophysical and microbial water quality parameters for pooled one year dataset. Parameter Intestinal enterococci Somatic coliphages Bacteroides (GB-124) HPC NH3eN Dissolved oxygen Conductivity Redox potential Turbidity pH Suspended solids E. coli NO3eN PO4eP Temperature Clostridium perfringens

Intestinal enterococci 1.00 .402a

SoC

Bacteroides GB-124

HPC

NH3eN

1.000 .429

1.000

DO

Cond

Redox

Turb

pH

SS

E. coli

1.000 1.000 .441a

1.000 .316a

1.000 1.000

.304a

.464a

1.000 1.000

.499a .312a .636a

.429a

.621a .432a

.457a .383a

1.000 1.000

.322a .803a

a

.464

a

.496

.341

a

.469a

NH3eN ¼ Ammoniacal-nitrogen; DO ¼ Dissolved oxygen; Cond ¼ Conductivity; Redox ¼ Redox potential; Turb ¼ Turbidity; SS ¼ Suspended solids; NO3eN ¼ Nitrate-nitrogen; PO4eP ¼ Orthophosphate; and Temp ¼ Temperature. a Correlation is significant at the 0.01 level (2-tailed). SoC ¼ Somatic coliphages; Bacteroides ¼ Phages of Bacteroides (GB-124); HPC ¼ heterotrophic plate count.

w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 2 3 5 e2 2 4 6

Conversely, levels of microbial parameters were lower, although not significantly, at Site 2. Presumptive E. coli, spores of C. perfringens, and somatic coliphages levels were significantly lower at Site 1 (downstream of Site 4), with the exception of presumptive intestinal enterococci, which were significantly higher at Site 1, whilst concentration of all the chemophysical parameters, with the exception of PO4eP and redox potential, were significantly higher at Site 1. Levels of all microbial parameters, except phages of Bacteroides GB-124, were significantly lower at Site 1, which was downstream of Site 10. When data from Site 6 (upstream) and Site 9 (downstream) were compared, all the microbial parameters, and all the chemophysical parameters, with the exception of DO and temperature, were significantly higher at Site 9. This was unsurprising given that Site 9 receives effluent returns from Scaynes Hill WWTW (Population equivalent, 37327). The lower DO level at this site may be a result of the organic content of the wastewater effluents from this WWTW. The observed increase in conductivity and other chemical parameters downstream is likely to be linked to natural

2241

processes that are characteristic of chalk streams (Neal et al., 2005), and is in agreement with the general behaviour of solutes in streams (Bengraı¨ne and Marhaba, 2003). The microbial parameters may also be adversely affected by environmental stressors such as ultraviolet radiation, elevated temperature and predation in the water environment. Therefore, and as observed in the present study, microbial die-off and settling may lead to significant reductions in their numbers downstream of their point of release. The high concentration of PO4eP at Site 9 demonstrates the impact of wastewater effluent on river water quality.

3.2. Correlations between measured water quality parameters The Spearman’s Rank Order Correlation revealed meaningful positive and negative (large and moderate) statistically significant associations between parameters (Table 4). The statistically significant positive correlations between FIO parameters were not surprising, in that faeces are their common

Fig. 2 e Principal component analysis on the chemophysical and microbial parameters for (a) autumn, (b) spring, (c) summer and (d) winter: graph of the parameters on plane (PC1, PC2).

Distance (Squared Euclidean Distance)

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transport processes) may have accounted for high positive correlations between these parameters.

138.60

3.3. Identifying principal microbial sources using principal component analysis (PCA)

92.40

46.20

C1

C4

C5

C2

C3

C6

0.00 1

2

4

10

9

5

6

8

7

11

12

13

3

14

Monitoring sites

Fig. 3 e A dendrogram from cluster analysis (using Ward’s hierarchical method) for the fourteen monitoring sites showing six distinct clusters (C1 to C6) based on similar annual chemophysical concentrations and microbial levels.

source. The positive correlations between turbidity, presumptive E. coli, and somatic coliphages suggest that these microorganisms were carried in suspension from either in-channel reservoirs, or from agricultural run-off, and that the process of sediment and microorganisms transport may be linked (Sinclair et al., 2009). The relationships observed between levels of somatic coliphages and PO4eP, and NO3eN and NH3eN, suggest a common source that may be wastewater from WWTW, or runoff from agricultural fields, to which phosphate and ammonium fertilisers have been applied. The negative correlation between temperature and DO concentration demonstrates their interdependence, whilst the negative correlations between DO, SS, and turbidity during winter may suggest that the SS and turbidity had a high organic content, and that their in-situ decomposition, depleted levels of DO in the water. The large correlations between SS and turbidity (P ¼ 0.01) occurred during the rainy months. Disturbances from rainfall, run-off, and inchannel disturbance of sediment (natural erosion and sediment

Two principal components (PCs), which contained most of the information in the original dataset based on the Kaiser’s criterion (eigenvalues above 1) (Kaiser, 1974) and inspection of the scree plots (eigenvalues on the y-axis and the parameter number, ranging from 1 to p, on the x-axis) generated (Cattell, 1966), were retained in each season. The eigenvalue of a PC is a measure of the variance of all the parameters accounted for by the PC. The two PCs explained 46.7% (autumn), 51.9% (spring), 47.8% (summer), and 479.0% (winter) of the variance present in the original dataset. The PCs represented classification axes (Field, 2009) along, which determined parameters were plotted, the co-ordinates of parameters along each axis representing the strength of relationship (loading) between that parameter and each PC (Fig. 2 (a, b, c, and d)). Parameters that had large loadings (the correlation coefficients between PCs and the original parameters) on the same axis were assumed to measure different aspects of some common underlying source or dimension (Field, 2009; Rogerson, 2010). Therefore, the greater the value of a parameter loading, on a PC axis, the more important that parameter is in accounting for the underlying source. As we were interested in finding common underlying sources within the data, the primary interest was only in the variance shared (common variance or communality) with other parameters. Only PC loadings with an absolute value greater than 0.5 (which explained around 25%, i.e. (0.52  100%) of the common variance in the parameter) were considered most important and were those from which inferences were drawn of the possible main sources of microbial contamination in each season. The two PCs in autumn, suggested that humans and/or municipal wastewater were the main sources of microbial contamination, as demonstrated by the large loading of the human specific phages of Bacteroides GB-124. In spring, the main source of microbial contamination probably was wastewater

Table 5 e Loadings of parameters to clusters. Parameter Intestinal enterococci (cfu/100 ml) Somatic coliphages (pfu/100 ml) Phages of Bacteroides GB-124 (pfu/100 ml) Escherichia coli (cfu/100 ml) Spores of Clostridium perfringens (cfu/100 ml) Dissolved oxygen (mg/l) Conductivity (mS/cm) Redox potential (mV) Turbidity (NTU) pH Suspended solids (mg/l) NH3eN (Ammoniacal-nitrogen) (mg/l) NO3eN (Nitrate-nitrogen) (mg/l) PO4eP (orthophosphate) (mg/l) Temperature ( C)

C1

C2

C3

C4

C5

C6

0.595 0.278 .967 0.462 0.250 0.133 0.701 1.724 .619 0.563 0.240 0.441 0.690 0.419 0.253

0.848 2.900 1.379 0.976 1.078 1.061 0.529 1.389 2.424 2.143 1.115 0.834 2.018 2.011 1.431

0.192 0.031 0.287 0.264 0.277 0.476 0.832 0.064 0.167 0.226 0.577 0.348 0.211 0.126 0.154

0.496 0.702 0.690 0.579 1.123 2.239 0.706 1.293 0.591 1.805 0.562 0.160 1.366 1.536 0.337

0.244 0.663 0.321 0.417 0.810 0.030 0.644 0.846 0.354 0.038 0.347 0.251 0.191 .922 1.319

2.954 .875 2.551 2.807 1.934 2.033 1.833 0.623 0.537 0.226 2.497 3.208 1.310 0.698 1.431

Italic numbers denote the highest positive loading of a parameter.

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Table 6 e FIO-based classification system of microbial water quality. Classification of faecal pollution

Class I

II

III

IV

V

Parameter

Faecal pollution

low

moderate

critical

strong

Excessive

Presumptive E. coli Presumptive intestinal enterococci

Log cfu/100 ml Log cfu/100 ml

2.0 1.6

>2.0e3.0 >1.6e2.6

>3.0e4.0 >2.6e3.6

>4.0e5.0 >3.6e4.6

>5.0 >4.6

Adapted from Kavka et al. (2006). Faecal indicator levels are in log median colony forming units (cfu) per 100 ml.

from WWTW, and possibly from in-channel stores, as revealed by strong presumptive E. coli, presumptive intestinal enterococci, SS, and turbidity loadings on PC1. In summer, agricultural run-off was probably the dominant microbial source, as suggested by the strong loadings of NH3eN, presumptive E. coli, presumptive intestinal enterococci, and turbidity on PC1, and NO3eN on PC2. In winter, the main source of microbial contamination appeared to be agricultural run-off, following flushing by rainfall.

3.4. Identifying ‘sentinel’ monitoring sites using cluster analysis (CA) The CA revealed six clusters of monitoring sites (Fig. 3). Monitoring sites 1 and 2 (Cluster C1) were located on the River Ouse and on a tributary (Bevern stream), respectively. Site 7 (C2) was located on another tributary (Longford stream). Sites 4e6, 8e10 (C3) were located on the River Ouse (apart from sites 5 and 8, which were located on River Uck and Plumpton stream tributaries, respectively). Site 3 (C4) was located on the Ditchling spring. Sites 11e13 (C5) were all located on the Bevern stream tributary. Site 14 (C6) was located on a subtributary of the Bevern stream. The parameters that characterised each cluster are shown in Table 5. The clustering procedure appeared to classify reliably monitoring sites in the catchment based on sources of faecal pollution. Therefore, CA could inform the design of an optimised river catchment spatial monitoring strategy. For example, for preliminary rapid water quality assessment studies, only ‘sentinel’ (representative) sites from each cluster need be used. This approach could facilitate more sustainable water quality monitoring strategies, by reducing laboratory

workload and reducing monitoring costs. It may also allow more costly methods such as molecular techniques to be more effectively targeted at specific sites.

3.5. Human-specific MST and delineation of microbial risk zones The detection of FIOs, and bacteriophages at all the fourteen monitoring sites clearly indicated that all the sites were impacted by faecal material. Human and non-human inputs were both likely contributors of faecal contamination to the River Ouse catchment. Phages capable of infecting Bacteroides GB-124 were detected in 68% of all the river water samples. At twelve (1e2, 4e13) of the fourteen monitoring sites, phages of Bacteroides GB-124 were detected in at least 60% of river water samples, but were detected in only 28% of samples at site 3, and 12% of samples at site 14. They were scarcely detected at site 14 (Median value ¼ 0.00 log pfu/100 ml), which is predominantly impacted by faecal contamination from agriculture. The application of the phages of Bacteroides GB-124 to all monitoring sites and samples added a beneficial MST component to the study. Based on the correlation approach of Kirschner et al. (2008), the low correlation coefficient (r ¼ 0.05) between levels of presumptive E. coli and phages of Bacteroides GB-124 suggests that a relatively small component of the observed presumptive E. coli in the catchment can be traced to human faecal material. If the presumptive E. coli were derived mainly from humans, there would be a strong positive significant correlation between presumptive E. coli and phages of Bacteroides GB-124 levels, since they would have been released simultaneously. The non-detection of phages of Bacteroides GB-

Table 7 e Comparison of levels of FIO and chemophysical concentrations during dry and wet weather conditions. Parameter

Escherichia coli (cfu/100 ml) Intestinal enterococci (cfu/100 ml) Somatic coliphages (pfu/100 ml) Ammoniacal-nitrogen (mg/l) Nitrate-nitrogen (mg/l) Orthophosphate (mg/l) Dissolved oxygen (mg/l) Conductivity (mS/cm) Suspended solids (mg/l) Turbidity (mg/l)

Dry weather sample (n ¼ 140)

Wet weather sample (n ¼ 140)

Median

Median

3

1.8  104 1.1  104 1.3  104 0.52 2.17 2.92 11.24 107.71 24.62 21.54

1.5  10 1.2  103 6.3  103 0.19 1.42 2.13 16.01 86.84 10.41 12.71

P-value refers to Wilcoxon Signed Test for each parameter. Italic numbers denote significant P-values, P < 0.05.

P-value

<.01 <.01 <.01 <.01 <.01 .362 <.01 .039 <.01 <.01

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124 in samples presenting high levels of traditional FIO was considered indicative of non-human faecal input.

3.5.1.

Identification of microbial risk zones

Classification of monitoring sites based on a system of five microbiological water quality categories (relating to levels of presumptive E. coli and presumptive intestinal enterococci) has been devised by Kavka et al. (2006). Faecal pollution levels of quality class I and II meet the ‘good’ bathing water standards (in accordance with the revised 2006 EU Bathing Water Directive), but quality classes III, IV, and V, exceed the faecal pollution threshold level for ‘good’ bathing water quality (Table 6). ‘Moderate’ pollution was observed at 7 of the 14 monitoring sites (1, 3, 5e7, 10, and 12). ‘Critical’ (Class III) pollution was observed in 6 of the 14 sites (2, 4, 8, 9, 11, and 13), whilst one of the 14 monitoring sites (14) showed ‘strong’ (Class IV) pollution. Though the pollution at site 14 was classified ‘strong’, the site may pose a lesser risk to human health because it receives mainly non-human inputs from a dairy farm. Sites 2, 9, and 11, which were classified as ‘critical’, could pose a risk to human health greater than site 14 given that they are located downstream of WWTW (Table 1).

3.6. levels

consuming) content (Kafi et al., 2008). Nutrients are likely to have originated from wastewater effluents of WWTW and from agricultural fields to which chemical fertiliser had been applied.

Impact of rainfall on microbial and chemophysical

Levels of FIO and concentration of nutrients, suspension load, and some chemophysical parameters increased significantly after rainfall (Table 7). Based on the log10-transformed arithmetic means, levels of presumptive E. coli (1.2 logs), presumptive intestinal enterococci (1.2 logs), and somatic coliphages (1.1 logs) in samples collected during heavy rainfall tended to be 1.1 to 1.2 logs higher than those collected during dry weather conditions. Concentrations of NH3eN and NO3eN tended to be 1.2 to 2.4 logs higher than during dry weather conditions, whilst concentrations of SS and turbidity tended to be 1.6 to 2.4 logs higher than during dry weather conditions. Conversely, DO and conductivity concentrations were each 1.1 logs higher during dry weather conditions. There was no significant difference in PO4eP concentration under the two meteorological conditions. The results showed that water quality deterioration was induced by heavy rainfall in the river catchment. Rainfall and run-off are essential to the mobilisation and transport of catchment surface stores, subsurface and resuspension of river sediment of NPS of FIOs, nutrients, and suspension load to receiving waters (Muirhead et al., 2004). Substantial quantities of animal faecal material may have accumulated within the catchment during the antecedent period of dry weather, and therefore the run-off generated during heavy rainfall would have been highly contaminated with FIOs. Rainfall run-off increased SS and turbidity levels in river water samples, both as a result of erosion and the transportation of erosional particulate matter from land sources within the catchment, and from erosion on the streambed and banks. The observed significant decrease in DO concentration may be as a result of biological oxygen demand. Coulliette and Noble (2008) reported DO increase during rainfall and attributed this to associated biological activity. However, in our study the high SS and turbidity levels recorded during heavy rainfall were probably indicative of a high organic (and oxygen-

3.7. Improved low-cost river catchment monitoring and management design Fig. 4 outlines a water quality assessment protocol that could aid attempts to distinguish seasonal patterns in water quality from short-term fluctuations. We suggest that this may support future river catchment monitoring and management

Reconnaissance,

sanitary

survey

and

ground-truthing of the river catchment, including initial identification of potential sources of NPS and PS of faecal pollution.

River catchment delineation, including sub-catchments where appropriate.

Selection of initial monitoring sites based on identified potential sources of pollution, environmental factors and accessibility.

Design and operation of appropriate 12 month monitoring programme (to include at least biweekly sampling, plus storm event monitoring).

Identification of ‘sentinel’ monitoring sites for longer-term catchment monitoring.

Long-term

catchment

monitoring

programme based on sentinel monitoring sites with option to adapt programme in response to significant changes in local climate or pollution sources. Fig. 4 e Proposed low-cost decision-tree approach to preliminary water quality assessment, and long-term monitoring and management of a river catchment.

w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 2 3 5 e2 2 4 6

frameworks. The first step in this approach is a sanitary survey to identify potential PS and NPS of FIOs within the river catchment. The next step is to delineate catchment into appropriate sub-catchments, and propose initial monitoring sites based on the pattern of significant PS and NPS, environmental factors, and safe access. Seasonal microbial dynamics for one year are then assessed using a regular (preferably biweekly) sampling regime. Event-triggered sampling should be carried out during storm events. PCA and CA are applied to the 12 month chemophysical and microbial dataset in order to extract the parameters that are the most indicative of faecal pollution in each season, and those parameters that are important year-round indicators of pollution. From these data, ‘sentinel’ monitoring sites can be suggested, and appropriate surrogates for FIOs may be determined. As demonstrated in this study, turbidity could act as a surrogate for presumptive E. coli levels, and presumptive intestinal enterococci, under many conditions. Using turbidity as a surrogate for FIOs may be a cheaper, simpler and more rapid approach, if initial data suggest that turbidity correlates significantly with levels of FIO in the study area. When the threshold set for the surrogate is exceeded, detailed enumeration of FIOs could be carried out promptly. The next step is to select the most important parameters and surrogates to be included in any longer-term, low-cost water quality monitoring framework. For example, if a monitoring site has not been identified as demonstrating significant levels of human faecal contamination in the initial study, water samples would not be analysed for phages of Bacteroides GB-124 as frequently, until possible contamination is suspected or identified. This monitoring approach could result in significant financial savings, as only those parameters that are present at levels associated with an increased risk to health at a particular site would be analysed routinely, while other parameters would be analysed perhaps once every two months in temperate climates, or less often in tropical regions, where there are distinctive wet and dry seasons. Our protocol therefore sets out the framework of a tiered approach to sustainable river catchment water quality monitoring that aims to achieve optimal human health gain at relatively low-cost. This approach could have significant implications for improved river catchment management in both LEDC and MEDC.

4.

Conclusions

1. All river water monitoring sites in the model river catchment were faecally-impacted, and microbial water quality deteriorated significantly following rainfall events. Faecal contamination of surface waters in Europe following predicted extreme storm events could have significant implications for future compliance with the EU revised Bathing Water Directive and the Water Framework Directive. 2. Contamination of the River Ouse and its tributaries by FIOs resulted from contributions from both PS (primarily WWTW effluents) and NPS (primarily agriculture and secondarily wild animals, e.g. birds). 3. Enumeration of phages of Bacteroides GB-124 may be used as a reliable and relatively simple method to demonstrate the presence of human faecal pollution in river catchment studies.

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4. Multivariate statistical and MST methods are potentially useful tools for unravelling the complex nature of chemophysical and microbial water quality issues in river catchments. 5. Turbidity measurements may in some instances act as a useful and low-cost surrogate for presumptive E. coli in river catchment studies, particularly as an early-warning of storm-related faecal contamination. Disturbances from rainfall, run-off, and in-channel disturbance of sediment may have accounted for the high positive statistically significant correlation (r ¼ 0.43, P ¼ 0.01) between presumptive E. coli and turbidity in this study. However, there might be the need to develop a turbidity model (a regression model relating turbidity to levels of presumptive E. coli) to determine whether during storm events, presumptive E. coli levels could be adequately predicted using turbidity as the only predictor parameter. 6. The suggested river catchment water quality management protocol resulting from this study has ready applications in future Water Safety Plans, and EU River Basin Management Plans.

Acknowledgments This work was partly funded by the European Regional Development Fund Interreg IVA Programme as part of the collaborative project AquaManche. The authors thank colleagues at the UK Environment Agency for their kind provision of river flow and meteorological data.

references

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quality and DNA-based quantitative microbial source tracking. In: Joint Danube Survey 2 Final Scientific Report. ICPDR e International Commission for the Protection of the Danube River, Vienna. Kleinheinz, G.T., McDermott, C.M., Leewis, M.-C., Englebert, E., 2006. Influence of sampling depth on Escherichia coli concentrations in beach monitoring. Water Research 40, 3831e3837. Mill, A., Schlacher, T., Katouli, M., 2006. Tidal and longitudinal variation of faecal indicator bacteria in an estuarine creek in south-east Queensland, Australia. Marine Pollution Bulletin 52, 881e891. Moore, M.V., Pace, M.L., Mather, J.R., Murdoch, P.S., Howarth, R.W., Folt, C.L., Chen, C.Y., Hemond, H.F., Flebbe, P.A., Driscoll, C.T., 1997. Potential effects of climate change on freshwater ecosystems of the New England/Mid-Atlantic region. Hydrologic Processes 11, 925e947. Muirhead, R.W., Davies-Colley, R.J., Donnison, A.M., Nagels, J.W., 2004. Faecal bacteria yields in artificial flood events: quantifying in-stream stores. Water Research 38 (5), 1215e1224. Muniani-Mujika, I., Lucena, F., Girones, R., Calvo, M., 2003. Comparative analysis of viral pathogens and potential indicators in shellfish. International Journal of Food Microbiology 83, 75e85. Neal, C., House, W.A., Jarvie, H.P., Neal, M., Hill, L., Wickham, H., 2005. Phosphorous concentrations in the River Dun, the Kennet and Avon Canal and the River Kennet, Southern England. Science of the Total Environment 344, 107e128. Payan, A., Ebdon, J., Taylor, H., Gantzer, C., Ottoson, J., Papageorgiou, G.T., Blanch, A.R., Lucena, F., Jofre, J., Muniesa, M., 2005. Method for isolation of Bacteroides bacteriophage host strains suitable for tracking sources of faecal pollution in water. Applied and Environmental Microbiology 71 (9), 5659e5662. Pedley, S., Howard, G., 1997. The public health implications of microbiological contamination of groundwater. Quaterly Journal of Engineering Geology and Hydrogeology 30 (2), 179e188. Plummer, J.D., Long, S.C., 2007. Monitoring source water for microbial contamination: evaluation of water quality measures. Water Research 41, 3716e3728. Rogerson, P.A., 2010. Statistical Methods for Geography. A Student’s Guide, third ed. SAGE Publications Ltd, London, ISBN 978-1-84860-003-4. Servais, P., Billen, G., Goncalvez, A., Garcia-Armisen, T., 2007. Modelling microbiological water quality in the Seine river drainage network: Past, present and future situations. Hydrology and Earth Systems Sciences 11, 1581e1592. Sinclair, M., 2009. Recreational access to catchments. Public Health Newsletter of Water Quality Research Australia (Issue 54). Sinclair, A., Hebb, D., Jamieson, R., Gordon, R., Benedict, K., Fuller, K., Stratton, C., Madani, A., 2009. Growing season surface water loading of faecal indicator organisms within a rural watershed. Water Research 43, 1199e1206. SOCS, 2010. www.sussex-ouse.org.uk/bmwp/bmwp-spatham. htm Accessed on 19th July 2010. Tartera, C., Araujo, R., Michel, T., Jofre, J., 1992. Culture and decontamination methods affecting enumeration of phages infecting Bacteroides fragilis in sewage. Applied and Environmental Microbiology 58 (8), 2670e2673. UNICEF, WHO, 2009. Diarrhoea: Why Children Are Still Dying and what Can Be Done. The United Nations Children’s Fund (UNICEF)/World Health Organization (WHO). UN, 2009. The Millennium Development Goals Report. United Nations, New York. WHO, 2008. Guidelines for Drinking-water Quality, third ed. World Health Organisation, Geneva, ISBN 978-92-4-154761-1.

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Available at www.sciencedirect.com

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A consistent modelling methodology for secondary settling tanks in wastewater treatment Raimund Bu¨rger a, Stefan Diehl b,*, Ingmar Nopens c a

CI2MA and Departamento de Ingenierı´a Matema´tica, Facultad de Ciencias Fı´sicas y Matema´ticas, Universidad de Concepcio´n, Casilla 160-C, Concepcio´n, Chile b Centre for Mathematical Sciences, Lund University, P.O. Box 118, SE-221 00 Lund, Sweden c BIOMATH, Department of Applied Mathematics, Biometrics and Process Control, Ghent University, Coupure Links 653, 9000 Gent, Belgium

article info

abstract

Article history:

The aim of this contribution is partly to build consensus on a consistent modelling

Received 17 July 2010

methodology (CMM) of complex real processes in wastewater treatment by combining

Received in revised form

classical concepts with results from applied mathematics, and partly to apply it to the

23 December 2010

clarification-thickening process in the secondary settling tank. In the CMM, the real

Accepted 25 January 2011

process should be approximated by a mathematical model (process model; ordinary or

Available online 1 February 2011

partial differential equation (ODE or PDE)), which in turn is approximated by a simulation model (numerical method) implemented on a computer. These steps have often not been

Keywords:

carried out in a correct way. The secondary settling tank was chosen as a case since this is

Clarifier

one of the most complex processes in a wastewater treatment plant and simulation

Thickener

models developed decades ago have no guarantee of satisfying fundamental mathematical

Continuous sedimentation

and physical properties. Nevertheless, such methods are still used in commercial tools to

Partial differential equation

date. This particularly becomes of interest as the state-of-the-art practice is moving

Simulation model

towards plant-wide modelling. Then all submodels interact and errors propagate through

Numerical method

the model and severely hamper any calibration effort and, hence, the predictive purpose of the model. The CMM is described by applying it first to a simple conversion process in the biological reactor yielding an ODE solver, and then to the solideliquid separation in the secondary settling tank, yielding a PDE solver. Time has come to incorporate established mathematical techniques into environmental engineering, and wastewater treatment modelling in particular, and to use proven reliable and consistent simulation models. ª 2011 Elsevier Ltd. All rights reserved.

1.

Introduction

Wastewater treatment (WWT) systems are widely studied with the aid of mathematical models (Gujer, 2008; Henze et al., 2000). Detailed models exist for the biological processes occurring in the system. However, a biological WWT system also includes a secondary settling tank (SST) for the separation of the cleaned liquid from the activated sludge. It has also a thickening function to recycle and retain the solids and

thereby the biological activity in the system. A typical WWT model consists of a very complex biological submodel and a rather simplified sedimentation submodel. The reason for the latter is mainly a practical one. Indeed, biological models typically consist of ordinary differential equations (ODEs), whereas a sedimentation model includes both time and space dependence, turning it into a partial differential equation (PDE). The main commercial simulators, however, do not provide reliable simulation methods for these PDEs; there is

* Corresponding author. Tel.: þ46 2220920; fax: þ46 2224010. E-mail addresses: [email protected] (R. Bu¨rger), [email protected] (S. Diehl), [email protected] (I. Nopens). 0043-1354/$ e see front matter ª 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2011.01.020

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Nomenclature A B C Cc F Fnum H KS Q SS V YH Z a b dcomp ddisp g m n

cross-sectional area of SST [m2] depth of thickening zone [m] concentration in SST [kg/m3] critical concentration [kg/m3] (convective) flux function [kg/(m2s)] numerical flux update [kg/(m2s)] height of clarification zone [m] model parameter in (2) [kg/m3] volumetric flow rate [m3/s] readily biodegradable substrate concentration [kg/m3] volume of bioreactor [m3] model parameter in (2) [e] substance concentration (substrate or biomass) [kg/m3] model parameter in (15) [m2/s] model parameter in (15) [m] compression function in (6) [m2/s] dispersion function in (11) [m2/s] acceleration of gravity [m/s2] number of substances in bioreactor model parameter in (13) [m3/kg]

no guarantee that the simulations satisfy fundamental physical properties. One reason for this has been the lack of established solvers for the particular type of nonlinear PDE that models continuous sedimentation. Therefore, many workarounds have been proposed for the simulation of integrated WWT models in an ODE environment (Abusam and Keesman, 2009; Chatellier and Audic, 2000; David et al., 2009a,b; De Clercq et al., 2003; Dupont and Dahl, 1995; Dupont and Henze, 1992; Giokas et al., 2002; Hamilton et al., , 1992; Ha¨rtel and Po¨pel, 1992; Koehne et al., 1995; Nocon 2006; Otterpohl and Freund, 1992; Ozinsky et al., 1994; Plo´sz et al., 2007, in press; Queinnec and Dochain, 2001; Taka´cs et al., 1991; Vaccari and Uchrin, 1989; Verdickt et al., 2005; Vitasovic, 1989; Watts et al., 1996; Zheng and Bagley, 1998). Although acceptable at the time of their development, these simulation models should be reconsidered as both knowledge and computational power have evolved significantly. In short, the problem is not the ODE environment, but rather the heuristic unreliable workarounds in the numerical implementation of the PDE model for sedimentation. De Clercq (2006) and De Clercq et al. (2008) utilize the provably reliable PDE solver by Bu¨rger et al. (2005) for the secondary settling tank (SST). An example of a combination of PDE and ODE solvers is the simulation model by Diehl and Jeppsson (1998), which utilizes the Activated Sludge Model no 1 (ASM1) by Henze et al. (1987) and a PDE solver for the SST based on the Godunov numerical flux. The PDE solver handles the sedimentation of flocculated multi-component particles (Jeppsson and Diehl, 1996b). The Godunov flux has also been used in the simulation model by Plo´sz et al. (2007, in press), however, this simulation model unfortunately contains other heuristic ingredients in the numerical method. Dispersion and

r n0 nhs ns z

reaction rate [kg/(m3s)] model parameter in (13) [m/s] hindered settling velocity [m/s] settling velocity [m/s] depth from feed level in SST [m]

Greek letters F (total) flux in (8)e(9) [kg/(m2s)] a model parameter in (14) [Pa] b model parameter in (14) [kg/m3] d Dirac delta distribution [1/m] g characteristic function in (9) and (11), equals 1 inside and 0 outside SST model parameter in (2) [kg/(m3s)] mmax density of solids [kg/m3] rs effective solids stress [Pa] se Subscripts e effluent f feed u underflow Superscript in incoming t time [s]

compression effects are modelled by one single constant. In the present paper, these two effects are modelled in more detail by separate functions. SSTs often cause problems in the daily operation of wastewater treatment plants (WWTPs). Factors influencing the solideliquid separation include hindered and compression settling, flocculation-breakup, non-settleable solids fractions, sludge viscosity and density. Furthermore, hydrodynamic impacts (geometry/design of the SST, horizontal density currents, solids influent and removal) have been studied in more detail and resulted in additional knowledge that has not yet been included into integrated WWT models. Hence, the problematic behaviour often observed in practice cannot be explained by current state-of-the-art models. Moreover, new pressures on WWT systems have come into the picture. With respect to the SST, there are extreme hydraulic events most possibly induced by climate change. The development of mitigation strategies calls for improved settler models. When developing the latter, it is advisable to start from the state-of-the-art in the modelling of continuous sedimentation that has been achieved in different disciplines. Knowledge in applied mathematics, chemical engineering and environmental engineering should be combined and utilized with the aim of building SST models efficiently, but first of all consistently. The clarification-thickening process also appears in several other applications, such as the mineral, chemical, food, pulp-and-paper and other industries. Researchers in different disciplines therefore tackled basically the same problem, gained a lot of insight and produced new results during several decades. From our experience there persists a wide gap between different fields, particularly between

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mathematics and environmental engineering science, which we would like to bridge. New results in mathematical publications require fairly advanced skills to be understood fully, so applied mathematicians need to “translate” and explain how the results can be used in the applications. The specific nonlinearities of the continuous sedimentation process have led to intense mathematical research during the last two decades. The environmental engineering field should now benefit from these results. Another reason for the gap lies in the traditional modelling approaches. In the WWT field, a “settler model” often means a simulation model implemented on a computer. These models are in many cases postulated by writing down the numerical method directly from physical reasoning and experience. No analysis is provided that would explain why such a method would produce any reliable simulation. On the contrary, such a numerical method is often inconsistent in one or another way. For example, the traditional Taka´cs layer model (Vitasovic, 1989; Taka´cs et al., 1991) has several shortcomings, see Jeppsson and Diehl (1996a,b), David et al. (2009a), Plo´sz et al. (in press). An example of failure is given in Section 5. At the same time, there exists a fundamental modelling methodology that is usually not written out but understood among applied mathematicians and utilized in some applied fields, e.g. chemical engineering. This methodology would be beneficial for the SST modelling future and this is the reason for the present paper. Moreover, it is in the interest of the environmental engineering field to use proven reliable models when moving to plant-wide modelling. As stipulated by Hug et al. (2009), a broad spectrum of modellers exists: basic and advanced model users, model and software developers, and teachers. It is clear that basic model users are far away from the mathematics behind a model and they often innocently use the software without questioning its correctness. Hence, more advanced model users and software developers are faced with the task to ensure the correctness of the simulation models and thereby to make the engineering community aware of the potential dangers of improper simulation models, which may imply faulty decisions. In this paper, we propose a consistent modelling methodology (CMM), within which future model extensions can be developed and thereby unnecessary pitfalls avoided. We make a clear distinction between a mathematical model and a simulation model. The CMM makes it easier to determine sound and unsound ways of modelling. The paper is organized in the following way. In Section 2, the CMM is described and illustrated on a principle process in the biological reactor. The outcome is an ODE solver. In Section 3, the CMM is applied to the continuous sedimentation process and the outcome is a PDE solver. Section 4 contains further remarks on the CMM and Section 5 contains some illustrative simulations. Section 6 collects the main conclusions of this paper.

begins. In this section, we describe the six steps of the CMM and apply it to a biological conversion process in a compartment of the bioreactor within the activated sludge process. The purpose of the CMM is to create a simulation model that produces reliable simulated data with respect to the constitutive assumptions made and the fundamental physical principles.

2.1.

Step 1: construction of a mathematical model

The starting point is usually a physical law. Often it is the conservation law of mass (mass balancing) which postulates that the increase of mass per time unit of a substrate in a region equals the net flux into the region (“transport in” minus “transport out”) plus the net production within the region (production minus consumption). Let the region be one of the compartments of the biological reactor. For simplicity, we consider an intermediate compartment of fixed volume V with the in- and out-going volumetric flow rate Q. If we also make the idealizing assumption that the compartment is always completely mixed, then the concentration of a single substance (substrate or biomass) Z is the same in the whole reactor at each time point t. Denoting the incoming concentration of the substance in the compartments by Zin, we can write down the conservation law exactly, namely as the following ODE: V

dZ ¼ QZin  QZ þ rV: dt

Here, r is the reaction rate and the term rV the net production of the substance within the compartment per time unit. The given variables in (1) are V, Q and Zin and the sought variable is Z. To solve the equation, one needs an additional relation between r and Z, and possibly concentrations of other substances. This is called a constitutive relation, or a constitutive assumption, and contains the model parameters. A common such is the Monod relation, which for readily biodegradable substrate, Z ¼ SS, can be written as follows, where for simplicity we ignore any dependence on other substances: mmax SS r¼ : YH ðKS þ SS Þ

The CMM is illustrated in Fig. 1. The terminology is explained in more detail in the Appendix. After initial observations and experience of the real process, the modelling procedure

(2)

INPUTS • Idealizing assumptions • Constitutive relations {model parameters}

MATHEMATICAL MODEL: ODE or PDE {model parameters}

PHYSICAL LAW mass conservation

no

observation no Validation. Successful?

2. A consistent modelling methodology (CMM)

(1)

Well -posed? (Additional physical principles?) yes

REAL PROCESS

SIMULATION MODEL Numerical scheme {model parameters}

real data Calibration simulated data

OUTPUT Numerical solution

Fig. 1 e Schematic overview of the consistent modelling methodology (CMM). The dashed arrows indicate the initial observations of the real process. Note that {model parameters} refers to the same set of parameters defined in the constitutive relations.

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The model parameters, which in this example are mmax, YH and KS, may have some physical meaning and they can sometimes be determined by laboratory experiments. We may model m substances (Zi ¼ 1,.,m) in the compartment by m ODEs of the form (1), which are coupled via the reaction rates similar to (2). We have a nonlinear system of ODEs, which makes up the mathematical model or process model, or just the model of the reactor. A well-known example is the ASM1 by Henze et al. (1987), in which m ¼ 13.

2.2.

Step 2: establishing well-posedness

In engineering, the system of ODEs of the form (1) would simply be simulated with an ODE solver in a software platform, which many users completely trust with respect to its correctness. One may not realize that the solver actually is derived from the mathematical model behind the software platform. The actual solution of the mathematical model, the exact solution, consists of a vector of substance concentrations as function of time, Zi(t), which satisfy the ODEs at every time point t (given initial data Zi(0)). In many cases it is impossible to write down these functions explicitly in terms of simple expressions like exponentials, power laws, trigonometric functions, etc. This is often referred to as “the equations cannot be solved (explicitly)”. Nevertheless, the question whether an exact solution exists or not is still open. If there exists one, it is physically and computationally important that it should be unique for given initial data. Furthermore, small changes in the initial data should only cause small changes in the solution. In other words, a solution should exist, be unique and depend continuously on initial data e the model is then said to be well-posed. The existence of a solution can often be established by proving convergence of a numerical method, see Step 3 below. Uniqueness of a solution is usually proved by starting with two solutions; both satisfying the same initial data, and then one shows that they are actually identical. A similar procedure can often be used to establish the continuous dependence on initial data. For the CMM, well-posedness is of key importance since it ensures that the mathematical model describes the real process in a relevant way. Then there is a good hope to find a reliable numerical method.

2.3.

Step 3: numerical method and simulation program

Fortunately, most ODE models arising from real processes are well-posed and can be solved approximately by efficient and reliable numerical methods, such as RungeeKutta methods, which are utilized in commercial software packages (e.g. the example in Step 1). The terminology “ODE solver” is well established for such a numerical method, although it only delivers approximate solutions of the unique exact solution of the mathematical model, which is defined at every time point t. The numerical approximate solution is only given at discrete time points. However, any reliable numerical method should produce numerical solutions that are increasingly better approximations of the exact solution as the resolution of the discrete time points becomes finer. In other words, the numerical solutions converge to the exact solution as the time step tends to zero.

2.4.

Step 4: calibration

Identification, calibration or fine tuning of the model is done by adjusting the model parameters in the constitutive relations. Some parameters may be found with specifically designed batch experiments in laboratories (e.g. respirometry). Otherwise real (full-scale) and simulated data are compared. The method of least squares and some suitable optimization algorithm are often used to find the optimal parameters, i.e. to solve the calibration problem. When the process includes biological material that changes over time it is an ultimate goal to develop an on-line calibration method of the full process. If the outcome of the calibration is not satisfactory, one could try new constitutive relations (instead of the Monod expression). The more parameters these have, the more is the freedom of adjustment which adds to the cost of computations, a more ill-conditioned calibration problem (difficult to find unique parameters) and sometimes also an illposed calibration problem (some parameters are not identifiable; different values of the parameters may yield the same simulated data; the calibration problem is not uniquely determined, hence not well-posed). More parameters will also induce larger output uncertainty.

2.5.

Step 5: validation

As one set of data has been used for calibration, another independent set should be used for validation of the model. A validation enables the modeller to assess the predictive power of the calibrated model.

2.6.

Step 6 and 1: rebuilding or extension of the model

In the validation step (or already in the calibration step) the real process often behaves in a way that cannot be explained sufficiently accurately by the simulation model. Then the only sound way to handle this problem is to change the mathematical model by changing the idealizing assumptions and restarting from Step 1. Note that the simulation model is never changed directly, only indirectly via the mathematical model. This procedure is often violated in the previous simulation models referred to in Section 1, which have been built without a proper connection to PDE theory. For example, introducing new factors or terms with more parameters directly in the numerical method may imply a better fit to a certain set of experimental data. However, since there is no connection to the PDE and hence no guaranteed connection to the underlying physical principles, it is likely that other data sets require totally different values of the parameters, also physical parameters, which are expected to be the same. This ad-hoc introduction of parameters will then result in a model that can fit a data set in a certain case, but that is not generic and cannot be used in other cases (i.e. low predictive power). Such a model will have low predictive power and is dangerous to use for subsequent optimization studies. In summary, this section provides a kind of ‘recipe’ for building a mathematically sound simulation model. Some issues might seem trivial, but are often ignored or taken for granted. These guidelines are useful for advanced modellers

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to improve existing models or build new ones, e.g. for new technologies in wastewater treatment.

3. The CMM applied to the continuous sedimentation process 3.1.

Step 1: construction of a mathematical model

The physical law is again the conservation of mass. We want to model the solideliquid separation of activated sludge driven by gravity and it is well known that the particulate concentration depends on both space and time; C(z, t), where the z-axis points downwards, see Fig. 2. We thus make the idealizing assumption that the SST is one dimensional, which albeit restrictive, will lead to a model that can capture the fundamental features of gravity settling and compression, since these phenomena are essentially one dimensional. Considering the sludge, we make idealizing assumptions such as: there is no biological activity in the SST; the sludge has flocculated in the preceding reactor and consists of particles of the same size and shape; outside the SST, i.e. in the outlet and effluent pipes, the sludge and water have the same speed. To capture the processes of gravity settling and compression, consider temporarily batch sedimentation. Then the conservation of mass can be expressed by the PDE: vC v þ ðCnS Þ ¼ 0 vt vz

(3)

where ns  0 is the downward settling velocity of the particles. This is one equation with two unknowns (C and ns). Hence, a constitutive relation is needed between ns and C. We make the following constitutive assumptions:

2. For high concentrations the sludge may be compressed by its own weight. More specifically, above a critical concentration, denoted by Cc, the particles are in constant contact and form a network that can bear a certain stress, the effective solids stress se, which is assumed to be an increasing function of the concentration above Cc and zero below (Aziz et al., 2000; Bu¨rger et al., 2000a; De Kretser et al., 2001):
¼ 0 for 0  C  Cc : se ðCÞ and s0e ðCÞ >0 for C > Cc In accordance with the continuum mechanical derivation by Bu¨rger et al. (2000b), we assume that the downward settling velocity of the particles in batch sedimentation can be written as the following constitutive relation:

(

ns ¼

nhs ðCÞ

Fig. 2 e Schematic overview of an ideal 1D SST.

(4)

where rs is the density of the solids, g is the gravity of acceleration and Dr is the density difference between the solids and the liquid. Thus, for concentrations greater than Cc, the settling velocity is reduced by a compression effect when the concentration increases with depth. For the solution the compressibility effect is the same as that of nonlinear diffusion. Indeed, inserting (4) into (3) we get the following degenerate parabolic PDE with one unknown variable C:   vC v v vC þ ðCnhs ðCÞÞ ¼ dcomp ðCÞ ; vt vz vz vz

(5)

where the compression function is
dcomp ðCÞ ¼

1. The hindered settling velocity nhs(C ) is a function of the local concentration only (Kynch, 1952). Commonly used formulae for activated sludge are those by Vesilind (1968) and Taka´cs et al. (1991).

nhs ðCÞ   for 0  C  Cc rs s0e ðCÞ vC ; 1 for C > Cc CgDr vz

0

rS n ðCÞs0e ðCÞ gDr hs

for 0  C  Cc for C > Cc :

(6)

The flux function in (5), Cnhs(C ), is the batch-settling flux function originating from Kynch (1952). A consequence of (4) is that hydrodynamic diffusion is a much slower process and need not be modelled. Now we again consider continuous sedimentation in the ideal 1D SST, see Fig. 2. The height of the clarification zone is denoted by H and the depth of the thickening zone by B. The volume flows leaving the SST at the effluent and underflow are denoted by Qe and Qu, respectively. We assume that there is either an upward (Qe) or a downward (Qu) volumetric flow at each point of the 1D axis, except for a single point where the feed source is assumed to be situated (z ¼ 0). The 1D assumption also implies that no horizontal effects are considered; wall effects are neglected; etc. We may assume that the cross-sectional area depends on depth, but for simplicity of presentation we assume here that it is a constant A. A third constitutive assumption is the following: 3. Modelling dispersion effects: the horizontal flows of an SST are substantial and difficult to capture in a 1D model. Turbulent currents cause a mixing of lower and higher concentrations of sludge, in particular, around the feed inlet because of its velocity field. This hydrodynamic dispersion phenomenon smoothes the deptheconcentration profile. By analogy with Fick’s constitutive relation for diffusion, we assume that

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the corresponding flux is equal to ddisp vC=vz with the dispersion coefficient ddisp  0 (David, 2009a; De Clercq et al., 2003, 2005; Lee et al., 2006; Lev et al., 1986; Plo´sz et al., 2007; Verdickt et al., 2005; Watts et al., 1996). If mixing currents are expected at certain heights, for example at the feed inlet, we may assume that ddisp depends on z and let it be positive in a neighbourhood of the feed inlet. One of the idealizing assumptions is that the mixture follows the bulk flows in the outlet pipes. This means that as the mixture has left the SST it cannot return, which in turn implies that we must require ddisp ðzÞ ¼ 0 for z < H and z > B:

(7)

The hydrodynamic dispersion resulting from the velocity field of the feed inlet can be modelled by letting ddisp be a function of the volumetric flow rate Qf in addition to z. As the constitutive assumptions are set, the conservation law of mass is used to derive an equation that captures this law exactly. The increase of mass per time unit in an arbitrary interval (z1, z2) equals the flux in minus flux out plus the production inside the interval: d dt

Zz2

 Zz2  ACðz; tÞdz ¼ A Fjz¼z1  Fjz¼z2 þ Qf ðtÞCf ðtÞdðzÞdz:

z1

(8)

z1

The last term is a source term containing the feed volumetric flow Qf, the feed concentration Cf and the Dirac delta distribution d. The flux F contains all constitutive functions:    vC  vC F C; ; z; t ¼ FðC; z; tÞ  gðzÞdcomp ðCÞ þ ddisp ðzÞ : vz vz

(9)

The convective flux function F incorporates the hindered settling velocity within the SST and the two volumetric upward and downward flows: 8 > > <

Qe ðtÞC=A nhs ðCÞC Qe ðtÞC=A FðC; z; tÞ ¼ > > nhs ðCÞC þQu ðtÞC=A : þQu ðtÞC=A

for z < H for  H < z < 0 for 0 < z < B for z > B

(10)

The depth axis is thus divided into four zones: the effluent zone (z < H ), clarification zone (H < z < 0), thickening zone (0 < z < B) and underflow zone (z > B). The function g(z) is equal to 1 inside the SST, i.e. in the interval (H, B), and 0 outside. Hence, outside the SST there is neither sedimentation nor compression, only bulk flows. If the solution C (z, t) of (8) is continuously differentiable, then Equation (8) is equivalent to the following convection-diffusion PDE (secondorder derivative terms are often referred to as ‘diffusion’ terms, although they may model other phenomena), defined for all z along the real axis:   vC vC v v  þ FðC; z; tÞ ¼ gðzÞdcomp ðCÞ þ ddisp ðzÞ vt vz vz vz þ

Qf ðtÞCf ðtÞ dðzÞ A

(11)

Since the solution C (z, t) may have discontinuities, Equation (11) cannot be interpreted in the classical sense (discontinuous functions are not differentiable). In particular, it should

not be used for the derivation of numerical methods. Instead, Equation (11) should be considered in the weak sense, which is a mathematical concept similar to distribution theory. Thus, the PDE (11) is only a symbol for the conservation of mass and the constitutive relations we have assumed. The physical conservation Equation (8) is built into this concept. The solutions are called weak solutions and may contain discontinuities. The analysis and derivation of numerical methods takes part within the weak sense. The fundamental features of (weak) solutions of Equation (11) are the following. In regions where ddisp > 0, the equation has a second-order derivative term, which implies that the solution has no discontinuity. In a mathematical model, we would like to handle all special cases, also ddisp ¼ 0. Recent analysis shows that even in this case (11) is still well-posed (Bu¨rger et al., 2005). For concentrations below Cc, the compression term vanishes (dcomp ¼ 0), the equation becomes hyperbolic and the solution may have discontinuities. This happens normally above and at the sludge blanket level. For higher concentrations the compression term smoothes the solution, which then is continuous. This occurs normally below the sludge blanket. The location of the sludge blanket, at which the concentration is Cc, is unknown beforehand, and is part of the exact or numerical solution. It is therefore of paramount importance that the sludge blanket, which here acts as a type-change interface, be approximated automatically. This is safely achieved by the method put forward by Bu¨rger et al. (2005). In this first step of the CMM, the mathematical model is given by Equation (11) together with initial data C (z, 0). The sought variable is the concentration C (z, t) for N < z < N, t > 0. The interesting output concentrations are C (z, t) for H < z < B and the effluent and underflow concentrations (cf. Fig. 2): Ce ðtÞ ¼ lim3/0þ CðH  3; tÞ; Cu ðtÞ ¼ lim3/0þ CðB þ 3; tÞ:

3.2.

Step 2: establishing well-posedness

The well-posedness analysis for PDEs of the form (11) and hence the development of a reliable numerical method are particularly involved. The possible presence of discontinuities in solutions implies that (11) does not have a unique solution for given initial data. This is resolved by requiring an additional physical principle, an entropy condition, to be fulfilled. Such an entropy condition should account for shock waves not only within each zone, but also at the space discontinuities (the feed inlet and the outlets). We do not go into the details here; see LeVeque (2002) for a general theory for shock waves within each region, and Bu¨rger et al. (2005) and Diehl (2009) for equations of the form (11). In the special case when dcomp ¼ ddisp ¼ 0, Equation (11) was first presented and analyzed independently (with different mathematical approaches) by Chancelier et al. (1994) and Diehl (1996). More general results were later presented by Bu¨rger et al. (2004b). A major break-through concerning the well-posedness of a version of (11) was made by Bu¨rger et al. (2005). They consider the case ddisp ¼ 0, however, the case ddisp > 0 causes no new complication in the analysis. By an exact solution of

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the mathematical model we mean a solution of (11) that satisfies a suitable entropy condition (Bu¨rger et al., 2005; Diehl, 2009).

3.3.

Step 3: numerical method (simulation model)

Since the concentration depends on two variables, the discretization has to be made along both the z- and t-axis. The z-axis is thus divided into intervals, or layers, that correspond to 1D finite volumes. The fundamental principles for PDE solvers of (11) include the following (see e.g. LeVeque (2002) for further details): I. There is an upper limit of the time steps in relation to the size of the layer, the so-called CFL condition (CouranteFriedrichseLewy). II. The numerical update of the convective flux function F, called the numerical flux, is critical and should have a certain form, which in mathematical terminology is called consistent. This means that the numerical flux is a function of the concentrations in certain neighbouring layers; setting these concentrations equal yields the original flux function F; an example is provided by (12). III. The numerical flux should automatically take into account the entropy condition (see Step 2). Certain standard choices of the numerical flux, such as the Godunov and EnquisteOsher numerical fluxes, have the so-called monotonicity property, which is the only known easily verifiable property that ensures that the entropy condition is taken into account. This built-in property qualifies such a scheme as robust and makes it potentially attractive as a building block for the CMM. However, this property comes at a price: namely, monotone schemes are only first-order accurate and require a relatively fine mesh to guarantee that the numerical solution is free of artefacts. We will expand on the question of appropriate choice of a numerical method, with the detail necessary, in a forthcoming paper. The Godunov numerical flux is derived from the unique exact solution, see Diehl (1996) and Jeppsson and Diehl (1996a). An explicit and a semi-implicit numerical method for (11) with the EnquisteOsher numerical flux were presented by Bu¨rger et al. (2005). This model has also been used for the calibration and simulation of batch and continuous sedimentation of activated sludge by De Clercq (2006) and De Clercq et al. (2008). The well-known numerical flux (Stenstro¨m, 1975; Vitasovic, 1989; Taka´cs et al., 1991; Taka´cs, 2008) from layer i to i þ 1 reads, for batch sedimentation: Fnum ðCi ; Ciþ1 Þ ¼ minðCi nhs ðCi Þ; Ciþ1 nhs ðCiþ1 ÞÞ:

(12)

This is consistent (satisfies item II above), since Fnum(C,C ) ¼ Cnhs(C ), which is equal to the batch-settling flux of the PDE. However, (12) does not satisfy III. We demonstrate with an example in Section 5 that (12) does not always take the entropy condition into account. This results in an unphysical numerical solution.

3.4.

2253

Step 4: calibration

The model parameters for calibration are the critical concentration Cc and those contained in the expressions for nhs(C ), se(C ) and ddisp (z, .). There are numerous reports on the calibration of different hindered settling formulae. This is sufficient for determining the convective flux F. Only a few experiments have been reported on the compressibility properties for activated sludge, see De Clercq (2006) and De Clercq et al. (2008).

3.5.

Step 5: validation

The mathematical model consisting of (3) and (4), modelling batch sedimentation of minerals, has been validated, e.g. Bu¨rger et al. (2000a, 2004a), Garrido et al. (2000). For the SST operation, some partial results were presented by De Clercq (2006).

3.6.

Step 6 and 1

Rebuilding or extension of the model. An inherent problem with our mathematical model is that the idealizing assumptions made do not take into account several influential features of the real process. Some of these are related to the feed and discharge mechanisms. A modification with a feed distributed over a set of layers in the feed zone, still in 1D, has  (2006). In steady state, the underflow been presented by Nocon and effluent concentrations are the same as in a model with  . It is only the concena point source, as concluded by Nocon tration profile around the inlet that is smeared out. We prefer to model this phenomenon in an easier way with the function ddisp. In summary, this section illustrates how the CMM methodology can be applied to the SST. It stresses the importance of each step and where these have often been violated in previous reported simulation models. Emphasis is mostly on the first three steps as normal modelling practice in the WWT community starts with the simulation model directly. The calibration and validation steps for the SST need further research.

4.

Further comments on the CMM

4.1.

A simple necessary convergence test of simulation models

It is difficult to prove whether a numerical method produces approximate solutions that converge to the exact solution of the model equation as the mesh size tends to zero (the number of layers tend to infinity). However, the method should at least pass the following convergence test: For given initial data, feed concentration, etc., run the method with an increasing number of layers, e.g. 10, 50, 100, 200.. The numerical solutions obtained should roughly be the same, with differences to a limit solution that become smaller as the number of layers is increased. If a method does not pass this test, then it should be discarded. We emphasize that passing this test is a necessary, however not sufficient, condition for

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being a reliable simulation model. Jeppsson and Diehl (1996a, 1996b) demonstrated partly that traditional layer models (Taka´cs and OtterpohleFreund) produce numerical solutions that are not qualitatively the same for different number of layers, partly that a numerical method using the numerical Godunov flux update passes this convergence test. The latter simulations correspond to the case when dcomp ¼ ddisp ¼ 0 in Equation (11). In a subsequent publication, we shall demonstrate the convergence in more general cases. Unfortunately, the number of layers is sometimes used as a model parameter. This is violating the CMM (since model parameters should only be introduced in Step 1). For example, Taka´cs (2008) performs simulations of a batch-settling test with a numerical flux given by (12), and argues that the optimal number of layers in an example was 9.

4.2.

Robustness tests of simulation models

Any simulation model should be able to handle all physically realistic initial data and feed inputs, even if these are uncommon or extreme. For example, simulation tests should be made with one volumetric flow set to zero or a large value. Batch sedimentation is a special case of continuous sedimentation, where Qu ¼ 0 and the thickening zone is the vessel. Therefore, a simulation model for the SST should correctly simulate a batch-settling test with any initial data. The physics is much simpler without the feed inlet and bulk flows; all particles settle to the bottom. We give such an example in Section 5, which our model passes but the Taka´cs model does not.

4.3.

The traditional 10-layer-model approach

The traditional layer model (Stenstro¨m, 1975; Vitasovic, 1989; Taka´cs et al., 1991) could be seen as a simulation model outcome in two ways. First, it can be fitted into the CMM in the following way. One makes the idealizing assumption that the SST consists of a fixed number of well-mixed compartments, usually 10, and that there are flows between these. Then, the conservation of mass yields 10 ODEs, which are coupled due to the fluxes between the compartments. The problem is how to model these fluxes in a physically correct way. If this were done in a satisfactory way, standard ODE solvers could be used as the simulation model. The first approach that also included the clarification zone was presented by Vitasovic (1989), who suggested the minimum-flux condition (12) for the numerical flux updates with some additional heuristic conditions. The same approach was also used by Taka´cs et al. (1991) in their simulation model, which still today is the most common one in the WWT field, but not in others. However, an inherent problem is that the mass balance is not sufficient to determine the fluxes uniquely between the compartments. This is the reason for the additional entropy condition. In Section 5, we demonstrate that the numerical flux (12) may yield unphysical solutions that do not satisfy the entropy condition. From a modelling point of view, one may question that the SST is subjectively discretized first (idealizing assumption) and then the mass balance is used. Indeed, there are no compartments in the SST.

The second way is the following. In many of the publications where layer models are used or created, one can indeed find a PDE as the mathematical model. This means that the layer model is used as a numerical method (PDE solver), which has been created without a proper connection to the PDE. Such a procedure severely violates the CMM. For example, the Taka´cs model does not pass the necessary convergence test described above. This has been illustrated by Jeppsson and Diehl (1996a), who also showed how the minimum-flux update by Vitasovic, cf. (12), should be adjusted to become a consistent and entropy satisfying numerical flux update, namely the Godunov method. Looking at these two flux updates without having the PDE background, it is not easy to judge which one is correct. The findings by Vitasovic and Taka´cs et al. put forward around 1990 were in the right direction, however, we now strongly recommend that correct numerical fluxes are used instead.

4.4.

The solids-flux theory and extensions

For more than half a century, the paper by Kynch (1952) has been the origin of a platform often referred to as the solidsflux theory from which many conclusions on the operation and design of SSTs have been drawn, see Ozinsky et al. (1994), Ekama et al. (1997), Diehl (2001) and references therein. With the assumptions by Kynch, the solids-flux theory is in fact based on a PDE which is a special case of the mathematical model (11), namely by setting dcomp ¼ ddisp ¼ 0, i.e. only hindered settling is considered. We refer to Diehl (2008) for the classical and extended results interpreted by means of operating charts for both stationary and dynamic situations. Hence, the CMM allows for deriving submodels. Another such is provided by the steady-state calculations by Bu¨rger and Narva´ez (2007), who consider (11) with ddisp ¼ 0 but dcomp > 0 for concentrations above CC.

4.5.

Non-flocculated particles

The constitutive relation for the hindered settling velocity can be expressed as any function of the concentration within the CMM. Equation (11) models the concentration of particles that have the same properties (density, size, shape). However, to take into account the non-flocculated particles that do not settle at all and follow the water streams, an appealing approach was put forward by Taka´cs et al. (1991), who suggested that the settling velocity function should be zero for small concentrations.

4.6.

Varying sludge properties

Some of the properties of the sludge are known to depend slowly on time, such as the sludge density and particle size distribution. Then the settling and compression behaviours are influenced. Such phenomena can be captured by letting the model parameters in the constitutive relations depend slowly on time. The main problem here for the future is to develop on-line calibration methods.

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5.

Illustration by simulation

5.1.

Simulations with a reliable numerical method

To demonstrate the behaviour of the mathematical model (11) for the SST we use the PDE solver by Bu¨rger et al. (2005). We have used the following data: H ¼ B ¼ 2 m, A ¼ 400 m2, and the hindered settling velocity is described by the Vesilind formula: nhs ðCÞ ¼ n0 enC ;

(13) 4

where n0 ¼ 9.6  10 m/s and n ¼ 0.37 l/g, see Fig. 3. At time t ¼ 0, we assume that the SST is full of sludge at the concentration C ¼ 2 g/l. The feed concentration is constant in time Cf ¼ 4.4 g/l and so are the volumetric flow rates Qe ¼ 3.9  102 m3/s and Qu ¼ 1.7  102 m3/s. In Fig. 4(a), the case when dcomp ¼ ddisp ¼ 0 m2/s is shown. This means that neither compression of the sludge at high concentrations nor dispersion effects are modelled. These two effects are modelled by second-order derivative terms which imply that the solution is smoothed. Without these terms, Equation (11) is hyperbolic and models only hindered settling and bulk flow transport. The solution may contain discontinuities anywhere. It is clearly seen that the solution has

vhs(C) [m/s]

−3

1

x 10

several discontinuities among which the sludge blanket is the most distinct one propagating upwards from the bottom as the initially homogenously distributed sludge settles. The high concentration in the underflow pipe is shown in the small interval below z ¼ B ¼ 2 m. Note that there is also a jump between the concentration at the bottom of the thickening zone and in the outlet pipe, which is in accordance with the classical solids-flux theory (Diehl, 2008). This is a result of the mass conservation when there is no second-order derivative term in the PDE. The initial amount of sludge in the clarification zone, together with the feed load, implies that some amount of sludge is built up in the clarification zone during approximately the first hour. Then all sludge in the clarification zone settles and after about 2 h there is no sludge left. To illustrate the effect of compression (Fig. 4(b)), we let ddisp ¼ 0 whereas dcomp is determined by, see (6), the constant rs/(gDr) ¼ 2.1 s2/m and the effective solids stress function by De Clercq et al. (2008):   C  Cc þ b se ðCÞ ¼ aln ; (14) b where we have chosen a ¼ 4 Pa, b ¼ 4 g/l and Cc ¼ 6 g/l, see Fig. 3. Thus, dcomp > 0 for concentrations higher than Cc ¼ 6 g/l. This means that for concentrations above Cc the settling flocs

σe(C) [Pa]

6 5

0.8

4

0.6

3 0.4

2

0.2 0

1

0

5

C [g/l] d

−4

10

15

(C) [m2/s] 1.5

2

1

1

0.5

0

0

5

C [g/l]

10

0

5

15

10

15

(z) [m2/s]

disp

x 10

0 −2

C [g/l] d

−4

comp

x 10

0

−1

0 z [m]

1

2

Fig. 3 e Graphs of the constitutive relations. Note that the maximum concentration is Cmax [ 15 g/l and the critical concentration is Cc [ 6 g/l. The graphs of ddisp are shown in the case b [ 0.5 m (solid) and b [ 1 m (dashed).

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Fig. 4 e Numerical solutions of Equation (11). The concentrations shown in the figure just below x [ 2 m are those in the underflow pipe. (a) The hindered settling and bulk flow transport are considered only (ddisp [ dcomp [ 0). (b) Compression is turned on at high concentrations (ddisp [ 0, dcomp > 0 for C > Cc [ 6 g/l). (c) Dispersion around the inlet is turned on (ddisp > 0 for -0.5 m< z < 0.5 m) in addition to the compression as in b. (d) As in c but with dispersion in the larger region -1 m< z < 1 m.

form a network that can bear a certain stress when it is compressed. The solution in such a region has no discontinuities. In the solution shown in Fig. 4(b), it is seen that Cc is reached below the sludge blanket. For higher concentrations, which occur below the sludge blanket, the concentration increases continuously all the way into the underflow pipe because of the compression of the floc network. For concentrations below Cc ¼ 6 g/l, dcomp ¼ 0 holds which means that there is no compression, only hindered settling as in Fig. 4(a). Consequently, for concentrations less than Cc there are discontinuities. In particular, the temporary presence of sludge in the clarification zone during the first 2 h is the same in Fig. 4(a and b) since this concentration (about 4 g/l) lies below Cc ¼ 6 g/l (note the scales on the z-axes). Finally, in addition to the values above, we now introduce a dispersion effect limited to a region around the inlet by using (in m2/s)
ddisp ðzÞ ¼

0  for jzj  b ; acos pz for jzj < b 2b

(15)

with a ¼ 1.4  104 m2/s and in two cases with b ¼ 0.5 m and b ¼ 1 m, respectively, see Fig. 3. This implies that the secondorder derivative term in the PDE containing ddisp is nonzero

around the inlet. This causes a smoothing effect such that discontinuities are not present and the feed mass is smeared out; see Fig. 4(c and d). In Fig. 4(c), one can in the solution clearly see the region where dispersion occurs; 0.5 m < z < 0.5 m. The lower part of the solution with the sludge blanket lies in z > 0.5 m where ddisp(z) ¼ 0 and hence the solution is the same in Fig. 4(b and c). In Fig. 4(d), the dispersion region is enlarged to 1 m < z < 1 m, which causes the feed mass to be smeared out even more. Furthermore, as the sludge blanket rises above z ¼ 1 m, it is smeared out and is no longer a discontinuity.

5.2.

An example of the failure of the Taka´cs model

A common ingredient in traditional layer models is the minimum-flux update (12). Taka´cs (2008) uses this for the simulation of batch sedimentation of an initially homogeneous suspension. Then the solution is always monotone; it is non-decreasing with depth. In fact, (12) is then equivalent to the reliable Godunov numerical flux. A non-decreasing concentration profile with depth is the most common one in both batch sedimentation and during dry weather conditions for continuous sedimentation. Taka´cs’ model is indeed found to behave satisfactory for such conditions. However, say that for some reason an operator wants to simulate the filling of an

w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 2 4 7 e2 2 6 0

SST, previously filled up with plain water, to simulate the development of the sludge blanket. We perform therefore the following robustness test. Consider batch sedimentation in the 2 m-deep thickening zone, which initially has a region of concentration 4 g/l on top of clear water:
Cðz; 0Þ ¼

4 for 0 < z < 0:5 : 0 for 0:5 < z < 2

2257

conservation of mass is satisfied for both approximate solutions in Fig. 5, but the discontinuity between 4 g/l and 0 g/l is unphysical and does not satisfy the entropy condition. The simulations have been performed with 60 layers. The same qualitative behaviour occurs for any number of layers and for any other positive concentration than 4 g/l.

(16)

Of course, the physically relevant solution shows that the sludge settles to the bottom. The model is Equation (11) with dcomp ¼ ddisp ¼ 0 within the thickening zone together with the initial data (16) and zero-flux boundary conditions at z ¼ 0 and z ¼ B. Fig. 5(a) shows a simulation where the Godunov flux has been used. This is an approximate solution of the exact one, which can be found in Diehl (2007). In Fig. 5(b), a simulation with the minimum-flux update (12) is shown. The sludge does not settle to the bottom of the vessel. In fact, the initial discontinuity between 4 g/l and 0 g/l is maintained undisturbed. This can be understood by calculating the numerical flux between a layer with Ci ¼ 4 g/l and the next one below with Ciþ1 ¼ 0 g/l with (12): Fnum ð4; 0Þ ¼ minð4nhs ð4Þ; 0nhs ð0ÞÞ ¼ 0: Since this numerical flux is zero, no mass is transported down to the layers with zero concentration. Note that the

Fig. 5 e Numerical solutions of a batch-settling test with sludge on top of clear water with (a) a reliable numerical method and (b) the Taka´cs method.

6.

Conclusions

The conclusions of this work can be summarized as follows:  A consistent modelling methodology (CMM), which can be used to construct models for all processes in WWT systems, was presented. A key principle of the CMM is that for a real process that occurs in continuous time and space, the modelling should be done in continuous time and space, resulting in a PDE as mathematical model. Supported by PDE theory, a simulation model (numerical method) is then defined at discrete time and space points (or layers). Another key principle is that the model parameters are introduced only in the first step of the CMM and appear in the simulation model automatically. Usually, they are contained in the physical constitutive relations. Parameters should never be introduced directly into the simulation model. If calibration of the model parameters is not satisfactory, then the mathematical model should be rebuilt. A simulation model (ODE or PDE solver) should never be changed as a result of a poor fitting of simulated data to real.  Following the CMM, a 1D model for the SST was presented. It takes into account most of the previously published physical phenomena considered for 1D models, such as hindered settling, compression and dispersion. Most importantly, simulations can be made with a proven consistent and reliable numerical method (PDE solver). In a subsequent publication, we will present this in detail and how it can be used together with established ODE solvers for the biological reactors.  The impacts of the three constitutive assumptions (on settling, compression and dispersion) were demonstrated by means of simulations. The simulated numerical solutions are close to the exact solutions of the PDE. Numerical errors can be made arbitrarily small by increasing the number of layers sufficiently. This property is the main advantage above any traditional layer model, which is a numerical method that has been constructed without utilizing PDE theory. Consequently, there is no proved connection to the model PDE and hence no proved connection to the basic physical principles that govern the real process.  Our robust (simulation) model for the SST can handle all types of physically possible initial conditions and feed inputs. We have in an example illustrated that the Taka´cs model generates an unphysical solution, which is a consequence of the fact that Taka´cs’ minimum-flux update does not always take an important physical principle (the entropy condition) into account.  As a consequence of the CMM, together with the fact that there are proven reliable PDE solvers available now, it is highly recommended that the traditional layer models should be replaced by reliable ones.

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Acknowledgements The authors are grateful to Sebastian Fara˚s, Centre for Mathematical Sciences, Lund University, who has provided the simulations after careful implementations. Raimund Bu¨rger acknowledges support by Fondecyt project 1090456, and BASAL project Centro de Modelamiento Matema´tico, Universidad de Chile and Centro de Investigacio´n en Ingenierı´a Matema´tica (CI2MA), Universidad de Concepcio´n.

Appendix. The terminology of the CMM Real process: The physical/biological/chemical process to be modelled. Idealizing assumptions: Simplifying assumptions made in order to define a mathematical model that is not too complicated but still captures the main features of the real process. Examples: 1D, neglecting wall effects, particles are spherical, instantaneously well-mixed compartment. Constitutive assumption ¼ constitutive relation: An assumed relation between physical (biological/chemical) variables needed to obtain a mathematical model that is not underdetermined. Examples: the Monod relation, the Vesilind expression for the settling velocity as a function of the concentration, Fick’s law of diffusion. The constitutive relations contain the model parameters, both kinetic and stoichiometric. Mathematical model ¼ model ¼ model equation ¼ process model: The system of equations that describes the physical law(s). It is a simplification of the real process, taking into account only some of the features in reality, but it models these exactly (and at every time point). Model parameters: Parameters introduced in the first step of the CMM, usually contained in the constitutive relations. Exactly the same set of parameters is present in the mathematical model and the simulation model. Numerical method ¼ numerical algorithm ¼ numerical scheme ¼ numerical model ¼ simulation model ¼ simulation method ¼ simulation program ¼ computer model (the prefixes ‘numerical’ and ‘simulation’ can often be used as synonyms): A sequence of instructions for computing real numbers. It can be defined explicitly or implicitly. Examples are RungeeKutta methods for ODEs, finite-element methods for PDEs. It is often seen as a discretized version of the (continuous-in-time) mathematical model. Therefore, the terminology ‘simulation model’, ‘numerical model’ or ‘computer model’ is common despite the fact that it is really not a model (unless the real process is discrete in time). The danger of using ‘model’ here is the common misinterpretation that a model can be built directly by numerical algorithms, which sharply contradicts the CMM (for continuous-in-time systems). Nevertheless, we have chosen to use the common terminology ‘simulation model’. Entropy condition: An admissibility criterion related to physical principles. It is needed for nonlinear PDEs in conservation law form to obtain the physically relevant unique solution. It can be expressed by inequalities relating concentrations and fluxes on both sides of a discontinuity. A numerical flux should take this into account automatically so that

only physically relevant (stable) discontinuities appear in the approximate solution. Well-posedness: A mathematical model, defined by an ODE or PDE (or system of such) together with initial data at time zero, is well-posed if there exists precisely one solution (existence and uniqueness), and this solution depends continuously on the initial data, i.e. a small change in the initial data will only cause a small change in the solution. Solution ¼ exact solution: This refers to the solution of the mathematical model subject to the condition that the model is well-posed, defined for all time points. In the case of (11), the solution is C(z, t). Numerical solution ¼ simulation output ¼ approximate solution: The output data from a simulation program constitute an approximate discrete-in-time solution of the exact one. Reliable numerical method: The word reliable means that the simulated data are consistent with the idealizing assumptions made at the beginning of the CMM (whether simulated data agree with experimental observations is a completely different issue; see Steps 4 and 5 in the CMM). A reliable numerical method is robust (consistently handles any physically reasonable input data), conservative (no loss of mass), has no overshoots (the concentration is never negative or above a prescribed maximum value), convergent (approximate solutions converge to the exact solution as the time step and layer thickness tend to zero). For a PDE that models continuous sedimentation, an additional requirement is that the approximate solutions should converge to the unique physically admissible solution (which satisfies an entropy condition).

references

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w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 2 6 1 e2 2 6 9

Available at www.sciencedirect.com

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Kinetics modeling and reaction mechanism of ferrate(VI) oxidation of benzotriazoles Bin Yang, Guang-Guo Ying*, Li-Juan Zhang, Li-Jun Zhou, Shan Liu, Yi-Xiang Fang State Key Laboratory of Organic Geochemistry, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou 510640, China

article info

abtract

Article history:

Benzotriazoles (BTs) are high production volume chemicals with broad application in

Received 11 October 2010

various industrial processes and in households, and have been found to be omnipresent in

Received in revised form

aquatic environments. We investigated oxidation of five benzotriazoles (BT: 1H-benzo-

27 January 2011

triazole; 5MBT: 5-methyl-1H-benzotriazole; DMBT: 5,6-dimethyl-1H-benzotriazole hydrate;

Accepted 28 January 2011

5CBT: 5-chloro-1H-benzotriazole; HBT: 1-hydroxybenzotriazole) by aqueous ferrate (Fe(VI))

Available online 4 February 2011

to determine reaction kinetics as a function of pH (6.0e10.0), and interpreted the reaction mechanism of Fe(VI) with BTs by using a linear free-energy relationship. The pKa values of

Keywords:

BT and DMBT were also determined using UVeVisible spectroscopic method in order to

Benzotriazoles

calculate the species-specific rate constants, and they were 8.37
0.01and 8.98
0.08

Ferrate(VI)

respectively. Each of BTs reacted moderately with Fe(VI) with the kapp ranged from 7.2 to

Oxidation

103.8 M1s1 at pH 7.0 and 24
1  C. When the molar ratio of Fe(VI) and BTs increased up to

Kinetics

30:1, the removal rate of BTs reached about >95% in buffered milli-Q water or secondary

Linear free-energy relationship

wastewater effluent. The electrophilic oxidation mechanism of the above reaction was illustrated by using a linear free-energy relationship between pH-dependence of speciesspecific rate constants and substituent effects (sp). Fe(VI) reacts initially with BTs by electrophilic attack at the 1,2,3-triazole moiety of BT, 5MBT, DMBT and 5CBT, and at the NeOH bond of HBT. Moreover, for BT, 5MBT, DMBT and 5CBT, the reactions with the species HFeO 4 predominantly controled the reaction rates. For HBT, the species H2FeO4 with dissociated HBT played a major role in the reaction. The results showed that Fe(VI) has the ability to degrade benzotriazoles in water. ª 2011 Elsevier Ltd. All rights reserved.

1.

Introduction

Benzotriazoles (BTs) are high production volume chemicals that find broad application in various industrial processes and in household products as anti-corrosion agents (Weiss et al., 2006). BTs are a class of polar heterocyclic compounds containing the benzotriazole skeleton with a benzene ring on which a vicinal pair of carbon atoms covalently bonded to three nitrogen atoms in a five membered ring. BTs are

characterized by high water solubility (3e70 g/L), low octanol water distribution coefficients (log Kow 0.11e2.26), and low soil adsorption coefficient (log Koc 1.2e2.2) and they are also weakly basic compounds (pKa 7.39e8.98) (US EPA, 2008), thus they are expected to be quite mobile in the aquatic environment. BTs are classified as toxic to aquatic organisms. The acute toxicity EC50 or LC50 of BTs to aquatic organisms (Microtox bacteria, fathead minnow and water flea) are in the range of

* Corresponding author. Tel./fax: þ86 20 85290200. E-mail addresses: [email protected], [email protected] (G.-G. Ying). 0043-1354/$ e see front matter ª 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2011.01.022

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0.72e118 mg/L (Cancilla et al., 1997; Pillard et al., 2001). BT has been shown to exhibit antiestrogenic activity in vitro in a yeast assay but not in vivo to fish (Harris et al., 2007). Moreover, it can interfere with the regulation of embryo development in protochordates such as Ciona Intestinalis (Kadar et al., 2010). BTs in aquatic environment may cause negative effects in the aquatic organisms. Therefore, it is necessary to remove BTs in effluents by applying various treatment technologies. BTs have been incompletely removed in conventional wastewater treatment plants due to their resistance to biodegradation (Weiss and Reemtsma, 2005; Weiss et al., 2006; Giger et al., 2006). Due to their incomplete removal and discharge of effluents, BTs have been reported in various aquatic environments. The concentration levels of BTs ranged from 0.01 to 0.2 mg/L in drinking water samples (van Leerdam et al., 2009) and between 0.1 and 6.3 mg/L in surface waters (Giger et al., 2006) as well as 0.8e18 mg/L in effluents of sewage treatment plants (Weiss and Reemtsma, 2005; Weiss et al., 2006; Reemtsma et al., 2010). 1H-Benzotriazole (BT) has been listed as one of the most widely detected polar organic pollutants in European surface water and ground water during pan-European reconnaissance (Loos et al., 2009, 2010). Various oxidation processes have been studied in the laboratory for BTs. Ozonation has been found quite effective in removal of BT from the treatment plant effluent (Weiss et al., 2006). The second-order rate constants for BT with molecular ozone were determined to be 36.4 M1s1 (the log-reduction of BT with ozone in excess) or 18.4 M1s1 at pH 2 and 22.0 M1s1 at pH 5 (the competition kinetic model) (Vel Leitner and Roshani, 2010), but the rate constants were found to be above 109 M1s1 for the reaction of hydroxyl radicals with BT using the pulse radiolysis technique (Naik and Moorthy, 1995). Photoelectrocatalytic degradation of BT by liquid phase deposited TiO2 film mainly proceeds by cleavage of the azo bond leading to decolorization, followed by opening of the benzene ring to form small molecular organic products (Ding et al., 2010). BT can also be degraded by UV irradiation in aqueous solution (Andreozzi et al., 1998; Hem et al., 2003; Wang et al., 2000). Reaction kinetics in photochemical transformation are significantly affected by pH because of its influence upon benzotriazole dissociation (Andreozzi et al., 1998). Moreover, previous studies of oxidation processes have mainly focused on BT, but few on its derivatives. Ferrate (Fe(VI)) is another powerful oxidizing agent in water treatment, which has the oxidation-reduction potential of 2.20 V at acidic pH condition and 0.57 V at basic pH condition (Lee et al., 2004). Due to its dual functions of an oxidant and a subsequent coagulant/precipitant as ferric hydroxide (Fe(III)) (Jun and Wei, 2002), Fe(VI) is regarded as an environmentally friendly oxidant in water and wastewater treatment process (Jiang and Lloyd, 2002; Sharma, 2002, 2010; Jiang, 2007). However the instability of Fe(VI) has limited its use in water treatment applications, but recent development in the production of Fe(VI) in situ using the electrochemical method makes it a promising oxidant for the real application of Fe(VI) in water and wastewater treatment plants (Yu and Licht, 2008; Alsheyab et al., 2009; Macova et al., 2009). As the oxidant of iron series, Fenton reactions compose reactions of peroxides with iron ions to form active oxygen species at the low pH of 2.8e3.0 that limit their

widespread usage (Pignatello et al., 2006), but Ferrate works well in a broader pH range. At circumneutral pH solution, H2FeO4, 2HFeO 4 , FeO4 are the predominant species with the oxidizing power of these oxidants increasing in the order nonprotonated ferrate < monoprotonated ferrate < diprotonated ferrate (Kamachi et al., 2005). These pH-dependent variations of Fe(VI) reaction with organic contaminants could be explained by considering species-specific reactions between Fe(VI) species and acid-base species of an ionizable substrates (Lee et al., 2005a; Sharma et al., 2006). Fe(VI) has been known to react with electron-rich organic moieties (ERM) through electrophilic oxidation mechanism (Lee et al., 2009). The proposed mechanism for the oxidation of aniline (Huang et al., 2001b) or phenol (Huang et al., 2001a) by Fe(VI) involves a free radical reaction mechanism through an associative type of mechanism with hydrogen bond formation in the activated complex accompanied by intermolecular electron transfer. Nevertheless, there has so far been no study on the oxidation of BTs by Fe(VI) available in the literature, especially on their reaction mechanisms. The objectives of the present study were to assess the potential of Fe(VI) oxidation of five benzotriazoles (BT: 1H-benzotriazole; 5MBT: 5-methyl-1H-benzotriazole; DMBT: 5,6-dimethyl-1H-benzotriazole hydrate; 5CBT: 5-chloro-1Hbenzotriazole; HBT: 1-hydroxybenzotriazole) and determine the reaction rate constants. A linear free-energy relationship was used to explain the reaction mechanism of Fe(VI) with BTs. Besides, the pKa values of BT and DMBT were also determined by UVeVisible spectroscopic method in order to calculate the species-specific rate constants.

2.

Experimental section

2.1.

Standards and reagents

1H-Benzotriazole (BT, 99%) was purchased from Tokyo chemical industry (Tokyo, Japan). 5-Methyl-1H-benzotriazole (5MBT, 98%), 5,6-dimethyl-1H-benzotriazole hydrate (DMBT, 99%) and 5-chloro-1H-benzotriazole (5CBT, 99%) were purchased from Acros Organics (New Jersey, USA). 1Hydroxybenzotriazole anhydrous (HBT, 98%) was purchased from J＆K Chemical (Guangzhou, China). The basic physiochemical information on the five benzotriazoles is listed in Table 1. Diammonium 2,20 -azinobis-(3-ethylbenzothiazoline6-sulfonate) (ABTS, 98%) was obtained from Tokyo chemical industry (Shanghai, China). Potassium ferrate (Fe(VI)) was prepared by wet chemical synthesis (Delaude and Laszlo, 1996). It has a purity of above 95% as Fe(VI) (w/w), which was determined by the direct 510 nm method (3 ¼ 1150 M1 cm1) at the pH value of 9.1
0.1 (5 mM K2HPO4/1 mM borate) (Rush and Bielski, 1986). All solutions were prepared with Milli-Q water from a Millipore Water Purification System. Buffer chemicals and all other reagents used for solutions were of analytical grade. Stock solutions of Fe(VI) (0.8e1.5 mM) was prepared by dissolving solid potassium ferrate in Milli-Q water (pH z 9.2) and used immediately. Stock solutions of BT and HBT were prepared in Milli-Q water at concentrations of 100 mg/L. Stock solutions of 5MBT, DMBT and 5CBT were prepared in Milli-Q water assisted by 2% (v/v) acetonitrile at

Table 1 e Species-specific second-order rate constants for the reactions of Fe(VI) with benzotriazoles in the pH range of 6.0e10.0 and at 24 ± 1  C. Benzotriazoles

pKa

spd

k12

k21

k22

R2

kapp at pH 7.0 (M1s1)

t1/2 (s)e

1H-benzotriazole (BT)

8.37a

0

1.9(
0.4)  101

1.9(
0.2)  102

0.95

19.9

689.8

5-Methyl-1H-benzotriazole (5MBT)

8.5b

0.17

2.7(
0.5)  101

4.3(
0.5)  102

0.95

28.2

486.1

5,6-Dimethyl-1H-benzotriazole (DMBT)

8.98a

0.34

8.5(
1.7)  101

7.3(
1.4)  102

0.91

76.5

179.6

5-Chloro-1H-benzotriazole (5CBT)

7.5b

0.23

2.0(
0.2)  100

6.6(
0.6)  101

0.88

7.2

1917.3

1-Hydroxybenzotriazole (HBT)

7.39c

0.37

7.7(
0.6)  101

0.99

103.8

132.3

1.6(
0.1)  106

w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 2 6 1 e2 2 6 9

a b c d e

Chemical name

Supplementary Information, SI-word. Hart et al. (2004). http://www.chemicalbook.com/ProductMSDSDetailCB2420172_EN.htm. sp values were obtained from the literature (Hansch et al., 1991). Estimated by assuming pseudo-first-order conditions with a Fe(VI) excess ([Fe(VI)] ¼ 10 mg/L, pH ¼ 7.0).

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concentrations of 100 mg/L. In Fe(VI) degradation processes, the experiments were not affected by the presence of trace acetonitrile (Lee et al., 2009). A secondary wastewater effluent was grab sampled from the Liede municipal wastewater treatment plant which uses primary sedimentation and activated sludge treatment. The plant is the biggest wastewater treatment plant which serves 2 million people in metropolitan Guangzhou, China. The pH, UV254, DOC, conductivity and alkalinity of the effluent were 6.88, 0.08, 4.93 mg/L, 512 mS/cm and 3 mM as HCO 3 , respectively. The samples were filtered with a 0.45 mm cellulose-nitrate membrane and used within 24 h. Besides, the trace pollutants did not interfere the experiments because of the high spiked concentrations for each target compound of BTs (10 mM).

2.2.

Determination of pKa values of BT and DMBT

The absorbencies of BT and DMBT in different pH buffer solutions were determined by a UVeVIS spectrophotometer at the wavelength of 280 nm at room temperature (24
1  C) (Castro et al., 2003; Fagel and Ewing, 1951). For detailed information of determination of pKa values of BT and DMBT, please refer to Supplementary Information, Text S1.

2.3.

Kinetics of BTs oxidation by Fe(VI)

Second-order rate constants for the reaction of Fe(VI) with the five BTs were determined in the pH range of 6.0e10.0. Reagents 10 mM phosphate/acetic acid were used as the pH 6.0e9.0 buffer solutions and 10 mM phosphate/NaOH used to adjust the pH 9.5 and 10.0 of reaction solutions. The kinetics of BTs oxidation by Fe(VI) was conducted under pseudo-firstorder conditions with Fe(VI) in excess to BTs. In the 150 mL reaction mixture solutions, the initial concentration of Fe(VI) was 100 mM while the concentration for each of BTs was 2 mM. The experiments were performed in a 200 mL beaker equipped with a magnetic stirrer (500 r/min) at the room temperature (24
1  C). The Fe(VI) stock solution was quickly filtered through a 0.45 mm hydrophilic polyethersulfone (PES) syringe filter (Shanghai ANPEL, China) and then standardized spectrophotometrically at 510 nm. Reactions were initiated by adding an aliquot of the Fe(VI) stock solution to suspensions containing each of BTs under rapid mixing. At proper time intervals, 5 mL of the reaction solution with an ABTS solution to measure residual Fe(VI) concentrations using a ABTS method at 415 nm (Lee et al., 2005b), and 1 mL of the reaction solution was sampled and quenched with a thiosulfate solution (5 mM, 0.1 mL) to measure residual concentrations of BTs. The absorbance was measured with a Lambda 850 spectrophotometer (PerkinElmer, USA). The pH values were determined using a Thermo Orin 5 star pH meter (Thermo Fisher Scientific, USA), which was calibrated using standard buffers (pH 4.0, 7.0, and 10.0, Thermo China). The pH variation was below 0.1 units during the experiments. Each of BTs was analyzed on an Agilent 1200 series high performance liquid chromatography (HPLC) fitted with a diode array detector (Santa Clara, CA). An SGE C18 RS column (100  4.6 mm, 5 mm) (Melbourne, Australia) with a guard column (C18, 4.6  7.5 mm, 5 mm) was used for the separation. The column temperature was set at 30  C. Methanol and water

(acidified with 0.1% acetic acid) were used as the mobile phase. The eluent ratio for BT, 5MBT, DMBT, 5CBT and HBT was 30:70, 40:60, 50:50, 50:50 and 20:80, respectively. The injection volume was 100 mL and the flow rate was set at 1 mL/min. The UV wavelength for BT, 5MBT, DMBT, 5CBT and HBT was 254 nm, 260 nm, 265 nm, 265 nm and 204 nm, respectively. The limit of quantitation of each target compound was 10 mg/L.

2.4.

Elimination of BTs at various Fe(VI) dose

The elimination level of BTs at various Fe(VI) dose was determined in buffered milli-Q water (10 mM phosphate buffer) and secondary wastewater effluent (20 mM borate buffer) at pH 8.0 and room temperature (24
1  C). In a series of 25 mL amber volumetric flasks, 10 mM for each of BTs was spiked in the buffer solution and then the filtered and standardized stock solution of Fe(VI) was added to yield the various concentrations of 0, 50, 100, 200 and 300 mM. The solutions were shaken to have sufficient reaction. Reaction time was 3 h in the darkness. One milliliter of the reaction solution was sampled and quenched with 100 mL of a thiosulfate solution (50 mM). If the reaction solution had iron-precipitates, the treated samples were quenched and centrifuged at 4000 r/min to remove the precipitates. The residual concentration for each of BTs was then determined by the HPLC methods as described above. All experiments were performed in triplicate.

3.

Results and discussion

3.1.

Kinetics for the reaction of BTs with Fe(VI)

According to the previous studies on Fe(VI) reaction with electron-rich organic moieties (ERM) (Lee et al., 2005a; Sharma et al., 2006; Lee et al., 2008; Hu et al., 2009; Lee et al., 2009), second-order reaction kinetics for the reaction of Fe(VI) with BTs were designed under pseudo-first-order conditions with Fe (VI) in excess to BTs ([Fe(VI)]0 ¼ 50[BTs]0, and [BTs]0 ¼ 2 mM). Second-order reaction rate law can be described by eq (1). d½BTs=dt ¼ kapp ½FeðVIÞ½BTs

(1)

Eq (1) is rearranged and d[BTs]/[BTs] is integrated to become eq (2).
 ln ½BTs=½BTs0 ¼ kapp

Zt ½FeðVIÞdt

(2)

0

Rt Where the term 0 ½FeðVIÞdt is the Fe(VI) exposure, the time integrated concentration of Fe(VI) due to the instability (Lee et al., 2005a) and kapp is the apparent second-order rate constant. As a representative example, Fig. 1 (a) shows the oxidation of BT (2 mM) by excess Fe(VI) (100 mM) at pH 7.5 and 24
1  C. The present study used the second-order rate constant (kself, eq (3)) for the Fe(VI) self-decomposition to predict the Fe(VI) self-decomposition as a function of time (Lee et al., 2009).

d½FeðVIÞ=dt ¼ kself ½FeðVIÞ2

(3)

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BT [Fe(VI)]

1.8

1/[Fe(VI)]

0.035

80

0.030

60

1.4 40

1.2

0.020 0.015

1.0

20

1/[Fe(VI)]=3.3E-5t+0.0091 R =0.99 0

100

200

300

400

500

10

0.010

2

0.8

0.025

BT 5MBT DMBT 5CBT HBT

100 1/[Fe(VI)]

1.6

100

-1 -1

2.0

k(M s )

a

600

700

0

1

t(s)

b

0.0

7.5

-0.5

8.0

8.5

9.0

9.5

10.0

Fig. 2 e Apparent second-order rate constants and associated model simulation for the reactions of benzotriazoles with Fe(VI) as a function of pH (6.0e10.0) at the room temperature (24 ± 1  C).

-0.4

-0.7

7.0

pH

-0.3

-0.6

6.5

Linear fit of Ln(C/C0)

-0.2 Ln(C/C0)

6.0

Ln(C/C0)

-0.1

-1 -1

k=17.1 M s 2

R =0.99

-0.8 0.00

0.01

0.02

0.03

0.04

Fe(VI) exposure(Ms)

Fig. 1 e (a) Fe(VI) oxidation of 1H-benzotriazole. [BT]0 [ 2 mM, [Fe(VI)]0 [ 100 mM, pH 7.5, 24 ± 1  C. (b) Fit of 1H-benzotriazole oxidation by Fe(VI) with second-order reaction kinetics (eq (2)).

 2 a1 ¼ ½H2 FeO4 =½FeðVIÞtot ¼ Hþ =T

(7)

    a2 ¼ HFeO ½FeðVIÞtot ¼ Hþ Ka;H2FeO4 =T 4

(8)

  ½FeðVIÞtot ¼ Ka;H2FeO4 Ka;HFeO4 =T a3 ¼ FeO2 4

(9)

 2   T ¼ Hþ þ Hþ Ka;H2FeO4 þ Ka;H2FeO4 Ka;HFeO4

(10)

Accordingly separating the variables and integrating give eq (4) (Fig. 1 (a)).

 
 þ   b1 ¼ ½BTs=½BTstot ¼ Hþ H þ Ka;BTs

(11)

1=½FeðVIÞ ¼ kself t þ C


   b2 ¼ ½BTs =½BTstot ¼ Ka;BTs = Hþ þ Ka;BTs

(12)

(4)

Then substituting eq (4) into Fe(VI) exposure leads to eq (5). Zt ½FeðVIÞdt ¼ 0

  kself t ln 1 þ C kself 1

(5)

Therefore the slope of the plots gave the kapp as 17.1 M1s1 (R > 0.99) at pH 7.5 and 24
1  C (Fig. 1 (b)). The values of rate constants kapp for the reaction of Fe(VI) with BTs as a function of pH (6.0e10.0) are presented in Fig. 2. The rate constants of the reaction decreased with increasing pH values except 5CBT. The oxidation rate of 5CBT showed a general increase with decreasing pH between pH 10.0 and 7.5, but the rates decreased obviously with a decrease in acidic media with a maximum at pH 7.5 as Fe(VI) reaction with glycine (Noorhasan et al., 2010). These pH-dependent variations in kapp could be distributed by considering species-specific reactions between Fe(VI) species (, pKa,H2FeO4 ¼ 3.50 (Rush et al., 1996), pKa,HFeO4 ¼ 7.23 (Sharma et al., 2001)) and acidbase species of an ionizable BTs (pKa, Table 2) by eqs (6e12).

2 , ½BTstot ¼ Where ½FeðVIÞtot ¼ ½H2 FeO4  þ ½HFeO 4  þ ½FeO4  ½BTs þ ½BTs . ai and bj represent the respective species distribution coefficients for Fe(VI) and BTs, i and j represent each of the three Fe(VI) species and BTs species respectively, and kij represents the species-specific second-order rate

2

kapp ½FeðVIÞtot ½BTstot ¼

X i ¼ 1; 2; 3 j ¼ 1; 2

kij ai bj ½FeðVIÞtot ½BTstot

Table 2 e The determinted kapp values for reactions of BT with various oxidants. Oxidants

pH

kapp (M1s1)

Fe(VI)

7.0 9.8 2.0 5.0 2.0 10.2 5.8 10.5

19.9 0.6 18.4a/36.4b 22.0 1.7  1010 6.2  109 7.6  109 9.0  109

O3 

OH

Reference This study This study Vel Leitner and Roshani, Vel Leitner and Roshani, Vel Leitner and Roshani, Vel Leitner and Roshani, Naik and Moorthy, 1995 Naik and Moorthy, 1995

(6) a : the competition kinetic model. b : the log-reduction of BT with ozone in excess.

2010 2010 2010 2010

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constant for the reaction between the Fe(VI) species i with the BTs species j. Based on previous investigations (Lee et al., 2005a; Lee et al., 2008; Hu et al., 2009; Lee et al., 2009), the reactions of FeO2 4 species with the compounds studied were omitted due to the low reactivity of FeO2 4 species, and did not significantly affect the model accuracy. For BT, 5MBT, DMBT and 5CBT, eqs (13e15) were used to model the kinetics. Hence the species-specific second-order rate constants, k21 and k22, were calculated from least-squares nonlinear regressions of the experimental kapp data by using the software SigmaPlot 10.0 (Systat Software Inc.). The model could explain the experimental kapp well (R2 ¼ 0.88e0.95). Table 1 summarizes the determined k21 and k22 values for BTs except HBT. k22 was magnitude higher than k21 because the deprotonated species are better electron donors. However just like Fe(VI) reaction with 4-cyanophenol and 4-nitrophenol (Lee et al., 2005a), the model kinetics (eqs (13e15)) could not interpret the observed pH dependency of kapp for HBT. The kapp of HBT steadily increases even at the whole investigated pH range. Thus we also utilized the eq (16,17) between H2FeO4 species and the dissociated HBT species (k12a1b2) to explain the pH dependency of kapp of HBT. k21

HFeO 4 þ BTs/products

(13)

k22

 HFeO 4 þ BTs /products

(14)

kapp ¼ k21 a2 b1 þ k22 a2 b2 for BT; 5MBT; DBMT; 5CBT

(15)

k12

H2 FeO4 þ HBT /products for HBT

(16)

kapp ¼ k12 a1 b2 þ k21 a2 b1 þ k22 a2 b2

(17)

Fig. 3 shows a successful model for the observed pH dependency of kapp for HBT (R2 ¼ 0.99) using eq (16,17) by leastsquares nonlinear regressions. The k12 and k22 values were 1.6

3.5 3.0

log(kij)

2.5 k22

2.0 1.5 1.0

-

HFeO4+HBT

k21

0.5 0.0

-0.4

-0.3

-0.2

-0.1

0.0

0.1

0.2

Fig. 3 e Correlations between the second-order rate constants of the reactions between HFeOL 4 with the undissociated benzotriazoles(k21) and the dissociated benzotriazoles (k22) vs Hammett constants.

0.3

(
0.1)  106 M1s1 and 7.7(
0.6)  101 M1s1 for HBT respectively. But the value of k21 could not be determined accurately because of low contribution of protonated Fe(VI) with undissociated HBT. Besides the k12 is 104 times higher than k22, which indicates H2FeO4 has a higher reactivity than HFeO 4 (Kamachi et al., 2005). As a consequence, each of BTs can be oxidized at circumneutral pH, with t1/2 ranging from 132 s to 1917 s for an Fe(VI) concentration of 10 mg/L at pH 7.0 and 24
1  C. In addition, the second-order rate constants for BT reaction with Fe(VI) (19.9 M1s1, pH 7.0) are similar to that with molecular ozone which was determined to be 36.4 M1s1 (the log-reduction of BT with ozone in excess) or 18.4 M1s1 at pH 2 and 22.0 M1s1 at pH 5 (the competition kinetic model) (Vel Leitner and Roshani, 2010), but magnitude lower than that with the hydroxyl radicals during ozonation (Vel Leitner and Roshani, 2010) or the pulse radiolysis (Naik and Moorthy, 1995). The second-order rate constants of the BT reaction with the hydroxyl radicals were determined to vary from 6.2  109 M1s1 to 1.7  1010 M1s1. The determinted kapp values for reactions of BT with various oxidants were shown in Table 2.

3.2.

Linear free-energy relationships

A linear free-energy relationship was performed to predict the effect of the substituents on the species-specific second-order rate constants of the BTs reaction with Fe(VI). Although other Hammett parameters (i.e., sþ) have previously been used to test substituted phenols reactivity with Fe(VI) (Lee et al., 2005a), the present study used sp as free-energy descriptors on account of their suitability for characterizing electrophilic reactions. The sp terms for BTs were obtained from the literature (Hansch et al., 1991). Fig. 3 shows the obtained Hammett-type correlations for k21 versus sp and for k22 versus sp (except for HBT). The linear regressions for both the undissociated and the dissociated BTs are logðk21 Þ ¼ 1:00ð
0:08Þ  2:86ð
0:38Þsp R2 ¼ 0:95 n ¼ 4

(18)

logðk22 Þ ¼ 2:27ð
0:02Þ  1:94ð
0:10Þsp R2 ¼ 0:99 n ¼ 4

(19)

A negative Hammett slope (r) illustrated the electrophilic oxidation mechanism. The magnitude of the r value reflects the sensitivity of the reaction to the substituent effect (Hansch et al., 1991). However, the reaction between HFeO 4 with the undissociated BTs (2.86) is more sensitive to the substituent effect than that with the dissociated BTs( 1.94).The Hammett constant (s) reflects the effects of substituents on the electron density of the aromatic ring by inductive and resonance effects. Fig. 3 indicates that electron-donating substituents (s < 0) activate the BT structures toward attack by Fe(VI), whereas electron-withdrawing substituents (s > 0) result in deactivation. With the increasing amount of methyl substituents of BT, the stronger the activation, and the higher the reaction rate of BTs with Fe(VI) except HBT. Thus, with the success of the linear free-energy relationships (eq (18,19)) we can suppose that Fe(VI) reacts initially with BT by electrophilic attack at the 1,2,3-triazole moiety. For HBT, it deviates from the fitted straight line. Because the hydroxyl replaces

w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 2 6 1 e2 2 6 9

hydrogen atom connecting with nitrogen rather than the benzene ring, the reaction mechanism is electrophilic attack at the NeOH bond of HBT by Fe(VI).

3.3.

BTs degradation by Fe(VI)

Fig. 4 demonstrates the degradation efficiency of BTs oxidation by Fe(VI) individually under different molar ratios in buffered milli-Q water (Fig. 4 (a)) and secondary wastewater effluent (Fig. 4 (b)) at pH 8.0 and 24
1  C. With the dosage of Fe (VI) gradually increasing, the concentration of each BTs was decreasing. Due to the competition reaction occurred between BTs and wastewater matrix, the removal rate of each BTs in secondary wastewater effluent was less then in buffered milliQ water when the dosage of Fe(VI) was lower than 100 mM (Fig. 4). However, when the dosage of Fe(VI) was more than 100 mM, the competition disappeared after the wastewater matrix were consumed (Lee and von Gunten, 2010), the removal rate of each BTs in secondary wastewater effluent was similar to that in buffered milli-Q water. With the molar ratio of Fe(VI) and BTs increasing up to 30:1, the removal rate of each BTs reached about >95% in buffered milli-Q water or secondary wastewater effluent. Besides, intermediates of the oxidation of benzotriazole by Fe(VI) were investiagted and independently determined by gas chromatography-mass

a

10 8 0 50 100 200 300

6 4 2 0

BT

5MBT

DMBT

5CBT

HBT

2267

spectrometry (GCeMS) and rapid resolution liquid chromatography-tandem mass spectrometry (RRLC-MS/MS), for detailed information of byproducts identification, please refer to Supplementary Information, Text S2. But no obvious intermediates were found in the present study. The present study and previous studies (Lee and von Gunten, 2010; Noorhasan et al., 2010; Sharma, 2010) showed that Fe(VI) can react with dissolved organic nitrogen compounds. Oxidation of nitrogen-containing compounds by Fe(VI) can proceed through either one-electron or two-electrons processes to yield non-hazardous oxidation products (Sharma, 2010). The k values decrease in the order of aniline > glycine (primary amine) > dimethylamine (secondary amine) > trimethylamine (tertiary amine) in the pH range 6e8 (Lee and von Gunten, 2010; Noorhasan et al., 2010). The present study demonstrated that Fe(VI) can also react with the nitrogen-containing heterocyclic compounds BTs. The k of BTs are found ranging between secondary amine and tertiary amine, depending on the substituents on BTs. In a word, BTs can be degraded by Fe(VI) oxidation completely.

4.

Conclusions

Second-order reaction kinetics was used to model the data obtained from the Fe(VI) oxidation of BTs and species-specific second-order rate constants were determined for the reaction as a function of pH. For BT, 5MBT, DMBT and 5CBT, the reactions with the species HFeO 4 predominantly controled the rates. For HBT, the species H2FeO4 with dissociated HBT played a major role in the reaction. Each of BTs reacted moderately with Fe(VI) with the half life (t1/2) ranged from 132 s to 1917 s as estimated by assuming pseudo-first-order conditions with a Fe (VI) excess. When the molar ratio of Fe(VI) and BTs increasing up to 30:1, the removal rate of BTs reached about >95% in buffered milli-Q water and secondary wastewater effluent. A linear free-energy relationship could interprete the electrophilic oxidation mechanism. Fe(VI) reacts initially with BTs by electrophilic attack at the 1,2,3-triazole moiety of BT, 5MBT, DMBT and 5CBT, and at the NeOH bond of HBT.

b

10

Acknowledgments 8 0 50 100 200 300

6 4 2 0

BT

5MBT

DMBT

5CBT

HBT

Fig. 4 e Oxidation of benzotriazoles in buffered milli-Q water (a, 10 mM phosphate buffer) and secondary wastewater effluent (b, 20 mM borate buffer) as a function of the Fe(VI) dose (0e300 mM). Experimental conditions: pH [ 8.0, T [ 24 ± 1  C, [BTs]0 [ 10 mM, and contact time 3 h.

The authors thank for the financial support from National Natural Science Foundation of China (NSFC 40688001, 40821003 and 40771180) and Ministry of Environmental Protection of the People’s Republic of China (2008ZX07528001-02), Guangdong Provincial Natural Science Foundation (8251064004000001) and the Earmarked Fund from the State Key Laboratory of Organic Geochemistry (sklog 2009A02). The authors thank Y.H. Lee (EAWAG) for his guidance in data processing. This is the contribution no. 1290 from GIG CAS.

Appendix. Supplementary data Supplementary data associated with this article can be found in online version at doi:10.1016/j.watres.2011.01.022.

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Jun, M., Wei, L., 2002. Effectiveness of ferrate (VI) preoxidation in enhancing the coagulation of surface waters. Water Research 36 (20), 4959e4962. Kadar, E., Dashfield, S., Hutchinson, T.H., 2010. Developmental toxicity of benzotriazole in the protochordate Ciona intestinalis (Chordata, Ascidiae). Analytical and Bioanalytical Chemistry 396 (2), 641e647. Kamachi, T., Kouno, T., Yoshizawa, K., 2005. Participation of multioxidants in the pH dependence of the reactivity of ferrate(VI). Journal of Organic Chemistry 70 (11), 4380e4388. Lee, C., Lee, Y., Schmidt, C., Yoon, J., Von Gunten, U., 2008. Oxidation of suspected N-nitrosodimethylamine (NDMA) precursors by ferrate (VI): kinetics and effect on the NDMA formation potential of natural waters. Water Research 42 (1e2), 433e441. Lee, Y., Cho, M., Kim, J.Y., Yoon, J., 2004. Chemistry of ferrate (Fe (VI)) in aqueous solution and its applications as a green chemical. Journal of Industrial and Engineering Chemistry 10 (1), 161e171. Lee, Y., von Gunten, U., 2010. Oxidative transformation of micropollutants during municipal wastewater treatment: comparison of kinetic aspects of selective (chlorine, chlorine dioxide, ferrateVI, and ozone) and non-selective oxidants (hydroxyl radical). Water Research 44 (2), 555e566. Lee, Y., Yoon, J., Von Gunten, U., 2005a. Kinetics of the oxidation of phenols and phenolic endocrine disruptors during water treatment with ferrate (Fe(VI)). Environmental Science & Technology 39 (22), 8978e8984. Lee, Y., Yoon, J., von Gunten, U., 2005b. Spectrophotometric determination of ferrate (Fe(VI)) in water by ABTS. Water Research 39 (10), 1946e1953. Lee, Y., Zimmermann, S.G., Kieu, A.T., von Gunten, U., 2009. Ferrate (Fe(VI)) application for municipal wastewater treatment: a novel process for simultaneous micropollutant oxidation and phosphate removal. Environmental Science & Technology 43 (10), 3831e3838. Loos, R., Gawlik, B.M., Locoro, G., Rimaviciute, E., Contini, S., Bidoglio, G., 2009. EU-wide survey of polar organic persistent pollutants in European river waters. Environmental Pollution 157 (2), 561e568. Loos, R., Locoro, G., Comero, S., Contini, S., Schwesig, D., Werres, F., Balsaa, P., Gans, O., Weiss, S., Blaha, L., Bolchi, M., Gawlik, B.M., 2010. Pan-European survey on the occurrence of selected polar organic persistent pollutants in ground water. Water Research 44 (14), 4115e4126. Macova, Z., Bouzek, K., Hives, J., Sharma, V.K., Terryn, R.J., Baum, J.C., 2009. Research progress in the electrochemical synthesis of ferrate(VI). Electrochimica Acta 54 (10), 2673e2683. Naik, D.B., Moorthy, P.N., 1995. Studies on the transient species formed in the pulse-radiolysis of benzotriazole. Radiation Physics and Chemistry 46 (3), 353e357. Noorhasan, N., Patel, B., Sharma, V.K., 2010. Ferrate(VI) oxidation of glycine and glycylglycine: kinetics and products. Water Research 44 (3), 927e935. Pignatello, J.J., Oliveros, E., MacKay, A., 2006. Advanced oxidation processes for organic contaminant destruction based on the Fenton reaction and related chemistry. Critical Reviews in Environmental Science and Technology 36 (1), 1e84. Pillard, D.A., Cornell, J.S., Dufresne, D.L., Hernandez, M.T., 2001. Toxicity of benzotriazole and benzotriazole derivatives to three aquatic species. Water Research 35 (2), 557e560. Reemtsma, T., Miehe, U., Duennbier, U., Jekel, M., 2010. Polar pollutants in municipal wastewater and the water cycle: occurrence and removal of benzotriazoles. Water Research 44 (2), 596e604. Rush, J.D., Bielski, B.H.J., 1986. Pulse radiolysis studies of alkaline iron(III) and iron(VI) solutions. Observation of transient iron

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w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 2 7 0 e2 2 8 0

Available at www.sciencedirect.com

journal homepage: www.elsevier.com/locate/watres

Sorption of antibiotics to biofilm David B. Wunder a,*, Valerie A. Bosscher a, Rhiana C. Cok a, Raymond M. Hozalski b a b

Calvin College, Department of Engineering, 1734 Knollcrest Circle SE, Grand Rapids, MI 49546 4493, USA University of Minnesota, Department of Civil Engineering, 500 Pillsbury Dr. SE, Minneapolis, MN 55455 0116, USA

article info

abstract

Article history:

Using a continuous-flow rotating annular bioreactor, sorption of three selected antibiotics

Received 25 June 2010

(sulfamethoxazole (SMX), ciprofloxacin (CIP), and erythromycin (ERY)) to bacterial biofilm

Received in revised form

was investigated. CIP had the greatest biofilm partition coefficient (Koc ¼ 92,000  10,000 L/

5 November 2010

kg) followed by ERY (Koc ¼ 6000  1000 L/kg) and then SMX (Koc ¼ 4000  1000 L/kg).

Accepted 8 November 2010

Antibiotic sorption to biofilm did not correlate with experimentally-determined Kow values

Available online 19 November 2010

(CIP: 0.4; ERY: 0.98; SMX: <-0.59 at pH 7), suggesting that hydrophobic interactions do not drive the sorption of these relatively hydrophilic compounds to the biofilm. It appears that

Keywords:

speciation (i.e. charge) and molecular size of the antibiotics are important in explaining

Sulfamethoxazole

their sorption to typically negatively charged biofilm. SMX is neutral to negatively charged

Ciprofloxacin

at circumneutral pH while CIP and ERY are both positively charged. The decreased extent

Erythromycin

of sorption of ERY relative to CIP is likely due to the larger molecular size of ERY that results

Environmental fate

in a decreased rate of mass transfer (i.e. diffusion) to and through the biofilm. In conclu-

Octanolewater

sion, the results of this research suggest that hydrophobic interactions (predicted by Kow)

Acid dissociation constant

do not control sorption of relatively hydrophilic antibiotics to biofilm and that antibiotic speciation and molecular size are important factors affecting the interactions between antibiotics and biofilm. ª 2010 Elsevier Ltd. All rights reserved.

1.

Introduction

Antibiotics have been detected in surface waters around the world at concentrations up to 1.9 mg/L (Kolpin et al., 2002; Pena et al., 2007). The presence of antibiotics in surface waters is of concern for several reasons. First, antibiotic resistance can develop in bacteria with exposure to sub-inhibitory concentrations (Ash et al., 2002). Also, aquatic organisms (algae, nitrifying bacteria, zooplankton) can be adversely affected by mixtures of antibiotics at low concentrations (i.e. 0.1e50 mg/L) (Flaherty and Dodson, 2005; Yang et al., 2008; Ghosh et al., 2009). Finally, although the human health effects of sustained exposure to antibiotics at sub-therapeutic doses are currently unknown, there is heightened public awareness over the presence of antibiotics and other pharmaceutical compounds

in drinking water supplies (Benotti et al., 2009). Thus there is interest in approaches to remove antibiotics and other pharmaceutical compounds from water supplies. Antibiotics are not effectively removed via conventional water treatment (i.e. coagulation/flocculation/sedimentation/ filtration) or lime softening (33%, Adams et al., 2002; Westerhoff et al., 2005). Free chlorine (1 mg/L for 40 min) effectively removes (90%) some antibiotics (sulfonamides, carbodox, and trimethoprim) from surface water, although sulfamethoxazole (SMX) may reform during dechlorination (Adams et al., 2002; Dodd and Huang, 2004). Unfortunately, little is known about the products of antibiotic chlorination and their activity and toxicity. Chlorine dioxide and ozone also effectively remove antibiotics (e.g., sulfonamides and macrolides) at reasonable doses and contact times (Huber et al., 2005; Westerhoff et al., 2005), but this is not the case

* Corresponding author. Tel.: þ1 612 526 6337; fax: þ1 612 526 6501. E-mail addresses: [email protected] (D.B. Wunder), [email protected] (R.M. Hozalski). 0043-1354/$ e see front matter ª 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2010.11.013

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for chloramines (Chamberlain and Adams, 2006). Fresh granular activated carbon (GAC) effectively removes erythromycin (ERY) and SMX (7.6-min empty-bed contact times), with spent GAC still exhibiting some removal (<40% for SMX and <55% for ERY) (Westerhoff et al., 2005; Snyder et al., 2007). Regarding membrane filtration, only reverse osmosis and nanofiltration are effective at rejecting antibiotics (Ngheim et al., 2005; Snyder et al., 2007). Finally, photodegradation of tetracyclines, quinolones and ionized sulfonamides occurs in low turbidity waters (Torniainen et al., 1997; Moore and Zhou, 1994) and may contribute to observed removals in treatment facilities using UV disinfection systems. Biofiltration systems, including slow rate filtration (i.e. slow sand filtration, bank filtration) and biologically-active rapid filtration, have been used in the water industry for many decades, especially in Europe (Bouwer and Crowe, 1988; Hiscock and Grischek, 2002). Interest in biofiltration in the U.S. has increased in recent years because of the many potential water quality benefits these systems provide. For example, biofilters effectively remove a variety of organic pollutants including: disinfection byproduct precursors, pesticides, and pharmaceuticals (Eighmy et al., 1993; Collins et al., 1989; Hiscock and Grischek, 2002; Weiss et al., 2003). Removal mechanisms include biodegradation and sorption, with potential sorbents including the filter media (e.g., GAC), natural organic matter (NOM) sorbed onto the filter media, and biofilm. Although there are reports of antibiotic sorption to GAC (Westerhoff et al., 2005; Snyder et al., 2007), sand (Thiele-Bruhn, 2003), manure, and digested sludge (Loke et al., 2002; Carballa et al., 2008), we are unaware of any studies concerning antibiotic sorption to biofilm. Understanding antibiotic sorption to biofilm could be useful for predicting the fate of antibiotics in biofiltration systems used for treatment of water or wastewater. Herein, we report on the results of laboratory experiments performed to quantify the sorption of three selected antibiotics to biofilm as a first step in characterizing the fate of antibiotics in biofilters.

2.

Materials and methods

2.1.

Antibiotics, chemicals, and reagents

The sorption of three selected antibiotics (Table 1) to biofilm was investigated using a continuous-flow rotating annular bioreactor (CFRAB). SMX, ERY, and Ciprofloxacin (CIP) were selected for the following reasons: (1) they represent three prominent classes of antibiotics with differing chemical characteristics and (2) they have been detected in surface water. SMX (a sulfonamide) and ERY (a macrolide) occur in surface waters at concentrations up to 1.9 and 1.7 mg/L, respectively (Kolpin et al., 2002). CIP is a fluoroquinolone with reported surface water concentration of up to 119 ng/L (Pena et al., 2007). The octanolewater partition coefficient (Kow), defined as the concentration of a chemical in octanol to that in water at equilibrium, is a commonly used parameter for predicting the fate of a chemical in the environment or of a pharmaceutical compound in the human body. Compounds with relatively high Kow are more likely to partition to natural organic matter, bioaccumulate in aquatic organisms, or partition into hydrophobic compartments in the human body such as lipid bilayers. Reported logKow (pH 7.4 and 25  C) values for SMX, ERY, and CIP are 0.9, 1.58, and 1.1, respectively (Hansch et al., 1995; Drakapoulos and Ioannou, 1997). The logKow determined at pH 7.4 (i.e. the physiological pH of blood serum) is often termed the octanolewater distribution coefficient (logD7.4). For ionizable compounds, the logKow value determined at a pH where the neutral chemical species predominates is also called the octanolewater partition coefficient but often expressed as logP. Reported logP values for SMX, ERY, and CIP are 0.89, 3.06, and 0.4, respectively (Drakapoulos and Ioannou, 1997; McFarland et al., 1997; Congliang et al., 2007). The pH values where these compounds are predominantly non-ionic (i.e. >95% of species; 4.3 for SMX, 10.2 for ERY, and ∼10.0 for CIP), however, are obviously quite different

Table 1 e Antibiotics Selected for Study. Antibiotic a

pKa(s) Molecular weight

Molecular structure

a Qiang and Adams (2004).

Sulfamethoxazole

Erythromycin

Ciprofloxacin

1.85, 5.60 253.3

8.90 733.9

3.0, 6.1, 8.7, 10.6 331.3

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from the circumneutral pH values typical of most natural waters. SMX (98% purity), ERY (95% purity), CIP (98% purity), and octanol (>99% purity, reagent grade) were purchased from SigmaeAldrich (St. Louis, MO, USA). Sulfamerazine (SMR; 99% purity; SigmaeAldrich), Oleandomycin phosphate (OLE; 96% purity; MP Biomedicals, Solon, OH, USA), and Clinafloxacin hydrochloride (CLN; 98% purity; LKT Laboratories, Saint Paul, MN, USA) served as internal standards. The internal standards were selected based on their structural similarity to the respective antibiotics of interest and their use in comparable work (Zabinski et al., 1995; McArdell et al., 2003; Scribner et al., 2002; Go¨bel et al., 2004; Renew and Huang, 2004). All antibiotic compounds were used as received. Suwannee River NOM (SRNOM) was purchased from the International Humic Substances Society (Saint Paul, MN, USA).

2.2.

Experiments

2.2.1.

Sorption experiments

Sorption experiments were conducted using a continuousflow rotating annular bioreactor (CFRAB) system (Fig. 1; Model 1120 LS, BioSurface Technologies Bozeman, MT) following the method of Headley et al. (1998). Sometimes referred to as rototorque reactors, CFRABs have been used for biofilm research for about two decades including investigations of biofilm heterogeneity (Gjaltema et al., 1994), organic contaminant sorption to biofilm (Headley et al., 1998), and the kinetics of organic compound biodegradation by biofilms (Gagnon and Huck, 2001). The CFRABs used in this work consisted of a rotating inner cylinder (140 mm diameter) with 20 flushmounted polycarbonate plastic slides inside of a stationary glass outer cylinder with an inner diameter of 155 mm. The total open or liquid volume was 1 L. The advantages of the CFRAB system for biofilm studies are the high surface area to volume ratio, removable slides for biofilm examination and quantification, and the ability to control the wall shear stress, determined by the rotational speed of the inner cylinder, independently of the hydraulic residence time.

The CFRAB was autoclaved prior to use. Trickling filter rock media from the Clean Water Plant in Wyoming, MI was used for seeding the reactor in order to establish a biofilm. This biofilm source was chosen because it provided a diverse community for colonization of the reactor and was proximate to the laboratory. For seeding, mineral medium and acetate (100 mg C/L) were continuously pumped through a 5-L polyplopylene reservoir (also used for collection) containing five biofilmcoated tricking filter rocks upstream of the CFRAB. The mineral medium (pH 7.25  0.3) contained (per liter of water) 43.8 mg/L K2HPO4, 17 mg/L KH2PO4, 62.4 mg/L Na2HPO4, 45.0 mg/L MgSO4, 0.5 mg/L FeCl3$6H2O, 5.4 mg/L NH4Cl, and 55 mg/L CaCl2. The upstream reservoir was taken off-line after a day of seeding. The CFRAB was operated with continuous feeding of mineral medium and 2 mg C/L acetate for 20 days to reach steady-state conditions (defined as <20% variation in consecutive effluent plate counts) before initiating the sorption experiments. The CFRABs were operated with a 30-min residence time, 100 rpm mixing, and at a temperature of 25  C. To minimize the potential for antibiotic photodegradation, all CFRAB apparatus components were covered, amber bottles were used for sample collection, and the laboratory was unlighted except for low background light during maintenance and sampling. Sorption experiments were conducted by initiating a step input of either a single antibiotic or a mixture of the three antibiotics and measuring the effluent concentration over time for up to 25 h. The influent antibiotic concentrations applied in our experiments were 0.33 mg/L (i.e. low concentration) and 3.33 mg/L (i.e. high concentration). Antibiotic and acetate feed solutions were prepared daily by diluting concentrated stock solutions. At the initiation of an experimental run, the reactor was spiked to the target antibiotic feed concentration (either 0.33 or 3.33 mg/L of antibiotic) using an autopipette and the antibiotic feed was turned on. One-liter samples were collected every 30 min for the first 5e6 h , and then with decreasing frequency through 25 h. For most runs effluent samples were collected after ceasing antibiotic feed; effluent concentrations decreased to below detection limits after 24 h. The CFRAB was operated without antibiotic feed for at least 5 days between

Fig. 1 e CFRAB apparatus.

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runs and effluent samples were collected before the next run to ensure that no residual antibiotics remained from the previous run. The effect of NOM on antibiotic sorption to biofilm was investigated by pre-equilibrating the CFRAB with NOM (1 mg/L as C) for 24 h prior to initiating the feed of antibiotic and continuing to feed NOM throughout the experimental run. Upon completing the last sorption experiment, the reactor biofilm was collected for determination of total organic carbon (TOC). CFRAB biofilm was detached from the removable slides via scraping and washing with distilled water and transferred to a glass flask. The biofilm was liquefied via acidification to pH 2 with phosphoric acid prior to TOC analysis (APHA, 1995).

with SMR, CLN, and OLE serving as internal standards in water samples for SMX, CIP, and ERY, respectively. Each sample was analyzed in duplicate and the results were averaged. Relative standard deviations (RSD) for the internal standards were between: 11 and 21% for OLE; 10 and 22% for SMR; and 13 and 22% for CLN. Method detection limits (MDL) were 0.05 mg/L for CIP and 0.1 mg/L for both SMX and ERY as determined according to the approach described in Standard Methods (APHA, 1995). A similar LCeMS approach was used for octanol samples, but without internal standards. For octanol samples, 5-point calibration (R2 > 0.99) of antibiotic samples in octanol was used for quantification, with an MDL of 0.25 mg/L for each antibiotic.

2.2.2.

2.4.

Data analysis

2.4.1.

Sorption experiments

Octanolewater partitioning experiments

A modified shake flask method (USEPA, 1996) was used to determine the octanolewater partition coefficient (Kow) of antibiotics as a function of pH (6, 7, and 8), calcium hardness (65 and 185 mg CaCO3/L), and NOM (0 and 0.5 mg/L as C). The water phase consisted of phosphate-buffered mineral medium and the pH was adjusted by titration with either HCl or NaOH. For each experiment, 14 mL of water was spiked with 1 mg/L of each antibiotic into a 17 mL polypropylene centrifuge tube and then shaken at 240 rpm on an orbital shaker for 12 h to allow for equilibration prior to the addition of octanol. Then either 1.5 or 2.0 mL of octanol was added and the tube was shaken at 240 rpm on an orbital shaker for 12 h to allow for equilibration. Each sample was then centrifuged at 3000 rpm for 100 min to separate the water and octanol phases and then 1 mL of octanol was transferred to an amber vial which was stored at 4  C in the dark until analysis.

For each sorption experiment the antibiotic concentration (C ) versus time (t) data were fit to a non-steady state mass balance model assuming a well-mixed reactor and linear sorption (Headley et al., 1998). dC Q Cfeed  C ¼ Koc M þ V dt

 Cfeed V  Cfeed ekt Koc M þ V



Analytical methods

Immediately after collection from the CFRAB, the 1-L aqueous samples were filtered (0.45 mm glass fiber filter, Millipore, Billerica, MA, USA) to remove biofilm fragments and bacteria and then stored for up to 2 days at ∼4  C prior to subsequent processing. The filtered samples were pre-concentrated via solidphase extraction using 200 mg Oasis hydrophilic-lipophilicbalanced water-wettable copolymer cartridges (Waters, Milford, MA, USA). The methanol extracts (6 mL) were reduced to 1 mL via evaporation using nitrogen gas prior to analysis. Octanol samples were analyzed directly without any processing. Liquid chromatographyemass spectrometry (LCeMS) (Agilent 1100 series) with positive ion electrospray detection in selective ion monitoring mode was used for analysis of antibiotics in water and in octanol. Retention times and m/z for the studied antibiotics and internal standards were: SMX (6.9 min and 254); SMR (6.4 min and 265); ERY (3.9 min and 716.5); ERY$H2O (4.2 min and 734); OLE (3.8 min and 688.4); CIP (3.8 min and 332); and CLN (3.9 min and 366). The two sets of values for erythromycin reflect the anhydrous (ERY) and hydrated (ERY$H2O) forms, the concentrations of which were summed to get the total erythromycin concentration. A Luna 5 m C18(2) ˚ 150  3 mm LC column (Phenomenex, Torrence, CA, USA) 100 A was used for separation. The LCeMS mobile phase consisted of 40% of 0.3% (v/v) formic acid in acetonitrile and 60% of 0.3% (v/v) formic acid in HPLC-grade water. An internal standard calibration method was used for antibiotic quantification in a et al., 2007), aqueous samples (Lindberg et al., 2004; Radjenovic

(1)

where: Q is the volumetric flow rate (L/min), C is the effluent antibiotic concentration (mg/L), Cfeed is the influent antibiotic concentration (mg/L), Koc is the biofilm organic carbon partition coefficient (L/kg), M is the mass of the biofilm organic carbon (kg), and V is the CFRAB volume (L). Integrating equation (1) yields, C ¼ Cfeed þ

2.3.




(2)

where: k is the rate constant (1/min) and t is the time in minutes. The rate constant is a function of the partition coefficient Koc (L/kg) as shown below. k¼

Q Koc M þ V

(3)

TableCurve2D version 5.01 (Systat Software, Chicago, IL) was used to fit a simplified form of equation (1) (C ¼ Cfeed þ (a  Cfeed)ekt) and solve for the two parameters, a and k. Because the magnitude of regressed values for the a term (i.e. CfeedV/KocM þ V) were negligible compared to Cfeed (i.e. a < 1  1012 mg/L for runs with Cfeed ¼ 0.33 mg/L, and ranging from 1  1012 to 0.43 mg/L for Cfeed ¼ 3.33 mg/L) in the initial fits, equation (2) was further simplified by setting a ¼ 0. Hence, a final round of data fitting was done with a oneparameter model of the form (C ¼ Cfeed(1  ekt)) and then the Koc values were determined from the regressed values for k and known values for Q, M, and V. It should be noted that no specific sorption mechanism is implied by the model and this study was not designed to elucidate underlying sorption mechanisms or the sorption sites involved. The assumption of linear sorption was believed to be reasonable because of the low antibiotic concentrations employed in the experiments. Biodegradation was not considered in the mass balance model because reported biodegradation rates for the studied antibiotics are slow (even after allowing for acclimation) compared to the duration of each experimental run (Al-Ahmad et al., 1999; Alexy et al., 2004; Drillia et al., 2005a).

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2.4.2.

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Octanolewater partition coefficient experiments

Octanolewater partition coefficients (Kow) were determined from a system mass balance. The antibiotic mass in octanol at equilibrium was computed from the measured antibiotic concentration and known volume of octanol. Knowing the total mass of antibiotic added, the antibiotic mass in water at equilibrium was determined by difference. The Kow is simply the ratio of the concentration of antibiotic in octanol to that in water.

3.

Results

3.1.

Antibiotic sorption experiments

Representative plots of effluent antibiotic concentration (normalized to the feed concentration) versus time are provided in Fig. 2 and the sorption parameters and correlation coefficient (R2) values for all experimental runs are provided in

, model s control - no biofilm

, model s control - no biofilm

CIP , model s control - no biofilm

Fig. 2 e Representative plots of effluent antibiotic concentration (normalized to Cfeed) versus time for SMX, ERY, and CIP, fed as a mixture (0.33 mg/L, each antibiotic). The symbols represent the experimentally-determined values (open [ with biofilm and closed [ control) and the solid lines represent the model fits.

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Table 2 e Regression model biosorption parameters. Feed Condition CIP (alone) CIP (alone) CIP (mixture) CIP (mixture) ERY (alone) ERY (alone) ERY (mixture) ERY (mixture) SMX (alone) SMX (alone) SMX (mixture) SMX (mixture) SMX (mixture), 1 mg C/L NOM ERY (mixture), 1 mg C/L NOM CIP (mixture), 1 mg C/L NOM

Concentration (mg/L)

k (hr1)

Koc (L/kg)

R2

Koc (L/kg)a

0.33 3.33 0.33 3.33 0.33 3.33 0.33 3.33 0.33 3.33 0.33 3.33 3.33

0.10 0.11 0.11; 0.09 0.06; 0.06 0.95 0.74 0.69; 0.66 1.25; 0.68 0.72 3.49 0.97; 0.74 1.05; 2.41 1.28 1.55 0.107

75,000 74,000 71,000; 84,000 126,000; 124,000 5000 7000 8000; 8000 2000; 8000 7000 0b 4000; 7000 4000; 0c 2000 1000 4000

0.952 0.815 0.802; 0.979 0.976; 0.992 0.706 0.925 0.703; 0.969 0.601; 0.836 0.946 0.883 0.414; 0.941 0.988; 0.954 0.975 0.696 0.966

92,000  10,000

6000  1000

4000  1000

Note: two values reported for replicate runs. a Mean standard error. b Negative value for Koc (2000) obtained from Equation (2) was set to zero. c Negative value for Koc (1000) obtained from Equation (2) was set to zero.

Table 2. No loss of antibiotics was observed in control experiments run with a clean (i.e. no biofilm) reactor (Fig. 2), indicating that biofilm was responsible for the observed losses in the other experiments. The R2 values ranged from 0.414 to 0.992 (with a mean of 0.858) and the quality of the fit increased with increasing extent of sorption as CIP exhibited the highest Koc and R2 values. Given the dynamic and complex nature of the biological sorbent (i.e. biofilm), the R2 values were deemed to be acceptable. With the exception of ERY and SMX in the high concentration (3.33 mg/L) mixture runs, reproducibility of replicate runs was good. The sorption rate constant (k) values for CIP (0.06e0.11 h1) were markedly slower than for ERY (0.66e1.25 h1) and SMX (0.74e3.49 h1). CIP consistently had the greatest Koc values (74,000e126,000 L/kg), regardless of feed conditions. The Koc values for ERY and SMX were similar and about an order of magnitude lower than that for CIP. In general, the Koc values were unaffected by changes in feed concentration and were similar whether the compounds were fed alone or as a mixture of the three antibiotics. In limited testing, antibiotic sorption to biofilm did not appear to be affected by the presence of SRNOM. The Koc values from this work are plotted versus Kow in Fig. 3 along with the data from other studies in which sorption of organic compounds to biofilm was investigated.

3.2.

for these conditions is reported in Table 3. The logKow values for CIP were unaffected by the variation in pH, hardness, and NOM.

4.

Discussion

The sorption of antibiotics to biofilm was observed over a range of environmentally-relevant concentrations in this study. In general, the reproducibility in Koc values for replicate runs was reasonable for a biological system. There were, however, considerable differences between Koc values determined for

Octanolewater partition coefficient experiments

Octanolewater partition coefficients were determined for SMX, ERY, and CIP as a function of pH (6, 7, or 8), calcium hardness (65 or 185 mg CaCO3/L), and NOM (Table 3). ERY consistently exhibited the greatest logKow values. The logKow values for ERY increased with increasing pH but were unaffected by changes in hardness and NOM concentration. For SMX, logKow decreased with increasing pH. At pH 7 and 8, the SMX concentration in the octanol phase was below the analytical detection limit (0.25 mg/L). Thus, a value of (<0.59)

Fig. 3 e logKoc versus logKow for organic compounds and biofilm and comparison with a correlation developed by Baker et al. for sorption of organic compounds to soil organic carbon. The solid line represents the correlation equation (logKoc [ 0.903logKow D0.094) and the dashed lines represent the 90% confidence intervals. For the antibiotics data from this study, the logKow values are at pH 7.

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Table 3 e Effect of of pH, hardness, and NOM on the octanolewater partition coefficient for three selected antibiotics. pH

Hardness (mg CaCO3/L)

NOM (mg C/L)

65

0.5 e 0.5 e 0.5 e 0.5 e 0.5 e 0.5 e

6

185 7

65 185

8

65 185

logKow SMX

CIP

ERY

0.05 0.20 0.11 0.09 <0.59

0.43 0.43 0.44 0.45 0.40 0.39 0.40 0.40 0.41 0.40 0.42 0.43

0.44 0.38 0.41 0.39 0.95 0.97 1.02 0.96 1.66 1.59 1.66 1.64

<0.59

ERY (2000 and 8000 L/kg) and SMX (0 and 4000 L/kg) in replicate runs when fed in a mixture at high concentration (3.33 mg/L). This discrepancy might be due to variations in biofilm quantity, composition, or both between runs. The Koc values were not affected by changes in antibiotic feed conditions (i.e. fed alone or in a mixture at low and high concentrations), suggesting that inter- and intra-species competitions for sorption sites were not factors in these experiments. Thus, all of the values for a given antibiotic were used to compute a mean Koc value. The relative standard errors (i.e. standard error/mean) for the mean Koc values (11%e25%) are comparable to those reported by Wicke et al. (2007) for compound sorption to biofilm. The sorption rate constant (k) is an effective sorption rate that reflects the difference between sorption and desorption rates (Headley et al., 1998). There was no attempt to isolate the two rates in this investigation. Reported k values for the sorption of organic pesticides to biofilm (Headley et al., 1998) were of the same order of magnitude (0.05e3.6 h1) and with similar variability between replicate runs as observed in this work. The k values for ERY and SMX were similar but much

lower for CIP. The significantly different Koc and k values for CIP suggest that there may be a difference between the sorption mechanism for CIP and the other two antibiotics. More work is needed to elucidate the mechanisms and sites involved in the sorption of antibiotics to biofilm. In developing the mass balance model describing the fate of antibiotics in the CFRAB, it was assumed that biodegradation was negligible. The assumption was verified by the approach of the effluent antibiotic concentration to the feed concentration (i.e. C/Cfeed ¼ 1) in the sorption experiments (Fig. 2). The lack of biodegradation in our system is not surprising as previous studies have shown that the selected antibiotics biodegrade slowly if at all and the duration of each sorption experiment in this work was only 48 h. For example, ERY was not biodegraded at mg/L concentrations in wastewater treatment plant effluent with 40-day closed-bottle tests (Alexy et al., 2004), while Gavalchin and Katz (1994) reported slow (t1/2 of 11.5 days) aerobic biodegradation in soil. Published results concerning SMX biodegradation are mixed. SMX was not aerobically biodegraded in soil (Gavalchin and Katz, 1994), but sulfonamides, including SMX, were degraded by activated sludge (t1/2 of 0.2e3 days) following 7e10 days of acclimation at 20  C under aerobic conditions (Ingerslev and HallingSørensen, 2000). Drillia et al. (2005a) also observed biodegradation of SMX by aerobic activated sludge biomass but only in the absence of another carbon source or nitrogen source, or both. Finally, a half-life of ∼2-days was reported for CIP in activated sludge (Rabolle and Spliid, 2000), but Al-Ahmad et al. (1999) reported that CIP was inhibitory to wastewater bacteria at concentrations as low as 80 mg/L. The Koc values determined in this study compared favorably with the Koc values reported by others for antibiotic sorption to various sorbents (Table 4); mimicking the relative order of Koc (CIP > ERY > SMX). Although our average Koc value for ERY (6000  1000 L/kg) is lower than reported Koc values for sediment (30,600 and 50,550 L/kg), others studying the fate of ERY in activated sludge processes have reported no sorption of ERY to biomass (Li and Zhang, 2010). Our mean Koc values for SMX (4000  1000 L/kg) and CIP (92,000  10,000 L/kg) are

Table 4 e Koc values for sorption of ERY, SMX, and CIP to organic matter of a biofilm compared to other organic sorbents (literature data). Antibiotic

This study Koc (L/kg)

ERY

6000  1000

SMX

4000  1000

CIP

a b c d e f

92,000  10,000

Other studies Koc (L/kg) a

30,600 50,550b 530c 114e2951 674d 13,350c 61,000e 320,000f

Sorbent

pH of water

Reference

Sediment Marine sediment Soil Digested sludge Activated sludge Marine sediment Soil Sediment

6.5e7.5 Not specified 4.3 5.5e6.6 7.0e7.5 Not specified 5.0 7.5

Kim and Carlson, 2007 Xu et al., 2009 Drillia et al., 2005b Carballa et al., 2008 Go¨bel et al., 2005 Xu et al., 2009 Nowara et al., 1997 Belden et al., 2007

From mean of soil organic matter (0.69%) and pseudo Kd (211 L/kg). Reported value of Koc in dynamic flume experiment. 7.1% organic carbon (OC). From Kd, assuming 38% OC in TSS. Sandy soil with 0.7% OC. Reported pH not directly given, but assumed based on reported conditions for similar tests for enrofloxacin. From Kd, converted to Koc using reported value of OC in sediment of 5.19%.

w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 2 7 0 e2 2 8 0

within the ranges of reported Koc values for SMX (114e13,350 L/kg) and CIP (61,000 and 320,000 L/kg). We are aware of only a few studies concerning the sorption of organic compounds to biofilm (Headley et al., 1998; Wicke et al., 2007) and the compounds used in those studies (i.e. pesticides and polycyclic aromatic hydrocarbons) were considerably more hydrophobic than the antibiotics used in this research. For relatively hydrophobic compounds (logKow > 1.7), the biofilm Koc values are in reasonable agreement with a correlation developed by Baker et al. (1997) for sorption of organic compounds to soil organic carbon (Fig. 3), suggesting that: (1) the sorptive behavior of biofilm is similar to that of soil organic matter and (2) logKow is a useful predictor of the sorption of hydrophobic compounds to biofilm. The hydrophilic antibiotics in this work (logKow < 1.7), however, were not consistent with the Baker et al.’s (1997) correlation. Similarly, the sorption of antibiotics to manure (Loke et al., 2002) and digested sludge (Carballa et al., 2008) also did not correlate with logKow. This result is not surprising as the hydrophilic organic compounds (logKow < 1.7) in the Baker et al.’s (1997) study were not consistent with the correlation. The reason for the different behavior of these low logKow compounds is that the sorption of hydrophilic compounds is not controlled by hydrophobic interactions, predicted by partitioning into octanol, but other types of interactions (e.g., ionic). Thus, in order to understand the partitioning behavior of antibiotics into biofilm, the potential for ionic interactions needs to be explored. The physicochemical character of bacterial biofilm is controlled by the chemistry of the extracellular polymeric substances (EPS). EPS include proteins (measured from 10 to 82% by weight of total extracted EPS), humic substances (30e60%), carbohydrates (7e30%), uronic acids (3e22%), and DNA (2e15%) (Jahn and Nielsen, 1995; Flemming et al., 1998). Heterotrophic EPS exhibits pKa values associated with carboxylic (5.8e7.6) and phenolic or amino (8.4e9.5) functional groups, with an isoelectric point for EPS of 7.3e7.7 (Lee and Davis, 2001; Guibaud et al., 2005). Because EPS contains functional groups that are anionic (e.g., eCOO, eSH, eSO 4, e þ ) and cationic (e.g., eNH ), it has exchange potential for HPO 4 3 both cations and anions, and the apolar functional groups (e.g., aromatic) of EPS proteins may sorb apolar organic compounds (Flemming et al., 1996). Uronic acids that comprise 20e50% of EPS polysaccharides provide high binding potential for cationic solutes (Kennedy and Sutherland, 1987). Although only a small fraction of the biofilm composition, bacterial cells also provide sorption sites. Cationic exchange occurs with anionic sites of the peptidoglycan and teichoic acid of gram positive bacteria, with anionic exchange from positively charged ammonium sites (Beveridge, 1984). Anionic sites of the peptidoglycan in the cell envelope of gram negative bacteria provide for cationic exchange (Beveridge and Koval, 1981). At pH >7, EPS is generally negatively charged and hydrophilic (Bryers, 2000). Contact angles ranging from 15 to 37 have been reported for EPS from activated sludge with surface charges ranging from 0.41 to 0.21 meq/g VSS (Liao et al., 2001). The functional groups of antibiotics greatly influence their activity in biological and chemical systems, with speciation dictated by compound pKa values and solution pH (Fig. 4). SMX

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Fig. 4 e Antibiotic speciation as a function of pH.

exhibits pKa values of 1.85 and 5.60 associated with the aromatic amine and the sulfonamide groups, respectively. SMX is anionic above pH 5.6. The pKa values of 3.0, 6.1, 8.7, and 10.6 for CIP are associated with the carboxylic acid group and the three nitrogen groups, respectively. Thus, between pH 6.1 and 8.7, CIP is predominantly cationic. ERY has a single pKa of 8.9 associated with the dimethylamine and is predominantly cationic at pH 7. Consequently, the relative extent of sorption to biofilm (CIP [ ERY > SMX) is expected given the extent of species ionization for each antibiotic. More sorption might be expected for ERY since it is predominantly protonated at circumneutral pH. The reason for the greater extent of sorption for CIP as compared to ERY, when both exhibit comparable protonation at circumneutral pH, is unclear. The logKow values at pH 7 for ERY and CIP were 0.98 and 0.4, respectively which suggest that ERY is more hydrophobic and might sorb via both ionic and hydrophobic interactions. One possible explanation is the decreased rate of mass transfer of ERY to and through the biofilm, as the molecular weight of ERY is more than twice that for CIP. Because of the effects of pH on antibiotic and EPS speciation, it is expected that fluctuations in system pH would affect the retention (or desorption) of antibiotics sorbed to biofilm. More research is needed to investigate the mechanism(s) of antibiotic sorption to biofilm including the relevant sorption sites as well as the potential for antibiotic desorption from biofilms.

5.

Conclusions

In this study, the biofilm organic carbon sorption coefficients for three selected antibiotics (ERY, SMX, and CIP) were determined using a CFRAB reactor. The main conclusions of this work are as follows:  The rate and extent of antibiotic sorption to biofilm are dependent on antibiotic structure. CIP exhibited a greater extent (Koc) and lower rate (k) of sorption compared to ERY or SMX.  The Koc values describing antibiotic partitioning to biofilm did not correlate with the antibiotic Kow values suggesting that hydrophobic interactions are not important for sorption of these relatively hydrophilic compounds to biofilm.

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 Antibiotic speciation and molecular size are important for explaining the interaction between antibiotics and biofilm. The results of this work may be useful for:  modeling the fate of antibiotics in biologically-active filtration systems used for drinking water production, including slow sand filtration, bank filtration, and rapid filtration;  predicting the fate of antibiotics in other systems where biofilms are present including drinking water distribution networks, aquifer recharge systems, soil-aquifer treatment installations, wastewater fixed-film bioreactors, and septic system leach fields; and  selecting or developing antibiotics for inactivation of bacterial biofilms.

Acknowledgements The authors gratefully acknowledge the financial support from the Water Research Foundation (project number 4135) for this research. Also, part of this work was supported by a Calvin Research Fellowship and the Newhof Environmental Engineering Fund, a Kuiper Fellowship for Undergraduate Research, and EPA Greater Research Opportunities Fellowships for Undergraduate Research. The authors also thank Michael Seymour and Hope College for the use of LC-MS instrumentation, and Scott Hekman for refining the LC-MS methodology.

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Yang, Li-.H., Ying, G.-G., Su, H.-C., Stauber, J.L., Adams, M.S., Binet, M.T., 2008. Growth-inhibiting effects of 12 antibacterial agents and their mixtures on the freshwater microalga Pseudokirchneriella subcapitata. Environmental Toxicology and Chemistry 27 (5), 1201e1208. Zabinski, R.A., Walker, K.J., Larsson, A.J., Moody, J.A., Kaatz, G.W., Rotshafer, J.C., 1995. Effect of aerobic and anaerobic environments on antistaphylococcal activities of five fluoroquinolones. Antimicrobial Agents and Chemotherapy 39 (2), 507e512.

w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 2 8 1 e2 2 8 9

Available at www.sciencedirect.com

journal homepage: www.elsevier.com/locate/watres

Electrochemical sulfide removal from synthetic and real domestic wastewater at high current densities Ilje Pikaar, Rene´ A. Rozendal, Zhiguo Yuan, Ju¨rg Keller, Korneel Rabaey* The University of Queensland, Advanced Water Management Centre (AWMC), St. Lucia, QLD 4072, Australia

article info

abstract

Article history:

Hydrogen sulfide generation is the key cause of sewer pipe corrosion, one of the major

Received 6 July 2010

issues in water infrastructure. Current abatement strategies typically involve addition of

Received in revised form

various types of chemicals to the wastewater, which incurs large operational costs. The

20 October 2010

transport, storage and application of these chemicals also constitute occupational and

Accepted 22 December 2010

safety hazards. In this study, we investigated high rate electrochemical oxidation of sulfide

Available online 8 January 2011

at Ir/Ta mixed metal oxide (MMO) coated titanium electrodes as a means to remove sulfide from wastewater. Both synthetic and real wastewaters were used in the experiments.

Keywords:

Electrochemical sulfide oxidation by means of indirect oxidation with in-situ produced

Sewer corrosion

oxygen appeared to be the main reaction mechanism at Ir/Ta MMO coated titanium elec-

Electrochemical systems

trodes. The maximum obtained sulfide removal rate was 11.8
1.7 g S m2 projected anode

Sulfide oxidation

surface h1 using domestic wastewater at sulfide concentrations of 30 mg L1 or higher.

In-situ oxygen generation

The final products of the oxidation were sulfate, thiosulfate and elemental sulfur. Chloride and acetate concentrations did not entail differences in sulfide removal, nor were the latter two components affected by the electrochemical oxidation. Hence, the use of electrodes to generate oxygen in sewer systems may constitute a promising method for reagent-free removal of sulfide from wastewater. ª 2011 Elsevier Ltd. All rights reserved.

1.

Introduction

Hydrogen sulfide is ubiquitously found in domestic and industrial wastewaters (Dutta et al., 2008). It is a toxic, corrosive and odorous compound, often requiring removal from the aqueous or gaseous phase before discharge into the environment. Hydrogen sulfide is of particular concern in sewer systems since it causes corrosion of sewer pipes. Current technologies for sulfide abatement in sewer systems involve adding chemicals to wastewater to prevent sulfide formation, or its transformation from liquid to gas phase (WERF, 2007). The commonly used chemicals include oxidants such as oxygen (Gutierrez et al., 2008; Zhang et al., 2008) and nitrate (Hvitved-Jacobsen, 2002; Mohanakrishnan et al., 2009) for

sulfide oxidation, iron salts for sulfide precipitation (Firer et al., 2008; Nielsen et al., 2008; Zhang et al., 2009) and magnesium hydroxide to elevate pH (Gutierrez et al., 2009; Zhang et al., 2008). Other chemicals used include chlorine, hydrogen peroxide, caustic and nitrite, which are toxic to sewer biofilms (Mohanakrishnan et al., 2008; Zhang et al., 2008). These strategies are considered expensive ($1.7e7.2 kg S removed1) (Zhang et al., 2008) and often come with a number of limitations such as sludge generation or loss of organics, the latter are needed for nutrient removal in downstream WWTPs. Recent advances in electrode development and operation have increased the interest in electrochemical abatement strategies. Electrochemical techniques offer several advantages including no requirement for dosing, transport and storage of

* Corresponding author. Tel.: þ61 7 3365 7519; fax: þ61 7 3365 4726. E-mail address: [email protected] (K. Rabaey). 0043-1354/$ e see front matter ª 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2010.12.025

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hazardous chemicals, robustness, versatility, controllability ´ ngela et al., 2009). A typical and the amenability to automation (A electrochemical reactor consists of an anode, a cathode and a membrane separating both. At the anode, electrochemical oxidation of a pollutant (here sulfide) can be achieved. Electrochemical oxidation can be achieved either by direct oxidation at the electrode surface or by indirect oxidation. During indirect electrochemical oxidation, oxidants including OH, O2 and Cl2, are generated at the anode surface; these subsequently oxidize the pollutant in the bulk solution. The reaction mechanism and selectivity of the oxidation process are mainly determined by the electrode material, the flow regime and the applied anode potential. Thus, depending on the conditions, sulfide can be oxidized at the anode or oxidized by in-situ generated oxidants. Sulfide can be oxidized to elemental sulfur, thiosulfate or sulfate. The oxidation of sulfide to sulfur is preferable since this requires the least amount of electrons and thus energy input (Dutta et al., 2009). A number of studies on aqueous electrochemical sulfide oxidation have been performed in the past (Ateya and Al-Kharafi, 2002; Ateya et al., 2007; Dutta et al., 2008, 2009, 2010; Farooque and Fahidy, 1978; Waterston et al., 2007). However, most of these studies reported on the direct oxidation of sulfide to elemental sulfur at carbon based anode materials using high strength/conductivity solutions such as alkaline media and brine solutions, at high sulfide concentrations and predominantly operated at low current densities (Ateya and Al-Kharafi, 2002; Ateya et al., 2007; Dutta et al., 2009; Farooque and Fahidy, 1978). However, for sewer systems, low current densities using high surface area carbon based electrodes are not feasible due to the large reactor size that would be required. Domestic wastewater typically has a sulfide content of around w10 mg HS L1, which is much lower than the concentrations tested in the aforementioned studies (Nielsen et al., 2003). Furthermore, since the electrochemical cell has to treat raw unfiltered sewage, flat mesh shaped electrodes are needed to avoid blockage and ragging of the system. Taking into account the low sulfide concentration and the use of flat mesh shaped electrodes (low surface area) indirect electrochemical reactions are likely to play a dominant role due to the limited reactant availability at the electrode surface. IrOx coated titanium electrodes are extensively used as oxygen evolution electrodes in electroflotation and electrocoagulation reactors (Chen, 2004). Miller and Chen (2005) reported on the direct anodic sulfide oxidation using titanium based Ti/Ta2O5eIrO2 electrodes from a caustic medium. This implies that, depending on the operational conditions (e.g. anode potential, sulfide concentrations), simultaneous sulfide oxidation and in-situ oxygen generation should be achievable. This oxygen can be used as a downstream control measure by inhibiting either the activity of sulfate reducing bacteria (SRB) and/or the oxidation of the sulfide that has been produced (Gutierrez et al., 2008). The kinetics and stoichiometry of the oxidation of sulfide by oxygen is well described in literature (Kuhn et al., 1983; Nielsen et al., 2003). Therefore, the aim of this study was to examine the feasibility of simultaneous oxidation of aqueous sulfide to elemental sulfur and water to oxygen at high current densities, using defined synthetic feed and domestic wastewater and an Ir/Ta MMO coated titanium electrode as anode.

2.

Materials and methods

2.1.

Electrochemical cell and operation

The two-chambered electrochemical cell consisted of two parallel Perspex frames (internal dimensions 14  12  2 cm) separated by a cation exchange membrane (Ultrex CM17000, Membranes International Inc., USA). In the anode chamber, a mesh shaped Ir/Ta MMO (IrO2/TaO2: 0.65/0.35) coated titanium electrode with a projected surface area of 100 cm2 was used (Magneto Anodes BV, The Netherlands). Stainless steel fine mesh (168 cm2) with a stainless steel current collector (6 mm mesh size, 0.8 mm wire) was used as electrode material in the cathode chamber. The anode liquid medium was constantly recirculated over an external buffer vessel, allowing a total anode liquid volume of 5 L. We operated the reactors in fed batch mode, as a once through system would have required cubic meter volumes of defined media/sewage per day to enable operation of the reactors at the desired current densities. The latter was not practical in the laboratory. The disadvantage of this recirculatory mode, however, is that the “influent” sulfide concentration into the reactor will slowly increase if 100% removal efficiency is not achieved. In the experiments, the sulfide concentrations increased from 30 to w90 mg L1, depending on the current applied and anode medium used, as the experiments progressed. An example of the typical sulfide concentration profile during the course of the experiments is shown in Fig. 4. The influent flow rate through the anode chamber was maintained at 15 L h1 using a peristaltic pump (Watson Marlow, UK). The off gas coming from the external buffer vessel was sent through a water-lock containing a 0.2 M NaOH solution. The recirculation flow in the anode chamber was kept at 22 L h1 using a peristaltic pump (Watson Marlow, UK) to obtain a higher mixing rate in the anode chamber. PVC tubing with an internal diameter of 4.5 mm was used for the feeding and recirculation lines. In all experiments, an Ag/AgCl (RE-5B, Bio Analytical, USA) was used as the reference electrode. Its potential was estimated at þ197 mV versus standard hydrogen electrode (SHE). An external buffer flask of 2 L was used in the recirculation of the cathode chamber. A 0.1 M NaOH solution in the cathode chamber was used in all experiments to trap any possible crossover of hydrogen sulfide. The recirculation flow of the cathode solution was kept at 22 L h1 using a peristaltic pump (Watson Marlow, UK). Experiments were initially performed using a defined synthetic feed (with composition to be described later) and subsequently domestic wastewater. Both for the synthetic and real domestic wastewater experiments, sodium sulfide (Na2S$9H2O) was supplied continuously to the incoming line of the anode chamber via a syringe pump (NE-1600, New Era Pump Systems, Inc., USA) at a dosing rate of 149
20 mg S h1, i.e. sufficient to give an anode influent concentration of w10 mg S L1. Before use, Na2S$9H2O crystals were washed with MilliQ (18 MU) water to remove oxidized sulfur species on the surface of the crystals (Dutta et al., 2008). Due to the production of protons at the anode, a decrease of the anode pH is expected 1 over time. To compensate for this, NaHCO 3 was added (5 g L ) in the experiments using synthetic feed to maintain pH values

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high current density from synthetic and real domestic wastewater. Each time, the performance was assessed in 6 h experimental runs using galvanostatic control at different current densities. During the experiments with continuous sulfide dosing, sulfide was fed at a rate of 149
20 mg S h1 to the reactor during 6 h. This is equivalent to a loading rate of 444
60 mg S L1anode volume h1, or a current (density) of w250 mA (25 A m2) (i.e. based on a 2-electron oxidation of sulfide to elemental sulfur). Prior to every run, the Ir/Ta MMO coated titanium electrode was rinsed in an alkaline sodium sulfide solution to remove deposited elemental sulfur (i.e. formation of soluble polysulfides) and subsequently rinsed with miliQ (18 MU) water to remove any aqueous (poly)sulfides left in the electrochemical cell. Aqueous sulfur species (i.e. sulfide, sulfite, thiosulfate and sulfate), acetate and COD concentrations were measured in 1.5 h intervals during the continuous experiments and every 1 h during the batch experiments. IC analysis of the anode water-lock was performed at the end of every experiment to account for the amount of any H2S stripped from the buffer vessel. IC analysis of the cathode compartment was performed during initial experiments to determine possible transfer of

commonly found in sewer systems. As the buffer capacity of domestic wastewater was not sufficient the pH was maintained at 7.5 by a PLC controlled dosage of a 0.5 M NaOH solution. The latter is not required in a practical situation since a once through system would be implemented there. An overview of the experimental setup is presented in Fig. 1.

2.2.

Measurements and calculations

Galvanostatic measurements and controls were performed using a Wenking potentiostat/galvanostat (KP07, Bank Elektronik GmbH, Germany). The anode potentials and the current were recorded every 60 s using an Agilent 34970A data acquisition unit. All calculations were performed according to Logan et al. (2006) and Rabaey et al. (2005a). Coulombic efficiencies were determined on the basis of sulfide to elemental sulfur conversion.

2.3.

Experimental procedures

Experiments, divided into 4 different sets, were conducted to verify the feasibility of electrochemical sulfide oxidation at

13 Power supply

12

12

15

14

11 16

3 H+ Na+ 1

4

K+

10 8

+

H 2

Na

+

+

K

9 5 6

S2- solution

1 2 3 4 5 6 7 8

anode water-lock (0.1M NaOH) influent buffer (5L) influent anodic compartment recirculation flow anodic compartment anode cathode sulfide feeding line recirculation flow cathode compartment

7

9 10 11 12 13 14 15 16

cathode external buffer (2L) cathode water-lock cathode vent gas (H2) sampling points potentiostat / galvanostat anode vent gas (H2S, O2, CO2) effluent anode compartment effluent cathode compartment

Fig. 1 e Schematic overview of experimental setup.

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w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 2 8 1 e2 2 8 9

H2S gas from the anode to the cathode through the membrane. These results indicated negligible transfer of H2S from anode to cathode, which is in agreement with Dutta et al. (2008) and with what would be expected from a cation exchange membrane (as sulfide is an anion). Therefore, in the experiments from thereon it was assumed that no transfer of H2S through the membrane occurred.

2.3.1.

Experiment 1

In the first set of experiments synthetic feed was used to determine the impact of current density on the kinetics on the sulfide oxidation process and the final products of sulfide oxidation. Experiments were performed in triplicate at fixed current densities of 25, 50 and 100 A m2, respectively.

2.3.2.

Experiment 2

In the second set of experiments, synthetic feed was used to investigate the impact of the presence of alternative electron donors (i.e. acetate) and chloride. Furthermore, trace elements in concentrations normally found in domestic wastewater, were added to investigate their catalytic effect (i.e. catalysts for auto-oxidation of sulfide) on chemical sulfide oxidation with in-situ oxygen generation. Trace elements composition and concentrations normally found in domestic wastewater were used according to Rabaey et al. (2005b). Two different experiments were performed: (a) sulfide oxidation in presence of trace elements and the absence of both acetate and chloride (in triplicate), (b) sulfide oxidation in the presence of acetate (424
8 mg L1), chloride (170
5 mg L1) and trace elements (in quintuplicate). Experiment (a) was performed at a fixed current density of 50 A m2 to investigate their catalytic effect on chemical sulfide oxidation with in-situ oxygen generation whereas experiment (b) was performed to investigate if organics (e.g. acetate) and/or chloride (i.e. in-situ production of chlorine) are electrochemically oxidized during sulfide oxidation.

2.3.3.

Experiment 3

The third set of experiments was performed (in quintuplicate) at a fixed current density of 50 A m2 to determine the kinetics, coulombic efficiency and reaction products of sulfide oxidation in domestic wastewater.

2.3.4.

Experiment 4

The fourth set of experiments was performed to investigate the oxidation kinetics of sulfide at concentrations close to 1 mg L1 (i.e. target concentration in sewer systems). Therefore, batch tests were performed (in triplicate) using domestic wastewater at a fixed current density of 50 A m2 spiked with sulfide to obtain an initial concentration of (8.3
0.75 mg L1).

2.4.

Chemical analyses

Sulfide, sulfite, thiosulfate and sulfate concentrations were measured with Ion Chromatography (IC), using the Dionex 2010i system, according to Keller-Lehmann et al. (2006). Samples collected from the reactors were immediately filtered by a 0.22 mm syringe filter (Millipore, USA) and preserved in previously prepared Sulfide Antioxidant Buffer (SAOB) solution prior to ion chromatography analysis. SAOB solution was

also used to dilute the samples when necessary. SAOB solution was prepared using nitrogen purged MilliQ (18 MU) water, 3.2 g L1 NaOH and 2.8 g L1 a-ascorbic acid. After preparation, the solution was kept refrigerated, shielded from light and not used beyond 24 h. Sulfide concentrations were also measured with a handheld ion selective electrode for sulfide measurements (Sentek, Sentek Type 3225, United Kingdom). Samples from the reactor were immediately filtered by a 0.22 mm syringe filter (Millipore, USA) and preserved in previously prepared Sulfide Antioxidant Buffer (SAOB) to obtain a SAOB to sample ratio of 1:1. SAOB solution was prepared using nitrogen purged MilliQ (18 MU) water, 80 g L1 NaOH, 67 g L1 EDTA and 35 g L1 a-ascorbic acid (as recommended by supplier). Calibration curves were obtained by combining the sulfide concentrations from IC analysis with the observed redox potentials using the ion selective electrode. In this way, possible drift of the electrode is ruled out since the electrode is internally calibrated every time an analysis is performed. COD concentrations were determined by means of COD cuvette tests (Merck, range 25e1500 mg L1). Volatile fatty acids (VFAs) concentration was determined by High Performance Liquid Chromatography (HPLC). The pH was either controlled online or measured using a handheld meter (Cyberscan PC 300, Eutech Instruments). Produced gas was collected in gas collection bags (SKC Tedlar 1 L Sample Bag). The collected gas was analyzed for O2 concentrations using a gas chromatograph (Shimadzu, molecular sieve, stainless steel, 6 ft  1  800 OD).

3.

Results

3.1. Sulfide oxidation from synthetic feed in the absence of trace elements, chloride and acetate The results of the experiments from synthetic feed at current densities of 25, 50 and 100 A m2 are detailed in Table 1. The sulfide removal rates increased from 6.1
0.3 to 9.2
0.4 g S m2anode surface h1 when the current density was increased from 25 to 100 A m2. Fig. 2A shows the total amount of sulfide removed (mg S) at the applied current densities. Increasing the current density from 50 to 100 A m2 did not result in significantly higher sulfide removal rates. However, it did result in higher gas production, indicating more oxygen was produced in-situ. The pH at the start of all experiments using synthetic wastewater was 8.3. As oxidation progressed, the pH decreased to w7 due to the release of protons generated as the sulfide is removed from the solution. The observed in-situ gas production (i.e. oxygen) at current densities of 25, 50 and 100 A m2 were 319
1, 808
29 and 1843
64 mL, respectively, which is higher than the maximum attainable oxygen (i.e. 343.75, 687.5 and 1375 mL) if all electrons are used for oxygen generation. Analysis of the gas composition performed in the second set of experiments confirmed that the extra gas produced originated from stripping of CO2. Over the course of the experiments the anode potentials remained fairly constant.

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Table 1 e Sulfide oxidation from synthetic feed at current densities of 25, 50 and 100 A mL2 using a NaHCOL 3 buffer solution. Parameter

Unit

Value

2

Current density Coulombic efficiency Removal rate

Am % mg S h1 mg S m2anode surface h1 % % % % mL

SO3-S produced S2O3-S produced SO4-S produced S0 produceda Gas production

25 44 63.5 6.06 6.4 25.8 36.1 29.3 319











50 30 80.7 8.13 3.5 21.3 36.1 38.3 808

3 5 0.25 3.0 6.9 7.7 17.2 1











100 14.3 98.9 9.16 2.4 9.8 27.1 53.6 1843

5.5 11 1.26 1.6 3.6 3.5 5.3 29











0.8 10 0.0.37 0.7 0.6 2.9 1.3 64

a The difference in sulfide-S added and sulfide-S removed (i.e. oxidized to SO3-S, S2O3-S and SO4-S) was assumed to be elemental sulfur present in solid form or in soluble form as polysulfide.

3.2. Sulfide oxidation from synthetic feed in presence of (a) trace elements and (b) trace elements, acetate and chloride The results of the experiments from synthetic feed at a current density of 50 A m2 in the presence of trace elements, chloride and acetate are detailed in Table 2. The average obtained sulfide removal rate was 10.9
0.6 in the presence of trace elements and 11.2
1.6 g S m2anode surface h1 in the presence of trace elements, chloride and acetate, respectively. This was 34
13% and 37
18% higher than the removal rate obtained with synthetic feed without (a) trace elements and (b) acetate, chloride and trace elements. Fig. 2B shows the total amount of sulfide removed (mg S) in the presence of (a) trace elements and (b) trace elements, acetate and chloride at a fixed current density of 50 A m2. Acetate analysis confirmed that acetate was not significantly removed (i.e. 1.9
3.0%). The presence or absence of chloride did not result in either an increase or a decrease in obtained sulfide removal rate. Analysis of the gas composition showed that 35.4
1.2 mg h1 of excess oxygen was generated. In Table 3, a summary of the electron balance is presented.

3.3.

as the removal rates obtained from synthetic feed in the presence of trace elements (i.e. 11.8
1.7 versus 10.9
0.6 and 11.2
1.6 g S m2anode surface h1). Analysis of the gas composition showed that 13
1 mg h1 of oxygen was produced. COD analyses revealed that the average COD removal was 9
4%. During the experiments the anode potentials remained fairly constant at 3.4
0.4 V. In Table 3, a summary of the electron balance is presented.

3.4. Batch test for sulfide oxidation at low concentrations The results of the batch tests for sulfide oxidation at low concentrations at a fixed current density of 50 A m2 are shown in Fig. 3. The average sulfide removal rate was 16.8
5.0 mg S h1, which is equal to a to a removal rate of 1.7
0.5 g S m2anode surface h1, approximately one eighth of the removal rates obtained during the continuous experiments.

4.

Discussion

4.1.

Sulfide oxidation and in-situ oxygen generation

Sulfide oxidation from domestic wastewater

The results of the experiments from domestic wastewater at a current density of 50 A m2 are detailed in Table 2. The average obtained sulfide removal rate was in the same order

In this work, we investigated the simultaneous aqueous sulfide oxidation and in-situ oxygen generation using Ir/Ta MMO coated titanium electrodes. The impact of organics (i.e. acetate), chloride and trace elements on the kinetics on sulfide

600

600

sulfide removed (mg S)

B 800

sulfide removed (mg S)

A 800

400

200

0

400

200

0 0

1

2

3

4

time (hours)

5

6

7

0

1

2

3 4 time (hours)

5

6

7

Fig. 2 e (A) Comparisons of the amount of sulfide removed (mg S) in the absence of trace elements, acetate and chloride at fixed current densities of 25, 50, and 100 A mL2 (n [ 3): (C) 25 A mL2, (B) 50 A mL2 and (;) 100 A mL2. (B) Comparisons of the amount of sulfide removed (mg S) in the presence of (V) trace elements (n [ 3), (;) trace elements, acetate and chloride (n [ 5) and (C) domestic wastewater (n [ 5) at a fixed current density of 50 A mL2.

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Table 2 e Sulfide oxidation from synthetic feed in (a) absence of trace elements, acetate and chloride, in presence of (b) trace elements and (c) trace elements, acetate, and chloride and (d) domestic wastewater at a current density of 50 A mL2. Unit

HCO 3 buffer solution

Trace elements

% mg S h1 mg S m2anode surface h1 % % % % % mL mg

30
5.5 80.7
11 8.13
1.26 3.5
1.6 21.3
3.6 36.1
3.5 38.3
5.3 n.a. 808
29 Not measured

32
1 123.5
0.6 10.86
0.6 4.1
1.1 32.4
0.6 26.1
1.2 37.0
1.7 n.a. 800
70 Not measured

Parameter Coulombic efficiency Removal rate SO3-S produced S2O3-S produced SO4-S produced S0 produceda Acetate removal Gas production Oxygen produced

Trace elements, acetate and chloride

Domestic wastewater













40
2 136
18 11.76
1.68 2.1
0.9 62.4
11.1 13.9
4.6 21.6
14.4 9
4 283
5 76
6b

33 129 11.38 2.8 34.4 28.8 44 1.9 750 212

2 12 1.15 1.3 4.9 5.2 22 3 4 7b

a The difference in sulfide-S added and sulfide-S removed (i.e. oxidized to SO3-S, S2O3-S and SO4-S) was assumed to be elemental sulfur present in solid form or in soluble form as polysulfide. b Measured in duplicate.

domestic wastewater was 23.5
3 mg S L1 h1, indicating indirect oxidation of sulfide with in-situ generated oxygen is the main reaction mechanism. This is in agreement with Bard and Faulkner (2001) who found that the maximum direct oxidation rate can be estimated from:

oxidation and in-situ oxygen generation was successfully investigated. To investigate the kinetics of sulfide oxidation at low concentrations, batch experiments with low sulfide concentrations were performed as well. Aqueous sulfide removal by electrochemical oxidation with concomitant in-situ oxygen generation was demonstrated using defined synthetic media as well as domestic wastewater. Increasing the applied current from 50 to 100 A m2 did not proportionally enhance sulfide oxidation, hence the coulombic efficiency decreased. The higher current caused more diversion of the electrons toward gas production (oxygen), which in a practical situation can be used for downstream sulfide control (i.e. prevention of sulfate reduction) or aqueous phase based sulfide oxidation (Gutierrez et al., 2008; Tanaka and Takenaka, 1995). The expected sulfide oxidation rate (mg S L1 h1) from domestic wastewater with pure oxygen at a sulfide concentration of 30 mg L1 and a dissolved oxygen concentration of 40 mg L1 would be 28.7 mg S L1 h1 (Sharma and Yuan, 2010). The observed sulfide removal rate in the experiments using

JL ¼ nFDc=d

(1.1) 2)

where JL is the mass transfer limited current density (A m , n is the number of electrons involved (i.e. 2 for the oxidation of sulfide to sulfur), F is the Faraday constant (96,485.3 C mol1), D is the diffusion coefficient (cm2 s1), c is the concentration of the component in the bulk solution (mol cm3 and d is the thickness of the Nernst diffusion layer (cm). Mills and Lobo (1989) found that the diffusion of sulfide species at 25  C is 1.73  105 cm2 s1. Hence, under the conditions found in sewer systems with sulfide concentrations around 2.82  107 mol m3 (i.e. w10 mg L1), the direct sulfide oxidation rate is approximately 1 A m2. Furthermore, the batch experiments showed that at low concentrations the sulfide oxidation rate was approximately 8 times lower compared to the continuous

Table 3 e Electron balance during sulfide oxidation from (a) synthetic feed in presence of trace elements, acetate, and chloride and (b) domestic wastewater at a current density of 50 A mL2 (n [ 2). Synthetic feed Input Added

HS-S (mg) Sulfur (mg) S2O3-S (mg) SO3-S (mg) SO4-S (mg) O2 (mg) Organics (mg) Electrons (C)

939
51 0 0 0 0 0 2085
45a 10 800
0

Electron balance Coulombs (C) 686
270 % 106
3%

Domestic wastewater

Output Removed (mg)

Produced (mg)

Input Energy requirement (C)

603
87 173 195 24 211 212 15
20








56 86 9 65 23

1042 2347 440 5096 2561








337 1041 169 1560 283

0 11,486
270

Added

883
67 0 0 0 0 0 2565
5121 10,800
0

Output Removed (mg)

Produced (mg)

Energy requirement (C)

681
92 110 431 11 125 78








81 18 1 24 7

665 5203 200 3013 946


489
219
11
571
79

n.d. 10,027
772

Electron balance 773
772 93
7%

w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 2 8 1 e2 2 8 9

sulfide concentration (mg/L)

10

8

6

4

2

0 0

1

2

3

4

5

6

7

time (hours)

Fig. 3 e Sulfide concentration (mg LL1) profile using at a current density of 50 A mL2 using domestic wastewater spiked with sulfide.

experiments, with an obtained sulfide oxidation rate of 3.4
1.0 mg S L1 h1. This is in agreement with values found in literature (1.5e5.2 mg S L1 h1) for chemical sulfide oxidation with oxygen in domestic wastewater at low concentrations (Gutierrez et al., 2008; Nielsen et al., 2003, 2005). This suggests that under the applied experimental conditions, indirect oxidation of sulfide by in-situ generated oxygen is the predominant reaction mechanism, whereas direct sulfide oxidation appears negligible. Therefore, under the conditions normally found in sewer systems, Ir/Ta MMO coated titanium electrodes appear suitable for in-situ oxygen generation. Oxygen injection is presently considered as an attractive option for sulfide abatement in sewer systems. Oxygen can both inhibit the activity of sulfate reducing bacteria (SRB) and oxidize the sulfide that has been produced (Gutierrez et al., 2008). It is less expensive than most other chemicals and

sulfide concentration (mg/L)

200

150

100

50

0 0

1

2

3

4

5

6

7

time (hours)

Fig. 4 e Typical sulfide generation profile during galvanostatic control at a current density of 50 A mL2 in the presence of acetate, chloride and trace elements (n [ 3): (C) sulfide dosed (mg/L) and (B) sulfide concentration.

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can target rising mains where the SRB activity is the highest (Hvitved-Jacobsen, 2002). Advantages of generating oxygen insitu compared to traditional methods for oxygen supply are the fine dispersion, high controllability, the ease to monitor and no requirement for transport and storage. The disadvantage is the cost of the oxygen per unit weight; for a coulombic efficiency of 95%, a cell voltage of 5 V and a cost of $0.06 per kWh the estimated cost is $1.06 kg1, relative to a delivery cost of $0.54e0.82 kg1 for standard oxygen purchases (de Haas et al., 2008). However, standard oxygen injection in sewer systems has a limited efficiency due to inefficiencies during dosing (i.e. coarse bubbles), which can result in a significant loss of undissolved gas from air in gas release valves downstream (de Haas et al., 2008). The latter is avoided when oxygen is generated in-situ due to the high transfer efficiency and fine dispersion of in-situ generated oxygen.

4.2.

Final product of oxidation

We aimed to oxidize sulfide to elemental sulfur, as this reaction minimizes the electron and thus energy input (Dutta et al., 2009). Possibly formed sulfite, thiosulfate and sulfate remain in the liquid phase and can therefore be reduced again to sulfide in rising mains downstream of the electrochemical cell. In all experiments a mixture of sulfur, thiosulfate and sulfate was produced (sulfite was negligible). This means that sulfide cannot be selectively oxidized to elemental sulfur using Ir/Ta MMO coated titanium at high current densities. This is in contrast with the selective oxidation to sulfur observed in carbon based, low current density systems (Ateya et al., 2003; Dutta et al., 2008), where no oxygen is formed and sulfide is oxidized directly at the electrode surface. However, for application in sewer systems, low current densities (and hence low anode potential) are not practical due to the large reactor size that would be required.

4.3.

Influence of trace elements, organics and chloride

The presence of metals can increase the chemical sulfide oxidation significantly, even when present in trace concentrations (Kuhn et al., 1983). Indeed, during the experiments with the addition of trace elements, a 37
18% increase in sulfide removal rate was observed, whereas the acetate and chloride concentrations remained constant. The presence or absence of chloride did not increase or decrease sulfide removal rates during the experiments. In addition to anodic sulfide oxidation and in-situ oxygen generation, organics such as acetate and chloride, which are commonly contained in wastewater, may be oxidized depending on the electrode material used and the operating conditions. These reactions are unwarranted since they increase the required energy input and possibly form methyl radicals and ethane from acetate oxidation (i.e. through the Kolbe reaction) (Sun et al., 2009; Vassiliev and Grinberg, 1991). The experiments with synthetic medium showed that the acetate concentrations remained unchanged. This is because acetate oxidation requires potentials higher than the potential of oxygen evolution at Ir/Ta MMO coated titanium electrodes (Chen, 2004). In the experiments using domestic wastewater, a small

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w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 2 8 1 e2 2 8 9

decrease in COD was observed. Whether there was some direct oxidation of organics or indirect oxidation with released oxygen cannot be discerned at this stage. Similar to the acetate, during all experiments the chloride concentrations remained constant. If chlorine was formed it could react with sulfide to form sulfur and chloride. Hence, chlorine production cannot be excluded during the experiments using defined medium. However, Ir/Ta MMO coated titanium electrodes are known to have a very high catalytic effect toward oxygen. Furthermore, chloride levels found in domestic wastewater are normally relatively low (i.e.
150e200 mg L1) and therefore it is likely that any production would be negligible (Chen and Chen, 2005; Vassiliev and Grinberg, 1991). The presence of chloride did not increase sulfide removal rates during the experiments.

4.4.

Practical implications and future research

Taking into account the slow kinetics of direct sulfide oxidation at low sulfide concentrations, direct oxidation of sulfide in sewer system presently appears not feasible at Ir/Ta MMO coated titanium electrodes. Therefore, either indirect oxidation of sulfide with in-situ generated oxygen or the prevention of sulfide build-up (i.e. prevent sulfate reduction by maintaining aerobic conditions) seem more suitable for application. One of the possible disadvantages of this strategy is the fact that the formed sulfur species can be reduced again downstream. However, this is also the case for other approaches such as conventional oxygen injection and nitrate dosing, and will be of importance when determining the position of the treatment in the sewer network. In-situ generated oxygen possesses several advantages compared to traditional methods for oxygen supply, including a high transfer efficiency, fine dispersion, high controllability, ease of monitoring and the lack of chemicals transport and storage. In this study, we used a current density of 50 A m2. In order to reduce investments costs, higher current densities are preferred. While direct sulfide oxidation is limited due to the diffusion of sulfide to the electrode, oxygen generation from water is less constrained. The Ir/Ta MMO coated titanium electrodes used have a high electrochemical stability and catalytic activity for oxygen evolution (Chen et al., 2002b). The expected electrode lifetime is strongly dependent on the applied current density. A Ti/IrOxeSb2O5eSnO2 electrode containing only 10 mol% of IrOx was predicted to have a lifetime over 9 years in a strong acidic solution at a current density of 1000 A m2 (Chen et al., 2002a). As the current densities in the sewer based system are lower, and the solution less corrosive, lifetimes of over 10 years can be expected, unless physical erosion of the electrode occurs due to wastewater particulates. The installation of an electrochemical system in sewer systems is subject to a number of restrictions including prevention of particle settling and accumulation and precipitation of inorganics. Whether the system cell will be placed inside the sewer pipe or as a bypass system is yet to be determined. If placed inside the sewer pipe the mesh shaped anode will be placed onto the sewer wall to prevent particle accumulation and settling. Biofilm formation on the electrode surface will not take place due to the applied anodic potentials

(strong oxidizing potential). Due to the local acidifying effect at the anode, precipitates such as calcium carbonate are not expected, although this may happen at the cathode. Possible solutions are periodic chemical cleaning (i.e. acid dosage) or by switching the polarity of the electrode. To minimize ohmic losses, anodes and cathodes will need to be spaced as closely as possible; therefore a flat membrane electrode assembly (mesh structure) is proposed. Passivation of the electrode surface by sulfur during long-term continuous application is not expected since the applied current densities will result in in-situ generation of oxygen.

5.

Conclusions

In this study, simultaneous sulfide oxidation and in-situ oxygen generation was demonstrated using Ir/Ta MMO coated titanium electrodes with both synthetic and real domestic wastewater at high rates. The maximum observed sulfide removal rate was 11.8
1.7 g S m2electrode surface h1 at sulfide concentrations 30 mg L1 from domestic wastewater. The final products of oxidation were sulfur, thiosulfate, sulfate and oxygen. Under the experimental conditions the indirect oxidation of sulfide with in-situ generated oxygen appears the main reaction mechanism. Acetate and chloride concentrations remained constant, indicating that Ir/Ta MMO coated titanium electrodes are very suitable electrodes for the indirect oxidation of sulfide with in-situ generated oxygen.

Acknowledgments Ilje Pikaar, Rene´ Rozendal and Korneel Rabaey thank the University of Queensland for scholarship and fellowship support. This work was funded by the Australian Research Council (ARC Linkage project: LP0882016 “Optimal Management of Corrosion and Odour Problems in Sewer Systems”). The authors also want to acknowledge Dr. Beatrice KellerLehmann and Ms. Kar Man Leung for their helpful collaboration with the chemical analyses.

references

´ ngela, A., Ane, U., Inmaculada, O., 2009. Contributions of A electrochemical oxidation to waste-water treatment: fundamentals and review of applications. Journal of Chemical Technology and Biotechnology 84 (12), 1747e1755. Ateya, B.G., Al-Kharafi, F.M., 2002. Anodic oxidation of sulfide ions from chloride brines. Electrochemistry Communications 4 (3), 231e238. Ateya, B.G., AlKharafi, F.M., Al-Azab, A.S., 2003. Electrodeposition of sulfur from sulfide contaminated brines. Electrochemical and Solid-State Letters 6 (9), C137eC140. Ateya, B.G., AlKharafi, F.M., Alazab, A.S., Abdullah, A.M., 2007. Kinetics of the electrochemical deposition of sulfur from sulfide polluted brines. Journal of Applied Electrochemistry 37 (3), 395e404. Bard, A.J., Faulkner, L.R., 2001. Electrochemical Methods Fundamentals and Applications. John Wiley & Sons Inc., New York.

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Chen, G., 2004. Electrochemical technologies in wastewater treatment. Separation and Purification Technology 38 (1), 11e41. Chen, X., Chen, G., 2005. Stable Ti/RuO2-Sb2O5-SnO2 electrodes for O2 evolution. Electrochimica Acta 50 (20), 4155e4159. Chen, G., Chen, X., Yue, P.L., 2002a. Electrochemical behavior of novel Ti/IrOx-Sb2O5-SnO2 anodes. Journal of Physical Chemistry B 106 (17), 4364e4369. Chen, X., Chen, G., Yue, P.L., 2002b. Novel electrode system for electroflotation of wastewater. Environmental Science and Technology 36 (4), 778e783. Dutta, P.K., Rabaey, K., Yuan, Z., Keller, J., 2008. Spontaneous electrochemical removal of aqueous sulfide. Water Research 42 (20), 4965e4975. Dutta, P.K., Rozendal, R.A., Yuan, Z., Rabaey, K., Keller, J., 2009. Electrochemical regeneration of sulfur loaded electrodes. Electrochemistry Communications 11 (7), 1437e1440. Dutta, P.K., Rabaey, K., Yuan, Z., Rozendal, R.A., Keller, J., 2010. Electrochemical sulfide removal and recovery from paper mill anaerobic treatment effluent. Water Research 44 (8), 2563e2571. Farooque, M., Fahidy, T.Z., 1978. The electrochemical oxidation of hydrogen sulfide in the tafel region and under mass transport control. Journal of the Electrochemical Society 125 (4), 544e546. Firer, D., Friedler, E., Lahav, O., 2008. Control of sulfide in sewer systems by dosage of iron salts: comparison between theoretical and experimental results, and practical implications. Science of the Total Environment 392 (1), 145e156. Gutierrez, O., Mohanakrishnan, J., Sharma, K.R., Meyer, R.L., Keller, J., Yuan, Z., 2008. Evaluation of oxygen injection as a means of controlling sulfide production in a sewer system. Water Research 42 (17), 4549e4561. Gutierrez, O., Park, D., Sharma, K.R., Yuan, Z., 2009. Effects of long-term pH elevation on the sulfate-reducing and methanogenic activities of anaerobic sewer biofilms. Water Research 43 (9), 2549e2557. de Haas, D.W., Sharma, K.R., Corrie, S., O’Halloran, K., Keller, J., Yuan, Z., 2008. Odour control by chemical dosing: a review. Journal of the Australian Water Association 35 (02), 138e143. Hvitved-Jacobsen, T., 2002. Sewer systems and processes. In: Sewer Processes – Microbial and Chemical Process Engineering of Sewer Networks. CRC Press, Boca Raton. Keller-Lehmann, B., Corrie, S., Ravn, R., Yuan, Z., Keller, J., 2006. Preservation and simultaneous analysis of relevant soluble sulfur species in sewage samples. In: 2nd International IWA Conference on Sewer Operation and Maintenance, Vienna, Austria. Kuhn, A.T., Chana, M.S., Kelsall, G.H., 1983. A review of the air oxidation of aqueous sulphide solutions. Journal of Chemical Technology and Biotechnology 33 (8), 406e414. Logan, B.E., Hamelers, B., Rozendal, R., Schroder, U., Keller, J., Freguia, S., Aelterman, P., Verstraete, W., Rabaey, K., 2006. Microbial fuel cells: methodology and technology. Environmental Science and Technology 40 (17), 5181e5192. Miller, B., Chen, A., 2005. Effect of concentration and temperature on electrochemical oscillations during sulfide oxidation on Ti/Ta2O5-IrO2 electrodes. Electrochimica Acta 50 (11), 2203e2212.

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Mills, R., Lobo, V.M.M., 1989. Self-diffusion in Electrolyte Solutions: a Critical Examination of Data Compiled from the Literature. Elsevier, Amsterdam. Mohanakrishnan, J., Gutierrez, O., Meyer, R.L., Yuan, Z., 2008. Nitrite effectively inhibits sulfide and methane production in a laboratory scale sewer reactor. Water Research 42 (14), 3961e3971. Mohanakrishnan, J., Gutierrez, O., Sharma, K.R., Guisasola, A., Werner, U., Meyer, R.L., Keller, J., Yuan, Z., 2009. Impact of nitrate addition on biofilm properties and activities in rising main sewers. Water Research 43 (17), 4225e4237. Nielsen, A.H., Vollertsen, J., Hvitved-Jacobsen, T., 2003. Determination of kinetics and stoichiometry of chemical sulfide oxidation in wastewater of sewer networks. Environmental Science and Technology 37 (17), 3853e3858. Nielsen, A.H., Hvitved-Jacobsen, T., Vollertsen, J., 2005. Kinetics and stoichiometry of sulfide oxidation by sewer biofilms. Water Research 39 (17), 4119e4125. Nielsen, A.H., Hvitved-Jacobsen, T., Vollertsen, J., 2008. Effects of pH and iron concentrations on sulfide precipitation in wastewater collection systems. Water Environment Research 80 (4), 380e384. Rabaey, K., Boon, N., Hofte, M., Verstraete, W., 2005a. Microbial phenazine production enhances electron transfer in biofuel cells. Environmental Science and Technology 39 (9), 3401e3408. Rabaey, K., Ossieur, W., Verhaege, M., Verstraete, W., 2005b. Continuous microbial fuel cells convert carbohydrates to electricity. Water Science and Technology 52 (1e2), 515e523. Sharma, K. and Yuan, Z. (2010) Kinetics of chemical sulfide oxidation under high dissolved oxygen levels. Submitted for oral presentation, 6th International Conference on Sewer Processes and Networks, 7e10 November 2010. Sun, Y.X., Wu, Q.Y., Hu, H.Y., Tian, J., 2009. Effects of operating conditions on THMs and HAAs formation during wastewater chlorination. Journal of Hazardous Materials 168 (2e3), 1290e1295. Tanaka, N., Takenaka, K., 1995. Control of hydrogen sulfide and degradation of organic matter by air injection into a wastewater force main. Water Science and Technology 31 (7), 273e282. Vassiliev, Y.B., Grinberg, V.A., 1991. Adsorption kinetics of electrode processes and the mechanisms of Kolbe electrosynthesis. Part III. Mechanism of the process. Journal of Electroanalytical Chemistry and Interfacial Electrochemistry 308 (1e2), 1. WERF, 2007. Minimization of Oders and Corrosion in Collection Systems Phase 1. Water Environment Research Foundation. Waterston, K., Bejan, D., Bunce, N., 2007. Electrochemical oxidation of sulfide ion at a boron-doped diamond anode. Journal of Applied Electrochemistry 37 (3), 367e373. Zhang, L., De Schryver, P., De Gusseme, B., De Muynck, W., Boon, N., Verstraete, W., 2008. Chemical and biological technologies for hydrogen sulfide emission control in sewer systems: a review. Water Research 42 (1e2), 1e12. Zhang, L., Keller, J., Yuan, Z., 2009. Inhibition of sulfate-reducing and methanogenic activities of anaerobic sewer biofilms by ferric iron dosing. Water Research 43 (17), 4123e4132.

w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 2 9 0 e2 2 9 6

Available at www.sciencedirect.com

journal homepage: www.elsevier.com/locate/watres

Uptake of methylated arsenic by a polymeric adsorbent: Process performance and adsorption chemistry Yu-Ting Wei, Yu-Ming Zheng, J. Paul Chen* Department of Civil and Environmental Engineering, National University of Singapore, 10 Kent Ridge Crescent, Singapore 119260, Singapore

article info

abstract

Article history:

Methylated arsenic in groundwater has caused a series of health problems to human

Received 14 October 2010

beings. A N-methylglucamine modified chitosan polymeric adsorbent was successfully

Received in revised form

developed for efficient adsorption of methylated arsenic from water solution. Adsorption

23 December 2010

behaviors of two common methylated arsenic species, monomethylarsonic acid (MMA)

Accepted 6 January 2011

and dimethylarsinic acid (DMA), onto the adsorbent were investigated in this paper. The

Available online 13 January 2011

surface modification increased the adsorption capabilities for the arsenic. The uptake of MMA was higher than that of DMA throughout all pH values. The maximum adsorption

Keywords:

capacities were 15.4 mg/g for MMA and 7.1 mg/g for DMA, exhibiting competitive advan-

Methylated arsenic adsorption

tages with other reported materials. The affinity of these arsenic species for the adsorbent

Chitosan

followed a pattern of MMA > DMA. The adsorption equilibrium was achieved within 20 h.

Polymeric adsorbent

The uptake of MMA and DMA was dependent upon the concentration of background

Spectroscopic analysis

electrolytes, indicating the formation of outer-sphere complexes of both organoarsenic species with the adsorbent during the adsorption. The existence of natural organic matter and competitive anions cause decrease in the uptake of both arsenic species. Furthermore, the simultaneous uptake of organic contaminants such as humic acid was observed. The spectroscopic analysis demonstrated the strong attachment of both organic arsenic species onto the amine functional group of the adsorbent. ª 2011 Elsevier Ltd. All rights reserved.

1.

Introduction

Arsenic, a naturally occurring and ubiquitous element in the environment exists in both inorganic and organic forms. Its speciation differs depending on solution chemistry such as pH and redox potential. The most common species are inorganic arsenate As(V) and arsenite As(III), as well as methylated arsenic species of monomethylarsonic acid (MMA) and dimethylarsinic acid (DMA). Methylation of arsenic by various microorganisms ranging from fungi to bacteria can influence the organoarsenic species in the natural systems (Cullen and Reimer, 1989). Methylated arsenic compounds can be introduced into the environment through industrial and agricultural activities. In the 1990s, MMA and DMA were extensively

used as herbicides in the U.S. to control weeds for cotton production and occurred as important contaminants in surface and groundwater (Bednar et al., 2002). The U.S. statistics demonstrated that 2 to 4 million pounds of MMA were used in the U.S. by industrial, commercial and government sectors in 1999 (Xu et al., 2008). A number of water sources have been polluted by the organic arsenic. Nearly 24% of the total dissolved arsenic was detected as the methylated species in the lakes of California, where the predominant arsenic species was the DMA (Pokhrel and Viraraghavan, 2008). Besides, the organic arsenic concentration accounted for up to 53e60% of the total dissolved arsenic in river and estuarine waters from southwest Spain (Sanchez-Rodas et al., 2005).

* Corresponding author. E-mail addresses: [email protected], [email protected] (J.P. Chen). 0043-1354/$ e see front matter ª 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2011.01.002

w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 2 9 0 e2 2 9 6

The inorganic arsenic species have usually been considered to be more toxic than methylated arsenic. However, some recent studies have reported that some methylated arsenic species can be more toxic than inorganic species (e.g., Lafferty and Loeppert, 2005). A long-term/chronic exposure to the DMA can cause DNA damage (Ahmad et al., 1999); it would lead to the multi-organ tumor promoting activities including lung, live, kidney and urinary bladder (Kenyon and Hughes, 2001; Salim et al., 2003; Arnold et al., 2006; Kinoshita et al., 2007) A series of extensive researches have been conducted to decontaminate inorganic arsenic from aqueous environmental systems; however, a few studies are reported on the development of technologies to remove the organic arsenic species. A clay modified by the polymeric Al/Fe could remove the DMA with a sorption capacity of 18.19 mg/g (Ramesh et al., 2007). A maximum MMA adsorption of 8.57 mg/g at pH 3-4 was reportedly achieved by a calcium alginate encapsulated magnetic adsorbent, where both adsorption and redox reaction were involved (Lim et al., 2009). A nanocrystalline TiO2-based adsorbent was developed for the simultaneous remediation of organic and inorganic arsenic in contaminated groundwater (Jing et al., 2009). Approximately 9900 bed-volume groundwater was treated before the MMA concentration in the effluent increased to 10 mg/L. Almost no DMA uptake was observed. In this study, we investigated the adsorption behavior of methylated arsenic by a polymeric material with NMDG moiety, which was developed by the surface modification of chitosan via the atom transfer radical polymerization (ATRP) technique. A series of batch experiments were conducted to investigate the adsorption characteristics of organoarsenic compounds onto the novel adsorbent. Effects of natural organic matter and coexisting anions on the uptake of organic arsenic were studied. The X-ray photoelectron spectroscopy (XPS) was used for the elucidation of the interactions between the arsenic and the functional groups on the adsorbent.

2.

Materials and methods

2.1.

Materials

Disodium methylarsenate (CH3AsNa2O3, 98%), one of MMA species, was purchased from Chem Service (USA). Sodium cacodylate (CH3)2AsNa2O2,
98%), one sodium salt of DMA was obtained from SigmaeAldrich (Singapore). Suwannee River humic acid was provided by the International Humic Substances Society (IHSS). Nitric acid and sodium hydroxide from Merck were used to adjust the solution pH. Sodium perchlorate, sodium fluoride, sodium phosphate, and sodium sulfate were analytical grades from SigmaeAldrich. The novel adsorbent was prepared as follows. First, the crosslinked chitosan (CCTS) was first prepared through electrostatic extrusion technique (Zhou et al., 2005). The ATRP surface initiator was then anchored onto the CCTS through the interaction with hydroxyl and amine groups of chitosan. Subsequently, the polymerization of glycidyl methacrylate (GMA) was started from the initiator sites in the presence of the ATRP catalysts. Finally, the NMDG reacted with the epoxide groups of poly(glycidyl methacrylate) (PGMA) to obtain the polymeric adsorbent designated as CTS-MG, which

2291

possessed both polyhydroxyl and tertiary amine functional groups for boron binding.

2.2.

Adsorption experiments

The stock solutions of MMA and DMA were prepared by dissolving certain amount of CH3AsNa2O3 and (CH3)2AsNaO3 in ultrapure water. The stock solutions were then diluted to prepare arsenic solutions with different desired concentrations. The MMA and DMA solutions with initial pH ranging from 2 to 11 were used in the pH effect study. Both initial and equilibrium pH values were measured by an ORION 920Aplus pH meter. The arsenic concentrations were analyzed by an inductively coupled plasma emission spectrometer (ICP-ES; Perkin-Elmer Optima 3000). Adsorption isotherm experiments were conducted by using different initial arsenic concentrations (0 e125 mg/L) with a dosage of 0.5 g/L adsorbent. Initial pH of the solution was adjusted to 3.4 and 5.0 for MMA and DMA, respectively. The CTS-MG and the CCTS were used to determine the uptake capacities for organoarsenic species. During the batch adsorption kinetics study, the arsenic solution with constant concentration was used; and sodium perchlorate ranging from 0 to 5 mM was selected as the background electrolyte (ionic strength, I). The solution pH was initially adjusted before the adsorbent was added. The samples were taken at predetermined time intervals for the measurement of arsenic concentration by the ICP-ES. The effect of natural organic matter on arsenic removal was investigated with various concentrations of humic acid (HA). A total organic carbon (TOC) analyzer (Shimadzu TOC Analyzer, Japan) was used to determine the concentrations of HA before and after adsorption. Different concentrations of three common anions in groundwater (fluoride, phosphate and sulfate) were prepared from 0 to 1 mM to study the effects of coexisting anions on the adsorption of organoarsenic species. Solution pH was adjusted to 7.0 for both MMA and DMA. All batch experiments were performed at 20  C. All mixtures in the equilibrium studies were shaken for 3 d before the samples were taken for the determinations of concentrations. All the experiments were repeated at least 3 times and the average of values was taken. The experimental error was controlled within 5%. The experimental data were presented in the format of solid/hollow points in the figures in Section 3.

2.3.

Spectroscopic analysis

The samples of the adsorbent before and after arsenic adsorption were analyzed using the X-ray photoelectron spectroscopy (Kratos XPS system-Axis His-165 Ultra, Shimadzu, Japan), with a monochromatized Al Ka X-ray source (1486.7 eV). For the wide scan spectra, an energy range from 0 to 1100 eV was used with pass energy of 80 eV and step size of 1 eV. The high-resolution scans were conducted according to the peak being examined with pass energy of 40 eV and step size of 0.05 eV. To compensate for the charging effects, all spectra were calibrated with graphitic carbon as the reference at a binding energy (BE) of 284.6 eV. The XPS results were

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w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 2 9 0 e2 2 9 6

collected in binding energy forms and fitted using a nonlinear least-square curve fitting program (XPSPEAK41 Software).

Results and discussion

3.1.

Effect of pH

As shown in Fig. 1, the MMA and DMA adsorption behave similarly in pH ranging from 2 to 11. The uptake of DMA is less than that of MMA throughout all pH values. At pH < 3.5, the uptake of organic arsenic species is very low. The optimal arsenic removal can be achieved at pH 4 and pH 6 for MMA and DMA, respectively, and then gradually declines as the pH is further increased. The pH-dependent adsorption behavior can be explained by the methylated arsenic speciation and the surface property of CTS-MG. The CTS-MG has a point of zero charge (PZC) of 7.8 (Wei et al., 2011a). The species distribution as a function of pH was calculated by the MINEQL 4.5, a commercial program by Schecher (2002). At an extremely low pH, both of the predominant species exist as neutral species (H2AsO3CH3 and HAsO2(CH3)2). The uptake of MMA and DMA thus becomes insignificant, as neither chemical adsorption nor electrostatic

15

100

12

80

9

60

6

40

3

20

b 12 10 8 6 4 2

0 1 2 3 4 5 6 7 8 9 10 11 12

0

Adsorption isotherm

The experimental results of adsorption isotherms of two methylated arsenic species are demonstrated in Fig. 2. The

Species distribution (%)

q (mg/g)

a

3.2.

Equilibrium pH

3.

interaction exists between non-ionic species and the positively charged surfaces. As pH increases, more arsenic species become negatively charged in the forms of monovalent arsenic species (namely,  HAsO3CH 3 and AsO2(CH3)2 ). The methylated arsenic adsorption onto the positively charged surfaces of CTS-MG becomes favorable according to Reactions (a)e(f) of Table 1. However, with a further increase in pH, the contents of protonated tertiary amine and hydroxyl groups reduce, leading to smaller amount of MMA and DMA adsorption. The decrease in adsorption of MMA and DMA above PZC of CTS-MG is probably attributed to rising competition between hydroxyl ions and arsenic species for active sites. A buffering effect is observed for both MMA and DMA removal (Fig. 1b and d). The final pH increases slightly at acidic condition, which may result from Reactions (a) and (b) in Table 1. The pH decreases slightly under the alkaline solution, which may be due to Reaction (g) in Table 1. A similar trend is observed in the inorganic arsenic sorption onto the CTS-MG (Wei et al., in press).

2

4

6 8 Initial pH

10

12

2

4

6 8 Initial pH

10

12

Equilibrium pH

80 60

2 40 1

0

d 12

100

4

3

q (mg/g)

2-

AsO3CH3

20 0 1 2 3 4 5 6 7 8 9 10 11 12 Equilibrium pH HAsO2(CH3)2

Species distribution (%)

c

-

HAsO3CH3

10 Equilibrium pH

H2AsO3CH3

8 6 4 2

-

AsO2(CH3)2

Fig. 1 e (a) Uptake of MMA as a function of pH, (b) Effect of MMA adsorption using CTS-MG on solution pH, (c) Uptake of DMA as a function of pH, (d) Effect of DMA adsorption using CTS-MG on solution pH. ([As]0 [ 20 mg/L; m [ 0.5 g/L; T [ 293 K; contact time [ 3 d).

2293

w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 2 9 0 e2 2 9 6

Table 1 e List of possible adsorption reactions of organic arsenic.

Table 2 e List of model parameters of the adsorption isotherms. Langmuir isotherm

Freundlich isotherm

(a)

r2

Kf

1/n

r2

0.988 0.986 0.997 0.939

4.546 0.784 0.411 0.344

0.260 0.426 0.506 0.419

0.991 0.987 0.986 0.856

qmax (mg/g) b (L/mg) R OH þ Hþ 5R OHþ 2

(b)

(c)

 þ   R OHþ 2 þ HAsO3 CH3 5R OH2 $HAsO3 CH3

(d)

(e)

  þ  R OHþ 2 þ AsO2 ðCH3 Þ2 5R OH2 $AsO2 ðCH3 Þ2     R OH þ OH 5R O þ H2 O

(f) (g)

Noted that the preferred pH for Reactions (a) and (b) is below 7.8, that for Reactions (c) and (d) is 2e8, and that for Reactions (e) and (f) and R OH represent the two functional

Eqs. (5) is 4e8. groups on the adsorbent.

Langmuir and Freundlich isotherms given below are used to analyze the experimental data. qeq ¼

qmax bCeq 1 þ bCeq

(1)

qeq ¼ Kf ðCeq Þ1=n

(2)

where qmax is the maximum adsorption capacity (mg/g), b is the constant related to the affinity between the adsorbent and the adsorbate (L/mg), qeq is the adsorption capacity (mg/g), Ceq is the equilibrium concentration of adsorbate in solution (mg/L), Kf is Freundlich constant and 1/n is heterogeneity factor. As shown in Table 2, both Langmuir and Freundlich equations are suitable in the description of the adsorption behaviors of MMA and DMA on the modified chitosan. The maximum adsorption capacities of CTS-MG for MMA

CTS-MG MMA DMA CCTS MMA DMA

15.4 7.1 5.9 0.66

0.132 0.033 0.024 0.052

and DMA are 15.4 and 7.1 mg/g, respectively, much higher than those of CCTS (5.9 mg/g for MMA and 0.66 mg/g for DMA). These clearly demonstrate that the chemical modification for the CCTS greatly enhances the organic arsenic adsorption. The comparison of adsorption of organic arsenic with that of inorganic arsenic shows that the substitution of hydroxyl by methyl groups directly affects the adsorption behavior (Wei et al., 2011b). The adsorption of arsenate is nearly 4 and 10 times higher than MMA and DMA, respectively. In addition a decrease in affinity of arsenic species for CTS-MG exhibits similar trends: inorganic As(V) >MMA >DMA. This finding is consistent with that by the TiO2 (Xu et al., 2007). The difference in the adsorption of these three arsenic species might be caused by the presence of additional methyl groups of MMA and DMA, which have different molecular geometries (Cheng et al., 2005; Ramesh et al., 2007). The comparisons of our adsorbent with other adsorbents reported in the literatures are given Table 3. The CTS-MG seems to have a better performance than many other types of adsorbents for methylated arsenic. Low-cost sorbents such as iron oxide coated sand have poor sorption for the methylated arsenic. However, the sorbents that have better uptake for arsenic are costly as the fabrication of sorbents is complicated and the raw materials may be expensive. As our sorbent not only can treat the arsenic contaminated streams but also can remove such pollutants as boron and natural organic matter (Fig. 4), it is anticipated that it would have a wider application in water treatment.

24 MMA, CTS-MG DMA, CTS-MG

20

Table 3 e List of adsorbents for methylated arsenic adsorption.

Langmuir fitting Freundlich fitting

16

qe (mg As / g)

MMA, CCTS DMA, CCTS

Adsorbent 12

MMA pH

8

CTS-MG Magnetic adsorbent Iron filings

4

DMA

qmax pH qmax (mg/g) (mg/g)

3.4 15.4 34 8.57

5 e

7.1 e

This work (Lim et al., 2009)

e

0.65

e

0.02

e

6

18.19

(Cheng et al., 2005) (Ramesh et al., 2007)

6.44 e

6.8 2.77 7.6 0.008

0 0

25

50

75

100

125

Ce (mg/L) Fig. 2 e Methylated arsenic adsorption isotherms onto CTS-MG and CCTS (m [ 0.5 g/L; pH [ 3.4 (MMA); pH [ 5 (DMA); T [ 293 K; contact time [ 3 d).

References

Polymeric Al/Fe e modified montmorillonite Degussa P25 TiO2 6.8 Iron oxide e coated sand

(Xu et al., 2007) (Thirunavukkarasu et al., 2002)

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w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 2 9 0 e2 2 9 6

3.4.

15 MMA, I = 0 DMA, I = 0

q (mg As / g)

12

MMA, I = 5 mM DMA, I = 5 mM

9

6 3

0 0

5

10

15

20

25

Time (h) Fig. 3 e Adsorption kinetics of MMA and DMA at different ionic strength ([As]0 [ 20 mg/L; m [ 0.5 g/L; pH [ 3.4 (MMA); pH [ 5 (DMA); T [ 293 K; contact time [ 3 d).

Natural organic matter is generally present in groundwater, and could potentially affect arsenic species removal through its competitive binding onto adsorbent. When the HA is present in the solution (pH 7), the adsorption capabilities become lower for both MMA and DMA as shown in Fig. 4. The decrease for MMA adsorption is less obvious than that for DMA. The adsorption amount for DMA is reduced by around 76% when the TOC (HA) is increased to approximately 14 mg/L. A simultaneous uptake of HA is observed in this study. Since humic substances are negatively charged, it can be attracted by the positively charged adsorbent. The carboxyl and phenolic groups of HA may interact with the protonated amine groups to form organic complexes. The direct competition for binding sites may contribute to the decline of organic arsenic removal. The influence of HA on the arsenic uptake follows the sequence of: inorganic As (V) <MMA MMA >DMA, which is consistent with the findings from our isotherm studies (Table 2).

3.5.

Effect of coexisting anions

Adsorption kinetics

a

5

5

4

4

3

3

2

2

1

1

0 0

2

4

6

8

10

12

q (TOC mg /g)

Fluoride, phosphate and sulfate are usually present in the groundwater, which may lead to competitive adsorption. As shown in Fig. 5, at the same concentration level of these three

q (As mg /g)

0 14

HA concentration (TOC mg/L)

b

3

6 5

2

4 3 2

1

q (TOC mg/g)

Fig. 3 demonstrates the time-dependent adsorption of MMA and DMA onto the CTS-MG at two different ionic strengths. In the absence of electrolyte backgrounds (NaClO4), over 80% of MMA and DMA adsorption rapidly occurs in the first 8e9 h, then followed by a relatively slow process. The equilibrium time is around 20 h. It is clearly demonstrated that the uptake of two methylated arsenic species on the CTS-MG is electrolyte-dependent. Arsenic removal is inhibited by an increasing concentration of NaClO4. The adsorption capacities for MMA and DMA are around 10.6 mg As/g and 2.7 mg As/g, respectively in the absence of NaClO4. The MMA removal is sharply reduced by 75% and DMA adsorption approaches to nearly zero when the concentration of NaClO4 is increased to 0.005 M. Adsorption of ionic species can be catalogued into innersphere and out-sphere adsorption. It is assumed that, in the out-sphere adsorption, the adsorbed ions are distributed at the same plane as the electrolyte ions and an increasing solution ionic strength would suppress the removal of nonspecifically adsorbed ions (Zhang et al., 2007; Hayes et al., 1988). The ionic strength-dependent adsorption in Fig. 3 clearly indicates that the process is an outer-sphere adsorption. Similar trends have been found for inorganic arsenic adsorption onto the CTS-MG and the shells of crab (Wei et al., 2011b; Vijayaraghavan et al., 2009). Another important finding in this study is that higher ionic strength slows the adsorption rate and extends the equilibrium time. The possible explanation is that the presence of a large amount of background electrolytes interrupts the transfer of adsorbates from bulk solution to the adsorbent surface and also limits their accessibility to active sites (Hamdaoui et al., 2008). A similar observation was reported that the ionic strength led to a significant decrease in the diffusivity for adsorption of amino acids with an anionexchange resin (Moreira and Ferreira, 2005).

q (As mg/g)

3.3.

Effect of natural organic matter

1 0 0

2

4

6

8

10

12

0 14

HA concentration (TOC mg/g) Fig. 4 e Effect of humic acid on the adsorption of methylated arsenic: (a) MMA, (b) DMA ([As]0 [ 20 mg/L; m [ 1 g/L; pH [ 7.0; T [ 293 K; contact time [ 3 d).

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w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 2 9 0 e2 2 9 6

a

0 mM 0.1 mM 0.5 mM 1 mM

4

q (mg/g)

anions, the DMA uptake is hindered much more than the MMA. When sulfate content is increased to 1 mM, the adsorption of MMA is reduced by nearly 75%; and almost no adsorption is observed for the DMA. The degree of the uptake is dependent upon the binding strength of methylated arsenic species onto the adsorbent. As the DMA has much lower affinity for CTS-MG than the MMA, its adsorption is hindered more in the presence of the competitive anions. Among these three competitive anions, the extent of inhibition for arsenic adsorption demonstrates the following order: sulfate > phosphate > fluoride. The similar adverse effects were found in arsenate adsorption by the weak-base anion-exchange fibrous adsorbent and the CTS-MG (Awual et al., 2008; Wei et al., 2011b). The reasons might be due to the fact that organic arsenic species is adsorbed on the polymeric adsorbent to form the outer-sphere complexes through electrostatic interaction, ion exchange and surface complexes, and the divalent anions are generally more preferred to be adsorbed than the monovalent anions according to the electroselectivity (Awual et al., 2008).

5

3 2 1 0 F

P

S

Competitive ions

b

5 0 mM 0.1 mM 0.5 mM 1 mM

q (mg/g)

4 3 2

3.6.

1

High-resolution spectra of As3d of methylated As-loaded adsorbents are given in Fig. 6. The As3d XPS spectrum at 45.5 eV corresponds to the characteristic peak of As(V), which confirms the adsorption of organic arsenic onto the adsorbent (Lim et al., 2009; Zhang et al., 2010). The valence of arsenic remains unchanged during the adsorption. The decomposition of N 1s spectra of pristine and Asloaded adsorbents yields two individual component peaks in Fig. 7. The peaks at binding energy of 399.0 eV and 401.8 eV can be assigned to the nitrogen atoms in neutral amine (eN) and protonated amine (eNþ) groups, respectively (Xu et al., 2005; Yu et al., 2004). The ratio of –Nþ/–N of pristine adsorbent is 0.21, however, it is increased to 0.91 and 0.36 after the adsorption of MMA at pH 3.4 and DMA at pH 5, respectively. The increase in the content of protonated amine groups may play an important role for the methylated arsenic binding through chemical and physical interactions.

0 F

P

S

Competitive ions Fig. 5 e Competitive adsorption of coexisting anions with methylated arsenic ([As]0 [ 20 mg/L; m [ 1 g/L; pH [ 7.0; T [ 293 K; contact time [ 3 d).

a

As 3d

43

44

45

46

47

b

As 3d

48

43

44

45

46

47

Spectroscopic analysis

4.

48

Conclusions

Binding Energy (eV) The present study reveals that MMA and DMA have similar adsorption edges in the pH range of 2 e11. The uptake of methylated arsenic by the CTS-MG is pH-dependent and the

Fig. 6 e As3d core-level XPS spectra of (a) MMA-loaded adsorbent, (b) DMA-loaded adsorbent.

a

N 1s +

N 1s +

-N /-N=0.21

-N /-N=0.91

b

c

N 1s +

-N /-N=0.36

-N +

-N

-N

-N +

-N

+

-N

396

398

400

402

404

396

398

400

402

404

396

398

400

402

404

Binding Energy (eV) Fig. 7 e N1s core-level spectra of (a) pristine adsorbent, (b) MMA-loaded adsorbent, (s) DMA-loaded adsorbent.

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maximum adsorption occurs at pH 4 (MMA) and pH 6 (DMA). Under optimum pH conditions, the adsorption capacities of CTS-MG are 15.4 mg/g for MMA and 7.1 mg/g for the DMA, respectively, which are much higher than the CCTS. The affinity of different arsenic species to the CTS-MG is in the order: inorganic As (V) > MMA > DMA. The adsorption kinetics study shows that the equilibrium can be established within 20 h. The presence of background electrolytes causes a decline in adsorption capacity and an extension in equilibrium time. The presence of humic acid, fluoride, phosphate and sulfate inhibits organic arsenic adsorption. The spectroscopic analyses confirm the strong binding of methylated arsenic onto the adsorbent. The amine functional groups of the adsorbent may be responsible for the arsenic uptake.

Acknowledgements The authors would like to express their appreciation to Agency for Science, Technology and Research of Singapore (Grant No. 0 921 010 059, and R-288-000-066-305) for the financial support of this study.

references

Ahmad, S., Anderson, W.L., Kitchin, K.T., 1999. Dimethylarsinic acid effects on DNA damage and oxidative stress related biochemical parameters in B6C3F1 mice. Cancer Letters 139 (2), 129e135. Arnold, L.L., Eldan, M., Nyska, A., van Gemert, M., Cohen, S.M., 2006. Dimethylarsinic acid: results of chronic toxicity/ oncogenicity studies in F344 rats and in B6C3F1 mice. Toxicology 223 (1e2), 82e100. Awual, M.R., Urata, S., Jyo, A., Tamada, M., Katakai, A., 2008. Arsenate removal from water by a weak-base anion exchange fibrous adsorbent. Water Research 42 (3), 689e696. Bednar, A.J., Garbarino, J.R., Ranville, J.F., Wildeman, T.R., 2002. Presence of organoarsenicals used in cotton production in agricultural water and soil of the southern United States. Journal of Agricultural and Food Chemistry 50 (25), 7340e7344. Cheng, Z.Q., Van Geen, A., Louis, R., Nikolaidis, N., Bailey, R., 2005. Removal of methylated arsenic in groundwater with iron filings. Environmental Science & Technology 39 (19), 7662e7666. Cullen, W.R., Reimer, K.J., 1989. Arsenic speciation in the environment. Chemical Reviews 89 (4), 713e764. Hamdaoui, O., Saoudi, F., Chiha, M., Naffrechoux, E., 2008. Sorption of malachite green by a novel sorbent, dead leaves of plane tree: equilibrium and kinetic modeling. Chemical Engineering Journal 143 (1e3), 73e84. Hayes, K.F., Papelis, C., Leckie, J.O., 1988. Modeling ionic-strength effects on anion adsorption at hydrous oxide solution interfaces. Journal of Colloid and Interface Science 125 (2), 717e726. Jing, C.Y., Meng, X.G., Calvache, E., Jiang, G.B., 2009. Remediation of organic and inorganic arsenic contaminated groundwater using a nanocrystalline TiO2-based adsorbent. Environmental Pollution 157 (8e9), 2514e2519. Kenyon, E.M., Hughes, M.F., 2001. A concise review of the toxicity and carcinogenicity of dimethylarsinic acid. Toxicology 160 (1e3), 227e236. Kinoshita, A., Wanibuchi, H., Morimura, K., Wei, M., Nakae, D., Arai, T., Minowa, O., Noda, T., Nishimura, S., Fukushima, S., 2007. Carcinogenicity of dimethylarsinic acid in Ogg1-deficient mice. Cancer Science 98 (6), 803e814.

Lafferty, B.J., Loeppert, R.H., 2005. Methyl arsenic adsorption and desorption behavior on iron oxides. Environmental Science & Technology 39 (7), 2120e2127. Lim, S.F., Zheng, Y.M., Chen, J.P., 2009. Organic arsenic adsorption onto a magnetic sorbent. Langmuir 25 (9), 4973e4978. Moreira, M.J., Ferreira, L.M., 2005. Kinetic studies for sorption of amino acids using a strong anion-exchange resin: effect of ionic strength. Journal of Chromatography A 1092 (1), 101e106. Pokhrel, D., Viraraghavan, T., 2008. Organic arsenic removal from an aqueous solution by iron oxide-coated fungal biomass: an analysis of factors influencing adsorption. Chemical Engineering Journal 140 (1e3), 165e172. Ramesh, A., Hasegawa, H., Maki, T., Ueda, K., 2007. Adsorption of inorganic and organic arsenic from aqueous solutions by polymeric Al/Fe modified montmorillonite. Separation and Purification Technology 56 (1), 90e100. Salim, E.I., Wanibuchi, H., Morimura, K., Wei, M., Mitsuhashi, M., Yoshida, K., Endo, G., Fukushima, S., 2003. Carcinogenicity of dimethylarsinic acid in p53 heterozygous knockout and wildtype C57BL/6J mice. Carcinogenesis 24 (2), 335e342. Sanchez-Rodas, D., Gomez-Ariza, J.L., Giraldez, I., Velasco, A., Morales, E., 2005. Arsenic speciation in river and estuarine waters from southwest Spain. Science of the Total Environment 345 (1e3), 207e217. Schecher, W.D., 2002. MINEQLþ: a Chemical Equilibrium Program for Personal Computers, User Manual, Version 4.5. Environmental Research Software, Hallowell, ME. Thirunavukkarasu, O.S., Viraraghavan, T., Subramanian, K.S., Tanjore, S., 2002. Organic arsenic removal from drinking water. Urban Water 4 (4), 415e421. Vijayaraghavan, K., Arun, M., Joshi, U.M., Balasubramanian, R., 2009. Biosorption of As(V) onto the shells of the crab (Portunus sanguinolentus): equilibrium and kinetic Studies. Industrial & Engineering Chemistry Research 48 (7), 3589e3594. Wei, Y.T., Zheng, Y.M., Chen, J.P., 2011a. Design and fabrication of an innovative and environmental friendly adsorbent for boron removal. Water Research. doi:10.1016/j.watres.2011.01.003. Wei, Y.T., Zheng, Y.M., Chen, J.P., 2011b. Enhanced adsorption of arsenate onto a natural polymer based sorbent by surface atom transfer radical polymerization. Journal of colloid and Interface Science. doi:10.1016/j.jcis.2010.12.020. Xu, F.J., Cai, Q.J., Kang, E.T., Neoh, K.G., 2005. Covalent graft polymerization and block copolymerization initiated by the chlorinated SiO2 (SiO2-Cl) moieties of glass and oriented single crystal silicon surfaces. Macromolecules 38 (4), 1051e1054. Xu, T.L., Cai, Y., O’Shea, K.E., 2007. Adsorption and photocatalyzed oxidation of methylated arsenic species in TiO2 suspensions. Environmental Science & Technology 41 (15), 5471e5477. Xu, Z.H., Jing, C.Y., Li, F.S., Meng, X.G., 2008. Mechanisms of photocatalytical degradation of monomethylarsonic and dimethylarsinic acids using nanocrystalline titanium dioxide. Environmental Science & Technology 42 (7), 2349e2354. Yu, W.H., Kang, E.T., Neoh, K.G., 2004. Functionalization of hydrogen-terminated Si(100) substrate by surface-initiated RAFT polymerization of 4-vinylbenzyl chloride and subsequent derivatization for photoinduced metallization. Industrial & Engineering Chemistry Research 43 (17), 5194e5202. Zhang, J.S., Stanforth, R.S., Pehkonen, S.O., 2007. Effect of replacing a hydroxyl group with a methyl group on arsenic (V) species adsorption on goethite (alpha-FeOOH). Journal of Colloid and Interface Science 306 (1), 16e21. Zhang, S.J., Li, X.Y., Chen, J.P., 2010. Preparation and evaluation of a magnetite doped activated carbon fiber for enhanced arsenic removal. Carbon 48 (1), 60e67. Zhou, Y., Sun, T., Chan, M., Zhang, J., Han, Z.Y., Wang, X.W., Toh, Y., Chen, J.P., Yu, H., 2005. Scalable encapsulation of hepatocytes by electrostatic spraying. Journal of Biotechnology 117 (1), 99e109.

w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 2 9 7 e2 3 0 5

Available at www.sciencedirect.com

journal homepage: www.elsevier.com/locate/watres

Design and fabrication of an innovative and environmental friendly adsorbent for boron removal Yu-Ting Wei, Yu-Ming Zheng, J. Paul Chen* Department of Civil and Environmental Engineering, National University of Singapore, 10 Kent Ridge Crescent, Singapore 119260, Singapore

article info

abstract

Article history:

Boron can pose adverse effects on human beings and plants species. It exists in various

Received 27 October 2010

water environments and is difficult to be removed by conventional technologies. In this

Received in revised form

study, an efficient and environmental friendly sorbent was fabricated by the functionali-

26 December 2010

zation of a natural biopolymer, chitosan, with N-methylglucamine through atom transfer

Accepted 10 January 2011

radical polymerization. The SEM and BET studies revealed that the sorbent had a rougher

Available online 15 January 2011

surface and a more porous structure than the chitosan. At the optimum neutral pH, the maximum sorption capacity was as high as 3.25 mmol/g, much higher than the com-

Keywords:

mercial boron selective resins (e.g., Amberlite IRA-743) and many other synthesized

Sorption

sorbents. Almost 90% of boron sorption occurred within 8 h and the equilibrium was

Boron

established in 12 h, which was well described by an intraparticle surface diffusion model.

Atom transfer radical

The presence of sodium chloride and sodium nitrate had no effect on the boron removal.

polymerization

The boron concentration in seawater could be reduced to less than 0.5 mg/L from 4.8 mg/L

Chitosan

when a sorbent dosage of 1.2 g/L was used. It was therefore concluded that the sorption

N-Methylglucamine

technology from this study could be promising for boron removal from aqueous solutions. ª 2011 Elsevier Ltd. All rights reserved.

Electrostatic extrusion

1.

Introduction

Boron is widely distributed in our environments. It naturally exists in water, soils, plants and animals. They can come from natural sources (e.g., seawater) anthropogenic activities. Generally, boron in aqueous environment is found in the form of boric acid and partial borate salts (Xu and Jiang, 2008). Since boron acts as an essential micronutrient, its level in irrigation water plays a crucial role for normal growth of most crops. There is a narrow range between its deficiency and its toxicity. When the boron concentration is high in irrigation water, some toxic symptoms will occur in plants, including marginal and tip necrosis in leaves, followed by the loss in photosynthetic capacity and plant productivity (Parks and Edwards, 2005; Kabay et al., 2010).

The World Health Organization (WHO) once set a guideline limit of 0.5 mg/L for boron in drinking water. The value in the guideline however is revised to 2.4 mg/L, which will be incorporate into the Guidelines for Drinking-water Quality (4th edition) published in 2011 (Kabay et al., 2010). Although this new change seems more relaxed for the drinking water than before, the requirement of 0.5 mg/L is still kept for irrigation water since the boron demonstrates the herbicidal effect. As the freshwater sources become increasingly diminished, seawater containing an average boron concentration of 5 mg/L has become attractive as an alternative water source (e.g. for drinking and agricultural use). It is therefore important to control the boron level in the treated effluent for different applications. Several traditional technologies have been used for boron removal, such as chemical precipitation,

* Corresponding author. E-mail addresses: [email protected], [email protected] (J. Paul Chen). 0043-1354/$ e see front matter ª 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2011.01.003

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electrocoagulation, membrane filtration, and activated carbon adsorption. These technologies generally have certain limitations, especially for boron at low-concentration levels. For example, reverse osmosis (RO) is insufficient to reject boron from seawater under normal pH conditions. Although elevated pH can facilitate boron rejection, it would lead to scaling, corrosion and higher cost (Bektas et al., 2004; Glueckstern and Priel, 2003; Itakura et al., 2005; Karahan et al., 2006; Kluczka et al., 2007; Nadav et al., 2005; Prats et al., 2000; Remy et al., 2005). The polymers with the vicinal polyalcohol function groups are reportedly the most efficient ligands for the complexation of boron in aqueous solutions. Amberlite IRA-743 with the N-methylglucamine functions has possibly been one of few commercial resins for the boron-specific removal since the 1960s (Gazi et al., 2008). However, it exhibits less satisfactory performance for the boron uptake. The operational cost and capacity loss during the regeneration make the resin less economically attractive. Thus, a series of researches have been carried out through the surface modification of both inorganic and organic substrates, aiming at development of effective resins for the boron removal (Bicak et al., 2001; Inukai et al., 2004; Kaftan et al., 2005; Sabarudin et al., 2005; Wang et al., 2007). Most polymer-supported resins are based on the copolymers of styrene and divinylbenzene, which often limit their efficiencies due to their strong hydrophobic properties (Wang et al., 2007). Chitosan is one of abundant, cheap and environmentalfriendly biopolymers. It is obtained by deacetylation of chitin, a major component of shrimp, crab and other crustaceans. The amine and hydroxyl groups of chitosan usually serve as the primary sites for the surface modification, which leads to such applications as biomedical engineering and water treatment. Grafting specific groups onto native chitosan can result in desired surface chemical functionality, and therefore becomes an efficient way to enhance sorption of contaminants. Surface initiated polymerization provides an approach to covalently graft a wide range of polymer chains onto substrates. As one of the most successful processes, atom transfer radical polymerization (ATRP) has rapidly attracted growing interests. It is versatile in polymerization of various vinyl monomers with specific properties. As it can offer a living/controlled polymerization, the ATRP does not need stringent experimental conditions (Jin et al., 2005; Yu et al., 2004; Zheng and Stover, 2003). In this study, a low-cost and environmental-friendly chitosan was chosen as the substrate and ATRP as a grafting approach, in order to develop a novel sorbent with high content of vicinal hydroxyls for enhanced boron removal. The major objectives of this research were: (1) to design and synthesize an innovative chitosan-based sorbent, consisted of two key steps: modification of chitosan with GMA through ATRP technique to obtain epoxide groups, and opening rings of poly(glycidyl methacrylate) (PGMA) in the presence of Nmethylglucamine (NMDG) to introduce vicinal polyalcohol functional groups; (2) to characterize the sorbent via Brunauer-Emmett-Teller (BET) analysis, Scanning Electron Microscopy (SEM) observation, and potentiometric titration; (3) to investigate its boron sorption behavior by batch experiments including isotherm, kinetics, pH effect, ionic strength effect and boron removal from simulated seawater.

2.

Experimental section

2.1.

Materials

Acetic acid, nitric acid and sodium hydroxide from Merck, boric acid from Fisher Scientific, and ethylenediamintetraacetic acid disodium salt (EDTA) from J.T.Baker were used. Methanol, acetone, tetrahydrofuran (THF) and N, N-dimethylformamide (DMF) of HPLC-grade were supplied by the TEDIA. Flake-type chitosan (
85% and deacetylated), ethylene glycol diglycidyl ether (EGDE), 2,20 -bipyridine (BPY,
99%), triethylamine (TEA, >99%) and copper (I) bromide (CuBr, 98%), sodium chloride (
98%) and sodium nitrate (
99%) were purchased from the SigmaeAldrich. 2-Bromoisobutyryl bromide (2-BIB, >97%), glycidyl methacrylate (GMA,
97%) and N-methyl-D-glucamine (NMDG,
97%) were purchased from the Fluka. Nitric acid and sodium hydroxide solutions were used for the pH adjustment.

2.2.

Sorbent preparation

2.2.1.

Preparation of crosslinked chitosan beads

The crosslinked chitosan beads (CCTS) were prepared by the electrostatic extrusion method (Zhou et al., 2005). 3-gram chitosan was first dissolved in acetic acid solution at 70  C for 6 h. The chitosan solution dripped through a needle connected to a high-voltage power generator that controlled the size of droplets. The beads were immediately formed in the sodium hydroxide solution. The nascent beads formed (Fig. 1) were hardened overnight, and then collected, and rinsed with distilled water. The operational parameters are given in Table 1. The chitosan beads were suspended in a water solution. EGDE was then added into the chitosan suspension. The mixture was shaken for 6 h for crosslinking reaction at 70  C. After the reaction was completed, the mixture was cooled down to the room temperature and then washed with the DI water repeatedly until the pH became the same as the DI water. The crosslinked beads were finally dried in a freeze dryer for two days.

2.2.2.

Immobilization of the initiator on the chitosan beads

The CCTS were weighed and added into a solution containing triethylamine and THF, followed by slowly dropwise addition

Fig. 1 e Illustration of chitosan beads prepared by the electrostatic extrusion technology.

w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 2 9 7 e2 3 0 5

Table 1 e Operational parameters in electrostatic extrusion for preparation of chitosan beads. Applied voltage Flow rate Needle size Distance between needle and liquid Chitosan concentration Gelling ion concentration

10 kv 1 ml/min 25 G 4 cm 1.5% w/v 2 M NaOH

of 2-bromoisobutyryl bromide in THF. The mixture was gently stirred and then left at the room temperature overnight. Finally, the surface-initiated chitosan beads (CTS-Br) were collected and washed successively with THF, methanol and DI water; and then dried by a freeze drier for 2 days.

2.2.3.

Surface-initiated atom transfer radical polymerization

In the polymerization of GMA on the initiated chitosan surface, GMA, CuBr, and BPY were added into a round-bottom flask containing a mixed solvent. The solution was stirred and bubbled with an argon gas. The surface-imitated beads were then added into the solution. The flask was tightly sealed and the mixture was shaken at the room temperature for 48 h. After the polymerization, the chitosan beads grafted with the PGMA were separated and washed with excess acetone, EDTA and DI water. They were placed in a freeze drier for 2 days. The collected particles were bigger than the initial size and were designated as chitosan-gPGMA (CTS-PGMA).

2.2.4.

Preparation of NMDG modified CTS-PGMA (CTS-MG)

The CTS-PGMA was reacted with NMDG in 20 ml DMF at 80  C for 14 h. The mixture was subsequently cooled, filtered, washed with methanol and DI water, respectively. The particles called as CTS-MG were dried at a freeze dryer for 2 days. The CTS-MG was stored in an air-tight container and used in this study.

2.3.

Sorbent characterization

2.3.1.

Scanning electron microscopy

The surface morphology of CTS-MG was visualized by an SEM (JEOL, JSM-5600V, Japan). The analysis enables the direct observation of the changes in the surface microstructures of the sorbents due to the chemical modification.

2.3.2.

Specific surface area

The specific surface areas of particles were determined using nitrogen adsorption/desorption and the Brunauer-EmmettTeller (BET) algorithm on a BET Analyzer (NOVA 4200e, Quantachrome Instrument, USA). Prior to the measurements, the samples were degassed at 70  C overnight. The specific surface areas were calculated by the BET method.

2.3.3.

Surface charge density

The surface charge density of CTS-MG was obtained by an acidebase titration. 50-mg sorbent was shaken with 100 ml CO2free aqueous solution for 1 day in an air-tight bottle. A recorded volume of 0.01 M/0.1 M nitric acid or 0.01 M sodium hydroxide was added into the mixture. The time interval between each addition was 20 min to stabilize the solution pH and then pH

2299

value of the mixture was recorded. During the whole process, the solution was shaken and bubbled with the nitrogen gas in order to prevent CO2 dissolution from the atmosphere. The experiments were conducted at the room temperature. The acid and base titration experiments were performed separately.

2.3.4.

Stability of sorbents

The stability of sorbents is important for the operation and regeneration. The sorbents were added into solution of pH 1 to test the stability. The sorbents were left in the solution for 1 days. The shape and solubility of sorbents were observed.

2.4.

Batch sorption experiments

In the pH-effect study, initial pH values of boron solutions were adjusted by nitric acid or sodium hydroxide. The sorbent was added to the boron solution with a known concentration. All mixtures were shaken at the room temperature for 3 days. The samples were taken at the end of the experiments to analyze boron concentration by an inductively coupled plasma emission spectrometer (ICP-OES; PerkineElmer Optima 3000). Both initial and equilibrium pH values were measured by an ORION 920Aplus pH meter. The sorption isotherm experiment of CTS-MG was conducted by a dosage of 0.5 g/L sorbent and various concentrations of boron. The initial pH of the boron solution was adjusted to 7.0. All bottles were shaken at the room temperature for 3 days to reach the adsorption equilibrium. At the end of the experiment, 20-ml samples were collected and filtered for the measurement of boron concentrations by the ICP-OES. The crosslinked chitosan and the Amberlite IRA-743 were also tested for its uptake capacity for boron by the procedure used for the CTS-MG. The sorption kinetics experiments were conducted to determine the equilibrium time for boron sorption. The initial pH was adjusted to the optimum value of 7.0. The CTS-MG with a desired amount was added to the boron solution with a known concentration. The samples were taken for the determination of the boron concentrations by the ICP-OES. In order to test the effects of competing anions on the boron uptake, a few experiments were conducted with sodium chloride and sodium nitrate (ionic strength of 0e100 mM). The solution pH was initially adjusted to 7.0 before the sorbent was added. The sorbents of 0e2 g/L were added into the boron solution. Other procedures were the same as those used in the above mentioned pH-effect experiment. Moreover, simulated seawater was prepared to evaluate the efficiency of the adsorbent for boron removal. The sorbents of 0e2 g/L were added. Other procedures were the same as the pH-effect experiment. The chemical compositions of the simulated seawater were determined by ion chromatography system (ICS-2000 RFIC, Dionex) and total organic carbon (TOC) analyzer (Shimadzu TOC Analyzer, Japan).

3.

Results and discussions

3.1.

Characterization of sorbents

3.1.1.

Scanning electron microscopy and BET surface area

The scanning electron microscopy was used to observe the surface morphology of CCTS and CTS-MG. Fig. 2 clearly

2300

w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 2 9 7 e2 3 0 5

3.1.2.

Surface charge density

The potentiometric titration experiments were used for the determination of the surface charge density (s0) of sorbent. Its value can be calculated from the following equation (Chen and Lin, 2001):    
cA  cB þ OH þ Hþ F (1) s0 ¼ Sa where cA and cB (M ) are the concentrations of acid and base needed to reach a point on the titration curve. [Hþ] and [OH] (M ) are the concentrations of Hþ and OH, F is Faraday’s constant (96490 C/mol), S (m2/g) is the specific surface area of adsorbent, and a (g/L) is the concentration of adsorbent. Fig. 3 depicts the surface charge density as a function of solution pH. It decreases as pH is increased, which is similar to other sorbents reported in the literatures (e.g., Lim et al., 2008). The pHzpc of the sorbent is 7.8. At pH < 7.8, the surface is positively charged, indicating a higher affinity for anions; at pH > 7.8, the surface becomes negatively charged and thus unfavorable for anions removal. pH plays a key role in both adsorption reactions and electrostatic interactions, leading to specific and non-specific adsorption respectively.

3.1.3.

Stability of sorbents

Both CCTS and CTS-MG were left in an acidic solution (pH 1) for 1 day. It was observed that there was no dissolution of the sorbents. The shape was unchanged. This clearly demonstrates that the sorbents are stable in its application/regeneration at a very acidic solution.

3.2.

Effect of solution pH

At total concentration less than 25 mM, the boron exists as H3BO3 and B(OH) 4 instead of polyanionic species (Choi and

4

Surface charge density ( C/m2)

demonstrates that the surface of CCTS is rather smooth. However, many pores and protuberances are found in the CTSMG. The surface modification greatly contributes to a rough surface and porous structure of the sorbent, which can facilitate the diffusion of boric acid or borate ions during the sorption. The BET study further confirms the conclusion from the SEM study. According to the analysis, the CTS-MG has a specific surface area of 16.7 m2/g, more than 10 times higher than the CCTS (1.2 m2/g, around the equipment detection limit).

3 2 1 0 -1 4

5

6

7

8

9

10

pH Fig. 3 e Surface charge density of CTS-Mg as a function of pH (m [ 0.5 g/L; T [ 20  C).

Chen, 1979). A chemical equilibrium program (MINEQL þ by Schecher, 2002) was used to calculate the distribution of boron species in aqueous solution as a function of pH. As shown in Fig. 4a, the fraction of borate anion is less than boric acid at pH < 8; particularly it becomes negligible at pH < 7. When pH is increased from 8 to 9.5, the percentage of borate anion greatly increases; it becomes 100% at pH > 10.5. Solution pH affects both boron speciation and surface properties of sorbent. As shown in Fig. 4a, the boron can be removed not only as negatively charged borate ion but also as neutral boric acid. The boron uptake on the sorbent exhibits a typical parabolic sorption envelope. Under an acidic condition, an increase in pH results in an enhanced sorption. However, after a maximum sorption (around 0.4 mmol/g) is achieved at pH 6.9, a subsequent decrease in the sorption is observed in the curve. Hence, the solution pH of all subsequent experiments was adjusted to around 7. The sorption peak at neutral pH demonstrates clearly that the sorbent can be applied in treatment of water, of which pH ranges 6.5e7.5. Similar trend can also be found in the literatures (Goldberg et al., 1993; Liu et al., 2009; Qi et al., 2002; Wang et al., 2007; Xu and Peak, 2007). Furthermore, the figure indicates that

Fig. 2 e SEM surface morphology: (a) CCTS, (b) CTS-MG.

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w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 2 9 7 e2 3 0 5

a 0.6

Table 2 e List of adsorption reactions.

100

0.5 H3BO3

60 0.3 40 0.2 20 0.1 B(OH)-4 0

Species distribution (%)

q (mmol/g)

80 0.4

d

0.0 2

4

6

8

10

12 where sorbent.

Equilibrium pH

and R* OH represents the functional group on the

b 12

Equilibrium pH

10 8 6 4 2 2

4

6

8

10

12

Initial pH

a decrease in hydrogen ions shifts Reactions (c) and (d) to the right side, which forms more complexes. Thus more boric acid is removed. Under the alkaline condition (pH > 7 in Fig. 4a), borate ions become the dominant species in the aqueous solution. Besides, the deprotonation becomes dominated, which leads to the net negatively surface charges (demonstrated in Fig. 3). An increase in pH enhances the repulsive electrostatic interactions between B(OH) 4 and negatively charged sorbent surface. Meanwhile, more hydroxyl ions are present in the solution, which may have certain affinities for the hydroxyl functional groups in the sorbent (e.g. alkoxide formation) (Garcia-Soto and Camacho, 2005). As strong competition for active sites possibly makes boron complex reaction more difficult, the sorption decreases.

Fig. 4 e Effect of pH on boron sorption: (a) adsorption capacity and boron speciation as a function of pH, (b) initial pH and equilibrium pH ([B]0 [ 0.39 mM; m [ 0.5 g/L; t [ 20  C; contact time [ 3 days).

3.5 3.0

0.8

2.0 qe (mmol/g)

qe (mmol/g)

2.5 the regeneration of the used sorbent may be conducted by either acidic or alkaline solution. Selection of pH of elution solution is important as it affects the adsorption capacity of the regenerated sorbent and operational cost, which will be in the future studies. The pH dependent boron uptake is mainly related to the surface functional groups of CTS-MG. Reactions (a)e(d) of Table 2 are proposed to explain the sorption of boron in acid medium. Under the acidic condition (pH < 7 in Fig. 4a), both tertiary amine and hydroxyl functional groups become protonated, resulting in positively charged surfaces on the sorbent (also demonstrated in Fig. 3). However, the neutral species R* OH may still be present partially. Meanwhile, the predominant species of boron in the aqueous solution is neutral boric acid B (OH)3 (the H3BO3 curve by the MINEQL þ shown in Fig. 4a). Boric acid can complex with R* OH or R OHþ 2 to release H2O and/or Hþ, as shown in Table 2. As pH is increased (up to 7),

1.5 1.0 0.5 0.0

0.6 0.4 0.2 0.0

0

4

8

0

2 4 6 Ce (mmol/L)

12

16

8

20

Ce (mmol/L) Fig. 5 e Boron sorption isotherms of CTS-MG, CCTS and commercial resin Amberlite IRA-743. C CTS-MG, - CCTS, :Amberlite IRA-743, dd Langmuir fitting, —— Freundlich fitting (m [ 0.5 g/L; pH [ 7.0 ± 0.1; T [ 20  C; contact time [ 3 days).

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w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 2 9 7 e2 3 0 5

Table 3 e List of isotherm parameters for boron sorption onto sorbents.

Langmuir

Freundlich

qmax (mmol/g) b (L/mmol) r2 Kf 1/n r2

CTS-MG

CCTS

Amberlite IRA-743

3.25 0.33 0.968 0.87 0.43 0.991

0.39 0.35 0.995 0.10 0.55 0.971

0.71 1.86 0.996 0.41 0.27 0.951

In general, the change in pH during sorption is insignificant as illustrated in Fig. 4b. The final pH becomes slightly higher than initial pH in acidic region, which may be due to the adsorption of hydrogen ions onto the surface functional groups as shown in Reactions (a) and (b) of Table 1. In the alkaline region, less sorption of hydroxyl ions and boron onto the sorbent (Figs. 3 and 4a) occurs. As such no change in the pH is observed.

3.3.

Adsorption isotherm

Fig. 5 shows the adsorption isotherms of CTS-MG, CCTS and Amberlite IRA-743. Generally, the boron uptake increases with an increase in equilibrium concentration for these three materials. However, the boron sorption capacities onto the CCTS and IRA-743 are very low, ranging from 0 to 0.3 mmol/g

and from 0 to 0.6 mmol/g, respectively. The CTS-MG greatly outperforms both CCTS and IRA-743, indicating successful improvement in the boron uptake as a result of grafting of multi-hydroxyl functional groups onto the CCTS. Langmuir and Freundlich adsorption isotherms used to describe the sorption performance are shown as follows: qe ¼

qmax bCe 1 þ bCe

(2)

 1=n qe ¼ Kf Ceq

(3)

where qmax is the maximum sorption capacity (mmol/g), b is the constant related to the affinity between the adsorbent and the adsorbate (L/mmol). Kf is Freundlich constant and 1/n is heterogeneity coefficient. qe and Ce are the sorption capacity (mmol/g) and the equilibrium concentration of adsorbate in solution (mmol/L). The fitted results and the equilibrium constants are given in Fig. 5 and Table 3, respectively. Both isotherms well describe the sorption with high r2 values. Slightly higher r2 value in the fitting by Freundlich isotherm for the CTS-MG indicates that the sorption may be heterogeneous. However, the sorption by CCTS and IRA-743 may be homogeneous because of slightly higher r2 values in the fitting by Langmuir isotherm. These findings are consistent with the SEM studies given in Fig. 2. As shown in Table 3, the maximum sorption capacities of CTS-MG, CCTS and IRA-743 are 3.25, 0.39 and 0.71 mmol/g,

Table 4 e Comparison of boron sorption capacities on several synthesized materials. Resin

Bio-polymer based materials Organic based materials

Inorganic based materials

Base Material

Moiety

Sorption Capacity (mmol/g)

References

3.25 1.1 2.1 0.7
0.9a 0.7a 0.6a 3

This work (Inukai et al., 2004) (Sabarudin et al., 2005) This work (Hilal et al., in press) (Hilal et al., in press) (Hilal et al., in press) (Senkal and Bicak, 2003)

CTS-MG NMDG-type cellulose derivatives CCTs-NMDG Amberlite IRA-743 Diaion CRB02 DOWEX BSR-1 Purolite S108 Polymer supported iminodipropylene glycol poly(styryl sulfonamide) based resin Polymer supported 2-hydroxyethylamino proplene glycol poly(GMA-co-TRIM)-NMDG Multi-hydroxyl functional hairy polymer Functionalized mesoporous solid

CCTS Cellulose CCTS Polystyrene PS-DVB PS-DVB PS-DVB GMA-MMA-DVB

NMDG NMDG NMDG NMDG NMDG NMDG NMDG Glycidol

PS-DVB

Glucamine hydrochloride 2-hydroxyethylamino propylene glycol

2.365

(Gazi et al., 2004)

1.82

(Gazi and Bicak, 2007)

poly(GMA-co-TRIM) PS-DVB

NMDG HEP

1.84 3.28

(Wang et al., 2007) (Gazi et al., 2008)

Mesoporous silica

MCM-41 SBA-15

0.67 0.76 0.49 1.85 0.8 0.63

(Rodriguez-Lopez et al., 2004)

NMDG-MCM-41 Polyol-grafted SBA-15

Glucose Fructose Galactose Mannose NMDG Glucose

GMA-MMA-DVB

(Kaftan et al., 2005) (Wang et al., 2006)

NMDG: N-methylglucamine; GMA-MMA-DVB: glydicyl methacrylate-methyl methacrylate-divinylbenzene; PS-DVB: poly(styreneedivinylbenzene); poly(GMA-co-TRIM): poly(glydicyl methacrylate-co-trimethylolpropane trimethacrylate); HEP: 2-hydroxyethylamino 2,3propanediol. MCM-41, SBA-15: silica mesoporous materials. a Capacity unit (eq/L).

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w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 2 9 7 e2 3 0 5

respectively. The sorption capacities of reported adsorbents and ion exchange resins are between 0.49 and 3.28 mmol/g, as shown in Table 4. It can be concluded that our synthesized CTS-MG greatly outperforms most of adsorbents and resins for boron removal from aqueous solutions.

of Ds and Kf can be determined by the best-fitting between the experimental data and the modeling output. As shown in Fig. 6, this model can well describe the experimental data. The external mass transfer coefficient and the surface diffusivity are in ranges of 104 m/s and 1012 m2/s, respectively.

3.4.

3.5.

Sorption kinetics

Fig. 6 illustrates the experimental data of the adsorption kinetics studies together with the modeling simulation result. Most of boron species are adsorbed rapidly, which is followed by a slightly slow process. It can be observed that more than 90% of the ultimate sorption can be reached within 8 h and the sorption equilibrium time is 12 h. The sorption history was simulated by an intraparticle diffusion model with assumptions of a “two-step mass transport mechanism” and constant physical properties. As the specific surface area of the sorbent was very limited, the hypothesis of surface diffusion was used. The control equation and its corresponding initial and boundary conditions are given as follows (Tien, 1994):   1 vq 1 v 2 vq ¼ 2 r 0  r  ap ; t
0 Ds vt r vr vr

(4)

q ¼ 0 0  r  ap ; t < 0

(5)

vq ¼0 r¼0 vr

(6)

vq Ds rp ¼ Kf ðC  C Þ r ¼ ap vr

(7)

NaCl and NaNO3 widely exist in seawater and surface/ groundwater. Their presence may affect the performance of the CTS-MG. Several experiments were conducted with the ionic strength varied from 0 to 100 mM. As illustrated in Fig. 7, no obvious change of boron uptake in the presence of both NaCl and NaNO3 is observed at the ionic strength <5 mM. Slightly higher removal occurs at the ionic strength of 5e100 mM. The enhancement on boron sorption at high ionic strength is possibly due to the compression of electrical double layer (Chen and Lin, 2001). Furthermore, the observation indicates that the boron uptake may be due to the formation of a strong inner-sphere surface complex. The similar findings were reported for the boron sorption on goethite, gigbsite and kaolinite (Goldberg et al., 1993; Goldberg, 2005).

3.6.

Adsorption in simulated seawater

An adsorption study was conducted for the removal of boron in the simulated seawater. The chemical compositions of the

a

0.7 0.6

q (mmol/g)

where C and q are the concentrations of boron in the bulk and solid phases, respectively; C* is the aqueous phase concentration at the particle surface, in equilibrium with the corresponding concentration in the solid phase q*; Ds is the surface diffusivity within the particles; rp is the particle density; r is radius distance from the center of particle; ap is the particle radius; Kf is the external mass transfer coefficient, and t is the time. The values

Effect of ionic strength

0.5 0.4 0.3 0.2 0.1 0.0 0

1

5

10

50

10 0

salt conc. (mM)

0.5

b

0.4

0.7

0.3

q (mmol/g)

q (mmol/g)

0.6

0.2 Experimental data ---- Modeling prediction -4 kf = 2.0x10 m/s

0.1

-12

0.5 0.4 0.3 0.2 0.1

2

DS= 1.8x10 m /s

0.0

0.0

0

0

5

10

15

20

25

Time (h) Fig. 6 e Boron sorption kinetics of the CTS-MG (m [ 0.5 g/L; pH [ 7.0 ± 0.1; [B]0 [ 0.45 mM; T [ 20  C).

1

5

10

50

100

salt conc. (mM) Fig. 7 e Effects of ionic strength on boron adsorption: (a) NaCl; (b) NaNO3 (m [ 0.5 g/L; pH [ 7.0 ± 0.1; [B]0 [ 0.46 mM; T [ 20  C; contact time [ 3 days).

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w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 2 9 7 e2 3 0 5

Table 5 e Chemical composition of simulated seawater. Species

Concentration*



Cl SO2 4 Boron Naþ Kþ Ca2þ Mg2þ IC TOC pH

17567.0 2243.4 4.8 9794.8 281.7 294.1 1038.8 21.8 3.1 8.1

*All are in milligrams per liter except pH.

simulated seawater are listed in Table 5. Noted that the initial concentration of boron in simulated seawater is 4.8 mg/L (0.44 mM). As shown in Fig. 8, the final boron concentration (at equilibrium) of less than 0.5 mg/L (0.046 mM) is achievable at the dose of sorbent > 1.2 g/L, of which the removal efficiency is above 90%. One can conclude that this polymeric sorbent is applicable in the direct removal of the boron from seawater. Only 1.0 g/L sorbent is needed for the complete removal of boron from the aqueous solution as shown in the boron-only curve, which further confirms the high efficiency of our sorbent. Generally the most of membranes for the seawater desalination can only remove 60e80% of boron in the first-stage RO (Jacob, 2007). If we assume the boron concentration in the seawater is 5 mg/L, the boron concentration in the permeate after the first-stage RO treatment would still be between 1 and 2 mg/L. As a result, a second-stage RO must be used to further remove the boron, which leads to high energy consumption and removal of trace elements beneficial to human beings (e.g., Ca2þ, Mg2þ and CO2 3 ). It should be more economic if a second-stage RO is substituted with a sorption unit of the CTS-MG. If we assume that the boron concentration after the first-stage RO is 2 mg/L,

only a dose of 0.3 g/L is needed in order that the boron concentration becomes less than 0.5 mg/L (0.046 mM) (Fig. 8). The combination of RO with adsorption would significantly reduce the capital and operating costs, and become more attractive than the currently practiced “all RO” technology.

4.

Conclusions

In this study, a novel functionalized polymeric sorbent for the effective boron sorption is successfully designed and fabricated through electrostatic extrusion and atom transfer radical polymerization techniques. The CTS-MG exhibits a remarkably high capability for boron removal with the maximum capacity of 3.25 mmol/g, much higher than those of commercial resins and many other materials previously reported. The optimal sorption occurs near neutral pH region, indicating no pH adjustment needed in the treatment of boron contaminated water. Boron sorption is suppressed by hydrogen ions at low pH and weakened at high pH. The adsorption kinetics study indicates that 90% of boron is adsorbed within 8 h. The adsorption history is well described by an intraparticle surface diffusion model. The presence of sodium chloride or sodium nitrate does not affect boron sorption, implying formation of inner-sphere complexes at the water/solid interface. The sorbent demonstrates its greater potential for the boron removal in desalination of seawater.

Acknowledgements The authors would like to express their appreciation to Agency for Science, Technology and Research of Singapore (Grant No. 0 921 010 059 and R-288-000-066-305)) for the financial support of this study.

100

0.4

80

0.3

60

0.2

40

0.1

20

0.0 0.0

0.5

1.0

1.5

2.0

Removal efficiency (%)

[Boron] (mmol/L)

references 0.5

0 2.5

Dose of sorbent (g/L)

Fig. 8 e Removal of boron from simulated seawater and single-species boron solution (C and B Simulated seawater, initial pH [ 8.1, O single-species boron solution, initial pH [ 7.0, T [ 20  C, contact time [ 3 days).

Bektas, N., Oncel, S., Akbulut, H.Y., Dimoglo, A., 2004. Removal of boron by electrocoagulation. Environmental Chemistry Letters 2 (2), 51e54. Bicak, N., Bulutcu, N., Senkal, B.F., Gazi, M., 2001. Modification of crosslinked glycidyl methacrylate-based polymers for boronspecific column extraction. Reactive & Functional Polymers 47 (3), 175e184. Chen, J.P., Lin, M.S., 2001. Surface charge and metal ion adsorption on an H-type activated carbon: experimental observation and modeling simulation by the surface complex formation approach. Carbon 39 (10), 1491e1504. Choi, W.W., Chen, K.Y., 1979. Evaluation of boron removal by adsorption on solids. Environmental Science & Technology 13 (2), 189e196. Garcia-Soto, M.D.D., Camacho, E.M., 2005. Boron removal by processes of chemosorption. Solvent Extraction and Ion Exchange 23 (6), 741e757. Gazi, M., Bicak, N., 2007. Selective boron extraction by polymer supported 2-hydroxylethylamino propylene glycol functions. Reactive & Functional Polymers 67 (10), 936e942.

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Gazi, M., Senkal, B., Bicak, N., 2004. Modification of crosslinked poly(styrene) based polymers for boron-specific extraction. Macromolecular Symposia 217, 215e221. Gazi, M., Galli, G., Bicak, N., 2008. The rapid boron uptake by multi-hydroxyl functional hairy polymers. Separation and Purification Technology 62 (2), 484e488. Glueckstern, P., Priel, M., 2003. Optimization of boron removal in old and new SWRO systems. Desalination 156 (1e3), 219e228. Goldberg, S., 2005. Inconsistency in the triple layer model description of ionic strength dependent boron adsorption. Journal of Colloid and Interface Science 285 (2), 509e517. Goldberg, S., Forster, H.S., Heick, E.L., 1993. Boron adsorption mechanisms on oxides, clay-minerals, and soils inferred from ionic-strength effects. Soil Science Society of America Journal 57 (3), 704e708. Hilal, N., Kim, G.J., Somerfield, C. Boron removal from saline water: a comprehensive review. Desalination in press, doi:10. 1016/j.desal.2010.05.012. Inukai, Y., Tanaka, Y., Matsuda, T., Mihara, N., Yamada, K., Nambu, N., Itoh, O., Doi, T., Kaida, Y., Yasuda, S., 2004. Removal of boron(III) by N-methylglucamine-type cellulose derivatives with higher adsorption rate. Analytica Chimica Acta 511 (2), 261e265. Itakura, T., Sasai, R., Itoh, H., 2005. Precipitation recovery of boron from wastewater by hydrothermal mineralization. Water Research 39 (12), 2543e2548. Jacob, C., 2007. Seawater desalination: boron removal by ion exchange technology. Desalination 205 (1e3), 47e52. Jin, Y.Z., Gao, C., Kroto, H.W., Maekawa, T., 2005. Polymer-grafted carbon spheres by surface-initiated atom transfer radical polymerization. Macromolecular Rapid Communications 26 (14), 1133e1139. Kabay, N., Guler, E., Bryjak, M., 2010. Boron in seawater and methods for its separation - a review. Desalination 261 (3), 212e217. Kaftan, O., Acikel, M., Eroglu, A.E., Shahwan, T., Artok, L., Ni, C.Y., 2005. Synthesis, characterization and application of a novel sorbent, glucamine-modified MCM-41, for the removal/ preconcentration of boron from waters. Analytica Chimica Acta 547 (1), 31e41. Karahan, S., Yurdakoc, M., Seki, Y., Yurdakoc, K., 2006. Removal of boron from aqueous solution by clays and modified clays. Journal of Colloid and Interface Science 293 (1), 36e42. Kluczka, J., Trojanowska, J., Zolotajkin, M., Ciba, J., Turek, M., Dydo, P., 2007. Boron removal from wastewater using adsorbents. Environmental Technology 28 (1), 105e113. Lim, S.F., Zheng, Y.M., Zou, S.W., Chen, J.P., 2008. Characterization of copper adsorption onto an alginate encapsulated magnetic sorbent by a combined FT-IR, XPS and mathematical modeling study. Environmental Science & Technology 42 (7), 2551e2556. Liu, H.N., Ye, X.S., Li, Q., Kim, T., Qing, B.J., Guo, M., Ge, F., Wu, Z.J., Lee, K., 2009. Boron adsorption using a new boron-selective hybrid gel and the commercial resin D564. Colloids and Surfaces A Physicochemical and Engineering Aspects 341 (1e3), 118e126. Nadav, N., Priel, M., Glueckstern, P., 2005. Boron removal from the permeate of a large SWRO plant in Eilat. Desalination 185 (1e3), 121e129.

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w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 3 0 6 e2 3 1 4

Available at www.sciencedirect.com

journal homepage: www.elsevier.com/locate/watres

Monitoring organic loading to swimming pools by fluorescence excitationeemission matrix with parallel factor analysis (PARAFAC) _  ska-Sobecka a, Colin A. Stedmon b, Rasmus Boe-Hansen c, Bozena Seredyn Christopher K. Waul a, Erik Arvin a,* a

Department of Environmental Engineering, Technical University of Denmark, Miljoevej, Building 113, 2800 Kgs. Lyngby, Denmark Department of Marine Ecology, National Environmental Research Institute, Aarhus University, Frederiksborgvej 399, 4000 Roskilde, Denmark c Kru¨ger A/S, Gladsaxevej 363, 2860 Søborg, Denmark b

article info

abstract

Article history:

Fluorescence ExcitationeEmission Matrix spectroscopy combined with parallel factor

Received 31 August 2010

analysis was employed to monitor water quality and organic contamination in swimming

Received in revised form

pools. The fluorescence signal of the swimming pool organic matter was low but increased

8 December 2010

slightly through the day. The analysis revealed that the organic matter fluorescence was

Accepted 12 January 2011

characterised by five different components, one of which was unique to swimming pool

Available online 20 January 2011

organic matter and one which was specific to organic contamination. The latter component had emission peaks at 420 nm and was found to be a sensitive indicator of organic

Keywords:

loading in swimming pool water. The fluorescence at 420 nm gradually increased during

Swimming pool

opening hours and represented material accumulating through the day.

Fluorescence

ª 2011 Elsevier Ltd. All rights reserved.

ExcitationeEmission Matrix (EEM) Wastewater Parallel factor analysis (PARAFAC)

1.

Introduction

Monitoring of water quality in swimming pools is important in order to avoid health risk to swimmers and swimming pool staff. In general, there are three sources of organic matter: the water supplied to the pool, a passive loading of organics leached from the bodies of bathing guests, and a more direct loading of bodily wastes in the form of urine and faeces. The latter is most harmful, however, all three contribute to the organic loading and hence the microbial quality in pools. Microbial safety of swimming pool water is required by law

(Directive, 2006). Moreover, the organic matter concentration should be maintained low as it reacts with chlorine and produces a suite of chlorinated organic compounds (e.g. trihalomethanes THMs) which are known to be harmful. To be effective, a water quality monitoring system needs to detect contamination at the initial stage. At present, for chemical and microbial water quality monitoring, a combination of sampling and subsequent analysis is usually applied and may not assure the health of the bathers. Therefore, there is a strong need for on-line sensors providing immediate information on water quality which enables a quick remedial

* Corresponding author. Tel.: þ45 45251472; fax: þ45 932850.  ska-Sobecka), [email protected] (C.A. Stedmon), [email protected] (R. Boe-Hansen), E-mail addresses: [email protected] (B. Seredyn [email protected] (C.K. Waul), [email protected] (E. Arvin). 0043-1354/$ e see front matter ª 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2011.01.010

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action. Fluorescence might be a promising technique that fulfils the required criteria. Fluorescent properties of organic matter have been widely studied in various aquatic systems for many years (Coble et al., 1990; Muller et al., 2008; Henderson et al., 2009; Johnstone and Miller, 2009). However, limited studies have been carried out on the fluorescence properties of chlorinated aquatic organic matter and these have mainly been focused on the chlorination of drinking (Fabbricino and Korshin, 2004; Johnstone and Miller, 2009; Roccaro et al., 2009) and recycled water (Hambly et al., 2010a, b). To our knowledge, there is no information on fluorescent organic matter in swimming pools. Drinking water purification studies suggest that in chlorinated waters organic matter fluorescence will be low (Johnstone and Miller, 2009). In swimming pools, water has to be disinfected with chlorine and adequate free chlorine level has to be maintained to assure the microbial safety (Uhl and Hartmann, 2005). Chlorine dosages used for swimming pool disinfection are higher than those applied in drinking water treatment. Moreover, pool water is recycled and therefore chlorinated on continuous basis (Lee et al., 2009). Due to fluorescence quenching properties of chlorine and its reactivity (Henderson et al., 2009 and references therein), one can expect a very low background fluorescence of swimming pool organic matter, which may be ideal for using fluorescence to monitor for excessive organic loading, and indicate when further water quality treatment or other intervention are required. Based on this assumption, a series of experiments employing both swimming pool water and wastewater has been performed aiming at estimating the detection limit for anthropogenic contamination in swimming pool water. Wastewater fluorescence has been previously successfully investigated for detecting cross-connections between drinking and recycled water systems (Hambly et al., 2010a, b). The authors reported promising role of peak T1 (lex/em ¼ 300/350 nm) in distinguishing recycled water samples from potable water samples (Hambly et al., 2010a). Moreover, combination of peak T1 and C1 (lex/em ¼ 325/426 nm) was able to further separate recycled water samples at various treatment stages (Hambly et al., 2010b). In our study, the obtained fluorescence ExcitationeEmission Matrices (EEMs) of swimming pool organic matter were evaluated by parallel factor analysis (PARAFAC) modeling which delivered information on both qualitative and quantitative aspects of the obtained fluorescence signal.

2.

Materials and methods

2.1.

Sampling and storage

Two swimming pools at the Gladsaxe Sport Centre (Søborg, Gladsaxe council, Denmark) were sampled during this study. One was a full length cold water pool (2700 m3) and the other a smaller warm water basin (50 m3). Each pool has a separate water treatment system. To minimize the adverse effect of chlorine on human skin, sodium chloride is normally added to the water. A sodium chloride concentration of about 0.4% is maintained in both basins. Water temperature is maintained in range of 26e27  C and 31e34  C in the cold water and warm

water basin, respectively. The pH was 7.4 in both pools. The warm water pool had a 13-time higher number of guests per m3 than the cold water pool, which corresponded to 4.6 and 0.35 persons/m3/day, respectively (Table 1). A set-up of the water recirculation in the two systems is shown in Fig. 2. This set-up was similar for both systems; therefore it is shown as one. In this set-up, only elements of interest are shown. Both pool treatment systems contained coarse filtration and sand filtration. Chlorine is produced from electrolysis of sodium chloride. This is an in-line and an onsite production and dosage in the warm water pool system and in the cold water system, respectively. In both systems, the water is recirculated and about 3e5 m3 of water is added per day. Both filter beds were backwashed a week before the study period. During the experiment, samples were taken from a variety of sites to minimize sampling error and generate a representative set of samples. All sampling sites for both pool systems are marked in Fig. 1. For the cold water basin system, the sampling sites were as follows: directly in the pool (site 1), pipe collecting water from the basin, before the equalizing tank and sand filters (site 2), and a small pipe at the analytic board (site 3). For the warm water pool, samples were taken directly from the pool (site 4), the equalizing container collecting water from the basin (site 5) and a pipe at the analytic board (site 6). The difference between sampling sites between the two systems (sites 2 and 4) was caused by their accessibility in terms of sampling and applying water quality sensors. In the first series of the experiment, water quality was monitored for 5 days (3 days for site 2 only and 2 days for sites 1 and 3e6) within the opening hours (07e21 on weekdays and 08e15 on weekends) of the sport centre so the daily variability in fluorescence could be assessed. Conductivity was measured on-site with Hach HQ 14d meter (Hach Co., USA). For fluorescence, absorbance, adsorbable organic halogens (AOX), and non-volatile organic carbon (NVOC) analyses, water samples were collected in acid washed and precombusted (550  C) 40 ml glass vials with Teflon-lined silicone caps. A total of 100 ml of

Table 1 e Characteristics of the two investigated swimming pools. Factor Volume of pool Area of pool Temperature Retention time Bathers per day Person volumetric load Make-up water Filter area Filter run time pH Chlorine conc. Combined chlorine conc. THM conc. AOX conc. NVOC

Unit m3 m2  C h pe/d pe/m3/d m3/d m2 weeks e mg Cl2/L mg Cl2/L mg/L mg Cl2 mg C/L

Warm water Cold water pool pool 50 39 31-34 0.5 228 4.6 3-5 7-8 1 7.4 1.1 0.8-1.1 32e50 1.7e2 2.2e3.2

2700 1050 27 5 900e1000 0.35 3e5 21 1 7.4 0.65 0.4e0.6 23 1.0e1.3 1.8e1.9

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warm-/cold-water basin

2

make-up water

analytic board 3, 6

chlorination

sand filters

5 equalization tank

1, 4

coarse filter

Fig. 1 e Set-up of the warm and cold water swimming pool systems. Sampling sites (described in Section 2.1) are marked with numbers.

Na2S2O3 solution (concentration of 5 g/l) was added to every sample to bind free chlorine and stop further reaction with organic matter during storage (Johnstone and Miller, 2009). Before sampling this preservation method had been evaluated for its effects on organic matter fluorescence. No adverse effects were seen (Supplementary Information, SI 1.). The collected samples were kept refrigerated at 4  C, transported to the laboratory and analyzed within 3 days for NVOC and absorbance/fluorescence, and within 2 weeks for AOX.

2.2.

Wastewater experiment

In addition to the pool sampling a laboratory experiment with wastewater additions was carried out. Raw municipal wastewater was used as the source of domestic waste including fractions released directly from human bodies thus equivalent of anthropogenic organic matter in swimming pools (saliva, sweat, skin, hair, urine, faeces, etc.). A 5 L swimming pool water sample was taken from the warm water basin. The sample was kept refrigerated (4  C) and used in the experiment on the following day. Before starting the experiment the pool water was spiked with sodium hypochlorite solution to reestablish in situ chlorine concentrations of 1.2 mg/L free Cl2. The wastewater used in the experiment was raw sewage from a municipal wastewater treatment plant serving 135,000 persons (Lundtofte, Kgs. Lyngby, Denmark). The wastewater is mainly of residential origin, only 8e10% is industrial wastewater. It was filtered through a 1.6-mm pore size glass fiber filter before use. The characteristics of the water are shown in Table 2. The analyses were performed by a commercial laboratory (Miljoelaboratoriet I/S, Glostrup, Denmark) except

the NVOC analyses which were carried out at DTU Environment (Kgs. Lyngby, Denmark). Total and combined chlorine concentrations were measured by the DPD (N,N-diethylp-phenylenediamine) colorimetric method in a Allcon Test spectrophotometer (APHA, 2005).

2.3.

Analyses

In total, 103 samples of swimming pool water have been analyzed for fluorescence EEM and NVOC to gain an understanding of how the fluorescence signal of pool organic matter varies in intensity and characteristics throughout the day. NVOC was measured using a Shimadzu TOC-V WP analyzer with ASI_V autosampler. The analyzer uses sodium persulfate solution, UV radiation and a temperature of 80  C to oxidize organic carbon. A 10-fold auto-dilution was used for analysing swimming pool water samples which had been previously found to show better reproducibility for samples containing chloride ions (data not shown). For wastewater analysis, a manual dilution of wastewater was prepared before analysis without further auto-dilution. Fluorescence was measured in a 1-cm cuvette using a Varian Cary Eclipse Fluorescence Spectrophotometer. Wavelength range for excitation spectra was 240e450 nm while for emission 300e600 nm, with 5-nm and 2-nm steps, respectively. Excitation and emission slit widths were set to 5 nm and photomultiplier tube voltage to 1000 V. The excitation and emission spectra measured from each sample were combined to create excitation emission matrices (EEMs). In such obtained EEMs both excitation and emission wavelengths were corrected using Rhodamine B and a ground quartz diffuser,

Table 2 e Characteristics of filtered and unfiltered wastewater used in the experiment. Parameter BOD COD Total nitrogen Total phosphorus Coliform bacteria Thermotol. coliform bacteria HPCyeast, 22  C, 72 h HPCyeast, 37  C, 48 h NVOC

Unfiltered WW

Filtered WW

Unit

Method/Standard

330 1.2Eþ03 60 12 1.3E þ07 1.3E þ07 1.5E þ07 0.5E þ07

73 180 42 6.5

mg O2/L mg O2/L mg N/L mg P/L CFU/100 mL CFU/100 mL CFU/mL CFU/mL mg C/L

DS/EN 1899-1 (1999) DS/ISO 15705 (2002) DS/EN ISO 11905-1 (1997) DS 292 (1985) DS 2255 (2001) DS 2255 (2001) DS/EN ISO 6222 (2000) DS/EN ISO 6222 (2000)

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respectively. Sample inner filter effects were also corrected using absorption measurements. Absorption measurements were performed on Varian Cary 50 Bio UV-visible Spectrophotometer in a 1 cm quartz cuvette and UV-visible spectra recorded from 240 to 700 nm with 0.5 nm slit. The correction was followed by Raman calibration according to Lawaetz and Stedmon (2009). The calibrated and corrected fluorescence data were then modeled using the DOMFluor Toolbox in Matlab according to the procedure recommended in Stedmon and Bro (2008). The number of fluorescence components was found by a validation method including split half and residual analysis. Short term changes in organic matter fluorescence immediately after addition to chlorinated water were monitored by measuring 20 successive EEMs within 160 min (repeated measurements without refilling the cuvette). A blank sample where swimming pool water was replaced by MilliQ water was also measured. AOX concentration was measured using a rapid analysis test kit from Hach-Lange (LCK391). The method is based on the same pre-treatment principle as the standard ISO method (ISO 9562, 2004), but with wet-oxidation of the carbon disc and photometric determination of halogen ions (Cl).

2.4.

Detection limit

Detection limits were calculated using a method based on t-distribution test (Harris, 2003). This method generates a detection limit that has a 99% chance to be greater than the blank. Fluorescence EEMs of a blank sample which was swimming pool water collected the day before the experiment was performed. A sample close to the DL (0.75% wastewater addition) was generated and measured 7 times. In addition a series of wastewater additions to swimming pool water were made in the concentration range of 0e2% and their fluorescence measured. Fluorescence intensity of wastewater-like peaks, found during PARAFAC modeling were used as signal response for calculations. The signal detection limit was calculated according to Eq. (1). The concentration detection limit ( yDL) was calculated using the obtained calibration curve.

yDL ¼ yblank þ t  s

(1)

yblank corresponds to the average fluorescence intensity for wastewater fluorescence in blank samples. The t value is from t-test (equal to 3.14 for seven measurements) and s is the standard deviation of the wastewater fluorescence in a 0.75% v/v wastewater addition sample.

3.

Results and discussion

3.1. PARAFAC components and correlation to wastewater addition Kinetics study of fluorescence in swimming pool water with wastewater addition showed that all components were quite stable within the measurement time (160 min) (Supplementary Information, SI 2). Comparison of the EEMs of swimming pool water with and without wastewater addition

2309

showed that swimming pool water exhibited very low fluorescence. Consequently, the fluorescence spectrum of a swimming pool with wastewater added is clearly dominated by the wastewater organic matter fluorescence (Fig. 2). PARAFAC modeling of the swimming pool samples, including samples with and without wastewater addition revealed that the fluorescence of organic matter in swimming pools could be characterized by five different fractions (Table 3 and Fig. 3). There are no earlier studies of swimming pool water organic matter fluorescence to which to compare these data directly. However, the swimming pool water spectrum resembled the EEM of recycled water subjected to deep bed sand filtration, ultraviolet disinfection and super-chlorination which has been reported by Hambly et al. (2010b). Moreover, the swimming pool water organic matter fluorescence signals are similar to those seen in natural waters. The broad and long wavelength peaks of component 1 have also been found in a variety of contrasting environments and it is thought to represent terrestrial material (Stedmon et al., 2003 and references therein). Its presence in the wastewater is likely due to surface run off (drainage) being present. Component 2 had a proteinlike fluorescence. This type of fluorescence signals is often associated with microbial activity found in many surface waters and related to either biological productivity or sewage contamination and referred to as the T peak, or protein-like peak (Coble, 1996; Baker et al., 2004; Coble, 2007; Cumberland and Baker, 2007). Component 3 is a ubiquitous fluorescence signal known as C-peak, found in almost all types of waters, including surface, ground and marine waters (Coble, 1996). However, additional peaks in this EEM region, originating from optical brighteners have been reported (Henderson et al., 2009 and references therein). These peaks are characterized by excitation maxima at 375, 350 and 330 nm, and emission maxima at 410e450 nm. Beside their industrial applications (e.g. paper brightening), optical brightening agents are commonly used in household detergents and thus can be found worldwide in sewage and sewage-contaminated waters (Takahashi and Kawamura, 2007). Component 4 resembles the previously identified M-peak. Originally, it was associated with surface water productivity (Coble, 1996) but then it was found to be more of a ubiquitous component (Coble, 2007). Component 5 exhibited a shape and form similar to the protein-like peak but both its emission and excitation maxima were shifted towards longer wavelengths. Among the fractions, component 5 was specific to swimming pool water and components 1 and 3 were specific to wastewater. Components 2 and 4 were present in both water types, however, at much higher concentrations in wastewater (Fig. 4). Components 1e4 showed strong correlation with wastewater concentration (R2 0.985; 0.989, 0.987 and 0.995, respectively) (Fig. 5). The corresponding R2 values for the fixed wavelength pairs (without PARAFAC) were close and equaled 0.991; 0.986; 0.989 and 0.993 (data not shown). The best linear relationship between wastewater concentration and fluorescence was found for component 4. Among the five fluorophores only component 5 was not correlated with wastewater concentration, hence associated with swimming pool organic matter. Considering component 5’s “position” in the EEM, which is between protein-like and humic-like

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Fig. 2 e Example EEMs of the swimming pool water and wastewater samples: top row e measured data, bottom row e PARAFAC model: (a) cold water pool sample, (b) warm water pool sample with 0.75% addition of wastewater, (c) MQ water with 0.75% addition of wastewater, (d) warm water pool sample.

regions, this component is most likely a combination of swimming pool microbial activity products and swimming pool humic-like substances. Fluorescence intensity of component 5 exhibited an average intensity of 0.047 R.U. and

Table 3 e Excitation and emission maxima of PARAFAC components found for swimming pool e wastewater samples. Component number 1 2 3 4 5

Excitation wavelength, nm

Emission wavelength, nm

260, <240, 370 280 <240, 330 <240 <240, 310

520 330 420 370 360

0.011 R.U. for the warm and the cold pool, respectively, with standard deviation of 0.002 R.U. in both cases.

3.2. pool

Daily variability of fluorescence in the swimming

The possibility of using components 1e4 for monitoring anthropogenic contamination in swimming pools was tested on fluorescence daily variation data. For the cold water pool, almost all the wastewater indicator components showed fluorescence below the wastewater detection limit. Only one sample exceed the detection limit (Table 4 and Fig. 6). In contrast, the warm water basin was expected to contain more contamination due to the higher person volumetric load (Table 1). An example of daily variation of fluorescence in the warm water basin is shown in Fig. 6. Among the four wastewater-like peaks component 3 shows the biggest increase through the day (almost doubled on both sampling days). Similarly, component 4 almost doubled through both

w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 3 0 6 e2 3 1 4

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Fig. 3 e EEMs of PARAFAC components (contour plots) found for the swimming pool water (warm pool) and wastewater samples.

sampling days. However, component 4 fluorescence exceeded the wastewater detection limit for two samples only, whereas component 3 was above the detection limit for the both entire days (Table 4, Fig. 6). Component 5, a swimming pool organic matter-like peak, showed some variation but no systematic trend during the day. Fluorescence of components 1 and 2 was below the wastewater detection limit through the whole sampling period (Table 4, Fig 6).

3.3. Correlation between fluorescence, NVOC, AOX and combined chlorine As indicated with the fluorescence data, there was little variability in the NVOC concentrations in the cold water pool. The NVOC values were relatively constant (1.8e1.9 mg C/L on both

measurement days). As a result no correlation was found between fluorescence and NVOC, and fluorescence and combined chlorine. Combined chlorine concentration was in range of 0.4e0.6 mg/L, and AOX content varied from 1.0 to 1.26 mg/L, whereas THM concentration was 23 mg/L. For the warm water pool, the NVOC content was in range of 2.2e2.8 mg C/L on the first sampling day, and higher (range 2.5e3.2 mg C/L) on the second sampling day, and increased through the day. The increase in NVOC was caused by input of two organic fractions, assigned as components 3 and 5 which were correlated with NVOC (Fig. 7a). The material released from bodies of bathers in swimming pools contains both organic matter and ammonia and can react with chlorine. Both chlorinated and non-chlorinated organic matter can be detected as total or non-volatile organic

2312

Fluorescence [R.U.]

w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 3 0 6 e2 3 1 4

c1

0.06

c2

c3

c4

c5

Table 4 e Wastewater detection limits in the swimming pool water for the PARAFAC components. Detection limit, DL

0.04

DL [R.U.] DL [% WW]

0.02 0 0.75% WW

100% SP Sample type

SP + 0.75%WW

Fig. 4 e The fluorescence intensity of each of the five components (c1ec5): Milli Q water with 0.75% v/v wastewater, warm swimming pool water (stored overnight at 4  C without addition of Na2S2O3) and warm swimming pool water with 0.75% v/v wastewater. Values presented are averages of seven replicate measurements and the error bars represent one standard deviation.

carbon. Therefore, in this study the concentration of combined chlorine in the swimming pools was correlated with NVOC (R2 of 0.626, data not shown). Consequently, a correlation between fluorescence components 3 and 5 and combined chlorine concentration in the warm water pool was found (Fig. 7b). High concentration of disinfection by-product in the swimming pool water, showed by chloramines, was also confirmed by the AOX content, which was in range of 1.73e2.03 mg/L for the warm water pool. Negative correlations between AOX and NVOC, and AOX and fluorescence of components 3 and 4 were also observed (Supplementary Information, Figs. SIe5).

The trend in component 3 observed for the warm water pool represents organic loading to the pool which can originate from either direct release of organic matter to the pool and/or

c3

c4

0.035 0.8

0.030 0.5

0.019 0.6

0.023 0.2

Wastewater detection limit

The detection limits of wastewater in the swimming water were calculated for all the wastewater components (Table 4). It shows that the lowest detection limit was found for

0.1

0.12 c2

c3

c4

c1

c5

Fluorescence [R.U.]

c1 Fluorescence [R.U.]

c2

a product of its initial oxidation. This component is stable and accumulates during the day but becomes oxidized during the night when the organic loading to the pool has stopped (i.e. there are no guests in the pool). No similar accumulation was observed in the cold water swimming pool. Most probably, this is due to the high number of bathers in the warm water pool (Table 1). Moreover, a higher temperature in the warm water pool (31e34 and 27  C in the warm and the cold water pool, respectively) could both promote release of organic substances from the bather’s skin and stimulate the production rate of the oxidized fraction of component 3. The higher organic matter release and oxidation extent are confirmed by higher NVOC, THM and AOX concentration in the warm water pool (Table 1). Similarly, Glauner et al. (2005) found that total organic carbon and THM concentration were well correlated with the overall bather number in a pool. Component 5 consists of substances permanently present in swimming pool water, which most likely can be associated with products of oxidation reactions in the swimming pool water. A relatively high concentration of chlorine in the investigated water excludes significant microbial production that had been previously related to this fluorescence region (Henderson et al., 2009). Therefore, component 5 is likely produced in chemical rather than microbial reactions proceeding in the swimming pool water.

3.5.

3.4. Monitoring organic matter loading and accumulation in swimming pool water

c1

0.09

0.06

0.03

c2

c3

c4

c5

0.08 0.06 0.04 0.02 0

0 0

0.5

1

1.5

2

Wastewater concentration [%]

Fig. 5 e Correlation between fluorescence of the PARAFAC components (c1ec5) and wastewater concentration in the swimming pool e wastewater samples.

6

8

10

12

14

16

18

20

22

Time [o'clock] Fig. 6 e An example of the daily variation of the PARAFAC fluorescence components 1e5 (c1ec5) in the warm water pool.

w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 3 0 6 e2 3 1 4

Fluorescence [R.U.]

a

reactions on the organic matter released in the pools does occur and one of the reaction products has a humic-like fluorescence signal, similar to what is found in many natural waters.

0.1 c3

c5 2

0.08

R = 0.930

0.06

4.

0.04

2

R = 0.715

0.02 0 2.0

2.5

3.0

3.5

NVOC [mg/L]

b

0.1

Fluorescence [R.U.]

c3

c5

0.08 R2 = 0.606 0.06 0.04

2313

Conclusions

 Organic matter fluorescence has a potential for monitoring swimming pool water quality.  Among the two investigated pools, only the warm water pool exhibited detectable waste contamination.  In swimming pool-wastewater organic matter, five fluorescence components have been found, one of which was unique to swimming pool organic matter (component 5, exhibiting excitation maximum at <240 and 310 nm and emission maximum at 360 nm).  Component 3 was a very good indicator for anthropogenic release to swimming pool water. It exhibited emission maximum at 420 and had two excitation peaks: one below 240 and the other at 330 nm.

R2 = 0.520

Acknowledgements

0.02 0 0.2

0.4

0.6

0.8

1

Combined chlorine [mg/L]

Fig. 7 e (a) NVOC, (b) combined chlorine vs. fluorescence of PARAFAC components 3 and 5 (c3 and c5) in samples collected from the warm water pool.

Jørgen Vienberg, Tom Østmar, and other staff at the Gladsaxe Sport Center, for allowing access to the swimming pools and helping with the sampling. Sinh Hy Nguyen and Peter Kofoed for their technical assistance. Henrik R. Andersen for his helpful comments. This research was supported by the Danish Agency for Spatial and Environmental Planning, Ministry of the Environment (Ref. BLS-403-00043 Aqua Fingerprint).

Supplementary data component 4 and equaled 0.2% v/v of wastewater. The highest detection limit has been obtained for component 1 and it was four times higher than for component 4. The detection limit for component 3 was 0.6% v/v of wastewater, which for this experimental set-up corresponded to 0.13 mg C/L. The five facts: (i) specificity of component 3 to wastewater, (ii) its stability in chlorinated water, (iii) the increase in fluorescence of this component through the opening hours, (iv) its correlation with NVOC and combined chlorine, and (v) its sensitivity to wastewater additions, confirms its usefulness for anthropogenic organic matter detection. This means that among the three wastewater components, component 3 was found to contain a fraction that might be released to water by the swimming pool guests. Therefore, the excitation and emission maxima of component 3 are recommended for monitoring of organic loading in swimming pool water. The fact that a humiclike fluorescence peak is a waste indicator in swimming pool water, is a novel finding. Previous works on detecting waste contamination in surface and potable waters showed that the protein-like peaks are the best waste indicators (Henderson et al., 2009 and references therein) and that peak C plays a supplementary role (Hambly et al., 2010a, b). However, in the highly oxidative environment of swimming pool water, protein-like material does not persist. Most likely, the suite of

Supplementary data associated with this article can be found, in the online version, at doi:10.1016/j.watres.2011.01.010.

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management of bathing water quality and repealing Directive 76/160/EEC. DS/EN 1899-1, 1999. Water quality e determination of biochemical oxygen demand after n-days (BODn) e part 1: dilution and seeding method with allylthiourea addition. DS 2255, 2001. Water quality e enumeration of coliform bacteria and thermotolerant coliform bacteria e multiple-tube fermentation method (most-probable number method). DS 292, 1985. Water analysis e total phosphorus e photometric method. DS/EN ISO 11905-1, 1997. Water quality e determination of nitrogen e part 1: method using oxidative digestion with peroxodisulfate. DS/EN ISO 6222, 2000. Water quality e enumeration of culturable microorganisms e colony count by inoculation in a nutrient agar culture medium. DS/ISO 15705, 2002. Water quality e determination of chemical oxygen demand index (ST-COD) e small-scale sealed-tube method. Fabbricino, M., Korshin, G.V., 2004. Probing the mechanisms of NOM chlorination using fluorescence: formation of disinfection by-products in Alento River water. Water Science and Technology: Water Supply 4, 227e233. Glauner, T., Waldmann, P., Frimmel, F.H., Zwiener, C., 2005. Swimming pool waterdfractionation and genotoxicological characterization of organic constituents. Water Research 39, 4494e4502. Harris, D.C., 2003. In: Quantitative Chemical Analysis, sixth ed. W. H. Freeman and Company, Michelle Russel Julet, New York, pp. 726e727. Hambly, A.C., Henderson, R.K., Baker, A., Stuetz, R.M., Khan, S.J., 2010a. Fluorescence monitoring for cross-connection detection in water reuse systems: Australian case studies. Water Science & Technology 61 (1), 155e162. Hambly, A.C., Henderson, R.K., Storey, M.V., Baker, A., Stuetz, R. M., Khan, S.J., 2010b. Fluorescence monitoring at a recycled water treatment plant and associated dual distribution system e implications for cross-connection detection. Water Research 44 (18), 5323e5333.

Henderson, R.K., Baker, A., Murphy, K.R., Hambly, A., Stuetz, R.M., Khan, S.J., 2009. Fluorescence as a potential monitoring tool for recycled water systems: a review. Water Research 43, 863e881. ISO 9562, 2004. Water quality e determination of adsorbable organically bound halogens (AOX). Johnstone, D.W., Miller, C.M., 2009. Fluorescence excitationeemission matrix regional transformation and chlorine consumption to predict trihalomethane and haloacetic acid formation. Environmental Engineering Science 26, 1163e1170. Lawaetz, A.J., Stedmon, C.A., 2009. Fluorescence intensity calibration using the Raman scatter peak of water. Applied Spectroscopy 63, 936e940. Lee, J., Ha, K.-T., Zoh, K.-D., 2009. Characteristics of trihalomethane (THM) production and associated health risk assessment in swimming pool waters treated with different disinfection methods. Science of The Total Environment 407, 1990e1997. Muller, C.L., Baker, A., Hutchinson, R., Fairchild, I.J., Kidd, C., 2008. Analysis of rainwater dissolved organic carbon compounds using fluorescence spectrophotometry. Atmospheric Environment 42, 8036e8045. Roccaro, P., Vagliasindi, F.G.A., Korshin, G.V., 2009. Changes in NOM fluorescence caused by chlorination and their associations with disinfection by-products formation. Environmental Science and Technology 43, 724e729. Stedmon, C.A., Markager, S., Bro, R., 2003. Tracing dissolved organic matter in aquatic environments using a new approach to fluorescence spectroscopy. Marine Chemistry 82, 239e254. Stedmon, C.A., Bro, R., 2008. Characterizing dissolved organic matter fluorescence with parallel factor analysis: a tutorial. Limnology and Oceanography: Methods 6, 572e579. Takahashi, M., Kawamura, K., 2007. Simple measurement of 4, 40 -bis (2-sulfostyryl)-biphenyl in river water by fluorescence analysis and its application as an indicator of domestic wastewater contamination. Water, Air &; Soil Pollution 180, 39e49. Uhl, W., Hartmann, C., 2005. Disinfection by-products and microbial contamination in the treatment of pool water with granular activated carbon. Water Science and Technology 52, 71e76.

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Parameters predictive of Legionella contamination in hot water systems: Association with trace elements and heterotrophic plate counts Annalisa Bargellini a, Isabella Marchesi a, Elena Righi a, Angela Ferrari a, Stefano Cencetti b, Paola Borella a, Sergio Rovesti a,* a b

Department of Public Health Sciences, University of Modena and Reggio Emilia, Via Campi 287, I-41125 Modena, Italy University Hospital Policlinico of Modena, Via del Pozzo 71, I-41124 Modena, Italy

article info

abstract

Article history:

The contamination of hot water samples with Legionella spp. was studied in relation to

Received 24 September 2010

temperature, total hardness, trace element concentrations (iron, zinc, manganese, and

Received in revised form

copper) and heterotrophic plate counts (HPC) at both 22 and 37
C. Factor analysis and

21 December 2010

receiver operating characteristic (ROC) curves were used to establish the cut-off of water

Accepted 12 January 2011

parameters as predictors for Legionella contamination. Legionella spp. was isolated in 194

Available online 20 January 2011

out of 408 samples (47.5%), with Legionella pneumophila being the most common (92.8%). After multiple logistic regression analysis, the risk for legionellae colonisation was posi-

Keywords:

tively associated with Mn levels >6 mg l1, HPC at 22
C >27 CFU l1, and negatively with

Legionella

temperature >55
C and Cu levels >50 mg l1. Multiple regression analysis revealed that

Heterotrophic bacteria

Legionella spp. counts were positively associated with Mn, HPC at 37
C and Zn and nega-

Trace elements

tively associated with temperature. Only 1 out of the 97 samples (1%) having a Mn

Manganese

concentration, an HPC at 22
C and an HPC at 37
C below the respective median values

Hot water distribution system

exhibited a Legionella spp. concentration exceeding 104 CFU l1 vs. 41 out of the 89 samples

Water quality parameters

(46.1%) with the three parameters above the medians. Our results show a qualitative and quantitative relationship between Legionella spp., the Mn concentration and heterotrophic plate counts in hot water samples from different buildings, suggesting that these parameters should be included in a water safety plan. The role of manganese in biofilm formation and its possible involvement in the mechanisms favouring Legionella survival and growth in water niches should be investigated further. ª 2011 Elsevier Ltd. All rights reserved.

1.

Introduction

Drinking water usually meets the chemical and microbiological quality standards, but chemical changes and microbiological colonisation that reduce the water quality often occur within buildings and human-made structures (Kilb et al., 2003; Lehtola et al., 2004; Wingender and Flemming, 2004). The

majority of microbial growth occurs on the pipe wall in biofilms predominantly formed by autochthonous aquatic microflora that have no relevance to human health (World Health Organization, 2003). This microbial colonisation may be even more relevant in hot water leaving from building plumbing systems, where opportunistic pathogens such as Legionella pneumophila find optimal growth conditions. Once integrated

* Corresponding author. Dipartimento di Scienze di Sanita` Pubblica, Via Campi 287, 41125 Modena, Italy. Tel.: þ39 (059) 2055222; fax: þ39 (059) 2055483. E-mail address: [email protected] (S. Rovesti). 0043-1354/$ e see front matter ª 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2011.01.009

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in a water biofilm, and because of their ability to replicate in protozoa hosts, these potential pathogens are protected from external stresses, such as the action of disinfectants, and can persist and multiply (Borella et al., 2005a; Moritz et al., 2010). Water temperature, flow, stagnation, pipe materials, degree of pipe corrosion, high water shear stress and flushing are wellknown factors favouring Legionella spp. growth (Exner et al., 2005), whereas water characteristics such as trace element concentrations and hardness have been only suggested (Borella et al., 2005b). The relationship between heterotrophic bacteria and Legionella colonisation has begun to be investigated only recently (Edagawa et al., 2008; Vo¨lker et al., 2010). The concentrations of iron (Fe) and zinc (Zn) might indicate corrosion processes in plumbing systems in which FeeZn pipes are used. Given that the plumbing systems are frequently metallic in nature, at least in Italy (Borella et al., 2004), a question arises concerning the effects of metals leached from hot water tanks and pipes on the survival and growth of Legionella (States et al., 1985). Iron is needed for the laboratory growth of Legionella; it serves as a cofactor in enzymes and is relevant for Legionella infection. In addition, L. pneumophila replication in the mammalian host is dependent upon Fe, and multiple pathways for Fe acquisition has been discovered (Cianciotto, 2007). The L. pneumophila major secretory protein (Msp), a zinc metalloprotease, has been found to significantly alter human phagocyte functional responses, thus contributing to the pathogenesis of Legionnaires’ disease (Sahney et al., 2001; Banerji et al., 2005). Copper (Cu) is a well-known anti-infective element (Gordon et al., 1994), and methods based on coppersilver injection have been successfully used to control Legionella contamination of water (Stout and Yu, 2003). Although manganese-oxidising bacteria are a consistent part of biofilms in drinking water distribution systems (Dickinson et al., 1997; Kielemoes et al., 2002; Baker et al., 2003) and although there is evidence that manganese (Mn) is essential for bacterial growth and pathogenesis (Kehres and Maguire, 2003; PappWallace and Maguire, 2006; Arirachakaran et al., 2007), data on the involvement of Mn in Legionella virulence and growth are lacking. The heterotrophic plate count (HPC) was among the first parameters used to monitor drinking water and has become an indicator of general water quality within distribution systems (World Health Organization, 2003). An increase in the HPC indicates treatment breakthrough, post-treatment contamination, growth within the water conveyed by the distribution system or the presence of deposits and biofilms. A sudden increase in the HPC above baseline values should trigger actions to investigate and, if necessary, remediate the situation. The role of the HPC monitoring the presence of Legionella in hot water distribution systems is less studied (Kusnetsov et al., 2003; Edagawa et al., 2008; Moritz et al., 2010). In this study, we evaluated the relationship between warm water Legionella contamination and a number of water parameters suspected to play a role in the presence and/or growth of the bacterium. These parameters included microbial parameters (HPC at 22 and 37
C), trace element concentrations (Fe, Zn, Mn, and Cu), hardness, and temperature. The final aim was to suggest new parameters that are useful in predicting the risk of Legionella contamination in hot water distribution systems.

2.

Materials and methods

2.1.

Sample collection

In total, 408 hot water samples were collected between 2002 and 2009 from different public and private structures in Modena province lacking a disinfecting procedure or at a distance of any disinfecting procedure. In the area, both spring water and groundwater are used for drinking supply, the majority of them treated with chlorine dioxide (Fantuzzi et al., 2003). Four hospitals (1 public and 3 private) composed by eight different buildings, 32 hotels (including 2 bed & breakfast and 3 residences), and 88 private homes were investigated in the study. In total 143 samples were collected from hospital storage tanks, return loops and distal outlets (showers or taps), in order to verify bacteria contamination at each floor and/or each ward at different distance from central heater. Similar criteria were used to sample hotel hot water (n ¼ 124); two to six water samples, depending on the number of rooms and building floors, were taken from bathroom outlets (shower heads or bath-tap). In addition, 141 samples were collected from private homes, and sampling points ranged from 1 to 2 depending on the number of baths. Water was sampled without flaming and after 1 min of flushing. To neutralise residual free chlorine, sodium thiosulphate was added to sterile bottles for bacteriological analysis, whereas acid-preserved glass bottles were used for chemical determinations. Bottles were returned to our laboratory immediately after sampling for chemical and bacteriological analyses. A strict protocol was established to standardise the procedures for sample collection, transport, handling and storage until analysis according to ISO 19458 (ISO, 2006).

2.2.

Microbiological analysis

Culture and identification of Legionella spp. were carried out following the ISO 11731 (ISO, 1998) method as described in previous studies (Borella et al., 2005b). Briefly, 1 l of water was filtered (0.2-mm-pore-size polyamide filter, Millipore, Billerica, MA), the filtrate was resuspended in 10 ml of the original sample water by vortexing for 10 min, and 5 ml of the sample heat-treated (50
C for 30 min in a water bath) to reduce contamination by other microorganisms (Leoni and Legnani, 2001). Two aliquots of 100 ml of the original or concentrated samples (heat-treated and untreated, diluted 1:10 and undiluted) were plated onto MWY selective medium (Oxoid Ltd., Basingstoke, Hampshire, UK). The plates were incubated at 36  1
C with 2.5% CO2 for 10 days and analysed on day 4 with a dissecting microscope. Presumptive Legionella colonies were subcultured on BCYE (with cysteine) and CYE (cysteine-free) media (Oxoid) to test their inability to grow in the absence of this amino acid; these cultures were incubated at 36  1
C for 48 h. Colonies grown on BCYE were subsequently identified using an agglutination test (Legionella latex test, Oxoid). This test allows separate identification of L. pneumophila serogroup 1 and serogroups 2e14 and detection of seven species of nonL. pneumophila legionellae (polyvalent) that have been implicated in human disease. The results were expressed as

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CFU l1, and the detection limit of the procedure was 25 CFU l1 (mean of two plates). Along the study period our laboratory participated to an external quality control programme (Legionella EQA Scheme, Health Protection Agency, UK) to verify the proficiency of reference cultural method. An internal quality control gave a coefficient of variation (CV) <5% performing counts by two different persons (Bargellini et al., 2010). Heterotrophic plate counts (HPCs) at 37
C and 22
C were determined in duplicate by the pour plate method using standard Plate Count Agar (PCA, Oxoid). The plates were incubated at 37
C for at least 48 h and at 22
C for at least 72 h.

2.3.

Physical and chemical analyses

At the time of sampling, the water temperature in
C was registered using a calibrated thermometer placed in the middle of the water stream. The total water hardness was measured by the standard technique and expressed in
f. Water Mn, Zn, Fe and Cu concentrations were determined in acidified samples (1% HNO3) using a PerkineElmer A Analyst 200 flame atomic absorption spectrometer (AAS, Shelton, USA) with deuterium arc background correction. Instrument calibration standard solutions were prepared from commercial materials. As part of the quality control protocol, analyses of reagent blanks and the certified reference water standard SRM 1640 were carried out by means of AAS, and the results were in good agreement with the certified values (mean CV within 10%). All labware used for metal analysis was cleaned with detergent, thoroughly rinsed with tap water, soaked in a 10% nitric acid solution overnight and finally rinsed with ultrapure water.

2.4.

Statistical analysis

Statistical calculations were performed using SPSS, version 17.0 (SPSS, Inc. Chicago, IL, USA). Non-normally distributed parameters were logarithmically transformed, and the results are presented as geometric means. The bacteriological data were converted into log10 (x þ 1). Student’s t test was used to compare the mean values of the measured variables with respect to Legionella spp. presence. Pearson’s correlation

coefficients and multivariate linear regression analysis using a forward likelihood ratio procedure (Wald test) were performed to study the relationship between Legionella counts and continuous variables. Receiver operating characteristic (ROC) curve analysis was then performed to define the cut-off levels for the predictors of Legionella contamination. The optimal cut-off point was defined as the point on the ROC curve nearest to the point where both the sensitivity and specificity were 1. On this basis, we established the cut-offs as follows: Fe > 42 mg l1 (n ¼ 188); Mn > 6 mg l1 (n ¼ 188); Zn > 375 mg l1 (n ¼ 199); HPC at 37
C  150 CFU ml1 (n ¼ 202); and HPC at 22
C  27 CFU ml1 (n ¼ 207). The ROC curves gave a non-significant result for all other parameters; thus, cut-offs of 50 mg l1 for Cu (n ¼ 36), 25
f for total hardness (n ¼ 197), and 55
C for temperature (n ¼ 35) were selected based on previous results (Borella et al., 2005b). The c2 and odds ratios (OR) with 95% confidence intervals (CI) were calculated to compare the proportions of contamination with respect to these dichotomous variables. Variables that were significant or near significance according to this analysis were entered into a multiple logistic regression. Using the conditional model, independent predictors of colonisation were established. Variables were retained in the model if the likelihood ratio test was significant ( p < 0.05). Finally, one-way analysis of variance (ANOVA) was used to assess differences in the Legionella spp. concentrations with respect to the predictive variables categorised into quartiles.

3.

Results

Legionella spp. was isolated in 194 out of 408 samples (47.5%) with a geometric mean of 4.5  103 CFU l1 (range ¼ 25e1.25  106 CFU l1). Among positive samples, L. pneumophila was detected in 180 samples (92.8%). Levels of L. pneumophila between 103 and 104 CFU l1 were found in 63 samples (32.5%), and levels above 104 CFU l1 were found in 73 (37.6%) samples; levels above 104 CFU l1, according to the Italian Guidelines, require intervention (Italian Guidelines, 2000). Table 1 shows the physicochemical and microbiological characteristics of the water samples in relation to Legionella spp. colonisation. Water samples positive for Legionella

Table 1 e Mean values and ranges of the measured parameters in water samples with respect to Legionella spp. presence. Parameters

Total (n ¼ 408)

Legionella spp.

p value

Negative (n ¼ 214)

Positive (n ¼ 194)

Geom. Mean (range)

Geom. Mean

Geom. Mean

Mn (mg l1) Zn (mg l1) Fe (mg l1) Cu (mg l1) HPC at 22
C (CFU ml1) HPC at 37
C (CFU ml1)

6.8 (0e124.8) 258.6 (1e6000) 49.3 (0.1e5116) 15.0 (0e800) 35.0 (0e8  105) 101.1 (0e8  105) Mean  SD

5.2 167.2 33.6 15.8 15.3 56.6 Mean  SD

9.1 418.3 75.2 14.2 67.4 191.6 Mean  SD

<0.001 <0.001 <0.001 ns <0.001 <0.001

Temperature (
C) Total hardness (
f)

44.3  9.4 24.2  11.9

45  10.4 25.3  12.6

43.4  8.1 23.1  11.1

ns ns

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spp. exhibited significantly higher Fe, Mn, and Zn concentrations and HPCs compared to negative samples. The association between the studied parameters and Legionella spp. counts is presented in Table 2. Legionella was positively associated with the concentrations of Mn, Fe, and Zn and with the HPC at both 22 and 37
C and was negatively associated with temperature and total hardness. Trace element concentrations were highly related each other: FeeMn, r ¼ 0.725, p < 0.001; FeeZn, r ¼ 0.629, p < 0.001; MneZn, r ¼ 0.574, p < 0.001; CueFe and CueMn exhibited the same lower correlation (r ¼ 0.150, p < 0.002). The HPCs at 22 and 37
C were highly related (r ¼ 0.674, p < 0.001). After applying the multiple regression analysis, variables associated with Legionella concentrations were (in decreasing order) the Mn concentration, the HPC at 37
C, the Zn concentration, and the temperature, which explained 20% of the variation in the Legionella counts (F ¼ 26.98, p < 0.001). A second analysis performed only on Legionella-positive samples confirmed only the association with the Mn concentration and the HPC at 37
C (b ¼ 0.399 and 0.223, respectively, p < 0.001). Table 3 shows the risk for Legionella colonisation in relation to water parameters categorised into dichotomous variables. According to the univariate analysis, the risk significantly increased in water exceeding the cut-off values for the concentrations of Mn, Zn, and Fe; the HPC at 22
C; and the HPC at 37
C. The risk significantly decreased with respect to increasing Cu concentration, temperature and total hardness. After applying a multiple conditional logistic regression, the Mn concentration and the HPC at 22
C remained the main predictors of Legionella contamination, followed by temperature and Cu, which have a protective effect. A U curve was observed for total hardness: the percentage of positive samples was lower in the samples with hardness values of 25e35
f (n ¼ 111) compared to samples (n ¼ 297) having hardness values of <25 or >35
f (29.7% vs. 54.2%, c2 ¼ 19.4, p < 0.001). Fig. 1 shows the increasing trend of Legionella spp. counts with the quartiles of the Mn concentration, the HPC at 22
C and the HPC at 37
C, both separately and grouped into three categories (low, intermediate and high) according to their median levels. Only 1 out of 97 samples (1.0%) in the low category exhibited a Legionella concentration >104 CFU l1 vs.

Table 2 e Association between Legionella spp. concentration and measured variables as determined by correlation and multivariate linear regression analysis (forward likelihood ratio procedure). Predictive variables

Mn Zn Fe Cu HPC at 22
C HPC at 37
C Temperature Total hardness

Linear correlation

Multivariate linear regression

r

p

b

0.359 0.301 0.337 0.007 0.278 0.294 0.107 0.115

<0.001 <0.001 <0.001 ns <0.001 <0.001 <0.05 <0.05

0.246 0.131 0.094 0.032 0.108 0.229 0.096 0.042

p <0.001 <0.05 ns ns ns <0.001 <0.05 ns

Table 3 e Predictive variables associated with Legionella spp. presence as determined by univariate and multiple logistic regressions. Characteristics

Mn >6 mg l1 (n ¼ 188) Zn >375 mg l1 (n ¼ 199) Fe >42 mg l1 (n ¼ 188) Cu >50 mg l1 (n ¼ 36) HPC at 22
C > 27 CFU ml1 (n ¼ 207) HPC at 37
C > 150 CFU ml1 (n ¼ 202) Temperature >55
C (n ¼ 35) Total hardness >25
f (n ¼ 197)

Univariate regression

Multiple logistic regression

OR (95% CI)

OR (95% CI)

3.19 2.35 2.25 0.34 2.68

(2.13e4.79)a (1.58e3.49)a (1.51e3.35)a (0.15e0.74)b (1.80e4.00)a

2.91 (1.90e4.44)a

0.37 (0.16e0.84)c 2.24 (1.47e3.42)a

2.31 (1.55e3.43)a 0.35 (0.16e0.77)b

0.38 (0.17e0.88)c

0.54 (0.36e0.80)b

a p < 0.001. b p < 0.005. c p < 0.05.

41 out of 89 samples (46.1%) in the high category. When selecting only the samples in the extreme quartiles (<25th percentile and >75th percentile) for all three considered parameters, 5 out of 16 samples (31.3%) in the lowest quartile were positive vs. 19 out of 23 (82.6%) in the highest quartile (c2 ¼ 10.52, p < 0.001); the geometric means were 351 CFU l1 vs. 6.9  105 CFU l1, respectively (t ¼ 6.09, p < 0.001). No sample exceeded 103 CFU l1 in the first group, whereas 84.2% of samples were above 104 CFU l1 in the highest quartile.

4.

Discussion

The results of our study on a large number of hot water samples confirm the existence of frequent Legionella spp. colonisation (47.5% of examined samples), mainly represented by L. pneumophila (92.8%), in both public and private Italian buildings (Borella et al., 2004, 2005b; Leoni et al., 2005). Trace element concentrations, heterotrophic bacteria counts, temperature and hardness were measured to identify reliable indicators of hot water quality able to predict Legionella risk. In this study, we found that both the presence and concentration of Legionella were positively associated, in decreasing order, with the concentrations of Mn, Zn and Fe. Mn > 6 mg l1, Zn > 375 mg l1 and Fe > 42 mg l1 were sufficient to increase the colonisation risk 3.2, 2.3 and 2.2 times, respectively. These levels are frequently present in hot water as the result of factors such as corrosion or leaching from tanks, plumbing materials and solders of the examined buildings. Our results confirm those obtained in a previous Italian multicentric study (Borella et al., 2003), suggesting that the association between Legionella and metals is dependent on phenomena occurring inside hot water distribution systems, more than on water source and treatment which can differ according to the geographic area. Studies on drinking water confirm that stagnation and corrosion in pipelines can affect

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4

-1

Legionella spp. (Log CFU l )

F = 25.5, p <0.001

F = 13.5, p <0.001

F = 44.2, p <0.001

F = 12.0, p <0.001

3

2

1

0 m Mediu

00 -1

HPC at 37°C (CFU ml )

High

Low

>600

151-6

-1

HPC at 22°C (CFU ml )

0 15-15

<15

>250

0 30-25

2-29.9

<2

a

>13

6-13

2-5.9

<2

-1

Mn (µg l )

Mn+HPC 22°C+HPC 37°Ca

Low = Mn <6 µg l-1 + HPC 22 °C <30 CFU ml-1 + HPC 37 °C <150 CFU ml-1 (n = 97) Medium = one or two parameters in the higher range (n = 222) High = Mn >6 µg l-1 + HPC 22 °C >30 CFU ml-1 + HPC 37 °C >150 CFU ml-1 (n = 89)

Fig. 1 e Legionella spp. counts with respect to the Mn concentration, the HPC at 22 and the HPC at 37
C quartiles (<25, 25e50, 50.1e75, >75 percentiles). The black line indicates 100 CFU lL1, a level that is often used to classify Legionella risk. the water concentrations of metals such as Fe (Sarin et al., 2004) and Cu (Lehtola et al., 2007). A positive association between Legionella spp. contamination and the Fe concentration, but not the Zn and Cu concentrations, was reported in a recent study of hot water samples from public buildings in Japan (Edagawa et al., 2008). In experimental studies, concentrations of Fe and Zn similar to those found in our water samples enhanced growth of L. pneumophila, whereas higher concentrations became toxic (States et al., 1985). The different sensibility of Legionella species and serogroups to water content in Cu and Mn observed in the previous Italian multicentre survey (Borella et al., 2003) was not confirmed in the present investigation, probably for a high variability of legionellae isolates. After applying a multivariate analysis, only Mn entered in the model, suggesting that Mn may be a better indicator of Legionella spp. contamination. Indeed, manganese-oxidising bacteria, and not sulphate-reducing and iron-oxidising bacteria, have been identified as the major contributors of biofilm occurrence in drinking water distribution systems (Dickinson et al., 1997; Kielemoes et al., 2002; Baker et al., 2003; Scheifhacken et al., 2010). Anaerobic conditions and excessive microbiological growth can favour the Mn release from pipe surfaces into the water stream (Hoehn et al., 1987). In addition, Mn is increasingly recognised as an essential element for the growth and pathogenesis of bacteria species, including Salmonella typhimurium and Streptococcus mutans (Kehres and Maguire, 2003; PappWallace and Maguire, 2006; Arirachakaran et al., 2007). Recent studies have highlighted the essential role of Fe and Zn in Legionella spp. pathogenesis (Sahney et al., 2001; Cianciotto, 2007), but investigations on Mn involvement in Legionella virulence and growth are lacking. Our results contribute to the establishment of the concentration of Cu able to protect from Legionella contamination, confirming that Cu > 50 mg l1 is associated with a lower risk (Leoni et al., 2005). In treated water distribution systems, copperesilver ionisation has been shown to be effective against the formation of Legionella and other

waterborne pathogens in biofilms and planktonic phases at Cu levels exceeding 200 mg l1 and in combination with silver (Shih and Lin, 2010). This inhibitory effect was not confirmed by Mathys et al. (2008), who compared the pipe materials, and plumbing systems with Cu pipes were more contaminated than those made of synthetic materials or galvanised steel. Heterotrophic plate count measurements are assumed to provide a general indication of the hygienic conditions of piped water distribution systems (World Health Organization, 2003). In this study on hot water, Legionella presence and counts were highly associated with the HPC at both 22 and 37
C, and the cut-off for positivity was low (27 and 150 CFU ml1, respectively). The HPC at 22
C was highly related with Legionella presence, as expected for environmental bacteria, whereas the HPC at 37
C was predictive of the Legionella count, in agreement with the capability of this bacterium to easily reproduce at high temperatures. In another similar study, in which 11 physical, chemical and microbiological characteristics were analysed, significant positive associations with Legionella contamination were observed for turbidity, the Fe concentration, and the HPC at 30
C (Edagawa et al., 2008). Additionally, in Finnish hospital water systems, legionellae occurrence was explained by conductivity (positive), hardness (negative) and the HPC at 30
C (positive) (Kusnetsov et al., 2003). The association between the HPC and Legionella is not surprising, as a prerequisite for Legionella colonisation of water systems is the presence of other heterotrophic organisms that establish the biofilm (Borella et al., 2005a). HPCs are in fact used to follow biofilm development in both drinking and hot water distribution systems (Bagh et al., 2004; Moritz et al., 2010). These biofilms provide the habitat for the interaction between Legionella and protozoa (Lau and Ashbolt, 2009). In a recent investigation, HPCs were found to differ with respect to Acanthamoeba presence/absence in cold and hot spring waters (Huang and Hsu, 2010), suggesting that heterotrophic bacteria from natural sources or from biofilms may allow the predation of protozoa needed for Legionella survival.

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By grouping the samples according to the Mn concentration and HPC percentile distributions, the trend of increasing Legionella counts was even more evident, suggesting that these parameters contribute together in favouring Legionella colonisation in water distribution systems. The relationship between legionellae, water Mn concentrations, manganeseoxidising and -reducing bacteria, biofilms and heterotrophic plate counts is intriguing and needs of further investigation. The protective effect of temperatures higher than 55
C confirmed the results of previous studies (Darelid et al., 2002; Borella et al., 2005b; Hruba´, 2009), but due to the Italian legislative requirement of maintaining hot water at 48  5
C in public structures, temperatures >55
C were infrequently measured in our samples collected at the distal sites (35/408). We agree with Hruba´ (2009) that an operating temperature of at least 55
C might be optimal to reduce the risk of Legionella contamination, avoiding the economic and safety limitations of temperatures as high as 60
C. Finally, a U trend was observed with respect to water hardness, with lower Legionella colonisation in the interval 25e35
f. The published data are contradictory, as positive associations (Lasheras et al., 2006), negative associations (Kusnetsov et al., 2003) and no association (Bonetta et al., 2010) have been reported between Legionella and total hardness. In addition, the association of Legionella presence with this parameter was stronger than that of Legionella growth (Lasheras et al., 2006), and variations according to species have also been observed (Borella et al., 2005b). We should point out that the interpretation of our results was complicated by the use of softening systems, as water softening reduces the potential of the system to form biofilms but may increase corrosion (World Health Organization, 2007).

5.

Conclusions

Our results show a qualitative and quantitative relationship between Legionella spp., the manganese concentration and heterotrophic plate counts in hot water samples collected in both private and public buildings from Modena area. Manganese concentrations below 6 mg l1, HPCs below 27 CFU ml1 at 22
C and HPCs below 150 CFU ml1 at 37
C were found to be good predictors of Legionella absence, although also a temperature >55
C and Cu levels >50 mg l1 can contribute to the lack of contamination. These parameters might be useful to monitor the quality of hot water supplies if included in a comprehensive water safety plan, indicating the possibility of contamination with Legionella and other opportunistic pathogens. The role of Mn in biofilm formation and its possible involvement in the mechanisms favouring Legionella survival and growth in water niches should be investigated further.

Acknowledgements This work was supported by the Italian University Minister (PRIN, 2000, 2002, and 2005).

references

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w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 3 2 2 e2 3 3 0

Available at www.sciencedirect.com

journal homepage: www.elsevier.com/locate/watres

Biodegradation potential of bulking agents used in sludge bio-drying and their contribution to bio-generated heat Ling Zhao, Wei-Mei Gu, Pin-Jing He*, Li-Ming Shao State Key Laboratory of Pollution Control and Resources Reuse, Key Laboratory of Yangtze River Water Environment, College of Environmental Science and Engineering, Tongji University, 1239 Siping Road, Shanghai 200092, PR China

article info

abstract

Article history:

Straw and sawdust are commonly used bulking agents in sludge composting or bio-drying.

Received 14 July 2010

It is important to determine if they contribute to the biodegradable volatile solids pool. A

Received in revised form

sludge bio-drying process was performed in this study using straw, sawdust and their

1 January 2011

combination as the bulking agents. The results revealed that straw has substantial

Accepted 13 January 2011

biodegradation potential in the aerobic process and sawdust has poor capacity to be

Available online 22 January 2011

degraded. The temperature profile and bio-drying efficiency were highest in the trial that straw was added, as indicated by a moisture removal ratio and VS loss ratio of 62.3 and

Keywords:

31.0%, respectively. In separate aerobic incubation tests, straw obtained the highest oxygen

Dewatered sludge

uptake rate (OUR) of 2.14 and 4.75 mg O2 g
1VS h
1 at 35  C and 50  C, respectively, while

Straw

the highest OUR values of sludge were 12.1 and 5.68 mg O2 g
1VS h
1 at 35  C and 50  C and

Sawdust

those of sawdust were 0.286 and 0.332 mg O2 g
1VS h
1, respectively. The distribution of

Bio-drying

biochemical fractions revealed that soluble fractions in hot water and hot neutral deter-

Biodegradability

gent were the main substrates directly attacked by microorganisms, which accounted for

Bio-generated heat

the initial OUR peak. The cellulose-like fraction in straw was transformed to soluble fractions, resulting in an increased duration of aerobic respiration. Based on the potential VS degradation rate, no bio-generated heat was contributed by sawdust, while that contribution by straw was about 41.7% and the ratio of sludge/straw was 5:1 (w/w, wet basis). ª 2011 Elsevier Ltd. All rights reserved.

1.

Introduction

Sludge bio-drying is a novel alternative method of composting to treat dewatered sludge (Velis et al., 2009). In sludge biodrying, the wastes are dried by the thermal energy released from aerobic decomposition of degradable organic matters. Moisture removal makes the lower calorific value be increased and the viscosity and odors be reduced. It has been demonstrated that bio-drying is a prospective method of volume reduction and pre-stabilization that benefits shortterm storage, transportation and incineration (Zhang et al.,

2008; Navaee-Ardeh et al., 2010). However, dewatered sludge contains few biodegradable organic substances, a higher moisture content and poor porous biomass matrix. Due to their widespread availability, straw and sawdust are commonly used as bulking agents in sludge bio-drying or composting to provide favorable free air space (FAS) and regulate the moisture content (Mohajer et al., 2009; Iqbal et al., 2010). Most studies attribute the enhancement of composting efficiency by bulking agents to optimization of the physical structure of composting material, but few studies have considered the biodegradation of bulking agents

* Corresponding author. Tel./fax: þ86 21 6598 6104. E-mail address: [email protected] (P.-J. He). 0043-1354/$ e see front matter ª 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2011.01.014

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w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 3 2 2 e2 3 3 0

during the process (Zorpas and Loizidou, 2008; Tre´mier et al., 2009; Yanez et al., 2009). Indeed, some studies assume that these materials are recalcitrant, and their contribution to organic degradation can be neglected (Yamada and Kawase, 2006). Straw and sawdust are rich in lignocellulose and the degradation of the plant cell walls of these materials is often inefficient. This is because most polymers of cellulose and hemicellulose are either insoluble or blocked by the insoluble matrix (Dinis et al., 2009). However, given a specific environment and sufficient time, such recalcitrant organic materials might be exploited more or less. It has been reported that cellulose was more extensively degraded than hemicellulose and lignin by microbial isolates as inoculants during composting of pepper plant waste and rice straw. Furthermore, if the microorganisms were selected appropriately for the characteristics of the raw material, the final lignin content could be decreased by 14.3e24.3% (Vargas-Garcı´a et al., 2007). Petric studied poultry manure composting using different percentages of added wheat straw and reported that in 13 days, the organic matter losses were 34.3, 38.8 and 25.1% in samples containing 26.5, 17 and 12% (dry basis) added straw, respectively. However, it was difficult to explain that adding more wheat straw gave higher biodegradable carbon (Petric et al., 2009). In fact, many reports have implied that bulking agents play a significant role as carbon sources or a substrate (Vuorinen, 2000). These studies have all mentioned that C/N ratio is an important parameter influenced by bulking agents, indicating that the bulking agents participate in mineralization (Chang and Chen, 2010). However, all of these studies have focused on the biochemical evolution of the mixtures, while the contribution of the bulking agents has been unclear until now (Marche et al., 2003; Zhang et al., 2010). The composting materials are divided into easily biodegradable fraction, slowly biodegradable fraction and inert fraction without distinguishing the substrate and bulking agents (de Guardia et al., 2008). Only Mason stated that woodchips likely made a substantial contribution to the biodegradable volatile solids pool in bovine manure composting with a high moisture content of 80% (Mason et al., 2004). Sludge bio-drying is an approach of biomass energy utility and temperature is the most important indicator. High temperatures (>55  C) enable the moisture to shift to vapor and enhance the vapor pressure of the air-flow passing through the matrix to carry more moisture out (Frei et al., 2004; Navaee-Ardeh et al., 2006). Thus the biodegradation potential of a bulking agent would significantly influence the bio-drying process by the bio-generated heat. Also the physical structure and moisture content of the materials are influenced by the decay of bulking agents. Moreover, when compared with composting (40e60 d), sludge bio-drying requires much less time (13e18 d) (Roca-Pe´rez et al., 2009). Therefore, the objectives of this study were as follows: 1) To investigate the biodegradation potential of straw and sawdust separately compared with sludge itself under conditions similar to those used for sludge bio-drying; 2) To analyze the mechanisms resulting in the difference in biomass degradability; 3) To evaluate the bio-generated heat contributed by bulking agents in bio-drying.

2.

Materials and methods

2.1.

Dewatered sludge and bulking agents

The dewatered sewage sludge was obtained from a local municipal wastewater treatment plant in Shanghai, China. The plant treats 75,000 m3 d
1 of wastewater (93% domestic and 7% industrial sewage) using anaerobiceanoxiceoxic process. Sludge was dewatered by centrifuge with addition of organic flocculating agents. Chopped rice straw of 0.5e3 mm and powdery sawdust were used as bulking agents. Sawdust was the by-product of a wood working manufacturing facility produced by crushing various types of wood. The characteristics of the raw materials are presented in Table 1.

2.2.

Experimental method

2.2.1.

Sludge bio-drying process with different bulking agents

The bio-drying process was conducted in column reactors made of PVC plastic with volumes of 85 L (Fig. 1). The outer wall of each column was wrapped with 100 mm thick hollow cotton for thermal insulation. A layer of straw was covered on the materials at the top of the column to prevent heat loss and vapor condensation. A perforated baffle with a 2 mm mesh was fixed above the bottom to support the materials and facilitate aeration. A whirlpool pump (XGB-8, Penghu Co., China) and a gas-flow meter (LZB-10, Shanghai Instrument Co., China) were used for aeration. A time-based aeration control system was adopted for intermittent O2 supply with a frequency of 10 min run/20 min stop. The airflow rate was 0.084 m3 h
1 kg
1 (wet basis), which was relatively higher in bio-drying than composting to enhance the moisture loss. Three trials with different bulking agents were run: sludge/ straw/sawdust (Trial A), sludge/straw (Trial B) and sludge/ sawdust (Trial C). The ratio of sludge/bulking agent was 5:1 (w/w, wet basis) and the total weight of feedstock was 18 kg. For Trial A, the proportion of sludge:straw:sawdust was 5: 0.33: 0.67. Because this study was designed to evaluate the degradation potential of bulking agents, attempts were made to avoid differences in physical structures resulting from straw and sawdust. Namely, the volume of the feedstock was not very large and the materials were turned every 2 days to homogenize the materials. During the bio-drying process, matrix temperature was monitored using a thermometer (WMY-01 C, Huachen Co., China) with sensors located at the top, middle and bottom of the matrix.

Table 1 e The characteristics of the raw materials. Sludge Moisture content (%) VS (%, dry basis) TOC (%, dry basis) TN (%, dry basis) Calorific value (MJ kg
1dry matter)

78.4 66.3 35.6 6.24 163

    

0.53 0.18 0.35 0.10 0.71

Straw 11.0 88.5 39.6 1.01 158

    

0.25 0.19 0.05 0.08 1.83

Sawdust 9.01  96.2  47.7  0.514  179 

0.31 0.21 0.01 0.002 0.99

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Fig. 1 e Experimental equipment for sludge bio-drying.

The aerobic degradability of straw and sawdust was investigated separately through incubation at constant temperature (Barrena et al., 2009). To provide the bulking agents with a similar environment as that used in bio-drying, the moisture content of straw and sawdust was adjusted using the supernatant of mixed sludge from the aeration tank, which simultaneously functioned as an inoculant. The solid content of the supernatant was 0.528 g L
1 and the adjusted moisture content of the straw and sawdust was 77.3  0.053 and 71.6  0.012, respectively. About 14 g (dry weight) of sludge, straw and sawdust were placed into 1.5 L Erlenmeyer flasks and incubated hermetically in 35  C and 50  C incubators. To ensure aerobic respiration of the materials, the sludge was spread on the inner wall of the flask to form a thin layer (Fig. 2). The straw and sawdust were spread dispersedly on the bottom of the flask and enough free air space can be formed. Before sealing, the flasks were aerated with fresh air. During the incubation, about 50 mL of gas was extracted from the flasks at intervals of 12 h or 24 h and the O2 content was measured using a detector (CYS-1, Xuelian Co., China). After sampling, fresh air was aerated into the flasks again and the incubation was continued. The total

incubation time was 16 days and all tests were conducted in duplicate. The OUR (mg O2 g
1VS h
1) was calculated as:

OUR ¼

ð21%
CÞ  V  32  1000 22:4  Wvs  t

(1)

where C is the O2 content in the flask after a period of time(t); 21% is the O2 content of the fresh air, which is regarded as the initial value by the O2 detector; 32 is the molecular weight of O2 (g mol
1); V is the volume of the flask (L); Wvs is the weight of the volatile solid at time t (g); t is the period of time (h).

70 Average temperature of matrix ( °C )

2.2.2. Separate aerobic respiration and degradation test of the three materials

65 Turning

60 55 50 45 40 35 30 25 20

Room temperature 0

2

4

6

8

10

12

14

Time (d) Trial A: Sludge/Straw/Sawdust Trial B: Sludge/Straw Trial C: Sludge/Sawdust

Fig. 2 e Erlenmeyer flasks for aerobic incubation of sludge, straw and sawdust separately with the intermittent replacement of fresh air.

Fig. 3 e Temperature evolution of sludge bio-drying with different bulking agents (Trial A: Straw and sawdust; Trial B: Straw; Trial C: Sawdust).

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Table 2 e Mass balance of the sludge and bulking agents mixture after 13 days of bio-drying. Trials

Trial A a

Total mixture (kg) Dry matter (kg) Moisture (kg) VS (kg) TOC (kg) TKN (kg) COD (kg)

Trial B a

Trial C a

Initial

Final

Removal ratio

Initial

Final

Removal ratio

Initial

Final

Removal ratio

18.0 6.04 12.0 4.64 2.15 1.31 3.26

11.1 4.95 6.15 3.50 1.54 0.927 2.24

38.3% 18.0% 48.8% 24.5% 28.7% 29.0% 31.3%

18.0 5.67 12.3 4.20 1.92 1.39 3.06

9.34 4.69 4.65 2.90 1.39 0.988 2.09

48.1% 17.3% 62.3% 31.0% 27.9% 28.9% 31.6%

18.0 5.96 12.0 4.68 2.29 1.23 3.50

11.2 5.10 6.10 3.77 1.67 0.981 2.45

37.8% 14.4% 49.3% 19.4% 26.8% 20.1% 29.9%

a Due to the heterogeneity of the mixture, there is a little variance between the measured value and the calculated value based on the proportion of the raw materials (SD < 0.169). Here is the measured value.

2.3.

Analytical methods

oxygen demand (COD) was determined by dichromate oxidation (GB 9834e1988). A calorimeter (MTUM-A4, China) was used to evaluate the calorific value. After air-drying at 60  C, the samples were fractionated by a modified version of the method proposed by Van Soest. (Van Soest and Wine, 1967; Parnaudeau et al., 2004). The hot water soluble fraction (W100  C) was extracted with distilled water at 100  C for 30 min, after which it was extracted with a neutral detergent at 100  C for 60 min as an additional soluble fraction (SOL). The hemicellulose-like (HEM), cellulose-like (CEL) and lignin-like (LIG) fractions were sequentially obtained

35

Sludge Straw Sawdust

Oxygen uptake rate (mg O2

12 10 8 6 4 2 0 0

50

100

150

200

250

300

350

Time (h)

Oxygen uptake rate (mg O g VS)

7 50

6

Sludge Straw Sawdust

5 4 3 2 1 0 0

50

100

150

200

Time (h)

250

300

350

Cumulative oxygen consumption (mg O g VS)

VS h )

14

Cumulative oxygen consumption (mg O g VS)

Determination of the moisture content of samples was conducted after drying at 105  C for 24 h. Volatile solids (VS) were analyzed at 550  C for 5 h. The C and N content of the materials were analyzed using an element analyzer (Vario EL Ⅲ, Elementar, Germany). The total organic carbon (TOC) was analyzed using a TC/TN analyzer with a solid sample module (TOC-V CPN, TNM-1, SSM-5000A, SHIMADZU, Japan). The total Kjeldahl nitrogen (TKN) was analyzed using an auto Kjeldahl determination system (8400, FOSS, Sweden). The chemical

700

35

Sludge Straw Sawdust

600 500 400 300 200 100 0 0

50

100

150

200

250

300

350

Time (h) 50

Sludge Straw Sawdust

700 600 500 400 300 200 100 0 0

50

100

150

200

250

300

350

Time (h)

Fig. 4 e The respective oxygen uptake rate and cumulative oxygen consumption of sludge, straw and sawdust at 35  C and 50  C.

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Results and discussion

3.1. Operation of the sludge bio-drying process with different bulking agents The average temperature of the bottom, middle and top of the matrix during the sludge bio-drying process is presented in Fig. 3. The temperature increased rapidly and reached the peak values of 54.6  C, 51.4  C and 54.7  C in Trial A, B and C, respectively in 2 days, after which a tendency to decrease was observed, despite of turning. An obvious second temperature peak appeared at 5e6 d during trial B when straw was used as the only bulking agent. The temperature for all trials was maintained at a medium level for about 9 days, while it decreased rapidly during the last 2 days. Overall, trial B conducted using straw as the sole bulking agent produced the highest temperature, while trial C with sawdust produced the lowest value. The initial higher temperatures of Trial A and C can be attributed to the larger specific surface area of the sawdust, while straw increased the duration of the process. Shin and Jeong explained that the secondary temperature peak is an indication of cellulose degradation after readily degradable matter is consumed (Shin and Jeong, 1996). Another possible reason was speculated for the second peak is recovery of the thermophilic microbial population, and turning can also improve the aerobic conditions and cause a second temperature rise (Petric et al., 2009). However, in this bio-drying process, the matrix temperature was relatively low than the usual composting process due to the greater aeration, so the influence of recovered activity of thermophilic microbial population should be neglected. Turning also likely had little influence on the second temperature increase due to the frequent turning made a favorable aerobic conditions and homogeneous mixture. Table 2 presents the mass balance of the mixture before and after the process. After 13 days of bio-drying, the water removal ratio of trial A, B and C was 48.8%, 62.3% and 49.3%

Biochemical fractions ( g 100 g-1 DM)

100

a

b

c

a

b

c

a

b

c

90 80

a: Sludge b: Straw c: Sawdust

70 60

ASH LIG CEL HEM SOL W100°C

50 40 30 20 10 0

FAS ¼ 1
BD$

100

16 d, 35°C

16 d , 50°C

Fig. 5 e Biochemical fractions (Van Soest method) of sludge, straw and sawdust before and after 16 days of aerobic incubation at 35  C and 50  C.

(2)

a b

a b

a b

a b

a b

90 80

35°C

70

a: Straw b: Sawdust

60 50

ASH LIG CEL HEM SOL W100°C

40 30 20 10 0

0

96

192

288

384

a b

a b

Time (h) 100

a b

a b

a b

90 80

50 °C

70

a: Straw b: Sawdust

60 50

ASH LIG CEL HEM SOL W100 °C

40 30 20 10 0

0d

  1
DM DM$VS DM$ð1
VSÞ þ þ dW dVS dASH

In which, BD is the total bulk density on a wet basis (kg m
3). dW, dVS and dASH are the density of water, volatile fraction and inorganic fraction (ash) respectively. dVS and dASH were assumed to equal 2.5  103 and 1.6  103 kg m
3. The results were that the initial FAS for Trial A, B and C was 60.2%, 69.7% and 52.0%. After 4 days bio-drying, the values were 61.7%, 71.8% and 53.3%. At the end of the process, the values were 62.6%, 73.5% and 55.2% respectively. They were in a range of adaptive requirement in the composting of mixtures (Ruggieri et al., 2008). On one hand, water removal increases the FAS; on the other hand, degradation of organic matters makes the materials collapse and decreases the FAS. The change of FAS depends on that which effect is predominant. The FAS in this study all increased indicating that the influence of water removal was larger than that of biodegradation.

Biochemical fractions ( g 100 g -1 DM )

3.

and the total water loss was 5.88, 7.42 and 5.94 kg respectively. The VS loss ratio of the mixture was 24.5, 31.0 and 19.4% for trial A, B and C, respectively. During the process, the moisture and VS content all decreased most rapidly for Trial B when straw was used as the bulking agent. The loss ratio of the VS, TOC, TKN and COD of the mixture were all highest for Trial B. More biodegradable organic matters generated more heat resulting higher temperature, which in return enhanced the organic matters degradation. The free air space (FAS) of the matrix was calculated by Eq. (2) (Richard et al., 2004; Ruggieri et al., 2009; Iqbal et al., 2010).

Biochemical fractions ( g 100 g-1 DM)

following the AOAC standard using a crude fiber extractor (FiberteCap, 2023; Foss, Sweden). The biochemical fractions were expressed as g 100 g
1 of DM (dry matter).

0

96

192

288

384

Time (h)

Fig. 6 e Evolution of biochemical fractions (Van Soest method) of straw and sawdust during 16 days aerobic incubation at 35  C and 50  C.

2327

w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 3 2 2 e2 3 3 0

Table 3 e The volatile solid (VS) loss ratio of the sludge, straw and sawdust after 16 days of aerobic incubation at 35  C and 50  C. Materials 0d

Sludge VS (%, dry basis) VS (g) VS (%, dry basis)

16 d

VS (g) VS loss ratio (%)

35 50 35 50



35 50 35 50



C  C  C  C C C  C  C 

3.2. The aerobic respiration of bulking agents compared with sludge After the raw materials were mixed and degraded, it was difficult to isolate them from each other completely. Even though the sludge could be washed off from the straw, the residual straw only represented the fractions that had not been degraded. Specifically, the wheat straw apex was more readily attacked than the basal portion. After the bio-drying process, visual evidence of degradation was observed at the exposed surface of the straw, while no visible decay was observed at the cut surface (Dresboll and Jakob Magid, 2006). Therefore, in this study, the bulking agents were incubated separately in a similar environment with in sludge bio-drying to investigate the respective degradability. The oxygen uptake rate (OUR) and cumulative oxygen consumption of the three materials during 16 days of incubation are presented in Fig. 4. The results revealed that sludge obtained its highest OUR of 12.1 mg O2 g
1VS h
1 during the initial 0e6 h when incubated at 35  C, after which the OUR decreased rapidly to below 2 mg O2 g
1VS h
1 in 50 h. At 50  C, the highest OUR of 5.68 mg O2 g
1VS$h
1 was delayed until 144 h. This was likely because additional time was required for the microorganisms to acclimatize themselves to the higher temperature. For sludge, the rapid decrease in the OUR after peak respiration and the lack of a second peak indicated that the sludge bio-drying process cannot last long unless the bulking agents provide additional bio-generated heat. Straw showed strong biodegradability as a bulking agent. The maximum OUR of 2.14 and 4.75 mg O2 g
1VS h
1 was obtained at 18 h at 35  C and 50  C, respectively. Moreover, there were two peak values observed when straw was used as the bulking agent, regardless of temperature, and the second peak appeared at around 120 h at 35  C and 200 h at 50  C.

Straw

Sawdust

66.3 2.61 2.68 57.3 57.1

    

0.18 0.09 0.00 0.58 0.65

88.5  7.39  6.99  85.6  84.7 

0.19 1.36 0.29 0.12 0.18

96.2  14.1  14.2  96.5  96.4 

1.78 1.74 31.9 35.1

   

0.10 0.20 1.61 0.00

5.70  5.00  22.9  28.5 

1.00 0.27 0.72 1.01

15.6  0.48 15.1  0.09
10.7  0.67
6.66  1.27

0.21 0.35 0.25 0.02 0.04

During the bio-drying process, there was also a second peak in the temperature profile. These results suggest that the organic fraction of straw was degraded during the bio-drying process and contributed to the biomass heat, even though the conditions of flask incubation and the bio-drying process were not completely the same. Sawdust in flask showed poorer biodegradability than straw and sludge. The maximum OUR of sawdust was achieved at the 18 h, and was 0.286 and 0.332 mg O2 g
1VS h
1 at 35  C and 50  C, respectively. Although the OUR of sawdust was low, a second peak value also existed. The cumulative oxygen consumption of sludge during 16 days of incubation was greater than that of straw and sawdust (Fig. 4). However, it is important to note that the VS content of the sludge was much lower than that of straw and sawdust. Therefore, during the bio-drying process, the available carbon source from the bulking agent for heat generation should not be ignored.

3.3. Organic matter change for interpreting the difference of biomass degradability The distribution of Van Soest fractions makes it possible to characterize the bio-accessibility of different organic materials. As shown in Fig. 5, the easily soluble fractions W100  C and SOL in sludge were obviously larger than in straw, and much larger than in sawdust. This difference explains why the highest initial OUR was observed for sludge. Previous studies have also reported that the soluble fraction from wood chips was negligible when compared to the soluble fractions from sludge (de Guardia et al., 2008). During incubation, the CEL fraction in straw showed a remarkable reduction, while in sawdust, little reduction was observed (Fig. 6). The content of W100  C and SOL in sawdust

Table 4 e The initial and end calorific value of the sludge, straw and sawdust after 16 days aerobic incubation. Calorific value

Sludge Initial

MJ kg
1 DM MJ kg
1 VSa

163 246

Straw Final

Initial

35  C

50  C

159 277

161 264

158 179

Sawdust Final

Initial

35  C

50  C

156 182

155 183

a The calorific value per unit VS was calculated based on the VS content in Table 3.

179 186

Final 35  C

50  C

195 202

185 192

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w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 3 2 2 e2 3 3 0

Table 5 e Evaluation of bio-generated heat contributed by every material including sludge, straw and sawdust based on the feedstock of the bio-drying process. Trial A

Dry matter of feedstock (kg) Initial VS (kg) Final VS (kg)a Bio-generated heat (MJ) Contribution (%)

Trial B

Trial C

Sludge

Straw

Sawdust

Sludge

Straw

Sludge

Sawdust

3.24 2.15 1.43 142 80.6

0.89 0.788 0.585 34.2 19.4

1.82 1.75 1.75 0.00 0.00

3.24 2.15 1.43 142 58.3

2.67 2.36 1.76 102 41.7

3.24 2.15 1.43 142 100

2.73 2.63 2.63 0.00 0.00

a It is the average of the values at 35  C and 50  C.

decreased markedly, while in straw and sludge it changed little. There was also a small change with the content of HEM in the three materials (Fig. 5). These findings demonstrate that the soluble fractions of W100  C and SOL were the main substrates attacked directly by microorganisms, which accounts for the initial OUR peak. The CEL fraction in straw was transferred to the soluble fractions and was then utilized by microorganisms, resulting in a high level of aerobic respiration, while CEL in sawdust was more recalcitrant (Komilis, 2006; Amir et al., 2008). Leconte stated that the carbonous materials determined the length of the thermophilic phase, regardless of the mixing ratio (Leconte et al., 2009). A previous report also stated that the soluble and cellulose-‘like’ fractions in straw were the most biodegradable (Vargas-Garcı´a et al., 2007; Mottet et al., 2010). Komilis once stated that the retardation of CEL biodegradation is thought to be primarily due to physical inhibition related to the sheathing of cellulose by lignin rather than due to a chemical inhibition (e.g. sorption of cellulolytic enzymes onto lignin). Lignin is present between cellulose fibrils, decreasing the available surface area and preventing ready access to the relatively easily degradable cellulose by the invading microbes and enzymes (Komilis and Ham, 2003). After 16 days of aerobic incubation, the VS loss ratio of straw was not much lower than that of sludge (Table 3), while the VS of sawdust was almost unchanged and even increased slightly. It may have adsorbed some organic matters during the incubation.

3.4. The potential contribution of bulking agents to biogenerated heat The bio-drying process aimed to exploit the energy available in biomass for water removal. Due to the high solid content and VS content of straw, the contribution of its biomass energy should be considerable. The bio-generated heat was calculated based on the biodegraded volatile solid (BVS), namely the VS loss (Gea et al., 2007). Many previous studies have recommended different values of combustion heat (Hc, MJ kg
1BVS) based on the different nature of wastes. However, all recommended Hc values were about the mixture of substrates and bulking agents (Haug, 1993; Navaee-Ardeh et al., 2006; Mason, 2009). In this study, the calorific value of the initial and final material was measured and then used to calculate the combustion heat per unit VS was calculated (Table 4). The VS loss ratios of the mixture in Trial A, B and C during 13 days of bio-drying were 24.5, 31.0 and 19.4% (Table 2),

respectively, which were in the range of the values observed during 16 days of incubation (Table 3). Therefore, the average VS loss ratios obtained in the incubations at 35  C and 50  C could be used to evaluate the potential heat bio-generated during the bio-drying process (Table 5). Because there was no VS loss measured in sawdust incubation, it was assumed that sawdust contributed no bio-generated heat to the bio-drying system. The contribution of straw was approximately 19.4% in Trial A and 41.7% in Trial B. Moreover, as greater amounts of straw were added, higher temperatures were obtained and more energy was exploited. When the lignocellulose-rich materials were used as bulking agents during composting of easily biodegradable substrates, the contribution of their biomass degradation was negligible. Adhikari stated that for food waste composting with chopped wheat straw, a higher temperature profile was achieved under a higher food waste/straw ratio (2:1e8.9:1) (Adhikari et al., 2009). Conversely, sludge contains a low proportion of easily biodegradable organic materials (Lu et al., 2008). Based on the results presented here, the contribution of biomass energy in straw could be exploited due to its large biodegradation potential. Therefore, straw is of great importance for sludge bio-drying. In this study, the dewatered sewage sludge used was raw sludge without anaerobic digestion. According to Gea et al. (2007), the organic matter content of anaerobic digestion sludge (52.6%, dry basis) was lower than raw sludge (60.4%, dry basis) and, consequently, organic matter suitable for degradation could be more abundant in raw sludge. Initial value of OUR for raw sludge was clearly higher than that for anaerobic digestion sludge (Barrena et al., 2005; Gea et al., 2007). So if the anaerobic digestion sludge was used for bio-drying, the contribution of bulking agent degradation will be more significant.

4.

Conclusions

The agriculture and wood wastes are preferable bulking agents for sludge bio-drying, while these lignocellulosic-rich materials are typically assumed to be poorly degraded. When compared with composting, sludge bio-drying starts at a relatively high moisture content (68e72%) and lasts for a short time (12e16 days). This study investigated the biodegradation potential of straw and sawdust separately through aerobic incubation tests under conditions similar to sludge bio-drying. The results showed that straw presented strong degradability, especially at 50  C, while the

w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 3 2 2 e2 3 3 0

degradability of sawdust was poor. The initial OUR of sludge was high, but it decreased rapidly within 50 h. The respiration of straw lasted longer and showed a second peak value at about 120 h, which was considered to be related to the CEL fractions in straw. Based on these findings, it can be inferred that in sludge composting or bio-drying process, slowly biodegradable fraction is from some bulking agents rather than sludge itself, which contribute substantial amounts of bio-generated heat to enhance the matrix temperature. Therefore, when selecting a bulking agent it is important to consider its physical structure as well as its biodegradability to improve the efficiency of bio-drying.

Acknowledgment The authors express appreciation to the following: 1) The Key Special Program on the S&T for the Pollution Control and Treatment of Water Bodies (No.2008ZX07317, No.2008ZX07316); 2) China Postdoctoral Science Foundation (No.20090450732); 3) Science and Technology Commission of Shanghai Municipality (No.09R21415600).

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w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 3 3 1 e2 3 4 1

Available at www.sciencedirect.com

journal homepage: www.elsevier.com/locate/watres

Evaluation of selected ubiquitous contaminants in the aquatic environment and their transformation products. A pilot study of their removal from a sewage treatment plant M.J. Martı´nez Bueno a, S. Ucle´s a, M.D. Hernando a,b, E. Da´voli c, A.R. Ferna´ndez-Alba a,d,* a

Pesticide Residues Research Group, Department of Hydrogeology and Analytical Chemistry, University of Almerı´a, 04120 La Can˜ada de San Urbano, Almerı´a, Spain b National Reference Centre for Persistent Organic Pollutants, University of Alcala´, 28871 Alcala´ de Henares, Madrid, Spain c Istituto di Ricerche Farmacologiche “Mario Negri”, Environmental Health Sciences Department, Via Giuseppe La Masa 19, 20156 Milan, Italy d Fundacio´n IMDEA-Agua, C/Punto Net 4, 2a planta, Edificio ZYE, Parque Cientı´fico Tecnolo´gico de la Universidad de Alcala´. 28805 Alcala´ de Henares. Madrid, Spain

article info

abstract

Article history:

A simple method using direct sample injection combined with liquid chromatography

Received 4 November 2010

tandem mass spectrometry has been developed for the simultaneous analysis of six

Received in revised form

alkaloid compounds in environmental samples. The target list includes two psychosti-

10 January 2011

mulants (nicotine and caffeine), three metabolites (cotinine, nicotinic acid and para-

Accepted 13 January 2011

xanthine) and a coffee chemical (trigonelline). The analytical method was evaluated in

Available online 25 January 2011

three different matrices (surface water, influent and effluent wastewater). The method developed showed an adequate sensitivity, below 0.6 mg L
1 for wastewater and 0.1 mg L
1

Keywords:

for river matrices, without any prior treatment of the samples. Finally, the methodology

Nicotine

was applied to real samples for evaluation of their removal from a sewage treatment plant

Trigonelline

and their persistence/fate in the aquatic environment. All compounds studied in this work

Nicotinic acid

were detected at all sampling points collected along the Henares River. However, nicotinic

Anthropogenic markers

acid was only detected three times in treated sewage samples at levels above its detection

Environmental water

limit.

Direct sample injection

1.

Crown Copyright ª 2011 Published by Elsevier Ltd. All rights reserved.

Introduction

Coffee is one of the most popular beverages worldwide, appreciated not only for its characteristic taste and aroma, but

also recently for its potentially beneficial effects on human health (Ranheim and Halvorsen, 2005). Caffeine, trigonelline and nicotinic acid are some of the coffee’s constituent compounds with the most relevant biological activity (Trugo,

Abbreviations: AcN, Acetonitrile; CAD, Collision gas; CE, Collision energy; CUR, Curtain gas; CXP, Collision cell exit potential; DP, Declustering potential; EP, Entrance potential; ESI, Electrospray interface; FIA, Flow injection analysis; GC/MS, Gas chromatographymass spectrometry; GS1/GS2, Ion source gas; HPVC, High production volume chemicals; IDL, Instrumental detection limits; Koc, Soilewater partition coefficient/organic carbon adsorption coefficient; Kow, Octanolewater partition coefficient; LC/MS, Liquid chromatography-mass spectrometry; LLE, Liquideliquid extraction; LOD, Detection limit; LOQ, Quantification limit; MeOH, Methanol; pKa, Acid dissociation constant logarithmic; QqQLIT, Triple quadrupole/linear ion trap; R.S.D, Relative standard deviation; SPE, Solid-phase extraction; SRM, Selected reaction monitoring; STP, Sewage treatment plant; WWTP, Wastewater treatment plant. * Corresponding author. Pesticide Residues Research Group, Department of Hydrogeology and Analytical Chemistry, University of Almerı´a, 04120 La Can˜ada de San Urbano, Almerı´a, Spain. Tel.: þ34 950015034; fax: þ34 950015483. E-mail address: [email protected] (A.R. Ferna´ndez-Alba). 0043-1354/$ e see front matter Crown Copyright ª 2011 Published by Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2011.01.011

2332

w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 3 3 1 e2 3 4 1

2003). Caffeine is a xanthine alkaloid found in tea, cocoa, chocolate, energy drinks and other derived food. However, coffee has the highest caffeine content with regards to other dietary products. It is suggested that values range from 840 to 1500 mg of caffeine/100 g of commercial coffee (Perrone et al., 2008). In humans, caffeine is a central nervous system stimulant, and it is present in a large number of prescriptions because of its diuretic properties and benefits associated with improvements in alertness, learning capacity and exercise performance. About 80% of the caffeine dose is metabolized in the liver to paraxanthine (1,7-dimethylxanthine), 10% to theobromine (3,7-dimethylxanthine), and 4% to theophylline (1,3-dimethylxanthine). Trigonelline is an alkaloid found in barley, cantaloupe, corn, onions, peas, soybeans, tomatoes, crustaceans, fish, and mussels. It is also highly available in the coffee, the average content is 280e950 mg/100 g in commercial coffee (Perrone et al., 2008). In humans, this alkaloid possesses anti-cancer (cervix and liver), anti-migraine, antiseptic, hypoglycaemic and mutagenic properties. About 50e80% of the trigonelline is decomposed during coffee roasting, forming nicotinic acid, a water-soluble B vitamin also known as niacin, and other aromatic nitrogen compounds. However, only 5% of the nicotinic acid consumed is metabolized to trigonelline in humans (Zeiger, 1997). Nicotinic acid (vitamin B3 or niacin) is also an organic compound found in a variety of foods including liver, chicken, beef, fish, cereal, peanuts and legumes, but it is also present in prepared coffee (10e30 mg/ 100 g of commercial coffee) (Perrone et al., 2008). Niacin is produced as a vitamin supplement in tablet form as hypocholesterolemic and antihyperlipidemic (Zeiger, 1997). Although nicotine is the most abundant alkaloid in tobacco (98% of the total alkaloids, 13e25 mg nicotine/cigarette) (Wu et al., 2002), there are some other plants which likewise contain nicotine, e.g. other members of the Solanaceae family such as potatoes, tomatoes, egg plants or chilli peppers; and members of the Camellia sinensis family such as tea products. A variety of black as well as green teas have been previously investigated by Siegmund et al. (1999) for estimation of the nicotine content in tea. Between 0.002 and 1.695 mg nicotine/ kg were found in several kinds of teas with a mean value 0.5 mg/ L. On the other hand, nicotine is rapidly and extensively metabolized in humans: approximately 70e80% of the nicotine absorbed by a smoker is transformed to cotinine and excreted in the urine (Bramer and Kallungal, 2003). Nicotine and cotinine can be measured in various biological fluids (blood, saliva, and urine) (Benowitz et al., 2002). In its pure form it is fast acting and has often been used as an agricultural insecticide. However, although it is a natural insecticide generated by plants as a defense against insects, nicotine-based insecticides have been banned in the U.S. since 2001 in order to prevent residues from contaminating foodstuffs (Environmental Protection Agency, EPA). Although nicotine can be oxidized to nicotinic acid with nitric acid (McElvain, 1941), a few publications have reported studying nicotine biosynthesis in cell-free systems at the enzymatic level, caused by a reduction reaction of nicotinic acid with NADPH (Friesen and Leete, 1990). Due to the worldwide consumption of coffee and tobacco, or food and beverages which contain caffeine and nicotine, these organic compounds and their active metabolites may be continually introduced into the aquatic environment, via

numerous and varied routes, such as industrial waste related to coffee processing or untreated, and treated, municipal wastewater. Fig. 1 shows a schematic overview of some of the possible routes of introduction of these contaminants into the aquatic system. A potential tracer or indicator of human impact is that substance clearly of anthropogenic origin and often has been detected in wastewater and surface water. A widespread detection of these chemicals in the environment may give weight to their potential relationship with water contamination due to anthropogenic sources. Therefore, a good marker should allow magnitude quantification as well as the unambiguous elucidation of the pollution source. Some of the requirements for being an adequate chemical marker are: (i) constant consumption, considering the population’s consumption habits or that the compound is not phased out in future and (ii) a regular detection: the quantities discharged into the environment should be sufficient to permit their detection after dilution/dissipation in the aquatic medium. As well as this, some of the possible removal processes to which these compounds may be subjected (sedimentation, volatilization or biotic/abiotic degradation in the environment) should be evaluated. Human endogenous metabolites, constituents in pharmaceuticals, personal care products or food are some of the substances which might be evaluated as possible markers (Buerge et al., 2003). Thus, in the last few years, caffeine, paraxanthine, nicotine and cotinine have been widely proposed as chemical markers for anthropogenic contamination processes (Buerge et al., 2003, 2008; Martı´nez Bueno et al., 2010). To our knowledge, up until now there has been no scientific literature on monitoring data for nicotinic acid or trigonelline in aquatic environment, in contrast to the wide-ranging information concerning the occurrence, persistence and fate of many pharmaceuticals and emergent contaminants in the environment processes (Martı´nez Bueno et al., 2010; HuertaFontela et al., 2007, 2008; Castiglioni et al., 2006; Ternes, 1998), as is the case for nicotine, cotinine, caffeine and paraxanthine. The analysis of these compounds in aqueous environmental samples has been performed using different methodologies, such as enzyme-linked immunosorbent assay (Nicolardi et al., submitted for pub), gas chromatography-mass spectrometry (GC-MS) (Buerge et al., 2003; Weigel et al., 2004; Go´mez et al., 2007) or liquid chromatography-mass spectrometry (LC-MS) (Buerge et al., 2008; Martı´nez Bueno et al., 2010; Huerta-Fontela et al., 2007, 2008; Castiglioni et al., 2006; Ternes, 1998). The continuing advances in analytical instrumentation, detection systems and separation techniques have provided analytical tools to reduce the treatment and pre-concentration of the sample, that allows very low detection limits (LODs). However, most of the above methods generally require time-consuming sample preparation procedures, such as liquideliquid extraction (LLE) or solid-phase extraction (SPE) (Buerge et al., 2003, 2008; Martı´nez Bueno et al., 2010; HuertaFontela et al., 2007, 2008; Castiglioni et al., 2006; Ternes, 1998; Nicolardi et al., submitted for pub; Weigel et al., 2004) prior to analysis of the sample, which is often tedious and time consuming. Direct injection methods offer the advantage of reduced sample preparation steps and therefore improved reproducibility and minimized potential contamination of the sample. Nevertheless, to date, the direct analysis of

w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 3 3 1 e2 3 4 1

2333

Fig. 1 e Schematic summary of some possible ways of introducing of anthropogenic markers into the aquatic environment from human activities.

wastewater without prior sample treatment has not come into widespread use because of typically low sensitivity with respect to the levels present in the aquatic environment. The aim of this work was to develop and validate a rapid LC-MS/MS method by direct sample injection to determine and evaluate nicotine, caffeine and some of their transformation products, ubiquitous in the aquatic environment as possible anthropogenic tracers. For that, the direct injection of a sample into a liquid chromatography-linear ion trap mass spectrometry (LC-QqQLIT-MS/MS) system was the procedure developed in this study. The more demanding requirements regarding mass spectrometric confirmation currently set by EU regulations were taken into account in carrying out the confirmation and quantification of target compounds (Commission Decision (2002/657/EC), 2002).

2.

Experimental

2.1.

Chemicals and reagents

Nicotine, nicotinic acid, cotinine, caffeine, paraxanthine, trigonelline hydrochloride, nicotine-d3 of analytical grade (purity  98%) were purchased from SigmaeAldrich (Steinheim, Germany). Individual stock standard solutions were prepared at

a concentration of 2 mg mL
1 in methanol. Mixtures of all chemicals were prepared at different concentration levels for the preparation of calibration standards and to fortify samples by appropriate dilution of the individual stock solutions in acetonitrile or acetonitrile:water (10:90, v/v). All the standard and working solutions were stored at
20  C. Table 1 shows some properties of target analytes evaluated in this study. Methanol (MeOH) and HPLC-grade acetonitrile (AcN) were supplied by Merck (Darmstadt, Germany). Water used for LC-MS analysis was generated from a Direct-Q 5 Ultrapure Water System from Millipore (Bedford, MA, USA) with a specific resistance of 18.2 MU cm and Formic Acid (98% purity) were purchased from Fluka (Buchs, Germany).

2.2.

Sample collection

Wastewater samples used in this study were collected from a municipal sewage treatment plant (STP) located in the south-east of Spain (Almerı´a). The plant is connected to a sewage system servicing a municipal area of w78.000 inhabitants. It is strategically situated in a very productive agricultural area and very close to a hospital which discharges into the urban network. This plant applies a pre-treatment for solid removal, a primary treatment to eliminate suspended material, an activated sludge biological treatment and a final

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Table 1 e Physicochemical properties of target analytes evaluated in this study.

clarification. Analysis of the corresponding daily composite samples e influents and treated effluents e were performed on seven consecutive days in two consecutive months (AprileMay 2010). Integrated samples were representative of one day’s work in the STP and were taken at 1-h intervals. Sampling was carried out by an automatic device (0.5 L/3 h).

The river samples analyzed were collected from the Henares River, located in the centre of Spain (Madrid). This area is one of the most developed and densely populated in Spain, with cities such as Madrid (>3,200,000), Alcala´ de Henares (>200,000) or Guadalajara (>83,000) together with heavy industrial activity and several wastewater treatment

w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 3 3 1 e2 3 4 1

plants (WWTPs) along the river. Sampling points were selected in order to evaluate the effects of the influence of human activities and population density on the river’s pollution. Henares River sampling points 1, 2 and 3 were located between two small STPs (35 km, 24 km and 6 km before point 4, respectively), also an area with very industrial surroundings. Sampling point 4 receives the treatment effluents from an important WWTP and it is a densely populated area. Sampling points 5 and 6 were situated after a discharge zone from the sewage plant and downstream from a heavily populated area with large contributions from urban and industrial zones (20 m and 1.15 km after point 4, respectively). Grab samples (2 L) were collected from the 6 sites described above during a sampling campaign carried out in May 2010. All samples were collected using pre-rinsed amber glass bottles, and then, they were sent in boxes packed with ice to the laboratory for posterior analysis. Upon reception, samples were filtered through a 0.7 mm glass fiber filter (Teknokroma, Barcelona, Spain), in order to remove particles that might interfere with the extraction procedure. Subsequently, samples were adjusted to pH 3 with 37% HCl and stored at
20  C in the dark prior to analysis in order to prevent degradation of analytes during storage.

2.3.

Sample preparation and extraction method

Direct injections and solid phase extractions (SPEs) were the procedures tested with different matrices. Prior to treatment, all samples were spiked with a labeled standard of nicotine-d3. For the SPE method, OasisTM HLB cartridges (60 mg, 3 cc, divinylbenzene/N-vinylpyrrolidone copolymer) from Waters (Mildford, MA, USA) were used. To optimize the extraction method, three pH conditions were tested: pH 3, 6, and 8. For the recovery studies, HPLC-grade water and environmental samples were spiked with all the target compounds at concentrations ranging between 5 and 20 mg L
1. The procedure for carrying out the extraction of target compounds was as follows. The Oasis HLB cartridges were preconditioned with 2 ml of MeOH and 2 ml of deionized HPLC-grade water (pH adjusted to 3, 6 or 8, employing NH4OH or H2SO4). After the conditioning step, aliquots of environmental samples, 10 ml, (pH adjusted 3, 6 or 8) were loaded into the cartridge. The cartridges were dried using a nitrogen stream for approximately 10 min in order to remove excess water and finally, the retained analytes were eluted with 2  2 ml of MeOH. The extract was evaporated until almost dryness using a Turbo-Vap from Zymark (Hopkinton, Massachusetts), with a water temperature at 35  C and reconstituted with 1 ml of AcN:water, 20:80 (v/v). In order to assess whether SPE pre-concentration was necessary, direct injections of sample without prior treatment at three different pHs (3, 6 and 8) were tested. Before analysis, all extracts were filtered using a 0.45-mm PTFE syringe filter (Millipore, USA) to remove suspended solids and particulate matter.

2.4. Liquid chromatography-QLIT-mass spectrometry analysis A hybrid triple quadrupole/Linear Ion Trap mass spectrometer system (5500 QTRAP LC/MS/MS, AB Sciex Instruments, Foster City, CA) was used for the analysis of the target compounds. The

2335

triple quadrupole/linear ion trap (QqQLIT) is a hybrid system in which the final quadrupole can operate as a conventional mass filter or as a linear ion trap (Martı´nez Bueno et al., 2009). The LC equipment was an HPLC binary solvent delivery system (Agilent Series 1200) equipped with a reversed-phase C-8 analytical column of 150 mm length  4.6 mm I.D and 5 mm particle size (Zorbax Eclipse XDB-C8, Agilent Technologies). Mobile phases A and B were acetonitrile and HPLC-grade water containing 0.1% formic acid, respectively. The flow rate was kept constant at 500 ml min
1. A linear gradient was set from 10% A to 100% A in 5 min, and then maintained at 100% A for 5 min. The re-equilibration time was 5 min. The injection volume was 10 ml. The HPLC system was connected to a QqQLIT-MS/MS with an electrospray interface (ESI) and was operated in positive ionization mode. The TurboIonSpray source settings were: IonSpray Voltage (IS), 5500 V; Source Temperature, 500  C; Curtain Gas (CUR), 20 (arbitrary units); Collision Gas (CAD), Medium; Ion Source Gas (GS1 and GS2) at 55 psi. Nitrogen was used as the nebulizer gas, curtain gas and collision gas. In order to obtain maximum sensitivity for identification and detection of the target compounds, a careful optimization of all MS parameters e declustering potential (DP), entrance potential (EP) for precursor ions, collision energy (CE) and collision cell exit potential (CXP) for product ions e was performed by flow injection analysis (FIA) in the spectrometer. The MS operated in selected reaction monitoring (SMR) mode, under time-scheduled conditions to achieve the best sensitivity and the time window was 90 s. The scheduled SRM enables optimized cycle time and maximized dwell times to be used during acquisition to provide higher multiplexing with good analytical precision. The quadrupoles Q1 and Q3 were set at low and unit resolution, respectively. For confirmation of target analytes, the EU guidelines for LC-MS/MS analysis were considered (Commission Decision (2002/657/ EC), 2002). The following criteria were used: the acquisition of two SRM transitions for each compound, retention time and the monitoring of the SRM ratio (which is the relationship between the abundances of transitions selected for identification and for quantification, SRM2/SRM1) in order to get a suitable confirmation and thus avoid overestimations or false positive findings in quantitative analysis. Table 2 shows the values of the parameters optimized and the SRM transitions used for the confirmation and quantification of all compounds studied. The most intense SRM transition was selected for quantitation purposes (SRM1). The data acquisition and processing was carried out using commercial software (Analyst 1.5.1, Applied Biosystems/MDS SCIEX).

2.5.

Method validation

Linearity, sensitivity (detection and quantitation limits, LODs and LOQs, respectively), repeatability and reproducibility were established to determine the accuracy and precision of the LCESI-MS/MS method. In the light of the results, direct injection of all samples at pH 3 was the procedure selected for carrying out all the validation studies. Because of the impossibility of obtaining blanks, the samples were previously analyzed and the presence of the target compounds considered. The matrix effect was assessed by comparing standard calibration curves prepared in water, effluent, influent and river extracts. To

2336

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Table 2 e Values of the optimized parameters with the developed method by LC-QLIT-MS/MS. Compound

tR (min) (% R.S.D)

Precursor Ion (m/z)

SRM 1 Quantitation

CE 1

SRM 2 Confirmation

CE 2

Trigonelline Nicotine Cotinine Nicotinic acid Paraxanthine Caffeine

2.7 (3) 2.8 (3) 3.1 (3) 3.2 (2) 6.4 (2) 6.7 (1)

138 163 177 124 181 195

94 130 80 78 124 138

30 27 36 30 25 25

92 117 98 80 69 110

30 35 26 33 43 30

[SRM2]/[SRM1] (% R.S.D) 0.8 0.7 0.4 0.9 0.2 0.4

(3) (3) (2) (6) (2) (4)

DP: declustering potential, 100 V; CE: collision energy (eV); EP: entrance potential, 10 V; CXP: collision cell exit potential, 5 V.

minimize matrix effects, a consequence of the presence of sample matrix components, matrix-matched calibration curves were used for quantitative determinations. Each point was obtained as the average of three injections. The linearity in the response was studied by using matrix-matched calibration solutions prepared by spiking each matrix at six concentration levels, ranging from the quantitation limit of each analyte to 100 mg L
1 in wastewater extracts or 1 mg L
1 in river extracts, depending on the concentration level usually present in the samples. The detection limits (LODs) and quantification limits (LOQs) were determined experimentally by extrapolation as: the minimum concentration of analyte which showed signal-to-noise ratios of 3 (for the SRM2 transition) and 10 (for the SRM1 transition), respectively, from spiked extracts in the three different matrices. The instrumental detection limits (IDLs) were estimated from the injection of a standard solution successively diluted until reaching a concentration level corresponding to a signal-tonoise ratio of 3 (SRM2). Intra- and inter-day variability, determined as relative standard deviation (R.S.D), were obtained from repeated analysis (n ¼ 5) of spiked effluent samples at 50 mg L
1, from run-to-run over 1 day (repeatability) and over 5 days (reproducibility), respectively.

3.

Results and discussion

3.1.

Analytical procedure

The continuing advances in analytical instrumentation have provided very sensitive analytical tools capable of achieving detection limits in the range of mg L
1 or c¸g L
1. Fig. 2 shows a typical chromatogram for separation of all target compounds in a spiked sewage extract at 25 mg L
1 obtained from the analytical methodology developed in this study (direct sample injection combined with an LC-QqLIT-MS/MS system).

3.1.1.

Sensitivity, matrix effect and precision

The results obtained by different SPE procedures tested (pH ¼ 3, 6 and 8) were not satisfactory (data not included). Trigonelline and nicotine were not retained in the cartridge to pH ¼ 3, while at pH ¼ 6 nicotinic acid and trigonelline were not. Finally, the direct injection of all samples adjusted at pH 3 was the procedure selected for carrying out all the validation studies, since significant differences were not found in the results obtained with the three pHs tested, it was also the pH selected for the storage of samples. The analytical method developed showed

a satisfactory performance in terms of sensitivity with instrumental detection limits (IDLs) in the range of 0.1e0.6 pg (injecting 10 ml), and linearity of the analytical response with correlation coefficients (r2)  0.994 for all compounds in solvent or matrix (over four orders of magnitude). The LODs obtained were at or below 0.4 mg L
1 for all compounds in sewage matrices, except nicotinic acid, paraxanthine (both 0.5 mg L
1) and trigonelline (0.6 mg L
1) in influent extracts. In surface water, LODs were below 0.1 mg L
1 in all cases. The LOQs were in the range of 0.3e0.8 mg L
1, 0.2e0.65 mg L
1 and 0.02e0.17 mg L
1, in influent, effluent and river extracts, respectively. The results of the analytical method are summarized in Table 3. It must be considered that detection limits depend on the nature of the sample and the pre-concentration factor reached during the sample treatment. Most of the papers previously cited analyzed environmental samples applying pre-concentration factors (Buerge et al., 2003, 2008; Martı´nez Bueno et al., 2010; Huerta-Fontela et al., 2007, 2008; Castiglioni et al., 2006; Ternes, 1998; Nicolardi et al., submitted for pub; Weigel et al., 2004). Although lower LOQs and LODs could be obtained by increasing the pre-concentration factor, this is not recommended since it leads to more complex extracts, making the use of additional clean-up steps necessary. Matrix effects can compromise quantitative analysis by LC-ESI-MS/MS. This occurs because the ESI source is highly susceptible to other components present in the matrix, which may result in a suppression or signal enhancement leading to erroneous results. Several different strategies have been proposed in the literature to reduce matrix effect, e.g. the use of an external calibration using matrix-matched samples, an internal standard, standard addition methods or dilution of sample extracts (Gros et al., 2006). Matrix effects were investigated in river, influent and effluent matrices. In order to minimize matrix effects, a consequence of the presence of sample matrix components, matrix-matched calibration curves were used for quantitative determinations. The relative ion suppression was small for all compounds (j1e28j%) in all matrices studied, except in the case of nicotine (j42e55j%). For this compound, the addition of an internal standard (nicotine-d3), added in all extracts, was the strategy used for overcoming the problem of high suppression. The corresponding slopes (peak area ratio of analyte/internal standard vs concentration ratio analyte/ internal standard) obtained in extracts were compared to those in standards. In order to evaluate the precision of the proposed method, within-laboratory repeatability and reproducibility were estimated. To do that, a spiked extract at 50 mg L
1 was analyzed 5 times in the same day and over different days. The

w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 3 3 1 e2 3 4 1

2337

Fig. 2 e XIC chromatograms (extracted ion chromatograms) for all target analytes in a sewage extract spiked at 25 mg LL1 a SRM1, quantitation transition.

repeatability, expressed as a percentage of relative standard deviation (R.S.D.), varied between 1 and 5%, a slightly higher variability was achieved for the reproducibility, from 3 to 10%.

3.2.

Ubiquitous occurrence in real samples

The presence of these substances in the aquatic environment is ubiquitous and has been previously documented (Martı´nez Bueno et al., 2010; Huerta-Fontela et al., 2007, 2008; Castiglioni et al., 2006; Ternes, 1998)., except for the compounds, nicotinic acid and trigonelline, for which there are no monitoring data in environmental samples up to the present.

3.2.1.

Analysis of WWTPs

To demonstrate the applicability of the optimized method, fourteen sewage samples were analyzed. All the target compounds were detected in most of the wastewater samples analyzed, except for nicotinic acid in effluent samples. A constant consumption of caffeine and nicotine was

observed during the period investigated (two week). Maximum concentrations were detected for caffeine, paraxanthine and trigonelline, with mean concentrations between 67 and 16 mg L
1, 49e11 mg L
1 and 48e0.9 mg L
1, respectively, in influent and effluent samples. Nicotine was found in the samples at mean concentrations of 22.4 mg L
1 (input) and 5.6 mg L
1 (output). Cotinine was found in the samples at concentrations higher than 4 mg L
1 for influent and 0.3 mg L
1 for effluent samples. Nicotinic acid was the compound present at lower concentrations, both in influent and effluent samples, ranging from 2 to 20 mg L
1, and was only detected three times in treated sewage samples at levels above its detection limit (0.4 mg L
1). A summary of the results achieved is displayed in Table 4, where the concentration ranges and the median values, both in influents and in effluents, are indicated. The mean concentration values detected for nicotine and cotinine in treated effluent samples (5.6 mg L
1 and 3.4 mg L
1, respectively) were in the range of those reported by Huerta-Fontela et al. (2008) from several WWTPs in north-

2338

w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 3 3 1 e2 3 4 1

Table 3 e Validation results of the analytical method. Compounds

IDL (pg injected)

Trigonelline Nicotine Cotinine Nicotinic acid Paraxanthine Caffeine

Influent

0.1 0.4 0.4 0.4 0.6 0.1

Effluent

River

Signal suppression (
), enhancement (þ), %

LOQ (ug/L)

LOD (ug/L)

LOQ (ug/L)

LOD (ug/L)

LOQ (ug/L)

LOD (ug/L)

Influent

Effluent

Rivers

0.80 0.76 0.30 0.73 0.50 0.50

0.60 0.40 0.15 0.50 0.50 0.30

0.54 0.65 0.20 0.55 0.40 0.40

0.26 0.35 0.10 0.40 0.40 0.20

0.03 0.17 0.02 0.07 0.05 0.08

0.02 0.09 0.01 0.04 0.05 0.04


19
55
25
10
23
28


21
52
23
12
20
25


5
42
12
1
12
7

Repeatability/ Reproducibility % R.D.S (n ¼ 5)

3/7 5/4 1/9 2/10 1/7 2/3

IDL: instrumental detection limit; LOQ: detection limit; LOD: quantification limit.

eastern Spain (3.5 mg L
1 and 2.4 mg L
1, respectively). However, mean concentrations for caffeine and its metabolite (16.7 mg L
1 for caffeine and 11.4 mg L
1 for paraxanthine) were an order of magnitude higher than those reported in the same work in effluent samples (2.5 mg L
1 and 1.3 mg L
1, respectively).

3.2.2.

Analysis of surface waters

To study the presence and fate of target compounds in the environment, we analyzed surface water samples from a river. The Henares River basin comprises a densely populated area with large contributions from urban and industrial zones. The river was sampled at six sites, before and after an important WWTP and a densely populated area (site 4), 36 km, 24 km and 6 km, before the plant (site 1, 2 and 3, respectively), 20 m after it (site 5) and 1.15 km further down (site 6). A summary of the results is shown in Table 5. Each result was obtained as the average of two injections. Caffeine and its metabolite, paraxanthine, were detected at all sampling points at concentrations between 512e524 hg L
1 and 98e109 hg L
1, respectively. Nicotinic acid and cotinine were also detected in all samples but at concentrations lower than those found for caffeine and paraxanthine (104e370 hg L
1 for nicotinic acid and 23e48 hg L
1 for cotinine). However, although nicotine was detected at three sampling sites with a mean concentration of 186 hg L
1, at the other points the concentration levels were below the LOQ (170 hg L
1). As it has been commented before, nicotine is an alkaloid found in

Table 4 e Concentration range and mean values found in the influent and effluent of an STP in the south-east of Spain. Compounds

Influent (mg/L)

Effluent (mg/L)

Samplesa Range Mean Samplesa Range Mean Trigonelline Nicotine Cotinine Nicotinic acid Paraxanthine Caffeine

12 14 14 12 14 14

4e134 8e28 4e27 2e20 18e89 21e97

48.8 22.4 15.4 7.1 49.7 67.1

12 10 14 3 14 14

0.7e2.0 0.9 0.6e17 5.6 0.3e10 3.4 0.6e0.9 0.7 14e27 11.4 10e33 16.7

a Number of samples with concentrations higher than LOQ value.

tobacco and other nightshade plants (such as potatoes, tomatoes, egg plants, tea products, etc.). For that, nicotine may enter surface waters not only via discharge of domestic wastewater, but also directly such as with cigarrette stubs or industries relating with processing of this kind plants or tobacco, and therefore achieving the aquatic environment. Specifically, at point 4 where the concentration of nicotine detected is higher than the average background detected. This area corresponds to a heavily populated area and probably those high values can be related with other such as sources industrial and agricultural activities. The coffee chemical, trigonelline, was found in all samples at loads higher than 115 hg L
1, the maximum concentration being detected at point 4, which receives the treatment effluents from the WWTP and at point 5 situated 20 m after the discharge zone (above 357 hg L
1). Although concentration values found for nicotine, cotinine, caffeine and paraxanthine were lower than the mean values reported by Huerta-Fontela et al. (2007) in a river from northeast Spain (595, 331, 1926 and 1756 hg L
1, respectively), the levels detected in this work were of the same order to those found in a previous monitoring campaign performed in the same area (Martı´nez Bueno et al., 2010). The profiles observed for the detected compounds along the river showed constant concentration levels for trigonelline, nicotine and their metabolites (cotinine and nicotinic acid) when moving downstream, from points 1 to 3 and 5 to 6 with a general increase at sampling point 4, which corresponding with the discharge zone from an important STP. Nevertheless, caffeine and paraxanthine concentrations remained stable along the river.

3.3.

Efficient elimination in STPs

The removal of target contaminants undergoing conventional biological treatment during their passage through a municipal STP was investigated. Analysis of the corresponding daily composite water samples, influent and effluent, were performed over seven consecutive days in two consecutive months. Fig. 3 shows the estimated removal efficiencies for an STP in south-eastern Spain. Elimination rates ranged from 75% for nicotine and caffeine to up to 98% for trigonelline. Most of the compounds investigated showed acceptable removal efficiencies (75%). In spite of the high removal efficiency found for nicotinic acid (90%) and trigonelline (98%),

2339

w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 3 3 1 e2 3 4 1

Table 5 e Concentration values found in the Henares River during a sampling campaign carried out in May 2010. Compounds

Trigonelline Nicotine Cotinine Nicotinic acid Paraxanthine Caffeine

River LOQ (ng/L)

LOD (ng/L)

30 170 20 70 50 80

20 90 10 37 50 44

Point 1 (35 km) (ng/L)

Point 2 (24 km) (ng/L)

124
115
Point 3 (6 km) (ng/L)

Point 4 (Effluent) (ng/L)

Point 5 (20 m) (ng/L)

126 198 28 104 97 505

378 186 48 370 98 512

357 175 35 224 94 421

Point 6 (1.15 km) (ng/L) 141

both compounds were detected in the treated effluents of the WWTPs and even in the river samples (see Table 5). The removal rates reported for caffeine, nicotine, paraxanthine and cotinine varied between the different STPs studied by various authors, but in general, the values obtained in this work were slightly lower than others previously reported, which are up to 98% (Huerta-Fontela et al., 2008; Rosal et al., 2010).

3.4.

Suitable use as an anthropogenic marker

An ideal marker should allow the quantification of the magnitude and the elucidation of the source of pollution. In addition, an adequate chemical marker should be stable in terms of its persistence and/or constant input into the environment (regular consumption and detection). Another relevant criteria to select a compound as a marker is its solubility in water. Table 1 shows some physicochemical properties related to solubility, persistence and environmental fates. To determine all these parameters, PBT Profile Software (PBT Profiler, U.S EPA; United States National Library of Medicine) was applied and the results are summarized in Table 1. The pKa of caffeine is 10.4, indicating that this compound will primarily exist in the cation form in the environment and cations do not volatilize from water. If released into water, caffeine is not expected to adsorb to suspended solids and

Fig. 3 e Mean concentrations and removal efficiencies estimated for target compounds detected in a sewage treatment plants in the south-east of Spain.

sediment, based upon the estimated Koc. With an estimated half-life of 15 days in water, biodegradation may be an important environmental fate process. Paraxanthine is the major metabolite of caffeine, and therefore in spite of caffeine’s short half-life, it is transformed into paraxanthine in the environment. The pKa of nicotinic acid is 4.75, indicating that this compound will primarily exist in the anion form in the environment and anions generally do not adsorb more strongly to soils containing organic carbon and clay than their neutral counterparts. In soils, nicotinic acid is expected to have very high mobility based upon an estimated Koc of 37. Hydrolysis is not expected to be an important environmental fate process since this compound lacks functional groups that hydrolyze under environmental conditions. If released in soil, nicotine is expected to have high mobility, based upon an estimated Koc of 100. However, nicotine is a base and protonation under neutral and acidic conditions may result in greater adsorption and less mobility than either its estimated Koc or water solubility indicates. If released into water, nicotine is not expected to adsorb to suspended solids and sediment based upon the estimated Koc. Similarly to nicotinic acid, nicotine has no functional groups that hydrolyze under environmental conditions and, therefore, hydrolysis is not expected to be relevant in abiotic degradation processes. With relation to the detection frequency in the environment, all the compounds studied in this work were detected at all the sampling points located along the Henares River, as is reported in Table 5, except nicotinic acid, which was only detected three times in treated sewage samples at levels above its detection limit. These results highlight the interest in using such compounds as suitable anthropogenic markers for water contamination caused by human activities, since they fulfill the two requirements for being considered as adequate chemical markers: all of them are consumed worldwide (coffee, tea, cocoa, chocolate, tobacco, energy drinks, etc.) and besides that, all of them are regularly detected in surface waters; the concentrations discharged into the environment being sufficient to permit their detection after dilution/dissipation in the aquatic medium. The use of target compounds (trigonelline, caffeine, nicotine and some of their transformation products) as anthropogenic markers is, of course, limited to areas where there is an influence of human activities, in particular for nicotine, caffeine and trigonelline. Caffeine and nicotinic acid have been reported by EU Industry as high production volume chemicals (HPVC) (European Commision Joint Research Centre).

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4.

w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 3 3 1 e2 3 4 1

Conclusions

The direct analysis of environmental samples presented, without prior sample treatment is attractive because of the reduced sample preparation steps. Direct injection avoids the time consuming activity typically necessary with SPE as well as its associated errors. Another advantage is in saving the need to measure recoveries, thereby increasing the overall robustness of the analysis. While achievable limits of detection are poorer than with SPE pre-concentration, it has been possible to achieve sufficient sensitivity using a high sensitive analytical system capable of getting detection limits within the range of mg L
1 or hg L
1 in environmental water samples: this is enough to fulfill the more restricted regulation. A constant presence of caffeine and nicotine was observed during the investigated period, both in wastewaters and in surface waters. Therefore, in light of the results obtained, trigonelline, caffeine, nicotine and their transformation products could be considered as appropriate indicators of water contamination from human activities, since this study reveals the ubiquity of such compounds in the different aquatic environments. In spite of their low-medium stability in water, their constant presence is consequence of their persistent input into the environmental water cycle.

Acknowledgments The authors wish to acknowledge the Spanish Ministry of Education and Science (Programa Consolider Ingenio 2010 CECSD2006-00044) for their economic support. M.J. Martı´nez Bueno acknowledges the research fellowship from the Junta de Andalucı´a (Spain) associated to the project (Ref. TEP2329).

references

Benowitz, N.L., Jacob, P., Ahijevych, K., Jarvis, M.J., Hall, S., LeHouezec, J., Hansson, A., Lichtenstein, E., Henningfield, J., Tsoh, J., Hurt, R.D., Velicer, W., 2002. Biochemical verification of tobacco use and cessation. Nicotine Tob. Res. 4, 149e159. Bramer, S.L., Kallungal, B.A., 2003. Clinical considerations in study designs that use cotinine as a biomarker. Biomarkers 8, 187e203. Buerge, I.J., Poiger, T., Mu¨ller, M.D., Buser, H.R., 2003. Caffeine, ananthropogenic marker for wastewater contamination of surface waters. Environ. Sci. Technol. 37, 691e700. Buerge, I.J., Kahle, M., Buser, H.R., Mu¨ller, M.D., Poiger, T., 2008. Nicotine derivatives in wastewater and surface waters: application as chemical markers for domestic wastewater. Environ. Sci. Technol. 42, 6354e6360. Castiglioni, S., Bagnati, R., Fanelli, R., Pomati, F., Calamari, D., Zuccato, E., 2006. Removal of pharmaceuticals in sewage treatment plants in Italy. Environ. Sci. Technol. 40, 357e363. Commission Decision (2002/657/EC), 2002. Implementing council directive 96/23/EC concerning the performance of analytical methods and the interpretation of results. Off. J. Eur. Communities L221, 8e36. Brussels, Belgium. Environmental Protection Agency (EPA). EPA 40 CFR Part 180. Nicotine; Proposed Revocation of Tolerances. [OPP-301192; FRL-6810e3].

RIN 2070-AB78. http://pmep.cce.cornell.edu/profiles/insect-mite/ mevinphos-propargite/nicotine/nicotine_tol_1201.html. European Commision Joint Research Centre. ESIS European Chemical Substance Information System. http://ecb.jrc.ec. europa.eu/esis/. Friesen, J.B., Leete, E., 1990. Nicotine synthase: an enzyme from Nicotiana species which catalyzes the formation of (S)nicotine from nicotinic acid and 1-methyl-D1 pyrrolinium chloride. Tetrahedron Lett. 31, 6295e6298. Go´mez, M.J., Agu¨era, A., Mezcua, M., Hurtado, J., Mocholı´, F., Ferna´ndez-Alba, A.R., 2007. Simultaneous analysis of neutral and acidic pharmaceuticals as well as related compounds by gas chromatography-tandem mass spectrometry in wastewater. Talanta 73, 314e320. Gros, M., Petrovic, M., Barcelo, D., 2006. Development of a multiresidue method for the analysis of pharmaceuticals based on solid phase extraction and LC-tandem mass spectrometry in surface and wastewaters. Talanta 70, 678e690. Huerta-Fontela, M., Galceran, M.T., Ventura, F., 2007. Trace determination of cannabinoids and opiates in wastewater and surface waters by ultra-performance liquid chromatographytandem mass spectrometry. Anal. Chem. 79, 3821e3829. Huerta-Fontela, M., Galceran, M.T., Martin-Alonso, J., Ventura, F., 2008. Occurrence of psychoactive stimulatory drugs in wastewaters in north-eastern Spain. Sci. Total Environ. 397, 31e40. Martı´nez Bueno, M.J., Agu¨era, A., Hernando, M.D., Go´mez, M.J., Ferna´ndez-Alba, A.R., 2009. Evaluation of various liquid chromatography-quadrupole-linear ion trap-mass spectrometry operation modes applied to the analysis of organic pollutants in wastewaters. J. Chromatogr. A 1216, 5995e6002. Martı´nez Bueno, M.J., Hernando, M.D., Herrera, S., Go´mez, M.J., Ferna´ndez-Alba, A.R., Bustamante, I., Garcı´a-Calvo, E., 2010. Pilot survey of chemical contaminants from industrial and human activities in river waters of Spain. Int. J. Environ. Anal. Chem. 90, 321e343. McElvain, S.M., 1941. Nicotinic acid. Org. Synth. Coll. 1, 385. Nicolardi, S., Herrera, S., Ferna´ndez-Alba, A.R., A new direct competitive elisa for determination of caffeine and cotinine in river and wastewater. Int. J. Environ. Anal. Chem. Submited for pub. PBT Profiler. U.S EPA. Persistent, Bioaccumulative, and Toxic Profiles Estimated for Organic Chemicals. http://www. pbtprofiler.net/. Perrone, D., Marino Donangelo, C., Farah, A., 2008. Fast simultaneous analysis of caffeine, trigonelline, nicotinic acid and sucrose in coffee by liquid chromatography-mass spectrometry. Food Chem. 110, 1030e1035. Ranheim, T., Halvorsen, B., 2005. Coffee consumption and human health e beneficial or detrimental? e mechanisms for effects of coffee consumption on different risk factors for cardiovascular disease and type 2 diabetes mellitus. Mol. Nutr. Food Res. 49, 274e284. Rosal, R., Rodrı´guez, A., Perdigo´n-Melo´n, J.A., Petre, A., Garcı´aCalvo, E., Go´mez, M.J., Agu¨era, A., Ferna´ndez-Alba, A.R., 2010. Occurrence of emerging pollutants in urban wastewater and their removal through biological treatment followed by ozonation. Water Res. 44, 578e588. Siegmund, B., Leitner, E., Pfannhauser, W., 1999. Determination of the nicotine content of various edible nightshades (Solanaceae) and their products and estimation of the associated dietary nicotine intake. J. Agric. Food Chem. 47, 3113e3120. Ternes, T., 1998. Occurrence of drugs in German sewage treatment plants and rivers. Water Res. 32, 3245e3260. Trugo, L.C., 2003. Analysis of coffee products. In: Encyclopedia of Food Scien. Nutri. Academic Press, pp. 1498e1506.

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United States National Library of Medicine. TOXNET e Databases on toxicology, hazardous chemicals, environmental health, and toxic releases. www.toxnet.nlm.nih.gov. Weigel, S., Kallenborn, R., Hu¨hnerfuss, H., 2004. Simultaneous solid-phase extraction of acidic, neutral and basic pharmaceuticals from aqueous samples at ambient (neutral) pH and their determination by gas chromatography-mass spectrometry. J. Chromatogr. A 1023, 183e195.

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Wu, W., Ashley, D.L., Watson, C.H., 2002. Detemination of nicotine and other minor alkaloids in international cigarettes by solid-phase microextraction and gas chromatography/mass spectrometry. Anal. Chem. 74, 4878e4884. Zeiger, E., 1997. Trigonelline. Review of Toxicological Literature. National Institute of Environmental Health Sciences. P.O. Box 12233. Contract No. N01-ES-65402.

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Available at www.sciencedirect.com

journal homepage: www.elsevier.com/locate/watres

Method for rapid and sensitive detection of Enterococcus sp. and Enterococcus faecalis/faecium cells in potable water samples Andre´e F. Maheux a,b, Luc Bissonnette a,b, Maurice Boissinot a,b, Jean-Luc T. Bernier a, Vicky Huppe´ a, E`ve Be´rube´ a, Dominique K. Boudreau a, Franc¸ois J. Picard a, Ann Huletsky a,b, Michel G. Bergeron a,b,* a b

Centre de recherche en infectiologie de l’Universite´ Laval, Centre de recherche du CHUQ, Que´bec City, Que´bec, Canada De´partement de microbiologie-infectiologie et immunologie, Faculte´ de me´decine, Universite´ Laval, Que´bec City, Que´bec, Canada

article info

abstract

Article history:

We have developed a rapid and robust technological solution including a membrane

Received 27 September 2010

filtration and dissolution method followed by a molecular enrichment and a real-time PCR

Received in revised form

assay, for detecting the presence of Enterococcus sp. or Enterococcus faecalis/faecium per

22 January 2011

100 mL of water in less than 5 h and we compared it to Method 1600 on mEI agar in terms of

Accepted 24 January 2011

specificity, sensitivity, and limit of detection. The mEI and the Enterococcus sp.-specific

Available online 1 February 2011

assay detected respectively 73 (64.0%) and 114 (100%) of the 114 enterococcal strains tested. None of the 150 non-enterococcal strains tested was detected by both methods with the exception of Tetragenococcus solitarius for the Enterococcus sp. assay. The multiplexed

Keywords: Drinking water analysis

E. faecalis/faecium assay efficiently amplified DNA from 47 of 47 (100%) E. faecalis and 27 of 27

Enterococcus sp.

(100%) E. faecium strains tested respectively, whereas none of the 191 non-E. faecalis/faecium

Enterococcus faecalis/faecium

strains tested was detected. By simultaneously detecting the predominant fecal entero-

Filtration

coccal species, the E. faecalis/faecium-specific assay allows a better distinction between

membrane

dissolution

procedure

enterococcal strains of fecal origin and those provided by the environment than Method

Whole genome amplification

1600. Our procedure allows the detection of 4.5 enterococcal colony forming units (CFU) per

Real-time PCR

100 mL in less than 5 h, whereas the mEI method detected 2.3 CFU/100 mL in 24 h (95% confidence). Thus, our innovative and highly effective method provides a rapid and easy approach to concentrate very low numbers of enterococcal cells present in a 100 mL water sample and allows a better distinction between fecal and environmental enterococcal cells than Method 1600. Crown Copyright ª 2011 Published by Elsevier Ltd. All rights reserved.

1.

Introduction

Enterococci, previously included in a group known as fecal streptococci, are now regrouped into the bacterial genus Enterococcus (Schleifer et al., 1984), Enterococcus faecalis and

Enterococcus faecium being the predominant species of the genus found in human feces (Ruoff et al., 1990). In fact, all mammals carry these microorganisms in the colon at concentrations in the order of 105e107 per gram of feces (Noble, 1978) (i.e. approximately 100- to 10,000-fold less than

* Corresponding author. Centre de recherche en infectiologie de l’Universite´ Laval, Centre de recherche du CHUQ, 2705 Laurier Blvd., Que´bec City, Que´bec, Canada G1V 4G2. Tel.: þ1 418 656 4141x48753; fax: þ1 418 654 2715. E-mail address: [email protected] (M.G. Bergeron). 0043-1354/$ e see front matter Crown Copyright ª 2011 Published by Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2011.01.019

w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 3 4 2 e2 3 5 4

Escherichia coli). Although some enterococcal species are naturally found in the environment and not necessarily related to fecal pollution, the presence of enterococci in water is considered by the United States Environmental Protection Agency (USEPA) as an indication of fecal pollution and of the possible presence of enteric pathogens (USEPA, 2005; Cabelli et al., 1982; Franz et al., 1999; Kjellander, 1960). However, detecting Enterococcus sp. is of limited significance for determining the source of fecal contamination in water since the broad spectrum of species cannot be used to distinguish non-fecal (environmental) from fecal contamination (Bonds et al., 2006; Converse et al., 2009). Indeed, there are many possible sources of Enterococcus sp. in water including animal waste (Devriese and De Plesmaecker, 1987; Devriese et al., 1991; Sinton et al., 1993; Harwood et al., 2001), soil (Fujioka et al., 1999), invertebrates (Martin and Mundt, 1972; Svec et al., 2002), and plants (Muller et al., 2001). The usefulness of enterococci as indicators of the risk of waterborne disease for humans is complicated, but not eliminated, by their broad environmental distribution. Thus, environmental water quality assessment may benefit from focusing on a group of Enterococcus sp. that is associated with sources of fecal pollution rather than relying on the entire Enterococcus genus. Consequently, E. faecalis and E. faecium are potentially good fecal species as they have been consistently identified as predominant enterococcal species in warm-blooded animal feces and sewage, but not from environmental sources (Chenoweth and Schaberg, 1990; Ruoff et al., 1990; Gelsomino et al., 2003; Manero et al., 2002). The most widely used procedure to detect the presence of enterococci is the membrane filtration-based USEPA Method 1600 on mEI agar (Messer and Dufour, 1998; USEPA, 2005). However, it is acknowledged that growth on mEI medium leads to rates of false-positive and -negative results of 6.0 and 6.5%, respectively (Messer and Dufour, 1998). Moreover, problems associated with growing bacteria on artificial media might be attributable to the poor culturability of injured and stressed organisms (Lleo et al., 2005). This problem is exacerbated when selective media are used, their selective agents probably exerting an inhibitory or toxic effect on injured target bacteria (Scheusner et al., 1971). The method also has limitations such as long duration of incubation, lack of ubiquity (Maheux et al., 2009), and poor detection of slow-growing or viable but nonculturable (VBNC) microorganisms (Joux and Lebaron, 2000; Lleo et al., 2005; Roszak and Colwell, 1987). Furthermore, the detection of Enterococcus sp. on a standard microbiological medium such as mEI provides no information about their origin (Fuentefria et al., 2006). Fecal enterococcal species of human origin comprise E. faecalis, E. faecium, E. casseliflavus, E. durans, E. gallinarum, E. hirae, and E. raffinosus (Ruoff et al., 1990; Stern et al., 1994; Pinto et al., 1999; Tannock and Cook, 2002; Teixeira and Facklam, 2010) and fecal enterococcal species originating from animals comprise E. faecalis, E. faecium, E. hirae, E. cecorum, E. gallinarum, E. casseliflavus, E. durans, E. avium, and E. raffinosus (Devriese and De Plesmaecker, 1987; Devriese et al., 1991; Stern et al., 1994; Mac et al., 2003). Among these, E. faecalis and E. faecium are the predominant species (Ruoff et al., 1990; Aarestrup et al., 2002). Other species are often found in the environment and may not be associated with fecal pollution (Ashbolt et al., 1997; Fujioka and Hardina, 1995). Because of the

2343

non-selective nature of the USEPA Method 1600, this method fails to discriminate between fecal and non-fecal enterococci in water. However, identifying the contributing sources of Enterococcus is critical for an accurate assessment and appropriate control measures. In the field of water microbiology, the development and implementation of more rapid, sensitive, specific, and affordable tests to protect health is warranted to prevent or investigate the transmission of waterborne gastrointestinal pathogens (Harwood et al., 2005; Reiff et al., 1996). Molecular microbiology methods constitute a suitable avenue for such tests and, for example, the detection of enterococci by realtime polymerase chain reaction (rtPCR) has been achieved by targeting the 23S rRNA gene (Frahm and Obst, 2003). However, the application of rapid molecular testing applied to the microbiological quality of potable water is hampered by the lack of simple solutions for concentrating and recovering very low numbers of microbial particles (indicators and pathogens) present in a relatively large water sample. Using Enterococcus sp. and E. faecalis/faecium as target microorganisms, we demonstrate in this study that, coupling a highly effective microbial particles concentration and recovery method to whole genome amplification (WGA) and rtPCR amplification, allows the detection of as few as 4.5 enterococcal cells per 100 mL (2.3 CFU/100 mL for mEI agar method) of potable water, in less than 5 h. By detecting species mostly found in mammal feces, the E. faecalis/faecium-specific rtPCR assay allows a better distinction between enterococcal strains of fecal origin and those provided by the environment than Enterococcus sp.-based detection methods than culturebased Method 1600 on mEI agar.

2.

Materials and methods

2.1.

Bacterial strains

The analytical sensitivity (ability to detect all or most enterococcal strains) of the Enterococcus sp. and the E. faecalis/ faecium rtPCR primer sets was verified by using 115 different strains of enterococci representing 32 species (Table 1). The analytical specificity of each rtPCR assay was demonstrated by testing a battery of strains consisting of 36 Gram-positive (Table 2) and 114 non-enterococcal Gram-negative (Table 3) bacterial species. The species identification of the strains used in this study was reconfirmed using an automated MicroScan Autoscan-4 system (Siemens Healthcare Diagnostic Inc., Newark, DE, USA) or a Vitek 2 system (bioMe´rieux SA, Marcy l’E´toile, France). Bacterial strains were grown from frozen stocks, kept at 80  C in brain heart infusion (BHI) medium (Beckton, Dickinson and Company, Mississauga, Ontario, Canada) containing 10% glycerol, and cultured on sheep blood agar, chocolate or BCYE agar depending upon the specific growth requirement of each species.

2.2.

Bacterial cell suspension preparation

The bacterial strain used for spiking experiments was E. faecalis ATCC 19433. Enterococcal cells were grown to logarithmic

2344

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Table 1 e Ability of the culture-based mEI agar method and of the 2 primers and probes sets to detect enterococcal strains. Enterococcus species (n ¼ 114)

E. aquimarinus E. avium E. caccae E. canintestini E. canis E. casseliflavus E. casseliflavus E. casseliflavus E. casseliflavus E. casseliflavus E. casseliflavus E. cecorum E. columbae E. devriesi E. dispar E. durans E. faecalis E. faecalis E. faecalis E. faecalis E. faecalis E. faecalis E. faecalis E. faecalis E. faecalis E. faecalis E. faecalis E. faecalis E. faecalis E. faecalis E. faecalis E. faecalis E. faecalis E. faecalis E. faecalis E. faecalis E. faecalis E. faecalis E. faecalis E. faecalis E. faecalis E. faecalis E. faecalis E. faecalis E. faecalis E. faecalis E. faecalis E. faecalis E. faecalis E. faecalis E. faecalis E. faecalis E. faecalis E. faecalis E. faecalis E. faecalis E. faecalis

Origin

Environmental Clinical Clinical Animal Animal Environmental N/A Clinical Clinical Clinical N/A Animal Animal Animal Clinical Clinical Clinical Clinical Clinical N/A Clinical Clinical Clinical Clinical Clinical N/A N/A N/A Clinical Clinical Clinical Clinical Clinical Clinical N/A Clinical Clinical Clinical N/A N/A N/A N/A N/A N/A Clinical N/A N/A N/A N/A N/A Clinical Clinical Clinical Clinical Environmental Clinical N/A

Reference no.

CCRI-15963 ATCC 14025 ATCC-BAA.1240 CCUG 37867 CCUG 46666 ATCC 25788 ATCC 51328 ATCC 12819 CCRI-1434 CCRI-1566 CCRI-1588 ATCC 43198 ATCC 51263 CCUG 37865 ATCC 51266 ATCC 19432 ATCC 19433 ATCC 23241 ATCC 29212 ATCC 33186 ATCC 49533 ATCC 51299 CCRI-1376 CCRI-1435 CCRI-1471 CCRI-1474 CCRI-1476 CCRI-1489 CCRI-1490 CCRI-1491 CCRI-1498 CCRI-1500 CCRI-1516 CCRI-1517 CCRI-1589 CCRI-1906 CCRI-1908 CCRI-1910 CCRI-2092 CCRI-9725 CCRI-9732 CCRI-9738 CCRI-9912 CCRI-9914 CCRI-9931 CCRI-9932 CCRI-9933 CCRI-9954 CCRI-9955 CCRI-9956 CCRI-12847 CCRI-12848 CCRI-12849 CCRI-15139 CCRI-16012 CCRI-16617 LSPQ 5192

mEI agar

  þ   þ þ þ þ þ þ  þ  þ þ þ  þ þ þ  þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ  þ

Enterococcus sp. assay

Multiplex Enterococcus faecalis/feacium assay

Enterococcus sp. specific primer and probe setsa

E. faecalis-specific primer and probe setb

E. faecium specific primer and probe setc

þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ

                þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ

                                                        

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w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 3 4 2 e2 3 5 4

Table 1 (continued) Enterococcus species (n ¼ 114)

E. faecalis E. faecalis E. faecalis E. faecalis E. faecalis E. faecium E. faecium E. faecium E. faecium E. faecium E. faecium E. faecium E. faecium E. faecium E. faecium E. faecium E. faecium E. faecium E. faecium E. faecium E. faecium E. faecium E. faecium E. faecium E. faecium E. faecium E. faecium E. faecium E. faecium E. faecium E. faecium E. flavescens E. flavescens E. gallinarum E. gallinarum E. gallinarum E. gallinarum E. gallinarum E. gallinarum E. gilvus E. haemoperoxidus E. hirae E. hirae E. italicus E. malodoratus E. moraviensis E. mundtii E. pallens E. phoeniculicoli E. pseudoavium E. raffinosus E. ratti E. sileciacus E. saccharloyticus E. sulfureus E. termitis E. villorum

Origin

N/A N/A N/A N/A N/A Clinical Clinical N/A Clinical N/A Clinical Clinical N/A Clinical N/A N/A Clinical N/A N/A N/A N/A Clinical Clinical Clinical Clinical Clinical Environmental Environmental Environmental N/A N/A Clinical Clinical Clinical N/A Clinical N/A Clinical Clinical Clinical Water Clinical Clinical Food Food Water Environmental Clinical Animal Animal Clinical Animal Water Clinical Environmental Animal Animal

Reference no.

LSPQ 5378 LSPQ 5548 LSPQ 5570 LSPQ 5638 LSPQ 5660 ATCC 19434 ATCC 700221 CCRI-1472 CCRI-1473 CCRI-1475 CCRI-1479 CCRI-1733 CCRI-8824 CCRI-9726 CCRI-9727 CCRI-9728 CCRI-9766 CCRI-9911 CCRI-9936 CCRI-9937 CCRI-9938 CCRI-14889 CCRI-15140 CCRI-16347 CCRI-16348 CCRI-16354 CCRI-16518 CCRI-19447 CCRI-19448 LSPQ 5155 LSPQ 5656 ATCC 49996 ATCC 49997 CCRI-1433 CCRI-1436 CCRI-1486 CCRI-9737 LSPQ 3364 LSPQ 5375 ATCC-BAA.350 CCUG 45916 ATCC 8043 CCUG 37829 CCUG 50447 ATCC 43197 CCUG 45913 ATCC 43186 ATCC-BAA 351 CCUG 48923 ATCC 49372 ATCC 49427 ATCC 700914 CCUG 53830 ATCC 43076 ATCC 49903 CCUG 53831 CCRI-8858

mEI agar

þ þ þ þ þ þ   þ      þ þ þ  þ þ þ      þ þ þ  þ   þ þ  þ þ þ   þ     þ        þ  

Enterococcus sp. assay

Multiplex Enterococcus faecalis/feacium assay

Enterococcus sp. specific primer and probe setsa

E. faecalis-specific primer and probe setb

E. faecium specific primer and probe setc

þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ

þ þ þ þ þ                                                    

     þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ                          (continued on next page)

2346

w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 3 4 2 e2 3 5 4

Table 1 (continued) Enterococcus species (n ¼ 114)

Origin

Reference no.

mEI agar

Enterococcus sp. assay Enterococcus sp. specific primer and probe setsa

All enterococcal strains:

73/114 (64.0%)

Multiplex Enterococcus faecalis/feacium assay E. faecalis-specific primer and probe setb

E. faecium specific primer and probe setc

114/114 (100%)

All Enterococcus faecalis strains: All Enterococcus faecium strains:

47/47 (100%) 26/26 (100%)

N/A : not available. CCRI: collection of the Centre de recherche en infectiologie de l’Universite´ Laval. a Frahm and Obst (2003): primers ECST784F and ENC854R, probe GPL813TQ. b This study: primers Mefs569 and Mefs670, probe Mefs-TL1-A1. c This study: primers Defm273 and Defm468, probe Mefs-T1-F2.

phase (0.5e0.6 OD600) in BHI and adjusted to a 0.5 McFarland standard (Thermo Fisher, Ottawa, Ontario, Canada), before being serially diluted ten-fold in phosphate-buffered saline (PBS; 137 mM NaCl, 6.4 mM Na2HPO4, 2.7 mM KCl, 0.88 mM KH2PO4, pH 7.4). An aliquot of the 105 dilution was spiked in ozonated spring water from Sainte-Marie-de-Blandford (Comte´ de Be´cancour, Que´bec, Canada; total dissolved þ2 mineral salt content: 60 ppm [40 mg/L HCO 3 , 11 mg/L Ca ,   þ2 þ 1 mg/L Cl , 0.1 mg/L F , 2.7 mg/L Mg , 1 mg/L K , 3 mg/L Naþ, and 8 mg/L SO 4 ]) to produce suspensions containing approximately 100, 50, 25, 16, 8, 4, 2, and 1 colony forming unit(s) (CFU) per 100 mL of water. Bacterial counts were verified by filtering 100 mL of each spiked water sample through a GN-6 membrane filter (47 mm diameter, 0.45 mm pore size; Pall Corporation, Mississauga, Ontario, Canada) with a standard platform manifold (Millipore Corporation, Billerica, MA, USA). Tests to confirm the sterility of filter membranes and buffer used for rinsing the filtration apparatus were also performed. Sewage water was harvested at the entrance of the municipal treatment plant of St-Nicolas (Que´bec, Canada) and conserved at 4  C for a maximum of 3 days. Sewage water was serially diluted ten-fold in PBS. To determine the ability of the

concentration and recovery method coupled with a WGA-rtPCR assay to detect enterococcal cells in different potable water samples, 10 different well water samples harvested in the Que´bec City area during fall 2008 were spiked with sewage to produce suspensions having approximately 20 CFU/100 mL of water. To determine the detection limits of mEI agar and that of the WGA Enterococcus sp. and E. faecalis/faecium-specific rtPCR assays, another well water sample was spiked with sewage to produce suspensions with titers of 50, 10, 5, 1, 0.5, and 0.1 CFU/ 100 mL of water. The enterococcal concentration on the sewage was previously estimated by the mEI agar method. Titers lower that 1 CFU/100 mL were used because the spiking count is theoretical and could lead to higher or lower count on plate. For all spiked water samples, a process control consisting of approximately 60 Bacillus atrophaeus subsp. globigii spores per 100 mL was added prior the filtration. Spores were prepared according to Picard et al. (2009).

2.3.

Membrane filtration

Membrane filtration method was performed according to Maheux et al. (2009). For each spiked sample, two (2) 100 mL

Table 2 e Gram-positive bacteria used for specificity analysis (n [ 36). Abiotrophia defectiva Clostridium lavalense Gemella haemolysans Granulicatella adiacens Kocuria rhizophila Lactobacillus acidophilus Leifsonia aquatica Listeria grayi Listeria innocua Listeria ivanovii Listeria monocytogenes Listeria seeligeri Ruminococcus gauvreauii Staphylococcus aureus Staphylococcus capitis subsp. capitis Staphylococcus epidermidis Staphylococcus haemolyticus Staphylococcus hominis subsp. hominis

ATCC 49176 CCRI-9842 ATCC 10379 ATCC 49175 ATCC 9341 ATCC 4356 ATCC 14665 ATCC 19120 ATCC 33090 ATCC 19119 ATCC 15313 ATCC 35967 CCRI-16110 ATCC 25923 ATCC 27840 ATCC 14990 ATCC 29970 ATCC 27844

Staphylococcus lugdunensis Staphylococcus saprophyticus subsp. saprophyticus Staphylococcus simulans Staphylococcus warneri Streptococcus agalactiae Streptococcus anginosus Streptococcus bovis Streptococcus constellatus subsp. constellatus Streptococcus cristatus Streptococcus intermedius Streptococcus gordonii Streptococcus mutans Streptococcus parasanguinis Streptococcus pneumoniae Streptococcus pyogenes Streptococcus salivarius Streptococcus sanguinis Streptococcus suis

ATCC ATCC ATCC ATCC ATCC ATCC ATCC ATCC ATCC ATCC ATCC ATCC ATCC ATCC ATCC ATCC ATCC ATCC

43809 15305 27848 27836 13813 33397 33317 27823 51100 27335 33399 25175 15912 6303 19615 7073 10556 43765

w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 3 4 2 e2 3 5 4

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Table 3 e Gram-negative bacteria used for specificity analysis (n [ 114). Acinetobacter baumannii Acinetobacter haemolyticus Aeromonas caviae Aeromonas hydrophila Burkholderia cepacia Citrobacter amalonaticus Citrobacter braakii Citrobacter farmeri Citrobacter freundii Citrobacter gillenii Citrobacter koseri Citrobacter murliniae Citrobacter sedlakii Citrobacter werkmanii Citrobacter youngae Enterobacter aerogenes Enterobacter agglomerans Enterobacter amnigenus Enterobacter asburiae Enterobacter cancerogenus Enterobacter cloacae Enterobacter dissolvens Enterobacter gergoviae Enterobacter hormaechei Enterobacter intermedius Enterobacter nimipressuralis Enterobacter pyrinus Escherichia blattae Escherichia coli Escherichia fergusonii Escherichia hermannii Escherichia vulneris Haemophilus haemolyticus Haemophilus influenzae Haemophilus parahaemolyticus Haemophilus parainfluenzae Hafnia alvei Klebsiella oxytoca Klebsiella pneumoniae Leclercia adecarboxylata Legionella pneumophila subsp. fraseri Moraxella atlantae Moraxella catarrhalis Neisseria caviae Neisseria elongata subsp. elongata Neisseria gonorrhoeae Neisseria meningitidis Neisseria mucosa Pantoea agglomerans Pasteurella aerogenes Photorhabdus asymbiotica Proteus mirabilis Proteus vulgaris Providencia alcalifaciens Providencia heimbachae Providencia rettgeri Providencia rustigianii

ATCC 19606 ATCC 17906 CCUG 44411 ATCC 7966 ATCC 25416 ATCC 25405 ATCC 43162 ATCC 51112 ATCC 6879 ATCC 51117 ATCC 27156 ATCC 51641 ATCC 51115 ATCC 51114 ATCC 29935 ATCC 13048 ATCC 27989 ATCC 33072 ATCC 35953 ATCC 33241 ATCC 7256 ATCC 23373 ATCC 33028 ATCC 49162 ATCC 33110 ATCC 9912 ATCC 49851 ATCC 29907 ATCC 11775 ATCC 35469 ATCC 33650 ATCC 33821 ATCC 33390 ATCC 9007 ATCC 10014 ATCC 7901 ATCC 13337 ATCC 13182 ATCC 27736 ATCC 29916 ATCC 33156 ATCC 29525 ATCC 25238 ATCC 14659 ATCC 25295 ATCC 35201 ATCC 13077 ATCC 19696 ATCC 27155 ATCC 27883 ATCC 43948 ATCC 25933 ATCC 29513 ATCC 9886 ATCC 35613 ATCC 9250 ATCC 12013

volumes were filtered on a GN-6 membrane filter. The first filter was deposited on mEI agar and the second filter was treated using the membrane dissolution and concentration procedure as described below. Following membrane filtration, enterococcal cell counts were determined by the culture-based USEPA Method 1600 (USEPA, 2005), performed

Providencia stuartii Pseudomonas aeruginosa Pseudomonas alcaligenes Pseudomonas fluorescens Pseudomonas oryzihabitans Pseudomonas putida Pseudomonas stutzeri Raoultella ornithinolytica Raoultella planticola Raoultella terrigena Salmonella bongori Salmonella enterica subsp. enterica Choleraesuis Salmonella enterica subsp. enterica Enteritidis Salmonella enterica subsp. enterica Gallinarum Salmonella enterica subsp. enterica Heidelberg Salmonella enterica subsp. enterica Paratyphi A Salmonella enterica subsp. enterica Paratyphi B Salmonella enterica subsp. enterica Pullorum Salmonella enterica subsp. enterica Putten Salmonella enterica subsp. enterica Typhi Salmonella enterica subsp. enterica Typhi Salmonella enterica subsp. enterica Typhimurium Salmonella enterica subsp. enterica Virchow Salmonella enterica subsp. houtenae Salmonella enterica subsp. indica Salmonella enterica subsp. salamae Serratia entomophila Serratia ficaria Serratia fonticola Serratia grimesii Serratia liquefaciens Serratia marcescens Serratia odorifera Serratia plymuthica Serratia proteamaculans subsp. proteamaculans Serratia proteamaculans Serratia rubidaea Shigella boydii Shigella dysenteriae Shigella flexneri Shigella sonnei Stenotrophomonas maltophilia Vibrio alginolyticus Vibrio cholerae Vibrio fluvialis Vibrio parahaemolyticus Vibrio vulnificus Yersinia aldovae Yersinia bercovieri Yersinia enterocolitica subsp. enterocolitica Yersinia frederiksenii Yersinia intermedia Yersinia kristensenii Yersinia mollaretii Yersinia pseudotuberculosis Yersinia rohdei Yersinia ruckeri

ATCC 33672 ATCC 27853 ATCC 14909 ATCC 2219 ATCC 43272 ATCC 12633 ATCC 17588 ATCC 31898 ATCC 33531 ATCC 33257 ATCC 43975 ATCC 7001 ATCC 13076 ATCC 9184 ATCC 8326 ATCC 9150 ATCC 8759 ATCC 9120 ATCC 15787 ATCC 10749 ATCC 27870 ATCC 14028 ATCC 51955 ATCC 43974 ATCC 43976 ATCC 43972 ATCC 43705 ATCC 33105 ATCC 29844 ATCC 14460 ATCC 25641 ATCC 8100 ATCC 33077 ATCC 183 ATCC 19323 ATCC 33765 ATCC 27593 ATCC 9207 ATCC 11835 ATCC 12022 ATCC 29930 ATCC 13637 CCRI-14794 ATCC 25870 CCRI-14795 ATCC 17802 ATCC 27562 ATCC 35236 ATCC 43970 ATCC 9610 ATCC 29912 ATCC 29909 ATCC 33638 ATCC 43969 ATCC 29833 ATCC 43380 ATCC 29473

on mEI agar for 24  2 h at 35.0  0.5  C. Subsequently, colony count and color were determined. Each preparation of mEI plates was tested for performance using pure cultures of target and non-target microorganisms, as recommended by the USEPA microbiology methods manual (USEPA, 1978). Tests to confirm the sterility of the filter membranes and

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w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 3 4 2 e2 3 5 4

buffer used for rinsing the filtration apparatus were also performed.

2.4. Dissolution of the filtration membrane and concentration of enterococcal cells Following filtration, the membrane was aseptically removed from the filtration manifold, transferred to a 15-mL polypropylene tube (Sarstedt, Newton, NC, U.S.A.), exposed for 10 s to 8.5 mL of HPLC-grade methanol (SigmaeAldrich, St. Louis, MO, U.S.A.), and vigorously agitated on a vortex mixer during 10 s. After this step, the reaction tube and its content were centrifuged for 3 min at 2100g. The supernatant was removed and 1 mL of histological-grade acetone (EMD Chemicals, San Diego, CA, U.S.A.) was added to the pellet and complete dissolution was achieved by vigorous agitation on a vortex mixer. The resulting clear acetone solution was transferred to a 2-mL tube containing a mixture of glass beads (150e212 mm and 710e1180 mm; SigmaeAldrich, St. Louis, MO, U.S.A.), centrifuged for 3 min at 15 800g, and the supernatant was removed. To maximize the recovery of filtered cells, the 15-mL polypropylene tube was briefly rinsed with 1.0 mL of histological-grade acetone and the resulting mixture was transferred to the glass beads tube previously used. The tube was then centrifuged for 3 min at 15 800g. The resulting pellet was washed with 1.0 mL of TE (TriseHCl 100 mM, EDTA 1 mM, pH 8.0) and centrifuged for 3 min at 15 800g. After centrifugation of the washed filtrate-glass beads suspension in the presence of TE buffer, the supernatant was removed. The dead volume in the glass beads is estimated to be approximately 25 mL. At this point, the tube containing the concentrated enterococcal cells was treated to evaluate the recovery rate and efficiency of the membrane dissolution method or submitted to a molecular enrichment by WGA for the sensitive detection of enterococcal cells contained in the 100 mL of water sample (see next section). To evaluate the recovery rate and efficiency of the membrane dissolution step, fifteen (15) mL of TE (TriseHCl 100 mM, EDTA 1 mM, pH 8.0) was added to the tube and the lysis of the cells contained in the pellet was achieved by vigorous mixing, at maximum speed, on a vortex mixer for 5 min. The reaction tube containing the cell lysate was then incubated 2 min at 95  C, briefly spun in a microcentrifuge, and kept at 20  C until rtPCR amplification. Then, 1 mL of the 40 mL final volume obtained after DNA extraction was directly used to perform an Enterococcus sp. and E. faecalis/faeciumspecific rtPCRs. Consequently, the colony count obtained with the membrane filtration technique was divided by 40 for the corresponding comparison with rtPCR signals (Table 4).

2.5.

Whole genome amplification

Forty (40) mL of Illustra GenomiPhi V2 sample buffer (part of the Illustra GenomiPhi DNA Amplification Kit; GE Healthcare Life Sciences, Baie d’Urfe´, Que´bec, Canada) was added to the 25-mL reaction mixture. The cells contained in the pellet were mechanically lysed by vigorous mixing, at maximum speed, on a vortex mixer for 5 min at room temperature. The reaction tube containing the crude cell extract was then incubated 3 min at 95  C, and kept on ice for a minimum of 3 min. A

Table 4 e Comparative recovery of the targeted microorganisms by counting procedures and the membrane dissolution step. Expected microbial mEI agar CFU/rtPCR counts (CFU/100 mL) reaction (CFU) 80 80 40 40 20 20 Negative ctrl.

82.7 77.7 30.7 29.0 14.7 13.3 0

 7.1  11.6  4.9  3.3  1.9  1.8

2.1 1.9 0.8 0.7 0.4 0.3 0

rtPCR signal þ þ þ þ þ þ 

þ þ þ þ   

þ þ þ þ  þ 

mixture of forty-five (45) mL of GenomiPhi reaction buffer and 4 mL of Phi29 (429) DNA polymerase (Illustra GenomiPhi DNA Amplification Kit) was added to the extract, gently mixed by finger tapping, before being briefly spun in a microcentrifuge. The WGA reaction mixture was incubated for 3 h at 30  C. The enzymatic reaction was then arrested by a 10-min incubation at 65  C. One (1) ml of WGA-amplified products was then used as template for enterococci and B. atrophaeus subsp. globigii rtPCR amplification using the conditions described below. To ensure that the tested water samples were free of enterococcal cells, WGA-rtPCR negative controls were also performed using unspiked water. The detection of B. atrophaeus subsp. globigii serves to monitor for the integrity of the procedure and the absence of rtPCR inhibition.

2.6.

Real-time PCR primers and probes

The sequence of rtPCR primers and probes are shown in Table 5. The rtPCR primers (ECST784F, ENC854R) and probe (GPL813TQ) for the Enterococcus sp. assay have been described by Frahm and Obst (2003). The rtPCR primers and probe sets for the simultaneous detection of E. faecalis and E. faecium were developed by retrieving sequence files from public databases and constructing multiple sequence alignments with GCG programs (version 8.0; Accelrys, Madison, WI, USA). The mtlf and ddl genes respectively code for the EIIA domain of the 6-pyruvoyltetrahydropterin synthase (PTS) enzyme and the cytoplasmic enzyme D-alanyl D-alanine ligase. The E. faecalis-specific rtPCR primers, Mefs569 and Mefs670, and the specific probe, Mefs-TL1-A1, were designed as a part of this study. For E. faecium we have selected the rtPCR primers Defm273 and Defm468 and the specific probe Defm-T1-F2. All those primers and probes were used together in a multiplex rtPCR assay. The rtPCR primers (ABgl158, ABgl345a) and probe (ABgl-T1-B1) for the B. atrophaeus subsp. globigii assay are described elsewhere (Picard et al., 2009). Oligonucleotide primers and probes were synthesized by Integrated DNA Technologies (Coralville, IA, USA).

2.7.

Real-time PCR assays

Real-time PCR amplifications for specificity and ubiquity assessment were performed using a bacterial suspension adjusted to a 0.5 McFarland standard. The cells were lysed using the BD Diagnostics-GeneOhm Rapid Lysis kit as

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Table 5 e Real-time PCR primers and probe used in this study. Assay Genetic Primers and Primers and probe sequence (5’ / 3’) target probe A

B

C

23S rRNA ECST784F ENC854R GPL813TQ mtlf Mefs569 Mefs670 Mefs-TL1-A1 ddl Defm273 Defm468 Defm-T1-F2 atpD ABgl158 ABgl345a Abgl-T1-A1

Reference

AGAAATTCCAAACGAACTTG CAGTGCTCTACCTCCATCATT FAMa-TGGTTCTCTCCGAAATAGCTTTAGGGCTA-BHQ-1b GAACAGAAGAAGCCAAAAAA GCAATCCCAAATAATACGGT FAMa-CALGGAATLCTGTLGTALGTGLCAAG-BHQ-1b TGCTTTAGCAACAGCCTATCAG TAAACTTCTTCCGGCACTTCG CalFluorRed610c-CTCGAGCAATCGTTGAACAAGGAATTG-BHQ-2d CACTTCATTTAGGCGACGATACT TTGTCTGTGAATCGGATCTTTCTC FAMa-CGTCCCAATGTTACATTACCAA-CCGGCACT-(BHQ-1b)-GAAATAGG

Frahm and Obst, 2003

This study

Picard et al., 2009

L

N: locked nucleic acid (LNA) nucleotide analog a FAM, 6-carboxyfluorescein, fluorescence reporter dye. b BHQ-1, Black Hole Quencher-1, fluorescence quencher dye. c CalFluorRed610, fluorescence reporter dye. d BHQ-2, Black Hole Quencher-1, fluorescence quencher dye.

recommended by the manufacturer (BD Diagnostics-GeneOhm, Que´bec City, Que´bec, Canada). One (1) mL of the standardized lysed bacterial suspension or of the WGA amplification products was transferred directly to a 24 mL PCR mixture containing 50 mM KCl, 10 mM TriseHCl (pH 9.0), 0.1% Triton X-100, 2.5 mM MgCl2, 0.4 mM of Enterococcus sp., E. faecalis/faecium, or B. atrophaeus subsp. globigii primers, 0.2 mM of Enterococcus sp., E. faecalis/faecium, or B. atrophaeus subsp. globigii probe, 200 mM each deoxyribonucleoside triphosphate (GE Healthcare Life Sciences Inc., Baie d’Urfe´, Que´bec, Canada), 3.3 mg per mL of bovine serum albumin (BSA; SigmaeAldrich Canada Ltd., Oakville, Ontario, Canada), 0.025 enzyme unit (U) of Taq DNA polymerase (Promega, Madison, WI, USA), and TaqStart antibody (Clontech Laboratories, Mountain View, CA, USA). For each experiment, one (1) mL of sterile water was added to the rtPCR mixture as negative control. The rtPCR mixtures were subjected to thermal cycling (1 min at 95  C and then 45 cycles of 15 s at 95  C, 10 s at 60  C and 20 s at 72  C for E. faecalis/faecium rtPCR assay and 1 min at 95  C and then 45 cycles of 15 s at 95  C and 60 s at 60  C for Enterococcus sp. and B. atrophaeus subsp. globigii rtPCR assays) with a Rotor-Gene thermocycler (Corbett Life Science, Sidney, Australia, now a QIAGEN company).

2.8.

Statistical analysis

Logistic regression statistical analysis was done using softwares JMP v8.0 (JMP, 1989e2007) and R (R Development Core Team, 2008).

3.

Results and discussion

3.1. Analytical sensitivity of the culture-based mEI method and real-time PCR assays The analytical sensitivity of the culture-based mEI method and the Enterococcus sp.-specific and the E. faecalis/faecium-

specific rtPCR assays was demonstrated by testing genomic DNA isolated from 114 enterococcal strains of different serotypes and of different geographic origins comprising 47 E. faecalis and 26 E. faecium strains (Table 1). The mEI method detected b-glucosidase activity for 73 (64.0%) of the 114 enterococcal strains tested. The Enterococcus sp.-specific rtPCR primers and probe efficiently amplified DNA from all 114 enterococcal strains tested whereas the multiplexed E. faecalis/faecium rtPCR assay efficiently amplified DNA from 47 of 47 (100%) E. faecalis and 26 of 26 (100%) E. faecium strains tested respectively. Thus, against all enterococcal strains, the Enterococcus sp. rtPCR assay is 100% sensitive in its ability to detect all enterococcal strains, whereas the multiplex E. faecalis/faecium rtPCR assay is 100% sensitive for the detection of E. faecalis and E. faecium.

3.2.

Specificity of the mEI agar method and rtPCR assays

The analytical specificity of the mEI agar method and the Enterococcus sp. and the E. faecalis/faecium-specific rtPCR assays was verified by testing 150 non-enterococcal strains representing 36 species of Gram-positive and 114 species of Gramnegative bacteria frequently encountered in clinical and environmental settings and including species phylogenetically close to enterococci (Tables 2 and 3). None of the 150 nonenterococcal strains tested was detected on mEI agar. With the exception of Tetragenococcus solitarius, none of the 150 nonenterococcal strains tested was detected by the Enterococcus sp.-specific rtPCR assay. Phylogenetically, T. solitarius is very closely related to enterococci (Ke et al., 1999; Ennahar and Cai, 2005) and controversy in its taxonomical classification persists. The analytical specificity of the multiplexed E. faecalis/faecium rtPCR assay was verified by testing 217 non-E. faecalis and 238 non-E. faecium species, respectively. The multiplexed E. faecalis/faecium rtPCR was 100% specific for E. faecalis and E. faecium respectively since it did not amplify DNA from any of the non-E. faecalis/faecium strains tested.

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3.3. Membrane dissolution procedure followed by molecular detection method

When an rtPCR assay is optimized, the LOD can be as low as a single copy of DNA. However, it is impossible, based on Poisson probability, to guarantee that single copies get into a particular reaction tube (Bustin et al., 2009). Bustin et al. (2009) stated that the most sensitive limit of detection theoretically possible is 3 genome copies per rtPCR, assuming a Poisson distribution, a 95% chance of including at least 1 copy in the rtPCR, and single copy detection. In this study, we always detected at least 3 genome copies per mL after membrane dissolution. Thus, we assume that the loss of enterococcal cells, during this part of the procedure, is negligible.

Three parameters were tested in this study. First, we tested the detection limit of our real-time PCR assays with genomic DNA. We also tested the percentage of recovery of the membrane dissolution procedure from water sample until resuspension in TE buffer. In this part, 1/40 of the recovered cells were tested directly (without WGA) by rtPCR. Finally, we calculated the detection limit of the entire procedure (filtration of the water sample, filtration membrane dissolution, DNA extraction, WGA, and rtPCR).

3.3.3. 3.3.1.

Analytical detection limit of real-time PCR assays

Analytical sensitivity of the entire procedure

Testing the equivalent of 2.5 mL of the original water sample is insufficient for monitoring drinking water quality and this is mostly attributed to the limitations imposed by the final volume of 25 mL obtained after the sample preparation procedure (dead volume of glass beads). Since common DNA purification procedures are not efficient in recovering DNA at low concentrations, molecular enrichment by WGA was used to increase the amount of enterococcal DNA at a level detectable by rtPCR. The inclusion of this step significantly lowered the detection limit for the assay to approximately 4.5 CFU/100 mL (95% confidence; JMP, 1989–2007) of potable water whereas 2.3 CFU/100 mL of enterococcal cells has been detected with mEI agar method for the same water samples (Table 6). Culture enrichment steps, requiring an incubation time of 8e16 h, are often used in molecular environmental microbiology to reach a level of sensitivity sufficient to assess drinking water quality (Scheusner et al., 1971; Feng and Hartman, 1982; Frahm and Obst, 2003). Coupling molecular enrichment by WGA with rtPCR amplification provides an alternate and faster strategy to detect and identify bacteria from drinking water samples in only 5 h, without culture enrichment. Our method, using WGA prior to specific real-time PCR to increase the sensitivity of the assays, is not a quantitative procedure. By using the specific real-time PCR directly after membrane dissolution (without WGA), we are able to quantify enterococcal cells present in water. However, our detection limit (w20 CFU/100 mL; Table 4) is not sufficient to assess potable water quality since 1 CFU/100 mL must be detected. By adding the WGA step, we amplified non-specifically all the

The analytical detection limit of both molecular assays was verified by using purified E. faecalis and E. faecium genomic DNA. Both rtPCR assays were able to detect as few as one purified genome copy of E. faecalis/faecium per rtPCR reaction.

3.3.2. Recovery rate and efficiency of the membrane dissolution step Tests were performed to determine the limit of detection (LOD) and the repeatability of the membrane dissolution step without WGA. One (1) mL of the concentrate, corresponding to 2.5 mL of the original water sample, was tested by rtPCR. A preliminary experiment was done to determine the concentrations where enterococcal cells were always detected. This experiment showed that enterococcal cells were always detected for concentrations as low as 10 CFU per rtPCR reaction. The same experiment was also realized with 10 different well water samples randomly collected from the region of Que´bec City during fall 2008. Recovery levels were similar to those obtained during the preliminary experiment. Following these experiments, tests were performed to determine the LOD and the repeatability of the recovery protocol. This was monitored by testing replicates at low level (between 0.5 and 10 CFU per rtPCR reaction; Table 4). No signal was observed with negative controls, and the process control tested positive for every sample. The LOD at 95% for enterococci, calculated by logistic regression, corresponds to 0.60 CFU (2.4 genome copies since E. faecalis contains four 23S rRNA genome copies per cell; Paulsen et al., 2003) per rtPCR reaction ( p value: 0.0210; R Development Core Team, 2008).

Table 6 e Comparative recovery of enterococcal cells by mEI agar and the membrane dissolution method coupled to the WGA-specific Enterococcus sp. real-time PCR assay. Target enterococcal Average bacterial mEI agar count (CFU/100 mL) count (CFU/100 mL) (presence or absence for each replicate) 100 50 25 16 8 4 2 1 ND: not done.

84.7 46.2 22.4 10.0 7.7 3.2 2.0 1.2

       

5.2 4.7 2.4 1.6 1.3 1.2 1.6 1.2

þ þ þ þ þ þ þ þ

þ þ þ þ þ þ þ þ

þ þ þ þ þ þ þ þ

ND ND ND ND ND þ þ 

ND ND ND ND ND þ þ 

ND ND ND ND ND þ  

WGA-rtPCR (presence or absence for each replicate) þ þ þ þ þ þ þ þ

þ þ þ þ þ þ þ þ

þ þ þ þ þ þ þ þ

ND ND ND ND ND þ  þ

ND ND ND ND ND   

ND ND ND ND ND   

w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 3 4 2 e2 3 5 4

genomic DNA presents in water sample, including the 1 CFU of enterococci (if present), thousands of times. Hence, although only a fraction of the amplified DNA is used for the specific realtime PCR assay, we are able to detect the presence of 1 CFU of Enterococcus, if present. Thus, by adding the WGA step, our assay is qualitative but much more sensitive so it can be used as a presence/absence assay. Until 1992, most drinking water regulations were based on some numerical index of the biological indicator. For many years, Clark (1969) had reported that concentrations of microbial indicators in potable water, especially at the low concentrations in which they occurred, were not reproducible, and quantitation produced a false sense of security. His investigations found that using the simple Present or Absent (P/A) mode better protected the public’s health by permitting a large number of tests and increasing the simplicity of the procedure. Pipes et al. (1986) found that there were significant changes in the microbial quality which occurred week-to-week and month-to-month and that the P/A mode was more efficient at detecting these changes than quantitative methods. Accordingly, the USEPA adopted the P/A mode as part of its regulations (USEPA, 1992). A preliminary study performed in our laboratory, using the same procedure, has shown that no rtPCR amplification is obtained from drinking water samples spiked with up to 0.05 ng (equivalent to 104 genome copies) of purified genomic DNA of E. coli (data not shown). This suggests that free DNA found in a drinking water sample flows through the filter during the filtration step, thus confirming that no free enterococcal DNA in water can be detected by the concentration and recovery method coupled to a WGA-rtPCR assay. Previous studies have used organic solvent to dissolve filters prior to molecular analysis (Chung et al., 1998, Kostrzynska et al., 1999; Udeh et al., 2000; Faezel et al., 2009; de Evgrafov et al., 2010). The procedures used by Faezel et al. (2009) and de Evgrafov et al., 2010, employing a classic phenolechloroform extraction, required at least 80 min to process from water to DNA and do not allow to recover 100% of the extracted DNA (Bostro¨m et al., 2004). In comparison, our recovery and concentration method allows the recovery of 100% of microbial DNA in less than 20 min. Chung et al. (1998), Kostrzynska et al. (1999), and Udeh et al. (2000) described the use of acetone to dissolve membrane filters prior to molecular analysis. These 3 studies were all based on the membrane dissolution protocol previously described by Aldom and Chagla (1995). In the Aldom and Chagla procedure, a cellulose acetate membrane is dissolved in organic solvents in order to recover Cryptosporidium oocysts from a large amount of raw water. In the Aldom and Chagla procedure, the filter is first completely dissolved in acetone then sequentially exposed to 95% ethanol and 70% ethanol. The residual pellet is resuspended into eluting fluid prior the detection of oocysts by direct immunofluorescence. This method comprises 4 centrifugation steps of 15 min each at 650g and requires more than 80 min to complete from filtration to resuspension in eluting fluid. In contrast, the recovery method presented in this article was designed to detect the presence of bacteria in 100 mL of potable water sample and requires 4 centrifugation steps of only 3 min and takes 20 min to accomplish from filtration to resuspension in TE buffer. In terms of recovery, Aldom and Chagla (1995) reported detection of Cryptosporidium oocysts with a mean recovery of

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70.5%, calculated from a range of 61e87% with raw water. In a subsequent study, McCuin et al. (2000) observed with the same procedure a highly variable recovery of Cryptosporidium and Giardia (oo)cysts, ranging from 0.4 to 83.9%. Compared to these studies, our concentration and recovery method is sufficiently sensitive to be used with potable water since the loss of microbial particles is considered negligible with a recovery rate close to 100% with this type of water. This capacity may be explained by a combination of factors. Indeed, the initial methanol step produces small membrane pieces that apparently create conditions that favor the confinement of microbial particles during centrifugation, limiting losses. Furthermore, the methanol step reduces the amount of acetone required to completely dissolve filtration membrane fragments. Thus, the remaining 1 mL of acetone can be easily transferred in a 2-mL microtube where glass beads contribute to the efficiency of microbial particles recovery, acting a secondary confinement matrix. This twostep dissolution and confinement approach considerably increase the microbial particles recovery rate obtained by the original Aldom and Chagla membrane dissolution procedure. To determine the ability of the concentration and recovery method coupled with the WGA-rtPCR assay to detect enterococcal cells in different drinking and potable water samples, 10 different well water samples harvested in the Que´bec City area during fall 2008 were spiked with sewage to produce suspensions having approximately 20 CFU per 100 mL of water. All well water samples were submitted to the concentration and recovery method coupled to the WGA-rtPCR assay before and after spiking. The 10 well water samples tested negative to the specific-enterococcal rtPCR, whereas they all tested positive after spiking with sewage. As process control, B. atrophaeus subsp. globigii, was detected in all cases and the cycle thresholds for all enterococcal amplification profiles were similar indicating that inhibitors contained in the 10 well water samples tested were (bio)chemically equivalent and not in sufficiently high concentrations to inhibit the enzymatic processes of WGA-rtPCR assay. The direct comparison between culture- and molecularbased methods is critical to estimate their functional and analytical equivalence. To address this, we have tested well water spiked with sewage. Briefly, a well water sample harvested in the Que´bec City area during fall 2008 was spiked with sewage to produce suspensions having 50, 10, 5, 1, and 0.5, and 0.1 CFU per 100 mL of water. The enterococcal cell concentration in the sewage was previously estimated by Method 1600. The WGA Enterococcus sp.-specific rtPCR assay tested positive for all concentrations tested whereas Enterococcus colonies were only observed on mEI agar with samples containing approximately 50, 10 and 5 CFU/100 mL. On the other hand, the WGA E. faecalis/faecium-specific rtPCR assay tested positive only with the suspensions containing approximately 50 and 10 CFU/100 mL (Table 7). Maheux et al. (2009) reported that among a panel of enterococcal species, Enterococcus avium, Enterococcus cecorum, Enterococcus pseudoavium, Enterococcus raffinosus, Enterococcus ratti, Enterococcus saccharolyticus, and Enterococcus villorum do not grow on mEI plates. Since the Enterococcus sp. rtPCR assay amplifies all the above-mentionned species as well as all other species belonging to the Enterococcus genus, it is not surprising to detect more enterococcal cells using the Enterococcus sp. rtPCR assay than by

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w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 3 4 2 e2 3 5 4

Table 7 e Detection limit of mEI agar as compared to WGA Enterococcus sp.- and Enterococcus faecalis/faecium real-time PCR assays, for the analysis of water samples spiked with sewage. Target enterococcal count (CFU/100 mL) 50 10 5 1 0.5 0.1 Unspiked

mEI agar (CFU/100 mL)

WGA Enterococcus sp.-rtPCR

WGA Enterococcus faecalis/faecium rtPCR

20 10 3 0 0 0 0

þ þ þ þ þ þ 

þ þ     

culture on mEI agar. For the same reason, we detected less enterococci using the E. faecalis/faecium rtPCR assay than the standard culture-based method. Consequently, the 2 rtPCR assays described in this report appear more efficient than the culture-based Method 1600 in the detection of enterococcal strains of fecal origin over those provided by the environment.

4.

Conclusion

In this report, we have demonstrated that, by coupling a highly efficient membrane filtration-based method for the concentration and recovery of microbial particles to the molecular enrichment of extracted nucleic acids by WGA and robust rtPCR assays targeting the Enterococcus sp. or the species of fecal origin E. faecalis and E. faecium, we have developed a molecular microbiology approach enabling the detection of 4.5 CFU/100 mL (95% confidence) in less than 5 h without culture enrichment. Although further validation studies will be needed to confirm its equivalence to culture-based methods and establish its usefulness in the water quality monitoring process, this innovative and highly effective method provides a rapid and easy approach to concentrate very low numbers of enterococcal cells present in potable water and allows a better discrimination between environmental and fecal enterococcal contamination than the culture-based method on mEI agar (USEPA Method 1600). Replication studies, comprising a large number of natural samples, are however needed to confirm our results and the acceptability of the procedure to assess water quality according to criteria established by regulatory authorities.

Acknowledgments We thank Louise Coˆte´, director of the Microbiology Laboratory of CHUL (Centre Hospitalier Universitaire de Que´bec), Pierre Harbec (Laboratoire de Sante´ Publique du Que´bec), Daniela Centron (Facultad de Medicina, Universidad de Buenos Aires, Paraguay), Jan Bell (Microbiology and Infectious Diseases, Women’s and Children’s Hospital, North Adelaide, Australia), Barbara Murray (Division of Infectious Diseases, The University of Texas-Houston Medical School, Houston, Texas, USA), Donald Low as well as Barbara Willey (Department of Microbiology, Mount Sinai Hospital, Toronto, Ontario, Canada), Patrice Courvalin (Institut Pasteur, Paris, France), Kristin Hegstad

Dahl (Department of Microbiology and Virology, University of Tromsø, Tromsø, Norway), N. Woodford (Antibiotic Reference Unit, Central Public Health Laboratory, London, UK), Wang Fu (Huashan Hospital), Michael Mulvay (Nosocomial Infections and Antimicrobial Resistance Canadian Science Center for human and Animal Health, Winnipeg, Manitoba, Canada), and Pat Campbell as well as Fred C. Tenover (Centers for Disease Control and Prevention, Atlanta, GA, USA), Howard S. Gold (Beth Israel Deaconers Medical Center, Boston, MA, USA), Libera dalla Costa (Universidade Federal do Parana´, Brazil), Hilary-Kay Young (University of Edinburgh, UK), and Jang-Jih Lu (Tri-Service General Hospital and National Defense Medical Center, Taiwan, China) for providing enterococcal strains. We also thank Luc Trudel (Universite´ Laval) for providing sewage sample. This research project was supported by grants PA-15586 from the Canadian Institutes of Health Research (CIHR) and FCI-5251 from the Canada Foundation for Innovation (CFI). Andre´e Maheux, Jean-Luc T. Bernier, and Vicky Huppe´ were supported by a scholarship from Nasivvik (Center for Inuit Health and Changing Environment; Canadian Institutes for Health Research).

references

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Reply to comment on “Using Bayesian Statistics to Estimate the Coefficients Of a Two-Component Second-order Chlorine Bulk Decay Model for a Water Distribution System” by Huang, J.J., McBean E.A. Water Res. (2007) Hailiang Shen a, Jinhui Jeanne Huang b, Edward McBean a,* a b

School of Engineering, University of Guelph, Guelph, Ontario, Canada N1G2W1 School of Civil Engineering, Tianjin University, PR China

article info Article history: Received 3 November 2010 Received in revised form 3 January 2011 Accepted 11 January 2011 Available online 19 January 2011

1.

Introduction

The authors wish to thank Fisher et al. (2010) for their interest in the paper and who identified a valuable correction in Huang and McBean (2007). In the comments of Fisher et al. (2010), they pointed out that: (i) the final analytical solution from Clark (1998) is correct, and (ii) there are chlorine decay models other than the one applied by Clark (1998) including, for example, a four-reactant second-order model, which can be simplified to a two-reactant model for drinking water while retaining modeling accuracy.

2.

Model development

The two-component second-order decay model was based on the concept of competing reacting substances, and on the

assumption that the balanced reaction equation can be represented by aA þ bB/gG where A and B are reacting substances. Here, A represents chlorine, B represents chlorine demand, and G is the product of the reaction, and parameter a/b is the stoichiometric ratio. The derivation in Huang and McBean (2007) contained an incorrect conclusion due to the inappropriate initial condition imposed after Equation (15). Corrections for the incorrect equations are listed in Table 1. As stated in Huang and McBean (2007), TOC is appropriate to represent the reactant with chlorine in the above model. However, TOC can be in various forms and not all forms of TOC participate in the chlorine decay reaction. One solution is to introduce another ‘fraction factor’, f to represent the percentage of TOC which contributes to the chlorine decay

DOI of original article: 10.1016/j.watres.2010.01.039. * Corresponding author. Tel.: þ1 5198244120; fax: þ1 5198360227. E-mail address: [email protected] (E. McBean). 0043-1354/$ e see front matter ª 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2011.01.008

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w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 3 5 5 e2 3 5 7

Table 1 e Equation corrections. Equations

Original equations shown on Huang and McBean (2007) x  C A0

Between equations (15) and (16)

ln

(16)

x  CA0

x

x

aCB0 b

aCB0 b

Corrected equations x  CA0 CA ln  lnaCB0 ¼ ut aCB 0 x b0 b

¼ ut

x  CA0

¼ eut

x aCB0 b Þ

(17)

x  CA0 ¼ eut ðx 

(18)

ð1  eut Þx ¼ CA0 

aCB0 b

aCB0 b

aCB0

 ln b ¼ eut C A0

x  CA0 ¼ eut ðx  C

ð1  eut aCAB0 Þx ¼ CA0  CA0 eut

eut

b

aC

(19)

x¼

CA0  bB0 eut ð1  eut Þ

x¼

0

CA0  CA0 eut CA

ð1  eut aCB0 Þ b

aCB

(20)

CA ¼ CA0  aC

h (21)

CA ¼

CA

ð a=b0 fCB0 ÞkA t

pðXi ; Yi jqÞ ¼

CA0i  ab fCB0i

CA0  CA0 eut ð1 

C eut A0 Þ aCB

1e

0i fC B0i ÞkA t a=b

qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 1  s 2ðCAi CAi Þs 2pe

h

0

aCB0 b aCB0 ut bCA0 e

CA0  1

CA0  ab fCB0 A0 a fCB0 ðCa=b fCB0 ÞkA t 1 e b C A0

CAi ¼ CAi ei ¼

CA

ð

pðXi ; Yi jqÞ ¼

CA0i  ab fCB0i A0i a fCB0i ðCa=b fCB0i ÞkA ti 1 e b CA0i

qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 1  2 s 2ðCAi CAi Þ s 2pe

vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi u CA0i  ab fCB0i u  1ð  CAi Þ2 s u CA 0i 2 fCB t 1  ab CA 0i eð a=b fCB0i ÞkA ti s 0i pðXi ; Yi jqÞ ¼ 2pe

vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi a u u 1ð CA0i  bfCB0i  C Þs t CA Ai 2 ð a=b0i fCB0i ÞkA t s pðXi ; Yi jqÞ ¼ 2pe 1  e

(25)

0

b

CAi ¼ CAi ei ¼

(24)

CA ¼ CA0 

CA0  ab fCB0 1e

(22)

CA0  b 0 eut ð1  eut Þ

CA0  bB0 1  eut

CA ¼

aCB0 CA0 b ÞðaCB0 Þ b

Note: the corrected equation (20) is equivalent to the equation (10) in Fisher et al. (2010).

reaction process. Introducing ‘f’ changes Equation 20 to Equation 21. As apparent in Equation 21, two groups of parameters a/ b and f, and kA and a/b are always together, which make the inter-group parameters dependent upon each other in any parameter evaluation. It needs a large data set to break the connections among them. However, due to the underlying assumption of homogenous characteristics of the water samples including, temperatures, CA0 and CB0, only short period data can be utilized. Therefore, to estimate less connected and fewer parameters is a sound approach in the implementation of this chlorine decay model. To eliminate dependency, an alternative form of Equation (21) was developed and Equation (21) becomes

CA ¼

  CA0  abfCB0 CA0  abf CB0
 ¼ 
CA C kA 0 fC a CB0 ðCA0 Þ 1ðabf ÞCAB0 a=b t a fCB0  a=b B0 kA t 0 1 e 1  b f CA e 0 b CA0 (26)

where two new parameters (a/b)f and kA/(a/b) are reformulated in Equation (26). These are denoted as D and E respectively, as a D¼ f b E¼

(27)

kA a=b

(28)

Table 3 e Parameter estimation results. Table 2 e Reconstructed parameters estimation results. Parameters D E

f

Parameters a/b kA a/b kA

Mean

10%

Median

90%

1

0.781 0.051

0.395 0.007

0.587 0.048

2.635 0.119

0.5

Mean

10%

Median

90%

0.781 0.029 1.561 0.058

0.395 0.017 0.790 0.034

0.587 0.028 1.174 0.056

2.635 0.049 5.129 0.098

w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 3 5 5 e2 3 5 7

Fig. 1 e Validation results of B1 when f [ 1.

confidence levels corresponding to f ¼ 1 and f ¼ 0.5, are displayed in Figs. 1 and 2 respectively. As apparent from the results depicted in Figs. 1 and 2, there is a marginal discrepancy between the two figures. The small differences arise from Markov Chain Monte Carlo random error, which indicates that model validation is independent on the value of f. For a given set of data, the value of a prior ‘f’ is strongly connected to the values of a/b and kA. A large data set is needed to break the connections amongst the variables. However, with two new parameters D and E defined and estimated, the negligible discrepancy between Figs. 1 and 2 indicates that the given data set can predict the new values of substance CA or CB well without knowing the three parameters a/b, f, and kA individually. However, if the value of ‘f’ is determined, the estimates of a/b and kA are available which is of value since knowledge of the stoichiometric ratio and the reaction rate coefficient is available, providing excellent insight into chlorine changes.

3.

Fig. 2 e Validation results of B1 when f [ 0.5

Model validation is completed by using the data of the B1 group from Huang and McBean (2007). The parameter estimation results for D and E are listed in Table 2, which illustrates the values for the four statistical parameters, namely mean, 10% lower confidence level, median, and 90% upper confidence level. Given estimated values for D and E, there are an infinite number of combinations of values of a/b, f, and kA. To evaluate the three parameters, one of them has to be set to a constant a` priori, to compute the other two. The parameter f is a fraction factor, and has constrained between 0 and 1; the ranges of a/ b and kA are unknown; and of the three, f is better defined. Therefore, consider parameter f as fixed at two different levels 1.0 and 0.5, to allow estimation of the other two parameters a/ b and kA, to validate the model. As shown in Table 3, all statistical parameters for a/b and kA are doubled by reducing the f value from 1 to 0.5. The predicted free chlorine mean value, as well as the 10% and 90%

2357

Summary and conclusions

This reply corrects the derivation of equations in the original paper of Huang and McBean (2007). As well, since the three parameters namely stoichiometry, reaction rate coefficient, and fraction factor ( f ) cannot be estimated separately, they are re-estimated by fixing f at different levels. The validation results show the chlorine concentration predicted values from the model are directly related to the value of f. However, once a fraction factor value is determined, estimation of statistics (such as mean, median, confidence) of stoichiometry and reaction rate coefficient can be obtained from the values corresponding to a known f value, e.g. 0.5.

references

Clark, R.M., 1998. Chlorine demand and TTHM formation kinetics: a second order model. Journal of Environmental Engineering, ASCE 124 (1), 16e23. Fisher, I., Kohpaei, A.J., Sashasivan, A., 2010. Comments on “Using Bayesian statistics to estimate the coefficients of a twocomponent second-order chlorine bulk decay model for a water distribution system” by Huang, J.J., McBean, E.A. Water Res.. (2007). Water Research 44 (10), 3309e3310. Huang, J.J., McBean, E.A., 2007. Using Bayesian statistics to estimate the coefficients of a two-component second-order chlorine bulk decay model for a water distribution system. Water Research 41 (2), 287e294.