a case study of bioremediation of petroleum-hydrocarbon contaminated soil at crude oil spill site

7/29/2019 A Case Study of Bioremediation of Petroleum-hydrocarbon Contaminated Soil at Crude Oil Spill Site

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Advances in Environmental Research 7 (2003) 767782

1093-0191/03/$ - see front matter 2002 Elsevier Science Ltd. All rights reserved.PII: S109 3-0 191 0 2 .0 0 0 2 9 - 1

A case study of bioremediation of petroleum-hydrocarboncontaminated soil at a crude oil spill site

B.K. Gogoi , N.N. Dutta , P. Goswami , T.R. Krishna Mohan *a a a b,

Regional Research Laboratory, Jorhat 785 006, Indiaa

CSIR Centre for Mathematical Modelling and Computer Simulation (C-MMACS), Bangalore 560 037, Indiab

Accepted 10 March 2002

Abstract

Laboratory and field pilot studies were carried out on the bioremediation of soil contaminated with petroleumhydrocarbons in the Borhola oil fields, Assam, India. The effects of aeration, nutrients (i.e. nitrogen and phosphorus)and inoculation of extraneous microbial consortia on the bioremediation process were investigated. The beneficialeffects of these parameters on the bioremediation rate were realised equally in laboratory and field pilot tests. Thefield tests revealed that up to 75% of the hydrocarbon contaminants were degraded within 1 year, indicating thefeasibility of developing a bioremediation protocol. A complementary computer simulation study was carried out toenhance the understanding of the basic processes and the rate determining factors for bioremediation under the

practically relevant conditions of Borhola oil fields. The simulations indicated that due to the high initial contaminantconcentrations, the bioremediation process was restricted mostly to the macropores of the system within the periodof 1 year and had not penetrated into the soil aggregates sufficiently. Certain shortcomings of the model have beenidentified and possible refinements suggested. 2002 Elsevier Science Ltd. All rights reserved.

Keywords: Biodegradation; Fuel contamination; Field pilot study; Aeration; Nutrients; Extraneous microbial consortia; Modelingand simulation

1. Introduction

Borhola oil fields, under the Damodar Valley project

(DVP) of Eastern (India) Regional Business Centre(ERBC), Oil and Natural Gas Corporation Limited(ONGCL), India, with an operational area of approxi-mately 300 acres, have been producing crude oil at anestimated capacity of two million tonnes per annumsince 1972. During normal operation, leakage and spill-age of crude oil result in soil contamination at suchlocations as oil wells, sumps and pits, tank batteries,gathering lines and pump stations. Depending on the

*Corresponding author. Tel.: q91-80-527-4649; fax: q91-80-526-0392.

E-mail address:[email protected](T.R. Krishna Mohan).

site location, the level of oil contaminants in the soilmay be as high as 10% wyw. Furthermore, an apprecia-ble amount of waste crude oil is generated during field

operations and is collected in a waste pit constructednear the drilling site. The upper layer of oil in the wastepit is removed and transferred to Group GatheringStations (GGS) and the combustible portion of theremaining contaminated soil in the pit is removedmanually to an isolated place and subjected to opendump burning. Finally, there is some contaminated soilstill remaining in the waste pit which is left to bedegraded by natural processes. This practice of disposalhas limitations. During the rainy season, flooding andyor accidents, crude oil from the waste pit may spreadto the surrounding fields causing pollution. Althoughopen dump burning may be simple and easily adaptable,this technique has undesirable health and safety hazards


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768 B.K. Gogoi et al. / Advances in Environmental Research 7 (2003) 767782

from air pollution. Furthermore, due to incompletecombustion, the residual hydrocarbons may graduallypercolate into acquifers, causing long-term environmen-tal problems. Bioremediation is an alternative technol-ogy capable of achieving permanent remediation at

waste sites without such associated problems, as recog-nised by the US EPA for implementation under theSuperfund Amendments and Reauthorisation Act(SARA) of 1986 (Sims et al., 1990); acceptance by thegeneral public is another major advantage of this tech-nology (Skladney and Metting, 1993). To achieve man-aged in situ bioremediation, nutrients and air aresupplied to the subsurface and the indigenous bacterialmix is often augmented to obtain enhanced bioremedia-tion. The design of an efficient bioremediation systemrequires a set of careful studies of the local conditions.

Accordingly, this paper reports on a systematic lab-

oratory treatability and field pilot scale study conductedtowards the development of a bioremediation protocolfor the oil contaminated soil at Borhola oil fields. Also,we report on a mathematical model that was employedfor simulation purposes for forming a better insight intothe rate limiting steps involved in the remediationprocesses. The laboratory exercises require periods ofthe order of 1 year which makes it difficult to designan optimal system by tuning the parameters in thelaboratory. Computer simulations, hand in hand with thelaboratory exercises, provide the achievable solutions.

2. Experimental study

2.1. Sampling and site assessment

Development of a bioremediation protocol requires acomplete site characterisation to define the subsurfacegeology and the distribution of contaminants. The mag-nitude of soil contamination in the drill site of CS areaof Borhola oil fields is very high and the soil variabilityin this zone appears to be minimal. In addition, thiszone is more prone to cause oil spillage to the nearbypaddy fields than are the oil well head sites. The wellheads are distributed throughout the oil fields in different

sites of which the geological and hydrological conditionsvary.Representative soil samples were collected from dif-

ferent locations following appropriate statistical proce-dures reported in the literature (Huesemann, 1994a). Inthe selected site of 0.25 acres, the contaminant (crudeoil) distribution on the surface was observed to beuniform. Accordingly, eight non-overlapping squaregrids were selected of equal, 4=4 ft. surface area andsamples collected from the centre of each grid. Visualobservation and random estimates of the oil contentindicated uniformity of infiltration rates upto a depth of2 ft. (0.6 m). Accordingly, three samples, weighing 3kg each, were collected from the centre of each grid,

from different depths going upto a maximum of 2 ft.Samples were stored in plastic buckets and homogenisedin a mixer before selecting a subsample for analysis.

2.2. Contaminant characterisation

While a number of different methods of soil analysisfor compounds like BTEX (benzene, toluene, ethylben-zene and xylene) or PNAS (polynuclear aromatic hydro-carbons) may be required by regulatory agencies, onlyoil and grease (O&G) and total petroleum hydrocarbons(TPH) concentrations are required for the design ofoptimal land treatment conditions. The success of bio-remediation (i.e. TPH removal) depends largely on thecontaminant characteristics. Therefore, it is essential tohave a comprehension of the same in respect of totaloil content and its constituents in terms of the different

hydrocarbons present. Usually, the hydrocarbons presentcomprise saturates (alkanes and cycloalkanes) and aro-matics (mono- and polynuclear). The polar fraction ofthe petroleum containing nitrogen, sulfur and oxygen iscomprised mostly of ashphaltenes and resins. Moisturecontent in the soil is important because it has relevanceto the biodegradation process.

2.2.1. Oil and moisture content in soil samples

Dried, crushed and sieved contaminated soil sampleswere subjected to Soxhlet extraction using freonychlo-roform for 8 h. The extracted oil was concentrated in avacuum rotary evaporator and weighed. The extract was

treated with solice gel to remove the polar compounds.The resulting TPH was then collected in a pre-weighedbeaker, dried at 60 8C for 18 h, and weighed accordingto EPA methods 413.1 (USEPA, 1979a) and 418.1(USEPA, 1979b).

For determination of moisture content, the contami-nated soil samples, 50100 g each, were dried at 60 8Covernight and then kept in a dessicator. The sampleswere then crushed in a mortar, sieved from a 36-meshsieve, redried and weighed. The loss in weight wascalculated as the moisture content.

Typical values of oil and moisture contents in five

samples are shown in Table 1; samples 68 were almostidentical in colour, consistency, etc., and were omittedfrom further investigations. All analyses were carriedout in triplicate and reproducibility was found to be"5%.

2.2.2. Oil component analysis

Component analyses of the samples were used todraw inferences regarding natural biodegradability ofthe crude oil. For the component analysis, the ashphal-tenes were precipitated from the extracted oil samplesand Borhola crude oil using n-pentane as the antisolvent.After the separation of ashphaltenes, the extracted oiland Borhola oil were further fractionated into saturated


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769B.K. Gogoi et al. / Advances in Environmental Research 7 (2003) 767782

Table 1Oil (on a dry weight basis) and moisture content of collectedsoil samples

Sample Oil Moistureno. content content

(% wyw) (% wyw)

1 13.35 7.882 8.55 18.803 2.97 4.544 9.86 3.145 6.77 25.22

Table 2Types of components in extracted oil samples and crude

Sample % wyw ofno.

Saturates Aromatics Resins Ashphaltenes Hc Non-Hc

1 50.88 12.56 27.83 8.73 63.44 36.562 58.60 19.04 18.97 3.39 77.64 22.363 30.29 27.16 37.80 4.75 57.45 42.554 31.10 39.14 19.14 10.05 70.81 29.195 58.80 19.10 13.29 8.79 77.92 22.08

Crude 69.38 18.89 5.07 6.66 88.27 11.73

and aromatic hydrocarbons and resins by column chro-matography on an activated alumina column by elutionwith petroleum ether (4060 8C boiling range)benzenemixture (2:1 vyv) and methanol in sequence. Table 2

shows the various fractions of petroleum compoundspresent in this extracted oil in samples 15 as well asin the crude.

The lower percentage of saturates in the samples,with a relative increase in their aromatic content, maybe attributed to natural biodegradation of the crude. Itis known that saturates are degraded faster than aromat-ics and others, so that aromatic contents increase in theweathered samples over that found in the crude oil.Increases in non-hydrocarbon content (as compared tothe crude oil) in the samples are also indicative ofnatural biodegradation phenomena as these compoundsare difficult to degrade by microorganisms (Bossert andPartha, 1984).

Sample nos. 1, 2 and 5, collected from a fresh spill,are characterised by a high level of saturated hydrocar-bon content that may be attributed to the sandy matrixof the sample, which poorly adsorbs ashphaltenes andresins (Weissenfels et al., 1992). Component analysisof sample nos. 3 and 4 shows substantial degradationwhile sample nos. 1, 2 and 5 exhibit relatively littledegradation.

In order to draw further inferences regarding naturalbiodegradation, chromatographic analysis of the saturatefraction was conducted with a Shimadzu GC RIA gas

chromatograph, with a metallic capillary column (30

m=0.32 mm i.d.) coated with OV-101 and temperatureprogrammed from 100 to 280 8C at 4 8Cymin. Chro-matograms of sample nos. 1 and 3 are shown in Fig. 1while a chromatogram for the fresh crude oil saturatefraction is shown in Fig. 2. As evident from the

chromatograms, sample no. 1 is undegraded since itwas a fresh sample; its chromatogram is quite similarto that of the crude oil sample. Sample no. 3 was takenfrom an approximately 10-year-old waste pit and at adepth of 1 ft. (0.3 m) below the surface. It showssubstantial degradation as evident from the presence ofa high concentration of non-hydrocarbon components.This may be attributed to the low oil content in thesample (2.9% wyw) leading to reduced substrate inhi-bition effects and a consequent high biodegradation rate.

Thus, two general inferences could be drawn:

i. Component analysis of the extracted oil samples

gives a fair idea of the state of degradation, andii. chromatographic analysis of the saturate fraction of

the extracted oil samples provides a semi-quantitativeestimate of the extent of biodegradation. A similarchromatographic approach has been reported else-where to explain biodegradation capability of con-taminated soil during an in situ study (Gruiz andKriston, 1995).

2.3. Estimation of soil physico-chemical properties

Though at present details are not known about theinfluence of soil type on biodegradation kinetics, it islikely that the highly sorptive surfaces of some clay andorganic matter fractions limit the bioavailability ofpetroleum hydrocarbons to soil microorganisms (Hue-semann, 1994b; Tang et al., 1998). This may be espe-cially the case for intensely weathered soils where thecontaminants have had time to migrate into the micro-pores, which are less accessible to microbial attack. Ingeneral, bioavailability of hydrocarbons declines withageing. The rate and extent of sequestration as measuredby the extent of mineralisation of phenanthrene by anadded bacterium has been shown to be appreciable in

soil samples with )2.0% organic carbon (Nam et al.,


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Fig. 1. G C of saturate fractions of extracted oil samples.

1998). In other words, soils from various sources andlocations exhibit differences in both rate and extent ofsequestration. We have not conducted a study to deter-mine the levels of sequestration at Borhola oil fields.

Given the high levels of contaminant concentrationin the field, it is expected that there should be significantlevels of bioavailable contaminants.

In the case of intensely weathered soils, the kineticsare not limited by the number of hydrocarbon degradersor the intrinsic petroleum hydrocarbon biodegradability,but rather by mass transport (desorption, diffusion andconvection) phenomena. It is known that the PNAbiodegradation rates are affected by the fraction of fines(-0.075 mm) in the soil. Soil characterised by more

than 10% fines exhibited lower PNA biodegradationrates and the extent of bioremediation during landtreatment was lower than that of soils with smaller finesfractions, i.e. -10% (Huesemann, 1994b). Theincreased sorptive surface area of soil with larger finesfractions may affect the bioavailability of certain hydro-carbon contaminants.

The soil chemistry is equally important in developinga biodegradation potential for contaminated soil (Rogerset al., 1993). For instance, the soil pH should be adjustedto within the range 68 to enhance microbial activity(Hicks and Caplan, 1993). The levels of nitrogen andphosphorus in the soil may also be very critical as thesemay limit the biodegradation rates because of an inter-


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Fig. 2. G C of saturate fraction of Borhola oil.

Table 3Soil chemical properties

Parameter Measuredvalues

PH 4.50Ammonia-N, mgykg 300.00Orthophosphate, mgykg 9.80Water soluble potassium, mgykg 127.00Water soluble iron, mgykg 61.40Water soluble Mg, mgykg 8.00

Water soluble Ca, mgykg 8.60

active process occurring between the nutrients (Wal-worth and Reynolds, 1995).

The levels of N, P, K, Fe, Mg and C, etc., thatdetermine the soil physico-chemical properties alongwith the pH were estimated by standard text bookprocedures (Partha and Bossert, 1984) and are shownin Table 3. It is apparent that concentrations of inorganicnutrients, i.e. N, P, Fe and Mg, are low and this could

limit the metabolism of the existing microorganismscapable of degrading hydrocarbons in the soil environ-ment; application of extraneous nutrients may berequired for developing a feasible bioremediationmethod.

Major soil physical characteristics that may influencethe bioremediation process are porosity, bulk densityand air permeability. The permeability determines therate of transfer of electron acceptors to the contaminatedsoil. It is believed that the reduction of permeabilitybecause of microbial biofilms in the soil macrovoids, aswell as in the smaller pores of the soil matrix (micro-voids), is a major hurdle in managed in situ bioreme-diation; the problem may be more acute at the point of

nutrient application or injection. Air permeability of thecontaminated soil was determined by a method basedon Darcys law for steady flow in a packed bed asdescribed in the literature (Johnson et al., 1990). Thepermeability value so determined was found to be 159

Darcy as compared to the reported value of 145270Darcy for hydrocarbon contaminated soil (Dupont,1993). The experimental value obtained here is indica-tive of clean sand and gravel and thus permeability maynot be the limiting factor at the beginning of bioreme-diation. This nature of the soil bed is also substantiatedby the observed values of soil porosity, density andparticle size (see below). It is expected that permeabil-ities under growth conditions in presence of nutrientsmay decrease by 20% from the above value.

The porosity and particle density of soil samples,determined by the usual gravimetric methods, were

found to be 38% and 2.6 gy

cm , respectively. The

3

average soil particle size determined by sieve analysiswas found to be 0.5 mm. The sand and clay-silt contentof the sand were in the ranges of 6879% and 2531%,respectively.

2.4. Estimation of soil microbiological properties

The soil microbial properties are, perhaps, the vitalfactors determining natural biodegradability. Microbialcharacterisation of soil includes enumeration of totalmicrobes and contaminant-specific degraders. Soils usu-ally contain large numbers of native or indigenous

microorganisms that are able to degrade petroleumhydrocarbons. Microbial inhibition may occur in thepresence of high salt concentration and heavy metals,i.e. Ni, Cr, Pd, Cd, As, etc. (Balrich and Stotsky, 1985).In addition, hydrocarbon levels higher than 10% wt. areassociated with varying degrees of inhibitory effects onsoil microbes (Huesemann, 1994b). Total microbialcontent was determined by the Agar Plate Method forTotal Microbial Count as described in the literature(Clark, 1965). The total microbial count was comparedwith an estimate of the population that will degrade thecontaminant. The preliminary screening of the contam-

inant-specific degraders was performed in a medium


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that provides only the necessary inorganic nutrientsrequired for microbial growth, but with no intrinsiccarbon source; the contaminant supplement serves asthe sole source of carbon.

Cultures of selected potential biodegraders were pre-

pared in supplemented and unsupplemented (control)media. Following incubation, the cultures were evalu-ated for the presence and relative abundance of micro-bial growth. Isolates demonstrating little or no growthwere judged to be poor or non-degraders of the contam-inants and eliminated from further studies. The remain-ing isolates were then evaluated for the effects ofenvironmental parameters and for performance in abench scale bioremediation test as discussed in a sub-sequent section. The information on microorganismsand contaminant-specific degraders provides an indica-tion of microbial activity of the soil for existing (una-

mended)

site conditions.The basal medium used for isolation of hydrocarbondegrading microbes had the following composition (gyl): 4.74 K HPO ; 0.56 KH PO ; 0.50 MgSO 7H O; 12 4 2 4 4 2NaCl; 0.01 FeCl ; 0.01 CaCl 2H O; 2.5 NH NO ; 13 2 2 4 3ml of a trace element solution consisting of (gyl) 10CuSO 5H O; 10 H BO ; 10 MnSO 5H O; 704 2 3 3 4 2ZnSO 7H O; 10 MOO with a medium pH of 7.0.4 2 3Fifty millilitres of the medium taken in a 250-ml flaskwas sterilised by autoclaving at 120 8C for 15 min. Thedegradation capacity of the indigenous microorganismswas assessed qualitatively from the observation ofgrowth and colour change of the enriched culture media.

One gram each of the oil contaminated soil sampleswas then added to 50 ml of medium and 1% vyv crudeoil in a flask. The flask was then incubated in a shakerat 30 8C and 280 revymin for 12 days. Thereafter, 1 mlof culture broth from each flask was transferred to afresh medium of the same composition and incubatedunder the same conditions for another 12 days. Thegrowth of bacterial population (consortia) and the extentof degradation of crude oil in each flask was assessedvisually.

A comparative assessment of the potential of bacterialconsortia to utilise individual classes of hydrocarbon

compounds was made by using n-hexadecane, BTEXand phenanthrene as representatives for the groups ofaliphatic, aromatic and PNA hydrocarbons, respectively.The medium and cultivation conditions were the sameas that used for the isolation of the hydrocarbon degrad-ing consortia except that crude (as carbon and energysource) was replaced by hexadecane (1% vyv), BTEX(1% vyv) and phenanthrene (0.05% wyv). Each of theconsortia was tested for its capacity to degrade aliphatic,aromatic and PNA hydrocarbons. Flasks were inoculatedwith 1 ml of culture broth from each of the 12-day-oldconsortia, which were maintained by subculturing every12th day. Growth and hydrocarbon degradation in allcases were assessed from visual observation of hydro-

carbon dispersion in the aqueous medium, turbidity andcolour formation of the culture broth.

The effectiveness of the microbial consortia obtainedfrom different oil-contaminated soil samples varied fromsample to sample in their capabilities to degrade differ-

ent types of hydrocarbons. Consortia developed fromsample no. 4 had the best crude oil degrading capacity.Regarding degradation capacity for individual hydrocar-bons, all the consortia were found to degrade hexade-cane and the BTEX fractions of crude oil; however, theconsortium from soil sample no. 4 was better than theothers. Consortia from soil sample nos. 4 and 5 degradephenanthrene (PNA) preferentially. It may be inferredthat aliphatic degrading microorganisms are more ubiq-uitous than PNA hydrocarbon degraders. As the aro-matic and PNA hydrocarbons are more resistant tomicrobial degradation, they may accumulate in soil, and

in time they may contaminate the aquifers(

Huesemann,1994b). In the contaminated soil samples, alkanedegrading microorganisms were more prevalent thanaromaticypolyaromatic hydrocarbon degraders.

3. Laboratory evaluation of bioremediation potential

Biodegradation potential can be assessed by perform-ing a laboratory treatability study or extensive wastecharacterisation combined with the simulation of biore-mediation potential based on biodegradability data fora given type of compound. In this section, isolation ofpure cultures and their efficacy in contaminant degra-

dation, surfactant effect in contaminant solubilisationand biodegradation determined from laboratory shakeflask experiments have been documented. Results froma complementary laboratory microcosm study are alsopresented in order to draw inferences regarding biode-gradation potential from a technological perspective.

3.1. Isolation of pure culture

The relative potentials for bioremediation of the majortypes of petroleum compounds decrease in the order,monoaromatics)straight chain alkanes)branched

alkanes)naphthenes)polynuclear aromatics)polars(Huesemann, 1994b). Futhermore, microorganisms areselective and attack specific hydrocarbons rather thanall the components of the oily waste (Brown, 1987).An appropriate mixture of different microbial species isneeded to form a commensurable association that candegrade all of the components to the same extent (Gruizand Kriston, 1995). To create such an artificial mixtureis not easy, but a suitable adaptation technique may beuseful. From the result of the soil microbiologicalenumeration study presented above, it may be inferredthat artificial mixtures of microbial consortia will benecessary for biodegradation of petroleum compoundsin the contaminated soil of Borhola oil fields.


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Table 4Properties of biosurfactant produced in a n-hexadecane grown culturea

Strain Surface Functional Partial chemical(N) tension characterisation characterisation

(dynesycm)

10.5 32 Oil soluble, produce Glycolipidcreamy oyw emulsion

12 38 Oil soluble, produce Similar tocreamy oyw emulsion phospholipid

CRL-4 31 Water soluble, produce Glycoproteinstable oyw emulsion

7.2 28 Water soluble, produce stable oyw emulsion

Rhamnolipid isolated from Pseudomonas aurigenosa G6.a

Table 5Effect of biosurfactant on soil desorption and biodegradation

Sample Treatment Desorption Biodegradationno. (% oil) (% oil)

1 Control (sterilised soil) 13.27 2 Soilqbiosurfactant 71.05 3 Soilqsynthetic

surfactanta 77.274 Soilqconsortia 21.415 Soilqconsortiaq 29.24

biosurfactant6 Soilqconsortiaq 28.98

synthetic surfactant

Tween 80: polyoxyethylene sorbitan monoaleate (non-a

ionic).

Pure cultures were isolated from the consortia of soilsample nos. 4 and 5 (which exhibited biodegradationcapability for hexadecane and BTEX) as discussed be-low. Each consortium, developed in crude oil containingliquid medium (12 days old), was spread on agar platesafter appropriate dilution; sterilised basal medium agarwas used for preparing the agar plates. n-Hexane (0.2ml) or phenanthrene (0.2 ml of 0.05% w in ether) wasthen spread on the agar surface. The plates were thenincubated at 30 8C in an incubator for 4 days. The well-separated bacterial colonies were transferred to agarslants containing hexadecane or phenanthrene, and were

then allowed to grow for 4 days at 308C. Each culturewas tested for its capacity to degrade hexadecane and

phenanthrene in liquid medium.Pure cultures from these consortia having the capacity

to degrade aliphatic hydrocarbons as well as phenan-threne were isolated. n-Hexadecane degrading pure cul-ture was obtained from sample no 5, whereasphenanthrene degrading pure bacterial culture wasobtained from consortia of sample no. 4. These purecultures were maintained in the laboratory.

3.2. Hydrocarbon solubilisation and soil desorption

testing

As hydrocarbons are mostly insoluble in water, bac-terial cultures producing biosurfactant will be useful insolubilising andyor emulsifying hydrocarbons leading todesorption and thereby enhancing biodegradation rate(Rogers et al., 1993). Biosurfactant production was onlydetected in the n-hexadecane grown culture; the detec-tion method was based on measuring the surface tensionof the medium using a Du-Nouy ring method (De Grootand Monson, 1931). The pertinent properties of thedetected biosurfactant (from Pseudomonas sp. 7.2) areshown in Table 4. Due to surface tension of aqueousbiosurfactant solution, oil solubilisation and consequent

desorption from the soil matrix may be expected to behigher.

The effects of the isolated biosurfactant and syntheticsurfactant on desorption and degradation were separatelystudied in a microslurry reactor operated for 48 h. Forthis study, 5 g of homogeneous, contaminated soil wasstirred with 150 ml of aqueous medium in a flask andthe content was mixed in a shaker bath. The extent ofbiodegradation was estimated from the total oil contentof the soil (on dry basis), before and after the slurrytreatment. The extent of desorption was estimated fromthe total oil content in contaminated soil before treat-

ment and that present in the soil as well as in the liquiddispersion after the treatment. The oil content in thesupernatant was determined by a standard gravimetricmethod (Franson, 1976a).

Typical results of desorption and biodegradation areshown in Table 5 from which it is apparent that,although the biosurfactant effects are realised signifi-cantly in the desorption process, the enhancement ofbiodegradation can be considered to be only marginal


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Table 6Degradation of crude oil contaminated seashore sand in a batchreactor

Sample Time variation of oil content (gyg)consortia

0 days 15 days 30 days 60 daysno.

Control 0.048 0.048 0.044 0.0444 0.048 0.038 0.035 0.0355 0.048 0.040 0.034 0.030Mix 0.048 0.038 0.036 0.038

Fig. 3. Schematic diagram of field pilot study test cell.

under the ideal conditions of the slurry treatment. Itmay be possible, based upon the hypothesis that masstransfer limitations dominate contaminant fate in thesoil matrix, to determine the treatment end-point of thehydrocarbons. Under the ideal conditions of the slurry

reactor, desorption and solubilisation with culturedmicroorganisms are maximal which, in turn, maximisesthe extent of biodegradation.

3.3. Batch reactor microcosm test

In general, laboratory pan microcosm andyor columntests are conducted to determine the kinetics of biode-gradation, depending on whether an ex- or in situapproach is desired. Alternately, a microcosm test canbe used to model essential characteristics of the envi-ronment to predict the consequences of bioremediation

treatment. However, due to the constraints of conductingthe demonstration within the budgetary and time limi-tations, the objectives of this test have been confined toseeing whether the inoculation of bacterial culture hasany effect on the extent of biodegradation. The testshave been conducted using fresh sand contaminatedwith crude oil in an open batch reactor as describedbelow.

Sand (700 g) was mixed thoroughly with 35 g ofBorhola crude oil. The sandoil mixture was distributedin seven open flasks (100 g of sand in each flask)where 10 ml each of inoculum was added. For the

inoculum, a consortium was developed in basal mediumcontaining 1% crude oil for 8 days at 30 8C and 200revymin in a rotary shaker. The culture broth (50 mleach) was passed through glass wool to remove lumpsof undegraded crude oil. The collected culture brothwas then centrifuged, the cell mass collected and sus-pended in 20 ml of basal medium (used as inoculum).Inocula of pure cultures were also prepared using therespective hydrocarbons (either n-hexadecane or phe-nanthrene) in place of crude oil. A control experimentwas also carried out with 10 ml of the basal medium.The flasks were kept in an incubator at 30 8C and amoisture level of 1015% was maintained with sterilediluted water. The residual oil content was determined

after Soxhlet extraction of the dried soil sample by theprocedure described in Section 2.2.

Results of degradation are presented in Table 6; whatis provided are average values from analysis in triplicateand the reproducibility was within "5%. It is apparent

that added bacteria degraded the hydrocarbon contentof the crude oil, but the extent of degradation ofindividual hydrocarbons needs to be quantified.

4. Field pilot test

A practical bioremediation technology should lenditself to design of a controlled and regulated system ofoptimum efficiency through optimal aeration (oxygenor another electron acceptor), optimal nutrient supply(N, P, etc., supplementation) and the best possiblebacteria. In order to assess the optimal combination of

these parameters, a field pilot study was carried out asdiscussed below.

Six test cells of dimensions shown in Fig. 3 with anair sparger were used for test experiments. The cellsdesignated as A, B, C, D, E and F were filled withapproximately 500 kg each of crude oil contaminatedsoil prepared by mixing in a Banbury mixer. The soilpH was maintained between 6 and 8 by adding approx-imately 300 g of calcium carbonate during the mixingoperation. The experimental conditions in each cell(Table 7) were different such that effects of aeration,nutrient supply and added bacterial consortia could be

assessed. In test cells E and F, 25 kg each of oilcontaminated soil collected from the top surface of the


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Fig. 4. Variation of normalised oil concentrations with time intest cells; normalisation is done with respect to the concentra-tion in the control cell. The errors in all cases are within 10%.The legends are defined in Table 7.

Table 7Experimental conditions in bench scale test cells

Test Soil Crude Nutrient (kg) Consortia Aerationcell (kg) (% wt.) w(NH) HPO x2 4

A (control) 500 4.8 No No NoB 500 4.8 0.5 No NoC 500 4.4 0.5 Yes YesD 500 4.7 0.5 Yes NoE 500 4.4 0.5 Yesa YesF 500 3.5 No Yesa Yes

Naturally adapted.a

drainage channel at the waste pit were added as a sourceof naturally adapted hydrocarbon degrading microbialconsortia. Aeration was provided for 1 h every day at arate of 100 m yh. The moisture content in each cell was3

maintained between 50 and 65% during the entire period

by adding water from time to time; the moisture levelwas monitored by weight loss (see Section 2.2 underoil and moisture content in soil samples) taking 1012random samples from each cell. It may be noted thatthe moisture content of field samples was in the 1215% range. In each of the cells E and D, consortia froma bacterial suspension obtained from sample no. 5(discussed earlier) were added, at levels of 1.2 mlykg.During the course of investigation, the progress ofbioremediation was monitored by estimating periodical-ly the oil content, pH, moisture content, nitrogen andphosphorus levels along with the total microbial content

of the soil samples. The samples were withdrawn atvarious depths, i.e. 0, 3, 6, 9 and 12 inches from thetop surface and at these positions: one sample at thecentre, and four at diagonally opposite positions, 9inches away from the corners. Averaged values of theparameters from a sampling in triplicate were consideredfor data interpretation. The number of soil microbeswas estimated by the most probable number (MPN)enumeration test (Franson, 1976b).

4.1. Results of the field pilot test

The field pilot test was conducted for the duration of

1 year. The time variations of the oil content in thesoils in the test cells, over this period, are shown in Fig.4; this is a plot of time variations of normalised oilconcentrations defined as the ratio of the oil concentra-tion (C) in the test cell to that (C ) in the control cellcat any time. As evident from the figure, nutrient additionhas an advantageous effect on the bioremediation pro-cess; also, there is an advantage in the addition ofextraneous consortia. The advantageous effect of aera-tion seems to be marginally greater than nutrition if wego by the lower final concentration achieved by the soilin cell F as compared with that in cell D. The biodegra-

dation capability of the indigenous microorganisms

seems to be adequate for the process under conditionsof added nutrients (which may also be further enhancedthrough aeration) since the performance of the soil incell B is not very far behind those in C, D, E and Fwhere extraneous consortia were employed. It is seenthat under such ideal conditions, the extent of degrada-tion of the crude oil was as high as 75% during thisperiod.

Fig. 5 shows the microbial growth pattern during thetreatment period and it is apparent that the biodegrada-tion process is associated with microbial growth espe-cially under the influence of nutrients and aeration; themicrobial growth in the control cell remained essentiallyconstant during the test period. These results suggestthat the conditions in the test cells with nutrients andaeration are conducive to increased microbial growthand metabolism of the petroleum hydrocarbon utilisers.

5. Mathematical model and computer simulation

5.1. Model

Modeling and simulation offer promising means for

assessing the migration and attenuation of the contami-


10/16


Fig. 5. Variation of microbial population with time in test cells; the legends are defined in Table 7. Errors are within 10% in allcases.

nants, being treated in situ, in the subsurface. Sorption,convective flow, and biological transformation are threemajor processes controlling the ultimate fate and trans-port of contaminants in the subsurface. A completelymixed pore model, accounting for bioremediation in theinterstitial spaces among soil aggregates (macropores),has been combined with another model, which accounts

for diffusion and biodegradation in the microporesamong the soil particles of the aggregates, developed ina previous work (Dhawan et al. (1993); hereafterreferred to as Dh93). This model has been employedby us in the present study and analysed further in lightof the experimental results. A brief sketch of the modelis provided here for convenience. The basic assumptionsmade in deriving the model are:

1. The bed is composed of spherical soil aggregatesof a uniform radius, R.

2. The aggregates are saturated, homogeneous and

isotropic, and are composed of soil particles of siltand clay.3. The temperature is constant in the bed.4. Transport in the aggregates is by diffusion only.5. The mobile liquid in the macrovoids of the bed is

completely mixed. Thus, the concentrations of oxy-gen, biomass and substrate leaving the solid bed areequal to those in the macropores of the bed.

6. The adsorption process is sufficiently rapid so thatan equilibrium exists at the soil surface between theconcentrations of components in the liquid andadsorbed phases.

7. A concentration equilibrium is maintained betweenthe components in the liquid phase in the macro-

pores and those on the surface of the aggregates inthe bed.

8. The contaminants are not toxic and do not inhibitbiodegradation.

9. The contaminants are biodegraded to carbon dioxideand water. In general, the effects of by-products onbioremediation rates are considered negligible.

10. The reaction rate follows the Monod kinetic model(see, e.g. Bailey and Ollis, 1986; see also Rashidand Kaluarachchi, 1999 for a comprehensive dis-cussion on its role in biodegradation models) anddepends only on the concentrations of three com-ponents: substrate (contaminants), oxygen, andbiomass.

11. The transport resistances of substrate and oxygen toand through the microcolonies attached to the sur-face of soil particles are negligible. Therefore, themicrocolonies respond to the variations in the bulkconcentrations in the pore liquid.

12. Transport of microorganisms within the aggregatescan be represented by a diffusion term in theequation for the biomass.

13. The effect of biofilms in the macrovoids on theconvective flow rate is neglected.

The justification for the above assumptions have beendiscussed in Dh93 (see also Dhawan et al., 1991,hereafter referred to as Dh91). The effects of sequestra-tion and consequent non-bioavailability of contaminantsfor biodegradation are not considered herein; it may beremembered that the field samples are high in initialcontaminant concentrations. The equations for themacropores (see Dh93 for a full derivation), with S, X


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Table 8Parameter values for numerical simulation; in brackets are given for those few parameters, whose values have been changed fromthe ones used by Dh93, their Dh93 values for comparison

b9 s1.0=10 gycm0 y7 3 s0.0D9s,so s1.0=10 cm ysy6 2D9b

X9 s8.0=10 gycmi y6 3 K s1.0=10 gycmy8 3x s2.0=10 cm ysy5 2D9x

Y s0.5 gygs Y s1.0 gyg0 K s30.0 cm yg3

db

K s45.0 cm yg3ds Rs0.5 cm s43 500.0 mgykg0qs

m s5.5=10 ysy5m s0.45a s0.45l(2.78=10 ys)y5 (0.37) (0.37)

s2.5=10 cm ysy6 2D9s Qs4.5 cm ys3 Vs62 000.0 cm3

(4.0=10 cm ys)y6 2 (2.16=10 cm ys)y3 3 (30.48 cm )3

k s1.0=10 ysy7d K s5.0=10 gycmy7 3

s rs2.5 gycm3

(2.78=10 ys)y7 (1.0=10 gycm )y6 3 (1.72 gycm )3

ts2.0 (1.4)

and B indicating concentrations of substrate, oxygenand biomass, respectively, are given below; s, x and b

indicate corresponding quantities in the micropores. Theequations are being presented in the dimensionless formand the notations are listed in the Appendix A.

B E ZdS R ss Zi 2C Fsg S yS yf Bhy D (1) . 1 m s ZZD Gdu R rb rs1Z

B E ZdX W xZ2C Fsg 1yX yf Bhy D (2) . 1 m x ZZD Gdu R rb rs1Z

ZdB bZi 2 2sg B yB qf Bhyf By D (3) . 1 2 m b ZZdu r rs1Z

where the Monod reaction term, h, is given by

B EB ES XC FC FhsD GD G1qb S 1qb Xs x

The correspondence with the physical (dimensional)quantities are as follows:

B9R X9 S9 b9R x9b bBs , Xs , Ss , bs , xs ,

o i 0 0 iR Y s X s R Y s Xs s s s0 is9 m ts X k t tm d

ss , f sR , f sR , us ,1 20

y ys K K D9 D9 us x s s r

i 0X s rKdbb s , b s , R s1q ,x s b

K K x s a

2 0rK R t s R Yds s sR s1q , u s , Ws ,s r i

D9 X Ya s x

D9ytqD R y1 .s s,so s D9 D9x bD s , D s , D s ,s x b

D9yt D9 D9s s s3 1y .l a V r9l

u s , s , rsl mQ Rl

Further, we have introduced gsu yu .r l

The value of the retardation factor, R , for the contam-sinants depends on the linear soilwater partition (or

distribution) coefficient K ; it is large for compoundsdsthat adsorb strongly to soil particles. The concentrationof the adsorbed component can be given, with theequilibrium relation for adsorptiondesorption describedby a linear adsorption isotherm, as

q sK s9s ds

where q is the concentration in the solid phase. Thesinitial conditions are as follows:

0At us0, Ss1.0, Xs0.05 and BsB

For the micropores, the mass balance equations areas follows (detailed derivation in Dh91).

2B EB Es D 2 s ss 2C FC Fs q yf bh9 (4)12D GD Gu R r r rs

2B Ex 2 x x2C FsD q yf Wbh9 (5)x 12D Gu r r r

2B EB Eb D 2 b bb 2 2C FC Fs q qf bh9yf b (6)1 22D GD Gu R r r rb

where the Monod reaction term is

B EB Es xC FC Fh9sD GD G1qb s 1qb xs x

The initial and boundary conditions are:

0At us0, ss1.0, xs0.05, bsB for 0FrF1

VAt rs0, s0, where Vss, x or b for uG0

r

At rs1, ssS, xsX, bsB for u)0


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Fig. 6. Evolution of concentrations inside the aggregate; (a) atrs0.0, and (b) at rs0.5.

5.2. Simulation

Table 8 gives the parameter values which match thesituation at Borhola oil fields, Assam wsee RegionalResearch Laboratory (1997) for discussions; this is

referred to as RRL97 hereafterx; the parameter valuesin the studies reported hereafter will be these unlessotherwise specifically mentioned. Many of these valuesare the same as in Dh93 where their choice wasdiscussed and justified. We have had to alter only a fewof them to suit the Borhola conditions. Where they havebeen altered, we have given the Dh93 values in bracketsfor easy comparison (except for , R and K , which0qs dswere varied for sensitivity studies in Dh93). This modeldoes not reproduce closely the experimental resultsusing the laboratory-estimated parameters (KrishnaMohan, 1998). Nevertheless, the parameter values from

the model that give output that compares favourablywith experimental results are not outside realistic rangesof these parameters.

The coupled set of ODEs is integrated by using IMSL(1991) subroutine DIVPAG. This employs Greens algo-rithm for stiff equations and is based on backwarddifferentiation formulas. It requires an algebraic systemof equations to be solved at each time step and resortsto the chord iteration method that calculates the Jacobianinternally by finite differences.

The coupled set of PDEs is integrated by using IMSL(1991) subroutine DMOLCH. This algorithm is based onthe method of lines and employs a series of cubic

Hermite polynomials to obtain the solution. This schemerequires a large number of mesh points for spatialaccuracy and consequently large computation times. Inour simulations, we have also employed a NAG (1993)routine equivalent to DIVPAG, viz. DO2NBF, for integrat-ing the set of ODEs leaving DIVPAG for exclusive useby DMOLCH; this is called for because sharing ofDIVPAGby both the ODEs and PDEs necessitates downloadingof the routine, each time, from memory before makingit available for the other which will make it expensivein terms of time.

5.2.1. Essential behaviour of the equationsThe simulation is terminated when the contaminant

concentration drops below 1 ppb everywhere within thesoil bed; we refer to the time taken to achieve this asthe bioremediation time (T ). The results of a typicalbsimulation are shown in Fig. 6. For illustration we haveused a much reduced initial contaminant concentration,

s60 mgykg and, also, Rs1.0 cm and K s150 cm y0 3qs dsg. In Fig. 6a,b, the evolution of the concentrations ofsubstrate, oxygen and biomass is shown for two pointsin the aggregate. It is seen that the degradation ofsubstrate takes place much later at the centre (Fig. 6a),than at the halfway point to the centre (Fig. 6b). It isalso seen that the start of degradation coincides with

the increase in oxygen and concomitant increase inbiomass. Therefore, the delay in the oxygen frontmoving into the aggregate is what causes the delayeddegradation of substrate at the centre of the aggregate.Furthermore, the degradation of substrate takes placevery early in the macropores (in this particular case, wenoted that by us0.2, substrate concentration, S isapprox. 0) and subsequently, biomass and oxygen con-centrations stay at high levels.

In summary, it can be stated that the bioremediationprocess is a moving front that advances from themacropores into the micropores along with the oxygenfront which enables the bacteria to start consuming thecontaminants and multiply. It is to be expected then thatthe model itself can be made more efficient by convert-ing it to a moving boundary problem. Such a strategycan be expected to reduce the computation time signif-icantly (by orders of magnitude) since wasteful integra-tion would be carried out during most times outside thezone of the oxygen front where the activity levels arepractically zero. This strategy is being worked out by


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Fig. 7. Time evolution of substrate concentration, on a loga-rithmic scale, inside the aggregate at rs0.0.

Fig. 8. Laboratory results for biodegradation plotted on a log-arithmic scale; variation of (suitably) averaged substrate con-

centration with time for a period of 360 days.

Table 9Comparison of bioremediation times obtained with the Dh93and Borhola parameter sets

Dh93 Dh93 Borhola Dh93 Borholacase Tb Tb exponent exponentno. (days) (days)

2 22.6 24.3 y0.2887 y0.28119 8.1 11.39 y1.2415 y1.2139

10 41.3 72.2 y0.1340 y0.0850

designing moving grid methods and will be reportedelsewhere. Note that for the concentrations encounteredat Borhola oil fields (higher by a couple of orders ofmagnitude than typically encountered in the literature;see Table 8), numerical integration is extremely costlyeven on a Convex C3820 computer; 1 years integrationtakes approx. 24 h real time and the concentrations havebeen reduced by just an order of magnitude (see below;reduction by seven orders is required for completeremediation to levels of 1 ppb or less).

In Fig. 7, we have depicted the decay of the substrate

concentration on a logarithmic scale at the centre of theaggregate (Fig. 6). What is emphasised here, is theexponential nature of the degradation once the oxygenfront arrives. Notice that reduction by several orders ofmagnitude is needed to make the concentration levelsacceptable, in spite of the low value of employed for0qsthese simulations.

5.2.2. Comparison with experimental results

An important feature of the situation at Borhola oilfields is that the initial contaminant concentrations( ;10 mgykg, see Table 8) are higher by a couple0 4qs

of orders of magnitude than the values typically foundin the literature ( ;10 mgykg in Dh93). This implies0 2qsthat numerical integration is expensive (the equationsare stiff) and it becomes impractical to run the model,in the present form, till the contaminant concentrationdrops below 1 ppb everywhere. However, the field pilotstudies (see RRL97) were conducted only over a periodof 1 year and, as such, the simulation results have alsoto be compared over the same length of time which iswithin the practical limits. Note that some of theassumptions made in the model cannot be expected tohold over the length of 1 year; for example, thetemperature cannot be expected to be constant and thereare bound to be associated variations in diffusion coef-

ficient. Nevertheless, the model can be expected to givesome useful bounds on the bioremediation time. Wewill refer to the value of the contaminant concentrationat the end of 360 days as the year-end value in thefollowing.

To see the effect of the laboratory parameters on T ,bit was decided to run some sample cases reported byDh93 with the Borhola values of parameters as givenin Table 8. The Dh93 values of R, K and , for the0qds sspecific cases we have tested (we retain the same case

numbers as Dh93)

are as follows: is 600 mgykg for

0

qscase 2, and 150 and 1500 mgykg for cases 9 and 10,respectively, while K is 15 cm yg and Rs1 cm for3dsthese selected cases. We found it convenient to comparethe results using the exponents of exponential degrada-tion (see Fig. 7); the results of the field pilot studyplotted on a semi-log graph (Fig. 8) fit the exponentialpattern well within the available decade and yield anexponent of y0.004. Table 9 gives the results of thecomparison tests. It is seen that for the three casesinvestigated, the value of the degradation exponent withthe laboratory parameters is lower than with the Dh93parameters. At the same time, we also see that the

percentage differences vary much from case to case. Ithas been demonstrated that the sensitivity to most


14/16


parameters is non-linear in nature (Krishna Mohan,1998).

Dh93 had studied the sensitivity of the model to K ,dsR and and found that T is directly proportional to0qs ball of them. Our studies have revealed, however, that

the dependence of T on K is non-linear in nature andb dstheir conclusion of linear proportionality is a result ofthe restricted range in K , that they had investigatedds(Krishna Mohan, 1998). We have observed a quadraticbehaviour of K with T ; a monotonous increaseyds bdecrease of K brings it to a turnaround value wheredsT once again starts increasingydecreasing after thebinitial decreaseyincrease with change in K . It may bedsexpected that the presenceyabsence of surfactants (bioor synthetic) will alter the force of adsorption ofcontaminants to the surfaces, thus affecting the speedof remediation and K should be capturing this effect.ds

However, what should be noted here is that its role islimited and there is a turnaround value after which theamount of remediation decreases with further loweringyraising of K ; also, the exponent value remains lowdsregardless of the value of K for the Borhola caseds(Krishna Mohan, 1998). In general, the turnaroundvalue will be different for different sets of parameters.

We have also observed, as we have reported else-where (Krishna Mohan, 1998), that the model is notvery sensitive to changes in the coefficients of eitherthe input terms (first terms in the R.H.S. of Eqs. (1)(3)) or the biodegradation terms (second terms in theR.H.S. of Eqs. (1)(3)). Furthermore, the model is not

sensitive to changes in the diffusion coefficients ofbiomass and oxygen (D and D , respectively).b x

The model is sensitive to changes in the diffusioncoefficient of substrate. Our results indicate that areduction of the whole coefficient of the third term ofEq. (1) by approximately 25%, to a value 0.78 timesitself, results in the experimentally correct exponent ofy0.004 being obtained. However, this change cannotbe effected through alteration in D 9 because of the formsof definition of D involving D and R ; D is forceds s,so s sto be at 1.0 or greater in the present form (see KrishnaMohan, 1998, for a different form). The change can be

effected by altering and (for , s0.57, thel a l asame exponent is obtained). However, such a changeaffects other terms in the equations as well (though theresults are not sensitive to changes in those terms asreported earlier) and also, the value of 0.57 for , l adoes not agree well with the Borhola values.

A reduction in the substrate diffusion coefficient (toobtain the correct exponent) implies that the leachingof substrate from the aggregates to the macropores isreduced. It has also been seen from the model output(Krishna Mohan, 1998) that the degradation had notyet reached the aggregates and that macropore activitywas still going on after 1 year. This would imply thatthe reduced leaching prevents an extra load being put

into the macropore and that this enhances remediation;the extra load probably induces an inhibitory effect.However, since inhibition has not been explicitly mod-elled here, we see this as an artefact of the boundaryconditions in the model. Instead of matching the variable

concentrations at the surface of the aggregate with thecorresponding macropore values, it seems that the inclu-sion of an exchange factor for the transfer would bemore appropriate; this is being pursued further. Leachingof the contaminant into the macropores indicates thatthe desorption and diffusion mechanisms are operative,which should lead to more rapid bioremediation, in theabsence of inhibitory terms, because of the increasedavailability of contaminant in the liquid medium.

In the pilot study, the contaminant concentration wasestimated from thoroughly homogenised samplesextracted from the different parts of the soil column;

only averaged(

over the micropores and macropores)

concentration values were available. We have assignedonly 15% weightage to the macropores in the averagingand, with more weightage, the remediation activity willshow an increase because all the activity is concentratedin the macropore in this 1 year; this is another one ofthe sensitive factors.

Further studies are required to fine tune the modelwith experimental results. In the present form of themodel, the optimal values (in the sense of least changein and ) of K , D 9 and ( , ), for attaining thel a ds s l adegradation exponent of 0.004, are 60, 2.6=10 andy4

(0.54, 0.54), respectively; the corresponding values

estimated for the Borhola samples in the laboratory,were 45, 2.5=10 and (0.45, 0.45).y6

6. Conclusions

The present report is essentially the result of the firstphase of study on the design, construction and operationof above-ground biodegradation facilities; the completestudy will include earthwork design and installation ofpiping, liners, drain etc., and finally, detailed costestimation of the technology. The results and discussionpresented above are expected to provide a meaningful

insight into the biodegradability of the crude oil contam-inated soil of Borhola oil fields. The experimental resultsof this study can be utilised to assess the role of variousfactors which control the success of bioremediation.These factors include the availability of microorganismsthat can metabolise the contaminant utilising it as acarbon source through solubilisationydesorption underfavourable conditions of temperature, pH, sufficientmetabolic nutrients and moisture.

The field pilot study showed that by applying nutrientand microbe enriched solution, the crude oil componentswere reduced under aerated conditions by 75% withina time span of 1 year. This observation is supplementedby a laboratory enumeration of adequate soil microbial


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and chemical properties, which can be exploited fordesigning a large-scale process system for field appli-cation. However, further studies may be required to tunethe system suitably so as to achieve an even moreaccelerated process of biodegradation; this is necessary

considering the high levels of contaminant concentrationpresent at the site.

In the above context, the role of computer simulationis of paramount importance. The laboratory experimentstake periods of the order of 1 year, and it is difficult totune the system efficiently through such a process.Computer simulation studies carried out alongside thelaboratory work help in tuning the system satisfactorily.Our computer simulation study indicated, with the setof parameters measuredyemployed in the laboratory,that the degradation activity is restricted to the macro-pores during the initial period of 1 year. Mathematically,

the necessity for an exchange factor in the matching ofconcentrations at the surface of the aggregates with themacropore values in the boundary conditions has beenseen. The need for a better model, especially in thecontext of such high initial concentrations, is felt. It isexpected that a moving boundary model will run effi-ciently; work in this direction is on going and will bereported elsewhere. We have also pointed out thatcontrary to what is suggested in Dhawan et al. (1993),bioremediation time is non-linearly related to several ofthe parameters in this model.

Acknowledgments

Financial support for this work from ERBC-ONGCL,Nazira, Assam, India, is hereby gratefullyacknowledged.

Appendix A: Notation

B9, b9 Biomass concentration in macropores and insideaggregates, respectively, MyL 3

D 9j Diffusion coefficient of component j in poreliquid, where jss, x or b, L yT2

D9s,so Diffusion coefficient of substrate in the solid

phase, L yT2Kds Linear partition coefficient for substrate, L yM

3

Kj Saturation constant of component j, where jsSor X, MyL 3

kd Reaction rate constant for the decay of biomass,Ty1

Q Volumetric flow rate in the bed, L yT3

qs Concentrations of substrate in the solid phase,MyM

R Radius of the aggregate, LRj Retardation factor for component j, where jsS or

B

r9 Radial position in the aggregate, LS9ys9 Substrate concentration in macropores and inside

aggregates, respectively, MyL 3

S , X , B0 0 0 Initial concentrations of substrate, oxygen andbiomass, respectively

S , X , Bi i i Concentrations of substrate, oxygen and biomass,respectively, at the inlet

Tb Time taken for remediation of soil bed, Tt Time, TV Volume of the soil bed, L 3

W Oxygen supply factorX9, x9 Oxygen concentration in macropores and inside

aggregates, respectively, MyL 3

Yj Yield factor of component j, where jsS or X,MyM

Appendix B: Greek letters

b , bs x Saturation parameters of substrate and oxygen,respectively

a Volumetric fraction of liquid in the aggregatel Volumetric fraction of liquid in the macrovoids of

the bedmm Maximum specific growth rate of biomass, T

y1

r Bulk density of the aggregate particle, MyL 3

u Dimensionless timeh,h9 Monod reaction term for macropores and aggregates,

respectivelyul Time to process one macrovoid volume of the bed, Tur Characteristic time for diffusion, Tf ,f2 2 Thiele moduli for the growth and decay, respectively,

of biomass (note that the corresponding quantity in

Dh93 is lesser by a factor of three)t Tortuosity of pores in the aggregate

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Binod Kumar Gogoi has an M.Sc. and Ph.D. inChemistry with over 22 years of research experience inindustrial and environmental biotechnology.

Narendra Nath Dutta holds a Masters and Ph.D.(Tech) degree in Chemical Engineering from BombayUniversity. During his 25 years of service, he has beeninvolved in R&D activities on chemical and biochemical

processes, particularly in the field of petroleum technol-ogy. He has several papers and patents to his credit.

Pranab Goswami has an M.Sc. and Ph.D. in Chem-istry, and has worked for the last 15 years in the areaof environmental biotechnology, particularly on themechanism of microbial degradation of aliphatichydrocarbons.

T.R. Krishna Mohan is a Physicist in the area of non-linear dynamical systems and chaos theory. At C-MMACS, apart from his basic interest, he also worksin the area of mathematical modelling applied to variousnatural and industrial systems: modelling the growth of

Indian transport sector, biochemical oscillations(

bifur-cations and various dynamical patterns), bioremediationetc.

a case study of bioremediation of petroleum-hydrocarbon contaminated soil at crude oil spill site

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