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Evaluation of risks produced by high noise exposure levels of workers in the industrial washing area. (#980)

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Date of Conference

July 17-19, 2024

Published In

"Sustainable Engineering for a Diverse, Equitable, and Inclusive Future at the Service of Education, Research, and Industry for a Society 5.0."

Location of Conference

Costa Rica

Authors

Millones Montero, Mauricio Esteban

Rojas Del Águila, Sael

Rodríguez Santos, Marycarmen

Infante Takey, Henry Ernesto

Abstract

Noise is more than present in our lives, it can cause auditory and non-auditory health effects. Noise is defined by the World Health Organization (WHO) as an unpleasant and annoying sound that is potentially harmful to health. The health effects were first recognized in occupational environments, such as looms, where high noise levels were associated with noise-induced hearing loss. Occupational noise is the type of noise exposure that has been studied the most. This review aims to investigate and review information on devices or procedures that help to attenuate high noise values, with the objective of safeguarding the hearing health of workers by preventing possible occupational diseases. Seventy-three original articles were found in the Scopus search engine up to the year 2023, of which 43 articles met the inclusion criteria. Noise assessment by dosimetry is the most commonly used in the selected studies. Different methods were found to reduce noise from machinery in the main industries, applying noise-reducing materials, algorithms, among others, and cases where energy savings have achieved the reduction. Risk assessment in industry helps organizations to know what factors affect workers, in the case presented the loud noises produced by machines affect both psychologically and physically to workers. The methods presented as implementation of noise insulating materials, which reduce noise by a large percentage with polyurethane material (earmuffs, air gun silencers, among others). silencers of air guns, among others), mathematical methods (algorithms and measurements) and energy saving would help the organization to reduce these risks.

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