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1474
Advances in Environmental Biology, 6(4): 1474-1476, 2012
ISSN 1995-0756
This is a refereed journal and all articles are professionally screened and reviewed
ORIGINAL ARTICLE
Applicability of CDE Equation for Simulation of Escherichia coli Bacterial Leaching
from a Silty Clay Soil
1
Maryam Maneshdavi, 2Alireza Jafarnejadi, 3Hossein Shirani, 4Gholamabbas Sayyad
1
Department of Soil Science, Science and Research Branch, Islamic Azad University, Khouzestan, Iran.
Soil and Water Research Department of Agricultural and Natural Resources Research Center of Khouzestan,
Iran.
3
Department of Soil Science, Vail-E-Asr University of Rafsanjan, Rafsanjan, Iran.
4
Department of Soil Science, Shahid Chamran University of Ahvaz, Ahvaz, Khouzestan, Iran.
2
Maryam Maneshdavi, Alireza Jafarnejadi, Hossein Shirani, Gholamabbas Sayyad; Applicability of
CDE Equation for Simulation of Escherichia coli Bacterial Leaching from a Silty Clay Soil
ABSTRACT
In this study, the equilibrium equation was used to predict the transfer and absorption of bacteria in the
column of soil in during leaching time. The coefficient determination for direct modeling through the
equilibrium equation was recognized 0.58 respectively. But, in the inverse modeling, bacterial movement in soil
was predicted by using equilibrium equation had higher coefficient to amount 0.91 respectively. These results
show the high efficiency of model through using the equilibrium equation (CDE) in the inverse method than its
direct method in simulating the bacterial movement in the soil treated. The evaluation of model sensitivity was
carried out by changing the soil hydraulic parameters in a specific range, it was revealed that the model has been
sensitive to some changes of the soil hydraulic parameters and also it undergoes drastic changes when the
parameters of the curve simulated by the model change. Finally, the validation of the inverse method using the
equilibrium equation was 0.98. Both show the accuracy and correctness of the model.
Key words: Escherichia Coli bacteria, simulation, HYDRUS-1D model.
Introduction
Materials and Methods
Manure is a valuable biological source and it has
some ecologically and environmentally positive and
negative effects, and it is much used in agriculture
[2]. There are different kinds of poisonous gases,
combination of mutation and solution, pathogenic
microorganisms, drug-resistant bacteria, protozoa,
and viruses in the waste matters of animals. For
instance, one can refer to the fecal forms such as
salmonella, shigella, escherichia coli, and dangerous
protozoa such as cryptosporidium and giardia which
are among pathogenic bacteria present in the waste
matters of animals [4]. The model HYDRUS-1D is
one of the advanced models in relation with the
movement of water, minerals, and warmth in soil.
This model was developed by Simonk et al. in
Russian soil laboratory in the United States of
America [1]. Through the use of HYDRUS-1D
model, one can simulate the movement of bacteria
and bacterial contamination resulting from using
manure contaminated by different kinds of pathogens
and some strategies can be presented for the correct
use of manure and preventing the soil, surface and
underground water from being contaminated by
manure.
The samples of soil under study were put in
PVC columns which were 35 centimeters high and
10.5 centimeters in diameter. Then, some weighted
hen manure was added to the columns and it was
mixed with 5 centimeters of soil. Then, the columns
were studied for irrigation for 5 days. After every
rinse, the drained water coming out the soil columns
was put in sterilized containers. Afterward, the
environment of culture was taken out from the
incubator and some bacterial colonies were observed
[3]. For every column of soil, the software of
operation and the density of Escherichia coli bacteria
in the drained water during the time have been
simulated. The needed main inputs of the model for
simulation consist of: information related to time and
printing the results, soil hydraulic characteristics,
introducing the primary and boundary conditions for
the water flow.
Results and Discussion
Sensitivity analysis:
Corresponding Author
Maryam Maneshdavi, Department of Soil Science, Science and Research Branch, Islamic Azad
University, khouzestan, Iran. E-mail: [email protected] 1475
Adv. Environ. Biol., 6(4): 1474-1476, 2012
Some of the results obtained from the
determination and analyzing the sensitivity of the
model have been shown in Fig 1.
Model was sensitive to the changes of soil
hydraulic parameters (i.e.) and the amount of model
predictions changed as the parameters were changed.
Fig. 1: The sensitivity analysis model with the modified data: A (saturated water content (qs) with the use
equilibrium equation), B (water retention in soil (n) with the use equilibrium equation).
Calibration:
The simulated BTC of E.coli with CDE using
direct and invers method vs observed E.coli BTC are
shown in Fig 2.
There was a weak correlation between the
observed BTC and the simulated by the model using
the CDE in the direct modeling (Fig 2-A). The value
of coefficient 0.58 shows that the equilibrium
equation with the direct method was not able to
estimate exactly the amount of bacterial movement in
soil (Fig 2-A). while the value of coefficient 0.91
with equilibrium equation( in the inverse method
shows a high correlation between the measured data
and the simulation (Fig 2-B).
The fitted hydraulic parameters with CDE using
invers method are shown in Table 1.
Fig. 2: Simulation of the E.coli bacteria using of equilibrium equations (CDE) treated by hen manure: A) the
direct method. B) The inverse method.
Table 1: Some fitted Hydraulic parameters of the model.
Parameter
r
s
Equation
CDE
0.1068
0.4873
(0.0712-0.1068)
(0.344-0.516)
Accuracy measures (statistical measures):
In order to evaluate the accuracy of the model
predictions, some statistical measures were
calculated using model predictions and measured

n
ks
0.008
(0.008-0.012)
1.476
(0.984-1.476)
2.016
(1.344-2.016)
data. The statistics included correlation coefficient
(R2), Mean difference between predicted and
observed data (Md), and root mean square error
(RMSE) with the following equations. The statistics
are shown in Table 2.
Table 2: Statistical analysis to evaluate the accuracy of model predictions.
Method
direct method
Equation
Md
RMSE
R2
Equilibrium
-12800
42.81
0.58
Md
-5390
inverse method
RMSE
16.81
R2
0.91
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Adv. Environ. Biol., 6(4): 1474-1476, 2012
Md values shows model overestimation, while
negative Md values shows model underestimation.
Md values for equilibrium equation in using direct
modeling and inverse modeling had negative
amounts.
Less RMSE values shows more accuracy in
model prediction. RMSE values for model
predictions equilibrium equation in direct method
compared with invers method was less. This shows
more accuracy invers method than the direct method
whit using equilibrium equation.
Testing (validation) step:
To check the model performance and to know if
the model can be used for the same cases, a
validation steps was run using the data obtained from
other replications in the same treatment. The results
are shown in Fig 3.
The testing results showed that model could
predict Ecoli leaching with good accuracy both using
CDE equations.
Fig. 3: Validation of model predictions using equilibrium equation treated by hen manure.
Conclusion:
Comparing the direct and inverse methods of
HYDRUS-1D for predicting Ecoli transport through
the soil using CDE equation, the inverse method was
more effective than direct method. with the ability of
synchronic estimation, estimation of the needed
coefficients for the model with the hydraulic
characteristics in a specific environment and putting
the coefficients in those defined limits the results
were by large more precise and had a high
correlation with the measured data along with the
equilibrium equation compared with the direct
method.
References
1.
2.
Abbasi, F., 2005. Simulation of water movement
in soil using HYDRUS-1D. Proceedings of
Workshop on Modeling in Irrigation and
Drainage, Iranian National Committee on
Irrigation and Drainage.15 December, pp: 65-82.
Fallah, S., A. Ghalavand, D. Ghanbarian, A.R.
Yadavi, 2009. Effects of Poultry Manure and its
Incorporation Methods with Soil on Soil
Properties and Corn Yield. Journal of Water and
Soil, 3: 78-87.
3.
4.
Shirvani, S., 2009. Effect Of Salinity and
Manure on Bacteria Transport in Disturbed Soil
Columns. M.Sc. Thesis, Faculty of Agriculture,
Vali-E-Asr University of Rafsanjan.
Unc, A. and M.J. Goss, 2003. Movment of
faecal bacteria through the vadose zone. Water
Air Soil Poll., 149: 327-337.