U n i v

U n i v
University of Pretoria etd – Maila, M P (2005)
Faculty of Natural and Agricultural Sciences
Academic Year 2004
Microbial Ecology and Bio-monitoring of Total Petroleum Contaminated Soil
Environments
by
MP Maila
Thesis submitted in fulfilment of the requirement for the degree of Doctor (PhD) in
Applied Biological Science specialisation in Biotechnology
Rector: Prof CW Pistorius
Dean:
Prof A Ströh
Promotor:
Prof TE Cloete
University of Pretoria etd – Maila, M P (2005)
The author and promoter give the authorisation to consult and to copy parts of this work
for personal use only.
Any other use is limited by the laws of Copyright. Permission to reproduce any material
contained in this work should be obtained from the author.
Pretoria, November 2004
The promoter:
The author:
Prof TE Cloete
MP Maila
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University of Pretoria etd – Maila, M P (2005)
In memory to my parents
SL Maila (1945-1996)
PR Maila (1952-1996)
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University of Pretoria etd – Maila, M P (2005)
‘So little is known about most of the microbial world that no one has ever documented
the extinction of a bacterium’.
New Scientist, “Save a bug for Biotechnology”, 1 August 1992 p. 7.
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University of Pretoria etd – Maila, M P (2005)
Acknowledgements
I express my gratitude to Prof T E Cloete (University of Pretoria), Prof V Torsvik
(University of Bergen, Norway) and Dr J De Beer of the CSIR for giving me the
opportunity to carry out my PhD in their laboratories.
I’m also grateful to Dr EM Top (University of Idaho, US), Dr K Drønen (University of
Bergen), Dr P Wade (Phokus Technologies, SA) and Prof W Verstraete (University of
Gent, Belgium) for their spontaneous and free of charge co-operation in reviewing the
work and in microbiological investigations.
I thank my family (especially my brothers Chipu and Motinyane Maila) for the support
and understanding they have given me throughout the duration of the studies.
I would also like to thank all my colleagues at the CSIR and the University of Pretoria for
their support and assistance in microbiological investigations and preparing the thesis.
Finally, yet importantly, I thank God for giving me the wisdom, strength and
perseverance, which enabled me to reach this stage of my career.
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University of Pretoria etd – Maila, M P (2005)
Summary
The contamination of environmental media by total petroleum hydrocarbons (TPH) is a
concern in many parts of the world; particularly as most petroleum components like
polycyclic aromatic hydrocarbons (PAHs) are either toxic or carcinogens. In South
Africa, the sale of major petroleum products by the South African Petroleum Industry
Association (SAPIA) reveals that about 21 billion litres of petroleum products are sold
per year. These products include bitumen, diesel, fuel oil, illum paraffin, jet fuel and
petrol. In addition, 19.5 million tonnes of crude oil are brought into South Africa annually
to feed the country’s four refineries. The production of oily sludges at refineries,
transportation, storage, and handling of petroleum products by end users, results in
environmental contamination. The soil environment is particularly vulnerable to
hydrocarbon contamination as most of the accidental spillages by trucks, rail
locomotives and pipelines have a direct impact on the soil medium. As most of the
petroleum compounds are either toxic or carcinogenic, their removal from the soil is
necessary.
The literature reveals that biological treatment of hydrocarbons is cost effective
compared to other treatment options. However, in order to improve the efficiency of
biological treatments, there is a need to understand the microbial diversity of TPH
stressed environments and how simple biomonitoring ‘instruments’ can be used to
evaluate the removal of hydrocarbons from the soil. The message from the literature
indicates some potential solutions to the existing problems associated with soil microbial
diversity and biotreatment of hydrocarbon contaminated soil, which must be
investigated.
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University of Pretoria etd – Maila, M P (2005)
The main aim of this work was to evaluate the microbial diversity of the different soil
environments disturbed by Total Petroleum Hydrocarbons (TPHs) and the potential use
of plants and microorganisms in monitoring and removing hydrocarbons from the soil. In
addition, the potential of the culture-independent methods in complementing, the culturedependent methods when evaluating soil microbial diversity were also evaluated.
The polyphasic approach was successfully used in evaluating microbial diversity in both
hydrocarbon-contaminated and uncontaminated soils. The approach involved the use of
community level physiological profiles (CLPP) and polymerase chain reaction-denaturing
gradient gel electrophoresis (PCR-DGGE) to evaluate the effects of hydrocarbons on the
soil microbial communities of both the contaminated and non-contaminated soil layers at
a diesel contaminated site. Because of the ability of the molecular methods (PCRDGGE) to complement the CLPP, the polyphasic approach is recommended when
evaluating soil microbial diversity and the effect of pollutants on microbial community
structure as the approach appears to compensate for the limitations of each of the
methods of evaluating microbial diversity. However, further work is needed to improve
the recovery of bacteria from the soil, particularly where the interest is to evaluate the
availability of the indigenous microbial populations for bioremediation.
The substrate utilisation pattern and 16S DNA fragments of the soil microbial
communites in different soil layers at a diesel contaminated site were different. The
substrate utilisation pattern of the topsoil was different from the substrate utilisation
pattern of the soil layers below 1m. In addition, the substrate utilisation pattern of the
contaminated and uncontaminated soil layers were different. 16S DNA fragments of the
different soil layers were also different. While the metabolic activities of different samples
as reflected by CLPP does not necessarily imply the difference in community structure of
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the samples, PCR-DGGE revealed differences in 16S DNA fragments and this
complemented the results of the culture based methods. The results suggest that the
use of functional and genetic approaches (in combination) have a better chance of
revealing a ‘clearer’ picture of soil microbial diversity.
The distribution of hydrocarbon-utilising bacteria and the efficiency of biodegradation of
hydrocarbons vary with soil depth. The biodegradation rate of hydrocarbon was highest
in the topsoil compared to other soil layers and this was supported by the high number of
hydrocarbon-degrading bacteria in the topsoil compared to soil layers at and below 1m.
The results suggest that the biological removal of hydrocarbons varies in different soil
layers and that microbial diversity as measured by CLPP and PCR-DGGE varies with
depth in hydrocarbon-contaminated soil. The information about metabolic activities of
different soil layers is important when assessing the footprints of degradation processes
during monitored natural attenuation (MNA). However, further studies are required to
understand the effect of (not only) other pollutants, but the influence of soil components
(pore volume, level of adsorbents and other environmental factors) on the microbial
diversity of different soil layers in both ‘shallow’ and deep aquifers.
The microbial diversity of different environments contaminated by hydrocarbons has
different community level physiological profiles. At diesel depots where similar
hydrocarbons are used for maintenance of locomotives, the number of bacteria (both
total culturable heterotrophic bacteria and hydrocarbon-degrading bacteria) was
proportional to the level of hydrocarbon contamination. However, there was no
significant difference in the level of total culturable heterotrophs (TCHs) and the
hydrocarbon degrading bacteria. In addition, the biological activities as evaluated by CO2
production were higher in nutrient amended treatments in which high numbers of TCHs
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University of Pretoria etd – Maila, M P (2005)
were present. Microbial diversity of polluted surfaces needs to be studied further to
investigate the concentration or the thickness of the hydrocarbons layer on the rock
surfaces that encourages the attachment or colonization of the TCHs and the
hydrocarbon-degrading bacteria.
The hydrocarbons rather than the geographical origin of the soil sample appear to be
more important in determining functional or species diversity within the bacterial
communities. The samples from different locations were as different as samples from the
same location but from contaminated versus uncontaminated soil. The results of the
soils from different locations artificially contaminated by different hydrocarbons also
reached the same conclusion. However, further work is required to investigate the
importance of soil heterogeneity in community studies of soil environments contaminated
by similar hydrocarbons.
The removal of Polycyclic Aromatic Hydrocarbons (PAHs) in multi-planted soil
microcosm was higher compared to PAHs removal in monoculture soil microcosms. In
addition, the PAH removal was higher in the vegetated soil microcosms compared to the
non-vegetated microcosms. There was however, no significant difference in the PAH
removal in the soil microcosms planted with Branchiaria serrata and the microcosm with
Eulisine corocana. The Principle Component Analysis (PCA) and Cluster analysis used
to analyse the functional diversity of the different treatments revealed differences in the
metabolic fingerprints of the PAH contaminated and non-contaminated soils. However
the differences in metabolic diversity between the multi-planted and mono-planted
treatments
were
not
clearly
revealed.
The
results
suggest
that
multi-plant
rhizoremediation using tolerant plant species rather than monoculture rhizoremediation
have the potential to enhance pollutant removal in moderately contaminated soils.
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University of Pretoria etd – Maila, M P (2005)
Lepidium sativum, a plant with short germination period, was successfully used to
monitor, the removal of Polycyclic Aromatic Hydrocarbons (PAHs) from the soil. The
sensitivity of L. sativum decreased with increasing concentration of the polycyclic
aromatic hydrocarbons in the artificially contaminated soil while no germination occurred
in the historically polluted soil. When used during phytoremediation of PAH, the
germination level of L. sativum was inhibited during the first weeks, after which
germination increased, possibly due to PAH dissipation from the soil. The methodology
based on the sensitivity of L. sativum to PAH can be used as a monitoring tool in
bioremediation of soil contaminated with PAH. However, the methodology should be
developed further to gain more knowledge on aspects of bioavailability of PAH in both
the aged as well as the freshly spiked soil. Also critical is the sensitivity of the seeds to
other pollutants (e.g. heavy metals), which are most likely to occur in the presence of the
PAHs. Although the biological activities have the potential to monitor the removal of
hydrocarbons from the soil, the methodologies have not been developed sufficiently to
cater for the heterogeneity of the soil and to differentiate toxicity by the parent compound
and the metabolites. At present, it is best that they be used to complement existing
conventional monitoring instruments.
Finally, the biological removal of hydrocarbons is cost-effective compared to other
treatments. However, inherent physical, chemical and biological limitation hampers the
efficient utilisation of the bioremediation technologies. Biostimulation approaches
involving the stimulation of indigenous pollutant-degrading bacteria should be preferred
ahead of bioaugumentation.
The latter approach should be considered when the
contaminated site does not have the indigenous pollutant-degrading bacteria. Even in
this case, the aim should be to ‘seed’ the biodegradation knowledge to the indigenous
microbial populations due to poor survival of the added strains.
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TABLE OF CONTENTS
Page
Chapter 1 :
General Introduction .............................................................................. xii
Chapter 2 :
Evaluation of Microbial Diversity of Different Soil Layers at a
Contaminated Diesel Site ........................................................................25
Chapter 3 :
Evaluation of Microbial Communities Colonizing Stone Ballasts at
Diesel Depots ..........................................................................................44
Chapter 4 :
Soil Microbial Diversity: Influence of Geographic Location and Hydrocarbon Pollutants
………………………………………………………..60
Chapter 5 :
Multiplanted and Monoculture Rhizoremediation of Polycyclic Aromatic
Hydrocarbons (PAHS) from the Soil. .......................................................76
Chapter 6 :
Germination of Lepidiun Sativum as a Method of Evaluating the
Removal of Polyaromatic Hydrocarbons (PAHS) from Contaminated
Soil...........................................................................................................94
Chapter 7 :
The Use of Biological Activities to Monitor the Removal of Fuel
Contaminants: Perspective for Monitoring Hydrocarbon Contamination.
110
Chapter 8 :
Bioremediation of Petroleum Hydrocarbons through Landfarming: Are
Simplicity and Cost-Effectiveness the Only Advantages? .....................130
Chapter 9 :
Conclusions and Perspectives...............................................................157
---oOo---
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Chapter 1
GENERAL INTRODUCTION
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TABLE OF CONTENTS
CHAPTER 1: GENERAL INTRODUCTION .................................................................. xii
1.
2.
3.
SOIL MICROBIAL DIVERSITY- AN OVERVIEW ..................................................1
RESEARCH IN MICROBIAL DIVERSITY .............................................................3
EVALUATION OF DIFFERENT METHODS OF ESTIMATING MICROBIAL
DIVERSITY IN SOIL..............................................................................................4
3.1
3.2
3.3
Community Level Physiological Profiles (CLPP) ...................................................4
Phospholipid Fatty Acid Analysis (PLFA) ..............................................................5
Molecular Techniques ...........................................................................................6
4.
THE USE OF BIOLOGICAL ACTIVITIES TO MONITOR THE REMOVAL OF
TPH FROM SOIL ..................................................................................................8
BIOTREATMENT OF HYDROCARBONS IN SOIL.............................................10
RESEARCH OBJECTIVES .................................................................................13
REFERENCES ....................................................................................................16
5.
6.
7.
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1.
SOIL MICROBIAL DIVERSITY- AN OVERVIEW
Microbial diversity can be defined as “the variety of bacterial species in ecosystems, as
well as the genetic variability within each species” (Conservation, 1987). Theoretically,
microbial diversity can also be regarded as the amount and distribution of genetic
information in a natural community. A representative estimate of microbial diversity is
therefore a prerequisite for understanding the phenotypic diversity of microorganisms in
ecosystems (Garland and Mills, 1994; Zak et al., 1994).
In the soil, the number of different species can be 104 or higher per gram of soil (Torsvik
et al., 1990; Klug and Tiedje, 1994). Because of the immense genotypic and phenotypic
diversity, soil microbial communities remain some of the most difficult to characterise.
Two approaches generally used to characterise microbial diversity includes the culturedependent (or phenotypic) and the culture independent (genomic) based methods.
However, due to the heterogeneity of the soil and the inherent limitations of the existing
methodologies, most of the bacterial species in the soil remains unidentified.
Microbial diversity can further be considered from a variety of perspectives. It has been
suggested that ‘trophic, physiological or functional diversity, intraspecific genetic
diversity, or phylogenetic diversity of species or higher taxa’ are all levels of diversity of
concern to the microbial ecologist (Delong, 1996). Kawanabe (1996), proposed that the
diversity of ecological relationships among life forms (e.g. competition, cooperation, etc)
is a more important part of biodiversity than ‘simply the diversities among creatures’.
Thus, synergistic and antagonistic interactions play a critical role in community functional
diversity (Atlas, 1984).
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It is generally assumed that in the soil ecosystem, in which most of the niches or tasks
are fulfilled, microbial diversity is high. However, the microbial community structure
changes and diversity decreases due to environmental stress or disturbances (Atlas et
al., 1991). The change in community structure results in the emergence of dominant
populations within disturbed communities, which have enhanced physiological
tolerances and substrate utilisation capabilities (Atlas et al., 1991; Wünsche et al., 1995).
The soil microbial diversity is critical to the maintenance of good soil health, because
microorganisms are involved in many important functions such as soil formation, toxin
removal, and elemental cycles of carbon, nitrogen, phosphorus and others (Brock et al.,
1984; Fredrickson and Hagedorn, 1992; Leung et al., 1994). However, environmental
stresses, can alter microbial populations and therefore endanger soil health.
Environmental stresses caused by Total Petroleum Hydrocarbons (TPH), like other
pollutants, can cause microbial community structure changes and a decrease in
microbial diversity. However, as most of the low molecular weight hydrocarbons are
volatile and most of the high molecular weight alkanes are biodegradable, the impact of
hydrocarbons on microbial community structure can be mitigated by treating the
contaminated soil. It is necessary to understand microbial diversity in soil as existing
bioremediation technologies can be optimised if the current knowledge of both functional
and genetic microbial diversity can be improved.
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2.
RESEARCH IN MICROBIAL DIVERSITY
In the field of microbial ecology, more specifically soil microbial diversity, slow progress
has been made during the last two decades since the discovery that culture-dependent
methods are only capable of enumerating 1% of the bacteria and that the microbial
diversity in soil can be as high as 104 species per gram of soil (Alexander, 1977; Torsvik
et al., 1990; Klug and Tiedje, 1994; Borneman et al., 1996). Indeed, (99.5 to 99.9%) of
the soil bacteria observed using the fluorescence microscope cannot be isolated and
cultured on laboratory media (Torsvik et al., 1990; Aman et al., 1995). They will therefore
be excluded when phenotypic diversity is estimated.
Previous studies on microbial diversity involved the use of culture-dependent methods or
the phenotypic characterisation of isolated strains (Kaneko et al., 1977; Bell et al., 1982;
Margulis et al., 1986). These culture-dependent methods included the numerical
taxonomic studies, which use either profiles of cellular constituents (Mallory and Saylor,
1984; Lambert et al., 1990), or phenotic characteristics (Kaneko et al., 1977) of isolates
to define operational taxonomic units as defined by Sneath and Sokal (1973). However,
the analysis of microbial diversity using culture-dependent methods alone limits insight
into the ecological relevance of microbial community structure due to the inability of most
bacteria to grow on laboratory media. As a result of this limitation other techniques are
needed to study microbial diversity.
It was largely due to the discovery of the high diversity of DNA of soil bacteria (Torsvik et
al., 1990) and the limitations of culture-dependent methods that emphasis on microbial
diversity included the use of molecular techniques to estimate microbial diversity in soil.
Molecular biological techniques offer new opportunities for the analysis of the structure
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and species composition of microbial communities. In particular, sequence variation in
rRNA has been exploited for inferring phylogenetic relationships among microorganisms
(Woese, 1987) and for designing specific nucleotide probes for the detection of
individual microbial taxa in natural habitats (Giovannoni et al., 1990; Amann et al., 1992).
However both culture-dependent and independent approaches have limitations, which
must be considered when investigating microbial diversity in ecosystems.
3.
EVALUATION OF DIFFERENT METHODS OF ESTIMATING MICROBIAL
DIVERSITY IN SOIL
3.1
Community Level Physiological Profiles (CLPP)
Biolog is a redox-based technique that was originally developed for classification of
bacterial isolates based on the ability of the isolates to oxidise 95 different carbon
sources (Bouchner, 1989). Garland and Mills (1991) adapted the method and used it to
characterise the functional potential of microbial communities. The data from the carbon
utilisation patterns have been used in two ways (i) to quantify differences among specific
environmental samples, or (ii) to assess the functional diversity of microbes in
ecosystem (Zak et al., 1994). Using Biolog in such a way is called Biolog-generated
community level physiological profiles (CLPP) and is thus used to estimate the ex-situ
metabolic potential of members of the microbial community from a variety of
environments.
Community level physiological profiles provide an indication of the metabolic diversity
present in an environment with respect to the number of defined substrates that can be
oxidised. Table 1 summarises the advantages and disadvantages of the CLPP. Recent
studies have suggested that the faster growing species such as Pseudomonas produce
the generated patterns and not the numerically dominant members of the microbial
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communities (Garland, 1997; Konopka et al., 1998). Smalla et al. (1998), reported that
carbon source utilisation profiles obtained with Biolog GN plates do not necessarily
reflect the functional potential of the numerically dominant members of the microbial
community used as the inoculum.
Table 1: Advantages and disadvantages of CLLP
Advantages
•
Disadvantages
•
Easy to use & generates large data
methods
set
•
•
•
Have the same limitations of culture
•
Sensitive to soil management
Present functional rather than
treatment effects on soil
structural information about
communities
microbial communities
•
Inoculation of plates is less labour
Not known if changes in substrate
intensive and less costly in time
utilisation pattern represent
and materials
changes in community composition
*SUP provides data set amenable
to multivariate statistical analysis
that can quantify sample
differences
*SUP = substrate utilisation pattern
3.2
Phospholipid Fatty Acid Analysis (PLFA)
Characterisation of phospholipid fatty acid (PLFA) profiles and substrate utilisation
pattern are increasingly common in studies of microbial communities. PLFA is a valuable
approach, because it is a biochemical method that provides direct information about the
structure of the active microbial community, free of the limitations inherent in culturing
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University of Pretoria etd – Maila, M P (2005)
microorganisms (Bossio and Scow, 1998). Table 2 summarises the advantages and
disadvantages of PLFA. PLFA can be used to interpret effects at several levels. The
most general level is to use the entire profile of fatty acids as a fingerprint. This allows a
more detailed assessment of changes at the community level than bulk measures. With
the appropriate statistics, the degree of similarity among communities or environmental
effects on a community can be quantified and tested for significance. Major taxonomic
groups such as eukarya vs. bacteria, can be differentiated at the next level. A higher
level of resolution can be achieved by focusing on the specific fatty acids that act as
biomarkers of certain functional groups or species of microorganisms (Vestal and White,
1989).
Table 2: Advantages and disadvantages of PLFA
Advantages
Disadvantages
•
Can differentiate taxonomic groups
•
Present structural information about
derived from information on pure
microbial diversity
cultures
•
Free
of
limitations
inherent
•
•
in
Database
for
fingerprinting
is
Biomarker not always universal to a
specific group
culturing microorganisms
•
Difficulty in interpreting specific
PLFA peaks
3.3
Molecular Techniques
The identification of organisms has traditionally been achieved by cultivation techniques
such as plate counting. However, because typically >99% of naturally occurring
microorganisms are not cultivated by standard techniques (Amann et al., 1995),
alternative methods are needed to describe community constituents. Molecular methods
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University of Pretoria etd – Maila, M P (2005)
used to study microbial divesity includes Polymerase Chain Reraction- Denaturing
Gradient Gel Electrophoresis (PCR-DGGE), Automated method of ribosomal intergenic
spacer analysis (ARISA), amplified rDNA restriction analysis (ARDRA), reassociation
kinetics and many other modified molecular methods (Torsvik et al., 1990; Øvreås and
Torsvik, 1998; Nusslein and Tiedje, 1999; Fisher and Triplett, 1999 and many others).
During the last decade, methods based on direct Polymerase Chain Reaction (PCR)
amplification and analysis of ribosomal RNA genes were developed and allowed a more
comprehensive analysis of microbial communities in comparison with cultivation based
techniques. The amplified fragments of 16S or 18S rRNA genes and especially the
analysis of these genes by temperature or denaturing gradient gel electrophoresis
(DGGE) have been frequently used to examine the microbial diversity of environmental
samples and to monitor changes in microbial communities (Curtis and Craine, 1998;
Muyzer and Smalla, 1998; van Elsas et al., 1998; Eichner et al., 1999).
In a DGGE gel, the number, precise position, and intensity of the bands in a gel track
give an estimate of the number and relative abundance of numerically dominant
ribotypes in the sample. This approach allows a comparison of different microbial
communities but not without specific problems. The advantages and disadvantages of
methods based on analysis of ribosomal RNA genes are summarised in table 3.
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Table 3: Advantages and disadvantages of methods based on rRNA genes
Advantages
•
•
Disadvantages
Characterise
high
portion
•
of
community constituents
microbial communities are usually
Identifies rRNA genes (rDNA) in
very
DNA extracted directly from the
primers are used.
•
environment
•
Banding patterns of high diverse
Permits
the
phylogenetic
detection
identification
complex
Only
major
when
bacterial
populations
are
represented on the DGGE patterns
and
•
of
Less abundant species may not be
detected by the method
fastidious and as yet uncultured
•
organisms
Lack of rDNA sequences from
close relatives in the databases
results in un-identification of ‘new’
species
4.
THE USE OF BIOLOGICAL ACTIVITIES TO MONITOR THE REMOVAL OF
TPH FROM THE SOIL
The increasing concern about the cost of soil remediation has necessitated the need to
explore not only cost effective technologies but also alternative monitoring tools.
Conventional chemical analytical instruments like GC-MS usually monitor the progress
of remediation of hydrocarbon-contaminated soil, which can be expensive (Maila and
Cloete, 2002). Due to the cost associated with traditional monitoring tools, focus is now
shifting towards using biological activities for monitoring of bioremediation of
hydrocarbon-polluted soil. The use of bioindicators to evaluate hazardous chemical
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University of Pretoria etd – Maila, M P (2005)
waste sites provides a direct, inexpensive and integrated estimate of bioavailability and
contaminant toxicity (Mueller et al., 1991; Wang and Freemark, 1995; Maila and Cloete,
2002). Table 4 summarises the advantages and disadvantages of bioindicators.
Many promising approaches using bioindicators as monitoring instruments have been
reported (Athey et al., 1989; Siciliano et al., 1997; Dorn et al., 1998; Marwood et al.,
1998; Margesin et al., 1999; Maila and Cloete, 2002). These include the use of
enzymes, earthworm survival, microbial bioluminescence and seed germination.
Table 4: Advantages and disadvantages of using bioindicators as monitoing
instruments
Advantages
•
•
Disadvantages
•
Can detect both toxicity of parent
•
distinguish
toxicity
resulting from parent compound
Readily
and metabolites;
available
materials
are
•
Bioindicator response don’t always
The test can be performed ex or in-
correspond
situ;
concentration;
•
The test period in most cases is
Uncomplicated
with
contaminant
Different tests respond differently to
individual toxicants;
short;
•
to
compounds and toxic metabolites;
required to do the test;
•
Inability
methodology
•
is
Sensitivity depends on the toxicant
used to assess the extent of
and soil (i.e. the test can be
pollution reduction.
sensitive to other factors of the
soil).
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The use of bioindicators to monitor the removal of hydrocarbons in soil should be
considered when planning the monitoring programmes for soil remediation as it has the
potential to reduce soil cleanup costs.
5.
BIOTREATMENT OF HYDROCARBONS IN SOIL
The technologies, which involve the biological removal of hydrocarbons from
contaminated soil, are today well established and many are applied commercially in
large scale. During the 1970’s, when environmental concerns associated with
uncontrolled disposal became apparent, and environmental regulations were established
and applied in North America and Europe (aimed at minimising the risk of air and
groundwater contamination), landfarming gained popularity. This ‘low tech’ biological
treatment method involves the controlled application and spread-out of a more or less
defined organic bio-available waste on the soil surface and the incorporation of the
waste into the upper soil zone (Genou et al., 1994). In 1983 it was estimated that at least
one-third of all United States refineries operated full-scale or pilot scale landfarmers
(American Petroleum Institute, 1983). The technology has been widely used as it is
simple and cost effective to implement compared to other treatments (Genou et al.,
1994; Balba et al., 1998; Marijke and van Vlerken, 1998; Picado et al., 2001).
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University of Pretoria etd – Maila, M P (2005)
Other biological treatment technologies include phytoremediation, bioventing, biopiles,
composting and bioslurping. Phytoremediation uses the plants and associated
microorganisms to degrade or immobilise contaminants in soil and groundwater (Figure
1d). The technology is used to decontaminate moderately contaminated soil, as highly
contaminated soil can be toxic to plants (Lin and Mendelssohn, 1998). Bioventing
integrates physical removal (soil venting) and enhanced aerobic biodegradation of
hydrocarbons. The technology is recommended at contaminated sites with soil gas
permeability greater than 0.1 Darcy (1 Darcy = 1 * 10-8 cm2) and a radius of influence or
radius of remediation of between 8 and 49 m (Johnson et al., 1990; Long, 1992; Li,
1995).
Biopiles refer to the piling of the material to be biotreated by adding nutrients and air into
piles or windrows usually to a height of 2-4 m. Biopiles may be static with installed
aeration piping or they may be turned or mixed by special devices for this purpose
(Figure 1c). Biopiles may be amended with bulking agents, usually with straw, sawdust,
bark or wood chips or some other organic material. If organic material is added, the
technology is termed composting. The different biological treatments technologies used
for removing hydrocarbons in soil are shown in Figure 1.
Bioslurping combines the two remedial approaches of bioventing and vacuum enhanced
free product recovery. Bioventing stimulates the aerobic bioremediation of hydrocarboncontaminated soil. Vacuum enhanced free product recovery extracts the light nonaqueos
phase liquids (LNAPLs) from capillary fringe and the water table.
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University of Pretoria etd – Maila, M P (2005)
a) Landfarming
b) Bioventing
c) Biopile
d) Phytoremediation
e) In-situ Bioslurping
System
Figure 1. The biological treatment technologies used for treating organic compounds in the soil (Black, 1999; US EPA, 1994).
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For efficient full-scale bioremediation of hydrocarbons, four important questions (adapted
from Jørgsen et al., 2000) that must be answered are:
•
How low a concentration of the contaminant can one obtain (microbial activity
and bioavailability)?
•
What is the fate of the contaminant (volatilisation, biotransformation, build-up of
microbial biomass, adsorption or incorporation to the bound residues)?
•
How much time is needed to obtain the set goal (removal or degradation rate)?
•
What are the costs?
Biological removal of hydrocarbons is cost effective compared to other remediation
technologies and a large range of microbial genera have been reported to degrade
hydrocarbons (Atlas, 1981; Rosenberg, 1992).
6.
RESEARCH OBJECTIVES
Petroleum compounds are known environmental pollutants. They contaminate the soil
through spillages of oil or diesel pipelines, maintenance activities at workshops,
leakages from locomotives at diesel depots and underground storage tanks, spillages
from oil trucks accidents, and many others.
The literature reveals that stresses exerted by hydrocarbons on soil microbial
communities results in a decrease in microbial diversity and an emergence of the
dominant populations, which have enhanced physiological tolerances and substrate
utilisation patterns (Atlas et al., 1991; Wünsche et al., 1995). However, these studies
only investigated the influence of hydrocarbons using mainly the topsoil. Information
about the microbial diversity of different soil layers at a given site is lacking. Because oil
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contamination normally penetrates deeper than the top layer, it is important to
understand the distribution of degrading populations with soil depth and how the
distribution patterns influence the efficiency of biodegradation. The biological removal of
hydrocarbons in different soil layers is evaluated in Chapter 2.
Previously, microbial community structure was evaluated using culture dependent
methods and most recently culture independent methods. In addition to the biological
removal of hydrocarbons, Chapter 2 further evaluates microbial diversity in hydrocarboncontaminated soil using both the community level physiological profiles (CLPP) and
Polymerase Chain Reaction-Denaturing Gradient Gel Electrophoresis (PCR-DGGE).
The objective was to assess the potential of the two methods to complement each other
in assessing environmental disturbances brought by hydrocarbon contamination. As the
contaminated soil is often poor in organic matter and can have a general low microbial
activity, the hydrocarbon removal capacity and microbial diversity of different soil layers
is also evaluated (Chapter 2).
The impact of environmental stresses on microbial diversity has been well documented
(Atlas et al., 1991). However, information about the importance of geographical origin of
the soil samples and hydrocarbons is lacking. It is not known if the geographical origin of
the samples or the pollutants is more important in determining functional or species
diversity within bacterial communities. Chapter 4 assesses the importance of
geographical origin of the soil contaminated by similar pollutants. In addition, the effect
of different hydrocarbons on microbial diversity of similar soil environments was also
investigated.
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The heterotrophic bacteria and more specifically the hydrocarbon utilising bacteria play
an important role in the restoration of hydrocarbon-contaminated soils. However, the
distribution and heterotrophic diversity of these bacteria at different sites conducting
similar anthropogenic activities is not known. Chapter 3 evaluates the heterotrophic
diversity and microbial activities at different diesel depots contaminated by similar
hydrocarbons.
Plant-mediated removal of organic pollutants has been reported with relative success
(April and Sims, 1990; Lee and Banks, 1993; Walton et al., 1994; Günther et al., 1996;
Reilly et al., 1996).
Laboratory and greenhouse experiments on plant-mediated
dissipation of polycyclic aromatic hydrocarbons (PAHs) have concentrated mainly on the
use of monoculture rhizoremediation of PAHs from the soil (April and Sims, 1990; Lee
and Banks, 1993; Walton et al., 1994; Günther et al., 1996; Reilly et al., 1996). The
information about the effectiveness of multi-plant rhizoremediation of PAHs is lacking.
The use of multi-plant microcosms or microcosms with mixed planted species has the
potential to increase soil heterogeneity (Angers and Caron, 1998) and microbial
diversity, which can improve the microbial competence of the soil bacteria for effective
pollutant removal. Chapter 6 evaluates the effectiveness of multi-plant rhizoremediation
compared to monoculture rhizoremediation of PAH contaminated soil.
Soil remediation is expensive. While biological removal of pollutants is often regarded as
cost-effective compared to other non-biological technologies, there are inherent
constraints to biological treatment technologies. Chapter 8 reviews the simplest of
technology (landfarming) and how best to implement it (for effective pollutant removal)
and the prevention of potential health and environmental problems.
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The progress of soil remediation is usually monitored by conventional chemical analysis
of pollutants using Gas Chromatography-Mass Spectrometer (GC-MS). However, these
form of monitoring is not only expensive, it also requires a high degree of skill to use the
analytical instruments. The use of bioindicators to monitor the removal of hydrocarbons
is an alternative. In Chapter 6, we assess the potential of Lepidium sativum as a
bioindicator of hydrocarbon removal from the soil. The review of potential bioindicators of
hydrocarbon removal is also reviewed in Chapter 7.
Biological process cannot only be used for soil remediation only; they can also be used
as potential bioindicators of biological removal of pollutants and can therefore act as
monitoring ‘instruments’.
The main aim of this work was to evaluate the microbial diversity of the different soil
environments disturbed by Total Petroleum Hydrocarbons (TPHs) and the potential use
of plants and microorganisms in monitoring and removing hydrocarbons from the soil. In
addition, the potential of the culture-independent methods in complementing the culturedependent methods when evaluating soil microbial diversity were also evaluated.
7.
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Chapter 2
EVALUATION OF MICROBIAL DIVERSITY OF DIFFERENT SOIL LAYERS AT A
CONTAMINATED DIESEL SITE
A modified version of this text was accepted for publication as:
Mphekgo P. Maila, P Randima, K Drønen, K Surridge and Thomas E Cloete
(2004) Evaluation Of Microbial Diversity Of Different Soil Layers At A Contaminated
Diesel Site. International Biodegradation and Biodeterioration.
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EVALUATION OF MICROBIAL DIVERSITY OF DIFFERENT SOIL LAYERS AT A
CONTAMINATED DIESEL SITE
Abstract
In this study, we evaluated the hydrocarbon removal efficiency and microbial diversity of
different soil layers. The soil layers with high counts of recoverable hydrocarbon
degrading bacteria had the highest hydrocarbon removal rate compared to soil layers
with low counts of hydrocarbon degrading bacteria. Removal efficiency was 48% in the
topsoil compared to 31% and 11% in the 1.5 m and 1 m respectively. There was no
significant difference between the Total Petroleum hydrocarbon (TPH) removal in the
nutrient amended treatments and the controls at 1 m and 1.5 m soil layers. The
respiration rate reflected the difference in the number of bacteria in each soil layer and
the availability of nutrients. The high O2 consumption rate corresponded positively with
the high TPH removal rate. Analysis of the microbial diversity in the different soil layers
using functional diversity (community level physiological profile using Biolog) and genetic
diversity using Polymerase Chain Reaction-Denaturing Gradient Gel Electrophoresis
(PCR-DGGE) of 16 SrDNA revealed differences in substrate utilisation patterns and
DGGE profiles of 16 SrDNA fragments respectively. The microbial diversity as revealed
by DNA fragments was reduced in the highly contaminated soil layer (1.5 m) compared
to the topsoil and the soil layer at 1 m.
Introduction
The effect of hydrocarbon contamination on soil microbial communities has been studied
(Atlas et al., 1991; Wünsche et al., 1995; Lindstrom et al., 1999; MacNaughton et al.,
1999; Stephen et al., 1999; Juck et al., 2000; Bundy et al., 2002). However, these
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studies only investigated the influence of hydrocarbons using mainly the topsoil.
Information about the microbial diversity of different soil layers at a given site is lacking.
Because oil contamination normally penetrates deeper than the top layer, it is important
to understand the distribution of degrading populations with soil depth and how the
distribution patterns influence the efficiency of biodegradation.
The subsurface soil environment, though devoid of sufficient nutrients, oxygen and other
factors, harbors an array of soil microorganisms that plays an important role in
decomposition and the recycling of nutrients (Krumholz, 1998). It is widely presumed
that the number of heterotrophic bacteria changes with increasing depth. This can be
attributed to spatial and resources factors which can influence the microbial diversity of
the soil (Zhou et al., 2002). Shallow subsurface micro-flora appears to be predominantly
prokaryotic, appears to be specially adapted for growth and survival in nutrient poor
conditions, includes strains that can function throughout a wide range of nutrient
concentrations and may sometimes exert significant effect on groundwater chemistry
(Ghiorse and Balkwill, 1983; Balkwill and Ghiorse, 1985; Bone and Balkwill, 1988;
Ghiorse and Wilson, 1988; Balkwill et al., 1989).
The availability of hydrocarbons in the vadose zone can alter the diversity of the
heterotrophic community due to an increase in the carbon substrate. According to Atlas
(1981), Leahy & Colwell (1990), the number of hydrocarbon bacteria and their relative
abundance in the bacterial communities increases significantly in the presence of readily
available hydrocarbons. Also the changes in hydrocarbon content in soil results in
characteristic shifts of the substrate utilisation patterns by the microorganisms and that
the altered pattern of substrate utilisation corresponds with similar changes in
abundance of hydrocarbons in the soils (Wünsche et al., 1995). This is not surprising,
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and in accordance with the theories about gene accumulation and selection pressures,
we can predict lower abundance of hydrocarbon degraders with depth as selection
pressure and growth conditions in general lowers with depth.
In this study, we investigated the hydrocarbon removal capacity and the microbial
diversity of different soil layers after diesel contamination. The capacity of the soil layers
in removing hydrocarbons was evaluated (using simple microbial assays), while
microbial diversity was evaluated using functional diversity (community level
physiological profiles using Biolog micro plates) and genetic diversity (PCR-Denaturing
Gradient Gel Electrophoresis of 16SrRNA).
Materials and Methods
Soil: The contaminated soil layers were collected in sterile bags from a dieselcontaminated site at Coalsbrook, in the Free State Province, South Africa. The soil
collected was a loam soil with depth to ground water, 2m. The organic carbon of the
uncontaminated topsoil was 0.9%. The electron acceptors were not measured. The soil
layers were collected one month after contamination by a leaking diesel pipeline. Direct
push drilling to 2 m was used to sample the contaminated soil layers at a depth of 1 m
(CS1m) and 1.5 m (CS1.5m). The contaminated topsoil layer (CTS) was collected within
10 cm of the soil surface. Uncontaminated topsoil (UCTS) was also collected from the
same site. Samples were kept at 4°C until analysis, which was completed within 24 h.
Microbiological analysis: 100 mℓ of 0.2% tetra-sodium pyrophosphate was added to
250 mℓ Erlenmeyer flask containing 10 g of the soil from each sample. The flasks were
placed on a shaker (140 rpm) for 45 min. The mixtures in the flasks were allowed to
settle for 5 min after mixing. Serial dilutions (with saline solution) were done using the
samples before inoculating both the agar plate and the Biolog GN plates. The Total
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Recoverable Heterotrophs (TRHs) were enumerated by spread plate technique using
nutrient agar (Biolab Diagnostics). The hydrocarbon-degrading bacteria were isolated as
described by Margesin and Schinner (1999a) with diesel being the only source of carbon
and energy. Both agar plates were incubated at 28°C and counted after 24 h and 7 d
respectively. The bacterial counts were not corrected for the dry mass of the soil.
Bacterial counts were done in triplicates. Analysis of Variance (ANOVA) was used to
determine the difference between the treatments.
Carbon source utilization pattern determination: Sample dilutions were done as
described above. Heterotrophic plate count data were used to adjust the samples to
similar cell density for Gram Negative Biolog plate inoculation. 100 µℓ of the each sample
was added to each well. The Biolog plates were read at 600 nm using Bio-Tek Elx800
microreader (Bio-Tek Instruments Inc) before incubation at 28°C. The plates were further
read after 24, 48 and 72 h. Readings of the micro plates were made in triplicate.
Statistical analyses were done using STATISTICA for Windows release 5.1.
Respiration rate determination: The biological activity of the different samples was
evaluated by monitoring oxygen consumption using a Micro-Oxymax Respirometer
(Columbus Instruments). 100 g of each soil layer was added to a 250 mℓ bottle
containing 10 mℓ nutrients (mineral salt medium) with no carbon and energy source. The
nutrients were added to the soil layers to stimulate bioremediation in hydrocarbon
contaminated layers. The treatments in which no nutrients were added to the soil, served
as control. The O2 consumption was measured over 5 d. The composition of the nutrient
solution was (g ℓ-1 in the medium): 10 g ℓ-1 Na2HPO4, 10 g ℓ-1 KH2PO4, 2.5 g ℓ-1
(NH4)2SO4, 0.4 g ℓ-1 MgSO4, 0.05 g ℓ-1 CaCl2.2H2O, 0.0086 g ℓ-1 EDTA, 0.01 g ℓ-1
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FeSO4.7H2O, 0.004 g ℓ-1 ZnSO4.7H2O, 0.01 g ℓ-1 MnSO4.H2O, 0.0015 g ℓ-1 CuSO4.5H2O,
0.0008 g ℓ-1 Co(NO3)2 .6H2O, 0.0001 g ℓ-1 (NH4)6Mo7O24.4H2O.
Chemical Analysis: The contaminated soil layers were analysed using the Total
Petroleum Hydrocarbons (TPH) method described in Margesin et al. (1999). 10 g of the
contaminated soil were used for the analysis. The analyses were done in triplicates.
DNA extraction and purification: Total DNA was isolated from the soil using the
Bio101 extraction kit (Bio Inc.)
PCR conditions: A 1 µℓ volume of the extracted DNA was amplified by PCR with a 9600
thermal cycler (Perkin-Elmer/Cetus). The PCR mixture used contained 100 µm each
primer, 100 mM each deoxy-nucleoside triphosphate, 5 µℓ of 10x PCR buffer, 0.25 µℓ
(5 U/µℓ) of hot start polymerase, (Perkin-Elmer, Roche Molecular Systems, Branchburg,
NJ), 2.5 µℓ of 2% Bovine Serum Albumin, 40 µℓ sterile water to a final volume of 50 µℓ.
The 16S rRNA genes from soil microbial communities were amplified by PCR using the
primers, pA8f-GC (5'-CGC-CCG-CCG-CGC-GCG-GCG-GGC-GGG-GCG-GGG-GCACGG-GGG-GAG-AGT-TTG-ATC-CTG-GCT-CAG-3')
and
KPRUN518r
(5'ATTACCGCGGCTGCTGG-3’). The primers were found to be useful for 16S rRNA
gene amplification in ecological and systematic studies (Øvreås and Torsvik, 1998).
Samples were amplified as follows: 95°C for 10 min, 30 cycles of denaturation (1 min at
94°C), annealing (30 sec at 51°C), and extension (1 min at 72°C) and a final extension
at 72°C for 10 min. Amplified DNA was examined by horizontal electrophoresis in 1%
agarose with 5 µℓ aliquots of PCR product.
30
University of Pretoria etd – Maila, M P (2005)
DGGE:
DGGE was performed using Hoefer SE600 vertical dual cooler system (Hoefer
Scientific, San Francisco, CA). PCR samples were loaded onto 8% (wt/vol)
polyacrilamide gels in 0.5x TAE (20 mM Tris, 10 mM acetate, 0.5 mM Na-EDTA,
pH 7.4). The 8% (wt/vol) polyacrylamide gels (bisacrylamide gel stock solution, 37.55:1;
BioRad Laboratories, Inc) were prepared with a 20 to 55% gradient of denaturant (urea
and formamide) (and allowed to polymerise). The electrophoresis was run at 60°C, first
for 10 min at 20 V, and subsequently for overnight at 70 V. After electrophoresis, the
gels were stained for 15 min in SYBR Green I nucleic acid gel stain rinsed in distilled
water for 1 min and photographed with a Polaroid MP4 Land camera. The gels were
analysed using a software program developed by Svein Norland (Department of
Microbiology, University of Bergen), where presence/absence of bands was recorded.
Clustering was based on the simple matching algorithm, while the dendrogram was
drawn applying the group average method.
Diversity Indices
The Shannon index, H’ (Shannon, 1948), was calculated (log 2) on the basis of biotypes
defined in the cluster analysis on data retrieved from PCR-DGGE. The equitability J
(Pielou, 1966) index was also calculated (Watve and Gangal, 1996).
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University of Pretoria etd – Maila, M P (2005)
Results
Microbiological analysis
The number of Total Recoverable Heterotrophs (TRHs) decreased with soil depth
(Figure 1). Similar results were obtained for the hydrocarbon-degrading bacteria. There
was not much difference in the number of TRHs and hydrocarbon-degrading bacteria in
the 1 m and 1.5 m samples.
Log No. of bacteria (CFU/g soil)
7
6
5
Total Culturable Heterotrophs
(TCHs)
4
Hydrocarbon-Degrading-Bacteria
3
Hydrocarbon-Degrading-Bacteria
after 5d incubation
2
1
0
Top Soil
Uncont.Top
Soil
Soil at 1m
Soil at 1.5m
Different Soil Layers
Figure 1. Bacterial counts of different soil layers at Coalsbrook diesel contaminated site
The Total Petroleum Hydrocarbon (TPH) concentration was highest in the soil layer at
1.5 m, followed by the topsoil and then at 1 m (Figure 2). However, TPH removal during
the period of incubation reflected the difference in the number of bacteria in the samples
as it was highest in the topsoil compared to the other soil layers (1 m and 1.5 m).
32
University of Pretoria etd – Maila, M P (2005)
There was no significant difference (p<0.01) between the TPH removal in the nutrient
amended treatments and the controls (at 1 m and 1.5 m soil layers) Figure 2. Removal
efficiency was 48% in the topsoil compared to 31% and 11% in the 1.5 m and 1 m
respectively. Soil layers with the high recoverable hydrocarbon degrading bacteria had
the highest removal capacity compared to soil layers with low counts of hydrocarbon
degrading bacteria.
35000
30000
TPH (mg/kg soil)
25000
TPH concentration during day 0
20000
TPH concentration after 5d
incubation
15000
TPH concentration after 5d inc. in
nutrient amended soils
10000
5000
0
Top Soil
Soil at 1m
Soil at 1.5m
Different Soil Layers
Figure 2. The concentration of hydrocarbons in Coalsbrook's different soil layers
Respiration rate determination
The respiration rate reflected the difference in the number of bacteria in each soil layer
and the availability of nutrients. The O2 consumption rate was highest in the
contaminated topsoil (CTS) compared to other soil layers (Figure 3a). Oxygen
consumption rate was also highest in the nutrient amended treatments compared to the
‘controls’ (Figure 3a and b). The respiration rate in the uncontaminated topsoil was low
33
University of Pretoria etd – Maila, M P (2005)
compared to the contaminated soil layers at the top, 1.5 and 1 m. The respiration rate
corresponded positively with the high TPH removal rate (Figure 2).
0.8
Oxygen(mg/h)
0.7
0.6
CTS
0.5
UCTS
0.4
CS1M
0.3
CS1.5M
0.2
0.1
96
10
2
10
8
11
4
90
84
78
72
66
60
54
48
42
36
30
24
18
6
12
0
0
Time (Hours)
a)
Oxygen (mg/h)
1.2
1
CTSn
0.8
UCTSn
0.6
CS1Mn
0.4
CS1.5Mn
0.2
10
2
10
8
11
4
96
90
84
78
72
66
60
54
48
42
36
30
24
18
12
6
0
0
Time (Hours)
b)
Figure 3. Oxygen consumption by microorganisms in different diesel contaminated soil
layers. a) Soil layers with no nutrients, b) Soil layers with nutrients. CTSn-Contaminated
topsoil
with
nutrients,
CTS-Contaminated
topsoil
with
no
nutrients,
UCTS-
Uncontaminated topsoil, UCTSn-Uncontaminated topsoil with nutrients, CS1MnContaminated soil at 1m depth with nutrients, CS1 m-Contaminated soil at 1 m with no
nutrients, CS1.5Mn-Contaminated soil at 1.5 m depth with nutrients, CS1.5 MnContaminated soil at 1.5 m with no nutrients.
34
University of Pretoria etd – Maila, M P (2005)
Biolog Analysis
The Principle Component Analysis (PCA) of the colour response data of the soil layers
revealed different substrate utilisation pattern (Figure 4).
PCA was performed to characterize the associations amongst samples, taking into
account the absorbance values for all 96-response wells at the different incubation
times. Two principal factors were isolated from the individual UCTS, CTS, CS 1 m and
CS 1.5 m patterns, which explained 58% of the variation. This low percentage of
variation, explained by the two factors, can be a result of the few samples used in the
analysis. The use of more samples would probably improve the variation explained by
the two factors. For the four samples, factor one was related to the absorbance values
for the wells, while factor 2 was related to the incubation time.
Factor Loadings, Factor 1 vs. Factor 2
Rotation: Varimax normalized
Extraction: Principal components
1.2
CTS
1.0
Factor 2
0.8
0.6
0.4
UCTS
0.2
0.0
0.1
CS1_5M
CS1M
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
Factor 1
Figure 4. The substrate utilization pattern of different soil layers as depicted by Principle
Component Analysis (PCA). CTS-Contaminated topsoil, UCTS-Uncontaminated topsoil,
CS1 M-Contaminated soil at 1 m, CS1.5M-Contaminated soil at 1.5 m.
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University of Pretoria etd – Maila, M P (2005)
The relationship between the substrate utilization patterns was further analysed using
hierarchical clustering. In a dendrogram (Figure 5), the results of cluster analysis also
showed that the metabolic activities of CS1m were ‘closely’ related to CS1.5m than to
UCTS and CTS. The uncontaminated topsoil (UCTS) was different from the
hydrocarbon-contaminated topsoil (CTS).
Both the dendrogram and the PCA illustrate that the substrate utilization pattern of the
microbial communities in different soil layers are different.
Tree Diagram for 4 Variables
Single Linkage
Euclidean distances
CTS
UCTS
CS1M
CS1_5M
4.5
5.0
5.5
6.0
6.5
7.0
7.5
8.0
8.5
9.0
9.5
Linkage Distance
Figure 5: Cluster Analysis of the different soil layers. CTS-Contaminated topsoil, UCTSUncontaminated topsoil, CS1 M-Contaminated soil at 1 m, CS1.5M-Contaminated soil at
1.5 m.
PCR-DGGE
DGGE profiles of amplified 16S rDNA fragments from DNA extracted from the soil
bacterial fractions revealed differences in the DNA fingerprint of the different soil layers.
36
University of Pretoria etd – Maila, M P (2005)
The profiles of the contaminated and uncontaminated samples (5 and 6) were different
(Figure 6) while the profile of the soil layer at 1.5m was difficult to resolve.
a)
b)
Figure 6.(a) Cluster analysis of microbial communities at different soil layers and (b)
DGGE fingerprints. UCTS - Uncontaminated topsoil, CTS - Contaminated topsoil, CS1m
- Contaminated soil layer at 1 m, CS1.5m - Contaminated soil layer at 1.5 m. M - Marker.
Lines beneath the numbers represent the band detected.
Table 1: Diversity Indices
*Soil Type
Shannon Index (H’)
Equitability Index (J’)
UCTS
2.120667
0.826787
CTS
2.52069
0.909147
CS1m
1.981935
0.731868
CS1.5m
1.84471
0.947993
*Abbreviations as in Figure 4.
Cluster analysis using a dendrogram revealed that the soil at 1 m was closely related to
the contaminated topsoil than to uncontaminated topsoil. The Shannon diversity index
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University of Pretoria etd – Maila, M P (2005)
(H’) revealed high diversity in the topsoil compared to other soil layers (table 1). The
equitability (J) of all populations ranged from 0.73 to 0.95.
Discussion and Conclusion
In this study the number of TRHs and hydrocarbon-degrading bacteria decreased with soil
depth. This can be attributed to nutrient and oxygen limitations to the biota of the soil
subsurface. According to Zhou et al. (2002), spatial and resources factors influence microbial
diversity in soil. Similar results were obtained with the hydrocarbon degrading bacteria after
incubation for five days. However, there was little difference in the number of TRHs and
hydrocarbon degrading bacteria in the 1-m and 1.5-m samples, possibly owing largely to
similarities of nutrient levels in the soil layers.
The TPH concentration was greatest in the soil layer at 1.5 m, followed by the topsoil and the
soil at 1 m. The high concentration of hydrocarbons at a depth of 1.5 m indicates the potential
mobility of the pollutants to deeper soil layers. The removal of TPH reflected the number of
bacteria in each soil layer. Removal efficiency was 48% in the topsoil compared with 31% and
11% in the soil at 1.5m and 1m, respectively. There was no significant difference between the
TPH removal in the nutrient amended treatments and the controls (at 1 m and 1.5 m soil
layers). The depth to ground water was 2 m and owing to the proximity of the 1.5 m layer to
water, microbial activity could be higher at 1.5 m than at 1 m.
The respiration rate corresponded positively with TPH removal rate and reflected the difference
in the number of bacteria in each soil layer and also the availability of nutrients. The nutrients
were added to the contaminated soil to stimulate the biological removal of hydrocarbons
(Churchill et al., 1995; Braddock et al., 1997; Seklemova, 2001).
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University of Pretoria etd – Maila, M P (2005)
Since it is well known that different soil layers harbour different numbers of bacteria, it was
expected that the soil layers would have a different pattern of substrate utilisation, especially as
both PCA and cluster analysis revealed differences between the soil layers.
The relationship between the substrate utilisation patterns was further analysed using
hierarchical clustering. In a dendrogram, the results of cluster analysis showed that cs1 m was
more ‘closely’ related to cs1.5 m than to ucts and cts. This can be attributed to the similarities
in nutritional (organic matter, limiting nutrients) and environmental conditions (pH, temperature)
of the two soil layers. However, the closeness of the 1-m and 1.5-m samples was not evident
when clustering the DGGE profiles of the 16S rDNA fragments.
The difference between ucts and cts evident in both substrate utilisation patterns and DGGE
(Figures 4 and 6) can be attributed to the changes in the composition of microbial populations
brought about by changes in hydrocarbon content (Atlas et al., 1991; Wünsche et al., 1995).
The Shannon diversity index revealed high diversity in the topsoil compared with other soil
layers. This can be attributed to the relatively high amount of nutrients (organic matter and
possibly limiting nutrients) in the topsoil compared with the other soil layers. The diversity
indices corroborated the results of functional diversity. High removal capacity of the topsoil can
be attributed to the different number of bacteria that are capable of degrading the
hydrocarbons.
The DGGE profiles of amplified 16S rDNA fragments for the 1.5-m layer were difficult to
resolve, however, the bands detected by the software program revealed few fragments
relative to the other soil layers. The use of Archae primers instead of bacterial primers
may enhance the chance of revealing the differences between the DGGE profiles of the
39
University of Pretoria etd – Maila, M P (2005)
different soil layers (Øvreås and Torsvik, 1998). The data on both the functional and
genetic diversity revealed that the two approaches of studying microbial diversity can
complement each other, as the community level physiological profiles (CLPP) measures
the metabolic activities of different environmental samples while the PCR-DGGE
provides information about the microbial structure. The data on CLPP and PCR-DGGE
suggest that in combining the functional and genetic approaches of assessing microbial
diversity there is the potential to provide a clear picture about the abundance of a variety
of species in an ecosystem. Further studies are required in order to understand the
effect of not only other pollutants but also the influence of soil components (pore volume,
level of adsorbents and other environmental factors) on the microbial diversity of
different soil layers in both ‘shallow’ and deep aquifers. The results suggest that in
hydrocarbon-contaminated soil biological removal of hydrocarbons differs with soil layer,
and also that the microbial diversity (as measured by CLPP and PCR-DGGE) varies with
depth. However, as the study was conducted using soil that was contaminated for a
‘relatively short period’, further studies are required using soil that has been
contaminated for longer period as this can yield further information about the structure
and function of a stable microbial community in hydrocarbon contaminated soil. It was
also not clear in the current study, if the Biolog test was reflecting the real functionality of
a fast changing microbial community. This requires further evaluation by comparing the
‘aged’ and ‘non-aged’ hydrocarbon contaminated sites.
It will also be important to
investigate the influences of different soil types, groundwater level, total organic carbon
and the electron acceptors on microbial diversity of different soil layers. Further studies
of microbial diversity of contaminated soil layers should also include the study on
microbial diversity of uncontaminated soil layers. Information about metabolic activities
of different soil layers is critical when assessing the footprints of degradation processes
during monitored natural attenuation (Smets et al., 2002).
40
University of Pretoria etd – Maila, M P (2005)
References
1.
Atlas, R.M., Horowitz, A., Krichevsky, M. and Bej, A.K.
1991. Response of
microbial populations to environmental disturbance. Microbial Ecology. 22: 249256.
2.
Balkwill, D.L., Fredrickson, J.K. and Thomas, J.M. 1989. Vertical and horizontal
variation in the physiological diversity of the aeroboc chemoautotrophic bacterial
microflora in the deep southeast coastal plain subsurface sediments. Applied and
Environmental Microbiology. 55: 1058-1065.
3.
Balkwill, D.L. and Ghiorse, W.C. 1985. Characterisation of subsurface bacteria
associated with two shallow acquifers in Oklahoma. Applied and Environmental
Microbiology. 50: 580-588.
4.
Bone, T.L. and Balkwill, D.L. 1988. Morphological and cultural comparison of
microorganisms in surface soil and subsurface sediments at a pristine study sites
in Oklahoma. Microbial Ecology. 16: 49-64.
5.
Braddock, J., Ruth, M., Catteral, P., Walworth, J. and Mcarthy, K. 1997.
Enhancement and inhibition of microbial activity in hydrocarbon contaminated
aerctic soils: implications for nutrient amended bioremediation. Environmental
Scence and Technology. 31: 2078-2084.
6.
Bundy, J.G., Paton, G.I. and Campell, C.D. 2002. Microbial communities in
different soil types do not converge after diesel contamination. Journal of Applied
Microbiology. 92: 276-288.
7.
Churchill, S.A., Griffin, R.A., Jones, L.P. and Churchill, P.F. 1995. Biodegradation rate enhancement of hydrocarbons by an oleophilic fertilizers and
rhamnolipid biosurfactant. Journal of Environmental Quality. 24: 19-28.
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8.
Ghiorse, W.C. and Balkwill, D.L. 1983. Enumeration and morphological
characterisation
of
bacteria
indigenous
to
subsurface
environments.
Developments in Industrial Microbiology. 24: 213-224.
9.
Ghiorse, W.C. and Wilson, J.T. 1988. Microbial ecology of the terrestrial subsurface. Advances in Applied Microbiology. 33: 107-172.
10.
Juck, D., Charles, T., Whyte, L.G. and Greer, C.W. 2000. Polyphasic microbial
community analysis of petroleum hydrocarbon-contaminated soils from two
northern Canadian communities. FEMS Microbiology Ecology. 33(3) 241-249.
11.
Krumholz, L.R. 1998. Microbial ecosystems in the earth’s subsurface. ASM
News. 64:197-202.
12.
Leahy, J.G. and Colwell, R.R. 1990. Microbial degradation of hydrocarbons in
the environment. Microbiological Reviews. 54: 305-315.
13.
Lindstrom, J.E., Barry, R.P. and Braddock, J.F. 1999. Long-term effects on
microbial communities after a subarctic oil spill. Soil Biology and Biochemistry.
31: 1677-1689.
14.
MacNaughton, S.J., Stephen, J.R., Venosa, A.D., Davis, G.A., Chang, Y.J. and
White, D.C. 1999. Microbial population changes during bioremediation of an
experimental oil spill. Applied and Environmental Microbiology. 65(8): 3566-3574.
15.
Margesin, R. and Schinner, F.
1999a. A feasibility study for the in situ
remediation of a former tank farm. World Journal of Microbiology and
Biotechnology. 15: 615-622.
16.
Margesin, R., Zimmerbauer, A. and Schinner, F. 1999. Soil lipase activity – a
useful indicator of oil biodegradation. Biotechnology Techniques. 13: 859-863.
17.
Øvreås, L. and Torsvik, V. 1998. Microbial diversity and community structure in
two different agricultural soil communities. Microbial Ecology. 36: 303-315.
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18.
Pielou EC. 1966. The measurement of diversity in different types of biological
collections. Journal of Theoretical Biology. 13:131-144.
19.
Seklemova, E., Pavlova, A. and Kovacheva, K. 2001. Biostimulation–based
bioremediation of diesel fuel: field demonstration. Biodegradation. 12: 311-316
20.
Shannon, C.E. 1948.
A mathematical theory of communication. Bell System
Technology. 27: 379-423.
21.
Smets, B.F., Siciliano, S.D. and Verstraete, W. 2002. Natural attenuation: extant
microbial activity forever and ever? Environmental Microbiology. 4(6): 315-317.
22.
Stephen, J.R, Chang. Y.J., Gan, Y.D., Peacock, A., Pfiffner, S.M., Barcelona,
M.J., White. D.C. and MacNaughton, S.J. 1999. Microbial characterisation of a
JP-4 fuel contaminated site using a combined lipid biomarker/polymerase chain
reaction-denaturing gradient gel electrophoresis (PCR-DGGE)-based approach.
Environmental Microbiology. 1(3): 231-241.
23.
Watve, M.G. and Gangal, R.M. 1996. Problems in measuring bacterial diversity
and a possible solution. Applied and Environmental Microbiology. 62:4299-4301.
24.
Wünsche, L., Bruggemann, L. and Babel, W. 1995. Determination of substrate
utilisation patterns of soil microbial communities: An approach to assess
population changes after hydrocarbon pollution. FEMS Microbiology Ecology. 17:
295-306.
25.
Zhou, J., Xia, B., Treves, D.S., Wu, L.Y., Marsh, T.L., O’Neill, R.V., Palumbo,
A.V. and Tiedje, J.M.
2002. Spatial and resources factors influencing high
microbial diversity in soil. Applied and Environmental Microbiology. 68(1): 326334.
43
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Chapter 3
EVALUATION OF MICROBIAL COMMUNITIES COLONIZING STONE BALLASTS
AT DIESEL DEPOTS
A modified version of this text has been accepted for publication as:
Mphekgo P. Maila, Thomas E. Cloete (2004) Evaluation of Microbial Communities
Colonizing Stone Ballasts at Diesel Depots. Journal of Water, Air and Soil Pollution
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University of Pretoria etd – Maila, M P (2005)
EVALUATION OF MICROBIAL COMMUNITIES COLONIZING STONE BALLASTS AT
DIESEL DEPOTS
Abstract
In this study, we evaluated the heterotrophic microbial communities colonising stone
ballasts at diesel depots. The number of bacteria (both total culturable heterotrophic
bacteria and hydrocarbon-degrading bacteria) was proportional to the level of
hydrocarbon contamination. However, there was no significant difference (p<0.01) in the
level of total culturable heterotrophs (TCHs) and the hydrocarbon degrading bacteria.
Addition of nutrients to the ballast stimulated the biological activity and possibly the
removal of hydrocarbons. However, this was only evident in the highly contaminated
stone ballasts samples. The biological activity was evaluated using CO2 production. The
production of CO2 was higher in nutrient amended treatments in which high numbers of
TCHs were present. Characterisation of heterotrophic communities using Biolog
revealed differences in the microbial metabolic profiles for the different sites. The results
suggest that the heterotrophic microbial communities at different diesel depots are
different.
Introduction
Diesel oil is a complex mixture of normal, branched and cyclic alkanes, and aromatic
compounds obtained from the middle–distillate, gas-oil fraction during petroleum
separation (WHO, 1996). These hydrocarbons have the potential to cause considerable
damage not only to the soil but also to water intakes or groundwater reservoirs due to
the mobility of some of the hydrocarbon compounds. At diesel depots, the contamination
of stone ballasts which are used as support structures for railroads also pose a
45
University of Pretoria etd – Maila, M P (2005)
significant risk to both the ground water resources and to humans as they can be
exposed to both volatile and non-volatile hydrocarbons.
The heterotrophic bacteria, or more specifically the hydrocarbon degrading bacteria, can
play an important role in mitigating these environmental problems as they can limit the
mobility of petroleum contaminants by degrading the pollutants to avoid, for example,
groundwater contamination. According to Atlas (1981), Leahy and Colwell (1990), the
number of hydrocarbon degrading bacteria and their relative abundance in the bacterial
communities increases significantly in the presence of readily available hydrocarbons.
Hydrocarbon utilizing bacteria are ubiquitously distributed in natural environments and
their proportions in the heterotrophic bacterial soil communities ranges from 0.13% to
50% (Jones et al., 1970; Pinholt et al., 1979).
Most knowledge into the effect of hydrocarbons on microbial diversity has been
generated using soil as a medium (Urzí et al., 1999; Bundy et al., 2002). The information
about microbial diversity of hydrocarbons polluted rocky surfaces is not well
documented. There is a need to study heterotrophic diversity on rocky surfaces as this
information can be useful in the cleanup of hydrocarbon contaminated stone ballasts.
Stone surfaces tend to accumulate inorganic and organic substances from the
surrounding
environment,
most
of
which
can
serve
as
nutrients
for
many
microorganisms (Urzí et al., 1999).
At diesel depots, the ballasts are continuously contaminated with hydrocarbons from the
parked locomotives and general maintenance work. These rocky surfaces used on
railroads are usually of the quartzite, fine-grained basic plutonic rock and other rock
types. The loading rate of the hydrocarbons on the ballast varies depending on the traffic
load at the depots and therefore the degree of contamination of the ballasts reflects the
46
University of Pretoria etd – Maila, M P (2005)
intensity of the activities at the depot. The concentration or thickness of hydrocarbons on
the ballast that encourages the attachment or colonization of the heterotrophs and the
hydrocarbon degrading bacteria will vary from site to site. The information about
heterotrophic diversity at contaminated rocky surfaces such as the hydrocarboncontaminated ballasts can be useful for bioremediation purposes.
In this study we investigated the heterotrophic diversity of polluted stone ballast at
different diesel depots using culture dependant methods, community level physiological
profiles and respiration. The aim of the experiment was to test the hypothesis that
hydrocarbons deposition on the stone ballast at different diesel depots selects for similar
microbial communities dominated by hydrocarbon degrading bacteria.
Materials and Methods
Stone Ballast: The contaminated stone ballasts were collected in sterile bags from
Koedoespoort (Kc), Sentrarand (SRc), Pyramid (Pc) and Springs (SPc) diesel depots, in
Gauteng province, South Africa. The uncontaminated stone ballasts were collected from
the Pyramid (Pu) and Sentrarand (SRu) diesel depots. The ballasts were ground using
Keegor jaw crusher (Dickie and Stockler Pty, SA) under sterile conditions to reduce the
size of the stones before analysis. The diameter of the ground stone ballasts used in the
experiment averaged 1cm. The ballasts had size distribution when collected and the
proportion of exterior contaminated stones after grinding was much higher than the
interior uncontaminated stones. The exterior contaminated stones averaged 1cm in
diameter while the interior uncontaminated stones averaged less than 0.5cm.
The
ground ballasts averaging 1cm in diameter were used in the experiment. The stone used
as ballasts at diesel depots were prepared using fine-grained basic plutonic rocks.
Samples were kept at 4°C until analysis. All analyses were done within 24 hours.
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University of Pretoria etd – Maila, M P (2005)
Enumeration of Microorganisms: 250 mℓ of 0.2% tetrasodium pyrophosphate was
added to 500 mℓ Erlemeyer flask containing 25 g of the grinded stone ballasts from each
diesel depots. The ballasts were grinded using Keegor jaw crusher (Dickie and Stockler
Pty, SA) under sterile conditions to reduce the size of the stones before adding them into
the flasks. The diameter of the grinded stone ballast averaged 1cm. The flasks were
placed on a shaker (140 rpm) for 45 min. The mixtures in the flasks were allowed to
settle for 5 min after mixing. Serial dilutions (with saline solution) were done using the
samples before inoculating both the agar plates and the Biolog GN plates. The total
culturable heterotrophs (TCHs) were enumerated by spread plate technique using
nutrient agar (Biolab Diagnostics). The hydrocarbon-degrading bacteria were isolated
using the mineral salt medium in which the filter-sterilised diesel 3% (v/v) was used as
the sole carbon and energy source. The composition of the mineral salt medium is
shown in table 1. Bacteriological agar (15g/ℓ, Biolab Diagnostics) was autoclaved
(121°C, 15 min) before adding the sterile solutions (table 1). Cycloheximide (200 mg/ℓ)
was added in both media to inhibit fungal growth. Both plates were incubated at 28°C
and counted after 24 h and 7 d respectively. Bacterial counts were done in triplicate
(three true replicates stones were ground to prepare the dilution series). ANOVA
(Analysis of Variance) was used to determine the difference between the TCHs and
hydrocarbon degrading bacteria.
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University of Pretoria etd – Maila, M P (2005)
Table 1: Composition of Mineral Salt Medium
Trace mineral
solutions
A
B
C
D
E
Compounds
g/ℓ stock
Na2HPO4
KH2PO4
( NH4 )2SO4
MgSO4
CaCl2.2H2O
EDTA
FeSO4.7H2O
ZnSO4.7H2O
MnSO4.H2O
CuSO4.5H2O
Co(NO3)2 .6H2O
(NH4)6Mo7O24.4H2O
141.96
136.09
100
19.7
1.15
0.64
0.55
0.23
0.34
0.075
0.047
0.025
Concentration
in media g/ℓ
10
10
2.5
0.4
0.05
0.0086
0.01
0.004
0.01
0.0015
0.0008
0.0001
Carbon source utilisation pattern determination: Sample dilutions were done as
described above. Dilutions yielding similar bacterial counts in the samples from SRc, Kc
and SPc were used for carbon utilization pattern analysis. 100 µℓ of the each sample
was added to each well (Biolog GN microplates). The optical density (OD600) of the
plates was measured using Bio-Tek Elx800 microreader (Bio-Tek Instruments Inc) at
time 0 and after 24, 48, and 72 h of incubation at 28°C. Three Biolog plates were used
per treatment. Statistical analyses were done using STATISTICA for Windows release
5.1.
Respiration rate determination: The biological activity of the contaminated ballasts
was evaluated by monitoring carbon dioxide production using a Micro-Oxymax
Respirometer (Columbus Instruments). 20 g of the grinded contaminated stone ballasts
were added to a 250 mℓ bottle containing the sterile 150 mℓ nutrients (mineral salt
medium) with no carbon and energy source. The nutrients were added to the
contaminated ballast to stimulate bioremediation. The treatments in which sterile
deionised water was added to the ballast, instead of nutrients, served as controls. The
49
University of Pretoria etd – Maila, M P (2005)
CO2 production was measured over 46 h. The composition of the different solutions used
to prepare the nutrient solution is shown in table 1.
Chemical Analysis: The contaminated stone ballasts were ground as described above
and Total Petroleum Hydrocarbons (TPH) was analysed using the EPA 418.1 method
(US Environmental Protection Agency, 1979). 25 g of the ground ballasts were used for
the analysis. After extraction of the ballasts with 1,1,2-trichloro-trifluoro-ethane, the
hydrocarbon content was quantified by infrared spectroscopy. The analyses were done
in triplicate.
Results
Microbiological and chemical analysis
The number of bacteria (both total culturable heterotrophs and hydrocarbon-degrading
bacteria) on the polluted ballast was proportional to the level of hydrocarbon
contamination (Figure 1). The number of total culturable heterotrophs (TCHs) on the
polluted stone ballast was highest in the Koedoespoort ballast (Kc), followed by the
Spring ballast (SPc), Sentrarand (SRc) and the Pyramid (Pc) ballast. Similar bacterial
counts for TCH and hydrocarbon-degrading bacteria were counted at both Pc and SRc.
No hydrocarbon-degrading bacteria and TCHs were isolated in the uncontaminated
Pyramid stone ballast (Pu) and uncontaminated Sentrarand ballast (SRu). The
hydrocarbon concentration at the diesel depots was highest at Koedoespoort depots
(Kc) followed by SPc, SRc and the Pyramid diesel depots. The number of bacteria (both
total culturable heterotrophic bacteria and hydrocarbon-degrading bacteria) was
proportional to the level of hydrocarbon contamination. Kc, the diesel depot sample with
the highest TPH concentration had the highest number of bacteria while Pc, the diesel
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University of Pretoria etd – Maila, M P (2005)
depot sample with the lowest concentration of TPH had the lowest number of culturable
6
70000
5
60000
50000
4
40000
3
30000
2
20000
1
TPH (mg kg ballast-1)
Log No. of Bacteria (CFU g
ballast-1)
heterotrophic bacteria (Figure 1).
10000
0
0
SPc
Pc
Pu
SRc
SRu
Kc
Diesel Depots
Total Culturable Heterotrophs (TCHs)
Hydrocarbon-Degrading Bacteria
Total Petroleum Hydrocarbons (TPH)
Figure 1. The number of culturable heterotrophic bacteria and the concentration of
hydrocarbons at different diesel depots. Values are the means of three replicates
(SD ± 1.4 or less). SPc-Springs contaminated ballast, Pc-Pyramid contaminated ballast,
Pu-Pyramid uncontaminated ballast, SRc-Sentrarand contaminated ballast, SRuSentrarand uncontaminated ballast, Kc-Koedoespoort contaminated ballast.
51
University of Pretoria etd – Maila, M P (2005)
Respiration rate determination
1
0.9
Pc
CO2 rate (mg h-1)
0.8
Kc
0.7
SPc
0.6
SRc
LKc
0.5
LPc
0.4
LSRu
0.3
LPu
LSRc
0.2
LSPc
0.1
44
40
36
32
28
24
20
16
12
8
4
0
0
Tim e (hours)
Figure 2: CO2 production by microorganisms in different diesel depots samples. PcPyramid contaminated stone ballast, Kc- Koedoespoort contaminated ballast, SPcSprings contaminated ballast, SRc-Sentrarand contaminated ballast, LKc- nutrient
amended Koedoespoort contaminated ballast, LPc- nutrient amended Pyramid
contaminated ballast, LSRu- nutrient amended Sentrarand uncontaminated ballast, LPunutrient amended Pyramid uncontaminated ballast, LSRc- nutrient amended Sentrarand
contaminated ballast, LSPc- nutrient amended Springs contaminated ballast.
Addition of nutrients stimulated the biological activity of the samples and possibly the
removal of hydrocarbons. However, this was only evident in the LSPc and LKc. The
biological activity as measured by CO2 production was very low in the LSRc, LPu, LSRu,
LPc and the control samples (Figure 2). The rate of CO2 production in the diesel depots
samples was higher in the nutrient amended samples in which high numbers (≥ 104
CFU/g ballast) of TCHs were present. No significant production of CO2 was evident in
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University of Pretoria etd – Maila, M P (2005)
the samples with low or no culturable heterotrophs (LSRc, LPc, LPu, LSRu and the
control samples).
Carbon source utilization Profiles
Factor Loadings, Factor 1 vs. Factor 2
Rotation: Varimax normalized
Extraction: Principal components
1.0
0.8
KC72
SRC72
SPC72
Factor 2
0.6
0.4
0.2
SRC24
SRC48
KC48
KC24
SPC24
0.0
-0.2
-0.1
SPC48
0.1
0.3
0.5
0.7
0.9
1.1
Factor 1
Figure 3. The relationship between microbial communities of different diesel depots as
depicted by principal component analysis. Kc-Koedoespoort contaminated ballast, SPcSprings contaminated ballast, SRc-Sentrarand contaminated ballast (readings for
different incubation times are shown).
Principle Component Analysis (PCA) was performed to characterize the correlation
amongst samples, taking into account the absorbance values for all 96-response wells.
Two principal factors were isolated from the individual SRc, Kc, SPc (Figure 3) patterns,
which explained 63% of the variation. For the three samples, factor one was related to
the absorbance values for the wells, while factor 2 was related to the incubation time.
The Kc and SRc samples (at 24 h and 48 h incubation periods) had a similar pattern of
carbon source utilization based on PCA (Figure 3). However, the similarities in carbon
53
University of Pretoria etd – Maila, M P (2005)
source utilization patterns appeared to diminish with increasing incubation time (after
48 h, Fig. 3). At 72 h, Kc and SRc were less similar than at the shorter incubation times,
and SPc is much less similar to Kc and SRc. The trend over time is greater divergence
of carbon source utilisation patterns, more so for SPc than for Kc and SRc.
The relationship between the substrate utilization patterns was further analysed using
hierarchial clustering. In a dendrogram (Figure 4), the results of cluster analysis
confirmed the degree of similarities between the samples (Figure 4).
Both the dendrogram and the PCA illustrate that the substrate utilization pattern of
microbial communities at different diesel depots were different.
Tree Diagram for 9 Variables
Single Linkage
Euclidean distances
SRC72
SRC48
SRC24
SPC24
KC24
KC72
KC48
SPC72
SPC48
2
3
4
5
6
7
Linkage Distance
Figure 4. The relationship between microbial communities of the different polluted stone
ballasts resulting from cluster analysis (readings for different incubation times are also
shown, abbreviations as in Figure 3).
54
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University of Pretoria etd – Maila, M P (2005)
Discussion and Conclusion
The microbiological analysis indicated that the number of culturable bacteria on the
polluted ballasts was proportional to the level of hydrocarbon concentration. Kc, the
diesel depot sample with the highest TPH concentration had the highest number of
culturable heterotrophic bacteria while Pc, the diesel depot sample with the lowest
concentration of TPH had the lowest number of culturable heterotrophic bacteria. This
can be attributed to the availability of the readily biodegradable hydrocarbons on the
ballast, which can encourage the colonisation of the heterotrophs on the ballasts.
According to Atlas (1981), Leahy and Colwell (1990), the number of hydrocarbon degrading bacteria and their relative abundance in the bacterial communities increases
significantly in the presence of readily biodegradable hydrocarbons. The analysis of
variance indicated that the number of total culturable heterotrophs (TCHs) were not
significantly higher than the number of hydrocarbon-degrading bacteria. The results
suggest that the number of hydrocarbon degrading bacteria forms the majority of the
total culturable heterotrophic bacteria in the hydrocarbon-contaminated stone ballasts.
There was no hydrocarbon degrading bacteria and the TCHs that were isolated in the
uncontaminated Pyramid and uncontaminated Sentrarand ballasts. This can be
attributed to the lack of the readily biodegradable organic compounds on the diesel
ballasts.
The production of CO2 by microorganisms in the diesel depots samples was higher in
only two of the nutrient amended samples compared to the controls. This ‘divergent’ CO2
data can be attributed to the probable heterogeneousness of the samples in terms of the
real amount of hydrocarbon ‘added’ to the bottles. The results also suggest that the
activity of LSRc and LPc was very low and the community cannot be marked as ‘active’.
In addition, the high CO2 production in LKc and LSPc appears to reflect the higher
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University of Pretoria etd – Maila, M P (2005)
carbon source (hydrocarbon) content and larger bacterial inoculum at the beginning of
the test. The addition of nutrients (mainly nitrogen and phosphorus) has been reported to
stimulate the biological activity in hydrocarbon contaminated soil environments (Churchill
et al., 1995; Braddock et al., 1997; Seklemova, 2001).
The number of carbon source of Biolog plates used by the SRc sample was four times
the number of substrates used by the Springs contaminated stone ballast (SPc) after 24
hours of incubation. This suggests higher degradation capacity in the SRc sample
compared to the SPc sample as both samples were adjusted to have similar cell density
for Biolog plate inoculation. According to Wünsche et al. (1995), at lower incubation
periods, Biolog patterns reflect the metabolic activities of the quantitatively dominating
components of the microbial communities. However, current information reveals that
carbon source utilisation profiles obtained with Biolog GN plates do not necessarily
reflect the functional potential of the numerically dominant members of the microbial
community used as the inoculum (Smalla et al., 1998).
Influence of incubation time on the development of the substrate utilization pattern of the
samples was similar to that reported elsewhere (Garland and Mills, 1991; Winding, 1994;
Haack et al., 1995; Kersters et al., 1997). The Biolog substrate oxidation response and
average well color developments often exhibit a lag phase, an exponential phase and a
stationary phase. This non-linearity implies that the substrates to be most significant in
discriminating microbial communities may change over the course of the experiment. A
cursory glance at the results suggests that this is responsible for the dendrogram
pattern.
Microbial colonization of rocky surfaces by heterotrophs is made possible by (among
other things) the availability of the selective substrates that can be used to sustain the
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University of Pretoria etd – Maila, M P (2005)
heterotrophic community. In diesel depots in which similar human activities results in the
contamination of stone ballasts by hydrocarbons, patterns of substrates utilization of the
heterotrophs was expected to be similar. This was, however, not the case in this study
as indicated by the substrate utilisation patterns. Both the principle component analysis
(PCA) and cluster analysis indicated the degree of dissimilarity between the different
diesel depots samples. Using cluster analysis, SRc and Kc were closely related
compared to the SPc. However, using the PCA, the three samples (SRc, Kc and SPc)
after 72 hours of incubation were lumped together, suggesting the extent of similarity in
the pattern of substrate utilisation.
Microbial diversity of polluted surfaces needs to be studied further to investigate the concentration or the thickness of the hydrocarbons layer on the rock surfaces that encourages the attachment or colonization of the TCHs and the hydrocarbon-degrading bacteria. It is also not clear how the heterotrophs acquire the micronutrients from the surrounding environment or what is the structure of microbial communities under the stone
ballasts. However, it is probable that the heterotrophs acquire the miconutrients from airborn pollutants. Knowledge of microbial diversity of contaminated rocky surfaces is
essential as it can be applied in bioremediation of contaminated rocky surfaces as in
contaminated diesel depots and contaminated rocky surfaces caused by oil spills.
Acknowledgements
We thank the Risk and Environmental managers at
(Spoornet) Diesel depots for
allowing and helping us to collect the samples. Special thanks also goes to Dr P Wade
for assisting us with the Statistical analysis. The National Research Foundation and the
CSIR supported this work.
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University of Pretoria etd – Maila, M P (2005)
References
1.
Atlas, R.M. 1981. Microbial degradation of petroleum hydrocarbons: an
environmental perspective. Microbiological Reviews. 45: 180-209.
2.
Braddock, J., Ruth, M., Catteral, P., Walworth, J. and Mcarthy, K. 1997.
Enhancement and inhibition of microbial activity in hydrocarbon contaminated
aerctic soils: implications for nutrient amended bioremediation. Environmental
Sciience and Technology. 31: 2078-2084.
3.
Bundy, J.G., Paton, G.I. and Campell, C.D. 2002. Microbial communities in
different soil types do not converge after diesel contamination.
Journal of
Applied Microbiology. 92: 276-288
4.
Churchill,
S.A.,
Griffin,
R.A.,
Jones,
L.P.
and
Churchill,
P.F.
1995.
Biodegradation rate enhancement of hydrocarbons by an oleophilic fertilizers and
rhamnolipid biosurfactant. Journal of Environmental Quality. 24: 19-28
5.
Garland, J.L. and Mills, A.L. 1991. Classification and characterization of
heterotrophic Microbial communities on the basis of patterns of community-level
sole-carbon-source utilization. Applied and Environmental Microbiology. 57(8):
2351-2359
6.
Haack, S.K., Garchow, H., Klug, M.J. and Forney L.J. 1995. Analysis of factors
affecting the accuracy, reproducibility, and interpretation of microbial community
carbon source utilization patterns. Applied and Environmental Microbiology. 61:
1458-1468.
7.
Jones, J.G., Knight, M. and Byron, J.A.
1970.
Effect of gross pollution by
kerosene on the microflora of a moorland soil. Nature (London) 227, 1166.
8.
Kersters, I., Van Vooren, L., Verschuere, L., Vautern, L., Wouters, A., Mergaert,
J., Swings, J. and Verstraete, W. 1997. Utility of the Biolog System for the
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characterization of heterotrophic microbial communities. Systematic and Applied
Microbiology. 20: 439-447.
9.
Leahy, J.G. and Colwell, R.R. 1990. Microbial degradation of hydrocarbons in
the environment. Microbiological Reviews. 54: 305-315.
10.
Pinholt, Y., Struwe, S. and Kjoller, A. 1979. Microbial changes during oil
decomposition in soil. Holarctic Ecology. 2: 195-200.
11.
Seklemova, E., Pavlova, A. and Kovacheva, K. 2001. Biostimulation–based
bioremediation of diesel fuel: field demonstration. Biodegradation. 12: 311-316.
12.
Smalla, K., Wachtendorf, U., Heuer, H., Liu, W. and Forney, L. 1998. Analysis of
Biolog GN substrate utilisation patterns by microbial communities. Applied and
Environmental Microbiology. 64 (4): 1220-1225.
13.
Urzí, C., Garcia–Valles, M., Vendrell, M. and Pernice, A. 1999. Biomineralisation
processes on rock and monument surfaces observed in field and in laboratory
conditions. Geomicrobiology Journal. 16: 39-54.
14.
US Environmental Preotection Agency. 1979. Methods for chemical analysis of
water and wastes, Revised 1983, EPA 600/4-79-020, Washington DC.
15.
Winding, A. 1994. Fingerprinting bacterial soil communities using Biolog
microtiter plates, pp. 85-94. In: Beyond the biomass: Compositional and
functional analysis of soil microbial communities K. Ritz, J Dighton, KE Giller,
eds) Chichester, Wiley.
16.
Wünsche, L., Bruggemann, L. and Babel, W. 1995. Determination of substrate
utilisation patterns of soil microbial communities: An approach to assess
population changes after hydrocarbon pollution. FEMS Microbiology Ecology. 17:
295-306.
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Chapter 4
SOIL MICROBIAL DIVERSITY: INFLUENCE OF GEOGRAPHIC LOCATION AND
HYDROCARBON POLLUTANTS
A modified version of this text was sent for publication as:
Mphekgo P. Maila, P Randima, and Thomas E Cloete
(2003) Soil Microbial Communities: Influence of Geographic Location and Hydrocarbon
Pollutants. Journal of Soil Biology & Biochemistry.
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University of Pretoria etd – Maila, M P (2005)
SOIL MICROBIAL DIVERSITY: INFLUENCE OF GEOGRAPHIC LOCATION AND
HYDROCARBON POLLUTANTS
Abstract
The importance and relevance of the geographical location of the soil sample and the
petroleum hydrocarbons in determining the functional or species diversity within different
bacterial communities was evaluated using the community level physiological profiles.
The petroleum hydrocarbons rather than the geographical location of the sample appear
to be more important in determining functional or species diversity within the bacterial
communities. Cluster analysis of the different community profiles revealed that the
samples from different locations were as different as samples from the same location but
from contaminated versus uncontaminated soils. The results of the soils from different
locations artificially contaminated by different hydrocarbons also reached the same
conclusion. The samples from different soils were as different as samples from the same
soil contaminated by different hydrocarbons. In addition, the removal rate of the different
hydrocarbons in the artificially contaminated soil was different. The results suggests that
the pollutants rather than the geographical origin of the sample might be more important
in determining the functional or species diversity within bacterial communities.
Introduction
The impact of petroleum hydrocarbons on soil microbial diversity has been the subject of
investigation in recent years. The changes in hydrocarbon content in soils results in
characteristics shifts in microbial populations and the abundance of hydrocarbon utilising
bacteria (Atlas et al., 1991; Wünsche et al., 1995). Hydrocarbon contamination selects
for a less diverse but catabolically versatile bacterial community (Atlas et al., 1991;
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University of Pretoria etd – Maila, M P (2005)
Lindstrom et al., 1999). However, information about the importance of geographical
origin of the soil and the hydrocarbons in determining the functional and species
diversity within bacterial communities is not well documented. There is a need to
understand the importance of geographical origin of the soil and the hydrocarbons when
assessing the different soil environments contaminated by hydrocarbons. The improved
knowledge of the influence of the geographical origin of the soil and the hydrocarbons on
microbial diversity can help to improve microbial process used in the removal of
hydrocarbons from the soil.
Bundy et al. (2002), used community level physiological profiles (CLPP) and
phospholipids fatty acid (PLFA) to study the effect of diesel on microbial communities
and reported that microbial communities in different soil types do not converge after
diesel contamination. However, the soil used in the study was artificially contaminated
and could therefore, not adequately reflect the contaminated field sites.
Juck et al. (2000), found that at two oil contaminated Arctic sites investigated by DGGE
and Biolog analysis, absolute diversity was decreased at one site and remained the
same or increased at the other. However, the study was conducted using the cold
adapted microbial communities.
In this study, we evaluated soil microbial diversity of different geographic locations
contaminated by similar hydrocarbons. We also investigated the biodegradation
efficiency and microbial diversity of different soils artificially contaminated by different
hydrocarbons. The aim of the study was to characterise the funtional diversity of different
hydrocarbon contaminated soil environments to establish the importance and relevance
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University of Pretoria etd – Maila, M P (2005)
of the geographic locations in relation to the stressor. The community level physiological
profile was used to characterise the microbial communities.
Materials and Methods
Soil: The soil samples were taken in sterile bags from a hydrocarbon-contaminated site
in Secunda (Mpumalanga Province), Coalsbrook (Free State Province), and Rosslyn
(Gauteng Province), South Africa. The soils were predominantly sandy loam and had the
Total Petroleum Hydrocarbon (TPH) concentration during the day of sampling of 1.2 g/kg
soil, 2.5 g/kg soil and 1 g/kg soil respectively. Both the contaminated and
uncontaminated soils were collected. The uncontaminated soil was also collected from
the CSIR (Council for Scientific and Industrial Research) site.
Influence of different hydrocarbons on functional diversity: Crude oil (obtained from
Petronet, Durban, SA), mineral oil and diesel (both from Exel Pty Ltd) were used to
contaminated both the CSIR soil and the Coalsbrook uncontaminated soils. The different
treatments used for the experiment are shown in table1. Each hydrocarbon was added
to the soil to make the initial concentration of the artificially contaminated soil 40000
mg/kg soil. The oil and the soil were thoroughly mixed before preparing the replicates for
each treatment. The replicates, containing 500g of the contaminated soil of each
treatment were prepared in 10 cm pots.
All the pots were incubated at room
temperature in the greenhouse with normal day-night cycle. The pots were watered
three times a week with 200 mℓ of water to maintain the ideal soil mositure for microbial
activity. In instances where leachates were produced, the leachate was used to water
the same pots. One replicate from each treatment was sacrificed once every two weeks
to determine the level of hydrocarbons in the soil. The soil from each treatment (after
nine weeks) was used for determining the community level physiological profiles.
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University of Pretoria etd – Maila, M P (2005)
Chemical Analysis: The contaminated soils were analysed using the Total Petroleum
Hydrocarbons (TPH) method described in Margesin et al. (1999). 10 g of the polluted
soil was used for the analysis. The analyses were done in triplicate.
Table 1: Treatments used during Bioremediation
Treatments
Additions/Preparations
COCOIL
Coalsbrook soil + Crude Oil
CSCOIL
CSIR soil + Crude Oil
CSD
CSIR soil + Diesel
COD
Coalsbrook soil + Diesel
CSMO
CSIR soil + Mineral Oil
COMO
Coalsbrook soil + Mineral Oil
COUN
Uncontaminated Coalsbrook soil
CSUN
Uncontaminated CSIR soil
Viable counts and Biolog assays: Microbial suspensions were prepared from soil as
described by Wünsche et al. (1995). After appropriate dilutions in sterile saline solution,
the cell suspensions were used to determine the number of culturable heterotrophs and
to inoculate BIOLOG micro plates. The number of culturable heterotrophs, expressed as
CFU, was determined by spreading 0.1 mℓ cell suspension on to a nutrient agar (Biolab
Diagnostics, Pty Ltd, SA) medium, amended with cycloheximide (200 µg/mℓ) to suppress
fungal growth. Three replicates were spread on agar plates and incubated for 24 hours
at 28°C.
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University of Pretoria etd – Maila, M P (2005)
To obtain a substrate utilisation fingerprinting of the microbial communities, three
replicates of all the soil extracts were inoculated in BIOLOG GN microtiter plates (Biolog
Inc., Hayward Calif) containing 95 different sole-carbon sources and a control without a
carbon source. The BIOLOG GN plates were incubated at 28°C and readings done
using a Bio-Tek Elx800 (Bio-Tek Instruments Inc) micro plate reader at 600 nm after 24,
48 and 72 h. Statistical analyses were done using STATISTICA for Windows release
5.1.
Results
The Principle Component Analysis (PCA) revealed differences in the substrate utilisation
patterns of both the contaminated and uncontaminated soil from each geographic
location (Figure 1). However, dendrogram analysis only clustered the Rosslyn sample
site based on geographic location (Figure 2). The contaminated and uncontaminated
soils from the other three sites were not clustered together based on geographic
location.
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University of Pretoria etd – Maila, M P (2005)
Factor Loadings, Factor 1 vs. Factor 2
Rotation: Varimax normalized
Extraction: Principal components
1.0
ROCS
0.9
CSUS
0.8
ROUS
Factor 2
0.7
0.6
0.5
COCS
0.4
CSCS
SEUS
0.3
COUS
0.2
0.1
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
SECS
0.8
0.9
Factor 1
Figure 1. Principle component analysis of the different soil samples. COCShydrocarbon-contaminated Coalsbrook soil, COUS-Coalsbrook uncontaminated soil,
CSCS-hydrocarbon-contaminated CSIR soil; CSUS-CSIR uncontaminated soil, ROCShydrocarbon-contaminated Rosslyn soil, ROUS-Rosslyn uncontaminated soil, SECSydrocarbon-contaminated Secunda soil, SEUS-Secunda uncontaminated soil.
The samples from different locations were as different as samples from the same
location but from contaminated versus uncontaminated soils. Because of soil usage and
heterogeneity, which can influence microbial diversity, it was expected that the
geographical origin of the sample rather than the hydrocarbons was more important in
determining functional or species diversity within the bacterial communities. However,
the results appears not to reinforce the suggestion that geographical origin of the
samples, rather than the hydrocarbons, is important in determining functional or species
diversity
within
the
mesophilic
bacterial
66
communities,
as
contaminated
and
University of Pretoria etd – Maila, M P (2005)
uncontaminated samples from the majority of the sites were not closely related (Figure 1
and 2).
Tree Diagram for 8 Variables
Single Linkage
Euclidean distances
SECS
ROUS
ROCS
CSUS
SEUS
CSCS
COCS
COUS
4.0
4.5
5.0
5.5
6.0
6.5
7.0
7.5
Linkage Distance
Figure 2. Cluster analysis of the different soil samples from the different geographic
locations.
COCS-hydrocarbon-contaminated
uncontaminated
soil,
Coalsbrook
CSCS-hydrocarbon-contaminated
soil,
CSIR
COUS-Coalsbrook
soil;
CSUS-CSIR
uncontaminated soil, ROCS-hydrocarbon-contaminated Rosslyn soil, ROUS-Rosslyn
uncontaminated soil, SECS-hydrocarbon-contaminated Secunda soil, SEUS-Secunda
uncontaminated soil.
Influence of different carbon substrates on functional diversity
The importance of geographical origin of the samples and the hydrocarbons in
determining functional diversity within bacterial communities were further evaluated
using two different soils artificially contaminated by different hydrocarbons. The
community level physiological profiles (CLPP) of the different contaminated and
uncontaminated soils were analysed after incubation using the Principle Component
Analysis (PCA) and cluster analysis.
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University of Pretoria etd – Maila, M P (2005)
The PCA revealed differences in the substrate utilisation pattern of the contaminated
and the uncontaminated soils (Figure 3). The uncontaminated soil from each of the CSIR
and Coalsbrook soil (COUN and CSUN) was not closely related to any of the respective
artificially hydrocarbon contaminated soils. The crude oil contaminated CSIR soil
(CSCOIL) was also not closely related the crude oil contaminated Coalsbrook soil
(COCOIL). However, the mineral oil contaminated CSIR (CSMO) and Coalsbrook soils
(COMO) were closely related. Also related were the diesel contaminated CSIR (CSD)
and Coalsbrook soil (COD, Figure 3).
The difference in the community level physiological profiles of the different hydrocarbon
contaminated soils were further analysed using hierarchical clustering (Figure 4). In a
dendrogram, the mineral oil contaminated CSIR soil was clustered together with the
mineral oil contaminated Coalsbrook soil, while the diesel contaminated CSIR soil was
also clustered with the diesel contaminated coalsbrook soil. No clustering was evident in
the case of the crude oil contaminiated CSIR and coalsbrook soil.
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University of Pretoria etd – Maila, M P (2005)
Factor Loadings, Factor 1 vs. Factor 2
Rotation: Varimax normalized
Extraction: Principal components
1.0
COUN
0.9
CSUN
0.8
COCOIL
Factor 2
0.7
COMO
CSMO
0.6
0.5
CSCOIL
0.4
CSD
0.3
COD
0.2
0.1
0.0
0.2
0.4
0.6
0.8
1.0
Factor 1
Figure 3. PCA of the different soils contaminated by different hydrocarbons. COD-diesel
contaminated coalsbrook soil, COUN-uncontaminated coalsbrook soil, COCOIL-crude oil
contaminated Coalsbrook soil, COMO-mineral oil contaminated Coalsbrook soil, CSMOmineral oil contaminated CSIR soil, CSUN-uncontaminated CSIR soil, CSCOIL-crude oil
contaminated CSIR soil, CSD-diesel contaminated CSIR soil.
Different soil environments may harbor different microbial diversity. However, with the
availability of petroleum hydrocarbons, the functional diversity of the soil, as revealed by
the substrate utilisation patterns appears to be similar. There was no clustering of either
the CSIR uncontaminated soil or Coalsbrook uncontaminated soil with the contaminated
soil to suggest the influence or importance of geographic location in relation to the
stressor in determining functional diversity within the bacterial communities. Even though
the uncontaminated CSIR and the uncontaminated Coalsbrook soil were clustered
together, the linkage distances illustrated their differences. The results suggest that the
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University of Pretoria etd – Maila, M P (2005)
hydrocarbons rather than the geographical origin of the samples are important when
determining the functional diversity within bacterial communities.
Tree Diagram for 8 Variables
Single Linkage
Euclidean distances
COCOIL
CSCOIL
CSD
COD
CSMO
COMO
COUN
CSUN
3.0
3.5
4.0
4.5
5.0
5.5
6.0
6.5
Linkage Distance
Figure 4. Cluster analysis of the soils contaminated by different hydrocarbons. CODdiesel contaminated coalsbrook soil, COUN-uncontaminated coalsbrook soil, COCOILcrude oil contaminated Coalsbrook soil, COMO-mineral oil contaminated Coalsbrook
soil, CSMO-mineral oil contaminated CSIR soil, CSUN-uncontaminated CSIR soil,
CSCOIL-crude oil contaminated CSIR soil, CSD-diesel contaminated CSIR soil.
The removal of different hydrocarbons in the ‘same soil’ is different (Figure 5). Diesel
was removed much faster than the crude oil and the mineral oil. In addition, the removal
of mineral oil and crude oil was much higher in the CSIR soil than in the Coalsbrook soil.
However, similar removal rate of diesel was found in both soils. The CSIR soil had a
higher number of both the total culturable heterotrophs (TCHs) and the culturable
hydrocarbon-utilisation bacteria than the Coalsbrook soil (results not included). The
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University of Pretoria etd – Maila, M P (2005)
removal of hydrocarbons from the soils was highest in the first two weeks of the
‘treatment’.
45000
40000
TPH (mg/kg soil)
35000
30000
COCOIL
CSCOIL
25000
COMO
CSMO
20000
COD
CSD
15000
10000
5000
0
0
2
4
7
9
Time (Weeks)
Figure 5. Biodegradation of different hydrocarbons in different soils. COD-diesel
contaminated coalsbrook soil, COCOIL-crude oil contaminated Coalsbrook soil, COMOmineral oil contaminated Coalsbrook soil, CSMO-mineral oil contaminated CSIR soil,
CSCOIL-crude oil contaminated CSIR soil, CSD-diesel contaminated CSIR soil.
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University of Pretoria etd – Maila, M P (2005)
Discussion and Conclusion
The results indicate that different locations contaminated by different hydrocarbons have
different microbial communities. The Principle Component Analysis (PCA) revealed
differences in the substrate utilisation patterns of both the contaminated and
uncontaminated soil from each geographic location. This is inline with similar findings
that the increase in hydrocarbon content in soil results in significant changes in the
microbial communities of the affected soil environments (Wünsche et al., 1995; Atlas et
al., 1991).
The dendrogram analysis clustered only the Rosslyn sample site based on geographic
location. The contaminated and uncontaminated soils from the other three sites were not
clustered together. In addition, the contaminated and uncontaminated soils from each
geographical location were also not closely related. This results contrast those reported
by Juck et al. (2000), who reported clustering of samples based on the geographic origin
of the samples. However, the study worked on the cold-adapted bacterial communities
while the current study worked on the mesophillic bacterial communities.
Because of soil heterogeneity and usage that can influence microbial diversity, it was
expected that the geographical origin of the sample rather than the hydrocarbons was
more important in determining functional diversity within the bacterial communities.
However, the results did not support this hypothesis as the samples from different
locations were as different as samples from the same location but from contaminated
versus uncontaminated soils.
The removal of hydrocarbons from the soils was highest in the first two weeks of the
‘treatment’. This can be attributed to the contribution of other removal mechanisms other
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University of Pretoria etd – Maila, M P (2005)
than biodegradation. According to Harmsen et al. (1994) and Hejazi et al. (2003), the
dominant removal mechanism of hydrocarbon during the initial phase involves the
volatilisation of the low molecular weight volatile compounds. The principal removal
mechanism for the non-volatile hydrocarbons appears to be biodegradation.
The removal of different hydrocarbons in the ‘same soil’ is different. Diesel was removed
faster followed by crude oil and mineral oil. This was not surprising as the different
substrates have different compositions of hydrocarbons or different aliphatic chains
which can influence biodegradation (Dias and Alexander, 1971). In addition, the
biodegradation of mineral oil and crude oil was much higher in the CSIR soil than in the
Coalsbrook soil. However, similar biodegradation of diesel was found in both soils. This
can be attributed to the significant role played by other removal mechanisms other than
biodegradation. According to Morgan and Watkinson (1989), up to 40% of the
hydrocarbons may evaporate in hotter climates. The differences in the biodegradation
efficiency in the two different soils can be attributed to the higher number of both the
total culturable heterotrophs (TCHs) and the culturable hydrocarbon-utilisation bacteria
in the CSIR soil than the Coalsbrook soil.
The importance of geographical origin of the samples and the hydrocarbons in
determining functional diversity in bacterial communities were further evaluated using
two different soils contaminated by different hydrocarbons. As with the soil samples from
the different contaminated sites, the uncontaminated soil from each of the CSIR and
Coalsbrook soil was not closely related to any of their respective artificially hydrocarbon
contaminated soils. However, the mineral oil-contaminated and diesel contaminated
soils were closely related. In addition, the hydrocarbon-contaminated soils were
clustered together. The results suggest that hydrocarbons rather than the geographical
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University of Pretoria etd – Maila, M P (2005)
origin of the sample are more important in determining the functional diversity within the
bacterial communities.
In conclusion, the study did not support the hypothesis that the geographical origin of the
sample rather than the hydrocarbons is important in determining functional or species
diversity within bacterial communities. However the study opened the possibility of
applying CLPP for determination of natural attenuation. Further work is required to
investigate the importance of soil heterogeneity in community studies of soil
environments contaminated by similar hydrocarbons using both functional and genetic
diversity methods. In addition, similar studies should incorporate the physico-chemical
characterisation of the various soil samples.
References
1.
Atlas, R.M., Horowitz, A., Krichevsky, M. and Bej, A.K.
1991. Response of
microbial populations to environmental disturbance. Microbial Ecology. 22: 249256.
2.
Bundy, J.G., Paton, G.I. and Campell, C.D. 2002. Microbial communities in
different soil types do not converge after diesel contamination. Journal of Applied
Microbiology. 92: 276-288.
3.
Dias, F.F. and Alexander, M. 1971. Effect of chemical structure on
biodegradability of aliphatic acids and alcohols. Applied and Environmental
Microbiology. 22:1114-1118.
4.
Harmsen, J., Velthorst, H.J. and Bennehey, I.P.A.M. 1994. Cleaning of residual
concentrations with an extensive form of landfarming. In: Applied Biotechnology
for Site Remediation. Hinchee RE, Anderson DB, Blaine FB, Sayles GD. Eds,
Lewis Publishers, Boca Raton, USA, 84-91.
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5.
Hejazi, R.F., Husain, T. and Khan, F.I.
2003. Landfarming operation of oily
sludge in arid region-human health risk assessment. Journal of Hazardous
Materials. B99: 287-302.
6.
Juck, D., Charles, T., Whyte, L.G. and Greer, C.W. 2000. Polyphasic microbial
community analysis of petroleum hydrocarbon-contaminated soils from two
northern Canadian communities. FEMS Microbiology Ecology. 33(3) 241-249.
7.
Lindstrom, J.E., Barry, R.P. and Braddock, J.F.
1999. Long-term effects on
microbial communities after a subarctic oil spill. Soil Biology and Biochemistry.
31: 1677-1689.
8.
Margesin, R., Zimmerbauer, A. and Schinner, F. 1999. Soil lipase activity – a
useful indicator of oil biodegradation. Biotechnology Techniques. 13: 859-863.
9.
Morgan, P. and Watkinson, R.J. 1989. Hydrocarbon degradation in soils and
methods for soil biotreatment, CRC Critical Reviews in Biotechnology. 8 (4): 305332.
10.
Wünsche, L., Bruggemann, L. and Babel, W. 1995. Determination of substrate
utilisation patterns of soil microbial communities: An approach to assess
population changes after hydrocarbon pollution. FEMS Microbiology Ecology. 17:
295-306.
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Chapter 5
MULTI-SPECIES AND MONOCULTURE RHIZOREMEDIATION OF POLYCYCLIC
AROMATIC HYDROCARBONS (PAHS) FROM THE SOIL
A modified version of this text was accepted for publication as:
Mphekgo P. Maila, P Randima and Thomas E Cloete (2004) Multi-Species and
Monoculture Rhizoremediation of Polycyclic Aromatic Hydrocarbons (PAHs) from the
Soil. International Journal of Phytoremediation
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University of Pretoria etd – Maila, M P (2005)
MULTI-SPECIES AND MONOCULTURE RHIZOREMEDIATION OF POLYCYCLIC
AROMATIC HYDROCARBONS (PAHS) FROM THE SOIL
Abstract
In this study, we investigated the potential of multi-species rhizoremediation and
monoculture rhizoremediation in decontaminating polycyclic aromatic hydrocarbon
(PAH) contaminated soil. Plant-mediated PAH dissipation was evaluated using monoplanted soil microcosms and soil microcosms vegetated with several different grass
species (Brachiaria serrata and Eleusine corocana). The dissipation of naphthalene and
fluorene was higher in the ‘multi-species’ vegetated soil compared to the mono-planted
and non-planted control soil. The concentration of naphthalene was undetectable in the
multi-species vegetated treatment compared to 96% removal efficiencies in the monoplanted treatments and 63% in the non-planted control after 10 weeks of incubation.
Similar removal efficiencies were obtained for fluorene.
However, there was no
significant difference (p<0.05) in the dissipation of pyrene in both the mono- and multispecies vegetated treatments. There was also no significant difference (p<0.01) between
the dissipation of PAHs in the mono-planted treatments with different grass species.
Principle Component Analysis (PCA) and Cluster analysis were used to evaluate
functional diversity of the different treatments during phytoremediation of PAHs. Both
PCA and Cluster analysis revealed differences in the metabolic fingerprints of the PAH
contaminated and non-contaminated soils. However the differences in metabolic
diversity between the multi-species vegetated and mono-planted treatments were not
clearly revealed. The results suggest that multi-species rhizoremediation using tolerant
plant species rather than monoculture rhizoremediation have the potential to enhance
pollutant removal in moderately contaminated soils.
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University of Pretoria etd – Maila, M P (2005)
Introduction
Phytoremediation, the use of plants and associated microorganisms to degrade or
immobilise contaminants in soil and groundwater is increasingly being considered for
rehabilitating moderately contaminated soils. The technology represents a potential lowcost, effective and low maintenance alternative for waste management (Aprill and Sims,
1990). The mechanisms of phytoremediation for hazardous organic contamination in soil
include, direct plant uptake, microbial degradation stimulated by plant roots, cometabolism of contaminants in the rhizosphere and adsorption to humic or organic
matter (Schnoor et al., 1995; Cunningham and Ow, 1996; Chen et al., 2003).
Laboratory and greenhouse experiments on plant-mediated dissipation of polycyclic
aromatic hydrocarbons (PAHs) have concentrated mainly on the use of monoculture
rhizoremediation of PAHs from the soil (Aprill and Sims, 1990; Lee and Banks, 1993;
Walton et al., 1994; Günther et al., 1996; Reilly et al., 1996). In these studies, the extent
of organic pollutant removal in planted soil has been significantly greater than in nonplanted soil. However, the information about the effectiveness of multi-species
rhizoremediation of PAHs is lacking. The use of soil microcosms with mixed planted
species has the potential to increase soil heterogeneity (Angers and Carnon, 1998) and
microbial diversity, which can improve the microbial competence of the soil bacteria for
effective pollutant removal.
In this study, the effectiveness of both monoculture rhizoremediation and multi-species
rhizoremediation of PAH-contaminated soil was evaluated. In addition, the study also
evaluated the functional diversity of the PAH-contaminated and non-contaminated
rhizosphere and non-rhizosphere soil using community level physiological profiles.
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University of Pretoria etd – Maila, M P (2005)
Materials and Methods
Chemicals
All solvents (dichloromethane 99% and trichloromethane 99%) and PAH (Naphthalene
98%, fluorene 98% and pyrene 98%) were purchased from Sigma-Aldrich, SA.
Soil
The predominantly sandy loam soil used in the experiments was taken from the Pretoria
Campus of the Council of Scientific and Industrial Research (CSIR), SA. The artificial
contamination of the soil with PAH was done as described by Leyval and Binet (1998).
The PAHs were first dissolved in trichloromethane before being mixed with 1% of the
total soil to be polluted. Trichloromethane was allowed to volatilise under fumehood, and
the amended soil was mixed thoroughly with the remaining 99% of the soil. After mixing,
three samples of the artificially contaminated soil were collected in sealable glass
containers for PAHs analysis. The concentration of the individual PAHs in the soil
averaged 300mg/kg soil. Further soil sampling and storage of the rhizosphere and nonrhizosphere soil in the experiments were done as described by Wollum (1982). The
rhizosphere soil (soil that adheres to the roots) was collected in sterile bags by gently
shaking loose soil from the intermingled roots of the planted treatments. This
rhizosphere soil was used for determining the community level physiological profiles and
for PAH analysis. The non-rhizosphere soil was collected from the pots and mixed
thoroughly before further analysis. Analyses were made within 24 hour of sampling.
However, in cases were this was not feasible, the soil samples were stored overnight at
4°C.
Plants
The seeds of Brachiaria serrata (Velvet signal grass) were bought from Agricol Pty Ltd,
SA and those of Eleusine coracana (African millet) were obtained from Plant Genetic
Resources Directorate (Agriculture Department, SA). B. serrata is a perennial tufted
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University of Pretoria etd – Maila, M P (2005)
grass with shrub-like growth form. E. coracana is a robust annual tufted grass with slated
culms and has an exceptionally dense and strong root system. The grass seeds were
sown in trays (38 cm x 38 cm) containing the soil. The trays were incubated at room
temperature in the greenhouse with natural day-night cycles until germination of both
plant seeds occurred. No supplemental lighting was supplied. However, the average
natural cycle was 9 hours of daylight. The room temperature in the greenhouse
averaged 28°C during the period of incubation. The grass seedlings were used in the
phytoremediation experiment (vegetated microcosms).
Vegetated and Non-vegetated microcosms
Plants mediated removal of PAH was evaluated using mono-planted and multi-planted
soil microcosms. The soil was artificially contaminated with PAHs as described above.
The concentration of each PAH in the soil was 300mg/kg soil. This ‘time zero’
concentration is the average of the individual PAHs detected. The mono-planted and
multi-planted treatments were prepared as shown in table 1. Six hundred grams of each
soil preparation (table 1) were placed in 10 cm pots with saucers for leachate collection.
The grass seedlings were planted in different pots as shown in table 1. The density of
the plants was one plant per pot for the mono-cultured treatments while two plants (as
shown in table 1) were used for the multi-species treatment. The pots were placed in the
green house at room temperature and natural day-night cycles. No supplemental lighting
was supplied. However, the average natural cycle was 9 hours of daylight. The room
temperature in the greenhouse averaged 28°C during the period of incubation. Two
hundred millilitres of water was used to water the plants every two days. In instances
where leachates were produced, the leachate was used to water the same pots. Each
treatment had 11 pots and two pots for each treatment were sacrificed after two, six and
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ten weeks of incubation to determine concentrations of PAHs in the soil.
ANOVA
(Analysis of Variance) was used to determine the difference between the treatments.
Chemical Analysis
Residual PAH in treatments T0 to T3 were quantified as described by Maila and Cloete
(2002). PAHs were extracted from 25 g soil of each treatment using dichloromethane.
PAHs were quantified after extraction with dichloromethane, using a Varian Saturn 2000
Ion Trap Gas Chromatography/Mass Spectrometer equipped with a Chrompack CP-SIL
8CB-MS (5% phenyl) Fused Silica Capillary Column (30 m* 0.25 mm* 0.25 µm). The
detector was tuned according to EPA 8270C using DFTPP. Injector temperature was
230°C, oven temperature program: 30°C (6 min), 10°C/min, 300°C (7 min). Analyses
were done in triplicate.
Table 1: Treatments used in the experiments
Treatments
Additions/Preparations
T0 (Control)
Soil + PAHs
T1 (CSEC)
Soil + PAHs + E.corocana
T2 (CSBS)
Soil + PAHs + B.serrata
T3 (CSBSEC)
Soil + PAHs + E.corocana + B.serrata
T4 (UCS)
Soil
T5 (UCSEC)
Soil + E.corocana
T6 (UCSBSEC)
Soil + B.serrata + E.corocana
Community Level Physiological Profiles (CLPP)
The microbial community level physiological profiles of the different treatments shown in
table 1 was done using the soil samples sacrificed after 4 weeks of incubation. This
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period was deemed to be sufficient to allow the acclimatisation of the plants in the
contaminated and non-contaminated soils. In addition, the PAH dissipation analysis
were also done (as described above) using the samples sacrificed after 4 weeks of
incubation (week 4 PAH dissipation results not included). For CLPP determination, three
pots per treatment were used. The rhizosphere and non-rhizosphere soils were sampled
as described above and microbial suspensions were prepared from the soil for BiologTM
inoculation as described by Wünsche et al. (1995). 10g of soil and 100 mℓ of 0.2 % tetrasodium pyrophosphate solution were shaken in an Erlenmeyer flask for 30 min on a
rotary shaker at 140 rpm. The mixture was allowed to settle for 5 min and the
supernatant used for serial dilutions in a physiological saline solution.
After appropriate dilutions in sterile saline solution, the cell suspensions were used to
determine the number of culturable heterotrophs and to inoculate BIOLOGTM GN micro
plates. The number of culturable heterotrophs (results not included), expressed as CFU,
was determined by spreading 0.1 mℓ cell suspension on to a nutrient agar (Biolab
Diagnostics Pty Ltd, SA) medium, amended with cycloheximide (200 µg/mℓ) to suppress
fungal growth. Plate counting was done in triplicate and incubation was at 28°C for
24 hours. Dilutions giving the same number of culturable heterotrophs were used for
BiologTM inoculation.
To obtain the metabolic fingerprints of the microbial communities in different treatments
(table 1), three replicate (three pots per treatment) of all the soil extracts were inoculated
in BIOLOGTM GN microtiter plates (Biolog Inc., Hayward Calif) containing 95 different
sole-carbon sources and a control without a carbon source. 100 µℓ of each soil extract
was added to each well. The BIOLOGTM GN plates were incubated at 28°C and readings
done using a Bio-Tek Elx800 (Bio-Tek Instruments Inc) micro plate reader at 600 nm
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University of Pretoria etd – Maila, M P (2005)
after 24, 48 and 72 h. Statistical analyses were done using STATISTICA for Windows
release 5.1.
Results
Plant mediated PAH dissipation was evaluated using mono-planted and multi-planted
soil microcosms. PAH dissipation was higher in the planted soils compared to the nonplanted soil (table 2-4). There was on average a 97% reduction of naphthalene in
planted soils compared to 63% in the non-planted soil. The dissipation of naphthalene
and fluorene was higher in the multi-planted soil compared to the mono-planted and
non-planted soil (table 2 and 3). The concentration of naphthalene was undetectable in
the multi-planted treatment compared to 96% reduction in the mono-planted treatments
and 63% reduction in the non-planted control after 10 weeks of incubation. The standard
deviations for the average values are shown in table 2. There was a 96% reduction in
fluorene, from the multi-planted treatment compared to 81% reduction in the monoplanted treatment with E. corocana and 47% of the control treatment. However, there
was no significant difference (P<0.01) in the dissipation of pyrene in both the mono- and
multi-planted treatments (table 4). There was also no significant difference between the
dissipation of PAHs in the treatment with Brachiaria serrata and the treatment with
Eleusine corocana (table 2-4).
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Table 2: Naphthalene concentrations (mg/kg soil) in Monocultured and Multiplanted soil treatments (Average ± Std. Dev)
Weeks
of Control (T0)
CSEC (T1)
CSBS (T2)
CSECBS (T3)
293
224
202
198
± 0.632
± 0.089
± 1.032
± 0.019
248
110
98
66
± 0.059
± 1.112
± 0.221
± 1.532
111
12
9
0
± 0.632
± 0. 321
± 1.209
Incubation
2
6
10
As the development of microbial communities inhabiting the root zone is influenced by
plant species, it was expected that the dissipation of PAHs in treatments vegetated by
different plant species would be different. However, this was not observed, as there was
no significant difference (p<0.01) in PAH dissipation in treatments with B. serrata (T2)
and treatment with E. corocana (T1).
Table 3: Fluorene concentrations (mg/kg soil) in Monocultured and Multi-planted
soil treatments (Average ± Std. Dev)
Weeks
of Control (T0)
CSEC (T1)
CSBS (T2)
CSECBS (T3)
296
241
246
236
± 0.089
± 0.087
± 0.009
± 0.127
235
178
164
110
± 0.635
± 1.612
± 0.112
± 0.255
160
56
38
10
± 1.541
± 0.065
± 2.872
± 1.432
Incubation
2
6
10
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University of Pretoria etd – Maila, M P (2005)
Table 4: Pyrene concentrations (mg/kg soil) in Monocultured and Multi-planted
soil treatments (Average ± Std. Dev)
Weeks
of Control (T0)
CSEC (T1)
CSBS (T2)
CSECBS (T3)
298
262
258
261
± 0.283
± 0.064
± 0.832
± 0.432
266
203
218
198
± 0.642
± 1.412
± 1.414
± 0.432
255
166
128
117
± 0.565
± 1.632
± 0.876
± 1.555
Incubation
2
6
10
In addition, as plants have the potential to increase soil heterogeneity and soil microbial
diversities, it was expected that the treatment with ‘multi-plants’ would have a higher
PAH dissipation compared to mono-planted (T1 and T2) treatments. This was observed
as both naphthalene and fluorene dissipation was significantly higher (p<0.05) in the
multi-planted treatments compared to the mono-planted treatments. However, different
results were obtained for pyrene degradation.
Community Level Physiological Profiles (CLPP)
Heterotrophic microbial communities were evaluated during phytoremediation of the
PAHs by determining the metabolic fingerprints of the different treatments. Both Principle
Component Analysis (PCA) and Cluster analysis were used to evaluate the difference in
substrate utilisation patterns of the different treatments. PCA was performed to
characterize the correlation between samples, taking into account the absorbance
values for all 96-response wells. Two principal factors were isolated from the individual
patterns (Figure 1), which explained 63% of the variation. For the samples, factor one
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University of Pretoria etd – Maila, M P (2005)
was related to the absorbance values for the wells, while factor 2 was related to the
incubation time.
Factor Loadings, Factor 1 vs. Factor 2
Rotation: Varimax normalized
Extraction: Principal components
1.0
UCBSEC
UCSEC
0.8
UCS
Factor 2
0.6
0.4
CSBSEC
CS
CSBS
0.2
CSEC
0.0
0.0
0.2
0.4
0.6
0.8
1.0
Factor 1
Figure 1. Metabolic diversity of PAH contaminated and non-contaminated rhizosphere
and non-rhizosphere soils. UCS-uncontaminated Soil, CS-contaminated soil, CSBScontaminated soil with Brachiaria serrata, UCSEC-uncontaminated soil with Eleusine
corocana, UCBSEC-uncontaminated soil with Brachiaria serrata and Eleusine corocana,
CSBSEC-contaminated soil with Brachiaria serrata and Eleusine corocana.
The metabolic fingerprints of the PAH-contaminated soils (CS, CSBS, CSBSEC, CSEC)
were not closely related to the metabolic fingerprints of the non-contaminated soils,
(UCS, UCSEC, UCBSEC; Figure 1). The metabolic fingerprint of the PAH-contaminated
soil (CS) was more closely related to the contaminated mono-planted treatment with
Brachiaria serrata (CSBS) and contaminated multi-planted soil with Brachiaria serrata
and Eleusine corocana (CSBSEC) than to other treatments (Figure 1). The
uncontaminated soils were more ‘closely’ related to each other than to the PAH
contaminated soils.
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University of Pretoria etd – Maila, M P (2005)
Tree Diagram for 7 Variables
Single Linkage
Euclidean distances
CS
CSBS
CSBSEC
CSEC
UCS
UCBSEC
UCSEC
4.5
5.0
5.5
6.0
6.5
7.0
7.5
Linkage Distance
Figure 2. Cluster analysis of the PAH-contaminated and non-contaminated rhizosphere
and non-rhizosphere soils. UCS-uncontaminated Soil, CS-contaminated soil, CSBScontaminated soil with Brachiaria serrata, UCSEC-uncontaminated soil with Eleusine
corocana, UCBSEC-uncontaminated soil with Brachiaria serrata and Eleusine corocana,
CSBSEC-contaminated soil with Brachiaria serrata and Eleusine corocana.
The metabolic fingerprints of the different treatments were further evaluated using
hierarchical clustering (Figure 2). In a dendrogram, the contaminated soils (CS, CSBS
and CSBEC) were clustered together while CSEC was clustered with the
uncontaminated soils. Both PCA and cluster analysis indicated that the metabolic
fingerprints of PAH contaminated soils (with the exception of CSEC) were closely related
as were the metabolic fingerprints of the uncontaminated soils.
As plants have the potential to increase soil heterogeneity and possibly soil diversity, it
was expected that treatments with more plants will have high microbial competence,
reflected by higher PAH dissipation and higher number of Biolog substrates used by the
multi-planted treatments compared to mono-planted treatments. Higher PAH dissipation
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University of Pretoria etd – Maila, M P (2005)
was only evident with naphthalene and fluorene (table 2 and 3), while there was no
significant difference in the amount of Biolog substrates (results not included) used by
the multi-planted and mono-planted contaminated treatments after 72 hours of
incubation. However, more Biolog substrates were used by microorganisms in the
planted treatments compared to the non-planted treatments.
Discussions and Conclusion
The study investigated plant mediated PAH dissipation using mono-planted and multiplanted soil microcosms. PAH dissipation was higher in the planted soils compared to
the non-planted control soil. This finding was in line with other findings, which indicated
enhanced PAHs dissipation in vegetated soils compared to non-vegetated soil (Aprill
and Sims, 1990; Reilly et al., 1996). According to Muratova et al. (2003), significant PAH
dissipation is attained in the vegetated soils as plants stimulate the rhizosphere micro
flora, which degrade the pollutants.
The dissipation of naphthalene and fluorene was higher in the multi-planted soil
compared to the mono-planted and non-planted control soil. However, there was no
significant difference in the dissipation of pyrene in the mono and multi-planted
treatment. This was not surprising as the rate of PAH degradation is inversely
proportional to the number of rings in the PAH molecule (Cerniglia and Heitkamp, 1989;
Cerniglia, 1992). In addition, the period of incubation was probably not sufficient for the
complete dissipation of pyrene in both treatments or the lack of pyrene biodegraders
was responsible for this difference.
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University of Pretoria etd – Maila, M P (2005)
There was also no significant difference (p>0.01) between the dissipation of PAHs in the
treatment with Brachiaria serrata and the treatment with Eleusine corocana. As plant
species influence the development of microbial communities inhabiting the root zone
(Rovira, 1956; Rovira, 1959), it was expected that the dissipation of PAHs in treatments
vegetated by different plant species will be different. This expectation emanated from the
fact that the two grasses have slightly different root structure and also due to the
probable difference in the root exudates, which may influence the ‘competency’ of the
rhizosphere microbial community inhabiting the plants. However, this was not realised,
as there was no significant difference (p>0.01) in PAH dissipation between treatments
with B. serrata and treatment with E. corocana. This finding was in contrast to findings
by Muratova et al. (2003), who reported differences in the degradation of PAH with
different plant species. However, alfalfa and reed species were used in their study while
different grass species were used in this study.
The data on enhanced dissipation of both naphthalene and fluorene in multi-planted
treatments compared to mono-planted treatments suggests that phytoremediation can
be enhanced by ‘bioaugumenting’ moderately contaminated soil with multi-plants instead
of monocultures. However, as many organisms are known to produce toxins designed to
minimise competition (Curl and Truelove, 1986), plants that are able to coexist or the socalled co-occurring plants, should be evaluated for their potential to enhance
rhizoremediation of PAHs. Also longer-term studies should be considered to corroborate
these results.
The metabolic fingerprints of the PAH-contaminated soils were different from the
metabolic fingerprints of the non-contaminated soils. This can be attributed to the
change in microbial diversity due to the presence of PAH. The availability of hydrophobic
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University of Pretoria etd – Maila, M P (2005)
pollutants has been reported to cause significant changes in soil microbial communities
(Saxton and Atlas, 1977; Atlas et al., 1991; Wünsche et al., 1995). The metabolic
fingerprint of the PAH-contaminated soil was more closely related to the contaminated
mono-planted treatment with Brachiaria serrata and contaminated multi-planted soil with
Brachiaria serrata and Eleusine corocana than to other treatments. This can be
attributed to the ‘similarities’ in metabolic diversity of the contaminated treatments
caused by the presence of the stressor (PAHs).
The functional diversities of the different treatments were further evaluated using
hierarchical clustering. In a dendrogram, the contaminated soils (CS, CSBS and
CSBEC) were clustered together while CSEC was clustered with the uncontaminated
soils. These results were perplexing as it was expected that CSEC, a planted
contaminated soil was going to be clustered with other contaminated soil due to
presence of the stressor, which can cause significant changes in soil microbial
communities (Rathbone et al., 1998).
In conclusion, plant–mediated dissipation of PAHs was enhanced in the multi-planted
treatments compared to the mono-planted treatments. However, enhanced PAH
dissipation in the multi-planted treatments compared to the mono-planted treatments did
not correspond to high functional diversity as revealed by the number of Biolog
substrates used. The results suggest that multi-plant rhizoremediation should be
considered when evaluating remedial approaches for moderately contaminated soils.
The results further strengthen the suggestion that ‘bioaugumentation’ in moderately
contaminated soils can be achieved by using multi-planted instead of monocultures to
enhance the competence of the soil bacteria. However, further work using both
functional and molecular techniques is required to understand microbial diversity in
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multi-planted soil treatments, particularly as root exudates by one plant species may
suppress or encourage the bacterial species ‘predominant’ when each plant grows in
isolation.
Acknowledgement
We thank the National Research Foundation and RCN of Norway (project no
152243/730) for funding this project.
References
1. Angers, D.A. and Caron, J. 1998. Plant-induced changes in soil structure:
processes and feedbacks. Biogeochemistry. 42, 55-72.
2. Aprill, W. and Sims, R.C. 1990. Evaluation of the use of prairie grasses for
stimulating polycyclic aromatic hydrocarbon treatment in soil. Chemosphere. 20
(1-2), 253-265.
3. Atlas, R.M., Horowitz, A., Krichevsky, M. and Bej, A.K. 1991. Response of
microbial populations to environmental disturbance. Microbial Ecology. 22, 249256.
4. Cerniglia, C.E. and Heitkamp, M.A. 1989. Microbial degradation of polycyclic
aromatica hydrocarbons in the aquatic environment. In: Varanasi U (Ed)
Metabolism of polycyclic aromatic hydrocarbons in the aquatic environment. Pp
41-68. CRC Press, Boca Raton, FL.
5. Cerniglia, C.E. 1992. Biodegradation of Polycyclic aromatic hydrocarbons.
Biodegradation. 3, 351-368.
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6. Chen, Y.C., Banks, M.K. and Schwab, A.P. 2003. Pyrene degradation in the
rhizosphere of Tall Fescue (Festuca arundinacea) and Switch grass (Panicum
virgatum L.). Environmental Science and Technology. 37, 5778-5782.
7. Cunningham,
S.D.
and
Ow,
D.W.
1996.
Promise
and
prospects
of
phytoremediation-Update on biotechnology. Plant Physiology. 110, 715-719.
8. Curl, E.A. and Truelove, B. 1986. Rhizosphere in relation to plant nutrition and
growth. In: The Rhizosphere. 6. Springer-Verlag, Berlin, Germany, pp. 167-189.
9. Günther, T., Dornberger, U. and Fritsche, W. 1996. Effects of rye grass on
biodegradation of hydrocarbons in soil. Chemosphere. 33(2), 203-215.
10. Lee, E. and Banks, M.K. 1993. Bioremediation of petroleum contaminated soil
using vegetation: A Microbial study. Journal of Environmental Health. A28(10),
2187-2198.
11. Leyval, C. and Binet, P. 1998. Effect of polyaromatic hydrocarbons in soil on
arbuscular mycorrhizal plants. Journal of Environmental Quality. 27, 402–407.
12. Maila, M.P. and Cloete, T.E. 2002. Germination of Lepidium sativum as a method
to evaluate polycyclic aromatic hydrocarbons (PAHs) removal from contaminated
soil. International Biodeterioration and Biodegradation. 50, 107-113.
13. Muratova, A., Hübner, T., Tischer, S., Turkovskaya, O., Möder, M. and Kuschk,
P. 2003. Plant-Rhizosphere-microflora association during phytoremediation of
PAH-contaminated soil. International Journal of Phytoremediation. 5(2), 137-151.
14. Rathbone, K., Fuchs, J., Anderson, K., Karthikeyan, R. and Nurhidayat, N.1998.
Effects of PAHs on microbial activity and diversity in freshly contaminated and
weathered soils. In: Proceedings of the 1998 Conference on Hazardous Waste
Research. Snowbird, Utah, May 18-21, pp. 383-402.
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15. Reilly, K.A., Banks, M.K. and Schwab, A.P.
1996. Organic chemicals in the
environment: Dissipation of PAHs in the rhizosphere. Journal of Environmental
Quality. 25, 212-219.
16. Rovira, A.D. 1956. Plant roots excretions in relation to the rhizosphere effect.1.
The nature of root exudates from oats and peas. Plant and Soil. 47, 178-194.
17. Rovira, A.D. 1959. Plant roots excretions in relation to the rhizosphere effect.
IV. Influence of plant species, age of plant, light, temperature, and calcium
nutrition on exudation. Plant and Soil. 9, 53-64.
18. Saxton, A.J. and Atlas, R.M. 1977. Response of microbial populations in Arctic
tundra soils to crude oil. Canadian Journal of Microbiology. 23, 1327-1333.
19. Schnoor, J.L., Licht, L.A., McCutcheon, S.C., Wolfe, N.L. and Carreira, L.H.
1995. Phytoremediation of organic and nutrient contaminants. Environmental
Science and Technology. 29, 318A-323A.
20. Walton, B.T., Guthrie, E.A. and Hoylman, A.M. 1994. Toxicant degradation in the
rhizosphere. In: Bioremediation through Rhizosphere Technology, Anderson, T.,
Coats, J., Eds., American Chemical Society Symposium Series, pp.11-26.
21. Wollum, A.G. 1982. Cultural methods for soil microorganisms. In: Methods of
soil analysis Part 2, (page AL, RH Miller and DR Keeney). pp. 783-787. Madison,
Wisconsin, USA.
22. Wünsche, L., Bruggemann, L., Babel, W., 1995. Determination of substrate
utilisation patterns of soil microbial communities: An approach to assess
population changes after hydrocarbon pollution. FEMS Microbiology Ecology. 17,
295-306.
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Chapter 6
GERMINATION OF LEPIDIUN SATIVUM AS A METHOD OF EVALUATING THE
REMOVAL OF POLYAROMATIC HYDROCARBONS (PAHS) FROM
CONTAMINATED SOIL
A modified version of this text was published as:
Mphekgo P. Mailaa, Thomas E. Cloeteb (2002) Germination of Lepidium sativum as a
method to evaluate polycyclic aromatic hydrocarbons (PAHs) removal from
contaminated soil. International Biodeterioration and Biodegradation 50: 107-113.
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University of Pretoria etd – Maila, M P (2005)
GERMINATION OF LEPIDIUM SATIVUM AS A METHOD TO EVALUATE
POLYCYCLIC AROMATIC HYDROCARBONS (PAHS) REMOVAL FROM
CONTAMINATED SOIL
Abstract
The sensitivity of Lepidium sativum germination to polycyclic aromatic hydrocarbons
(PAHs) was investigated in soil(s) artificially and historically contaminated with mixtures
of PAH. The level of germination of L. sativum decreased with increasing concentration
of the PAH in the artificially contaminated soil, while no germination occurred in the
historically polluted soil. At a concentration of 1000 and 50 ppm, the germination levels
were <16% and >75%, respectively. The same germination levels, as a function of PAH
concentration, were observed when a non-ionic surfactant was present in the soil(s).
When used during phytoremediation of PAH, the germination level of L. sativum was
inhibited during the first weeks, after which germination increased, possibly due to PAH
dissipation from the soil. The data suggest that the germination of L. sativum can be
used to monitor the removal of PAH pollutants from soil.
Introduction
Polycyclic aromatic hydrocarbons (PAHs) are pollutants. They occur as common
constituents of petroleum, coal tar and shale oil, but are most frequently formed by
incomplete combustion of fossil fuels (Pothuluri and Cerniglia, 1994). Their fate in nature
is of great environmental concern due to their toxic, mutagenic and carcinogenic
properties (LaFlamme and Hite, 1978; Pahlmann and Pelkonen, 1987).
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University of Pretoria etd – Maila, M P (2005)
Although GC-MS analysis of PAH has been successfully used to determine the
concentration of PAH during the remediation of the pollutants in soil, the methodology
remains expensive and requires a high degree of skill in the use of the instruments.
Previously, it was indicated that photomodified PAH had a marked inhibition of root fresh
weight of the Canola plant (Ren et al., 1996). The phytotoxicity of these pollutants could,
therefore, possibly be used to establish a monitoring method that would indicate the
presence of this compound in soil. This suggested that germination efficiency of selected
plants could be used as a bioindicator of pollutants.
In this study, our objective was to select and use an appropriate plant with a short
germination period and to evaluate it as a potential bioindicator of PAH pollution. In this
study, we investigated the germination efficiency of Lepidium sativum exposed to
different concentrations of PAH in both the presence and absence of a surfactant in soil.
Surfactants have been shown to increase the desorption and bioavailability of PAH in
soil (Aronstein et al., 1991). We also evaluated the possibility of using germination of
L. sativum as a bioindicator of PAH removal from contaminated soil.
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Materials and methods
Chemicals: All solvents (dichloromethane 99% and trichloro-methane 99%), non-ionic
surfactant (Triton X-100) and PAH (naphthalene 98%, pyrene 98%, coronen 98%,
phenanthrene 98% and anthracene 98%) were purchased from Sigma-Aldrich, SA.
Soil: The soils used in the experiments were labeled A, B, C and D. Soil A was a
predominantly sandy loam soil taken from the CSIR site (Pretoria, SA), soil B was an
industrial soil taken from an industrial oil site (Secunda, South Africa), soil C was also an
industrial soil (Kwazulu-Natal, SA) containing ilmenite (6%), rutile (0.4%), zircon (1%),
leucoxene (0.3%), magnetite (1%) and kyanite (1%). Soil D was a white playpen sand
bought from Lion Bridge SA (Pty) Ltd. Soils C and D were used in the experiment to test
the germination of L. sativum in different soils that are not contaminated with PAH. The
industrial soil (soil B) consisted of heterogeneous soil material from an oil refinery with a
contamination of ≈1.2 g PAH per kg soil. The artificial contamination of soil A with PAH
was done as described by Leyval and Binet (1998).
Plants: The seeds of white buffalo grass (Panacum maximum) were purchased from
AGRICOL (Pty) Ltd, SA and those of L. sativum were purchased from Lion Bridge (Pty)
Ltd, SA. The grass seeds were sown in trays (38 cm * 38 cm) containing soil A. The
seedlings of P. maximum were used in the phytoremediation experiment (vegetated
microcosm) while L. sativum seeds were used as potential bioindicator of PAH removal.
L. sativum bioindicator: The seeds of L. sativum were exposed to different
concentrations (50, 150, 300, 500 and 1000 ppm) of PAHs in soil A. For each treatment,
75 mℓ of deionised water was added to 375 g soil to bring the soil moisture to 75% field
capacity (14% on wet weight). The soil was mixed and then divided into three large
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polystyrene Petri dishes (150 mm* 25 mm) after which 50 seeds of L. sativum were
sown in each Petri dish. Three replica plates were used for each treatment. The plates
were placed next to the window allowing sufficient light for photosynthesis. After 3 days,
the seedlings were counted. The sensitivity of L. sativum germination to PAH was
assessed by seedling count as well as by the weight of the seedlings. Germination was
defined as a visible cracking of the seed coat with a measurable root or shoot
production. Five seedlings from each of the three plates were weighed and the average
taken. The above experiment was repeated with the addition of Triton X-100 (a
surfactant) at a concentration of 100 µg/g soil. The treatment in which PAHs were
absent, as well as the treatment containing Triton X-100 with no PAH served as controls.
The germination of L. sativum was also evaluated using different soil types. The
germination procedure was repeated using the uncontaminated soil A, industrial soils (B
and C) as well as soil D. The statistical processing was performed with SPSS for
Windows release 7.5.2.
Table 1: Treatments used during Rhizoremediation
Treatments
Additions/preparations
T0 (Control)
Soil A + 100 µg/g Triton + grass
T1 (Treatment 1)
Soil A + 100 µg/g Triton + PAH(1000 ppm) + grass
T2 (Treatment 2)
Soil A + 100 µg/g Triton + PAH(1000 ppm)
T3 (Treatment 3)
Soil A + PAH (1000 ppm)
T4 (Treatment 4)
Diluted Soil B + 100 µg/g Triton + grass
T5 (Treatment 5)
Diluted Soil B + 100 µg/g Triton
Vegetated and non-vegetated microcosms: The use of L. sativum germination as a
bioindicator of PAHs removal from contaminated soil was evaluated during
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phytoremediation of PAH in different soils (table 1). Treatments 4 and 5 were prepared
by mixing 350 g of soil B with 150 g of soil A to make the diluted soil B shown in the
table. PAHs were added to the soil as described earlier. Five hundred grams of each soil
preparation (table 1) were placed in pots with saucers for leachate collection. Two
seedlings of the white buffalo grass were planted in each pot and the pots placed in the
green house at ambient temperature and natural day–night cycles. Hundred milliliters of
water was used to water the plants every 2 days. In instances were leachates were
produced, the leachate was used to water the same pots. Each treatment had 10 pots
and one pot for each treatment was sacrificed every week and the L. sativum
germination method carried out as described previously. Three replicates plates were
used for each treatment.
Chemical analysis: Residual PAH in treatments T1, T2 and T3 were quantified after
extraction with dichloromethane, using a Varian Saturn 2000 Ion Trap Gas
Chromatography/Mass Spectrometer equipped with a Chrompack CP-SIL 8CB-MS (5%
phenyl) Fused Silica Capillary Column (30 m* 0.25 mm* 0.25 µm). The detector was
tuned according to EPA 8270C using DFTPP. Injector temperature was 230°C, oven
temperature program: 30°C (6 min), 10°C/min, 300°C (7 min). Chemical analysis was
not carried out for T4 and T5.
Results
L. sativum bioindicator
The ability of L. sativum seeds to germinate in soil contaminated with different
concentrations of PAHs was investigated. Germination levels of the seeds decreased
with an increase in the concentration of the PAH (Figure 1a). At higher PAH
concentration (1000 ppm), the germination level of L. sativum was lower by a factor of
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120
Germination level (%)
100
80
60
Germinated seedlings
40
20
0
0
50
150
300
500
1000
PAH (m g/kg soil)
(a)
120
Germination level (%)
100
80
60
Germinated seedlings
40
20
0
0
50
150
300
500
1000
PAH (mg/kg soil)
(b)
Figure 1. The level of germination of L. sativum in soil contaminated with different
concentrations of PAH: (a) germination of L. sativum in the absence of the surfactant
(Triton X-100) and (b) germination levels in the presence of surfactant (100 µg/g soil).
The surfactant was added to make the PAH bioavailable. Error bars represent standard
deviations. There was a significant difference between the treatments used (P<0.05).
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three, compared to germination levels at 50 ppm. The presence of Triton X-100 in the
soil did not increase the toxicity of the PAH to seed germination (Figure 1b) as the
surfactant by itself had no effect on the seed germination. The level of germination also
decreased with increasing concentration of PAH. The effect of PAH on the fresh weight
of the seedlings was also investigated in the experiment. The seedling fresh weight of
L. sativum decreased with an increase in the concentration of PAH (Figure 2a and b). At
higher PAH concentration, the weight of the seedlings was lower by a factor of two
compared to the seedlings exposed to lower PAH concentration.
Germination of L. sativum in different soils shows that, where PAHs are absent, the level
of germination exceeds 95% while in the case where the concentration of PAH are high
(1.2 g/kg soil as in soil B), no germination of L. sativum occurs (Figure 3). The composition of soil C did not appear to have any inhibition on the germination of L. sativum
seeds, as germination levels were comparable to the levels in soils with no PAH (A and
D).
Germination over time in vegetated and non-vegetated soils
The potential of L. sativum germination as a bioindicator of PAH removal was investigated during phytoremediation of soil contaminated with PAH. Soil A was artificially contaminated with PAH mixtures of naphthalene, anthracene, phenanthrene, coronen and
pyrene and the grass seedlings of P. maximum were planted in treatments T0, T1 and
T4.
The level of germination in the soil of vegetated treatment (T4) was significantly (p<0.05)
higher compared to germination levels in the non-vegetated treatment T5 (Figure 4).
However, there was no significant difference in the germination level of both the
vegetated treatment (T1) as well as the non-vegetated treatments (T2 and T3)
containing the artificially contaminated soil A. The data suggest that P. maximum may
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have a positive effect on the removal and or detoxification of the PAH from the soil. The
extent of PAH disappearance in vegetated soil is significantly greater than in
unvegetated soil (Aprill and Sims, 1990). Initial germination levels were below 15% but
increased with the time of the experiment, possibly due to the reduction in the
concentration of PAH in the soil (table 2).
0.3
Weight (g)
0.25
0.2
0.15
Weight of the seedlings
0.1
0.05
0
0
50
150
300
500
1000
PAH (mg/kg soil)
(a)
0.25
Weight (g)
0.2
0.15
Weight of the seedlings
0.1
0.05
0
0
50
150
300
500
1000
PAH (m g/kg soil)
(b) Figure 2. The fresh weight (wet) of L. sativum seedlings in the presence of different
concentrations of PAH in soil A: (a) the seedlings fresh weight of L. sativum germinated
in soil with no Triton X-100 and (b) seedling fresh weight of L. sativum in soil with Triton
X-100.
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University of Pretoria etd – Maila, M P (2005)
120
Germination level (%)
100
80
Soil Types
60
40
20
0
Soil D
Soil A
Soil B
Soil C
Types of Soil
Figure 3. Germination of L. sativum in different soil types. Soil A (sandy loam soil not
contaminated with PAH), soil B (historically PAH polluted industrial soil), soil C (soil
contaminated with heavy minerals but with no PAH), soil D (white playpen sand free of
PAH). Error bars represent standard deviations. There was no significant difference
between the treatments in which germination occurred (p>0.01).
120
100
Germination (%)
T0
80
T1
T2
60
T3
T4
40
T5
20
0
0
7
14
21
28
35
42
49
56
63
70
Tim e (days)
Figure 4. L. sativum germination during phytoremediation of contaminated industrial soil
as well as soil artificially contaminated with PAH. Error bars represent standard
deviations. T1 was not significantly different from T2 and T3. However, T4 was
significantly different from T5 (p<0.05).
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Table 2: PAH Concentrations (mg/kg soil) in planted and non-planted soil using
EPA analytical method 8270C
Naphthalene
Fluorene
Phenathrene
Anthracene
Pyrene
Days of
Incubation
T1
T2
T3
T1
T2
T3
T1
T2
T3
T1
T2
T3
T1
T2
T3
7
143
106
197
433
536
572
679
757
711
737
696
703
720
824
696
42
2.46
3.81
20.4
82.7
146
183
296
510
502
571
437
512
335
402
397
70
0.82
0.52
1.7
17.1
20.3
37.4
50.4
56.5
63.4
182
135
193
184
205
211
0.3
0.25
Weight (g)
0.2
T0
T1
T2
0.15
T3
T4
T5
0.1
0.05
0
0
7
14
21
28
35
42
49
56
63
70
Time (days)
Figure 5: The average weight (wet) of L. sativum seedlings during phytoremediation of
PAH in contaminated soil. T0-uncontaminated soil A with Triton X 100 and grass, T1contaminated soil A with Triton X 100 and grass, T2-contaminated soil A with Triton X
100, T3-contaminated soil A, T4-diluted soil B with Triton X 100 and grass, T5-diluted
soil B with Triton X 100.
The effect of PAH on the weight of the seedlings of L. sativum was also investigated
over an experimental period of 70 days (Figure 5). As with the germination of the
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seedlings, the weight of the seedlings germinated in the respective soils of the vegetated
treatments gave similar results. The weight of the seedlings in the industrial soil (soil B)
remained low throughout the experiment, possibly because of toxicity due to the
presence of recalcitrant PAH.
Discussion
PAH phytotoxicity has been described as a physiological toxicity (Bossert and Bartha,
1985; Huang et al., 1996; Ren et al., 1996), and an indirect effect on the ability of the
contaminated soil to provide water and nutrients to the plants (Reilley et al., 1996). In
this study, L. sativum showed sensitivity to PAH as the level of germination decreased
with an increase in the concentration of the PAH. The level of germination in soil with no
surfactant (Triton X-100) was significantly lower (p<0.05) compared to the level of
germination in soil with Triton X-100. As light dramatically enhances the toxicity of PAH
(Ren et al., 1994), the presence of the surfactant in the soil might have interfered with
the absorption of radiation by the PAH and thereby rendering the PAH less toxic. This
was contrary to the findings that bioavailability/desorption of PAH in treatments with
surfactant tends to be higher at certain surfactant concentration compared to treatments
with no surfactant (Aronstein et al., 1991). According to Guha et al. (1998), for a given
surfactant concentration, the bioavailability appears to be higher for the lower molecular
PAH and there is very little difference in the bioavailability of the same compound as
single solute or in different binary or ternary mixtures. The findings may be explained
thus, if the PAH were of low molecular weight.
The fresh weight of L. sativum seedlings also decreased with an increase in the
concentration of PAH in the soil. At a concentration of 300 ppm, the weight of the
seedlings was lower compared to the control (0 ppm). There were no noticeable
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differences in the seedling weight of L. sativum in both the absence and the presence of
Triton X-100. PAH have been suggested to cause germinated seeds to produce fewer
roots (Ren et al., 1996). At the early stages of plant development, root growth is due
primarily to cell expansion and not cell division (Taiz and Zeiger, 1991). Cell expansion
is probably being impeded, which could for example, be by inhibition of hormone action
(auxin) or interference with cellular metabolism (e.g. mitochondrial function).
The data on the germination of L. sativum in different soils shows that where PAHs are
absent, the level of germination exceeds 95% while in the case of soil B, no germination
of L. sativum occurs. The composition of soil C did not appear to have any inhibition on
the germination of L. sativum seeds as germination levels were comparable to the levels
in soils with no PAH (soils A and D). The data suggest that L. sativum germination is a
potential PAH bioindicator.
The data of phytoremediation showed that plants may be playing a significant role in the
removal and or detoxification of PAHs in the soil. Germination level of L. sativum in soils
of vegetated treatment (T4) was significantly higher compared to the germination level in
treatment T5 while there was no significant difference between the vegetated treatment
(T1) and the non-vegetated treatments containing artificially contaminated soil A. The
data suggest that P. maximum enhanced the detoxification and or dissipation of the PAH
in the soil. Vegetated treatments enhance pollutant removal in soil compared to nonvegetated treatments possibly due to the increased microbial activity as well as the
deposition of root exudates in the rhizosphere (Siciliano and Germida, 1999).
The germination level of L. sativum seeds, increased with the reduction of the PAHs in
the soil artificially contaminated with the PAHs. The level of germination in the artificially
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contaminated soil was nearly the same as the germination level in the control treatment
after 3 weeks of the experiment, possibly due to the dissipation of PAH in the soil.
The methodology based on the sensitivity of L. sativum (that has a short germination
period) to PAH can be used as a monitoring tool in remediation treatments of soil
contaminated with PAH. The methodology should be further developed to gain more
knowledge on aspects of bioavailability of PAH in both the aged as well as the freshly
spiked soil. As one can never be sure if only a specific pollutant inhibits seed
germination, it is important to know if the bioindicator is also sensitivity to other pollutants
(e.g. heavy metals), which are most likely to occur in the presence of the PAHs. This
bioindicator is very useful to examine the bioavailability of pollutants in contaminated
soil, however, extra chemical analysis are needed to complement the methodology.
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stimulating polycyclic aromatic hydrocarbon treatment in soil. Chemosphere. 20:
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sativus (cucumber) in simulated solar radiation. Ecotoxicology and Environmental
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arbuscular mycorrhizal plants. Journal of Environmental Quality. 27: 402–407.
8.
Pahlmann, R. and Pelkonen, O. 1987.
Mutagenecity studies of different
polycyclic aromatic hydrocarbons: the significance of enzymatic factors and
molecular structures. Carcinogenesis. 8: 773–778.
9.
Pothuluri, J.V. and Cerniglia, C.E. 1994. Microbial metabolism of cyclic aromatic
hydrocarbon.
In:
Chaundry,
G.R.
(Ed.),
Biological
degradation
and
bioremediation of toxic chemicals. Dioscorides Press, Portland, OR, pp. 92–124.
10.
Reilley, K.A., Banks, M.K. and Schwab, A.P. 1996. Dissipation of polycyclic
aromatic hydrocarbons in the rhizosphere. Journal of Environmental Quality. 25:
212–219.
11.
Ren, L., Huang, X.D., McConkey, B.J., Dixon, D.G. and Greenberg, B.M. 1994.
Photoinduced toxicity of three polycyclic aromatic hydrocarbons (fluoranthene,
pyrene and naphthalene) to the duckweed Lemma gibba L.G3. Ecotoxicology
and Environmental Safety. 28: 160–171.
12.
Ren, L., Zeiler, L.F., Dixon, G. and Greenberg, B.M. 1996. Photoinduced effects
of polycyclic aromatic hydrocarbons on Brassica napus (Canola) during
germination and early seedling development. Ecotoxicology and Environmental
Safety. 33: 73–80.
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13.
Siciliano, S.D. and Germida, J.J. 1999. Enhanced phytoremediation of
chlorobenzoates in rhizosphere soil. Soil Biology and Biochemistry. 31: 299–
305.
14.
Taiz, L. and Zeiger, E. 1991. Plant Physiology. Benjamin/Cummings, Redwood
City, CA.
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Chapter 7
THE USE OF BIOLOGICAL ACTIVITIES TO MONITOR THE REMOVAL OF FUEL
CONTAMINANTS: PERSPECTIVE FOR MONITORING HYDROCARBON
CONTAMINATION
A modified version of this text was accepted for publication as:
Mphekgo P. Maila, Thomas E. Cloete (2003) The use of biological activities to monitor
the removal of fuel contaminants: Perspective for monitoring hydrocarbon contamination.
International Biodeterioration and Biodegradation.
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THE USE OF BIOLOGICAL ACTIVITIES TO MONITOR THE REMOVAL OF FUEL
CONTAMINANTS: PERSPECTIVE FOR MONITORING HYDROCARBON
CONTAMINATION
Abstract
Soil biological activities are vital for the restoration of soil contaminated with
hydrocarbons. Their role includes the biotransformation of petroleum compounds into
harmless compounds. In this paper, the use of biological activities as potential
monitoring tools or bioindicators during bioremediation of hydrocarbon-contaminated soil
are reviewed. The use of biological activities as bioindicators of hydrocarbon removal in
soil has been reported with variable success. This variability can be attributed partially to
the spatial variability of soil properties, which undoubtedly plays a role in the exposure of
organisms to contaminants. Widely used bioindicators have been enzyme activities,
seed
germination,
earthworm
survival
and
microorganisms
or
microbial
bioluminescence. A mixture of some successful utilization of biological activities and
several failures and inconsistencies reported, shows that at this stage there is no
general guarantee for a successful utilization of biological activities as monitoring tools.
Wherever possible, the use of biological activities as bioindicators of hydrocarbons
removal must be used to complement existing traditional monitoring tools.
Introduction
The increasing concern about the cost of soil remediation has necessitated the need to
explore not only cost effective technologies but also alternative monitoring tools.
Conventional chemical analytical instruments like GC-MS usually monitor the progress
of remediation of hydrocarbon-contaminated soil, which can be expensive. Due to the
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cost associated with traditional monitoring tools, focus is now shifting towards using
biological activities for monitoring of bioremediation of hydrocarbon-polluted soil. The
use of bioindicators to evaluate hazardous chemical waste sites provides a direct,
inexpensive and integrated estimate of bioavailability and contaminant toxicity (Mueller
et al., 1991; Wang and Freemark, 1995, Maila and Cloete, 2002). Table 1 summarises
the advantages and disadvantages of bioindicators.
Many promising approaches using bioindicators as monitoring instruments have been
reported (Athey et al., 1989; Siciliano et al., 1997; Dorn et al., 1998; Marwood et al.,
1998; Margesin et al., 1999, Maila and Cloete, 2002).
Table 1: Advantages and disadvantages of using bioindicators as monitoing
instruments
Advantages
•
•
Disadvantages
Can detect both toxicity of parent
•
to
distinguish
toxicity
resulting from parent compound
Readily
and metabolites;
available
materials
are
•
Bioindicator response don’t always
The test can be performed ex or in-
correspond
situ;
concentration;
The test period in most cases is
•
Uncomplicated
with
contaminant
Different tests respond differently to
individual toxicants;
short;
•
Inability
compounds and toxic metabolites;
required to do the test;
•
•
methodology
is
•
Sensitivity depends on the toxicant
used to assess the extent of
and soil (i.e. the test can be
pollution reduction.
sensitive to other factors of the
soil).
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The list of bioindicators that have been tested as potential monitoring tool of
hydrocarbon removal is shown in table 2. These approaches include the use of
enzymatic activities, seed germination, earthworm survival and microorganisms or
microbial bioluminescence as bioindicators. Both these biological processes have
varying degrees of success as monitoring tools. Reliable bioindicators must give
interpretable response curves across a range of environmental parameters (Adema and
Henzen, 1989; Hund and Traunspurger, 1994), otherwise, environmental effects upon
bioindicator response may confound extrapolations meant to depict the bioavailability
and toxicity of contaminants in soil.
This paper reviews the types of potential bioindicators, including enzymes, seed
germination, earthworm survival and microbial bioluminescence used for monitoring the
remediation of soil contaminated with petroleum compounds.
Enzymes
Soil enzymes activities are attractive as indicators for monitoring various impacts on
soils because of their central role in the soil environment: soil enzymes are the catalysts
of important metabolic process functions including the decomposition of organic inputs
and the detoxification of xenobiotics. Besides hydrocarbons, soil biological activities
have been used as biological indicators of pollution with heavy metals or pesticides
(Bayer et al., 1982; Dick, 1997; Top et al., 1999).
The degradation of hydrocarbons to simple molecules like water and carbon dioxide
involves many chemical reactions in which catalytic proteins are involved. Because of
their central role in hydrocarbon degradation, it is not surprising that focus is now shifting
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towards using them as potential monitoring tools during bioremediation. Enzymes that
have been tested for their potential to monitor hydrocarbon removal include soil lipases,
dehydrogenases, catalases and ureases (table 3). However, their use has been confined
to lab studies.
The lab-scale studies show great potential for the use of these proteins as bioindicators
of hydrocarbon removal. Of the catalytic proteins tested, soil lipases showed great
potential in monitoring bioremediation of hydrocarbon (Margesin et al., 1999). The
dehydrogenases, catalases and urease were found only to be useful to indicate the
onset of the biodegradation process as their activities declined rapidly after the
biodegradation rate has decreased (Frankenberger and Johanson, 1982; Janke et al.,
1992; Van der Waarde et al., 1995; Margesin and Schinner, 1997).
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Table 2: Different bio-indicators that were used in monitoring hydrocarbons removal
Bioindicator
Enzymes
Pollutant specificity
Sensitivity and Range Tested
References
Diesel oil, mineral oil
Sensitive. Up to 1 mg/g soil
Margesin et al., 1999;
Margesin et al., 2000
Soil Lipase
Soil Dehydrogenase
Urease & catalase
Crude oil and refined
Moderately sensitive. 20-60% w/w
petroleum products
oil/dry soil.
Mineral oil
Less sensitive. Detectable at high
Frankenberger and Johanson, 1982
Margesin et al., 2000
TPH concentration (5000 mg/kg
soil)
Seed germination
Prairie grass (Canada
Aromatics
blue grass & Slender
(Halogenated)
Sensitive. 13-133 µg/kg soil
Wang and Freemark, 1995; Siciliano
et al., 1997
wheatgrass
Lepidium sativum
PAHs
Moderately sensitive
Maila and Cloete, 2002
50 – 1000 mg/kg soil
Microbial biomass
Oil contaminated soil
Moderately sensitive
Kandeler et al., 1994
‘Batteries’ of
Creosote, Heavy,
Moderately sensitive.
Wang and Freemark, 1995; Dorn et
bioindicators
medium and light crude
Earthworm>seed germination>
al., 1998;
Microbial
oils.
bioluminescence. 25 - 17400 µg/g
Marwood et al., 1998;
soil.
Phillips et al., 2000;
bioluminescence,
Shakir et al., 2002
earthworm & seed
germination
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In addition, the increase in soil dehydrogenase activity in hydrocarbon-contaminated soil was
in proportion to the rates of oil application in that activity increased with increasing loading
rates (Frankenberger and Johanson, 1982). Any influence the oils may have on soil
dehydrogenase activity is dependent on the chemical composition of the oil itself. In his
review Cole (1983), showed that oxygenation is a common process in pesticide and
herbicide metabolism and it is an important initial mode of attack when organisms encounter
what are often highly lipophilic compounds. However, not much work has been done on this
particular process as a potential bioindicator of pollutant removal in soil.
Table 3: Measurements of enzymatic activities in hydrocarbon contaminated soil
Process
Enzyme
Methodology
References
Hydrocarbon
Soil Lipase
Titration
Porkona, 1964;
mineralisation
Schinner et al., 1996;
Margesin et al., 1999
Soil
Spectrophotometer Stevenson, 1959;
dehydrogenases (Color intensity
Catalases
Frankenberger and
measurement)
Johanson, 1982
Titration
Margesin and
Schinner, 1997
Ureases
Colorimetry
Margesin and
Schinner, 1997
Most enzymatic tests are artificial and refer to the potential activity of soil enzymes. A defined
amount of soil is incubated in aqueous environment with specific substrate and sometimes a
buffer (Bitton and Koopman, 1992). The enzyme converts the specific substrate to another
compound that can be extracted and quantified by spectroscopy for example. For lipases,
tributyrin is used as a substrate, which is catalytically converted to butyric acid, which can be
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extracted and quantified (table 3). The main advantage of enzymatic tests is the easy way of
measuring but a disadvantage might be their indirect approach.
Not all enzymes are synthesized by a cell in the same amounts; some enzymes are present
in far greater copy number than others. In bacterial cells, regulation of enzyme amount by the
phenomenon called induction and repression occurs at the gene level whilst the enzyme
activity is regulated through product inhibition, covalent modification and feed back inhibition
(Brock and Madigan, 1991). The required amount of hydrocarbons in soil that can induce the
necessary enzymes to bring about the metabolism of specific hydrocarbons is not known.
However, based on the ATP required to synthesise the proteins of bacterial cells and the
diffusion, limited by the volume of water surrounding cells, it is estimated that at least about
150 mg of substrate per liter of soil water should be present (Sims et al., 1991).
Microorganisms
The immediate concern of the rehabilitation practitioners when assessing the strategy and
outcome of bioremediation is the availability and capacity or degradative potential of the
autochthonous microbial communities. The use of microorganisms as monitoring instruments
of
hydrocarbon-contaminated
soil
is
not
well
established.
However,
microbial
bioluminescence, microbial biomass/counts and soil respiration have been evaluated as
potential monitoring tools of hydrocarbons (Delistraty, 1984; Kandeler et al., 1994; Steinberg
et al., 1995; Van Beelen and Doelman, 1997; Phillips et al., 2000).
Microbial bioluminescence involves the activities of electron transport systems, which
produce substrates for the production of light. This monitoring tool has been evaluated as a
potential bioindicator of a number of organic compounds (table 4).
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Table 4: The use of bioluminescence to monitor/detect hydrocarbons
Primary reported
Organisms/System
Reference
compound/class
Photobacterium
Creosote
Phillips et al., 2000
phosphoreum
King et al., 1990; Heitzer et
Pseudomonas fluorescens
Naphthalene
al., 1994
P phosphoreum
Organics
Kaiser and Palabrica, 1991
P phosphoreum, V harveyi
Synfuel by-products
Delistraty, 1984
The use of bioluminescence is attractive since it more closely reflects toxicity than does the
use of chemical analysis (Steinberg et al., 1995). In addition, the application of
bioengineering to produce or enhance bioluminescence properties of organisms may lead to
new systems for assessing environmental toxicity. The disadvantage of using bioluminescent
is the possibility of bacteria adsorbing to the soil particles and thereby being filtered out of
suspension, resulting in lower luminescence than would correctly represent the level of soil
toxicity (Hund and Traunspurger, 1994; Benton et al., 1995; Cook and Wells 1996; Ringwood
et al., 1997). For further information on bioluminescence, the reader is referred to Steinberg
et al. 1995.
It is widely assumed that the number of indigenous biodegraders increases with the
reduction of hydrocarbons and that microbial population changes after hydrocarbon pollution.
Wünsche et al. (1995), reported that changes in hydrocarbon content in soil resulted in
characteristic shifts of the substrate utilization patterns by the microorganisms and that the
altered pattern of substrate utilization corresponded with similar changes in abundance of
hydrocarbon-utilizing bacteria and the occurrence of specific bacterial groups in the soils.
Increases of hydrocarbon degrading bacteria during bioremediation have been reported
elsewhere (Pearce et al., 1995; Margesin et al., 1999).
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The concern with using microorganisms as bioindicators is that changes in bacterial numbers
might be an indicative of a stimulated biodegradation process, but it does not necessarily
represent an accurate measurement of the actual biodegradation process. In addition, the
added biodegradative strains (in bioaugumentation process) are notoriously unreliable in
their ability to compete with native microorganisms when released into the natural
environment (Van Veen et al., 1997). There are cases, however, where microorganisms
have been used with relative success in detecting the removal of hydrocarbons in soil
(Steinberg et al., 1995).
Microbial processes have also been used elsewhere in monitoring pesticide removal from the
soil (Top et al., 1999). The most widely used microbial process to detect biotoxicity and
biodegradation of contaminants is respiration (Martin et al., 1978; Weissenfels et al., 1992;
Margesin et al., 2000; and many others). This process cannot be used reliably, however, to
monitor hydrocarbons removal, as it is difficult to distinguish biological hydrocarbons removal
from other decomposition of soil organic compounds. Microbial respiration in soil is usually
evaluated using respirometer and through titration. Phillips et al. (2000), evaluated six soil
toxicity tests (including Microtox) to monitor bioremediation in creosote contaminated soil and
found that the toxicity testing results did not always correlate with contaminant
concentrations, nor were the trends indicated by each test consistent for any of the soil types
used in the study.
Plants and Earthworms
Two tests that are widely used for measuring soil toxicity are the seed germination and
earthworm survival assays (Green et al., 1988). Other tests for water have been adapted for
soil (Kwan and Dutka, 1992; Quillardet and Hofnung, 1993; Dutka et al., 1995; Cook and
Wells, 1996; Ringwood et al., 1997). Seed germination and earthworm survival assays does
also have the potential to be used as bioindicators of hydrocarbons removal in soil. The
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sensitivity of earthworms to hydrocarbon-contaminated soil has been reported elsewhere
(Shakir et al., 2002). Earthworm survival and seed germination tests are sensitive to changes
in soil toxicity during bioremediation of PAH and petroleum hydrocarbon contaminated soil
(Athey et al., 1989; Dorn et al., 1998; Marwood et al., 1998; Knoke et al., 1999).
Seed germination and earthworm survival tests are useful as bioindicator response
endpoints because of their simple methodology, moderate sensitivity to toxicants and their
potential to be used both in situ and ex situ. The use of these tests as potential bioindicators
has, however, only been confined to lab scale studies. Maila and Cloete (2002), reported that
the level of germination of Lepidium sativum decreased with increasing concentration of the
PAH in the artificially contaminated soil, while no germination occurred in the historically PAH
polluted industrial soil. When used during phytoremediation of PAH, the germination level of
L. sativum was inhibited during the first weeks, after which germination increased, possibly
due to PAH dissipation from the soil.
Bioindicator response to organic pollutants varies in different plant species. Siciliano et al.
(1997), reported a 12-fold difference in 2CBA (2-chloro benzoic acid) and 10-fold differences
in Aroclor 1260 sensitivity among grass species. According to Cairns (1993), and Chapman
(1995), the use of indigenous species (as bioindicators) will increase the relevance and
reliability of bioindicator testing.
Dorn et al. (1998), evaluated the sensitivity of earthworm, microbial bioluminescence and
seed germination to oil contaminated soil. Earthworms were 1.4 to 14 times more sensitive
than microbial bioluminescence and 1.3 to >77 times more sensitive than seed germination
to the oily soils. Overviews on the use of plants (vascular macrophytes) phytotoxicity testing
and its role in environmental monitoring and assessment are available elsewhere (Wang and
Freemark, 1995).
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Phillips et al. (2000), reported that although total petroleum hydrocarbons (TPHs) in the soil
was reduced following treatment, results of the earthworm and seed germination tests still
showed an increase in toxicity, an indication that toxic intermediary metabolites may have
formed during biodegradation.
Influence of technology and environmental conditions
The use of biological activities to evaluate hazardous chemical waste sites provides a direct,
inexpensive, and integrated estimate of contaminant toxicity (Mueller et al., 1991; Wang and
Freemark, 1995). However, it appears that apart from the pollutants, remediation
technologies do have an effect on the bioindicator response (Siciliano et al., 1997; Margesin
et al., 1999).
Biostimulation of hydrocarbon-contaminated site had an effect on the activity of the
extracellular enzyme lipase (Margesin et al., 1999). The presence of inorganics (N and P)
accelerated the activity of the extra-cellular enzyme lipase.
Biological treatments can have a negative effect on the bioindicator response (Belkin et al.,
1994; Hund and Traunspurger, 1994; Siciliano et al., 1997). Siciliano et al. (1997), reported
that the effect of soil type on bioindicator response varies in different plants. The effect of soil
types on bioindicator response of Canada blue grass to 2CBA (2-chlorobenzoic acid) was
significant while no significant difference was observed in the germination response of wheat
grass. Differences in either one of the soil components will alter the toxicological hazard
associated with a contaminated site. Implicit in this measurement of bioavailability and
toxicity is the independence of bioindicator response to other organisms in the ecosystem. It
is well documented that many organisms produce toxins designed to minimize competition
(Curl and Truelove, 1986). It is thus important to understand the effect of the treatment
technology and environmental conditions on bioindicator response.
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Soil components like organic matter; pore space and pH play an important role in pollutant
bioavailability in soil (DeVliegher and Verstraete, 1996). It is estimated that about one third of
the initial contamination is lost through abiotic process such as volatilisaton, sorption
processes and chemical transformation (Margesin et al., 1999). Bioavailability, degradation
and toxicity of soil contaminants are all influenced by sorption, which is influenced by time,
and the physico-chemical properties of individual soils (Manilal and Alexander, 1991;
Weissenfels et al., 1992; Erickson et al., 1993; Loehr and Webster, 1996; White and
Alexander, 1996).
Effective bioindicators require a rapid and reliable methodology that characterizes the extent
of contamination, minimizes worker exposure, and reduces artifacts induced by sampling the
soil. Bioindicators integrate measurement of contaminant bioavailability and toxicity.
Conclusion
A number of methods exists that can be used to assess the extent of hydrocarbon
contamination in soil. However, the uniqueness or heterogeneity of the soil, formation of toxic
metabolites and the influence of technology contribute towards ‘poor’ bioindicator response
of the different biological activities. Chemical and toxicity data do not always corroborate one
another nor do the results of each toxicity test in a battery always agree due to the fact that
each soil is unique in the response it induces and each toxicity test unique in its ability to
detect different contaminant levels in different soils.
There is therefore a need to understand the influence of these factors on bioindicator
response. For enzymes to be successfully used as monitoring tools during bioremediation of
hydrocarbons, enzyme induction by the pollutant (hydrocarbons) as well as other soil
compounds must be investigated. Not all inducers and co-repressors are substrates or end
products of the enzyme involved (Brock and Madigan, 1991). Two other important aspects
that must be investigated are (i) the effect of technology as in N and P addition during
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biostimulation and rhizodeposition during phytoremediation and (ii) the influence of soil
components that play critical roles in the bioavailability of the hydrocarbons to the biota.
The limiting concentration such as required to induce enzyme synthesis and potential
product inhibition must also be evaluated. The other possibility is to monitor bioremediation
processes using a ‘battery’ of bioindicators as attempted by Phillips et al. (2000). The battery
must be made of different bioindicators with different sensitivities to hydrocarbon
contamination. A comparison to an uncontaminated soil with identical texture must be used
when determining the hydrocarbon toxicity.
In as far as microorganisms are concerned, the use of molecular techniques in characterizing
both microbial communities and functional genes during soil remediation needs to be
evaluated as potential monitoring tools. Substrate utilization techniques can also be used to
evaluate population changes during bioremediation, however their limitations in evaluating
most soil microbes offers some drawbacks.
It is not well documented that plants sensitivity to toxicants can vary substantially with
environmental conditions such as organic matter, pH, ligands and toxicant interactions, and if
this sensitivity can vary on a species by species basis.
The extent to which bioindicators respond to non-bioavailable poorly extractable pollutants is
not well documented. The effect of non-bioavailable hydrocarbons in soil on enzyme
synthesis and seed germination needs to be investigated.
In conclusion, there is still work that must be done on bioindicators before they can be used
on their own to monitor hydrocarbon contamination and removal. The recommendations
made by Freemark et al. (1990) and Freemark and Boutin (1994) are valid. There is a need
to use more relevant ecological test species or activity, existing protocols must be
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modified/new methods developed, tests need to be standardized and relevant test end points
must be selected. At present, it is best that they be used to complement existing
conventional monitoring instruments.
Acknowledgement
The author would like to thank Dr K Drønen, Bergen University (Norway) and Dr P Wade
Phokus Technologies (SA) for the constructive suggestions received from them.
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Chapter 8
BIOREMEDIATION OF PETROLEUM HYDROCARBONS THROUGH LANDFARMING:
ARE SIMPLICITY AND COST-EFFECTIVENESS THE ONLY ADVANTAGES?
A modified version of this text was accepted for publication as:
Mphekgo P. Maila, Thomas E. Cloete (2004) Bioremediation through Landfarming: Is
simplicity and cost effectiveness the only draw cards? Reviews in Environmental Science
and Biotechnology.
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BIOREMEDIATION OF PETROLEUM HYDROCARBONS THROUGH LANDFARMING:
ARE SIMPLICITY AND COST-EFFECTIVENESS THE ONLY ADVANTAGES?
Abstract
The biological removal of petroleum products using landfarming has been applied
commercially in large scale with relative success. The technology has been widely used due
to its simplicity and cost-effectiveness. However, together with these advantages, there are
physical, chemical and biological aspects of the technology that can hamper the remediation
process. The dominant pollutant removal mechanisms involved in landfarming are
volatilisation of low molecular weight volatile compounds during the early days of
contamination or treatment, biodegradation and adsorption. However, volatilisation and
leaching of the petroleum products present both health and environmental challenges to the
rehabilitation practitioners when designing the landfarming technology. Bioaugmentation and
biostimulation are promising bioremediation approaches involving landfarming. However, due
to the inherent problems related to bioaugmentation such as poor survival of augmented
strains, biostimulation should be preferred in contaminated sites with indigenous pollutantdegrading bacteria. Although simplicity and cost-effectiveness are the major advantages for
using landfarming, other factors generally regarded as disadvantageous to implementing the
technology can be addressed. These includes requirements for large land area for treatment,
availability of the pollutant degrading bacteria, effectiveness of the technology at high
constituent concentration (more than 50 000 ppm), improved concentration reductions in
cases requiring more than 95% of pollution reduction and the flexibility of the technology in
integrating the removal of petroleum hydrocarbons with other contaminants that may occur
with the petroleum products.
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Introduction
The technologies that involve the biological removal of petroleum products from
contaminated soil environments are today well established, and many are applied
commercially on a large scale. During the 1970’s, when environmental concerns associated
with uncontrolled disposal became apparent, and environmental regulations were
established and applied in North American and Europe (aimed at minimising the risk of air
and groundwater contamination), landfarming gained popularity. This ‘low tech’ biological
treatment method involves the controlled application and spread-out of a more-or-less
defined organic bio-available waste on the soil surface, and the incorporation of the waste
into the upper soil zone (Genou et. al., 1994). In 1983 it was estimated that at least one-third
of all United States refineries operated full-scale or pilot scale landfarmers (American
Petroleum Institute, 1983). The technology has been widely used, as it is simple and costeffective to implement compared to other treatments (American Petroleum Institute, 1983;
Harmsen, 1991).
Landfarming lost its popularity in 1984 when the United States Environmental Protection
Agency (US EPA) issued the land disposal restriction (LDR) as part of the hazardous and
solid waste amendments (HSWA) to the resource conservation and recovery act (RCRA).
The US EPA went further on 18 August 1992, by publishing a final rule, (57 FR 37194,
37252), establishing treatment standards under the land disposal restrictions program for
various hazardous wastes that included petroleum products. Landfarm operators had to
either operate their facilities to treat their waste below the EPA specified contaminant levels
(referred to as treatment standard), or to submit a petition demonstrating that there was no
migration of hazardous constituents from the injection zone (US EPA, 1984). As a result,
most of the traditional landfarms in North America were closed.
Although there have been some restrictions on the application of the technology, it is still
being used to treat petroleum products, with added measures for minimising or treating
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volatiles and leachates (Genouw et al., 1994; Harmsen et al., 1994; Balba et al., 1998;
Picado et al., 2001; Maila, 2002).
The petroleum products from the soil during landfarming are largely removed through
volatilisation, biodegradation and adsorption (Morgan and Watkinson, 1989; Devliegher and
Verstraete, 1996; Margesin et al., 1999; Hejazi et al., 2003). Lighter (more volatile) petroleum
products like gasoline tend to be removed by volatilisation during landfarm aeration process
and to a lesser extent, degraded by microbial respiration (EPA, 1994). The mid-range
petroleum products like diesel fuel and kerosene contain lower percentage of lighter
constituents than does gasoline. Biodegradation of these petroleum products is more
significant than volatilisation. The more heavier or non volatile petroleum products like
heating oil and lubricating oils do not volatilise during landfarm aeration, the dominant
mechanisms that breaks down these petroleum products is biodegradation. Adsorption also
plays an important role in the dissipation of petroleum products from the soil. According to
Margesin et al. (1999), a third of diesel was removed from the contaminated soil by
physicochemical means (adsorption and volatilisation).
The volatile organic compounds (VOCs) from the landfarm area can present air pollution
problems if the treatment area is not properly covered to minimise the emissions (Hejazi et
al., 2003). Apart from the VOC emissions, other constraints faced by the rehabilitation
practitioners considering landfarming as a treatment option include, requirements for large
land area for treatment, availability of the pollutant degrading bacteria, effectiveness of the
technology at high constituent concentration (more than 50 000 ppm), improved
concentration reductions in cases requiring more than 95% of pollution reduction and the
flexibility of the technology in integrating the removal of petroleum hydrocarbons with other
contaminants that may occur with the petroleum products. Although problems associated
with depth of pollution can be solved by ex-situ treatment, the polluted soil often requires a
large treatment area, which can increase the risk of human exposure to the contaminants.
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However, such exposure is only temporary, as contaminants will be degraded if
environmental conditions are optimal (Ausma et al., 2002).
Although simplicity and cost-effectiveness are the major advantages of the technology, the
treatment has physical, chemical and biological ‘constraints’, which must be addressed. In
this paper, we discuss these limitations, benefits, and possible solutions to the constraints.
Benefits and constraints of the technology
Bioremediation through landfarming is both simple and cost-effective to implement compared
with other treatment technologies (Pearce and Ollerman 1998; Kelly et al., 1998). On
average, the costs associated with treating petroleum hydrocarbon-contaminated soil ranges
from $30 to $70 per ton of contaminated soil compared with a physical treatment like soil
venting which is relatively expensive ($70 to $200) per ton (Marijke and van Vlerken, 1998;
Environment Canada, 2003). However, as a result of costs associated with soil excavation
and transporting the contaminated soil, in situ techniques can be in general about 40 to 50%
of ex-situ techniques (SCG, 2004). The technology is simple in that typical equipments,
which are used for landfarming, is used widely in the farming community and is therefore
‘readily’ available. As most of this equipment is designed to till the soil to a depth of ≤ 0.5m,
additional costs can be incurred during soil excavation for ex-situ treatment (Kelly et al.
1998). Different forms of the technology are shown in Figure 1.
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Aeration equipment
Contaminated soil
a)
b)
c)
Figure 1. Different landfarm layouts a) Traditional ’landfarming’ system b) ‘Complex’ landfarm
system (Figure 1b, adapted from Picado et al., 2001) c) Landfarm system without a
greenhouse structure (Figure 1c, adapted from EPA, 1994).
For additional landfarm layouts or designs, the reader is referred to Doelman and Breedveld
(1999) and to Battelle series (Alleman and Leeson, 1999). However, together with these
advantages (table 1), there are physical, chemical and biological aspects of the technology
that can hamper the remediation process.
The physical aspects include the land area
required for treatment, the ability and limitations of aeration equipments, mobility of pollutants
in the soil, water requirements; chemical aspects include toxicity, transformation and
partitioning of the petroleum products in different environmental media while biological
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aspects include biostimulation or bioaugumentation for optimal biotransformation of
petroleum products in the soil. The constraints of landfarming are listed in table 1.
Table 1: Benefits and constraints of Landfarming
Technology
Benefits
Constraints
•
Very low capital input required
•
Technology is simple to design
Large treatment area is
•
Involves risk of pollutant
exposure
•
Both in/ex-situ can be applied
•
Has small environmental
•
Substantial cost can be
incurred during excavation
impact
•
•
needed
Large soil volumes can be
treated
Landfarming
Limited to removal of
biodegradable pollutants
and implement
•
•
•
Limited knowledge of
Energy efficient
microbial process
Physical and Chemical aspects of Landfarming
Landfarming requires a sizeable area to treat the contaminated soil in cases where the
volume of the excavated contaminated soil is large, and this can increase the risk of
exposure to pollutants if ex-situ treatment is applied. The potential health hazards due to the
volatilisation of lighter petroleum products from the soil during the treatment can be avoided
by designing the landfarms as shown in Figure 1b. In this way exposure to harmful pollutants
and dust will be minimised. However, volatilisation is only important during the loading of the
greenhouse, particularly in mild climates.
The treatment of contaminated soil using landfarming can also be limited by the capacity of
the aeration equipment. It is important to design landfarms in such a way that the tilling
equipments are able to reach the ‘subsurface’ contaminated soil. The depth of the
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contaminated soil varies, depending on the capacity of the tilling equipments (30 to 60 cm is
commonly used, EPA, 1994). Also of importance during the treatment design is the need to
incorporate an impermeable membrane with a drainage layer (as shown in Figure 1b). This
membrane (high-density polyethylene membrane, ≥ 250µm thickness) prevents groundwater
contamination.
Soil moisture can also impact the efficiency of removing petroleum compounds from the soil.
The level of moisture in most landfarms is kept between 30 and 80% field capacity (Block et
al., 1992; Pope and Mathews, 1993; Malina et al., 2002). The moisture level ensures the
survival of the pollutant-degrading bacteria and enables dust control. However, as the size of
the treatment area increases, the amount of water required to maintain the level of moisture
ideal for biological activity can be enormous, especially in dry countries, and this can
increase the treatment costs.
The interaction between the pollutant and micro-biota can result in the transformation of
parent compounds to toxic metabolites which can lead to abortive pathways (Leisinger et al.,
1981; Haugland et al., 1990; Lee et al., 1994), while adsorbents like clay and organic matter,
which are site-specific can decrease the bioavailability and therefore a lower risk for higher
organisms (reduction in toxicity) and lower biodegradation efficiency as contaminants are
tightly bound to the soil matrix (Volkering, 1996; Hatzinger and Alexander, 1995; Guerin and
Boyd, 1992). The interaction between the pollutant and soil components is shown in Figure
2.
While the physical and chemical constraints of landfarming can hamper the efficiency of
landfarming, the knowledge that has been generated during the last two decades, which
addresses these limitations (Verstraete and Top, 1999; Holden and Firestone, 1997), has
made it possible for the treatment of petroleum products in an environmentally safe manner.
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Physical
Chemical
vii
i
iv
Particulate pollutant
absorbed
ii
v
chemically bound
liquid film
iii
in water phase
in pores
vi
surface adsorbed
as separate phase in pores
Extractability
Easily extractable
poorly extractable
non-extractable
(aged compounds)
Bound pollutants
High
low
bioavailability
Figure 2. Different physical and chemical forms of organic pollutants in soil i: solid particles,
ii: liquid film, iii: adsorbed onto soil, iv: in the water phase of soil pores, vi: as a separate
phase in soil pores, vii: chemically bound to soil (adapted from Rulkens, 1992; Volkering
1996; Devliegher and Verstraete, 1996).
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Bioaugmentation and Biostimulation
Bioaugmentation, the process of introducing pollutant-degrading bacteria to contaminated
site, has been reported with mixed success (Van Veen et al., 1997). The limitation to
successful bioaugmentation in soils have been cited as being due to suppression of added
strains by indigenous microbial community (poor survival of the introduced strains) and the
use of readily degradable substrates, due to low concentrations and non-biodegradability of
targeted pollutants (Alexander, 1994). Various efforts have been attempted to improve the
success of bioaugmentation in contaminated sites (Del’Arco and de França, 1999).
Strategies employed to improve bioaugmentation process for the effective removal of
contaminants from the soil include the use of adapted strains or the Field Application Vector
(as tested by Lajoie et al., 1994). However, the most promising approach with regard to
bioaugmentation has been attempted by ‘seeding’ the biodegradation knowledge to the
indigenous microbial populations (Miethling and Karlson, 1996; El Fantroussi et al., 1997;
Kästner et al., 1998; Top et al., 1999). This involves the genetic transfer from the augmented
strains to the indigenous bacteria.
With biodegradable pollutants like petroleum products (table 2), biostimulation of
microbiological processes at the contaminated site is encouraged. This usually involves the
modification of the site by adjusting pH, addition of limiting nutrients to achieve an ideal
C:N:P ratio and improving the soil moisture. High petroleum hydrocarbon removal rates have
been reported using the ratio of 100:10:1 (Genouw et al., 1994). Table 3 shows some of
cases in which biostimulation and bioaugmentation were attempted with relative success.
The availability of petroleum hydrocarbon-degrading bacteria should be investigated during
the biotreatability studies. The presence of these bacteria at contaminated site indicates that
remedial approaches involving biostimulation can be used to ‘encourage’ the biological
removal of petroleum hydrocarbons from the soil.
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Table 2: The biodegradability of different hydrocarbons
Hydrocarbons and Biodegradability
Biodegradability
Example Constituents
Products in Which
Constituent is Typically
Found
More degradable
n-butane, n-pentane, n-
m Gasoline
octane
nonane
m Diesel fuel
Methyl butane,
m Gasoline
dimethylpentenes,
methyloctanes
Benzene, toluene,
m Gasoline
ethylbenzene, xylenes
Less degradable
propylbenzenes
m Diesel, Kerosene
decanes
m Diesel
dodecanes
m Kerosene
tridecanes
m Heating fuels
tetradecanes
m Lubricating oils
naphthalenes
m Diesel
fluoranthenes
m Kerosene
pyrenes
m Heating oil
acenaphthenes
m Lubricating oils
Biostimulation of indigenous petroleum hydrocarbon-degrading bacteria in landfarms should
be encouraged ahead of bioaugmentation, as the former process relies on the degrading
bacteria that have already adapted to the site’s conditions.
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Bioaugmentation should be implemented in contaminated sites where no indigenous
petroleum hydrocarbon degrading bacteria exists, such as sites contaminated by high
molecular weight polyaromatic hydrocarbons. The process of bioaugmentation should aim at
‘seeding’ the knowledge of degrading the pollutants to the indigenous bacteria (Fulthorpe
and Wyndham, 1992; Brokamp and Schmidt, 1991; De Rore et al., 1994; Top et al., 1999;
Top et al., 1998; Verstraete and Top, 1999). As the number of microorganisms tends to
increase during biostimulation, the increase in the number of degrading bacteria can be used
as potential bioindicators during bioremediation (Margesin et al., 1999).
Table 3: Efficiency of Full-scale Landfarming of TPH sites
Efficiency
Microbial Process
(%)
and Pollutants
Duration
Technology
References
Biostimulation
82-90
12 months
Balba et al., 1998
(oil)
Bioaugumentation
43
Del’Arco and del Franca,
28 days
(oil)
1999
Berends and Kloeg, 1986;
Biostimulation
80-90
Landfarming
3 years
Bossert and Bartha, 1986;
(PAHs)
Kincannon and Lin, 1985
Biostimulation
78
3 months
Picado et al., 2001
7 months
Schenk et al., 1992
(PAH)
Biostimulation
15
(heavy molecular
weight PAHs)
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Lesson learned
The objective of landfarming is to treat petroleum-contaminated sites in an environmentally
safe manner by harnessing the removal efficiencies of biological, physical and chemical
processes in the soil. This objective is sometimes not realised due to the constraints of the
technology. In addition, no standard procedure is available for determining the allowable
loading of landfarms and the time required for biodegradation of the petroleum compounds in
the soil. This lack of procedure makes many landfarm designs to become a trial and error
procedure with no assurances that the design will be successful in remediating the
contaminated soil.
While the bio-treatability protocol recommended by Sabaté et al. (2004) is relevant, the
urgency of the bio-treatability studies makes it difficult to gather the relevant information
about optimising the processes involved in the removal of higher molecular weight petroleum
compounds or the removal of poorly available part of the contaminants that are removed
after the dissipation of the low molecular weight or the easily degradable petroleum
compounds. There is a need to incorporate, in the biotreatability studies, investigations
aimed at gathering information about the unravelling of the subsequent limiting factors during
bioremediation. As this type of study may require a longer time than the ‘generic’ or well
documented bio-treatability studies (EPA, 1994; Sabaté et al., 2004), the studies can run
concurrently with the full scale treatment of the contaminated site. With this approach, the
information obtained from the ‘urgent’ bio-treatability studies, can be used to initiate the full
scale treatment, while the information from the ‘extended’ studies about the subsequent
limiting factors, used to optimise the treatment after the removal of the easily degradable
petroleum compounds.
Picado et al. (2001) reported a 63 % reduction in total PAHs (polyaromatic hydrocarbons)
concentration after the first three months of the treatment. The majority of the PAH removed
during the treatment period were the 2, 3 and 4 ringed polyaromatic hydrocarbons. High
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molecular weight PAHs were not removed, probably due to lack of the degrading strains,
unfavourable bacterial growth conditions or due to the fact that they required a longer
treatment time to dissipate, as they are difficult to degrade. Knowledge about enhancing the
removal of the remaining high molecular weight hydrocarbons after the dissipation of low
molecular weight hydrocarbons can help in improving the efficiency of landfarming.
Bossert et al. (1986) studied landfarming of 16 PAHs present in oil-contaminated sludge and
reported a reduction of about 80-90% after 3 years of treatment. Low removal rates of high
molecular weight petroleum compounds and the long treatment periods were experienced in
some of the studies (table 3) due to the lack of process optimisation. According to Harmsen
et al. (1994) landfarming include two steps; the first step involves an intensive treatment in
which the readily available contaminants are removed. During the second step an extensive
(intrinsic) treatment, the poorly available part of the contaminant is removed. In most
landfarm operations, these two steps are not properly optimised by either biostimulation, in
which an ideal C:N:P ratio is applied or by bioaugmentation in which the biodegraders are
added to degrade petroleum compounds that are difficult to degraded by the site’s
indigenous biota. In addition, subsequent limiting factors (nutrients, pH, biodegraders, toxic
metabolites) during landfarming are not adequately addressed, resulting in long treatment
periods.
While landfarming has been able to reduce the concentration of petroleum compounds in
contaminated soil (table 3), concern remains about its effectiveness in reducing the level of
recalcitrant hydrocarbons and the potential toxicity of the metabolites generated during the
degradation process. Also critical is the amount of time needed to reduce the concentration
of petroleum compounds to levels acceptable by the regulators.
Apart from the generic approach of implementing landfarming, to treat petroleum
compounds, it is important to take into account the ‘added or non-additive effect’ of potential
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limiting factors on bioremediation. This can be achieved by a detailed bio-treatability studies
which can run concurrently with the full scale treatment process, or by incorporating an
improved monitoring program that include investigation of the unravelling limiting factors.
Possible solutions to the constraints
One of the earlier concern about using landfarming to treat petroleum contaminated soil has
been the risk of transferring environmental pollutants from one environmental compartment
(soil) to another (air or groundwater). This necessitated the need to find solutions to both the
physical, chemical and biological constraints associated with landfarming. Treatment
standards had to be met when applying the technology to remove petroleum compounds
from the soil. The concern for further environmental contamination due to landfarming led to
better treatment designs as shown in Figure 1 (b and c) from the traditional treatment
approach (Figure 1a). Landfarming should be designed as shown in Figure 1b. This
treatment design is able to prevent or minimise the transfer of contaminants from one
environmental media to another. The design encompass a greenhouse structure that avoid
or minimise dust and volatilisation of lighter petroleum compounds from the soil and also
include an impermeable membrane with an impermeable layer (high density polyethylene
membrane, ≥ 250 µm thick) which prevents ground water contamination. However, this
‘physical structure’ alone does not guarantee the efficient removal of petroleum compounds
from the soil. The condition conducive to the proliferation of petroleum degrading bacteria in
the soil has to be created for the efficient removal of petroleum compounds. This has to be
evaluated during the feasibility studies. In addition, as treatment standards vary from one
country to another, the success of one treatment design in one country is not a guarantee
that different treatment standards will be met in another country. Landfarming principles are
shown in table 4.
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Table 4: The land treatment principles
Parameter
Ideal Characteristics
Well drained soil (bulky agents needed in
Soil
clayey soils)
Pollutants should be biodegradable (by
Nature of pollutants
existing microbiota)
Greenhouse type structure (required to
Climatic conditions
minimise erosion and precipitation effects)
Indigenous pollutant degrading bacteria and
Microbiological
conducive environmental conditions (pH,
nutrients, moisture content etc.)
As the technology ‘relies’ on the biological process to remove petroleum compounds, the key
to successful remediation is to implement removal approaches that are inline with the
petroleum degrading bacteria. It is important to first conduct the feasibility studies which will
yield the information about the type and metabolic activity of the indigenous microorganisms
at the site, presence of possible inhibitors, biodegradability of contaminants under optimal
conditions, influence of nutrients and bioavailability of pollutants in soil. This information will
also help the rehabilitation practitioner to decide if biostimulation or bioaugmentation is the
relevant approach for cleaning the contaminated soil. However, while this information is
useful for intensive treatment of petroleum compounds, it provides very little information
about the unravelling of limiting factors during bioremediation and this can have an impact on
the efficiency of landfarming. Landfarming design should include a monitoring plan, which
addresses the limiting factors that may occur during bioremediation, particularly as both the
biological, physical and chemical processes in the soil have the potential to alter soil
conditions, which may become unfavourable to petroleum degrading bacteria.
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University of Pretoria etd – Maila, M P (2005)
Also, the petroleum products are often present in combination with other pollutants (e.g.
heavy metals as in motor washbay areas) and this creates problems, as the metals can be
toxic to hydrocarbon-degrading bacteria. In this case, a bio-separation process as shown in
Figure 3 is recommended. However, soil washing is recommended if the sand fraction of the
contaminated soil is large, as clay matrix can be destroyed at low pH (Tichy et al., 1996).
With this process, metals can be removed by extraction while the petroleum hydrocarbons
can be treated biologically using landfarming (Figure 3).
Hydrocarbon
and metal
Contaminated
Soil
METAL EXTRACTION
Wet
Classification
with Leachant
Oversize (rock,
gravel, sand)
Leachate recycle
Clay/humus
Leach
Slurry
Leachate
recycle
BIOREMEDIATION
Clean
soil
Metal
Dewater
Slurry
Mud
Bioaugument
fertiliser pH
adjust
Clay/humus
Landfarm
Metal
Recovery
Dewater
slurry
Slurry
bioreactor
Water
Water recycle
Carbon dioxide
Figure 3. Metal Leaching and Bioremediation Process (adapted from US EPA, 1992).
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University of Pretoria etd – Maila, M P (2005)
Ecological risk management
The volatilisation of lighter petroleum products and the mobility of petroleum pollutants from
landfarms constitute a threat to humans and groundwater resources. The risk to humans and
groundwater can be minimised by designing landfarms as shown in Figure 1b or 1c, in which
the volatiles and the downward migrating pollutants are minimised or treated.
According to Hejazi et al. (2003), landfarming at the site poses risk of detrimental effects
through the air pathway (through the inhalation exposure route) to site workers during the
initial period of landfarming. Contaminated soils are excavated and spread on a pad with a
built-in system to collect any "leachate" or contaminated liquids that seep out of contaminantsoaked soil. In some cases, reduction of contaminant concentrations actually may be
attributed more to volatilisation than biodegradation (Morgan and Watkinson, 1989). When
the process is conducted in enclosures controlling escaping volatile contaminants,
volatilisation losses are minimized.
Bioremediation through landfarming aims to remove pollutants through conversion to CO2
and water. However, in many cases, an important fraction of pollutant and its metabolites
remain untouched by the cleaning process (Devliegher and Verstraete, 1996). This amount
of pollutant remaining in the soil constitutes a major concern and source of debate in relation
to risk assessment. The threat posed by the pollutant residues can be minimised by adding
adsorbents to form the non-bioavailable residues as suggested by DeVliegher and
Verstraete (1996). Non-bioavailable pollutants can be considered as representing no direct
harm to the environment. The different physical and chemical forms of organic pollutants are
listed in Figure 2.
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Future R&D needs
Landfarming is a cost-effective method of treating biodegradable petroleum products in the
soil. However, it is important to design the treatment system in such a way that the transfer of
pollutants to other environmental media is minimised or prevented. It is also important to
modify the contaminated site’s conditions to be ‘inline’ with the normal activities of the
indigenous pollutant-degrading bacteria as this can improve the biological removal of
petroleum products.
One of the disadvantages of landfarming is the inability of the technology to have
concentration reductions of more than 95% (EPA, 1994). This pollution reduction may (in
some instances) not be adequate to meet regulations or standards from specific petroleum
constituencies in some countries. As this can be attributed to the unavailability of the
pollutant to biota, agents (like surfactants) that improve the bioavailability of petroleum
products in soil must be considered during the design phase of the technology. This should
be particularly encouraged where there is a significant risk posed by the remaining residues.
However, the effectiveness of this approach must be compared with the addition of
adsorbents, which can make the pollutant residues, less available and therefore not harmful
to higher organisms.
Landfarming may also not be effective for high constituent concentrations in the soil. As high
concentration of the pollutants can be toxic to soil microorganisms, studies should be
undertaken during the biotreatability studies to determine the minimum amount of soil or
adsorbents (e.g. straws which can also improve soil aeration) that can be added to the soil to
reduce toxicity. It is therefore important to corroborate (using other petroleum products) the
findings of Del’Arco and de França (2001), who reported that the extent of oil biodegradation
is inversely proportional to increasing oil contamination.
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University of Pretoria etd – Maila, M P (2005)
Landfarming has been used to treat volatile and biodegradable pollutants with relative
success. However, the technology has not been greatly used to treat persistent organic
pollutants like the high molecular weight polyaromatic hydrocarbons. There is a need to
understand microbial processes and environmental conditions conducive for ‘seeding’
biodegradation information to the indigenous microbial communities. Remedial approaches
involving bioaugumentation with the aim of increasing the removal capacity of the indigenous
bacteria should therefore be evaluated at both pilot and large scale to improve the biological
removal of persistent petroleum compounds using landfarming. It is also important to
understand the unravelling of the subsequent limiting factors during bioremediation of both
the low and high molecular weight petroleum compounds.
In conclusion, although simplicity and cost-effectiveness are the major advantages of using
landfarming, the technology has ‘inherent’ physical, chemical and biological constraints.
However, these constraints which are generally regarded as disadvantageous to
implementing the technology can be addressed by applying the current wealth of knowledge
on biodegradation and bioavailability of petroleum hydrocarbons, partitioning of petroleum
hydrocarbons between environmental media, genetic transfer of the biodegradation
knowledge to indigenous microbial communities, impact of petroleum products on soil
microbial diversity and the intensive treatment of contaminated soil where space is a
constraint. This wealth of knowledge on biodegradation and bioavailability of pollutants adds
on to the advantages that have been well documented about landfarming. Hence, simplicity
and cost effectiveness are not the only advantages associated with landfarming. Stimulated
biological process and co-metabolism of recalcitrant (heavy molecular weight PAHs) are the
other advantages associated with the technology. It is however, important to implement the
technology in such a way that ‘side effects’ are minimised (i.e. there is less risk of
transferring the pollution to other environmental media like the air and groundwater).
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University of Pretoria etd – Maila, M P (2005)
Acknowledgement
The author would like to thank Dr K Drønen and Dr P Wade for their constructive inputs to
the paper. This work was also partially funded by the National Research FoundationResearch Council of Norway grant.
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Chapter 9:
CONCLUSIONS AND PERSPECTIVES
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CONCLUSIONS AND PERSPECTIVES
The methods used to study soil microbial diversity, as indicated in Chapter 1, include culturedependent and culture-independent techniques. However, both methods have inherent
limitations, which hamper efforts of understanding the genetic and functional diversity of
different ecosystems. There is therefore a need to investigate the extent to which the two
approaches can be used to complement each other. The literature revealed that culturedependent methods are ideal for assessing the metabolic activities or the functional diversity
of the soil while the molecular techniques and the phospholipid fatty acid are ideal for
assessing the microbial community structure (Amann et al., 1995; Vestal and White, 1989).
Also of importance is the ability and capability of the different techniques to evaluate the
effect of environmental pollutants on soil microbial communities.
In order to seek clarification on the extent to which both cultured-dependent and cultureindependent methods can be used to complement each other, we used the community level
physiological profiles (CLPP) and polymerase chain reaction-denaturing gradient gel
electrophoresis (PCR-DGGE) to evaluate the influence of total petroleum hydrocarbons
(TPH) on soil microbial communities. Also investigated was the influence of the
hydrocarbons on both the rhizosphere and non-rhizosphere soil microbial communities.
The community-level physiological profiles (CLPP) and PCR-DGGE were used to evaluate
the influence of hydrocarbons on soil microbial diversity of the different soil layers. An
additional aim was to understand the distribution of hydrocarbon-degrading populations with
soil depth and how the distribution patterns influence the efficiency of biodegradation. The
substrate utilisation pattern of the topsoil was different from the substrate utilisation pattern of
the soil layers below 1m. In addition, the substrate utilisation patterns of the contaminated
and uncontaminated soil layers were different. 16S rRNA gene fragment patterns of the
different soil layers were also different. While the metabolic activity of different samples as
reflected by CLPP do not necessarily imply the difference in community structure of the
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samples, PCR-DGGE revealed differences in 16S rRNA gene fragments of the different
soils, and this complemented the results of the CLPP. The results suggest that the use of
functional and genetic approaches (in combination) have a better chance of revealing a
‘clearer’ picture of soil microbial diversity.
The biodegradation rate of hydrocarbon was highest in the topsoil compared to other soil
layers and this was supported by the high number of hydrocarbon-degrading bacteria in the
topsoil compared to soil layers at and below 1 m. The results suggest that the biological
removal of hydrocarbons varies in different soil layers and that microbial diversity as
evaluated by CLPP and PCR-DGGE varies with depth in hydrocarbon-contaminated soil.
Information about metabolic activities of different soil layers is critical when assessing the
footprints of degradation processes during monitored natural attenuation (Smets et al.,
2002). Further studies are required to understand the effect of (not only) other pollutants, but
the influence of soil components (pore volume, level of adsorbents and other environmental
factors) on the microbial diversity of different soil layers in both ‘shallow’ and deep aquifers. It
will also be important to investigate the influences of soil type, groundwater level, total
organic carbon and the electron acceptors on microbial diversity of different soil layers, as
these are important factors in soil remediation.
Heterotrophic bacteria play an important role in the restoration of hydrocarbon-contaminated
soil. However, there is very little information about the distribution of the heterotrophic
bacteria in different environments contaminated by similar contaminants. To get an
understanding of the distribution of the heterotrophic bacteria in different contaminated
environments, the stone ballasts at different diesel depots contaminated by hydrocarbons
were used as ‘models’. Hydrocarbon-utilizing bacteria colonizing the stone ballast at different
diesel depots formed the majority of the total culturable heterotrophs (TCHs). The number of
total culturable heterotrophs (TCHs) and the culturable hydrocarbon-utilizing bacteria were
proportional to the concentration of the hydrocarbons on the ballasts. Characterisation of
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heterotrophic communities using Biolog (Chapter 3) revealed differences in the microbial
metabolic profiles for the different sites.
Microbial diversity of polluted surfaces needs to be studied further to investigate the
concentration or the thickness of the hydrocarbons layer on the rock surfaces that
encourages the attachment or colonization of the TCHs and the hydrocarbon-degrading
bacteria. It is also not clear how the heterotrophs acquire the micronutrients from the
surrounding environment. Knowledge of microbial diversity of contaminated rocky surfaces is
essential as it can be applied in bioremediation of contaminated rocky surfaces as in
contaminated diesel depots and contaminated rocky surfaces caused by oil spills.
The importance of the geographical origin of the samples and the hydrocarbons were
evaluated using the contaminated and uncontaminated soils from the different soil locations.
The contaminated and uncontaminated soils from the different locations were not clustered
together by cluster analysis of the different community profiles (CLPP). In addition, the
contaminated and uncontaminated soils from each geographic location were also not closely
related. Because of different soil usage and heterogeneity, which can influence microbial
diversity, it was expected that the geographical origin of the sample rather than the
hydrocarbons will be more important in determining functional or species diversity within the
bacterial communities. However, the results did not support this hypothesis as the samples
from different locations were as different as samples from the same location but from
contaminated versus uncontaminated soil. The results of the soils from different locations
artificially contaminated by different hydrocarbons also reached the same conclusions.
Further work is needed to investigate the importance of geographic location and
hydrocarbons using molecular techniques.
Multi-planted rhizoremediation of polycyclic aromatic hydrocarbons (PAHs) was more
effective compared to monoculture rhizoremediation (Chapter 5). However, selecting the
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correct mixture of plant species for multi-plant rhizoremediation could create problems,
particularly as organisms are known to produce toxic compounds to minimise competition
(Curl and Truelove, 1986). It is therefore necessary to use plants which can coexists or the
so-called co-occuring plants for multi-plant phytoremediation purposes.
The effect of PAHs on the non-rhizosphere microbial communities was found to be in line
with similar findings on the impact of organic pollutants on soil microbial communities (Atlas
et al., 1991; Wünsche et al., 1995). The metabolic diversity of the contaminated and noncontaminated soil was different. In addition, the rhizosphere microbial communites appears
to be impacted in the same way as the non-rhizosphere microbial communities (Chapter 5).
The Principle component analysis and cluster analysis revealed differences in the metabolic
diversity of the contaminated and non-contaminated rhizosphere soil. However, the
differences in the metabolic diversity of the multi-planted and monoplanted treatments were
not revealed. There is a need to study further the microbial diversity in multi-planted
treatments aimed at decontaminating hydrocarbon-contaminated soil using both functional
and molecular approches.
The knowledge of genetic and functional diversity of the soil plays an important role during
the planning and remediation of contaminated soils. However, equally important is the
availability of reliable monitoring instruments to ‘gauge’ the progress of bioremediation (Maila
and
Cloete,
2002).
Bioremediation
as
evaluated
by
Gas
Chromatography-Mass
Spectrometer (GC-MS), can be expensive. Recently, biological activities have been
investigated as potential monitors of the removal of organic compounds from the soil
(Margesin et al., 1999; Athey et al., 1989; Siciliano et al., 1987; Dorn et al., 1998; Marwood
et al., 1998; Maila and Cloete, 2002). Enzymes, earthworm survival, microbial
bioluminescence and seed germination using grass species have been evaluated for their
potential, but not much has been done on the use of plants with short germination period
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(Margesin et al., 1999; Wang and Freemark, 1995; Siciliano et al., 1987; Phillips et al., 2000;
Shakir et al., 2002).
The sensitivity of Lepidium sativum, a plant with short germination period, was investigated in
soil(s) artificially contaminated and historically contaminated with mixtures of PAH
(Chapter 6). The level of germination of L sativum decreased with increasing concentration of
the PAH in the artificially contaminated soil while no germination occurred in the historically
polluted soil. When used during phytoremediation of PAH, the germination level of L sativum
was inhibited during the first weeks, after which germination increased, possibly due to PAH
dissipation from the soil. The data suggests that the germination of L sativum can be used to
monitor the removal of PAH pollutants from soil. The method based on the sensitivity of
L. sativum (with a short germination period) to PAH can be used as a monitoring tool in
remediation treatments of soil contaminated with PAH. The methodology should be further
developed to gain more knowledge on aspects of bioavailability of PAH in both the aged as
well as the freshly spiked soil. Also critical is the sensitivity of the seeds to other pollutants
(e.g. heavy metals), which are most likely to occur in the presence of the PAHs.
The review article (Chapter 7) reveals that although several biological activities have the
potential to monitor the removal of hydrocarbons from the soil, the methodologies have not
been developed sufficiently to cater for the heterogeneity of the soil and to differentiate the
toxicity by the parent compound and the metabolites. There is still work that must be done on
bioindicators before they can be used on their own to monitor hydrocarbon contamination
and removal. The recommendations made by Freemark et al. (1990) and Freemark and
Boutin (1994) are valid. There is a need to use more relevant ecological test species or
activity, existing protocols must be modified or new methods developed, tests need to be
standardized and relevant test end points must be selected. At present, it is best that they be
used to complement existing conventional monitoring instruments.
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Apart from the knowledge of genetic or functional diversity of contaminated soils and
monitoring instruments, the selection of the relevant treatment technology is also important
for soil rehabilitation. The selection of the remediation technology should be based on: site
characteristics, nature and extent of contamination, risk assessment and economic
considerations. For bioremediation technologies, the pollutant of interest must be
biodegradable by the existing indigenous or the added strains. As indicated in Chapter 8,
bioremediation approaches involving the stimulation of the indigenous pollutant-degrading
bacteria should be preferred above bioaugumentation. The latter approach should be
considered when the contaminated site does not have the indigenous pollutant-degrading
bacteria. Even in this case, the aim should be to ‘seed’ the biodegradation knowledge to the
indigenous microbial populations due to poor survival of the added strains (Van Veen et al.,
1997; Top et al., 1999).
As indicated in Chapter 8, the biological removal of hydrocarbons is cost-effective compared
to other treatment technologies. Also cost-effective is the use of biological activities to
monitor the removal of hydrocarbons from the soil. However, because of soil heterogeneity,
bioindicator response to pollutants can be ‘masked’ by the availability of different adsorbents
in the soil. Because of this limitation, the remediation and monitoring strategy illustrated in
Figure 1 is recommended. Indeed chemical analysis is ideal for determining the levels of
hydrocarbons in soil, while toxicity can be determined by using bioindicator response to not
only the parent compounds, but also to toxic metabolites which are not easy to determine
analytically.
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University of Pretoria etd – Maila, M P (2005)
Phases
Phase 1
Site Assessment
Phase 2
Treatment & Monitoring
Program
Phase 3
Rehabilitation Closure
Target
Levels/Standards
Site characteristics
Pollutant
Biodegradability
Treatment Option
Outcomes
Contamination
Assessment Report
Biotreatment
• Bioventing
• Land
treatment
• Biopile
• Etc
Monitoring
Traditional
Monitoring
Instruments
e.g. GC-MS
Remediation Action Plan &
Monitoring Plan
C= Complementation of Monitoring Instruments
Figure 1. Strategy for bioremediation of TPH contaminated soil with an integrated monitoring program
164
C
Toxicity of
Residues
Biomonitoring
e.g. Biological
Activities
Site Rehabilitation Closure
Report
University of Pretoria etd – Maila, M P (2005)
In conclusion, the polyphasic approach is recommended when evaluating soil microbial
diversity and the effect of pollutants on microbial community structure, as the approach
compensates for the limitations of each existing method of evaluating microbial diversity.
However, further work is needed to improve the recovery of bacteria from the soil, particularly
where the interest is to evaluate the availability of the indigenous microbial populations for
bioremediation. Also critical is the need to standardise or create new (biomonitoring)
approaches to evaluate the removal of organic pollutants from the soil. The inherent
(physical, chemical and biological) limitations of bioremediation technologies must also be
investigated to improve the efficiency of bioremediation.
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