Biodegradation (2014) 25:867–879 DOI 10.1007/s10532-014-9706-1

ORIGINAL PAPER

Microrespirometric characterization of activated sludge inhibition by copper and zinc Ivonne Esquivel-Rios • Ignacio Gonza´lez Frederic Thalasso

•

Received: 27 February 2014 / Accepted: 5 August 2014 / Published online: 19 August 2014 Ó Springer Science+Business Media Dordrecht 2014

Abstract We have developed a novel microrespirometric method to characterize the inhibitory effects due to heavy metals on activated sludge treatment. This method was based on pulse dynamic respirometry and involved the injection of several pulses of substrate and inhibitors, of increasing concentration. Furthermore, we evaluated the inhibitory effects of heavy metals (copper and zinc), substrate and biomass concentrations, and pH on activated sludge activity. While higher biomass concentrations counteracted the inhibitory effects of both copper and zinc, higher substrate concentrations predominantly augmented the inhibitory effect of copper but no significant change in inhibition by zinc was observed. pH had a clear but relatively small effect on inhibition, partially explained by thermodynamic speciation. We determined the key kinetic parameters; namely, the half saturation constant (KS) and the maximum oxygen

I. Esquivel-Rios  F. Thalasso (&) Centro de Investigacio´n y de Estudios Avanzados del Instituto Polite´cnico Nacional (Cinvestav), Departamento de Biotecnologı´a y Bioingenierı´a, Av. IPN 2508 San Pedro Zacatenco, 07360 Mexico City, Mexico e-mail: [email protected] I. Gonza´lez Departamento de Quı´mica, Unidad Iztapalapa, Universidad Auto´noma Metropolitana, San Rafael Atlixco No. 186, Col. Vicentina, Iztapalapa, 09340 Mexico City, Mexico e-mail: [email protected]

uptake rate (OURmax). The results showed that higher heavy metal concentrations substantially decreased KS and OURmax suggesting that the inhibition was uncompetitive. Keywords Activated sludge  Micro-24  Kinetic parameters  pH  Chemical speciation

Introduction Industrial wastewater are very changeable in load and composition, and it has been shown that wastewater treatment by activated sludge is often subject to peak inhibition by several heavy metals (Alkan et al. 2008; Cokgor et al. 2007) like copper, zinc, lead, mercury, chromium, cadmium, iron, nickel, aluminum, manganese, and cobalt (Katsou et al. 2011; Fu and Wang 2011; Oliveira et al. 2007; Ustun 2009). Among these, copper and zinc, are the most commonly found heavy metals in wastewater (Fu and Wang 2011; Ahluwalia and Goyal 2007). Cecen et al. (2010) reported total Cu concentrations from 0.03 to 540 mg L-1 in wastewater from electroplating industries, while Sciban et al. (2007) reported wastewater containing Cu and Zn at average concentrations of 19 and 76 mg L-1, respectively, also in an electroplating industry. In industrial wastewater Algarra et al. (2005) reported Cu, Zn and other heavy metals concentration of up to several hundreds of mg L-1 while Wang et al. (2005) reported

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Cu concentrations of 120–2051 mg L-1 and Zn concentrations of 325–6719 mg L-1. Heavy metals toxicity in activated sludge processes primarily depends on the nature and concentration of the heavy metal (Ong et al. 2005; El Bestawy et al. 2013; Malamis et al. 2012), type of microorganisms (Gikas and Romanos 2006; Ochoa-Herrera et al. 2011; Madoni and Romeo 2006), biomass concentration (Cokgor et al. 2007; Vankova et al. 1999), exposition time (Zhou et al. 2011), and pH (Van Nostrand et al. 2005; Smolyakov et al. 2010). Complex interactions with other metallic (Gikas 2007) and nonmetallic ions (Pai et al. 2009) that affect heavy metals speciation (Smolyakov et al. 2010; Stasinakis et al. 2003), also in turn affect heavy metal toxicity. A wide variety of factors affect heavy metal toxicity in activated sludge processes. Consequently, significant differences in terms of inhibitory effects have been previously reported. Table 1 lists values of half inhibitory concentrations (IC50) of Cu(II) and Zn(II) in wastewater treatment described in the literature. The high variability has been attributed to the massive differences in wastewater composition, differences in the process operational conditions (batch, continuous, with or without sludge recycling), the concentration and the age of the biomass, the relative acclimation of activated sludge to heavy metals, and the experimental conditions as well as the methods used to assay toxicity (Ochoa-Herrera et al. 2011; Vankova et al. 1999; Gikas et al. 2009). For wastewater plants managers, it is of crucial importance to avoid biomass inhibition or death and therefore to detect sudden inhibitory effects of toxic compounds on non-adapted biomass, with a rapid, sensitive and economical method (Wong et al. 1997). Several methods based on the determination of enzymatic activity, microbial growth rates, substrate or oxygen uptake, and bioluminescence are used to evaluate metal toxicity in activated sludge (Alkan et al. 2008; El Bestawy et al. 2013; Zhou et al. 2011; Wang et al. 2010). With the exception of bioluminescence methods, which are techniques using specific model microorganisms (Polo et al. 2011), these techniques are usually time-consuming and require long experiments. Furthermore, each condition needs to be tested separately. Consequently, a majority of previous studies in this area (Table 1) merely determined the relative toxicity of heavy metals in activated sludge under a limited set of experimental conditions

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and often restricted their analysis to the determination of IC50 for a specific experimental condition. Despite noteworthy advances in environmental toxicology, our understanding of inhibition of wastewater treatment due to heavy metals is still very limited. There is a need for a method that would allow rapid quantification of inhibition under a wide range of experimental conditions. Respirometry, which is the measurement of the biological oxygen consumption rate under well-defined conditions (Spanjers et al. 1999), might be a partial solution to this problem. It has been commonly used to estimate kinetic and stoichiometric parameters of biological processes and to determine inhibitory effects of heavy metals (Table 1). However, standard respirometry is time consuming and does not allow for the characterization of large numbers of samples. Nevertheless, respirometry based on microreactor arrays (microrespirometry) have recently been shown to be a convenient tool for simultaneous evaluation of large number of samples in a relatively short experimental time and with a significant decrease in experimental error (EsquivelRios et al. 2014). The objective of this study was to develop a microrespirometric method for the characterization of wastewater treatment inhibition by copper and zinc. This study was also done to assess the effect of pH, heavy metals, biomass and substrate concentrations, and metal speciation on wastewater treatment inhibition by copper and zinc. To the best of our knowledge, this is the first time these parameters have been studied in a single experimental design.

Materials and methods Activated sludge culture and media A glass bubble column (0.12 m diameter, 0.66 m height, and 6 L working volume) was used as a reactor. The reactor was inoculated with activated sludge from an industrial wastewater treatment plant (Liconsa, Mexico). Prior to inhibition assays, this reactor was operated continuously for 90 days under pseudo steady-state; i.e. reactor maintained under stable operation and with effluent substrate concentration not changing over time. The reactor was fed with synthetic wastewater containing (mg L-1); peptone, 160; meat extract, 110; urea, 30; NaCl, 7;

Biodegradation (2014) 25:867–879 Table 1 Previously reported of half inhibitory concentrations (IC50) for Cu(II) and Zn(II) in wastewater treatment

PME Peptone-Meat Extract, PP Proteose-Peptone

869

Reference

Heavy metal

Range (mg L-1)

pH

Substrate

IC50 (mg L-1)

Target

El Bestawy et al. (2013)

Cu(II)

2–60

–

Triptone

8.00

Enzymatic activity

Malachowska-Jutsz et al. (2011)

Cu(II)

0–8

–

–

1.30

Enzymatic activity

Utgikar et al. (2004)

Cu(II)

0.125–1

–

–

0.50

Bioluminescence

Gutierrez et al. (2002)

Cu(II)

–

–

–

0.19

Bioluminescence

El Bestawy et al. (2013)

Cu(II)

2–60

–

Triptone

11.00

Respirometry

Gutierrez et al. (2002)

Cu(II)

–

–

Triptone

32.07

Respirometry

Kelly et al. (2004)

Cu(II)

0–100

–

PME

15.00

Respirometry

Malamis et al. (2012)

Cu(II)

0–40

7.3

Acetate

10.00

Respirometry

Wong et al. (1997) Ozbelge et al. (2007)

Cu(II) Cu(II)

0–11.63 0–4.5

– 7.0

Acetate PP

7.80 –

Respirometry COD

El Bestawy et al. (2013)

Cu(II)

2–60

–

Triptone

27.00

TOC

Pamukoglu and Kargi (2007) Gutierrez et al. (2002)

Cu(II)

15

7.0

Molasses

29–200

COD

Zn(II)

–

–

–

0.76

Bioluminescence

Utgikar et al. (2004)

Zn(II)

0.5–4

–

–

1.48

Bioluminescence

Kong et al. (1994)

Zn(II)

12–72

6.8–7.5

PME

45.00

Respirometry

Gutierrez et al. (2002)

Zn(II)

–

–

PME

55.79

Respirometry

Kelly et al. (2004)

Zn(II)

0–100

–

PME

40.00

Respirometry

Kong et al. (1994)

Zn(II)

0–70

7.3

Acetate

40.00

Respirometry

Wong et al. (1997) Ozbelge et al. (2007)

Zn(II) Zn(II)

0–29.07 0–27

– 7.0

Acetate PP

27.70 –

Respirometry COD

CaCl2H2O, 4; MgSO47H2O, 2; K2HPO4, 28 (ISO 8192 1995). After inoculation, the synthetic wastewater was fed to the reactor with a constant flow rate of 0.125 L h-1 (hydraulic residence time of 48 h) using a peristaltic pump (Masterflex L/S precision, ColeParmer, USA). Air was supplied continuously through a porous plate placed at the bottom of the reactor at an airflow rate of 360 L h-1 (1 vvm). The pH was controlled at 7.0 ± 0.5 using H3PO4 1 M. The reactor was maintained at room temperature (21 ± 2 °C). Effluent biomass was partially recycled to increase biomass concentration for respirometric assays. Reactor operation was monitored using total and soluble Chemical Oxygen Demand (COD) tests.

Analytical techniques COD was measured using standard reflux colorimetric assay (APHA 1999). Total COD was determined by measuring the COD of a homogenized sample while soluble COD was determined by measuring the COD of a filtered sample (0.45-lm filter). Insoluble COD values were estimated by calculating the difference between total and soluble COD values. In the activated sludge culture, the culture media contained no other suspended solids than biomass. Insoluble COD was therefore considered to be biomass concentration (X), while the substrate concentration (S) was considered to be soluble COD. In few cases biomass

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concentration was also expressed as total solid (mixed liquor suspended solid; MLSS) measured by comparing the mass of a known biomass sample, previously centrifuged and washed with distilled water, before and after drying at 105° until constant weight. Inoculum preparation Prior to respirometry, inoculum was prepared to ensure repeatability of respirometry tests and the absence of readable biodegradable substrate. A 1-L sample of the activated sludge was taken from the reactor and allowed to settle for 30 min. Supernatant was discarded and settled biomass was resuspended in fresh media with no carbon source. Absence of respiration in excess of endogenous decay confirmed the depletion of carbon. The stock biomass solution was diluted to obtain several biomass concentrations, as required. The unused amount of biomass stock solution was poured back to the reactor. Heavy metals We selected zinc and copper since they are the most prevalent heavy metals in wastewater (Fu and Wang 2011; Ahluwalia and Goyal 2007). Stock solutions of heavy metal ions were prepared by dissolving Cu(NO3)22.5H2O and ZnSO47H2O in deionized water. The solutions were further diluted to the required concentration. All chemicals were of analytical grade (Sigma-Aldrich or Baker, Me´xico). We first did some preliminary respirometric experiments, with a wide range of heavy metals concentrations; namely 0–500 mg L-1 for Cu(II) and 0–200 mg L-1 for Zn(II). From these preliminary results we adjusted the concentration range of each heavy metal, for inhibition tests, as 0–24 mg L-1 for Cu(II), and 0–120 mg L-1 for Zn(II), which were identified to cover the full range of inhibition. These concentrations are within the range reported by the literature (Table 1). Microreactor system The microreactor system used was an unbaffled 24-well (16 mm diameter, 18 mm depth) microflask system (PreSens, Mexico). Each well included a pre-calibrated fluorometric dissolved oxygen (DO) sensor (OxoDish, PreSens, Mexico). The sensors were read with a 24-channel Sensor Dish Reader (SDR-281, PreSens,

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Mexico) connected to a personal computer for data acquisition using the PreSens software (SDR_v37). The microflask system included a ‘‘sandwich’’ cover that consisted of a stainless steel cover, 0.2-lm filter, and microfiber inlays with a flexible silicone layer for sealing. The microreactor system used did not include an aeration device. Oxygen transfer to the 24 microwells was therefore achieved using superficial aeration. To improve mixing and mass transfer, the microreactor system was placed in an orbital shaker-incubator (100 rpm, orbital diameter, 1.91 cm; SK-737R, Amerex Instruments, Mexico) and one glass bead (5 mm diameter) was introduced into each well. Similarly, the microreactor system used did not include any pH sensor. During the respirometric experiments, pH was measured before and after each experiment with a pH sensor (Corning 440, Mexico). All experiments were done at 25 °C. Mass transfer in each well of the microreactor system was characterized through the Volumetric Oxygen Transfer Coefficient (KLa, h-1), which was measured in replicate of three, before each respirometric experiment, by a dynamic method adapted from Moo-Young and Blanch (1981). Briefly; first, a stock of anoxic mineral medium was prepared using a continuous flow of nitrogen. Then, 1.5 mL of anoxic mineral medium and one glass bead were introduced into each well. The microwell plate was closed and set on the orbital shaker-incubator at 25 °C. DO concentration was recorded on-line until reaching 100 % saturation concentration. Inhibition experiments Assays were conducted using pulse dynamic respirometry, which consists of on-line measurement of the DO profile after injecting a substrate pulse of known concentration. This method has been previously described for standard respirometers (Oliveira et al. 2011; Ordaz et al. 2012) as well as a microreactor system (Esquivel-Rios et al. 2014). Briefly, (i) each well of the microreactor system was filled with 1.4 mL of biomass solution with no carbon source and operated until stable DO concentration readings were obtained, which corresponds to the endogenous respiration state (Kong et al. 1994); (ii) a pulse of 0.1 mL of a known substrate concentration (synthetic wastewater with carbon source) containing the desired concentration of heavy metal [Cu(II) or Zn(II)] was then injected into each well; and (iii) the DO concentration was

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measured until it returned to steady state; i.e. DO concentration not changing over time. Well contents were discarded after each pulse. We injected six different substrate pulse concentrations, including a control with no added substrate, for each of the seven heavy metals concentrations (also including a control with no heavy metal). To increase precision in substrate and heavy metal concentration injected in each well, the same stock solution was prepared, sterilized and kept refrigerated for use in all experiments. All conditions were tested in replicates of three. Total concentration of inhibitor species (sum of all species) was considered for inhibition experiments and data interpretation, unless otherwise specified. During respirometric experiments the initial substrate to biomass ratio was kept below 0.06 g COD g-1 COD, to discard significant growth and to measure intrinsic parameters, according to Grady et al. (1996). Data interpretation We used respirometry to determine two kinetic parameters namely, the maximum oxygen uptake rate (OURmax, mg O2 L-1 h-1), and the substrate affinity constant (KS, mg L-1), which are important parameters of the Monod model (Eq. 1). OUR is defined as the observed oxygen uptake rate of the culture (mg O2 L-1 h-1). OUR ¼ OURmax 

S KS þ S

ð2Þ

KS and OURmax were determined by plotting 1/ OUR0 max against 1/Sp according to Lineweaver–Burk plot (Eq. 3). 1 Ks 1 1 ¼  þ 0 0 OURmax OURmax Sp OURmax

I¼

OUR0maxControl  OUR0maxInhibitor  100 OUR0maxControl

ð4Þ

Metal speciation Metal speciation was calculated theoretically, taking into account the heavy metal concentration used in each assay, the concentration of anions and cations of the culture medium, and the pH value. This estimation was done with open access chemical equilibrium software ‘‘MEDUSA’’ (Puigdomenech 2001). The impact of complex substrates (peptone and meat extract) was not considered since the corresponding thermodynamic data were not included in the chemical database. Statistical analysis Significance difference between parameters was calculated using the Tukey–Kramer’s multiple comparison (TK) test performed after analysis of variance (p \ 0.05) using the R language (R Development Core Team 2008).

ð1Þ

The method used to estimate OURmax and KS was based on injecting pulses of increasing concentration. This method has been previously described for standard respirometers (Ramirez-Vargas et al. 2013) and consists of determining observed OURmax (OUR0 max) at several pulse concentrations (Sp). OUR0 max at each pulse was determined from Eq. 2, where Cb is the baseline DO concentration (mg L-1), and Cmin is the minimum DO concentration after pulse injection (mg L-1). OUR0max ¼ KL a  ðCb  Cmin Þ

The percentage of inhibition (I), in presence of heavy metals, was determined using Eq. 4 and the difference in OUR0 max observed values for the control experiment (no inhibitor, OUR0 max-Control) and in presence on inhibitor (OUR0 max-Inhibitor). IC50 was calculated by polynomial interpolation of I versus inhibitor concentration.

ð3Þ

Results and discussion Substrate degradation in the activated sludge reactor was observed, and after day 27, a steady-state was reached with a soluble COD removal efficiency of 89.9 ± 5.6 %. Biomass was partially recycled over the steady-state period and varied in concentration over time, with an average of 1811 ± 724 mg COD L-1. The COD over MLSS ratio for 15 biomass samples at different concentrations was 1.18 ± 0.10 g COD g-1 MLSS. Before biological inhibition experiments, we tested exhaustively mass transfer in the microreactor system, through KLa determination, which is of critical importance for respirometry testing and data interpretation. Under the conditions tested, KLa was 11.28–11.93 h-1 with an average of 11.61 ± 0.17 h-1. The standard deviation of KLa measurements

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performed in the same well was 1.76 %, while the standard deviation of measurements done in different wells was 2.12 %. These standard deviations correspond to a standard error of the mean (SEM; standard deviation divided by the square root of the number of experiments) of 1.02 % in the same well and 1.23 % in different wells, for triplicate measurements. These results are in agreement with previously reported values (Esquivel-Rios et al. 2014) and show that KLa should be preferably measured independently in each well and before each respirometric experiment, as done further on Respirometry experiments were started at day 90. First, control experiments (no heavy metals) were done in the 24 wells of the microreactor system. We did not observe any spatial variation pattern between wells, as those previously reported by (Pacheco et al. 2013) in a 96-well microplate; namely, significant differences in border wells compared to wells located in the center of the plate. Similarly, we did not observe spatial trend of KLa. Second, initial respirometric pulses both, with and without heavy metals, and at several pulse concentrations were injected to assess pulse repeatability. It was observed that the respirometric response to control pulses was slowly changing over time. This may be due to changes in biomass concentration and composition in the inoculum reactor. Consequently, we decided to do all tests for a given heavy metal in the shortest possible experimental time, no greater than one week. We did not observe significant difference between parameters determined from control experiments (no heavy metals) done during this experimental time frame. Preliminary tests were also done to select a convenient range of heavy metals that showed significant inhibition but avoided complete inhibition. Last but not least, we checked for pH variations during pulse experiments. Variation of pH ranged from 0.2 to 0.4 units, depending on the duration and concentration of the pulses. This pH change is similar to other previously reported in microreactor (Munz et al. 2008; Carvallo et al. 2002; Ordaz et al. 2011) and it should be noted that except when titrimetry is associated to respirometry (Gernaey et al. 2001), which is a method requiring a strict pH control, most respirometers do not include pH control. All respirometry tests in presence of heavy-metals and under the different experimental conditions were done from day 90 to day 120, with a total of 250 independent experiments being performed for each

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Fig. 1 Examples of dissolved oxygen profiles observed after the injection of a single substrate pulse (60 mg COD L-1), in presence of; a Cu(II) concentrations from 0 to 24 mg L-1 and b Zn(II) concentrations from 0 to 80 mg L-1

heavy metal. Figure 1 shows examples of respirograms, observed after the injection of substrate pulses of 60 mg COD L-1, in presence of heavy metals concentrations of up to 24 and 120 mg L-1 for Cu(II) and Zn(II), respectively. In presence of increasing Cu(II) and Zn(II) concentrations, the respirograms showed lower response to substrate pulses, compared to control experiments as indicated by the longer duration of respirograms and higher Cmin value. It should be noted that at higher heavy metal concentrations tested, the final DO baseline was superior to the initial baseline. This is an indication of biomass death and/or inhibition of the endogenous respiration. These results clearly demonstrate that the microrespirometry method can be used to observe inhibitory effects. Figure 2 shows I as a function of heavy metal concentration for several biomass concentrations as well as the best third or second-degree polynomial

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Fig. 2 Percentage of inhibition observed versus heavy metal concentration; a Cu(II) at biomass concentrations of 2.1 (filled circle), 3.7 (filled square) and 5.6 (filled triangle) g COD L-1 and b Zn(II) at biomass concentrations of 1.9 (filled circle), 3.7 (filled square) and 6.3 (filled triangle) g COD L-1. Dashed lines show the best third or second degree polynomial fitting

fitting. In some cases, I was asymptotically correlated to heavy metal concentration, as previously described by Ong et al. (2005) with inhibition of wastewater treatment by Ni(II), Cr(III), and Zn(II). In some other cases, I was linearly correlated to heavy metal concentration as previously described by Pai et al. (2009) when assessing the inhibitory effect of cadmium and copper on activated sludge. As observed in Fig. 2, the biomass concentration had a clear effect on I. For a given heavy metal concentration, I was inversely proportional to the biomass concentration. This inhibition of activated sludge is consistent with previous reports by Stasinakis et al. (2003) with arsenic and mercury, and by Vankova et al. (1999) with chromium. From the results presented in Fig. 2, IC50 for each biomass concentration was determined. We observed that IC50 for Cu(II) as well as Zn(II) was

873

Fig. 3 Examples of Lineweaver–Burk plots observed with no inhibitor (control experiments, filled circle) and in presence of; a 16 mg L-1 of Cu(II) and b 60 mg L-1 of Zn(II) concentration of 60 mg L-1; heavy metals (circle)

linearly correlated to the biomass concentration (Eqs. 5 and 6, respectively; where n = 4, including control at X = 0). IC50 ¼ 5:16X; r 2 ¼ 0:9887

ð5Þ

IC50 ¼ 19:11X; r 2 ¼ 0:906

ð6Þ

0

The OUR max value obtained from substrate pulses of increasing concentrations fitted well with the Monod model regardless of the heavy metals. Figure 3 shows some examples of Lineweaver–Burk plot generated from experimental data for the control experiment (no heavy metals) and for the selected heavy metal concentrations closest to IC50. The presence of heavy metals changed the abscissa-intercept as well as the ordinate-intercept, which indicate that both OURmax and KS were altered (See Eq. 3). Similar Lineweaver– Burk plots were obtained with all tested biomass and heavy metals concentrations (data not shown).

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Fig. 4 OURmax and KS determined at several Cu(II) concentrations, with biomass concentrations of 2.1 (a, d); 3.7 (b, e) and 5.6 (c, f) g COD L-1

Figures 4 and 5 show OURmax and KS values as a function of heavy metal concentration for three biomass concentrations we tested. KS was ranging from 2.15 to 61.07 mg COD L-1 for Cu(II) and from 10.37 to 50.89 mg COD L-1 for Zn(II). OURmax was ranging from 9.16 to 115.16 mg O2 L-1 h-1 for Cu(II) and from 5.90 to 53.67 mg O2 L-1 h-1 for Zn(II). KS values were similar to those reported by Insel et al. (2006) in activated sludge fed with synthetic wastewater containing Cr(VI) and Ni(II); namely 23 mg COD L-1. The KS values determined in the present work were also higher than those reported by Kong et al. (1996), who used acetate containing model wastewater; i.e. 0.5–2.0 mg COD L-1. Such difference between our results and those obtained by Kong et al. (1996) can be accounted for substrate complexity. The OURmax values reported here cannot be compared to previous reports because, to the best of

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our knowledge, no OURmax values under similar conditions have been reported and most of the previous works on heavy metal inhibition using respirometry (Kelly et al. 2004; Cokgor et al. 2007; Wong et al. 1997) reported observed OUR, not OURmax. Increasing heavy metals concentrations generally led to a decrease in OURmax and KS values. Lower KS and OURmax values in presence of an inhibitor indicate uncompetitive inhibition, also known as anti-competitive inhibition. While, Kong et al. (1996) reached a similar conclusion for wastewater treatment inhibition by Cu(II), our data are contradictory to Insel et al. (2006) who suggested non-competitive inhibition of wastewater treatment by heavy metals. Uncompetitive inhibition is usually observed when the inhibitor binds to the enzyme-substrate complex, which is unlikely to occurs with a complex multi-substrate, a diverse

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Fig. 5 OURmax and KS determined at several Zn(II) concentrations, with biomass concentrations of 1.9 (a, d); 3.7 (b, e); 6.3 (c, f) mg COD L-1

Fig. 6 Percentage of inhibition observed at pH from 5.0 to 8.0 with a Cu(II) and Zn(II) concentration of 4 and 80 mg L-1, respectively

microbial community and a structurally simple inhibitor. Consequently, the uncompetitive inhibition observed in this work may be predominantly due to complex matrix of inhibition and toxic effects on a microbial community, which are difficult to describe with simple enzymatic kinetics. The effect pH on inhibition was assessed by measuring I. We injected a substrate pulse (30 mg COD L-1) with single heavy metal concentration (4 and 80 mg L-1 for Cu(II) and Zn(II), respectively) at initial pH ranging from 5.0 to 7.5. Figure 6 shows I observed from four replicate experiments in each condition as well as the OUR0 max observed for control experiments with no heavy metals. Control OUR0 max indicated a moderate respirometric response to pH

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Fig. 7 Diagram of chemical speciation for; a Total Cu(II) concentration of 4 mg L-1 and b Total Zn(II) concentration of 80 mg L-1

Fig. 8 Effect of the substrate concentration on the percentage of inhibition in presence of; (filled circle) 24 mg L-1 of Cu(II) and (filled square) 120 mg L-1 of Zn(II)

with higher response at pH of 7.0 and 7.5. Additionally, I was found to decrease with increasing pH. With Cu(II), we observed a significant decrease in I between

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pH of 5.5 to 7.0 (p \ 0.05), while in presence of Zn(II), I decreased the most between pH of 5.0 to 6.5. This decrease in I with an increase in pH may be due to metal speciation. Alternatively, a pH closer to the optimum value might increase the resistance of the microbial community to heavy metal toxicity. To further assess the effects of pH, speciation of both heavy metals was determined theoretically, considering the composition of the synthetic wastewater. It is worthwhile to note that while our analysis included most of the substrate components, we neglected the complex carbon sources used (peptone and meat extract), as commonly done (Rathnayake et al. 2013), because their equilibrium are unknown. Figure 7 shows the molar distribution of chemical species for heavy metal concentration of 4 mg L-1 for Cu(II) and 80 mg L-1 Zn(II). Most of the Cu equilibrium variations occurred between a pH of 5.5 to 6.5, which matched partially with the pH range for which I varied the most (Fig. 6). Similarly, most of the Zn equilibrium variations occurred at a pH ranging from 5.5 to 8.0, which matched with the pH range where I varied the most. In both cases, I decreased where the speciation predicted a lower concentration of divalent metal cations, Cu2? and Zn2?. This might indicate that toxicity is partially but not completely explained by the concentration of the free metals cations, reported as the most toxic form of heavy metals, able to permeate cells (Hu et al. 2002; Nies 1999; Gikas 2008). Moreover, from pH 5.0 to 7.5, the fraction of free Cu2? passed from 0.97 to 0, while I decreased from 76 to 32 %. Similar results were observed with Zn2?. Complex substrates were not accounted for in our analysis; this may have had an important effect on heavy metal speciation, which should be studied using a more sensitive technique, such as anodic stripping voltammetry (ASV) (Alonso et al. 2004). However, the limited change of I with pH suggests complex inhibitory mechanisms such as intracellular and extracellular sequestration, metal exclusion, active transport, protein binding, and enzymatic detoxification, as reviewed by Bruins et al. (2000). Respirometry tests were done at several substrate concentrations, for each biomass and heavy metal concentration. Based on the results, we have estimated the effect of substrate concentration on inhibition. This has been scarcely reported in the literature. Figure 8 shows I as a function of the substrate concentration for Cu(II) and Zn(II) concentrations of 24 and

Biodegradation (2014) 25:867–879

120 mg L-1, respectively. The effect of substrate concentration on Cu(II) toxicity was clearly observed but no such effect was observed for Zn(II) toxicity. The absence of such an effect is consistent with findings of Cokgor et al. (2007) with activated sludge inhibition with Ni(II) and Cr(VI). To the best of our knowledge, augmentation of Cu(II) toxicity due to an increase in substrate concentration has never been reported before.

Conclusions The microrespirometric method was effective for the characterization of inhibition of wastewater treatment by heavy metals. With moderate experimental efforts, the effect of pH, metal speciation, and substrate and biomass concentrations on inhibition of wastewater treatment by Cu(II) and Zn(II) was successfully determined. Moreover, we have also analyzed the effect of these inhibitors on kinetic parameters of activated sludge. To the best of our knowledge, this is the first time that so many experimental conditions including the kinetic parameters have been reported. This demonstrates the potential of the microrespirometric method for exhaustive experimentation. Higher biomass concentrations increased microbial resistance to the inhibitory effect of both heavy metals, while higher substrate concentrations increased the inhibitory effect of Cu(II) but not of Zn(II). pH also had a clear but relatively small effect on inhibition that can not be explained completely using thermodynamic speciation. The results of this work confirm that the inhibitory effect of heavy metals on wastewater treatment in an extremely complex matrix that will generate a unique response to Cu(II) and Zn(II) concentrations for each wastewater treatment plant. However, the results obtained here suggest that low substrate concentration and relatively high biomass concentration, as well as neutral pH, are the best conditions to ensure higher microbial resistance to Cu(II) and Zn(II) toxicity. Acknowledgments This work was supported by ‘‘Consejo Nacional de Ciencia y Tecnologı´a’’ (project 133338). We also gratefully acknowledge the ‘‘Consejo Nacional de Ciencia y Tecnologı´a’’ for the financial support to Ivonne Esquivel-Rios (Grant # 225319). The authors are thankful to Victoria T. Vela´zquez-Martı´nez, Juan Corona-Herna´ndez and Joel AlbaFlores for their technical assistance. The authors declare no conflict of interest.

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References AdS Oliveira, Bocio A, Trevilato TMB, Takayanagui AMM, Doming JL, Segura-Munoz SI (2007) Heavy metals in untreated/treated urban effluent and sludge from a biological wastewater treatment plant. Environ Sci Pollut Res 14(7):483–489. doi:10.1065/espr2006.10.355 Ahluwalia SS, Goyal D (2007) Microbial and plant derived biomass for removal of heavy metals from wastewater. Bioresour Technol 98(12):2243–2257. doi:10.1016/j.biortech. 2005.12.006 Algarra M, Jimenez MV, Rodriguez-Castellon E, Jimenez-Lopez A, Jimenez-Jimenez J (2005) Heavy metals removal from electroplating wastewater by aminopropyl-Si MCM-41. Chemosphere 59(6):779–786. doi:10.1016/j.chemosphere. 2004.11.023 Alkan U, Eleren SC, Nalbur BE, Odabas E (2008) Influence of the activated sludge system configuration on heavy metal toxicity reduction. World J Microbiol Biotechnol 24(8):1435–1443. doi:10.1007/s11274-007-9629-7 Alonso E, Santos A, Callejon M, Jimenez JC (2004) Speciation as a screening tool for the determination of heavy metal surface water pollution in the Guadiamar river basin. Chemosphere 56(6):561–570. doi:10.1016/j.chemosphere. 2004.04.031 APHA (1999) Standard methods for the examination of water and wastewater, 20th edn. American Publishers Health Association, Washington DC Bruins MR, Kapil S, Oehme FW (2000) Microbial resistance to metals in the environment. Ecotoxicol Environ Saf 45(3):198–207. doi:10.1006/eesa.1999.1860 Carvallo L, Carrera J, Chamy R (2002) Nitrifying activity monitoring and kinetic parameters determination in a biofilm airlift reactor by respirometry. Biotechnol Lett 24(24):2063–2066. doi:10.1023/a:1021375523879 Cecen F, Semerci N, Geyik AG (2010) Inhibitory effects of Cu, Zn, Ni and Co on nitrification and relevance of speciation. J Chem Technol Biotechnol 85(4):520–528. doi:10.1002/ jctb.2321 Cokgor EU, Wdemir S, Karahan O, Insel G, Orhon D (2007) Critical appraisal of respirometric methods for metal inhibition on activated sludge. J Hazard Mater 139(2):332–339. doi:10.1016/j.jhazmat.2006.06.038 Development Core Team R (2008) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna El Bestawy E, Helmy S, Hussein H, Fahmy M (2013) Optimization and/or acclimatization of activated sludge process under heavy metals stress. World J Microbiol Biotechnol 29(4):693–705. doi:10.1007/s11274-012-1225-9 Esquivel-Rios I, Ramirez-Vargas R, Hernandez-Martinez GR, Vital-Jacome M, Ordaz A, Thalasso F (2014) A microrespirometric method for the determination of stoichiometric and kinetic parameters of heterotrophic and autotrophic cultures. Biochem Eng J 83:70–78 Fu F, Wang Q (2011) Removal of heavy metal ions from wastewaters: a review. J Environ Manage 92(3):407–418. doi:10.1016/j.jenvman.2010.11.011 Gernaey AK, Petersen B, Ottoy JP, Vanrolleghem P (2001) Activated sludge monitoring with combined respirometric-

123

878 titrimetric measurements. Water Res 35(5):1280–1294. doi:10.1016/s0043-1354(00)00366-3 Gikas P (2007) Kinetic responses of activated sludge to individual and joint nickel (Ni(II)) and cobalt (Co(II)): an isobolographic approach. J Hazard Mater 143(1–2):246–256. doi:10.1016/j.jhazmat.2006.09.019 Gikas P (2008) Single and combined effects of nickel (Ni(II)) and cobalt (Co(II)) ions on activated sludge and on other aerobic microorganisms: a review. J Hazard Mater 159(2–3):187–203. doi:10.1016/j.jhazmat.2008.02.048 Gikas P, Romanos P (2006) Effects of tri-valent (Cr(III)) and hexa-valent (Cr(VI)) chromium on the growth of activated sludge. J Hazard Mater 133(1–3):212–217. doi:10.1016/j. jhazmat.2005.10.023 Gikas P, Sengor SS, Ginn T, Moberly J, Peyton B (2009) The effects of heavy metals and temperature on microbial growth and lag. Global Nest J11(3):325–332 Grady CPL, Smets BF, Barbeau DS (1996) Variability in kinetic parameter estimates: a review of possible causes and a proposed terminology. Water Res 30(3):742–748. doi:10. 1016/0043-1354(95)00199-9 Gutierrez M, Etxebarria J, de las Fuentes L (2002) Evaluation of wastewater toxicity: comparative study between Microtox (R) and activated sludge oxygen uptake inhibition. Water Res 36(4):919–924. doi:10.1016/s0043-1354(01)00299-8 Hu ZQ, Chandran K, Grasso D, Smets BF (2002) Effect of nickel and cadmium speciation on nitrification inhibition. Environ Sci Technol 36(14):3074–3078. doi:10.1021/ es015784a Insel G, Karahan O, Ozdemir S, Pala L, Katipoglu T, Cokgor EU, Orhon D (2006) Unified basis for the respirometric evaluation of inhibition for activated sludge. J Environ Sci Health A Tox Hazard Subst Environ Eng 41(9):1763–1780. doi:10.1080/10934520600778895 ISO 8192 (1995) Water quality—test for inhibition of oxygen consumption by activated sludge Katsou E, Malamis S, Loizidou M (2011) Performance of a membrane bioreactor used for the treatment of wastewater contaminated with heavy metals. Bioresour Technol 102(6):4325–4332. doi:10.1016/j.biortech.2010.10.118 Kelly CJ, Tumsaroj N, Lajoie CA (2004) Assessing wastewater metal toxicity with bacterial bioluminescence in a benchscale wastewater treatment system. Water Res 38(2):423–431. doi:10.1016/s0043-1354(03)00432-9 Kong Z, Vanrolleghem P, Verstraete W (1994) Automated respiration inhibition-kinetics analysis (ARIKA) with a respirographic biosensor. Water Sci Technol 30(4):275–284 Kong Z, Vanrolleghem P, Willems P, Verstraete W (1996) Simultaneous determination of inhibition kinetics of carbon oxidation and nitrification with a respirometer. Water Res 30(4):825–836. doi:10.1016/0043-1354(95)00232-4 Madoni P, Romeo MG (2006) Acute toxicity of heavy metals towards freshwater ciliated protists. Environ Pollut 141(1):1–7. doi:10.1016/j.envpol.2005.08.025 Malachowska-Jutsz A, Matyja K, Ziembinska A (2011) Cadmium and copper toxicity assessment in activated sludge using ttc bioassay. Arch Environ Prot 37(4):85–94 Malamis S, Katsou E, Daskalakis N, Haralambous KJ (2012) Investigation of the inhibitory effects of heavy metals on heterotrophic biomass activity and their mitigation through the use of natural minerals. J Environ Sci Health A Tox

123

Biodegradation (2014) 25:867–879 Hazard Subst Environ Eng 47(13):1992–1999. doi:10. 1080/10934529.2012.695266 Moo-Young M, Blanch HW (1981) Design of biochemical reactors: mass transfer criteria for simple and complex systems. Adv Biochem Eng 19:1–69 Munz G, Gori R, Cammilli L, Lubello C (2008) Characterization of tannery wastewater and biomass in a membrane bioreactor using respirometric analysis. Bioresour Technol 99(18):8612–8618. doi:10.1016/j.biortech.2008.04.004 Nies DH (1999) Microbial heavy-metal resistance. Appl Microbiol Biotechnol 51(6):730–750 Ochoa-Herrera V, Leon G, Banihani Q, Field JA, Sierra-Alvarez R (2011) Toxicity of copper(II) ions to microorganisms in biological wastewater treatment systems. Sci Total Environ 412:380–385. doi:10.1016/j.scitotenv.2011.09.072 Oliveira CS, Ordaz A, Ferreira EC, Alves M, Thalasso F (2011) In situ pulse respirometric methods for the estimation of kinetic and stoichiometric parameters in aerobic microbial communities. Biochem Eng J 58–59:12–19. doi:10.1016/j. bej.2011.08.001 Ong SA, Toorisaka E, Hirata M, Hano T (2005) The behavior of Ni(II), Cr(III), and Zn(II) in biological wastewater treatment process. Acta Hydroch Hydrob 33(2):95–103. doi:10. 1002/aheh.200400559 Ordaz A, Oliveira CS, Alba J, Carrion M, Thalasso F (2011) Determination of apparent kinetic and stoichiometric parameters in a nitrifying fixed-bed reactor by in situ pulse respirometry. Biochem Eng J 55(2):123–130. doi:10.1016/ j.bej.2011.03.015 Ordaz A, Oliveira CS, Quijano G, Ferreira EC, Alves M, Thalasso F (2012) Kinetic and stoichiometric characterization of a fixed biofilm reactor by pulse respirometry. J Biotechnol 157(1):173–179. doi:10.1016/j.jbiotec.2011.10.015 Ozbelge TA, Ozbelge HO, Altinten P (2007) Effect of acclimatization of microorganisms to heavy metals on the performance of activated sludge process. J Hazard Mater 142(1–2):332–339. doi:10.1016/j.jhazmat.2006.08.031 Pacheco A, Hernandez-Mireles I, Garcia-Martinez C, Alvarez MM (2013) Microplates as a microreactor platform for microalgae research. Biotechnol Prog 29(3):638–644. doi:10.1002/btpr.1721 Pai TY, Wang SC, Lo HM, Chiang CF, Liu MH, Chiou RJ, Chen WY, Hung PS, Liao WC, Leu HG (2009) Novel modeling concept for evaluating the effects of cadmium and copper on heterotrophic growth and lysis rates in activated sludge process. J Hazard Mater 166(1):200–206. doi:10.1016/j. jhazmat.2008.11.009 Pamukoglu MY, Kargi F (2007) Mathematical modeling of copper(II) ion inhibition on COD removal in an activated sludge unit. J Hazard Mater 146(1–2):372–377. doi:10. 1016/j.jhazmat.2006.12.033 Polo AM, Tobajas M, Sanchis S, Mohedano AF, Rodriguez JJ (2011) Comparison of experimental methods for determination of toxicity and biodegradability of xenobiotic compounds. Biodegradation 22(4):751–761. doi:10.1007/ s10532-010-9448-7 Puigdomenech I (2001) HYDRA MEDUSA: make equilibrium diagrams using sophisticated algorithms. KTH Royal Institute of Technology, Stockholm Ramirez-Vargas R, Ordaz A, Carrion M, Hernandez-Paniagua IY, Thalasso F (2013) Comparison of static and dynamic

Biodegradation (2014) 25:867–879 respirometry for the determination of stoichiometric and kinetic parameters of a nitrifying process. Biodegradation 24(5):675–684. doi:10.1007/s10532-012-9615-0 Rathnayake IVN, Megharaj M, Krishnamurti GSR, Bolan NS, Naidu R (2013) Heavy metal toxicity to bacteria—are the existing growth media accurate enough to determine heavy metal toxicity? Chemosphere 90(3):1195–1200. doi:10. 1016/j.chemosphere.2012.09.036 Sciban M, Radetic B, Kevresan D, Klasnja M (2007) Adsorption of heavy metals from electroplating wastewater by wood sawdust. Bioresour Technol 98(2):402–409. doi:10.1016/j. biortech.2005.12.014 Smolyakov BS, Ryzhikh AP, Bortnikova SB, Saeva OP, Chernova NY (2010) Behavior of metals (Cu, Zn and Cd) in the initial stage of water system contamination: effect of pH and suspended particles. Appl Geochem 25(8):1153–1161. doi:10.1016/j.apgeochem.2010.05.001 Spanjers H, Takacs I, Brouwer H (1999) Direct parameter extraction from respirograms for wastewater and biomass characterization. Water Sci Technol 39(4):137–145. doi:10.1016/s0273-1223(99)00068-2 Stasinakis AS, Thomaidis NS, Giannes AS, Lekkas TD (2003) Effect of arsenic and mercury speciation on inhibition of respiration rate in activated sludge systems. Environ Sci Pollut Res 10(3):177–182. doi:10.1065/espr2002.05.121 Ustun GE (2009) Occurrence and removal of metals in urban wastewater treatment plants. J Hazard Mater 172(2–3):833–838. doi:10.1016/j.jhazmat.2009.07.073 Utgikar VP, Chaudhary N, Koeniger A, Tabak HH, Haines JR, Govind R (2004) Toxicity of metals and metal mixtures:

879 analysis of concentration and time dependence for zinc and copper. Water Res 38(17):3651–3658. doi:10.1016/j. watres.2004.05.022 Van Nostrand JD, Sowder AG, Bertsch PM, Morris PJ (2005) Effect of pH on the toxicity of nickel and other divalent metals to Burkholderia cepacia Pr1(301). Environ Toxicol Chem 24(11):2742–2750 Vankova S, Kupec J, Hoffmann J (1999) Toxicity of chromium to activated sludge. Ecotoxicol Environ Saf 42(1):16–21 Wang C, Hu X, Chen ML, Wu YH (2005) Total concentrations and fractions of Cd, Cr, Pb, Cu, Ni and Zn in sewage sludge from municipal and industrial wastewater treatment plants. J Hazard Mater 119(1–3):245–249. doi:10.1016/j.jhazmat. 2004.11 Wang X-H, Gai L-H, Sun X-F, Xie H-J, Gao M-M, Wang S-G (2010) Effects of long-term addition of Cu(II) and Ni(II) on the biochemical properties of aerobic granules in sequencing batch reactors. Appl Microbiol Biotechnol 86(6):1967–1975. doi:10.1007/s00253-010-2467-9 Wong KY, Zhang MQ, Li XM, Lo WH (1997) A luminescencebased scanning respirometer for heavy metal toxicity monitoring. Biosens Bioelectron 12(2):125–133. doi:10. 1016/s0956-5663(97)87058-3 Zhou X-H, Yu T, Shi H-C, Shi H-M (2011) Temporal and spatial inhibitory effects of zinc and copper on wastewater biofilms from oxygen concentration profiles determined by microelectrodes. Water Res 45(2):953–959. doi:10.1016/j. watres.2010.09.035

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