research article statistical modeling of environmental...

Research ArticleStatistical Modeling of Environmental Factors on MicrobialUrea Hydrolysis Process for Biocement Production

Hamed Khodadadi Tirkolaei and Huriye Bilsel

Department of Civil Engineering, Eastern Mediterranean University, Famagusta, Northern Cyprus, Mersin 10, Turkey

Correspondence should be addressed to Hamed Khodadadi Tirkolaei; [email protected]

Received 25 May 2015; Revised 3 August 2015; Accepted 9 August 2015

Academic Editor: Ying Li

Copyright © 2015 H. Khodadadi Tirkolaei and H. Bilsel. This is an open access article distributed under the Creative CommonsAttribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work isproperly cited.

Calcium carbonate is a widely used raw material by many industries. It can be precipitated through microbial process within soilpores as cementitious bonding agent between grains for geotechnical applications. It is calledmicrobially induced calciumcarbonateprecipitation (MICP). Designing an appropriate biogroutmaterial for injection into soil is essential for controlling the amount, type,time, and place of the biocement production within pores. For this purpose, understanding the active reactions and the kineticsof bacterial growth and urea hydrolysis is necessary. A conductometric method and spectrophotometry were used in this study to,respectively, monitor the urea hydrolysis reaction progress and bacterial growth in S. pasteurii-inoculated urea-NB-NH

4Cl solution

at different level of the environmental factors that are initial cell concentration, urea concentration, and temperature. Variation inconductivity of the solution versus logarithmic scale of time was depicted as microbial ureolysis characteristic curve (MUCC)through which lag duration, specific rate, and potential of urea hydrolysis at each condition were obtained. Central composite face-centered (CCF) design, which is one of the response surface methodologies, was employed to statistically fit polynomial modelsexplaining the bacterial growth and the characteristics obtained from MUCCs in terms of the environmental factors and theirinteractions. An optimization analysis based on the urea-normalized responses was also carried out.

1. Introduction

Calcium carbonate is a widely used raw material by manyindustries. It can be precipitated in an aqueous calcium richenvironment by mediation of microorganisms as catalyzer.This process is called “microbially induced calcium carbonateprecipitation” (MICP) which is a kind of biocementation.Injecting an appropriate biogrout material (or treatmentsolution) into soil can provide the MICP as a cementitiousbonding agent between grains in the pores. Geotechnicalengineering application of the MICP in soil pores for groundmodification has been a concern of many studies since themiddle of the last decade [1–13]. Amongmany differentMICPprocesses, urea hydrolysis process has been more favorabledue to energy efficiency [11] and ubiquity of urease enzyme-producing microorganisms [14]. Regulation and estimationof the amount, type, time, and place of this biocementproduction are necessary for application of the ureolyticMICP technique in soil engineering. For this purpose,

understanding the active reactions and the kinetics of bac-terial growth, urea hydrolysis, and calcium carbonate precip-itation is essential.

Based on the recent insights, within the MICP process,the microbial urease enzyme accelerates the ammoniumcarbamate production by urea degradation. The ammoniumcarbamate is decomposed into ammonium and bicarbonateions through a nonenzymatic and buffer-dependent reaction[15]. Higher ammonium increases the pH of the medium.Concentrated carbonate ions also start precipitating asCaCO

3in the presence of calcium ions at pH=8.3 up to 9 [16].

The pH is then reduced back to neutral during precipitation[5]. Sporosarcina pasteurii is urease-active bacteria whichhave been more focused in the studies.

Temperature, pH, urea concentration, calcium ion con-centration, initial cell concentration, presence of othermicroorganisms, ionic strength of precipitation solution,existence of other types of ions (e.g., Ni+, Na+, and Mg2+),oxygen availability, type and concentration of nutrient

Hindawi Publishing CorporationAdvances in Materials Science and EngineeringVolume 2015, Article ID 340930, 14 pageshttp://dx.doi.org/10.1155/2015/340930

2 Advances in Materials Science and Engineering

sources (i.e., protein and nitrogen sources, vitamins), pre-treatment, and mutating of bacterial cells are the factorsinfluencing the kinetics of bacterial growth, its urease activity,and calcium carbonate precipitation. Many studies wereperformed to investigate the effect of one or some of thesefactors on S. pasteurii [5, 9, 16–25].

The kinetic studies in this subject use acid-base titra-tion, ammonium ion selective electrode, spectrophotometricmethod, calorimetry, and electrical conductometry to mon-itor the reaction progress [15]. Conductometry which is aninexpensive, robust, easy-to-use, and continuous-measuringassay method was applied in this study. This method ismore compatible with civil and geotechnical engineeringapplications.

Most of the studies investigating the kinetics of themicrobial urea hydrolysis system have been conducted basedon initial measurements through which the further progressis estimated. Actually the ureolysis rate is considered equal tothe initial rate in this method. This method is true under theassumption of the first-order kinetics for ureolysis rate [17,26]. In the present study, the extended measurement methodwas employed to check the soundness of the assumption atdifferent environmental conditions.

Obtaining themaximum rate and amount of urea hydrol-ysis and calcium carbonate precipitation were the main con-cern of almost all the kinetic investigations in the literature,regardless of minimizing the amount of nonhydrolyzed ureaand ammonium byproduct. A urea-normalized measure-ment was carried out in the current study to evaluate thevariation of maximum rate and amount of urea hydrolysisalong with amount of nondegraded urea in the system.

Except a few studies which applied advanced statisticalmethods for optimization of S. pasteurii growth condition[22] and calcium carbonate precipitation rate [27], thetraditional one factorial method was used in the kineticstudies on MICP. The traditional method is only able tointerpret the effect of an individual factor regardless of itspossible interactions with other influencing factors. Sucha method is usually costly and time consuming as well.In order to overcome the drawbacks of the conventionalmethods, response surface methodology (RSM) which is anefficient statistical method [28, 29] was employed in thisstudy. Central composite face-centered (CCF) design, whichis one of the designs describing the response surface, was usedto fit a second-order model relating each response with theeffect of initial cell concentration, urea concentration, andtemperature. The responses were the bacterial growth, lagduration, urea hydrolysis potential, and specific rate of ureahydrolysis. Optimum condition at which the combination ofthe specific rate and potential of urea hydrolysis is maximizedwas also obtained. This paper presents the findings of such amultiresponse kinetic study using RSM, which has not beenfound elsewhere in the literature.

2. Materials and Methods

2.1. Bacterial Culture Medium. The urease producing bac-teria used throughout the study were S. pasteurii (DSM33)

grown in yeast extract-ammonium-Tris liquid medium. Themedium was prepared by dissolving 20 g/L yeast extract and10 g/L ammonium sulfate into 0.13M Tris buffer solution(Trizma base, pH 9) separately. The solutions were thenautocalved at 121∘C for 20 minutes and mixed afterward.200mL of the mixture was inoculated with the bacteria andincubated in a 1000mL flask for around 70 hours at 30∘C and200 rpm shaking speed to reach the desired cell concentration(OD600

= 1.4 equal to 1.2 × 109 cells/mL). It was stored at 4∘Cfor further usage, not more than a week.The same but abiotic(without microbe) medium was also incubated in parallel tocontrol the contamination.

2.2. Colony Counting and OD Measurement. Serial dilutionmethod was used to find the cell concentration in the liquidgrowth medium. It was obtained by counting the singlecolonies grown on solid medium which has the same recipeas bacterial culture solution as well as 1.5% agar.

Optical density of the bacterial solution at the wavelengthof 600 nm (OD

600) was also measured using spectropho-

tometer. The OD600

value of the solution with known cellconcentration (obtained from the serial dilution method)was later used to prepare bacterial solution with the sameconcentration.

2.3. Electrical Conductography of Microbial Ureolysis Process.The electrical conductometric method was used to monitorthe microbial enzymatic urea hydrolysis reaction progress.A probe was dipped into S. pasteurii-inoculated urea-NB-NH4Cl solution in order to simultaneously measure the

temperature and electrical conductivity (E.C.) at a givenconstant temperature and 200 rpm shaking speed. The solu-tion consisted of 3 g nutrient broth (NB), 10 g ammoniumchloride, 2.12 g sodium bicarbonate, and varied amount ofurea per liter of distilled water. The pH of the solutionwas adjusted to 6.5 for all the experiments. The initialconcentration of bacterial cell in the solution was adjustedto be 106, 107, and 108 cells/mL. The bacterial solution takenfor inoculation was earlier centrifuged (at 4000 rpm for 15minutes) and the supernatant was also replaced with thefresh urea-NB-NH

4Cl solution. Pellets were mixed in the

fresh solution using vortex mixer. The centrifugation processwas found to have negligible effect on bacterial cell lossby counting the bacterial cells in the solution using serialdilution method before and after centrifugation. Parallel toeach test, a noninoculated control solutionwas also observed.Since electrical conductivity of the solution was found to betemperature-dependent per se and the solution was takingsome minutes to reach the given constant temperature, theelectrical conductivity readings were corrected for varioustemperatures by using the related graphs (see Figure 1).Thesegraphs were obtained through recording the electrical con-ductivity of noninoculated solution at different temperatures.

Plotting the electrical conductivity changes caused bymicrobial activity versus logarithmic scale of time for eachrun resulted in a characteristic curve, which is called micro-bial ureolysis characteristic curve (MUCC), from which theresponses investigated in the present study (exceptΔ(OD

600))

Advances in Materials Science and Engineering 3

E.C.

(mS/

cm)

E.C. = 27.65 − 0.058 ∗ temp.

R2= 0.9980

24.515 20 25 30 35 40 45 50 55

25

25.5

26

26.5

Temperature (∘C)

(a)

E.C. = 26.81 − 0.051 ∗ temp.

R2= 0.9989

E.C.

(mS/

cm)

24.5

25

24

25.5

26

15 20 25 30 35 40 45 50 55Temperature (∘C)

(b)

Figure 1: Electrical conductivity (E.C.) of urea-NB-NH4Cl solution with 0.1M urea (a) and 1M urea (b) versus temperature.

Table 1: Experimental range of amount and levels of the factors.

Factors Unit Symbol Range of amount and concentrationInitial cell concentration Cell/mL 𝐴 106 107 108

Urea concentration M 𝐵 0.1 0.55 1Temperature ∘C 𝐶 20 35 50Level coded −1 0 +1

were graphically obtained (Figure 2). The MUCC is actuallyan indirect kinetic analysis result of the microbial ureahydrolysis process. Urea hydrolysis pattern can be deter-mined through applying transformation to MUCC. Thetransformation function is the calibration curve relating theconductivity to ammonium concentration. Considering thefirst-order linear calibration curve [15, 18], the transformationfunction is a constant conversion coefficient. It means theurea hydrolysis pattern is equal to MUCC multiplied by theconstant coefficient.

Bacterial growth (Δ(OD600

))wasmeasured through spec-trophotometry of the solution at the wavelength 600 nm.In order to eliminate the effect of color changes caused byvarious levels of degraded urea in the solutions, the solutionswere earlier centrifuged and the supernatants were replacedwith normal saline solution.

2.4. Design of Experiments (DOE). A series of CCF designedexperiments with one repetition has been carried out toinvestigate the effects of three factors (Table 1) and theirinteractions on the responses (Table 2). Each factor was con-sidered at three levels of maximum,mid-level, andminimumwhich were coded as +1, 0, and −1, respectively. The CCFdesign contained 4 factorial points, 6 star points, and 3 centerpoints. The design matrix was presented in Table 3. All thetests were run randomly and the data obtained from therepeated runs were averaged. Urea hydrolysis potential and

specific urea hydrolysis rate are the main responses whichwere studied for optimization.

2.5. Statistical Data Analysis. The multiple regression cal-culations were carried out to fit a polynomial model toeach response. Inverse transformation was applied to all theresponses as it results in more realistic fit for asymptoticsystems like biological systems [30]. The “Design-Expert”software (Stat-Ease, Inc., USA) was utilized for the analyses.The program calculates the effects for all model terms usingthe analysis of variance (ANOVA). A statistically significantmodel was detected through comparing statistics such as𝑃 value, lack of fit, and 𝑅-squared values for each model.As a higher order model explicitly maximizes accuracy, thehighest order model with 𝑃 value less than 0.05, insignificantlack of fit, and reasonable agreement between adjusted 𝑅-squared and predicted 𝑅-squared (within 0.2 of each other)values was finally selected as a representative model. Normalprobability plot and residual plots provided by the softwarewere examined to check the assumptions underlying the dataanalysis and model fitting.

2.6. Optimization. A multiple response method called desir-ability [31] was used to find the optimum condition at whichthemost desirable combination of themain responses occurs.Potential and specific rate of urea hydrolysis were selectedas the main responses. The optimization method which


Table 2: Responses investigated and their definitions.

Responses Unit Abbreviation Definition

Bacterial growth — Δ(OD600

)Difference between initial and final optical density ofthe bacterial cell suspension at the wavelength 600 nm.It represents the bacterial cell growth for eachexperiment.

Urea hydrolysis potential mS⋅cm−1⋅M 𝑃𝑈

The proportion of the maximum change in electricalconductivity of the solution to the initial ureaconcentration for each experiment, (ΔE.C.)max/[𝑈]0.

Lag duration min. 𝑇Lag

Time corresponding to the intersection betweentangent lines of lag phase and log phase for eachexperiment.

Specific urea hydrolysis rate mS⋅cm−1⋅min−1⋅M 𝑟

Rate of urea hydrolysis per initial urea concentration,𝑟𝑈/[𝑈]0. 𝑟𝑈was considered as urea hydrolysis rate at log

phase, (𝑟𝑈)Log, when (𝑟𝑈)0/(𝑟𝑈)Log was negligible

(around 0.15 or less); otherwise it was taken as secantrate, 𝑟

𝑈(see Figure 2).

Table 3: Design matrix and corresponding observed Δ(OD600

).

Test number 𝐴 𝐵 𝐶 Δ(OD600

)1 0 0 0 0.4162 1 −1 1 0.1343 1 0 0 0.3484 0 0 −1 0.8085 0 1 0 0.2716 0 0 0 0.3817 0 −1 0 0.5738 0 0 1 0.1289 0 0 0 0.410 −1 0 0 0.3411 1 1 −1 0.32512 −1 1 1 0.05113 −1 −1 −1 1.165

considers the desirability as an objective function rangingfrom zero (outside of the limits) to one (at goal) finds a pointthat maximizes the function.

3. Results and Discussions

In this study, the interdependent kinetics of bacterial growthand urea hydrolysis within the microbial ureolysis processwere monitored by utilizing conductometry of the solutionat different environmental conditions. The outputs versuslog-time were presented as MUCC. Urea hydrolysis poten-tial (𝑃

𝑈), lag duration (𝑇Lag), and specific rate of urea

hydrolysis (𝑟) were the responses obtained from MUCC ofeach experiment. The environmental variables investigatedin the present study were initial cell concentration (𝐴), ureaconcentration (𝐵), and temperature (𝐶). The dependenceof the environmental variables and their interactions onthe aforementioned response as well as bacterial growth(Δ(OD

600)) was evaluated using the RSM with CCF design.

𝑃𝑈and 𝑟 were considered the responses for the optimization.

1 10 100 1000

ΔE.

C. (m

S/cm

)

Time (min)

0

10

20

30

40

50

60

70

80

90

(rU)0

rU

TLag

(rU)Log

(rU)0

ΔE.C.Lag

ΔE.C.max

Figure 2: Graphically obtaining the urea hydrolysis potential,lag duration, and specific urea hydrolysis rate; for example,in the above curve which is related to test number 11 (seeTable 3), (ΔE.C.)max = 67.5mS⋅cm−1; 𝑃

𝑈= (ΔE.C.)max/[𝑈]0 =

67.5/1 = 67.5mS⋅cm−1⋅M−1; 𝑇Lag = 280min; (𝑟𝑈)Log =

0.036mS⋅cm−1⋅min−1; 𝑟𝑈= 0.034mS⋅cm−1⋅min−1; (𝑟

𝑈)0

=0.048mS⋅cm−1⋅min−1; (𝑟

𝑈)0/(𝑟𝑈)Log = 1.33 > 0.15 (not negligible)

so 𝑟𝑈

= 𝑟𝑈

= 0.034mS⋅cm−1⋅min−1; 𝑟 = 𝑟𝑈/[𝑈]0= 0.034/1 =

0.034mS⋅cm−1⋅min−1⋅M−1.

Microbial ureolysis characteristic curves (MUCCs) of allthe experiments were presented in Figure 3. It was observedthat all the curves follow a similar pattern as bacterial growthcurve including four phases: lag phase, log phase, stationaryphase, and decline phase (Figure 4). At lag phase, there isnot a considerable amount of urea hydrolysis. The microbialurea degradation exponentially increases at log phase. Itthen drastically drops at the end of the log phase where thestationary phase starts. It finally begins to decline after aperiod. Excluding the last phase, the curves can be presentedas modified logistic functions. They reveal that the microbialurea hydrolysis process which is a function of simultaneouscontribution of bacterial growth and urease generation by


0

10

20

30

40

50

60

70

80

90

1 10 100 1000Time (min)

ΔE.

C. (m

S/cm

)

B: 108; U: 1; T: 20

B: 107; U: 1; T: 35B: 106; U: 1; T: 50B: 108; U: 0.1; T: 50B: 106; U: 0.1; T: 20 B: 107; U: 0.1; T: 35B: 107; U: 0.55; T: 35B: 108; U: 0.55; T: 35

B: 106; U: 0.55; T: 35B: 107; U: 0.55; T: 20

B: 107; U: 0.55; T: 50

Figure 3: Microbial ureolysis characteristic curve (MUCC) of eachexperiment.

B: 107; U: 0.55; T: 35

E.C.

(mS/

cm)

20

30

40

50

60

70

80

90

100Lag phase

Log

phas

e

Stat

iona

ry p

hase

Dec

line

pha

se

10 100 1000Time (min)

TLag TLog

Figure 4: Description of microbial urea hydrolysis pattern which issimilar to microbial growth curve.

cells is mainly governed by bacterial growth within the rangeof the study. The same growth-base logistic pattern for thekinetics of calcite precipitation through microbial ureolysisprocess had been proposed by Stocks-Fischer et al. [16]. Sucha pattern can be attributed to the starting pH less than 8 [21].

Bacterial growth was examined for each experiment bymeasuring Δ(OD

600) of the solutions. Highest and lowest

bacterial growth were, respectively, observed at minimumand maximum level of both urea concentration and tem-perature. The RSM analyses exhibited that bacterial growthis more significantly affected by urea concentration and

Table 4: Summary of ANOVA for the bacterial growth model fit.

Source Mean square 𝐹-value 𝑃 value, prob. > 𝐹Model 8.292E − 003 645.87 <0.0001Lack offit 7.188E − 006 0.34 0.8053

𝑅2 0.9994

Adj. 𝑅2 0.9974Pred. 𝑅2 0.9938C.V. % 0.69

Pred.equ.

1

(Δ (OD600) + 1.55)

= 0.51 − 1.115𝐸 − 3 ∗ 𝐴 + 0.039 ∗ 𝐵

+ 0.086 ∗ 𝐶 + 6.736𝐸 − 3 ∗ 𝐴𝐵

− 9.890𝐸 − 3 ∗ 𝐴𝐶 − 0.035 ∗ 𝐵𝐶

+ 0.018 ∗ 𝐴2

Table 5: Summary of ANOVA for the urea hydrolysis potentialmodel fit.


𝑅2 0.9995

Adj. 𝑅2 0.9991Pred. 𝑅2 0.9988C.V. % 0.14

Pred.equ.

1

(𝑃𝑈+ 500)

= 1.766𝐸 − 3 − 5.245𝐸 − 5 ∗ 𝐴 + 1.994𝐸 − 5

∗ 𝐵 + 7.453𝐸 − 5 ∗ 𝐶 − 5.250𝐸 − 5 ∗ 𝐴𝐶

− 9.279𝐸 − 6 ∗ 𝐵𝐶 + 4.162𝐸 − 5 ∗ 𝐵2

temperature and their interactions (Table 4; Figure 5). Itindicated the inhibitory effect of higher temperature and ureaconcentration on bacterial growth.

Urea hydrolysis potential (𝑃𝑈), which was defined as the

maximum conductivity change to the initial urea concentra-tion, is one of the factors expressing the cost and ecologicalefficiency of the process. In this study, it was detected thata lower proportion of available urea was hydrolyzed at theutmost level of urea concentration, although a greater quan-tity of degraded urea was obtained in the solution containingfurther initial urea concentration. It was statistically figuredout that urea concentration had quadratic effect on ureahydrolysis potential (Table 5). The urea level correspondingto the maximum potential can be attributed to an optimumconcentration at which the urease activation influence ofurea [5, 18] and its growth-repressive impact were tradedoff. Temperature raise was detected to have mitigative effecton the ureolysis potential although it (up to 70∘C) enhancesthe urease expression of S. pasteurii [5, 18]. It representsthat growth-inhibiting role of temperature increase prepon-derates over its strengthening effect of urease expression.The impact of temperature change faded at rather initial cellconcentration. The mutual effects of the factors are shown inFigure 6.


C: b

acte

rial g

row

th

A: initial cell concentration B: temperature−0.5−1

−0.5

−0.5

−0.5

−1

−1

−1

X1 = A: initial cell concentrationX2 = C: temperature

00 0

0

0

0.20.40.60.8

1

1

11

1

0.5 0.50.5

0.5

1.21.4

Design-Expert softwareFactor coding: actualOriginal scaleBacterial growth

Actual factor= −1B: urea concentration

A: initial cell concentration

C: bacterial growth

B: t

empe

ratu

re

−0.5

−0.5

−1

−1

0

0

1

1

0.5

0.5


C: bacterial growth

B: t

empe

ratu

re

−0.5

−0.5

−1

−1

0

0

1

1

0.5

0.5


C: bacterial growth

B: t

empe

ratu

re

1.165

0.051C

: bac

teria

l gro

wth

A: initial cell concentration B: temperature−0.5−1

−0.5−1


0

0 0

0.20.40.60.8

1

1

10.5

0.5

1.21.4


Actual factor= 0B: urea concentration

C: b

acte

rial g

row

th

A: initial cell concentration B: temperature−0.5

−1

−0.5−1


0

0 0

0.20.40.60.8

1

1

10.5

0.5

1.21.4


Actual factor= 1B: urea concentration

0.4

0.6

0.8

1

1.2

0.2

0.1 0.1

0.2

0.3

0.4

al cell co mperat−0.5−1

−0.5−

0 01

0.50.5

ial atu−0 51

−0.5−

0 01

0.50.5

0.2

0.4

0.6

0.8

1.165

0.051

1.165

0.051

Figure 5: Response surface plots of the mutual effect of the factors on bacterial growth.


Table 6: Summary of ANOVA for the lag duration model fit.


𝑅2 0.9989

Adj. 𝑅2 0.9977Pred. 𝑅2 0.9898C.V. % 0.21

Pred.equ.

1

(𝑇Lag + 5𝐸 + 3)= 1.912𝐸 − 4 + 6.700𝐸 − 6 ∗ 𝐴

− 1.444𝐸 − 6 ∗ 𝐵 + 9.226𝐸 − 6 ∗ 𝐶

− 6.239𝐸 − 6 ∗ 𝐴𝐶 + 1.688𝐸

− 6 ∗ 𝐵𝐶 − 2.253𝐸 − 6 ∗ 𝐶2

Duration of lag phase, as a time point at which the ureahydrolysis starts increasing exponentially, was graphicallyobtained from MUCC of each experiment. The correspond-ing conductivity of 𝑇Lag in MUCC represents the minimumamount of hydrolyzed urea at the onset of log phase. Itmanifested that a minimum limit of urease enzyme shouldbe produced to trigger the exponential degradation. Actuallythe lag phase corresponded to the time required for pHshift caused by urea degradation [18]. So, starting pH ofthe medium is a determining parameter on 𝑇Lag. A logisticureolysis pattern can be transformed into an exponentialpattern at negligible lag duration. The RSM analyses showeda reduction in temperature and initial cell concentrationdelayed the generation of the minimum required amountof urea hydrolysis. It was also detected that the effect ofchange in initial cell concentration was gradually diminishedby raising temperature. Urea also showed a slight directconsequence on lag duration. The results were illustrated inFigure 7 and Table 6.

The semilogarithmic scale of MUCC discloses two dif-ferent rates of urea hydrolysis at lag phase and log phase(Figure 3).This is what is overlooked in the common illustra-tion way of kinetic curves with natural time scale when thelag duration or the time domain of study is short. In a morepreciseway, urea hydrolysis rate in the present studywasmea-sured through defining the terms (𝑟

𝑈)Log and 𝑟𝑈 as previously

explained (Table 3). Specific urea hydrolysis rate representsthe affinity of bacteria for urea. Evaluation of the specific ureadegradation rate using RSM analyses indicated the quadraticeffects of the initial cell concentration and urea concentrationon 𝑟 (Table 7). The specific rates were detected to be between0.001mS⋅cm−1⋅min−1⋅M and 0.56mS⋅cm−1⋅min−1⋅M whichwould be equivalent to 0.00066 h−1 and 0.37296 h−1, respec-tively, based on the conversion factor demonstrated afterWhiffin [18]. It was depicted that the maximum ratesoccurred around the mid-level of initial cell concentration.Comparing the response prediction surfaces of 𝑟 and 𝑟

𝑈,

it was also observed that higher urea concentration andtemperature led to reduction in 𝑟 while they increased 𝑟

𝑈

(Figure 8 and Figure S1 in Supplementary Material avail-able online at http://dx.doi.org/10.1155/2015/340930). In other

Table 7: Summary of ANOVA for the specific urea hydrolysis ratemodel fit.

Source Mean square 𝐹-value 𝑃 value, prob. > 𝐹Model 0.66 643.55 <0.0001Lack offit 1.399E − 003 2.96 0.2624

𝑅2 0.9989

Adj. 𝑅2 0.9973Pred. 𝑅2 0.9717C.V. % 1.37

Pred.equ.

1

(𝑟 + 0.3)

= 2.29 − 0.21 ∗ 𝐴 + 0.65 ∗ 𝐵 + 0.053 ∗ 𝐶

− 0.12 ∗ 𝐴𝐶 − 0.086 ∗ 𝐵𝐶 + 0.61 ∗ 𝐴2

− 0.5 ∗ 𝐵2

words, higher proportion of available urea degraded per unittime at lower temperature and urea concentration while theamount of degraded urea per unit time decreased. Regardinginitial specific urea degradation rate (Figure 9) and 𝑟, it wasshown that a greater initial rate does not necessarily representa higher rate of urea degradation. Therefore, initial ratemeasurement cannot be an appropriate index for affinity ofthe bacteria for urea except at the condition with too shortlag duration (starting pH around 8). Initial specific rate wasdetected to be mainly affected by initial cell concentration.

3.1. Statistical Analysis and Optimization. The summary ofstatistical analysis of each response was presented in Tables4–7 and S1. Multiple regression analyses were applied to fit amodel to the results of CCF designed experiments for eachresponse. Statistical significance of each model, parameterestimates, and lack of fit were checked using ANOVA (𝐹-test). Considering the significance level of 5%, the valuesof 𝑃 < 0.05 and 0.05 < 𝑃 < 0.10 were, respectively,accepted as significant and marginally significant [32]. Themodel presented for each response exhibited 𝑅2 equal to 0.99which is much greater than the minimum acceptable valueof 0.60 for the RSM [33]. It means that the model can cover99% of the possible occurring responses. Moreover adjusted𝑅2, which is another statistic confirming the significance of

a model, was determined to be bigger than 0.99 for all themodels in this study [33, 34]. It indicated that the modelscan explain 99% of the variation around the mean of theresponses. Prediction 𝑅2 value obtained was larger than 0.97implying the high goodness of all the models in predictionof a response value, as it is also within 0.2 of adjusted 𝑅2.Coefficient of variation (C.V. %) of 1.37% and less confirmedthe RSM and reproducibility of its results. Smaller coefficientof variation shows more closeness of the predicted valuesto the actual ones (see the predicted versus actual curves inFigure S2).

Variation of desirability function in terms of the envi-ronmental factors was described in Figure 10. It was shownthat about 97% desirability (optimum point) was achievedat the mid-level of initial cell concentration (107 cells/mL)


Ure

a hyd

roly

sis p

oten

tial

A: initial cell concentrationB: urea concentration

−0.5−1

−0.5−1

X1 = A: initial cell concentrationX2 = B : urea concentration

020406080

100

0 01

10.5

0.5

Design-Expert softwareFactor coding: actualOriginal scaleUrea hydrolysis potential



Actual factor= −1C: temperature

= 0C: temperature

90

0.5


Actual factor= 1C: temperature

Ure

a hyd

roly

sis p

oten

tial

A: initial cell concentration B: urea concentration

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020406080

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Urea hydrolysis potential



90

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cent

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rea c

once

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tion


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B: u

rea c

once

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tion


7580

85

90

90

40

50

60

604020

70

80

50

Figure 6: Response surface plots of the mutual effect of the factors on the urea hydrolysis potential.


Lag

dura

tion

A: initial cell concentrationB: temperature

−0.5−1

−0.5−1


0200

400600800

10001200

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Lag

dura

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890

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001

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−0.5−

00

10.5

0.5

200

400

600

800

200

400

600

800

200

400

600

8001000

Figure 7: Response surface plots of the mutual effect of the factors on the lag duration.


A: initial cellconcentration B: urea concentration

−0.5−1

−0.5−1


00.2

0.40.60.8

0 0

1

1

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Design-Expert softwareFactor coding: actualOriginal scaleSpecific rate of urea hydrolysis

Actual factor−1=C: temperature


Specific rate of urea hydrolysis

Actual factor0=C: temperature




A: initial cellconcentration B: urea concentration

−0.5−1

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00.2

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concentration B: urea concentration−0.5

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cent

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ific r

ate o

f ure

ahy

drol

ysis

Spec

ific r

ate o

f ure

ahy

drol

ysis

Spec

ific r

ate o

f ure

ahy

drol

ysis

0.56

0.001

0.56

0.001

Figure 8: Response surface plots of the mutual effect of the factors on the specific urea hydrolysis rate.


0

2

4

6

8

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14

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20

0 50 100 150 200 250 300Time (min)

ΔE.

C./[u

rea]0

(mS/

cm·M

)

B: 108; U: 1; T: 20B: 108; U: 0.1; T: 50B: 108; U: 0.55; T: 35

(a)

0

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ΔE.

C./[

urea] 0

(mS/

cm·M

)

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B: 107; U: 0.55; T: 35B: 107; U: 0.55; T: 20

B: 107; U: 0.55; T: 50B: 107; U: 0.1; T: 35B: 107; U: 1; T: 35

(b)

0

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4

6

8

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0 100 200 300 400 500 600 700 800 900Time (min)

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urea] 0

(mS/

cm·M

)

B: 106; U: 1; T: 50B: 106; U: 0.1; T: 20B: 106; U: 0.55; T: 35

(c)

Figure 9: Specific initial urea hydrolysis rate (the specific urea hydrolysis rate at lag phase) for the experiments with 106 (a), 107 (b), and 108(c) cells/mL initial cell concentration.

and lowest level of urea concentration (0.1M) and tem-perature (20∘C). It was predicted to acquire Δ(OD

600) =

1.307, 𝑃𝑈

= 86.9mS⋅cm−1⋅M−1, 𝑇Lag = 469min, and 𝑟 =0.70mS⋅cm−1⋅M−1⋅min−1 under the optimum environmental

condition. Some experiments including the optimum pointand a control mediumwere run in duplicate in order to checkthe validity of the result. The control medium was chosenunder the same condition of test number 4 from the CCF


0.97

0.00

0

00.5

0.51 1

0.200.400.600.801.00

Des

irabi

lity

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−1

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0.00

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00.5

0.51 1

0.200.400.600.801.00

Des

irabi

lity

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−1B: urea concentration A: initial cell concentration

0.00

000.5

0.51 1

0.200.400.600.801.00

Des

irabi

lity

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0

00.5

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ce0

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.00

000.5

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.20406080

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concentr l ell concentrat

0.200.10

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Desirability

Desirability

0.5


−0.5−1 0 10.5A: initial cell concentration

B: u

rea c

once

ntra

tion

−0.5

−1

0

1

0.5

B: u

rea c

once

ntra

tion

Desirability

−0.5−1 0 10.5A: initial cell concentration

−0.5

−1

0

1

0.5

B: u

rea c

once

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tion


Design-Expert softwareFactor coding: actualDesirability

Actual factor−1=C: temperature







1.00

0.00

Prediction 0.97

1.00

0.00

1.00

0.00

Figure 10: Desirability variation at different levels of the factors.


Table 8: Observed and predicted responses at the optimum envi-ronmental condition.

𝐴 𝐵 𝐶 Δ(OD600

) 𝑃𝑈𝑇Lag 𝑟

𝑈

Observed 0 −1 −1 1.346 87.7 510 0.082Predicted 1.307 86.9 469 0.070

design. The results of the experiment verified the predictedvalue (Table 8). The desirability versus environmental factorsdetected that increasing the temperature and urea concentra-tion causes desirability reduction. It was also shown that aninitial concentration around 107 cells/mL is more desirableat a given temperature and urea concentration. Generally, itis worth mentioning that the conditions favoring the ureasegeneration of S. pasteurii, quantity of hydrolyzed urea, andurea hydrolysis rate inhibited the cell growth, urea hydrolysispotential, and specific urea hydrolysis rate and vice versa.

4. Conclusions

Results obtained through statistical investigation of effectof the environmental factors on microbial ureolysis processin this study assist a better understanding of the MICPprocess. They provide useful information on designing anappropriate biogroutmaterial with environmental conditionsin order to facilitate controlling the amount, type, time, andplace of biocement production within soil. It was generallyfound that the conditions forcing the bacteria to producemore urease enzyme and higher quantity of urea degradationsuppress the bacterial growth, potential, and specific rate ofurea hydrolysis. The finding is in agreement with the natureof all the biological systems in which the organisms do notwork properly and efficiently under the condition which isprovided for slaving not growth.

A new illustration fashion of conductometric kineticstudy on microbial ureolysis process was developed. In thisfashion the conductivity was shown versus logarithmic scaleof time. The semilogarithmic way of description signalizedthe initial rate of urea hydrolysis and lag duration which wasoften neglected in the common kinetic curves. The curvesrevealed that the initial urea hydrolysis rate cannot alwaysbe an appropriate index for evaluation of the potential ureaseactivity of the bacteria at a given condition. It was also foundthat all the curves follow the samepattern asmicrobial growthcurve.

The statistical analysis employed in this study wasdetected to be a robust method to evaluate the effect of thementioned environmental factors and their interactions onthe investigated responses of the microbial urea degradationprocess.

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper.

Acknowledgments

The authors would like to thank Associate Professor BaharTaneri and Associate Professor Sukru Tuzmen from theDepartment of Biological Sciences andDr. Zulal Yalınca fromthe Department of Chemistry of the Eastern MediterraneanUniversity, who have provided technical help and access tothe Molecular Biology and Genetics Laboratory for conduct-ing the experimental study of this paper.

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