Removal of copper from aqueous solutions by biosorption onto pine sawdust

Sustainable Chemistry and Pharmacy 32 (2023) 101016 Available online 6 February 2023 2352-5541/© 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Contents lists available at ScienceDirect Sustainable Chemistry and Pharmacy journal homepage: www.elsevier.com/locate/scp Removal of copper from aqueous solutions by biosorption onto pine sawdust Clara Isabel Orozco, M. Sonia Freire, Diego Gómez-Díaz, Julia González-Álvarez* Department of Chemical Engineering, School of Engineering, Universidade de Santiago de Compostela, Santiago de Compostela, 15782, Spain ARTICLE INFO Handling Editor: Klaus Kümmerer Keywords: Copper Heavy metal Sawdust Pinus radiata Biosorption ABSTRACT Untreated Pinus radiata sawdust was investigated for the removal of Cu+2 ions from aqueous solutions. The biomass was characterized by Inductively Coupled Plasma-Mass (ICP-MS) spectrometry and by Scanning Electron Microscopy with an Energy Dispersive X-ray spectroscopy (SEMEDX), X-Ray crystalline powder Diffraction (XRD) and Fourier Transform Infrared (FTIR) spectroscopy, before and after adsorption. The influence of contact time (up to equilibrium), adsorbent dose (1–50 g/L), initial metal ion concentration (5–300 mg/L) and pH (2–8) on copper sorption efficiency was studied through batch experiments. The results demonstrated that adsorption equilibrium is reached in less than 2 h and the best conditions (Cu+2 removal percentage, 93.4% and adsorption capacity, 0.82 mg/g) were achieved by increasing the adsorbent dose up to 5 g/L and the solution pH up to 7, and decreasing the initial metal concentration to 5 mg/L. The adsorption was optimized by means of a Doehlert experimental design analyzing the influence of adsorbent dose (5–15 g/L) and copper initial concentration (5–45 mg/L) on adsorption efficiency. Kinetic data were satisfactorily fitted to the second-order kinetic model. Intraparticle diffusion model demonstrated that different stages are involved in the adsorption process. Langmuir isotherms fitted satisfactorily the copper bioadsorption equilibrium data. Desorption studies achieved high efficiencies up to 94.5% and the possibility of sawdust regeneration was studied with four adsorption-desorption cycles. Thus, this study evidenced that sawdust is a promising efficient, renewable and economic adsorbent for metal removal and its use for that purpose constitutes an alternative for its management and valorization. 1. Introduction In the past decades, the fast growth of the industrial activity has promoted the discharge of pollutants in the surface and groundwater. Heavy metals can be distinguished from other pollutants for their serious implications on the environment and human health. A problem that is aggravated if it is considered that heavy metals are present in the discharge of numerous industries such as metal processing, mining, energy sector, surface treatment of metals and plastics, organic chemical industry, etc. (European Environment Agency, 2016). This kind of pollutants is not only a serious environmental issue due to their high toxicity in trace concentrations but also due to their non-biodegradability, accumulation in the food chain, and mutagenic and carcinogenic effects (World Health Organization, 2006). For example, in the case of copper which is the metal with the largest toxicity factor in European waters (European Environment Agency, 2019), exposure to large doses in humans provokes degeneration or even necrosis of the kidneys, the liver, and *Corresponding author. E-mail address: julia.gonzal[email protected] (J. González-Álvarez). https://doi.org/10.1016/j.scp.2023.101016 Received 27 October 2022; Received in revised form 16 January 2023; Accepted 26 January 2023 Sustainable Chemistry and Pharmacy 32 (2023) 101016 2 C.I. Orozco et al. the gastrointestinal system. Moreover, it is related to nervous system alterations or mental problems like insomnia or anxiety (Briffa et al., 2020). Therefore, the current study will be focused on copper elimination due to its high emissions in industrial discharges (European Environment Agency, 2019). The ion selection is reinforced by copper properties, such as the ionic radius or electronegativity, which make it an ion within the group of heavy metals with average removal trends in current technologies, and a reference for the removal of other metals, although a specific study is recommended for each heavy metal (Gorgievski et al., 2013). In response to these adverse effects, governments have implemented diverse regulations and standards to limit heavy metal discharges. In the European Union, remarkably, the last decision about these limits corresponds to the organizations of each member country, and the discharge limits depend mainly on the medium of discharge: the sewage system or a direct discharge to a river or sea. For example, in the case of Spain, the limits for copper discharge are between 0.25 and 3 mg/L for direct discharges and between 1 and 5 mg/L for sewerage (MARM et al., 2009). Although, the European trend for copper discharges is generally more restrictive, for example, in Poland or Belgium the limits discharges are under 1 mg/L (BiPRO, 2012;Poland Ministry of Agriculture, 2013). In other countries, the emission value trend is similar to Spain, like in the US where the direct discharges limits are between 1 and 5 mg/L, meanwhile, the indirect emissions are lower than 1 mg/L (U.S. EPA;ZDHC, 2015). However, all these limits can be altered in certain cases such as the presence of sensitive aquatic species, nearby drinking water intakes or specific industrial regulations (MARM et al., 2009). In addition, it is necessary to consider the presence of different pollutants in industrial wastewater: biodegradable organic matter, suspended solids, salts and other metals ions that could interfere with copper ions elimination (European Environment Agency, 2019;Lim et al., 2008). Anyway, the composition of industrial effluents presents a great variety taking into account the diversity of industries, processes or raw materials, being a variable difficult to investigate and requiring specific studies applied to each particular industry (Baskar et al., 2022). Actual technologies for removing metals from wastewaters such as precipitation, ion exchange, membrane separation, etc. do not allow to achieve the discharge limits imposed due to several difficulties and limitations. These include high costs, sludge production, high reagent usage or energy requirements and low efficiency for heavy metals concentrations below 100 mg/L (Delgado Sancho et al., 2016). This is why efforts have been focused on developing more economical and effective methods, such as adsorption which arises nowadays as an opportunity for the removal of heavy metals and other pollutants for having numerous advantages, such as its low energy requirements or its operation simplicity (Abdolali et al., 2014). Activated carbons are the most widely used adsorbents for removing water pollutants, but their high production cost and difficult regeneration have caused the search for cheaper and more eco-friendly options (Sahmoune and Yeddou, 2016). Compared to other adsorbents, waste biomass meets those requirements and its use as adsorbent provides an alternative for its valorization achieving a more environmentally friendly process and contributing to the circular economy. Thus, various studies have reported good results using various biomass for effluent metal removal: oyster shell waste powder (Wu et al., 2014), olive stone and pine bark (Blázquez et al., 2011), orange peels (Pérez-Marín et al., 2007), rice husk (Bansal et al., 2009), Pinus sylvestris sawdust (Taty-Costodes et al., 2003), etc. Among a wide range of low-cost adsorbents, sawdust can be considered a promising alternative for being abundant, renewable and rich in different functional groups such as carbonyl, carboxylic, amine, and hydroxyl, which have shown favorable interactions between the metals ions and adsorbent surface (Meez et al., 2021). Sawdust also shows various potential characteristics of a good adsorbent, compared with agricultural wastes, such as high surface areas (Carreño-De León et al., 2017) or high cellulose and lignin contents, which are related to adsorption capacity (Abdolali et al., 2014;Basso et al., 2010). Pinus radiata is one of the coniferous species most widely spread across northern Spain and is common in other countries, such as USA, New Zealand or Chile, so large quantities of wastes are generated from its industrial processing (Castro et al., 1999;CIF Lourizan, 2017). Despite the high amount of Pinus radiata sawdust produced, studies focused on its use for the removal of pollutants from wastewater are limited, especially those in which the sawdust has not been pre-treated. Since lignocellulosic materials without previous modification have presented good removal efficiencies for metals removal (Acemioǧlu and Alma, 2004;Mannaï et al., 2021;Semerjian, 2018), this research proposes the use of pine sawdust as a cheap and safe adsorbent, while reducing the utilization of hazardous reagents. For that reason, this work aims to investigate the feasibility of using Pinus radiata sawdust (PS) without any pre-treatment for the removal of Cu+2 from aqueous solutions. To attain this objective, firstly, the adsorbent was characterized by different techniques (ICP-MS, SEM-EDX, FTIR and XRD). In a second stage, the influence of adsorbent dose (1–50 g/L), initial Cu+2 concentration (5–300 mg/L), pH (2–8), and contact time (till 24 h) on adsorption efficiency and capacity was analyzed using the one factor at a time method, and the effect of the most significant variables was studied using response surface methodology (RSM). In addition, adsorption kinetics and equilibrium were studied to understand the process. Finally, the efficiency of metal desorption and adsorbent reuse was analyzed. 2. Material and methods 2.1. Adsorbent and adsorbate 2.1.1. Adsorbent preparation and characterization PS was provided by a regional sawmill (Lugo, Spain). It was sun-dried for 24 h (moisture content 10% dry basis), subsequently sieved and the fraction between 0.5 and 1 mm was selected. PS was used in the different studies without any further treatment. In previous studies, the point of zero charge (pHPZC = 4.8) was determined as described by Nordine et al. (2014) and Şentürk and Alzein (2020). Also, sawdust BET surface areas from N2adsorption isotherm at 77 K (1.55 ± 0.81 m2/g) and CO2adsorption isotherm at 273 K (17.83 ± 0.032 m2/g) were determined (sent to publication). Heavy metals in sawdust that could interfere with the adsorption process were quantified by Inductively Coupled Plasma (ICP) with a plasma mass spectrometer (ICP-MS Agilent 7900). Sustainable Chemistry and Pharmacy 32 (2023) 101016 3 C.I. Orozco et al. Additionally, the surface morphology and elemental composition of the sawdust before and after copper adsorption were analyzed by Scanning Electron Microscopy and Energy Dispersive X-ray spectroscopy (SEM-EDX) (ZEIS EVO LS 15 with EDX). In addition, Fourier transform infrared (FTIR) spectra for pine sawdust, before and after adsorption, were recorded with a VARIAN FTIR 670 spectrometer to examine those functional groups responsible for copper adsorption. KBr pellets (1%, g of sample per 100 g of pellet) were prepared with samples after grinding and drying under vacuum for five days. Cu-loaded sawdust sample analyzed was obtained at an adsorbent dose of 12.5 g/L, pH = 7 and an initial copper concentration of 45 mg/L for 2 h at 25 °C. X-ray crystalline powder diffraction (XRD) data were collected at room temperature, in Bragg-Brentano geometry using a BRUKER D8 ADVANCE type X-ray diffractometer (40 kV, 40 mA, theta/theta) equipped with a sealed X-ray tube (CuKα1, λ= 1.5406 Å), and a LYNXEYE XE-T type detector. The diffractograms were obtained in the angular range of 5 < 2Ɵ < 50° with a step of 0.02° at 2 s per step. The samples were rotated during the measurement to obtain the most optimal peak profiles for the analysis, as well as to minimize the effect of the preferred orientation (Yao et al., 2020). They were deposited on a base of an oriented crystal (Si 511 plate) to avoid the background noise caused by a glass-like support. The crystallinity index (CI) was determined as Bala and Mondal (2018). 2.1.2. Preparation of metal solutions Cu+2 solutions for adsorption experiments were prepared by dissolving Cu(NO3)2·3H2O in distilled water at the corresponding concentration. Copper solutions for calibration were prepared from a copper stock solution (1 mg/mL) in 1% nitric acid. When necessary, the solution pH was modified using 0.01 M and 1 M HNO3or 0.1 M and 1 M NaOH solutions. 2.2. Batch adsorption experiments Batch adsorption experiments were performed by placing 50 mL of the copper solution and the appropriate amount of adsorbent according to the dose selected in an orbital shaking bath (J.P Selecta Unitronic Vaivén C) at 25 °C and 100 rpm. After reaching the target contact time, the samples were vacuum filtered using glass microfiber filters (1.2 μm) and the residual copper concentration was determined by Flame Atomic Absorption Spectrophotometry (GBC 932 A A). All experiments were duplicated, and copper concentration was measured in triplicate to ensure the repeatability of the results. Adsorption efficiency (%) and capacity (qt, mg/g) were calculated using the equations Eq. (1) and Eq. (2), respectively: Adsorption efficiency (%)=((C0−Ct)∕C0)x100 (1) qt(mg∕g)=((C0−Ct)∕m)xV (2) where C0and Ctare the initial and residual metal concentration after a predetermined time (mg/L), respectively, m (g) is the mass of dry adsorbent and V (L) is the volume of the metal aqueous solution. 2.2.1. Influence of different variables on copper adsorption In this investigation, the influence of several variables on adsorption efficiency was studied, namely, contact time, adsorbent dose, initial Cu+2 concentration, and pH (Afroze and Sen, 2018;Meez et al., 2021). Firstly, the effect of contact time (up to 24 h) on copper adsorption was studied, fixing the other variables: natural pH (5.4); adsorbent dose (1 or 50 g/L) and initial copper concentration (5, 45 and 300 mg/L). In a second stage, the effect of the adsorbent dose (1, 5, 10, 25 and 50 g/L) was studied for a 5 mg/L copper initial concentration at natural pH and a contact time of 2 h. Then, the effect of the initial metal concentration was studied for a copper concentration of 1 and 5 mg/L with an adsorbent dose of 5 g/L at natural pH and a contact time of 2 h. Finally, the effect of pH (2, 4, natural (5.4), 6, 7, 8) was investigated for an initial copper concentration of 5 mg/L, an adsorbent dose of 5 g/L and a contact time of 2 h. All the experiments were performed following the procedure previously described. 2.2.2. Experimental design A Doehlert experimental design was applied, following the Response Surface Methodology (RSM), to appraise the combined effects of the factors that most influence adsorption efficiency. The variables selected for the optimization were the adsorbent dose (X1), which was studied at five levels (5, 7.5, 10, 12.5, and 15 g/L) and the initial metal concentration (X2), evaluated at three levels (5, 25, and 45 mg/L). The other parameters to consider in this investigation, pH (natural or 7) and time (2 h, time till equilibrium determined in Section 3.2.1), were fixed. The values selected for the independent variables and their coded values are presented in Table 1. The mathematical-statistical analysis was performed with the IBM SPSS Statistics software (Version 25 for Windows). The experimental data can be described, with the next quadratic equation (Eq. (3)) for two variables (Bezerra et al., 2008). Table 1 Doehlert experimental design parameters for copper adsorption efficiency. Factor Variable Unit Coded level −1−0.5 0 0.5 1 −0.866 0 0.866 X1Adsorbent dose g/L 5 7.5 10 12.5 15 X2Initial copper concentration mg/L 5 25 45 Sustainable Chemistry and Pharmacy 32 (2023) 101016 4 C.I. Orozco et al. Y=𝛽0+𝛽1X1+𝛽2X2+𝛽12X1X2+𝛽11X2 1+𝛽22X2 2 (3) where Y is the response (copper adsorption efficiency); β0, the constant term; β1and β2, the regression coefficients for the linear effects; β12, for the interaction effect; β11 and β22, for the quadratic effects and X1and X2represent the independent variables (the adsorbent dose and the initial copper concentration, respectively). In order to determine the significant factors, an analysis of variance (ANOVA) was carried out establishing a 95% confidence level (p < 0.05). Meanwhile, the model significance was evaluated with the coefficient of determination (R2) and the adjusted R2adj. 2.3. Adsorption kinetics The adsorption kinetic studies allow to determine the mechanism of the adsorption process. Various adsorption kinetic models have been proposed, being the most employed for identifying the adsorption dynamics the pseudo-first-order (PFO) model or Lagergren model, the pseudo-second-order (PSO) or Ho's model and the intra-particle (IP) model (Lima et al., 2015;Kajjumba et al., 2018). The pseudo-first-order kinetic model, which is often related to physical sorption, can be described by the differential and linearized equations (4) and (5): dqt∕dt =k1(qe−qt) (4) ln (qe−qt)= ln qe−k1t (5) The pseudo-second-order kinetic model, which considers the chemical sorption as the rate-limiting step, uses the differential and linearized equations (6) and (7): dqt∕dt =k2(qe−qt)2 (6) t∕qt=1∕k2qe 2+t∕qe (7) where qeand qtare the amount adsorbed at equilibrium and at time t (mg/g), respectively; k1, the PFO constant (min−1); k2, the PSO constant (g/mg min); and t, time (min). Nevertheless, these models don't provide information for the diffusion mechanisms and since adsorption is controlled by the slowest step, it is interesting to know which is the rate-limiting step. Intraparticle diffusion, compared with the other steps, possibly offers higher resistance to mass transfer so it is important to confirm this assumption applying the IP model expressed by Eq. (8) (Lima et al., 2015;Sahoo and Prelot, 2020): qt=kct0.5+C (8) where kcis the intraparticle diffusion constant (mg/g min0.5) and C is a constant that determines the thickness of the boundary layer (mg/g). If the plot corresponding to the equation passes through the origin, intraparticle diffusion is the rate-limiting step. However, when the plot shows multiple linear sections it implies that the adsorption process is controlled by different mechanisms (Lima et al., 2015;Kajjumba et al., 2018). To modelize copper adsorption kinetic data from experiments described in Section 2.2.1 were used. 2.4. Adsorption equilibrium Adsorption isotherms provide a wide variety of information such as the affinity and interactions between the adsorbent and adsorbate or the value of the adsorbent capacity. In the present study, the copper adsorption on the PS surface was analyzed with Langmuir and Freundlich isotherm models. Langmuir model assumes that adsorption is monolayer and the adsorbents have a finite number of active sites, which are homogeneously arranged on the surface and are energetically equivalent. The Langmuir isotherm equation is described by Eq. (9) and its linearized expression in Eq. (10) (Lima et al., 2015): qe=(qmKLCe)∕(1+KLCe) (9) Ce∕qe=(1∕KLqm)+Ce∕qm (10) where qeand qmare the equilibrium and maximum adsorption capacity (mg/g), respectively; KL, the Langmuir adsorption constant (L/mg) and Ce, the adsorbate concentration in the equilibrium (mg/L). Furthermore, the dimensionless separation or equilibrium parameter, RL, which can be calculated using Eq. (11), can be used to know more characteristics about adsorption. According to literature (Afroze and Sen, 2018) adsorption can be: irreversible (RL= 0), favorable (0 < RL< 1), linear (RL= 1) or unfavorable (RL> 1). RL=1∕(1+KLC0) (11) Sustainable Chemistry and Pharmacy 32 (2023) 101016 5 C.I. Orozco et al. Freundlich model is characteristic of adsorbents that have active sites with different affinities or adsorption energies. Furthermore, it is assumed that the adsorption can be multilayer. This model can be expressed with Eq. (12) or with its linearized version in Eq. (13): qe=KFCe 1 n (12) ln qe= ln KF+1∕nln Ce (13) where KFis Freundlich adsorption constant which is an indicator of adsorption capacity (mg/g (mg/L)−1/n); and n, describes the affinity between adsorbent and adsorbate. If the value of n is lower than 1, adsorption is unfavorable; by contrast, when n > 1, the adsorption of the molecules onto the adsorbent surface is favorable. High values of n suggest a strong sorption intensity. Equilibrium studies were performed at 25 °C and natural pH with an adsorbent dose of 5 g/L, varying the initial copper concentration between 5 and 360 mg/L and with a contact time of 2 h following the procedure previously described. 2.5. Adsorbent regeneration Regeneration experiments were performed using adsorption conditions selected from the previous experiments (solid/liquid ratio of 5 g/L, initial copper concentration of 5 mg/L, natural pH and a contact time of 2 h). Batch desorption experiments were carried out contacting copper-loaded sawdust with a HNO30.1 M solution at a solid/liquid ratio of 50 g/L. The samples were shaken at 25 °C and 400 rpm in an orbital shaking bath (VWR Incubating Mini Shaker) for 30 min. Then, the sawdust was separated by vacuum filtration with 1.2 μm glass microfiber filters and the liquid sample was analyzed by atomic absorption spectrophotometry as previously explained. After desorption, the sawdust was washed with distillated water several times to remove any acid residue, measuring the pH of the water after washing until reaching pH of distilled water. Four adsorption/desorption cycles were performed. Desorption and regeneration efficiencies were calculated using Eq. (14) and Eq. (15), respectively: Desorption efficiency (%)=[1−((ma−md)∕ma)]x100 (14) Regeneration efficiency (%)=maon a specific cycle∕main the first cycle (15) where maand md, respectively, are the mass of copper adsorbed at equilibrium and in the sample after desorption (mg), respectively, for every cycle. 3. Results and discussion 3.1. Characterization of the adsorbent material The levels of trace metals in Pinus radiata sawdust (Table 2) are significantly lower than those of other tree parts of pine species from nearby locations, such as Pinus pinaster from Portugal (Viana et al., 2018). However, although the concentration ranges differ widely, the trends are the same. That is, the most abundant elements in both species are iron, manganese and aluminum, and in smaller proportions are arsenic, cadmium, chromium, or lead. Moreover, the large amounts of aluminum or iron found in PS suggest that the presence of certain elements in the sawdust can be due to the contact of the wood surface wood with processing machines (Akhouairi et al., 2019). Based on the results, the possible interference of the metals present in the sawdust in the adsorption process was dismissed due to the low levels found. As mentioned above, SEM and EDX analysis of the pine sawdust were performed in a previous work (sent to publication). Thereby, as an example, Figs. 1a and 2a show, respectively, one SEM image before adsorption and the corresponding results of the EDX analysis. After copper adsorption, changes in the biomass were detected (Figs. 1b and 2b, respectively). Thus, SEM shows that the sawdust surface became rougher after adsorption. Moreover, the porosity seems to keep still low, which suggests that in this case porosity does not play an important role in adsorption compared to other more porous materials. Finally, EDX analysis confirmed copper adsorption as copper weight % increased from 0.3 (Fig. 2a) to 0.93 (Fig. 2b) after adsorption. Additionally, the decrease in calcium, potassium, and magnesium weight % suggests that ion exchange is possibly one of the mechanisms involved in adsorption (Fawzy et al., 2022;Meez et al., 2021). No changes were detected for other elements such as carbon and oxygen. FTIR analysis was used to evaluate the changes of functional groups in Pinus radiata sawdust, before and after copper adsorption. It can be observed that the pine spectrum (Fig. 3) shows peaks characteristic for lignocellulosic materials due to the presence of cellulose, lignin and hemicelluloses (Kovacova et al., 2019). According to literature (Xie et al., 2017;Vázquez et al., 2009) the broad and strong band at 3413 cm−1may be due to overlapping of –OH and –NH stretching. Furthermore, peaks at 1157 and 1031 cm−1(C–O stretching of ether and alcoholic and C–N stretching vibration) show the presence of hydroxyl and amine groups, respectively, and peaks at 1637 and 1735 cm−1can be assigned, respectively, to a C=O stretching in carboxylic acids and their esters. The peak at 1510 cm−1is attributed to amine groups, whereas the band at 2923 cm−1can be due to C–H stretching and N–H bending. Finally, the Table 2 Heavy metal concentration (mg/kg) in Pinus radiata sawdust compared to Pinus pinaster wood and bark stem (Viana et al., 2018). Pine species Fe Al As Cd Pb Co Cu Cr Mn Zn Ni Pinus radiata (this study) 552.6 43.6 0.02 0.03 0.2 0.1 1.0 0.2 111.6 11.5 0.2 Pinus pinaster (wood stem) 29,375.0 5884.4 3.8 0.9 13.4 17.1 926.3 109.4 9750.0 1250.0 37.5 Pinus pinaster (bark stem) 9794.0 8231.2 3.0 2.1 0.4 26.3 85.1 8.5 7239.0 1618.1 21.3 Sustainable Chemistry and Pharmacy 32 (2023) 101016 6 C.I. Orozco et al. Fig. 1. SEM image of sawdust before (sent to publish) (a) and (b) after copper adsorption (Co: 45 mg/L, adsorbent dose: 12.5 g/L, pH: 7, contact time: 2 h). Fig. 2. EDX analysis of sawdust before (sent to publish) (a) and (b) after copper adsorption (Co: 45 mg/L, adsorbent dose: 12.5 g/L, pH: 7, contact time: 2 h). peaks in the range of 1300–1400 cm−1can be assigned to the link between the oxygen and the carbon in the cellulose (Andalia et al., 2020). It was demonstrated that many of these functional groups identified in the spectra as the carboxyl, hydroxyl and amine ones are involved in the copper adsorption (Xie et al., 2017;Vázquez et al., 2009). Although FTIR spectra before and after adsorption are similar (Fig. 3), it can be observed that, in general, peaks diminished in absorbance after copper binding, which can indicate that those groups participated via complexation and/or ion exchange as mentioned previously (Mongioví et al., 2022). With respect to XRD analysis, the diffractograms for pine sawdust before and after copper adsorption are shown in Fig. 4. Peaks corresponding to cellulose in its amorphous and crystalline forms, and to lignin and hemicelluloses, the amorphous material (Bala and Mondal, 2018), were observed. Thus, the peak of the crystalline plane was found at 2θ ≈ 22°, and the lowest value of the amorphous region was at 2θ ≈ 16°. The intensity of crystalline cellulose was practically unaffected by copper adsorption, being the crystallinity index (CI) for raw and copper loaded sawdust, 0.42 and 0.41, respectively. These low values indicate the important contribution of the amorphous part in the sawdust structure. Sustainable Chemistry and Pharmacy 32 (2023) 101016 7 C.I. Orozco et al. Fig. 3. FTIR spectra of pine sawdust before and after copper adsorption. Fig. 4. XRD diffractograms of pine sawdust before and after copper adsorption. 3.2. Influence of different parameters on copper adsorption 3.2.1. Effect of contact time Firstly, the effect of contact time on copper adsorption was studied at the natural pH (5.4) for a 5 mg/L initial concentration and adsorbent dose of 1 g/L. The results shown in Fig. 5 demonstrated that copper adsorption was relatively fast, achieving a 36% adsorption efficiency in 15 min, and equilibrium conditions (42% adsorption efficiency) were attained in 1 h. Increasing the adsorbent dose to 50 g/L, the adsorption equilibrium was attained also in 1 h and the adsorption efficiency rose to 80%. The experiments performed with a higher initial concentration of 45 mg/L at 1 and 50 g/L of adsorbent doses showed the same trend of fast adsorption in the first minutes, also reaching equilibrium in approximately 1 h. Nevertheless, for 1 g/L a low adsorption efficiency was obtained (13.5%), whereas when the adsorbent dose was increased to 50 g/L, the efficiency increased to 84.0%. However, at this dose the equilibrium adsorption efficiency decreased to 26% when the copper initial concentration was raised to 300 mg/ L, although the equilibrium time was kept at 1 h. In addition, for all experiments the equilibrium time seemed to be independent of the initial concentration of the metal. This behavior was also found in other studies (Geng et al., 2018;Taman et al., 2015). High adsorption rates in the first minutes were also found for other biosorbents (Lim et al., 2008;Mannaï et al., 2021;TatyCostodes et al., 2003). This behavior can be related to the adsorption mechanism that is affected by the initial metal concentration and the available adsorbent actives sites. When the adsorption starts, a large part of the adsorbent surface is available and there is a high concentration of the metal that is still not adsorbed which favors the mass transfer. As the adsorption process takes place, the adsorbent surface becomes more saturated, so there are fewer and harder accessible active sites. In addition, the low concentration gra- Sustainable Chemistry and Pharmacy 32 (2023) 101016 8 C.I. Orozco et al. Fig. 5. Effect of contact time on Cu+2 adsorption efficiency at natural pH until 24 h (a) and (b) at the first 2 h (Co: 5–300 mg Cu+2/L, adsorbent dose: 1–50 g/L). dient lowers the driving force. All this entails that the metal removal rate increases more slowly till equilibrium (Meez et al., 2021; Sahmoune and Yeddou, 2016). 3.2.2. Effect of the adsorbent dose As expected, the removal efficiency increased with increasing the adsorbent dose from 1 to 10 g/L (Fig. 6) due to the higher availability of binding sites, but the change being much more pronounced from 1 to 5 g/L. However, from 10 to 50 g/L a decrease was observed that may be explained by overlapping adsorption sites or particle interactions such as aggregation, leading to a decrease in the adsorbent surface area (Sahmoune and Yeddou, 2016). At 5 g/L an equilibrium adsorption efficiency of 82.6% (adsorption capacity 0.72 mg/g) was reached which was not significantly improved by increasing the adsorbent dose to 10 g/L. For this reason, 5 g/L was the dose selected to continue experimentation. 3.2.3. Effect of initial metal concentration At the adsorbent dose previously selected (5 g/L) and natural pH the effect of the initial metal concentration was studied in a range that ensure compliance with regulation standards (Delgado Sancho et al., 2016) (1 and 5 mg/L). The best adsorption efficiency (91%) was achieved at 1 mg/L (Fig. 7) demonstrating that reducing initial concentration has a good effect on adsorption. Fig. 6. Effect of pine sawdust dose on Cu+2 adsorption efficiency (Co: 5 mg Cu+2/L, natural pH, contact time: 2 h). Sustainable Chemistry and Pharmacy 32 (2023) 101016 9 C.I. Orozco et al. Fig. 7. Effect of copper initial concentration on Cu+2 adsorption efficiency (adsorbent dose: 5 g/L, natural pH, contact time: 2 h). Therefore, the decrease in the adsorption efficiency observed with the rise of the copper concentration can be explained by the adsorbent saturation. However, the opposite effect was found in the adsorption capacity (Fig. 7) which can be explained by the presence of more metal ions available to interact with the adsorption sites (Meez et al., 2021). 3.2.4. Effect of pH One of the most important factors controlling the adsorption is pH, especially for metals, because it is related to metal speciation. Moreover, pH also affects other aspects such as the adsorption capacity or the surface charge of the adsorbent (Wu et al., 2014). Regarding metal speciation, there are a great diversity of speciation plots for metals as a function of pH. For that reason, a copper speciation plot as a function of pH was generated with MINTEQ 3.0 (Fig. 8). Although there may be differences due to temperature, concentration, etc., most authors agree that the predominant species is Cu+2 below pH = 4–5. When pH rises above 6, the amount of Cu(OH)+species starts to increase and Cu(OH)+and Cu(OH)22+ are the dominant species at pH = 6–7. Close to pH = 8, precipitation of Cu(OH)2occurs and at pH greater than 10, new species such as Cu(OH)3–or Cu(OH)42−begin to appear (Sočo and Kalembkiewicz, 2015;Wu et al., 2014). Consequently, pH plays an important role in the choice of the mechanism of metal removal (adsorption or precipitation). However, in certain pH ranges it's difficult to establish the removal mechanism, even some authors concluded that micro-precipitation occurs during the biosorption process (Abdolali et al., 2014). The increase of the removal percentage with increasing pH up 7 (Fig. 9) could be partially due to the presence of other species different from Cu2+ as mentioned earlier. The hydrated species of the metal ions such as Cu(OH)+ and Cu(OH)2 (aq) have more mobility than Cu+2 due to their smaller size because of their lower load. 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