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Hubbe, M. A., Hasan, S. H., and Ducoste, J. J. (2011). "Cellulosic substrates for removal of pollutants from aqueous systems: A review. 1. Metals," BioRes. 6(2), 2161-2287.

Abstract

Recent years have seen explosive growth in research concerning the use of cellulosic materials, either in their as-recieved state or as modified products, for the removal of heavy metal ions from dilute aqueous solutions. Despite highly promising reports of progress in this area, important questions remain. For instance, it has not been clearly established whether knowledge about the composition and structure of the bioadsorbent raw material is equally important to its availability at its point of use. Various physical and chemical modifications of biomass have been shown to boost the ability of the cellulose-based material to bind various metal ions. Systems of data analysis and mechanistic models are described. There is a continuing need to explain the mechanisms of these approaches and to determine the most effective treatments. Finally, the article probes areas where more research is urgently needed. For example, life cycle analysis studies are needed, comparing the use of renewable biosorbents vs. conventional means of removing toxic metal ions from water.


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CELLULOSIC SUBSTRATES FOR REMOVAL OF POLLUTANTS FROM AQUEOUS SYSTEMS: A REVIEW. 1. METALS

Martin A. Hubbe,* Syed Hadi Hasan, b and Joel J. Ducoste c

Recent years have seen explosive growth in research concerning the use of cellulosic materials, either in their as-recieved state or as modified products, for the removal of heavy metal ions from dilute aqueous solutions. Despite highly promising reports of progress in this area, important questions remain. For instance, it has not been clearly established whether knowledge about the composition and structure of the bioadsorbent raw material is equally important to its availability at its point of use. Various physical and chemical modifications of biomass have been shown to boost the ability of the cellulose-based material to bind various metal ions. Systems of data analysis and mechanistic models are described. There is a continuing need to explain the mechanisms of these approaches and to determine the most effective treatments. Finally, the article probes areas where more research is urgently needed. For example, life cycle analysis studies are needed, comparing the use of renewable biosorbents vs. conventional means of removing toxic metal ions from water.

Keywords: Cellulose; Remediation; Pollutants; Heavy metals; Adsorption; Biosorbents

Contact information: a: Department of Forest Biomaterials, North Carolina State University, Campus Box 8005, Raleigh, NC 27695-8005; b: Department of Applied Chemistry, Institute of Technology, Banaras Hindu University, Varanasi – 221005, U. P., India; c: Department of Civil, Construction and Environmental Engineering, Campus Box 7908, Raleigh, North Carolina 27695-7908;

* Corresponding author: hubbe@ncsu.edu

INTRODUCTION

This article reviews publications in which lignocellulosic materials have been used, either “as-received” or in modified form, to remove various heavy metals from dilute aqueous solution. There have been an impressive number of relevant publications in this field. The preparation of the present article was made easier by the existence of earlier reviews, some of which are listed in Table 1. As shown, certain reviews have dealt with the biosorption of metal ions in general, while others have focused on specific ionic species or classes of biomass. Some of the articles have reviewed chemical or thermochemical modifications of cellulosic raw materials to render them more effective for the collection and binding of various metal ions. Readers interested in certain metals, certain types of sorbents, or certain aspects of metal bioadsorption are encouraged to scan the columns of Table A (see Appendix), as well as chapters in Wase and Forster (1998). In addition, a book by Cooney (1998) describes engineering principles and strategies for implementation of absorbent-based water treatment systems. Kurniawann et al. (2006a) and Owlad et al. 2009) reviewed systems other than biosorption for removal of metals.

Table 1. Selective List of Relevant Review Articles and Chapters

Table 2 displays the main organization of the present article. An attempt was made to gather metal sorption data from many individual studies, bearing in mind that conditions of sample preparation, treatment, and testing verried greatly among the published studies. A second main goal of this review article is to provide a fairly complete overview of several mathematical formulas that have been employed to fit metal adsorption data. By using Table 2, readers can select topics of highest interest within the article.

Table 2. Organization of the Present Article

Metals in soluble form have raised increasing concerns in recent years. Toxic effects of various metals have been described in detail by Chang (1996), and more recently by Babula et al. (2008) for less common metals. Metal-induced neurological disorders in particular are covered in a book edited by Zatta (2003). Progress has been achieved recently in understanding the attributes of metal ions that contribute to their toxicity (Yoon et al. 2008). Most metal ions become harmful when their concentration exceeds a certain threshold, which depends on the sensitivity of the consuming organism. At the same time, a majority of the same metal species can be considered as essential nutrients, and serious adverse health effects would result if they were completely eliminated from an environment or from a drinking water/food supply system. The most dangerous metals are those that tend to bioaccumulate, building up in the fatty tissues of animals in a food chain (Luoma 2008; Chojnacka 2009, 2010). Chromium(VI) is of particular concern in this regard, since the chromate ion (CrO42-) is easily transportable across cell membranes. The species is readily reduced to the Cr(III) form, which tends to form insoluble complexes that cannot easily be expelled by the affected organism (Cabtingan et al. 2001; Srinath et al. 2002; Aravindhan et al. 2004b; Deng et al. 2006). Metal speciation and the analysis of metal ion species in water have been reviewed by Ali and About-Enien (2006).

Many of the published studies considered in this review article may have been motivated by a desire to find profitable uses of specific waste streams or under-utilized materials produced during industrial operations. When considered separately, almost every such study can be considered successful. However, there has been a need to answer some practical questions, such as those that follow:

  • Are cellulosic materials universally effective at removing hazardous metal ions from aqueous solutions?
  • Are there rules of thumb that can lead to the selection of suitable biomass for use in sorption of metal ions from solution?
  • What are the most useful mathematical expressions that can be used to fit adsorption isotherm data?
  • What mechanisms governing metal uptake have been well established? Where are there opportunities for progress in useful theories?
  • Can the biosorption of metal ions be improved by mechanical treatments of the cellulosic material?
  • What kinds of chemical extractions, derivatizations, or grafting can greatly improve the efficiency of metal ion uptake?
  • Should one attempt to regenerate or incinerate cellulose-based biosorbent materials after they have been used to remove metals from water and thereby change the material’s life-cycle?

Guide to the Tabulation of Data

As a first step in attempting to answer questions such as those listed above, an extensive literature search was performed, and information reported in the various articles are collected in Table A, which due to its size is placed in the Appendix to this article. Because Table A will be mentioned frequently during subsequent discussions, a description of its organization is provided here. Columns in Table A indicate the type of biomass, the type of modification (if any), the studied metal species, the adsorption capacity (listed both on a mass basis and a molar basis per unit mass), an abbreviated summary of key findings, and the author-year information, which can be used to find the full citation in the “Literature Cited” section. Going down the table, the entries are organized according to biomass type (first column) and then alphabetically by author name within each category. An exception is made when considering studies in which the biomass was so profoundly modified that the nature of the original biomass was judged to be unimportant in comparison. Thus, the various kinds of chemical modifications, as well as production of activated carbon products from cellulose-derived resources, are given unique groups with no regard for the biomass type that was used as the starting material. Starting at the top of the table, the biomass types are organized as follows: Wood: (hardwood, softwood, unspecified), wood fibers, bark, foliage, cones, nut shells; Crop residuals: husk, stalks; Food residuals: sugar cane bagasse, sugar beet pulp, other, seeds, fruit stone, fruit peel, tea leaves; straw and grasses; weeds and plants; Aquatic plants: fresh water, seaweed, loofa; Microbiota, etc.: algae, bacterial biomass, yeast; Fungal biomassLignin-related: isolated lignin, lignite and humic matter, peat moss, sludge and biogas residuals; Chemically modified: alkali-treated, oxidized, with adsorbed materials, derivatized (succinylated, citric acid-treated, carboxymethylated, aminated, other), grafted; Activated carbons; and Ash.

Criteria for Success

A wide range of criteria have been considered by different authors when judging the relative success of methods to remove heavy metal ions from water. Most authors list adsorptive capacity of the biosorbent among their top concerns. It has been pointed out, however, that one of the most advantageous applications of cellulose-derived sorbents is in the treatment of very dilute solutions and in the reduction of aqueous metal concentrations to very low levels (Gaballah and Kilbertus 1998; Gupta et al. 2000; Amuda et al. 2007; Demirbas 2008). None of the reviewed works expressed the opinion that adsorbtion was not rapid enough for any envisioned usage, though the speed of uptake is mentioned by many authors. Rather, much attention has been paid to modeling the kinetics of metal uptake (see, for instance Table A), and the obtained rate expressions have been used in modeling water treatment systems based on both packed-bed operations and batch treatment (Ho and McKay 1999a; Ho et al. 2000b).

Far less attention has been paid to a number of other criteria that might be used to judge the success of a metal remediation strategy. One such criterion is the stability of partially or fully saturated biosorbent. A question remains as to whether the bioadsorbent will continue to hold onto adsorbed metal ions during long-term storage. Another issue that has received relatively little attention is the practical handling of the biomass, including its efficient collection from an aqueous mixture for proper disposal or regeneration without discharging into a surrounding waterbody (Kapoor and Viraraghavan 1998b). Some powdered biomass tends to become soft when placed into water. Its low density and fine particle size can make it difficult to separate from treated wastewater, and fixed bed reactors filled with biomass powders have a tendency to clog (Kapoor and Viraraghavan 1998b).

Using the cellulose-derived material as a support for a primary adsorbent

Another way to define successful use of cellulose-based matter in removal of heavy metals involves the concept of “support”. In other words, the biomass may serve as a backbone structure upon which the main adsorbent material is attached. Zhu et al. (2009b) demonstrated such a concept in their use of zero-valent-iron (ZVI) nanoparticles supported on activated carbon. The combination was found to be effective for the removal of arsenic from water. The ZVI nanoparticles act as a strong reducing agent, having the potential to change the valence state of such metals as arsenic and chromium to less toxic forms. The cited article is a prime example of how it is possible to address such problems without needing to release a strong reducing agent directly into aqueous streams or groundwater, which would create an additional contribution to the pollutant load.

Life-cycle issues

It is important, for both environmental and economic reasons, to consider in detail what happens to an absorbent material after it has been employed to remove heavy metals from water. As evidenced by numerous entries in Table A, most cellulose-based sorbents can be “regenerated” by treatment with acid solution (see, e.g., Chang et al. 1997), though some studies also evaluated the feasibility of using an alkaline solution or brine. In each case, the idea is to displace the metal ions back into a relatively concentrated solution, which either can be disposed of or further processed as a source of valuable metals or inorganic compounds (Cui and Zhang 2008). Another approach is to incinerate the metal-containing biomass, so that the metal content can be concentrated in the ash (Gaballah and Kilbertus 1998).

Relatively little attention has been paid by researchers to landfilling as an alternative fate for used sorbent material. Unlike the options considered in the previous paragraph, landfilling does not require the use of either chemical treatment or incinera-tion of the contaminated sorbent. Treatments with acid or brine can have environmental consequences, even if the pH is subsequently neutralized. Energy may be required to dry sorbent material before it can be incinerated. Thus, as a potential end-of-use strategy for metal-containing biosorbent material, landfilling should be an option in future life-cycle analyses. Issues that need to be considered include the degree to which typical biosorbents will hold onto their metal content during long-term storage and the likely concentrations of metal ions in leachate from such operations. Considering the case where material in a landfill is subjected to rainfall, research results suggest that typical biosorbents will release relatively low concentrations of metals (Gaballah and Kilbertus 1998; Gupta et al. 2000; Amuda et al. 2007; Demirbas 2008). None of the cited studies, however, addressed what might happen as the biomaterial breaks down in the soil.

BIOMASS TYPES AND KEY FACTORS

Based on the reviewed literature, it appears that almost every possible category of biomass material has been evaluated for the uptake of heavy metal ions. As indicated in Table A, multiple respresentatives from many different classes of cellulose-derived materials have been evaluated and judged to be successful as biosorbents. Individual studies have generally tended to be narrow in scope, considering relatively few sorbents, relatively few heavy metal ions, and a limited range of aqueous conditions. Taken together, however, a voluminous collection of scientific work has been published, most of it within the last 20 years. In addition, effects of a great many chemical and thermo-chemical modifications of cellulosic materials have been used in an attempt to achieve higher adsorption capacities. The take-away message is that there is a large selection of suitable sorbent materials with which one can remove heavy metals from water.

Evaluation of First Hypothesis: The Type of Biomass is Important

The first question to consider is whether there are clear differences in metal uptake, depending on the type of untreated biomass support. Figures 1A and 1B display the amounts of lead and chromate ions that were taken up by unit mass of different classes of cellose-based materials under the conditions specified in the cited works. Each plotted “X” symbol in the figure corresponds to the results of an individual study. In general, the data taken from studies considered in this review show that the adsorbed amounts varied over very wide ranges, even within each class of sorbent. For instance in the case of “Wood, sawdust” (as represented by the left-most column), the results for Cr(VI) sorption (Fig. 1B) spanned a factor of about 300). Subsequent sections of this article will describe a variety of reasons that each might account for part of these differences. Note that the rectangular “boxes” in these figures indicate the 25%, 50%, and 75% levels based on the relative frequency of articles reporting different values. The “stems” in the diagram extend upwards and downwards to the highest and lowest reported values of metal sorption in each case.

Figure 1A. Graphical summary of reported amounts of lead ion, Pb(II), adsorbed by diffent classes of cellulose-based matter according to the conditions specified in articles cited in Table A .

Figure 1B. Graphical summary of reported amounts of adsorbed Cr(VI) taken up by diffent classes of cellulose-based sorbents according to the conditions specified in articles cited in Table A. Note that Cr(VI) can be present as the CrO42- (cromate) anion, depending on the pH.

Visual inspection of Fig. 1A and B suggests that certain biomass classes were somewhat more promising in terms of achieving relatively high levels of sorption of specific metal ions. Though there were important differences attributable to the metal species (especially when comparing adsorption of the chromate ion vs. cationic metal species), such differences appeared to be dominated by effects attributable to differences among the substrate samples, even when the nominal material was similar. As a class, average adsorptive capacities reported for “Wood” did not appear to be as high as those reported for such classes as “Bark, cones, leaves, and nuts,” “Crop/food residuals,” “Aquatic plants,” “Lignin-related,” and “Pyrolysis products.”

Figure 2 addresses a related question: How did different metal ions generally compare against each other, with respect to their tendency to be taking up by a given class of cellulosic material? To make this comparison, seven kinds of metal ions were compared with respect to their sorption by the “crop/food residuals” types of biomass. Though later sections of this article will refer to studies providing evidence of significant “metal selectivity,” such effects are not apparent when one looks at the assemblage of data plotted in Fig. 2.

One of the most fascinating aspects of these results is the finding that the chromate ion (data plotted farthest to the right) compared very well with the other ions, even though the chromate ion (CrO42-) has a negative charge, which is the same net charge as that of typical cellulosic surfaces.

Figure 2. Graphical summary of reported amounts of seven types of metal ions taken up by one class of cellulose-based sorbents (“crop/food residuals) according to the conditions specified in articles cited in Table A

Because the proportions of cellulose, hemicellulose, and lignins can differ to a great extent, not only among the biomass categories mentioned above, but also from species to species, it makes sense to compare the metal sorption efficiency of biomass samples showing large differences in compostion. Various authors have proposed that lignin-rich samples, such as composts, would be expected to have a high affinity for metals ions due to an expected high level of carboxylation (Harman et al. 2007). Indeed, various studies support this hypothesis (Srivastva et al. 1996; Lalvani et al. 1997; Dizhbite 1999; Crist et al. 2002, 2003; Acemioğlu et al. 2003; Babel and Kurniawan 2003; Basso et al. 2004; Demirbas 2004, 2005; Sciban and Klasnja 2004a,b; Celik and Demirbas 2005; Mohan et al. 2006; Harman et al. 2007; Guo et al. 2008; Quintana et al. 2008; Wu et al. 2008; Harmita et al. 2009). By contrast, there has been a notable lack of attention paid to hemicelluloses and extractive components of biomass in this regard; this is surprising, since these components of biomass are known generally to be rich in carboxylic acid groups (Sjöström 1993).

Cost and Availability

A possible lesson that can be drawn from Table A is that almost any biomass-derived product can be used for metal ions removal from solution. It is difficult, however, to claim that any one type of source material is consistently superior to others, though large differences have been observed between different types of biosorbents. That being the case, it is worth questioning whether it is sometimes adequate to make one’s selection based only on cost and local availability. For instance, if one were able to obtain peanut shells, fungal biomass, and pine sawdust from local sources, what factors other than performance ought to guide one’s choice?

In principle, transportation costs and associated usage of energy can be minimized by using locally-collected biomass as the basis for a biosorbent system. But the overall cost and energy expenditure will also depend on the performance of the material. One needs to consider that a higher-performing biosorbent may achieve one’s objectives for metal removal with much less biosorbent material, thus reducing labor costs and operational costs. There may be savings related to safe disposal or regeneration of the spent material.

The ideal biosorbent should be very cheap, an unwanted byproduct that currently has to be hauled away and landfilled or burnt in heaps. On the other hand, the ideal biosorbent should have a huge appetite for a broad range of metal ions, binding them quickly, tightly, and dependably. One approach is just to test various readily available materials in “as-received” form. However, as will be shown in the course of this review, other investigators have employed a more proactive option, treating the biomass in various ways to improve its performance. Though the detailed prices of various biosorbents, both in their as-received and treated forms, lie beyond the scope of the present review article, it is expected that some of the collected data will permit subsequent investigators to make judicious choices among existing biosorbents and to develop additional variations to further enhance performance of biosorbents for different applications.

Cost

The words “low cost” have been used frequently, especially in review articles, by authors summarizing the main motivations prompting the use of biosorbent technology (Scheider et al. 1995; Gupta et al. 1998; Bailey et al. 1999; Brown et al. 2000; Gérente et al. 2000; Kumar et al. 2000; Marchetti et al. 2000a; Wartelle and Marshall 2000; Yu et al. 2000; Reddad et al. 2002b; Babel and Kurniawan 2003; Fiol et al. 2003; Ulmanu et al. 2003; Krishnani et al. 2004; Chuah et al. 2005; Horsfall and Spiff 2005b; Karthikeyan et al. 2005; Agarwal et al. 2006; Ali and Gupta 2006; Kumar 2006; Kumar and Bandyopadhyay 2006a; Kurniawan et al. 2006b; Lodeiro et al. 2006; Mohan and Pittman 2006; Parab et al. 2006a; Pino et al. 2006; Sarin and Pant 2006; Singh et al. 2006; Upendra and Manas 2006; Abdel-Ghani et al. 2007; Dubey and Krishna 2007; Garg et al. 2007; Ghodbane et al. 2007; Nouri et al. 2007; Soleimani and Kghazchi 2007; Zafar et al. 2007; Ahmady-Asbchin et al. 2008; Arief et al. 2008; Chakravarty et al. 2008; Demirbas 2008; Farinella et al. 2008; Igwe et al. 2008; Sud et al. 2008; Anandkumar and Mandal 2009; Gadd 2009; Rai 2009; Shukla et al. 2009; Wang and Chen 2009; Parab et al. 2010; Zahra 2010). Though Gupta et al. (2000) claimed that the operating costs involved in the usage of biosorbents for metal removal can be low relative to various alternative pollution abatement measures, there has been insufficient attention to operating costs, including the costs of transporting the sorbent material to the point of use, as well as costs associated with transportation to a site of final disposal, regeneration, or other beneficial use.

Gadd (2009) raised the following challenge to those attempting to make distinctions, other than cost, among alternative cellulose-derived sorbents: Because the composition of biomass does not vary a great deal between different species, it would seem pointless to spend a lot of effort testing many different representatives within a given class of biomass.

As an alternative, it was suggested that researchers should focus on biomass types that have distinct chemical differences from other types. Fungal biomass was mentioned as a key example, since it contains chitin within its cell walls (Ahluwalia and Goyal 2005b). The amino groups within chitin may have the potential to bind certain metal ion species in a different way from other kinds of biomass. Details about the use of fungal biomass for metal remediation have been reviewed (Sag 2001; Bishnoi and Garima 2005).

Local availability of large quantities

When the attributes of a needed commodity include “very low cost,” and often “bulky,” it can be a great advantage to minimize transportation costs. One promising low transportation strategy is to position one’s bioremediation facility adjacent to a business that produces a suitable lignocellulosic waste stream. For example, substantial quantities of microbial biomass are produced during industrial-scale fermentation processes (Ahluwalia and Goyal 2005b). Likewise, there may be opportunities to accumulate such bypoducts as bark, sawdust, carcoal, or ash adjacent to a facility that produces wood products or paper pulp as a primary product.

Performace Factors

Having already noted that cellulosic biomass is mainly composed of cellulose, hemicellulosics, lignin, and some extractives – the next challenge is to try to account for the huge ranges of metal ion adsorptive capabilities, such as those that are indicated in Fig. 1. Indeed, while certain cellulose-based sorbents represented in the figure were not very effective on a unit mass basis, others can equal or exceed the sorption capability of commercial ion exchange resins (e.g. Chang and Hong 1994; Ariff et al. 1999; Chamarthy et al. 2001; Saliba et al. 2002b; Choi and Yun 2004; Bishnoi and Garima 2005; Cochrane et al. 2006; Arshad et al. 2007; Ziagova et al. 2007).

Surface area

Cellulosic materials of biological origin tend to be organized with systems of interconnecting pores, thus providing a relatively high surface area per unit mass. However in such cases, there may be questions about: (a) whether parts of that surface area are inaccessible to the metal ions in question, and (b) whether parts of that surface area are lacking in potential binding sites for the metal ions. The importance of maximizing the accessible surface area has been demonstrated by studies that considered effects of particle size of the sorbent (Bai and Abraham 2001). However, there has been a lack of detailed study to compare surface area parameters vs. metal uptake under well-defined conditions. Future research might use, for instance, the BET nitrogen adsorption method (Faur-Brasquet et al. 2002; Budinova et al. 2006; Demiral et al. 2008; Hanafiah and Ngah 2009) to quantify the surface area of sorbent materials. A solvent-replacement method and freeze-drying could be used to minimize collapse of submicroscopic pores as water is removed (Stone and Scallan 1996).

A shown in Figs. 1 and 2, the typical data for adsorption of specified metal ions on nominally similar biomass samples range over three orders of magnitude. Including all the factors to be considered in this article, such a wide spread of data would appear to be best explained by differences in accessible surface area. Indeed, the cited work of Stone and Scallan (1996) showed that the drying of cellulosic material has the potential to decrease the apparent surface area by at least a factor of 100. Stone and Scallan revealed that about half of the mesopores that collapsed during drying of cellulosic pulp failed to re-open when the same fibers were rehydrated under the conditions of testing. Future studies related to metal remediation could address this point by scrupulously avoiding inadvertent drying of fresh biomass material at any point before the start of experimentation. This approach has the potential to show whether or not “un-dried biomass,” in contrast to more typically available samples of unknown drying history, might offer substantially higher metal uptake capacity.

Agitation

Another possibility that researchers have studied is whether the uptake of metal ions may be limited by the rate of diffusion of metal ions to surfaces. Some researchers have found that the amount of metals adsorbed increased with increasing agitation during batch testing (Ahalya et al. 2005; Basci et al. 2003). Though both of the cited studies observed higher adsorbed amounts associated with higher rates of agitation, the mechanism is not completely clear. Agitation can be expected to facilitate convective transport of metal ions to sorbent surfaces, and several studies indeed showed positive effects of agitation on the rate or extent of metal sorption (Basci et al. 2003; Ahalya et al. 2005; Shen and Duvnjak 2005b,c; Malkoc 2006; Martinez-Garcia et al. 2006b; Chaves et al. 2009; Garg et al. 2009). Alternatively, the associated pressure pulses acting on suspended particles in a mixture might also create a pump-like action, creating intermittent flow into and out of pore spaces within cellulosic materials in suspension. Such questions have not been adequately resolved and will require further research.

Ion exchange capacity

The issue of “binding sites,” as mentioned previously, can be addressed by considering the ion exchange capacities of candidate sorbent materials (Gadd 2009). Strictly speaking, ion exchange capacity can be measured by determining how much of one type of ion desorbs from a unit mass of a given sorbent when the system is saturated with a specified metal ion. The desorbed ion is usually either the proton, sodium, or an alkaline earth ion, such as calcium. The term “ion exchange” usually implies that the researchers are considering non-specific, electrostatic mechanisms of metal binding. Though such approaches often can be used as a first approximation, later sections of this article will consider alternative approaches that can help to explain deviations from an anticipated 1:1 stoichiometry between the ion exchange capacity of a sorbent and the adsorption capacities of different metal ions, even those having the same valence.

The major proportion of the ion exchange capacity of a biosorbent material usually can be attributed to surface-bound carboxylic acid groups. In principle, the content of carboxylic acid groups can be estimated by titrating a mixture of the sorbent material in water between two levels of pH, such as 3 and 9, and comparing the result with a blank determination (Gill 1989; Herrington and Petzold 1992; TAPPI 1993; Lindgren et al. 2002). An alternative method is required if one needs to determine carboxylic acid groups in the presence of other sources of acidity (e.g., Chai et al. 2003). The importance of carboxyl groups in sorbing metal ions has been demonstrated in many studies (Maranon and Sastre 1992b; Gloaguen and Morvan 1997; Jia and Thomas 2000; Kadirvelu et al. 2000; Merdy et al. 2002; Tiemann et al. 2002; Chubar et al. 2003; Davis et al. 2003; Pagnanellil et al. 2003; Karunasagar et al. 2005; Leyva-Ramos et al. 2005; Southichak et al. 2009b; Gurgel et al. 2008; Lodeiro et al. 2008; Bakir et al. 2009; Iqbal et al. 2009b; Jaramillo et al. 2009; Martin-Lara et al. 2008, 2009).

MODIFICATION OF BIOSORBENTS

Once a decision has been made to use a certain type of biosorbent , perhaps due to favorable performance of the as-received material, the next decision may involve whether and how to modify that material to improve its efficiency. The following discussion will start with gross mechanical and thermal treatments, then proceed to chemical treatments. Of the latter, relatively superficial “rinsing” strategies will be considered first, then more pervasive treatments such as oxidation, polymer adsorption, and formation of chemical derivatives or graft polymers at the surface of cellulosic materials. Activated carbon, which is often prepared from cellulosic biomass, will be considered last.

Size Reduction

In principle, a more finely ground sample of a given cellulose-based material is expected to adsorb more metal ions from solution, under specified conditions, compared to coarser particles. Indeed, this statement has been proven in a few studies (Ajmal et al. 1998; Blazquez et al. 2005). A further question is whether the effort and expense of size reduction can be justified. Here the answer is less clear. The study by Ajmal et al. (1998) observed an increase in metal sorption capacity by only a factor of about 2 when the particle size of sawdust was decreased from 500 µm to 100 µm. In addition to the high energy requirements, especially if one aims to achieve particles much smaller than 1 mm, one can anticipate increased problems with the handling of very fine material, including greater difficulties in later separation from the water phase, clogging of filters, and even dust and fire hazards if and when the material is dried. Sawdust actually represents a favorable case, since the energy to reduce the particle size has already been expended, perhaps in the production of lumber. Unfortunately, there have been few studies dealing systematically to determine under what circumstances one can justify the energy and time needed to reduce the particle size of a selected biomass sample in preparation for its use as a metal sorbent.

Living vs. Dead Biomass

One of the likely consequences of mechanically or chemically treating a biomass sample (see next sections) is its conversion from living organisms into dead biomass. Several investigators have investigated whether a change from living to dead has a significant effect on the ability of the material to adsorb metals ions (Kapoor et al. 1999; Srinath et al. 2002; Chen et al. 2005; Methta and Guar 2005; Yan and Viraraghavan 2003). Mehta and Guar (2005) reviewed relevant studies and concluded that dead biomass samples typically outperform living biomass for the uptake of heavy metals. Only in some cases did the investigators find substantially higher uptake when utilizing live cells (Yan and Viraraghavan 2003; Chen et al. 2005). The ability of some cells to accumulate metals internally by active biological processes has been suggested (Chen et al. 2005), and interest in the use of living cells for biosorption has been predicted to increase (Wang and Chen 2009). Other researchers have found cases where metal sorption was actually increased after such processes as autoclaving (Kapoor et al. 1999; Srinath et al. 2002; Deepa et al. 2006). The explanation for the latter findings may be that killing the cells opens internal surfaces, making the material more accessible. Kapoor and Viraraghavan (1998a) found mixed results; pretreatment of live Aspergillus niger biomass with various reagants yielded the best results for uptake of lead, cadmium, and copper, but the best uptake of nickel ions was observed with dead cells.

As noted by Ahluawalia and Goyal (2005b), non-living biosorbants have many potential advantages, including insensitivity to growth conditions or toxins, easier handling, easier storage, and easier disposal. Also, as noted by Ozdemir et al. (2004) and Hasan et al. (2007), the growth of living cells may be inhibited in the presence of significant concentrations of the specific metal that one would like to remove from an aqueous system. Kurek and Majewska (2004) found that autoclaving of different strains of fungal biomass tended to minimize differences among them in terms of metal sorbency.

Heat Treatment

Rocha et al. (2006) observed that the drying conditions used to prepare algal biomass may impact its sorptive capacity. In general, drying tended to shrink the material and resulted in the closing of pores. Various authors have reported increases in metal sorption capacity in the case of heat-inactivated biomass samples (Kacar et al. 2002; Bayramoglu et al. 2003; Gurisik et al. 2004; Arica et al. 2005; Tunali et al. 2005).

Drying of biomass also has been reported by many researchers as having a positive effect on metal uptake (Rocha et al. 2006), and many of the articles cited in this work specified the use of dried biomass. However, there has been almost no systematic study of this important issue.

Alkaline Treatment

Treatment with alkaline solution has been shown to enhance metal uptake in a few cases (Azab and Peterson 1989; Luef et al. 1991; Fourest and Roux 1992; Addour et al. 1999; Kapoor et al. 1999; Mameri et al. 1999; Kumar et al. 2000; Reddad et al. 2002d; Spanelova et al. 2003; Min et al. 2004; Tuanli et al. 2005; Sciban et al. 2006b; Southichak et al. 2006a; Afkhami et al. 2007; Nasir et al. 2007; Gupta and Rastogi 2008b; Argun et al. 2009). The mechanism has not been confirmed in detail. It seems likely that saponification of various ester groups may be involved, increasing the number of carboxylate groups on the treated surfaces (Reddad et al. 2002e; Xuan et al. 2006; Li et al. 2007, 2008).

Oxidation

One kind of treatment that has been consistently shown to increase the ability of cellulose-derived substrates to adsorb cationic metal species is oxidation (Maekawa and Koshihima 1984; Jia and Thomas 2000; Rangel-Mendez and Streat 2002; Chen and Zeng 2003; El-Hendawy 2003; Park et al. 2003; Park and Kim 2004; Babel and Kurniawan 2004; Saito and Isogai 2005; de Mesquita et al. 2006; Kikuchi et al. 2006; Argun et al. 2008a; Chavez-Guerrero et al. 2008; Baccar et al. 2009; Berenquer et al. 2009; El-Hendawy 2009; Foglarova et al. 2009; Han et al. 2009; Jaramillo et al. 2009; Klasson et al. 2009; Shukla et al. 2009). As already has been noted, oxidation of cellulose-derived material may result in increased numbers of carboxyl groups, which can dissociate to their negatively charged carboxylate form as the pH is increased in a range of 3 to 6.

In contrast to oxidation, chemical reduction may be useful for metal ion uptake, but only in isolated circumstances. Harry et al. (2008) observed enhanced adsorption on carbon cloth following electrochemical reduction, especially in the case of Cr(VI). Reduction would be expected to render the biomass-derived surfaces less negative in charge, thus favoring the adsorption of a negative species. Consistent with this finding, Aggarwal et al. (1999) found that oxidation of activated carbon samples suppressed the adsorption of the Cr(VI) chromate anion, though it favored the adsorption of the Cr(III) cation. However, other authors observed higher adsorption onto substrates that had been oxidized, even for the adsorption of Cr(VI) (Babel and Kurniawan 2004). Redox interactions during the adsorption of Cr(VI) and certain arsenic and mercury ions will be discussed in a later section.

Polymer Adsorption

Although polymer adsorption can be considered as a gentle treatment in the sense that no covalent reactions need to take place with the substrate, the effects on metal adsorption capacity can be profound. There has been a notable lack of research attention paid to adsorption of carboxyl-containing species onto biosorbents, for purposes of enhancing metal uptake.

Surprisingly, increases in adsorption of metal cations have been observed when using positively charged polyelectrolytes such as polyamines (Deng and Ting 2005b,c). Such systems are especially effective for adsorption of the chromate anion (Deng et al. 2006; Fang et al. 2007). Analogously, a cationic surfactant has been used to enhance the uptake of chromate ions onto fungal biomass (Mungasavalli et al. 2007). Chitosan, a positively charged polymer of natural origin, was likewise found to be effective for adsorption of Cr(VI) (Nomanbhay and Palanisamy 2005). The chitosan was loaded onto a charcoal support in the cited work. Tschabalala et al. (2004) observed, similarly, that a cationized cellulosic support was effective for removing phosphate, a negatively charged material, from aqueous solution. Gérente et al. (2007) have provided context from some of these findings in their review of research related to the use of chitosan for removal of heavy metals from aqueous solution.

Chemical Derivatization

Chemical derivatization of cellulosic material can be defined as the covalent attachment of various functional groups. This approach makes it possible for technologists to select chemical functionalities that may be expected to enhance metal uptake. From a scientific standpoint, derivatization also can be considered as a way to evaluate different hypotheses regarding which chemical groups, some of which may be present naturally, are likely to contribute to observed metal-binding effects.

Carboxylic acid derivatives

Earlier it was noted that carboxylic acid groups contribute directly to the ion exchange capacity of sorbent materials. Accordingly, many authors have reported favorable effects on metal uptake when using biosorbents that have been derivatized to increase their carboxylic acid content. Xie et al. (1996) describe the use of chloroacetic acid for this purpose. Other authors have achieved similar effects by reacting the cellulosic material with succinic anhydride (Marchetti et al. 2000a; Nada and Hassan 2006; Karnitz et al. 2007; Gurgel et al. 2008; Parab et al. 2008; Belhalfaoui et al. 2009; Chandlia et al. 2009; Garg et al. 2009). In general, such approaches have been shown to increase the adsorption capacity of a biosorbent for the target metal(s).

Multifunctonal carboxylic acid derivative

Esterification of a cellulose-based polymer with 1,2,3-propanetricarboxylic acid was found to yield strong binding of a wide range of heavy metals (Sugur and Babaoglu 2005). By such a reaction, there is potential to create adjacent carboxylic acid sites at the sorbent surface. Ideally, if one were to use just the right monomer, there is a theoretical possibility to approach the strong metal binding capabilities of a chelating agent. However, one needs to keep in mind that the most effective chelating agents require three to six carboxylic acid groups in a specific arrangement that is best suited for coordinating with a given type of metal ion (Lawrance 2010).

Phosphate derivative

Alternatively, it has been shown that a phosphate group can be attached to the surface of activated carbon (Nada et al. 2002a; Puziy et al. 2002). The product was found to be stable and offering a good ion-exchange capability and ability to bind heavy metal ions.

Sulfur-containing derivatives

When the goal is to adsorb such metals as Hg, Ag, and As, it may make sense to prepare sulfur-containing derivatives. Such an approach has been demonstrated in several cases (Tashihiro and Shimura 1982; Igwe et al. 2008). Thus, Aoki et al. (1999a) derivatized cellulose with a variety of groups, including isothiouronium and mercapto groups and achieved higher adsorption of Ag(I) and Hg(II). Macias-Garcia et al. (2004) and Marshall et al. (2007) introduced sulfur groups – presumably sulfonate – by use of SO2 gas during propration of activated carbon. Kim et al. (1999) observed a three-fold increase in the adsorption of lead after derivatizing algal biomass with xanthate groups.

Grafting

The term “grafting” will be used to represent a polymeric or oligomeric group that is attached to the cellulosic surface, usually by a covalent reaction involving the hydroxyl groups. Researchers have demonstrated the potential to create high-performing biosorbents by such treatments (Kubota and Shigehisa 1995; Yu et al. 2007), and the field has been reviewed by O’Connell et al. (2008). In particular, researchers have attached acrylamide-related chains to cellulosic surfaces (Aoki et al. 1996b; Raji and Anirudhan 1998; Bicak et al. 1999; Marchetti et al. 2000b; Shibi and Anirudhan 2002, 2005, 2006; Guclu et al. 2003; Choi et al. 2004; Unnithan et al. 2004; Chauhan et al. 2005a,b, 2006; Deng and Ting 2005a; Hashem 2006; Hashem et al. 2006a; Nada and El-Wakil 2006; Nada et al. 2007a; Sharma and Chauhan 2009; Sokker et al. 2009). The major increases in metal-binding capability that have been observed by many of these authors are consistent with the chelating effects that can be achieved by multifunctional carboxylic acids, as in the case of substrates that have been grafted with acrylic acid chains. In principle, grafting technologies enable the technologist to attach a wide range of highly specific functional groups to the substrate surface. As such, the approach can be compared to the use of the underlying biomaterial as a kind of support, rather than necessarily being a significant individual contributor to metal uptake.

Pyrolizing to Produce Activated Carbon Products

Very strong heating in the relative absence of oxygen is known to convert cellulosic materials into carbon. Careful control of the pyrolysis conditions, including the temperature and the composition of the surrounding gases, make it possible to achieve a very high accessible surface area per unit mass, in addition to providing significant control regarding the chemical sites at the carbon surface (Dias et al. 2007; Chen et al. 2008). For instance, the pore structure often can be enhanced by the use of steam during preparation of the activated carbon (Budinova et al. 2006). Many authors have demonstrated the potential to remove metal ions from aqueous systems by the use of such products, and these are listed in the appropriate section of Table A. Because of the generally hydrophobic nature of many activated carbon products, some authors regard such products as being especially suitable for removal of organic pollutants from aqueous systems, whereas removal of metal ions might be considered as a secondary benefit (O’Connell et al. 2008).

Numerous strategies have been used to render activated carbon products more effective in the uptake of one or more kinds of metal ions, and these are likewise indicated in Table A (see the “Activated Carbon” section indicated in the first column, near to the back of the table, and see the “Modifications” as indicated in the second column). Many of these treatments have already been discussed in preceding paragraphs. In general, these modifications can be achieved by the following strategies: First, the gaseous conditions can be adjusted, e.g. by the addition of steam, controlled amounts of oxygen, and by controlling the temperature, etc. Second, the mixture fed into the pyrolysis operation can be treated, for instance, with such materials as phosphoric acid to achieve a higher proportion of carboxylic acid groups or phosphorous-containing groups on the resulting carbon surface. Finally, the resulting powder can be post-treated, e.g. with nitric acid, to oxidize the surface, thus increasing the number of carboxyl groups (see, e.g. Lyubchik et al. 2004).

Ash

Ash often is available as an under-utilized waste product of the combustion of lignocellulosic materials. Though the properties of the ash itself are seldom a prime consideration in its genesis, it still makes sense to consider its possible beneficial applications. Chonjacka and Michalak (2009) found, for instance, that ash from wood was three times as effective for the removal of metal ions, compared to bone ash. Other studies that have considered the use of ash for metal removal can be cited (Rao et al. 2002; Banarjee et al. 2004; Chaves et al. 2009; Chu and Hashim 2002; Gupta et al. 1998, 1999, 2003; Gupta and Ali 2000, 2004; Pehlivan et al. 2006; Srivastava et al. 2006a,b,c, 2007, 2008a, 2009a,b).

MECHANISMS OF INTERACTIONS WITH METAL SPECIES

Ion-Exchange

“Ion exchange” refers to a class of mechanisms in which adsorbing metal ions take the place of other species already associated with the sorbent surface. For instance, these entitities may be metal ions such as Na+, Ca2+, or the proton, etc. Let’s suppose that a given biosorbent material has been prepared by equilibration in either NaOH solution or NaCl brine. In such cases, its reasonable to expect that acidic sites on the substrate will be mainly associated with Na ions. If the Cu2+ ion, for instance, is then introduced to the system, it may have a higher affinity for acidic sites in comparison to a monovalent cation. The resulting competition will lead to a net desorption of Na+ from surface sites and a net uptake of Cu2+.

Recent studies reviewed by Sag (2001) with fungal biomass in general and seaweed in particular have indicated a dominant role of ion exchange in metal binding. The classical ion-exchange concept, based on exchange-equilibrium constants and separation factors, can be applied. For a generalized ion-exchange reaction for dissolved species A being exchanged for a bound species B, with the underlined character representing the bound species,

 (1)

where the equilibrium constant KAB and the separation factor γAB are given as follows, for the case of ideal behaviour of the exchanging species (1:1 ion exchange, activity = 1) in both of the phases:

 (2)

 (3)

For a binary ion-exchange system, the value of the equilibrium constant KAB can be determined from the slope of the plot of qA/qB versus CA/CB. Biosorbents can also be prepared in different ionic forms, and the sorption analysis is often reduced to considering a series of simple binary ion-exchange systems. By eliminating qB through substitutions, the following expression is obtained:

 (4)

As qA / Q represents the fraction of the binding sites occupied by A, this equation may be used to evaluate the decrease of the equilibrium uptake of the species A by the biosorbent caused by the presence of species B. Using simple dimensionless concentration fractions as variables, Eq. (4) can be re-written as follows:

 (5)

This equation is the most generalized description of the ion-exchange sorption equilibrium for binary systems (Kratochvil and Volesky 1998). Modeling multi-metal ion exchange in biosorption has been applied to the brown alga Sargassum fluitans, which contains the carboxyl groups of alginate and the sulfate groups of fucoidan. An ion-exchange-based two-site model has been developed (Schiewer and Volesky 1995) and extended to describe multi-site and multi-ion system behavior (Schiewer and Volesky 1996).

Substantial published evidence supports the validity of the concept as just described, when it is applied to cellulosic-derived substrates. Desorption of displaced ionic species has been demonstrated during the uptake of a charge-equivalent amount of the heavy metal of interest. For instance calcium or magnesium ions may be released, depending on how the sorbent material has been prepared (Akthar et al. 1995; Diniz and Volesky 2005).

As an example, alkali-treated A. niger biomass was used to sequester silver ions from dilute as well as concentrated solutions (Akthar et al. 1995). The bound Ag+ was fully desorbed by dilute HNO3, and the biosorbent was regenerated by washing with Ca2+/Mg solution. The binding of Ag+ was attended by a stoichiometric release of Ca2+ and Mg2+ ions together (the sum of the two released being equal to Ag+ bound in molar terms, as expected on the basis of the univalency of the Ag+ ion). This equivalence established the mechanism of Ag+ sorption as being quantitatively due to exchange with (Ca2+ + Mg2+) ions of the biosorbent.

Biosorption of the lanthanides, lanthanum (La3+), europium (Eu3+), and ytterbium (Yb3+) from single component and multi-component batch systems using Sargassum polycystum Ca-loaded biomass was studied (Diniz and Volesky 2005). The ion exchange sorption mechanism was confirmed by the release of calcium ions from the biomass that matched the total number of metal and protons removed from the solution.

In other cases, the adsorption of metal ions is accompanied by dissociation of a stoichiometrically equivalent amount of protons. Metal (Pb, Cu, Zn, Cd, Ca) uptake by kraft lignin was also found to occur by displacement of protons or bound metals (Crist et al. 2002), as shown by the following equations, where X stands for a mono-valant bonding site on the substrate. Square brackets indicate concentrations in solution, while terms in parentheses represent the sorbate on the sorbent after ion exchange.

 (6)

 (7)

 (8)

 (9)

Crist et al. (2003) also conducted a detailed study with a kraft pine lignin powder. This material has acid functions that can act as ion exchange sites, which showed that uptake of divalent toxic metals was accompanied by a release of protons or existing metals from the lignin. A demonstrated stoichiometry of one mole Ca displaced for one mole of metal (Sr or Cd) sorbed was fully consistent with a chemical reaction of ion exchange and difficult to explain by frequently used adsorption models.

Adsorption of Hg(II), Cr(III), Cu(II), Cd(II), Ni(II), Ca(II), Sr(II), Zn(II), Co(II), Mn(II), Mg(II), K(I), and Na(I) by activated carbon made from pecan shells (Dastgheib and Rockstraw 2002a), showed that the Slips and Freundlich equations (see later) were satisfactory for explaining the experimental data. The ratio of equivalent metal ions adsorbed to protons released was calculated for the studied metal ions over a range of concentrations. In most cases, particularly at low concentrations, this ratio approached one, confirming that ion exchange of one proton with one equivalent metal ion was the dominant reaction mechanism.

Other studies have shown correlations between the number of weak-acid sites on the substrate in comparison to the amount of metal ions that can be adsorbed. The concept of ion exchange is also supported by the fact that a sufficiently high concentration of salt, acid, or base can cause a reversal of the process; this type of phenomenon will be discussed later in this article with respect to the regeneration of sorptive materials that have been used at least once in the sorption of heavy metals.

Figure 3 contrasts the ion exchange concept, as just described, with a site-specific concept. Ion exchange is mainly concerned with the stoichiometry of displacement of bound ions by dissolved ions, as displayed in the left-hand frame of the figure. For instance, adsorption of a trivalent metal ion is shown to occur from the displacement of three mono-valent species. By contrast, a complexation model (see later discussion) focuses on the underlying interactions of ions with surface sites. Thus, the right frame of Fig. 1 envisions the adsorption of a hydrated divalent ion, the simultaneous release of some of the waters of hydration, and the formation of a bi-dentate complex having moderate stability. Concepts related to such complexation are considered near the end of this article.

Fig. 3. Pictorial comparison of ion exchange concept (left frame) vs. metal adsorption involving chemical complexation (right frame)

To further understand implications of the foregoing observations, the next sections deal with mathematical, as well as theoretical fits between adsorption character-istics and solution concentrations, i.e. sorption isotherms.

SORPTION ISOTHERMS

When any sorption system reaches a state of equilibrium, there is a defined distribution of sorbate molecules at the solid-liquid interface and also in the bulk at a particular temperature. This provides an idea of the capacity of the sorbent for the sorbate. The maximum possible accumulation of the sorbate at the solid surface is a function of its concentration at a constant temperature, and it can be expressed by the following generalized relationship,

qe = f(Ce) (10)

where qe is the amount of sorbate sorbed at equilibrium (mg/g), Ce is the equilibrium concentration of the sorbate (mg/L), and “f” can be equated to the phrase “is a function of”. This type of relation is termed a ‘sorption isotherm’, which represents equilibrium between the concentration of a solute in solution and its concentration on the sorbent, at a given temperature. For assessing the maximum sorption capacity of a given biosorbent, the derivation of sorption isotherms is the most appropriate method. Further, the study of sorption isotherms is useful not only to evaluate to what extent a sorption system can be improved, but also to help predict conditions for working in open reactors and estimate optimal operating conditions.

The mathematical modeling of sorption is a very powerful tool for understanding the sorption process and essential for process design and optimization (Esposito et al. 2002). Several equilibrium-based models have been used to describe the metal transfer between the solution and solid phase during the sorption process (Vijayaraghavan et al. 2006a).

Langmuir Sorption Isotherm

The Langmuir model, which is one of the most widely used (see Table A), was initially proposed for the adsorption of a gas on the surface of a solid. Nevertheless, it has been extended to include the sorption of solute at a solid–liquid interface. The Langmuir model suggests that the sorption occurs on the surface of the solid that is made up of elementary sites, each of which can adsorb one sorbate molecule, i.e. monolayer sorption. It was also assumed that every sorption site is equivalent and the ability of sorbate to get bound there is independent of whether or not the neighbouring sites are occupied (Langmuir 1918).

The Langmuir model is given as follows (Langmuir 1918):

 (11)

Equation (11) can be linearized as follows:

Ceqe = 1/ Qob + CeQo (12)

where qe (mg/g) and Ce (mg/L) are the sorbed metal ions on the sorbent and the metal ion concentration in the solution at equilibrium, respectively, b (L/mg) is the constant related to the affinity of binding sites, i.e. the affinity of sorbent for the sorbed species. Qo (mg/g) is known as the Langmuir constant, which represents the monolayer sorption capacity, i.e. a practical limiting sorption capacity when the surface is fully covered with metal ions. Qo assists in the comparison of sorption performances. In general, for good sorbents, high values of Qo and low values of b are required (Kratochvil and Volesky 1998). Equation 11 also can be linearized to other forms. The final linearized form will be a function of the data distribution.

The affinity between adsorbate and adsorbent can be predicted using the Langmuir parameter b from the dimensionless separation factor RL,

RL = 1/ (1 + bCo) (13)

where Co is the initial metal ion concentration and b is the Langmuir isotherm constant. The adsorption process as a function of RL may be described as follows: When RL is greater than one, then the sorption reaction is unfavourable, and it is linear when RL is equal to one. When RL is between zero and one, the reaction is favourable, while the reaction is supposed to be irreversible when RL is equal to zero. This can be summarized as follows:

RL > 1 unfavorable

RL = 1 linear

0 < RL < 1 favorable

RL = 0 irreversible

Freundlich Sorption Isotherm

The Freundlich isotherm model, which is also very widely used, describes the sorption of solute from liquid to solid surface and assumes that the stronger binding sites are occupied first and that the binding strength decreases with an increasing degree of site occupation. The Freundlich model proposes a monolayer sorption with a heterogeneous energetic distribution of active sites, and/or interactions between sorbed species, i.e. multilayer sorption (Freundlich 1907).

The Freundlich model can expressed by the following empirical equation:

 (14)

Equation (14) can be expressed in logarithmic terms to obtain the following form,

 (15)

where KF (mg1-n/g Ln) and n (dimensionless) represent the Freundlich constants characteristic of the system. KF is indicative of the relative sorption capacity, whereas is the measure of the nature and strength of the sorption process and the distribution of active sites. If (n) < 1, then the bond energies increase with the surface density. If (n) > 1, the bond energies decreases with the surface density. When n = 1, all surface sites are equivalent. Alternatively, it has been shown using mathematical calculation that n values between 1 and 10 represent beneficial sorption. These parameters are empirical constants, and they depend on several factors (Bajpai et al. 2004; Febrianto et al. 2009).

Dubinin-Radushkevich (D-R) Sorption Isotherm

The Dubinin–Radushkevich isotherm model (Dubinin and Radushkevich 1947) is postulated within a sorption space close to the sorbent surface to evaluate the sorption free energy and to help determine the nature of bonding, i.e. either physisorption or chemisorption.

The D–R isotherm can be presented as follows,

 (16)

where qm is the amount of sorbate sorbed (mmol/g), Xm is the maximum sorption capacity of the sorbate retained (mmol/g), KDR is the activity coefficient constant related to the sorption free energy of the transfer of the solute from the bulk solution to the solid sorbent (mol2 kJ2), and F is the Polanyi potential, which is given by the equation,

 (17)

where R is the universal gas constant (0.0834 kJ/mol/K) and T is the absolute temperature in Kelvin. Ce was defined earlier.

Assuming that the surface of the sorbent is heterogeneous and when choosing an approximation to a Langmuir isotherm model as a local isotherm for all sites that are energetically equivalent, the quantity KDR, which is related to the mean free energy (E) of the transfer of 1 mol of solute from infinity to the surface of the sorbent, can be expressed by the equation:

 (18)

If the magnitude of E is between 8 and 16 kJ/mol, then the sorption process is supposed to proceed via chemisorption, while for values of E < 8 kJ/mol, the sorption process is of physical nature (Basar 2006; Hasany and Chaudhary 2001; Saeed et al. 1996; Tunali et al. 2006; Vijayaraghavan et al. 2006a).

Temkin Isotherm

The Temkin isotherm equation (Temkin and Pyzhev 1940) contains a factor that explicitly takes into account adsorbing species–adsorbate interactions. It assumes that the heat of adsorption of all the molecules in the layer decreases linearly with coverage due to adsorbate–adsorbate repulsions and that adsorption involves a uniform distribution of maximum binding energy. In addition, it assumes that the fall in the heat of sorption is linear rather than logarithmic, as implied in the Freundlich equation. The Temkin isotherm has commonly been written in the following form,

 (19)

Equation 19 can also be represented as follows:

(20)

where, T is the absolute temperature in Kelvin, and R is the universal gas constant, 8.314 J/mol/K. The constant bTe is related to the heat of adsorption (J/mol), and aTe is the equilibrium binding constant (L/g) corresponding to the maximum binding energy.

Flory-Huggins Isotherm

Another two-parameter isotherm is the Flory-Huggins model, which can be represented as follows (Padmesh et al. 2006):

 (21)

The equilibrium constant, KFH has been used to compute the Gibbs free energy (ΔG):

G = –RT lnKFH (22)

where θ = (1−C/Co) is the degree of surface coverage, KFH is the Flory-Huggins equilibrium constant, and nFH is the Flory-Huggins exponent.

Redlich–Peterson Isotherm

The R–P isotherm can be described as follows (Padmesh et al. 2006),

 (23)

where KRP is a first R–P isotherm constant (l/g), aRP is a second R–P isotherm constant (L/mg), β is an exponent, the value of which lies between 0 and 1, and Ce is the equilibrium liquid phase concentration (mg/L).

If β = 1, then the Langmuir will be the preferable isotherm, while if β = 0, the Freundlich isotherm will be preferred. Although the two parameters in the Langmuir and Freundlich equations can be graphically determined, Redlich–Peterson constants are not computed by graphing, because there are three unknown parameters. However, the values of the three parameters in the equation can be obtained using non-linear regression analysis.

Toth Isotherm

The Toth isotherm is derived from the potential theory, and it is applicable for heterogeneous adsorption (Toth 1971). This model assumes a quasi-Gaussian energy distribution, where most sites have adsorption energies lower than the peak or maximum adsorption energy. The Toth isotherm is represented as follows (Padmesh et al. 2006),

 (24)

where qmax is the maximum dye sorption (mg dye/g biomass), bT is the Toth model constant, and nT is the Toth model exponent.

Determining Isotherm Parameters

By linearization

The simplest approach to determining isotherm constants for two-parameter isotherms is to transform the isotherm variables so that the equation is converted to a linear form and then to apply linear regression (Ho et al. 2002). Although a linear analysis is not possible for a three-parameter isotherm, a trial and error procedure has previously been applied to a pseudo-linear form of the Redlich-Peterson isotherm to obtain values for the isotherm constants (McKay et al. 1984), and this involves varying the isotherm parameter, KRP, to obtain the maximum value of the correlation coefficient for the regression.

By non-linear regression

Due to the inherent bias resulting from linearization, alternative isotherm parameter sets can be determined by non-linear regression (Ho et al. 2002). This provides a mathematically rigorous method for determining isotherm parameters using the original form of the isotherm equation (Seidel and Gelbin 1988; Seidel-Morgenstern and Guiochon 1993; Malek and Farooq 1996; Khan et al. 1996). Most commonly, algorithms based on the Levenberg-Marquardt or Gauss-Newton methods (Edgar and Himmelblau 1989; Hanna and Sandall 1995) are used.

The optimization procedure requires the selection of an error function in order to evaluate the fit of the isotherm to the experimental equilibrium data. The choice of error function can affect the parameters derived. Error functions based primarily on absolute deviation bias the fit towards high concentration data, and this weighting increases when the square of the deviation is used to penalize extreme errors.

This bias can be offset partly by dividing the deviation by the measured value in order to emphasize the significance of fractional deviations. In the cited study (Ho et al. 2002), five non-linear error functions were examined and in each case a set of isotherm parameters were determined by minimizing the respective error function across the concentration range studied. The error functions employed were as follows:

The Sum of the Squares of the Errors (ERRSQ):

 (25)

A Composite Fractional Error Function (HYBRD):

 (26)

A Derivative of Marquardt’s Percent Standard Deviation (MPSD):

 (27)

The Average Relative Error (ARE):

 (28)

The Sum of the Absolute Errors (EABS):

 (29)

As each of the error criteria is likely to produce a different set of isotherm parameters, an overall optimum parameter set is difficult to identify directly. Hence, in order to try to make a meaningful comparison between the parameter sets, a procedure of normalizing and combining the error results was adopted, producing a so-called ‘sum of the normalized errors’ for each parameter set for each isotherm.

The calculation method for the ‘sum of the normalized errors’ was as follows:

(a) select one isotherm and one error function and determine the isotherm parameters that minimize that error function for that isotherm to produce the isotherm parameter set for that error function;

(b) determine the values for all the other error functions for that isotherm parameter set;

(c) calculate all other parameter sets and all their associated error function values for that isotherm;

(d) select each error measure in turn and ratio the value of that error measure for a given parameter set to the largest value of that error from all the parameter sets for that isotherm; and

(e) sum all these normalised errors for each parameter set.

The parameter set thus providing the smallest normalised error sum can be considered to be optimal for that isotherm, provided that:

  • There is no bias in the data sampling – i.e. the experimental data are evenly distributed, providing an approximately equal number of points in each concentration range; and
  • There is no bias in the type of error methods selected.

APPLYING ISOTHERM EQUATIONS TO METAL SORPTION DATA

The following subsections describe a number of cases in which authors have provided justification for different types of isotherm models for the analysis of metal sorption onto cellulosic materials under different experimental conditions.

Langmuir Isotherm

As has been noted, the use of the Langmuir adsorption isotherm implies an assumption of uniform, non-interacting adsorption sites. When one considers the impure nature of typical biomass-derived sorbents, it is remarkable how large a proportion of the publications considered in this review reported that good fits were achieved by means of the Langmuir equation (see Table A). Possible ways to explain the goodness of fit, in so many of the listed cases, are as follows:

  • Many studies tend to be dominated by effects due to one kind of chemical group, e.g. a certain kind of carboxylate group present on that type of modified biomass.
  • In addition, it is likely that in many cases the adsorption experiments were performed at sufficiently high ionic strength such that the adsorption of a metal ion at one site did not have an appreciable influence on the adsorption of the next metal ion at an adjacent site. The likely range of influence can be roughly estimated based on the Debye-Hückel reciprocal length parameter (Hiemenz and Rajagopalan 1997).

Multifunctional Langmuir Adsorption Models

Aksu et al. (1997, 1999) studied adsorption onto Chlorella vulgaris for Fe(III), Cr(VI) and Cu(II) as single-component systems, as well as Fe(III)–Cr(VI) and Cu(II)– Cr(VI) binaries. They concluded that single-component isotherms could be modeled by either the Freundlich or Langmuir isotherms. The binary Freundlich equation proposed by Fritz and Schlünder was appropriate for fitting the data of both binary systems, while the extended Langmuir equation was used successfully for only the Fe(III)–Cr(VI) system.

The simultaneous biosorption of copper(II) and chromium(VI) to C. vulgaris from binary metal mixtures was investigated by Aksu et al. (1999) in a single-staged batch reactor as a function of Vo/Xo ratio (volume of wastewater containing heavy metal mixture/quantity of biosorbent) at different orders of second metal ion addition and at pH values of 2.0 and 4.0 chosen as the optimum biosorption pH values for chromium(VI) and copper(II), respectively. The sorption phenomenon was expressed by a competitive, multi-component Freundlich adsorption isotherm, which was then used for calculating each residual or adsorbed metal ion concentration at equilibrium (Ceq,i or Cad,eq,i) at a constant Vo/Xo ratio for a given combination of heavy metals in a single-staged batch reactor. In the cited study, the non-competitive Freundlich isotherm model (Eq. 14) was used for describing the short-term and mono-component adsorption of heavy metal ions by algal cells. However, for binary mixtures, an empirical extension of the Freundlich model has been proposed where the coefficients relating to isotherms could be determined from mono-component isotherm data, except for the biosorption competition coefficients, which had to be determined experimentally. The Freundlich models for the first and the second components restricted to binary mixtures are given by Eqs. (30) and (31),

 (30)

 (31)

where KFIKFII, nI, and nII are derived from the corresponding individual Freundlich isotherm equations and the six other parameters (noting that xI, yI, zI, xII, yII, and zII are the competitive Freundlich adsorption constants of the first and second metal ions, respectively, for the binary system) are the competition coefficients for two metal ion species.

The biosorption of heavy metal ion mixtures by the biomass in a batch reactor can be considered as a single-staged equilibrium operation. Consideration of the single-stage equilibrium operation would depend on two basic constraints, that of equilibrium (shown in Eqs. (30) and (31)) and that of a mass balance. The mass balance for the first component in the mixture is given by,

 (32)

 (33)

 (34)

where C0I is the initial concentration of the first component (mg L-1); CeqI is the residual concentration of the first component at equilibrium (mg L-1); q0I is the amount of the first component adsorbed per unit weight of algae at the beginning (mg g-1); qeqI is the amount of the first component adsorbed per unit weight of algae at equilibrium (mg g-1); V0 is the volume of solution containing heavy metal ion mixture in the batch reactor (l); and X0 is the amount of biosorbent in the batch reactor (g)

Equation (34) belongs to a straight line for the first metal ion, and the line passes through points (C0Iq0I) and (CeqIqeqI) with (-V0:X0) slope. This is the operating line for this stage at a known concentration of the second metal ion. As qeqI and CeqI values are known from experimental data for the first metal ion in the mixture, the single-staged batch operation can be shown in a figure on the same coordinates by drawing the operation line and equilibrium curve for the first metal ion at a known combination and pH value. V0:X0 for a desired purification or CeqI and qeqI values at a given V0:X0 and a second metal ion concentration can be determined. The initial metal ion concentration of the first metal ion must be equal to,

 (35)

where Cad,eqI is the adsorbed concentration of the first component at equilibrium (mg L-1). The value of Cad,eqI can be calculated easily from Eq. (35). If a calculation is required, then Eq. (34) can be rearranged as:

 (36)

The amount of the first metal adsorbed per unit weight of biomass at the beginning of the biosorption (q0I) is equal to 0.0, so Eq. (36) can be rewritten:

 (37)

As KFInIKFII, and nII can be found from experimental data, Eq. (37) also provides the V0:X0 ratio for desired purification or CeqI and CeqII (or indirectly Cad,eqI and Cad,eqII) at a given V0:X0 ratio for a given heavy metal mixture at a known combination. Equation (37) can also be rewritten for the second metal ion in the same manner.

Thus the copper(II)-chromium(VI) multi-ion system was defined with the multi-component Freundlich adsorption isotherm and used to model the adsorption of a binary system to C. vulgaris in a single-staged batch reactor as a function of V0:X0 ratio and second metal ion concentration at pH 2.0 and 4.0. The pH of the biosorption medium, the order of addition of the metal ions, and the amount of biosorbent (V0:X0 ratio) strongly affected the equilibrium uptake of the first metal ion by the algae. The individual Freundlich constants evaluated from the non-competitive isotherms were used to find the competitive Freundlich constants in a competitive Freundlich model describing multicomponent adsorption equilibrium. These constants were used in Eq. (37) to calculate the residual concentration of the first metal ion at a known second metal ion concentration and the V0:X0 ratio in a single-staged equilibrium operation.

The equilibrium isotherms for the first metal ion at the known second metal ion concentrations with the operation line with V0:X0 slope were also developed to predict the residual concentrations of the first metal ion. It was considered that these two methods may be used successfully to estimate the residual concentrations of the first and second metal ions in a mixture at equilibrium. C. vulgaris biomass offers a practical approach for removing mixtures of copper(II) and chromium(VI) ions from waste waters containing mainly these two components. Using low V0:X0 ratios, high purification yields can be obtained for the first metal ion at its optimum pH value and at low second metal ion concentrations or for desired purifications of the first metal ion, V0:X0 ratios, pH, and second metal ion concentrations. Parameters can be chosen according to Eq. (37) by using individual and competitive Freundlich constants in a single-staged batch reactor up to 150 mg/L initial metal ion concentration for each metal ion. Multi-staged reactors can also be designed and operated by estimating a sufficient amount of algae for a known volume of waste water (choosing V0:X0) with a known heavy metal ions combination, especially if required purification cannot be provided in a one-staged reactor when studying higher metal ion concentrations.

In the work of Leyva et al. (2001), single and simultaneous Cd(II) and Zn(II) adsorption isotherms from aqueous solution onto activated carbon were determined experimentally. Single isotherms for these ions were fitted to Langmuir isotherms, while the simultaneous adsorption isotherms was fitted to the bisolute Langmuir isotherm modified with an interaction factor. Experimental data for single adsorption isotherms for Zn(II) and Cd(II) onto C (carbon) were fitted to the Langmuir isotherm (Eq. 11).

The constants for this isotherm were obtained by a least-squares method based on the optimization algorithm of Rosenbrock-Newton. The average percent deviation was calculated, and a reasonable fit to the experimental data was obtained based on application of the Langmuir isotherm. The Freundlich isotherm (Eq. 15) was also tested, but produced a weaker goodness of fit compared to the Langmuir isotherm. The max-imum molar uptake of Zn(II) averaged 1.6 times that of Cd(II). This result was explained by the author as probably being related to the electrostatic attraction between the very heterogeneous surface of the activated carbon and the metal ions in solution. Another possible explanation for the relatively high Zn(II) selectivity was related to the ability of both Cd(II) and Zn(II) to be adsorbed at one class of surface sites, while Zn(II) was exclusively adsorbed on other class of surface sites. Thus the single adsorption isotherm of Zn(II) can be represented by a dual-site Langmuir isotherm, known as the bi-Langmuir isotherm. However, a Scatchard plot (q/C vs. q, presented in the paper) of the single solute adsorption data of Zn(II) did not suggest that Zn(II) was adsorbed on two kinds of sites. Thus, it was said that the single-site Langmuir isotherm was appropriate to represent the single adsorption of both metal ions (Leyva et al. 2001).

The simultaneous adsorption of Cd(II) and Zn(II) was also studied, as these ions usually occur together in industrial wastewaters. In multicomponent systems, the adsorption isotherm of a certain solute also depends on the concentration and characteristics of the other solutes in the aqueous solution. The solute of interest may be in competition with other solutes for the same active adsorption sites. The experimental data for simultaneous Cd(II) and Zn(II) adsorption were interpreted with the bisolute Langmuir isotherm. The competitive Langmuir isotherms for Cd(II) and Zn(II) are represented as follows:

 (38)

 (39)

The constants of these two isotherms are from the single-solute Langmuir isotherms. The experimental molar uptake values of Cd(II) and Zn(II) were compared to the molar uptake values of Cd(II) and Zn(II) predicted with the bisolute Langmuir isotherm, and it was found that the bisolute Langmuir isotherm overestimated the molar uptake of Zn(II) with an average percent deviation of 94.0%. However, it underestimated the molar uptake of Cd(II), and the average percent deviation was 33.36%. Thus, the binary adsorption data were not properly described with the bisolute Langmuir isotherm. The literature reports that the bisolute Langmuir model provides a reasonable fit to the multicomponent adsorption data when the qm,i values for each metal evaluated from single-solute Langmuir isotherm are similar to each other. As previously noted in this study, qm,Zn is approximately 1.6 times greater than qm,Cd. Jain and Snoeyink (1973) assumed that some adsorption occurs without competition, because not all sites were available to all solutes. Consequently, the bisolute Langmuir isotherm was modified for systems in which the qm,i values of components were different, proposing the following isotherm for the solute with the higher qm,i, which in the cited study was Zn(II):

 (40)

The isotherm for the solute with the lower qm,i is the same as that represented in Eq. (38), and the constants are from the single-solute Langmuir isotherms. In Eq. (40), the difference between the maximum molar uptakes is the number of sites with noncompetitive adsorption. The modified bisolute Langmuir model was applied to the experimental data for simultaneous Cd(II) and Zn(II) adsorption and it overpredicted the molar uptake of Zn(II), with an average deviation of 112.7%. Thus the modified bisolute Langmuir isotherm failed to predict the molar uptake of Zn(II), and its prediction had a higher average percent deviation than that obtained with the bisolute Langmuir isotherm.

Ho and McKay (1999b) modified the bisolute Langmuir isotherm with an interaction factor, η, and obtained an excellent fit of the adsorption data of Cu(II) and Ni(II) onto peat. The model proposed by these authors can be represented as follows,

 (41)

 (42)

where ηi,j is the interaction factor of metal i for the adsorption of metal j. This interaction factor is specific to each metal ion in a given system and depends upon the other metal ions present. In the cited study, the best interaction factor was obtained by fitting the isotherm models to the experimental data with a least-squares method that employs an optimization algorithm.

The values of the interaction factors were calculated and found to be as follows: ηCd,Cd = 1.02; ηZn,Cd = 3.29; ηCd,Zn = 0.089; and ηZn,Zn = 1.26. The experimental molar uptake and the molar uptake predicted with the bisolute Langmuir isotherm that had been modified with the interaction factor were compared, and it was found that the bisolute Langmuir isotherm interpreted the experimental data for both ions reasonably well. The average percent deviation was 18.8% for Cd(II) and 31.0% for Zn(II), which are the lowest values for all the models tested in the cited work. Thus, the experimental data for simultaneous Cd(II) and Zn(II) adsorption onto carbon correlated well with the bisolute Langmuir isotherm modified with an interaction factor. The simultaneous adsorption isotherms for Cd(II) and Zn(II) were always reduced compared to the single adsorption isotherms for these ions. The Zn(II) adsorption isotherm was affected more by the presence of the other ion than Cd(II). The adsorption isotherm for a given ion is always reduced by the presence of the other because the two ions compete for some of the same active sites.

The adsorption behavior of Cu(II) and Mn (II) cations in the presence of other metal ions that display strong or intermediate affinities for adsorption sites was system-atically investigated, taking into consideration the following factors: (1) metal ion site competition; (2) charge accumulation near the carbon surface; and (3) speciation of the metal ions. Two multicomponent adsorption models were proposed, and the results were then compared to two models presented in the literature (Dastgheib and Rockstraw 2002b).

For modeling multicomponent systems comprised of species whose single–solute isotherms obey the Freundlich Isotherm, a multicomponent Freundlich equation was used. The first equation of this type was proposed for binary systems by Fritz and Schlünder (1974, 1981), as provided in Eq. (43),

 (43)

where q1 and C1 are the concentrations of solute 1 in the solid and liquid phases, respectively; C2 is the concentration of the solute 2 in liquid phase; K1 and n1 are Freundlich equation constants in the single solute 1 system; and β11α12 and β21 are constants that are determined from the least squares analysis of the binary data.

The second multicomponent Freundlich equation, which was proposed by Sheindorf et al. (1981), was derived under the assumption that: (1) each component in a single system obeys the Freundlich model and (2) for each component in multicomponent system, the adsorption energies of different sites are distributed exponentially, with the distribution function being identical to that for the single-component system. The proposed binary system equation is shown in Eq. (44),

 (44)

where η12 is the interaction parameter (with other parameters are defined in the same manner as Eq. (43)).

Thus the proposed equation, written for solute 1 in a binary system of solutes 1 and 2, can be shown as,

 (45)

where q1 and C1 are the concentrations of solute 1 in the solid and liquid phases, respectively; C2 is the concentration of solute 2 in the liquid phase; K1n1K2, and n2 are the single component Freundlich constants, and a12 , b12, and n12 are interaction constants obtained from a least squares analysis of the binary data. The term inside the bracket on the right hand side of Eq. (45) represents the overall competition and interaction factor, and has a value of less than or equal to unity (when C2 → 0, this term is equal to 1). The term a12K2 can be condensed to a single term, and was considered as one constant.

It was found in most cases that the value of 0.5 for n12 in Eq. (45) gave acceptable results. Using this assumption, Eq. (45) was reduced to a form that has only two interaction constants, as shown by Eq. (46),

 (46)

The general case of the proposed multicomponent Freundlich model is Eq. (47),

 (47)

where qi and Ci represent concentrations of solute i in the solid and liquid phases, respectively; Cj represents the concentration of other solutes in liquid phase; KiniKj, and nj are the single-component Freundlich constants; aijbij, and nij are binary interaction constants obtained from aii = bii = 0; and m is the number of solutes.

Least squares analysis was used to find the constants of Eqs. (43 through 46). In each case, the objective function, as defined in Eq. (48), was minimized

 (48)

In Eq. (48), qexp,i is the experimental value of the metal ion uptake in binary system at data point Iqcal,i is the calculated value of metal ion uptake (from the selected model), and m is the number of data points.

To evaluate and compare the performance of each model, average relative error (ARE%) and root mean squares error (RMSE%) were calculated for each binary system. The large calculated values of RMSE% corresponding to Eq. (44) demonstrated that this model did not predict the metal ion adsorption isotherms in binary systems particularly well. Equation (43) and the proposed model in Eq. (45) were both found to be good models for predicting adsorption isotherms of metal ions in binary systems, as demonstrated by low RMSE% of different binaries.

Samples of dead biomass from the marine brown algae Fucus ceranoidesFucus vesiculosus, and Fucus serratus were studied for their ability to remove cadmium from aqueous solutions by Herrero et al. (2006). A non-ideal competitive adsorption isotherm model (NICCA) (Kinniburgh et al. 1999), which described very well the competition between protons and metal ions, in contrast to a simpler discrete competitive Langmuir model, was applied. This model is a semi-empirical, thermodynamically consistent model, which implicitly accounts for a variable degree of heterogeneity of the sorbent. The basic NICCA equation for the overall binding of species i in the competitive situation is,

 (49)

where i is the coverage fraction of the species i, Ki is the median value of the affinity distribution for species i, p is the width of the distribution (usually interpreted as a generic or intrinsic heterogeneity seen by all ions), and ni is an ion-specific non-ideality term. Strictly speaking, ci should be the local concentration of species i at the binding site, i.e., the bulk concentration (or activity) corrected for the double layer effect (for instance, the concentrations in the Donnan phase). In this work, the bulk concentrations was used instead, and therefore, the metal binding constants calculated were conditional parameters (referred to 0.05 M ionic strength). The following normalization condition was used to calculate the amount of species i bound, qi,

 (50)

where qmax,H is the maximum binding capacity for protons, which can be calculated from the equivalence point of the acid-base titrations in absence of heavy metal. The ratio ni/nH was interpreted by Kinniburgh et al. (1999) in terms of stoichiometry and cooperativity. When this ratio is less than one, then the maximum binding of species i is lower than the total amount of sites (defined as the amount of titratable protons), which would be a consequence of some degree of multi-dentism. On the other hand, a value of ni/nH greater than one would reflect some degree of cooperativity. Finally, if ni/nH=1, it can be demonstrated that the maximum proton/metal exchange ratio is one, and the NICCA isotherm reduces to the generalized (multicomponent) Langmuir-Freundlich isotherm (GLF):

 (51)

If only the proton binding is considered (i.e., absence of competing ions) in Eqs. (50) and (51), then the LF isotherm is recovered,

 (52)

where now the heterogeneity parameter mH describes the combined effect of nH and p (mH=nHp). In the case of a homogeneous system (no chemical heterogeneity), mH=1, and then the Langmuir isotherm is obtained. For instance, the ideal Langmuir competitive isotherm for the binding of Cd2+ (assuming a 1:1 stoichiometry) would be,

 (53)

with qCd = Cdqmax,H.

It was found that the fit of the NICCA model to the cadmium binding data (discarding the data at pH 6 and lower metal concentrations) was satisfactory.

Other Models

In addition to the relatively well known isotherm approaches summarized on previous pages, one of the goals of the present review is to provide some guidance on alternative equations that have been demonstrated in at least one study involving metal removal but not widely used as those discussed earlier. Such approaches may have potential to become more widely used in the future.

Incorporation of Donnan relationships

Schiewer and Volesky (1997a) used biomass of the brown alga Sargassum for the biosorption of Cd2+ ions. This work provided a mathematical model for predicting the equilibrium of proton and metal ion binding as a function of metal ion concentration, pH, and ionic strength. Since the presence of sodium significantly influenced Cd binding, it is recommended to use models that incorporate ionic strength effects. Although swelling of the biomass particle was observed, a simple Donnan model that assumed a rigid particle already yielded a good prediction of the experimental data. A combined Donnan-Biosorption Isotherm equation was derived that allowed for direct calculation of cation binding without interactions. Three versions of the Donnan model were considered: one that assumes a rigid particle (DORI), one (DOSWa) that accounts for swelling by a linear correlation (Eq 54), and one (DOSWb) that accounts for swelling by a more complex relation (Eq 55).

Since the swelling of sorbent increased with the number of free sites C, the following simple linear relationship between the specific particle volume and C was assumed,

 (L/g) (54)

where Yv is a constant that has to be determined from the experimental data. For C approaching zero (i.e., all sites are occupied), electrostatic effects and therefore the volume are irrelevant (i.e., it does not matter that the value calculated for Vm approaches zero). Equation 54 expresses that the charge density per volume is constant, independent of the degree of site occupation.

Since, the swelling not only increased significantly with C but, additionally, it decreased with M(the metal ion binding (mequiv/g)), the following swelling correlation was considered:

 L/g (55)

Schiewer and Wong (2000) investigated the binding of protons and metal ions by three brown seaweeds Sargassum hemiphyllum, Colpomenia sinuosa, and Petalonia fascia as well as the marine green alga Ulva fascia as a function of metal concentration, pH, and ionic strength. Differences in overall biosorption behavior were explained as a result of different numbers of binding sites, affinities for metal complexation, and charge density. These relationships were predicted using the Donnan model combined with an ion exchange biosorption isotherm for covalent binding of metals (Cu and Ni) and protons.

The concentration factor [H]p/[H] was modeled according to the Donnan equilibrium, whereby the concentration of any ion was assumed to be homogeneous throughout the biomass particle, and the negative charge of the biomass was balanced by counter-ions such as protons (H), sodium (Na), or divalent metal ions (M), whose concentration factor was determined by:

 (56)

One main factor determining K is the ionic strength. K decreases with increasing ionic strength, approaching a value 1.0.

A pH-sensitive isotherm equation was derived, which allows for the calculation of the amount of metal and protons bound covalently.

 (mequiv/g) (57)

 (mequiv/g) (58)

The Donnan model with particle swelling (DOSW) was represented by the following equation:

 (L/g) (59)

where YV is a ftting parameter and C is the number of free carboxyl groups (i.e., assumed not covalently bind to any cation). The following equations were derived by combining the Donnan model Eq. (56) with the isotherm equations (60) and (61):

 (mequiv/g) (60)

 (mequiv/g) (61)

The Donnan model was successfully used to account for the ionic strength effects in pH titrations and in metal binding. In metal binding experiments at high ionic strength swelling of the biomass particles was observed. The model fit improved when compared to the Donnan model for rigid particles when particle swelling proportional to the number of free binding sites was assumed.

Sundman et al. (2008) characterized the interactions between Ca2+, Cu2+, and two different fibre materials—a fully bleached softwood kraft pulp, and a chemically modified fully bleached softwood kraft fibre material—aiming for a better understanding of the interactions between water suspended cellulose fibres and metal ions. The study was conducted as a function of pH (2 to 7), both in the absence and presence of an excess of Na+ ions (0 to 100 mM NaCl). For both fibre materials, adsorption data collected in the absence of Na+ were fully explained by the non-specific Donnan ion-exchange model. However, in the presence of an excess of NaCl, the data clearly indicated that higher amounts of divalent metal ions adsorbed in comparison to the prediction of the Donnan model. Therefore, to model these data, specific metal ion–fibre surface complexes were assumed to form, in addition to the Donnan ion-exchange. It was found that the Donnan ion exchange model satisfactorily described Ca2+ and Cu2+ ion adsorption by both fibre materials when no excess of Na+ ions was present in the fibre suspensions. On the other hand, in an excess of ionic medium, the Donnan model underestimated the Ca2+– and Cu2+-ion uptake in all experiments. The deviation was greatest for the native low-charged fibre material and at the highest ionic medium.

Lumped parameter isotherm model

Schiewer and Wong (1999) used a lumped parameter isotherm model, where they emphasized the need to incorporate pH effects into the isotherm model. Since pH is one of the key parameters in biosorption, it is desirable to use isotherm equations that can accommodate pH as one model variable. Their model also incorporates ion exchange constants, reflecting that the biosorbent is initially saturated with some ions that have to be released when the metal ion is consumed. Ion exchange constants do not, however, take into account that the degree of binding site occupation may change. Therefore, multicomponent isotherm models have to be proposed that can account for ion exchange and pH effects. The model needs to be based on a 1:2 binding stoichiometry, whereby one divalent metal ion M binds to two binding sites B. Two different approaches have been proposed, i.e., to use ion exchange constants that assume the formation of B2M complexes or a multicomponent isotherm assuming the formation of BM0.5 complexes.

These isotherm models can be represented as:

 for B+M = BM (62)

 for 2B+M = 2BM0.5 (63)

 for 2B+M B2M (64)

The main advantage of the BM0.5 and B2M models is that they adequately represent the possible occurrence of divalent metals occupying two binding sites. Therefore, these stoichiometric assumptions are better suited to model the exchange between metals and protons. The BM0.5 and B2M models yield slightly lower deviations from the experimental data in comparison to the BMb model, but all have similar magnitudes. The cited authors found that for Cu binding, the B2M model was better than the BM0.5 stoichiometry, while the reverse was true for Ni. This improved performance for BM0.5 for Ni was due to different isotherm shapes, whereby the BM0.5 model showed rather gradual changes of ion binding with metal concentration. In either case, the stoichiometric assumption that yielded the better fit displayed a slope in the exchange plot much closer to 1.0 compared to the other model. It can be concluded that the slope in the log/log plot for metal proton ion exchange is the best indicator of the appropriateness of the stoichiometric assumptions.

The B2M model can be advantageous at very low metal concentrations, where the BM0.5 model sometimes tends to overpredict the metal binding. Since however both stoichiometric assumptions typically fit equally well and since the BM0.5 model offers the additional advantage of being much simpler to use (no iterations are required), it is recommended to use the BM0.5 model. An exception is when utilizing the assumption that binding sites must be a suitable distance from each other in order to form a stable complex. Thus, the lesser affinity in Ulva in the cited work may be caused by a lack of suitably spaced sites (i.e., the individual carboxyl sites may be too far apart to allow bidentate binding).

Other multicomponent fits

Srivastava et al. (2006) studied the competitive adsorption of Cd(II) and Zn(II) ions onto bagasse fly ash (BFA) from binary systems and used different isotherm models to study the equilibrium of systems.

Various monocomponent isotherm equations such as those of Freundlich (Eq. 14), Langmuir (Eq. 11), and Redlich–Peterson (R–P) (Eq. 23) were used to describe the equilibrium characteristics of adsorption; the Redlich–Peterson (R–P) and the Freundlich models represented the single ion equilibrium adsorption data better than the Langmuir model. Equilibrium isotherms for the binary adsorption of Cd(II) and Zn(II) ions on BFA had been analyzed by non-modified Langmuir, modified Langmuir, extended-Langmuir, Sheindorf–Rebuhn–Sheintuch (SRS), non-modified R–P, and modified R–P adsorption models. These multicomponent isotherm equations that have been used are presented as follows:

Non-modified competitive Langmuir model

The extension of the basic Langmuir model for component i in a N-component system to competitive adsorption can be formulated as follows:

 (65)

where qm,i and KL,i are derived from the corresponding individual Langmuir isotherm equations.

Modified competitive Langmuir isotherm

Individual adsorption constants may not define exactly the multi-component adsorption behavior of metal ion mixtures. For that reason, better accuracy may be achieved by using modified isotherms related to the individual isotherm parameters and the correction factors. An interaction term, ηL,i, which is a characteristic of each species and depends on the concentrations of the other components, has been added in the competitive Langmuir model. The modified competitive Langmuir isotherm is given as,

 (66)

where qm,i and KL,i are derived from the corresponding individual Langmuir isotherm equations, and ηL,i values are estimated from competitive adsorption data. For binary mixtures, this equation can be rewritten as the first and the second component, respectively, and the two equations can be solved simultaneously to obtain the multicomponent Langmuir adsorption constants for each component.

Extended Langmuir isotherm

Assuming that the surface sites are uniform, and that all the adsorbate molecules (ions) in the solution compete for the same surface sites, the extended Langmuir equation for multicomponent systems can be written as

 (67)

Sheindorf–Rebuhn–Sheintuch (SRS) model

A Freundlich-type multi-component adsorption isotherm known as the Sheindorf–Rebuhn–Sheintuch (SRS) equation was derived by Sheindorf et al. (1981), to represent experimental data. A general SRS equation for the adsorption isotherm for component i in a N-component system is given as:

 (68)

The pre-exponential coefficient KF,i and the exponent ni are determined from the mono-component systems. The competition coefficients aij describe the inhibition to the adsorption of component i by component j, and can be determined from the thermodynamic data, or more likely, from the experimental data of multicomponent systems. The SRS equation assumes that (I) each component individually obeys the Freundlich isotherm; (II) that for each component in a multicomponent adsorption system, there exists an exponential distribution of site adsorption energies,

 (69)

where ai and bi are constants; and (III) the coverage by each adsorbate molecule (or ion) at each energy level Q is given by the multicomponent Langmuir isotherm equation:

 (70)

where,

Integration of Ni(Q)θi (Q) over energy levels in the range of – ∞ to + ∞ yields Eq. (71), and the competition coefficients are defined as aij = K0j/K0i and thus aji = 1/aij. The SRS equation was successfully applied to a multicomponent equilibrium adsorption of different types of contaminants,

 (71)

where

 and aij = b0j b0i

Non-modified competitive Redlich–Peterson model

The competitive non-modified R–P model related to the individual isotherm parameters only is given as follows,

 (72)

where KR,iaR,I, and βi are the R–P parameters derived from the corresponding individual R–P isotherm equations. The competitive non-modified R–P model is rearranged to the following modified competitive R–P Model to take the characteristics of each species into account,

 (73)

where values of R,i are estimated from competitive adsorption data.

Marquardt’s percent standard deviation (MPSD) was used to test the adequacy and accuracy of various isotherm model fits with the experimental data. Based on a linearly regression analysis, Srivastava et al. (2006) showed that R2 was closer to unity for the R–P and the Freundlich models compared to the Langmuir model. Thus it was concluded that equilibrium adsorption data of single component adsorption, i.e. Cd(II) and Zn(II) ion, could be represented more appropriately by the R–P and the Freundlich models in the studied concentration range and at lower concentrations, since the Langmuir isotherm did not adequately represent the equilibrium sorption. The single-component Langmuir constants are Qo (monolayer saturation at equilibrium) and b (corresponding to the concentration where the amount of metal ion bound to adsorbent is equal to Qo/2 and which indicates the affinity of the metal ions to bind with adsorbent). The results of this study showed that the amount of Zn(II) ions per unit weight of BFA for the complete monolayer surface coverage was higher than that of Cd(II), and a large value of b implied strong bonding of Zn(II) ions to BFA. KF and n, the single-component Freundlich constants (indicating the adsorption capacity and adsorption intensity, respectively) were also calculated, and the BFA displayed greater heterogeneity for Cd(II) than for Zn(II) ions. The value of n was found to be 1, which implied that both the Cd(II) and Zn(II) ions were favorably adsorbed by BFA at pH 6.0. The magnitude of KF also showed higher uptake of Zn(II) than Cd(II) ions by BFA at pH 6.0. It was noted that the Redlich–Peterson constant b normally lies between 0 and 1, indicating a favorable adsorption. The experimental and predicted equilibrium uptake (qe) evaluated from the single-component Langmuir, Freundlich, and Redlich–Peterson models for the individual adsorption of Cd(II) and Zn(II) onto BFA at pH 6.0 were also compared, and the MPSD values were calculated. Based on the lower MPSD values, the R–P and Freundlich models displayed better fit to the experimental adsorption data than the Langmuir model.

Studies comparing alternative multi-component models to fit data

The simultaneous adsorption data of Cd(II) and Zn(II) on the BFA was also fitted to multi-component isotherm models (Srivastava et al. 2006). The multi-component non-modified Langmuir model displayed a poor fit to the experimental data (MPSD = 101.6). All the modified Langmuir coefficients (ηL,i) estimated were much greater than 1.0, indicating that the non-modified multi-component Langmuir model related to the individual isotherm parameters could not be used to predict the binary-system adsorption. However, the use of the interaction term, ηL,i, in the modified Langmuir model (MPSD = 28.3) improved the fit of the non-modified Langmuir model. The use of the multi-component extended-Langmuir model in the cited study showed its inadequacy to represent the experimental data (MPSD values were large). The Ki values, reflecting the affinity between the adsorbent and the metals in the binary systems by using the BFA were found to be 0.04 L/mg for both Cd(II) and Zn(II). The overall total metal ions uptake (qmax) by BFA is 7.24 mg/g. These values were found to be considerably lower than the sum of the maximum total capacities of Cd(II) and Zn(II) ions resulting from the single component adsorption systems. For that reason, it was concluded that the adsorption sites of Cd(II), and Zn(II) in binary systems onto BFA may likely be partially overlapped. These lower metal ion uptake results also implied that there may be a variety of binding sites on the adsorbents showing partial specificity to the individual metal ions. The information obtained from the maximum capacities seems to violate the basic assumptions of the Langmuir model, i.e. that the entire adsorbent surface is homogeneous and that there is no lateral interaction between the adsorbate molecules. Consequently, the affinity of each binding site for the adsorbate molecules should be uniform. The use of interaction terms, ηR,i, for the modified R–P model (MPSD = 24.1) improved the fit of the non-modified R–P model (MPSD = 52.0); however, the SRS model (MPSD = 15.4) provided the best-fit to the binary adsorption data of Cd(II) and Zn(II) onto BFA. Thus, the SRS isotherm was found to best represent the binary system adsorption. This improved SRS performance was expected, as BFA has a heterogeneous surface, and the adsorption of the single metal ions had also been well represented by the Freundlich isotherm equation. It was evident that the modification of the Freundlich equation, as given by the SRS model, took into account the interactive effects of individual metal adsorbate ions between and among themselves and the adsorbent reasonably well. Therefore, the binary adsorption of metal ions onto BFA can be represented satisfactorily and adequately by the SRS model. The multicomponent SRS model is applicable to those systems where each component individually obeys the single-component Freundlich isotherm.

The isotherm coefficients can be determined from the mono-component isotherm except for the adsorption competition coefficients, aij, which have to be determined experimentally. The competition coefficients, aij, describe the inhibition to the adsorption of component i by component j. The two components for the cited study were found to obey the single-component Freundlich model individually. The competition coefficients aij and aji were estimated from the competitive adsorption data for Cd(II), Ni(II), and Zn(II) ions by using the MS EXCEL 2002 program. A comparison of the competition coefficients in the adsorption isotherm equation shows that the uptake of the strongly adsorbed Zn(II) was significantly inhibited by the presence of Cd(II) (a21 = 2.70). Similarly, the uptake of Cd(II) by BFA was suppressed in the presence of Zn(II) ion in the solution (a12 = 2.15).

Three-dimensional (3-D) adsorption isotherm surfaces were used to evaluate the performance of the binary metal ions adsorption system. It was found that the SRS model predictions for the simultaneous adsorption of Cd(II) and Zn(II) ions by BFA from aqueous solution were very satisfactory.

Srivastava et al. (2009a) analyzed the competitive adsorption of Cd(II), Ni(II), and Zn(II) ions onto rice husk ash (RHA) from ternary metal ion mixtures. Various isotherm equations such as those of Freundlich (Eq. 6), Langmuir (Eq. 2), and R-P (Eq. 14) were used to describe the monocomponent equilibrium characteristics of adsorption of individual ions onto RHA. The MPSD error values were the lowest for the Freundlich model, followed by the R-P and Langmuir models. Therefore, the equilibrium adsorption data of Cd(II), Ni(II), and Zn(II) ion adsorption on RHA could be represented appropriately by the Freundlich model within the studied concentration range. RHA has a heterogeneous surface. It is, therefore, expected that the Freundlich and R-P isotherm equations can better represent the equilibrium sorption data than the Langmuir isotherm model. The simultaneous sorption data of Cd(II), Ni(II), and Zn(II) from the ternary mixture onto RHA was fitted to the multicomponent isotherm models, viz., nonmodified, modified, and extended Langmuir models (Eq. 2); nonmodified and modified R-P models (Eq. 14); and the SRS model. On the basis of the MPSD error function, it was found that the simultaneous sorption phenomena of Cd(II), Ni(II), and Zn(II) ions on the RHA could be adequately represented by the SRS model.

The sorption of heavy metals (lead, copper, and cadmium) by a marine algal biomass Sargassum sp. was studied in single and multiple metal-ion systems (Sheng et al. 2007). The equilibrium data for the single metal ion system was studied with the help of the Langmuir adsorption isotherm model (Eq. 11). The effect of the presence of multiple metal ions on the biosorption performance was investigated, and the results were evaluated using the modified competitive Langmuir model and modified Jain-Snoeyink model.

The extension of the basic Langmuir model to account for competitive adsorption in multiple-metal systems can be formulated as follows,

 (74)

where the terms qmax,i (monolayer sorption capacity) and b(affinity of sorbent for the sorbed species) are derived from the corresponding individual Langmuir isotherm equations; qe,i and Ce,i are, respectively, the uptake and final concentration when adsorption equilibrium is reached, and is the number of metal ions in solutions. The Langmuir model assumes that each component is adsorbed onto the surface according to ideal solute behaviors, where there is no interaction or competition between molecules involved under homogeneous conditions. To account for nonideal systems using the Langmuir theory, Jain and Snoeyink introduced an additional term into Eq. 74 for binary metal systems,

 (75)

where qmax,1 > qmax,2. The additional term on the right-hand side of Eq. 75 (proportional to the quantity qmax,1 – qmax,2) is the Langmuir expression for the amount of solute 1 adsorbed on to the surface without competition. The second term on the right hand side represents the amount of solute 1 adsorbed onto the surface in competition with solute 2. The amount of solute 2 adsorbed onto the sorbent surface can be calculated from Eq 74.

All the model parameters in these competitive isotherms for multiple-metal systems may be derived from single-component isotherms. Indeed, better accuracy may be achieved by extracting additional coefficients from the multiple-metal isotherms. For instance, an interaction term η, which is a characteristic of each species and is dependent on the sorption properties of the sorbents, has been defined in the modified competitive isotherms.

The modified competitive Langmuir model takes the form

 (76)

For a binary system, the modified Jain-Snoeyink model becomes

 (77)

The root-mean-square error (RMSE) was used to check the adequacy of the model. The sorption data for single metal system at different pH values were well-modeled by the Langmuir isotherm. However, in case of binary and tertiary metal systems, the original competitive Langmuir model and the Jain-Snoeyink model failed to fit the experimental data adequately, with all the R2 values being less than 0.70. Experimental data fitted both the modified competitive Langmuir model and the modified Jain-Snoeyink model well. It was evident that the modified models, with the introduction of the interaction coefficient (η), considerably improved the accuracy of the modeling. Furthermore, it was also shown that the interaction coefficient ηderived from the binary metal system could be successfully applied to the ternary metal system, thus indicating the possibility of predicting biosorption performance of such complex systems, based on the modeling parameters obtained from simpler experiments.

The lead (II) biosorption potential of Aspergillus parasiticus fungal biomass was investigated in a batch system (Akar et al. 2007b). Freundlich (Eq. 14), Langmuir (Eq. 11), and Dubinin–Radushkevich (D–R) isotherms (Eq. 16) were used for the biosorption isotherm modelling. Results indicated that the Langmuir, Freundlich, and D–R isotherm models are suitable for describing the lead (II) biosorption equilibrium by A. parasiticus in the studied concentration range with the regression coefficient (R2) values more than 0.97. The RL (affinity between sorbent and sorbate using Langmuir constants) value for this study was 1.73 × 10–2, indicating that the biosorption of lead (II) was favorable. The Freundlich constants KF and indicate the biosorption capacity of the biosorbent and a measure of the deviation from linearity of the biosorption, respectively. The adequate description of the experimental results with all of the isotherm models investigated in this study implied that the biosorption of lead (II) ions onto A. parasiticus biomass was complex, involving more than one mechanism. The biosorption process could be described by ion exchange as the dominant mechanism, in addition to complexation with groups at the surface of this biosorbent. The ion exchange mechanism was confirmed by the E value obtained from D-R isotherm model as well.

The biosorption of chromium(VI) from saline solutions onto dried Rhizopus arrhizus was studied as a function of pH, initial chromium(VI), and salt (NaCl) concentrations in a batch system by Aksu and Balibek (2007). The equilibrium sorption data were analysed by using Freundlich (Eq. 14), Langmuir (Eq. 11), Redlich–Peterson (Eq. 23), and Langmuir–Freundlich (Sips) models. The two- and three-parameter adsorption models, using non-linear regression technique and isotherm constants, were evaluated depending on salt concentration.

The Langmuir–Freundlich (Sips) model used in the cited study is another three-parameter empirical model for the representing equilibrium biosorption data (Eq. 78). This model suggests that the equilibrium data follow Freundlich isotherm at lower solute concentration, and thus, do not obey Henry’s law, but that they follow a Langmuir pattern at higher solute concentration,

 (78)

where AB, and are the Langmuir–Freundlich parameters. Values for m>>1 indicate heterogeneous adsorbents, while values closer to or even equal to 1.0 indicate a material with relatively homogenous binding sites. In this case, the Sips model is reduced to the Langmuir equation.

Thus the equilibrium data were fitted to these isotherm models, and the values of average percentage errors and linear regression coefficients were the criteria for the selection of the most suitable isotherm model. On the basis of lower average percentage errors (in the range 0.8 to 2.4) and higher linear regression coefficients (in the range 0.998 to 1.000), the three-parameter Langmuir–Freundlich (Sips) model best described the chromium(VI) sorption isotherm data compared to other models examined, which suggested the monolayer, homogeneous sorption in single as well as salt-added binary-systems. The relatively lower percentage errors also indicated that both the two-parameter Langmuir and three-parameter Redlich–Peterson models were also very suitable for describing the biosorption equilibrium of chromium(VI) by the fungal cells in all cases. The other two-parameter model of Freundlich exhibited a poor fit to the biosorption data of chromium(VI) with an average percentage error more than 8.3.

The value of (Freundlich constant), which was significantly higher than unity, indicated that chromium(VI) ions were favorably adsorbed under all the experimental conditions examined. The values of at different salt concentrations also indicated that decreased chromium(VI) biosorption intensity was affected by salt addition into biosorption medium. The magnitude of the constant KF (Freundlich constant) showed a relatively easy uptake of chromium(VI) ions from aqueous solution, with high adsorptive capacity of biomass for chromium(VI) in both single and binary systems. The presence of salt at any initial concentration was found to reduce the KF constant significantly. The salt added at different levels also affected the Langmuir constants (Qo and b). Dried R. arrhizus exhibited the maximum biosorption capacity (Qo) for single chromium(VI) biosorption. The addition of salt decreased the Qo value of chromium(VI) biosorption to an insignificant extent. A high value of the other Langmuir parameter, b, indicated a high affinity of the biosorbent for the sorbate. The highest value obtained for monometal conditions also decreased with the addition of salt, indicating its negative effect on chromium(VI) biosorption. Related biosorption parameters were also calculated according to the three-parameter isotherm of Redlich–Peterson using a non-linear regression method for chromium(VI) biosorption at different salt levels. The Redlich–Peterson constant, KRP, indicated that the adsorption capacity of biosorbent also diminished with increasing salt concentration. It is noted that β normally lies between 0 and 1, indicating favorable biosorption. In the case considered, the value of β was 1.0 for 50 g/L salt containing medium and tended to unity for other salt concentrations studied, suggesting that the isotherms approached the Langmuir form. The corresponding Langmuir–Freundlich parameters of AB, and for different salt concentrations were also calculated. Constant A, indicating the biosorption capacity and affinity of biosorbent to chromium(VI) ions, also decreased with salt addition. The value of m, an indicator of heterogeneity index, which was calculated to be about 1.0 for all levels of salt, showed that the chromium(VI) sorption data obtained in the cited study tended towards the Langmuir form rather than the Freundlich form, and thus, the fungus had a homogeneous surface. The results showed that three-parameter models represented the biosorption isotherm data much better than two-parameter models for all cases, with low percentage error values.

Again all these parameters changed with respect to the level of salt, and the results could be used to predict the adsorption behavior of chromium(VI) in an aqueous solution at a specific salt concentration. When isotherm constants were plotted against the salt concentration, the functional relationship between isotherm constants and salt concentration were not linear for the entire range of salt concentration. The results showed that the Freundlich and Langmuir parameters decreased following a second-order polynomial function of salt concentration with high linear regression coefficients. An exponential relationship between the Redlich–Peterson parameters (aRP and KRP) of chromium(VI) and salt concentration was obtained (β is assumed as 1 for all cases) with the correlation coefficients of 0.935 and 0.981, respectively. The relationship between the Langmuir–Freundlich model constants of and of chromium(VI) and salt concentration also followed an exponential equation with a high linear regression coefficient, while other Langmuir–Freundlich parameter, m, varied linearly with salt concentration.

The sorption of hexavalent chromium by marine brown algae Cystoseira indica, which was chemically-modified by cross-linking with epichlorohydrin (CB1, CB2), oxidized by potassium permanganate (CB3), or only washed by distilled water (RB), was studied with variations in the parameters: contact time, pH, initial metal ion concentration, and solid/liquid ratio (Basha et al. 2008). Langmuir (Eq. 11), Freundlich (Eq. 14), and Dubinin–Radushkevich models (Eq. 16) were used to describe the equilibrium between the Cr(VI) sorbed on the four pretreated biomasses of C. indica (q) and Cr(VI) ions in the solution. It was found that the Dubinin–Radushkevich isotherm model was in good agreement with all the experimental data as compared to the Langmuir and Freundlich isotherm models. The magnitude of E (mean free energy, kJ/mol), as obtained in the cited work, was useful for estimating the type of sorption reaction. The E values obtained were around 15 kJ/mol, which is in the energy range of an ion-exchange reaction, i.e., 8 to 16 kJ/mol. This E value suggests that biosorption of Cr(VI) by C. indica may be classed as an ion exchange reaction.

Chakravarty et al. (2008) used newspaper pulp as an adsorbent for the removal of copper from aqueous medium. The experimental data were analyzed using Freundlich (Eq. 14), Langmuir (Eq. 11), Dubinin–Radushkevich (D–R) (Eq. 16), and Redlich-Peterson (R–P) (Eq. 23) isotherm models. Their results showed that adsorption data fit reasonably well to the Langmuir, Freundlich, and R–P isotherms, as was reflected by the high correlation coefficients (R2). The Freundlich constant n increased with increasing initial Cu concentration. The D–R isotherm showed a definite trend for the KDR value. However, the R2 in D–R isotherm decreased with increasing initial concentration of Cu. No definite trend was observed for the Langmuir constants b and Qo. The calculated RL value (affinity) for adsorption of Cu(II) on the newspaper pulp adsorbent were found to be in the range of 0 to 1 at all initial Cu(II) concentrations, which confirms the favorable uptake of Cu(II) in the sorption process.

El Nemir et al. (2008) used a new activated carbon developed from date palm seed wastes, generated in the jam industry, for removing toxic chromium from an aqueous solution. The equilibrium data were tested using several isotherm models, including the Langmuir (Eq. 11), Freundlich (Eq. 14), Redlich–Peterson (Eq. 23), Temkin (Eq. 19), Dubinin–Radushkevich (Eq. 16), Koble–Corrigan (79), and generalized isotherm (80) equations.

The Koble–Corrigan equation used in the cited study is another isotherm model that depends on the combination of the Langmuir and Freundlich equations in one non-linear equation for representing the equilibrium adsorption data. It is represented as follows,

 (79)

where ab, and n are the Koble–Corrigan parameters, which were obtained by solving Eq. (79) using SPSS version 10.0 computer program.

The generalized isotherm equation was also tested for correlation of the equilibrium data. The linear form of the generalized isotherm is given by,

 (80)

where KG is the saturation constant (mg L-1); Nb the cooperative binding constant; Qo the maximum adsorption capacity of the adsorbent (mg g−1) (obtained from the Langmuir isotherm model); and qe (mg g−1) and Ce (mg L−1) are the equilibrium chromium concentrations in the solid and liquid phases, respectively.

In the cited study, four different linear forms of Langmuir isotherm were used, which are presented as follows (El Nemir et al. 2008):

Form Linear equation

Langmuir-1  (81)

Langmuir-2  (82)

Langmuir-3  (83)

Langmuir-4  (84)

Parameters related to each isotherm were determined by using linear regression analysis, and R2 was calculated. Their results showed that the linear forms 1 and 2 of the Langmuir isotherm were best fitted for the equilibrium data in comparison to 3 and 4. The experimental data were also found to fit well to the Freundlich model, with nF > 1, indicating that adsorption of Cr6+ onto DSC is a favorable physical process. The Koble-Corrigan equation also displayed high R2 values, which indicates that the Koble-Corrigan equation had a strong goodness of fit to the experimental data. The values of b were 0.01 and 0.02, indicating the combination between heterogeneous and homogeneous adsorption of Cr6+ on DSC. The three isotherm constants (A, B, and g) of the Redlich-Peterson isotherm model were also calculated using non-linear regression analysis. The correlation coefficients obtained were comparable to the Langmuir and Freundlich equations, indicating that the Redlich–Peterson isotherm can be representative of the data obtained from the adsorption of Cr6+ on DSC. According to the R2 values, the Temkin isotherm can also characterize the equilibrium adsorption data. However, this isotherm appeared to be less suitable than both the Koble-Corrigan and Redlich-Peterson isotherm models. The results using the D-R isotherm indicated that the D-R model had a poorer fit to the experimental data compared to the Langmuir, Freundlich, Koble-Corrigan, and Temkin isotherm models. The values of E (mean free energy) were also calculated and were found to be in the range of ion-exchange mechanisms, indicating that the adsorption process of Cr6+ ion onto DSC was physisorption. The results also showed that the generalized adsorption isotherm represented the equilibrium data reasonably well. The results showed that most of the tested isotherm models fitted well to the experimental data obtained for the adsorption of Cr6+ on DSC. Only the Dubinin-Radushkevich isotherm showed less agreement with the experimental data obtained.

The removal of poisonous Pb (II) from wastewater by different low-cost abundant adsorbents, e.g. rice husks, maize cobs, and sawdust was investigated, and the equilibrium adsorption capacity of adsorbents used for lead were measured and extrapolated using linear Freundlich, Langmuir, and Temkin isotherms (Abdel-Ghani et al. 2007). The experimental data were found to best fit the Temkin isotherm model.

Biosorption of chromium using suspended and immobilized cells of Rhizopus arrhizus was studied by evaluating the physicochemical parameters of the solution such as initial chromium ion concentration in both batch and packed bed reactor. The Langmuir, Freundlich, and Redlich-Peterson adsorption isotherm models were used in the equilibrium modeling. The Freundlich and Redlich-Peterson adsorption isotherm models were found to fit accurately with the experimental data (Preetha and Viruthagiri 2007b).

A need for further comparative evaluation of different isotherm models

Based on the discussion in the foregoing section, it is clear that data from metal sorption studies can be well fitted to a large number of different isotherm models. These models consider various aspects of the problem, including surface characterisitcs of the sorbent, affinity between sorbate and sorbent, potentiality of the sorbent, and the nature of the sorption process. However, two of the earliest models, those of Langmuir and Freundlich, continue to dominate the attention of the majority of researchers. This situation suggests that there may be important opportunities for researchers to compare the goodness of fit of existing or newly generated data to a wider variety of the available isotherm models, as outlined in this article. A goal of such efforts can be to determine which of the more recently developed models offers sufficient benefits in terms of fitting accuracy and mechanistic insights to justify their more frequent usage, with some emphasis placed on making the methods available to engineers in a user-friendly format.

CHEMICAL FACTORS AFFECTING SORPTION

Chemical Complexation

The concept of chemical complexation presupposes that there will be a site-specific interaction between particular kinds of metal ions and functional groups at the sorbate surface (Fourest and Volesky 1996; Kim et al. 1998; Merdy et al. 2002; Vijayaraghavan and Yun 2008; Lawrance 2010). Based on such concepts, some authors (Paagnanelli et al. 2005b; Zhang et al. 2005; Lodiero et al. 2006; Valex et al. 2006) have advocated an approach in which chemical complexation, rather than ion exchange, is used to account for adsorbed amounts as a function of solution concentrations.

Metal specificity

Some of the most cogent evidence in support of a chemical complexation concept of metal ion sorption consists of a dependency of molar adsporption capacities on the identity of the tested metal ion. Such differences have been widely reported, and the following citations are representative (Qadeer et al. 1996; Puranik and Paknikar 1999; Schiewer and Wong 1999; Kogej and Pavko 2001; Iqbal et al. 2002; Pardo et al. 2003; Chauhan et al. 2005b; Kobya et al. 2005; Saito and Isogai 2005; Romera et al. 2006; Zhang and Banks 2006; Afkhami et al. 2007; Arslan and Pehlivan 2008; Chen and Wang 2008b; Krishnani et al. 2008b). According to theories of chemical complexation (Lawrance 2010), differences in the ability of a surface site to bind different metals are often attributed to matching the radius of the metal ion, as well as the symmetry (octahedral, etc.) of its valence electron orbitals, to the positions of the surface-bound atoms (e.g. carboxylate groups) at the site of adsorption.

Hard and soft ions

The concept of hard and soft ions (Pearson 1963) has been used effectively to explain why certain metal ions tend to have greater affinity for certain types of sorbent surfaces (Avery and Tobin 1993; Brady and Tobin 1995; Chen and Wang 2007b,c, 2010; Gadd 2009). To summarize, ions that are called “soft” are those that have relatively loosely held, polarizable outer electrons, so that greater contributions of covalent character can be expected in their interaction with surface sites. Examples include lead and mercury. By contrast, “hard” ions have more closely held, less polarizable outer electrons, so that their interactions are more simply dominated by electrostatic factors. Nickel is a prime example. In some cases the molar amounts of adsorbed ions have been shown to be related to the ionic radius of the metal (Chen and Wang 2007b,c). Avery and Tobin (1993) observed that soft ions can be expected to favor sites containing S and N atoms, whereas hard ions can be expected to favor sites with oxygen atoms.

Demonstration of Metal Ion Interaction with Functional Groups

A large number of studies have provided evidence in support of metal ions interaction with specific functional groups at the substrate surface. Many authors have used shifts in the maxima of infrared light adsorption as evidence for specific interactions (Ashkenazy et al. 1997; Guibaud et al. 2003; Ahluwalia and Goyal 2005a; Deng and Ting 2005a; Ahalya et al. 2006, 2007; Chen and Yang 2006; Murphy et al. 2007; Arief et al. 2008; Li et al. 2008; Bakir et al. 2009; Garcia-Reyes et al. 2009; Iqbal et al. 2009a; O’Connell et al. 2010). NMR spectra also have been used to substantiate the involvement of specific chemical sites in the binding of metal ions (Araujo et al. 2007). One question that has not been completely resolved by these investigations is whether the observed shifts in IR absorbance maxima might be an effect, rather than indicating a cause of metal ion binding. This lack of certainty regarding cause/effect is because anything that changes the electron density significantly near a functional group can be expected to have an impact on the energy content of the associated covalent bonds. It follows that the presence of a metal ion in a fixed position on a substrate surface will change the IR spectra associated with adjacent functional groups, whether or not they each have a positive effect on the bonding mechanism.

In particular, IR evidence has supported the participation of carboxylate groups in metal ion binding (Ahluwalia and Goyal 2005a; Gardea-Torresdey et al. 2002). Several studies have supported such a conclusion by selectively converting the existing carboxylic acid groups to another form and displaying a substantial decrease in metal uptake (Beveridge and Murray 1980; Fourest and Volesky 1996; Ashkenazy et al. 1997; Kapoor and Viraraghavan 1997; Tiemann et al. 1999; Romero-Gonzalez et al. 2001; Bai and Abraham 2002; Tiemann et al. 2002; Chubar et al. 2003; Meunier et al. 2003b; Sheng et al. 2004b; Southichak et al. 2006b; Suksabye et al. 2007; Iqbal et al. 2009b; Murphy et al. 2009a). Chen and Wang (2008a) and Park et al. (2008d) used X-ray methods to display evidence of oxygen atom participation in the binding of zinc to yeast cell biomass. Other authors have used the pH-dependency of adsorption as contributing evidence to support the importance of carboxylic acid groups in metal ion binding (Tiemann et al. 2000; Malik et al. 2002; Guo et al. 2008). A related approach, involving immersion calorimetry and shifts in pH, has been pioneered by Lopez-Ramon et al. (1999).

Effects of pH on Metal Ion Sorption

Although a great deal of attention has been paid to dissociation constants associated with different kinds of acidic groups at sorbent surfaces, inadequate attention has been paid to the simultaneous effects of pH on the ionic species of metal ions that are present in the bulk solution.

Close inspection of the next to last column in Table A indicates a common theme for the effects of pH on adsorption of metal cations. Authors of several studies have indicated that adsorption is favored by “increasing pH,” but only up to a certain limit. The trend with increasing pH (i.e., 3 to 6) is generally explained by increasing dissociation of carboxylic acid groups on the cellulosic substrate, which results in an increasing ion exchange capacity of the material. The climactic upper end of a typical curve of adsorptive capacity vs. pH is generally understood to entail an equilibrium between the soluble hydrated metal ion and a corresponding insoluble neutral hydroxide species (Chang et al. 1997; Leyva-Ramos et al. 1997; Schneider et al. 2001; Dastgheib and Rockstraw 2002a; Mohan and Pittman 2006; Sciban et al. 2006b; Sheng et al. 2007; Demirbas 2008). In addition, certain ions tend to form polynuclear species as they progressively interact with OH ions in solution (O’Connell et al. 2010).

Essentially the opposite pH dependency is often observed when evaluating the adsorption of Cr(VI) species onto biosorbent surfaces. As shown by many entries in Table A, the highest sorption of Cr from Cr(VI)-containing solutions is generally found within a pH range of about zero to 2 (see, for instance Gupta et al. 1999). Under such conditions the cellulosic carboxyl groups are expected to be fully protonated, thus minimizing any electrostatic barrier to sorption of negative chromate ions.

Redox Effects

Changes in the valence state of the metal

During the past decade significant progress has been achieved in understanding the adsorption of metal ions that are prone to changes in their oxidation state. Though chromium has received the greatest attention, valence changes also can play a role in the removal of platinum (Chen et al. 2007), mercury (Lloyd-Jones et al. 2004; El-Shafey 2010); copper (Chandran et al. 2002), gold, silver, and palladium (Cox et al. 2005) from aqueous solution. The following researchers have reported evidence for Cr(VI) reduction to Cr(III) accompanying its adsorption (Raji and Anirudhan 1997; Selomulya et al. 1999; Han et al. 2000; El-Shavey and Canepa 2003; Deng and Ting 2005c; Deng et al. 2006; El-Shafey 2005; Gao et al. 2005; Park et al. 2004b, 2005b,c, 2006a, 2007a,b, 2008d; Murphy et al. 2009b; Suksabye et al. 2007, 2009). In certain cases, reduction from Cr(VI) to Cr(V) was observed (Suksabye et al. 2009). The reduction of Cr(VI) to Cr(III) has been found to proceed under highly acidic conditions (i.e., pH between 1 and 3) (Candela et al. 1995). The reduction of metal ions by microbes has also been demonstrated as a mechanism of metal removal from water (Chandran et al. 2002).

Changes in the valence state of the substrate

Research has confirmed an expected balance between reduction and oxidation whenever there is a valence change of a metal during the course of its adsorption. In other words, if the metal is reduced, something else in the system must be oxidized. Several researchers have observed oxidation of surface groups of a cellulose-based sorbent material when reductively adsorbing various precious metals or hexavalent chromium (Cox et al. 2005; Park et al. 2005a,b, 2006c, 2007b, 2008b; Elangovan et al. 2008a; Murphy et al. 2009b). Oxidation of the substrate as part of the adsorption mechanism is consistent also with the form of the rate expression that governs the uptake of chromium (Park et al. 2005c). Further support for this type of mechanism was provided by Praghakran et al. (2009), who showed that the rate of reductive sorption of Cr(VI) was highly dependent on the nature of the substrate, with superior results being achieved using coffee dust. Studies involving the blocking of specific sites showed that both carboxyl goups and amino groups can participate in the reductive adsorption process of Cr(VI) (Park et al. 2005d). In the case of chromium (VI) adsorption, it can be considered highly fortuitous that oxidation of the surface often increases the number of carboxylate groups, which are well suited to the bonding of the positively charged Cr(III) ions that result from the process.

Figure 4 provides a schematic of some of the concepts just described. The question mark at the left of the figure represents an intial challenge in accounting for the adsorption of a negative ion onto a predominantly negative cellulose-based material. The rightward arrow represents a combined process in which (a) groups at the substrate surface (or in the bulk phase) are oxidized, (b) the chromium(VI) is reduced to Cr(III), and (c) the positively charged Cr(III) ion is simultaneously or sequentially bound to negative sites at the substrate surface.

A worthy question for future research is to explore whether intentional changes to the state of oxidation of a substrate can be used in order to induce subsequent changes in valence of metal ions, thus facilitating their collection. Such a proposal is consistent with work reported by Berenquer et al. (2009) who applied galvanic reduction and oxidation to activated carbon. Changes in the surface groups were observed, similar to what can be achieved by treatment with reducing agents or oxidizing agents. However, it is not yet known whether related approaches could facilitate the conversion of, say, Cr(VI) to the less toxic and more adsorbable Cr(III) form. Such observations raise questions as to whether surface sites also could be manipulated by selecting anaerobic vs. aerobic conditions of digestion (Joseph et al. 2009).

Reversibility of Metal Binding

In order to optimize conditions of regeneration or disposal, it is important to understand what circumstances can lead to the reversal of metal sorption onto cellulose-dried surfaces The most straightforward way to encourage metal ions to go back into solution is to replace those ions with a large excess of something else. Hydrochloric acid is the most commonly specified reagent, as indicated by the many entries in the next to last column of Table A. Regeneration, however, also has been achieved by strong base, or less commonly by a concentrated salt solution. Many studies have demonstrated restoration of a majority of the original sorption capacity after the restorative treatment. Increases in adsorption capacity, relative to the original capacity, have been observed in some cases (Chang et al. 1997).

Figure 5 illustrates one way that an ion-exchange material can be regenerated. The left-hand part of the figure depicts a metal ion bound in bi-dentate fashion to a pair of carboxylate groups at a surface. Addition of salt brine (or acid, NaOH, etc.) causes the target metal(s) to be released into the brine, while sodium ions are left weakly associated with the carboxylate groups on the sorbate.

CLOSING COMMENTS

Any review of such an active and multifaceted field as biosorption of metals is bound to leave out information or to oversimplify critical details. Fortunately, as noted in the Introduction, subtopics of the field have already been reviewed, often with a particular focus that may be of interest to readers (see Table 1).

The explosive growth in publications in this area, with expected faster pace of technological developments to come, may still be difficult to grasp. Combinations of biomass types, chemical or thermal modificationis, and target metals are often being studied in relative isolation and often without a strong theoretical justification for the combinations of factors that are compared. Some studies appear to just apply well-known methods to different species of biomass or different metal ions. The scene sometimes can be compared to a wild frontier, in which the first researcher to generate, say, a Langmuir isotherm, can lay claim to having found something new.

The field of study will hopefully continue to mature during the coming decades, and in our opinion two kinds of studies would be very valuable to lead the way into the future. On the one hand, there is a need for mechanistic studies that deal more cogently with the molecular mechanisms, including such issues as binding sites, valence states, coordination chemistry, and the speciation of metals in solution. On the other hand, there need to be a life cycle analysis, considering the wasteproducts of metal remediation operations.

A Need for Studies Related to Proteinacious Binding Sites

One apparent gap in research that can be expected to attract increasing attention during the coming decade concerns the use of engineered proteins for advanced adsorption technologies. A study by Kostal et al. (2005) points the way in this direction, showing how biotechnological and nanotechnological principles can be applied. A study by Vinopal et al. (2007) showed that certain peptide sequences have highly specific affinity for certain metal ions. In principle, the sorptive properties of enzymatic materials could be optimized by genetic manipulation. Once the optimized biomaterial has been created, the genes could be inserted into a suitable organism to produce suitable quantities for the needed application. It is possible that such proteinaceous biosorbent products could be supported on cellulosic scaffolds, facilitating their use in either batch-type or column-type treatment schemes.

A Need for Application of Nanotechnology Concepts

Given the major attention to nanotechnology during the past decade, there has been a notable lack of consideration of related concepts in the case of biosorption. In particular, the concept of self-assembly (Ninham and Lo Nostro 2010) should be considered relative to the manner in which adjacent hydrated metal cations arrange themselves in various instantaneous configurations at a substrate surface. Attention also should be focused on the nucleation of colloidal precipitates of metal hydroxides as the pH is raised in the neighborhood of a solubility limit (Chang et al. 1997; Leyva-Ramos et al. 1997; Schneider et al. 2001; Sciban et al. 2006b; Demirbas 2008). Presumably such precipitated metal hydroxides could either remain as a colloidal suspension or it may be adsorbed onto the biosorbent material. In the latter case, the mechanisms of adsorption may be entirely different from those governing adsorption of the cationic forms of the same metals.

Another aspect of nanotechnology involves accessibility. Metal-binding sites at the surfaces of biomass samples can be effective only if the metal ions are able to reach them. Mesopores in the biomass may have closed up irreversibily at some point due to an uncertain history of the drying of the material (Stone and Scallan 1966). Such effects have the potential to explain the huge ranges (typically three orders of magnitude or more) that have been reported by different researchers for the metal-uptake capacities of nominally similar biomass samples. Future studies need to address these issues science at the nanoscale level.

A Need for Life Cycle Analysis Research

An article by Chojnacka (2010) has helped to define the scope of some needed research related to the life cycle of bioremediation. For a healthy ecosystem it is necessary to account not only for biosorption (which mainly involves dead biomass), but also for bioaccumulation, which involves ingestion and retention within living organisms. The two phenomena can be expected to interact in complex ways, keeping in mind that some metals are prone to building up over time to toxic levels in living tissues.

As was noted in the introduction, there has been inadequate research towards the merits of landfilling or land application as an alternative strategy for dealing with spent cellulose-based sorbent material. Though it is reasonable to be concerned with leachate that contains metals, research is needed to determine whether the metal-binding capacity of the cellulosic material is sufficient such that the resulting concentrations of soluble species remain in a generally safe or beneficial range, especially in the case of metals that can be considered as essential minerals when they are present at low levels. By contrast, the most widely used regeneration schemes require major shifts in pH, leading to the requirement that the pH be adjusted prior to discharge of the water, which will then have a higher level of salinity. Another potentially beneficial strategy that deserves exploration is the incineration of spent biosorbent, making it possible to concentrate the collected metals in the resulting ash. Though there is potential to make use of biosorption as a fuel source, there is need to be concerned about the energy requirement for evaporation of water, as well as measures to reduce airborne particulates.

While there is clear evidence that bio-sorption could be a viable alternative for the remediation of contaminated aquifers, there are several decision factors that companies must consider when selecting an appropriate treatment technology. These factors include but are not limited to treatment cost, volume of contaminated material to be treated, treatment time, site location, the complexity of the target pollutant, and the surrounding water quality. Technologies that merely change the form of toxic wastes, such as in adsorption processes without proper removal, may lead to future problems and costs. Therefore, effective remediation treatment technologies must consider the entire fate of wastes over a longer period. It would be easy to say that these factors are independent. However, it’s more likely that the decision complexity is increased due to the interdependency of these factors. Further, companies that would like to select in-situ soil remediation options must weigh that approach against dig-and-haul operators, who remove contaminated soil to landfill sites at a possibly cheaper cost. Consequently, the decision to utilize bio-sorption processes, regardless of whether it is a proven technology, is a complex decision.

A Need for Non-Selective Sorption

The mining industry has provided early stimulus for research into metal removal technology. Perhaps as a consequence, many of the studies cited in this review dealing with simultaneous adsorption of more than one type of metal contain the implicit assumption that highly specific adsorption of certain classes of metal may be advantageous. However, it would appear that the multi-component nature of typical biomass samples renders them more suitable, in principle, for a significantly different application, the removal of a wide range of pollutants simultaneously. Biosorbents have the potential for use during polishing treatments of both freshwater and wastewater. One can expect that the public will become increasingly aware, of the importance of metal ion control in both tap water and in water discharged to the environment. By contrast, in cases where highly selective sorption of certain metal ions is required, perhaps as a preliminary step in the isolation of pure metals or compounds, it would make sense to employ relatively pure chelating agents. As one option, chelating functional groups can optionally be bound to cellulosic supports, depending on the needs of the process.

Another area that requires research attention is the use of cellulosic materials to remove not only heavy metals, but also organic toxins. It is reasonable to expect that various industrial outfalls and municipal wastewater streams may contain both heavy metals and organic contaminants. The subject of biosorption of organic materials such as dyes will be addressed in Part 2 of this series.

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