# Study of Noise Effect of Slag Storage Technology on Surrounding Environment
## 1 Introduction
The authors introduced noise reduction measures that can improve the environment, check the technology for abnormalities, and increase the reliability of the equipment through preventive maintenance. The study reported in [6] aimed to find an identify, from a practical point of view, major policy aspects that can stimulate effective hearing conservation in working environments and are also feasible within a real industrial context. The findings were very relevant for the industry, because they identify the major influential factors for use in hearing protection on the work floor. This way, the results can serve as a base and inspiration for focused hearing conservation programs, improving the prevention of noise-induced hearing loss with effective efforts. In [7], environmental factors that can have a negative impact on workers were introduced. The purpose was to investigate the impact of temperature, noise, and lighting on factory workers' productivity and day-to-day health in a rubber compound manufacturing plant. The results revealed that higher levels of temperature and noise inside the factory can cause lower productivity levels. High temperatures may not only reduce productivity but also contribute to illnesses or heat stress symptoms such as headaches. According to [8], noise is a harmful pollutant that leads to an unsanitary environment in urban areas. This study identified workers' public health in a technological plant in Mosul City, Iraq. Workers' health was assessed by measuring blood parameters and other biochemical parameters, such as TP, ALB, F.B.S, bilirubin, blood urea, and cholesterol. The results showed a significant change in the blood samples studied compared to the control sample. Depending on the results obtained, up to 30% of the workers tested had severe hearing troubles. This percentage was classified as acute hypoacusis (57%), severe hypoacusis (40%), and complete deafness (3%). [PERSON], VD. [9] discussed noise mapping in Indian mines and proposed three different models: one based on baseline operating conditions in two shifts (Situation A) and two other virtual situations where production targets were improved either by extending the working hours to three shifts (Situation B) or only by increased mechanization and no change in the duration of work (Situation C). In all situations, the predicted noise levelsexceeded the noise limits of the process plant in the adjacent residential area. Residential areas close to crushing and raw material processing plants were susceptible to increased noise pollution. The authors recommended placing an acoustic barrier to reduce noise propagation in the vicinity of the crusher. The objective of the paper by authors [PERSON], [PERSON] et al. [10] was to identify the contribution of noise from external sources to the noise pollution generated by the process plant by comparing the sound pressure levels measured in its surroundings and those calculated by noise mapping. A metalworking manufacturing plant was selected for the measurements, and the measured data were entered into Sound Plan noise mapping software to identify the main noise sources by iteration. The placement of sub-barriers along some critically noisy walls was found to be effective, especially in the reference time interval of night, when the measures in place resulted in not exceeding the permissible levels of the noise determinants as defined by the relevant legislation. [PERSON] [11] investigated industrial noise mapping for a metal processing plant and the ambient noise pollution from its operation. The complete study evaluated two different scenarios: in the first scenario, all the individual noise sources of the process plant were considered as point sources, while, in the second scenario, the entire plant was considered an area source. A regression plot was generated between the predicted and measured values for both scenarios individually. This study showed that the second scenario provided a more accurate noise prediction map than the first one.