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2024
pp. 1209-1220
Vol. 23
p-ISSN: 0972-6268
(Print copies up to 2016) No. 2
Nature Environment and Pollution Technology
An International Quarterly Scientic Journal
Original Research Paper
e-ISSN: 2395-3454
Open Access Journal
Original Research Paper
https://doi.org/10.46488/NEPT.2024.v23i02.057
Enhanced Natural Attenuation Technique, Edaphic and Microbiological Changes
in Oil-Impacted Soil of Odhiaje Community, Rivers State
P. N. Muonye*† and C. C. Nnaji**
*Department of Civil Engineering, University of Nigeria, Nsukka, Nigeria
**Faculty of Engineering and Built Environment, University of Johannesburg, South Africa
†Corresponding author: P.N. Muonye; engr_muonye@yahoo.com
ABSTRACT
Oil spills in the Niger Delta could exert environmental pressures on the soil component. We
investigated the impacts of oil spills and the effect of the Enhanced Natural Attenuation (ENA)
remediation method on contaminated soil and resident microbial populations in the Odhiaje
community in Rivers State, Nigeria. Soil samples for microbiological studies were collected
weekly during a 17-week remediation period, while those for edaphic parameters were taken
before and after remediation, all at 4 sampling points (SPs). Serial dilution of the oil-impacted
soils for microbial density enumeration was carried out according to standard methods.
Results revealed that mean concentrations of Total Petroleum Hydrocarbon Contents (THC)
(Sig.value = 0.009), SO4
2- ions (Sig.value = 0.001), and sand compositions (Sig.value = 0.045)
all differed markedly across the sampling points at p<0.05. Mean levels of EC (Sig.tvalue =
0.039) and ΣN (Sig.tvalue = 0.058) & K+ ions (Sig.tvalue = 0.004) differed significantly before
and after the remediation exercise at the 95% confidence interval. Application of nutrients
was rapidly accompanied by microbial population increases, leading to the consumption of
oil contaminants in soils to levels comparable to control over the remediation period. Total
Heterotrophic Bacteria counts correlated with pH (r = 0.501) and SO4
2- ions (r = 0.500)
(p<0.05), and K+ ions (r = -0.800) (p<0.01); Total Heterotrophic Fungi correlated with pH
(r = 0.520) (p<0.05), and Mg2+ ions (r = 0.820) (p<0.01); Hydrocarbon Utilizing Bacteria
correlated with available P (r = 0.530) and silt composition (r = -0.504) (p<0.05), and K+ (r =
0.626) and Mg2+ ions (r = 0.733) (p<0.01); and Hydrocarbon Utilizing Fungi correlated with
K+ (r = 0.500) & Mg2+ ions (r = 0.506) (p<0.05). Results indicate improvement in C/N ratios
and effectiveness of the current cost-effective bioaugmentation technique in the restoration
of arable soil productivity in the Odhiaje community.
INTRODUCTION
Crude oil drives the bulk of the Gross Domestic Product
(GDP) of Nigeria, as the product currently generates the
bulk of the country’s foreign exchange and serves as an
energy source as well as industrial raw materials used in
producing several products and services. The extraction
process of this natural resource in the environment of the
Niger Delta region of the country could be very damaging.
As a result, and over the last decade, oil exploration and
exploitation have impacted harmfully on the socio-physical
environments of oil-bearing communities in the Nigerian
Delta, largely threatening their subsistent peasant economy,
the environment, livelihood, and hence the basic survival of
the people (Eni & Okpiliya 2011).
Incidence of oil spills in the Niger Delta areas have
become rampant, and according to Bob-Manuel & Johnson
(2001), they are mainly from fractured pipelines due to
corrosion or company operational errors in the environment,
as well as from sabotage of pipelines by locals for economic
and political reasons. Further, Ebuehi et al. (2005) have
identified other minor causes of spills, including the low
level of technological know-how, the weakness of our laws
and their feeble enforcement, the callousness of multinational
enterprises participating in the oil business in the country,
and the carelessness of various personnel within and outside
the industry.
Oil spills have impacts, and their effects on the
environment on the biota are also diverse. Additionally,
widespread spillages on soil in rivers, creeks, ponds, and
wells in the riverine areas of the country have rendered arable
soils and good drinking water scarce, and many victims of the
pollution have suffered from diarrhea and dysentery (Albert
et al. 2018). Joseph et al. (2021) reported that the impact
has severely degraded borehole water samples in a partially
remediated oil spill site. Many products from oil spills are
Nat. Env. & Poll. Tech.
Website: www.neptjournal.com
Received:
29-09-2023
Revised: 02-11-2023
Accepted:
06-11-2023
Key Words:
Bioremediation
Oil spill
Water pollution
Attenuation
Bacteria
Niger Delta
1210 P. N. Muonye and C. C. Nnaji
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toxic to wildlife, which, when incorporated into the food
chain, will also be poisonous to humans. This knowledge has
increased scientific interest in studying the distribution, fate,
and behavior of oil and its derivatives in the environment
(Semple et al. 2006). Fishing and farming, which are the
traditional means of livelihood of the people of the oil-
producing communities, are adversely affected. According
to (Ogbuagu et al. 2019), deaths of fishes, crustaceans, and
other aquatic organisms, which form the main sources of
animal protein in the areas, have been reported.
Natural attenuation is the monitoring of natural
processes in environmental segments that act without
human intervention to reduce the mass, toxicity, mobility,
volume, or concentration of contaminants (Jorgensen et al.
2011). It could be utilized as a bioremediation method to
treat polluted environments, in which case microorganisms
will contribute to pollutant degradation without deliberate
human interventions. However, where site evaluations
need rapid removal of pollutants, enhanced natural
attenuation bioremediation, classified into biostimulation
(addition of nutrients and chemicals to stimulate innate
microorganisms) and bioaugmentation (inoculation with
exogenous microorganisms), can be applied. Of the available
remediation techniques, the enhanced natural attenuation is
the least expensive in environmental management, because
the technique could be practiced with little or no expertise and
in a natural environment. The problem of oil spillage in the
oil-producing areas of Nigeria has proved as challenging as
the inability the recover the spill and remediate the impacted
environment. In this regard, Mafiana et al. (2021) reported
that over 73% of oil spills are unrecovered. Mafiana et al.
(2021) identified non-supplemented in-situ remediation as a
potentially cost-effective method for mitigating the impact
of oil spillage in oil-producing communities and other
impacted sites. Ebuehi et al. (2005) had earlier reported that
remediation by enhanced natural attenuation (RENA) with
spiking and tilling could be used for the reclamation of oil
spill-impacted farm settlements in Rivers State, Nigeria.
Chikere et al. (2017) reported successful remediation of
oil-impacted soil in Bayelsa State, Nigeria, using enhanced
natural attenuation with a significant reduction in total
petroleum hydrocarbon (TPH) and polyaromatic hydrocarbon
(PAH) as well as a spike in hydrocarbon utilizing bacteria
(HUB) during remediation.
The current work evaluated the efficiency of the
Enhanced Natural Attenuation (ENA) remediation
technique on oil-impacted soil of Odhiaje communities in
Rivers State, Nigeria. The main focus was on key indicator
physicochemical and microbiological parameters of the
impacted soil over a timeline. The objectives included the
assessment of some edaphic variables in contaminated soil
and population dynamics of relevant resident microbiological
organisms before, during, and after the remediation exercise.
STUDY AREA
An Overview of the Study Area
Odhiaje community is located at latitudes 0532‘11°” and
0415‘60°” N and longitudes 0630’32°” and 0625‘40°” E
(Fig. 1) and is within the tropical rainforest zone of Nigeria,
with much rainfall and thick vegetation. Presently, the
vegetation is dwindling due to population growth, persistent
farming, and rapid socio-economic development, including
but not limited to mineral exploitation. The climate, typical of
the tropics, has an average rainfall of 200 mm, mean ambient
temperature of 28°C, and relative humidity of between 88 and
98 % (Shell Petroleum Development Company of Nigeria
2002). The wet season lasts between March and November,
while the dry season lasts the remaining four months. The soil
is mainly of sandy loam, and economic trees such as Elaeis
guineensis (oil palm) and Hevea brasiliensis (rubber) grow
well in the area. Annual crops, mainly cassavas and maize,
as well as pineapples, okra, and other vegetables, are also
grown extensively in farmlands.
Crude oil exploration and exploitation activities are
ongoing in the area, and currently, several oil wells and
pipelines are traversing the area. An oil spill incident that
occurred in June 2018 at Odhieje Community, Ahoada
East Local Government Area in Rivers State of Nigeria,
resulted in the discharge of large volumes of crude oil into
several hectares of adjoining farmlands and forest. The
environmental damages inflicted by this spill alone created
environmental stress.
MATERIALS AND METHODS
Field Methods and Sampling Locations
The longitudes and latitudes of four sampling points,
including a control where the study was conducted, are
presented in Table 1. SP 2 was both the northernmost
and easternmost sampling point (SP). Sampling was done
according to the method of (Iwegbue 2007) at 0-15 and 15-
30cm soil depths and samples were collected with a hand-
held stainless auger. However, composite samples were
collected for post-remediation assessment of the edaphic
variables. Quality assurance procedures were strictly adhered
to in sample collections and laboratory analyses.
Soil Remediation Exercise
Oil-impacted soil was excavated up to 0.5 m depth, spread,
mixed with plants and animal dung, and plowed to promote
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EDAPHIC AND MICROBIOLOGICAL CHANGES IN OIL-IMPACTED SOIL
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hydrocarbon degradation in situ. The procedures of plowing
and tilling were done twice with a digger and spade and then
homogenized. Composted plants and poultry dung, as well
as nitrate-phosphate-potassium (NPK) fertilizer, were added
during the homogenization procedure. Ridges (windrows)
were constructed and then leveled severally at alternate
times. Soil samples for petroleum hydrocarbons and other
edaphic variables assessment were taken before and after a
17-week remediation period.
Laboratory Analyses
This was in keeping with the standard methods of (APHA
2002). THC content was obtained by shaking 10 g of a
representative soil sample with 20 mL toluene, and oil
was extracted. The extracted oil was determined with
absorbance at 420nm wavelength in a Spectronic 21-D
spectrophotometer. Concentration was then calculated with
reference made to the standard curve that was prepared using
5
Fig. 1: Map of Odhiaje community and environs in Rivers State, Nigeria, showing the four sampling
points (BH 1&SP 1, BH 2&SP 2, BH 3&SP 3, and BH Control/SP Control).
Crude oil exploration and exploitation activities are ongoing in the area, and currently, several
oil wells and pipelines are traversing the area. An oil spill incident that occurred in June 2018
at Odhieje Community, Ahoada East Local Government Area in Rivers State of Nigeria,
resulted in the discharge of large volumes of crude oil into several hectares of adjoining
Fig. 1: Map of Odhiaje community and environs in Rivers State, Nigeria, showing the four sampling points (BH 1&SP 1, BH 2&SP 2, BH 3&SP 3,
and BH Control/SP Control).
Table 1: Longitudes and latitudes of the sampling locations.
Location Longitude Latitude
SP 1 06°37'38.423''E 05°1'22.067''N
SP 2 06°37'50.022''E 05°2'30.607''N
SP 3 z06°37'26.824''E 05°0'16.69''N
SP Control 06°33'15.863''E 04°59'57.71''N
1212 P. N. Muonye and C. C. Nnaji
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a known concentration of hydrocarbons in the extractant.
Multiplication was made by the appropriate dilution factor.
To determine the soil pH, Pansu & Gautheyrou (2006)
were used. The air-dried soil sample was sieved through a
2 mm sieve, and then 20 g of it was placed in a 50 mL beaker
with 40 milliliters of distilled water. A glass rod was used to
stir the mixture vigorously and made to stand for 30 min before
reading off the pH value on a Corning pH meter (Model 7).
Pansu & Gautheyrou (2006) modified Bougoucous
hydrometer method was used to determine textural
classifications. A Solution containing sodium carbonate
(8 g.L-1) and hexametaphosphate (Calgon 44 g.L-1) were used
to disperse the soil samples. The pH of the solution stayed
retained at approximately 8.3, and the textural triangular
diagram was used to determine the textural classes.
For the exchangeable cations, 1 g sample of soil was
put into a digesting tube, followed by an addition of 10 mL
conc. HNO3. At 96°C, the sample was put in the digester
with intermittent stirring for 8 h. When the digestion process
was completed, Whatman No. 42 filter papers were used to
filter the sample into a 100 mL volumetric flask. The sample
was prepared up to the 100 mL mark in the volumetric
flask with distilled deionized water. The concentrations
of K+, Ca2+, Mg2+and Na+ions in the supernatant solution
were determined using a Varian Spectr-AA 600 Atomic
Absorption Spectrophotometer (AAS), with air acetylene
flame connected to it.
The sulfate ions presence in the soil sample was
confirmed by the monocalcium phosphate extraction method.
The soil sample is exposed to the air to become dried and
then sieved. 10 g of the dried and sieved sample was put in a
50 mL Erlenmeyer flask. Monocalcium phosphate extracting
solution is measured to just 25 mL and added the solution
in the Erlenmeyer flask. The solution is shaken for 30 min
at a frequency of 200 oscillations per minute. Charcoal
measuring 0.25 g was added to each sample, and an extra
3 min of shaking was done. A Whatman No. 42, which is
free from sulfate, was used to filter the solution. 10 mL of
the filtrate from the extraction process was pipetted and
transferred to a 50 mL Erlenmeyer flask. One milliliter of
seed was added, and the solution was agitated. Addition of
0.5 g of BaCl2. 2H2O crystals were done, and the solution
was put in a steady position for 1 min before a magnetic swirl
was used to continuously swirl the flask until the crystals
dissolved. HACH DR 2010 UV-visible spectrophotometer at
a wavelength of 420 nm was used to read the transmittance at
3-8 min intervals. A linear graph to plot absorbance against
the concentration and the absorbance reading was recorded.
The concentration of sulfate in 10g of the soil sample was
then calculated as:
mg S/ L × 0.025L
MgSO - S / kg of soil = = mg S / L × 2.5
40.010 kg soil
mg S/ L × 0.025L
MgSO - S / kg of soil = = mg S / L × 2.5
40.010 kg soil
For available phosphorus, First, 15 mL of 1M ammonium
fluoride and 25 mL of 0.5N HCl were added to 460 mL
distilled water to prepare the extracting solution. Then 1g
soil sample is air-dried and sieved through a 2mm mesh
size. After weighing the dried-up soil sample, it was placed
into a centrifuge tube, followed by the addition of extracting
solution measuring 7 mL. This mixture stood stirred for 1
minute and then centrifuged. Two milliliters of the clear
supernatant were transferred into a 20 mL test tube, and
5 mL of distilled water was added, followed by 2 mL of
ammonium solution. 1 mL of chloride solution was added
to the mixture. A spectrophotometer at 660 nm wavelength
was used to measure the percentage transmittance in 20 min.
A standard curve prepared with phosphate in soil standard
solution was used in the determination of the amount of
available PO4
3- ion in the soil sample.11
The electrical conductivity (EC) of soil samples was
determined on the filtrate obtained after filtering the suspension
used for pH determination. The Lovibond conductivity meter
(Model CM-21 bridge) was used in measuring conductivities
in µS.cm-1 (Pansu & Gautheyrou 2006).
Total nitrogen was determined by the Macro Kjeldahl
method, as described by Pansu & Gautheyrou (2006). A
typical soil sample weighing 5 g was shaken with 50 mL of
1N K2SO4. The phenol sulphonic Acid method was employed
in determining the nitrogen content using the Aliquot of the
resulting extract.
Organic carbon was determined by the wet combustion
method of Pansu & Gautheyrou (2006). In duplicate, 2 g of
soil sample was put into a 250 mL Erlenmeyer flask after
it was weighed. 10 mL of 1N K2Cr2O7 solution was added
into the flask and swirled gently for the soil to be dispersed.
An automatic pipette was used to make a rapid addition of
20 mL conc. H2SO4, directing the stream into the suspension.
Instantaneously, the flask was shaken gently, make soil and
reagents were mixed. After which it was shaken vigorously
for 1 minute. The flask was then made to stand for 30 min
on a sheet of asbestos. When thirty min had elapsed, 100 mL
of distilled water was added. The addition of Four drops of
o-phenanthroline-ferrous indicator and titration with 0.5N
ferrous sulfate solution was carried out. The approach to the
end-point of titration was marked by the solution changing
from a greenish cast to a dark green color. At this point, the
ferrous sulfate was added drop-wise until the color changed
sharply from blue to red (maroon color) in reflected light against
a white background. A blank titration was also made devoid
1213
EDAPHIC AND MICROBIOLOGICAL CHANGES IN OIL-IMPACTED SOIL
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Soil variables and the microbial community in the soil, the
Pearson correlation coefficient (r) was utilized.
RESULTS AND DISCUSSION
Edaphic Variability and Oil Spill Remediation
Mean pH changed from 6.0±0.1, 5.4±0.2, 4.7±0.01 and
5.7±0.1 before remediation to 5.0±0.1, 5.5±0.1, 5.1±0.01 and
5.2±0.1 after remediation at SP 1, SP2, SP3 and the control
(Table 2). Electrical conductivity generally increased from
53±2.5, 54.5±4.0, 28±2.0 and 33±4.0 at SP1, SP2, SP3 and
control point, respectively, before remediation to 85±2.0,
90±3.5, 88±2.0 and 86±5.4 µS.cm-1 after remediation.
Carbon-Nitrogen (C/N) ratios also varied from 16.5±1.6,
37.5±0.3, 66.0±2.5 and 9.0±3.0 at SP1, SP2, SP3 and control
point respectively before remediation to 26.0±1.1, 37.5±0.3,
28.25±2.3 and 60.0±2.7 after remediation.
THC had decreased from 351.77±97.5 before remediation
to 161.40±60.5 mg.kg-1 after remediation at SP 1; from
5035.30± 61.8 before remediation to 2587.30±55.1 mg.kg-1
after remediation at SP 2; from 2990.70±37.8 before
remediation to 497.40±21.5 mg.kg-1 after remediation at SP
3 and from 65.96 ± 9.9 to 67.50±5.5 mg.kg-1 at the control
point (Table 2). Available phosphorus contents decreased
from 13.1±8.2 before remediation to 12.52±3.4 µg.g-1 after
remediation at SP 1; from 33.95±1.35 before remediation
to 10.70±1.1 µg.g-1 after remediation at SP 2; from 38.85
7.3 before remediation to 7.5±3.4 µg.g-1 after remediation
at SP 3 and from 15.80± 1.7 before remediation to 11.25±
1.0 µg.g-1 after remediation at SP Control. Potassium
concentration at SP 1, SP 2, SP 3, and the control point decreased
from 64.1±1.3±71.8 ,7.7±75.6 ,14.1 and 1.3±59.0µg.g-1,
respectively, before remediation to 2.51±6.7, 1.79±4.5, 1.42±1.0
and 0.87±0.2 µg.g-1 respectively after remediation.
Organic carbon content decreased from 3.22±1.24
and 3.62±0.23% at SP 2 and SP 3, respectively, before
of soil samples to homogenize the dichromate. The result was
computed and organic carbon was expressed in percentage.
Microbial Analysis
Soil samples were air-dried, ground, and sieved through a
2mm mesh size sieve. The oil-contaminated soil samples
were serially diluted in ten folds, according to the methods
of (Vallabhaneni 2012). Each sample of previously air-dried
soil was vigorously shaken in 10 mL of sterile water to
prepare a soil suspension. The soil suspension was put into
the test tubes, and then, ten-fold serial dilution was done
up to 10-5.
In triplicate and from the dilution of 10-3 and 10-4 of each
soil sample, 0.1mL aliquot was aseptically placed against
Nutrient (NA) and Sabouraud Dextrose Agar (SDA) plates
in clean form by pour plate methods of Brenner et al. (2005)
and the spread plates methods of (Vallabhaneni 2012).
Incubation of inoculated plates was done at 37°Centigrade
for a duration of 18 to 24 h at ambient temperatures of 48 to
72 h for the details of total heterotrophic bacterial (THB) and
fungal (THF) counts, respectively. Colonies that are distinct
in incubated plates were counted and then expressed as
colony-forming units per gram (cfu.g-1) of the sampled soil.
The Hydrocarbon-Utilizing Bacteria (HUB) and Fungi
(HUF) were cultured and enumerated on solid oil agar.
Statistical Analyses
The data analysis was carried out using SPSS v.23.0 and
MS Excel 2020 software. Descriptive statistics and plots
were employed to express variations in hydrocarbons and
soil parameters. To determine variations in concentrations of
hydrocarbons and other soil parameters across the locations
at p<0.05, The one-way ANOVA test was employed,
and mean separation was done with the Duncan Multiple
Range test. To explore possible relationships between the
Table 2: Summary of physico-chemical characteristics of impacted soil before and after remediation.
Sample
ID
Before Remediation After Remediation
pH EC [µS.
cm-1]
THC
[mg.kg-1]
Organic
C [%]
C/N
Ratio
pH EC [µS.
cm-1]
THC
[(mg.kg-1]
Organic
C [%]
C/N
Ratio
SP1 5.95 53 351.8 0.97 16.5 5 85 161.4 1.56 26
SP2 5.4 54.5 5035.3 3.215 63.5 5.5 90 2587.3 1.5 37.5
SP3 4.7 28 2990.7 3.615 66 5.1 88 497.4 1.13 28.25
Control 5.7 33 66.0 0.545 9 5.2 86 67.5 1.2 60
Sample
ID
Before Remediation After Remediation
Av. P
[µg.g-1]
SO4
2-
[µg.g-1]
K+
[mg.kg-1]
Na+
[mg.kg-1]
Ca2+
[mg.kg-1]
Mg2+
[mg.kg-1]
Av. P
[µg.g] -1
SO4
2-
[µg.g-1]
K+
[mg.kg-1]
Na+
[mg.kg-1]
Ca2+
[mg.kg-1]
Mg2+
[mg.kg-1]
SP1 13.1 8.45 64.1 0.75 1.75 1.15 12.52 9 2.51 1.09 1.1 0.3
SP2 33.95 3.3 75.6 1.2 0.75 2.5 10.7 4 1.79 1.08 1.4 0.5
SP3 38.85 5 71.8 0.9 1.15 3.4 7.5 6 1.42 1.13 1.3 0.2
Control 15.8 6.2 59 0.85 0.9 2.05 11.25 5 0.87 1.17 0.8 0.5
1214 P. N. Muonye and C. C. Nnaji
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remediation to 1.50±1.0 and 1.13±0.1% respectively after
remediation, but increased from 0.97±0.22 and 0.55±0.2%
at SP 1 and the control point respectively before remediation
to 1.56 and 1.2±0.05% respectively after remediation.
Sulfate ion concentrations increased from 8.45±0.4, 3.3±0.3
and 1.0±0.3 µg.g-1 at SP 1, SP 2, and SP 3, respectively,
before remediation to 9.0±0.1, 4.0±0.1, 6.0±0.1 µg.g-1
after remediation. Still, they decreased from 6.2±0.2 before
remediation to 5.0±0.1 after remediation at the control point.
Magnesium ions decreased from 1.15±0.2, 2.5±0.1, 3.3±0.2
and 2.05±0.1 mg.kg-1 at SP 1, SP 2, Sp 3, and the control
point, respectively, before remediation to 0.3±0.1, 0.5±0.1,
0.2±0.1 and 0.5±0.1respectively after remediation. Total
Nitrogen remained fairly unchanged at SP 1 (0.06±0.001%)
but decreased from 0.05±0.001, 0.06±0.001, 0.06±0.001%
at SP 2, SP 3 and the control point, respectively, before
remediation to 0.04±0.001, 0.04±0.001 and 0.02±%0.001
respectively after remediation. Sodium concentration
increased from 0.08±0.01, 1.2±0.01, 0.9±0.02 and 0.9±0.01
mg.kg-1 at SP 1, SP 2, SP 3, and the control point, respectively,
to 1.09, 1.08±0.03, 1.13±0.001 and 1.17±0.01 mg.kg-1.
Calcium ions had increased from 0.8±0.02 and 1.2±0.05
mg.kg-1 at SP 2 and SP 3, respectively, before remediation to
1.4±0.001 and 1.3±0.02 mg.kg-1 after remediation. Still, they
decreased from 1.8±0.3 and 1.8±0.02 at SP 1 and the control
point, respectively, before remediation to 1.1±0.1 and 0.8±
0.001 mg.kg-1, respectively, after remediation (Fig. 2).
Of the textural classes, the composition of sand increased
very slightly from 49±2 to 51±2 at SP 2. From 64±0.5
to 65±0.2% at SP Control before and after remediation
(Fig. 3). However, it decreased from 60±1.5 to 47±1.0%
at SP 1 and 74±1.3 to 63±1.0% at SP 3 before and after
remediation respectively. Silt composition increased from
17±1.0 to 34±1.0 % at SP 1, 0.5±9 to 0.2± 26 at SP 3, and
14±0.4 to 28±0.1% at the control point before and after
remediation. Clay composition decreased from 23±1.0 to
19±1.0 at SP 1, 25±1.2 to 23±1.0 at SP 2, 17±0.5 to 11±0.2
at SP 3, and 22±0.3 to 7±0.1% at the control point before
and after remediation respectively.
13
Fig 2: Attenuation of physico-chemical parameters due to remediation.
Fig 3: Variations in soil characteristics before and after remediation.
The One-way ANOVA test revealed that the concentrations of THC (Sig.
value
= 0.009), SO
42-
ions (Sig.
value
= 0.001), and sand compositions (Sig.
value
= 0.045) all differed markedly across
the sampling points at p<0.05. A post-hoc mean separation technique with the Duncan Multiple
Range test (Table 2) revealed that the observed difference in THCs was between SP 1 = SP
Control and SP 2; that in SO
42-
ions was between SP 1 and the rest sampling points, while that
in Sand composition was between SP 2 and SP 3. Using the Student’s t-test to carry out a pair-
wise comparison in concentrations of the soil variables showed that mean levels of EC
Fig. 2: Attenuation of physico-chemical parameters due to remediation.
13
Fig 2: Attenuation of physico-chemical parameters due to remediation.
Fig 3: Variations in soil characteristics before and after remediation.
The One-way ANOVA test revealed that the concentrations of THC (Sig.
valu e
= 0.009), SO
42-
ions (Sig.
value
= 0.001), and sand compositions (Sig.
value
= 0.045) all differed markedly across
the sampling points at p<0.05. A post-hoc mean separation technique with the Duncan Multiple
Range test (Table 2) revealed that the observed difference in THCs was between SP 1 = SP
Control and SP 2; that in SO
42-
ions was between SP 1 and the rest sampling points, while that
in Sand composition was between SP 2 and SP 3. Using the Student’s t-test to carry out a pair-
wise comparison in concentrations of the soil variables showed that mean levels of EC
Fig. 3: Variations in soil characteristics before and after remediation.
1215
EDAPHIC AND MICROBIOLOGICAL CHANGES IN OIL-IMPACTED SOIL
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The One-way ANOVA test revealed that the
concentrations of THC (Sig.value = 0.009), SO4
2- ions
(Sig.value = 0.001), and sand compositions (Sig.value = 0.045)
all differed markedly across the sampling points at p<0.05.
A post-hoc mean separation technique with the Duncan
Multiple Range test (Table 2) revealed that the observed
difference in THCs was between SP 1 = SP Control and SP
2; that in SO4
2- ions was between SP 1 and the rest sampling
points, while that in Sand composition was between SP 2
and SP 3. Using the Student’s t-test to carry out a pair-
wise comparison in concentrations of the soil variables
showed that mean levels of EC (Sig.tvalue=0.039) and
ΣN (Sig.tvalue = 0.058) & K+ ions (Sig.tvalue = 0.004)
differed significantly before and after the remediation
exercise at the 95% confidence interval.
Variations in Microbial Population
The counts of microorganism groups in oil-impacted soil
during the 17-week remediation exercise are shown in
Tables 4 - 7. For the Total Heterotrophic Bacteria (THB)
counts (Table 4), the population of the microorganisms
increased in SP 1 from 5.3 × 105 at the commencement of
the remediation exercise to 4.0 × 107 cfu.g-1 in week 9. Then,
it decreased to 3.5 × 105 cfu.g-1 at the end of the exercise.
At SP 2, microorganism counts increased from 6.8 × 106 at
the commencement of the exercise to 3.1 × 108 cfu.g-1 in
week 10 and then decreased to 4.4 × 106 cfu.g-1 at the end.
At SP 3, THB increased from 4.4 × 106 to 2.5 × 108 cfu.g-1
in week 11 and then decreased to 4.1 × 106 cfu.g-1 at the end
(Table 4). However, at the control location, microbial
populations increased from 4.9 × 106 to 3.5 × 107 cfu.g-1 in
week 8 and then decreased to 4.8 × 106 cfu.g-1 at the end of
the exercise.
Table 5 shows that the Total Heterotrophic Fungi (THF)
counts in the impacted site increased at SP 1 from 7.4 × 103 to
6.8 × 105 cfu.g-1 in week 10 and then decreased to 2.3 × 103
cfu.g-1 at the end of the exercise. THF counts also increased
at SP 2 from 6.3 × 103 to 4.5 × 105 cfu.g-1 in week 12 and
then decreased to 2.5 × 103 cfu.g-1 at the end. They increased
at SP 3 from 6.4 × 103 to 5.5 × 105 cfu.g-1 in week 5 and then
decreased to 2.5 × 103 cfu.g-1 at the end of the exercise. At
the control location, THF counts increased from 4.5 × 103
to 3.7 × 104 in week 5 and then decreased to 4.2 × 103 at the
end of the exercise.
The Hydrocarbon-Utilizing Bacteria (HUB) counts
increased from 9.9 × 104 to 8.8 × 105 cfu.g-1 in week 10 and
decreased to 9.3 × 103 cfu.g-1 at the end of the exercise in SP
1 (Table 6). At SP 2, it increased from 1.2 × 106 to 1.3 × 108
cfu.g-1 in week 11 and then decreased to 1.9 × 104 cfu.g-1 at
the end. They increased from 9.9 × 105 to 7.6 × 107 cfu.g-1 in
week 10 and then decreased to 7.4x 103 cfu.g-1 at the end of
SP 3. HUB counts at the control sampling point also increased
from 6.5 × 103 to 6.1 × 104 cfu.g-1 in week 6 and then decreased
to 6.7 × 103 at the end of the remediation exercise.
Table 7 shows that the Hydrocarbon-Utilizing Fungi
(HUF) counts increased from 9.1 × 102 to 8.4 × 104 cfu.g-1
in week 9 and then decreased to 2.1 × 102 cfu.g-1 at the end
of the exercise at SP 1. At SP 2, HUF counts increased from
1.1 × 103 to 1.8 × 104 cfu.g-1 in week 11 and then decreased
to 3.0 × 102 cfu.g-1 at the end. At SP 3, counts increased from
9.7 × 102 at the commencement of the experiment to 8.0 ×
104 cfu.g-1 in week 11 and then decreased to 2.4 × 102 cfu.g-1
at the end. At the SP Control location, counts increased from
1.1 × 102 to 1.0 × 103 cfu.g-1 in week 10 and then decreased
to 1.1 × 102 cfu.g-1 at the end of the remediation exercise.
Relationships Between Edaphic Variables and Microbial
Community in Impacted Soils
Table 8 shows the Pearson’s correlation coefficients (r)
between the edaphic variables and microbial communities
in impacted soils during the remediation period. At p<0.05,
THB counts correlated positively with pH (r = 0.501) and
sulfate ions (r = 0.500). At p<0.01, it correlated negatively
with electrical conductivity (EC) (r = -0.701) and K+ ions
(r = -0.800). At p<0.05, THF counts correlated positively
Table 3: Mean separation in edaphic variables impacted by the oil spill
in Odhiaje community by Duncan Multiple Range (DMR) Test (p<0.05).
Sampling points
Parameters SP 1 SP 2 SP 3 SP Control
pH 5.633a5.433a4.833a5.533a
EC 63.666a66.333a48.000a50.666a
THC 288.310a4219.296b2159.600ab 66.73a
Organic C 1.166a2.643a2.786a0.763a
∑N 0.060a0.046a0.050a0.046a
C/N ratio 19.666a54.833a53.416a26.000a
Av. P 12.906a26.200a28.400a14.283a
SO4
2- 8.633c3.533a5.333b5.800b
K+43.570a50.996a48.340a39.623a
Na+0.863a1.160a0.976a0.956a
Ca2+ 1.533a0.966a1.200a0.866a
Mg2+ 0.866a1.833a2.333a1.530a
Sand 55.333ab 49.666a70.000b64.333ab
Silt 23.000a26.000a 14.656a8.666a
Clay 21.666a24.333a15.333a17.000a
Values with the same superscript along the same row are not significant-
ly different at
p<0.05; EC=Electrical conductivity; THC=Total Petroleum Hydrocar-
bons;
Av. P=Available phosphorus
1216 P. N. Muonye and C. C. Nnaji
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Table 4: Total Heterotrophic Bacteria (THB) counts (cfu.g-1) in impacted soils of Odhiaje community during the remediation period.
Sampling
Points
Time (Weeks)
Wk 1 Wk 2 Wk 3 Wk 4 Wk 5 Wk 6 Wk 7 Wk 8 Wk 9 Wk 10 Wk 11 Wk 12 Wk 13 Wk 14 Wk 15 Wk 16 Wk 17
SP 1 5.3 ×
105
5.1 ×
105
5.3 ×
105
4.5 ×
106
4.1 ×
106
4.4 ×
106
3.1 ×
107
3.7 ×
107
4.0 ×
107
4.8 ×
106
3.2 ×
107
3.1 ×
107
4.5 ×
106
4.7 ×
106
4.5 ×
106
4.8 ×
105
3.5 ×
105
SP 2 6.8 ×
106
6.2 ×
106
5.0 ×
107
5.5 ×
107
6.0 ×
106
4.1 ×
107
4.3 ×
107
4.0 ×
107
3.0 ×
108
3.1 ×
108
6.7 ×
107
6.4 ×
107
6.0 ×
107
5.8 ×
106
4.8 ×
106
4.5 ×
106
4.4 ×
106
SP 3 4.4 ×
106
4.4 ×
106
4.8 ×
106
3.5 ×
107
3.0 ×
107
4.0 ×
106
3.1x
107
2.5 ×
108
2.2 ×
108
2.1 ×
108
2.5 ×
108
3.8 ×
107
4.9 ×
106
3.0 ×
107
4.1 ×
106
4.6 ×
106
4.1 ×
106
SP
Control
4.9 ×
106
4.8 ×
106
4.1 ×
106
5.1 ×
106
4.8 ×
106
4.3 ×
106
3.1 ×
107
3.5 ×
107
5.0 ×
106
5.1 ×
106
3.0 ×
107
4.1 ×
106
3.1 ×
107
4.4 ×
106
4.4 ×
106
4.1 ×
106
4.8 ×
106
Table 5: Total Heterotrophic Fungi (THF) counts (cfu.g-1) in impacted soils of Odhiaje community during the remediation period.
Sampling
Points
Time (Weeks)
Wk 1 Wk 2 Wk 3 Wk 4 Wk 5 Wk 6 Wk 7 Wk 8 Wk 9 Wk 10 Wk 11 Wk 12 Wk 13 Wk 14 Wk 15 Wk 16 Wk 17
SP 1 7.4 ×
103
7.1 ×
103
7.2 ×
103
6.5 ×
104
6.1 ×
104
6.3 ×
104
6.1 ×
104
7.0 ×
104
7.1 ×
104
6.8 ×
105
6.0 ×
105
7.0 ×
104
6.8 ×
104
7.0 ×
103
4.8 ×
103
4.5 ×
103
2.3 ×
103
SP 2 6.3 ×
103
6.4 ×
103
7.9 ×
102
7.0 ×
102
6.8 ×
103
6.5 ×
103
6.4 ×
103
6.1 ×
103
5.5 ×
104
5.2 ×
104
5.5 ×
104
4.5 ×
105
4.1 ×
105
5.1 ×
104
6.8 ×
103
4.0 ×
103
2.5 ×
103
SP 3 6.4 ×
103
6.4 ×
103
6.1 ×
104
6.5 ×
104
5.5 ×
105
5.1 ×
105
4.8 ×
105
4.8 ×
105
4.7 ×
105
5.5 ×
104
5.1 ×
104
5.2 ×
104
5.6 ×
104
6.0 ×
103
4.1 ×
103
2.6 ×
103
2.5 ×
103
SP
Control
4.5 ×
103
4.1 ×
103
4.0 ×
103
4.5 ×
103
3.7 ×
104
4.1 ×
103
4.1 ×
103
4.0 ×
103
4.7 ×
103
4.5 ×
103
4.1 ×
103
4.1 ×
103
3.5 ×
104
4.4 ×
103
4.1 ×
103
4.0 ×
103
4.2 ×
103
Table 6: Hydrocarbon Utilizing Bacteria (HUB) counts (cfu.g-1) in impacted soils of Odhiaje community during the remediation period.
Sampling
Points
Time (Weeks)
Wk 1 Wk 2 Wk 3 Wk 4 Wk 5 Wk 6 Wk 7 Wk 8 Wk 9 Wk 10 Wk 11 Wk 12 Wk 13 Wk 14 Wk 15 Wk 16 Wk 17
SP 1 9.9 ×
104
9.7 ×
104
9.7 ×
104
8.5 ×
105
8.2 ×
105
8.4 ×
105
8.1 ×
105
8.8 ×
104
8.4 ×
105
8.8 ×
105
8.0 ×
105
8.2 ×
105
7.5 ×
104
7.6 ×
104
8.7 ×
103
8.8 ×
103
9.3 ×
103
SP 2 1.2 ×
106
1.3 ×
106
1.7 ×
106
1.1 ×
107
1.3 ×
107
1.4 ×
107
1.1 ×
107
1.0 ×
108
1.1 ×
108
1.0 ×
108
1.3 ×
108
1.1 ×
108
1.7 ×
107
1.9 ×
106
1.8 ×
105
1.5 ×
104
1.9 ×
104
SP 3 9.9 ×
105
9.5 ×
105
9.8 ×
105
8.4 ×
106
8.3 ×
106
8.1 ×
106
7.2 ×
107
7.0 ×
107
7.3 ×
107
7.6 ×
107
7.1 ×
107
8.0 ×
106
8.5 ×
106
9.4 ×
105
9.7 ×
104
9.1 ×
103
7.4 ×
103
SP
Control
6.5 ×
103
6.6 ×
103
6.7 ×
103
7.1 ×
103
7.0 ×
103
6.1 ×
104
6.0 ×
104
7.8 ×
103
7.8 ×
103
7.4 ×
103
7.3 ×
103
7.1 ×
103
7.5 ×
103
7.0 ×
103
6.8 ×
103
6.7 ×
103
6.7 ×
103
1217
EDAPHIC AND MICROBIOLOGICAL CHANGES IN OIL-IMPACTED SOIL
Nature Environment and Pollution Technology • Vol. 23, No. 2, 2024
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Table 7: Hydrocarbon Utilizing Fungi (HUF) counts (cfu.g-1) in impacted soils of Odhiaje community during the remediation period.
Sampling
Points
Time (Weeks)
Wk 1 Wk 2 Wk 3 Wk 4 Wk 5 Wk 6 Wk 7 Wk 8 Wk 9 Wk 10 Wk 11 Wk 12 Wk 13 Wk 14 Wk 15 Wk 16 Wk 17
SP 1 9.1 ×
102
9.3 ×
102
9.1 ×
102
9.5 ×
102
8.7 ×
103
8.5 ×
103
8.1 ×
103
8.5 ×
103
8.4 ×
104
8.1 ×
104
8.0 ×
104
8.1 ×
103
8.5 ×
103
8.7 ×
102
5.5 ×
102
3.7 ×
102
2.1 ×
102
SP 2 1.1 ×
103
1.2 ×
103
1.2 ×
103
1.6 ×
103
1.7 ×
103
2.0 ×
103
1.5 ×
104
1.4 ×
104
1.5 ×
104
1.5 ×
104
1.8 ×
104
1.6 ×
104
1.8 ×
103
1.5 ×
103
3.4 ×
102
3.1 ×
102
3.0 ×
102
SP 3 9.7 ×
102
9.5 ×
102
8.8 ×
103
8.7 × 8.5 ×
103
8.6 ×
103
8.1 ×
103
7.5 ×
104
7.1 ×
104
7.5 ×
104
8.0 ×
104
7.8 ×
103
7.3 ×
103
4.7 ×
102
5.1 ×
102
4.5 ×
102
2.4 ×
102
SP
Control
1.1 ×
102
1.0 ×
102
1.7 ×
102
1.2 ×
102
1.4 ×
102
1.2 ×
102
1.8 ×
102
1.5 ×
102
1.8 ×
102
1.0 ×
103
1.0 ×
103
1.5 ×
102
1.2 ×
102
1.7 ×
102
1.4 ×
102
1.7 ×
102
1.1 ×
102
Table 8: Correlation (r) matrix between the edaphic variables and Microbial groups in oil-impacted soils of the Odhiaje community.
Parameters pH EC THC Org.C ∑N C/N Av.P SO4
2K+Na+Ca2+ Mg2+ Sand Silt Clay
THB 0.501* -0.701** -0.111 -0.174 0.200 -0.270 0.024 0.500* -0.800** 0.123 0.027 0.151 0.072 -0.218 0.283
THF 0.520* -0.280 -0.134 -0.259 -0.050 -0.267 -0.186 -0.173 0.067 -0.053 -0.032 0.820** 0.081 -0.157 0.133
HUB -0.131 -0.707** 0.436 0.336 0.153 0.349 0.530* -0.390 0.626** -0.243 -0.429 0.733** 0.347 -0.504* 0.226
HUF -0.193 -0.538* 0.233 0.254 0.137 0.232 0.456 -0.050 0.500* -0.086 -0.249 0.506* 0.221 -0.324 0.150
*values are significant at p<0.05; **values are significant at p<0.01; THB=Total Heterotrophic Bacteria; THF=Total Heterotrophic Fungi; HUB=Hydrocarbon-Utilizing Bacteria;
HUF=Hydrocarbon-Utilizing Fungi; EC=Electrical conductivity; THC=Total Petroleum Hydrocarbons; Org.C=Organic Carbon; C/N=Carbon/Nitrogen ratio; Av.P=Available
with pH (r=0.520), and at p<0.01, they correlated positively
with Mg2+ ions (r = 0.820). At p<0.05, HUB counts
correlated positively with available phosphorus (r = 0.530)
and negatively with the composition of silt (r = -0.504).
However, at p<0.01, it correlated positively with K+ (r =
0.626) and Mg2+ ions (r = 0.733) and negatively with EC
(r = -0.707). At p<0.05, HUF correlated positively with K+
ions (r = 0.500) and Mg2+ ions (r = 0.506) and negatively
with EC (r = -0.538).
Effect of Remediation on Soil Physicochemical Properties
The current work revealed high concentrations of petroleum
hydrocarbons in soil and drastic decreases after remediation,
similar to the work of Mafiana et al. (2021) and Chikere
(2017) in the Niger Delta area of Nigeria. Soil-oil
contaminant generally decreased by up to 50.41% at SP 2
after the 17-week remediation exercise. (Liu et al. 2010) also
observed total petroleum hydrocarbon content reduction by
58.2% in treated plots after bioremediation for 360 days. The
current work also revealed corresponding and appreciable
improvement in the carbon-nitrogen ratio of soil, which got
progressively lowered as the remediation proceeded and with
the decay and release of more organic nitrogen in the soils.
Essien and John (2010) also observed a similar trend in their
work in Akwa Ibom State, Nigeria, carried out on the alluvial
soils of the coastal plains of the Qua Iboe river wetlands.
By nature, organic carbon in soil is normally derived from
flora and fauna, such as peat formation over time, plant fine
roots yield, and microbial renewable organic materials from
plants, animals, and others (Wang et al. 2013). However,
the total organic carbon in the soil might be from crude oil
contamination in the oilfield soils. The high concentration
of THC in the oil-impacted soil might have resulted in the
elevated total organic carbon content recorded. Wang et
al. (2010) reported that a significant increase in the total
organic carbon contents due to oil contamination is most
likely because of the much higher THC concentration in
spilled sites.
On a spatial basis, the current work reveals both slight
increases and decreases in the pH of soil after the remediation
exercise. Previous results from studies on oilfields in China
also revealed an increase in soil pH as a result of oil pollution
(Wang et al. 2010, Jia et al. 2009). The reason for the higher
pH values in crude oil-contaminated soil in this study may
be due to two factors: firstly, the hydrophobic nature of
crude oil might encourage a potential scarcity in the shallow
and underground layers of contaminated soil (Njoku et al.
2009), which could intensify the concentration of salt in the
soil, and thereby raising the pH values when matched with
the values in the control location. Secondly, the buildup of
1218 P. N. Muonye and C. C. Nnaji
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exchangeable ions such as Calcium and sodium ions and a
decrease in the exchangeable amount of acid and real cation
exchange capacity have been revealed to be associated
with oil-polluted soil (Osuji et al. 2006, Agbogidi et al.
2007). These mechanisms might also be responsible for
the increases in pH values in oil-polluted soil. However,
the mechanisms may not have operated in locations where
decreases in pH were recorded in the current work.
The levels of petroleum hydrocarbons, SO4
2- ions,
and sand composition all differed significantly on a
spatial basis. Edaphic properties that showed significant
increases in their concentrations after remediation include
electrical conductivity, total nitrogen, and K+ ions. These all
contributed to improved soil quality in the area. However,
results of a previous study (Wang et al. 2010) showed that
oil contamination could decrease available phosphorus
concentration in soil by various degrees. A study carried out
on the Momoge wetlands of China (Wang et al. 2010) showed
a decrease in the concentration of available phosphorus as
the time of oil exploration and production increased. Similar
decreases were, however, observed in the current work only
after remediation. In another experimental oil study, the
available phosphorus in crude oil-polluted soil was reduced
to as much as 66% in concentration in comparison with
the control site as the crude oil content touched 30 mg.kg-1
(Eneje et al. 2012). However, Liu et al. (2010) reported that
available phosphorus concentrations are not considerably
influenced by oil contamination.
From the extant study, lowered available phosphorus
concentrations after remediation in all the impacted locations
may have been enhanced for two reasons. First, THC in
the soil could increase the carbon concentration. This may
disturb the balance of soil nutrients. Soil microbes that utilize
THC as a carbon source may well consume considerable
amounts of available phosphorus when the hydrocarbons are
degraded (Wang et al. 2013). Second, phosphorus solubility
of phosphorus is exploited at pH near the neutral value,
and higher pH values in some locations of the work may
have also lowered the available phosphorus concentration
when compared with that obtained in the control location.
Phosphorus is an important macro-nutrient for plants and
soil microorganisms. Decreased available phosphorus
concentrations in impacted locations could alter the structural
composition of vegetation and microorganisms in the soil,
as well as reduce soil ecosystem services and values (Bello
& Anobeme 2015).
Effects of Remediation on Microbial Populations
The endpoint and achievement of the oil spill and its
bioremediation are dependent on the capability to start and
preserve conditions that help enhance oil biodegradation
rates in the contaminated environment. Scientific review
that discussed various factors that affect the rate of oil
biodegradation, including that by (Das & Chandran 2011),
showed that the presence of microorganisms with suitable
metabolic capabilities is an important requirement. Optimal
rates of growth and biodegradation of hydrocarbon occur
when these microorganisms exist. This process can be
sustained if the pH is between 6 and 9 and the concentrations
of nutrients and oxygen are sufficient. The physicochemical
characteristics of the oil and the oil surface area are also vital
factors of successful bioremediation. There are two basic
approaches to oil spill bioremediation. The first, which was
applied in the current study, is bioaugmentation, in which
known oil-degrading bacteria are added to supplement the
existing microbial population. The second is biostimulation,
in which the addition of nutrients or other growth-limiting
co-substrates stimulates the growth of indigenous oil
degraders. Microbial counts in oil-impacted soils did not
show appreciable change over weeks 1-3. Still, they did
after week 3 when nutrients, consisting of composted plants
and animal dung, as well as nitrate-phosphate-potassium
(NPK) fertilizers, were introduced. Generally, counts peaked
between weeks 6 and 14 and were least in week 17, marking
the end of the remediation exercise in all the sampling points.
The hydrocarbon-utilizing bacteria (HUB) counts peaked
in week 10, and at week 17, all the microbial communities
attained counts comparable to those of their respective
control locations.
These exponential increases in microbial population
are due to stimulatory effects by the composted plants and
animal dungs, as well as NPK fertilizer introduced on the
impacted soils. This effect has been explained to be due
to the proliferation of microbes in soil, especially in the
presence of growth nutrients (Semple et al. 2006, Hollender
et al. 2003, Walworth et al. 2007). Findings by Ogbonna et
al. (2007) revealed that there is a more rapid bioremediation
of crude oil-contaminated soils with a combination of
microorganisms, poultry manure, and fertilizers other than
microorganisms or fertilizers alone. Selective enrichment
of the soil with microbial species that have tolerance for
extremely high oil concentrations could spontaneously be
caused by the high oil content.
The soil became selectively enriched with very high oil
concentration tolerant microbial species due to the excessive
petroleum hydrocarbon content of the polluted area.
The general increases recorded in the various microbial
community counts also confirm that hydrocarbon pollution
does not only enrich the hydrocarbon utilizers but also
enriches additional populations that utilize the by-products
1219
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that are not intact hydrocarbons, as observed by Walworth
et al. (2007). According to (Das & Chandran 2011), bacteria
are the most active agents in petroleum degradation,
and they work as primary degraders of spilled oil in the
environment. Microbial degradation is the major and
ultimate natural mechanism by which one can clean up the
petroleum hydrocarbon pollutants from the environment.
Several bacteria are even known to feed exclusively on
hydrocarbons. The hydrocarbon-utilizing bacteria and
fungi also increased during the remediation period. These
observations are consistent with the knowledge that inputs
of hydrocarbon pollutants stimulate increases in microbial
numbers (Ogbonna et al. 2007). These categories of
microbes utilize hydrocarbons as their sole carbon sources
of metabolism. Bello & Anobeme (2015) observed that
hydrocarbonoclastic bacteria and fungi as oil degraders are
ubiquitous in both the temperate and tropical environments of
oil-polluted and unpolluted locations. This also explains the
presence of hydrocarbonoclastic microbes even in the control
sites. Ramdass & Rampersad (2021) have also reported the
presence of a diverse microbial population, including novel
oil-degrading filamentous fungi at eight oil-impacted sites
in Trinidad.
Interactions of Edaphic variables and Microbial Groups
This study revealed that pH and Mg2+ ions had positive
effects on the growth of the THF, while EC and K+ ions
appeared to inhibit the growth of the HUF. The recorded
decrease in the pH of soil after remediation could be a result
of the metabolic activities of the microorganisms, which
produced pH-depressing metabolites. The utilization of crude
oil by these organisms, which resulted in their population
growth, also produced and accumulated acidic metabolites
(Essien & John 2010). Electrical conductivity appeared to
significantly inhibit the growth of HUF. This observation
corresponded with decreases recorded in concentrations of
K+ ions, which usually contribute to the conductivity of soil
and water media after the remediation exercise. Potassium
and EC also showed inhibitory effects on growths of the
THB, HUB, and HUF.
CONCLUSION
The current research set out to employ conventional
enhanced natural attenuation techniques in the remediation
of oil-impacted soil. This study reveals the effectiveness
of bioaugmentation with simple locally-available manure,
as well as synthetic fertilizer (NPK), in the restoration of
the productivity of arable soil in the Odhiaje community in
the Delta area of the River Niger, Nigeria. The addition of
organic and inorganic nutrients was rapidly accompanied by
microbial population growth in the soil, and this subsequently
led to the consumption of the oil contaminant in the soil
to comparable levels over a 17-week test period. There
was a general decrease in the soil-oil contaminant by up to
50.41% after 17-week remediation, with a corresponding
improvement in the carbon-nitrogen ratio of soil.
RECOMMENDATIONS
Enhanced Natural Attenuation (= Landfarming) technique
should be recognized as the least expensive of the other
bioremediation treatment methods in environmental
management. The technique could be practiced with little
or no expertise and in a natural environment.
REFERENCES
Agbogidi, O.M., Eruotor, P.G., Akparobi, S.O. and Nnaji, G.U. 2007.
Evaluation of crude oil contaminated soil on the mineral nutrient
elements of maize (Zea mays L.). J. Agron., 6(1): 188. Doi: 10.3923/
ja.2007.188.193.
Albert. O.N., Amaratunga, D. and Haigh, R.P. 2018. Evaluation of the
Impacts of Oil Spill Disaster on Communities and Its Influence on
Restiveness in Niger Delta, Nigeria. Proced. Eng., 212: 1054-1061.
Doi:10.1016/j.proeng.2018.01.136.
American Public Health Association (APHA). 2002. Standard Methods for
the Examination of Water and Wastewater. Twentieth edition. APHA/
AWWA/WEF, Washington DC.
Bello, O.S. and Anobeme, S.A. 2015. The effects of oil spillage on the
properties of soil and environment around the marketing outlets of
some petroleum marketing companies in Calabar, Cross River State,
Nigeria. Mayfair J. Soil Sci.,1(1): 1-14.
Bob-manual, K.D.H. and Johnson, K.T. 2001. Oil Spill clean-up techniques
for sustainable environmental protection in the Niger Delta. NSE Tech.
Trans., 36: 2.
Chikere, C.B., Azubuike, C.C. and Fubara, E.M. 2017. Shift in the microbial
group during remediation by enhanced natural attenuation (RENA) of a
crude oil-impacted soil: a case study of Ikarama Community, Bayelsa,
Nigeria. Biotech., 7: 152. https://doi.org/10.1007/s13205-017-0782-×
Das, N. and Chandran, P. 2011. Microbial Degradation of Petroleum
Hydrocarbon Contaminants: An Overview. Biotechnology Research
International, Article ID 941810, 13 pages. doi:10.4061/2011/941810.
Ebuehi, O.A.T., Abibo, I.B., Shekwolo, P.D., Sigismund, K.I., Adoki, A.
and Okoro, I.C. 2005. Remediation of Crude Oil Contaminated Soil
by Enhanced Natural Attenuation Technique. J. Appl. Sci. Environ.
Manag., 9(1): 103-106.
Eneje, R.C., Nwagbara, C. and UwumarongieIlori, E.G. 2012. Amelioration
of chemical properties of crude oil-contaminated soil using compost
from Calapoigonium mucunoides and poultry manure. Int. Res. J.
Agric. Sci., 2(6):246-251.
Eni, D.D. and Okpiliya, F.E 2011. Evaluation of on-shore oil spill
remediation operations in Port Harcourt, Nigeria. Int. J. Dev. Manag.
Rev., 6: 161-171.
Essien, O.E. and John, I.A. 2010. Impact of crude-oil spillage pollution and
chemical remediation on agricultural soil properties and crop growth.
J. Appl. Sci. Environ. Manage., 14(4): 147-154.
Hollender, J., Althoff, K., Mundt, M. and Dott, W. 2003. Assessing the
microbial activity of soil samples, their nutrient limitation, and the toxic
effects of contaminants using a simple respiration test. Chemosphere,
53(3): 269-275.
1220 P. N. Muonye and C. C. Nnaji
Vol. 23, No. 2, 2024 • Nature Environment and Pollution Technology This publication is licensed under a Creative
Commons Attribution 4.0 International License
This publication is licensed under a Creative
Commons Attribution 4.0 International License
Iwegbue, C.M.A. 2007. Heavy Metal and Physio-chemistry of Soils,
Sediments, and surface water in the environment of an oil-impacted
area of Delta State. M.Phil Thesis. RSUST, Port Harcourt.
Jia, J., Liu, Y., Li, G. and Zhang, ×. 2009. Contamination characteristics
and its relationship with physicochemical properties of oil-polluted
soils in oilfields of China. Chem. In. Eng. Soc. China, 60(3): 726-732.
Jørgensen, K.S. Comprehensive Biotechnology. Science Direct Publications/
Elsevier B.V, The Netherlands.
Joseph, A., Udofia, U. and Ajang, R. 2021. Evaluation of the water quality
of borehole water from a partially remediated oil spill site in Ikot
Ada Udo, Akwa Ibom State, South–South Nigeria. Environ. Technol.
Innvo., 24: 101967.
Liu, W., Luo, Y., Teng, Y., Li, Z. and Ma, L.Q. 2010. Bioremediation of oily
sludge-contaminated soil by stimulating indigenous microbes. Environ.
Geochem, Health, 32(1): 23-9. Doi: 10.1007/s10653-009-9262-5.
Mafiana, M.O., Bashiru, M.D. and Erhunmwunsee, F. 2021. An insight
into the current oil spills and on-site bioremediation approaches to
contaminated sites in Nigeria. Environ. Sci. Pollut. Res., 28: 4073-4094.
https://doi.org/10.1007/s11356-020-11533-1
Njoku, K., Akinola, M. and Oboh, B. 2009. Phytoremediation of crude
oil contaminated soil: The effect of the growth of Glycine max on the
physico-chemistry and crude oil contents of soil. Nature Sci., 7(10):
79-87.
Ogbonna, D.N., Iwegbue, C.M.A., Sokari, T.G. and Akoko, I.O. 2007. Effect
of bioremediation on the growth of okra (Abelmoscus esculentus) in
the Niger Delta. Environmentalist, 27:303-309.
Ogbuagu, D.H., Idewele, M.O. and Nzekwue, K.P. 2019. Bioaccumulation
of persistent environmental pollutants (PEPs) in the mud catfishes
(Clarias gariepinus and Heterobranchus longifilis) from the Ogu Creek,
Rivers State, Nigeria. Int. J. Marine Sci., 9(7): 54-65.
Osuji, L.C., Egbuson, E.J. and Ojinnaka, C.M. 2006. Assessment and
treatment of hydrocarbon inundated soils using inorganic nutrient
(NPK) supplements: Ⅱ. A case study of Eneka oil spillage in Niger
Delta, Nigeria. Environ. Monit. Assess., 115(1): 265-278. Doi: 10.1007/
s10661-006-6552-6.
Pansu, M. and Gautheyrou, J. 2006. Handbook of Soil Analysis:
Mineralogical, Organic and Inorganic Methods. Springer Berlin
Heidelberg, New York, p. 995.
Ramdass, A.C. and Rampersad, S.N. 2021. Diversity and oil degradation
potential of culturable microbes isolated from chronically contaminated
soils in Trinidad. Microorganisms, 9(6): 117. https://doi.org/10.3390/
microorganisms9061167
Semple, K.T., Dew, N.M., Doick, K.J. and Rhodes, A.H. 2006. Can
microbial mineralization be used to estimate the microbial availability
of organic contaminants in soil? Environ. Pollut., 140(1): 164-172.
Shell Petroleum Development Company of Nigeria (SPDC). 2002. Report
on Fourth Quarter 2002-Gaseous Emission Monitoring Report. Quality
Environmental Management Company Limited.
Vallabhaneni, S. 2012. Soil Microbiology- A Laboratory Manual.
LAMBERT Academic Publishing., NY.
Walworth, J., Pond, A., Snape, I., Rayner, J., Ferguson, S. and Harvey, P.
2007. Nitrogen requirements for maximizing petroleum bioremediation
in a sub-Antarctic soil. Cold Reg. Sci. Technol., 48(2): 84-91.
Wang, ×.Y., Feng, J. and Zhao, J.M. 2010. Effects of crude oil residuals on
soil chemical properties in oil sites, Momoge Wetland, China. Environ.
Monit. Assess., 161(1): 271-280. Doi: 10.1007/s10661-008-0744-1.
Wang, Y., Feng, J., Lin, Q., Lyu, ×., Wang, ×. and Wang, G. 2013. Effects
of crude oil contamination on soil physical and chemical properties in
Momoge Wetland of China. Chin. Geogra. Science, 23(6): 708-715.
Doi: 10.1007/s11769-013-0641-6.
