scholarly journals The relationship of Depression Anxiety and Stress Scale and Harmon Jones with Noise in Isfahan Steel Industry Workers

2021 ◽  
Author(s):  
Hadi ALIMORADI ◽  
Ruhollah FALLAH MADAVARI ◽  
Mahsa NAZARI ◽  
Reza JAFARI NODOSHAN ◽  
Mohammad Javad ZARE SAKHVIDI ◽  
...  
Keyword(s):  
Cognitive Dissonance ◽  
Sound Pressure Level ◽  
Steel Industry ◽  
Sound Pressure ◽  
Noise Pollution ◽  
Pressure Level ◽  
Control Group ◽  
Case Group ◽  
Wide Range ◽  
Relationship Of

Introduction: Loud noise is one of the harmful factors that affects industry workers seriously. In the steel industry, a wide range of equipment and machinery are used in the production processes, which are considered as the sources of annoying noise. Sound has immediate and delayed harmful effects on the process of concentration and increases blood pressure. The aim of this study was to investigate the effect of noise in two different ranges in the control and case groups within the authorized (between 60 to 85 dB) and unauthorized (above 85 dB) categories in the steel industry. Methods: This cross-sectional study was conducted among 300 workers in Isfahan Steel Industries. Environmental sound assessment was performed to determine the distribution of sound pressure level according to the ISO 9612 standard in the company's production units. In this method, the number of exposed people, the exposure time, and the weight factor corresponding to the sound pressure level were calculated in 30 minutes. The DASS-42 and Harmon Jones (DARQ) questionnaires were used to predict the mental state of the participants and to measure the severity of mood swings and arousal. The collected data were analyzed using SPSS statistical software (ver22). Results: Based on the findings, age had a significant effect on depression, marital status had a significant effect on anxiety, and work shift had a significant effect on the level of stress and cognitive dissonance of employees. The stress mean was significantly higher in the case group (14.40 ± 1.66) than the control group (p <0.001). This indicates the effect of sound intensity level on the increase of stress and cognitive dissonance of workers in a noisy environment. With increasing exposure to sound, the participants’ stress decreased (p <0.05). Conclusion: Considering the positive and significant relationship of noise level with stress and cognitive inconsistency of workers in the case group, it is necessary to take effective preventive measures to prevent psychological harm and maintain the workers' health in this industry. In order to reduce noise, a number of applicable solutions have been proposed including spatial planning, selection of suitable materials, control of noise pollution related to outdoor construction, control of noise pollution related to indoor construction, and training.

scholarly journals Status of noise pollution in urban area of Dharan Sub- Metropolitan City and Inaruwa Municipality of Sunsari District, Nepal

2017 ◽  
Vol 7 (1) ◽  
pp. 35-40
Author(s):  
Ranij Shrestha ◽  
Alankar Kafle ◽  
Kul Prasad Limbu
Keyword(s):  
Sound Pressure Level ◽  
Sound Pressure ◽  
Pollution Assessment ◽  
Noise Pollution ◽  
Pressure Level ◽  
Noise Levels ◽  
Night Time ◽  
Level Measurement ◽  
Standard Value ◽  
Autumn And Winter

The environmental noise level measurement in Dharan and Inaruwa cities of eastern Nepal were conducted and compared with the ambient noise standards provided by Government of Nepal. The noise pollution assessment was performed in autumn and winter seasons by the indicator average day time sound pressure level (Ld, during 7.00 to 22.00 hrs) and average night time sound pressure level (Ln, during 22.00 to 7.00 hrs). The Ld and Ln values at the commercial, silence and residential zones of Dharan were 78 to 82 and 72 to 73, 65 to 73 and 60 to 70, 65 to 76 and 62 to 64 dB(A) in autumn and 78 to 79 and 72 to 76, 64 to 71 and 58 to 68, 63 to 74 and 60 to 62 dB(A) in winter, respectively whereas for Inaruwa, measurement were 75 to 77 and 73 to 75, 59 and 57, 67 and 60 dB(A) in autumn and 66 to 70 and 63 to 68, 55 and 53, 65 and 58 dB(A) in winter, respectively. The results showed that noise levels exceeded the standard value at most of the sites.

scholarly journals Investigation of Noise Pollution Distribution in Different Parts of Yazd Textile Factories

2021 ◽  
Author(s):  
Mohammad Javad Zare Sakhvidi ◽  
Hamideh Bidel ◽  
Ahmad Ali Kheirandish
Keyword(s):  
Occupational Exposure ◽  
Sound Pressure Level ◽  
Textile Industry ◽  
Sound Pressure ◽  
Noise Pollution ◽  
Sound Level ◽  
Pressure Level ◽  
Cross Sectional ◽  
Sound Pressure Levels ◽  
Different Parts

 Background: Chronic occupational exposure to noise is an unavoidable reality in the country's textile industry and even other countries. The aim of this study was to compare the sound pressure level in different parts of the textile industry in Yazd and in different parts of the textile industry. Methods: This cross-sectional study was performed on 930 textile workers in Yazd. A questionnaire was used to obtain demographic information and how to use protective equipment. Then, to obtain the sound pressure level of each unit and device and to use the measurement principles, a calibrated sound level meter was used. Then the results were analyzed using SPSS Ver.29 software. Results: The participants in this study were 714 males and 216 females with a mean age of 35.27 and 33.63 years, respectively. Seven hundred fifty-six participants (81.29%) were exposed to sound pressure levels higher than 85 dB. Among the participants, only 18.39% of the people used a protective phone permanently. Except for factory E, with an average sound pressure level of 77.78 dB, the rest of the factories had an average sound pressure level higher than the occupational exposure limit. The sound measurement results of different devices show that the sound pressure levels above 90 dB are related to the parts of Dolatab, Ring, Kinetting (knitting), Chanel, Autoconer, Dolakni, Open End, MultiLakni, Tabandegi, Texture, and Poy. Conclusion: Based on the results of the present study, noise above 90 dB is considered as one of the main risk factors in most parts of the textile industry (spinning and weaving), which in the absence of engineering, managerial or individual controls on it causes hearing loss in becoming employees of this industry

scholarly journals Sound Levels Forecasting in an Acoustic Sensor Network Using a Deep Neural Network

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 903 ◽  
Author(s):  
Juan M. Navarro ◽  
Raquel Martínez-España ◽  
Andrés Bueno-Crespo ◽  
Ramón Martínez ◽  
José M. Cecilia
Keyword(s):  
Neural Network ◽  
Sensor Network ◽  
Sound Pressure Level ◽  
Deep Neural Network ◽  
Sound Pressure ◽  
Noise Pollution ◽  
Pressure Level ◽  
Acoustic Sensor ◽  
Short Term ◽  
Sound Levels

Wireless acoustic sensor networks are nowadays an essential tool for noise pollution monitoring and managing in cities. The increased computing capacity of the nodes that create the network is allowing the addition of processing algorithms and artificial intelligence that provide more information about the sound sources and environment, e.g., detect sound events or calculate loudness. Several models to predict sound pressure levels in cities are available, mainly road, railway and aerial traffic noise. However, these models are mostly based in auxiliary data, e.g., vehicles flow or street geometry, and predict equivalent levels for a temporal long-term. Therefore, forecasting of temporal short-term sound levels could be a helpful tool for urban planners and managers. In this work, a Long Short-Term Memory (LSTM) deep neural network technique is proposed to model temporal behavior of sound levels at a certain location, both sound pressure level and loudness level, in order to predict near-time future values. The proposed technique can be trained for and integrated in every node of a sensor network to provide novel functionalities, e.g., a method of early warning against noise pollution and of backup in case of node or network malfunction. To validate this approach, one-minute period equivalent sound levels, captured in a two-month measurement campaign by a node of a deployed network of acoustic sensors, have been used to train it and to obtain different forecasting models. Assessments of the developed LSTM models and Auto regressive integrated moving average models were performed to predict sound levels for several time periods, from 1 to 60 min. Comparison of the results show that the LSTM models outperform the statistics-based models. In general, the LSTM models achieve a prediction of values with a mean square error less than 4.3 dB for sound pressure level and less than 2 phons for loudness. Moreover, the goodness of fit of the LSTM models and the behavior pattern of the data in terms of prediction of sound levels are satisfactory.

scholarly journals Identification and analysis of noise sources in the Noor-Abad gas compressor station, Iran

2015 ◽  
Vol 4 (1) ◽  
pp. 196
Author(s):  
Nader Mohammadi ◽  
Kami Mohammadi
Keyword(s):  
Sound Pressure Level ◽  
Sound Pressure ◽  
Acoustic Noise ◽  
Noise Pollution ◽  
Residential Area ◽  
Pressure Level ◽  
Compressor Station ◽  
Noise Condition ◽  
Noise Sources ◽  
Noise Map

The objective of this study is to identify the sources of acoustic noise (noise pollution) in the Noor-Abad gas compressor station and then to prioritize the station equipment based on noise pollution. First, the key locations inside the station as well as in the surrounding residential area, aka the study area, are determined for the measurement of sound pressure level. Then, the sound pressure level is measured at those points, and the related noise map is produced. Based on the noise map, the noise condition in the study area is evaluated by comparing the measured acoustic parameters with allowable standard values. Dangerous regions and critical points are thus identified. The major noise sources consist of main blowdown, units’ blowdowns, scrubbers, and turbo-compressors. The sound pressure level of main blowdown is measured at two intervals from its position: 80 m inside the station and 600 m outside the station (at the edge of the surrounding residential area). Also, the sound pressure level for a unit blowdown and a scrubber is measured at respectively 25 m and 40 m from their positions. Finally, the station equipment is prioritized based on noise pollution. The analysis of measurement results showed that the main noise sources are, respectively, the station main blowdown, units’ scrubbers, units’ blowdowns, turbo-compressors, and gas pipelines.

scholarly journals A Study on Performance and Noise Analysis of 4 Stroke 4 Cylinder Diesel Engine

2020 ◽  
pp. 105-117
Author(s):  
Surabhi Kumari ◽  
Suresh Kumar Badholiya
Keyword(s):  
Diesel Engine ◽  
Sound Pressure Level ◽  
Sound Pressure ◽  
Noise Analysis ◽  
Noise Pollution ◽  
Pressure Level ◽  
Modern World ◽  
Octave Band ◽  
Frequency Spectrum Analysis ◽  
Growing Environment

In our modern world, rapidly growing environment one of the developing problems is that of “Noise”. This has lead to overcrowded or jammed roads and noise pollution. Engine noise is one of the major sources of noise in vehicles. So, it is necessary to study noise generated by four stroke four cylinder diesel engine at different loads. First the sound pressure level is measure in dB(A) near the engine at four different locations at distance of 1.5m from centre of each side of an engine to find out that location where sound pressure level is maximum. Sound power is calculated using rectangular parallelepiped at different loads. Frequency spectrum analysis is done to measure sound pressure level in 1-1 octave band.

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