Integrated Noise Management Strategies in Industrial Environments: A Framework for Occupational Safety, Health, and Productivity

Abstract

Noise exposure in rubber manufacturing poses significant risks to worker health, safety, and productivity. This study explores these challenges via an integrated approach that combines task-based and group noise measurements as required by Directive 2003/10/EC, noise mapping, real-time monitoring, and worker feedback. Noise levels were found to range from 81 to 89 dB(A) across worker groups, with task-specific peaks exceeding 91 dB(A) near high-noise machinery. To mitigate these risks, engineering controls, including acoustic barriers and machine isolation, were proposed alongside administrative measures such as task rotation and improved access to personal protective equipment. The analysis demonstrated a link between noise levels and reduced productivity, emphasizing the need for targeted interventions. Integrating IoT sensors and AI for real-time noise monitoring offers opportunities to increase compliance, reduce exposure, and improve safety outcomes. Whilst engineering controls involve higher initial investments, cost-benefit analysis highlights their long-term advantages in reducing health-related expenses and improving productivity. This study reinforces the importance of a proactive, worker-centered approach to noise management in rubber manufacturing, emphasizing sustainability, risk mitigation, and the adoption of innovative technologies to create safer and more efficient workplaces.

1. Introduction

Occupational noise exposure is a significant concern in many industries, and the rubber manufacturing sector exemplifies the challenges of high-noise environments. The production process relies on machinery such as mixers, extruders, and molding presses, which frequently exceed permissible exposure limits specified for employees. Exposure levels exceeding the specified limits not only damage workers’ hearing but also contribute to stress, fatigue, cardiovascular strain, and increased risk of workplace accidents. Addressing these noise hazards is especially urgent in rubber manufacturing, which supports key global industries, such as automotive, construction, and healthcare [1].

Technological advancements in the rubber industry have increased productivity and product quality, but they have also led to increased noise emissions [2]. More powerful machinery often means higher noise levels, which require updated safety protocols and targeted noise reduction strategies. Although regulatory frameworks such as the European Union’s Directive 2003/10/EC [3] and the SR EN ISO 9612:2010 standard [4] provide guidance, they often overlook the specific noise conditions in industries such as rubber manufacturing. This creates a growing need for assessments tailored to the complex, task-specific nature of noise exposure in these environments.

Noise affects hearing more than just hearing. Noisy environments disrupt workplace communication, leading to errors, accidents, and reduced productivity [5]. Workers may struggle to hear warnings or react to alarms, increasing safety risks. Studies also link noise exposure to higher levels of stress and more frequent workplace accidents, highlighting the need for effective noise management strategies that protect both safety and health [6].

Research on noise in the rubber industry is limited compared with that in other sectors. While general data on noise exposure are available, they often overlook the specific challenges of rubber manufacturing, where noise levels vary on the basis of tasks and machinery [7,8].

This study employs a dual-assessment approach, integrating measurements of equivalent continuous sound levels (L_EX,8h) for homogeneous worker groups with task-specific noise evaluations. Homogeneous group assessments determine the average exposure levels for workers performing similar tasks, while task-based measurements capture variability in noise levels associated with specific activities. This combined methodology facilitates the identification of areas and tasks with high noise exposure, guiding targeted intervention.

Noise mapping provides a visual representation of zones with increased noise exposure, calculated as equivalent continuous sound levels (L_EX,8h). High-exposure areas near mixing and molding equipment, where raw materials are combined and shaped using industrial presses, highlight the need for targeted engineering controls, such as acoustic barriers and noise-dampening measures. These measures can be supplemented by administrative interventions, such as task rotation, and personal protective equipment (PPE) to address residual risks. Although developed for rubber manufacturing, this approach is adaptable to other sectors, including construction, textiles, and mining, where similar challenges with machinery-generated noise are prevalent. By combining noise mapping with homogeneous group and task-specific assessments, this framework supports efficient noise management and reduces occupational noise risks [9].

Recent developments in noise control technologies, such as real-time monitoring systems and advanced noise-dampening materials, have helped address these challenges. Integrating these technologies into industrial settings can improve noise mitigation, especially when combined with the assessment strategies used in this study. These tools enable proactive interventions, reducing risks before they escalate and supporting continuous improvements in occupational safety and health [10].

An important aspect of noise management is worker training and engagement [11]. Educating employees about noise risks and the proper use of protective equipment enhances safety and increases compliance with mitigation measures. Task-specific training programs empower workers to reduce their exposure, improving the overall effectiveness of noise control strategies [12].

This study provides a framework with European applicability, adaptable to various industrial contexts within the European Union and beyond. The proposed methodologies align with Directive 2003/10/EC [3] and follow the principles outlined in SR EN ISO 9612:2009 [4]. In Romania, the national regulations fully transpose Directive 2003/10/EC [3], ensuring that the applicable thresholds for occupational noise exposure are identical to those defined at the European level. By addressing noise exposure through this unified framework, the study highlights its relevance not only to Romanian industrial environments but also to broader European contexts, offering a consistent and adaptable approach. Recognizing the complexity of occupational noise exposure, the study emphasizes the importance of collaboration among industry stakeholders, regulatory bodies, and researchers. Sharing data, best practices, and solutions can accelerate efforts to reduce noise risk effectively. Such partnerships also support policy advancements, ensuring that regulations keep pace with industry changes [13].

2. Materials and Methods

This study used a longitudinal design, combining both qualitative and quantitative methods, to understand noise exposure in the rubber industry. The longitudinal approach allowed observation of changes over time, providing insights into both the immediate and long-term effects of noise exposure. This comprehensive design sets the stage for detailed data collection through various assessment techniques.

To create a robust research framework, this study conducted a comprehensive literature review on occupational noise exposure in the rubber industry. This review aimed to consolidate existing findings, identify research gaps, and guide data collection methods. The focus was on understanding typical noise levels, evaluating health impacts, and assessing mitigation strategies. Searches were conducted in Scopus, Web of Science, and PubMed, applying strict inclusion criteria to ensure relevance. Only peer-reviewed studies from 2010–2024 that focused on occupational noise in rubber manufacturing or similar industries were included, ensuring reliable data.

The PRISMA framework guided the review process for transparency and rigor. The process followed structured steps: first, studies were identified through database searches; next, studies were screened on the basis of predefined inclusion and exclusion criteria; and finally, full-text analysis was conducted for relevance and quality. Titles and abstracts were reviewed to filter out studies not directly related to occupational noise or rubber manufacturing. Data extraction focused on identifying common trends, such as typical noise exposure levels, health effects, and existing mitigation strategies. The review’s structured methodology ensured the selection of studies that provided a solid basis for addressing gaps in current research and informed the development of appropriate methodologies for the study.

Noise exposure data were collected at a rubber manufacturing plant equipped with diverse machinery, including mixers, extruders, and molding machines. This variety allowed for a representative assessment of noise exposure under real working conditions. Measurements were conducted via calibrated dosimeters, following the SR EN ISO 9612 standard [4], to continuously record sound pressure levels over a standard workweek. This standard specifies the 3 dB exchange rate, which reflects a doubling of noise energy with each 3 dB(A) increase in sound level. This approach, adopted in the European framework, reflects the principle that even small increases in sound level can significantly impact cumulative noise exposure over time.

The study adheres to the occupational noise criteria defined in Directive 2003/10/EC [3]. At the lower exposure action value of 80 dB(A), preventive measures, such as worker training and noise monitoring, are required to mitigate potential risks. At the upper exposure action value of 85 dB(A), noise reduction measures become mandatory, alongside the compulsory use of hearing protection and health surveillance programs. The exposure limit value of 87 dB(A) represents the maximum permissible noise level, which must not be exceeded. If noise exceeds this level, employers are required to take immediate corrective actions to reduce exposure levels and safeguard their workforce’s hearing threshold and well-being.

To capture comprehensive data, two methodologies were employed: homogeneous group assessments and task-based measurements. Homogeneous group assessments provided an overview of average noise exposure across worker categories, whereas task-based measurements revealed noise risks associated with specific activities. Workers were grouped into exposure categories via random sampling to ensure representativeness. Task-based measurements focus on activities such as operating mixing machines or conducting quality inspections, capturing specific exposure scenarios.

The study involved 72 workers, 46 of whom were under direct observation. The participants represented various roles and exposure levels, ensuring that the results reflected the plant’s operational diversity. Ethical compliance was maintained by anonymizing the participants and briefing them on the study’s purpose. No personal identifiers were collected to encourage genuine participation.

Table 1. Homogeneous groups characteristics.

Homogeneous Group Code	Number of Workers in the Group	Number of Workers Under Observations	Total Measurement Time per Group (Minutes)
A	28	14	840
B	16	8	640
C	5	5	350
D	19	10	700
E	4	4	280

summarizes the characteristics of the homogeneous groups, including the number of workers and total observation time per group.

Task-based measurements focused on specific activities, enabling detailed analysis of noise exposure linked to each task. The results are shown in

Table 2. Representative tasks to monitor noise exposure.

Task Code	Number of Workers Under Observations	Total Measurement Time per Task (Minutes)	Task Description
A1	1	30	Operating rubber mixing machine
B1	1	30	Monitoring extrusion process
C1	1	30	Overseeing molding operations
D1	1	30	Quality control and inspection
E1	1	30	Maintenance of heavy machinery

, which outlines the duration and nature of these tasks.

The dosimeters were calibrated to simulate human hearing sensitivity, and measurements were standardized to an 8-h workday for comparability. The calculation of L_eq,8h integrates variations in noise levels throughout the workday, providing a standardized approach to assess daily noise exposure in compliance with Directive 2003/10/EC. This principle ensures that fluctuations in noise levels during different tasks or time periods are accounted for in the exposure assessment. The data were collected under controlled indoor conditions at temperatures between 23–24 °C. The measurements spanned five homogeneous exposure groups and multiple job tasks, providing a detailed overview of the plant’s noise environment.

This study used observational methods to track workplace conditions and worker behavior over time, focusing on noninvasive techniques to assess environmental and behavioral factors. Data collection included periodic noise level measurements with calibrated sound level meters and dosimeters placed strategically within the facility. These measurements captured fluctuations in noise levels across different work areas and tasks throughout the workday. High-risk zones, including material shaping processes, were prioritized for observation based on preliminary noise level assessments. This approach ensured that the most affected areas received focused attention during data collection.

Task performance was monitored with structured checklists, and indicators such as task completion times, error rates, and worker activity patterns were recorded. Specific tasks, including rubber mixing, molding, and quality control, were selected for observation because of their varying exposure levels and potential impacts on productivity. Observations were conducted discreetly to minimize interference with normal operations, ensuring the authenticity of worker behavior and productivity data.

A timeline for follow-up assessments was established to evaluate the long-term effects of noise exposure on workplace efficiency. These periodic assessments ensured that the study could track both immediate and long-term changes, offering a complete picture of noise-related impacts on productivity. Monthly noise level measurements and quarterly observational reviews captured trends and cumulative impacts. Specific metrics, such as noise level variations, task duration, and frequency of interruptions, were tracked to identify correlations between noise and productivity. The analysis determined thresholds where noise began to negatively affect worker focus and efficiency. The approach avoided direct health measurements to maintain a noninvasive, operational focus.

By prioritizing a contextual understanding of how noise exposure affects workplace dynamics, this study provides actionable insights into environmental and behavioral impacts without intruding on workers’ privacy. Regression modeling helps identify specific points at which noise begins to interfere with productivity, providing clear targets for interventions. Statistical analyses quantified the relationship between noise levels and productivity, offering a robust framework for evaluating long-term effects. This methodology balances precision and practicality, minimizing disruption while delivering reliable data on the impact of noise.

Noise mapping was employed to visualize the noise distribution across the rubber manufacturing facility. Continuous noise level measurements were collected via calibrated sound level meters and dosimeters strategically placed throughout the facility to capture representative data. Measurement points were selected to cover areas of high activity, proximity to noise-generating machinery, and reflective surfaces, ensuring a comprehensive representation of noise levels.

Data collection accounted for key factors such as equipment placement, room dimensions, surface materials, and environmental conditions. Measurements were conducted during standard 8-h work shifts, reflecting typical operational conditions and capturing daily noise variations. The devices were calibrated daily via a standard acoustic calibrator to ensure consistency and accuracy.

Noise mapping followed a periodic assessment schedule, with measurements conducted monthly to evaluate short-term variations and cumulative trends. Maps were updated whenever significant changes occurred in the facility layout, machinery, or production processes, ensuring that the data remained relevant. The resulting maps used color gradients to indicate varying noise intensities, highlighting high-risk zones where noise levels exceeded the 85 dB(A) threshold.

The collected data were processed via specialized software that applied spatial interpolation techniques to generate noise maps. The collected data were processed using software developed for occupational noise analysis. This application is designed to create and present measurement data as contour maps with accuracy. These maps provide a visualization of noise distribution, offering a framework for assessing exposure and planning interventions. The software applies spatial interpolation techniques, including an advanced triangulation method with optional smoothing, to calculate and display noise contours (noise maps) and exposure levels (L_ex,8h). Input parameters include noise levels measured with sound level meters, task durations at each workstation, facility layout schematics, and other data. The interpolation process is interactive, automatically updating results as input data are modified, ensuring precision and adaptability during noise analysis. Additionally, the application supports simulations to evaluate and compare noise mitigation solutions, such as installing acoustic barriers or adjusting task rotations in areas where worker exposure exceeds recommended limits. Reports generated by the software include input data and results, facilitating decision-making for noise management strategies. It also visualizes the acoustic field distribution of noise sources within the workplace, aiding in the prioritization of interventions. Validation against real-world measurements offer accuracy in noise control planning. This approach aligns with workplace noise standards, providing a tool for managing occupational noise exposure adequately.

A cost-benefit framework was employed to evaluate the financial impact of noise control measures in the workplace. The analysis compared the costs of various interventions, such as installing acoustic barriers, implementing task rotation schedules, and providing personal protective equipment (PPE), with their projected benefits. These benefits included reductions in absenteeism, healthcare costs, and productivity losses.

The framework incorporates both direct costs, such as material and installation expenses, and indirect savings from improved worker efficiency and reduced health complaints. The data inputs included installation costs, maintenance estimates, and performance metrics derived from observational studies and noise mapping. By quantifying the economic outcomes of different scenarios, cost-benefit analysis provides practical insights into the financial viability and sustainability of noise control strategies.

This study proposed a framework that integrates the Internet of Things (IoT) and artificial intelligence (AI) tools into a plant’s monitoring and reporting systems to enhance noise management. IoT sensors were strategically placed to collect continuous noise data, which were transmitted to a centralized server for analysis. AI algorithms process data, identify patterns and predict risks such as equipment malfunctions or increased noise exposure in specific zones. A dashboard visualizes noise levels in real time, uses heatmaps and alerts to highlight areas above safety thresholds. Predictive analytics utilize historical data to guide proactive adjustments, ensuring timely interventions. The system also automated compliance reporting and trend analysis to support regulatory requirements and long-term planning.

Worker feedback was an essential step in adapting the decision-making framework to practical workplace conditions. The structured discussions involved 25 employees selected to represent diverse roles, exposure levels, and experiences. Semi-structured interviews explored perceived noise levels, challenges with protective equipment, and the effectiveness of current control measures. Confidentiality was maintained to encourage honest participation, and no personal identifiers were collected. The feedback highlighted recurring issues, such as inconsistent task rotation, limited PPE availability, and insufficient awareness of noise protocols. These insights help align the proposed interventions with real-world needs.

The decision-making framework combines IoT data, AI analysis, and worker feedback to create a dynamic approach to noise control. Engineering controls, such as acoustic barriers and isolation measures to reduce noise at the source, were prioritized for their long-term benefits. These were complemented by administrative measures, such as task rotation, while PPE was used as a supplementary solution for high-risk zones. Feedback ensures that interventions are practical and tailored to workplace realities, making the framework adaptive to changing conditions and effective in reducing noise exposure.

3. Results

Noise exposure in the rubber industry poses a significant risk to worker health and safety. Studies consistently show how noise affects both work performance and health, demonstrating its widespread impact on employees. These findings emphasize the necessity for tailored monitoring and control strategies to protect workers’ well-being [14].

The literature review identified 25 studies on occupational noise exposure in rubber manufacturing, following a rigorous search strategy. The PRISMA framework ensured transparency throughout the identification, screening, and selection stages. From an initial pool of 325 records retrieved from Scopus, Web of Science, and PubMed, 75 duplicates were excluded. Titles and abstracts were screened for relevance, and 175 records unrelated to occupational noise, empirical data, or the rubber industry were excluded. Among the remaining 75 full-text articles, 45 were excluded because of unrelated industrial contexts or a lack of empirical data. Ultimately, 25 studies met all the inclusion criteria, as summarized in the PRISMA flow diagram (Figure 1).

Research highlights the dual impact of noise exposure: direct health effects and interactions with other occupational factors. Material combining and forming tasks often exceed 90 dB(A), exceeding the 85 dB(A) safety limit. Variations in exposure levels are attributed to specific machinery, task types, and factory layouts [14,15,16]. Prolonged exposure at these levels is strongly linked to noise-induced hearing loss (NIHL), heightened stress, and cardiovascular issues [17,18]. In combination with other stressors, such as shift work, noise increases risks, increasing the incidence of hypertension and work fatigue [19,20,21].

A comparison with the construction and mining industries highlights transferable strategies for rubber manufacturing. While average noise levels in construction exceed those in rubber manufacturing, effective control measures, such as low-noise machinery and regular maintenance schedules, significantly reduce worker exposure [22,23]. Conversely, the mining industry experiences lower average noise levels but often struggles with inconsistent control measures, providing lessons into what practices to avoid. Rubber manufacturing occupies an intermediate position, with moderate noise levels and opportunities to improve strategies by adopting best practices from these sectors.

Control measures from other industries illustrate the value of combining source-level and administrative interventions. For example, construction frequently employs sound-absorbing materials and acoustic barriers alongside task scheduling and worker training programs. These strategies can be adapted to rubber manufacturing to increase worker protection and productivity. Figure 2 illustrates the differences in average noise levels and the effectiveness of control measures across these industries.

The mitigation strategies discussed in the reviewed studies include engineering solutions such as acoustic barriers and sound-dampening materials, complemented by administrative measures such as task rotation and exposure limitations [24,25]. Tailored insulation designs and job rotation schedules have proven effective in reducing noise exposure and improving productivity [14,26]. Innovations such as rubber-infused acoustic barriers address both noise and waste management issues [25,26]. Despite these advancements, implementation remains inconsistent, particularly in SMEs [27,28].

Research gaps persist. Several studies provide detailed, task-specific noise exposure assessments, limiting the understanding of how operations contribute to noise variability and health impacts [15,29]. Longitudinal studies are also scarce, leaving questions about the long-term effects of chronic exposure, such as progressive hearing loss or cardiovascular diseases [17,18].

The limited application of advanced monitoring technologies, such as IoT-enabled real-time tracking, hinders the development of adaptive noise control frameworks [26,27]. Few studies have investigated the interaction between worker behavior, such as PPE compliance, and the effectiveness of engineering or administrative controls [16,30]. Addressing these issues is essential for ensuring that noise control measures effectively reduce risks.

The findings from the literature review underscore the need for direct noise exposure measurements to address gaps specific to the rubber manufacturing sector. While existing studies highlight general risks and mitigation strategies, sector-specific data are essential for quantifying exposure levels and designing targeted interventions. Building on this, the study conducted homogeneous group and task-based measurements to provide a detailed assessment of noise exposure levels [31].

This study quantified noise exposure levels experienced by workers in the rubber industry through direct measurements, addressing gaps identified in the literature. By standardizing noise exposure to an 8-h workday and employing a homogeneous exposure group and task-based assessments, the study confirmed the significant noise levels reported in previous research and provided detailed insights into the risks faced by specific worker groups. These findings are essential for developing effective, tailored noise mitigation strategies [32,33].

A total of 51 measurements were collected from homogeneous exposure groups, summarizing daily noise exposure levels and their associated uncertainties (

Table 3. Results of the homogeneous exposure group measurements.

Homogeneous Group	Daily Exposure (LEX,8h) dB(A)	Associated Uncertainty (U (LEX,8h)) dB(A)	Final Exposure Value (LEX,8h + U (LEX,8h)) dB(A)
A	86.8	2.2	89
B	80.3	2.5	82.8
C	85.4	2.4	87.8
D	78.4	3	81.4
E	81	2.6	83.6

). The results reveal notable variations in exposure, highlighting specific groups requiring immediate noise control interventions.

Groups A and C experienced the highest daily exposure levels, averaging 89 dB(A) and 87.8 dB(A), respectively. These findings suggest that workers near high-noise equipment, such as mixers and molding machines, face the greatest risk of noise-induced health impacts. Although Group D recorded a lower exposure of 81.4 dB(A), preventive measures are still needed to mitigate potential cumulative impacts over time.

Task-specific measurements provide detailed noise exposure profiles for various job functions, including mixing, extrusion, molding, quality control, and maintenance.

Table 4. Results of the task-based measurement.

Task Code	Daily Exposure (LEX,8h) dB(A)	Associated Uncertainty (U (LEX,8h)) dB(A)	Final Exposure Value (Lp,Aeq,T) dB(A)
A1	88.8	2.3	91.1
B1	84.7	2.6	87.3
C1	87.2	2.5	89.7
D1	83.3	3.1	86.4
E1	83.5	2.7	86.2

presents the daily noise exposure levels (L_EX,8h), associated uncertainties (U (L_EX,8h)), and final exposure values (Lp,A_eq,T) for each task.

Tasks A1, B1, and C1, involving operations such as rubber mixing, extrusion, and molding, consistently exceeded the safety threshold of 85 dB(A), with final exposure values of 91.1, 87.3, and 89.7 dB(A), respectively. These elevated levels underscore the need for engineering controls, such as sound-dampening solutions. Although tasks D1 and E1 remained below the safety threshold, they exceeded the 80 dB(A) action level, indicating the potential for administrative controls, such as reduced exposure times or personnel rotation, to further reduce risks.

Figure 3 illustrates the distribution of noise exposure across homogeneous groups and task-specific measurements. The data reveal distinct exposure patterns, with higher levels observed in tasks involving machinery operations and in specific worker groups exposed to high-noise equipment. These findings highlight the variability in exposure within the plant. The exposure limit value of 87 dB(A) represents the 8-h equivalent continuous sound level (L_eq,8h), as defined by Directive 2003/10/EC [3]. This threshold accounts for the cumulative effect of noise exposure over the workday. This approach ensures compliance with occupational noise exposure standards across different tasks and worker groups.

The observational study offered detailed data on noise exposure levels across workstations and their effects on worker performance [34]. Periodic noise measurements revealed variation in noise levels throughout the workplace, as shown in Figure 4. The mixing and molding stations recorded noise levels that frequently exceeded 90 dB(A), surpassing the recommended threshold of 85 dB(A). Quieter areas, such as quality control and maintenance areas, generally remained below 85 dB(A). However, noise levels in these zones showed less variability and sustained lower intensities over time.

Behavioral data collected alongside noise monitoring established a measurable relationship between noise levels and productivity indicators. Regression analysis, presented in Figure 5, quantified the impact of noise levels on task performance, task duration, and error rates. Task efficiency decreased by 1.5% for every additional decibel above baseline, whereas task duration increased by 0.24 min per decibel. The error rates rose by 0.1% per decibel, with a sharp increase observed above 87 dB(A).

The mixing and shaping stations presented the most significant declines in performance, corresponding to the highest noise levels recorded. In contrast, areas with lower noise levels, such as quality control and maintenance, presented more stable task performance metrics across the observation period. These results indicate variability in noise exposure and its effects on worker productivity across different workstations.

The longitudinal analysis highlighted cumulative impacts, showing that workers in high-noise areas experienced higher declines in performance over time than those in quieter zones did. This dataset provides a foundation for understanding noise-related impacts on worker productivity and health in rubber manufacturing environments.

The noise mapping process provided a detailed spatial representation of the noise distribution across the rubber manufacturing facility. By incorporating data on machinery noise, workplace layout, room dimensions, and worker positions, the maps identified high-exposure zones concentrated near machinery and reflective surfaces. These zones were characterized by noise levels that consistently exceeded the recommended threshold of 85 dB(A), with certain areas reaching or surpassing 90 dB(A).

Figure 6 presents the facility’s noise map, illustrating the distribution of noise levels and highlighting high-risk areas requiring intervention. High-risk zones are areas where noise exposure levels exceed the upper exposure action value of 85 dB(A), as defined in noise exposure regulations. These zones pose an increased risk of noise-induced hearing loss and other health impacts, highlighting the need of implementing noise control measures. The map uses color gradients to represent noise levels across the workplace. Noise hotspots are observed in zones adjacent to high-noise machinery, such as mixing and shaping equipment. Quieter areas, including quality control and maintenance zones, presented noise levels below the threshold. However, their potential cumulative risks remain under observation. The contours in the noise map represent L_eq,8h levels, reflecting the 8-h equivalent continuous sound levels across the workplace, in alignment with applicable noise exposure regulations.

The noise mapping results provide a perspective on how spatial factors influence noise exposure in rubber manufacturing facilities [35]. By visualizing high-risk zones, the maps translated raw noise measurements into actionable insights, revealing patterns not evident through task-based or group-level assessments. These findings underscore the importance of spatial data in identifying specific areas where noise mitigation efforts should be concentrated.

By integrating noise mapping with other measurement methods, this study demonstrates how spatial data enhance the understanding of noise exposure dynamics. This approach bridges gaps in traditional assessments, providing a comprehensive framework for designing interventions that balance worker safety with operational needs.

The cost-benefit analysis assessed the financial implications of implementing various noise control strategies in rubber manufacturing facilities. Costs and benefits were compared for engineering controls, administrative measures, and PPE, with a focus on reductions in absenteeism, lower healthcare expenses, and increased productivity. The findings, summarized in Figure 7, provide a clear comparison of these strategies [36].

Engineering controls, such as barrier-based noise reduction, have emerged as the most effective long-term solution. These interventions significantly reduce noise exposure by approximately €100 per square meter, including installation and maintenance, over five years. This led to lower absenteeism rates and measurable productivity gains, making engineering controls a cost-effective investment despite their higher initial costs.

Administrative measures, including task rotation and scheduled quiet periods, offered a cost-effective alternative with moderate benefits. Costs were associated with scheduling, training, and compliance monitoring. These measures improved worker focus and reduced error rates, demonstrating their value in managing the behavioral aspects of noise exposure. Although less impactful than engineering controls, administrative measures complemented these interventions by addressing exposure duration and variability.

Personal protective equipment provides immediate and inexpensive noise protection but has limited long-term economic benefits [37]. Its effectiveness was contingent on consistent worker adherence, which varied across tasks. As a result, the PPE served primarily as a supplementary measure rather than a standalone solution.

The analysis highlights the financial advantages of prioritizing engineering controls, supported by administrative measures and PPE. These combined strategies offer a comprehensive approach to noise management, balancing initial costs with long-term benefits.

The proposed framework for integrating the IoT and AI-driven tools into workplace noise management demonstrates its ability to enhance monitoring, responsiveness, and long-term planning [38]. Figure 8 outlines the components and interactions within this system, providing a roadmap for modernizing noise control in industrial environments. By combining real-time data collection, predictive analysis, and automated reporting, the framework addresses gaps in traditional noise management methods.

IoT sensors installed in high-risk zones enable the continuous collection of noise data. This information was processed centrally, where AI algorithms detected patterns and identified hazards. Supervisors accessed visualizations through dashboards that provided actionable insights, such as heatmaps and alerts for noise levels exceeding 85 dB(A). These tools facilitate immediate interventions to mitigate risks in high-exposure areas.

Predictive analytics uses historical noise data to identify trends and anticipate future risks, such as potential equipment malfunctions or changes in production conditions. Automated compliance reporting supported regulatory adherence, offering summaries of noise trends without increasing administrative burdens. These features streamlined the implementation and evaluation of noise control strategies, ensuring efficiency in workplace safety management.

Although conceptual, the framework effectively highlights the role of integrated technologies in addressing workplace noise hazards. This study illustrates a pathway to reduce worker exposure, improve compliance, and modernize operational safety protocols, demonstrating potential for real-world applications.

The structured discussions with 25 employees highlighted several key challenges in workplace noise management. Approximately 65% of the participants reported inconsistencies in task rotation schedules, particularly in high-noise zones, leading to prolonged exposure, fatigue, and reduced productivity. Insufficient access to PPE was identified by 70% of workers, with issues related to availability, fit, and comfort. Additionally, 55% of the respondents indicated limited awareness of noise control protocols, citing gaps in training and communication.

The participants also provided feedback on current noise management measures. While 60% acknowledged efforts to implement controls, many expressed concerns about their consistency and overall effectiveness. Suggestions included more structured task rotation, improved PPE access, and regular training on noise hazards and protective measures.

The findings were quantified to inform targeted interventions. High-priority areas included addressing inconsistencies in task rotation (65%), ensuring adequate PPE access (70%), and enhancing training and communication about noise control protocols (55%). These data served as essential inputs for refining the decision-making framework, ensuring that the proposed measures aligned with workplace realities and worker needs.

Building on these insights, the proposed decision-making framework integrates key inputs such as worker feedback and noise analysis data into a systematic and responsive process. Figure 9 illustrates the structured workflow, beginning with the identification of high-risk areas and proceeding through intervention, monitoring, and continuous training.

High-risk zones and tasks were identified via noise data, enabling targeted evaluation of interventions. Engineering controls, such as isolating noisy equipment and modifying layouts, were prioritized for their long-term effectiveness. Administrative measures, including task rotation and restricted access, complemented engineering solutions, whereas PPE was implemented as an immediate safeguard in high-exposure areas. Monitoring and periodic updates ensured that the framework remained adaptable to changing workplace conditions.

The workflow emphasizes the integration of worker feedback with technical analysis to refine and prioritize interventions. Supervisors were trained to interpret noise data and adapt plans accordingly, whereas workers received guidance on adhering to noise protocols and optimizing PPE use. This structured approach aligns technical findings with practical workplace realities, ensuring the effective implementation of noise control measures.

4. Discussion

These findings confirm that noise exposure remains a major challenge in the rubber industry. Noise levels frequently exceed recommended thresholds, especially during tasks involving mixers and molding equipment, posing significant risks to worker health. These include noise-induced hearing loss (NIHL), increased stress, and hypertension, underscoring the broader health impacts of occupational noise exposure. These results highlight the need for tailored monitoring and targeted control measures in this sector [39].

A detailed analysis of task-specific and machinery-driven noise exposure reveals variations linked to specific tasks, machinery conditions, and workplace layouts. Tasks such as molding and mixing consistently exceed 90 dB(A), highlighting the necessity for focused interventions. Engineering solutions, such as acoustic barriers and machine modifications, effectively reduce noise levels when implemented correctly. These findings provide a foundation for developing approaches for mitigating noise tailored to high-risk tasks and machinery in rubber manufacturing.

Comparative insights from the construction and mining industries offer practical lessons. Despite higher average noise levels, the construction industry achieves superior outcomes due to proactive measures, such as low-noise equipment and regular preventive maintenance [22,26,27,40]. Conversely, the mining sector, despite experiencing lower noise levels, presents inconsistencies in applying control measures. Rubber manufacturing can adopt best practices from these industries, such as isolating noise sources, using sound-absorbing materials, and improving worker training, to develop more efficient workplace noise management plans.

Several research gaps identified during the review further emphasize the need for enhanced noise management. The lack of task-specific noise assessments limits the understanding of how individual operations contribute to noise exposure. Similarly, the absence of longitudinal studies hinders comprehensive insights into the cumulative effects of noise, such as progressive hearing loss and cardiovascular diseases [41]. Addressing these gaps will require in-depth studies focused on task-level exposure and long-term health impacts.

Challenges in implementing protective measures were also apparent. Limited access to PPE and inconsistent task rotation practices point to the need for more structured and enforceable protocols. Worker compliance with existing measures, often overlooked, plays a key role in ensuring the effectiveness of noise control strategies. The incorporation of worker feedback into the design and evaluation of noise management measures can increase compliance and improve overall outcomes.

The integration of engineering controls, administrative measures, and advanced monitoring technologies is crucial for effective noise management. Innovative tools, such as IoT-enabled monitoring systems and predictive analytics, could improve compliance, provide real-time data, and enable dynamic interventions. Combining these technologies with insights from related industries and addressing research gaps can pave the way for adaptive and sustainable noise control systems in rubber manufacturing. Future research should validate these proposed interventions and explore innovative approaches to improve workplace safety and productivity.

These findings build on a literature review, extending its conclusions by quantifying the variability in noise exposure through a dual-method approach. This detailed perspective addresses gaps in understanding specific high-risk tasks and worker groups, offering actionable insights for targeted interventions.

The results of noise measurements provide important insights into noise exposure risks in the rubber industry. Homogeneous group measurements identified Groups A and C as having the highest average daily exposure levels, highlighting that worker near high-noise machinery, such as mixers and molding machines, face risks. This uneven distribution of noise exposure emphasizes the need for targeted controls in high-risk zones.

Task-based measurements offered granular insights into exposure variability, revealing that tasks such as rubber mixing, extrusion, and molding consistently exceed safety thresholds, with exposure levels reaching 91.1 dB(A). These findings emphasize the limitations of relying solely on group-level data, which cannot capture task-specific risks. By identifying the activities and machinery contributing most to elevated noise levels, task-based measurements allow for precise interventions, such as equipment isolation or soundproofing.

Combining homogeneous group assessments with task-based measurements enhances the technical accuracy and practicality of noise exposure assessments. Homogeneous group data establish a baseline for average exposure trends across worker categories, enabling the identification of systemic risks. Task-based measurements add precision by isolating the specific operations that drive elevated noise levels. This integrated methodology bridges the gap between general and specific noise risks.

For example, while homogeneous group data confirmed consistently high exposure levels for Groups A and C, task-based measurements pinpointed tasks, such as rubber mixing and shaping, that are the primary contributors to these risks. This distinction supports the development of targeted interventions, such as installing acoustic barriers in high-noise areas or optimizing task schedules to limit exposure duration.

This integrated methodology bridges the gap between general and specific noise risks. Homogeneous group data align with regulatory requirements by providing systemic insights, whereas task-based measurements address the finer details necessary for optimizing resource allocation and designing tailored noise control solutions. Without this combined approach, significant task-specific risks might be overlooked, limiting the effectiveness of interventions.

Integrating these methods into a practical noise management framework can be further enhanced through advanced technologies, such as IoT-based noise sensors. These tools provide real-time exposure data, enabling immediate adjustments to mitigate risks. Predictive analytics can support proactive scheduling of preventive actions, reduce exposure peaks and improve compliance with safety standards. This combination of methodologies and technologies supports a more adaptive and efficient noise control strategy tailored to the unique challenges of the rubber manufacturing industry.

The findings from the observational study demonstrate the significant impact of noise exposure on worker performance and health. Tasks requiring sustained focus, such as rubber mixing and shaping, are particularly affected. This finding reinforces the need for targeted interventions in high-risk zones, especially in operations involving high-noise machinery. Over time, prolonged exposure not only reduces productivity but also contributes to chronic health risks, such as noise-induced hearing loss (NIHL) and cardiovascular issues.

The integration of observational data with homogeneous group and task-based noise measurements provides a more comprehensive understanding of workplace noise risk. While homogeneous group assessments identified systemic patterns, such as the high exposure of Groups A and C, the observational study offered unique insights into how fluctuating noise levels disrupt worker efficiency. The use of a noninvasive observational method ensured that worker behavior was captured authentically, avoiding any disruptions to normal operations. This approach provides reliable data on how noise directly impacts task performance in real-world conditions. For example, regression analysis revealed a direct correlation between noise levels and task performance, confirming that even small increases in noise reduce productivity and increase error rates. These findings validate the need for a dual approach to monitoring and managing noise exposure.

Behavioral data further emphasize the cascading effects of noise on worker productivity and well-being. High-noise zones not only prolong task durations but also increase error rates, particularly when noise exceeds 87 dB(A). These results align with broader research linking excessive noise to reduced concentration and operational inefficiencies. In contrast, quieter zones, such as quality control and maintenance zones, presented more stable performance levels, highlighting the potential benefits of noise reduction measures in high-exposure areas. The nonintrusive nature of the observational method ensured that these performance metrics reflected genuine workplace dynamics, strengthening the reliability of the study’s findings.

These findings suggest that effective noise control requires a combination of administrative and engineering interventions. Limiting the exposure time in high-noise zones and implementing consistent task rotation can mitigate the immediate impacts of noise on performance. Moreover, engineering controls, such as machinery modifications, address the root causes of elevated noise levels. Integrating these strategies with real-time monitoring systems would enable dynamic adjustments, ensuring both worker safety and operational efficiency.

By linking noise exposure to performance metrics, this observational study bridges the gap between occupational health and workplace productivity. Using a noninvasive method offers a comprehensive view, aiding the creation of adaptable noise reduction strategies for changing production environments. Continuous monitoring and adaptive interventions are essential for creating safer, more efficient workspaces in the rubber manufacturing industry.

Building on these insights, the noise mapping process adds a spatial dimension to the understanding of noise exposure. While observational methods revealed the impacts of noise on behavior and productivity, the maps provided a clear visualization of high-risk zones, transforming raw noise measurements into actionable data. This integration of methods ensures that both worker-specific and spatial factors are addressed, allowing for targeted and effective noise control strategies.

The maps highlighted the direct relationship between equipment placement and noise levels, emphasizing the role of facility layout in shaping exposure risk. Reflective surfaces and proximity to high-noise machinery emerged as contributors to raised noise levels. This spatial perspective supports the prioritization of engineering controls, such as isolating noisy machinery or using soundproofing materials in targeted zones, as an effective strategy to reduce overall noise exposure.

One of the key insights from the mapping process was the ability to guide administrative interventions. Identifying high-exposure zones enables better scheduling of task rotations and limits the worker time spent in these areas, mitigating cumulative risks. These measures are particularly relevant for zones where engineering controls may not fully address exposure levels.

Regularly updating noise maps is essential for maintaining their effectiveness, as facility layouts and operational conditions change. The ability to adapt noise control strategies on the basis of updated spatial data ensures that interventions remain relevant and effective over time, reducing worker exposure while supporting productivity.

This adaptive approach aligns with cost-benefit analysis, which emphasizes prioritizing interventions that balance effectiveness with financial sustainability, ensuring that noise control measures remain both practical and impactful over time.

The economic evaluation of noise control measures adds an important dimension to understanding the impact of workplace noise exposure. Unlike homogeneous group assessments, task-based measurements, or noise mapping, cost-benefit analysis focuses on the financial implications of interventions, offering a practical framework for decision-making. This approach connects technical findings with actionable strategies, ensuring that noise control measures are both effective and economically viable.

Engineering controls, such as soundproof barriers, stand out as the most impactful intervention, addressing the root causes of noise exposure. While previous methods highlighted high-risk zones and tasks, the economic evaluation quantified the long-term benefits of reducing noise at its source. These controls contribute to improved productivity, fewer absences, and reduced healthcare costs, making them a sustainable investment in worker safety and organizational performance.

Administrative measures, such as task rotation, complement engineering controls by managing the behavioral and temporal aspects of noise exposure. The economic analysis demonstrated their cost-effectiveness and flexibility, particularly in dynamic work environments. Unlike task-based or group-level measurements, which focus on exposure patterns, this evaluation highlights how administrative interventions reduce operational disruptions and cumulative risks over time.

PPE, although limited in standalone effectiveness, plays a supplementary role in noise management strategies. The analysis underscores its utility in providing immediate protection where engineering and administrative controls are less feasible. This aligns with insights from noise mapping and observational studies, which identified specific zones and tasks where supplementary measures are necessary.

By integrating cost-benefit analysis with the applied methods, this study offers a comprehensive framework for noise management. While technical measurements and mapping provide a detailed understanding of exposure risks, economic evaluation ensures that interventions are prioritized on the basis of both their effectiveness and financial sustainability. This approach supports the development of noise control strategies that balance immediate needs with long-term gains, ultimately fostering a safer and more efficient workplace.

The proposed IoT-based framework introduces a proactive approach to workplace noise management, addressing the limitations of traditional reactive strategies. By leveraging real-time monitoring and predictive analytics, the framework aims to minimize prolonged noise exposure and improve overall workplace safety. The ability to identify and address noise hazards before they escalate represents a significant advancement in noise management practices, fostering a safer and more efficient work environment.

Figure 10 illustrates a practical case study, demonstrating how this framework could be implemented in a rubber manufacturing facility. The visualization highlights the integration of IoT sensors, AI-driven analytics, and worker feedback to create a dynamic and adaptive noise management system. This real-world application underscores the potential of the framework to address specific challenges, such as high-exposure zones and task scheduling.

The IoT-based monitoring system generates high-resolution spatial and temporal data on noise exposure, which can be systematically applied to improve noise control strategies. Spatial interpolation of sensor data enables the precise identification of noise propagation patterns, supporting localized mitigation measures, such as acoustic barriers or sound-absorbing materials. Temporal data trends, derived from predictive analytics, anticipate periodic noise peaks, enabling proactive scheduling adjustments to reduce exposure during high-risk periods. Additionally, integrating real-time noise data with worker-specific exposure profiles allows adaptive task allocation algorithms, optimizing task rotations to minimize cumulative exposure. This data-driven approach prioritizes interventions, focusing resources on the most effective and cost-efficient measures.

Integrating worker feedback into the IoT framework ensures that the proposed measures align with the practical realities of the workplace. Feedback on issues such as task rotation and PPE availability allows the system to adapt to specific worker needs, making noise management more responsive and effective. This collaborative approach fosters worker engagement and increases compliance with noise control protocols, contributing to the sustainability of interventions.

The economic evaluation highlights the dual benefits of the IoT framework: reducing health-related costs and improving productivity. By mitigating noise-induced health issues and absenteeism, the framework demonstrates its potential for both cost-effectiveness and long-term organizational gains. Additionally, the ability to automate compliance reporting and provide actionable insights streamlines regulatory adherence, reducing administrative burdens while maintaining a focus on worker safety.

One of the key strengths of the framework is its adaptability. Real-time data and predictive analytics allow for immediate interventions in high-risk zones, such as rescheduling tasks or deploying additional PPE. These capabilities not only improve responsiveness but also provide a basis for long-term planning, enabling continuous refinement of noise control strategies as workplace conditions evolve.

However, the framework also presents challenges. High initial and maintenance costs may limit its accessibility for smaller organizations. Additionally, reliance on technology introduces risks of system failure due to connectivity or technical issues, potentially disrupting noise management efforts. To address these limitations, organizations must establish contingency plans and prioritize infrastructure reliability.

By combining real-time monitoring, worker feedback, and advanced analytics, the IoT-based framework offers a comprehensive solution to workplace noise management. Its ability to integrate technical precision with practical insights ensures that interventions are both effective and grounded in worker realities. This adaptive and collaborative approach positions the framework as a sustainable model for reducing noise exposure and associated health risks, contributing to a healthier and more productive workforce.

The decision-making framework integrates worker feedback and real-time noise data into a structured, adaptive approach to noise management. By linking worker insights to technical analysis, the framework ensures that interventions are targeted and practical. Engineering controls, such as isolating noisy equipment or redesigning workflows, are prioritized for their long-term effectiveness. Complementary administrative measures, including task rotation and restricted access to high-noise zones, help distribute exposure evenly, whereas PPE serves as an immediate safeguard in high-risk areas. The adaptability of the framework, driven by continuous monitoring and feedback, ensures its relevance as workplace conditions evolve.

Worker feedback highlighted practical challenges that informed the development of the framework. Issues such as task rotation inconsistencies, insufficient PPE access, and limited awareness of noise control protocols emphasize the need for tailored interventions. These findings underline the importance of incorporating worker perspectives to align technical measures with real-world needs and improve compliance.

The framework also incorporates advanced noise mitigation tools tailored to specific risks. Tools such as active noise control (ANC) systems, smart acoustic barriers, and wearable noise monitoring devices provide dynamic, real-time interventions to reduce exposure. AI-driven noise mapping and customized hearing protection devices enhance precision and adaptability, ensuring both immediate protection and long-term health benefits. These technologies are summarized in

Table 5. Technologies to manage occupational noise exposure.

Tool	Benefits	Implementation	Target Areas
Active noise control (ANC) systems	Adaptable to dynamic industrial environments Improves worker concentration by reducing background noise	In areas with persistent low-frequency noise, such as near heavy machinery	Machinery operating zones, such as rubber mixing and molding areas
Smart acoustic barriers	Provides real-time adjustments to changing noise levels. Enhances noise reduction efficiency in high-risk areas.	Deploy barriers around high-noise equipment, focusing on mobile or modular designs that can adjust to workspace changes	Enclosures around extruders, mixers, and conveyors
Machine noise isolation pods	Significantly reduces noise emissions at the source Improves overall workspace noise levels. Protects nearby workers from harmful exposure	Fully enclose high-noise machinery using soundproofing materials, ensuring minimal interference with equipment operations	Fixed machinery such as rubber mixers, vulcanizers, and compressors
Dynamic noise control systems	Automatically optimizes machinery operation for quieter performance Reduces noise without impacting productivity	Integrate sensors and control systems into machinery to adjust operational settings, such as speed and power, to minimize noise emissions without compromising efficiency	Equipment prone to operational noise fluctuations, such as fans, pumps, and conveyor belts
AI-driven noise mapping tools	Identifies high-risk zones with precision. Allows preemptive scheduling of quieter work periods Enhances decision-making for targeted interventions	Deploy AI algorithms to analyze spatial noise data, identify high-risk zones, and predict exposure trends	Facility-wide analysis, focusing on areas with complex noise patterns
Wearable noise monitoring devices	Offers personalized noise exposure tracking Provides immediate alerts for unsafe levels	Distribute wearable devices to workers operating in high-risk zones	Workers in close proximity to machinery or those performing prolonged tasks in noisy environments
Customized hearing protection devices (HPDs)	Tailors’ protection to specific noise profiles Improves comfort and usability Encourages higher compliance among workers.	Conduct individual noise exposure assessments to select or design hearing protection tailored to worker needs	Workers performing tasks with variable or peak noise levels

, offering practical solutions to address noise-related challenges in high-risk industrial environments. This integrated approach demonstrates the value of combining technical solutions, worker feedback, and ongoing monitoring. By continuously adapting to workplace changes and evolving risks, the framework provides a sustainable model for noise management. Regular training for both workers and supervisors ensures that noise control protocols are understood and followed, further strengthening the system’s efficacy.

Noise control solutions are essential in industries with high-noise environments. Advanced technologies such as wearable noise monitoring and AI-driven mapping tools are widely used in sectors such as mining and electronics manufacturing to identify high-exposure zones and address noise risks. Industries such as pharmaceutical production and food processing utilize smart acoustic barriers and isolation pods to reduce machinery noise. In oil, gas and construction, customized hearing protection devices play a supplementary role in minimizing worker exposure, addressing residual risks when engineering and administrative controls are insufficient. Real-time noise monitoring systems integrated with IoT devices enable continuous assessments and immediate interventions when thresholds are breached. Predictive maintenance technologies enhance these systems by identifying and mitigating noise sources, such as worn-out machinery or calibration issues. These technologies ensure compliance with safety regulations, improve worker protection, and enhance operational efficiency [26,27,34,38,42].

The decision-making flow offers a structured framework for managing noise exposure risk, integrating technical measures with practical applications. Engineering controls, address noise at its source and form the backbone of the framework. Administrative measures complement these efforts by managing worker exposure. IoT-based noise monitoring and AI-driven analytics add precision by identifying high-risk zones and predicting potential hazards. Worker feedback is a key input, ensuring that interventions are practical and aligned with workplace realities. PPE offers supplementary protection in high-risk areas, mitigating residual risks while long-term engineering and administrative solutions are being developed or implemented.

The structured process for implementing noise control measures, illustrated in Figure 11, involves a step-by-step approach. It starts by identifying high-risk zones and tasks through noise mapping and measurement data. This step provides a clear understanding of areas requiring immediate attention. The process continues with evaluating the feasibility of engineering and administrative controls to prioritize interventions. Real-time monitoring and worker feedback refine these measures, ensuring that they are both effective and practical. The iterative nature of the framework allows for continuous monitoring and dynamic adjustments, keeping interventions relevant as workplace conditions evolve. This adaptability ensures compliance with safety standards and long-term protection for workers.

Once interventions are selected, they are implemented and continuously monitored to ensure their effectiveness and adaptability to changing workplace conditions. Monitoring provides real-time insights into how well the measures reduce noise exposure and whether adjustments are needed. The final step involves evaluating the outcomes of these interventions and assessing their impact on noise reduction and compliance with safety standards. This iterative process ensures that the measures remain relevant over time, supporting long-term worker protection and sustained adherence to regulations.

Table 6. Structured noise management framework—engineering, administrative, and PPE measures.

Control type	Description
Engineering controls	Install acoustic barriers and noise isolation pods to contain high-noise areas Optimize machinery design and placement to minimize sound propagation Implement active noise control systems to cancel harmful low-frequency sounds.
Administrative controls	Rotate workers to balance exposure across tasks and zones Establish quiet periods during peak operational hours to reduce cumulative exposure Enforce adherence to noise limits through regular supervision and feedback loops
Personal protective equipment (PPE)	Provide customized hearing protection devices tailored to task-specific exposure profiles. Train workers on proper PPE usage and maintenance to improve compliance. Ensure PPE availability in all high-risk zones for immediate access.

summarizes the structured approach to noise management, integrating engineering controls, administrative measures, and PPE into a cohesive strategy. This comprehensive framework balances technical precision with practical application, ensuring that solutions are not only effective but also scalable across diverse industrial settings. By addressing both technical challenges and workplace realities, the approach fosters safer, more efficient work environments.

Noise control measures are necessary for reducing worker exposure to noise while improving workplace performance [43]. These measures include a range of solutions, from engineering controls, such as sound-dampening solutions and isolation pods, to advanced monitoring technologies. Together, they provide a structured framework for identifying high-risk zones and mitigating exposure, fostering safer and more efficient work environments across industries.

Effective noise control strategies not only protect workers from hearing loss and noise-induced stress but also enhance task performance. By reducing distractions and fatigue, these measures improve concentration, accuracy, and productivity. Workers perform tasks more efficiently in controlled noise environments, with fewer errors and faster completion times [44]. This contributes to smoother operations and long-term organizational success.

Table 7. Comparative noise reduction measures.

Noise Reduction Measure	Estimated Initial Cost (€) *	Estimated Annual Maintenance Cost (€) *	Potential Noise Reduction	Benefits	Expected Worker Acceptability
Acoustic barriers (materials and installation)	5000	500	High	Reduces direct exposure in high-risk areas, improves productivity	High
Machinery soundproofing (soundproofing materials and installation)	8000	600	Medium	Improves overall noise levels in workspaces, reduces stress	Medium
IoT sensors integration for monitoring (hardware and installation)	7000	1000	High	Continuous monitoring, prompt and adaptive interventions	High
Task rotation (planning and training costs)	1000	0	Low	Even distribution of noise exposure, reduces fatigue risk	Medium
PPE (initial PPE purchase)	500	200	Variable	Immediate individual protection in high-noise areas	Low

* All costs listed in this table are estimated values on the basis of available data and are subject to change.

summarizes the noise reduction measures and compares their costs, effectiveness, and worker acceptability. This evaluation helps organizations identify practical interventions tailored to their operational needs, ensuring that noise management strategies are both efficient and appropriate.

Organizations that prioritize noise management gain significant economic advantages. By reducing noise-related health issues such as hearing loss and hypertension, companies can lower healthcare expenses, absenteeism, and disability claims [45]. Quieter work environments improve employee morale, leading to higher retention rates and reduced turnover costs. Demonstrating a commitment to noise control enhances an organization’s reputation by showing compliance with safety standards, fostering stakeholder trust, and improving the company’s public image.

The financial benefits of noise reduction extend beyond worker safety and health. Low noise levels reduce wear and tear on equipment, prolong its lifespan and minimize maintenance costs. Optimizing machinery to reduce noise often results in energy savings, further decreasing operational expenses. Adherence to noise regulations helps organizations avoid fines, lawsuits, and higher insurance premiums while also supporting corporate social responsibility initiatives by prioritizing worker well-being and environmental sustainability.

Effective noise control drives both financial and operational success [46]. It reduces costs associated with health issues, absenteeism, and equipment maintenance while enhancing productivity, worker satisfaction, and compliance. By adopting these measures, organizations create a healthier, more efficient workforce and foster a sustainable work environment that supports long-term growth.

Similar studies have been conducted in industries such as construction, mining, and general manufacturing, where noise exposure poses significant risks. Research in the mining sector has utilized task-based and homogeneous group noise assessments to highlight the impact of high noise levels on worker health. In the construction industry, the use of noise maps and real-time monitoring technologies has proven effective in identifying high-risk zones and implementing mitigation measures. Adapting these approaches to the specific context of rubber manufacturing provides valuable insights for enhancing noise control strategies and improving worker safety [47,48].

This study focuses on the rubber manufacturing industry; however, its findings and methodologies are broadly applicable to other industrial sectors with noise exposure challenges. The approach integrates noise mapping, task-specific exposure assessments, and cost-benefit analysis, creating a versatile framework adaptable to industries such as pharmaceuticals, food processing, and electronics manufacturing. These sectors share challenges related to equipment noise and task variability. For example, noise mapping could identify high-exposure areas near mixers and centrifuges in pharmaceutical production, while task-specific assessments could quantify worker exposure during packaging processes [49]. Similarly, in food processing, the methods could locate noise hotspots caused by grinders and conveyor belts, guiding targeted mitigation measures.

The framework uses IoT-based monitoring and AI-driven analysis to evaluate noise exposure dynamically. This makes it particularly relevant for industries with complex layouts and machinery configurations, such as automotive manufacturing, steel production, and logistics [50]. For instance, IoT sensors in automotive plants could monitor assembly lines, allowing AI to analyze trends and suggest interventions such as acoustic barriers or task adjustments. In logistics, real-time monitoring could track exposure in loading areas, supporting task rotation to reduce risks.

Additionally, the cost-benefit analysis offers a structured approach to assess the economic impact of noise control measures. This aspect is valuable for sectors such as chemical processing, energy production, and transportation, where investments in quieter equipment or soundproofing require financial justification [51]. For example, chemical plants could use this framework to prioritize quieter pumps or insulation, while energy facilities could evaluate the cost-effectiveness of soundproofing turbines.

Despite its valuable contributions to understanding noise exposure in the rubber manufacturing industry, this study has certain limitations that affect its broader applicability. Its focus on a specific industrial context means that findings may not translate seamlessly to other sectors with differing operational characteristics, such as construction or food processing, where noise sources and work processes vary significantly. The limited sample size of workers and tasks monitored may also reduce the representativeness of the noise exposure profiles, restricting generalizability to larger populations or diverse industrial settings. Additionally, while the study proposes advanced technologies such as the IoT and AI for real-time noise management, these innovations have not been empirically validated within the research scope. The practical implementation and effectiveness of these methods in reducing noise exposure and improving worker safety require further assessment in real-world scenarios.

Future research should address these gaps by expanding the scope of noise exposure studies to include a wider range of industries. Cross-sector comparisons identify both common challenges and unique risks, providing a broader foundation for developing effective noise mitigation strategies. Worker-centric approaches, such as customized PPE, targeted training programs, and the integration of worker feedback, should also be explored. Evaluating these solutions on the basis of their influence on compliance and worker experience in high-noise environments could yield practical insights for improving safety protocols. Collaborative efforts between industries, regulatory bodies, and researchers are crucial for refining noise management standards and promoting best practices globally. Sharing successful strategies across sectors can enhance safety measures and worker well-being in diverse operational contexts.

5. Conclusions

The findings emphasize the need for a comprehensive approach to occupational noise management that integrates advanced technologies, worker input, and adaptability to evolving industrial conditions. This study highlights the potential of data-driven tools, such as predictive models, to anticipate and address noise risks proactively. Interdisciplinary collaboration, which combines expertise from engineering, occupational health, and behavioral sciences, is essential for developing effective and sustainable noise control strategies. By promoting a proactive safety culture, industries can enhance worker well-being, optimize operational efficiency, and meet regulatory standards. These insights support the vision of noise management as a fundamental component of industrial design and strategic planning, moving beyond reactive measures to integrate forward-thinking solutions.