The Impact of Mining Activities on the Sleep Quality of Adjacent Residential Areas (Case Study: Gold Mine)

doi:10.21203/rs.3.rs-4286209/v1

Sleep, as a complex neural state, is crucial for restoring the body's energy levels and encouraging rest. Few studies have investigated the effects of mining on mental health, especially on the quality of sleep in residential areas near mines. This study seeks to identify these effects and consequences as thoroughly as possible. External physical factors can influence sleep patterns, leading to disturbances that manifest as sleep disorders. Sleep disorders are prevalent clinical issues, adversely affecting overall health, safety, and quality of life by disrupting natural sleep patterns. Inadequate or non-restorative sleep can disrupt normal physical, mental, social, and emotional functioning. The primary approach to addressing sleep disorders involves identifying their root causes and dealing with the associated conditions. This study explores sleep disorders arising from mining activities, focusing on the impact of explosions on residents near gold mines in Iran. Conducted over one month, the research aimed to assess sleep quality using the Pittsburgh Sleep Quality Index (PSQI) questionnaire. After collecting data through this standardized questionnaire, analysis was conducted using SPSS26 and Mplus softwares. Results from the questionnaire analysis revealed that 72.5% of individuals residing in the mining area reported experiencing sleep disorders. Significant differences in PSQI indices between men and women were identified, suggesting that women in the studied rural area perceived mining activities as significantly impacting their sleep quality and daily functioning. Furthermore, no significant differences were observed in sleep quality indices between singles and married participants, except for the use of sleep-inducing medications among employed and unemployed groups in the study community. Noteworthy, many workers, particularly those from the rural male population employed in mining, face various harmful factors such as explosions and noise, potentially contributing to the impact of mining on residents in the area. The study results can inform the development of effective strategies to mitigate the negative consequences of mining.

Mining Activities

Mine Blasting

Pittsburgh

Sleep Disorder

Mining activities, from transportation to infrastructure and from energy to information technology, make a significant contribution to society. However, the value extraction from such operations does not mitigate the substantial impact this industry has on the lives of millions inhabiting areas of mineral extraction [1]. Hence, these mining activities serve as key fulcrums of sustainable development indicators [2]–[4] Sustainable development, often perceived as a nuanced concept, is seen as an eco-economic progression that integrates social indicators, primarily seeking to safeguard our planet, foster prosperity and peace among all individuals, enhance the quality of life for future generations, and encompass the principles of social responsibility and a deep concern for public health [5]–[9]. Mining, on one side, serves as a significant source of income and economic benefits, driving employment and engaging local residents in industrial development. However, it is also linked with substantial challenges, including low living standards, degradation of local livelihood resources, and a negative impact on human rights and overall quality of life. For instance, in Sub-Saharan Africa, over 8 million individuals are directly engaged in mining activities, supporting more than 45 million dependents [10]. Thus, mining operations serve as a lifeline for millions of disadvantaged individuals worldwide [11]. Simultaneously, due to the nature of its economic objectives, mining is undeniably linked to the degradation of natural resources, harm to the natural environment, and a consequent decline in individual health [12], [13]. These repercussions affect ecosystems, communities, and miners alike, posing health risks not only to those directly involved but also to those residing in proximity to these operations [14]. Furthermore, research findings have indicated that the release of toxic pollutants from coal mining activities can potentially pose substantial threats to human health [15]. Carvalho (2017) [16] and Diringer et al. (2015) [17] suggested that gold extraction processes, particularly those comprising the use of mercury and cyanide, could impair human health through exposure to such toxic pollutants. Despite this, mining remains one of the most perilous occupations globally, marked by both short-term injury and fatality risks and long-term health effects. A study by Baah-Ennumh et al. (2020) employed a questionnaire in the western region of Ghana to gauge the quality of life among individuals residing near mineral areas. The findings revealed cancer and acute respiratory issues as the most common diseases linked to mineral activities, with 52.30% of respondents attributing these health issues to excessive dust pollution and 3% to explosions [18]. Leuenberger et al. (2018) further highlighted that the impacts of mining extended beyond environmental concerns. The structural damage to homes and the high noise levels generated by mine blasting would lead to worry and fear, notably among women and children [19], evidently imposing significant impacts on the health of residents in mining areas. Moreover, beyond the detrimental effects on physical health, mining activities have a profound impact on mental health. Research carried out by Hossain et al.(2013) in rural coal mining communities in the western region of Queensland underscores that coal mining is fundamentally linked to the rise of mental illnesses such as stress, depression, and suicide, which significantly alter the lifestyle, personality, and demographics of rural communities [20]. The findings from a study conducted on the inhabitants of mining areas in Appalachia indicated that significant adverse psychological impacts were widespread among those living near mining operations. The intensive environmental changes, environmental stress, and anxiety induced by hazardous and harmful mining activities have resulted in symptoms of traumatic stress, anxiety, insomnia, and depression within these communities [21]. One of the salient impacts of mining activities pertains to public health, with mental health disorders being notably prevalent. The blasting operation in mining, a key step in the operational cycle, triggers the release of seismic energy into the surrounding environment, significantly contributing to sleep disorders. Moreover, due to the inherent characteristics and locations of mines, various social and environmental factors such as social cohesion, safety, area disturbance, light, noise, traffic, and pollution can influence sleep patterns in both adults and children [22], [23]. Sleep quality, a fundamental facet of human health, is emerging as a crucial issue and one of the five factors considered in the healthy sleep evaluation. A myriad of factors, including work-related accidents and stress, can induce sleep disorders [24], subsequently disrupting normal sleep patterns, as some of the most common clinical problems, resulting in insufficient or non-restorative sleep. These disturbances can affect overall health, safety, and quality of life, as well as normal physical, mental, emotional, and social functioning [25]. Given that sleep plays a critical role in maintaining the body's homeostasis, any imbalance can lead to changes in the body's normal routine and contribute to the onset of mental illnesses, stress, and disabilities [26]. The results of the study by Mysliwiec et al. (2018) show that people suffer from sleep disorders and trauma-related nightmares after being exposed to stressful environments and traumatic experiences [27]. Research conducted by Halperin (2014) revealed that environmental noise would disrupt sleep, leading to a redistribution of time spent in various sleep stages. It is typical for environmental noise to increase wakefulness and slow-wave sleep, which can result in shallow sleep [23]. However, an increasing body of research indicates that certain environmental factors can alter healthy sleep. In a study that involved measurements and objective sleep reporting, Johnson et al. (2018) found that adverse environmental factors such as exposure to inadequate light, vibrations, disturbing noise, temperature, and humidity could disrupt healthy sleep [22]. Signs of sleep disorders are also evident in shift workers [28]. A study by Drake et al. (2004) found that 16% of workers involved in shift work and those in certain environments experienced sleep disorders and excessive sleepiness [29]. Recognizing the importance of sleep as a primary physiological necessity in the circadian cycle and a vital aspect of life quality, HokmAbadi et al. (2022) conducted a descriptive and analytical study to explore the relationship between sleep disorders and the physical and mental performance of individuals assessed by the Work Ability Index in workers engaged in construction workshops. The findings indicated that high-risk jobs, due to their strenuous and harsh nature, posed threats to not only safety but also individual health [30]. Furthermore, the study conducted by Hefez et al. (1987) on war survivors underscored the correlation between high-risk environments and traumatic experiences and their detrimental effects on sleep quality. These survivors reported decreased sleep quality and frequent nightmares [31]. Overall, noise and vibrations arising from noise pollution and mine blasts contribute to mental disorders, depression, anxiety, and sleep disorders in individuals. However, a review of previous studies revealed a research gap regarding the direct relationship between mining activities and sleep disorders in individuals residing near mines. This study aimed to investigate and evaluate the indicators and components of sleep disorders influenced by mining activities, specifically vibrations resulting from noise pollution and mine blasting. This evaluation was conducted as a field study in a gold mine in Iran.

The current research is a cross-sectional analysis, conducted following the strategy shown in Fig. 1.

The current research is a descriptive study investigating the factors influencing sleep quality due to mining activities, with a focus on the perspectives of rural residents in mining regions, specifically within one of the gold mines in Iran, through a comparative analysis. Field data were collected from a representative sample of the community, and the sample size was determined using G-POWER software [32] based on scientific principles. Individuals needed to have at least one year of residency, be free from underlying diseases and sleep disorders, and complete an informed consent form to be eligible for participation. With a probability of error set at 0.05, a test power exceeding 90%, and an effect size of 0.15, the sample size was calculated to be 135 individuals using the G-POWER software for sample size measurement (Fig. 2).

In this study, 135 questionnaires were distributed among participants. A total of 102 cases were validated following an 86% response rate and subsequent data preprocessing.

1-2- Data Collection

In this study, the Pittsburgh Sleep Quality Index (PSQI) questionnaire, a standardized tool for sleep quality assessment, was employed to evaluate sleep patterns. This questionnaire is specifically designed to gauge sleep quality and disturbances over the preceding months. First introduced by Buysse et al. in 1989 [33], the validity of the PSQI questionnaire has been rigorously examined through various methods, including internal validity, inter-rater reliability, and content validity. Buysse et al. (1989) reported an internal consistency coefficient (Cronbach's alpha) of 0.83 for the total score, indicating a high level of internal validity[33]. Furthermore, Backhaus et al. (2002) reported the reliability of the test for assessing insomnia patients to be 0.87 [34]. Additionally, in the study conducted by Smith & Wegener (2003), the estimated Cronbach's alpha ranged from 0.74 to 0.78 [35], underscoring the favorable internal consistency of the PSQI questionnaire. The PSQI questionnaire consists of 19 self-assessment questions and 5 questions related to sleep habits. These inquiries are divided into 7 sections, covering mental sleep quality, sleep onset latency, sleep duration, habitual sleep efficiency, sleep disorders, use of sleeping medication, and daytime dysfunction. The overall sleep quality score is calculated by summing the measured components [36]

2-2- Data Analysis

Upon collecting the questionnaires, the sleep quality for each participant was quantified numerically between 0 and 21 by summing the scores for each dimension of sleep quality. Subsequently, the data were entered, preprocessed, and subjected to descriptive statistics using SPSS26 software. Furthermore, an evaluation was conducted to explore and present a Structural Equation Model (SEM) utilizing MPLUS 7.4 software.

The study findings revealed that 40.2% (n = 41) were females, and 59.8% (n = 61) were males. The majority of participants (30.4%) had a lower level of education. Moreover, 92.2% of participants had more than 15 years of residency experience in the proximity of the mine. A significant portion of residents (59%) were employed. Descriptive statistics for the dimensions of the sleep quality variable are presented in Table 1.

Table 1. Descriptive Statistics of Sleep Quality Structure Components
	N	Min.	Max.	Mean	SD	Skewness		Kurtosis
	Statistic	Statistic	Statistic	Statistic	Statistic	Statistic	Std. Error	Statistic	Std. Error
Subjective sleep quality	102	0	3	1.31	0.933	0.150	0.239	-0.848	0.474
Sleep latency		0	3	1.36	1.106	0.176	0.239	-1.299	0.474
Sleep duration		0	3	0.96	0.878	0.525	.239	-0.565	0.474
Habitual sleep efficiency		0	3	0.63	0.688	1.014	0.239	1.263	0.474
Sleep disorders		0	3	1.19	0.461	1.273	0.239	2.310	0.474
Use of sleeping medications		0	3	0.17	0.646	3.873	0.239	3.844	0.474
Daytime dysfunction		0	2	0.26	0.525	1.882	0.239	2.741	0.474
Overall Sleep Quality Score		1	2	1.73	0.448	-1.026	0.239	-0.967	0.474

The cumulative scores across the 7 sub-scales will fall within the range of 0 to 21. A total score exceeding 5 in the entire questionnaire indicates suboptimal sleep quality. As illustrated in Fig. 3, a frequency distribution chart delineates the distribution of sleep quality among the sampled individuals in the study area. Notably, 72.5% of participants exhibited poor sleep quality, indicative of sleep disorders. Based on Table 1, the skewness and kurtosis coefficients of the primary research variables were within the acceptable range (-5 to 5) and (-3 to 3), respectively. This indicates that the data distribution in this study met the necessary conditions for normality, consequently, justifying the employment of parametric tests and software tools [37]. Table 2 presents the frequency distribution of sleep quality composition among the participants in the study area.

Table 2

Frequency Distribution of Sleep Quality Composition Among Sampled Individuals in the Study Area
	Frequency	Percent Frequency	Valid Percent	Cumulative Percent
Normal	28	27.5	27.5	27.5
With a sleep disorder	74	72.5	72.5	100.0
Total	102	100.0	100.0

The significance of sleep quality indices, assessed after implementation in the SPSS software, was examined using an independent samples t-test to explore differences between the study groups. Table 3 presents descriptive statistics for sleep quality components in the study sample for male and female groups. Despite variations in the means of several sleep quality indices in the sample, only daytime dysfunction in the male and female groups of the study community exhibited a statistically significant difference at a 95% confidence level, implying that female participants in the studied rural community significantly believed that mining activities disrupted their sleep quality and daily functioning. No significant differences were observed in other sleep disorder indices between the two study groups. Table 4 indicates a lack of statistically significant differences in sleep quality indices between the two cohorts of unmarried and married individuals.

Table 3

Comparison of Average Sleep Quality Index Scores in Two Groups of Women and Men
		Levene's Test for Equality of Variances		t-test for Equality of Means
		F	Sig	t	df	Sig. (2-tailed)	Mean Difference	Std. Error Difference	95% Confidence Interval of the Difference
		F	Sig	t	df	Sig. (2-tailed)	Mean Difference	Std. Error Difference	Lower	Upper
Subjective sleep quality	Equal variances assumed	0.489	0.486	1.259	100	0.211	0.236	0.187	-0.136	0.607
Subjective sleep quality	Equal variances not assumed			1.287	94.676	0.201	0.236	0.183	-0.128	0.599
Sleep latency	Equal variances assumed	0.036	0.851	-0.043	100	0.966	-0.010	0.244	-0.453	0.434
Sleep latency	Equal variances not assumed			-0.043	89.340	0.966	-0.010	0.223	-0.453	0.433
Sleep duration	Equal variances assumed	0.871	0.353	-1.230	100	0.222	-0.217	0.176	-0.566	0.133
Sleep duration	Equal variances not assumed			-1.252	93.522	0.214	-0.217	0.173	-0.560	0.127
Habitual sleep efficiency	Equal variances assumed	0.692	0.407	1.067	100	0.289	0.148	0.138	-0.127	0.422
Habitual sleep efficiency	Equal variances not assumed			1.057	85.482	0.293	0.148	0.140	-0.130	0.425
Sleep disorders	Equal variances assumed	4.256	0.042	0.949	100	0.345	0.088	0.093	-0.096	0.272
Sleep disorders	Equal variances not assumed			0.905	72.587	0.386	0.088	0.097	-0.106	0.282
Use of sleeping medications	Equal variances assumed	0.000	0.989	0.000	100	1.000	0.000	0.131	-0.259	0.259
Use of sleeping medications	Equal variances not assumed			0.000	86.889	1.000	0.000	0.131	-0.261	0.261
Daytime dysfunction	Equal variances assumed	17.756	0.000	-1.989	100	0.049	-0.207	0.104	-0.414	-0.001
Daytime dysfunction	Equal variances not assumed			-2.171	97.302	0.032	-0.207	0.095	-0.396	-0.018

Table 4

Comparison of Average Sleep Quality Index Scores in Two Groups of Single and Married Participants
		Levene's Test for Equality of Variances		T-test for Equality of Means
		F	Sig.	t	df	Sig. (2-tailed)	Mean Difference	Std. Error Difference	95% Confidence Interval of the Difference
		F	Sig.	t	df	Sig. (2-tailed)	Mean Difference	Std. Error Difference	Lower	Upper
Subjective sleep quality	Equal variances assumed	0.209	0.649	0.473	100	0.637	0.118	0.249	-0.376	0.611
Subjective sleep quality	Equal variances not assumed			0.501	24.254	0.621	0.118	0.235	-0.367	0.602
Sleep latency	Equal variances assumed	0.265	0.608	0.920	100	0.360	0.271	0.294	-0.313	0.854
Sleep latency	Equal variances not assumed			0.949	23.581	0.352	0.271	0.285	-0.318	0.859
Sleep duration	Equal variances assumed	0.043	0.837	1.111	100	0.269	0.259	0.233	-0.203	0.721
Sleep duration	Equal variances not assumed			1.040	21.580	0.310	0.259	0.249	-0.258	0. 776
Habitual sleep efficiency	Equal variances assumed	0.057	0.812	0.128	100	0.898	0.024	0.184	-0.341	0.388
Habitual sleep efficiency	Equal variances not assumed			0.115	20.939	0.909	0.024	0.204	-0.401	0.448
Sleep disorders	Equal variances assumed	4.243	0.042	0.478	100	0.633	0.059	0.123	-0.185	0.303
Sleep disorders	Equal variances not assumed			0.352	18.554	0.729	0.059	0.167	-0.292	0.409
Use of sleeping medications	Equal variances assumed	6.833	0.010	1.307	100	0.194	0.224	0.171	-0.116	0.563
Use of sleeping medications	Equal variances not assumed			0.898	18.010	0.381	0.224	0.249	-0.299	0.747
Daytime dysfunction	Equal variances assumed	2.713	0.103	-0.757	100	0.451	-0.106	0.140	0.383	0.172
Daytime dysfunction	Equal variances not assumed			-0.943	30.002	0.353	-0.106	0.112	0.335	0.124

Table 5 reveals that, despite variations in the means of several sleep quality indices within the sample, only the utilization of sleep medications in the employed and unemployed cohorts in the investigated community exhibited a statistically significant difference at a 95% confidence level. This suggests that the employed subgroup in the study village perceived a substantial impact of mining activities on their sleep quality, compelling them to resort to sleep medications to mitigate the ensuing sleep disorders. Nevertheless, it is crucial to note that many employed individuals, particularly those engaged in the male community of the village and employed in mining, confront various detrimental factors, such as noise, which could influence these individuals and contribute to the repercussions of mining activities.

Table 5: Comparison of Average Sleep Quality Index Scores in Two Groups of Employed and Unemployed participants
		Levene's Test for Equality of Variances		t-test for Equality of Means
		F	Sig.	t	df	Sig. (2-tailed)	Mean Difference	Std. Error Difference	95% Confidence Interval of the Difference
		F	Sig.	t	df	Sig. (2-tailed)	Mean Difference	Std. Error Difference	Lower	Upper
Subjective sleep quality	Equal variances assumed	0.041	0.840	-0.391	100	0.696	-0.074	0.189	-0.448	0.300
Subjective sleep quality	Equal variances not assumed			− .0392	88.871	0.696	-0.074	0.188	-0.448	0.300
Sleep latency	Equal variances assumed	0.187	0.667	0.224	100	0.824	0.050	0.224	-0.394	0.494
Sleep latency	Equal variances not assumed			0.222	85.846	0.825	0.050	0.225	-0.398	0.498
Sleep duration	Equal variances assumed	0.041	0.840	0.537	100	0.592	0.095	0.177	-0.256	0.447
Sleep duration	Equal variances not assumed			0.533	85.688	0.596	0.095	0.179	-0.260	0.451
Habitual sleep efficiency	Equal variances assumed	.162	0.688	-0.480	100	0.633	-0.067	0.139	-0.342	0.209
Habitual sleep efficiency	Equal variances not assumed			-0.473	84.053	0.637	-0.067	0.141	-0.347	0.213
Sleep disorders	Equal variances assumed	2.343	0.129	-0.077	100	0.939	-0.007	0.093	-0.192	0.178
Sleep disorders	Equal variances not assumed			-0.072	68.879	0.943	-0.007	0.099	-0.204	0.190
Use of sleeping medications	Equal variances assumed	0.000	0.989	0.000	100	1.000	0.000	0.131	-0.259	0.259
Use of sleeping medications	Equal variances not assumed			0.000	86.889	1.000	0.000	0.131	-0.261	0.261
Daytime dysfunction	Equal variances assumed	11.487	0.001	1.589	100	0.115	0.167	0.105	-0.041	0.375
Daytime dysfunction	Equal variances not assumed			1.718	98.971	0.089	0.167	0.097	-0.026	0.359

The TVALUE or SIG value indicates the confirmation of the null hypothesis (H₀) and the rejection of the alternative hypothesis (H₁). In contrast to the sample, where cognitive performance indices exhibited significant differences between day and night shifts across multiple metrics, with a slightly more pronounced discrepancy in the night shift, the broader community did not show any significant differences in cognitive functions between day and night shifts.

$$\left\{\begin{array}{c}{H}_{0}:{\mu }_{1}={\mu }_{2}\\ {H}_{1}:{\mu }_{1}\ne {\mu }_{2}\end{array}\right.$$

The coefficients of skewness and kurtosis for the main variables in the research fall within the acceptable range (-5 to 5 for skewness and − 3 to 3 for kurtosis), suggesting a sufficient condition for the normality of the data distribution in this study. In other words, both variables exhibit a relative distance measurement scale, indicating a quantitative nature. Since they conform to the normal distribution pattern in the frequency distribution of data, the necessary and sufficient conditions are met, consequently justifying the employment of parametric tests and software. Given the research objectives, which ultimately aim to predict the behavior of dependent or endogenous variables related to sleep disorders in the studied community, a Multiple Indicators Multiple Causes (MIMIC) model has been formulated. Figure 4 illustrates the MIMIC model and research assumptions. The research poses two hypotheses: The research hypothesis (H₀) in statistical language asserts that exposure does not impact sleep. The second hypothesis (H₁), or the researcher's hypothesis, posits the influence of mining on the studied indicators. As researchers, the goal is to reject H₀ and accept H₁. The criterion for accepting the H₁ hypothesis is when α < 0.05. If α > 0.05, H₀ is accepted. Here, α represents the research error from the sample data to the population. Therefore, these hypotheses are tested considering the influence of the community sample on the study indicators.

Table 6 presents the examination results of research hypotheses concerning the influence of mining activities on the sleep quality of residents in the vicinity of the mine. Although 74 individuals (72.5%) among the residents near the mining site reported experiencing sleep disorders, no significant differences were observed across demographic indicators, including age, gender, and marital status. For instance, the comparison of sleep disorders between the two groups of men and women in the studied community did not reveal a statistically significant difference, even though such a difference was observed in the sample under study. The table indicates that instances with a p-value of < 5% have had a substantial impact on the studied community.

Table 6

Hypothesis Testing in the Relationship between the Impact of Mining Activities on the Sleep Quality of Residents around the Mine
Hypothesis	β	S.E.	Est./S.E	P-Value
Age	0.051	0.072	0719	0.472
Gender	-0.199	0.174	-1.145	0.252
Marital status	-0.198	0.175	-1.130	0.259
Education Residence records	-0.004 0.081	0.050 0.087	-0.081 0.930	0.935 0.352
Employment	-0.235	0.197	-1.195	0.232

The current investigation, titled "The Impact of Surface Mining on Sleep Disorders in Nearby Residential Areas," was conducted to explore the adverse effects of mining activities on the residents' quality of life and the influence of mining-induced explosions on sleep quality. Descriptive statistics of sleep quality components in the study sample for both men and women revealed a statistically significant difference at a 95% confidence level only in the domain of daily functioning disturbance among, despite variations in the means of several sleep quality indices within the sample. This suggests that women in the studied village significantly attributed sleep disorders affecting their daily functioning to mining activities. Moreover, no notable disparities were identified in sleep quality indices between single and married participants. The sole statistically significant difference at a 95% confidence level was attributed to the utilization of sleep medications in the employed and unemployed groups within the studied community. This indicates that the employed subgroup in the study village significantly associated mining activities with an impact on their sleep quality, prompting them to resort to sleep medications to mitigate the resultant sleep disorders. It is noteworthy that many employed individuals, especially those from the male community in the village engaged in mining, contended with various adverse factors such as explosions and noise, potentially contributing to the impact of mining on residents in the area. While approximately 72.5% or 74 individuals in the mining area reported experiencing sleep disorders, no statistically significant differences were observed in demographic indicators such as age, gender, and marital status. Despite observed differences in sample averages, these demographic factors did not exhibit a significant impact. Exposure to noise, particularly in public living environments, is on the rise, both in industrialized and developing regions globally [38]. Peplow et al.'s (2021) study, focusing on continuous noise exposure, revealed that approximately 125 million Europeans endured sound pressure levels exceeding 55 decibels, contributing as a detrimental factor to residents' health. Disturbing noise is associated with cognitive and behavioral disorders, as well as more pronounced impairments in hearing and sleep deprivation [39]. Elgstrand et al.'s (2017) investigation further highlighted that the consequences of noise on mine workers, including issues like hearing loss, could be regarded as a chronic ailment, causing considerable annoyance and stress for those exposed to prolonged noise. Moreover, the inability to hear moving machinery and warnings among mine workers poses a significant safety hazard [40]. Research conducted by Mokhtar et al. (2007), aiming to assess the impact and extent of noise on workers in the rubber and metal industries in Malaysia, utilized a questionnaire focusing on the effects of sound on individuals. The findings indicated that 88% of workers in the rubber industry and 66% in the metal industry experienced hearing problems attributable to noise. Additionally, 35% of rubber industry workers and 55% of metal industry workers reported sleep disorders [41]. In a cross-sectional study conducted by Omidi et al. (2017) to investigate the relationship between shift work and health effects and job satisfaction among mining industry workers in southwest Iran, the questionnaire-based research demonstrated a statistically significant difference between shift workers and those with fixed daily schedules. This difference was associated with fatigue and sleep disorders, with approximately 13% of shift workers resorting to sleeping pills to facilitate daytime sleep [42]. The results of Baffoe et al.'s (2022) study, conducted through a triple questionnaire in the Tarkwa mining area in southwest Ghana to examine noise levels and their effects using a sound meter, revealed that noise had a 58% impact on mental stress, 62% on sleep, 84% on hearing, 76% on lack of concentration, and 79% on cardiovascular effects [43]. Bakker et al.'s (2012) investigation, which aimed to analyze the impact of environmental noise on residents living near wind turbines in the Netherlands through a questionnaire, indicated that 48% of respondents reported experiencing sleep disorders. As sound pressure levels increased, the prevalence of sleep disorders also rose, highlighting the adverse effects of noise on individuals residing near wind turbines [44]. In a cross-sectional observational study by Test et al. (2011), exploring the influence of hearing damage on the sleep quality of industrial workers exposed to disruptive noise in Israel, results from a validated questionnaire revealed that 30% of the impairment score for sleep was linked to workers over the age of 50. Additionally, 75% of workers identified tinnitus as the primary cause of sleep disorders. While tinnitus played a significant role, hearing disorders independently contributed to sleep disorders, especially insomnia, in workers exposed to occupational noise, regardless of age and years of exposure [45]. Ribet & Derriennic (1999) investigated the effects of occupational factors on the incidence of sleep disorders over a 5-year period in 7 regions of France through a survey, suggesting that among the objective occupational risk factors, shift work, working weeks often exceeding 48 hours, and exposure to vibrations were the main contributors to sleep disorders. The prevalence of sleep disorders increased from 19.1–21.0% among men and from 25.7–29% among women [46]. Taoussi et al. (2022) conducted a cross-sectional study to assess the impact of noise exposure on industrial workers at the N'Djamena power plant in Chad, highlighting that the noise pollution levels in this area were exceptionally high and posed a serious threat to human health. Workers at the power plant were exposed to elevated noise levels, leading to adverse health effects. The consequences of noise pollution included fatigue, hearing issues (38%), tinnitus (32.6%), hearing loss (15.2%), nervousness (45.7%), headaches (33.7%), and insomnia (14.1%) [47]. In Farooqi et al.'s (2020) study, investigating noise pollution levels in different areas of a major industrial city in Pakistan, a survey was conducted near sampling points to gauge the general perception of local residents. The results indicated that sound pressure levels measured during morning, afternoon, and evening hours exceeded acceptable limits. According to the survey, 94% of respondents reported experiencing headaches, 76% insomnia, 74% high blood pressure, 74% physiological stress, 64% an increase in blood pressure levels, and 60% dizziness [48]. Demirtaş et al. (2021) conducted a study to assess the hearing levels, sleep quality, depression, and quality of life among employees at an ammunition factory in Kirikale province using standard questionnaires. Questionnaire scores indicated that 60.9% of the workers experienced severe sleep problems, 11.50% moderate, 8.80% mild, and 18.80% no sleep problems [49]. In a cross-sectional descriptive-analytical study by Mokarami et al. (2020), conducted in an Iranian brick factory, the relationships between occupational and environmental factors, along with various social and psychological risks, with sleep disorders were analyzed. The study investigated disturbing noise, respirable particles, light, and heat stress, demonstrating that sleep disorders, particularly the prevalence of sleepiness, were higher among workers exposed to elevated noise levels than those exposed to lower noise levels. The Epworth Sleepiness Scale (ESS) revealed that participants at a higher risk of sleepiness (ESS > 10) were more exposed to environmental and psychosocial stressors, especially factors like sound and heat stress. Respirable dust, measured using standard objective methods, showed a moderate correlation with sleep disorders [50]. A comprehensive review of previous studies revealed that noise pollution and persistent exposure to irritating and unwanted sound levels may affect both humans and other living organisms adversely. Prolonged exposure to low or high noise levels can elevate stress hormone levels, contributing to detrimental effects on mental health, including psychological stress associated with cardiovascular complications, sleep disorders, concentration problems, nervousness, depression, anxiety, fatigue, uncertainty, irritation, reduced work capacity, and disturbances in interpersonal relationships [23]. It is important to note that few studies have explored the effects of industrial activities, especially in large industries like mines, on sleep disorders among residents living in proximity to such sites. Residents in these environments, more than predictable and controlled sounds, are disturbed by uncontrolled human activities, potentially leading to sleep disorders and psychological problems.

One of the most significant impacts of mining activities on individuals is the prevalence of mental health disorders, with sleep disorders being among the most common conditions. The blasting operations in mining extraction are considered a crucial stage in the operational cycle. The released energy from a mining explosion results in seismic energy spreading in the surrounding environment. Environmental noise is identified as a major factor contributing to sleep disorders. The nature of mines, their location, social and environmental characteristics, social cohesion, safety, noise pollution, regional disturbances, and physical features such as light, noise, traffic, and pollution can influence sleep patterns in both adults and children. Therefore, the present study explored and investigated the consequences of mining activities on sleep quality indices in one of the gold mines in Iran. Relevant data were collected from the target sample through a literature review and utilizing standard questionnaires. The study results revealed that 72.5% of individuals residing in the mining area reported complaints of sleep disorders, indicating that mining activities, including physical factors, noise, explosions, and vibrations, had the most significant impact on sleep quality indices. Moreover, only daily functioning disturbances in the studied group of men and women showed a significant difference, implying that the female group in the studied rural area significantly attributed mining activities to sleep disorders, consequently affecting their daily functioning. Additionally, no significant difference was observed in sleep quality indices between the two groups of single and married participants. Furthermore, the use of sleep medications in the employed and unemployed groups showed a significant difference in the studied community, suggesting that the employed group in the rural area significantly attributed mining activities to a meaningful impact on their sleep quality, forcing them to use sleep medications to address the resulting sleep disorders. Therefore, mines should define and implement goals and plans based on the realities of their coverage area to minimize their consequences. The present study, like other research, had limitations, with one of them being the small sample size. Future studies can explore sleep quality indices in larger sample sizes. Another limitation is that this research solely relied on standard questionnaires, and future studies may benefit from various tools such as observation and examination of common illnesses and health records from health centers. The target group in this study was not homogeneous, lacking, for example, a high level of literacy, which made understanding the questions difficult for them. To address this issue, detailed explanations for each question were necessary for the sample members. Moreover, the Pittsburgh Sleep Quality Index questionnaire was utilized in this study, which had a high number of questions. The multitude of variables and, consequently, the large number of questions may not affect the accuracy of participant responses. Therefore, an attempt was made to manage this limitation by categorizing reflective and compound questions and using short-item questions to the extent possible, in line with current research conventions.

Author Contribution

Methodology, H.D. and A.K.; Software, K.A.; Validation, H.D., A.K.; Formal analysis, P.A.and K.A.; Investigation, H.D., K.A.; Data curation, A.K.; Writing—original draft, K.A.;Supervision, H.D. All authors have read and agreed to the published version of the manuscript.

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No competing interests reported.

The Impact of Mining Activities on the Sleep Quality of Adjacent Residential Areas (Case Study: Gold Mine)

Status:

Version 1

Abstract

Figures

1-Introduction

2-Methods

1-2- Data Collection

2-2- Data Analysis

3- Results

4-Discussion

5- Conclusion

Declarations

Author Contribution

References

Additional Declarations

Status:

Version 1