Experimental Research on the Influence of Short-Term Noise Exposure on Miners’ Physiology

Abstract

Coal mine noise affects human physiology, psychology, and behavior. It causes errors at work and increases accidents. In this study, we built a coal mine noise simulation experiment system. The system not only included an experimental environment simulation system and a physiological indicator test system, but it also added a miners’ working simulation system. This paper aimed to study the effect of different short-term (25 min) noise levels (60 dB, 70 dB, 80 dB, 90 dB, and 100 dB) on human physiology (skin conductivity and heart rate). Critical analysis showed that the stronger the noise intensity is, the shorter the contact time it takes for physiological indicators to present significant changes, and by setting different noises and measuring the skin conductivity and heart rate of human body, it was concluded that the noise level should be reduced to 90 dB to reduce accidents of miners.

1. Introduction

All countries in the world regard coal as an important energy source. Coal consumption accounts for 60% of the total energy consumption in China. However, coal mine accidents in China represent more than 70% of the world’s [1]. As we all know, coal mining is recognized as one of the most dangerous industries [2,3]. The national statistics of coal mine accidents and injuries can show the danger of coal mine operations [4]. In addition, mining companies may even go bankrupt or suffer heavy losses due to accidents [5]. Human factors are related to industrial accidents and heavy losses [6,7,8,9]. Statistics show that major coal mine accidents in China were caused by unsafe human behavior [10]. The unsafe behavior of miners is related to various environmental factors (such as high temperature, low light, and high dust density), and noise is one of them [11].

Modern coal mines often use noisy mechanical equipment, but thick walls, narrow roadways, and closed working spaces make it difficult for noise to disperse or be absorbed. Usually, the noise level in coal mines exceeds the maximum noise value (85 dB/A)(work 5 d a week, 8 h a day, the steady-state noise limit is 85 dB (A)) in the “Occupational Exposure Limits for Hazardous Agents in The Workplace-Physical Factors” (GBZ2.2-2007). In such a high-level noise environment, human psychology and physiology change. Human psychological and physiological systems are interrelated and interact with each other. If a person is in poor physical condition, it affects his mental state. On the other hand, poor mental condition affects the disorder of the physiological system to a large extent. In addition, the external environment affects the overall state of the psychological and physiological system and ultimately affects behavior and operation [12]. Noise can easily lead to the formation of pressure, thus damaging personal physical and mental health, affecting work efficiency, and increasing the probability of accidents.

Previous studies have shown that noise affects human physiology, psychology, and behavior. Physiologically, studies [13,14,15,16] opined that noise could cause human hearing loss and affect verbal communication. Liu et al. [17] expressed that noise affects miners’ blood tension and hearing. Babisch et al. [18] stated that the incidence rate of cardiovascular diseases increased significantly with the increase of noise. Additionally, noise affects human physiological indicators. Alanalyn N et al. [19] used the change in pupil dilation response of participants caused by each type of noise as an assessment of physiological stress response to noise stimulation. Lusk, SL et al. [20,21] found that heart rate was significantly positively associated with noise exposure. Sajad Zare et al. [22] found that increasing noise SPL resulted in increased serum cortisol concentrations; S, H. Park et al. [23] found that the noise level was significantly associated with skin conductivity and RR changes but not to heart rate. Jing Guoxun et al. [24] found that RRI and HRV decreased, sympathetic nervous system activity increased, and HR increased with increasing noise level. Psychologically, Mohapatra, H et al. [25] believed that the human annoyance increased with increasing noise levels. Wang, FS et al. [26] found that noise induces tension, depression, boredom, and anger, which bring about unpleasant moods and reduce accuracy in judging the degree of environmental safety, causing a breach of regulations. In terms of safety behavior, C. Clark et al. [27] pointed out that exposure to noise increases children’s noise problems, and reading and understanding ability also depends on exposure to noise. Jing et al. [28] said that the behavioral reliability of fully mechanized coal mining operators is high with a low accident occurrence rate under the noise of 50~70 dB. Under 70~90 dB, their behavioral reliability is with potential accident risks. The behavioral reliability is low when noise is 90~110 dB, under which accidents may easily take place; Li et al. [29] opined that increased levels of environmental noise affect indicators of safe behavior capacity, and the test indicators of attention, fatigue, and reaction significantly change when the noise exceeds 70–80 dB.

The above research shows that a person’s physical and mental health is affected by noise, which can easily lead to unsafe behaviors of miners. However, previous studies have had two limitations. First, some focused on the analysis of the occupational hazard of noise and failed to study the impact of noise on humans from an objective perspective. Second, they were unable to research whether and how short-term noise exposure under different noise levels affects human physiological indicators. To make up for the lack of previous studies, we built a coal mine noise simulation system and studied how short-term noise exposure (0–5 min, 10–15 min, 20–25 min) affects human indicators at five noise levels (60 dB, 70 dB, 80 dB, 90 dB, 100 dB). At the same time, we hope to provide new ideas to take noise protection measures (especially shifting systems for workers), prevent unsafe behaviors, and reduce accidents.

2. Methods

2.1. Selecting Physiological Indicators

In this quantitative research, skin conductivity and heart rate were selected as research indicators according to the experimental conditions and the relevant literature [21,23].

Skin conductivity (SC) is the potential difference between two points on a person’s complexion. It is closely related to human emotions. When people are nervous or eager, caring nerves are activated, perspiration discharge increases, complexion freedom fighters decrease, and skin conductivity increases. Psychologists believe that skin conductivity can be used as a physiological indicator for measuring emotions. This is because the skin’s electrical conductivity is controlled by the autonomic nervous system [30].

Pulse is the number of beats per minute. It is closely related to stress. When a human is under pressure, their blood flow becomes faster, which increases the heart rate.

2.2. Establishing an Experimental System

Our experiment system consisted of a simulation system and a physiological indicator test system.

(1)

Miners’ working intensity simulation system

According to studies [31], the labor intensity of miners is moderate or high. Reflections on West German coal mines’ labor intensity [32] defined that the metabolic value of medium labor intensity ranges from 232~348 W, and the high labor intensity is above 348 W. Thus, we put the above data into the metabolism formula established by Pandolf [33] (Equation (1)) and calculated that the speed of the treadmill should be 5 km/h. In the experiment, as shown in Figure 1, the work intensity of underground miners was simulated by letting the subjects walk on a treadmill.

$$M_W=1.5W+2.0\left(W+L\right){\left(\frac{L}{W}\right)}^2+\eta \left(W+L\right)\left(1.5V^2+0.35VG\right)$$

(1)

where M_W is metabolic rate (watts); W is subject weight (kg); L is load carried (kg); η is terrain factor (η = 1.0 for treadmill); V is speed of walking (m·s⁻¹); G is grade (%).

(2)

Coal mine noise simulation experiment system

In this experiment, we recorded the real coal mine noise as the noise source and controlled noise levels through a speaker and a sound level meter (see

Table 1. Noise test equipment.

Noise System Device Name	Equipment Model
Computer	Dell–Inspiron 7557
Louder speaker box	JBL–Charge4
Sound meter	TES1350A

). The experiment temperature was at 25 °C, the humidity 50%, and the wind speed was 0.25 m/s. These can make sure experimental results are free from any changes in temperature, humidity, and wind speed [34].

Our experiment originally set 8 noise levels, a control group (50 dB), and seven experimental groups (60 dB, 70 dB, 80 dB, 90 dB, 100 dB, 110 dB, and 120 dB). First, we tested the calm state in the laboratory and found the noise level was 50 dB; thus, we set the control group as 50 dB. Second, we set 80 dB, 90 dB, 100 dB, 110 dB, and 120 dB as experimental groups because our field investigation of Shanxi Province, China found that the range of coal mine noise is mainly 80 dB~120 dB. Thirdly, we added 60 dB and 70 dB because we believe it helps to discuss the general effects on miner physiology and would improve the credibility of the experiment’s credibility. Unfortunately, 7 subjects of 12 showed serious nausea, felt dizzy, or vomited after 5 min under 100 dB. For safety concerns, the 110 dB and 120 dB tests were canceled, and the test of 100 dB was 0–5 min.

(3)

Physiological indicator test system

The physiological indicator test system consisted of an ErgoLAB human–machine environment synchronization platform and wireless physiological sensors. First, the skin conductivity, heart rate, and other physiological indicators of the subjects were measured by wireless physiological sensors (see

Table 2. Test indicators and equipment.

Test Indicator	Equipment Name	Equipment Model	Manufacturer
Heart rate	ErgoLAB wireless blood volume pulse sensor	ErgoLAB-PPG/BVP	KINGFAR, Beijing, China
Skin conductance	ErgoLAB wireless skin conductance sensor	ErgoLAB-EDA

and Figure 2). Then, the data were synchronously collected and stored by the ErgoLAB human–machine environment platform.

2.3. Selecting Subjects

Based on this study [35], researchers selected 12 participants. Considering the particularity of the miners, the volunteers were men. They were (22.5 ± 2.5) years old and weighed (56 ± 10) kg, which avoids, to a certain extent, the individual differences due to age, body type, etc. In addition, participants were healthy. Volunteers should have voluntarily participated in the study without compulsion. A total of 12 subjects were numbered sequentially. They were not allowed to take any medicine during the test to make sure not to overstimulate their nervous system. They kept an adequate sleep of no less than 8 h [36] before the 24 h of this experiment and wore comfortable clothes.

2.4. Experimental Process

The experiment was divided into two stages, before the experiment and while experimenting.

Preparation before the experiment was as follows: (a) We contacted the subjects, explained what they need to pay attention to, and taught them how to use instruments. (b) We set the experimental environment, adjusted the laboratory temperature, humidity, and wind speed, and debugged tools.

During the experiment, except for researchers, volunteers, and recorders, other personnel were temporarily evacuated from the experimental site. The proper experimental steps were as follows: (a) 2 subjects wore the EDA wireless skin sensors and PPG/BVP wireless blood volume pulse sensors with the help of the researcher. (b) The volunteer adapted to a quiet test environment (50 dB) for 30 min before the test to ensure they were in perfect condition. (c) After 30 min, the researcher measured volunteers’ physiological indicators in a quiet environment (50 dB) for about 3 min. At the same time, two subjects were asked to continuously watch the indicators displayed on the computer screen within three minutes. (d) The researcher changed the noise level to 60 dB and collected the subjects’ physiological indicators when subjects were watching the computer screen (as a working condition). This step continued for 30 min. (e) After the first test, we removed the sensors, debugged them, and asked the 2 subjects of the second group to wear the sensors. We conducted the second group by repeating (a)–(d). We all together performed 6 tests and tested the indicators of 12 subjects. (f) After all the 12 tests, we saved the test results. (g) We turned off the ErgoLAB human-machine environment platform, debugged all the instruments, and put everything in the experimental environment in its right place. (h) We conducted 60 dB/70 dB/80 dB/90 dB/100 dB tests at the same period (8.00–11:30 and 2:00–5:30) in the following 4 days. The experiment process was the same as (a)–(g).

It is very noteworthy that we performed the following things to make sure the results were credible: (a) The tests of the 5 experimental groups were carried out separately. That is to say, we tested indicators on only one noise level in 1 d to avoid noise adaptability. (b) We conducted a control group test before the tests of each experimental group. This minimized the possible errors caused by subjects’ different conditions every day. (c) The test time was between 8:00–11:30 and 14:00–17:30 every day, and the experiment lasted for 5 days. (d) Each subject placed the wireless physiological sensors in the same place.

2.5. Analytical Method

In this paper, noise levels and exposure time were independent variables; skin conductivity and heart rate were dependent variables. A bar graph was used to show the distribution of all indicators and the variation trend of their mean values. SPSS26.0 was used to perform paired sample t-test analysis. It could determine when the hands were statistically significant and when they were not under different noise levels. We checked the normality of the physiological data before the paired sample t-test because we can perform a paired sample t-test only when statistics show normal distribution.

3. Results

3.1. Skin Conductivity

Figure 3 shows how skin conductivity changed with time under five noise levels. We can see that overall skin conductivity increased exposure time. Skin conductivity under all five noise levels changed relatively rapidly at a particular time, but the specific time when the rapid changes occurred was somehow different. When the noise was at 60 dB, skin conductivity sharply rose in 20–25 min. At 70 dB and 80 dB, skin conductivity did not sharply rise until 10–15 min. At 90 dB and 100 dB, skin conductivity sharply increased in the first 0–5 min. We further determined the statistical significance of the above changes through the paired sample t-test results to speculate the reasons for the trend change.

Table 3. Normality test results of skin conductivity.

Items	Shapiro-Wilk			Decision-Making
	Statistics	df	Significant p
Control	0.947	12	0.598	Accept H0
t0–5/60 dB	0.938	12	0.478	Accept H0
t10–15/60 dB	0.955	12	0.703	Accept H0
t20–25/60 dB	0.956	12	0.732	Accept H0
Control	0.940	12	0.501	Accept H0
t0–5/70 dB	0.949	12	0.621	Accept H0
t10–15/70 dB	0.951	12	0.646	Accept H0
t20–25/70 dB	0.957	12	0.740	Accept H0
Control	0.933	12	0.414	Accept H0
t0–5/80 dB	0.938	12	0.470	Accept H0
t10–15/80 dB	0.954	12	0.694	Accept H0
t20–25/80 dB	0.941	12	0.508	Accept H0
Control	0.921	12	0.294	Accept H0
t0–5/90 dB	0.950	12	0.638	Accept H0
t10–15/90 dB	0.929	12	0.371	Accept H0
t20–25/90 dB	0.954	12	0.700	Accept H0
Control	0.957	12	0.736	Accept H0
t0–5/100 dB	0.960	12	0.786	Accept H0

shows the normality test results of skin conductivity. The null hypotheses H0 are the skin conductivity of the control group, 0–5 min at 60 dB/70 dB/80 dB/90 dB/100 dB, 10–15 min at 60 dB/70 dB/80 dB/90 dB and 20–25 min at 60 dB/70 dB/80 dB/90 dB, and they were normally distributed. We can see that under the test standard α = 0.05, the p-value of the S-W test for skin conductivity in different noise levels and their different test periods was greater than 0.05; thus, the null hypotheses H0 were accepted. That is to say, the distribution of skin conductivity was normal, and we were able to perform the paired sample t-test.

Table 4. Paired sample t-test results of skin conductivity.

Matching Design	Pairing Difference					t	Degree of Freedom	Significant p (Two-Tailed)	Decision-Making
	Average Value (E)	Standard Deviation	Standard Error Mean	95% Confidence Interval for the Difference
				Lower Limit	Upper Limit
Control-t0~5/60 dB	−0.04167	0.15976	0.04612	−0.14318	0.05984	−0.903	11	0.386	Accept H0
Control-t10~15/60 dB	−0.11417	0.23956	0.06916	−0.26638	0.03804	−1.651	11	0.127	Accept H0
Control-t20~25/60 dB	−4.14667	1.18646	0.34250	−0.490051	−3.39283	−12.107	11	0.000	Reject H0
Control-t0~5/70 dB	−0.11500	0.22359	0.06454	−0.25706	0.02706	−1.782	11	0.102	Accept H0
Control-t10~15/70 dB	−3.72250	1.10470	0.31890	−4.42439	−11.673	−11.673	11	0.000	Reject H0
Control-t20~25/70 dB	−5.03833	1.74591	0.50400	−6.14763	−9.997	−9.997	11	0.000	Reject H0
Control-t0~5/80 dB	−0.08583	0.14817	0.04277	−0.17997	0.00831	−2.007	11	0.070	Accept H0
Control-t10~15/80 dB	−3.11500	0.87780	0.25340	−3.67273	−2.55727	−12.293	11	0.000	Reject H0
Control-t20~25/80 dB	−5.31000	0.27414	0.27414	−5.91337	−4.70663	−19.293	11	0.000	Reject H0
Control-t0~5/90 dB	−4.31000	1.33697	0.38595	−5.15947	−3.46053	−11.167	11	0.000	Reject H0
Control-t10~15/90 dB	−6.04167	1.47611	0.42612	−6.97954	−5.10379	−14.178	11	0.000	Reject H0
Control-t20~25/90 dB	−6.82917	1.83239	0.52897	−7.99341	−5.66492	−12.910	11	0.000	Reject H0
Control-t0~5/100 dB	−6.32333	1.64988	0.47628	−7.37162	−5.27505	−13.667	11	0.000	Reject H0

shows the results of the paired sample t-test analysis of skin conductivity. The null hypotheses H0 were that there was no significant difference between the skin conductivity of 0–5 min/10–15 min/10–15 min at 60 dB/70 dB/80 dB/90 dB/100 dB and that of the control group. We can see that the higher the noise levels were, the shorter the contact time it took for skin conductivity to present significant changes. In detail, under the test standard α = 0.05, p < 0.05 was in 20–25 min at 60 dB; p < 0.05 was in 10–15 min at both 70 dB and 80 dB; p < 0.05 was in the first 0–5 min at both 90 dB and 100 dB. This shows that the changes in the box plot in Figure 3 were statistically significant. Thus, it can be imagined that the increase in noise intensity and exposure time makes the human body easily nervous, anxious, and mentally sweating, resulting in a decrease in skin resistance and an increase in skin conductivity.

3.2. Heart Rate

Figure 4 shows how heart rate changed with time under five noise levels. We can see that, overall, heart rate increased with the rise of exposure time, but this trend was not as strong as skin conductivity. The heart rates under all five noise levels changed relatively rapidly at a particular time, but the specific time when the rapid change occurred was somehow different. When noise was at 60 dB, the heart rate slightly increased, but there was no drastic change within 25 min. At 70 dB, the heart rate sharply rose in 20–25 min. At 80 dB and 90 dB, heart rate drastically grew in 10–15 min. At 100 dB, the heart rate sharply grew in the first 0–5 min. We further determined the statistical significance of the above changes through the paired sample t-test results to speculate the reasons for the trend change.

Table 5. Normality test results of heart rate.

Items	Shapiro-Wilk			Decision-Making
	Statistics	df	Significant p
Control	0.949	12	0.626	Accept H0
t0–5/60 dB	0.950	12	0.631	Accept H0
t10–15/60 dB	0.962	12	0.806	Accept H0
t20–25/60 dB	0.954	12	0.689	Accept H0
Control	0.931	12	0.392	Accept H0
t0–5/70 dB	0.946	12	0.574	Accept H0
t10–15/70 dB	0.927	12	0.349	Accept H0
t20–25/70 dB	0.951	12	0.648	Accept H0
Control	0.943	12	0.537	Accept H0
t0–5/80 dB	0.959	12	0.773	Accept H0
t10–15/80 dB	0.918	12	0.267	Accept H0
t20–25/80 dB	0.938	12	0.470	Accept H0
Control	0.947	12	0.590	Accept H0
t0–5/90 dB	0.940	12	0.499	Accept H0
t10–15/90 dB	0.932	12	0.401	Accept H0
t20–25/90 dB	0.960	12	0.778	Accept H0
Control	0.941	12	0.505	Accept H0
t0–5/100 dB	0.937	12	0.456	Accept H0

shows the normality test results of heart rate. The null hypotheses H0 are the heart rate of the control group, 0–5 min at 60 dB/70 dB/80 dB/90 dB/100 dB, 10–15 min at 60 dB/70 dB/80 dB/90 dB, and 20–25 min at 60 dB/70 dB/80 dB/90 dB, and they were normally distributed. We can see that, under the test standard α = 0.05, the p-value of the S-W test for heart rate in different noise levels and their different test periods was greater than 0.05; thus, the null hypothesis H0 was accepted. That is to say, the distribution of heart rate was normal, and we were able to perform the paired sample t-test.

Table 6. Paired sample t-test analysis results of heart rate.

Matching Design	Pairing Difference					t	Degree of Freedom	Significant p (Two-Tailed)	Decision-Making
	Average Value (E)	Standard Deviation	Standard Error Mean	95%Confidence Interval for the Difference
				Lower Limit	Upper Limit
Control-t0~5/60 dB	−0.25000	0.75378	0.21760	−0.72893	0.22893	−1.149	11	0.275	Accept H0
Control-t10~15/60 dB	−0.33333	1.07309	0.30977	−1.01514	0.34847	−1.706	11	0.305	Accept H0
Control-t0~5/70 dB	−0.34323	0.88763	0.25624	−0.89730	0.23064	−1.301	11	0.220	Accept H0
Control-t10~15/70 dB	−0.41637	0.99620	0.28758	−1.04963	−0.21629	−1.449	11	0.175	Accept H0
Control-t0~5/80 dB	−0.41667	1.08362	0.31282	−1.10517	0.27184	−1.332	11	0.210	Accept H0
Control-t10~15/80 dB	−4.58333	1.78164	0.51432	−5.71533	−3.45133	−8.912	11	0.000	Reject H0
Control-t0~5/90 dB	−0.58333	1.08362	0.31282	−1.27184	−0.10517	−1.865	11	0.089	Accept H0
Control-t10~15/90 dB	−5.41667	1.67649	0.48396	−6.48186	−4.35148	−11.192	11	0.000	Reject H0
Control-t0~5/100 dB	−6.83333	2.08167	0.60093	−8.15596	−5.51071	−11.371	11	0.000	Reject H0

shows the results of the paired sample t-test analysis of heart rate. The null hypotheses H0 were that there was no significant difference between the heart rate of 0–5 min/10–15 min/10–15 min at 60 dB/70 dB/80 dB/90 dB/100 dB and that of the control group. We can see that the higher the noise levels were, the shorter the contact time it took for the heart rate to present significant changes. In detail, under the test standard α = 0.05, when the noise was at 60 dB, the p-value was greater than 0.05 within 25 min. At 70 dB, p < 0.05 was in 20–25 min. At 80 dB and 90 dB, p < 0.05 was 10–15 min in both. At 100 dB, p < 0.05 was in the first 0–5 min. Thus, the changes in the box plot in Figure 4 were statistically significant. Thus, it can be imagined that the increase in noise intensity and exposure time increases the psychological load on humans, making it easy to be nervous and impetuous and resulting in an increase in heart rate.

4. Discussion

In the high-risk coal mine environment, due to the particularity of their work, miners are under much greater pressure than ordinary workers, and the potential unintended consequences of accidents caused by stress disorders are even greater. As external influences and internal psychological conditions are easy to damage human performance, whether high-risk coal mine workers can take effective measures to mitigate and adjust their emotions and attitudes under noise conditions in a timely manner is crucial to recover losses, reduce losses, and control the scale of disasters. In risk assessment, environmental factors must be scientifically assessed.

Previous studies explored the impact of noise on miners through cognitive level data of attention, reaction time, and mental fatigue level. For a more in-depth study, we analyzed the impact of noise on miners based on data produced, making use of the principles of neuroscience. Through studying whether and how short-term noise exposure affects human physiological indicators, we compared when dramatic changes occurred in skin conductivity and heart rate under different noise levels and found that: (a) When the noise was at 60 dB, skin conductivity did not present significant growth until 20–25 min, while heart rate showed no significant change within 25 min. (b) When noise was at 70 dB, skin conductivity significantly increased in 10–15 min, while heart rate significantly rose in 20–25 min. (c) When noise was at 80 dB, skin conductivity and heart rate both presented as significantly increased in 10–15 min. (d) When the noise was at 90 dB, skin conductivity significantly changed in 0–5 min, while heart rate significantly changed in 10–15 min. (e) When the noise level was at 100 dB, skin conductivity and heart rate presented significant changes in 0–5 min. Relevant studies [21,23] also found that human heart rate, heart rate variability, skin conductivity, and other physiological indicators all change in a noisy environment. The center rate, heart rate variability, skin conductivity, and respiratory rate can reflect human comfort. Physiological indicators are widely used in the study of environmental factors.

In short, the noise intensity standard is not absolute, and it cannot be said that a noise environment lower than the noise intensity standard will not affect humans. An intensity of 60 dB~80 dB affects skin conductivity and heart rate relatively short-term, although this range is not beyond what is required by occupational health standards. Moreover, sometimes time spent working in a noisy environment is not long, but it may still have an impact on the human body. Additionally, noise protection is necessary despite shorter working hours or an environment of low-level noise(60 dB~80 dB), and the noise level should preferably be kept no higher than 90 dB in coal mines. We hope that this study can provide scientific basis for formulating noise occupational health standards and preventing accidents.

Considering the experimental operation, the researchers just selected the mining college students as volunteers. By reason of the influence of the cultural level, physiological and psychological quality, work experience, and other factors, students’ reactions to coal mine accidents may be different from miners’ reactions, and the changes of physiological indicators may also be different under different noise levels. In future research, the experiment can be further expanded to make the selected research volunteers closer to the actual situation of miners. In real coal mines, the walls have noise-absorptive and reflective effects, and this experiment did not consider this. However, it still can be proven that the collected coal mine noise is an adequate stressor. This aspect needs further research to use virtual reality technology to simulate a natural coal mine noise environment.

This research can also be further explored later. For example, in real accidents, individuals may suffer from physical injury and irritable psychological and irrational factors, which may lead to accidents. The possibility and extent of operational errors, inattention, and unreasonable behaviors caused by physiological and psychological changes of volunteers under different noise levels were not taken into account.

5. Conclusions

Based on human factor engineering, we selected two physiological indicators and studied how five noise levels affect the indicators in a short time. The results are as follows:

a

Skin conductivity and heart rate presented an overall increasing trend, with the increase of noise exposure time under five different noise levels, although the noise exposure time was short. The higher the noise level, the shorter the contact time required for significant changes in skin conductivity and heart rate.

b

An intensity of 60 dB~80 dB affects skin conductivity and heart rate in a relatively short-term aspect, although this range is not beyond what is required by occupational health standards. Thus, it is necessary to properly carry out safety regulations for noise prevention.

c

When the noise reached 90 dB or above, skin conductivity or heart rate significantly changed in the first 0–5 min. Without protection, it is necessary to control noise levels below 90 dB and reduce the long-term exposure of miners in such an environment.

d

We compared how much time it takes for skin conductivity and heart rate to present significant changes. We found that skin conductivity needs relatively less time to show substantial change under the five noise levels.

To conclude, from the perspective of accident prevention, we suggest coal mines pay attention to the noise protection of miners even though miners are in an environment of low-level noise. Additionally, noise protection is necessary despite shorter working hours. Notably, the noise level should preferably be kept no higher than 90 dB in coal mines without protection. These can effectively reduce accidents.