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Abstract

Noise, emitted by wind turbines, is one of the main health risk factors which has been recently considered in many researches. Noise annoyance is among the most important human responses to noise. The aim of this work was to modeling of annoyance due to noise at workplace coming from wind turbines in workers. All workers of a wind power plant consisted the study sample. The equivalent noise level was measured using a task-based method. Moreover, data related to noise annoyance and noise sensitivity were acquired by standardized methods. Based on the results, noise exposure, noise sensitivity, visibility, age, and experience affected noise annoyance. According to path analysis, the most indirect and direct effect on noise annoyance were attributed to noise exposure. Age, sensitivity, and noise exposure were positively associated to annoyance. It can be concluded that there is a significant relationship between age, experience, sensitivity to noise, and exposure to the wind turbine noise with noise annoyance.

Keywords

Noise annoyance wind turbine noise noise sensitivity workers

Introduction

Wind farms as a new method of generating renewable energy¹ with minimal environmental impact are considered as a new source of noise pollution. The air turbulence in the vicinity of wind turbine (WT) blades and mechanical interactions between its components produce high levels of sound.^2–4 Jeffery et al.⁵ stated that sound emitted from WTs can cause anxiety and stress. It seems that industrial WTs can cause adverse health effects such as headaches, fatigue, nausea, vomiting, insomnia, and palpitations in the people living close to wind farms.⁶ Pierpont⁷ named the health effects of WTs as WT syndrome and stated that health-related problems may be caused by low-frequency noise emitted by WTs. One of the potential impacts of wind turbine noise (WTN) on people’s health is related to its low frequency and infrasound nature.^8–10 Moreover, Lee et al.¹¹ in a study mentioned that amplitude modulation from WTN can considerably increase noise annoyance. Impulsive noise from the WTs is another potentially harmful effect.¹²

Noise annoyance is one of the most basic human responses to low frequency noise.^12,13 The emitted noise from the WTs can cause annoyance or inconvenience to the residents near the WTs.^14,15 Janssen et al.¹⁶ compared the noise annoyance of WTs and other noise sources and stated that in equal Lden, noise annoyance caused by WTs is more than that caused by environmental sources such as road and rail traffic noise. Leventhall¹⁷ expressed that the time-varying sounds are much more annoying than steady noise with an equal equivalent noise level. Impulsive noises emitted from the WTs are also considered as time-varying noises and they are uniquely annoying due to the frequent changes in its frequency and loudness during the changes in the velocity of the wind and weather conditions.¹⁸ WTN is audible at great distances and the real reason has not been known yet. Additionally, this noise is also heard at distances greater than 3 km and can create annoyance.^19,20 Low-frequency sounds such as WTN do not require high level of loudness to cause annoyance.²¹ Also a previous study showed that the visual impacts of the WTs can lead to the elimination of annoyance while their color has a weak positive correlation with the level of annoyance.²² Noise sensitivity is another variable that can influence annoyance of individuals.²³ It is observed that high sensitive people are more annoyed in the case of noise exposure.²⁴ Noise sensitivity refers to the attitudes of people to noise source and is a subjective dimension of human response to the noise.

According to the findings of World Health Organization, noise annoyance has harmful effects on health-related quality of life.²⁵ Furthermore, noise annoyance is one of the indicators of possible adverse health effects resulting from noise exposure. Stansfeld and Clark²⁶ stated that noise annoyance and psychological distress have reciprocal effects on each other.⁴ Noise annoyance can be regarded as one of the contributing factors for sleep disturbances which impact sleep quality.²⁷ All previous studies have examined the effect of the WTN on noise annoyance among people living near wind farms and up to our knowledge no research has investigated workers of wind farms, who are exposed to high level of noise. Considering this fact that people working in wind power plants are close to WTs, their noise exposure levels are higher than those of general people. Thus, it can be assumed that they are more affected by WTN. In this regard, targeting wind power plant workers is an advantage of this study, since they play a major role in the preservation and maintenance of the facilities. What is more, survey of WTN, due to its unique features and its adverse effects comparing to other sound sources, can be considered as strengths of this research. Hence, the aim of this work was to modeling of annoyance due to noise at workplace coming from WTs in workers.

Material and methods

This study was conducted in the Manjil wind farm due to the highest number of workers as well as the largest number of turbines in Iran. The study population consisted of all personnel working in the plant, was divided into three occupational groups according to the type of their job and their distance to the WTs, including maintenance, security, and administrative staff. This article was extracted from a master thesis and subject’s description and noise measurement method are presented in another work as well.

Subjects

In total, 53 subjects, who were working in Manjil wind farm, participated in this study. In general, two women were working in this plant, one of whom was not willing to participate in the study. So the results of investigating only one woman was not representative, hence the gender variable was excluded. It is suggested that studies be considered in future regarding gender. Other individuals, who participated in the study, were divided into three groups based on their tasks. The maintenance workers were responsible for repairing the WTs and therefore they were always working in the vicinity of turbines and their noisy parts such as inside the turbines’ trunk. The security personnel were in charge of guarding the plant. This occupational group had rotating shifts in the guard station, where WTs were placed around, and in some cases they were assigned to patrol the sites in the plant. This group of workers had further distance from WTs and was exposed to lower level of noise comparing to the maintenance group. Office staff was always inside the office building, which was located far from the WTs (comparing to the maintenance and security staff) because their job was to handle the administrative and financial affair in 8 h shifts.

Noise measurement

To assess noise exposure of workers, below steps were conducted:

Determination of occupational groups including maintenance workers, guards, and administrative staff based on the similarity of their work;

Determination of all people traffic area and workstations in each group;

Determination of the time of presence (T) in designated area;

Noise measurement: In order to generalize the measurement results to all individuals in each of the occupational groups, three people were randomly selected from each job category. Then for each of these selected people the equivalent continuous A-weighted sound pressure level over the duration T (L_Aeq,T) in the designated workstations was measured at a height of 1.5 m based on the standard ISO9612: 2009 method.²⁸ To increase the accuracy, the measurement was repeated three times per person in each workstation. Given that three participants were selected from each group and at each selected workstation the L_Aeq,T measurements were repeated three times, at each workstation nine L_Aeq,T were obtained. Finally, the average of all nine L_Aeq,T was taken as a representative of the noise exposure level in that workstation. Finally, based on the equivalent continuous A-weighted sound pressure level formula, the daily noise exposure at each occupational group was obtained which was extended to all participants in that occupational group.

In the locations where the personnel had the greatest exposure, sound frequency analysis was performed in octave band using a calibrated sound meter, TES, 1358 sound analyzer.

Questionnaires

Demographic and background information including age, work experience, type of working shift, education, etc. were collected by a general questionnaire. Noise annoyance scale and Weinstein noise sensitivity scale (WNSS) were used to investigate the level of personal noise annoyance and noise sensitivity, respectively. A brief overview of each tool is provided below.

Noise annoyance scale

Noise annoyance was determined by the “Acoustics-Assessment of noise annoyance by means of social and socio-acoustic surveys” questionnaire which is provided in ISO standard 15666.²⁹ Validity of the annoyance questionnaire obtained by expert’s comments and preliminary tests were performed to confirm reliability (Cronbach’s alpha: 0.81).³⁰ A Likert scale was used, range 0–10, with the higher score the higher level of annoyance. The question regarding the noise annoyance scale was phrased as follows:

“Thinking about the last (12 months or so), what number from 0 to 10 best shows how much you are bothered, disturbed or annoyed by wind turbine noise?”

“If you are not at all annoyed choose 0; if you are extremely annoyed choose 10; if you are somewhere in between, choose a number between 0 and 10.”

WNSS

Noise sensitivity is defined as “the internal state of any individual which increases their degree of reactivity to noise in general.”³¹ Noise sensitivity is a factor explaining differences in reactions to noise. WNSS was used to measure the noise sensitivity. Reliability and validity of Persian version of WNSS was confirmed in a previous study by Alimohammadi et al.³² The questionnaire consisted of 21 questions with a 6-point Likert scale (0 = strongly agree, 5 = strongly disagree). The range of scores for WNSS is 0–105; the higher the WNSS score, the higher the rate of noise sensitivity. The scale comprised of four subscales including attitude to noise (five questions), interference with concentration (six questions), became sensitive to noise (10 questions), and “attitude to noise control” (six questions). It is worthwhile to mention that some questions were repeated within the subscales. Based on the total score of noise sensitivity, individuals are categorized into nonsensitive (score below the 25th percentile), middle sensitive (score between 25th and 75th percentiles), and highsensitive (score over 75th percentile).

Statistical analysis

In the last stage, the collected data were analyzed at a confidence level of 95% using SPSS software version 20 and R software. Spearman and Pearson correlation coefficients and ANOVA/Chi-square statistical tests were used to investigate the relationship between the study variables and to understand the significant differences between groups. Following, paired comparisons of significant effects were conducted by Scheffe’s post hoc test. Multiple regression analysis with dummy coding method was applied in order to examine the effect of noise exposure on noise annoyance and compare the differences among the occupational groups. Additionally, the predictability of noise exposure, noise sensitivity, age, experience, and visibility of WT to noise annoyance were determined by path analysis with maximum likelihood estimation method. For this aim, based on theoretical considerations, a model was proposed to show hypothesized structural relationships among study variables. Model fit indices were investigated and their amounts were determined based on hypothetical models. Normed fit index, comparative fit index, and incremental fit index are some types of model fit indices and the closer to 1 the value of the indices, the better the fit of the model. Following, based on the expressed routes by scientific evidence-based modified indices, the routes were proposed to improve the model fitness.

Results

Based on the results, 8 h equivalent noise level in A network for maintenance workers, security and office staff were obtained 83 ± 9 dB (min = 76 dB, max = 101 dB), 66 ± 8 dB (min = 75 dB, max= 60 dB), and 60 ± 11 dB (min = 47 dB, max= 66 dB), respectively. According to results of noise frequency analysis, the noise pressure level in the workplace of repairing workers is at the highest level at all frequencies, compared to workplaces of the other two groups (see Figure 1). Moreover, the figure showed that the sound energy was dominant in the frequency range of less than 250 Hz and it was much higher than frequencies above 250 Hz. As well as WTN had a low frequency characteristic because sound pressure level had greater value at lower frequencies (lower frequencies of octave band).

Figure 1.

Octave band frequency analysis for the three occupational groups.

In total, 53 subjects, working in Manjil wind farm, participated in this study. The mean (SD) age, work experience, and noise sensitivity of the participants were 30.8(5.9) years, 14.1(5.5) years, and 70.2(14), respectively. The mean (SD) noise annoyance for the whole personnel was 6(2.5). The highest and lowest noise annoyance, respectively, belonged to maintenance workers (mean = 8.4) and office staff (mean = 2.6).

In this study, participants with job experience less than 12 years, age less than 36 years old, education level of lower than diploma, workers who could not see WT, nonsensitive workers, and those with rotating shifts showed the least level of noise annoyance. Descriptive statistics related to individuals’ noise annoyance according to demographic and background variables are presented in Table 1.

Table 1.

Noise annoyance for participants with different demographic and background characteristics.

Variables	Category	Number of participants	Percent (%)	Noise annoyance		Analysis methods
Variables	Category	Number of participants	Percent (%)	Mean	Standard deviation	Analysis methods
Occupational group	Repairman	22	41.5	8.4	1	ANOVAp-value=0.001
	Security	17	32	5.8	0.85
	Official staff	17	26.5	2.6	1.3
Job tenure	<12	19	36	4.9	2.2	ANOVAp-value=0.003
	12–19	23	43.5	6.3	2.4
	19<	11	20.5	8.2	1.9
Age	<36	23	43.5	4.9	2.3	ANOVAp-value=0.001
	36–41	19	36	6.3	2.5
	41<	11	26.5	8.2	2
Education	<diploma	12	22.5	5.9	2.9	Chi-squarep-value>0.05
Education	Diploma≤	41	77.5	6.1	2.4	Chi-squarep-value>0.05
Noise sensitivity	Not sensitive	6	11.3	2.5	1.7	ANOVAp-value=0.001
	Middle sensitive	13	24.5	4.3	2
	High sensitive	34	64.2	7.4	1.8
Shiftwork type	Fixed	38	71.5	6.1	3	Chi-squarep-value>0.05
Shiftwork type	Rotation	15	28.5	5.8	0.83	Chi-squarep-value>0.05
Visibility of WT	Yes	34	64.1	7.6	1.5	Chi-squarep-value=0.001
Visibility of WT	No	19	35.9	3.4	1.7	Chi-squarep-value=0.001

ANOVA: analysis of variance; WT: wind turbine.

Regarding the comparison of noise annoyance of employees at different levels of exposure to WTN, ANOVA analysis manifested significant differences among the three occupational groups (P-value < 0.05) which means that the noise-annoyed workers were exposed to higher noise level (see Table 1). The Scheffe post hoc test was used for further comparisons and the results showed a significant difference between the mean noise annoyances among all three levels of noise exposure.

Comparison of noise annoyance among workers of different job experience and age groups (using ANOVA tests) showed that the mean noise annoyance was not similar among different groups (P-value < 0.05). The mean noise annoyance between workers with age of more than 41 years and those with age of less than 36 years had a significant difference, according to Scheffe post hoc test. Moreover, the only significant difference of noise annoyance among different groups of work experience was between workers with less than 12 years of experience and those with more than 19 years of experience.

Using ANOVA tests, it was observed that the mean noise annoyance was significantly different among workers with different levels of noise sensitivity (P-value < 0.05). The Scheffe post hoc test was used for further comparisons and the results showed a significant difference between the mean noise annoyance of nonsensitive and high-sensitive workers. In this regard, there was a statistically significant difference between middle- and high-sensitive workers.

Based on the results of Chi-square test, a statistically significant difference of mean noise annoyance was observed between workers who could see WT and those who could not. Significant differences of annoyance were found between different educational level groups and type of shift work. The pertinent results are presented in Table 1.

Pearson correlation analysis showed a significant positive correlation between worker’s noise annoyance and noise exposure with a correlation value of 86%. Additionally, simple linear regression was applied to examine the effect of noise exposure on worker’s noise annoyance and based on the results each dB increase in noise exposure was accompanied by 0.22 increase in worker’s noise annoyance.

In order to examine the effect of noise exposure on noise annoyance and also to compare these effects among occupational groups, dummy coding method of multiple regressions was used. In this method, office staffs were selected as the control group due to the lower level of annoyance. Results showed that the type of noise exposure could explain 83% of variations of noise annoyance. Noise exposure for maintenance workers had the greatest effect on their noise annoyance in a way that the effect of noise exposure on noise annoyance of maintenance workers was almost 2.2 times greater than office staff and 1.8 greater than security staff. This effect for the security group was 1.2 times greater than office staff as well. This result indicates that the three occupational groups experienced different levels of annoyance based on their noise exposure, and effect of noise on the annoyance is more at the higher noise levels (Table 2).

Table 2.

The effect of noise exposure on noise annoyance based on occupational groups.

Model	Model compatibility		Unstandardized coefficients		Standardized coefficients	T	P-value	R	R2
Model	F	P-value	B	Std. error	Beta	T	P-value	R	R2
Control (60 dBA)	118.33	0.001	2.64	0.29	–	0	0.001	0.90	0.83
Security (66 dBA)			3.23	0.39	0.6	8.17	0.001
Repairman (83 dBA)			5.76	0.37	1.11	15.35	0.001

Pearson correlation analysis showed a significant positive correlation between levels of noise annoyance and workers age (P-value < 0.05, r = 0.44). The effect of age on the worker’s noise annoyance was examined using simple linear regression (forward method) and a linear association was reported between age and noise annoyance. In this sense, it was observed that for each year increase in age, 0.19 units will be added to the level of noise annoyance.

The relationship between noise annoyance levels of employees and their job tenure was analyzed using Pearson correlation coefficient. A significant positive association was observed between work experience and noise annoyance (r = 0.44). Employing simple linear regression analysis, the effect of work experience on noise annoyance was investigated. Accordingly, each year increase in work experience resulted in 0.21 units increase in the level of noise annoyance.

Pearson correlation analysis showed a significant positive correlation between levels of noise annoyance and sensitivity (P-value < 0.05, r = 0.82). Additionally, simple linear regression was applied to examine the effect of noise sensitivity on worker’s noise annoyance and based on the results each unit increase in noise sensitivity is accompanied by 0.67 increase in the worker’s noise annoyance.

Based on the results of path analysis, the results showed that final model appropriately was fitted to explain the relationship between predictor variables and the dependent variable. The Pearson correlation coefficient showed a significant positive correlation between noise annoyance and noise sensitivity, age, experience, and noise exposure. Based on the results of path analysis, the most indirect effect of noise annoyance belonged to exposure to WTN (β = 0.57, p < 0.001). Age had the greatest direct effect on sensitivity (β = 0.55, p < 0.001). Moreover, the effect of noise sensitivity on the annoyance was statistically significant (see Table 3).

Table 3.

Direct effects, indirect effects, and total effects of predictors on noise annoyance.

Predictor	Standardized direct effects	Intermediate variable	Standardized indirect effects	Standardized total effects	P-value
Noise sensitivity	0.52	Noise annoyance	–	0.52	<0.001
Age	0.55	Noise sensitivity	–	0.55	<0.001
Age	0.03	Noise annoyance	0.29	0.32	0.568
Experience	− 0.3	Noise sensitivity	–	−0.3	<0.001
Experience	–	Noise annoyance	−0.15	−0.15	<0.001
Noise exposure	0.47	Noise sensitivity	–	0.45	<0.001
Noise exposure	0.57	Noise annoyance	0.24	0.81	<0.001

The results showed that final model appropriately was fitted to explain the relationship between predictor variables and the dependent variable. Schematic view of the model is presented in Figure 2.

Figure 2.

Path diagram association of noise sensitivity, age, experience, and noise exposure with noise annoyance.

The analytical path diagram showed that there were statistically significant standardized direct effects (path coefficient) of noise exposure, age, and noise sensitivity on the noise annoyance. Standardized indirect effects of age, experience, and noise exposure on noise annoyance are depicted as well (Chi-square = 80.826, df = 4, P-value=.001). The results show that noise sensitivity acts as an indicator for noise annoyance.

Discussion

Based on the findings of the present research, the average worker noise annoyance was 6 which represent relatively high WT noise annoyance. Moreover, 32% of participants reported noise annoyance scores of 8 and above which indicate extremely high noise annoyance of workers with high noise exposure. This result is inconsistent with those reported by Ali,³³ which was done among workers of different industries. In the study by Song et al., responses of “rather annoyed” and “very annoyed” were categorized as “annoyed,” representing 81.5% of the study population. Also, 51.5% of the people rated “highly annoyed.” In the present study, with a similar classification, it can be noted that 56.6 and 49% of participants rated “annoyed” and “highly annoyed,” respectively. Of the reasons for this difference, the sensitivity of general population, resistance of the employees, and employees’ conflict of interest, and therefore their compromise can be mentioned.¹⁴ In the study by Di et al.³⁴ in China, which was conducted to investigate the effect of road and rail traffic noise on annoyance of residents in the level less than 50 dB, the results showed 32% of individuals to be extremely annoyed. The reasons for discrepancies of the results of the present study and that by Di et al. can be attributed to differences of subjects and different acoustic characteristics of study noise.

The results showed a significant positive linear relationship between exposure to WTN and worker’s noise annoyance. What is more, the ANOVA tests manifested that annoyance varied among studied occupational groups, with the highest and lowest noise annoyance belonged to the maintenance workers and office staff, respectively. The highest level of noise annoyance for maintenance workers was due to their adjacency to the source of sound and exposing to higher level of noise. This result is in accordance with that reported by Ali,³³ which was performed among various job groups in Egypt. He examined the worker’s noise annoyance among various occupational groups, exposed to different noise levels, and found that noisier the work environment the higher noise annoyance. The equivalent sound level of the present study ranged from 60 to 83, while it was 70–100 dB in the study by Ali. Nevertheless, he also confirmed that in a common range with our study (70–83 dB), by increasing the level of noise, the percentage of individuals who have been annoyed extremely increased. Despite several differences including the type of profession, noise source, personal characteristics, and working conditions, in both studies, increasing level of noise exposure resulted in an increase in the level of noise annoyance. Thus, it can be concluded that the high level of noise annoyance among maintenance workers is due to high levels of noise exposure. Similar results have been reported in previous studies.^35–37 Pawlaczyk-Łuszczyńska et al.³⁷ investigated the effects of low frequency and broadband noise on workers in the control room of a power station and cement factory and reported a linear association between noise exposure and noise annoyance. Also, they expressed that the impact of low frequency noise on noise annoyance is more than that of the noise with broadband frequency. The frequency range of noise was 60–85 dB in the Pawlaczyk-Łuszczyńska study which is equal to the range of our research. However, a higher slope of curve was observed in the current study which indicates a greater impact of WTN on the annoyance. This could be resulted from the impulsive nature of noise, time-varying nature of WTN, difference in subjects’ sensitivity, and so on. Accordingly, previous studies have shown that residents, who live near WTs, experience more annoyance.^16,27 Pedersen and Larsman²² carried out a study on residents of wind power plants and stated that the visibility of turbines had negative effect on the annoyance of residents that was in accordance with the present study. This was confirmed also in the study by Doolan et al.,³ Arezes et al.,³⁸ and Van den Berg et al.³⁹ Although the present study has been conducted among workers, the noise source is similar to the mentioned researches. Therefore, it can be stated that perhaps the reason for further annoyance of maintenance and security workers, comparing to office staff, is a combination of the two factors including adjacency to noise source and also psychological discomfort caused by visibility of WTs as the noise source.

The results showed a significant positive correlation between the level of noise annoyance and worker’s age. According to World Health Organization, aging makes people having hearing loss at high frequencies and this will reduce the sensitivity to mid and high frequencies. Hearing loss at high frequencies eliminates the coating effect of background noise to the WTN.³⁹ Thus, it can be noted that older people perceive high level of noise at lower frequencies due to the reduction in their coating effect for background noise and they are annoyed more in response to low-frequency noise emitted from WTs.²⁵ Van Gerven et al.⁴⁰ reported an inverted U-shaped relationship between the age and traffic noise annoyance so that noise annoyance reached a high level for people with age of 45 years and it declines somewhat for people above 45 years old. Van Gerven et al. conducted their study among the general population while the working population was targeted in the present study, who are more resistant to the effects of noise compared to the general population. Moreover, this study is different with van Germen’s study in terms of the frequency spectrum of the noise source. Despite the mentioned differences, the van Gerven study confirms the results of the current study since in our study, individuals aged more than 41 years old also had the highest level of noise annoyance comparing to other age groups.

By ignoring the impact of age, by increasing each decibel to equivalent noise, noise annoyance would go up by 0.21 units. Similarly, by ignoring the impact of equivalent noise, each year increase of age leads to increase in worker’s noise annoyance by 0.15. These results confirm that the effect of noise level on noise annoyance is greater than that of age and its resultant changes. In this context, Jakovljevic et al.⁴¹ showed a significant relationship between noise annoyance and noise levels, personal characteristics, and some of the houses conditions. Although the noise sources, population, and tools and methods are different, results reported by Jakovljevic and the current study confirm that the impact of noise exposure levels and noise characteristics on the annoyance is greater than that of personal characteristics.

Additionally, annoyance has a significant correlation with visibility of WTs.⁴² Pedersen’s study confirmed the results of the present research since in our study by increasing the level of noise, amounts of noise annoyance increased and administrative staff experienced less annoyance due to absence of WTs, compared to the guards and maintenance workers.

Due to the high level of worker’s exposure level compared to the residents around the WTs, it is expected that worker’s noise annoyance will be much higher than residents living near WTs. However, this study did not achieve this result and this issue requires further researches. This may be explained by the fact that general populations are more sensitive than workers. Furthermore, workers are considered as economically beneficiaries of wind power plants and previous studies confirmed that beneficiaries have been less annoyed than others.^7,16

The results of the path analysis showed that noise sensitivity as a personal factor had a large effect on noise annoyance. Given that noise sensitivity is related to personal attitude, it can be possible that workers working at the wind farm have a negative attitude toward their workplace and subsequently they will be annoyed. Also, age, job experience, and noise exposure caused an increase in sensitivity and they intensified the effect of sensitivity on annoyance. Based on the path coefficient we can conclude that noise exposure as an exterior factor had greatest impact on annoyance and sensitivity as an interior factor can affect annoyance.

Conclusion

We can conclude that there is a strong association between age, experience, sensitivity to noise, and exposure to the WTN with noise annoyance. Thus, selecting the appropriate workers with regard to personal characteristics such as sensitivity and attitude toward workplace noise for working in the wind farm can be an essential countermeasure in reducing the noise annoyance.

Footnotes

Acknowledgment

We gratefully appreciate Manjil power plant staffs for their kind cooperation.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research,authorship,and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research,authorship,and/or publication of this article: This article is extracted from Master’s Thesis No.25306. The authors would like to acknowledge the financial support of Tehran University of medical sciences for this research.

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