Journal of Environmental Treatment Techniques

2019, Volume 7, Issue 1, Pages: 166-170

J. Environ. Treat. Tech.

ISSN: 2309-1185

Journal web link: http://www.jett.dormaj.com

Comparison of Regression Model and Modified

Monod Kinetic Model to Predict the Removal of

Ethanol in Trickling Biofilter

Amin Goli , Susan Khosroyar , Behroz Vaziri , Fatemeh Sadat Dehghani , Reza Sanaye ,

Mohammad Mohammadi *

- Department of Engineering, Quchan Branch, Islamic Azad University, Quchan, Iran

- Department of Medical Nanotechnology, Shiraz University of Medical Sciences, Shiraz, Iran

- Department of Cancer proteomics, Shiraz University of Medical Sciences, Shiraz, Iran

Received: 10/12/2018

Accepted: 17/02/2019

Published: 30/03/2019

Abstract

Ethanol is a toxic compound and a member of volatile organic compounds (VOCs). Ethanol is emitted to the atmosphere

by several industries worldwide. Biotrickling filter technology is a well-known technology for removal of VOCs from air. The

aim of this study is to compare two regression and modified monod models to predict the removal of ethanol using a

biotrickling filter reactor (BTFR). The data of the previous study on ethanol vapor removal by bio-trickling filter were used for

determination of r_m_a_xand K . Also by these data, a simple regression model was developed. Eventually, ethanol removal

efficiency was predicted by both regression and kinetic models. All results were compared with actual data. Our results show

that regression model could only predict the average of ethanol removal efficiency. However, kinetic model could additionally

predict all changes in ethanol removal efficiency: it has had some good alignment with actual data.

Keyword: Ethanol, Kinetic coefficient, Modeling, Biotrickling filter, Biodegradation

applied by utilizing chemical scrubbers, are often

Introduction

expensive–these possess even less efficiency in ethanol

reduction (19). In contrast, biochemical methods, despite

their complications, could prove to be highly appropriate

substitutes for the physical and chemical methods (20).

The biological methods mostly involve bio-filters. It is to

be noted that amongst all these, the trickling biofilters are

deemed the most optimum for the elimination of ethanol.

The models proposed by researchers to determine the best

conditions of applying trickling biofilters, are actually

divided into two groups of Micro kinetic and Macro

kinetic (21, 22). In Micro kinetic models, it is attempted to

take into consideration all parameters involved (23).

These may well include the specific surface of the

substrate, the thinness of the applied biofilm, the

dispersion coefficient of input polluted air into the

biofilter, and the constant coefficient of Henry in Mass

Transference. These models are usually very complex;

they require a vast array of parameters and coefficients

which are normally unavailable to the engineers (24).

Ottengraf and Van den Over offered one of the most

referred-to microkinetic models. The macrokinetic

models often avoid defining or investigating partial

parameters and would only focus on the most prominent

parameters including the concentration of the pollutant,

input pollutant debit, and the degree of moisture and

temperature. Studying the effects of these parameters on

the system efficiency and providing the mathematical

relations in the form of macro kinetic models require

laboratory experiments; thence they are referred to as the

The rapid evolution of industries during the last few

centuries has left a significant impact on the environment

1-3). The momentous scale of today’s environmental

(

challenges has urged countless number of researchers

continuously working in order to provide the best solution

for various types of man-made harms dealt to the

environment (4-7). Among these is the issue of air

pollution caused by the exhaust gasses including ethanol

emitted from various industries such as petrochemical and

alcoholic drinks manufacturers, often beyond the

acceptable capacities (8-10). Ethanol is one of such

pollutants and it is categorized into the volatile organic

compounds (VOCs), posing threats to the environment

(11). Ethanol is widely used in the production of

petrochemical compounds. Thus it is only natural to

address this issue by considering the threats of this

pollutant and its wide-scale usage and generation. In order

to achieve reductions in air pollution to attain air quality

standards, a set of specific techniques and measures

should be identified and implemented. In this particular

case, various systems of physical, chemical and

biochemical nature have already been utilized so as to

treat this pollutant (12-16). However, some of procedures

including the physical methods applied by utilizing

various adsorbents (17) and the chemical methods (18)

Corresponding author: Mohammad Mohammadi,

Department of Medical Nanotechnology, Shiraz

University of Medical Sciences, Shiraz, Iran.

"Experimental models". Generally, in macro kinetic

Journal of Environmental Treatment Techniques

2019, Volume 7, Issue 1, Pages: 166-170

models having lower concentration of the input pollutant,

the shifts in the fixation rate of the input pollutant within

the system is linear. However, by increasing the

concentration of the input pollutant, the fixation rate of the

pollutant within the system shifts towards zero so that it

can no longer remain linear. Strauss et al in 2000 provided

the Eq. 1 for the determination of the biofilter's efficiency

in eliminating VOCs, where a and b are the constant

coefficients, t is the retention time within the biofilter, and

ultimately u is the overall efficiency of the system. The

constant coefficients in this equation are determined by

calculating the log form from both sides of the equation

and eventually by fitting the data based on the results

obtained in the experiments within the various retention

intervals.

regression method, the daily efficiency of the system was

obtained. After each cycle, the average efficiency within

the upload was calculated. Therefore, in the end, 3

different efficiency rates for every 3 upload were arrived

at. By applying the linear regression amongst these data, a

simple model is achieved. In order to practicalize this

simple model in the design of the biofilter, or even for

purposes of anticipating various states of the biofilters, it

is combined with another model.

2.2 The calculation of the kinetic parameters

In the first step of analyzing the resulting data from

the experiments, the development of a suitable model is

necessary. The development of such a model is conducted

in the following steps:

The removal capacity (r) of the biofilter is

determined by the following equation.

)

μ = a (1-e

Similar studies have also been conducted by Duplacis

et al in 2003, leading to the successful modulation of an

experimental model based on the completion of the

previous equations. In all macro kinetic models, the

efficiency of the biofilter depends on the concentration of

input pollutant which is also, in its own turn, dependent on

the input contamination rate into the system. It is worth

mentioning that the rate of the input pollutant entering the

biofilter is associated with the debit of the input

contaminated air that gets into the totality of the system.

The main purpose of this study is investigating into

the kinetic parameters of a biotrickling filter and also

providing a simple regression model. Furthermore, a

comparison between the obtained anticipatory results of

the biokinetic equations and the regression model is

provided. In the first step, piloting the biotrickling filter

was kept under examination for 61 days after which the

biosynthetic parameters were calculated by the

resulting data. The re-examined monod equation

and the regression model are propounded, too. In the

end, the resolution of these models in anticipating

the efficiency of the system within various

conditions has come to the fore.

r = ((Cin-Cout)Q)/V

Where Q is the debit of the input flow into the biofilter in

-1

-3

m h , the concentration is in gm , and the volume of the

reactor is in cubic meter. Furthermore, based on the

monod equation, the removal capacity could also be

expressed by the following equation:

r = (rmax×Cg)/(Km+Cg)

where C is the average concentration of the pollutant in

gm , r

is the maximum reaction speed of the

max

bio-decomposition associated with the bed volume in

3 -1

gm h and K is the saturation constant in the gas phase

in gm . By combining the equation 1 and 2, and

equalizing both, Eq.3 is attained for the calculation of the

kinetic parameters.

(

ꢀ ꢃꢀ )ꢇ ꢊ

ꢎꢀ_ꢏ

ꢋꢌꢍ

ꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒ(ꢓ)

ꢁ

ꢂ

ꢄꢅꢆ

ꢉ

ꢈ

ꢐ ꢑꢀ_ꢏ

ꢋ

By simplifying the Eq.3, Eq.4 is achieved:

Materials and Methods

ꢈ

ꢛ_ꢜ

ꢠ

We began with designing a pilot for the biofilter,

ꢉ

ꢎ ꢑ

ꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒ(ꢢ)

(

ꢔ ꢃꢔ )ꢚ ꢝ

ꢔ

ꢡ

^ꢝꢜꢞꢟ

ꢕꢖ

ꢗꢘꢙ

ꢜꢞꢟ

based on the descriptions provided by Goli et al. This was

examined in 3 debits of input air into the ethanol vapor in

where C is the average logarithmic concentration in the

0, 291 and 1512 liters per hour. Previous studies have

biofilter. The following equation could be applied in order

to obtain the average logarithmic concentration. In the

first step, Eq.1 is solved, which would then lead to Eq.5:

shown that the efficiency of the biofilter is highly

associated with the hydraulic retention time and the pH of

the environment. Therefore, based on the resulting

conclusions, within 61 days the biofilter was subjected to

(

ꢀ ꢃꢀ )ꢇ

ꢁꢂ ꢄꢅꢆ

ꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒ(ꢣ)

different uploads. Since the previous studies by Goli et

al established the optimum pH for the highest efficiency at

, the pH of system in this study is constantly maintained

ꢈ ꢉ

ꢊ

on 7 in all uploads. The uploads were maintained until the

system came to be stabilized. The stable conditions for

this study are defined in terms of stable or insignificant

changes in system efficiency for at least 6 days. In each

upload, the parameters of retention time, the input

pollutant and the output pollutant were investigated. A

simple model based on the linear regression and the

reexamined monod equation was also studied based on the

obtained data.

The researchers also show that both r and the volume of

the reactor could be expressed through the equations 6 and

ꢐꢤ ꢎꢀꢏ

ꢊ ꢉ

ꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒ(ꢦ)

ꢏ

ꢠꢑꢐ ꢎ

ꢥ

^ꢀꢁ_ꢂ

^ꢀꢄꢅꢆ

ꢧꢨꢩ

ꢪꢑꢐꢥ(ꢀꢁꢂ ꢃꢀꢄꢅꢆ)ꢇ

ꢐ_ꢤ

ꢈ ꢉ

ꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒ(ꢫ)

.1 The calculation of the regression model

In order to provide a simple model based on the

Journal of Environmental Treatment Techniques

2019, Volume 7, Issue 1, Pages: 166-170

By equalizing the two equations, Eq.8 is obtained:

retention time is obtained by Eq.13 in which V is the

volume of the filter in l, Q is the debit of the input polluted

air into the filter in l/h, and t is the hydraulic retention time

in seconds. By replacing Eq.12 with F factor and by

replacing Eq.13 with t factor in Eq.11 and the simplifying

thereof, we can achieve Eq.14. Eq.14 allows us to

determine the volume of the filter in industrial scale by

providing it with the input and output concentration of

ethanol and the debit of the input polluted air by ethanol.

Enough attention has to be paid to the fact that this

equation will only be applicable if the utilized bed is

identical to the bed used in this study. This is because

different beds are characterized by different porosity rates

and especial surfaces. As a result, the cultured biomass on

these beds could be different. The input air into the system

only contains ethanol. The bio-decomposition rate of the

biofilter is of course different in the presence of other

compounds. Finally, the concentration of ethanol must be

within the scope of this study. In order to prove the

interpolation capacity of this model and also the

extrapolation resolution, further experiments are required.

ꢀ_ꢁ_ꢂ

^ꢀꢄꢅꢆ

ꢧꢨꢩ

ꢪꢑꢐ (ꢀ ꢃꢀ )ꢇ

(

ꢀ ꢃꢀ )ꢇ

ꢥ

ꢁꢂ

ꢄꢅꢆ

ꢁ

ꢂ

ꢄꢅꢆ

ꢉ

ꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒ(ꢬ)

ꢊ

ꢐ_ꢤ

By replacing the Eq.6 with the r factor in Eq.8, Eq.9 is

arrived at:

^ꢀꢁ_ꢂ

ꢀ_ꢄ_ꢅ_ꢆ

ꢧꢨꢩ

ꢪꢑꢐ (ꢀ ꢃꢀ )ꢇ

(

ꢀ ꢃꢀ )ꢇ

ꢐ ꢎꢀ_ꢏ

ꢤ

ꢥ

ꢁꢂ

ꢄꢅꢆ

ꢁ

ꢂ

ꢄꢅꢆ

ꢉ

ꢒꢒꢒꢒꢒꢒꢒꢒꢒ(ꢭ)

ꢐ_ꢤ

ꢠꢑꢐ ꢎꢀ_ꢏ

ꢥ

The expansion of the Eq.9, simplifying it and

ultimately solving it for C leads to the identification of

the following equation as the average logarithmic

concentration in bioreactor which is expressed as Eq.10:

^ꢀ^ꢁꢂ

^ꢃ^ꢀꢄꢅꢆ

ꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒ(ꢠꢮ)

ꢀ_ꢏ

ꢉ

^ꢀꢁ_ꢂ

ꢧꢨꢩ

ꢪ

^ꢀꢄꢅꢆ

By applying Eq.10 we can attempt to calculate the

y = 0.0524x + 94

R² = 0.9098

average logarithmic concentration, and by replacing it in

Eq.4 we can obtain the synthetic parameters. In order to

determine the kinetic coefficients in this study, the

parameters of V/[(C -C )Q] is plotted against 1/C in

in out

Eq.4 which leads to ꢝ_ꢜ_ꢞ_ꢟand ꢛ_ꢜ.

.3 Experimental methods

In this study, an ethanol measurement device was

utilized (Inters can company, model: 4160) which

provided fast and direct examination of the ethanol

present in the air.

Results and Discussions

.1 Regression model

100

150

The previous studies by Goli et al suggested that the

Retention Time

efficiency of the biological filter studied here is

statistically associated with retention time and the pH of

the environment. Since the optimum pH for the studied

biofilter was established at 7, it is only natural that any

future navigation is also the most efficient in neutral pH;

therefore, the calculation of Eq.11 by the regression

method is conducted based on this pH. It is noticeable that

Eq.11 is achieved by considering the average efficiency of

the system in each upload obtained during the 61

experiments conducted on each upload on a daily basis.

The results of this regression are shown in Fig.1. As

demonstrated there, the correlation coefficient in this

equation is equal to 0.909.

Fig.1: The dependence of filter efficiency on retention

time

ꢔ ꢃꢔ

ꢕꢖ

ꢗꢘꢙ

ꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒ(ꢠꢱ)

ꢳ ꢉ

^ꢔꢕꢖ

ꢵ

ꢴ

ꢉ ꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒ(ꢠꢓ)

ꢚ

ꢚ[ꢠꢮꢮꢎ(ꢔ ꢃꢔ )ꢃꢭꢦꢰꢱ]

ꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒ(ꢠꢢ)

ꢕꢖ

ꢗꢘꢙ

ꢵ

ꢉ

ꢮꢰꢮꢦꢣꢔ_ꢕ_ꢖ

ꢯꢉꢮꢰꢮꢣꢱꢲꢒꢑꢒꢭꢢꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢠꢠꢒꢒꢒꢒꢒꢒ

.2 Calculation of the kinetic parameters

In this study, Eq.12 is applied in order to obtain the

^e^f^fⁱ^cⁱ^eⁿ^c^y^o^f^t^h^e^s^y^s^t^e^m^.^W^h^e^r^e^Cout is the output ethanol

In this study, Eq.4 was used in order to determine the

kinetic parameters. In Eq.4 the rates of ꢝ_ꢜ_ꢞ_ꢟand ꢛ_ꢜis

obtained by plotting the V/[(C -C )Q] against 1/C . ꢒ

from the system in mg/l and C is the concentration of the

out

input ethanol into the system in mg/l. Moreover, the

Journal of Environmental Treatment Techniques

2019, Volume 7, Issue 1, Pages: 166-170

Table1: The calculation results of kinetic parameters

Debit of

the

input

flow

0.09

0.291

1.512

Average

logarithmic

concentration (V/Q)/(C -C_o_u_t)

Outlet

Ethanol

concentration

Inlet Ethanol

concentration

Volume

1/Cg

(Cg)

-3

.1130ꢀ

.00319

485

53.26

18.32

26.54

195.449

142.447

157.790

8.209*10

2.348*10

4.601*10

5.116*10

7.020*10

6.337*10

The above values are then applied in order to provide

The data retrieved from various experiments were

then inserted into these equations and the results were

then compared with the real experimental results. It is

worth mentioning that these data were applied to the

system in three different loadings. The load shift was

applied by changing the hydraulic retention time by

increasing the debit. The range of retention time changes

includes: 90, 291 and 1512 lit/h. The results of these

experiments are presented in Fig.3. As it is illustrated in

this figure, the real percentage of formaldehyde

elimination by the biofilter shows severe shifts. On the

other hand, the regression model has only determined the

average of the formaldehyde removal without the capacity

to anticipate the possible shifts. However, the accuracy of

these models is still remarkable. Based on Fig.3, the

provided results by Eq.4 are about 10% lower than the real

experimental results; nonetheless, they were successful in

anticipating the various shifts that occurred within the

biofilter. This is considered as a big advantage in applying

this equation.

the linear regression between the V/[(C -C_o_u_t)Q] and 1/C_g

columns and calculating the associated equation. Equation

5 is the result of the respective regression model in which

the intercept was 1/r_m_a_x, and the constant coefficient of X

was K /r (thus allowing easy calculation of the kinetic

max

parameters needed).

ꢯꢒꢉꢒꢱꢢꢰꢣꢬꢲꢒꢃꢒꢮꢰꢮꢠꢠꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒ(ꢠꢣ)

The results of this table illustrate that the

3 -1

biofilter r_m_a_xis equal to - 0.011 gm h and K_mis

equal to 24.58 gm . The r

is less than zero

max

because of the relative increase of the efficiency

against the increase of the formaldehyde

concentration.

.06

.05

.04

.03

.02

.01

Financial supports

y = 24.588x - 0.0111

R² = 0.9862

Jami Institute of Technology and Shiraz University of

Medical Sciences financially supported this study (Vot.

No. 000105).

Competing interests

The authors declare that there is no conflict of interest

that would prejudice the impartiality of this scientific

work.

Author’s contributions

It is certified that all of the authors have made the

same contribution in the experiments and manuscript

writing.

0.001

0.002

0.003

/Cg (gm )

Fig.2: The results of the linear regression in 3

different loadings.

Acknowledgements

This study is the result of the Bachelor’s degree thesis

by Amin Goli, air conditioning engineering student in the

Jami Institute of Technology (JIT), Isfahan, Iran. The

authors of this study appreciate the financial and spiritual

support provided by JIT which specified the requirements

of this study.

.2 Comparing the results of the regression model and

the kinetic model in anticipating the biofilter’s efficiency

At this stage, equation 4 (the kinetic model) and 14

the regression model) were solved in order to determine

the efficiency of the biofilter. These equations were then

applied in the forms of Eq.16 and Eq.17.

(

Ethical considerations

Authors are aware of, and have complied with, best

practices in ethics, specifically with regard to authorship

ꢈ

ꢀ_ꢄ_ꢅ_ꢆ

ꢉ

ꢑꢔ ꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒ(ꢠꢦ)

ꢕꢖ

^ꢛꢜ

(avoidance of guest authorship), dual submission,

ꢝ_ꢜ_ꢞ_ꢟ

ꢠ

ꢝ_ꢜ_ꢞ_ꢟ

ꢚꢶ

ꢑ

ꢷ

manipulation of figures, competing interests and

compliance with policies on research ethics. Authors

adhere to publication requirements that the submitted

work is original and has not been published elsewhere in

any language.

^ꢔꢡ

ꢕꢖ

^ꢔ⁽^ꢃ^ꢠ^ꢢ^ꢣ^ꢰ^ꢬ^ꢸ^ꢒ^ꢑ^ꢒ^ꢭ^ꢭ^ꢰ^ꢭ^ꢬ⁾^ꢑ^ꢠ^ꢮ^ꢮ^ꢎ^ꢔꢕꢖ

ꢀ_ꢗ_ꢘ_ꢙ

ꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒꢒ(ꢠꢫ)

ꢠꢮꢮ

Journal of Environmental Treatment Techniques

2019, Volume 7, Issue 1, Pages: 166-170

logarithmic equation applied to a bio-trickling filter

reactor for formaldehyde removal from synthetic

contaminated air. RSC Adv. 2013;3(15):5100-7.

4. Talaiekhozani A, Talaei MR, Fulazzaky MA, Bakhsh

HN. Evaluation of contaminated air velocity on the

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Pyrene-Contaminated Soils Using

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the

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biotrickling filter reactor. J Air Pollut Health.

016;1(3):171-80.

017;26(2):141-56.

5. Beheshtkhoo N, Kouhbanani MAJ, Savardashtaki A,

Amani AM, Taghizadeh S. Green synthesis of iron

oxide nanoparticles by aqueous leaf extract of Daphne

mezereum as a novel dye removing material. Appl

Phys A. 2018;124(5):363.

6. Mousavi SM, Hashemi SA, Esmaeili H, Amani AM,

Mojoudi F. Synthesis of Fe3O4 nanoparticles

modified by oak shell for treatment of wastewater

containing Ni (II). Acta Chimica Slovenica.

Goli A, Shamiri A, Talaiekhozani A, Eshtiaghi N,

Aghamohammadi N, Aroua MK. An overview of

biological processes and their potential for CO2

capture. J Environ Manage. 2016;183:41-58.

Alaee S, Talaiekhozani A, Ziaei GR, Lohrasbi P.

Evaluation of Iranian college students’ awareness

about infertility risk factors. Jundishapur J Health Sci.

016.

Bazrafshan M. Comparing the ZnO/Fe(VI), UV/ZnO

and UV/Fe(VI) Processes for Degradation of Reactive

Blue 203 in Aqueous Solution. Isfahan, Iran: Jami

Institute of Technology; 2018.

Eskandari Z. Control of hydrogen sulfide and organic

compounds in municipal wastewater by using ferrate

018;65(3):750-6.

7. Mousavi S, Hashemi S, Zarei M, Amani A, Babapoor

A. Nanosensors for Chemical and Biological and

Medical Applications. Med Chem (Los Angeles).

018;8:205-17.

8. Gabriel D, Deshusses MA. Retrofitting existing

chemical scrubbers to biotrickling filters for

H<sub>2</sub>S emission control. Proceedings of

(VI) produced by electrochemical method. Isfahan,

Iran: Jami Institute of Tecxhnology; 2016.

Alaee S, Monsefi M. Effect of cadmium on oxidative

stress of testes in adult male mice. Iranian Journal of

Reproductive Medicine. 2013.

the

National

Academy

Sciences.

003;100(11):6308-12.

9. Fulazzaky MA, Talaiekhozani A, Majid MZA.

Formaldehyde removal mechanisms in a biotrickling

filter reactor. Ecol Eng. 2016;90:77-81.

0. Amani A. Synthesis and biological activity of

piperazine derivatives of phenothiazine. Drug Res.

Alaee S. Air Pollution and Infertility–A letter to

Editor. J of Environ Treat Tech. 2018;6(4):72-3.

Talaiekhozani A. Preparing Emission Factors of

Carbon Dioxide, Carbon Monoxide, Hydrocarbons

and Nitrogen Oxides for Cigarette. J Air Pollut

Health. 2018;3(4).

015;65(01):5-8.

1. Talaiekhozani A, Banisharif F, Eskandari Z, Talaei

MR, Park J, Rezania S. Kinetic Investigation of 1,

Talaiekhozani A, Bagheri M, Najafabadi NR, Borna

E. Effect of nearly one hundred percent of municipal

solid waste recycling in najafabad city on improving

-Dimethyl-Methylene Blue Zinc Chloride Double

Salt Removal from Wastewater using Ferrate (VI) and

Ultraviolet Radiation. JKSUS. 2018;In Press.

2. Talaiekhozani A, Jafarzadeh N, Fulazzaky MA,

Talaie MR, Beheshti M. Kinetics of substrate

utilization and bacterial growth of crude oil degraded

of its air quality.

016;1(2):111-22.

Air Pollut Health.

0. Talaiekhozani A, Amani AM. Enhancement of

cigarette filter using MgO nanoparticles to reduce

carbon monoxide, total hydrocarbons, carbon dioxide

and nitrogen oxides of cigarette. J Environ Chem Eng.

by of

Environmental Health Science and Engineering.

015;13(64):1-8.

Pseudomonas

aeruginosa.

Journal

019;7(1):102873.

1. Goli A, Talaiekhozani A, Talaie M. Identification of

prodominant microorganisms of biotrickling filters to

treat polluted air with formaldehyde and ethanol.

Tolooebehdasht. 2016;14(6):163-70.

2. Fulazzaky MA, Talaiekhozani A, Ponraj M, Abd

Majid M, Hadibarata T, Goli A. Biofiltration process

as an ideal approach to remove pollutants from

3. Mousavi M, Hashemi A, Arjmand O, Amani AM,

Babapoor A, Fateh MA, et al. Erythrosine Adsorption

from Aqueous Solution via Decorated Graphene

Oxide with Magnetic Iron Oxide Nano Particles:

Kinetic and Equilibrium Studies. Acta Chimica

Slovenica. 2018;65(4):882-94.

4. Motaghedifard M, Behpour M, Amani AM.

Electrochemical Growth of Sponge/Raspberry-Like

Gold Nanoclusters at the Carbon Rod. Russ J

Electrochem. 2018;54(8):629-35.

polluted

air.

Desalin

Water

Treat.

014;52(19-21):3600-15.

3. Fulazzaky MA, Talaiekhozani A, Hadibarata T.

Calculation of optimal gas retention time using a