Journal of Environmental Management 133 (2014) 309e314
Contents lists available at ScienceDirect
Journal of Environmental Management journal homepage: www.elsevier.com/locate/jenvman
Arsenic and chromium removal from water using biochars derived from rice husk, organic solid wastes and sewage sludge Evita Agrafioti a, Dimitrios Kalderis b, Evan Diamadopoulos a, * a b
Department of Environmental Engineering, Technical University of Crete, 73100 Chania, Greece Department of Natural Resources and the Environment, Technological Educational Institute of Crete, 73100 Chania, Greece
a r t i c l e i n f o
a b s t r a c t
Article history: Received 25 June 2013 Received in revised form 3 December 2013 Accepted 9 December 2013 Available online 8 January 2014
Biochars derived from rice husk, the organic fraction of municipal solid wastes and sewage sludge, as well as a sandy loam soil, were used as adsorbents for As(V), Cr(III) and Cr(VI) removal from aqueous solutions. The kinetic study showed that sorption can be well described by the pseudo-second order kinetic model, while simulation of sorption isotherms gave better fit for the Freundlich model. The materials examined removed more than 95% of the initial Cr(III). However, removal rates for As(V) and Cr(VI) anions were significantly lower. Biochar derived from sewage sludge was efficient in removing 89% of Cr(VI) and 53% of As(V). Its ash high Fe2O3 content may have enhanced metal adsorption via precipitation. Soil was the most effective material for the removal of As(V), yet it could not strongly retain metal anions compared to biochars, as a significant amount of the adsorbed metal was released during desorption experiments. Ó 2013 Elsevier Ltd. All rights reserved.
Keywords: Biochar Chromium Arsenic Adsorption Desorption
1. Introduction The presence of heavy metals in the environment is of global concern due to possible adverse effects on human health, as well as on aquatic flora and fauna. Heavy metals direct or secondary disposal to soils and waters poses significant environmental risks, as they are non-degraded and, in high concentrations, are also toxic. Among the heavy metals, arsenic (As) is a rather important environmental pollutant with severe carcinogenic impacts on human beings (Choong et al., 2007). The Environmental Protection Agency (EPA), as well as the European Commission guidelines set up a limit of 10 mg/L for As concentration in drinking water (Council Directive 98/83/EC, 1998; US EPA, 2009). Inorganic As exists predominately in the þ3 and þ5 oxidation states. Its presence in the environment is related not only to volcanic deposits, geothermal sources and sedimentary rocks, but also to several anthropogenic activities including pesticide manufacturing, wood preservatives production, glass industry, semiconductor production and pigmentation (Alvarado et al., 2008). Apart from As, chromium (Cr) is also widely used in various industries and is released to the environment through wastewater of industrial leather tanning, electroplating, photography, pigmentation and metal cleaning
* Corresponding author. Tel.: þ30 28210 37795. E-mail address: [email protected] (E. Diamadopoulos). 0301-4797/$ e see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.jenvman.2013.12.007
(Ucun et al., 2002). The most important Cr oxidation states in the environment are þ3 and þ6. Cr(III) is an essential trace element for human health that plays an important role in metabolic disorders (Staniek et al., 2010), reducing blood glucose and cholesterol levels while controlling diabetes. On the other hand, Cr(VI) is hazardous and toxic, having adverse effects on humans (Costa, 2003). Based on drinking water guidelines recommended by the EPA, total chromium concentration should not exceed 100 mg/l (US EPA, 2009). The corresponding limit set by the European Commission is 50 mg/l (Council Directive 98/83/EC, 1998). The conventional methods used for heavy metal removal from water include adsorption on activated carbons, precipitation, use of ion exchange resins and membrane filtration (Srivastava and Thakur, 2006). In the case of a heavy metal contaminated soil, soil removal and landfilling, physicochemical extraction, stabilization/solidification, soil washing, phytoremediation and bioremediation are usually applied (Jeyansingh and Phillip, 2005; Polti et al., 2011). However, lately there is an intense interest regarding heavy metal immobilization using biochars in waters and soils. Biochar is a carbon rich, solid by-product resulting from the pyrolysis of biomass under oxygen-free and low temperature conditions. Biochar’s proven ability to remain stable against chemical and biological degradation, when applied to soils, makes it a pioneer means of mitigating climate change (Lehmann, 2007). In addition, biochar can improve soil productivity, not only because it may be a valuable nitrogen and phosphorous source, but also it
310
E. Agrafioti et al. / Journal of Environmental Management 133 (2014) 309e314
affects soil cation exchange capacity, pH and retention of water and nutrients (Steiner et al., 2008; Glaser et al., 2002). Finally, biochar has the potential to restore and remediate contaminated soils as it can adsorb both organic and inorganic pollutants (Beesley and Marmiroli, 2011). Biochar’s ability to adsorb heavy metals is possibly attributed to electrostatic interactions between carbon negative surface charge and metal cations, as well as to exchange of ions between biochar surface protons and metallic cations. In addition, the presence of mineral impurities (e.g. ash and metal oxides), acidic oxygen groups (e.g. carboxylic and lactonic groups) and basic nitrogen groups could further enhance the adsorption capacity of carbonaceous materials (Machida et al., 2006). The use of biochar, as a cost effective sorbent for heavy metal removal from contaminated water and soils, has already been reported by many researchers. The majority of studies are focused on the immobilization of metal cations, such as Pb, Cu, Ni and Cd (Inyang et al., 2012; Jiang et al., 2012; Liu and Zhang, 2009; Park et al., 2011; Regmi et al., 2012; Uchimiya et al., 2010), while limited research has been conducted on As(V) and Cr(VI) removal by biochar. For instance, Mohan et al. (2007) studied As(III) removal from water by woody biomass-derived biochars, showing that oak bark char has a significant potential for As(III) adsorption, whereas Beesley et al. (2011) and Gomez-Eyles et al. (2011) dealt with As immobilization in multi-element contaminated soils. In addition, Shen et al. (2012), Dong et al. (2011) and Mohan et al. (2011) studied the use of biochars for Cr(VI) removal from water and found that biochar can efficiently adsorb chromate, while its maximum sorption capacity was, in some cases, 123 mg/g. The purpose of the present study is to investigate the feasibility of using biochars for the removal of both anionic and cationic metals from water. For this reason, batch kinetic, as well as equilibrium sorption and desorption experiments were conducted using As(V), Cr(III) and Cr(VI) as adsorbates. The biochars used were derived from rice husk, the organic fraction of municipal solid wastes and sewage sludge. The first was chosen as one of the most abundant types of biomass worldwide, while the other two in order to find alternative innovative uses of these wastes. In addition, soil was also used as an adsorbent for As(V), Cr(III) and Cr(VI) removal, in order to compare its adsorption efficiency with those of biochars. Knowing the separate behavior of biochars and soil towards metal sorption, it could be the first step in explaining the fate of heavy metals in biochar-amended soil. The concentrations of heavy metals examined were rather low, in the range of mg/L, in order to simulate a moderate water contamination (based on ground/ drinking water standards). 2. Materials and methods 2.1. Feedstocks Rice husk, the organic fraction of municipal solid wastes and sewage sludge were used for biochar production. Rice husk was collected from a rice mill located in northern Greece. The organic fraction of solid wastes was collected from the Chania municipal solid waste materials recovery facility. Finally, sewage sludge was obtained from the Chania municipal wastewater treatment plant. In this plant municipal wastewater receives secondary treatment by activated sludge system, whereas sludge treatment is practiced via anaerobic digestion and belt-filter-press dewatering. This sludge sample has already been used for biochar production in a previous study (Agrafioti et al., 2013). All feedstocks were dried at 103 C for 24 h, they were then milled to 99% for the case of BC-SW, whereas the corresponding percentage for As(V) was approximately 40% for the case of soil. This could be attributed to the higher Cr(III) concentration examined but is mostly related to interactions between biochar negative surface charge and Cr(III) cations. Kinetic equilibrium times determined contact times for the subsequent sorption isotherm studies. 3.2. Sorption isotherm studies The simulation of sorption isotherms of As(V), Cr(III) and Cr(VI) on the three biochars and soil was based on the Langmuir and Freundlich models. The Langmuir model reflects the standard equilibrium process behavior assuming that the adsorbent has a constant number of adsorption sites and sorption on adsorbent surface is monolayer. It also assumes that the adsorbent surface is homogeneous and there is no interaction between adsorbed molecules. The Langmuir model is described by Equation (3):
qe ¼
2.5. Surface area analysis
(1)
(2)
where qt and qe (mg/g) are the amount of metal adsorbed at time t and equilibrium time, respectively, and k1 (1/h) and k2 (1/h) are the rate constants for the pseudo-first and pseudo-second order kinetics, respectively. Linear plot of log (qe qt) against t gives k1 and qe values for the pseudo-first order model, whereas the plot of t/qt against t gives k2 and qe for the pseudo-second order model. For all metal and adsorbents examined the pseudo-second order kinetic model gave a better fit and provided the best correlation to the data (R2 > 0.999) compared to pseudo-first order. For the latter, correlation coefficients were rather low reaching even 0.518 (data not
311
QbCe 1 þ bCe
(3)
where qe (mg/g) is the amount of metal adsorbed per unit weight of adsorbent, Ce (mg/L) is the equilibrium solution concentration of the adsorbate, Q (mg/g) is the maximum amount of adsorbed metal ions needed to form a monolayer on adsorbent surface and b (L/mg) is the Langmuir adsorption constant related to binding energies. On the other hand, the Freundlich model assumes that the adsorbent surface is heterogeneous and sorption on its surface is multilayer (Eq. (4)). 1=n
qe ¼ KCe
(4)
where qe (mg/g) is the amount of metal adsorbed per unit weight of adsorbent, Ce (mg/L) is the equilibrium solution concentration of the adsorbate, K ((mg/g)(L/mg)1/n) is a constant related to adsorbent maximum adsorption capacity and 1/n is a constant measuring the strength of adsorption. Plotting the linear forms of Langmuir and Freundlich equations, the corresponding constants and correlation coefficients were obtained (Table 3). The adsorption isotherms of As(V), Cr(III) and Table 2 Parameters of pseudo-second order kinetic model for As(V) and Cr(III) adsorption onto BC-RH, BC-SS, BC-SW and soil. Sample
Heavy metal
qe (mg/g)
k2 (1/h)
R2
BC-RH
As(V) Cr(III) As(V) Cr(III) As(V) Cr(III) As(V) Cr(III)
2.59 15.13 4.25 30.12 3.54 42.37 10.46 39.53
0.17 1.56 0.22 1.00 0.09 1.11 0.02 0.71
0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99
BC-SS BC-SW Soil
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E. Agrafioti et al. / Journal of Environmental Management 133 (2014) 309e314
Fig. 1. Effect of adsorption time on (a) As(V) and (b) Cr(III) removal by biochars and soil (initial As(V) concentration: 90 mg/l, initial Cr(III) concentration: 170 mg/l, adsorbent dose: 4 g/l).
Cr(VI) are presented in Fig. 2. Based on the correlation coefficients, the best-fit model for all metals and adsorbents examined was the Freundlich model. The R2 values for the Langmuir model were significantly lower and in some cases (such as in the case of As(V) adsorption on BC-RH) did not fit to experimental data at all, even though the Langmuir isotherm plot (Fig. 2) was very close to Freundlich isotherm and experimental data. The R2 values for the Freundlich model varied from 0.655 to 0.983. The failure of the Langmuir model was possibly attributed to the heterogeneous surface of the initial feedstocks and thus of the produced biochars. Besides, soil was a mixture of fine aggregates and this further supports its heterogeneity. The analysis of experimental data for Cr(III) adsorption on BC-SS could not fit either the Freundlich or the Table 3 Langmuir and Freundlich constants and correlation coefficients. Sample
Heavy metal
Langmuir model Q
BC-RH
BC-SS
BC-SW
Soil
a
As(V) Cr(III) Cr(VI) As(V) Cr(III) Cr(VI) As(V) Cr(III) Cr(VI) As(V) Cr(III) Cr(VI)
a
NF NF NF 13.42 94.34 64.10 NF NF 44.05 NF NF 17.06
Freundlich model
b
R2
K
1/n
R2
NF NF NF 0.01 0.01 0.01 NF NF 0.001 NF NF 0.002
NF NF NF 0.318 0.202 0.873 NF NF 0.564 NF NF 0.543
0.01 6 1011 5 104 0.21 5.21 1.95 0.001 NF 0.14 9 104 0.01 0.26
1.3 6.57 1.56 0.71 0.50 0.61 2.07 NF 0.75 2.52 2.95 0.54
0.983 0.894 0.842 0.747 0.700 0.914 0.940 NF 0.937 0.783 0.665 0.655
NF: No satisfactory fit by any model.
Langmuir isotherm model as even low doses of BC-SS (1 g/L) were able to adsorb approximately all the amount of the adsorbate. Based on 1/n Freundlich constants values, the use of BC-RH for the adsorption of heavy metals resulted in unfavorable isotherms (1/n > 1), BC-SS in favorable isotherms (1/n < 1), BC-SW in unfavorable isotherms for the case of As(V) and in favorable for the case of Cr(VI), while isotherms of soil were unfavorable for As(V) and Cr(III) removal and favorable for Cr(VI) adsorption. Table 4 presents the maximum removal rates of the three heavy metals for each adsorbent examined. As far as As(V) is concerned the maximum removal observed was 65% in the case of soil, followed by 55% for BC-SW, 53% for BC-SS, and 25% for BC-RH. The high adsorption efficiency of BC-SW may be attributed to the interactions of arsenate anion with the oxides in biochar solid matrix. The XRF analysis of BC-SW ash showed that the CaO content was 49.8%, whereas the Fe2O3 and Al2O3 contents were negligible (
Contents lists available at ScienceDirect
Journal of Environmental Management journal homepage: www.elsevier.com/locate/jenvman
Arsenic and chromium removal from water using biochars derived from rice husk, organic solid wastes and sewage sludge Evita Agrafioti a, Dimitrios Kalderis b, Evan Diamadopoulos a, * a b
Department of Environmental Engineering, Technical University of Crete, 73100 Chania, Greece Department of Natural Resources and the Environment, Technological Educational Institute of Crete, 73100 Chania, Greece
a r t i c l e i n f o
a b s t r a c t
Article history: Received 25 June 2013 Received in revised form 3 December 2013 Accepted 9 December 2013 Available online 8 January 2014
Biochars derived from rice husk, the organic fraction of municipal solid wastes and sewage sludge, as well as a sandy loam soil, were used as adsorbents for As(V), Cr(III) and Cr(VI) removal from aqueous solutions. The kinetic study showed that sorption can be well described by the pseudo-second order kinetic model, while simulation of sorption isotherms gave better fit for the Freundlich model. The materials examined removed more than 95% of the initial Cr(III). However, removal rates for As(V) and Cr(VI) anions were significantly lower. Biochar derived from sewage sludge was efficient in removing 89% of Cr(VI) and 53% of As(V). Its ash high Fe2O3 content may have enhanced metal adsorption via precipitation. Soil was the most effective material for the removal of As(V), yet it could not strongly retain metal anions compared to biochars, as a significant amount of the adsorbed metal was released during desorption experiments. Ó 2013 Elsevier Ltd. All rights reserved.
Keywords: Biochar Chromium Arsenic Adsorption Desorption
1. Introduction The presence of heavy metals in the environment is of global concern due to possible adverse effects on human health, as well as on aquatic flora and fauna. Heavy metals direct or secondary disposal to soils and waters poses significant environmental risks, as they are non-degraded and, in high concentrations, are also toxic. Among the heavy metals, arsenic (As) is a rather important environmental pollutant with severe carcinogenic impacts on human beings (Choong et al., 2007). The Environmental Protection Agency (EPA), as well as the European Commission guidelines set up a limit of 10 mg/L for As concentration in drinking water (Council Directive 98/83/EC, 1998; US EPA, 2009). Inorganic As exists predominately in the þ3 and þ5 oxidation states. Its presence in the environment is related not only to volcanic deposits, geothermal sources and sedimentary rocks, but also to several anthropogenic activities including pesticide manufacturing, wood preservatives production, glass industry, semiconductor production and pigmentation (Alvarado et al., 2008). Apart from As, chromium (Cr) is also widely used in various industries and is released to the environment through wastewater of industrial leather tanning, electroplating, photography, pigmentation and metal cleaning
* Corresponding author. Tel.: þ30 28210 37795. E-mail address: [email protected] (E. Diamadopoulos). 0301-4797/$ e see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.jenvman.2013.12.007
(Ucun et al., 2002). The most important Cr oxidation states in the environment are þ3 and þ6. Cr(III) is an essential trace element for human health that plays an important role in metabolic disorders (Staniek et al., 2010), reducing blood glucose and cholesterol levels while controlling diabetes. On the other hand, Cr(VI) is hazardous and toxic, having adverse effects on humans (Costa, 2003). Based on drinking water guidelines recommended by the EPA, total chromium concentration should not exceed 100 mg/l (US EPA, 2009). The corresponding limit set by the European Commission is 50 mg/l (Council Directive 98/83/EC, 1998). The conventional methods used for heavy metal removal from water include adsorption on activated carbons, precipitation, use of ion exchange resins and membrane filtration (Srivastava and Thakur, 2006). In the case of a heavy metal contaminated soil, soil removal and landfilling, physicochemical extraction, stabilization/solidification, soil washing, phytoremediation and bioremediation are usually applied (Jeyansingh and Phillip, 2005; Polti et al., 2011). However, lately there is an intense interest regarding heavy metal immobilization using biochars in waters and soils. Biochar is a carbon rich, solid by-product resulting from the pyrolysis of biomass under oxygen-free and low temperature conditions. Biochar’s proven ability to remain stable against chemical and biological degradation, when applied to soils, makes it a pioneer means of mitigating climate change (Lehmann, 2007). In addition, biochar can improve soil productivity, not only because it may be a valuable nitrogen and phosphorous source, but also it
310
E. Agrafioti et al. / Journal of Environmental Management 133 (2014) 309e314
affects soil cation exchange capacity, pH and retention of water and nutrients (Steiner et al., 2008; Glaser et al., 2002). Finally, biochar has the potential to restore and remediate contaminated soils as it can adsorb both organic and inorganic pollutants (Beesley and Marmiroli, 2011). Biochar’s ability to adsorb heavy metals is possibly attributed to electrostatic interactions between carbon negative surface charge and metal cations, as well as to exchange of ions between biochar surface protons and metallic cations. In addition, the presence of mineral impurities (e.g. ash and metal oxides), acidic oxygen groups (e.g. carboxylic and lactonic groups) and basic nitrogen groups could further enhance the adsorption capacity of carbonaceous materials (Machida et al., 2006). The use of biochar, as a cost effective sorbent for heavy metal removal from contaminated water and soils, has already been reported by many researchers. The majority of studies are focused on the immobilization of metal cations, such as Pb, Cu, Ni and Cd (Inyang et al., 2012; Jiang et al., 2012; Liu and Zhang, 2009; Park et al., 2011; Regmi et al., 2012; Uchimiya et al., 2010), while limited research has been conducted on As(V) and Cr(VI) removal by biochar. For instance, Mohan et al. (2007) studied As(III) removal from water by woody biomass-derived biochars, showing that oak bark char has a significant potential for As(III) adsorption, whereas Beesley et al. (2011) and Gomez-Eyles et al. (2011) dealt with As immobilization in multi-element contaminated soils. In addition, Shen et al. (2012), Dong et al. (2011) and Mohan et al. (2011) studied the use of biochars for Cr(VI) removal from water and found that biochar can efficiently adsorb chromate, while its maximum sorption capacity was, in some cases, 123 mg/g. The purpose of the present study is to investigate the feasibility of using biochars for the removal of both anionic and cationic metals from water. For this reason, batch kinetic, as well as equilibrium sorption and desorption experiments were conducted using As(V), Cr(III) and Cr(VI) as adsorbates. The biochars used were derived from rice husk, the organic fraction of municipal solid wastes and sewage sludge. The first was chosen as one of the most abundant types of biomass worldwide, while the other two in order to find alternative innovative uses of these wastes. In addition, soil was also used as an adsorbent for As(V), Cr(III) and Cr(VI) removal, in order to compare its adsorption efficiency with those of biochars. Knowing the separate behavior of biochars and soil towards metal sorption, it could be the first step in explaining the fate of heavy metals in biochar-amended soil. The concentrations of heavy metals examined were rather low, in the range of mg/L, in order to simulate a moderate water contamination (based on ground/ drinking water standards). 2. Materials and methods 2.1. Feedstocks Rice husk, the organic fraction of municipal solid wastes and sewage sludge were used for biochar production. Rice husk was collected from a rice mill located in northern Greece. The organic fraction of solid wastes was collected from the Chania municipal solid waste materials recovery facility. Finally, sewage sludge was obtained from the Chania municipal wastewater treatment plant. In this plant municipal wastewater receives secondary treatment by activated sludge system, whereas sludge treatment is practiced via anaerobic digestion and belt-filter-press dewatering. This sludge sample has already been used for biochar production in a previous study (Agrafioti et al., 2013). All feedstocks were dried at 103 C for 24 h, they were then milled to 99% for the case of BC-SW, whereas the corresponding percentage for As(V) was approximately 40% for the case of soil. This could be attributed to the higher Cr(III) concentration examined but is mostly related to interactions between biochar negative surface charge and Cr(III) cations. Kinetic equilibrium times determined contact times for the subsequent sorption isotherm studies. 3.2. Sorption isotherm studies The simulation of sorption isotherms of As(V), Cr(III) and Cr(VI) on the three biochars and soil was based on the Langmuir and Freundlich models. The Langmuir model reflects the standard equilibrium process behavior assuming that the adsorbent has a constant number of adsorption sites and sorption on adsorbent surface is monolayer. It also assumes that the adsorbent surface is homogeneous and there is no interaction between adsorbed molecules. The Langmuir model is described by Equation (3):
qe ¼
2.5. Surface area analysis
(1)
(2)
where qt and qe (mg/g) are the amount of metal adsorbed at time t and equilibrium time, respectively, and k1 (1/h) and k2 (1/h) are the rate constants for the pseudo-first and pseudo-second order kinetics, respectively. Linear plot of log (qe qt) against t gives k1 and qe values for the pseudo-first order model, whereas the plot of t/qt against t gives k2 and qe for the pseudo-second order model. For all metal and adsorbents examined the pseudo-second order kinetic model gave a better fit and provided the best correlation to the data (R2 > 0.999) compared to pseudo-first order. For the latter, correlation coefficients were rather low reaching even 0.518 (data not
311
QbCe 1 þ bCe
(3)
where qe (mg/g) is the amount of metal adsorbed per unit weight of adsorbent, Ce (mg/L) is the equilibrium solution concentration of the adsorbate, Q (mg/g) is the maximum amount of adsorbed metal ions needed to form a monolayer on adsorbent surface and b (L/mg) is the Langmuir adsorption constant related to binding energies. On the other hand, the Freundlich model assumes that the adsorbent surface is heterogeneous and sorption on its surface is multilayer (Eq. (4)). 1=n
qe ¼ KCe
(4)
where qe (mg/g) is the amount of metal adsorbed per unit weight of adsorbent, Ce (mg/L) is the equilibrium solution concentration of the adsorbate, K ((mg/g)(L/mg)1/n) is a constant related to adsorbent maximum adsorption capacity and 1/n is a constant measuring the strength of adsorption. Plotting the linear forms of Langmuir and Freundlich equations, the corresponding constants and correlation coefficients were obtained (Table 3). The adsorption isotherms of As(V), Cr(III) and Table 2 Parameters of pseudo-second order kinetic model for As(V) and Cr(III) adsorption onto BC-RH, BC-SS, BC-SW and soil. Sample
Heavy metal
qe (mg/g)
k2 (1/h)
R2
BC-RH
As(V) Cr(III) As(V) Cr(III) As(V) Cr(III) As(V) Cr(III)
2.59 15.13 4.25 30.12 3.54 42.37 10.46 39.53
0.17 1.56 0.22 1.00 0.09 1.11 0.02 0.71
0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99
BC-SS BC-SW Soil
312
E. Agrafioti et al. / Journal of Environmental Management 133 (2014) 309e314
Fig. 1. Effect of adsorption time on (a) As(V) and (b) Cr(III) removal by biochars and soil (initial As(V) concentration: 90 mg/l, initial Cr(III) concentration: 170 mg/l, adsorbent dose: 4 g/l).
Cr(VI) are presented in Fig. 2. Based on the correlation coefficients, the best-fit model for all metals and adsorbents examined was the Freundlich model. The R2 values for the Langmuir model were significantly lower and in some cases (such as in the case of As(V) adsorption on BC-RH) did not fit to experimental data at all, even though the Langmuir isotherm plot (Fig. 2) was very close to Freundlich isotherm and experimental data. The R2 values for the Freundlich model varied from 0.655 to 0.983. The failure of the Langmuir model was possibly attributed to the heterogeneous surface of the initial feedstocks and thus of the produced biochars. Besides, soil was a mixture of fine aggregates and this further supports its heterogeneity. The analysis of experimental data for Cr(III) adsorption on BC-SS could not fit either the Freundlich or the Table 3 Langmuir and Freundlich constants and correlation coefficients. Sample
Heavy metal
Langmuir model Q
BC-RH
BC-SS
BC-SW
Soil
a
As(V) Cr(III) Cr(VI) As(V) Cr(III) Cr(VI) As(V) Cr(III) Cr(VI) As(V) Cr(III) Cr(VI)
a
NF NF NF 13.42 94.34 64.10 NF NF 44.05 NF NF 17.06
Freundlich model
b
R2
K
1/n
R2
NF NF NF 0.01 0.01 0.01 NF NF 0.001 NF NF 0.002
NF NF NF 0.318 0.202 0.873 NF NF 0.564 NF NF 0.543
0.01 6 1011 5 104 0.21 5.21 1.95 0.001 NF 0.14 9 104 0.01 0.26
1.3 6.57 1.56 0.71 0.50 0.61 2.07 NF 0.75 2.52 2.95 0.54
0.983 0.894 0.842 0.747 0.700 0.914 0.940 NF 0.937 0.783 0.665 0.655
NF: No satisfactory fit by any model.
Langmuir isotherm model as even low doses of BC-SS (1 g/L) were able to adsorb approximately all the amount of the adsorbate. Based on 1/n Freundlich constants values, the use of BC-RH for the adsorption of heavy metals resulted in unfavorable isotherms (1/n > 1), BC-SS in favorable isotherms (1/n < 1), BC-SW in unfavorable isotherms for the case of As(V) and in favorable for the case of Cr(VI), while isotherms of soil were unfavorable for As(V) and Cr(III) removal and favorable for Cr(VI) adsorption. Table 4 presents the maximum removal rates of the three heavy metals for each adsorbent examined. As far as As(V) is concerned the maximum removal observed was 65% in the case of soil, followed by 55% for BC-SW, 53% for BC-SS, and 25% for BC-RH. The high adsorption efficiency of BC-SW may be attributed to the interactions of arsenate anion with the oxides in biochar solid matrix. The XRF analysis of BC-SW ash showed that the CaO content was 49.8%, whereas the Fe2O3 and Al2O3 contents were negligible (
