i

BIOREMEDIATION OF SPENT AUTOMOBILE

ENGINE OIL CONTAMINATED SANDY LOAM

SOIL IN SOUTH-EASTERN NIGERIA USING

MAIZE AND COWPEA TEST CROPS

BY

AHAMEFULE HENRY EMEKA

(PG/PhD/06/41421)

DEPARTMENT OF SOIL SCIENCE

FACULTY OF AGRICULTURE

UNIVERSITY OF NIGERIA NSUKKA

JULY 2013

BIOREMEDIATION OF SPENT AUTOMOBILE

ENGINE OIL CONTAMINATED SANDY LOAM SOIL

IN SOUTH-EASTERN NIGERIA USING MAIZE AND

COWPEA AS TEST CROPS

BY

AHAMEFULE HENRY EMEKA

(PG/PhD/06/41421)

A THESIS SUBMITTED IN PARTIAL FULFILMENT

OF THE REQUIREMENTS FOR THE AWARD OF THE

DEGREE OF DOCTOR OF PHILOSOPHY (Ph.D.) IN

SOIL PHYSICS/CONSERVATION

DEPARTMENT OF SOIL SCIENCE

FACULTY OF AGRICULTURE

UNIVERSITY OF NIGERIA NSUKKA

JULY 2013

CERTIFICATION

This is to certify that Ahamefule, Henry Emeka, a Postgraduate student in the Department of

Soil Science, with Registration Number PG/Ph.D./06/41421, has satisfactorily completed the

requirements for research work for the degree of Doctor of Philosophy (Ph.D.) in Soil

Science (Soil Physics/Conservation). The work embodied in this thesis is original and has not

been published in part or full for any other diploma or degree of this, or any other university.

Prof. M.E. Obi Dr. P.I. Ezeaku

(Supervisor) (Ag. Head of Department)

DEDICATION

This study is dedicated first to God Almighty and then to my beloved wife and friend

Dr.(Mrs)Pearl Ogechukwu Ahamefule and my darling daughter Miracle Akachukwu Henry.

ACKNOWLEDGEMENT

My gratitude goes to Jehovah, the 'brain' behind the success of this study. Out of nothing o

Lord you brought forth this increase. To the Lord most high be ascribed the praise and

honour, Amen. Shame to you o death for you were not considerate to lay your ugly hands on

an academic colossus who supervised the initiation of this study. Prof. J.S.C. Mbagwu (late)

you are remembered, the academic seed you sowed six years ago has germinated. Let me

record my deep appreciation to Prof. M.E. Obi who took me in at a time when I was

overwhelmed by grieve and taunted by confusion. Prof., your fatherly supervision and

immense contributions brought back hope and stirred this study to a logical conclusion. I will

ever remain most grateful and indebted to you my supervisor. My warmest appreciation goes

to Prof. Walford I. Chukwu who threw his financial weight behind me at a time when I was

in financial distress and at the verge of throwing in the academic towel, Prof. thanks also for

the statistical analysis. I wish to express my deep appreciation to Prof. C.A. Igwe for his

fatherly advice and for squeezing out time from his very busy schedule to read through this

work with cogent remarks. My appreciation also goes to Prof. C.L.A. Asadu and Prof. F.O.R.

Akamigbo for their immense professional support and fatherly advice. I deeply acknowledge

the contributions of Bro. Peter Prince C., miss. Nkechi Aka, Bro. Ifeanyi Onyema and

mummy Comfort Okoro who took upon themselves the duty of typing this work. Let me at

this point bring my loving appreciation to my very caring and thoughtful wife for her

invaluable support which culminated in the success of this work. My sincere thanks to my

mother Mrs. Matilda Akomaye for her profound support. I cannot forget the contributions of

my dear siblings Glory, Shulammite, Peter, Paul, Caleb and Uzoma. I am also grateful to Dr.

Chizoba Okoro, Dr. Ifeyinwa Asogwa, the Olumides' family, Bro. Monday and all members

of Scripture Union Nigeria (barracks group) for their prayers and moral support.

Ahamefule, H.E.

TABLE OF CONTENTS

CHAPTER ONE

1.0 Introduction

CHAPTER TWO:

2.0 LITERATURE REVIEW

2.1 Description of Contaminants

2.2 Contaminant sources

2.3 Properties of spent and crude oil

2.4 Physical and chemical properties of petroleum hydrocarbon

2.5 Petroleum components and their biodegradation

2.6 Properties affecting the fate and transport of organic contaminants in the environment

2.6.1 Solubility

2.6.2 Biodegradability

2.7 Effects of petroleum hydrocarbons on soil physical properties

2.8 Effects of petroleum and oil based products on soil chemical properties

2.9 Effects of petroleum hydrocarbon on soil microbial ecology

2.10 Effects of petroleum hydrocarbon on crop development

2.11 The test crops

2.12 Remediation of petroleum Hydrocarbon contaminated soils

2.12.1 Excavation and off-site disposal

2.12.2 In-situ soil venting

2.12.3 In-situ bioremediation

2.12.4 Above ground or in-situ chemical oxidation

2.13 Assessment of petroleum hydrocarbon and heavy metals hazard

2.13.1 Benzene,toluene,ethyl benzene and xylene

2.13.2 Poly-Aromatic Hydrocarbons (PAHs)

2.13.3 Heavy metals

2.14 Micro-organisms in bioremediation

2.15 Properties of the organic amendments

CHAPTER THREE:

3.0 MATERIALS AND METHODS

3.1 Site description

3.2 Field methods

3.2.1 Experimental design

3.2.2 Experimental layout

3.2.3 Field preparations

3.3 Data collection

3.4 Laboratory studies

3.4.1Texture and bulk density

3.4.2 Soil porosity

3.4.3 Mean weight diameter (aggregate stability)

3.4.4 Saturated hydraulic conductivity

3.4.5 Total Hydrocarbon Content (THC)

3.4.6 Heavy metal analysis

3.4.7 Biodegradation rate (hydrocarbon loss)

3.4.8 Remediation Efficiency (R.E)

3.4.9 Microbial count

3.4.10 Determination of Leaf Area Index (LAI) and dry matter yield

CHAPTER FOUR:

4.0 RESULTS AND DISCUSSION

4.1 Soil physical properties

4.1.1 Texture

4.1.2 Soil bulk density

4.1.3 Soil porosity

4.1.4 Aggregate stability (Mean Weight Diameter-Wet and –Dry)

4.1.5 Saturated hydraulic conductivity (Ks)

4.2. Soil chemical properties

4.2.1 Total hydrocarbon content (THC) of soil

4.2.2 Distribution of heavy metals and contaminant/pollution limit (C/P index)

4.3 Biological enhancement

4.4. Effects on crop performance

CHAPTER FIVE

5.0 Summary and Conclusion

LIST OF TABLES

Table 1: Concentrations of Heavy Metals in Soils

Table 2: Microbial Genera Degrading Hydrocarbons in Soil.

Table 3: Properties of Palm Oil Mill Effluent (POME)

Table: 4 Chemical properties of cassava peels

Table 5: Some characteristics of the top (0 - 30 cm) soil of the experimental site and

spent oil used in the experiment.

Table 6: Effect of Treatments on Bulk Density (gcm-3

) of soil (0 – 20 cm depth zone).

Table 7: Effect of Treatments on Bulk Density (g cm-3

) of soil (20 – 40 cm depth zone).

Table 8: Effect of Treatments on Macro -porosity (%) of Soil (0 – 20 cm depth zone).

Table 9: Effect of Treatments on Macro-porosity (%) of Soil (20 – 40 cm depth zone).

Table 10: Effect of Treatments on Micro-porosity (%) of Soil (0 – 20 cm depth zone).

Table 11: Effect of Treatments on Micro-porosity (%) of Soil (20 - 40 cm depth zone)

Table 12: Effect of Treatments on Wet Mean Weight Diameter (MWDW) (mm) of top

(0 – 20 cm) soil.

Table 13: Effect of Treatments on Wet Mean Weight Diameter (MWDW) (mm) of sub

(20 – 40 cm) soil.

Table 14: Effect of Treatments on Dry Mean Weight Diameter (MWDD) (mm) of top

(0 – 20 cm) soil.

Table 15: Effect of Treatments on Dry Mean Weight Diameter (MWDD) (mm) of Sub

(20– 40 cm) soil.

Table 16: Effect of Treatments on the Potential Structural Enhancement Index (PSEI) of

Wet Aggregates of Soil (0 – 20cm depth zone).

Table 17: Effect of Treatments on the Potential Structural Enhancement Index (PSEI) of

Wet Aggregates of soil (20 – 40 cm depth zone).

Table 18: Effect of Treatment on Potential Structural Enhancement Index (PSEI) of Dry

Aggregates of soil (0 – 20 cm depth zone).

Table 19: Effect of Treatment on Potential Structural Enhancement Index (PSEI) of Dry

Aggregates of soil (20 – 40 cm depth zone).

Table 20: Effect of Treatments on Saturated Hydraulic Conductivity (Cm h-1

) of Soil

(0 – 20cm depth zone)

Table 21: Effect of Treatments on Saturated Hydraulic Conductivity (Cm h-1

) of Soil

(20 –40cm depth zone)

Table 22a: Variation in Total Hydrocarbon Content of treated Soils (0 – 20 cm depth

zone).

Table 22b: Variation in Total Hydrocarbon Content of treated Soils (20 – 40 cm depth

zone)

Table 22c: Effect of Treatment on Total Hydrocarbon Content of the Soil

Table 23: Effect of Treatments on Zinc (Zn) Concentration (mg kg-1

) of Soil (0 – 20 cm

depth zone)

Table 24: Effect of Treatments on Lead (Pb) Concentration (mg kg-1

) of Soil (0 - 20 cm

depth zone)

Table 25: Effect of Treatments on Chromium (Cr) Concentration (mg kg-1

) of Soil

(0 - 20 cm depth zone)

Table 26: Effect of Treatments on Iron (Fe) Concentration (mg kg-1

) of Soil (0 – 20 cm

depth zone)

Table 27: Effect of Treatments on Aluminium (Al) Concentration (mg kg-1

) of Soil (0 –

20 cm depth zone)

Table 28: The C/P (Zinc) Index of the Soil (0 – 20 cm depth zone) as Influenced by the

Treatments

Table 29: The C/P (Iron) Index of the Soil (0 – 20 cm depth zone) as Influenced by

Treatments

Table 30: The C/P (Chromium) Index of the Soil (0 – 20 cm depth zone) as Influenced

by Treatments

Table 31: The C/P (Lead) Index of the Soil (0 – 20 cm depth zone) as Influenced by the

Treatments

Table 32: Effect of Treatments on Viable Count of Hydrocarbon Degrading Micro

Organisms Population (CFUg-1

) 0 – 20 cm Soil depth zone

Table 33: Effect of Treatments on Viable Count of Hydrocarbon Degrading Micro

Organisms Population (CFUg-1

) 20 – 40 cm Soil depth zone

Table 34: Effect of Treatments on Germination Count of Maize (%)

Table 35: Effect of Treatments on Germination Count of Cowpea (%)

Table 36: Effect of Treatment on Dry Matter Accumulation of Maize (tonnes ha-1

)

Table 37: Effect of Treatments on Dry Matter Accumulation of Cowpea (tonnes ha-1

)

Table 38: Effect of Treatments on Leaf Area Index of Maize at Tasselling

Table 39: Effect of Treatments on Leaf Area Index of Cowpea at Flowering

Table 40: Effect of Treatments on Grain Yield of Maize (Tonnes ha-1

)

Table 41: Effect of Treatments on Grain Yield of Cowpea (Tonnes ha-1

)

Table 42: Regression Models Relating Three Years Average Maize and Cowpea Yield

(Y) in tonnes ha-1

to Applied Spent Oil (X) in mg kg-1

Under Various Organic

Amendments on an Ultisol.

.

LIST OF FIGURES

Figure 1: Experimental layout

LIST OF PLATES

Plate 1: Palm oil mill Effluent from local oil palm processing unit

Plate 2: local Processing of cassava which produces peels as waste

Plate 3: General Field layout after application of treatments

Plate 4: Unpolluted/un-amended (control) plot

Plate 5: Palm bunch refuse amended plot

Plate 6: Cassava peels amended plot

Plate 7: POME amended plot

Plate 8: Zero hours of oil contamination

Plate 9: Depth of spent oil contamination after 36 months

LIST OF APPENDICES

Appendix I: Sample Calculation of Remediation Efficiency (RE)

Appendix II: Sample Calculation of Degradation Rate of Spent Oil

ABSTRACT

Some agricultural wastes which constitute nuisance to the environment have been found to be

rich sources of nutrients. Hence this study evaluated their applicability as bioremediation

agents for soils contaminated with spent automobile engine oil. Main plot treatments were

0% (control), 1%, 2%, and 3% spent oil (Sp) applied in a single dose at 0, 12, 24 and 36

Mg/ha respectively. The sub-plot treatments were control (No Amendment = NA), Palm Oil

Mill Effluent (PE), Palm Bunch Refuse (PR) and Cassava Peels (CS) applied at 12 Mg/ha

each, per year. Treatments were arranged in a split-plot in Randomized Complete Block

Design (RCBD) with three replications. Samples were collected from top (0 - 20 cm) and sub

(20 – 40 cm) soil at 3, 6, 12, 18, 24, 30 and 36 months and analyzed for aggregate stability,

bulk density (BD), soil porosity, saturated hydraulic conductivity (Ks), total hydrocarbon

content (THC), heavy metals (Al, Cr, Fe, Pb and Zn) and viable microbial count (VMC).

Maize and cowpea were planted in three seasons to evaluate crop response to the treatments.

The texture of the soil was sandy loam. The stability of top soil aggregates under wet

conditions was observed to have increased with increased spent oil, with mean weight

diameter (MWD) values ranging from 1.30 mm – 1.88 mm. Plots under PE and CS organic

amendments showed significantly higher stability(P < 0.05) compared to PR amended and

the un-amended (NA) soils. Bulk density increased with increasing oil contamination but was

restored to control values (1.50 g cm-3

) 12 months following amendment with PE and CS.

The Ks of the soils decreased from 25cmh-1

(very rapid permeability) before oil

contamination to 6cm h-1

(moderately rapid permeability) following contamination but

increased to 25cm h-1

(very rapid permeability) following 36 months of organic amendment.

Reduction in THC was shown to be achieved not only by biodegradation, as commonly

reported by other researchers but also by its combination with loss due to gravity (downward

seepage). Reduction in THC in un-amended top (0 - 20 cm) soils was 3 times more by gravity

compared to amended plots in which THC reduction was 3 times more on the average by

microbial degradation. The PE amended plots reached a peak of 11-fold loss in THC relative

to un-amended equivalent. The rate of hydrocarbon degradation was highest in the first 3

months, increasing from 43.0 mg Kg-1

d-1

in un-amended soils to 125.5 mg Kg-1

d-1

in PE

amended soils. The PE treatment was the most efficient amendment of the three organic

amendments applied with remediation efficiency ranging from 19.1–111.3 % compared to PR

amendment with the least remediation efficiency ranging from 1.3 – 51.3 %. Increased heavy

metal (Al, Fe, Cr, Pb and Zn) concentration in soil followed increased oil contamination. The

C/P index values obtained in this experiment were < 1, thereby indicating that the resultant

contaminations were within tolerable levels for soil micro-fauna, -flora and higher plants.

The population of viable hydrocarbon degrading micro-organisms grew with increase in oil

contamination, nutrient and time. Microbial population in amended plots followed the order

PE > CS > PR > NA. Viable microbial count declined with soil depth. The 3% oil treatment

reduced maize germination from 86% (control) to 41 while it reduced cowpea germination

from 92% (control) to 74 %, in the first year. Germination in amended plots was higher than

that in un-amended plots, with PE treated plots showing highest germination rate. Increasing

oil beyond 1% significantly reduced dry matter yield to between 41.7 % and 83.9 % in maize

and to between 2.3 and 8.0 % in cowpea 12 months after contamination. Increased oil

contamination led to decrease in leaf area of maize and cowpea plants. Plants grown in

amended plots showed significantly larger leaf area compared to those in un-amended plots.

Maize yield reduced by 94.7 % and 99.3% in 2 % and 3 % oil treated plots respectively while

there was a 70 % cowpea yield decline under 3 % oil. The yield of maize and cowpea in

amended plots were between 19.3 – 43.0 % and 1.6 – 32.9 % respectively higher than yield in

un-amended plots. All maize plants under 3 % oil showed yield failure except those in plots

amended with PE. The crop parameters examined showed that oil had more deleterious effect

on the maize than on cowpea plant.

CHAPTER ONE

1.0 INTRODUCTION

Pollution caused by petroleum and its derivatives (like spent engine oil) is a prevalent

problem in the environment. In Nigeria, however, common forms of pollution come from

household wastes, agricultural wastes, gas flaring, oil spills and spent lubricating oil. Spent

engine oil, usually obtained after servicing and subsequently draining used oil from

automobiles and generator engines, is indiscriminately disposed into gutters, water drains,

open vacant plots and farms in Nigeria by auto mechanics and allied artisans with workshops

on the road sides and open places (Anoliefo and Vwioko, 2001). The pollution incidence of

spent oil in the environment has been shown by Atuanya (1987) to be more widespread than

crude oil pollution. Nigeria was reported to account for more than 87 million litres of spent

automobile engine oil annually (Anon, 1985), and adequate attention has not been given to its

disposal (Anoliefo and Vwioko, 1994).

Furthermore, both crude oil and spent engine oil contain potentially phyto-toxic polycyclic

aromatic hydrocarbons (Sharifi, et. al., 2007). Lubricating oil contains heavy metals.

However, the proportion and type of these heavy metals increase in the used lubricant

depending on the process generating the waste. Edebiri and Nwanokwale (1981) reported that

metals present in spent oil were not necessarily the same as those present in the unused

lubricants. Whisman et. al., (1974) observed that heavy metals like Vanadium (Va),Lead

(Pb),Aluminium (Al),Nickel (Ni) and Iron (Fe) that were below detection in unused lubricant

oil showed high values in the used oil. Atuanya (1987) and Agbogidi and Ejemete (2005)

noted that oil in soil has deleterious effects on biological, chemical and physical properties of

the soil depending on the dose, type of the oil and other factors. Benka-Coker and Ekundayo

(1995) and Benka-Coker and Ekundayo (1997) also reported that the microbiological

components of soil were usually negatively affected following oil application to the soil.

According to Ekundayo et. al., (2001), germination of seeds planted in crude oil polluted soil

area is delayed while percentage germination is also significantly affected. Agbogidi (2010)

reported poorer germination response of cowpea with increasing dose of spent engine oil.

Also, the effects of crude oil on plants according to Sharma et. al., (1980) have been found to

include morphological aberration, reduction in biomass and stomata abnormalities while

yellowing and dropping of leaves to complete shedding of leaves in areas of heavy pollution

have been reported by Opeolu (2000). Crude oil spilled in soils has also been found to inhibit

cowpea growth due to impaired water drainage and oxygen diffusion (Amadi et. al., 1993).

Worse still, the natural recovery of oil from polluted soils is slow and communities affected

by such problems are denied utilization of their agricultural lands for a long time (Gradi,

1985). The problem is further compounded by the vast number of sites that need to be treated

(McGugan et. al., 1995) and coupled with the fact that there are areas of the world

(developing countries) that cannot afford expensive remediation. Therefore, bioremediation is

now being considered as a viable alternative for this purpose.

Bioremediation has been defined by Madsen (1991) as “a managed or spontaneous process in

which biological, especially microbial catalysis, acts on pollutant compounds, thereby

remedying or eliminating environmental contamination”. It also refers to the enhancement of

the native capability of micro-organisms by the addition of oxygen and nutrients to the soil

system to support biological growth and improve the degradation (Catallo and Portier, 1992).

Petroleum contaminants which are classified into saturated hydrocarbons, aromatic

hydrocarbons and polar organic compounds can be converted by this method to inert or less

harmful materials (Ram, et. al., 1993). Though this technology is yet evolving in Nigeria, it

will prove most useful in the remediation of spent oil polluted soils. It was observed that the

bioremediation process depended on nutrient supply among other factors (Ladousse and

Tramier,1991; Leahy and Cowell,1990). This calls for concerted research efforts geared at

tapping the fertility potentials of some of the agricultural wastes abundant in the various

agro-ecological zones of Nigeria. These wastes can be employed to enhance the nutrient

status of polluted soils for remediation in such places.

The south-eastern part of Nigeria falls into the oil palm belt and cassava producing region in

the country. Nigeria, with the year 2006 production of 49 million tonnes of cassava, is the

largest producer of the crop in the world (NPC, 2008). The processing of these produce,

generates quite an enormous quantity of wastes in this area. During the processing of the ripe

fruits of oil palm to extract cooking oil, a lot of waste water (Palm Oil Mill Effluent-POME)

is generated. Observations show that most of the POME is not treated before discharge into

the surrounding environment especially by small scale mills, causing pollution problems. For

cassava, its processing results in the production of peels, chaff, fibre and spoilt or otherwise

unwanted tubers. A relatively small quantity of peels and unwanted tubers are fed directly to

ruminants. However, the much larger remaining proportion of cassava solid wastes are

indiscriminately discharged into the environment and amassed as waste dumps on sites where

cassava is processed, with increased production of peels and other cassava-derived wastes.

This constitutes an enhanced risk of pollution to the environment. There is, therefore, an

urgent need to find an alternative productive use of these agricultural wastes. One area of

possibility is to investigate the potentials of POME, oil palm bunch refuse and cassava peels

as bioremediation tools and by so doing, reduce their nuisance to the environment. Hence this

research is aimed at assessing the effectiveness of these wastes for the bioremediation of an

Ultisol (in eastern Nigeria), contaminated with waste crank-case oil (spent engine oil). The

specific objectives of the study were to:

1. Determine the physico-chemical properties of spent oil-contaminated, organic waste

amended and control soils.

2. Evaluate the applicability of some nutrient supplements as bioremediation technology.

3. Assess the toxicity of some spent oil-induced heavy metals to cowpea, maize and soil

micro-fauna.

CHAPTER TWO

2.0. LITERATURE REVIEW

2.1 Description of Contaminants

Contaminants are all solutes introduced into the environment as a result of human activities

regardless of whether or not the concentrations reach levels that cause significant

degradation or any harm (Lacatusu, 1998). Pollution results when contaminant concentration

reaches a level that is considered to be objectionable (Freeze and Cherry, 1979), or a

situation in which the concentration of a substance is higher than would naturally occur but

also indicates that the substance is causing harm of some type. Lacatusu (1998) observed

that when soil contamination continued to increase, there was the possibility that it would

exceed the soil capacity to fix chemical elements (by well known phenomena of adsorption

and complexing) and also the possibility of inducing negative effects on soil functions; thus,

the pollution phenomenon starts off. Common chemical contaminants include: organic

(pesticides, solvent and petroleum hydrocarbons) and inorganic (heavy metals and other

trace elements). Used oil, spent solvents, cleaning compounds, discarded paints, by-products

of chemical processes and discarded chemical formulations are classed as wastes (OCHCA,

2002). Wastes that contain toxic compounds in excess of specified concentration are

considered hazardous wastes. The term toxic or hazardous waste or substance refers to

pollutants. The added distinction of toxic or hazardous is used for substances that can be

acutely or chronically toxic to humans.

2.2 Contaminant Sources

Contaminant sources can be broadly defined as originating from anthropogenic or natural

sources. For example, a volcano may place a greater quantity of noxious gas and particulate

matter into the atmosphere than the combined output from a large number of electric power

plants, but some would not consider the output from the volcano as a pollutant because of its

natural origin (Pierzynski et. al., 2000). Similarly metal mining may pollute soils with heavy

metals, yet soils with high concentration of heavy metals can occur naturally because of

their proximity to metal ore deposits.

Potential sources of contaminants include both point and non-point sources. Point sources

are direct discharges or emissions from discrete sources, or identifiable point of discharge

such as land fill, or spill. According to Lacatusu (1998) point sources of pollution within

urbanized areas include industries and municipalities which discharge directly into streams

and rivers, as well as releases from chemical spills, and leaking underground storage tanks

(LUSTS).

The chemical sources that are not specifically characterized which may encompass

numerous individual discharges or emissions, or an obvious single point of discharge are

non-point sources. Examples of non-point sources include agricultural, urban storm runoff

and construction sites as well as automobile emissions. Non-point sources have the potential

to contribute significant pollutant load from runoff and the atmospheric deposition into the

river (Lacatusu, 1998).

2.3 Properties of Spent Oil and Crude Oil

The spent or waste engine oil is usually obtained after servicing and subsequent draining

from automobile and generator engines by auto-repairers. It includes mono and multi-grade

crankcase oils from petrol and diesel engines, together with gear oils and transmission fluids

with significant levels of hydrocarbons and other properties present in all petroleum

products (Omoluobi, 1998). In Nigeria, the government has not been able to monitor and/or

control the discharge of petrol oils and grease from the thousand auto-repair workshops,

because it has proved very difficult to regulate their activities by virtue of their small size.

It has been observed that Nigeria produces more than 87 million litres of spent automobile

oil annually and that most heavy metals, such as Va, Pb, Al, Ni and Fe, which are below

detection in unused lubricating oil, showed high values in waste automobile oil (Anon,

1985). Engine oil, is a petroleum product which aids in the reduction of friction between

engine parts’ surfaces. It is produced by vacuum distillation of crude oil and usually contains

chemical additives including amines, phenols, benzenes, calcium, zinc, barium, magnesium,

phosphorus, sulphur and lead (Obidike, 1985; Kirk et. al., 2005).

Crude oil on the other hand, is a complex naturally occurring mixture of organic compounds

that is produced by the incomplete decomposition of biomass over a long period of

geological time. Petroleum compound can occur in a gaseous form that is often called

natural gas, as a liquid called crude oil, and a solid or semi-solid asphalt or tar associated

with oil sands and shale’s (Freedman, 1981).

Hydrocarbons are quantitatively the most important constituents of petroleum (Freedman,

1981), while the term “Total Petroleum Hydrocarbons” (TPH) is generally used to describe

the measurable amount of petroleum-based hydrocarbons in the environment (USDHHS,

1999). Hydrocarbons can be classified into three broad groups, each with various subclasses

(Clark and Brown, 1977):

• Aliphatic hydrocarbons are open-chain compounds. If there is only one bond between

all adjacent carbon atoms, the molecule is said to be saturated. Unsaturated molecules have

at least one double or triple bond. Saturated aliphatics are known as paraffin or alkanes, and

they are chemically more stable than unsaturated aliphatics. The latter are not produced in

crude oil, but they can be produced secondarily during an industrial refining process, or

photo chemically after crude oil is spilled.

• Alicyclic hydrocarbons have some or all their carbon atoms arranged in a ring structure,

and they can be saturated or unsaturated.

• Aromatic hydrocarbons are those that contain at least one six-carbon ring in the

molecular structure. The basic C6H6 ring is known as benzene.

Crude oil from different locations varies in hydrocarbon composition while the elements in

crude oil include sulphur (<0.1% to 6% by weight), nitrogen (<0.1% to 0.9%) and oxygen at

up to 2%. The most important trace elements in petroleum are Va and Ni, both at

concentrations of up to 300ppm, and present as organometallic complexes (Clark and

Brown, 1977).

During the refining of crude oil, the various hydrocarbon products are separated by

fractional distillation at specific temperatures. Typical yields are: natural gas, gasoline,

kerosene, middle distillates (including heating oil, and jet and rocket fuels), wide-cut gas oil

(lubricating oils, waxes, feedstock for catalytic cracking), and residual fuel oil (bunker fuel

for ships and for electrical utilities (Freedman, 1989). While these products are generally

considered as single entities, each is actually a complex mixture of many organic chemicals

with its own properties and behavior when in contact with soil and water (Nyer and

Skladany, 1993).

2.4 Physical and Chemical Properties of Petroleum Hydrocarbons

Once a liquid petroleum product is released into the ground, it partitions into three separate

phases: dissolved, liquid and gas (Nadim et. al., 2000). Furthermore a small fraction of

petroleum hydrocarbon dissolves in the soil moisture or groundwater and a part remain in

the soil pore space in its pure liquid form as residual saturation while some of it evaporates

into the air or soil pores. Pure liquids that do not readily dissolve in water are called non-

aqueous phase liquids (NAPLs).The NAPLs are sub- divided into two classes: namely those

that have densities less than that of water (LNAPLs), and those with a density greater than

that of water (DNAPLs). Hydrocarbon fuels such as gasoline, heating fuel, kerosene, jet fuel

and aviation fuel are LNAPLs. Therefore, above ground or in-situ remediation of

hydrocarbon contaminated soils or water must address the specific organics present (Nyer

and Skladany, 1993).

The number of carbon atoms present in a compound has a major effect on its properties.

According to Nyer and Skladany (1993), alkane chains up to 17 carbons in length are liquids

and have densities less than that of water (< 1), while alkane chains with 18 or more carbons

in length are solid waxes. Solubility of alkanes rapidly decreases as the number of carbon

present increases; vapour pressure decreases as alkane carbon number increases (Nyer and

Skladany, 1993). High vapour pressure indicates that a compound can be easily volatile; low

vapour pressure is associated with chemicals that are semi-volatile or non-volatile. Boiling

point temperatures for alkanes increases with the number of carbons present (Nyer and

Skladany, 1993). Characterizing environmentally acceptable endpoints for soil requires an

understanding of the impact of chemicals on soil and the subsequent effects on terrestrial

ecosystems (Stegmann et. al., 2003).

2.5 Petroleum Components and their Biodegradation

Crude oil is a complex mixture of several different structural classes of compounds such as

alkanes, aromatics, heterocyclic polar compounds, and asphaltenes. The rate of microbial

degradation of crude oil depends on the interaction between the physical and biochemical

properties of these compounds (Uraizee et. al., 1998). The distribution of the various

structural classes and compounds present in petroleum influences the biodegradability of

individual hydrocarbon components. Katsivela (2005) reported that the ability of mixed

microbial cultures to utilize hydrocarbons present in four crude oils depended not only on

the concentration of the n-saturated fraction but also on the asphaltene and nitrogen, sulphur

and oxygen (NSO) fraction of the oil.

2.6 Properties Affecting the Fate and Transport of Organic Contaminants in the

Environment

The environment plays a key role in the transport and ultimate fate of contaminants. The

specific fate of contaminants, following release into the environment, depends on the

chemical structure of the contaminants, which is highly variable (Brady and Weil, 2002).

Abiotic factors within the receiving environment (e.g. organic carbon, pH, water, etc), and

interactions with the biotic environment, can result in degradation, transformation or bio-

concentration of the contaminants (Aichberger et. al., 2005). Furthermore, when one of

these critical components was sub-optimal for conversion of organic contaminants,

biodegradation was slow or did not take place. Vezzulli et. al., (2004) and Gallizia et. al.,

(2005) observed that the rate of transformation of organic pollutants in soils was a function

of the availability of chemicals to the micro-organisms that can degrade them, the population

of those micro-organisms and the activity level of the organisms. Thus, contaminant

properties and soil characteristics can often provide a general indication of the applicability

of the treatment technologies available for remediation of the particular contamination

incident .At given environmental conditions, the degree of hydrocarbon biodegradation is

mainly affected by the type of hydrocarbons in the contaminant matrix. Huesemann (1995)

observed that, of the various petroleum fractions, n-alkanes and branched-alkanes of

intermediate length (C10-C20) were the preferred substrates for micro-organisms and were

the most readily degradable. Longer chain alkane (>C20) are hydrophobic solids and are

difficult to degrade due to their inherent recalcitrance and their poor water solubility. Crude

petroleum and many of the refined petroleum products contain thousands of hydrocarbons

and related compounds. Some oils contain toxic hydrocarbons which may prevent or delay

microbial attack, whereas, some refined oils have additives, such as lead, which according to

Katsivela et. al., (2005), inhibited microbial degradation of polluting hydrocarbons.

Under favourable conditions (Katsivela et. al., 2005), micro-organisms will degrade 30 to

50% of crude oil residue. With favourable conditions and the proper organisms virtually all

kinds of hydrocarbons like straight chain, branched-chain, cyclic, simple aromatic, poly-

nuclear aromatic, and asphaltic, have been found to undergo oxidation (Mesarch et. al.,

2000). Each of these organic compounds has unique characteristics that dictate which

mechanism or a combination of mechanisms controls its movement and degradability. In

another study, Davis et. al., (2003) reported that for a successful biodegradation programme,

the natural heterogeneity of the soil system must be over -come, the rate limiting factors

must be removed, and the microbial population promoted to remove the organic

contaminants.

According to Ram et. al., (1993), significant characteristics of organic wastes affecting their

biodegradation include chemical composition of the waste, its physical state (i.e. liquid,

slurry, and sludge), its carbon-nitrogen ratio, water content and solubility, volatility, pH,

biochemical oxygen demand (BOD) and chemical oxygen demand (COD). Boopathy (2002)

and Massias et. al., (2003) observed that the behaviour of toxic pollutants in the

environment also depended upon a variety of chemical processes (e.g. hydrolysis,

photolysis, oxidation, reduction, hydration) and physical or transport processes (e.g.

advection, dispersion, and diffusion, sorption, volatilization, solubilization, viscosity and

density).

Strong interactions between the soil matrix and hydrophobic pollutants, causing pollutant

retention or even irreversible binding to sorbents, have been observed (Huang et. al., 2003).

This phenomenon, known as aging, increases with time and has been reported to

significantly reduce bioavailability of hydrophobic contaminants in the soil. Several studies

reported the degree of hydrocarbon degradation was mainly affected by the type of

hydrocarbon in the contaminant matrix and only to a less extent by soil characteristics

(Nocentini et. al., 2000; Breadveld and Sparrevik, 2001). This was true in particular, for

soils derived from further depths in the subsoil, where relatively low amounts of soil organic

matter (SOM) were present. Pollutant retention over time was governed by physical-

chemical characteristics of the pollutant and soil characteristics strong or even irreversible

sorption onto soil (Huang et. al., 2003) was attributed to the soil organic matter. Other

factors, such as availability and type of electron acceptors, temperature, pH, moisture

content, availability of mineral nutrients and contaminants concentration, have been reported

to affect the degree of hydrocarbon degradation (Mohn and Stewart, 2000). Most petroleum

related hydrocarbons are readily degraded by aerobic micro-organisms and a number of

studies have shown that in the absence of oxygen with alternate electron acceptor, such as

nitrate, manganese (IV), iron (III), sulphate and carbondioxide (Boopathy, 2002; Massias et.

al., 2003), hydrocarbons can be biodegraded. Addition of nutrients was reported to have a

beneficial effect on hydrocarbon degradation in the soil (Chaineau et. al., 2003),whereby a

carbon:nitrogen:phosphorus (C:N:P) ratio of 100:10:1 was commonly proposed (Atagang

et.al;2003). Microbial activity proceeded optimally in the presence of water at between 50%

and 70% field capacity (Margesin et. al., 2000).

The “quality” of organic matter (OM) is widely recognized to affect the rate and extent of

OM decomposition and re-mineralization. Within the bulk of OM, proteins(PRT) and

carbohydrates (CHO) have been identified by several authors as the most bio-available food

source for microbial metabolism (Danovaro et. al., 1999; Meyer-Reil and Koster, 2000); in

particular, PRT are more labile than CHO, and are considered the first organic polymers to

be degraded for bacterial metabolism, while CHO are more refractory to consumption.

According to Vezzuli et. al., (2003), PRT and CHO concentrations can be utilized as

indicators of the biodegradation accruing in organic-rich soils.

Non-ionic and non polar organic pollutants are normally absorbed on soil humic materials

(Alloway and Ayres, 1997). Since most soil organic matter is found in the surface horizon,

there is a tendency for organic pollutions to be concentrated in the topsoil. Alloway and

Ayres (1997) further observed that migration of organic contaminants down the profile only

occurred to any marked extent in highly permeable sandy or gravelly soils, with low organic

matter contents and where large pores (Macro pores) and fissures were present. Several

other physico-chemical parameters, which are useful in predicting the behaviour of organic

contaminants in soils, include a substance’s solubility in water (MgL-1

), its soil-water

distribution coefficient (Kd), its specific gravity (dimensionless), its octanol-water partition

coefficient (K0w) and its organic carbon partition coefficient (Koc) and biodegradation.

2.6.1 Solubility

Solubility is one of the most important properties affecting the fate and transport of organic

compounds in the environment. The solubility of a compound is described as the maximum

dissolved quantity of compound in pure water at a specific temperature (Nyer et. al., 1993).

The extent to which an organic compound (solute) dissolves in a solvent (water) is referred

to as the water-solubility of the compound and ranges from 1-100,000 mgL-1

at ambient

temperature for most common organic compounds. Highly soluble compounds are easily

transported by the hydrologic cycle. Alloway and Ayres (1997) observed that the rate at

which highly soluble compounds moved through the unsaturated zone was controlled to a

greater extent by the unsaturated hydraulic conductivity in the porous media.

Compounds (from spills) which have high water solubility are reported to have shorter

downward travel times, low adsorption coefficients for soils and low bio-concentration

factors in aquatic life (Nyer et. al., 1993). Highly soluble compounds also tend to be more

readily bio-degradable. For oil spills (Pfannkuch, 1985), the hydrocarbon components, with

differing solubilities, dissolve out differentially and produce simultaneous aging and

leaching effect on the spills. Alloway and Ayres (1997) observed that solubility usually

decreased as temperature increased due to an increase in water vapour pressure at the

liquid/gas interface. Degradation of polynuclear aromatic hydrocarbons (PAHs), in general,

is limited because of their low solubility. Wiesel et. al., (1993) observed that the order of

degradation of PAHs was related to their water solubility, which is invariably related to ring

concentration. They reported that the tetra cyclic compounds were less available than di-

and-tri-cyclic compounds.

2.6.2 Biodegradability

This parameter is used to determine whether a compound is degradable, the most effective

biodegradation mechanism (aerobic VS anaerobic), and the biodegradation rate. Organic

compounds that are completely degradable, but slow, can be persistent in the soil

environment for a long period of time (Nyer et. al., 1993). Biodegradation potential of

organic contaminants has been studied and classified as: degradable, persistent and

recalcitrant (USEPA, 1990). Readily degradable contaminants refer to compounds that have

passed biodegradability tests in a variety of aerobic environments. Persistence refers to

materials or substances that remain in the environment for long periods of time. These

compounds, according to USEPA (1990), are not necessarily “non-degradable”, but

degradation requires long periods of acclimation or modification of the environment to be

induced. The USEPA (1990) studies further stressed that each organic compound must be

evaluated to determine the estimated time to complete the transformation of the chemical

under optimal conditions.

Bossert and Bartha (1994) reported an inverse correlation between the numbers of PAH

rings and their loss from soil. Biodegradation correlated positively with water solubility

rather than with the degree of condensation cluster against linear arrangement of the same

number of rings. Biodegradation has been shown to be a major removal mechanism for

many PAHS from soil. Its augmentation to accelerate hydrocarbon decomposition is an

effective means of hydrocarbon removal from the soil (Bossert and Bartha, 1994).

Therefore, knowledge of the mechanism of degradation and the factors controlling it are

necessary to achieve bioremediation.

2.7 Effects of Petroleum Hydrocarbons on Soil Physical Properties

The presence of oily wastes makes soil constituents hydrophobic, but if the soil is properly

managed, the impact on the soil environment can be minimal. However, Rasiah et. al.,

(1990) reported that oil tends to accumulate in disposal sites in the long-term. Disposal of

oily wastes or oil spill may lead to formation of oily scum, which according to Amadi et. al.,

(1993), impedes O2 and water availability to biota and creates anaerobic conditions in the

subsoil, which aids the persistence of the oil.

Anoliefo and Vwioko (1995) observed that oil in soil created unsatisfactory conditions for

plant growth, probably due to insufficient aeration of the soil. They further reported that this

condition was caused by the displacement of air from pore spaces by oil, and an increase in

the demand for oxygen brought about by activities of oil-decomposing micro-organisms.

McGill (1976) observed that oil occupied the macro-pores and coated macro-aggregates,

reduced the water film thickness around macro aggregates and retarded the movement of

water into and out of micro-aggregate. Rasiah et. al., (1990) in the same vein, observed that

oil interacted with clay surfaces to form hydrophobic micro-aggregates. This suggests that

hydrophobicity and modification in hydraulic properties occur at the micro-aggregate level.

A general conclusion from studies on the effects of oil-based wastes on soil hydraulic

properties is that water retention is increased by their application to soil (Stevenson, 1987).

Rasiah et. al., (1990) studied the soil physical conditions of a clay loam soil which had

received about 167,000 L ha-1

yr-1

of oil from a waste water treatment plant, and observed

that water retention in the oily waste contaminated soil was significantly low compared to

the non-contaminated soil. The low water retention suggested that oil had succeeded water

in the competition for pore space. According to the study, the fact that the decrease in water

retention occurred at high potentials (-10 to-200 Kpa) suggested that the competition

occurred in the macro-pores. These researchers concluded that oily wastes in the soil

reduced water retention at high water potentials while increasing the saturated hydraulic

conductivity and total pore volume. The unsaturated hydraulic conductivity was drastically

reduced by the oil waste. According to West et. al., (1992) a reduction in porosity from 90 to

30% resulted from the formation of structural crusts. Associated with the porosity decrease

by structural crust was a reduction in the mean size of pores .On the ability of soil micro-

organisms to remediate oil-contaminated soils, Glick (2003) observed that the activity of soil

micro-organisms on decomposition processes was found to be higher at high water potential

than at low water potentials. Chenu et. al., (2001) observed that plant nutrition in oil

contaminated soils was controlled, in part, by the availability of nutrients within specific

layers or regions of soil aggregates and preferential movement of water. Soils contaminated

with oil, appeared waxy and usually did not allow water to penetrate from above.

2.8 Effects of Petroleum and Oil-based Products on Soil Chemical Properties

Depletion in the nutrient status (nitrogen and phosphorous) has been reported in spent oil-

contaminated soils (Atlas and Bartha, 1993). Amadi et. al., (1993) studied the effects of

heavy and moderate oil spill on soils, and observed that the pH status of the soils in the

contaminated zones varied from acidic (4.0) to near neutral (6.0). The C content of the soils

decreased from 3.6% at the heavily impacted to 2.84% at the moderately impacted zones.

According to the study, total N in the heavily impacted and moderately impacted zones

differed by a fraction of 0.10%. Available P was higher at the moderately than heavily

impacted zone, while CEC decreased from a combined mean of 6.48 at the heavily impacted

zones to 4.46 at the moderately impacted zones.

Bossert and Compeau (1995) observed inhibition of microbial activities, such as nitrogen

fixation, algal photosynthesis and bacterial chemo-taxis, in soils impacted with oily wastes.

Studies of Amadi et. al., (1993) reported that organic C, total N, C/N ratio, available P,

exchangeable K and CEC were adversely affected in oil-contaminated lands. Alloway and

Ayres (1997) observed that the effect of oil and other pollutants on soil chemical properties

was determined by the soil pH, temperature, supply of oxygen, the structure of the

contaminant molecules, their toxicity and that of their intermediate decomposition products,

the water solubility of the contaminant and its adsorption to the soil matrix. They further

observed that oxidation of organic pollutants occurred by the action of oxygenase enzymes

secreted by micro-organisms. In alkane hydrocarbons, the initial step in oxidation is the

conversion of terminal CH3 groups to CO2H group. According to the study, aromatic rings

were cleaved by the addition of OH to adjacent carbon atoms.

The decomposition products of some organic molecules are more toxic to soil micro-

organisms, animals and humans than the initial compound. For example, Alloway and Ayres

(1997) observed that the microbial oxidation products of PAH molecules were carcinogenic

because they were bonded to cellular DNA. Many organic pollutants are more rapidly

decomposed after they have been adsorbed on to the soil organic matter. Alloway and Ayres

(1997) reported that some xenobiotic organo-chlorine molecules, such as DDT, PCBs and

PCDDs were generally regarded as being highly persistent in soil, with residence times of at

least 10 years. They have a very low decomposition rate because the carbon-chlorine bond is

not found in nature and so most micro-organism species do not possess the enzymes to break

this bond.

2.9 Effects of Petroleum Hydrocarbon on Soil Microbial Ecology

Sensitivity of soil micro-flora to petroleum hydrocarbons is a factor of the quantity and

quality of oil spilled and previous exposure of the native soil microbes to oil (Bossert and

Compeau, 1995). Petroleum hydrocarbon utilizers can tolerate oil- contaminated

environments because they possess the capacity to utilize oil as energy source (Song et. al.,

1990). Odu (1981) reported that contamination of soil with 1% to 5% oil normally served as

a boost to soil organic matter. Schwindinger (1968) observed that beyond 3% concentration,

oil becomes increasingly deleterious to soil biota and crop growth. Among several bio-

indicators of soil health and quality are micro-organisms; this is due to their capability to

respond quickly to environmental changes. According to Doran and Safly (1997), "the soil

health is the continued capacity of the soil to function as a vital living system, within

ecosystem and land-use boundaries, to sustain biological productivity, promote the quality

of air and water environments and maintain plant, animal and human health".

Microbial indicator has been defined as a microbial parameter that represents properties of

the environment or impacts on the environment which can be interpreted beyond the

information that the measured or observed parameter represents itself (Nielsen et. al., 2002).

Microbial bio-indicators could be based on functional land structural diversity of the

bacterial community. Functional diversity, according to Zak et. al., (1994), is the number,

type, activity and rate at which a set of substrate is utilized by a bacterial community.

Among the functional diversity indicators, the carbon utilization pattern and the

measurement of enzymatic activities, expressed by the whole bacterial community, have

been suggested as useful tool to evaluate the soil status (Nielsen et. al., 2002). Structural

diversity is the number of parts or elements within a system, indicated by such measures as

the number of species, genes, communities or ecosystem. Several indices, such as species

richness, diversity and evenness have been used to describe the structural diversity of a

community and to monitor changes in microbial diversity due to environmental fluctuations,

land management practices and oil pollution as well as industrial activities (Ovreas, 2000),

and was found to be very sensitive to environmental pollution. Variation in microbial

population and activity was reported to function as a predictor of changes in soil health.

Avidano et. al., (2005) observed a shift in carbon substrate utilization patterns in soil

contaminated with oil and related it to the development of hydrocarbon utilizing bacterial

community. The study further showed that Pseudomonas and Bacillus micro-organisms

were prevalent in the oil contaminated sites, whereas dramatic reduction occurred in the total

microbial community due to the additions of petroleum waste sludge. Katsivela et. al.,

(2005) reported that petroleum waste sludge adversely affected the microbial population by

depleting essential inorganic nutrients and growth factors and lowering the pH immediately

around negatively charged surfaces.

2.10 Effects of Petroleum Hydrocarbon on Crop Development

According to Kirk et. al (2005), in petroleum contaminated soils, plant growth is typically

limited by nitrogen and phosphorus as a result of the overabundance of carbon from the

petroleum hydrocarbons. Kirk et. al,( 2005) further stated that because of the hydrophobic

nature of the contaminants, water and water-soluble nutrients are often limited. It was

suggested that arbuscular mycorrhizal fungi (AMF) may reduce plant stress through an

increase in water availability and enhanced oxidative enzyme production (Joner and Leyval,

2004), thereby increasing the volume of soil being remediated by the mycorrhizosphere.

In the same vein, the effect of oil on seed germination has been shown to be inhibitory due

to unfavourable soil conditions (Anoliefo and Vwioko, 1995; Agbogidi, 2010; Adewole and

Moyinoluwa, 2012). Anoliefo and Vwioko (1995) further reported that upon drying, the

soils contaminated with oil became too hard to allow germination. Also the reduced oxygen

content of the soil due to the blockage of pores in the soil and increased water stress on the

seed imposed negative effects on germination (Atuanya, 1987). Adewole and Moyinoluwa

(2012) observed that the growth rate of cowpea showed a decrease in stem height, leaf

number and percentage protein content with increasing crude oil contamination. Anoliefo

and Vwioko, 1995) also reported that there was more than 50% reduction in the height of

Lycopersicon esculenta when grown in soil treated with only 1% spent oil compared to the

control with Capsicum annum as the test crop. The authors also noted a decline in leaf area

as oil contamination increased. They, however, observed that the oil contamination effect

was more pronounced on L. esculenta compared to C. annum. They further observed that 84

days after sowing, the few plants which had germinated died prematurely. Agbogidi (2010)

working on cowpea, observed that beyond 25ml spent oil per plot, there was reduction in

plant height, leaf area, number of leaves, stem diameter, days to 50% flowering, number of

nodes on main stem, number of branches and number and length of peduncles. Udo and

Fayemi (1975) also reported that growth of cereals was adversely affected in oil polluted soil

causing chlorosis of leaves and plant dehydration. Amadi et. al., (1993) and Ekundayo et.

al., (2001) working on maize, supported the findings of Udo and Fayemi (1975) stating that

increasing the concentration of oil beyond 3% in soil, reduced percentage germination, by

oil coating on seed surfaces thereby affecting physiological functions within the seed.

However, by decreasing the soil bulk density with sawdust, in their experiment Amadi et.

al., (1993), noted that the soil volume available for contact with oil was reduced.

Consequently, the degree of inhibition of the physiological function was reduced. On a

general note, the effects of crude oil on plants, according to Sharma et. al. (1980), have been

found to include morphological aberration and reduction in biomass to stomata

abnormalities while yellowing and dropping of leaves to complete shedding of leaves in

areas of heavy pollution have been reported by Opeolu (2000). It is generally agreed that

contamination of soil with petroleum and its derivative has pronounced effect on plant

growth and that the extent to which plants were affected differed due to an innate genetic

response of the plant system as modified by environmental influences (Atunanya, 1987).

2.11 Test Crops

There are several types of toxicity studies involving plant processes. According to Fletcher

(1991), the tests with plants can be used in 5 different categories: biotransformation, food

chain uptake, sentinel, surrogate, and phytotoxicity. Among these tests, the phytotoxicity is

receiving more attention during the last years. Some species recommended by the United

States Environmental Protection Agency (USEPA) and Federal Department of Agriculture

(Fletcher, 1991) are radish (Raphanus sativus, carrot (Daucus carota); rice (Oryza sativa);

turnip (Brassica rapa); soybean (Glycine max); Oats (Avena sativa); cabbage (Brassica

campestri); maize (Zea mays); tomato (Lycopersicon esculentum); bean (Phaseolus aureu;

Phaseolus vulgari); onion (Allium cepa); sorghum (Sorghum bicolor); and lettuce (Lactuca

sativa). This however does not indicate that other crops peculiar to impact areas cannot be

used as test plants.

Cowpea (Vigna unguiculata L.) is a popular leguminous food crop in Nigeria (Adelaja, 2000;

Adaji et al., 2007). Cowpea belongs to the family fabaceae and sub-family faboideae. The

Igbo in Nigeria locally call it “Agwa” while the Yoruba tribe calls it “Ewa” and the Hausa

tribe “wake”. It is cultivated extensively in West Africa and it is the principal source of

dietary protein in Nigeria (Brantley, 1992) from the swampy rain forest of the Niger Delta to

the sparse savannah grassland of northern Nigeria. It is available throughout the year either as

vegetable or as pulse (Singh and Rachie, 1985; Asumugha, 2002; Olapade et al., 2002). Islam

et al., (2006) noted that cowpea was more tolerant to drought, water logging, infertile soils

and acid stress than common beans. Thiaw, et al., (1997) however, reported that moisture

stress could reduce productivity considerably during the period from emergence to first

flower, but with determinate cultivars might not significantly affect yields when water stress

occurred thereafter. They also found that nodulation was reduced by water stress, particularly

when combined with experimentally lengthened days (16 hours). Most cultivars are adapted

to temperature ranges of 25-290C and elevations of up to 1000m, which provides satisfactory

conditions for growth (Wien and summer field, 1980). They also noted that most cultivars

began to flower 35-70 days from germination while plant spacing varied from 25x50cm or 75

x 20cm for semi erect type to 16 x 34cm or 17 x 40cm for erect, low branching cultivars.

Maize does well in a wide range of soils, but performs best in well drained, well aerated,

deep, warm loams and silt loams containing adequate organic matter and well supplied with

available nutrients (Samuel et. al., 1975). The authors also reported that flowering time was

influenced by photo-period and temperature while optimum germination temperature ranged

between 18- 210C and below 13

0C it was greatly reduced; then failed below 10

0C.

2.12 Remediation of Petroleum Hydrocarbon Contaminated Soil

Methods used in the clean-up of petroleum contaminated soil are often developed and

evaluated in order to conform to regulatory demands, which may require that residual total

petroleum hydrocarbon (TPH) concentrations in the soil are reduced to below 100mg kg-1

(USEPA, 1991). There are many technologies available for treating sites contaminated with

petroleum hydrocarbons. However selection of any treatment method would depend on the

contaminant properties itself, site characteristics, regulatory requirements, cost and time

constraints. There are four major ways to remediate soils contaminated with petroleum

hydrocarbons (Nyer and Skladany, 1993):

• Excavation and off-site disposal

• In-situ soil venting

• In-situ biodegradation

• Aboveground or in situ chemical oxidation

2.12.1 Excavation and off-site disposal

Excavation of site soils may result in the loss of the volatile compounds present. As these

soils are exposed to the atmosphere, petroleum products with high vapour pressures and low

boiling points tend to volatilize. The USEPA (1996) described chemicals that have boiling

points lower than 2200C in the environment with one atmosphere as volatile compounds.

Benzene, the most significant compound in terms of human health effects, is reported to be

one of the most volatilized compounds (Nyer and Skladany, 1993).

2.12.2 In-situ soil venting

In-situ soil venting is to move air past the contaminated soils and to transfer the organic

contaminant from the liquid phase to vapour phase. This technique is described by Nadim et

al., (2000) as soil vapour extraction (SVE), that is in-situ clean-up process to remove volatile

and some semi-volatile organic compounds (VOCs and semi-VOCs) by an induced vacuum.

This mass transfer process would effectively remove the hydrocarbons from the soil (Nyer

and Skladany, 1993). They further reported that compounds of gasoline and fewer

components of diesel and fuel oil are amenable to soil venting technologies.

2.12.3. In-situ bioremediation

Naturally occurring micro-organisms may be able to biodegrade hydrocarbons and other

organic compounds in unsaturated soil and aquifers if the level of contaminants is low and

does not produce toxicity for the active bacteria (Nadim et al., 2000). All of the compounds

found in gasoline, diesel and fuel oil are degradable by bacteria (Nyer and Skladany, 1993).

However, enhanced bioremediation requires improvement in sub-surface growth environment

of the indigenous micro-organisms (Nyer and Skladany, 1993). The ability of naturally

occurring bacteria to degrade organic contaminants is enhanced by the addition of nutrients,

oxygen and water (Nadim et al., 2000). Furthermore, Nadim et. al., (2000) noted that the soil

stratum should have sufficient permeability to allow movement of oxygen and nutrients, and

extreme pH ranges in soil can reduce microbial diversity and activity.

2.12.4 Aboveground or in-situ chemical oxidation

Chemical oxidation relies on the use of hydrogen peroxide and catalysts to destroy the

hydrocarbon present (Nyer and Skladany, 1993). They further observed that this treatment

process might have to be repeated until all the hydrocarbon constituents reach acceptable

concentration. When dealing with petroleum- contaminated soils, one can measure the

concentration of individual petroleum constituents or the Total Petroleum Hydrocarbon

(TPH) concentration.

Furthermore, analytical test procedures normally used to assess soil contamination by

petroleum products are petroleum hydrocarbon and heavy metals determination (Kelly and

Tate, 1998; Massoud et al., 1996; Onianwa, 1995; Hewari et al., 1995; Hayes et al., 1985).

However, the degree of soil contamination by petroleum hydrocarbons and heavy metals can

impact soil ecosystems sufficiently to result in significant losses in soil quality (Coyne, 1999;

Kelly and Tate, 1998; Amadi et al., 1996). These have their attendant toxicological and

health implications as the pollutants find their way into the food chain (Adeniyi and Afolabi,

2002). Therefore, ecotoxicological effects of contaminants are often used as a measure of

environmental health (Stegmann et al., 2003).

2.13. Assessment of Petroleum Hydrocarbons and Heavy Metals Hazard

Common hydrocarbon fractions and heavy metals that have deleterious effects on living

organisms are the Benzene, toluene, ethyl-benzene and xylene (BTEX), PAHs and Cadmium

(Cd), Chromium (Cr),Copper (Cu),Nickel (Ni),Lead (Pb) and Zinc (Zn). The TPH

concentration has little to do with risk but can be used as an indicator of the degree of

contaminantion and the success of remediation process (Pierzynski et al., 2000).

2.13.1. Benzene,toluene,ethyl benzene and xylene

Benzene, toluene, ethyl benzene, and xylene (BTEX) are light aromatic hydrocarbons with

relative high water solubilities of 150 to 1800mg/L (Howard et al., 1991). Nyer and Skladany

(1993) observed that the aromatic fraction of petroleum products is the most important group

of chemicals from an environmental point of view. Oral-rat LD50 of pure benzene (C6H6) is

3800mg/L and oral – mouse LD50 is 4700mg/L (Nadim et al., 2000). LD50 stands for 50%

Lethal Dose. The basic idea of the test is to slowly feed healthy animals such as mice or rats

sufficient quantities of a specified chemical to exterminate approximately 50% of them.

Furthermore, prolonged ingestion of benzene can lead to lymphocytic leukemia in children

and liver cancer in adults (Nadim et al., 2000). Budavari et al., (1989) reported that long –

term gastrointestinal and respiratory exposure of mouse and rats to BTEX have produced

liver cancer, and leukemia in humans or rats.

2.13.2. Poly-aromatic hydrocarbons (PAHS)

PAHs are more abundant in heavy petroleum fuel oils (Nadim et. al., 2000). Studies have

shown the carcinogenicity of PAHs in laboratory mice and rats (Hughes et. al., 1993).

2.13.3. Heavy metals

Heavy metal is a general collective term used in describing the group of metals and

metalloids with atomic density greater than 6.0 g cm-3

(Alloway and Ayres, 1997). It is

widely recognized as describing elements such as Mercury (Hg), Cd, Cr, Ni, Pb and Zn,

which are harmful or hazardous oftentimes causing pollution and toxicity problems. The

extent to which metals are adsorbed depends on the properties (valancy, radius, degree of

hydration and coordination with oxygen) of the metal concerned, the physico- chemical

environment (pH and Redox potentials), the nature of the adsorbent (permanent and pH

dependent charge, complex-forming ligands), other metals present and their concentration

and the presence of soluble ligands in the sorrounding fluids (Alloway and Ayres, 1997).

Heavy metals tend to reach the environment from a vast array of anthropogenic sources as

well as natural geochemical processes.

Elements with no known essential biochemical functions are called non-essential elements

but are sometimes also referred to as ‘toxic’ elements. According to Ernst (1996), these

elements which include As, Cd, Hg and Pb, cause toxicity at concentrations which exceed the

tolerance level of the organisms, but do not cause deficiency disorders at low concentrations

like micronutrients. Ernst (1996) further reported that the toxic effects caused by excess

concentrations of these metals include competition for sites with essential metabolites,

replacement of essential ions, damage to cell membrane and reactions with phosphate groups.

Organism have homeostatic mechanisms which enable them to tolerate small fluctuations in

the supply of most elements but prolonged excesses eventually exceed the capacity of the

homeostatic system to cope and toxicity occurs; which, if severe can cause the death of

organisms.

The danger of heavy metals is aggravated by their almost indefinite persistence in the

environment. Garbisu and Alkorta (2001) observed that some metals are essential for life (i.e.

they provide essential cofactors for metalloproteins and enzymes) but at high concentrations,

they can act in a deleterious manner by blocking essential functional groups, displacing other

metal ions or modifying the active conformation of biological molecules. In addition, they are

toxic to both higher organism and microorganism. Many metals affect directly various

physiological and biochemical processes, causing reduction in growth, inhibition of

photosynthesis and respiration as well as degeneration of main cell organelles (Vangronsveld

and Clijsters, 1994). Some metals(especially Pb) have been reported to accumulate in roots,

probably due to some physiological barriers against metal transport to aerial parts, while

others (example Cd) were easily transported in plants (Udom et al., 2004).

According to Garbisu and Alkorta (2001) heavy metals cannot be destroyed biologically

(non-degradable) but are only transformed from one oxidation state or organic complex to

another. The authors observed that as a consequence of the alteration of its oxidation state,

metal may become either: (i) more water soluble and are removed by leaching, (ii) inherently

less toxic (iii) less water soluble, so that they precipitate and then become less bio-available

or removed from the polluted area. Devez et al., (2005) reported that, at high concentrations

copper inhibited plant growth and interfered with several cellular processes, including

photosynthesis, respiration, enzyme activity, pigment and protein synthesis and cell division.

Heavy metals exhibit toxic effects towards soil biota: they can affect key microbial processes

and decrease the number and activity of soil micro-organisms, thus affecting the biological

properties of such soils. Conversely; long term heavy metal effects have been reported to

increase bacterial community tolerance (Baath et al.,1998) as well as the tolerance of fungi,

such as Arbuscular Mycorrhizal Fungi (AMF), which can play an important role in the

restoration of contaminated ecosystem (Joner and Leyval, 2001). As a result of the adverse

effects of heavy metals and other contaminants, environmental agencies set critical levels in

soils, above which toxicity is considered to be possible. Nevertheless, micro-organisms

respond quickly to changes and can rapidly adapt to environmental conditions. Changes in

microbial population or activity can precede detectable changes in soil physical and chemical

properties, providing an early sign of soil improvement or an early warning of soil

degradation.

Micro-organisms can detoxify metals by valence transformation, extra cellular chemical

precipitation, or volatilization (Garbisu and Alkorta, 2001). The study further showed that

some micro-organisms obtained energy for growth by coupling the oxidation of simple

organic acids and alcohols, hydrogen, or aromatic compounds, to the reduction of Fe (III), or

Mn (IV). They suggested that bacteria that use U(IV) as a terminal electron acceptor may be

useful for removing uranium from contaminated sites and that the reduction of the toxic

selenate and selenite to the insoluble and much less toxic elemental selenium in the study

may be exploited to enhance removal of these anions from contaminated sites.

According to studies of Garbisu et. al., (1997), the more toxic form of chromium Cr (IV),

could also be detoxified by bacterially – mediated reduction of Cr (IV) to Ci (III) which was

at the time being studied for commercial bioremediation. Another natural reduction process,

being developed for commercial application, is the transformation of mercuric ion [Hg (II)],

to volatile metallic mercury, [Hg (O)]. The studies of Lovely (1995) showed that micro-

organisms can also enzymatically reduce other metals such as vanadium, molybdenum, gold,

silver and copper.

Although it is true that micro-organisms that use metals as terminal electron acceptors or

reduce them as a detoxification mechanisms can be of use for the removal of metal

pollutants from the environment (Garbisu and Alkorta, 2001), it is certainly not less true that

when considering the remediation of a metal polluted soil, metal –accumulating plants offer

numerous advantages over microbial processes since plants can actually extract metals from

the polluted soils, theoretically rendering them clean (metal-free soils).

Heavy metals, with soil resilience times of thousands of years have been reported to present

numerous health dangers to higher organisms (Garbisu and Alkorta, 2001).

They are also known to decrease plant growth, ground cover and have negative impact on

soil micro-flora. However, a small group of plants can tolerate uptake, and translocation of

high levels of certain heavy metals that would be toxic to any other known organism. Such

plants are termed “hyper accumulators”. According to Brown et al. (1995), hyper

accumulator species are those plants whose leaves may contain>100mg kg-1

Zn and Mn (dry

weight) when grown in metal – rich soils.

Toxic materials have adverse effects on health. Heavy metals belong to these types of

materials due to many diseases resulting from them (Al-Asheh et. al., 1999).It is reported

(Environment Canada,1996) that heavy metals might adversely affect specific tissues,

reproduction and development and may also cause anaemia, nervous system disorders and

depressed immunity, resulting in mortality and effects on population levels. Copper in

particular acts as an irritant to the skin causing itching and dermatitis, and may cause

keratinization of hands and sole of the feet (Sitting, 1981). Nickel (by inhalation, exposure

and ingestion) can result in chronic bronchitis, emphysema, asthma, lung cancer, reduced

body weight, foetal toxicity, and allergic contact dermatitis (DEFRA and Environment

Agency, 2002). According to DEFRA and the Environment Agency (2002), Pb exposure is

associated with reduced cognitive development and intellectual performance, ( neurotoxicity)

Cr is carcinogenic by inhalation, and has been shown to have mutagenic potential; renal

damage is the most sensitive indicator of toxicity resulting from chronic oral exposure to Cd,

whilst lung cancer and kidney diseases have also been associated with inhaled Cd.

As a result of the adverse effects of heavy metals and other contaminants, environmental

agencies set critical levels in soil above which toxicity is considered to be possible. For

example, Table 1 shows the widely used normal range and critical total concentration of

heavy metals for soil given by Bowen (1979) and Kabata-pendias and Pendias (1984) as cited

by Alloway (1990). Since trace metals are absorbed at particle surfaces, bound to carbonates,

occluded in iron and / or manganese oxyhydroxides, bound to organic matter, sulphide, soil

matrix, or dissolved in the interstitial water (Jones et al., 1997), the complexity of trace metal

bio-availability associated with these phases hinders the prediction of effects (Campbell and

Tessier, 1997). According to McLaughlin et al., (2000), a number of challenges present

themselves before accurate, reliable and precise assessment of contaminant hazard criteria for

soils and plants can be made. These include: sampling, extraction and analytical obstacles

associated with the determination of trace levels of metals in environmental media; quality

assurance and quality control issues associated with both extraction and analytical procedures

(especially for metals where non-compliance with regulatory standards may be penalized);

and confounding environmental effects (e.g. rooting depth, soil salinity, Eh, pH, plant

species, metal species) which limit the usefulness of the relationship between current tests

and actual hazards. The authors further observed that these difficulties have combined to

produce soil tests for heavy metals that are often poorly correlated with hazard, whether crop

uptake of a contaminant, or the adverse effects of metals or metalloids on human or

environmental health. Since assessment of an “available” fraction of a particular soil nutrient

is the accepted norm of soil testing for crop nutrition, McLaughlin et al., (2000) posited that,

Table 1: Concentrations of Heavy Metals in Soils

Element Normal range in soil Critical soil total

(mg kg-1

) concentration(mg kg-1

)

Ag 0.01- 8 2.0

As 0.1-40 20-50

Au O.001-0.02 -

Cd 0.01-2.0 3-8

Co 0.5-65 25-50

Cr 5-1,500 75-100

Cu 2-250 60-125

Hg 0.01-0.5 0.3-5.0

Mn 20-10,000 1500-3000

Mo 0.1-40 2-10

Ni 2-750 100

Pb 2-300 100-400

Sb 0.2-10 5-10

Se 0.1-5 5-10

Sn 1-200 50

Ti 0.1—0.8 1.0

U 0.7-9 -

V 3-500 50-100

W 0.5-83 -

Zn 1-900 70-400

Source: Alloway (1990).

in many countries, assessment of metal is still inappropriately based on the total soil metal

concentration, despite increasing recognition that the concept of elemental availability is just

as relevant for environmental hazard as for crop nutrition. Therefore, tests aimed to assess

metal “bioavailability”, i.e., the fraction of the chemical that can be absorbed by the body

through the gastrointestinal system, the pulmonary system and the skin, are now gaining

widespread acceptance by regulators as a means to characterize hazards from contaminants in

soil (McLaughlin et. al., 2000).

The goal of bioremediation is not only to enhance the degradation, transformation,

remediation, or detoxification of these pollutants by biological means, but also to protect soil

quality (Andriano et. al., 1999). However, the combination of biological indicators with

quantitative description and interpretation of physical –chemical soil conditions may be an

important step forward in the direction of soil quality assessment (Dettaan, 1996).

2.14. Micro-organisms in Bioremediation

Micro-organisms are the principal agents responsible for the recycling of carbon in nature.

Atlas and Bartha (1993) observed that in many soils, there is already an adequate indigenous

hydrocarbonoclastic microbial community, capable of extensive oil biodegradation, provided

that the environmental conditions are favourable for oil-degrading metabolic activity. It was

suggested by some researchers (Shailubhai, 1986; Atlas and Bartha, 1993) that all soils

except those that are very acidic, contained the organisms capable of degrading oil products,

and that the problem was actually one of supplying the necessary nutrients at the site.

According to Gibson (1982), the ability of micro-organisms to utilize hydrocarbons is widely

distributed among diverse populations. Many species of bacteria, cyanobacteria, filamentous

fungi, and yeast co-exist in natural ecosystems and may act independently or in combination

to metabolize aromatic hydrocarbons. Some of the common microbial genera that can

degrade hydrocarbons in the soil (Shailubhai, 1986) are shown in Table 2.

The overall effect of an organism on a complex substrate is measured by its capacity to attack

only certain substances or to accumulate intermediates that it cannot degrade. Gibson (1982)

observed that extensive degradation of petroleum pollutants generally was accomplished by

mixed microbial populations, rather than single microbial species. Combinations of bacteria

and fungi provided twice, as much degradation of mixed hydrocarbon substrates as do

bacterial or fungi strains individually.

Table 2: Microbial Genera Degrading Hydrocarbons in Soil.

Bacteria Actinomycetes Fungi Yeast

Achronobacter Actinomyces Aspergillus Candida

Bacillus Endomyces Saccharomyces Rhodotorula

Beigerinckia Nocardia Cephalosporium Torula

Clostridium Cunninghamella

Desulfovibrio Trichoderma

Escherichia

Methanobacterium

Micrococcus

Mycobacterium

Pseudomonas

Source: Shailubhai (1986)

It has been observed that in aquatic and terrestrial environments, micro-organisms are the

chief agents of biodegradation of environmentally important molecules (Alexander, 1980).

Nearly 100 species of bacteria, yeasts, and mould representing 30 microbial genera had been

discovered to have hydrocarbon oxidizing properties (Alexander, 1980). Although many

micro-organisms appear limited to degradation of a specific group of chemicals, others have

demonstrated a wide diversification of substrates they are capable of metabolizing. Thus,

heterotrophic bacteria are the most important organisms in the transformation of organic

hazardous compounds, and soil treatment schemes may be directed toward enhancing their

activity.

2.15. Properties of the Organic Amendments

The palm oil industry is a major agro based one in Nigeria especially in the southern part

where oil palm trees are found both in the wild and plantations and hence still a major player

in the economy of this region. The industry also generates large amount of wastes such as

empty fruit bunch (23%), mesocarp fibre (12%), shell (5%) and Palm Oil Mill Effluent

(POME) (60%) for every tonne of fresh fruit bunches processed in the mills (Najafpour et al.,

2005).

During processing of the ripe fruits to extract the cooking oil, a lot of waste water (POME) is

generated (Plate1). Much of the POME results from water used in processing (Okwute and

Isu, (2007), Abdul et al. (2003)). Observations show that most of the POME is not treated

before discharge into the surrounding environment especially by the small scale mills,

causing pollution problems. The use of POME in agriculture and for land reclamation is

common practice in regions with abundant supply especially for irrigation, soil conditioning,

amendment and conservation purposes (Binder et al., 2002). Though POME is a land and

aquatic pollutant when discharged directly into the environment, it is amenable to

biodegradation. According to Azhari et al., (2010) POME is composed of high organic

content (Table 3) mainly oil and fatty acids and is able to support bacterial growth to reduce

its pollution strength. Therefore the conversion of POME to organic fertilizer can be a

sustainable strategy for its disposal only when its effects on soil microbial and biochemical

properties are well known (Nwoke and Ogunyemi, 2010). It is known that POME application

to soil can result in some beneficial soil chemical and physical characteristics, such as

increases in organic matter, organic carbon, major nutrients like N and P (Table 3), water–

holding capacity and porosity (Logan et al., 1997, Navas et al., 1998, Mantzavinos and

Plate 1: Palm Oil Mill Effluent (POME) from local oil palm processing unit

Table 3: Properties of Palm Oil Mill Effluent (POME)

PARAMETERS FRESH RAW POME POME ANAEROBIC SLUDGE

Moisture (%) 98.21±0.2 94.03±2.3

pH 4.33±0.3 7.41±0.2

C (%) 36.36±3.8 37.51±5.1

N (%) 2.71±0.9 4.68±0.7

C/N 13.4 8.0

Oil and grease (mgL-1

) 2151.0 ± 50.1 183.0±10.1

Electrical conduct (dsm-1) - -

COD (mgL-1

) 113191.0 40563.0

BOD (mgL-1) 35580.0 15180.0

Volatile suspended solid (mgL-1

) 14530.0 21110.0

Total suspended solids (mgL-1

) 18980.0 34720.0

Total solid (mgL-1) 41022.0 55884.0

Cellulose (%) 38.36 ±5.0 10.45±5.1

Hemicelluloses (%) 23.21 ±2.9 6.01±1.8

Lignin (%) 26.72± 3.4 48.13±9.2

Composition of Nutrients and Metal Elements

Phosphorus (%) 1.01±0.2 1.25±0.1

Potassium (%) 2.49±0.2 5.16±2.2

Calcium (%) 1.56±0.1 2.55±0.1

Sulphur (%) 0.57±0.2 1.21±0.3

Iron (%) 1.03±0.3 1.09±0.4

Magnesium (%) 1.21±0.2 1.41±0.2

Zinc (mg kg-1) 118.82±22.1 151.0±14.5

Manganese (mg kg-1

) 339.0±20.0 495.24±48.3

Copper (mg kg-1

) 73.24±8.1 174.9±20.3

Boron (mg kg-1) 95.59±8.2 65.0±10.1

Molybdenum (mg kg-1

) n.d 5.0±1.0

Cadmium (mg kg-1

) 1.2±0.1 n.d

Nickel (mg kg-1

) n.d. 14.0±2.2

n.d. not detectable (all % are in dry weight). BOD = biochemical oxygen demand, COD = chemical oxygen

demand

Source: Azhari et al., (2010).

Kalogerakis(2005). However it brings about undesirable changes such as decreases in pH,

and increases in salinity etc. (Kittikun et al., 2005).These effects occur very slowly and need

many years to provide significant results. Soil microbiological and biochemical properties

have been considered early and sensitive indications of soil changes and can be used to

predict long-term trends in the quality of soil (Ros et al., 2003). In line with this, Nwaugo, et.

al., (2008) found that light application of POME caused significant increase in total

heterotrophic, phosphate solubilizing, nitrifying and lipolytic bacteria counts, while heavy

application caused a decrease in all of them. Nwoke and Ogunyemi (2010) also found that

fermented POME increased soil microbial carbon, basal respiration and metabolic quotient.

Oil palm bunch refuse is used in the South eastern part of Nigeria as organic manure and for

soap making it is rich in phosphorus and potassium. According to Nahrawi et al., (2011) % C

was 43.2; %N was 0.81 and % lignin was 17.5.

Cassava is a very important crop grown for food and industrial purposes in several parts of

the tropics. Nigeria, with the year 2006 production of 49 million tonnes of cassava, is the

largest producer of the crop in the world (NPC, 2008). The ongoing encouragement of

cassava cultivation by the federal government of Nigeria is gradually raising the profile of the

crop as a significant cash crop. According to IFAD/FAO (2000), cassava is the fourth most

important staple crop in the world after rice, wheat and maize. The processing of cassava

results in the production of peels, chaff, fibre, and spoilt or otherwise unwanted tubers. A

relatively small quantity of peels and unwanted tubers are fed directly to ruminants.

However, the much larger remaining proportion of cassava solid wastes are indiscriminately

discharged into the environment and amassed as waste dumps on sites where cassava is

processed, with increased production of peels and other cassava – derived wastes (Plate 2).

This constitutes an enhanced risk of pollution to the environment. There is therefore an

urgent need to find an alternative productive use of the peels. One area of possibility is to

investigate the potential of cassava peels as bioremediation tool and by so doing, reduce its

nuisance value to the environment. The chemical properties (nutrients) of cassava peels

which could justify its use in bioremediation technology are shown in Table 4.

Plate 2: local Processing of cassava which produces peels as waste

Table: 4 Chemical properties of cassava peels

Parameters Peels

% organic carbon 48.7

% total nitrogen 1.0

C/N ratio 48.7

% K 1.1

% P 1.6

% N03 0.16

Zn (mg kg -1

) 125

Cu (mg kg -1

) 15

Mn (mg kg -1

) 180

Ph 6.4

% Na 0.15

% Ca 0.9

Pb (mg kg -1

) 16.7

% Ash 52.6

Source: Adelekan and Bamigboye (2009)

CHAPTER THREE

3.0. MATERIALS AND METHODS

3.1. Site Description

This experiment was conducted between the years 2006 and 2008 in the University of

Nigeria Nsukka campus Teaching and Research farm, located by latitude 05052’N and

longitude 07024’E and at an elevation of 400m asl. The mean annual maximum temperature

in this location ranges between 270C and 32

0C in the period from March to May while the

mean daily sunshine hours in the area are between 5 and 6 hrs in the dry season and 3 to 5 hrs

in the wet season (Inyang, 1978). Rainfall in the area occurs between March and October.

More than 80 % of the total annual rainfall is received between the months of May and

October, with mean annual total in excess of 1700 mm (FORMECU, 1998). The rainfall is

bimodally distributed with peaks in July and September and a short dry spell around mid

August. The average slope of the site is 5 %. The soil is an Ultisol belonging to the Nkpologu

series (Nwadialo, 1989). It is very deep, dark-reddish brown in the top soil and red in the sub

soil. It is coarse to medium textured, granular in structure, acid in reaction and low in nutrient

status. Its mineralogy is composed mainly of kaolinite and quartz (Akamigbo and Igwe,

1990).

3.2 Field Methods

3.2.1. Experimental design

The design of the experiment was a split – plot in Randomized complete Block Design

(RCBD) with four (4) treatments in each main plot and four (4) in each sub plot. Each

treatment was replicated three (3) times giving a total of 48 sub plots. The main plot

treatments comprised four (4) levels (0, 1, 2, and 3 %) of spent oil. The subplots were treated

with three types of organic amendments (oil palm bunch refuse, palm oil mill effluent and

cassava peels) at 12 Mg/ha and un- contaminated – non-amended (control).

3.2.2. Experimental layout

A land area of 0.0256 ha was used for this study. The main plots measured 15.125 m2 (5.50

m x 2.75 m) while the sub-plots measured 2.75 m2 (2.75 m x 1.0 m).

NA

PE

CS

PR

PE

NA

PR

CS

PE

CS

NA

PR

CS

PR

PE

NA

PE

NA

PR

CS

NA

PR

CS

PE

PE

PR

CS

NA

PE

NA

CS

PR

NA

PR

CS

PE

NA

PR

CS

PE

NA

PE

CS

PR

NA

CS

PR

PE

Block I Block II Block III

Where: NA = Un-amended plots, PE = Palm oil mill effluent amended plots, CS = Cassava peels amended

plots and PR = Palm bunch refuse amended plots.

SP0, SP1, SP2 and SP3 = 0 %, 1 %, 2 % and 3 % spent oil contamination respectively.

Fig 1: Experimental Layout

Main Plot Treatments Designation

0% spent automobile engine oil (W/W) Sp0 (control)

1 % spent automobile engine oil (W/W), equivalent, 10,000 mg kg-1

SP1

2 % spent automobile engine oil (W/W), equivalent, 20,000 mg kg-1

SP2

3 % spent automobile engine oil (W/W), equivalent, 30,000 mg kg-1

SP3

Application Rates

Main plots

i. Sp0 = No contamination

ii. Sp1 = 6.1 kg spent automobile engine oil /plot, equivalent to 12 Mg ha-1

iii. Sp2 = 12.2 kg spent automobile engine oil /plot, equivalent to 24 Mg ha-1

iv. Sp3 = 18.3 kg spent automobile engine oil /plot, equivalent to 36 Mg ha-1

Sub plots

NA = No amendment

PE = 3.3 kg/plot, equivalent to 12 Mg ha-1

PR = 3.3 kg/plot, equivalent to 12 Mg ha-1

CS = 3.3 kg/plot, equivalent to 12 Mg ha-1

Sub Plot Treatments Designation

No amendment (control) NA

Palm oil mill effluent PE

Oil palm bunch refuse PR

Cassava peels CS

3.2.3. Field preparations

Soil samples were collected in a grid of 4 m x 2 m bulked and a composite sample taken for

laboratory analyses to determine the initial chemical, physical and biological properties of the

site. Glyphosate, a post emergence herbicide (a.i ispropylamine) and butachlor, a pre-

emergence herbicide (a.i.2-chloro-2, 6- diethyl – N (butoxy methyl) acetanilide) were used to

control weeds. Plots were tilled in the first year of the experiment and were zero tilled in the

subsequent two years. The nutrient supplements (organic amendments) were applied to both

oil contaminated and uncontaminated plots 7 days after oil treatment. After nutrient

application, the treated and untreated plots were left for two weeks before planting to allow

for incubation. Oil treatment was applied in a single dose at the start of the experiment while

the organic amendments were applied in repeated doses, once every year through the three

years of this work (Plate 3 - 9). The test crops comprised of cowpea (Vigna unguiculata L.

Walp, var 355), and maize (Zea mays. Oba II). They were grown for three planting seasons

(2007, 2008 and 2009) with each planting season spanning from May to August. The cowpea

was the erect and low branching type. Each sub-plot contained 20 plants (10 stands of maize

and cowpea each) at one plant per stand in two rows (a row each for maize and cowpea)

giving a plant population of 80, 000 plants ha-1

. Sowing was done manually at the rate of two

seeds per hole, to depth of 2.5cm and spacing of 50 cm x 25 cm, and thinned down to one

plant per stand after emergence.

Plate 3: General Field layout after application of treatments

Plate 4: Unpolluted /Unammended (control) plot

Plate 5: Oil Palm bunch refuse ammended plot

Plate 6: Cassava peels ammended plot

Plate 7: POME ammended plot

Plate 8: Zero hours of spent automobile engine oil contamination

Plate 9: Depth of spent automobile engine oil contamination after 36 Months

3.3 Data Collection

Germination count was taken 8 days after planting while samples from cowpea and maize

test crops for monitoring dry matter accumulation and leaf area responses to treatments were

collected at flowering and tasseling stages respectively, each year. Grain yield data was

collected at maturity. Harvesting took place in each year when the maize and cowpea had

sufficiently dried in all treatments, thus, harvesting was done on different dates each year.

The dry maize cobs and dry cowpea pods were shelled, and the grain weighed at 14 %

moisture content to obtain the yield. Bulk and core samples of soil were collected from 0 – 20

cm and 20 – 40 cm depths at 90, 180, 360, 540, 720, 900 and 1080 DAA (Days after

application) for determination of physical, chemical and biological properties. All data

collected were subjected to statistical analysis (using SPSS version 16.0 computer statistical

package) to determine treatment effects and interactions.

3.4 Laboratory Studies

The following laboratory procedures were used for the determination of the some physical,

chemical and biological properties of the soil and crop samples.

3.4.1Texture and Bulk density

The texture of the soil was determined according to Bouyoucos (1975) while bulk density

was determined by the core method (Blake and Hartge, 1986).After oven drying the soil

samples used for saturated hydraulic conductivity measurements to constant weight at 1050C

bulk density was calculated using the formular:

Db = ( )3sMgcm

V

−

Where:

Db = Bulk density

Ms = Mass of soil sample (g)

V = Volume of soil sample (cm3) which is volume of core

3.4.2 Soil porosity

The macro and the micro-pores are the two types of pore size measured. The samples used

for the determination of saturated hydraulic conductivity were also used to determine the

macro-porosity of the samples. This involved dividing the difference between the weight of

sample at saturation (Wsat) and the weight after 24 hours of mounting on the tension table at

60 cm tension (W60) by the volume of core (VC), then multiplying by 100 %.

Macro-porosity = sat 60W W100%

Vc

−×

Then also in the same sample, micro-porosity was determined by dividing the difference

between the weight of sample after 24 hours of mounting on the tension table at 60 cm

tension (W60) and the weight of oven dried sample at 1050C for 48 hours (WO) by the volume

of the core (VC), then multiplied by 100 %.

Micro-porosity = 60 0

c

W W 100%

V

− ×

3.4.3 Mean weight diameter

The distribution of aggregates was estimated by the wet-sieving technique, described in detail

by Kemper and Rosenau (1986). In this procedure 25 g of the < 4.75 mm, air dried soil

samples were put in the topmost of a nest of four sieves of diameters 2, 1, 0.5 and 0.25 mm,

and pre-soaked in distilled water for 10 mins., before oscillating in water, 20-times (along a 4

cm amplitude) at the rate of one oscillation per second. Care was taken to ensure that the soil

particles on the topmost sieve were always below the water surface during each oscillation.

After wet sieving, the resistant aggregates on each sieve were quantitatively transferred into

beakers, oven dried at 1050C for 24 hours and then reweighed. The percentage ratio of the

aggregates in each sieve represents the water stable aggregates of size classes: 4.75 – 2.00,

2.00 - 1.00, 1.00 - 0.50, 0.50 - 0.25 and < 0.25 mm.

The method of van Bavel (1950), as modified by Kemper and Rosenau (1986), was used to

determine the mean weight diameter of the water –stable aggregates. Thus,

Where MWD is the mean weight diameter of water stable aggregates, X the mean diameter of

each size fraction (mm) and W the proportion of the total sample weight (WSA) in the

corresponding size fraction, after deducting the weight of stones (upon dispersion and passing

through the same sieve) as indicated above. The determination of the mean weight diameter

of dry aggregates followed same procedures as stated above with the only difference being

that rotary sieving technique described in detail by Kemper and Rosenau (1986) was used

rather than the wet sieving technique. Higher values of MWD indicate the dominance of the

less erodible, large aggregates of the soil (Piccolo et al., 1997).

For ease of comparing the effects of the treatments on aggregate stability (AS), the change in

MWD between control and the treated soils was normalized as follows:

PSEI = [1-( MWDc / MWDt)] x 100

Where PSEI is the Potential Structural Enhancement Index, MWDc is the mean weight

diameter for control, and MWDt is the mean weight diameter for treated soil. Positive value

indicates contribution to structural enhancement, and negative value means no contribution.

3.4.4. Saturated hydraulic conductivity:

Undisturbed soil samples were collected from two depths (0 – 20 cm and 20 – 40 cm) in each

sub-plot using cylindrical metal cores of sizes 5.0cm x 5.5 cm for the length and internal

diameter, respectively. Soil loss was prevented by muslin gauze at the bottom of the column.

After 24 hrs water saturation, saturated hydraulic conductivity (Ksat) was determined

(Bouwer, 1986). The transposed Darcy’s formular for vertical flows of liquid was used to

calculate Ksat thus:

Where: Q = steady state volume of outflow (cm3)

A = interior cross sectional area of core sample (cm2)

T = time of flow (h)

L = Length of core sample (cm)

H = change in hydraulic head (cm)

3.4.5. Total hydrocarbon content

Total hydrocarbon content at each sampling date was determined gravimetrically by toluene

extraction (cold extraction) method as described by Odu et al., (1989). In this procedure, 10 g

of soil sample was weighed into 50 ml flask and 20 ml toluene added. After shaking for 30

mins the liquid phase of the extract was measured spectrophotometrically at 420 nm, and

fitted into standard curve derived from fresh spent oil treated with toluene. This provides an

estimate of organic and bio-available forms of Total Hydrocarbon Content (THC).

3.4.6. Heavy metal analysis

Bio-available or soluble concentration of heavy metals was determined by Aqua Regia

method. The procedure involved digestion of 3 g air dried, pre-sieved (< 2 mm), soil samples

with 10 ml HCl and 3.5 ml HN03. Every digest batch included two blanks and one

International Atomic Energy Agency (IAEA) reference sample. The mixtures were left

overnight in the digestion block without heating under the switch–on fume cupboard. The

following day, they were heated for 2 hours to 1400C, gradually increasing the temperature to

control foaming. Distilled water was added to cool the digestates and then filtered with

Whattman No. 542 filter paper(pre-washed with 0.5 M HN03 and wash solution discarded)

and topped up to 100 ml with distilled water.

The filtrates were analyzed for Al, Cr, Fe, Pb and Zn using Atomic Absorption

Spectrophotometer (AAS). The values were compared with the widely used normal and

critical levels of total concentrations of heavy metals for soil by Environmental Agencies

given by Kabata-pendias and Pendias (1984) as cited by Alloway (1990) (Table 1). The

contaminant limit (C/P index) was calculated as the ratio between the heavy metal content in

the soil and the toxicity criteria (the tolerable levels) as shown in Table 1 Alloway (1990).

The C/P index values < 1 indicate soil contamination(C) range values > 1 indicate pollution

(P) range. The result was further classified according to Lacatusu (1998) as:

a = very slight contamination (C/P < 0.1)

b = slight “ (0.1 - 0.25)

c = moderate “ (0.26 - 0.50)

d = severe “ (0.51 - 0.75)

e = very severe “ (0.76 - 1.00)

f = slight pollution (1.1 - 2.0)

g = moderate pollution (2.1 - 4.0)

h = severe pollution (4.1 - 8.0)

i = very severe pollution (8.1 - 16.0)

j = excessive pollution (< 16.0)

3.4.7 Biodegradation rate (hydrocarbon loss)

Average biodegradation loss rates (mg kg-1

day-1

) of hydrocarbons under different treatments

were estimated according to Yeung et al., (1997) as:

∆HL = (HCi - HCe) / Time inc

Where, ∆HL = the average hydrocarbon loss (mg kg-1

day-1

)

HCi = the initial hydrocarbon content in soil (mg kg -1

)

HCe = the hydrocarbon content when the experiment ended (mg kg -1

)

Time inc = the degradation time (days)

3.4.8 Remediation efficiency

The Remediation Efficiency (R.E) which shows (in percentage) the effectiveness of the

organic amendments (nutrients) relative to the un-amended plots in reducing the total

hydrocarbon content (THC) of plots treated with same quantity of spent oil was calculated

thus:

. 100

Where:

THCci = total hydrocarbon content in control plot under a given oil loading.

THCti = total hydrocarbon content in an amended plot under a given oil loading.

3.4.9 Microbial count

In this procedure, 10 g of the soil sample was mixed with 90 ml of sterile Ringers saline to

get the stock solution. Then 1ml of the stock solution was taken and serial dilution done using

ten fold dilutions. 1ml aliquot was plated out with 19mls of sterile nutrient agar, allowed to

gel and incubated for 48 hrs at 370C. The viable count of hydrocarbon degrading micro-

organisms (H-dm) was recorded using a colony counter and results expressed as Colony

Forming Units (CFUg-1

soil).

3.4.10 Determination of leaf area index and dry matter yield

Forty eight (48) plants, one each of maize and cowpea from the sub-plots were tagged at

random and their leaves collected. The leaves were passed through an area measurement

system (machine) connected to an area metre (monitor screen), which displayed the total leaf

area and the number of leaves of each sample. The Leaf Area Index (LAI) was calculated as

the total leaf area per unit ground area.

Dry matter determination involved oven drying the plant materials at 1100C (leaves, stem,

roots and flowers) to constant weight (about 48 hrs) and then, the final weight was

determined as the dry matter content of the sample.

CHAPTER FOUR

4.0 RESULTS AND DISCUSSION

Results were obtained after three (3) years of field and laboratory studies involving soil

sampling from two depths (0 – 20 cm and 20 - 40 cm) and crop samples from cowpea and

maize.

4.1 Soil Physical Properties

4.1.1 Texture

Table 5 shows that the texture of the experimental site was sandy-loam at the start and the

end of the experiment. No change in soil texture was expected following treatment since the

dominant particles from parent materials determine soil textural class (Akamigbo and Asadu,

1983).

4.1.2 Soil bulk density

Table 6 shows the effects of treatments on the soil bulk density of the main and sub-plots.

The bulk density of main plots generally increased significantly with increase in oil over the

36 months of sampling. Adewole and Moyinoluwa (2011) reported that oil increased soil

bulk density due to its coagulatory properties. Three (3) months after oil contamination the

bulk density of plots under 2 and 3% oil treatments were 1.58 g cm-3

and 1.60 g cm-3

but 12

months of organic amendment with PE and CS significantly reduced the bulk density of the

soils to 1.50 g cm-3

and by the 36th

month values had further reduced to 1.39 and 1.40 g cm-3

respectively. Bulk density values in 1% oil treated plots did not differ (P < 0.05) from the

uncontaminated plots. This trend was observed through the 36 months of this study. In terms

of efficiency of the organic amendments as bioremediation tool, PE and CS were more

efficient in reducing the bulk density of the soil which was increased by increased oil

concentration. The two treatments restored the plots to their condition prior to oil

contamination (1.50 g cm-3

in control plot) within 12 months, suggesting that these organic

amendments decomposed and incorporated faster into the soil thereby reducing the volume of

soil in contact with the hydrocarbon (oil).

Table 7 shows that following the contamination of top soils with oil only the bulk density of

sub-soils contaminated with 3 % oil and not treated with any of the organic amendments

showed significant (P < 0.05) oil treatment effect(1.62 g cm-3

mean value) compared to

values in control soil (1.57 g cm-3

) indicating increased oil seepage at higher concentrations

especially in un-amended top-soils.

Table 5: Some characteristics of the top (0 - 30 cm) soil of the experimental site and

spent oil used in the experiment.

Parameter Soil Spent oil

Sand (%) 67 -

Silt (%) 15 -

Clay (% 18 -

Textural class Sandy-loam -

pH (1:2.5 H20) 4.7 -

pH (0.01M KCl) 3.8 -

organic carbon (%) 3.71 31.1

Total N (%) 0.76 2.70

Exchangeable bases (C mol kg-1

)

Na 0.69 -

K 0.02 -

Ca 1.12 -

Mg 3.16 -

Exchangeable acidity (C mol kg-1

)

Al3+ 1.20 -

H+ 2.40 -

ECEC (C mol kg-1) 8.59 -

Available P (mg kg -1

) 8.67 -

Saturated hydraulic conductivity (cmh-1

) 32.48 -

Aggregate stability (MWD) (mm) 1.30 -

Bulk density (gcm-3

) 1.50 -

Macro-porosity (%) 21.0 -

Micro porosity (%) 31.0 -

Specific gravity - 0.92

Pba (mg kg -1) - 2.45

Ala (mg kg

-1) - 265

Zna (mg kg -1) - 5.81

Fea (mg kg

-1) - 258

Cra (mg kg

-1) - 6.8

a = metal concentration in soil was by Aqua Regia–soluble extraction method

66

NA = Control, PE = Palm oil mill effluent, CS = Cassava peels and PR = Oil Palm bunch refuse. ns = non significant,

* = significant at 5 %, - = no interaction

Table 6: Effect of treatments on bulk density (g cm -3

) of top (0 - 20 cm depth zone) soil

Months

Oil concentrations (A) % 0 3 6 12 18 24 30 36

0 1.50 1.50 1.49 1.47 1.45 1.43 1.41 1.39

1 1.50 1.52 1.50 1.48 1.46 1.44 1.42 1.41

2 1.50 1.58 1.57 1.55 1.54 1.51 1.50 1.47

3 1.50 1.60 1.59 1.58 1.57 1.55 1.52 1.50

LSD0.05 ns 0.05 0.06 0.06 0.07 0.05 0.05 0.06

Organic amendments (B)

NA 1.50 1.55 1.55 1.55 1.55 1.54 1.54 1.54

PE 1.50 1.56 1.53 1.50 1.48 1.45 1.42 1.39

CS 1.50 1.54 1.53 1.50 1.48 1.46 1.43 1.40

PR 1.50 1.55 1.55 1.53 1.52 1.49 1.46 1.44

LSD0.05 ns Ns Ns 0.04 0.05 0.04 0.03 0.04

Interactions

(A × B) ns Ns Ns * * * * *

67

NA = Control, PE = Palm oil mill effluent, CS = Cassava peels and PR = Oil Palm bunch refuse. ns = non significant,

* = significant at 5 %, - = no interaction

Table 7: Effect of treatments on bulk density (g cm -3

) of sub (20 - 40 cm depth zone) soil

Months

Oil concentrations (A) % 0 3 6 12 18 24 30 36

0 1.57 1.57 1.57 1.57 1.57 1.61 1.57 1.57

1 1.57 1.57 1.57 1.58 1.58 1.58 1.59 1.58

2 1.57 1.57 1.58 1.59 1.59 1.61 1.62 1.61

3 1.57 1.57 1.58 1.61 1.62 1.63 1.63 1.62

LSD0.05 ns Ns ns 0.04 0.05 0.06 0.04 0.07

Organic amendments (B)

NA 1.57 1.57 1.58 1.62 1.62 1.65 1.63 1.62

PE 1.57 1.57 1.57 1.58 1.58 1.58 1.59 1.58

CS 1.57 1.57 1.58 1.58 1.58 1.59 1.58 1.59

PR 1.57 1.57 1.58 1.59 1.59 1.62 1.61 1.35

LSD0.05 ns Ns ns 0.04 0.04 0.05 0.04 0.03

Interactions

(A × B) ns Ns ns ns * * * *

4.1.3 Soil porosity

Results presented in Table 8 show that the treatments generally had significant effects on macro–porosity

at the sampling dates. There was a general trend of decreasing macro-porosity with increasing spent oil

contamination. Plots under 3% oil treatment showed macro-porosity values ranging from 7.5 - 14.0 %

between the 3rd

- 36th

month of sampling while macro-porosity in the 2% oil treatment ranged from 9.0 to

16.0 % and that under the 1 % treatment was between 9.5 and 17.0 % and in the uncontaminated (0 %)

plots the values ranged between 22.0 and 25.5 %. Also generally observed was that macro-porosity

increased with time as the organic amendments decomposed and mineralised and the oil which clogged

the macro-pores was gradually depleted by the hydrocarbon degrading microbial population. Plots treated

with PE and CS amendments showed consistently higher (P < 0.05) macro-porosity over the contaminated

and un-amended (NA) plots. The macro-porosity of PE treated plots increased from 13.5 % in the 3rd

month to 21.0 % in the 36th

month and CS from 12.0 to 21.0 %. In the PR plots the increase was 12.0 -

19.0 % while in the contaminated and un-amended (NA) plots it was from 11.0 - 12.0 %. It was observed

that while oil contamination reduced the high macro-porosity values of 21.0 % in control soil to as low as

6.5 % in 3 months, 36 months of organic amendment, on the other hand, increased the macro-porosity

values of the contaminated soil to as high as 21.0 %. Macro-porosity was restored to control value of 21.0

% in plots contaminated but amended with PE and CS. The PR treated plots showed 90.5% increase while

macro-porosity in NA treatment increased by 57.1% by the 36th

month. The response of macro-porosity to

organic amendment indicates that PE and CS decomposed and incorporated faster than PR into the soil

following yearly amendment. It was earlier observed that the C: N ratio of PE and CS were comparatively

lower than that in PR and this was responsible for their faster decomposition and incorporation into the

soil. Nahrawi et.al (2011), reported a high correlation between C:N ratio and rate of decomposition of

plant residues. Organic matter incorporated into the soil reduces the volume of soil per unit area hence

increasing the soil porosity. According to Mbagwu (1995) macro-pores may consist of inter-aggregate

pore space, shrink-swell cracks, root channels or faunal channels. Hence the vigorous rooting activity of

the test crops supported by the higher nutrient status of PE and CS amended plots may have contributed to

the increased macro-pores observed in the plots.

Table 9 shows that the effect of spent oil treatment and organic amendment on macro-porosity was not

significant in the sub (20 – 40 cm) soil, indicating that much of the oil was biodegraded in the top soil and

the incorporation of the organic amendments was limited to the surface soils.

69

NA = Control, PE = Palm oil mill effluent, CS = Cassava peels and PR = Oil Palm bunch refuse. ns = non significant,

** and * = significant at 1 % and 5 %, - = no interaction

Table 8: Effect of treatments on macro - porosity (%) of top (0 - 20 cm depth zone) soil

Months

Oil concentrations (A) % 0 3 6 12 18 24 30 36

0 21.0 22.0 22.5 23.5 23.5 24.0 24.5 25.5

1 21.0 9.5 10.0 12.0 12.0 14.0 16.0 17.0

2 21.0 9.0 9.0 10.5 11.5 13.0 14.0 16.0

3 21.0 6.5 8.0 9.0 10.0 11.0 13.0 14.0

LSD0.05 ns 1.45 1.81 1.01 1.03 1.10 1.07 1.17

Organic amendments (B)

NA 21.0 11.0 11.0 11.0 11.0 11.0 11.0 12.0

PE 21.0 13.5 15.0 15.0 17.0 18.0 20.0 21.0

CS 21.0 12.0 13.0 15.0 16.0 17.5 19.0 21.0

PR 21.0 12.0 12.0 13.5 14.0 16.0 17.0 19.0

LSD0.05 ns 1.25 1.48 1.31 1.15 1.61 1.28 1.16

Interactions

(A × B) ns ** ** * * ** * *

70

NA = Control, PE = Palm oil mill effluent, CS = Cassava peels and PR = Oil Palm bunch refuse. ns = non significant,

- = no interaction

Table 9: Effect of treatments on macro - porosity (%) of sub (20 - 40 cm depth zone) soil

Months

Oil concentrations (A) % 0 3 6 12 18 24 30 36

0 16.0 16.0 16.0 16.0 16.0 16.0 16.0 16.0

1 16.0 16.0 16.0 16.0 15.5 15.5 15.5 15.5

2 16.0 16.0 16.0 15.5 15.5 15.5 15.0 15.5

3 16.0 16.0 16.0 15.5 15.0 15.0 15.0 15.0

LSD0.05 ns Ns ns ns Ns ns 1.07 ns

Organic amendments (B)

NA 16.0 16.0 16.0 15.5 15.5 15.0 15.0 15.0

PE 16.0 16.0 16.0 16.0 15.5 16.0 16.0 15.5

CS 16.0 16.0 16.0 16.0 16.0 16.0 15.5 16.0

PR 16.0 16.0 16.0 16.0 15.5 15.5 15.5 16.0

LSD0.05 ns Ns ns ns Ns Ns ns ns

Interactions

(A × B) - - - - - - - -

Table 10 shows that micro-porosity increased with increase in oil contamination. Oil contamination

increased micro-porosity from 31.0 % (control) to 44.0 % (3 % oil treated plots) in 36 months. All the

organic amendments significantly reduced soil micro-porosity in contaminated plots relative to the

control. The PE treated plots consistently showed the lowest micro-porosity values with mean values

ranging from 34.0 % - 38.5 % from the 3rd

– 36th

month. The effect of CS amendment did not vary (P <

0.05) from that of PR. It was observed that the contaminated but un-amended plots had relatively higher

(P < 0.05) micro-porosity which remained consistent through these studies. The high micro- to macro-

porosity ratio observed in the oil- contaminated but un-amended plots (NA) was due to clogging of

macro-pores by oil. This condition may be detrimental to certain crops because it could lead to build – up

of Co2 and / or toxicity to both plant roots and micro-organisms which may retard biodegradation of the

oil. Pore size affects the rate of growth of organisms (McInerney, et. al., 1993).Growth of Escherichia coli

is reduced in smaller pore sizes, possibly due to a restriction of bacterial cell division (Eve Riser-Roberts,

1998).Low infiltration and high risk of soil erosion are also associated with soils under such conditions

(Anoliefo and Vwioko, 1995).

Table 11 shows that both the oil treatment and the organic amendment of the top (0 – 20 cm) soil did not show

significant effect (P < 0.05) on the micro porosity of sub (20 – 40 cm) soils.

4.1.4 Aggregate stability (wet and dry)

Table 12 shows that the stability of wet soil aggregates increased as spent oil contamination increased

from 0 – 3 %. The increase was between 16.3 – 30.4 % three months following contamination. The

stability of soil aggregates of the contaminated plots was 11.4 - 22.0 % higher than that of the

uncontaminated plots. Amended soils had significantly larger (p < 0.05) aggregates compared to the un-

amended soils. Aggregates of PE amended plots were larger (P < 0.05) than those of plots under other

organic amendments; the CS amended plots showed significantly larger aggregates compared to

aggregates of soils of the PR amended plots. Organic materials with low C: N ratio decomposed faster

than those with higher ratios (Nahrawi et. al., 2011). Consequently, the comparatively higher stability

observed in the amended plots especially from the 18th

– 36th

month could be attributed to the cementing

effect of the decomposing organic amendments. The improved wet aggregate stability in uncontaminated

but amended plots was apparently due to the modifying effects of the organic amendments while that on

the contaminated and amended plots was due to the added influence of oil contamination (especially in the

first 12 months). Within the first 12 months, the contribution of spent oil (main plot treatment) to

increased mean weight diameter of wet aggregates ranged between 62.5 - 89.1 % while that of the organic

amendments was between 10.9 - 37.5 %. Between the 18 - 24th

months the influence of spent oil dropped

to between 29 – 57 % while that of the organic amendments increased to between 43 - 71 %. In the last 30

- 36 months the effect of oil further dropped to between 6.2 - 41.1% while that of the organic amendments

further increased to between 58.9 - 93.8%. The above trend indicates that though oil contributed to

increased stability of soils, this oil induced stability reduced as the oil was biodegraded.

58

NA = Control, PE = Palm oil mill effluent, CS = Cassava peels and PR = Oil Palm bunch refuse. ns = non significant,

** and * = significant at 1 % and 5 % probability levels, - = no interaction

Table 10: Effect of treatments on micro - porosity (%) of top (0 - 20 cm depth zone) soil

Months

Oil concentrations (A) % 0 3 6 12 18 24 30 36

0 31.0 31.0 30.0 28.8 28.0 28.0 31.0 30.0

1 31.0 42.0 42.0 40.0 39.0 38.0 42.5 42.0

2 31.0 43.0 42.5 41.0 40.0 39.0 43.0 42.0

3 31.0 44.0 44.0 43.0 41.0 41.0 44.0 44.0

LSD0.05 ns 1.93 1.68 2.82 2.00 1.60 1.68 1.84

Organic amendments (B)

NA 31.0 41.0 41.0 41.0 41.0 41.0 41.0 41.0

PE 31.0 38.5 38.0 37.0 35.0 34.0 38.5 38.0

CS 31.0 40.5 39.0 37.0 36.0 34.0 40.5 39.0

PR 31.0 40.0 40.0 38.0 37.0 35.0 40.0 40.0

LSD0.05 ns 1.56 1.83 2.10 2.00 2.88 1.86 1.46

Interactions

(A × B) ns ** ** * * * * *

59

NA = Control, PE = Palm oil mill effluent, CS = Cassava peels and PR = Oil Palm bunch refuse. ns = non significant,

- = no interaction

Table 11: Effect of treatments on micro - porosity (%) of sub (20 - 40 cm depth zone) soil

Months

Oil concentrations (A) % 0 3 6 12 18 24 30 36

0 28.0 28.0 28.0 28.0 28.0 28.0 28.0 28.0

1 28.0 28.0 28.0 28.0 28.5 28.5 28.5 28.5

2 28.0 28.0 28.0 28.5 28.5 29.0 28.5 28.5

3 28.0 28.0 28.0 28.5 29.0 29.0 29.0 29.0

LSD0.05 Ns Ns Ns ns Ns Ns ns ns

Organic amendments (B)

NA 28.0 28.0 28.0 28.5 29.0 29.0 29.0 29.5

PE 28.0 28.0 28.0 28.0 28.5 28.0 28.0 28.5

CS 28.0 28.0 28.0 28.0 28.0 28.0 28.5 28.0

PR 28.0 28.0 28.0 28.0 28.5 28.5 28.5 28.0

LSD0.05 Ns Ns Ns ns Ns Ns ns ns

Interactions

(A × B) - - - - - - - -

60

NA = Control,

PE = Palm oil mill effluent, CS = Cassava

peels and PR = Oil Palm bunch refuse. ns = non

significant,

** and * = significant at 1 % and 5 %, - = no interaction

Table 12: Effect of treatments on mean weight diameter (mm) of wet aggregates of top (0 - 20 cm depth zone)soil

Months

Oil concentrations (A) % 0 3 6 12 18 24 30 36

0 1.30 1.30 1.44 1.35 1.45 1.53 1.61 1.75

1 1.30 1.57 1.64 1.63 1.54 1.64 1.74 1.78

2 1.30 1.66 1.77 1.64 1.73 1.80 1.84 1.88

3 1.30 1.76 1.82 1.66 1.74 1.80 1.83 1.86

LSD0.05 ns 0.18 0.16 0.12 0.10 0.13 0.15 0.16

Organic amendments (B)

NA 1.30 1.48 1.54 1.42 1.29 1.38 1.37 1.37

PE 1.30 1.75 1.82 1.78 1.93 1.98 2.04 2.12

CS 1.30 1.57 1.67 1.52 1.65 1.75 1.87 1.96

PR 1.30 1.57 1.64 1.56 1.58 1.66 1.66 1.81

LSD0.05 ns 0.08 0.06 0.08 0.06 0.07 0.10 0.12

Interactions

(A × B) ns ** ** * * * ** *

Therefore the stability of contaminated and un-amended soils reduced with time while that of the amended

soils increased. The spent oil induced soil structural stability observed in this experiment is due to the

hydrophobic properties of oil which do not allow disaggregation of soil particles by rain drop impact.

Since all the treated plots were exposed to rain, plots treated with oil had their aggregates protected from

the shattering effects of raindrops.

Under 3% oil treatment significant effect on aggregate stability was observed in the 20 – 40 cm soil depth

12 months after contamination (Table 13). At this time a significant amount of spent oil from the top 0 -

20 cm of the plots had seeped down to the sub-soil thereby causing a significant impact on the mean

weight diameter of its particles. Hence increased rate of spent oil applied on the surface soil also increased

the stability of the sub-soils with time. However 24 months following organic amendment of the

contaminated top soils, the decomposition and mineralisation of the organic materials stimulated much

biodegradation of the oil and as a result less oil seeped downward in amended plots. The sub-soils of

amended plots (PE, CS and PR) with less oil showed lower stability compared to the un-amended (NA)

sub-soils. Means et. al., (1980) reported that increased aggregate stability observed under oil treated soils

was due to the hydrophobicity of oil which led to the formation of a hydrophobic oil film on the soil

particles thereby increasing water repellence which, in-turn, increased soil structural stability.

Results of the mean weight diameter of dry aggregates (MWDD) of the top (0 - 20 cm) soil (Table 14)

showed that 6 – 36 months after oil application the stability of dry aggregates (MWDD) increased as spent

oil contamination increased. The dry aggregates of contaminated and un-amended (NA) plots showed

significantly higher stability compared to aggregates in amended plots most likely due to the higher oil

concentration in such plots following slower biodegradation of oil as a result of comparatively lower

nutrient supply.

The sub( 20 – 40 cm ) soil of the oil treated plots (Table 15) showed that from the 18th

month, plots

treated with 2 and 3% spent oil had significantly higher structural stability in dry state, while plots treated

with 1% did not differ from the uncontaminated (0 %) plots. The dry aggregates of contaminated and un-

amended (NA) plots (20 – 40 cm) showed significantly higher stability compared to aggregates of the

amended plots.

Though the results obtained for wet and dry soil aggregates generally indicate that spent oil could stabilize

the soil against water and wind erosion, this, however, should be weighed against the many deleterious

effects of spent automobile engine oil on the soil and some crops.

62

NA=Control, PE = Palm oil mill effluent, CS = Cassava peels and PR = Oil Palm bunch refuse. ns = non significant, - = no interaction

Table 13: Effect of treatments on mean weight diameter (mm) of wet aggregates of sub (20 - 40 cm depth zone) soil

Months

Oil concentrations (A) % 0 3 6 12 18 24 30 36

0 1.35 1.36 1.37 1.36 1.36 1.34 1.35 1.33

1 1.35 1.36 1.36 1.36 1.42 1.48 1.43 1.44

2 1.35 1.36 1.36 1.37 1.46 1.55 1.58 1.58

3 1.35 1.37 1.45 1.48 1.55 1.58 1.61 1.62

LSD0.05 Ns Ns Ns 0.10 0.08 0.09 0.07 0.09

Organic amendments (B)

NA 1.35 1.36 1.38 1.42 1.47 1.54 1.56 1.58

PE 1.35 1.37 1.39 1.38 1.43 1.43 1.45 1.44

CS 1.35 1.36 1.38 1.39 1.44 1.46 1.44 1.45

PR 1.35 1.37 1.38 1.39 1.45 1.51 1.52 1.50

LSD0.05 Ns Ns Ns ns ns 0.06 0.05 0.08

Interactions

(A × B) Ns - - - - - - -

63

NA = Control, PE = Palm oil mill effluent, CS = Cassava peels and PR = Oil Palm bunch refuse. ns = non significant, - = no interaction

Table 14: Effect of treatments on mean weight diameter (mm) of dry aggregates of top (0 - 20 cm depth zone) soil

Months

Oil concentrations (A) % 0 3 6 12 18 24 30 36

0 1.20 1.20 1.19 1.18 1.17 1.15 1.13 1.11

1 1.20 1.41 1.38 1.36 1.33 1.31 1.26 1.23

2 1.20 1.44 1.44 1.42 1.40 1.37 1.34 1.31

3 1.20 1.52 1.51 1.50 1.46 1.43 1.40 1.37

LSD0.05 ns 0.16 0.12 0.10 0.12 0.14 0.13 0.09

Organic amendments (B)

NA 1.20 1.40 1.40 1.39 1.38 1.37 1.36 1.15

PE 1.20 1.41 1.36 1.35 1.30 1.28 1.23 1.20

CS 1.20 1.39 1.38 1.35 1.32 1.27 1.23 1.20

PR 1.20 1.40 1.39 1.38 1.36 1.08 1.31 1.27

LSD0.05 ns 0.11 Ns Ns ns 0.14 0.11 0.10

Interactions

(A × B) ns - - - - - - -

64

NA=Control,PE=Palm oil mill effluent, CS = Cassava peels and PR = Oil Palm bunch refuse. ns = non significant, - = no interaction,

* = significant at 5 % probability level.

Table 15: Effect of treatments on mean weight diameter (mm) of dry aggregates of sub (20 - 40 cm depth zone)soil

Months

Oil concentrations (A) % 0 3 6 12 18 24 30 36

0 1.24 1.24 1.24 1.24 1.24 1.24 1.25 1.25

1 1.24 1.24 1.26 1.30 1.32 1.33 1.33 1.33

2 1.24 1.24 1.30 1.34 1.36 1.38 1.38 1.39

3 1.24 1.24 1.30 1.35 1.41 1.45 1.46 1.46

LSD0.05 ns Ns Ns Ns 0.11 0.10 0.12 0.11

Organic amendments (B)

NA 1.24 1.24 1.27 1.32 1.36 1.41 1.43 1.44

PE 1.24 1.24 1.27 1.29 1.30 1.30 1.31 1.31

CS 1.24 1.24 1.28 1.31 1.33 1.33 1.33 1.33

PR 1.24 1.24 1.28 1.32 1.34 1.34 1.35 1.35

LSD0.05 ns Ns Ns Ns ns 0.10 0.09 0.10

Interactions

(A × B) ns - - - - - * *

To evaluate the effects of individual treatment on aggregate stability (AS), the change in MWD between

the control (untreated) and the treated plots was normalized using the Potential Structural Enhancement

Index (PSEI).

The results obtained for wet aggregates of the top (0 – 20 cm) soil presented in Table 16 showed that oil

had a positive contribution to the enhancement of wet aggregate stability of the soil and the level of

contribution increased as oil concentration increased. The mean effect ranged from 16.80 - 25.57 % in the

3rd

month, from 20.77 - 27.86% in the 6th

month, 19.29 - 21.12 % in the 12th

month, 13.63 - 23.37% in the

18th

month, 19.65 - 26.76% in the 24th

month, while in the 30th

month it was from 23.24 - 27.58 % and

24.61 - 28.55 % in the 36th

month. Positive values indicate contribution to structural enhancement while

negative or zero values indicate no contribution. Relative to the control, all the organic amendments

generally contributed to structural enhancement. The PE treatment showed the highest contribution which

ranged from 24.14 % in the 3rd

month to 38.52 % in the 36th

month while the PR treatment was the least

ranging from 16.63 % in the 3rd

month to 27.95 % in the 36th

month. The PSEI results obtained therefore

strengthens the earlier results of aggregate stability obtained for oil treated and organically amended soils

which indicated that the treatments increased the structural stability of soils. The organic amendment

contributed to structural enhancement by their cementing effect following decomposition. Enhanced

structural stability would mean that the nutrients from the mineralisation of the subsequent (2nd

– 3rd

year)

amendment will not be washed away by erosion or leaching, but rather remain to boost microbial

degradation of the contaminant (oil), encourage vigorous rooting activity of the test crops and increase

porosity.

66

NA = Control, PE =Palm oil mill effluent, CS = Cassava peels and PR = Oil Palm bunch refuse.

Table 16: Effect of treatments on the potential structural enhancement index of wet aggregates of top (0 - 20 cm

depth zone) soil

Months

Oil concentrations (A) % 0 3 6 12 18 24 30 36

0 - 7.14 11.63 5.61 13.73 19.15 24.13 31.16

1 - 16.80 20.77 19.56 13.63 19.65 23.24 24.61

2 - 21.50 26.20 19.29 22.60 26.03 27.30 28.61

3 - 25.57 27.86 21.12 23.37 26.76 27.58 28.55

Organic amendments (B)

NA - 15.50 19.35 11.34 0.57 8.63 6.79 6.38

PE - 24.14 27.63 26.07 32.08 33.94 36.08 38.52

CS - 16.83 21.36 13.81 20.51 25.28 30.27 33.73

PR - 16.63 20.04 15.78 16.87 21.11 24.76 27.95

As shown in Table 17, the structural enhancement exhibited by both the oil and organic amendments was

negligible in the lower 20 – 40 cm of the plots in the 3-12 months interval with only the 3 % oil treatment

showing significant effect during this period. This was because the comparatively higher oil concentration

in top-soils under 3 % oil treatment permitted more rapid seepage of oil downwards. The treatments

however showed increased contribution to the structural enhancement of the lower soil aggregates

between the 18th

- 36th

months. It was observed that the sub-soils of uncontaminated plots were not

structurally enhanced by the organic amendments while the sub-soils of contaminated and un-amended

plots showed structural enhancement, indicating that the structural enhancement of the organic

amendments (cementing effects) on wet aggregates was to a greater extent limited to the top (0 - 20 cm)

soil while the oil treatment was largely responsible for the enhanced soil structure observed in the sub-

soils of the plots. The mineralisation of the organic amendments and hence nutrient mediation of the

bioremediation process which depleted the oil in the top soil, however determined the extent to which oil

affected the stability of the sub-soils.

The organic amendments made positive contribution to the enhancement of the structural stability of dry

aggregates in the uncontaminated plots, as shown by the positive values observed from the 18th

- 36th

month of this experiment (Table18).Though all the spent oil treatments and organic amendments made

positive contributions to soil structural enhancement which declined with time, the effect of oil in the

contaminated but un-amended plots was higher compared to its effect in the contaminated but amended

plots. This indicates higher rate of degradation of oil following the organic material amendment of

contaminated soils. Generally the contamination of this soil with spent oil did not show adverse effect on

soil structure. The amendments however reduced the effect of the oil on soil structure due to their

effectiveness as bioremediation tools on the oil contaminated soil. Thus, as the oil was biodegraded there

was a consequent loss of oil-enhanced structural stability.

The spent oil treatment and organic amendments did not contribute to structural enhancement of dry soil

aggregates of the sub (20 – 40 cm) soil 3 months following treatment (Table 19). This was observed to be

due to insignificant amount of the oil which seeped into the sub-soil by this time. The effect of oil

however increased from the 6th

– 36th

month. The sub-soils of uncontaminated top-soils did not show

structural enhancement which suggests that it was the seepage of the spent oil into the sub-soils that

brought about its structural enhancement.

68

NA = Control, PE = Palm oil mill effluent, CS = Cassava peels and PR = Oil Palm bunch refuse.

Table 17: Effect of treatments on the potential structural enhancement index of wet aggregates of sub (20 - 40 cm

depth zone) soil

Months

Oil concentrations (A) % 0 3 6 12 18 24 30 36

0 - 0.74 0.74 0.73 0.49 -0.51 0.21 -1.50

1 - 0.74 0.55 0.92 4.87 8.38 14.81 5.84

2 - 0.74 0.55 1.49 7.62 12.84 14.81 14.58

3 - 1.45 7.06 8.47 12.75 14.20 16.06 16.29

Organic amendments (B)

NA - 1.22 2.97 5.92 10.70 15.92 6.79 18.39

PE - 1.10 2.28 2.20 5.08 5.43 36.08 5.56

CS - 0.74 2.07 2.38 6.21 30.11 30.27 5.98

PR - 0.74 2.28 2.59 6.31 10.13 24.76 10.63

69

NA = Control, PE = Palm oil mill effluent, CS = Cassava peels and PR = Oil Palm bunch refuse.

Table 18: Effect of treatments on the potential structural enhancement index of dry aggregates of top (0 - 20 cm

depth zone) soil

Months

Oil concentrations (A) % 0 3 6 12 18 24 30 36

0 - 0.00 -0.84 -1.70 -2.56 -4.30 -6.19 -8.11

1 - 14.99 13.04 13.04 9.78 8.04 4.76 2.22

2 - 18.41 16.67 15.49 13.04 12.41 10.45 8.04

3 - 21.05 20.40 20.00 17.81 16.08 13.04 12.41

Organic amendments (B)

NA - 14.29 14.29 12.95 13.04 12.12 11.68 11.12

PE - 14.99 11.76 11.12 7.69 5.28 1.40 0.00

CS - 12.95 13.04 11.12 9.09 4.70 2.00 0.00

PR - 13.46 12.95 13.04 13.04 9.37 7.48 4.70

70

NA = Control,PE = palm oil mill effluent, CS = Cassava peels and PR = Oil Palm bunch refuse.

Table 19: Effect of treatments on the potential structural enhancement index of dry aggregates of sub (20 - 40 cm

depth zone) soil

Months

Oil concentrations (A) % 0 3 6 12 18 24 30 36

0 - 0.00 0.00 0.00 0.00 0.00 0.60 0.20

1 - 0.00 1.59 4.61 6.16 6.77 6.51 6.64

2 - 0.00 4.61 7.28 8.82 9.73 10.17 10.47

3 - 0.00 4.61 8.15 10.19 13.96 14.68 13.04

Organic amendments (B)

NA - 0.00 2.36 6.06 8.59 11.47 12.48 12.85

PE - 0.00 2.36 3.88 4.71 4.73 5.11 5.11

CS - 0.00 3.12 5.34 4.40 6.49 6.51 4.42

PR - 0.00 3.12 6.06 6.91 7.23 7.86 8.17

4.1.5. Saturated hydraulic conductivity.

Table 20 show results of the effect of treatments on the saturated hydraulic conductivity (Ksat) of the top

(0 – 20 cm) soil which indicates an inverse relationship between spent oil rate and Ksat. The values of Ksat

obtained in 0 % oil treated plots ranged between 32. 60 - 36.26 cm h-1

from the 3rd

to the 36th

month, the

values in 1 % treatment ranged from 11.68 - 28.42 cm h-1

while values in 2 % oil treated plots ranged

between 9.56 - 20.90 cm h-1

and the values in 3 % oil treated plots from 6.29 - 14.76 cm h-1

. The observed

differences in these mean values were significant. It was earlier observed (Table 16) that the reduction in

macro-porosity followed the clogging of the macro-pores by oil. The extent of clogging of the macro-

pores determined the pore space available for water conduction and since the macro-pores are generally

responsible for water movement under saturated conditions, the Ksat of oil treated soils reduced to

6.29cmh-1

due to increased clogging of the pores following increase in oil concentration. The results

according to Rasiah et. al., (1990) indicate that oil succeeded water in the competition for pore spaces,

leading to reduction in water film thickness around the macro-aggregates.

The organic amendments significantly improved the Ksat of contaminated plots which deteriorated

following oil contamination. It also modified the Ksat of uncontaminated plots especially because of its

repeated doses (12 Mg ha-1

yr-1

). Through 36 months of the studies, CS treated plots consistently showed

higher improvement in Ksat (P < 0.05) relative to other amended plots and control. Plots under PE

amendment showed significantly higher Ksat compared to PR amended plots. A general observation was

that the Ksat of plots contaminated with oil changed from very rapid (> 25 cm h-1

) to moderately rapid

'permeability' (< 6 cm h-1

) but when the plots were amended this change in Ksat was reversed with time.

Over 36 months the comparatively higher oil concentration of the sub (20 - 40 cm) soil of contaminated

but un-amended plots (Table 21) significantly reduced Ksat compared to soils with upper 0 - 20 cm

contaminated but amended. The Ksat of sub-soils of plots under PE and CS organic amendments remained

relatively unchanged through the 36 months of these studies, indicating higher biodegradation rate of oil

in top-soils treated with PE and CS. The observed differences in the sub-soils of the amended plots

depended largely on the bioremediation efficiency of the organic materials which determined the amount

of oil degraded in the top (0 – 20 cm) soil and that which seeped down to the lower soils.

The Ksat values reflected how much the bioremediation process had succeeded by the amount of oil

removed from the macro-pores and the extent to which the organic amendments had increased the

porosity of the soil. Low Ksat would mean that the crops (maize and cowpea) under this condition would

face higher production risk and may suffer water stress related injuries, which may ultimately lead to

death.

72

Table 20: Effect of treatments on saturated hydraulic conductivity (cm h-1

) of top (0 - 20 cm depth zone) soil

Months

Oil concentrations (A) % 0 3 6 12 18 24 30 36

0 32.48 32.6 33.22 33.84 34.72 35.2 35.72 36.26

1 32.48 11.68 13.91 16.24 19.48 21.49 24.49 28.42

2 32.48 9.56 11.05 12.83 14.75 16.73 18.82 20.9

3 32.48 6.29 7.68 9.43 10.58 11.68 12.83 14.76

LSD0.05 Ns 1.21 1.52 1.86 2.11 1.78 2.05 1.39

Organic amendments (B)

NA 32.48 14.53 14.77 15.52 16.48 16.91 17.3 17.9

PE 32.48 14.27 16.74 18.83 20.79 22.57 24.75 27.39

CS 32.48 16.63 18.5 20.56 22.95 24.73 26.73 29.48

PR 32.48 14.7 15.84 17.41 19.31 20.91 25.8 25.57

LSD0.05 Ns 1.14 1.06 1.37 1.22 1.16 1.08 1.52

Interactions

(A × B) Ns * ** ** * * ** *

NA = Control, PE = Palm oil mill effluent, CS = Cassava peels and PR = Oil Palm bunch refuse. ns = non significant,

** and * = significant at 1 and 5 %

73

NA = Control, PE = Palm oil mill effluent, CS = Cassava peels and PR = Oil Palm bunch refuse. ns = non significant,

** and * = significant at 1 and 5 %

Table 21: Effect of treatments on saturated hydraulic conductivity (cm h-1) of sub (20 - 40 cm depth zone) soil

Months

Oil concentrations (A) % 0 3 6 12 18 24 30 36

0 25.83 25.83 25.84 25.83 25.84 25.84 25.84 25.84

1 25.83 25.83 25.84 25.73 25.40 25.33 25.33 25.33

2 25.83 25.83 25.67 25.01 22.72 22.27 22.01 22.01

3 25.83 25.83 25.65 23.49 22.45 22.51 22.30 22.27

LSD0.05 ns ns ns 1.27 2.21 1.15 1.59 1.36

Organic amendments (B)

NA 25.83 25.83 25.72 23.13 22.51 21.45 21.01 20.98

PE 25.83 25.83 25.83 25.65 25.82 25.74 25.73 25.73

CS 25.83 25.83 25.73 25.56 25.78 25.08 25.58 25.05

PR 25.83 25.83 25.71 25.23 25.05 23.84 23.68 23.68

LSD0.05 Ns ns ns 1.10 1.36 1.22 1.18 1.31

Interactions

(A × B) ns ns ns ns * * * *

4.2 Soil Chemical Properties

4.2.1 Total hydrocarbon content of soil

The results obtained (Table 22a) followed a single dose treatment of oil in the first year (1 % = 10,000 mg

kg-1

, 2 % = 20,000 mg kg-1

and 3 % = 30,000 mg kg-1

) and a split application of organic amendment of 12

Mg ha-1

yr-1

for 3 years. The top (0 – 20 cm) soil showed that (Total Hydrocarbon Content) THC of the

soil increased with increasing rate of oil application. The observed differences were significant (P <

0.05). The THC in plots treated with 0, 1, 2 and 3 % oil ranged from 954 - 998 mg kg-1

, 7455 - 1325 mg

kg-1

, 15889 - 6362 mg kg-1

and 24429 - 11550 mg kg-1

respectively, in 36 months.

Amendment of the contaminated plots showed that for the PE treated plots the top (0 – 20 cm) soil

exhibited significant (P < 0.05) reduction in THC over the control (NA) and the other organic

amendments in the 3rd

month, indicating quick onset of nutrient mediated biodegradation in PE treatment

following faster decomposition and mineralisation. In the 6th

month PE and CS treated plots showed

significant reduction in THC values compared to those under PR and control. From the 12th

– 36th month

a clear order of reduction (P < 0.05) in the THC was established by the organic amendments thus: PE >

CS > PR > NA. The mean THC values in the PE amended plots fell from 9553 mg kg-1

in 3 months to

1997 mg kg-1

in 36 months, THC values in CS amended plots fell from 3712 to 1204 mg kg-1

while values

in PR fell from 13426 to 6373 mg kg-1

and THC values in NA treated plots fell from 13697 to 8152 mg

kg-1

. In an experiment to determine the rate of leaf and fine root decomposition, Nahrawi et.al (2011),

reported a high correlation between C/N ratio and rate of decomposition of plant residues. Therefore the

behaviour of these organic amendments with respect to THC reduction may be connected to their C/N

ratios (Tables 3 and 4). The PE amendment with comparatively lower C/N ratio decomposed and

mineralised faster compared with CS and PR, hence releasing the needed nutrients for enhancement of

fast microbial degradation of the hydrocarbon. The CS amendment with lower C/N ratio than PR

decomposed comparatively faster. The nutrients N and P were reported to be most critical in

bioremediation (Morgan and Watkinson, 1989; Margesin and Schinner, 2001; Jelena, 2009). The order of

reduction of THC by these organic amendments was also observed to reflect their N and P contents.

75

NA = Control, PE = Palm oil mill effluent, CS = Cassava peels and PR = Oil Palm bunch refuse. ns = non significant,

** and * = significant at 1 % and 5 % probability levels.

Table 22a: Variation in total hydrocarbon content (mg kg-1

) of treated top (0 - 20 cm depth zone) soil

Months

Oil concentrations (A) % 0 3 6 12 18 24 30 36

0 905 954 987 919 948 957 986 998

1 905 7455 5963 4615 4227 3560 2105 1325

2 905 15889 14034 12010 10681 8541 7528 6362

3 905 24429 21166 20910 19906 17223 14345 11550

LSD0.05 Ns 5100 4850 3012 2971.6 2138.3 1009 761.8

Organic amendments (B)

NA 905 13697 12992 12042 11381 10821 9393 8152

PE 905 9553 7121 6538 5809 3195 2488 1997

CS 905 12047 9794 8919 8176 6819 5010 3712

PR 905 13426 12244 10955 10396 9447 8073 6373

LSD0.05 Ns 1127.3 1006.8 951.0 946.2 712.5 664.1 539.0

Interactions

(A × B) Ns ** ** * * ** * * *

The PE amendment with the highest content of these nutrients, showed the highest reduction of soil THC

while PR with the lowest content showed the least reduction.

A test for the presence of the hydrocarbon (spent automobile engine oil) in the 20 – 40 cm soil depth, 3

months following contamination of the top (0 - 20 cm) soil yielded negative results (Table 22b).

However, 6 months later, significant quantity of oil was observed at this depth (20 – 40 cm) in plots under

3 % oil treatment except for plots treated with PE amendment. The observation in the PE amended plots

showed that there was comparatively less oil seepage into the sub-soils due to higher biodegradation of oil

in the surface soils. The organic amendments PE, CS and PR had remediation efficiencies of 44.5 %,

16.5 % and 4.6 % respectively in the 6th

month. Higher remediation efficiency of an organic amendment

translated to higher biodegradation rate. The THC values under the main-plot treatments (spent oil)

followed the order 3 % > 2 % > 1 % > 0 % whereas in the sub-plots (organic amendments) the order was

NA > PR > CS > PE.

Table 22c shows that the degradation rate of spent oil increased with increasing oil rate as also reported by

Eve Riser-Roberts (1998) and with increased nutrient status of the soil that followed yearly application of

the organic amendments. This suggests that the microbial consortium which degraded oil increased as the

substrate (oil) and nutrient supply increased. This was confirmed by the results of the viable count of

hydrocarbon degrading micro-organisms. Delille et al. (2002) and Jelena et al. (2009) reported similar

findings. The experiment showed that THC reduction was achieved by loss due to gravity and loss due to

microbial activity. Loss of oil to the sub-soil due to gravity increased from 0 % in the 3rd

month to 28.3 %

in the 36th

month depending on the level of oil contamination and type of amendment. Loss of THC due to

microbial activity in amended plots was comparatively higher than that in un-amended plots, with values

in 36 months ranging from 6.4 – 18.8 % in un-amended plots while values in amended plots ranged from

14.1 – 85.2 % under 3% oil contamination. The remediation efficiencies of the organic amendments were

directly related to the degradation rate and THC loss due to microbial degradation of the hydrocarbon

(oil). Under 3% oil contamination, the PE amendment showed the highest remediation efficiency of

81.7 % while the lowest remediation efficiency of 28.4 % was observed for PR amendment in 36 months.

The result suggests that PE is a better nutrient source compared to CS and PR. There was therefore more

reduction of oil by biodegradation than by gravity in PE amended soils. The faster of the processes:

biodegradation of oil and loss of oil by gravity, was observed to determine the extent of damage done to

soil physical, chemical and biological properties, and to what depth. With optimum supply of nutrients,

microbial degradation of oil out-runs the downward seepage, hence reducing collateral damage to the soil.

Complete remediation was achieved in 36 months only in plots contaminated with 1 % oil and amended

with PE with almost no loss of oil to the lower (20 - 40 cm) soil depth. This indicates that the quantity of

the amendments should be increased beyond 12 Mg ha-1

for all levels of the contaminations (1, 2 and 3%)

for complete and faster remediation.

77

NA = Control, PE = Palm oil mill effluent, CS = Cassava peels and PR = Oil Palm bunch refuse. ns = non significant,

* = significant at 5 % probability levels.

Table 22b: Variation in total hydrocarbon content (mg kg-1

) of treated sub (20 - 40 cm depth zone) soil

Months

Oil concentrations (A) % 0 3 6 12 18 24 30 36

0 801 801 800 801 801 801 802 801

1 801 801 801 1410 1308 1543 1547 1542

2 801 801 861 1670 2208 3194 3328 3407

3 801 801 887 2358 3452 3844 4072 4248

LSD0.05 ns ns 57.8 120.6 201.2 319.0 516.8 811.0

Organic amendments (B)

NA 801 801 850 3016 3494 4115 4376 4562

PE 801 801 801 903 1071 1157 1194 1227

CS 801 801 849 1062 1304 1521 1553 1588

PR 801 801 850 1401 1901 2590 2625 2622

LSD0.05 ns ns 48.1 108.3 187.0 251.6 290.2 385.9

Interactions

(A × B) ns ns ns * * * * *

Table 22c: Effect of Treatment on Total Hydrocarbon Content of the Soil Treatments Spent oil Residual Total loss Loss by Loss due Degradation Remediation

Loading THC in THC gravity to microbial rate efficiency

(mgkg-1

(mgkg-1

) (%) (%) activity (%) (mg kg-1

d-1

) (%)

3rd

Month

SP3NA 30,000 26131 12.9 0.00 12.9 43.0 -

SP3PE 30,000 18709 37.6 0.00 37.6 125.5 28.4

SP3CS 30,000 23473 21.8 0.00 21.8 72.5 10.2

SP3PR 30,000 25782 14.1 0.00 14.1 46.9 1.3

SP2NA 20,000 17047 14.8 0.00 14.8 32.8 -

SP2PE 20,000 11432 42.8 0.00 42.8 95.2 32.9

SP2CS 20,000 15001 25.0 0.00 25.0 55.5 12.0

SP2PR 20,000 16454 17.7 0.00 17.7 39.4 3.5

SP1NA 10,000 7990 20.1 0.00 20.1 22.3 -

SP1PE 10,000 4320 56.8 0.00 56.8 63.1 45.9

SP1CS 10,000 6036 39.6 0.00 39.6 44.0 24.5

SP1PR 10,000 7846 21.5 0.00 21.5 23.9 1.8

6th Month

SP3NA 30000 24237 19.2 0.38 18.8 32.0 -

SP3PE 30000 13459 55.1 0.00 55.1 91.9 44.5

SP3CS 30000 20237 32.5 0.37 32.1 54.2 16.5

SP3PR 30000 23113 23.0 0.40 22.6 38.3 4.6

SP2NA 20000 18542 4.9 0.41 4.5 8.1 -

SP2PE 20000 9123 54.4 0.00 54.4 60.4 50.8

SP2CS 20000 11611 42.0 0.41 41.6 46.6 37.4

SP2PR 20000 15239 23.8 0.41 23.4 26.5 17.8

SP1NA 10000 7646 23.5 0.00 23.5 13.1 -

SP1PE 10000 2143 78.6 0.00 78.6 43.7 72.0

SP1CS 10000 3528 64.7 0.00 64.7 36.0 53.9

SP1PR 10000 6915 30.9 0.00 30.9 17.1 9.6

12th Month

SP3NA 30000 23502 21.6 12.5 9.1 18.1 -

SP3PE 30000 19019 36.7 1.01 35.7 30.5 19.1

SP3CS 30000 21203 29.3 2.4 26.9 24.4 9.8

SP3PR 30000 23175 22.8 4.8 18.0 19.0 1.4

SP2NA 20000 14971 25.2 15.4 9.8 14.0 -

SP2PE 20000 6906 65.5 0.51 65.0 36.4 53.8

SP2CS 20000 10169 49.2 1.5 47.7 27.3 32.1

SP2PR 20000 12374 38.1 2.9 35.2 21.2 17.4

SP1NA 10000 6129 38.7 10.2 28.5 10.8 -

SP1PE 10000 2391 76.1 0.00 76.1 21.1 61.0

SP1CS 10000 1602 84.0 0.29 83.7 23.3 73.9

SP1PR 10000 4717 52.8 3.8 49.0 14.8 23.0

18th

Month

SP3NA 30000 22272 25.8 19.4 6.4 14.3 -

SP3PE 30000 13634 54.6 2.4 52.2 30.3 38.8

SP3CS 30000 19188 36.0 4.7 31.3 20.0 13.8

SP3PR 30000 21411 28.6 8.8 19.8 15.9 3.9

SP2NA 20000 13952 30.2 18.2 12.0 11.2 -

SP2PE 20000 5819 70.9 1.6 69.3 26.3 58.3

SP2CS 20000 8135 59.3 2.6 56.7 22.0 41.7

Table 22c Continued

SP2PR 20000 11199 44.0 5.7 38.3 16.3 19.7

SP1NA 10000 5710 42.9 13.2 29.7 7.9 -

SP1PE 10000 662 93.4 0.25 93.1 17.3 88.4

SP1CS 10000 1638 83.6 0.83 82.8 15.5 71.3 SP1PR 10000 5276 47.2 6.0 41.2 8.7 7.6

24th Month

SP3NA 30000 21739 27.5 20.6 6.9 11.5 -

SP3PE 30000 5453 81.8 3.0 78.8 34.1 74.9

SP3CS 30000 17909 40.3 6.5 33.8 16.8 17.6

SP3PR 30000 20170 32.8 7.1 25.7 13.7 7.2

SP2NA 20000 12734 36.3 24.6 11.7 10.1 -

SP2PE 20000 3448 82.8 2.4 80.4 23.0 72.9

SP2CS 20000 4920 75.4 4.1 71.3 21.0 61.4

SP2PR 20000 9443 52.8 16.8 36.0 14.7 25.8

SP1NA 10000 5212 47.9 21.7 26.2 6.7 -

SP1PE 10000 311 96.9 0.3 96.6 13.5 94.0

SP1CS 10000 623 93.8 1.0 92.8 13.0 88.1

SP1PR 10000 4475 55.3 6.7 48.6 7.7 14.1

30th Month

SP3NA 30000 19109 36.3 22.3 14.0 12.1 -

SP3PE 30000 4228 85.9 3.4 82.5 28.6 77.9

SP3CS 30000 12317 58.9 7.3 51.6 19.6 35.5

SP3PR 30000 18106 39.7 7.7 32.0 13.2 5.3

SP2NA 20000 11954 40.2 27.1 13.1 8.9 -

SP2PE 20000 2006 90.0 2.7 87.3 20.0 83.2

SP2CS 20000 3617 82.0 3.7 78.3 18.2 69.7

SP2PR 20000 8916 55.4 17.1 38.3 12.3 25.4

SP1NA 20000 2924 35.4 22.0 13.4 7.9 -

SP1PE 10000 158 98.4 0.2 98.2 10.9 94.6

SP1CS 10000 267 97.3 0.9 96.4 10.8 90.9

SP1PR 10000 1425 85.5 6.8 78.7 9.5 51.3

36th

Month

SP3NA 30000 18048 39.8 24.0 15.8 11.1 -

SP3PE 30000 3310 89.0 3.8 85.2 24.7 81.7

SP3CS 30000 8304 72.3 7.7 64.6 20.1 54.0

SP3PR 30000 12918 56.9 8.2 48.7 15.8 28.4

SP2NA 20000 10247 48.8 28.3 20.5 9.0 -

SP2PE 20000 1204 94.0 2.8 91.2 17.4 88.3

SP2CS 20000 2322 88.4 3.8 84.6 16.4 77.3

SP2PR 20000 8053 59.7 17.2 42.5 11.1 21.4

SP1NA 10000 710 92.9 21.9 71.0 8.6 -

SP1PE 10000 -80 101.1 0.0 100.9 9.3 111.3

SP1CS 10000 358 96.4 0.9 95.5 8.9 49.6

SP1PR 10000 690 93.1 6.7 86.4 8.6 2.8

SP1, SP2,and SP3 = 1, 2 and 3% spent oil, NA = no amendment, PE = palm oil mill effluent, CS = cassava peels,

PR = palm bunch refuse

Delille et al. (2002) working with 1.0 Mg ha-1

fish compost in 1.5 % oil contaminated plots achieved

complete remediation of Total Petroleum Hydrocarbon (TPH) in 6 months and Jelena et al. (2009)

achieved complete remediation in a soil under 30 g TPH/Kg in 5.5-months with a combination of poultry

manure and sawdust in an open 150 m3

bioreactor.

According to Jelena et al (2009) the speed and efficiency of biodegradation of a soil contaminated with

petroleum and petroleum products depend on the number of indigenous hydrocarbon-degrading micro-

organisms. They further noted that the most important factors for the growth of these micro-organisms

are temperature, oxygen, pH, nutrient status (N and P) hydrocarbon class and their effective concentration.

Rittman and McCarty (2001) and VanHamme et al. (2003) added that the degree and rate of

bioremediation were influenced by the type of soil in which the process occurred. Since these factors are

not constant, there is bound to be variation in results of similar experiments across the globe. Also of

note is the fact that the organic amendments used in this experiment are very cheap agricultural wastes.

Generally, it is important to have information on the rates of contaminant/pollutant biodegradation under

specified environmental conditions to be able to assess the potential fate of the compounds (Pfaender and

Klump, 1981), to evaluate the efficacy of the in-situ biodegradation treatment and to assign appropriate

approaches to enhance the degradation rates (Fu et. al, 1996). This work reveals, however, that when in-

situ bioremediation experiments are conducted and conclusions on bioremediation rate of non-volatile

petroleum hydrocarbons are drawn from observations in the top-soil without recourse to the THC of the

sub-soil, such conclusions are bound to be over blown and, hence, projections made with such results may

be misleading. The reduction in THC was earlier shown to be achieved not only by biodegradation, as

commonly reported by most researchers conducting similar experiments, but by its combination with loss

due to gravity (downward seepage).This finding was due to the examination of the sub-soil over time,

which showed that plots under no amendment and organic amendments with low remediation efficiency

lost comparatively higher proportion of the hydrocarbon to deeper soil layers. This portion lost due to

gravity is often-times erroneously reported as part of that lost by biodegradation. Though there was no

significant loss of THC by seepage in the 1 % oil contaminated plots under PE amendment nevertheless

the sub-soils of treated plots should always be examined and tested in such experiments to account for loss

due to seepage or at least to eliminate loss via seepage and hence ensure accurate reportage .This becomes

even more necessary when spent oil contamination is up to 2 %. In this experiment seepage down the

lower soil layer was noted in 6 months. This work notably partitioned and also quantified (Table 22c) the

loss of THC in an in-situ bioremediation experiment. The remediation efficiency (Table 22c) evaluates the

efficacy of organic amendments as tool for bioremediation of soils contaminated with petroleum

hydrocarbons. Differences in remediation efficiency values would indicate comparative advantage of an

organic amendment compared to another.

4.2.2 Distribution of heavy metals and contaminant/pollution limit (C/P index)

Table 23 shows that spent automobile engine oil contamination increased Zinc (Zn) concentration of the

plots from 20.02 to 27.76 mg kg-1

three (3) months after application. This increase however did not

exceed the normal range in soils (Alloway, 1990). Complete biodegradation of Zn was achieved under PE

and CS treated plots within 30 months while PR achieved about 90 % biodegradation in 36 months. The

Zn reduction under NA plots was marginal.

Table 24 shows the effect of organic amendments on Pb concentration following four levels of oil

contamination. It was observed that Pb concentration in the main plots increased with increase in oil

contamination and decreased with time. The increase was of the order: 3 % > 2 % > 1 % > 0 % and the

differences in treatment means were significant at 5 % probability level. The concentration of Pb under

3% oil treated plots ranged from 6.26 - 11.38 mg kg-1

and concentration under 2 % oil treatment was from

9.65 - 4.95 mg kg-1

while under 1 % oil treatment the values were from 3.07 - 6.14 mg kg-1

. For the 0 %

oil treatment the concentration ranged between 1.16 - 1.44 mg kg-1

. According to Alloway (1990) the

values all fall within normal range in soil (Table 1). This implies that though the spent oil used in this

experiment led to significant increase in the concentration of Zn and Pb, no adverse effect was expected

on crops, micro-flora and –fauna following their low concentrations. This observation should not be

generalised since "the proportion and type of these heavy metals depend on the process generating the

spent oil" (Edebiri and Nwanokwale, 1981). All the amended plots generally showed evidence of

significant (P < 0.05) reduction in Pb concentration relative to un-amended plots. In the first 12 months

plots treated with PE showed significant decrease in Pb relative to plots under other organic amendments

and the un-amended. In the 3 months following contamination, plots under CS, PR and NA did not vary

(P < 0.05) in their Pb content.In the 6th

month there was significant drop in Pb concentration in CS plots

relative to PR and NA with further reduction in Pb concentration in the 12th

month. Between 18 – 36

months the organic amendments reduced Pb concentration in the plots following the order: PE > CS = PR

> NA.

82

NA = Control, PE = Palm oil mill effluent, CS = Cassava peels and PR = Oil Palm bunch refuse. ns = non significant,

** and * = significant at 1 % and 5 % probability levels.

Table 23: Effect of treatment on Zinc concentration (mg kg-1

) of top (0 - 20 cm depth zone) soil

Months

Oil concentrations (A) % 0 3 6 12 18 24 30 36

0 20.02 19.46 19.34 19.06 18.33 18.16 17.22 16.13

1 20.02 24.74 24.14 23.11 22.84 22.11 20.74 19.74

2 20.02 26.68 26.68 25.26 24.80 24.37 23.31 22.68

3 20.02 27.76 27.76 25.97 25.61 24.58 22.57 19.94

LSD0.05 Ns 1.35 1.58 1.86 2.03 1.55 1.21 1.46

Organic amendments (B)

NA 20.02 25.33 25.27 25.24 25.21 25.14 25.12 25.05

PE 20.02 23.32 22.03 21.87 20.30 20.24 15.74 15.60

CS 20.02 24.82 23.93 22.25 22.11 20.68 20.00 17.64

PR 20.02 25.17 24.98 24.03 23.96 23.16 23.00 20.20

LSD0.05 Ns 1.38 1.15 1.10 1.49 1.20 1.52 1.66

Interactions

(A × B) Ns * * * * ** * * *

83

NA = Control, PE = Palm oil mill effluent, CS = Cassava peels and PR = Oil Palm bunch refuse. ns = non significant,

** and * = significant at 1 % and 5 % probability levels.

Table 24: Effect of treatment on Lead concentration (mg kg-1

) of top (0 - 20 cm depth zone) soil

Months

Oil concentrations (A) % 0 3 6 12 18 24 30 36

0 1.50 1.44 1.41 1.38 1.34 1.31 1.26 1.16

1 1.50 6.14 5.74 4.76 4.43 3.62 3.51 3.07

2 1.50 9.65 9.12 8.77 7.88 7.31 6.47 4.95

3 1.50 11.38 10.90 10.31 9.46 8.33 7.68 6.26

LSD0.05 Ns 0.57 0.63 0.84 0.72 0.95 0.61 0.50

Organic amendments (B)

NA 1.50 7.83 7.82 7.79 7.77 7.75 7.71 7.68

PE 1.50 5.33 5.12 5.07 3.34 3.35 1.94 1.86

CS 1.50 7.67 6.89 5.92 5.43 4.47 4.36 2.75

PR 1.50 7.76 7.40 6.43 6.20 5.01 4.92 3.16

LSD0.05 Ns 1.38 1.15 1.10 1.49 1.20 1.52 1.66

interactions

(A × B) Ns * ** ** ** ** * * **

Table 25 shows that increasing oil concentration increased Cr concentration which declined with time.

Soils under organic amendments generally showed greater reduction in Cr content compared to the

un-amended plots. The results generally showed that PE was the best of the organic amendments, with PE

treated plots showing a Cr concentration of 20.69 - 34.66 mg kg-1

in 36 months. Over this period

(36months) Cr concentration in CS treated soils ranged from 22.77 - 38.16 mg kg-1

and that in PR treated

soils from 25.05 - 38.76 mg kg-1

while that in NA plots ranged from 38.24 - 39.22 mg kg-1

.

The organic amendments showed significant reduction of Fe concentration which increased due to oil

contamination of the plots (Table 26). The order of reduction of Fe concentration in the amended and un-

amended plots followed the order PE > CS > PR > NA. It was observed that PE and CS amendments

reduced Fe concentration in plots significantly faster than PR and this may be related to their

comparatively lower C:N ratio. Organic materials with low C:N ratio decomposed faster and hence

provided binding bases for heavy metals in the soil (Jones et al., 1997). The concentration of Fe in PE,

CS, PR and NA plots from the 3rd

- 36th

month ranged from 4517 - 6290 mg kg-1

, 4929 - 6458 mg kg-1

,

5223 - 6492 mg kg-1

and 6424 - 655mg kg-1

respectively.

The results in Table 27 show that Al contamination of the plots increased with increasing oil and reduced

with time. Al concentration ranged from 3651 - 4068 mg kg-1

three months following spent oil

contamination but 36 months of soil amendment showed reduction in Al concentration to as low as

2949mg kg-1

. The amendments reduced Al concentration in the order PE > CS > PR > NA. The results

showed that soils under organic amendments had significantly (P < 0.05) lower Al concentration

compared to un-amended plots.

85

NA = Control, PE = Palm oil mill effluent, CS = Cassava peels and PR = Oil Palm bunch refuse. ns = non significant,

** and * = significant at 1 % and 5 % probability levels.

Table 25: Effect of treatment on Chromium concentration (mg kg-1

) of top (0 - 20 cm depth zone) soil

Months

Oil concentrations (A) % 0 3 6 12 18 24 30 36

0 25.28 24.77 23.66 23.10 22.40 21.17 20.14 19.99

1 25.28 36.95 33.89 33.15 31.44 29.89 27.19 26.23

2 25.28 40.23 38.02 37.01 35.37 33.51 29.90 27.13

3 25.28 48.84 47.57 46.31 44.53 44.53 39.15 33.90

LSD0.05 ns 2.98 3.44 3.26 3.02 3.18 3.75 3.51

Organic amendments (B)

NA 1.50 7.83 7.82 7.79 7.77 7.75 7.71 7.68

PE 1.50 5.33 5.12 5.07 3.34 3.35 1.94 1.86

CS 1.50 7.67 6.89 5.92 5.43 4.47 4.36 2.75

PR 1.50 7.76 7.40 6.43 6.20 5.01 4.92 3.16

LSD0.05 ns 2.16 2.37 2.10 2.25 2.11 2.10 2.08

Interactions

(A × B) ns * * * * ** * * *

86

NA = Control, PE = Palm oil mill effluent, CS = Cassava peels and PR = Oil Palm bunch refuse. ns = non significant, * = significant

at 5 % probability levels

Table 26: Effect of treatment on Iron concentration (mg kg-1) of top (0 - 20 cm depth zone) soil

Months

Oil concentrations (A) % 0 3 6 12 18 24 30 36

0 4520 4492 4474 4440 4369 4322 4116 3937

1 4520 5007 4906 4825 4782 4703 4212 3974

2 4520 7215 7022 6769 6767 6600 6223 5810

3 4520 9076 8916 8834 8657 8515 7927 7372

LSD0.05 ns 391.6 372.7 330.0 386.2 279.1 185.6 208.8

Organic amendments (B)

NA 4520 6551 6540 6535 6535 6530 6527 6424

PE 4520 6290 6097 5703 5703 5695 4561 4517

CS 4520 6458 6295 6094 6094 5860 5544 4929

PR 4520 6492 6386 6241 6241 6055 5846 5223

LSD0.05 ns 225.5 196.3 150.6 145.4 161.0 165.8 186.2

Interactions

(A × B) ns * * * * * * *

87

NA = Control, PE = Palm oil mill effluent, CS = Cassava peels and PR = Oil Palm bunch refuse. ns = non significant, * = significant

at 5 % probability levels

Table 27: Effect of treatment on Aluminium concentration (mg kg-1) of top (0 - 20 cm depth zone) soil

Months

Oil concentrations (A) % 0 3 6 12 18 24 30 36

0 3189 3132 3093 3055 3033 2977 2963 2938

1 3189 3651 3621 3550 3471 3398 3338 3110

2 3189 3915 3832 3771 3689 3547 3475 3370

3 3189 4068 4025 3940 3834 3699 3613 3495

LSD0.05 ns 505.7 395.8 381.2 365.9 330.0 321.6 315.3

Organic amendments (B)

NA 3189 3755 3752 3745 3743 3735 3733 3730

PE 3189 3564 3465 3456 3209 3198 2983 2949

CS 3189 3703 3637 3410 3477 3224 3212 2975

PR 3189 3744 3718 3615 3598 3465 3461 3259

LSD0.05 ns 27.2 170.1 128.6 107.0 104.7 105.0 101.3

Interactions

(A × B) ns ns ns ns ns Ns Ns ns

Shown in Tables 28 to 31 are the contaminant – pollution indices (C/P index) calculated for Fe, Cr, Pb and

Zn. Table 28 shows that oil contamination did not change the status of the soil that was moderately

contaminated with Zn. In 30 months following treatment, the Zn concentration in oil contaminated but PE

amended plots dropped to slight contamination while plots under PR attained the “slight contamination”

level in 36months.

Three (3) months after oil application, it was observed that the 1 % oil treatment did not lead to

appreciable contamination of plots with Fe (Table 29), whereas the 2 % and 3 % oil levels led to severe

and very severe contamination of plots. Nevertheless, the PE amendment was able to restore the soil back

to its moderate contamination level after 36 months. Un-amended but contaminated plots remained under

severe Fe contamination through the experiment. Mean treatment values generally ranged between 0.58 -

0.59 mg kg-1

.

From Table 30 it was observed that the 2 and 3% oil levels led to severe contamination of plots with Cr in

3 months in all the plots that were not PE amended. However Cr concentration in plots treated with PE

was moderate while plots under CS and PR amendments showed moderate Cr contamination in 6 months.

Mean treatment concentrations of Cr ranged from 0.27 - 0.65 mg kg-1

within the 36 months of this

experiment.

Only the 3 % spent oil treatment slightly contaminated the plots with Pb and even without organic

amendment, the plots were decontaminated within 3 months (Table 31). The mean concentration of Pb in

the plots ranged from 0.01 - 0.12 mg kg-1

.

In appraising the levels of soil contamination and pollution with spent oil-induced heavy metals, a

distinction between soil contamination range and soil pollution range was established by means of

contamination/pollution index (C/P). This index represents the ratio of the heavy metal content

effectively measured in soil by chemical analysis and the toxicity criteria (the tolerable levels) as shown in

Table 1. The C/P index value lower than 1 characterizes soil contamination range while values higher

than 1 characterize the pollution range (Lacatusu, 1998). The results obtained, therefore, generally showed

that though the oil contamination brought about significant increase in heavy metals concentration, the

increases were still within tolerable levels for soil micro-fauna, -flora and higher plants (Lacatusu, 1998).

89

NA = Control, PE = Palm oil mill effluent, CS = Cassava peels and PR = Oil Palm bunch refuse.

b = slight “ (0.1 - 0.25)

c = moderate “ (0.26 - 0.50)

Table 28: The C/P (Zinc) index of the soil (0 - 20 cm depth zone) as influenced by the treatments

Months

OIL CONCENTRATIONS (A) % 0 3 6 12 18 24 30 36

0 0.29c 0.29

c 0.28

c 0.27

c 0.26

c 0.26

c 0.25

c 0.23

c

1 0.29c 0.35

c 0.35

c 0.33

c 0.33

c 0.32

c 0.30

c 0.28

c

2 0.29c 0.38

c 0.37

c 0.36

c 0.36

c 0.35

c 0.34

c 0.33

c

3 0.29c 0.40

c 0.38

c 0.37

c 0.37

c 0.35

c 0.33

c 0.39

c

ORGANIC AMENDMENTS (B)

NA 0.29c 0.36

c 0.28

c 0.27

c 0.26

c 0.26

c 0.25

c 0.23

b

PE 0.29c 0.33

c 0.35

c 0.33

c 0.33

c 0.32

c 0.30

c 0.28

c

CS 0.29c 0.36

c 0.36

c 0.36

c 0.36

c 0.35

c 0.34

c 0.33

c

PR 0.29c 0.36

c 0.37

c 0.37

c 0.37

c 0.35

c 0.33

c 0.29

c

90

NA=Control, PE = Palm oil mill effluent, CS = Cassava peels and PR = Oil Palm bunch refuse.

a = very slight contamination (C/P < 0.1)

b = slight “ (0.1 - 0.25)

c = moderate “ (0.26 - 0.50)

d = severe “ (0.51 - 0.75)

e = very severe “ (0.76 - 1.00)

Table 29: The C/P (Iron) index of the soil (0 - 20 cm depth zone) as influenced by the treatments

Months

OIL CONCENTRATIONS (A) % 0 3 6 12 18 24 30 36

0 0.41c 0.41

c 0.41

c 0.40

c 0.40

c 0.39

c 0.37

c 0.36

c

1 0.41c 0.45

c 0.45

c 0.44

c 0.43

c 0.43

c 0.38

c 0.36

c

2 0.41c 0.65

c 0.64

d 0.61

d 0.61

d 0.60

d 0.56

d 0.53

d

3 0.41c 0.82

e 0.81

e 0.80

e 0.79

e 0.35

c 0.72

d 0.67

d

ORGANIC AMENDMENTS (B)

NA 0.41c 0.59

d 0.58

d 0.59

d 0.59

d 0.59

d 0.59

d 0.58

d

PE 0.41c 0.57

d 0.55

d 0.55

d 0.52

d 0.52

d 0.41

c 0.41

c

CS 0.41c 0.59

d 0.57

d 0.56

d 0.55

d 0.53

d 0.50

c 0.45

c

PR 0.41c 0.59

d 0.58

d 0.57

d 0.57

d 0.55

d 0.53

d 0.47

c

91

NA=Control, PE = Palm oil mill effluent, CS = Cassava peels and PR = Oil Palm bunch refuse.

a = very slight contamination (C/P < 0.1)

b = slight “ (0.1 - 0.25)

c = moderate “ (0.26 - 0.50)

d = severe “ (0.51 - 0.75)

Table 30: The C/P (Chromium) index of the soil (0 - 20 cm depth zone) as influenced by the treatments

Months

OIL CONCENTRATIONS (A) % 0 3 6 12 18 24 30 36

0 0.34c 0.33

c 032

c 0.013

a 0.30

c 0.29

c 0.29

c 0.27

c

1 0.34c 0.50

c 0.45

c 0.44

c 0.42

c 0.40

c 0.36

c 0.35

c

2 0.34c 0.54

d 0.51

d 0.49

c 0.47

c 0.45

c 0.40

c 0.36

c

3 0.34c 0.65

d 0.64

d 0.62

d 0.59

d 0.45

c 0.52

d 0.45

c

ORGANIC AMENDMENTS (B)

NA 0.34c 0.52

d 0.52

d 0.44

c 0.52

d 0.39

c 0.52

d 0.52

d

PE 0.34c 0.46

c 0.45

c 0.37

c 0.39

c 0.39

c 0.30

c 0.28

c

CS 0.34c 0.51

d 0.47

c 0.37

c 0.43

c 0.39

c 0.36

c 0.30

c

PR 0.34c 0.52

d 0.48

c 0.38

c 0.45

c 0.42

c 0.39

c 0.34

c

92

NA = Control, PE = Palm oil mill effluent, CS = Cassava peels and PR = Oil Palm bunch refuse.

a = very slight contamination (C/P < 0.1)

b = slight “ (0.1 - 0.25)

c = moderate “ (0.26 - 0.50)

d = severe “ (0.51 - 0.75)

Table 31: The C/P (Lead) index of the soil (0 - 20 cm depth zone) as influenced by the treatments

Months

OIL CONCENTRATIONS (A) % 0 3 6 12 18 24 30 36

0 0.015a 0.01

a 0.01

a 0.01

a 0.01

a 0.01

a 0.29

c 0.27

c

1 0.015a 0.06

a 0.06

a 0.05

a 0.04

a 0.04

a 0.36

c 0.35

c

2 0.015a 0.07

a 0.09

a 0.09

a 0.08

a 0.07

a 0.40

c 0.36

c

3 0.015a 0.12

b 0.11

b 0.10

a 0.10

a 0.10

a 0.52

d 0.45

c

ORGANIC AMENDMENTS (B)

NA 0.015a 0.080

a 0.076

a 0.08

a 0.08

a 0.08

a 0.08

a 0.08

a

PE 0.015a 0.054

a 0.051

a 0.05

a 0.03

a 0.03

a 0.02

a 0.02

a

CS 0.015a 0.078

a 0.069

a 0.06

a 0.07

a 0.05

a 0.04

a 0.03

a

PR 0.015a 0.054

a 0.074

a 0.07

a 0.06

a 0.05

a 0.05

a 0.03

a

4.3 SOIL BIOLOGICAL PROPERTIES

4.3.1 Biological enhancement

The studies revealed that the population of viable hydrocarbon degrading micro-organisms grew as the

spent oil dose increased and also increased with time (Tables 32). The populations which were

represented in colony forming units per gram of soil ranged from 1.4 x 104 – 2.3 x 10

7cfug

-1 in 3 % oil

treated plots, 1.3 x 104 – 1.1 x 10

7 Cfug

-1 in 2 % oil treated plots, 1.2 x 10

4 – 2.0 x 10

6 Cfug

-1 in plots

under 1 % oil and 1.3 x 104 - 3.0 x 10

4 Cfug

-1 in the uncontaminated plots through 36 months.

Uncontaminated plots showed significantly lower microbial growth relative to the contaminated plots.

This indicates that the presence of spent oil either attracted hydrocarbon degrading organisms or served as

substrate for the multiplication of some indigenous hydrocarbon degrading microbes. Minas and Gunkel

(1995) and Jelena et al. (2009) reported similar findings. Odu et al. (1989) also observed that the

presence of gasoline (a hydrocarbon) in the soil resulted in significant increase in microbial population

and metabolic activities. Odu et al. (1989) further reported that the number of hydrocarbon-utilizing

organisms were higher in oil polluted sites than in the unpolluted sites.

When these contaminated plots were subjected to yearly organic amendments, it was observed that there

was a significant bloom in microbial population following each yearly amendment. It was also observed

that viable count of spent oil degrading micro-organisms in amended plots yielded significantly (P < 0.01)

higher populations as against that in un-amended plots. Following 36 months of soil amendment PE

treatment showed the highest significant count of viable microbes, while count in PR treated soils was

least. The viable counts for plots under PE through the 36 months of this experiment ranged from 1.5 x

106

- 2.5 x 107cfug

-1 and those in PR treated plots were from 1.3 x 10

4 - 1.7 x 10

6 cfug

-1 . For the NA plots

the counts were from 1.1 x 104

- 4.2 x 106

cfug-1

. The viable count of micro-organisms in the un-amended

plots (NA) 36 months following oil contamination (4.2 x 104

cfug-1

) was less than that in the amended

plots (1.5 x105, 6.4 x10

4 and 6.0 x10

4 cfug

-1 respectively, for PE, CS and PR) in 12 months. This

showed that the decomposition and mineralization of the organic amendments provided nutrients which

supported the growth of hydrocarbon degrading micro-organisms. Huesemann and Moore (1993) reported

that “counts of hydrocarbon degraders were usually higher in soil with addition of nitrogen and

phosphorus sources”, which were also contained in these organic amendments.

94

NA = Control, PE = Palm oil mill effluent, CS = Cassava peels and PR = Oil Palm bunch refuse. ns = non significant,

* * = significant at 1 % probability levels.

Table 32: Effect of treatments on viable count of hydrocarbon degrading micro organisms population (CFU g-1) of

top (0 - 20 cm depth zone) soil

Months

OIL CONCENTRATIONS (A) % 0 3 6 12 18 24 30 36

0 1.2 × 104

1.3×104 1.4×10

4 1.5×10

4 1.7×10

4 2.1×10

4 2.6×10

4

3.0×104

1 1.2 × 104 1.2×10

4 3.0×10

4 7.3×10

4 6.2×10

4 2.3×10

5 5.7×10

5 2.0×10

6

2 1.2 × 104 1.3×10

4 4.3×10

4 8.2×10

4 2.4×10

5 3.2×10

5 2.3×10

6 1.1×10

7

3 1.2 × 104 1.4×10

4 5.3×10

4 1.3×10

5 2.5×10

5 1.4×10

6 2.9×10

6 2.3×10

7

LSD0.05 ns 421.8 580.2 835.0 950.7 1000.8 1201.4 1686.0

ORGANIC AMENDMENTS (B)

NA 1.2×104 1.1×10

4 1.5×10

4 1.8×10

4 2.3×10

4 3.2×10

4 3.7×10

4 4.2×10

4

PE 1.2×104 1.5×10

4 6.2×10

4 1.5×10

5 4.1×10

5 1.6×10

6 4.8×10

6 2.5×10

7

CS 1.2×104 1.3×10

4 3.3×10

4 6.4×10

4 7.0×10

4 2.2×10

5 5.4×10

5 9.7×10

6

PR 1.2×104 1.4×10

4 3.1×10

4 6.0×10

4 6.8×10

4 1.0×10

5 4.9×10

5 1.7×10

6

LSD0.05 ns 401.5 611.0 762.5 740.3 918.0 1356.2 2188.0

INTERACTIONS

(A × B) ns ** ** * * ** ** * * **

For the sub (20 – 40 cm) soil (Table 33) there was no appreciable growth of hydrocarbon degrading

micro-organisms in the first 3 months following contamination and amendment of the top (0 – 20 cm)

soil. A culture of the 6th

month sample showed significant (P < 0.05) growth of hydrocarbon degrading

micro-organisms in the sub-soils of all contaminated top-soils, with sub-soils under PE amendment

showing significantly higher growth relative to other amendments while there was no growth under the

un-amended (NA) plots. The soils of the uncontaminated and un-amended (control) plots did not show

significant (P<0.05) growth of oil degrading micro-organisms when their samples were cultured

throughout this experiment. The results, therefore, suggest that the presence of oil attracted oil degrading

microbes and/or stimulated the growth of indigenous oil degrading microbes which count was

insignificant prior to oil contamination. Significantly higher viable count followed increasing oil

contamination while the order of increase in viable count among the amendments followed the order PE >

CS > PR > NA. Comparing results obtained in the top (0– 20 cm) and sub (20 – 40 cm) soil showed that

viable counts of micro-organisms declined with increasing soil depth. The results obtained support those

of Bossert and Compeau (1995), Avidano et al. (2005) and Katsivala et. al. (2005).These researchers

reported that microbial population decreased with soil depth. This phenomenon is due to increasing bulk

density with soil depth which determines pore space through which water and air can move (Hornick,

1983).Pore size affects the rate of growth of organisms (McInerney, et. al., 1993).Growth of Escherichia

coli is reduced in smaller pore sizes, possibly due to a restriction of bacterial cell division (Eve Riser-

Roberts, 1998).

96

NA = Control, PE = Palm oil mill effluent, CS = Cassava peels and PR = Oil Palm bunch refuse. ns = non significant, * *and * =

significant at 1 % and 5 % probability levels.

Table 33: Effect of treatments on viable count of hydrocarbon degrading micro organisms population (CFU g-1) of

sub (20 - 40 cm depth zone) soil

Months

OIL CONCENTRATIONS (A) % 0 3 6 12 18 24 30 36

0 - - - - 1.0×104 1.0×10

4 1.1×10

4

1.1×104

1 - - 1.0×104 1.3×10

4 1.8×10

4 2.2×10

4 5.7×10

4 2.5×10

5

2 - - 1.1×104 1.3×10

4 2.0×10

4 3.3×10

4 2.1×10

5 5.3×10

5

3 - - 1.1×104 1.3×10

4 1.8×10

4 4.5×10

4 2.6×10

5 6.0×10

5

LSD0.05 ns Ns 816.6 Ns 833.9 846.0 883.6 925.4

ORGANIC AMENDMENTS (B)

NA - - - 1.0×104 1.0×10

4 1.3×10

4 1.4×10

4 1.5×10

4

PE - - 1.1×104 1.5×10

4 2.5×10

4 6.0×10

4 4.0×10

5 7.2×10

5

CS - - 1.0×104 1.2×10

4 1.8×10

4 2.5×10

4 4.9×10

5 3.4×10

5

PR - - 1.0×104 1.3×10

4 1.7×10

4 1.9×10

4 4.7×10

4 4.2×10

5

LSD0.05 ns Ns 820.2 812.0 822.0 830.0 851.2 908.5

INTERACTIONS

(A × B) ns ns * * * ** * * * **

4.4. Crop Performance

Increased oil contamination inhibited the germination of maize seeds (Table 34). In the first year (2006),

plots under 3 % and 2 % oil contaminations showed reduced maize germination compared to the

control(41 and 50 % respectively compared to 86 % under the control) while plots under 1 % oil

treatment did not show significant reduction in germination compared to the control. The germination

count of maize improved over the years (2007 - 2008). The germination count in plots under 3 % oil

treatment improved from 51 % in 2007 to 60 % in 2008, while that under the 2 % oil treatment improved

from 55 % in 2007 to 64 % relative to control in 2008.

The germination count in amended plots was significantly higher than that in un-amended plots. In the

first year, germination in plots under PE (72 %) was significantly (P < 0.05) higher compared to that in CS

(69 %), PR (63 %) and NA (58 %). The germination count in PR plots did not differ (P < 0.05) from that

in NA. The effect of the organic amendments on germination of maize in the second year followed the

order PE > CS = PR > NA while in the third year the order was PE = CS > PR > NA.

The results obtained for cowpea (Table 35) showed that in the first year oil contamination levels of 3%

and 2 % reduced the germination of cowpea in this soil to 74 % compared to 92 % germination recorded

for the control plot. In the second year, germination count improved to 77 % and 83 % for 3 and 2 % oil

treated plots respectively. Further improvement in germination was observed in the third year with plots

under 3 % and 2 % oil recording 83 and 90 % germination respectively. Germination count in the 1 % and

0 % oil treated plots did not show significant (P < 0.05) difference.

Germination count in amended plots was significantly higher compared to that recorded for un-amended

plots in the first year. In the second and third years it was noted that only plots under PE amendments

consistently showed significantly higher germination count while that in PR plots did not differ (P < 0.05)

from the NA (control) plots.

Generally, PE appears a better soil amendment material by enhancing germination in oil contaminated

soils and providing sustaining nutrients for hydrocarbon degraders. Also on the average, cowpea

germination was less inhibited (70 – 100 % germination) by oil contamination than maize seeds (32 - 98%

germination). Similar findings we reported by Ekundayo et al. (2001) and Adewole and Moyinoluwa

(2012).

Table 34: Effect of Treatments on Germination Count of Maize (%)

Organic amendment (B)

Year Oil Concentration (A)% NA PE CS PR MEANS

2006 0 86 82 89 85 86

1 82 90 88 88 87

2 32 61 54 50 50

3 33 55 45 29 41

MEANS 58 72 69 63

LSD0.05 A = 9.5, B = 8.4, AxB = **

2007 0 95 100 95 100 98

1 81 92 90 89 88

2 36 70 62 50 55

3 36 58 56 52 51

MEANS 62 80 76 73

LSD0.05 A = 10.2, B = 3.5, AxB = *

2008 0 100 100 98 92 98

1 88 96 97 92 93

2 48 78 70 60 64

3 41 69 68 60 60

MEANS 69 86 83 76

LSD0.05 A = 90, B = 5.1, AxB = *

NA = no amendment, PE = palm oil mill effluent,CS = cassava peels,PR = palm bunch refuse

Table 35: Effect of Treatments on Germination Count of Cowpea (%)

Organic amendment (B)

Year Oil Concentration (A) % NA PE CS PR MEANS

2006 0 92 95 98 91 94

1 90 91 92 100 93

2 71 75 79 70 74

3 65 80 75 76 74

MEANS 80 85 86 84

LSD0.05 A = 18.1, B = 4.0, AxB = **

2007 0 95 95 91 92 93

1 90 96 100 90 94

2 76 86 85 84 83

3 70 82 81 75 77

MEANS 83 90 89 85

LSD0.05 A = 10.0, B=4.7, AxB = *

2008 0 96 100 97 98 98

1 92 100 93 91 94

2 89 92 90 90 90

3 78 88 85 81 83

MEANS 89 95 91 90

LSD0.05 A = 7.0, B=4.3, AxB=*

NA = no amendment, PE = palm oil mill effluent,CS = cassava peels,PR = palm bunch refuse

Shown in Table 36 are the results obtained on the effect of the treatments on dry matter accumulation

(DMA) of maize. Increasing oil contamination beyond 1 % significantly reduced the DMA of maize

planted in such plots by 41.7 - 83.9 % relative to control in the first year. In the second year maize grown

in 2 and 3 % oil treated plots showed 20.4 to 74.2 % reduction in DMA while in the third year DMA

reduction was between 0.4 to 54.6 % lower than that in the control. Oil concentrations beyond 1 % may

have led to the accumulation of oil in the plant tissues and damage thereby reducing dry matter

accumulation. The results also show that plots under organic amendments produced maize plants with dry

matter content 19.3 - 43.0 % higher than those in un-amended plots in the first year while in the third year

dry matter yield in amended plots was 44.2 - 67.8 % higher compared to those in un-amended plots. In

three years of this study maize plants grown in soils amended with PE and CS showed significantly higher

dry matter content compared to plants grown in soils amended with PR and the un-amended soils.

Table 37 indicates that increasing oil contamination to 3 % reduced the dry matter yield of cowpea.

Decline in dry matter yield of cowpea following 3 % oil contamination was 23.0 %, 17.9 % and 15.7 %

relative to control in the first, second and third years respectively. The dry matter contents of cowpea

grown in amended plots (PE, CS and PR) were between 16 - 32.8 % higher than those of the plants in un-

amended (NA) plots in the first year while in the third year the dry matter yields of plants grown in

amended plots were between 19.4 - 39.0 % higher than those of the un-amended (NA) plots. The first

year results followed the order PE = CS > PR = NA while in the second and third years the order was PE

> CS > PR > NA. A comparison of the results obtained for maize and cowpea plants shows that oil

contamination impacted more negatively on the DMA of maize than that of cowpea. The soil

amendments also benefited cowpea better than maize in terms of dry matter yield. This could have been

due to the leaf cover of cowpea which reduced water evaporation from the soil and also due to the action

of root nodule bacteria which modified the rhizosphere.

The leaf area index (LAI) of maize at tasselling (Table 38) decreased with increase in spent oil

contamination. Plants grown in amended plots showed significantly larger leaf area compared to those in

un-amended plots. First year result shows that the leaf area of plants in treated plots followed the order

PE = CS = PR > NA and in the second year CS = PE > PR > NA while in the third year PE = CS > PR >

NA. The LAI of maize grown in PE amended plots ranged from 369.5 – 429.6 while in un-amended (NA)

plots values ranged from 254.8 – 258.7 from the first to the third years.

Table 39 shows that the leaf area index of cowpea plants was reduced as oil contamination increased from

1–3%. Cowpea plants grown in amended plots had significantly higher leaf area index with values ranging

from 2.40 – 2.92 compared to 2.29 in un-amended in 36 months. Plants in PE and CS treated plots showed

significantly higher leaf area indices compared to plants in PR and NA plots.

Table 36: Effect of Treatment on Dry Matter Accumulation of Maize (tonnes ha-1

)

Organic amendment (B)

Year Oil Concentration (A)% NA PE CS PR MEANS

2006 0 5.08 5.48 5.36 5.21 5.28

1 7.12 7.41 7.21 7.21 7.24

2 0.69 5.10 3.03 3.00 2.96

3 0.60 1.30 0.71 0.65 0.82

MEANS 3.37 4.82 4.08 4.02

LSD0.05 A = 1.52, B = 1.02, A x B = *

2007 0 5.11 5.48 5.40 5.37 5.34

1 7.00 7.37 7.25 7.20 7.21

2 0.63 5.83 6.00 3.81 4.07

3 0.66 1.83 1.56 1.24 1.32

MEANS 3.35 5.13 5.05 4.41

LSD0.05 A = 1.80, B = 0.61, A x B = *

2008 0 5.00 5.39 5.52 5.16 5.27

1 7.00 6.89 7.11 7.15 7.04

2 0.98 7.25 6.51 5.17 4.98

3 0.68 3.42 2.75 2.24 2.27

MEANS 3.42 5.74 5.47 4.93

LSD0.05 A = 1.73, B = 0.65, A x B = *

NA = no amendment, PE = palm oil mill effluent, CS = cassava peels, PR = palm bunch refuse

Table 37: Effect of Treatments on Dry Matter Accumulation of Cowpea (tonnes ha-1

)

Organic amendment (B)

Year Oil Concentration (A)% NA PE CS PR MEANS

2006 0 3.09 4.20 3.81 3.59 3.67

1 5.16 5.60 5.95 4.88 5.40

2 2.12 4.01 3.85 2.09 3.02

3 2.06 2.71 2.66 2.07 2.38

MEANS 3.11 4.13 4.07 3.16

LSD0.05 A = 1.53, B = 1.35, A x B = **

2007 0 3.01 4.21 4.03 3.65 3.73

1 5.09 5.62 5.21 5.28 5.30

2 2.25 4.26 3.92 2.58 3.25

3 2.06 2.85 2.69 2.27 2.47

MEANS 3.10 4.24 3.96 3.45

LSD0.05 A = 0.27, B = 0.47, A x B = **

2008 0 3.12 4.08 3.75 3.84 3.70

1 4.83 5.52 5.36 5.26 5.24

2 2.28 4.48 4.17 3.16 3.52

3 2.10 3.04 2.84 2.53 2.63

MEANS 3.08 4.28 4.03 3.70

LSD0.05 A = 0.41, B = 0.22, A x B = **

NA = no amendment, PE = palm oil mill effluent, CS = cassava peels, PR = palm bunch refuse

Table 38: Effect of Treatments on Leaf Area Index of Maize at Tasselling

Organic amendment (B)

Year Oil Concentration (A) % NA PE CS PR MEANS

2006 0 408.3 420.8 418.0 413.6 415.2

1 500.7 526.8 520.4 521.0 400.2

2 60.1 409.5 254.9 250.2 243.7

3 50.1 120.8 65.3 55.9 73.0

MEANS 254.8 369.5 314.7 310.2

LSD0.05 A = 149.3, B = 56.1, A x B = *

2007 0 411.1 420.3 420.0 415.7 416.8

1 501.1 520.0 522.1 520.2 515.9

2 60.0 424.5 486.3 285.4 314.3

3 52.6 190.0 162.5 110.7 130.0

MEANS 256.2 389.0 397.7 333.0

LSD0.05 A = 125.0, B = 40.8, A x B = **

2008 0 405.2 418.6 422.6 411.8 414.6

1 500.3 516.9 519.6 518.3 513.8

2 75.3 502.6 495.8 411.6 371.3

3 53.8 280.4 236.2 210.6 195.3

MEANS 258.7 429.6 418.6 388.1

LSD0.05 A = 118.5, B = 30.0, A x B = **

NA = no amendment, PE = palm oil mill effluent, CS = cassava peels, PR = palm bunch refuse

Table 39: Effect of Treatments on Leaf Area Index of Cowpea at Flowering

Organic amendment (B)

Year Oil Concentration (A)% NA PE CS PR MEANS

2006 0 2.30 3.13 2.59 2.72 2.69

1 3.86 3.88 3.43 4.00 3.79

2 1.61 1.95 2.36 2.13 2.01

3 1.16 1.79 1.70 1.46 1.53

MEANS 2.23 2.69 2.52 2.58

LSD0.05 A = 0.62, B = 0.11, A x B = *

2007 0 2.32 3.01 2.74 2.53 2.65

1 3.52 4.16 3.98 3.21 3.72

2 1.38 2.49 2.38 3.21 2.16

3 1.24 1.92 1.65 1.43 1.56

MEANS 2.12 2.90 2.69 2.39

LSD0.05 A = 0.79, B = 0.36, A x B = *

2008 0 2.21 3.08 2.96 2.55 2.70

1 3.55 3.91 3.97 3.08 3.63

2 1.86 2.67 2.14 2.18 2.21

3 1.52 2.03 1.91 1.58 1.76

MEANS 2.29 2.92 2.75 2.40

LSD0.05 A = 0.82, B = 0.40, A x B = *

NA = no amendment, PE = palm oil mill effluent, CS = cassava peels, PR = palm bunch refuse

Table 40 indicates that the yield of maize reduced as oil contamination increased from 1- 3 %. A yield

decline as high as 99.3 % relative to control was observed in 3 % oil contaminated plots. It must be noted

at this point that though the 2 % and 3 % oil treated plots showed germination count of up to 61 % under

PE amendment in the first year, the plants visibly suffered chlorosis and some died before maturity while

those that matured either failed to yield or yielded very low. In the first year the results obtained following

the amendment of 2 % oil treated plots with PE, CS and PR indicated decreased maize yield of 92.7, 93.4

and 93.4 % respectively, while the amendment of 3 % oil contaminated plots with PE showed decreased

yield of 98.7 % with complete yield failure in the CS and PR treated plots relative to control. The results

further showed that 3 years following 3 % oil contamination, maize grown on such plots without organic

amendment failed to yield; this was due to the persistence of the oil and the consequent deleterious effects

in such plots. However 36 months of organic amendment increased maize yield in PE,CS and PR treated

plots by 32.1%,18.6 %and13.6 % respectively.

Generally 3-years following the amendment of the contaminated soils, amended plots significantly out-

yielded the un-amended by at least 54.5 % with PE amended soils showing the highest yield (1.85 t/ha)

while NA amended soils showed the lowest yield (1.03 t/ha).The implication is that these common

organic materials used, which could be acquired at almost no cost, have great remediation potentials.

Table 41 show that there was 0.62 t/ha cowpea yield decline when oil concentration increased to 3 %.

However following 3years of organic amendment and consequent biodegradation of the oil, there was

cowpea yield increase of 90.2 % relative to control in the contaminated plots. The best fit regression

models shown in Table 42 were obtained from a three year average yield data of maize and cowpea plants.

The models accounted for 90 - 95 % and 69 - 90 % of the variation in grain yield of maize in cowpea been

the possible contributions of applied spent oil to yield reduction. The negative slope of the models

however indicates that the contribution was generally negative (yield reduction). Though the models

showed excellent predictive abilities (as measured by the magnitude of R2) they were better in maize

compared to cowpea.

From the first to the third year, the effects of the organic amendments on the yield of cowpea showed

significant difference (P < 0.05) among their treatment means and followed the order PE > CS > PR >

NA. According to Samuel et.al (1975) the expected yield of maize in the southern and forest zones of

Nigeria range from 3.5 – 5.0 tonnes while Omotugba et.al (2008) puts the expected yield of cowpea in this

region at a range of 0.3 – 1.5 tonnes. The results therefore indicate that following 3-years of soil

amendment cowpea yield increased to acceptable range while maize yield failed to reach the acceptable

range for the zone.

Table 40: Effect of Treatments on Grain Yield of Maize (Tonnes ha-1)

Organic amendment (B)

Year Oil Concentration (A) % NA PE CS PR MEANS

2006 0 1.51 1.72 1.68 1.31 1.56

1 1.75 2.22 1.95 1.87 1.95

2 0.00 0.11 0.10 0.10 0.08

3 0.00 0.02 0.00 0.00 0.01

MEANS 0.82 1.02 0.93 0.82

LSD0.05 A = 0.07, B = 0.10, AxB = *

2007 0 1.56 1.85 1.43 1.58 1.61

1 1.76 1.97 1.84 1.78 1.84

2 0.85 1.81 1.38 1.28 1.33

3 0.00 0.86 0.75 0.36 0.49

MEANS 1.04 1.62 1.71 1.25

LSD0.05 A = 0.10, B=0.08, A x B = *

2008 0 1.40 1.86 1.72 1.80 1.70

1 1.75 1.88 1.73 1.76 1.78

2 0.98 2.16 1.95 1.80 1.72

3 0.00 1.51 1.23 1.00 0.94

MEANS 1.03 1.85 1.66 1.59

LSD0.05 A = 0.08, B = 0.11, A x B = *

NA = no amendment, PE = palm oil mill effluent, CS = cassava peels, PR = palm bunch refuse

Table 41: Effect of Treatments on Grain Yield of Cowpea (Tonnes ha-1)

Organic amendment (B)

Year Oil Concentration (A) % NA PE CS PR MEANS

2006 0 0.30 1.01 0.85 0.68 0.71

1 0.52 1.28 1.09 0.96 0.96

2 0.30 1.00 0.71 0.65 0.67

3 0.05 0.12 0.10 0.10 0.09

MEANS 0.29 0.85 0.68 0.60

LSD0.05 A = 0.04, B = 0.07, A x B = **

2007 0 0.42 1.11 1.01 0.92 0.87

1 1.07 1.81 1.51 1.42 1.45

2 0.45 1.54 1.42 1.20 1.15

3 0.10 1.05 0.80 0.60 0.64

MEANS 0.51 1.38 1.19 1.04

LSD0.05 A = 0.13, B = 0.15, A x B = **

2008 0 0.51 1.05 1.03 0.73 0.83

1 1.10 1.85 1.79 1.41 1.54

2 0.55 1.41 1.36 1.15 1.61

3 0.51 1.37 1.15 0.83 0.97

MEANS 0.67 1.42 1.34 1.03

LSD0.05 A = 0.18, B = 0.21, A x B = **

NA = no amendment, PE = palm oil mill effluent, CS = cassava peels, PR = palm bunch refuse

Table 42: Regression Models Relating Three Years Average Maize and Cowpea Yield (Y) in tonnes

ha-1

to Applied Spent Oil (X) in mg kg-1

Under Various Organic Amendments on an Ultisol.

Crop Treatment Regression Equation R2a

Maize PE Y = 0.21-6.1 x 10-5

X 0.95

CS Y = 0.03-5.9 x 10-5

X 0.92

PR Y = -0.25-6.8 x 10-5

X 0.90

Cowpea PE Y = 2.07-4.0 x 10-5

X 0.90

CS Y = 1.88-3.9 x 10-5

X 0.88

PR Y = 1.68-3.8 x 10-5

X 0.69

aAll R

2 values are significant at 5 % probability level.

The results indicate that oil contamination suppressed the yield of maize more than that of cowpea. The

organic amendments also had better effects on the yield of cowpea over that of maize. This may be

connected to the cover properties of cowpea, which reduced the evaporation of water in these plots. The

action of the root nodule bacteria in cowpea may have also modified the rhizosphere. Several factors may

have contributed to the death of some of the maize plants and the low yield observed in maize and earlier

in cowpea. The factors may include lack of adequate oxygen, decrease in soil water retention capacity,

surface crusting, penetration and accumulation of oil in plant tissues causing damage to cell and cell

leakage (Udo and Fayemi, 1975; Anoliefo and Vwioko, 1995) and other undesirable soil physical and

chemical conditions elicited by the spent oil contamination.

There are several types of toxicity studies involving plant processes. According to Fletcher (1991), the

tests with plants can be used in 5 different categories: biotransformation, food chain uptake, sentinel,

surrogate, and phytotoxicity. Among these tests, the phytotoxicity is receiving more attention during the

last years. Plants that are sensitive to poisonous substances can be used as bioindicators (Banks and

Schultz, 2005). Some species recommended by the United States Environmental Protection Agency

(USEPA) and Federal Department of Agriculture (Fletcher, 1991) are rice (Oryza sativa); soybean

(Glycine max); maize (Zea mays); tomato (Lycopersicon esculentum); bean (Phaseolus aureu; Phaseolus

vulgari); onion (Allium cepa) and sorghum (Sorghum bicolor). Cowpea and soybean however belong to

the same family: Fabaceae, sub-family: Papilonoideae, tribe: Phaseoleae.

CHAPTER FIVE

5.0 SUMMARY AND CONCLUSION

There is need to find alternative uses of wastes from the processing of agricultural produce like cassava

and oil palm nuts to reduce their potential risk of pollution and environmental nuisance. This study was

driven towards employing these agricultural wastes as tools in bioremediation technology to enhance the

decontamination of soils polluted with hydrocarbons from spent automobile engine oil.

The results showed that both the oil treatment and organic amendments brought about increase in Mean

Weight Diameter of Wet (MWDW) aggregates and Mean Weight Diameter of Dry (MWDD) aggregates

of treated soils. The improvement of MWDW by spent oil dropped while that by the organic amendments

increased with time. It took 12 months for oil contamination of the top (0 – 20 cm) soil to elicit a

significant change in the MWDW of the sub (20 - 40 cm) soil. Sub-soils under PE and CS amendments

showed the highest MWDW compared to sub-soils under other organic amendments. Increased spent oil

concentration led to increase in the soil bulk density in the short run. Two of the organic amendments PE

and CS reduced the bulk density of top (0 – 20 cm) soil to initial conditions prior to oil contamination

within 12 months. Increased oil contamination decreased macro-porosity both in surface and sub –soils.

Significant increase in micro-porosity followed increased concentration of spent oil while amendment of

contaminated soils reduced micro-porosity relative to control from the 12th

- 30th

month. The organic

amendments, PE and CS, showed significantly higher reduction of micro-porosity of top (0 – 20 cm) soil

compared to PR amended and un-amended plots. The organic amendments significantly increased the Ksat

of contaminated plots which deteriorated following oil treatment. The Ks of the soils reduced from 25 cm

h-1

(very rapid permeability) before oil contamination to 6cm h-1

(moderately rapid permeability) following

contamination but increased to 25cm hr-1

(very rapid permeability) following 36 months of organic

amendment.

The results further showed that increased Total Hydrocarbon Content (THC) which followed increased oil

contamination was lost in two ways: by gravity and by microbial degradation. Between 12 and 36 months

following oil treatment, un-amended plots lost 3-times more oil to gravity than amended plots. It was also

observed that the rate of hydrocarbon degradation increased with increasing oil and nutrient supply.

Increased heavy metal (Al, Fe, Cr, Pb and Zn) concentration in soil followed oil contamination. Complete

decontamination of Al, Cr and Zn was achieved in PE treated soils in 30 months. The

contaminant/pollution (C/P) index showed that the soil remained slightly contaminated with Zn after oil

contamination but showed severe to very severe contamination with Fe, severe contamination with Cr and

slight contamination with Pb. Though oil contamination led to increased heavy metal contamination of

the soil, the increase did not exceed plant tolerable limits in soils (Alloway, 1990). The population of

viable hydrocarbon degrading micro-organisms grew as spent oil dose increased and also increased with

time. Significant microbial bloom followed yearly amendment of contaminated plots in the order PE > CS

> PR > NA. Viable microbial count declined with soil depth. Since oil contamination was in a single

dose, the first year recorded the highest impact. Oil contamination with 3 % and 2 % reduced maize

germination from 86 % (control) to 41 and 50 % respectively. There was however improved germination

in the second and third years. The germination of cowpea under 3 % spent oil treatment was reduced from

92 % (control) to 74 % in the first year but improved in subsequent years. Amended plots showed higher

germination count of cowpea and maize crops compared to un-amended plots, with PE treated plots

showing significantly higher germination counts. Increasing oil treatment beyond 1 % significantly

reduced dry matter yield in maize by 41.7 - 83.9 % and by 2.2 - 8.0 % in cowpea, 12 months after

contamination. Increasing oil contamination led to decrease in leaf area of maize and cowpea plants, while

plants grown in amended plots showed significantly higher leaf area index of both crops. Amended plots

recorded higher yield compared to the un-amended by 19.3 - 43.0 % in maize and 1.6 - 32.8 % in cowpea

grain yield, especially in the first 12 months when oil impact was highest. The results showed that the 2 %

oil treatment reduced maize grain yield by 94.7 % while plots treated with 3 % oil showed maize grain

yield failure. Regression models from oil rate and yield data showed a negative slope indicating a general

yield decline with increasing spent oil. The crop parameters examined showed that spent auto-engine oil

had more deleterious effect on the maize than cowpea plant.

Conclusions drawn from this study are that:

(i) Application of spent auto-engine oil to this sandy loam Ultisol increased viable count of

hydrocarbon degrading microbes, aggregate stability, bulk density, micro-porosity; and reduced

macro-porosity and saturated hydraulic conductivity.

(ii) The spent oil clearly had detrimental effects on germination, growth, development and yield of

maize and cowpea crop. The effects of the oil persisted in the soil where remediation efficiencies

of the organic amendments were low.

(iii) Though the oil contamination led to increase in heavy metal concentrations in soil, there was no

evidence of detrimental effects on soil micro-fauna and-flora and higher plants, since

concentrations did not exceed plant tolerable limits.

(iv) The effect of spent auto-engine oil on soil physical and biological properties reduced with soil

depth especially when the contaminated surface was amended and if un-amended, the effects were

observed in the lower soils within 6 - 12 months.

(v) Palm oil mill effluent (PE), cassava peels (CS) and oil palm bunch refuse (PR) are effective for use

as tools in bioremediation technology.

REFERENCES

Abdul, L.A, Suzylawati, I. Norliza, I. and Subhas, B. (2003). Removal of suspended solids

and residual oil from palm oil mill effluents J. Chem. Tech. Biotech 78 (9): 971-

978.

Adaji M.J., Olufaja. 0.0 Aliyu, L. (2007). Effect of intra-row spacing and stand density on

the growth and yield of cowpea (Vigna unguiculata (L) walp). In: Olufaja, 0.0,

Omokore, D.F., Akpa, G.N and sanni, S.A.(eds). Proceedings of the 4Ist Annual

conference of the Agricultural Society of Nigeria (ASN) held at the Institute for

Agricultural Research, Samaru, Ahmadu Bello University, Zaria, 22nd

- 26th

October, 2007.153-157.

Adelaja S.O. (2000).Development and evaluation of some quality parameters of cowpeas

(Vigna unguiculata snack). In Nkama, L., Jideani, J. A. and Ayo, J.A (eds).

Proceedings of the 24th

Annual Conference of NIFST held at Bauchi State, 20th -

24th November, 2000.258-259.

Adelekan, B.A. and Bamgboye A.I. (2009). Comparison of biogas productivity of cassava

peels mixed in selected ratios with major livestock waste types. Afr. J. Agri.

Res.Vol. 4 (7): 571-577.

Adeniyi, A.A. and Afolabi, J.A. (2002). Determination of total hydrocarbons and heavy

metals in soils within the vicinity of facilities handling refined petroleum products

in Lagos metropolis. Environ. Inter. 28:79-82.

Adesodun, J.K. (2004). Bioremediation of an Alfisol Contaminated with Spent Oil and Its

Quality Assessment Using Micro-morphological Analysis. Ph.D Thesis,

Department of soil Science, University Nigeria, Nsukka.

Adewole, M.G. and Moyinoluwa, D.A., (2012). Effect of crude oil on the emergence and

growth of cowpea in two contrasting soil types from Abeokuta south western

Nigeria. Asian J. Appl. Sci. 5 (4): 232-239.

Ae, N, Osazawa , S., Kubota, J. (1987). Effect of phenolic constituents on soil aggregates.

Jpn. J. Soil Sci. Plant Nutr. 58: 233-236.

Agbogidi, O. M. (2010). Response of six cultivars of cowpea (Vigna unguiculata (L.)

Walp) to spent engine oil. Afri. J. Food Sci. Technol. Vol. 1(6):139-142.

Agbogidi, O.M. and Ejemete, O.R. (2005). An assessment of the effects of crude oil

pollution on soil properties, germination and growth of Gambaya albida (L.).

Uniswa Res.J.Agric.Sci.Technol.8(2):148 – 155.

Aggelides, S.M. and Londra, P.A. (2000). Effects of compost produced from town wastes

and sewage sludge on the physical properties of a loamy and clay soil. Bio. Res.

Tech. 71:253-259.

aa

Aichberger, H. Hasinger, Braun, R and Loibner, A.P. (2005). Potential for preliminary test

methods to predict biodegradation performance of petroleum hydrocarbon: Soil

Biodegradation.Biotechnol. 16:115-125.

Akamigbo,F.O.R and Asadu,C.L.A.(1983).Influence of parent materials on the soils of

south eastern Nigeria .East. African.Agric. Forest.J.48:81-91.

Akamigbo, F.O.R. and Igwe, C.A. (1990). Morphology, geography, genesis and taxonomy

of three soil series in eastern Nigeria. Samaru J. Agric. Res. 7: 33-48.

Al-Asheh, S., Banat, F., Mohal, F. (1999). Sorption of copper and nickel by spent animal

bones. Chemosphere 39:2087-2096.

Alexander, M.(1981). Biodegradation of chemicals of environmental concern. Science

211:132-138.

Alloway, B. J. and Ayres, D.C. (1997). Chemical principles of environmental pollution.

London,Champman and Hall Publ.

Amadi, A., Dickson, A.A., Maate, G.O. (1993). Remediation of oil polluted soils: I. Effects

of organic and inorganic nutrient supplements on the performance of maize (Zea

mays L). Water, Air and Soil Pollution 66:59-76.

Andriano, D.C., Bollag, J.M. Frankenberg, Jr. W.T., Sims, R. C. (1999). Bioremediation of

contaminated soils. Am. Soc. Agron.37:772.

Anoliefo, G. O., Isikhuemhen, O., Ohimain, E. (2006). Sensitivity studies of the common

bean (Vigna unguiculata) and maize (Zea mays) to different soil types from the

crude oil drilling site at Kutchalli, Nigeria. J. Soils and Sediments 6(1): 30-36.

Anoliefo, G.O. and Vwioko, D. E., (1995). Effect of spent lubricating oil on the growth of

Capsicum annum I. and Lycopersicon esculentum mill. Environ. Pollution

99:361-364.

Anoliefo, G.O. and Vwioko, D.E. (2001). Tolerance of Chromolaen odorata (L) grown in

soil contaminated with spent lubricating oil. J. Trop. Biosci. 1(1): 20-24.

Anon. (1985). Collection and Disposal of Oily Wastes in Nigeria. Report prepared by RRI

(Nig.) Ltd. for the petroleum oil wastes disposal committees, 22nd April 1985.

Asumugha V.U. (2002). Sensory and functional properties of dry vegetable cowpea product

(Akara). In: Proceedings of the 26th Annual Conference of the Nigerian Institute

of Food Science and Technology (NIFST) held at the Federal University of

Technology, Owerri, Imo State between 4th and 8

th of November, 2002.66-67.

Atagang, H.I, Haynes, R.J. and Wallis, F.M. (2003). Optimisation of soil physical and

chemical conditions for the bioremediation of creosote-contaminated soil.

Biodegradation 14:297-307.

Atlas, R.M. and Bartha, R. (1993). Stimulated biodegradation of oil slicks using oleophillic

fertilizer. Environ. Sci. Technol. 7:538-540.

Atuanya, E.I. (1987). Effect of Waste engine oil pollution on physical and chemical

properties of soil. A case study of Delta soil in Bendel State. Niger. J. Appli. Sci.

5:115-176.

Avidano, L., Gamalero, E., Cossa, G.P. and Carraro, E. (2005). Characterization of soil

health in an Italian polluted site by using micro-organisms as bioindicators. Appl.

Soil Ecol. 30:21-33.

Awe, O.A. (2008). Preliminary evaluation of three Asian yards long bean cowpea lines in

Ibadan, South-western Nigeria. In: proceedings of the 42nd

Annual conference of

Agricultural Society of Nigeria held at Ebonyi State university, Abakaliki,

Nigeria between 19th and 23

rd of October, 2008. PP 246-249.

Azhari, S.B, Lim S.H. Mohd, Z.Y, Nor, A.A, Umi K.S, Mohd, A.H, Minato, W, Kenii, S.

and Yoshihito, S. (2010). Effects of palm oil mill effluent (POME) anaerobic

sludge from 500m3 of closed anaerobic methane digested tank on pressed-

shredded empty fruit bunch (EFB) composting process. Afri. J. Biotechnol. Vol.

9(16):2427-2436.

Baath, E., Frostegard, A., Diaz-Ravina, M. and Tunlid, A. (1998). Microbial community-

based measurements to estimate heavy metal effects in soil: The use of

phospholipids fatty acid patterns and bacterial community tolerance. Ambio.

27:58-61.

Banks M.K and Schultz K.E. (2005). Comparison of plants for germination toxicity test in

petroleum contaminated soil, water and air. Soil pollution,167: 211 – 219.

Bartoli, F., Phillippy, R., Burtin, G. (1988). Aggregation in soils with small amounts of

swelling clays. I. Aggregate stability. J. Soil Sci. 39:593-616.

Benka-Coker, M.O, and Ekundayo, J.A. (1995). Effect of an oil spill on soil physico-

chemical properties of a spill site in the Niger Delta area of Nigeria.

Environmental Monitoring and Assessment 36:93-104.

Benka-Coker, M.O. and Ekundayo, J.A. (1997). Applicability of evaluating the ability of

microbes isolated from an oil spill site to degrade oil. Environ. Monitor. Assess.

45:259-272.

Binder D.L, Dobermann, A, Sander, D.H. and Cassman, K.G. (2002). Biosolids as

Nitrogen Source for irrigated maize and rainfed sorghum. Soil Sci. Soc. Am. J.

66:531-543.

Biradar S, Salimath P.M, Sridevi, O. (2007). Association Studies in the three segregating

populations of cowpea (Vigna unguiculata (L.) Walp.). Karnataka J. Agric. Sci,

20 (2): 252-254.

Blake, G.R. and Hartge, K.H. (1986).Bulk density. In: Methods of Soil Analysis, 2nd ed.,

part 1, Klute A. (Ed).Soil Sc. Soc.Amer.Madison, Wis.9: 363-375.

Boopathy, R. (2002). Use of anaerobic soil slurry reactors for the removal of petroleum

hydrocarbons in soil. Int. Biodeterioration, Biodegradation 52:161-166.

Bossert, I.D. and Compeau, G.C. (1995). Clean Up of Petroleum Hydrocarbon

Contamination in Soil: Microbiology Transformation and Degradation of Toxic

Organic Chemicals. L.Y. Young and C.E. Cernigha(eds). New York. Wiley-Liss.

Bossert, R. and Bartha, R. (1994). The fate of petroleum hydrocarbon in soil environment.

In: Petroleum Microbiology, Atlas, R.M. (ed). New York. Macmillan.

Bouwer, H. (1986). Intake rate: Cylinder infiltrometer. In: Methods of Soil Analysis, part I,

Klute A. (Ed.). 9: 826-844.Madison, Wis. Amer.Soc. of Agron.

Bouyoucos,G.J.(1975).A recalibration of hydrometer for testing mechanical analysis of

soils. J.Agric.43:434 – 438.

Bowen, H.J.M. (1979). Environmental Chemistry of the Elements. London. Acad. Press.

Brady, N.C. and Weil, R.R. (2002). The Nature and Properties of Soils. 12th

ed. New-

Jersey, Pearson Education, Inc.

Brantley Jr., B.B. (1992). McGraw-Hill Encyclopedia of Science and Technology. New

York, McGraw-Hill.

Breedveld, G.D. and Sparrevik, M. (2001). Nutrient-limited biodegradation of PAH in

various soil strata at a creosote contaminated site. Biodegradation 11:391-399.

Brown, S.L, Chaney, R.L, Angle, J.S. and Baker, A.J.M. (1995) Zinc and cadmium uptake

by hyper-acumulators. J.Environ. Toxicol. Chem. 16:46-52.

Budavari, S., O’Neil, M.J, Smith, A., Heckelman, P.E. (eds.). (1986). The Merc Index: An

Encyclopedia of Chemicals, Drugs, and Biologicals. NJ, Mercks, Rahway.

Campbell, P.G.C. and Tessier, A. (1991). Biological availability of metals in Sediments:

Analytical approaches. In: Heavy Metals in the Environment, Vernet, J.P. (ed.),

Amsterdam. Elsevier Publ.

Catallo, W.J. and Portier, R.J. (1992). Use of indigenous and adapted microbial

assemblages in the removal of organic chemicals from soils and sediments. Water

Sci. Technol. 25(3): 229-237.

Chaineau, C.H., Yepremian, C., Vidalie, J.F., Ducreux, J. and Batterini, D. (2003).

Bioremediation of a crude oil-polluted soil: Biodegradation, Leaching and Toxicity

Assessments. Water, Air, Soil poll. 144:419-440.

Chen, Y., and Schnitzer, M. (1978). The surface tension of aqueous solutions of soil humic

substances. Soil Sci. 125: 7-15.

Chenu, C., Hassink, J. and Bloem, J. (2001). Short-term changes in the spatial distribution

of micro-organism in soil aggregates as affected by glucose addition. Biol. Fert. Soils

34:349-356.

Clark, R.C., and Brown, D.W. (1977). Petroleum properties and analysis in biotic and

abiotic systems. In: Effects of Petroleum on Arctic and Subarctic Marine

Environments and Organisms. Malins (ed.).1: 1-90. New York. Academic Press.

Coyne, M.S. (1999). Soil Microbiology-Exploratory Approach. London. Delman.

Danovaro, R., Marrole, D., Delta, C.N., Parodi, R. and Fabiano, M. (1999). Biochemical

composition of sedimentary organic matter and bacterial distribution in the Argean

Sea: Trophic slate and pelagic-benthic coupling. J. Sea Res. 42:117-129.

Davis, C., Cort. T., Dai, D., Illangasekare, T.H. and Munaka-Marr, J. (2003). Effects of

heterogeneity and experimental scale on biodegradation of diesel. Biodegradation

14:373-384.

De Haan, F.A.M. (1996). Soil quality evaluation. In: Soil Pollution and Protection. de

Haan, F.A.M., Visser-Reyneveld, M.I. (eds). PHLO, Wagenigen.

DEFRA (Department for Environment, Food and Rural Affairs) and the Environment

Agency. (2002). Assessment of Risks to Human Health from Land Contamination:

An Overview of the Development of Soil Guideline Values and Related Research.

Environment Agency Rio House, Bristol.

Delille, D., Delille, B., Pelletier, E. (2002). Effectiveness of bioremediation of crude oil

contaminated subantarctic intertidal sediment: The microbial response. Microb Ecol.

44:118-126.

Doran, J.W. and Safley, M. (1997). Defining and assessing soil health and sustainable

productivity. In: Pankhurst, C., Doube, B.M. and Gupta, V.V.S.R. (Eds.), Biological

Indicators of Soil Health, Oxon, CAB international.

Duke, J.A. (1981). Handbook of Legumes of World Economic Importance. 1st ed. New-

York, Plenum Press.

Edebiri, R.A.O. and Nwanokwale, E. (1981). Control of pollution from internal combustion

engine used lubricant. The Petroleum Industry and the Nigerian

Environment.Proceedings of 1981International Seminar held in Port Harcourt,1981.

Ekundayo, E.O., Emede, T.O. and Osayande, D.I., (2001). Effects of Crude oil spillage on

growth and yield of maize (Zea mays L.) in soils of Mid-western Nigeria. Plant

Foods Hum. Nutri., 56:313-324.

Environment Canada. (1996). The state of Canada’s environment, (1996) Ottawa: Public

Works and Government Services Canada.

Ernst, W.H.O. (1996). Bioavailability of heavy metals and decontamination of soils by

plants. Appl. Geochem. 11:163-167.

Fletcher, J. (1991). A brief overview of plant toxicity testing, In: Plants for Toxicity

Assessment, Goruch, J.W., Lower W.R., Lewis M.A. and Wang, W. (eds).

Philadelphia, ASTM.

FORMECU (The Forestry Management, Evaluation and Co-ordination Unit). (1998). An

assessment of vegetation and land use changes in Nigeria.Proc.1st National Seminar

on Agricultural Land Resources, in Kaduna,13 – 18th

Sept.1998.44P.

Freedman, B. (1981). The Impact of Pollution and Other Stresses on Eco-system Structure

and Function: Environmental Ecology. New York, Acad. Press.

Freeze, R.A and Cherry, J.A. (1979) Groundwater. Englewood Cliffs, Prentice Hall publ.

Co.

Fu,C., Pfanstiel, S., Gao,C., Yan,X., Govind, R., and Tabak,H.H.(1996).Studies on

contaminant biodegradation in slurry, water, and compacted soil tube

reactors.Environ.Sci.Technol.30(3):743 – 750.

Gallizia, I., McKlean, S. and Banat, I.M. (2003). Bacterial degradation of phenol

and 2,4-dichlorophenol. J.Chem. Technol. Biotechnol. 78: 959-963.

Garbisu, C and Alkorta, I. (2001). Phytoextraction: Cost-effective plant-based technology

for the removal of metals from the environment. Bioresource Technol. 77:229-236.

Gibson, D.T. (1982). Microbial degradation of hydrocarbons. Environ. Toxicol. Chem.

5:237-250.

Glick, B.R. (2003). Phytoremediation: synergistic use of plants and bacteria to clean up the

environment. Biotechnol. Adv. 21:283-393.

Gradi, P.C. (1985). Biodegradation: its management and microbiology basis.

Biotechnology and Bio-engineering 27:660-674.

Haynes, M.O., Michael, J., Murdy, M. (1985). Final Report on Environmental Baseline

Studies for the Establishment of Control Criteria and Standard Against Petroleum

Related Pollution in Nigeria, Lagos: Petroleum Inspectorate, Nigeria National

Petroleum Corporation (NNPC).

Haynes, R.J and Swift, R.S. (1990). Stability of soil aggregates in relation to organic

constituents and soil water. J. Soil Sci. 41:73-83.

Haynes, R.J. (1993). Effect of sample pretreatment on aggregate stability measured by wet

sieving and turbidimetry on soils of different cropping history. J.Soil Sci. 44:261-

270.

Hewari, J., Beaulie, C., Oullete, D., Pontbriand, Y., Hasaz, A.M., Vanata, H. (1995).

Determination of Petroleum hydrocarbons in soil: SFE versus soxhlet and water

effect on recovery. Int. J. Environ. Anal. Chem. 60:123-137.

Howard, P.H, Sage, G.W., Javis, W.F., Gray, D.A. (1991). Handbook of Environmental

Fate and Exposure Data for Organic Chemicals 2. Chelsea,Lewis Publ.

Huang W., Pent, P., Yu, Z. and Fu, J. (2003). Effects of organic matter heterogeneity on

sorption and desorption of organic contaminants by soils and sediments. Appl.

Geochem. 18:955-972.

Huesemann, M.H, and Moore, K.O. (1993). Compositional changes during land farming of

weathered Michigan crude oil-contaminated soil. J. Soil Contam. 2:245-264.

Huesemann, M.H. (1995). Predictive model for estimating the extent of petroleum

hydrocarbon biodegradation in contaminated soils.J. Environ. Sci. 19:93 - 99.

Hughes, N.C., pfau, W., Hewer, A., Jacob, J., Grimmer, G., Phillips, D.H. (1993) Covalent

binding of polycyclic aromatic hydrocarbon components of coal tar to DNA in

mouse skin. Carcinogenesis 14:135-144.

IFAD/FAO. (2000). The World Cassava Forum. International Fund for Agricultural

Development/Food and Agricultural Organization of the United Nations. Rome.

Inyang, P.E.B. (1978). Climate regime In: Nigeria in Maps. Eastern States. (ed.). Ofomata,

G.E.K., Benin City. Ethiope Publ. House.

Islam, S; Cowmen, R.C, and Garner, J.O. (2006). Screening for tolerance of stress

temperature during germination of twenty-five cowpea (Vigna unguiculata L. Walp)

cultivars. J. Food, Agric. and Environ. 4(2): 189-191.

Jelena, S. Milic; Vladimic, P. Beskoski;Mila V. ilic; Samira A.M. Ali; Gordana D. Goigic-

Cvijouic and Miroslav M. Vrvic. (2009). Bioremediation of soil heavily

contaminated with crude oil and its products: Composition of the microbial

consortium. J.Serb. Chem. Soc. 74(4) 455-460.

Joner, E.J. and Leyval, C. (2003). Rhizosphere gradients of polycyclic aromatic

hydrocarbons (PAHs) dissipation in two industrial soils and the impact of arbuscular

mycorrhiza. Environ. Sci. Technol. 37:2371-2375.

Joner, E.J. and Leyval, C. (2004). Influence of arbuscular mycorrhiza on clover and

ryegrass grown together in a soil spiked with polycyclic aromatic hydrocarbons.

Mycorrhiza 10: 155-159.

Jones, D.S, Suter H.G.W, Hull. R.N. (1997). Toxicological Benchmark for Screening

Potential Contaminants of Concern for Effects on Sediment Associated Biota:

ES/ER/TM-95/R3, Oak Ridge National Laboratory, Oak Ridge, Tenn.

Kabata-pendias, A. and Pendias, H. (1984). Trace Elements in Soil and Plants. Boca Raton,

CRC press.

Katsivela, E., Moore, E.R.B., Maroukli; D., Strompl, C., Pieper, D. and Kalogerakis, N.

(2005). Bacterial community dynamics during in-situ bioremediation of petroleum

waste sludge in land farming sites. Biodegradation 16:160-180.

Kelly, J.J., Tate, R.L. (1998). Effects of heavy metals contamination and remediation on

soil microbial communities in the vicinity of a Zn smelter. J. Environ. Qual. 27: 609-

617.

Kemper, W.D., Rosenau, R.C. (1986). Aggregate stability and size distribution. In:

Methods of Soil Analysis. Klute A., (ed.), Part I. Am.Soc. Agron. Monograph 9: 425

– 442.

Khaleel, R., Reddy, K.P. and Overcash, M.R.(1981).Changes in soil physical properties

due to waste applications: A review.J.Environ.Qual.10:133 – 141.

Kirk, J.L., Montoglis, P., Klironomos, J. Lee, H. and Trevors, J.T. (2005). Toxicity of

diesel fuel to germination, growth and colonization of Glomus intraradices in soil

and in- vitro transformed carrot root cultures. Plant and Soil. 270:23 – 30.

Kittikun, A.H., Prasertsan, P., Srisuwan, G., and Krause, A. (2000). Environmental

Management for Palm Oil Mill Material Flow Analysis of Integrated Biosystems.

Retrieved from http://ias.unu.edu/proceedings (ic mfa/kittikun/paper.html.)

Kostiakov, A.N. (1932). On the dynamics of the coefficient of water percolation in soils

and on the necessity of studying it from a dynamic point of view for purpose of

amelioration. Trans-Int.Congr. Soil Science, Paris.

Lacatusu, R. (1998). Appraising levels of soil contamination and pollution with heavy

metals. European Soil Bureau Research Report No. 4

(www.researchgate.net/publication/23) accessed Sept.25th,2013.

Ladousse, A and Tramier, B. (1991). Results of 12 years of research in spilled oil

bioremediation. Inipole EAP.1991 Oil Spill Conf., Washington D.C, Amer.

Petroleum Inst.

Lambot, C. (2003). Industrial potential of cowpea. In: Fatukun, C.A., Taranwall S.A.,

Singh, B. B., Kormawa P.M. and Tamo, M. (eds) Challenges and Opportunities for

Enhancing Sustainable Cowpea Production IITA, Ibadan.

Leahy, J.G., Colwell, R.R. (1990). Microbial degradation of hydrocarbons in the

environment. Microbial Rev. 54: 305 – 315.

Logan, T.J., Lindsay, B.J., Goins, L.E, Ryan, J.A. (1997). Field assessment of sludge metal

bioavailability to crops: Sludge rate response J. Environ. Qual.26:534-550.

Lovely, D.R. (1995). Minimum threshold for hydrogen metabolism in methanogenic

bacteria. Appl. Environ. Microbial 49: 1530 – 1531.

Madsen, E.I. (1991). Determining in-situ bioremediation; facts and challenges. Environ.

Sci. Technol. 25(10) 1663 – 1673.

Mantavinos, D. and Kalogerakis, N. (2005). Treatment of olive mill effluents. Part I.

Organic matter degradation by chemical and biological processes:An overview.

Environ. Int. 31: 289 – 295.

Margesin, R., Walder, G. and Schinner, F. (2000). The impact of hydrocarbon remediation

(diesel oil and polycyclic aromatic hydrocarbons) on enzyme activities and microbial

properties of soil. Acta Biotechnol. 20: 313 – 333.

Margesin, R. and Schinner, F. (2001).Bioremediation (Natural attenuation and

biostimulation) of diesel-oil contaminated soil in an alpine glacier skiing area.

Applied and Environ. Microbiol.67(7): 3127 – 3133.

Massias, D., Grossi, V. and Bertrand, J.C. (2003). In-situ anaerobic degradation of

petroleum alkanes in marine sediments: Preliminary results. Geosciences 335: 435 –

439.

Massoud, M.S., Al-Abdali, F., Al-Ghadbam, A.N., Al-Sarawi, M. (1998). Bottom

sediments of the Arabian Gulf: III. TPH and TOC contents as indicators of oil

pollution and implication for the effect and fate of the Kuwait oil slick. Environ.

Pollution. 93:27 – 28.

Mbagwu, J.S.C. (1995). Saturated hydraulic conductivity in relation to physical properties

of soils in the Nsukka plains, south eastern Nigeria. Geoderma 68: 51 – 66.

McGill, W.R. (1976).Disposal of industrial and oily sludges by land cultivation.Resource

Systems Mgt.Assoc.103 – 122.

McGugan, B.R., Lees, Z. M. and Senior, E. (1995). Bioremediation of an oil-contaminated

soil by fungal intervention.: Bioaugmentation for site Remediation. Pap. 3rd

Int.

Symp. on on-site Bioreclam. (1995).Hinchee, R.E., Fredrickson, J. and Alleman,

B.C. eds. Columbus, OH, Battelle press.

Mckay, D.M., Roberts, P.V. and Cherry, J.A. (1985). Transport of organic contaminants in

ground water. Environ. Sci. Technol. 19: 384 – 392.

Mclaughlin, M.J., Zarcinas, B.A., Stevens, D.P., Cook, N. (2000). Soil testing for heavy

metals. Comm. Soil Sci. & Plant Anal. 31: 1661 – 1700.

Means, J.L.,Kucak,T., and Crerar, D.A.(1980).Relative degradation rates of NTA, EDTA,

and DTPA and environmental implications.Environ.Pollution.,B.1:45 – 60.

Mesarch, B.M., Nakatsu, H.C. and Nies, L. (2000). Development of catechol 2, 3 –

dixygenase specific primers for monitoring bioremediation by competitive

quantitative PCR. Appl. Environ. Microbiol. 66:678 – 683.

Meyer – Reil, L.A. and Koster, M. (2000). Eutrophication of marine waters: effects on

benthic microbial communities. Marine Pollution Bull. 41:255 – 263.

Millioli, V.S., Servulo, E.L.C., Sobral, L.G.S., Decarvalho. (2009). Bioremediation of

crude oil-bearing soil: Evaluation of the effect of rhamnolipid addition to soil

toxicity and to crude oil biodegradation efficiency. Global NEST. J. 2 (2): 181 – 188.

Minas, W. and Gunkel, W. (1995). Oil pollution in the North sea. A microbiological point

of view. Helgol Meeres 49: 143 – 158.

Mohn, W.M. and Stewart, G.R. (2000). Limiting factors for hydrocarbon biodegradation at

low temperature in Arctic soils. Soil Biol.32: 1161 – 1172.

Morgan,P. and Watkinson,R.J.(1989).Hydrocarbon degradation in soils and methods for

soil biotreatment.Biotechnol.8:305 – 333.

Nadim, F., Hoag, G.E., Liu, S., Robert, J.C., and Zack, P. (2000). Detection and

remediation of soil and aquifer system contaminated with petroleum products: An

overview J. Petroleum Sci.Eng. 26: 169 – 178.

Nahrawi,H; Husni,M.H.A; Othman, R and Bah, A.(2011). Decomposition of leaf and fine

root residues of three different crop species in tropical peat under controlled

condition.Malaysian J. Soil.Sc.15:63 – 74.

Najafpour, G.D., Zinatizadeh, A.A.L., Mohamed, A.R., Isa, M.H., and Nasrollah Z.H.

(2005). High rate anaerobic digestion of palm oil mill effluent in an up-flow sludge-

fixed film bioreactor. Proc. Biochem. 41: 370 – 379.

National Planning Commission (NPC). (2008). Nigeria’s crop production for 1999 – 2006.

The fifth National Development plan (2008 – 2011). Federal Republic of Nigeria.

Nelson, D.W. and Sommers, L.E. (1982). Total carbon, organic carbon and organic matter.

In: Page, A.L. (ed). Methods of Soil Analysis part 2. 2nd

ed. 9: 539 – 594 ASA,

Madison Wis.

Nielsen, N.W., Winding A., Binnerup, S., Hansen, B.M and Kroer, N. (2002). Micro-

organisms as Indicator of Soil Health. National Environmental Research Institute

(NERI) Technical Report No. 388.

Nocentini, M., Danielle, D. and Fava, F. (2000). Bioremediation of a soil contaminated by

hydrocarbon mixtures: The residual concentration problem. Chemosphere 41: 1115 –

1123.

Nwadialo, B.E. (1989). Soil – landscape relationship in the Udi – Nsukka plateau. Catena

16: 111 – 120.

Nwoko, C.O. and Ogunyemi, S. (2010). Effect of palm oil mill effluent (POME) on

microbial characteristics in a humid tropical soil under laboratory conditions. J. Int.

Environ. Sci. and Dev.1(4):307 – 314.

Nyer, K and Skladany, K.G. (1993). Relating the Physical and Chemical Properties of

Petroleum Hydrocarbons to Soil and Aquifer Remediation: Practical Techniques for

Groundwater and Soil Remediation. Nyer, K. (ed), Boca Raton, CRC press.

Obidike, D.I. (1985). Oil spill contingency planning. Proc. Int. seminar Pet. Ind. and the

Nig. Environ. 11 – 14 Nov., 1985. Pp. 145 – 156.

OCHCA (Orange County Health Care Agency). (2002). Hazardous waste. The Basics. A

resource for small and medium sized business in Orange County.

(www.calepa,cahwnet.gov/dtsc) accessed on Oct. 13th, 2010.

Odu, C.T. I. (1981). Degradation and weathering of crude oil under tropical condition. The

petroleum industry and the Nigerian environment.Proceedings of 1981International

Seminar held in Port Harcourt,1981.

Odu, C.T.I., Nwoboshi, L.C., Fagade, S.O. and Awani, P.E. (1989). Post impact study of

Shell Prod. Dev.Corp.(SPDC’s) Nun River 8” delivery line oil spillage. Final report.

SPDC, Nig.

Ogbo, E.M. (2009). Effects of diesel fuel contamination on seed germination of four crop

plants – Arachis hypogaea, Vigna unguiculata, sorghum bicolor and zea mays.

African J. Biotech 8(2): 250 – 253.

Okwute, O.L. and Isu, R.N. (2007). Impact analysis of palm oil mill effluent on the aerobic

bacterial density and ammonium oxidizers in a dumpsite in Anyigba, Kogi State.

Afr. J. Biotechnol. 6(2):116 – 119.

Olaleke, A.O., Olorunfemi, O. and Akintayo, T.E. (2006). Compositional evaluation of

cowpea (Vigna unguiculata) and scarlet runner bean (Phaseolus coccineus) varieties

grown in Nigeria.J.Food.Agric.Environ.4(2):39 – 43.

Omoluobi, A. (1998). Artisans and Nigerian Environment. NEST Annual workshop, 1998.

Pp 1 – 8.

Oniwanwa, P.C. (1995). Petroleum hydrocarbon pollution of urban top soil in Ibadan City,

Nigeria. Environ. Int. 21: 341 – 343.

Opeolu, B.O. (2000). Effects of lead on the performance and nutrient quality of two

cowpea varieties.Unpublished M.Sc. Thesis, Department of Crop Protection and

Environmental Biology, University of Ibadan, Nigeria.

Ovreas, L. (2000). Population and community level approaches for analyzing microbial

diversity in natural environments. Ecol. Lett. 3: 236 – 251.

Pfaender, F.K. and Klump,J.V.(1991).Assessment of environmental biodegradation of

organic pollutants.Abstr.Annu.Confr.Am.Soc.Microbiol.214.

Philips, P.,Bender, J.,Word, J.,Niyogi, D.,and Denovan,B. (1994). Mineralization of

naphtalene, phenanthrene, chrysene and hexodecane with a constructed silage microbial

vat. In: Applied Biotechnology for Site Remediation. Hinchee, R.E., Anderson, D.B.,

Meting, F.B. Jr, and Sayles, G.D., (eds). Boca Raton. Lewis publishers.

Piccolo, A. and Mbagwu, J.S.C. (1999). Role of hydrophobic components of organic matter

in soil aggregate stability. Soil Sci. Soc. Am. J. 63: 1810 – 1816.

Piccolo, A., Piethramellara, G. and Mbagwu, J.S.C. (1997). Use of humic substances as soil

conditioners to increase aggregate stability. Geoderma 75: 265 – 277.

Pierzynski, G.M., Sims, J.T. and George, F. (2000). Soils and Environmental Quality. Boca

Raton. CRC, Press.

Ram, N.M., Bass, D.H., Falotico, R. and Leahy, M. (1993). A decision framework for

selecting remediation technologies at hydrocarbon contaminated sites. J. Soil

Contam. 2: 167 – 189.

Rasiah, V., Voroney, R.P., Greenbelt, P.H. and Kachanoski, R.G. (1990). Modifications in

soil water retention and hydraulic conductivity by an oily waste. Soil Technol. 3:

367 – 372.

Rittman, B.E and McCarty, P.L. (2001). Environmental Biotechnology: Principles and

Applications. New York. McGraw-Hill.

Ros, M. Hermandez,M.T and Garcia, C. (2003). Soil microbial activity after restoration of

a semi-arid soil by organic amendments. Soil Biology and Biochemistry. 35: 463 –

469.

Samuel, R.A., Walter, O.S. and Earl, R.L. (1975). Modern Corn Production. 2nd

ed. Publ. A

and L Publication. USA.

Schwendinger, R.B. (1968). Reclamation of soil contaminated by oil. J. Inst. Petr. 53:

182 – 197.

Shailubhai, K. (1986). Treatment of petroleum oil sludge in soil. Trends Biotechnol. 4: 202

– 206.

Sharifi, M, Sadeghi, Y. and Akharpour, M. (2007). Germination and growth of six plant

species on contaminated soil with spent oil. Inter. J. Environ. Sci. Technol. 4(4):

463 – 470.

Sharma, G.K., Chandler, C. and Salemi, L. (1980). Environment pollution and leaf cultivar

variation in Kudzu (Puereria lobata Willd). Ann. Bot. 45: 77 – 80.

Singh, S.R., Rachie, K.O. (1985). Cowpea Research and Utilization. New York. John

Wiley and Sons.

Sitting, M. (1981). Handbook of Toxic and Hazardous Chemicals. Park Ridge, New Jersey.

Noyes publ.

Stegmann, R. R., Brunner, G., Clamano, W and Martz, G., (eds). (2003). Treatment of

Contaminated Soil: Fundamental Analysis; Applications. New York. Springler –

Verlag.

Stevenson, D.S. (1987). Effects of three soil conditioners on water contents in two soils at

three pressure plate matric potentials. Can. J. Soil Sci. 67: 395 – 397.

Sullivan, L.A. (1990). Soil organic matter, air encapsulation and water stable aggregation.

J. Soil Sci. 41: 529 – 534.

Thiaw, S., Hall, A.E., Ismail, A.M. and Ehlers, J.D. (1977). Water use efficiency and

drought adaptation:Advances in Cowpea Research. Publ. IITA and JIRCA.

Tisdall, J.M. Oades, J.M. (1982). Organic matter and water-stable aggregates in soils. Soil

Sci. 33: 141 – 163.

Udo, E.J, and Fayemi, A.A.A. (1975). The effect of oil pollution of soil on germination,

growth and nutrient uptake of corn. J. Environ. Qual. 4: 537 – 540.

Udom, B.E. (2008). Bioremediation of spent oil – contaminated soil using legume plants

and poultry manure. Unpublished Ph.D. Thesis, Department of Soil Science

University of Nigeria, Nsukka.

Udom, B.E., Mbagwu, J.S.C, Adesodun, J.K. and Agbim. (2004). Distributions of zinc,

copper, cadmium and lead in a tropical Ultisol after long-term disposal of sewage

sludge. Environmental International 30: 467 – 470.

Uraizee, F.A, Venosa, A.D. and Suidan, M.T. (1998). A model for diffusion controlled

bioavailability of crude oil components. Biodegradation 8: 287 – 296.

USDHHS (U.S. Department of Health and Human Services). (1999).Toxicological profile

for total petroleum hydrocarbons(TPH).Agency for Toxic Substances and Disease

Registry,Atlanta,Georgia.

USEPA (United States Environmental Protection Agency). (1990). Basics of pump-and-

treat ground-water remediation technology. EPA 600-8-90/003. Rober, S.K.

Environmental Research Laboratory.

USEPA (United States Environmental Protection Agency). (1991). Research and

Development (RD – 681). EPA/600/M-91/049. Alternative Treatment Technology

Information Centre (ATTIC).

Van Hamme, J.D., Singh, A. and Ward, O.P. (2003). Microbiology. Mol. Biol. 67:503 -

509.

VanBavel, C.H.M. (1950). Mean weight diameter of soil aggregates as a statistical index of

aggregation. Soil Sci. Soc. Am. Proc. 14:20-23.

Vangronsveld, J. and Clijsters, H. (1994). Toxic effect of metals in plants. Farayo, M.G.

and Weinhein, V.C.H. (eds.). Plant and the Chemical Elements. New York.

Cambridge.

Vezzulli, L., Marrale, D., Moveno, M.P. and Fabiano, M. (2003). Sediment organic matter

and mesofauna community response to long-term fish-farm impact in the Ligunan

sea (Western Mediterranean) Chem. Ecol. 19:431-440.

Vezzulli, L., Pruzzo, C. and Fabiano, M. (2004). Response of the bacterial community to

in-situ bioremediation of organic-rich sediments. Mar. Poll. Bull. 49:740-751.

Walker, W., Beyer, M., Klein, J. and Rehn, H.J. (1991). Degradation of pyrene by

Rhodococcus Spp. UWI. Appl. Microbiol. Biotechnol. 34:671-676.

West, L.T., Chiang, S.C. and Norton, L.D. (1992). The Morphology of surface crusts. In:

Summer, M.E. and Stewart, B.A. (eds.) Soil crusting, Chemical and Physical

process. Prov. 1st Int. Symposium on Soil crusting. Adv. Soil Sci.

Whisman, M.L., Goetzinger, J.W. and Cotton, F.O., (1974). Waste Lubricating Oil

Research: An Investigation of Services. Re-refining Methods. Bureau of Mines,

Bartlesville, Energy Research Centre.

Wien, H.C., and Sumerfield, R.J. (1980). Adaptation of cowpeas in West Africa: Effects of

photoperiod and temperature response in cultivars of diverse origin. Advances in

Legume Sci. R.J. Summerfield, A.H. Bunting (eds.) HMSO, London.

Wiesel, I., Wuchker, S.M. and Rehn, H.J. (1993). Degradation of polycyclic aromatic

hydrocarbons by an immobilized mixed bacterial culture Appl. Microbiol.

Biotechnol. 30: 110-116.

Yeung, P.Y., Johnson, R.L. and Xu, J.G. (1997). Bioremediation of petroleum

hydrocarbons in soil as affected by heating and forced aeration. J. Environ. Qual.

26:1511-1516.

Zak, J.C., Willing, M.R., Moorhead, D.L. and Wildman, H.G. (1994). Functional diversity

of microbial communities: A quantitative approach. Soil Biol. Biochem. 26:1100-

1108.

APPENDIX I

Remediation efficiency(%) = Residual THC in control –Residual THC in treated plot

Residual THC in control

Remediation efficiency for Sp3PE in 3rd

month = 26131 – 18709 X 100

26131

= 7422 X 100

26131

= 28.4%

Where control = -NA

APPENDIX II

Degradation rate (mg Kg-1

day-1

) = Initial spent oil concentration – Residual THC

[` Degradation time (days)

Degradation rate in Sp3PE in the 3rd

month = (30000 – 18709) mg Kg-1

90 days

= 125.5 mg Kg-1

day-1