Environ Sci Pollut Res DOI 10.1007/s11356-017-0248-z

CONTAMINATED SITES, WASTE MANAGEMENT AND GREEN CHEMISTRY: NEW CHALLENGES FROM MONITORING TO REMEDIATION

Effect of pH on hexavalent and total chromium removal from aqueous solutions by avocado shell using batch and continuous systems Erick Aranda-García 1 & Eliseo Cristiani-Urbina 1

Received: 29 April 2017 / Accepted: 19 September 2017 # Springer-Verlag GmbH Germany 2017

Abstract Solution pH appears to be the most important regulator of the biosorptive removal of chromium ions from aqueous solutions. This work presents a kinetic study of the effects of solution pH on Cr(VI) and total chromium removal from aqueous solution by Hass avocado shell (HAS) in batch and continuous packed bed column systems. Different Cr(VI) and total chromium removal performances of HAS were obtained in pH-shift batch, pH-controlled batch, and continuous systems. These results emphasize the great importance of determining the most appropriate pH for Cr(VI) and total chromium removal, considering the operational mode of the proposed large-scale treatment system. Total chromium biosorption batch kinetics was well described by the Elovich model, whereas in the continuous system, the fitness of the kinetic models to the experimental data was pH dependent. Xray photoelectron spectroscopy and kinetic studies clearly indicated that the reaction mechanism of Cr(VI) with HAS was the reductive biotransformation of Cr(VI) to Cr(III), which was partially released to the aqueous solution and partially biosorbed onto HAS. Keywords Avocado shell . Biosorption . Hexavalent chromium . pH . Total chromium . Wastewater treatment Responsible editor: Guilherme L. Dotto Electronic supplementary material The online version of this article (https://doi.org/10.1007/s11356-017-0248-z) contains supplementary material, which is available to authorized users. * Eliseo Cristiani-Urbina [email protected] 1

Escuela Nacional de Ciencias Biológicas, Departamento de Ingeniería Bioquímica, Instituto Politécnico Nacional, Avenida Wilfrido Massieu s/n, Unidad Profesional Adolfo López Mateos, Delegación Gustavo A. Madero, 07738 Mexico City, Mexico

Introduction Chromium [Cr] is a redox-sensitive heavy metal, and its physical properties, bioavailability, and toxicity depend on its oxidation state (Li et al. 2017; Morales-Barrera et al. 2008). Hexavalent [Cr(VI)] and trivalent [Cr(III)] are the two oxidation states of chromium most stable in the natural environment and are found in the wastewaters of different industrial processes (Chen et al. 2012). Among them, Cr(VI) species have higher solubility and mobility in aqueous systems (Fellenz et al. 2017) and are many times more toxic, mutagenic, carcinogenic, and teratogenic than Cr(III) (Costa 2003; MoralesBarrera et al. 2008; Park et al. 2005). Thus, Cr(VI) has been designated as a priority pollutant in many countries (MoralesBarrera et al. 2008). Considering the deleterious effects of toxic Cr(VI) ions on human and environmental health, as well as the value of recycling chromium from polluted water and wastewater, several different technologies to remove or recycle chromium from industrial effluents have been used in recent decades, such as chemical reduction of Cr(VI) to Cr(III) followed by precipitation under alkaline conditions, ion exchange, reverse osmosis, and adsorption onto activated carbon. However, these technologies are either ineffective or expensive when chromium ions are present in low concentrations or when low chromium concentrations must be achieved in treated waters. In addition, they may also generate toxic chemical sludge or other residues that are difficult and costly to manage and treat (Netzahuatl-Muñoz et al. 2015). Biosorption has great potential to replace conventional methods of removing heavy metals, due to its high flexibility, efficiency, profitability, environmental friendliness, simplicity of design and operation, and minimization of secondary pollution (Kuppusamy et al. 2016a; Park et al. 2010; Spasojevic et al. 2016). Furthermore, biosorption also offers the

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advantages of regenerating biosorbents and recovering the heavy metals following biosorption (Park et al. 2010). Previous studies have shown that the biosorptive removal of Cr(VI) from acidic aqueous solutions is challenging because it involves a mixed mechanism of biosorption and bioreduction of the highly toxic Cr(VI) to the less toxic Cr(III) (Aranda-García et al. 2014; Lopez-Nuñez et al. 2014; Netzahuatl-Muñoz et al. 2010, 2012a, b, 2015; Park et al. 2005). Under acidic conditions, the Cr(III) generated by reduction of Cr(VI) can be found in the aqueous solution and/or bound to the biosorbent surface; hence, the total chromium removal capacity of a biosorbent could be lower than its capacity to remove Cr(VI) (Aranda-García et al. 2014; Netzahuatl-Muñoz et al. 2012b, 2015). Solution pH appears to be the most important environmental factor for chromium biosorption (Das et al. 2008; Park et al. 2010), since it affects the chemical speciation, charge, and solubility of the chromium compound in solution; the chemical state of functional groups (state of the chemically active sites) responsible for chromium biosorption and Cr(VI) bioreduction; and the competition with coexisting ions in solution (Aranda-García et al. 2014; Das et al. 2008; LopezNuñez et al. 2014; Netzahuatl-Muñoz et al. 2012b; Park et al. 2010). Most studies on the effects of solution pH on Cr(VI) and total chromium removal from aqueous solution by biosorbents have been carried out in batch systems. However, the data obtained under batch conditions are generally not applicable to most treatment systems such as continuous packed bed column operations (Farooq et al. 2013). Likewise, to the best of our knowledge, there are no reported findings on this issue for continuous biosorption systems. As the operating conditions of batch and continuous biosorption systems differ significantly, the optimum pH for the removal of target pollutants may vary between both systems; therefore, it is crucial to determine the optimum pH for Cr(VI) and total chromium removal, using the operational mode of the treatment system that will be used at larger scale. Avocado (Persea americana Mill.) is an edible, oleaginous fruit of high economic importance (Marahel et al. 2015). Industrial processing of avocados and consumption of avocado as fresh fruit by consumers generate a large amount of byproducts, such as avocado shells, which are highly underutilized and are a source of environmental pollution (Calderón-Oliver et al. 2016; Rodríguez-Carpena et al. 2011). A potentially beneficial use of avocado shells is as a biosorbent for the removal of toxic heavy metals from aqueous solutions. In this context, a recent preliminary study showed that Hass avocado (Persea americana Mill. var. Hass) shell has great potential as a simple and inexpensive biomaterial for removing Cr(VI) and total chromium from aqueous solutions (Cristiani-Urbina et al. 2011).

The main aim of this work was to assess the effect of solution pH on Cr(VI) and total chromium removal from aqueous solution by Hass avocado shell (HAS) in batch and continuous packed bed column systems. Furthermore, the removal mechanism of Cr(VI) from aqueous solutions by HAS was investigated using X-ray photoelectron spectroscopy (XPS).

Material and methods Biomaterial HAS samples were obtained for free from a local wholesale market. HAS samples were first washed thoroughly with tap water and then with distilled deionized water. Afterwards, they were oven-dried at 60 °C until they reached a constant dry weight. The dried HAS samples were subsequently milled using a hammer mill (Glen Creston, Ltd.), and the particles obtained were screened using ASTM standard sieves. The fraction with particle sizes between 0.15 and 0.50 mm was used in Cr(VI) and total chromium removal experiments. The sieved biosorbent without any further chemical or physical treatment was kept in an airtight plastic container until being used for the experiments. HAS chemical composition was determined in compliance with methods described by the Association of Official Analytical Chemists (Horwitz and Latimer 2005), and the results obtained are as follows (dry matter basis): total protein, 7.22%; ether extract, 2.54%; total ash, 5.66%; crude fiber, 31.76%; and nitrogen-free extract, 52.83%, indicating that HAS has low fat, protein, and ash content and high carbohydrate content. Likewise, HAS is known to contain catechins, procyanidins, hydroxycinnamic acids, and flavonols (Calderón-Oliver et al. 2016; Rodríguez-Carpena et al. 2011). Previous Fourier transform infrared (FTIR) studies showed the presence of absorption bands corresponding to aromatic compounds such as lignin and other phenolic compounds, as well as cellulose molecules, alkanols, and –OH and carboxyl functional groups on the HAS surface (Vázquez-Palma et al. 2017). HAS pH at the point of zero charge (pHpzc) was determined to be 6.87 ± 0.0784, using a batch equilibrium method, procedures for which were outlined by Hasan et al. (2010). Furthermore, HAS density was 0.6523 ± 0.0072 g cm−3. Cr(VI) stock solution A 1000 mg L−1 Cr(VI) stock solution was prepared by dissolving an accurately weighed amount of analytical grade K2CrO4 (J.T. Baker; 99.9% purity) in distilled deionized water. Test Cr(VI) solutions were prepared by diluting the Cr(VI)

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stock solution. The pH of the test Cr(VI) solutions was adjusted to the desired value with 1.0 M HCl or NaOH solutions. Effect of solution pH on Cr(VI) and total chromium removal by HAS in batch systems To analyze the influence of solution pH on Cr(VI) and total chromium removal from Cr(VI) aqueous solutions by HAS, two set of batch kinetic biosorption experiments were performed: (1) pH-shift (pH-uncontrolled) experiments and (2) pH-controlled experiments. All of the experiments were performed in 500-mL Erlenmeyer flasks containing 100 mL of 100 mg L−1 Cr(VI) solution and 1 g (dry weight) L−1 of HAS. In pH-shift experiments, Cr(VI) solutions were initially adjusted to selected pH values (1.0, 1.5, 2.0, 2.5, 3.0, or 4.0) with 1.0 M HCl or NaOH solutions. Thereafter, pH was not adjusted during the test period. Throughout the course of the pH-controlled experiments, the pH of each solution was maintained at a constant value (1.0, 1.5, 2.0, 2.5, 3.0, or 4.0) by periodic checking and adjusting with 1.0 M HCl or NaOH solutions when necessary. Flasks were agitated in a shaker at 140 rpm and room temperature (20 ± 2 °C) for 120 h. To check for glass adsorption of Cr(VI) and/or chromium precipitation, HAS-free controls were run simultaneously under the same conditions used for the batch Cr(VI) and total chromium removal experiments. No measurable changes were detected in Cr(VI) or total chromium concentrations in the HAS-free controls, indicating that the observed Cr(VI) and total chromium removals in the HAS experiments were due solely to the biosorbent. Samples were collected at different times of contact between HAS biomass and Cr(VI) solutions and were filtered through Whatman paper (grade 42). The obtained filtrates were subsequently analyzed for Cr(VI) and total chromium concentrations. The removal capacity of Cr(VI) or total chromium (qt, mg g−1) represents the amount of Cr(VI) or total chromium ions removed at time t by per unit (dry weight) of HAS biomass, and was estimated using the following mass balance equation: qt ¼

ðC 0 −C t Þ X

ð1Þ

Here, C0 and Ct (mg L−1) are the initial and residual Cr(VI) or total chromium concentration at time t0 = 0 h and t = t, respectively, and X is the HAS concentration (g L−1). Effect of solution pH on Cr(VI) and total chromium removal in a continuous packed bed up-flow column Continuous Cr(VI) and total chromium removal experiments were conducted in a Pyrex glass column of 15.5-cm length

and 1.2-cm internal diameter (Fig. 1). The cylindrical column was packed with 2 g of HAS particles. The bulk density, porosity, and height of the packed bed were 0.4226 g cm−3, 0.6458, and 4.2 cm, respectively, and the volumes of the packed bed and the interstitial liquid were 4.75 and 3.07 cm3, respectively. Spherical glass beads (diameter 3 mm) were placed on both ends of the column (with a height of 3.5 and 6 cm at the top and bottom of the packed bed) to ensure even distribution of the influent across the whole cross section of the column and also to prevent the HAS biomass floating. A 500-mesh sieve was placed between the glass beads and the HAS biomass to avoid any loss of biosorbent. The column was joined to a Pyrex glass lid and base by metallic clamps and was sealed with silicone rubber gaskets. At the center of the base, there was a porous glass liquid diffuser with a pore size of 30–40 μm. A Cr(VI) solution of concentration 200 mg L−1 was pumped from the influent reservoir upward through the column at 0.75 mL min−1, using a peristaltic pump (Master Flex L/S 7528-30). The influence of solution pH on Cr(VI) and total chromium removal was examined from Cr(VI) solution at different pH, namely 1.0, 1.5, 2.0, 2.5, and 3.0. The outlet of the column was attached to a fraction collector (LKB Bromma, Ltd.) to collect the effluent samples intermittently, which were then filtered through Whatman paper (grade 42) and analyzed for residual Cr(VI) and total chromium concentrations. All packed bed column experiments were conducted at room temperature (20 ± 2 °C) for 96 h. HAS-free controls were run concurrently and under the same conditions as those used for the continuous Cr(VI) and total chromium removal experiments, to check for Cr(VI) adsorption onto glass. No significant differences were found between the influent and the effluent Cr(VI) and total chromium concentrations, indicating that the observed Cr(VI) and total chromium removals in the continuous experiments with HAS were due solely to the biomaterial. The performance of the packed bed column at different pH of feed solution was evaluated from breakthrough curves, which were plotted as ratio of effluent concentration (Ct, mg L−1) to influent concentration (C0, mg L−1) of Cr(VI) or total chromium versus process time (t, h). The amount of Cr(VI) or total chromium removed in the column (mr, g) was calculated from the area over the breakthrough curve multiplied by solution flow rate, as follows:   Q t¼tT Ct C 0 dt 1− ð2Þ ∫ mr ¼ C0 1000 t¼0 Here, Q is the volumetric flow rate of the Cr(VI) solution (mL min−1), C0 is the inlet metal concentration (mg L−1), Ct is the effluent metal concentration (mg L−1), and tT is the total operation time (96 h).

Environ Sci Pollut Res Fig. 1 Schematic diagram of experimental setup for packed bed column studies [influent Cr(VI) solution reservoir (A), peristaltic pump (B), packed bed of biosorbent (C), glass beads (D), mesh sieve (E), and fraction collector (F)]

Then, the Cr(VI) or total chromium removal capacity (qb, mg g−1) was calculated by dividing mr by HAS biomass in the column (mb, g), as follows: qb ¼

mr mb

ð3Þ

Here, q e2 is the biosorption capacity at equilibrium (mg g−1), qt is the biosorption capacity at any time t (h), and k 2 is the pseudo-second-order model rate constant (g mg−1 h−1). The Elovich model is given by Eq. (6) (Plazinski et al. 2009): 1 lnð1 þ Ae Be t Þ Be

Modeling of batch biosorption kinetics

qt ¼

The variation of total chromium removal (biosorption) capacity as a function of experimental duration at differing solution pH was analyzed using pseudo-first-order, pseudo-second-order, Elovich, intraparticle diffusion, and fractional power kinetic models. The nonlinear expression of Lagergren’s pseudo-first-order model (Febrianto et al. 2009) is given by Eq. (4):

Here, Ae is the initial biosorption rate (mg g−1 h−1), Be is the desorption constant, which is related to the extent of surface coverage and to the activation energy for chemisorption (g mg−1), qt is biosorption capacity (mg g−1) at any time t (h), and t is the contact time (h) between HAS and the aqueous solution. The intraparticle diffusion model can be expressed by Eq. (7) (El Nemr et al. 2015):

qt ¼ qe1 1−e−k 1 t




ð4Þ

qt ¼ k id t 0:5 þ c

ð6Þ

ð7Þ

−1

Here, qt and qe1 are the biosorption capacities (mg g ) at any time t (h) and at equilibrium, respectively, and k1 is the pseudo-first-order model rate constant (h−1). The pseudo-second-order kinetic model can be expressed by Eq. (5) (Ho and McKay 1999): qt ¼

t 1 t þ k 2 q2e2 qe2

ð5Þ

Here, k id is the intraparticle diffusion rate constant (mg g−1 h−0.5), c is the model intercept, which is related to the boundary layer thickness, and qt is the biosorption capacity at any time t (h). The biosorption kinetics can be described by a fractional power model as follows (Eq. 8) (Basha and Murthy 2007): qt ¼ k fp tν

ð8Þ

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Here, kfp is a constant of the fractional power model (mg g−1) and v is the rate constant (h−1).

Here, α and β are the dose–response model parameters. β represents the time when effluent concentration reaches 50% of the influent concentration (min).

Modeling of continuous packed bed biosorption kinetics The kinetics of total chromium biosorption in the packed bed column was modeled using the Bohart–Adams, Thomas, Yoon–Nelson, Yan, and dose–response models. The Bohart–Adams model is given as follows (Eq. 9) (Calero et al. 2009): Ct ek AB C0 t ¼ k N Z=v AB 0 C0 e −1 þ ek AB C0 t

ð9Þ

Here, C0 is the inlet total chromium concentration in the solution (mg L−1), Ct is the outlet effluent concentration of total chromium (mg L−1) at any time t (h), kAB is the model kinetic constant (L mg−1 min−1), N0 is the maximum volumetric biosorption capacity (mg L−1), Z is the biosorbent bed depth (cm), and v is the linear flow rate (cm min−1). The Thomas model can be described by Eq. (10) (Calero et al. 2009): Ct ¼ C0 1 þ e

k Th Q

1


ðqTh mb −C 0 QtÞ

ð10Þ

Here, kTh is the Thomas model rate constant (mL min−1 mg−1), Q is the volumetric flow rate of inlet Cr(VI) solution (mL min−1), qTh is the maximum concentration of the solute in the biosorbent (mg g−1), and mb is the biosorbent mass in the column (g). The Yoon–Nelson model can be represented by Eq. (11) (Farooq et al. 2013): Ct ek YN ðt−τ Þ ¼ C 0 1 þ ek YN ðt−τ Þ

ð11Þ

Here, kYN is the Yoon–Nelson model rate constant (min−1) and τ is the time when effluent concentration reaches 50% of the influent concentration (min). The Yan model is given by Eq. (12) (Ghasemi et al. 2011): Ct 1   ¼ C 0 1 þ C0 Q t a q mb

ð12Þ

0

Here, q0 is the maximum biosorption capacity (mg g−1) and a is the Yan model constant. The dose–response model can be expressed as follows (Eq. 13) (Chatterjee and Schiewer 2011): Ct 1   ¼ C0 1 þ t α β

ð13Þ

Statistical and data analysis Cr(VI) and total chromium removal data were statistically analyzed by analysis of variance (ANOVA) followed by a group comparison with Tukey’s test (overall confidence level = 0.05) in GraphPad Prism software (version 6.0; GraphPad Software, Inc.), which was also used to estimate all of the kinetic model parameters by nonlinear regression analysis of the experimental data. Determination coefficient (r2), sum of squared error (SSE), root mean squared error or standard error (RMSE), Akaike information criterion (AIC), and 95% confidence intervals were used to measure the goodness of fit of the kinetic models. Values of r2 close to 1.0; small SSE, RMSE, and AIC values; and narrow 95% confidence intervals indicate better curve fitting. Analytical techniques Cr(VI) and total chromium quantification Cr(VI) and total chromium concentrations in aqueous solutions were quantified by photocolorimetric methods using a Genesys 10 UV/Visible spectrophotometer (Thermo Electron Scientific Instruments Corp.), following the procedures outlined in the Hach methods 8023 and 8024 of the Hach Water Analysis Handbook (Hach Company 2008). Although other chromium species such as Cr(II), Cr(IV), and Cr(V) can be present, they are very unstable at room temperature and have very short half-life (Ponce et al. 2015); consequently, in this work, it was assumed that total chromium concentration was equal to the sum of the concentrations of Cr(VI) and Cr(III) species only. Therefore, Cr(III) concentration in solution was estimated by subtracting residual Cr(VI) concentration from residual total chromium concentration. X-ray photoelectron spectroscopy analysis Raw (i.e., not loaded with chromium) and chromium-loaded HAS samples were analyzed using XPS in order to detect the presence of chromium at the HAS surface and to ascertain the oxidation state of the chromium bound to the HAS biomass. The chromium-loaded HAS biomass was obtained through contact with a 1000 mg L−1 of Cr(VI) solution (pH 3.0) for 120 h. Then, the chromium-loaded samples were washed several times with deionized distilled water, oven-dried at 60 °C until constant weight, and kept in an airtight plastic container until the moment of the analysis.

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XPS analysis was carried out on a Thermo Fisher Scientific K-Alpha XPS spectrometer, using a monochromatic Al Kα incident X-ray beam (excitation energy = 1486.6 eV). The vacuum in the analysis chamber was 10 −9 mbar. Lowresolution survey spectra were acquired in the binding energy range of 1370 and − 10 eV at 200 eV pass energy, whereas Cr 2p high-resolution spectra were acquired in the binding energy range of 597 and 568 eV at 40 eV pass energy. Analytical grade compounds (J.T. Baker, Mexico) were used as hexavalent chromium (K2CrO4 and K2Cr2O7) and trivalent chromium (Cr2O3, Cr(NO3)3, and CrCl3·6H2O) references. The presence of the element chromium was determined by comparison of XPS spectra of raw and chromium-loaded HAS samples. Analysis of biosorbed chromium oxidation state was deduced from the analysis and comparison of experimental XPS spectra with those of Cr(III) and Cr(VI) references.

Results and discussion Influence of solution pH on Cr(VI) and total chromium removal from Cr(VI) aqueous solution in batch systems Figures 2 and 3 show the kinetic profiles of Cr(VI) and total chromium removal obtained in pH-shift and pH-controlled experiments, respectively, in which HAS was brought into contact with Cr(VI) solution at the different pH values (range 1.0–4.0) assayed herein. It was observed in both the pH-shift and pH-controlled experiments that Cr(VI) and total chromium removal by HAS depended strongly on solution pH and contact time. The concentrations of residual Cr(VI) and residual total chromium diminished progressively as experimental duration proceeded, and the decline in concentrations was more pronounced during the first hours of contact between HAS and the Cr(VI) aqueous solution. In the pH-shift experiments, all the Cr(VI) initially present in the aqueous solution were completely removed by HAS at initial pH values ranging from 1.0 to 2.5. In contrast, under pH-controlled conditions, the complete removal of Cr(VI) occurred at pH values from 1.0 to 3.0. A longer contact time was needed for the complete removal of Cr(VI) as the solution pH increased, which indicates that Cr(VI) removal rate was greater in lower pH solution (Figs. 2 and 3). Qualitatively similar behavior was reported for Cr(VI) removal by Quercus crassipes acorn shell (Aranda-García et al. 2014), Prunus domestica bark (Lopez-Nuñez et al. 2014), Cupressus lusitanica bark (Netzahuatl-Muñoz et al. 2012b), brown seaweed Ecklonia (Park et al. 2004), fruiting bodies of the jelly fungus Auricularia polytricha (Zheng et al. 2014), and by an amphoteric sorbent based on cellulose-rich biomass (Zhong et al. 2014). This behavior can be well explained on the basis

that as the solution pH decreased, the concentration of protons (H+ and H3O+) in solution increased, and the net surface charge of HAS became positive due to the adsorption of protons, which favored the biosorption of Cr(VI) oxyanions onto HAS due to electrostatic attraction (Aksu et al. 2002; El Nemr et al. 2015; Park et al. 2004; Ponce et al. 2015). Conversely, as the solution pH increased to 4.0, the HAS surface binding sites for Cr(VI) oxyanions became deprotonated and then the availability of Cr(VI) oxyanion binding sites decreased, resulting in lower Cr(VI) removal (Figs. 2 and 3) (Mishra et al. 2015; Ponce et al. 2015). The lower Cr(VI) removal obtained at the higher solution pH values can also be ascribed to the competition between OH− ions and Cr(VI) oxyanions for occupancy of the binding sites (Arslan and Pehlivan 2007). It was noticeable that lower residual Cr(VI) concentrations were obtained at all assayed contact times and solution pH levels in experiments conducted under pH-controlled conditions than in pH-shift conditions. Likewise, it was observed that during the process of Cr(VI) removal by HAS, the solution pH tended to increase, mainly in experiments performed at pH values of 2.5, 3.0, and 4.0. Therefore, the higher Cr(VI) removal achieved under pH-controlled conditions may have been due to the increase in the number of protons in solution and consequently to the increase in the number of positively charged binding sites on HAS surface for negatively charged Cr(VI) oxyanions by the addition of protons (1.0 M HCl) to maintain the desired pH level constant. On the other hand, although HAS was able to remove all the Cr(VI) initially present in the aqueous solution at lower solution pH levels, it did not remove the entire total chromium (Figs. 2 and 3). Within the pH range 1.0–2.5 and 1.0–3.0 for pH-shift and pH-controlled experiments, respectively, and at all contact times assayed, the concentration of residual total chromium was higher than that of residual hexavalent chromium; in addition, the difference between these two concentrations increased as the solution pH decreased. In contrast, the difference between residual Cr(VI) concentration and total chromium concentration was minimal at pH values of 3.0– 4.0 for pH-shift and 4.0 for pH-controlled conditions, respectively. The differences found between residual Cr(VI) concentration and total chromium concentration of aqueous solution indicate that some reducing compounds present in the HAS biomass were capable of reducing Cr(VI) to chromium ions of lower valence. As the stable forms of chromium in the environment are the trivalent and the hexavalent, it seems more likely that HAS biomass was able to reduce the highly watersoluble and toxic Cr(VI) to the much less soluble, mobile, toxic, mutagenic, and cytotoxic Cr(III) (Morales-Barrera et al. 2008). Reduction of Cr(VI) to Cr(III) by HAS biomass occurred very rapidly in both the pH-shift and pH-controlled experiments, since Cr(III) was detected in the aqueous solution in

Environ Sci Pollut Res Fig. 2 Variations of Cr(VI) (circles), total chromium (squares), and Cr(III) (triangles) concentrations with respect to contact time at different solution pH values in pH-shift batch experiments [solution pH: a 1.0, b 1.5, c 2.0, d 2.5, e 3.0, f 4.0]. Conditions: initial Cr(VI) concentration = 100 mg L−1, biosorbent concentration 1 g L−1, temperature 20 ± 2 °C. When not shown, standard deviation bars are smaller than symbol size

the first minutes after contact of the Cr(VI) solution with HAS and its concentration increased as experimental time proceeded. The highest levels of Cr(III) were found at solution pH 1.0, and they decreased with increasing pH (Figs. 2 and 3), which indicates that Cr(VI) reduction to Cr(III) consumes protons (Park et al. 2004). Likewise, higher levels of Cr(III) were formed under pH-controlled conditions than in pH-shift conditions, and this may be due to the addition of protons/HCl for maintaining constant pH throughout the experiments. It is well known that when Cr(VI) comes into contact with natural biomaterials, especially in an acidic solution, Cr(VI) can be easily or spontaneously reduced to Cr(III), because Cr(VI) has high redox potential value, exceeding + 1.3 V under standard conditions (Park et al. 2007). Cr(VI) exists in four different soluble species in aqueous solution (CrO42−, HCrO4−, H2CrO4, and Cr2O72−); the proportions of these species vary with solution pH. Within the pH range 1 to 6, the predominant form of Cr(VI) is HCrO4−, whereas higher solution pH leads to transformation of HCrO4− to other Cr(VI) forms, such as Cr2O72− and CrO42−

(Yang and Chen 2008). At low pH values, there are more protons in solution, which participate in the redox reaction to reduce Cr(VI) to Cr(III) as follows (Park et al. 2004; Zheng et al. 2014): þ − 3þ CrO2− þ 4H2 O 4 þ 8H þ 3e ↔Cr

E0

¼ 1:48 V HCrO−4 þ 7Hþ þ 3e− ↔Cr3þ þ 4H2 O

ð14Þ E0

¼ 1:35 V H2 CrO4 þ 6Hþ þ 3e− ↔Cr3þ þ 4H2 O

ð15Þ E0

¼ 1:48 V þ − 3þ Cr2 O2− þ 7H2 O 7 þ 14H þ 6e ↔2Cr

¼ 1:33 V

ð16Þ E0 ð17Þ

Environ Sci Pollut Res Fig. 3 Variations of Cr(VI) (circles), total chromium (squares), and Cr(III) (triangles) concentrations with respect to contact time at different solution pH values in pH-controlled batch experiments [solution pH: a 1.0, b 1.5, c 2.0, d 2.5, e 3.0, f 4.0]. Conditions: initial Cr(VI) concentration = 100 mg L−1, biosorbent concentration 1 g L−1, temperature 20 ± 2 °C. When not shown, standard deviation bars are smaller than symbol size

From the above chemical reactions, it is evident that the electron donor groups present on the biosorbent surface, and the concentration of protons in solution, are key factors determining the rate and extent of Cr(VI) reduction to Cr(III). It is known that polyphenols, polysaccharides, low molecular weight carbohydrates, and proteins, whose redox potentials are generally lower than those of chromate species, are good electron donors and thus have the capacity to reduce Cr(VI) to Cr(III) under acidic conditions (Cabatingan et al. 2001). Lignocellulosic materials have also proved to be able to reduce Cr(VI) at low pH values (Daneshvar et al. 2002; Dupont and Guillon 2003; Fiol et al. 2008). HAS is comprised of several of these compounds, for instance, polyphenols, polysaccharides, and lignocellulose (Calderón-Oliver et al. 2016; Rodríguez-Carpena et al. 2011; Vázquez-Palma et al. 2017), which are possibly the components of HAS that cause Cr(VI) to reduce to Cr(III), as described here. On the other hand, from results shown in Figs. 2 and 3, it is evident that HAS was also capable of removing much of the

total chromium initially present in the aqueous solution, indicating that it was also capable of biosorbing chromium. In view of the above findings, it can be concluded that HAS is capable of removing Cr(VI) from aqueous solution by means of a mixed mechanism involving bioreduction of Cr(VI) to Cr(III) and biosorption of total chromium and that the rate and extent of Cr(VI) bioreduction and total chromium biosorption depend strongly on the solution pH. A mixed mechanism of biosorption and reduction of Cr(VI) to Cr(III) has been previously reported for Cr(VI) removal from Cr(VI) aqueous solution by diverse biomaterials such as acorn shell of Q. crassipes Humb. & Bonpl. (ArandaGarcía et al. 2014), grape stalks and yohimbe bark (Fiol et al. 2008), Quercus robur acorn peel (Kuppusamy et al. 2016b), P. domestica bark (Lopez-Nuñez et al. 2014), Schinus molle bark (Netzahuatl-Muñoz et al. 2010), Prunus serotina bark (Netzahuatl-Muñoz et al. 2012a), C. lusitanica bark (Netzahuatl-Muñoz et al. 2012b, 2015), the brown seaweed Ecklonia sp. (Park et al. 2004), and the use of gum karaya

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(Sterculia urens) to stabilize zero-valent iron nanoparticles (Vinod et al. 2017). In the pH-shift and pH-controlled experiments performed at pH 1.0–2.0, residual total chromium concentration was found to reach its lowest level at a certain contact time, and subsequently increased when no more Cr(VI) was found in the aqueous solution (Figs. 2 and 3), which indicates that part of the chromium that had been biosorbed by HAS was subsequently released into the aqueous solution as Cr(III). Based on these results, it seems that the chromium bound to HAS was in the trivalent form. This hypothesis was confirmed further by XPS results. The desorption of Cr(III) at low solution pH levels from chromium-loaded biosorbents has been previously observed in Cr(VI) removal experiments performed with Q. crassipes acorn shell (Aranda-García et al. 2014) and C. lusitanica bark (Netzahuatl-Muñoz et al. 2012b). Cr(III) desorption has been attributed to the fact that the Cr(III) complexes formed between some functional groups of the biosorbents and Cr(III) ions are unstable at low pH (Netzahuatl-Muñoz et al. 2012b). Figures 4 and 5 display the kinetic profiles of Cr(VI) and total chromium removal capacity for the six different values of solution pH assayed in the pH-shift and pH-controlled experiments, respectively. At solution pH of 1.0–2.0 for the pHshift experiments and 1.0–2.5 for pH-controlled experiments, Cr(VI) removal capacity progressively increased with experiment duration, until Cr(VI) removal capacity reached a maximum constant value, which was maintained until the end of the experiment (Figs. 4a and 5a). In contrast, at pH values higher than those mentioned above, Cr(VI) removal capacity gradually increased with contact time and reached its maximum value at the end of the experiment (120 h) (Figs. 4a and 5a). The highest Cr(VI) removal capacities (approximately 100 mg g−1) were achieved at solution pH values ranging from 1.0 to 2.5 in the pH-shift experiments and from 1.0 to 3.0 in the pH-controlled experiments. Contrastingly, at pH values of 3.0 and 4.0 for the pH-shift experiments and pH of 4.0 for the pH-controlled experiments, the Cr(VI) removal capacities were significantly lower. It was noted that with decreasing solution pH, less contact time was needed to reach maximum Cr(VI) removal capacity, which supports the observation that Cr(VI) removal rate increased as solution pH decreased (Figs. 4a and 5a). Depending on the biosorbent and on experimental conditions assayed, optimum pH values within the range 1–2 have been previously reported for the efficient removal of Cr(VI) (Aksu et al. 2002; El Nemr et al. 2015; Rangabhashiyam et al. 2015; Tunali et al. 2005). The kinetic profiles of total chromium removal (biosorption) capacity obtained in the pH-shift experiments and pH-controlled experiments differed significantly from those of Cr(VI) removal capacity. Over the entire range of

Fig. 4 Effect of solution pH on Cr(VI) (a) and total chromium (b) removal capacity in pH-shift batch experiments [solution pH: 1.0 (filled circles), 1.5 (squares), 2.0 (triangles), 2.5 (inverted triangles), 3.0 (diamonds), 4.0 (empty circles)]. Conditions: initial Cr(VI) concentration, 100 mg L −1 ; biosorbent concentration, 1 g L −1 ; temperature, 20 ± 2 °C. When not shown, standard deviation bars are smaller than symbol size

experimental contact times, Cr(VI) removal capacity was greater than total chromium biosorption capacity (Figs. 4 and 5), which was attributed to the amount of Cr(VI) that was reduced to Cr(III) by HAS and released into the aqueous solutions. In both the pH-shift and pH-controlled experiments, optimal pH for total chromium biosorption onto HAS was dependent on the contact time between HAS and the Cr(VI) solution. In pH-shift experiments, during the first 4 h of contact, the highest total chromium biosorption capacity was obtained at pH 1.5, while from 4 to 48 h and from 72 to 120 h of contact, optimum pH values were 2.0 and 2.5, respectively. In contrast, in pH-controlled experiments, during the first 2 h of contact, the greatest total chromium biosorption level was obtained at pH values of 1.5 and 2.0, whereas at 3 to 12 h, 12 to 24 h, and 48 to 120 h of contact time, optimal pH values were 2.0, 2.5, and 3.0, respectively. These findings agree with those reported by Park et al. (2004), Netzahuatl-Muñoz et al.

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Fig. 5 Effect of solution pH on Cr(VI) (a) and total chromium (b) removal capacity in pH-controlled batch experiments [solution pH: 1.0 (filled circles), 1.5 (squares), 2.0 (triangles), 2.5 (inverted triangles), 3.0 (diamonds), 4.0 (empty circles)]. Conditions: initial Cr(VI) concentration, 100 mg L−1; biosorbent concentration, 1 g L−1; temperature, 20 ± 2 °C. When not shown, standard deviation bars are smaller than symbol size

(2012b), and Aranda-García et al. (2014), who observed that optimal pH for total chromium biosorption from aqueous solution by Ecklonia sp., C. lusitanica bark, and Q. crassipes acorn shell rose from 1.0 to 4.0 as contact time increased from 1 to 480 h, from 1 to 168 h, and from 1 to 120 h, respectively. The dependence of optimum pH on contact time for achieving total chromium biosorption can be explained on the basis of the reduction rate of Cr(VI) to Cr(III) at the different solution pHs. As solution pH increases, the rate of Cr(VI) reduction slows and the Cr(III) formed from Cr(VI) is easily bound to the biosorbent. Although the reduction of Cr(VI) to Cr(III) is slow at high solution pH, Cr(VI) can be completely reduced to Cr(III), which is subsequently bound to the biosorbent as the contact time increases (NetzahuatlMuñoz et al. 2012b; Park et al. 2004). Thus, the rise in maximum total chromium biosorption capacity at higher solution pH and longer contact time may be ascribed to the fact that the Cr(III) formed from the Cr(VI) reduction was more easily biosorbed by HAS, as has been proposed for other biosorbents

(Aranda-García et al. 2014; Cimino et al. 2000; NetzahuatlMuñoz et al. 2012b; Park et al. 2004). Contrastingly, only one optimal pH value has been reported for the biosorption of total chromium from Cr(VI) aqueous solutions by Ceramium virgatum (red algae) (Sari and Tuzen 2008), Sargassum polycystum (Senthilkumar et al. 2010), olive stone (Blázquez et al. 2009), a lignocellulosic substrate extracted from wheat bran (Dupont and Guillon 2003), Palmaria palmata, Polysiphonia lanosa, Fucus spiralis, Ulva spp., Ulva lactuca, Fucus vesiculosus (Murphy et al. 2008), and Pinus brutis Ten. (Ozdes et al. 2014), among others. Nevertheless, it should be noted that in the abovementioned studies, chromium removal was assessed at only one contact time, and hence, optimal pH is only reported for that particular time. It is evident from our results, as well as from the results reported by Park et al. (2004), NetzahuatlMuñoz et al. (2012b), and Aranda-García et al. (2014), that optimal pH values can be appraised more precisely when evaluated at several contact times throughout an extended period of time that allows completion of the Cr(VI) reduction and total chromium biosorption reactions (Netzahuatl-Muñoz et al. 2012b). The highest total chromium biosorption capacities exhibited by HAS were 75.96 mg g−1 (pH shift; solution pH 2.5; 96-h contact) and 76.77 mg g−1 (pH controlled; pH 3.0; 120-h contact). A comparison between different biosorption systems, even when tested on the same heavy metal, is very difficult, due to diverse operating conditions used in experiments. Despite this, we have compared the total chromium biosorption capacity of HAS with that of several biosorbents reported in the literature. The results of the comparison made at similar initial Cr(VI) concentrations clearly indicate that the total chromium biosorption capacity exhibited by HAS in batch systems is similar to that reported for HCl-pretreated P. domestica bark (Lopez-Nuñez et al. 2014) and S. molle bark (NetzahuatlMuñoz et al. 2010), slightly lower than that obtained for C. lusitanica bark (Netzahuatl-Muñoz et al. 2012b) and acorn shell of Q. crassipes Humb. & Bonpl. (Aranda-García et al. 2014), and significantly higher than that for S. polycystum (Senthilkumar et al. 2010), C. virgatum (Sari and Tuzen 2008), coconut coir (Shen et al. 2010), Arthrobacter viscosus (Silva et al. 2012), grape stalks, yohimbe bark (Fiol et al. 2008), waste plant biomass (Mishra et al. 2015), Corynebacterium glutamicum (Park et al. 2008a), and acorn peel of Q. robur (Kuppusamy et al. 2016b). As mentioned above, most studies have evaluated the effect of pH on Cr(VI) and/or total chromium removal in batch systems, either controlling or not controlling pH throughout the experiments, and an optimum pH has been selected. However, our results show for the first time that different kinetic profiles of Cr(VI) and total chromium removal are obtained in pHshift batch and pH-controlled batch experiments. Clearly,

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these differences emphasize the great importance of the way in which the pH effect is determined, either under controlled or uncontrolled conditions. Furthermore, our results confirm that the optimum pH for total chromium removal is dependent on contact time and show that it also depends on the way in which the effect of pH is determined. Modeling the batch kinetic process of total chromium biosorption onto HAS In order to elucidate the mechanism and potential ratecontrolling steps involved in the process of total chromium biosorption onto HAS, five kinetic models were used to evaluate the experimental data: pseudo-first-order, pseudo-second-order, Elovich, fractional power, and intraparticle diffusion. The data obtained at contact times where total chromium desorption occurred in the pH-shift and pH-controlled experiments were not considered for modeling the biosorption process. Tables S1 and S2 in Electronic Supplementary Material show the experimental equilibrium biosorption capacity (qe exp), the kinetic parameter values of the pseudo-first-order (k1 and qe1), pseudo-second-order (k2 and qe2), Elovich (Ae and Be), fractional power (kfp and v), and intraparticle diffusion (kid and c) kinetic models, along with the corresponding r2, RMSE, SSE, AIC, and 95% confidence interval values. The determination coefficients were generally higher with the Elovich kinetic model, and the RMSE, SSE, and AIC values were generally lower than those obtained with the pseudo-first-order, pseudo-second-order, fractional power, and intraparticle diffusion models at the solution pH values assayed in both the pH-shift and pH-controlled experiments. The good fit of total chromium biosorption kinetics to the Elovich model suggests that the rate-limiting step in biosorption of chromium ions onto HAS is probably a chemical sorption (chemisorption), involving a variation of the energetics of chemisorption with the extent of surface coverage (Netzahuatl-Muñoz et al. 2012b). The Elovich model assumes that the active sites of biosorbents are heterogeneous and hence exhibit different activation energies for biosorption. Elovich’s model has also been successfully used to describe the biosorption of heavy metals from aqueous solutions by biosorbents, such as Cr(III) by C. lusitanica bark (Netzahuatl-Muñoz et al. 2012b); Cd(II), Cu(II), and Zn(II) by bone char (Cheung et al. 2000, 2001); and Cu(II) by sphagnum moss peat (Ho and McKay 2002). It can be seen from the data in Tables S1 and S2 (Electronic Supplementary Material) that the initial biosorption rate of total chromium (Ae) and the desorption constant (Be) of the Elovich model varied as a function of solution pH. The constant Ae decreased as solution pH increased, both in pH-shift and pH-controlled experiments. Likewise, the constant Be decreased as solution pH increased in the pH-controlled

experiments; similar behavior was reported by NetzahuatlMuñoz et al. (2012b) for the biosorption of Cr(III) from aqueous solutions by C. lusitanica bark under pH-controlled conditions. In contrast, in the present pH-shift experiments, the constant Be decreased as solution pH increased from 1.0 to 2.0 and then increased as solution pH increased from 2.5 to 4.0. These results indicate that the extent of surface coverage of HAS and consequently the extent of available biosorption surface for chromium ions are strongly affected by solution pH and by whether or not the solution pH is controlled throughout the biosorption process. Effect of feed Cr(VI) solution pH on Cr(VI) and total chromium removal in a continuous packed bed column Chromium removal data obtained from batch experiments are useful for providing fundamental information about the effectiveness, efficiency, and overall performance of metal– biosorbent systems. Nevertheless, these data are generally not applicable to continuous treatment systems such as packed bed column operations, where contact time is not sufficiently long to attain equilibrium (Chauhan and Sankararamakrishnan 2011; Farooq et al. 2013). The assessment and the analysis of the influence of operating conditions on Cr(VI) and total chromium removal from aqueous solutions in a continuous packed bed process are crucial for establishing the treatment process on an industrial scale. The sparse literature focused on the dynamic behavior of biosorption columns for chromium removal provides no data on the removal of both Cr(VI) and total chromium nor on the effect of feed solution pH on both Cr(VI) and total chromium removal. To the best of our knowledge, only two previous works have addressed the influence of feed solution pH on Cr(VI) removal in packed bed columns (Chen et al. 2012; Malkoc et al. 2006). However, those studies did not measure total chromium, and consequently, they did not realize the occurrence of Cr(VI) reduction to Cr(III) by the biomaterials tested. In addition, as shown above, the bioreduction of Cr(VI) to Cr(III) and biosorption of chromium ions, which occur simultaneously on the biosorbent surface, are strongly related to solution pH. Therefore, there is a need to explore the influence of influent solution pH on both Cr(VI) and total chromium removal in a continuous-flow system. Figure 6 displays breakthrough curves for Cr(VI) and total chromium removal at different feed solution pH values. It is apparent that Cr(VI) and total chromium concentrations of the effluents varied widely with the pH of the influent solution. The lowest effluent Cr(VI) and total chromium concentrations were obtained in the first hours of operation, after which the effluent concentrations increased with time, indicating a decrease in Cr(VI) and total chromium removal capacities due to the gradual exhaustion of active sites for Cr(VI) reduction and chromium biosorption (Vieira et al. 2008).

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Fig. 6 Effect of feed solution pH on the breakthrough curve of Cr(VI) (a) and total chromium (b) removal by HAS [feed solution pH: 1.0 (circles), 1.5 (squares), 2.0 (triangles), 2.5 (inverted triangles), 3.0 (diamonds)]. Conditions: initial Cr(VI) concentration, 200 mg L−1; amount of biosorbent, 2 g; flow rate, 0.75 mL min−1. When not shown, standard deviation bars are smaller than symbol size

No Cr(VI) was detected in the effluent in the first 6 h of operating the packed bed column with influent solution pH values of 1.0, 1.5, and 2.0. In contrast, at feed solution pH of 2.5 and 3.0, Cr(VI) was detected from the beginning of the process. It should be noted that during the first 24 h of operation, the Cr(VI) concentration in the effluent decreased as solution pH decreased. Furthermore, at operation times 8–28 and 32–96 h, the lowest effluent Cr(VI) concentrations were obtained at influent solution pH of 1.0 and 1.5, respectively. Coincidentally, the highest Cr(VI) removal capacities were also obtained at feed solution pHs of 1.0 (181.55 mg g−1) a n d 1 . 5 ( 1 8 4 . 3 9 m g g − 1 ) ( Ta b l e S 3 , E l e c t r o n i c Supplementary Material; p > 0.05). At feed solution pH > 1.5, Cr(VI) removal capacity decreased with increasing pH. Malkoc et al. (2006) reported that the highest Cr(VI) removal capacity (26.65 mg g−1) by a bed packed with waste acorn of Quercus ithaburensis was achieved at feed solution

pH 2.0, which was the lowest pH value assayed in that study. The Cr(VI) removal capacity exhibited by HAS in the packed bed column was significantly higher than that of Q. ithaburensis at all feed solution pH values assayed. It is evident from Fig. 6b that the steepness of the breakthrough curves for total chromium removal is a strong function of feed solution pH. In contrast to what was observed in the removal of Cr(VI), total chromium was detected in the effluent from the beginning of packed bed column operation, at all feed solution pH values assayed. These differences further emphasize the great importance of quantifying both Cr(VI) and total chromium in chromium removal systems. The lowest levels of total chromium in the effluent were obtained in the first 16 h of operation with feed solution pH of 2.0. In contrast, at operation times from 36 to 96 h, the lowest concentration of total chromium in effluent occurred at influent solution pH 1.5. At all assayed operation times, total chromium concentration of the effluent was higher than Cr(VI) concentration for feed solution pH 1.0–2.5, which was ascribed to the reduction of Cr(VI) to Cr(III). In contrast, at feed solution pH 3.0, the effluents showed minimal difference in Cr(VI) and total chromium concentrations. The ratio between effluent and influent Cr(VI) or total chromium concentration (Ct/C0; Fig. 6a, b) did not reach 1.0 at any of the pH values assayed, revealing that the HAS active sites for Cr(VI) reduction and total chromium biosorption were not completely exhausted (Vieira et al. 2008). Table S3 in Electronic Supplementary Material shows that the highest total chromium biosorption capacity (127.63 mg g−1) was achieved at influent solution pH 1.5, followed by those at pH 1.0 (108.21 mg g 1 ) and 2.0 (108.39 mg g−1 ) (no significant difference; p > 0.05). Furthermore, as feed solution pH increased from 1.5 to 3.0, total chromium biosorption capacity decreased from 127.63 to 71.11 mg g−1. It should be noted that the difference between Cr(VI) removal capacity and total chromium removal capacity of packed bed HAS decreased from 73.34 to 0.27 mg g−1 as the feed solution pH increased from 1.0 to 3.0, which indicates that the amount of Cr(III) that was formed from Cr(VI) reduction and that was released to the aqueous solution increased as the feed solution pH decreased. The Cr(VI) and total chromium biosorption capacities obtained in the packed bed column system were higher than those achieved in batch systems, indicating more efficient utilization of HAS capacity in the continuous-flow system (Chatterjee and Schiewer 2011; Ghasemi et al. 2011). Furthermore, the total chromium biosorption capacities shown by HAS in the packed bed column are significantly higher than for S. polycystum (Senthilkumar et al. 2010) and Sargassum sp. (Vieira et al. 2008). Thus, low cost, widespread availability, renewable nature, and efficient and effective total chromium biosorption make HAS a highly attractive and

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promising biomaterial for remediation of Cr(VI)-contaminated water and wastewater. The above results show that the highest total chromium biosorption capacities were obtained at solution pH values of 2.5, 3.0, and 1.5 in the pH-shift batch, pH-controlled batch, and continuous systems, respectively. These results underscore the importance of determining the most suitable pH for the removal of Cr(VI) and total chromium using the mode of operation of the treatment system to be used on a large scale. Modeling total chromium breakthrough curves The application of mathematical models to predict and simulate the kinetic performance of dynamic sorption column systems is an important area of environmental engineering (Romero-González et al. 2009), and these models have an important role in transferring biosorption process technology from laboratory scale to pilot and industrial scales (Fiol et al. 2006). However, predicting the biosorption kinetics of continuous packed bed systems is not easy (Malkoc et al. 2006). In addition, there are no reported kinetic models of influent solution pH influence on total chromium biosorption. In the present work, the Bohart–Adams, Thomas, Yoon– Nelson, Yan, and dose–response models were applied to the experimental data to represent the total chromium breakthrough curves at the different feed solution pH values assayed. The nonlinear regression parameters of the breakthrough curve models and the corresponding r 2 , SSE, RMSE, AIC, and 95% confidence interval values are listed in Table S4 (Electronic Supplementary Material). In the present work, the fitness of the kinetic models to the experimental data was pH dependent (i.e., the various kinetic models described the breakthrough curves at differing influent solution pH values). The r 2 values were higher with the Bohart–Adams, Thomas, and Yoon–Nelson models, and the RMSE, SSE, and AIC values were lower than those obtained with the Yan and dose–response models at feed solution pH of 1.0, 2.5, and 3.0. In contrast, at influent solution pH 1.5 and 2.0, the Yan and dose–response models rendered slightly higher r2 values than the Bohart–Adams, Thomas, and Yoon–Nelson models. These differences may be attributed to the different shape of the breakthrough curves obtained at feed solution pH 1.0, 2.5, and 3.0 compared with those obtained at pH 1.5 and 2.0. The Bohart–Adams model is mainly used to describe the initial part of the breakthrough curve, usually until the adsorbate concentration in the effluent is less than 15% of the influent concentration (Calero et al. 2009). However, in the present work, the Bohart–Adams model described suitably the complete breakthrough curves obtained at all assayed feed solution pH values. Although the Thomas model had a high determination coefficient and low RMSE, SSE, and AIC values, it was unable

to predict satisfactorily the experimental biosorption capacity of total chromium at most of the feed solution pH values assayed (p < 0.05), except at pH 2.0 (p > 0.05; Table S4 in Electronic Supplementary Material). Likewise, the Yan model was also incapable of accurately predicting the experimental biosorption capacity data obtained at the five different influent solution pH values assayed (p < 0.5; Table S4 in Electronic Supplementary Material). Therefore, the Thomas and Yan models are not suitable to describe the biosorption behavior of total chromium onto HAS in the packed bed column. The values of parameters τ and β in the Yoon–Nelson and dose–response models, respectively, represent the time required to retain 50% of influent total chromium (C t / C0 = 0.5). There were no significant differences between the experimental values (t 1 / 2 ; Table S4 in Electronic Supplementary Material) and the τ values of the Yoon– Nelson model at influent pH 1.0 and 2.5, nor between the experimental and β values of the dose–response model at influent pH 1.5, 2.0, and 2.5 (p > 0.05). Therefore, the Yoon– Nelson and dose–response models showed good agreement with the experimental breakthrough curves obtained at feed solution pH 1.5 and 2.0 and for pH 1.5, 2.0, and 2.5 compared with model predictions (p > 0.05), respectively. The Yoon– Nelson model assumes that the rate of decrease in the probability of biosorption for each adsorbate molecule is proportional to the probability of adsorbate biosorption and the probability of adsorbate breakthrough on the biosorbent (Gokhale et al. 2009). The dose–response model has been commonly used in pharmacology to describe different types of processes and is currently used to describe the biosorption in packed bed columns (Calero et al. 2009). At influent solution pH 3.0, the effluent chromium concentration was higher than 50% of the influent chromium concentration throughout the course of the continuous operation, and the Yoon–Nelson and dose–response models predicted unrealistic values and consequently are incapable of describing the breakthrough curve at this feed solution pH. XPS studies Batch and continuous kinetic experiments performed in this work demonstrate the presence of both Cr(III) and Cr(VI) in the aqueous phase, which indicates that HAS is able to reduce Cr(VI) to Cr(III). Identifying the oxidation state of the chromium bound to HAS is crucial for characterizing the main mechanism involved in Cr(VI) removal from aqueous solutions by the biomaterial, and XPS was employed here for this purpose. Wide-scan XPS spectra of raw and chromium-loaded HAS were obtained to identify the surface elements present on the HAS biomass surface (Fig. 7a). Both XPS spectra revealed the presence of carbon, oxygen, and nitrogen. The XPS spectrum of raw HAS revealed that there was no chromium associated

Environ Sci Pollut Res Fig. 7 XPS survey scanning spectra (a) and high-resolution Cr 2p XPS spectra (b) of raw HAS (gray continuous line), chromium-loaded HAS (black continuous line), and standard Cr(III) and Cr(VI) compounds

with the biomaterial surface, whereas that of chromiumloaded HAS indicated significant contributions of the chromium biosorbed onto the biomaterial. Significant bands appeared at binding energies ranging from 590 to 570 eV, corresponding to Cr 2p orbital. The Cr 2p high-resolution XPS spectra of raw and chromium-loaded HAS are shown in Fig. 7b. No bands were detected in the Cr 2p region for raw HAS, which confirms the absence of chromium in the biomaterial; in contrast, significant bands appeared in the spectrum of chromium-loaded HAS at binding energies of 587.7 and 577.8 eV, which correspond to Cr 2p1/2 and Cr 2p3/2 orbitals, respectively. It has been demonstrated that Cr 2p1/2 and Cr 2p3/2 orbitals for Cr(III) species are assigned to binding energies of 587– 587.5 and 577 eV, respectively (Moussavi and Barikbin 2010; Silva et al. 2012). These values are similar to the binding energies of the bands found in the XPS spectrum of chromium-loaded HAS. Likewise, Cr 2p XPS spectra of reference compounds of Cr(III) and Cr(VI) are shown in Fig. 7b. Cr2O3, Cr(NO3)3, and CrCl3·6H2O (Cr(III) standards) gave spectra with bands appearing at binding energies of 577.0, 578.0, and 577.6 eV, corresponding to Cr 2p3/2 orbital, and at 586.3, 588.1, and

587.3 eV, corresponding to Cr 2p1/2 orbital, respectively. These binding energies for the Cr 2p3/2 and Cr 2p1/2 orbitals are similar to those found in the XPS spectrum of chromiumloaded HAS (577.8 eV for Cr 2p3/2 orbital and 587.7 eV for Cr 2p1/2 orbital). In contrast, bands of standard Cr(VI) compounds, K2CrO4 and K2Cr2O7, appeared at binding energies of 579.7 and 580.0 eV, corresponding to Cr 2p3/2 orbital, respectively. No bands were found in the chromium-loaded HAS XPS spectrum at nearby binding energies for standard Cr(VI) compounds, and consequently, there was no evidence of the presence of Cr(VI) on the HAS surface. From the above, it is evident that the chromium-loaded HAS spectrum was coincident with the standard Cr(III) compounds and that there was no coincidence of chromiumloaded HAS with the standard Cr(VI) compounds. Therefore, XPS results indicated that the chromium species bound to the HAS biomass was Cr(III). Similar results have been previously obtained in Cr(VI) removal studies using various biosorbents, such as lignocellulosic substrate (Dupont and Guillon 2003), grape stalks, yohimbe bark (Fiol et al. 2008), soy hull (Blanes et al. 2016), Aspergillus niger (Park et al. 2005), fermentation waste of C. glutamicum (Park et al. 2008a), and A. viscosus (Silva et al. 2012).

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According to the XPS and kinetic results obtained in the present work, it can be clearly concluded that the reaction mechanism of Cr(VI) with HAS was the reductive biotransformation of Cr(VI) to Cr(III), which was partially released back to aqueous solution and partially biosorbed onto HAS biomass. Several studies have reported this same conclusion (Blanes et al. 2016; Park et al. 2008a, b; Shen et al. 2010; Silva et al. 2012).

Conclusions Cr(VI) and total chromium removal from aqueous solution by HAS is strongly affected by solution pH. The highest HAS Cr(VI) removal capacities were obtained at solution pH values of 1.0–2.5, 1.0–3.0, and 1.0–1.5 in the pH-shift batch, pHcontrolled batch, and continuous packed bed column systems, respectively. Optimum pH for total chromium biosorption by HAS varied according to contact time, both in the pH-shift batch and pH-controlled batch systems, whereas the packed bed column achieved highest total chromium biosorption capacity at influent solution pH 1.5. The results clearly show that the solution pH most suitable for Cr(VI) and total chromium removal is dependent on the mode of operation of the treatment system. The batch biosorption kinetics for total chromium is best described by the Elovich model, whereas in the continuous system, the fitness of the kinetic models varied with solution pH. XPS studies showed that the chromium bound to the HAS biomass was Cr(III). These results indicate that HAS is capable of removing Cr(VI) from aqueous solution by means of a mixed mechanism involving bioreduction of Cr(VI) to Cr(III) and biosorption of Cr(III). Acknowledgements The CONACyT awarded a graduate scholarship to one of the authors (E.A.-G.). E.C.-U. holds grants from COFAA-IPN, EDI-IPN, and SNI-CONACyT. Funding information The authors gratefully acknowledge the support provided by the scientific team of the Centro de Nanociencias y Micro y Nanotecnologías, IPN, as well as the financial support provided by the Secretaría de Investigación y Posgrado, IPN.

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