Abstract
This study prepared nine biochars from three biomass wastes (CompostA, CompostB and Sludge) through different carbonization conditions. The adsorption behaviors and mechanisms of these biochars for Pb(II) were tested by a series of adsorption experiments and properties analysis. Preliminary experiments showed biochars obtained from CompostA and Sludge had better Pb(II) adsorption performance than CompostB and the optimum carbonization temperature of CompostA was lower than that of Sludge. Adsorption experimental results demonstrated that CompostA600 (numbers represent carbonization temperatures) had the largest adsorption capacity of 57.34 mg/g for Pb(II) among samples, followed by Sludge800 of 50.00 mg/g. The kinetic adsorption of CompostA600 and Sludge800 were both described by the Nth-order model very well. Pb(II) adsorption of CompostA600 most appropriately followed the Langmuir–Freundlich model and the Redlich–Peterson model. Characterization analysis suggested diverse carbonization temperatures and precursors caused discrepant pore size distributions and element contents, which determined the deposition of lead compound crystals on materials. This study examined the effects of raw materials and carbonization temperatures on obtained biochars and provided an inexpensive and environmental-friendly way for biochar sorbent preparation and heavy metal wastewater treatment.
INTRODUCTION
With the rapid expansion of industry and the continuous increase of population, water pollution caused by toxic heavy metals has attracted worldwide attention (Arshadi et al. 2017). Lead is a highly toxic metal, and widely exists in the wastewater of electroplating, oil refining, paint, pigment production, electroplating, battery production and other industries (Wang et al. 2012). Different from organic contaminants, Pb(II) is nonbiodegradable and usually accumulates in organisms, which can destroy human hematopoietic systems, nervous systems and hearts and cause anemia, encephalopathy, nephropathy, and some other diseases (Wang et al. 2018). Therefore, it is essential to find valid methods to eliminate Pb(II) from aqueous solutions.
Scholars have conducted many types of research on the disposition of wastewater containing Pb(II) and proposed some useful treatment measures. Jing et al. (2018) prepared a poly-selenide through reaction of 1,4-bis(chloromethyl)benzene with the in situ-generated NaHSe to adsorb and recover lead, but the recycling of poly-selenide needs to be carried out in Na2SO3 solution. Foroughi & Zarei (2013) composed a hydroxyapatite nanoparticle to dispose of poisonous substances from industrial wastewaters; however, a large number of chemical reagents are needed in the process of material synthesis, which makes the cost of material synthesis very high. Silva et al. (2018) adopted an electrochemical method to remove lead ions from wastewater. Since this method requires the use of auxiliary equipment such as an aluminum electrode, it is not suitable for the treatment of large amounts of sewage. Otherwise, the chemical precipitation process adds hydroxide, carbonates or halides into wastewater, which can react with Pb(II) to form insoluble lead precipitation. However, this method consumes a large number of precipitants and causes secondary pollution. In contrast, adsorption can effectively avoid the defects of the above methods. It is a potential technique for eliminating trace impurities from aqueous solutions.
In addition to traditional humic acid and activated carbon, many researchers have used new materials as adsorbents in recent years. Guo et al. (2014) prepared a graphene magnetic material (Fe3O4-GS), but the adsorption capacity is only 27.95 mg/g for Pb(II). Graphene oxides (β-cyclodextrin decorated graphene oxides) prepared by Zheng et al. (2018) and nanofibrous MOF (metal organic framework) membranes prepared by Efome et al. (2018) have high adsorption capacities, but the preparation processes of them are very complicated, especially MOFs, which is currently in the experimental stage and needs to be optimized for large-scale application.
Among various materials, biochar is a potential adsorbent for eliminating heavy metals because of its proper pore size distribution, well-developed porosity, and variegated surface functional groups such as carboxylic groups, carbonyl groups, hydroxyl groups, and other electriferous groups. These groups serve as chemical complexation agents (Ahmad et al. 2012). Subsequently, Pb(II) can bind to the biochars through chemical complexation, cation exchange, electrostatic attraction, or direct adsorption on the pore surfaces. Therefore, highly efficient, low price, locally available, and easily prepared biochars are ideal materials to remove heavy metal in wastewater. During the preparation of biochar, different carbonization temperatures not only affect the yield of biochar but also affect the properties of biochar (Ahmad et al. 2012). Too low temperatures will result in less biochar and disordered microstructure. High temperatures can cause some functional groups on the surface of biochar to disappear. Carbonization temperature also affects the chemical composition, surface charge, thermal stability and specific surface area of biochar, thus leading to differences in the adsorption capacities of biochars. In general, the adsorption capacities and the feasible removal rates are directly related to raw materials and carbonization temperature, which are the main concerns during biochar synthesis of this study.
In recent years, the number and size of city sewage treatment plants has been increasing quickly, which brings about more and more waste sludge. The disposal and utilization of municipal sewage sludge have been identified as a significant social problem, so it is appropriate to investigate it as a raw material of biochars. In addition, composts are widely and cheaply used as scrap materials from agriculture and agro-industries. What is more, composts contain a large amount of biomass, which benefits their preparation into biochars. There is little research on the use of these waste materials to prepare biochars for the removal of lead ions in aqueous solutions.
Consequently, precursors and carbonization temperatures have a great impact on physicochemical properties and cost of obtained biochars, which are potential materials for heavy metal treatment. In this study, two composts and one sludge were chosen as raw materials to prepare nine biochars under different carbonization temperatures for the eliminate of Pb(II) from aqueous solutions. The main objectives of this research were to: (1) study the effects of carbonization temperatures and precursors on biochar properties and Pb(II) adsorption ability; and (2) discuss the potential mechanism of Pb(II) adsorption on different biochars by carrying out isotherm and kinetic experiments and X-ray diffraction (XRD) analysis and scanning electron microscope (SEM) analysis. Brunauer–Emmett–Teller (BET) analysis was performed by a Tristar 3000 surface area analyzer to obtain the specific surface area.
MATERIALS AND METHODS
In this study, CompostA, CompostB and wet sludge came from Beijing, China. Anhydrous lead nitrate Pb(NO3)2 was acquired from Beijing Chemical Works Co. Ltd. All solutions were prepared with deionized (DI) water and all reagents used were analytical grade.
Biochar preparation
CompostA and CompostB were baked at 80 °C in a drying oven for 24 h before putting into a controlled atmosphere furnace with N2 flow rate of 80 mL/min and heating rate of 10 °C/min. The highest pyrolysis temperature was selected as 400 °C, 600 °C and 800 °C, respectively, for 1 h. The carbonized materials were naturally cooled to 25 °C in the furnace. Then, the samples were washed to neutral pH with distilled water, and oven dried to get biochars of CompostA400, CompostA600, CompostA800, CompostB400, CompostB600, and CompostB800, respectively. The wet sludge was treated by the same processes at 700 °C, 800 °C and 900 °C, respectively, to obtain biochar samples of Sludge700, Sludge800, and Sludge900.
Characterization
The abundance of elements C, N, H and S was determined by a CHNOS elemental analyzer (Elementar, Germany) under high temperature catalytic combustion followed by infrared detection of the resulting CO2, N2, H2O and SO2 gases. Major inorganic metal elements were determined using inductively coupled plasma atomic emission spectroscopy (ICP-OES, Optima 8300; PerkinElmer). Dissolved matter tests were carried out through putting 0.05 g material and 25 mL DI water into plastic centrifuge tubes that were shaken in a table shaker for 24 h at 25 °C. Then, mixtures were immediately filtered through a 0.22 μm pore size cellulose membrane filter. The dissolved matter concentration was measured by ICP-OES.
The XRD analysis was carried out with a computer-controlled X-ray diffractometer (Rigaku Ultima IV, Rigaku, Japan), and data were collected in the 2 θ range of 10–90° at a scan rate of 8°/min to investigate the crystallographic structures and size of lead and its compounds on the biochars before and after adsorbing Pb(II). The surface morphology of the sample was analyzed using a Hitachi S-4800 scanning electron microscope, which is equipped with an energy dispersive X-ray fluorescence spectrometer (EDS, Oxford instruments) for evaluating surface element composition of materials.
Adsorption experiments
Adsorption experiments were performed in plastic centrifuge tubes under ambient conditions by using batch technique. The preliminary experiments were done by mixing 0.05 g biochars and 25 mL 50 mg/L Pb(NO3)2 adsorption solutions in plastic centrifuge tubes which were shaken on a table shaker at room temperature (22 ± 0.5 °C) at 200 rpm constant speed maintained for 24 h.
RESULTS AND DISCUSSION
Effect of carbonization temperature
Carbonization temperature is an important factor that could influence biochar structures and their adsorption capacities. Carbonization involves the growth and polymerization of aromatic structures, leading to the enrichment of carbon and the formation of pores with the removal of volatile substances. The change of carbonization temperature results in corresponding changes in yield, water sorption capacity, total nitrogen content, and ion exchange capacity of materials, because it can change pH, BET surface area, carbon content, and heavy metal stability of biochar (Agrafioti et al. 2013). The adsorption ability of CompostA600 that was carbonized at 600 °C significantly exceeded that of CompostA400 and CompostA800, which were carbonized at 400 °C and 800 °C, respectively, with the initial lead concentration of 50 mg/L (Figure 1). This is because when carbonization temperature is too low, only part of the biomass converts to graphite crystallite with low graphitization degree, which makes it difficult to form a proper structure to adsorb and store lead ions. The surface area, pore volume and graphite crystallites of as-prepared biochars increase gradually with the increase of the pyrolysis temperature (Tan et al. 2014). However, when the carbonization temperature is too high, the turbostatic stacking of hexagonal carbon layers transforms to graphitic stacking and the graphite crystallites grow both parallel and perpendicular to the carbon layers easily, which means graphite crystals are arranged more closely, which causes pore size shrinkage (Cantrell et al. 2012). The small pore size leads to small specific surface area, and high graphitization degree is often accompanied by surface energy and the number of effective functional groups on the surface of biochar reduction. All of these could also result in a decrease of adsorption capacities of biochars (Figure 1). The same regularity for the carbonization of CompostB and Sludge is also shown in Figure 1, which indicated the existence of optimal carbonization temperature for each raw materials as 600 °C for CompostA and CompostB, and 800 °C for Sludge in this study. Therefore, we selected six biochars (CompostA600, CompostA800, CompostB600, CompostB800, Sludge800 and Sludge900), which had better adsorption performance through preliminary experiments, for the following experiments and analysis.
Comparison of Pb(II) removal by different adsorbents: CompostA400, CompostA600, CompostA800, CompostB400, CompostB600, CompostB800, Sludge700, Sludge800, Sludge900. (Initial concentration of Pb(II), 50 mg/L; adsorbent dosage, 2 g/L; room temperature (22 ± 0.5 °C); contact time, 24 h.)
Comparison of Pb(II) removal by different adsorbents: CompostA400, CompostA600, CompostA800, CompostB400, CompostB600, CompostB800, Sludge700, Sludge800, Sludge900. (Initial concentration of Pb(II), 50 mg/L; adsorbent dosage, 2 g/L; room temperature (22 ± 0.5 °C); contact time, 24 h.)
Characterization of the adsorbents
According to the organic elemental analysis of biochar samples, the percentage of carbon content increased as the pyrolysis temperature rose from 600 °C to 800 °C, especially for CompostA, which increased by 53% from 23.02% to 79.02%. In the pyrolysis process, the loss of volatile substances took away a large number of surface functional group elements (O, H and N). The contents of remaining H and N were 0.909% and 1.22%, respectively, through pyrolysis temperature at 600 °C, which declined to 0.453% and 0.04% at 800 °C for CompostA. The low carbon content and high hydrogen content of sludge may be due to a large amount of refractory organic compounds in the remaining sludge of sewage treatment plants (Table 1). Metal element tests show that almost all carbonized materials did not contain hazardous heavy metal elements suggesting these samples are safe and sustainable (Table 1). Also, the dissolved matters of the six biochars only consisted of very little K, Ca and Na, while other heavy metal elements such as Cd, Mn, Li, Pb, Cr, Zn, and Fe were not detected in all six biochar samples, further demonstrating that the materials are environmentally friendly (Table 2).
Elemental analysis of the CompostA, CompostB, the sludge, and their associated biochar samples, respectively
Adsorbents . | (%) Mass based . | |||||||||
---|---|---|---|---|---|---|---|---|---|---|
C . | H . | S . | N . | K . | Ca . | Mg . | Al . | Na . | Ni . | |
Original CompostA | 15.95 | 1.927 | 0.66 | 1.41 | –a | – | – | – | – | – |
CompostA600 | 23.02 | 0.909 | 0.52 | 1.22 | – | – | – | – | – | – |
CompostA800 | 79.02 | 0.453 | 0.1 | 0.04 | – | – | – | – | – | – |
Original CompostB | 39.41 | 4.58 | 1.06 | 2.31 | – | – | – | – | – | – |
CompostB600 | 48.31 | 1.59 | 1.1 | 2.47 | – | – | – | – | – | – |
CompostB800 | 52 | 1.094 | 2.05 | 0.93 | – | – | – | – | – | – |
Original sludge | 17.17 | 0.438 | 0.15 | 0.9 | 0.012 | 0.014 | – | 0.017 | – | – |
Sludge800 | 35.82 | 5.322 | 0.72 | 6.58 | 0.012 | 0.014 | – | 0.017 | – | – |
Adsorbents . | (%) Mass based . | |||||||||
---|---|---|---|---|---|---|---|---|---|---|
C . | H . | S . | N . | K . | Ca . | Mg . | Al . | Na . | Ni . | |
Original CompostA | 15.95 | 1.927 | 0.66 | 1.41 | –a | – | – | – | – | – |
CompostA600 | 23.02 | 0.909 | 0.52 | 1.22 | – | – | – | – | – | – |
CompostA800 | 79.02 | 0.453 | 0.1 | 0.04 | – | – | – | – | – | – |
Original CompostB | 39.41 | 4.58 | 1.06 | 2.31 | – | – | – | – | – | – |
CompostB600 | 48.31 | 1.59 | 1.1 | 2.47 | – | – | – | – | – | – |
CompostB800 | 52 | 1.094 | 2.05 | 0.93 | – | – | – | – | – | – |
Original sludge | 17.17 | 0.438 | 0.15 | 0.9 | 0.012 | 0.014 | – | 0.017 | – | – |
Sludge800 | 35.82 | 5.322 | 0.72 | 6.58 | 0.012 | 0.014 | – | 0.017 | – | – |
aFor test results below 0.01%, the sample is considered to be free of the element.
Dissolved matters of CompostA600, CompostA800, CompostB600, CompostB800, Sludge800, and Sludge900 samples
Biochars . | mg/g . | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
K . | Ca . | Na . | Mg . | Ni . | Cd . | Mn . | Li . | Pb . | Cr . | Zn . | Fe . | |
CompostA600 | 1.97 | 1.62 | 0.98 | 2.07 | 0.07 | –a | – | – | – | – | – | – |
CompostA800 | 0.90 | – | 0.91 | 2.72 | 0.07 | – | – | – | – | – | – | – |
CompostB600 | 0.72 | 1.78 | 0.09 | 0.00 | 0.05 | – | – | – | – | – | – | – |
CompostB800 | 0.93 | 3.00 | 0.66 | 0.00 | 0.12 | – | – | – | – | – | – | – |
Sludge800 | 4.58 | – | 1.12 | 4.10 | 0.08 | – | – | – | – | – | – | – |
Sludge900 | 0.91 | – | 0.26 | 0.30 | 0.06 | – | – | – | – | – | – | – |
Biochars . | mg/g . | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
K . | Ca . | Na . | Mg . | Ni . | Cd . | Mn . | Li . | Pb . | Cr . | Zn . | Fe . | |
CompostA600 | 1.97 | 1.62 | 0.98 | 2.07 | 0.07 | –a | – | – | – | – | – | – |
CompostA800 | 0.90 | – | 0.91 | 2.72 | 0.07 | – | – | – | – | – | – | – |
CompostB600 | 0.72 | 1.78 | 0.09 | 0.00 | 0.05 | – | – | – | – | – | – | – |
CompostB800 | 0.93 | 3.00 | 0.66 | 0.00 | 0.12 | – | – | – | – | – | – | – |
Sludge800 | 4.58 | – | 1.12 | 4.10 | 0.08 | – | – | – | – | – | – | – |
Sludge900 | 0.91 | – | 0.26 | 0.30 | 0.06 | – | – | – | – | – | – | – |
aFor test results below 0.01 mg/g, the sample is considered to be free of the element.
The selected six samples were observed by SEM. The CompostA600 consisted of spherical particles which made biochar have high surface energy and provided enough active sites for lead crystals growth (Figure S1(A) and S1(B) in the online Supporting Information). What is more, pores among particles could provide spaces to store lead compound crystals. Therefore, there were a number of lead crystals evenly deposited on the surface of post-sorption CompostA600 materials (Figure 2). The surface of CompostB600 showed a lot of pore structures. Columnar crystals could be seen on the surface of post-sorption CompostB600 (Figure 3). The SEM images revealed well-developed micro-pore structures of Sludge800 (Figure S1(E) and S1(F) in the online Supporting Information). Lead-containing crystals were uniformly coated on the surface of Sludge800 after adsorption (Figure 4). Among the three materials, the pore channels of CompostA600 and Sludge800 were more uniform, which is a benefit for Pb(II) adsorption.
SEM images of CompostA600 after adsorption (a) and corresponding distribution diagram of lead (b) and EDS (c).
SEM images of CompostA600 after adsorption (a) and corresponding distribution diagram of lead (b) and EDS (c).
SEM images (a)–(c) and corresponding EDS (d) of CompostB600 after adsorption.
SEM images of Sludge800 after adsorption (a) (b) and corresponding distribution diagram of lead (c) and EDS (d).
SEM images of Sludge800 after adsorption (a) (b) and corresponding distribution diagram of lead (c) and EDS (d).
For further judging the crystal structure of precipitated granules after Pb(II) adsorption, the XRD tests were conducted. The results are shown in Figure 5. There is a strong band detected at around 27° in the pattern of original biochars, which is a typical amorphous carbon peak. The patterns also showed strong signals of lead compounds that were Pb4(SO4)(CO3)2(OH)2 and Pb3(CO3)2(OH)2 for CompostA600 and PbO2 and PbSO4 for CompostA800, respectively (Figure 5(a) and 5(b)). The peaks in XRD patterns of CompostB600 and CompostB800 after Pb(II) adsorption (Figure 5(c) and 5(d)) were indexed to PbSO4 crystals, which suggested that Pb(II) reacted with sulfate ions on the surface of the materials to form precipitation during the adsorption processes. The diffraction spectra of Sludge800 and Sludge900 (Figure 5(e) and 5(f)) also showed some lead sulfate crystalline peaks at the scan range 10–90◦, which were not as obvious as that of CompostB indicating Pb(II) exists in an amorphous phase on Sludge800 and Sludge900. The adsorption of Pb(II) on sludge biochars may mainly depend on physical adsorption such as van der Waals force and electrostatic attraction.
Powder XRD patterns of CompostA600 (a), CompostA800 (b), CompostB600 (c), CompostB800 (d), Sludge800 (e), and Sludge900 (f) before and after Pb(II) adsorption.
Powder XRD patterns of CompostA600 (a), CompostA800 (b), CompostB600 (c), CompostB800 (d), Sludge800 (e), and Sludge900 (f) before and after Pb(II) adsorption.
Adsorption kinetics
Some mathematical models were applied to simulate the adsorption kinetics data and study the characteristics of the Pb(II) adsorption process onto the selected biochars (Doğan & Alkan 2003).
The pseudo first-order model is based on mononuclear adsorption (Simonin 2016). The pseudo second-order model is established under the condition that the adsorption rate is controlled by the square of surface vacancy. This adsorption process is mainly controlled by the chemical adsorption between adsorbent and adsorbate, involving electronic sharing or electron transfer (Simonin 2016). The Nth-order model describes the dynamics of the solid–solution system based on N-nuclear adsorption (Tseng et al. 2014). The Elovich model is an empirical formula, describing a process that includes a series of reaction mechanisms, such as a spread of solute in solution phase or interface, surface activation and inactivation effect. It is very suitable for describing the process of reaction activation energy changing rapidly.
The transformation of lead adsorption with time also was studied, and the results are shown in Figure 6. The quantities of Pb(II) adsorption increased with time. By fitting, adsorption kinetics curves of CompostA600 and CompostA800 were much more close to the Nth-order model than to the other models (Table 3). The results also indicated that adsorption rate increased quickly at the beginning and became slow after just a few hours. The adsorption process of Pb(II) on the studied materials was two-phase adsorption, which was consistent with the models. The first rapid step was related to adsorption of the ions on the surface of adsorbent. The second step was attributed to the transfer of the adsorbed Pb(II) ions from external surface to the internal space of the adsorbent (Tan et al. 2014). The time required for CompostA600 and CompostA800 to reach the adsorption equilibrium situation is discrepant. The adsorption speed of Pb(II) on CompostA600 was faster than that on CompostA800. This distinction can be illustrated by the different surface characters of the two biochars. Adsorption kinetics largely depends on the chemical and physical properties of materials, which also affect the adsorption mechanism. The results of element distribution show that the content of H, O, N and S in CompostA600 is higher than that in CompostA800 (Table 1). These heteroatoms tend to form more functional groups on the surface of CompostA600, which can be bound to Pb(II) ions to remove them.
Kinetics model parameters for the adsorption of Pb(II) onto the different samples
Sample . | Parameter . | First-order . | Second-order . | Nth-order . | Elovich . |
---|---|---|---|---|---|
CompostA600 | Parameter 1 | k1 = 0.8029 (h−1) | k2 = 0.04181 (g/mg·h) | kn = 1.168 (gn−1/mgn−1·h) | β= 15.28 (g/mg) |
Parameter 2 | qe = 26.91 (mg/g) | qe = 28.42 (mg/g) | qe = 67.99 (mg/g) | α = 4.009 (mg/g·h) | |
Parameter 3 | n = 11.41 | ||||
R2 | 0.9358 | 0.9573 | 0.9693 | 0.9522 | |
CompostA800 | Parameter 1 | k1 = 0.3692 (h−1) | k2 = 0.02238 (g/mg·h) | kn = 0.002179 (gn−1/mgn−1·h) | β= 9.314 (g/mg) |
Parameter 2 | qe = 18.9 (mg/g) | qe = 21.34 (mg/g) | qe = 1.006e + 004 (mg/g) | α = 2.943 (mg/g·h) | |
Parameter 3 | n = 2371 | ||||
R2 | 0.9229 | 0.9469 | 0.9614 | 0.9072 | |
CompostB600 | Parameter 1 | k1 = 0.3608 (h−1) | k2 = 0.04122 (g/mg·h) | kn = 0.2 (gn−1/mgn−1·h) | β= 5.141 (g/mg) |
Parameter 2 | qe = 9.605 (mg/g) | qe = 10.94 (mg/g) | qe = 62.42 (mg/g) | α = 1.352 (mg/g·h) | |
Parameter 3 | n = 28.26 | ||||
R2 | 0.9275 | 0.937 | 0.9414 | 0.9021 | |
CompostB800 | Parameter 1 | k1 = 0.4154 (h−1) | k2 = 0.07768 (g/mg·h) | kn = 0.4415 (gn−1/mgn−1·h) | β= 2.411 (g/mg) |
Parameter 2 | qe = 5.444 (mg/g) | qe = 6.23 (mg/g) | qe = 5.608 (mg/g) | α = 1.06 (mg/g·h) | |
Parameter 3 | n = 1.281 | ||||
R2 | 0.9875 | 0.9873 | 0.9779 | 0.9692 | |
Sludge800 | Parameter 1 | k1 = 6.203 (h−1) | k2 = 0.4096 (g/mg·h) | kn = 1 (gn−1/mgn−1·h) | β= 13.71 (g/mg) |
Parameter 2 | qe = 16.68 (mg/g) | qe = 18.03 (mg/g) | qe = 1016 (mg/g) | α = 2.208 (mg/g·h) | |
Parameter 3 | n = 451.3 | ||||
R2 | 0.668 | 0.8366 | 0.9943 | 0.9933 | |
Sludge900 | Parameter 1 | k1 = 8.09 (h−1) | k2 = 1.54 (g/mg·h) | kn = 21.97 (gn−1/mgn−1·h) | β= 5.433 (g/mg) |
Parameter 2 | qe = 6.404 (mg/g) | qe = 6.826 (mg/g) | qe = 26.08 (mg/g) | α = 0.7595 (mg/g·h) | |
Parameter 3 | n = 28.03 | ||||
R2 | 0.635 | 0.8152 | 0.9804 | 0.9793 |
Sample . | Parameter . | First-order . | Second-order . | Nth-order . | Elovich . |
---|---|---|---|---|---|
CompostA600 | Parameter 1 | k1 = 0.8029 (h−1) | k2 = 0.04181 (g/mg·h) | kn = 1.168 (gn−1/mgn−1·h) | β= 15.28 (g/mg) |
Parameter 2 | qe = 26.91 (mg/g) | qe = 28.42 (mg/g) | qe = 67.99 (mg/g) | α = 4.009 (mg/g·h) | |
Parameter 3 | n = 11.41 | ||||
R2 | 0.9358 | 0.9573 | 0.9693 | 0.9522 | |
CompostA800 | Parameter 1 | k1 = 0.3692 (h−1) | k2 = 0.02238 (g/mg·h) | kn = 0.002179 (gn−1/mgn−1·h) | β= 9.314 (g/mg) |
Parameter 2 | qe = 18.9 (mg/g) | qe = 21.34 (mg/g) | qe = 1.006e + 004 (mg/g) | α = 2.943 (mg/g·h) | |
Parameter 3 | n = 2371 | ||||
R2 | 0.9229 | 0.9469 | 0.9614 | 0.9072 | |
CompostB600 | Parameter 1 | k1 = 0.3608 (h−1) | k2 = 0.04122 (g/mg·h) | kn = 0.2 (gn−1/mgn−1·h) | β= 5.141 (g/mg) |
Parameter 2 | qe = 9.605 (mg/g) | qe = 10.94 (mg/g) | qe = 62.42 (mg/g) | α = 1.352 (mg/g·h) | |
Parameter 3 | n = 28.26 | ||||
R2 | 0.9275 | 0.937 | 0.9414 | 0.9021 | |
CompostB800 | Parameter 1 | k1 = 0.4154 (h−1) | k2 = 0.07768 (g/mg·h) | kn = 0.4415 (gn−1/mgn−1·h) | β= 2.411 (g/mg) |
Parameter 2 | qe = 5.444 (mg/g) | qe = 6.23 (mg/g) | qe = 5.608 (mg/g) | α = 1.06 (mg/g·h) | |
Parameter 3 | n = 1.281 | ||||
R2 | 0.9875 | 0.9873 | 0.9779 | 0.9692 | |
Sludge800 | Parameter 1 | k1 = 6.203 (h−1) | k2 = 0.4096 (g/mg·h) | kn = 1 (gn−1/mgn−1·h) | β= 13.71 (g/mg) |
Parameter 2 | qe = 16.68 (mg/g) | qe = 18.03 (mg/g) | qe = 1016 (mg/g) | α = 2.208 (mg/g·h) | |
Parameter 3 | n = 451.3 | ||||
R2 | 0.668 | 0.8366 | 0.9943 | 0.9933 | |
Sludge900 | Parameter 1 | k1 = 8.09 (h−1) | k2 = 1.54 (g/mg·h) | kn = 21.97 (gn−1/mgn−1·h) | β= 5.433 (g/mg) |
Parameter 2 | qe = 6.404 (mg/g) | qe = 6.826 (mg/g) | qe = 26.08 (mg/g) | α = 0.7595 (mg/g·h) | |
Parameter 3 | n = 28.03 | ||||
R2 | 0.635 | 0.8152 | 0.9804 | 0.9793 |
Data and fitted models of adsorption kinetics and isotherms of Pb(II) onto CompostA600 and CompostA800 (a) (b), CompostB600 and CompostB800 (c) (d), and Sludge800 and Sludge900 (e) (f).
Data and fitted models of adsorption kinetics and isotherms of Pb(II) onto CompostA600 and CompostA800 (a) (b), CompostB600 and CompostB800 (c) (d), and Sludge800 and Sludge900 (e) (f).
For CompostB600 and CompostB800, the tendency of adsorption kinetics was similar to that of CompostA600. With lead ions adsorbed increasing, adsorption rate gradually reduced to the final balance. The time required to reach the equilibrium situation was similar to that of the examined CompostB. By fitting, CompostB600 and CompostB600 were also close to the Nth-order model, which suggested that the adsorption mechanism of CompostB biochars was similar to that of CompostA biochars.
For Sludge800 and Sludge900, during the first 3 hours, the adsorption of lead ions occurred very fast initially, and the time required to achieve equilibrium state was faster than that of compost biochars, with most heavy metal ions being dislodged within 3 h. This situation may be due to the strong attraction between lead ions and the adsorbent, which leads to the rapid diffusion of lead ions into the interlayer space of the adsorbent to achieve equilibrium. The correlation coefficients (R2) for Nth-order and Elovich kinetic models are higher than those for other kinetic models. These results suggest that the overall rates of the adsorption of Pb(II) are controlled by both chemical and physical adsorption.
Adsorption isotherms
The equilibrium isotherm models were used to simulate adsorption experiment data and adsorption reaction of lead. The equation parameters and basic assumption of thermodynamics of these equilibrium models usually provide some basis for the adsorption mechanism, surface properties and surface group affinity of adsorbents (Ali et al. 2016).
The Langmuir isotherm is based on a unified assumption that all adsorption sites are identical, and the adsorption of active sites is independent of whether adjacent sites are occupied. Therefore, in the case of low original concentration, the isotherm is nearly linear, which follows Henry's law (Harris & Rice 1988). The Freundlich isotherm is an empirical equation that assumes multilayer adsorption on heterogeneous surfaces. It can also be applied to single layer adsorption and miscellaneous adsorption on heterogeneous surfaces (Liu et al. 2004). The Langmuir–Freundlich isotherm model is a combination of the Langmuir and the Freundlich models derived to predict the uneven adsorption systems (Jeppu & Clement 2012). The Redlich–Peterson isotherm approximates to Henry's law and the Freundlich isotherm at low and high concentrations, respectively (Özer & Dursun 2007). The Temkin isotherm is suitable for uneven surface adsorption (Eriksson et al. 1997).
The adsorption isotherms of Pb(II) on biochars are shown in Figure 6. The adsorption capacity of Pb(II) on CompostA600 was 57.34 mg/g, which was higher than that of CompostA800. For CompostB600 and CompostB800, the equilibrium adsorption capability was 29.37 mg/g and 26.18 mg/g, respectively. The equilibrium adsorption capacity of Sludge800 was 50.00 mg/g, but that of Sludge900 was just 17.14 mg/g (Figure 6(f)). Isotherm model parameters determined by experimental data analysis are shown in Table 4.
Isotherms kinetic model parameters for the adsorption of Pb(II) onto the different samples
Sample . | Parameter . | Langmuir . | Freundlich . | Langmuir–Freundlich . | Redlich–Peterson . | Temkin . |
---|---|---|---|---|---|---|
CompostA600 | Parameter 1 | K = 4.761 (L/mg) | Kf = 25.7 (mg1−n·Ln/g) | Klf= 3.97e + 005 (Ln/mgn) | Kr= 6.6 (L/g) | b= 523.7 (J·g/mg) |
Parameter 2 | Q = 53.18 (mg/g) | n = 0.1274 | Q = 52.99 (mg/g) | a = 289.9 (Ln/mgn) | A= 350.9 (L/mg) | |
Parameter 3 | n = 5.54 | n = 0.964 | ||||
R2 | 0.9136 | 0.8392 | 0.952 | 0.9258 | 0.8912 | |
CompostA800 | Parameter 1 | K= 0.04693 (L/mg) | Kf = 8.35 (mg1−n·Ln/g) | Klf= 632.9 (Ln/mgn) | Kr= 0.02672 (L/g) | b= 560.6 (J·g/mg) |
Parameter 2 | Q = 26.08 (mg/g) | n = 0.1769 | Q = 23.58 (mg/g) | a = 0.9116 (Ln/mgn) | A= 0.5562 (L/mg) | |
Parameter 3 | n = 3.793 | n = 1.04 | ||||
R2 | 0.9709 | 0.9081 | 0.9369 | 0.9715 | 0.9613 | |
CompostB600 | Parameter 1 | K= 0.01408 (L/mg) | Kf = 3.276 (mg1−n·Ln/g) | Klf= 0.04144 (Ln/mgn) | Kr= 0.1625 (L/g) | b= 516 (J·g/mg) |
Parameter 2 | Q = 28.95 (mg/g) | n = 0.3229 | Q = 42.74 (mg/g) | a = 1.108 (Ln/mgn) | A= 0.3336 (L/mg) | |
Parameter 3 | n = 0.5696 | n = 0.7852 | ||||
R2 | 0.9608 | 0.9757 | 0.9935 | 0.9956 | 0.9796 | |
CompostB800 | Parameter 1 | K= 0.01071 (L/mg) | Kf = 2.201 (mg1−n·Ln/g) | Klf= 0.01379 (Ln/mgn) | Kr= 0.04334 (L/g) | b= 564.1 (J·g/mg) |
Parameter 2 | Q = 27.04 (mg/g) | n = 0.3547 | Q = 26.17 (mg/g) | a = 0.4541 (Ln/mgn) | A= 0.2922 (L/mg) | |
Parameter 3 | n = 0.98 | n = 0.8653 | ||||
R2 | 0.9859 | 0.9564 | 0.9845 | 0.9935 | 0.991 | |
Sludge800A | Parameter 1 | K= 0.006312 (L/mg) | Kf = 5.913 (mg1−n·Ln/g) | Klf= 0.02103 (Ln/mgn) | Kr= 0.008237 (L/g) | b= 399 (J·g/mg) |
Parameter 2 | Q = 54.14 (mg/g) | n = 0.2925 | Q = 60.86 (mg/g) | a = 0.37 (Ln/mgn) | A= 1.295 (L/mg) | |
Parameter 3 | n = 0.726 | n = 0.9753 | ||||
R2 | 0.9934 | 0.9657 | 0.9949 | 0.9936 | 0.9705 | |
Sludge900 | Parameter 1 | K= 0.09679 (L/mg) | Kf = 5.399 (mg1−n·Ln/g) | Klf= 0.006786 (Ln/mgn) | Kr= 1100 (L/g) | b= 1795 (J·g/mg) |
Parameter 2 | Q = 14.66 (mg/g) | n = 0.1478 | Q = 799.9 (mg/g) | a = 5942 (Ln/mgn) | A= 46.45 (L/mg) | |
Parameter 3 | n = 0.1478 | n = 0.8523 | ||||
R2 | 0.5824 | 0.9905 | 0.9918 | 0.992 | 0.9417 |
Sample . | Parameter . | Langmuir . | Freundlich . | Langmuir–Freundlich . | Redlich–Peterson . | Temkin . |
---|---|---|---|---|---|---|
CompostA600 | Parameter 1 | K = 4.761 (L/mg) | Kf = 25.7 (mg1−n·Ln/g) | Klf= 3.97e + 005 (Ln/mgn) | Kr= 6.6 (L/g) | b= 523.7 (J·g/mg) |
Parameter 2 | Q = 53.18 (mg/g) | n = 0.1274 | Q = 52.99 (mg/g) | a = 289.9 (Ln/mgn) | A= 350.9 (L/mg) | |
Parameter 3 | n = 5.54 | n = 0.964 | ||||
R2 | 0.9136 | 0.8392 | 0.952 | 0.9258 | 0.8912 | |
CompostA800 | Parameter 1 | K= 0.04693 (L/mg) | Kf = 8.35 (mg1−n·Ln/g) | Klf= 632.9 (Ln/mgn) | Kr= 0.02672 (L/g) | b= 560.6 (J·g/mg) |
Parameter 2 | Q = 26.08 (mg/g) | n = 0.1769 | Q = 23.58 (mg/g) | a = 0.9116 (Ln/mgn) | A= 0.5562 (L/mg) | |
Parameter 3 | n = 3.793 | n = 1.04 | ||||
R2 | 0.9709 | 0.9081 | 0.9369 | 0.9715 | 0.9613 | |
CompostB600 | Parameter 1 | K= 0.01408 (L/mg) | Kf = 3.276 (mg1−n·Ln/g) | Klf= 0.04144 (Ln/mgn) | Kr= 0.1625 (L/g) | b= 516 (J·g/mg) |
Parameter 2 | Q = 28.95 (mg/g) | n = 0.3229 | Q = 42.74 (mg/g) | a = 1.108 (Ln/mgn) | A= 0.3336 (L/mg) | |
Parameter 3 | n = 0.5696 | n = 0.7852 | ||||
R2 | 0.9608 | 0.9757 | 0.9935 | 0.9956 | 0.9796 | |
CompostB800 | Parameter 1 | K= 0.01071 (L/mg) | Kf = 2.201 (mg1−n·Ln/g) | Klf= 0.01379 (Ln/mgn) | Kr= 0.04334 (L/g) | b= 564.1 (J·g/mg) |
Parameter 2 | Q = 27.04 (mg/g) | n = 0.3547 | Q = 26.17 (mg/g) | a = 0.4541 (Ln/mgn) | A= 0.2922 (L/mg) | |
Parameter 3 | n = 0.98 | n = 0.8653 | ||||
R2 | 0.9859 | 0.9564 | 0.9845 | 0.9935 | 0.991 | |
Sludge800A | Parameter 1 | K= 0.006312 (L/mg) | Kf = 5.913 (mg1−n·Ln/g) | Klf= 0.02103 (Ln/mgn) | Kr= 0.008237 (L/g) | b= 399 (J·g/mg) |
Parameter 2 | Q = 54.14 (mg/g) | n = 0.2925 | Q = 60.86 (mg/g) | a = 0.37 (Ln/mgn) | A= 1.295 (L/mg) | |
Parameter 3 | n = 0.726 | n = 0.9753 | ||||
R2 | 0.9934 | 0.9657 | 0.9949 | 0.9936 | 0.9705 | |
Sludge900 | Parameter 1 | K= 0.09679 (L/mg) | Kf = 5.399 (mg1−n·Ln/g) | Klf= 0.006786 (Ln/mgn) | Kr= 1100 (L/g) | b= 1795 (J·g/mg) |
Parameter 2 | Q = 14.66 (mg/g) | n = 0.1478 | Q = 799.9 (mg/g) | a = 5942 (Ln/mgn) | A= 46.45 (L/mg) | |
Parameter 3 | n = 0.1478 | n = 0.8523 | ||||
R2 | 0.5824 | 0.9905 | 0.9918 | 0.992 | 0.9417 |
The results indicated that the heterogeneous surface adsorption of lead ions onto CompostA600 was more obvious than that of CompostA800. According to organic elements analysis, the oxygen content of the CompostA600 is much higher than that of the CompostA800 (Table 1). The reason for the change of non-uniform adsorption on the surface may be that with the increase of carbonization temperature, the oxygen content of carbon materials decreased, which led to the continuous reduction of oxygen-containing functional groups, further causing the diminution of active adsorption sites, and finally gave rise to the change of surface heterogeneity. What is more, complexes of oxygen-containing functional groups and lead ions could be formed due to the high oxygen element content. These results are consistent with SEM results. Compared with the CompostA800, the CompostA600 adsorbed crystals almost cover the surface of the material. In addition, BET results show that the CompostA600 has a specific surface area of 80 m2/g.
After fitting, CompostB600 conforms to the Langmuir–Freundlich model and the Temkin model better than other models, and CompostB800 conforms to the Redlich–Peterson model and the Temkin model better (Table 4). These results implied that the two materials both have a non-uniform adsorption layer and distribution of the adsorption layer was uneven. It can also be seen from the XRD results that lead ions and sulfate radical on the materials react to form insoluble lead sulfate crystals, which enhanced the adsorption capacity. For Sludge800 and Sludge900, difference in adsorption quantity for the two materials was not obvious initially, but when the initial concentration exceeded 250 mg/g, adsorption capacity became different. After fitting with isotherm models, Sludge800 conformed to the Langmuir, the Langmuir–Freundlich and the Redlich–Peterson model better than other models, while Sludge900 was close to the Freundlich, the Langmuir–Freundlich and the Redlich–Peterson model (Table 4), indicating that there is a strong van der Waals force between the materials and lead ions, which is conducive to the adsorption.
CONCLUSIONS
It was found that biochars prepared from different raw materials at a series of carbonation temperatures have various pore structures and surface element distributions, which leads to different adsorption capacities of Pb(II) in aqueous solution. Adsorption experiments showed that CompostA600 had the highest adsorption capacity among all biochar samples and carbonization temperature is lower than that of sludge biochars. The results of isotherm and kinetic fitting indicated that the adsorption of Pb(II) onto CompostA600 is not only based on physical adsorption, but also depends on chemical adsorption. Physical adsorption is mainly due to rich pore structures, and chemical adsorption is due to reaction between lead ions and the sulfate radical as well as complexation of lead ions and oxygen-containing groups. Therefore, we prepared a biochar with excellent properties and low cost for heavy metal adsorption in aqueous solution. At the same time, we studied the influence of temperature and precursors on the preparation of biochars, providing a basis for relevant research. As expected, the Pb(II) adsorbed on biochars could be extracted to prepare Pb(NO3)2, and the waste biochars have potential applications in fuel and energy recovery technologies.
ACKNOWLEDGEMENTS
This research was supported by the National Key R&D Program of China through Grant 2018YFC1900102 and the National Key Program for Basic Research of China through Grant 2015CB251100.
SUPPLEMENTARY DATA
The Supplementary Data for this paper are available online at http://dx.doi.org/10.2166/wst.2019.353.