Assessing the efficiency and mechanism of zinc adsorption onto biochars from poultry litter and softwood feedstocks

Abstract

The efficiency and adsorption mechanism of zinc removal was assessed in aqueous solution using four biochars from multiple biomass residues (poultry litter and three tree species). The effect of pH, kinetic effects, and isotherm fittings were investigated, as well as zinc-laden biochar using x-ray diffraction and absorption near edge structure. Sorbent load results showed softwood biochar exhibited the greatest zinc removal from both deionized (15 mg_Zn/L) and mining influenced river water (10 mg_Zn/L). The Langmuir isotherm was the best fit for the majority of the biochars. Exchangeable cations contributed most for the adsorption mechanism from the softwood biochars, while precipitation was greatest contribution for the poultry litter biochar. Overall, our results suggest that biochars from Douglas Fir trees are more efficient at removing zinc from aqueous solutions (up to 19.80 mg_Zn/g) compared to previously studied biochars (0.61 to 11.0 mg_Zn/g) and should be used for future remediation efforts.

1. Introduction

Biochar is a porous, high-carbon material obtained by heating biomass under low oxygen conditions like pyrolysis (Sizmur et al., 2017; Van Hien et al., 2020). During pyrolysis the biomass becomes dehydrated, volatile carbon is driven off as a gas, and aromatic structure and more stable bonds are formed (Jiang et al., 2019). Production of biochar is inexpensive, relatively energy efficient, and can provide an advantageous use of materials that would otherwise be discarded (Tan et al., 2015; Tomczyk et al., 2020). The structure of biochar produced after pyrolysis is highly heterogenous with a chemical composition that varies widely depending on the pyrolysis conditions and input biomass (Cantrell et al., 2012; Tomczyk et al., 2020). Pyrolysis conditions can vary by both changing the temperature (Al-Wabel et al., 2013; Cantrell et al., 2012) and by altering the thermochemical process (da Silva Veiga et al., 2021). Biomass materials used in the production of biochar are widely varying and include sewage (Goswami et al., 2019), manure (Jiang et al., 2018), tire (Shahrokhi-Shahraki et al., 2021), poultry litter (Acharya et al., 2014; Jiang et al., 2019), and plants (Bogusz et al., 2015; Poo et al., 2018). Further, biochars have been augmented through various laboratory methods (e.g. chemical treatments, impregnation, and steam activation) to improve desired properties compared to pristine biochars (Rajapaksha et al., 2016). The pyrolysis of biomass from animal litter has shown to produce a higher yield of biochar compared to plant-based materials, likely due to their increased inorganic component (Cantrell et al., 2012; Enders et al., 2012).

Interest in biochar materials has grown in recent years due to its effectiveness in a myriad of environmental applications, such as soil quality and fertility improvement, adsorption of pollutants from biowastes, and remediation of soils and water (Ahmad et al., 2014). Biochar has been found to be particularly useful in removing metals from aqueous solutions, largely due to its high cation exchange capacity, specific surface area (SSA), and pH values (Tan et al., 2015; Tomczyk et al., 2020). Various pyrolyzed biomass sources have been proven to successfully remove significant concentrations of metals from aqueous environments, but with differing efficiencies (Bogusz et al., 2015; Van Hien et al., 2020; Wang et al., 2019; Xu et al., 2013b, Xu et al., 2013a). The inconsistencies in these findings are likely due to differing mechanisms of metal adsorption onto the biochar.

Zinc has been indicated as one of the main pollutants of interest in water systems, depicted as a highly mobile metal, largely due to its high solubility (Dradrach et al., 2019; Tan et al., 2015) and toxicity when ingested (Zwain et al., 2014). Pollution caused by zinc is largely attributed to mining and fertilizer industries that are either directly or passively distributed through the environment (Bogusz et al., 2015; Juracek, 2013). Even though metal adsorption onto biochar has been studied by numerous researchers, the mechanisms of zinc adsorption are still of interest because several properties of biochar (e.g., source material, pyrolysis temperature, mineral content, particle size) affect its metal uptake capacity and the mechanisms involved in adsorption (Li et al., 2017; Zhao et al., 2020). Several authors have pointed out mechanisms like precipitation, cation exchange, complexation with oxygen-containing functional groups, coordination with π electrons, and cation exchange as the most important zinc adsorption mechanisms (Deng et al., 2020; Gao et al., 2019; Song et al., 2020; Zhao et al., 2018). Literature reports metal adsorption onto biochar having mixed results over different biochar types. Song et al. (2020) found that pyrolysis temperature is a major driver of zinc adsorption onto biochar coupled with montmorillonite by an adsorption experimental model. A study published by Van Hien et al. (2020) found that functional groups were the governing adsorption mechanism for adsorbing zinc from water for their biochar sourced from multiple biomasses. A follow up study found that aging increased the sorption of zinc from soil to biochar, and was also theorized based on previous studies that this result was due to an increased number of functional groups (Nie et al., 2021). Mineralogy of the biochar was found to be a main driver in other biochars, which caused metal precipitation as hydroxides and carbonates (Xu et al., 2013a, 2013b). Therefore, while all of these have found to be primary mechanisms behind zinc adsorption onto biochar, there is still a question regarding how large of a role they play for individual biochars before their application in field-scale (Gurwick et al., 2013; Wang et al., 2020a; Wang et al., 2020b) and in industrial (Gayathri et al., 2021) remediation efforts.

In this study, we aimed to better understand the mechanism of zinc removal by biochar in aqueous solutions using different biochars, three of which are commercially available. We performed batch experiments using four different types of biochar derived from poultry litter (PL) and softwood materials from pine (PW), mixed fir (MF), and douglas fir trees (DF; Table 1). We altered operational factors such as pH, concentration of zinc, and biochar solution contact time. From these experiments, we were able to look at the kinetics, isotherm models, and influence of pH to better understand the zinc removal mechanism from aqueous solution by biochars from varying biomass sources using both laboratory prepared and mining influenced water solutions. Additionally, we used XRD and XANES analyses to further elucidate the mechanism responsible for zinc removal.

Table 1.

Characteristics of each biochar.

Product	Biochar PL	Biochar PW	Biochar MF	Biochar DF
Matrix type	Poultry litter	Beetle kill pine wood	Coniferous tree wood	Douglas fir wood
Pyrolysis temperature (°C)	Proprietary	550–600	1400	900–1000
Bulk density (kg/L)	0.584	0.166	0.134	0.077
Natural pH	9.61	9.49	9.30	10.09
Particle size (mm)	0.7–3	0.7–3	Not available	Wide range
Specific surface area, m2/g	4.2	250	722	475
Mineral content, %	54	3	26	7

2. Materials and methods

2.1. Biochar materials

PL, PW, and DF are all commercially available biochars. The PL biochar was produced using a gasification process at temperatures and hold times that are proprietary by Josh Frye at his poultry farm in West Virginia. Biochar PW was acquired from Biochar Now and was produced by pyrolyzing Beetle Kill pine wood at a temperature range of 550 to 600 °C. The Black Owl Environmental Ultra biochar (DF) was purchased from Biochar Supreme. Biochar DF was pyrolyzed at 900 to 1000 °C from wood wastes, primarily composed of douglas fir trees that had organic carbon content greater than 80%. Biochar MF was produced using a gasification process and pyrolyzed at approximately 1400 °C on coniferous wood from mixed fir tree species.

2.2. Characterization of biochar

Matrix type, particle size, and pyrolysis temperature information was obtained from each material manufacturer. The bulk densities were measured by weighing the mass of biochar in a graduated cylinder compacted, as described in Bashour and Sayegh (2007). Natural pH was obtained following EPA Method 9045D using an Orion 370 pH meter, calibrated using three pH buffers (4, 7, and 10). Surface area was measured using liquid nitrogen with the Brunauer-Emmett-Teller (BET) equation using a Micromeritics accelerated surface area and porosimetry (ASAP) model 2020 (Norcross, GA, USA), after degassing for 16 h at 105 °C. The mineral content was measured by washing the biochar with 1 M HCl and tumbling it for 4 h, then rinsing it until the deionized water pH became constant (Gao et al., 2019). The mineral percentages were calculated by the mass of biochar after demineralization*100/original mass of biochar.

2.3. Batch experiments

Batch sorption experiments were run in triplicate and performed using 50 mL of solution in 50 mL vials tumbled at room temperature and 30 rpm for 24 h, except for the kinetic test. For the sorbent load test, increasing amounts of biochar (0, 0.1, 0.2, 0.5, 1, 2, 5, and 10 g/L) were used in two sets, 15 mg/L zinc solution prepared in deionized water (DI) and mining influence surface water from Short Creek (SCW) spiked to 10 mg/L of zinc. The SCW was collected at Short Creek (37.0542°N, −94.4003°W), a tributary to Spring River, in southeast Kansas allowed for a greater proxy for biochar application in the field. The Spring River watershed is part of the Tri-State Mining District Superfund site, a former mining area for zinc with persistent concentrations that are above human and ecological health limits (Juracek, 2013). A table in the supplementary material provides a summary of the chemical composition of the SCW water before being spiked. The 15 mg/L zinc solution was prepared from a 1000 mg/L zinc standard and having the pH adjusted to 7.14 with NaOH. The kinetic test used 0.1 g of biochar in 50 mL vials tumbled for 0.083, 0.25, 0.5, 1, 2, 4, 8, 24, 48, 72, and 168 h with 50 mL of SCW spiked with zinc to 15 mg/L. The zinc isotherms were performed by tumbling 50 mL vials with 0.1 g of biochar with zinc solutions at increasing concentrations (1, 5, 25, 50, 100, 150, 200 mg/L with initial pH adjusted to 7.0) for 24 h. The following isotherm fittings were then used from experimental data: Henry’s, Langmuir, Freundlich, Temkin, Jovanovic, Halsey, Harkings-Jura, and Elovich. The variable pH experiments were carried out using a 15 mg/L zinc solution in 50 mL vials with 0.1 g of each biochar at initial pH of 2, 4, 6, 8, 10, and 12 tumbled at 30 rpm for 24 h.

2.4. Aqueous phase analyses

The aqueous phase from all these experiments were filtered using 0.45 μm polyvinylidene fluoride (PVDF) membrane filter media and then acidified with 0.5 mL of nitric acid to obtain the dissolved elements composition with an ICP-AES following EPA Method 6010B. The detection limits of the ICP-AES for zinc was 0.024 mg/L. SPEX 30 preparatory assurance grade single-element standards were used as primary quality controls with recovery percentages ranging from 90 to 110%. The final pH was measured in the batch experiments using an Orion 370 pH meter, calibrated using three pH buffers (4, 7, and 10).

2.5. Solid phase analyses

The solid residues of the batch experiments treated with a 500 mg/L zinc solution were dried at 105 °C for at least 10 h and then analyzed for crystalline minerals with a Panalytical Aeris X-ray Diffractometer (XRD) from 2-Theta angles of 2 to 90 degrees. The X-ray Absorption Near Edge Structure (XANES) zinc speciation analysis were conducted on the solid residues of the 200 mg/L adsorption experiment using synchrotronbased x-ray absorption spectroscopy (XAS) at the Materials Research Collaborative Access Team (MRCAT) Sector 10-ID and 10-BM beamlines of the Advanced Photon Source at Argonne National Laboratory (Kropf et al., 2010; Segre et al., 2000). The sample spectra collected were simultaneously calibrated to a zinc foil in transmission. The XANES spectra were normalized using the zinc foil and replicate scans were averaged and fitted using linear combination fitting (LCF) procedures in the Athena software package IFEFFIT version 1.2.12 (Ravel and Newville, 2005). Visual Minteq 3.1 (Gustafson, 2013) was used for to obtain the chemical equilibrium data of simulations of the batch tests.

2.6. Partitioning of zinc sorption mechanisms

The mechanisms considered for zinc adsorption onto biochar were precipitation, cation exchange, complexation with oxygen-containing function groups, and coordination of Zn²⁺ with π electrons. The calculation for contribution of each mechanisms was performed based on the descriptions of Wang et al. (2019) and Gao et al. (2019) with the exception of adsorption capacity, which was instead calculated using the following equation:

$$q_e=(C_o-C_e)\times \frac{V}{m}$$ (1)

The sorption capacity by precipitation and cation exchange with minerals was determined using the following equation:

$$q_{pc}=q_e-q_{Ca}\times Y$$ (2)

where q_pc is the adsorption capacity generated by precipitation and cationic exchange; q_ca is the adsorption capacity of the demineralized biochar and Y is the yield of biochar demineralization. The biochar was demineralized by acid washing with 1 M HCl and rinsing until pH of two consecutive rinses was constant. The adsorption capacity by exchange with cations (q_exc) was calculated by the released of K⁺, Ca²⁺, Na⁺ and Mg²⁺ into the solution by the biochar in the zinc solution compared to DI water (with no zinc added) tumbled with biochar under the same conditions, while Na⁺ was compared to the concentration present in the zinc solution before being in contact with the biochar, and was calculated using the following equation:

$$q_{exc}=q_K+q_{Ca}+q_{Na}+q_{Mg}$$ (3)

where q_K, q_ca, q_Na, and q_Mg are the amounts of the respective cations released by the sorption of zinc (mg/g). The adsorption capacity from precipitation (q_pre) was calculated using the following equation:

$$q_{pre}=q_{pc}-q_{exc}$$ (4)

The adsorption capacity by complexation with oxygen-containing functional groups was calculated using the following equation:

$$q_{com}=q_{pH}\times Y$$ (5)

where q_com is the adsorption capacity of zinc by complexation with oxygen-containing functional groups and q_pH is the adsorption capacity by pH change during adsorption (pH was measured at time zero and at the end of each experiment).

The adsorption capacity by coordination of zinc with π electrons was calculated using the following equation:

$$q_{c\pi }=q_{ct}-q_{pre}-q_{exc}-q_{com}$$ (6)

where q_ct is the total adsorption capacity, q_cπ is the adsorption capacity due to coordination of zinc with π electrons.

3. Results and discussion

3.1. Biochar characterization

Biochar properties vary widely depending on the type of biomass that undergoes thermal decomposition. Metal adsorption on different biochars have been shown to be affected by material source and by pyrolysis temperature, which directly affect specific surface area, pH, pore volume, cation exchange capacity, volatile matter, mineral and carbon content (Li et al., 2017; Tomczyk et al., 2020; Wang et al., 2020a; Zama et al., 2017). However, mixed results have been reported as to what the main driver is for the metal adsorption. To better understand the mechanisms behind zinc adsorption onto different biochars, we looked at commercial and laboratory-produced biochars from multiple biomass sources. The bulk density from these biochars ranged from 0.077 (DF) to 0.584 kg/L (PL) (Table 1). Natural pH was found to be lowest from MF (9.30) and was highest for DF (10.09). Surface area ranged from 4.2 m²/g (PL) to 722 m²/g (MF). Lastly, the mineral content was found to be greatest in PL (54%) and smallest in Biochar PW (3%). Because the characteristics of these biochars vary widely, we were able to obtain a greater understanding of the adsorption mechanisms of zinc onto each type of biochar.

3.2. Effect of pH on zinc adsorption

MF and DF biochars presented a similar behavior at different pH values and had the greatest adsorption from the pH values of 2 to 4, with continued removal until the pH value 10 (Fig. 1). Increased zinc removal until the pH value 10 is likely due to the changing surface chemistry of the biochar. In low pH conditions, the surface of the biochar tends to be protonated and as the pH increases deprotonation occurs, opening sites for free zinc ions to bind onto the surface. At the pH value 10, we observed the minimum Zn concentration in solution, mostly due to zinc precipitation based on Visual Minteq simulations. At the pH value 12 there was an increase in zinc concentration in solution, suggesting that binding sites on the surface of the biochar surface are likely saturated.

Fig. 1.

Plots of initial pH and zinc concentration after sorption onto the poultry litter (PL), pine wood (PW), mixed firs (MF), and douglas fir (DF) biochars.

Biochar PW showed little change in zinc adsorption between the the pH values of 2 to 8 and then there was a drastic decrease in zinc concentration (Fig. 1). At a pH value of 10, Biochar PW had a similar zinc concentration in solution as the other two softwood biochars (MF and DF). The difference in adsorption may be due to carbonate in the water causing zinc to precipitate out into ZnCO₃. These findings suggest a more constrained aqueous pH is required from Biochar to significantly remove zinc from solution before precipitation becomes the dominant mechanism of removal.

The biochar derived from PL was the least effective in zinc removal overall compared to the softwood biochars. This difference could be due to the high mineral content found in PL, which has been seen in previous poultry litter biochar studies (Acharya et al., 2014; Enders et al., 2012). Visual Minteq simulations confirmed that zinc tends to precipitate as ZnCO₃ at pH value >7. In this case, some of the carbonates could come from the surrounding atmospheric carbon dioxide and some other portion from the calcium carbonate present in the biochar. Also, the presence of phosphates cause zinc precipitation possibly as Zn₃(PO₄)₂ at pH > 6.86, observed only in the Visual Minteq simulation of zinc sorption on Biochar A. Another possibility is that the zinc precipitated as ZnO, which could happen at pH value >7 and does not need minerals from the biochar to occur. Overall, the results from all four biochars showed the pH value for the optimal removal of zinc from solution is 10 and greater efficiencies occurred using the softwood biochar compared to the poultry litter biochar.

3.3. Sorbent load experiments

Sorbent load experiments using DI water found that MF and DF biochars had both the greatest and similar efficiencies at removing zinc, removing 99.6% and 98.7%, respectively (Fig. 2a). The PW biochar was not efficient as the other softwood biochars (MF and DF) but kept reducing the zinc concentration in the solution with a removal of 84.6%. The PL biochar reached a minimum at 0.5 g/L loading then plateaued at an overall efficiency of 48.4%. All four biochars had an initial decrease in pH with a load of 0.1 g/L and then had an increase of pH with increasing loads (Fig. 2b). This pH increase is crucial in determining which mechanisms are acting in zinc removal. At pH > 7.0 ZnCO₃ starts precipitating and at pH > 7.2 ZnO could precipitate according to Minteq simulations. Even though DI water was used for these tests, carbonates could dissolve from carbon dioxide present in air.

Fig. 2.

Plots of dissolved final zinc concentrations (mg/L) in the sorbent load experiments in DI a) and natural surface water c) and final pH in the sorbent load (g/L) experiments in DI b) and natural surface water d) on to the poultry litter (PL), pine wood (PW), mixed firs (MF), and douglas fir (DF) biochar.

We also tested the sorbent load with natural surface water spiked at 10 mg/L of zinc and pH adjusted to 7.14. This water was used specifically because it in the Tristate Mining District Superfund site and has been selected as the location where future remediation efforts will be focused due to its significantly high zinc concentration compared to the rest of the area (Juracek, 2013). Findings from this study should aid in future remediation efforts with these biochars, as it is a similar chemistry as when it is applied in the field (Table S1). Similar to the DI water findings, the MF and DF biochars were the most efficient in zinc removal (96.9% and 98.6%, respectively; Fig. 2b). Unlike the results from the DI water results, the PL biochar had a greater removal efficiency (71.6%) compared to the PW biochar (50.6%). Knowing that the PL biochar has the higher mineral content, it is possible that precipitation may be a main driver of its zinc removal capacity. The PL biochar reached the lowest zinc concentration at 1 g/L and then remained steady at increasing biochar amounts. On the other hand, all softwood biochars (PW, MF, and DF) showed that the curve keeps decreasing at increasing amounts of biochar, suggesting that adsorption onto the surface could have played an important role in zinc removal. Unlike the DI water experiments, we do not see that initial dip in pH at 0.1 g/L, instead most of the biochars increase immediately, which was likely due to buffering in the natural water (Fig. 2d). With the spiked natural surface water, we find the greatest initial increase in pH by the PL biochar. The MF and DF have similar curves, until 2 g/L, where the DF biochar started to have a faster increase in slope and had the highest end pH. The pH of the PW biochar was the lowest and had a similar trend to the DI water test.

3.4. Kinetic experiments

The rate at which metal adsorption occurs onto biochar is an important factor in designing and operating water treatment systems. Hence, it is crucial to determine the optimal biochar-solution contact time. We observed a massive decrease in zinc concentration in our kinetic experiment, which reached the maximum amount of removal by the first hour for all four biochars (Fig. S1). Similar results were seen in previous studies of zinc removal from aqueous solutions using commercially available and lab-prepared biochars (Bogusz et al., 2015). Biochar DF obtained the greatest decrease in zinc (96.3%), followed by the MF (86.4%), PL (83.2%), and PW (77.6%) biochar. Biochar DF also had the widest range of particle sizes and one of the highest SSA, suggesting physical surface conditions have a large influence on zinc adsorption. This is further supported by Biochar PW behavior, which was found to perform the worst at removing zinc and had a smaller particle size distribution and a smaller SSA. The mineral content between Biochar DF and Biochar PW were similar, augmenting the argument of a physical surface control rather than a chemical control on zinc adsorption. Biochars MF and DF tended to have similar zinc removal, which was likely due to the similarity in the biomass and pyrolysis temperatures. While Biochar PW also had a similar biomass to Biochars MF and DF, it was pyrolyzed at a much lower temperature, which could be also causing the difference in zinc removal. The fluctuation in zinc concentration after the first 4 h is likely due to saturation of the biochar surface and minor fluctuations in pH, as seen in a previous study of metal adsorption using sawdust (Yu et al., 2001). While rapid and efficient removal rates advocate for its use in the field, further research should be done to enhance the applicability of these biochars in the field. Numerous environmental factors such as temperature, precipitation, and microbial interactions can alter biochar remediation outcomes after the initial application (Wang et al., 2020b) and should be further explored for effective field-scale biochar applications. Additionally, regeneration should be investigated to look at how renewable these biochars could be for future field applications.

3.5. Isotherm fittings

Isotherm adsorption optimization is essential to assess how adsorbates relate and interact with sorbents. Isotherm fittings is a common and important technique used to elucidate the mechanism of sorption from an aqueous sample. High correlation values from isotherm models presumes that the assumptions of the model fit the mechanisms of adsorption. These fittings allow for better understanding, especially when looking at monolayer and multilayer adsorption. We focused on the most commonly used models to look at both mono (Henry’s, Langmuir, Temkin, and Jovanovic) and multi-layer (Freundlich, Halsey, Harkins-Jura, and Elovich) adsorption of zinc onto the four biochars (Al-Ghouti and Da’ana, 2020; Baltrėnaitė-Gedienė et al., 2020; Kaveeshwar et al., 2018). The simplest isotherm model is Henry’s isotherm, which assumes that the concentration of the absorbate is at a low enough concentration to where there are negligible interactions between molecules. The Langmuir isotherms apply to adsorption on homogeneous surfaces with negligible interaction between the adsorbed molecules. This equation assumes the adsorption is proportional to the available fraction of the surface while desorption is proportional to the occupied fraction of the surface. Expanded from the Langmuir assumptions, the Jovanovic isotherm assumes the possible mechanical contact between absorbant and adsorbate. The Temkin isotherm assumes that the heat of adsorption decreases linearly with increased surface cover on nonhomogeneous surfaces.

The Freundlich isotherm equation assumes that the surface of the adsorbate is heterogenous with different affinities to the adsorbent and that adsorption occurs in a multilayer pattern. The Halsey isotherm is a multilayer isotherm based on Dubinin’s potential theory that evaluates multilayer adsorption from a relatively large distance from the surface. The Harkins-Jura isotherm, another multilayer isotherm, assumes multilayer adsorption on a solid surface with heterogeneous pore distribution. Lastly, the Elovich isotherm, which is mainly used for chemisorption, is based on a kinetic principle which assumes adsorption sites increase exponentially with adsorption, implying multi-layer adsorption (Baltrėnaitė-Gedienė et al., 2020). The equation assumes the surface of the sorbent is heterogeneous and that activation energy increases with activation time. Originally used for modelling chemisorption of gas onto solid, during the past decade, the Elovich model has been used to represent liquid-solid systems.

Results from a wide range of isotherm fittings showed results were the best for biochars PL, PW, and MF with R² values of 0.97, 0.80, and 1.00, respectively using the Langmuir model (Table S2, Fig.4). Unlike the other three biochars (PL, PW, and MF), the DF biochar had the highest linear correlation using the Temkin isotherm (R² = 0.99; Fig. 3). The greatest adsorption capacity (q_max) was for biochar DF (19.80 mg/g), and then biochar MF (13.46 mg/g), PL (8.40 mg/g), and PW (1.20 mg/g; Table 2). The q_max from DF and are higher than recent biochar studies from plant materials (Chen et al., 2011; Poo et al., 2018; Van Hien et al., 2020; Zhao et al., 2020). These studies found q_max values of 0.61 mg/g (rice straw), 3.82 mg/g (rice husk), 4.02 mg/g (hardwood), 4.54 mg/g (hardwood), 5.20 mg/g (wood sawdust), 7.62 mg/g (bamboo), and 11.00 mg/g (corn straw) for zinc from their studied biochars (Chen et al., 2011; Poo et al., 2018; Van Hien et al., 2020; Zhao et al., 2020). These marked improvements support the proposed biochars use over the previously reported biochars. From the Langmuir model, a value of K_L > 0 represents favorable uptake of the sorbate in the material’s surface. Biochar PL had the most favorable zinc uptake K_L value of 8.395), followed by biochar PW (2.734), DF (2.147), and MF (0.693). The Langmuir model for zinc adsorption onto the surface of the biochar was found to be the best fit in previous studies of zinc adsorption on biochars from various biomass sources (Chen et al., 2011; Frišták et al., 2015; Kołodyńska et al., 2012). However, other studies of different biochars found the Freundlich isotherm (Doumer et al., 2016; Van Hien et al., 2020) and Harkins-Jura (Baltrėnaitė-Gedienė et al., 2020) were better fits for zinc adsorption. These contrasting results likely reflect the physicochemical differences inherent to biochar from various biomass sources and pyrolysis techniques. While there seems to be other factors from the various biochars affecting the adsorption of zinc onto different biochars, our findings suggest that the adsorption occurs as a monolayer on a homogeneous surface to the four studied biochars.

Fig. 4.

XRD diffractograms for a) poultry litter (PL), c) pine wood (PW), d) mixed firs (MF), and e) douglas fir (DF) for raw, demineralized, and b) zinc-laden and the relative abundances of different zinc speciation determined by XANES for all four biochars. The minerals that had patterns identified were K_2.8Na_1.2Cl_4.0 (A), struvite (B), quartz (C), coesite (D), and calcite (E).

Fig. 3.

Linear form of the Langmuir isotherm fitting for the poultry litter (PL), pine wood (PW), mixed firs (MF), and douglas fir (DF) biochars.

Table 2.

Results of the linear regression of the Langmuir isotherm, q_max and K_L values.

Biochar	a = 1/KL*q (mgZn/g)	b = 1/q	R2	qmax (mgZn/g)	KL
Biochar PL	0.9999	0.1191	0.9707	8.40	8.395
Biochar PW	2.2717	0.8309	0.8012	1.20	2.734
Biochar MF	0.0515	0.0743	0.9969	13.46	0.693
Biochar DF	0.1084	0.0505	0.6972	19.80	2.147

3.6. Contributions of adsorption mechanisms

The total relative contributions of the calculated mechanisms towards the total adsorption are summarized in Table 3. High values of coordination between zinc and cation exchange with minerals was seen in the softwood biochars, biochar PW (1.07 mg/g), biochar MF (6.60 mg/g), and biochar DF (11.08 mg/g). The relative contribution of q_cπ was highest for biochar DF (79.1%), followed by biochar MF (51.2%), and biochar PW (40.0%). These results are similar to previous findings from a study of biochar derived from rice straw that showed heavy metal adsorption was largely controlled by the exchangeable cation capacity (Park et al., 2017). Coordination of zinc with π electrons was the next largest contributor for biochars PW (0.97 mg/g), MF (3.45 mg/g), and DF (2.86 mg/g) with a relative contribution ranging from 20.4 to 36.5%. Adsorption caused by precipitation was almost negligible for biochar DF (0.4%) and minor contributions were found for the PW (23.6%) and MF (19.6%) biochar.

Table 3.

Distribution of different mechanisms in zinc adsorption with the tested biochars.

Material	qct	qcπ	qpre	qexc	qcom
	(mgZn/g)	(mgZn/g)	(mgZn/g)	(mgZn/g)	(mgZn/g)
Biochar PL	23.00	8.73	11.67	2.60	0.00
%	100%	37.9%	50.7%	11.3%	0.0%
Biochar PW	2.67	0.97	0.63	1.07	0.00
%	100%	36.5%	23.6%	40.0%	0.0%
Biochar MF	11.83	3.45	2.32	6.06	0.00
%	100%	29.2%	19.6%	51.2%	0.0%
Biochar DF	14.00	2.86	0.06	11.08	0.00
%	100%	20.4%	0.4%	79.1%	0.0%

Unlike the softwood biochars, precipitation was the dominant mechanism for the poultry litter biochar (PL) with a value of 11.67 mg/g contributing 50.7%, followed by coordination of zinc with π electrons at a value of 8.73 mg/g contributing 37.9%, and cation exchange with minerals yielding a value of 2.60 mg/g with a contribution proportion of 11.3%. The dominance of precipitation on the adsorption of metals and surface complexation and coordination with π electrons has also been seen in previous studies of biochars derived from animal litter (Cao et al., 2009; Gao et al., 2019; Xu et al., 2013a). This is likely due to the much higher mineralogic composition of biochar PL compared to the three softwood biochars. The adsorption capacity of zinc by complexation with oxygen-containing functional groups was negligible for all four biochars. This is likely due to biochar DF being produced at a high temperature (900–1000 °C), causing the removal of major oxygen-containing functional groups. The overall results from these adsorption mechanisms agree with the affinity of the zinc ions with the biochars’ surface suggested by the isotherms results.

3.7. Biochar residue analysis

The XRD patterns of the tested biochars were obtained for the raw biochar, for demineralized biochar, and for a zinc-laden biochar sample. The zinc-laden samples were the solid residues of a 500 mg/L zinc solution adsorption experiment to have a high zinc concentration detectable by XRD. Biochar PL, the biochar with the highest mineral content, showed most distinct patterns for several minerals including K_2.8Na_1.2Cl_4.0, struvite, quartz, and coesite (Fig. 4a). These identified minerals were among those previously reported in poultry litter (Acharya et al., 2014; Jiang et al., 2019). Biochar MF, which had the second highest mineral content, was determined to be mostly amorphous with calcite being the only identified minor contribution (Fig 4c). Biochars PW and DF both showed diffractograms of an amorphous substance with no identifiable peaks. XRD results from the three softwood materials are similar to those seen in previous literature (Fig. 4d and e; Acharya et al., 2014; Xu et al., 2013a). Conversely, Acharya et al. (2014) found a differing pattern for biochar produced from willow ash. These results further validate the need to characterize individual biochars even if they are from similar biomass sources before they are executed in the field.

Overall, diffractogram patterns were the most visible in those produced by the raw biochars. The count intensity and the number of peaks decreased in the demineralized biochar, and the zinc-laden biochar had the same basic profile as the demineralized with a few additional peaks. The patterns observed in the zinc-laden biochar were not enough to obtain a pattern that could be identified as a zinc mineral. XRD diffractograms are similar between the raw, demineralized, and zinc-laden biochars confirms that adsorption should have been prevalent over precipitation, substantiating the Langmuir isotherm results that showed monolayer adsorption was prevalent in the biochars. Additionally, the biochar samples taken from the isotherm batch tests were analyzed using XANES (Fig. 4b). Zinc from the biochar was found predominantly adsorbed to bentonite and alumina (62.7 to 100.0%) for all biochars, with minor speciation of zinc and carbonate in biochars PW (37.3%) and DF (36.9%). The XRD diffractograms being similar between zinc-laden and raw biochar samples coupled with the XANES results further suggests the dominance of adsorption removing zinc from aqueous solution rather than other reactions, such as precipitation.

4. Conclusions

Softwood biochars MF and DF had the most efficient zinc removal from solution in both DI and mining influenced river waters. We have proven its efficiency at zinc removal from natural and synthetic water, elucidated the main involved adsorption mechanisms, and used solid analyses to confirm zinc sorption onto the materials’ surfaces. We find the adsorption of zinc onto the biochar was mostly attributed to cation exchange with minerals, precipitation, and surface complexation and coordination with π electrons. These results encourage the implementation of biochar as a cost and time-efficient technique at field-scale remediation projects.