Research Article Volume 3 Issue 4
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
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
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
Nomenclature
C = Compressive strength
A(x) = Porosity of concrete
B(x) = Additive and cementious materials
α1x = Water Cement Ratio
x = Curing Age
(1)
Transform the above Bernoulli’s Equation to a linear first order DE gives:
Let I.F = (2)
Use I.F to Solve (2) above
Hence, the general Solution becomes:
(3)
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 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
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.
©2019 Eluozo, et al. This is an open access article distributed under the terms of the, which permits unrestricted use, distribution, and build upon your work non-commercially.