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N. Y. Quintero
Contamination of soil and water with U from mining and tailing activities has
been extensively treated using conventional methods such as membranes technology, electrolysis, ion exchange, solvent extraction, chemical precipitation and
adsorption (Li and Zhang 2012). However, these available treatment technologies
are either not effective enough or are very expensive and inadequate especially
when treating a large amount of waste waters containing U at low concentration
(1 to 100 mg/L, concentrations commonly found in radioactive wastes from) (Li
and Zhang 2012; Volesky 2001). These facts have limited the use of these methods
at large scale (Li and Zhang 2012). Likewise, these shortcomings led to a search
for more eco-friendly approaches like biotechnological methods; these approaches
based on the use of biosorbents have emerged in the last decade as one of the most
promising cost-effective alternatives (Volesky and Holan 1995). Their advantage
relies on the ability of biosorbents, i.e., organisms such as bacteria, fungi or algae,
to trap and immobilise the metals by mechanisms such as biosorption (Fourest
and Roux 1992). These organisms have been highlighted as potential accumulators
having high U uptake capacity.
Once, biosorbents have been used, to avoid spread of radioactive biomass
with U adsorbed, it is recommended its suitable treatment for final disposal as
in the case of other nuclear wastes (Volesky 2001). The result is helping to the
process of ecosystems restoration and to recover the radioactive metals like uranium
(McCullough et al. 2003). Compared to conventional physicochemical methods
assessed taking into account influencing factors such as pH, presence or absence
of carbonates in waste waters, biosorbents are cheaper and attractive because of
their low operating cost and high efficiency (Wang and Chen 2009).
Their performance has been assessed taking into account attributes such as
pH, metal concentration in solution, biomass concentration, age of biomass, temperature, percentage of metal removal, time requested for removing the metal
input and uptake capacity (Yi and Lian 2012). Because this work deals with the
biotechnological issue of determining which microorganisms could be better as U
trappers in aqueous systems, a comparison is required.
An important point for performing this task was to collect an appropriate
methodology. Due to the rapid increase of experimental information coming from
many biotechnological studies dealing with U trappers, there are many data in the
literature. Specifically, in the biosorption field of U, several studies propose the
suitability of biosorbents as U trappers (Wang and Chen 2009); nevertheless, it is
not known which are the best U bioaccumulator in aqueous solutions.
Since several attributes affect the biosorption process and they should be
simultaneously taken into account, the search of a suitable methodology turns
towards multi-criteria decision analysis methods (MCDA) (Lerche et al. 2002).
These MCDA approaches do not only collect and extract relevant information from
the pair-wise comparisons of attributes but also perform data analysis aiming to
screen, to assign priorities and to rank objects (Fattore and Bruggemann 2017).
These tasks are also in the focus of the current work, where the comparison of
N. Y. Quintero
Contamination of soil and water with U from mining and tailing activities has
been extensively treated using conventional methods such as membranes technology, electrolysis, ion exchange, solvent extraction, chemical precipitation and
adsorption (Li and Zhang 2012). However, these available treatment technologies
are either not effective enough or are very expensive and inadequate especially
when treating a large amount of waste waters containing U at low concentration
(1 to 100 mg/L, concentrations commonly found in radioactive wastes from) (Li
and Zhang 2012; Volesky 2001). These facts have limited the use of these methods
at large scale (Li and Zhang 2012). Likewise, these shortcomings led to a search
for more eco-friendly approaches like biotechnological methods; these approaches
based on the use of biosorbents have emerged in the last decade as one of the most
promising cost-effective alternatives (Volesky and Holan 1995). Their advantage
relies on the ability of biosorbents, i.e., organisms such as bacteria, fungi or algae,
to trap and immobilise the metals by mechanisms such as biosorption (Fourest
and Roux 1992). These organisms have been highlighted as potential accumulators
having high U uptake capacity.
Once, biosorbents have been used, to avoid spread of radioactive biomass
with U adsorbed, it is recommended its suitable treatment for final disposal as
in the case of other nuclear wastes (Volesky 2001). The result is helping to the
process of ecosystems restoration and to recover the radioactive metals like uranium
(McCullough et al. 2003). Compared to conventional physicochemical methods
assessed taking into account influencing factors such as pH, presence or absence
of carbonates in waste waters, biosorbents are cheaper and attractive because of
their low operating cost and high efficiency (Wang and Chen 2009).
Their performance has been assessed taking into account attributes such as
pH, metal concentration in solution, biomass concentration, age of biomass, temperature, percentage of metal removal, time requested for removing the metal
input and uptake capacity (Yi and Lian 2012). Because this work deals with the
biotechnological issue of determining which microorganisms could be better as U
trappers in aqueous systems, a comparison is required.
An important point for performing this task was to collect an appropriate
methodology. Due to the rapid increase of experimental information coming from
many biotechnological studies dealing with U trappers, there are many data in the
literature. Specifically, in the biosorption field of U, several studies propose the
suitability of biosorbents as U trappers (Wang and Chen 2009); nevertheless, it is
not known which are the best U bioaccumulator in aqueous solutions.
Since several attributes affect the biosorption process and they should be
simultaneously taken into account, the search of a suitable methodology turns
towards multi-criteria decision analysis methods (MCDA) (Lerche et al. 2002).
These MCDA approaches do not only collect and extract relevant information from
the pair-wise comparisons of attributes but also perform data analysis aiming to
screen, to assign priorities and to rank objects (Fattore and Bruggemann 2017).
These tasks are also in the focus of the current work, where the comparison of