Evaluation of individual and environmental sound pressure level and drawing noise-isosonic maps using Surfer V.14 and Noise at Work V.5.0

Noise pollution is one of the common physical harmful factors in many work environments. The current study aimed to assess personal and environmental sound pressure level and project the sound-Isosonic map in one of the Razavi Khorasan Paste manufacture using Surfer V.14 and Noise at work V.5.0. This cross-sectional, descriptive study is analytical that was conducted in 2018 in the Paste factory that contains Canister, production and Brewing unit. Following ISO 9612:2009, Casella Cel-320 was used to measure personal sound pressure level, while CEL-450 sound level meter (manufactured by Casella-Cel, the UK) was employed to assess environmental sound pressure level. Statistical analyzes was done using SPSS V.18 and Linear Regression test. The sound-isosonic maps were projected using Surfer V. 14 and Noise at work V.5.0. The results of assessing personal sound pressure level indicated that the highest received dose (172.21%) and personal equivalent sound level (87.36 dBA) were recorded for workers in the Canister unit. According to results of measuring of the environmental sound pressure level, out of 16 measurement stations in this unit, overall 87.5% were regarded as danger and caution areas. The lowest and highest sound pressure levels in this units were 61 dBA and 92 dBA that belong to Brewing and Canister units respectively. Results indicate Over 75% of the Canister and production units had a sound pressure level greater than 85 dBA and these two units were regarded as the most dangerous area in terms of noise pollution. It is therefore necessary to implement noise control measures, apply hearing protection program and auditory tests among workers in these units

Weighing and modelling factors influencing serum cortisol and melatonin concentration among workers that are exposed to various sound pressure levels using neural network algorithm: An empirical study

Background: Noise is one of the most common harmful agents in the workplace. Exposure to excessive noise can lead to complications such as cardiovascular disorders, disturbance of body hormones� rhythm and hearing loss. This study aimed at weighing and modelling factors influencing serum cortisol and melatonin concentrations of workers that are exposed to various sound pressure levels using neural network algorithm. Methodology: A case-control design was adopted in the current research. The required data were collected from 75 industrial and mining firm staff members. They were assigned to three groups with equal sample sizes (25 workers). In developing the conceptual model in regard to variables that may affect workers� serum cortisol and melatonin concentration, SPL, age, weight, and height were included. The influence of SPL on serum cortisol concentration as assessed in the three shifts. Moreover, radioimmunoassay (RIA) was utilized to assess serum cortisol and melatonin concentrations. Neural network algorithm was subsequently exploited to weigh and model predictor factors. IBM SPSS Modeler 18.0 was the software program used for data analysis. Results: The average cortisol concentration values for administrative, condensing, and pelletizing units respectively were 10.24 ± 2.35, 12.15 ± 3.46, and 14.91 ± 4.16Formula presented. On the other hand, the average melatonin concentration values for administrative, condensing, and pelletizing units respectively were 37 ± 12.52, 34 ± 13.15, and 27 ± 9.54Formula presented. According to the results of the developed model for cortisol, SPL3 (32%) and age (5%) respectively had the highest and lowest impact. On the other hand, considering the model developed for melatonin, height (27%) and SPL1 (10%) were the most and least influential factors in that order. The accuracy rates of the model were also found to be 95% for cortisol and 97% for melatonin. Conclusion: Comparing cortisol concentrations during various shifts revealed a significant reduction (from the beginning to the end of the shift) in all the three groups. Further, the rise of SPL would result in higher secretion of cortisol. Moreover, in all the three groups, the average serum melatonin concentration went up from the beginning to the middle of the shift and then declined to the end of the shift. Considering the accuracy rates of the models developed to predict hormones, neural network algorithm is a suitable and powerful tool for weighing and modelling factors influencing serum cortisol and melatonin concentrations. © 2020 The Author(s