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Applied Bionics and Biomechanics

Research Article Volume 3 Issue 4

Modeling and simulation to monitor cryptosporidium oocyst influenced by nitrogen in heterogeneous fine sand deposition

Eluozo SN,1 Bunonyo WK,2 Amadi CP3

1Department of Civil Engineering, Gregory University Uturu (GUU), Nigeria
2Department of Mathematics Faculty of Science, Federal University Otuoke Bayelsa State, Nigeria
3Department of Mathematics Faculty of Science, Rivers State University, Nigeria

Correspondence: Eluozo SN, Department of Civil Engineering, College of Engineering, Gregory University Uturu (GUU), Abia State of Nigeria

Received: May 04, 2019 | Published: July 2, 2019

Citation: Eluozo SN, Bunonyo WK, Amadi CP. Modeling and simulation to monitor cryptosporidium oocyst influenced by nitrogen in heterogeneous fine sand deposition. MOJ App Bio Biomech. 2019;3(3):77-80. DOI: 10.15406/mojabb.2019.03.00107

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Abstract

This study monitor the behaviour of cryptosporidium oocyst depositions in heterogeneous fine sand formation, several experts has monitored the transport of microbes in the stratum, but could not identified the heterogeneity of the predominant structural influence of the depositions, but this study try to monitor the migration rate of cryptosporidium oocyst depositions on heterogeneous structure of the formation, microelement nitrogen were observed to deposit in heterogeneous structure of the stratum, this were observed to develop its influential pressure on the migration process in the deposition, the study experienced linear trend to the optimum values recorded at thirty metres at the period of hundred days, despite the observed linear trend, the concentration monitored at different location of the study environment were not homogeneous, the study experience different concentration within the predominant stratum of the formation, these condition expressed the heterogeneous influenced on the transport process of the contaminant, the study experienced pressure from latitudinal flow path within the predominant stratum as influential to the rate of migration based on velocity of flow. Nitrogen deposition also developed its pressure, all these parameters express their various influences reflected on the migration and deposited various concentration in heterogeneous setting, validation of the model were carried with experimental data, and both parameters developed best fits correlation.

Keywords:modeling cryptosporidium oocyst, nitrogen, heterogeneous, and fine sand

Introduction

Regardless of technological developments in water decontamination as it has been established effectiveness of UV irradiation for the decontamination of C. parvum oocysts, the outbreak of disease from water born pathogen remain imperative to public health concern.1Tremendous challenges from water born pathogen such as C. parvum has been ubiquitous in aquatic environment, this has currently in the environmental express resilient oocyst form, it has also generate précised imperative difficulties in the protection of drinking water supply including chemical disinfection due to its resistance to conventional chemical applied. The application of Granular media filtration (GMF) is known as the conventional treatment techniques. This has developed high percentage of effective on the passage of pathogen in drinking waters for treatment. of C. parvum as reported by. Having such effectiveness GMF that a barrier on C. parvum oocyst, it has been observed that its treated passage into a drinking water is significantly impacted through design and operational factors, these are superiority of pre-treatment (coagulation), these are point between the filter cycle, hydraulic loading rate, media type, and raw water quality. Other concept that considered inorganic colloids or biocolloids such as C. parvum oocysts, the expansion of filtration theory that define colloid deposition within water-saturated porous media , these include granular media filters that has progressed considerably extend during the last several decades.2−20

Theoretical background

Nomenclature

C = Compressive strength

A(x) = Porosity of concrete

B(x) = Additive and cementious materials

α1x = Water Cement Ratio

x = Curing Age

dc dx + A (x) C d + B (x) C d n =0 MathType@MTEF@5@5@+= feaagKart1ev2aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqkY=Mj0xXdbba91rFfpec8Eeeu0xXdbba9frFj0=OqFf ea0dXdd9vqaq=JfrVkFHe9pgea0dXdar=Jb9hs0dXdbPYxe9vr0=vr 0=vqpWqaaeaabiGaciaacaqabeaadaqaaqaaaOqaamaalaaabaqcLb sacaWGKbGaam4yaaGcbaqcLbsacaWGKbGaamiEaaaacaaMc8UaaGPa VlabgUcaRiaaykW7caaMc8UaamyqaOWaaSbaaSqaaKqzadGaaiikai aadIhacaGGPaaaleqaaKqzGeGaaGPaVlaaykW7caWGdbGcdaWgaaWc baqcLbmacaWGKbaaleqaaKqzGeGaaGPaVlabgUcaRiaaykW7caWGcb GcdaWgaaWcbaqcLbmacaGGOaGaamiEaiaacMcaaSqabaqcLbsacaaM c8Uaam4qaSWaa0baaeaajugWaiaadsgaaSqaaKqzadGaamOBaaaaju gibiaaykW7caaMc8UaaGPaVlabg2da9iaaykW7caaMc8UaaGPaVlaa icdaaaa@6A51@    (1)

Transform the above Bernoulli’s Equation to a linear first order DE gives:

Let I.F = l α x 1x MathType@MTEF@5@5@+= feaagKart1ev2aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqkY=Mj0xXdbba91rFfpec8Eeeu0xXdbba9frFj0=OqFf ea0dXdd9vqaq=JfrVkFHe9pgea0dXdar=Jb9hs0dXdbPYxe9vr0=vr 0=vqpWqaaeaabiGaciaacaqabeaadaqaaqaaaOqaaKqzGeGaeS4eHW McdaahaaWcbeqaaKqzadGaeyOeI0caaOWaaWbaaSqabeaajugWaiab eg7aHjaadIhalmaaBaaameaajugWaiaaigdacaWG4baameqaaaaaaa a@424B@    (2) 

Use I.F to Solve (2) above

Hence, the general Solution becomes:

C d 1n = B A +C e α 1 x MathType@MTEF@5@5@+= feaagKart1ev2aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqkY=Mj0xXdbba91rFfpec8Eeeu0xXdbba9frFj0=OqFf ea0dXdd9vqaq=JfrVkFHe9pgea0dXdar=Jb9hs0dXdbPYxe9vr0=vr 0=vqpWqaaeaabiGaciaacaqabeaadaqaaqaaaOqaaKqzGeGaam4qaO Waa0baaeaajugWaiaadsgaaOqaaKqzadGaaGymaiabgkHiTiaad6ga aaqcLbsacaaMc8UaaGPaVlaaykW7cqGH9aqpcaaMc8UaaGPaVlabgk HiTiaaykW7kmaalaaabaqcLbsacaWGcbaakeaajugibiaadgeaaaGa aGPaVlaaykW7cqGHRaWkcaaMc8UaaGPaVlaadoeacaWGLbGcdaahaa qabeaajugWaiabgkHiTiabeg7aHTWaaSbaaOqaaKqzadGaaGymaaGc beaajugWaiaadIhaaaaaaa@5E08@    (3)

Materials and method

Standard laboratory experiment where performed to monitor Shigallae using the standard method for the experiment at different formation, the soil deposition of the strata were collected in sequences base on the structural deposition of the lithology at different locations, this samples collected at different location generated variations at different depths producing different Shigallae concentration through column experiment, from the pressure flow at different strata, the experimental result were compared with the theoretical values for the validation of the model.

Results and discussion

Results and discussion are presented in tables including graphical representation for Shigallae concentration. Figure on to seven shows the linearization of the contaminant to the optimum values recorded at thirty metres at the period of hundred days, the study observed heterogeneous setting as the figure developed different rate of concentration at various figures. The study express growth rate of the contaminant under exponential condition, this are based on the facts that there are several factors in the transport system, this can be attributed to the microelements that are observed to deposit heterogeneous in the fine sand formation, such structured deposition in different formation of the soil express these setting in different dimensions. The growth of the contaminant in this condition determined the factor that contributes to exponential phase of the transport in depth and time. The heterogeneity of the concentration experienced this linearization in all the figures but the determined factor are on the latitudinal flow part observed to influence the growth rate with respect to time in the figures, the predictive values in such non homogeneous applied system express every influence that deposit in the litho structures of the environment, thus generate the concentration observed from the derived model simulation. Validation of the system expressed best fits correlation as the predictive and experimental values were compared.

Depth [m]

Predictive Values

 Experimental Values

3

0.9

0.92

6

1.8

1.82

9

2.7

2.71

12

3.6

3.62

15

4.5

4.53

18

5.4

5.42

21

6.3

6.32

24

7.2

7.22

27

8.1

8.12

30

9

9.22

Table 1 predictive and Experimental Values of Cryptosporidium Concentration at Different Depth

Time [T]

Predictive Values

 Experimental Values

10

0.17

0.108

20

0.34

0.283

30

0.52

0.458

40

0.69

0.633

50

0.87

0.808

60

1.04

0.983

70

1.22

1.158

80

1.39

1.333

90

1.57

1.508

100

1.74

1.683

Table 2 predictive and Experimental Values of Cryptosporidium Concentration at Different Depth

Time [T]

Predictive Values

 Experimental Values

10

1.83E-03

0.00201

20

3.68E-03

0.00401

30

5.52E-03

0.00601

40

7.36E-03

0.00801

50

9.21E-03

0.01001

60

1.11E-02

0.01201

70

1.29E-02

0.01401

80

1.47E-02

0.01601

90

1.65E-02

0.01801

100

1.84E-02

0.02001

Table 3 predictive and Experimental Values of Cryptosporidium Concentration at Different Depth

Time [T]

Predictive Values

 Experimental Values

10

1.74E-04

0.00019993

20

3.48E-04

0.00039993

30

5.23E-04

0.00059993

40

6.97E-04

0.00079993

50

8.71E-04

0.00099993

60

1.04E-03

0.00119993

70

1.22E-03

0.00139993

80

1.39E-03

0.00159993

90

1.57E-03

0.00179993

100

1.74E-03

0.00199993

Table 4 predictive and Experimental Values of Cryptosporidium Concentration at Different Depth

Depth [m]

Predictive Values

 Experimental Values

3

2.83E-03

0.00272

6

5.65E-03

0.00542

9

8.48E-03

0.00812

12

1.13E-02

0.01082

15

1.41E-02

0.01352

18

1.73E-02

0.01622

21

1.98E-02

0.01892

24

2.26E-02

0.02162

27

2.54E-02

0.02432

30

2.83E-02

0.02702

Table 5 predictive and Experimental Values of Cryptosporidium Concentration at Different Depth

Depth [m]

Predictive Values

 Experimental Values

3

5.19E-04

0.0003

6

1.04E-04

0.0009

9

1.55E-03

0.0015

12

2.10E-03

0.0021

15

2.59E-03

0.0027

18

3.11E-03

0.0033

21

3.63E-03

0.0039

24

4.15E-03

0.0045

27

4.66E-03

0.0051

30

5.19E-03

0.0057

Table 6 predictive and Experimental Values of Cryptosporidium Concentration at Different Depth

Depth [m]

Predictive Values

 Experimental Values

3

3.75E-04

0.00034

6

7.51E-04

0.00064

9

1.25E-03

0.00094

12

1.51E-03

0.00124

15

1.88E-03

0.00154

18

2.25E-03

0.00184

21

2.63E-03

0.00214

24

3.01E-03

0.00244

27

3.38E-03

0.00274

30

3.75E-03

0.00304

Table 7 predictive and Experimental Values of Cryptosporidium Concentration at Different Depth

Figure 1 predictive and Experimental Values of Cryptosporidium Concentration at Different Depth.

Figure 2 predictive and Experimental Values of Cryptosporidium Concentration at Different Depth.

Figure 3 predictive and Experimental Values of Cryptosporidium Concentration at Different Time .

Figure 4 predictive and Experimental Values of Cryptosporidium Concentration at Different Time.

Figure 5 predictive and Experimental Values of Cryptosporidium Concentration at Different Depth.

Figure 6 predictive and Experimental Values of Cryptosporidium Concentration at Different Depth.

Figure 7 predictive and Experimental Values of Cryptosporidium Concentration at Different Depth.

Conclusion

The study has express the behaviour of Cryptosporidium oocyst in fines and deposition, such condition expressed the migration in heterogeneity at different figures, linear trend was observed but the concentration were heterogeneous in different depositions, such observed condition implies that the system are pressure by other factors that should influence the transport system to unconfined bed, based on this factors, the development of nonhomogeneous system to monitor the behaviour of the formation were appropriate, the developed system express the relationship between the heterogeneity of the formation and that of the migration level of the contaminant. These conditions have tremendously expressed other influential factors that may have caused the rate of concentration to generate exponential phase all the figures based on time and depth. These conditions observed variations on the flow path between intercedes of the strata. The litho structure of the formation experienced heterogeneous in slight condition but predominantly influenced by microelement nitrogen and flow path under latitudinal setting in the depositions, the derived model simulation from non-homogeneous system has definitely express the required pressured from these variables as an influential factors for the growth rates of the contaminant.

Acknowledgments

None.

Conflict of interest

The authors declare no conflict of interest.

References

  1. Aboytes R, Di Giovanni GD, Abrams FA, et al. Detection of infectious Cryptosporidium in filtered drinking water. Journal American Water Works Association. 2004;96(9):88−98.
  2. Bradford SA, Bettahar M. Straining, attachment, and detachment of Cryptosporidium oocysts in saturated porous media. J Environ Qual. 2005;34(2):469−478.
  3. Bradford SA, Bettahar M, Simunek J, et al. Straining and attachment of colloids in physically heterogeneous porous media. Vadose Zone Journal. 2004;3(2):384−394.
  4. Bradford SA, Simunek J, Bettahar M, et al. Straining of colloids at textural interfaces. Water Resources Research. 2005;41(10):W10404.
  5. Bradford SA, Simunek J, Bettahar M, et al. Modeling colloid attachment, straining, and exclusion in saturated porous media. Environ Sci Technol. 2003;37(10):2242−2250.
  6. Bradford SA, Simunek J, Bettahar M, et al. Significance of straining in colloid deposition: Evidence and implications. Water Resources Research. 2006;42(12):W12S15.
  7. Bradford SA, Torkzaban S,Walker SL. Coupling of physical and chemical mechanisms of colloid straining in saturated porous media. Water Research. 2007;41(13):3012−3024.
  8. Emelko MB. Removal of viable and inactivated Cryptosporidium by dual- and tri-media filtration. Water Research. 2003;37(12):2998−3008.
  9. Elimelech M. Effect of particle-size on the kinetics of particle deposition under attractive double-layer interactions. Journal of Colloid and Interface Science, 1994;166(1):266−266.
  10. Emelko MB, Huck PM, Douglas IP. Cryptosporidium and microsphere remova during late in-cycle filtration. Journal American Water Works Association. 2003;95(5):173−182.
  11. Emelko MB. Removal of viable and inactivated Cryptosporidium by dual- and tri-media filtration. Water Res. 2003;37(12):2998−3008.
  12. Elimelech M. Effect of particle-size on the kinetics of particle deposition under attractive double-layer interactions (vol. 164, pg 190, 1994). Journal of Colloid and Interface Science. 1994;166(1):266−266.
  13. Emelko MB, Huck PM, Coffey BM. A review of Cryptosporidium Oocyst Removal by granular media filtration. Journal American Water Works Association. 2005;97(12):101.
  14. Huck PM, Coffey BM, Emelko MB, et al. Effects of filter operation on Cryptosporidium Removal. Journal American Water Works Association. 2002;94(6):97−111.
  15. Harrington GW, Xagoraraki I, Assavasilavasukul P. Effect of filtration conditions on removal of emerging waterborne pathogens. Journal American Water Works Association. 2003;95(12):95-104.
  16. Nieminski EC, Bellamy WD, Moss LR. Using surrogates to improve plant performance. Journal American Water Works Association. 2000;92(3):67.
  17. Sunnotel O, Lowery CJ, Moore JE, et al. Cryptosporidium. Letters in Applied Microbiology. 2006;43(1):7−16.
  18. Tufenkji N, Elimelech M. Deviation from colloid filtration theory in the presence of repulsive electrostatic interactions: Implications to microbial transport. Abstracts of Papers of the American Chemical Society, 228, U605-U606, 2004.
  19. Johnson WP, Li X, Yal G. Colloid retention in porous media: Mechanistic confirmation of wedging and retention in zones of flow stagnation. Environmental Science & Technology. 2007;41(4):1279−1287.
  20. David JS. Cryptosporidium and Particle Removal from Low Turbidity Water by Engineered Ceramic Media Filtration MSC Thesis department of Civil Engineering University of Waterloo. 2004. p 1−4.
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