ARID ZONE JOURNAL OF ENGINEERING, TECHNOLOGY &
ENVIRONMENT

AZOJETE June 2020. Vol. 16(2):387-394
Published by the Faculty of Engineering, University of Maiduguri, Maiduguri, Nigeria.

Print ISSN: 1596-2490, Electronic ISSN: 2545-5818
www.azojete.com.ng

Corresponding author’s e-mail address: eguasbridge@gmail.com; eatikpo@delsu.edu.ng 387

ORIGINAL RESEARCH ARTICLE
EVALUATING THE POTENTIAL OF MICROORGANISMS IN

BIOREMEDIATION OF LEAD POLLUTED SOIL
E. Atikpo1*, and N. I. Ihimekpen2

(1Department of Civil and Environmental Engineering, Faculty of Engineering, Delta State University, Oleh Campus, PMB 1, Abraka, Delta
State, Nigeria.

2Department of Civil Engineering, Faculty of Engineering, University of Benin, PMB 1154, Benin City, Nigeria.)

*Corresponding author’s email address: eguasbridge@gmail.com; eatikpo@delsu.edu.ng

1.0 Introduction
Human activities like mining, agriculture and manufacturing have kept the environment at a high
risk of pollution through heavy metals (Gupta et al., 2016; Kure et al., 2018). Heavy metals have
been reported to be very injurious to plants and animals (Vijaya et al., 2010; Girma, 2015).
Metals are important for the performances of living organisms, but at concentrations above the
required; they could be very injurious to lives (Musa et al., 2017). Lead (Pb) is classified among
hazardous metals negatively affecting soil quality, crops yields; and public health (Gupta et al.,
2016; Upadhyay et al., 2017). At concentration above the threshold (100 mg/kg) in soils
(Chiroma et al., 2014), it can be injurious to plants; it can limit plant growth as a result of
hampered photosynthetic activities, nutrients, and performances of necessary enzymes (Kabata-
Pendias, 2010; Namatian et al., 2013). Metals are capable of bioaccumulation in food chain and
in this manner; their effects on lives and associated risk are wider (Tak et al., 2013). Effects of
lead include mental retardation, anemia, renal dysfunction, palsy, reproductive systems disorder
(Athar and Vohora, 2006).

The removal of metals from the environment requires effective and affordable techniques that
are better than the physical and chemical techniques with associated devastating post
remediation toxicity, unwanted sludge, very high cost, and under performance at very low
concentration (Ayangbenro and Babalola, 2017). These disadvantages of physical and chemical
techniques of remediation are the reasons for a push for further remediation alternatives,
which include bioremediation that involves the use of organism for the removal of
contaminants from the environment. The capacities of organisms to effect bioremediation is
tied to some vital factors (suitable pH, temperature, and moisture) which promote their
metabolism and growth (Verma and Jaiswal, 2016).

Beside the use of organisms that are genetically engineered, the use of indigenous micro
organisms is efficient for bioremediation (Gupta et al., 2016).

Human activities during mining in Amita agricultural forest in Ebonyi State of Nigeria has
contaminated the forest soils (Ezeh and Chukwu, 2011) and some crops (tubers and vegetable)

ARTICLE
INFORMATION

ABSTRACT
This study evaluated and compared the abilities of indigenous Bacillus sp. and
Micrococcus sp. for treating lead polluted soil. The organisms were inoculated
into the contaminated soil samples prepared with the optimum values of
screened determinant factors and experimented on 8, 16, 24, 32, 40, 48, and
56 days respectively for lead ion residual concentration using Atomic Absorption
Spectrophotometer. Bacillus sp. showed greater remediation strength than
Micrococcus sp. The results show that Bacillus sp. attenuated the Pb initial
concentration from 150.74 mg/kg to control level in 40 days while Micrococcus
sp. achieved that in 48 days. The 56th day residual concentrations and removal
efficiencies were 81.43 mg/kg and 46% for Bacillus sp., and 86.91 mg/kg and
42.34% for Micrococcus sp. The removal rate was higher for Bacillus sp. (-
0.0715 d-1) than for Micrococcus sp. (-0.0744d-1); and the remediation process
was transport controlled. This information will be useful for remediation
programs; and it will serve as a reference material for future research.

© 2020 Faculty of Engineering, University of Maiduguri, Nigeria. All rights reserved.

Submitted 08 Feb., 2020
Revised 24 May, 2020
Accepted 30 May, 2020

Keywords:
Lead
Soils
Contamination
Bioremediation
removal kinetics

mailto:eguasbridge@gmail.com
mailto:eatikpo@delsu.edu.ng
http://www.azojete.com.ng


Atikpo and Ihimekpen: Evaluating the Potential of Microorganisms in Bioremediation of Lead Polluted Soil. AZOJETE,16(2):387-394.

ISSN 1596-2490; e-ISSN 2545-5818, www.azojete.com.ng

Corresponding author’s e-mail address: eguasbridge@gmail.com; eatikpo@delsu.edu.ng 388

(Onyedika and Nwosu, 2008) with some heavy metals including lead. These soils need effective
and affordable remediation for their restoration for meaningful agricultural practice.

Microorganisms (Fungi, Bacteria and Algae) are known to be effective for removing heavy
metals from water and soils (Kure et al., 2018). Dixit et al. (2015) documented that microbes
like Pseudomonas, Escherichia, Bacillus, Yeasts and Enterobacter are useful for the removal of
heavy metals from soils and water.

This study focused on determining the potential application of indigenous Bacillus sp. and
Micrococcus sp. acquired from Amita forest soils for tackling the Pb pollution through the
process of bioremediation for safe food and commercial supply purpose. The work also
examined the processes controlling the bioremediation through rate-limiting steps established
by some kinetics models.

2.0 Materials and Methods
2.1 Materials and Reagents
The materials and reagents used in this study include: 1. soil samples collected from Amita
forest, Ebonyi State in Nigeria; 2. MacCartney bottles; 3. oxidase reagent; 4. Incubator; 5.
Kovac’s reagent; 6. cotton wool; 7. whatman filter papers; 8. sodium hydroxide; 9. measuring
cylinder; 10. atomic absorption spectrophotometer; 11. perchloric acid; 12. wire loops; 13.
refrigerator; 14. inoculating needles; 15. perchloric acid; 16. pipettes; 17.sulphuric acid; 18.
crystal violent; 19. hot plate; 20. methylene blue; 21. Lugo’s iodine; 22. Autoclave; 23. Ethanol;
24. hydrogen peroxide; 25. petri dishes; 26. hydrochloric acid; 27. Beakers; 28. Safranin; 29.
Microscope; 30. nitric acid; and 31. conical flasks.

2.2 Nutrients Preparation
To prepare the nutrients needed for isolation of organisms and biostimulation of the organisms
for effective bioremediation, 15g of peptone water powder; 28 g of nutrient ager; 65 g of triple
sugar iron agar; 24 g of Simon Citrate agar; and 52 g of powered MacConkey agar were
dissolved in 1000 cm3 each of water for 10 minutes according to Cheesebrough (2000) and
manufacturer’s manual. The formulated solutions were autoclaved at 10342.14 N/m2 for 15
minutes at 121 ºC and cooled to 45 ºC for use.

2.3 Acquisition of Microorganisms
At a microbiology laboratory of Delta State University in Abraka, Delta State of Nigeria,
bacteriological evaluation of the collected soil sample was conducted. The soil was serial
diluted by dissolving 1g of the soil in 10 ml of distilled water. One (1) ml of the resultant
solution was diluted with 9 ml of distilled water and the resultant solution was serial diluted
from step 10-1 to 10-5. Using the pure plate method, 0.1ml from 10-1, 10-3 and 10-5 serial
delusion was measured into several petrri-dishes and cover with nutrient and MacConkey agars
respectively (Baron et al., 1994); and incubated at 37 ºC for 24 h. Developed colonies of the
organisms were sub-cultured and recognized using the techniques in (Holt, 1994;
Cheesebrough, 2000).

2.4 Selection of Optimal Factors
Bioremediation is very effective when carried out with optimum values of determinant factors
(Murthy et al., 2012; Atikpo and Michael, 2018). This makes it imperative to select the optimum
values of these determinant (pH, temperature, stirring frequency, mass of organisms and
nutrient dosage) experimentally (Atikpo, 2016; Atikpo and Ezugwu, 2017).

The experiments were conducted by adopting the method in (Atikpo and Micheal, 2018). Five
grams of soil samples were measured to have 39 pairs of 5g of the soil. From the 39 pairs of
5g of soil samples, 8 pairs were conditioned with nutrient dosage of 4, 5, 6, 7, 8, 9, 10 and 11ml;
nine pairs were conditioned with 1, 2, 3, 4, 5, 6, 7, 8 and 9g of organisms’ masses; seven pairs
were conditioned with 10, 15, 20, 25, 30, 35 and 40 oC of temperature; eight pairs were
conditioned with 4, 5, 6, 7, 8, 9, 10 and 11 of pH; and seven pairs were conditioned with 0, 1,
2, 3, 4, 5 and 6 per week (pw) of stirring frequency. One out of each pair was for inoculation
with Bacillus sp. while the second one out of each pair was for inoculation with Micrococcus sp.

http://www.azojete.com.ng


Arid Zone Journal of Engineering, Technology and Environment, June, 2020; Vol. 16(2) 387-394. ISSN 1596-2490; e-ISSN 2545-5818;
www.azojete.com.ng

Corresponding author’s e-mail address: eguasbridge@gmail.com; eatikpo@delsu.edu.ng 389

These pairs of soil samples were inoculated with respective 24hr old Bacillus sp. and
Micrococcus sp. that were isolated and sub-cultured and identified with the methods in (Baron
et al., 1994; Holt, 1994; Cheesebrough, 2000). Adopting the least residual Pb ion content of soil
as the criteria for identifying the optimum values of pH, temperature, stirring frequency, mass
of organisms and nutrient dosage, their optimum values were then determined and recorded
by evaluating the residual lead (Pb) ion content of the pairs of soil samples with Atomic
Absorption Spectrophotometer (GBC SensAA Model no. A6358) on the 24th day.

2.5 Remediation Studies
The bioremediation studies were conducted following the method reported by (Atikpo and
Micheal, 2018). The optimum masses selected for the organisms were inoculated in 5g each of
soil sample in 50ml beaker and conditioned with the selected optimum values of pH,
temperature, stirring frequency, mass of organisms and nutrient dosage. The remediation
abilities of the organisms were determined at time 8, 16, 24 and 32, 40, 48 and 56 days by
analyzing the residual Pb ion in the soils using Atomic Absorption Spectrophotometer (GBC
SensAA Model no. A6358) after separating the organisms from the sample with a centrifuge.

The Pb ion removed with time; Pb ion removed at equilibrium; and the ionic removal
efficiencies were determined using Equations (1), (2) and (3) respectively (Badmus et al., 2007).

qt =
Co−Ct
m

.V (1)

qe =
Co−Ce
m

.V (2)

Efficiency = Co−Cf
Co

.100 (3)
where: qt (mg/kg) is Pb ion removed with time t; qe (mg/kg) is Pb ion removed at equilibrium;
Co (mg/kg) is the initial lead ion brought in contact with the organisms; Cf (mg/kg) is the final
Pb ion; V(m3) is the volume of soil used; and m (g) is the optimum mass of organism.

Difference in the organisms’ removal proficiencies was evaluated with Microsoft Excel 2016 at
(P < 0.05); and the rates of Pb ion removal by organisms were assessed with pseudo-first order
kinetics model (Owamah, 2014; Atikpo, 2016).

2.6 Removal Kinetics
This study was conducted to deduce the rate-limiting steps (Chen and Wang, 2007; Atikpo,
2016) useful for establishing process controlling the bio-removal of the metal ion from the soil
(Atikpo, 2016); and for establishing the rate of the ion removal by the bio-sorbents (Owamah,
2014). The kinetics were evaluated by fitting the elovich, pseudo-first order, intra-particle
diffusion, and pseudo-second order models with the batched experimental data using Equations
(4), (5), (6) and (7) (Chien and Clayton, 1980; Mckay and Poots, 1980; Ho et al., 2000;); and the
models’ coefficient of determination, R2 was engaged in determining the rate-limiting steps.

 t
t q

dt
dq   exp

(4)

 te
t qqk

dt
dq


(5)

XtKq t  2
1

2 (6)

 2
1 te

t qqk
dt
dq


(7)

where: qt in mg/kg is the ion removal capacity with time t in days; k is pseudo first order model
rate constant per day (d-1); qe in mg/kg is equilibrium ionic removal capacity; k1 is pseudo
second order model rate constant in kg. mg-1d-1;  in mg/kg is elovich model initial ion removal
rate;  in mg/kg is elovich model ion desorption rate constant; and k2 is intra-particle diffusion
model rate constant.
3.0 Results and Discussion
3.1 Organisms and Bioremediation Optimum Factors

file:///C:/Users/user/Downloads/azojete143/www.azojete.com.ng


Atikpo and Ihimekpen: Evaluating the Potential of Microorganisms in Bioremediation of Lead Polluted Soil. AZOJETE,16(2):387-394.

ISSN 1596-2490; e-ISSN 2545-5818, www.azojete.com.ng

Corresponding author’s e-mail address: eguasbridge@gmail.com; eatikpo@delsu.edu.ng 390

The developed and sub-cultured colony (3.2 x 102 cfu/ml) of microorganisms was found to
contain Bacillus sp. and Micrococcus sp. through various microbiology analyses.

Figure 1 shows the influences of pH values and the corresponding residual Pb concentration.
pH is very sensitive to bioremediation process. It affects negatives charges on organisms’ cells
and physiochemistry of metals (Lopez et al., 2000; Samarth et al., 2012). The organisms were
influenced differently by the pH. The optimum values were 6 for Micrococcus sp., and 8 for
Bacillus sp. The residual concentrations at these values were 83.91 mg/kg for Bacillus sp. and
103.81 mg/kg for Micrococcus sp.

Figure 1: Performance of Organisms under Varied pH Values

The performances of these organisms observed under varied temperature values of 10, 15, 20,
25, 30, 35 and 40 ºC were evaluated. The organisms performed differently under these
temperatures. Figure 2 showed a common optimum temperature value of 30 ºC for the
organism; but their removal potential differs markedly. Bacillus sp. was more sensitive to
temperature change than Micrococcus sp. This was evident from the residual concentrations of
85.47 mg/kg and 119.11 mg/kg relative to their performances. The optimum value is important
because temperature influences remediation process (Ajaykumar et al., 2009; Murthy et al.,
2012).

The influence of 4, 5, 6, 7, 8, 9, 10 and11ml of nutrients dosage; 2, 3, 4, 5, 6, 7, 8 and 9 g of
organisms and 0, 1, 2, 3, 4, 5 and 6 (pw) of stirring frequencies were studied. The observed
optimum values of these factors were 8ml for Bacillus sp. and 9 ml for Micrococcus sp.; 5 g for
Bacillus sp. and 4 g for Micrococcus sp.; and 6 pw for the organisms.

Figure 2: Performances of Organisms under Varied Temperature Values

http://www.azojete.com.ng


Arid Zone Journal of Engineering, Technology and Environment, June, 2020; Vol. 16(2) 387-394. ISSN 1596-2490; e-ISSN 2545-5818;
www.azojete.com.ng

Corresponding author’s e-mail address: eguasbridge@gmail.com; eatikpo@delsu.edu.ng 391

3.2 Bioremediation
The bioremediation abilities of the organisms were experimented using the batched techniques
in (Lin and Lin, 2005; Atikpo and Michael, 2018) for 8, 16, 24, 32, 40, 48 and 56 days. Their
abilities to bring the concentration of Pb from 150.74 mg/kg to below the maximum allowable
concentration value of 100 mg/kg set for agricultural soils by standards in (Chiroma et al., 2014)
were assessed. The difference between 150.74 mg/kg and 100 mg/kg showed that the forest
soil was unhealthy and needed remediation.

Figure 3: Comparative Removal by the Organisms

Figure 3 shows the organisms to be effectively useful for treating the lead contaminated soil.
They are capable of reducing the concentration from 150.74 mg/kg to below 100 mg/kg at
different rates and times. Removal by Bacillus sp. was more rapid than removal by Micrococcus
sp. Bacillus sp. attenuated the Pb concentration to control level in 40 days while Micrococcus
sp. did in 48 days. The respective residual concentrations and efficiencies on the initial safe
concentration dates were 96.78 mg/kg and 35.80% for Bacillus sp., 93.45 mg/kg and 38.01% for
Micrococcus sp. These safe concentration values were barely below the maximum allowable
concentration. The 56th day residual concentrations and removal efficiencies were 81.43 mg/kg
and 45.99% for Bacillus sp., 86.91 mg/kg and 42.34% for Micrococcus sp.
Two-ways ANOVA at (P < 0.05) conducted with Microsoft Excel, 2016 revealed the possibility
of greater sorption by a consortium of the organisms because there was a significance
difference between the organisms’ sorption capacities. The ANOVA also revealed a possibility
of relevant performances with time intervals shorter than the one adopted in this study. This
was evident from the significant difference in sorption with experimental times adopted.

From descriptive statistics, the mean residual ion is 109.18 mg/kg for Bacillus sp., and 113.82
mg/kg for Micrococcus sp. The maximum residual ion (MAXRI), minimum residual ion (MINRI),
and ion removed (IR56) on 56 day are shown in Table 1.

Table 1. Descriptive Parameters
Organism MINRI (mg/kg) MARI (mg/kg) IR56 (mg/kg)
Micrococcus sp. 86.91 144.53 63.83
Bacillus sp. 81.43 140.57 69.31

3.3 Kinetics of Removal
Deducible rate-limiting steps from kinetics models (Chen and Wang, 2007; Atikpo, 2016) are
useful for establishing whether biosorption process is reaction or transport controlled (Atikpo,
2016). Kinetics models are useful for establishing the rate of ions removal (Owamah, 2014).
Kinetics models: elovich, pseudo first order, pseudo second order, and intraparticle diffusion
were fitted with the experimental data to deduce the rate-limiting steps.

The relationships of ln(qe-qt) and t; t/qt and t; qt and ln(t); and qt and t1/2 from the linear
version of models in equations (4), (5), (6), and (7) respectively were plotted as shown in

file:///C:/Users/user/Downloads/azojete143/www.azojete.com.ng


Atikpo and Ihimekpen: Evaluating the Potential of Microorganisms in Bioremediation of Lead Polluted Soil. AZOJETE,16(2):387-394.

ISSN 1596-2490; e-ISSN 2545-5818, www.azojete.com.ng

Corresponding author’s e-mail address: eguasbridge@gmail.com; eatikpo@delsu.edu.ng 392

Figures 4, 5, 6 and 7 reflecting the regression models and R2. The high values of models’ R2
indicated that the experimental data were describable by the fitted kinetics models. The order
of models fit was the fit for Micrococcus sp. before the fit for Bacillus sp. using pseudo-first and
second order, intraparticle diffusion; and Bacillus sp. before Micrococcus sp. using elovich
model. The removal rate of pseudo first order was higher for Bacillus sp. (-0.0715 d-1) than for
Micrococcus sp. (-0.0744d-1).

Figure 4: Fit of Pseudo First Order Model

Figure 5: Fit of Pseudo Second Order Model

Figure 6: Fit of Elovich Model

Figure 7: Fit of Intraparticle Diffusion Model

The laboratory data fitted well with tested kinetics models. The R2 values comparisons in Table
2 displayed the intraparticle diffusion model as the best for describing the removals by the

http://www.azojete.com.ng


Arid Zone Journal of Engineering, Technology and Environment, June, 2020; Vol. 16(2) 387-394. ISSN 1596-2490; e-ISSN 2545-5818;
www.azojete.com.ng

Corresponding author’s e-mail address: eguasbridge@gmail.com; eatikpo@delsu.edu.ng 393

organisms, Micrococcus sp. with R2 of 0.992 and Bacillus sp. with R2 of 0.982. Therefore, the
diffusion process was the rate-limiting step for the removals by these organisms (Chen and
Wang, 2007) and the processes were transport controlled (Atikpo, 2016).

Table 2. Kinetic Parameters for the Fitted Kinetic Models
Organism Kinetic models and their parameters

Pseudo - first
order

Pseudo - second order Elovich Intraparticle diffusion

k (d-1) R2 k1 (kg.mg-1.d-1) R2 α (mg.kg-1) R2 k2
(mg/kg.d1/2)

R2

Micrococcus
sp.

-0.0744 0.8262 0.0088 0.950 0.0054 0.950 0.0133 0.992

Bacillus sp. -0.0715 0.8123 0.0079 0.844 0.0058 0.957 0.0134 0.982

Conclusion
This study is on bioremediation of lead contaminated soil in Amita forest in Ebonyi State of
Nigeria using indigenous and biostimulated Bacillus sp. and Micrococcus sp.

The organisms were effective for treating the lead contaminated soil. Bacillus sp. showed
greater remediation potential than the Micrococcus sp.; removal rate of (-0.0715 d-1) for
Bacillus sp. was higher than (-0.0744d-1) for removal with Micrococcus sp.; and the remediation
processes was transport controlled.
This study furnishes relevant insight that will be helpful for soil cleaning programs and further
research.

References
Ajaykumar, AV., Nalf, AD. and Hilal, N. 2009. Study of various parameters in the biosorption of
heavy metals on sludge. World Applied Sciences Journal, 5: 32-40.

Athar, M. and Vohora, SB. 2006. Heavy metals and environmental, New Age International (P)
Limited Publishers, 4835/24, Ansari Road, Daryaganji, New Delhi-110002.

Atikpo, E. 2016. Spatial distribution and attenuation of heavy metals pollution in Amaonye Ishiagu
forest soils. A PhD thesis submitted to the Department of Civil Engineering, University of Benin,
Benin City, Nigeria.

Atikpo, E. and Ezugwu, MO. 2017. Preferential removal of lead, cadmium and zinc from
contaminated agricultural soil by Bacillus subtilis. Journal of Nigerian Association Mathematical
Physics, 41: 339-346.

Atikpo, E. and Michael, A. 2018. Performance evaluation of six microorganisms ultilized for the
treatment of lead contaminated agricultural soil. Journal of Applied Science and Environmental
Management, 22(7):1105-1109.

Ayangbenro, AS. and Babalola, OO. 2017. A new strategy for heavy metal polluted environments: a
review of microbial biosorbents. International Journal of Environmental Research and Public Health,
14: 94.

Badmus, MAO., Audu, TOK. and Anyata, BU. 2007. Removal of lead ion from industrial
wastewaters by activated carbon prepared from periwinkle shells (Typanotonusfuscatus). Turkish
Journal Environmental Engineering and Science, 31: 251-263.

Baron, EJ., Peterson, LR. and Finegold, SM. 1994. Bailey and Scotts diagnostic microbiology, 9th
Edition. Mosby, Baltimore.

Chiroma, TM., Ebewele, RO. and Hymore, FK. 2014. Comparative assessment of heavy metal levels
in soil, vegetables and urban grey waste water used for irrigation in Yola and Kano. International
Refereed Journal of Engineering and Science, 3(2):01-09.

Dixit, R., Malaviya, D., Pandiyan, K., Singh, UB., Sahu A., Shukla, R., Singh, BP., Rai, JP., Sharma, PK.
and Lade, H. 2015. Bioremediation of heavy metals from soil and aquatic environment: an overview
of principles and criteria of fundamental processes. Sustainability, 7:2189-2212.

file:///C:/Users/user/Downloads/azojete143/www.azojete.com.ng


Atikpo and Ihimekpen: Evaluating the Potential of Microorganisms in Bioremediation of Lead Polluted Soil. AZOJETE,16(2):387-394.

ISSN 1596-2490; e-ISSN 2545-5818, www.azojete.com.ng

Corresponding author’s e-mail address: eguasbridge@gmail.com; eatikpo@delsu.edu.ng 394

Ezeh, HN. and Chukwu, E. 2011. Small scale mining and heavy metals pollution of agricultural soils:
the case of Ishiagu mining District, south eastern Nigeria. Journal of Geology and Mining Research,
3(4):87-104.

Girma, G. 2015. Microbial bioremediation of some heavy metals in soils: an updated review. Indian
Journal of Scientific Research, 6(1): 147.

Gupta, A., Joia, J., Sood, A., Sood, R., Sidhu, C. and Kaur, G. 2016. Microbes as potential tool for
remediation of heavy metals: a review. Journal of Microbial and Biochemical Technology, 8:364-372.

Ho, YS., JohnWase, DA. and Forster, CF. 2000. Study of the sorption of divalent metal ions onto
peat. Adsorption Science Technology, 18:639-650.

Holt, J.C. (ED) 1994. The shorter bergeys manual of determinative bacteriology, eight edition,
Williams and Willkins Company, Baltimore.

Kure, JT., Gana, M., Emmanuel, A., Isah, RM. and Ukubuiwe, CC. 2018. Bacteria associated with
heavy metal remediation: a review. International Journal of Applied Biological Research, 9(1): 134 -
148.

Lin, C. and Lin H. 2005. Remediation of soil contaminated with the heavy metal (Cd2+). Journal of
Hazardous Materials, 122 (1-2):7-15.

Lopez, A., Lazaro, N., Priego, JM. and Marques, AM. 2000. Effect of pH on the biosorption of nickel
and other heavy metals by Pseudomonas fluorescens 4F39. Journal of Industrial Microbiology and
Biotechnology, 24:146-151.

Mckay, G. and Poots, VJ. 1980. Kinrtics and diffussion processes in colour removal from effluent
using wood as an adsorbent. Journal of Chemical Technology, Biotechnology, 30:279-292.

Murthy, S., Bali, G. and Sarangi SK. 2012. Biosorption of lead by Bacillus cereus isolated from
industrial effluents. British Biotechnology Journal, 2(2):73-84.

Musa, JJ., Mustapha, HI., Bala, JD. Ibrahim, YY., Akos, MP., Daniel, ES., Oguche, FM. and Kuti, IA.
2017. Heavy metals in agricultural soils in Nigeria: a review. Arid Zone Journal of Engineering,
Technology and Environment, 13(5):593-603.

Nematian, MA. and Kazemeini, F. 2013. Accumulation of Pb, Zn, C and Fe in plants and
hyperaccumulator choice in galali iron mine area, Iran. International Journal of Agriculture and Crop
Sciences, 5:426-432.

Onyedika, GO. and Nwosu, GU. 2008. Lead, zinc and cadmium in root crops from mineralized
Galena-Sphalerite mining areas and environment. Pakistan Journal of Nutrition, 7(3):418-420.

Owamah, HI. 2014. Biosorptive removal of Pb (II) and Cu (II) from wastewater using activated
carbon from cassava peels. Journal of Material Cycle and Waste Management, 16(2):347-358.

Samarth, DP., Chandekar, CJ. and Bhadekar 2012.Biosorption of heavy metals from aqueous
solution using Bacillus licheniformis. International Journal of Pure and Applied Sciences and
Technology, 10(2):12-19.

Tak, HI., Ahmad, F. and Babalola OO. 2013. Advances in the application of plant growth-promoting
rhizobacteria in phytoremediation of heavy metals. In Whitacre, D.M. (eds), Reviews of
Environmental Contamination and Toxicology, 223:3–52.

Upadhyay, N., Vishwakarma, K., Singh, J., Mishra, M., Kumar, V., Rani, R., Mishra, RK., Chauhan, DK.,
Tripathi, DK. and Sharma, S. 2017. Tolerance and reduction of chromium (VI) by Bacillus sp. Mnu16
isolated from contaminated coal mining soil. Frontiers in Plant Science, 8:778.

Verma, JP. and Jaiswal, DK. 2016. Book review: advances in biodegradation and bioremediation of
industrial waste. Frontiers in Microbiology, 6:1555.

Vijaya, CB., Kiran, K., and Nagendrappa, G. 2010. Assessment of heavy metals in water samples of
certain locations situated around Tumkur, Karnataka, India. CODEN ECJHAO E-Journal of
Chemistry, 7(2): 349-352.

http://www.azojete.com.ng